esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 In-Orbit Verification  
In this section, the in-orbit experimental results are presented. The satellite operates in a low-earth orbit, with the orbit height \(h\) being 400 km. The camera utilizes a CMOS imaging sensor with a pixel size of 5.5 \(\upmu\)m. The orbit and attitude information of the satellite is sent in real time to the camera to calculate the compensated image motion. Figure 9 presents two typical in-orbit images of stars that were captured using the IMC technique with the local area of the target star and gray-scale three-dimensional images of the corresponding image data. From the figure, it is evident that the imaging results were excellent after compensating for image motion through the pointing platform. The energy concentrations of 5 \(\times\) 5 pixels in Figure 9a,b are 71.8% and 73.4%, respectively. The calculation method of energy concentration is given in (14), where \(I(i,j)\) represents the DN value of a pixel. The energy concentration of the target stars in the overall captured images exceeds 70%, thus meeting the requirements for detecting high-magnitude celestial targets. As it is generated after the image motion compensated, the image is representative enough to illustrate the IMC method using the two-axis pointing platform for space cameras.  
\[E=\frac{\sum_{m,n=-1}^{1}I(i+m,j+n)}{\sum_{m,n=-2}^{2}I(i+m,j+n)}. \tag{14}\]  
Figure 8: Image with IMC.  
Figure 7: Image without IMC.
## 4 Results & Discussion  
### Evaluation Details  
The dataset was partitioned into train, test, and validation datasets at random. Sixty percent of the dataset was utilized to train, with the remainder shared evenly between test and validation samples. Furthermore, it was ensured that there would not be any overlap between the datasets. To train and assess the effectiveness of the suggested framework, a total of ten repetitions were performed. The pre-trained Inception-ResNet-V2 model was utilized, and the model was fine-tuned, utilizing the training dataset every time.  
The model was trained for 100 epochs with a batch size of eight for the training, validation and testing process. Mean square error was used as a loss function, while the Adam optimizer with a learning rate of \(1\times 10^{-5}\) was used as an optimizer.
# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
## 3 Proposed Methodology  
In the proposed work, the following stages are conducted: data collection, preprocessing, aspect/feature extraction, representation of word2 vec, and implementation of the swarm ensemble, as depicted in Figure 2.  
Data gathering begins with site scraping and online repository access, which yielded the dataset utilized in this paper. Another crucial step is the analysis of the data. It is imperative to refine the acquired data thoroughly. Secondly, the raw, unclean data undergo a preprocessing stage, during which it is refined and formatted in preparation for subsequent accurate processing. Thirdly, we extract and compare the features (aspects) of the enhanced data. We were working with aspect-based labels and multidimensional data, which can be challenging and prone to various interpretations. Fourthly, we employed the word2 vec tool to generate data vectors, serving as a tool for data representation. Before the multi-labeling application, the data undergo training utilizing the suggested method known as EDAO. Subsequently, an analysis is conducted. The following sections of this article provide a detailed elucidation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Approach** & **Advantages** & **Disadvantages** \\ \hline BR & Simple and fast & Does not model label correlations, affected by class imbalance, requires complete labeled data \\ \hline Pairwise (PW) & Conceptually straightforward & Time complexity concerns, ineffective for overlapping labels, needs complete labeled data \\ \hline Power Set Label & Accounts for label correlations & Computational complexity issues, prone to overfitting \\ \hline Pruned Sets Method & Handles irregular labeling, operates quickly, considers label correlations & Relics on predictive confidence function, challenges with unlabeled data \\ \hline Ensembles of Pruned Sets (EPS) & \begin{tabular}{c} Efficient predictive performance, \\ parallel processing \\ \end{tabular} & Does not utilize unlabeled data \\ \hline C4.5 & \begin{tabular}{c} Allows attribute selection, \\ enhances learnability \\ \end{tabular} & Does not consider class correlations, cannot use unlabeled data \\ \hline AdaBoost-MR & \begin{tabular}{c} AdaBoost-MH and minimize hamming loss \\ errors, improve accuracy \\ \end{tabular} & Poor performance, does not use unlabeled data \\ \hline ML-kNN & \begin{tabular}{c} Enhances performance works well with text \\ and image data \\ \end{tabular} & Does not exploit unlabeled data \\ \hline BP-MLL & Provides optimal generalization capabilities & \
\begin{tabular}{c} High computational complexity during \\ training, does not use unlabeled data \\ \end{tabular} \\ \hline CNN-HF &
\begin{tabular}{c} Considers correlations between data \\ and classes \\ \end{tabular} & Reduces accuracy with unlabeled data \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of multi-label text classification approaches.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 4 Communication Optimization Schemes
### Operation of the Parameter Server for Communication Optimization  
The left side of Figure 4 shows how the parameter server operates when performing distributed deep learning by applying layer dropping. When training starts, the parameter server initializes the model parameters and sends them to workers. After the first step, the parameter server sends compressed model parameters to the workers. When the workers send compressed gradients calculated using the model parameters sent by the parameter server, the parameter server searches the list [0] among the received gradients. When the parameter server finds the list [0], the parameter server converts the list [0] to the shape of the original layer and fills the converted list with zeros using the shape information of the layer that the parameter server has. After restoring all the list [0] to the original shape, the parameter server updates the model parameters using the restored gradients. Before sending the updated parameter to the worker, the parameter server compresses the parameters to be sent. To compress the parameters, the parameter server checks whether each layer has been updated or not. The parameter server compresses the parameters to be transmitted by replacing the un-updated layer with the list [0]. The compressed parameters are sent to the workers.
## 3 Results and Discussion
### Volatile Compounds Analysis
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{ \begin{tabular}{c} **Retention** \\ **Time (min)** \\ \end{tabular}  } & \multirow{2}{*}{**CAS**} & \multicolumn{5}{c}{**Concentration (ng/g)**} \\ \cline{5-10}  & & & & **BHCH \({}^{*}\)** & **SXZH \({}^{*}\)** & **FXZH \({}^{*}\)** & **LTWH \({}^{*}\)** & **FZWH \({}^{*}\)** \\ \hline \multicolumn{10}{p{42.7 pt}}{**Alcohols**} \\ \hline A1 & 2-Heptanol & 9.96 & 543-49-7 & 1816.45 \(\pm\) 5.39 & 3583.08 \(\pm\) 1.67 & n.d. \({}^{\prime}\) & n.d. & n.d. \\ \hline A2 & 1-Hexanol & 8.61 & 111-27-3 & 1318.91 \(\pm\) 4.45 & 2084.91 \(\pm\) 2.75 & n.d. & n.d. & 1776.81 \(\pm\) 0.64 \\ \hline A3 & 1-Octen-3-ol & 13.58 & 3391-86-4 & 970.42 \(\pm\) 3.49 & n.d. & 299.66 \(\pm\) 1.5  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
## 5 Discussion  
The rapid development in the field of IoT technologies facilitates wireless real-time ambient parameter monitoring in fresh fruit and vegetable supply chains. This creates possibilities for reducing the amount of products lost and wasted along the chain. Firstly, an IoT-based monitoring system can be used for getting insight into the current product status, performing weakness analysis on parameter profiles, and enhancing process control. Secondly, the combination of a monitoring system with predictive food models enables prediction of the remaining shelf life in each step of the chain on actual sensor data [112]. This approach facilitates optimization of storage and logistics [113; 78; 117]. For example, instead of the conventionally used FIFO (first in first out) principle, the EFO (first expired first out) principle could be applied in warehouse management, which allows for shelf life-driven distribution of products [17]. To realize this, shelf life models for respective products are required [112; 114]. If fresh fruit and vegetable supply chains should benefit from the combined use of IoT-based sensors and dynamic shelf life, a range of specific and accurate models is needed. This is a challenging task, since fresh fruit and vegetables are a complex product group with various quality and spoilage characteristics. It strongly depends on the product which ambient parameters have most influence on the quality and hence should be monitored
## 3 Results and Discussion  
A photo of the fabricated antenna is illustrated in Figure 4a. The measured and simulated results of return loss in terms of S11 of the 1-port and S22 of the 2-port are displayed in Figure 4b. For the two polarizations, the measured values of S11 are \(-\)30 dB and \(-\)34 dB at their corresponding center frequencies.  
Figure 4: **(a)** Photograph of the proposed antenna; **(b)** Simulated and measured S11 and S22 of the proposed antenna.  
Figure 3: Simulated return loss with different radiator shapes.  
The return loss achieves a bandwidth of 3.3% from 4.2 to 4.34 GHz (for a 10-dB return loss) at 1-port and a bandwidth of 5.1% from 3.8 to 4.0 GHz at 2-port. All the data are measured with an Agilent E8362C Vector Network Analyzer (VNA). A reasonably good agreement between the simulation results and measured results is observed.  
The far field radiation patterns of the proposed antenna are shown in Figure 5, which are obtained from HFSS and tested in an anechoic chamber. The simulated and measured E-plane and H-plane far field radiation patterns of 1-port at 3.9 GHz are presented in Figure 5a,c, respectively. The cross-polarization components are also illustrated. In this case, higher cross-polarization levels are observed at the E-plane than at the H-plane. The cross-polarization levels are approximately 24-dB and 22-dB lower than the co-polarization component at the boresight in the E- and H-planes, respectively, indicating excellent polarization purity. Figure 5b,d present the measured and calculated radiation patterns in the E- and H-planes of the 2-port, respectively. The cross-polarization components are approximately 24-dB and 21-dB lower than the co-polarization component at the boresight in the E- and H-planes. The measured gain of the fabricated antenna is 4.6 dBi at 3.9 GHz and 5.9 dBi at 4.3 GHz, respectively. In general, the calculated co-polarization patterns agree well with the measured ones.
## 4 Result and Discussion  
### Antenna Alignment  
The maximum amplitude of the TB is received by the GMR when the antenna beam points to the center of the sun. Then, the azimuth and elevation biases of the antenna beam between the peak TB and the predicted sun position are the corresponding calibration angle of antenna pointing. During the beam scanning of azimuth and elevation, the sun is located by an automatic search method until a maximum is found, and the pointing bias can be determined by this method. In addition, we can use this result to automatically check and calibrate the pointing of GMR that is deployed in the field. The difference between the observed antenna elevation (reading) and the calculated elevation of the sun is the calibration value of the antenna alignment, and the same as elevation calibration for the azimuth calibration.  
The scanning solar power as a function of the radial distance to the antenna beam axis is approximated by a Gaussian function. Because there are some biases in antenna alignment, the scanning solar power data exhibit a Gaussian pattern with a maximum in the center but slightly offset from the origin. This offset points to a small bias in the GMR antenna alignment. Therefore, Equation (7) becomes  
\[G(x,y)=\exp\left\{-4\ln 2\left[\frac{\left(x+\Delta_{az}\right)^{2}}{\theta_{ H}^{2}}+\frac{\left(y+\Delta_{el}\right)^{2}}{\theta_{E}^{2}}\right]\right\}, \tag{13}\]  
where \(\Delta_{az}\) and \(\Delta_{el}\) are the biases of the azimuth and elevation, respectively. Thus, the antenna gain can easily be fitted to this equation by the least-square method because a scanning antenna simply cannot point optimally at the sun all of the time. Figure 4 shows the pointing bias from fitting the Gaussian pattern at each frequency from December 2019 to January 2021. The pointing bias data exhibit a slight offset as is still seen in the center, the mean azimuth and elevation could be determined with 0.17\({}^{\circ}\) and 0.1\({}^{\circ}\). The results show that the solar radiation can be used for monitoring and calibrating the alignment of the GMR antenna.  
Figure 4: _Cont._
## 4 Results and Discussion  
### Experimental Environment  
To assess satisfaction and verify the system's reflection of grip strength, six participants (22.17 years old, \(\pm\)2.13), three men and three women, were recruited. The participants were students from Daegu University, and one of them had no prior experience using VR systems. The study was conducted with individuals who did not have any musculoskeletal disorders affecting the FDP. The participants were fully informed about the experiment beforehand and consented to participate.  
To evaluate their satisfaction with the proposed system, a mixed-methods research approach, incorporating both quantitative and qualitative evaluations, was used, and the system was assessed through semi-structured interviews. The questions designed for the quantitative evaluation of the system were as follows.  
Q1: I felt more sensory interaction using the system that reflects muscle activity.  
Q2: I did not need any special adaptation or learning to use the system.  
Q3: The input of muscle activity was sufficiently reliable and stable.  
Q4: I was satisfied while using the system.
## 3 Development of Virtual Sensor
### LSTM-Autoencoder
#### 3.1.2 Autoencoder  
An autoencoder is an RNN used for unsupervised learning that consists of a basic structure with input, hidden, and output layers. The hidden layer is divided into an encoder, latent vector, and decoder, as illustrated in Figure 3. The autoencoder has fewer neurons in the hidden layer than in the input and output layers owing to this structure, resulting in a bottleneck shape in which the number of neurons in the input and output layers is equal. The working principle of the autoencoder is as follows. The encoder learns the unique characteristics of the input data as they are input into the autoencoder and then compresses them to extract the latent vector. The decoder reconstructs the compressed data to produce output data similar to the original input data (data before compression). However, the output data cannot be identical to the original input data owing to compression by the encoder and reconstruction by the decoder. As a result, there is a reconstruction error between the original input and output data. The autoencoder is trained to minimize the reconstruction error.
# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 1 Introduction
In recent years, with the rapid development of intelligent optimization algorithms, various swarm intelligence optimization algorithms such as the Salp Swarm Algorithm (SSA) [19], Bobcat Optimization Algorithm [20], and Wombat Optimization Algorithm [21] have demonstrated remarkable advantages in the field of path planning due to their excellent global search capabilities and convergence performance. These algorithms, by simulating the intelligent behaviors of biological groups in nature, provide novel approaches for solving complex optimization problems. Therefore, this paper proposes an improved A* algorithm based on the SSA. The algorithm introduces a new A* heuristic function and optimizes its weight coefficients to enhance search efficiency while ensuring path feasibility. Furthermore, this paper also optimizes path smoothing to enhance the safety and smoothness of the final path. In practical applications, path smoothing is crucial. Traditional path smoothing methods include the Artificial Potential Field [22], Bezier curves [23], and B-spline interpolation [24], and others. Among these, the B-spline interpolation algorithm is the most widely applied. However, traditional methods like B-spline interpolation often struggle to maintain a safe distance from obstacles. To address this, our approach integrates an obstacle avoidance mechanism into the smoothing process, balancing both path smoothness and safety. We propose a two-stage optimized B-spline interpolation strategy: in the first stage, control point positions are adjusted to avoid collisions with obstacles; in the second stage, global obstacle avoidance is achieved. In conclusion, this paper focuses on how to optimize the A* algorithm using the SSA to improve the efficiency of path search and proposes an improved B-spline smoothing strategy to guarantee the safety and smoothness of the path. The following chapters will provide a comprehensive explanation of the proposed method, followed by the experimental results and comparative analysis to validate the effectiveness of the proposed approach. The main structure of this paper is as follows: Section 2 introduces the path planning algorithm, proposing an A* algorithm optimized by the SSA. Section 3 elaborates on the path smoothing algorithm, presenting an improvement of the B-spline interpolation method. The simulation experiments of the two proposed algorithms are demonstrated in Section 4. The conclusions are given in Section 5.
## 3 Acquired Data
### Disturbance Simulation
#### 4.3.2 Impulse Disturbance  
An impulse disturbance has an impact on the wheel detector signal. Such a phenomenon can be caused by the discharge on the catenary during rising or falling of the pantograph, driving with power through the neutral sections (change of the feeding system) or other commutations in the traction system.  
Impulse disturbance may be simulated as a Kronecker Delta signal, defined as:  
\[\mathrm{\delta(i)}=\left\{\begin{array}{ll}1,\ \mathrm{for\ i=0}\\ 0,\ \mathrm{for\ i}\ \
eq\ 0\end{array}\right. \tag{13}\]  
The disturbance can be added to the wheel signature as presented on the Figure 14, scaled by \"k\" and shifted by \"m\" samples to simulate different moment in time when the disturbance affects the signal (Equations (14) and (15)).  
\[\mathrm{u1_{d}(n)} =\mathrm{u1(n)+k\cdot\delta(n\ -\ m)} \tag{14}\] \[\mathrm{u2_{d}(n)} =\mathrm{u2(n)+k\cdot\delta(n\ -\ m)} \tag{15}\]  
Both signals u1 and u2 are affected in the same moment, which is typical for such types of disturbance (effect of commutations). To obtain effects on the detector signal that are observed in the field, Kronecker delta should be scaled by an extremely high factor (e.g., k = 1000) to achieve high enough energy. Such a signal is not transferred by the detector  
Figure 17: Example of disturbed signal (k = 0.05, \(\sigma\) = 50 mV, not filtered), u(t), d(t), u\({}_{\mathrm{d}}\)(t) respectively. (**A**) Original, wheel signal; (**B**) Disturbance; (**C**) Wheel signal with scaled disturbance.  
so a more natural approach is to replace it by the pulse with a lower amplitude (i.e., the maximum one which may be transferred by the wheel detector) and lasting longer.  
Pulse disturbances appear rarely so it is sufficient to add a single pulse per a whole wheel signature at various points in time (different \"m\"), as it is presented in Figure 18.  
The delay \"m\" may be generated randomly. Pulses shall be generated in the same, as well as in the opposite phase in the case of signals \(u1\) and \(u2\) (as observed in the field, Figure 19).
# Methodology for the Assessment of Multi-Hazard Risk in Urban Homogeneous Zones
## 2 Methodology
### Multi-Criteria Analysis and Decision-Making Approach to Risk Assessment  
In the analysis of natural hazards, impacts are often expressed in terms of hazard, vulnerability and exposure. A hazard (H) presents the probability that a harmful event will appear in a particular area and in a certain time interval. Vulnerability (V) is defined as the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard. Exposure (E) is the totality of people, property, systems or other elements present in hazard zones that are thereby subject to potential losses.  
In this research, the hazard (H) is presented by seismic hazard maps with a Peak Ground Acceleration--PGA for the earthquake, while, in the case of sea floods and extreme sea waves, it is based on a flood depth and a wave height in inundation areas, respectively. The exposure (E) that corresponds to the measure of hazard will be presented by intensity and Area Impact (e.g., area exposed to earthquake, flood or extreme waves). Furthermore, vulnerability is evaluated by vulnerability index on multiple levels (for the particular building, for the settlement, etc.) with the use of additional criteria set which is different for each level. Therefore, in this research, function f represents mathematical PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) method [29] that will connect all criteria and assess the risk for the observed area.  
Obviously, there are many other mathematical methods for multi-criteria analysis and decision-making, but some of them are better accepted and more widely used. Three of them have recently become the most popular: AHP [25], TOPSIS [26] and PROMETHEE [30; 31]. There is also a need to decide which of the available methods is the most adequate for a particular problem, but very often, the outranking methods like PROMETHEE are the most suitable choice [32]. This is especially because PROMETHEE method can be simplified to be used by non-expert users [33; 34].  
Furthermore, using the concept of vulnerability makes it more explicit that the impacts of a hazard are also a function of the preventive and preparatory measures that are employed to reduce the risk. Depending on the particular risk analyzed, the measurement of risk can be carried out with a greater number of different variables and factors, depending inter alia on the complexity of the chain of impacts, the number of impact factors considered and the requisite level of precision. The scheme of assessment of single-hazard and multi-hazard exposure for the investigated coastal urban area is shown in Figure 4.