esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless May Benefit Blockchain +
## 3 Consensus Among Wireless Nodes
### PoC  
The consensus mechanism employed by Helium, known as PoC [48], is a focal point of analysis in this paper. It stands out significantly from the dominant mechanisms, namely PoW and PoS. As elaborated in Section 5, PoC is found to amalgamate the benefits of PoW, such as enhanced decentralization due to fluctuations in wireless connectivity, and PoS, offering greater scalability as only specific nodes partake in the consensus process.  
Within the PoC framework, miners establish physical hotspots, typically small computers with internet connectivity and antennas for wireless communication with nearby hotspots. These hotspots receive wireless data. During each \"challenge\", 10 witnesses are randomly selected from all the hotspots within the beacon's transmission range. Notably, this random selection of witnesses can change with every challenge, occurring at 30 min intervals; shorter intervals enhance PoC's decentralization [49], aiming for a level akin to PoW.  
The fundamental principle of PoC is to validate that a given hotspot is genuinely operational and serving a real area. The term \"consensus\" in this context pertains to whether other hotspots can substantiate that the challenged hotspot is genuinely providing service. This verified decision by witnesses constitutes a single block in the Helium network.  
This paper focuses on quantifying the impact of uncertainties arising from wireless communications on the performance of PoC consensus. For example, PoC miners facing significant uncertainties may fail to provide accurate input to the consensus, thereby compromising the blockchain's scalability. Conversely, highly variable communication channels are likely to result in the replacement of current miners with others, effectively increasing decentralization [50].
## 2 Materials and Methods
### Loading of DOX onto MSN-Based Systems  
The solvent immersion technique for loading of native MSNs was adapted and modified from a previous study by [PERSON] and colleagues [32]. MSNs (5 mg) were ultrasonically dispersed in 5 mL solution of DOX in MilliQ water (DOX conc = 1 mg mL\({}^{-1}\)) for 15 min. The suspension remained under stirring for 24 h at room temperature, protected from light. DOX@MSN was collected using centrifugation (12,000 RPM, 30 min, 24 \({}^{\circ}\)C). The supernatant was collected separately; while solid DOX@MSNs were washed with MilliQ water (2 mL \(\times\) 3) until supernatant became clear, indicating the absence of free DOX particles in the wash. HPLC was then employed to quantify the amount of free/unloaded DOX molecules in the combined supernatants. The HPLC analytical method was adapted and modified from a previously published procedure [33]. From the free DOX determined by HPLC, loaded DOX can be quantified based on the initial amount of DOX in the original solution. A calibration curve was obtained from a serial dilution of stock solution containing pure DOX in MilliQ water (concentration 1 mg mL\({}^{-1}\)). Encapsulation efficiency (EE%) and drug content (DC%) were determined using the following Equations (1) and (2), respectively [34; 35].  
\[\text{EE\%}=\text{Weight of loaded DOX/Initial weight of DOX }\times 100\% \tag{1}\]  
DC% = Amount of loaded DOX/(Weight of MSNs + Weight of loaded DOX) \(\times\) 100% (2)  
A previous procedure from [PERSON] et al. [36] was adapted and modified for the post-surface grafted loading of MSN-PLH and MSN-PLH-TAM (Figure 2b). In short, MSN-PLH or MSN-PLH-TAM (15 mg) were ultrasonically dispersed in 6.25 mL of PBS buffer (pH 5) containing DOX (concentration 1.5 mg mL\({}^{-1}\)). The resulting solutions were then stirred vigorously (400 RPM) at room temperature and protected from light for 24 h. DOX@MSN-PLH and DOX@MSN-PLH-TAM were collected by centrifugation (12,000 RPM, 20 min, 24 \({}^{\circ}\)C), and washed with water (2 mL \(\times\) 3). Total supernatant and washing solvents were recovered and HPLC was then employed to quantify EE% and DC% of both systems following Equations (1) and (2) above.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 5 Experiments and Results
### Experimental Setting
#### 5.1.2 Datasets  
We evaluate our approach on four NIDSs datasets.  
**UNSW-NB15**[34] has 43 standardized features. Out of the 2,390,275 flows, 2,295,222 (96.02%) samples are benign flows, and the remaining 95,053 (3.96%) samples are attack flows. Nine attack types are distributed across the 3.98% of the flows representing attacks. **CSE-CIC-IDS2018**[35] contains 18,893,708 flows distributed between 16,635,567 (88.05%) benign samples and 2,258,141 (11.95%) attack samples. Within the 11.95% of attack samples are 6 different attack methods. **ToN-IoT**[36] is a netflow-based IoT network dataset, which includes different types of IoT data, such as operation system logs and telemetry data of IoT/IIoT services. The dataset consists of 796,380 (3.56%) benign flows and 21,542,641 (96.44%) attack flows, with a total of 22,339,021 flows. **BoT-IoT**[37] is comprised of 6 types of attacks and a total of 47 features with corresponding class labels. The dataset contains only 477 (0.01%) benign flows and 3,668,045 (99.99%) attack flows, with a total of 3,668,522 flows. These datasets are all updated versions of existing NIDS datasets that have been standardized into a NetFlow format [38].
## 4 Experiment and Result Analysis
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **P \%** & **R \%** & **F1 \%** \\ \hline \multirow{4}{*}{Winter Olympics Problem} & [PERSON] & 98.40 & 94.44 & 95.51 \\ \cline{2-5}  & [PERSON] & 97.87 & 95.68 & 96.47 \\ \cline{2-5}  & [PERSON] & 97.74 & 96.03 & 96.65 \\ \cline{2-5}  & [PERSON]+blatt & 98.18 & 95.30 & 96.22 \\ \cline{2-5}  & BCNBLMATT & 99.60 & 96.96 & 98.12 \\ \hline \multirow{4}{*}{MixATIS} & [PERSON] & 97.62 & 94.74 & 95.78 \\ \cline{2-5}  & [PERSON] & 97.81 & 96.13 & 96.78 \\ \cline{1-1} \cline{2-5}  & [PERSON] & 97.22 & 96.90 & 96.89 \\ \cline{1-1} \cline{2-5}  & [PERSON]+blatt & 97.61 & 95.82 & 96.51 \\ \cline{1-1} \cline{2-5}  & BCNBLMATT & 97.70 & 97.04 & 97.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative experiment. respectively. This indicates that [PERSON] has a strong generalization ability and can handle natural language understanding tasks in different domains. Subsequently, we introduced the BCNBLMATT model and observed its performance on the MixATIS dataset. The results showed that the BCNBLMATT model achieved 97.70%, 97.04%, and 97.20% in terms of the Macro_P, Macro_R, and Macro_F1 values. This further validates its superiority in solving multi-intention problems. Taken together, the BCNBLMATT model can maintain an excellent performance on multi-intent recognition tasks in different domains, laying a solid foundation for its generalization in practical applications and making it a reliable solution with wide applicability.
# Ultrasound Elastography in the Evaluations of Tendon-Related Disorders--A Systematic Review
## 3 Results
### Study Characteristics  
From the included studies, the most frequent evaluation technique was the longitudinal scan, used in 20 papers. Seventeen papers evaluated both longitudinal and transverse scans. Only one paper conducted only transverse scan. One paper did not declare the adopted probe's position. The general and methodological features of the studies are resumed in Supplementary Table S2.  
Sensitivity and specificity of US elastography were evaluated in 14 papers [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36].  
Accuracy value was evaluated in 6 papers [22; 30; 34; 35; 36; 37]. Moreover, inter-observer reliability with MRI was evaluated in 3 papers [38; 39; 40].  
From the included studies, the most reported outcome was the strain ratio, in 20 papers. Ten papers concerning shear wave elastography reported shear wave velocity expressed in meters/second; 11 papers instead reported shear modulus in kPa concerning tissue stiffness. One paper reported information concerning shear wave elastography in both shear wave velocity and tissue stiffness. Only 2 papers reported all three values of interest, i.e., strain ratio, shear wave velocity, and shear modulus.  
Reliability indexes of included studies, concerning specificity, sensitivity, and accuracy of included papers, are hosted in Table 2.  
Study characteristics, concerning the type of US machine, examination position of the patient, and location of the probe, are resumed in Supplementary Table S2.  
Specific values and results related to shear wave elastography are resumed in Supplementary Table S3. Supplementary Table S4 hosted values and results related to strain elastography.
## 3 Results
### Quantification of GAG and Collagen Content  
To evaluate the biochemical functionality of these scaffolds, chondrogenic differentiation behavior was investigated in terms of the accumulation of glycosaminoglycan (GAG) and collagen (COL) in the scaffold. Atelocollagen gels and spheroids cultured in 96-well plates, which are commodity scaffolds, were used as controls. Figure 3 shows the accumulation of GAG after 4 weeks of culture in atelocollagen gels, spheroids, and 3D silica fabrics under normal and differentiation conditions. GAG is one of the chondrogenic factors in cartilages. In spite of cultivation in both atelocollagen gel and spheroid culture under normal and differentiation conditions, no difference in accumulation was observed in either condition. Although accumulation in the 3D silica fabrics was inferior to those in the other scaffolds, accumulation under differentiation condition was significantly superior to that under normal condition.  
Figure 3: Accumulation of glycosaminoglycan (GAG) in normal medium (Diff (\(-\))) and chondrogenic differentiation medium (Diff (\(+\))) on 3D silica fibers, atelocollagen gels, and spheroids for 4 weeks.  
Figure 2: Number of cells in 3D silica fibers cultured in normal medium (Diff (\(-\))) and chondrogenic differentiation medium (Diff (\(+\))) for 4 weeks after seeding at a density of 3.0 \(\times\) 10\({}^{5}\) cells/well and preculturing for 1 week.  
Figure 4 shows the accumulation of COL after 4 weeks of culture in 3D silica fabrics, atelocollagen gels, and spheroids under normal and differentiation conditions. Here, because atelocollagen itself was detected by the hydroxyproline assay, COL accumulation could not be accurately quantified. The accumulation of COL under differentiation condition was superior to that under normal condition in both spheroid and 3D silica fabric culture. However, COL accumulation in 3D silica fabrics was inferior to that in spheroid culture under differentiation condition.
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Antimicrobial Activity of Piperine  
Piperine has not been extensively studied with respect to its antimicrobial potential, but several performed antimicrobial assays have demonstrated the wide range, but moderate inhibitory action, of this compound. In the study of [PERSON] [121], piperine was inhibitory against a spectrum of test microorganisms in the range 3.12-100 mg/mL and exhibited the highest activity against a fungal strain _Candida albicans_. In the study of [PERSON] et al. [52], it was found that its activity ranged from 100-600 ug/mL and that it exhibited the best inhibitory potential against _Pseudomonas aeruginosa_. Recently, this effect was confirmed for four tested bacterial strains [122]. It has been reported that piperine enhances the antimicrobial action of ciprofloxacin against _Escherichia coli_ and _Bacillus subtilis_[123]. Piperine decreases the efflux of ethidium bromide, thereby inhibiting mycobacterial efflux pump in _Mycobacterium smgematis_[124]. Piperine derivatives having pyridine scaffold were synthesized by [PERSON] et al. [125] and observed that they exhibit antimicrobial activity when tested against _B. subtilis, Streptavidins sp., Staphylococcus aureus, E. coli, Salmonella typhi, Aspergillus niger, A. flavus_, and _A. fumigatus_.  
To the best of our knowledge, scientific research on piperine in food models have not been reported yet, but studies have confirmed the efficacy of black pepper in microbiological preservation against _Listeria monocytogenes_ in fermented sausages [126] and _S. aureus_ in cheese [127], as well as against general microbial load [128; 129; 130]. The efficacy of black and white pepper in food preservation was also reported in the sense of lowering lipid oxidation [128; 131] and increasing the sensory quality and shelf life of the treated food samples [129; 132; 133]. Since piperine represents a major constituent of the pepper, it contributes at least partially to these activities. Therefore, future studies should focus on investigations of the pure piperine in this sense.  
Piperine derivatives with electronegative atoms, large volumes, an ester group, the absence of unsaturation on a side chain, carboxyl group possess anti-leishman activity against promastigotes forms [134]. Piperine inhibits promastigotes of _Leishman donovani_[135], while some of the piperine derivatives and analogs are anti-leishman towards _L. amazonensis_[136]. Piperine and its derivatives exert a toxic effect on epimastigotes and amastigotes of _T Trypanosoma cruzi_[137].
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 4 Experiments and Results  
### Experiment Setting and Training Details  
To simulate and verify the effectiveness of the decoding model, the program was written in Python language, and the network part was implemented in the Pytorch deep learning framework. The computer used an Ubuntu 16 operating system.  
We tested the DIR-Net method with five lengths of original codewords: 8, 16, 32, 64, and 128 code lengths, with the corresponding polar decode length of 16, 32, 64, 128, and 256, respectively. As for the training data, all the original codewords were generated randomly with the binary model. Then, they were encoded into polar codes for network training. In all training stages, the learning rate of these networks was set to 0.001 during training. To prevent the network from overfitting at the initial stage of training, the warmup training strategy was utilized. In the first 1000 iterations, the learning rate was gradually increased from 0.0001 to 0.001, while, in the subsequent training process, the learning rate remained constant until the model converged.  
The neural network training process we designed was divided into three steps. Firstly, only the denoising subnetwork was trained. Here, the input to the network was the received data processed by LLR, while the trained label was the data input to the channel. Furthermore, the label data contained no noise and attenuation via the channel. Secondly, after training the denoising subnetwork, the parameters of the network were fixed, and the decoding subnetwork was trained. At this point, the output of the denoising subnetwork was regarded as the input of the decoding subnetwork, and the original unencoded codeword was taken as the label to train the decoding subnetwork. Finally, the entire network was finetuned together. At this stage, the denoising subnetwork and the decoding subnetwork were released for training at the same time, so that the two subnetworks could better work together.
# The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
## 3 The Implementation of Recognition Algorithm and the Experimental Analysis
### Experiment Analysis of Actual Data  
To verify the algorithm, we designed experiments to collect actual data in Shangweidian Village, Mentougou District, Beijing. The tester takes four kinds of actions of mechanical drilling, mechanical picking, walking and picking respectively. Each action is divided into three groups and lasts for 2 min. The size of each group of data is 122,880 rows and 45 columns. In order to observe the detection and identification results, the collection of data is performed in a specific geographical area. It is evident from the results that the signals are mainly concentrated in columns 41 and 42. The abscissa represents the locations and the distance between adjacent columns is 16 m. The ordinate represents time and the unit is second. The number of false alarms is the major factor for the detection. For the identification, the mechanical drilling and mechanical picking signals are both classified as mechanical signals.
# NMR Analysis of Pulegone in Food Products
## 1 Introduction
In addition, previous studies reported a no observed effect level (NOEL) of 0.44 mg/kg bw and a no observed adverse effect level (NOEL) of 0.75 mg/kg bw [20,21]. To date, pulegone in food has been primarily analyzed using gas chromatography coupled with mass spectrometry (GC-MS). A review of the literature reveals that nuclear magnetic resonance (NMR) methods have been employed only for the analysis of essential oils. The measurement time for NMR was shorter than that for GC-MS, and the preparation of samples for NMR was less time-consuming than that for GC-MS [22]. Therefore, NMR is a promising candidate for routine analysis in testing laboratories for the analysis of pulegone in food. In the present study, the NMR measurement parameters of the pure substance pulegone were initially optimized, specifically including the solvent. In the literature, only chloroform-\(d_{1}\) (CDCl\({}_{3}\)) has been used as a solvent to date [1,3]. Subsequently, essential oils from trade and foods such as teas, herbs, and confectioneries are analyzed for pulegone. Furthermore, the method is validated in terms of selectivity, accuracy, and linearity, as well as the determination of the limit of detection (LOD) and limit of quantification (LOQ). Subsequently, a toxicological evaluation is performed.
## 5 Discussion
Its goal of creation was to enable a wide range of different applications and systems to be able to work together, regardless of their internal designs, standardizing enterprise messaging on an industrial scale. The work presented here is designed for a cell-based manufacturing environment. The traditional linear manufacturing production lines are not flexible and are generally designed for one product type. Development of the new smart manufacturing assembly lines, i.e., cell-based, allows flexibility of products during volume fluctuations. The smart manufacturing assembly lines are used for multiple products, having the capability of a flexible production system for all production operations. In cell-based manufacturing, different cells are connected by an automatic product delivery system that transports the product through the manufacturing process, from cell to cell as defined by detailed production sequences using an MES. The automatic product delivery system on the AIV can collect and deliver products to process cells in a format suitable for product feed. The proposed scheduler currently has support for a single AIV, but this scheduler can be updated to support a fleet of AIVs in a single manufacturing place. A detailed review of swarm robotics system and software for multi-robotic environment are given in Reference [51; 52], respectively. Many AIVs or AMRs (Autonomous Mobile Robots) available in the market with Fleet management already take care of scheduling but either these are very expensive and/or require expensive licenses/contracts for continuous technical support [53]. For industries that do not wish to use this expensive solution or want a bespoke solution, can take advantage of this work. The aim of this research was to provide a cost effective and customized solution for the industry partner to build an integrated and automated industrial environment which is a requirement for Industry 4.0. Here, a separate customized scheduler is discussed but the authors believe that this provides a platform to build a low cost fully integrated fleet manager with the scheduling feature integrated. The proposed scheduler is highly configurable and can be used to integrate any ROS-built AIV with an MES. The scheduler interface is user friendly and can be operated by a non-technical operator As mentioned in Sections 3.3.2 and 3.3.6, the selection criteria for pickup and drop-off locations selection is based on sequential logic. But the scheduler can be designed to support more selection criteria, like the selection of nearest pickup and drop-off locations, selection of the high priority locations/product, etc. The development of an increase in automation technologies will drive the creation of more profitable and efficient manufacturing. This is a vital requirement for the coming years, with increased pressure on production in high-cost countries. This research work will have a direct influence on the future product direction with low-cost AIVs and will potentially increase production at the plant and thus increased jobs.
## 3 System Overview
### Network Architecture  
We selected Wi-Fi communication based on its high availability, mobility, IP support, and leverage of low power architecture. The system is configured in a star topology. A high-level overview of the system is shown in Figure 7.  
Figure 6: Software architecture adaptive power algorithm in Node-Red.  
Figure 7: Network Architecture.
# Design and Experiment of Magnetic Antenna Vibration and Noise Reduction System
## 5 Conclusions  
In this paper, the research background and application of magnetic antenna are described. By analyzing the environmental vibration of magnetic antenna during underwater detection, we know from the vibration reduction requirement that the radial vibration transmissivity of the antenna should be less than 0.01 (\(-\)40 dB). On this basis, the principle model of the magnetic antenna damping system was established, and the main parameters to be optimized were defined. A magnetic antenna damping system based on silicone rubber elastic element was proposed, and its mechanical parameters were analyzed. Finally, the prototype design and performance analysis of the vibration damping system were completed. Dynamic simulation tests are carried out to verify that the performance of the prototype meets the requirements of vibration reduction, and the function comparison test confirms that the magnetic antenna equipped with the vibration absorption system can receive electromagnetic wave signal normally.  
Conceptualization, S.J. and C.S.; methodology, S.J., C.S. and W.J.; validation, S.J. and C.Y.; formal analysis, L.H.; investigation, H.Z. and C.Y.; resources, H.Z. and L.H.; Funding acquisition, W.J.; visualization, S.J.; project administration, C.S.; Writing--original draft, S.J. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Key R&D Program of China (No. 2020 YFB2007601), and the National Natural Science Foundation of China (No. 52075193).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 13: Environmental electromagnetic noise signal acquisition test results.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 2 Dataset
### Sentinel-1  
Sentinel-1 is a satellite constellation carrying C-band SARs, with Sentinel-1A (S-1A) and Sentinel-1B (S-1B) launched in April 2014 and April 2016, respectively. They operate with four exclusive modes, Interferometric Wide Swath (IW) mode, Extra Wide Swath (EW) mode, StripMap (SM) mode, and Wave (WV) mode. Among these modes, IW is the main acquisition mode over coastal and land areas. The spatial resolution of IW is 5 m by 20 m and its incidence angles range from 29\({}^{\circ}\) to 46\({}^{\circ}\) with a swath width of 250 km. IW can operate at dual (VV + VH/HH + HV) or single (VV/HH) polarization. In this study, we focus on S-1 IW acquisitions (VV + VH) from March 2015 to November 2020. This is the default mode over coastal areas. The leakage from VV to VH channels is considered to be negligible as described in [PERSON] et al. [26].