esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Advantages of Developed CET
#### 4.1.4 Detection of Spatio-Temporal Changes in Coastline Position  
In this study, CET was applied to the Iz-Rava island group, whose coast is made from coastal erosion-resistant carbonate rocks [53]. Due to carbonate rocks, little erosion-induced spatio-temporal changes can be observed over time. However, if applied for mapping of erosion-prone coastlines (e.g., pebble or sandy coast), CET could be used for accurately monitoring the overall coastline evolution. As accurate coastline extraction represents a basis for the quantification of erosion-induced spatio-temporal changes, CET has great potential for precise mapping of temporal shifts in coastline position.  
Figure 14: Example of CET application for the detection of small boats (**A**); the detection and mapping of fish farms (**B**); the detection of speedboats (**C**); the detection of aircrafts (**D**). Spectral bloom is caused by fast-moving objects due to temporal offset of the multispectral sensors of the WV-2 satellite. It can be used for determination of the speed of moving objects [73].
## 2 Materials and Methods
### Non-Hertzian Contact Model
Therefore, the numerical signal was simulated by hypothesizing three load levels below the fatigue threshold and quite different among them. The first condition encompassed the assumption of an infinite life design, whereas the second enabled discussions on the variability of the diagnostic content with respect to the load level, as well as to damage the severity. Examples of the obtained pressure distributions for those force levels and different defect sizes are visible in Figure 3. Indeed, the pattern of the pressure distribution is Hertzian only when no defect is present in the contact area, while introducing the defect in the middle of the contact area introduces further overpressures, due to the sharp edges of the defect and the lack of material underneath the contact area. Furthermore, since the dimension of the damage is larger than the contact area, it produces two separate contact patterns in which the maximum pressure values are around three times higher than under normal conditions. The shape and the curvatures of the contact surfaces are simulated in the MATLAB(r) code by referring to the bearing geometry specifications. The shapes and the sizes of the damages, for which an example is reported in Figure 2b, retrace the experimental data of Table 2. This model was then being used to extract the maximum contact pressure as a function of the rotation angle, numerically simulating around 100 revolutions in order to have a statistically significant sample, since the \(BPFI\) is not an integer with respect to a single revolution. Computations were carried out by simulating the relative motion between the race and the rolling element. No defect evolution due to plastic deformation was taken into account, since low loads and low numbers of cycles were simulated. The obtained time histories for three different load levels and damage dimensions are shown as examples in Figure 4 for the first few revolutions of the inner race. Figure 3: Examples of contact pressure distributions at different load levels and defect sizes. Table 3 reports the main features of the simulated contact pressure signals. The initial sampling frequency was rather high (74 kHz), given the fact that it was obtained numerically. Hence, the pressure signal was numerically resampled by means of interpolation and an antialiasing filter in order to make the damage detection analysis comparable with the experimental investigation. To the same end, numerical samples were cut by using \(M\) points.
## 4 Discussion
### Nutrient Dynamics across the Sediment-Water Interface
#### 4.1.1 Mechanisms Driving NO\({}_{3}\)- and NH\({}_{4}\)+ Concentrations and Fluxes
For the 3-10 min exposure, seasonal differences in water temperature and DO (\(p<0.001\)) affected NH4\({}^{+}\) fluxes. We assume this was due to microbial activity affecting the N-cycle and in turn, these seasonal differences affected bioactivity (\(F=17.276\), \(p<0.001\)). Regarding NO3\({}^{-}\), significant seasonal differences in flux associated with bioactivity were also observed (\(F=8.723\), \(p=0.031\)). We suspect this significant variation reflects NH4\({}^{+}\) flux participating in overall DNRA reactions. Concentrations of NO3\({}^{-}\) and NH4\({}^{+}\) were significantly different among the study rivers (\(F=12.737\), \(p<0.001\); \(F=4.760\), \(p=0.010\), respectively). The NH4\({}^{+}\) concentrations at the sites affected by the wastewater effluents (i.e., L1, T1) and the site affected by the tributary inflow (i.e., W2) were higher than at another site on each river. On the one hand, the effluents with elevated water temperature and organic matter promoted the mineralization of organic nitrogen by microbes likely promoting higher NH4\({}^{+}\) concentrations [92]. On the other hand, high concentrations of NH4\({}^{+}\) (\(>\)2 mg/L) of the River Ba flowing into the River Wenyu yielded higher NH4\({}^{+}\) concentrations at W2. For NO3\({}^{-}\), concentrations were negatively correlated with NH4\({}^{+}\) concentrations (\(r^{2}=-0.557\), \(p<0.001\)) and NO3\({}^{-}\) at L1 and T1 were lower than at L2 and T2, respectively. [93] pointed out that NO3\({}^{-}\) tend to increase downstream with increasing river discharge due to reduced water residence time which could explain our observations. Mean NO3\({}^{-}\) concentrations across all rivers were lowest in summer. We suspect that this observation is linked to NO3\({}^{-}\) uptake supporting biological activity in summer (e.g., phytoplankton). This idea is further supported by the fact that the highest mean NO3\({}^{-}\) concentrations were observed in winter. Mean NO3\({}^{-}\) concentration in autumn was higher than in spring, and the increased seasonal precipitation in North China is a likely factor because of the role of surface runoff as a non-point source of pollution [14]. Although increased precipitation and runoff can also dilute NO3\({}^{-}\) concentrations, pollution associated with surface runoff can be significantly stronger than dilution [94]. However, this needs further investigation for the study basins.
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
###### Abstract  
Feature extraction methods have been used to extract location features for indoor positioning in wireless local area networks. However, existing methods, such as linear discriminant analysis and principal component analysis, all suffer from the multimodal property of signal distribution. This paper proposes a novel method, based on enhanced local fisher discriminant analysis (LFDA). First, LFDA is proposed to extract discriminative location features. It maximizes between-class separability while preserving within-class local structure of signal space, thereby guaranteeing maximal discriminative information involved in positioning. Then, the generalization ability of LFDA is further enhanced using signal perturbation, which generates more number of representative training samples. Experimental results in realistic indoor environment show that, compared with previous feature extraction methods, the proposed method reduces the mean and standard deviation of positing error by 23.9% and 33.0%, respectively.  
Keywords: indoor positioning; wireless local area network; received signal strength; feature extraction +
Footnote †: journal:
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Real-Time Processing  
#### 5.3.1 Challenges  
In the realm of smart healthcare, the burgeoning Internet of Things (IoT) has led to an explosion of data that demand immediate processing to derive actionable insights. Traditional data analysis methods, which rely on centralized servers, often introduce latency that undermines the effectiveness of real-time applications Figure 4. This limitation has exposed the insufficiency of cloud computing alone in meeting the diverse and dynamic data analysis requirements of modern intelligent systems. As a solution, fog and edge computing have emerged as pivotal technologies. By decentralizing data processing and relocating computational resources closer to the data source, these approaches significantly reduce latency and enhance processing efficiency. For instance, edge computing allows for real-time analytics directly at the point of data generation, such as wearable health monitors analyzing patient vitals on the device itself. This minimizes the delay inherent in transmitting data to and from the cloud, facilitating quicker responses and more timely insights. Similarly, fog computing extends this capability by providing additional storage and computational power at intermediate nodes between the edge and the cloud. This model can handle more extensive data processing tasks, such as aggregating and analyzing health data from multiple sources within a smart hospital network, while still addressing the latency issues associated with cloud-based solutions [108].
# A 6 DOF Virtual Environment Space Docking Operation with Human Supervision
## 2 Real and Virtual Setup Description
### Co-Simulation Procedure  
The previous section gave a brief overview of both the LabVIEW and the VB.NET applications, while this section explains the cooperation between both applications. Both applications have the ability to influence each other, but only at specific points during the execution of the code, by reading and writing registry keys and by creating intermediary files to transfer large amounts of data. The communication via the registry keys is simply used as a confirmation and allows one application to know which part of the code the other application is executing. It can be said that the communication between LabVIEW and the VB.NET-application is a two-way communication process, but it is however not a simultaneous or continuous type of communication. Larger amounts of data are transferred between applications using both.csv-files and.txt-files, which results in another type of indirect communication. The motion sequence is recorded by six virtual sensors that are positioned at the center of the upper surface of the virtual platform. These sensors measure the displacement in three directions as well as the angular displacement around three axes. NX outputs the measurement data of the sensors as a binary file. These data are used as an input by LabVIEW to match the motion sequence of the real-world platform to that of the virtual platform. Because the data of the virtual sensors are used as a function of the virtual  
Figure 6: Flowchart of the operational procedures in the VisualBasic application (red path represents the manual override mode, the blue path represents the import motion mode and the green path represents the export motion mode). LV is the abbreviation of LabVIEW.  
world coordinate system, this data have to be transformed to the real world-platform coordinate system using the principle illustrated in Figure 4b. The real-world-platform motion-sequence is recorded by the Kinect sensor, which returns the platform orientation data. The data consist of three markers on the plate, which are detected by the visual sensor and are further used to calculate the platform position and attitude. The initial locations for each of these points is known, thus enabling us to calculate the transformation matrix between the initial location and the current location.  
This co-simulation consists of two types of simulations:  
1. The real-world simulation is in itself a real-time simulator: values that are changed on the front panel of the LabVIEW-app have an immediate effect on the orientation of the real-world platform;
2. The virtual-world simulation, however, is a slower-than-real-time-simulation because this application solves the entirety of the sequence at once before starting the animation.  
The program mode that most closely resembles a synchronous simulation is the auto-mode. It records small motion-sequences with a step duration of approximately one 60 th of a second, sends them to the virtual-world simulator and animates the simulation.
# Object and Event Detection Pipeline for Rink Hockey Games
## 3 Automatic Pipeline for Object and Event Detection in Rink Hockey Games
### Dataset Organization  
A major contribution of this work is the organization of the dataset. Our rink hockey dataset was annotated with the aid of a web-based computer vision annotation tool that helps to label video and images. The selected solution, Roboflow, is a popular computer vision development platform that facilitates data collection, preprocessing, and model training. This framework allows easy access to public datasets as well as the ability for users to upload their own custom data [23]. Additionally, it supports various annotation formats, including JSON, CSV, and XML.  
The dataset was divided into three subsets: training (70%), validation (20%), and testing (10%). The dataset included objects labeled with seven classes: ball, player, stick, referee, crowd, goalkeeper, and goal. The main goal of this annotation process was to produce a dataset that can be used to train object detection models, such as YOLOv7 [24], for transfer learning.  
From the total of 2525 annotated frames/images (1764 training, 501 validation, and 260 testing), the image augmentation technique was applied to the training images which, by applying horizontal flipping, cropping with a zoom up to 20%, and changing the brightness changing between \(-15\%\) (darker) and \(+15\%\) (brighter), resulted in a total of 5292 images for training. Beyond that, through these and other possible modifications, we artificially increased the size of the dataset. This technique is used not only to increase the size of the dataset and hence the robustness and generalization of the model, but also to try to avoid model overfitting, where a trained model performs better on trained data but poorly on unseen data. In addition to the previously mentioned image augmentation in the training set, we also used the default data augmentation techniques configured in the training process of YOLOv7. Of these techniques, we emphasize mosaic, which allows for the combination of several images to create a single training image with a mosaic appearance [25].  
As Figure 3 shows, the dataset is slightly unbalanced, with the player and stick classes being the most represented. This is not surprising, since these are the most visible objects in all images.  
The resulting dataset is available online [22]. It can definitely be useful for a wide range of applications, including player tracking and video analysis of rink hockey games. Being publicly available, this dataset will serve as a valuable resource for other researchers and practitioners within the field of computer vision and sports analysis.  
Figure 3: Dataset health check.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 4 Numerical Examples and Discussions
The greater the gap between the value of the amplitude mismatch and one, the greater the harmful effect of the amplitude mismatch on system performance. Figure 4: Outage probability versus the parameter of phase mismatch for the system with ipSIC and pSIC conditions. Figure 3: Outage probability versus the parameter of ipSIC for the system with IQI and ideal conditions. The effects of the three different conditions for the system throughput are evaluated in Figure 6, in which we fix the parameters of IQI as \(g_{t}=g_{r}=0.8\), \(\varphi_{t}=\varphi_{r}=5^{\circ}\) and the ipSIC parameters as \(\varepsilon=0.03\) for the IQI and ipSIC conditions. For only the ipSIC condition, we fix the parameters of IQI as \(g_{t}=g_{r}=1\), \(\varphi_{t}=\varphi_{r}=0^{\circ}\) and ipSIC parameters as \(\varepsilon=0.03\). For the ideal condition, we use the parameters of IQI as \(g_{t}=g_{r}=1\), \(\varphi_{t}=\varphi_{r}=0^{\circ}\) and ipSIC parameters as \(\varepsilon=0\). In the SNR range from \(-10\)dB to \(45\)dB, the analysis curves of the system throughput with IQI and/or ipSIC condition is always lower than that of the ideal condition. It should be noted that the system throughput enhances as the SNR increase. Moreover, both system throughputs gradually tend to be stable in high SNR region. It can be observed from Figure 6 that the system throughput under two channels is the same in the low SNR region, and at high SNRs, the Rayleigh fading is slightly better than the throughput of the Rician fading channels in the ideal condition. However, in other non-ideal conditions, it is obviously worse than Rician fading channels. Therefore, a conclusion can be obtained that there is almost no difference of the considered system throughput with the ideal condition in the two channel cases, and IQI and ipSIC have worse effects on the system throughput with Rayleigh fading channels than on Rician. Figure 5: Outage probability versus the amplitude mismatch for the system with ipSIC and pSIC conditions. Figure 6: System throughput versus the transmit SNR of the proposed TWR C-NOMA system for \(D_{1}\) and \(D_{2}\) with data rate thresholds \(\bar{R}_{1}=0.2 BPCU\) and \(\bar{R}_{2}=0.3 BPCU\).
## 1 Introduction
In this sense, a common alternative to prevent haze formation is cold stabilization [4; 10], by freezing the spirit at temperatures below \(-\)5 \({}^{\circ}\)C for a few days, followed by subsequent filtration before bottling [7; 11; 12]. Previous works carried out on fruit brandies (apricot, plum, and rye) studied different conditions of stabilization, such as the temperature and filtration systems [4; 5; 7; 12]. The results showed the influence of these factors on the final turbidity of the samples, obtaining the best results with intermediate values of temperature (from \(-\)1 to \(-\)4 \({}^{\circ}\)C) and filter pore size (filter sheets with a higher nominal retention rate, \(>\)0.7 \(\upmu\)m, and membranes with 800 nm pore size). However, most of the studies focused on the effect of the stabilization and filtration processes on the physical characteristics of the brandies, ignoring their effect on the aroma quality. In this sense, esters are one of the most important families of volatile compounds impacting the aroma of brandies, being the main known contributors to the flower and fruity notes [13]. In brandies, these compounds are mainly originated during wine fermentation and are modulated during wine maturation, distillation, and brandy ageing [14]. Their relevance for the organoleptic properties of brandies comes from their low perception threshold and their contribution to the brandy aroma through synergistic interactions, even at concentrations below their perception thresholds [15]. Thus, the objective of the present work was to compare the influence of two methods of stabilization on the ester profile of \"Brandy de Jerez\": the traditional method at room temperature for 1 year, to assure that all physicochemical reactions and insolubilizations that may occur during stabilization have been completed, followed by filtration; and cold stabilization for 7 days at \(-\)10 \({}^{\circ}\)C, followed by filtration. The ester profile obtained with both stabilization processes was also compared with the ester profile of brandies without stabilization.
## 2 The Whole Stage Stability of the ANC System with Online SPM
### Mean Square Behavior  
#### 2.2.1 SPM Filter  
The covariance matrix of \(\upsilon(n)\) is defined as \(\textbf{R}_{\upsilon\upsilon}(n)=E(\upsilon(n)\upsilon^{\mathsf{T}}(n))\). Multiplying \(\Delta\textbf{s}(n+1)\) by its transpose and taking the expectation on both sides of its updating equation, the covariance matrix of \(\Delta\textbf{s}(n+1)\) can be expressed as:  
\[\begin{array}{ll}\textbf{c}_{s}(n+1)&=E\big{(}\Delta\textbf{s}(n+1)\Delta \textbf{s}^{\mathsf{T}}(n+1)\big{)}\\ &=\textbf{c}_{s}(n)-\mu_{s}\textbf{c}_{s}(n)\textbf{R}_{\upsilon\upsilon}(n)\\ &-\mu_{s}\textbf{R}_{\upsilon\upsilon}(n)\textbf{c}_{s}(n)\\ &+\mu_{s}^{2}E(\upsilon(n)\upsilon^{\mathsf{T}}(n)\textbf{c}_{s}(n)\upsilon(n )\textbf{v}^{\mathsf{T}}(n))\\ &+\mu_{s}^{2}E(\upsilon(n)\upsilon^{\mathsf{T}}(n)\textbf{c}_{s}^{2}(n)+u^{2} (n))\end{array}, \tag{46}\]  
For the fourth term, by applying the method introduced in [30], it can be simplified as:  
\[\begin{array}{ll}E(\upsilon(n)\upsilon^{\mathsf{T}}(n)\textbf{c}_{s}(n) \upsilon(n)\upsilon^{\mathsf{T}}(n))\\ =2\textbf{R}_{\upsilon\upsilon}(n)\textbf{c}_{s}(n)\textbf{R}_{\upsilon \upsilon}(n)+\textbf{R}_{\upsilon\upsilon}(n)tr\big{(}\textbf{R}_{\upsilon \upsilon}(n)\textbf{c}_{s}(n)\big{)}\end{array}, \tag{47}\]where \(tr(\cdot)\) is the trace of the matrix. Because \(\
u(n)\) is Gaussian white noise, substituting (54) into (53) yields:  
\[\begin{array}{ll}\mathbf{c}_{s}(n+1)&=\mathbf{c}_{s}(n)-2\mu_{s}\sigma_{v}^{2} \mathbf{c}_{s}(n)\\ &+\mu_{v}^{2}\sigma_{v}^{4}(2\mathbf{c}_{s}(n)+tr(\mathbf{c}_{s}(n)\mathbf{I}) \\ &+\mu_{s}^{2}\sigma_{v}^{2}(E(\mathbf{\epsilon}_{x}^{2}(n))+\sigma_{u}^{2}) \mathbf{I}\end{array}, \tag{48}\]  
where \(\mathbf{I}\) denotes an identity matrix with compatible dimension. It can be observed from the fourth term in (55) that the error of the ANC filter generates a driving term for the differential equation of the secondary path. However, we assumed that the excitation caused by the fifth term is finite. It can be treated as an LMS algorithm for stability analysis from the literature [9], and the stability condition can be obtained as:  
\[\mu_{s}\leq\frac{2}{3 tr(\mathbf{R}_{vv}(n))}=\frac{2}{3L\sigma_{v}^{2}}. \tag{49}\]
## 1 Introduction
For riverbed deformation addressed in this study, the monitoring is focused on detecting and confirming minor abrupt-deformations at a few millimeters and slow gradual-deformations at a few millimeters per week. The term \"near real-time\" indicates a time delay between the occurrence of an event in the field and the recognition of the event in the monitoring center. The difference between the terms \"near real-time\" and \"real time\" can be vague because it always takes a certain amount of time to identify and confirm an event even if the data transformation and processing can be conducted in seconds.
## 5 Experimental Results and Evaluation
### Performance Evaluation  
This section evaluates the performance of executing the operations in the data authorization and verification. In the data authorization process, the desktop launches a transaction, which encapsulates the Swarm hash of the meta-data in the Blockchain, and then, the states of the SC can be updated until a block containing transactions committedby the miners. Thus, we evaluated the end-to-end latency and gas usage during a successful data authorization operation. According to Algorithm 1, the whole data integrity verification procedure is divided into three steps: (1) the client (Rpi or desktop) queries the data token containing the Swarm hash of the meta-data and the root from the Blockchain; (2) the client validates the Merkle root and Swarm hash in the data token; (3) the client retrieves the meta-data from the DDS and verifies them. Therefore, we evaluated the processing time of the individual steps on different platforms by changing the number of meta-data (\(N_{m}\)). Finally, we analyzed the computational overheads incurred by retrieving the meta-data from the DDS and performing symmetric encryption on the meta-data. We conducted 50 Monte Carlo test runs for each test scenarios and used the averages to measure the results.