esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Multimedia communication has attracted the interest of the research community [1]. Multimedia information includes several applications like television, chatting, gaming, internet, video/audio-on-demand, video conferencing, _etc._[2]. Due to the rapid growth of multimedia applications, security is an important concern [3].  
Authentication, confidentiality, integrity and non repudiation are the essential security requirements of multimedia communication in VANETs. [4] Security attacks (denial of service, malicious node attack, impersonation) and vulnerabilities (forgery, violation of copywrite and privacy) exist in multimedia applications due to the mobility and dynamic nature of VANETs [5].  
Video transmission in VANETs faces a lot of challenges due to the limited available bandwidth and transmission errors [6]. Security, interference, channel fading, dynamic topology changes and lack of infrastructure are some other factors that degrade the performance of video streaming in VANETs [7].  
In this paper we propose a sensor based framework for secure multimedia communication in VANETs. It removes redundant messages and reduces the network load and delays. Malicious nodes and malicious data are easily detected with the help of this framework, which is not possible in existing approaches. It also prioritizes the network and user traffic so high traffic gets more media than lower traffic.  
This paper is organized as follows: in Section 2, we will discuss the security issues of multimedia traffic in VANETS and how to detect malicious nodes and data with the help of signal strength and vehicle position. In Section 3, we discuss the proposed framework and the results obtained using the NS-2 simulator is presented in Section 4. Lastly in Section 5 our conclusion is given.
# A Low-Cost IoT-Based Bidirectional Torque Measurement System with Strain Gauge Technology
## 4 Results
The standard deviation quantifies the repeatability of the system at each torque level, while the final column displays twice the standard deviation, corresponding to an approximate 95% confidence interval for the repeated measurements (assuming a normal distribution). This analysis provides a transparent assessment of the measurement uncertainty and repeatability across the full bidirectional torque range. Notably, the standard deviation remains below 1.25 N\(\cdot\)m for all calibration points, confirming the system's high precision and suitability for applications requiring reliable bidirectional torque monitoring. The key calibration parameters are now described by the coefficients of the fifth-order polynomial regression, which accurately models the nonlinear relationship between the digital sensor output and the applied torque. The goodness of fit is quantified by the coefficient of determination (R\({}^{2}\)), which exceeds 0.999, confirming the suitability of the polynomial model for the full bidirectional measurement range. Negative torque values in the calibration data correspond to counterclockwise (CCW) torque, reflecting the system's ability to distinguish and quantify bidirectional loads. The standard deviation values, summarized in Table 3, further demonstrate the system's accuracy and repeatability across all calibration points. Figure 5 illustrates the correlation between the applied torque (horizontal axis) and the indicated torque measured by the developed sensor system (vertical axis) across the full measurement range, including both clockwise and counterclockwise directions. Each data point represents the mean indicated torque for a specific applied torque value, while the vertical error bars denote the 95% confidence intervals calculated from repeated measurements at each calibration point. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Applied Torque** & **Indicated Torque** & **MAE** \\
**[N\(\cdot\)m]** & **[N\(\cdot\)m]** & **[N\(\cdot\)m]** \\ \hline
50 & 49.88658495 & 0.113415 \\
45 & 45.64236065 & 0.642361 \\
40 & 38.48384461 & 1.516155 \\
35 & 34.59844455 & 0.401555 \\
30 & 29.54496305 & 0.455037 \\
25 & 27.42266205 & 2.422662 \\
20 & 21.39366942 & 1.393669 \\
15 & 13.15642551 & 1.843574 \\
10 & 8.926782281 & 1.073218 \\
5 & 5.188683855 & 0.188684 \\ \(-\)5 & \(-\)4.244757547 & 0.755242 \\ \(-\)10 & \(-\)9.750358511 & 0.249641 \\ \(-\)15 & \(-\)15.06403354 & 0.064034 \\ \(-\)20 & \(-\)20.14973559 & 0.149736 \\ \(-\)25 & \(-\)25.07085006 & 0.070850 \\ \(-\)30 & \(-\)29.96374104 & 0.036259 \\ \(-\)35 & \(-\)34.94836928 & 0.051631 \\ \(-\)40 & \(-\)40.00695376 & 0.006954 \\ \(-\)45 & \(-\)44.96933359 & 0.030666 \\ \(-\)50 & \(-\)49.9673941 & 0.032606 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation between applied torque and indicated torque. A detailed cost analysis is provided in Table 4.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 4 Applied OMA Methods
### Peak-Picking  
Peak-picking is the non-parametric method in the frequency domain that requires the lowest computational burden. It is based on the evaluation of the power spectral density [14; 15; 16]. The name of the method derives from the fact that the proper modes of the structure are identified by looking for peaks in the power spectral density (PSD) graph [1]. The PSD is estimated by the [PERSON] method [17] as follows.  
\[\hat{G}_{xx}(f)=\frac{2}{nNt_{s}}\sum_{i=1}^{n}\lvert X_{i}(f)\rvert^{2} \tag{2}\]  
where \(\hat{G}_{xx}(f)\) is the one-sided auto-spectral density function; \(n\) is the number of contiguous segments into which the signal is split; \(Nt_{s}\) is the length of each segment into which the signal has been divided; and \(\lvert X_{i}(f)\rvert^{2}\) is the FFT of the segment.  
A few preventive operations, such as the application of appropriate windows to the raw signal to avoid the leakage phenomenon, need to be performed on the raw signal before applying [PERSON]'s method [1; 7; 9; 14; 15; 18].  
The peak-picking method assumes that in proximity to the structure's resonance, close to the peak, the structure behaves like a one-degree-of-freedom system (1 DOF), as only one mode is dominant, and the contribution of the other modes can be neglected.  
This method has the advantage of providing an acceptable approximation of the modal parameters of the system quickly and easily if the modes are well separated, but it shows low accuracy and an inability to identify modes that are very close to each other, or cases of high damping [1]. It is especially useful for a first check on the sampled data and for a first estimation of the modal parameters of the system.  
In the present work, the peak-picking method is applied on a single sensor output. A MDOF approach with frequency-domain decomposition (FDD), based on the SVD of the 3D PSD matrix, is not performed.
## 4 The Development Prospect and Technological Breakthrough of DC
### Problems and Countermeasures in the Construction of New SC
Third, it is necessary to strengthen personnel training and build a contingent of high-quality personnel, introduce high-tech and interdisciplinary talent, form a talent development echelon, and build a contingent of intelligent talent. Fourth, it is necessary to energize the market, boost the drive of industry, give play to the decisive role of the market in resource allocation, innovate ways of government-enterprise cooperation, attract enterprises and design forces to participate, and expand financing channels. Fifth, it is necessary to enhance our awareness of service, improve services for the people, promote the integration of technological innovation and services for the people, and improve product quality and people's sense of experience in light of local conditions and people's needs. Seventh, the government should strengthen top-level design, introduce policies and measures, release unified standards for information, and strengthen guidance and coordination among various departments and industries. It is necessary to breakdown the fragmentation and thoroughly solve the problem of horizontal information \"fortress\" and vertical information \"chimney\". [PERSON] et al. [101] believed that the foundation of SC is DC. First, the big data center of SC should be established to accumulate and store all the data information of the city, which is the heart of SC. With the help of City Information Modeling (CIM), Building Information Modeling (BIM), Geographic Information Systems (GIS), and other modern means, it is easy to digitize the physical city and then load the data of the urban environment (water quality, air, pollution, etc.) and urban social and economic data (population, industry, assets, etc.) [102, 103, 104]. There is also real-time data acquisition, forming a huge city mass \"data pool\", to achieve the whole field and full coverage of urban informatization and digitization. Second, as the brain of SC, the command center of SC construction and operation should be established. It is responsible for the overall coordination and scheduling of SC construction and operation to form an integrated management and operation pattern with consistent goals, departmental linkage, high coordination, quick response, and efficient operation.
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 3 Layout Implementation and Post-Layout Simulation Results
Considering the LDO (Low-Dropout Regulator) required for subsequent on-board testing, the circuits designed in this project all operate with a 1.8 V supply voltage, similar to those described in references [19; 21], so the requirements for the LDO are quite comparable. Compared to the LDO in [19], this design may have slightly higher power consumption, though it will remain significantly lower than the power consumption reported in [20]. Except for reference [18], which mentions an input capacitance of 2 pF, other references do not provide detailed descriptions of this parameter. Reference [18] uses MOM (Metal-Oxide-Metal) capacitors, where the size of the unit capacitor is 1 pF. Such a small capacitor may introduce the risk of capacitor mismatch. Since the minimum capacitor available in the process library used for this design is a MIM (Metal-Insulator-Metal) capacitor with a value of 127.36 fF, the input capacitance of the circuit is 17 pF. Comparison shows that the area utilization efficiency of the CDAC in this paper is significantly higher than that of the CDAC structure in reference [18]. Compared to the design in [18], this may require the preceding AFE (Analog Front-End) to have stronger driving capability and consume more power. Designed and post-layout-simulated using a 180 nm process, the proposed SAR ADC benefits from compatibility with established biomedical chip manufacturing technologies, reducing production costs and facilitating integration into multi-channel systems. Moreover, it outperforms traditional SAR ADCs (e.g., the 11.12-bit ENOB in [21]) in terms of resolution, dynamic range, and power consumption, addressing the critical requirements of high-precision neural signal processing. In conclusion, the proposed SAR ADC, through its optimized architecture and circuit design, delivers high resolution and a dynamic performance while maintaining ultra-low power consumption, positioning it as a robust option for long-term implantable monitoring and high-accuracy signal acquisition in BCI systems, with significant potential for widespread adoption in biomedical applications.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 3 Analytical Model
### Acquisition Failure Probability Calculation
#### 3.1.2 Receiving SC in the Pre-Estimated UC
\(R_{m}\) can be calculated by Equation (5) and Equation (10), with the integration in Equation (5) performed along the dotted line \(s_{r}\) in Figure 6.  
\[R_{m}=\sqrt{-\log\left(\frac{N_{s,min}v_{t}hv}{2Q_{e}P_{0}\int_{0}^{\frac{V_{ tot}}{2}}e^{-\frac{\gamma_{0}^{2}}{\sigma_{0}^{2}}}dy}\right)}\frac{w_{0}^{2}}{2}. \tag{18}\]  
The speed of the scanning beam is limited by the angular rotation speed of the SC. If the time required for the beam to sweep across the receiving SC exceeds the integration time of the acquisition camera, the camera can detect the spot for more than one integration period. These multiple integration windows can effectively improve the success rate of spot acquisition. However, the number of integration windows cannot be predetermined due to the uncertainties in the position of the receiving SC and variations in track width selection. For ease of calculation, we consider the number \(k_{a}\) of integration windows as the number of times the beam moves in a straight line, and the center of the beam sweeps across the receiving SC. However, calculating \(k_{a}\) theoretically is challenging. Therefore, using the previous assumption that the receiving satellites are uniformly distributed across the range \(D_{t}\), we employ numerical methods to calculate the average value of \(k_{a}\) over the range of \(R_{m}\). However, due to the correlation between different integration windows when sweeping over the receiving satellites at low scanning speeds, we need to apply the following corrections to the calculated average number of integration windows \(k_{a}\):  
\[X_{t}+X_{n}<-\theta_{div},\ \ X_{t}+X_{n}>\theta_{div}. \tag{14}\]0  
where \(t_{c}\) is the time that correlation decreases to a certain level. In this paper, we choose 25% as the level. Therefore, we need to update the calculation of the acquisition failure probability further, as outlined above in Equation (16), by raising the capture failure probability of each trajectory to the power of \(k_{s}\). \[X_{t}+X_{n}<-\theta_{div},\ \ X_{t}+X_{n}>\theta_{div}. \tag{14}\]1  
Therefore, the total acquisition failure probability can be calculated using the following equation [24]:  
\[X_{t}+X_{n}<-\theta_{div},\ \ X_{t}+X_{n}>\theta_{div}. \tag{14}\]2
## 3 Results and Discussion  
### The Maximum Dry Density and the Optimal Moisture Content  
The cement stabilized macadam mixture was compacted under different conditions. Then the maximum dry density \(\rho_{\text{dmax}}\) and optimal moisture content \(w_{0}\) of the heavy compacted (HC) and vibration compacted (VC) mixture under different gradations and cement doses were counted. The results are shown in Tables 7 and 8.  
is squeezed out at the bottom of the test mold on the macro level. That will reduce the density lost due to water occupying, so \(\rho_{\text{dmax}}\) will increase correspondingly.  
On the other hand, \(w_{0}\) of GM gradation is smaller than that of the XM gradation, with a ratio of 0.804-0.860, and an average value of 0.829. Besides, \(\rho_{\text{dmax}}\) is 1.015-1.025 times that of the XM gradation and has an average time of 1.020. It shows that GM graded cement stable macadam mixture based on the vibration compaction method has the advantages of small best moisture content and compact arrangement. This is because GM gradation has an excellent embedded crowded frame structure. Coarse aggregate squeeze each other, which could form a stable skeleton structure in the small void. Besides, fine aggregate and cement form the set of cement mortar together, which could fill the void compact closely.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### Classification Results and Model Performance
In RF, the algorithm can exploit this redundancy by randomly selecting a subset of features at each split, which may lead to different bands being considered important in different trees. CART, on the other hand, may consistently choose one of the correlated bands over others, resulting in different feature importance rankings. Furthermore, RF tends to be more robust to overfitting compared to CART, especially when dealing with high-dimensional data like satellite images. As a result, RF may capture more complex relationships between features and the target variable, leading to different feature importance rankings compared to CART, which may prioritize simpler relationships. CART decision trees are generally easier to interpret compared to RF, as they represent simple decision rules. The simplicity of CART models may influence the feature importance rankings, as they tend to highlight features that have a more direct impact on the classification decision. Table 4 presents a cartographic comparison of two classification results for the study area, which were obtained using the two different classifier algorithms. The colors on the map correspond to six distinct categories of urban green spaces (lavender pink for agriculture, dark green for forest, yellow for grass, gold for mixed, deep saffron for shrubs, \begin{table}
\begin{tabular}{c c} \hline RF & \\ \hline PlanetScope only & Multiple images \\ \hline \hline PlanetScope only & Multiple images \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variable of importance. and pacific blue for wetland), while red indicates urban built-up areas with manmade structures and blue signifies water bodies, such as coastlines, lakes, rivers, or reservoirs. When using only PlanetScope bands, it becomes evident that the classification is heavily skewed toward urban built-up areas and manmade structures, resulting in a reduced accuracy in classifying urban green spaces. However, the inclusion of multiple bands from Sentinel-1, Sentinel-2, RTVI, and RTWI significantly enhanced the classification results. Both the classification maps generated by the RF classifier algorithm and the utilization of multiple images corroborated the superior performance of the classification outcomes.
## 2 Materials and Methods  
### Historical Background and Samples' Description  
In 1954, two ancient boat pits were found just south of the Great Pyramid of [PERSON] [PERSON], who ruled from 2589 to 2566 BCE [12, 47]. The boat called the \"First Boat\" came from the eastern pit, and the \"Second Boat\", from the western one. Due to the poor state of wood preservation, the Second Boat remained sealed in its pit until 2009, unlike the First Boat, which was lifted in 1954 [12, 47]. Since 2021, when the highly successful Egyptian-Japanese collaboration to assemble the Second Khufu Boat began, the stone blocks which covered the burial pit, were lifted for the first time since the reign of [PERSON]defre during the Old Kingdom. Similar to the First Khufu Boat lifted in 1954, the pit of the Second Boat was carefully sealed with limestone blocks, and the boat was typically dismantled into pieces, with each component carefully placed in a specific arrangement [12, 47]. The boat just after lifting stone blocks is shown in Figure 1a. The poor state of preservation of the boat was evident and was confirmed by the analyses reported in this study. The alteration of the wood layers (Figure 1a, red arrow) was pronounced in the pit, which decreased as a result of the loss of wood properties. The first wooden bar lifted, approximately 1 metre long, is shown in Figure 1b. A large-scale conservation and reconstruction project of the Second Boat is currently in progress. A key first step in conservation was to identify the types of wood used by the ancient builder, assess the boat's state of decay and determine the best conservation methods. The boat consisted of various parts, including the deckhouse, canopy, forecastle, deck beam, hull, ribs and oars [48, 49].  
The present work focuses in particular on the types of wood used in the construction of the deckhouse, canopy and bow castle. For this purpose, three small samples were collected, one from each of the specified parts (referred to as sample 1, 2 and 3). The shape of the samples was semi-cylindrical, with a diameter of 5-7 mm and a height of approximately 10 mm.
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 4 Analysis of the GPR-Based Channel Model
### Position Error Bound  
Figure 9 shows the predicted PEB throughout the floor plan, computed from (5), when using information from various SMCs. The information provided by each SMC is quantified by \(\text{SINR}_{k}(\mathbf{p}^{*})\). The sub-figures on the left-hand side include the LOS component from the PA, while the LOS is neglected in all sub-figures on the right. In the top row, all VAs are considered, while first-order VAs only are considered in the middle, and at the bottom, only VA 2, VA 4, VA 6, VA 7, and VA 8 are used, which contribute most to the PEB.  
It is observed that the second-order VAs play an important role in any localization algorithm, despite their low powers. With information from them, the PEB improves significantly even without the LOS component. With only first-order VAs, the PEB is worse.  
Figure 9: GPR-predicted PEB considering information from SMCs corresponding to: (**a**) all VAs and the PA, (**b**) all 1 st and 2 nd-order VAs, (**c**) 1 st-order VAs and the PA, (**d**) all 1 st-order VAs, (**e**) selected VAs and the LOS, (**f**) selected VAs.
## 1 Introduction
In AOI applications, the geometric relationship between a template image \(P\) and an inspected image \(S\) is constricted as shown in Figure 1.  
An indicated inspection pattern (such as the diamond templates shown in Figure 1) is usually used for the images, in order to alleviate the computational burden that is associated with real-time image alignment. Because the working distance between the lens and the inspected target is fixed in AOI applications, the scaling variation between \(P\) and \(S\) is tiny and can be neglected. Therefore, the image alignment problem for AOI can be formulated to identify the optimal 2D rigid transformation between \(P\) and \(S\), as constrained by translation vector \(\widetilde{\text{T}}\) and rotation angle \(\theta\). The method presented here locates the template image within the inspection image and provides precise rotation information during the template-matching process by using a novel rotation estimation scheme. This is the so-called ring-shifted technique which uses the architecture of a two-stage template-matching method. In the first stage (the candidate-searching stage), the only likely candidates are determined using rotation-invariant features. Subsequently, the ring-shifted technique is applied to the most likely candidate that is obtained from the first stage in order to estimate the rotation in the second stage (the rotation-estimation stage). Because the rotation is directly calculated using hybrid-projection data, the  
Figure 1: The geometric relationship between pattern image \(P\) and inspection image \(S\). proposed method minimizes computational complexity. Even though the newly matching method is proposed, it is not invariant to scale changes. The main contributions of this paper include the following: (1) The novel image alignment algorithm based on rotation-discriminating ring-shifted projection, which is efficiently applied to recognize and align the template and inspection images; (2) The proposed hybrid projection transformation based on image entropy can provide unique and robust features for rapidly selecting the most likely candidate based on the image pyramid searching technique; (3) A new rotation-estimation technique known as the ring-shifted technique is proposed to detect the rotation of only the best of the output of the candidates; (4) An image pyramid searching technique is applied to significantly enhance the computation performance.