esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Preliminaries and Notations  
#### 3.1.1 System Model  
The UWSN is represented by an undirected graph \(G(t)=(V,E(t))\) at time slot \(t\), where \(V\) is the set of sensor nodes and \(E(t)\) is the set of links between pairs of nodes within the communication range of each other at time slot \(t\). As depicted in Figure 1, \(N\) sensor nodes are tethered to the water bottom via wires, and move passively due to internal currents or vortices.  
The moving region is a semi-sphere with a radius of \(R_{i}\) while the communication range of sensor \(v_{i}\) is denoted by \(CR\). \(C_{i}(t)\) denotes the 3D-coordinate of \(v_{i}\in V\) at time slot \(t\), which is expressed as \((x_{i}(t),y_{i}(t),z_{i}(t))\). If \(|\mathbf{C_{i}(t)}-\mathbf{C_{j}(t)}|\leq CR\), then \((v_{i},v_{j})_{t}\in E(t)\) is a bidirectional link and \(v_{j}\) is a neighbor of \(v_{i}\). \(H(i,t)\) denotes the set of neighbors of \(v_{i}\) at time slot \(t\).  
Meanwhile, we have \(M\) sink nodes deployed on the water surface and the set of sink nodes are denoted by \(S\). Additionally, \(S(i,t)\) denotes the set of sink nodes which are within the communication range of \(v_{i}\). sink node \(s_{m}\in S\) is mounted on an autonomous draft so that \(s_{m}\) can hold its position. In addition, they are equipped with acoustic modems for sensors and RF modems for satellites, along with access to GPS localization. Data packets are periodically generated and \(P_{i,t}\) denotes the set of packets in \(v_{i}\) at time slot \(t\) while \(p_{i,t}\) denotes the \(p\)-th packet in \(v_{i}\) at time slot \(t\). Sensor nodes learn to forward packets to sink nodes in terms of Value of Information and the energy consumption of sensor nodes. Packets are supposed to be received by sink nodes via multi-hop relays.  
In order to leverage the broadcast property of the wireless channel, each packet is acknowledged implicitly. Specifically, after transmitting a packet, the sender starts listening to the channel. If it overhears the packet being retransmitted within a certain period of time, the packet is regarded as successfully transmitted; otherwise, the packet is considered to be lost and the sensor node will learn to retransmit it, which will be described in detail in Section 4.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 3 Multi-Parameter Optical Analysis and Design of PM Scanning Heads
### Scan Angles and FOV
The interval of \(\alpha\) is considered between 0 (for which the PM would become a circle) and \(\pi\), which would correspond to the case of the monogon (i.e., single facet pyramidal scanner). For \(\alpha\to 0\), the graphs of both \(\theta_{min}\) and \(\theta_{max}\) start from \(arcsinx\) for \(x\leq 1\) (and in particular from \(\pi/2\) for \(x=1\)). However, the smallest value of \(\alpha\) is actually \(\pi/n_{max}\) (where \(n_{max}\) is the maximum technologically appropriate value of \(n\), as discussed in Section 6). This gives the limit values of the scan angles, corresponding to the lowest admissible FOV. For \(x>1\), the minimum value of \(\alpha\) is, from Equation (12) in Table 1,  
\[\alpha_{m}=\arccos 1/x. \tag{21}\]  
For this interval of \(x\), the value of \(\alpha\) for which \(\theta_{min}=0\) is \(\alpha_{max}=\text{atan}x>\pi/4\). Thus, the \(\alpha e[\alpha_{m},\text{atan}x]\) interval includes the (common) case of the square PM. * A useful limit value of \(\alpha\) is \(\pi/3\), corresponding to \(1<x<\sqrt{3}\). However, as observed above, the case of a too high x (for a small n) must be avoided, to minimize vignetting. Therefore, in practice, \(n\) is equal to 4 or higher, as discussed in another context in [32]. An exception may be the \(n=2\) case (of a plane or pyramidal rotational mirror with two facets) or the \(n=1\) case (of the pyramidal monogon). To conclude this part of the study, the results of the analysis in Figure 6 are synthesized in Figure 5b, for the most relevant parameter intervals of \(x<1\) and of \(\alpha<\pi/4\) (therefore of \(n>4\)). The two surfaces correspond to the scan angles \(\theta_{\min}\) and \(\theta_{\max}\), and they meet in the O\(\theta\)x plane in the \(\theta=\arcsin(x)\) curve. One can obtain, with transversal sections in these surfaces in Figure 5b, the graphs in Figure 6(a1-a3),(b1).
## 2 Methodology
### PEV Arrival, Departure and State of Charge  
It is considered that the drivers that charge their PEV at OAKA are the drivers that work and exercise in the sport center, the drivers living in the nearby area as well as the drivers who charge their PEV at OAKA as a stopover before their destination. Most people go to work between 7:00 to 9:00. Furthermore, the majority of sport activities are between 9:00 to 13:00 and 17:00 to 20:00. Hence, most of the arrivals are estimated to take place in these hours as the majority of the PEV drivers who parked at the sport center are those who are training there. Moreover, most people return from their morning work between 15:00 to 17:00. Taking the above into consideration, the arrival profile of PEV is presented in Figure 1. Number of PEVs represent the number of PEV that charge at the sport center every day. Five different scenarios are considered as explained in Section 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PEV Type**} & \multirow{2}{*}{**PEV Model**} & \multirow{2}{*}{**PEV Appearance Possibility**} & \multicolumn{2}{c}{**On-Board Charger**} \\  & & & & **Location (kW)** & \\ \hline  & Renault Zoe [19] & 0.18 & 52 & 22 & 46 \\  & Tesla Model 3 [20] & 0.23 & 57 & 11 & 170 \\  & Volkswagen e-Golf [21] & 0.1 & 32 & 7.2 & 40 \\  & Hyundai Kona EV [22] & 0.08 & 64 & 11 & 77 \\  & Nissan Leaf [23] & 0.09 & 37 & 6.6 & 46 \\  & BMW i3 [24] & 0.07 & 37.9 & 11 & 49 \\ \hline  & Vespa Elettrica [25] & 0.09 & 4.2 & 1.9 * & 3 * \\  & Zero SR [26] & 0.06 & 12.6 & 3.3 & 6 \\ \hline   EB & Electric Minibuses [27] & 0.1 & 160 & 19.2 & 100 * \\ \hline \hline \end{tabular}
\end{table}
Table 1: PEV type, model, possibility appearance to the sport center, battery capacity and charging limitations.  
The duration of sport activities is usually between 1 to 4 h. In addition, most people work about 8 to 9 h. Hence, the parking duration probability depending on the arriving time is presented in Figure 2.  
The probability arriving SoC of the PEVs was estimated as in [1]. Figure 3 depicts the probability of the arriving PEV's SoC.  
Figure 1: PEV arrival profile.  
Figure 2: PEV parking duration probability when they arrive between (**a**) 00:00 to 8:00, (**b**) 9:00 to 13:00 or 17:00 to 20:00, (**c**) 14:00 to 16:00 and (**d**) 21:00 to 23:00.
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 4 Experiments and Analysis
### Implementation Details  
We use AdamW [36] as the optimizer. We initialize the model parameters by inflating the 2D ImageNet pre-trained weights to 3D. The initial setting is as follows: learning rate \(1\times 10^{-4}\); weight decay in AdamW \(5\times 10^{-2}\); dropout 0.5; training epoch 200. All experiments are implemented in Pytorch over 5-folds.  
In the training phase, we estimate average vehicle speed in each short period based on the motion of the vehicle. Therefore, we select frame sequence at equal intervals from the video samples. This process is different from that in video action recognition, where successive frames are extracted from video footage. The input of 16 frames remains unchanged in the training and test phases.  
**Data Augmentation**: It is acknowledged that data augmentation is of great importance in deep neural networks. Thus, we apply data augmentation in our model. In the training phase, we first resize a video image to \(256\times 256\) and then randomly crop a \(224\times 224\) patch from the \(256\times 256\) image. We randomly choose the starting frame among video images with the consideration that the starting frame is sufficiently early so that a desired number of frames is guaranteed. The time intervals between frames, chosen as model inputs, are fixed and uniform. We also apply random left-right flipping for each video in the training phase. In the test phase, we resize an image to \(256\times 256\) and then take the \(224\times 224\) center patch.
# Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average
## 5 Conclusions  
We proposed a novel action recognition network (ARN) using a short-term pixel-difference and bidirectional moving average. The proposed ARN generates deep features using the short-term pixel-difference image to combine spatio-temporal information and the temporal relationship between adjacent frames. The proposed network gives a higher weight to the middle frames to train important action elements. Finally, the previously generated information and result of C3D are fused to improve the performance of the proposed network. The late fusion result proves that the temporal relationship is important in improving the recognition performance.  
Experimental results showed that ARN succeeded in action recognition with a small dataset. A combination of short-term pixel-difference-based deep features and bidirectional moving average significantly improved the performance of the baseline network. Although the ARN additionally takes temporal information into account, it does not require additional computation compared with 2D CNN. As a result, the ARN is suitable for real-time action recognition in terms of both recognition accuracy and computational efficiency.  
[PERSON] initiated the research and designed the experiments, [PERSON] and [PERSON] acquired the test images and performed the experiments, and [PERSON] wrote and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported partly by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) [2017-0-00250, Intelligent defense boundary surveillance technology using collaborative reinforced learning of embedded edge camera] and partly by the ICT R & D program of MSIP/IITP [2014-0-00077, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis and supported by the Chung-Ang University Research Scholarship Grants in 2019.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The UCF101 datasets presented in this study are openly available in [13] and can be found here [[https://www.crcv.ucf.edu/data/UCF101.php](https://www.crcv.ucf.edu/data/UCF101.php)]([https://www.crcv.ucf.edu/data/UCF101.php](https://www.crcv.ucf.edu/data/UCF101.php)). The KTH datasets presented in this study are openly available in [14] can be found here [[https://www.csc.kth.se/cvap/actions/](https://www.csc.kth.se/cvap/actions/)]([https://www.csc.kth.se/cvap/actions/](https://www.csc.kth.se/cvap/actions/)).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Speed** \\ \hline RGB & 48.40 & 0.031 sec. \\ RGB+Diff & 54.62 & 0.075 sec. \\ RGB+Diff+Moving avg. & 55.97 & 0.075 sec. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational complexity comparison.
## 1 Introduction
They found that using a five-slice dynamic patch-based sampling algorithm resulted in high performance, with an AUC, sensitivity, and specificity of 97.1%, 96.7%, and 100%, respectively. [PERSON] et al. [19] compared the accuracy of CNN classification with classification by an experienced physician, and it was concluded that a CNN network can help to identify ACL tears from a single MRI slice with good sensitivity and specificity while being superior to physician classification. All of these studies have contributed to the development of intelligent ACL tear diagnosis. However, there are areas for improvement in the studies mentioned above. First, some of the studies [13; 17; 19] were trained and evaluated using slices, which is inconsistent with the actual clinical application in medicine. The exact circumstance should be categorized as a person using all their slices. The second is that most of the studies [12; 14; 18] define ACL tear classification as a binary classification, which cannot differentiate tear conditions according to the three given classes and requires manual judgment in clinical diagnosis. A complex training problem caused by the small sample size and data imbalance arises in the triple classification study of ACL tears in a human unit. The human-based ACL triple classification problem, which is crucial for clinical medical aid diagnosis, is effectively improved in this paper by utilizing channel correction and ordinal loss. This study effectively models MRI images of the ACL based on the KneeMRI dataset with a deep learning algorithm. This study also improves the evaluation metrics of sensitivity, specificity, and accuracy for diagnosing ACL tears at baseline based on triple classification training for ACL MRI images on a small data set, which is different from most binary classification tasks. In addition, this work can provide reference information for the selection of clinical treatment plans and surgery, which is promoted in clinical aid diagnosis. The main contributions of this paper are as follows:  
1. In this paper, a channel correction module is used for the correlative MRI dataset. The negative effects of different ACL MRI image distributions for various patients are attenuated. 2. An ordinal loss function is introduced into this task because ACL tear grades are as sequential as knee injury grades, and the ordinal loss function serves as a solution to the training difficulties caused by label imbalances in a small sample dataset. 3. The effectiveness of the designed network structure is tested in different backbone networks, and the generalizability of the framework is demonstrated, as it can be added to many backbone networks. 4. The ablation experiments on ResNet-18 show that the channel correction module, spatial attention module, and ordinal loss function used in this paper are practical and improve the accuracy by 6.6% over the baseline.
# Physical and Mechanical Properties and Damage Mechanism of Sandstone at High Temperatures
###### Abstract
[PERSON] [18] conducted cyclic loading tests on granite, monitored the thermal damage evolution of the rock during the warming and cooling process in real time using a specially designed acoustic emission testing system, and established a thermal damage evolution model considering both the warming and cooling processes; the numerical results were in good agreement with the experimental results. While grasping the changing pattern of such physicochemical properties, it is also necessary to explain this pattern through microscopic mechanisms. Currently, the commonly used means are scanning electron microscopy (SEM) [19], X-ray diffraction (XRD) [20], and computed tomography (CT) [21]. [PERSON] [22] explored the effects of high temperatures on the microstructure, mineralogy, and physicochemical properties of the Barakar sandstone, which is one of the largest underground coal mine fire zones in the world, using scanning electron microscopy (SEM) and electron microprobe microanalysis. Based on the experimental results, the effects of temperature on the sandstone were categorized into three temperature zones. Using SEM and XRD, [PERSON] [23] found that the compound effect of the creation of new cracks and pores and the chemical change in the mineral composition under the action of high temperatures led to a reduction in the strength of the rock. The chemical change in mineral composition is an important factor affecting the physical and mechanical properties of rocks. For example, quartz transforms from \(\alpha\)-quartz to \(\beta\)-quartz at 573 \({}^{\circ}\)C and then \(\beta\)-quartz to \(\gamma\)-quartz when the temperature reaches 870 \({}^{\circ}\)C [24]. Kaolinite will undergo a dehydrodxylation reaction at 480-600 \({}^{\circ}\)C, changing into the meta kaolinite phase; at about 850-1000 \({}^{\circ}\)C, the meta kaolinite phase transforms into the Al-Si spinel phase [25]. In this paper, the effects of different temperatures (20-1200 \({}^{\circ}\)C) on the physical properties (color, mass, volume, density, and elastic wave velocity) and mechanical properties (uniaxial compressive strength, peak strain, and modulus of elasticity) of sandstones are mainly investigated by using an acoustic emission testing system to analyze the damage information during uniaxial compression, and SEM investigates the changes in the microstructure. On this basis, the effect produced by the heating rate on the mechanical properties of sandstone is further discussed. Compared with the existing studies, this paper collectively analyzes the intrinsic connection between the physical and mechanical properties, acoustic emission information, and macro and delicate damage patterns of sandstone after high temperature so that the study of the thermal damage mechanism of sandstone is more systematic. The response characteristics of the macroscopic phenomenon and the microscopic mechanism are studied in more depth.
## 3 Results
### \(H_{2}O_{2}\) Concentration, Chl-a Content, and Carotenoid Content  
The H\({}_{2}\)O\({}_{2}\) content changes in _M. aeruginosa_ (Figure 2A(i)) and _P. foetida_ (Figure 2A(ii)) showed a similar pattern; a more pronounced effect on H\({}_{2}\)O\({}_{2}\) production was seen with an extended period of dark processing. The H\({}_{2}\)O\({}_{2}\) content in _M. aeruginosa_ cells had an extreme increase of 76.6% between the fifth month and the sixth month, while an increase of 117.0% in _P. foetida_ cells between the sixth month and the seventh month was observed. Based on these differences, the following classification of both cyanobacteria via the length of the dark treatment period was suggested. _M. aeruginosa_ can be divided into two groups: 0-5 months and 6-12 months, and _P. foetida_ can be divided into two groups: 0-6 months and 7-12 months. Within the groups, no significant differences were found (one-way ANOVA, \(p\) > 0.1); however, significant differences between the groups were identified (_t_-test, \(p\) < 0.01) (Figure 3A). It was revealed that the H\({}_{2}\)O\({}_{2}\) content per unit protein in _M. aeruginosa_ cells was, on average, 148.5% higher than that in _P. foetida_ (Figure 2A(iii)).  
Figure 2: H\({}_{2}\)O\({}_{2}\) concentration, chlorophyll-a, and carotenoid content in _M. aeruginosa_ and _P. foetida_ recultured following various dark treatment periods. H\({}_{2}\)O\({}_{2}\) contents per one mL of _M. aeruginosa_ (**A(i)**) and _P. foetida_ (**A(ii)**) culture solution was measured and normalized by the total protein content to compare the H\({}_{2}\)O\({}_{2}\) production for both species (**A(iii)**). Chl-a and carotenoid content in _M. aeruginosa_ (**B(i)**; **C(i)**) and _P. foetida_ (**B(ii)**; **C(ii)**), and the alteration of pigment per unit protein (**B(iii)**; **C(iii)**), were also estimated in the same manner. Error bars represent standard errors. Different letters represent significant differences between dark treatment durations (one-way ANOVA, \(p\) < 0.05).  
In _M. aeruginosa_, a slight decline over the dark treatment period was observed for both Chl-a and carotenoid content. It was shown that there was a sharp drop between the initial group (non-dark-treated group) and the one-month dark-treated group (Figure 2B(i),C(i)). In contrast to _M. aeruginosa_, a gradual increase in both Chl-a and carotenoid contents was seen in _P. foetida_. The lowest Chl-a and carotenoid contents of _P. foetida_ appeared in the initial group, with the peak for Chl-a in the last month (Figure 2B(ii)) and the peak for carotenoid in the eleventh month (Figure 2C(ii)). It was indicated that the Chl-a content in _M. aeruginosa_ cells was much higher than in _P. foetida_ cells (Figure 2B(iii)), matching the results of the Fv/Fm value mentioned above. However, regarding the variation in carotenoid content per unit protein, higher initial values were seen in _M. aeruginosa_ compared to _P. foetida_, but starting from the sixth month, an increase in carotenoid content in _P. foetida_ cells was observed, with the highest point in the seventh and eleventh months.
## 3 Data Analysis  
### Simulated Signals Description  
In order to verify the selectors' efficiency, we simulated four different types of the signals: \(s_{1},s_{2},s_{3}\) and \(s_{4}\). The first of the signal \(s_{1}\) is the Gaussian White Noise: \(s_{1}=N(t)\), which corresponds to the bearing vibration in a healthy condition, see Figure 1a. In such a case, we expect that any of the IFB selectors respond significantly. The signal's frequency is \(25,000\) Hz and its length is \(1\) s. Signal \(s_{2}\) corresponds to the locally damaged bearing vibration and it is defined as follows:  
\[s_{2}=ACI\cdot gauspuls(t_{0},fc_{0},bw_{0})+s_{1}, \tag{9}\]  
where \(gauspuls(t,fc,bw)\) is a unity-amplitude Gaussian RF (radio-frequency) pulse at the times indicated in array \(t\), with a center frequency \(fc\) in hertz and a fractional bandwidth \(bw\). \(ACI\) is the amplitude of the cyclic impulses (\(ACI=3\)), \(fc_{0}\) is set to \(2500\) Hz and the frequency modulation of cyclic impulses is equal to \(30\) Hz. Simulated signal \(s_{2}\) has been presented in Figure 1b.  
Signal \(s_{3}\) imitates the bearing operation of the loaded machine in a healthy condition and it is defined as follows:  
\[s_{3}=ANCI\cdot gauspuls(t_{1},fc_{1},bw_{1}), \tag{10}\]  
Figure 1: Exemplarysimulations of the considered signals: (**a**) Gaussian White Noise, (**b**) Gaussian White Noise with the cyclic impulses added, (**c**) Gaussian White Noise with non-cyclic impulses added, (**d**) Gaussian White Noise with cyclic and non-cyclic impulses added.  
where \(ANCI\) is the amplitude of the non-cyclic impulses (\(ANCI=30\)), \(t_{1}\) is the location of the non-cyclic impulses, which has the uniform distribution. Signal \(s_{3}\) has been presented in Figure 1c. The last of the signal is the mixture of previously mentioned, namely, it is defined as follows:  
\[s_{4}=s_{2}+s_{3}.\]  
It imitates the bearing vibrations in case of the loaded machine operating in the unhealthy condition of bearing, see Figure 1d.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 5 Critical Analysis and Open Research Challenges
### Future Research Directions
#### 5.8.7 Optical Wireless Communication and Interference Considerations [249; 250; 251; 252]  
Optical wireless communication, including visible light communication, light fidelity (LiFi), and free-space optical communication, is gaining attention as a complementary technology in B5G and beyond due to its large unlicensed BW and low electromagnetic interference. In dense indoor or urban environments, LiFi offers interference-free operations compared to radio frequency (RF) systems, but it also introduces new interference types, such as optical beam misalignment, multi-user access interference, and noise from ambient light sources. To manage these, AI-based RA and dynamic beam steering methods are proposed, similar to RF-domain IMI. Recent research explores hybrid RF/optical systems where coordinated IM across domains enhance reliability and QoS. Future B5G networks may benefit from cross-domain interference awareness involving both RF and optical channels, especially in UD or latency-critical applications.