esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Convolutional Neural Network (CNN)  
Convolutional Neural Networks (ConvNets or CNNs) [43] are one of the most common types of neural networks used to recognize and classify images. CNNs are commonly utilized in domains such as object detection, facial recognition, and so on. The convolution layer, pooling layer, activation layer, and fully connected layer are the major layers of CNN architecture.  
There are multiple layers in CNN that process and extract features from the data. To perform the convolution operation, there are several filters in the convolution layer. To perform operations on elements, there is a ReLU layer in CNN, and the rectified feature map is the output from this layer. Then, the rectified feature map passes to the pooling layer. Pooling reduces the dimension of the feature map. Then, the pooling layer converts the two-dimensional vector space into single-dimensional vector space by flatting it. The flattened vector space then passes to the fully connected layer and then classifies the input image.  
The applications of CNN are in the field of image recognition, video analysis, natural language processing, anomaly detection, drug discovery, health risk assessment and biomarkers of aging discovery, checkers game, computer go, time series forecasting, cultural heritage, and 3d datasets.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 3 Problem Description
### Detour Method
Note that \(l_{kijh_{1}}\) represents the total delivery distance of vehicle \(k\) from customer \(i\), through the detour around the vehicle-restricted zone \(h_{1}\), to customer \(j\). If the quantity is 2, select method 1, \(l_{kijh_{1}}=\min\Bigl{(}l_{ib}+l_{jb},l_{id}+l_{jd}\Bigr{)}\). If the quantity is 3, select method 2, \(l_{kijh_{1}}=l_{id}+l_{jd}\). If the quantity is 4, select method 3, \(l_{kijh_{1}}=\min\Bigl{(}l_{ib}+l_{bc}+l_{cj},l_{ia}+l_{ad}+l_{dj}\Bigr{)}\). As shown in Figure 2b, the drone follows a detour along the edge of the no-fly zone, with a central angle of \(\theta_{ijh_{2}}\). The flight length along the edge of the no-fly zone \(h_{2}\) is \(l^{1}_{ijh_{2}}\), and the distance flow within the no-fly zone without detouring is \(l^{2}_{ijh_{2}}\). The additional detour distance is \(l_{uijh_{2}}\). The total flight distance between the customer points for the drone is expressed by Equations (1) and (2) [29]. \[l_{u_{k}ij}=l_{ij}+\sum_{h_{2}\in H_{2}}l_{u_{k}ijh_{2}}\cdot z_{u_{k}ijh_{2}t },i\in V,j\in V,h_{2}\in H_{2} \tag{1}\]  
\[l_{u_{k}ijh_{2}}=l^{1}_{ijh_{2}}-l^{2}_{ijh_{2}}=\Bigl{(}r_{h_{2}}\cdot\min \Bigl{(}\theta_{ijh_{2}},360-\theta_{ijh_{2}}\Bigr{)}-l^{2}_{ijh_{2}}\Bigr{)} \tag{2}\]
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 3 Results
### Multiple Methods to Build Models Based on Vegetation Index
#### 3.3.4 Neural Network Method  
In the SPSS software, 76.7% of the data was utilized as training data, while 23.3% of the data was allocated for validation during the regression modeling process. The resulting model achieved a modeling accuracy of 0.743, which surpasses the threshold of 0.6, indicating a relatively high-quality model with favorable accuracy. In order to evaluate the generalization ability of the model more comprehensively, the accuracy of the model is 0.702 by K-fold cross-validation. This indicates that the model has strong generalization ability and is suitable for estimating wheat yield.
## 6 Results
### Single Transmission
#### 6.3.2 Impact of Packet Loss on the Throughput  
Throughput is one of the most important performance measures of transmission. We tested and recorded the throughputs of four schemes under different conditions with a bottleneck link bandwidth of 80 Mbps.  
Figure 7(a) shows the four schemes' throughput variation with the link packet loss rate. From this, it can be seen that all four schemes had good throughput performance when there was no packet loss and could approach the bandwidth limit of the bottleneck link. However, as the packet loss rate increased, the performance loss of the NewReno and Cubic schemes was significant, as both schemes use packet loss as a signal of network congestion. The BBR and Nodctl schemes do not use packet loss as a network congestion signal, and thus a small packet loss value did not affect their throughput performance.  
Figure 7(b) shows the bandwidth utilization of the four schemes as a function of the bottleneck bandwidth when the packet loss rate was 0.015%. Although NewReno and Cubic may significantly reduce their rates due to packet loss, they can quickly recover to the bottleneck link bandwidth when the bandwidth is low. However, with a bandwidth increase, the performance loss of the NewReno and Cubic schemes was severe. The BBR scheme also experienced a specific decrease in bandwidth utilization when the bandwidth was high, while the Nodctl scheme consistently maintained the best performance in bandwidth utilization.  
Figure 7(c) shows the impact of latency on the throughput of four schemes at a packet loss rate of 0.15%. The figure shows that as the delay increased, the transmission time increased, and the throughput capacity of all four schemes significantly decreased. The Nodctl scheme had the slowest performance degradation rate.  
Table 6 shows the four schemes and three parameters: the ratio of the throughput at a 0.3% packet loss rate to the throughput without packet loss; the ratio of bandwidth utilization in a 90 Mbps link to bandwidth utilization in a 10 Mbps link under a 0.015% packet loss condition; and the ratio of the throughput in a 150 ms delay link to throughput in a 10 ms delay link under 0.15% packet loss conditions. It can be seen that when the latency and bandwidth were small, the performance of BBR and Nodctl was not affected by the packet loss rate. As the bandwidth increased, the packet loss resistance performance of Nodctl remained stable, while the performance of BBR slightly decreased. As the latency increased, the performance of Nodctl decreased, but it still outperformed the other three schemes. The performance of BBR was reduced significantly, and it no longer had advantages compared with NewReno and Cubic.  
Figure 8: Transmission performance with packet loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fluctuation Range** & **NewReno** & **Cubic** & **Bbr** & **Nodctl** \\ \hline RTT & 41 ms & 25 ms & 9 ms & 0.2 ms \\ Throughput & 37.4 Mbps & 21.8 Mbps & 46.8 Mbps & 0.2 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 5: Range of transmission RTT and throughput fluctuations.
# A Mixed Clustering Approach for Real-Time Anomaly Detection
## 6 Experimental Analysis and Results
Moreover, the weight of each attribute is taken as equal which gives clusters more representation of the datasets and it can be seen from the obtained results. Figure 4: Comparative analysis of anomalies using KDDCUP’99. Figure 3: Comparative analysis of output clusters using Kitsune. Figure 5: Data size vs. output clusters using KDDCUP’99 and Kitsune. Figure 6: Similarity threshold vs. output clusters using KDDCUP’99. Figure 7: Similarity threshold vs. output clusters using Kitsune. Thirdly, the temporal attributes are treated like any other attributes by aforesaid algorithms. However, the proposed algorithm treats temporal attributes (time-stamps) separately and produces the time-dependent clusters. Thus, each clusters will have a fuzzy life-time, and a data instance not falling within the lifetime or falling with a very low membership value or belonging to sparse cluster can be treated as an anomaly. That is why the proposed algorithm can extract more anomalies than others. Finally, \(k\)-means and IF models give a specified number of clusters depending on the value of \(k\) chosen initially, which is not realistic in case of real-time clustering. If the time attribute is ignored or treated like any other attributes, PCM [14] gives better results, ACA does not give a reasonable solution as its output depends on order and number of inputs. OnCAD [31], needs a couple of input parameters to be specified. The proposed algorithm outperforms the aforesaid algorithms in the sense that it can handle higher dimensional data with numeric, categorical and temporal attributes better than others. Although the number of clusters \(k\) has to be specified in the partitioning phase, during phase two, the merge function minimizes the number of clusters. So, even if we start with a different number of input clusters, we arrive at quite a smaller number of stable clusters. For this experiment, the value of \(k\) is taken as 12 for KDDCup'99 [44] and 15 for Kitsune [45]. The similarity threshold has to be adjusted accordingly and in this case the values are taken as 0, 0.25, 0.5, 0.75. The obtained results corroborate our claim. Furthermore, the algorithm is scalable with respect to the data sizes. The algorithm's time complexity is expressed graphically in Figure 8.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods
### Input Data Sources
#### 2.3.1 Satellite Data
Afterwards, PAI, leaf bi-hemispherical reflectance and transmittance, together with constant values for soil reflectance in the visible (\(VIS\) = [400-700] nm, \(\rho_{soil,VIS}=0.15\)) and near infrared (\(NIR\) = [700-2500] nm, \(\rho_{soil,NIR}=0.25\)) were used to quantify the shortwave net radiation of the soil and canopy. Leaf chlorophyll concentration (i.e., \(C_{a+b}=CCC/LAI\)) was used to derive the leaf bihemispherical reflectance and transmittance in the visible spectrum after a curve fitting of 45,000 ProspectD (2017) simulations. Likewise, equivalent water thickness (i.e., \(C_{w}=CWC/LAI\)) was used to retrieve leaf bihemispherical reflectance and transmittance in the NIR spectral region. The thermal data needed to drive the evapotranspiration model was obtained from the Sea and Land Surface Temperature Radiometer (SLSTR) on board of the Sentinel-3A satellite (2017). SLSTR contains 3 thermal infrared (TIR) channels (with two dynamic range settings--for fire monitoring and for sea/land surface temperature monitoring) with 1 km spatial resolution and less than two days temporal resolution with one satellite (less than one day with both A and B satellites) at the equator. For each selected S2 scene, all the S3 scenes falling on the day of S2 overpass or within ten days before and after, were selected for processing. In the current study we used the L2A Land Surface Temperature (LST) product as downloaded from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/--last](https://scihub.copernicus.eu/--last)]([https://scihub.copernicus.eu/--last](https://scihub.copernicus.eu/--last)) accessed 10 September 2019). The accuracy of this product is reported to be below 1 K when comparing against in situ radiometer measurements and independent operational reference products (2017). Finally, the parameters in the ET models that could not be directly retrieved from shortwave observations (e.g., vegetation height or leaf inclination angle) were set based on a land cover map and a look-up table (see Table 2). The CCI landcover map from 2015 (2017), which was produced with a global coverage and 300 m spatial resolution, was used as the initial input layer before being reclassified into the smaller number of classes as shown in Table 2. Out of the parameters set according to the look-up table, the vegetation height (\(h_{C}\)) has the largest influence on the modelled fluxes as it effects aerodynamic roughness (2017; 2018). Therefore in herbaceous classes where it can change throughout the growing season (grasslands and croplands) it was scaled with PAI using a power law, with maximum value \(h_{C,max}\) indicated in Table 2 reached at a prescribed maximum PAI \(PAI_{max}\) (5 in croplands and 4 in grasslands) and a minimum value set to 10% of the maximum value.
# Optimal Configuration and Path Planning for UAV Swarms Using a Novel Localization Approach
## 5 Simulation Results
### Dynamic Emitter  
As for dynamic emitter, it is assumed that the emitter is uniform linear motion and the dynamic behavior of the state is described by:  
\[\mathbf{x}_{t}(k)=\mathbf{F}\mathbf{x}_{t}(k-1)+\mathbf{v}(k). \tag{60}\]  
Figure 6: Evolution of RMSE with different numbers of UAVs.  
The initial state is \(\mathbf{x}_{t}(1)=\left[0,\;50/\sqrt{2},\;0,\;50/\sqrt{2}\right]^{T}\), with the state transition matrix given by:  
\[\mathbf{F}=\left[\begin{array}{cccc}1&\Delta T&0&0\\ 0&1&0&0\\ 0&0&1&\Delta T\\ 0&0&0&1\end{array}\right], \tag{61}\]  
where \(\Delta T=1\)s. In order to simplify the calculation, it is assumed that the emitter flies at a speed of 50 m/s along the straight line and other constraints are the same as in the static emitter situation.  
Figure 7a shows the results of location and tracking for a dynamic emitter. Different from static emitters, each UAV starts to fly towards the estimated emitter position after obtaining a certain angle. Since the distance between the emitter and the UAV changes significantly at each moment, the distance rule exerts more influence on the UAV path at this time as compared with the static emitter localization. According to Figure 7b, the location accuracy after path optimization is still high and stable as compared with a location with straight-line paths.
## 1 Introduction
The best phases for acquiring strain data is during the compression and decompression phase, since no strain signal is transmitted when the stress is stable between these two phases. Compression and decompression of vital tissue may be caused by applied pressure from the probe or by natural internal movements from arterial pulsation, heart movements, or even breathing. Some strain elastography platforms provide feedback to the examiner about the phase of compression or decompression on which the image is acquired, enabling the use of \(SR\) from similar phases of tissue straining. One study on breast imaging concluded that peak \(SR\) performs better than average \(SR\) in breast tumor characterization [6]. Several studies reported high accuracy of \(SR\) in determining focal breast lesions as malignant or benign. In a meta-analysis, [PERSON] et al. reported a sensitivity and a specificity of 88% and 83%, respectively, for \(SR\) with a receiver operating characteristic-area under curve (ROC-AUC) of 0.92. Strain elastography with \(SR\) has been compared to evaluation using a strain histogram with similar good results [7]. \(SR\) was reported to be better than magnetic resonance imaging (MRI) for breast tumor characterization, but the combination of the two modalities had a better ROC-AUC of 0.914 [8]. Furthermore, for the evaluation of axillary lymph nodes in breast cancer patients, a combined evaluation of B-mode ultrasound (US) and Real-Time Elastography (RTE) increased specificity [9]. Also, in the characterization of thyroid nodules as malignant or benign, \(SR\) was reported to have sensitivity of 85-89% with specificity of 80-82% in two meta-analyses [10; 11]. In trans-rectal applications in prostate and rectal tumors, \(SR\) has been used to improve B-mode identification of malignant tumors with adequate accuracy (ROC-AUC > 90%) [12; 13]. The aim of this study was to retrospectively compare variability in strain-based elastography quantified by \(SR\), recorded in three different applications using Real-Time Elastography in homogeneous tissue-mimicking media, in resected tissue from bowel lesions, and during the endoscopic ultrasound (EUS) of focal pancreatic lesions. We chose to calculate the inter-quartile range (IQR) and median for all applications based on previous studies, since this has become a much-used quality-indicator in transient- and shear-wave elastography platforms. Our hypothesis is that \(SR\) measurement variability would increase substantially from phantom scanning to an endoscopic application on pancreatic lesions, which may limit the usefulness of the method in some applications.
###### Abstract  
This study introduces a novel optimization approach for airfoil-based flutter energy harvesters through installation angle adjustment, addressing a critical research gap in the field where previous studies have primarily focused on structural modifications. To investigate this unexplored avenue, we developed a flutter energy harvester with an adjustable installation angle mechanism, aiming to reduce critical flutter velocity, broaden operational bandwidth, and improve energy harvesting efficiency under low-speed airflow conditions. The performance characteristics of the harvester were comprehensively evaluated through both numerical simulations incorporating fluid-structure-electrical coupling and wind tunnel experiments conducted at four distinct installation angles (0\({}^{\circ}\), 3\({}^{\circ}\), 6\({}^{\circ}\), and 9\({}^{\circ}\)). The experimental results demonstrated a significant correlation between installation angle and critical flutter velocity, showing a consistent reduction from 7.8 m/s at 0\({}^{\circ}\) to 7.2 m/s at 6\({}^{\circ}\), and further decreasing to 6.3 m/s at 9\({}^{\circ}\). Notably, optimal performance was achieved at a moderate installation angle of 3\({}^{\circ}\), yielding a maximum output voltage of 12.0 V and power output of 0.58 mW, which substantially exceeded the baseline performance at 0\({}^{\circ}\) (10.9 V, 0.48 mW). However, further increasing the installation angle to 9\({}^{\circ}\) led to performance degradation, attributed to a premature aerodynamic stall, resulting in reduced output metrics of 7.9 V and 0.25 mW for voltage and power, respectively. These findings demonstrate a simple yet effective method for enhancing flutter energy harvesting performance in low-speed airflow conditions.  
Keywords:wind-induced vibration; flutter; piezoelectric energy harvester; numerical analysis _Article_
## 3 Network, Assumptions and Adversary Model  
### Network Model  
A wireless sensor network (WSN) is composed of large number of small sensor nodes that are of limited resource and densely deployed in an environment. Whenever end users require information about any event related to some object(s), they send a query to the sensor network via the base station. And the base station propagates that query to the entire network or to a specific region of the network. In response to that query, sensor nodes send back required information to the base station. A typical wireless sensor network scenario is shown in Figure 1. Links are bidirectional. Also, sensor nodes use IEEE 802.11 standard link layer protocol, which keeps packets in its cache until the sender receives an acknowledgment (ACK). Whenever a receiver (next hop) node successfully receives the packet it will send back an ACK packet to the sender. If the sender node does not receive an ACK packet during predefined threshold time, then the sender node will retransmit that packet.  
\begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline  & PFR [3] & PSR [4] & SAS \& CAS [5] & CEM [7] & SIGF [6] & GeRaF [8, 9] & SiFT [10] \\ \hline Required information for routing & ID of destination & Routing table (e.g., destination ID, \# of hops _etc._) & Routing caching & Depending on a routing scheme & Depending on a routing scheme & Own, destination, \& destination, \& destination, \& destination and own location & Destination and destination location \\ \hline Transmission mechanism & 1 st phase: Point-to-point; Broadcast & Point-to-point routing scheme & Depending on a routing scheme & Depending on a routing scheme & Point-to-point routing scheme & Broadcast & Broadcast \\ \hline Decision place for forwarding & 1 st phase: Transmitter; 2 nd phase: Receiver & Transmitter routing scheme & Depending on a routing scheme & Transmitter & Receiver & Receiver & Receiver \\ \hline Criteria for forwarding packet to next hop & 1 st phase: random; 2 nd phase: flooding hops & 1 st phase: random; 2 nd phase: flooding in terms of hops & 1 st phase: random; 2 nd phase: shortest in terms of hops & 1 st phase: random; 2 nd phase: shortest in terms of hops & 1 st phase: random; 2 nd phase: shortest in terms of region & Randomly select any trusted node lies in forwarding location & Node that is closer to the destination in terms of trajectory \\ \hline Identity privacy & Not Available & Not Available & Available & Not Available & Not Available & Not Applicable & Not Applicable \\ \hline Route privacy & Available & Available & Depending on a routing scheme & Depending on a routing scheme & Available & Available & Available \\ \hline Location privacy & Available & Available & Not Available & Available & Available & Not Applicable & Not Applicable \\ \hline Data privacy & Not Available & Not Available & Available & Available & Available & Not Applicable & Not Applicable \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of privacy preserving schemes.
# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
###### Abstract  
Predicting the motion of handwritten digits in video sequences is challenging due to complex spatiotemporal dependencies, variable writing styles, and the need to preserve fine-grained visual details--all of which are essential for real-time handwriting recognition and digital learning applications. In this context, our study aims to develop a robust predictive framework that can accurately forecast digit trajectories while preserving structural integrity. To address these challenges, we propose a novel video prediction architecture integrating ConvCARU with a modified DCGAN to effectively separate the background from the foreground. This ensures the enhanced extraction and preservation of spatial and temporal features through convolution-based gating and adaptive fusion mechanisms. Based on extensive experiments conducted on the MNIST dataset, which comprises 70 K pixel images, our approach achieves an SSIM of 0.901 and a PSNR of 29.31 dB. This reflects a statistically significant improvement in PSNR of +0.20 dB (\(p<0.05\)) compared to current state-of-the-art models, thus demonstrating its superior capability in maintaining consistent structural fidelity in predicted video frames. Furthermore, our framework performs better in terms of computational efficiency, with lower memory consumption compared to most other approaches. This underscores its practicality for deployment in real-time, resource-constrained applications. These promising results consequently validate the effectiveness of our integrated ConvCARU-DCGAN approach in capturing fine-grained spatiotemporal dependencies, positioning it as a compelling solution for enhancing video-based handwriting recognition and sequence forecasting. This paves the way for its adoption in diverse applications requiring high-resolution, efficient motion prediction.  
vision processing; ConvCARU; video processing; GAN; motion predicting 2025