esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technical Overview
### Implementation Overview  
Implementing the real-time function of the adaptive ARA mix model (see Figure 9) was a challenging procedure due to the following restrictions: (a) the low-latency of the model, and (b) the sound quality of the synthesized virtual sound source. The model was developed in Simulink, the programming environment of Matlab for simulations. The window size was set at 1024 samples and the sample rate at 48 kHz. The system was single-rate. The window size was restrictive for the FFT analysis and for the sampling time of the model. Tests have shown that the window size of 1024 samples can support the adaptive ARA mix model of a seven band analysis. Furthermore, the window size of 1024 samples affects the smoothness of mixed signals, because of the dynamic level changes in frequency bands. After empirical tests, the use of a filterbank with 50% overlapping between filters was chosen, as referred to in Section 3.4. Besides the programming environment, a TASCAM US-16x8 soundcard was used as an audio interface. Additionally, a mini mixer (Behringer XENYX502) was used for routing, and an i7 860 with 8 gb ram computer for processing.  
For the binaural recording of the real environment and the ear canal filter implementation, the evaluation board of analog devices--ADAU1761 Figure 10 and the in-ear headset of Philips SHN2500 Figure 11 with the noise cancellation system disabled were used.  
In addition, a module was added to the GUI to facilitate the control of the \(BL\) difference between the virtual and real component. The purpose of this was the investigation of different \(BL\) mix loudness modes, i.e., of the effect of different mix levels on the audience. In the final implementation there are three such \(BL\) mix loudness modes: one mode of zero difference between real to virtual acoustic environment, one of three and one of six phons difference (low \(BL\) mix, medium \(BL\) mix, high \(BL\) mix). Through the GUI that controls the model and the demo sessions, the virtual source and real environment recordings were selected to be propagated through the speakers, Figure 12. There is also the option of a 3-scale \(BL\) mix mode (low, mid, high). Finally, the option of automatic or manual spatialization of the virtual source is provided, as well as some advanced controls for extra adjustments of the \(BL\) mix. Essentially, the user does not need any of these controls in an ARA scenario, except for the knob of the audio augmentation level to personalize the mix.  
Figure 10: Binaural recording processor.  
Figure 9: This figure presents the implementation overview.
# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 4 Experiments and Analysis
### Ablation Experiments  
In this section, we probe the contribution of each model component experimentally by setting up three ablation models, which we then compare with the intact model termed multiscale backcast convolution neural network (MBCNN). The none-backcast variant of the model involved removal of the feedback branch. The none-dilation model was designed to test the role of the multiscale convolution module. This variant of MBCNN only implemented standard convolution instead of multiscale convolution. Finally, the none-dilation-backcast variant lacks both the backcast branch and the multiscale convolutional layer. We introduced this variant to study the combined effect of these two model components. Table 3 shows results from the ablation experiments, averaged over three experiments.  
It is clear that removing the feedback branch reduced performance: MSE, MAE, and CORR decreased by 2.0%, 2.5%, and 0.58%, respectively. After removing the multiscale convolution, performance decreased by 1.66%, 3.69%, and 0.64%, respectively. Removing both components caused the most obvious degradation in performance with losses of 3.01%, 4.19%, and 0.94% for the three metrics, respectively. However, thanks to the advantages brought by frequency domain angle analysis, the residual performance obtained after  
Figure 5: Part of the test set prediction results for sensors 100–500. The curves are the prediction results of each sensor. The “Input”, “GT”, and “Prediction” below each curve correspond to the frequency domain representation of the input time series, the ground truth series, and the model prediction result. The core frequency components are accurately captured by our model, especially the low-frequency components with large amplitude.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **MAE** & **CORR** \\ \hline MBCNN & 0.6272 & 0.3629 & 0.8592 \\ none-backcast & 0.6398 & 0.3722 & 0.8542 \\ none-dilation & 0.6376 & 0.3763 & 0.8537 \\ none-dilation-backcast & 0.6461 & 0.3781 & 0.8511 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of Ablation Experiments.  
removing the two components is still better than most baseline model indicators, and only MAE and CORR indicators are worse than those associated with LSTM.
## 2 Methods
### Selection of Studies and Eligibility Criteria
It presents the core keywords, target population, health outcomes, and exclusion criteria that shape the search process. This structured approach ensures the identification of relevant studies by systematically organizing the key terms for database queries. During the literature collection process, certain biases were introduced. For example, some studies did not use virtual reality (VR) device games but instead employed the CAVE system or used augmented reality (AR) devices such as the Hololens. Additionally, some studies did not use self-developed games but opted for existing commercially available games for their experiments. These studies were excluded in subsequent screenings. However, due to the large number of keywords and selection criteria, some articles that \begin{table}
\begin{tabular}{c c} \hline \hline
**Keywords** & **Related Words** \\ \hline Core keywords: VR games & “Virtual reality” OR “VR” OR “immersive game” \\  & “VR games” OR “virtual reality games” OR “exergames” \\ \hline Target group: older persons & Keywords for the target audience include: “elderly” OR “older adults” OR “senior” OR “aging population” OR “aged” \\  & Screening healthy older adult populations: “healthy” OR “without complications” OR “community-dwelling” \\ \hline Health effects: cognitive function” OR “memory” OR “attention” \\ function and physical health & “executive function” OR “cognitive choice” \\  & Physical Health and Motor Skills: “physical activity” OR “balance” OR “coordination” OR “motor skills” OR “gait” OR “fall prevention” \\ \hline Exclusion: VR games that do & “non-hardware-based VR” OR “without standalone hardware” \\ not include discrete hardware & “non-headset VR” OR “computer-based VR” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Related keywords. met the inclusion standards may not have been identified, which could lead to potential bias in the research findings. In the end, we designed the following search formula, which was entered into the database for retrieval:  
(\"virtual reality\" OR \"VR\" OR \"immersive game\" OR \"VR games\" OR \"virtual reality games\" OR \"exergames\")  
AND  
(\"elderly\" OR \"older adults\" OR \"senior\" OR \"aging population\" OR \"aged\" AND  
(\"cognitive function\" OR \"memory\" OR \"attention\" OR \"executive function\" OR \"cognitive decline\" OR  
\"physical activity\" OR \"balance\" OR \"coordination\" OR \"motor skills\" OR \"gait\" OR \"fall prevention\")  
NOT  
(\"non-hardware-based VR\" OR \"without standalone hardware\" OR \"non-headset VR\" OR \"computer-based VR\")
# A Domain-Based, Adaptive, Multi-Scale, Inter-Subject Sleep Stage Classification Network
## 3 Experiments
### Results
In addition, the sensitivity of the four classes (wake, REM, light sleep, deep sleep) on SHHS2, SHHS1, and MESA before DA is (77.4%, 14.5%, 50.3%, 13.2%), (84.8%, 20.5%, 49.7%, 24.7%), and (70.0%, 12.2%, 56.8%, 12.6%), respectively, while their sensitivity after DA is (98.0%, 69.7%, 86.7%, 69.3%), (97.5%, 73.0%, 79.4%, 72.0%), and (97.3%, 69.5%, 81.2%, 59.3%), respectively. Before DA, deep sleep and REM are difficult to distinguish because the model can easily overlearn on the other two classes, which are wake and light sleep, and ignore the experience of these two classes. Meanwhile, the domain difference leads the class experience learned by the model from the training set to apply to the test set with deviation. In the process of domain alignment, to reduce the distribution difference between the training set and the test set, the multi-class focal loss uses weights and the factor to increase the error loss of hard samples from different classes. When the prediction probability of difficult samples is biased toward other error classes, their loss value is much larger than the value calculated using the cross-entropy loss function, which causes the model to pay more attention to the samples that are difficult to learn. Therefore, the model learns the information on deep sleep and REM better with the help of domain alignment based on MMD with the multi-class focal loss. There are some subjects in the test set whose results are much higher or far lower than the average, as shown in Figure 7, depending on the matching degree between the class distribution of these subjects and the classifier. For some subjects whose feature distribution is too far from the feature distribution of the training set, the adjustment effect of features after DA is limited, which leads to the situation in which the prediction performance of these subjects is far below the average level. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **CNN Block** & **Residual Module** & **Residual Module** & **Residual Module** & **Multi-Scale Residual** \\  & **Module** & **(Kernel Size: 5)** & **(Kernel Size: 11)** & **(Kernel Size: 21)** & **Module (Proposed)** \\ \hline SHHS2 & 77.5\% (0.72) & 78.6\% (0.73) & 79.4\% (0.73) & 85.4\% (0.84) & 84.9\% (0.84) \\ SHHS1 & 72.1\% (0.61) & 67.4\% (0.56) & 77.5\% (0.76) & 76.5\% (0.71) & 82.7\% (0.79) \\ MESA & 78.7\% (0.69) & 81.4\% (0.76) & 82.9\% (0.77) & 80.7\% (0.72) & 86.8\% (0.84) \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of sleep staging with different feature extractors. Figure 6: Confusion matrix of inter-subject sleep staging before and after domain adaptation based on the proposed network. (**a**) Confusion matrix for SHHS2, (**b**) confusion matrix for SHHS1, and (**c**) confusion matrix for MESA. Figure 7: An example of the accuracy (ACC) and Kappa (CK) of each subject in the test sets.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design
### Receiver Model
Then, the receiver filter \(\mathbf{\Gamma}_{\pi\chi}^{(L)}=\left(\mathbf{\Gamma}_{\pi\chi}^{(L)}\right)^{*}\) with only \(LM\) filter coefficients is applied. Thus, the down-sampling process by a factor \(L\), represented by \(\left(\mathbf{R}^{(L)}\right)^{T}\), is needed to get \(M\) samples that match the transmitted data in terms of the number of symbols on the \(k\)th subcarrier. Finally, the resulting signal is converted to the time domain by applying IDFT matrix \(\mathbf{Z}_{M}^{H}\). All these operations can be represented by the Hermitian transpose of the same single matrix used in the transmitter \(\mathbf{A}^{H}\), which corresponds to the GFDM matched filter equalizer. In this case, the estimate of data bits sequence can be obtained after the de-mapping process, as  
\[\hat{\mathbf{D}}=\mathbf{A}^{H}\mathbf{Y}_{d}^{T}, \tag{19}\]  
noting that \(\hat{\mathbf{D}}=[\hat{\mathbf{d}}_{1},\dots,\hat{\mathbf{d}}_{U}]\in\mathrm{ C}^{KM\times U}\), where \(\hat{\mathbf{d}}_{u}=[\hat{\mathbf{d}}_{u,0}^{T},\hat{\mathbf{d}}_{u,1}^{T}, \dots,\hat{\mathbf{d}}_{u,K-1}^{T}]^{T}\in\mathrm{C}^{KM}\). The formulas from Equations (16)-(19), may be represented in an equivalent expression to Equation (19), as  
\[\hat{\mathbf{D}}^{T}=\sum\limits_{l=0}^{NM-1}\left(\mathbf{W}_{d,l}(\mathbf{W }_{a})^{H}\mathbf{H}_{l}\mathbf{D}^{T}\mathbf{A}^{T}\mathbf{E}_{l+1}\left( \mathbf{A}^{H}\right)^{T}\right)+\sum\limits_{l=0}^{NM-1}\left(\mathbf{W}_{d,l }(\mathbf{W}_{a})^{H}\mathbf{N}\mathbf{E}_{l+1}\left(\mathbf{A}^{H}\right)^{T }\right). \tag{20}\]
###### Abstract  
This paper proposes a multi-convolutional neural network (CNN)-based system for the detection, tracking, and recognition of the emotions of dogs in surveillance videos. This system detects dogs in each frame of a video, tracks the dogs in the video, and recognizes the dogs' emotions. The system uses a YOLOv3 model for dog detection. The dogs are tracked in real time with a deep association metric model (DeepDogTrack), which uses a Kalman filter combined with a CNN for processing. Thereafter, the dogs' emotional behaviors are categorized into three types--angry (or aggressive), happy (or excited), and neutral (or general) behaviors--on the basis of manual judgments made by veterinary experts and custom dog breeders. The system extracts sub-images from videos of dogs, determines whether the images are sufficient to recognize the dogs' emotions, and uses the long short-term deep features of dog memory networks model (LDFDMN) to identify the dog's emotions. The dog detection experiments were conducted using two image datasets to verify the model's effectiveness, and the detection accuracy rates were 97.59% and 94.62%, respectively. Detection errors occurred when the dog's facial features were obscured, when the dog was of a special breed, when the dog's body was covered, or when the dog region was incomplete. The dog-tracking experiments were conducted using three video datasets, each containing one or more dogs. The highest tracking accuracy rate (93.02%) was achieved when only one dog was in the video, and the highest tracking rate achieved for a video containing multiple dogs was 86.45%. Tracking errors occurred when the region covered by a dog's body increased as the dog entered or left the screen, resulting in tracking loss. The dog emotion recognition experiments were conducted using two video datasets. The emotion recognition accuracy rates were 81.73% and 76.02%, respectively. Recognition errors occurred when the background of the image was removed, resulting in the dog region being unclear and the incorrect emotion being recognized. Of the three emotions, anger was the most prominently represented; therefore, the recognition rates for angry emotions were higher than those for happy or neutral emotions. Emotion recognition errors occurred when the dog's movements were too subtle or to fast, the image was blurred, the shooting angle was suboptimal, or the video resolution was too low. Nevertheless, the current experiments revealed that the proposed system can correctly recognize the emotions of dogs in videos. The accuracy of the proposed system can be dramatically increased by using more images and videos for training the detection, tracking, and emotional recognition models. The system can then be applied in real-world situations to assist in the early identification of dogs that may exhibit aggressive behavior.  
convolutional neural networks; dog detection; dog tracking; dog emotion recognition; long short-term memory +
Footnote †: journal: Medical Image Processing
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 2 Broadband Quasi-Isotropic Antenna Design
### Parameter Studies  
To understand the working mechanism of the proposed antenna, parameter studies are carried out on the critical design parameters. These studies focus on achieving a nearly perfect isotropic radiation pattern while maintaining its broad bandwidth performance. The design parameters and values are illustrated and listed in Figure 3a and Table 1, respectively. In each study, only a single parameter is varied, while the values of all other parameters are kept the same. We plot the reflection coefficient and gain deviation.  
We first check the radius of the vacant ring, \(r_{c}\), since the behavior of the proposed quasi-isotropic radiation is strongly dependent on the phase difference between the two fork-shaped dipoles. The vacant rings extend the length of the dipole and create an orthogonal phase difference on the dipoles. In addition, CP waves can also be achieved due to the existence of a phase delay between the two dipoles. As shown in Figure 6a, when the radius of the vacant ring increases, the bandwidth is somehow improved, whereas the gain deviation worsens sensitively with respect to \(r_{c}\). This is due to the variation in the desired phase delay offered by the vacant ring. The next study is on the thickness of substrate \(h\) since the gain deviation is significantly affected by the vertical current component. As shown in Figure 6b, when \(h\) is smaller, the gain deviation improves due to the lower effect of the vertical currents. The impedance bandwidth is also improved. Lastly, the angle between the fork-shaped arms \(\alpha\) is examined. As previously mentioned, the proposed fork-shaped arm design possibly increases the impedance bandwidth while providing a more uniform current at the dipole conductors. Figure 6c illustrates again that changing \(\alpha\) has a huge impact on the antenna impedance bandwidth, as well as on the radiation properties. This is mainly due to variations in the resonant frequency and current distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value** \\ \hline \(L_{p}\) & Length of the primary dipole & 24.5 \\ \(Ls\) & Fork-shaped arm length & 22 \\ \(W\) & Width of the dipole & 1.5 \\ \(\alpha\) & Angle between the dipoles & 11 \\ \(w_{c}\) & Width of the vacant ring & 0.25 \\ \(r_{c}\) & Radius of the vacant ring & 2.5 \\ \(r\) & Radius of the substrate & 25 \\ \(h\) & Thickness of the substrate & 0.254 \\ \hline \hline \end{tabular} Note: All of the values are in millimeters except for \(\alpha\), which is in degrees.  
\end{table}
Table 1: Antenna design parameters.  
Figure 5: Simulation results of the performance of the crossed dipole antennas in the design stage shown in Figure 4. (**a**) Reflection coefficient. (**b**) Gain deviation.
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 2 Descriptive Statistics
### Traffic by Applications  
Let us divide the application classes into three groups, as shown in Table 6. Figures 1-3 illustrate the traffic profile in each group. Group No. 1 (Figure 1) is similar to the total traffic profile, and the time series is seasonal. For example, for \"Web applications\", the correlation coefficient for upload and download traffic is 0.961. Group No. 2 (Figure 2) is not similar to the total traffic profile and is non-seasonal with outliers. In \"Terminals\" application, the correlation coefficient is 0.935. Group No. 3 (Figure 3) is not similar to the total traffic profile and is seasonal. For example, in \"VoIP\", the coefficient is 0.708.
## 3 Materials and Methods
### Data
#### 3.1.2 Ground Measurements
Figure 1: The locations of the eddy covariance flux tower, field sampling plots, and target area defined by the flux footprint at the Bilý Kriz research site. Additionally, we obtained ground measurements of vegetation properties characterizing spruce forest conditions at the site, which were collected during a joint ground/airborne campaign on 31 August 2016. The location of the field sampling plots was pre-determined by previously existing forest inventory plots established by the Institute of Forest Ecosystem Research (IFER), Czech Republic, in the vicinity of the ecosystem station. The coordinates of the central point of each plot were measured using a differential global positioning system. The measured biochemical vegetation properties included leaf chlorophyll content (\(C_{\text{ab}}\)), leaf carotenoids content (\(C_{\text{ca}}\)), leaf water content (\(C_{\text{w}}\)), and leaf dry matter content (\(C_{\text{dm}}\)). The biochemical properties were measured in a laboratory (leaf pigments were extracted according to [PERSON] et al. (2016)) for needles sampled from the top and bottom part of the crown of three representative trees selected at each sample plot. The measured structural property was the leaf area index \(LAI\), which was determined at the plot level using a Plant Canopy Analyser LAI-2200 instrument (LI-COR, Lincoln, NE, USA). More details about the ground measurements and data pre-processing are reported in [PERSON] et al. (2016). These ground measurements were compatible with airborne hyperspectral data acquired on 31 August 2016. For this study, we used the minimum, mean, and maximum measurements of three plots that were located inside and right at the edge of the target area (Figure 1). These three plots represented a Norway-spruce-dominated forest stand. Table 2 provides the means of the measured vegetation properties. We validated the retrieved vegetation properties against the measured means.
# Experimental Study on the Permeability of Microbial-Solidified Calcareous Sand Based on MICP
## 2 Materials and Methods
### Experimental Strategy
#### 2.3.2 Permeability Test  
As shown in Figure 5, the grouting device was also adopted to carry out the variable head permeability test, where the perplex pipe on the scale plate is used as a variable head pipe. By utilizing the head difference, seepage flows can be formed in the mold from the bottom to top.  
The initial permeability coefficient and after each cycle of MICP treatment, all sand samples were measured according to the standard for variable head penetration tests [48]. The procedure of permeability test was as follows:  
1. The sand sample in the mold was sealed to be watertight and fully saturated.
2. The deionized water was injected into the variable head pipe until the fluid was overflowing without bubbles, then the pinchcock was closed.
3. The variable head pipe was injected with deionized water to the required height (1\(\sim\)1.5 m). Then, the pinchcock was opened, and the changing of the water head and time was recorded. The measurement was repeated 2\(\sim\)3 times at the same water head.
4. The measurement was repeated 5\(\sim\)6 times at different water heads for each sand sample. When the permeability coefficient measured was within the allowable error (\(\pm\)2.0 \(\times\) 10\({}^{-n}\) cm/s), the test was ended. The permeability coefficient (\(k\)) can be calculated using the following equation:  
\[k=2.3\frac{aL}{A(t_{2}-t_{1})}log_{10}\frac{h_{1}}{h_{2}} \tag{5}\]  
where \(k\) is the permeability coefficient of the sand sample, cm/s; \(a\) is the cross-sectional area of standpipe, cm\({}^{2}\); \(L\) is the center distance between two pressure-measuring tubes, cm; \(A\) is the cross-sectional area of sand column cm\({}^{2}\); \(t_{2}\) is the ending time of permeability test, \(s\); \(t_{1}\) is the beginning time of permeability test, \(s\); \(h_{1}\) is the initial water head, cm; and \(h_{2}\) is the ending water head, cm.