esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Coverage  
[PERSON] et al. [15] defined as item coverage, the proportion of items for which the system is able to generate recommendations (prediction coverage), or the proportion of available items that are recommended to a user (catalog coverage).  
It needs to be noted that the previous definitions refer to coverage in items. However, a less popular term is user coverage and is defined as the proportion of users to whom specific items have been recommended [16]. User coverage poses significant interest for lists of items, meaning that it is important for an item provider to be aware to what proportion of the users his items have been recommended. In this publication, one of the goals of the recommender system is to fulfill user coverage constraints for the different providers.  
Although the coverage has been mentioned in a variety of publications, in most cases, it is calculated after the recommendations have been provided and is used only for system evaluation, without being taken into consideration during the recommendation process [15; 16; 17; 18]. In [1], [PERSON] et al. include constraints for user coverage to the recommendation problem definition. Specifically, they formulate the system recommendation problem as a mathematical optimization problem with constraints for coverage. This approach is close to the approach followed in the context of this publication.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 4 Approach, LPWAN Communication Technology, and Energy Consumption Analysis
### LPWAN Communication Technology
#### 4.2.1 LoRaWAN
The measurement for current consumption, energy, and process time is shown in the Table 1. In Figure 8, state 1 exhibits the sleep mode; state 2 is the duration in which controller warms up and becomes active; state 3 is the transmission mode to send the message from end-device to the gateway; state 4 and 6 are the waiting period before opening first and second receive slot respectively; and state 5 and 7 are the receiving slots for the end-device where it waits to receive a message from the gateway.
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 5 Ablation Studies
### Guidance with Hard or Soft Label  
In SMG, we use the foreground soft label generated from the source model as the guidance. However, the hard label from the ground truth also can be utilized as the guidance. The hard label is the ground-truth foreground score, which is the combination of all ground-truth targets mapped to the heatmap. In every position of heatmap, the value of the hard label is either 0 or 1, which is different from the soft label in \([0,1]\).  
We fix the image resolution at \(512\times 512\) and compare the baseline (no guidance) with three different guidance methods, including hard, soft, and both of them in Table 9. The methods with guidance surpass the baseline more than 5% in mAP, which shows that the guidance is an important factor in performance improvement. Furthermore, the soft guidance obtains higher accuracy than other guidance methods. We attribute it to the fact that the soft label contains hidden information about how the source model distinguishes foreground from background, which is exactly what the target network needs to learn. Therefore, we choose the soft guidance in SMG other than hard guidance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{3}{c}{**AP (\%)**} & \multirow{2}{*}{**FPS**} \\ \cline{3-3} \cline{5-6}  & & & **Person** & & **Car** & **Bicycle** & \\ \hline \multirow{4}{*}{Visual detectors} & SSD [9] & 62.1 & 63.1 & 75.8 & 47.5 & 24 \\  & YOLOv3 [18] & 69.4 & 74.5 & 84.4 & 49.2 & 42 \\  & Faster-RCNN [8] & 70.9 & 71.3 & 75.8 & 61.8 & 8 \\  & CenterNet [19] & 71.1 & 76.6 & 85.4 & 51.2 & 107 \\  & RefineDet [39] & 74.3 & **79.4** & 85.6 & 58.0 & 22 \\ \hline \multirow{4}{*}{Infrared detectors} & MMTOD-CG [1] & 61.4 & 63.3 & 70.6 & 50.3 & - \\  & MMTOD-UNIT [1] & 61.5 & 64.5 & 70.7 & 49.4 & - \\ \cline{1-1}  & Effi-YOLOv3 [40] & 70.8 & 74.5 & 84.7 & 53.2 & 22 \\ \cline{1-1}  & Pseudo-two-stage [14] & 75.6 & 78.7 & 85.5 & 62.5 & 21 \\ \cline{1-1}  & SMG-C & 75.6 & 79.0 & 85.8 & 62.0 & **107** \\ \cline{1-1}  & SMG-Y & **77.0** & 78.5 & **86.6** & **65.8** & 40 \\ \cline{1-1}  & SMG-Y-1/4 & 74.5 & 76.6 & 84.4 & 62.5 & 40 \\ \hline \hline \end{tabular} SMG-Y-1/4 is trained on FLIR-1/4, and the other detectors are trained on FLIR.  
\end{table}
Table 7: Detection results of different detectors on the FLIR benchmark.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Size** & **Method** & **mAP (\%)** \\ \hline \(384\times 384\) & Baseline & 53.9 \\  & SMG-C & 59.0 \\ \hline \(512\times 512\) & baseline & 62.7 \\  & SMG-C & 68.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Detection results on the FLIR benchmark at different image resolutions.
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 4 Methodology  
We follow the online Multiple Object Tracking (MOT) methodology and have designed an end-to-end MOT framework (JDTHM), as shown in Figure 3. This framework mainly includes a feature extraction and object detection module, a feature aggregation module that integrates historical tracklet information, and a data association module.  
The feature extraction and object detection module preliminarily achieves the localization and description of detection results for objects. The deep feature aggregation module extracts temporal information from historical tracklet data, amalgamating features from object detection with those from feature extraction. This enhances the consistency between the current frame features and the tracklet features, preventing identity swaps and tracklet breaks caused by the loss or distortion of object detection. The data association module, based on hypergraph matching, receives information from object detection and feature extraction. It uses the tracklet information generated by the data association module to enhance the object-detection and feature extraction modules, thereby reducing false positives and false negatives. The aggregated features further assist the feature extraction module in generating discriminative features, thus minimizing variations caused by occlusions.  
Figure 2: Overview of graph/hypergraph matching pipeline. (**a**) Graph Matching. (**b**) Association Graph. (**c**) Association Hypergraph. The node-to-node matching problem in (**a**) can therefore be formulated as the node classification task on the association graph, whose edge weights can be induced by the affinity matrix. Similarly, the vertex-to-vertex matching problem can be formulated as a vertex classification task on the associated hypergraph, where the edge weights can be induced by the affinity tensor.
## 3 Experimental Results and Discussions
### Conclusions  
In conclusion, we have demonstrated a compact and efficient sub-nanosecond diode-end-pumped Nd:YVO\({}_{4}\) laser system. An off-axis four-pass diode-end-pumped Nd:YVO\({}_{4}\) amplification architecture was designed as a pre-amplifier. The main amplifier is composed of three stages, which include two single pass end-pumped Nd:YVO\({}_{4}\) sub-amplifiers. A maximum output energy of 65.4 mJ at 1 kHz without the stimulated Raman scattering (SRS) process was obtained with pulse duration of 600 ps, corresponding to 109 MW peak power. Good beam quality was obtained with M\({}^{2}<1.6\), and the excellent signal to noise ratio was more than 42 dB. Using LBO crystal, 40.5 W of 532 nm green light and a 0.28% power stability (RMS) were achieved. We also analyzed the influence of the SRS process on working parameters of diode-end-pumped bulk Nd:YVO\({}_{4}\) amplifier. At the high peak power amplifier, the limitation of diode-end-pumped pump power of bulk Nd:YVO\({}_{4}\) crystal due to suppression of the SRS process and thermal fracture. Our work offers an approach to estimate the maximum amplified ability of end-pumped Nd:YVO\({}_{4}\) crystal for high peak power laser amplification. This end-pumped Nd:YVO\({}_{4}\) sub-nanosecond laser system with high energy and good beam quality has simple and compact structure, which makes this laser an attractive light source for remote laser ranging.  
We confirm that all authors contributed substantially to the reported work. [PERSON] was the main originator of this study and conceived most of the experiments; [PERSON]; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] participated in the research design and laser system adjustment; [PERSON] supervised the project and provided the facilities. All the authors discussed and interpreted the results. All the authors read the final manuscript.  
The work was supported by the project of National a thousand program from Academy of opto-electronics, Chinese academy of sciences (Y80B22A13Y) and the Development of High Power Nanosecond Laser Precision & Detecting Instrument Foundation (Grant No.ZDYZ2013-2).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
* Multidisciplinary Digital Publishing Institute
* Directory of open access journals
* Three letter acronym
* linear dichroism
## 3 Results  
### Firmness, Ethylene Production, Respiration Rate, and SSC  
Apple flesh firmness was monitored during storage and the subsequent shelf-life period (Figure 1A). Firmness of air-stored fruit progressively decreased during the 56-day storage, especially within the initial 28 days of storage, as well as the 44-day shelf life. Compared with the air-stored group, apples treated with 2 kPa O\({}_{2}\) had a higher firmness throughout the experimental period, indicating that storage under low oxygen retarded the softening of the GD apples. Interestingly, no difference in SSC was observed between the 2 kPa O\({}_{2}\) and air groups during the storage and shelf-life periods (Figure 1B).  
Ethylene production in both groups increased in the early days of storage, then decreased during the late stage, which was consistent with the typical characteristics of climacteric fruit (Figure 1C). The apples in the 2 kPa O\({}_{2}\) group had a lower ethylene production than those in the air group during the storage and the shelf-life periods (Figure 1C). Ethylene production in the air-stored fruit peaked on day 56, while the peak of ethylene production in the fruit stored at 2 kPa O\({}_{2}\) was observed on day 56 + 23. Low-oxygen partial pressure, therefore, not only inhibited the production of ethylene during storage, but also delayed the ethylene peak during the subsequent shelf-life period. Similarly, the respiration rate in the apples gradually increased during the 56 days of storage and reached its peak  
Figure 1: Firmness (**A**), soluble solid content (**B**), ethylene production (**C**) and respiration rate (**D**) of ‘Golden Delicious’ apple fruit during 56-day storage under 2 kPa O\({}_{2}\) + 98 kPa N\({}_{2}\) at 4 °C and subsequent 44-day shelf life at 20 °C. Air represents apple fruit stored in air; 2 kPa O\({}_{2}\) represents apple fruit stored under 2 kPa O\({}_{2}\) + 98 kPa N\({}_{2}\). Bars indicate standard deviation for each mean value from at least three replicates. Asterisks indicate significant difference between air-stored and 2 kPa O\({}_{2}\)-stored fruit at \(p<0.05\) ([PERSON]’s multiple range test).  
on day 56 + 9 and day 56 + 23 for the air and 2 kPa O\({}_{2}\) groups, respectively (Figure 1D). The respiration rate in the fruit stored under 2 kPa O\({}_{2}\) was always lower than that of the air group (Figure 1D).
## 3 Results and Discussion
### Characterization of the Biochar
#### 3.3.3 Thermogravimetry (TG)  
Figure 7 shows the thermogravimetric curves of SDG and NaOH-activated and KOH-activated biochars. The weight loss of SDG started when the temperature was increased to 200 \({}^{\circ}\)C, and over 60% weight loss was observed at the temperature of 500 \({}^{\circ}\)C, indicating that the SDG contained a large number of volatile substances [21]. However, no weight losses were observed for the two biochars until the temperature reached 350 \({}^{\circ}\)C, and their total weight losses (to 800 \({}^{\circ}\)C) were less than 25%, suggesting good thermal stability of the two prepared biochars. By contrasting the two biochars, it was found that the temperature at which the main weight loss started for NaOH-activated biochar was slightly higher than that for KOH-activated biochar. This result can presumably be attributed to the higher content of functional groups in the KOH-activated biochar (FTIR results), which tend to decompose during TG measurements.  
Figure 6: XRD patterns of NaOH-activated biochar before (**a**) and after (**b**) ammonium nitrogen adsorption and KOH-activated biochar before (**c**) and after (**d**) ammonium nitrogen adsorption.
# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 2 Background and Related Work
### Edge-Aware Weighted Guided Filtering  
When the value of a certain pixel at a sharp edge is large, the pixel value in the neighborhood centered on this pixel is affected. The original pixel with a small value becomes larger, and the phenomenon of expansion occurs at the edge. Therefore, the filtered image appears as halo artifacts. If the halo artifacts are not processed, problems such as color distortion and blurred details in the filtered image occur. To overcome the halo artifacts, edge-aware weighted guided image filtering is proposed [13]. It inherits the advantages of both global and local filtering methods in terms that: (1) its complexity is same as that of guided image filtering, and (2) it can avoid halo artifacts such as the currently used global smoothing filters.  
Let \(\sigma_{G}^{2}(p^{\prime})\) be the variance of \(G\) in the \(3\times 3\) window, \(\Omega(p^{\prime})\). An edge-aware weighting \(\Gamma_{G}(p^{\prime})\) is defined by utilizing the local variances of all pixels within a \(3\times 3\) windows. It is given by  
\[\Gamma_{G}(p^{\prime})=\frac{\sigma_{G}^{2}(p^{\prime})+\varsigma}{\frac{1}{M} \sum\limits_{p=1}^{M}\sigma_{G}^{2}(p)+\varsigma}. \tag{5}\]  
Here, \(M\) is the number of the pixels and \(\varsigma\) is a small constant value to prevent the denominator from being 0. Consequently, they use \(\varepsilon=\lambda/\Gamma_{G}(p^{\prime})\) to perform edge enhancement. The weighting \(\Gamma_{G}(p^{\prime})\) measures the importance of pixel \(p^{\prime}\) in the overall guidance image. If the pixel \(p^{\prime}\) is at an edge, the value of \(\Gamma_{G}(p^{\prime})\) is usually larger than 1. Therefore, \(\varepsilon\) becomes smaller and \(a_{k}\) in Equation (4) becomes larger. This implies that the sharp edges of \(G\) in \(\omega_{k}\) are enhanced.  
Unlike the original guided filtering, the value of \(\varepsilon\) in edge-aware weighted guided filtering is no longer fixed, but adapts according to the different edge characteristics. Therefore, the edge features can be maintained well while smoothing the image.
## 4 Results and Discussion
### Analysis of Repair Strategies
#### 4.3.2 Temporal Analysis of Network Service Efficiency  
Based on the established recovery time relationships and the recovery order in Table 3, the temporal variation curves of network service efficiency under four recovery strategies--random repair, topology-based importance, service efficiency index, and resilience optimization--were calculated. Figure 8 illustrates the dynamic changes in the service efficiency temporal variation curves under the impacts of landslide and flood disasters.  
When the disaster causes all 10 key nodes to fail, the network service efficiency significantly decreases. Under the landslide disaster, the service efficiency drops to 0.00627; under the flood disaster, it decreases to 0.00975. Compared to the network service efficiency in normal conditions (0.0393), the service efficiency after node failure declines by 84.0% and 75.2%, respectively.  
In the landslide disaster (Figure (a)a), the resilience optimization and service efficiency index recovery strategies based on the genetic algorithm show that, after one day in the first recovery phase, the network service efficiency rebounds to 0.0137, representing an improvement of 19.0% compared to the failure state of 0.00627. In contrast, the random  
Figure 8: Time-varying curves of network service efficiency under four recovery strategies.  
repair strategy shows no improvement during the first recovery phase. During the second recovery phase, the resilience optimization strategy surpasses the service efficiency index strategy, demonstrating better recovery performance.  
In the case of the flood disaster (Figure 8b), the resilience optimization repair strategy recovers the network service efficiency to 0.0146 after one day in the first recovery phase, marking a 12.4% improvement, while the other three strategies do not see any increase in network service efficiency after the first recovery phase. As the recovery enters the second phase, the network service efficiencies of these three repair strategies begin to gradually improve. This indicates that the genetic algorithm-based network resilience optimization recovery strategy outperforms the random repair, topology importance, and service efficiency index strategies in terms of overall network performance.
# External Validation of Accelerometry-Based Mechanical Loading Prediction Equations
## 3 Results
\begin{table}
\begin{tabular}{l l l r r r} \hline \hline
**Vector** & **Activity** & **Accelerometer Placement** & **MAE** & **MAPE** & **RMSE** \\ \hline \multicolumn{5}{l}{**pGRF prediction**} \\ \hline Resultant & Walking & Ankle & 606.7 & 80.0\% & 617.3 \\  & & Lower back & 598.7 & 78.9\% & 609.9 \\  & & Hip & 577.4 & 75.9\% & 587.7 \\ \cline{2-5}  & Running & Ankle & 247.5 & 14.3\% & 314.8 \\  & & Lower back & 218.0 & 12.9\% & 271.0 \\  & & Hip & 189.9 & 11.6\% & 240.9 \\ \hline Vertical & Walking & Ankle & 621.7 & 83.3\% & 632.5 \\  & & Lower back & 596.8 & 79.6\% & 607.3 \\  & & Hip & 557.0 & 74.1\% & 567.2 \\ \cline{2-5}  & Running & Ankle & 243.5 & 14.4\% & 302.0 \\  & & Lower back & 239.8 & 14.1\% & 295.8 \\  & & Hip & 199.7 & 12.1\% & 248.5 \\ \hline \multicolumn{5}{l}{**pLR prediction**} \\ \hline Resultant & Walking & Ankle & 8847.6 & 116.0\% & 9706.2 \\  & & Lower back & 9460.4 & 134.0\% & 10,372.9 \\  & & Hip & 8713.0 & 114.8\% & 9653.9 \\ \cline{2-5}  & Running & Ankle & 16,638.6 & 40.6\% & 20,459.1 \\  & & Lower back & 17,539.7 & 43.1\% & 21,288.0 \\  & & Hip & 22,334.3 & 56.6\% & 26,363.9 \\ \hline Vertical & Walking & Ankle & 8042.0 & 106.5\% & 8826.3 \\  & & Lower back & 10,328.2 & 147.6\% & 11,247.3 \\  & & Hip & 8512.6 & 113.6\% & 9334.4 \\ \cline{2-5}  & Running & Ankle & 15,729.8 & 38.9\% & 19,313.6 \\  & & Lower back & 16,609.6 & 40.7\% & 20,319.9 \\  & Hip & 14,198.7 & 35.2\% & 17,408.8 \\ \hline \end{tabular} Abbreviations: MAE, mean absolute error; MAPE, mean absolute percent error; pGRF, peak ground reaction force; pLR, peak loading rate; RMSE, root mean square error.  
\end{table} 
Table 2: Accuracy indices for pGRF and pLR predictions for walking and running based on the [PERSON] et al. (2022) [20] equations. The high prediction errors for pLR reflect challenges in accurately predicting this variable using accelerometers. Factors such as low accelerometer sampling frequency and individual variation in biomechanics likely contribute to the larger discrepancies in pLR compared to pGRF.
# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 3 Characterization of Solar Irradiance Time Series
### Correlation between Statistical Parameters--Redundancy  
This section assesses whether the parameters defined above are not redundant and whether they all provide new information. If not, the static dependence will lead to a rejection of one of the two parameters to the detriment of the more complex one to be calculated. The goal is to be parsimonious and to propose the least complex method. The Spearman's Rank Correlation Coefficient [67] is a statistical tool that examines the degree to which two datasets are correlated, even if the relationship between these two sets is not linear (unlike the Pearson one). This parameter is defined from the ratio between the covariance of two rank variables R(\(X\)) and R(\(Y\)) (\(cov(\text{R}(X),\text{R}(Y)\)) and the product of their standard deviation (\(\sigma(\text{R}(X)\text{x}\ \sigma(\text{R}(Y))\). If the coefficient is greater than 0.75 (relationship in the same direction) or lower than \(-\)0.75 (relationship in the opposite direction), the correlation must be considered to be high. The task is then to use only descriptors with cross-correlations (in the Spearman sense) between \(-\)0.75 and 0.75. Figure 8 shows the Spearman correlation coefficient between the six descriptors of the kt series. A lot of values are outside the range of \"statistical independence\". To have only values between\(-0.75\) and \(0.75\), it is advisable to keep only three variables: mean, Hurst exponent (H), and forecastability coefficient (\(F\)). Figure 9 shows the median and quartiles (of the mean, H, and \(F\)) of the series. We note that \(F\) is relatively homogeneous on the dataset, and the two other parameters are more scattered mainly for the mean.
## 3 Numerical Method  
This study employs COMSOL Multiphysics (v5.5) to investigate the screening process by simulating the fluid-structure interaction of sewage water and components of the screening components. A time-dependent solver is employed to simulate the FSI interface of COMSOL Multiphysics with a k-\(\omega\) turbulence model for 300 s and at a time step of 0.001 s. A built-in physics-controlled mesh, which consists of tetrahedral mesh elements, was used to discretize the computational domain. A boundary-layer mesh with a dense mesh along the FSI boundaries was used to capture the flow fields in these regions precisely. Figure 3 shows the grid details: (a) for the entire WSM channel, (b) rake, (c) sub-screen, and (d) main screen. A grid independence test was performed by varying the mesh elements with a factor of three under the same computational resources to ensure a high accuracy of results. Three different mesh sizes were employed in this study: fine (324,712 domains and 48,836 boundary elements), finer (1,187,146 domains and 129,646 boundary elements), and extra fine (4,176,976 domains and 355,447 boundary elements). The obtained results in terms of local velocity are plotted in the upstream region (at location C) and downstream region (at location D) of the WSM. The velocity is plotted at a distance (X-axis) of 0.5 m away from the WSM in the upstream region (location C) in Figure 4a. Similarly, the velocity is plotted at a distance (X-axis) of 0.5 m away from the WSM in the downstream region (location D) in Figure 4b. We note a minimal difference in the calculation of velocity magnitude at the upstream and downstream channels of the WSM at a flow velocity of 0.2 m/s. Therefore, a finer mesh (1,187,146 domains and 129,646 boundary elements) was adopted in further simulations to save computation time.  
Figure 3: Mesh configuration: (**a**) for the entire WSM channel, (**b**) rake, (**c**) sub-screen, and (**d**) the main screen.
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 4 Some Special Cases  
As it is well known, the M distribution covers many well-known fading models as special cases, which is shown in Table 1. In this section, the derived secrecy analysis results in Section 3 will be extended to these fading channels.  
\begin{table}
\begin{tabular}{c c} \hline
**Distribution Models** & **Generation** \\ \hline K distribution & \(\Omega=0\), \(\rho=0\) \\ Exponential distribution & \(\Omega=0\), \(\rho=0\), \(\alpha\rightarrow\infty\) \\ Gamma-Gamma distribution & \(\rho=1\), \(\Omega^{\prime}=1\) \\ Lognormal distribution & \(\rho=0\), \({\rm Var}[|\{|