esa-sceva/satcom-chunk-collection huggingface dataset:
# Pile Base and Shaft Capacity under Various Types of Loading
## 2 Pile Bearing Capacity
### Concept of the New Method of Bidirectional Static Load Testing  
The bidirectional test allows for conducting the static load test without retaining structure as the loading force is balanced within the testing appliance (Figure 2c). This kind of test was originally introduced by [PERSON] [6]. Similar tests were also carried out in Austria [21], Slovakia [22], Poland [7], Serbia [23], and the USA [24].  
The proposed research method requires two steel pipes with different diameters and similar lengths for the tests. The outer pipe with a larger diameter is installed in the ground with a loose steel shoe beneath. Inside the installed pipe, a second pipe is placed. On the prepared stand, three stages of the test can be performed (Figure 2).  
Figure 2: Types of test performed with the new device: (**a**) load applied to the whole pile, (**b**) load applied to the pile base, (**c**) bidirectional test with a compressed base and uplift shaft.  
In the first stage, the load is applied to the outer pipe by means of a hydraulic cylinder (Figure 2a). The second stage is similar to the first one, although this time the load is applied only to the inner tube, which allows only for measurement of the pile base bearing capacity (Figure 2b). In the third stage, the load is also applied to the inner tube, but this time the support structure is bolted to the outer tube (Figure 2c).  
During the measurements, the steel shoe (inner pipe + shoe) is pressed into the ground, and at the same time the outer pipe, working solely by friction on its shaft, is uplifted vertically with the same force. The result of the tests, in the first stage, is the \(Q\)-\(s\) curve for the whole pile and, in the second stage, for its base. During the tests in the third stage, two \(Q\)-\(s\) curves are obtained, one for the uplift shaft and the other for the pressed base.  
Additionally, with the conducted tests, it is possible to plot a \(Q\)-\(s\) diagram for the pile shaft under compression loading. For this purpose, the Osterberg method [6], originally developed to determine an equivalent \(Q\)-\(s\) plot from the results of the bidirectional test, can be applied. The method for constructing the shaft curve is presented in Figure 3.  
The main assumption in the construction of the curve is that every point in the curve of a pile is a sum of load points on the base and shaft curves for the same displacement. The process of curve construction is divided into a few steps. At first, both curves for the whole pile and pile base are divided into a series of points. Then, two points from both curves are selected for the same displacement values and a new point is created. It has the same value of displacement, but the value of load results from subtracting the loads for these two points selected. This procedure is repeated for successive points until the whole curve \(Q\)-\(s\) of the pile shaft can be drawn.
## 5 Experiment and Analysis of Results
### Analysis of Experimental Data
#### 5.3.4 Video Stream Experimental Data Analysis  
In order to test the detection capability of the YOLOv3-DL algorithm in the video stream, we collected vehicles' driving videos for experiments in a variety of different scenarios and different weathers. The length of each collected video is uniformly 40 s. For further comparison, we take a Test-1 video at the roadside on a clear day at dusk, a Test-2 video on the flyover at noon on a clear day, and a Test-3 video on the sidewalk on a cloudy morning. The test results shown in Tables 5 and 6 have also presented that even in different weather conditions, the accuracy rates of the YOLOv3-DL algorithm are higher than those of the YOLOv3 algorithm.  
The test results show that it takes an average of 25 ms to count the vehicles per frame image, and the monitoring of traffic flows using the YOLOv3-DL algorithm is almost equal to the real traffic flow. As compared with the previous results of YOLOv3, the video monitoring accuracy rate of traffic flows has been improved, and we also note that the video collection location should not be too high and the field of view should not be too large. Because when the location is too high, it may cause missed detection, and the field of vision will cause false detection. The average accuracy rates of traffic statistics in Tables 5 and 6 are 90.8% and 98.8%, respectively, and the average statistical times of YOLOv3 and YOLOv3-DL are 32 ms and 25 ms, respectively. When these results are compared with other monitoring algorithms of traffic flows, such as Faster R-CNN and SSD, as the results in Table 7 show, the YOLOv3-DL algorithm has the maximum accuracy rate and shortest monitoring time.
# DAB Signal Preprocessing for Passive Coherent Location
###### Abstract  
Digital Audio Broadcast (DAB) transmitters can be successfully used as illumination sources in Passive Coherent Location (PCL). However, extending the integration time in such a configuration leads to the occurrence of periodical artifacts in the bistatic range/Doppler plots, resulting from the time structure of the DAB signal. In this paper, we propose some methods of signal preprocessing (based on symbol removal, substitution by noise, and duplication) that operate on the DAB transmission frame level and improve the received signal's correlation properties. We also demonstrate that two of these methods allow us to avoid the mentioned artifacts and thus to improve the quality of the range/Doppler plots with detection results. We evaluate the performance of the proposed methods using real DAB signals acquired in an experimental PCL platform. We also provide the analysis of the Signal to Noise Ratio (SNR) during the detection of a moving target which shows that the proposed solution, based on symbol duplication, can offer around 3 dB of gain in SNR. Finally, we carry out the computational complexity analysis showing that the proposed method can be implemented with a minimal cost after some optimizations.  
passive bistatic radar; passive coherent location; digital audio broadcasting; DAB illuminated PCL; signal preprocessing +
Footnote †: journal: _Sensors_**2022**, 22, 378. [[https://doi.org/10.3390/s2010378](https://doi.org/10.3390/s2010378)]([https://doi.org/10.3390/s2010378](https://doi.org/10.3390/s2010378))
# Stepped Spillways and Energy Dissipation: A Non-Uniform Step Length Approach
## 2 Materials and Methods
### Boundary Conditions  
For all models investigated in this article, four boundary conditions were defined as follows:  
1. The water inflow boundary was set as a velocity-inlet condition and obtained based on the range of flow rates \(1.1\leq d_{o}/h\leq 1.9\). The term (\(d_{o}/h\)) represents the ratio of the critical depth (\(d_{c}\)) to step height (\(h\)), in which critical depth is representative of the discharge.
2. The outlet boundary condition was defined as an outlet pressure to allow the water flow out freely.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Step Height** & **Step Length** & **Height of Dam** & **Number of Steps** & **Slope** \\
**h(m)** & **L(m)** & **H\({}_{\text{d}}\) (m)** & **(N)** & **V:H 1** \\ \hline
0.1 & 0.10 & 1.2 & 12 & 1:1 \\
0.1 & 0.15 & 1.2 & 12 & 1:1.5 \\
0.1 & 0.20 & 1.2 & 12 & 1:2 \\
0.1 & 0.22 & 1.2 & 12 & 1:2.2 \\
0.1 & 0.25 & 1.2 & 12 & 1:2.5 \\
0.1 & 0.30 & 1.2 & 12 & 1:3 \\
0.1 & 0.35 & 1.2 & 12 & 1:3.5 \\ \hline \hline \end{tabular} \({}^{1}\) V indicates the vertical step height and H is the horizontal step length.  
\end{table}
Table 1: Uniform configurations of the stepped spillways modelled in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Concave** & **Convex** & **Random 1** & **Random 2** & **Random 3** & **Semi-Uniform** \\ \hline i & L (m) & L (m) & L (m) & L (m) & L (m) & L (m) \\ \hline
1 & 0.1 & 0.35 & 0.2 & 0.25 & 0.1 & 0.1 \\
2 & 0.125 & 0.325 & 0.2 & 0.2 & 0.35 & 0.3 \\
3 & 0.15 & 0.3 & 0.3 & 0.1 & 0.125 & 0.1 \\ N = 12 & 4 & 0.175 & 0.275 & 0.1 & 0.35 & 0.325 & 0.3 \\ h = 0.1 m & 5 & 0.2 & 0.25 & 0.25 & 0.2 & 0.15 & 0.1 \\ H\({}_The walls were assumed to have a no-slip condition.  
4. As shown in Figure 1, the atmospheric pressure was considered at the free surface boundary.  
After validating the proposed numerical model by the experimental results obtained by [PERSON] [40] and [PERSON] [2], different uniform and non-uniform stepped spillways were investigated and, as shown in Figure 2, the optimized design was selected.
# Entropy-Regularized Federated Optimization for Non-IID Data
## Appendix B Convergence of ERFO Under Non-Convex Objectives
### Assumptions and Preliminaries  
We assume the following standard conditions for non-convex federated optimization:  
Assumptions for Non-Convex Federated Optimisation  
* **Smoothness.** Each client loss \(F_{i}(w)\) is \(L\)-smooth, i.e. \(\|\
abla F_{i}(w)-\
abla F_{i}(w^{\prime})\|\leq L\|w-w^{\prime}\|\) for all \(w,w^{\prime}\). Equivalently, \(F_{i}(x)\leq F_{i}(y)+\
abla F_{i}(y)^{\top}(x-y)+\frac{l}{2}\|x-y\|^{2}\). The aggregated objective \(F(w)=\frac{1}{N}\sum_{i=1}^{N}F_{i}(w)\) is therefore also \(L\)-smooth.
* **Bounded variance.** Mini-batch gradients have bounded variance: \(\mathbb{E}\big{[}\|\,g_{i}(w;\xi)-\
abla F_{i}(w)\|^{2}\big{]}\leq\sigma^{2}\).
* **Bounded client drift.** There exist constants \(\Gamma\geq 1\) and \(C_{0}\geq 0\) such that \(\frac{1}{N}\sum_{i=1}^{N}\|\
abla F_{i}(w)\|^{2}\leq\Gamma^{2}\|\
abla F(w) \|^{2}+C_{0}\). For clarity we set \(\Gamma=1\) and \(C_{0}=0\); general \(\Gamma\) produces only a constant-factor slowdown.
* **Learning-rate condition.** The step size satisfies \(0<\eta\leq 1/L\). A fixed \(\eta\) is assumed, although decay schedules are also admissible.
* **Entropy-regulariser smoothness.** Adding the entropy term \(-\lambda H(p)\) increases the smoothness constant by at most a factor of \(\mathcal{O}(1)\). We take \(\lambda\leq\lambda_{\max}\) so that the combined objective remains \(L^{\prime}\)-smooth with \(L^{\prime}=\mathcal{O}(L)\), ensuring \(\eta\leq 1/L^{\prime}\).  
Local Entropy-Regularised Update  
Each client performs \(\tau\) local steps per round:  
* **Initial step (\(k=0\)).**\(w_{t,1}^{j}\gets w_{t}-\eta\big{(}\
abla F_{i}(w_{t})-\lambda\;\
abla H_{i}(w_{t}) \big{)}\).
* **Subsequent steps (\(k=1,\ldots,\tau-1\)).**\(w_{t,k+1}^{i}\gets w_{t,k}^{i}-\eta\;\
abla F_{i}(w_{t,k}^{i})\).
* **Aggregation.**\(w_{t+1}=\frac{1}{N}\sum_{t=1}^{N}w_{t,\tau}^{j}\).  
For simplicity we assume full client participation; extensions to partial participation follow by taking expectations over the sampled set.
## 2 Related Work
For example, the SMOTE (synthetic minority oversampling technique) method is utilized to compensate for the minority class. Furthermore, three different neural networks are explored, multiple feature sets are selected, and the steps taken in the direction of optimization and more specifically to avoid overfitting are described. A detail that is interesting is the analysis of the challenges and the benefits that would arise in case a multi-class classification problem is targeted instead of a binary one. Both cases were examined with the binary classifiers resulting in a higher performance. A work that lies in the same context to our research and includes the process of training classifier models based on in-game data is that of [PERSON] et al. [25]. The scope of this study is much broader, since it also includes the process of creating the serious games, the selection of a suitable focus group, the inspection of collected data from a statistical point of view, the classifier training and finally the evaluation of the serious games based on participant's replies to the Game Experience Questionnaire. Regarding the classification models, three ML algorithms have been used, with a single dataset composed of features automatically selected based on their importance as calculated by a Random Forest based model. An evaluation study was performed with 16 seniors, including AD, MCI and healthy individuals as assessed by the MMSE scale. A dataset of 89 instances was assembled with several variables derived from the three games used. The binary classification model that was trained using logistic regression and support vector machine achieved an absolute prediction with no false negatives. Except for accuracy, the false positive and false negative ratios were measured, along with the metric of F-measure defined as the weighted harmonic mean of precision and recall. To summarize there are only a few studies that are targeting MCI detection leveraging on ML models trained on data collected from serious games. Moreover, between the existing approaches there are significant differences in terms of the screening tools and the cutoff scores employed for assessing ground truth cognitive states, the game tasks involved, the cognitive functions targeted, the features engineered for model training, the ML methods applied, the measures taken to prevent high model bias/variance and the provision of an endpoint to access online classification services for new data. This entails that a simple comparison between existing methods may not be practical and that the discussion should take into consideration several characteristics. Table 1 provides an overview of such characteristics in order to associate our work to similar studies on MCI detection.
# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 3 Application and Experiments  
### Application Description  
The operation and maintenance data are from a KPG component in the integrated navigation system of a TYW-001, a large fixed-wing UAV from BHUAS. The endurance of the TYW-001 is over 40 h with a ceiling of 8000 m, a take-off weight of 1500 kg, and an external payload of 370 kg. Apart from the fire control system and redundant backups, the flight data of the TYW-001 has a total of 457 variables, including sensor monitoring and operation input. The main functions of the KPG component include solving the orientation of the aircraft relative to the ground station; solving the information of the aircraft deviating from the predetermined course and the predetermined glide slope during microwave landing; solving the information of the aircraft deviating from the course and the glide slope during instrument landing; and providing the distance information of the runway entrance by the pointing beacon input and output signal power division, signal reception and transmission, etc.  
According to the spike testing of the target KPG component, 32 relevant data features can be obtained from onboard sensors, existing 5 fault modes, and 1 normal state. Table 2 shows the distribution and corresponding criticality values of these states in our spike testing.  
Figure 1: The basic workflow of the proposed two-step ensemble cost-sensitive diagnosis model (MC–LGB).
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 3 Experimental Results
### Dataset Description  
The experiments in this paper use four datasets of multi-labeled images for evaluating the efficiency of the proposed deep learning solution. The first two datasets pertain to the college of the computer and the information sciences building at the King Saud University (KSU), Saudi Arabia. They have been collected by our team using the BlindSys system described in Section 3.1. The second two datasets were collected by the authors of [8] in two different buildings of the University of Trento, Italy. The cameras used to capture these images are from a company called IDS-imaging [38]. For the KSU datasets, the camera used was a UI-1220 SE -C-HQ, USB 2.0, CMOS, 87.2 fps, 752 \(\times\) 480, 0.36 MPix, 1/3\", ON Semiconductor, Global Shutter. For the UTrento datasets, the camera used was a UI-1240 LE -C-HQ, USB 2.0, CMOS, 25.8 fps, 1280 \(\times\) 1024, 1.31 MPix, 1/1.8\", e2v, Global Shutter, Global Start Shutter, Rolling Shutter. Both cameras were equipped with a KOWA LM4 NCL 1/2\", 3.5 mm, F1.4 manual IRIS  
Figure 7: Graphic user interface (GUI) of the prototype implemented to collect dataset images at King Saud University. (**a**) Detailed GUI for testing and operator support. (**b**) Simple GUI on the mobile device to be used by the VI.  
C-Mount lens from RMA Electronics Inc. [39]. The details of these four datasets are given in Table 1, which also presents the list of objects considered in every dataset.  
The King Saud University 1 (KSU1) dataset is composed of 320 images, divided into 161 training images and 159 testing images. The King Saud University 2 (KSU2) dataset is composed of 174 images, divided into 86 training images and 88 testing images. The University of Trento 1 (UTrento1) dataset accounts for a total of 130 images acquired in two separate daytimes (morning and evening), and these were split into 58 training images (i.e., for training the NN model) and 72 for testing purposes. Finally, the University of Trento 2 (UTrento2) dataset is composed of 131 images, divided into 61 training images and 70 testing images. It is noteworthy that the training images for all datasets were selected in such a way to cover all the predefined objects in the considered indoor environment. Thereupon, we have selected the objects deemed to be the most important ones in the considered indoor environments. Figures 8 and 9 show sample images from the KSU2 and UTrento2 datasets with the list of objects contained within. Furthermore, Figure 10 presents the number of occurrences of objects in the training set and the test set of every dataset.  
Figure 10: Number of occurrences of objects in each dataset. The x axis represents the object number according to the list given in Table 1.  
Figure 9: Sample images from the University of Trento 2 dataset (UTrento2) and the corresponding labels (objects).
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 3 Overview of Drones  
The primary components of a drone system include a Ground Station Controller, physical sensors, actuators, a Power Management System (PMS), a Flight Control Board (FCB), a rotor system, an Electronic Speed Controller (ESC), and a Transceiver Control Unit (TCU). These crucial elements, such as ESC, FCB, TCU, and PMS, can serve as potential sources for drone forensics procedures. They store vital data related to flight control, flight records, internal monitoring, and information from transceivers and sensors mounted on the drone. It is important to note that the specific components may vary depending on  
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Paper**} & \multirow{2}{*}{
\begin{tabular}{c} **Network** \\ **Forensics** \\ \end{tabular} } & \multicolumn{4}{c}{**Security Aspects**} & \multicolumn{4}{c}{**Privacy Aspects**} & \multirow{2}{*}{**Models**} \\ \hline  & & & \multicolumn{4}{c}{**Communication**} & \multicolumn{4}{c}{**Software**} & \\ \cline{3-13}  & & & A & V & T & C & A & V & T & C & A & C \\
2016 & [16] & ✓ & ✓\(\times\) & & ✓\(\times\) & ✓\(\times\) & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
2017 & [11] & & ✓ & & & & & & & & & & & ✓ \\
2018 & [7] \(\bigstar\) & ✓ & ✓ & ✓ & & ✓ & & & & & & & ✓ \\
2018 & [17] & ✓ & ✓ & & ✓\(\times\) & & & ✓ & & & & ✓ & & ✓ \\
2019 & [10] \(\bigstar\) & ✓ & ✓ & & & ✓ & & ✓ & & & ✓ & & ✓ & ✓ \\
2019 & [18] \(\bigstar\) & & & & & & & ✓ & & ✓ & & ✓ & ✓ & ✓ \\
2019 & [19] & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & & & ✓ & ✓ \\
2019 & [20] & & & & & & & & ✓ & & & & ✓ & ✓ & \\
2020 & [15] \(\bigstar\) & ✓ & ✓ & & ✓ & & & ✓ & ✓ & & ✓ & ✓ & ✓ & \\
2020 & [12] & ✓ & ✓\(\times\) & ✓ & & ✓\(\times\) & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & \\
2020 & [21] & & & & ✓ & ✓ & & & & ✓ & ✓ & ✓ & ✓ & ✓ & \\
2021 & [9] & & & ✓ & & ✓\(\times\) & ✓ & ✓ & & ✓ & ✓ & ✓ & ✓ & \\
2021 & [22] & & & ✓ & ✓ & & ✓ & ✓ & & & & ✓ & ✓ & ✓ & \\
2021 & [23] & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & & ✓ & ✓ & \\
2022 & [4] & & & & ✓ & & ✓ & ✓ & & ✓ & ✓ & & & ✓ \\
2023 & Proposed \(\bigstar\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) & ✓\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Other related surveys and review articles on Drone Forensics (**A**: Attacks, **V**: Vulnerabilities, **T**: Threats, **C**: Countermeasures, \(\bigstar\): Forensic Investigation, \(\bigstar\): Partial discussion, \(\bigstar\): Full discussion).  
the drone's purpose or usage. Additionally, inertial sensors are responsible for altering control surfaces and thrust, while navigation components, such as GPS, Compass, Galileo, GLONASS, or other inertial sensors, aid in drone navigation by adjusting thrust and control surfaces.
# Fingerprints Classification through Image Analysis and Machine Learning Method
## 1 Introduction  
Recently, machine learning algorithms [1; 2; 3] have outpaced previous approaches in many problems in object classification [4], object tracking [5] and image segmentation problems [6]. Practical problems in energy systems [7] that require extremely high accuracy and very low system errors are thoroughly solved by machine learning. The fingerprint classification system is also one of the systems that has a strict requirements for this. Despite of the progress made in the use of machine learning for object classification or object tracking tasks, their use in real-world applications is error-prone particularly on quality degraded input images, which is mostly human error. Almost all fingerprint images are not of perfect quality. They can be deformed, damaged by noise, such as the skin properties of each object (temperature, the humidity of the skin) are collected fingerprints, position on the finger (fingerprint angle, fingerprint area), pressure (too strong or too light) when rolling out to sample. In this study, we apply computer vision techniques [8] to improve image processing quality, focusing on noise filtering, increasing edge information. At the same time, applying machine learning algorithms, namely Random Forest algorithm [9] and Support Vector Machine (SVM) algorithm [10] to classify fingerprints into 3 types: arch, loop, whorl. The purpose is to increase the performance of the automatic fingerprint recognition system.