esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, we developed a water vapor retrieval algorithm using two bands (865 and 910 nm) for PSAC onboard HJ-2 satellites, which is constructed based on the high correlation between the water vapor absorption transmittance of band 6 at 910 nm and water vapor content. The semi-empirical algorithm consists of three parts: (1) calculating the water vapor absorption transmittance of band 6 of PSAC; (2) constructing the relationship between the water vapor absorption transmittance of band 6 and CWV; (3) inverting CWV based on the constructed relationship. Since it can directly calculate CWV based on PSAC data and does not need to build the look-up table before retrieval and retrieve CWV by querying the look-up table, it has a very high retrieval efficiency.  
To clearly assess the quality of PSAC CWV data, we not only use AERONET data to validate PSAC CWV data but compare its accuracy with that of widely used MODIS CWV data. The validation results based on ground-based CWV data from 312 AERONET sites show that PSAC CWV has a very high accuracy, and its accuracy is significantly better than that of MODIS CWV data released by NASA. The RMSE, MAE, RE, and PER10 of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE, and a 372% increase in PER10. In addition, time-series comparisons between PSAC CWV data and AERONET data were performed to evaluate the time consistency between them. The results show that PSAC CWV data can effectively characterize the change trend of CWV.  
This work was carried out in collaboration with all the authors. Conceptualization, Y.X., W.H., Z.L. ([PERSON]), [PERSON] ([PERSON]), [PERSON], B.Y., X.S. and Y.Q.; methodology, Y.X., W.H., [PERSON] ([PERSON]), Y.M., C.F. and J.G.; data acquisition, Y.X., W.H., S.Z., X.L. ([PERSON]), [PERSON], [PERSON] ([PERSON]), Y.Z., M.S. and P.Z.; writing and revision, [PERSON], W.H., [PERSON] ([PERSON]), S.Z., Y.Z. and Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41871269), the National Outstanding Youth Foundation of China (Grant No. 41925019), the National Natural Science Foundation of China (Grant No. 41701407), and the K. C. Wong Education Foundation (Grant No. GJTD-2018-15).  
Not applicable.  
Figure 7: The distribution of the absolute error of PSAC CWV retrieval results from March 2021 to August 2021. The sloping red line is the linear regression line. The red points are the means for specific ranges of absolute error, and the vertical red lines are the mean \(\pm\) 2\(\sigma\) (standard deviation) of absolute error in a certain range. The color of a point is determined by the density of the point where it is located.
## Results  
### Greenhouse Cage Experiments  
Greenhouse cage experiments showed that the numbers of _B. tabaci_ adults and eggs were affected by different plant species (Figure 1). The maximum number of _B. tabaci_ adults per plant was on eggplant at 24 h (F\({}_{(4,\,25)}=11.763\), \(p<0.001\)), 72 h (F\({}_{(4,\,25)}=13.190\), \(p<0.001\)) and 120 h (F\({}_{(4,\,25)}=8.312\), \(p<0.001\)) (Figure 1A). Mean numbers of _B. tabaci_ adults markedly varied from 13.2 to 6.8 per eggplant, 10.5-6.7 per cucumber, 9.8-7.0 per tobacco, 4.3-2.2 per cotton, and 0.8-1.5 per tomato from 24 to 120 h after adults were released, respectively (Figure 1A). After _B. tabaci_ adults injured plants for 120 h, the number of eggs on eggplant was significantly higher than that on other plant species (F\({}_{(4,\,25)}=8.564\), \(p<0.001\); Figure 1B).  
The number of _S. japonicum_ adults on each _B. tabaci_-infested plant greatly differed across plant species (Figure 2). More adults were found on eggplant, cucumber and cotton at each time interval than on other plant species (1 h: F\({}_{(4,\,20)}=7.813\), \(p=0.001\); 3 h: F\({}_{(4,\,20)}=7.585\), \(p=0.001\); 6 h: F\({}_{(4,\,20)}=10.351\), \(p<0.001\); 24 h: F\({}_{(4,\,20)}=8.507\), \(p<0.001\); 72 h: F\({}_{(4,\,20)}=10.726\), \(p<0.001\); and 120 h: F\({}_{(4,\,20)}=3.020\), \(p=0.042\); Figure 2). Moreover, _S. japonicum_ adults clearly preferred eggplant during the investigation. In contrast, _S. japonicum_ adults were only found on tomato at 72 h, and the number was lowest compared with other plant species. No _S. japonicum_ adult was found on tobacco at 1 h, 6 h or 24 h after initial release (Figure 2).  
Figure 1: Numbers (mean \(\pm\) SE) of _Bemisia tabaci_ adults (**A**) and eggs after adults had infested plants for 120 h (**B**) for each plant in greenhouse cage experiments. Different lowercase letters mean significant differences among plant species at the same time at \(p<0.05\).
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 3 Methodology
### Window Function  
Each sequence in our dataset is of variable length since it depends upon the size and quality of the raw material. So the overall weight and time it runs for on the conveyor belt determine its respective outcome. However, for passing the data to machine learning algorithms, features for all the videos need to be of the same length. Hence, the concept of windows is applied, where features are collected for all video frames, and then aggregation is performed, namely sum and mean, at equal intervals over small subsets of the data.  
Furthermore, the total number of windows is selected beforehand, which always produces an equal data length. For instance, if a window is created every minute, all frames within that one-minute time frame are aggregated together. If we perform sum aggregation, all features in a one-minute time frame are summed together. Since we are also deciding the total number of windows, let us say that we only want five windows, then these five windows will only contain data for the first five minutes of the video. However, if the video length exceeds five minutes, the last window will encompass aggregation for all the remaining data. For example, Figure 17 below shows the windows created for a 6 min video.  
To summarize, as defined in Section 3.4, the total number of features for a single training example is equal to the number of features multiplied by the number of frames. Since the number of frames for each video is variable, by dividing the features into an equal number of bins, we ensures that our feature vector for all training examples is of equal length.  
Figure 17: Window function concept: dividing the video into smaller equal size chunks, except the last which might contain the leftover frames not enough to form a complete chunk.
# Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation
## 3 Proposed Model  
### Preliminaries  
Let \(U=\{u_{1},u_{2},\cdots,u_{m}\}\)\((|U|=m)\) represent the collection of users and \(I=\{i_{1},i_{2},\cdots,i_{n}\}\)\((|I|=n)\) represent the collection of items. Since we are focused on item recommendation, we define \(\mathbf{R}\in\mathbb{R}^{m\times n}\) to represent the user-item interaction binary matrix. For each pair \((u,i)\), \(r_{ui}=1\) indicates that user \(u\) has interacted with item \(i\) and, conversely, \(r_{ui}=0\) indicates that user \(u\) has not interacted with item \(i\) or that user \(u\) is not interested in item \(i\). We represent social relations using directed social networks, where \(\mathbf{S}\in\mathbb{R}^{m\times m}\) represents an asymmetric relation matrix. Additionally, \(\left\{\mathbf{Z}_{u}^{(1)},\mathbf{Z}_{u}^{(2)},\cdots,\mathbf{Z}_{u}^{(l)}\right\}\in \mathbb{R}^{m\times d}\) and \(\left\{\mathbf{Z}_{i}^{(1)},\mathbf{Z}_{i}^{(2)},\cdots,\mathbf{Z}_{i}^{(l)}\right\}\in \mathbb{R}^{n\times d}\) denote the embeddings of the size d-dimensional users anditems learned in each layer, respectively. This article uses bold uppercase letters for matrices and bold lowercase letters for vectors.  
**Definition 1**.: _Hypergraph._  
_Let \(G=(V,E)\) represent a hypergraph, \(V\) a vertex set containing \(N\) vertices in the hypergraph, and \(E\) an edge set containing \(M\) hyperedges. Each hyperedge \(\varepsilon\in E\) contains two or more vertices and is assigned a positive weight \(W_{\varepsilon c}\). All weights form a diagonal matrix \(W\in\mathbb{R}^{M\times M}\). A hypergraph is represented by an incidence matrix \(H\in\mathbb{R}^{N\times M}\), which is defined as follows:_  
\[H_{i,j}=\left\{\begin{array}{ll}1,&\text{if }v_{i}\in V\\ 0,&\text{if }v_{i}\
otin V\end{array}\right. \tag{1}\]  
_If the hyperedge \(\varepsilon_{j}\in E\) contains a vertex, then \(H_{i,j}=1\); otherwise, \(H_{i,j}=0\). The degrees of the vertices and edges of a hypergraph are represented as follows: \(D_{v}=\sum_{\varepsilon=1}^{M}W_{\varepsilon c}H_{i\varepsilon}\); \(D_{e}=\sum_{i=1}^{N}H_{i\varepsilon}\)._
## 4 Experiment and Comparison
### Evaluation  
The reusability of the tool is evaluated against the five contracts described in Section 4.1. First, we construct and store some reuse units for voting and transaction business in the reuse library. Second, a developer with experience in SC development is invited to develop the five SCs in a manual line-by-line manner, and the number of lines of code, the state variables, and the functions of each SC are determined. Then, the developer develops the five SCs with the proposed visualized reuse-supporting tool, using reusable units to generate code automatically. Finally, we also calculate the number of lines of code, state variables, and functions reused.  
Next, we use the ratio of the number of code lines reused to the total number of code lines manually developed by the developers as the reuse rate, as shown in Table 1. It can be seen from the experimental results that our tools can effectively support code reuse, thus improving the development efficiency and code correctness. As the number of reusable units gradually accumulates, the effectiveness of this reusable mechanism will become more obvious.
###### Abstract  
In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.  
RL; Q-learning; MISO-NOMA; KKT conditions +
Footnote †: journal: _Sensors_
# Joint Location-Dependent Pricing and Request Mapping in ICN-Based Telco CDNs For 5G
## 1 Introduction
Therefore, the integrated design has the features of both content-centric forwarding and powerful off-path caching, giving a cost-efficient and realistic choice for the deployment of ICN-based telco CDNs in the 5G era;
* Second, we propose a framework that consists of a location-dependent pricing (LDP) module, price-aware request mapping (PARM) module and status measurement (SM) modules. The LDP module dynamically sets prices of different CDN sites according to the sites' bandwidth usage measured by the SM modules. The PARM module calculates the request mapping rules according to the prices and the application access statistics measured by the SM modules;
* Third, we carefully design the LDP module and the PARM module. Specifically, an algorithm is presented to describe the LDP procedure, considering the spatial and temporal heterogeneity of different telco CDN sites. Then, an optimization problem that minimizes user perceived latency and the total payment is formulated to make the PARM decisions;
* Fourth, we conduct extensive simulations to evaluate the proposed design. The simulation results show that our design helps ICN-based telco CDNs flexibly price different CDN sites according to their congestion levels. When the congestion level of a CDN site increases to a certain level, LDP sets the price higher, helping the telco keep pace with its increasing bandwidth cost. Moreover, we observe the impact of some key parameter settings on the telco's revenue and cost, helping a telco to set proper parameters when adopting our design. The rest of the paper is organized as follows. In Section 2, we review the related work. In Section 3, we illustrate the integrated design of the ICN-based telco CDN architecture. Then, we demonstrate the framework that integrates the location dependent pricing and the price-aware request mapping. In Section 4, we present the details of the LDP algorithm design followed by the problem formulation of the optimization running in the PARM module. Then, in Section 5, we conduct extensive simulations, analyze the results in different traffic patterns and different parameter settings. In Section 6, we discuss parameter setting issues in an implementation point of view. Finally, we conclude the paper in Section 7.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Requests-Shaping
#### 4.1.2 Performance Evaluation
We can observe that when the congestion window size is fixed to three the service delay experienced with the request shaper is always better than the service delay experienced without the request shaper. But, of course, when there are concurrent requests in the network it is possible that some of them experience a larger service delay with respect to the case in which there is always only one pending request in the network. Indeed, we can observe that the maximum service delay experienced by the packets in the case of a window value equal to three is larger than the case of a window value equal to one.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 3 Data  
### Landsat 9 OLI Imagery  
This work uses the Landsat Collection 2 Level-1 TP (L1 TP) data for comparison with the ground measurements to evaluate the absolute radiometric calibration of the OLI sensor. The L1 TP imagery is produced by the Landsat Product Generation System (LPGS), where each image is radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation models (DEMs). The imagery is freely available to registered users on the USGS Earth Explorer web portal. As with the Landsat 8 OLI imagery, there are effectively two data radiometry products of interest in each L1 TP image as follows: the TOA spectral radiance and the TOA reflectance. The Landsat data archive was restructured into collections in 2016 to ensure that the radiometry and geometry of the Level-1 data products are consistent over the entire data record [108]. Collection 1 included Landsat 1-5, 7, and 8, and it was available until December 2022. It has been superseded with the release of Collection 2, which adds Landsat 9 to the data archive. Updates to Collection 2 include radiometric and geometric improvements, as well as the inclusion of Level-2 surface reflectance and surface temperature data products [109]. The Collection 2 L1 TP imagery is converted from a digital number (DN) to either TOA spectral radiance or TOA reflectance using radiometric scaling coefficients that are included in the metadata file accompanying the imagery. It should be noted that the TOA spectral radiance and TOA reflectance products follow separate radiometric calibration traceability chains, so unlike previous Landsat sensors, there is no official exoatmospheric solar irradiance model to convert from one to the other.  
Figure 13: A summary of the reflectance-based processing methodology.  
Preliminary L1 imagery was made available to the ground calibration teams after the Landsat 9 commissioning phase to monitor any post-launch radiometric issues with the OLI The calibration parameter files (CPFs) have since been updated, and the work presented here uses the most current CPFs available. As mentioned, the USGS also produces a Level-2 surface reflectance data product (L2 SR) using the Land Surface Reflectance Code (LaSRC), but the scope of this work is limited to radiometric validation.
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 3 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference and Year** & **Method** & **DR** & **C\({}_{\text{Dinitial}}\)** & **Count** \\ \hline [PERSON] et al., 2010 [91] & trapezoid wing & 12.00\% & 0.410 & 49 \\ [PERSON] et al., 2010 [98] & front splitter plate & 27.00\% & 0.293 & 79 \\ [PERSON] et al., 2010 [98] & rear splitter plate & 12.00\% & 0.293 & 35 \\ [PERSON] et al., 2011 [96] & rectangular flap & 9.00\% & 0.293 & 26 \\ [PERSON] et al., 2012 [92] & bump-shaped winglet & 2.00\% & 0.350 & 7 \\ [PERSON] et al., 2012 [100] & delta winglet & 90.00\% & 0.110 & 99 \\ [PERSON] et al., 2014 [78] & elliptical rear flap & 11.11\% & 0.646 & 72 \\ [PERSON] et al., 2014 [78] & rectangular rear flap & 6.85\% & 0.646 & 44 \\ [PERSON] et al., 2014 [78] & triangular rear flap & 6.43\% & 0.646 & 42 \\ [PERSON] et al., 2014 [78] & perforated elliptical rear flap & 8.86\% & 0.646 & 57 \\ [PERSON] et al., 2014 [78] & perforated rectangular rear flap & 5.37\% & 0.646 & 35 \\ [PERSON] et al., 2014 [78] & perforated triangular rear flap & 5.26\% & 0.646 & 34 \\ [PERSON] et al., 2014 [79] & spoiler & 2.02\% & 0.351 & 7 \\ [PERSON] et al., 2014 [79] & rectangular flap & 3.87\% & 0.351 & 14 \\ [PERSON] et al., 2014 [79] & delta winglet & 1.17\% & 0.351 & 4 \\ [PERSON] et al., 2014 [79] & spoiler with winglet & 4.35\% & 0.351 & 15 \\ [PERSON] et al., 2017 [97] & rear flap at top & 17.90\% & 0.201 & 36 \\ [PERSON] et al., 2017 [97] & rear flap at bottom & 11.20\% & 0.201 & 23 \\ [PERSON] et al., 2017 [97] & side flap & 21.20\% & 0.201 & 43 \\ [PERSON] et al., 2019 [94] & delta winglet & 9.04\% & 0.341 & 31 \\ [PERSON] et al., 2019 [19] & spoiler & 3.10\% & 0.080 & 2 \\ [PERSON] 2019 [99] & spoiler with winglet & 22.59\% & 0.415 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum DR by topology modification. beneficial. Downward facing flaps can also be categorized as a partial boat tail application method. Boat tails have 2% (DR = 9.02% [64] and DR = 9.10% [65]), more DR than downward flap in average DR = 7.31% [78]. From the 32 analysed works, 20 cases attained their results with CFD and 27 cases with wind tunnel measurement. From the wind tunnel measurement, only 16 used PIV. For numerical evaluation, only two papers used different methods than RANS [17]; for turbulence modelling, k-\(\varepsilon\) is a highly favoured method. It is also notable that only six papers included comparison of CFD and wind tunnel measurements.
###### Abstract
performed a damage analysis of a tunnel to define the damage state and corresponding damage indices (DIs) under seismic loading [15]. [PERSON] et al. performed shaking table tests to investigate the dynamic interaction between a subway station with an irregular section and silty-clay soil. Consequently, the movement of the subway station was controlled more by the surrounding silty clay soil in terms of phase and amplitude. In addition, the lower layers of the shallow buried subway structures, particularly the components of the central column, sustained cumulative damage [16]. [PERSON] et al. developed a finite element analysis model to evaluate the seismic performance of an underground subway station structure, considering the connection modes and diaphragm walls [17]. [PERSON] and [PERSON] conducted table tests to improve the seismic performance of the soft tunnel portal sections. Therefore, a structure strengthening and isolation method should be adopted for the seismic design of the soft portal section [18]. [PERSON] et al. [19] verified the seismic performance of large subway stations using numerical analysis of various soil foundations. [PERSON] et al. performed a seismic performance evaluation of damage in a three-dimensional (3D) subway structure based on probabilistic analysis and verified that the probabilistic earthquake generated through an intensity-frequency nonstationary ground motion model was consistent with the code spectrum [20]. [PERSON] et al. applied a rapid detection and analysis system to shield tunnels to investigate lining damage. The types, morphological characteristics, and distribution characteristics of the structural damage were quantitatively analyzed based on the field investigation results [21]. This study computed the subgrade reaction modulus of a subway box structure by applying the continuous elastic foundation beam model after converting the in situ leveling results into a displacement function. In addition, the deformation and damage of the subway box structure were examined by comparing and analyzing the results of the elastic beam and numerical analyses. As a result, damage and additional damage prediction can be achieved using simple analysis evaluation techniques when damage, such as cracks, occurs in subway box structures.