Bibliographic record
Abstract
Wireless and fiber-optic communication systems Communication systems transfer information between two points (point-to-point) or from one point to multiple points (point-to-multi-point ) located at a distance from each other. The distance may be anywhere from a few centimeters in personal area networks (PAN), to a few thousand kilometers in long-haul optical fiber communication systems. The information can be conveyed using carrier frequencies and energies occupying the audio, microwave, mm-wave, optical, and infrared portions of the electromagnetic spectrum. In this book, we refer to the range spanning GHz to hundreds of GHz as high-frequency. Although optical frequencies do not fall into this category, the baseband information content of most current fiber-optic systems covers the frequency spectrum from DC to tens of GHz. This makes the circuit topologies and design methodologies discussed in this book applicable to the electronic portion of fiber-optic systems. Wireless versus fiber systems Figure 2.1 illustrates the block diagrams of typical wireless and fiber-optic communication systems. They both consist of a transmitter and a receiver, a synchronization block, and a transmission medium. The information signal modulates a high-frequency (GHz to hundreds of GHz) or optical (hundreds of THz) carrier which is transmitted through the air, or through an optical fiber, to the receiver. The receiver amplifies the modulated carrier and extracts (demodulates) the information from the carrier. In both cases, an increasing portion of the system is occupied by analog-to-digital converters (ADC), digital to analog converters (DAC), and digital signal processors (DSP), operating with clock frequencies extending well into the GHz domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".