esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Leveraging insights from various studies on acoustic signals from keystroke clicks on physical keyboards [5], we explore the use of keystroke inference and acoustic fingerprinting techniques on touchscreens. Using the mobile device's on-board microphones, we surmise that acoustic signals arising from the interactions with the touchscreen can reveal the user's touch input location and can thus be used to eavesdrop sensitive input information. Such a pathway may inadvertently allow user data input associated with screen input location to be inferred without users noticing [18]. We extract acoustic features from the on-board microphone signals, supported with accelerometer and gyroscope movement data to separate and classify user touch input location on the touchscreen. Our contributions in this study are twofold. Firstly, we generate a dataset containing 2-channel (stereo) audio recordings and movement data of user touch input location on a touchscreen surface under both controlled and realistic conditions. Secondly, we compare the performance of acoustic features and movement data in categorizing touch input location using machine learning algorithms. To address the acoustic side channel presented in this paper one can consider mitigation techniques which can be broadly classified into three categories: prevention, jamming and shielding. Prevention techniques limit access to device sensors which can be implemented on both the hardware level with physical switches and in software with user access control policies. Jamming typically involves saturating sensor with noise or false information to mask the actual sound created by the touch input. Side-channel leakage can also be attenuated via shielding with physical means by altering or redistributing mass to guard against acoustic side channels. Accordingly, this paper is organized as follows: Detection of different sources of touch input and the data collection approach used is described in Section 2. In Section 3, we describe experiments conducted regarding the extracted features and the classification process. Section 4 contains results produced as part of this investigation. We discuss the results of various sensors and possible mitigation measures in Section 5 and finally conclusions in Section 6.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Synthesizing Antenna Arrays Using Taylor One-Parameter Distribution
#### 3.1.2 Select Design Parameter \(\alpha\)  
The design parameter \(\alpha\) in antenna array synthesis using the Taylor one-parameter distribution represents the main lobe width or beamwidth, crucial for controlling the radiation pattern's directivity and sidelobe levels.  
* The design parameter \(\alpha\) determines the overall shape of the amplitude distribution.
* Choose \(\alpha\) based on the desired trade-off between the side lobe level and the main lobe width.
* Higher values of \(\alpha\) result in lower side lobes but wider main lobes, and vice versa.
## 3 Flexural Fatigue Test Design for PB-DA  
### Loading Scheme  
The loading scheme aims to test two aspects: (1) the full-time variation in flexural fatigue behavior during the cyclic loading process and (2) the mechanical properties and crack distribution of the PB-DA after cyclic loading. The experiment was controlled in a pure bending state, using two-point concentrated loading, with the specific loading positions shown in Figure 5. The test equipment of this paper was a 1000 kN three-dimensional multi-point coordinated electro-hydraulic servo dynamic loading test system produced by Tianshui Red Shan Testing Machine Co., Ltd., Tianshui, China. The test data acquisition system adopts the DH3818 static strain test system and DHDAS dynamic signal acquisition and analysis system, which can realize the acquisition of strain, displacement, and acceleration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & **Design Strength** & **Compressive** & **D-A Content/\%** \\  & **Grade** & **Strength/MPa** & **D-A Content/\%** \\ \hline L1 & C50 & 59.9 & 10 \\ L2 & C50 & 60.03 & 20 \\ L3 & C50 & 61.5 & 30 \\ L4 & C80 & 80.2 & 24 \\ L5 & C80 & 81.9 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The 28-day compressive strengths of the cubic specimens.  
Figure 4: A total of 15 cubic specimens.  
Figure 5: Loading position of PB-DA.  
Based on the testing objectives of this paper, 18 loading stages were designed, with the loading schematic and each stage's loading method shown in Figure 6 and Table 6, respectively. The first loading stage is the preloading stage, aimed at testing the reliability of the experimental equipment. From the second stage onward, the cyclic loading stage begins. The upper limit of the fatigue load is 25 kN, determined through a depressurization loading test. The lower limit of the fatigue load is the same as the lower limit of the testing machine, set at 15 kN. Sinusoidal half-wave constant-amplitude stress cyclic loading was used, with 2 million cyclic loading cycles at 5 Hz. To capture the full-time evolution of flexural fatigue behavior, graded loading was performed after a certain number of cycle loads to obtain the PB-DA's flexural fatigue behavior after varying numbers of cycle loads. Finally, after completing all the cyclic loading numbers, a static load was applied to the PB-DA until cracks appear and the loading was stopped.
## 3 Current Sensor Fault Detection  
In this section, an LPV observer based residual generator is presented to detect current sensor faults. The fault detection threshold is based on the \(\mathcal{L}_{2}\) re-constructible condition proposed in [27].
## 4 Discussion
### Analysing the Proposed Approach  
Variations in the size of the polyps were analysed using images generated with different degrees of dilation and erosion applied to the masks. Table 2 shows the results, highlighting how the classifiers' confidence level decreases as the polyps' size is reduced. An inverse relationship was observed between the polyp size and the models' confidence in correctly classifying it as a polyp. This behaviour, consistent across both classifiers, was particularly pronounced in images where the polyps had been eroded, indicating that the models face significant challenges in classifying and accurately classifying smaller polyps.  
Classifiers trained with the control dataset had difficulty detecting and classifying smaller polyps. Both ViT and EfficientNet showed a progressive reduction in confidence as the size of the polyps decreased due to erosion. After multiple iterations of erosion, the models failed to classify the images correctly, highlighting their sensitivity to structural changes in the data. Although EfficientNet maintained slightly more stable confidence levels for enlarged polyps than ViT, both classifiers showed limitations in generalising to examples with smaller, less prominent polyps. These results reinforce the difficulty in dealing with deviations from the patterns present in the training data.  
On the other hand, the classifiers trained with the p2p dataset showed an improved ability to identify smaller polyps. However, these models also tended to classify some images generated without polyps as belonging to the polyp class. This suggests that whiletraining with synthetic data helped the models adapt to artificial variations in size, it may have introduced an unintended bias. A plausible explanation is that the models learned to recognise subtle visual artifacts from the Pix2 Pix generation process itself as a feature of the 'polyp' class. This reliance on generative artifacts, rather than purely anatomical features, would limit the models' generalisability to real-world data where such artifacts are absent.
## 3 Results and Discussion  
### Shortlisting of Climate Models  
The results of the shortlisting process for the downscaled General Circulation Models (GCMs) are presented in Figure 3. According to the Shared Socioeconomic Pathway (SSP) 245 scenario, the range of percentage change in mean annual precipitation (\(\Delta\)P) and change in mean temperature (\(\Delta\)T) in the future is between 2.3% and 13.5% and 0.7 \({}^{\circ}\)C to 2 \({}^{\circ}\)C, respectively. Under the SSP 370 scenario, the range is between 0% and 26.7% for \(\Delta\)P and 0.89 \({}^{\circ}\)C to 2.2 \({}^{\circ}\)C for \(\Delta\)T. For the SSP 585 scenario, the range is 3% to 41.69% for \(\Delta\)P and 1.2 \({}^{\circ}\)C to 2.67 \({}^{\circ}\)C for \(\Delta\)T. All available models predict an increase in mean rainfall and temperature in the future in all scenarios. During this process, the GCMs in all scenarios were evaluated based on the 10 th, 50 th, and 90 th percentile values of \(\Delta\)T (\({}^{\circ}\)C) and \(\Delta\)P (%). Based on this approach, the Cold-Wet, Cold-Dry, Warm-Wet, Warm-Dry, and central models are shown in Table 2. It is important to note that the phrase \"Cold\" in the \"Wet-Cold\" and \"Dry-Cold\" models refers to a lower level of warming compared to the Warm models, rather than a future temperature that is colder than the reference period. Correspondingly, the phrase \"Dry\" in the \"Dry-Cold\" and \"Dry-Warm\" models refers only to their position in comparison to other climate models.  
Figure 3: Predicted changes in average temperature (\(\Delta\)T) and annual precipitation (%\(\Delta\)P) from 2022 to 2100 compared to 1975 to 2015 using data from 13 different models. The blue crosses represent the 10 th, 50 th, and 90 th percentile values for %\(\Delta\)P and \(\Delta\)T.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Services and Platforms Prone to Malware Propagation
#### 3.2.1 Internet of Things (IoT)  
The Internet of Things (IoT) encompasses all devices capable of connecting to the internet and interact with physical processes, where human intervention may be minimal. However, this high level of connectivity brings a cybersecurity gap, increasing the attack surface in both domestic and industrial environments [30]. Many IoT devices lack security updates and present insecure default configurations [31].  
The exponential growth of IoT devices, direct internet access, and a lack of security updates represent significant risk factors that increase the attack surface in connected environments. Among the most vulnerable components are insecure default configurations, communication channels susceptible to compromise, and applications infected with malware. These elements have facilitated attacks against critical infrastructure, large-scale data theft, and acts of cyberrrorism, highlighting the high impact of the threats associated with these environments [32].
## 3 UVSQ-SAT Data Method and Map Reconstruction of the Variables (Observations and Model)
### UVSQ-SAT Data Processing to Obtain Observation Time Series
#### 3.1.3 Methodology for Obtaining UVSQ-SAT Attitude and Position Time Series  
UVSQ-SAT has a new 3-axis accelerometer/gyroscope/compass (Teach' Wear (TW)) for providing satellite attitude estimation [1]. Using the TW observations, it is possible to achieve sufficient attitude estimation accuracy for a satellite using regular Kalman filter algorithm. Another way is to use a new method based on a multilayer perceptron network to determine the UVSQ-SAT satellite attitude [18].  
Otherwise, we have a good knowledge of the UVSQ-SAT CubeSat position (latitude, longitude, altitude) according to the Two-Line Element sets (TLE) that are generally used to predict the position of a satellite. For each measurement carried-out by the instruments, we have its location on the world map. Figure 7 shows an example of ground track measurements location of the UVSQ-SAT satellite during February 2021, which represents the path on the surface of the Earth directly below the satellite's trajectory. The knowledge of the satellite position allows to calculate notably the solar zenith angle (from latitude, longitude, solar declination, Universal Time) and to rigorously locate the measurements area.
# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 3 Results
### Experimental Setup  
Concerning the deep learning classifiers, we used the Tensorflow 2.1 Library [48]. We used the default Python API of the library. Models are instantiated using the Keras [49] default implementation inside Tensorflow. Concerning the Ensemble voting, we implemented the majority voting approaches (soft and hard approaches) in Python language. We used Scikit-Learn [50] library for the SVM and the Random Forests models. We used Keras [49] for the Neural Network-based Ensemble Voting. All the experiments were made in Python [51] Language, and we used Jupyter Lab [52] for easy assessment of the results. We performed the training and the testing on Google Colab Professional account. The GPUs used were P100 and T4. The size of the RAM was 100 GB. For all the algorithms used, we performed the training using the Adam optimizer and the Cross-Entropy loss function. The image input sizes for all the networks are of (224 \(\times\) 224) pixels. For the learning rate, we started by 1.00 \(\times 10^{-4}\), and then we made some tuning by increasing the value of the learning rate to 1.00 \(\times 10^{-5}\) for all the models, except the VGG16 and EfficientNet-B0. Only for these two models, we noticed that the learning rate increase did not improve the convergence of the results.  
For the number of epochs, we started by 200 epochs. Then, we increased it or decreased it depending on the convergence results and the stabilization of the Training Loss and the Accuracy Curve. In Table 2, we presented the training epoch number and the epochs number of best convergence for each deep learning model used in the experiments. The batch size is a hyperparameter of gradient descent representing the number of training samples fed to the network in one iteration before updating its parameters. Its value depends on the size of the model, the GPU memory, and the convergence of the results.  
Figure 10: The ensemble classification model.
## 2 Materials and Methods
### Evaluation of TSNP Application in Biosensing and Diagnostics
#### 2.4.4 Fibronectin Functionalization of PEGAuTSNP and pH-Conformation Monitoring  
AuTSNPs were used to monitor fibronectin (Fn) conformational changes induced under different pH conditions. As described by [43], Fn has a compact, folded conformation under physiological buffer conditions (pH 7.4), but is extended at a more acidic pH. Disodium phosphate buffer (pH 4) and phosphate buffer (pH 7) were prepared to induce Fn conformational transitions. Phosphate buffer NaH2 PO4, 0.2 M at pH 4.16 was prepared by dissolving 3.12 g of NaH2 PO4 in 100 mL of NaCl 0.2 M. To prepare the buffer at pH 7.23, 40.5 mL Na2 HPO4 0.2 M, 9.5 mL NaH2 PO4 0.2 M, and 50 mL of ultrapure water were mixed. Both buffers were diluted to 0.01 M before use.  
PEG-coated AuTSNPs were functionalized with Fn by incubating 25 ug of Fn (1 mg/mL) with 50 \(\upmu\)L of PEGAuTSNPs. Upon functionalization, 37.5 \(\upmu\)L of Fn-PEGAuTSNPs were incubated with 200 \(\upmu\)L of phosphate buffer pH 7.2 and pH 4.1, respectively. UV-Vis measurements were performed to record the protein structural changes.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 4 Safety and Security for SafeCOP Co-CPSs
### Vehicles and Roadside Unit (RSU) Interaction  
In this use case, safety and security risks are related to creating links and communication between different actors: service providers, vehicles and roadside units. Roadside unit is hosting the road weather station measurements and sensors, vehicle unit the embedded vehicular sensors and service provider the general service data. In each of the elements, the same security risks remain: identification of counterpart, validating the runtime operation of sensors and services and avoiding malfunctions due to interference with parallel communication. Other safety aspects are related to ensure the complete data exchange procedures in local area vehicular networking, when vehicles are passing each other or roadside unit. IEEE 802.11p operates in dedicated 5.875-5.905 GHz band in Europe. In 3G, the operator is hosting the communication parameters in each link, ensuring the quality of service. Therefore, the communication failures are typically caused by capacity overload. Communication can be disturbed by intentional interference as well, which must be taken into account when aiming to ensure safety and security.
## 6 Analysis of Experiment Results
### Algorithm Accuracy
While it does achieve better accuracy on smaller datasets, it does take a dip on 250 samples for reasons unknown, and the accuracy is shown to decrease at 2500 and more samples due to a higher hyperparameter value in the pruning heuristic. TWNFI's computational time stays relatively the same regardless of the dataset size, which can be attributed to it building smaller local models for each data sample. However, its performance on smaller subsets appears to be worse than that of TRSVM, which could be explained by SVMs generally working better on smaller datasets. Its dependency on correctly chosen parameters may also complicate its deployment. However, it starts showing better results on larger subsets, likely due to more relevant samples being available for the construction of its local models. Overall, the relatively low performance of all algorithms (including the ones provided by _scikit-learn_) may indicate further undetected anomalies in the dataset, where further testing of both TRSVM and TWNFI on different datasets has them show much better results. Despite this, the performed calculations are enough to observe several patterns related to the algorithm. While TRSVM performs pretty well on smaller datasets, its computational time becomes impractical on larger ones, and the proposed solution by the original author  
Figure 7: Comparison of accuracy, fitting time, prediction time and mean square error between the chosen algorithm. trades off accuracy for speed, in ways that are not always acceptable. Meanwhile, TWNFI can perform better in certain conditions, but its extreme dependence on external parameters can make it more difficult to deploy, due to the difficulties of parameter picking in machine learning algorithms.
## 3 Results and Validation of Tree Height Estimation
The graph in Figure 15 shows the mean elevation differences between the heights calculated using Equation (1) and those measured via UAV for the four tests considered, along with the corresponding standard deviations. The results indicate that, on average, the calculated heights are in close agreement with the UAV measurements, with centimetric deviations and standard deviations of about 0.20 m. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Number of Trees**} & \multirow{2}{*}{**Mean Error**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Mean Error**} \\  & & & **(m)** & & **Value (m)** \\ \hline \multirow{4}{*}{1a} & Total & 612 & 0.015 & 0.190 & 0.140 \\  & \"1” class & 494 (80.7\%) & 0.014 & 0.195 & 0.144 \\  & \"0” class & 118 (19.3\%) & 0.021 & 0.169 & 0.127 \\
1b & Total & 612 & \(-\)0.024 & 0.288 & 0.175 \\
2 & Total & 20 & 0.043 & 0.135 & 0.115 \\
3 & Total & 20 & 0.038 & 0.121 & 0.101 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mean value, standard deviation, and absolute value of the mean difference between the heights calculated using Equation (1) and those measured via UAV point cloud. Figure 14: Frequency distributions of differences between heights calculated using Equation (1) and those measured via UAV surveys for Test 1a (**on the left**) and Test 1b (**on the right**). Figure 13: Comparison between heights calculated using Equation (1) and UAV-measured heights for Test 1a (**on the left**) and Test 1b (**on the right**). The dashed red line represents the 1:1 correspondence. A further validation of the tree height calculation obtained from Equation (1) was carried out for Areas 2 and 3 using the available high-resolution TLS survey over nine olive trees. The differences between the heights calculated using Equation (1) and those measured from the TLS point cloud were computed, obtaining 0.166 m, \(-\)0.079 m, and 0.147 m, respectively, for the mean error, standard deviation, and mean absolute error.
# Learning-Free Text Line Segmentation for Historical Handwritten Documents
## 4 Method
### Text Line Extraction with Energy Minimization Using Graph Cuts  
Binary blob lines that are detected in Section 4.2 correspond to the text line detection phase. Extraction level representation requires further assigning a text line label to each pixel in the document image. We use energy minimization [45] for assigning connected components to text line labels with the help of detected blob lines. It urges to assign components to the label of the closest blob line, while straining to assign closer components to the same label and not to assign any component to spurious blob lines. Let \(\mathcal{L}\) be the set of binary blob lines and \(\mathcal{C}\) be the set of connected components in the binary document image. Energy minimization finds a labeling \(f\) that assigns each component \(c\in C\) to a label \(l_{c}\in\mathcal{L}\), where energy function \(E(f)\) has the minimum.  
\[E(f)=\sum_{c\in\mathcal{C}}D(c,\ell_{c})+\sum_{\{c,c^{\prime}\}\in\mathcal{N}}d (c,c^{\prime})\cdot\delta(\ell_{c}\
eq\ell_{c^{\prime}})+\sum_{\ell\in\mathcal{ L}}h_{\ell} \tag{9}\]  
Energy function has three terms:  
Figure 4: The effectiveness of horizontally elongated filters on text lines with varying inclination. The visual results show that as the inclination increases the effectiveness of the method decreases.  
Data cost is the cost of assigning component \(c\) to label \(l_{c}\). For every \(c\in\mathcal{C}\), \(D(c,\ell_{c})\) is defined as the Euclidean distance between the centroid of \(c\) and the blob line \(l_{c}\). The closer the component to a blob line, the higher the chance the component will be assigned to the label of this blob line.
2 Smoothness cost is the cost of assigning closer components to different labels. Let \(N\) be the nearest component pairs. For every \(\{c,c^{\prime}\}\in\mathcal{N}\), \(d(c,c^{\prime})=\exp(-\alpha\cdot d_{e}(c,c^{\prime}))\) where \(d_{e}(c,c^{\prime})\) is the Euclidean distance between the centroids of the components \(c\) and \(c^{\prime}\). \(\alpha=(2\left\langle d_{e}(c,c^{\prime})\right\rangle)^{-1}\) where \(\left\langle\cdot\right\rangle\) denotes expectation over all pairs of adjacent elements [46]. \(\delta(\ell_{c}\
eq\ell_{c^{\prime}})\) is 1 if the condition inside the parentheses holds, and 0 otherwise.
3 Label cost For every blob line \(\ell\in\mathcal{L}\), \(h_{\ell}\) is defined as \(\exp(2\cdot r_{\ell})\) where \(r_{\ell}\) is the normalized number of foreground pixels overlapping with blob line \(\ell\). The higher the label cost, the higher the probability of discarding the blob line as spurious line.