esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Agent Dynamic Fog Service Placement Approach
## 3 Service Placement Orchestrator Implementation
### Security Maintenance  
As a default configuration, agent communication messages are neither encrypted nor signed. It may give an opportunity for a hacker using a malicious agent to sniff a message content or even modify it. It would lead to malicious instructions for a recipient agent. Different requests can be sent to AMS to eliminate some agents if there are no security checks. To maintain security, the JADE security add-on JADE-S was used. After  
Figure 4: Agent list synchronization messages.  
Figure 5: Agent list synchronization.  
it is installed, services such as SecurityService, PermissionService, SignatureService, and EncryptionService have to be enabled. SecurityService is a primary one, and the other ones are optional and can be enabled as required.  
Instead of using the method send(), the method sendMessage() is used. It allows us to specify whether the message has to be signed and encrypted. Credentials are checked by the method retrievePrincipal(). It is triggered first before a relevant message is sent. It first sends a _request_ message with the content \"get-principal\", and the answer is formed by a recipient to send it back as an _inform_ message. Please see Figure 6 for more details.  
In addition to the above information, further modules have to be enabled to adjust default security settings to preferred ones. The SignAlgorithm allows us to choose an algorithm that will be used to have the messages signed such as SHA1 withRSA, MD5 withRSA, DSA etc. The size for public and private keys can be defined by the module AsymKeySize, which ranges from 512 as a default value to 2048. There are a few more modules, in addition to the above ones, used to ensure additional customizability.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### COVID-19 Infection Detection Using Deep Learning  
Deep learning (DL) is a field of artificial intelligence focused on teaching computers to understand and interpret data using mathematical models. In our COVID-19 detection model, DL techniques are employed to classify chest X-ray scans into two categories: COVID-19 positive and COVID-19 negative, based on the features learned through the learning process [22].  
Figure 2: Output image after pre-processing and Canny edge algorithm where ’R’ indicates the right side of the chest.  
The core architecture of our model consists of convolutional layers responsible for capturing local features indicative of abnormalities present in the X-ray scans of patients. Normalization techniques are applied to enhance the processing of the extracted features obtained from the parallel 3 \(\times\) 3 Convolutional Neural Network layers. Pooling layers are then utilized to downsample and standardize distinctive characteristics, reducing computational complexity and preventing overfitting of the CNN architecture [47; 48; 49].  
To address overfitting, dropout layers are incorporated into our model. Dropout randomly deactivates a fraction of neurons during training, promoting generalization and preventing the model from relying too heavily on specific features. This ensures that the model can generalize well to unseen data.  
Concatenation layers are employed to improve learning accuracy and enable the discovery of new architectural features. By combining features learned from different parts of the network, concatenation facilitates the model's ability to capture complex patterns and relationships within the data.  
The final CNN architecture is depicted in Figure 3. After the pooling layer, all the layers in our model are flattened to generate the output, which comprises two neurons representing the binary classification of COVID-19 positive and COVID-19 negative cases.  
TensorFlow is used to implement the 1D convolution, achieved by wrapping the 2D convolution in reshaped layers to add an H dimension before the 2D convolution and remove it later, adapting the 2D Convolutional Neural Network for 1D data.  
The overall methodology combines the power of DL techniques, including normalization, pooling, dropout, and concatenation layers, to extract and learn meaningful features from chest X-ray images, enabling accurate and efficient detection of COVID-19 cases.
## 1 Introduction
First, the friction coefficients should be estimated online to provide information about the road surface. Second, the vehicle speed should be planned based on these estimated friction coefficients. On a normal road with a high friction coefficient, a constant desired speed is preferred. When a decrease in the friction coefficient is detected, a safe speed should be planned to avoid side slipping and accidents on curved roads with large curvatures. In this paper, a friction coefficient estimation algorithm is proposed based on torque injection [25; 26]. By means of torque injection, the slip ratios of the tires can be increased, which is essential for accurately estimating the friction coefficient. The friction coefficient is estimated based on the slip ratios and side-slip angles over a given sampling period. With the aim of calculating a safe vehicle speed, a speed planning algorithm is proposed that considers a changing friction coefficient on a curved road. The speed profile is planned to prevent potential accidents caused by high speeds and the saturation of friction on curved roads with changing friction coefficients, which is a topic that has seldom been discussed in previous studies. By combining the proposed speed-planning and friction coefficient estimation algorithms, an integrated speed-planning and friction coefficient estimation (ISPFCE) algorithm is proposed. This algorithm consists of two components, as shown in Figure 1. Note that the sensor system, motion controller, and electric vehicle are also included in this figure to illustrate the overall relationships in the system. The first component of the algorithm is the friction coefficient estimation, in which the friction coefficient is estimated through optimization. The second component is the speed planning, which is designed to calculate a safe speed considering the desired speed and safety constraints. A motion controller is used for lane keeping and cruise control; however, this controller is not the focus of this study. Thus, the designed algorithm can be utilized in combination with various motion controllers based on previously-designed lane-keeping and cruise control algorithms to improve their performance. The remainder of this paper is organized as follows. In Section 2, the friction estimation algorithm based on torque injection is proposed. In Section 3, the speed-planning method considering the road curvature and friction coefficient is presented. Simulations conducted to verify the proposed algorithm are reported in Section 4. Finally, conclusions are presented in Section 5.
## 2 Materials and Methods
### Data Sets and Evaluation Metrics  
We use the nine-species benchmark data set and evaluation framework first introduced by DeepNovo. The nine datasets were acquired from the Thermo Scientific Q-Exactive with the HCD technique. All the original data were first searched against the UniProt database by PEAKS DB software (version 8.0) [32] with a false-discovery rate of 1%. A total of about 1.5 million peptide-spectrum matches (PSMs) were obtained. We use the same cross-species evaluation scheme used by DeepNovo, in which eight data sets are used for training and a remaining data set is used for testing. This cross-species evaluation framework is widely used to evaluate the performance of de novo peptide sequencing, because the peptides in the training set are almost completely separated from the peptides of the remaining species. Most practical applications of de novo peptide sequencing use it to directly infer a peptide sequence that has never been seen.  
Figure 2: Denovo-GCN network architecture.  
We calculated the amino-acid precision, amino-acid recall, and peptide recall. A predicted amino acid matched a real amino acid if their mass difference was smaller than 0.1 Da and if the prefix masses before them varied by less than 0.5 Da. If the total number of matched amino acids was equal to the total length of the peptide sequence, the peptide sequence was recalled. This is also the evaluation metric adopted by DeepNovo. The \(N_{matched}^{AA}\) is defined as the total number of matched amino acids. The \(N_{matched}^{A}\) is defined as the total number of predicted amino acids. The \(N_{matched}^{Peptide}\) is defined as the total number of correctly predicted peptide sequences. The \(N_{total}^{Peptide}\) is defined as the number of all peptides in the test set. To evaluate the success of the model, we calculate three performance criteria:  
\[\text{Amino Acid precision}=\frac{N_{matched}^{AA}}{N_{ predicted}^{AA}} \tag{9}\]  
\[\text{Amino Acid recall}=\frac{N_{matched}^{AA}}{\sum_{i}^{N_{ total}^{Peptide}}{length_{i}}} \tag{10}\]  
\[\text{Peptide precall}=\frac{N_{matched}^{Peptide}}{N_{total}^{Peptide}} \tag{11}\]  
In addition, we describe the precision-recall curve by sorting the prediction results according to the confidence score provided by the model. At the same time, we calculate the area under the precision-recall curve. Finally, we provide the overall calculation process in Figure 3.
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 1 Introduction  
Polymethyl methacrylate (PMMA), generally known as plexiglass, is wildly used for optical windows material due to its good light transmission performance, high mechanical strength, good corrosion resistance and easy molding [1]. However, the surface of light-transmitting materials of underwater optical windows will be affected by various factors such as seawater erosion, corrosion and marine biological fouling, which seriously interferes with the performance and stability of underwater optical equipment. By coating self-cleaning coating on the surface of window materials, the cost of equipment maintenance and cleaning can be effectively reduced.  
Common self-cleaning coatings are mainly divided into two categories. One is hydrophobic coating with low surface energy, which is mainly composed of organic silane coating [2; 3; 4] and organic fluorine coating [5; 6; 7], and the other is photocatalytic coating containing TiO\({}_{2}\)[8; 9; 10]. It is worth mentioning that the self-cleaning coating applied to the PMMA surface in the above environment needs to meet several characteristics. In addition to good self-cleaning effects, the light transmission, adhesion strength and wear resistance of the coating must also be considered. More importantly, the heat resistance of polymethylmethacrylate is not high. Although its glass transition temperature reaches 104 \({}^{\circ}\)C, the maximum continuous use temperature varies from 65 to 95 \({}^{\circ}\)C depending on the working conditions, and the thermal deformation temperature is about 96 \({}^{\circ}\)C. Therefore,the forming temperature of self-cleaning coating shall not exceed 60 \({}^{\circ}\)C, and it is better to have the ability of cure at room temperature.  
Contributed to good adhesion, hydrophobicity and corrosion resistance, organic silane coating is an ideal material for preparing self-cleaning coating. Moreover, it also can be formed at room temperature by adjusting the composition of the siloxane monomer [11]. Among the commonly used alkoxysilane monomers, methyltrialkovysilane and orthosilicate are beneficial to hardness due to high crosslinking, and dimethyldialoxysilane is beneficial to hydrophobicity. In addition, inspired by the lotus leaves, a superhydrophobic surface can be achieved by a combination of low surface energy materials and micro-nanostructures [12; 13; 14; 15]. Thus, in this paper, targeting the self-cleaning coating of PMMA optical window, we prepared a hard, transparent and hydrophobic coating by using methyltrialkovysilane, orthosilicate and dimethyldialoxysilane as the raw material for organic silane coating and nano-silica as a modifier. The chemicals, surface microstructure, wettability, hardness, adhesion, transparency, water scouring resistance as well as microorganism attachment rate of the coatings were systematically investigated in this study, which might provide a new way to solve the problem of deep-see contamination of optical windows.
## 3 Results
### Linear Discriminant Analysis (LDA)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Hierarchy**} & **Data** & **Oil-Slicks’** & **Oil** & & **Oil** & **Oil** & **Oil** \\  & **Transformations** & **Signature** & & **Seeps** & & **Spills** & **Slicks** \\ \hline \multirow{2}{*}{1} & \(\log_{10}\) & _SIG.amp_ & \multirow{2}{*}{1296} & 64.99\% & \multirow{2}{*}{1829} & 71.22\% & \multirow{2}{*}{3125} & 68.50\% \\  & & & & & 63.69\% & & 72.38\% & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Cube root} & _SIG.amp_ & \multirow{2}{*}{1407} & 70.56\% & \multirow{2}{*}{1711} & 66.63\% & \multirow{2}{*}{3118} & 68.35\% \\  & & & & & 62.15\% & & 74.46\% & \\ \hline \multirow{3}{*}{3} & No & \multirow{2}{*}{_SIG.amp_} & \multirow{2}{*}{1570} & 78.74\% & \multirow{2}{*}{1344} & 52.34\% & \multirow{2}{*}{2914} & 63.88\% \\  & & & & & 56.19\% & & 76.02\% & \\ \hline \multirow{2}{*}{**Hierarchy**} & **Data** & **Size** & **Oil** & **Oil** & **Oil** & **Oil** & **Oil** \\  & **Transformations** & **Information** & **Seeps** & & **Spills** & & **Slicks** \\ \hline \multirow{2}{*}{1} & \(\log_{10}\) & PtoA and & \multirow{2}{*}{1288} & 64.59\% & \multirow{2}{*}{1841} & 71.69\% & \multirow{2}{*}{3129} & 68.59\% \\  & & Compact & & 63.92\% & & 72.28\% & \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Cube root} & PtoA and & \multirow{2}{*}{1417} & 71.06\% & \multirow{2}{*}{1667} & 64.91\% & \multirow{2}{*}{3084} & 67.60\% \\  & & Compact & & 61.13\% & & 74.29\% & \\ \hline \multirow{2}{*}{3} & No & PtoA and & \multirow{2}{*}{1575} & 78.99\% & \multirow{2}{*}{1338} & 52.10\% & \multirow{2}{*}{2913} & 63.85\% \\  & & & & 56.15\% & & 76.15\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Outcomes of the linear discriminant analyses (LDAs) from the first refined study [31; 32]. Top: oil-slicks’ signature (SAR signature: sigma–naught with no despeckle filter—_SIG.amp_). Bottom: size information (size only: PtoA and Compact). See also Tables 1–5. cube-transformed, improving its seep-spill categorization capacity: 67.60% and 68.35%, respectively. For the original not-transformed data, only one oil slick is differently classified. Similar patterns happen in our current analysis (Table 5). The accuracy behavior of the oil-slicks' signature (size and SAR), on the other hand, is actually slightly different once we account for the other SAR backscatter coefficients and SAR calibrated products (Table 5). In the relation to the data transformations, size only has the poorer performance of all instances in both no transformation (63.90%) and cube root (67.62%), but when the SAR signature is taken into account we obtain improved accuracies: no transformation (_GAM.dB.FF_: 65.67%) and cube root (_GAM.amp.FF_: 68.35%). Likewise, when we compare the log\({}_{10}\) instances size information without (size only: 68.59%) and with the SAR signature (_GAM.dB_: 65.85%), there is also an improvement; smaller though.
# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Enaptranspiration Estimation
DBN-LSSVM model outperformed two conventional models with higher prediction accuracy suggesting DBN as an efficient feature extractor. [PERSON] et al. [83] compares several ML algorithms of SVM, XGBoost, and ANN, including DNN, for estimating maize transpiration using meteorological variables, soil water and leaf area index as inputs. The result showed DNN outperformed other techniques by a slight margin.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 3 Key Aspects of the BSI Technical Guideline with Relevance for Energy Management
### Availability of Metering Data  
In summary, given the SMGW has been configured in a suitable way, i.e., meter measurements are collected sufficiently frequently, the _Consumer Interface_, which is located within HAN, allows detailed access to meter measurements as described above. Generally speaking, information available inside the WAN is more sparse, but depending on the tariff use case and how long billing and registration periods need to be, the EMP is also able to receive detailed meter readings every 15 min or less for electricity, and 60 min or less for gas.
## 3 Experimental Tests
### Test Results  
#### 3.2.1 Failure Mode  
The test results including ultimate moments (\(M_{u}\)), failure modes, and deflections at 30% of \(M_{u}\) (\(\Delta_{s}\)) are shown in Table 2. The typical failure modes of the specimens are given in Figure 8. Three typical  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Series** & **Specimens ID** & \(f_{cut}\) (MPa) & \(p_{f}\) (\%) & \(M_{u}\) (kN-m) & \(\Delta_{s}\)(mm) & \(F_{f}A_{f}\) (kN) & **Failure Mode 1** \\ \hline \multirow{4}{*}{Series I} & LC–G–5\#13.77 & 64.7 & 1.64 & 82.92 & 15.0 & 33,027 & C.C \\  & SLC–G–5\#13.77 & 82.3 & 1.64 & 98.75 & 11.3 & 33,027 & B.F \\  & LC–G–8\#13.77 & 62.5 & 2.66 & 97.96 & 12.5 & 52,843 & C.C \\  & SLC–G–8\#13.77 & 79.4 & 2.66 & 119.85 & 13.1 & 52,843 & C.C \\ \hline \multirow{4}{*}{Series II} & LC–G–3\#13.77 & 54.9 & 0.92 & 63.51 & 13.3 & 19,816 & B.F \\  & SLC–G–3\#13.77 & 81.6 & 0.92 & 73.54 & 7.1 & 19,816 & FRP.R \\  & LC–G–6\#9.87 & 64.4 & 0.98 & 43.30 & 6.6 & 21,452 & FRP.R \\  & SLC–G–6\#9.87 & 69.5 & 0.98 & 56.51 & 4.1 & 21,452 & FRP.R \\ \hline \multirow{4}{*}{Series III} & LC–G–4\#9.87 & 61.6 & 0.62 & 32.52 & 1.2 & 14,302 & FRP.R \\  & LC–S–4\#10.62 & 76.2 & 0.72 & 47.77 & - & 65,974 & C.C \\ \hline \hline \end{tabular}  
* C.C = Concrete crushing, B.F = Balanced failure, FRP = FRP rupture; \(f_{cut}\) = cube compressive strength of concrete; \(E_{f}\) = modulus of elasticity of the glass fiber-reinforced polymer (GFRP) bars; \(A_{f}\) = area of tension reinforcement.  
\end{table}
Table 2: Details and experimental results of specimens.  
Figure 7: Reinforcement details of specimens.  
failure modes were observed among the 10 specimens, namely, concrete crushing, balanced failure, and the rupture of GFRP bars. This result was corroborate with those of FRP-reinforced plain and fibrous normal weight concrete beams [27; 28; 29]. Specimens undergoing concrete crushing failure exhibited large deflection and crack widths in the midspan, and owing to its relatively gradual process, the damage incurred by this type of failure is less destructive than that of the other failure modes (Figure 8a). The balanced failure mode featured concrete crushing followed immediately by the rupture of GFRP bars. The concrete on the top surface at the midspan was compressed to crushed, and the beams collapsed along the crack caused by the sudden fracture of the GFRP bars, as shown in Figure 8b. Compared to the balanced failure, a distinct characteristic of the FRP rupture failure was that the concrete located in the compression zone was not crushed (Figure 8c). Hence, this type of failure involved fracturing without prior warning. Specimens that were classified into FRP rupture possessed relatively little deflection.
## 3 Results
### Dynamic Grasping
#### 3.3.1 Prediction Performance of the KF
The average detection publishing frequency is 25 ms, resulting in a total latency of 92 ms, which satisifies industrial-grade requirements. At all conveyor speeds, the Low-Pass filter method consistently produces more accurate and reliable predictions by suppressing high-frequency noise more effectively than the Moving Average. However, at higher conveyor speeds, as shown in Figure 10a, the performance gap becomes more apparent as the Moving Average method struggles with delays, while the Low-Pass filter maintains stability and precision. These results highlight the advantages of integrating the Low-Pass filter with the KF for dynamic oyster grasping tasks, particularly in high-speed scenarios. Figure 9: Dynamic detection results for oysters: (**a**) the oyster comes into view of the camera, (**b**) the oyster remains in motion, (**c**) the oyster moves out of the field of view (the green dots represent the location of the oyster grab detected by yolovii-obb, and the blue dots represent the predicted location of the oyster grab in the next frame determined by the KF and Low-Pass filter).
## 4 Model and Methodology of the Adaptive Protocol
### Adaptive Routing Protocol Based on Double-Q-Values  
In order to meet different business needs, this section proposes an adaptive weighting scheme based on the proposed double-Q-value model and the principles of maximizing network lifetime and adaptively meeting the functional requirements of various types of sensors, trying to consider both energy saving and delay reduction. Two methods are used to adapt to different business objectives. The multi-objective function formula is as follows:  
\[\max Q=\max(\alpha_{w}\times Q_{L}+(1-\alpha_{w})\times Q_{T}), \tag{19}\]  
where \(\alpha_{w}\) is a business adaptive weighting factor used to adjust the weight of a life cycle and real-time goals. As shown in Table 2, different services in the system have different requirements for life cycle and real-time performance. When the real-time performance requirement of the monitoring object is low, that is, the frequency of the device sending data is less than once an hour, it means that the object has higher requirements for the continuity of data transmission. The adaptive weight factor increases with the improvement of life requirements; and when the real-time requirement of the monitored object is high, that is, the frequency of the device sending data is higher than once every 10 min, the algorithm assigns priority to meeting its real-time requirements. Therefore, the design of the adaptive weight factor is as follows: in this formula, low, medium, and high values are 0.2, 0.5, and 0.8.  
\[\alpha_{w}=\left\{\begin{array}{cl}\frac{\log(\eta\times(\overline{D_{l}^{s} }/D_{l}^{s}))}{\log(\eta\times\max(\overline{D_{l}^{s}}/D_{l}^{s}))}&D_{t}^{s} =0.2\\ \frac{0.5\times\log(\eta\times(\overline{D_{l}^{s}}/D_{l}^{s}))}{\log\max( \overline{D_{l}^{s}}/D_{l}^{s})}&D_{t}^{s}=0.5\\ +\frac{\frac{0.5\times 1}{(\overline{D_{l}^{s}}-D_{l}^{s})\times\overline{D_{l}^{s}}} }{1+e^{\frac{\overline{\xi}(\overline{D_{l}^{s}}-D_{l}^{s})\times\overline{D_{l }^{s}}}{(\overline{D_{l}^{s}}-D_{l}^{s})\times\overline{D_{l}^{s}}}}}&D_{t}^{s} =0.8\end{array}\right., \tag{20}\]  
where the life cycle demand amplification factor \(\eta\) and the real-time demand amplification factor \(\xi\) are defined as follows:  
\[\eta=\frac{\max D_{l}^{s}}{\max D_{l}^{s}}\quad 0<D_{l}^{s}<1,0<D_{t}^{s}<0.5, \tag{21}\]\[\xi=\max(D_{t}^{s}-D_{l}^{s})\quad 0<D_{l}^{s}<1,0.5<D_{t}^{s}<1. \tag{22}\]