esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Scale Analysis of the Channel Bar in a Braided River and Its Internal Architecture
## 3 Results
### Quantitative Study on the Internal Architecture Elements of the Channel Bar
#### 3.3.2 The Architecture Pattern of Channel Bar  
After the determination of the inclination angle of the architecture interface of the accretion, as well as the width and length of the accretion, this study establishes the architecture model of the vertical deposited sand in PI3 sublayer in the dense well pattern area in the Nanwu area [39]. The cross-paleocurrent direction is nearly horizontal, while the paleocurrent direction is slightly inclined to the downstream direction (Figure 11). According to the characteristics of the architecture interface between the proluvial bodies, a single channel bar can be completely divided into four stages of proluvial bodies longitudinally, and each stage of the vertical deposited sand gradually becomes smaller from the bottom to the top, and the change is especially obvious near the top. However, the horizontal change of the thickness of each stage of the vertical deposited sand is not large, indicating that in the formation process of a channel bar, the scale of flood in each period is roughly the same. The silty layer and channel sedimentation can be seen locally in the single accretion, all of which are filled with fine sandstone. The continuity of the silty layer is good, but the sediment is not very thick, only with tens of centimeters of thickness. The sedimentary characteristics of channel sedimentation is similar to that of the braided river; that is, the top is flat, while the bottom is concave. However, the scale of channel sedimentation is relatively small and has poor continuity on the plane. It shows a crosscutting relationship with the silty layer, and has meter-level thickness.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Algorithm
#### 4.2.3 Appearance of PUs on Channels  
Three PUs reappear in the operating channels of the D2D communication randomly. The backbone route \(k_{1}\), which serves as a backup, is free from PU activities. When the channel of a route has PU activities, the route breaks and the source node must select another available route following the priority mechanism explained in Section 4.1.1. There are three scenarios related to the presence of PU activities in routes \(k_{2}\), \(k_{3}\), and \(k_{4}\). In all scenarios, the destination node is beyond the transmission range of the source node, and so the traffic stream must go through the intermediate nodes of the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Parameter** & **Value** \\ \hline Experiment & Duration & 900 s \\  & Number of channels & 11 \\  & Number of USRP/GNU radio nodes & 10 \\ \hline USRP/GNU radio & Transport layer & UDP \\ nodes & Transport layer & UDP \\ \hline USRP & Channel bandwidth & 40 MHz \\  & Sample rate & 1.1 MB \\ \hline USRP antenna & D2D transmission power among nodes & 10 dBm \\  & Transmission power between node and MC BS & 20 dBm \\  & Carrier frequency & 850 MHz \\ \hline RP3 & Operating system & Ubuntu-Mate \\ \hline PU activities & PU ON time & 50 s \\  & PU OFF time & \{50,100,150,200,250\}s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental setup parameters.
## 3 FANET Routing Algorithms
### Exact-Based Routing Algorithms  
Exact algorithms are a class of algorithms that can find the optimal solution to a problem [71]. The algorithm exhausts the space of all possible solutions according to a set of constraints. When the size of the problem is small, exact algorithms are able to find the optimal solution in an acceptable time [72; 73]. Some routing algorithm researchers are interested in exact algorithms because they can discover exact solutions and confirm the solutions' accuracy.  
[PERSON] et al. [74] proposed a group routing algorithm implemented using a distributed dynamic planning algorithm in 2021. This study uses dynamic planning for routing paths to achieve high throughput and load-balanced data forwarding. While the experiments proved that this approach could provide reliable and stable routing for UAVs, this study only discussed one performance metric. Furthermore, due to the high complexity of the algorithm, more time and computational resources are consumed when facing large-scale FANETs. In the same year, an adaptive routing algorithm based on linear programming (LP) was proposed by [PERSON] et al. [75] for the optimization of routing path lifetime and path length. This study applied the RWP and G-M migration models to evaluate network performance and achieved good results in terms of success rate and throughput. In addition, [PERSON] et al. [76] proposed the use of staged fuzzy logic, where a threshold is set and then the node is scored according to the state of the UAV node (e.g., its energy consumption). If a node scores above the threshold, this node is given the opportunity to participate in the next routing node. Such methods can be used in the routing process to select routes with high fitness, low latency, and low hop count for data transmission. The focus of the study by [PERSON] et al. [77] is also on UAV energy consumption. They effectively solved the UAV multi-trip routing problem by modeling the problem as a non-linear function of payload and flight distance. The researchers added a dual indicator formulation to the solution process and devised a branch-and-cut algorithm (B&C). Although the algorithm achieved good results, the effect of factors such as UAV speed and wind speed on the algorithm was not considered when optimizing for routing.
## 4 Space-Qualified RF Generator Techniques
### DDS and DAC Chip Repackaging  
A fourth option consists in repackaging an existing commercial off-the-shelf DDS-DAC integrated circuit. The technique offers a twofold benefit. On the one hand, this approach offers the functionality of the commercial device, and on the other hand it protects the chip against the harsh environment of space.  
The principle in which the DDS is combined with a DAC is shown in Figure 12. An external clock signal \(\mathrm{f_{CLK}}\) is needed, as well as additional filtering of the output signal RF\(\mathrm{{}_{OUT}}\) and a digital interface.  
Figure 11: SSB output signal for the VIS range.  
The die of the chip is embedded in a space-resistant enclosure, for which specific repackaging techniques are available [43]. Several commercial devices exist that are able to fulfil the requirements listed in Tables 1 and 2, e.g., AD9912 and the AD9957 from Analog Devices [44, 45].  
Commercial components are known for outgassing generation in the context of low pressures and high temperatures [46]. This could jeopardize a mission in which optical elements are used. Hence, care has to be taken if this approach is selected.
## 3 Results
### Comparison and Validation of Simulation and Experimental Results  
To compare and validate the simulation and experimental results, we performed an experiment on a mannequin leg. It was important to calculate the pressure transferred by the SPA chamber onto the leg and to evaluate the efficiency of the SPA chamber performance based on the selected material. This will depict the inflatable SPA chamber in contact with human skin in delivering the required pressure. Based on the simulation results, we fabricated the SPA chamber using food-grade silicone with a Shore hardness of A10 and A15. The material for the upper and bottom layers was A10 Shore food-grade silicone, and A15 Shore food-grade silicone, respectively. The experimental setup discussed in Section 2.4 was used to measure the pressure transmission of the SPA chamber. The interface pressure was expressed as a function of force exerted on the skin measured using an FSR sensor. The experimental result of the maximum deformation during the inflation of the SPA chamber was compared against the FEM results for maximum deformation in relation to the supplied pressure.  
The reliability showed a good correlation for material deformation ICC > 0.9 for both conditions. There was no significant difference in the average deformation between the two testing methods (\(p\) > 0.05). For the Bland-Altman plot, the differences between the simulation and experimental for material parameters (i.e., deformation) are illustrated in Figure 9. The three lines represent the mean differences. The solid line represent bias and the two dash line are limit of agreements mean +1.96 SD (upper interval) and mean -1.96 SD (lower interval). The LoA for material deformation was (95% CI 2.83 to -1.33). From the graph, the scatterplot also demonstrates that most of the points were evenly distributed within the interval in material deformation. There is only one outlier detected beyond the interval. However, most of the deformation points were within LoA.  
Figure 8: The behavior of three silicone elastomers from the simulation results: deformation vs. pressure.
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 7 Conclusions and Future Work  
In summary, we proposed novel semi-supervised methods for drivable road segmentation. Our method reaches a good performance by enforcing cross-consistency between the perturbed expanding features and pseudo labels so that they can leverage the information of unlabeled data. Our methods can almost reach the same accuracy and IoU values by only using 40% labeled data as fully supervised methods do with 100% labeled data. Furthermore, the experimental results demonstrate that compared to other semi-supervised methods, ours has better accuracy and real-time performance in the field of drivable area segmentation. Moreover, our methods remain effective when employing other networks, which illustrates the generalizability of our method.  
In the future, we will improve our methods by investigating new encoder-decoder-structured backbones that could reach the same IoU with fewer labeled data. If possible, we will deploy the model to an edge computing device such as Nvdia TX2 and perform experiments on real scenario and noisy data. In addition, experimentation regarding where perturbations are placed will be an appealing idea in models with a non-encoder-decoder structure, which can broaden the scope of applications of our semi-supervised method. Moreover, the main module should be as brief as possible to ensure real-time prediction. Finally, designing more efficient perturbations is also one of the focuses of future work.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Semi (40\% Labeled)** & **Full (40\% Labeled)** & **Full (100\% Labeled)** & **FPS (40\% Labeled)** \\ \hline UNet & 0.724 & 0.683 & 0.779 & 82.1 \\ ENet & 0.855 & 0.819 & 0.892 & 71.0 \\ ERFNet & 0.829 & 0.797 & 0.903 & 104.5 \\ DeepLabV3+ & 0.803 & 0.772 & 0.815 & 97.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of different base segmentation models on CamVid.  
Figure 11: Our method can achieve significant improvement when applied to different segmentation models (ResNet, UNet, ENet, ERFNet, and DeepLabV3).  
**Author Contributions:** Conceptualization, S.M. and C.S.; methodology, S.M.; software, S.M.; validation, S.M.; formal analysis, S.M.; investigation, C.S.; resources, C.S.; data curation, S.M.; writing--original draft preparation, S.M.; writing--review and editing, C.S.; visualization, S.M.; supervision, C.S.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** The authors used the publicly available datasets Cityscapes and CamVid for the experiments. The Cityscapes dataset is available in [50]. The CamVid dataset is available in [51].  
The authors declare no conflict of interest.
## 4 Discussion
### Model Complexity and Accuracy, Key Caveats, and Limitations
Despite the usefulness of this study's generated models and findings, we acknowledge that some caveats should be mentioned. First, this study's available data were somewhat limited due to data availability in both spatial and temporal domains. The data were available only from 2012 to 2018, covering a small number of districts (30 districts) in the study area. Further study still needs to utilize the hyperparameter tuning of the Decision Tree model in the following metrics: the total number of nodes, total number of leaves, tree depth, and number of attributes used to optimize model accuracy. Moreover, experimenting on different geographic characteristic areas will enhance understanding of the prediction model at the diverse remotely sensed dataset.
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 3 Quantum Immune Security Solutions
### Quantum Immune Physical Layer Security
In other words, an attacker is able to accurately estimate the radio signals at [PERSON]'s and [PERSON]'s locations at any given time, which renders the jamming signal useless. However, for the attacker to succeed, she still needs to have considerable computing resources at her disposal to compute maximum-likelihood (ML) estimates of the transmitted bits. Computing ML estimates may seem a prohibitive task at the moment, but, in the future, can be significantly sped up using quantum search algorithms (QSA).
## 3 Optimal Beamwidth Maximizing Uplink Coverage Probability Scheme  
The proposed optimal beamwidth maximizing the uplink coverage probability scheme aims to address the increasing FSPL and mitigate inter-beam interference simultaneously by tuning the active antenna element number to control the beamwidth and antenna gain. This scheme utilizes a satellite equipped with a phased array antenna to facilitate adjustable beamwidth. The phased array antenna allows beam direction management as the satellite moves, meeting the demand for steerable beams in the QEFC system. This section introduces the management of the beam boresight direction, the uplink coverage probability analysis model, and the optimization function of the proposed scheme.
# New Aspects of Solution Feasibility in a Context of Personalized Therapy Optimization
## 4 The Diagram of a General Solution Procedure  
One of the most frequently used approaches for solving dynamic optimization problems is a sequential approach [18]. The sequential approach to optimization is based on iteratively performing the following computational steps:  
1. Parameterize the control function \(u(t)\), such that \[\mathcal{P}\{u(t)\}=\mathbf{U}\] (31) where \(\mathcal{P}\) is a chosen parametrization operator defined as in [19] and try to indicate reasonable values of the parameters that constitute the matrix \(\mathbf{U}\).
2. For a given matrix of parameters \(\mathbf{U}\), the trajectories of the state variables \(y(t)\) and \(z(t)\), as well as the quality index (26) and the constraints functions (28), are calculated.
3. An influence of the selected parameters on the value of the quality indicator can be examined.
4. Choosing one of the available numerical gradient methods [20], the values of the matrix \(\mathbf{U}\) can be improved according to the rule \[\mathbf{U}=\mathbf{U}+\Delta\mathbf{U}.\] (32)  
The described steps of the procedure can be performed iteratively until the solution is found with the required accuracy or until the computational resources are used [21].  
An important issue in any constrained optimization task is the problem of determining, whether a proposed solution is feasible/acceptable or not. The term \"feasible\" should clearly state whether a given solution meets the limitations and whether it is a potential application. The scheme of the proposed general solution procedure is presented in Figure 1. It takes into account the required input data in the form of the objective function (Equation (9)) and model Equations (10) and (11) with the given initial conditions (20), as well as additional inequality constraints (such as, e.g., (21) and (22)) that can be used to determine the type of feasibility of the obtained solution.  
It should be emphasized, that the main elements of the presented scheme include:  
* Choice of the parametrization method,
* A method for solving systems of differential-algebraic equations,
* A gradient numerical optimization algorithm,
* Defining the rules, the fulfillment of which means that the solution is feasible, depending on the particular issue.  
The presented results were obtained after the implementation of the presented procedure in the Matlab environment. The constant function was used to parameterize the control function. The system of the DAE equations was solved using the ode15s solver. The fmincon function was chosen as the numerical optimization algorithm. The trajectories obtained as a result of the performed calculations were presented and discussed from the perspective of different levels of the solution feasibility.
## 3 Case Studies  
The examples presented in this paper are deliberately selected to demonstrate the framework and procedure of the methodology in a clear and structured manner. These examples, such as the beam-node system, enable explicit calculation of stiffness and mass matrices and effectively illustrate the core principles of the method. This simplicity is intentional to ensure that the key features of the methodology are highlighted without introducing unnecessary complexity, making the approach easier to validate against analytical solutions and numerical FEA.  
While the examples are simple, they are not meant to fully exhaust the potential of the methodology. Rather, they represent reduced-order approximations of more complex systems. The proposed GRCFBS method is scalable and can effectively analyze systems with intricate geometries by replacing complex substructures with points that carry the corresponding receptance matrix data. This approach allows for the analysis of highly complex systems in a reduced-order form, ensuring that the method remains applicable across a wide range of dynamic systems.  
It is important to note that this simple beam-node model can be used as a reduced-order representation to describe a more complex structure [28]. Since this approach is based on coupling receptance matrices at specific points, the complexity of the geometry does not directly influence the methodology. Instead, the complex substructures are effectively replaced by points that carry the receptance matrix data, allowing for the analysis of even highly complex systems in a reduced-order form.
## 2 Signal Model  
The UWB impulse radar can detect human targets mainly based on the chest motion caused by human respiration. The Gaussian pulse emitted by the UWB impulse radar can be formulated as follows [44]:  
\[x(t)=\frac{1}{\sqrt{2\pi}\sigma_{t}}e^{-(\frac{t^{2}}{2\sigma_{t}^{2}})}, \tag{1}\]  
where \(t\) represents fast-time, and \(\sigma_{t}\) is related to the pulse width. Slow-time is discrete with \(nT\), \(n=0,1,\ldots,N-1\) is the slow-time sampling point. \(T_{s}\) is the pulse repetition time, which is also the slow-time sampling interval. Thus, the sequential transmitted signal is:  
\[s(t)=\sum\limits_{n=0}^{N-1}x(t-nT). \tag{2}\]  
Consider a simple situation, and assume that there are \(P\) human targets and one stationary target in the detection area. The received signal can be expressed as:  
\[\begin{array}{rl}R(t)=&\sum\limits_{n=0}^{N-1}\sum\limits_{p=0}^{P-1}x(t-nT- t_{p})*h_{p}(t)\\ &+\sum\limits_{n=0}^{N-1}x(t-nT-t_{s})*h_{s}(t)+r(t)+w(t),\end{array} \tag{3}\]  
where \(h_{p}(t)\) denotes the respiration response of the \(p\)th human target with propagation time \(t_{p}\). \(h_{s}(t)\) is the impulse response of the static target with propagation time \(t_{s}\). \(r(t)\) represents non-static inferences in the same frequency range as respiration, and \(w(t)\) represents additive Gaussian white noise (AWGN). \(t_{p}\) is caused by the respiratory motion of the human chest, which varies approximately as harmonic vibration [45]. \(t_{p}\) can be expressed as:  
\[t_{p}=t_{p0}+t_{pd}\sin(2\pi f_{p}nT), \tag{4}\]  
where \(t_{p0}\) is the propagation time delay of chest vibration center in fast time, \(t_{pd}\) is the magnitude of the chest respiratory motion, and \(f_{p}\) is respiratory frequency.  
We denote the fast-time sampling interval by \(\delta_{T}\), then \(R(t)\) can be expressed in discrete form as:  
\[R[m,n]=h[m,n]+s[m,n]+r[m,n]+w[m,n], \tag{5}\]  
where \(m=0,1,\ldots,M-1\) is the fast-time sampling point. \(h[m,n]\) is the discrete form of the human respiration signal, \(s[m,n]\) is the discrete form of the static clutter, \(r[m,n]\) is the discrete form of non-static inferences, and \(w[m,n]\) is the discrete form of AWGN.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 5 Simulation Results and Discussion
### Pre-equalization Performance Evaluation  
The bandwidth of the AFE in the transmitter was analyzed to evaluate the performance of the passive equalizer. Figure 13a shows the frequency response of the designed passive equalizer. This passive equalizer served as a high-pass filter to extend the limited modulation bandwidth caused by the LED. As presented in Figure 13b, the \(-\)3 dB bandwidth of the transmitter including limitation from the LED was 52.42 MHz, and it could be extended to 313.39 MHz after adopting the passive equalizer with the sacrifice of low-frequency component loss. The corresponding eye diagrams of 2.7 Gbps and 3.6 Gbps PAM-8 signal transmissions without and with the NN-based FFE were detected at the output of the LED to verify the feasibility of the FFE. The first pre-tap weight and first post-tap weight for the 2.7 Gbps data transmissions were 0.05 and 0.35, respectively, while the values for 3.6 Gbps were 0.07 and 0.5. Figure 14a,c are the eye diagrams acquired only with the passive equalizer under the data rates of 2.7 Gbps and 3.6 Gbps separately. Meanwhile, Figure 14b,d are the eye diagrams acquired with both the passive equalizer and FFE under the data rates of 2.7 Gbps and 3.6 Gbps. When comparing Figure 14a to Figure 14b, it can be  
Figure 12: Schematic of TIA circuit.  
noticed that the eye quality increased dramatically with the equalization of the FFE, which proved the feasibility of the proposed NN-based FFE. However, when the data rate was increased from 2.7 Gbps to 3.6 Gbps, the top and bottom eyes diagrams of Figure 14 were almost closed. Nevertheless, the case of the data rate equal to 3.6 Gbps also suffered from nonlinearity caused by the LED. After transmission through the channels, the quality of the eye diagrams was further decreased. Therefore, these problems of bandwidth limitations and path loss remained to be solved by the RBF-NN-enabled post-equalization.  
Figure 14: Eye diagrams of PAM-8 signal transmissions with passive equalizers at LED output: (a) 2.7 Gbps data rate without FFE; (b) 2.7 Gbps data rate with FFE; (c) 3.6 Gbps data rate without FFE; (d) 3.6 Gbps data rate with FFE.  
Figure 13: (a) Frequency response of passive equalizer; (b) \(-\)3 dB bandwidth extension of transmitter using passive equalizer.