esa-sceva/satcom-chunk-collection huggingface dataset:
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Single-/** & **Frequency** & **Tunability** & **Tuning** & **Tuning** & & & & & \\  & **Dual-Band** & **(GHz)** & **Tuneability** & **Range (\%)** & 1S\({}_{\text{n1}}\)\({}_{\text{dB}}\)** & 1S\({}_{\text{2n1}}\)\({}_{\text{dB}}\)** & 1S\({}_{\text{5n1}}\)\({}_{\text{dB}}\)** & **1S\({}_{\text{41}}\)\({}_{\text{dB}}\)** & **Size** \\ \hline
[22] & Dual & 0.82 & No & - & \(>\)20 & \(>\)20 & \(<\)0.65 & \(>\)20 & 150 mm \(\times\) 100 mm \\ \hline
[23] & Dual & 1.04 & No & - & \(>\)20 & \(>\)20 & \(<\)1.3 & \(>\)20 & 0.35\({}_{\text{g}}\)\(\times\) 0.17\({}_{\text{g}}\) \\ \hline
[24] & Dual & 1.31 & No & - & \(>\)18 & \(>\)20 & \(<\)1.25 & \(>\)20 & 105 mm \(\times\) 45 mm \\ \hline
[25] & Single 1 & 1.63 to 1.93 & Yes & 15.8 & \(>\)17.9 & \(>\)21.1 & \(<\)0.57 & \(>\)28.5 & Not reported \\ \hline
[25] & Single 2 & 1.41 to 2.05 & Yes & 36.9 & \(>\)17.4 & \(>\)26.1 & \(<\)0.90 & \(>\)17.9 & Not reported \\ \hline
[26] & Single & 2.07 to 2.75 & Yes & 28.5 & \(>\)15 & \(>\)22 & 2.42 & \(>\)22 & 0.34\({}_{\text{g}}\)\(\times\) 0.34\({}_{\text{g}}\) \\ \hline
[27] & Single & 1.29 to 2.06 & Yes & 46 & \(>\)20 & \(>\)20 & \(<\) 1.22 & \(>\)20 & 0.25\({}_{\text{g}}\)\(\times\) 0.04\({}_{\text{g}}\) \\ \hline
[28] & Single & 2.2 to 5 & Yes & 78 & \(>\)19 & \(>\)15 & \(<\) 1.97 & \(>\)15 & 0.25\({}_{\text{g}}\)\(\times\) 0.11\({}_{\text{g}}\) \\ \hline
**This Work** & Dual & 2.06 to 2.4 & Yes & 15.3 & \(>\)15 & \(>\)16.5 & \(<\)1.12 & \(>\)15 & \\  & 5.44 to 5.84 & Yes & 7.1 & \(>\)20 & \(>\)15 & \(<\)1.6 & \(>\)17 & 45.5 mm \(\times\) 29.4 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the proposed crossover and published designs.
# MAC Layer Protocols for Internet of Things: A Survey
## 2 Short Range MAC Layer Protocols
### IEEE 802.11 a/b/g/n/ah
One more characteristic that deserves to be highlighted is the use of PHY preamble fields is to indicate the continuity of the channel utilization. It is used to minimize collisions and to adopt the use of the bidirectional transient capacity (BDT). In addition, the adoption of the short inter frame space (SIFS) between uplink and downlink communications within the same transmission opportunity (TXOP) to improve medium access. These features also contribute to energy saving. Another improvement brought in favor of energy saving is scaling the value of the Max Idle Period field. This field defines the terminal sleep time. In this way, the terminal is able to go into a sleep mode for more than five years. Reduction of processing, reduction of overhead in addition to reduction and optimization of access to the medium also drives the evolution of IEEE 802.11 PHY and MAC layers. In the PHY layer, the transmission recognition system that uses the acknowledgment (ACK) package has been optimized by reducing the ACK packet itself to the minimum necessary and also by creating the null data packet (NDP) carrying MAC. To keep the QoS parameters, the use of a new frame format dedicated to QoS called the Short QoS Data Frame. Its header was reduced to 12 bytes compared to the 30 bytes of the QoS data frame of IEEE 802.11n. In order to increase the number of stations up to a maximum of 8191, four encoding modes were defined in IEEE 802.11 ah. They allow IEEE 802.11 ah to compress the traffic indication virtual bitmap, used to signaling the association identifier of a station. A comparison of the IEEE 802.11 standards described is present in Figure 15.  
Separating the stations in different types, with different procedures, common channel access time periods and other characteristics was added to the MAC protocol. These characteristics permits up to 8191 connected end devices already considering the collision issues among them. Although there is a need to maintain connection and synchronization with the APs, the IEEE 802.11 ah terminals are equipped with mechanisms that provide energy saving during the period of inactivity. This ensures that the IEEE 802.11 ah features long range and low power consumption when compared to other WLAN technologies, but remaining different from LP-WANs [82]. Using proper antennas and its features, the protocol can also be used in fixed point-to-point arrays typically in ranges of more than 1 km whenever there is line of sight (LOS). IEEE 802.11 ah is also used for point-to-point communications or back-haul transport systems.
# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
###### Abstract  
Soil erosion worldwide is an intense, poorly controlled process. In many respects, this is a consequence of the lack of up-to-date high-resolution erosion maps. All over the world, the problem of insufficient information is solved in different ways, mainly on a point-by-point basis, within local areas. Extrapolation of the results obtained locally to a more extensive territory produces inevitable uncertainties and errors. For the anthropogenic-developed part of Russia, this problem is especially urgent because the assessment of the intensity of erosion processes, even with the use of erosion models, does not reach the necessary scale due to the lack of all the required global large-scale remote sensing data and the complexity of considering regional features of erosion processes over such vast areas. This study aims to propose a new methodology for large-scale automated mapping of fill erosion networks based on Sentinel-2 data. A LinkNet deep neural network with a DenseNet encoder was used to solve the problem of automated fill erosion mapping. The recognition results for the study area of more than 345,000 sq. km were summarized to a grid of 3037 basins and analyzed to assess the relationship with the main natural-anthropogenic factors. Generalized additive models (GAM) were used to model the dependency of fill erosion density to explore complex relationships. A complex nonlinear relationship between erosion processes and topographic, meteorological, geomorphological, and anthropogenic factors was shown.  
Keywords:fill; soil erosion; neural networks; remote sensing; relationship modeling +
Footnote †: 2022: _11_, 197. [[https://doi.org/10.3390/ijgi11030197](https://doi.org/10.3390/ijgi11030197)]([https://doi.org/10.3390/ijgi11030197](https://doi.org/10.3390/ijgi11030197))
## 2 Driver Road Performance Assessment Model
### Driver Perception Capability Assessment
A left lane change involves scanning the left rear-view mirror area, while a right lane change pertains to scanning the right rear-view mirror area. Within lane-changing scenarios, a score of 100 is assigned if the driver observes the rear-view mirror area, and a score of 0 is given if no observation is recorded. The system will maintain a record of the entire observation area polygon acquired during the observation process. Simultaneously, it will record the traffic participants observed by the driver during this process, encompassing pedestrians and vehicles, based on the outcomes of laser radar target identification. The observation analysis capability score, denoted as \(Score_{2}\), can be determined based on the number of observed and unobserved traffic participants according to Equation (2). \[Score_{2}=100\times\frac{(num_{spotted})^{0.5}}{(num_{spotted}+num_{ unspotted})^{0.5}} \tag{2}\]The inclusion of \(Score_{2}\) is motivated by the fact that when assessing the overall size of the area observed by a driver, consideration is limited to the scanning area over a particular timeframe without taking into account the presence of newly introduced traffic participants. This oversight can lead to situations in which drivers have scanned the entire area, yet have failed to notice new developments within areas they had previously observed. The purpose of \(Score_{2}\) is to assess a driver's dynamic environmental perception capabilities. The relationship between \(Score\), \(Score_{2}\), and the driver's ability is described in Figure 3.  
Finally, the overall score for a driver's environmental perception capability can be expressed as \(EnvS\) in Equation (3). \[EnvS=k*Score+(1-k)*Score_{2} \tag{3}\]  
In recognition of the influence of the number of traffic participants on a driver's willingness to observe, \(k\) is assigned a value lower than 0.5. In our experimental setup, \(k\) was set to 0.4 and \(1-k\) was 0.6.
## 4 Method
### IoT Network of Camera-Based Sensors  
The \"sensors analysis and selection\" phase relies on the proposed literature review. As previously stated, most analyzed recent applications aimed at optimizing energy performances and consumptions rather than building operation and use [9; 12; 16]. Nonetheless, existing studies allowed objectively comparing several available sensor types, supporting the selection of the most suitable type for occupancy monitoring.  
Camera-based sensors were selected considering their high accuracy and the possibility to perform other kind of analyses, such as security and safety monitoring, thus allowing for further implementations of new features in the system, increasing the scalability of the system itself.  
The limitations of camera-based sensors that have been presented in the literature review section, and how they have been overcome, are described as follows:  
Figure 1: Stages of the research project.  
* Detection only within field-of-view of the sensors: the BIM model was used to ensure the best positioning and orientation of sensors and to maximize the area covered by the sensors' field-of-view;
* Privacy issues and Hawthorne effect: the system was set to anonymously monitor users and not to store any images. The user is recognized as a human by the deep learning algorithm embedded in the camera-based sensors and translated into an anonymous agent that cannot be linked to a specific user identity. Consequently, the movements of the user can be anonymously monitored in real time and visualized in the online platform SophyAI, without storing any real image or video recording.  
The sensors can detect occupancy; in particular, they can visualize real time movements of users that are instantaneously transformed into anonymous virtual agents.  
The detection of anonymous real-time movements of users is limited to common spaces, i.e., circulation areas and corridors, and they can be visualized in the online platform SophyAI, but are not stored in the database (DB), to protect the users' privacy. On the contrary, sensors count and store the number of agents that are entering or leaving rooms, which are the main objects of monitoring.  
Two values are recorded by the sensors for each monitored room:  
* O: Occupancy values at room-level, i.e., the number of people (p) occupying a room in a certain period of time;
* T: Period of time in which one or more virtual agents occupy a room (minutes/hours).
## 3 Results  
### Modal Identification  
The repeatability results are listed in Table 4. Eight modes have been identified in the selected frequency range. The score stands for the number of appearances of the mode compared to the total number of measures. Modes 1, 3, 4, 6, 7 and 8 have been identified in more than 80% of the tests. Mode 2 was identified in 60% of the tests and Mode 5 in 30%.  
The mode frequencies exhibit a coefficient of variation below 5%. Frequencies 1, 3, 4, 6, 7 and 8 have been identified in at least 80% of the tests, and their coefficients of variation lie below 5%. Their identification is therefore considered repeatable. Dampings for Modes 2, 3, 4, 6 and 7 have similar values, around 7.57%. Damping of Mode 1 is 40% higher, while that of Mode 5 is about 50% lower and that of Mode 8 only 30%. The variation coefficients range from 16.2% for Mode 6 to 49.9% for Mode 5. The modal dampings are consequently considered not repeatable, contrary to the frequencies. The parameters considered by the protocol could not provide accurate measurement of damping. Therefore, damping will be discarded in the forthcoming exploitation of the results.  
The results of the modal identification for the 11 subjects are listed in Table 5.  
Eight modes have been identified among all of the subjects. The modal frequencies were estimated as identical for the eight identified modes with a coefficient of variation below 5%. However, modal damping had a high coefficient of variation (above 30.1%). This confirmed the previous results of the repeatability protocol.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 2 System Architecture and Mathematical Modeling
### Resource Scheduling Algorithm
#### 2.9.1 Edrx (Extended Discontinuous Reception) Model  
eDRX is an extended form of DRX to keep on longer in the idle or power-saving state between two pages. At first, it starts with some short active cycles and then enters into the long deep sleep cycles, which minimize the device power consumption. In the eDRX mode, devices are periodically available for DL or MT data transfer requests. From LTE Release 13 specifications, it is defined that the eDRX idle mode can be extended up to 3 h.  
Figure 6: Illustration of user device’s behavior in NB-IoT (PSM and eDRX).
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 3 Result and Discussion
### Results of Model-Pruning Experiments  
To prune the model, it is first necessary to train the model sparsely, as shown in Figure 10. We visualized the scaling factors of the BN layer. It can be seen from the figure  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of Images** & **Defect [EMAIL_ADDRESS]/\%** & **Scratch [EMAIL_ADDRESS]/\%** & **All mAP/\%** \\ \hline Ori & 1533 & 23.80 & 83.38 & 53.59 \\ Ori+aug & 4599 & 26.42 & 89.90 & 58.16 \\ Ori+aug+GAN & 6099 & 26.40 & 94.32 & 60.36 \\ Ori+aug+GAN+oversampling & 7276 & 96.21 & 94.93 & 95.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection results with different datasets.  
that the BN layer scaling factor is normally distributed before the model is sparse trained, and after the sparse training, the distribution of the BN layer scaling factor gradually converges to near zero due to the L1 regularization of the parameters. Then, the channels close to zero are removed to achieve the pruning of the model.  
In this paper, we pruned and fine-tuned the lightweight network YOLOv5n, and compared the performance of various aspects of the model with different pruning ratios. The results are shown in Table 4.  
From Table 2, it can be seen that, with the increase in pruning rate, the number of model parameters and inference time decreased, but at the same time, the detection accuracy of the model decreased, even after fine-tuning. When the pruning ratio exceeded 70%, the model performance decreased more obviously. Therefore, considering both the accuracy and speed of the model, this paper used a network with a pruning ratio of 70% as the final mini-network and named it YOLOv5n-slim, based on which a knowledge distillation algorithm was used to distill it to improve the performance of the model.
## 4 Findings
### Spatial Correlation and Regional Variations in Selected Economic Sectors
Nevertheless, the productivity per capita is highly concentrated in the Bangkok Metropolitan area and the Eastern region, reinforcing these areas as economic hubs for advanced services. _Administrative and support service_ activities' growth, while lacking spatial correlation globally, demonstrates regional high-growth clusters in the Bangkok Metropolitan area and parts of the Central and Eastern regions through the LISA Cluster maps. The pattern for productivity per capita mirrors that of professional activities, with high values clustered in Bangkok and the Eastern region. For the _leisure_ sector, encompassing arts, entertainment, and recreation, there is a consistent concentration of high productivity per capita in Bangkok and its neighbouring provinces, Nonthaburi and Pathum Thani. The central area of Thailand is noted for the sector's growth, whereas the Northeastern region is distinctly marked by low growth. The LISA cluster maps presented in Figure 3 are invaluable for discerning the distinctive characteristics and trends within Thailand's key economic sectors. These maps highlight the spatial variability and disparities that exist, providing crucial insights that can inform and shape development policies by public sector entities.
## 2 Overall Communication Solution of DSSS Transceiver
### Modulation and Demodulation  
The BPSK modulation and demodulation technique is used in this paper. BPSK has some advantages, such as high spectrum efficiency, strong anti-interference properties, good spectral characteristics, quick transmissibility and others. BPSK uses the 0 s and 1 s of the digital signal to switch between a 0 and \(\pi\) phase of the carrier signal [10; 11; 12; 13]. Equation (2) is the time-domain expression of the BPSK signal.  
\[s(t)=\biggr{[}\sum_{n=-\infty}^{\infty}a_{n}g_{T}(t-nT_{b})\biggr{]}\cos( \omega_{t}+\theta_{i}) \tag{2}\]  
where \(a_{n}\) denotes a bipolar sequence, whose statistic characteristic values are 1 and \(-1\). \(w_{i}\) denotes the carrier frequency. \(T_{b}\) denotes the binary symbol interval. \(\theta_{i}\) is the initial phase of the carrier. \(g_{T}(t)\) denotes a single rectangle pulse, whose pulse width is \(T_{b}\). \(g_{T}(t)\) has raised cosine characteristics.  
In this paper, as the selection method to generate the BPSK signal we used the baseband signal to control a switching circuit to select the input signal. The input signal of the switching circuit is two co-frequency carriers whose phase difference is \(\pi\). Figure 2 shows the block diagram of our BPSK modulation module:  
Coherent demodulation and incoherent demodulation are two common demodulation methods. Coherent demodulation has great advantages with regard to threshold detection, output signal to noise ratio, error rate and others, and is the method used in this paper. The block diagram of the BPSK demodulation module is shown in Figure 3.  
Figure 1: M-sequence generator.  
Figure 2: Block diagram of Binary Phase Shift Keying (BPSK) modulation.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Analysis of Statistical Distributions  
The selection of appropriate statistical distributions in healthcare DES models significantly impacts simulation accuracy and reliability. Our comprehensive analysis reveals patterns in distribution selection across different healthcare processes, while acknowledging the context-specific nature of these choices. Through detailed examination of the 349 case studies, we identified distinct patterns in distribution usage across various healthcare processes.  
The inclusion of travel time in simulations [46,47] becomes particularly relevant when modeling patient transfers to different facilities or hospitals or when transferring to other departments within the same hospital. Notably, we observed that registration times were not consistently addressed in the literature.  
Furthermore, our analysis showed that the distributions we identified may work well for some medical departments, but they may not be as suitable for others. This indicates that there is no one-size-fits-all statistical distribution that can be universally applied. Our aim was to investigate the distributions that are commonly used and recognized in the field of discrete event simulation (DES).  
Table 4 presents the statistical distributions that were identified in the reviewed literature.  
The events have been categorized into three main groups: patient flow events (relating to patient movement through the system), operational events (relating to direct care delivery), and support events (relating to auxiliary activities). This classification provides  
\begin{table}
\begin{tabular}{c c c} \hline
**Event** & **Discrete Event** & **Statistical Distribution (Number of Appearances)** \\ \hline Patient Flow & Arrival & Poisson (25); Normal (5); Johnson (2) \\ Patient Flow & Interarrival time & Exponential (11) \\ Patient Flow & Length of stay (LOS) & Lognormal (6); Erlang (1); Normal (4); Weibull (3); beta (1); \\  & & Pearson (2); Gamma (2); Exponential (2) \\ Operational & Process time & Lognormal (1); Triangular (4); Normal (1); Weibull (2); \\ Operational & Activity durations & Uniform (1); Triangular (3) \\ Operational & Service times & Beta (1); Uniform (1); Triangular (5); Exponential (4) \\ Support & Administrative tasks & Johnson (1) \\ Support & Travel times & Lognormal (1); Triangular (4); Normal (1); Weibull (2) \\ \hline \end{tabular}
\end{table}
Table 4: Statistical distributions and discrete events.  
a clearer understanding of how different statistical distributions are applied to distinct aspects of healthcare operations. For example, while patient arrivals predominantly follow Poisson distributions, clinical process times show more variation in distribution choice, reflecting their greater complexity and variability.
###### Abstract  
The twin-field (TF) protocol is a key protocol in quantum key distribution (QKD) that enables remote key distribution, achieving a maximum secure transmission distance of over 500 km. However, the TF protocol still faces several security issues in real-world environments. To address the issue of untrusted sources, one effective solution is to introduce a light-source monitoring module into the system. Analysis shows that a solution based on untagged bits (UBs) can achieve ideal monitoring performance. This solution can capture UB signals to accurately estimate key parameters in the protocol's security analysis, ultimately deriving a tight bound for the secure bit rate. Simulations show that this solution approximates the performance of ideal light sources in the presence of untrusted sources and effectively mitigates the impact of light-source fluctuations. It outperforms other solutions in key performance metrics, such as transmission distance.  
Keywords:quantum key distribution; twin fields; sending-or-not-sending; light source monitoring Article
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 5 Application  
FORCE is increasingly used to support a wide range of scientific to operational applications. Landsat ARD and hARD, as well as Landsat-improved MODIS phenometrics were generated to serve as baseline products for environmental monitoring purposes in Southern Africa [62]. Landsat ARD and higher-level products have been extensively used in the Miombo forest ecosystem in central Angola (i) to evaluate the trade-off between food and timber resulting from forest to agriculture conversion [63], (ii) to assess spatio-temporal changes of smallholder cultivation patterns [64], and (iii) to detect forest areas that are being degraded [50]. Landsat ARD were used to support illuminating the discrepancy between deforestation and its social perception in Zambia [65]. Landsat hARD products were used to map cropping practices on a national scale in Turkey [66]. FORCE has been used in a number of conference contributions, e.g., to characterize Mediterranean land degradation due to overgrazing [67], to highlight the benefit of topographically corrected ARD for improved land cover classification [68], or as an essential building block in prototypic operational forestry applications [69].