esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### Blockchain-Based Distributed Data Storage and File Sharing Projects  
Distributed and blockchain-based data storage solutions vary based on their security features and application domains. Table 11 provides a detailed overview of prominent projects, storage methodologies, data retention strategies, and industry applications associated with these solutions.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline  
Table 11 provides a detailed analysis of how blockchain and distributed storage solutions are applied across various use cases.  
Filecoin and Storj, which utilize off-chain storage, offer decentralized and secure cloud storage while ensuring data integrity through strong encryption techniques. However, these systems face scalability challenges and are dependent on node participation for reliability. Hybrid systems like Flux and Helium integrate blockchain infrastructure with their own specialized networks, providing high-security solutions for decentralized applications and IoT data transfer. While they offer flexibility and cost efficiency, complex configuration requirements pose integration challenges. On-chain storage solutions, such as Arweave and Civic, focus on permanent data storage and identity verification, addressing critical security needs. These platforms ensure long-term data retention and user-controlled data access. However, high transaction costs and regulatory compliance challenges limit their widespread adoption. Among off-chain systems, Sia and BitTorrent provide low-cost and fast content distribution but face security vulnerabilities and piracy risks. Hybrid and off-chain solutions like Ankr and Holo offer secure storage for decentralized application hosting and enterprise cloud services. However, they face challenges related to early-stage development and high market competition. These projects demonstrate how blockchain-based storage solutions can be adapted across different industries while highlighting the unique advantages and limitations of each approach.
# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 4 Evaluation  
In order to test our approach in a low-resource dataset, we use the training dataset provided during the Neural Information Processing Scaled for Bioacoustics (NIPS4B) bird song competition of 2013 that is publicly available and contains 687 recordings of maximum length of 5 s each [32]. For the NIPS4B dataset the recordings have already been weakly labelled and the labels are provided by the organisers along with the dataset recordings. The dataset contains a total of 87 classes, with each being active in only 7 to 20 recordings. Each recording has 0 to 6 classes active in it. Such a dataset can be considered low-resource since the total amount of training time is less than one hour and also there are 87 possible labels that have very sparse activations, 7 to 20 positive recordings for each.  
For our experiments, we split the NIPS4B 2013 training dataset into a training set and testing set. During the NIPS4B 2013 bird song competition, only the weak labels for the training dataset were released, hence we could only use these recordings and couldn't make any use of the NIPS4B 2013 testing dataset that consisted of more recordings. We enlisted an experienced bird watcher to manually annotate strong labels for most of the training dataset recordings [33]. For our training set, the first 499 recordings of the NIPS4B 2013 training dataset are used, while the rest are included in our testing set, excluding 14 recordings for which confident strong annotations could not be attained. Those 14 recordings were added to our training set for a total of 513 training recordings and 174 testing recordings.  
In order to efficiently use the data provided by the NIPS4B 2013 training dataset for our WHEN task, we first consider all 87 unique labels as one general label 'bird' and train an audio event detection network for this class. Another limitation of this dataset is the imbalance of positive and negative recordings: out of the whole dataset (687 recordings), only 100 of them are labelled as negative (not having any bird present in them). We provide a balanced training set by using our Half and Half training approach. For this dataset, the training set consists of 450 recordings in total (385 positive, 65 negative), each recording being 432 time frames long, totalling less than 40 min of total training audio. During training, HnH will randomly duplicate the negative recordings in order to balance their amount with the positive recordings, hence creating a training set of 770 recordings half of which are unique positive recordings and the other half are randomly duplicated negative recordings.
## 5 Results and Discussion
### Ratio of the Vertical Response Spectra to the Horizontal Response Spectra (V/H)  
The horizontal and vertical standards are commonly presented simultaneously, considering the vertical response standard is essential in seismic design. Many seismic design criteria (Table 3) include the vertical to horizontal (V/H) response spectra ratio over short and long periods.  
Figure 9 shows the average and individual V/H values of the 8 stations. The V/H was higher than 2/3 for most frequency bands except 0.3-0.4 Hz and \(\sim\)2 Hz, which were consistent with the V/H scales suggested by various seismic design standards (Table 3) and a previous study ([PERSON] and [PERSON], 2004) [12]. Although the frequency range of high responses including the maximum has a higher amplitude than that of inland areas, it shows similar characteristics to inland H/V, indicating that the vertical component response value increases simultaneously as the horizontal component response value increases.  
Moreover, this study also investigated the dependence of V/H values on the earthquake magnitude, as shown in Figure 10. The classification criteria of the three different levels of earthquake magnitude considered for this evaluation were the same as that in the previous section. Although some frequency bands were smaller than 3/2 in the V/H for all three seismic magnitude groups, they consistently showed values smaller than 1 in the entire frequency band and are independent of the magnitude of the earthquake, which is consistent with the various seismic design standards. Additionally, considering the vertical component is always smaller than the horizontal component response in the entire frequency band, no significant dependence of the V/H values on the earthquake magnitude for the horizontal and vertical components of the response spectra was observed.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Structure  
This work is organized as follows. Section 2 discusses the key steps in performing OCR. Section 3 describes the challenges in Arabic OCR. Section 4 illustrates deep-learning techniques that will be used as components of Arabic OCR and text correction. Section 5 presents the metrics used to evaluate OCR accuracy. Sections 6 and 7 are the core of the paper, in which recent works are critically analyzed and discussed. First, OCR deep-learning works are critically analyzed. These works are classified in three types: CNN-LSTM-CTC-based works, Transformers-based works, and GANs-based works. Second, post-OCR text-correction works are thoroughly analyzed. Section 7 discusses the analyzed works in more depth, highlighting the findings from our analysis. In Section 8, challenges and future trends are extrapolated from two sources: the gaps in recent works and the possibilities of applying findings from other areas in the Arabic domain. Section 9 finally recapitulates the whole paper and findings in a conclusion that can serve as a door-opener for other researchers and readers.
# HS-SPME Gas Chromatography Approach for Underivatized Acrylamide Determination in Biscuits
## 3 Results and Discussion
### Validation of the HS-SPME/GC-MS Methodology for Acrylamide Quantification  
An external calibration curve was built using increments of AA in model aqueous solutions (Figure 5). Representative chromatograms (standard solution and biscuits samples) and respective mass spectrum for AA were included as Supplementary Materials (Supplementary Figure S2).  
A good linearity was obtained with a coefficient of determination (R\({}^{2}\) > 0.99). Furthermore, the developed methodology showed high precision (RSD < 9%), with limits of detection, LoD = 0.11 ug/vial, and quantification, LoQ = 0.37 ug/vial. Considering that 4 g of biscuit in a 120 mL SPME vial are used for sample preparation, the LoD and LoQ can be estimated to be 27.4 ug/kg\({}_{\text{biscuits}}\) and 91.5 ug/kg\({}_{\text{biscuits}}\), respectively. Contrarily to the methods described in bibliography (Table 1) this work proposes a methodology that does not requires clean-up nor derivatization of acrylamide. Because the LoQ is still below the benchmark level for AA in biscuits (350 ug/kg\({}_{\text{biscuits}}\)) recommended by the European Food Safety Agency [5], the proposed methodology could be implemented in routine analysis for AA monitoring in biscuits.
## Materials and Methods  
### Materials and Mixture Proportions  
Polyvinyl-alcohol (PVA) was used as a reinforcing fiber for the HDCCs investigated in this study and the properties of PVA fiber are listed in Table 1. Type I ordinary Portland cement with a density of 3.15 g/cm\({}^{3}\) was used as the main binding material. The limestone powder was used as a replacement for cement. The density and fineness of the limestone powder was 2.83 g/cm\({}^{3}\) and 4521 cm\({}^{2}\)/g, respectively. Silica sand with a density of 2.65 g/cm\({}^{3}\) and an average size of 100 \(\upmu\)m was used as fine aggregate. Coarse aggregate was excluded to improve the tensile behavior because it increases the fracture toughness of the matrix. Superplasticizer (SP), viscosity modifying agent (VMA), and deformer blended with hydrocarbons and polyglycols were added as admixture to improve the dispersion of the PVA fiber within the mixtures.  
The mix proportions of the HDCCs investigated in this study are listed in Table 2. The HDCC0 mixture was the control mixture and did not contain limestone powder. HDCC15, HDCC30 and HDCC45 were designed to investigate the effect of replacement of cement with various amounts of limestone powder, on the composite properties and micromechanical properties of the HDCC.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fiber**} & **Density** & **Length** & **Diameter** & **Tensile** & **Young’s** & **Elongation** \\  & **(g/mm\({}^{3}\))** & **(mm)** & **(\(\upmu\)m)** & **Strength (MPa)** & **Modulus (GPa)** & **(\%)** \\ \hline PVA & 1.30 & 12 & 40 & 1560 & 41 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of polyvinyl-alcohol (PVA) fiber.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & **Water** & **Cement** & \multicolumn{2}{c}{**Limestone**} & \multirow{2}{*}{**Sand**} & \multicolumn{3}{c}{**Admixtures**} & **PVA Fiber** \\  & & & & **Powder** & & **SP** & **VMA** & **Deformer** & **(by vol.)** \\ \hline HDCC0 & 0.45 & 1 & 0 & 0.80 & 0.0031 & 0.0020 & 0.0020 & 2 \\ HDCC15 & 0.45 & 0.85 & 0.15 & 0.80 & 0.0026 & 0.0026 & 0.0020 & 2 \\ HDCC30 & 0.45 & 0.70 & 0.30 & 0.80 & 0.0025 & 0.0030 & 0.0020 & 2 \\ HDCC45 & 0.45 & 0.55 & 0.45 & 0.80 & 0.0023 & 0.0035 & 0.0020 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportion of highly ductile cement composites (HDCCs) (proportion by weight of cement and limestone powder).
# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 3 Proposed Method  
The segmentation of cervical cells is a tricky task, due to their large variation in size, shape, and staining, low contrast between target and background, and blurred and irregular boundaries. A high-performance network for extracting rich features from images is required to meet these challenges. In this paper, we propose a module for global information modeling and feature refinement (GDLA), which consists of three main components: a global dependency module, a spatial attention module, and a channel attention module. The global dependency module models the global context information to automatically extract a priori knowledge of the shape and location of cervical cells, such as the closeness and uniqueness of nuclei. The spatial attention module combines contextual information to extract boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information. The three networks are computed in parallel and finally applied to the original feature map to enhance the feature representation. Given an intermediate feature map \(X\in\mathbb{R}^{\mathbb{C}\times H\times W}\) as an input, GDLA infers in parallel a global dependency map \(F_{\text{G}}\in\mathbb{R}^{\mathbb{C}\times 1\times 1}\), a channel attention map \(F_{\text{C}}\in\mathbb{R}^{\mathbb{C}\times 1\times 1}\), and a spatial attention map \(F_{\text{S}}\in\mathbb{R}^{1\times H\times W}\). The overall framework structure of the GDLA Model is shown in Figure 2, which does not change the feature map size and can be easily applied to different layers of the backbone. The overall process can be summarized as:  
\[\widehat{X}=\{F_{\text{G}}(X)\oplus X\}\oplus\{F_{\text{C}}(X)\otimes X\} \oplus\{F_{\text{S}}(X)\otimes X\} \tag{1}\]  
where \(\otimes\) and \(\oplus\) denote element-wise multiplication and element-wise addition. During multiplication or addition, the tensor values are broadcast (copied), accordingly. \(\widehat{X}\) is the final refined output. Figure 3 depicts the computation process of each component. The following describes the details of each module.  
Figure 2: Overview of GDLA model.
###### Abstract  
As large-scale low Earth orbit (LEO) constellations continue to expand, the potential of their signal strength for positioning applications should be fully leveraged. For high-precision angle of arrival (AOA) estimation, current spectrum search algorithms are computationally expensive. To address this, we propose a method that downscales the 2D joint spectrum search algorithm by incorporating satellite ephemeris a priori information. The proposed algorithm efficiently and accurately determines the azimuth and elevation angles of NLOS (non-line-of-sight) signals. Furthermore, an NLOS virtual satellite construction method is introduced for integrating NLOS satellite data into the positioning system using previously estimated azimuth and elevation angles. Simulation experiments, conducted with a uniform planar array antenna in environments containing both LOS (line-of-sight) and NLOS signals, demonstrate the effectiveness of the proposed solution. The results show that the azimuth determination algorithm reduces computational complexity without sacrificing accuracy, while the NLOS virtual satellite construction method significantly enhances positioning accuracy in NLOS environments. The geometric dilution of precision (GDOP) improved significantly, decreasing from values exceeding 10 to an average of less than 1.42.  
s +
Footnote †: journal: Applied Sciences  
1  
e lect: 10 November 2024  
11 December 2024  
Academic Editors: [PERSON] and [PERSON]
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 1 Introduction
### Related Work
#### 1.3.2 Enhancing Energy Efficiency in WDM Backbone Networks  
Extensive research efforts focus on developing survivable and energy-efficient networks. The literature presents numerous power-efficient algorithms to address this goal. In [21], two distinct protection strategies, dedicated-link and dedicated-path protection, were proposed, with a focus on energy efficiency. They introduced an integer linear programming formulation to enable power-aware network design, utilizing sleep mode for devices involved in backup path provisioning. In [22], the authors introduced an energy-efficient WDM network capable of withstanding single link and node failures. This approach achieves energy efficiency through optimized resource allocation for working and protection lightpaths, as well as the reduction of power consumption associated with redundant resources. [PERSON] and colleagues [23] presented a scalable algorithm for static energy-aware lightpath provisioning in WDM networks, with a specific focus on dedicated 1:1 path protection. In [24], [PERSON] et al. compared four protection strategies, namely, shared-link, shared-path, dedicated-link, and dedicated-path protection. They provided mathematical models to support power-aware design in a sleep-mode scenario. Furthermore, in [25], the authors proposed a cognitive power management technique based on traffic prediction to efficiently activate or deactivate underutilized network elements. In [26], the authors introduced a hybrid architecture of the WDM network combining free space optics (FSO) and single-mode fiber (SMF)-based optics and achieved a significant reduction in energy consumption. Furthermore, in [27], the authors used a mixed-integer linear programming (MILP) model to compare different network topologies and optimize and minimize the embodied energy consumption of network equipment in the IP and optical layers. The authors of [28] presented a thorough examination of advancements in strategies aimed at enhancing the energy efficiency across various categories of networks. These encompass radio-and-fiber (R&F) networks, radio-over-fiber networks (RoF), hybrid fiber-wireless (FiWi) networks incorporating multi-access edge computing (MEC), and software-defined network (SDN)-based FiWi networks. Additionally, the paper explored potential future avenues for further enhancing energy efficiency within FiWi networks. In [6], the introduction of a new power-aware algorithm, which ensures energy efficiency by establishing the minimum number of backup links in the virtual topology to maintain tolerant connectivity, was presented. The protection scheme used was the \(1+1\) link protection scheme, where for every connection of two nodes, a backup connection is also provided that can be used in case of failure of the first one.  
The network paths that may be present in the area that will suffer a catastrophic earthquake, as can be seen in Figure 2, are the A-B, B-A, A-B-E, and E-B-A paths. The appropriate optimal backup paths for this case are the A-D-C-B, B-C-D-A, A-D-C-E, and E-C-D-A paths.
## 6 Palladium
### The Heck Reaction  
The palladium-catalyzed coupling of acrolein with aryl halides using the Heck reaction is usually fraught with difficulties due to the propensity for acrolein to polymerize during the reaction conditions. By using acrolein diethyl acetal as the substrate under so called Cacchi conditions [55], [PERSON] and [PERSON] have shown that cinnamic aldehyde derivatives can be prepared from aryl halides via the Heck reaction using a polystyrene-supported Kaiser oxime palladacycle (**28**, Scheme 20) [56]. Aryl iodides, bromides and chlorides could all be used as the aromatic halide precursor. 3-Arylpropanoates could also be prepared under somewhat different reaction conditions, using the same catalyst. The efficiency of the polymer-supported catalyst was found to be in line with the corresponding unsupported catalyst, and leaching of palladium into solution was negligible (0.06-0.08 ppm). A more complete study from the same group includes the use of other alkene substrates for the Heck reaction [57]. Other examples of Heck reactions using polymer-bound catalysts have been reported by [PERSON] _et al._[58], involving the use of poly(_N_-vinylimidazole) as the polymer, and [PERSON] and co-workers [59], who used mesostructured polymeric supports linked to a palladium catalyst.  
**Scheme 20.** Heck coupling of acrolein with 1-bromonaphthalene using a supported Kaiser oxime palladacycle.  
_N_-Heterocyclic carbenes (NHCs) are useful ligands to metals like palladium, but the free carbenes are unstable entities that can be difficult to handle. Carbon dioxide can be used as a protecting group for carbenes, however [60,61]. This practical technique has been exploited in a solid phase context by [PERSON] and [PERSON], who prepared a carbon dioxide adduct of a tetrahydropyrimidine-derived carbene attached to a polymeric support, which in turn was prepared _in situ_ via ring-opening metathesis of a pendant norbornene functionality attached to the protected carbene [62]. The protected polymer-bound ligand was then converted to a supported metal complex, the metal being rhodium, iridium or palladium. The palladium-supported complex **29** (Scheme 21) was found to be very efficient as a catalyst in the Heck reaction of styrene and butyl acrylate with various aryl bromides, with turn-over numbers as high as 100,000 (TOF 25,000) (Scheme 22). The corresponding iridium complex was applied in a hydrogen transfer reaction, while the rhodium complex was used for the polymerization of phenyl acetylene.  
**Scheme 21.** Preparation of a polymer-bound CO\({}_{2}\)-protected _N_-heterocyclic carbene and its direct conversion into a supported palladium catalyst.  
**Scheme 22.** Application of polymer-supported catalyst **29** in the Heck coupling of butyl acrylate with 4-bromoacetophenone.