esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Paired Detectors  
Figure 1 shows the paired Cherenkov detectors used in this study. The two Cherenkov detectors were placed face-to-face, 50 mm apart. The origin of the coordinate system, \((x,y,z)=(0,0,0)\), was defined at the center of the detector plane for \((x,y)\) and at the center between the pair of the detectors at the \(z\)-axis, as shown in Figure 1. For the Cherenkov radiator, we used a monolithic PbF\({}_{2}\) crystal with a size of \(40\times 40\)\(\times 10\) mm\({}^{3}\) in the \((x,y,z)\) direction. The PbF\({}_{2}\) crystal is appropriate as a Cherenkov radiator because it has a high density, high refractive index, and transparency toward the UV region, as shown in Table 1[24; 25]. Each photodetector was topped with 300-\(\upmu\)m-thick protection resin (\(n=1.5\)), and the photodetector array was connected to a radiator by 100-\(\upmu\)m-thick grease, as shown in Figure 1. All crystal surfaces were painted black to reduce the number of reflections, except the surface where a photodetector array was coupled. As stated in the previous section, because the number of Cherenkov photons is small, the position-time information of all photons should be read out individually to fully utilize limited information.  
For practical consideration, we modeled an SPTR of the photodetector as a Gaussian distribution of \(\sigma=0\) to 100 ps in intervals of 10 ps. The 10 ps SPTR is possible for MCP-PMT [26], but 50-100 ps is realistic for silicon photomultipliers (SiPM) [27]. Further, we changed the readout pitch of the photodetector in the range of 0 to 10 mm with an interval of 1 mm. The photodetection efficiency (PDE) was assumed to be 100 % for simplicity. The PDE does not affect whether the direct annihilation position classification is possible, although it affects the sensitivity of the system. The photon detection threshold was assumed to be five, except in the detection efficiency study, to confirm the feasibility of our proposed method under the same conditions as our previous studies [8; 14], wherein the same photon detection threshold value was employed.
###### Abstract
In fact, studies [20; 21] on psychophysics have already proven that the temporal misalignment between vision and hearing can lead to the misinterpretation of one modality by another. Furthermore, the study [22] on multisensory representation learning also shows that judging whether visual and audio signals are synchronized can be used to learn representative multimodal features. Hence, our idea is to adaptively assign attention to event-related audio-visual connections by considering the time difference between modalities. In light of the above analysis, this paper proposes a novel AVE localization framework consisting of two parts: a self-supervised audio-visual representation method called audio-visual Barlow Twins (AV-BT), and temporally adaptive cross-modal attention (TACMA). On the one hand, AV-BT takes both audio and visual data as inputs and trains the feature encoders using the intrinsic relation between audio and visual signals as supervision. It  
Figure 1: An illustration of the AVE localization task. helps to narrow the heterogeneity gap between the encoded features by matching the corresponding semantic information between synchronized audio and visual embeddings. On the other hand, a weighting method is introduced in TACMA to adaptively model the cross-modal relations by assigning different weights to audio-visual pairs with different time differences and semantic similarities. Specifically, the most attention is assigned between the synchronized visual and audio features which describe the same state of the event since they are the most relevant, while the association between those asynchronous signals (even if both contain the information of the same event) is relatively weakened because they are less relevant. The main contributions of this work are concluded as follows:  
* We propose a self-supervised audio-visual representation method that encodes features in the same latent space and matches the semantic information contained in the audio and visual modalities. Such a design can narrow the heterogeneity gap between different modal features and benefit subsequent modeling of cross-modal relationships;
* We propose a weighting-based cross-modal attention module that dynamically weakens the connections between different modal features that are unrelated to events or temporally asynchronous;
* The proposed methods are combined in a framework to perform the event localization task on the public audio-visual event dataset. When directly classifying features learned by our self-supervised method for event localization, significant improvements are achieved in both unimodal and multimodal cases. When further combining these features with the proposed cross-modal attention, our overall approach achieves state-of-the-art localization accuracy. The rest of this paper is organized as follows: Section 2 briefly introduces several related works about our method. In Section 3, the detailed system flow and discussions of AV-BT and TACMA are presented. Both the quantitative and qualitative results of our methods are given in Section 4. Conclusions and future directions are presented in Section 5.
## 3 Implementation of Raising Power Loss Equalizing Degree (PLED) by Convex Optimization Commutation
### Implementation of Raising PLED by Convex Quadratic Optimization Commutation
Supposing the mover is levitated above the stator at air gap of 1 mm, and moves at acceleration of 1g in the \(x\)-direction, ranging from -10.61 mm to 10.61 mm, the desired electromagnetic force/torque vector including control efforts, \(\rm W_{d}\), is:  
\[\rm W_{d}=(F_{xd},F_{yd},F_{zd},T_{xd},T_{yd},T_{zd})^{T}=(11N,\ 1N,\ 11N,\ 10N\cdot mm,\ 10N\cdot mm,\ 10N\cdot mm)^{T} \tag{26}\]  
When the commutations are optimized without boundary constraint \(\rm I_{m}\), with boundary constraint \(\rm I_{m}\) = 2.9 A, 2.6 A, 2.3 A, and 2.0 A, respectively, the PLED curves are as shown in Figure 8. As observed from this figure, along with the decrease of the boundary constraint \(\rm I_{m}\), the PLED is gradually raised in the \(x\)-direction, ranging from \(-\)10.  
shown in Figure 9, respectively. It can be seen from Figure 9, along with the decrease of the boundary constraint \(\rm I_{m}\), the maximum local power loss is gradually reduced and the local power loss distribution becomes more uniform. Figure 8: Power loss equalizing degree when the commutations are optimized without boundary constraint \(\rm I_{m}\), with boundary constraint \(\rm I_{m}\) = 2.9 A, 2.6 A, 2.3 A, and 2.0 A, respectively. Figure 9: Local power loss distribution in position (**a**) \(x=-4.244\) mm and (**b**) \(x\)= 4.244 mm when commutations are optimized without boundary constraint \(\rm I_{m}\), with boundary constraint \(\rm I_{m}\) = 2.9 A, 2.6 A, 2.3 A, and 2.0 A, respectively. can be changed. The permanent magnet array stage is mounted on an _X-Y_ plane motion mechanism, which is driven by two controlled step motors and thus the relative position of two stages in the horizontal direction can be controlled by its graphical user interface on the host computer. To eliminate disturbance of the coil cable, the permanent magnet array stage instead of the coil array stage moves relatively; the coil array stage and the sensor are always stationary. This does not change the relative motion relationship between the two stages. After the measurement platform is accurately calibrated, the position precision can attain a \(\upmu\)m order. The current precision of power amplifier can attain an mA order. The real force and torque resolutions of the multi-dimension force/torque sensor are \(\pm\) 0.5 N and \(\pm\) 50 N\(\cdot\)mm, respectively.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 3 Proposed Protocol
### Network Initialization  
The nodes are deployed one by one at random depths underwater having random \((x,y,z)\) coordinates, whereas the sink is placed at the surface of sea with zero depth. Initially, after deployment, nodes do not have any prior information about the address and location of the sink node.  
In the network initialization phase, the sink node broadcasts a control packet named \"HELLO\" packet in a defined transmission radius, containing base station ID and hop count which denotes the address and total number of wireless links from node to sink, respectively. After receiving the packet, the node increments the value, stores the hop count if it is not already present or is smaller than the stored hop count and relays the message with the updated value. In the case of the hop count being equal or larger than the current value, the node will discard the message. This process continues until the message reaches every node in the network.
# Variation in Crop Yield Depending on the Tractor Tire Contact Area and Clay Loam Soil Moisture
## 1 Introduction
However, reduction in tire pressure significantly influences the deformation and rolling resistance ratio in various states of soil [16]. A larger contact area is advantageous in loose soils as it helps minimize sinkage and soil deformation. Reducing soil compaction is another critical consideration, as less intensive tillage at shallow narrowing depths minimizes the risk of soil damage [17]. While tractors do compact the soil when pulling implements, the question is whether the heavy machinery is responsible for reducing yields. However, as we will see from our study, compaction is not always detrimental; on the contrary, low soil compaction leads to greater moisture retention and yield improvements. There are few tests that specifically measure the extent of soil damage caused nor how much crop yields by a slipping tractor pulling an implement are affected by a single pass. It remains unclear whether the soil is damaged to such a degree that plants suffer significant losses, except perhaps at the edges of the field where the tractor turns. To address this gap, a study was conducted to determine the cumulative effect of tractor chassis on crop yield, particularly when the soil is impacted by the slipping wheels of a loaded tractor across plots with too different soil moisture levels. The aim of the research is to establish the relationship between tractor tire contact area, slippage at constant traction, and grain yield. The study seeks to determine whether increasing the tire contact area and reducing slippage can enhance the environmental friendliness of tractors.
## 2 Background
### Overlays Revisited  
As we have mentioned above, \"overlay network\" is a generic term to designate an application-specific network, built on top of a generic, multi-purpose network such as the Internet. CDNs and P2P networks are two different examples of overlays. In the first case, CDN nodes must be deployed as caches close to edge routers and their use is transparent to end users, hidden through the use of Web redirection and domain-specific DNS mappings. In the second case the overlay functions actually run directly on user equipment and the network is a pure virtual reconstruction [16]. Yet proper operations may require cooperation from network providers, or the use of extra devices at the network edge, e.g., to overcome firewall or bandwidth restrictions.  
In both cases, we see instances of a three-tier model where some degree of processing is deployed at the edge of the network, preferably close to the user to improve her experience of some services. These features have become a fixture of the Internet and underlines its evolution.
## 2 Related Work
### Production Environment Constraints
In the domain of threat detection, considerations related to computational expense, encompassing factors such as energy consumption, execution time, and memory usage, often receive insufficient attention. Although highly effective detection systems can deliver strong performance, they may also impose substantial computational burdens as training times and memory demands escalate with an increasing number of classifiers. Addressing this challenge, [PERSON] et al. [44] investigated the processing costs of mobile phishing detection through an exclusive reliance on URL-based features. Their approach achieved an average detection time of approximately 621 milliseconds. Moreover, their measurements indicated that the real-time extraction of lexical and content features took 5.85 times longer to process legitimate pages relative to phishing ones. Nonetheless, their methodology did not account for page loading times, which can add additional seconds to the overall latency. Subsequently, [PERSON] et al. [27] and [PERSON] et al. [11] found that DNS-based feature collection imposes higher computational overheads compared to static features, given its dependence on network-level data capture. In a similar vein, more expensive feature sets, such as those derived from Public Key Infrastructure (PKI) components [45; 46], can also enrich phishing detection capabilities while increasing operational costs. In an alternative line of inquiry, [PERSON] et al. [47] evaluated the throughput associated with different detection models. Their findings revealed that LSTM networks required more time to evaluate URLs--roughly 280 URLs per second--compared to the 942 URLs per second achieved by Random Forest (RF) models. Although the LSTM model demanded approximately eighty-times-longer training periods, it maintained considerably lower memory usage (581 KB) than the Random Forest approach (288 MB). Furthermore, [PERSON] et al. [48] underscored the necessity of factoring in both feature processing and extraction times when scaling threat detection to accommodate billionsof users. By employing transfer learning, they demonstrated the potential to curtail computational costs while simultaneously enhancing the model's generalization capabilities.
## 4 Calibration Impact on the Estimated Residuals
Each pixel shows the averaged semivariance over \(4^{\circ}\) and covers the spatial variance over up to \(30^{\circ}\) azimuth and \(30^{\circ}\) elevation difference. All antennas have in common that the semivariance generally decreases after removing the elevation pattern. However, the residuals are still correlated (low semivariance) over short distances, independent of removing the antenna pattern. This is an indication that remaining errors are not just white noise. After removal of the elevation pattern (calibration), the semivariance for the TRM5 antenna is the smallest, with reduced correlation in both the elevation and azimuth direction, especially in elevation. This is followed by the AV28 antenna with the circular ground plane. For the AV28 antenna, with a rectangular adaptor, azimuth- and elevation-specific artifacts remain that could only partially be covered by the calibration. The semivariogram maps of all antennas can be found in the Appendix A.  
Figure 2: Ambiguity-fixed phase residuals on \(L_{1}\) (**left**) and \(L_{2}\) (**right**) before and after the calibration using \(0.1^{\circ}\), \(1^{\circ}\), and \(5^{\circ}\) binning widths. The abbreviations (c) and (r) in the antenna names correspond to the utilized mounting brackets (circular plane and rectangular bracket).
# Capturing a Space Target Using a Flexible Space Robot
## 3 Contact Model
### Contact Detection and Penetration Depth Calculating  
It can be seen from Figure 1 that the end effector is composed of three cylinders, and the target object is a cylinder too. Thus, the contact between the end effector and the target can be converted to the contact among the cylinders. The target object \(B_{8}\) is assumed to be inside the two hands \(B_{6}\) and \(B_{7}\) during the capturing process, so the point contact is the only existing form of contact between \(B_{8}\) and the two hands. The contact detection is introduced below.  
Figure 2 shows the contact of the target object \(B_{8}\) with one hand of \(B_{6}\), where \(HJ\) and \(EF\) are the cylinder axes of Hand 1-1, and the two cylinders of Hand 1-1 have the same radius and are represented by \(r_{1}\); \(XI\) is the axis of \(B_{8}\) and its radius is \(r_{2}\). To detect the distance between two axes, we need to calculate the closest point of the two axes first. Let the axes \(HJ\) and \(XI\) be specified parametrically by the points \(H,I,X\) and \(J\), given by [36].  
Figure 2: Schematic diagram of contact detection.  
\[\mathbf{L}_{1}(s) =\mathbf{H}+sd_{1},\ \mathbf{d}_{1}=\mathbf{J}-\mathbf{H} \tag{9a}\] \[\mathbf{L}_{2}(t) =\mathbf{X}+q\mathbf{d}_{2},\ \mathbf{d}_{2}=\mathbf{I}-\mathbf{X} \tag{9b}\]  
For some pairs of values, for example, for \(s\) and \(q\), \(L_{1}(s)\) and \(\mathbf{L}_{2}(q)\) correspond to the points on the lines, and \(\mathbf{v}(s,q)=\mathbf{L}_{1}(s)-\mathbf{L}_{2}(q)\) describes a vector between them. When the segments \(L_{1}\) and \(L_{2}\) do not intersect each other, if \(\mathbf{v}(s,q)\) is perpendicular to both lines \(\mathbf{L}_{1}\) and \(\mathbf{L}_{2}\), the points \(\mathbf{L}_{1}(s)\) and \(\mathbf{L}_{2}(q)\) are the closest points on the lines. For nonparallel lines, \(\mathbf{v}(s,q)\) is unique. Based on the definition of perpendicularity constrains, we can obtain  
\[\mathbf{d}_{1}\cdot\mathbf{v}(s,q)=0,\ \mathbf{d}_{2}\cdot\mathbf{v}(s,q)=0 \tag{10}\]  
Considering Equation (9) and the parametric equation of \(\mathbf{v}(s,q)\), we can get  
\[\mathbf{d}_{1}\cdot(\mathbf{L}_{1}(s)-\mathbf{L}_{2}(q)) =\mathbf{d}_{1}\cdot((\mathbf{H}-\mathbf{J})+sd_{1}-q\mathbf{d}_{2})=0 \tag{11a}\] \[\mathbf{d}_{2}\cdot(\mathbf{L}_{1}(s)-\mathbf{L}_{2}(q)) =\mathbf{d}_{2}\cdot((\mathbf{H}-\mathbf{J})+sd_{1}-q\mathbf{d}_{2})=0 \tag{11b}\]  
This can be expressed as the 2 \(\times\) 2 system of linear equations  
\[(\mathbf{d}_{1}\cdot\mathbf{d}_{1})s-(\mathbf{d}_{1}\cdot\mathbf{d}_{2})q =-\mathbf{d}_{1}\cdot(\mathbf{H}-\mathbf{J}) \tag{12a}\] \[(\mathbf{d}_{2}\cdot\mathbf{d}_{1})s-(\mathbf{d}_{2}\cdot\mathbf{d}_{2})q =-\mathbf{d}_{2}\cdot(\mathbf{H}-\mathbf{J}) \tag{12b}\]  
Solving Equation (12), we can get \(s\) and \(q\). Substituting them into Equation (9), we can obtain \(\mathbf{L}_{1}(s)\) and \(\mathbf{L}_{2}(q)\). Regarding \(d\) as the distance between \(\mathbf{L}_{1}(s)\) and \(\mathbf{L}_{2}(q)\), if \(d<r_{1}+r_{2}\), the two cylinders have contacted, and the penetration depth is \(\tilde{d}=r_{1}+r_{2}-d\).
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 1 Introduction
Specifically, innovations in wearables have made a wider range of measurements available, providing personalized information on individual activity, physical state, and even emotional state. Together with the improved quality of the collected data, this has opened up all sorts of new opportunities for the application of personalized Human Digital Twins in healthcare where predictions and interventions are based on the results of models generated using only personal data. With these personalized HDTs or patient Digital Twins, the focus lies more on personalizing and individualizing treatments and therapy aimed at changing the behavior of patients or participants [15]. Personalized models enable the achievement of precision healthcare, facilitating proactive patient care, early disease diagnosis and detection [16], and the possibility of preventive care. Taking these developments into account, our research group decided to investigate the possibilities of elevating our personalized physical activity coaching application, the Virtual Fitness Coach (VFC) platform [17; 18], to the level of a personalized HDT. In the first step to achieve this, we focused on evaluating the performance of machine learning techniques used to perform the predictive tasks, we established that the efficiency and usability of personalized applications such as the VFC platform depend not only on the quality and completeness of the data being used but also on the way this data is processed. This motivated us to determine the state of the art of the application and selection of ML techniques used in the AI cores of HDTs that perform the predictive and analytical tasks. The rest of this paper is organized as follows: In Section 2, a further motivation for this survey is presented. Next, in Section 3, we discuss the context, definitions, and direction of the survey. Related surveys are given in Section 4. In Section 5, the main findings of existing research on the topic are presented. Finally, the conclusion and suggestions for further research are discussed in Section 6.
# A Smart Rehabilitation Glove Based on Shape-Memory Alloys for Stroke Recovery
## 3 Method
### Wireless Connection of Mobile and Rehabilitation Glove  
In the prototype developed in this study, the integrated circuit board embedded in the smart rehabilitation glove has the capability to collect, process, and transmit data. The sensors and wireless-transmission module within the board transmit the detected physiological signals to the mobile application, allowing patients and doctors to remotely obtain these parameters. The sensors include a piezoresistive pressure sensor and infrared thermometers (with an uncertainty of \(\pm 2\%\)). The wireless-transmission module was selected for its stability, low-power consumption, and rapid response time in short-range communication [35]. This technology is widely employed in products that integrate wearable devices with mobile terminals.  
To facilitate the usage of the application by both patients and doctors, the software Figma Online (Version 2025.05.15, [[https://www.figma.com](https://www.figma.com)]([https://www.figma.com](https://www.figma.com)), accessed 15 May 2025) was used to design the mobile application's interface, due to its various functions, including multi-user collaboration, interface interaction, and online design [36]. To help doctors assess the health of patients and adjust training modes, an interface called \"patient health monitoring\" is included in the doctor-specific version of the application. This interface contains physiological signals received and transmitted by sensors, as well as basic information for the corresponding patient and physical analysis reports. When doctors receive data remotely, they can judge the patient's rehabilitation based on the data and adjust the rehabilitation training mode in a timely manner. This design makes communication between patients and doctors more convenient and cost-effective, benefiting the patient's recovery.