esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimedia Quality of Experience (QoE): Current Status and Future Direction
## 1 Introduction  
Quality of Experience (QoE) can be defined as the overall delight or annoyance of the user of an application or service. Typically, multimedia QoE is expected to offer improved user satisfaction in the audio-visual channels with which the users interact. Even though a significant number of novel multimedia applications are emerging, it is a challenge to provide better multimedia QoE for the end user. As a result, emerging multimedia applications fail to provide a minimum level of QoE required for end users to keep them engaged with the content. This is mainly due to the complexity of the content representation, processing, communication, security and visualisation. Therefore, in order to design QoE-proofed multimedia applications, researchers and developers need to overcome these challenging five pillars. The advancement of technology and multi-disciplinary research have enabled some multimedia applications to provide end-user satisfaction by overcoming technological and social barriers [1; 2]. It is interesting to explore how well novel multimedia applications meet end-user expectations or satisfy user QoE where the use of objective approaches of measuring quality (e.g., Quality of Service (QoS)) is no longer acceptable.  
The use of Artificial Intelligence (AI) and Machine Learning (ML) for improved multimedia QoE is on the rise [3]. Most of the current solutions for multimedia problems are based AI or ML. QoE can have a major role in these multimedia applications to measure end-user satisfaction. Soon QoE will be the decisive factor of User-Centred Design (UCD) approaches to maximise overall user satisfaction, rather than objective measures such as Delay, Jitter, Packet-Loss, Peak Signal-to-Noise Ratio (PSNR) and Structure SIMilarity (SSIM). In addition, User Quality of Security (QoSec) is an important aspect of QoE. The multimedia content should be original (i.e., authentic) and users shouldfeel safe when consuming multimedia content. With the increase in face swapping and deepfakes, the authenticity of multimedia content is being compromised more than ever [4; 5]. Therefore, future multimedia applications should deploy solutions to measure their authenticity and, in turn, satisfy end users' delight or overall satisfaction (i.e., QoE).  
With the challenges in ensuring high levels of user experience in emerging multimedia applications and making future applications fool-proof against security threats, we have put together this Special Issue on multimedia Quality of Experience (QoE). We have aimed to summarise the current activities in this field and the future projections.
## 3 Experiments and Results
### Training Protocol  
All experiments were conducted on a workstation equipped with an NVIDIA RTX 4090 GPU (NVIDIA Corporation, Santa Clara, CA, USA) and implemented using the PyTorch 2.5.0 framework. The proposed water body segmentation frameworks are based on a U-shaped architecture. During training, the Adam optimizer was employed. For the deep network, the initial learning rate was set to 5 \(\times\) 10\({}^{-4}\), with a momentum of 0.9 and a weight decay of 1 \(\times\) 10\({}^{-4}\). For the lightweight network, the initial learning rate was set to 2 \(\times\) 10\({}^{-4}\), with the same momentum and a weight decay of 1 \(\times\) 10\({}^{-5}\). Both the deep network and the lightweight LKF-DCANet adopted a cosine annealing learning rate schedule, with the minimum learning rate set to 1% of the initial value. The batch size was fixed at 8 for both, and all models were trained for 500 epochs.
# Kiwifruit Allergy--Molecular Basis, Diagnostics and Treatment
## 3 Protein Families
### Cysteine Protease  
Actinidin was reported as a major allergen from kiwifruit in different populations [28; 29]. It is a 30 kDa cysteine protease enzyme belonging to the peptidase C1 family of papain-like proteases [29]. Actinidin is found in both the green (_A. deliciousa_) and golden (_A. chinensis_) varieties (Figure 2A); however, actinidin levels in green kiwifruit are higher than those in the golden variety [30; 31]. As is common for most thiol group enzymes, actinidin is secreted in its inactive form as a zymogen (molecular weight of 39 kDa) and then converted into the active form (actinidin) after the cleavage of the precursor peptide [29]. As a proteolytic enzyme, actinidin breaks down complex proteins into peptides to aid in digestion and fruit ripening in the kiwifruit plant. This proteolytically active form is responsible for the allergenicity of this protein [29; 32]. Studies have shown that inactive actinidin has lower allergenity than the proteolytically active form [33]. Proteolytically active actinidin has higher thermal stability (Tm = 73.9 \({}^{\circ}\)C) than the inactive form and retains activity in a wide pH range [33; 34]. Studies have also shown that immunogenic and proteolytic properties, including the conformational fold of actinidin, are well preserved in a simulated gastric and intestinal digestion experiment, suggesting actinidin's resistance to pepsin and trypsin degradation [33]. Since actinidin can reach the intestinal mucosa in a proteolytically active form, it can increase the intestinal epithelial permeability by cleaving tight junctions' proteins in a similar fashion to clinically relevant inhalant allergens (e.g., Der p/ f 1), which disrupt the nasal epithelial barrier integrity, and allow facilitated allergen delivery to the intraepithelial dendritic cells [35; 36; 37]. This has been shown by studies in which actinidin was able to proteolytically truncate intestinal epithelial tight junctions' proteins like ZO-1 and occludin, in time- and concentration-dependent manners, and increase intestinal permeability in in vitro and in vivo models [34; 38]. It was also shown that actinidin upregulates the pro-TH\({}_{2}\) cytokines IL-25, IL-33, and thymic stromal lymphopoetin in mice intestines, suggesting its role in triggering innate immunity [39].  
Figure 2: A-RISC indices for (**A**) papain-like cysteine proteases and (**B**) thaumatin-like proteins. Sequences were selected from the WHO/IUIS (proteins with full allergen nomenclature) and AllFam (proteins without numbers) databases. All A-RISC diagrams in this work are scaled against each other, such that red indicates a high likelihood of IgE-mediated cross-reactivity, green indicates a low likelihood, and intermediate colors suggest intermediate cross-reactivity.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 2 Background
### Countermeasures
#### 2.4.1 Partition-Based Countermeasures  
Partition-based techniques [7; 8; 9; 11; 13; 14; 44; 45] ensure that there is no cache sharing between processes. A straightforward approach is set partitioning, which assigns different cache sets to different processes. This can be achieved at the OS level through page coloring [12; 13; 46] or dedicated hardware. The cache content can also be partitioned using cache ways, as in CATalyst [10]. This approach requires hardware support, such as Intel's Cache Allocation Technology (CAT) [47]. Since the number of ways is small, the number of security domains is very limited. As a consequence, the CATalyst design only supports two partitioning domains.
_applied_  
_sciences_  
_Article_  
Evaluation of Water Inrush Hazard in Karst Tunnel Based on Improved Non-Linear Attribute Variable Weight  
Recognition Model  
[PERSON]\({}^{\copyright}\), [PERSON],*, [PERSON], [PERSON], [PERSON] and [PERSON]  
1 School of Energy and Power Engineering, Xihua University, Chengdu 610039, China  
2 Key Laboratory of Fluid Machinery and Engineering, Xihua University, Chengdu 610039, China  
3 Key Laboratory of Fluid and Power Machinery, Xihua University, Ministry of Education, Chengdu 610039, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-151-9662-9973  
Footnote 1: email: [[http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci)]([http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci))  
**Abstract:** Water inrush in karst tunnels will cause casualties and economic losses. Thus, it is significant to objectively assess the water inrush risk level and adopt valid preventive measures to reduce losses from this disaster. The relationship between the factors affecting water inrush in the dynamic coupling system is strong nonlinear, so the attribute recognition model, which lessens the mutation points and error and causes the evaluation results to be more reasonable and accurate, is improved nonlinearly in this paper. Firstly, the assessment system was established by selecting seven factors: formation lithology, unfavorable geological conditions, attitude of rock formation, landform and physiognomy, contact zones of dissolvable and insoluble rock, layer and interlayer fissures, and groundwater level. Secondly, the multi-factor interaction matrix, C-OWA operator, and variable weight theory are used to calculate the constant weight and variable weight of each evaluation index. In addition, the linear attribute measurement function of the attribute identification model is optimized by using the non-linear trigonometric function to distinguish the risk level of the water inrush. Finally, the proposed model was successfully used in Qiyueshan Tunnel. The evaluation results of the risk level are more accurate than other methods, and they are in agreement with the excavation results. The proposed model provides a valuable reference for the risk assessment and project management of tunnel construction.  
**Keywords:** risk assessment; water inrush; variable weight; multi-factor interaction matrix; attribute recognition model
# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 4 Deployment on SELENE Platform
### AI-Inference SW Library  
The EDDL library is a general-purpose, open-source, deep-learning library used for the training and inference processes of NN models. One of the key features of EDDL is its ability to work with a wide variety of hardware, including CPUs, GPUs or FPGA. This allows users to take advantage of the best hardware for their specific use case and makes it easy to switch between different hardware platforms. Interoperability is provided with the EDDL Open Neural Network Exchange (ONNX) format support, as it allows pre-trained models in ONNX format to be loaded and it ensures compatibility with other frameworks.  
The SELENE platform relies on Linux as the default operating system. In particular, it uses a Debian-based RISC-V Linux adaptation to NOEL-V. The AI software toolchain integration is built on top of this Linux distribution. To deploy artificial intelligence models on the SELENE platform, the EDDL library has been extended by including the SELENE platform as a new computing target. The EDDL library is then deployed on top of the Linux OS running on the NOEL-V processor. This allows for the inference process to be executed entirely within the NOEL-V multi-core system. Additionally, SAF is used to offload heavy computations to the SELENE AI hardware accelerators to speed up the inference process.
# Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors
## 5 Prospects  
In recent years, an extensive volume of research related to cracked rotors diagnosis has been generated. Multiple works applying very different methods have been carried out that have obtained promising results; some of which have been mentioned in the present work. A high number of studies applied novel methods to experimental signals, these also provided quantitative information of the inverse process of crack detection, as a response of the highlights in [1]. Currently, despite the high number of methods applied and the different features used, none of them stands out above the rest. New methodologies appeared to solve the problem of the non-existing standard methodology to select the best wavelet function, the decomposition level, and the features. One of these methods proposed was based on the use of intelligent classification systems. However, this remedy can be worst than the disease since there are again a lot of parameters that must be selected when applying intelligent classification systems. There is no agreement when deciding which  
Figure 5: POD curve, showing mean value, 95% confidence bound, and 90/95 crack size \(a_{NDE}\).  
intelligent classification system is best, nor a methodology to select optimal functions or values for its parameters. The most common extended methodology is to optimize the success rate percentage.  
Several works have published quantitative data about success in results when experimentally diagnosing a specific fault, in a specific element, belonging to a specific machine, and working under specific conditions. It would be appropriate to study how many of the techniques applied are robust and still work when these very specific conditions are altered, or under what range of conditions these techniques still work; for example, what mounting; what speed; what load; whether they work when multiple faults are present; or when faults are in a different location. Another critical issue is the decision over what criteria should be used in the verification of whether a technique works or not. There is a need to benchmark the different techniques that have proven effectiveness in this area. So, it would be very useful to establish standard comparison parameters, statistically representative, such as POD curves and the computational cost. Benchmarking of the different methods under many different conditions would make it possible to check if one of them stands out. These kinds of studies are required in order to achieve the aim of a method of condition monitoring that is general while also being robust and reliable.
## 2 Numerical Model
\[\mathbf{H}(s)=\frac{1}{4\pi}\int_{V_{S}}\frac{\mathbf{J}_{\text{s}}(\mathbf{ r})\times(\mathbf{r}-\mathbf{r}^{{}^{\prime}})}{\parallel\mathbf{r}-\mathbf{r}^{{}^{ \prime}}\parallel^{3}}dV \tag{7}\]  
The principle of studying the system is based on the fact that only the parts carrying the source current or induced current are discretized. The excitation coil Hs is evaluated at each node of the finite difference grid through the average value of Equation (7). Due to the eddy currents generated in the CFRP plate, the coil impedance changes. This change is calculated by Equation (8), where the source current and the magnetic field scattered by the CFRP plate are calculated using the Biot-Savart Equation (9). \[\Delta\text{Z}=\frac{-1}{\mathbf{I}_{\text{S}}^{2}}\int_{V_{\text{S}}}\text{ EJ}_{\text{s}}\text{dV} \tag{8}\]  
\[\mathbf{H}_{i}(\mathbf{r})=\frac{-j\omega\mu_{0}}{4\pi}\int_{V_{C}}\frac{ \mathbf{J}(\mathbf{r}^{{}^{\prime}})}{\mathbf{r}-\mathbf{r}^{{}^{\prime}}}dV,\ \left(\mathbf{r}^{{}^{\prime}}\in V_{C}\right) \tag{9}\]We need to measure the relationship between the average value of the modulus of the current density within a certain area in the plane \(\rho_{\rm A}\) and \(\mathbf{H}_{i}\). Therefore, Equation (9) can be written as Equation (11). \[\mathbf{H}_{i}=\frac{-j\omega\mu_{0}s}{4\pi}\rho_{\rm A}\int\frac{\mathbf{J}( \mathbf{r}^{\prime})}{\mathbf{r}-\mathbf{r}^{\prime}}d\mathbf{r} \tag{10}\]  
The definition is as follows:  
\[\rho_{\rm A}=\frac{\iint JdS}{S}=\frac{\iint\frac{1+j}{\sqrt{2}}\sqrt{\omega \mu_{0}\sigma\mathbf{H}}dS}{S} \tag{11}\]  
In Equation (11), \(\omega\) represents the frequency control and \(S\) represents the specific cross-sectional area used to calculate the current density and magnetic field strength during the detection process. \(S\) is controlled by the test sample, \(\mathbf{H}\) is controlled by the excitation, and \(\sigma\) is related to the areal density of carbon content (denoted as \(\rho_{c}\)). Hence, the relationship between the coil impedance variation and the areal density of carbon content can be accounted for by Equation (12). \[\Delta Z=F(\rho_{c}) \tag{12}\]
# Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems
## 1 Introduction
High-order sliding mode-based control strategies are also significant ways to deal with the chattering elimination problem. Among them, second-order sliding mode (SOSM) control is promising, and it has been widely used in various applications, such as the regulation problem of a buck converter [16; 17], rigid spacecraft attitude control [18], and so on. The super-twisting algorithm, known as a kind of second-order sliding mode control, retains all the advantages of sliding mode and effectively reduces the chattering [19; 20]. By adding an integrator to the control input, the actual control signal and its derivative can be obtained explicitly. Instead of acting on the first-order sliding manifold time derivative, the discontinuous term proceeds on its second-order time derivative. Consequently, the actual control law will be a continuous integration of its derivative, and the chattering phenomenon can be eliminated. However, for most SMC-based methods, with respect to disturbances, a common assumption is that the disturbances are bounded by an unknown constant [21; 22], which is directly utilized in the controller. However, the disturbance bounds are often not easy to determine in real systems. Therefore, in this paper, we use an adaptive gain on the basis of the super-twisting algorithm, which helps to learn the upper bound of the disturbance by the adaptive law. As a result, the upper bound of the disturbance learned by the adaptive law will be close enough to the real bound of the disturbance to make the gain of the controller as small as possible, which further reduces the chattering. The main contributions of this paper are as follows:  
(1) The proposed adaptive super-twisting algorithm is capable of reducing the chattering phenomenon and noise amplification. (2) The stability analysis of the closed-loop system and convergence of the adaptive parameters are guaranteed by the Lyapunov theorem. (3) Experiments on a real MWIP system are conducted to show the effectiveness of the proposed method. It might be the first attempt to employ the adaptive STA to a real MWIP system. The rest of the paper is organized as follows. The MWIP system is modeled in Section 2. In Section 3, the controller based on adaptive STA for the MWIP system is proposed. Experiments are presented to demonstrate the effectiveness and robustness of the strategy in Section 4. Finally, a conclusion is given in Section 5.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
###### Abstract  
Terahertz (THz) band will play an important role in enabling sixth generation (6G) envisioned applications. Compared with lower frequency signals, THz waves are severely attenuated by the atmosphere temperature, pressure, and humidity. Thus, designing a THz communication system must take into account how to circumvent or diminish those issues to achieve a sufficient quality of service. Different solutions are being analyzed: intelligent communication environments, ubiquitous artificial intelligence, extensive network automation, and dynamic spectrum access, among others. This survey focuses on the benefits of integrating intelligent surfaces (ISs) and THz communication systems by providing an overview of IS in wireless communications with the scanning of the recent developments, a description of the architecture, and an explanation of the operation. The survey also covers THz channel models, differentiating them based on deterministic and statistical channel modeling. The IS-aided THz channels are elucidated at the end of the survey. Finally, discussions and research directions are given to help enrich the IS field of research and guide the reader through open issues.  
terahertz band; intelligent surface; reconfigurable intelligent surface; large intelligent surface; 5G; 6G +
Footnote †: journal: Sensors  
1][PERSON] 1,2][PERSON] 1,2][PERSON] 1,2][PERSON] 1,2][PERSON] 1,2][PERSON] 1,2][PERSON] 1,2]
# A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes
## 2 Materials and Methods
### Study Design  
Data from the KNHANES 2013-2016 dataset collected by the Centers for Disease Control and Prevention Korea (KCDC) were used to perform analysis and construct a DLM for predicting undiagnosed diabetes. The KCDC assesses trends in health risk factors and nutrition status. It also conducts surveillance of infectious and chronic diseases. Records collected from the surveillance system are analyzed for the development and evaluation of health policies [25].  
There were 31,098 subjects in the KNHANES 2013-2016 dataset, which is a non-duplicate sample. Those with an age \(\leq\) 19 years (n = 7003), null or unknown response (n = 6864), or any record of diabetes diagnosis, abnormal insulin level, or antidiabetic treatment prescription (n = 1331) were excluded. The study population was then divided into a development group (n = 11,456; 2013-2015) and a validation group (n = 444; 2016) according to the surveyed years shown in Figure 2.  
The diagnosis of diabetes was based on diabetes diagnosis criteria that referred to the Classification of Diabetes Mellitus 2019 WHO (world health organization) [2]. Undiagnosed diabetes was identified in the health interview survey for subjects with fasting plasma glucose (FPG) \(\geq\) 126 mg/dL), subjects without a previous diagnosis of diabetes made by a healthcare professional, and subjects who were taking insulin or oral antidiabetic agents [3]. Impaired fasting glucose was defined as an FPG of 100-125 mg/dL with above-constraint satisfaction. Subjects were classified into two categories in the model architecture. The primary dependent group was UDG, and NG and IFG were combined into a comparison group.
## 4 Conclusions
The results show that there are significant variations in the observable times for high-latitude regions in different years, while the variations are relatively small for mid-to-low latitude regions. In conclusion, with the advantage of wide swaths, continuous observation capabilities, and large coverage, the MS-BiSAR could provide temporally consistent and spatially continuous Earth observation data for large-scale geoscience phenomena, which will help us deepen our understanding of Earth system science. In future work, the precision of the observational geometry model established in this paper will be improved, and more optimized observation constraints will also be discussed. Meanwhile, other issues related to the MS-BiSAR, such as imaging spatial resolution, bistatic configuration, and Earth science phenomena suitable for MS-BiSAR observations, will be further considered. Conceptualization and summary H.G. and K.Z. ; methodology, K.Z. ; software, K.Z. and D.J. ; writing--original draft preparation, K.Z. ; writing--review and editing, C.H. ; All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2022 YFB3902100), the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (CBAS2022 IRP02), and the National Natural Science Foundation of China (Grant No. 41590853). Not applicable. We thank the IAU SOFA Collection for its use, copyright(r) International Astronomical Union Standards of Fundamental Astronomy ([[http://www.iausofa.org](http://www.iausofa.org)]([http://www.iausofa.org](http://www.iausofa.org)) accessed on 27 August 2021). We thank the JPL for the free access to DE430 and the IERS for the free access to EOP data. The authors declare no conflict of interest.