esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Shape Memory Alloy-Based Soft Wearable Robot for Assisting Wrist Motion
## 1 Introduction
However, the systems that use the pneumatic actuators require compressors and regulators, which in turn makes it hard to be used as a portable robot. Tendon-driven systems are also generally employed for designing wearable robots [42; 43; 44; 45]. The use of Bowden cables along with DC motors [42; 43] or twisted coil actuators [44] allows locating the actuators away from the point of interest, reducing the weight on the wearer and mobilizing the external joints that were constrained using the rigid exoskeleton. For example, [PERSON] et al. used a simple cable-driven wearable robot with flexible polymer structures and an active anchor to produce the dart-throwing motion of the wrist [45]. However, further improvement in the anchoring mechanism is needed to accomplish a precise and repetitive motion. Recently, Shape Memory Alloy (SMA) has been highlighted as a potential material to be used as an artificial muscle thanks to its unique characteristics of shape memory effects [46]. The shape change of the SMA is caused by the change in crystalline arrangement with change in temperature. The actuators that are fabricated using SMA can produce high force and can be rapidly actuated via Joule heating. Also, the SMA actuators that are shaped as a coil spring are capable of producing large strains, over 200% of their initially contracted length. The wearable robots targeting the UE that utilize wire type SMA actuators have been introduced [47; 48; 49]. However, slow response and the need of a passive force to pre-stretch the actuator makes practical implementation for such systems difficult. In this paper, we propose a new soft SMA-based wearable wrist robot aimed at portability and compliance, so that the wearer can use the robot independently and effortlessly. Our device focuses on the 2-DOF wrist motion occurring near the two non-aligned axis of the radiocarbon joint; namely extension, flexion and ulnar and radial deviation. In this paper, the design process of the highly stretchable, fast-response muscle-like actuator is described first by optimizing the parameters of the SMA coil spring and the cooling system of the actuator. Next, the design process and the performances of the wearable robot are described. The fabricated wearable robot can produce the 2-DOF motion of the wrist by selectively activating the muscle-like actuators. Finally, user tests were performed to observe the wearability and the range of motion (ROM). Time spent for self and assistive wearing were measured as well as the ROM of the SWA for five subjects.
## 2 Reflectarray Design and Structure
Furthermore, the reflection amplitude is about 0 dB at 16 GHz. Excellent transparent and reflection performances are obtained at the lower and upper frequency bands, respectively. It is also noted that this concept is particularly suitable for a dual-frequency system with a large frequency interval, such as a 12 GHz and 24 GHz system, because the wavelengths at the two frequencies are very different. According to the reflection phase response with the elements size depicted in Table 1, a 7 \(\times\) 11 element reflectarray operating at 12 GHz (H-Polarization) and 16 GHz (V-Polarization) was fabricated to validate the concept (normal incidence and 30\({}^{\circ}\) desired scattering angle). The photographs of the reflectarray are shown in Figure 5. The top layer is a cross-dipole reflectarray element layer, as shown in Figure 5a. As the main beam  
Figure 4: Modified square-loop element FSS (**a**) Geometry: Cell size: \(l\times w\) = 10 mm \(\times\) 10 mm, length of loop: \(l_{l}\) = 4.25 mm, width of strip line: \(w_{s}\) = 0.5 mm. (**b**) S parameters V.S. frequencies for Modified FSS. scans only in the xoz-plane (for 12 GHz)/yoz-plane (for 16 GHz), the dimensions of the cross-dipole length in \(x/y\) direction are the same within each column/row. The middle layer is the FSS layer, as shown in Figure 5b. Both of them were etched by using a milling and drilling LPKF prototype. The bottom layer is the metal ground of substrate 1 and not shown here, for clarity.
# Anisotropic Yield Criterion of Rolled AZ31 Magnesium Alloy via Nanoindentation
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline Angle (\({}^{\circ}\)) & \(0^{\circ}\) & \(45^{\circ}\) & \(90^{\circ}\) & ND \\ \hline \(\sigma_{y}\) (MPa) & 116 & 120 & 137 & 94 \\ \hline \end{tabular}
\end{table} 
Table 3: Strength of the magnesium AZ31 alloys. \[N_{y}=(\frac{2}{\sigma_{45}^{2}}-\frac{1}{2\sigma_{z}^{2}})\overline{\sigma}_{y} ^{2} \tag{16}\]  
Through-thickness anisotropic parameters related to shear \(L_{y}\) and \(M_{y}\) are assumed to be identical to the anisotropic parameters of \(N_{y}\) and calculated as follows [16]:  
\[L_{y}=M_{y}=N_{y} \tag{17}\]  
The yield stress clearly increases as the angle along the rolling direction increases. This result is consistent with the results of macroscopic compression yield strength. The anisotropic parameters in the yield function can be calibrated using the data in Table 2. The yield stress in the vertical direction \(\sigma_{z}\) is used to represent the effective yield stress \(\overline{\sigma}_{y}\). The calibrated anisotropic parameters are summarized in Table 4.  
The anisotropic yield function determined via the nanoindentation test is utilized to describe the yield behavior of rolled AZ31 magnesium alloys. The following expression can be acquired by obtaining the primitive function:  
\[ \begin{array}{c}f_{y}=0.406(\sigma_{22}-\sigma_{33})^{2}+1.184(\sigma_{33}- \sigma_{11})^{2}+0.468(\sigma_{11}-\sigma_{22})^{2}+0.723\sigma_{23}{}^{2}\\ +0.723\sigma_{31}{}^{2}+0.723\sigma_{12}{}^{2}-\overline{\sigma}_{y}{}^{2} \end{array}  \tag{18}\]
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 5 Simulation and Analysis  
Three representative functional areas in intelligent city construction were selected from Shanghai, China: commercial, residential, and industrial. The density of light poles varied from high to low with different road network information, which can cover most functional areas in urban construction. Moreover, there were many light poles in the commercial area, which is difficult for location selection. Due to different functional equipment requirements, the residential and industrial areas were also challenged to achieve synergistic location selection. Three representative functional areas were modeled in simulation experiments based on different coverage requirements to verify the feasibility and robustness of the location selection model. Therefore, on the premise of meeting the basic coverage rate, the difficulty of location selection and the degree of coordination of different location selection schemes can be obtained to provide detailed criteria for the practical project.  
The distribution information of light poles in the three functional areas is shown in Table 2. Each coupling pole was defined to contain a coupling device. The selection cases were all from Google Maps, and the basic data of road network information were obtained through online measurement.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Region Name**} & \multirow{2}{*}{**Effective Area**} & \multicolumn{3}{c}{**Number**} \\ \cline{3-5}  & & **One-Sided** & **Light Poles** & **Coupling Poles** \\ \hline Commercial area & 188,733 m\({}^{2}\) & 48 & 200 & 41 \\ Residential area & 509,356 m\({}^{2}\) & 64 & 392 & 97 \\ Industrial area & 681,662 m\({}^{2}\) & 28 & 297 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed details of three different functional areas.  
First, the three functional areas were numbered. Since the site location was based on the discrete points of the streetlights, carrying the 5G micro-base station only on the light poles may not meet the basic coverage requirement. We added an alternative point for a single independent pole of the 5G micro-base station between every two light poles. Light poles in the functional area were equipped with security surveillance cameras, traffic cameras, USB charging piles, and first-aid equipment at the corresponding positions according to the actual engineering needs. They were the representative equipment in smart security, smart transportation, smart energy, and smart management subsystems. Light poles are the basic equipment of the smart street lighting system. Security surveillance and traffic cameras can be coupled with 5G micro base stations to achieve lossless transmission of monitoring information and operation and maintenance of 5G micro base station equipment. The USB charging pile and first-aid equipment are fixed and have no coupling effect with the 5G micro base station.
## 3 Methodology
### Assessment of the ISS Images
#### 3.1.4 Classification with the RF Algorithm  
For low-light suburban areas, we chose three types of land surface, namely snowfields (Snow), trees/forests (Forest), and other types, city lights areas (Other types). We randomly selected 200 (100 samples for training, 100 samples for testing) and 160 (76 samples for training, 86 samples for testing) samples for the land-cover classification in the two study areas, respectively. Numerous classification methods, including decision trees (DTs), support vector machines (SVMs), and random forests (RFs), have been developed for land-type classification [51; 52; 53; 54]. RF algorithms have been widely used in various studies due to their flexibility and practicality. Many studies have found that random forest algorithms could produce better results than DT and SVM algorithms [55]. Therefore, RF algorithms were adopted in this study for object-oriented classification on the ISS images. The multi-resolution image segmentation algorithm and RF algorithm implemented in eCognition Developer 64 9.01 software was used. Some key parameters of the entire procedure are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Geometric** & \multirow{2}{*}{**Threshold Values**} & **Multi-Resolution Segmentation** & \multirow{2}{*}{**Samples**} & **Classification** \\  & **Correction** & & & **(Low-Light Areas)** & & **Method** \\ \hline ISS034-E-37257 & Landsat-7 & 50, 50, 45 & 40 & 100, 100 & RF \\ ISS045-E-155029 & Landsat-8 & 35, 30, 25 & 50 & 76, 86 & RF \\ \hline \hline \end{tabular} Note: Threshold Values for the red, green, and blue bands; samples: training samples and testing samples.  
\end{table}
Table 2: Parameters of the ISS image land surface classification.  
Figure 4: The images of the Calgary and Komsomsolsk-na-Amure after threshold segmentation.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 2 Methodology
### Fitness Function  
The effectiveness of an optimizer is evaluated by its corresponding fitness function. The fitness function in feature selection is dependent on the classification error rate and number of features used for classification. It is deemed to be a good solution if the selected feature subset reduces the classification error rate and number of features chosen. The following fitness function is considered in this paper [40]:  
\[\downarrow\ Fitness=\lambda\gamma_{S}(D)+(1-\lambda)\frac{|S|}{|F|} \tag{1}\]  
where \(\gamma_{S}(D)\) is the classification error computed by the classifier, \(|S|\) is the reduced number of features in the new subset, \(|F|\) is the total number of features in the dataset, and \(\lambda\in[0,\ 1]\) is a factor corresponding to the importance of the classification performance and the length of the reduced subset.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 4 Performance Evaluation  
In this paper, we have evaluated the proposed scheme by computer simulations using channel responses measured in an actual environment to clarify the potential of it.
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 1 Introduction
It provides feature importance scores that help in understanding which features contribute most to the deformation prediction. On the other hand, GPR offers several advantages not captured in the EE-DL and RF approaches. This includes the ability to encode prior knowledge, and the probabilistic approach of GPR provides an uncertainty metric for each prediction, which helps define anomalies (i.e., if a measurement is outside the uncertainty envelope). Finally, the performance of the EE-DL algorithm for identifying anomalous deformation behaviour is illustrated using the failed Cadia TSF as a case study. A deeper dive into a single case study was chosen, instead of superficial evaluations over multiple examples of TSFs. The depth and rigour invested in previous work [19] served as the foundation and validation for the input data in the present study. It provides an in-depth understanding of the failure mechanism and geotechnical validation of the InSAR measurements.
## 4 Data Collection and Storage
### IoT Module and Mobile Application  
The IoT module consists of a smartphone and a GPS. The GPS helps record the movement of the machinery in the field. A mobile application has been developed to record GPS data. The mobile application gives control to the machinery driver to start and stop the recording of the data. While carrying out the study the smartphone is kept with the driver to record the machinery movement. Initially, the data is stored inside the smartphones cache memory and as soon as the smartphone is connected to the internet, the data are sent over to the cloud platform.
## 2 Results
### Analysis of Real Samples
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **Molecular Formula** & **Retention Time** & **Pear Brandy** & **Plum Brandy** & **Apple Brandy** \\ \hline Ethanethiol & C\({}_{2}\)H\({}_{6}\)S & 1.56 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ Dimethyl-sulfide & C\({}_{2}\)H\({}_{6}\)S & 1.626 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\
1-Propanethiol & C\({}_{3}\)H\({}_{8}\)S & 2.177 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ Diethyl-sulfide & C\({}_{4}\)H\({}_{10}\)S & 3.555 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\
1-Butanethiol & C\({}_{4}\)H\({}_{10}\)S & 3.815 & 1.17\(\pm\)0.06 & \(<\)LOD & \(<\)LOD \\ Dimethyl-disulfide & C\({}_{2}\)H\({}_{6}\)S\({}_{2}\) & 4.521 & \(<\)LOD & \(0.14\pm 0.03\) & \(0.11\pm 0.03\) \\ Ethyl thioacetate & C\({}_{4}\)H\({}_{8}\)OS & 5.57 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\
1-Pentanethiol & C\({}_{5}\)H\({}_{12}\)S & 7.568 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ Dipropyyl sulfide & C\({}_{6}\)H\({}_{14}\)S & 9.895 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ Diethyl disulfide & C\({}_{4}\)H\({}_{10}\)S\({}_{2}\) & 10.478 & \(<\)LOD & \(<\)LQQ & \(<\)LOD \\ Thiophenol & C\({}_{6}\)H\({}_{6}\)S & 11.292 & \(<\)LQO & \(<\)LQQ & \(<\)LQQ \\
2-Methyltetrahydrodothiophene-3-one & C\({}_{5}\)H\({}_{6}\)OS & 11.539 & \(<\)LOD & \(<\)LOD & \(81.76\pm 1.06\) \\
3-Thiophenearboxaldehyde & C\({}_{5}\)H\({}_{4}\)OS & 11.578 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\
2-Thiophenecarboxaldehyde & C\({}_{5}\)H\({}_{4}\)OS & 11.785 & 3.39 \(\pm\) 0.17 & \(<\)LOD & \(<\)LOD \\ Ethyl 3-(methylthio)propionate & C\({}_{6}\)H\({}_{12}\)O\({}_{2}\)S & 13.941 & \(<\)LOD & \(<\)LOD & \(<\)LQO \\ Dibutyl sulfide & C\({}_{6}\)H\({}_{18}\)S & 14.007 & \(<\)LOQ & \(<\)LOD & \(<\)LOD \\ Dipropyl disulfide & C\({}_{6}\)H\({}_{14}\)S\({}_{2}\) & 14.254 & \(<\)LOQ & \(<\)LOQ & \(<\)LOD \\ Benzothiazole & C\({}_{7}\)H\({}_{8}\)NS & 15.839 & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average concentration (\(\upmu\)g/L) of VSCs found in tested brandy samples.
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 4 Discussion
### Quality Assessment and Meta-Analysis  
This review did not include a formal quality assessment using tools such as ROBINS-I or GRADE. These tools require comparable study designs and consistent outcome measures, which were not present across the included studies. The reviewed research varied widely in methodology, sensor setups, validation procedures, and definitions of stress or mental health outcomes. Because of this heterogeneity, applying a unified risk of bias tool would not have produced meaningful results. As the field progresses and research designs become more standardized, the use of formal quality assessment tools will become increasingly relevant.  
In addition, despite the fact that a quantitative synthesis could strengthen the overall conclusions of this review, a meta-analysis was not feasible. The primary reason was the significant heterogeneity across the included studies, both in terms of methodological design and reported outcomes. Studies varied in algorithmic models, sensor configurations, data collection durations, and outcome definitions. These inconsistencies prevented the meaningful aggregation of effect sizes or performance metrics.  
Instead, this review provides a structured qualitative synthesis, which allows for identifying common trends and challenges in smartwatch-based stress and mental health detection. Future work may consider more focused subgroups or standardized protocols to enable quantitative comparisons across studies.
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 1 Introduction
Although these research addresses coverage and lifetime in the deployment of the sensor networks, this research optimize both coverage and lifetime together for the WSN deployment in environmental and health application using artificial optimization algorithms. In this regard, several research studies have been conducted to optimize both coverage and lifetime in general WSN deployment [16; 17; 24; 25; 26]. Although such research has addressed both important issues in the sensor network deployment, these studies haven't compared two evolutionary optimization algorithms (Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms) for WSN deployment with considering coverage, lifetime and connectivity objectives. In addition, they haven't used a Minimum Spanning Tree (MST) algorithm for path and energy optimization in WSN deployment. This paper aims to apply GIS-based optimization algorithms for WSN deployment in the health and environmental applications for maximizing the coverage and lifetime by considering the connectivity constraint. To achieve this main goal, three sections of research are as follows. First, previous research studies on WSN deployment are reviewed in environmental and health applications. Second, the MST algorithm has been applied in the WSN deployment process to minimize energy consumption. MST algorithm is a routing protocol that minimizes WSN energy consumption with minimum total network lengths. Third, two optimization algorithms, BA and PSO, are compared for evaluating the GIS-based WSN deployment in health and environmental applications. BA and PSO are the evolutionary optimization algorithms that have been successfully applied for solving different multi-objective problems. To compare the algorithms, three parameters are used: convergence rate, constancy repeatability, and modeling complexity. Section 2 reviews WSN deployment research studies in several applications of environmental and health applications and shows the importance of coverage and lifetime in WSN deployment for these fields. Section 3 describes the methodology of the research. This section includes the GIS-based WSN deployment method for optimizing the network coverage and lifetime with applying the MST routing algorithm. In addition, the steps of BA and PSO optimization algorithms for GIS-based WSN deployment are explained in this section. Section 4 covers how the algorithms implemented in a simulated health and environmental region and the experimental results of comparing two algorithms using three criteria. Section 5 belongs to the discussion of the research. Finally, conclusions and future works are explained in Section 6.
## 3 Results
### Analysis of GPP Estimation Using Multisource SIF Satellite Products  
#### 3.2.1 Analysis of Applicability  
From Figure 3, it can be seen that there are differences in the linear regression fit between the RTSIF (Figure 3a), CSIF (Figure 3b), SIF_OCO2_005 (Figure 3c), and GOSIF (Figure 3d) satellite products and their corresponding station GPP data in arid regions. In the pasture grassland area, RTSIF demonstrated the highest R\({}^{2}\) fitting value (0.85), followed by CSIF (0.84), and GOSIF shows the lowest (0.41). The order of R\({}^{2}\) fitting values was the same for the cultivated and farmland area. In the desert vegetation area, the highest R\({}^{2}\) fitting value was for RTSIF (0.62), and the lowest was for SIF_OCO2_005 (0.36). Additionally, there are also differences in the RMSE and SD among the satellites in different underlying surfaces. RTSIF showed the smallest RMSE and SD in the pasture grassland area (0.01 and 0.11, respectively), followed by CSIF (0.01 and 0.13, respectively), and GOSIF showed the largest (0.01 and 24.46, respectively). In the cultivated and farmland area and the desert vegetation area, the order of the RMSE and SD was the same as that in the pasture grassland area.  
Furthermore, by comparing the R\({}^{2}\) fitting values of the four satellite products on three typical underlying surfaces with the linear regression optimal value (optimal value is 1), it was found that the difference between the R\({}^{2}\) fitting value and the optimal value was less than 0.38 for RTSIF, less than 0.41 for CSIF, less than 0.49 for SIF_OCO2_005, and less than 0.64 for GOSIF. This indicates that there are differences in the applicability of multisource SIF satellite products for GPP estimation in arid regions. The overall ranking of their applicability is RTSIF > CSIF > SIF_OCO2_005 > GOSIF, with RTSIF having an overall significance greater than 0.5 and a confidence interval of 95%, indicating that RTSIF satellite products have the best suitability.  
Figure 2: Interannual variation of monthly average GPP at each site in 2020 (excluding nighttime values).
## 4 Discussion and Future Work  
This study presents a vision-guided deep learning framework for dexterous robotic grasping that integrates YOLOv8-n-seg for object detection, Gaussian Processes (GP) for uncertainty-aware force predictions, and Transformer models for sequential data processing. The grasp configuration prediction system enhances adaptability by selecting optimal finger configurations based on object-specific characteristics.  
While the framework demonstrates improved efficiency, several challenges remain. The vision model exhibited misclassification errors for objects with subtle textures and irregular shapes, particularly for the Bottle in two- and five-finger configurations, the Lemon in two-finger configurations, and the Tomato in two- and three-finger configurations. Additionally, instability persisted in five-finger grasps for the Banana, Bottle, and Ball due to their irregular geometries and complex force modulation demands. These factors resulted in inconsistent force distribution across the fingers, leading to grasp failures in certain scenarios.  
Future research will focus on enhancing the vision system through improved object classification techniques and real-time tracking to mitigate misclassification issues. Five-finger grasp control strategies will be refined by incorporating advanced grasp stability metrics and force redistribution techniques. Further optimizations in stability-aware predictions using the Transformer model will be explored to improve force consistency, particularly for objects requiring fine-tuned adjustments. Additionally, integrating tactile sensing capabilities will provide real-time feedback on grasp stability, enabling precise and adaptive force modulation.