esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Design
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Clock** & **Clock Error Extraction Method** & **Syntonization** & **Autonomous** \\ \hline GPS Block IIR & Atomic clock \& & & \\ crosslink & VCXO & & Asynchronous two-way ranging & Yes & Yes \\ \hline RESSOX & VC-OCXO &
\begin{tabular}{c} Using GPS signals and the follow-up \\ processes on the ground station \\ \end{tabular} & Yes & No \\ \hline NAMURU V3.2 & VC-TCXO & Disciplining to GPS time & Yes & Yes \\ \hline GRACE & OCXO & Synchronous dual one-way ranging & No & No \\ \hline Proposed system & VC-OCXO & STDD \&FD dual one-way ranging & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of synchronization systems. Figure 3: Schematic of the proposed system.
###### Abstract  
To solve the conflict when multi-user packets are transmitted in a shared wireless link, a novel frameless slotted-ALOHA protocol is proposed. Signature codes are used to help the receiver identify the set of transmitting users, and successive interference cancellation technology is employed to recover conflicting packets. Thus, the information in the conflicting slot can be reused to reduce the number of retransmissions. Taking the number of backlogged users in each slot as a system state, a Markov chain model is established to analyze the protocol, in which the state transition probabilities are obtained based on the binomial distribution of packets sent in a slot. Under the maximum number of detectable conflicting users, the best value is taken, traffic balance equations are obtained, and the expressions of throughput, average number of backlogged users, average successful transmission probability and average memory size are derived. Finally, a numerical simulation is carried out to accurately analyze the influence of the first transmission probability of the packets on various performance indexes and the effectiveness of the theoretical analysis is further verified by the simulation results.  
signature code; successive interference cancellation (SIC); slotted-ALOHA; Markov chain +
Footnote †: journal:  
11 September 2022  
Accepted: 26 September 2022  
Published: 28 September 2022  
MCPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Results and Discussion
### Wrc  
The evolution of the moisture content with various ZP contents is shown in Figure 4. For the untreated samples, the initial moisture content was 25.6%. It decreased significantly with the increase in drying time from 0 h to 7 h; the moisture significantly decreased from 9% to 13%. After 17.5 h of drying, the moisture content of the compacted clay mixed with ZP of 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% were 4%, 7%, 8%, 9%, and 12%, respectively. The moisture of the soil treated with 1.0% ZP content was 193% higher as compared to the soil with 0.2% ZP. Studies have proven [37] that ZP has a large specific surface area with many hydrophilic groups. Due to the electrostatic attraction between the anions and the penetration disparity induced by the differing concentrations of cations inside and outside, PAM expands linearly to form a three-dimensional network structure [25; 26]. Thus, the polymer network of ZPhas a strong physical adsorption effect on water molecules. Accordingly, ZP can effectively improve the WRC of compacted clay and inhibit the evaporation of clay moisture during the drying process. After drying, the moisture content of the clay increased with increasing ZP content, and the modification was the most prominent when the ZP content was 1%.  
Figure 4: The moisture content of the compacted clay mixed with different PAM contents.  
Figure 3: Liquid limit of CCC mixed with ZP. **(a)** Influence of PAM content on the LL of the compacted clay; **(b)** effect of different materials mixed with clay on \(\rm LL/LL_{ck}\) of the compacted clay [58; 59].  
Figure 5 reflects the influence of ZP content on the water loss of the compacted clay after drying. After 17.5 h of drying at 100 \({}^{\circ}\)C, the water loss rates of the compacted clay mixed with different contents of ZP were 84%, 71%, 66%, 64%, and 54%, as the ZP content ranged from 0.2% to 1.0%. Therefore, increasing the content of modifier ZP can effectively reduce the water loss rate of clay and improve its WRC. Studies have indicated that the water loss of the clay mixed with various ordinary agricultural and forestry water-retaining agents varies from 55% to 63%, after they are dried for 360 min with a temperature of 60 \({}^{\circ}\)C [53]. However, when our research used 0.8% and 1.0% ZP for modification, the water loss rates of the clay after drying at 100 \({}^{\circ}\)C for 17.5 h were 64% and 54%. Furthermore, field monitoring results in the Changzhou contaminated site showed that [18] the highest atmospheric temperature was 45 \({}^{\circ}\)C, which was much lower than the test temperature of 100 \({}^{\circ}\)C in this paper. As a result, ZP plays a more prominent role in enhancing the WRC of CCC under low temperatures of 45 \({}^{\circ}\)C rather than 100 \({}^{\circ}\)C.  
Based on the preliminary test results, when the ZP content was than 1%, the clay with the same initial moisture content was too viscous to be blended homogeneously. Therefore, we did not test the soils with ZP content higher than 1.0%.
## 3 Results and Discussion
### Input/Output Data for Prediction of Penetration Condition
Moreover, in the burn-through group (class 3), it was measured as 265 A/31.0 V. For the same WFR, the average current remained consistent with variations in penetration; however, there were fluctuations in the average voltage. The length from the nozzle tip to the arc initiation point is defined as the wire extension (Figure 6a), which was then examined with respect to the form of penetration. It was observed that the wire extension varied based on the form of penetration: 12.0 mm for class 1, 14.5 mm for class 2, and 16.1 mm for class 3. With a constant wire diameter of 1.2 mm, the dynamic resistance during welding increased as the wire extension increased. In the case of a constant-current DC pulse, the extended wire extension increased the welding voltage. Variations in voltage are expected to induce \begin{table}
\begin{tabular}{c c c c c c c} \hline
**WFR (m/min)** & **7.0** & **8.0** & **9.0** & **10.0** & **11.0** & **12.0** \\ \hline Average (A/V) & 212/26.4 & 241/27.4 & 267/28.1 & 289/29.2 & 314/29.8 & 351/31.1 \\ Pulse cycle (ms) & 5.77 & 5.12 & 4.79 & 4.46 & 4.13 & 3.85 \\ \(I_{P}\) (A) & 466 & 465 & 464 & 465 & 463 & 466 \\ Current & \(I_{B}\) (A) & 36 & 38 & 43 & 47 & 55 & 65 \\ \(T_{IP}\) (ms) & 1.79 & 1.77 & 1.78 & 1.78 & 1.76 & 1.80 \\ \(T_{IB}\) (ms) & 2.14 & 1.67 & 1.09 & 0.92 & 0.36 & 0.12 \\ \(V_{P}\) (V) & 33.2 & 34.0 & 34.3 & 33.6 & 33.8 & 34.2 \\ Voltage & \(V_{P}\) (V) & 20.8 & 21.2 & 21.6 & 21.5 & 22.6 & 22.9 \\ \(T_{VP}\) (ms) & 1.60 & 1.62 & 1.58 & 1.61 & 1.62 & 1.57 \\ \(T_{VB}\) (ms) & 3.33 & 2.81 & 2.22 & 1.94 & 1.44 & 1.21 \\ \hline \end{tabular}
\end{table} 
Table 2: Welding current/voltage analysis results according to WFR changes. Figure 5: Schematic of analysis method with welding waveform. changes in the STFT of the dynamic resistance utilized as input data. In addition, anomalies in the abnormal voltage appeared in the base voltage owing to arc phenomena in the CJP and burn-through. These occurrences are also expected to reflect changes in the STFT of the dynamic resistance.
## 13 Materials Analysis  
The Role of Size and Hyperthermia Treatment on the Distribution of Magnetic Nanoparticles for Theranostic Applications  
**F. Pinakidou *, M. Katsikini, K. Simeonidis, E. C. Paloura and M. Angelakeris**  
School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece  
* Correspondence: fpina@physics.auth.gr  
The spatial distribution and nanostructure of magnetite magnetic nanoparticles (MNPs) implemented in hyperthermia applications is addressed by combining SR-based \(\upmu\)-XRF elemental mapping with XAFS measurements. Their excellent behaviour towards tumour destruction is attributed to their increased heat conversion efficiency, a property that correlates directly to their size and also the structural characteristics of iron. We investigated a series of Fe\({}_{3}\)O\({}_{4}\) MNPs with varying sizes (8 to 80 nm) prior and after hyperthermia treatment. As the 2D \(\upmu\)-XRF maps demonstrate, the smallest MNPs (8 nm) are spread homogeneously with no preference for segregation, while upon increasing the size of the MNPs, the tendency of Fe for extended clustering becomes more prominent. In contrast, between MNPs of the same size, hyperthermia does not have a prominent effect on their distribution; only when the diameter is equal to 40 nm does there exist a slight tendency for desegregation of the widely dispersed Fe-rich inclusions. These conclusions based on the \(\upmu\)-XRF 2D maps, can be quantitated by calculating the distribution of 15 \(\times\) 15 \(\upmu\)m\({}^{2}\) clusters of MNPs. According to the Fe-\(K\)-XAFS analysis results, the majority of Fe\({}^{2+}\) transform to Fe\({}^{3+}\) while no pronounced modification in site occupancy is detected.
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 4 Pairwise Error Probability Analysis
When the delay spread increases, the coherence bandwidth reduces and \(b_{c}\geq\Gamma\) can no longer be satisfied; however, \(b_{c}\geq\Gamma/2\) can still be satisfied. Thus, our proposed scheme exhibits improved BER performance compared to the DSFC scheme. When \(\tau_{rms}\) is higher than 3.2 \(\upmu\)s, the coherence bandwidth is very low such that coding in the frequency dimension introduces inter-carrier interference; thus, the BER performance of our proposed scheme and the DSFC scheme degrade significantly. In this condition, however, DSTC-OFDM outperforms the other schemes.
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 4 Performance Evaluation of the Proposed SET algorithm  
This section primarily aims to perform elaborate evaluations of the proposed SET algorithm to validate its performance compared to the DRX power-saving algorithm.
## 4 Experimental Validation and Analysis
### Experimental Validation
#### 4.1.3 AHP Determines the Weights of the Criterion Layer  
The qualitative AHP method is used to determine the weight of the criterion layer (transmitter performance (\(U_{1}\)), receiver performance (\(U_{2}\)), antenna performance (\(U_{3}\)), and communication performance (\(U_{4}\))). It reflects the performance of the target layer (target \(U\)). Table 5 shows the criterion level index.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental Sets** & **V1-PA-1** & **V1-PA-2** & **V1-PC** & **V2-PC** & **V2-PB** \\ \hline scheme & 1 & 2 & 3 & 4 & 5 \\ The maximum transmitting power \(u_{11}\) & 102 & 103 & 105 & 99 & 106 \\ The fundamental transmission & 670 & 750 & 720 & 580 & 780 \\ bandwidth \(u_{12}\) & 54 & 51 & 49 & 43 & 56 \\ The harmonic rejection ratio \(u_{13}\) & 47 & 53 & 58 & 49 & 57 \\ The intermodulation rejection ratio \(u_{14}\) & 47 & 53 & 58 & 49 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation indicator data of vehicular transmitter in each scheme.  
The weight vector \(\mathbf{W}\) in Equation (65) is derived by normalizing matrix \(\mathbf{C}\) using Equations (64), and (66) is the result for the consistency check of the index weight vector \(\mathbf{W}\).  
\[\mathbf{C}=\begin{bmatrix}1&2&3&2\\ 1/2&1&1/3&1\\ 1/3&3&1&2\\ 1/2&1&1/2&1\end{bmatrix} \tag{64}\]  
\[\mathbf{W}=[0.417,0.148,0.278,0.157]^{\mathrm{T}} \tag{65}\]  
\[\mathrm{CR}=0.085 \tag{66}\]  
Equation (66) shows that \(\mathrm{CR}<1\), confirming \(\mathbf{W}\) as the criterion layer weight vector \(\mathbf{A}\).  
\[\mathbf{A}=[0.417,0.148,0.278,0.157] \tag{67}\]
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantic-Guided Color Histogram (SCH) Loss
#### 4.3.2 Differentiable Color Histogram Estimation  
Traditional color histograms are discrete and cannot be directly used for gradient computation, making them difficult to use in end-to-end training of neural networks. Backpropagation relies on the chain rule to compute gradients, and the discrete nature of traditional histogram calculation makes it non-differentiable, preventing the gradient from propagating.  
To resolve this issue, this study borrows the concept of Kernel Density Estimation (KDE) and adopts a differentiable color histogram estimation method. This approach allows color histograms to be optimized end-to-end during training and seamlessly integrated into the neural network framework. Inspired by DeepHist [39], the method smooths the histogram calculation using a Gaussian kernel, making it differentiable.  
For the R channel of the c-th instance block, the color histogram estimation process is defined as follows:  
\[x_{ij}^{h} =x_{j}-\frac{i-0.5}{255} \tag{9}\] \[x_{ij}^{l} =x_{j}-\frac{i+0.5}{255}\]  
where \(x_{j}\) represents the j-th pixel value in the instance block, and i is the current pixel intensity being computed, with\(i\in[0,255]\). \(x_{ij}^{h}\)and \(x_{ij}^{l}\) represent the high and low anchor points for estimating the color histogram, respectively. Next, the differentiable color histogram estimate is computed as follows:  
Figure 4: Structure of SCH loss.  
\[H_{i}^{c}=\sum_{j}(Sigmoid(\alpha\dots x_{ij}^{h})-Sigmoid(\alpha\dots x_{ij}^{j}))H ^{c}=\{i,H_{i}^{c}\}_{i=0}^{255} \tag{10}\]  
where \(H_{i}^{c}\) represents the estimated number of pixels with intensity i, and \(\sigma(\cdot)\) is the Sigmoid activation function, which smoothly approximates the binary histogram calculation, making the process differentiable. The factor \(\alpha\) is set to 400 in this study to ensure higher calculation precision. In this formulation, the difference between the two Sigmoid functions estimates the number of pixels with a specific color intensity iii. For example, when \(x_{j}\) is exactly equal to iii, the contribution to \(H_{i}^{c}\) will be 1. Therefore, \(H^{c}\) represents the final instance-level differentiable color histogram, which can be optimized during back-propagation.
## 3 Materials and Methods
### Proposed MOIBBO-CNN-LSTM
MOIBBO further enhances this by optimizing the structure of the DL model, ensuring that the trade-off between computational efficiency, accuracy, and model complexity is properly managed. By leveraging multi-objective Pareto optimization, the proposed model does not settle for a single solution but instead provides a set of optimal solutions, allowing decision-makers to select the best configuration based on real-world constraints. This adaptability is crucial in wireless networks, where environmental factors constantly change, and a fixed model may not be optimal for different scenarios. The ability of MOIBBO to dynamically tune the model structure ensures that it remains robust and efficient across different IoT deployment conditions. Figure 4 shows the flowchart of the proposed MOIBBO-CNN-LSTM model. The flow of data through the model further highlights its effectiveness. Raw sensor data from IoT devices enters CNN, where initial transformations occur, removing noise and irrelevant features while preserving essential spatial structures. These refined features then pass through the LSTM layers, where short-term and long-term temporal correlations are modeled. The final layers of the network map the learned features to specific outputs, predicting optimal network configurations that maximize SEE. By consistently optimizing this process using MOIBBO, the model can adapt to different network conditions, ensuring that both energy efficiency and security requirements are met. In general, the proposed MOIBBO-CNN-LSTM model presents a highly adaptive and efficient solution to optimize SEE in CF-mMIMO-based IoT networks. It combines CNN's capability to extract spatial patterns, LSTM's ability to learn sequential delays, and MOIBBO's strength in global optimization to provide a powerful framework that balances precision, efficiency, and robustness. The integration of multi-objective optimization ensures that no single performance metric is prioritized at the expense of others, making the model flexible for real-world deployment where trade-offs between energy consumption, security, and computational constraints must be carefully managed.
## 5 Summary and Conclusions  
The present study developed a wave propagation model based on the electron motion equation in a hypersonic plasma sheath magnetized by a dipole magnetic field. The dipole magnetic field is considered to be generated by coils. The mechanic for wave dissipation is analyzed based on the present model, and the effects of uniform and dipole magnetic fields on the mitigation of wave attenuation are compared. In addition, the impacts of the parameters of the coils are investigated. The conclusions are drawn below.  
The wave dissipation in hypersonic plasma sheaths is mainly dominated by the absorption due to the collisions between free electrons and neutral particles. A dipole magnetic field generated by a coil is not as efficient as a uniform magnetic field for mitigating the wave attenuation for the blunt-coned plasma sheath. The reason is that the attenuation region in the plasma sheath is beyond the region of the maximum magnetic field. Nevertheless, a uniform magnetic field is not feasible enough in realistic scenarios. In such a case, the dipole magnetic field generated by coils could be a potential choice to mitigate high frequency wave attenuation in hypersonic plasma sheaths. The effects of dipole magnetic fields on wave attenuation mitigation dramatically increases with the coil radius while the radius is smaller than 0.06 m. The present study suggested that the communication system operating at 94 GHz or 140 GHz with a coil whose radius is 0.06 m could achieve good performance in terms of mitigating the communication blackout problem for hypersonic vehicles.  
Data curation, Z.Z.; Formal analysis, B.B. and K.Y.; Funding acquisition, K.Y. and R.T.; Investigation, Z.Z., M.M., J.X. and K.W.; Methodology, Z.Z. and K.Y.; Project administration, R.T. and X.D.; Supervision, R.T. and X.D.; Validation, K.W.; Visualization, B.B.; Writing--original draft, B.B.; Writing--review & editing, B.B., K.Y. and R.T.; Resources, X.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, under grants 41974195, 41727804, and 61861031, and the Interdisciplinary Innovation Fund of Natural Science from Nanchang University, under grant 9166-27060003-YB14.  
The datasets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# 3D Documentation with TLS of Caliphal Gate (Centa, Spain)
## Materials and Methods  
### Materials  
Two Leica GS-15 global positioning system (GPS) receivers were applied to observe the external control points and the vertices of the micro-geodetic network (see Sections 2.2 and 3). These two devices were used to observe in real time kinematic (RTK) mode, and the coordinates of each basis were measured four times. Final coordinates were obtained by calculating the average of the observed values, as long as the difference between them was less than 3.5 cm.  
For the capture of the field observables, a Leica ScanStation C5 TLS (Figure 1) was selected. The range of this device allows for measuring maximum distances of 300 m (it being conditioned by the albedo), with accuracies of about 4 mm in single measurements, and recording speeds that range between 25,000 and 50,000 points per second. Certain features, such as the screen or the user's interface, ease the register of data, avoiding the necessity to carry and use a laptop.  
The GPS and TLS devices guarantee the quality of the data that are measured in the field to generate the 3D model, in terms of absolute and relative positioning.  
For the management of GPS data, two different software programs were applied: Leica Ski-pro was used to solve the GPS coordinates, and the planimetric and altimetric networks were adjusted with Geored.  
Considering the management of the point-clouds obtained by means of TLS, Leica Cyclone was chosen, not only as it was the alternative designed by the same manufacturer, but also due to it robustness. Reconstructor and Geomagic Rapidform were applied to generate the mesh and to obtain graphic outputs from it.  
Given the size of the files, it was necessary to count with a powerful workstation for the management of point-clouds. The main characteristics of its set up are listed next:  
* Intel Core i7 3770 K, with CPU at 3.5 GHz, 3901 MHz;  
Figure 1: Leica ScanStation C5 terrestrial laser scanner (TLS).  
Four main processors and eight logical processors;  
RAM: 32 GB;  
Hard disk: 2 TB;  
Graphic Card: GeForce GTX560.
# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
## 3 Related Work
### Kriging Technique
It can be described by  
\[\hat{\gamma}(\mathbf{h})=\frac{1}{2N_{h}}\sum_{g_{i}-g_{j}=\mathbf{h}}\big{[} r(\mathbf{g}_{j})-r(\mathbf{g}_{i})\big{]}^{2}\forall\mathbf{g}_{i},i=1,2, \ldots,K, \tag{4}\]  
where \(N_{h}\) is the number of pairs of points distant of \(\mathbf{h}\). In Figure 2, we present the three principal parameters of a variogram. The first parameter is the nugget. It represents the discontinuity at the origin caused by the measurement error. The second parameter is the sill. It represents the maximum semivariance value. It is equal to the process variance. Finally, the last parameter is the range, which represents the distance of decorrelation where the two samples become decorrelated and, consequently,the variogram reaches the sill. It is also shown that, at the same location, the semivariance value is equal to zero, i.e., \(\gamma(0)=0\). In this work, we propose using automatic clustering to improve the RSRP prediction accuracy by enhancing the path loss exponent estimation used in (3). In the following, we describe our selected automatic clustering method. Figure 3 illustrates the flow of the framework in this paper. Graph (a) describes the general workflow without clustering, while Graph (b) shows the flow of our approach using KMC clustering, described in Section 5. As seen here, in our approach we aggregate the predicted RSRP values for the two clusters built. This latter offers a gain in the RSRP prediction as the model parameters are better estimated for each cluster. In fact, this clustering approach greatly improves the efficiency of the training process, which contributes to accurate prediction results. Figure 3: RSRP prediction comparison between the two approaches (**a**) without clustering, (**b**) with clustering. Figure 2: Variogram parameters.
# Data Exchange in Cluster Structure for Longevity of IoT
## 4 Performance Evaluation
### Average Energy Consumption Rate Per Round  
Figure 9 shows the average energy consumption rates per round according to the number of nodes. In the two methods, as the number of nodes increases, the energy consumption rates also increase.  
Figure 8: Percent of dead node per round.  
Figure 7: Dead round of the last node.  
As the number of nodes increases, the number of clusters increases. Accordingly, the probability of data interchanges between clusters is also increased. However, as shown in this figure, the non-CPDT method has an energy consumption rate more than 10 times faster than the CPDT method because all nodes in clusters participate in the data interchange. However, in the CPDT method, the data transfers are executed with only the nodes relevant to the property information for queries. Consequently, unnecessary network traffic is eliminated, and thus, the energy consumption rate is low.