esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Road inspection and maintenance require a large amount of data collection, where the main limiting factor is the time required to cover long stretches of road, having a negative impact on the optimization of the work. This article aims to identify modern tools for road maintenance and analysis. To carry out the research, recent methodologies are used to guide the work in different stages to adequately justify the processes involved. Using unmanned aerial vehicles (UAVs), cameras, and GPS, three-dimensional virtual models are reconstructed, which are useful for extracting the necessary information since they allow for accurate replication of the captured. In this way, it is possible to obtain longitudinal profiles associated with the road, and with it, the international roughness index (IRI) is calculated, which gives results within 0.1 (m/km) of the certified official results, which shows its potential use and development.  
road engineering; unmanned aerial vehicles (UAVs); photogrammetry; longitudinal profile; international roughness index; IRI +
Footnote †: journal: Applied  
_Article_  
Use of Unmanned Aerial Vehicles (UAVs) and Photogrammetry to Obtain the International  
Roughness Index (IRI) on Roads
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Inst. Strain**} & \multicolumn{5}{c}{**Located at y3 at Main Member**} \\ \cline{3-6}  & & & **Right** & **Percentage Error (\%)** & **Left** & **Percentage Error (\%)** \\ \hline \multirow{3}{*}{Current cross-arm} & Experimental data & \(\epsilon\) (\(10^{-3}\)) & 1.006 & - & 0.988 & - \\  & Findley model & \(\epsilon_{o}\) (\(10^{-3}\)) & 1.010 & 0.398 & 0.994 & 0.604 \\  & Burger model & \(\epsilon_{o}\) (\(10^{-3}\)) & 1.010 & 0.398 & 0.986 & 0.202 \\ \hline \multirow{3}{*}{Braced cross-arm} & Experimental data & \(\epsilon\) (\(10^{-3}\)) & 0.806 & - & 0.731 & - \\  & Findley model & \(\epsilon_{o}\) (\(10^{-3}\)) & 0.798 & 0.993 & 0.722 & 1.231 \\ \cline{1-1}  & Burger model & \(\epsilon_{o}\) (\(10^{-3}\)) & 0.806 & 0.000 & 0.756 & 3.420 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of instantaneous strain value between experimental outputs and numerical models located at y3 for current and braced wooden cross-arms.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 3 The Proposed Robust Attack Path Tracing Algorithm
### Assumptions and Features  
The algorithm assumes that any router within the attack path is willing to cooperate and be enabled with the RAPT algorithm; to this end, it becomes clear that the algorithm does not only have to distinguish the source of the attacking packet, but also to identify the entire path between the source and the victim. If a path can be traced through any number of flawless or faulty routers, it must either terminate at the source or pass through a faulty router. This is why it is important to construct the attack path that contains every router in which the potential daemon packet passes through its journey from source to victim. Additionally, since multiple packets may be injected into the network from different sources (daemons), the tracing algorithm must generate an attack graph composed of the attack path for each instance of the attack packets delivered to the victim. Figure 2 describes the network as seen by the victim with a possible attack on the victim from daemons D1 through D6 forming five different attack paths represented by the tuples {R1, R6, R7}, {R2, R6, R7}, {R2, R5, R7}, {R2, R5, R8}, and {R3, R4, R8}. The daemons are assumed to collude together to initiate a synchronized attack targeting the victim.  
Although there are several solutions proposed for DDoS attacks phenomenon in the literature, the features of the proposed solution make it unique. The objective of the RAPT algorithm is to find a feasible solution through an IP traceback approach to detect the source of the attack. The following criteria must be met in order to have a feasible solution:  
* It must track down and stop the SYN-Flood attack;
* It must be robust against distributed attacks;
* The detection time must be near-optimal;It should not dramatically affect the victim and/or network performance; and Administrative cost should be minimal.
## 3 Energy Management Strategy of FCHEV
### Speed Prediction  
An LSTM neural network is used to predict the future speed, which provides the speed data for the subsequent adaptive adjustment of the equivalence factor. To ensure that the proposed speed prediction model has a high prediction accuracy and effectiveness in a wide range of driving scenarios, eight combinations of driving conditions that cover most of the driving scenarios in daily life were used as the training sample database for the prediction model. The combined operating conditions are shown in Figure 7.  
The acceleration and historical speed data were selected as the input of the LSTM neural network speed prediction model, and a multi-step prediction of the future speed was obtained by recursive methods. The specific steps of the model training and prediction were as follows:  
1. Various driving conditions were combined to build a database of historical speed samples. The historical \(N\) s speed samples were selected in a sliding window and the acceleration corresponding to the vehicle speed at the current moment was introduced as a characteristic parameter to build the input \(X_{in}\) and the output vectors \(X_{out}\): \[X_{in}=\Big{[}v_{(t-N)^{\prime}},v_{(t-N+1)},\cdots,v_{(t-1)^{\prime}},v_{t},a \Big{]},\] (23) \[X_{out}=\Big{[}v_{(t+1)}\Big{]};\] (24)
2. The size of the training sample data set was \(S\). The sliding step was set to \(1\) s, and the model selected the first \(N\) s historical speed data of the training sample database to train based on the sliding step. The model training sample input matrix \(X_{in_{-}M}\) and output matrix \(X_{out_{-}M}\) were then obtained:  
Figure 7: Overall framework of energy management strategy.  
\[X_{in_{-}M}=\begin{bmatrix}v_{1}&\cdots&v_{N}&a_{N}\\ v_{2}&\cdots&v_{(N+1)}&a_{(N+1)}\\ \vdots&\vdots&\vdots&\vdots\\ v_{S-(N-3)}&\cdots&v_{(S-2)}&a_{(S-2)}\\ v_{S-(N-2)}&\cdots&v_{(S-1)}&a_{(S-1)}\end{bmatrix}, \tag{25}\] \[X_{out_{-}M}=\begin{bmatrix}v_{(N+1)}\\ v_{(N+2)}\\ \vdots\\ v_{(S-1)}\\ v_{S}\end{bmatrix}; \tag{26}\]
3. After the training of the LSTM speed prediction model was complete, the first N s historical speed data in the test sample database were selected as the sliding window, and the historical speed data were input into the trained model according to the input matrix of the test sample to predict the speed 1 s in the future. At this time, the sliding window was shifted to the right in accordance with the step length, and the 1 s speed data obtained from the prediction was added to the right side of the sliding window to form a new input, which was then input into the model to predict the speed 2 s in the future. The above operation was repeated to realize the multi-step prediction of the future speed.
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 4 Carrier-To-Noise Density Ratio
### C/N\({}_{0}\) Statistical Characteristics of IGSO Satellites  
Similar to GEO satellites, IGSO satellites are also high-orbit satellites. However, the orbital elevation and visibility of IGSO satellites are constantly changing, which is similar to MEO satellites. The results of all IGSO satellites (including 7 BDS-2 satellites and 3 BDS-3 satellites) are shown in Table 3. It can be found that the average elevation of all 7 IGSO satellites of BDS-2 is 54.67 degrees, which is almost the same as the average elevation of 54.88 degrees of the two IGSO satellites of BDS-3. At the B1I frequency, the C/N\({}_{0}\) value of BDS-3 is dB/Hz higher than that of BDS-2, while at the B3I frequency, the two are almost equal. The average C/N\({}_{0}\) value of the new signal B1C of the BDS-3 IGSO satellite is 45.76 dB/Hz, while the average C/N\({}_{0}\) value of B2a is 49.31 dB/Hz, which is significantly higher than the signals at all other frequency points. Therefore, the B1I and B3I signals of the BDS-3 IGSO satellites have less improvement than the BDS-2 satellite, while the B1C and B2a have better signal strength, especially the signal strength of B2a is nearly 3 dB/Hz higher than other frequencies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Satellite** &
\begin{tabular}{c} **Elevation** \\ **(Average)** \\ \end{tabular} & **B1I** & **B2I** & **B3I** \\ \hline \multirow{8}{*}{**BDS-2**} & average & 35.04 & 37.59 & 40.34 & 42.62 \\  & C01 & 35.31 & 39.01 & 40.36 & 42.94 \\  & C02 & 42.15 & 37.49 & 41.72 & 44.75 \\  & C03 & 50.27 & 41.30 & 43.51 & 46.05 \\  & C04 & 23.40 & 35.35 & 37.75 & 39.72 \\  & C05 & 24.06 & 34.80 & 38.37 & 39.64 \\  & average & 35.31 & 42.82 & & 44.57 \\
**BDS-3** & C59 & 35.42 & 41.95 & & 44.50 \\  & C60 & 35.20 & 43.69 & & 44.64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results of C/N\({}_{0}\) of all BDS-2 and BDS-3 GEO satellites (dB/Hz).
## 4 Experimental Evaluation
### User Study
\begin{table}
\begin{tabular}{p{42.7 pt} p{284.5 pt}} \hline \hline \# & **Statement** \\ \hline Q1 & I have the feeling of trust in the system without explanations regarding its prediction of the sleep quality. \\ \hline Q2 & I have the feeling of trust in the system with explanations regarding its prediction of the sleep quality. \\ \hline Q3 & The system without explanations can assist me in improving my sleep quality. \\ \hline Q4 & The system with explanations can assist me in improving my sleep quality. \\ \hline Q5 & The explanations would help me decide whether the forecast of the system is correct. \\ \hline Q6 & Explanations are easily understandable. \\ \hline Q7 & Explanations are detailed enough. \\ \hline Q8 & Explanations are not lengthy. \\ \hline Q9 & Explanations are useful to understand the reason for the system’s forecast. \\ \hline Q10 & Explanations help reduce the learning time on the system. \\ \hline Q11 & In general, I am satisfied with the explanations provided by the system. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Questionnaire for the user study. provided by the system are considered easily understandable and sufficiently detailed. Moreover, users appreciate the clarity and accessibility of the explanations. Furthermore, users believe that the explanations are useful in understanding the system's reasoning (Q9, average score 4.1) and reducing the time required to learn how the system functions. This suggests that the explanations provide valuable insights into the system's decision-making process, helping users to grasp its functioning more efficiently. However, it is worth noting that the average score for the question about the length of explanations (Q8) is 2.4. This suggests that users generally would like the explanations to be more concise, aiming for greater efficiency. Overall, the system with explanations appears to have a positive impact on the user experience compared to the system without explanations. Users find the explanations easily understandable, appreciate their level of detail, and perceive them as helpful in understanding the system's reasoning and reducing the learning time.
# FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection
## 3 InSAR Imaging via FDBPA
### Algorithm Flow
(2015)_ [PERSON]; [PERSON]; [PERSON] Frequency-Domain Backprojection Algorithm for Synthetic Aperture Radar Imaging. _IEEE Geosci. Remote Sens. Lett._**2015**, _12_, 905-909. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A synthetic bandwidth method based on frequency-domain back projection for stepped-frequency SAR. _Remote Sens. Lett._**2017**, \(8\), 743-751. [CrossRef]* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] A fast three-dimensional frequency-domain back projection imaging algorithm based on GPU. In Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA, 23-27 April 2018; pp. 1173-1177. * [PERSON] and [PERSON] (1998) [PERSON]; [PERSON] Synthetic aperture radar interferometry. _Inverse Probl._**1998**, _14_. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A tutorial on synthetic aperture radar. _IEEE Geosci. Remote Sens. Mag._**2019**, \(1\), 6-43. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Phase unwrapping in InSAR: A review. _IEEE Geosci. Remote Sens. Mag._**2019**, \(7\), 40-58. [CrossRef]
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# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis  
#### 5.3.1 Mutual Authentication  
In the proposed scheme, \(DC_{j}\) authenticates \(SM_{i}\) by confirming whether the equation \(M_{4}^{*}=M_{4}\) is established, and \(SM_{i}\) authenticates \(DC_{j}\) by confirming whether the equation \(M_{8}^{*}=M_{8}\) is established. After mutual authentication, SM and DC negotiate the session key \(SK\) to build a secure channel.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 2 Background
### Chinese Remainder Theorem [14]  
The Chinese Remainder Theorem (CRT) is a fundamental result in number theory that provides a solution to a system of simultaneous congruences with pairwise coprime moduli. The theorem can be stated as follows:  
Let \(n_{1},n_{2},\ldots,n_{k}\) be pairwise coprime positive integers, and let \(a_{1},a_{2},\ldots,a_{k}\) be integers. Then, there exists an integer \(x\) that simultaneously satisfies the system of congruences:  
\[x \equiv a_{1}\pmod{n_{1}},\] \[x \equiv a_{2}\pmod{n_{2}},\] \[\vdots\] \[x \equiv a_{k}\pmod{n_{k}}.\]The solution \(x\) is unique modulo \(N\), where \(N\) is the product of all moduli, i.e., \(N=n_{1}\cdot n_{2}\cdot\ldots\cdot n_{k}\). The solution can be constructed explicitly using the following formula:  
\[x\equiv\sum_{i=1}^{k}a_{i}\cdot N_{i}\cdot N_{i}^{-1}\pmod{N},\]  
where \(N_{i}=\frac{N}{n_{i}}\) and \(N_{i}^{-1}\) is the modular multiplicative inverse of \(N_{i}\) modulo \(n_{i}\), which satisfies the equation:  
\[N_{i}\cdot N_{i}^{-1}\equiv 1\pmod{n_{i}}.\]  
This construction ensures that each term \(a_{i}\cdot N_{i}\cdot N_{i}^{-1}\) contributes only to the congruence condition for \(n_{i}\), thus satisfying all the given congruences simultaneously. The Chinese Remainder Theorem is not only a powerful theoretical tool in number theory but also has practical applications in computer science, cryptography and coding theory, where it is used to simplify computations by breaking them into smaller, more manageable parts.  
In particular, in the process of finding the polynomials \(F\) and \(G\) that satisfy the condition \(fG-gF=1\) in the NTRUsolve algorithm (in the HAWK key-generation process), the method of handling polynomial coefficients independently under several small prime moduli and then combining them using the Chinese Remainder Theorem (CRT) simplifies the system's computations. This approach offers the advantage of compensating for computation failures in specific moduli with successful computations in other moduli.
# Remote Sensing Road Extraction by Road Segmentation Network
## 4 Conclusions and Future Work  
In this paper, an end-to-end road segmentation network is proposed to extract the road from high-resolution remote sensing images. Although the existing CNN architectures have made achievements, they have not explored enough the context features with adjacent layers of CNN. In this paper, an inter-layer self-attention mechanism was designed to  
Figure 4: Examples of road extraction obtained by the compared methods. (**a–c**) Selected from the DeepGlobe dataset, and (**d–f**) showed on the Massachusetts dataset.  
obtain road context information. The roads in images always have the shape of a thin stripe. The proposed method introduces the strip pooling in the refinement network to better restore the topology and long-distance dependence of roads. The whole network achieved better performance by enhancing the context feature in adjacent layer and strip feature of roads in refined processing. The quantitative results demonstrate the effectiveness of the proposed method and the superiority of road extraction from high-resolution remote sensing images. The improvements of our proposed method obtained on two datasets can be mainly attributed to two designs. The first factor is that the context feature enhanced by the attention mechanism used in adjacent layers guarantees the correct identification of road pixels. The second factor is that the consideration of the strip characteristics of the road in the refinement network makes the extracted roads retain a better topology.  
Although the proposed method is successfully applied to remote sensing road extraction, there are still some possible extensions in the future work. This paper only exploits remote sensing images to extract the road, which is difficult with complex scenarios [22] (dense urban area, different viewpoints). Future work will tackle the complex scenarios by integrating multiple data sources, such as multi-temporal images [24], and multi-modal data [25].  
Conceptualization, J.T. and M.G.; methodology, J.T.; software, J.T.; validation, J.T., K.Y., and T.D.; formal analysis, T.D.; investigation, J.T.; resources, M.G.; data curation, J.T.; writing--original draft preparation, J.T.; writing--review and editing, K.Y.; visualization, K.Y.; supervision, M.G.; project administration, M.G.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Research Fund of State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences under Grant SKLST202005.  
Not applicable.  
The authors would like to thank the editors and reviewers for their insightful suggestions.  
The authors declare no conflict of interest.
## 2 Localization Solution
### Considerations on Beacon Placement  
The optimal placement of beacons in a given environment is a complex problem that warrants its own dedicated study. It depends on various factors specific to each implementation scenario, including room geometry, potential obstructions, expected robot paths, and desired coverage area. While a comprehensive analysis of optimal beacon placement is beyond the scope of this paper, it is important to note that our system's flexibility allows for beacon positions to be optimized during the installation phase. This adaptability enables the system to be fine-tuned to best suit the particular requirements and constraints of each environment. Factors that might influence beacon placement include the following:  
* Maximizing coverage area;
* Minimizing geometric dilution of precision;
* Avoiding areas of potential signal interference;
* Accommodating architectural features or obstacles.  
The process of determining optimal beacon placement could involve techniques such as computer simulations, on-site testing, or even machine learning algorithms. However, such optimization methods represent a separate field of study and are not explored in detail in this work. Our focus remains on the fundamental principles and advantages of the proposed localization system, with the understanding that practical implementations would involve additional considerations for beacon placement.
# Reverse Engineering and Topology Optimization for Weight-Reduction of a Bell-Crank
###### Abstract  
This paper describes a new design method that was developed to achieve an optimal design method for weight reduction of a bell crank, sourced from a Louis Christen Road Racing F1 Sidecar. The method involved reverse engineering to produce a 3D model of the mechanical part. The 3D bell crank model was converted to a finite element (FE) model to characterize the eigenvalues of vibration and responses to excitation using the Lanczos iteration method in Abaqus software. The bell crank part was also tested using a laser vibrometer to capture its natural frequencies and corresponding vibration mode shapes. The test results were used to validate the FE model, which was then analysed through a topology optimization process. The objective function was the weight and the optimization constraints were the stiffness and the strain energy of the structure. The optimized design was converted back to a 3D model and then fabricated to produce a physical prototype for design verification and validation by means of FE analysis and laboratory experiments and then compared with the original part. Results indicated that weight reduction was achieved while also increasing the natural frequency by 2%, reducing the maximum principal strain and maximum von Mises stress by 4% and 16.5%, respectively, for the optimized design when compared with the original design. The results showed that the proposed method is applicable and effective in topology optimization to obtain a lightweight (~3% weight saving) and structurally strong design.  
bell crank; topology optimization; natural frequency; reverse engineering; vibrometer; design; Abaqus +
Footnote †: journal: Applied Soft. 2020, 10, 8568; doi:10.3390/app10238568