esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Computer Vision Module  
#### 3.4.1 YOLOv10  
As previously explained, all the cameras in the system use YOLOv10 for UAV detection, utilizing a custom model trained with a dataset comprising the Birds vs. Drones dataset [30], Maciej and Marek UAV dataset [31], and the GSV-Cities dataset [32]. From these three sources, 60,178 images were used, divided into 48,521 for training and 11,657 for validation.  
The reason for using such an extensive dataset is to represent as many real-world scenarios as possible. It includes images of UAVs of different sizes and types, at various distances and under different weather conditions, and in diverse flight environments. There are also background images and elements that could potentially confuse detection, such as birds.  
The training was carried out using the YOLOv10-S model, a small version that balances speed and accuracy. It is the most powerful version that could be trained using the created dataset and the selected hyperparameters given that the computer used for training did not have sufficient computational capacity for more complex architectures.  
The training was set to 400 epochs with a batch size of 32 and employed data augmentation techniques. The training process lasted approximately three days and yielded very good results when analyzing the metrics extracted by YOLOv10, as shown in Figure 7, achieving very high precision and recall values.  
Figure 6: Architecture of autoencoders used for drone detection. The green part, encoder, represents the compression of the input data. The blue is the latent space, which is the representation of compressed data, and the orange, decoder, represents the reconstruction up to the original dimension.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 2 Signal Model and Potential Error Sources
### Normalized Range Residuals (NRR)  
The geometrical distance between the satellite in the sky and the receiver on the ground, including the travel time of the signal, is referred to as pseudorange. It is estimatedby the least square method, which is the traditional technique for computing the position solution. This pseudorange measurement can be affected by MP/NLOS reception in urban environments, and that error is quantified by range residuals. Range residuals are a beneficial measurement for GNSS signal performance. In a severe MP case, the computed position always encloses a large error, so the range residual cannot differentiate LOS and NLOS accurately. Therefore, the range residuals at each observation are normalized and NRR is given as:  
\[NRR=\frac{RR_{s}-RR_{min}}{RR_{max}-RR_{min}} \tag{6}\]  
where  
* \(RR_{s}\) is the Range residuals of satellite \(s\).
* \(RR_{min}\) and \(RR_{max}\) denotes the minimum and maximum of range residuals at each observations respectively.  
For an accurate navigation and position solution, the normalized range residuals of the direct LOS signal are near zero, whereas in the complex environment case (MP), NRR increases due to additional propagation errors, i.e., reflections, blockage.
# Feasibility Study for Using Piezoelectric-Based Weigh-In-Motion (WIM) System on Public Roadway
## 2 Methods
### Installation of the P-WIMs  
To find if the P-WIM is capable of providing legitimate axle loading information of vehicle under the realistic condition, four fabricated prototype P-WIMs were installed at Troutville Weigh Station near Roanoke, VA. There are over 4000 trailer trucks passing the weigh station every day. The P-WIMs were installed at the weigh lane of the weigh station. The axle loading information of every vehicle passed through weigh lane will be recorded by VDMV. The data collected by VDMV is used to investigate the relationship between the vehicle axle loading and the generated voltage from P-WIMs.  
The installation spacing of P-WIMs is determined from the tire configuration of regular trailer trucks. The P-WIMs were installed along the wheel paths of the vehicles to have better chance to have full contact with the tires of vehicles. The layout of the installation is illustrated in Figure 3. Four P-WIMs are installed in two rows on both sides of the cable channel. The tiles with a darker color in the figures represent the P-WIMs, and the numbers are the identification of each P-WIM.  
Figure 2: The prototype P-WIM (**a**) and the conceptual sketch (**b**).  
Before installing the P-WIMs, square-shaped pits were cut on the pavement (Figure 4b). Then the bottoms of the pits are leveled parallel to the surface of the pavement using cement grout to have a uniform load distribution on the base of the P-WIMs. As suggested by VDOT, engineering epoxy is used to seal the space in pits and keep the P-WIMs steady in the pavement. A metal plated is placed on top of each P-WIM, and the top surface of the metal plate is precisely aligned to the surface of the lane to avoid any potential impact from passing vehicles. Figure 4a is a sketch of the side view of the installation, and Figure 4c is a picture showing the installed P-WIMs under real traffic.
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 2 Materials and Methods
### Data Treatment and Validation  
Each live insulation test was conducted three times, during which both internal and external temperatures, as well as humidity, were continuously monitored. To ensure consistency and facilitate cross-experiment comparisons, a standard starting temperature of 22 \({}^{\circ}\)C inside each beehive was established. From this set point, thermal activity within the beehive was calculated based on the area under the temperature curve from the moment 22 \({}^{\circ}\)C was achieved until one hour later. This method was uniformly applied across all replicates to evaluate the thermal insulation efficiencies of the different materials. The maximum temperature reached within this period was also recorded for each type of insulation.  
Data were collected and stored as separate text files for each test. Initial graphical assessments of these files helped pinpoint the commencement of the experiment when the target temperature in the chamber was met. The primary metric for comparison was temperature, considering no humidity control mechanisms were implemented during the experiments. However, humidity data were recorded and may be pertinent for future studies or specific applications.  
Statistical analysis was performed using R version 4.2.0 [42] and Statgraphics Centurion XVIII. We employed ANOVA to determine significant differences between the insulation materials' performance, ensuring robust comparative analysis. This method was specifically chosen for its ability to handle variance within and across groups, which is ideal for our experimental design. Following the identification of significant effects via ANOVA, [PERSON]'s least significant difference (LSD) test was utilised as a post hoc analysis to further explore pairwise differences between group means.  
Regarding data integrity, any outliers identified during the initial graphical assessment were scrutinised to ascertain whether they resulted from experimental errors or genuine anomalies. Since the experimental conditions were highly controlled, significant deviations were rare. In the absence of outliers, no data points were excluded from the analysis. Had there been missing data, imputation techniques would have been employed to maintain data completeness, ensuring no loss in data reliability. Thankfully, our data collection process was meticulous, resulting in a complete dataset without any missing values. Through these procedures, we ensured the reliability and accuracy of our findings.
## 6 Lab-Built Scenario and Benchmarking
### Security Testing journey
#### 6.2.2 Security Testing Scenario: After Initial Hardening  
This test was conducted after initial AZTRM-D hardening, which focused on eliminating the network attack surface while revealing physical vulnerabilities.  
**Outsider Perspective:**  
Our reconnaissance and scanning phases confirmed that the network-based attack surface had been effectively eliminated. Comprehensive port scans with Nmap revealed no open network ports, and vulnerability scanners returned no actionable findings. This shifted our efforts entirely to physical attack vectors. The unencrypted SD card was identified as the primary vulnerability. After physically removing the SD card, we mounted its root filesystem on an external Linux machine, gaining full access to all files, including /etc/shadow. We then used the chroot utility to set a new password for a local user offline, completely bypassing all logical authentication mechanisms. After reinserting the SD card into the Jetson Nano, we connected via the UART serial console (ttyTHS1), which remained physically accessible. We successfully authenticated with the new credentials and, because the user was still in the sudo group, escalated privileges to root. This demonstrated that while network access was secured, the lack of full-disk encryption created a critical physical vulnerability.  
**Insider Perspective:**  
From an insider perspective, our interaction with the Jetson Nano, even after its initial hardening with the AZTRM-D model, revealed a critical oversight related to physical access. While the logical security controls for user authentication, access to the Git Server, and development workflows were robust and consistently enforced, the unencrypted SD card presented a fundamental bypass. This meant that if an insider gained physical access to the device, they could remove the SD card, mount its filesystem externally, and gain unauthorized root access to the underlying operating system. This direct physical compromise would effectively circumvent the meticulous logical controls and audit trails designed for user interactions and software processes. The framework, at this stage, implicitly trusted the integrity of the hardware once physical access was gained. An insider with this level of access could then operate with unconstrained system privileges at the lowest level, outside the visibility and enforcement of AZTRM-D's software-based monitoring. This highlighted that while network attack vectors were successfully mitigated, the physical security of the storage medium remained a crucial unaddressed vulnerability, impacting the overall Zero Trust posture from an insider's viewpoint.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 3 Experiments  
### Experimental Platform  
The experimental platform is shown in Figure 5 and it consisted of a base station, a transmitting station and a mobile station. The base station comprised a GPS antenna and a receiver, which was used to obtain spatial correlation error information, to be used by the mobile station to correct its observations. The transmitting station, composed of a broadcast station and a broadcast antenna, broadcasted the acquired error information. The mobile station received the GPS signal and the error information broadcast by the transmitting station, and used them to quickly perform error correction and improve single-point positioning accuracy. In this experiment, the carrier phase collected by the base station was sent to the mobile station for precise positioning. This technology is called Real-Time Kinematic (RTK), and it can achieve single-point centimeter-level positioning accuracy. In the field experiments, in order for good single-point positioning accuracy to be obtained, the base station needed to be free of obstruction in the space above the 10\({}^{\circ}\) elevation mask angle and it was necessary that there were no strong electromagnetic radiation sources nearby. The mobile station could not be too far away from the base station. In this experiment, the distance between the mobile station and the base station was less than 500 m.
## 2 Related Technologies
### Merkel Hash Tree  
The Merkle hash tree (MHT), which was proposed by [PERSON] in 1988 [22], is a tree data structure used to verify data integrity. The structure of the MHT is illustrated in Figure 2. In this solution, the unique identifier (i.e., device ID) of each IoT device is generated using PUF technology, and then the hash value of each ID is calculated as a leaf node through a hash algorithm (SHA256). Next, the adjacent hash values are combined, and the hash values are recalculated as their parent nodes until only one root node remains, namely, Root Hash. Root Hash represents the integrity and reliability of the entire IoT network, stored on the blockchain and maintained and updated by all nodes.  
With the MHT, unique identifiers of IoT devices can be guaranteed against tampering and forgery. If the ID of any device changes, its hash value will change accordingly, resulting in changes in the entire MHT structure and Root Hash. In this way, any malicious attacker cannot alter the device ID without being detected, so as to guarantee the security and reliability of the IoT network.  
Figure 2: Merkel hash tree.  
Figure 3 exhibits the group authentication module of this paper. The MHT algorithm is mainly adopted in the upload node and the verification node, and the Root Hash of each device group is calculated by the ID of the space-time big data collection device. Root Hash (A) indicates the value obtained from all device IDs in the group stored in the initialization phase, and the default is a legal user. Root Hash (A\({}^{\prime}\)) reflects the value obtained from all device IDs in the group, and whether there are illegal users remains unclear. During the verification process, verification node B first receives all device IDs in group A to be verified and utilizes the MHT algorithm to obtain the Root Hash (A\({}^{\prime}\)) of the group to be verified. Afterward, the trusted Root Hash (A) in the blockchain is determined. Finally, Root Hash (A) and Root Hash (A\({}^{\prime}\)) are compared. If they are the same, the devices in the group are all legal. If they are different, there are illegal users in the group.
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 3 Proposed Method
### Description for Eclipse-Aware Routing (EAR)  
Algorithm 1 is Pseudo-code representing the Eclipse-Aware Routing method. The goal of this algorithm is to select a stable data transmission path while reducing energy consumption in the LEO satellite network. After performing the KSP algorithm, this algorithm takes K paths and compares the various conditions to select the optimal path. First, it initializes the energy consumption value for the basic work. Then it receives K paths and checks whether the satellites included in each path enter the eclipse zone. If a satellite exists in a solar eclipse zone, it calculates the duration of the eclipse and estimates the expected amount of communication during that period and the energy value used at that time. Next, the total energy consumption is calculated by summing the estimated energy value and the basic operating energy. Although the DoD is not explicitly calculated, it considers the risk of battery consumption for satellites with high traffic volume while staying in the eclipse zone and considers it indirectly. When evaluating a path, it calculates the maximum energy consumption of the path based on the satellite that consumes the most energy within each path and finally selects the path with the lowest value. This can prevent excessive load from being concentrated on a specific satellite and reducing battery efficiency and life. This approach is similar to previous studies predicting DoD through eclipse time and energy concentration without directly computing DoD [9; 11].  
The equation used in the EAR algorithm to estimate energy consumption during an eclipse period is as follows.  
\[Energy=A\ \times\ B+C\ \times\ (D+1) \tag{1}\]  
where the variables are defined as follows:  
\(A\): power consumption rate per unit time;  
\(B\): duration of the satellite eclipse;  
\(C\): energy required for a single data transmission or reception;  
\(D\): number of transmissions/receptions performed during the eclipse;
## 2 Materials and Methods
### Sampling and Measurements  
One representative soil sample was collected before the experiment for chemical soil analyses, in order to determine soil pH and the content of basic macro- and micronutrients in the soil. Soil samples for microbial and biochemical analyses were collected from each plot at three dates corresponding to the successive stages of spring barley development: 1--at the spring barley differing stage (BBCH 18-21), 2--at the grain filling stage (BBCH 74-75), and 3--after the harvesting. The samples were collected with an Egner's stick. In autumn, after the entire experiment had been set up, 15 soil samples were collected diagonally from a depth of 0-20 cm and thoroughly mixed. Next, one composite sample of about 0.5 kg was collected and placed in a marked plastic bag. The same method was applied to collect soil samples during the growing season of spring barley, although they were taken from the middle of each plot. On collection, the soil samples for microbial analyses were immediately placed in a refrigerator and stored at 2-5 \({}^{\circ}\)C. They were then placed in a portable cool box and transported to a laboratory at the Poznan University of Life Sciences.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{4}{c}{**Month**} & \multirow{2}{*}{**Average/Total**} \\ \cline{2-2} \cline{4-6}  & **April** & **May** & **June** & **July** & \\ \hline \multicolumn{6}{c}{Temperature (\({}^{\circ}\)C)} \\ \hline
2019 & 9.8 & 13.3 & 17.9 & 18.5 & 14.9 \\ \hline
2020 & 8.6 & 11.7 & 19.3 & 19.0 & 14.7 \\ \hline
2021 & 6.6 & 12.4 & 20.4 & 22.7 & 15.5 \\ \hline Long-term average 1980–2009 & 7.9 & 11.2 & 16.7 & 19.3 & 13.8 \\ \hline \multicolumn{6}{c}{Rainfall (mm)} \\ \hline
2019 & 5.9 & 59.8 & 35.9 & 29.7 & 131.3 \\ \hline
2020 & 6.0 & 63.5 & 118.5 & 67.7 & 255.7 \\ \hline
2021 & 42.0 & 29.5 & 33.8 & 50.0 & 155.3 \\ \hline Long-term average 1980–2009 & 49.6 & 48.2 & 60.7 & 45.7 & 204.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The temperature and rainfall during the growing season of spring barley according to the Weather Station in Zawady, Poland.
# Comparison of Three Biological Control Models of _Pycnoporus__sanguineus_ on Phytopathogenic Fungi
## 4 Discussion
It was not possible to observe growth restriction, as the pathogen was able to grow on the cinnabarin-impregnated discs, although it did slightly inhibit their growth, with values of 16.81% for _B. cinerea_ and 17.79% for _F. oxysporum_. These PIRG values were mostly lower than those of captan and benomyl(Table 1). While there are several studies evaluating the effect of cinnabarin on the growth of bacteria, its cytotoxic effects, and even its antiviral effects [30], very few studies have specifically evaluated the action of this compound on _B. cinerea_ and _F. oxysporum_ or other phytopathogenic fungi. Nevertheless, [PERSON] [6] and [PERSON] [31] evaluated the effectiveness of _P. sanguineus_ pigments against _Colletotrichum fragariae_, _Colletotrichum gloesporioides_, and _Botrytis cinerea_, finding considerable antifungal activity. In any case, based on the results of the present and previous studies, the use of complete extracts of _Pyc-noporus_ may represent a more effective action model against these or other fungi due to the inclusion of different components with potential antifungal activity, such as polyporin [32], ergosterol [33], 4H-pyran-4-one, and 2,3-dihydro-3,5-dihydroxy-6-methyl [25], as well as other enzymes and compounds involved in antioxidant processes [20; 34]. It is also important to highlight that the results demonstrate, as expected, the effectiveness of commercial products containing benomyl and captn, which generally resulted in the highest PIRG values among the tests conducted (Tables 1 and 2). However, it is essential to recognize the disadvantages of their use, as they may have a more significant impact in field application with all the associated repercussions [35; 36]. The results of this study highlight the importance of further research on the use of _Pyc-noporus_ compounds in biological pest and disease control. However, it is also important to consider expanding the number of phytopathogenic fungal species to be tested, delving into the compounds involved, and examining the differences that may exist between these in vitro models and in situ application contexts, which is a relatively unexplored but promising field.
## 2 Numerical Model and Calculation Scheme  
The calculation model employed in this study is a two-dimensional axisymmetric model derived from the real structure shown in Figure 3, as depicted in Figure 4, with respect to geometry. The cooling channel has a circular cross-section with an inner diameter of 1 mm and a length of 1000 mm. Along the cooling channel, two unheated adiabatic sections are used to ensure a fully developed flow and heat transfer of the fuel set at the inlet and outlet with a length of 100 mm. The tubes were subjected to a uniform heat flux. Within the unheated section, a section with a length of 800 mm was developed and heated using different uniform heat fluxes. The fuel was assumed to exhibit no slip at the wall.
# Generating Structurally Complete Stylish Chinese Font Based on Semi-Supervised Model
## 4 Experiments
### Experimental Results
#### 4.2.3 Comparison with Advanced Methods  
In this experimental section, we compared our proposed method with various alternative generative models. Since the zi2 zi method relied on paired datasets, we manually constructed two paired datasets for this paper. Due to the large number of Chinese characters, acquiring paired training datasets was challenging. Therefore, in these experimental methods, we focused solely on the task of generating _Regular Script_ and _Slu_ font styles from standard print. Figure 10 illustrates several examples of the generated artistic characters, revealing notable occurrences of spelling errors in the strokes when employing the CycleGAN model. Although StrokeGAN mitigated mode collapse and preserved stroke information, it still exhibited stroke redundancy. In comparison, zi2 zi produced better results due to the use of paired training data but still suffered from noise and missing strokes. In contrast, our proposed improved model effectively compensated for missing and redundant strokes in StrokeGAN, generating more realistic Chinese fonts than other methods, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Character Style Translation**} & \multicolumn{2}{c}{_Hanyi Linbo_} & \multicolumn{2}{c}{_Slu_} \\ \cline{2-5}  & **Content Acc** & **Stroke Error** & **Content Acc** & **Stroke Error** \\ \hline StrokeGAN & 86.1 & 7.47 & 89.5 & 5.94 \\ Without _S-S+SE_ & 86.9 & 7.01 & 90.3 & 5.55 \\ Without _Atten_ & 88.7 & 6.53 & 92.9 & 4.28 \\ Ours & **89.3** & **6.25** & **93.8** & **3.07** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of numerical results of ablation experiments.  
Figure 9: Our model underwent a series of ablation experiments. Red rectangles mark the gap in the results generated by different experiments.  
Table 5 presents the performance of the four methods. As expected, zi2 zi, which utilizes paired data, produces more recognizable results with lower stroke error rates compared to the other two methods. However, our method surpasses all others in these metrics and generates superior-quality artistic Chinese fonts with the lowest stroke error rate. Importantly, our method achieves these results using a limited amount of paired training data, confirming its effectiveness in generating high-quality artistic Chinese fonts.