esa-sceva/satcom-chunk-collection huggingface dataset:
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 4 Conclusions and Outlook  
The past few decades have witnessed a considerable growth in the synthesis and utilization of metal chalcogenide-based materials in different kinds of energy storage and conversion systems. The reliable and outstanding performance of these metal chalcogenides makes them the prime candidates for thermoelectric applications. In fact, their thermoelectric modules with ZT of around 1 have been successfully commercialized [133]. Nevertheless, to be competitive with other power generation systems, TEG materials should have a ZT of 2-3 at least. Further enhancement of conversion efficiency with these metal chalcogenide materials is being investigated through chemical doping and nanostructure engineering. This review mainly focused on the industry-friendly solution-based synthesis of nanoscaled or thin-film metal chalcogenides with controllable size, composition, morphology, and structure and their promising application as thermoelectric materials. The commonly used strategies can be categorized into the 'top-down' liquid exfoliation method and 'bottom-up' methods, including the hydrothermal/solvothermal process, template-assisted solution phase synthesis, chemical reduction-precipitation method, and electrodeposition method. In consideration of yield, production rate, crystallinity, and thermoelectric performance of the derived metal chalcogenide materials, the hydrothermal/solvothermal synthesis and wet chemical synthesis are most appropriate for practical large-scale production of these materials. The ink-based processing, together with various printing and coating methods, although still in the developing stage, is promising for cost-effective manufacturing of thin-film flexible thermoelectric devices.  
This research was funded by A*STAR, SERC, and the Thermoelectric Materials Programme. Grant numbers: 1527200020 and 1527200021.  
This work is also supported by the National University of Singapore, Department of Building.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: (**a**) Photograph of the Cu\({}_{2}\)Se thin film on the polyimide substrate. (**b**) Cross-sectional SEM analysis of the film on Al\({}_{2}\)O\({}_{3}\) substrate. (**c**) TEM analysis of the nanocrystals in the film. The dashed lines highlight the grain boundaries. Inset is the corresponding Fourier transform function (FFT) of the TEM image. (**d**) Electrical conductivity, (**e**) Seebeck coefficient, and (**f**) power factor of the Cu\({}_{2}\)Se film deposited on polyimide substrate. Readapted with permission from WILEY-VCH [117].
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Evaluation Indexes  
In order to evaluate the effectiveness of the proposed algorithm, the evaluation indexes [31] used in the experiments are described below.  
1. mIoU (mean Intersection over Union) is a commonly used performance index in semantic segmentation, which is the average of the ratio of the intersection and union of the two sets of real and predicted values. The value of mIoU is in the range of \([0,1]\). The closer the value is to 1, the higher the accuracy of segmentation is. mIoU was calculated using Equation (5). \[\text{mIoU}=\frac{1}{k+1}{\sum\limits_{i=0}^{k}\frac{p_{ii}}{\sum\limits_{j=0} ^{k}p_{ij}+\sum\limits_{j=0}^{k}p_{ji}-p_{ii}}}\] (5) where \(p_{ii}\) denotes the pixel whose real pixel is \(i\) predicted to be \(i\), and \(p_{ij}\) denotes the pixel whose real pixel is \(i\) predicted to be \(j\). \(k+1\) is the number of classes.
2. mPA (mean Pixel Accuracy) is a simple promotion of PA (Pixel Accuracy), which calculates the proportion of correctly classified pixels in each class and averages it over all classes. The value of mPA is in the range of \([0,1]\). The closer the value is to 1, the higher the accuracy of segmentation is. mPA was calculated using Equation (6). \[\text{mPA}=\frac{1}{k+1}{\sum\limits_{i=0}^{k}\frac{p_{ii}}{\sum\limits_{j=0} ^{k}p_{ij}}}\] (6)  
Figure 7: The samples of seven iris datasets: (**a**) JLU-6.0; (**b**) JLU-7.0; (**c**) CASIA-Iris-Interval-v4; (**d**) CASIA-Iris-Lamp-v4; (**e**) UBIRIS.V2; (**f**) MICHE-I; (**g**) Mmu2.  
where \(p_{ii}\) denotes the pixel whose real pixel is \(i\) predicted to be \(i\), and \(p_{ij}\) denotes the pixel whose real pixel is \(i\) predicted to be \(j\). \(k+1\) is the number of classes.
3. Precision and Recall are a pair of contradictory measures, and F1-score is the harmonic average of the two. The value of F1-score is in the range of [0, 1]. The closer the value is to 1, the higher the accuracy of segmentation is. F1-score was calculated using Equation (7). \[\text{F1}-\text{score}=2\cdot\frac{\text{precision}\cdot\text{ recall}}{\text{precision}+\text{ recall}}\] (7)
4. Time complexity: * FLOPs is the abbreviation of floating point operations. A smaller value of FLOPs indicates that the network model is less computational intensive. * Average Time is the average time to predict an iris mask image. The smaller the value of Average Time is, the better performance the practical application has.
5. Space complexity: * Params is the total number of weight parameters of all parameter layers in the network model. The smaller the value of Params is, the smaller the number of parameters of the network is. * Storage Space is the storage space of the network model. The smaller the Storage Space value is, the better the practical application of the model is.
###### Abstract  
This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang-Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.  
fuzzy PID controller; unmanned air vehicles; nano quadcopter; interval type-2 fuzzy controller; autonomy +
Footnote †: journal:  
00 2023  
Aerial Vehicle with Flexible Cable-Connected Payload
# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 5 Experiments and Discussion
### Comparison with Deep Learning (DL)-Based Models  
Additionally, the proposed LTCSP, \(\text{LTCSP}_{uni}\), and \(\text{LTCSP}_{ri}\) descriptors are further compared with the emerging deep learning (DL)-based models including ALEX [54], GoogleNet [55], VGGm128 [56], VGGm1024 [56], VGGm2048 [56], and VGGm4096 [56]. Referring to [57,58], firstly, the last full-connected layers in the pre-trained models are converted into the corresponding feature vector. Secondly, the converted feature vectors are sent to perform the L2 normalization. Thirdly, the normalized feature vectors are used for computing the similarity measure score.  
Figure 8 presents the comparisons among the proposed descriptors and the DL-based models. For \(\text{LTCSP}\), \(\text{LTCSP}_{uni}\), and \(\text{LTCSP}_{ri}\), it can be observed that the proposed descriptors product higher APRs rates than all DL-based models on VisTex, KTH-2a, Face-95, ETHZ and ZuBuD datasets. For Corel-1000, \(\text{LTCSP}\), \(\text{LTCSP}_{uni}\), and \(\text{LTCSP}_{ri}\) product lower APR rates than all DL-based models. There are two main reasons for this phenomenon: (1) Corel-1000 is a natural scene image dataset that include more complex scene semantic information; and (2) all DL-based models are pre-trained on the ImageNet that is the natural scene dataset. Comparing with the DL-based models, the superiorities of \(\text{LTCSP}\), \(\text{LTCSP}_{uni}\), and \(\text{LTCSP}_{ri}\) are summarized as follows:  
1. The DL-based models rely heavily on expensive hardware configurations (e.g., RAM and GPU), yet the proposed descriptors can be easily embedded into cheap hardware devices (e.g., chip and microcontroller).
2. The DL-based models are sensitive to rotation, scaling and illumination differences, while the proposed descriptors are robust against rotation, scaling, and illumination differences to some extent.
3. The DL-based models need to be pre-trained on large-scale and annotated datasets (e.g., ImageNet), which seriously limits its applications.
4. \(\text{LTCSP}\), \(\text{LTCSP}_{uni}\), and \(\text{LTCSP}_{ri}\) are superior to the DL-based models on five datasets out of six.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Feature Dimensionality (d)** & **Memory Cost (kB)** \\ \hline OCLBP & 1535 & 11.99 \\ IOCLBP & 3072 & 24.00 \\ maLBP & 1024 & 8.00 \\ mdLBP & 2048 & 16.00 \\ OC-LBP + CH & 108 & 0.84 \\ LPCP & 844/760/844/616/592/424 & 6.59/5.94/6.59/4.81/4.63/3.31 \\ \(\text{LTCSP}\) & 688/496/688/436/544/448 & 5.38/3.88/5.38/3.41/4.25/3.50 \\ \(\text{LTCSP}_{uni}\) & 491/347/419/203/299/203 & 3.84/2.71/3.27/1.59/2.34/1.56 \\ \(\text{LTCSP}_{ri}\) & 468/324/468/288/396/180 & 3.66/2.53/3.66/2.25/3.09/1.41 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Feature dimensionality (d) and memory cost (kB) among the proposed descriptors and six former descriptors.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 1 Introduction
### Motivation and Contributions  
The process of integrating the blockchain with IIoT systems is challenging due to the particular requirements involved. These challenges include the resource-constrained and  
Figure 1: Blockchain architecture for IIoT.  
low-power nature of IoT devices, the sensitivity of the information, low storage capacities, and the high numbers of devices contained inside large IIoT networks. To overcome these challenges, we proposed a trusted, highly scalable, low-power, transparent, and lightweight security scheme based on a private blockchain mechanism for a cement factory. Our architecture consists of three layers: a physical layer, a blockchain service layer, and an application layer. In the first of these layers, we utilized an STM32 development board to transmit data from sensors and actuators to the blockchain service layer, which contains private blockchain (permission) and lightweight nodes that are designed to give a faster performance for an asymmetric cryptographic algorithm (ECC). Proof of authentication (PoAh) is used for new blocks authentication in our architecture, as this eliminates the need for the reverse hash function used in proof of work (PoW) and makes the process lighter in weight [10; 12].  
The main contributions of the proposed architecture are as follows:  
* It provides a security scheme that can guarantee the security of data transmission between peers in a Cement industry environment.
* It is based on integration between a blockchain technology and IoTs to provide a lightweight, trusted, low-power, scalable, transparent, and decentralized IIoT network. It is also resistant to common IoT attacks such as Sybil, modification, impersonation, replay, repudiation, man in middle (MIM), denial of service (DoS), and distributed (DoS) attacks.
* It offers low computational complexity and high performance due to the adoption of a blockchain service layer that contains lightweight nodes.
* It successfully eliminates the need for a third party service (a trusted authority) through the use of PoAh and smart contracts that are executed autonomously.
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 2 Materials and Methods  
### Boruta Method  
In research, selecting the appropriate number of variables is crucial to ensure accurate prediction of pavement performance and to avoid the inclusion of irrelevant information. The Boruta feature selection method is one effective approach for feature selection. The primary objective of this method is to identify a comprehensive set of features that have a strong association with the dependent variable, thereby enhancing our understanding of the factors that influence pavement performance [20; 21].  
The Boruta method employs a technique called \"shadow features\", which involves permuting the actual features to generate additional ones. The significance of these shadow features is determined by comparing them with the real features based on variable importance measurements (VIM). This comparison assists researchers in identifying the most relevant and significant features that contribute to the prediction of pavement performance. The main steps of the Boruta feature selection method are as follows:  
1. Creation of shadow features: Randomly shuffle the real features R to create shadow features S, then combine the real features and shadow features to create a new training feature matrix N = [R, S].
2. Input the newly created feature matrix and train a tree-based model (such as RF, LightGBM, and others) to obtain the VIM for the real features and shadow features. The Z-score is calculated as Z-score = average (VIM)/SD(VIM).
3. Compare the Z-score of each real feature with the maximum Z-score of the shadow feature (S_max). Real features with a Z-score greater than S_max are labeled as \"important\", and those with a Z-score less than or equal to S_max are labeled as \"unimportant\".
4. Discard the \"unimportant\" features as well as all the shadow variables.
5. Repeat steps (1)-(4) until all feature attributes are labeled as \"important\" or \"unimportant\".
## 5 Potential Challenges
### Uplink NOMA  
Uplink transmission using 5G and its integration with new and current mobile technologies receive less attention than downlink transmission. As a consequence, the efficiency of our communication networks has decreased. Thus, it is important to investigate and recommend high-quality uplink transmission settings in NOMA from the perspectives of practical realization, security, and signaling.
## 2 Improved TOPSIS Method  
As a common multi-objective decision analysis method [21, 22], TOPSIS sorts the evaluation samples based on their proximity to the ideal effect. The algorithm constructs the virtual best solution and the worst solution, namely the positive ideal solution and the negative ideal solution, and calculates the relative Euclidean distance of the evaluation sample and the positive and negative ideal solutions, which is close to the positive ideal solution and far from the negative ideal solution as the optimal solution, and then sorts the evaluation samples in order.  
The positive ideal solution and the negative ideal solution are the ideal values of the hypothesis, that is, the positive ideal solution is the virtual best feasible solution, and the negative ideal solution is the virtual worst feasible solution. Figure 3 is a schematic diagram of the decision of the TOPSIS method for two objectives. In the figure, A\({}^{+}\) and A\({}^{-}\) are positive ideal solutions and negative ideal solutions, respectively, which are virtual optimal solutions and worst solutions. Although A\({}_{1}\) is closer to the positive ideal solution than A\({}_{2}\), it is not far from the negative ideal solution. It can be seen that A\({}_{2}\) is farther away from the negative ideal solution. Therefore, the advantage of TOPSIS method is that, among the solutions with the same distance from the positive ideal solution, the one with a longer distance from the negative ideal solution is selected as the satisfactory solution, which avoids the situation where a single ideal solution results in the same evaluation result.  
Figure 1: Improved process of the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method.  
Figure 2: Evaluation system diagram.  
Some researchers have improved the TOPSIS method by adding weights again in the calculation process of Euclidean distance. However, the above improvements are only supplemented by the weight issue, and the correlation between PQ indices is still not considered. If the correlation between the indices is neglected, the evaluation results obtained by traditional PQ evaluation methods will inevitably have deviations. In this paper, the Euclidean distance in the traditional TOPSIS method is replaced by the Mahalanobis distance. The Mahalanobis distance nonlinearly correlates the internal components of the evaluation matrix by its own covariance matrix. It can not only solve the problem of different dimensions of indices, but also eliminate the correlation interference between indices. It should be one of the preferred solutions for multi-objective evaluation.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 3 Intervention
### Cycling  
Experimental Setup  
All cycling sessions were conducted using electromagnetically braked stationary ergometers (e.g., Monark Ergomedic 828E or equivalent), which provided real-time monitoring of cadence, torque output, and heart rate. Participants were seated in an upright position with adjustable saddle height and handlebar positioning to ensure ergonomic alignment and comfort. Pedals were equipped with foot straps to maintain proper foot placement and bilateral engagement. Each ergometer was connected to a display that showed the target cadence (80-100 RPM), heart rate zone (70-80% of HRmax), and real  
Figure 1: (**a**) Screenshots of the exergames: Reflex Ridge (**left**), Space Pop (**middle**), and Just Dance (**right**). (**b**) Kinect-based motion tracking setup for VR-free interactive training.  
time feedback to guide effort. Heart rate was continuously recorded using a chest strap monitor (Polar H10), while the Borg Rating of Perceived Exertion (RPE) was noted at the end of each session. Sessions were performed in a controlled clinical environment under the supervision of a certified physiotherapist to ensure safety, appropriate intensity, and adherence to the HIIT protocol.  
The high-intensity cycling intervention aimed to improve lower limb strength, neuromuscular coordination, and cardiovascular endurance in individuals with Parkinson's disease. Using stationary cycling with real-time motion tracking and resistance modulation, sessions followed a high-intensity interval training (HIIT) framework, alternating between maximal effort and active recovery to optimize aerobic capacity and neuromuscular control. Participants cycled at 70-80% of HRmax, ensuring a sustained cardiovascular challenge while promoting dopaminergic activation and neuroplasticity. Training intensity was regulated using heart rate monitoring and the Borg Rating of Perceived Exertion (RPE). Ergometrically controlled bikes adjusted resistance based on cadence (80-100 RPM), torque output, and real-time feedback, addressing gait disturbances like bradykinesia and freezing of gait. Sessions emphasized bilateral motor engagement to enhance muscle coordination and limb symmetry. Dual-tasking consisted of performing cognitive tasks (e.g., simple arithmetic or word recall) while cycling. Physiological and biomechanical parameters, including heart rate variability, cadence stability, and torque fluctuations, were continuously monitored to ensure progressive overload while preventing excessive strain. This high-intensity cycling protocol acted as a neuromodulatory intervention, stimulating dopamine release, promoting neuroplasticity, and mitigating motor decline. By combining intensity modulation, cognitive-motor integration, and real-time performance feedback, it aimed to enhance locomotor stability, gait control, and functional independence, improving overall quality of life in individuals with [PERSON]'s disease [43].
## 3 Round Robin Test (RRT) and Reliability Analysis  
### Tube Weld Specimens  
For the tube welds of the reheater and superheater used in thermal power plants, various materials are used, such as carbon steel, austenitic stainless steel, and dissimilar metals. The materials are selected according to the allowable tensile strength and operating temperature of the facility, and tubes with different specifications are used depending on the usage environment. Taking into account this aspect, the test specimens were fabricated with the same materials and specifications as those used in real facilities. To obtain more accurate results for austenitic stainless steel specimens (for which it is generally difficult to apply the PAUT method), a large number of specimens were fabricated. The tube weld specimens were divided into a similar metal and dissimilar metal welds, and a total of 30 specimens were fabricated. Table 1 presents the specifications and quantities of the fabricated test specimens.  
The specimens were designed/fabricated by artificially inserting volumetric flaws (porosity, slag) and planar flaws (incomplete penetration, lack of fusion, crack). In particular, planar flaws, which are more detrimental than volumetric flaws to the integrity of the facilities, were fabricated at a high ratio. Table 2 presents the type and number of flaws designed according to the materials of the test specimens. The test specimens were fabricated via welding according to the Welding Procedure Specification (WPS) applied in power plant construction. This allowed the fabrication of the specimens under the same conditions as the tubes used in the field. A total of 109 flaws were fabricated in the specimens, which was a sufficient quantity to perform a reliability analysis (POD, RMSE).
## 7 Future Directions
### Unmanned Aerial Vehicles (UAVs)  
Unmanned Aerial Vehicles (UAVs), or drones, offer a promising new solution for improving HO and mobility performance in 5G and B5G HetNets. With UAVs acting as mobile BSs, network coverage and capacity can be extended to remote or disaster-stricken areas that are difficult to reach with traditional terrestrial BSs. UAVs provide several benefits, including improved QoS, reduced call drop rates, and HOF, by serving as mobile BSs for devices with poor signal quality. They can also provide connectivity in areas without ground-based BSs, offload traffic to reduce congestion and improve overall network performance, and serve as essential communication links in emergency situations where ground-based networks are disrupted. Moreover, UAVs can be deployed on-demand in situations where existing BSs are unable to provide optimal service, reducing costs and improving connectivity. In summary, UAV-based solutions have the potential to revolutionize HO and mobility management in 5G and B5G HetNets.