esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 4 Results  
### Setup  
We develop a classifier in this paper using a particular YOLOv7 architecture. The layer number and parameter adjustments listed in Table 2 are used to finetune the method. Plotting the figures and handling data are carried out using the Python platform. These experiments also use the modules numpy, open cv, and pandas. The standard Keras library is used to download the pretrained DenseNet parameters. However, because our computer's RAM is limited and since no graphics processing unit is being employed in the trials, the batch size will also be altered.  
Figure 4: The YOLOv7 framework features a lead head and an auxiliary head that are guided by a label assigner [21].  
Figure 5: Flow diagram of the ship detection model.  
Figure 3: The YOLOv7 architecture, the planned re-parameterized version of RepConv. RepConv is replaced by RepConvN as RepConv does not have an identity connection [21].
## 3 The Proposed Protocol
### Three Proposed Authentication Methods
#### 3.3.2 Signature-Concatenation Authentication (SCA)  
We introduce the second proposed method, Signature-Concatenation Authentication (SCA), using the example of Figure 8. We propose the Signature-Concatenation Authentication (SCA) method calculates the individual signatures for the receivers that belong to different zones and concatenates the signatures into one. For the case where the receivers are located in \(N\) different zones, the transmitter vehicle attaches to its message a concatenated signature of \(N\) different signatures, calculated for each zone. Then, the receivers in each zone verify only their signature corresponding to its zone, among the \(N\) signatures. The transmitter generates \(N\) signatures using the pre-stored secret keys of the transmitter within these communicated neighbor zones and attaches its corresponding public keys for verification. The transmitter provides the zone ID list \(L_{ZID}\), which indicates the ordering of the receiver zones to inform each receiver the signature that it should verify among the concatenated signatures. Each receiver verifies only the signature corresponding to their zone ID, using each receiver zone information, as shown in Figure 8.  
Figure 8: Multi-zone authentication using the signature concatenation (SCA) method.  
Algorithm 2 illustrates the authentication procedure of SCA for the example of Figure 6. Vehicles in zone 1 substitute \((g_{21})\)_and_\(pk_{1}\) in Equation (5), to verify \(\sigma_{1}\) over message m, while vehicles in zone 2 verify \(\sigma_{2}\) using \((g_{22})\) and \(pk_{2}\).This method suffers from a high communication overhead in high-density scenarios where the target receivers are located in multiple zones.  
```
Signature Generation:
1. Prepare message m= {\(L_{ZID},\;pid_{i},m_{i},T_{s}\)}
2. Generate \(\sigma_{1}=sk_{1}.H(m)\), \(\sigma_{2}=sk_{2}.H(m)\),... \(\sigma_{N}=sk_{N}.H(m)\)
3. Concatenate the signatures, and their corresponding public keys: \(\sigma_{\text{C}}=(\sigma_{1}||\;\sigma_{2},\ldots||\sigma_{N})\), \(pk_{\text{C}}=(pk_{1}||\;pk_{2},\ldots||pk_{N})\), where \(||\) represents the concatenation operation of two elements.
4. \(v_{i}\) broadcasts {\(L_{ZID},\;pid_{i},m_{i},T_{s},pk_{\text{C}},\sigma_{\text{C}}\)} Signature Verification:
1. All receivers in neighboring zones receive {\(L_{ZID},\;pid_{i},m_{i},T_{s},pk_{\text{C}},\sigma_{\text{C}}\)}
2. Each receiver checks the \(L_{ZID}\) to find its corresponding signature in \(\sigma_{\text{C}}\) and corresponding public key in \(pk_{\text{C}}\).
3. Then the receivers use the corresponding \(g_{2 ZID}\) to verify. If \(e\;(g_{2 ZID},\sigma_{i}\;)=e(pk_{i},H(m))\) accept the message, else reject the message.
```  
**Algorithm 2**Signature-Concatenation based Authentication (SCA)
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 3 Energy Deposition of Electrons in Materials
### Calculation Results of Energy Deposition of Multi-Energy Electron Beam  
Taking the parameters of the actual relativistic electron beam (REB) as the analog input source, the actual multi-energy-spectrum electron beam current, voltage, and energy spectrum waveforms are presented in Figures 5-7, and the corresponding analytical and Monte Carlo calculations are carried out.  
Figure 5: Electron beam current waveform (No. 21040).  
Figure 6: Electron beam voltage waveform (No. 21040).  
Figure 7: Multi-spectrum electron beam spectrum waveform (No. 21040).  
quite different. Compared with the experimental results, the calculated results of the Monte Carlo method are more real and reliable.  
The energy deposition of carbon phenolics is calculated by the Monte Carlo method under single-energy electron incidence. Figure 9a shows the variation of energy deposition profile at 0.6 MeV, Figure 9b shows the variation of energy deposition profile at 0\({}^{\circ}\).  
Figure 8: Comparison of the two calculation methods. (**a**) 1 Mev, Al. (**b**) 0.8 Mev, Al. (**c**) 1 Mev, C/PH. (**d**) 0.8 Mev, C/PH.  
Figure 9: Energy deposition profile in carbon phenolic. (**a**) Different incident angles. (**b**) Different incident energies.  
Through calculation and analysis, it can be concluded that energy deposition has the following characteristics:  
(1) The electrons with the same incident energy in carbon phenolic obviously have a deeper energy deposition than those in aluminum. The electrons with an initial incident energy of 1.0 MeV were deposited at a depth of about 1.9 mm in aluminum and peaked at 0.5 mm, while the electron deposited at a depth of 3.4 mm in carbonic phenol and peaked at 1.5 mm. At present, experiments on the thermodynamic effect of the electron beam are mainly carried out in the REB accelerator, which has a maximum electron energy of about 1.0 MeV. If thermal shock generation and propagation experiments are carried out on this machine, the thickness of the target should be greater than 3.5 mm, to avoid the impact of incident electrons on the sensor.  
(2) With the decrease of incident electron energy, the peak position and the depth of electron energy deposition gradually decrease, but the deposition peak becomes larger.  
(3) With the increase of the incident angle, the peak position of energy deposition gradually moves to the radiation surface. When the incident angle is 75\({}^{\circ}\), for the incident electron of 0.6 MeV, its energy deposition peak occurs on the incident surface, which is similar to the deposition profile of the X-ray.
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 3 Analysis and Verification
### Influencing Factors of Balancing Performance  
The quality of the balancing circuit is usually judged by balancing efficiency and balancing time. The relevant parameters in (33) for the effects of balancing efficiency are analyzed in this paper. From (3)-(5), (31), and (32), when a certain quantity of charges is transferred to the target cells, the average current will be larger, and the transfer power will be higher if the transfer current is larger, meaning the balancing time will be shorter. Therefore, the relationship between the relevant parameters in (31) and (32) and the balancing time is also analyzed in this paper.  
To analyze the relevant parameters in terms of the influence of balancing performance, a six-cell simulation balancing circuit was built in MATLAB/Simulink. The balancing circuit simulation parameters were set according to Table 1, and the voltage of the high-energy cells and the voltage of the low-energy cells involved in balancing were set according to Section 3.1.
## 2 Theoretical Background
### Support Vector Machines (SVMs)  
SVMs are supervised learning techniques used for classification, regression, novelty detection, and other purposes [38]. SVMs were conceived as linear models, capable of classifying data by creating a hyperplane to achieve maximum separation between the classes in the feature space that depends on the datapoints near the hyperplane, called support vectors [39]. Considering that real-world data tend to present nonlinear characteristics, SVMs have evolved to implement different kernel functions to deal with data distribution, achieving great accuracy, flexibility, robustness, and efficiency with high generalization from the training data [39].  
For a linearly separable case, the starting point is a data set \(D\), expressed as (36), formed by a \(n\)-dimensional vector of samples, \(\overrightarrow{x}\), and their labels, \(y\). The SVM classification function, or hyperplane, is defined with Equation (37), where \(\overrightarrow{w}\) is a weight vector, and \(b\) is the bias; this function separates the data as shown in Equation (38).  
\[D=\left\{\left(\overrightarrow{x_{1}},y_{1}\right),\left(\overrightarrow{x_ {2}},y_{2}\right),\ldots,\left(\overrightarrow{x_{n}},y_{n}\right)\right\} \tag{36}\]  
\[F\left(\overrightarrow{x}\right)=\overrightarrow{w}\cdot\overrightarrow{x}-b \tag{37}\]  
\[y_{i}\left(\overrightarrow{w}\cdot\overrightarrow{x}-b\right)>0,\ \forall\left(\overrightarrow{x_{i}},y_{i}\right)\in D \tag{38}\]  
The hyperplane needs to provide the greatest separation from both classes, called the margin; this can be achieved by modifying (38) by scaling \(\overrightarrow{w}\) and \(b\) as shown in Equation (39).  
\[y_{i}\left(\overrightarrow{w}\cdot\overrightarrow{x_{i}}-b\right)\geq 1,\ \forall\left(\overrightarrow{x_{i}},y_{i}\right)\in D \tag{39}\]  
The margin from the closest vector \(\overrightarrow{x}\) to the hyperplane is expressed in Equation (40). The vectors that satisfy (39) with an equality are the _support vectors_. To maximize the margin, the denominator of (40) needs to be minimized using Equations (41) and (42).  
\[margin=\frac{\left|F\left(\overrightarrow{x}_{i}\right)\right|}{\left\| \overrightarrow{w}\right\|}=\frac{1}{\left\|\overrightarrow{w}\right\|} \tag{40}\]  
\[minimize:\frac{1}{2}\left\|\overrightarrow{w}\right\|^{2} \tag{41}\]  
\[subject\ to:\ y_{i}\left(\overrightarrow{w}\cdot\overrightarrow{x_{i}}-b \right)\geq 1,\ \forall\left(\overrightarrow{x_{i}},y_{i}\right)\in D \tag{42}\]
## 2 Materials and Methods
### GMSC Characterization  
#### 2.2.1 Surface Marker Analysis  
Cultured GMSCs were analyzed using flow cytometry for the quantitative surface marker identification of CD73, CD90, CD105, CD34, and CD45. About 1 \(\times\) 105 cultivated cells were examined using a flow cytometer (Beckman Coulter, Fullerton, CA, USA) after being treated with monoclonal antibodies against the surface markers [50].
## 6 Adjacent Channel Interference
### Channel Switch  
After detecting the channels of target public APs, a safe wireless transmitter will switch its channel to interfere with the target public APs. However, there are four adjacent channels that can interfere with the working channel of the target public APs. From the above analysis in Section 4.1, we know that when the channel spacing between two neighbor APs is different, the channel interference between them is also different; thus, the packet loss rate caused by the channel interference will also be different.  
Therefore, which channel should the safe wireless transmitter be switched to so that the packet rate loss will be the maximum?  
Theoretically, when the channel spacing between two adjacent APs is one, the channel interference between the two channels is the largest, because the overlap between the two channels is the largest.  
However, in most cases, attackers can use two or more public APs to collect CSI measurements in order to improve the success rate of CSI-based attacks. Therefore, after detecting the channels of all target public APs, how does the safe wireless transmitter switch its channel to interfere with all the target public APs? There are two cases considered here: one is that the safe wireless transmitter only needs to switch its channel to a proper channel, and the other is that the safe wireless transmitter needs to switch its channel between two channels to interfere with all the target public APs.  
(a) Switch to a proper channel:  
There exists two cases in which the safe wireless transmitter can switch its channel to a proper channel to interfere with all target public APs. In the first case, there exists only one target public AP in the public place, and the channel of the safe wireless transmitter can be switched to an adjacent channel; for example, when the target public AP is working on Channel 6, the safe wireless transmitter can switch its channel to Channel 5 or Channel 7. In the second case, there exists two or more target public APs in the public place, and the channel spacing of target public APs is less than five. The channel of the safe wireless transmitter can be switched to the proper channel to interfere with the target public APs. For example, the channels of two target public APs are Channel 1 and Channel 6, and the safe wireless transmitter can switch its channel to Channel 3 or Channel 4.  
In the second case, the safe wireless transmitter switches its channel to the proper channel to interfere with all target public APs, but in order to maximize the packet loss rate, the safe wireless transmitter can also switch its channel between two channels.  
(b) Switch between two channels:  
If the channel spacing between the target public APs is greater than five, the safe wireless transmitter can switch its channel between two channels to achieve adjacent channel interference. For example, when the channels of the two target public APs are Channel 1 and Channel 11, respectively, the safe wireless transmitter needs to switch its channel between Channel 2 and Channel 10.
## 3 Results  
Figures 9 and 10 show a colour map of the spatial distribution of the SPL at 1 kHz based on the FEM 2D analysis of sections A-D. It is evident from the figures that before the planned intervention, the sound pressure level was high at most points in the sections, while after the noise mitigation, there was a remarkable reduction in the sound pressure levels. The chosen solutions significantly reduced the sound pressure level; on average, an IL of 10 dB (A) is obtained in the frequency range of interest (200-1000 Hz). This result is particularly significant when considering section C (Figure 10), corresponding to the road section where the measurements with the highest sound pressure levels were taken.  
In the preliminary measurements, the receivers were positioned according to the specific regulations [21; 23; 37]. Correspondingly, Figure 11 presents the IL from the A-weighted SPL spectrum in the frequency range of 200 Hz to 1 kHz in 5 Hz steps at the  
Figure 10: Spatial distribution of SPL based on the FEM 2D analysis of sections C (first row) and D (second row) at 1 kHz; left column: without the noise barrier; right column: with the noise barrier.  
Figure 9: Spatial distribution of SPL based on the FEM 2D analysis of sections A (first row) and B (second row) at 1 kHz; left column: without the noise barrier; right column: with the noise barrier.  
receiver point R of section A (see Figure 7). Positive values of the insertion loss correspond to a reduction in the noise disturbance at each frequency. The IL plot confirms an average IL of 10 dB (A), with a maximum sound level reduction of up to 25 dB (A) at some frequencies above 600 Hz. Moreover, the IL trend is further explained in Figure 11 through an exponential smoothing function (with a damping factor of 0.9), which highlights IL's significant increase (>10 dB(A)) from 500 Hz above. On the lower frequencies (between 200 and 500 Hz), the SCAB FEM analysis reports an IL ranging from 5 dB above.  
The numerical analysis results confirmed the analytical pre-dimensioning of the noise barrier (the Maekawa method with the Kurze-Anderson formulation) and the effectiveness of the selected low-noise road pavement in combination with sonic crystal noise barriers (SCAB), which would result in the view in Figure 12. Here, each SCAB colour represents a different height as follows: A (green) height is 5 m, B (purple) is 4 m, C (blue) is 8 m, and D (red) is 7 m.
# Psychoeducation on Stress and Anxiety Using Virtual Reality: A Mixed-Methods Study
## 3 Results  
### Sample Characteristics  
The study sample was composed of 20 participants (12 female, 60%), with a mean age of 43 years old (SD = 10). In total, 11 individuals (55%) were doctors, and 9 (45%) were nurses; 10 participants (50%) worked at the IRCCS Neurological Institute Carlo Besta and 10 (50%) at the Fondazione Europea Ricerca Biomedica (FERB). The sample reported high scores in the use (M = 5.6, SD = 1.2) and acceptance (M = 5.4, SD = 1.4) of technologies. Fourteen participants (70%) had never tried VR. The level of knowledge of this technology was low (M = 1.9, SD = 1.4), while general interest in its use was medium (M = 2.9, SD = 1.8). Seventeen participants (85%) reported that they had never used programs for psychological support and/or management of stress and anxiety. The sample reported good knowledge of the symptoms and causes of stress and anxiety, while lower knowledge of techniques to manage these conditions emerged (Table 2).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variables** & **Mean (SD)** & **N (\%)** & **Range** \\ \hline \hline \multicolumn{4}{l}{Have you ever used psychological support programs?} \\ Yes & & 3 (15\%) & \\ No & & 17 (85\%) & \\ \hline \multicolumn{4}{l}{Have you ever used stress and anxiety management} \\ programs? & & & \\ Yes & & 3 (15\%) & \\ No & & 17 (85\%) & \\ \hline \hline \multicolumn{4}{l}{Knowledge of the difference between stress and anxiety} & 4.1 (1.3) & 1–7 \\ \hline \hline \multicolumn{4}{l}{Knowledge of the causes of stress and anxiety} & 4.1 (1.2) & 1–7 \\ \hline \hline \multicolumn{4}{l}{Knowledge of the symptoms of stress and anxiety} & 4.1 (1.4) & 1–7 \\ \hline \hline \multicolumn{4}{l}{Knowledge of the main techniques to manage stress} & 3.5 (1.5) & 1–7 \\ \multicolumn{4}{l}{and anxiety} \\ \hline \hline \multicolumn{4}{l}{Importance of stress and anxiety management training} & 5.8 (1.4) & 1–7 \\ \hline \multicolumn{4}{l}{Have you ever searched the internet for information about stress and anxiety?} & 3.1 (1.8) & 1–7 \\ \hline \hline \multicolumn{4}{l}{Have you ever attended meetings on stress and anxiety?} & 2.2 (1.8) & 1–7 \\ \hline \hline \multicolumn{4}{l}{Have you ever read books on stress and anxiety?} & 2.5 (1.8) & 1–7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Background knowledge of stress and anxiety in the sample (N = 20).
## 2 Background
### Real-World Multi-Objective Problems  
Over the years, several artificially constructed test problems have been proposed to compose benchmarks for evaluating meta-heuristics. These problems offer many advantages over real-world problems for the purpose of general performance testing [70], by allowing users to compare the results of their algorithms (regarding effectiveness and efficiency) with others, over a spectrum of algorithms' instantiations [46].  
In the literature, one can find several of these MOP benchmarks, such as WFG, DTLZ [27], and UF [12]. However, even if an algorithm has successfully solved these problems, this does not guarantee effectiveness and efficiency in solving real-world problems [46].  
In terms of multi-objective hyper-heuristics, researchers have been using both benchmarks and real-world applications. However, few studies consider more than one real-world problem. This choice can, in fact, diminish the accuracy of evaluating hyper-heuristics on cross-domain applications.  
Table 1 presents the eighteen real-world problems that we have used in this work. Ten of them were picked from _Black Box Optimization Competition_ ([[https://www.ini.rub.de/P/EOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-201-7.zip](https://www.ini.rub.de/P/EOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-201-7.zip)]([https://www.ini.rub.de/P/EOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-201-7.zip](https://www.ini.rub.de/P/EOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-201-7.zip)), accessed date 10 April 2021) [38], a group of problems created/selected for the 9 th International Conference on Evolutionary Multi-Criterion Optimization (EMO'2017) ([[http://www.emo2017.org/](http://www.emo2017.org/)]([http://www.emo2017.org/](http://www.emo2017.org/)), accessed date 10 April 2021). We included the CrashWorthness problem due to the fact it was already considered in previous papers for all the hyper-heuristics. The problems Water, Machining, and CarSidel Impact were studied using MOABHH, but not for HHCF, HHLA, and HHRL. The other four problems were selected from the optimization literature and picked from the jMetal framework [71], the framework used by all studied hyper-heuristics. In the table, their number of objectives, variables, and constraints are detailed. All the problems are in continuous space.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods
### New Model
#### 2.4.4 QPSO-MKELM Model and Kmeans-QPSO-MKELM Model  
The QPSO-MKELM model is constructed by optimizing the hyperparameters of the MKELM using a Segmented QPSO improved by GA. These hyperparameters include the Gaussian kernel width (\(\sigma\)), polynomial kernel degree (d), regularization parameter C, and the weight parameters of the three kernel functions (c1,c2,c3).  
The Kmeans-QPSO-MKELM model first divides the training set into k clusters using K-means clustering. The k clusters are then treated as k subsets of the original dataset. For each k subset, a QPSO-MKELM sub-model is trained, and all k sub-models are combined to form the final model. In this study, the Kmeans-QPSO-MKELM model adopts a weighted scheme during the testing phase. First, the Euclidean distance between each data point in the test set and the centroids of the k clusters is calculated. The two clusters whose centroids are closest to the test point are selected, and the corresponding sub-models are used to make predictions. The final prediction is then computed using a weighted averaging approach, where the weights are determined by the formula 1/(d'6). In this study, k was chosen as 10, 20, 30, and 40 for comparative experiments.
## 4 Mathematical Analysis of Handover Delay and Packet Loss of CoMP
### Packet Loss Analysis  
Packet loss is the amount of packets dropped, lost, or corrupted during transfer. Because the packet loss is proportional to the handover delay, the packet loss P_HOprotocol_ of the handover protocol of _HOprotocol_ can be calculated as follows:  
\[\text{P}_{HOprotocol}=\lambda_{\text{p}}\;\text{D}_{HOprotocol} \tag{2}\]  
Here, \(\lambda_{\text{p}}\) is the packet arrival rate in packets per time units, and D_HOprotocol_ is the handover delay of the handover protocol of _HOprotocol_. A summary of the total packet loss for MIPv6, HMIPv6, and CoMP is shown in Table 4. In the case of CoMP, a PUT holding request message and an ACK response message between the CoAP B and the WMMS are required during a handover to maintain the hold mode. Because it is assumed that during the hold mode, almost no packet loss occurs, packet loss during the handover operation is zero.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 5 Experimental Evaluation
### Dataset Level Assessment
Nevertheless, the mean (across all different datasets and all values of \(\alpha\)) loss of our framework for \(5\log_{2}T\) inducing points is small, and keeping the best clustering for each dataset results in a small improvement in the average clustering quality of Algorithm 1 compared against the standard (and way more time demanding) \(k\)-means.