esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Decoupling Techniques (DTs) in Ultra-Wideband MIMO Antennas (UMAs)
### Metamaterial/Electromagnetic Band Gap (EBG) Structure  
Metamaterials are artificial materials with a subwavelength feature and spatially varying geometric parameters that manipulate the phase, amplitude, and polarization of a wave [73]. A metamaterial is organized between dual electromagnetically coupled patches to remove the surface wave coupling and improve isolation. In reference [65], the AEs consisted of microstrip-fed modified circular radiators and a partial ground with stubs. A metamaterial structure was loaded around both sides of the feed line for bandwidth improvement, and mutual coupling reduction is displayed in Figure 7a. In paper [66], a four-port MIMO antenna consisted of four circular AEs with modified defected ground plane and periodic EBG structures. The EBG structures were composed of grid patterns on the top patch, as presented in Figure 7b. They were printed on the center of the dielectric substrate to lower electromagnetic coupling between the parallel elements. Additionally, by etching four crescent ring-shaped resonant slots on the radiators, a sharp band-notched characteristic was achieved. In reference [67], by loading an \"EL\" slot into the radiating  
Figure 5: Neutralization line: (**a**) [60] and (**b**) [61].  
Figure 6: Defected ground structure: (**a**) [62], (**b**) [63], and (**c**) [64].  
element along with two identical stubs coupled to the partial ground, impedance matching and radiation characteristics across the band were improved, as presented in Figure 7c. To attain high port isolation, an un-protruded multi-slot (UPMS) isolating element was placed between two closely spaced AEs.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 2 System Description  
The overall structure of the MAI system is mainly composed of the transmission channel, the multichannel receiver and the antenna array. Among them, the transmission channel generates the sounding waveform. The sequence of the pseudo-random phase code is employed to modulate the transmitting waveform, which breaks through the monopulse working mode of the traditional MF radar. With the pulse compression of the complementary code and the gains of the coherent accumulation (pulse accumulation), the MAI system has strong anti-jamming and clutter suppression abilities, both of the mesospheric and ionospheric soundings. The multichannel receiver is responsible for analog processing and digital down-conversion (DDC) of the echo signals. In order to calibrate the amplitude and phase consistency between the receiving channels, an additional reference source is introduced into the device. The electrical length of the feeders connected to the receiver and antennas are strictly measured and adjusted to be equal. The method of these independent transceiver channels does not require high-performance transceiver switches, but also avoids the possible interference. A miniaturized designed antenna array is employed to meet the sounding and the analysis algorithms' requirements, which has the advantages of small size and low cost. In order to adapt to the large bandwidth required for ionospheric sounding, progressive-wave antennas are used as the elements of the array. The system structure diagrams and the main technical indicators of MAI system are shown in Figure 1 and Table 1, respectively.
# Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber-Physical Production Systems
## 2 Background and Related Works
### Cybersecurity Research on Manufacturing System  
Research studies in this domain primarily focus on exploring potential attack scenarios, illustrating the tangible risks posed to manufacturing integrity and security. For instance, [PERSON] et al. [31] demonstrated how NC files could be altered in an attack, leading to the production of defective parts. Similarly, [PERSON] et al. [32] examined the implications of tampering with STL files, which is crucial for additive manufacturing processes, while [PERSON] et al. [33] introduced a qualitative approach for assessing vulnerabilities in cyber-physical manufacturing systems, using compromised CAD files as a case study.  
Moving beyond isolated incident analysis, some researchers have concentrated on developing comprehensive taxonomies of attacks within manufacturing systems. [PERSON] et al. [34] proposed an attack taxonomy centered around quality control systems, suggesting that securing these systems could mitigate a broad spectrum of potential attacks. [PERSON] et al. [12] focused on creating an attack taxonomy for the additive manufacturing process. These contributions showcase the perspective of manufacturing engineers,  
Figure 1: A scenario of advanced persistent threats in the manufacturing system. (Note that the numbers with circles indicate different vulnerabilities).  
highlighting the operational impact of cyber security threats. However, from a cyber security engineering standpoint, these scenarios represent specific instances of broader attack methodologies, such as unauthorized database access leading to information tampering. This inconsistency underscores the need for a unified approach that enables both manufacturing and cyber security professionals to collaboratively address and mitigate cyber security challenges within manufacturing systems.  
Assessing the risk within manufacturing systems necessitates a comprehensive analysis of vulnerabilities that span the intricately linked IT and OT components. The mere identification of isolated vulnerabilities and threats falls short of addressing the multifaceted nature of contemporary systems [35]. Efforts to model these systems are aimed at identifying both discrete vulnerabilities, like outdated software, and systemic weaknesses, such as inadequate network segmentation. Techniques like tree structures, directed graphs, and logic diagrams have become prevalent for conducting overarching cybersecurity assessments or modeling potential exploits [36; 37]. Nonetheless, many existing models, often tailored to specific system architectures or network setups, focus on assessing the probability or impact of particular vulnerabilities, including denial-of-service (DoS) attacks. These models typically lack flexibility and scalability and do not prioritize these aspects during their design phase [36]. Consequently, adapting the current cybersecurity assessment frameworks to new types of vulnerabilities often requires significant modifications, rendering them neither cost-effective nor efficient [38].
## 3 Intelligent Contact Tracing Based on Transmission Characteristics
### Space-Based Indoor Wi-Fi Contact Tracing  
Unlike conventional Wi-Fi-based contact tracing methods that require comprehensive mapping of Wi-Fi fingerprints, our system employs a machine learning model that leverages spatial similarity derived from AP similarity metrics to determine co-location [6]. For space-based indoor Wi-Fi contact tracing, Wi-Fi fingerprints of a confirmed case (\(u\)) and a potential contact (\(v\)) are used to calculate similarity values, as shown in Figure 1. The status of exposed cases is determined through a machine learning model using calculated similarity values. The steps that we followed in the space-based indoor Wi-Fi contact tracing method are described in the following sub-sections.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 6 Sleep Pattern Extraction and Sleep Quality Analysis
### Background Research  
Quantity (sleep duration) and quality (absence of sleep disturbances or disorders) are among the most important metrics of a healthy sleep.  
Researchers found out that there is a U-shaped association between sleep duration and all-cause mortality, where those who sleep the least and the most are at higher risk of morbidity and all-cause mortality [45]. Recommendations [46] by the American Academy of Sleep Medicine and the Sleep Research Society state that less than 7 h of sleep per night on a regular basis is associated with adverse health outcomes, both physically and mentally. Meanwhile, sleep of more than 9 h per night is also not recommended, unless the subjects are young adults or individuals recovering from illnesses or sleep debt. The sleep duration recommended by the National Sleep Foundation [15] for older adults (\(\geqslant\)65 years) is 7 to 8 h per night, while less than 5 or more than 9 h is not recommended.  
In terms of sleep quality, 12 indicators were identified in the National Sleep Foundation's recommendations [16], among which there are 4 sleep continuity variables (sleep latency, number of awakenings, length of awakenings after sleep onset, and sleep efficiency), 5 sleep architecture variables (neural activity-related sleep stages), and 3 nap variables (number of naps per day, nap duration, nap frequency). Additionally, panelists claim that the optimal range of the sleep indicators depends on the age of the subject. They have agreed on 9 age categories and suggested different recommendations for each group. For this study, with the youngest patient being 68 years old, we consider the 'older adults' (\(\geqslant\)65 years) as the relevant age category for sleep quality guideline. Sleep architecture measurements (i.e., the identification of sleep stages) require sophisticated devices, and are out of the scope of this study. We consider sleep continuity, and nap variables to quantify sleep qualities in our system. Optimal conditions for high sleep quality in the target age group are: (i) falling asleep in less than 30 min, (ii) waking up no more than twice per night, (iii) time being awake after sleep onset of no more than 30 min, and (iv) the ratio of total sleep time to time in bed of \(\geqslant\)85%. In terms of naps, the fewer naps, and the shorter the nap duration per day the higher the sleep quality.
## 3 Controller and Observer
### Objective Sliding Mode Control (OSMC)  
We propose a sliding mode surface designed to progressively decelerate as it nears the endpoint. It is expressed as  
\[\mathbf{s}=\dot{\mathbf{e}}+(\alpha+\mathbf{e})f(\mathbf{e})^{m}+\big{(}\beta+\dot{\mathbf{e}} \big{)}f\big{(}\dot{\mathbf{e}}\big{)}^{n} \tag{5}\]  
\(\mathbf{e}\), \(\dot{\mathbf{e}}\) and \(\ddot{\mathbf{e}}\) are defined as  
\[\mathbf{e}=\mathbf{q}_{r}-\mathbf{q} \tag{6}\]  
\[\dot{\mathbf{e}}=\ddot{\mathbf{q}}_{r}-\dot{\mathbf{q}} \tag{7}\]  
and  
\[\ddot{\mathbf{e}}=\ddot{\mathbf{q}}_{r}-\ddot{\mathbf{q}} \tag{8}\]  
The parameters within the sliding mode surface were defined as follows: \(\alpha>0\), \(\beta>0\), \(m>0\), \(1<n<2\). The function f is defined as  
\[f(x)=x+\frac{ax}{\exp(x^{2})} \tag{9}\]  
To prove that the function \(f(x)\) is bounded, it can be expressed as \(\exists A>0\), \(|f(x)|\leq A\). **Proof. **  
\[|f(x)|=\left|x+\frac{ax}{\exp(x^{2})}\right|\leq|x|+\frac{a|x|}{\exp(x^{2})} \tag{10}\]  
Assume  
\[G(x)=x+\frac{ax}{\exp(x^{2})}<x+\frac{a}{x} \tag{11}\]  
When \(x>0\)  
\[G(x)=x+\frac{ax}{\exp(x^{2})}<x+\frac{a}{x} \tag{12}\]  
And \(x<0\)  
\[G(x)=-x-\frac{ax}{\exp(x^{2})}<-\Big{(}x+\frac{a}{x}\Big{)} \tag{13}\]  
Let  
\[H(x)=x+\frac{a}{x} \tag{14}\]  
Taking the derivative of this function yields  
\[H^{\prime}(x)=\left\{ \begin{array}{l}\frac{x^{2}-a}{x^{2}},x>0\\ \frac{-x^{2}-a}{x^{2}},x<0\end{array} \right. \tag{15}\]  
it can be obtained that  
\[H_{\text{min}}=H_{\text{max}}=\lambda\sqrt{a} \tag{16}\]  
Therefore, the function \(f(x)\) is bounded. \(\square\)Differentiating both sides of Equation (5) yields \(\dot{\textbf{s}}\). \[ \begin{array}{ll}\dot{\textbf{s}}=&\ddot{\textbf{e}}+\alpha mf(\textbf{e})^{m-1 }f(\textbf{e})^{\prime}\dot{\textbf{e}}+\textbf{e}f(\textbf{e})^{m}+\textbf{e} mf(\textbf{e})^{m-1}f(\textbf{e})^{\prime}\dot{\textbf{e}}\\ &+\beta nf\big{(}\dot{\textbf{e}}\big{)}^{n-1}f\big{(}\dot{\textbf{e}}\big{)}^{ \prime}\ddot{\textbf{e}}+\dot{\textbf{e}}f\big{(}\dot{\textbf{e}}\big{)}^{n}+ \dot{\textbf{e}}nf\big{(}\dot{\textbf{e}}\big{)}^{n-1}f\big{(}\dot{\textbf{e}} \big{)}^{\prime}\ddot{\textbf{e}}\end{array}
# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 5 Comparison with In-Situ Oceanic Data
This is again consistent with the findings of Figure 3, which shows a positive bias at all radii for category 4 and 5 events;
* H80 constrained by all wind-radii information strongly overestimates Hs when Maria is closest to the storm. This is also consistent with the results of Figure 3 (and the findings of [PERSON] and [PERSON] (2016)), which show that H80 tends to overestimate wind speeds close to the center of strong  
Figure 5: Study area. The computational domain is depicted with the dashed red contour. The dashed white line represents the track of hurricane Maria. The location of the buoys used for comparison is given in the upper-right corner box. H3, H4, and H5 represent the Saffir-Simpson category of hurricane Maria along its trajectory. (category 4-5) cyclones. The prediction is better when Maria moves further away, which is also expected and
* the best results are obtained for E11H80 and M16b, the two parametric models built to minimize the biases. NRMS values of the order of 9%-20% are obtained for significant wave heights, which is a good score considering the cyclone asymmetry due to the complexity of the area, characterized by a number of mountainous islands separated by just a few dozen kilometers. Figure 6: Significant wave height time series for different parametric models. “R64” denotes a model constrained only by the 64-kt wind radius. “ALL” indicates a model constrained with all the available information (34-kt, 50-kt, and 64-kt wind radii). E11H80 corresponds to a blend of the model E11 (for the inner core region) and H80 (for the outer region). M16b is the result of a blend between wind speeds obtained from M16 constrained by R64 only and with all wind radii information. Results for Fort-de-France, 42060, and the Sainte-Lucie station are displayed in the upper, middle, and lower panels, respectively. These results, obtained with observed and completely independent data, support our assumption that the ASCAT and CYGNSS products are relatively good proxies for moderate and strong surface wind speeds, respectively, with the exception of the region close to the core for which higher spatial resolution is needed.
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 5 Fuzzy Algorithm for the Assessment of 5G's impact on Health
### Fuzzy Logic Systems Type 2 and Type 3  
Fuzzy logic systems type 1 can handle uncertainties with some limitations related to higher levels of uncertainty and complexity. To address these limitations, fuzzy logic systems type 2 (FLS-T2) was developed [27; 28; 29]. FLS-T2 is widely used in control and decision-making algorithms and expert systems. FLS-T2 is more complex than FLS-T1. In FLS-T2, the membership functions themselves are fuzzy sets, and each membership function is defined by its upper and lower membership functions. Mamdani and Sugeno are the two main inference methods in FLS-T2.  
The Mamdani inference method is widely used and involves a set of _if-then_ rules. These rules consider the membership degree of inputs and compute the membership degree of the output. The Mamdani inference method uses a defuzzification process to convert the resulting fuzzy set back into a crisp output value. This inference method is suitable for applications with a high uncertainty degree.  
The Sugeno inference method is another popular method for FLS-2. It uses a linear combination of input values to compute the output. This approach is faster and more accurate than the Mamdani method for certain applications, especially when the rule base is small. Sugeno inference is more suitable when the system output mainly depends on the input values.  
When selecting between the Mamdani and Sugeno inference methods, the main factors to consider are the degree of dependency between inputs and outputs, the size of the rule base, and the speed and accuracy of the system response.  
One significant advantage of FLS-T2 is that it can handle higher levels of uncertainty and complexity compared to FLS-T1. The other one is that it can incorporate more expert knowledge, making the system more intuitive and easier to operate. However, a big disadvantage of FLS-T2 is that it requires more computational resources and complex calculations.  
Fuzzy logic systems type 3 (FLS-T3) is a relatively new area of research in the field of fuzzy logic. This system extends the classical type-1 and interval type-2 fuzzy sets to deal with more complex and uncertain environments. Until now, FLS-T3 only has a theoretical application, and the authors believe that the proposed algorithm would not be significantly improved by implementing it.
# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 2 Self-Organizing Map
### Network Algorithm  
As the core of model construction, the network algorithm is the key to the self-organization and the mapping characteristics of the model. The SOM neural network algorithm includes network initialization, input vector setting, etc.  
1. **Initialize**. Generally, the weight vector will be given any value in the interval [0, 1], represented by \(W_{i}\). The learning rate is \(\eta\).
2. **Set input vector input.** The input vector is the network model training sample: \[X_{n}=[x_{1},x_{2},x_{3},\cdots x_{(n-1)},x_{(n)}]^{T}\] (1)
3. **Derive Euclidean Distance.**\(W_{ij}\) represents the weight between the input layer neuron \(i\), and the mapping layer neuron \(j\). Derive the Euclidean distance between the input vector and the weight vector to get the specific position of the neuron. The Euclidean distance is calculated as:  
Figure 1: Model of SOM Network Structure.  
\[d_{i}(t)=\left\|X-W_{j}\right\|=\sqrt{\sum_{i=1}^{n}\left[x_{i}(t)-w_{ij}(t)\right] ^{2}} \tag{2}\]
4. **Label the winning neuron.** The winning neuron position is the position of the neuron with the minimum Euclidean distance between the input vector and the weight vector. The input vector is denoted by \(X\), the winning neuron is denoted by \(c\), Then its calculation formula is: \[\|X-W_{c}\|=\min_{i}\|X-W_{c}\|,i=1,2,3,\cdots n-1,n\] (3)
5. **Adjust weights.** Correct the input neuron and the neuron connection weights in the neighborhood according to Equation (3): \[\Delta w_{ij}=w_{ij}(t+1)-w_{ij}(t)=\eta(t)\left[x_{i}(t)-w_{ij}(t)\right]\] (4)  
Among, \(\eta(t)\) is the learning rate at \(t\), \(\eta(t)\in[0,1]\), \(\eta(t)\) gradually decreases with time, Inversely proportional to \(t\), its expression is:  
\[\eta(t)=0.2\times(1-\frac{t}{1000}) \tag{5}\]
6. Calculate the output value \(O_{k}\): \[O_{k}=f(\min\|X-W_{c}\|)\] (6)  
Among, \(f\) represents the function that takes the smallest Euclidean distance.  
Determine whether the output results meet the requirements. If the result meets the classification requirements, output the category; if the result does not meet the category requirements, return to step (2) to continue learning until the judgment result is met. Output and end learning.
## Modeling of the Temperature Profiles and Thermoelectric Effects in Phase Change Memory Cells  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] \({}^{\ast}\)**  
School of Optical and Electronic Information, Huazhong University of Science and Technology,  
Wuhan 430070, China; [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (J.H.);  
[EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (J.Z.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]  
Received: 31 May 2018; Accepted: 11 July 2018; Published: 27 July 2018  
**Featured Application: This work can be used for the optimal design of higher efficiency phase change memory (PCM) cells.**  
**Abstract:** Phase change memory (PCM) is an important element in the development and realization of new forms of brain-like computing. In this article, a three-dimensional finite element method simulation is carried out to study the temperature profiles within PCM cells for a better understanding of switching operations. On the basis of a finite difference method, the simulation consists of phase transition kinetics, electrical, thermal, percolation effect, as well as thermoelectric effects, using temperature-dependent material parameters. The Thomson effect within the phase-change material and the Peltier effect at the electrode contact are respectively considered for a detailed analysis of the impact on the temperature profiles and the programming current for switching processes. The simulation results show that switching operations are primarily implemented by the melting and quenching of the phase-change material close to the contact between the bottom electrode and phase change material, and its final phase distribution is determined by the cooling rate. With positive current polarity, thermoelectric effects improve heating efficiency and then reduce the programming current. Because of the different occurrence region, the Peltier effect significantly changes the temperature profile, which is more influential in switching operations. Additionally, the contribution of thermoelectric effects decreases with the cell size scaling because of the weakening of the Peltier effect. This paper aims at providing a more precise description of the thermoelectric phenomena taking place in switching operations for future PCM design.  
**Keywords:** phase change memory; finite element modeling; temperature profiles; thermoelectric effects; cooling rates
# Audio-Visual Action Recognition Using Transformer Fusion Network
## 4 Experimental Results
### Datasets  
For the performance evaluation of the proposed method, UCF-sound [15], Kinetics-sound [16; 17], and audio-visual datasets were adopted. The UCF-sound [15] is a dataset derived from UCF-101 [33], with or without an invalid soundtrack from UCF-101. The UCF-sound contains 6624 video clips with 50 classes. We divided the dataset into training and test sets, following the same split used in [15], resulting in 4733 samples for training and 1891 samples for testing. The classes in the UCF-sound dataset are diverse, covering a wide range of auditory activities, with each class incorporating both video and audio elements. These classes are listed below, with samples of these classes presented in Figure 5a.  
* applylipstick, archery, babycrawling, balancebeam, bandmarching, basketballdunk, blowdryhair, blowingcandles, bodyweightsquats, bowling, boxingpunchingbag, boxingspeedbag, brushingteeth, cliffdiving, cricketbowling, cricketshot, cuttinginkitchen, fieldhockeypenalty, floorgymnastics, frisbeecatch, frontcrawl, haircut, hammerthrow, hammering, handstandpushups, handstandwalking, headmassage, icedancing, knitting, longjump, moppingfloor, parallelbars, playingcello, playingdaf, playingdhol, playingflute, playingsitar, rafting, shavingbeard, shotput, skydiving, soccerpenalty, stillrings, sumowrestling, surfing, tabletennisshot, typing, unevenbars, wallpushups, writingonboardThe Kinetics-sound [16; 17] is a dataset derived from the Kinetics video dataset [34]. Among the subsets of the Kinetics-sound dataset, one contains 34 human action classes that clearly contain audio-visual information. However, some classes were removed, and only 32 classes were used in the experiment, as in [17]. We divided the dataset into training and test sets, resulting in 22,914 samples for training and 1585 samples for testing. The classes in the Kinetics-sound dataset are primarily centered around musical instruments and everyday actions, with each class incorporating both video and audio elements. These classes are listed below, with samples of these classes presented in Figure 5b.  
* blowingnose, blowingoutcandles, blowingoutcandles, bowling, choppingwood, dribbling-basketball, laughing, mowinglawn, playingaccordion, playingbagpipes, playingbassguitar, playingclarinet, playingdrums, playingguitar, playingharmonica, playingkeyboard, playingorgan, playinggpiano, playingvioulin, playingxylophone, playingsaxophone, rippingpaper, shufflingcards, singing, stompinggrapes, strummingguitar, tapdancing, tappingguitar, tappinggen, tickling, playingtrumpet, playingtrombone9