esa-sceva/satcom-chunk-collection huggingface dataset:
# Cable-Driven Exoskeleton for Ankle Rehabilitation in Children with Cerebral Palsy
## 2 Exoskeleton Development and Control Methodology
### Novel Ankle CDPR Module  
Initial clinical evaluations of the D2W revealed limitations in the original ankle actuation, particularly its inability to provide targeted assistance across key degrees of freedom involved in early gait development. Clinicians emphasized the need for a more versatile module capable of actuating not only ankle rotation but also translational movements essential for foot placement and load transfer. In response, a novel ankle control module  
Figure 4: ROS2 architecture implemented in three levels: high, medium, and low. Focusing on the ankle control module (purple box), it is subscribed to two topics. One of them (/GaitSetpoints) sends to it the three-dimensional trajectory generated by the middle level. The other topic (/foot_position) was implemented to test the new ankle control module, checking its ability to reach isolated positions and follow a variety of trajectories.  
was developed to address these requirements, enabling actuation in 3 degrees of freedom: longitudinal (\(x\)) and vertical (\(z\)) translations and \(pitch\) rotation corresponding to dorsiflexion and plantarflexion. This functionality is critical for modulating toe clearance, ground contact dynamics, and overall gait symmetry.  
In this section, we present the design and implementation of this new module, detailing its mechanical structure, custom end effector, electronic integration, kinematic modeling, and control architecture.
# Enhanced Path Planning by Repositioning the Starting Point +
## 3 Proposed Method
### Distance Analysis  
To illustrate our approach, we begin with a simple scenario shown in Figure 1. This scenario involves a single drone that must visit static ground stations with predefined coordinates in a specific order, collect data, and return to its starting point.  
The conventional approach involves finding the shortest path by applying optimization algorithms that consider all possible combinations of station-to-station flights. Using the TSP formulation, which forms the foundation of this study, we obtain the result depicted in Figure 2. The total distance of 1419.3 units, calculated as \(d(1)+d(2)+d(3)+d(4)+d(5)\), represents the optimal flight path under traditional TSP constraints.  
Figure 1: Simple scenario.  
While the TSP produces an optimal solution with a fixed station visiting order, further distance minimization remains possible through strategic starting point adjustment. Consider the sum of distances \(d(1)+d(5)=470.9\), which can be reduced by repositioning the starting point along the straight line between stations \(S3\) and \(S4\), as demonstrated in Figure 3. This repositioning yields an improved value of \(d(1)+d(5)=398.2\), representing the minimum possible distance between \(S3\) and \(S4\).  
This analysis reveals a fundamental distinction in flight path distance calculations. Two distinct distance types emerge: inter-station distances \(d(2),d(3),d(4)\) (shown as solid lines) and starting point distances \(d(1),d(5)\) (shown as dotted lines). The key insight is that starting point repositioning can significantly influence path optimization, opening new avenues for improvement beyond traditional TSP solutions.  
This principle extends naturally to multi-drone missions where all drones share a common starting point, as illustrated in Figure 4. The scalability of this approach demonstrates its practical relevance for complex operational scenarios.  
Figure 4: Starting point on a multi-drone mission.  
Figure 3: Flight path after moving starting point.  
Figure 2: Optimal flight path.
## 3 Results and Discussion
### ML Model Performance on Single Source and Multi-Source Data  
Relying on a single data source to estimate SM often introduces limitations, as factors such as vegetation cover, topography, soil properties, and meteorological conditions influence SM variability. Researchers increasingly integrate multi-source data, combining different RS variables with geophysical and meteorological data to improve ML model performance in SM estimation.  
[PERSON] et al. [91] estimated SM in China using multispectral, thermal, and RGB datasets. They found that incorporating thermal and RGB data into multispectral datasets increased the coefficient of determination (R\({}^{2}\)) from 0.69 to 0.78. [PERSON] et al. [92] estimated SM in Iran using Landsat reflectance values and ML models across diverse land-cover types. They concluded that SM is not solely a function of reflectance values but is also significantly influenced by land-cover characteristics. [PERSON] et al. [93] used CYGNSS (Cyclone Global Navigation Satellite System) reflectivity to estimate SM in the USA and Australia, showing that reflectivity is influenced not only by SM but also by geophysical factors such as vegetation canopy, topography, and soil texture, indicating the need for multi-source data in SM estimation. [PERSON] et al. [94] estimated SM in China by combining ultra-wideband radar echoes with multispectral vegetation indices, showing that incorporating multispectral RS data significantly enhanced the convolutional neural network (CNN) model's performance, increasing R\({}^{2}\) from 0.74 to 0.92. [PERSON] et al. [59] estimated SM using raw Landsat bands combined with multi-source data, including SSM from ERA5, precipitation, digital elevation model, and soil texture. Their findings showed that incorporating these additional datasets improved SM estimation, increasing R\({}^{2}\) from 0.79 to 0.95. [PERSON] et al. [62] estimated SM across the Chinese Loess Plateau and found that incorporating soil physical and hydraulic properties alongside RS variables significantly improved ML model performance, increasing R\({}^{2}\) from 0.10 to 0.62. Several studies conducted in different regions of the world have shown that incorporating multi-source data enhances the performance of ML models in SM estimation [95; 96; 97; 98]. Hence, future studies could use the benefits of multi-source data to enhance SM prediction accuracy using ML models.
## 2 Materials and Methods
### Hemocompatibility  
To determine the blood compatibility of the prepared samples, hemolysis analysis was performed. A sample of 5 mL of fresh blood from a healthy donor was collected and stored in a tube with the addition of ethylenediaminettetraacetic acid (EDTAA) as a decoagulant. The samples were soaked in 5 mL standard PBS solution in a test tube and kept at 37 \({}^{\circ}\)C for 45 min. After that, the diluted blood was held in a test tube and placed at 37 \({}^{\circ}\)C for one hour. Then, the blood-containing tube was taken out, and 3 mL of blood was added to 9 mL PBS and duly mixed. The mixed suspension was centrifuged for 10 min at 2400 rpm, and the supernatant was removed. This step was repeated four times. After the final wash, the supernatant was removed, and 200 \(\upmu\)L of blood was added to 9.5 mL PBS solution after extracting it from the bottom of the tube and mixing properly. The blood suspension was taken and added to tubes containing the samples in a way that the samples were dipped completely. Finally, the optical density (OD) was obtained via UV-vis spectrophotometry (Thermo Fisher, USA) by adding the suspension to a 96-well plate separately. Normal saline with 0.2 mL diluted blood and distilled water with 0.2 mL diluted blood were taken as negative and positive controls, respectively. Henceforth, the % hemolysis ratio (HR) was calculated by using Equation (3).  
\[\%\;\text{Hemolysis rate}=\frac{[OD_{t}-OD_{-\text{\tiny{rev}}}]}{[OD_{+\text{ \tiny{rev}}}-OD_{-\text{\tiny{rev}}}]}\;\times 100 \tag{3}\]  
where \(OD_{t}\) refers to the optical density of the test sample and \(OD_{+\text{\tiny{rev}}}\) and \(OD_{-\text{\tiny{rev}}}\) denotes the optical density of the positive and negative control, respectively.
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC)  
A DSC test was conducted to investigate the thermal properties of composites with varying compositions. Through this analysis, the semi-crystalline characteristics of both pure PLA and PLA composites were analyzed, including parameters like melting temperature (T\({}_{\text{m}}\)), cold crystallization temperature (T\({}_{\text{cc}}\)), and glass transition temperature (T\({}_{\text{g}}\)), comparing them with each other. The results, depicted in Figure 8, revealed that the addition of iron powders had no discernible impact on the glass transition temperature (T\({}_{\text{g}}\)) of PLA, with all samples maintaining a T\({}_{\text{g}}\) value of approximately 62 \({}^{\circ}\)C. Similarly, there were no significant alterations in the melting temperature (T\({}_{\text{m}}\)), which remained consistent at around 149 \({}^{\circ}\)C across all samples. However, a notable departure from this uniformity was observed in the degree of crystallinity. The T\({}_{\text{cc}}\) (cold crystallization temperature) for all PLA/iron samples exhibited an increase when compared to pure PLA. This phenomenon is vividly illustrated in Figure 8 and comprehensively documented in the accompanying Table 4. The degree of crystallinity of the samples (%X\({}_{\text{CFPLA}}\)) were calculated using Equation (3).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **T\({}_{\text{g}}\)**(\({}^{\circ}\)**C**) & **T\({}_{\text{cc}}\)**(\({}^{\circ}\)**C**) & **T\({}_{\text{m}}\)**(\({}^{\circ}\)**C**) & **\%X\({}_{\text{cr}}\)** \\ \hline Neat PLA & 62 & 114 & 149 & 5.63 \\ \hline PLA/Fe5 & 62 & 121 & 149 & 5.77 \\ \hline PLA/Fe10 & 62 & 119 & 149 & 6.79 \\ \hline PLA/Fe15 & 62 & 122 & 149 & 6.92 \\ \hline \end{tabular}
\end{table}
Table 4: Tabulated data of DSC test.  
Figure 7: TGA graph of neat PLA and PLA/iron composite samples.  
The addition of 5% iron led to an enhancement in PLA's degree of crystallinity, elevating it from 5.63% to 5.77%. Furthermore, the incorporation of 10% and 15% iron powders to PLA resulted in even more substantial increases, with the degree of crystallinity reaching 6.79% and 6.91%, respectively. This suggests that the iron particles served as nucleation agents for PLA and facilitated crystallization [30; 35; 36].
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 2 Related Works
### End-to-End Services of MQTT  
Existing research concerning end-to-end support within MQTT is predominantly focused on security investigations [41; 42; 43; 44; 45; 46], with some research works [47; 48; 49; 50] also touching on end-to-end services. [PERSON] et al. [42] introduced a security architecture and protocols designed to establish MQTT security within wireless sensor networks. [PERSON] et al. [43] presented a lightweight security mechanism for managing security levels in MQTT. [PERSON] et al. [46] proposed a security mechanism that uses lightweight cryptographic operations to provide end-to-end data confidentiality while efficiently managing patient mobility within hospital or home premises. SEEMQTT [41] is a framework that ensures end-to-end data confidentiality, integrity, and authorization in MQTT-based communication for mobile IoT systems. [PERSON] et al. [44] presented the design of a secure end-to-end encryption MQTT protocol using lightweight cryptographic algorithms and Galantucci secret sharing. [PERSON] et al. [45] discussed the importance of over-the-air (OTA) updates for secure IoT systems and highlighted the widespread use of MQTT as an IoT communication protocol.  
[PERSON] et al. [47] conducted comprehensive analysis of end-to-end service assurance parameters, with a special focus on content delay and probability of content delivery, for MQTT-SN in healthcare IoT applications. [PERSON] et al. [48] presented a comprehensive end-to-end IoT system that monitors information concerning road users, which is generated through wearable sensors. [PERSON] et al. [49] discussed the implementation of an improved TCP-based MQTT network for efficient communication in smart cities, emphasizing quality of service (QoS) considerations for reliable end-to-end service assurance. [PERSON] et al. [50] introduced a self-adaptive resource management framework for large-scale cyber-physical systems (CPS) that maintains low end-to-end monitoring delays while ensuring high monitoring resolution.
###### Abstract  
Tooth spalling is one of the most destructive surface failure models of the gear faults. Previous studies have mainly concentrated on the spalling damage of a single gear tooth, but the spalling distributed over double teeth, which usually occurs in practical engineering problems, is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, and the relative position of two faulty teeth. Moreover, to ensure the accuracy of dynamic model verification results and reduce the difficulty of fault feature analysis, a novel parameter-adaptive variational mode decomposition (VMD) method based on the ant lion optimization (ALO) is proposed to eliminate the background noise from the experimental signal. First, the ALO is used for the self-selection of the decomposition number K and the penalty factor a of the VMD. Then, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the ALO-VMD, and the IMFs whose effective weight kurtosis (EWK) is greater than zero are selected as the reconstructed signal. Combined with envelope spectrum analysis, the de-nosing ability of the proposed method is compared with that of the method known as particle swarm optimization-based variational mode decomposition (PSO-VMD), the fixed-parameter VMD, the empirical mode decomposition (EMD), and the local mean decomposition (LMD), respectively. The results indicate that the proposed dynamic model and background elimination method can provide a theoretical basis for spalling defect diagnosis of gear systems.  
gear systems; double-teeth spalling fault; fault feature analysis; ant lion optimization; variational mode decomposition +
Footnote †: journal: Applied Mechanics and Physics
## 3 Results and Discussion
### Electrochemical Analysis
When the copper concentration is further increased, no increase in current is observed as the surface reaches saturation (Figure 3f,g). By evaluating all results, it was understood that the most appropriate modification parameters for the best electrode response were HAP+0.5 M CuSO\({}_{4}\) and 0.5 M NaOH 1:5. Electroactive surface area of unmodified and modified PGE was calculated in the inner-sphere redox probe Fe\({}^{3+}\)/Fe\({}^{2+}\) in 1.0 M HCl by cyclic voltammetry. The voltammogram in Figure 4 was recorded in 2.0 mM FeCl\({}_{3}\) in 1.0 M HCl with a scan rate of 10 mV s\({}^{-1}\). The increase in the electroactive surface area after modification with HAP and CuO was determined according to the Randles-Sevcik Equation (1). \[\text{I}_{\text{pa}}=2.69\times 10^{5}\text{AD}^{1/2}\text{n}^{3/2}\text{v}^{1/2} \text{C} \tag{1}\]  
Figure 4: Cyclic voltammograms of unmodified and modified PGE with HAP and CuO in 2.0 mM FeCl\({}_{3}\) in 1.0 M HCl with a scan rate of 10 mV s\({}^{-1}\). I\({}_{\text{pa}}\) is anodic peak current. A refers to electroactive surface area of the electrode. D, n, v, and C show the diffusion coefficient, the number of transferred electrons, scan rate, and concentration of the redox probe, respectively [46; 47]. While the electroactive surface area of the unmodified PGE is 6.33 \(\times\) 10\({}^{-8}\) cm\({}^{2}\), after modifications with HAP and CuO, it was 8.13 \(\times\) 10\({}^{-8}\) cm\({}^{2}\) and 2.28 \(\times\) 10\({}^{-7}\) cm\({}^{2}\), respectively. Modification of PGE with HAP and CuO increased electroactive surface area by about 28% and 260%, respectively.
## 1 Introduction
Typically, IEEE 802.15.4 receiver consumes \(20\) mA for \(0.1\) Mbps, \(30\) mA for Bluetooth at \(0.3\) Mbps, \(100\) mA for WLAN at \(10\) Mbps. Power consumptions in analog and digital portion separately have been reported in some papers. Authors in [10] have reported that baseband of IEEE 802.15.4 consumes \(3.2\) mA at \(1.8\) V supply (\(5.76\) mW) in \(0.18\)\(\mu\)m technology whereas the analog portion consumes \(7.0\) mA. The authors in [9] have given break up of analog and digital portion of the receiver for UWB. Here analog portion consumes \(20\) mA compared with \(19.6\) mA of digital at \(200\) MHz. We start the next section by formulating an optimization problem for minimizing power while varying \(Q_{dig}\) and \(f_{s}\) for the digital baseband. Following this we explain our approach to minimize power based on this optimization. Section 3 explains the simulation and interference model used in subsequent sections. Section 4 discusses various blocks of the receiver, which are designed to accommodate variable \(Q_{dig}\) and \(f_{s}\) and to be compatible with adaptation procedure. Section 5 discusses the implementation specific details and dynamic power estimation of the design. Section 6 discusses experimental setup and results from the experimental setup to validate the concepts. Section 7 concludes the paper.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 3 Proposed Scheme
### Image Segmentation  
The purpose of image segmentation is to generate objects for classification by merging pixels with similar attributes into a region, typically referred to an object. We used the LiDAR features (e.g., intensity, nDSM, curvature) as input data for segmentation, considering both attribute and shape factors. The pixels with similar attributes were merged into an object, and the attributes were the pixel values of the input data. The shape factors were the geometric pattern of the segmented object. The segment criteria are based on the heterogeneity index [32], which combines the attribute and shape factors. Segmentation is a bottom-up method starting from a pixel, and the heterogeneity index is calculated from each pixel as a logical object according to neighborhood pixels. If the heterogeneity index meets the predefined criterion, these pixels are merged.  
We implemented image segmentation using Trimble eCognition software (Version 8.7 Trimble Inc., Westminster, CO, USA). All input features used the same coordinate system and pixel size, so different features are overlapped in the same coordinate system for segmentation. We used equal weight for all input features, so all input features have the same impact on segmentation. As the numerical value of all input features are not the same (e.g., nDSM is ranged from 0 m to 50 m while NDFI is ranged from \(-1\) to \(+1\)), the range of the value will influence the results of segmentation. This study normalized all the features into the same range. Therefore, all input features have same range of values for segmentation. Segmentation has three parameters, and we used the same empirical parameters for all cases: the segmentation scale was 50, shape was 0.1, and compactness was 0.5. It should be noted that the ideal segmentation parameters for different types of input data might not be the same. We observed the results of segmentation using different parameters to find proper segmentation parameters for this test area. The objective of this study was to compare the results of land cover classification by different features, so we used the same parameters for different types of input data.
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 2 System Overview and Problem Statement  
We consider a general system model in SIoT which consists of five ingredients: (1) service requester (SR), (2) service provider (SP), (3) intermediate object, (4) the context, and the (5) service/task. The life cycle of a task or service is shown in Figure 1. The first step that the SR will perform is to determine the content of the task, including the context, property and goal. It will also publish the task request information. Then, after receiving the message of the task request, the SPs will determine whether to respond to the request by evaluating the trustworthiness of the SR. If the SR is trustworthy from the perspective of the SP, the SP will send a respond message which contains the task price, which is calculated by considering the cost of the task performance. After receiving some response, the SR will delegate the task to the specific SP based on the trust model and the consideration of utility. Then, the delegated SP will perform the task and submit the result. After receiving the result of the task from the delegated SPs, the SR and SP will evaluate each other about their behaviors and update the trust model.  
Figure 1: The operation framework of the task/service cycle.  
Different from the traditional trust-based service delegation model in SIoT, we combine the bidirectional evaluation to construct the trust model and adopt the utility optimization to formulate the service delegation problem. On the one hand, most of the current literature assumed that SR is reliable, which means the trust between the SR and SP is unidirectional. This assumption may be reasonable and useful in the small-scale network or SR-centric situation. However, in the open and large-scale SIoT scenarios, the SR may not be reliable. If there is no bidirectional evaluation, a malicious SR may damage the SP's privacy, or it may delay a payment after the SP submits the task results. On the other hand, the current literature often employs trustworthiness to determine which SP should be delegated, but there is a lack of consideration for utility issues. To this end, we design the trust-based utility formulation for service delegation.  
In order to facilitate the formal description, we divide the entire process into four steps, focusing on the decision-making problem of the object in the process of IoT tasks or services.