esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Modal Identification
#### 3.1.2 Mode Shape Extraction
The authors achieved satisfactory results under the influence of track irregularities by combining the STFDD with the method of subtracting the signal from consecutive axles. The performance of the method was not evaluated as a function of the vehicle speed; however, very low speeds (in the order of 1 m/s) were used in the presented case study. Although it has not been tested, it is possible to infer that the increase in speed will be harmful to the method since there will be a significant reduction in the acquisition time and, consequently, in the frequency resolution obtained with the FFTs. Figure 8: Estimated mode shapes under different bridge damping ratios: (**a**) without correction and (**b**) with correction [29]. So far, most of the studies involving drive-by methodologies for mode shape extraction have been based only on the SDOF models without any experimental validation. Seeking to further advance in this topic, [PERSON] et al. [50] proposed a methodology for bridge mode shape extraction based on the vertical acceleration of a two-axle vehicle crossing a bridge. Firstly, the FFT of the acceleration signal was taken and the peaks corresponding to bridge frequencies were isolated from the rest of the response by frequency domain filtering. Then, the time-domain response corresponding to the bridge contribution was recovered and the mode shapes were estimated based on the signal's envelope. The method has proved to be effective for speeds up to 20 m/s and with different vehicle parameters and bridge damping values; however, track irregularities and noise had a negative impact on its accuracy. A scaled laboratory test was conducted, and the bridge's first two mode shapes were successfully identified for a low level of irregularities. In another study involving scaled experimental validation, [PERSON] et al. [51] proposed the Crowdsourced Modal Identification using the Continuous Wavelet (CMICW) technique for the extraction of the mode shapes of a bridge. This technique relies on the use of the Continuous Wavelet Transform (CTW) method averaged for a large number of vehicles (crowd) to extract the absolute value of the mode shapes, as it is presented in the flowchart of Figure 10a. The use of a large number of vehicle passages contributes for reducing the influence of track irregularities, measurement noise, and other sources of interference. The authors validated their methodology using a reduced-scale bridge and vehicles made of Lego(r) to measure vertical acceleration. The results obtained for the five first mode shapes are depicted in Figure 10b. Figure 9: Mode shape extraction by the Short Time Frequency Domain Decomposition technique [31].
## 4 Experimental Results and Discussion
### Relationship Between Fractal Dimension and Mechanical Properties  
The strength of concrete is one of its most important macroscopic performance indexes. The strength is closely related to pore structure parameters such as the air content, porosity and spacing factor [31]. The fractal dimension can synthetically characterize the micropore structure of concrete under certain conditions; therefore, the fractal dimension may have a certain relationship with the strength of concrete [30]. Figure 7 show results of the correlation analysis of the fractal dimension with compressive strength and splitting tensile strength. It can be seen that the fractal dimension has a good correlation with both compressive strength and splitting tensile strength, and the corresponding correlation coefficients are 0.883 and 0.777, respectively. This demonstrates that the fractal dimension as a pore size distribution parameter can well-describe the relationship between pore structure and the mechanical properties of BPFRC. The compressive strength and splitting tensile strength of BPFRC show the same changing trend with an increase in the fractal dimension. That is, compressive strength and splitting tensile strength show an obvious increasing trend with increasing fractal dimension. According to fractal theory, the larger the fractal dimension, the more complex the spatial distribution of pores in concrete and the stronger is the pores' ability to occupy space [44]. Therefore, when the specimens are subjected to stress, the internal stress is distributed evenly, which prevents concentration of premature stress and improves compressive strength and splitting tensile strength; this indicates that the complexity of the pore structure is an important factor affecting the macroscopic performance of concrete. The equation in Figure 7 expresses the relationship between the macroscopic mechanical properties and the micropore structure index and the mode of interaction. This finding is consistent with results reported in the literature [30; 31; 44].
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Generation of Simulation Scenario  
The reference point is obtained from the OSM, which depicts a realistic setting of Islamabad, Pakistan's capital (Sector F-5). The.osm file is fed into SUMO, which generates the mobility.tcl file, which contains information about every node (vehicle), such as the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(Th_{agg}\) & Average throughput \\ \(P_{s}\) & Packet received \\ \(P_{s}\) & Packet sent \\ \(T_{l}\) & Time of last received packet \\ \(T_{f}\) & Time of first transmit packet \\ \(EED\) & End-to-end delay \\ \(T_{delay}\) & Transmission delay \\ \(P_{delay}\) & Propagation delay \\ \(P_{delay}\) & Processing delay \\ \(Q_{delay}\) & Queuing delay \\ \(PDR\) & Packet delivery ratio \\ \(T_{sim}\) & Total simulation \\ kB & Packet size in kilobytes \\ kJ/day & Energy per day \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of notations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Value** \\ \hline Vehicles & 49, 79, 109, 10, 30, 50 \\ Mobile RSU & 2, 4, 6 \\ Dimensions & 2000 \(\times\) 2000 m \\ Initial Energy & 100 J \\ Vehicles Speed & Random \\ RSU Speed & 20 m/s \\ MAC Protocol & IEEE 802.11p \\ Packet Size (KB) & 500, 1000, 1500, 2000, 2500, 100, 200, 300, 400, 500 \\ Simulation Time in SUMO & 360 s \\ Routing Protocol & AODV \\ Transmission Power & 20 dBm \\ Transmission Range & 200 m \\ Simulation Time in NS-3 & 150 s \\ Data Rate & 1024 bps \\ Radio Range & DSRC \\ Frequency & 5.9 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
number of vehicles and their positions, speeds, and directions. The mobility.tcl file is used to run the simulation in NS-3. The 802.11p MAC/PHY, with a frequency of 5.9 GHz, is kept steady for the delivery of basic messages to analyze the surrounding BSM. Variations in transmission schemes and routing protocols depending on the simulation time and the number of nodes are used to evaluate the performance of the network (vehicles). Figure 9 shows a screenshot of a plausible scenario with the simulated SUMO scenario generated.
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
## 4 Simulation and Experiment
### Simulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Wavelet–Kalman Filtering** & **Compensation with a Threshold** & **The Proposed Method** \\ \hline Variance/m\({}^{2}\) & \(8.18\times 10^{-14}\) & 2.24 \(\times 10^{-14}\) & \(1.55\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variance of acoustic water surface waves using different methods in the simulation. greater than 100 Hz, it is judged as encoding 1; when the amplitude of the 100 Hz frequency is greater than 200 Hz, it is judged as encoding 0. We transmitted a 2 FSK signal with the bit information \"01010101010101\". The green box in the time-frequency graph shows the received bit results, while the red box marks the bits that are incorrect. The results show that phase compensation can reduce the number of discontinuous points, which is manifested as a decrease in vertical stripes in the time-frequency plot. The second-order differential joint outlier removal can reduce the spike phenomenon caused by discontinuous points, improve the clarity to obtain clearer time-frequency graphs, and be more conducive to extracting communication coding information and reducing communication bit error rates. To evaluate the effectiveness of the proposed method under different parameters, the simulations with different significant wave heights are conducted, as shown in Figure 12. When the significant wave height increases to 0.1 m with all other parameters held constant, the target crosses more radar range bins, as shown in Figure 12a. And, Figure 12b illustrates that in the phase history, phase jumps exist at the transitions of range bins. Most importantly, the proposed method is still effective. It is worth noting that when the wave height on the sea surface is high, the model of micro-amplitude waves excited by sound waves on the water surface (Section 2.1) may no longer be applicable, and more research on sound wave propagation is needed. If the depth of the underwater transmitters increases, the propagation attenuation of sound waves in the medium increases. Under the same emission power, the sound pressure level reaching the water surface will decrease, causing the amplitude of the acoustic water surface waves  
Figure 11: **Simulations**: STFT results using different methods for extracting acoustic water surface waves: (**a**) Wavelet–Kalman Filtering; (**b**) compensation with a threshold; (**c**) the proposed method. Figure 10: **Simulations**: acoustic water surface waves using different methods. to decrease. To ensure the reliability of communication, it is possibly advisable to increase the transmission power while increasing the depth of the underwater transmitters.
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### Grid Anomaly Detection Based on Satellite Positioning  
The satellite positioning described in the previous section can provide high-precision global coordinates. Therefore, in the application of power grid abnormality detection, it  
Figure 4: Diagram of GNSS positioning systems.  
is often used in power facility settlements, tower pole tilt detection, and high-precision positioning for various unmanned patrol systems.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Spectral Indices  
After pre-processing the PlanetScope satellite images, the NDVI and NDWI spectral indices were calculated based on their respective operations, and the results were analyzed using the Jeffries-Matusita distance (JMD) [56].  
NDWI serves as an effective indicator of active plant biomass or, otherwise, vegetation vitality. Developed by [PERSON] et al. [31], the NDVI ranges from \(-1\) to \(1\), obtained using Equation (1):  
\[\mathrm{NDVI}=\frac{(\mathrm{NIR-RED})}{(\mathrm{NIR+RED})} \tag{1}\]  
where NIR is the reflectance in the near-infrared band, and RED is the reflectance in the visible red band.  
This index helps differentiate vegetated areas from other land covers, such as artificial ones, and allows for the assessment of the overall vegetation condition [57]. Additionally, NDVI enables the demarcation and monitoring of vegetation zones, as well as the recognition of any anomalies or changes in the observed area. This indicator is useful for monitoring seasonal variations in vegetation, though its effectiveness depends on surface reflection characteristics [58].  
NDWI, using the green and near-infrared bands, is an efficient indicator for monitoring the presence and distribution of water in terrestrial and aquatic surfaces. This index is particularly effective in identifying water bodies and assessing moisture in vegetation,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image ID** & **Acquisition Date** & **Sensor** & **Spectral Range** \\ \hline
120231031\_120836\_24\_24a1 & 31 October 2023 & PSB.SD & Blue: 465–515 nm Green: 547–585 nm Red: 650–680 nm NIR: 845–885 nm \\ \hline
220181020\_122327\_0e0e & 20 October 2018 & PS2 & Blue: 455–515 nm Green: 500–590 nm Red: 590–670 nm NIR: 780–860 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of PlanetScope images.  
contributing to irrigation and water resource management studies [59]. NDWI is also helpful in detecting flooded areas and analyzing soil water saturation, making it a valuable tool for environmental and agricultural planning [32]. The NDWI ranges from \(-1\) to \(1\) and is calculated using Equation (2):  
\[\text{NDWI}=\frac{(\text{Green}-\text{NIR})}{(\text{Green}-\text{NIR})} \tag{2}\]  
where NIR is the reflectance in the near-infrared band, and Green is the reflectance in the visible green band.
# Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
## 3 Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
### Online Training
\begin{split}\mathbf{y}_{i}&=\mathbf{D}\mathbf{w}_{i}+ \boldsymbol{e}_{i},\quad\mathbf{w}_{i}=\mathbf{z}_{i}\odot\mathbf{s}_{i}\\ \mathbf{d}_{k}&\sim\mathcal{N}(0,P^{-1}\mathbf{I}_{P }),\quad\mathbf{s}_{i}\sim\mathcal{N}(0,\boldsymbol{\gamma}_{s}^{-1}\mathbf{I }_{K})\\ \boldsymbol{e}_{i}&\sim\mathcal{N}(0,\boldsymbol{ \gamma}_{e}^{-1}\mathbf{I}_{P}),\quad\pi_{k}\sim\mathbf{Beta}(\frac{a_{0}}{K}, \frac{b_{0}(K-1)}{K})\\ \boldsymbol{\gamma}_{s}&\sim\mathbf{Gamma}(c_{0},d_{0 }),\quad\boldsymbol{\gamma}_{e}\sim\mathbf{Gamma}(e_{0},f_{0})\\ \mathbf{z}_{i}&\sim\prod_{k=1}^{K}\mathbf{ Bernoulli}(\pi_{k}),\quad\boldsymbol{\pi}\sim\prod_{k=1}^{K}\mathbf{Beta}(a_{0}/K,b_{0}(K-1)/K)\end{split}  \tag{5}\]  
In Equation (5), \(\mathbf{w}_{i}\) is a sparse vector, \(\alpha\odot\) is the dot product, and \(\mathbf{I}_{P}(\mathbf{I}_{K})\) is an identity matrix. \(a_{0},b_{0},c_{0},d_{0},e_{0}\), and \(f_{0}\) are hyperparameters. As the variables in the above model are from the conjugate exponential function, a variational Bayesian or Markov chain Monte Carlo methods [20] like Gibbs sampling could be used for inference. ```
Input \(\boldsymbol{\phi}_{i}\) (computed by Equation (3)), \(J\) (the required minimum number of segments for each cluster), \(V\) (the initial number of clusters), \(N\times L\) (the number of segments), \(N_{k}=0\), \(\Omega=\varnothing\) for\(i\gets 1\)to\(N\times L\)do  cluster the \(i^{th}\) segment into cluster \(C_{i}\) by \(C_{i}=max_{j}(\phi_{ij})\); \(\phi_{ij}\) represents the \(j^{th}\) element of the column vector \(\boldsymbol{\phi}_{i}\);  end for while\(N_{k}<J\)do for\(c\gets 1\)to\(V\)do  find the smallest cluster \(k\), the number of segments in which is \(N_{k}(N_{k}\
eq 0)\); \(\Omega\triangleq\Omega\cup k\);  end for for\(i\gets 1\)to\(N\times L\)do if\(C_{i}==k\)then  merge the segments of cluster \(k\) into new cluster by \(C_{i}=max_{j}(\phi_{ij}),j\
ot\in\Omega\). end for  end for Output \(C_{i}\)
```  
**Algorithm 1**Cluster Merging
# Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning +
## 5 Performance Evaluation  
A comprehensive set of experiments was conducted on three real-world graph stream datasets in order to validate the effectiveness of the Dichotomy Graph Sketch (DGS). Our method was compared to two state-of-the-art graph sketches: TCM [1] and GSS [2]. A laptop equipped with Intel Core i5-9300H processors (4 cores, 8 threads), 8 GB of RAM and NVIDIA GeForce RTX 2060 GPU was used for all experiments. Except for GSS, all sketches were implemented in Python. For GSS, we used the C++ source code provided on the Github ([[https://github.com/Puppy95/Graph-Stream-Sketch](https://github.com/Puppy95/Graph-Stream-Sketch)]([https://github.com/Puppy95/Graph-Stream-Sketch](https://github.com/Puppy95/Graph-Stream-Sketch))). Because GSS does not limit the memory usage of its adjacency list buffer, we disabled it for fair comparison. Our proposed edge classifier was implemented using the PyTorch library [43].
## 3 Results and Discussion
### Algorithm Tuning and Validation
#### 3.2.2 Algorithm Validation
We found no sensitivity to these factors (\(\mathrm{r}^{2}<0.04\) in each case, \(\mathrm{n}=29.757\) for Aqua, Terra, and SeaWiFS combined), indicating that the algorithm is not optimized in a way that would limit its performance in, for example, high salinity, offshore waters, or fresher waters with high inputs of fresh CDOM. A geographic distribution of algorithm retrieval error (percent error) for \(a_{g}\)(412) and \(S_{g}\)(412-600) is shown in Figure 10.  
A similar sensitivity analysis was evaluated for MLR2 (DOC retrieval) performance at validation stations to help identify limitations of the algorithm. We tested the correlation between the DOC retrieval error (percent difference between retrieved and measured DOC) and salinity, water column depth, and DOC concentration, but found no strong trends in the distribution of error (\(\mathrm{r}^{2}=0.17,0.19\), \(0.43\), respectively, see included figures below), although it could be argued that absolute retrieval error increases somewhat (overestimates) at the extremely high salinity stations, and at extremely low DOC stations. In general, it appears that shallow stations underestimate DOC, and deeper stations tend to overestimate. The geographic distribution of error in algorithm retrievals (Figure 10) revealed no patterns with respect to distance from shore or nearby fluvial sources, but MODIS Aqua retrievals did overestimate DOC in southern oceans (south of 40\({}^{\circ}\) S, 41% \(\pm\) 16%, \(\mathrm{n}=18\)) compared to minor underestimates from other sensors and at latitudes north of 40\({}^{\circ}\) S (-8% \(\pm\) 18%, \(\mathrm{n}=1054\)). Figure 9: Taylor diagrams (top row) and target plots (bottom row) depicting comparative algorithm performance for retrieving DOC from MODIS Aqua and Terra, and SeaWiFS. Care should therefore be taken when evaluating algorithm retrievals in these areas. Only 1,090 stations (all sensors combined) were available for validation of the MLR2 and this analysis of sensitivity, and their distribution is not uniform across the world's oceans, but, as shown in Figure 1, a broad spectrum of water types with a large dynamic range of DOC were represented in both the optimization and validation datasets. Unfortunately, no validation stations for MLR2 were identified for mid-ocean gyres, and therefore the performance of the MLR2 in those waters remains poorly defined, and caution is advised in the interpretation of DOC retrievals in those areas. Figure 10: Geographic distribution of error in MLR algorithm retrievals of CDOM absorption and slope in the VIS (top and center), and MLR2 retrievals of DOC (bottom) using validation stations and satellite imagery. Differences between retrieval statistics across satellite platforms using the MLR approaches were generally small, with Aqua and Terra outperforming SeaWiFS for CDOM absorption (Table 6). All three sensors performed comparably for \(S_{g}\) and DOC (Tables 7 and 8).
# Optimal Energy Management within a Microgrid: A Comparative Study
## 2 Model for Microgrid Components
### Transformer  
The primary winding is considered an ideal transformer with relation to complex tap \(T_{v}\):1 and \(T_{i}\):1 in series with the impedance \(Z_{p}\) (refers to Figure 2) where \(T_{v}=T_{i}{}^{\star}=T_{v}\angle\phi_{tv}\), and the symbol \({}^{\star}\) denotes a complex conjugate. The secondary winding is also represented as an ideal transformer with relation to complex tap \(U_{v}\):1 and \(U_{i}\):1 in series with the impedance \(Z_{s}\) where \(U_{v}=U_{i}{}^{\star}=U_{v}\angle\phi_{uv}\). The relationship between voltage \(V_{p}\) and electricity \(I_{p}\) of the primary winding and the voltage \(V_{s}\) and electricity \(I_{s}\) of the secondary one is given by Equation (8) [21].  
Figure 1: \(\pi\) circuit for the feeders.  
Figure 2: T model for the transformers.  
\[\left[\begin{array}{c}I_{p}^{t_{z}}\\ I_{s}^{t_{z}}\end{array}\right]=\left[\begin{array}{cc}G_{PP}&G_{PS}\\ G_{SP}&G_{SS}\end{array}\right]+j\left[\begin{array}{cc}B_{PP}&B_{PS}\\ B_{SP}&B_{SS}\end{array}\right]\left[\begin{array}{c}V_{P}^{t_{z}}\\ V_{s}^{t_{z}}\end{array}\right];\forall t_{z}\in T \tag{8}\]  
where  
\[G_{PP}=\frac{F1(U_{v}^{2}+R1)+F2R2}{F1^{2}+F2^{2}},B_{PP}=\frac{F1R2-F2(U_{v}^ {2}+R1)}{F1^{2}+F2^{2}}\]  
\[G_{SS}=\frac{F1(T_{v}^{2}+R3)+F2R4}{F1^{2}+F2^{2}},B_{SS}=\frac{F1R4-F2(T_{v}^ {2}+R3)}{F1^{2}+F2^{2}}\]  
\[B_{PS}=\frac{T_{v}U_{v}(F2\cos(\phi_{1})+F1\sin(\phi_{1})}{F1^{2}+F2^{2}},G_{ SP}=\frac{-T_{v}U_{v}(F1\cos(\phi_{2})+F2\sin(\phi_{2})}{F1^{2}+F2^{2}}\]  
\[\begin{array}{c}B_{SP}=\frac{T_{v}U_{v}(F2\cos(\phi_{2})+F1\sin(\phi_{2})}{F 1^{2}+F2^{2}},F1=T_{v}^{2}R_{S}+U_{v}^{2}R_{P}+R_{eq1},\\ F2=T_{v}^{2}X_{S}+U_{v}^{2}X_{P}+X_{eq1}\end{array}\]  
\[R_{eq1}=(\xi)G_{0}-(\xi)B_{0},X_{eq1}=(\xi)B_{0}-(\xi)G_{0},\xi=R_{P}R_{S}-X_{ P}X_{S},\xi=R_{P}X_{S}-R_{S}X_{P}\]  
\[R1=R_{S}G_{0}-X_{S}B_{0},R2=R_{S}B_{0}-X_{S}G_{0},R3=R_{P}G_{0}-X_{P}B_{0},R4= R_{P}B_{0}-X_{P}G_{0}\]  
\[\phi_{1}=\phi_{tv}-\phi_{uv},\phi_{2}=\phi_{uv}-\phi_{tv}\]  
As the feeders, the injections of active and reactive power in the nodes of connection \(i\) and \(j\), where \(i=p\), \(s\); \(j=p\), \(s\); \(i\
eq j\) are represented by Equations (6) and (7), but considering the conductance and susceptance matrices of Equation (8), it is important to highlight that the decision variables associated to transformers correspond to the nodal voltages in its terminals \(\left[V_{p}^{t_{z}},V_{s}^{t_{z}},\theta_{p}^{t_{z}},\theta_{s}^{t_{z}}\right] \in y_{RD}\) (\(\forall t_{z}\in T\)).
## 4 Experiments  
### Concept  
To fulfill the generic goal of process monitoring, a suitable pipeline is to be demonstrated and, central to the research, a classification model for distinguishing relevant classes of the condition of the printing process results is to be developed, evaluated and tested. Figure 2 illustrates a conceivable structure for the in situ monitoring routine. The concept envisages utilizing the laser line profile sensor to measure the surface topography of the printed parts during the printing process in the phases between the printing of individual layers and feeding this data into an analysis routine incorporating a DL classifier. In order to successfully develop and test the method, certain auxiliary functions of data preparation must still be carried out manually in the course of this study in order to close the entire pipeline required for the overall objective for demonstrative purposes. The routine is able to combine the following sub-functions:  
* Initialize the scanning process;
* Prepare raw data (read in and pre-process);
* Prepare data for classification;
* Classify scanned layer in separate patches and evaluate results;
* Transmit feedback to the printer.  
To ensure that the model makes accurate decisions, it is necessary to create a target plan for the component to be printed. This specification plan specifies how the part should  
Figure 2: Flowchart of an in situ monitoring routine based on classification of height information of printed layers using convolutional neural networks.  
be built up layer by layer. In this way, the classification model can compare its predictions with the expected results. If there are deviations between the prediction and the expected layer, the system interrupts the printing process and issues a warning.  
In order to generate the required database, various settings for printing thermoplastic with integrated wire must be tested in preliminary trials. With the help of this preliminary knowledge, it is necessary to construct a demo component. The next step is to develop a suitable network architecture for classifying the components. This involves designing a CNN, taking into account various factors such as layer architecture, data pre-processing and optimization methods. The CNN model is trained and evaluated with image data collected in order to assess the classification performance. Attention is paid to ensuring that the model has the desired accuracy and reliability to correctly classify the component layers.