esa-sceva/satcom-chunk-collection huggingface dataset:
## 10. Numerical Simulation in Matlab
### Discussion  
The path taken for the three strategies is indicated in red color, as shown in Figures 10-12 with the maximum speed value for each node.  
A position-speed phase diagram is used to visualize the relationship between speed and position (distance traveled) for each strategy, as shown in Figures 13-15. Three dots with different colors (red, orange, green, and blue) are depicted in each phase diagram.  
Figure 11: Map Strategy 2: oriented graph nodes.  
Figure 12: Map Strategy 3: oriented graph nodes.  
These dots will help illustrate a specific situation or task along the path. The red and orange dots indicate the nodes where the direction changes (i.e., when the movement changes from horizontal \"Node-Axis-X\" to vertical \"Node-Axis-Y\" or vice versa). Three green dots normally correspond to a node stop, first to load baggage, second to the dropping of the baggage, and the last to node \"0\" in parking. Two waiting nodes, noted as blue points, represent the two points waiting to enter battery charging. However, the energy consumed for each strategy is illustrated in Figures 16-18.  
Figure 16: Energy consumed in _Strategy1_.  
Figure 15: Position–speed phase diagram: _Strategy3_.  
When the AIVs moves with a limited speed to the average and when the energy consumed is low, as _Strategy1_ (cf.--Figures 13 and 16), despite making two stops at this level waiting node. An abrupt variation in speed between the maximum and average involves high levels of energy consumption; this situation is represented by _Strategy3_ (cf.--Figures 15 and 18).  
Figure 17: Energy consumed in _Strategy2_.  
Figure 18: Energy consumed in _Strategy3_.  
The average energy consumption is determined by maintaining the maximum speed along almost the entire length of the circuit, as shown in _Strategy2_ (cf.--Figures 14 and 17).  
During an AIV mission, two constraints are imposed, waiting baggage and traffic, which are considered inputs into our fuzzy logic model for strategy selection.  
Three strategies generated via our methodology effectively reflect these constraints. The goal is to find an optimal compromise between energy consumption, baggage waiting times, and traffic conditions.  
As shown using numerical simulations, _Strategy1_ focuses on optimal energy consumption. _Strategy2_ allows for a slight increase in energy usage to address waiting baggage. _Strategy3_ prioritizes alleviating energy constraints related to traffic while addressing waiting baggage.
## 3 Security Communication Model of OPC UA Based on Whirlpool
### Procedure of Security Channel Establishment  
In the procedure of building security communication channel between client and server, both of the ends will authenticate each other through checking whether the digital signature of CA is right. This model uses Whirlpool to obtain a message digest, then uses the private key of RSA in certificate to encrypt the message digest to generate digital signature. The procedure of building a security communication channel between client and server is illustrated in Figure 4.  
(1) Before building a security channel, the client requests CA to authenticate whether the server is legal. The contents of authentication include CA signature, issue and expiry date, and CA revocation list.  
(2) The CA returns the result of authentication to the client.  
(3) The client requests the server to build security connection. The client provides its certificate and a nonce to the server. The data to be sent are signed with the private key of the client, and then encrypted by the public key of the server.  
Figure 4: Procedure of security channel establishment.  
Figure 3: Architecture of security communication model.  
(4) After the connection request is received by the server, the server requests CA to authenticate the client. The contents of authenticate also include CA signature, issue and expiry date, and CA revocation list.  
(5) The CA returns the result of authentication to the server.  
(6) After the client is authenticated, the server responses the connection request from the client. The server sends a nonce, security token, and the lifetime of the token to the client. The contents to be sent is encrypted by the private key of the server, and then encrypted by the public key of the client. After that the client and the server can communicate with each other through the security channel. In the subsequent communication, the receiver of the message calculates the digest of the received message using Whirlpool, then compares it with the digest in the signature to check whether the message is intact or not.
# An Improved YOLO Model for Traffic Signs Small Target Image Detection
## 4 Experiment and Discussion
### Comparative Experiments with Mainstream Algorithms  
In order to validate the detection performance of the improved algorithm described in this paper, an experimental analysis was conducted. This involved a comparison of the improved algorithm with mainstream algorithms such as SSD, Faster R-CNN, RetinaNet, YOLOv3, YOLOv4, and YOLOv5. The backbone of the utilized Faster R-CNN is ResNet-101, while the SSD is based on ResNet-50. The input size was identical in both width and height. The experimental results are showcased in Table 9 and illustrated further in Figure 13.  
From the experimental data, it can be inferred that there is an inverse proportion between the number of parameters and detection speed. Among all of these compared algorithms, YOLOv5 has the least parameters, at just 7.13 \(\times\) 10\({}^{6}\), and offers the fastest speed. After the improvements were made to our algorithm, it added approximately 17.17 \(\times\) 10\({}^{6}\) parameters, subsequently dropping the speed to 80.7 frames per second. Despite this decline in speed, it is still remarkably competitive among these algorithms,  
Figure 13: mAP for different modules.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Parameters/M** & **P\%** & **R\%** & **mAP\%** & **FPS** \\ \hline SSD & 25.0 & 30.2 & 35.6 & 45.3 & 83.7 \\ Faster & 167.7 & 81.0 & 85.2 & 82.4 & 3.4 \\ R-CNN & 67.6 & 73.2 & 75.4 & 71.9 & 17.6 \\ YOLOv3 & 61.8 & 56.3 & 59.2 & 58.1 & 29.6 \\ YOLOv4 & 96.9 & 73.6 & 78.4 & 75.2 & 41.7 \\ YOLOv5 & 7.13 & 92.9 & 86.8 & 91.8 & 107.5 \\ Ours & 24.3 & 95.0 & 91.6 & 95.4 & 80.7 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Contrast with advanced algorithms.  
assuring its capability for real-time detection. Regarding accuracy analysis, experiments showed that when the input size was standardized at 640, YOLOv5 offered superior precision over YOLOv3, YOLOv4, SSD, Faster R-CNN, and RetinaNet. However, our paper's improved algorithm excelled, improving by approximately 2.1 and 4.6 percentage points in average precision and average recall for smaller detection targets, respectively. Furthermore, it outperformed YOLOv5 in terms of overall target accuracy, showing an improvement of approximately 3.6 percentage points.  
In summary, the experimental analysis indicates that the improved algorithm proposed in this paper demonstrates certain superiority, both in terms of accuracy and speed, on the TT100K traffic sign detection dataset.
# Influence of Sugar Beet Pulp Supplementation on Pigs' Health and Production Quality
## 2 Materials and Methods
### Experimental Design and Diets  
The pigs (81 days old) were distributed into two groups at the beginning of the experiment, namely (i) a basal diet groups (control group--CG) and (ii) basal diet with dried SBP group (treated group--TG-I). The basal feed was formulated according to the Nutrient Requirements of Swine [17]. The feed composition and nutritional values are shown in Table 1. Dietary contents were analysed according to the Association of Official Agricultural Chemists (AOAC) recommendations [34]. Other nutritional value parameters, including ME, were calculated by using a feed optimization program and tables of composition and nutritional value of feed materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CG** & **TG-I** & **CG** & **TG-I** \\ \cline{2-5}
**Ingredients (\%)** & \multicolumn{3}{c}{**First Fattening Period**} & \multicolumn{3}{c}{**Second Fattening Period**} \\  & \multicolumn{3}{c}{**(81–116 Days)**} & \multicolumn{3}{c}{**(116–163 Days)**} \\ \hline Barley & 69.90 & 66.90 & 63.00 & 60.00 \\ Barley malt sprouts & - & - & 6.50 & 6.50 \\ Grain mix (barley-buckwheat 60:40) & - & - & 6.79 & 6.79 \\ Oats & 3.00 & 3.00 & 5.00 & 5.00 \\ Rape meal & - & - & 6.00 & 6.00 \\ Wheat bran & 5.00 & 5.00 & 6.00 & 6.00 \\ Hulled soybean meal GMO & 7.20 & 7.20 & 2.87 & 2.87 \\ Dried sugar beet pulp & - & 3.00 & - & 3.00 \\ Molasses & 0.57 & 0.57 & - & - \\ Rapeseed oil unrefined & 1.91 & 1.91 & - & - \\ Peas & 3.00 & 3.00 & - & - \\ Rape seed expeller & 5.00 & 5.00 & - & - \\ NaCl & 0.35 & 0.35 & 0.35 & 0.35 \\ Calcium carbonate & 1.38 & 1.38 & 1.18 & 1.18 \\ Monocalcium phosphate & 0.39 & 0.39 & 0.32 & 0.32 \\ DL-Methionine & 0.19 & 0.19 & 0.04 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diet composition of control (CG) and treated (TG-I) groups.  
All animal groups were fed with wet feed (water and feed ratio 3/1); the equipment used for feeding was WEDA (Dammann & Westerkamp GmbH, Goldenstedt, Germany).  
The pigs' growth performance was evaluated by testing all pigs from both groups. For the evaluation of blood parameters, 15 pigs from each group were randomly selected. For the microbial profiling of faeces, samples from 15 randomly selected pigs were taken before the distribution of pigs into two groups (day 81), and 15 samples from each group were taken after the feeding experiment on day 163.
## 5 Discussion  
### Accuracy of the Albedo Background  
In the data assimilation algorithm, the background field is a preliminary estimate of target parameters, which is obtained from historical and empirical data, and reflects the general variation trend of the estimator [54]. Our EnKF algorithm results were obtained based on the optimal estimation of the current time. The background field trend introduces an observation increment in the optimal weight, so its accuracy is very important. We conducted spatial registration before constructing the background; after processing, the projection mode and resolution of MODIS data were consistent with Landsat. MODIS data actually provides background fields and dynamic models (preliminary estimates), so errors do exist, but they are allowed under the assimilation algorithm. Surface albedo presents a certain regularity in the growing season. We averaged the historical MODIS data, instead of the contemporary MODIS data, as the albedo background field because MODIS data were missing across our study area [55]. Consider the AU-DaP site as an example: The background field albedo has the same change trend as the original MODIS albedo, but with no missing data and an overall smoother structure. To this effect, the background field is more suitable for data assimilation.  
The background field encompasses periodic albedo characteristics, but the albedo is overestimated or underestimated due to the accuracy of MODIS products (especially in heterogeneous pixels) [56]. Under the assimilation scheme, however, the background field only provides an initial prediction and the weight of background data is determined according to its accuracy. When the background accuracy is low, the weight is decreased and the weight of the TM observation is increased. Therefore, the estimation results gradually approach the field observations as the quantity of TM observations increases; using the mean albedo of the time series as a background field is feasible and effective.
## 5 Analysis of Coal Gangue Image Processing and Recognition Results
### Coal and Gangue Image Defogging and Segmentation Field Application  
To test the applicability of the proposed coal gangue segmentation model and dust defogging algorithm, underground dust coal gangue images were collected and recognized before and after defogging. The recognition results are shown in Figure 14.  
As shown in Figure 14, the recognition performance of dust images before defogging was poor, with only partial recognition in areas with low dust concentrations, while no recognition results were obtained in areas with higher dust concentrations. After defogging, the recognition performance of the entire image improved significantly, verifying that the segmentation model and defogging algorithm fully meet the requirements for recognizing coal and gangue in underground dust-laden environments.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 4 Analysis of Eavesdropping Attacks in UASNs
### Eavesdropping Probability  
We model the successful chance of eavesdropping attacks by the _eavesdropping probability_, denoted by \(P(E)\). To derive \(P(E)\), we need to calculate the probability of no node being eavesdropped first. We then consider an _effective eavesdropping area_\(D\), which is the expected value of the critical region where the communication can be wiretapped by eavesdroppers only when a node falls in this region. Since the eavesdropper is equipped with a single isotropic hydrophone, it wiretaps acoustic signals uniformly in all directions, as shown in Figure 6. In particular, we have  
\[D=\pi d_{\max}^{2} \tag{17}\]  
where the value of \(d_{\max}\) can be calculated by Equation (16).  
We next denote the number of nodes in the eavesdropping area by a random variable \(Y\). Since nodes are randomly distributed in a 2-D area according to a homogeneous Poisson point process, we then have the probability of no node falling in the eavesdropping area, which is given by  
\[P(Y=0)=e^{-\rho D} \tag{18}\]  
Substituting \(D\) in Equation (18) by RHS of Equation (17), we finally obtain the _eavesdropping probability_\(P(E)\) by the following equation:  
\[P(E)=1-P(Y=0)=1-e^{-\rho\pi(d_{\max})^{2}} \tag{19}\]From the aforementioned analysis, the higher spreading factor \(k\) and the higher wind speed \(w\), the shorter the maximum eavesdropping distance \(d_{\max}\) is. Therefore, we can see from Equation (19) that with the fixed node density \(\rho\), the higher spreading factor \(k\) and the higher wind speed \(w\), the lower probability of eavesdropping attacks \(P(E)\) is. Our empirical results will further confirm this observation.
###### Abstract  
China's rural houses are mostly courtyard-style independent houses. Such houses have certain characteristics, e.g., small mutual influences between houses, strong transformation flexibility, and an easier approach to using the natural environment to develop passive energy-saving characteristics. Therefore, rural houses have large energy-saving potential. In this study, for the first time, the cold alleys between buildings were used as an energy source for passive cooling and ventilation. Traditional houses in Shuhe, China, were used as a case study. The cold alleys in the settlements were used to compensate for the natural conditions in summer, and the cold air in the cold alley was introduced into each room using hot-press ventilation and by employing an accumulation effect from a corresponding patio. The room was ventilated and cooled, and air ducts were used to connect the rooms on both sides of the patio to improve the cooling efficiency. The research variables included the existence or non-existence of wall heat radiation (WHR), and the importance and influence of the WHR on the indoor conditions were verified. The cold air trapped in the new system formed an air partition wall, effectively blocking the direct influence of solar radiation on the room, reducing the heat transfer rate of the residential wall, and consuming part of the heat. In winter, based on using air ducts as supporting members, a glass roof was added to the patio, which improved the heat storage capacity of the patio and turned it into a constant-temperature heater for heating the building interior. Based on calculations, in the new system without WHR, the annual cooling load reduction was 55,417.33 kWh. With WHR, the annual cooling load reduction was 28,537.57 kWh. The annual cooling load of the air insulation wall of the new system was reduced to 1133.7 kWh. In winter, using the glass roof to increase the heat storage capacity of the patio reduced the heating load to 54,537.78 kWh.  
CFD; passive cooling; passive heating; air insulation wall; cold alley; rural house +
Footnote †: journal: Applied Sciences
## 3 Results
### Characterization of Extracted Proteins from DB-Yeast and Se-Enriched DB-Yeast
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{H_{2}O_{2}}\)\({}^{\mathbf{a}}\)} & \multicolumn{3}{c}{**DB-Yeast Extracted Protein \({}^{\mathbf{b}}\)**} & \multicolumn{3}{c}{**Se-enriched DB-Yeast Extracted Protein \({}^{\mathbf{b}}\)**} \\ \cline{2-9}  & \(\boldsymbol{\beta}\)-Sheet & \(\boldsymbol{\beta}\)-Turn & \(\boldsymbol{\alpha}\)-Helix & **Random Coil** & \(\boldsymbol{\beta}\)-Sheet & \(\boldsymbol{\beta}\)-Turn & \(\boldsymbol{\alpha}\)-Helix & **Random Coil** \\ \hline
0 & 47.12 & 17.67 & 11.54 & 23.67 & 47.55 & 17.22 & 11.51 & 23.72 \\
0.05 & 46.46 & 17.81 & 11.75 & 23.98 & 46.89 & 17.54 & 11.67 & 23.90 \\
0.25 & 45.97 & 17.89 & 11.92 & 24.22 & 46.15 & 17.75 & 11.89 & 24.21 \\
1 & 44.71 & 18.13 & 12.17 & 24.99 & 45.23 & 18.05 & 12.13 & 24.59 \\
4 & 42.88 & 18.54 & 12.79 & 25.79 & 44.37 & 18.24 & 12.45 & 24.94 \\
16 & 40.19 & 19.06 & 13.23 & 27.52 & 43.08 & 18.34 & 12.86 & 25.72 \\ \hline \hline \end{tabular} \({}^{\mathbf{a}}\) mM; \({}^{\mathbf{b}}\) secondary structure composition (\(\%\)).  
\end{table} 
Table 2: The secondary structure composition of extracted proteins from DB-yeast and Se-enriched DB-yeast after treatment with H\({}_{2}\)O\({}_{2}\). Figure 5: Characterization of DB-yeast and Se-enriched DB-yeast extracted proteins. (**A**) pH of extracted proteins (20 mL) after mixing with 0.017 mol/L HCl (0 to 5 mL). (**B**) Reducing power of extracted proteins (10.0 mg/mL of extracted proteins). (**C**) Hydroxyl radical removal capacity of extracted proteins (0.4 mg/mL of extracted proteins). (**D**) Effect of different H\({}_{2}\)O\({}_{2}\) concentrations on total sulfhydryl content (3.6 mg/L of extracted proteins). \({}^{\mathbf{*}}\) Statistically significant (\(p\)-value \(<\)0.05).
# Detection of Unknown DDoS Attacks with Deep Learning and Gaussian Mixture Model
## 3 Proposed Framework--BI-LSTM-GMM
### BI-LSTM Module  
The LSTM is a Recurrent Neural Network (RNN) architecture with a memory cell and gates for the explicit control of input, output, and forget, as shown in Figure 3. With its unique design, LSTM is particularly suited for applications related to data sequences. The operation of an LSTM is governed by (1), as follows  
\[\left\{\begin{array}{c}i_{t}=\sigma(W_{t},\ [h_{t-1},x_{t}]+b_{i})\\ o_{t}=\sigma(W_{o},\ [h_{t-1},x_{t}]+b_{o})\\ f_{t}=\sigma\Big{(}W_{f},\ [h_{t-1},x_{t}]+b_{f}\Big{)}\\ \widetilde{C}_{t}=tanh(W_{c},\ [h_{t-1},x_{t}]+b_{c})\\ C_{t}=f_{t}\ \cdot\ C_{t-1}+i_{t}\ \cdot\ \widetilde{C}_{t}\\ h_{t}=o_{t}\ \cdot\tanh(C_{t})\end{array}\right. \tag{1}\]  
where \(t\) is the index of LSTM stage; \(x_{t}\) is the input; \(h_{t}\) is the output; \(W\) is the weight; \(b\) is the bias, \(C_{t}\) and \(\widetilde{C}_{t}\) are the current and the last states, respectively; and \(\sigma(\cdot)\) is the sigmoid function.  
A BI-LSTM is a DL architecture with dual LSTMs, one for the forward direction and the other for the reverse direction, as shown in Figure 4. A BI-LSTM becomes a non-causal system in the sense of data sequences. It is capable of gripping correlations on both sides along the time axis, which is a desired attribute for the detection of DDoS attacks.  
Figure 3: The internal structure of an LSTM stage.  
The output of the BI-LSTM is directed to a fully connected layer serving as a classifier for the discernation of normal and malicious traffic. This classifier is gated by the outcome of the GMM. The classifier's output is counted only when the GMM reports the given instance falling within the training data distribution.
## 2 Background and Related Work
### Error Rate Pattern according to P/E Cycles of 3D-NAND Flash  
[PERSON] et al., designed a NAND flash test platform with Xilinx Zynq-7020 and tested blocks on different chips of 3D-TLC flash memory [16]. Figure 2 depicts the distribution of error rates revealed in the papers by [PERSON] et al. [16]. In this figure, we show the average error rate as a function of block number, ranging from 50 to 5000 P/E cycles. The figure shows that the difference in average BER by block does not appear significantly below 1000 P/E cycles but as the number of P/E increases, the difference enlarges.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 4 Cryptographic Background
### Public-Key Encryption  
In public-key cryptography [26], each party possesses a key pair composed of a private key, which is kept secret, and a public key, which is made public.  
Given a public key \(Pub\), a message \(M\) can be encrypted into a ciphertext \(\text{Enc}_{Pub}(M)\). Such a ciphertext can only be decrypted by a party possessing the corresponding private key.
# Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
## 4 Experimental Setup
### Implementation Details  
In our work, we employed a 60-40% dataset partitioning scheme for training and testing phases. Usually, 80-20%, 75-25% or sometimes 70-30% training and validation dataset partitioning are present in the literature. In our case, the MalImg, malware-based dataset is limited and highly imbalanced, as shown in Figure 4. Therefore, we used the scheme (60-40) to make our model more robust for unseen malware data. The training samples were further divided for training samples and validation samples for hyper-parameters selection using a hold-out cross-validation scheme. The detail of hyper-parameters is available in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Optimization Method & SGD \\ Momentum Value & 0.95 \\ Weight Decay & 0.0005 \\ Learning Rate & 0.0001 \\ Epoches & 10 \\ Loss Function & cross-entropy \\ Batch-size & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter details.  
Figure 7: MalImg dataset families.  
The simulations were carried out on MATLAB-2021a using a Dell Core I i5-7500 with GPU-enabled Nvidia(r) GTX 1060-T. It took ~1-2 h to train a model on the said settings. One epoch took 7-10 min on Nvidia-Tesla K-80, while a single malware image took approximately 2 s for analysis.