esa-sceva/satcom-chunk-collection huggingface dataset:
With the goal of efficient coexistence in a shared spectrum, a significant number of approaches promise to increase the overall wireless network efficiency, expressed in terms of limiting interference, optimizing resource utilization, ensuring fair spectrum sharing and, eventually, increasing Quality of Service (QoS) or user quality of experience and the \"social welfare\" factor. However, the viability of such mechanisms is based to a large extent on the cooperation among autonomous network entities and their conformance to protocol-specified spectrum sharing rules. A critical question that naturally emerges is how such behavior can be checked and/or enforced. The vast majority of the body of research in this area, implicitly or explicitly, assumes that all participating entities demonstrate full cooperation [8]. However, there exist approaches focusing on studying selfish behavior, often using tools from game theory. A common approach is to offer competing nodes the proper incentives for fair spectrum use and conformance to agreed-upon rules. At the same time, efforts have been made to limit the benefits of non-conformant behavior. The proposed mechanisms are based on reputation schemes [9], tit-for-tat models [10], punish/reward schemes or transfer models [11], where some kind of compensation is offered in return for using a spectrum portion. Still, an important assumption is the existence of a mechanism for effective detection of non-conformant operation and identification of misbehaving nodes (and even malicious intent). We believe that in future spectrum sharing systems, it is necessary to put significant effort into the direction of modeling and quantifying the effects of uncooperative or anomalous behavior and preventing it, or making the system robust against it. As a first step, in this work we identify such misbehavior scenarios and relevant attacks to various phases of the spectrum sharing process. The remainder of this article is structured as follows. In Section 2 we consider the building blocks of the spectrum sharing process, namely negotiation among interested parties, dissemination of the derived spectrum sharing rules and their implementation, as well as sensing the environment for data collection or to monitor rule conformance. Before we delve into the identification of cases of misbehavior for each of the above processes, in Section 4, we briefly describe the profiles of potential attackers in Section 3. We conclude the article in Section 5 with a brief summary and outlook.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 3 Violation of the Helmholtz Equation
### Azimuthal Direction  
The validity of the azimuthal direction is also examined. We use \(\mathbf{V}(\phi)\) to represent the scaled exponential function and have  
\[\mathbf{V}(\phi)=e^{jmu\phi}. \tag{16}\]  
By taking (16) into the left-hand side of (9a), we have  
\[-m^{2}u^{2}e^{jmu\phi}+m^{2}e^{jmu\phi}. \tag{17}\]  
From (17), we can find that only when \(u=1,~{}V\) satisfies (9a).  
Based on the proof provided above, it is evident that SSHs can satisfy the Helmholtz equation only when \(q_{1}=1\), \(q_{2}=0\), and \(u=1\). In this special case, SSHs become SHs, which are indeed solutions of the Helmholtz equation. However, for any other values of \(q_{1}\), \(q_{2}\), and \(u\), SSHs do not fulfill the Helmholtz equation. The violation of the Helmholtz equation can affect the application of SSHs in certain scenarios, such as sound field radial extrapolation, which is investigated in the following section.
## 3 Method
### Types of Analyses
#### 3.3.2 Natural Frequency Analysis  
The natural frequency of a structure is defined as the rate at which the structure vibrates naturally when disturbed. The vibration rate depends on the structure's stiffness, mass, and vibration mode.  
This relationship can be formulated using Equation (2), where \([K]\) is the global stiffness matrix, \([M]\) is the global mass matrix, \(\{x\}\) is the vibration mode vector, and \(\omega\) is the natural frequency.  
\[[K]\{x\}=\omega^{2}[M]\{x\} \tag{2}\]  
The equation above shows that the natural frequency is directly proportional to the stiffness and inversely proportional to the mass. This can provide critical insights into controlling the behaviour of the structures. For example, increasing the mass leads to a decrease in the natural frequency of the structure. Therefore, damping is typically used in tall buildings to reduce the vibration due to the wind and seismic load.  
The vibration mode, also called mode shape, is another critical factor in the natural frequency analysis, which is defined as the pattern of vibration the structure follows when excited [46]. Structures can have multiple mode shapes, and each mode shape is associated with a specific natural frequency [47].  
In practice, the natural frequency analysis is used to obtain the natural frequencies and mode shapes, which can be used to find the relative displacement. In addition, the natural frequency can be used to check the frequency range of the structure in different conditions [47]. For example, natural frequency analysis is often used to check the frequency range of the structure against the external frequencies caused by the wind and seismic load. Having a similar structure and external frequency can reinforce the vibration, leading to the inconvenience of the occupants and potential failure of the structure.  
The natural frequency solver is performed by considering the stiffness and mass matrices for each individual element in the discretised structure. These matrices are assembled through the assembly to form the global stiffness and mass matrices. The frequencies and the mode shapes are obtained by solving the eigenvalue problem, using the method of sub-space iteration [48].
## 5 Conclusions  
A high-gain axisymmetric Minkowski fractal reflectarray is designed and fabricated for Ku-band satellite internet communications. High gain is achieved here by carefully optimising the number of unit cells, their shape modifier, focal length, feed position and scan angle. The space-filling properties of the Minkowski fractal aid in the miniaturisation of the fractal. Due to the compact nature of this reflectarray, it helps limit the space used for transmitting or receiving antennas in a satellite, ultimately reducing launch costs. The scan angle of the reflectarray varied by adjusting fractal scaling for each unit cell in the array. The designed reflectarray with these performance characteristics and beam scanning capabilities is found to be an excellent choice for Ku-band satellite internet communications. Due to its simple design and ease of analysis, it also boosts the chance of its use in satellite communications. The interference analysis of the proposed reflectarray at the ground station in a real-time scenario proves the concept to a greater extent. Although considerable development has already been carried out in this area, there is still considerable scope available in reconfigurable reflectarray design, which requires further extensive research.  
Conceptualization, P.K.P., H.C.M., D.C.P. and M.M.; methodology, P.K.P., H.C.M., R.A.P. and D.C.P.; software, P.K.P., R.A.P. and H.C.M.; validation, P.K.P.; formal analysis, P.K.P. and H.G.M.; investigation, D.C.P., H.C.M., R.A.P., M.M. and H.G.M.; resources, P.K.P., H.C.M. and D.C.P.; data curation P.K.P.; writing--original draft preparation, P.K.P.; writing--review and editing, D.C.P., H.C.M. and M.M.; visualisation, P.K.P., R.A.P. and H.G.M.; supervision, H.C.M., D.C.P. and M.M.; project administration, H.C.M. and H.G.M.; funding acquisition, H.G.M. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R140), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
# KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation
## 4 Experimental Results
### Experimental Setup  
The experiments were accelerated using a single GTX TITAN XP GPU (12 GB) in the PyTorch 1.12.0 framework. We employed the LFW [37] and CASIA-WebFace [38] datasets for training facial UAPs. LFW and CASIA-WebFace are widely used datasets in the field of face recognition, encompassing a diverse range of facial poses, expressions, lighting conditions, and occlusions, thereby authentically reflecting the challenges of face recognition in various scenarios. LFW serves as a common benchmark dataset for face verification, extensively employed to evaluate the performances of facial feature extraction and matching algorithms. CASIA-WebFace contains a larger scale of data and is primarily used to aid models in learning facial feature representations. The research task of this study focused on generating universal adversarial perturbations for face recognition. By conducting experiments using these two datasets, we could obtain more objective and effective evaluation results. Both datasets contain paired images of the same individuals. The training set contains 6000 pairs of facial images, while the test set contains 3000 pairsof facial images. These facial images were resized to \(112\times 112\). We used Arcface to pre-train three backbone feature extraction networks: IResNet50 [39], MobileFaceNet [40], and MobileNetV1 [17]. In order to make the adversarial perturbation invisible to the human eye, the perturbation intensity \(\xi\) was set to 0.08. Considering the computational capabilities of our hardware resources and the training time for the task, we set a batch size of 10 pairs of images and a learning rate of 0.01 to achieve optimal perturbation generation results. By empirically fine-tuning the weight parameter of the loss function, we found that setting \(\lambda\) to 0.05 effectively balances the attack ability and stealth. For the evaluation on different datasets and backbone networks, we employed two objective stealthiness metrics, namely, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR).
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications
### Natural Interaction with Gestures (V-Must.net)  
Keys to Rome was an international museum exhibition developed through the V-Must.net project, which took place in four cities in 2014 (Rome, Amsterdam, Alexandria, and Sarajevo) [46]. Its goal was to depict the differences and uniqueness of the Roman empire. The museums' collections were available in a virtual journey through computer animations, natural interaction installations, as well as multimedia and mobile applications. Four cities were connected in the story, which gave visitors the ability to explore Roman culture in the characters of the old merchant [PERSON] and his nephew [PERSON]. Each historical site had a virtual reconstruction of the site and the objects in it. Each object had a story that was narrated by [PERSON] and [PERSON].  
Part of this exhibition was the Admotum installation (Figure 1). Users were able to walk through virtual environments, collect objects, and hear the stories. After all objects from one location were collected, other locations were unlocked for exploration. The Admotum installation consisted of a computer, projector, projector screen, and Kinect sensor. The interaction was gesture-based. The main problem with this type of interaction was that users had to perform gestures precisely, and they were required not to move from the static position where the Kinect sensor had the best accuracy. In the exhibition, the users often moved and Kinect lost traction. This was often confusing for a user and may have caused the loss of focus on the storytelling.  
Another part of the Admotum installation was Holobox (Figure 2). This was a reinterpretation of an immersive theater technique called \"[PERSON]'s ghost effect\", which gives an illusion of a 3D object floating in space. The user was able to transfer the object by using a gesture to Holobox where the object was controlled by Leap sensor controls. Leap sensor is a small device that detects a user's hand gestures through a system of small infrared lights and cameras. This type of interaction was much more precise, but it lacked tangible feedback.  
Figure 1: Admotum setup [46].
# A Low-Band Multi-Gain LNA Design for Diversity Receive Module with 1.2 dB NF
## 3 Structure of the Voltage Generator  
In order to keep the design independent of PVT variations, the supply voltage is supported by an LDO. The structure of the LDO can be found in [5]. Furthermore, a digitally controllable bias voltage generator has been designed and presented in Figure 3[6]. This structure provides all of the required bias voltage levels for VB1 and also VB2 for the different gain modes. These voltages can be selected and changed automatically when a change in the gain modes is required. Another intrinsic feature of this block is that for different corner cases, the voltage level changes automatically. With ideal bias voltage, for example for a slow-slow case, the current reduces which consequently degrades other characteristics of the LNA, such as S\({}_{11}\). However, the designed bias generator automatically increases the bias voltage of the input transistor to compensate for the current reduction and increases the degraded current to its original value and keeps the performance in the required range.
## 2 Road Lighting Distributions  
### Simulated Road Lighting Scenes  
There are three main kinds of road lighting distributions: single (luminaire on one side of road only), double (luminaires on both sides of road, opposite), and staggered types (luminaires alternating between the two sides of the road). We simulate these three types of road lighting distributions in DIALux with various luminaires that differed in their photometric curves, and report a randomly chosen luminaire as an example. These luminaire poles are 8 m in height. The luminaires are of 250 W and 20,000 lm. All three types of simulated roads are 25 m in longitudinal distance (d\({}_{\text{l}}\)) between two luminaires. The single-type road contains two lanes and the transverse distance (d\({}_{\text{t}}\)) is 7 m, the double-type road contains four lanes with d\({}_{\text{t}}\) of 14 m, and the staggered-type road contains three lanes with d\({}_{\text{t}}\) of 10.5 m. The road lighting setups of single type is shown in Figure 1. For each different type of road lighting configuration, 10 \(\times\) 7 points are measured, as shown in Figure 2, respectively. The illuminance distributions in the longitudinal directions of the three kinds of road types conform to a sine function with high coefficients of determination R\({}^{2}\) over 0.90, except for the illuminance distribution of the staggered type at the transverse distance d\({}_{\text{t}}\) = 5.25 m in the center of the road. This is because the illuminances in the center are influenced by luminaires on both sides equally. However, these illuminances in the center are relatively close, and there might not be great errors with sine fitting. The maximum illuminances are likely to appear beneath or around the luminaires, and minimum illuminances at the midpoint between two luminaires. All our simulated illuminance scenes using different luminaires conform to the sine-like illuminance distributions in the longitudinal direction.  
Figure 1: Road lighting setup taking the single type for example. Most setups, except the number of lanes, for the double and staggered types are the same as the single type.  
Figure 2: The illuminance distributions in longitudinal directions at different transverse distance \(\rm d_{\rm t}s\) of three kinds road types: (**a**) single type, (**b**) double type, and (**c**) staggered type. They are fitted with sine functions at different \(\rm d_{\rm s}\) reaching \(\rm R^{2}\) 0.95–0.99 for the single type, 0.94–0.99 for the double type, and 0.90–0.99 for the staggered type, excluding the one at \(\rm d_{\rm t}\) = 5.25 m of 0.68.
## 6 Discussion and Conclusions
### Future Research Directions
#### 6.2.4 Caching  
Effective caching strategies are integral to achieving high QoS and QoE in ICN. Optimal cache placement, size, and replacement policies can significantly reduce content retrieval times and network congestion, improving overall network performance and user satisfaction. Plus, understanding content popularity and access patterns is crucial for developing adaptive caching mechanisms that enhance QoE. In addition, preventing cache pollution and ensuring fair content distribution are also essential for maximizing the benefits of in-network caching. Furthermore, ICN-specific QoS/QoE metrics, such as SRAM size, interest satisfaction rate, content store search time, etc., should be more thoroughly investigated.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 1 Introduction
However, Li exploration with the resource of remote sensing data and techniques represents an emergent field, with several difficulties and unknown possibilities. Taking this into account, this review aims at (i) providing information about what can be accomplished in Li exploration using remote sensing methods; (ii) identifying the main difficulties associated with this kind of deposits; and (iii) providing insights on how to overcome these difficulties as well as future research perspectives. This paper presents the first summary of the developments made in the field of remote sensing applied to Li exploration and we consider it to be timely and appropriate due to the high global demand of this metal to the production of Li-ion batteries [58]. Additionally, this review can help to promote new applications and to solve new exploration problems.
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 1 Introduction
The gestures are not only played with hands but also include other different nonverbal communication means such as facial expressions and body movements [7]. Different research works have investigated the use of sensors and device-based techniques for ArSL [5; 8; 9]. These studies have developed a speech recognition engine to convert common Arabic words into ArSL and vice versa. Some studies were designed to measure sign language motion using data gloves [8; 9]. To the best of our knowledge, few research works have investigated ArSL vision-based methods which rely on AI techniques including image processing, computer vision, machine learning [3], and deep learning [7; 10; 11; 12]. Therefore, the contributions of this work can be highlighted in the following points:  
* This study explores the use of transfer learning approach using pretrained deep learning models such as VGG, ResNet, MobileNet, Inception, Xception, DenseNet, and InceptionResNet for ArSL classification using a large-size dataset. Additionally, a state-of-the-art pretrained model, BiT (Big Transfer), by Google Research, was evaluated for ArSL classification. * This study investigates the use of vision transformers namely ViT and Swin, with the transfer learning approach for ArSL classification. Since the literature review studies have shown successful vision-based transformer models applied to other sign languages, these vision transformers are also investigated and evaluated for ArS classification. * Finally, this research work investigates several deep learning convolutional neural network (CNN) architectures trained from scratch for ArSL classification and compares their results with the proposed transfer learning models for ArSL classification. Anablation study was conducted to compare different architectural models of CNN for the classification of Arabic sign language alphabets. The rest of the paper is organized as follows. Section 2 gives an extensive review of SL recognition methods and resources, moving from machine learning to deep learning methods, and transfer learning-based methods for SL classification. Section 3 introduces the methodology framework. Section 4 presents the experimental setup, datasets for ArSL and evaluation metrics. Section 5 presents our experimental results using different architectures of deep learning CNN models, pretrained models, and vision transformers, and finally, in Section 6, we give the conclusion and future remarks to be continued.
## 2 Materials and Methods
### Analysis of Volatile Compounds by SPME-GC-MS  
The volatile components of Parmigiano Reggiano cheese were extracted and identified by the headspace SPME-GC-MS method. A Shimadzu Gas Chromatograph GC2010 PLUS (Kyoto, KYT, Japan) interfaced with a Shimadzu single quadrupole Mass Spectrometer MS-QP2010 (Kyoto, KYT, Japan) ultra and a HT280T auto sampler was used to analyze the cheese headspace compounds.  
All extractions were carried out using a DVB/carboxen/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) stable flex (50/30 \(\upmu\)m) (Supelco Co. Bellefonte, PA, USA) SPME triphasic fiber. The extracted volatile compounds of the cheese samples were separated by a low-polarity capillary column (DB-WAX, Agilent Technologies, Santa Clara, CA, USA). The conditioning of the sample was carried out maintaining the sample at 50 \({}^{\circ}\)C for 10 min in the HTA280T oven, the extraction of the compounds from the headspace was done by exposing the triphasic fiber for 15 min at the same temperature. The chromatogram was recorded with the following temperature program: 40 \({}^{\circ}\)C held for 8 min, linear gradient 4 \({}^{\circ}\)C/min up to 190 \({}^{\circ}\)C and held for 5 min, followed by a rise from 190 \({}^{\circ}\)C to 210 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, for thermal desorption of the analytes. The carrier gas used was helium with a flow rate of 1 mL/min. The selection of the fiber type, extraction and operation conditions were the same as in this study [5]. The eluted compounds were identified by comparing them with the compounds listed in the National Institute of Standards and Technology (NIST) mass spectral database. Peak areas were calculated from the total ion current. For volatile compounds analysis, the samples of cheese (2 g) were weighed in 20 mL chromatographic vials sealed with Silicon-PTFE septum, crimped with an aluminum crimp, and then subjected to SPME-GC-MS experiment.
# Power System State Estimation Approach Considering Transmission Line Temperature
## 5 Case Studies  
The IEEE 14-, 39-, and 118-bus systems are utilized to verify the effectiveness of the proposed AJ-ETC-SE and ITS-ETC-SE approaches. The WLS-SM method in [19] is used for comparison. Moreover, the algorithms are developed on the MATLAB R2019b. Further, the calculation is conducted on a 2.33 GHz Intel (R) Core (TM) 2 Quad CPU Q8200 processor with 16 GB of RAM. Additionally, the convergence threshold \(\xi\) for the Newton method is set as 0.0001.  
Voltage amplitude, power injection, branch power flow, and currents are incorporated in the measurement vector. Then, 2% and 4% Gaussian-distributed white noise are added to the voltage and power measurements, respectively. Meteorological parameters remain constant, which is reasonable for the short simulation time.  
Furthermore, the root mean square (RMS) error of estimation results \(e_{\text{RMS}}\) is calculated, and can be expressed as,  
\[e_{RMS}=\sqrt{\frac{1}{NM}\sum_{i=1}^{N}\sum_{j=1}^{M}\left(\frac{x_{j}-x_{ij, r}}{x_{j}}\right)^{2}} \tag{39}\]  
where \(N\) is the group of measurement data, \(M\) is the number of buses, \(x_{j}\) is the value of the \(j\)th state variable, and \(\mathbf{x}_{i,j,r}\) is the estimated value of the \(j\)th state variable with the \(i\)th group of measurement data.