esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Figure 1: **(a)** Crystal structure of amphibole minerals: in green the [SiO\({}_{4}\)] tetrahedra, in blue the (OH)’ groups, in yellow and in violet the Na, Ca, Mg, Fe\({}^{2+}\), or Fe\({}^{3+}\) cations; **(b)** Crystal structure of serpentine minerals: in green the [SiO\({}_{4}\)] tetrahedra (T layers), in yellow the octahedral Mg cations (O layer), and in blue and white the OH’ groups; **(c)** In chrysotile T and O layers roll up around a hole forming long fibers. The last mineral regulated as \"asbestos\" is:  
chrysotile, theoretical chemical formula Mg\({}_{3}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\). This is a serpentine mineral [14], whose crystal structure is constituted by tetrahedra [SiO\({}_{4}\)] layers (T layer) and octahedra (Mg O, OH)\({}_{6}\) layers (O layer), where Mg\({}^{2+}\) are linked to the [SiO\({}_{4}\)] tetrahedra and to OH\({}^{-}\) ions, Figure 1b. Chrysotile exhibits a fibrous habit consequent to the rolling up of the T and the O layers around a hole, Figure 1c[15]. In this work, we describe the application of micro-Raman spectroscopy to fibers of the different \"asbestos\", proving its reliability not only in the identification of the minerals, but also of other phases lying on the fiber surfaces. In fact, the spectral range 4000-100 cm\({}^{-1}\) has been recorded and presence on the fiber surfaces of particles, not necessarily crystalline, could be demonstrated. The reasons of the carcinogenic effects of the \"asbestos\" has been extensively studied by different authors [4; 16; 17; 18] and the dose of the inhaled fibers but also their morphology and the iron in the chemical formula have been described as factors determining their harmfulness [19; 20]. As described before, growing these compounds in natural environment, iron can be present also in phases theoretically iron free. In vitro studies proved that, among \"asbestos\", the phases richest in iron, crocidolite and amosite, showed higher carcinogenic effects [21]. In addition to \"asbestos\", other minerals exhibiting fibrous habit have been proved to be responsible of asbestos-related diseases. Among all, erionite, a zeolite mineral showing an average chemical formula K\({}_{2}\)(Na, Ca\({}_{0.5}\))\({}_{8}\)[Al\({}_{10}\)Si\({}_{26}\)O\({}_{72}\)]\(\cdot\)30H\({}_{2}\)O has been considered responsible of the peak of mesothelioma, detected in Cappadocia (Turkey) during the 1970s [22; 23; 24]. On this, mineral iron is not a main chemical component, but erionite has been defined by the International Agency for Research on Cancer (IARC) more carcinogenic than asbestos [25]. Micro-Raman spectroscopy has therefore been applied also to samples of erionite from different localities (Oregon and North Dakota- USA- and Karlik- Cappadocia).
# Using Deep Learning for Collecting Data about Museum Visitor Behavior
## 3 Proposed Method
For each video frame and for each object in the video frame, the model provides a score \(0<p\leq 1\), expressing the likelihood of an object being detected, the class \(c\) of the object, and a 4-dimensional vector containing the coordinates of the upper left and the lower right vertices of the box inside the video frame where the object \(c\) is detected. Therefore, for each video frame, the output of the detection process consists in a set of triples \((c_{i},p_{i},\mathbf{b}_{i})\). Hereafter, vector \(\mathbf{b}_{i}\) will be referred to as the bounding box of the object of class \(c_{i}\) and we will denote the coordinates of the upper left and lower right corners of the bounding box by \((\mathbf{b}_{i}(x_{1}),\mathbf{b}_{i}(y_{1}))\) and \((\mathbf{b}_{i}(x_{2}),\mathbf{b}_{i}(y_{2}))\), respectively. If the value of \(p\) for a class \(c\) is higher than a prefixed threshold \(\sigma\) (we empirically set \(\sigma=0.8\) in our experimental tests), we assume that the object of class \(c\) is detected inside the video frame. The value of \(\sigma\) is a hyperparameter of the system. For high values of the \(\sigma\) parameter, we can have a high number of false negatives, whilst, for low values, we can have a high number of false positives.
# Development and Evaluation of Doppler Ultrasound Training Phantom for Human Vessel Simulation
## 3 Results
### The Color Doppler Results
#### 3.2.3 Gain  
In this experiment, color Doppler imaging was performed with gain settings of 2 dB, 5 dB, 10 dB, 35 dB, 60 dB, and 100 dB. Additionally, color Doppler images were acquired via steering the color box to the left and right. Figures 6 and 7 display the resulting images obtained through adjusting the gain to 2 dB, 5 dB, 10 dB, 35 dB, 60 dB, and 100 dB, with the color box steered to the left and right, respectively.  
Figure 5: Color Doppler images based on scale settings. (**a**) Color Doppler with a scale setting of 4 kHz and leftward steer of the color box. (**b**) Color Doppler with a scale setting of 0.6 kHz and leftward steer of the color box. (**c**) Color Doppler with a scale setting of 4 kHz and rightward steer of the color box. (**d**) Color Doppler with a scale setting of 0.6 kHz and rightward steer of the color box.  
Figure 6a represents the image obtained with a gain setting of 2 dB, showing a significant loss in blood flow information. On the other hand, Figure 6f displays the image acquired with a gain of 100 dB, indicating an overestimation of blood flow, as indicated by the arrow. As the gain increases, blood flow appears excessively, and becomes difficult to evaluate accurately, due to artifacts such as bleeding, as indicated by red arrows in Figure 6e,f. Conversely, as the gain decreases, blood flow information becomes attenuated, making it challenging to assess properly. Similar observations were made in Figure 7, where the color box was set in the reverse direction. Among the tested gain settings, 35 dB appeared to be the most suitable, as shown in Figures 6d and 7d. To adjust the gain appropriately, it was increased until artifacts appeared, and then decreased until the artifacts disappeared, indicating the optimal Doppler gain [40].
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 3 Structural Damage Identification Methods
### Data Preprocessing  
Considering that in the testing process of the structural health testing system, due to the existence of adverse factors such as testing environment, measurement errors and test technical defects, the test data often contain different degrees of measurement noise and test errors. In order to obtain better damage recognition accuracy, data preprocessing is often necessary. The commonly used methods include the threshold method and average method, which can eliminate mixed noise and errors in test data by preprocessing test signals.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### Competing/Isolation  
In situ capping involves covering the contaminated soil volume with one or more layers of sand, gravel, silt, or even geomembranes [351] to chemically or physically isolate and immobilise the contaminants and eliminate the risk of their dispersion into the aquatic environment [352]. If the cover does not fulfil the remediation objectives, certain additives can be added to the cover material. These additives reduce water infiltration through the cap, increase the sorption capacity, and improve the removal of contaminants. Reactive components added between the layers include apatite [353], zeolites [354], organophilic clays [355], and activated carbon [356], which also leads to improved stability [357]. Numerical simulations of contaminant transport through confining materials have been carried out since the 1980s [358; 359; 360; 361; 362; 363]. These models consider both common processes of contaminant transport, such as advection and dispersion, as well as less common processes such as bioturbation, consolidation, or ion exchange.
## 3 Monitoring Results and Analysis  
### Internal Displacements of Surrounding Rocks  
Figures 10 and 11 depict time history plots of the internal displacements in class V surrounding rocks at positions B and C (see Figure 8) in the K89+729 section. As shown in the figures, the changing trend is closely related to the construction procedures, which mainly includes three stages of change: (1) after the multi-point extensometers had been installed at the right places, the measured values of the internal displacements at each location of surrounding rocks kept increasing rapidly and the growth rate was on the rise as well. After the construction of the initial supports, the displacements at each point reached to about 50% of the ultimate stable values; (2) the internal displacements at each location of surrounding rocks continued to increase whereas the increasing rate gradually slowed down after the initial supports were completed. Till the completion of secondary linings, the increments of the displacements accounted for about 40% of the final values; (3) after keeping small growth rate for a short period, the internal displacements of surrounding rocks did not reach the stable state until the construction of secondary linings.  
Figure 12 shows the final distribution of the internal displacements in the cross sections corresponding to three different classes of rock masses (class III, class IV and class V) (layouts of the measuring points are shown in Figure 4). As we can see from Figure 12: at the same location in the monitoring section, the internal displacements of surrounding rocks are larger in the area that is closer to the tunnel excavation contour line. The displacements are relatively larger in the area within 3 m from the tunnel excavation contour line, which should be seen as the key monitoring area. At different locations in the same monitoring section, the internal displacements of surrounding rocks at the tunnel vault are greater than that at the tunnel spandrel while the displacements at the tunnel spandrel are greater than that at the side wall of the tunnel. In addition, the internal deformations of surrounding rocks are relatively larger in the cross section with lower-level surrounding rock masses.  
Figure 8: Layout diagram of the measuring points in class IV and V surrounding rocks (K99+105, K89+729 respectively).  
Figure 9: Layout diagram of the measuring points in class III surrounding rocks (K89+949).  
For example, the final stable-state value of the internal displacement at the vault in the cross section K89+949 (class III) is about 20 mm while the value becomes about 40 mm in the section K90+105 (class IV) and it reaches up to 60 mm in the section K89+729 (class V).
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
###### Abstract  
The Imaging Atmospheric Cherenkov technique has opened up previously unexplored windows for the study of astrophysical radiation sources in the very high-energy (VHE) regime and is playing an important role in the discovery and characterization of VHE gamma-ray emitters. However, even for the most powerful sources, the data collected by Imaging Atmospheric Cherenkov Telescopes (IACTs) are heavily dominated by the overwhelming background due to cosmic-ray nuclei and cosmic-ray electrons. As a result, the analysis of IACT data necessitates the use of a highly efficient background rejection technique capable of distinguishing a gamma-ray induced signal through identification of shape features in its image. We present a detailed case study of gamma/hadron separation and energy reconstruction. Using a set of simulated data based on the ASTRI Mini-Array Cherenkov telescopes, we have assessed and compared a number of supervised Machine Learning methods, including the Random Forest method, Extra Trees method, and Extreme Gradient Boosting (XGB). To determine the optimal weighting for each method in the ensemble, we conducted extensive experiments involving multiple trials and cross-validation tests. As a result of this thorough investigation, we found that the most sensitive Machine Learning technique applied to our data sample for gamma/hadron segregation is a Stacking Ensemble Method composed of 42% Extra Trees, 28% Random Forest, and 30% XGB. In addition, the best-performing technique for energy estimation is a different Stacking Ensemble Method composed of 45% XGB, 27.5% Extra Trees, and 27.5% Random Forest. These optimal weightings were derived from extensive testing and fine-tuning, ensuring maximum performance for both gamma/hadron separation and energy estimation.  
machine learning; ensemble learning; imaging atmospheric Cherenkov telescopes; gamma/hadron separation; image analysis; pattern recognition 2023  
14 June 2023  
10 July 2023  
11 July 2023  
13 July 2023
# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
###### Abstract  
The efficacy of early fire detection hinges on its swift response and precision, which allows for the issuance of timely alerts in the nascent stages of a fire, thereby minimizing losses and injuries. To enhance the precision and swiftness of identifying minute early flame targets, as well as the ease of deployment at the edge end, an optimized early flame target detection algorithm for YOLOv8 is proposed. The original feature fusion module, an FPN (feature pyramid network) of YOLOv8n, has been enhanced to become the BiFPN (bidirectional feature pyramid network) module. This modification enables the network to more efficiently and rapidly perform multi-scale fusion, thereby enhancing its capacity for integrating features across different scales. Secondly, the efficient multi-scale attention (EMA) mechanism is introduced to ensure the effective retention of information on each channel and reduce the computational overhead, thereby improving the model's detection accuracy while reducing the number of model parameters. Subsequently, the NWD (normalized Wasserstein distance) loss function is employed as the bounding box loss function, which enhances the model's regression performance and robustness. The experimental results demonstrate that the size of the enhanced model is 4.8 M, a reduction of 22.5% compared to the original YOLOv8n. Additionally, the mAP0.5 metric exhibits a 2.7% improvement over the original YOLOv8n, indicating a more robust detection capability and a more compact model size. This makes it an ideal candidate for deployment in edge devices.  
small flame target detection; YOLOv8n; edge deployment; loss function +
Footnote †: journal: Proceedings
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
### Notations  
For any matrix \(\mathbf{A}\), denoted by boldface capital letters, or for any column vector \(\mathbf{a}\), denoted by boldface lowercase letters, \(\text{tr}(.)\), \((.)^{*}\), \((.)^{T}\), and \((.)^{H}\) denote the trace, the conjugate, transpose and Hermitian of a matrix, respectively. The operator \(\text{sign}(a)\) represents the sign of real number \(a\); it is applied element-wise to matrices and \(\text{sign}(c)=\text{sign}(\Re(c))+j\text{sign}(\Im(c))\) if \(c\in\mathbb{C}\). The functions \(\Re(c)\) and \(\Im(c)\) represent the real part and imaginary part of \(c\), respectively. \(\left\{\alpha\right\}_{l=1}^{L}\) represents an \(L\) length sequence. The function \(\text{diag}(\mathbf{a})\) gives a diagonal matrix \(\mathbf{A}\), where the entries of the diagonal of \(\mathbf{A}\) are equal to vector \(\mathbf{a}\). The function \(\text{diag}(\mathbf{A})\) gives a vector equal to the entries of diagonal of the matrix \(\mathbf{A}\). The vector \(\mathbf{a}=\left[\mathbf{a}_{q}\right]_{1\leq q\leq Q_{1}}\in\mathbb{C}^{Q_{1} Q_{2}}\) and the matrix \(\mathbf{A}=\left[\mathbf{A}_{q}\right]_{1\leq q\leq Q_{1}}\in\mathbb{C}^{Q_{1} Q_{2}\times L}\) are the concatenation of vector \(\mathbf{a}_{q}\in\mathbb{C}^{Q_{2}}\) and matrix \(\mathbf{A}_{q}\in\mathbb{C}^{Q_{2}\times L}\), respectively. The element of row \(n\) and column \(l\) of \(\mathbf{A}\) is denoted by \(\mathbf{A}(n,l)\). The matrix \(\mathbf{I}_{N}\) is the identity matrix \(N\times N\). Finally, the indices, \(t\), \(k\), and \(u\) represent the time domain, subcarrier in the frequency domain, and user terminal, respectively.
## 4 Results
### Bathymetry  
In addition to the flow velocities and direction, the method presented here also allows for easy modeling of the river bathymetry, especially because of the dense survey pattern. The surface elevation and water depth were included in the interpolation algorithm presented in Section 3.2. Subtracting depth from the surface elevation gives river bed elevation. The points were interpolated to a surface by a triangulated irregular network interpolation. Figure 12 shows the evolution of the river bed at the meander bend AOI over the study period.  
The bathymetric model created from the ADCP points gives a synoptic view of the shape of the river bed, albeit with some small artifacts caused by the measurement pattern. The time series of river bed elevations reveals how the pool on the upstream end of the AOI erodes deeper every day and slowly progresses downstream to connect to the deeper part of the channel near the outside of the bend. At the same time, the submerged point on the outside edge on the upstream end decreases over time.  
Figure 12: River bathymetry. Bed topography is shown as elevation in meters above sea level.
# Feasibility of LoRa for Smart Home Indoor Localization
## 3 Experiment and Evaluation
### Experimental Setup
#### 3.1.2 Data Collection  
In our experiments, we use Adafruit Feather 32u4 RFM95 LoRa radio, capable of only operating on 868 MHz (European ISM) and 915 MHz (American ISM). We chose 915 MHz band to be compliant with the Federal Communications Commission (FCC) regulations. We equipped each board with a 900 MHz external antenna to achieve better signal strength. As shown in Figures 1 and 2, we use three anchors and five tags. Tags are connected through USB cables to a laptop, where the experiment is controlled by a Python script. The script sends a command to each tag at a time, requesting a set of RSSI measurements with all anchors with a specified TX power. The tag then sends a packet with the specified TX power to each anchor at a time, requesting an RSSI measurement. The packet contains the TX power number that the anchor should use. On the reception of this request by the anchor, it records RSSI and is included in the response packet. The tag receives the response packet and records the RSSI. Finally, the tag logs this measurement by specifying RSSI measured at both sides. In our experiments we set the LoRa nodes to use a bandwidth of 125 kHz, a coding rate of 4/5, a spreading factor of 8, and a preamble length of 8. We collect various data with different TX power levels between 5 dB and 23 dB. For localization experiments, we collect approximately 2000 RSSI measurements at each five tags from each three anchors.
## 3 Research and Results
### AIS Results  
Selected examples of vessel movement parameter visualizations are presented in Figure 10. The figure shows the speed over ground (SOG), course over ground (COG), and the position of one of the analyzed vessels.  
The figure above shows the normal operating parameters of a vessel, including a slight decrease in speed to 15 knots, which does not raise suspicion during the initial stage of analysis and is only subject to routine monitoring. Figure 11, on the other hand, presents actual graphs of the speed over ground (SOG) and course over ground (COG) of a ship behaving suspiciously. The ship is on a transit route, directly above submarine power cables. A sudden and significant reduction in speed to zero is visible, which is considered abnormal at this location.  
At this point, the ship, moving at a course of approximately 135\({}^{\circ}\), makes a sharp turn to port (left) to approximately 080\({}^{\circ}\), and then to starboard (right) to approximately 155\({}^{\circ}\). According to experienced maritime navigators, this is a typical indication of dropping the port anchor, which is considered unjustified at this location. The ship then quickly increases its speed to 10 knots and proceeds along the recommended route, moving away from the area. Such a vessel is immediately subjected to a full MCDA, and appropriate measures are taken by law enforcement and naval units.  
Figure 10: Recorded movement parameters of the analyzed unit.  
Since the described incidents are relatively infrequent, simulation studies using machine learning were also conducted to predict ship movements. Figure 12 shows a simulated maritime infrastructure area (highlighted in yellow) marked on the chart, which several vessels are approaching. Ship traces are presented in blue.  
Vessel 1, moving on a westerly course, does not alter its heading early enough to avoid the area and is proceeding directly toward it. Vessel 2, following a west-southwest course near the boundary, is being monitored and warned.  
The forecast trajectory, marked in red, half an hour before entering the area, combined with the MCDA, indicates the need for decisive action by the authorities supervising the maritime area.  
Figure 11: Parameters of the suspicious vessel’s movement with visible anomalies.  
Figure 12: Suspicious vessel with predicted trajectory.
## 3 Experiments and Results
### Experimental Parameters
#### 3.1.3 Evaluation Index  
To quantify the performance of our model in the road extraction task, we employed a confusion matrix as a robust measure of binary classification outcomes. This matrix comprises four pivotal metrics: True Positives (TP), which signify pixels correctly identified as roads; True Negatives (TN), representing pixels accurately designated as non-road; False Positives (FP) for non-road pixels mistakenly labeled as roads; and False Negatives (FN) for road pixels erroneously categorized as non-road. Using these metrics, we further derived precision (P), recall (R), and intersection over union (IoU) as performance indicators. Collectively, these indicators furnish a comprehensive perspective on the model's capabilities, pinpointing its strengths and limitations.  
Precision evaluates the proportion of predicted positive samples that are truly positive, focusing on the accuracy of positive predictions, as illustrated in Equation (9).  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{9}\]  
Recall quantifies the fraction of genuine positive samples predicted correctly by the model, emphasizing the model's capacity to capture positive samples. This is detailed in Equation (10).  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{10}\]  
The intersection over union, or IoU, gauges the overlap between predicted and actual regions, often serving as a critical metric in image segmentation and object detection tasks. This measure of overlap is elucidated in Equation (11).  
\[\mathrm{IOU}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}+\mathrm{FN}} \tag{11}\]  
In this study, we deployed these three metrics to conduct a rigorous quantitative assessment of the MixerNet-SAGA network and five comparative models, juxtaposing their respective performances.