esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 Grey Relational Analysis  
The correlation degrees of the WUE with the LAI and climate factors were quantified through grey relational analysis. The importance of the influencing factors to the WUE was determined. The time series of the WUE was chosen as the reference factor sequence {\(X_{0}\)(t)} (t = 0, 1, 2,..., m), while LAI, temperature, precipitation, wind speed, relative humidity, sunshine duration, and water vapor pressure were used as the comparative factor sequence {\(X_{\text{i}}\)(t)} (t = 0, 1, 2,..., m; i = 1, 2,..., n). For the comparison of the variables with different units and orders of magnitude, the factor sequence was first normalized:  
\[X^{\prime}=\frac{X-\bar{X}}{\sigma} \tag{2}\]  
where \(X^{\prime}\) refers to the normalized value of the factor sequence. \(X\) is the factor sequence. \(\bar{X}\) is the mean of the factor sequence and \(\sigma\) is the standard deviation of the factor sequence. The grey relational coefficient is calculated as follows:  
\[\xi_{0i}(t)=\frac{\min_{\text{i}}\bigl{(}\min_{\text{t}}\bigl{(}\bigl{|}X_{0}^ {\prime}(t)-X_{\text{i}}^{\prime}(t)\bigr{|}\bigr{)}+\varepsilon\max_{\text{i }}\bigl{(}\max_{\text{t}}\bigl{(}\bigl{|}X_{0}^{\prime}(t)-X_{\text{i}}^{ \prime}(t)\bigr{|}\bigr{)}\bigr{)}}{\bigl{|}X_{0}^{\prime}(t)-X_{\text{i}}^{ \prime}(t)\bigr{|}+\varepsilon\max_{\text{i}}\bigl{(}\max_{\text{t}}\bigl{(} \bigl{|}X_{0}^{\prime}(t)-X_{\text{i}}^{\prime}(t)\bigr{|}\bigr{)}\bigr{)}} \tag{3}\]  
where \(X_{0}^{\prime}\)(t) is the normalized reference factor-sequence value at t. \(X_{\text{i}}^{\prime}\)(t) refers to the normalized comparative factor-sequence value at t. i represents the influencing factors. \(\varepsilon\) is the differential coefficient and it is usually 0.5 [37; 38; 39; 40]. \(k_{0i}\) refers to the grey relational coefficient of the WUE and \(X_{\text{i}}\)(t) at t. Generally speaking, a grey relational degree is the mean of the full-process correlation coefficient. Since there are differences among the indicators or spatial importance, it is necessary to calculate the weighted mean of the grey relational coefficient during the computation of the correlation degree. The weights of the indicators can be determined through the entropy-weight method [53; 54]. The procedure to determine the weights is as follows:  
\[\text{r}_{\text{i}}(t)=\frac{X_{\text{i}}^{\prime}(t)}{\sum_{\text{i}=1}^{ \text{n}}X_{\text{i}}^{\prime}(t)} \tag{4}\]  
\[\text{z}(t)=-\ln(n)^{-1}\times\ \sum_{\text{i}=1}^{\text{n}}\text{r}_{\text{i}}(t) \times\text{lnr}_{\text{i}}(t) \tag{5}\]  
\[\lambda(t)=\frac{1\ -\ \text{z}(t)}{\sum_{\text{t}=1}^{\text{m}}(1\ -\ \text{z}(t))} \tag{6}\]  
where \(\lambda(t)\) is the weights of indicators. The grey relational degree was calculated according to the weights:  
\[\text{G}_{0i}=\sum_{\text{t}=1}^{\text{m}}\lambda(t)\xi_{0i}(t) \tag{7}\]where \(\rm G_{0i}\) is the grey relational degree between the WUE and \(\rm X_{i}(t)\) after the weighted mean of \(\rm\xi_{0i}\) was calculated, with a range of 0\(\sim\)1. The influencing factor becomes more important to the WUE as \(\rm G_{0i}\) approaches 1 [35].
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
###### Abstract  
Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial-temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km/h and mean square error (MSE) as 14.62.  
vehicle speed estimation; 3D ConvNets; non-local blocks; optical flow; multi-scale +
Footnote †: journal:
## 2 Smart Grids System
### Microgrid Trading  
Microgrids are small-scale power networks that provide a more flexible and reliable energy distribution in limited geographic regions [131]. For fulfilling local demands, they generally use DERs such as distributed generating units and energy storage facilities. As a result, they can minimize dependency on the traditional centralized power grid (also known as a microgrid or primary grid in power system literature) that generally relies on massive central station generation [132]. Besides, the environmental benefits of using locally accessible RESs such as solar panels, fuel cells, or WTs also have economic benefits because if DERs and loads are physically close together, microgrids can minimize transmission and distribution losses [133; 134; 135]. A microgrid system was used to maintain the energy arbitrage, balance, reserve frequency regulation and transmission-level for voltage control, investment deferral, grid capacity support at the distribution level, time-of-use (TOU) cost management, etc. [136; 137; 138]. Furthermore, it considered as a detection device from the connected grid and operate autonomously in island mode if technical or economic situations demand, which is considered as local energy in the surrounding area [139]. Power delivery from a distance is inefficient because part of the electricity--as much as 8% to 15%--evaporates in route. A microgrid solves this inefficiency by generating power close to the people it serves; the generators are either nearby or within [140; 141]. A microgrid system warrants research attention for several reasons: first, it is local, making electricity close to the people you serve; generators are near or within the building or on the roof in solar panels. The tiny network addresses inefficiencies in significant networks, which lose energy during transmission from producing units to transmission and distribution lines across vast distances. Second, it is independent and can be disconnected from the primary grid and run on its own. When the electrical system goes down due to a storm or other disaster, they must deliver power to their consumers. Third, the generators, batteries, and surrounding building energy systems are all controlled by microgrid intelligence. In addition, the controller coordinates a variety of resources in order to meet the energy goals of the microgrid's consumers, which can be searching for the cheapest energy, the cleanest energy, the most reliable electricity, or something else entirely. The controller accomplishes these objectives by raising or decreasing any of the microgrid's resources or combinations of those resources for optimum impact, as shown in Figure 6[142].  
Figure 5: Schematic illustrating concepts of distributed energy resources.
## 3 Results and Discussion
### Fabrication of Long Tubular Products
When the applied temperature exceeds the designated temperature for diffusion bonding (Case E), the liquid phase induces dimensional distortion and degrades the bonding qualities, as shown in Figure 13b. If the temperature is lower than the designated temperature for diffusion bonding, insufficient junctures are observed. After heating for diffusion bonding, bonded tubular products are slowly cooled in the furnace to prevent thermal shock. Rapid cooling can damage the heating coils and cause failures of the refractory bricks in the heating zone. In the product extract, the hydrogen atmosphere is substituted with nitrogen atmosphere. Then, the product is moved to the cooling zone and cooled by nitrogen circulation. Figure 14 shows a diffusion bonded tube obtained using controlled the heating schedule shown in Figure 11. The outer diameter, wall thickness, and length of the tube are 205.0 mm, 20.0 mm, and 832 mm, respectively. In the stabilization of the material properties of the diffusion bonded tube, a post heat treatment was performed at 1150 \({}^{\circ}\)C for 8 h [34; 35]. The bonding quality of diffusion bonded products was estimated in terms of mechanical properties such as the hardness, tensile strength, and elongation. Table 2 shows the mechanical properties of the diffusion bonded tube after heat treatment. As shown in Table 2, no remarkable differences in mechanical properties are observed, and the bonded zone has the characteristics of the non-bonded base zone. As shown above, the proposed method was successfully implemented, and its feasibility has been well confirmed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Temp (\({}^{\circ}\)C)** & **Microstructure** & **Bonding Quality** \\ \hline A & 1427 & Figure 12a,b,c & Bad \\ B & 1438 & Figure 12d,e & Bad \\ C & 1441 & Figure 12f & Acceptable \\ D & 1443 & Figure 12g & Good \\ E & 1454 & Figure 12h,i & Overheat (distortion) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Diffusion bonding test result. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Hardness (HRC)**} & \multicolumn{2}{c}{**Tensile Properties (ASTM E8M)**} \\ \cline{3-4}  & & **Tensile Strength (kg/mm\({}^{2}\))** & **Elongation (\%)** \\ \hline Bonded Zone & 29.75 & 95.2 & 29.1 \\ Non-bonded base & 29.69 & 95.9 & 28.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mechanical properties of the diffusion bonded tube after heat treatment.
## 4 Experiments
### Experimental Results
#### 4.3.2 Performance Evaluation of a Classroom Student Object Detection Dataset  
To further evaluate the inference performance of the emotion recognition algorithm under different hardware configurations to meet the needs of classroom applications for real-time feedback [34], this study tested the inference time and computational efficiency under different GPU configurations (Table 7).  
This study uses a self-made dataset of student classroom object detection to carry out more validation experiments that test how well the YOLOv8-Improved model works for detection in a real classroom setting. One frame was randomly selected from each of the 100 classroom videos to obtain a total of 100 validation images. Each image contained 30 to 60 students and was accurately labeled using the LabelImg tool (v.1.8.6), as shown in Figure 17.  
With the help of the trained model, a validation script was run to quantitatively analyze the detection ability of the model in a real classroom environment. Table 8 summarizes the obtained performance metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hardware** & **CPU** & **GPU** & **Inference Time** & **FPS** \\
**Configuration** & **CPU** & **GPU** & **per Image (ms)** & **FPS** \\ \hline
1 & Intel i7-12700 KF & NVIDIA RTX 2080 & 24.39 & 41 \\
2 & Intel i7-12700 KF & NVIDIA RTX 2080 Super & 22.22 & 45 \\
3 & Intel i7-12700 KF & NVIDIA RTX 3080 & 20.83 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different GPU configurations.  
Figure 17: Annotation example diagram of self-made student classroom object detection dataset.  
We used the YOLOv8x-COCO model in this study, which we downloaded from the official YOLOv8 website and pre-trained on the COCO dataset. The experiment only evaluates its detection effect on the 'Person' category and compares it with the performance of YOLOv8x and YOLOv8x-Improved in the Visible BBox. The results show that the YOLOv8x-COCO model performs well on general-purpose datasets, but lacks the ability to generalize to specific classroom scenarios. YOLOv8x and YOLOv8x-Improved, specifically trained for crowd detection, demonstrate superior precision and recall. YOLOv8x-Improved, in particular, excels in precision, recall, and mAP50 and mAP50-95 metrics, indicating the model's strong generalization across various scenarios and its exceptional detection performance in natural classroom scenarios following training in the CrowdHuman dataset.  
In addition, this paper tests the model through different classroom scenarios (Classroom A and Classroom B). Figure 18 shows the detection results of YOLOv8x-COCO and YOLOv8x-Improved in different classroom scenarios, respectively. YOLOv8x-Improved excels in identifying students at the rear of the classroom and in areas on both sides, while also minimizing missed detection, demonstrating its adaptability in complex classroom environments.
# Optical and Transport Properties of Ni-MoS\({}_{2}\)
## 2 Experimental Details  
The MoS\({}_{2}\) single crystals were grown by the CVT method for this study, using I\({}_{2}\) as a transporting agent. The iodine transport agent and the elements including Mo (99.99%), S (99.99%) and Ni (99.99%) were used for the crystal growth. The molar ratio of Mo/S is 1:2. The intentional doping concentration of Ni is 0.5%. Afterward, whole reactants were placed in quartz tubes which were evacuated to 2 \(\times\) 10\({}^{-5}\) torr and sealed. For growing the crystals, the quartz tube was placed in a three-zone furnace and grown at 1000 \({}^{\circ}\)C for 720 h. In order to obtain an optimal diffusion gradient for crystal growth, the temperature gradient was adjusted at 1050 to 935 \({}^{\circ}\)C. We carried out electron spectroscopy for chemical analysis (ESCA) for Ni-MoS\({}_{2}\) to confirm existence of elements like nickel or iodine. Figure 1a,b shows the ESCA results which indicate our Ni-MoS\({}_{2}\) actually has Ni atoms with low percentage of 0.2%. No iodine was detected for both samples using ESCA analysis which provides evidence that probably only a few iodine atoms in both samples. The sample surface was observed using an homemade optical microscope (OM) system. A 150 W Xenon lamp equipped with a PTI 0.25 m monochromator was used to provide monochromatic light which was chopped by a mechanical chopper at frequency of 200 Hz for R measurement. The monochromatic light reflected from the sample surface. Consequently the light was collected by lens, detected by a photomultiplier tube (PMT) and recorded by a lock-in amplifier. Meanwhile, a closed-cycle cryostat was utilized to control the temperature changing from 20 to 300 K for R experiment. The same light source and similar setup were also employed in PC and transmittance experiments. As for PC measurements, the sample was connected in series to a resistor (10 k\(\Omega\)) and a constant voltage (1 V) to complete a circuit loop. The frequency of probe light was chopped and fixed 7 Hz. We utilized van der [PERSON] method to carry out Hall effect measurement at room temperature. The electrical properties of MoS\({}_{2}\) and Ni-MoS\({}_{2}\) were investigated including carrier concentration, resistance and mobility. Since the shape of sample is irregular, the four contacts were made to sides in square shape located at the edges of the sample with the same distance between each contact [21]. The temperature-dependent I-V measurements from 20 to 300 K were also carried out in a closed-cycle He-crysostat for temperature conductivity analysis. The samples were illuminated with monochromatic light of 1.2 eV filtered from a xenon lamp.
## 1 Introduction
Most of these findings were separately explored at different spatio-temporal scales over different vegetation types and commonly based on simulated results [31]. Few studies were conducted to simultaneously assess their impacts on SIF, especially based on the continuous ground measurements over various types of plant. Meanwhile, the relationship of SIF yield (and apparent SIF yield), a more direct signal of photosynthesis than SIF, with these parameters has never been reported using ground measurements. Thus, further work needs to be carried out to bridge the gap toward the correct interpretation of the SIF signal in the sequential application for the health assessment of a terrestrial ecosystem. In order to compare and analyze the impacts of growth and environmental parameters on SIF (including SIF, SIF yield, and apparent SIF yield), high-frequency time series of canopy SIF over four plots of wheat and maize were collected in 2017 (from 28 March to 19 May for wheatand 29 July to 28 September for maize) with an automated observation system. Environmental parameters including solar radiation, air temperature, and relative humidity were collected from a nearby micro-meteorological station. Canopy structure (leaf area index and mean leaf inclination angle) and leaf physiological parameters (chlorophyll content and leaf water content) were also collected during the field campaign. With these measurements, we aimed to address the following questions: (1) How does the SIF of wheat and maize change at seasonal and diurnal scales? (2) What is the detailed relationship between SIF and each environmental or biophysical parameter? Answers to these questions can provide deep understanding about the effect of environmental and biophysical parameters on SIF variations, both at seasonal and diurnal scales. This paper is structured as follows: Section 2 describes the experimental design, the measurements of environmental and growth parameters, the spectrum collection method, and data processing. The results and discussions are detailed in Sections 3 and 4, respectively. Finally, Section 5 provides the conclusions.
# Application of Machine Learning Methods for Pallet Loading Problem
## 4 Application and Dataset Description
### Dataset  
In this paper, the dataset comprised 15,187 data points, which contained various types of boxes and pallet-sizes. This dataset was used for the training and testing of different models, which utilized the aforementioned five heuristic algorithms to place the boxes on pallets according to their variable lengths, heights, and widths to obtain optimal results for their arrangements. This dataset is available as listed within an Excel file. The pre-processing of the dataset was first performed by data filtering to create a meaningful classification and eliminate unnecessary data points. The pre-processed dataset is extremely important for the proper training and testing process of the ML models. For instance, from this dataset, the data in accordance with the most commonly-used standard pallet sizes 42 \(\times\) 42 (P1), 45 \(\times\) 45 (P2), and 48 \(\times\) 40 (P3) were selected for training and testing. Thus, our algorithm is applicable only for the P1, P2, and P3 pallet sizes, with only approximately 1 in 15 instances being further considered. While selecting these data, the PUV (pallet utilization volume) ratio for both the P1 and P2 pallet sizes was 85%, and the PUV ratio for the P3 pallet was 95% or above. Thus, a total of 1110 data points were finalized, with 314 for the P1 pallets, 333 for the P2 pallets, and 463 for the P3 pallets. Table 2 presents the dataset details used in this research study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Total Data Points**} & \multirow{2}{*}{**Data Points after Pre-Processing**} & \multicolumn{2}{c}{**P1 Pallets**} & \multicolumn{2}{c}{**P2 Pallets**} & \multicolumn{2}{c}{**P3 Pallets**} \\ \cline{3-8}  & & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline
15,187 & 1110 & 283 & 31 & 299 & 34 & 417 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The datasets used in model training and testing.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 4 Ground Subsidence Risk Prediction Model Based on Machine Learning
### Random Forest  
RF is a tree-based ensemble model that is suitable for solving regression and classification problems [22]. Numerous studies have investigated the development of classification models using RF [23; 24; 25; 26]. The RF algorithm derives results by averaging or obtaining the best outcome from multiple single trees that are repeatedly trained via voting and bagging. The risk of overfitting in RF is relatively low, allowing for greater freedom in variable selection. Moreover, even if the correlation between the data is not high, RF exhibits excellent performance in deriving the results, making it advantageous for application to various types of data [27].
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Application Case Studies in Industrial Environments  
#### 3.2.1 Industrial Applications of Wireless Sensor Networks (IWSN)  
The application of WSN in the oil and gas industries was presented in [38; 39]. The authors discussed the implementation of WSN in the three major sectors of the oil and gas industries. This includes the challenges and requirements for the deployment of WSN. While WSNs are still faced with real-time reliability issues and security, Reference [40] designed and implemented a low-powered ZigBee technology used to monitor and provide a predictive maintenance routine in the oil and gas industry. An example of Industrial Wireless Sensor Network (IWSN) Automation was described in [41]. The performance metrics required for IWSN have necessitated its development as compared to the already existing technologies such as cellular communications, Wi-Fi, etc. These metrics include Low power consumption, the distance between communication points, security, service reliability, data transmission rate, and latency.
## 4 Deep Learning-Based Minimum Timeline Cover
All these snapshots of the graph \(G\) are then passed to the graph embedding block, which uses them to train the parameters of a GraphSAGE model (i.e., to learn how to aggregate information to build a more nuanced representation of each node). This provide a representation for each node in each timestamp \(h_{v_{i}}=[x_{1},x_{2},\ldots,x_{d}]\quad\forall v_{i}\in V\). All these representations are then combined in a unique vector for each node \(H_{v_{i}}=[h_{v_{1}}^{t_{1}},h_{v_{2}}^{t_{2}},\ldots,h_{v_{d}}^{t_{d}}]\quad \forall v_{i}\in V\). Note that \(<\)_PAD_> is a symbol used to pad the sequence to its maximum length. Then, these vectors (sequences of embeddings produced for each node and timestamp, for which—from now on—for simplicity, we use \(v\) omitting the subscript, even if they are computed for each \(v_{i}\in V\)) are used to train the parameters of the positional encoding which produces vectors \(H_{v}\) and the Temporal Encoder Transformer, which perform temporal aggregation, first producing \(H_{v}^{t}\) and then producing \(P_{v}\) as output. \(P_{v}\) is actually a set of vectors \(P_{v_{i}}=[P_{v_{i}}^{t_{i}}\quad\forall t\in T]\quad\forall v_{i}\in V\). These vectors are then used to train the PN. The input sequence of the PN is a sequence of representations for for each node through time, which represents the evolution of that node through the timestamps. These are used to compute a pair of timestamps \(I_{v_{i}}=[s_{v_{i}},e_{v_{i}}]\) for each of these sequences, which represents the start and end of each timeline. This timeline produced may not be valid; thus, it is then completed with the iterative algorithm. This integrated approach leverages the strengths of GNNs, Transformers and PNs. By transforming the graph data with GNNs and Transformer into a state representation that reflects both their structural properties and temporal changes, the PN can select the starting and ending points of each node sequence which correspond to the interval. Finally, an iterative procedure can optimize this interval, providing a valid coverage. In the following, we will refer to this approach as DLMinTC+.