esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Suggestions and Recommendations
#### 9.2.4 Implementing Strong Security Measures  
Deploying robust monitoring systems (i.e., IDS and Honeypots), securing remote access using advanced access controls and authorization, and using Virtual Private Networks (VPNs) to connect securely without compromising the system's integrity [222]. This includes cybersecurity measures such as AI and physical security solutions in addition to dynamic honeypots.  
1. **Adopting the Security-by-Design Concept:** This involves adding security to the smart grid system's design while adopting a defense-in-depth strategy that deploys multiple security control layers to provide resiliency and robustness against potential cyber-physical attacks.
2. **AI Solutions for Cybersecurity:** Through constant analysis of enormous volumes of data, artificial intelligence (AI) technologies improve cybersecurity in smart grids by quickly identifying anomalies and possible threats. By adjusting to changing assault tactics, machine learning algorithms can increase the precision and speed of threat identification. AI also automates incident response, reducing risks quickly and effectively to preserve smart grid infrastructure security and resilience against sophisticated cyberattacks. Therefore, employing advanced machine learning and reinforcement learning safety and security measures in addition to federated learning [242] approaches to develop efficient solutions can quickly recognize, identify, and address abnormalities.
3. **Enhanced Physical Security.** This step is recommended for smart grid hardware components, including antennas, grids, substations, and other critical infrastructure. In other words, to safeguard vital infrastructure from both digital and physical threats, smart grids must integrate powerful encryption, firewalls, and intrusion detection systems with physical barriers, surveillance, and access controls. This all-encompassing security plan reduces risks and strengthens resilience against possible incidents while guaranteeing the grid's availability, integrity, and confidentiality.
4. **Dynamic Honeypots:** To ensure a greater level of safeguarded deceptive technology that will be able to increase the detection level with a higher level of engagement, honeypots should be deployed at the client's end or the centralized server with the dynamic variable selection of vulnerabilities. This makes it possible to gather and evaluate real-time information about the attacker more rapidly and precisely. To attract and identify malicious actors and their techniques and proactively strengthen the defensive mechanisms of the smart grid, honeypots are deliberately placed to mimic susceptible components. This strategy not only offers early-warning signals and real-time threat intelligence but also deters attackers from targeting important assets, enabling thorough forensic investigation and increased defense against cyber attacks.
## 4 Using the Compact Topological Maps to Localize the Robot
### Resolution of the Localization Problem in a Model That Has Not Been Compacted  
In this case, the map is composed of a straightforward set of descriptors (i.e., this set has not been treated to create a hierarchical map through any clustering process). Once this straightforward map is available, the localization process starts:  
1. The robot captures a new image at time instant \(t\) from an unknown position (\(im_{t}\)).
2. It calculates the global appearance descriptor of the captured image \(\vec{d_{t}}\).
3. The distances between this new descriptor and the set of descriptors in the map are obtained. The comparison between descriptors is carried out through one of the distance metrics presented in Section 4.1.
4. A distance vector \(l_{t}=\{l_{t1},...,l_{tN}\}\) is obtained where \(l_{tj}=dist\{\vec{d_{t}},\vec{d_{j}}\}\) according to any distance measure.
5. Considering the position of the robot as the position of the closest neighbour within the map (the problem known as image retrieval [53]), the corresponding position of the robot is the position in the map that minimizes the distance \(arg\min_{j}l_{tj}\). This way, the position \((x,y)\) of the robot in the instant \(t\) is estimated.
# The Role of the CFO of an Industrial Company: An Analysis of the Impact of Blockchain Technology
## 4 Analysis of the Results
#### Improvement of Working Capital Management  
Blockchain technology facilitates full transparency and integrity of the sensors' telemetry data measuring a company's assets. According to the surveyed experts, this considerably improves the working capital management of a CFO. Firstly, blockchain-enabled predictive advanced data analytics support the CFO in minimizing the warehouse stock. Consequently, the tied-up capital declines and therefore working capital is reduced, which in turn increases the company's liquidity.  
Secondly, in addition to minimizing the warehouse stock, the blockchain enables the CFO to capitalize a company's assets on the balance sheet. If, for instance, the silo is situated at the customer's plant, it is currently rather complex to gain real-time and verified insight into the filling level of the silo. However, if these measurement data are now transmitted in a fully transparent and verified manner, the CFO can capitalize the available assets on the balance sheet at any time. Consequently, previously hidden reserves are disclosed which positively impacts the asset side and therefore the working capital.  
The integration of machines into payment transactions also exerts a positive influence on a CFO's working capital ratio. The utilization of blockchain-enabled autonomous M2M payments shortens the supplier's DSOs, as payments are instantly settled. This, in turn, positively impacts the cash-to-cash cycle time and liquidity, while ultimately lowering the working capital associated with the increase in transaction speed.
###### Abstract  
The classification of maritime boats and ship targets using optical satellite imagery is a challenging subject. This research introduces a unique and rich ship dataset named Very High-Resolution Ships (VHRShips) from Google Earth images, which includes diverse ship types, different ship sizes, several inshoe locations, and different data acquisition conditions to improve the scalability of ship detection and mapping applications. In addition, we proposed a deep learning-based multi-stage approach for ship type classification from very high resolution satellite images to evaluate the performance of the VHRShips dataset. Our \"Hierarchical Design (HieD)\" approach is an end-to-end structure that allows the optimization of the Detection, Localization, Recognition, and Identification (DLRI) stages, independently. We focused on sixteen parent ship classes for the DLR stages, and specifically considered eight child classes of the navy parent class at the identification stage. We used the Xception network in the DRI stages and implemented YOLOv4 for the localization stage. Individual optimization of each stage resulted in Fl scores of 99.17%, 94.20%, 84.08%, and 82.13% for detection, recognition, localization, and identification, respectively. The end-to-end implementation of our proposed approach resulted in F1 scores of 99.17%, 93.43%, 74.00%, and 57.05% for the same order. In comparison, end-to-end YOLOv4 yielded F1-scores of 99.17%, 86.59%, 68.87%, and 56.28% for DLRI, respectively. We achieved higher performance with HieD than YOLOv4 for localization, recognition, and identification stages, indicating the usability of the VHRShips dataset in different detection and classification models. In addition, the proposed method and dataset can be used as a benchmark for further studies to apply deep learning on large-scale geodata to boost GeoAI applications in the maritime domain.  
deep learning; optical satellite image; ship classification; end-to-end approach; dataset +
Footnote †: journal: Geo-Informat
## 2 Materials and Methods
### Computational Analysis  
The crystal structures of CaLB and _Rhizomucor miehei_ lipase (RM) were downloaded from the Protein Data Bank (PDB) database (PDB IDs: 1 TCA for CALB and 4 TGL for RM). Substrate molecules were generated with ChemDraw and converted to 3D by using Avogadro software 1.2.0 ([[https://avogadro.cc](https://avogadro.cc)]([https://avogadro.cc](https://avogadro.cc)), accessed on 1 February 2023) [22]. Both lipases were prepared using PyMOL software 2.5.4 ([[https://pymol.org/2/](https://pymol.org/2/)]([https://pymol.org/2/](https://pymol.org/2/)), accessed on 15 January 2023) [23] by eliminating all water molecules, heteroatoms, any co-crystallized solvent, and substrates from the structure. Before docking, the molecules were further processed for the minimization step using the \"dock prep\" tool in Chimera software 1.16 ([[https://www.cgl.ucsf.edu/chimera/](https://www.cgl.ucsf.edu/chimera/)]([https://www.cgl.ucsf.edu/chimera/](https://www.cgl.ucsf.edu/chimera/)), accessed on 15 January 2023) and by addingpolar hydrogens and partial charges to the lipase structures. The Autodock Vina tool [24] integrated into Chimera software [25] was used for the docking. The grid box was set by using the center of mass calculated in PyMol software, based on the catalytic triads of the enzymes (Ser105, Asp 187, and His 224 for CalB [8], and Ser 144, Asp 203, and His 257 for RM [26,27]) and by considering that the ligand's extended conformations fit well in the grid box (25 \(\times\) 25 \(\times\) 25 A). The best candidates were visualized and further analyzed (distances, interactions, and surface hydrophobicities) using the Chimera software.
## 5 Evaluation of Major Design Codes Based on Results of FE Analysis
### Influence of Rise-to-Span Ratio  
The influence of the rise-to-span ratio on the normalized critical axial force can be seen in Figure 14. In most cases, according to the Chinese and European codes, the normalized critical axial force slightly decreases with increasing rise-to-span ratio, while it increases with increasing rise-to-span ratio in the Japanese code. This rising tendency of the Japanese code is more apparent when the stiffness ratio is large. These findings indicate that the Chinese code and Eurocode overestimate the negative influence of the rise-to-span ratio on critical axial force, while the Japanese code underestimates it. They can be explained as follows. Since the rise-to-span ratio has only a slight influence on the critical axial force in the FE analysis results, the influence of the rise-to-span ratio on the normalized critical axial force is almost the same as on the critical axial force in the codes. Moreover, the influence of the rise-to-span ratio on the critical axial force in the codes can be explained as follows. The Chinese code treats the arch rib as a Vierendeel truss with the length of the arch axis, and the length of the arch axis \(s\) and panel length \(a\) increase with increasing rise-to-span ratio, thereby causing the decrease in critical axial force calculated by Equations (2)-(4). According to the Eurocode, the \(h_{r}\)-value increases with increasing rise-to-span ratio, which leads to a higher \(\beta\)-value, so as to decrease the out-of-plane critical axial force obtained by Equation (9). The Japanese code provides that the \(\beta_{z}\)-value in Equation (6) increases with increasing rise-to-span ratio, which results in a higher slenderness ratio of the arch rib, \(l/r\), so the allowable axial compressive stress \(\sigma_{\text{ca}}\) decreases slightly. These changes keep the horizontal component of arch axial force almost constant. However, the increases in the rise-to-span ratio led to the increasing angle between the arch axis and the horizontal; thus, the critical axial force increases.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
## 3 Results and Discussion
### Transport Vibration Levels in the Pre-Planned Test Route
The overall rms G value for the top 30% of the recorded data was approximately 0.31 G for the leaf-spring truck and 0.18 G for the air-ride truck, and that for the lower 70% of the data recorded, which was muted by averaging high-amplitude events included in the measured transport vibration signals, was approximately 0.18 G and 0.08 G, respectively (Table 4). The overall rms G value for the lower 70% of the data recorded was approximately 42% less in the leaf-spring truck and 56% less in the air-ride truck than that for the top 30% of the data recorded. Therefore, because high-amplitude events that occur during truck transport have a significant impact on product damage [3; 38], the high-amplitude events included in the measured transport vibration signal should not be excluded in the development of the PSD profile for lab-based simulation. The overall shape of the PSD profile in the leaf-spring truck was higher in the low-frequency range of 1-10 Hz, lower in the medium-frequency range of 15-40 Hz, and again relatively higher in the high-frequency range of 40-250 Hz. However, in the case of the air-ride truck, the overall shape of the PSD profile was lower with increasing frequency. The shape of the PSD profile for the leaf-spring truck was similar to that reported by [PERSON] and [PERSON] [13]. In the low-frequency range of 1-10 Hz, the shape and peak intensity and frequencies of the high-level PSD profile of leaf-spring truck, representing the top 30% of the data recorded, were roughly similar to those of ASTM D4169:16 (medium level) [26] and ISTA 3A (over-the-road trailer spectrum) [28]. However, they showed significant differences in the higher-frequency regions. The shape and peak intensity and frequencies of the high-level PSD profile of the air-ride truck were roughly similar to those of the ISTA 3H (air-ride suspension truck) [39], although significant differences were noted at the higher-frequency regions. The reason for the relatively substantial difference in these regions between the results of this study and the existing standardized test protocols is attributed mainly to the differences in test subjects and conditions [40]. In addition, the results of this study were measured in unload conditions for general trucks, and there are many sudden speed changes of trucks during transport and the characteristics of national roads and highways in Korea having many curved routes.
## 5 Results  
### Experimental Setup  
To verify the effectiveness and applicability of the methodology proposed in this study, the research team conducted field validation in the production workshop of a 110 kV dry-type transformer coil at Company A (The company is located in Changji, China). The aim was to assess the capability of this methodology in managing the coil manufacturing process and information traceability. The workshop is equipped with five smart warehouses, two intelligent pouring devices, nineteen smart curing ovens, five AGVs, two RGVs, eight sets of automatic KBK, one raw material testing center, fourteen CNC winding machines, and two sets of beam robots for spool loading. In previous digital transformation efforts, comprehensive digital business systems were established, utilizing remote calling interfaces such as Webster and Restful for data exchange and integrating platforms, including SCADA, MES, WMS, QMS, AGV control systems, and the Industrial Internet. Each device, workstation, material, personnel, and work-in-progress  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process** & **Characteristics** & **Corresponding to the Place of** **Pett Net** \\ \hline \multirow{8}{*}{Casting and curing process} & Vacuum degree (mbar); temperature (\({}^{\circ}\)C); duration (min); & \\  & bottom temperature 1 of the tank (\({}^{\circ}\)C); bottom temperature 2 of the tank (\({}^{\circ}\)C); front temperature of the tank (\({}^{\circ}\)C); middle & \\  & temperature of the tank (\({}^{\circ}\)C); rear temperature of the tank (\({}^{\circ}\)C); & \\  & mold temperature (\({}^{\circ}\)C); vacuum degree (\({}^{\circ}\)C); duration (min); & \\  & epoxy resin ratio in resin tank A; toughening agent ratio in resin & \\  & tank A; silica future ratio in resin tank A; curing agent ratio in & \\  & resin tank B; silica fume ratio in resin tank B; pressure; vacuum & \\  & degree; failure; casting shrinkage; local softening & \\ \hline Coil grinding, appearance & Finished product coil surface finish, roundness, & \\  & inspection, etc. & perpendicularity, height, etc. & \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
Figure 10: Example of extendable Petri net for identification resolution in coil quality traceability.  
was assigned a unique identification code. Smart sensors and gateways were employed to automate the collection and association of essential data, including operational data from key equipment, environmental monitoring data, material information, and quality inspection data. Based on this, the implementation and validation of the research method took place primarily from February to June 2024, with detailed content and processes introduced in the following sections.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Short-Term Traffic Flow Combination Model Based on CEEMDAN
#### 4.1.3 Construction of KNN Models  
The low-frequency component sequence fluctuates smoothly, the data change amplitude is small, the principle of k-nearest neighbor (KNN) model is simple and easy to use, and its prediction mainly depends on the adjacent K samples. In contrast, the neural network gradually approaches the true value through the backpropagation of error and does not spend a lot of time looking for hyperplanes, such as the support vector machine, which will greatly reduce the computational complexity. The flowchart of the KNN prediction model is shown in Figure 3 using the five-fold cross-validation method to solve the optimal hyperparameter K and the KNN prediction model.
# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 3 Problem Statement and System Framework
### Problem Statement
Additionally, the 3 GPP NR Release-15 [14] supports the _K_-repetition (Krep) scheme, which allows for a predefined number of consecutive replicas of the same packet without waiting for feedback. On the other hand, there exist numerous factors that exert an influence on the BLER. The Channel Quality Indication (CQI) is transmitted from the terminal to the BS with the information measurement. It primarily represents the quality of the downstream channel. The LTE protocol defines the quality of the channel as CQI and quantizes it into a sequence of 0-15. The larger the CQI value, the better the channel quality, and the higher the utilization rate of the modulation coding method. It also means a greater efficiency and a larger corresponding transmission block, thus providing a higher downstream peak throughput. The opposite is true for small CQI values. Distance is another important factor affecting the BLER and transmission latency [15; 16; 17; 18]. As the distance between the TX and the RX increases, the Signal-and-Interference-to-Noise Ratio (SINR) continues to decrease, and the probability of packet loss due to packet collision also increases [18]. Moreover, the rate of packet loss experiences a rapid increase once the received signal power reaches its perceived power threshold. In addition, the BLER is also influenced by various network load factors. Therefore, based on making full use of these characteristics of the 5G standard and the above analysis of the factors influencing the communication performance of the 5G NR-V2X, this study proposes an alternative optimization transmission scheme to address the issues that arise when the BLER exceeds the threshold.
###### Abstract  
This study introduces a novel deep learning-based model, UniU-Net, designed to achieve the high-precision segmentation of area palms in remote sensing imagery. UniU-Net incorporates an auxiliary encoder and a unified attention fusion module (UAFM), enhancing the model's anti-overfitting capabilities to improve its overall segmentation performance. Specifically, the primary and auxiliary encoders, through isomorphic parallel processing, leverage the principles of structural reparameterization to enhance the model's effective learning of area palm features while reducing the risk of overfitting. The UAFM utilizes a spatial attention mechanism to facilitate the effective fusion of multi-scale features. This architecture enables the model to capture intricate morphological details and accurately delineate the boundaries of area palms, even under complex and heterogeneous environmental conditions such as mixed vegetation and varying illumination. To validate the effectiveness of UniU-Net, comprehensive experiments were conducted on a specialized area palm dataset, demonstrating superior performance compared to several state-of-the-art semantic segmentation models. The proposed method achieves significant improvements in key evaluation metrics, such as the F1-score and intersection over union (IoU), highlighting its robustness and precision in automated area palm extraction tasks. The integration of advanced attention mechanisms not only enhances the model's ability to focus on relevant regions but also improves the segmentation accuracy in challenging scenarios. Beyond the specific application of area palm segmentation, the methodologies introduced in this study hold substantial practical significance for broader agricultural applications, such as precision farming and crop monitoring.  
arcca palm extraction; remote sensing; deep learning; attention mechanism; UniU-Net +
Footnote †: journal: Applied sciences  
Article
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs  
FANET has distinguishing features (e.g., high dynamicity of network topology, large coverage, and limited energy) as compared to other kinds of ad hoc networks (e.g., MANETs and VANETs). Hence, in FANETs, flying nodes can join and leave a highly dynamic ad hoc network, which causes frequent link disconnections and route breakages. This has motivated researchers to investigate FANETs.
## 3 Algorithm Description  
In this section, we introduce our novel DTS-AC resource distribution strategy, aimed at improving performance, achieving a stable system, and ensuring efficient allocation of frequency and time resources. The primary goal of this strategy is to address the Reader-to-Reader Interference (RRI) and Reader-to-Tag Interference (RTI) collision problems.  
This protocol utilizes a novel TDMA notification system to address RTI concerns while also incorporating the frequency resource management aspect of the FTSMAC (FDMA) [24] protocol to resolve RRI collision issues.