esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Evaluation Metrics
#### 3.3.2 Accuracy  
The accuracy of a model is measured by the fraction of all true predictions by all predictions of the model. The formula to compute the accuracy is represented below in Equation (1).  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FP}+\text{TN}+\text{ FN}) \tag{1}\]  
It is a standard measure for evaluating classification tasks. However, accuracy is not always a reliable metric, especially in scenarios where the class distribution is imbalanced. For example, in a cancer diagnosis dataset where only 1% of the instances are cancerous, a model that predicts all instances as non-cancerous would still have a high accuracy rate.
# Identifying Human Daily Activity Types with Time-Aware Interactions
## 4 Experiment & Results
### Results  
The built graph structure has 41,917 nodes, including 17,918 word nodes, 23,701 post nodes, and 298 user nodes. These nodes are linked by 12,436,864 word-word edges, 1,666,595 word-post edges, and 23,701 user-post edges. We conducted experiments on the built dataset using baseline models and the proposed model. The initial feature vectors of unknown words are random initialized during evaluation, and the evaluation results of these models on the test dataset are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**ALSTM-DE**} & \multicolumn{2}{c}{**TG-HAGCN**} \\ \cline{2-7}
**HAT** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline Food & 0.806 & 0.832 & **0.963** & 0.966 & **0.963** & **0.970** \\ Beauty & 0.167 & 0.192 & 0.719 & 0.689 & **0.914** & **0.883** \\ Entertainment & 0.585 & 0.441 & 0.756 & 0.691 & **0.831** & **0.788** \\ Travel & 0.622 & 0.578 & 0.822 & 0.833 & **0.849** & **0.859** \\ Shopping & 0.742 & 0.654 & 0.786 & 0.829 & **0.876** & **0.874** \\ Services & **0.782** & **0.760** & 0.530 & 0.557 & 0.708 & 0.709 \\ Sports & 0.649 & 0.577 & 0.740 & 0.755 & **0.841** & **0.866** \\ Health & 0.083 & 0.124 & 0.765 & 0.491 & **0.963** & **0.825** \\ Car & 0.640 & 0.689 & 0.800 & 0.706 & **0.832** & **0.804** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with baseline models (where “Acc” means accuracy, “F1” means F1-score, “Mac” and “Mic” are the prefixes of “Acc” and “F1” to indicate macro/micro-average accuracy and F1-score). For each row, the best Acc and F1 are indicated in bold.  
As shown in Table 3, TG-HAGCN model achieves the best performance on both Mic accuracy and Mac accuracy. It should be noted that CNN models make very lower Mac accuracy, which may be raised by the unbalance of training sample.  
To observe the misclassify item, we plotted the confusion matrixes of different models in Figure 3. The figure shows that our model performed the best for dealing with category confusion in the unbalanced dataset. We argue that TG-HAGCN model may perform better in other tasks.  
Comparatively, the proposed model captures the network structure (interaction) between nodes and edges by exchanging features with their neighbors in training process. In this way, the samples in each category are not trained independently any more, hence, achieving best Mac accuracy in those models. In addition, the TG-HAGCN model has enough features from interactions to make accurate activity classification in a small dataset. Besides, the model performs well in the recognition of activity types in the unbalanced dataset. The results suggest that interactions and date information can supplement additional information, and this information together with methods to fuse them should be considered on improving the model performance in related works.
## 4 Experimental Results and Discussions
### Calculation of Focal Length and Beam Size  
The measured focal length of the sample fiber with L\({}_{\text{C}}\) = 308 \(\upmu\)m and R = 72.8 \(\upmu\)m was 375 \(\upmu\)m; on the other hand, the theoretical focal length was 406 \(\upmu\)m, corresponding to a difference of 31 \(\upmu\)m. This difference between the measured and calculated focal lengths may be due to the uncertainty in the MFD of the SMF. The MFD of the SMF-28 communication optical fiber (Corning Corp.) used in this study was 10.4 \(\pm\) 0.5 \(\upmu\)m. Figure 8 shows the graph obtained by substituting the values of the nearby MFDs within the error range. We note that as the MFD value decreases by 0.1 \(\upmu\)m, the focal length is sensitively changed by 10 \(\upmu\)m. The red dotted line in Figure 8 indicates the focal length obtained by substituting the average MFD value of 5.2 \(\upmu\)m to obtain the theoretical focal length. We note here that the error further depends on the optical-alignment conditions.  
The beam size for the focal length of 406 \(\upmu\)m was also measured using the knife-edge method, and it was estimated as 27 \(\upmu\)m at 1/e\({}^{2}\) of the peak intensity. When compared with the theoretical beam size of 23 \(\upmu\)m at the 2\(\omega_{x}\) point, the error was 4 \(\upmu\)m.
# A Comprehensive Review of Micro UAV Charging Techniques
## 4 Wireless Power Transfer (WPT)
### Laser Power Transfer (LPT)
#### 4.1.1 PV Cell Selection  
The receiver component of any LPT system should be carefully designed for the efficient conversion of optical signals into electricity. The most common conversion technologies are photovoltaic, pyroelectric and thermoelectric. Among these technologies, photovoltaic technology is considered the most mature and delivers the highest efficiency when delivering high power at longer distances. For efficient performance, laser power, wavelength, PV cell material and temperature should all be taken into account. For any PV cell receiver, the photon energy must be equal to or higher than the bandgap energy of the material. This photon energy is proportional to its frequency. Hence, a monochromatic light with an ideal frequency is considered an ideal light source. Figure 11 illustrates the spectral response of various PV cells [90].  
Figure 10: Redranging of multiple UAVs through laser beams [89].  
It can be seen from the graph curves that commonly available GaAs and Si PV cells give the highest conversion efficiencies at 850 nm and 900 nm, respectively. While AlGaAs gives the highest response from 550-600 nm. A PV cell gives efficient response for a given temperature and generates more power if it is illuminated with an intense light beam [91]. Thus, PV cells operate efficiently at high laser power intensity. Generally, GaAs [92] and Si [93] are often used, but it is also important to develop the optical receiving device according to the wavelength of the optical source in order to enhance the overall efficiency of the system.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 3 Protocol Specification  
### Overview  
This section describes the redesigned Modbus-UDP protocol in detail. In particular, Section 3.2 discusses its frame format, its encapsulation down to the physical layer, and the way Modbus-UDP datagrams are addressed to support broadcast beyond unicast communication. Moreover, it contains some frame length calculations used in Section 4 for performance evaluation. Then, Section 3.3 specifies how the MBAP header, already present in Modbus-TCP, has been redefined in a backward-compatible way to support broadcast communication and multi-master systems.  
The discussion continues with Section 3.4, which explains how the Transaction Identifier (TID) field of the MBAP header is generated and checked to assist the automatic retransmission of UDP datagrams, whose mechanism is illustrated in Section 3.5 and which makes Modbus-UDP robust with respect to network errors (as shown in Section 4.4). Finally, Section 3.6 describes the packet prioritization mechanisms that Modbus-UDP leverages at L3 and L2 to reach a satisfactory real-time performance (evaluated in Section 4.3) and make it virtually insensitive to other network traffic (see Section 4.6).
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
###### Abstract  
Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function's call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of \(13.7\times\), \(1.50\times\), \(9.78\times\), and \(2.04\times\) faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools.  
software security; software testing; fuzzing Article
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 2 Background
### GNSS Fundamentals  
The navigation link component utilizes GNSS technology. Current GNSS systems include Global Positioning System (GPS), GLONASS, Galileo, BeiDou and NavIC [22].  
GNSS satellites transmit navigation signals in the L-band across multiple frequencies, enabling users to determine the signal's travel time and the satellites' position. These signals consist of several key components. The first is the carrier, an RF sinusoidal wave transmitted at a specific frequency. Another essential element is the ranging code, also called Pseudo-Random Noise sequences. These codes allow the receiver to calculate the signal's time of flight (TOF), a technique used in both GPS Course Acquisition (C/A) and P codes. Lastly, the binary message navigation data provide important information such as satellite ephemeris, clock bias parameters, and health status.  
These elements are fundamental for commercial drones, especially for the \"return to home\" function that activates upon signal loss. Among the systems, GPS and Galileo provide interoperability in the EU.  
GPS works on three bands: L1 (1575.42 MHz, 30 MHz bandwidth), L2 (1227.60 MHz, 30 MHz bandwidth), and L5 (1176.45 MHz, 26 MHz bandwidth) [23].  
Galileo operates on three primary frequency bands: E1, E5, and E6. The E1 band has a bandwidth of 24.552 MHz with a carrier frequency of 1575.42 MHz. The E5 band consists of two sub-bands: E5a (20.460 MHz bandwidth, 1176.45 MHz carrier) and E5b (20.460 MHz bandwidth, 1207.14 MHz carrier). The E6 band offers a bandwidth of 40.920 MHz at a carrier frequency of 1278.75 MHz [24].
## 4 Simulation Results
### Network Modeling  
The following assumptions are made about the network:  
1. Each node has a distinct identification;
2. Links between nodes are symmetric; if there is a link from \(i\) to \(j\), there is a reverse link from \(j\) to \(i\);
3. Nodes do not know their geographical position;
4. The sink is located at (0,0), the origin;
5. Sensor nodes are equal in terms of processing power, radio, battery and memory, except the sink node;
6. The CSMA/CA (carrier sense multiple access/collision avoidance) protocol is used for medium access.  
We model the WSN as a connected graph \(G=(V,E)\). A graph \(G=(V,E)\) consists of a finite nonempty set \(V\) of vertices and a finite set \(E\subseteq V\times V\) of arcs [20]. Vertices are simple objects that can have names and attributes, and edges are connections between two vertices. Sets \(V\) and \(E\) represent, respectively, the node set and the link set. There is an edge (_i,j_) if the maximum transmission radius \(r\) of both \(i\) and \(j\) is at least their Euclidean distance \(((i,j)\Leftrightarrow|dist_{i,j}|\leq r)\). The average degree \(k=\frac{2*|E|}{|V|}\) is the relation between the number of edges and the number of nodes. We consider grid network topologies with random disturbance in the position of nodes. To perform the simulations, we used the software, Mathematica [26], along with the SensorSim library [27]. The SensorSim library uses the transmission parameters of the IEEE 802.15.4 [25].
## 4 Training and Simulation
### Comparative Analysis of the Control Effects in the MBS Simulations
#### 4.3.2 Curve-Negotiation Performance  
Figure 9 shows the simulation results for the IRW vehicle running on a 1500 m radius curve at 150 km/h. The curved line includes a circular arc track between short, straight lines and transition curves. Compared with the Hoo robust controller, the PER-MADDPG controller effectively reduced the maximum lateral displacement of the front IRW from 2.61 mm to 2.39 mm and the maximum lateral displacement of the rear IRW from 0.91 mm to 0.64 mm. The yaw angles of both independently rotating wheelsets also decreased significantly, from 1.62/0.79 mrad to 1.05/0.73 mrad. This reduction in yaw angle, resulting in diminished wheel-rail forces, led to a notable decrease in the maximum wear number of the wheelsets on the curve, from 644.4 N and 113.54 N under Hoo control to 458.7 N and 65.4 N under PER-MADDPG control, representing reductions of 28.8% and 42.4%, respectively. The peak torques for both the front and rear wheelsets remained below the  
Figure 8: Straight line cosimulation results (\(V_{0}\) = 120 km/h). (**a**) Lateral displacement of the wheelsets, (**b**) Yaw angle of the wheelsets, (**c**) Wear number of the wheelsets, (**d**) Controller output.  
2 kN\(\cdot\)m maximum threshold. Additionally, the wheelset dynamic response indicated that upon exiting the curve, the active control swiftly reestablished straight-line centering.  
Through this comparative analysis of the running performance of the front and rear IRWs under various straight and curved conditions, it is evident that the PER-MADDPG controller significantly enhances the dynamic performance of the IRWs. This improvement is revealed by better stability on straight tracks, improved curve-negotiation capabilities, and reduced wheel-rail wear, all while ensuring that the controller's outputs remained within the design limit.
# Characteristics of the Damping Ratio of Undisturbed Offshore Silty Clay in Eastern Guangdong, China
## 4 Conclusions  
1. Soil-pile interaction damping plays a significant role in reducing loads on offshore wind turbine structures, and the soil-element damping ratio is fundamental for studying this interaction. Silty clay, which is widely distributed along China's coastline, is an important soil type. However, current research on soil damping has primarily focused on clay and sand, with relatively little attention given to silty soils. Therefore, targeted studies on the damping ratio of typical silty clay are essential for optimizing offshore wind power designs in China.
2. The damping behaviors of both overconsolidated and normally consolidated silty clay are nearly identical. The influence of cyclic stress history on the damping ratio is significant.
3. Currently, no empirical model accurately predicts the damping ratio of silty clay across the entire range of low-to-high strains. While the [PERSON] and [PERSON] et al. models provide relatively accurate predictions at low strain levels, they tend to overestimate the damping of silty clay at medium-to-high strains. This limitation should be considered when empirical models are used in practical engineering applications in place of test data.
4. Given the significant challenges and high costs associated with obtaining undisturbed silty clay samples, this study is not exhaustive and does not encompass parametric or mechanistic analyses. Future research should aim to further investigate the damping characteristics of silty clay and silty sand, with the objective of developing a universal empirical damping ratio model for typical silty soils in China. Such a model would have substantial implications for offshore seismic engineering and the optimization of offshore wind power designs.  
Figure 12: Comparison between Experimental Results and Predicted Results for Silty Clay Damping Ratio (Normally Consolidated) [15; 16; 28].  
**Author Contributions:** Conceptualization, P.G. and Y.Z.; methodology, P.G.; validation, Q.B.; formal analysis, P.G.; investigation, P.G.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, P.G.; writing--review and editing, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant no. 52471273.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors would like to acknowledge the financial support received through the China Natural Science Foundation (grant 52471273).  
The authors declare no conflict of interest.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.5 Monte Carlo Tree Search  
In the context of solving the 3D-Bin Packing Problem (3D-BPP), the application of the Monte Carlo Tree Search (MCTS) algorithm has proven to be a pivotal technique in various research approaches.  
In their study [22], [PERSON] et al. harness MCTS as a cornerstone of their 3D-BPP solution driven by reinforcement learning. MCTS, known for its ability to navigate combinatorial spaces represented as trees, offers an effective strategy for decision making. In the context of 3D-BPP, the nodes of this tree represent different problem states or configurations. Notably, the authors adapt the AlphaGo algorithm for operation in single-player and score-based scenarios. They introduce a buffer to facilitate multi-item action selection and employ a data augmentation strategy. This comprehensive approach demonstrates MCTS's adaptability and potential in reinforcement-learning-driven solutions for complex optimisation problems.  
The study by [PERSON] and [PERSON] [34] introduces MCTS as a key component of another approach to tackle the 3D-BPP, specifically under incomplete information. The algorithm begins by selecting the most optimal node at the current state to expand the search tree. Subsequently, it conducts simulations to evaluate potential outcomes of various actions, which, in the context of 3D-BPP, equates to estimating the effectiveness of stacking actions. The outcomes of these simulations play a pivotal role in updating the status of nodes in the search tree. This information is then propagated back up the tree, informing the selection process in subsequent iterations. In this way, MCTS enhances the decision-making process, especially in scenarios where information is not complete, and decisions must rely on estimating outcomes.  
MCTS's efficiency and forward-looking capabilities are leveraged in the 3D-BPP solution presented by [PERSON] et al. [35]. In a unique integration, MCTS collaborates with a robotic system tasked with packing objects into pallets. This application of MCTS in a robotic context is particularly valuable for its ability to simulate potential future moves and evaluate their consequences. In the context of packing, it means considering how placing an object in a specific location may impact the available space for subsequent objects, thereby optimising the packing process and enhancing efficiency.
## 2 Measurement Principle of Photoacoustic Spectroscopy  
Wavelength modulation spectroscopy is widely used in various spectroscopy systems to reduce noise interference. In wavelength modulation photoacoustic spectroscopy, the second-harmonic PAS signal (\(S_{2f}\)) is used to infer the target gas concentration by the following expression [29]:  
\[S_{2f}=S_{m}cFI_{0}cos(\psi)\times H_{2}(v_{0}) \tag{1}\]  
where \(S_{m}\) is the microphone sensitivity, \(c\) is the trace gas concentration, \(F\) is the PAS cell constant, \(I_{0}\) is the light intensity of laser, \(\psi\) is the phase difference between the driving signal and the laser amplitude modulation, \(v_{0}\) is absorption line center, and \(H_{2}\) is the second-harmonic magnitude derived from the Fourier cosine series expansion of the absorption profile at the absorption line center. This formula indicates that the signal intensity is directly determined by the cell constant (see Formula (3) below) and the intensity of the laser beam.  
The acoustic signal within a resonator highly depends on the spatial overlap between the propagating laser beam and the pressure distribution of the nth acoustic eigenmode of the resonator. The amplitude (\(A_{n}\)) of the nth intrinsic mode component sound pressure signal generated by light absorption is expressed by the following formula [30]:  
\[A_{n}=C_{n}(\omega_{n})\alpha W_{L} \tag{2}\]  
where \(C_{n}(\omega_{n})\) denotes the sensitivity of the PA resonator at a given resonance frequency, \(W_{L}\) denotes the light power, and \(\alpha\) is the absorption coefficient.  
The cell constant \(C_{n}\) at the resonance frequency \(\omega_{n}\) of the nth eigenmode can be written as [30]:  
\[C_{n}(\omega_{n})=\frac{(\sigma-1)LF_{n}p_{n}(r_{M})}{V_{cell}}\frac{Q_{n}}{ \omega_{n}} \tag{3}\]  
where \(\sigma\) is the adiabatic coefficient of the gas, \(L\) is the absorption length, \(F_{n}\) is the normalized overlap integral, \(p_{n}(r_{M})\) is the dimensionless eigenmode distribution, \(V_{cell}\) is the volume of the cavity, and \(Q_{n}\) is the quality factor of the resonance. The sound pressure is inversely proportional to an effective cross-section defined by \(\frac{L}{V_{cell}}\). For a cylindrical PA cell, \(\frac{L}{V_{cell}}\) is equal to its cross-sectional area, which is positively correlated with radius of resonant cavity.
## 1 Introduction
Nevertheless, the research on the application of digital image photogrammetry to the monitoring of MSE walls is relatively limited. This study aims to determine whether digital image photogrammetry equipment, including a digital camera and commercial program, can accurately measure the MSE wall deformation in areas with restricted-access and security due to the current situation in South Korea. The applicability evaluation was conducted by comparing and analyzing the measurement results of traditional monitoring and digital image photogrammetry methods for MSE wall displacement in restricted-access and controlled areas. That is, this study consists of field investigations, monitoring results and analyses, error rate evaluations, and discussions, and the experimental study was conducted using the following methodology. The electrical resistivity survey was conducted to evaluate the cause of the displacement of the MSE wall, and the monitoring target location was determined from the results. Then, the monitoring of the MSE wall using the traditional monitoring method and digital image photogrammetry was conducted. In addition, the displacement facing the MSE wall was analyzed using the monitoring results, and the error was evaluated. Finally, the limitations of this study and the need for additional research were suggested based on the error rate analysis results. The results are directed toward increasing the utility of the applied technology by enabling rapid measurements of infrastructure in areas with very limited access.