esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 2 The Error Model  
This paper primarily addresses the robot's geometric errors and end-effector deformation errors. Establishing a model is the initial step in analyzing the positioning errors of the robot's end-effector. The Denavit-Hartenberg (D-H) model [15] is a widely used method for robot kinematic modeling. In the D-H model, each link is represented by a coordinate system with its origin at the corresponding joint, and the orientation of the coordinate system defines the link's motion direction. The D-H parameters facilitate the description of the motion relationships between adjacent links.  
The standard D-H parameters typically consist of four values: link length (\(a\)), joint offset (\(d\)), twist angle (\(\alpha\)) and joint angle (\(\theta\)). When two adjacent axes of a robot are parallel or nearly parallel, the traditional D-H model can produce substantial changes in geometric parameters, leading to a singular Jacobian matrix. To address the singularity issues inherent in the traditional model, [PERSON] [16] introduced a new parameter, \(\beta\), which represents the rotation angle around the \(y\)-axis. The pose transformation between two consecutive coordinate frames, \(O_{i-1}X_{i-1}Y_{i-1}Z_{i-1}\) and \(O_{i}X_{i}Y_{i}Z_{i}\), can then be represented using a homogeneous transformation matrix, \(T_{i}^{i-1}\), as shown in Equation (1).  
\[T_{i}^{i-1}=R_{z}(\theta_{i})T_{z}(d_{i})T_{x}(a_{i})R_{x}(\alpha_{i})R_{y}( \beta_{i})=\begin{bmatrix}c\theta_{i}c\beta_{i}-s\beta_{i}s\alpha_{i}s\theta_{ i}&-c\alpha_{i}s\theta_{i}&s\beta_{i}c\theta_{i}+c\beta_{i}s\alpha_{i}s\theta_{ i}&a_{i}c\theta_{i}\\ s\theta_{i}c\beta_{i}+s\beta_{i}s\alpha_{i}c\theta_{i}&c\alpha_{i}c\theta_{i} &s\beta_{i}s\theta_{i}-c\beta_{i}s\alpha_{i}c\theta_{i}&a_{i}s\theta_{i}\\ -s\beta_{i}c\alpha_{i}&s\alpha_{i}&c\beta_{i}c\alpha_{i}&d_{i}\\ 0&0&0&1\end{bmatrix} \tag{1}\]  
This paper focuses on the KR5 R1400 robot, manufactured by KUKA. The robot is a six degree-of-freedom (DOF) serial manipulator, with its geometric parameters displayed in Figure 1. The D-H parameters are shown in Figure 2.  
Figure 1: KUKA KR5 R1400 robot geometric parameters. The dimension units in the figure are in millimeters.  
The pose of the end-effector coordinate frame relative to the base coordinate frame can be expressed as Equation (2). Here, \(T_{t}^{0}\) represents the homogeneous transformation matrix from the robot's base coordinate system to the end-effector, while \(T_{t}^{6}\) denotes the homogeneous transformation matrix from the sixth link of the robot to the end-effector.  
\[T_{t}^{0}=T_{1}^{0}T_{2}^{1}T_{3}^{2}T_{4}^{3}T_{5}^{4}T_{6}^{5}T_{t}^{6} \tag{2}\]
# Wastewater Treatment by Polymeric Microspheres: A Review
## 3 Natural Polymer
### Alginate
The adsorption force decreased with rising temperatures. SA microspheres can also adsorb organic pollutants [61]. An N-doped reduced graphene oxide/sodium alginate/polyvinyl alcohol (NRGO/SA/PVA) microsphere was fabricated to remove anthracene (ANT), its oxygenated-polycyclic aromatic hydrocarbon (OPAH), and 2-methylthaquinone (2-MAQ) and reduce the risk of the application of nanomaterials in a water environment. It was prepared with 2.0 wt% SA, 1 wt% PVA, and 3.22 wt% NRGO through a simple embed method. NRGO was embedded instead of GO because it has a higher affinity for hydrophobic organic pollutants than GO. TGA and DTG analysis showed that among three steps of thermal degradation, the second stage showed the greatest weight loss of 42.7% at 275-600 \({}^{\circ}\)C due to the degradation of PVA and calcium alginate. The residual polycyclic aromatic hydrocarbons (PAHs) concentration was measured using a gas chromatograph-mass spectrometer. The sorption kinetics followed the PSO model and equilibrium data followed the Freundlich and D-R isotherm model, indicating that the adsorption was chemical adsorption. Hydrogen bonding and \(\pi\)-\(\pi\) interactions mainly attributed to the adsorption mechanism.
## 3 Machinability Analysis
### Temperature
The use of air was therefore essential not only to improve the process stability, but also to preserve the surface quality by avoiding chip adhesion on the workpiece. The influence of cutting parameters in thermal behavior is in good agreement with findings reported across different material classes, supporting the generality of the observed trends. In thermoplastics such as PEEK, temperature has been shown to increase with \(S\) and decrease with \(f\) due to reduced exposure time and improved chip removal at higher feed values [51]. Similarly, in fiber-reinforced composites like CFRP, higher _vc_ has been associated with elevated cutting temperatures, which are critical for surface integrity when operating near the glass transition temperature [52]. Recent studies on aerospace-grade metals further confirm this behavior. In the machining of Ti6 Al4V and Inconel 718, _vc_ was identified as the primary factor affecting tool temperature, while \(f\) played a secondary role in heat accumulation [53]. These observations across polymers, composites, and metals reinforce the idea that the thermal behavior observed is an inherent characteristic of the cutting process itself. The mechanisms of heat generation are fundamentally governed by cutting dynamics, making these effects material independent and intrinsically linked to process parameters.
## 4 Conclusions and Future Work  
The distributed clustering algorithms present a series of advantages over the centralized ones in terms of lower cost of the sensors and better adaptation to small scenarios. Centralized algorithms usually get good metrics, but they rely on a vast knowledge of the network, which is not feasible to get in many cases due to the excessive number of messages that should be exchanged or the size, or cost of the additional hardware required in the nodes (e.g., GPS). Our proposal (DCAGBS) presents a BS-guided distributed algorithm that uses an interval Type-2 fuzzy system to adapt the selection  
Figure 8: First Node Dead (FND), Half Node Dead (HND), and Last Node Dead (LND) for scenario 3.  
of CHs to the changing characteristics of the network like, for example, the lack of accuracy in many control variables. In our method, the BS is able to change the algorithm configuration dynamically. Thus, with the occasional intervention of the BS, the network adapts its configuration according to the death of sensors, changing their behavior after the first and half of the deaths. The algorithm has been compared with other proposals and the goodness of it is demonstrated by simulation in several scenarios. This improvement is especially important in terms of the lifetime of the network, since DCAGBS significantly outperforms all the other proposals.  
As future work, we will investigate the proposed approach in larger networks and with a different number of nodes to validate it. Additionally, an optimization of the \(skip\) values will be accomplished to see how it is dependent on the new number of nodes and the size of the deployment field.  
Conceptualization, A.-J.Y.-D., [PERSON]-C.C.-M., and A.T.-C.; methodology, A.-J.Y.-D. and [PERSON]-C.C.-M.; software, A.-J.Y.-D. and [PERSON]-C.C.-M.; validation, A.-J.Y.-D., J.-C.C.-M., and A.T.-C.; formal analysis, A.-J.Y.-D., J.-C.C.-M. and A.T.-C.; resources, A.-J.Y.-D. and [PERSON]-C.C.-M.; data curation, A.-J.Y.-D. and [PERSON]-C.C.-M.; writing--original draft preparation, A.-J.Y.-D. and [PERSON]-C.C.-M.; writing--review and editing, J.-C.C.-M. and A.T.[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Plan Propio de Investigacion de la Universidad de Malaga (Spain).  
In memoriam [PERSON]. We are thankful for his support and help in this research.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
###### Abstract
Ecosystems with higher WUE can sequester more carbon, which contributes to climate change mitigation. Typically, WUE can be characterized at two levels: the canopy level (canopy water use efficiency, WUEc) and the ecosystem level (ecosystem water use efficiency, WUEe) [20]. Factors such as plant community structure, soil water availability, and microbial activities significantly affect carbon and water fluxes, which results in variations in the response of WUE to temperature increases in alpine ecosystems [21]. Studies of the entire QTP have indicated that GPP is a major factor mediating changes in WUEe [22; 23]. In an alpine meadow in Hongyuan County in the northeastern QTP, ET shows greater sensitivity to warming than GPP, which results in a decrease in WUE under warming conditions [24]. The extent to which water fluxes and carbon fluxes mediate changes in WUE due to climate warming appears to depend heavily on plant and soil characteristics, although the underlying mechanisms have not yet been fully characterized. Several recent studies have examined carbon and water fluxes, as well as WUE dynamics. However, whether carbon or water fluxes primarily drive changes in WUE under different levels of warming has remained unclear. To address this research gap, we conducted an open-top warming experiment in the peatlands of the Zoige Plateau to simulate various warming levels. Our objectives were to (1) assess the response of carbon and water fluxes, as well as WUE, to gradient warming treatments in an alpine peatland; (2) determine whether carbon or water dynamics are the main factors affecting WUE; and (3) elucidate the underlying mechanisms. Understanding the effects of warming on WUE is imperative for predicting the interactions between carbon and water cycles as climate change progresses. The results of this study offer valuable perspectives on the adaptive strategies of plant communities and the resilience of peatland ecosystems, which are vital for the development of carbon management and climate adaptation strategies.
# Numerical Investigation on the Water Entry of Several Different Bow-Flared Sections
## 1 Introduction
developed a global hydroelastic model for the prediction of springing and whipping responses of flexible vessels [20]. The flow field inside the fluid domain was solved using the open-source CFD software package OpenFOAM. Structural deformation was described by a modal superposition of dry mode shapes expressed in a local floating frame of reference. The interaction between the fluid solver and the structural solver were in a strongly coupled scheme. [PERSON] et al. developed a one-way fluid structure interaction method for the whipping response of a hull girder [21]. Hydrodynamic loads were calculated using the CFD solver StarCCM+. Added mass was estimated by empirical formulation, while transient structural response was solved using the mode superposition method. The slamming pressure and the whipping response showed good agreement with the published experimental results [22]. [PERSON] et al. developed a one-way coupling model to simulate hydroelastic response under severe wave conditions [23]. Commercial solvers StarCCM+ and LS-DYNA were used to solve the fluid domain and the structural response, respectively. The whipping response of the hull girder and the local dynamic response of the double bottom structure were simulated. Compared with the CFD method based on RANS equations, slamming loads can be predicted more quickly using the boundary element method (BEM) based on the potential flow theory, especially for the simulation of slamming loads and whipping responses of ships in waves. However, it is difficult to use BEM to correctly predict the secondary slamming phenomenon. Therefore, it is inevitable to use simplified sections to replace the original hull section for prediction of slamming loads ([16; 17; 18]). For a bow-flared section with a bulbous bow, [PERSON] and [PERSON] presented three simplified sections [16]. However, the simplification of the original section may lead to deviation in predicting slamming loads due to inaccurate prediction of the separation point using BEM. To our knowledge, the difference in hydrodynamic characteristics during the water entry of the simplified sections and the original section has not been well documented. In this study, the water entry of several different bow-flared sections, including the original section and the simplified sections, was numerically investigated using the CFD method based on RANS equations. The validity of the CFD method was checked by comparing the predicted results with published experimental data under the same flow conditions. The influences of simplification of the bow-flared section on hydrodynamic characteristics, free surface evolution, pressure field, and impact force were investigated and are discussed here.
## 5 Experimental Studies
### Sensitivity of Program Embeddings to the Used Classification Algorithm
The comparison of sensitivity to the used classifier of the considered embeddings on a small dataset containing 100 programs using the _Accuracy_ (13), _Precision_ (14), _Recall_ (15), and _F1 Score_ (16) metrics computed according to weighted SD (17) is shown in Figure 21. According to Figure 21, code2 vec and first-order Markov chains are least sensitive to the used classification algorithm on a dataset containing 100 programs solving \(11\) different tasks in the task detection problem. The comparison of sensitivity to the used classifier of the considered embeddings on a dataset containing 1000 programs using different classifier quality metrics computed according to (17) is shown in Figure 22.  
Figure 21: The comparison of sensitivity to the used classifier of the considered program embeddings on a small dataset with 100 programs based on the data from Table 4 (lower is better). Figure 22: The comparison of sensitivity to the used classifier of the considered program embeddings on a dataset with 1000 programs based on the data from Table 5 (lower is better). According to Figure 22, first-order Markov chains and word2 vec are the embeddings that are least sensitive to the used classification algorithm on a dataset containing 1000 programs solving 11 different tasks in the task detection problem. This matches the observations discussed in Section 5.2, and the quality of the token-based word2 vec embedding improves with the increase in the dataset size. According to both Figures 21 and 22, first-order Markov chain is the embedding that is least sensitive to the used classification algorithm regardless of the dataset size. The increase in Markov chain order increases the sensitivity of the embeddings to the used classifier. According to Figures 19 and 20, the KNN algorithm shows worse performance with the increase in the Markov chain order.
## 4 Security Analysis of the MAPP Protocol
### Security Requirement Analysis
#### 4.1.3 Non-Repudiation  
Any proposed security protocol must allow the non-repudiation service by providing the identity of the message sender, the accurate sending time, and the accurate location. The non-repudiation can prevent any sender from denying sending of any malicious message. If this service is not guaranteed, any driver can disseminate malicious messages without any punishment. Our proposed MAPP protocol attaches a pseudo-identity \(pid_{i}\) and a timestamp \(T_{s}\) to each message \(\mathsf{m}=\{L_{ZID},\ pid_{i},m_{i},T_{s},pk_{i},\sigma_{i}\}\), to prove the non-repudiation requirement.
## 2 SGX-Based TEE
### Basic Concept About SGX
#### 2.1.2 Attestation  
In cloud-based IoT scenarios, users transmit code and data to the cloud for processing. Attestation can ensure that users' sensitive data are not tampered by malicious software in the cloud. There are two kinds of attestation technology in SGX, local attestation and remote attestation. Local attestation is used to confirm the identity between two enclaves on the same platform while remote attestation is used to protect the data integrity on remote servers. The code, data, and some metadata within the enclave are hashed to create a unique abstract, which serves as the enclave's identity for attestation [29].
## 4 Discussion
### Grad-CAM Analysis  
Evaluating the results obtained by applying Grad-CAM, we can say that the VGG16 network largely focuses on the most relevant regions for detecting anomalies, such as areas with lesions, ulcers or visible signs of infection and ischaemia in the feet. The activations shown by Grad-CAM indicate that the model is learning to correctly identify patterns associated with pathological conditions. This is a positive sign that VGG16 is able to correlate the most pertinent visual characteristics of the images with the final classification.  
As we can see in Figure 8, the activations are fully not concentrated in the lesioned areas, but they highlight relevant areas for decision, including non-obvious areas from a human perspective. This reflects a good match between the model's decision and the clinical importance of the areas identified.  
Overall, the Grad-CAM visualisations provide important visual validation that the VGG16 model is correctly targeting the regions of greatest clinical interest, reinforcing the model's effectiveness in the task of classifying diabetic feet.  
The Grad-CAM results applied to VGG19 showed superior performance compared to VGG16, especially in identifying pathological areas in diabetic feet. VGG19 activations are more concentrated in relevant regions, such as ulcerations and lesions. To summarise, VGG19 shows greater accuracy in identifying lesions, with more focused and less dispersed activations.  
The Grad-CAM results applied to MobileNetV2 show consistent performance in identifying pathological areas in diabetic foot ulcers. MobileNetV2 activations are more scattered in some images but still manage to highlight important areas associated with lesions, although with less precision compared to deeper architectures such as VGG19. MobileNetV2 performs satisfactorily, with activations consistent with pathological areas, but with a slight tendency to activate uninjured regions. The focus of the activations is more diffuse, which reflects a lower ability to capture fine clinical detail, especially compared to VGG19.
## 3 Materials and Methods
### Proposed Remote Sensing Detection Framework  
The EYMR-Net model in this paper mainly contains four parts, the input, backbone, neck, and prediction head, and its network structure is shown in Figure 1. Firstly, the backbone network module consists of several layers, including the CBS layer, ELAN layer, MP-1 layer, and the Swin Transformer layer introduced in this paper. The CBS layer consists of a convolutional layer, a BN layer, and a LeakyReLU activation function for extracting image features at different scales. The ELAN layer in this paper is used to enhance the learning ability of the network by incorporating computational modules from different feature groups. This integration allows the layer to learn diverse feature information without compromising the original gradient path. The MP-1 layer uses a maximum pooling operation to expand the receptive field of the current feature layer, which is then fused with the feature information from normal convolutional processing to improve the feature extraction capability of the model. In this paper, we add the Swin Transformer [44] module to the feature extraction backbone network, located behind the second and third ELAN modules, in order to enhance the network's ability to collect fine-grained information about multi-scale targets and to obtain richer gradient fusion information, especially detailed information about small targets.  
The neck module in this study adopts the structure of a Path Aggregation Feature Pyramid Network (PAFPN) [45], which incorporates bottom-up paths to facilitate information flow from lower layers to higher layers, thus efficiently fusing features at different levels. To further enhance information fusion, we add the CBAM [46] attention mechanism into the neck of YOLOv7. CBAM's Channel Attention Module (CAM) strengthens channel-wise feature interactions, while the Spatial Attention Module (SAM) focuses on capturing target positions. By encoding feature map information along channel and spatial dimensions, CAM and SAM enable the output to be more focused on relevant details. In this paper, we replace the CBS module, originally present behind the four ELAN-W modules for feature analysis in the neck of YOLOv7, with the CBAM attention mechanism.  
Furthermore, an angle classification prediction branch is added to the YOLOv7 prediction header. Hence, multi-task losses are calculated during training to optimize the angle loss, including classification loss, horizontal bounding box regression loss, confidence loss, and angle loss. In the testing phase, prediction boxes are generated by applying non-maximal suppression (NMS) after rotating them according to the predicted angles.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Enhanced ResNet Architecture
#### 3.2.1 Multiscale Residual Block (MSRB)  
Forested landscapes, especially in mountainous regions, exhibit substantial spatial heterogeneity due to variations in canopy structure, forest type, elevation, and topographic complexity. Standard residual blocks in ResNet typically rely on a single convolutional scale (e.g., \(3\times 3\)) and may struggle to capture such diverse spatial patterns. To address this, we introduce a Multiscale Residual Block (MSRB) (see Figure 3) that explicitly extracts features at multiple spatial resolutions. Each MSRB contains three parallel convolutional branches:  
* A standard \(3\times 3\) convolution for fine-grained local details (e.g., leaf texture, small shadows);
* A \(5\times 5\) convolution for medium-scale patterns such as tree crowns and canopy gaps;
* A dilated \(3\times 3\) convolution (dilation rate = 2) for capturing broader contextual cues (e.g., tree clusters, terrain contours).  
The outputs of the three branches are concatenated along the channel dimension and fused using a \(1\times 1\) convolution to reduce dimensionality and enable feature interaction:  
\[\mathbf{F}_{\text{ms}}=\text{Conv}_{1\times 1}([\text{Conv}_{3\times 3}(\mathbf{ X}),\,\text{Conv}_{5\times 5}(\mathbf{X}),\,\text{DilatedConv}_{3\times 3}(\mathbf{X})]) \tag{2}\]  
where \(\mathbf{X}\) is the input feature map and \([\cdot]\) denotes channel-wise concatenation. This multi-scale structure offers several advantages:It allows the network to capture both local and global spatial patterns, improving robustness to canopy size variation and forest fragmentation.
* It enhances the residual pathway with multi-receptive field aggregation, which improves spatial representation without a significant increase in parameters.
* It contributes to stable gradient propagation and faster convergence due to preserved residual connections.  
In summary, MSRB empowers the model to effectively extract hierarchical spatial features under diverse ecological conditions, thereby improving the accuracy and generalization of carbon stock estimation across heterogeneous forested landscapes.