esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment
## 3 The Driving Envelope for Candidate Driving Maneuvers
### The Reachable-Set Considering Driver's Input  
The reachable-set is a useful tool for human-in-the-loop systems to determine the boundary of executable trajectories [27]. For a system \(f\), the system dynamic is \(\dot{x}(t)=f(x(t),u(t))\) under an allowable control signal \(u\). Given an initial state \(x_{0}\), an input signal \(u(t)\), and the system dynamics \(\dot{x}(t)=f(x(t),u(t))\), the reachable set \(\mathcal{R}(t)\) is defined as the set of all states that can be reached from an initial set \(\mathcal{X}^{0}\) at time \(t\).  
\[\mathcal{R}(t)=\left\{x_{0}+\int_{0}^{t}f(x(\tau),u(\tau))d\tau|x_{0}\in \mathcal{X}^{0},\forall t:u(t)\in\mathcal{U}\right\} \tag{14}\]  
Due to the constraint of the machinery steering structure, the curvature of the trajectory should not exceed the maximum boundary \(\kappa_{mech,max}\). To avoid severe sideslip, the curvature is limited by maximum lateral acceleration \(a_{lat,max}\).  
\[\kappa_{max}=\min(\kappa_{mech,max},a_{lat,max}/v^{2}) \tag{15}\]  
Considering the driving characteristics, the steering maneuver should not be too hasty, and the path curvature should changes smoothly.  
\[\kappa(s)=\kappa_{0}+k_{rate}\cdot s \tag{16}\]  
where \(k_{rate}\) represents the change rate of curvature as the trajectory length increases, \(\kappa_{0}\) represents the initial curvature, and it is influenced by the front wheel angle. \(s\) represents the arc length of the trajectory.  
\[\kappa_{0}=\tan\delta/L \tag{17}\]  
If the vehicle moves at constant acceleration \(a\), then the length \(s\) can be calculated by the uniform acceleration motion formula.  
\[s=v_{0}t+\frac{1}{2}at^{2} \tag{18}\]  
The curvature and the change rate of curvature are limited in a reasonable range, \(\kappa\in[-\kappa_{\max},\kappa_{\max}]\), \(k_{rate}\in[-\kappa_{rate,max},\kappa_{rate,max}]\). By discretizing the curvature rate \(k_{rate}\), the  
Figure 10: The minimum and maximum longitudinal position for lane-change abortion.  
executable trajectory range of the vehicle in the finite time domain can be obtained, as shown in Figure 11.  
\[x(s) =\int_{s_{0}}^{s}\cos(\theta(\tau))d\tau+x(s_{0})\] \[y(s) =\int_{s_{0}}^{s}\sin(\theta(\tau))d\tau+y(s_{0}) \tag{19}\] \[\theta(s) =\int_{s_{0}}^{s}\kappa(\tau)d\tau+\theta(s_{0})\]
# Numerical Analysis of Seismic Pounding between Adjacent Buildings Accounting for SSI
## 2 Theoretical Model Framework
### Equation of Motion
Furthermore, the damping matrices of Building \(A\) are given as \(C_{ax}\), \(C_{ay}\), and \(C_{\theta R}^{a}\), in Equation (1), considered proportionally to the stiffness matrices [47]. The displacement vectors of Building \(A\) in the related directions without the effects of SSI can be calculated as  
\[ \begin{split}\{x_{ic}^{t}\}&=x_{o}^{a}\{1\}+x_{g} \{1\}+\varphi_{o}^{a}\{h_{i}\}+\{x_{i}\}-f_{a}\{\theta_{ic}\}\\ \{y_{ic}^{t}\}&=y_{o}^{a}\{1\}+y_{g}\{1\}+\varphi_{ o}^{a}\{h_{i}\}+\{y_{i}\}+e_{a}\{\theta_{ic}\}\\ &\qquad\qquad\{\theta_{ic}^{t}\}=\theta_{o}^{a}\{1\}+\{\theta_{ ic}\}\end{split}  \tag{2}\]  
where \(x_{i}\) and \(y_{i}\) are vectors of displacement with relation to the CM of the superstructure; \(x_{o}^{a},\ y_{o}^{a},\ \varphi_{o}^{a}\), and \(\varphi_{o}^{a}\) represent the DOF at the foundation that is related to translations and rocking about the \(x\) and \(y\) axes; and \(\theta_{o}^{a}\) is the rotation around the \(z\) axis. In each building, the five equations of motion of the foundation are described as translational and rocking distances in the \(x\) and \(y\) directions as well as a torsional mode of vibration.
## 3 Material and Method
### Experimental Design
#### 3.2.4 Dataset  
Table 1 shows a sample of the dataset used in this study, which was collected through real-time monitoring of a single battery at intervals of seconds (s). Data Training will be taken from a sample of data with a time range of 26 June 2023 12:44:12.403 until 26 June 2023 17:59:54.437 or equivalent to 5 h of monitoring, which has a data composition resulting in 7200 data rows with a portion of 80% or 5760 data rows for training and 20% or 1440 data rows for testing.  
The input matrix (X) has dimensions of 7200 (N observations) \(\times\) 3 (D features), namely:  
* N Observations (7200): This means that 7200 observations or datasets are included in the matrix. Each observation represents 1 s from the previous 24 h for a single battery unit.
* D Features (3): There are 3 features or variables were recorded for each observation, namely, Voltage, Temperature, and Degradation Value.  
Thus, the input matrix (X) will have 7200 rows and three columns, where each row represents data from one second in the previous 24 h and each column represents the Voltage, Temperature, and Degradation Value.  
Here is a more detailed explanation of data parameters:  
* Datetime: This is the timestamp indicating when the measurement data were obtained.
* Voltage: This parameter measured the voltage of the battery at the time of measurement.
* Temperature: This refers to the ambient temperature where the battery is located.
* Degradation Value: This value is calculated based on the baseline value minus the measured voltage. \(Degradation\ Value=V_{baseline}-V_{n}\). These degradation values may indicate changes in the battery. However, there is not always a direct correlation between voltage changes and deeper degradation values.
## 5 Results and Discussion
### Flatness  
With DEPP and traditional CMP, the flatness of the sapphire substrates is shown in Figure 18. For substrate polished with traditional CMP, its height increases from the edge to the center, with the center significantly higher than other locations. The flatness (RMS value) of the substrate is 0.6863 waves. While with DEPP, the height of the substrate also gradually increases from the edge to the center, however with a much smaller magnitude. The flatness (RMS value) is 0.3078 waves. Thus, it can be concluded that the polished sapphire substrate surface obtained by DEPP is smoother and flatter than that by traditional CMP.  
The reason why the flatness of the sapphire substrate polished by DEPP is better than that polished by the traditional CMP is similar to that for surface roughness. Abrasives move in the direction towards  
Figure 17: Final state of the sapphire substrate: (**a**) surface micrograph; (**b**) 2D surface morphology; and (**c**) 3D surface morphology.  
the sapphire substrate and congregate on the surface of the substrate, and this effectively increases the friction between the polishing slurry and the sapphire substrate, slowing down the effect of the polishing slurry thrown out by centrifugal force. Comparing with the traditional CMP, the polishing slurry on the sapphire substrate surface is distributed more evenly in the DEPP process and there are more abrasives participating in material removal at the central location. Therefore, DEPP results in better flatness compared to the traditional CMP.
# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 2 Methodology and Materials
### Selection of Predictive Models
#### 2.3.1 ML Methods
The dataset was divided into two subsets to train and test the chosen algorithms. The 70% and 80% training proportions are most often used in the literature [46; 47; 48; 64; 65]. To determine the most appropriate ratios for the dataset, values ranging from 50% to 80% were tested in this study. The results confirmed the use of the two proportions mentioned above. Similar performances in terms of RMSE and R\({}^{2}\) were observed for these proportions (see Appendix B). ANN modeling was conducted using the neural network toolbox in MATLAB-based software, considering a dataset divided into 70% for training, 15% for validation, and 15% for testing. All the other algorithms were developed based on the python statistical computation language. The hyper-parameters were maintained at their default values, considering a dataset distribution of 70% for training and 30% for testing. The input and output variables used for the models are summarized in Table 2. The temperature history is a matrix of parameters with a difference of 30 min between its different columns. For example, if the temperature was recorded at a time t, the history corresponds to t-0.5 h, t-1 h, t-1.5 h, and t-2 h.  
The accuracy of these forecasting models was evaluated, and their performances were compared based on the following criteria: \begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameters** & **Output Parameters** \\ \hline Heat Power & Indoor Temperature (at the center) \\ \hline Outdoor Temperature Wall 1 & Indoor Temperature Wall 1 \\ \hline Outdoor Temperature History Wall 1 & Indoor Temperature Wall 2 \\ \hline Indoor Temperature History & Indoor Temperature Wall 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input and output parameters of the ML methods. The root mean square error (RMSE) that can provide information on the magnitude of the deviations [3; 65; 66]:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}}{n} \tag{1}\]  
The coefficient of determination (R\({}^{2}\)) that can be a measure of the adequacy between the predicted and the observed data [16; 55; 52]:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{(y_{i}-\hat{y}_{i})}^{2}}}}{\sum\limits_ {i=1}^{n}{{{(y_{i}-\overline{y})}^{2}}}} \tag{2}\]  
where \(\hat{y}\) is the predicted vector, \(y\) is the reference vector, and \(n\) is the number of parameters.
## 2 Proposed PMSM-SCS Based on DTC Strategy
### PMSM-SCS Based on Variable-Flux DTC Strategy
#### 2.2.2 FOSMC-Type Controller for PMSM-SCS Based on DTC Strategy Using Variable Flux  
To improve the performance of PMSM-SCS based on DTC strategy, the synthesis of a FOSMC-type controller is proposed to obtain \(i_{qref}\) and then flux \(\psi_{ref}\), based on Equations (40) and (41). For this purpose, the sliding surface \(S\) is selected as follows:  
\[S=k_{p}x_{1}+k_{d}D^{\mu}x_{1}=k_{p}x_{1}+k_{d}D^{\mu-1}x_{2} \tag{44}\]  
By calculating the derivative of the surface \(S\), the next equation can be obtained:  
\[\dot{S}=k_{p}\dot{x}_{1}+k_{d}D^{\mu+1}x_{1}=k_{p}x_{2}+k_{d}D^{\mu-1}\dot{x}_ {2} \tag{45}\]  
Based on the descriptive equations of the PMSM mathematical model, the following equation can be written:  
\[\dot{x}_{2}=\ddot{\omega}_{ref}-1.5\frac{n_{p}\lambda_{0}}{J}\dot{i}_{q}+\frac {1}{J}\dot{T}_{L}+\frac{B}{J}\dot{\omega} \tag{46}\]  
Substituting Equation (46) into Equation (45) gives  
\[\dot{S}=k_{p}x_{2}+k_{d}D^{\mu-1}\bigg{(}\ddot{\omega}_{ref}-1.5\frac{n_{p} \lambda_{0}}{J}\dot{i}_{q}+\frac{1}{J}\dot{T}_{L}+\frac{B}{J}\dot{\omega} \bigg{)} \tag{47}\]  
According to the SMC methods for \(\dot{S}=0\), the following equation can be obtained:  
\[-\varepsilon H(S)-qS-k_{p}x_{2}=k_{d}D^{\mu-1}\bigg{(}\ddot{\omega}_{ref}-1.5 \frac{n_{p}\lambda_{0}}{J}\dot{i}_{q}+\frac{1}{J}\dot{T}_{L}+\frac{B}{J}\dot{ \omega}\bigg{)} \tag{48}\]Applying the operator \(D^{1-\mu}\) to Equation (48) to both members, the following equation can be written:  
\[D^{1-\mu}\big{(}-\varepsilon H(S)-qS-k_{p}x_{2}\big{)}=k_{d}\bigg{(}\ddot{\omega }_{ref}-1.5\frac{n_{p}\lambda_{0}}{J}\dot{i}_{q}+\frac{1}{J}\dot{T}_{L}+\frac{B }{J}\dot{\omega}\bigg{)} \tag{49}\]  
After processing Equation (49), the following equation can be obtained:  
\[\frac{1}{k_{d}}D^{1-\mu}\big{(}-\varepsilon H(S)-qS-k_{p}x_{2}\big{)}=\ddot{ \omega}_{ref}-1.5\frac{n_{p}\lambda_{0}}{J}\dot{i}_{q}+\frac{1}{J}\dot{T}_{L}+ \frac{B}{J}\dot{\omega} \tag{50}\]  
By rewriting Equation (50), the next equation can be written as  
\[1.5\frac{n_{p}\lambda_{0}}{J}\dot{i}_{q}=\ddot{\omega}_{ref}+\frac{1}{J}\dot{ T}_{L}+\frac{B}{J}\dot{\omega}-\frac{1}{k_{d}}D^{1-\mu}\big{(}-\varepsilon\,H(S)-qS-k _{p}x_{2}\big{)} \tag{51}\]  
The current equation \(i_{qref}\) is achieved from Equation (51), as follows:  
\[i_{qref}(t)=\frac{1}{1.5\frac{n_{p}\lambda_{0}}{J}}\cdot\int\limits_{0}^{t} \bigg{[}\ddot{\omega}_{ref}+\frac{1}{J}\dot{T}_{L}+\frac{B}{J}\dot{\omega}- \frac{1}{k_{d}}D^{1-\mu}\big{(}-\varepsilon\,H(S)-qS-k_{p}x_{2}\big{)}\bigg{]}dt \tag{52}\]  
Then, as in the case of the integer-order SMC-type controller, the reference value of the flux \(\psi_{ref}\) is obtained by using Equations (40) and (41).
## 2 Application of Borehole Camera Technology in Engineering  
### Digital Panoramic Borehole Camera System (DPBCS)  
The DPBCS is a new set of advanced and intelligent exploration equipment which integrates electronic technology, video technology, digital technology, and computer technology. The system records and analyzes the borehole wall without disturbance in situ from a panoramic angle. The disturbing influence of the drilling core is avoided by directly studying the hole wall. The structural plane condition inside the borehole can be accurately detected. The state of the rock formation inside the hole can be reflected in detail. The system can observe the 360\({}^{\circ}\)-hole wall at the same time. The system can display and analyze the entire drilling data on site in real-time monitoring.  
The key component of the panoramic borehole camera system is the panoramic camera probe, as shown in Figure 1. It contains a truncated cone reflector that can obtain panoramic images, a light source for detecting illumination, a magnetic compass for positioning, and a miniature CCD camera. The entire probe adopts high-pressure sealing technology, which enables the probe to work in water. The depth encoder is one of the positioning devices of the system, which consists of a measuring wheel, a photoelectric corner encoder, a depth signal acquisition board, and an interface board. The software part of the system includes data acquisition software and data analysis software. The software can digitize the recorded borehole video, analyze the panoramic borehole image, identify and interpret the structural plane, realize the storage and maintenance of borehole image data, and provide reliable borehole data and rock mass structure parameters for various kinds of drilling engineering.
## 4 Our Proposed Framework  
In this section, we present a privacy-preserving learning framework based on Restricted Boltzmann Machines and Instance Reduction Algorithms. The goal of this framework is to preserve privacy while maintaining model accuracy and reducing the computation and communication overhead in collaborative learning settings. Our work differs from other privacy-preserving frameworks in the reviewed literature by addressing the privacy-utility trade-off, encryption, communication, and computation overheads, and the inability to handle complex deep learning models.  
We assume that the deep learning framework is collaborative deep learning, where two or more training parties share their local data to jointly learn a deep learning model [6]. Each training party (user or client) applies an RBM model to their training data to encode it in a way that retains the relevant features. Then, a reduction technique is used to select a reduced subset of the encoded data to send to the server. This reduced and encoded set is sent to a central server without too many concerns about data privacy or computation and communication overhead, as the majority of the data are not sent depending on the retention rate of the reduction technique used. The central server combines the reduced datasets into one training set and then builds and trains an MLP or a CNN global model using the combined dataset. Figure 2 shows our privacy-preserving deep learning framework. The central server does not need to share any hyperparameters of the model with other parties. Similarly, in the prediction phase, the training party applies the RBM model to a data point to encode data and then sends it to a central server for prediction. The server then runs the deep learning models and returns the prediction output's label, not the prediction score.  
The server might be owned by a service provider operating in sectors such as healthcare or finance, or a Machine Learning as a Service (MLaaS) platform [9], which provides predictive services to clients. We also make the common assumption that the server and participants are honest but might be curious since they follow the same training protocol but might be curious about other participants' training datasets. If they are not honest, then they may deviate from the protocol rules or even falsify data.
# Automatic Selective Encryption of DICOM Images
## 2 The DICOM Image Encryption: Motivations and Limitations  
The Health Insurance Portability and Accountability Act (HIPAA) recommended that all data that might be used to identify all entities (e.g., patient, physician, etc.) must be confidential and only authorized entities can access them. However, the DICOM standard does not support encryption for pixel data. DICOM secures patients' information using the Application Level Confidentiality Profile. However, this does not necessarily guarantee confidentiality. For instance, if an attacker already has access to the pixel data or part of it, it can be used to identify the patient's identity [1]. The supplementary documents of the DICOM standard highlight these risks on the NEMA website, as shown in [26; 27; 28].  
Several research works have been conducted in the literature to extend the DICOM standard with pixel encryption mechanisms. However, these mechanisms increase the processing time overhead, which causes additional delay and requires more computational resources. Hence, it is very important to provide an efficient and simple mechanism, especially in the era of telemedicine and handheld devices.
## 3 SRB Activities in Acidic Environments
### Response of SRB to Acidic Environments
#### 3.1.4 Other Factors Support Survival and Growth of SRB  
In addition to SRB's own strategy to acid stress, the existence of micronuclehes can attribute to SRB acid tolerance. Microniches are small sites which are different from the bulk environment. [PERSON] et al. reported the presence of micronuclehes of higher pH around bacteria in an acid mine tailing environment [59]. Microniches can only be sustained thermodynamically by an active energy consumption process. If the pH inside micronuclehes need to maintained at neutral pH in acidic bulk environment, bacteria need to have proton consuming process [25]. sulphate reduction is a proton consuming process, thus SRB can maintain their micronuclehes at elevated pH. Additionally, in natural environment, SRB can be protected by the protective layer caused by the entrapment of precipitation of sulphide and iron/other metals in the matrix of biofilm. This precipitation layer can act as diffusion barrier to proton from environment [59; 60; 61].  
Another factor that can be taken into account for bacterial survival and growth is the change in environmental pH. As sulphate reduction is a proton consuming process, thus the concentration of proton in environment decreases which generates neutral/alkaline pH of the environment [62] which is suitable for optimising bacterial growth. The growth of D. vulgaris_ was arrested at pH 4, however regrew after a few days when the bulk solution pH increases [17].  
The application of this change in environmental pH has been used in bioremediation where to neutralize environmental pH. As pH increases, this results in precipitation of metals which is used in heavy metal removal [14; 51; 63].
# Structuring Reference Architectures for the Industrial Internet of Things
## 5 Considered Reference Architectures
### Fiware  
The FIWARE Foundation promotes an open-source software stack to accomplish interoperability also beyond IoT use cases. The Next Generation Service Interface (NOSI) is a standardized Web API for the IoT restricted to RESTful interactions. Any IoT protocol can be connected by suitable agents or wrappers, providing data towards the Orion Context Broker as the intermediary component for data and command transformation and translation. The currently specified NGSI-LD [41] provides a semantically annotated JSON syntax for context modeling and guidelines to interact with the respective resources.  
The FIWARE reference architecture provides documentation for developers and system architects on cloud computing and how Big Data possibly enhances IIoT architectures on the higher network levels instead of regarding physical assets where concepts from for example, RAMI4.0 or IIRA are more detailed. In addition to HTTP serving as the suggested protocol with specified bindings FIWARE defines protocol-agnostic methods and context representations [41].  
The strong orientation on HTTP and RESTful interaction patterns at the level of NGSI interfaces and the delivery of software libraries puts its emphasis on the different platform character of FIWARE. Whereas IIRA and RAMI4.0 comprise reference frameworks to categorize IIoT architectures, FIWARE proposes certain patterns and interfaces based on a RESTful integration layer.  
As a consequence of the more software-focused view, the architecture directly illustrates certain components and specify necessary interaction patterns. Similar to other IIoT frameworks, IIRA and RAMI4.0 view HTTP as a possible communication protocol among others, whereas FIWARE puts it in the center of its integration and (user) interaction process.
## 2 Analysis and Design of the Active Phase Shifter Circuit
### Circuit Architecture  
Figure 1 shows the basic structure of the proposed phase shifter. The single-ended RF input signal is first converted into a differential signal by the input balun, which then enters the quadrature signal generator to produce four orthogonal IQ signals. To achieve wideband performance while maintaining good orthogonality, the quadrature signal generation circuit utilizes a second-order RC-CR network structure. This design sacrifices insertion loss to expand the operating bandwidth. The four IQ signals generated by the quadrature signal generator are fed into the variable-gain amplifier (VGA). The VGA adopts a common-source configuration, and the four output signals are directly combined into two differential signals. Finally, the desired 64-state phase-shifted signals are obtained through the output balun.  
Figure 1: Schematic of the proposed active phase shifter.
## 8 Practical Deployment Considerations
### Comparison with Alternative Modalities  
In Table 8, we provide a high-level comparison of WiFi CSI-based sensing with other commonly used sensing modalities.  
Compared to vision-based or physical contact sensors, WiFi CSI offers a practical trade-off between cost, privacy, and setup simplicity, though its performance is more sensitive to spatial and environmental dynamics.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 6 Synchronization by Conflict Resolution
### The Synchronization Algorithm
#### Correctness of Algorithm 1  
As discussed after the proof of Proposition 12, \(A^{o}\smallsetminus C\) and \(B^{o}\smallsetminus C\) are refluent canonical sets, and \(C\) is an initial segment of every merger of \(A^{o}\) and \(B^{o}\). Moreover, if \(M^{o}\) is a merger of \(A^{o}\) and \(B^{o}\), then \(M^{o}\smallsetminus C\) is a merger of \(A^{o}\smallsetminus C\) and \(B^{o}\smallsetminus C\). From here the correctness of the algorithm follows easily.
## 2 Problem Formulation
### UWB Localization with the Adaptive Distributed Student's T Extended Kalman Filter Scheme  
We begin by introducing the UWB localization scheme with the adaptive distributed Student's t EKF, as shown in Figure 2. The adaptive filter employs a distributed filter structure, consisting of \(N\) Allan variance-assisted local t EKFs. Each local filter estimates the target object's position \(\mathbf{P}_{j}\), \(j\in[1,N]\), by fusing the distance measurements \(r_{j}\), \(j\in[1,N]\). The main filter then combines the outputs of the local filters to compute the final position of the target. The design of the local Allan variance-assisted t EKF filter and the data fusion model used by the local filters will be discussed in the next section.