esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Ge Photodetector Light Coupling Schemes
### Resonant Cavity Enhanced (RCE) Detectors  
To overcome the tradeoff between bandwidth and efficiency in NI detectors, one method is to sandwich a thin layer of photo absorbing material between two light reflectors so that cavity resonance is enhanced [45, 46]. In this structure, light is ideally trapped between the two reflectors and travels through the center light absorber multiple times until fully absorbed. At the same time, the photoabsorption layer can be thin enough to achieve high bandwidth. Another advantage of RCE detectors is the wavelength selectivity. When the light reflector is fabricated in the form of a Bragg reflector, only light in a small range of certain wavelengths is reflected effectively so as to produce high efficiency. The RCE device's light selectivity makes it especially useful for wavelength division multiplexing (WDM) systems.  
Ge RCE Schottky photodetectors (Figure 4) were demonstrated by [PERSON] _et al_. [45] in 2005. The resonant cavity was formed between the Au reflecting top metal contact and the SOI substrate. The backside of the SOI wafer was polished to facilitate light coupling. Schottky contact was formed between the top contact Au and the Ge layer while the bottom contact of Au and p+-Si was ohmic contact. The resonant wavelength was found at around 1,538 nm, leading to an increased quantum efficiency of 59%.  
Although RCE photodetectors solve the bandwidth-efficiency tradeoff to some extent, the fabrication of high reflectivity mirrors increase the design and process complexity significantly. The multiple layers needed for effective reflection also make RCE detectors difficult integrate with other functional devices. Therefore, other methods with more process and integration friendliness are still required.  
Figure 4: Cross-sectional view of the back-illuminated Ge-SOI Schottky photodetector. From [45].
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Policy Impact and Integration  
Can Low-Emission Zones Be Managed More Dynamically and Effectively? [34]  
This study introduces the concept of Green Activity Zones (GAZs), a novel dynamic pricing model designed to improve the efficiency of LEZs by leveraging real-time emission data. The GAZ system dynamically adjusts charges based on continuous emission measurements from vehicles operating within the zone, providing a more accurate and equitable framework compared to traditional static LEZ policies. The main contributions are as follows:  
* Dynamic Pricing Based on Real Emissions: Charges are calculated using a differentiated pricing model for pollutants such as NOx, PM, HC, and CO. The system accounts for spatial and temporal variations in environmental sensitivity, allowing for adaptive tolling during periods of high pollution.
* Incentives for Behavioral Change: By implementing real-time data collection and feedback mechanisms, the GAZ model encourages carriers to adopt eco-friendly practices, such as optimizing routes, increasing load factors, and adopting cleaner vehicles.
* Fairness and Transparency: The system adheres to the Polluter Pays Principle, ensuring that charges are proportionate to the environmental impact of each vehicle. Stakeholders can also document their environmental performance, creating incentives for sustainable competition.
* Stakeholder Engagement and Acceptability: Surveys conducted among retailers, wholesalers, and carriers highlight variations in acceptance levels. Wholesalers aremore willing to adopt the GAZ framework than carriers, who express concerns over cost implications and limited perceived effectiveness.
* Technological Feasibility: The study emphasizes the readiness of current technologies, such as onboard sensors and real-time monitoring systems, to support the implementation of GAZ.  
The GAZ model exemplifies a next-generation approach to LEZ management by integrating dynamic pricing mechanisms with real-time data. Its focus on fairness, behavioral incentives, and stakeholder engagement aligns with the goals of dynamic LEZs to enhance environmental and operational outcomes.
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 4 Conclusions  
This paper presents a low-power, high-resolution SAR ADC tailored for BCI systems. Leveraging a segmented monotonic switching scheme, dynamic logic architecture, and an optimized CDAC, the design achieves a 13.44-bit ENOB and 27.9 uW of power consumption at a supply voltage of 1.8 V, with a compact core area of 0.24 mm\({}^{2}\). The experimental results demonstrate an SNDR of 81.94 dB and an SFDR of 91.69 dBc at a sampling rate of 320 kS/s, making it well suited for neural signal acquisition and biomedical sensing applications. While the 180 nm CMOS process imposes limitations on voltage scaling and current leakage reduction, the design maintains a competitive performance in power efficiency and area optimization. Future work, including process migration to advanced nodes and further circuit-level enhancements, is expected to extend SAR ADC's applicability to implantable medical devices and portable instrumentation.  
Conceptualization, H.Y.; Methodology, H.Y.; Software, H.Y.; Validation, H.Y.; Formal analysis, H.Y.; Investigation, H.Y.; Resources, H.Y.; Data curation, H.Y.; Writing--original draft, H.Y.; Writing--review & editing, H.Y. and J.C.; Visualization, H.Y.; Supervision, J.C.; Projectadministration, J.C.; Funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Lingang Laboratory through Key Project LG-GG-202402-05-02.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because the research is supported by Lingang Laboratory.  
Author [PERSON] was employed by the company MTRIX. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Goals
#### 3.2.3 Dynamic Authentication Key Sharing with the Verification Nodes  
Whenever FASIN selects intermediate verification nodes, the member nodes in the event cluster generate authentication keys and share them with the chosen verification nodes afresh. This minimizes the chances of stale keys being captured by the adversary to create False MACs or forge false reports and restricts the previous verification nodes from duplicating the authentication keys.
## 3 Materials and Methods  
This section describes the basic theoretical concepts of the two components of the SimKG BERT model (Sim BERT model, knowledge graph entity similarity calculation), and also describes the enhanced data processing and pseudo code of the algorithm in combination with the diabetes complication misdiagnosis medical record data enhancement task.  
A generation model is used to generate additional training samples while maintaining the original label. Still, direct generation will affect the similarity between the misdiagnosed medical records and the positive samples. We required a hard-negative sample with both negative sample labels and high similarity with respect to the positive sample. We integrated the SimBERT model and a knowledge graph to enhance the data. The processing flow is shown in Figure 1. Firstly, we divided the electronic medical records into two parts. One part is the critical symptom phrase K in the misdiagnosed cases selected by specialists, constituting the key symptoms diagnosed as negative samples. These core symptom phrases K are directly used in the Sim BERT model to generate new symptom phrases. The other part constitutes the other noncore phrases X, which are highly similar to the positive samples. We compared their cosine similarity with the embedding vector E of knowledge graph entities and selected entities in the knowledge graph for generation through Top-K sampling. In this way, using the generation model, we could retain the negative sample labels of the critical variables of misdiagnosis cases and the high similarity between the overall sample and the positive samples. In addition, the knowledge graph also incorporates domain knowledge related to diabetes complications into the generation task.  
Take a case of the misdiagnosis of diabetes nephropathy as an example. The case history is \"diffuse mesangial hyperplasia, diffuse mesangial immunoglobulin and complement deposition, accompanied by diabetes retinopathy, acute onset, sudden increase of urinary protein, high edema of the whole body, severe concave edema of both lower limbs, massive proteinuria, pleural effusion, elevated blood pressure, 24 h urine protein quantification: 9.8 g, diabetes duration: 12 years, serum creatinine elevation, fasting blood glucose: 8.2 mmol/L\". Among these symptoms, \"diffuse mesangial hyperplasia, diffuse mesangial immunoglobulin and complement deposition, acute onset, and sudden increase of urinary protein\" are the keys to the diagnosis of non-diabetes nephropathy. They are the core disease phrase K, which will be directly used in the Sim BERT model to generate new disease phrases. Other non-core disease phrases are generated through entities in the knowledge graph.
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 3 Methodology
### Validation of ML Models  
The soil dataset comprised 96 sites, which were divided into two groups: a calibration dataset containing 77 sites (80%), and a validation dataset containing 19 sites (20%). For the purpose of cross-validation and evaluating the efficacy of each model, we used three evaluation metrics: mean absolute error (MAE) (Equation (1)); root-mean-squared-error (RMSE) (Equation (2)); and determination coefficient (R\({}^{2}\)) (Equation (3)). MAE measures absolute model accuracy, whereas RMSE measures the accuracy within the model. The MAE was less affected by the outliers than the RMSE. The R\({}^{2}\) value, which falls within the range 0-1, signifies the extent of agreement between the observed and predicted values along the fitted regression line.  
\[MAE\ =\ \frac{1}{n}\sum_{i\ =\ 1}^{n}\left|o_{i}-p_{i}\right| \tag{1}\]  
\[RMSE\ =\ \sqrt{\frac{\sum_{i\ =\ 1}^{n}\left(o_{i}-p_{i}\right)^{2}}{n}} \tag{2}\]  
\[R^{2}\ =\ 1-\frac{\sum_{i\ =\ 1}^{n}\left(p_{i}-o_{i}\right)^{2}}{\sum_{i\ =\ 1}^{n}\left(o_{i}-\overline{o}\right)^{2}} \tag{3}\]  
where \(p_{i}\) and \(o_{i}\) are the predicted and true values of the validation phase of the sample points, respectively; and \(\overline{o}\) is the true mean value.
## 5 Discussions and Future Perspectives  
It is evident from the analysis of the current landscape that HDR reconstruction, whether from a single frame (single-frame) or multiple frames (multi-frame), has advanced significantly through innovations in areas such as network architectures (CNNs, GANs, Transformers, and Diffusion Models), improved evaluation metrics, and the creation of more robust datasets. This section discusses the main insights, current challenges, emerging strategies, and future perspectives guiding the field.
# Analysis of Gut Microbiome Structure Based on GMPR+Spectrum
## 5 Conclusions  
In this study, we present a new method of GMPR+Spectrum to analyze the gut microbiome from the patients with MHE/HE. The results show that GMPR+Spectrum can more effectively identify structural differences in the gut microbiota of different patients, and extracting critical bacteria, and provide a reference for clinical screening and diagnosis of MHE/HE. Methodology, X.X. ; formal analysis, X.X. and Y.R. ; writing--review and editing, X.X. and Y.R. ; data curation, Y.R. ; visualization, Y.R. and J.H. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number: 82060329, Yunnan Province Science and Technology Department Projects, grant number: 202101 AT070310. The study was approved by according to the Ethics Committee of the First People's Hospital of Yunnan Province. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data is available from the Department of Gastroenterology, First People's Hospital of Yunnan Province. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Index** & **Spectrum** & **GMPR+Spectrum** & **M3C** & **iClusterPlus** \\ \hline \multirow{4}{*}{N} & NMI & 0.1524 & 0.1521 & 0.00046 & 0.2678 \\  & DBI & 2.7807 & 3.0169 & 2.8357 & 5.7933 \\  & CH & 2.5914 & 1.6433 & 0.0103 & 1.1882 \\  & Runtimes/second & 23.18 & 20.39 & 157.36 & 38.69 \\  & Cluster number & 2 & 2 & 4 & 3 \\ \hline \multirow{4}{*}{H} & NMI & 0.1555 & 0.1558 & 0.00018 & 0.27001 \\  & DBI & 3.2233 & 3.0738 & 1.8580 & 6.1212 \\  & CH & 1.6681 & 2.3817 & 20.451 & 1.2989 \\  & Runtimes/second & 17.14 & 15.86 & 151.75 & 28.83 \\  & Cluster number & 2 & 2 & 2 & 3 \\ \hline \multirow{4}{*}{M} & NMI & 0.1623 & 0.1647 & 0.00059 & 0.2705 \\  & DBI & 2.7388 & 2.8420 & 2.9583 & 5.9948 \\ \cline{1-1}  & CH & 2.7118 & 2.6339 & 0.0102 & 1.4089 \\ \cline{1-1}  & Runtimes/second & 18.00 & 15.29 & 154.06 & 33.76 \\ \cline{1-1}  & Cluster number & 2 & 2 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OTUs contained in each Cluster after GMPR+Spectrum. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N** & **OTUID** & **SCORE** & **Family** & **Genus** \\ \hline \hline Cluster1 & \ \begin{tabular}{c} OTU45 \\ OTU104 \\ OTU230 \\ OTU125 \\ OTU26 \\ OTU336 \\ OTU329 \\ OTU346 \\ OTU350 \\ OTU367 \\ OTU473 \\ \end{tabular}  &
\begin{tab
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
## 2 US-Powered IMDs: A System Overview
The first one regards the presence of a clamping circuit that often makes the amplitude demodulation on the incoming acoustic waves difficult; indeed, a variation of the voltage amplitude on the driving _TX-PIEZO_ could not be proficiently detected once impinged on the implanted IC. The second motivation is the necessity of a continuous powering of the IMDs, making the efficient use of the On-Off Shift Keying (OOK) proposed in the Refs. [19; 21; 22] difficult. Finally, the last motivation arises from inspection of the graph reported in Figure 2. It shows the Bit-Error-Rate (BER) for different modulation schemes as a function of the Signal-to-Noise Ratio (SNR) defined as the ratio \(E_{b}/N_{o}\), where \(E_{b}\) is the energy-per-bit and \(N_{o}\) is the noise power. Specifically, the compared schemes modulate the received signal amplitude (BASK), its frequency (BFSK), its phase (BPSK) or its phase with differential modulation (DPSK). It is possible to observe that the BPSK presents the lowest BER, for a given value of the SNR. Furthermore, if the BPSK demodulation system does not require any synchronization between the data transmitter and the data receiver, it is defined as a non-coherent BPSK modulation. Assuming BPSK modulated the input signal, the external powering and modulating electronic system depicted in the left-most side of Figure 1 is essentially based on an FPGA that generates the sinusoidal signal at the piezo-transducers electrical resonance frequency, \(f_{0}\), as well as a power amplifier (PA) which drives the _TX-PIEZO_ with the suitable power level. The FPGA acts as a modulator exploiting a proper digital signal, named _MOD_, that exchanges the two sinusoidal phases, \(\varphi_{0}\) and \(\varphi_{180}\), generating the following transmitted signal  
\[V_{PZT}(t)= \begin{cases}\sin{(2\pi f_{0}t+\varphi_{0})}=\sin{(2\pi f_{0}t)}& \quad\text{if }MOD\text{ is low}\\ \sin{(2\pi f_{0}t+\varphi_{180})}=\sin{(2\pi f_{0}t+180^{\circ})}&\quad\text{if }MOD \text{ is high}\end{cases}  \tag{1}\]  
As shown in Figure 1, the implanted demodulator of the integrated system requires a robust oscillator (i.e., current starved ring (CSRO) oscillator, as proposed in the Ref. [6]). However, it is worth noting that the internal oscillator of the implanted processing unit could be equivalently used, thus reducing both the area occupation and the power consumption of the entire data-receiving system.
## 3 Results
### Relationship of GPP with VIs and SIF, 2001-2020
#### 3.3.2 Correlation between GPP and SIF  
The SIF-GPP relationship (meanR\({}^{2}\) = 0.85) improved compared to VIs. Moreover, SIF performs worse than VIs in some land classes and on certain time scales, which denied  
Figure 8: The correlation between VIs, SIF, and GPP. Sp, Su, Au, Ngs, Gs, Rw, and Am are spring, summer, autumn, non-growing season, growing season, removal of winter, and the entire study period, respectively.  
the original hypothesis that SIF performed best. Specifically, in sparse vegetation, it was lower than at least one of the indices in the VIs at other time scales except summer and autumn and was especially lowest in the non-growing season (R\({}^{2}\) = 0.13). In addition, it was lower than NIRV in the non-growing season in grassland and forest-shrub-grass vegetation mosaic belts and lower than NDVI in summer in deciduous and evergreen forests.
# Effects of Short Fibers on the Long-Term Behavior of RC/FRC Beams Aged under Service Loading
## 3 Experimental Results and Discussion
### Long-Term Behavior
#### 3.1.2 Cracking Behavior  
The cracking behavior of beams of Set #2 (exposed for 17 months) and of Set #3 (exposed for 72 months) is presented and discussed in this section. The discussion mainly concerns the crack width, since the crack distance was governed by stirrup distance. The measurements of the crack width during the sustained load of 50 kN (Set #2 and #3) showed that a smaller crack width was observed in the FRC beams (50-60% with respect to PC beam). Unfortunately, the last continuative measurement of crack width on the beams of Set #3 was taken after 23 months of exposure (due to the impossibility to gain access in the area at the time), when the average values of the crack width were equal to 0.26, 0.17 and 0.14 mm for PC, ST and POL beams, respectively.  
Figure 5 shows a comparison between the beams of Set #2 and #3 in terms of crack opening in the constant moment region versus load, during the final loading test in the laboratory (after exposure);  
Figure 4: Load vs. mid-span deflection curves for beams aged for 72 months.  
Figure 3: Mid-span deflection values under a service load of 50 kN at the end of the test (17 months for _E17 and 72 months for _E72 beams).  
once again, it can be noticed that these FRC beams show lower values of crack width than the PC beam, especially in the last stages of loading. It can be also noticed that, at the service load (50-60 kN), polyester fibers have a similar effect to steel fibers.  
Other valuable considerations arise when taking into account the crack length and the crack width measured in the constant moment zone for all of the exposed beams of Set #3 under the sustained load of 50 kN. In particular, in the constant moment region, the total crack width, calculated as the sum of the crack widths in the constant moment zone of each beam, is 30% smaller in FRC beams than that measured in the PC beams. The comparison between Set #3 and Set #2, in terms of mean crack width in the constant moment region, did not provide substantial differences.  
Figure 6 also reports relevant results recorded in the field, in terms of crack width versus time. The mean crack was computed as the average value between the maximum crack openings in the constant moment region. It is clear, from the plots, that those concrete beams incorporating short fibers show a cracking behavior which is mitigated in terms of width. Moreover, the stabilization of the crack width is reached earlier for the FRC beams with respect to the ordinary RC beams.  
Figure 7 shows that, in cracked sections, the PC beams had the higher value of the maximum carbonation depth (49.1 mm) as compared to the FRC beams (45.1/34 mm for STI/ST2 and 37.4/36.2 for POL1/POL2); therefore, the beneficial effect of short fibers in the concrete mix is confirmed [23].  
Figure 5: Mean crack width in the constant moment region—Set #2 and #3.  
Figure 6: Mean crack width over the constant moment zone vs. time.
## 4 Experiment and Results
### Experimental Assumptions  
(1) Roadside equipment can obtain real-time vehicle information through vehicular wireless communication technology (V2X). Communication delays and information errors are not considered.  
(2) The study focuses only on passenger cars, ignoring the impact of vehicle size. From the perspective of vehicle power type, vehicles are categorized into two types: gasoline-powered and electric vehicles.  
(3) All vehicles are equipped with the same devices, with the only difference being their power sources. Given the instability of start-stop devices in the current traffic environment [52; 53], the impact of these devices on the model results is neglected.  
(4) According to the 2023 Global Electric Vehicle Outlook [54], the current mix of gasoline and electric vehicles in traffic is set at 71% and 29%, respectively.  
(5) The impact of pedestrians, non-motorized vehicles, and weather conditions on the road system is not considered [55].  
(6) The study scenario is an urban intersection with a flat road surface, where factors such as slope have a negligible effect on the VSP-based power model [49].
# Overview of Mobile Communications in Colombia and Introduction to 5G
## 1 Introduction
### Current Networks  
Mobile wireless communications have undergone a rapid evolution over four decades, starting in the 1980s with the first generation (1G), based on analog systems, to the current fifth-generation (5G), which will occupy a dominant position on the global horizon over the current decade, while the next will be 6G, which is expected to be available from 2030 [4]. Colombia is no stranger to this transformation of the telecommunications industry and currently has 2G, 3G, and 4G networks deployed in its territory. In the case of 5G, the state has launched a set of actions to enable its implementation in the short term. Figure 1 shows the evolution of mobile communications with the main milestones of each generation, and a comparison of the advantages and disadvantages of each is presented in Table 1.  
Figure 1: Evolution of mobile communications networks [5]. Data from the Colombian Ministry of Information and Communications Technologies (MinICT) indicate that in 2019, close to 9.5 million users, corresponding to 31% of the total, accessed mobile telephony services with 2G and 3G technologies [11]. The technology of 2G is extremely limited in mobile data transfer, focusing on voice and text messaging services, which are the most common ways to connect to the network in the most remote areas of Colombia, mostly characterized as rural scenarios. The Colombian Chamber of Information Technologies and Telecommunications (CCIT) estimated in 2019 that approximately 10 million users in Colombia only had access to the mobile network through 2G, being one of the few countries in the world where this type of network is in operation [12]. The lack of high-speed connectivity hurts information access and competitiveness indicators in the most remote and backward areas of the country. Completing the total \"switch-off\" of 2G and 3G as soon as possible is necessary and inevitable in Colombia for its economy and population to continue to evolve in the digital society, where mobile networks play a fundamental role in the exchange of information, including that generated from millions of data obtained from monitoring and measurement systems, such as the Internet of Things (IoT). In addition to the superiority of 4G and 5G in terms of capacity and data transfer speed, the disconnection of 2G and 3G will free up significant portions of the radio spectrum that could be used in more efficient systems (4G and 5G). For these reasons, in June 2020 the MinICT launched the Plan for Transition to New Technologies, which presents strategies to modernize the ecosystem of mobile