esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Clearing Prices
#### 3.1.3 Pricing  
Once an allocation is found, let us consider the following network: The set of nodes consists of nodes \(0,1,\ldots,n\); there is an arc from each node \(1\leq i\leq n\) to node \(0\) with weight \(v_{ii}=v_{i}\); there is an arc from node \(i\) to node \(j\) for all \(1\leq i,j\leq n\) such that \(i\
eq j\) with weight \(v_{ii}-v_{ij}=\delta_{ij}\). Thus, the shortest paths from nodes \(1\leq i\leq n\) to node \(0\) correspond to the envy-free prices \(p_{1},\ldots,p_{n}\)[8]. The network is shown in Figure 3.  
Instead of using the Bellman-Ford algorithm to find the shortest paths to node \(0\), we will explore the metric structure of the cost set to simplify the calculations, but, before that, we will see that it is possible to find a particular price without having to calculate the corresponding shortest path.  
Figure 2: An instance of the EFPM.  
**Lemma 1**.: _If \(v_{1}\leq v_{2}\leq\ldots\leq v_{n}\), then \(p_{1}=v_{1}\)._  
Proof.: In fact, if \(p_{1}>v_{1}\), the envy-freeness condition of non-negative maximum utility is violated. If \(p_{1}<v_{1}\), the other prices would be forced down until the other buyers' utilities were at least \(v_{i1}-p_{1}\) for all \(2\leq i\leq n\), and then the prices would not maximize the revenue, from which the results follow.  
We are now able to iteratively find the remaining prices from this first discovery.  
**Lemma 2**.: _If \(v_{1}\leq v_{2}\leq\ldots\leq v_{n}\), then \(p_{i}=\min_{1\leq j<i}\{v_{i},v_{j}+\delta_{ij}\}\) for all \(2\leq i\leq n\)._  
Proof.: From the metric structure of the cost set, we have \(\delta_{ij}\leq\delta_{ik}+\delta_{kj}\) for all \(1\leq i,j,k\leq n\). Thus, the shortest path from node \(i\) to node \(0\) consists of either the arc connecting node \(i\) to node \(0\) with weight \(v_{i}\) or the arcs connecting node \(i\) to some node \(j\) and node \(j\) to node \(0\) with weights \(\delta_{ij}\) and \(v_{j}\), respectively. Since \(v_{i}\leq v_{k}\) for all \(i\leq k\), we have \(v_{i}\leq v_{k}+\delta_{ik}\) for all \(i\leq k\), and then \(\min_{1\leq j<i}\{v_{i},v_{j}+\delta_{ij}\}\leq v_{k}+\delta_{ik}\) for all \(2\leq i\leq n\) with \(i\leq k\), from which the results follow.
## 3 System Model
### SDN-Based Space Information Network Architecture  
#### 3.1.1 Overall Networking Architecture  
Based on the core idea of SDN, this paper establishes a hierarchical and domain-controlled SIN architecture, whose overall network architecture is shown in Figure 1.  
Figure 1: Overall networking architecture of the hierarchical and domain-controlled space information network (SIN) architecture.  
From a hierarchical point of view, the SIN architecture is divided into three parts: space-based, air-based, and ground-based. The space-based network is mainly composed of satellites with different orbits, which are geostationary orbit satellites (GEO), medium orbit satellites (MEO), and low orbit satellites (LEO) from far to near. Air-based networks include stratospheric airships, balloons, manned or unmanned aerial vehicles, etc. Ground-based networks are mainly composed of gateway base stations, caches, and MEC servers, as well as space-based network controllers, space-based network controllers, ground-based network controllers, and an SIN resource management and scheduling center. Because the resource management and scheduling center of the SIN plays an important role, a backup center should be set up.  
From the point of view of sub-domain, the ground-based network, space-based network, and air-based network are divided into several domains according to the region; each domain is controlled by a network controller. Among them, there are three kinds of network controllers, space-based network controllers, air-based network controllers, and ground-based network controllers, which control space-based networks, air-based networks, and ground-based networks, respectively. In order to make full use of the global coverage capability of GEO satellites and the high-speed computing capability of ground controllers, space-based network controllers are divided into space-based network controllers on the ground and space-based network controllers on the GEO satellite. Space-based network controllers on the ground are responsible for computing and storing large amounts of data and other complex functions. Space-based network controllers on the GEO satellite are responsible for collecting global views, completing simple routing storage, distributing flow tables, and other functions. Each network controller constitutes a single-domain controller, and multiple single-domain controllers are uniformly controlled by the SIN resource management and scheduling center [25].
## 4 Results of the Experiment
### Comparison of Inversion Results  
#### 4.2.1 Image Comparison  
In order to verify the superiority of TVDI in vegetation drought monitoring in Laibin City, this article compares its monitoring results with RVI, NDWI, and VTCI monitoring.RVI is a drought monitoring index calculated by dividing the reflectance value of the near-infrared band by the reflectance value of the red band. The NDWI index provides an effective and timely assessment of changes in vegetation water content, aiding in drought monitoring by identifying water-deficient vegetation and its growth status [29]. VTCI utilizes the different spectral reflectance characteristics of soil and vegetation in the red and near-infrared bands to construct a drought detection model based on surface temperature and vegetation index, monitoring drought severity through vegetation moisture content. This study focuses on the drought inversion results of vegetation areas in Laibin City in January 2023 for comparison. The results are illustrated in Figure 7.  
Figure 6: Maps of inversion results of Temperature Vegetation Drought Index (TVDI).  
Figure 7: Drought inversion results of various indices in the vegetation area of Laibin City in January 2023.
## 3 Results and Discussion  
### Satellite Datasets  
The images that were used for analysis have been calibrated, geometrically and radiometrically corrected, and topographically normalized. Examples of the images that went through all the pre-processing are depicted in Figure 5. The topographic normalization outcome is also shown in Figure 6. The study found that these processes are necessary and must be done on any SAR images before further analyses are carried out.  
Figure 5: Corrected images for all polarizations of (**a**) PALSAR-2 HV; (**b**) PALSAR-2 HH; (**c**) Sentinel-1A VH; and (**d**) Sentinel-1A VV, displayed in backscatter values.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 3 Background of V2X
### NR-V2X Resource Allocation  
The 3 GPP extended 5G NR-V2X standard [34] builds on the LTE-V2X resource allocation mechanism to support the ultra-low latency and high reliability needed for autonomous vehicles. NR-V2X introduces a flexible resource allocation structure to meet these stringent demands. The physical layer of NR-V2X communication resources consists of Physical Resource Blocks (PRBs), fundamental units of resource allocation. A PRB comprises 12 subcarriers in the frequency domain and Orthogonal Frequency Division Multiplexing (OFDM) symbols in the time domain, as shown in Figure 4, enabling the transmission of data over designated frequency bands and time periods [35]. Flexible numerology, defining subcarrier spacing and slot duration, allows NR-V2X to adjust resource allocation for varying service requirements.  
NR-V2X supports two sidelink communication modes: Mode 1 (centralized scheduling) and Mode 2 (distributed scheduling). These modes ensure compatibility with LTE-V2X and meet the diverse communication needs of autonomous vehicles. Mode 1, a centralized method, operates under base station coverage. Vehicles transmit Scheduling Requests (SR) via the Physical Uplink Control Channel (PUCCH), and the base station allocates resources using Downlink Control Information (DCI) over the Physical Downlink Control Channel (PDCCH). Resource allocation combines Dynamic Grant (DG), which provides real-time flexibility, and Configured Grant (CG), which pre-allocates resources for periodic transmissions [36]. This hybrid approach enhances resource efficiency and reliability. However, Mode 1 relies heavily on base stations, making it susceptible to failures in areas with poor network infrastructure or during network overload [37]. Mode 2 is a distributed scheme where vehicles autonomously allocate resources, ensuring independence from base station coverage, and it uses a sensing and selection window to minimize collisions. Vehicles employ a Semi-Persistent Scheduling (SPS) scheme for periodic resource reservation [38; 39]. NR-V2X Mode 2 supports unicast, groupcast, and broadcast communications, unlike LTE-V2X, and incorporates feedback-based retransmission with Hybrid Automatic Re  
Figure 4: Structure of resource block.  
peat reQuest (HARQ) to enhance reliability. This approach reduces resource conflicts and supports efficient communication in high-density vehicular environments.
###### Abstract
Combining spectral and image texture information from optical hyperspectral data on plant leaf nitrogen can improve the accuracy of nitrogen prediction models and the generalization ability [12,13]. [PERSON] et al. [14] constructed a chlorophyll content prediction model based on the fusion of spectral and texture features by comparing the back propagation neural network (BPNN) and support vector regression (SVR) algorithms, and its prediction set R\({}^{2}\) was 0.9571. In summary, the fusion of spectral and texture features is beneficial to alleviate the disadvantage of the low sensitivity of spectral analysis techniques and to improve the accuracy and robustness of nitrogen prediction models [15]. Algorithms play a decisive role in plant nitrogen prediction. The partial least squares (PLS) regression algorithm is an extension of the multiple linear regression model, which can effectively reduce the covariance between data variables and thus is applied extensively. On the other hand, the SVR algorithm can find the set of variables containing minor redundant information in the spectral information to minimize the covariance among variables, which is widely used in regression problems. [PERSON] et al. [16] proposed a binary particle swarm optimization-support vector regression (BPSO-SVR) machine learning method, which was based on a binary particle swarm optimization algorithm, to predict soil nitrogen content with good prediction results. [PERSON] et al. [17] proposed a PLS regression-based model for predicting the leaf nitrogen content of winter wheat with a validation set prediction accuracy of R\({}^{2}\) of 0.84. [PERSON] et al. [18] used an aerial hyperspectral spectral imaging technique, and a partial least squares regression (PLSR)-based nitrogen content prediction model for corn plants was constructed with a prediction accuracy of R\({}^{2}\) of 0.85. Therefore, constructing plant prediction models based on PLSR and SVR algorithms has a certain reliability and research significance. Few studies have been conducted to build plant inversion models based on feature fusion. In this study, we combined spectral imaging and image analysis techniques to predict LNC in corn accurately. We successively processed the spectral data using first-order derivatives, standard normal variance (SNV), and Savitzky-Golay (S-G) smoothing and normalization and extracted feature wavelengths using an improved fusion algorithm with a competitive adaptive reweighting algorithm (CARS) and a successive projection algorithm (SPA). The predictive regression model of the corn LNC was developed by fusing the spectral features and image texture features from hyperspectral data. In addition, we also compared the application of PLSR and SVR modeling methods in corn LNC monitoring. Therefore, the fusion based on texture features and spectral features can break through the limitations of single-feature prediction, effectively obtain more comprehensive phenotypic characteristics of plants, improve the credibility of data, further enhance the accuracy of prediction models, and provide a new idea and method for detecting the growth pattern of plants.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 3 Implementation
### Wi-Fi Hotspot Module  
A single-board Raspberry pi 3 model B (RP3B) computer was selected. This is because the hardware uses a low-power design quad-core processor that minimizes power consumption but is powerful enough to host access point applications. It is also cheaper than those single-board computers that run an x86 system. The RP3B model is the first Raspberry Pi that has an integrated Wi-Fi network interface card (NIC), allowing the software on the single board computer to run Wi-Fi hotspot service.  
The Raspberry Pi was installed with Raspbian stretch Operation System, which is a Raspberry Pi distro. The system was installed the following software packages, which are used to set up the Wi-Fi Hotspot on Raspberry Pi.  
* Dnsmasq: provides domain name system (DNS) and DHCP services;
* Hostapd: manages and broadcasts Wi-Fi signal via the wireless network interface card. In this study, the service set identifier (SSID) assigned is \"satnet.\"
## 4 Simulation and Experimentation
### Blind Source Separation of Images  
Three grayscale images are selected as the source signals S, each with 512 \(\times\) 512 pixels, and a 3 \(\times\) 3 mixing matrix A is randomly generated to mix the source signals to obtain the observed signals X. The BAS-BCA, BCA, SCA, and FastICA algorithms are used to separate the observed signals, respectively. The correlations among the source signals are shown in Table 8.  
Figure 15 shows the simulation results, and Table 9 compares the correlation  
Figure 15: Effect drawing of image signal separation. (**a**) The image of source signals; (**b**) the image of observed signals; (**c**) the image of FastICA separated signals; (**d**) the image of SCA separated signals; (**e**) the image of BCA separated signals; (**f**) the image of BAS-BCA separated signals.  
According to Figure 15, the three images separated by the BAS-BCA algorithm are the closest to the original images, whereas the images separated by the other methods have different degrees of ambiguity. A more objective look at Table 9 shows that the BAS-BCA algorithm's average similarity coefficient is the one that is closest to 1, while also having the smallest PI value and highest SIR value. This indicates that the BAS-BCA algorithm proposed in this paper has higher accuracy and better performance in processing blind source separation of images, as well as a shorter running time.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Number of Vehicles
#### 4.3.2 Simulation Results of Three-Log Distance Propagation Model regarding Number of Vehicles  
In the three-log distance propagation model, the mRSU has a smaller average delay than the sRSU at all x-axis points because the spacing is limited in the three-log distance or log distance propagation model; hence, the mRSU packet is obtained at the node relatively early. For the packet delivery ratio, the mRSU also gives good outcomes because the probability of packet loss decreases as the RSU is in motion together with the vehicles.
## 5 Simulation Results  
Simulations were performed to substantiate the considered topology and related controls. Load and source parameters are presented in Table 1.  
For compactness, the positive and negative halves of each port are identified with a plus and minus (e.g., 5+ represents the electric port constituted by the fifth port positive pole and middle point, while 5\(-\) represents the electric port constituted by the fifth port middle point and negative pole). Converter and control parameters are shown in Table 2. Four events were considered in order to show the control's effectiveness in managing abrupt load variations, main grid failure, and external faults. Each of these events is discussed separately in the following subsections.  
\begin{table}
\begin{tabular}{c c c} \hline
**Port** & **Connection** & **Details** \\ \hline
1 & Main AC Grid & 400 V, 50 Hz \\
2 & Lithium batteries & 3.3 V, 60 Ah per module; 143 series connected modules \\
3 & AC load & 400 V, 50 kW, cos \(\varphi\) = 1 \\
4+ & PV panels & 75 kW, no-load voltage 450 V (assumed constant 50 kW in simulation) \\
4\(-\) & PV panels & 75 kW, no-load voltage 450 V (assumed constant 50 kW in simulation) \\
5+ & DC Load & 400 V, 40 kW, variable resistive load \\
5\(-\) & DC Load & 400 V, 40 kW, variable resistive load \\
6+ & DC Load & 400 V, 40 kW, variable resistive load \\
6\(-\) & DC Load & 400 V, 20 kW, variable resistive load \\ \hline \end{tabular}
\end{table}
Table 1: Loads and sources.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 3 Identity-Based Encryption Scheme
### Efficiency Analysis of Our IBE Scheme  
We compared the parameters of the proposed IBE scheme with the most classic ABB10 scheme and another scheme with better parameters, focusing on critical parameterssuch as the lattice dimension, the size of the master private key, the identity public key, and the ciphertext, the comparison results are presented in Table 1, where our scheme demonstrates improvements in all these key parameters. Additionally, we provide a set of instantiated parameters \(\left(Q=393216,\ p=1536,\ q=2^{8},b=16,\ n=1024\right)\) to more intuitively compare the efficiency of the scheme. the comparison results are presented in Table 2.
## 4 Data Sources  
We use 15 multi-GNSS experiment (MGEX) stations with BDS-GEO observations and four ionosonde stations over low-latitude areas to study temporal-spatial variation of the ionosphere during the geomagnetic storm on 25-27 August 2018. Table 1 shows the detailed geographic and geomagnetic coordinates of MGEX stations. In addition, the specific locations of these stations are marked by blue triangles and green squares in Figure 1. As shown in Figure 1, the MGEX stations are distributed in latitude from 30\({}^{\circ}\)S to 30\({}^{\circ}\)N, and longitude from 0\({}^{\circ}\)E to 180\({}^{\circ}\)E. The ionosonde stations are located at 18.3\({}^{\circ}\)N and 109.4\({}^{\circ}\)E (SANYA), 13.6\({}^{\circ}\)N and 144.9\({}^{\circ}\)E (GUAM), 12.4\({}^{\circ}\)S and 130.9\({}^{\circ}\)E (DARWIN), and 21.8\({}^{\circ}\)S and 114.1\({}^{\circ}\)E (LEARMONTH), respectively. The standard GIMs are used to construct the upper and lower bounds for studying the ionospheric disturbance responses. Moreover, the solar wind velocity, _F10.7_ solar flux index, the interplanetary magnetic field (IMF) Bz, By, and geomagnetic indexes Dst and Kp are used to analyze the different performance of geomagnetic conditions.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 2 Related Work
### Research Gap  
While traditional cloud-based anomaly detection methods have been extensively studied, they often fall short of meeting smart home applications' real-time processing and privacy requirements. The reliance on centralized data processing introduces latency and potential security vulnerabilities, which are unacceptable in scenarios where immediate action is necessary.  
Although Edge AI presents a promising alternative, its application in anomaly detection within smart homes remains underexplored. Challenges such as developing lightweight models operating efficiently on resource-limited devices, ensuring model accuracy, and maintaining energy efficiency must be addressed.  
Recent studies have explored distributed AI architectures for anomaly detection, demonstrating that it is possible to create a scalable, federated anomaly detection system without significant loss of accuracy [7]. However, more research is needed to optimize inference efficiency, particularly for transformer-based models that require substantial computational resources. Future work will focus on refining hybrid models that combine Isolation Forest and LSTM Autoencoders with lightweight transformer architectures, striking a balance between computational efficiency and high anomaly detection accuracy.  
This research aims to fill these gaps by proposing a lightweight anomaly detection framework optimized for edge devices in smart home settings. By leveraging recent advancements in Edge AI and machine learning techniques, the framework seeks to provide real-time, privacy-preserving anomaly detection without the drawbacks of cloud-based solutions.  
While studies such as [14; 15; 16] have explored anomaly detection using autoencoders or transformer-based methods, these solutions are often computationally intensive and poorly suited for low-power edge deployment. In contrast, our proposed system is optimized for resource efficiency, offering a fully quantized implementation tested on real embedded platforms.  
To the best of our knowledge, this is one of the first works to combine real-time reconstruction-aware thresholding, hybrid model fusion, and deployment-ready quantized Edge AI for anomaly detection in smart homes.
# Microbial Colony Detection Based on Deep Learning
## 4 Experiment
### Evaluation Metric  
We use the common evaluation metrics of the COCO [43] dataset as the assessment metrics for colony detection in this experiment. For the tasks of object detection and instance segmentation for the COCO dataset, the most often employed evaluation measures are the Average Precision (AP) and Average Recall (AR).  
The Average Precision quantifies the accuracy of the model in detecting various classes of objects at different Intersection over Union (IoU) thresholds. For each category, objects are initially ranked based on the model's prediction confidence. Subsequently, the accuracy is computed at various IoU thresholds (typically 0.50, 0.55, 0.60, \(\ldots\), 0.95). The AP for that category is then obtained by averaging the accuracies at different IoU thresholds. Finally, the APs for all of the categories are averaged to calculate the mean Average Precision (mAP). The AP metric comprehensively assesses the model's performance by considering its accuracy across different classes and IoU thresholds, offering valuable insights into its object detection and instance segmentation capabilities. Higher mAP values indicate better detection accuracy and are indicative of a more versatile and effective model for practical applications.  
In addition, the AR is another essential metric that is used in object detection to assess the model's ability to correctly detect targets under different IoU thresholds. The AR measures the degree of coverage of correctly detected targets by the model. Similar to the AP, the AR calculates the accuracy at different IoU thresholds, representing the proportion of correctly detected targets at varying levels of IoU thresholds. By averaging the AR values across different IoU thresholds, we obtain a comprehensive measure of the model's detection performance. The AR complements the AP in providing a more holistic evaluation of the model's ability to recall true targets accurately. Together with the AP, the AR offers valuable insights into the model's overall detection capabilities, making it an important parameter for evaluating object detection models.  
Before providing the specific formula for each indicator, it is necessary to establish the meaning of some confusion matrix symbols because many indicators rely on them for computation. _TP_, _TN_, _FP_, and _FN_ stand for true positive, true negative, false positive, and false negative, respectively. As a result, Equation (8) is used to compute the precision rate and Equation (9) is used to obtain the recall rate.  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]