esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Architecture
Furthermore, the macro base station (MBS) are typically linked with the core network via wires that have huge bandwidth. * **Layer 2** comprises a team of M-UAVs equipped with the essential gadgets, these being cameras, IMU, sensors, and a GPS unit, among others, for monitoring the assigned zone. Moreover, M-UAVs are connected with each other using Bluetooth 5 (2.4 GHz) link and with the RMEC-UAV with 802.11 ac (5 GHz) Wi-Fi link. * **Layer 3** is composed of RMEC-UAV that is used to collect data from M-UAVs and forwards it to the base station. RMEC-UAV can also connect with the ground-level IoT devices and collect data from them.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 2 Preliminaries
### Example  
Combined with the description of the mathematical model, an example of a DSTT is shown in Figure 2.  
According to the value matrix, under the condition of obtaining the maximum value for the fitness function of the Schedule, the task-dynamic-scheduling result is obtained through the intelligent algorithm. The result data are the equipment-code sequence. The two-dimensional table of the Schedule is shown in Figure 2. The abscissa is the sequence code of the period, the ordinate is the sequence code of the equipment, and the data are the frequency band transmitted by the equipment during the period. For example, the number of position 0 in the result value is 1, indicating that task0 is assigned to equipment1. The frequency-band code of task0 is 3, the start time is 0, and the delay time is 3. With time from 0 to 2 in equipment1 in the _Schedule_, the frequency-band code is 3.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**Explain**} \\ \hline \(m\) & number of _Schedule_ definition periods \\ \(n\) & number of equipment defined in _Schedule_ \\ \(p\) & number of _Schedule_ tasks \\ \(Task_{k}\) & task of the \(k\)-th Schedule, including \(<TaFr,Start,Span>\) \\ \(TaskResult_{k}\) & data description of the \(k\)-th result, including \(<Task,TransNo>\) \\ \(Va_{ij}\) & evaluation value of the transmitting effect of the \(i\)-th equipment in the \(j\)-th frequency band \\ \(FreqBand_{ij}\) & _Schedule_ node data, frequency code of the \(i\)-th equipment working in the \(j\)-th time period \\ _ScheduleResult_ & The output result of _Schedule_ is composed of \(p\) piece of \(TransNo\) code values \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter table of DSTT mathematical model.  
Figure 2: Example of DSTT.
# An Improved YOLOv8 Model for Strip Steel Surface Defect Detection
## 3 Experiment and Analysis  
### Experimental Data Set  
This study utilizes the publicly available NEU-DET dataset, which contains surface defect images of rolled steel strips.As shown in Figure 12, the dataset includes six defect types: rolled-in scale (RS), patches (Pa), cracks (Cr), pitted surface (PS), inclusions (In), and scratches (Sc), comprising a total of 1800 grayscale JPG images. Each defect type is represented by 300 images, each with a resolution of 200 \(\times\) 200 pixels. The dataset is randomly split into a training set and a validation set in a 7:3 ratio, yielding 1260 images for training and 540 images for validation.  
The experiments are conducted using a GPU in an Ubuntu 22.04.3 environment. The GPU utilized is an NVIDIA GeForce RTX 3090 with 24 GB of VRAM. The software environment includes CUDA 12.1 and the deep learning framework PyTorch version 2.1.1, with programming performed in Python 3.10.14. The model is trained for 120 epochs, with images resized to 640 \(\times\) 640 pixels, a batch size of 16, an initial learning rate of 0.01, a weight decay of 0.0005, and a learning rate momentum of 0.937.
# Performance Prediction and Design of Stratospheric Propeller
## 5 Finite Element Analysis  
### Airfoil Model  
ANSYS ICEM was used to generate a two-dimensional O-Block mesh of the fluid domain, in which the number of grid points was 300 \(\times\) 100. A density-based solver was applied in ANSYS Fluent, and the Reynolds average Navier-Stokes (RANS) equation and the S-A turbulence model were adopted. The Green-Gauss node-based scheme was selected as the discretization scheme, and the second-order upwind scheme was selected for turbulence equation discretization. The computational domain was 15 times the chord length. The far-field pressure was used as the boundary condition, and the airfoil was set as the wall boundary. The residual value was required to be below 1 \(\times\) 10\({}^{-6}\) to ensure convergence. The mesh of the airfoil is shown in Figure 3.
## 4 Average Information Capacity of Link  
Figure 2 shows the discrete memoryless channel among the energy levels of signal OAM and received OAM, which is characterized by channel (transition) probabilities. Let \(X=\{m_{00},m_{01}\cdots,m_{0i-1}\}\) and \(Y=\{m_{0},m_{1}\cdots,m_{J-1}\}\) be the input and the output letters channel, respectively; the channel is completely characterized by the following set of transition probabilities [36]:  
\[p\big{(}m_{j}/m_{oi}\big{)}=P\big{(}Y=m_{j}/X=m_{0i}\big{)},0\leq p\big{(}m_{ j}/m_{0i}\big{)}\leq 1, \tag{12}\]  
where \(i\in\{0,1,\cdots,I-1\},j\in\{0,1,\cdots,J-1\}\), while \(I\) and \(J\) denote the sizes of input and output alphabets, respectively. The transition probability \(p\big{(}m_{j}\mid m_{0i}\big{)}\) represents the conditional probability that \(Y=m_{j}\) for given input \(X=m_{0i}\). The information capacity is obtained by maximization of mutual information over all possible input distributions [36]:  
\[C=\max_{\{p(m_{0i})\}}I(X,Y)=\max_{\{p(m_{0i})\}}[H(X)-H(X\mid Y)], \tag{13}\]  
where \(H(X)-\langle\log_{2}P(X)\rangle\) represents the uncertainty about the channel input before observing the channel output, while \(H(X\mid Y)\) represents the uncertainty of the channel input after it has been received. In Equation (13), \(I(X;Y)\) is defined as mutual information. The mathematical expression for mutual information is expressed as follows:  
\[ \begin{split} I(X;Y)&=H(X)-H(X\mid Y)\\ &=\sum_{i=1}^{M}p(m_{0i})\log_{2}\biggl{[}\frac{1}{p(m_{0i})} \biggr{]}-\sum_{j=1}^{N}p\bigl{(}m_{j}\bigr{)}\sum_{i=1}^{M}p\bigl{(}m_{0i} \mid m_{j}\bigr{)}\log_{2}\biggl{[}\frac{1}{p\bigl{(}m_{0i}\mid m_{j}\bigr{)} }\biggr{]}.\end{split}  \tag{14}\]  
Equation (14) represents that the mutual information is obtained as the output information minus information lost in the channel. Considering that (1) POV constitutes an infinite OAM channel and the channels used for communication are finite: (2) when POV is transmitted in weakly turbulent seawater, the difference between the energy level corresponding to OAM crosstalk formed by seawater turbulence disturbance and the energy level of OAM signal is very small. Therefore, the OAM channel composed of \(N=2m+1\) input and \(N=2m+1\) output is a symmetric channel (MSC). Further, let constellation points be marked with gray codes, and the symbol error causes the corruption of only 1 bit out of \(\log 2M\) bit comprising the symbol [38]. We have that \(p\bigl{(}m_{j}\mid m_{0i}\bigr{)}=P_{B}\log_{2}M/(M-1)\) and \(p\bigl{(}m_{j}\mid m_{0j}\bigr{)}=1-P_{B}\log_{2}M\), where \(P_{B}\log_{2}M\) is symbol error probability, \(P_{B}\) is the bit-error rate (ABER). The AIC of a bandwidth-constrained link is described by the following formula  
\[C=\log_{2}N+(1-P_{B}\cdot\log_{2}M)\cdot\log_{2}(1-P_{B}\cdot\log_{2}M)+P_{B} \cdot\log_{2}M\cdot\log_{2}\frac{P_{B}\cdot\log_{2}M}{N-1}. \tag{15}\]  
Taking into account Equations (11) and (15), we can obtain the AIC of the POV carrier link with M-QAM as  
\[
## 3 Microwave Photonics Applications of DFT
### DFT for Microwave Arbitrary Waveform Generation  
Microwave arbitrary waveforms have found wide applications in radar, wireless communications, imaging, and instrumentation systems. The difficulties of electrical method for generating microwave arbitrary waveforms include low frequency, small bandwidth and high cost, due to the limited sampling rate and analog bandwidth of electronic circuitry. Photonically assisted microwave arbitrary waveform generation (AWG) techniques provide the capability of generating high-frequency and broadband microwave arbitrary waveforms which may not be easily accomplished by conventional electronic techniques [23,93]. Various techniques, such as direct space-to-time (DST) pulse shaping [94,95], photonic microwave filtering [96,97], and DFT pulse shaping, have been proposed and demonstrated for photonic generation of microwave arbitrary waveforms. DST pulse shaping method splits a single optical pulse into a temporally distributed pulse sequence matching a spatially distributed pattern. Arbitrary envelope and phase profiles of the generated microwave waveform are determined by controlling the amplitude, pulse-to-pulse spacing and repetition rate of the pulse sequence. In photonic microwave filtering method, since the filter can be designed to have an arbitrary spectral response, the employment of the filter for the generation of an arbitrary microwave waveform can be realized.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.13 I-Leach  
One disadvantage of the LEACH protocol is that a new CH is selected in each round, requiring energy to form a new cluster. This also leads to excessive energy use through routing overhead, which is unacceptable for any IoT device [87]. [PERSON] et al. [2] proposed an effective CH replacement method, which can be used to avoid extra energy during the formation of the cluster and the transmission of advertising messages to the cluster members. A threshold value is calculated, and only when the remaining energy of the CH is lower than the threshold does a new cluster and CH need to be formed; otherwise, the same CH will continue to the next round. It can be considered that the minimum energy level that is the best for the CH replacement method is evaluated as:  
\[P_{Th}=Count_{Rnd}(P_{RTx}+P_{RRx})P_{TX} \tag{15}\]\(Count_{Rnd}\) is the number of rounds to act as member nodes within a cluster. \(P_{kTx}\) and \(P_{kRx}\) are the transmission and reception packet sizes, and \(P_{Tx}\) is the power required to transmit 1 byte of data.
## 5 Simulations
### Simulation Results
#### 5.2.3 Performance of DecantFed by Optimizing the Workload Among Clients  
We next evaluate how the workload optimization affects the performance of DecantFed. Here, we have two settings for DecantFed: (1) UniformDecant [42] performs uniform local training among the clients, i.e., all the clients use the same number of data samples (i.e., \(d_{i}=10,\forall i\in\mathbf{\mathcal{I}}\)) to train their local models. (2) DecantFed optimizes \(d_{i}\) by solving \(\mathbf{\mathcal{P}}\)2. Other settings for UniformDecant and DecantFed are the same. Figure 5 shows the learning curves for UniformDecant and DecantFed by using CIFAR-10 when \(\beta=0.1\) and \(\beta=1\). From the figures, we can find that DecantFed can achieve not only a higher final model accuracy but also a faster convergence rate. This basically demonstrates that dynamic workload optimization enables clients with high computational capacities to train their local models over more data samples can significantly improve the FL performance.
## 4 Conclusions  
The installation of a novel silica-aerogel-incorporated composite insulation board in the roof, coupled with the application of silica-aerogel-incorporated composite cement paste as render on the roof-top surface, was studied to explore the prospect of adopting nanoporous silica-aerogel-incorporated composite materials in thermal-energy-efficient pitched roof assemblies in the tropical region. A morphology study on a silica aerogel sample revealed that the sample had no definite form and a porous structure. Characterization of the sample revealed that the sample was moderately amorphous, with silica gel as the main constituent, where the SiO\({}_{2}\) content was the highest, which was 74.48%. Then, 50 mm \(\times\) 50 mm \(\times\) 50 mm cement paste cubes were prepared with OPC and free water, where a control sample without silica aerogel and four silica-aerogel-incorporated samples with varying silica aerogel content were prepared. Composite insulation samples were prepared with HDPE and kapok fiber, where a control sample without silica aerogel and five silica-aerogel-incorporated samples with varying silica aerogel content were prepared. For optimization of thermal and strength performances, the silica aerogel contents of 4 wt.% and 3 wt.% were selected for the cement render and insulation board, respectively. A simulation study performed on a BIM with a pitched roof to project annual cooling load and  
Figure 14: Annual cooling load and energy savings of the BIM at varying roof configurations.  
energy savings of its indoor environment revealed that the application of 20-mm cement render, coupled with the installation of a 100-mm thick insulation board, is recommended as the optimum roof configuration.  
Conceptualization, S.A.F.; methodology, F.I.I.; software, S.U.A.; validation, N.H.; formal analysis, M.T.S.; investigation, N.H.; resources, F.I.I.; data curation, M.T.S.; writing--original draft preparation, S.A.F.; writing--review and editing, F.I.I., A.K.V.; visualization, S.U.A.; supervision, N.S.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Yayasan Universiti Teknologi PETRONAS, Malaysia, via grant number 015 LC0-155, as administered by Research Management Center of Universiti Teknologi PETRONAS.  
Not applicable.  
The data presented in this study are available on request from the corresponding author, S.A.F.  
The authors are thankful to [PERSON] and team of the Institute of Science of Universiti Teknologi MARA Shah Alam, Malaysia, as well as the team members of Syed Uthman Expert Systems (SUES) (003032518-D), for the support and collaboration.  
The authors declare no conflict of interest.
# Quantum Key Distribution in Kubernetes Clusters
## 3 Quantum Software Stack 2.0
### Trusted Repeaters  
QKD commercial technologies are currently limited to PTP exchanges and cannot exceed a hundred kilometres of range. Because of this, repeaters are required to make possible communication over longer distance. As presented in [21], the lack of quantum repeaters based on entanglement swapping in commercial devices leads to the necessity of having trusted repeaters. These rely on the weak assumption that nodes within a QKD network are trusted, and thus they could be used as relays to transmit the key material over long distances.  
Figure 2: The architecture of the new Quantum Key Server (QKS) version.  
The current version of the QKS introduces the possibility of using trusted repeaters and performing long-distance exchanges. All QKSs within a QKD network are provided with routing information coming from their routing modules Section 3.3 that keep this information updated according to the state of the network. When a long-distance exchange is required, i.e., an endpoint needs to pass through one or more QKSs to reach its destination, all the QKSs along the route are informed and reserve two keys to allow the flow of key material (during the KRP). We adopted the \"store and forward\" strategy as described in [22], where data are encrypted and decrypted on each relay. Clearly, this strategy has its flaws, though it is out of the scope of this paper to find an optimal algorithm for performing exchange in the case of trusted repeaters. However, due to the modular software architecture of our solution, it is possible to rely upon the KRP over the trusted repeaters and develop a custom exchange strategy. It is worth mentioning that, from a QKS user perspective, the presence of trusted repeaters is entirely transparent as they could send a request as in the PTP case, only indicating the destination SAE.
# Design and Calibration of a Tri-Directional Contact Force Measurement System
## 4 Static Loading Experiment
### Decoupling Matrix  
From Equation (1), the following expression was derived to compute the decoupled applied forces, \(\{\widetilde{F}\}=\left(\widetilde{F}_{x},\widetilde{F}_{y},\widetilde{F}_{z} \right)^{T}\)  
\[\{\widetilde{F}\}=[C]^{-1}\{F^{*}\}=[D]\{F^{*}\} \tag{2}\]  
where the inverse of the calibration matrix \([C]\) forms \([D]\) representing the decoupling matrix. The decoupled applied forces \(\widetilde{F}_{x},\ \ \widetilde{F}_{y},\ \ \widetilde{F}_{z}\) for Sensor 1 along \(x\)-axis, Sensor 2 along \(y\)-axis and Sensor 3 along \(z\)-axis, respectively, were calculated by multiplying the decoupling matrix with the vector of measured force values for each case. Finally, these decoupled applied forces \(\left(\widetilde{F}_{x},\ \widetilde{F}_{y},\ \widetilde{F}_{z}\right)\) were compared with the actual applied forces \(\left(F_{x},\ F_{y},\ F_{z}\right)\) for each sensor to validate the effectiveness of the calibration test and the decoupling matrix.
## 5 Blast Analysis  
### Blast Scenario  
A CFD analysis was performed using the validated numerical model of the protective tunnel to investigate the effects of the slope angle of the external tunnel entrance and blast pocket on overpressure mitigation, as described in Section 4. The blast scenario was established considering external entrance slope angles, \(\alpha_{s}\), of 90\({}^{\circ}\), 75\({}^{\circ}\), 60\({}^{\circ}\), and 45\({}^{\circ}\) and the ratios of blast pocket length to tunnel width, \(L_{b}/L_{w}\), of 0.5, 1.0, 1.5, and 2.0, respectively. The shape of the external tunnel entrance was archived for all the models. The simulation matrix for the parametric study is presented in Table 3, where a total of seventeen cases were considered. Model Arch-90-0 is the original model of the protective tunnel used in Section 4 for model validation and has a slope angle, \(\alpha_{s}\), of 90\({}^{\circ}\) and no blast pocket (\(L_{b}/L_{w}=0\)). The maximum overpressures were monitored at a point 55 m inward from the entrance of the main tunnel, as shown in Figure 13; that is, the monitoring location was placed far enough away from the main tunnel entrance based on the stability analysis presented in Section 4.2.  
Figure 12: Validation of numerical results in comparison to design charts in UFC 3-340-02.  
The shapes of the external tunnel entrance varying the slope angle for \(L_{b}/L_{w}\) = 1.0 are shown in Figure 14, and these are the same for \(L_{b}/L_{w}\) = 0.5 and 1.5. The shapes of the tunnel model varying the ratio of blast pocket length, \(L_{b}\), to the tunnel width, \(L_{w}\), are shown in Figure 11. Three blast pockets were arranged where the direction of the blast waves changes; that is, at the corners, as shown in Figure 15b-d. Figure 16 shows the length of the blast pocket, \(L_{b}\), and the tunnel width, \(L_{w}\), for the blast pocket A with a ratio of 0.5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Model** &
\begin{tabular}{c} **Entrance** \\ **Shape** \\ \end{tabular} & \(\mathbf{\pi_{s}}\) & \(\mathbf{L_{b}/L_{w}}\) & **Note** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: Simulation matrix.  
Figure 13: Monitoring location in the main tunnel.  
Figure 15: Models varying the depth of blast pocket. (**a**) Arc-90 (original); (**b**) Arc-90-05; (**c**) Arc-90-15-10; (**d**) Arc-90-15.
## 2 Results and Discussion
### Volatile Components in HAB
#### 2.3.4 Cyclic Components  
Furans, phenols, and lactones were classified under this group. In this research, a total of 29 furans were detected. Compounds of 1-(2-furanyl)-ethanoneone, 5-methyl-furfurral, 2-acetyl-5-methylfuran, and 2-furanmethanol have been described as contributing a honey, caramel odor [27]. Dihydro-2-methyl-3(2_H_)-furanone reveals sweet, bread and buttery aroma with a sensory threshold of 5 ng/L [44]. This kind of compound mainly contributes to the sweet aroma of Baijiu. Eight out of the 18 phenols are first detected in HAB. 2-Methoxy-4-ethylphenol, 2-methoxy-4-methylphenol and 2-methoxy phenol were reported to be important aroma compounds in HAB, revealing smoke, sweet, and spice aroma with odor thresholds of 123 ug/L, 315 ug/L, and 13 ug/L, respectively. 2-Methyl-phenol and 3-ethyl-phenol are key food odorants [34], and they have not been reported in HAB before.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods  
For testing the wireless data relay system in the field, we selected basic water quality indicators for routine water quality monitoring, including pH, electrical conductivity (EC), dissolved oxygen (DO), and water temperature.
# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
## 4 Results and Analysis
### Privacy  
Concerning privacy (Figure 3d), neither centralized optimization nor full contribution grant any privacy to the users, as they require full knowledge about the state of the system. Conversely, localized contribution strategies allow a fraction of the user to keep their data private. This fraction increases with the population size for aspiration learning, while Q-Learning trades lower privacy off for higher fairness. Random contribution offers the highest privacy--around 50% of users (as each user has a 50% chance of contributing)--at the expenses of other measures, e.g., success rate and efficiency.
## 5 Experiment and Analysis
### Comparative Analysis of Different Datasets in Different Models
#### 5.4.3. The Classification Performance of Balanced Datasets on Different Models  
Figure 13a shows the diagnostic results of the method proposed in reference [13] under balanced data, with an average accuracy of 91.68% for each category. Figure 13b shows the diagnostic results of the method proposed in this paper, with an accuracy of 95.86%. Figure 13c shows the diagnostic results using only the GLFC method proposed in this paper, with an average accuracy of 89.29%. Figure 13d shows the diagnostic results using the BiGRU method, with an average accuracy of 88.43%. Figure 13e shows the diagnostic results using the CLA method, with an average accuracy of 81.00%. Figure 13f shows the diagnostic results using the CNN method, where the average accuracy is 82.14%. For details, please refer to Table 8. This indicates that the diagnostic performance of the ST-CBA model proposed in this article is superior to that of other methods. The method proposed in this article can be used for the fault diagnosis of plunger pumps, with high diagnostic efficiency and accuracy.  
Figure 13: Test results after equalizing data based on the SMOTE-Tomek Link. (**a**) Reference [13], (**b**) the ST-CBA, (**c**) GLFC, (**d**) BiGRU, (**e**) CLA, (**f**) and CNN methods.