esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Twin System for Adaptive Aligning of Large Cylindrical Components
## 3 Digital Twin Multi-Dimensional Modeling for the DT Aligning System
### Physical Modeling for the DT Aligning System  
Physical modeling necessitates that the virtual aligning system model reflects the physical properties of the physical entity system, such as the actual process, process parameters, forces, and so on, and perform dynamic mathematical approximation simulation and characterization at the macro and micro scales. For the aligning system, the physical properties closely related to the aligning process are first selected, i.e., the physical modeling is carried out around the pose adjustment of large components and the feed process of the aligning platform. Note that the aligning process mainly involves the displacement adjustment of the dynamic aligning platform and the pose angle adjustment of large components in the rough aligning process stage; on the other hand, the assembly of the aligning features and the pose fine-adjustment of large components are mainly involved in the finish aligning process stage. In total, the physical properties for the aligning system mainly include the pose information of large components in the whole process and the assembly stress/torque in the finish aligning process stage. Therefore, from the perspectives of kinematics and dynamics, mathematical characterization of the aligning process of the physical execution system is carried out to achieve physical modeling of the aligning system.  
The working mechanism of the aligning process platform, as shown in Figure 6, consists of a fixed base, attitude adjustment mechanisms in various directions, and connectors. Note that the motion transmission is from the servo motor to the end of the dynamic aligning platform. The lead screw is divided into three groups according to the combination of the pose-adjustment mechanism and moves along their respective axes to achieve displacement and pose angle changes in that direction, jointly driving the translation and rotation of the dynamic aligning platform and realizing the adjustment of the five degrees of freedom pose of the large component to be docked.  
In addition, in the finish aligning stage, the aligning reference of the aligning features is inconsistent under the influence of the aligning error, the aligning stress is generated (e.g., the axis of the pin and the hole of the large cylindrical component are not collinear), which affects the quality of large component aligning. Therefore, it is necessary to analyze the stress conditions of aligning features (i.e., pins and holes) under different poses of large components, establish the relationship between the aligning stress and pose change, and obtain the physical simulation model of contact stress, so as to provide engineering constraint support for the adjustment and optimization of the aligning pose of large components.  
Figure 6: Working mechanism of the aligning process platform.
# Safety Challenges in Battery Swapping Operations of Electric Underground Mining Trucks  
[PERSON]  
1 Faculty of Engineering Technology, University of Twente, 7522 NB Enschede, The Netherlands 1  
[PERSON]  
2 Faculty of Mining and Geology, University of Belgrade, 11000 Belgrade, Serbia; [EMAIL_ADDRESS] (O.P.); [PERSON]DDRESS] (A.C.); [EMAIL_ADDRESS] (V.M.) 2  
[PERSON]  
3 Department of Applied Science and Technology, Polytechnic of Turin, 10125 Turin, Italy; [EMAIL_ADDRESS] 3  
[PERSON]  
4 Department of Environment, Land and Infrastructure Engineering, Polytechnic of Turin, 10125 Turin, Italy; [PERSON]4  
[PERSON]  
Incorporating battery-powered electric trucks in underground mining operations also aligns with global trends toward sustainability of raw materials industry and energy efficiency [7]. As mineral resources are increasingly scrutinised for their environmental impact, mining companies are under pressure to invest in cleaner technologies. Electric trucks not only reduce carbon footprints but also lower operating costs due to lesser maintenance requirements and lower energy expenses compared to traditional IC engine vehicles [6].  
Furthermore, advancements in battery technology, including quicker charging times and extended range, have made battery-powered electric underground trucks more viable for day-to-day mining operations [8]. Many manufacturers are actively developing specialised trucks that can operate efficiently in the steep gradients and unique terrain conditions found underground [9].  
Integrating digital solutions, such as telemetry and automated systems [10], with electric trucks amplifies their effectiveness and safety, such as improved air quality and ventilation demands [11; 12]. As the industry continues to evolve, battery-powered electric underground trucks represent a promising pathway to achieving both operational excellence and environmental stewardship in the extraction of vital mineral resources [13]. Overall, the adoption of electric vehicles in underground mining represents a significant innovation that addresses environmental concerns, improves safety and efficiency, and aligns with evolving regulatory requirements in the mining industry. However, the transition to battery-powered vehicles brings some challenges that need to be carefully investigated. Research presented in this paper is related to health and safety challenges during the swapping and charging of batteries in the conditions of underground mining of mineral resources.
# Hash Indexing-Based Image Matching for 3D Reconstruction
## 3 The Proposed Method
### Feature Detection  
Many feature point detectors can be candidates for image matching in 3D reconstruction, such as SIFT [9], ORB [12], KAZE [11], SuperPoint [57], and SuperGlue [17]. In this paper, the SURF [10] feature is adopted to find feature points and compute feature descriptors due to its fast speed and comparable matching precision to SIFT. Figure 2 presents the pipeline of the SURF detector for locating feature points from the given image. Specifically, for the input image \(I\), the box filter is utilized to construct multi-scale spaces via different kernel sizes, and the integral image operation is also adopted to accelerate convolutional operation.  
\[I_{\alpha}(x,y)=\sum_{\begin{subarray}{c}0\leq x^{\prime}<kernel\_cols\\ 0\leq y^{\prime}<kernel\_rows\end{subarray}}kernel\left(x^{\prime},y^{ \prime}\right)*I\left(x+x^{\prime}-anchor.x,y+y^{\prime}-anchor.y\right) \tag{1}\]  
The position of the feature point can be computed by the Hessian matrix in multi-scale spaces. The Hessian matrix \(H(x,y,\sigma)\) in \((x,y)\) at scale, \(\sigma\) is defined as  
\[H(x,y,\sigma)=\begin{bmatrix}L_{xx}(x,y,\sigma)&L_{xy}(x,y,\sigma)\\ L_{xy}(x,y,\sigma)&L_{yy}(x,y,\sigma)\end{bmatrix} \tag{2}\]  
where \(L_{xx}(x,y,\sigma)\) is the convolution of the Gaussian second-order derivative \(G(x,y,\sigma)\) with image \(I\) in point \((x,y)\).  
\[L_{xx}(x,y,\sigma)=\frac{\partial^{2}g(x,y,\sigma)}{x^{2}}\otimes I(x,y,\sigma) \tag{3}\]  
Notice that \(L_{xy}(x,y,\sigma)\) and \(L_{yy}(x,y,\sigma)\) can be calculated similarly.  
To this end, we can judge whether \((x,y)\) is a feature point according to the approximate determinant \(\text{Det}(H^{\prime}(x,y,\sigma))\) of the Hessian matrix \(H(x,y,\sigma)\).  
\[\text{Det}(H^{\prime}(x,y,\sigma))=L_{xx}(x,y,\sigma)L_{yy}(x,y,\sigma)-\left( 0.9L_{xy}(x,y,\sigma)\right)^{2} \tag{4}\]  
If \(\text{Det}(H^{\prime}(x,y,\sigma))\) is greater than all pixel values in the neighborhood of \((x,y)\), then \((x,y)\) is a feature point, otherwise, it is not. As depicted in Figure 2, the circles containing yellow are the located feature points.
# Reinforcement Learning Based Query Routing Approach for P2P Systems
## 4 Performance Evaluation
### Evaluation Measures  
We use the **Recall** metric [30] in order to evaluate the retrieval effectiveness of RLQR vs its competitors. The recall \(R(q_{i})\) for the query \(q_{i}\) is defined as follows:  
\[R(q_{i})=\frac{RRD}{RLD}, \tag{2}\]  
where, \(RRD\) and \(RLD\) are the numbers of relevant retrieved documents and the number of relevant ones, respectively. The cumulative average recall up to \(n\) sent queries (\(CAR_{n}\)) is defined as follows:  
\[CAR_{n}=\frac{\sum_{i=1}^{n}R(q_{i})}{n}. \tag{3}\]  
To assess the search cost of the different routing algorithms we use the following metrics:  
\(\bullet\): **CP(\(q_{i}\))**: it refers to the number of contacted peers for the query \(q_{i}\). The Cumulative Average of the number of Contacted Peers up to \(n\) sent queries (\(CACP_{n}\)) is defined as follows:  
\[CACP_{n}=\frac{\sum_{i=1}^{n}CP(q_{i})}{n}. \tag{4}\]* **Overhead(\(q_{i}\))**: it refers to the number of exchanged messages for the query \(q_{i}\). The Cumulative Average Overhead up to \(n\) sent queries (\(CAO_{n}\)) is defined as follows: \[CAO_{n}=\frac{\sum_{i=1}^{n}Overhead(q_{i})}{n}.\] (5)
# A Decentralized Receiver in Gaussian Interference
## 3 Bounds on Communications Rates
### Achievable Rate through Gaussian Distortion
Maximizing \(R_{G}\) over \(\mathcal{R}_{LAN}(L)\) gives a lower bound on the system capacity:  
\[R_{G,\max}(L)\triangleq\max_{\mathbf{r}\in\mathcal{R}_{\mathrm{LAN}}(L)}R_{G}(\mathbf{ r}). \tag{29}\]  
Unfortunately, this expression cannot be simplified much in general. Maximization of \(R_{G}\) over \(\mathcal{R}_{\mathrm{LAN}}(\cdot)\) can be performed efficiently using quasi-convex optimization algorithms:  
**Remark 4**. : _The max of \(R_{G}(\mathbf{r})\) follows the maximum of \(\mathbf{h}^{\dagger}\mathbf{D}(\mathbf{r})^{-1}\mathbf{h}\), which is quasi-concave in \(\mathbf{r}\)._  
Proof. : The maximum of \(R_{G}\) follows the maximum of \(-\mathbf{h}^{\dagger}\mathbf{D}(\mathbf{r})^{-1}\mathbf{h}\) by the matrix determinant lemma. It suffices to show that the restriction of this functional on the intersection of any line with \(\mathbb{R}_{+}^{N\times 1}\) is quasi-concave (see Reference [21]). Further, a one-dimensional function is quasi-concave if anywhere its derivative is \(0\) and its second derivative is below \(0\). Fix any \(\mathbf{a},\mathbf{b}\in\mathbb{R}^{N\times 1}\), denote \(\mathbf{D}_{t}\triangleq\mathbf{D}(\cdot)_{|t\mathbf{a}+\mathbf{b}}\) for any \(t\) where \(t\mathbf{a}+\mathbf{b}\in\mathbb{R}_{+}^{N\times 1}\). Define \(\mathbf{A}_{t}\triangleq\frac{d}{dt}\mathbf{D}_{t}\), and \(f(t)\triangleq-\mathbf{h}^{\dagger}\mathbf{D}_{t}^  
\(R_{G,\max}(L)\) is tight with the upper bound in Equation (16), in limit with LAN throughput \(L\). **Remark 5**: \(R_{G,\max}(L)\to C\) _as \(L\to\infty\)._  
**Proof. **\(R_{G\max}(L)\geq R_{G}(L/N\cdot\mathbf{1}_{N})\), and if \(L\to\infty\) then  
\[R_{G}(\,L/N\cdot\mathbf{1}_{N}\,)\to\log_{2}\left[\frac{|\Sigma+\boldsymbol{h} \boldsymbol{h}^{\dagger}|}{|\Sigma|}\right] \tag{32}\]  
by continuity of \(\log_{2}\), sums, and the matrix determinant (in terms of matrix coordinates). The limit in Equation (32) is greater than or equal to Equation (16), establishing the remark. This strategy has the benefit of not needing much coordination between receive nodes. The transmitter does not have to share codes with helpers because here the helpers do not perform any decoding. Each helper needs only its encoding rate and its own channel state to form its messages properly.
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 2 Related Works
### Object Detection in Aerial Remote Sensing Images  
In recent years, with the development of deep convolutional network applications in images, many scholars performed much valuable research in the field of multi-scale object detection in aerial remote sensing images. [PERSON] et al. [26] embedded the transition module, the residual network and the spatial pyramid pooling structure into the original YOLOv3 network, which could improve the detection precision of tiny ships in remote sensing images. [PERSON] et al. [21] improved the detection precision of aircraft objects in remote sensing images by reducing the feature fusion output ports of neck network in YOLO algorithm. [PERSON] et al. [27] optimized the CSP framework in the YOLO backbone network, enhanced the non-linear classification ability of the network with Mish activation function, and embedded the pyramid pooling framework in the neck network. The above methods strengthened the ability of feature fusion, and made the detection accuracy of objects increase. [PERSON] et al. [28] proposed an effective object detection method in low altitude environment for remote sensing images. Although it has high detection accuracy for low-altitude UAV objects, the detection result of aerial remote sensing images is poor. [PERSON] et al. [29] embedded CBAM [30] attention module into the conventional YOLOv5 network to strengthen the ability of the algorithm to detect tiny objects in 2021. Although the algorithm makes the detection accuracy improve, It requires a lot of computation and reduces the operation efficiency. On this basis, [PERSON] et al. [31] introduced the ECA [32] module and the SAHI [33] framework in YOLOv5 and tested them on three aerial remote sensing image datasets, achieving excellent detection results. However, they ignored the initial clustering points of anchor box and position features of multi-size objects, therefore, the detection accuracy could still be improved.
## 5 Conclusions  
This paper investigates the multi-energy interconnection, interaction and coordination in ME-VPP. A bi-objective optimization problem is formulated, which simultaneously saves economic cost (EC) and enhances power quality (PQ). EC consists of energy-purchasing cost and environmental governance cost, and PQ is the weighed aggregation of voltage stability and active power loss. Various realistic factors are considered, which include multi-energy coupling, pollutant emission tariff, power balance of buses, transmission capacity, equipment output limits, etc.  
A realistic case of Hongfeng Eco-town in Southwestern China is studied. LSTM is adopted for wind prediction. Based on the deep-learning of nine-year historical data, we tested LSTM performances on various prediction steps: from 15-min ahead to 6-h ahead. Numeric results demonstrate that LSTM is superior to the traditional grey model in terms of prediction accuracy, robustness, and computational efficiency. MOPSO is applied as the solving algorithm, and three scenarios are investigated: \"money-oriented\", \"user-first\", and \"Pareto-optimal\". It is verified that the bi-objective modeling is capable of compromising two objectives, as compared with the mono-objective counterparts.  
Two strategies--\"improvisational\" and \"foresighted\"--were proposed to test the overall performances. Both incorporate LSTM wind prediction and bi-objective optimization, but are over different receding horizons: 1-h and 5-h, respectively. Test results on IEEE 118-bus system indicated that the \"foresighted\" strategy takes much more time of Pareto dominance in a day.  
Conceptualization, Z.X. and W.X.; Data curation, F.Z.; Funding acquisition, Z.X. and W.X.; Methodology, J.Z.; Resources, M.F.; Software, J.Z. and F.Z.; Supervision, W.X.; Validation, X.L.; Writing--original draft, J.Z.; Writing--review & editing, Z.X.  
This research was funded by National Natural Science Foundation of China, grant number 61773292, 71401125; Research & Development Program of Guizhou Power Grid Co., Ltd., grant number GZKJXM20160635.  
The authors gratefully acknowledge [PERSON] of Guizhou Power Grid Co., Ltd. for the technical seminars. Special thanks to system operators for providing historical data of Hongfeng Eco-town. The authors greatly appreciate the two autonomous reviewers for their constructive suggestions that make the manuscript improved. Since gratitude also goes to [PERSON] and [PERSON] of the University of Sydney for their elaborate and timely helps in proofreading.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### R-B-NIR Digital Imaging  
A digital commercial camera, Canon Powershot SX260 HS (Canon USA Inc., NY, USA), including a 12-megapixel backlit CMOS sensor, was used in order to capture images that later had the red (R), blue (B), and near infrared (NIR) values extracted. This type of camera used a green-blocking filter and created a NIR-blue-red digital image ([PERSON] et al., 2012), which was processed by a suitable software (Mutispec v4.4, 2015) and the R, B, and NIR values of the selected sections were extracted. The light conditions and tuber distance were similar as described above. Two sections at two opposite areas around the equatorial diameter of each potato tuber covering \(>75\%\) of the area were selected and an average for each tuber was calculated. The calibration was performed similarly to RGB images, but in this case the equation was R = (NIR - Bl)/(W - Bl), where NIR is the raw NIR image, and Bl and W are the signals for black and white references, respectively.
# A Test Platform for Managing School Stress Using a Virtual Reality Group Chatbot Counseling System
## 2 Literature Review
### Patent Review  
This research collects patent data from Innovation Q+. Table 1 shows the keywords used for the search and the International Patent Classification (IPC) and annual filter settings of the search strategy. A total of 3178 patents with a relevance level of four or more were collected and analyzed. The results showed that the leading technologies have been developed in the United States and China with few related patents before the year 2000. Although there were some applications, they were not registered and published. The number of related patent applications and publications increased after 2000 and began to accelerate in growth after 2015.  
Using IPC analysis, the technical focus of VR applied to psychological service and the scope of technical development can be accurately described. The top IPC classification results showed three dominant technical fields. The largest field was the G categories related to physics, including G06: computing, G09: educating and display, and G16: information and communication technology especially adapted for specific application fields. These patents are applied to VR environment design and the display of VR headsets. The second largest field was category A, which is related to human needs. A61 stands for medical science, and A63 relates to sports and amusement. The third largest field was category H, which is electricity. H04 refers to electric communication techniques used for signal transmission between devices.  
The results show the trend of the leading IPCs from 1991 to 2015. Most of the patents belong to category G06, which is electric digital data processing. After 2015, the number of medical science related patents in category A61 increased. By 2018, the category G16 for information and communication technology became the leader. The technology trend shows the development of related patents shifting from digital data processing to applications in the medical field and especially inventions related to communication and interaction between devices. The communication technology is relevant to group counseling and for developing chatbots. Chatbots automate the means to effectively interact with people who are suffering stress recording what they say so these constructs can be related to information stored in the communication databases. By relating conversational constructs to stored treatment solutions, accurate responses can be modelled and used during psychological consultation. The figures show the results of the patent analysis in Appendix A.
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Discussion  
Blockchain-oriented subject-sensitive hashing should emphasize tamper sensitivity, especially to maintain tamper sensitivity for subtle tampering at the pixel level, which is different from existing subject-sensitive hashing algorithms. At the same time, it should maintain robustness to operations that do not affect users at all, which is different from traditional blockchains.  
We propose a subject-sensitive hashing algorithm based on MultiRes-RCF, which borrows the MultiRes mechanism of MultiResU-Net to improve the RCF network. Through the experiments in this paper, we can draw the following conclusions:  
1. Tamper Sensitivity.  
From Table 6 to Table 8, the tamper sensitivity of the subject-sensitive hash algorithm based on MultiRes-RCF is better than that of other subject-sensitive hashing algorithms. Of course, the tamper sensitivity of subject-sensitive hashing algorithms is not as good as cryptographic hashing. After all, subject-sensitive hashing algorithms do not perform binary-level integrity authentication, and it will not be considered that the image has been tampered just because one bit of the image has changed.  
1. Robustness.  
The MultiRes-RCF-based subject-sensitive hashing algorithm can maintain a good robustness to operations such as format conversion and digital watermark embedding that do not affect the user's use. It has no advantages over the existing subject-sensitive hashing algorithms but still overcomes the lack of robustness of the cryptographic hashing algorithms. Robustness is also an advantage of our improved blockchain over the existing blockchains entirely based on cryptography.  
As subject-sensitive hashing needs to measure the degree of change in the image content, instead of only detecting whether the image has changed, the hash sequence length of subject-sensitive hashing is not as immutable as the cryptographic hash algorithm function. If the feature compression and encoding process of the algorithm is modified, a longer hash sequence can be obtained.  
Overall, the overall performance of our proposed MultiRes-RCF-based subject-sensitive hashing algorithm is more suitable for HRRS image blockchains than for existing algorithms.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Other MIPv6 Variants  
Over time, variants of MIPv6 and PMIPv6 have been designed in order to deal with a number of shortcomings. An assessment of mobility approaches is provided in [16]. We provide here a summary within the context of our proposal, particularly in keeping with those proposals that are aimed at real deployment by being progressed via the IETF.  
A key drawback of all of these approaches is that none of them are designed to support multihoming together with mobility, and they all introduce increased OAM overhead:  
* _Fast handover for Mobile IPv6 (FMIPv6)_[17]. This seeks to improve handover performance by reducing latency at the network layer by using a proxy to aid the handover. FMIPv6 has a number of enhancements in the areas of address management, handover procedure, dealing with packet loss during change in a PoA, and high rates of movement. However, in order to provide such capability, it introduces additional complexity and OAM overhead. Extensions to the control plane with new messages types are defined, and a proxy with tunnelling is also used as part of the control plane architecture.
* _Hierarchical Mobile IPv6 (HMIPv6)_[18]. The main goals of HMIPv6 are to improve signalling overhead for the MN and to allow smoother and more efficient transition for a MN from one network to another. However, the approach introduces an additional network entity--the Mobility Anchor Point (MAP)--which the MN must communicate with. The MAP now takes responsibility for communication of the control plane interaction between the MN and its HA. The MAP is yet another proxy in the MIPv6 architecture, and so it has the general drawbacks of using a proxy, as already discussed.  
In comparison, our approach does not use proxies or tunnels, nor does it introduce further complexity into the control plane--signalling is end-to-end directly between the MN and CN.