esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 3 Digital Twin-Based Security Assessment for CPPS
### Reference Architecture for CPPS
#### 3.2.2 Control Layer of CPPS  
The control layer introduces a critical distinction between IT and OT components, enriching the CPPS framework [54]. OT components directly impact physical processes and include devices such as HMI, IED, PLC, and RTU. HMIs serve as control panels, enabling human operators to interact with PLCs and IEDs, which are integral to automating and monitoring physical tasks. PLCs, which are specialized computers within the OT spectrum, execute programs to automate tasks based on sensor inputs, while IEDs, connected to sensors and actuators, facilitate automatic actuation, showcasing the intricate interplay  
Figure 3: Leveraging Digital twin in security assessment and virtual patching.  
between cyber and physical components. For example, the control layer empowers human operators to oversee assembly operations through the _PLCController_, with dedicated computers in the control center collecting and displaying production data, as presented in Figure 5. Other important components of this layer include a historian server for historical data retrieval, an application server for data analysis and software support, and supervisory control and data acquisition (SCADA) server and timer for CPPS monitoring and control.
## 2 Materials and Experimental Results  
First, a mathematical model of grain growth was developed based on the diffusion model described in the next section. Using this model, several numerical calculations were performed. The results of these calculations (grain size) were compared with the results obtained from the experiment. For this purpose, a two-component eutectic alloy Mg72 Zn8 was produced by casting in a resistance furnace under the protection of argon as an inert gas. Both magnesium and zinc were 99.9% pure. Liquid Mg72 Zn8 alloy was poured into the steel mould cavity. In this way, a cylindrical sample with a diameter of 20 mm and a height of 50 mm was obtained. Then, this sample was rolled to a diameter of 10 mm. The sample was melted using a melt-spinning device to cast a ribbon with an amorphous structure. The thickness of the obtained ribbon was 150 um, and the peripheral speed of the wheel was 40 m s\({}^{-1}\). Both processes, i.e., melting and casting, took place under the protection of argon as an inert gas. The obtained ribbons were subjected to X-ray diffraction tests to confirm the amorphous structure. The X-ray diffraction (XRD) results were collected using a Panalytical Empyrean diffractometer (Almelo, Nederland) equipped with a Cu K\(\alpha\) X-ray source. The next step was to verify the simulation results with an experiment. For this purpose, the amorphous ribbons were subjected to isothermal annealing heat treatment at selected temperatures. Heat treatment was carried out with high-purity argon using a TA DSC Q20 differential scanning calorimeter (Eschborn, Germany). The rate of heating the amorphous ribbon to the isothermal annealing temperature was 80 K min\({}^{-1}\). The ribbon was held at an isothermal annealing temperature for 300 s and then cooled to ambient temperature at a rate of 80 K min\({}^{-1}\). Grain size tests were performed using a JEOL JSM-7600F field emission scanning electron microscope (FE-SEM).
# Precision Manufacturing of Patterned Beryllium Bronze Leaf Springs via Chemical Etching
## 2 Principle and Process Flow  
### Principle  
The grade of the beryllium bronze sheet used in this study is C17200. The beryllium bronze's main element is Cu, other gradient includes: beryllium (1.9-2.15%), cobalt (0.35-0.65%), nickel (0.2-0.25%), silicon (<0.15%), iron (<0.15%) and aluminum (<0.15%). Cu can be corroded through a solution of Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\). The chemical reaction equation is as follows:  
\[\mathrm{Cu}+\mathrm{Na}_{2}\mathrm{S}_{2}\mathrm{O}_{8}=\mathrm{CuSO}_{4}+ \mathrm{Na}_{2}\mathrm{SO}_{4} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **Lithography** & **Electotyping** & **Sputtering** &
\begin{tabular}{c} **Wire-Electured Discharge** \\ **Machining (EDM)** \\ \end{tabular} & **Laser Cutting** & **Chemical Etching** \\ \hline precision & \textless{}10 nm & \textless{}0.5 \(\upmu\)m & 10 nm & 5-20 nm & 50 \(\upmu\)m & \textless{}10 \(\upmu\)m \\ deformation & no & non-uniform thickness & no & thermal and stress & thermal and stress & no \\ complex patterns & easy & easy & difficult & easy & easy \\ equipment cost (§) & 800,000+ & 1500+ & 1500+ & 1500+ & 1500+ & 1500+ & 150- \\ power (kW) & 1–100 & 3-10 & 3-20 & 2-5 & 2-10 & \textless{}0.6 \\ operation & very complex & very complex & very complex & complex & very simple \\ space usage (m\({}^{2}\)) & \textgreater{}50 & \textgreater{}50 & \textgreater{}50 & 10–15 & 10–15 & \textless{}5 \\ production periodic & \textgreater{}5 h & \textgreater{}5 h & \textgreater{}5 h & \textless{}1 h & \textless{}1 h & \textless{}2 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different manufacturing methods.  
A beryllium bronze sheet is always used as the raw material of a leaf spring. A photoresist film (negative photosensitive material) can be used to protect the beryllium bronze sheet from corroding. The photoresist film used to cover a beryllium bronze sheet has to conform to the pattern of the designed leaf springs in advance. Therefore, the part of a beryllium bronze sheet with a photoresist film will be protected and maintained, whereas other parts will be etched off in a Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\) solution. Thus, patterned leaf springs can be obtained.
## 3 Method
### Overview of the Subject-Sensitive Hashing Algorithm  
As shown in Figure 4, the flow of our proposed subject-sensitive hash algorithm mainly included: preprocessing of the image, image feature extraction based on the improved Swin-Unet, and feature encoding based on the mapping mechanism and PCA.  
1. Preprocessing was designed to process the HRRS image so that it met the requirements of the input size of the improved Swin-Unet, which was 224 \(\times\) 224 pixels. If the size of the HRRS image was large (for example, larger than 512 \(\times\) 512 pixels), it could be divided into non-overlapping grid cells by grid division to ensure the accuracy of integrity authentication. Since grid division-based methods have been discussed repeatedly by existing algorithms [6; 26], we chose not to repeat them in this paper.
2. For feature extraction, the preprocessed HRRS image was input into the trained improved Swin-Unet to obtain the feature map of the corresponding image. The training process of the improved Swin-Unet was discussed in Section IV.
3. Feature coding: the feature extracted by the improved Swin-Unet was essentially a two-dimensional matrix of pixel gray values. After feature encoding, based on the mapping mechanism and PCA, the obtained one-dimensional 0-1 sequence was encrypted by the AES (Advanced Encryption Standard) algorithm [40; 41] to get the hash sequence, denoted as _SH_.
## 3 Materials and Methods
### Model Architectures
#### 3.3.2 Model Quantization  
The second strategy to reduce memory footprint and computational complexity is model quantization, a process of model transformation into an equivalent representation but using parameters and computations at a lower precision. According to [54], the usage of low-precision fixed integer value representation has the potential to reduce the memory footprint and latency by a factor of 16\(\times\). Despite being fast and very easy to use, the  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Refinement Methods** \\ \hline
**Selection** & **Quantization** \\ Keep energies above the threshold & Selection bit \\ Keep layer maximum & Uniform quantizer \\ Capping & Non-uniform quantizer \\ Reduction of longitudinal profile & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data refinement methods.  
post-training quantization approach is not an option because it suffers from significant degradation in model accuracy in case of precision lower than 8 bits [55].  
Please note that any quantization (and, in some cases, data reduction, as introduced previously) results in loss of data accuracy, i.e., quantization error. However, since we aim to implement a selection algorithm for L1T and not a (final) data analysis algorithm, our work was not affected by this issue in a traditional manner. We aimed to reduce data complexity to the point where it was just about sufficient to differentiate said data from other signal types (with high accuracy) while reducing their impact on NN memory and resource footprints (within defined timing and resource constraints). In this process, we worked with data copies and were thus not limited by data accuracy in the final analysis since if the proposed algorithm flagged an event as being of interest (i.e. EM shower), the complete set of data (with full precision) would be sent for storage via other channels for later analysis. This should be kept in mind when interpreting the proposed (aggressive) quantization.  
The QKeras library enables quantization-aware training using a simple replacement for Keras layers, greatly simplifying the quantization process. QKeras allows heterogeneous quantization: it is possible to use different quantization levels and different quantized activation functions on each layer. The concept of a straight-through estimator presented in [56] is used; hence, the forward pass applies the quantization functions, and the backward pass adopts the quantization as the identity function to make the gradient differentiable [57]. After exhaustive research, it was decided to use the quantizer _quantized_bits_ defined as follows  
\[2^{int-b+1}clip(round(x*2^{b-int-1}),-2^{b-1},2^{b-1}-1) \tag{3}\]  
for weights and kernels, where \(x\) = input, \(b\) = number of bits for the quantization, \(int\) = how many bits are to the left of the decimal point.  
_Quantized_relu_ is chosen as a quantized replacement for the ReLu activation function, used in the inner layers of models.
## 3 Experimental Results
### Performance Measures
It is formally stated as follows:  
\[TPR=\frac{TP}{TP+FN}=\textit{Specificity}\]  
The ideal classifier will have a ROC curve where the contour reaches a rate of 100% certified positives with zero improvements in the center x point (true positives). The area under the curve (AUC) measures the ability of a classifier to distinguish between clusters and serves as a summary of the ROC curve across all cutoffs, which is invariant to disease prevalence and diagnosis threshold choice. Generally, there are 4 cases: When the AUC is equal to 1, then the classifier is perfectly and correctly capable of discriminating all negative and positive class items. If the AUC corresponds to 0, then the classifier would predict all positives as negative, and all negatives as positive. * When AUC = 0.5, the classifier is unable to differentiate between positive and negative class points. This means that it predicts a random or a steady class for all data points. * When 0.5 < AUC < 1, it is likely that the classifier can discriminate positive class values from negative ones. This stems from the ability of the classifier to detect a greater number of true negatives and true positives than false negatives and false positives. In general, the wider the AUC, the better the model's ability in discriminating between negative and positive classes. Figure 17 illustrates the ROC curves of the ML/DL algorithms tested. As illustrated in the Figure, the areas under the curve (AUC) of the different algorithms were greater than 76.85%; some even reached 99.73%. In Figure 17, The RF algorithm scored the best performance, with the AUC of the crop leaves hitting. While the ReNet50 network and the NB algorithm performed poorly compared to the other ML/ DL models tested.
## 3 Results
### Results of the Supervised Method
#### 3.2.3 Data Sufficiency Analysis and Generalisation Analysis  
In the previous section, the results of predicting occupancy from consumption using the model based on the Random Forest algorithm have been shown. Since those results came from a single testing period, which was chosen to be the last half of the month in the dataset, additional verification was done by applying the algorithm during other periods from the dataset and with different training lengths. For this purpose, half a month of testing periods were selected, starting from 1 May until 31 August. Model training period length was varied from three days before the testing period until the beginning of the dataset by three-day steps.  
Occupancy prediction errors were calculated, as RMSE and EV, for these eight testing periods and are presented in Figures 8 and 9. In these figures, each line represents the occupancy prediction error for a single testing period dependent on the length of model training. First, it can be seen that during vacation time (two testing periods in July), the lowest EV values were shown, because of that period being untypical compared to its training dataset. On the other hand, the lowest RMSE value during the July period was because of the lower office occupancy overall. Similarly, this was valid for the first period in August, which used first the untypical period of July for
## 4 Results and Discussion
### The Influence of Perturbed Film Thickness Included in Turbulence Coefficients  
For assembled radius clearance \(C_{b}=143.5\)\(\upmu\)m and at assembled eccentricity ratio \(\varepsilon^{\prime}=0.1\), the variations of dimensionless equivalent dynamic coefficients of a tilting pad bearing with rotating speed under dimensionless perturbation frequency \(\Omega=0.5\) and 1 are shown in Figure 5. Various kinds of solving situations were presented together for reflecting the contributions of perturbed film thickness included in turbulence coefficients perturbation. The results shown in red and black lines were obtained by the partial derivative method embracing turbulence perturbation. Though the turbulence was considered in the results shown in the blue line, the influence of dynamical variation of turbulence coefficients is not considered.  
Comparison of red (or black) line to blue line indicates that neglecting the perturbed film thickness \(\widetilde{h}_{d}\) included in turbulence coefficients perturbation \(k_{x}\), \(k_{z}\) leads to obviously excessive results of equivalent stiffness coefficients at rotating speed beyond a certain value. Under different dimensionless perturbation frequencies \(\Omega\), the regulation of the influence of turbulence perturbation on a certain stiffness or damping coefficient is similar.  
The dimensionless equivalent stiffness coefficients under dimensionless perturbation frequency \(\Omega=0.5\) are larger than that under \(\Omega=1\). However, the dimensionless equivalent damping coefficients present the opposite relationship. Under different \(\Omega\), the variation regulation of each dynamic coefficient with rotating speed is consistent.  
Figure 5: Variations of the dimensionless equivalent dynamic coefficients with rotating speed at assembled clearance \(C_{b}=143.5\)\(\upmu\)m.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 2 Related Work
### Opportunistic Routing Protocols
CTEA increases the packet delivery ratio, residual energy, and network lifetime by mitigating the nodes' misbehavior in the presence of energy drain and bad mouth attacks. In contrast to the existing opportunistic routing approaches, the proposed DEOR is a non-cluster and non-predefined opportunistic routing algorithm for post-disaster MIoT networks, which aims to address the issue of intermittent connectivity of wireless transmission links caused by the failure of surviving nodes during post-disaster network data transmission in planar accident area rescue scenarios, such as a fully mechanized coal face. DEOR improves the energy efficiency of the post-disaster network by dynamically adjusting the number of the candidate forwarding nodes. Moreover, DEOR overcomes the void routing node problem and improves the robustness of the post-disaster network by selecting available relay nodes in a downward path to the sink.
###### Abstract  
Electronic medical records (EMRs) contain a variety of valuable medical entities and their relations. The extraction of medical entities and their relations has important application value in the structuring of EMR and the development of various types of intelligent assistant medical systems, and hence is a hot issue in intelligent medicine research. In recent years, most research aims to firstly identify entities and then to recognize the relations between the entities, and often suffers from many redundant operations. Furthermore, the challenge remains of identifying overlapping relation triplets along with the entire medical entity boundary and detecting multi-type relations. In this work, we propose a Specific Relation Attention-guided Graph Neural Networks (SRAGNNs) model to jointly extract entities and their relations in Chinese EMR, which uses sentence information and attention-guided graph neural networks to perceive the features of every relation in a sentence and then to extract those relations. In addition, a specific sentence representation is constructed for every relation, and sequence labeling is performed to extract its corresponding head and tail entities. Experiments on a medical evaluation dataset and a manually labeled Chinese EMR dataset show that our model improves the performance of Chinese medical entities and relation extraction.  
Chinese electronic medical record; joint entity and relation extraction; attention mechanism; graph neural network Article
## 2 The Simulation Framework
### Inputs to the Simulation Framework  
The framework was designed to accept measurement data from the bus as inputs in an offline process. In ASSURED, the various OEMs and CBOs involved in the demonstrations were responsible for the data collection process and then forwarding those data to the simulation team. However, different OEMs and CBOs used different data logging and data processing techniques. Therefore, it was not possible to apply a standardized methodology for data collection, making the offline validation the most suitable option. Table 1 gives a concise overview of the measurement data collected in each city. As can be seen, the collected data seems rather arbitrary; it is due to different stakeholders being involved in the data collection process. However, each stakeholder was required, at minimum, to provide the vehicle's speed profile (to be used as the simulation input) and battery SoC profile (to be compared with the simulation output), at a data logging frequency of 1 Hz, to ensure reasonable tuning and validation of the simulation framework. Beyond these constraints, each stakeholder communicated, according to their data sharing policies,  
Figure 1: Overview of the simulation framework illustrating the forward-facing e-bus powertrain and grid infrastructure.  
a subset of the following parameters: energy usage rate, mileage, charging state, charging time, ambient temperature, road inclination, GPS coordinates, and altitude.  
Sensor data in vehicles are mainly communicated via the CAN bus network and logged via CAN-based dataloggers attached to the vehicle's CAN network and wirelessly communicated to a central server via the GSM (3G/4G) or Wi-Fi. The data is then decoded from the CAN message format (.blf), which is binary, into a more user readable format, including comma separated values (.csv), excel (.xdsx), or a simple text (.txt) file, using a CAN database (.dbc) file structure. The next step is to convert them into a common format, the MATLAB data (.mat) file, after which the parameter values are brought to a common sampling rate of 10 Hz, using up-and-down sampling techniques; wherein 10 Hz was chosen as a simulation time step of the Lo-Fi model. The data is then pre-processed to remove noise from the data, especially those which were measured via the GPS module, since GPS user accuracies, even with augmentation and when operated in wide open areas, are in \"meters\" for horizontal (i.e., longitude and latitude) measurements, and much worse for vertical (i.e., altitude) measurements [6]. In an urban setting featuring many obstacles (i.e., buildings, bridges etc.) and a multipath signal environment due to reflected signals, these accuracies are further degraded. Finally, the data is thoroughly checked to ensure that the speed and acceleration do not exceed the vehicle maximum for those parameters, and that the road inclination and difference in altitude between the lowest and highest point of the route were within known ranges.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 3 Experimental Settings and Results
### Evaluation Indicators
#### 3.4.2 Ch  
The CH is also an indicator for evaluating good or bad clustering effects that is calculated much faster than the SC, and is calculated as follows.  
\[CH=\frac{SS_{B}}{K-1}/\frac{SS_{W}}{N-K} \tag{16}\]  
\[SS_{B}=\text{tr}(B_{k}),B_{k}=\sum_{q=1}^{k}n_{i}\big{(}c_{q}-c_{e}\big{)} \big{(}c_{q}-c_{e}\big{)}^{T} \tag{17}\]  
\[SS_{W}=\text{tr}(W_{k}),W_{k}=\sum_{q=1}^{k}\sum_{x\in C_{q}}n_{i}\big{(}x-c_{ q}\big{)}\big{(}x-c_{q}\big{)}^{T} \tag{18}\]  
where \(SSB\) is the variance between clusters and \(SSW\) is the variance within clusters. The ratio of compactness to separation is the \(CH\) index, where \(c_{q}\) represents the center point of cluster \(q\), \(c_{e}\) represents the center point of the data set, \(n_{q}\) represents the number of data in cluster \(q\), and \(C_{q}\) represents the data set of cluster \(q\). The sum of the square distances between each cluster point and its center is used to determine the cluster's compactness. The sum of the square distances between the cluster centers and the center of the data set as a whole is used to determine the degree of separation between the datasets. A higher \(CH\) index indicates improved clustering efficiency.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 5 Experimental Results and Discussion
### Comparative Analysis
#### 5.2.3 Packet Loss Rate  
However, the packet loss rate must be lower to show that the network has obtained higher _QoS_. In general, the packet loss rate is reduced due to the lack of awareness of the buffer rate and network size. Furthermore, the rate of packet drop increases when malicious node presence is high, and in this case, wireless channels are vulnerable. Lack of knowledge about the network increases the packet loss rate. This metric is computed by the total number of packets transmitted from the source to the number of packets sent by the destination.  
A comparison of the packet loss rate with respect to the simulation rounds is depicted in Figure 12. The proposed B-DEAH architecture addresses all the previous issues of higher packet losses. An optimal channel selection method is introduced by considering multiple  
Figure 11: End-to-end delay vs. simulation rounds (periodic packets.  
Figure 10: End-to-end delay vs. simulation rounds (emergency packets).  
objectives, such as RSS, SNR, channel capacity and radio power. All sensed packets from body sensors are represented with their _QoS_, and reliable transmission of packets occurs between the source and destination pairs. Previous works E-HARP and CF-EHARP are loss packets without the information of packet type. In B-DEAH, two sinks are deployed in which PBA forward emergency requests to the first sink and nonemergency requests forward to the second sink, which reduces the packet loss rate efficiently.