esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Loss Function  
Similar to MSCANet, our model also computes the cross-entropy loss function for the three change features output by the multi-scale decoder. Specifically, the three change feature maps generated by the multi-scale decoder are denoted as \(P_{\text{s4}}\), \(P_{\text{s8}}\), and \(P_{\text{s16}}\). These feature maps correspond to change detection results at different scales. By calculating the cross-entropy loss between these feature maps and the ground truth Y, the model parameters are optimized to improve prediction accuracy. The expression for the cross-entropy loss is as follows:  
\[L=-\sum_{i=1}^{N}\sum_{c=1}^{C}y_{ic}\log(p_{ic}) \tag{13}\]  
where N represents the total number of samples, C denotes the total number of classes, \(y_{ic}\) is the ground truth label for sample i in class c, and \(p_{ic}\) is the predicted probability that sample i belongs to class c. The model calculates the cross-entropy loss for each scale's change feature map separately, and then it performs a weighted sum of these losses to obtain the overall loss function. Specifically, let \(\alpha_{4}\), \(\alpha_{8}\), and \(\alpha_{16}\) denote the loss weights for the three scales' change feature maps. The overall loss function \(L_{total}\) is expressed as  
\[L_{total}=\alpha_{4}L(P_{s4},Y)+\alpha_{8}L(P_{\text{s8}},Y)+\alpha_{16}L(P_{ \text{s16}},Y) \tag{14}\]  
where \(L(P_{si},Y)\) denotes the cross-entropy loss between the change feature map at scale \(L(P_{si},Y)\) and the ground truth Y. Through this multi-scale supervision mechanism, the model is able to learn rich contextual information from feature maps at different scales,thereby improving performance in the farmland change detection task. The Transformer decoder effectively captures global information, enhancing feature representation capabilities.
# Bio-Corrosion Behavior of Ceramic Coatings Containing Hydroxyapatite on Mg-Zn-Ca Magnesium Alloy
## 3 Results and Discussion
### Effect of HA Particles on the Blood Compatibility of the Coatings  
Figure 6 shows the effect of HA particles on the amount and shape of blood platelet adhesion of the coatings. It can be seen that less blood platelet counts were observed on the HA-containing coating compared with that of the MAO coating. Furthermore, the platelets on the MAO coating were unevenly dispersed, with some regions exhibiting higher platelet density than those on the HA-containing coating. Generally, the sample with less platelet density adhering on the surface indicates improved blood compatibility [46]. The SEM micrographs also provided information on the morphology of adhered platelets and their activation state. The platelets on the HA-containing coating were round or oval without pseudopodia-like protrusions. However, platelets on the MAO coating were spread as dendritic with one or more pseudopodia. These results indicate that the HA-containing coating possessed good blood compatibility.  
Figure 5: Potentiodynamic polarization curves obtained in [PERSON]’s solution at 37 \({}^{\circ}\)C.  
Figure 6: SEM images of blood platelet adhesion on the (**a,b**) micro-arc oxidation (MAO) coating and (**c,d**) HA-containing coating.  
Figure 7 shows the change in the static water contact angles of the coatings with and without HA particles. The water contact angle of the MAO coating was 40.6\({}^{\circ}\) whereas that of the HA-containing coating decreased to approximately 18.7\({}^{\circ}\), indicating that the HA-containing coating possessed improved hydrophilicity. Normally, the percentages of round platelets and the total number of adhering platelets are related to the degree of hydrophilicity: the more hydrophilic the surface is, the lower the number of platelets adhering to it and the smaller are the changes in platelet shape. The improved hydrophilicity causes a less tendency of absorption in the protein, blood platelet, and blood clotting factors and resulting improvement in the blood compatibility of implant material [47; 48].
## 8 Conclusions
\begin{tabular}{l l} AES & Advanced Encryption Standard \\ AMI & Advanced Metering Infrastructure \\ DOS & Denial Of Service \\ EG & Eschenauer-Gligor \\ ETP & European Technology Platform \\ HAN & Home Area Network \\ IDS & Intrusion Detection System \\ IDPS & Intrusion Detection and Prevention System \\ IEC & International Electrotechnical Commission \\ IEEE & Institute of Electrical and Electronics Engineers \\ ISM & Industrial, Scientific and Medical \\ MANET & Mobile and Adhoc NETwork \\ MIMO & Multiple Input Multiple Output \\ NAN & Neighborhood Area Network \\ NIST & National Institute of Standards and Technology \\ OFDM & Orthogonal Frequency Division Multiplexing \\ PHEV & Plug in HybridElectric Vehicle \\ PLC & Power Line Communication \\ SCADA & Supervisory Control And Data Acquisition \\ TDMA & Time Division Multiple access \\ WAN & Wide Area Network \\ Wi-Fi & Wireless Fidelity \\ WSN & Wireless Sensor Network \\ \end{tabular}
# Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
## 3 WF-Based Bit Allocation
In advance, the maximal transmission rate \(R^{\prime}_{k}\) per shot in the \(k\)-th channel can be expressed as:  
\[R^{\prime}_{k}=C+\frac{1}{2}log_{2}I_{k} \tag{14}\]  
where  
\[C=log_{2}\frac{ln2}{2\lambda} \tag{15}\]  
Considering (14), the sum of all \(R^{\prime}_{k}\) must be \(NR\) such that the total signal power in the left term of (10) can be  
\[\sum_{k=1}^{N}(C+log_{2}I_{k})=NR \tag{16}\]  
Thus, the channel capacity can be derived as:\[C=R-\frac{1}{2}log_{2}\left(\prod\limits_{k=1}^{N}{I_{k}}\right)^{\frac{1}{N}} \tag{17}\]  
Considering (14), the bit allocation can be  
\[\min\biggl{\{}\frac{1}{N}\sum_{k=1}^{N}\frac{I_{k}}{2\times\ 2^{R_{k}}}\biggr{\}} \tag{9}\]0  
However, the rate \(R_{k}\) provided by the WF algorithm is floating. Instead of direct rounding, the bit-level optimization can satisfy the requirement of digitization for communication or signal processing. In the proposed Algorithm 1, the output \(R_{k}^{\text{bit}}\) is the best bit allocation in the \(k\)-th channel. The allocation of the bit-wise process can evenly assign the desired bits to the most available channels with the aim of fewer transmission errors. Therefore, the channel diversity is realized in this MIMO VLC system. ```
1:Initialization: \(B_{p}=\sum\limits_{k=1}^{N}{R^{\prime}{}_{k}}\); \(R^{\text{bit}}=\left[R_{1}^{\text{bit}}\,R_{2}^{\text{bit}}\,\ldots,R_{N}^{\text{bit}}\right]=[0,0,\ \ldots 0]\);
2:Calculate \(R^{\prime}{}_{k}=R-log_{2}\left(\prod\limits_{k=1}^{N}{I_{k}}\right)^{\frac{ 1}{N}}+log_{2}I_{k}\) for each \(k\)
3:While\(B_{p}>0\),do
4:\(k^{\prime}=\text{arg}\max(R^{\prime}{}_{k}|k=1,\ldots,N)\)
5: If \(R^{\prime}{}_{k^{\prime}}\)< 0 then break and output failure;
6:\(R^{\prime}{}_{k}\)=\(R^{\prime}{}_{k^{\prime}}\ -1\);
7:\(B_{p}=B_{p}-1\);
8:\(R_{k^{\prime}}^{\text{bit}}=R_{k^{\prime}}^{\text{bit}}+1\);
9:endwhile
10:Output:\(R^{\text{bit}}\)
```  
**Algorithm 1** WF-based bit allocation algorithm.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 2 Proposed Method 1: Robust Models with Gabor Dictionary Analysis and Machine Learning  
The cough sound signals are projected as \(Z\) of length \(L\), and this is conducted in a very-high-dimensional subspace. Sometimes, the projection can be over an overcomplete dictionary also, which comprises column vectors arranged as normalized atoms and represented as \(C=[c_{1},c_{2},\dots,c_{N}]\) of size \(L\times N\), with \(L<N\). With the help of an ordinary least square term, this can be conducted efficiently. A sparsity-inducing regularizer could also be designed efficiently using such a technique. By means of solving the following optimization issue, the best coefficient vector \(b\) of length \(N\) is attained:  
\[\min_{b}\lVert z-Cb\rVert_{2}\text{ subject to }\lVert b\rVert_{0}\leq K\ll N \tag{4}\]  
where the energy norm is represented by \(\lVert\cdot\rVert_{2}\) and the number of non-zero entries is represented by \(\lVert\cdot\rVert_{0}\) in the vector \(b\). Therefore, the most descriptive collection of atoms is obtained by the model. A high-end analysis dictionary technique and a robust regularization scheme are required to model the atoms. With the help of greedy matching, the tackling of the issue is conducted efficiently. By means of projecting the residual onto the specific atoms of the dictionary, the coefficient vector \(b\) is computed iteratively. Atoms that mitigate the residual energy the most are chosen, and then, the residual is updated. Unless a termination criterion is met, the procedure is repeated often. Traditionally, the concept of matching pursuit was used with the Gabor dictionary; hence, in our proposed work, three different schemes were used to replace the matching pursuit concept [30]. The sparsity degree of the model is controlled, and the regularization parameter handles the multicollinearity very well.  
For the cough audio signals, a dictionary along with a particular resolution based on its time-frequency atoms is considered, and then, the coefficient vector \(b\) is estimated so that the regularization parameter is explored well. This optimization step aids the accurate classification of cough audio signals. Then, using the estimated coefficient vector \(b\) of the respective cough audio signals, the time-frequency matrices are obtained so that it is useful for classification. The coefficient vectors are reshaped efficiently so that appropriate mathematical functions are implemented correctly. The proposed framework is given in Figure 1, as follows.
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
## 2 Antenna System Design  
As mentioned above, the satellites supporting UHF communication need to cover multiple bands for different geographical areas. When the satellite reaches different areas, it will adjust its transmission and reception frequencies to the bands allowed in those regions, such as 868 MHZ in Europe, 915 MHz in the Americas, and 915 MHz and 923 MHz in Asia. Therefore, antennas used for satellites need to have multi-frequency band features to ensure worldwide continuous operation. In addition, to optimize transmission quality and to reduce the interference caused by ground-based systems, a high gain with circular polarization and a wide beamwidth radiation pattern are required. For these reasons, Lacuna Space decided to use right-hand circular polarization (RHCP) for uplink and downlink communications.  
To integrate the radiation structure on the 1U face of the 3U CubeSat, the maximal dimensions of the structure were 77 \(\times\) 77 mm\({}^{2}\) and 10 mm in height. The radio frequency connector was placed on the bottom layer of the bottom substrate. Based on the literature, a single resonance quadrifilar structure with such a form factor limitation would reach a 30 MHz frequency bandwidth. In order to extend the frequency band operation, a reconfigurable solution based on digital tunable capacitors or pin diodes was considered too risky. In order to provide a passive dual-band antenna, parasitic elements have been extensively used with an inverted F antenna [32]. Then, to meet the project requirements, we proposed to combine a quadrifilar structure with parasitic elements. Similar to [32], two thin printed circuit board (PCB) substrates were used. The feeding circuit and connector were placed on the bottom PCB, and antenna traces were placed on the top PCB. The two substrates were connected using 10 mm long vertical wires soldered to both PCBs. To provide good mechanical characteristics and a reduced production time, 1.6 mm thick FR-4 substrate material (\(\epsilon_{r}\) = 4.4 and tan \(\delta\) = 0.02) was chosen to manufacture the two substrates. The use of FR-4 material is not optimal to maximize the radio frequency performance of the antenna but it is qualified for space operation and ensures good mechanical properties to handle the vibration at the satellite launching phase. In addition, a 25 mm diameter ceramic ring was placed in the center of the structure to improve mechanical robustness.
## 4 Conclusions  
This study aims to increase the accuracy of the prediction concentration by selecting the excitation wavelengths and emission wavelengths from the EEM. To eliminate the redundant information, the excitation wavelengths were selected based on the clustering method; to refine the most useful emission wavelengths, the emission wavelengths corresponding to the selected excitation wavelengths were determined based on the Monte Carlo method. Comparing to the predicted concentration with all spectra, the accuracy of the prediction concentrations with the selected spectra is increased by selecting the irrelevant excitation spectra and reserving the emission wavelengths contributing most to predict component concentration based on the proposed method. The RMSEP for Naphthalene, 1-naphthol, and 2-naphthol with the selected wavelengths are 1.54%, 0.72%, and 0.16% lower than those with all wavelengths, respectively. Furthermore, the running time of the concentration prediction is also reduced because only the selected spectra were used instead of the full spectra. The running time for Naphthalene, 1-naphthol, and 2-naphthol with the selected wavelengths is 228.7, 274.88, and 255.27 s shorter than that with all wavelengths, respectively.  
Conceptualization and methodology, Y.W.; software, C.H.; validation, Y.W., C.H., Z.Z., and G.W.; formal analysis, Y.W. and C.H.; writing--original draft preparation, C.H.; writing--review and editing, Y.W., G.W., and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (No. 2018 YFB2003803).  
The authors declare no conflicts of interest.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 1: Collect System Information
#### 4.1.3 Step 1.3: Define Critical Loads and MDI Scores  
Table 2 represents the scaling of loads developed from the NAVFAC, United States Coast Guard (USCG) Office of Civil Engineering, and National Aeronautics and Space Administration (NASA)'s MDI and modified to fit our design [58]. All the loads simulated within our design are deemed critical and therefore a relative scale was developed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Load**} & \multirow{2}{*}{**Facility Type**} & **Average Maximum** & **Average Normal** \\  & & **Load (kW)** & **Load (kW)** \\ \hline
1 & Small Office & 5.8 & 2.8 \\
2 & Small Office & 5.8 & 2.8 \\
3 & Medium Office & 71.3 & 32.3 \\
4 & Large Office & 524.9 & 267.5 \\
5 & Warehouse & 31 & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Building Load Data. This data is derived from the DOE Commercial Reference Building dataset hourly load data [56]. This data is used to extract the average maximum load and average normal load for associate facility types.  
Figure 4: Microgrid and Nanogrid Operating Modes [2]. The figure shows how a microgrid or nanogrids can move between different modes of operation.
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 2 Review of Simulators  
The hardware and software implementation of ready-made solutions in the field of testing mobile networks, taking into account the behavior of its nodes in various conditions, is not rational both from an economic point of view and from the point of view of provision guaranteed reliability data transmission. As a consequence, it is customary to perform software simulations of processes occurring in mobile networks. Given this, a review of the available ready-made software solutions that would maximally cover the issues raised above was carried out. These questions can be formulated as follows:  
* Provision and modernization of reliable data transmission in continuously evolutionary networks;
* The ability to scale the topology and behavior of nodes in the network;
* The possibility of implementing threats and countermeasures to them with the use of methods of information security of telecommunication networks (these criteria are described in more detail in the works [1; 2]);
* Using the maximum number of possible layers of the data communication protocol stack.  
As a review, all the proposed solutions with implemented simulation are divisible into software use of simulators and emulators. For the reasons already shown earlier, these are the arising hardware implementation difficulties.  
A simulator is a part of the software that simulates the topology of a network. The simulated network devices are not real devices and cannot carry real network traffic. The network devices in the simulator are limited by the commands and functions programmed into them. For this reason, many of the additional features presented on real network devices are absent in the simulated analogues. The main advantage of simulators is in their less demanding computational resources.  
In its turn, an emulator is software that works with virtual network devices. Emulators virtualize real network devices, so the behavior of virtual network devices reflects the behavior of real-world physical devices.
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 3 Route Nonuniformity Path Modelling
### How to Measure Route Feature Values for RA Calculation
#### 3.2.1 How to Measure Route Feature Values for AODV  
Route feature values are measured based on two types of trigger: the time when the destination node address is added to the route entry of the source node and the time when the destination node address is deleted from the route entry of the source node. In AODV, these events occur when the source node receives an RREP message or an RER (Route ERRor) message, or when the source node receives or fails to receive a HELLO message. The sequences of measuring the connected time and disconnected time for AODV are shown in Figure 4.  
As shown in Figure 4a, an addition to the route entry occurs when the source node receives an RREP, indicating that a route has been established. A deletion from the route entry occurs when the source node receives an RER, indicating that the route has been disconnected. The period between an addition and the deletion for the same route is connected time. The period between a deletion and the next addition, i.e., reception of the next RREP, is disconnected time. When a source node tries to establish a route, it may receive multiple RREPs because multiple routes can be established. If the source node receives multiple RREPs, the route with the smallest number of hops, for example, is selected in accordance with AODV, and the destination node address is added to its entry. The source node may receive multiple RERRs when a route is disconnected. When the source node receives multiple RERRs, the first one is selected, and the destination node address is deleted from its entry.  
Disconnections and re-establishments of routes through entry updates with HELLOs occur only when the source node communicates with the destination node directly, i.e., without using relay nodes. This occurs when the source node and the destination node are located close to each other. In such a case, no route requests arise and communication can start with exchanges of only HELLO messages, which are used to maintain and update routes. Connected time and disconnected time can be measured by detecting additions to and deletions from the route entry as shown in Figure 4b in the same manner as the case where routes are established and disconnected by receptions of RREPs and RERs. However, use of HELLO messages is optional in AODV.  
Route connections and disconnections are determined in accordance with the sequence of entry events that have occurred as shown in Table 2. Consecutive additions to the entry or consecutive deletions from the entry are treated as exceptions. An event once used is not used in the next attempt to determine connection or disconnection. The end of communication is defined as the time when the application of the sending side is closed.
# A Dynamic Model Updating Method with Thermal Effects Based on Improved Support Vector Regression
## 4 Thermophysical Parameters Identification  
At present, is it common for methods to assume that modified thermophysical parameters are constants or known function forms. However, the variation in thermophysical parameters with temperature dependence is much more complicated in practical engineering. In this paper, a set of linear combinations of basis functions is used to define the function of thermophysical parameters in a temperature-dependent manner, in which the basis functions can be expressed by SVR:  
\[k_{j}=\sum_{i=0}^{N_{T}}{\alpha_{ji}\psi_{ji}(T)}\;j=1,\cdots,s \tag{29}\]  
where \(s\) is the number of thermophysical parameters, \(\psi_{i}\) is the basis function and \(\alpha_{i}\) is the coefficient of the basis function. Then, the thermophysical parameters can be defined as the optimization variables, and the residual error between the experimentally measured datum and the datum calculated during simulations can be defined as the objective function; the objective function can be minimized to obtain the optimal results.  
\[J=\sum_{i=1}^{n}\abs{T_{i}^{a}(\mathbf{k})-T_{i}^{t}} \tag{30}\]  
where \(\mathbf{k}\) is the thermophysical parameter vector, \(n\) is the number of temperature measuring points, and \(T_{i}^{a}\) and \(T_{i}^{t}\) are the \(i\)th simulation calculated datum and experimentally measured datum, respectively.  
Then, the thermophysical parameter identification problem is solved as an optimization problem; that is, the thermophysical parameters are defined as a temperature-dependent function, which satisfies the following:  
\[\mathbf{K}=\mathbf{QT} \tag{31}\]  
where \(\mathbf{K}\) is the thermophysical parameter matrix, \(\mathbf{T}\) is the temperature matrix and \(\mathbf{Q}\) is the coefficient matrix.  
According to the coefficient matrix identification model, which is used to minimize the residual error between the experimentally measured datum and simulation calculated datum, the surrogate model is used for further calculation. The flow chart of thermophysical parameter identification with temperature dependence is shown in Figure 2.  
Figure 2: Flow chart of thermophysical parameter identification with temperature dependence.