esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 4 Infrared and Visible Image Fusion Dataset  
The infrared and visible image fusion datasets, which typically include both infrared and visible image pairs and the two images recorded in different modes (infrared and visible) of the same scene, are a crucial component in the training and testing process of infrared and visible image fusion. The TNO dataset, the RoadScene dataset, the MSRS dataset, etc. are examples of common visible and infrared image fusion datasets. The fundamental elements of each dataset are introduced in this section. The basic elements of each dataset are displayed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Datasets** & **Basic Content** \\ \hline TNO & \begin{tabular}{c} Contains enhanced vision, near infrared and long wave infrared night images for different military and surveillance scenarios \\ \end{tabular} \\ RoadScene & \begin{tabular}{c} Contains 221 image pairs of highly repetitive scenes \\ \end{tabular} \\ MS-COCO & \begin{tabular}{c} Contains photos of 91 object types, 82 of which have more than 5000 tagged instances \\ \end{tabular} \\ MSRS & \begin{tabular}{c} Contains 1444 pairs of high-quality aligned infrared and visible light images \\ \end{tabular} \\ LLVIP & \begin{tabular}{c} Contains 15,488 pairs of images that are strictly aligned in time and space, mostly taken in dark settings \\ \end{tabular} \\ RGBT210 & \begin{tabular}{c} Contains 210 video sets that are strictly aligned at the pixel level \\ \end{tabular} \\ RGBT234 & \begin{tabular}{c} Contains 234 video sets, expanding the diversity of the scene \\ \end{tabular} \\ OTCBVS & \begin{tabular}{c} Contains 7 infrared datasets, 1 visible light dataset, and 6 visible-infrared datasets \\ \end{tabular} \\ INO & \begin{tabular}{c} Contains 10 different categories of images, a total of 2000, the size of 256 \(\times\) 256 pixels \\ \end{tabular} \\ LITV & \
\begin{tabular}{c} Contains a variety of different sensor data and multiple subdata sets \\ \end{tabular} \\ GTOT &
\begin{tabular}{c} Contains 50 videos of different scenarios, such as LABS, pools and roads \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Infrared and visible image fusion datasets.
## 2 NB-PLC and RF Characterization
### NB-PLC Characterization  
PLC suffers from harsh conditions such as unexpected variations in channel attenuation, multipath effects, and uncommon noise characteristics [27]. In this section, the authors overview NB-PLC attenuation and its noise characteristics.
# An Automatic Detection Method for Cutting Path of Chips in Wafer
## 2 Materials and Methods
### Automatic Detection Algorithm of Chip Cutting Path
#### 2.3.3 Automatic Detection of Chip Region
Calculating the frequency domain correlation between the template image and the target image avoids traversing every sub-image in the image, and only processes the frequency spectrum of the template image and the target image, avoiding a great deal of calculation. **Step 3**: Non-Maximum Suppression (NMS) is used to process the correlation matrix \(R\) to determine the position and region of the chip's center of gravity. The correlation matrix \(R\) was processed by NMS [24] to obtain the location \(A_{ij}\) with the highest similarity in each region. In the correlation matrix \(R\), the larger the gray value is, the greater the possibility that this position is the center of gravity of the chip region, and the area with a low gray value is distanced from the chip region. Therefore, we set the area in the correlation matrix where the gray value is lower than the threshold \(T_{d1}\) to 0, determined the region where each chip is located, as shown in Figure 7d, and then took the position of the maximum value in each area as the center of gravity of the chip region, as shown in Figure 7e. We determined that the maximum value position in the correlation matrix of the thresholding process is \((x_{i},y_{i})\), used the red cross mark, determined the search radius \(r_{0}=\sqrt{m^{2}+n^{2}}\) around the maximum value position, set all gray values within the  
Figure 7: Detection results of the chip region for each step. (**a**) The result of the parameterized template; (**b**) the result of the parameterized wafer image; (**c**) correlation result; (**d**) the result of thresholding; (**e**) the result of extremum search; (**f**) the center of gravity position of the chip region. radius r0 around the maximum value to 0, and then repeated the process until all regions had been searched to obtain the center of gravity positions of all chips in the target image, as shown in Figure 7f. The size of the chip region in the image can be determined by the size of the template. The range and center of gravity of the chip region were used to find the interlayer in the next section.
###### Abstract  
This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders' treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth.  
computer-aided speech diagnosis; face image segmentation; object detection; semi-supervised deep learning; image fuzzification; stereo stream +
Footnote †: journal: Medical Image Analysis
However, there are uncertainties in the description of certain mechanisms, and different parameters have significant impacts on the simulation results [13]. Additionally, all the models rely on different datasets and intensive computational cost. Data-oriented approaches are the complements to process-based models, and their algorithms and tools are critically important for the robustness of the approaches. Machine learning (ML) methods have been widely adopted in investigating land cover change [20], environmental monitoring [21], carbon flux assessment [22], water resource monitoring [23], and gap-filling for eddy covariance dataset [24]. The ML algorithms use an advanced statistical approach to extract critical information contained in large datasets without explicit instructions [25]. Artificial neural network (ANN), supports vector machines for regression (SVR), and tree-based model (e.g., regression tree, model tree ensembles, random forest, and gradient boosting regression) are three widely used approaches for NEE prediction. Some novel algorithms such as deep learning [26] and symbolic regression [27; 28] also offer the possibility of NEE prediction. The ANN algorithm has been used for CO\({}_{2}\) flux prediction in different ecosystems over the past 20 years [29; 30; 31]. More recent studies [32; 33] combined ANN with SVR [34] to develop gross primary productivity (GPP) and NEE databases at site-level and continental-scale with EC data and remote sensing datasets. Tree-based models are the other type of ML method, which includes regression tree (RT, [35]), model tree ensembles (MTE, [36]), random forest (RF, [15]), and gradient boosting regression (GBR, [37]). Multiple ML methods also have been applied to estimate carbon flux at various scales [38; 39]. Each ML approach has its strengths and limitations. For example, the ANN algorithm is simple but requires a large training dataset and tremendous computational resources [40]. The SVR algorithm can produce high quality outputs but it mostly depends on the choice of the kernel function; its limitations also include difficulties in choosing a good kernel function and long training time [41]. The RF approach has been the most widely used ML algorithm in ecological studies, but it comes with a large computational cost [42]. In summary, computational inefficiency is a common problem of these ML models. XGBoost is a state-of-the-art ensemble ML algorithm based on gradient boosting proposed by [PERSON] and [PERSON] [43] after improving the gradient boosting decision tree (GBDT) algorithm, and it is well known that the XGBoost has high efficiency [43]. The objectives of this study are (1) to compare the RF and XGBoost algorithms in predicting site-level NEE; (2) to rank the environmental variables in controlling the NEE as shown by both RF and XGBoost; (3) to investigate the RF and XGBoost models in terms of predicting NEE under extreme climate conditions; and (4) to further evaluate the performance curving of two models against the observational NEE.
## 4 Discussion  
The validation results of the SUFRA_1 models in the whole Torto River basin showed outstanding AUC values but with limited specificity compared to the very high values of sensitivity. Considering that the false positives are not only errors but also future positives, these results gave us a warning about the accuracy of the predicted landslide scenario. The validation in the Sciara sub-basin, where new systematic inventories for flow and slide were detected, showed that the quality of the prediction images produced was inaccurate. In fact, the sensitivity dramatically decreased here, especially for the flow model, clearly reflecting the limited skill of the models to detect new unknown phenomena. Considering the geomorphological setting of the Sciara sub-basin is representative of a very large part of the Torto River catchment, the limits of SUFRA_1 were considered relevant. On the other hand, the SUFRA_2 models maintained outstanding AUC values with very high sensitivity and good specificity and, differently from SUFRA_1, the new models still showed outstanding AUC values in the Sciara basin. More importantly, the sensitivity reached the maximum performance with good to excellent specificity. The false-positive rates still suggest the basin is characterized by relevant proneness to both flow- and slide-type slope failures. The same high-model performance was observed for both the landslide typologies, confirming that the goodness of this model procedure is independent of the landslide typology and number of cases (provided the inventory is representative).  
According to our test, the proposed two-step approach is suitable for optimizing landslide susceptibility evaluation when the source inventory is affected by incompleteness or mapping inaccuracy. In fact, the second step of mapping (the susceptibility level-driven checking) permitted us to increase the quality of the calibration inventory and to cost-effectively correct the potential misleading results of the SUFRA_1 models. Obviously, the percentage of slope units checked (30% in this test) is not a standard but needs to be tuned case by case. At the same time, the selection of a single test sub-basin could be insufficient in the case of a more articulated geomorphological setting of the whole study area, and criteria for selecting the number and extension of such sectors need to be optimized (see [7] for a deeper inside of this issue). Indeed, different criteria for selecting the checking areas to improve the original inventory could be also explored. In our study, we precautionarily decided to maintain the same percentage of random extraction for each SUFRA_1 susceptibility class.
# Adaptive Emergency Call Service for Disaster Management
## 5 Simulation Results
### Test Environment
#### 5.1.2 Neighbor Realying Threshold: \(R5_{\text{th}}\)  
The maximum threshold for neighbor relaying, \(RS_{\text{th}}^{\text{max}}\), is set to a constant and \(\mid\eta\mid\) denoted the number of pending call requests in the UE buffer. The latter is used to estimate the number of different emergency calls being relayed in the neighboring area. Further, the maximum limit for \(\mid\eta\mid\) is assumed to be a constant. Hence, when the value of \(\mid\eta\mid\) changes from a minimum to maximum, the \(RS_{\text{th}}\) adapts its value according to (6). In comparison, the value of \(RS_{\text{th}}\) is fixed in the M-HELP protocol.  
Figure 6: Studied scenario of the Traverse city in Michigan, USA, with 7 gNBs and 15,000 randomly distributed UEs in the AnyLogic\({}^{\text{\textregistered}}\)software. Linked UEs (resp. UE-to-gNB links) represent D2D (resp. traditional) communication possibilities. Dark Red circles represent the gNBs’ covering areas in which the gNBs are at the center. Victim (emergency) devices, relay devices, in-coverage devices, and idle devices are represented respectively by the colors, red, green, yellow, and blue.
## 3 Results
### Category and Positional Differences for Total Distance, HSR, Sprint, and HMLD  
Table 2 shows category and positional comparisons for TD, HSR, sprint, and HMLD. Category-level differences were found in TD between senior and U-17 (\(p\) \(<\) 0.001; d = 1.75) and between U-20 and U-17 (\(p\) \(<\) 0.001; d = 1.26). HSR was significantly higher in seniors compared to U-17 (\(p\) \(<\) 0.001; d = 2.01) and U-20 (\(p\) \(<\) 0.001; d = 1.36). Sprint distance also favored seniors over U-17 (\(p\) \(<\) 0.001; d = 1.59) and U-20 (\(p\) \(<\) 0.001; d = 0.95). For HMLD, senior players outperformed both U-17 (\(p\) \(<\) 0.001; d = 4.76) and U-20 (\(p\) \(<\) 0.001; d = 4.29) (Figure 1). By playing position, significant differences in TD between the senior and U-17 groups were found for CB (\(p\) = 0.001; d = 1.88), FB (\(p\) = 0.001; d = 1.11), CM (\(p\) = 0.023;  
d = 0.97), WG (\(p\) = 0.006; d = 1.11), and ST (\(p\) = 0.005; d = 1.21). For HSR, differences were also significant between senior and U-17 players in CB (\(p\) < 0.001; d = 1.60), FB (\(p\) < 0.001; d = 1.15), CM (\(p\) = 0.005; d = 0.71), WG (\(p\) = 0.004; d = 1.68), and ST (\(p\) = 0.022; d = 0.59). Regarding sprint distance, significant differences were found for CB (\(p\) < 0.001; d = 1.36), FB (\(p\) < 0.001; d = 1.02), WG (\(p\) = 0.046; d = 1.46), and ST (\(p\) = 0.016; d = 0.46), with no significant difference observed for CM. For HMLD, senior players showed significantly higher values than U-17 across all positions: CB (\(p\) < 0.001; d = 5.11), FB (\(p\) < 0.001; d = 4.73), CM (\(p\) < 0.001; d = 6.16), WG (\(p\) < 0.001; d = 4.67), and ST (\(p\) < 0.001; d = 2.46). Comparisons between U-20 and U-17 revealed significant differences in TD for CB (\(p\) = 0.033; d = 1.34) and WG (\(p\) = 0.023; d = 1.02), and in sprint distance for WG (\(p\) = 0.046; d = 1.10). Between senior and U-20 players, significant differences in HSR were found for CB (\(p\) = 0.001; d = 2.01), FB (\(p\) = 0.007; d = 0.87), and CM (\(p\) = 0.031; d = 1.11); in sprint distance for CB (\(p\) = 0.012; d = 2.07) and FB (\(p\) = 0.002; d = 1.08); and in HMLD across all positions: CB (\(p\) < 0.001; d = 4.76), FB (\(p\) < 0.001; d = 2.54), CM (\(p\) < 0.001; d = 3.06), WG (\(p\) < 0.001; d = 1.73), and ST (\(p\) < 0.001; d = 5.22).  
Figure 1: Match external load metrics across age categories during the FIFA Women’s World Cup tournaments.
## 1 Introduction  
The management cycle of tangible cultural heritage--either archaeological findings, buried and open-air sites, monuments, historical buildings, or movable objects--includes at least the following phases: discovery, documentation, study, conservation, and promotion. Remote sensing (RS) is nowadays an acknowledged technological asset contributing to each of these phases and a means to collect digital data and records allowing not only non-invasive measurements at a given accuracy but also replicability and reproducibility of the features' and sites' geometry, of their context, and of their location in space. These properties are key features to support monitoring activities remotely, at various spatial and temporal scales, and in both ordinary and crisis times. For conservation purposes,monitoring through RS techniques enables the assessment of potential threats before harmful events occur, as well as the documentation of damages and impacts once an incident has happened, due to an either natural or anthropogenic process. As such, RS--via sensors mounted onto terrestrial, airborne, and satellite platforms--offers an instrument for institutions and organizations in charge of heritage conservation and promotion, alongside the scientific community, to undertake a variety of tasks, including the regular monitoring of the on-the-ground condition, risk assessment, and damage mapping.  
However, the extent to which RS and Earth Observation (EO) technologies are effectively and systematically exploited by end users outside the scientific research field and, if so, whether this use applies to all (or at least a broad range of) hazards and types of damage, or whether there is a polarization towards certain specific use-cases and scenarios, are yet to be fully unveiled.  
Therefore, to fill this gap, in this paper, we will first provide an in-depth, state-of-the-art analysis of the previous research and literature assessments on the topic, from which current issues and limitations will be highlighted. Based on these findings, in the second part, we will illustrate the specific objectives (i.e., the three research questions) and the methodology developed. The latter will have a dedicated section about the terminology used in the paper, followed by the step-by-step processes of analysis for both the indexed literature assessment and the Grey literature assessment. The third part of the article will be dedicated to the results. Here, we will separately describe the trends that have emerged from both the indexed literature assessment and Grey literature assessment. Within each type of assessment, the results will be distinguished on the basis of the research questions, focusing on the one hand on the types of hazards and damage and on the other hand on the geometric technologies used. In the discussion, we will evaluate the results separately, thus showing the main findings for the indexed literature and the Grey literature. The conclusion will underline the common problems that emerged from the two analyses and consequently the possible recommendations and way forward.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Random Telegraph Noise (RTN)
#### 3.2.1 RTN Amplitude
The impact of Coulomb scattering on the RTN and variability characteristic of such MOSFETs has been studied in [114; 115; 116; 117], showing that those effects are barely relevant for NAND Flash, that are read in the subthreshold regime. Charge quantization was instead accounted for via the density-gradient correction to the drift-diffusion formalism [118; 119]. Its relevance from the variability viewpoint was discussed in [120]. Figure 5 shows results for the current density profile in the channel of a decananometer Flash memory biased around a threshold. The two figures on the left-hand side correspond to a case of high \(V_{T}\) shift: transport in the empty trap case (upper figure) has a percolative nature because of RDF and field intensification around the active area corners. The RTN trap (black square in the lower figure) is located right above one main percolation path, close to one edge: its charging will shut off such path, leading to a large variation in current and \(V_{T}\). On the other hand, if the trap is located over a region in which the current density is already negligible, its charging and discharging will barely affect the overall current flow, as shown by the figures on the right-hand side. Figure 4: RTN fluctuation amplitude measured on a decananometer Flash cell (points = experimental data, dashed line = predictions from (2) when mobility modulations are neglected) [91], © 2007 IEEE. Figure 5: Current density profiles in the channel of decananometer Flash memories with and without a charged RTN trap. Left-hand side = high \(V_{T}\) shift case, right-hand side = low \(V_{T}\) shift case. Upper figures represent the empty trap case, lower figures the charged trap ones. The trap position is marked by the black squares in the lower figures. Monte Carlo results for the statistical distribution of the \(V_{T}\) shift due to a single RTN trap, \(\Delta V_{T}^{1}\), are shown in Figure 6: an exponential distribution was found, which can be easily characterized by its slope \(\lambda\), usually expressed in mV/decade. A thorough parametric analysis of \(\lambda\) was carried out in [113; 121], resulting in the following dependence:  
\[\lambda=\frac{K}{\alpha_{G}}\frac{t_{ox}^{\alpha}\sqrt{N_{A}}}{W\sqrt{L}}, \tag{3}\]  
where \(t_{ox}\) is the tunnel oxide thickness,\(N_{A}\) the average substrate doping, \(\alpha_{G}\) the control-gate coupling coefficient and \(K\) is a constant dependent on device design, doping profile and so on. This relation has proven itself to reproduce quite successfully the scaling dependence of the RTN slope over several different technologies.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 4 Isara
### Telecommunication Requirements and Design  
The ISARA spacecraft includes two telecommunication systems: a more traditional UHF system developed by Aerospace Corporation to transmit health telemetry and to receive spacecraft commands and the Ka-band telecommunication payload developed by JPL. This article focuses on the Ka-band system only. The requirement for the Ka-band system was to develop a transmit only technology to achieve 100 Mbps in Low Earth Orbit from a 3U CubeSat. The challenge was to develop a technology that would allow to achieve this data rate at the Ka-band and that it was compatible with the form factor of a CubeSat. In terms of compatibility, the Ka-band ground receiver for ISARA was specifically designed for this mission at JPL, so there was no particular need to be compatible with a specific ground network or protocol [12].  
Given these requirements, the Ka-Band system (Figure 9) is a very simple design including a low power transmitter connected to a switch that is connected to the reference antenna and the high gain antenna.