esa-sceva/satcom-chunk-collection huggingface dataset:
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 2 Related Work  
The YOLO series has seen continuous development, with recent research focusing on addressing challenges in dense and small object detection. In 2020, [PERSON] et al. [22] enhanced YOLOv3, resulting in a 5.43% increase in detection accuracy. However, this improvement came at the cost of increased computational requirements due to the addition of an auxiliary network, leading to longer detection times. Similarly, [PERSON] et al. [23] integrated DenseNet with YOLOv3 to achieve higher accuracy while maintaining real-time performance for remote sensing target detection. Despite these advancements, the current YOLO models, up to version 10, have significantly outperformed earlier versions in terms of both accuracy and efficiency. In 2021, [PERSON] et al. [24] introduced TPH-YOLOv5, combining a Transformer detection head and CBAM attention mechanism to increase attention in dense small target areas. However, this led to a significant increase in the number of parameters. [PERSON] et al. [25] improved YOLOv4 by using dense blocks instead of residual blocks and integrating SPPnet and PANnet in the Neck. Despite these modifications, the improvements were limited. In 2022, [PERSON] et al. [26] augmented YOLOv5 with BAM to enhance attention to small target information in shallow feature maps, whih performed well in the detection of small figures but with a large increase in parameter scale. [PERSON] et al. [27] developed TCA-YOLOv5m by integrating the Transformer algorithm with the Coordinate Attention mechanism, which significantly enhanced target detection capabilities. However, the model still exhibited instances of missed and false detections, particularly with small targets. In 2023, [PERSON] et al. [28] integrated a small target detection head and an attention mechanism into YOLOv7, enhancing the detection performance of small targets on the sea surface. However, they struggled to effectively control the increase in computational load. [PERSON] et al. [29] addressed the significant information loss of small and medium-sized targets in remote sensing images, reconstructed the feature fusion pyramid, and improved the up-sampling method to enhance the detection of small targets. This improvement, however, also increased the model's complexity. In 2024, [PERSON] et al. [30] enhanced YOLOv8s by integrating BiFPN and Dynamic Head, resulting in the improved detection of small targets, but the proposed algorithm still has shortcomings such as long training time and long model convergence time.  
These advancements primarily focus on incorporating modules tailored for detecting small targets. While these enhancements boost detection accuracy, they also come with a significant increase in computational cost. Considering that YOLOv7 achieves high detection accuracy in the detection of small targets in the recent version of the YOLO algorithm, this paper proposes an improved YOLOv7 algorithm for dense small targets. This algorithm enhances the detection accuracy of small targets while reducing model parameters, resulting in improved overall performance.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 2 Related Work
[24] address vulnerabilities to adversarial attacks in fault zone prediction using a Bayesian Neural Network (BNN) framework. The model improves prediction robustness and introduces an uncertainty-based detection scheme to counter adversarial threats, achieving high accuracy. Other works, such as [PERSON] et al. [25], investigate fault propagation in cyber-physical systems, proposing a neural network model that achieves high predictive accuracy in the IEEE 14-bus and 57-bus topologies, enabling early fault detection and preventive actions in SGs. Finally, [PERSON] et al. [26] propose a resilient microgrid formation strategy, combining distributed generator control and topology reconfiguration to optimize load restoration after outages using CPLEX, validated on the IEEE 33-bus and 615-bus test systems. However, these works focus on optimizing a model for a single topology, and the use of real datasets is missing. Therefore, after analyzing the state of the literature, the main contributions of our work consist of (1) a fault prediction solution for SG reconfiguration, (2) based on real datasets, and (3) generalized for any type, shape, or size of topology (i.e., evaluated with the random topology generator BRITE, according to real parameters). For this purpose, ML and DL models are analyzed, followed by their optimization.
# Performance of Concrete with Waste Granite Powder: The Effect of Superplasticizers
## Materials
### Granite Waste  
Waste granite powder (WGP), a by-product of the granite processing industry, was used in this research. Its specific gravity is (2.5) lower than that of the cement (3.2). In addition, Figure 3 shows its grain size distribution. The average diameter of cement is lower than that of granite powder and this can be considered as one of the reasons to decrease the mechanical characteristics of concrete.  
The chemical analysis, using X-ray Fluorescence technique, showed that WGP contains about 72.9% of soluble silica (SiO\({}_{2}\)), 14.65% of alumina (Al\({}_{2}\)O\({}_{3}\)) and 1.70% of ferric oxide (Fe\({}_{2}\)O\({}_{3}\)). Based on ASTM C618 [7], since their sum is over 70%, WGP can be introduced to concrete as pozzolanic material. The chemical characteristics of granite waste and cement is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Composition** & **Cement (\%)** & **Granite Powder (\%)** \\ \hline SiO\({}_{2}\) & 21.63 & 72.9 \\ Al\({}_{2}\)O\({}_{3}\) & 4.27 & 14.65 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.45 & 1.7 \\ CaO & 63.25 & 1.5 \\ Na\({}_{2}\)O & - & 3.85 \\ K\({}_{2}\)O & - & 3.98 \\ MgO & 2.77 & 0.37 \\ TiO\({}_{2}\) & - & 0.235 \\ MnO & - & 0.026 \\ SO3 & 2.02 & 0 \\ C3A & 5.48 & - \\ P\({}_{2}\)O\({}_{5}\) & - & 0.088 \\ LO.I & 1.5 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical characteristics of cement and waste granite powder (WGP).  
Figure 3: Grain size distribution of waste granite powder obtained by laser diffraction.  
Figure 2: Particle size distribution of cement.
## 5 Open Loop Processing Architecture
### Trade-Off between Noise Average and Observation Rate  
The signal \(w[n]\) will be processed by the scintillation analysis block that will perform a statistical analysis of the samples. For this reason, the noise contribution still affecting the signal after the accumulation has to be sufficiently small, so that it does not mask or bias the results.  
Given a fixed sampling frequency \(f_{s}\), increasing \(L\), the noise contribution tends to be averaged out, but this would require many segments of received signal, i.e., a large observation window \(LT_{code}\). Due to the non-stationarity of the scintillation process, the statistical distribution of the signal amplitude changes over time. This limits the increasing of \(L\) that, if chosen to be too large, would not catch the significant changes on the statistic distribution since they would be all included in the same observation window. The parameter \(L\) must trade off this _resolution in time_ of the analysis with the capability of reducing the noise effect.  
As a trade-off between the noise averaging and the resolution in time for the GPS C/A code, \(L=5000\), corresponding to five seconds of the received signal, was heuristically chosen. Therefore, from now on, signals obtained accumulating over 5000 periods of \(T_{code}=1\,\)ms of signal, are adopted for scintillation analysis of the GPS C/A code.
## 4 Ubunye Service Classifier
### Data Preprocessing
Shades of dark red represent a strong positive representation (close to \(+1\)), while shades of dark blue represent a strong negative representation (close to \(-1\)). Gray areas suggest weak or nonexistent (close to \(0\)), evidencing little or no linear relationship between the corresponding variables. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Metric** & **Description** \\ \hline Traffic volume per IP pair & Sums the packet sizes exchanged between IP pairs, helping to identify high-bandwidth flows. \\ \hline Packet number per IP pair & Counts the number of packets exchanged between IP pairs, indicating communication intensity. \\ \hline Total traffic time per IP pair & Calculates the duration of traffic based on the difference between the first and last timestamp. \\ \hline Average packet per IP pair & Calculates the average size of packets exchanged, helping to identify traffic types. \\ \hline Packet size variation per IP pair & Measures the variation in packet size, indicating traffic diversity and different application types. \\ \hline Packet rate per second & Measures the average packet rate per second, distinguishing low-latency traffic from high-bandwidth traffic. \\ \hline Protocol Distribution per IP pair & Calculates the distribution of TCP/UDP protocols, indicating reliable vs. fast traffic types. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Metrics per IP Pair. Figure 3: Ubunye service classification—correlation matrix. Based on the results presented in Figure 3, it can be concluded that the selected metrics in Table 2 are essential to capture important network traffic characteristics, integrating temporal, volumetric, and protocol-related information. Analyzing the results of these metrics allows for identifying patterns that differentiate low-latency and high-bandwidth flows, helping to build more robust and accurate models. The inclusion of attributes such as variation in packet size and protocol distribution highlights the diversity of traffic and the presence of different types of applications, while metrics such as packet rate per second and total volume help to distinguish between types of traffic with distinct characteristics. Thus, these metrics contribute significantly to improving the performance of the classification model by providing a comprehensive view of network traffic behavior.
## 3 Proposed Approach  
In this section, we present our proposed design that integrates blockchain-based smart contracts and deep-learning-based malware detection to protect smart systems. As illustrated in Figure 1, a smart contract possesses a structured life-cycle on the Ethereum blockchain. This life-cycle consists of a number of phases:  
* Creation: the smart contract is programmed and compiled.
* Deployment: the contract is deployed to the Ethereum blockchain, so it becomes public and immutable.
* Execution: the contract interacts with external systems and users through calling the contract's functions.
* Completion: the contract finishes executing normally, or it reaches an end state (i.e., self-destructs or obsoletes).  
Throughout this life-cycle, smart contracts are vulnerable to security threats, including malware attacks attempting to alter their behavior or exploit weaknesses. To explain more, we propose a simple scenario related to a malware that modifies a blockchain node to falsify recorded data. The nodes are installed on the internal servers of an organization that uses a blockchain for transaction traceability. The attacker sends a phishing email containing a malware-infected attachment to a system administrator. Once opened, the malware silently installs itself on the machine, accessing the file system. It identifies directoriesrelated to the operation of the local blockchain node. The malware then modifies the configuration files to redirect traffic to an attacker's node. Then, it alters local data to falsify transactions or falsify the history. It can also remotely control the machine or retrieve all files on the network. Thus, our proposed solution, as depicted in Figure 2, integrates an AI-powered security component into the blockchain network. Our framework employs a deep-learning-based model for detecting and preventing malicious behavior attempting to compromise smart contracts at any stage of their life-cycle. The blockchain module ensures secure and transparent transaction management between various smart system networks. Our solution is also strengthened with the latest feature selection techniques based on the GAN algorithm to enhance the malware detection rate.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 1 Introduction
Currently, little information is given to the properties of graphene modified concrete; unique experimental work has been done and demonstrated that GNP could improve transport resistance of concrete, but it reported that graphene has no influence on the compressive properties of concrete [12]. This is inconsistent with the effect of graphene on cement paste; therefore, much research about that graphene modified concrete and effects of graphene on durability of concrete should be done. The objective of the paper is to research the effects of GNP addition on F-T resistance. Experiments were conducted on the durability of concrete including 0%, 0.02%, 0.05%, 0.1%, 0.2%, 0.3% and 0.4% GNP, using the rapid freeze-thaw method. Compressive strength and mass loss were carried out on specimens with and without GNP that were exposed to F-T actions. Moreover, the influence of GNP on the micro-structure of concrete using Scanning electron microscopy (SEM) was characterized.
## 3 Results and Discussion
### Outdoor Conditions and Challenges Facing Standalone EAHX Cooling
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **June** & **July** & **August** & **September** & **Summer (June-September)** \\ \hline \multirow{4}{*}{\(T_{\text{a0}}\) [\({}^{\circ}\)C]} & max. & 40.2 & 38.8 & 36.1 & 31.9 & 40.2 \\  & \(Q_{3}\) & 29.3 & 31.1 & 28.2 & 24.7 & 28.3 \\  & median & 23.6 & 25.7 & 23.5 & 20.5 & 23.3 \\  & \(Q_{1}\) & 19.3 & 21.0 & 19.3 & 17.5 & 19.1 \\  & min. & 11.5 & 14.9 & 14.4 & 11.3 & 11.3 \\ \hline \multirow{4}{*}{\(\phi_{\text{a0}}\) [\%]} & max. & 93 & 88 & 90 & 92 & 93 \\  & \(Q_{3}\) & 57 & 62 & 67 & 71 & 65 \\ \cline{1-1}  & median & 41 & 44 & 51 & 54 & 47 \\ \cline{1-1}  & \(Q_{1}\) & 26 & 28 & 36 & 39 & 32 \\ \cline{1-1}  & min. & 9 & 9 & 8 & 15 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics for outdoor air temperature and relative humidity (\(T_{\text{a0}}\) and \(\phi_{\text{a0}}\), respectively). Obtained from summer monitoring data between 1 June and 30 September. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(T_{\text{rIII}}^{\text{U}}\) [\({}^{\circ}\)C] & \(T_{\text{rSI}}^{\text{U}}\) [\({}^{\circ}\)C] & \(T_{\text{rSI}}^{\text{L}}\) [\({}^{\circ}\)C] & \(T_{\text{rSI}}^{\text{L}}\) [\({}^{\circ}\)C] \\ \hline max. & 31.9 & 29.9 & 25.9 & 23.9 \\ median & 30.8 & 28.8 & 24.8 & 22.8 \\ min. & 28.7 & 26.7 & 22.7 & 20.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Upper and lower free-floating room setpoint temperatures (categories III and I). Obtained for summer monitoring data between 1 June and 30 September. was concluded that room heating was unnecessary. Figure 6 presents violin- and box-plots for outdoor air temperatures measured at different hours of the day. Figure 6 confirms that the lowest outdoor air temperatures, with median values lower than 19 \({}^{\circ}\)C, happen during nighttime, when office rooms are unoccupied. The significant reduction in median temperatures between daytime and nighttime also confirms that forced nighttime cooling with outdoor air can be used as a passive cooling technique, as described Table 2 item (n) and as mentioned in references [16; 18; 39]. Returning to the analysis of Figure 5, a line for the estimate of the undisturbed soil temperature (\(T_{\text{soo}}\)) at 5.5 m (the built EAHX burial depth) is also presented. Given the large temperature difference between this soil temperature estimate (17 \({}^{\circ}\)C) and Table 5 room upper setpoint medians (31 \({}^{\circ}\)C/29 \({}^{\circ}\)C for Category III/I), the feasibility of EAHX use for standalone room cooling is confirmed. Considering the temperature difference between these medians and the maximum outdoor air temperature (40 \({}^{\circ}\)C; see Table 4), it is concluded that for standalone EAHX cooling, the inflow to outflow temperature gradient (\(\Delta T_{\text{AL,a0}}\)) should be larger than 9 K/11 K (Category III/I). EAHX design, i.e., pipe length, depth, material and/or layout, should be such that the soil succeeds in reducing the outdoor air temperature in this order of magnitude, avoiding region D conditions.
## 4 Simulation and Analysis
### Curvature Radius \(R=120\ m\)  
In this particular scenario, all conditions remain the same as those specified in Section 4.1, except for the curvature radius. Figure 11a-h present the results of the simulation comparison for this test scenario, where the initial speed is set at \(V=60\) km/h. All three control systems avoid collision successfully, maintaining distances of 1.08 m, 1.03 m, and 1.10 m to the front obstacle vehicle when stationary. In System 0, the emergency braking process results in a lateral offset of the center of mass of the vehicle by 0.77 m. On the other hand, both System 0 and System 0 achieve lateral offsets of 0.25 m and 0.12 m, respectively, ensuring the vehicle body remains close to the lane centerline while stationary.  
However, when the initial speed is decreased to 50 km/h, the vehicle's lateral displacement is smaller, leading to the conclusion that there is no risk of deviating from the lane during the braking process in System 0. As a result, the LKA system is not activated, resulting in the same simulation results as in System 0. Table 5 displays the simulation results for the scenario with a curvature radius of 120 m. In this scenario, all three control systems successfully avoid collisions when activated.  
Figure 10: Simulation results (\(V=60\) km/h, \(R=90\) m).  
Figure 11: _Cont._  
The simulation results above, comparing three different curve radii and two distinct initial vehicle speed conditions, clearly demonstrate the effectiveness of all three designed control systems in collision avoidance. In System 0, where the LKA system is not engaged, a noticeable lateral offset of the center of mass is observed during emergency braking. Conversely, with the involvement of the LKA system, Systems 0 and 0 exhibit a substantial reduction in lateral offset, ensuring the vehicle remains closely aligned with the lane's centerline throughout the emergency braking process. Additionally, System 0 showcases superior lateral stability when compared to System 0. Moreover, the lateral offset during emergency braking for System 0 significantly decreases as the curve radius increases. However, the relationship between the lateral offset and the curve radius for the other two control systems is less pronounced.
# An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression
## 4 Simulation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Huffman**} & \multicolumn{2}{c}{**Arithmetic**} & \multirow{2}{*}{**Proposed**} \\ \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Size**} & \multicolumn{2}{c}{**Encoding**} & \multicolumn{2}{c}{**Coding**} & \multicolumn{2}{c}{**Algorithm**} \\ \cline{5-8}  & & & \multicolumn{2}{c}{**Angoffman**} & \multicolumn{2}{c}{**Angoffman**} & \multicolumn{2}{c}{**[PERSON]**} \\ \cline{5-8}  & & & \multicolumn{2}{c}{**NoBPP**} & **CP** & **NoBPP** & **CP** & **NoBPP** & **CP** \\ \hline Nature 1 & Color & 1920 \(\times\) 1080 & 7.69 & 3.83 & 7.66 & 4.21 & 7.43 & 7.08 \\ Nature 2 & Color & 1280 \(\times\) 720 & 7.93 & 0.88 & 7.91 & 1.17 & 7.64 & 4.53 \\ Map 1 & Color & 2428 \(\times\) 183 The algorithms were run on eight raw, randomly selected images from the Structured Analysis of the Retina database [39], and the results are presented in Table 9.  
In the evaluation of the algorithm, images in three different groups were used. The developed encoding algorithm was compared with the Huffman encoding algorithm and arithmetic coding algorithm for evaluation. The results are presented in tables with CP and NoBPP values. The obtained results were analyzed in detail in the next step of the study.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Control Plane
#### 3.1.2 Routing Protocol
LSR Message: used to acquire the corresponding node for the complete information of lacking LSAs, which includes a brief description of them. Figure 5: State machine of signal protocol. 4. LSU Message: used to send the complete information of the required lacking LSAs it to the corresponding node. 5. LSAck Message: used to reply to the LSU message to ensure the reliability of synchronization. 6. Unidirectional Message: added to support the use of the unidirectional links. This is used to inform the information of the unidirectional link to the corresponding node. 7. NonUnidirectional Message: added to support the use of unidirectional links. This is used to inform the corresponding node that the unidirectional link has been broken. Based on the design mentioned above, the routing protocol can discover neighbors automatically, collect and maintain network resources, and support the use of unidirectional links. The resources collected are stored in an LSDB, which consists of LSAs that are advertised by all nodes in the network. Each LSA describes the link states of a corresponding node. Furthermore, after the collection and maintenance of the network resources, the routing protocol can also use SPF (shortest path first) algorithm to calculate routes. In order to meet more service demands, we added the CSPF (constrained shortest path first) algorithm here, which can calculate the route path under constraints such as bandwidth, path delay and so on. Based on this, the routing protocol can calculate the route path according to the demands of services.