esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Major Components of Network Slicing System
### Intent-Based Networking (IBN) Tool
#### 4.2.2 Intent Manager  
The second component of the IBN framework is the Intent manager which acts as a manager of the system. This intent manager can interact with all components of the IBN system and Network orchestrators as well with the help of REST API. The step by step working of this manager is as follows; first of all, it receives the user contracts from the top and then extracts the contract information and validates the required resources by querying the available resources from the policy store. The policy store is a database repository that contains information about all underlying resources. Thus, the intent manager will validate the required resources with available resources from the database. After validating the resources, extracted contract information could be used to make a service graph for the deployment of resources. it will make two service graphs for RAN and core network resources. Finally, the intent manager will send the service graphs to the policy configurators. Both the service graphs (RAN, core) are sent to the RAN policy configurator and core policy configurators for further processing. Furthermore, these policy configurators convert the service graph information according to the underlying platform compatible format.
## 3 Results and Discussion  
### Physical and Chemical Properties Assessment  
According to the results of this research, the average wood dry density was approximately 0.60 g/cm\({}^{3}\) and ranged from 0.57 g/cm\({}^{3}\) to 0.63 g/cm\({}^{3}\) (Table 1). The mean maximum moisture content of chestnut wood was measured to be 100.43%.  
Extractive content, as well as other factors such as the presence of tyloses, especially in the heartwood area, are considered to be strongly related to increased wood density especially in the heartwood area, and as a result, to a decrease in surface roughness. The roughness and, in general, the surface quality of wood, is closely related to its density, as well as its anatomical characteristics and chemical composition [9]. More specifically, the higher the density of the wood, the smoother its surface, most of the time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Tree**} & \multicolumn{1}{c}{**Dry Density g/cm\({}^{3}\)**} & \multicolumn{1}{c}{**Basic Density g/cm\({}^{3}\)**} & \multicolumn{1}{c}{**Maximum MC \%**} & \multicolumn{1}{c}{**Extratives \%**} \\ \hline \multirow{3}{*}{1} & x & 0.565 & 0.526 & 110.236 & 13.208 \\ \cline{2-6}  & s\(\pm\) & 0.022 & 0.022 & 7.683 & 3.179 \\ \cline{2-6}  & n & 20 & 20 & 20 & 4 \\ \hline \multirow{3}{*}{2} & x & 0.595 & 0.599 & 102.235 & 11.385 \\ \cline{2-6}  & s\(\pm\) & 0.033 & 0.030 & 7.063 & 2.326 \\ \cline{2-6}  & n & 23 & 23 & 23 & 4 \\ \hline \multirow{3}{*}{3} & x & 0.599 & 0.561 & 100.696 & 14.133 \\ \cline{2-6}  & s\(\pm\) & 0.019 & 0.0202 & 4.736 & 2.6441 \\ \cline{2-6}  & n & 21 & 21 & 21 & 4 \\ \hline \multirow{3}{*}{4} & x & 0.607 & 0.564 & 96.651 & 15.084 \\ \cline{2-6}  & s\(\pm\) & 0.025 & 0.026 & 5.958 & 3.2797 \\ \cline{2-6}  & n & 20 & 20 & 20 & 4 \\ \hline \multirow{3}{*}{5} & x & 0.633 & 0.583 & 92.354 & 10.637 \\ \cline{2-6}  & s\(\pm\) & 0.037 & 0.030 & 6.951 & 1.6802 \\ \cline{2-6}  & n & 23 & 23 & 23 & 4 \\ \hline Mean & x & 0.600 & 0.599 & 100.434 & 12.889 \\ \hline \hline \end{tabular} \(\chi\): mean value, s\(\pm\): standard deviation value, n: number of examined samples.  
\end{table}
Table 1: Density (dry and basic), maximum moisture content values (%), and extractive content (%) of the chestnut wood studied.  
Therefore, as can be seen in Table 1, chestnut is a species with a high content of extractives and this is in line with medium to high values of its density, especially in the heartwood area [8; 27].
## 5 Curriculum Data Scheduling
### Optimized Data Scheduling with an Adaptive Window  
Next, we introduce our approach based on the above principles. To begin with, we first define our score function for reflecting the difficulty of any instance. Motivated by [41], we choose a loss-based measure as the score. Let \(y_{n,j,l}\in y_{n,i}=\{y_{n,i,1},\cdots,y_{n,i,L}\}\) denote the true label indicator of the \(i\)th instance in \(d_{n}\): \(y_{n,i,l}=1\) if the ground-truth class of the instance is \(l\); otherwise, \(y_{n,i,l}=0\). Let \(g_{n,i,l}\in\hat{y}_{n,i}=\{\hat{y}_{n,i,1},\cdots,\hat{y}_{n,i,L}\}\) be the probability value predicted from the softmax layer of global model \(\mathbf{w}_{r}\). In any round \(r\), the difficulty score of the \(i\)th instance in \(\mathbf{d}_{n}\), denoted by \(S_{i,n,r}\in\mathcal{S}_{n,r}=\{S_{1,n,r},\cdots,S_{d_{n,n,r}}\}\), is defined as  
\[S_{i,n,r}=-\sum_{l=1}^{L}y_{n,i,l}\ln(\hat{y}_{n,i,l}). \tag{20}\]  
A greater \(S_{i,n,r}\) indicates a higher difficulty level.  
Once \(d_{n}\) is sorted in ascending order according to \(\mathcal{S}_{n,r}\), the next step is to use a proper pacing function to balance the trade-off between effectiveness and latency. Let \(\hat{d}_{n,r}\) denote the sorted \(\mathbf{d}_{n}\) in round \(r\). For any round \(r\) and smartphone \(n\), we assume that \(t_{r}\) is a system parameter and that the latency requirement \(T_{n}\) is known before training. By examining (3), the upper bound of the number of rounds, denoted by \(R_{n,max}\), that smartphone \(n\) can participate in is calculated by  
\[R_{n,max}=\frac{T_{n}}{\min(t_{r})},\forall n\in\mathcal{N},r\in\mathcal{R}. \tag{21}\]  
Unlike the pacing functions described in [41], we use a moving window that gradually increases and selects more difficult instances for training. Let \(W_{n}^{\text{initial}}\), \(W_{n,r}^{\text{start}}\) and \(W_{n,r}^{\text{end}}\) be the initial number of data samples and start and end index of the sorted \(\mathbf{d}_{n}\) in round \(r\), respectively. Considering the marginal utility of data samples, as described in Section 5.1, we propose the following moving window as our pacing function for any round \(r\):  
\[W_{n}^{\text{initial}}=\frac{\bar{d}_{n}}{1+\ln(1+R_{n,max})}, \tag{22}\]  
\[W_{n,r}^{\text{start}}=\lceil d_{n}^{\text{initial}}\log_{10}(1+r)\rceil, \tag{23}\]  
\[W_{n,r}^{\text{end}}=\lceil d_{n}^{\text{initial}}\ln(1+r)\rceil. \tag{24}\]  
Note that only a subset of private local data is selected per round, i.e., \(\mathbf{d}_{n,r}\subset\mathbf{d}_{n}\). Our approach is shown to be effective in Section 7 with improved QoS. Then, the optimized dataset \(\mathbf{d}_{n,r}^{*}\) consists of instances selected from \(\bar{d}_{n}\), which has been sorted based on difficulty. These instances are chosen from within a moving windows defined by Equations (22)-(24).
## 3 Wireless Sensor Networks (Internet of Things--IoT)
### Sensor Networks: Wireless Transmission Systems
For example, after Apple Inc. began to sell its smartphone devices equipped with the Bluetooth Low Energy (BLE) protocol, several fitness products were developed with the same communication protocol to be compatible with these devices. In contrast, using ANT+ or ZigBee transmission protocols with a smartphone would require additional hardware [52]. Today, the design of a sensor network suited for sports applications must guarantee low power consumption of the wireless sensor nodes, wide transmission range between them, reconfigurability of the network allowing the easy addition of extra nodes, and, finally, the possibility of using personal portable terminals. Moreover, and primarily when the sensors must be placed at a great distance from each other, the design must care about the attenuation of the transmitted signal due to the obstacles between them as sports equipment, often manufactured with radio-shielding materials (e.g., among the others the carbon fiber), and the body of the athletes themselves. In addition, the design must consider the disturbance due to other radio transmissions in the nearby area and the similar frequency band as Wi-Fi or Bluetooth networks. In this case, the problem could be overcome by using more transmission nodes nearer distances and working as signal repeaters [44].
## 6 Experimental Results  
### Experimental Environment  
In this study, the Tsinghua University gesture public dataset [12] was applied, which was sent and received by two laptops, each equipped with an Intel 5300 wireless network card and a Linux CSI tool installed at the receiver side. Before sending and receiving the data, they were tuned to 5.825 GHz band because there is less channel interference in this band. The transmitter was broadcasted at a rate of 1000 packets per second. Three experimental locations were selected: classroom, hall, and office. The size of the classroom was 4.5 m \(\times\) 5.5 m, the office was 2.5 m \(\times\) 4.0 m, and the hall was 4.5 m \(\times\) 2.5 m. Based on the specific environment portrayed in Figure 9 (below), data were collected in five directions. However, no distinction was made between the directions to improve the robustness of this experiment. When the data were collected, the equipment was set 110 cm from the ground, and 16 experimenters (12 males and 4 females) had participated in the experiment (height: 155-185 cm; weight: 44-89 kg; and age: 22-28 years old). Data obtained from six users were selected for the experiment. Data pre-processing, feature recognition, and action classification were performed by using MATLAB.  
In this experiment, the representative gestures in the dataset were selected for classification and recognition, which are push, draw triangle, draw N, draw O, clap, sweep, etc. The specific actions are illustrated in Figure 10. The data of five people in three different experimental environments were selected for recognition verification.
# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 2 Related Work
### Maize Leaf Severity Analysis
#### 2.2.1 Color Image Processing  
The motivation for using color image processing techniques for leaf disease severity analysis stems from the fact that it is possible to segment the leaf image based on the color of the pixels [39]. Basically, we subdivide the image into its constituent regions or objects until all the regions of interest have been isolated [39]. In what follows, we review works that adopt color changes to segment leaf images.  
In 2006, [PERSON] et al. assessed the impact of the two-spotted spider mite on host plants using third-party image processing software (Mathematica). In their work, they converted the leaf images to greyscale and then measured the total damaged area based on a pre-determined threshold. Then, they expressed the result as a ratio of the total number of pixels of the leaf image to obtain a severity estimate [40]. Their results show that this method is reliable and can identify significant differences in pixel damage due to different concentrations of spider mites.
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Crop Grading
In experimental testing on eight different grades of tobacco leaves, the final model achieved a classification accuracy of 91.30%. However, dynamic testing was not addressed. Study [99] also employs a dual-branch approach, embedding the Channel-Based Attention Mechanism (CBAM) into the ResNet50 network. The designed FPN-CBAM-ResNet50 architecture integrates low-level detail features with high-level semantic features, enhancing the model's ability to capture subtle differences. VGG16 serves as the global branch network, employing a bilinear pooling strategy for feature fusion. This method was tested on a dynamic production line, achieving dynamic high-speed grading of tobacco leaves. However, the model has a large number of parameters, resulting in high computational complexity to maintain accuracy. Additionally, training on laboratory data may struggle to generalize to tobacco from other regions or years, posing domain adaptability issues. In addition to the aforementioned studies, ref. [100] proposed a method for evaluating the quality of carrots. In this study, a deep convolutional generative adversarial network (DCGAN) was used to generate high-quality carrot images, and a differential hashing algorithm was employed to remove low-quality or duplicate samples from the generatedimages. The study utilized a ResNet-18 as the main network, incorporated a squeezed incentive attention module to enhance the weighting of key features, and optimized the classifier module to retain more details. While progress was made in cross-crop classification, high-precision detection was achieved in carrot quality evaluation. Ref. [101] also utilized the channel-based attention module (CBAM) to enhance key feature extraction. The improved network framework enabled multi-scale feature fusion, achieving efficient and precise classification of mangosteens.
# An Enhanced Fusion Strategy for Reliable Attitude Measurement Utilizing Vision and Inertial Sensors
## 1 Introduction
To ensure precision and convergence, special approaches to correct pure inertial attitude errors may need to be developed. To avoid the attitude divergence phenomenon created by the attitude differential equation when only depending on inertial data during the intersampling of slow visual data, the traditional single frequency data fusion method needs to be improved. Nowadays, the deep learning algorithms are widely used in signal trend prediction to enhance the performance of data fusion in different applications. An artificial neural network [23] is used to enhance the attitude accuracy, which has the characteristic of strong machine learning. Long-short term memory [24] is employed to fuse the inertial and vision data, which can provide high precision for attitude measurement. These methods focus on reducing the attitude error by training the neural networks algorithm when the visual data are available. However, when the visual data are unavailable, these methods cannot ensure the high accuracy of attitude measurement due to the differences in sampling frequency. A convolutional neural network-support vector machine [25] method is used to remove the faults in data fusion, which have impactful effect. A convolutional neural network [26] is proposed to improve the accuracy of data fusion, which is robust and reliable. These methods can effectively fuse data, but they have some limitations in multi-frequency integrated attitude measurements. To achieve a reliable attitude in a vision and inertial integrated system during visual data unavailability, especially when the interval lasts for a period of time, a data fusion methodology based on radial basis function (RBF) and CKF for enhancing the performance of attitude measurements is developed. The algorithm can reduce the attitude divergence affected by an inconsistent sampling frequency between inertial and visual data. Combining CKF and RBF can fully utilize the advantages of these two techniques. In this way, the problem of divergence resulting from the sampling frequency discrepancy is ameliorated. The rest of the paper is organized as follows. Section 2 explains the problem formulation and system modeling. Furthermore, the core algorithms of the enhanced fusion strategy based on RBF and CKF is given in Section 3. Experimental testing and results are discussed in Section 4. Finally, Section 5 concludes the article.
## 3 Data, Methods and Results
### Validation of InSAR Time Series with GNSS Data  
To compare our InSAR displacements (velocities) with other geodetic data, we used the GPS observations of the permanent station KRIN of the Corinth Rift Laboratory (CRL) NFO of EPOS ([[http://crlab.eu/](http://crlab.eu/)]([http://crlab.eu/](http://crlab.eu/)) (accessed on 20 December 2021).). Published GNSS data (30-s daily positions; [25]) were used. We visited the station in May 2021, and we confirmed the stability of the antenna (see Figure S7). We transformed the GNSS components into LOS through the equation [63]:  
\[\mathrm{D_{r}=d_{u}\times cos(inc)-(sin(inc)\times(d_{n}\times cos(ah\text{-}3 \times pi/2)+d_{e}\times sin(Ah\text{-}3\times pi/2)))} \tag{1}\]  
Ah-3 \(\times\) Pi/2 corresponds to the angle to the azimuth look direction, which is perpendicular to the satellite heading; inc is the radar incidence angle; and \(d_{u}\), \(d_{n}\), \(d_{e}\) are the corresponding up, north and east displacements derived from GNSS solutions (Figure S8).  
The GNSS time series and the InSAR time series analysis show slightly different displacement rates (Figure 9, descending orbit). The GNSS rate is slightly greater than the InSAR rate; the displacement rate from GNSS is 28 mm/yr and from InSAR time series it is 25 mm/yr (see Figure S9), providing a deviation of 3 mm/yr or 12% of the GNSS rate. This rate difference is expected as it is related to the difference in absolute terms between the point measurement of displacement, which corresponds to the GNSS time series (i.e., the motion of the antenna reference point or ARP), and the 'pixel' measurement on the ground, which corresponds to the InSAR time series. Note that the InSAR time series represent the mean value of displacement within a pixel size 100 \(\times\) 100 m, i.e., enclosing the building where the GNSS antenna is located. This small deviation in rates may indicate: (a) the \"smoothing\" effect of InSAR; and (b) the possible occurrence of differential movements inside subareas which each SAR pixel covers. To complete the comparison--validation--we also projected the KRIN GNSS data into the LOS-ascending orbit geometry, confirming similar rates of motion (48 vs. 55 mm/yr, respectively; see time-series diagram in Figure S10).
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
###### Abstract  
The reliability of urban transportation systems is crucial for ensuring smooth traffic flow and minimizing disruptions caused by external factors. This study focuses on improving the stability and efficiency of transportation systems through the calibration of a refined link performance function while building upon the U.S. Bureau of Public Roads (BPR) model. To achieve this, we propose three customized algorithms--[PERSON]'s method, Bayesian optimization, and the differential evolutionary algorithm--to calibrate the key parameters. Additionally, we conducted a sensitivity analysis to assess the influences of the model parameters on link performance. Numerical experiments conducted in Yuyao City demonstrate the applicability and efficacy of the proposed model and solution algorithms. Our results reveal that the Newton approach is notably more efficient than the Bayesian optimization algorithm and the differential evolutionary algorithm.  
parameter calibration; link performance function; BPR function; license plate recognition data; parallel computing Article
## 1 Introduction  
The Facility for Antiproton and Ion Research (FAIR) will be unique and one of the most complex international accelerator facilities for the research on antiprotons and ions. It is already under construction by an international collaboration at the GSI Helmholtzzentrum fur Schwerionenforschung near Darmstadt, Germany. Its core, a double-ring NbTi superconducting-technology-based SIS100 heavy-ion synchrotron, with a circumference of 1100 m, will be associated with a complex system of cooler, storage rings and experimental setups (see Figure 1). This accelerator will deliver ion beams of unprecedented intensities and energies. Thus, intensive secondary beams can also be produced, providing antiprotons and exotic nuclei for groundbreaking experiments.  
A cryogenic system is required to cool the magnet system into a superconducting state. It distributes the cryogenic refrigerator's cooling capacity to the superconducting devices by supercritical or liquid helium. The design of exemplary supercritical helium cryogenic transfer lines is presented in [1, 2], and an overview of the European X-ray Free Electron Laser (XFEL), with a similar cryogenic system, is provided in [3].  
The cryogenic systems for the SIS100 synchrotron are comprised of a helium refrigerator, a distribution system, and the SIS100 local cryogenic system. The local cryogenics consist of the following:  
* three feed boxes (FBs), each distributing subcooled liquid helium and electric current to two sectors;
* six current lead boxes (CLBs) connected to the feed boxes, providing electric current from normal conduction to the superconducting transfer system;
* six bypass lines (BPLs), where each distributes the cooling and electric current from the feed box to the magnets.  
Design, manufacturing, and installation of all local cryogenic components of SIS100 is a Polish in-kind contribution to the FAIR project, realized by Wroclaw University of Science and Technology. The in-kind contribution is coordinated by a Polish shareholder--the Jagiellonian University in Krakow. In the paper, several technical solutions were presented, including two types of internal floating-suspension system based on stainless steel rods and aramid cables. The aramid cable suspension has been previously used in cryogenics applications in [4, 5, 6, 7]; a trial of the application in SIS100 BPL was presented.
## 3 Materials and Methods
### Ablation Study of the Proposed Model (Optimized ANN)
#### 3.7.1 Ablation Study 1: Changing Dense Layer  
In this work, Table S4 displays the train and validation accuracy of several dense layer types. With a training accuracy of 98.12% and a validation accuracy of 96.25%, dense layer 3 had the best accuracy. However, when the dense layer was changed, the validation accuracy of the suggested model dropped to 61.87%, 71.48%, and 65.00%.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 5 Toward Fog-Based Smart City Data Management and Analytics
### Fog-Based Data Management and Analytics
The authors in [35] proposed an IoT-based solution to monitor car park occupancy in a city by carrying out data analytics at the fog level. Edge gateways perform data aggregation and preprocessing. The proposed solution uses Hadoop and MapReduce [36; 37] for data processing and runs on a cluster of commodity computers at each fog node. This work is limited to batch processing as data are stored in log files. The authors in [38] proposed a big data architecture that distributes data processing at the three layers--edge, fog, and cloud--while taking advantage of each layer's capabilities. The edge layer is responsible for managing devices and actuators; the fog layer ensures data ingestion and performs data aggregation; the cloud is responsible for carrying out massive analytics that require more computational resources. The architecture's components are comparable to the ones we suggested in Section 7 that describes our proposed fog-based data management pipeline. These components include tools for data ingestion, data stream processing and analytics, and data visualization. The next section describes three use cases of fog computing-based solutions proposed to cope with many smart cities' challenging issues--in particular data management. The use cases are on (1) smart transportation and vehicular fog computing, (2) smart healthcare, and (3) smart grids.