esa-sceva/satcom-chunk-collection huggingface dataset:
# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 1 Introduction
Inspired by hinge loss in SVM, this paper enables the backpropagation of the gradient in a CNN by the hinge loss, thus learning lower-level features. Inspired by CA, features can be enhanced by embedding location information in channel attention, without a large overhead, providing a method for efficient image classification. The method fuses a CNN embedded in a CA mechanism with an SVM for image classification, which we call CA-support vector machine-CNN (CASVM). A CNN based on an attention mechanism is used to extract features classified by linear SVM. An empirical analysis of commonly used image datasets verifies the accuracy of the model for image classification. This study makes the following contributions:  
1. An SVM classifier replaces the softmax classifier commonly used in CNNs to improve the robustness of image classification; the hinge loss function of SVM is used to backpropagate the CNN to improve the generalization performance of the classifier on images, which reduces overfitting and improves classification results. 2. The CA mechanism used by CASVM has two components, coordinate information embedding (CIE) and CA generation (CAG), to enable the network to acquire information over a larger area without significant overhead. CIE captures location information and establishes long-range dependencies in the spatial direction by decomposing channel attention. CAG transforms the generated features to locate the position of the object of interest. 3. CASVM combines a CNN with an SVM that introduces a CA mechanism to improve classification accuracy. CASVM can improve feature representation performance in different network models, which improves image classification accuracy in different device environments.
# Estimation of Energy Storage Requirements in an Independent Power System from an Energy Perspective
## 1 Introduction  
In recent years, Taiwan has set a goal of achieving a 20% renewable energy proportion in its power generation by 2025 by actively promoting the development of wind and solar photovoltaic (PV) energy. By 2025, the installed capacity of offshore wind power is expected to reach 5.6 GW, with plans to add 1.5 GW of capacity annually until 2035. For solar PV, the goal is to achieve 20 GW of installed capacity, with an annual increase of 2 GW until 2030, totaling an additional 11 GW between 2026 and 2030. However, the development of renewable energy faces several challenges. As the penetration of renewable energy in the power system increases, controlling the system becomes more difficult [1, 2]. Unlike conventional power generation, renewable energy generation is affected by climate and weather and cannot be controlled by adjusting fuel input [3]. Hence, with high renewable penetration, situations may arise where generation exceeds consumption, leading to curtailment. Although shutting down conventional generators can alleviate this issue, sufficient traditional units are needed to stabilize the grid frequency and voltage [4], so they cannot be entirely shut down. In situations where there is a large gap between generation and consumption, the appropriate addition of energy storage systems can effectively reduce or even resolve curtailment issues [5, 6, 7]. Energy storage systems can also supply electricity when renewable generation is low [8], reducing traditional generation and thereby lowering CO2 emissions [9, 10].  
Due to the instability of renewable sources, traditional units will need a faster response capability [11]. Gas units, compared to coal units, can start up more quickly. When the proportion of renewable energy is high, power systems will mainly rely on gas generation [12]. Coal-fired units will be reduced during allowed conditions.  
This study utilized a full year's hourly data from Texas to evaluate the hydrogen storage needs for various renewable energy penetrations [13]. Additionally, an analysis of 35 years of hourly data was conducted to determine the energy storage requirements within a 100% renewable penetration in Germany [14]. This study used nine years of hourly data in the UK to evaluate the energy storage needs and economic costs of different solar-to-wind ratios under the penetration rate of 100-125% (curtailment) [15]. As far as the author knows, the mainstream literature usually considers the situation where the proportion of renewable energy is close to 100%. The relevant articles do not consider the energy storage needs of an independent system that was planned by the government in recent years.
## 3 Materials and Methods
### Calculation of Energy Consumption  
#### 3.4.1 Energy Consumption of CD and VMD Methods  
The energies consumed during CD (\(E_{C}\)) and VMD (\(E_{VM}\)) were determined on the basis of Equations (3) and (4).  
\[E_{C}=\left(\frac{N_{f}}{6}+N_{h}\right)\times t, \tag{3}\]  
where \(N_{f}\) (kilowatts) is the power consumed by the fan supplying air to six pipes that were fixed with electric heaters with a power consumption of \(N_{h}\) (kilowatts), and \(t\) is the drying duration (seconds).  
\[E_{VM}=\left(\frac{N_{M}}{\eta_{M}}+N_{V}+N_{e}\right)\times t, \tag{4}\]  
where \(N_{M}\) is the output power of magnetrons (kilowatt), \(\eta_{M}\) is the efficiency of magnetrons, \(N_{V}\) is the power consumption (kilowatts) of the vacuum pump, \(N_{e}\) is the power consumption of the engine (kilowatts) that rotates the container in the dryer, and \(t\) is the drying duration (seconds).
## 2 Related Work
Recent advancements have highlighted the potential of VLC in vehicular networks. [PERSON] et al. [29] proposed a vehicular VLC network architecture facilitating wireless infrastructure-to-vehicle (I2V) and V2V communication. This architecture employs a tree-based network access scheme designed to maximize network coverage and enhance rapid connectivity beyond the reach of conventional lighting gateways. Furthermore, [PERSON] et al. [30] explored the application of VLC in aeronautical networks for in-flight entertainment distribution. Their proposed architecture combines advanced wavelength assignment methods to mitigate intra- and inter-cell interference. Although the research provides valuable insights into optical interference across multiple physical domains, it lacks an in-depth exploration of the effects of modulation schemes and MIMO techniques. The work by [PERSON] and [PERSON] [31] introduces an ambient light noise filtering technique for MIMO-VLC systems, with a focus on mitigating the adverse effects of ambient light interference during high-speed multimedia data transmission. However, their study is limited to specific filtering configurations, leaving room for more extensive analyses, such as the inclusion of various modulation techniques and evaluation across different interference levels. In contrast, the present paper evaluates MIMO-VLC system performance using different modulation schemes (NRZ, RZ, and QPSK), offering a detailed quantitative analysis of BER, Q-factor, and received power under varying ambient noise conditions. In [32], [PERSON] et al. assess the performance of a MIMO-VLC system designed for data transmission, focusing on modulation schemes and signal processing techniques under typical communication conditions. However, their work does not explicitly consider the impact of ambient light interference on the system's performance. Conversely, the current study provides a comprehensive evaluation of ambient light interference's effect on MIMO-VLC systems, specifically addressing the performance of various modulation schemes (NRZ, RZ, QPSK) under significant ambient noise. Finally, [PERSON] and [PERSON] [33] examine the use of MIMO technology in VLC systems for high-data-rate indoor wireless communication. Their research evaluates different types of LEDs (phosphor-converted, organic, micro-LED, RGB, and white LEDs) and analyzes their performance in terms of luminous flux, luminous efficacy, switching time, data rate, and brightness. While this work focuses on the performance of different LED types in a MIMO-VLC system, it does not consider the influence of ambient light interference on system performance. In contrast, the present study specifically explores how ambient light interference affects the BER, Q-factor, and transmission range of MIMO-VLC systems. Additionally, the current research offers a comparative analysis of various modulation schemes under these interference conditions, which is not covered in [33]. Table 1 provides a summary of the related work, highlighting the key objectives, technologies used, performance metrics, and main limitations of each study.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 3 IoT Vulnerability Layers
### Network Vulnerabilities
#### 3.3.2 Wireless Sensor Networks
Lastly, protocol distortion mode alters routing rules to attract traffic to malicious nodes [98]. Each mode exploits WSNs' reliance on decentralized coordination, turning trust into a weapon. Additional threats include eavesdropping attacks, where sniffers--prebuilt for UWB, Zigbee, Z-Wave, or Bluetooth--capture packets over the air. Open source tools for Arduino or Raspberry Pi extend this capability to any protocol, decoding transmissions to reveal data like GPS coordinates or sensor readings [92]. Sybil attacks introduce fake node identities, tricking the network into routing through them, while sinkhole attacks lure traffic to a compromised node to manipulate or discard it [95]. These join traffic analysis attacks, where signal patterns--like a 5G node's burst rate--hint at activity without decrypting payloads [97]. Each leverages WSNs' wireless nature, where every transmission is a potential leak. To mitigate these advanced threats, encrypted communication protocols such as AES-based encryption for data packets should be enforced. Identity-based authentication can prevent Sybil attacks by requiring node verification. For eavesdropping threats, regularly changing encryption keys and employing physical-layer security techniques such as directional antennas and shielding can reduce interception risks. The tools for such exploits mirror WiFi's ecosystem; commercial sniffers abound, and DIY options thrive on open source code. An attacker could wield a Zigbee sniffer to log packets or craft a Raspberry Pi rig to jam Z-Wave signals. This accessibility, paired with WSNs' sprawling attack surface, ensures that vulnerabilities evolve alongside defenses, rendering these networks a persistent challenge in IoT security. However, by integrating encryption, anomaly detection, secure routing, and frequency-hopping mechanisms, WSNs can enhance their resilience against these evolving attacks. Figure 13: Wormhole attack tunnel. This image has been completely re-modified; however, the idea of the image was inspired by [98].
## 3 Results
### Land Use and Land Cover Distribution
#### 3.3.2 Conservation Units  
Within conservation units, the total area of deforestation and forest degradation revealed complex patterns across different management types and administrative levels in both Manaus and Porto Velho in 5 years. Consistently, forest degradation surpassed deforestation in most conservation units.  
In Manaus, Federal sustainable use units experienced minimal change, with no deforestation and only 2 ha converted to Secondary Forest. Federal full protection units faced more significant challenges, with 40 ha deforested and 4163 ha degraded (4.0% of the total unit area) (Figure 3(a)). State-level units in Manaus showed greater areas of change, with State sustainable use units experiencing 2223 ha of deforestation and 13,889 ha of degradation, while State full protection units saw 14 ha deforested and 349 ha degraded (Figure 3(b)). Local-level units in Manaus exhibited smaller but still notable areas of forest loss (Figure 3(c)).  
Figure 3: Maps representing areas of deforestation and forest degradation in consecutive years within the study sites: **(a)** Deforestation and **(b)** forest degradation in Manaus, and **(c)** deforestation, and **(d)** forest degradation in Porto Velho.  
Porto Velho displayed a different pattern, with Federal sustainable use units experiencing 2449 ha of deforestation and 7957 ha of degradation (6.0% of the total unit area). Federal full protection units in Porto Velho showed lower deforestation (35 ha) but significant degradation (2177 ha) (Figure 3(a)). The State sustainable use units in Porto Velho, the only state-level category present, faced substantial challenges, with 9834 ha deforested and 5849 ha degraded (Figure 3(b)). Local-level units in Porto Velho experienced minimal changes, likely due to their small size (Figure 3(c)).  
Across both cities, sustainable use units consistently showed larger areas of deforestation and degradation compared to full protection units. Porto Velho's sustainable use units consistently experienced more substantial deforestation than degradation, unlike Manaus, where degradation significantly outpaced deforestation. The primary driver of forest loss in conservation areas mirrored the broader trend, with conversion to Agriculture/Pastureland being the main factor.  
Analysis at the administrative level revealed that local-level units generally experienced less disturbance, while state-level units in Manaus and federal-level units in Porto  
Figure 4: Total areas of deforestation and forest degradation inside Conservation Units in both study sites per administrative types. Each management type (full protection and sustainable use) belonging to the (**a**) Federal Level; (**b**) State Level; and (**c**) Local Level.  
Velho faced the largest deforestation and degradation. Specific units stood out for their extensive changes, such as the Environmental Protection Area on the Left Bank of the Negro River in Manaus (State sustainable use) and the Bom-Futuro National Forest (Federal sustainable use) in Porto Velho.
# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 2 Materials and Methods
### Annual NPP in China from 1982 to 2015  
We collected multiresolution satellite remote sensing (MUSES) global vegetation productivity dataset with a temporal resolution of 8 days and a spatial resolution of 0.05\({}^{\circ}\). The gross primary productivity (GPP) and NPP products of this dataset were estimated with the global land surface satellite (GLASS) leaf area index (LAI)/fraction of absorbed photosynthetically active radiation (FPAR) products and multi-source data synergized quantitative (MuSyQ) algorithm, which is a light use efficiency (LUE) model [25]. Compared with moderate-resolution imaging spectroradiometer (MODIS) LAI/FPAR products, GLASS FPAR/LAI products are more continuous and complete in terms of their temporal and spatial distributions and have better accuracy [26; 27]. MuSyQ algorithm integrates high quality GLASS data, and adds clearness index (CI) to LUE estimation [28], which is used to express the effect of the fraction of diffuse solar radiation on LUE, so as to improve the estimation accuracy of GPP and NPP. Validation of the MUSES NPP was carried out using BigFoot NPP data. The objective of BigFoot was to provide ground validation of MODLand (MODIS Land Discipline Group) land cover, leaf area index (LAI), FAPAR, and net primary production (NPP) products. The BigFoot NPP product was an image extrapolated from field measurements based on biome bio-geochemical cycle (Biome-BGC) model, using Landsat ETM+ derived land cover and LAI, tower measured meteorological data and eco-physical parameters [29]. The verification results showed that for the estimated NPP based on the MuSyQ algorithm and BigFoot NPP, the R\({}^{2}\) value was 0.80, and the root mean square error (RMSE) was 214.6 g C/m\({}^{2}\)/yr. Currently, the detailed algorithm and global vegetation productivity products are shared in [[https://zenodo.org/record/3996814](https://zenodo.org/record/3996814)]([https://zenodo.org/record/3996814](https://zenodo.org/record/3996814)).  
This study extracted NPP products for China from 1982 to 2015 and aggregated NPP products with a temporal resolution of 8 days into the annual total NPP.
_applied sciences_  
_Article_  
**Analyses of the Ground Surface Displacement under Reinforcement Construction in the Shield Tunnel End Using the Artificial Ground Freezing Method**  
**[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1,3,*, [PERSON] 4 and [PERSON] 4**  
1 School of Civil Engineering and Architecture, Hainan University, Haikou 5700228, China;  
[EMAIL_ADDRESS] (H.X.)  
2 State Key Laboratory of Water Resources Engineering and Management, Wuhan University,  
Wuhan 430072, China  
3 Collaborative Innovation Center of Marine Science and Technology, Hainan University, Haikou 570228, China  
4 Hainan Hydrogeology and Engineering Geology Survey Institute, Haikou 570206, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.+86-183-8959-7985  
**Abstract:** There are stringent requirements on the vertical movement of a ground surface when using artificial ground freezing method to reinforce a shield tunnel in a city. This paper focused on the tunnel of Nanjing Metro Line Two between Yixianqiao and Daxinggong. Based on the discrete element thermo-mechanical coupling theory, the horizontal freezing reinforcement project was numerically simulated. The numerical results of the soil temperature field and displacement field are approximately compatible with the field measurements. When the tunnel was frozen for 40 days, an effectively frozen soil wall was created and satisfied the construction requirements. During the freezing construction, both frost heave and thawed settlement obviously occurred. Above the tunnel, the vertical deformation of the ground surface was symmetrical about the center of the tunnel and decayed towards the ends. The maximum vertical displacement of ground surface frost heave was 8 mm, and the maximum vertical displacement of ground surface thaw settlement was 18 mm. Increasing the depth of the tunnel embedment can result in a decline in ground surface displacement. The study serves as a viable means of predicting ground surface displacements.  
**Keywords:** artificial ground freezing method; frost heave; thaw settlement; discrete element method; numerical simulation
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis  
#### 5.3.1 Power Consumption  
Figure 4 shows the power consumption of the entire network by varying the number of nodes in the network for CAA-ERPL versus E-RPL. It is seen that the power consumption of CAA-ERPL decreases with an increasing number of nodes as compared to E-RPL. The reason for such a decrease in power consumption in CAA-ERPL is that aggregators are defined based on the number of nodes. In E-RPL, all sensor nodes form DODAG based  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range of Value** \\ \hline OS & Instant Contiki 3.0 \\ Network area & 500 m \(\times\) 500 m \\ \multicolumn{2}{c}{Mote type} & Sky mote \\ \multicolumn{2}{c}{MAC} & 802.15.4 \\ Interval time for transmission & 60 s \\ \multicolumn{2}{c}{Propagation model} & Unit disk graph \\ \multicolumn{2}{c}{Time of simulation} & 45 min \\ \multicolumn{2}{c}{Data transmission Interval} & 1 min \\ \multicolumn{2}{c}{RPL objective function} & ETX, Power \\ \multicolumn{2}{c}{Topology} & Hierarchical \\ \multicolumn{2}{c}{Number of nodes} & 10, 20, 30, 40, 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation setup.  
on an objective function, which is residual power for communicating to the border router. So, as the number of nodes increases, the sensor nodes will consume more power for communicating to the border router, which is far away. In CAA-ERPL, the aggregator is formed based on the number of nodes in the region for aggregating and compressing the data for transmission to the border router. This ultimately reduces the amount of power dissipated by sensor nodes. Aggregators play a vital role in aggregating and compressing the sensor data for communication with the border router. This can be achieved by forming DODAG with less power consumption and reduced communication.
# Reasonable Support Parameters for Loess Tunnel Under Different Water Contents: A Case Study
## 4 Numerical Simulation
### Simulation Analysis of Original Design Support Parameters
#### 4.2.2 Comparison of Stress Results  
According to the numerical simulation calculation results, when calculating the axial force, the bending moment of the steel rib and shotcrete, the total axial force, and the total bending moment assumed by the primary support were distributed to the steel rib and the shotcrete, according to Equations (6)-(9).  
\[N_{\text{h}}=N\frac{A_{\text{h}}E_{\text{h}}}{A_{\text{h}}E_{\text{h}}+A_{g}E_{ g}} \tag{6}\]  
\[M_{\text{h}}=0 \tag{7}\]  
\[N_{\text{g}}=N\frac{A_{\text{g}}E_{\text{g}}}{A_{\text{h}}E_{\text{h}}+A_{g}E_ {g}} \tag{8}\]  
Figure 11: Curves diagram of crown settlement deformation.  
Figure 12: Percentage distribution of crown settlement under different processes.  
\[M_{\rm g}=M \tag{9}\]  
where \(N_{\rm h}\) is the axial force assumed by the shotcrete (kN); \(N\) is the total axial force of the cross section in the unit length (kN); \(A_{\rm h}\) is the shotcrete area; \(E_{\rm h}\) is the elastic modulus of the shotcrete (kPa); \(A_{\rm g}\) is the calculated section area of the steel rib (m\({}^{2}\)); \(E_{\rm g}\) is the elastic modulus of the steel rib (kPa); \(M_{\rm h}\) is the bending moment of the shotcrete (kN\(\cdot\)m); \(N_{\rm g}\) is the axial force assumed by the steel rib (kN); \(M_{\rm g}\) is the bending moment of the steel rib (kN\(\cdot\)m); and \(M\) is the total bending moment of the cross section in the unit length (kN\(\cdot\)m).  
The stress cloud map of the supporting structure was obtained through calculation, and the normal stress calculation method was used for each measuring point to obtain the internal force distribution of the supporting structure [44]. The vertical stress cloud map and internal force distribution map are shown in Figure 13.  
The internal force distribution diagram shown in Figure 13 indicates that the internal forces at the measuring point in the crown of the primary support are greater than those  
Figure 13: The vertical stress cloud diagrams and the diagrams for the distribution of the internal force.  
at the other measuring points, and they are symmetrically distributed along the tunnel's central axis. This observation aligns with the on-site-measured data, further validating the rationality of the numerical model. Upon analysis of the internal force values, it was observed that the internal force exhibits distinct variation patterns: the axial force is highest under saturated water content and lowest under median water content, and the bending moment is smallest under natural water content, and largest under saturated water content.