esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates
## 2 Yield Optimization Problem and Benchmark Algorithms
### Yield Optimization. Benchmark Surrogate-Assisted Algorithms  
This section delineates the benchmark surrogate-assisted techniques: one-shot approach (Algorithm 1) and sequential approximate optimization SAO (Algorithm 2), the main features of which are juxtaposed in Table 1. Algorithms 1 and 2 exploit kriging data-driven surrogates [61], yet the actual choice of metamodeling technique is of secondary importance (other possibilities are, e.g., RBF [62] or PCE [29]).  
The overall idea of Algorithm 1 is to build a single surrogate for which its domain is spread over an ample neighborhood of the nominal design solution, within which the yield may be estimated in a reliable manner. This method is unsophisticated; however, the training data acquisition cost may be sizeable (proportional to the domain size). On the other hand, in Algorithm 2, yield estimation is replaced by an iterative process. Here, the aim is to set up the metamodel over a domain of a lower size, and then reposition it from iteration to iteration. Consequently, the CPU cost of the surrogate setup is reduced  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Algorithm 1** & **Algorithm 2** \\ \hline Method & One-shot approach & Sequential \\  & & approximate optimization \\  & Solve a single task & Render series \(\mathbf{x}^{(0)}\), \(i=0,1,\ldots,\) of \\ Solving & \(\mathbf{x}^{*}=\text{argmin}[\mathbf{x}\in X_{S^{c}}:-Y(\mathbf{x})]\) & approximations to \(\mathbf{x}^{*}\) \\ optimization task & within the surrogate domain \(X_{S}\) & by solving \\  & & \(\mathbf{x}^{(i+1)}=\text{argmin}[\mathbf{x}\in X_{S^{c}}:-Y_{s}{}^{(0)}(\mathbf{x})]\) \\ Yield & \(Y(\mathbf{x})\) evaluated once using single & In each iteration, \(Y_{s}{}^{(i)}\) is evaluated using the \\ estimation & surrogate & \(X_{S}=[\mathbf{x}^{(0)}-\delta,\mathbf{x}^{(0)}+\delta]\), \(X_{S^{c}}=[\mathbf{x}^{(0)}-\delta,\mathbf{x}^{(0)}+\delta]\), \\  & \(\delta=[\delta_{1}\ldots\delta_{n}]^{T}\), & \(\delta=[\delta_{1}\ldots\delta_{n}]^{T}\), \\  & \(\delta_{k}=10\delta_{\text{max}}\(as compared to Algorithm 1); however, the optimization process typically takes several iterations to converge.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Cloud Server Configuration
#### 3.1.2 Server Hardware and Software Configuration  
The hardware and software configuration of the server deployed on Alibaba Cloud is shown in Table 1. As this system is currently in the experimental stage, the rented configuration is relatively basic, which is enough to meet the functional requirement. In the future, the hardware and software configurations can be upgraded to meet larger computing and storage requirement.
# Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure
## 1 Introduction
Furthermore, the object detection method can optimize auto-identification by combining classification and bounding box regression into a multi-task learning manner. As a result, the auto-detected foot identification in the left and right feet of CP patients can be beneficial, reducing clinical workloads, increasing cost-efficacy, standardizing treatment, and improving patient care [23]. Based on the above description, CP primarily has lower-limb impairments, causing abnormal functional performance of activities of daily living and ambulation. Orthosis can effectively manage lower-limb impairments. Clinical deficiencies, such as dominant or non-dominant leg proprioception error in spastic CP, might cause complex footprints. Identifying the dominant leg can help evaluate foot balance index related postural control. Detecting the left and right feet can provide information on positive changes in decreased energy expenditure in the dominant or non-dominant leg in CP patients. Determining the left and right feet might also improve the efficacy of dominant-leg orthosis in CP treatment. Unusual footprints may impede left and right foot identification in spastic CP, caused by complex footprints resulting from foot problems such as scissor gait with abnormal foot progression angle and toe walking  
Deep learning based on object detection can perform many tasks such as localizing, tracking, and image discrimination in defective footprints. Thus, initial research is essential to analyze healthy people's foot profiles in left and right detection, providing a basis for understanding incomplete foot profiles in specific cases of CP. This study is a primary investigation in discovering new information on deep learning performance of defective footprint detection in CP. In addition, this study may have implications for providing new solutions for assessing the efficacy of ankle-foot orthosis in people with spastic CP.
## 4 Results and Analyses
### Internal Force of the Lining of the Preceding Tunnel  
#### 4.4.1 Axial Force  
Figure 10 presents the axial force distribution of the lining of the preceding tunnel under different reinforcement strategies, where the axial force refers to the internal force on the tunnel lining that coincides with the hoop axis. In the figure, the negative value of axial force corresponds to the tension of tunnel lining. The force on the center of the circle is \(-1000\) kN/m, while the force on the outermost circle is \(-2200\) kN/m.  
Figure 9: Variation of the maximum convergence of the preceding tunnel with \(t\): (**a**) horizontal direction; (**b**) vertical direction.  
The axial force of the lining of the preceding tunnel upon the completion of the following tunnel when the soil layer is reinforced with the strategies of RR is shown in Figure 10a. It is seen that, with the increase in \(t\), the axial force of the preceding tunnel lining generally decreases. In the case of \(t\) = 0.00 \(D\), the maximum and minimum absolute values of axial forces are 2115.1 and 1432.5 kN/m, respectively, while in the case of \(t\) = 0.30 \(D\), the corresponding values are 1785.3 and 1352.6 kN/m, respectively. For reinforcement with \(t\) = 0.30 \(D\), the maximum absolute axial force decreases by about 15.6%, and the minimum absolute axial force decreases by about 5.6%, which is not a significant change.  
Figure 10b shows the axial force of lining of the preceding tunnel upon the completion of the following tunnel when the soil layer is reinforced with the strategies of LR. It can clearly be seen that the variation trend of axial force with the increase in \(t\) is similar to Figure 10a, but the decrease rate is faster. The LR can reduce the disturbance to the preceding tunnel due to the excavation of the following tunnel more effectively. When \(t\) = 0.30 \(D\), the maximum and minimum absolute values of axial forces are 1516.9 and 1039.8 kN/m. Compared with the case \(t\) = 0.00 \(D\), the maximum absolute axial force decreases by about 28.3%, and the minimum absolute axial force decreases by about 27.4%.  
For the cases of TR, the maximum and minimum absolute values of axial forces are 1513.9 and 1034.8 kN/m when \(t\) = 0.30 \(D\). Compared with the case of \(t\) = 0.00 \(D\), the maximum absolute axial force decreases by about 28.5%, and the minimum absolute axial force decreases by about 27.7%. The degree of impact of reinforcement in TR is similar to that in LR, which also indicates that reinforcement of the preceding tunnel plays a minor role.  
For the cases of CR, the maximum and minimum axial forces are 2010.7 and 1390.8 kN/m, respectively, when \(t\) = 0.30 \(D\). Compared with \(t\) = 0 \(D\), the maximum absolute axial force decreases by about 5.0%, and the minimum absolute axial force decreases by about 27.4%.  
Figure 10: Axial force of the lining of preceding tunnel under different cases: (**a**) RR; (**b**) LR; (**c**) TR; (**d**) CR.  
3%. This shows that, during the excavation of the following tunnel, the CR has a weak ability to reduce the axial force of the preceding tunnel lining.
## 3 Beacon-Based Hazard Alarm System Design
### Design Rationale  
The system is designed to connect workers with the work environment through an IoT sensor system facilitated by beacon sensors, cloud platforms, and mobile devices. Figure 1 depicts the conceptual design of the proposed mobile communication system.  
A few types of data can be extracted from beacon sensors, including the temperature, distance, position, and acceleration, depending on the beacons used. This research used proximity data produced by LTE beacon sensors manufactured by the Estimote Inc. Based on the proximity data collected by the sensor, an alarm process is activated when the distance between the hazard source and the user is closer than the pre-set threshold. To achieve this, one beacon sensor that can broadcast BLE signals can be arranged at a suitable location for the hazard source. If the hazard source itself is an object with a large volume or covering a large area, such as large-sized mobile equipment, multiple beacon sensors can be deployed.  
Once a proximity case is detected, the system activates an alarm process to ensure that the user perceives the hazard existing in the surrounding environment. There can be several ways to communicate the hazard information to the end user, including visual cues, auditory cues, and tactile cues. This research uses a mobile phone as the end user device to support the availability of all three types of cues with a single device. When the distance between the user and a hazard source is closer than the threshold, the mobile phone can sound an alarm, vibrate, and show a notification on the screen. The end user system interface can ensure that workers do not ignore the alarm by requiring a confirmation of reception of the hazard alarm.  
To achieve this design concept, three specific objectives were developed: (1) design an IoT application for the LTE beacon sensor to receive the Bluetooth Low Energy signal (so-called \"BLE package\") from other beacons and parse the BLE package; (2) build a connection between the LTE beacon sensor and a personal mobile phone using APIs, Estimote Cloud, and Google Firebase Cloud Messaging platforms; and (3) develop a mobile phone application for the mobile phone to receive an alarm message through the Google messaging service and produce the visual, auditory, and tactile cues, as explained above. The next section explains each of the structural elements of the system.
## 1 Introduction
Furthermore, microcapsules with a mean diameter between 1 to 2 \(\upmu\)m, with narrow size distribution and a loading content of 60%, were obtained. In single nozzle electrospraying, a solvent able to solubilize both polymer shell and PCM is used to form the working solution. Then, this solution is pumped out from the electro-sprayed nozzle with an applied voltage to prepare mPCM. During the evaporation of the solvent, the solvent's richest phase diffuses to form a shell, and poor solvent phase stays in the inner part of the microcapsules to form the core, due to the solubility differences between matrix and PCM in the working solvent. The cone-jet mode is challenging to reach when compounds with low electrical conductivity, such as PCM, are used. Therefore, the use of a suitable solvent was required to be solubilized and improve its electro-sprayability. Thus, the formation and the final properties of electro-sprayed mPCM particles are not only determined by the feed ratio between the polymeric shelling the core, but also by the breakup of electro-sprayed droplets, the evaporation of solvent, the diffusion of PCM and the phase separation, to lead the formation of the shell around the PCM during the electrospraying process. Until now, there is no publication to investigate the effects of these processes mentioned later on the structure characteristics (mean diameter, morphology, and loading content of PCM) and thermal properties of the electrospray mPCM. In this work, the design of PCM microencapsulation from a single nozzle electrospraying process, based on the entrapment of PCM-loaded microparticles in a polyester matrix or shell was proposed, and its impact on the resulting thermal properties was assessed. \(N\)-hexadecane and \(n\)-eicosane have been selected as liquid and solid PCMs at room temperature, ethyl acetate (EA), and chloroform (Chl)as solvents for the working solutions, and polycaprolactone (PCL) as shell material. The loading content, as well as the encapsulation efficiency, were also investigated according to the core/shell ratio. The structures, morphologies, and properties of electro-sprayed MPCM were characterized by optical and scanning electron microscopies (OM and SEM), the chemical structure of the obtained microparticles were assessed by Fourier transform infrared spectroscopy (FT-IR), and the thermal properties by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA).
# An Overview of GEO Satellite Communication Simulation Systems
## 2 Coverage Analysis
### Summary and Comparative Analysis  
The coverage analysis methods reviewed above, i.e., geometry-based methods, fast evaluation and approximate inference techniques, simulation-based modeling, and data-driven adaptive approaches, each exhibit distinct strengths and limitations as shown in Table 1. Geometry-based methods offer analytical insights and computational efficiency, making them well-suited for initial feasibility studies; however, they fail to capture channel fading and dynamic conditions. Fast evaluation and approximate inference techniques provide rapid estimations beneficial for real-time decision-making, but their accuracy significantly degrades under heterogeneous or dynamic traffic scenarios. Simulation-based modeling, exemplified by STK-MATLAB integrations, achieves high-fidelity results at the expense of extensive computational overhead, limiting its applicability in real-time adaptive scenarios. Meanwhile, data-driven adaptive approaches can dynamically respond to user distributions and service demands, though they depend heavily on historical data availability and face generalization challenges in previously unseen operational conditions.  
Therefore, no single methodology fully satisfies the diverse and dynamic requirements of modern GEO satellite communication systems. This motivates integrated approaches,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Key Assumptions** & **Strengths** & **Limitations** & **Typical Use Cases** \\ \hline \multirow{2}{*}{Geometry-based} & Line-of-sight (LOS); static beams & Closed-form solutions; low computational cost & Ignores fading and adaptive beam patterns & Quick feasibility screening \\ \hline \multirow{2}{*}{Fast/Approximate} & \multirow{2}{*}{Simplified metrics} & \multirow{2}{*}{Fast evaluation} & Accuracy degrades under heterogeneous traffic conditions & Real-time link admission control \\ \hline \multirow{2}{*}{Simulation-based} & \multirow{2}{*}{Full physical modeling} & \multirow{2}{*}{High fidelity and realism} & High computational runtime & Mission rehearsal and system verification \\ \hline \multirow{2}{*}{Data-driven} & Historical traffic patterns and channel state information (CSI) & Learns adaptive behavior from data & Requires labeled data; risk of poor generalization & Dynamic beam hopping and online scheduling \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of coverage evaluation methods.  
combining geometry-based and simulation-based methods for baseline accuracy, further refined by data-driven models to accommodate adaptive and real-time coverage and scheduling demands.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 2 Model of the Three-Translation PM
### Position Inverse Solution  
According to the geometric relationship \(L_{i}M_{i}\bot M_{i}N_{i}\), the simplified mechanism from the Pythagorean theorem is shown in Equation (1):  
\[\overline{LN}^{2}=\overline{LM}^{2}+\overline{MN}^{2} \tag{1}\]  
where point coordinate \(L_{i}\):\(L_{i}=R\begin{bmatrix}cos\alpha_{i}\\ sin\alpha_{i}\\ 0\end{bmatrix}\); point coordinate \(N_{i}\):\(N_{i}=\begin{bmatrix}x+rcos\alpha_{i}\\ y+rsin\alpha_{i}\\ z\end{bmatrix}\); point coordinate \(M_{i}\): \(M_{i}=\begin{bmatrix}(l_{b}cos\theta_{i}+R)cos\alpha_{i}\\ (l_{b}cos\theta_{i}+R)sin\alpha_{i}\\ l_{b}sin\theta_{i}\end{bmatrix}\); \(\theta_{i}\) is the rotation angle of the active arm; and \(i=1\), \(2\), and \(3\). The calculation results from known coordinates are shown in Equation (2):  
\[\left\{\begin{aligned} \overline{LN}^{2}=[(x+rcos\alpha_{i})-R cos\alpha_{i}]^{2}+[(x+rsin\alpha_{i})-Rsin\alpha_{i}]^{2}+z^{2},\\ \overline{LM}^{2}=l_{b}^{2},\\ \overline{MN}^{2}=[(x+rcos\alpha_{i})-(l_{b}cos\theta_{i}+R) cos\alpha_{i}]^{2}+[(x+rsin\alpha_{i})-(l_{b}cos\theta_{i}+R)sin\alpha_{i}]^{2}\\ +(z-l_{b}sin\theta_{i})^{2}\end{aligned}\right. \tag{2}\]  
\(\overline{LN}^{2}\), \(\overline{LM}^{2}\), and \(\overline{MN}^{2}\) are substituted into Equation (1) to obtain the inverse solution of the mechanism. The inverse solution of the mechanism is shown in Equation (3):  
\[\theta_{i}=2\text{arctan}(t_{i}) \tag{3}\]
# Smart Facility Management System Based on Open BIM and Augmented Reality Technology
## 4 Design of an AR-Based Smart FMS
### System Function Display Configuration and UI  
In essence, AR systems consist of cameras, image converters, tracking, and video background renderers. The camera component ensures that the camera frame is delivered efficiently and automatically in accordance with the image format and size. When an AR marker is recognized by the smartphone camera, the user defines the object to be viewed through AR by means of the pixel format conversion. The target defined within the database configured with the COBie system detects the object through a tracking module. After identifying the condition of the target object, a 3D virtual object is modeled, and the information is provided on the display. Auxiliary information on the augmented 3D virtual object in the display is managed by web-based FMS and is augmented through extensible markup language (XML). The web service application program consists of a management system and a two-way communication system for auxiliary information on the augmented object. The auxiliary information management system resolves the issue of an existing 3D object shown with simple augmentation and is configured to provide various information to the user rapidly, while the two-way communication system is configured to provide information in real time that is not provided in the support information management system through bidirectional communication between the user of the AR system and the manager.  
The UI of an AR application that emphasizes interaction function with the real world requires a different design than the UI of traditional mobile applications. Mobile devices used on site are mostly smartphones and tablets that provide touch-based interfaces. The on-site FM support application proposed in this study targets the use of these mobile devices and takes advantage of existing touch operation methods. Therefore, operations using simple controls are necessary to allow ease of operations, and this in turn requires a  
Figure 8: Defect reporting BPMN.  
more user-friendly and intuitive UI. Due to this, the menu button was placed on the left, and required attribute data was placed at the bottom of the screen. Because AR interacts with the real world, transparency control is provided to support the projection of the visualized information to real-world imagery.  
This prevents the possibility of sight obstruction when the operator is either moving or carrying out tasks, and a navigation map of the building interior was placed on the upper right corner of the screen to allow the operator to verify his or her location. Figure 9 shows an example of a prototype implementation screen in which the site inspector has visualized the specifications for a door. The temperature and humidity sensory information of the space, as well as a checklist document for the interior finish inspection, are provided at the bottom of the screen. The inspector can view the door, windows and other target objects in this manner and input the inspection results for each inspection category through the checklist.
# Directional Active Noise Control with a Local Minimax Error Criterion
## 1 Introduction
Apart from [PERSON] and [PERSON], [PERSON] and [PERSON] used a planar microphone array, perpendicular to the desired control direction. In addition, the input of the active controller was the sum of the error signals of the array, which was equivalent to a single channel and minimized the complexity of the ANC system. Like the above methods, weighted sum methods show less potential for reductions at other positions. As mentioned above, ANC with the MSE method produces a residual acoustic field that can exhibit large differences between its maximum and minimum levels, producing a directivity. [PERSON] originally proposed an iterative minimax error criterion to produce the acoustic field in an enclosure that was more uniform than that achieved with the classic MSE criterion [11]. An iterative algorithm was developed that minimizes the maximum value of each of the error signals. In this approach, only the error microphone with the highest level is considered to design the ANC controller in each iteration, which reduces the computational load compared with that of the conventional least mean squares (LMS) algorithm. [PERSON] used an iterative algorithm to design an active T-shaped noise barrier and obtained a uniform residual sound field [19]. To implement an iterative algorithm based on a minimax error criterion in real applications, [PERSON] adopted the partial update (P-U) minimax algorithm and modified the iterations, further reducing the computational load [20]. Previous methods cannot balance the reductions at the desired points and those at other locations. To solve this problem, we introduce a local minimax error based on the Nelder-Mead algorithm as a new criterion. Given the computational complexity and stability of real-time processing in an ANC system, a fixed controller is designed offline with the proposed method. A controllable directional reduction and an overall reduction can be realized by minimizing the maximum error of certain points. The remainder of the paper is organized in five sections. Section 2 derives a fixed optimal filter that is designed by the MSE criterion in a feedforward multi-channel system. Section 3 describes a fixed optimal filter designed with the local minimax error criterion based on the Nelder-Mead algorithm in a feedforward multi-channel system. Section 4 introduces the experimental setup. Section 5 shows the performance of the two error criteria. Experimental results are presented and discussed. In addition, conclusions are provided in Section 6.