esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trackside Measurement through Field Instrumentation  
### Trackside Measurement Site Condition  
The trackside measurement for the dynamic stability evaluation was conducted on 5 different types of ballasted track structures, and 5 types of ballast-less (hereby referred to as concrete tracks) track types. Specifications of the respective test beds are outlined in Table 2. Track sites were selected from newly opened sites (from 2018-2019) for high-speed passenger transportation lines in Korea under the assumption that the tracks have undergone minimal degradation effects such that cracks and rail conditions would not affect the measurement results. In the case of ballasted track structures in Korea, the superstructure design specification of high speed tracks is compliant with the identical format, with exceptions for sleeper spacing in certain cases [16]. For the concrete track structures, design specifications with regards to component usage usually varies, all the while ensuring that safety design requirements are met [6; 17; 16; 18].  
For each track type, 20 separate measurements were conducted and 5 sets of ranges in intervals of 10 km/h from 120\(\sim\)170 km/h were selected such that the measurements can be derived at incremental intervals in accordance with increasing speed. Only straight line track sections were considered, to reduce the effect of the variables of cant and curvature during comparative analysis, and track condition factors (\(\Phi\)) of 0.3 were selected. In the Korea's case, ballast structures are constructed with the same fastening systems for high speed tracks (Tables 2 and 3 for details).  
Trackside measurement was conducted for only one specific vehicle type throughout the entire investigation (KTX-Sancheon). KTX operates in the site where field measurements were performed; it is composed of 18 passenger trains. Test trains without passengers were measured to reduce variability in the results. Specifications of the KTX are outlined in Table 4.
# Research on Three-Dimensional Scanning Path Planning of Casing Parts Based on Industrial Robot
## 4 Scanning Simulation Analysis
### Establishment of Scanning Simulation Model  
To verify the rationality of the above scanning path planning, the robot studio robot simulation software is used to simulate the virtual scanning trajectory motion under the condition of 3D scanner parameter constraints. The ABB-IRB6700 model robot and the casing part model were imported into the software, and the robot system was created by the 'layout' method. Select the frame creation method to establish the tool and workpiece coordinate system, generate the scanner tool and install it on the robot end effector. Adjust the position and angle of the imported model to make it in the robot workspace.  
In the workpiece coordinate system, the scanning motion parameters were set, the running speed is \(v\) = 0.04 m/s, the acceleration is \(a\) = 0.5 m/s and the posture adjustment time is \(t\) = 0.5 s. The obtained scanning trajectory coordinate points were used to set the robot's motion trajectory, generate the robot's motion path and complete the robot's motion trajectory offline programming. Run the scanning trajectory program to perform automatic scanning trajectory simulation motion, and the scanning motion process is shown in Figure 13.  
Figure 12: Schematic diagram of the circular scan trajectory.  
In the process of simulation scanning motion, the robot and the 3D scanner did not interfere or collide with the casing. The scanning range conforms to the scanning space constraint condition of the scanner, and the running time is close to the theoretical analysis running time. The off-line scanning trajectory programming based on the geometric characteristics of the measured parts was realized, and the feasibility and correctness of the scanning path planning were also proved.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 2 Analysis and Research of Blood Pressure Measurement Methods  
### The Arterial Tonometry Method  
For the arterial tonometry method for measuring blood pressure, superficial arteries, such as the radial artery, femoral artery, and carotid artery, are usually selected as measurement sites. In this study, the radial artery was chosen as the measurement location because it has a larger diameter and is supported by rigid bone tissue at the bottom [40].  
As shown in Figure 1, the pressure sensor is placed above the radial artery and an external pressure \(F_{c}\) is applied to the artery to obtain the blood pressure \(F_{p}\). The red region represents the blood vessel, \(T\) represents the circumferential tension in arterial wall, and \(\theta\) is the angle between tension \(T\) and the horizontal direction. The blood pressure \(F_{p}\) and the external force \(F_{c}\) satisfy the equation:  
\[F_{p}=F_{c}+2T\sin\theta \tag{1}\]  
During the measurement process, the external pressure \(F_{c}\) gradually increases until it equals the blood pressure \(F_{p}\). When the external pressure \(F_{c}\) is too small, the blood vessel does not reach a flattened state, resulting in a smaller pulse peak value and unclear waveform feature points. As the external pressure \(F_{c}\) gradually increases to the blood pressure level \(F_{p}\), the blood vessel becomes flattened, resulting in clear waveform feature points and a maximum pulse peak value. At this point, the pulse peak value corresponds to the systolic blood pressure (SBP), and the pulse trough value corresponds to the diastolic blood pressure (DBP).  
The arterial tonometry method requires the long-term application of external dynamic pressure to keep the blood vessels in a flattened state. Therefore, existing monitoring devices rely on air pumps or control motors to determine the pressure range, resulting in a bulky, complex, and expensive measurement system. In addition, the arterial tonometry method only uses the peak and trough values of the pulse waveform for calibration and does not fully utilize the features that are highly correlated with blood pressure values for calibration.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 6 Performance Evaluation
### Results: Intrusion Detection  
This analysis aims to evaluate the accuracy of the method used to estimate \(p\) on [PERSON]'s side. The assumption is that \(QBER_{SN}\) is known, for example, through measurement campaigns, as previously discussed. The intrusion detection procedure involves calculating \(Q\bar{B}ER\) on [PERSON]'s side and then, by inverting Equation (14), estimating \(\hat{p}\), which provides [PERSON] with information on the interception density. This information can help [PERSON] decide whether the shared key can be considered secure or if the BB84 protocol needs to be rerun. This study only considered the fake_brooklin simulator because it is important to account for the effects of the system noise in the estimation process.  
In this regard, the \(QBER_{SN}\) value refers to the result obtained in the scenario with \(n=4096\), as shown in Table 5, i.e., \(QBER_{SN}=0.0189\). The rationale behind this choice is that the higher the value of \(n\), the higher the accuracy of the \(QBER\) estimate.  
Figures 12 and 13 show the results for the cases \(n=1024\) and \(n=4096\), respectively. The figures display the set of points (\(p\),\(\hat{p}\)) obtained for each of the 50 runs. The analysis of the figures suggests that the average estimate is close to the set \(p\) value. However, the different estimations are spread over a relatively wide interval. This interval widens further as \(p\) approaches 1.  
Figure 11: fake_brooklin simulator model, \(n=4096\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**n** & **Slope** & \(Q\bar{B}ER_{SN}\) \\ \hline
1024 & 0.237 & 0.01835 \\
1536 & 0.247 & 0.01705 \\
2048 & 0.244 & 0.01918 \\
4096 & 0.243 & 0.0189 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for the fake_brooklin simulator model.  
The analysis of these tables reveals that the estimation becomes progressively more accurate with the increase in \(n\). Particularly, the last table, Table 9 provides almost perfect average estimations, with a mean standard deviation (\(\sigma\) in the tables) that is always less than 4% for all values of the interception density.  
It is important to note that, while the accuracy of determining the real value of \(p\) is certainly valuable, in practical scenarios, simply knowing that \(p>0\) may indicate a high likelihood of [PERSON]'s presence. In summary, the method can detect [PERSON]'s action as long as information on \(QBER_{SN}\) is available.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 3 Method and Implementation  
There are four main requirements to consider in terms of evaluating the efficacy of our approach with eNIDs.  
1. **A flow privacy metric.** To evaluate the privacy of flows, i.e., a way to measure flow correlation. This must work across multiple flows and over both spatial and temporal dimensions.
2. **Implementation in IPv6.** A demonstration that eNIDs can be implemented on a node within the scope of the current IPv6 specification as proposed.
3. **Communication across IPv6 networks.** A test to show that the modified packets can be transmitted successfully across existing IPv6 infrastructure as proposed.
4. **Use by existing IPv6 applications.** A demonstration that eNIDs can be used by existing, unmodified IPv6 applications.
## 3 Results
### Poisson's Ratio \(v\) and Dilation Angle \(\psi\)  
The Poisson's ratio \(v\) and dilation angle \(\psi\) were calculated based on the volumetric strain \(\varepsilon_{v}\)-axial strain \(\varepsilon_{1}\) relationship according to the equations below:  
\[v\ =\ \frac{1-k_{\mathrm{C}}}{2} \tag{4}\]  
\[\sin\psi\ =\ \frac{k_{\mathrm{D}}}{-2+k_{\mathrm{D}}} \tag{5}\]  
where \(k_{C}\) and \(k_{\mathrm{D}}\), respectively, represent the slopes of the contraction phase and dilation phase of the volumetric strain-axial strain curve.  
The variation in [PERSON]'s ratio \(v\) with \(w\) under different \(\sigma_{3}\) levels are illustrated in Figure 11a. As can be observed, at each \(w\) value, the \(\sigma_{3}\) increase led to a decrease in \(v\). This could be explained by the fact that the stiffness of the sample at higher \(\sigma_{3}\) value was larger, which led to a smaller lateral strain. In addition, the increase in \(w\) led to an increase in \(v\): when \(w\) increased from 19% to 35%, \(v\) grew from 0.25-0.28 to 0.31-0.39. The dependency of dilation angle \(\psi\) on \(w\) is shown in Figure 11b. It can be seen that no dilation behavior was observed under \(\sigma_{3}\) = 1200 kPa. In addition, the dilation angle \(\psi\) was larger at higher \(w\) values.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Iterative Training Algorithm
#### 3.5.1 Consistency Assessment  
Within the realm of multi-model collaborative learning, the temporal intervals forecasted by various models can at times diverge, resulting in inconsistency. To assess and quantify this inconsistency, we introduce the Intersection over Union (IoU) as a measure of consistency. Concretely, IoU is calculated by dividing the intersection of the two predicted time spans by their union, expressed as follows:  
\[\text{IoU}(A,B)=\frac{\max\left(0,\min(t_{e}^{A},t_{e}^{B})-\max(t_{s}^{A},t_{ s}^{B})\right)}{\left(t_{e}^{A}-t_{s}^{A}\right)+\left(t_{e}^{B}-t_{s}^{B} \right)-\max(0,\min(t_{e}^{A},t_{e}^{B})-\max(t_{s}^{A},t_{s}^{B}))} \tag{20}\]  
In this context, \(\left[t_{s}^{A},t_{e}^{A}\right]\) and \(\left[t_{s}^{B},t_{e}^{B}\right]\), respectively, signify the predicted time intervals for Model A and Model B. On the basis of the IoU value, we define an IoU threshold \(\lambda\) to gauge the degree of concordance between the models' predictions. When IoU \(>\lambda\), the alignment between the two models' predictions is relatively strong, prompting a decrease in the transfer loss weight. Conversely, if IoU \(\leq\lambda\), the predictions are deemed inconsistent, warranting an increase in the transfer loss weight so as to guide both models' results toward consistency. By quantifying the degree of consistency, the MMCL framework can dynamically adjust inter-model learning intensity, ensuring the effectiveness of collaborative learning. Consistency evaluation offers a dynamic adjustment signal that directly influences the adaptive loss adjustment procedure (see Section 3.5.2), guiding transfer loss weighting and providing a basis for iterative training.
## 3 Results and Discussion  
### Effect of Precipitation on Crystal Growth  
The morphology of the precipitated Ni(OH)\({}_{2}\) particles was followed and is presented in Figure 1. According to the FESEM imaging, the higher precipitation temperature created more closely packed primary particles and started cracking the secondary particles. The particle morphology shown in Figure 1 is comparable to that reported by [PERSON] et al. (2012). During the reaction-crystallization precipitation process in an aqueous solution, the Ni(OH)\({}_{2}\) particles are known to evolve from an amorphous state to spherical particles with sizes ranging from nanometers to several microns. According to the proposed mechanism, growth on crystallite templates in the radial direction is free and fast, but the growth rate in other directions is restricted due to a short supply of monomers. As only radially arranged crystallites grow quickly, the resulting macrospherical Ni(OH)\({}_{2}\) particles were characterized by large radially arranged platelet-like crystallites ([PERSON] et al., 2016).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Cycle Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7-30** & **31** & **32-59** & **61** & **62** \\ \hline \multirow{2}{*}{Charge C-rate} & 0.1 + & 0.1 + & 0.1 + & 0.1 + & 0.1 + & 0.1 + & 0.2 + & 0.2 + & 0.2 + & 0.2 + & 0.1 + \\  & 0.015 & 0.015 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 \\ \hline \multirow{2}{*}{Discharge C-rate} & 0.1 + & 0.1 + & & & & & & & & & & \\  & 0.015 & 0.015 & 0.2 & 0.33 & 0.5 & 1 & 2 & 0.2 & 2 & 0.2 & 0.1 \\ \hline \multirow{2}{*}{Voltage range} & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– & 4.3 V– \\  & 2.6 V & 2.6 V & 3.0 V & 3.0 V & 3.0 V & 3.0 V & 3.0 V & 3.0 V & 3.0 V & 3.0 V & 3.0 V \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coin cell testing program.  
The PSD is correspondingly visible in Figure 2, with the measured PSD curves after precipitation times of 6, 8, 10, and 12 h as well as after a maturation period of 12 h. Table 2 shows the tap densities of the samples prepared at different precipitation temperatures, illustrating that higher temperatures created a higher tap density. The precursor precipitated at 40 \({}^{\circ}\)C was subsequently lithiated at different temperatures, indicating a good tap density for LiNiO\({}_{2}\) after the lithiation process (Table 2).  
Figure 1: SEM images of the Ni(OH)\({}_{2}\) particles precipitated at 40, 50, or 60 \({}^{\circ}\)C. Magnitudes are 5000\(\times\) and 75,000\(\times\).
## 2 Materials and Methods
### Soil Analysis  
#### 2.3.1 Experimental Analysis of Undistributed Soil  
The undistributed soil samples were used to determine bulk density (BD), shear stress (\(\tau\)), plasticity index (\(wL_{p}\)), SSR, etc.  
1. Soil bulk density was determined by the cutting ring method [32].
2. The stress increased frictional force between soil particles and enhanced resistance to shear failure. Under constant stress, the shear stress of soil is linearly related to the normal stress of the section, which can be described by the Mohr-Coulomb formula [33]: \[\tau=C_{q}+\sigma\ tan\ \psi_{q}\] (1) where \(\tau\) is the shear stress (kPa); \(C_{q}\) is the cohesion (kPa); \(\sigma\) is the normal stress on the failure surface (kPa); \(\psi_{q}\) is the angle of internal friction (\({}^{\circ}\)); and \(tan\ \psi_{q}\) represents the friction coefficient; and \(\varphi\) are determined by soil properties, which are defined as soil shear stress parameters.
3. The liquid and plastic limits of every soil sample were measured by using a liquid-plastic limit tester. The plasticity index was determined according to the liquid and plastic limits (Equation (2)). \[wL_{p}=IP_{L}-w_{p}\] (2)  
Figure 1: The location of Zuoma small catchment (**a**), the sampled gully (**b**), sampling points (**c**), and exhibition of crack (**d**), pipe (**e**), and slide (**f**) along the gully wall in Yudu county, Jiangxi province.  
where \(wL_{p}\) is the plasticity index, \(IP_{L}\) is the liquid limit (%), \(w_{p}\) is the plastic limit (%). For every soil sample, soil cylinders in stainless steel sample retainers were submitted to three water absorption and desiccation processes in 20 to 30 \({}^{\circ}\)C. The soil was weighted with a table balance every 30 min in the water absorption process and every 3 days in the desiccation process to determine the SWC. At the same time, the height of the soil sample was measured using a caliper. This study hypothesizes that a zero shrink-swell ratio appears when the SWC is minimal. The shrink-swell (SSR) was computed according to Equation (3).  
\[SSR=\frac{h_{i}-h_{0}}{h_{0}}\times 100\% \tag{3}\]  
where \(SSR\) is the shrink-swell ratio (%), \(h_{i}\) is the height of soil at \(i\) time (0.1 mm), \(h_{0}\) is the height of soil at the start (0.1 mm).
## 7 Research Opportunities for Medical XAI
### Alternative Ways to Incorporate Medical Knowledge  
We have already commented on the difficulties of capturing and modelling knowledge from experts, especially in healthcare, due to the uncertainty and incompleteness of knowledge in clinical practice [137]. However, it is undeniable that knowledge would help in building more robust AI systems, with enhanced clinical interpretability. For that reason, research on alternative ways of embedding clinicians' expertise in AI models will be most useful (RQ3 and RQ4).  
One way of incorporating medical knowledge can involve human-in-the-loop evaluations of these systems [2]. Interaction with medical experts during development, or discussing the results of XAI models, can help detect errors, validate these systems, and identify possible underlying causes of the model's behaviour that would go unnoticed by technical developers. The use of the novel technique ChatGPT could also be helpful to automatize this process or part of it.  
Another interesting research path is information fusion. Combining different types of multimodal data, such as medical images, family histories, genomic data or electronic health records can help specify, define, and incorporate clinical context into a model, improving not only its diagnostic accuracy, but also its interpretability [35; 138; 139]. Moreover, using data collected from different centres can also help with domain variability and shift and enhance AI models [35].  
There are already some studies on how to achieve this fusion of different kinds of data [138] as well as studies taking this kind of approach [140], however, more research and comprehensive data collection and annotation should be made to facilitate the building of these enhanced AI systems [35].
###### Abstract  
**Objective:** This study aimed to identify the effects of a submaximal jump training program using accentuated eccentric loading (AEL) on punching performance and countermovement jump (CM) force-time characteristics in amateur boxers. **Methods:** Twenty-nine amateur boxers (age: 24.9 \(\pm\) 5.4 years; height of 175.9 \(\pm\) 5.2 cm; body mass: 76.2 \(\pm\) 10.5 kg) were randomly assigned to three groups: AEL group (n = 9), CMJ group (n = 10), and control group (n = 10). The AEL group performed countermovement jumps using handheld dumbbells equivalent to 10-20% of body mass, followed by unloaded concentric phases. All participants were evaluated pre- and post-intervention on punching peak force and countermovement jump performance. **Results:** Significant differences were found in favor of the AEL group for the peak force of the jab punch (pre: 1050 \(\pm\) 203; post: 1158 \(\pm\) 189 N), straight punch (pre: 1685 \(\pm\) 393; post: 1861 \(\pm\) 429 N), right cross punch (pre: 2005 \(\pm\) 362; post: 2150 \(\pm\) 417 N), and left cross punch (pre: 1836 \(\pm\) 312; post: 1977 \(\pm\) 393 N), along with greater gains in jump height, propulsive impulse, and absolute and relative peak power than the CMJ and control groups. **Conclusions:** A submaximal accentuated eccentric jump training program enhances punching peak force and lower-limb power output in amateur boxers, offering a practical strategy for improving power-oriented performance during preparatory training phases.  
combat sports; boxing; accentuated eccentric loading; plyometric training
## 2 Background: Steps of RVE Simulation for Fracture Analysis at the Microscale
### Representative Volume Element (RVE)-Based Simulations
To summarize, matrix/precipitate or matrix/particle interfaces are 2D interfaces that offer crack nucleation and propagation sites in various metals [6]. The mechanical properties of these interface elements, which describe this phenomenon, can be modeled using a traction-separation law [33]. The elements at the matrix/precipitate elements constitute a 2D mesh. However, commercial FE software does not provide the option to automatically generate cohesive elements at the interfaces. MATLAB software can be used to manipulate the ABAQUS input file in order to insert the cohesive elements at the interfaces. RVE-type models typically involve microstructures at a single length-scale, but they also offer effective material properties and local microstructural responses. RVEs of metals such as steel and aluminum have been created and imported into FE software to study the effects of microstructures on the nonlinear elastoplastic stress-strain response of the material [6; 24]. Also, RVE-based approaches have been applied to plane stress (2D) to different stages of ductile fractures of high-strength steels containing randomly distributed particles such as void nucleation, growth, and coalescence [6] (Figure 3). Moreover, a real microstructure of a typical sheet material can be created using OOF2 software, as shown in Figure 4[24], where an RVE is subjected to the uniaxial tension and damage evaluation, and the stress-strain response curves are shown and compared with experimental data. Figure 3: Stages of a ductile fracture of a high-strength steel [6]. The black dots and black squares resemble the fine and large particles of the second phase, which are randomly distributed within the matrix material.