esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Satellite Characteristic Fundamentals
### Satellite Transponder Technologies  
The structures/appliances installed on the satellite that guarantee the consolidation and retransmission of the satellite signal are called transponders, as they transpose the reception channel (called uplink) with the transmission channel (called downlink), shifting the used frequencies. The device sends a signal in response to the received one. This system allows for transmitting a downlink channel, providing a wide range of services. There are two types of satellite transponders [14]:  
* Transparent, also known as Bent Pipe (BP): the signals that reach the satellite through the radio channels can be sent back to Earth without undergoing any changes. As shown in Figure 2a, the main component of this satellite system is represented by the frequency converter block connected in input to a low-noise amplifier (LNA) receiver, and in output to a high-power amplifier (HPA) element. The signal is received by a receiving antenna and forwarded by the transmitting one after being handled by the three above blocks without changing its characteristics [15]. The Carrier-to-Noise (C/N) ratio is the main link parameter, and it represents the difference in decibels (dB) between the acceptable carrier signal strength and the unwanted noise power at the receiver;
* Regenerative, also known as On-Board Processing (OBP): using digital signals, this type of transponder is able to receive information from the ground and to re-transmit only after having processed the signals. In particular, the signals sent by users (uplink) that arrive at the satellite with distortions or noise will undergo down-conversion, demodulation, de-multiplexing, and reconstruction processes before they are retransmitted to the ground terminal (downlink) after performing modulation, multiplexing, and up-conversion processes [14]. The main OBP satellite blocks are depicted in Figure 2b, and they contribute to improving the overall satellite performance. Generally, for this typology of satellite platforms, the bit error rate (BER) parameter is considered in terms of bit error probability.  
The satellite network is composed of a control station, called a network control center (NCC), that has the task of managing satellites, allowing the transmission. The users inside the coverage area can exploit satellite services available for both fixed and mobile users with or without the use of a gateway or a ground station that is able to manage aggregated traffic flows. Clearly, the mobile user terminals are small devices that are limited in power with a radio link that suffers from the variations in the space of the mobile-satellite user devices. User devices and gateways compose the ground seg  
Figure 2: (**a**) Bent pipe transponder satellite; (**b**) on-board processor transponder satellite.  
segment consists of satellite objects that orbit around the Earth and ground stations used for monitoring and controlling the satellites [14; 15].
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 3 The Principle of Dual-Wavelength Method and Theoretical Calculation of Fiber-Optic SPR Sensor
Therefore, the variation of the two defined output signals with the simultaneous changes in RI and temperature can be expressed as:  
Figure 6: SPR reflected signal shifts versus RI for the temperature at 290 K and 320 K.  
Figure 7: SPR reflected signal shifts versus temperature for RI equalling 1.333 RIU and 1.340 RIU. \[\left( \begin{array}{c}\Delta S(\lambda_{1})\\ \Delta S(\lambda_{2})\end{array} \right)=K\left( \begin{array}{c}\Delta n\\ \Delta T\end{array} \right) \tag{22}\]  
\[K=\left( \begin{array}{cc}k_{n1}&k_{T1}\\ k_{n2}&k_{T2}\end{array} \right) \tag{23}\]  
where \(\Delta S(\lambda_{1})\) and \(\Delta S(\lambda_{2})\) are the output changes of the two defined signals. \(\Delta n\) and \(\Delta T\) represent the RI variation and temperature change, respectively. \(K\) is the sensitivity matrix, taking into account the cross-sensitivity between the changes of RI and temperature. The sensitivity matrix \(K\) is composed of the linear fitting coefficients between the output signals versus the RI and temperature as shown in Figures 6 and 7. However, the changes of RI and temperature are invisible in an actual sensing case of measuring the resonance spectrum to detect the variations of RI and temperature. Therefore, by the inverse matrix of \(K\) and measuring the defined output signals at the dual wavelengths, the changes of RI and temperature can be calculated simultaneously as:  
\[\left( \begin{array}{c}\Delta n\\ \Delta T\end{array} \right)=K^{-1}\left( \begin{array}{c}\Delta S(\lambda_{1} )\\ \Delta S(\lambda_{2})\end{array} \right)=\left( \begin{array}{cc}0.0201&-0.03 76\\ -0.1519&-0.0651\end{array} \right)\left( \begin{array}{c}\Delta S(\lambda_{1} )\\ \Delta S(\lambda_{2})\end{array} \right) \tag{24}\]  
With the full use of the detected spectrum data, both the variations in RI and temperature can be obtained with the dual-wavelength method. Further, this technique can be applied with traditional single-channel fiber-optic SPR sensors and extended to a detection situation of both the transmission-based and the reflection-based fiber-optic SPR probes.
# Influence of Dielectric Plate Parameters on the Reflection Coefficient of a Planar Aperture Antenna
## 2 Analysis of the Parameters of the Dielectric Plate on the Antenna Aperture  
### The Thickness and Width \(\times\) Length of the Antenna Aperture Dielectric Plate  
Although a waveguide of a circular cross-section does not form a plane wave (TEM) but transverse electric (TE) wave or transverse magnetic (TM) wave reflection, the transmission and absorption of the EM wave when encountering the dielectric layer is identical. Namely, reflection at the first boundary and all transmitted multiple reflected waves created from the transmitted wave into the dielectric planar layer; these multiple reflected waves return to the waveguide and, at the same time, constructively or destructively interfere with the existing reflected wave at the first boundary result in a reflection coefficient.
## 4 Discussion
### Uncertainty and Limitation  
This research investigated the spatiotemporal dynamics of vegetation CUE and WUE in the NMR and analyzed how these efficiencies are affected by climate change. The findings offer important perspectives on the processes governing ecosystem carbon and water dynamics. However, several uncertainties and limitations should be acknowledged. Firstly, although CUE and WUE were estimated using remote sensing products, the NPP, GPP, and ET datasets were derived from worldwide datasets. The lack of flux tower observations may impact the accuracy of the results. However, as flux tower networks in the NMR are developed and expanded, this limitation is expected to be mitigated. The uncertainty associated with WUE and CUE calculations based on MODIS data is generally within an acceptable range. [PERSON] et al. [66] validated the MODIS-derived WUE results using flux tower data from 32 sites, reporting R\({}^{2}\) values ranging from 0.74 to 0.963 across different vegetation types. [PERSON] et al. [67] validated MODIS-derived CUE estimates using eddy covariance flux tower data (Fluxnet), demonstrating a strong correlation between MODIS estimates and tower-based measurements across different vegetation types, with R\({}^{2}\) values ranging from 0.68 to 0.91. Additionally, this study primarily focused on precipitation and temperature as the primary climatic variables influencing CUE and WUE. However, other potential factors, such as human activities (e.g., land use changes) [64], increased CO\({}_{2}\) concentration [6], and soil physicochemical properties (e.g., soil nutrient availability, pH, and moisture content) [68], can also affect vegetation CUE and WUE. The transition from high vegetation cover to low vegetation cover can lead to a relative decline in WUE. This shift may influence vegetation WUE by altering soil water retention capacity and vegetation transpiration [69]. Elevated CO\({}_{2}\) concentrations typically enhance vegetation WUE, as plants can utilize water more efficiently in high-CO\({}_{2}\) environments while reducing stomatal opening to minimize water loss [70]. Increased soil nutrients can enhance vegetation photosynthetic efficiency, promoting an improvement in CUE, while changes in soil moisture content directly influence plants' water uptake capacity and subsequently affect WUE [71]. To further enhance the accuracy of identifying the principal drivers of CUE and WUE, it is crucial to integrate flux tower observational data with remote sensing information. This comprehensive method would enable a clearer insight into the regional and temporal variations in vegetation CUE and WUE, particularly in more localized regions.
## 4 Results & Discussion
### Cross-Corpora Experiments  
Since ItalianPVS has a lower sampling rate in its audios (16 kHz), we downsampled the Neurovoz audios to match this frequency. Figure 12 shows the results of these experiments when the models trained with ItalianPVS and Neurovoz are tested with the other corpus. The model obtained with Neurovoz and evaluated with ItalianPVS led to higher accuracy, reaching up to 83.8% for the vowels i: and ii:. These results are not observed when the model is trained with ItalianPVS and evaluated with Neurovoz, where the accuracy never goes beyond 55.9%. One possible factor influencing these results is that Neurovoz contains a larger amount of recordings per vowel than ItalianPVS (176 vs. 99), leading to a more robust model. The other reason, aligned with the results obtained in the baseline trials, is that the voices of PwPD of ItalianPVS are more clearly affected by the disease.In other words, the dysphonia of these PwPD is more evident and probably easier to detect with the models trained with Neurovoz.  
Figure 11: Effect of onset/offset removal on classification accuracy. In most experiments, accuracy decreases with the removal of the beginning and ends of the recordings.
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 3 Results and Discussion  
This study implemented an R-CNN model using the Keras deep learning framework with a Tensorflow backend. The feature extractor part of the model extracted feature maps, and these features were scanned for region proposals with anchors at different levels with different scales of \(8\times 8\), \(16\times 16\), \(32\times 32\), \(64\times 64\), and \(128\times 128\) to make sure that the model could detect all the sizes of polyps with aspect ratios of 1:2, 2:1, and 1:1. The final region proposals contained positive and negative anchors. Each of these proposals were then processed to generate masks.  
The masks were evaluated based on metrics as proposed by the MICCAI sub-challenge in order to make comparisons against other detection techniques. The location of the polyp marker in an image was considered as the baseline for polyp detection. A polyp marker that was inside the ground truth was considered as a true positive (TP). When there were multiple detections inside the same ground truth object, it was considered as one true positive. False positives (FP) were assigned to the detected polyp markers that fell outside the given ground truth. Every polyp misdetection was counted as a false negative (FN). There were no true negatives (TN), since there were no images with a complete absence of polyps. The metrics were calculated as follows:  
\[P=\frac{TP}{TP+FP} \tag{1}\]  
\[R=\frac{TP}{TP+FN} \tag{2}\]  
\[F1=\frac{2*P*R}{P+R} \tag{3}\]  
\[F2=\frac{5*P*R}{4*P+R} \tag{4}\]  
where \(P\) denotes precision, is recall, \(F1\) represents the F1 score, and \(F2\) represents the F2 score.  
The detection points for TP, FP, and FN were marked based on the probability of each pixel in the final mask. A binary mask was created by thresholding the probability mask with the highest  
Figure 4: Loss curves (loss vs. epochs) during the (**a**) training phase and (**b**) validation phase.  
probability value within that mask. The centroid of the region formed was considered the detection point. The pixel-wise probability masks were analyzed and converted to heat maps to locate the best pixel position that represented the presence of the polyp in the image. This also helped to better understand the occurrence of false positives.
## 3 Weighted Local-World Evolution Model
### Edge-weight evolution mechanism
#### 3.2.3 Evolution Model Implementation Process  
Combining the inseparability of topology structure and evolution, we believe that the topology model of SIN is the initial moment of its dynamic evolution model, that is, the model with \(t=t_{0}=0\). Therefore, based on the previous research contents, the implementation process of the weighted local-world dynamic evolution model of SIN is formulated, and the specific content is as follows.  
* **Step 1:** At the initial time \(t_{0}=0\), the initial topology model of SIN is generated, the initial time is a fully-coupled network with \(n\) nodes and \(e_{0}\) edges, and each edge is given an initial value \(w_{0}\). The hierarchy is denoted by the symbol \(H(H\in[1,4])\).
* **Step 2:** At the time \(t=t_{0}+\Delta t\), randomly select \(M(M<n)\) nodes as a local-world from the generated SIN, and proceed with the following operation with a certain probability. Assume that the time \(t\) obeys the exponential distributions of the parameter of \(\beta\), and when the evolution time \(t=t_{n}\), the evolution is complete. 1. Adding a new node \(v_{i}\) to the local-world with a probability of \(p_{1}\), and this node establishes a connection with the existing \(m\) nodes in the local-world. The connecting nodes are preferably taken in accordance with the probabilistic Equation (8). The dynamic evolution of edge weights is the same as that of rule 4. 2. Adding \(m_{1}\) as a new edge to the local-world with the probability of \(p_{2}\), and in the local-world, one node is randomly selected as one end of the edge, and the other end is selected in the local-world by the content of rule 5. 3. Increasing the number of new edges \(m_{2}\) inside the local-world and outside the local-world with a probability of \(p_{3}\), and increasing the number of connections inside and outside the local-world. In the local-world, the node of the network is selected as the end of the edge by Equation (9), and the other end is selected outside the local domain by Equation (10). 4. Deleting \(m_{3}\) links with a probability of \(p_{4}\), randomly selecting one point as the edge of the edge in the local-world, and selecting the other using Equation (11) with rule 5 in the local-world. Among them, \(p_{1}+p_{2}+p_{3}+p_{4}=1\), and \(p_{1},\ p_{2},\ p_{3},\ p_{4}>0\).
* **Step 3:** Repeating Step 2 until \(t=t_{n}\), ending the evolution.
## 2 Background
### Multi-Agent Systems
This is particularly useful in scenarios involving unmanned aerial vehicles (UAVs) and autonomous vehicle platoons, where maintaining precise formations is crucial for efficiency and safety. In addition to robotics, MASs are employed in fields such as distributed sensing, where multiple sensors work together to monitor and collect data from large areas. This approach enhances the coverage and accuracy of the sensing system, making it suitable for applications like environmental monitoring and surveillance. Despite their advantages, multi-agent systems also face several challenges. One of the primary challenges is ensuring effective communication and coordination among agents, especially in dynamic and unpredictable environments. Developing robust algorithms that can handle these complexities is an ongoing area of research. Another challenge is the scalability of MASs, as the number of agents is incremented, the system's complexity grows exponentially. Researchers are exploring techniques such as hierarchical organization and distributed control to address scalability issues [12]. Looking ahead, the integration of deep reinforcement learning (DRL) with multi-agent systems holds great promise. DRL enables agents to learn and adapt their behavior through interactions with their surroundings, making it a powerful tool for enhancing the capabilities of MASs. Future research will likely focus on developing more efficient and scalable DRL algorithms for multi-agent systems, paving the way for advanced applications in areas such as autonomous transportation, smart grids, and collaborative robotics [37].
## 2 Materials and Methods
### Evaluation of Landslide Influencing Factors  
The accuracy of the model output strongly depends on the quality of influencing factors, which require prior knowledge of the terrain. Adding a large number of influencing factors into the model does not always ensure the reliability and accuracy of the modeled results and can introduce abnormalities in modeling processes. Therefore, it is necessary to evaluate the incorporated factors before using them for susceptibility modeling. For this purpose, in this study, all 12 selected influencing factors were scanned through the two tests, i.e., information gain ratio (IGR) [52] and multicollinearity [53]. The IGR test, as provided in Equation (1), is a well-known feature selection method in ML, which is used to assess the contribution of factors, whereas the multicollinearity test was performed to evaluate the interassociation (the linear relationship between two or more factors) [54].  
\[\mathrm{IGR}(\mathrm{E}_{\mathrm{xA}})=\frac{\mathrm{IG}}{\mathrm{IV}} \tag{1}\]  
where information gain is IG and the intrinsic information is IV. Higher values of IGR represent the higher contribution of a particular factor for modeling and vice versa. For the multicollinearity test, variance inflation factor (VIF) and tolerance (TOL) were calculated using Equation (2).  
\[\mathrm{VIF}_{\mathrm{i}}=\frac{1}{1-\mathrm{R}_{\mathrm{i}}^{2}}=\frac{1}{ \mathrm{TOL}}\text{ for }\mathrm{i}=1,2,\ldots,\mathrm{k} \tag{2}\]  
where \(\mathrm{R}_{\mathrm{i}}^{2}\) is the coefficient of determination of the explanatory variables. A VIF value greater than 10 or a TOL value of less than 0.1 is considered to have a serious multicollinearity problem [55]. Figure 6 shows the obtained IGR, TOL, and VIF values of each influencing factor and it is found that none of the factors exhibit a collinearity problem. To calculate VIF and TOL, we used 'car' and 'VIF' packages, whereas for the IGR test, 'PSelector' package was used.
# Study and Design of Distributed Badminton Agility Training and Test System
## 4 System Communication Design
### Wireless Communication Design  
The core function of the Android program design is to wirelessly connect and communicate [29,30], because only wireless communication within the local area network is required, which is all based on the design of JAVA Socket technology to create and implement wireless connectivity and communication based on the TCP/IP protocol [31]. The upper computer in this design completes the function of creating a TCP server and opens a listener waiting for the client to connect. To facilitate the management of individual clients, it is also necessary to create a SocketList to store the corresponding Socket channels of each client. The Android application needs to display the IP address of the local machine on the LAN, so that the client can connect to the specified IP address and port on the server. The system design uses the WiFiManager class to obtain the hardware WIFI information and then the IP address of the local machine. The wireless communication flow chart is shown in Figure 4.  
In order to obtain the local IP address, the system creates a WiFi Manager object to obtain the information of the WiFi hardware and passes this information to the WifiInfo object, and then receives the IP address from the get Ip Address object of the WifiInfo  
Figure 4: Wireless communication flow chart.  
object. A service thread is created in the above thread for each socket channel to manage the communication. The run method of the service sub-thread is the function that handles the socket channel, including determining and acting on incoming messages and sending the specified messages. In the message handling function, the host forces the message sent by the sub-thread, i.e., the object, into a String type and assigns it to a String object, content, which is finally updated on the TextView component.  
In the creation of the TCP Server, the upper computer creates a thread and opens it. After creating the server socket, the program enters a loop and waits for a connection from the client socket. Once a client connection is made, the program adds the connected channel socket to the list queue and creates a separate thread for this socket channel to handle the communication. The flow chart for creating the TCP server sub-thread is shown in Figure 5.  
The main thread in this design is responsible for the UI response. If the network access is performed in the main thread, a forced shutdown will occur if the duration exceeds 5 s. The Handler is defined in the main thread but used in the sub-thread. In this design, the Handler is defined in the main thread and the HandlerMessage function receives messages from the sub-threads to perform the appropriate actions, including updating the UI.
## Abbreviations  
\begin{tabular}{l l} QC-LDPC & Quasi-Cyclic Low-Density Parity-Check. \\ NR & New-Radio. \\ MS & Min-Sum. \\ eMBB & enhanced Mobile BroadBand. \\ SOLL & Sub-Optimal Low-Latency. \\ URLLC & Ultra-Reliable Low-Latency Communication. \\ BER & Bit Error Rate \\ HDL & Hardware Description Language. \\ LUTs & Look-Up Tables. \\ FPGA & Field-Programmable Gate Array. \\ SNR & Signal-to-Noise Ratio. \\ E\({}_{\text{b}}\)/N\({}_{0}\) & Bit Energy-to-Noise Power. \\ FEC & Forward Error Correction. \\ CRC & Cyclic Redundancy Check. \\ IAMS & Improved Adapted Min-Sum. \\ BG & Base Graph. \\ PCM & Parity Check Matrix. \\ MSU & Min-Sum Unit. \\ Shamt & Shift-amount. \\ LLR & Log-Likelihood Ratio. \\ CN & Check Node. \\ OCN & Old Check Node \\ NCN & New Check Node. \\ CPNU & Check Node Processing Unit. \\ VN & Variable Node. \\ MB & Min Block. \\ MMB & Min-Max Block. \\ DLL & Dynamic-Link Library. \\ \end{tabular}