esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Analytical Comparison of the Models  
To illustrate the differences between HSALA and the three-layered architecture, we examine several parameters: accessibility, cybersecurity, scalability, user experience (UX/UI), and additional considerations such as technology choices and development overhead.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 3 5G Services and Technical Requirements of Other Groups
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **User Experienced** & **End-to-end** & **Mobility** & **Device** & **Connection** & **Traffic** \\  & **Data Rate** & **Latency** & **Mobility** & **Autonomy** & **Density** & **Density** \\ \hline
**Broadband access in** & DL: 300 Mbps & On demand, & & & 2500/km\({}^{2}\) & DL: 750 Gbps/km\({}^{2}\) \\
**dense area** & UL: 50 Mbps & 100 km/h & & (Activity factor: 10\%) & UL: 125 Gbps/km\({}^{2}\) \\ \hline
**Indoor ultra-high** & DL: 1 Gbps & 10 ms & Pedestrian & \(>\)3 days & 75,000/km\({}^{2}\) & DL: 15 Tbps/km\({}^{2}\) \\
**broadband access** & UL: 500 Mbps & & & (Activity factor: 30\%) & UL: 2 Tbps/km\({}^{2}\) \\ \hline
**Broadband access** & DL: 25 Mbps & & & & 150,000/km\({}^{2}\) & DL: 3.75 Tbps/km\({}^{2}\) \\
**in a crowd** & UL: 50 Mbps & & & (Activity factor: 30\%) & UL: 7.5 Tbps/km\({}^{2}\) \\ \hline
**50+ Mbps everywhere** & DL: 20 Mbps & 10 ms & 120 km/h & \(>\)3 days & 400/km\({}^{2}\) in suburban & DL: 20/5 Gbps/km\({}^{2}\) (suburban/rural) \\  & UL: 25 Mbps & & & 100/km\({}^{2}\) in rural & UL: 10/25 Gbps/km\({}^{2}\) (suburban/rural) \\ \hline
**Ultra low-cost** & DL: 10 Mbps & 50 ms & On demand, & \(>\)3 days & 16/km\({}^{2}\) & DL: 16 Mbps/km\({}^{2}\) \\
**access for low ARPU areas** & UL: 10 Mbps & 50 ms & 50 km/h & \(>\)3 days & (Activity factor: 10\%) & UL: 16 Mbps/km\({}^{2}\) \\ \hline Mobile broadband & DL: 50 Mbps & 10 ms & On demand, & \(>\)3 days & 200/km\({}^{2}\) & DL: 10 Gbps/km\({}^{2}\) \\
**in vehicles** & UL: 25 Mbps & up to 500 km/h & & (Activity factor: 10\%) & UL: 50 Gbps/km\({}^{2}\) \\ \hline
**Airplanes connectivity** & DL: 15 Mbps & & Up to 1000 km & N/A & 80 per plane & DL: 1.2 Gbps/plane \\  & UL: 75 Mbps & & & & 60 planes/18,000 km\({}^{2}\) & UL: 600 Mbps/plane \\ \hline
**Massive low-cost** & Low & & & & \\ long-range/low-power MTC & (typically 100 kbps) & Seconds to hours & On demand, & Up to 15 years & Up to 200,000/km\({}^{2}\) & Not critical \\ \hline
**Broadband MTC** & DL: 300 Mbps & & & & \\  & UL: 50 Mbps & & & & 2500/km\({}^{2}\) & DL: 750 Gbps/km\({}^{2}\) \\ \hline
**Ultra low latency** & DL: 50 Mbps & & & & \\  & UL: 25 Mbps & \(<\)1 ms & Pedestrian & \(>\)3 days & Not critical & Potentially high \\ \hline
**Resilience and** & DL: 1 Mbps & & & & \\ _traffic surge_ & UL: 1 Mbps & & & & \\ \hline Ultra-high reliability & DL: 10 Mbps & 1 ms & On demand, & & \\  & UL: 10 Mbps & & 500 km/h & Not critical & Not critical \\ \hline
**Ultra-high availability** & DL: 10 Mbps & & On demand, & & \\  & and reliability & UL: 10 Mbps & & & \\ \hline
**Broadcast like services** & DL: Up to 20 Mbps & & & & \\  & UL: Modest & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Technical requirements for 5G services in NGMN [10]. ARPU, average revenue per user.
## 3 Methods and Simulation  
### Decay Process and Electrostatic Collection  
\({}^{222}\)Rn and \({}^{220}\)Rn decay by emitting \(\alpha\)-particles with energy of 5.49 MeV and 6.30 MeV respectively. \({}^{222}\)Rn \(\alpha\)-emitter daughters are \({}^{218}\)Po (6.02 MeV) and \({}^{214}\)Po (7.68 MeV); while those of \({}^{220}\)Rn are \({}^{216}\)Po (6.77 MeV), \({}^{212}\)Bi (6.09 MeV) and \({}^{212}\)Po (8.77 MeV). The \(\alpha\)-particles in air lose some or all of their energy before to hit the detector, except those emitted nearby that can be detected with full energy [23]. It is well known that the daughter nuclei of \({}^{222}\)Rn and \({}^{220}\)Rn become positively charged in air: at atmospheric pressure and temperature, with a standard relative humidity of 45%, the fraction of ionized daughters of radon e thoron is 88%, the remaining 12% are neutral [24]. Therefore, it is possible to electrostatically collect the 88% part that will decay with full \(\alpha\)-energy on detector, inside a metallic chamber. To achieve the electrostatic collection, a high potential difference is applied between chamber and detector: an electric field is formed inside the detection chamber and the positive charged ions can be collected on the detector surface grounded. Instead, \({}^{222}\)Rn, \({}^{220}\)Rn and about their 12% daughters are neutrally charged in air; a direct detection of them is possible and the emitted \(\alpha\)-particles arrive with energy depending on the sizes of the chamber [18; 24].  
From the simulation point of view, to reproduce the physical process above described in Geant4, the pre-package physics list \(Q\)_GSP_BERT_HP_, recommended for low energy dosimetric application, is used, which contains the process _G4 RadioactiveDecay_ to simulate radioactive decay processes. The radionuclides production is made by _G4 VUserPrimaryGeneratorAction_ class, where through the use of the _GeneratePrimaries()_ method they are defined by specifying the atomic number, mass number, ion charge and excitation energy. Each radionuclide is simulated separately, distinguishing between ionized and non-ionized and respecting the percentage proportions of the decay chain to which it belongs. For our purposes, \({}^{222-220}\)Rn and their progeny are generated randomly with a uniform probability density function (PDF) within the whole chamber volume. This is performed with the random number generator by _G4 UniformRand()_. Emitted \(\alpha\)-particles produced during decay are generated by choosing a random emission direction in the whole 4\(\pi\) solid angle.
## Appendix A Cellular Network Technologies
### Architecture
#### Physical-Layer Cell Identifiers  
In 2G GSM, cells are identified at the physical layer via the base station ID code (BSIC), transmitted over the synchronization channel (SCH) about 25 times per second, giving the opportunity to quickly gather enough information to discriminate the received cells. It consists of 6 bits, which are usually structured as follows (such an organization is not mandatory):  
* The three most significant bits are the network color code (NCC), which can be used to distinguish one operator from the other, as in a country, a distinct NCC is assigned to each operator;
* The latter three bits are the base station color code (BCC), which can be used to discriminate among the cells associated with the same network operator.  
In principle, neighboring cells should have a different BCCs, but, since there are only \(2^{3}\) distinct values available, there is often the need to reuse codes. In order to uniquely identify the cells, one can pair the BSIC with the absolute radio frequency channel number (ARFCN). The ARFCN is a short description for the radio carrier resources (a pair of downlink/uplink), and its value may vary from 0 to 1023 (not all values are used). The same pair (BSIC, ARFCN) cannot be reused within the same region of the network.
## 1 Introduction  
Since the recent Fukushima Daiichi nuclear disaster, concerns about nuclear mass-casualty events has intensified. In the event of a large-scale radiological incident affecting populated areas,a quantitative assessment of the consequences for human health requires the availability of suitable techniques and procedures for dose reconstruction. Therefore, retrospective assessment of the radiation exposure is vital to the analysis of the radio-epidemiological studies and radiation risk. In addition, retrospective dosimetry is a useful tool for timely assessments of exposures to the general population and the evaluation of individual doses as a basis for the selection of appropriate countermeasures.  
For a large-scale radiological accident, the exposure of radiation workers can be found from personal monitors. However, for personnel not equipped with such a device, pursuing an alternative approach is required. Electron paramagnetic resonance (EPR) dosimetry is a method of quantitating the radiation-induced radicals, making it possible to measure the radiation dose absorbed by the irradiated material [1; 2; 3; 4; 5; 6; 7]. Owing to this feature, EPR-based dosimetry has been established for many applications such as radiation processing [8], identification of irradiated foods [9; 10], radiation oncology [11], evaluation of radiation risk [12], and archeological dating [13].  
The tooth enamel is one of the substances established as a suitable dosimeter for individual dose reconstruction following radiation accidents [14; 15; 16]. [PERSON] and [PERSON] [17] have considered the use of human fingernail for measurements of free radical production resulting from radiation damage. However, these materials are only applicable if the human teeth or the fingernail can be readily obtained from accident victims now deceased or from patients in the normal course of dental treatment. The aim of the study is to investigate the use of materials contained in creatures from sea or land potentially as an environmental dosimeter for a retrospective dose analysis in areas and locations where radiation-monitoring measurements were not performed. In this work, EPR technique was applied to irradiated biological samples, investigating the effect of irradiated dose, grain size, and storage temperature with the period following irradiation on the EPR intensity.
# An Anchor-Free Location Algorithm Based on Transition Coordinates
## 4 Computational Complexity  
Computation complexity is an important factor to consider when choosing a technique for the unanchored location. The higher the accuracy of the positioning required, the more times the algorithm has to be calculated and the longer the algorithm takes to calculate. Similarly, the more nodes are located, the more times the algorithm is computed and the longer the algorithm takes to compute. The computational complexity of the existing centralized algorithm is generally \(O(N^{3})\), the computational complexity of the semi-centralized algorithm is generally \(O(Nk^{3})\), and the computational complexity of the distributed algorithm is generally \(O(NL)\), while the complexity of the algorithm proposed in this paper is \(O((N-1)L)\). The symbols \(N\), \(k\), and \(L\) in the above computational complexity represent the number of nodes, the number of neighbors, and the number of iterations, respectively, where the number of nodes is greater than the number of neighbors [20]. Table 2 summarizes the computational complexity of the existing algorithms and the algorithms proposed in this paper. Compared with the existing localization algorithms, the proposed algorithm has less computational complexity and can quickly locate the location coordinates of nodes.
# Home-Based Lower Limb Exercises for Individuals with Diabetes: A Scoping Review
## 2 Materials and Methods
### Eligibility  
This review focused on studies that analyzed home-based lower limb exercises in individuals with DM. It included only full-text articles in English, with applied the eligibility criteria.  
The inclusion criteria were as follows: (1) only human adults participated in studies; (2) participants with DM and/or with peripheral neuropathy induced by diabetes; (3) studies where the main intervention was exercised relevant to the lower limb; (4) home-based lower limb exercises performed by the study participant on their own after receiving instructions in various forms, e.g., using verbal instruction, booklets, cards, online applications, etc.; and (5) studies evaluated longer-term outcomes, i.e., exercises performed for at least 4 weeks or more.  
The exclusion criteria applied in this review were as follows: (1) non-scientific publications; (2) articles in other language than English; (3) not full-text articles; (4) non-experimental articles, such as reviews or meta-analyses; (5) studies conducted for participants without DM or with DPN caused by other causes than diabetes; (6) pregnant participants of study; (7) studies that analyzed the effects of exercises not relevant to the lower limbs or combined with exercises for other parts of the body; (8) lower limb exercises combined with an additional type of intervention, i.e., pharmacological or electrical stimulation or physical therapy (i.e., combined therapies); (9) exercises performed in medical facilities or/and under supervision; (10) exercises conducted for improvement of healing the DFU; (11) exercises performed for less than 4 weeks; (12) home-based exercises performed only remotely or as follow-up to support supervised exercise protocol; (13) exercises conducted for/by amputees; and (14) papers analyzing the same data already included in this review, as well as proof-of-concept studies, feasibility studies, Delphi studies, or study protocols. Articles focusing on aspects outside the scope of this review, such as cost-effectiveness, were also removed (Figure 1).  
Figure 1: A flowchart demonstrating the selection of articles through the review process [9; 10].
## 4 Hierarchical Incremental Maps
### Node Merging  
According to the previous processes, the number of clusters constantly increases. Therefore the resulting number of clusters is sometimes higher than necessary and images that represent adjacent and/or visually similar zones may be included in a unique cluster. The proposed method has the possibility to decrease the number of clusters by merging similar nodes when necessary. That is possible because the method detects when two clusters are similar and the map would be more consistent if they are merged. A new condition is introduced and a newly created node \(N_{q}\) has to exceed a specific dissimilarity threshold to the other clusters. If an existing cluster and a new one are similar, they are merged in a unique cluster. To solve this problem the Mahalanobis distance is used as in Node Level Loop Closure process. Equation (10) shows this condition in the node merging process  
\[\Delta_{N_{q}}^{N_{q}}=(\vec{\mu}^{N_{q}}-\vec{\mu}^{N_{o}})^{\text{T}}(\sum \limits_{d}{}^{N_{o}})^{-1}(\vec{\mu}^{N_{q}}-\vec{\mu}^{N_{o}}) \tag{10}\]  
where I = 1,2,...,C and \(C\) is the number of clusters before creating \(N_{q}\). To detect if the node \(N_{q}\) should merge with another node \(N_{l}\), \(\Delta_{N_{q}}^{N_{l}}\) is evaluated. This distance has to satisfy the condition of similarity presented in Equation (11), where \(\mu_{ns}^{N_{o}}\) and \(\sigma_{ns}^{N_{o}}\) are the mean and the standard deviation of the Gaussian distribution calculated with the data of node \(N_{o}\), calculated as detailed in Section 4.1. \(y\) is a parameter which has to be empirically tuned (Table S4). The higher it is, the less restrictive the condition is, so the algorithm is more prone to merge similar clusters. Experiments show that this threshold should take a value between 1 and 2. If \(y=1\) the condition is very restrictive so less mergers are done and more clusters will be in the final map, but when \(y=2\) the condition is less restrictive, so more mergers are done and less clusters will be at the end. Experiments to test the values of \(y\) are carried out in Section 6.  
\[\left|\Delta_{N_{q}}^{N_{o}}-\mu_{ns}^{N_{o}}\right|\leq\left|y\sigma_{ns}^{N_ {o}}\right| \tag{11}\]
## 4 Discussion  
The Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework has been rigorously tested across complex industrial datasets, such as the Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB), demonstrating substantial improvements in fault-diagnosis capabilities. ATASML is particularly effective in few-shot learning scenarios that often challenge traditional models due to limited data availability.  
A key innovation of ATASML is its use of adversarial examples integrated within a meta-learning structure, which significantly enhances the model's ability to generalize from limited samples. Unlike traditional approaches such as MAML and GOPML, which, while robust, do not specialize in rapid adaptation to new and complex tasks, ATASML's strategic task sequencing further refines this capability by arranging learning tasks to optimize both the accuracy and speed of the learning process, utilizing adversarially augmented data to effectively challenge the model under diverse conditions.  
To underscore the efficacy of the ATASML framework, this study compares it with renowned deep learning models such as VGG-11 and ResNet-18, which are benchmarked for their ability to generalize from extensively pretrained features to new, unseen fault conditions. These models are thoroughly adapted to specific fault-diagnosis tasks by fine-tuning on targeted datasets. Our experiments concentrate on demonstrating that the ATASML framework, when subjected to comprehensive network fine-tuning, achieves superior accuracy compared to merely fine-tuning the classifiers, particularly under few-shot learning conditions. Moreover, to ensure a fair comparison across all evaluated methods, an N-way K-shot setup was employed. This setup not only maintains the integrity of the experimental comparisons but also aligns with the research paradigms of few-shot learning, allowing for a precise assessment of the ATASML framework's performance in complex fault-diagnosis tasks.  
However, the framework's computational intensity, particularly when processing large, complex datasets, highlights an area for potential improvement. Future work will focus on optimizing the computational efficiency of ATASML to enhance its scalability and practical applicability in industrial settings. This will involve refining the adversarial training components and exploring more efficient ways to implement task sequencing that reduces computational demands while maintaining high diagnostic accuracy.
A vast majority of them are related to plant fibres because of their good mechanical properties. [PERSON] et al. [17] used the addition of modified and non-modified flax fibres in injection-moulded composites to improve their mechanical properties. The use of flax fibres resulted in an improved Young's modulus compared to that of pure PLA. [PERSON] et al. [18] investigated the effect of weather conditions on composites with flax fibres compared to PVC. The initial samples showed properties better than PVC, and, despite ageing, their properties were still comparable. During testing in high humidity, they noticed swelling of the fibres, which caused cracking of the matrix. [PERSON] et al. [19] comparedflux composites with biodegradable and PP matrixes. They confirmed in their study the possibility of designing materials with a mechanical performance suitable for industry and are additionally able to offer faster biodegradation. In another study, [PERSON] et al. [20] investigated the effect of humidity on flax composites, where only at high humidity values (98%RH) did the mechanical properties decrease significantly. [PERSON] and [PERSON] [21] tested flax/PLA composites against flax/epoxy for their thermo-mechanical properties. The use of epoxy resin has advantages in terms of thermal stability, but the high Young's modulus obtained for PLA suggests that its composites have a good chance of being used in automotive applications. Much fewer studies address the use of wool fibres in composites. [PERSON] et al. [22] studied the mechanical and thermal properties of wool/epoxy composites. They obtained composites with good interfacial adhesion and improved tensile strength. [PERSON] et al. [23] conducted a study in which they created hybrid composites with date palm fibre/sheep wool in a polyester matrix. The compound containing 20 wt.% of the date palm fibre and wool with a mass ratio 50/50 showed the best properties in mechanical tensile and impact tests. Its properties were good enough for use in materials of daily use, while keeping the material very lightweight, which is one of the greatest advantages of using sheep wool. [PERSON] et al. [24] made sheep wool/PCL (polycaprolactone) composites suitable for 3D printing. The composite with the addition of 10% wool showed increased biodegradability and higher yield strength. The main objective of this research was to determine and compare the properties of biocomposites based on PLA and wool or flax fibres before and after the degradation process and to compare the effects of different natural fibres on the degradation process of the polymer matrix.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 2 Literature Review
### Review Conclusions  
Although research has been conducted on speech reconstruction from silent videos, there are two crucial issues. Firstly, while ensuring the quality of speech prediction, the model's performance also needs to be considered. This includes factors such as the model being lightweight and robust, as these are essential for practical deployment. To the best knowledge of the authors, there is no relevant research addressing this problem in this domain, as the key focus of current research is on speech reconstruction with deep neural networks without considering lightweight solutions. Secondly, speaker-independent speech reconstruction results are currently unsatisfactory. Reconstructing speech from silent videos involving multiple speakers requires supplementary audio or textual inputs. There is a need for a direct speech reconstruction model reconstructing speech from multiple speakers without additional supplementary data.