esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 GBRAS-Net  
GBRAS-Net is a steganalysis scheme based on convolutional neural networks (CNNs) and was proposed by [PERSON] et al. [34] in 2021. This deep learning scheme receives an image of 256 \(\times\) 256 pixels. The model has a preprocessing layer, which consists of a 2D convolutional layer with 30 filters. This step has 780 non-trainable parameters with preset weights and a custom 3 Tanh activation function. The feature extraction stage is composed of various layers: four 2D depthwise convolutional, four 2D separable convolutional, and nine 2D convolutional layers. Furthermore, the model uses batch normalization layers to improve the performance and avoid overfitting, followed by an average pooling layer to reduce the dimensionality. Moreover, the model uses skip connections. At the end of the model's process, global average pooling is carried out, resulting in two values. For the classification stage, these values are input to a softmax activation function, which predicts whether the element is a cover or stego image.  
GBRAS-Net was first experimented with in Python 3.8.1 and was developed using the TensorFlow framework version 2.2.0. The authors provided the code on GitHub. The metric  
Figure 4: Simple weight clustering.  
used for the evaluation of the method was the accuracy, and the model was compared with seven other steganalytic methods. At the time, the network had achieved the best accuracy results. The proposed method was implemented and assessed with 10,000 pairs of cover and stego images of BOSSBASE 1.01 for the steganographic algorithm S-UNIWARD, with 73.6% accuracy for 0.2 bpp and 87.1% for 0.4 bpp, and for WOW, with 80.3% accuracy for 0.2 bpp and 89.8% for 0.4 bpp. The data distribution in the experiment was as follows: 4000 pairs of images for training, 1000 pairs for the validation set, and 5000 pairs for testing. In addition, the authors assessed the performance of the proposed model against spatial domain steganography algorithms such as HUGO, HILL, and MiPOD. In terms of model size, GBRAS-Net has one of the smallest storage requirements compared to other steganalysis deep learning models, with only 0.65 MB of occupied memory.
## 3 Experimental Program  
### Materials  
The iron tailings used in the testing were taken from the Iron Quadrangle region, located in the province of Minas Gerais, Brazil (see Figure 2). The grain size distribution of the iron mine tailings is given in Figure 3. The iron mine tailings' physical properties are displayed in Table 1, being classified [16] as silty sand (SM). Mineralogical characterization of the iron tailings, acquired using an X-ray diffractometer, detected the presence of a few compounds: quartz [SiO\({}_{2}\)], hematite [Fe\({}_{2}\)O\({}_{3}\)], goethite [FeHO\({}_{2}\)], kaolinite [Al\({}_{2}\)H\({}_{4}\)O\({}_{9}\)Si\({}_{2}\)], and muscovite [Al\({}_{3}\)H\({}_{2}\)KO\({}_{12}\)Si\({}_{3}\)]. Regarding the chemical composition of the studied iron tailings, the following element concentrations were found after X-ray fluorescence: 69.7% of SiO\({}_{2}\), 24.0% of Fe\({}_{2}\)O\({}_{3}\), 4.8% Al\({}_{2}\)O\({}_{3}\), 0.40% of MnO, 0.25% of P\({}_{2}\)O\({}_{5}\), 0.15% of K\({}_{2}\)O, and 0.1% of SO\({}_{3}\), amongst others. The results of standard (600 kN.m/m\({}^{3}\)) and modified (2700 kN.m/m\({}^{3}\)) Proctor compaction tests are displayed in Figure 4.  
Figure 4: Compaction curves of iron ore tailings at standard and modified energies.  
Figure 3: Iron ore tailings grain size distribution.  
Figure 2: Location of the Iron Quadrilateral on the map of Brazil and Minas Gerais (MG) province.  
High early strength (Type III) Portland cement [17] was used throughout this investigation. Its rapid strength gain allows blends to achieve important strength thresholds from short curing periods. Cement grains' specific gravity is 3.15.  
Distilled water was utilized both for characterization tests and moulding specimens for the triaxial tests.
## 3 Results  
### Failure Characteristics of Anchors  
Anchor failure can be classified as one of four types based on different causes: insufficient tensile strength, resulting in fracture of the anchor body; low mortar strength, leading to shear failure at the interface between the anchor body and grouting; weak soil or rock mass strength, causing conical pull-out failure where the anchor is extracted in a conical shape from the surrounding soil or rock mass; and shear failure occurring at the interface  
Figure 5: Strain gauge position of anchors: (**a**) composite anchor; (**b**) tension anchor.  
between the soil or rock and the grouting material [34; 35]. In the conducted experiment, shear failure at the second interface, specifically at the interface between the soil or rock mass and the grouting material, was observed in both the composite anchor and the 3 m, 4 m tension anchors. Conversely, the failure of the 5 m tension anchor was attributed to steel yielding, whereby the ultimate bearing capacity exceeded the limited tensile strength of the anchor body. The anchor failure diagram is depicted in Figure 6.  
Upon application of the load, the cushion layer experiences a reaction force from the support system, and as depicted in Figure 6a, the groundwater present beneath the cushion layer continuously seeps out from the anchor hole under pressure. Once the anchor reaches its ultimate bearing capacity, the second interface undergoes failure, resulting in a significant reduction in the anchor's bearing capacity. Consequently, the load acting on the cushion layer diminishes, causing the groundwater to reverse its flow and either return to the lower portion of the cushion layer or infiltrate the lower region of the anchoring section through the second interface of the anchor, rather than continuing to seep out from the anchor hole. This phenomenon serves as a crucial criterion for determining the occurrence of anchor failure.
# FE-Aided Synchronization Analysis of Line-Start Synchronous Reluctance Motors
## 2 Quasi Steady State Model  
The _dq_ rotor reference frame is defined in Figure 1. The _dq_ voltage equation with reference to the rotor are:  
\[\begin{cases}v_{\text{sd}}&=R_{\text{s}}i_{\text{sd}}+\frac{d\lambda_{\text{sd} }}{dt}-\omega_{\text{r}}\lambda_{\text{sq}}\\ \\ v_{\text{sq}}&=R_{\text{s}}i_{\text{sq}}+\frac{d\lambda_{\text{sq}}}{dt}+ \omega_{\text{r}}\lambda_{\text{sd}}.\end{cases} \tag{1}\]  
\[\begin{cases}0&=R_{\text{rd}}i_{\text{rd}}+\frac{d\lambda_{\text{rd}}}{dt}\\ \\ 0&=R_{\text{rq}}i_{\text{rq}}+\frac{d\lambda_{\text{rq}}}{dt},\end{cases} \tag{2}\]  
where \(\omega_{\text{r}}\) is the rotor electrical radial speed, \(\lambda\) is the flux linkage, \(R\) is the resistance, and the subscript s,r denotes the stator and the rotor, respectively. At a constant slip speed, the steady state condition can be expressed by replacing the time derivatives term in (1) and (2) with _js_\(\omega\)[21]:  
\[\begin{cases}V_{\text{sd}}&=R_{\text{s}}I_{\text{sd}}+j\text{s}\omega\Lambda_ {\text{sd}}-\omega_{\text{r}}\Lambda_{\text{sq}}\\ V_{\text{sq}}&=R_{\text{s}}I_{\text{sq}}+j\text{s}\omega\Lambda_{\text{sq}}+ \omega_{\text{r}}\Lambda_{\text{sd}}\end{cases} \tag{3}\]  
\[\begin{cases}0&=R_{\text{rd}}I_{\text{rd}}+j\text{s}\omega\Lambda_{\text{rd}} \\ 0&=R_{\text{rq}}I_{\text{rq}}+j\text{s}\omega\Lambda_{\text{rq}},\end{cases} \tag{4}\]  
where the capitals denote phasors of which the angular frequency is \(s\omega\). The \(d\) and \(q\) components of the voltage in the time domain are described by:  
\[\begin{cases}v_{\text{sd}}&=-\tilde{V}\sin(s\omega t+\delta_{0})\\ v_{\text{sq}}&=\tilde{V}\cos(s\omega t+\delta_{0}),\end{cases} \tag{5}\]  
where \(\delta_{0}\) is the load angle at the synchronous speed. The voltage phase angle with rotor slip is steadily varied such that \(\delta(t)=s\omega t+\delta_{0}\). The _dq_ voltage phasors are:  
\[\begin{cases}V_{\text{sd}}&=j\tilde{V}\angle\delta_{0}\\ V_{\text{sq}}&=\tilde{V}\angle\delta_{0}.\end{cases} \tag{6}\]  
The electromagnetic torque of a LS-SynRM in quasi steady state consists of the average and the oscillating components pulsating with angular speed of \(2s\omega\) due to the rotor saliency [21]:  
Figure 1: Definition of the _dq_ frame, and the load angle \(\delta\).  
\[T_{e} =T_{0}+T_{1}\cos(2s\omega t+\alpha),\] (7) where \[\left\{\begin{aligned} T_{0}&=\frac{3}{4}p \cdot\mathcal{R}_{cal}(\Lambda_{\text{sd}}^{*}I_{\text{sq}}-\Lambda_{\text{sq }}^{*}I_{\text{sd}})\\ T_{1}&=\frac{3}{4}p\cdot|\Lambda_{\text{sd}}I_{ \text{sq}}-\Lambda_{\text{sq}}I_{\text{sd}}|.\end{aligned}\right.\] (8) The \(\alpha\) is the torque phase angle given by: \[\alpha=\tan^{-1}\left(\frac{\mathcal{I}_{mag}(\Lambda_{\text{sd}}I_{\text{ sq}}-\Lambda_{\text{sq}}I_{\text{sd}}}{\mathcal{R}_{cal}(\Lambda_{\text{sd}}I_{ \text{sq}}-\Lambda_{\text{sq}}I_{\text{sd}})}\right) \tag{9}\]  
The average torque includes the time-averaged torque components due to the rotor saliency and the asymmetrical rotor cage [21].
## 2 Materials and Methods
### Measured Data Processing  
Signal processing was performed for the STS experiment using data from each IMU attached to the body segments and MATLAB (The MathWorks Co., Release 2016a, Tokyo, Japan). The Madgwick algorithm is a method for accurate estimations of IMU orientations, and this method was used to obtain information regarding body segment orientation and joint angles. The algorithm calculates the attitude angle based on a fusion of measurements from an accelerometer, gyroscope, and magnetometer [30]. Figure 6 shows the coordinate system and the segment orientations and joint angles calculated in this study. The direction of the axis of the IMU, which was attached such that it matched the line along with the bone as closely as possible, was determined as the line of each segment. Pitch data was used to calculate the inclination angle for the flexion/extension of each body segment at the point of the attached IMUs: the joint angle of the ankle, knee, hip, and between the trunk and pelvis at the point between two IMUs. To calculate the inclination angle, the vertical axis in the sagittal plane was defined as 0 degrees, as a reference axis. On the other hand, the joint degree was defined as 0 degrees in the anatomically upright position. Equation (1) was used to calculate the joint angles. Before inputting the results of the motion analysis to the musculoskeletal model, skeletal and muscular information for the musculoskeletal model of a pregnant woman were modified to fit the measurements of the participant in this study, as described in Section 2.3. Then, the data from the change in the joint angle from the IMUs and the reaction forces from the ground and chair obtained from the WBBs were inputted to BoB to estimate the joint torque. Finally, the muscle torque was estimated using the joint torque through optimization in the model:  
\[\begin{split}\theta_{1}&=\phi_{1}\,,\\ \theta_{2}&=180-\phi_{2}\,,\\ \theta_{3}&=\phi_{3}+\phi_{2}\,,\\ \theta_{4}&=\phi_{4}-\phi_{3}\,,\\ \end{split} \tag{1}\]  
Figure 5: Points of additional mass for the musculoskeletal model for pregnant women. The red circles indicate the points where there is increased mass.
## 1 Introduction  
Access to safe and clean drinking water is a cornerstone of public and animal health. Globally, the provision of clean drinking water has improved substantially over the past decades, with significant progress driven by initiatives such as the United Nations Sustainable Development Goals (SDGs). SDG 6 aims at universal and equitable access to safe and affordable drinking water by 2030 [1]. However, despite these advancements, striving to ensure clean and safe drinking water, particularly in low-income and developing regions, remains a challenge. To this end, approximately 2 billion people globally still rely on contaminated drinking water sources, exposing them to a variety of diseases, including cholera and typhoid fever [2, 3].  
The quality of drinking water varies significantly depending on water management practices [4, 5]. In high-income countries, regulatory frameworks and advanced water treat ment facilities and technologies ensure that drinking water meets stringent safety standards. For instance, countries in North America and Europe routinely achieve compliance rates above 95% for microbial and chemical parameters [6], whereas many low- and middle-income countries often fail to control drinking water contamination from anthropogenic activities for reasons that are ultimately associated with underfunding [7; 8]. Pathogens, including bacteria, viruses, and protozoa, remain widespread contaminants, particularly in areas with limited sanitation infrastructure [9; 10]. Furthermore, untreated or poorly treated water sources contribute significantly to the spread of antibiotic resistance (AMR) genes that can be readily transferred to human and animal pathogens [11]. The emergence of AMR is exacerbated by antibiotics entering water systems through various pathways, including wastewater discharge from households, hospitals, and pharmaceutical industries, as well as agricultural runoff containing veterinary drugs [12; 13]. It is noteworthy that the presence of antibiotics, even at trace concentrations in drinking water, has been shown to contribute to the emergence of resistance, with significant implications for public and animal health [11; 14].  
Based on the above, routine monitoring for microbial contamination and drug resistance is of key significance in maintaining high drinking water quality. Therefore, this study focused on the phylogroup and genus assignment of drinking water isolates of _Escherichia coli_ and coliforms, respectively, and the assessment of their AMR.
## 2 Background and Related Works
### Uncertain Data Mining  
Let (i) Item be a set of \(m\) domain items and (ii) \(X=\{x_{1},x_{2},\ldots,x_{k}\}\) be a \(k\)-itemset (_i.e._, a pattern consisting of \(k\) items), where \(X\subseteq\texttt{Item}\) and \(1\leq k\leq m\). Then, a transactional database is the set of \(n\) transactions, where each transaction \(t_{j}\subseteq\texttt{Item}\) (for \(1\leq j\leq n\)). The projected database of \(X\) is the set of all transactions containing \(X\). Each item \(x_{i}\) in a transaction \(t_{j}=\{x_{1},x_{2},\ldots,x_{h}\}\) in an uncertain database is associated with an existential probability \(P(x_{i},t_{j})\), which represents the likelihood of the presence of \(x_{i}\) in \(t_{j}\)[41], with value  
\[0<P(x_{i},t_{j})\leq 1\]The existential probability \(P(X,t_{j})\) of a pattern \(X\) in \(t_{j}\) is then the product of the corresponding existential probabilities of every item \(x_{i}\) within \(X\) when these items are independent [41,42]:  
\[P(X,t_{j})=\prod_{x_{i}\in X}P(x_{i},t_{j})\]  
Finally, the expected support _expSup_(_X_) of \(X\) is the sum of \(P(X,t_{j})\) over all \(n\) transactions in the database:  
\[\textit{expSup}(X)=\sum_{j=1}^{n}P(X,t_{j})=\sum_{j=1}^{n}\left(\prod_{x_{i}\in X }P(x_{i},t_{j})\right) \tag{1}\]  
With this notion of expected support, existing tree-based algorithms--such as UF-growth [32], tube-growth [33] and BLIMP-growth [34]--mine frequent patterns from uncertain data by first scanning the uncertain database once to compute the expected support of all domain items (_i.e._, singleton patterns). Infrequent items are pruned as their extensions/supersets are guaranteed to be infrequent. The algorithms then scan the database a second time to insert all transactions (with only frequent items) into a tree (e.g., UF-tree [32], TPC-tree [33], or BLIMP-tree [34]). Each node in the tree captures (i) an item \(x\); (ii) its existential probability \(P(x,t_{j})\); and (iii) its occurrence count. At each step during the mining process, the frequent patterns are expanded recursively.  
A pattern \(X\) is frequent in an uncertain database if \(\textit{expSup}(X)\geq\textit{minsup}\). Given a database and _minsup_, the research problem of frequent pattern mining from uncertain data is to discover from the database a complete set of frequent patterns having expected support \(\geq\textit{minsup}\).
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 6 Conclusions  
In this paper, a new architecture is proposed for SAC-OCDMA system by employing 2D-MD codes and laser optical source for LR-PON context. The system performance of our proposal is addressed by numerical investigation as function of MAI, bit rate, SMF length and fiber mode effective area. Moreover, optical coherent source noise, channel nonlinearity, shot noise and thermal noise of the PIN photodetector are considered for system performance analysis. In the case of a single user (i.e., without considering MAI), we observe that a Q-limit is achieved for bit rates around 55 Gb/s and 40 Gb/s when the first and the second (same results for the third) order dispersion are taken into consideration, respectively. Furthermore, the proposed system provides a 30 Gb/s bit rate with performance above the Q-limit, while increasing the effect of fiber nonlinearity. We also demonstrated that our new proposed architecture extends the fiber length up to 600 km and 480 km, while considering linear and nonlinear effects, respectively. In the case of considering the MAI effects, an aggregate bit rate of 20 Gb/s for 4, 8 and 16 simultaneous users are achieved with a Q-factor greater than 15.5 dB while considering optical laser source noise, linear effects, nonlinear effects and photo-detector noise. Likewise, we show, through extensive simulations, that our proposal outperforms alternate solutions in literature by offering 20 Gb/s as an aggregate bit rate and a transmission reach up to 50 km for 4, 8 and 16 simultaneous users. As a conclusion, the proposed 2D-MD SAC-OCDMA system in LR-PON configuration can be viewed as a potential candidate for future access network by offering high level of both performance and security [36].  
Conceptualization, H.M. and F.B.; methodology, A.S.K., software, H.M.; validation, F.B., A.S.K. and A.C.; formal analysis, A.C.; investigation, A.B.; resources, F.B.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, H.M.; visualization, A.B.; supervision, A.S.K.; project administration, F.B.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experiment Verification  
Based on the simulation results, the superior types I and II were selected for test production and integration with hardware configurations that include heat pipes, cooling fans, cooling fins, TECs, and sensors. The implemented general experimental structures were classified into four major systems: computer system, measurement system, power supply, and cooling system. The purpose of the experiment was to replace an actual T/R module with a heat generator that supplies a stable heat source to test the temperatures on the surface of the test system as well as the internal measuring point of the heat source. The temperature changes in the system's passive and active heat dissipation mechanisms were observed to verify whether the simulation result trends were identical and the heat dissipation performance when an active cooling system was added. This confirmed whether the heat dissipation type designed in this study could meet heat dissipation performance requirements.
# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 5 Applications of Underwater Target Detection and Recognition Technology
### Underwater Equipment of Target Detection and Recognition  
[PERSON] [76] proposed a method for underwater cable detection and tracking using autonomous underwater vehicles. The edges of the image were first extracted, and then classified using texture information identified with a multilayer perceptron (MLP) neural network and SVM. Finally, the filtered edge was repaired using the morphological operator, and the cable was detected using the Hough transform with high accuracy. [PERSON] [77] compared the classification effects of 5 deep learning models on underwater cable images and found that MobileNetV2 performed the best, with the highest accuracy rate of 93.5% for underwater cable image classification.  
In the offshore oil and gas industry, pipeline corrosion problems can lead to cracks and leaks in pipelines. The use of human divers to monitor pipelines is increasingly dangerous as mining platforms travel deeper into the ocean. [PERSON] [78] proposed a new method for subsea pipeline corrosion estimation using the corroded pipeline color. The images were first segmented, then the color-corrected and contrast-enhanced images were fused using a wavelet-based fusion algorithm, and the corroded surface area of the pipeline was estimated using a clustering algorithm, with an accuracy of over 90%. Hull corrosion is a major problem that affects the safety and health of ships. [PERSON] [79] used a deep neural network to identify ship corrosion levels, and classified underwater images into four corrosion levels of high, medium, low, and no corrosion stages. This network could be embedded in ROVs to monitor ship corrosion conditions.  
[PERSON] [80] proposed a crack detection and classification method for underwater dams, which uses a dodging algorithm to eliminate uneven light brightness in underwater visible light images and has a good detection effect on smaller cracks. [PERSON] [81] used a laser vision sensor to detect welds. After a series of processing, such as the Hough transform, the effects of image enhancement and denoising were achieved. [PERSON] [82] proposed a robotic system scheme for weld seam recognition based on edge computing. After the weld image was filtered and preprocessed by edge detection, it was input into the CNN model for identification, which satisfies the requirements of maintaining a balance between high accuracy and the real-time performance requirements of underwater welding robots.