esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Organic and Organic-Inorganic Hybrid Polymeric Supports for Catalytic Applications
## 2 Organocatalysed Non-Asymmetric Reactions
### Miscellaneous Examples
The synthesis of different types of diethyl and dimethyl phosphonates under solvent free conditions in presence of 20 mol % of dimethylperhydro-1,3,2-diazphosphorine-supported on polystyrene (PS-BEMP) **51**[44; 45] as heterogeneous catalyst was carried out (Scheme 23). Good yields (78%-85%) were obtained using two phosphorus nucleophiles, diethyl phosphite and dimethyl phosphite, and a wide array of aromatic and non-aromatic \(\alpha,\beta\)-unsaturated ketones as electrophiles. This protocol was also extended to \(\alpha,\beta\)-unsaturated esters and nitriles. Good yields were obtained with methyl acrylate (79%-85%), less satisfactory results with methyl crotonate (34%-52%). Excellent results were obtained with the \(\alpha,\beta\)-unsaturated nitriles, acrylonitrile and cinnamonitrile (yields: 76%-90%). Catalyst **51** was not recyclable: all attempts to reuse the recovered catalyst in consecutive reaction runs failed.
## 4 Experimental Analysis
### Log Evaluation
Table 3 presents a quantitative analysis of four datasets derived from the Hadoop Distributed File System (HDFS), differentiated by their data volume. The suffix \"HC\" refers to the log version processed with the hierarchical classification method described earlier. The \"totalLines\" attribute refers to the total number of lines per dataset, with \"HDFS_2k\" and \"HDFS_2k_HC\" containing 2000 and 16 lines, respectively, and \"HDFS_20M\" and \"HDFS_20M_HC\" containing 14,947,770 and 22 lines, respectively. The \"tokensCount\" indicates the total number of tokens, being substantially higher in the larger datasets. The mean (\"tokensMeanBatch\") and standard deviation (\"tokensStdBatch\") of tokens per batch provide a measure of centrality and dispersion, respectively, in the distribution of the number of tokens per batch within each dataset. Notably, the \"HC\" subsets show a higher mean of tokens per batch, indicating higher lexical density or information concentration. Table 4 compares the performance metrics of two main datasets \"HDFS_2k\" and \"HDFS_20M\" along with their corresponding \"HC\" subsets. The \"lineCount\" metric indicates the number of lines in each dataset, providing context for interpreting the subsequent metrics. \"Tokenized Time\" indicates the time in seconds required to tokenize the datasets, with the \"HC\" subsets requiring less time due to their smaller size. \"Modelized Time\" denotes the time needed to process the 'bert-base-uncased' model, with the \"HDFS_20M\" set requiring significantly more time, consistent with its larger size. Finally, \"Mean Batch Time\" represents the average time taken per batch, which remains relatively constant across all cases, suggesting that the time per batch is independent of the dataset's total size and that the \"HC\" subsets have characteristics that require more processing time per batch, such as greater data complexity.
## 2 Related Works  
As we know, Cryptography, derived from the Greek words for \"secret\" and \"writing\", encompasses a broad array of techniques designed to ensure the privacy, authenticity, integrity, and non-repudiation of information. The rise of digital networks has facilitated unprecedented levels of interconnectedness and data exchange; however, it has also led to a corresponding surge in the demand for secure communications. Cryptography provides a framework to meet this need, but its understanding, application, and evolution remain subjects of intense scrutiny and research.  
GANs and cryptography may initially appear as disparate fields, but there is a growing intersection between them that is forging new directions in secure data processing and protection. GANs, a class of machine learning models, are designed to mimic and generate data distributions that are similar to real data. When applied to cryptography, GANs can introduce robust methods for enhancing cryptographic algorithms, creating new ways to secure data, and even providing innovative approaches to breaking cryptographic systems.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 2 Materials and Methods
### Secure SASV Module
#### 2.2.1 Keys Manager  
The Keys Manager is responsible for generating and managing the set of keys required by the SASV module. The public key is used to encrypt the voiceprint generated during enrollment, while the private key is employed to decrypt the biometric and anti-spoofing scores computed during the authentication phase. In addition to the public and private keys, the Keys Manager also generates Galois and relinearization (relin) keys to support HE operations. These two auxiliary keys enable specific encrypted computations that cannot be performed using standard keys alone. In particular, the Galois key allows for encrypted vector rotations and other Galois automorphisms on ciphertexts, which are essential for implementing matrix-vector multiplications during the authentication phase. Conversely, the relin key is used to reduce the size and computational complexity of ciphertexts after homomorphic multiplications, thereby improving efficiency and maintaining manageable noise levels [55].  
Figure 3: Overall architecture and flow chart of the secure SASV module implemented in SIBERIA. VC: voice credential.  
The set of cryptographic keys is generated using Microsoft SEAL [56], a lattice-based HE library that supports secure computation by enabling addition and multiplication operations directly on encrypted integers or real numbers. To ensure secure and auditable key storage, the Keys Manager integrates an instance of Vault [57], which provides robust, policy-based access control and secure storage for the generated encryption keys.  
Beyond key generation and storage, the Keys Manager is also responsible for computing the final SASV score (SS) returned to the SSI module. It receives a pair of encrypted biometric and anti-spoofing scores, which are individually decrypted using the private key. These decrypted scores are then combined using Equation (2), where \(b_{s}\) and \(a_{s}\) represent the decrypted biometric and anti-spoofing probability scores, respectively, while \(\sigma\) denotes the sigmoid function used to normalize the final score [58]. \(\alpha_{a}\) and \(\alpha_{b}\) are learned weights used for the fusion.  
\[\text{SS}=\sigma\Big{(}-\log\Big{|}\alpha_{a}e^{-a_{s}}+\alpha_{b}e^{-b_{s}} \Big{|}\Big{)} \tag{2}\]
## 2 Materials and Methods
### Volatile Compound Analysis  
The virgin olive oil samples (5 g) were introduced into glass screw top vials, with laminated Teflon-rubber disks in the caps. The vials were left in a water bath at 40 \({}^{\circ}\)C for 10 min to equilibrate the volatile compounds in the headspace. Then, a solid-phase microextraction (SPME) needle was inserted through the disk, and a 1-cm 50/30 \(\upmu\)m thickness DVB/Carboxen/PDMS fibre (Supelco, Bellefonte, PA, USA) was exposed to the headspace for 40 min while the vial was kept in the 40 \({}^{\circ}\)C-water bath. Later, the fibre was transferred to the gas-chromatograph inlet (splitless mode, 250 \({}^{\circ}\)C).  
The chromatographic separation of the compounds was carried out using a HB-5 (50 m \(\times\) 0.32 mm i.d, 1.05 \(\upmu\)m) column (Agilent, Avondale, AZ, USA) placed into a gas-chromatograph (Agilent 6890 series) equipped with a mass spectrum detector (Agilent 5973). The oven temperature was held at 40 \({}^{\circ}\)C for 10 min and risen at 3 \({}^{\circ}\)C min\({}^{-1}\) to a temperature of 160 \({}^{\circ}\)C, and then at 15 \({}^{\circ}\)C min\({}^{-1}\) to a final temperature of 220 \({}^{\circ}\)C, where it was held for 10 min (total run time: 64 min). Mass spectra were generated by electronic impact at 70 eV, with a multiplier voltage of 1756 V. Data were collected at a rate of 1 scan s\({}^{-1}\) over the 30-300 \(\upmu\)z range. The transfer line to the mass spectrometer was maintained at 280 \({}^{\circ}\)C. The Agilent MSD Chemstation software was used. \(n\)-alkanes (C5-C18) were analysed under the same conditions to calculate the linear retention indices (LRI).  
The identification was performed by matching mass spectra (MS) and LRI with those of reference compounds analysed under the same conditions (a total of 62 Sigma-Aldrich reference compounds were used), or with those included in the Flavornet (www.flavornet.org) or NIST [19] databases. Two samples of each oil batch were analysed, and results were expressed as total area counts.
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 5 CoSiWiNeT Evaluation  
The CoSiWiNeT algorithm's performance was evaluated using the four network traces with different conditions, summarized in Table 1. Various parameters such as predicted time and frequency offsets, offset error, and execution time were used for the algorithm evaluation. The proposed algorithm's performance was also compared with NTP and state-of-the-art methods based on KF and LS to derive the baselines. To make the proper comparison, raw or uncorrected offsets from free-running clocks in server and client devices were captured as described in Section 3. The uncorrected offset values captured over a long time were fed as an input to all the algorithms implemented in a Matlab simulator. The behavior of each algorithm captured in terms of how effectively they correct the offset was visualized and analyzed in Matlab.
## 3 Results and Discussion
### Nutritional Analysis
multipartita_ (18.43 \(\pm\) 0.07 g/100 g) exhibited higher CP values than the brown _S. muticum_ from San Marcos (10.13 \(\pm\) 0.07 g/100 g) and Buarcos (10.25 \(\pm\) 0.35 g/100 g). It can thus be concluded that green and red macroalgae have the potential to serve as an alternative source of protein for human food and animal feed. In addition, there are seasonal shifts in the protein content, with macroalgae containing higher amounts of protein during the late winter and early spring and lower amounts during the late summer and early autumn periods [56; 59]. The carbohydrate (CH) content of the seaweeds ranged from 42.48 \(\pm\) 0.15 g/100 g to 53.27 \(\pm\) 1.11 g/100 g in dw for the species _Ulva_ sp. and _S. muticum_ from San Marcos, respectively. The findings of this study indicate that the sample from San Marcos contains a significantly higher amount of carbohydrates than all of the other samples from Buarcos. Finally, the energy values (kJ/kcal) of the taxa analysed showed small differences between samples. _G. multipartita_ (1195.75 \(\pm\) 5.94 kJ/100 g dw and 282.12 \(\pm\) 1.37 kcal/100 g dw) had the highest energy value, followed by _S. muticum_ from Buarcos (1134.75 \(\pm\) 10.84 kJ/100 g dw and 267.79 \(\pm\) 2.44 kcal/100 g dw) and _S. muticum_ from San Marcos with the lowest values (1122.74 \(\pm\) 0.36 kJ/100 g dw and 265.51 \(\pm\) 0.07 kcal/100 g dw) and _Ulva_ sp. (1079.72 \(\pm\) 0.39 kJ/100 g dw and 254.77 \(\pm\) 0.09 kcal/100 g dw), which presented the lowest values. Thus, _G. multipartita_ provides the highest energy value for an adult but was closely followed by _S. muticum_, whereas _Ulva_ sp. shows significantly less carbohydrates and energy content. Table 2 shows the main results obtained for the mineral composition. The results support previous studies showing that the mineral content of macroalgae varies depending on the taxon, and it can also change within the same species due to factors like its location, environmental conditions, health, and life cycle [60]. The K content of the San Marcos brown macroalgae revealed the highest concentration (86.19 \(\pm\) 9.06 mg/g dw), being the most prevalent mineral among the macrominerals examined and even surpassing Na concentration. The concentration of K is much lower in
## 1 Introduction  
Remote sensing is a technique used to acquire data such as temperature, photographs, growth, pressure, and fire without direct contact with the target material. Remote sensing methodology has been increasingly applied in forest fire detection, iceberg detection, ship detection, floating object detection, agriculture monitoring, gathering pictures of the earth, etc. [1]. Many methods and tools such as radars are commonly used to capture remotely sensed image datasets [2], of which SAR image datasets have been playing a vital role. SARs can be used to construct two-dimensional images, which can be further reconstructed into multidimensional images if necessary. When the SAR focuses on the target application, such as biomass detection in the forest, ship detection in the ocean, and vehicle count in land, it comprises many small objects [3]. Detecting these small images is less effortes than general object detection in typical images. Specifically, observing ships or any biomass-detecting objects in applications such as marine surveillance has been challenging. The reason behind the difficulty is the number of pixels available in the small objects in a large image [4].  
Over the past decade, new techniques have been developed for small object detection in remote sensing images. Due to image processing and definition complexity, detectingicebergs and ships still needs improvement. This detection technique has been studied for a long time. Deep learning has made rapid progress in the computer vision field.
## 2 Presentation of Proposed FPO-Guided FMLT Method
### Pre-IF Estimation via FPO Algorithm
\tag{6}\]  
where  
\[\omega_{1}(\ )=-\left|\frac{f_{m}(\tau_{n})-f_{d}(\tau_{n-1})-m[\Delta f_{d}]}{IQR [\Delta f_{d}]}\right| \tag{7}\]  
\[\omega_{2}(\ )=-\left|\frac{f_{m}(\tau_{n})-m[f_{d}]}{IQR[f_{d}]}\right| \tag{8}\]  
\[m[\ ]=perc_{0,5}[\ ],\quad IQR[\ ]=perc_{0,75}[\ ]-perc_{0,25}[\ ] \tag{9}\]  
and where \(f_{d}(\tau_{n-1})\) is the candidate frequency value at \(\tau_{n-1}\), \(\Delta f_{d}\) is the derivative of \(f_{d}\), \(perc_{p}[f(t)]\) is the \(p\)th quantile of \(f(t)\),\(f_{d}\) represents the frequencies of a set of candidate ridge points in \([\tau_{1},\tau_{2},\ldots,\tau_{n-1}]\), \(m[\ ]\) stands for the median and \(IQR[\ ]\) represents the interquartile range, as defined in Equation (9). Solutions of the support function can be given as:  
\[q(m,\tau_{n})=\underset{k}{\text{argmax}}\{F[SP_{m}(\tau_{n}),f_{m}(\tau_{n}),f_{k}(\tau_{n-1})]+U(k,\tau_{n-1})],n>1 \tag{10}\]  
\[U(m,\tau_{n})=\left\{ \begin{array}{l}F[SP_{m}(\tau_{n}),f_{m}(\tau_{n}),f_{ m}(\tau_{n})],n=1\\ F[SP_{m}(\tau_{n}),f_{m}(\tau_{n}),f_{q(m,\tau_{n})}(\tau_{n-1})]+U(q(m,\tau_{n }),\tau_{n-1}),n>1\end{array} \right. \tag{11}\]  
where \(n\) = 1,2,....\(N\), \(m\) = 1,2,....\(N_{p}(\tau_{n})\), \(k=1,2,\ldots,N_{p}(\tau_{n-1})\), and \(q(m,\tau_{n})\) determines which peak at the previous time bin \(\tau_{n-1}\) should be connected to peaks at the current time bins \(\tau_{n}\) (\(n\) = 2,....\(N\)). Then, all possible paths are generated, as presented in Figure 1b. The \(U(m,\tau_{n})\) is an intermediate vector which is devoted to the optimization, i.e., pick the frequency value corresponding to the peak at time \(\tau_{N}\) where \(U(m,\tau_{N})\) takes the maximum and the path corresponding to the picked frequency value at \(\tau_{N}\) determined by \(q(m,\tau_{n})\) is extracted as the IF ridge, as shown in Figure 1c. Based on the above analysis, it can be seen that the FPO algorithm takes all local peaks in the TFR into account and generates all possible optimization paths among amplitude local peaks at the previous and current moment, which can ensure the continuity and accuracy of the extracted IF ridge to some extent. However, it should be noted that the accuracy of extracted IF ridges also relies upon the readability of TFR, in addition to the extraction algorithm. The underlying reason of TFR readability being impaired by smearing effects is that the base frequencies do not match the frequencies of the signal component, as reported in ref. [18]. Thus, the FMLT is proposed to improve the readability of TFR.
# Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams
## 4 Discussion
### Effect of Dune Sand Replacement Rate  
The correlation between the ultimate shear stress and the DS replacement rate is shown in Figure 8d. When the shear span-to-depth ratio, concrete strength and stirrup ratio remain constant, the ultimate shear stress increased by 5.5% and decreased by 3.0% for the specimens with DS replacement rates of 30% and 50%, respectively, compared to the ultimate shear stress of the normal specimens (\(v_{\text{u}}\) = 4.72 MPa). This indicated that the dune sand replacement rate strongly affected the shear strength of the RC beam. The pores between the coarse and fine aggregates are filled with DS, a very fine aggregate. Therefore, a small amount of dune sand can improve the compactness and compressive strength of concrete. A large amount of DS, on the other hand, reduces mortar strength and the bond between the mortar and the coarse aggregate. The shear strength contribution of DSRC beams is mainly composed of stirrup and concrete. When diagonal cracks were observed in the DSRC deep beams, the DSC failed earlier and the stirrup took up most part of the shear force.
## 5 Conclusions  
The development of an effective method of GDL separation from biotransformation media seems to be important in the context of the attempts to apply biotechnological synthesis of peach lactone in industry. Due to the existing gap in the research on GDL separation, an attempt was made to compare the effectiveness of three techniques: liquid-liquid extraction, hydrodistillation, and adsorption, in fragrance recovery. The relatively low concentration of GDL in the fermentation broth (approx. 3 g/L) and the presence of an unreacted lipid substrate (castor oil or ricinoleic acid), in which the lactone is partially dissolved, make its recovery difficult.  
Bearing in mind the selectivity of the process, its efficiency, and the speed of execution, among the three techniques used, adsorption on Amberlite XAD-4 is recommended. The six-hour adsorption on this resin resulted in less than 1% of lactone remaining in the bio-transformation medium. GDL adsorption on resin is based, inter alia, on the use of Van der Waals forces, which enable quick adsorption and easy desorption of the fragrance. Moreover, in the context of industrial GDL bioproduction and its separation from the medium, Amberlite XAD-4 has several advantages: it is inexpensive, does not chemically react with the fragrance, is easily regenerated, and can be easily adapted on an industrial scale. In addition, considering the aspect of environmental protection, it must be remembered that the desorption of GDL from Amberlite XAD-4 resin requires only small amounts of ethanol and takes place with an efficiency of approximately 80%.  
In the context of the purity of the released GDL, the hydrodistillation process seemed to be more advantageous, as the purity of the distillate was about 10% higher than that of the ethanol extract after lactone desorption from the resin (88 \(\pm\) 3.4% and 77 \(\pm\) 2%, respectively). However, by choosing a separation method, one should consider the resultant of many factors.  
This article only outlines the direction of further research that is necessary to optimize the GDL adsorption process, both in economic terms (studies on higher adsorption efficiency by selecting, e.g., the amount of adsorbent in relation to the adsorbed compound) and ecological terms (minimizing the amounts of reagents used).  
Conceptualization, J.M.; methodology, J.M. and J.B.; formal analysis, J.M., M.W.-W. and E.O.-L.; investigation, J.M. and J.B.; writing--original draft preparation, J.M.; writing--review and editing, J.M. and A.G.; visualization, J.M., E.O.-L. and M.W.-W.; supervision, J.M. and [PERSON]; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
The study was financially supported by the Ministry of Education and Science within funds of the Institute of Food Sciences of Warsaw University of Life Sciences (WULS), for scientific research.  
Not applicable.  
The authors declare no conflict of interest.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 3 Synthesis of Boronic Derivatives from Reagents Containing 1,3,4-Oxadiazole Moiety
### Indirect Connection of Boron with 1,3,4-Oxadiazole Ring via Phenotype Linker
#### 3.2.2 Derivatives of Meta-Substituted (1,3,4-Oxadiazol-2-yl)phenylboronic Acid  
1,3\(A\)-Oxadiazole-phenylboronic acid derivatives bearing a boronic group in the _meta_ position relative to the oxadiazole ring can be synthesized via three methods similar to those used for _para_-substituted derivatives. Table 4 summarizes the reaction conditions, catalysts, ligands, solvents, and corresponding yields, along with relevant references. The most common approach, based on metal-catalyzed borylation, typically utilizes aryl bromides (X = Br) (Scheme 11, method B). These reactions are usually carried out at elevated temperatures (>90 \({}^{\circ}\)C) using bis(pinacolato)diboron (B\({}_{2}\)(pin)\({}_{2}\)) as the boron source, potassium acetate (AcOK) as the base, and palladium catalysts such as Pd(dppf)Cl\({}_{2}\) or its DCM adduct (Table 4, Entries 1-11). Reactions conducted in 1,4-dioxane afforded the derivatives of 3-(1,3,4-oxadiazol-2-yl)phenylboronic acid pinacol ester in yields ranging from 44% to 99% (Table 4, Entries 1-4). Alternatively, the reaction can be performed in DMSO (Table 4, Entries 5-7), although only a single publication reports the yield (62%) for this solvent system (Table 4, Entry 5). Reactions in DMF have yielded 76% (Table 4, Entry 8) and 71% (Table 4, Entry 9), while THF has been used to achieve an 80% yield (Table 4, Entry 10). Additionally, the reaction can be conducted in refluxing dimethoxyethane (Table 4, Entry 11). A broader range of catalysts can be used for the conversion of 2-(3-bromophenylyl)-1,3,4-oxadiazole to _meta_-substituted 1,3,4-oxadiazol-phenylboronic acid derivatives. For example, Pd(PPh\({}_{3}\))\({}_{2}\)Cl\({}_{2}\) (bis(triphenylphosphine)palladium chloride) in 1,4-dioxane can be utilized to obtain the desired product in 35% overall yield over three steps, including hydrazide formation and 1,3,4-oxadiazol ring cyclization (Table 4, Entry 12). Alternatively, the same catalyst can be used in DMSO at 80 \({}^{\circ}\)C for 4.5 h to afford the boronic ester in 22% yield (Table 4, Entry 13). As another option, Pd(OAc)\({}_{2}\) (palladium(II) acetate) with