esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Response Characteristics of the TVSG
### Transient Oscillation Mechanism of TVSG
Increasing \(D_{\omega}\) or decreasing \(J_{\omega}\) can enhance the damping ratio \(\xi\), which effectively suppresses the transient oscillations in the TVSG output power. The main parameters of the TVSG are listed in Table 1.  
Figure 3 illustrates the root locus of the closed-loop poles and zero points of the APL for the TVSG under various parameter selections. Analysis of the trend in the system's closed-loop pole distribution reveals that the APL closed-loop system possesses a pair of conjugate poles. As the damping coefficient \(D_{\omega}\) varies in the same trend, the initial positions of the conjugate poles \(s_{1}\) and \(s_{2}\) approach the imaginary axis with an increase in \(J_{\omega}\). This decreases the system's damping ratio, weakening the stability and slowing the regulation rate of the TVSG. When \(J_{\omega}\) remains constant, an increase in \(D_{\omega}\) causes the conjugate poles \(s_{1}\) and \(s_{2}\) to gradually move towards two real-axis poles, as indicated by the arrows. This movement alters the system characteristics from underdamped to overdamped, which reduces the system's response rate and steady-state control accuracy. These effects are consistent with the physical implications outlined in Equation (7). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(U_{\text{g}}\) & Peak voltage of grid & 311 V \\ \(f_{\text{s}}\) & Switching frequency & 10 kHz \\ \(L_{\text{f}}\) & Filter inductance & 3.5 mH \\ \(R_{\text{f}}\) & Inductance spurious resistance & 0.1 \(\Omega\) \\ \(f_{\text{N}}\) & Rated frequency & 50 Hz \\ \(L_{\text{g}}\) & Grid inductance & 1.2 mH \\ \(R_{\text{g}}\) & Spurious resistance & 0.02 \(\Omega\) \\ \(U_{\text{dc}}\) & DC bus voltage & 800 V \\ \(X\) & Equivalent impedance & 1.49 \(\Omega\) \\ \(P_{\text{N}}\) & Rated power & 15 kW \\ \(K_{\omega}\) & PFR coefficient & 2389 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: VSG main circuit parameters.
# Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
## 4 Conclusions  
A complete server-based application has been developed to estimate biomass abundance through underwater image processing, accounting for small items and improving the previous processing methodology. The processing carried out by the system automatically detects small-sized organisms in each of the frames of the multimedia file, subjecting the image to a series of filters whose steps are specifically designed for this particular case. The program offers a user-friendly interface that empowers users to execute a wide array of tasks, such as data analysis, parameter fine-tuning, and video playback. Among its capabilities, it delivers a temporal analysis of object detection in videos for statistical insights. Furthermore, it maintains a chronological log of events, promptly notifying users of the presence of large organisms or sudden surges in detection counts. This feature serves as a valuable resource for conducting taxonomic post-analysis or manual verification as needed.  
This software was developed for the estimation of relative biomass abundance in the DSL. However, its applicability can be broadened to other purposes. The new software, tailored to the unique characteristics of underwater images, represents a significant advancement over manual analysis of existing tools [28].  
Considering the disciplines of the users for whom the application is intended, mainly marine studies, a user manual was prepared with a detailed explanation and series of steps for the installation of the tool, as well as a detailed description of the elements of the graphic interface and its operation. This user manual is published in the git repository where the application is hosted.  
In order to assess the accuracy and reliability of the automatic detection algorithm, videos from the DeepCom project have been used. Our software demonstrates good reliability in detecting small items, surpassing human visual inspection in certain cases. We evaluated its performance on a dataset of underwater videos, revealing occasional high-frequency errors that do not affect the final output due to the program's temporal resolution reduction capability. The identification of sudden density fluctuations in temporal analysis aids in pinpointing potential false positives. These findings highlight the software's effectiveness, particularly in scenarios involving partially translucent or bioluminescent organisms.  
Finally, a concise usability study was conducted to assess the application's effectiveness, efficiency, and user satisfaction. The findings revealed a high level of acceptance and satisfaction among users, highlighting their positive experience with the application. Furthermore, this study served as a valuable platform for gathering user feedback, suggestions, and insights for future enhancements and refinements of the application.
## 3 Survey of Techniques for Updating
### Traffic Reduction
#### 3.2.3 Deluge with Delta Coding  
A greedy delta algorithm is used to produce minimal delta file sizes based on the differences between two software versions (the original version and the update) [45]. This delta file is then disseminated throughout the WSN using Deluge. The delta file is based on four _VCDIFF_ instructions: _shift_, _run_, _copy_ and _add_, but uses a simpler file format designed to optimize sequential EEPROM accesses. These are then applied by each node to its original software version in order to produce the update. Energy use is reduced by the effectiveness of the compression algorithm--the authors report figures between 30% and 99% for representative programs and updates under TinyOS.  
As for the [PERSON] scheme, the only features relevant to autonomous updates are the inherent advantages in terms of extra flexibility introduced by the size reduction with reference to autonomous updates.
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 2 Materials and Methods
### Behavior Based Algorithm
#### 2.3.2 Distance Estimation Using GPS Data
Using the above functions, we can calculate the distance which the goat can travel during 1 s. A goat's maximum speed is 17 kph (17 kph = 4.72 mps), so the maximum possible traveling distance per second for a goat is 4.72 m. If the second point received from the GPS module is located further than 4.72 m, the system removes and creates a new point which is located 4.72 m away from the first point.
## 4 Expressway PTV Detection Algorithm for Over-the-Horizon
### Related Definitions  
To complete our method, the relevant definitions are given below:  
1. ETC transaction data (EData): The EData of the expressway mainly comprises three fields: SID, VID, and TTime, which refer to toll station or gantry ID, vehicle ID, and transaction time, respectively. According to the SID field, the longitude (LNG) and latitude (LAT) of ETC toll station or gantry can be further determined. Based on the VID field, the type of the vehicle and MAC address of the vehicle's OBU device in the ETC transaction data can be further determined. In this paper, the toll station is regarded as a particular portal.
2. Expressway network (LW): All sections within the scope of this expressway research constitute the expressway network, abbreviated as the road network. The expressway network is shown in Figure 2, where blue indicates the expressway toll station and red indicates the expressway ETC gantry, and the blue line indicates the expressway network.
3. Expressway section (QD): Each gantry and toll station entrance and exit (including inter-provincial entrances and exits) of the expressway is called Node, and two contiguous Nodes constitute a section QD, referred to as a section: QD = <Node1,Node2>,  
Figure 1: Overall composition of elements.  
where Node\({}_{1}\) represents the beginning of the section and Node\({}_{2}\) represents the end of the section.  
A schematic diagram of an expressway section is shown in Figure 3.  
4. Travelling trajectory (Trai): The sequence of nodes formed by a particular vehicle on an expressway gantry is called Traj: Traj = \(<\)Node\({}_{1}\),...,...Node\({}_{n}\)?, where Node\({}_{1}\) is called the starting point of the trajectory and Node\({}_{n}\) is called the end point.  
5. Vehicle speed (V\({}_{\rm car}\)): The average speed of a single vehicle passing through sections.  
\[\rm V_{car}=\frac{L}{\Delta t} \tag{1}\]  
where L represents the actual length of the section and \(\Delta t\) represents the time difference between vehicles passing through a certain section.
## 4 Application and Results
### CSE Corpus Annotation
#### 4.2.6 Inter-Annotator Agreement  
The inter-annotator agreement (IAA) was validated using Cohen's Kappa, which is one of the most popular statistics to measure the agreement between two annotators of N terms on m categories [40]. The formula to calculate Cohen's Kappa for two annotators is:  
\[k=\frac{p_{0}-p_{e}}{1-p_{e}}=1-\frac{1-p_{0}}{1-p_{e}}\]  
where \(p_{0}\) is the relative observed agreement among annotators and \(p_{e}\) is the hypothetical probability of chance agreement.  
The produced CSE corpus has N = 4500 terms and m = 3 categories, and both annotators (A and B) agreed for the _personal_ category 1665 times, for the _enterprise_ category 1442 times and for the _IT_ category 1194 times. Table 6 contains a contingency matrix where each xij represents the multitude of terms that annotator A classified in category i, but Annotator B is classified in category j, with i, j = 1, 2, 3. The proportions on the diagonal, xij, represent the proportion of terms in each category for which the two annotators agreed on the assignment.  
The observed agreement is calculated as: \(P(o)\ =\ \frac{1665+1442+1194}{4500}=0.956\ (95.6\%)\).  
The expected change agreement, thus the proportion of terms which would be expected to agree by chance is:  
\[P(e)\ =\ \frac{\frac{(1741\times 1738)}{4500}+\frac{(1532\times 1524)}{4500}+ \frac{(1227\times 1238)}{4500}}{4500}=0.339\]  
so, we conclude that the [PERSON]'s Kappa metric is, \(k=\frac{p_{0}-p_{e}}{1-p_{e}}=\frac{0.617}{0.661}=0.933\).  
Interpreting the [PERSON]'s kappa value of 0.933 [41], we can safely conclude that the level of agreement for the CSE corpus annotation task was almost perfect.
## 1 Introduction
### Smart Logistics System and Its Utilization by Different Actors
\begin{table}
\begin{tabular}{c c c} \hline
**Action** & **Actor** & **Description of Actions to Be Performed** \\ \hline \multirow{2}{*}{Add new user} & \multirow{2}{*}{Administrator} & Logistics manager who administers the smart system \\  & & can add new users to the system. New label codes will be \\  & & assigned to new actors. \\ \hline \multirow{2}{*}{Add new pruning} & \multirow{2}{*}{Farmers} & Farmers have to input pruning quality parameters required \\  & & by end users. \\ \hline \multirow{2}{*}{Edit pruning} & Farmers & Farmers can update input information such as pruning quality \\  & & parameters while it is in “pending” status. \\ \hline \multirow{2}{*}{Delete pruning} & Farmers & Farmers can delete pruning as long as it is still pending and has \\  & & not been flagged as “ready”. \\ \hline \multirow{2}{*}{Make ready} & Farmers & Farmers can flag pruning as “ready” so that consumers can view \\  & & that pruning and order it. \\ \hline \multirow{2}{*}{Generate QR code} & Farmers & Farmers can generate a quick read (QR) code for “pending”, \\  & & “ready”, and “ordered” pruning. \\ \hline \multirow{2}{*}{Order pruning} & \multirow{2}{*}{Consumers} & Consumers order pruning based on quality parameters. \\  & & Consumers select a transporter from a list registered in \\  & & the central system. \\ \hline \multirow{2}{*}{Add to shipment} & \multirow{2}{*}{Transporters} & Transporters select orders to be included in one shipment in \\  & & order to plan the route between locations of chosen orders. \\ \hline \multirow{2}{*}{Plan route} & \multirow{2}{*}{Transporters} & Transporters generate driving instructions for source and \\  & & destination locations for all orders included in one shipment. \\ \hline \multirow{2}{*}{Add to pruning} & \multirow{2}{*}{Traders} & Traders use the tool to select which orders are part of a new \\  & & treated pruning offer. Trader can add new pruning quality \\  & & parameters, e.g., if bales are chipped. \\ \hline \end{tabular}
\end{table} 
Table 1: Actions to be performed by main actors of the pruning supply chain. Figure 3: Major components of the smart logistics system. Reproduced from permission of [2] (MDPI, 2018).
# Experimental Investigation of a Device to Restrain the Horizontal Sliding of U-FREIs
## 2 Similarities and Differences between SREIs and U-FREIs  
The behavior of U-FREIs is similar to that of SREIs but with some differences, both under simple compression and under combined compression and shear.  
are produced, which is different from SREIs. Hence, U-FREIs can deform without damage under displacements of seismic magnitude. While the detached portions are approximately unstressed in U-FREIs, the portion remaining in contact performs a nearly pure shear deformation. By increasing the maximum imposed shear strain, the area of the isolator surfaces in contact with the support surfaces decreases and, therefore, the isolator average horizontal stiffness also decreases [19].  
This is positive for the purpose of seismic isolation because it lengthens the isolation period. In contrast, in SREIs, the horizontal stiffness remains almost constant up to a deformation of about 150% and then increases.  
Due to the absence of anchorages, U-FREIs, differently from SREIs, can be subjected to horizontal sliding, depending on the amount of applied horizontal force, the acting compression load, and the friction conditions at the isolator-support contact surface.
# Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
## 5 Conclusions  
In this paper, a neural network-based projection data enhancement algorithm is proposed to address the problem of low accuracy in TDLAS detection reconstruction using limited detection projection angles. The network model incorporates GRU and multi-level residual learning based on U-Net to realize the multi-view synthesis of sparse projection data. The cross-connection structure of the U-type deep learning network ensures a shallow correlation structurality with the premise of guaranteeing deeper feature information of the projection data. The multilevel residual learning of the higher-level feature representation, which improves the efficiency and generalization ability of the network, makes it easier to optimize and converge the network. The GRU is capable of learning the sequence properties among projection angles.  
The feasibility of the network model was evaluated using numerical simulation methods, and an experimental study was conducted to synthesize 32 multi-angle projection data from a finite number of \(2^{n}\) projection angles. Realizing the \(2^{n}\)-fold enhancement of the projection data, the experimental results show that the GMResUNet algorithm has an excellent projection data synthesis ability. Its 8-fold synthesis ability was especially effective (sparse projection N = 4); the \(PSNR\) was 40.726, the \(SSIM\) was 0.997, and the average relative error was 0.35%. At the same time, the sparse projection and synthetic projection are used separately for temperature field reconstruction, and the results show that the synthetic ability of the algorithm can significantly improve the reconstruction accuracy of the high-energy combustion temperature field, especially in cases of a small number of detection perspectives. The algorithm can not only realize high-quality temperature distribution reconstruction with a small number of projections but also has a potential market economic value for simplifying the hardware composition of detection systems in the future.  
Conceptualization, Y.S.; methodology, Y.S.; software, Y.S.; validation, Y.S.; formal analysis, Y.S. and X.H. ([PERSON]); investigation, Y.S. and X.H. ([PERSON]); writing--original draft preparation, Y.S. and P.P.; writing--review and editing, Y.S. and P.P.; visualization, Y.S.; funding acquisition, X.H. ([PERSON]), S.L. and T.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 52075504; the State Key Laboratory of Quantum Optics and Quantum Optics Devices (No. KF202301); the Shanxi Province Graduate Practice Innovation Project (2023 SJ209); the Shanxi Province Graduate Practice Innovation Project (2023 KY608); the Shanxi Provincial Key Research and Development Project (202302150101016); and Shanxi 1331 Project Key Subject Construction.  
**Institutional Review Board Statement:** Not applicable.  
Figure 17: Error distributions of sparse views with different noise levels. (**a**) Projection domain; (**b**) temperature field.
## 2 Modeling
### Effective Radiation Coefficient
The partial differential Equations (15) and (16) as well as the boundary Equations (17)-(19) are discretized in space by finite-difference-method and the resulting set of differential algebraic equations are solved in time with a commercial simulation tool (Matlab). Based on the described detailed model for the individual heat transport mechanisms, a parametrized formulation of the effective radiation parameter is determined. Its derivation (see Section 3.1) for a wider range of geometric configurations is new and of central importance to allow time-efficient simulation studies for such a system under investigation. For this purpose, the transient heating process is calculated for both models: the porosity based as described in Section 2.1 and the detail based as described in Section 2.2, whereby \(k_{rad}\) is determined in an iterative way by minimizing the temporal (spatial-averaged) temperature deviations. Thereby, adiabatic conditions with \(k_{Ins,z}=0\) and \(k_{Ins,r}=0\) are used for the porosity model to avoid boundary effects.
## 2 Materials and Methods
### Variables and Instruments
#### 2.3.5 Physical Activity  
In the second assessment time, to evaluate the patients' physical activity levels, the Spanish version of the IPAQ was administered [34; 35]. The questionnaire refers to different activities performed in the preceding 7 days to identify the intensity of the activity carried out, the duration, and the frequency. Consequently, each participant's level of physical activity was calculated according to the guidelines for data processing and the analysis of the IPAQ-short and long forms [36]. High levels were reached when performing \(\geq\)3 days of vigorously intense activity, achieving \(\geq\)1500 metabolic equivalents of task (MET) minutes a week, or completing \(\geq\)7 days of walking and/or moderate-intensity activities and/or vigorous-intensity activities, realizing \(\geq\)3000 MET minutes a week. Moderate physical activity levels were accomplished by patients involved in \(\geq\)3 days of vigorous-intensity activities and/or walking \(\geq\)30 min per day or carrying out \(\geq\)5 days of moderate-intensity activities and/or walking \(\geq\)30 min per day, making a combination of walking and moderate-intensity or vigorous-intensity activities \(\geq\) 5 days, reaching \(\geq\)600 MET minutes a week. Lastly, participants with low levels are those who did not meet the criteria for the other two levels [34; 36]. The version utilized in this study underwent validation within the Spanish population by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [35]. It exhibited a commendable reliability coefficient across various metrics, including total physical activity (r = 0.82, \(p<\) 0.05), vigorous activity (r = 0.79, \(p<\) 0.05), moderate activity (r = 0.83, \(p<\) 0.05), and time devoted to walking (r = 0.73, \(p<\) 0.05). As for the internal consistency in our sample, the Cronbach's alpha was 0.871 in vigorous activity, 0.721 in moderate activity, and 0.753 in walking activity. Moreover, the questionnaire asked how much time the patient spent sitting down on a working day to analyze the sedentary behavior.
## 4 Discussion
### Discussion of Research Findings
#### 4.1.3 Methodology Performance Compared to Other Classification Models  
According to Table 2 and the accompanying Figure 6 diagram, \(\mathrm{CNN}_{\mathrm{SGDM}}\) performs better than VGG-16, demonstrating that it can significantly contribute to fully automated disease assessments. On the other hand, the AlexNet model achieves better performance. However, the proposed deep CNN classifier is focused on short training processes from scratch as well as on minimizing the number of layers (4 layers in total compared to 8 layers of AlexNet and 16 layers of VGG-16). On the contrary, the poor, compared to the CNN architectures, performance of conventional MLP-ANN is one of the main reasons that have led the research community to make the transition to deep learning algorithms.  
Table 3 summarizes all the deep neural networks used in the liver biopsy dataset classification process along with their training times. It is emphasized that these training times cannot be directly compared since transfer learning updates have been applied to AlexNet [21] and VGG-16 [22] networks, which have been trained from scratch with the ImageNet dataset. Specifically, AlexNet was trained for 6 days on two NVIDIA Geforce GTX 580 GPUs and VGG-16 on four NVIDIA Titan Black GPUs for 2-3 weeks. Although these architectures are two of the most preferred options for extracting image features, they consist of a huge number of trainable parameters (AlexNet: 60 million, VGG-16: 138 million), leading to a very demanding processing procedure for the average hardware systems. It is shown that the AlexNet transfer learning process lasted 45 seconds, while for the much deeper VGG-16 model, it took 5 min and 13, which is longer than the \(\mathrm{CNN}_{\mathrm{SGDM}}\) training from scratch process, which was completed in 2 min thanks to its 16,825,876 trainable parameters. All these conclusions justify the effort of novel research works to develop new deep models that could achieve new and shorter training performances on specific classification problems, without the employment of high-budget hardware equipment.  
Figure 8: Verification of the classification results. In this figure, the boxplots depict the prediction error probabilities for each hepatic class, produced by the \(\mathrm{CNN}_{\mathrm{Adam}}\) and \(\mathrm{CNN}_{\mathrm{SGDM}}\) deep models. According to the two diagrams, classes with higher accuracy have a mean (the second quartile located at the boundary between the two colors) close to zero and less variance, while outliers are marked with black circles, pointing to greater than normal error values that affect the overall data observation.