esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 2 Establishment of SPH-FEM Model
### Model Verification  
To verify the effectiveness of the SPH-FEM coupled model established in this paper, the variation curves of installation torque and force with installation depth obtained from the simulation were compared with the calculation results of the theoretical formula by [PERSON] and [PERSON] [8], as shown in Figure 4. It can be observed that the simulation results from the SPH-FEM model are consistent with the theoretical formula-derived curves in terms of overall trends for both the variation in installation torque with installation depth and the variation in installation force with installation depth. However, there are some differences: the theoretical calculations yield smoother results, while the simulated curves exhibit certain fluctuations. These fluctuations are reasonable as they more closely resemble the actual installation process.  
After the installation of the helical anchor foundation is completed, the ultimate uplift capacity is determined by applying a displacement at the reference point on the top of the anchor rod, and it is standardized as the uplift capacity factor \(N_{r}\) (\(N_{r}=Q_{u}/\gamma AH\), where \(Q_{u}\) is the ultimate uplift capacity, \(\gamma\) is the unit weight of the soil, \(A\) is the area of the anchor plate, and \(H\) is the depth of burial of the anchor plate). Figure 5 presents a comparison between the numerically simulated uplift capacity factor obtained in this study for the embedment ratios \(H/D\) of 4.6 and 5.6, and the theoretical calculations by [PERSON] et al. [10] and [PERSON] et al. [14]. It can be observed from the figure that at \(H/D\) = 4.6, the simulated uplift bearing capacity coefficient in this study is 30.9, and at \(H/D\) = 5.6, it is 41.5. In contrast, the theoretical values calculated by [PERSON] et al. [10] are 35.5 and 45.3, and those by [PERSON] et al. [14] are 33.6 and 45.2. Although the simulated values in this study are  
Figure 3: Curves illustrating the variation in installation torque and compressive installation force with installation depth at different rotational speeds. (**a**) Installation torque \(T\) vs. installation depth \(z\). (**b**) Compressive installation force \(F\) vs. installation depth \(z\).  
slightly lower than the theoretical values, the discrepancies are within 5%. Therefore, it can be concluded that the model used in this study for simulating the uplifting process is reasonable and feasible.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
###### Abstract  
Deciding binding, routing, and scheduling within system synthesis for hard real-time systems can be a challenging task. Symbolic methods leveraging results from the area of satisfiability modulo theories (SMT) solving have shown to be scalable methods for this by splitting the work between a logic solver for routing and binding, and a background theory solver performing schedulability analysis. For these methods, in order to prune the search space of infeasible implementations efficiently, a feedback by the background theory is required. It can be observed that previous approaches might fail here as feedback cannot be derived within a reasonable amount of time. We propose a _coordinated_ synthesis approach that overcomes this issue. Here, we leverage an answer set solver as logic solver that is enhanced with a scheduling-aware binding and routing refinement. Based on the answer set solver's decisions for binding and routing, a background theory solver then computes time-triggered schedules to resolve resource access conflicts. If no feasible schedule exists, a feedback to the answer set solver can be derived within reasonable time. Our experiments synthesizing massively parallel hardware architectures show that our approach increases the applicability of symbolic synthesis considerably. While more than half of the investigated instances in our experiments cannot be solved in the non-coordinated approach already at small 2-dimensional \(3\times 3\) tiled mesh hardware architectures with 60% average computational utilization per tile, the _coordinated_ synthesis approach scales up to \(5\times 5\) architectures with average utilization of 70% per tile (2.8\(\times\) the hardware architecture size than before). Furthermore, we increase the scalability and the robustness of our approach by encoding our domain-knowledge within domain-specific heuristics in our designated answer set solver. Within our experiments, we observe that the domain-specific heuristics enable us to scale up to \(6\times 6\) architectures with 70% average utilization per tile.  
Keywords:system synthesis; background theory; answer set programming +
Footnote †: journal: _electronics_
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 2 RNN and LSTM Models for Structural Response Prediction
### LSTM Model  
As shown in Figure 3, the LSTM unit adds three filter gates (forget gate, input gate, and output gate) to control the transmission and rejection of features based on the RNN model. This allows for the transmission and expression of information from a lengthy data sequence without the loss of important information from long ago. The LSTM is composed of a series of LSTM units. In each LSTM unit, the forget gate is used to decide whether to retain the unit state \(c_{t-1}\) of the \(t-1\) time step, and its output value ranges within [0, 1]. The input gate is used to update the unit state \(c_{t}\). The output gate is responsible for calculating the hidden state \(h_{t}\). The forward propagation process of the LSTM can be expressed as:  
\[\left\{\begin{array}{l}\text{Input gate}:i_{t}=\sigma(w_{ix}x_{t}+w_{ih}h_{t -1}+b_{i})\\ \text{Input node}:c=\tanh(w_{cx}x_{t}+w_{fh}h_{t-1}+b_{c})\\ \text{Forget gate}:f_{t}=\sigma(w_{fx}x_{t}+w_{fh}h_{t-1}+b_{f})\\ \text{Output gate}:o_{t}=\sigma(w_{ox}x_{t}+w_{oh}h_{t-1}+b_{o})\\ \text{Unit state}:c_{t}=f_{t}\otimes c_{t-1}+i_{t}\otimes c\\ \text{Hidden unit}:h_{t}=o_{t}\otimes\tanh c_{t}\\ \text{Output layer}:\hat{y}_{t}=\sigma(w_{hy}h_{t}+b_{y})\end{array}\right. \tag{3}\]  
where \(w_{ix}\), \(w_{ih}\), \(w_{cx}\), \(w_{fh}\), \(w_{fx}\), \(w_{fh}\), \(w_{ox}\), \(w_{oh}\), and \(w_{hy}\) are the network weights, \(b_{i}\), \(b_{c}\), \(b_{f}\), \(b_{o}\), and \(b_{y}\) are the bias terms, \(\otimes\) is the element-wise multiplication, and \(\tanh\) is the hyperbolic tangent function.  
The LSTM model can be extended in both the vertical and horizontal dimensions and finally connected to the fully connected layer. Based on the LSTM, the response prediction models for linear and nonlinear structures under different seismic waves were established (see Figure 4). As in Section 2.1, the input of the models is the seismic wave sequence \(\ddot{a}_{g}=[\ddot{a}_{g,}^{1}\dot{a}_{g,}^{2}\dot{a}_{g}^{3},\ldots,\ddot{a} _{g}^{n}]\) and the output is the structural response \([u,\dot{u},\ddot{u}]\).  
Figure 3: Schematic diagram of LSTM unit.
### Speech-Sound Disorders and Malocclusions  
The American Speech-Language-Hearing Association (ASHA) defines speech sound disorder (SSD) as an \"impairment in articulation, fluency or voice\" and may include distortions, repetitive sounds, and omissions in speech sounds [10]. SSD is an umbrella term that can be linked to a number of causes, including organic and learned. Proper articulation of speech relies on precise interactions among the tongue, lips, teeth, alveolus, and jaws, which interact for speech production. These articulators are abnormally positioned in patients with a \"handicapping\" malocclusion or dentofacial disharmony (DFD), who make up 2.5% of the population [11]. Patients with DFD have severely aberrant skeletal and dental relationships that can interfere with mastication, temporomandibular joint function, facial esthetics, and speech [12; 13; 14]. DFD patients are commonly afflicted with articulation disorders due to abnormalities in their organic, anatomical structures. DFD patients are grouped by malocclusion including Class II (excess \"overbite\", a colloquial term for overjet), Class III (underbite), and anterior open bite (AOB), as shown in Figures 1 and 2, though some patients present with both vertical and anterior-posterior (AP) discrepancies (e.g., Class III open bites). The treatment for full correction of DFD includes orthodontics and orthognathic surgery, with dental decompensation and surgical movement of the maxilla, mandible, or both into an ideal relationship, often with enhancement of facial esthetics [11]. By altering the dental and skeletal relationships in DFD patients, jaw surgery can also improve masticatory function and reduce pain in the temporomandibular joint [15; 16]. Another functional benefit is improvement in respiration, particularly for patients receiving maxillomandibular advancement for sleep apnea [17]. Many patients postoperatively report improvements in sleep and general quality of life [18]. Improved speech may also be a functional benefit of jaw surgery but is still an active area of research requiring further evaluation.
## 4 Hybrid Beamforming and RIS Phase Shift Matrix Design
### RIS Phase Shift Matrix Design  
To simplify the problem, we consider the use of a fully digital beamforming matrix \(F_{k}^{f}\in\mathbb{C}^{N_{F}\times N_{k}}\) and a fully digital combination matrix \(W_{k}^{f}\in\mathbb{C}^{N_{R}\times N_{k}}\) to replace the hybrid beamforming matrix \(F_{RF}\), the \(F_{BB,k}\) and the hybrid combiner matrix \(W_{RF,k}W_{BB,k}\). The previous optimization problem can be expressed as follows:  
\[\begin{array}{c}\underset{F,W,\Phi}{\max}\log_{2}det\left(I_{kN_{s}}+\frac{p ^{{}^{\prime}}}{kN_{s}\sigma_{n}^{2}}W_{k}^{f-1}\dot{M}_{k}^{H}\Phi\times\tilde{ R}_{k}F_{k}^{f}{F_{k}^{H}}\tilde{R}_{k}^{H}\Phi^{H}\ddot{M}_{k}W_{k}^{f}\right)\\ s.t.\quad\left\|F_{k}^{f}\right\|_{F}^{2}=kN_{s,k}\\ \theta_{t}\epsilon\mathcal{L},\forall t\end{array} \tag{65}\]  
RIS reflection matrix \(\Phi\) is set as fixed. The mixed channel matrix is constructed as \(H_{kc}=\ddot{M}_{k}\Phi\ddot{R}_{k}\). By decomposing the effective mixed channel matrix \(H_{kc}\) performing a singular value decomposition, the optimal \(F_{k}^{f}\) and \(W_{k}^{f}\) can be obtained. \(H_{kc}=\overbrace{Q_{kc}\widetilde{\Sigma}_{kc}\widetilde{\mathbf{f}}_{kc}^{ \prime}}^{H}\), where \(\overbrace{Q_{kc}\in\mathbb{C}^{N_{R}\times N_{R}}}^{\infty}\), \(\widetilde{\Gamma}_{kc}^{\prime}\in\mathbb{C}^{N_{T}\times N_{T}}\), and \(\widetilde{\Sigma}_{kc}\) is a rectangular diagonal matrix composed of singular values in descending order. Due to the sparsity of the UAV channel for millimeter-wave massive MIMO, the mixed channel matrix \(H_{kc}\) usually has a low rank. \(H_{kc}\) approximation can be obtained as \(H_{kc}\approx\overbrace{Q_{kc1}\widetilde{\Sigma}_{kc1}\widetilde{\mathbf{f}}_{ kc1}^{\prime}}^{H}\). It is simultaneously satisfies \(\overbrace{Q_{kc1}\triangleq\overbrace{Q_{kc}}\left(;1:N_{s,k}\right)}^{ \infty}\), \(\widetilde{\Sigma}_{kc1}\triangleq\widetilde{\Sigma}\left(1:N_{s,k}\right)\) and \(\widetilde{\Gamma}_{kc1}^{\prime}\triangleq\widetilde{\Gamma}_{kc1}^{\prime} \triangleq\widetilde{\Gamma}_{kc1}^{\prime}\left(;1:N_{s,k}\right)\). Afterwards,
## 3 Image Processing  
The point cloud data obtained by the TLS is compressed by the octree data structure, and it can be converted to an image. To detect cracks from the converted image, a series of image processing techniques are used. Image processing for crack detection on concrete surfaces involves the following seven steps: (1) K-means clustering, (2) conversion into the gray-level image, (3) median filtering and improved subtraction, (4) removal of unnecessary parts using K-means clustering, (5) binarization, (6) morphological closing, and (7) blob removal using area and eccentricity. These seven steps are tabulated in Table 1.
# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 3 Materials and Methods
### System Architecture
#### 3.1.3 Basic Model Selection  
Based on Equation (3) and Equation (4), \(t_{CDSTT}\) and \(t_{TPWT}\) can be obtained via a many-to-one prediction, while the values of \(t_{BSDT}\) and \(t_{SSTT}\) can be achieved via a many-to-many prediction.  
It is widely acknowledged that the LSTM model is suitable for solving many-to-one problems [9]. Therefore, it is selected to predict \(t_{CDSTT}\) and \(t_{TPWT}\) in this paper, as depicted in Figure 4a. It should be noted that the length \(l\) of the input time series for \(t_{CDSTT}\) prediction is set to 3, while that for \(t_{TPWT}\) prediction is set to 10.  
On the other hand, MDARNN is based on DA-RNN [12]. It introduces an input attention mechanism to weight local factors at each time point and incorporates macro  
Figure 4: The inputs and outputs of the LSTM and MDARNN models. (**a**) LSTM for Current Driving Segment Travel Time and Transfer Point Waiting Time prediction. (**b**) MDARNN for Bus Stop Dwelling Time and Stop-to-Stop Travel Time prediction.  
factors at the decoder stage. This allows it to capture macro factor dependencies and extract key information about local influences, making it a viable solution for predicting \(t_{BSDT}\) and \(t_{SSTT}\). Suppose at time \(t\) there are m + 1 subsequent bus stops. The inputs and outputs of the MDARNN are shown in Figure 4b. The length of input time series for both \(t_{BSDT}\) and \(t_{SSTT}\) is set to 5, and the length of output time series is variable, which refers to the travel time of subsequent stops.  
It should be noted that the basic prediction models mentioned above do not take into account the influence of traffic. Therefore, the model outputs need to be adjusted by corresponding weight factors, which will be further discussed in Section 3.2.2.
## 1 Introduction
### Derivative-Based Methods
It is clear now that the integration of the iterative process of the Nelder-Mead algorithm into the iterative process of the genetic algorithm would improve its convergence speed. 4. **Practical applicability and generalizability:** The proposed methodology holds practical applicability and generalizability. Genetic algorithms are widely used in various fields and domains for optimization problems. By addressing the premature convergence problem and enhancing exploration capabilities, our research contributes to the broader applicability and effectiveness of genetic algorithms in real-world scenarios. The integration can potentially be applied to a wide range of optimization problems, providing practitioners and researchers with a valuable tool to improve their optimization processes. In summary, our research paper makes a significant contribution by integrating the opposition Nelder-Mead algorithm into the selection phase of genetic algorithms. This integration addresses the premature convergence problem, enhances exploration capabilities, improves convergence speed and solution quality, and offers practical applicability and generalizability. The proposed approach has the potential to advance the field of optimization and evolutionary computation, empowering practitioners and researchers with an effective tool for solving complex optimization problems. This paper is organized into four sections: Section 2: Background; Section 3: Proposed Methodology; Section 4: Experimental Results and Discussion; and Section 5: Conclusions and Future Scope. Section 2 provides an overview of the relevant concepts used to establish the context for the research. Section 3 outlines the specific approach used to integrate the opposition Nelder-Mead algorithm into the selection phase of genetic algorithms and highlights its potential benefits. Section 4 presents the empirical evaluation of the proposed methodology, including the experimental setup, results, and analysis. Finally, Section 5 summarizes the key findings, discusses the implications of the research, and suggests potential future directions for further exploration.
# Stereo-DIC Measurements of a Vibrating Bladed Disk: In-Depth Analysis of Full-Field Deformed Shapes
## 2 Optical System Setup  
The measurement system presented here was devised to obtain high-speed full-field 3D measurement by exploiting a stereo optical setup. To this extent, two CMOSIS cameras were used, equipped with a 2024 \(\times\) 2024, 1-inch black and white sensor (Optomotive TREX). The maximum available frame rate of the cameras was 178 fps. This would limit the application of the sensors to 89 Hz, according to the Nyquist-Shannon theorem if transient signals would be acquired. However, it was proven that a low speed sensor can be used to acquire high-frequency signals, provided that a single harmonic component is excited, as recently performed by [PERSON] et al. [27; 28; 29]. In this scenario, the frame rate does not represent a limitation anymore. Whereas, in order to avoid blur images, the shutter time is critical. This can be solved either by using a short shutter time or by introducing a fast illumination source which provides short time light pulses. Since this latter solution required additional costly hardware, the first approach was adopted in this work, by exploiting the minimum shutter time of the cameras, which was 2 us. This really short \(\Delta t\) was fifty times lower then the period of a 10 kHz signal, which was assumed as the highest frequency of interest for the system. Furthermore, this ratio was similar to other examples in the literature, such as [PERSON] et al. [30].  
The two cameras were mounted on spherical joints, which were then fixed to a bar through a slide. This allowed for the fine tuning of the stereo system baseline and orientation. The cameras were also equipped with multifocal 1-inch, C-mount lenses (Azure Photonics 1632 ZL5M, 5 Mpixel), which could be used to set aperture, zoom and focal length to adapt the system to different scales. Since a really short shutter time was used, high intensity light sources were needed. The StratusLed 100 W module (Version 3) was selected for this purpose, and four devices were equipped with parabolic reflectors and mounted on the same bar of the stereo system. The described setup is shown in Figure 1. The stereo system was calibrated exploiting a planar checkerboard and using the procedure described by [PERSON] et al. [31].
## 2 Outline of the Existing Research Conducted by Authors
### Material Testing and Specim Design  
The compressive strength of the specimens used for the structural test was set to 24.4 MPa. Cylindrical specimens for the strength test were cast in molds (diameter: 100 mm, height: 200 mm) in compliance with ASTM C39/C39M [20], and the 28-day average  
Figure 4: Relationship between the performance degradation factor and average potential difference in terms of voltage: (**a**) flexural failure type beams and (**b**) shear failure type beam.  
compressive strength was 24.4 MPa. D16 and D13 were used for vertical rebars, and D10 for stirrups. To identify the material properties of rebars, three rebar tensile test specimens were prepared in accordance with ASTM E8/E8M [21]. As a result, the average yield strength and tensile strength of rebars were 460 and 495 MPa, respectively.  
To investigate the impact of rebar corrosion on the flexural and shear behavior of RC beam members, 16 specimens in total (flexural and shear beams, two types each) were designed in accordance with ACI 318 Building Code [22]. As shown in Figure 5, the total length of all specimens amounted to 2200 mm. The cross section of the flexural failure type beam was set to 150 \(\times\) 250 mm, and the shear failure type beam to 200 \(\times\) 300 mm. The shear span ratio (a/d) of the shear failure type beam was set to 3.  
The flexural failure type beam was designed to have vertical rebars using 2-D13 in the tension part and 2-D10 in the compression part. For the shear reinforcement, D10 was used in the closed-loop stirrup form. The shear failure type beam was designed to have 3-D13 rebars in the tension part and 2-D10 rebars in the compression part, and the shear reinforcement was provided in the form of an open-loop stirrup. F-B0 and S-B0 are flexural and shear beam comparison specimens without rebar corrosion, respectively. FB-C0 through FB-C7 and SB-C0 through SB-C7 are specimens degraded by the sequentially applied impressed current technique.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 4 IEEE 802.11 Technology for TSN
### Transmission Reliability  
Transmission power control and redundant transmission techniques are used to ensure reliability within the TSN. Increment of the transmission power improves the transmission reliability because the resistance to bit errors increases. However, transmission power increment increases the interference range. Therefore, the transmission power is adjusted to minimize the interference while increasing the transmission reliability in an environment where multiple access points (APs) are installed [38; 39]. As mentioned above, a wireless NIC has multiple physical layer interfaces, such as those of 802.11 ac or 802.11 ad [36]. By activating multiple interfaces simultaneously, a packet is redundantly transmitted over multiple paths to ensure transmission reliability to eliminate the retransmission time due to packet loss.  
In 802.11 ax, a spatial reuse (SR) was proposed to prevent communication interference between adjacent APs [7]. As shown in Figure 7, If AP1 and AP2 has equal clear channel assessment carrier sense (CCA/CS) threshold, both APs cannot transmit simultaneously because of carrier sensing. In SR, AP2 increases overlapping basic service set (OBSS) preamble detection (PD) threshold to ignore weak interference from the neighboring AP. Because increasing OBSS/PD threshold reduces the transmission power, the transmission range of AP2 decreases and interference from AP2 to AP1 also decreases. Therefore, transmission reliability is improved.  
Figure 6: Example of OFDMA transmission.  
Figure 7: SR and CSR.