esa-sceva/satcom-chunk-collection huggingface dataset:
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 3 Method Design and Implementation
### Loss Function  
The conventional method for addressing multi-class classification problems commonly involves the utilization of the cross-entropy loss function to quantify the disparity between predicted outcomes and true labels as demonstrated explicitly in Formula (3):  
\[\mathcal{L}_{CE}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\log{(p_{i,c} )} \tag{3}\]  
In the given context, \(\mathcal{L}_{CE}\) represents the cross-entropy loss function, where \(N\) denotes the total number of samples, and \(C\) represents the total number of classes. The variables \(y_{i,c}\) indicate the probability (0 or 1) that sample \(i\) corresponds to the true label \(c\), while \(p_{i,c}\) signifies the probability assigned by the model to sample \(i\) belonging to class \(c\).  
The label categories utilized for the multi-class problem as described in Formula (3) are discrete and independent. The depth classification issue examined in this paper necessitates the consideration of both the imbalanced distribution of depth categories and the ordinal relationships among these categories. The handling of class imbalance has been introduced in Section 3.3. For example, when the true depth value lies within the range of [10; 20], a forecasted depth range of [5; 10] is more proximate to the actual label compared to [1; 2]. Consequently, as the predicted category approaches the actual depth category, the associated penalty should decrease. When formulating the loss function, it is essential to thoroughly contemplate the influence of this matter on the overall optimization of the model. Based on the aforementioned considerations, the imbalance-aware ordinal loss function proposed in this study is presented in Formula (4):  
Figure 8: Mapping between continuous depth values and depth categories.  
\[\begin{split}&\mathcal{L}(w,h,\chi,\Theta)=\sum_{k=0}^{l_{(w,h)}-1} \left(1-\mathcal{P}_{(w,h)}^{k}\right)^{\gamma}\log\left(\mathcal{P}_{(w,h)}^{ k}\right)+\sum_{k=l_{(w,h)}}^{K-1}\left(\mathcal{P}_{(w,h)}^{k}\right)^{\gamma}\log \left(1-\mathcal{P}_{(\omega,h)}^{k}\right),\\ &\mathcal{P}_{(w,h)}^{k}=P\left(\hat{l}_{(w,h)}>k|\chi,\Theta \right)\end{split} \tag{4}\]  
In Formula (4), \(\chi\) represents the resulting feature map corresponding to the input image \(I\); \(w\) and \(h\) denote the horizontal and vertical coordinates of a pixel, respectively; \(l_{(w,h)}\) denotes the actual depth category, while \(p\) represents the predicted value for the depth category greater than \(k\); and \(\hat{l}_{(w,h)}\) is the predicted depth category.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
###### Abstract
[PERSON] et al. [24] highlighted that cloud cover significantly reduces the accuracy of satellite remote sensing, and relevant data cannot be obtained during heavy rainfall. In GIS and cartography, accurate topographic maps are essential. [PERSON] et al. [25] demonstrate the application of drone-based high-resolution photogrammetry, using PPK methods to achieve model and rockfall analysis accuracies below 5 cm to assess the geomechanical properties and rockfall potential of rock scarps. It offers a safer, more efficient, and cost-effective alternative to traditional field surveys for analyzing discontinuities, failure mechanisms, and fracturing patterns across large areas. Additionally, susceptibility to rockfall was analyzed using CloudCompare 2.13 and ArcMap 10.8 software. [PERSON] et al. [26] generated digital orthophoto maps, digital elevation models, and three-dimensional geomorphological models based on UAV images. They analyzed the landslide area to identify potential landslides and developed the GeoStudio numerical model to evaluate the seepage field and the stability coefficient change curve of the landslide. Existing UAV slope monitoring and analysis methods are relatively homogeneous; fewer people use the AHP method for slope assessment. This study combines UAV remote sensing technology with AHP to analyze slope stability and improve the accuracy of slope assessment. This study explores the application of UAV remote sensing technology for identifying and monitoring potential geological hazards on slopes, aiming to develop efficient technical processes for hazard identification, monitoring, and stability analysis [26; 27; 28]. A slope in Shandong Province, China, was selected for the study, where a UAV-mounted camera was used to capture two sets of high-definition images of the slope. These images were used to construct point-cloud models, three-dimensional real-time models, and digital elevation models. Geological information, including contours, slope gradient, and slope shape, was extracted, and hazardous areas were identified and analyzed for stability. The identification of hazardous areas and the analysis of their stability. This study optimizes and enhances the methods for slope monitoring and stability assessment. This approach will ensure the safety of residents and infrastructure surrounding the slopes while providing technical support for disaster prevention and mitigation efforts in China.
## 4 Proposed Approach for Forecasting the Wind Power Intervals
### Wind Power Probability Interval Prediction
#### 4.2.2 PSO of the Prediction Interval in Different Power Segments  
The characteristics of wind power differ in different wind power segments. To eliminate these differences, the prediction interval in different power segments is optimized using PSO. First, the wind power sequence is equally divided into power segments and PSO is used to optimize the different power segments. The specific process for each power segment is as follows.  
The optimal prediction interval is divided into a training stage and a prediction stage. The wind power prediction data are divided into a training dataset \(p_{1}\) and a testing dataset \(p_{2}\). In the training stage, the training dataset \(p_{1}\) represents the input. The observation values of the prediction data are multiplied by the initial upper-limit coefficient \(\beta_{up}^{0}\) and the initial lower-limit coefficient \(\beta_{low}^{0}\) to represent the initial upper limit \(U_{0}\) and the initial lower limit \(L_{0}\) of the prediction interval, respectively. Next, the initial upper-limit coefficient \(\beta_{up}^{0}\) and the initial lower-limit coefficient \(\beta_{low}^{0}\) are optimized using PSO to minimize the objective function \(F\). The optimal upper-limit coefficient \(\beta_{up}^{best}\) and the optimal lower-limit coefficient \(\beta_{low}^{best}\) are obtained. In the prediction stage, the testing dataset \(p_{2}\) is multiplied by \(\beta_{up}^{best}\) and \(\beta_{low}^{best}\) to obtain the final wind power prediction interval \([U_{t},L_{t}]\).
## 6 Conclusions  
In this study, the overestimation of snow cover by MOD10A1 within the Tarim River Basin was verified to be correlated with high values of SZA and the mechanism responsible for the SZA effect was analyzed. A facile modification technique was proposed to modify the MOD10A1 dataset. The following conclusions can be drawn from this work:  
(i) A clear overestimation of SCA resulting from high SZA values was confirmed in various regions of the Tarim River Basin. Hydrological statistics (e.g., amount of snow cover) spanning 10 years in the region corroborated the existence of a positive correlation between high SCP and high SZA during snow-melting periods, which was documented from five different subwatersheds in the region.  
(ii) Analysis of the mechanism responsible for this SZA effect showed that reflectance at Band 4 and Band 6 of MODIS changes unevenly throughout the same range in SZA variation, which led to an increase in NDSI and an overestimation of SCA in pixels with large SZA values. A threshold SZA value of 22.37\({}^{\circ}\) was determined as the minimum at which a statistically significant response in snow cover overestimation occurred.  
(iii) By adopting an appropriate SZA threshold and using a multiday refilling method, a modified snow cover dataset could be produced with ease. After removal of the cloud cover, the calculated decreases in SCF and SCP from this modified snow cover data further indicated the importance of this SZA effect.  
(iv) Data collected by the HJ-1A/B satellite (with pixel resolution of 30 m) was compared with the snow cover data within our modified MOD10A1 dataset (with pixel resolution of 500 m). It showed that the removal of areas of overestimation led to an increase in the accuracy of the calculated snow cover from about 63% to 67%. This increase indicated that our modified dataset could be used for reliable daily analysis of snow cover identification by minimizing the detrimental effects of SZA.  
The following are available online at www.mdpi.com/2072-4292/8/9/750/s1, Table S1: Comparison of the overall accuracies between MOD10A1 and the modified snow cover product in 30 typical areas of Tarim river basin.  
Department of Geography of Kansas University provided the lab and equipment. Nanjing University covered the publication costs. The National Natural Science Foundation of China (Grant No. 41271353) provided support for this work. English language was revised by [PERSON].  
[PERSON] and [PERSON] designed and conceived the research target; [PERSON] designed the methods and analyzed the data; [PERSON] wrote the paper; and [PERSON] contributed to the revision of the paper.  
The authors declare no conflict of interest.
# A Wireless EEG System for Neurofeedback Training
## 3 System Architecture of the EEG Device
### Transmission Module
#### 3.2.4 Receiving Block  
The receiving unit was implemented based on a commercial module USB to nRF24L01 [27]. During the active RX mode, the nRF24L01+ radio, as a receiver, presents the demodulated data to the baseband protocol engine. It constantly searches for a valid packet, which can be cut by the protocol engine, when an address and a valid cyclic redundancy check match. The packet payload is presented in a free slot at the RX FIFOs (Figure A1). A received power detector (RPD) signal is available when RF signals \(>-64\) dBm are inside the receiving frequency channel. The state of the RPD is latched upon receiving a valid packet, which then indicates the signal strength from our transmitter.  
The module was a complete device that plugged directly into a computer's USB port. All settings were determined using AT commands to a serial interface for cellular modules (so-called modem instructions). The communication set-up software package called USB nRF24L01 + Config v0.9 through a USB adapter is a free web-based tool. The USB communication was based on the CH340, a widely available chip with an available free driver running on all versions of Windows.
# Kinematic Determination of the Aerial Phase in Ski Jumping
## 2 Materials and Methods
### The Rationale behind the Determination of the Steady Glide Phase  
For a jump to be successful, we hypothesize that a steady glide is obtained during a certain time period and distance. A steady glide phase entails a period in which changes in drag and lift forces and, hence, the \(LD\)-ratio, are small, but exist to a certain extent, while the variability in the \(LD\)-ratio is substantially larger prior to and following the steady glide phase. The change in \(LD\)-ratio variability at the start and end of the steady glide phase is deployed in this study to identify this phase. Hence, the main challenge in identifying the steady glide phase is the setting of \(LD\)-ratio variation boundaries to successfully divide the'steady' from the non-steady phases. An algorithm was built to search for the start and end of the steady glide phase. The search started in the mid-section of where the steady glide phase was assumed. For that purpose, the starting position of the search (\(\mathrm{p_{m}}\)) was set at 40 m after the take-off for all hill sizes included in this study. Then, the algorithm searched the \(LD\)-ratio rate-of-change signal towards the take-off; i.e., backward in time. The start of the steady glide was defined as the first point between \(\mathrm{p_{m}}\) and 0 m (inrun edge), where the rate-of-change exceeded a set band-width described by a threshold (\(\tau\)) and the end point was defined as the first point exceeding \(\tau\) after \(\mathrm{p_{m}}\). The steady glide phase (distance) was hence defined as the period in which the \(LD\)-ratio was continuously within that \(\tau\)-range, both backward (toward take-off, 0 m) and forward (toward the landing zone) from \(\mathrm{p_{m}}\).  
Furthermore, we assumed the steady glide period to be reasonably consistent, both in terms of distance and actual \(LD\)-ratio, between jumps and jumpers. The rate of change in the \(LD\)-ratio at \(\mathrm{p_{m}}\) is (by definition) close to zero in all jumps, and its (inter-jump) standard deviation in \(LD\)-ratio is used as the initial threshold value (\(\tau_{0}\)) for the the \(LD\)-ratio range. This threshold was used as the initial input parameter in the algorithm that searched for the minimization of four parameters: the standard deviation of the start and end positions of the steady glide phase (\(\mathrm{SD_{1,2}}\)) and the standard deviation of the \(LD\)-ratios at these points(SD\({}_{3,4}\)). The obtained standard deviations over a range of \(\tau\)-values were normalized with their own mean as  
\[\text{nSD}_{1-4.\tau}=\frac{\text{SD}_{1-4.\tau}}{\text{SD}_{1-4}}. \tag{1}\]  
The mean value of the four nSD\({}_{1-4.\tau}\) from (1) was the cost function (\(\overline{\text{nSD}_{1-4.\tau}}\)), and the optimal \(\tau\) (\(\tau_{opt}\)) was defined as at the \(\tau\) where the absolute value of the cost function was minimal.
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 4 Performance Evaluation
### Performance of the MOTSO Model
#### 4.2.2 Minimizing the Total Travel Costs (TTCs)  
The TTCs are a fundamental component, given the nature of the moves taken by a worker from one location to another in order to achieve spatial tasks. Table 10 presents the total travel costs achieved by the proposed model.  
The effectiveness of the MOTSO model in terms of the minimizing the total travel cost objective was compared with the different algorithms mentioned in [2; 10] using only the Gowalla dataset because there were no differences between the MOTSO results between the synthetic and real datasets. In order to conduct the comparison, we calculated the average \(TTC\) (i.e., the ratio of total travel costs per number of completed tasks using the equation mentioned in our previous work [19]):  
\[\textit{Average Travel Cost }(\textit{ATC})=\ \frac{TTC}{|V|} \tag{5}\]  
where \(TTC\) is the total travel cost and \(|\,V\,|\) is the number of completed tasks \(v_{ij}\). The results of the average travel cost (\(ATC\)) using the Gowalla dataset for all algorithms are presented in Table 11.  
Figure 7: The percentage of completed tasks using different algorithms. A&S = baseline algorithm; GALS = global assignment and local scheduling algorithm; NLALA-L = naive local assignment local scheduling based on location; NLALS-T = naive local assignment local scheduling based on task-oriented partitioning; BLALS-K = bisection-based local assignment and local scheduling based on K-means; BLALS-T = bisection-based local assignment and local scheduling; BLA = baseline algorithm; LBA = load-balancing algorithm; DCA = divide-and-conquer algorithm.
# 16-Channel Wavelength Division Multiplexers Based on Subwavelength Grating
## 4 Conclusions  
In this work, we designed and simulated a 16-channel wavelength WDM based on SWG. Our design utilized an SWG perturbation structure to provide inverse couple condition for each channel, which could lower the transmission loss and enhance fabrication tolerance compared to a WDM on an SWG waveguide structure. Besides, with specific structure optimization, our design was capable of 16-channel WDM, covering both O-band and C-band. In our design, the loss and crosstalk of each channel was lower than 0.5 dB and 17 dB. When demultiplexing the O-band, the transmission loss of the C-band was lower than 0.05 dB. Our results imply that the proposed device is promising for broadband and low-loss wavelength demultiplexing with compact footprint and broad bandwidth.  
Conceptualization, Y.B.; data curation, Y.B. and L.Z.; formal analysis, Y.B. and L.W.; investigation, L.W. and L.Z.; methodology, Y.B. and L.W.; resources, L.W., P.W. and B.P.; software, Y.B., L.W. and L.Z.; supervision, P.W.; writing--original draft, Y.B.; writing--review & editing, L.W., P.W. and B.P. All authors have read and agreed to the published version of the manuscript.  
This document is the results of the research project funded by Zhejiang Lab Research Funds (2022 QA0 AL01).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experimental Results
### Evaluation Measurement  
To evaluate the performance of the MBHO, we utilized several metrics, such as the duration of the FS process, _Accuracy_, the \(F1\) score, precision, recall, execution time (formatted as h:mm:ss:ms), and the area under the curve (AUC). These metrics, except for the duration of the FS process, are based on the confusion matrix, a crucial tool for gauging the effectiveness of machine learning algorithms. The confusion matrix consists of four main components: true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), and false negative (\(FN\)). In a scenario where we have a group of individuals, some of whom are infected with a specific disease, \(TP\) refers to those who are indeed infected and correctly diagnosed, TN to those who are not infected and correctly identified as healthy, \(FP\) to those who are healthy but mistakenly diagnosed as infected, and \(FN\) to those who are infected but incorrectly identified as healthy.  
This classification task helped us understand the significance of these four components in the context of machine learning evaluation. Our main metrics are given as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
\[Recall=\frac{TP}{TP+FN} \tag{19}\]  
\[Precision=\frac{TP}{TP+FP} \tag{20}\]  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{21}\]  
**Statistical significance:** We employed the Wilcoxon signed-rank test, a non-parametric statistical method. This test is used to compare two paired observations (FS algorithms in our evaluation here) across multiple cases (here, datasets). The goal is to determine whether there is a significant difference in the median values of these two observations [135]. The test uses a \(p\)-value and a null hypothesis to ascertain this difference. The null hypothesis, which assumes no significant difference between the two observations, is only rejected if the \(p\)-value falls below a specified significance level, \(SL\). Here, we consider the \(Acc\) as the main parameter for this statistical significance test. [PERSON], as a non-parametric test, operates on non-uniform distribution values, thus bypassing the need for normality assumptions in decision-making. Its purpose is to determine whether there are significant differences among at least three measurements within the same group of subjects concerning a skewed variable of interest, such as accuracy [136].
## 4 Coulomb Focused Bremsstrahlung Cross Section
The scaled form of the Coulomb focused nonrelativistic electron-atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}^{CF}[=(d\sigma_{b}/ de)^{MM}_{e-a}f_{CF}(\overline{E}_{0},\ \overline{\varepsilon},\ \overline{Z}_{eff})/(\pi a_{0}^{2}/ Ry)]\) per photon energy in units of \(\pi a_{0}^{2}/ Ry\) including the influence of bound atomic electrons and Coulomb focusing at the spectral cutoff is then represented by:  
\[ \begin{array}{ll}\left(\frac{d\overline{\sigma}_{b}}{d\overline{\varepsilon }}\right)_{e-a}^{CF}=&\frac{16}{3\pi}\frac{\overline{\varepsilon}^{2}a^{3}}{ \sqrt{E_{0}}\sqrt{E_{0}}-\overline{\varepsilon}}\left\{\frac{1}{2}\ \ln\left[\frac{\overline{ \varepsilon}^{2}+(\sqrt{E_{0}}+\sqrt{E_{0}-\overline{\varepsilon}})^{2}}{ \overline{\varepsilon}^{2}+(\sqrt{E_{0}}-\sqrt{E_{0}-\overline{\varepsilon}}) ^{2}}\right]\right.\\ &-\left.\frac{2\overline{\varepsilon}^{2}\sqrt{E_{0}}\sqrt{E_{0}-\overline{ \varepsilon}}}{[\overline{\varepsilon}^{2}+(\sqrt{E_{0}}+\sqrt{E_{0}- \overline{\varepsilon}})^{2}][\overline{\varepsilon}^{2}+(\sqrt{E_{0}}- \sqrt{E_{0}-\overline{\varepsilon}})^{2}]}\right\}\\ &\times\frac{1-\exp\left(-\frac{2\pi}{\sqrt{E_{0}}}\frac{\
u^{3}}{2^{2}+ \overline{\varepsilon}^{2}}\right)}{1-\exp\left(-\frac{2\pi}{\sqrt{E_{0}}- \overline{\varepsilon}^{2}}\frac{\
u^{3}}{2^{2}+\overline{\varepsilon}^{2}} \right)}\.\end{array}  \tag{20}\]  
Since the electron-atom bremsstrahlung cross section [Equation (20)] has been obtained by the Thomas-Fermi solution \(X_{MM}(x)\) and the modified Elwert-Sommerfeld Coulomb focusing factor \(f_{CF}(\overline{E}_{0},\ \overline{\varepsilon},\ \overline{Z}_{eff})\) with the mean effective charge \(\overline{Z}_{eff}\) for the electron-atom interaction, the modified electron-atom bremsstrahlung cross section \((d\overline{\sigma}_{b}/d\overline{\varepsilon})_{e-a}^{CF}\) would be quite reliable for investigating the physical properties of atomic bremsstrahlung spectra over wide range of electron energy including the spectral cutoff region. The bremsstrahlung emission rates in nonthermal Lorentzian astrophysical plasmas will also be discussed in the following section.
## 4 Comparison of Wind Loads on Bare and Ice-Covered Overhead Conductors  
### Expression of Wind Load  
In this chapter, for simplicity, the wind load was considered as static wind load. Based on wind profile, the average speed of wind in different altitudes was calculated from [21]:  
\[\overline{v}(z)=\overline{v}_{b}\left(\frac{z}{z_{b}}\right)^{\alpha} \tag{3}\]  
where \(\overline{v}(z)\) is the average speed of winds in target altitude (m/s), \(\overline{v}_{b}\) is the standard speed of wind (m/s), \(z\) is the target altitude (m) and \(z_{b}\) is standard target altitude (m). \(\alpha\) is the ground roughness exponent.  
The static wind load on a bare conductor or suspension cable from:  
\[F_{g}=\frac{1}{2}\rho v^{2}C_{D}A_{n} \tag{4}\]  
where \(F_{g}\) is static wind load in structure in per length (N/m), \(\rho\) is the density of the air (1.25 kg/m\({}^{3}\) in this case), \(\overline{v}\) is the average speed of the winds in target altitude calculated from (3), \(C_{D}\) is the drag coefficient of given cross-section and \(A_{n}\) is the projected area of structure along wind direction in per length (m\({}^{2}\)/m). For the long-span CRS, the cross-section of the suspension cable and conductor are smooth circular, and their length is  
Figure 8: First asymmetric vertical bending modes of multi-span CRS. (**a**) Two spans, (**b**) 3 spans, (**c**) 4 spans, (**d**) 5 spans.  
Figure 10: First asymmetric lateral bending modes of multi-span CRS. (**a**) Two spans, (**b**) 3 spans, (**c**) 4 spans, (**d**) 5 spans.  
Figure 9: First symmetric lateral bending modes of multi-span CRS. (**a**) Two spans, (**b**) 3 spans, (**c**) 4 spans, (**d**) 5 spans.  
significantly longer than their diameter. Thus their drag coefficient \(C_{D}\) was given as 0.6 according to available experimental data.  
The most unfavorable condition was considered to be when the wind direction was perpendicular to the conductor or suspension cable. The calculation condition of wind speed ranged from 0 m/s to 27 m/s, which is the maximum wind speed in a 50-year-based-period for this case. A diagram of wind loading is shown in Figure 11.