esa-sceva/satcom-chunk-collection huggingface dataset:
# The Dynamic Response of an Arch Dam During a Record Low-Intensity Earthquake
## 3 Numerical Model  
### General Aspects  
Numerical models representing the complete dam-foundation-reservoir system are currently employed to analyze the evolution of the dynamic behavior of concrete dams. The present model was created with the code 3 DEC [21], based on the discrete element method, which is used mostly in rock engineering projects but also allows for the analysis of structural problems. The code has often been applied to the safety assessment of concrete dams, given its ability to simulate the failure modes involving the jointed rock foundation, induced by either static or seismic loading. In the present work, the discontinuities represented are the dam's contraction joints, the dam-foundation joint, and the dam-reservoir interface. The rock mass is assumed to be an equivalent continuum since the level of dynamic action considered here is not sufficient to induce non-elastic behavior in the foundation. For this low-intensity earthquake, a simpler model would be sufficient. The use of 3 DEC is justified to allow the model to also be applied to higher dynamic loading, which might open the contraction joints, and possibly to insert rock mass joints for a more detailed foundation analysis [13]. The models also explicitly incorporate the reservoir in order to include the effect of water on the dam's upstream face. Therefore, simplified approaches, such as the Westergaard added mass method [22], often used in dam analyses, are not necessary. It has been shown that in arch dams, this simplified method overestimates the masses to be added to the nodal points of the dam's face [23; 24]. As a consequence, it is usually necessary to scale the associated masses with a factor that can vary between 0.5 and 1.0, with no objective criterion for its selection. The numerical models used in this work eliminate this subjective factor by incorporating the geometric discretization of the reservoir and the water dynamic response. The constitutive model of the contraction joints allows for the consideration of non-linear structural behavior in case the joints open, as is the case under intense shaking. Given the low level of vibration caused by the forced vibration tests or the low-intensity earthquake, these joints remain in the elastic range.
# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 3 Determining the Hyperparameters for the Developed Model with Q-Learning Scheme
### Hyperparameter Selection for the TCN Model Using Q-Learning Algorithm
Selection of the optimal parameters (\(\theta\)) for the developed TCN model is illustrated in Figure 5.  
Figure 5: Parameter selection for the TCN model using the Q-learning algorithm. In summary, the detailed Q-learning algorithm for wind power forecasting with the TCN learning model described by the PDL is as Algorithm 1. ```
Repeat The state vector \(S\) consists of \(N\) independent states \(S=[s_{1},s_{2},\ldots,s_{N}]\) Set \(R=0\) For each state \(s_{i}\) in \(S\) Initialize \(Q(s_{i},a_{i}),\ \forall s_{i}\in S_{i},\ a_{i}\in A(s_{i})\), arbitrarily, and \(Q(terminal-state,0)=0\) Repeat (for each episode of \(i\)th state \(s_{i}\)) Initialize \(S_{i}\) Repeat (for each step of episode) Choose \(A\) from \(S_{i}\) using policy derived from \(Q\) Take action \(A\), observe \(R\), \(S^{\prime}_{i}\) \(S^{\prime}=[s_{1},\ldots,s^{\prime}_{i^{\prime}},\ldots,s_{N}]\) \(R=TCN_{Loss(s)}-TCN_{Loss(s^{\prime})}\) \(Q_{k+1}(S_{i},a;\theta)\gets Q_{k}(s_{i},a;\theta)+\alpha[R+\gamma\underset{ a_{i}}{\max}Q_{k}(s^{\prime}_{i^{\prime}}a^{\prime}_{i^{\prime}};\theta)-Q_{k}(s_{i},a _{i^{\prime}};\theta)]\) \(S_{i}\gets S^{\prime}_{i}\) Until \(S_{i}\) is terminal Until \(\Delta L(\theta)<\varepsilon\) (preset constant \(\varepsilon\))
```  
**Algorithm 1** Q-learning for Hyperparameter Tuning of TCNModel
## 5 Conclusions  
In this paper, we have presented a novel data hiding approach based on LSB substitution using a mapping bits strategy. In the proposed method, first cover pixel bits and secret data bits were logically grouped into the number of pairs. Next, in the embedding process, these logical groups of four secret data bits were mapped with the (4-MSBs of) cover pixel bits. To maintain the mapping status between the cover and secret data, the method employed the (2-bit) LSB substitution. The contributions of the proposed method are high embedding capacity, less distortion quality, and robustness. First, the proposed method achieved high embedding capacity (up to 3.24 bpp) due to the maximum (4 bits) mapping between the cover and secret data bits. Second, the proposed method maintained a high (42.15 dB) PSNR value due to limiting the number of bits modification per pixel up to two bits. Third, the proposed method was able to resist the RS and histogram steganalysis attacks due to low visual distortion. In the future, we plan to take into consideration an open challenge to achieve adaptable mapping between the cover and secret data bits.  
All authors discussed the contents of the manuscript and contributed to its preparation. [PERSON] and [PERSON] designed and implemented the proposed scheme; [PERSON] and [PERSON] contributed input for this research and reviewed the manuscript; [PERSON] provided the experimental setup. [PERSON] designed and conducted the literature review.  
The APC was funded by CyberSecurity Malaysia. The work was supported by the Special Assistance Research Grant of University of Malaya (BKS107-2017) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A09081842).  
We thank the anonymous reviewers for their valuable suggestions that improved the clarity of this article.  
The authors declare no conflict of interest.
## 2 Design and Methods
### Design Optimization  
Following the initial FEA, simulation parameters were refined through an optimization process. The Ansys Design Exploration Response Surface feature was used for this step. This feature involved analytical and visualization techniques to assess the impact of input variables on model performance. The Response Surface simulation was utilized to systematically generate key optimization metrics, ensuring a data-driven refinement of the design.  
The Goodness of Fit metric was used to validate the accuracy of the simulation by comparing predicted values with experimental data. This ensured model reliability before further optimization, particularly in predicting stress-strain distributions for improved screw fixation strategies. This statistical measure ensured that the predictions were reliable before further optimization, particularly in assessing stress-strain distributions critical for THA screw fixation.  
The Response Graphs provided insight into how output variables (such as stress, strain, and deformation) varied in response to changes in design parameters. These graphs helped visualize non-linear trends, guiding parameter selection for improved implant performance.  
To further refine the design, a Local Sensitivity Analysis was conducted to quantify the impact of small variations in parameters such as screw length, cup positioning, and material stiffness. The results were visualized using Local Sensitivity Curves, which illustrated how specific parameters influenced implant stability, identifying the most critical factors for performance enhancement.  
Lastly, Spider Charts (Radar Charts) were employed to analyze the combined effect of multiple design variables. By mapping interactions between factors, such as bone quality, implant orientation, and fixation techniques, these charts provided a comprehensive assessment of trade-offs, aiding in the selection of an optimal screw configuration.
## 1 Introduction
Another pretreatment method to improve biomass quality is the ash rejection process developed by Korea Institute of Energy Research (KIER). This acetic acid-based pretreatment method has been used to extract the alkali mineral inside biomass and may contribute to a significant reduction in chloride-induced corrosion and slag/fouling caused by the deposition of the boiler during combustion [14]. [PERSON] et al. [14] proposed that the optimal pretreatment conditions for maximum ash rejection were a pH of 1.76, temperature of 60 \({}^{\circ}\)C, and a residence time of 10 min, at which point the losses of hemicellulose, cellulose, and lignin were minimized and investigated for several kinds of biomass. In addition, with an aim to reduce PM emission from biomass combustion, [PERSON] et al. [15] have proposed torrefraction and [PERSON] et al. [16] have proposed ashless biomass pre-treatment methods. Since K, Na, and Cl are sources of PM, techniques have been developed by prior researchers to use less fuel or remove these substances. Therefore, the ashless biomass technique used in this study is a state of the art pre-treatment method to remove ash and alkali matter in herbaceous as well as woody biomass. In addition, it is commercially applicable for the production of ashless biomass, which is seeing increasing potential as an acidic treatment of biomass compared to other pretreatment methods. To the best of our knowledge,detailed studies on the physicochemical properties of ashless biomass produced by the above method have not been performed and compared with other pretreatment methods thus far. Biomass can be classified into woody and herbaceous in terms of the carbon components and life cycles. Herbaceous biomass is regarded as an abundant and relatively inexpensive fuel source. Its short life cycle and low production costs imply that it can replace wood-based biomass in the market due to its high price competitiveness [17]. However, before marketing herbaceous biomass, its physical and chemical properties require further investigation. Despite recent torrefaction research that has focused on improving the fuel properties of woody biomass, very little research has thoroughly investigated and compared the structural and physicochemical properties of woody and herbaceous biomass, especially with ash rejection pretreatment. In addition, physicochemical research on herbaceous biomass pretreated by the ashless process has not been performed precisely. The purpose of this study was to investigate not only the fuel characterization and composition of woody and herbaceous biomass pretreated by torrefaction and the ashless method, but also to determine their physical and chemical characteristics. Characterization methods included scanning electron microscopy (SEM) for morphology examination, and thermogravimetric and elemental analysis to study the changes in the O/C ratio and components in the biomass. The surface area and pore size distribution were also investigated using the Brunauer-Emmett-Teller (BET) method. Furthermore, the hydrophobicity and grindability of each type of biomass were investigated.
### Monitoring Plan and Monitoring Data Analysis
#### 2.2.2 Monitoring Data Analysis  
Through the above monitoring plan, the monitoring results of water pressure outside the lining of the two monitoring planes are shown in Figure 8.  
From the analysis in Figure 8, it can be seen that the water pressure outside the lining on the same level was unevenly distributed; that is to say, the water pressure values of different measuring points were not equal, and the maximum difference was close to 0.44 MPa, of which, the water pressure range at \(-\)660 m was 1.49 \(\sim\) 1.94 MPa, and the water pressure range at \(-\)735 m was 1.67 \(\sim\) 2.17 MPa.  
It can be seen from the analysis of monitoring data (as shown in Figure 8) that the water pressure outside the lining on the same level was unevenly distributed, which is inconsistent with common sense. After analysis, it is believed that, due to the uneven distribution of steeply dipping large fissures in the Gaoligongshan stratum (as shown in Figure 9), the hydraulic conductivity of each part of the \"slurry-rock mass\" stone body formed by the surrounding rock after grouting is different, and the water pressure of the groundwater outside the grouting circle is reduced differently after passing through the grouting circle, thus resulting in different water pressures at different measuring points.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Vision Positioning Systems (VPS)  
The vision technology mainly utilizes a camera or other scanning devices for distinguishing different characteristics in the environment. Vision positioning systems (VPS) aim to match real-time images with stored frames or pre-constructed models, for relative or global pose estimation. Although it is a widely available technology with no additional infrastructure required, high storage capacity is still needed for building the images' database annotated with the environment map [24]. Moreover, the camera should be used at a low transmission rate, so the target must be stationary or slow moving [44].  
For localization purposes, it can offer submeter accuracy; however, it is easily affected by the pose and orientation of small devices, as well as the latency caused by the real-time communication between the server and smart devices. Specifically, a small change in the camera angle may significantly change the captured image, which can potentially influence the localization performance. This can be eliminated by adding the camera angle and orientation in the database and matching them with the captured images, which requires high storage capacity. The fingerprinting technique is commonly used for the image analysis and relies on the camera acquired images, segmented to extract features and estimate the closest match of extracted features against the database features. The feature extraction involves the processing of an image for extracting a set of numerical values, which can uniquely describe the image [24]. During the fingerprinting online phase,the image similarities are identified by decoding their transmission identifiers for their location [26].  
For instance, the perspective-n-point (PnP) pose problem [108] aims to estimate the relative pose, i.e., 3D position and orientation between a calibrated camera and 3D object, or the entire 3D scene, from a set of \(n\) visible 3D points with known scene coordinates and their 2D projections with known pixel coordinates [109]. Therefore, the geometric relations between the object's 3D coordinates and their 2D projection coordinates on the image sensors can be used to determine the position [108]. Additionally, the VPS can provide useful information based on the captured images or by using 2D tags, such as barcodes with encoding information, recorded and processed by a smart device with built-in cameras. For instance, Google maps navigation can provide visual guidance; thus, the users can spot their surroundings using their smart device's camera and visually interpret their direction with great accuracy [110].
## 1 Introduction
They found that the Mohr-Coulomb model introduces slightly larger errors in results compared to the Drucker-Prager model. Furthermore, they employed finite element numerical simulations to analyze the force distribution on the main cutting teeth during drill bit rock cutting, allowing them to predict the wear trend of the drill bit's cutting teeth. When drilling into rock using a drill bit, friction is generated that wears away the rock as the drilling reaches a steady state [20]. The contact stress acting on the rock drill bit and the inclination angle of the drill bit are important indicators that reflect the degree of drill bit wear [21]. [PERSON] et al. [22] conducted laboratory drilling experiments to study drill bit wear during the drilling process. At present, the rock-breaking mechanism and theoretical research on the drilling process are not yet complete. Most of the previous studies used static or quasi-static equilibrium analysis methods [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36], which resulted in large errors between the data monitored during the drilling process and the theoretical analysis results. The complex physical and mechanical changes in the rock-drilling process cannot be accurately explained [37; 38; 39; 40; 41; 42]. The process of drilling and breaking rock is carried out by controlling the drilling speed and rotation speed of the drill bit. Changes in the drilling speed, rotation speed and rock properties will cause changes in the force on the drill bit. ABAQUS is a powerful finite element analysis software that can be used to simulate a variety of engineering and scientific problems, including the techniques used to drill bits into rock. Using ABAQUS to simulate drilling into rock with a drill bit can help engineers optimize the drilling process, reduce costs and risks, and ensure the successful completion of the project. Therefore, it is necessary to use ABAQUS to study the changing rules of drill bit stress during drilling under different drilling speeds, rotational speeds, confining pressures and rock structure conditions. In this paper, the dynamic process of drilling and breaking rock is analyzed using the ABAQUS software. According to the stress-strain state of the rock and the force of the drill bit, the rock-breaking mechanism of drilling is revealed. The effect of the size of the drill bit, and the characteristics of the rock mass on the drilling parameters is studied during the drilling process.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 4 Experimental Results
### Evaluation Metrics
#### 4.3.6 F1-Score  
F1-Score, which is also called F-score or F-measure, indicates the balance which exists between recall and precision for a classifier. The closer it is to one, the more balance between precision and recall exists. F1-Score can be obtained by Equation (12).  
\[\text{F1}-\text{Score}=\frac{\text{2 TP}}{\text{2 TP}+\text{FP}+\text{FN}} \tag{12}\]
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 1 Introduction  
Magneto-inductive (MI) communication is of growing interest in communication environments, where radio, optical wireless, acoustical, and molecular communications are challenging or not possible [1]. Examples include underwater communication [2; 3], underground communication [4; 5], process engineering such as communication in chemical reaction tanks, and medical applications [6]. MI communication is characterized by low propagation delay and small susceptibility to the surrounding environment [7]. Furthermore, in the near field there are no issues such as multipath propagation and fading. However, near-field MI communication has inherent restrictions. Due to a high attenuation of 60 dB per decade only short distances are supportable with reasonable data rates. According to the state-of-the-art, for communication purposes coils are used at the transmitter and receiver sides. The coils can be described as antennas. Inductive coupling enables a communication link between the transmitter and the receiver. The transmitter coil induces a modulated electrical current in the receiver coil. The voltage at the clamps of the receiver coil is evaluated as the received signal (Figure 1a). To reach reasonable distances, the transmitter and the receiver coil should operate in a tuned resonant circuit. This principle generates a significant gain for signal frequencies around the resonant frequency, but reduces the possible bandwidth depending on the quality factor of the resonant circuit. Since the communication range is limited by the radius of the transmitter and the receiver coils, mobile applications such as autonomous underwater vehicles (AUVs) or sensor networks are not efficient, if small coils are required at both ends.  
In this publication, an innovative MI communication system approach is introduced: the receiver coil of a conventional MI communication system is replaced by a high-sensitivity magnetic field detector, in our prototype an anisotropic magneto-resistive (AMR) detector (Figure 1b). These detectors enable small and lightweight receiver units suitable for mobile applications. This proposal promises many benefits that could lead to cheap and reliable system design, among other advantages. Precise magnetic detectors have been applied for localization and navigationpurposes, see for example [8; 9; 10]. To the authors best knowledge, however, high-sensitivity magnetic field detectors have not been studied in the area of data communication. In this sense, our contribution is original.  
The paper is structured as follows. First, characteristics of magnetic field detectors are assessed in the context of MI communication. Afterwards, the sensitivity of coil-to-coil and coil-to-AMR communication is analyzed and compared. Finally, the results are summarized, and an outlook is given.
# Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things
## 4 The Secure Service Proxy  
The approach followed in this work allocates one reverse CoAP(s) proxy per constrained device. The CoAP specification [9] defines a reverse proxy as \"an endpoint that stands in for one or more other server(s) and satisfies requests on behalf of these, doing any necessary translations\", and it also states that \"The client may not be aware that it is communicating with a reverse-proxy; a reverse-proxy receives requests as if it were the origin server for the target resource.\" The reverse proxy approach enables splitting the end-to-end communication between a constrained device and its client at the proxy with no need for any additional configuration on the client (as mentioned in the CoAP specification). While the resulting communication is no longer end-to-end, indeed the proxy will share DTLS security contexts with both parties and will translate CoAP messages, the resulting system has many benefits and is able to overcome all of the issues that are discussed in the previous section. Additionally, our reverse proxy approach implements a virtual device for every constrained device. This enables the reverse proxy to extend a constrained device (beyond only proxying) by hosting functionality on the corresponding virtual device. Finally, by enabling the reverse proxy to be deployed on any system (see design), it is not restricted by the limitations common to constrained IoT devices. In the next subsections, we argue that the benefits of this approach far outweigh the downsides of splitting the end-to-end communication, and we present our design for such a reverse proxy.  
Figure 3: In a smart building scenario, there is a wide variety of different users. Constrained devices are unable to offer all necessary security and application features to cater to these users. In the approach followed by this work, unconstrained systems (e.g., border routers (BRs)) assist by offering these missing features. CBOR: Concise Binary Object Representation, ACL: Access Control List.
## 3 Materials and Methods  
The initial testbed utilized for our protocol evaluations was outlined in our previous work [26]. This testbed, which is also shown in Figure 1, is logically divided into three different sections: the test controller, 5G network, and Device Under Test (DUT). Two Intelligent Electronic Devices (IEDs) from two reputable manufacturers were utilized for our initial GOOSE tests. Each device was configured to close or open a contact in relation to a specific boolean value within a GOOSE packet. The test controller operates on a Raspberry Pi 4 single-board computer (SBC), allowing access to the physical measurements on General-Purpose Input/Output (GPIO) pins. Packet generation for this step was completed with the Libiec61850 [27] software library, which handles all scheduling and updating of packets. For the Modbus and DNP3 protocols, Scapy [28] was utilized with an additional user-contributed DNP3 library [29]. The User Equipment (UE) is described in detail in the subsection below. However, we are refraining from publicly disclosing the precise devices due to security considerations related to ongoing efforts.