esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Materials
### Theoretical Background for Data Processing
#### 2.1.2 Heat Equation and Infrared Thermography
Conservation of energy implies that the accumulation of the thermal energy at a given point of the specimen volume is equal to the negative spatial derivative of heat flow at that point, plus any other energy put into the system. Combination of these equations gives the familiar heat conduction equation:  
\[\rho c_{p}\frac{\partial T}{\partial t}=\dot{q}+k\
abla^{2}T \tag{10}\]  
The term on the left-hand side is the change in the internal heat energy, \(\dot{q}\) represents a heat source function that comprises contributions from a variety of existing heat sources or sinks, and the last term on the right-hand side describes the heat energy change due to the heat flow. The term \(\dot{q}\) accounts for a wealth of contributors to the energy change (except conduction), such as radiative loss, heat convection on a surface, heat dissipation due to plastic work, thermoelastic effect, and, possibly, other mechanisms that can be involved in a specific case. When applying Equation (10) to metals undergoing deformation, heat conduction is usually significantly larger than radiative loss, or convection [63] which can often be neglected. In the case of a large temperature difference between the metal and its surroundings, the heat loss due to these terms can be accounted for if necessary. Once these terms are neglected or accounted for, the remaining part of \(\dot{q}\) will consist of the thermoelastic contribution, and the part of mechanical power dissipated as heat \(p_{D}\). The thermoelastic effect occurs as a reversible temperature change caused by the change in the first stress invariant \(s\), and is usually described by the linear equation for the infinitesimally small increment of \(T\) as \[dT=-K_{t}Tds \tag{11}\]  
The thermoelastic parameter \(K_{t}\) is usually assumed to be constant [64] and expressed as  
\[K_{t}=\frac{\alpha_{T}}{\rho c_{p}} \tag{12}\]  
where \(\alpha_{T}\) is the coefficient of the linear thermal expansion, \(c_{p}\) is the specific heat capacity (at constant pressure), and \(\rho\) is the materials density. An integral of \(p_{D}\) with respect to time gives the energy density dissipated as heat \(w_{D}\). The same applies to \(p_{M}\) from Equation (5)--the time integral of \(p_{M}\) yields the mechanical work (per unit volume) \(w_{M}\) expended on the deformation. Both the heat energy dissipated by the system \(w_{D}\) and the mechanical work \(w_{M}\) enter the first law of thermodynamics, which relates these two quantities to the change in the stored energy density \(w_{S}\) as  
\[dw_{S}=\delta w_{M}-\delta w_{D} \tag{13}\]
# Development of a Wearable Finger Exoskeleton for Rehabilitation
## 5 Discussion  
The use of a robotic device for hand rehabilitation is a new development to support the recovery of hand functions following a stroke. Exoskeleton design in the literature requires deep knowledge of physical human-robot interactions, biomechanics, rehabilitation, actuators, sensors, and control. The main goal is to design effective, lightweight, and portable robotic devices for hand rehabilitation. However, most of the reported hand exoskeleton rehabilitation devices, with very few exceptions, lack portability, have many DOF, and, at the same time, they are voluminous, heavy, complex, and expensive [2; 3; 4; 5; 6].  
The exoskeleton proposed in this work is designed to achieve a lightweight device for the hand of the patient so that it can be worn more easily. It can be manufactured with a 3D printer and its lightness allows it to be used as a splint. The exoskeleton consists of a linkage-slide mechanism with one DOF that provides extension/flexion assistance for the finger. The use of the linkage-slide mechanism not only realizes the purpose, but also makes the rotation center of the linkage structure coincide with the rotation axis of the MCP, PIP, and DIP finger joints. The structure is, therefore, more compact and lightweight than those mentioned in other works [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28].  
Currently, the cost of complex robotic devices makes them unsuitable for use in a domestic or local clinical setting (e.g., [2]). Developing lower-cost devices could help to address this and the development of a low-cost prototype robotic device for rehabilitation to help people to recover finger functions and practice normal daily activities would be of use.
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 2 Optical Packaging
### Fiber-to-PIC Coupling
#### 2.1.1 Edge-Coupling
The broadband nature of the edge-coupler means that the relative strengths of the pump, signal and idler channels can be measured accurately (because all three wavelengths experience the same insertion-loss), and this allows the non-linear conversion efficiencies to be calculated precisely. To prevent damage to the mode-adapter, modules for non-linear applications with edge-couplers must be fabricated in a hermetically sealed package. Otherwise, the electric-field gradient of the 'hot-spot' formed by the high optical power being focused by the lensed fiber can result in an optical tweezer effect that attracts micro-particles and organic contaminants in the air to the facet of the mode-adapter. If these contaminants absorb a significant fraction of the optical-power, then it can give rise to a catastrophic failure of the mode-adapter, due to localized heating of the facet. Such failures are more common for SU-8 and PMMA mode-adapters, rather than for integrated SiON mode-adapters, because the dielectric material is more resistant to thermal decomposition than the polymer photoresists. A number of research groups and companies are working to develop schemes that reduce the alignment tolerance of edge-coupling [21; 22]. The aim is to relax the alignment tolerance (or strengthen the fabrication tolerance [23]) to a level that supports multi-channel edge-coupling for telecom and datacom applications. This activity is motivated by the lower insertion losses and more broadband coupling that would be offered by these edge-coupler arrays, compared to the current (dominant) alternative of grating-coupler arrays. The losses and bandwidth of current MPW grating-couplers are routinely identified by companies and standardization bodies as significant barriers to the commercial adoption of photonic devices.
## 3 Results and Discussion  
### The Law of Acceleration Response Characteristics  
Seismic inertia force is the main cause of slope deformation and failure under earthquake action. The acceleration amplification factor (_AAF_), which roughly corresponds to the maximum seismic inertia force experienced by the slope throughout the entire excitation process, is defined as the ratio of the peak acceleration (\(A_{E}\)) of the dynamic response at each measurement point of the slope to the peak acceleration (\(A_{C}\)) of the measured input at the table surface. In this manner, the analysis of the slope's _AAF_ and its distribution pattern can disclose the deformation properties of the slope under earthquake activity [33]. Furthermore, by contrasting the acceleration amplification coefficients of the shaking table test with the numerical simulation, the accuracy of the latter can be confirmed.  
Figure 4: Monitoring point layout. (unit: mm).  
Figure 5: Time history curve and Fourier spectrum of 4 seismic wave compressed 4 times: (a) time history curve and (b) Fourier spectrum.  
Figure 6 displays the results of a comparative analysis of the \(AAF\) obtained from the slope shaker test and numerical simulation under the action of seismic waves of the same amplitude (using 0.1 g and 0.2 g seismic waves as an example). They show that, generally speaking, the variation law of the slope's \(AAF\) from the slope shaker test and the numerical simulation that we previously conducted is the same. Specifically, the \(AAF\) at different elevation measurement points within the slope of both showed an overall increasing trend along the elevation from the bottom to the top of the slope (Figure 6a). Simultaneously, the \(AAF\) of the same elevation measurement points within the slope of both also showed an overall increasing trend along the slope direction from the interior of the slope to the slope surface (Figure 6b). In other words, the \(AAF\) showed an elevation effect and a tendency to surface effect. It should be noted that the boundary conditions on both sides of the X-direction of the model in the shaking table physical model test are insufficient to fully simulate the infinite boundary. As a result, the seismic wave produces reflection waves on both sides of the boundary, interfering with the seismic response inside the slope and producing the difference between the two data. This further demonstrates the superiority of numerical simulations in handling the boundary. As a result, the work that follows is predicated on excellent numerical simulation data.
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 5 Experiment Results
### Spatial B-Line Localization  
The spatial localization mechanism highlights regions in LUS frames, which contribute most to the detection task. Here, we verify the hypothesis that such regions are indeed the location of B-lines.  
Evaluating spatial localization is a challenging task that heavily depends on the available annotations. For instance, [PERSON] et al. [33] applied thresholding on the saliency mask for activity recognition in videos with bounding box annotations. The tightest bounding box that contains the thresholded saliency map is considered the predicted localization bounding box for each frame. Then, the predicted localization boxes were compared with the ground truth bounding boxes at different intersection over union (IoU) levels. Differently, in our study, B-line regions were annotated with a straight line as shown in Figure 5. Further, we noticed that our attention maps tend to highlight the beginning of the B-line as shown in Figure 6, which may correlate with the lung structure generating the B-line, yielding a correct localization even if the attention does not span over the entire length of the artefact. As a result, thresholding and IoU were not suitable for our evaluation.  
Instead, we focused on the aspects of localization that were useful in the clinical context. B-lines normally extend from the top to the bottom of the image following a radial direction, hence the relevant parameter is the angular position of the B-line. As a result, we assessed spatial localization by comparing the angle at which maximum attention was learned to the ground truth angle from expert annotators. To that end, images were converted to polar coordinates as shown in Figure 7, top. In polar representation, the mean attention over depth was computed for each angle, yielding a 1D attention vector that indicated which angle received more attention from the network, as shown in Figure 7, bottom.  
Then we employed the Pearson correlation coefficient [32] between the 1D attention vector and their related ground truth data, exemplified in Figure 7, centre row. This analysis showed that 1D attention vectors and their related ground truths are positively correlated, \(r=0.67\), \(p<0.0001\). Another example is shown in Figure 8, where both spatial and temporal (further evaluated in the next section) attentions are shown together.  
To help interpret the quality of this correlation, Figure 6 shows an illustration of typical attention maps and the ground truth B-line location overlaid, suggesting that our spatial attention effectively attends to the B-line regions. These maps confirm the existence of more dominant features closer to the lung surface, where B-lines are generated.
# Carbon, Water, and Light Use Efficiency Under Conservation Practice on Sloped Arable Land
## 3 Results
### Water Use Efficiency  
The three years of this study showed substantial differences in annual precipitation. Total precipitation amounted to 682 mm in 2021, 413 mm in 2022, and 404 mm in 2023. The year 2021 was classified as wet, with the probability of exceeding the recorded annual precipitation total estimated at 27%, and only 4% for the autumn-winter period (October-April). The distribution of precipitation and reference evapotranspiration (ETo) clearly indicates that 2022 and 2023 were significantly drier than 2021 (Figure 3a,b), with the most severe drought conditions occurring between July and October.  
Figure 3: Actual evapotranspiration (mm), reference evapotranspiration (mm), precipitation (mm), 2021–2023. Rotations: (**a**) wheat–maize–wheat and (**b**) maize–wheat–maize.  
Figure 2: Cumulative GPP—gross primary productivity (kgC m\({}^{-2}\)), NPP—net primary productivity (kg C m\({}^{-2}\)), AR—autotrophic respiration (kg C m\({}^{-2}\)), simulated on the basis of measured aboveground biomass; (**a**) plot 1—conventional tillage, rotation w-m-w; (**b**) plot 2—minimum tillage, rotation m-w-m; (**c**) plot 3—minimum tillage w-m-w; (**d**) plot 4—conventional tillage, rotation w-m-w; m—maize, w—wheat.  
Actual evapotranspiration (ETa) decreased for both wheat and maize in 2022 and 2023, with the reduction being particularly pronounced in maize. Water use efficiency (WUE) exhibited a significant increase during the years with reduced summer precipitation, as shown in Table 5. Although accumulated biomass was lower during this period due to limited moisture availability, WUE increased relative to evapotranspiration, with the most substantial improvement observed in maize (Table 5).
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 2 Related Work
However, because of the long preamble of acoustic modems [21] and the long propagation delay in the underwater acoustic channel, when the network load is heavy, the pure handshaking-based solution will spend a lot of time on channel reservation. Different from the abovementioned protocols, hybrid protocols make use of the positive characteristics of both contention-free and contention-based. [PERSON] et al. [22] propose a handshake-based ordered scheduling MAC (HOSM) protocol for underwater acoustic local area networks, which utilizes a variant Max-Min Ant System algorithm to calculate the optimal order for each data transmission round to improve channel utilization while maintaining low delay and good spatial fairness. Two MAC protocols, namely, Transmit Delay Allocation MAC (TDA-MAC) and Accelerated TDA-MAC, are proposed to provide time division multiple access to sensor nodes without the need for centralized clock synchronization, and to utilize ping packets to calculate the propagation delays between nodes, achieving the ordered scheduling [4]. [PERSON] et al. [23] propose a sequence-scheduled and query-based MAC (SQ-MAC) protocol for UANs with a mobile node, which is receiver-initiated [24], and can effectively avoid the sender-receiver collision [25] caused by the long propagation delay. Additionally, in this protocol, the ant colony algorithm [26] is adopted as Ref. [22] to schedule the transmission order of data packets to improve channel utilization. However, to ensure the real-time of mobile node data as much as possible, it increases the delay of static node data. However, none of these protocols consider the different data requirements of different nodes in practical applications. To some extent, the above protocols are capable of achieving their performance goals in the network model that they are concerned with and mainly based on theory analysis and simulation tests. Moreover, their performance is limited for UW-WiFi networks comprised of nodes with various task types. Accordingly, our work is significant for this kind of practical network scenario.
# A Preliminary Study on Machine Learning Techniques to Classify Cardiovascular Diseases in Mexico
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Characteristics and Advantages** \\ \hline Forward Selection & \begin{tabular}{c} Examine all the predictive attributes (features from dataset) selected in a database. Its main task is to find the best attributes to develop an algorithm [8]. \\ \end{tabular} \\ \hline Logistic Regression & This algorithm works correctly in problems related to cardiovascular diseases [9]. \\ \hline Random Forest & \
\begin{tabular}{c} It is a supervised algorithm in which multiple \\ decision trees are used to subsequently develop a \\ random forest. Random forest can handle large \\ numbers of attributes as input and, in addition, it is \\ able to identify the most significant attributes [10]. \\ \end{tabular} \\ \hline Support Vector Machines &
\begin{tabular}{c} In this algorithm, each element of the data is plotted \\ or mapped as a point in the n-dimensional space and \\ the value of each attribute is the value of each \\ coordinate. Support vector machine is efficient in a \\ space where the number of dimensions is greater than \\ the number of samples [11]. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics and advantages. clinical and demographic data from 303 patients. The classifier focuses on detecting the risk of cardiovascular disease (CVD) by analyzing key non-invasive attributes, such as age, sex, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, maximum heart rate, and exercise-induced angina. To achieve this, a six-stage methodology is proposed, incorporating a novel step that distinguishes between invasive and non-invasive attributes, optimizing both diagnostic time and cost. The classifier is implemented using three classical machine learning techniques: logistic regression, random forest, and support vector machine. The main contribution of this research is the development of a low-cost system, called Buzzduino-001, which enables cardiovascular disease risk classification through a step-by-step implementation procedure. The principal advantage of this classifier is its reliance on non-invasive attributes, reducing the need for expensive and time-consuming laboratory tests. Additionally, as this is a preliminary study, the selected patient sample was relatively small. Despite this, a high classification accuracy of 87% was obtained using the random forest algorithm, demonstrating the feasibility of this approach. The results of this research can provide a viable solution for the early detection of cardiovascular diseases in populations with restricted access to specialized medical services, promoting the use of affordable technology in healthcare.
## Results
### Spatial Evolution of Forest Productivity and Carbon Sequestration in the TGRA  
As shown in Figure 4, regions with higher forest NPP values from 1992 to 2012 were mainly concentrated in the four counties in western Hubei in the eastern area of the TGRAT, and forest productivity gradually increased from west to east. The most significant change occurred in the counties Wuxi, Wushan, and Yunyang in the eastern hinterland. From 1992 to 1996 and further to 2002, forest NPP rose. Up to 2012, the forest productivities of Wulong County and Fengdu County, located in the Karst Mountains, also significantly improved from 80.046 Gg T and 52.313 Gg T in 1992 to 179.673 Gg T and 118.145 Gg T in 2012, respectively. The spatial variation of forest productivity was consistent with that of the forest and the fragmented forest patches. Therefore, increases in forest area and optimization of the forest landscape pattern can promote the improvement of forest productivity.  
Figure 4: Spatial changes in forest NPP in the TGRA from 1992 to 2012.  
As shown in Figure 5, the forest carbon stock in this area also gradually increased from west to east, and the regions with significant carbon stock variation were Wuxi County, Wushan County, and Yunyang County in the eastern hinterland, Wulong County, Fengdu County, and Shizhu County in the western hinterland, and Jiangjin District and Banan District in the western area of the reservoir area. From 1992 to 1996, the forest carbon stock in the eastern hinterland of the reservoir area increased significantly with continuously high values. From 2002 to 2006, the forest carbon stocks of the Jiangjin District and the Banan District, in the hilly areas at the paralleled ridge valley of the western part, increased significantly; while from 1992 to 2012, the forest carbon stocks of Wulong County, Fengdu County, and Shizhu County, in the southwestern hinterland of the reservoir area, increased gradually; this area became the green barrier of the southern reservoir area.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications
### Open-Cut Mining  
[PERSON] et al. [56] described an integrated Leica/Locata system for open-cut mining operations. In this loosely-coupled system, the Leica GNSS-RTK receiver was used to initialise the Locata receiver's position. Following this initialisation, Locata operated independently of GNSS, and it demonstrated accuracies that were comparable to GNSS-RTK.  
From late 2006 through to early 2007, the first commercially available integrated Leica and Locata devices were installed and tested at DeBeers Venetia Mine in South Africa. Leica/Locata enabled positioning technology was installed on drill, dozer, and backpack systems. Extensive data analysis of data collected over two months showed that the overall positioning accuracy was within 10 centimetres in a horizontal sense, and 20 centimetres vertically [40].
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 2 Feature Point-Based Change Detection Method
### Performance Analysis  
For performance analysis, we manually measured the boundaries of changed objects and unchanged objects. They were used as references to analyze the performance of change detection. Accuracy, precision, recall, false alarm rate, \(F1\)-score, and Area Under the Curve (AUC) were used as performance indices. Accuracy is the ratio of correctly classified groups--true positive (_TP_) and true negative (_TN_)--versus all groups, including incorrectly classified groups--false positive (_FP_) and false negative (_FN_). In this paper, positive is the decision that change occurred. Precision is the ratio of true positives to all detected changes (_TP_ and _FP_). The recall is the ratio of true positives to all changed references (_TP_ and _FN_). The false alarm rate is the ratio of false positives to all unchanged references (_FP_ and _TN_). \(F1\)-score is the weighted average of precision and recall. Performance indices were calculated using Equations (4)-(8) [6; 15; 17].  
An AUC [27] value is calculated as the area under the Receiver Operating Characteristic (ROC) curve [34], which represents the relationship between the values for normalized recall and false alarm rate. When the false alarm value is low and the recall value is high, detection performance is good. Therefore, a higher AUC value indicates good performance. For this paper, AUC was calculated from the ranges between zero and 0.1 false alarms to compare feature-based and pixel-based change detectors in low-false-alarm regions.  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[False\ Alarm\ rate=\frac{FP}{FP+TN} \tag{7}\]  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]