esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Metrics for Adaptive Samples
## 3 Results
### Smooth Adaptive Distance and Homology Inference
#### 3.3.4 Homology Inference  
We have now introduced all the necessary pieces to prove our main homology inference result.  
**Theorem 3**.: _Given \(\alpha,\beta\in(0,1)\), consider compact sets \(\hat{L}\subseteq L\subset\mathbb{R}^{d}\) and compact sets \(\hat{X}\subseteq X\subset\mathbb{R}^{d}\setminus L^{\beta}\), such that \(\mathrm{d}_{H}^{\hat{X}}(L,\hat{L})\leq\delta<1\) and \(\mathrm{d}_{H}^{L}(\hat{X},X)\leq\varepsilon<1\). Define the real-valued functions \(\Psi\) and \(\Phi\) as:_  
\[\Psi(r):=\frac{r+\alpha r+\varepsilon}{(1-r-\alpha r-\varepsilon)(1-\delta)}\]  
_and:_  
\[\Phi(r):=\frac{r}{(1-\alpha)(1-\delta-r)}+\frac{\varepsilon}{1-\alpha}.\]  
_Given any \(\eta>0\), such that \(\Phi\Psi\Phi\Psi(\eta)<1\), if \(\mathrm{wfs}_{L}(X)>\Phi\Psi\Phi\Psi(\eta)\), then:_  
\[H_{*}(\tilde{A}_{X}^{L}(\eta))\cong\mathrm{im}\ (H_{*}(B_{\hat{X}}^{\widehat{L}}( \Psi(\eta)))\hookrightarrow B_{\hat{X}}^{\widehat{L}}(\Psi\Phi\Psi(\eta))).\]  
Proof.: Given \(\eta>0\) such that \(\Phi\Psi\Phi\Psi(\eta)<1\), we have the following sequence of inclusions as a result of Lemma 17.  
(3)  
As we assume that \(\mathrm{wfs}_{L}(X)>\Phi\Psi\Phi\Psi(\eta)\), by the definition of the weak feature size, Lemma 16 implies that the inclusions \(b\circ a\) and \(d\circ c\) are homotopy equivalences. We remind the reader that if two spaces are homotopy equivalent, all the induced homology maps between the spaces are isomorphisms. By applying homology to each space and inclusion in the previous sequence, we have the following sequence of homology groups, where \(b_{*}\circ a_{*}\) and \(d_{*}\circ c_{*}\) are isomorphisms.  
(4)  
\[H_{*}(B_{\hat{X}}^{\widehat{L}}(\Psi\Phi\Psi(\eta)))\xrightarrow{d_{*}}H_{*}( \tilde{A}_{X}^{L}(\Phi\Psi\Phi\Psi(\eta))).\]  
The aforementioned isomorphisms \(b_{*}\circ a_{*}\) and \(d_{*}\circ c_{*}\) factor through \(H_{*}(B_{\hat{X}}^{\widehat{L}}(\Psi(\eta)))\) and \(H_{*}(B_{\hat{X}}^{\widehat{L}}(\Psi\Phi\Psi(\eta)))\), respectively, proving that \(b_{*}\) is surjective and \(c_{*}\) is injective. We then have that \(H_{*}(\tilde{A}_{X}^{L}(\eta))\cong H_{*}(\tilde{A}_{X}^{L}(\Phi\Psi(\eta))) \cong\mathrm{im}\ b_{*}\cong\mathrm{im}\ (c_{*}\circ b_{*})\).
## 2 Data and Methods  
### Study Area  
The Xinjiang Uygur Autonomous Region (35\({}^{\circ}\)-50\({}^{\circ}\) N, 73\({}^{\circ}\)-96\({}^{\circ}\) E) (Figure 1a) is located in northwest China. Its north end is located in the Altay Mountains, its south end is located in the Kunlun Mountains, and its central area has the Tianshan Mountains running through it. It covers a total area of 1.6649 million km\({}^{2}\)[29; 30] and is part of China's arid region. Due to the region being far from the ocean and surrounded by mountains, the areas have a large temperature difference and are dry with little rain. The annual average temperature is about \(-\)4.9-14.9 \({}^{\circ}\)C [31]. The rivers and lakes in the region (such as the Tarim River and Abi Lake) mainly come from glacial meltwater. Owing to its unique geographical location, the region has diverse land surfaces, including desert vegetation, pasture grasslands, cultivated and farmlands, coniferous forests, and other underlying surfaces, the ecosystem is very typical.  
This study focuses on the northern region of Xinjiang as the research area. Three typical ecosystems in this region (cultivated and farmlands, pasture grasslands, and desert vegetation) are selected for the analysis of the coupling between SIF satellite products and GPP in arid regions.
###### Abstract  
Thermoelectric generator (TEG) modules generally have a low conversion efficiency. Among the reasons for the lower conversion efficiency is thermoelectric (TE) material mismatch. Hence, it is imperative to carefully select the TE material and optimize the design before any mass-scale production of the modules. Here, with the help of Comsol-Multiphysics (5.3) software, TE materials were carefully selected and the design was optimized to achieve a higher conversion efficiency. An initial module simulation (32 couples) of unsegmented skutterudite Ba\({}_{0.1}\)Yb\({}_{0.2}\)Fe\({}_{0.1}\)Co\({}_{3.9}\)Sb\({}_{12}\) (n-type) and Ce\({}_{0.5}\)Yb\({}_{0.5}\)Fe\({}_{3.25}\)Co\({}_{0.75}\)Sb\({}_{12}\) (p-type) TE materials was carried out. At the temperature gradient T\(\Delta\) = 500 K, a maximum simulated conversion efficiency of 9.2% and a calculated efficiency of 10% were obtained. In optimization via segmentation, the selection of TE materials, considering compatibility factor (s) and \(ZT\), was carefully done. On the cold side, Bi\({}_{2}\)Te\({}_{3}\) (n-type) and Sb\({}_{2}\)Te\({}_{3}\) (p-type) TE materials were added as part of the segmentation, and at the same temperature gradient, an open circuit voltage of 6.2 V matched a load output power of 45 W, and a maximum simulated conversion efficiency of 15.7% and a calculated efficiency of 17.2% were achieved. A significant increase in the output characteristics of the module shows that the segmentation is effective. The TEG shows promising output characteristics.  
Keywords:modelling; segmentation; thermoelectric generator; skutterudites; efficiency +
Footnote †: journal: Applied Soft. 2020, 10, 408
# Shooting Prediction Based on Vision Sensors and Trajectory Learning
## 3 Basketball Movement Trajectory Acquisition and Shooting Prediction Method
### Determination of the Initial Position of the Basketball  
During the shooting process, the determination of the initial position of the basketball is the premise of accurately constructing the trajectory of the basketball. In this paper, image registration technology is used to determine the initial position of the basketball in the video. The image to be searched is a sub-image obtained by cutting the basketball in the first frame of the video, and the sub-image contains all the features of the target basketball. As shown in Figure 3, the first frame image in the shooting video is used for the target to  
Figure 1: The movement of basketball when shooting.  
Figure 2: Basketball position acquisition process.  
be searched. The task of the search algorithm is to find the position of the template image at the target to be searched, and use this position as the reference position of the basketball, so as to complete further operations on the remaining video.  
Assuming that the size of the image to be searched is N \(\times\) N, the size of the target to be searched is M \(\times\) M, the image to be searched moves pixel by pixel on the image to be searched S, and the part where the target to be searched and the image overlap is called sub-image. S(i,j) are the coordinates of the pixel in the upper left corner of the sub-image in the image to be searched, which are called reference points, where the value ranges of i and j are both (1, N \(-\) M + 1). In order to improve the operation speed, this paper adopts the stacked sparse denoising auto-encoders (SSDA) to complete the matching degree detection [19]. At the same time, we use the formula to explain the principle of SSDA more intuitively. First, the absolute error value is defined as:  
\[\varepsilon(i,j,m_{k},n_{k})=\left|S^{ij}(m_{k},n_{k})-\hat{S}^{ij}(i,j)-T(m_{k },n_{k})+\hat{T}\right| \tag{1}\]  
\[\hat{S}(i,j)=\frac{1}{M^{2}}\sum_{m=1}^{M}\sum_{n=1}^{M}S^{ij}(m,n) \tag{2}\]  
\[\hat{T}=\frac{1}{M^{2}}\sum_{m=1}^{M}\sum_{n=1}^{M}T(m,n) \tag{3}\]  
Take a constant threshold value \(T_{k}\), randomly select pixel point \((m_{k},n_{k})\) in sub-picture \(S^{ij}(m,n)\), calculate the error value \(\varepsilon(i,j,m_{k},n_{k})\) between it and the corresponding point in T, and then add up the difference value and the difference value of other point pairs. When the accumulated error exceeds \(T_{k}\) for r times, the accumulation is stopped and the number r is recorded.  
Define the detection surface of SSDA as shown in (4):  
\[I(i,j)=r \tag{4}\]  
The point \((i,j)\) corresponding to \(I(i,j)\) with the largest value is used as the matching point \((x,y)\), so that the position point \((x,y)\) of the basketball in the first frame image is obtained as the initial position.
# Numerical Simulation and Engineering Application of Temporary Stress Field in Coal Mine Roadway
## 2 Rapid Tunneling System Area Division  
Based on the spatial relationship between the equipment and the surrounding rock, the tunnel is divided into four areas: the unexcavated area, the unsupported area, the temporary support area, and the permanent support area, as shown in Figure 1. Among these, the unexcavated area refers to the area that has not yet been excavated. The unsupported area is where the excavation has been completed but the temporary support is missing. The temporary support area is the section where the support equipment has a direct effect. The permanent support area refers to the location where bolting and other works have been completed, resulting in a stable surrounding rock formation [61; 62; 63; 64; 65].  
Due to the presence of temporary support, excavation and permanent support can be carried out in parallel, significantly improving tunneling efficiency [66; 67; 68; 69; 70; 71]. A reasonable unsupported roof distance can effectively reduce the frequency of equipment relocation, further enhancing efficiency. When the stability of the surrounding rock is ensured, a larger unsupported roof distance allows for longer excavation sections, reducing the frequency of equipment movement. In addition, the unsupported roof distance also affects the temporary support force and the length of the temporary support area.
## 2 Monitoring Dynamic Shear Ruptures in the Laboratory
### Digital Image Correlation to obtain Displacement Fields  
We employed the local digital image correlation (DIC) software Vic-2D (Correlation Solutions Inc.) to analyze the sequence of images acquired with the HPV-X high-speed camera, and to produce evolving displacement maps, computed with respect to a selected reference configuration. In local approaches, the correlation is performed on local \"subsets\" separated by a distance, referred to as \"step\", which can be less than half the subset size (i.e., subsets can overlap) [4; 11; 12]. The 2D-DIC algorithms provide the two in-plane displacement components at each subset center. A parametric study performed in [3] showed that a subset size of 41 pixels and step size of 1 are needed to accurately resolve the spatial and temporal features of dynamic ruptures. In order to capture the discontinuous displacement field across the interface, the correlation is performed separately for the domains above and below the interface. While standard local DIC approaches are able to produce the displacement map up to half a subset away from the interface, the \"fill boundary\" algorithm of Vic-2D uses affine transformation functions to extrapolate the displacements from the center of the subset up to the interface.
# Electrocardiographic Fragmented Activity (II):
## 3 Materials and Methods
### Processing and Features Spaces  
The data processing comprises several steps in our ECG data: (i) Low-order band-pass filtering from 0.5 to 100 Hz, in order to reduce the out-of-band noise without endanger the characteristics of fragmentation (fibrosis); (ii) Notch filtering to reduce the power-line interference; (iii) Baseline wander removal method, based on cubic spline interpolation [22]; (iv) QRS-detector stage, based on a tailored version of the Pan-Tompkins algorithm developed by our group [23]; (v) Beat template stage, where the beat templates are created by averaging the well-correlated QRS complexes, which reduces the total amount of noise present in the ECG without noticeable distortion [24].  
Once the signals have been properly adjusted and key variables such fiducial points have been carefully isolated, the environment is set for the feature space analysis. To do so, features-spaces calculation is divided into three additional ladders: (i) Transformation stage, where a signal is processed according to the multivariate transformations presented in the companion paper [8]; (ii) Signal selection stage, where setting the slot between 140 ms or 700 ms around the ECG main peak and the normalization is applied, if it is required; And (iii) feature computation, which is presented in two different possible ways, namely, statistical features and signal-samples related features.  
First, in order to broadly analyze the signal, statistics (stats) were computed in four separated situations, namely, the statistic of the whole signal, the statistic of the 25 Hz low-pass filtered signal, the statistic of the 25 to 75 Hz band-pass filtered signal and the statistic of 75 Hz high-pass filters signal. For each of them we computed: (i) the average; (ii) the standard deviation, which represents the power  
Figure 4: Examples of attribute test condition. (a) Non-binary attribute test condition. (b) Binary attribute test condition.  
of the signal; (iii) the skewness of the windowed signal, which takes into account the position of the maximum of the windowed signal distribution; (iv) the kurtosis, which represents the shape of the windowed signal distribution; (v) and the number of maxima present in the windowed signal. All of these features were computed according to Reference [25]. On the other hand, the features based on the signal-samples were three, namely, the aggregation of the ECG samples across all components (Sum), the aggregation of the squared values of the ECG samples across all components (PowSum) and the concatenation of the samples of each ECG component in one single vector (Concat).
# Development Status of Key Technologies for Optoelectronic Integrated Circuit Manufacturing
## 2 Design of Optoelectronic Integrated Circuits  
Because the optoelectronic integrated circuit contains optoelectronic devices in addition to the macro-module circuit of the integrated circuit, the circuit transmits not only electrical signals but also optical signals, which render the design of optoelectronic integrated circuits complex, resulting in diversified design indicators and indistinct design levels and complex topology, among others [24]. Therefore, computer-aided software is necessary to help the design of optoelectronic integrated circuits. The design software of optoelectronic integrated circuits is categorized into three classes, namely, chip design aid software, programmable chip design aid software, and system design aid software, including Protel 99 SE, PSPICE 17.2, multiSIM10, OrCAD 17.4, PCAD X4.6.2, LSILogic LSI2032, MicroSim V8.0, ISE14.7, and modelsim 2020.4 SE.  
Functionally, optoelectronic integrated circuits can be categorized into the following classes: photoelectric conversion, electro-optical conversion, and optical control circuits [25]. Photoelectric conversion converts optical information to electrical information. The optical signal is converted into an electrical signal by a detector and is subsequently processed by amplifiers, consisting of a photodetector, a transistor amplifier, and a bias circuit. Photoetectors require high speed, high sensitivity, high responsiveness, low noise, small capacitance, and easy integration. Studies have focused on the development of transistor amplifiers with high transconductance, high transimpedance, high current gain cut-off frequency, and maximum oscillation frequency. Electro-optical conversion in a circuit converts electrical information to optical information. These circuits consist of an LED, a driving circuit, and a bias circuit. The optical transmitter device is composed of a laser diode (LD), LED, and driving circuit. Studies have focused on the integration of high-rate LD and driver circuits. Optical control circuits consist of guided wave optical circuits, optical isolators, optical modulators, optical switches, optical filters, and other active and passive optical devices connected with intelligent control circuits, such as modulation, amplifier, and temperature field control circuits to guide the optical signal in the optoelectronic integrated circuit for superior integration and interconnection with the optical transmitter module and optical receiver module.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 3 Results and Discussion
### Equation of the Straight Line of the FSR Sensor Using Conductivity Data
Although some dispersion is observed, likely because of experimental errors, the overall linearity of the Interlink FSR-402 sensor is considered to be good. When comparing the linear expressions from other fixed resistances, the results generally follow the same trend as the 10 k\(\Omega\) reference line, except for slight deviations at 47 k\(\Omega\) and 200 k\(\Omega\) (Figure 8d). Overall, the conductivity characteristics of both FSR sensors demonstrated considerable linearity to the applied loads. Despite some deviations observed at higher resistance values, the sensors' outputs remained almost parallel to the reference straight line, confirming their reliability.
## 4 Results and Analysis
### Model Uncertainty Analysis  
The ground reflectance (\(\rho_{S}\)) is a necessary input parameter in our method; therefore, incorrect input value may lead to errors in LAI estimation. Ground reflectance often varies among different sites or even within sites due to different environmental conditions, such as water content and ground cover  
Figure 14: The contribution ratio of different height level LAI (i.e., LAI integration from 0–4 m, 4–8 m, 8–18 m) to the total LAI (LAI = \(4\pm 0.1\)).  
Figure 15: (**a**) Scatter plot of averaged GLAS LAI against MODIS LAI at the 500 m pixel level. The legend explains the number of data points that fell into certain square areas. (**b**) Histogram of LAI difference between GLAS and MODIS; the red line indicates the bias (0.89).  
We used a mean value from the non forest-covered GLAS footprints as the input for the whole study area, which may have introduced errors to the results. To see what role the ground reflectance played in our method, we analyzed the sensitivity of our method to variations in input ground reflectance. We varied the ground reflectance from 1.5 to 2.9 to evaluate its effect on LAI and found that its effect on the inversion results was minimal and nearly negligible (Figure 16).
## 2 Materials and Methods
### Total Phenolic Content and Antioxidant Activity  
Ground bread samples (1 g) were extracted with 80% methanol (10 mL) in a shaking incubator (Mikrotest, MSC-30, Ankara, Turkey) at 37 \({}^{\circ}\)C for 1 h. The mixtures were then centrifuged (PCE Instruments, CFE100, Meschede, Germany) at 6000 rpm for 10 min, and the supernatants were collected.  
The total phenolic content (TPC) in the methanol extract of the samples was determined using the Folin-Ciocalteau method and the procedure reported by [24]. All spectrometric measurements were performed in triplicate. The calibration curve was prepared using gallic acid, and the results were expressed as gallic acid equivalents (mg GAE/g dry weight).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **FPH** & **PHP** & **FGS** & **GSP** \\ \hline Moisture (\%, wet basis) & 71.40 \(\pm\) 0.01 & 6.855 \(\pm\) 0.09 & 21.90 \(\pm\) 0.16 & 6.035 \(\pm\) 0.04 \\ Protein (\%, dry basis) & 10.51 \(\pm\) 0.08 & 8.14 \(\pm\) 0.02 & 5.36 \(\pm\) 0.09 & 7.83 \(\pm\) 0.026 \\ Oil (\%, dry basis) & 6.35 \(\pm\) 0.12 & 8.79 \(\pm\) 0.04 & 9.10 \(\pm\) 0.12 & 12.14 \(\pm\) 0.01 \\ Ash (\%, dry basis) & 7.88 \(\pm\) 0.05 & 9.30 \(\pm\) 0.18 & 1.62 \(\pm\) 0.01 & 1.65 \(\pm\) 0.06 \\ \(L^{*}\) & 48.74 \(\pm\) 1.23 & 53.86 \(\pm\) 1.29 & 24.26 \(\pm\) 1.17 & 31.67 \(\pm\) 2.10 \\ \(a^{*}\) & 20.30 \(\pm\) 2.62 & 17.09 \(\pm\) 0.49 & 15.92 \(\pm\) 1.34 & 17.84 \(\pm\) 0.53 \\ \(b^{*}\) & 21.61 \(\pm\) 1.89 & 21.83 \(\pm\) 0.52 & 14.50 \(\pm\) 0.60 & 24.09 \(\pm\) 0.59 \\ Antioxidant activity (mg & 40.22 \(\pm\) 0.03 & 59.39 \(\pm\) 0.09 & 32.68 \(\pm\) 0.01 & 54.74 \(\pm\) 0.10 \\ Trolox/g dry weight) & & & & \\ Total phenolic content (mg & 104.9 \(\pm\) 0.16 & 137.3 \(\pm\) 0.25 & 66.48 \(\pm\) 0.06 & 96.73 \(\pm\) 0.15 \\ GAE/g dry weight) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate compositions, color values, antioxidant activity, and total phenolic content of fresh and powdered pistachio hulls and grape seeds.  
The antioxidant activity of the samples was measured using the DPPH (1,1-diphenyl-2-picrylhydrazyl) radical-scavenging method according to [PERSON] et al. [25] with some modifications. The extracts (0.1 mL) were added to a 2.9 mL DPPH solution (100 ppm). The mixture was shaken vigorously and left in the dark at room temperature for 30 min. Then, the absorbance was measured at 517 nm. The antioxidant activity was expressed as Trolox equivalents (mg Trolox/g dry weight).
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Popular Low-Power Wireless Technologies  
#### 2.1.1 Bluetooth Low Energy (BLE)  
As part of the Bluetooth 4.0 standard, an alternative to classic Bluetooth known as Bluetooth Low Energy (BLE) was introduced [54]. BLE was initially developed by Nokia in 2006. It was designed to provide an extremely low power idle mode, uncomplicated device discovery and highly reliable transfer of data. BLE is able to wirelessly connect miniature, low-power devices to mobile terminals which make it an appropriate candidate for the health-monitoring (BAN) applications. BLE is hardware-optimized version of Bluetooth because of its main differences such as data packet format, radio transceiver and baseband digital signal processing compared to classic Bluetooth. BLE is able to provide up to 1 Mbps data rate. Since BLE utilizes fewer numbers of channels for pairing BLE devices, it consumes considerably less time (few milliseconds) for device discovery and synchronization compared to seconds for Bluetooth. This is significantly valuable for resource-limited and latency-critical devices such as those used in health-monitoring applications. BLE employs a simplified protocol stack and is mainly concerned on short-range, star-topology network with uncomplicated routing algorithms.
## 5 Discussion
### Expert Opinions on PCA Integration with AHP-QFD for Product Design Decision-Manking  
The structured interviews with experts from various fields provided valuable insights into the applicability and limitations of PCA integrated with AHP-QFD. Environmental technical artists in the game industry highlighted that while the PCA-AHP-QFD method (Method 2) is faster, the traditional AHP-QFD method (Method 1) might provide more accurate answers in specific contexts. Senior IT specialists and front-end developers noted significant time reductions (up to 90%) with Method 2, emphasizing its effectiveness for quick evaluations.  
Experts from the Product and Innovation Design Research Center mentioned that Method 2 is more effective for quick evaluations, but Method 1 might be better for detailed analysis when more time is available. Interaction design and innovation master students indicated that Method 2 is suitable for managerial decision-making due to its speed and clarity.  
Converging opinions from environmental technical artists, senior IT specialists, front-end developers, and traffic and route designers generally found PCA-AHP-QFD to be beneficial in improving evaluation speed and clarity. They appreciated the method's ability to streamline decision-making processes, especially in the early design phases.  
However, diverging opinions from product and innovation design researchers, interaction design and innovation students, and educational designers acknowledged the effectiveness of PCA-AHP-QFD in reducing complexity and improving prioritization. They pointed out potential limitations in capturing detailed requirements. For phases requiring intricate detail, such as prototype development or testing, these experts suggestedintegrating complementary methods alongside PCA-AHP-QFD to ensure comprehensive decision-making.