esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multitask Cross-Lingual Summary Method Based on ABO Mechanism
## 2 Related Work  
### Cross-Lingual Summarization  
The primary task of cross-lingual summarization is to extract the essential content of a document in one language and express it in another language. Previous methods have typically combined translation tasks and single-text summarization tasks in different orders. There are two main approaches: (1) translating the corpus first and then extracting the abstract from the translated content [10; 11; 12]; (2) extracting the abstract first and then translating it into the target language [13; 14; 15]. Such a pipeline method is heavily influenced by the performance of the model and lacks any connection between tasks, which can easily lead to content deviation.  
The Transformer-based deep neural network can improve the effectiveness of each component of the task. For instance, [PERSON] [16] introduced a learnable linear mapper to the multitask framework to enhance the isomorphism between different languages and improve the cross-lingual transfer ability of the model. Similarly, [PERSON] [17] added a translation layer to the Transformer model to compute candidate translation words in the source document and added high-scoring candidates into summary generation to improve the summary quality. [PERSON] [18] introduced a cross-attention module to the Transformer encoder to establish interdependence between languages and enhance cross-lingual information correlation. Another approach proposed by [PERSON] [19] involved combining cross-lingual summarization tasks with single-language summarization tasks and translation tasks. This was achieved by sharing the encoder and using two independent decoders to process the two different tasks, enabling the model to learn the input of both tasks and improve the performance of cross-lingual summarization models. [PERSON] [20] spliced the MS output from the decoder and the CLS output sequentially while sharing the CLS encoder. This approach enhanced the interaction between different languages, implicitly considering cross-lingual alignment, semantic similarity, and patterns between summaries in different languages, which facilitate knowledge transfer from high-resource languages to low-resource languages. [PERSON] [21] employed a conditional variational autoencoder [22] with a shared encoder and decoder for multitask learning of machine translation (MT), MS and CLS tasks. The authors constructed two local-level latent variables for translation and summarization, respectively, and a global-level latent variable for CLS.  
Previous studies have primarily focused on enhancing the model's cross-lingual learning capability during training for various tasks. However, an essential aspect that impacts summary quality is how the model transforms discrete predicted word distributions into continuous sequences during the generation phase.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Prototype Design  
The objective is the design of an attitude and positioning system for static and dynamic solids that provides the highest possible accuracy. The technique that gives a larger accuracy is RTK, and the fact that the solid may be static makes three the minimum number of receivers that must be used.  
It is important to keep in mind some design requirements that describe the prototype development and the attitude and position computation:  
* The attitude information is computed as Euler angles (yaw, pitch, and roll) and as cuaternions in the NED (North, East, Down) reference system.
* The position information is given as latitude, longitude, and ellipsoidal height in the WGS4 system.
* The attitude is given with respect to the plane formed by the three antennas of the receivers. The antenna 3 cannot be aligned with antennas 1 and 2, and these two latter define the heading of the solid.  
The attitude computation of the solid given the relative positions of the three antennas is done following standard procedures [31; 32; 33].
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 3 Overview of Non-Terrestrial Networks
### NTN Challenges and Solutions  
The propagation delay for GEO satellites in NTN networks can be hundreds of milliseconds because of the incredibly large gap between the gNB and the UE, particularly in bent pipe configurations [2]. The UE Doppler effect and the rapid relative motion of spaceborne objects, such as LEO satellites, pose significant technological hurdles for NTN in achievinghigh mobility, such as transonic speed mobility of around 1000 km/h [1; 2]. For a 2 GHz carrier frequency, there is a Doppler change of more than 7 km/s and more than 20 ppm [4; 19; 20]. The Doppler effect and transmission delays are not intended to be considered by current NR standards, which are primarily intended for cellular systems.  
Directionality is required while operating at mmWaves to achieve an adequate link budget and maintain high-capacity connections [24]. Fine beam alignment substantially influences the design of control activities in this situation, including user tracking, handover, and radio link failure recovery [22]. Because of the speed of aerial and space platforms, these issues are more critical in the extra-terrestrial domain because beam alignment may be lost before a data transfer is completed. The channel may become non-reciprocal at high speeds owing to the higher Doppler experience [23], resulting in less feedback than a broadcast channel [29]. Therefore, NR-NTN is critical for 3D spatial mobile connectivity investigations [13].  
The physical layer management procedure and scheduling concerns are the primary technical issues that must be resolved [25]. As a result, power control, AMC/CSI feedback, beam switching, and uplink transmission time can all be used to overcome difficulties with the physical layer control technique [14]. While timing issues in 6G-NTN can be addressed through downlink and uplink synchronization, the new physical random-access channel (PRACH) protocol for the RACH process, and HARQ for improved mobility [15; 16].
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 2 IS in Wireless Communication
### A Historical Perspective
Each element of the RFocus IS could operate in one of two possible states: \"on\" or \"off\". The \"on\" state meant that the element would reflect the signal, and \"off\" that the signal would pass through it. The experiments in [86] revealed that the RFocus yielded an improvement of the median signal strength by 9.5\(\times\) and the median channel capacity by 2\(\times\), and robustness to elements failure: for one-third of elements cutting out, the performance improvement drops 50%. The smart glass mentioned in [87] was a glass substrate covering a two-dimensional surface that had a large number of sub-wavelength unit elements placed in a periodic arrangement. The motion of the glass substrate enabled three modes of operation concerning the arriving radio waves: full penetration, partial reflection, and total reflection. Measurements obtained transmittance values in the range of \(-0.3\) to \(-15\) dB for frequencies varying from 26.8 to 27.3 GHz. Recently proposed, intelligent omni-surface (IOS) represents an improvement for IRS because the latter leads to incomplete wireless coverage because it either reflects or does not the arriving signal. In [88], the authors studied an IOS-aided downlink cellular system, in which signals received through the IOS were transmitted and reflected concurrently. This behavior was obtained after embedding \(N\) diodes in the IRS metal patch element and connecting each one to the ground. They stated that 2\({}^{N}\) phase shifts in total were possible for each element. Based on Monte Carlo simulation, the authors showed that by using IOS, the spectral efficiency could be doubled in comparison with the use of IRS. Finally, the EU-funded Pathfinder project was started in 2021, aiming to set the theoretical and algorithmic bases of IS-empowered wireless 2.0 networks [89]. Carrying on the advancements in the area of electromagnetic metamaterials, the project's long-term vision is to enable wireless channels to adapt to the operation of cellular networks. This will create a SRE in which radio waves circumvent physical obstacles; the signals will be combined at the receiver through the phase alignment of the IS elements, and sensors and IoT devices will \"recycle\" and backscatter the radio waves with IS.
## 3 Results and Discussion  
### Sugar Analysis by HPLC-Refractive Index Detector (RID)  
The results of the sugar (fructose, glucose, and sucrose) analysis in Talh, Athel, Sidr, Spring flower, and Langnese honey samples by HPLC-RID are shown in Figure 1 and reported in Table 1. The results showed that fructose and glucose (monosaccharides) are the major components in all honey samples; however, none of the samples contained sucrose. Honey samples generally contain \(<\)1% of sucrose concentration, but this may increase if the bees are overfed by the beekeepers using sugar solutions during spring. Quantitatively, fructose is the predominant sugar after glucose [35]. Fructose and glucose concentrations in the honey samples of the Hail province ranged between 28.35-37.81 g/100 g and 20.21-32.28 g/100 g, respectively. Fructose (37.81 g/100 g) was the highest sugar in Sidr (_Ziziplus spina-christi_ L.) honey, whereas glucose (32.2 g/100 g) was the highest in Athel (_Tamarix aphylla_) honey in comparison with other samples of honey. Total sugar content was the highest in Langnese honey (68.9 g/100 g), and Talh (_Acacia gerrardii_ Benth) honey had the lowest total sugar content (48.56 g/100 g). The range of sugars in the honey samples were between 48.56-68.9 g/100 g, and these results are similar to those obtained for honey samples from Australia [36]. The Asir province (Saudi Arabia) honey samples contained 33.1-44.8% fructose and 26.7-37.9% glucose [24]. Sugar composition in honey depends on the bee consumption of plant secretions or flowers (nectar sources) [37], therefore, the sugar contents are sometime used to identify the geographical origin of honey. Honey's phenolics, proteins, and sugars can combine to form high molecular weight melanoidins, multi-component polymers with antioxidant properties [38]. The results of this study on Hail province honey samples indicates their high quality, as they are free from sucrose adulteration.
## Methods
### ML Approach  
This study aimed to calculate the SSI from GK2A/AMI data using an ML approach. Because in situ SSI measurements consider global solar radiation, it is necessary to characterize not only the direct component but also the diffuse component of solar radiation. Thus, for producing the optimized SSI model, we applied a convolutional neural network (CNN), which could characterize the surrounding environment conditions.  
Because the SSI in this study represents global solar radiance, it includes the direct component and diffuse component of solar radiation. For improving the accuracy of SSI measurements from GK2A/AMI, it was useful to account for the nearest cloud and solar conditions and adjacent cloud and solar conditions using the convolution method. CNN trains contextual features of images at different scales. While CNNs were initially developed for image classification, they have recently proven to be effective in various applications related to satellite images, including object detection and super resolution imaging [31; 32; 33]. A CNN model comprises convolution layers and pooling layers with a number of neurons, and dense layers are often added. We applied a 1d-CNN model, which used a 3-by-3 array of input variables in the input layer and added a flatten layer and dense layer. The 1d-CNN model for extracting the patches in the flattened spectrum vector identifies descriptive local features of adjacent pixels [34]. The 1d-CNN model could be useful for identifying fixed-length signal data such as spectral sequential data and time series data [35].
## 2 Background
### Double Pulses in FROG
Equation (13) holds true even when the relative phase and separation between the two pulses changes. Note that \(M_{c}\) and \(M_{s}\) refer to the total energy in each lobe, not merely their intensity. Thus, even though changing the separation of the two pulses in the double pulse will change the position of the side lobe, the energy will always be recorded in the trace. The total energy in the side lobes is unchanged, even though their peak intensity is lower when the pulse separation varies. This expression also holds true when the two pulses have different spectra or temporal profiles, although the relationship between the pulse energy and the pulse intensity will be different for each pulse in that case. If the relative energy of the two pulses varies, then the calculated relative energy will be the average relative energy. This very simple analysis can provide an additional check on FROG for double pulses. If the retrieved temporal pulse shape has pulses with a different relative energy from the one derived from the energy in the lobes of the trace, then there is further evidence of instability. Our simulations will attempt to confirm the FROG post-processing approach presented above and study the FROG algorithm's response to traces generated from unstable double pulses. With simulations, we can also investigate cases that are difficult to address analytically, such as the presence of small variations in the relative phase or of correlations between the pulse relative phase and separation.
## 3 Calculation Results and Analysis
### The Influence of Rotational Velocity on Frequencies  
Figure 3 studies the influence of the dimensionless rotational velocity on the first three order dimensionless natural frequencies of the nano-annular plates under two boundary conditions. \(\tau=0.05\), \(s=0.1\), \(\Delta T=0\) K. It can be seen from Figure 3 that the influence of rotational velocity on natural frequencies is related to boundary conditions. During the rotation of the nano-annular plates under the C-C boundary condition, the increase in the rotational velocity weakens the equivalent stiffness of the nano-annular plates and reduces their natural frequencies. When the rotational velocity reaches a certain value, the natural frequencies drop to zero, which leads to the instability of the nano-plates. For the nano-annular plates with the C-F boundary condition, the natural frequencies gradually increase with the increase in the rotational velocity, which means that the increase in the rotational velocity enhances the equivalent stiffness of the nano-annular plates. Analyzing the essential reason through these two phenomena, the centrifugal forces linearly distributed along the radial direction are generated when the nano-annular plates rotate. From expression 16b, it can be analyzed that the internal forces \(N^{R}\) in the local unit width along the radial direction in the annular plates is negative, and local pressures are generated. As the rotational velocity increases, the local pressure values gradually increase. When the rotational velocity increases to a certain extent, the radial equivalent pressure values of the nano-annular plates exceed the critical load, resulting in instability, and the free vibration frequencies become zero. Therefore, the rotational velocity of the nano-annular plates with the C-C boundary condition should not be too high when working, otherwise they will be unstable and cannot work normally. It can be analyzed from expression 16a that the linearly distributed centrifugal forces \(N^{R}\) generated in the radial direction are positive when the annular plates rotate, so that the radial cross-sections are stretched, and as the rotational velocity increases, the tensile deformation increases. Thereby, the equivalent stiffness of the nano-annular plates is enhanced, and their natural frequencies increase.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 3 Bitwise Transform
### TAPKI System  
In order to discuss bitwise transform, we first must discuss the TAPKI system. TAPKI (Ternary Addressable Public Key Infrastructure) is the system by which we leverage the inherent entropy of physical devices, PUFs, to generate cryptographic keys. In short, the TAPKI system provides a consistent, repeatable, and secure means of PUF key generation by identifying the inconsistencies of each device and using that knowledge to purify the final key.  
To keep the focus within scope, and on QKD, we concern ourselves with only the relevant outputs of TAPKI. If a deeper understanding of the details and mechanics of the TAPKI system is desired, see [38; 39; 40; 41].  
The three TAPKI outputs to consider are the stream (\(S\)), mask (\(M\)), and consequent key (\(K\)). The stream is the raw bitstream extracted from the addressed cells of the PUF. The mask denotes the fuzzy bits of the stream (mnemonic: \"mask marks\"); \"fuzzy\" meaning the bit's state fluctuates non-deterministically between 0 and 1. To get a key (\(K\)), you apply the mask to the stream using the mask operator (\(\otimes\)). The mask operator is reductive: \(S\otimes M=K\) such that \(K<S\). Any fuzzy bit, marked with a 1 in the mask, is removed from the stream (\"masked\"), and we are left with a consequent key:  
\[\begin{array}{ccc}S:01010101&&S:01010101\\ \otimes M:00100100&\longmapsto&\otimes M:00100100\\ \hline K:011001\end{array} \tag{4}\]  
Normally, the key is 256 bits long and is the result of applying a 512 bit mask to a 512 bit stream. All of these parameters can be adjusted, but the mask must be the same size as the stream. Note that the longer the stream is than the key, the more selective you can be, and the more stable the output key becomes. With a 256 bit key and a 256 bit stream, you have no buffer and must proceed with all selected 256 bits. With a 256 bit key and a 512 bit stream, you have a 256 bit buffer and can cherry pick the best 256 bits to make up your key.
## 2 Preliminaries and Problem Formulation  
### Preliminaries  
#### 2.1.1 Graph Theory  
The interaction of a group of unmanned underwater vehicles is naturally modeled by a graph. For this reason, the vehicles can be represented as nodes (or vertices) of a graph, and the interactions, such as sensing and communication among the vehicles, can be represented as edges of the graph. Graph theory is a useful mathematical tool to design a distributed control for multi vehicle systems. A connected directed/undirected graph \(G\) can be mathematically denoted as, \(G=\{\mathscr{V},\mathcal{E},A\}\), where \(\mathscr{V}\) is the vertices set and \(\mathcal{E}\subseteq\mathscr{V}\times\mathscr{V}\) is the edges set, \(A\) is the adjacency matrix of \(G\) with the elements \(a_{ij}\), which is defined as \(a_{ii}=0\) and \(a_{ij}>0\) if the \(j^{th}\) vehicle interacts with the \(i^{th}\) vehicle, otherwise \(a_{ij}=0\). A matrix \(L=[\ell_{ij}]\in\mathbb{R}^{n\times n}\) is the Laplacian of a graph \(G\), which be defined as, \(\ell_{ii}=\sum_{j=1}^{n}a_{ij}\) and \(\ell_{ij}=-a_{ij}\ \forall i\
eq j\).
# Bee Swarm Metropolis-Hastings Sampling for Bayesian Inference in the Ginzburg-Landau Equation
## 2 Classical BSO Algorithm and MH Sampling
### Characteristics and Limitations of Classical MH Sampling  
The MH algorithm is a classical representative of MCMC methods that is widely used in Bayesian inference and probabilistic modeling. Its fundamental idea is to construct candidate samples based on the current state and decide whether to accept them according to an acceptance probability, thereby forming a Markov chain that converges to the target distribution.  
However, in practical applications, the efficiency of MH is constrained by several factors. First, MH is inherently a local sampling strategy: candidate generation depends on the current state, lacking the ability to perform large jumps across the state space. This makes it prone to getting trapped in local modes, especially in multimodal or high-dimensional distributions, resulting in strong sample autocorrelation and slow convergence. Second, the form of the proposal distribution and the choice of step-size parameters critically affect performance--poorly chosen values can lead to unacceptably low acceptance rates or insufficient exploration. Moreover, MH lacks built-in adaptive adjustment mechanisms and information-sharing capabilities, making it difficult to dynamically optimize sampling trajectories and posing clear disadvantages when dealing with complex posterior structures.  
Figure 1: Illustration of the search mechanism in BSO.  
Figure 2 illustrates the standard workflow of the MH algorithm, including initialization, candidate generation, acceptance probability computation, state updates, and iterative sampling. Although the MH procedure is conceptually straightforward, it lacks mechanisms for integrating and coordinating information across samples.  
Given these limitations, incorporating bee swarm-inspired mechanisms that offer parallel search, information sharing, and dynamic adjustment capabilities has the potential to retain the theoretical strengths of MH while significantly enhancing its ability to efficiently explore complex posterior landscapes. Motivated by this, the present work proposes a hybrid sampling algorithm that integrates swarm intelligence with the MH framework, aiming to balance sampling quality with global exploration capability and to build a more robust and adaptive tool for Bayesian inference.
## 5 Case Study: Panel Test and E-Nose for the Determination of VOOs' Shelf Life
### Virgin Olive Oil Shelf Life Assessment
(2010) [131] were able to detect aromatic defects in olive oil (namely gusty, moldy, muddy, rancid and winey) and their value with MOS sensors. The sensory analysis performed by trained panelists was shown to be more robust if associated with the E-nose. The authors of [135] demonstrated that, thanks to a combination of an E-nose, an E-tongue and an E-eye, the analysis of olive oil bitterness can be successfully performed. As such, many applications of E-nose have been tested for olive oil authenticity assessment and SL monitoring in recent years, and are summarized in Table 4. Of course, further research is still needed to develop advanced sensors with superior capabilities and sensitivity for olive-oil-specific traits; thus, making the evaluation faster and more accurate [117]. This topic is increasingly earning attention, since there is the necessity to develop and refine the existing analytical techniques to make them more reliable, fast and inexpensive. In particular, it is crucial to guarantee high quality traits over time, accessible to all the players of the olive oil production chain. In this sense, the applications of the E-nose, such as those discussed above, represent a promising opportunity to overcome the many limitations which still characterize the traditional techniques. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Application** & **Sensor Arrays** & **Chemometrics** & **Quantitative Classification** & **Classical Methods** & **Reference** \\  & & & **Approach** & **Performance** & & \\ \hline \multirow{8}{*}{**Discrimination**} & Discrimination & & & & & \\  & & edible and & & & & \\  & non-edible VOO & & & & & \\ \cline{1-1}  & Classification of & & & & & \\  & vegetable oils & & & & & \\ \cline{1-1}  & Discrimination & & & & & \\  & between VOO, & & (piezoelectric & & \\  & non-virgin and & seed oil & & & & \\ \cline{1-1}  & Discrimination & & & & & \\  & quality, variety of & & & & & \\  & olive and & & & & & \\  & geographic origin & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main applications of the electronic nose in the olive oil discrimination and evaluation.