esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CubeSats and Inter-Satellite Links (ISLs)
### Future Constellations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Company** & **Origin** & **Constellation Type** & **Objective** & **Achievements** \\ \hline Amazon (Kuiper Systems) & USA & LEO & Provides broadband internet services with low latency & Plans to launch 3236 LEO & satellites as part of the Kuiper Systems project. \\  & & & Provides broadband internet services with low latency & Intends to employ laser communication technology for enhanced connectivity. \\ \hline Facebook (Athena) & USA & E-Band (71–86 GHz) & \begin{tabular}{c} Contacts a satellite \\ communication experiment \\ focused on high-speed data. \\ \end{tabular} & \begin{tabular}{c} Filed an application for \\ E-band frequency usage with \\ the Federal Communications \\ Commission (FCC) and aims \\ to deliver data 10 times faster \\ than Space-X’s Starlink \\ internet satellites using laser \\ communications. \\ \end{tabular} \\ \hline Laser Light Communications & USA & MEO & \begin{tabular}{c} Create sa 12-MEO satellite \\ constellation with high \\ capacity and data rates. \\ \end{tabular} & \begin{tabular}{c} Collaborated with Australian \\ telecommunications \\ company Optus to prepare \\ satellites and ground stations. \\ Aims to establish a global \\ network connecting \\ terrestrial fiber systems with \\ satellite systems. \\ \end{tabular} \\ \hline ILLUMA-T & USA & LEO & \begin{tabular}{c} Laser Communication \\ Terminals for use in the ISS \\ and Orion manned space \\ vehicle utilizing the LCRD \\ data relay with a data rate of \\ 1.2 Gbps \\ \end{tabular} & \begin{tabular}{c} Plans to distribute 50 optical \\ ground stations with \\ software-defined modems \\ worldwide to provide laser \\ communication services, \\ satellite and unmanned \\ aerial vehicles. Also invested \\ in governmental and 5G \\ network communication \\ needs. \\ \end{tabular} \\ \hline Kaskilo (KLEO Connect \\ GmbH) & Germany & LEO & \begin{tabular}{c} Build a 288-satellite LEO \\ constellation primarily for \\ constellation with laser \\ communication services. \\ \end{tabular} & \begin{tabular}{c} Will utilize laser \\ communication for \\ IoT services under \\ Germany’s Industry 4.0. \\ \end{tabular} & \
\begin{tabular}{c} Will utilize laser \\ communication for \\ inter-satellite links to support \\ IoT connectivity. \\ \end{tabular} \\ \hline Huawei (Massive VLEO) & China & LEO &
\begin{tabular}{c} Plants to use low satellite \\ altitudes of 300 km for \\ ultra-reliable, low-latency \\ communications, covering \\ massive machine-type \\ communications. Broadband \\ achieved using terahertz and \\ laser communications. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: (Known) planned future constellations of small satellites in mesh networks.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 4 Uplink Transmission  
### Uplink Training and Channel Estimation  
The uplink training methodology employed in this study follows the approach detailed in [7], wherein each User Equipment (UE) simultaneously transmits its pilot sequence to the Access Points (APs) once per coherence block. It is assumed that there are \(\tau_{p}\leq K\) mutually orthogonal pilot signals, each of length \(\tau_{p}\), allocated to the UEs. The pilot sequence \(\phi_{i_{k}}\in\mathbb{C}^{\tau_{p}\times 1}\) is transmitted by UE \(k\), where \(i_{k}\) denotes the index of the pilot used by UE \(k\)The set \(P_{k}\subseteq\{1,\ldots,K\}\) includes all UEs, including UE \(k\), that utilize the same pilot as UE \(k\), ensuring orthogonality among all pilot sequences.  
The received signal \(Z_{l}\in\mathbb{C}^{N\times\tau_{p}}\) at AP \(l\) is expressed as  
\[Z_{l}=\sum_{k=1}^{K}\sqrt{p_{k}}h_{k}\phi_{i_{k}}^{H}+N_{l}, \tag{61}\]  
where \(N_{l}\in\mathbb{C}^{N\times\tau_{p}}\) is a Gaussian noise matrix with i.i.d. \(\mathcal{NC}(0,\sigma^{2})\) elements, and \(p_{k}\) represents the transmit power of UE \(k\) for uplink training. The MMSE estimate of \(h_{kl}\) given \(Z_{l}\) is provided in Equation (62) [2]:  
\[\hat{h}_{kl}\triangleq\frac{c_{kl}}{\sqrt{\tau_{p}}}Z_{l}\phi_{i_{k}}, \tag{62}\]  
where \(c_{kl}\) is defined as  
\[c_{kl}\triangleq\frac{p_{k}\tau_{p}\beta_{kl}}{\tau_{p}\sum_{l\in P_{k}}p_{l} \beta_{kl}+\sigma^{2}}. \tag{63}\]  
It can be verified that the estimate \(\hat{h}_{kl}\) and the estimation error \(\hat{h}_{kl}=h_{kl}-\hat{h}_{kl}\) are independent Gaussian vectors with distributions \(\hat{h}_{kl}\sim\mathcal{NC}(0,\gamma_{kl}I_{N})\) and \(\hat{h}_{kl}\sim\mathcal{NC}(0,(\beta_{kl}-\gamma_{kl})I_{N})\), respectively, where  
\[\gamma_{kl}\triangleq\mathbb{E}\bigg{[}\Big{\|}\hat{h}_{kl}\Big{\|}^{2}\bigg{]} =\frac{p_{k}\tau_{p}\beta_{kl}^{2}}{\tau_{p}\sum_{l\in P_{k}}p_{l}\hat{p}_{kl} +\sigma^{2}}. \tag{64}\]  
The channel estimate \(\hat{H}_{l}=Z_{l}\Phi\in\mathbb{C}^{N\times\tau_{p}}\) is utilized, where \(\Phi=\left[\phi_{l},\ldots,\phi_{\tau_{p}}\right]\in\mathbb{C}^{\tau_{p}\times \tau_{p}}\) as defined in Equation (38). By applying DMD, \(\hat{H}_{l}\) is processed to estimate \(\hat{h}_{kl}\) as  
\[\hat{h}_{kl}=c_{kl}\hat{H}_{l}e_{i_{k}}, \tag{65}\]  
where \(e_{i_{k}}\) denotes the \(i_{k}\)-th column of the identity matrix \(I_{\tau_{p}}\).  
The second layer of centralized decoding involves processing the local estimates \(\{\hat{s}_{kl}:l=1,\ldots,L\}\) along with LSFD coefficients \(\{a_{kl}:l=1,\ldots,L\}\) to compute \(\hat{s}_{k}=\sum_{l=1}^{L}a_{kl}^{*}\hat{s}_{kl}\). In this study, DMD is employed to enhance efficiency by capturing and utilizing the reduced eigenstructures of consecutive pilot snapshots.
## 5 Correlation Analysis
### Correlation of Travel Time and Disturbance Factors
When observing the indicators showing travel time variability, for early vehicles, there are more significant correlations for \(v_{50/10}\) compared to \(v_{10/T}\), and for late vehicles, \(v_{90/T}\) shows more significant correlations than \(v_{90/50}\). As previously suggested, the closeness of \(v_{10/T}\) to minimum travel time makes it less suitable for representing early vehicles. To ensure the validity of predictors by checking whether the predictors are intercorrelated, all the predictors were subject to intercorrelation analysis to help confirm our suspicions of collinearity. The results confirmed that the elimination of the predictors was sound. For the remaining significant predictors, the results of the intercorrelation analysis are shown in Table 6, such that the acceptable tolerance is above 30%, and variance inflation factor above 3 shows mild collinearities, and above 5, the collinearities are strong. According to the table, there is only a possibility for mild collinearities to show among \(t\), PRIJ and \(t\), TRAM. Therefore, the chosen predictors are not intercorrelated, and this confirms the hypothesis H1: \"It is possible to correlate travel time discrepancy and variability with disturbance factors\".
# Application of Iron-Modified Activated Carbon for Phosphate Removal in Aqueous Systems
## 3 Results and Discussion
### Sorption Isotherm of Iron-Functionalized Activated Carbon  
To investigate the phosphate adsorption characteristics of SMAC, we conducted batch adsorption experiments using various modification concentrations, and the results are presented in Table 1 and Figure 2. While the adsorption pattern resembles the Langmuir isotherm, the Freundlich isotherm provides a better mathematical representation. The phosphate removal was 0.64 mg/g for AC and 1.29 mg/g for SMAC, indicating over twice the adsorption capacity for SMAC. This outcome is due to an increased binding of iron on the AC surface. Under neutral pH, the AC surface exhibits a negative zeta potential, which restricts its capacity to effectively adsorb anionic and inorganic substances [36]. However, when iron oxide particles bind to the AC surface, they create new functional groups that enhance bonding with anionic substances [37], resulting in a positively charged surface. In this study, it is assumed that anionic phosphate is adsorbed and removed through binding with iron, which acts as a cationic functional group. When the ferric chloride modification concentration increases beyond 0.2 M, the modification efficiency decreases, showing no significant increase in phosphate removal. SMAC sorption capacity of phosphate is shown by its adsorption coefficient. The results of the calculated Freundlich constants are summarized in Table 2. \"K\" is related to adsorption capacity (L/mg), and a large value indicates better adsorption ability. Meanwhile, \"n\" is associated with adsorption intensity, and a higher value of n signifies stronger adsorption strength [38]. The value of K shows a continuous increase with rising modification concentrations. Likewise, n rises to 1.90 at a concentration of 0.2 M but declines to approximately  
Figure 1: (**a**) Sediment Measurement Network Site 1 in Chudong water area, Daecheong Lake. A red marking is sampling point. (**b**) Particle size distribution of the collected soil samples obtained through dry sieve analysis. 1.85 at higher modification levels. Although AC exhibits good adsorption capacity for phosphate, its adsorption strength is weak. In SMAC modified at low concentrations, the number of available sites for phosphate adsorption decreases, which reduces adsorption capacity. However, the adsorption strength appears to increase due to the influence of cationic metals. When the modification concentration surpasses a certain level, the increase in adsorption sites causes the K value to continue rising. However, this increase becomes slower as the limited surface of the activated carbon reaches saturation with iron. Simultaneously, the adsorption capacity for phosphate declines and stabilizes at a consistent level.
## 4 Results  
In this section, GSRPnet was compared with other five segmentation models, including U-net [27], PSPnet [28], Segnet [29], DeeepLab V3+ [30], and Separable Graph Convolutional Network (SGCN-Net) [31]. A comparison of these models is listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loss Type** & **Pre \(\uparrow\)** & **IoU \(\uparrow\)** & **OA \(\uparrow\)** & **Best Epoch \(\downarrow\)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Best Epoch \(\downarrow\)** \\ \hline Cross entropy & 92.845 & 86.178 & 94.113 & 160 \\ Binary cross entropy & 93.408 & 86.145 & 94.145 & 154 \\
2 \(\times\) Focal + Dice & **93.665** & **86.702** & **94.393** & **136** \\ \hline \hline \end{tabular} The \(\uparrow\) and \(\downarrow\) indicate the larger the better and the smaller the better, respectively.  
\end{table}
Table 2: The UGS semantic segmentation model obtained the best accuracy and epoch on the validation set with different loss functions. The best and second-best results are highlighted in bold and underlined, respectively.  
Figure 10: Ablation study for different loss functions on the GS2018 validation dataset.
# Decimal Multiplication in FPGA with a Novel Decimal Adder/Subtractor
## 4 Decimal Multiplier  
Considering two operands, A and B, with \(n\) decimal digits (\(a_{i}\)) and (\(b_{i}\)), respectively, given by  
\[A=a_{n-1}a_{n-2}\ldots a_{0}=\sum_{i=0}^{n-1}a_{i}\times 10^{i} \tag{12}\]  
\[B=b_{n-1}b_{n-2}\ldots b_{0}=\sum_{i=0}^{n-1}b_{i}\times 10^{i} \tag{13}\]  
The product of A \(\times\) B is a number with \(2n\) decimal digits (\(p_{i}\)) given by \(\sum_{i=0}^{2n-1}p_{i}\times 10^{i}\). A decimal digit, \(x_{i}\), is coded with four bits according to expression  
\[\sum_{j=0}^{3}x_{i}[j]\times w_{i}[j] \tag{14}\]  
where \(x_{i}[j]\) is the bit of \(x_{i}\) at position \(j\) and \(w_{i}[j]\) is the weight of bit \(x_{i}[j]\) determined by the codification.  
The most common codification of a decimal digit is BCD (Binary-Coded Decimal) that adopts the weights of a pure binary representation (8421). However, several other representations have been considered in the literature, like (5421), (4221), (5221), (4311) and (3321). A representation is chosen as the one that optimizes a particular arithmetic algorithm. As observed in Section 3.3, we are particularly interested in the excess-3 code (see Table 2).  
In this paper, the design of the multipliers follows the algorithm of most previous approaches. Partial products are first generated and then reduced with a tree of decimal adders. Each partial product results from the multiplication of a multiplier digit by the multiplicand. The partial product is obtained by the addition or subtraction of two multiples of the multiplicand. This paper considers two different sets of multiples for the partial product generation that leads to different tradeoffs between delay and area. In the following, both methods for partial product generation are described.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Digit** & **BCD** & **Excess-3** \\ \hline
0 & 0000 & 0011 \\
1 & 0001 & 0100 \\
2 & 0010 & 0101 \\
3 & 0011 & 0110 \\
4 & 0100 & 0111 \\
5 & 0101 & 1000 \\
6 & 0110 & 1001 \\
7 & 0111 & 1010 \\
8 & 1000 & 1011 \\
9 & 1001 & 1100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Representation of a decimal digit with excess-3.
# Optimal Base Station Density of Dense Network:
## 4 Throughput of MAC Layer in SmBS Tier  
Since SmBSs are working in LBT mode, the factors that influence the network performance are not only link rate but also the load of an SmBS. Aggressively offloading data to the SmBS tier is not reasonable since heavy load incurs frequent collision and backoff, and degrades the utilization of the channel. Thus, it is necessary to work out load-aware offloading strategies to make full use of the channel and maximize the throughput.
## 5 Results
### Inspection of Certain Predictions
For instance, a high specificity system ensures that only those with a true likelihood of relapse are assigned aggressive therapeutic strategies, reducing the risk of overtreatment. The MCC further contextualizes these trade-offs by offering a more holistic measure of classifier quality, incorporating all four confusion matrix components (true positives, false positives, true negatives, and false negatives). The MCC scores indicate that while Isotonic Regression and Beta provide strong recall-driven certainty, Spline calibration optimizes overall balance. A high MCC ensures that the classifier is not disproportionately favoring one class over the other, which is critical in clinical settings where both false positives are likely to be false positives. Figure 17: Friedman-Nemenyi significance test results regarding Precision for certain data instances of Split conformal prediction and calibration methods on the CatBoost model across 200 bootstrapping iterations. positives and false negatives can be costly. For example, underdiagnosing a condition like post-surgical infection (false negative) can result in complications, whereas overdiagnosing it (false positive) leads to unnecessary antibiotic usage and increased resistance concerns. Figure 19: Friedman-Nemenyi significance test results regarding MCC for certain data instances of Split conformal prediction and calibration methods on the CatBoost model across 200 bootstrapping iterations. Figure 20: Friedman-Nemenyi significance test results regarding Minority Class Ratio of Split conformal prediction and calibration methods on the CatBoost model across 200 bootstrapping iterations. Figure 18: Friedman-Nemenyi significance test results regarding Specificity for certain data instances of Split conformal prediction and calibration methods on the CatBoost model across 200 bootstrapping iterations. Figure 20 (Minority Class Ratio) sheds light on another important consideration--the proportion of high-confidence positive predictions assigned to the minority class. A well-calibrated system should maintain a balance where underrepresented but clinically critical cases are neither overlooked nor overly emphasized. Isotonic Regression assigns approximately 5.6% of confidently labeled instances to positive cases, while Spline's 5.0% indicates a slightly more restrictive but stable classification boundary. Overall, the results underscore the necessity of calibration-aware decision-making in predictive clinical modeling. The choice of calibration method should align with the clinical setting's tolerance for false negatives versus false positives. If the primary objective is to ensure that no high-risk patients are overlooked, Isotonic Regression may be preferable. If the aim is to maintain high precision and reduce overdiagnosis, Spline or Platt Scaling should be considered. Given the variations observed, hybrid strategies--such as dynamically adjusting calibration approaches based on incoming patient profiles or using ensemble calibration techniques--may offer additional improvements in real-world deployments.
## 3 System Descriptions and Assumptions
### DESCO Task Processing Model  
Algorithm 3 outlines the workflow of the mechanism. Initially, the server gathers individual user requests and searches its local cache pool \(\mathbb{R}\) for corresponding application data \(\omega_{k}\). If the data are not already stored, the server then executes Algorithm 1 to locate the server node managing that application within the Cache Chord network by retrieving the cache IP list. If \(\text{ServerIP}_{Y}^{t}\
eq 0\), the IP of the node storing the application is returned to server \(d\). Subsequently, the server derives the RCO strategy \(\text{V}^{d,t}\) based on the local cache status and Cache Chord information. In the final phase, Algorithm 2 updates the resource index within the Cache Chord network. The output is the user layer energy consumption at that moment, which comprises computational and communication energy costs.  
```
1:Initialize MEC server cluster \(\Delta\), Cache Chord \(\mathbb{C}\), MEC server local cache buffer \(\mathbb{R}\)
2:for each server \(d\) in cluster \(\Delta\)do
3: server \(d\) gathering user request set \(U_{\text{N}_{d}}^{t}\)
4:for each user request \(\mu_{n}^{t}\)do
5: search corresponding application data \(\omega_{k}\) in cache buffer \(\mathbb{R}\)
6:if\(p_{k}^{d,t}=0\)then
7: play Algorithm 1 in Cache Chord \(\mathbb{C}\) to search application data of task \(k\)
8: server \(d\) compute offloading strategy \(\text{V}^{d,t}\) based on application caching state and Cache Chord by playing RCO decision
9:endif
10:for each offloading vector \(v_{n}^{d,t}\)do
11:if\(v_{n}^{d,t}\
eq 0\)and\(\text{ServerIP}_{k}\lor p_{k}^{d,t}=0\)then
12: user transmit task data \(\omega_{k}\) and task data \(\theta_{k}\) to server \(d\), execute task on MEC server
13:elseif\(v_{n}^{d,t}\
eq 0\)and\(\text{ServerIP}_{k}\lor p_{k}^{d,t}=0\)then
14: user transmit task data \(\theta_{k}\) to server \(d\), execute task on MEC server
15:else
16: local compute
17:endif
18: user \(n\) generated energy consumption \(\text{Y}_{n,d,k,t}\)
19:endfor
20:endfor
21: update application state in Cache Chord through Algorithm 2
22:endfor
23:return user layer energy consumption \(\sum\limits_{d\in\Delta}\sum\limits_{n\in\text{N}_{d},k\in U_{\text{N}_{d}}^ {t}}\text{Y}_{n,d,k,t}\)
```  
**Algorithm 3** DESCO Network Overall Mechanism
# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 3 Proposed Method
### CNN Test and Evaluation Metrics
#### 3.5.2 Evaluation Metrics  
By training the Train and Validation dataset, and testing the trained CNN models on the Test dataset, the confusion matrices were obtained. Based on [32], from these confusion  
Figure 7: Confusion matrix in CNN Test. (**a**) Confusion matrix in InceptionV3 testing. (**b**) Confusion matrix in VGG16 testing. (**c**) Confusion matrix in ResNet50V2 testing.  
matrices the evaluation metrics used are: Precision (Equation (2)), Recall (Equation (3)), F1-score (Equation (4)) and Accuracy (Equation (5)), that define the system behavior.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1-score=\frac{2*precision*recall}{precision+recall} \tag{4}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
where, TP as true positive, FP as false positive, TN as true negative and FN as false negative.
## 2 Bridge Structure  
This study investigates the implementation of the SACP system on a concrete structure, specifically a coastal bridge in South Korea, as shown in Figure 1. The bridge, stretching 331.24 m and featuring 60 piers across 10 spans, was completed in 1992 and has been in operation for approximately 30 years. The bridge spans from east to west. Piers with a 1.2 m diameter and heights varying from 1 to 6 m above the ground experience a tidal variation of approximately 2 m. Daily exposure to water and drying cycles during ebb tides creates a corrosive environment. Initially, the SACP system was applied to all 60 piers, and the performance was poor in many of the piers within a short period of time owing to the low humidity and water level. Thus, the SACP system with activated carbon was applied to seven piers later to evaluate its effectiveness in performance enhancement.
## 2 Preliminaries  
### System Model  
Let \(V=\{P_{1},P_{2},...,P_{n}\}\) be a set of \(n\) processes and \(E\subseteq V\times V\) be a set of bidirectional communication links in a distributed system. Each communication link is FIFO. Then, the topology of the distributed system is represented as an undirected graph \(G=(V,E)\). By \(N_{i}\), we denote the set of neighboring processes of \(P_{i}\). That is, \(N_{i}=\{P_{j}\mid(P_{i},P_{j})\in E\}\). By \(\mathit{dist}(P_{i},P_{j})\), we denote the distance between processes \(P_{i}\) and \(P_{j}\). We assume that the distributed system is asynchronous, i.e., there is no global clock. A message is delivered eventually, but there is no upper bound on the delay time, and the running speed of a process may vary.  
A set of local variables defines the local state of a process. By \(Q_{i}\), we denote the local state of each process \(P_{i}\in V\). A tuple of the local state of each process \((Q_{1},Q_{2},...,Q_{n})\) forms a configuration of a distributed system.
## 1 Introduction
The description of the merit(s) of each protocol makes the selection of the right protocol by researchers, scientists and engineers convenient for the right application. The description of the demerit(s) of every protocol, on the other hand, leads to the foundation for the design of new and novel protocols free of the addressed demerit(s). The latency information helps the researcher to select novel protocols. * The addressed routing protocols are classified into two categories: localization-based and localization-free protocols. The former class of protocols requires the two- or three-dimensional position coordinates information of the sensor nodes to find routing trajectories. The latter class requires only the depth or pressure of the sensor nodes to identify routing paths. Each category of the protocols is further classified based on the problem(s) it addresses or the major parameter(s) it considers during routing. The parameters for subdivision include nodes' mobility, energy balancing, channel properties, energy consumption and void zone. This classification helps the researchers in the selection of the appropriate protocols for the desired applications. For instance, in applications where scalability is required, such as underwater exploration, a localization-free routing protocol is a better choice than a localization-based routing protocol. * Challenges associated with the underwater medium and during packet forwarding from the bottom to the surface of water are described. The operation of every protocol is linked with how these challenges are addressed and mitigated. * Open research challenges and future research directions are specified based on the described protocols, existing issues and challenges.