esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Prior Work
RED executes at fixed intervals of time, and in each time period, witness nodes are selected as a function of a random seed broadcast by the base station. Because of the randomized nature of choosing witness nodes, the adversary is unable to identify and compromise these witness nodes ahead of time. While the costs of RED are lower than that of line-selected multicast per time period, the overall costs are higher, since RED repeats itself periodically. [PERSON] _et al._[16] proposed two variants of a localized-multicast scheme called single deterministic cell (SDC) and parallel multiple probabilistic cells (P-MPC). In these schemes, the witness nodes for a sensor are randomly selected from the nodes that are located within a particular geographic region in the sensor field. First, the scheme deterministically maps a node ID to a specific region in the field and then randomly chooses witnesses within this region. SDC and P-MPC differ in whether the location claims are broadcast to a single region or to multiple regions probabilistically. They show that these schemes achieve a higher detection rate than line-selected multicast with comparable communication costs. [PERSON] _et al._[17] proposed a duplicate detection scheme called SETwith lower communication and storage costs than the line-selected multicast protocol proposed by [PERSON] _et al._[10]. SET detects clones by first treating the sensor network as non-overlapping regions and forming exclusive subsets in each of these regions. Then, the scheme performs set operations, namely unions and intersections, of these exclusive subsets in the network. If the intersections of these subsets are non-empty, this indicates the presence of a replica in those regions. Based on the assumption that nodes in a sensor network are deployed in groups, [PERSON] _et al._[18] proposed a distributed, efficient scheme to detect replicas with lower communication, computation and storage overheads than the schemes listed earlier. They argue that this assumption is reasonable since many sensor networks are deployed in groups, either by dropping them over the field via airplanes or scattered by hand. Their scheme reduces communication overhead, since most nodes are able to communicate without generating location claims, as long as they are able to directly send messages to one of their group members. In our work, we make use of this assumption to design our detection scheme called \(\mathbf{Disccard}\). For a more comprehensive treatment of node replica detection schemes in wireless sensor networks, we refer the reader to the survey by [PERSON] _et al._[12].
# Numerical Simulation of Non-Linear Models of Reaction--Diffusion for a DGT Sensor
## 2 The Model
### Dimensionless Formulation  
To adapt scales to the order of magnitude of data, the variable changes that lead to the nondimensional problem, are described.  
* For all species in solution, that is, for all the species that have a bulk solution concentration, obtained from the solution of the equilibrium problem, we define standard concentrations, \[q_{i}=\frac{c_{i}}{c_{i}^{*}}\.\]
* For the rest, that is to say, the resin and its complexes, \[q_{j}=\frac{c_{j}}{c_{\rm TR}}\.\]
* For the spatial coordinate, instead of nondimensionalisation, a change of scale is used, \[z=\frac{x}{\sqrt{D_{\rm max}}}\,\] where \(D_{\rm max}\) is the highest diffusion coefficient. It makes it necessary to define \(r^{*}=r/\sqrt{D_{\rm max}}\); \(g^{*}=g/\sqrt{D_{\rm max}}\); \(d_{i}=D_{i}/D_{\rm max}\); and \(d_{i}^{\rm R}=D_{i}^{\rm R}/D_{\rm max}\).
* Given that the aim is to simulate experiments involving different time scales, the time axis is not changed.  
With these new variables, the reaction-diffusion equations described in Equation (14) are rewritten as follows:  
\[\frac{\partial q_{i}}{\partial t}=\left\{\begin{matrix}d_{i}^{\rm R}\\ d_{i}\end{matrix}\right\}\frac{\partial^{2}q_{i}}{\partial 2^{2}}+\sum_{j=1}^{numrec}(\hat{A}_{i,j}q_{nreac (j,1)}q_{mreac(j,2)}+\hat{\beta}_{i,j}q_{mreac(j,3)})\, \tag{15}\]  
where \(d_{i}^{\rm R}\) acts as a diffusion coefficient \(\mathrm{in}(0,r^{*})\) and \(d_{i}\) in \((r^{*},r^{*}+g^{*})\), and where the new coefficients \(\hat{A}_{i,j}\) and \(\hat{B}_{i,j}\) are calculated as  
\[\hat{A}_{i,j}=A_{i,j}\frac{c_{mreac(j,1)}^{*}c_{mreac(j,2)}^{*}}{c_{i}^{*}}; \ \ \hat{B}_{i,j}=B_{i,j}\frac{c_{mreac(j,3)}^{*}}{c_{i}^{*}}. \tag{16}\]For the concentrations of the resin and its complexes, \(c_{i}^{*}\) must be replaced by \(c_{\text{TR}}\).  
The initial and boundary conditions for all substances that do not contain resin, that is to say, for all those whose diffusion coefficient is different from zero, are now  
\[q_{i}(z,0)=0;\ \ \ q_{i}(r^{*}+g^{*},t)=1\, \tag{17}\]  
and  
\[\left.\begin{aligned} \frac{\partial q_{i}}{\partial z} \right|_{z=0}&=0\,\\ q_{i}(r^{*-},t)&=q_{i}(r^{*+},t)\,\\ \left.\frac{\partial q_{i}}{\partial z}\right|_{z=r^{*-}}& =\left.\frac{d_{i}}{d_{i}^{R}}\frac{\partial q_{i}}{\partial z} \right|_{z=r^{*+}}\.\end{aligned} \tag{18}\]  
The remaining substances only need an initial condition: for the resin  
\[q_{\text{R}}(z,0)=\left\{\begin{array}{ccc}1&\text{si}&0\ \ <z<r^{*}\\ 0&\text{si}&r^{*}<z<r^{*}+g^{*}\end{array}\right.\]  
For complexes of the resin \(q_{i}(x,0)=0\) in \((0,r^{*}+g^{*})\).
## 4 Experiments  
To investigate the performance of the proposed method in real applications, experiments of real-time noise control were conducted. In this experiment, the real sound of car horns was used as  
Figure 8: Robustness analysis (**a**) ANR curves; (**b**) changing process of \(\varepsilon\left(n\right)\); (**c**) Euclidean norm of weight vector \(\mathbf{\Theta}\left(n\right)\).  
the reference signal. Recordings were taken in Songbei Area of Harbin, Heilongjiang province, China. An NI 4472 data acquisition card was used for recording, and the horn is that of a Volkswagen car.  
In real-time experiments, the FxLMS, FxlogLMS, PFxNLMS, and DMPFxNLMS were applied to Intervalzero RTX 64 real-time system, respectively. The standard FxLMS was introduced to evaluate the performance of the proposed DMPFxNLMS. The FxRMC was deprived in the real-time experiments due to its heavy computational load [21; 22; 23; 24]. The estimated secondary path using off-line LMS algorithm was modelled as a FIR filter with 16 orders [25]. The sampling frequency was 8000 Hz. The length of weight vector was 32 taps. The diagram of the real-time experiment is shown in Figure 9. Figure 10a,b show the analysis of the record sound in the time domain and frequency domain, respectively.  
The error learning curves are shown in Figure 11. The FxLMS nearly diverged at 11 s, thus it was stopped manually after that time. It shows that DMPFxNLMS had better attenuation performance than FxlogLMS and PFxNLMS. To show clearly, the ANR performance was considered to give further results in Figure 12.  
In Figure 12, the attenuation of FxLMS was inferior to that of other methods applied in this experiment when impulsive interference occurred, it diverged at about 11 s, which also confirmed the conclusion in [8; 9; 10; 11; 12; 13; 14; 15] theoretical analysis that the FxLMS cannot cancel impulsive  
Figure 10: Reference signal of real-time experiment (**a**) time domain; (**b**) frequency domain.  
Figure 9: Diagram of the real-time experiment.  
noise. Although the FxlogLMS could maintain the steady state after the impulsive interference, the average attenuation of it was inferior to that of PFxNLMS(Opt) and DMPFxNLMS. Comparing to PFxNLMS(Opt), DMPFxNLMS had a better initial convergence rate. Between 6 s and 15 s, it performed more stably in this experiment. When the impulsive interference disappeared, it had better average attenuation than the PFxNLMS. These proved that the proposed DMPFxNLMS had better performance in the Gaussian noise environment with accidental impulsive interferences.
# Optimal Design of Aperiodic Reconfigurable Antenna Array Suitable for Broadcasting Applications
## 2 Geometry Definition
In order to calculate the theoretical value of \(d\) we use the following equations [19]:  
\[G_{1}=\frac{1}{120\pi^{2}}\int_{0}^{\pi}\left[\frac{\sin\left(\frac{k_{0}W_{th }}{2}\cos\theta\right)}{\cos\theta}\right]^{2}\sin^{3}\theta d\theta, \tag{6}\]  
\[G_{12}=\frac{1}{120\pi^{2}}\int_{0}^{\pi}\left[\frac{\sin\left(\frac{k_{0}W_{ th}}{2}\cos\theta\right)}{\cos\theta}\right]^{2}J_{0}(k_{0}L_{th}\sin\theta)\sin^{3} \theta d\theta, \tag{7}\]\[R_{in}=\frac{1}{2(G_{1}\pm G_{12})}\cos^{2}\big{(}\frac{\pi}{L_{th}}s_{th}\big{)}, \tag{8}\]  
where \(G_{1}\) is the self conductance of one of the radiating slots of a microstrip patch, \(k_{0}\) is the free space wavenumber, \(G_{12}\) is the mutual conductance between the two radiating slots of a microstrip patch, \(J_{0}\) is the Bessel function of the first kind of order zero and \(R_{in}\) is the resonant input resistance of a patch. By solving (8) for \(R_{in}=50\)\(\Omega\) we obtain the theoretical value \(s_{th}=44.2\) mm and therefore \(s\) is considered to be restricted between 0.6 \(s_{th}\) and 1.4 \(s_{th}\). As mentioned earlier, eight feeding weights must be found in order to achieve the desired radiation pattern. The boundaries for the amplitudes of the feeding weights \(A_{n}\) (\(n=1,2,...,8\)) are set between 0.1 and 1 (considering an upper limit equal to ten times the lower limit), while the phases \(P_{n}\) are considered to be between 0\({}^{\circ}\) and 360\({}^{\circ}\). The optimal value for \(g\) according to Reference [21] is considered to be between \(W/40\) and \(W/10\). As \(W\) takes different values during the optimization and \(g\) relies on \(W\), it was decided to confine \(g\) between 1/40 and 1/10 of the current value of \(W\) (\(W_{cur}\)) during the optimization process. All the boundaries for the optimization parameters are summarized in Table 1. In total, there are 26 parameters to be optimized (\(Q=26\)), thus creating a complex problem to solve.
## 5 Conclusions  
A smart food control and monitoring system was introduced which helps the experimenters conveniently schedule and control an animal's access to food and monitor their status. It is a programmable, sensorized, and easy-to-handle system that is designed to provide tightly controlled conditions where the food intake, behavior, and intensity of the animal's activity must be accurately measured. It eliminates the need for technicians to manually control the feeding and fasting schedule of the animal. It also gives the experimenter remote access for condition control and monitoring.  
The feeding mechanism is designed to be mounted on conventional cages used in most research centers and has a sliding door that can enable and disable the animal's access to food. The mechanism will change the food access according to the schedule set by the experimenter. Regardless of the preset schedule, the experimenter is also able to change the food access by opening and closing the food access door using the built-in function of the interface program. The vision unit takes advantage of a set of light sensors, a camera, and a trained YOLOv5s CNN real-time object detection model. We can confirm that a modern CNN model can provide satisfactory results (0.9825 precision) if provided with a large enough dataset (-27,000). The camera can be used for remote monitoring of the animal, which is needed in many applications, such as post-surgical monitoring, humane endpoint monitoring, and pregnancy monitoring. The vision unit also provides a live video stream of the animal on the experimenter's computer. In addition, as is required in many biomedical research studies, the vision unit can detect the animal's location within the cage, provide a history of the animal's activity, and compile statistics and analytics for the experimenter. The feeding mechanism and the vision unit are connected to the interface program using wired or wireless connections.  
In short, the key values of the described technology can be summarized as follows:  
* Eliminating the need for an in-person presence to change the animals' access to food;
* No scheduling of staff at irregular hours;
* Remote visual monitoring;
* The ability to check on animal well-being at any time (monitoring anywhere);
* Availability of recorded and logged animal activities for analysis and comparison to other procedures;
* Automated and reliable food control;
* Reduced need for safety-trained staff to risk exposure to radiation through remote monitoring;
* Increased capacity of conducting the experiments on different days of the week.  
Future work will focus on the description, validation, and testing of an algorithm that was used to approximate the distance traveled by a mouse, as well as more information on the dataset while comparing and reporting different object detection CNN performances for mice inside the cage.
## 2 Versatile Video Coding (VVC)
### Inverse Quantization (IQ) and Transform (IT)  
In VVC, both the residual intra- and inter-CBs are encoded with an MTS scheme with three transform types: DCT-II, DCT-VIII and DST-VII. Rectangular \(H\times W\) blocks are allowed with \(H\leq 64\), \(W\leq 64\) for DCT-II and \(H\leq 32\), \(W\leq 32\) for DCT-VIII and DST-VII (e.g., \(4\times 64\) or \(32\times 16\)). To reduce the computational complexity of large transforms, the high frequency coefficients of \(H\times 64\) and \(64\times W\) DCT-II transforms are _zeroed_; the same happens with the high frequency coefficients of the \(H\times 32\) and \(32\times W\) DCT-VIII and DST-VII transforms. After this primary transform, a LFNST was applied before quantization. LFNST was added to VVC for performing further transform in low-frequency primary transform coefficients [27]. These coefficients are transform coefficients that come from directional intra prediction [28].
## 3 Results  
The first stage of this work was to measure real sampled data in real buildings and to validate our calculation procedure. To do this, the ISO 13790 standard was implemented in a spreadsheet and its results were compared with the tools recognized by the Spanish Ministry of Industry (CE3X software version 2.3), both applied in an isolated residential building of Figure 6. It is interesting to highlight that this building, after an initial sample of its energy demand, was modified towards a reduction of its energy consumption. In consequence, an initial validation respect CE3X software and standards was done in its initial conditions and, a second validation after a retrofit process, with a more detailed information, was done too.  
As it was explained before, the energy certification model designed contemplates the architectural aspects of the building, the characteristics of the equipment and facilities of the building. This information was summarized to be introduced in the CE3X software and in our calculation procedure as:  
* Climate zone: C1 (according to D.B. HE1 Spanish construction law)
* Living area: 2343 m\({}^{2}\)
* Number of floors: 5
* Year of construction: 1987 (applicable Spanish standard NBE CT 1979)
* Facade wall (double ceramic brick wall with non-ventilated chamber): U = 1.69 W/m\({}^{2}\cdot\)K
* Building roof (roof with concrete slab and tile): U = 2.27 W/m\({}^{2}\cdot\)K
* Soil: U = 0.47 W/m\({}^{2}\cdot\)K
* Air changes: 0.63 Ach/h
* DHW: 1771 liters/day (at 60 \({}^{\circ}\)C)
* Boiler: standard diesel boiler with 1 burner stage (made in 1980)
* Walls transmittance: 1.69
* Roof cover transmittance: 2.27 W/m\({}^{2}\cdot\)K
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Satellite Image Data  
In this study, remote sensing imagery obtained from the GF-7 satellite, a Chinese satellite, was used as the primary data source. The panchromatic stereoscopic image pair has a resolution of 0.8 m, whereas the multispectral image has a resolution of 3.2 m. The imagery was acquired from the terrestrial observation satellite service network ([[https://data.cresda.cn](https://data.cresda.cn)]([https://data.cresda.cn](https://data.cresda.cn)) (accessed on 1 November 2021)). Considering the climate-based vegetation characteristics, image data should be collected during the vegetation growth period to obtain more accurate results. This research primarily used four GF-7 remote sensing image datasets collected between 15 April and 15 October 2021. The data processing procedure encompassed several stages, including orthographic correction, atmospheric correction, geometric correction, fusion, and mosaic processing. Following the resampling phase, images with submeter precision and a four-band resolution of 0.6 m were produced.
## 2 Related Work
### Multi-Person Counting and Tracking Systems  
True foreground extraction, i.e., human pixels, is only one of the primary steps for accurate counting and tracking of humans in crowded scenes [89; 90; 91; 92; 93]. Several approaches and systems have been introduced by many researchers for multi-person counting and tracking. In [94], [PERSON] et al. proposed a pixels-based people counting model using the fusion of a pixel map-based algorithm along with human detection to count only human classified pixels. They applied a depth map, image segmentation, and a human presence map that was updated with a human mask for the purpose of counting people; however, the system has misclassification problems due to interclass similarities. [PERSON] et al. in [95] proposed a new color and intensity patch segmentation approach for tracking and detection of human body parts and for the full body. They applied fusion of color space segmentations for the detection of body parts and for the full body. For tracking, based on the velocity of a target, they adaptively selected the track gate size. A target's likely forward position was predicted based on the target's previous velocity and direction. The proposed algorithm achieved satisfactory results only when the count of peoples was limited in the view, i.e., efficiency decreases as the crowd increases. In [96], [PERSON] et al. introduced a head tracking-based directional people counter. Using several circular patterns and preprocessing steps, people's heads were detected. For the tracking application, a Kalman filter was used, and counting was achieved on the bases of head detection and tracking. The effectiveness of the proposed algorithm decreases during serious occlusions, arbitrary movements, and overlaps. [PERSON] et al. in [97] introduced object tracking and counting using new morphological techniques. The frame-difference technique, followed by morphological processing and region growing, was used for counting people. Moving objects were extracted by determining their movements, and then tracking was performed using color features. As the illumination of the scene changed, the efficiency of the proposed algorithm decreased. [PERSON] et al. in [98] proposed a tracker based on a correlation filter. Kalman filter applications were used for tracking. They designed a tracker that detects numerous positions and alternate templates. However, the system was not efficient in dealing with complex situations, such as occlusions and random movements. [PERSON] et al. in [99] used deep learning to track multi-persons. Using CNN, they extracted features and then introduced a weighted triple loss strategy to assign weights during training. Their system was computationally complex, and a huge dataset was essential for training. [PERSON] et al. in [100] located humans by their shoulders and heads, and, for tracking, they used trajectory analysis and the Kalman filter, but the system was not effective for arbitrary movements or overlaps.
# Advanced Analysis of Electoretinograms Based on Wavelet Scalogram Processing
## 6 Discussion  
The proposed method is based on the use of a continuous wavelet transform. In comparison with Fast Fourier Transform, linear prediction, Windowed Fourier Transform, and other common methods, continuous wavelet transform provides a variable window size, a high resolution at low and high frequencies, and the ability to obtain detailed information during rapid frequency changes; it is also suitable for extracting features from non-stationary signals.  
In order to determine the ERG parameters, [PERSON] [25] compared the results of the time domain analysis and the continuous wavelet transform. It was confirmed that the continuous wavelet transform could distinguish between normal and abnormal ERGs, particularly in the early diagnosis of glaucoma, and that it was more accurate than the time domain analysis methods.  
A research group led by [PERSON] [26] focused on extracting information about the time-frequency characteristics of the ERG \(a\)- and \(b\)-waves using wavelet analysis. According to their results, low-frequency components dominated (both in \(a\)-waves and \(b\)-waves), and their temporal distribution was affected by the brightness of the light pulses.  
[PERSON] [27] used wavelet analysis to extract the characteristics of the ERG a-wave. The work was focused on diagnosing two pathologies: achromatopsia and congenital stationary night blindness. The results showed that the number of dominant frequencies and the times of their occurrences in both of the studied diseases could reflect the state of the retinal photoreceptors. A comparison of individual pathological cases with a healthy control group revealed that both the components of the incidence of the disease were shifted toward the lower frequencies. Other studies [28; 29; 30; 31; 32] also point toward the use of wavelet analysis. It should be noted that wavelet analysis is used directly in the above studies. In other words, there is no extraction of parameters from the wavelet scalograms.
## 5 Results and Discussion
### Image Enhancement Results
#### 5.1.4. Minimum Noise Fraction (MNF) and Decorrelation Stretch (DS)  
In this study, our results revealed that the MNF5 represents mantle peridotite information and MNF3 represents topographic information. Therefore, we selected MNF3-MNF5-MNF2 and placed them in the RGB channel, respectively; the RGB image showed an exquisite contrast between different rock units in the study area. This contrast enabled better detection of the range of ophiolitic mantle peridotite as fluorescent green in color (Figure 9a). Other geological units, such as conglomerate, were purple in color, mainly distributed in the northeast corner of the study area and distinguished from the Gangdese granite based on texture features, and metamorphic slate in brown in the southeast corner of the image. Moreover, using the MNF bands as inputs for the decorrelation stretch technique results in convenient discrimination for all the exposed rock units through DS of b7-MNF5-MNF3 FCC in RGB (Figure 9b). Their combined composite delivered images with better saturation and color contrast.