esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Time Convolutional Network
#### 2.2.1 Causal Convolution  
Causal convolution [26] serves two essential functions in TCN [27]: ensuring that the network generates an output of the same length as the input and preventing leakage from future to past. Causal convolution differs from a standard convolutional neural network in that it utilizes a unidirectional structure, ensuring that the model input and output are the same size. However, it is important to note that causal convolution is often limited by its receptive field, which restricts its ability to incorporate long-term historical information for accurate predictions.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 5 Distance-Based Interconnect Side-Channel  
The increase in the number of processor cores and the complexity of interconnects inevitably leads to greater differences in distances between different cores. As the lengths of interconnect links reach a certain scale, the time differences exhibited by electrical signals propagating over links of different lengths can be significantly observed and utilized to construct distance-based side-channel attacks [73].
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 4 Optimization Scheduling  
### Rolling Optimization for Local MGs  
The scheduling of the ESS is optimized based on the long term forecast information on PV generation, load demands and internal prices, which can be expressed as \(p_{i}^{h,f},l_{i}^{h,f},p_{cb}^{h,f}\) and \(p_{cs}^{h,f}\), respectively. The primary target of a local MG is to maximize the operation utility. According to (2), the target of the rolling optimization is equivalent to minimize the cost of energy trading with the EMO, which can be expressed as  
\[\min\!C_{i}^{roll}=\sum_{h}^{h+K\Delta h}[p_{cb}^{h,f}nl_{s,i}^{h}+p_{cs}^{h,f} nl_{b,i}^{h}+c_{i}e_{i}^{h2}], \tag{10}\]  
where \(k\) is the length of rolling optimization, and \(\Delta h\) is the rolling step. In addition, the objective function in (10) needs to satisfy not only the constraints in (3)-(6) but also the constraints of battery charging and discharging, which can be expressed as follows.  
\[e_{i}^{h}=e_{ch,i}^{h}-e_{dis,i}^{h}, \tag{11}\]  
\[0\leq e_{ch,i}^{h}\leq D_{ch,i}^{h}e_{ch,i}^{\max}, \tag{12}\]  
\[0\leq e_{dis,i}^{h}\leq D_{dis,i}^{h}e_{dis,i}^{\max}, \tag{13}\]  
\[D_{ch,i}^{h}+D_{dis,i}^{h}\leq 1, \tag{14}\]  
\[SOC_{i}^{h+1}=SOC_{i}^{h}+D_{ch,i}^{h}\cdot e_{ch,i}^{h}\cdot\eta_{ch,i}-D_{ dis,i}^{h}\cdot e_{dis,i}^{h}/\eta_{dis,i}, \tag{15}\]  
\[SOC_{i}^{\min}\leq SOC_{i}^{h}\leq SOC_{i}^{\max}, \tag{16}\]  
where \(e_{ch,i}^{h}\) and \(e_{dis,i}^{h}\) are the charging and discharging energy of the ESS, respectively. \(D_{ch,i}^{h}\) and \(D_{dis,i}^{h}\) indicate the charging and discharging state of the ESS, \(e_{ch,i}^{\max}\) and \(e_{dis,i}^{\max}\) are the maximum charging and discharging energy in time slot \(h\), respectively. Equation (15) shows the state of charge (SOC) of the ESS during its charging and discharging at the end of the time slot \(h\), \(\eta_{ch,i}\) and \(\eta_{dis,i}\) are the charging and discharging efficiency of the ESS and \(SOC_{i}^{\min}\) and \(SOC_{i}^{\max}\) are the lower and upper limits of the SOC. Therefore, the rolling optimal scheduling in (10) can be modeled as a mixed integer programming (MIP) problem.
## 3 Remote-Sensing System Framework and Problem Defined  
The proposed intelligent aerial remote-sensing system for constructing the aerial SWIR image map is composed of five layers for flying control and data acquisition in an onboard computer, as shown in Figure 2. The sensor layer includes all physical sensors equipped by the drone, such as an SWIR camera (Raptor OWL 640 camera), GPS, inertial measurement unit (IMU), and altitude and heading reference system (AHRS). All sensors are connected to the onboard computer, which is an Nvidia Jetson NANO, and obtain the data to send to the perception layer. The perception layer aims to capture the data from sensors and synchronize all frequencies. Our proposed LMFM was tested in the AI layer and generated a drone control command to send to the action layers. Finally, the storage layer records all data and sends it to the ground control station (GCS). This section describes the flying system settings and data acquisition in the sensor and perception layers.  
Figure 2: The structure of the whole system has five layers in the onboard computer. The aerial map construction process was performed in the ground control station.
## 4 Data and Results
### Utilization of Technology in On-Site Projects for Particular Purposes  
Table 11 indicates the frequency with which each technology used for a specific purpose, where 3D modeling was the most popular technology used in our sample cases. Moreover, 3D laser scanning, VR, oblique photography, and IoT were also frequently used digitization technologies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Time Performance**} \\ \hline \multirow{2}{*}{**Case No**} & **PTS** & **ATS** & \multirow{2}{*}{**Case No**} & **PTS** & **ATS** \\  & **(days/m2)** & **(days/m2)** & & & **(days/m2)** & **(days/m2)** \\ \hline
1 & 2.6\% & 3.0\% & 14 & 6.4\% & 5.4\% \\
2 & 2.7\% & 1.4\% & 15 & 10.0\% & 3.0\% \\
3 & 2.7\% & 2.7\% & 16 & 10.1\% & 3.5\% \\
4 & 4.6\% & 0.0\% & 17 & 10.3\% & 8.0\% \\
5 & 4.8\% & 0.0\% & 18 & 11.0\% & 11.0\% \\
6 & 5.0\% & 5.0\% & 19 & 11.2\% & 11.1\% \\
7 & 5.0\% & 4.0\% & 20 & 16.0\% & 0.0\% \\
8 & 5.0\% & 5.0\% & 21 & 18.9\% & 8.4\% \\
9 & 5.0\% & 4.4\% & 22 & 18.9\% & 6.1\% \\
10 & 5.6\% & 2.9\% & 23 & 25.0\% & 6.0\% \\
11 & 6.0\% & 1.5\% & 24 & 32.0\% & 30.0\% \\
12 & 6.0\% & 3.4\% & 25 & 44.0\% & 44.0\% \\
13 & 6.0\% & \(-\)34.6\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Time performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Cost Performance**} \\ \hline \multirow{2}{*}{**Case No**} & **PCS** & **ACS** & \multirow{2}{*}{**Case No**} & **PCS** & **ACS** \\  & **(yuan/m2)** & **(yuan/m2)** & & & **(yuan/m2)** & **(yuan/m2)** \\ \hline
1 & 3.0 & 2.5 & 15 & 56.0 & 7.0 \\
2 & 3.4 & 1.7 & 16 & 71.8 & 35.9 \\
3 & 4.2 & 28.9 & 17 & 79.6 & 74.3 \\
4 & 7.4 & 6.3 & 18 & 95.5 & 0.0 \\
5 & 9.4 & 5.7 & 19 & 95.7 & 46.4 \\
6 & 11.6 & 8.4 & 20 & 147.5 & 2.7 \\
7 & 13.2 & 2.8 & 21 & 190.5 & 95.2 \\
8 & 16.4 & 0.0 & 22 & 192.1 & 0.0 \\
9 & 18.3 & 12.5 & 23 & 195.4 & 56.2 \\
10 & 18.5 & 2.6 & 24 & 382.5 & 127.5 \\
11 & 23.9 & 0.0 & 25 & 522.7 & 418.2 \\
12 & 28.5 & 19.9 & 26 & 939.3 & 155.6 \\
13 & 34.0 & 33.8 & 27 & 3500.0 & 1610.0 \\
14 & 42.4 & 32.6 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Cost performance.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 1 Introduction
### Research Contributions and Framework  
This systematic review addresses these challenges by focusing on five specific areas that span the complete spectrum from foundational data issues to cutting-edge technological applications:  
1. A comprehensive data quality taxonomy that categorizes quality issues into collection-stage and analysis-stage challenges, providing a structured framework for understanding and addressing data limitations (Section 3).
2. A methodological evolution framework that traces the historical development from descriptive crash analysis to sophisticated system-based approaches, demonstrating how traditional statistical methods and emerging AI techniques can be integrated (Section 4).
3. Domain-specific intervention synthesis that demonstrates how advanced methodological approaches address real-world safety challenges across infrastructure design, vulnerable road users, and targeted countermeasures (Section 5).
4. Evidence-based implementation guidelines that bridge the research-practice gap by translating methodological advances into actionable recommendations for safety management and policy development (Section 6).
5. A future-oriented technology roadmap that examines emerging research frontiers in big data analytics, deep learning, real-time prediction systems, and connected/autonomous vehicle safety, identifying pathways for next-generation crash analysis capabilities (Section 7).  
These contributions are unified by a central organizing principle: methodological sophistication must be balanced with practical applicability to achieve meaningful improvements in road safety outcomes. The framework progresses systematically from foundational data and methodological concerns through specific applications and policy implications, culminating in emerging technologies that will define the future of crash analysis.  
This systematic approach serves two purposes: providing researchers and practitioners with a comprehensive methodological reference while identifying pathways for advancing sophisticated analytical approaches that enhance our ability to analyze crash data at scale and translate findings into effective safety interventions.
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 2 Related Work
### Cross-Layer Context Fusion  
The encoder in a segmentation network learns global context information, including the surroundings and category characteristics of objects [24; 25]. However, in complex scenarios, the encoder often struggles to adequately capture the edges, shapes, and structures of polyps, and it has limitations in extracting multi-scale contextual information. Toaddress these challenges, we designed the CCF module, which enhances the network's understanding of complex polyp morphology by fusing feature maps from different layers and combining contextual information across multiple scales and receptive fields. As illustrated in Figure 2a, the workflow of the CCF module consists of four main steps: feature maps \(\{x_{1},x_{2},x_{3}\}\) from three different scales are passed through a standard \(3\times 3\) convolution operation to standardize them to the same number of channels as \(x_{1}\). The feature maps \(x_{2}\) and \(x_{3}\) are scaled to the same spatial resolution as \(x_{1}\) using \(2\times\) downsampling and \(2\times\) upsampling, respectively. The three adjusted feature maps are concatenated along the channel dimension, generating a richer multi-scale feature representation. On the concatenated feature map, four parallel dilated convolutions with dilation rates of [1, 6, 12, 18] are applied. These dilation rates capture multi-scale contextual information from varying receptive fields, enriching the feature map's contextual expression and diversity. Mathematically, this process can be summarized as:  
\[C_{i}\ =\ Concat\big{\{}Dsample(f_{i}^{r}),f_{i+1}^{r},Upsample\big{(}f_{i+2}^{r} \big{)}\big{\}},\ i=1,2,3 \tag{1}\]  
where \(C_{i}\) represents the feature extracted from the \(i\)th stage of the encoder. By fusing feature maps from adjacent scales, the feature map \(C_{i}\) with enhanced contextual expression is obtained. Finally, the MSFBlock [26] is introduced to fuse \(C_{i}\) into \(f_{i}^{c}\), as shown in Figure 2b. The MSFBlock processes feature maps from varying receptive fields and performs hierarchical fusion, integrating multi-scale contextual information and enhancing the feature maps' expressive power.  
In MSEANet, three CCF modules are strategically placed between the encoder and decoder. These modules guide high-level global semantic information to different feature extraction stages, significantly improving the network's ability to extract multi-scale contextual information and understand the complex edges and morphology of polyps. This design enhances the accuracy and robustness of polyp segmentation, particularly in challenging scenarios.  
Figure 2: (**a**) Structure of Cross-layer Context Fusion (CCF) module. (**b**) Structure of Multi-scale Selective Fusion (MSF).
## 3 Results and Discussion
### Characterization and Properties of CRMFoams
#### 3.1.3 Porosity and Thermal Properties  
The experimental results of the density, porosity, water absorption, and thermal conductivity measured from the CRMFoams are presented in Table 2. In general, both foams showed low bulk density (B! 0.55 g/cm\({}^{3}\)) and high porosity (P? 65%) values. Their water absorption (A) was calculated at 179.2% for the CRMFoam-5 sample and 121.7% for CRMFoam-2.5, whereas the specific gravity (T) was 1.76 and 1.55, respectively. These differences can be explained by the fact that the initial resin/RM suspension used for the preparation of CRMFoam-2.5 was more viscous and concentrated due to the higher solid loading of RM (40 wt%) in comparison to that of CRMFoam-5 (20 wt%). Thermal conductivity experiments were carried out using two different pieces from each kind of sample (i.e., four pieces). Five independent measurements were performed to each piece, i.e., ten measurements to each kind of sample. Experimental data were treated using the IBM SPSS ver. 26 software, and the results are reported in Table 2. The equality of mean values was tested via the t-test method and no equality found for a confidence interval of 95%.
## 1 Introduction  
The high fibre content in ultra-high performance concrete (UHPC) provides a positive ductility under compressive loading and a considerable post-cracking tensile strength under tensile loading. Because of the increasing importance of resource conservation, it is no longer appropriate to allow \"unused capacities\" in building materials. Taking into account the obliged performance, the best way to prevent unused capacities is the optimal utilisation of the mechanical properties, such as e.g., the post-cracking tensile strength. The level of post-cracking tensile strength directly links to the fibre content and the fibre design. The fibre design, in turn, comprises the choice of the fibre material and fibre geometry as well as the fibre tensile strength.  
Empirical values, experiences, and suitability tests are often the current practice in regards to selecting a fibre type for fibre reinforced concrete members. They all have little mechanical background and do not consider complex load scenarios. Therefore, fibre design in terms of an optimal application to the performance requirements should explore for new methods.  
This paper demonstrates a mechanical approach for a performance-based fibre design of high-strength micro steel fibres for UHPC. Besides experiences in fibre design, the novel approach intends to cover the effects of fibre orientation and cyclic loading in addition to empirical values and suitability tests. The novelty of the approach is the gained knowledge of the fibre stresses in tensioned UHPFRC. Until now, it has remained largely unclear how fibres are utilised in cracked UHPFRC, i.e., it might be possible that the fibre stresses are close to fibre rupture or relatively low so that the fibre design is inefficient. Especially inclined fibres (compared to the direction of loading) face completely other stress distributions as straight fibres. These stress distributions have a great influence in the fibre design. The novel approach considers the stresses that occur from the oblique position of the fibres and shows, e.g., the reserves of a UHPFRC-component against failure. Furthermore, and besides static loading, it is unknown how inclined fibres respond to cyclic stresses. The approach tries to give information on the maximum applicable stresses for fibres under cyclic loading in UHPFRC.  
The basis of the approach rests upon pullout tests of high-strength micro steel fibres from UHPC with different orientations and embedded lengths carried out under quasi-static as well as cyclic loading at iBMB, Division of Concrete Construction, TU Braunschweig.
## 4 Field Experiment
### Quantitative Validation of the Proposed Image Processing Method  
The proposed image processing method was quantitatively validated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) in Google Colab [36]. PSNR is a metric used to measure the quality of an image compared to an original image. It is expressed in decibels (dB) and calculated using the Mean Squared Error (MSE) between the original and processed images. MSE measures the mean square differences between corresponding pixel values of the original image I and the processed image K (image size m \(\times\) n). MSE and PSNR are as follows:  
\[MSE=\frac{1}{mn}=\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\Bigl{[}I(i,j)-K(i,j)\Bigr{]} ^{2}, \tag{4}\]  
Figure 16: Comparison of experimental images and 3D modeling results after image processing: (**a**) Experimental image; (**b**) 3D modeling after image processing.  
\[PSNR=10\text{log}_{10}\left(\frac{MAX_{1}{}^{2}}{MSE}\right), \tag{5}\]  
where \(MAX_{1}\) is the maximum possible pixel value of the image, such as 255 in 8-bit grayscale image.  
A higher PSNR value generally indicates less distortion and therefore generally better image quality. SSIM is designed to evaluate the perceptual similarity between two images by considering luminance, contrast, and structural similarities. Unlike PSNR, which measures absolute errors, SSIM evaluates how similar the processed image is to the reference image from a human visual perspective.  
\[SSIM(x,y)=\frac{\left(\mu_{x}{}^{2}+\mu_{y}{}^{2}+C_{1}\right)}{\left(\sigma_{ x}{}^{2}+\sigma_{y}{}^{2}+C_{2}\right)\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2 \sigma_{xy}+C_{2}\right)}, \tag{6}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean intensities of images \(x\) and \(y\). \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of images x and y. \(\sigma_{xy}\) is the covariance between the two images. \(C_{1}\) and \(C_{2}\) are small constants to prevent division by zero. The SSIM value ranges from -1 to 1, with 1 indicating perfect similarity, 0 suggesting no similarity, and negative value implying structural dissimilarity.  
Table 3 shows the PSNR and SSIM measurements for the images to which the proposed method in Figures 11-15 was applied. Figure 11 shows the highest PSNR, followed by Figure 14. In the case of SSIM, it was evaluated to be the highest in Figure 14.  
In this study, the entry-level UAV models with low price were used to verify the proposed method. A camera resolution, sensor quality, GPS, and IMU are the main items that determine the price of the UAV. High-level UAV models provide high resolution, distortion correction functions, and stable aircraft flight capabilities, so more accurate images can be obtained through the use of high-level UAVs. Although it is difficult to generalize the performance shown in entry-level UAVs to other UAVs, it can be expected that better results can be obtained if a high-level UAV is used.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 6 Discussion
However, it was still valid if the bistatic angle was in the slant range (across track) direction or their hybrid. It must be admitted that the TCR does have limitations in bistatic SAR calibration. First, the feasibility of the TCR is limited to the mode that the bistatic angle is fixed. The bistatic SAR imaging modes are diversified, such as the tandem mode, translation invariant mode, constant velocity mode, and general mode [29]. However, the TCR has a very narrow bistatic pattern regarding the bistatic angles [8] and the 3-dB bistatic beamwidth corresponding to the bistatic angles is less than 4\({}^{\circ}\) for a 0.4-m TCR at the X-band, so it cannot be configured to calibrate all of the bistatic modes, especially bistatic modes with a large time-variable bistatic angle. Second, the TCR cannot be configured for all of the bistatic angles due to their rippled bistatic RCS pattern or lower SCR. We concluded that TCR can only be used as a radiometric calibration target for relatively small bistatic angles (less than 6\({}^{\circ}\)). If the TCR is aligned with the optimum boresight, it can also work well, as the bistatic angle is less than 20\({}^{\circ}\). For the time-variable bistatic angle or larger bistatic angle (more than 20\({}^{\circ}\)), maybe we should analyze an active calibration target.
## 2 Basic Theory
### Variational Mode Decomposition  
VMD is a novel non-recursive signal decomposition algorithm that is particularly well suited to the analysis and processing of nonlinear and nonstationary signals. VMD can be calculated as follows:  
\[\left\{\begin{matrix}min_{\{\omega_{k}\},\{u_{k}\}}\Big{\{}\sum_{k=1}^{K}\| \partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}u_{k}(t)\Big{]}e^{-j \omega_{k}t}\|^{2}\Big{\}}.\end{matrix}\right. \tag{1}\]  
where \(\{u_{k}\}=\{u_{1},u_{2},u_{3},\cdots,u_{k}\}\) represent mode components, with \(k\) being the mode number; \(\{\omega_{k}\}=\{\omega_{1},\omega_{2},\omega_{3},\cdots,\omega_{k}\}\) corresponds to the center frequency of \(\{u_{k}\}\); and \(f(t)\) denotes the original signal to be decomposed. To compute the solution of Equation (1), penalty factor \(\alpha\) and Lagrange multiplier \(\lambda(t)\) are introduced, transforming the constrained problem into an unconstrained variational problem. The augmented Lagrange, \(L\), can be described as follows:  
\[L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum_{k=1}^{K}\|\partial_{t}\bigg{[} \bigg{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\bigg{]}e^{-j\omega_{k}t}\|_ {2}^{2}+\|f(t)-\sum_{k=1}^{K}u_{k}(t)\|_{2}^{2}+\Big{(}\lambda(t),f(t)-\sum_{k =1}^{K}u_{k}(t)\Big{)}. \tag{2}\]  
where \(\alpha\) is utilized to ensure signal reconstruction accuracy, while \(\lambda\) is employed to maintain the strictness of the constraint conditions. According to Equation (2), the Lagrange expressions are sought by iteratively updating \(u_{k}^{n+1}\), \(\omega_{k}^{n+1}\), and \(\lambda^{n+1}\):  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum_{i\
eq k}\hat{u}_{i}( \omega)+\frac{\lambda(\omega)}{2}}{1+2\alpha\big{(}\omega-\omega_{k}\big{)}^{ 2}}, \tag{3}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d\omega }{\int_{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}d\omega}, \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}(\omega)+\tau\Big{[}f(\omega)- \sum_{k=1}^{K}\hat{u}_{k}^{n+1}(w)\Big{]}. \tag{5}\]  
We update with repeated iterations until the iteration stopping condition is satisfied:  
\[\sum_{k=1}^{K}\frac{\|\hat{u}_{k}^{n+1}-\hat{u}_{k}^{n}\|_{2}^{2}}{\|\hat{u}_ {k}^{n}\|_{2}^{2}}<\varepsilon. \tag{6}\]  
where \(n\) represents the number of iterations and \(\tau\) denotes the iteration step length.
## 3 DiRPL: RPL-Based Resource and Service Discovery for Constrained IoT Devices  
The RD/SD mechanism proposed in this work, denoted as DiRPL, relies on RPL, as highlighted in Section 1, in turn exploiting the handshake among a constrained IoT node newly joining a RPL-based network, and a sink node, referred to as Border Router (BR), handling the coordination tasks of the RPL-based network. We will refer to the joining IoT node as Last Entered Node (LEN). In the following, a description of the technique through which a new node discovers and joins an existing RPL-based DODAG is provided in Section 3.1, while Section 3.2 introduces our novel RD/SD mechanism, detailing how it exploits the RPL handshake phase in order to discover nodes, resources, and services. Hence, it is possible to separate the proposed DiRPL approach in two phases, both relying on a key behavior of CoAP, namely, the possibility to _observe_ a CoAP resource (here defined as the \"special\" CoAPresource /network/last_entered_node through the OBS option), thus avoiding continuous polling of appearing constrained nodes and to their resources and services. As will be better detailed in the following, this behavior helps in reducing the traffic among the IoT constrained network handled by the BR, together with a simplification of the tasks to be carried out by each LEN, which reduce to the need for being discovered by the BR itself and to reply with a list of the maintained CoAP resources once the joining phase has completed. It follows that this approach scales well and can be applied to several scenarios in which a direct human intervention could be prevented, e.g., in smart city scenarios [22] where technicians have to install/substitute SOs quickly without having to care about the interactions among themselves.