esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
To study the corrosion behavior of the three types of samples depending on the immersion time in the fire extinguishing solution, they were placed in hermetically sealed glass containers, in which 40 mL of FES was added. Every week, the aeration of the solution was performed. The samples were removed from the containers after 1 h, 36 days, and 78 days, and the surface layer structure was analyzed by electrochemical impedance spectroscopy (EIS). Additionally, the samples immersed for 78 days were removed from the FES, dried, and the surface morphology and chemical composition were analyzed using a scanning electron microscope (SEM) and the electron dispersive spectroscopy (EDS).  
Depending on the samples' immersion time in the fire extinguishing solution, they were covered with a thinner or thicker layer of corrosion products and/or adsorbed or absorbed components from the corrosion environment.  
Considering that the corrosion products can change the pH of the corrosion environment in which the samples are immersed [19], the evolution of this parameter must be checked during the experiment. Therefore, pH measurements of the fire extinguishing solution were performed using a pH-METER OP-2641/1 (Radelkis, Budapest, Hungary) for the three immersion periods studied, and the obtained values are presented in Table 1.  
Figure 1: Sample preparation flow chart.  
A relatively small change in the pH of the corrosive medium was observed, which increased with the samples' immersion time.  
The structural changes of the superficial surface layers were analyzed by electrochemical impedance spectroscopy (EIS) using a PGZ 301 potentiometer (Radiometer Analytical SAS, Lyon, France). The data acquisition was performed with VoltaMaster 4 software, and the ZSimpWin software was used to process the collected data [21]. The values presented in the following tables were determined in accordance with the theoretical procedure presented in [9].  
For the EIS determinations, we used:  
* a glass cell with three electrodes type C145/170 (Radiometer Analytical SAS, Lyon, France);
* the Teflon washers for fixing the samples on working electrode, the surface of exposure of the samples to the corrosion environment being S = 0.503 cm\({}^{2}\);
* a platinum electrode (S = 0.8 cm\({}^{2}\)): auxiliary electrode;
* a saturated calomel electrode: reference electrode.
* The parameters used for EIS determinations were:
* frequency range = 10\({}^{5}\)\(\div\) 2.5\(\cdot\)10\({}^{-2}\) Hz; and
* potential amplitude = 10 mV.  
The morphology of the superficial surface structure and its chemical composition were analyzed with a Vega Tescan LMH II scanning electron microscope (30 kV, SE detector) to which an EDX QUANTUMX QX2 detector (Bruker/Roentec Co., Berlin, Germany) was connected.
# Evaluation of Nitrogen and Phosphorus Removal
## 4 Discussion
### Phosphorus Removal Performance
#### 4.3.2 Woodchip bioreactors  
Woodchip bioreactors are typically designed to target N, so P removal reported in woodchip bioreactor studies is highly variable. Some studies found moderate removal [21], while others observed a net P export [39; 40]. The exact removal mechanism for TP and SRP in the woodchip bioreactors (Figure 5, Tables 2 and 3) in this study is uncertain, but possibilities include sorption to woodchips and microbial immobilization of P. High removal of TP concentrations was observed in a woodchip bioreactor treatment system, which reduced influent concentrations by 71%, but most reduction was observed from a settling tank [41]. [PERSON] [42] found a high TP removal rate in pilot-scale woodchip biofilters with high influent TP concentrations from stream flow. Removal rates increased at higher loading rates, a promising finding for storm event flow. [PERSON] [40] reported that non-sterile woodchips, i.e., with microbial growth, removed more dissolved P than sterile woodchips. Incorporating a P-binding additive into the bioreactor, such as drinking water treatment plant residuals (DWTR) has been successful in removing both TP and SRP via sorption onto these materials, however removal efficiencies were greater for SRP [43]. The aforementioned study also compared pilot scale woodchip bioreactors amended with DWTR and without, and for the non-amended woodchip bioreactors authors saw an average of 19% (TP) and 12% (SRP) removal efficiency, which is comparable to the average RE observed in the WB from this study (Table 3). Removal of P in these non-DWTR bioreactors was thought to have been due to physical filtration [43]. A possibility to further enhance removal of P in this system would be to incorporate a P-binding additive into one woodchip bioreactor, which could be compared with this paired system.  
Notably, System Inflow P concentrations (Table 1) were much higher than other woodchip bioreactor studies mentioned above. The treatment system from [PERSON] [41] received vegetable wash water with observed TP concentrations from 5 to 10 mg L\({}^{-1}\). Dissolved P concentrations were 1.12 mg L\({}^{-1}\) for the woodchip sorption experiment from [PERSON] [40]. In the lab study from [PERSON] [43] influent concentrations were 0.6 mg L\({}^{-1}\) and 0.2 mg L\({}^{-1}\) for TP and dissolved P, respectively. Higher influent concentrations in this system may have an impact on P sorption dynamics, however further consideration of these processes is needed.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### Overview of WBAN
#### 2.1.3 Applications  
WBAN applications are classified according to their area of use. In the following, we present the major categories [42].  
* Medical treatment and diagnosis: there are several cases of WBAN use in diagnostic and treatment of diseases, and many researches are conducted in this field. Use case example: cardio-vascular disease (CVD), diabetes, asthma, Parkinson's disease, etc.
* Training schedules of professional athletes: through the use of equipments helping athletes in training and monitoring of the progress and advancement of their performances.
* Public safety and prevention of medical accidents: a large number of people dies every year due to medical accidents, then, using a sensor node to maintain a log of previous medical accidents can reduce the number of deaths.
* Safeguarding of uniformed personnel: WBAN can be used by firefighters, policeman or military to keep them monitored in hazardous environments. For example, in the case of firefighters involved in stopping a fire, the WBAN can detect the existence of a dangerous gas and its level in the air, and relay this information to these team members.
* Consumer electronics and entertainment: some WBAN devices can be integrated in electronic equipments, head-mounted displays, microphone, camera, etc.  
Figure 1: Example of a patient monitoring using a wireless body area network (WBAN).
## 3 Results and Discussions
### SEM Analysis  
The morphology of Au-20 and Au-PVP-20 samples were investigated by a ZEIS Sigma 500 Field Emission Electron Microscope (FE-SEM). Dried drop-casted samples of Au-20 and Au-PVP-20 colloids were prepared on glass slides. Without PVP, the sizes of the majority of nanostructured particles were not uniform and were of irregular shapes, as shown in Figure 5a. However, a small number of AuNPs had a uniform size and shape. With PVP capping, the morphology of the particles changed as we expected from the PVP association. A strong shape-regulating behavior of PVP with AuNPs was observed, as shown in Figure 5c. This is in agreement with the previous investigations [31]. Now, the majority of the nanostructured particles seemed spherical with a controlled size range, whereas a minority of the particles also seemed to have an irregular shape. In Figure 5a,c,  
Figure 5: SEM Micrograph of (**a**) Au-20 and (**c**) Au-PVP-20 (**b**) EDX of Au-20 and (**d**) EDX of Au-PVP-20. The inset in (**a,c**) exhibit the respective size distribution of the nanostructured Au particles and nanostructured gold-PVP composites.  
the inset histogram shows the size distribution of Au-20 and Au-PVP-20, respectively, extracted from SEM micrographs.  
The Energy Dispersive X-ray (EDS) spectroscopy of both samples was executed for the elemental analysis of Au-20 and Au-PVP-20. The spectrum in Figure 5b confirms metallic gold, masked by C and O peaks present in D-Fructose. Cl is the constituent of the gold precursor (HAuCl4.3H2O), while the Si peak is due to the substrate. Argon is present because of being a base gas to produce plasma. Figure 5d is the EDX spectrum of Au-PVP-20, where the same elemental peaks of Au-20 appear with an extra N peak due to PVP capping. The N peak confirms the PVP capping [30]. The inset histogram shows the size distribution of nanostructured gold particles and nanostructured gold-PVP composites extracted from SEM micrographs. It depicts that in both the cases, the size ranged from 1 nm to 270 nm, with the maximum number particles in the size range of 30 nm to 60 nm.
## 3 WPT System: Experimental and Simulated Data
### WPT System Impedance
These values are measured before the AIMN intervention for impedance matching. A huge amount of data was collected through both experimentation and Cadence AWR simulation, encompassing variations in coil distance, lateral misalignment, and rotation angle. Three primary experimental configurations are included in the dataset described in [41], each featuring \(\theta=0\) and distinct values of \(\Delta x=0\) mm, 20 mm, and 30 mm and \(\Delta y=0\) mm, 20 mm, 25 mm. For each configuration, 57 data points were acquired by varying \(d\) from 14 mm to 70 mm in a 1 mm step. Each point was measured five times to minimize noise. The experimental dataset includes \(|S_{11}|\), \(|S_{21}|\), the input impedance, the complex value \(Z_{in}\), and the reflection coefficient \(\Gamma\) along with the AIMN capacitance values before and after matching. Complementing the experimental data, simulation results were generated for the same three configurations and an additional 229 configurations with different \(\Delta x\), \(\Delta y\), and \(\theta\). In all simulated cases, data were acquired across 57 distances, and the complete complex \(S\)-parameter matrix was recorded for 1001 frequency points. Figure 6: Transmission scattering parameter, port2–port1 measured from the transmitter side of the WPT system. Measurements are shown for different values of the distance between the two WPT coils, in the case 1 configurations, with \((\Delta x)\), \((\Delta y)\), and \((\theta)\) equal to zero. These values are measured before the AIMN intervention for impedance matching. Figure 7: Real part of impedance parameter with distance between the two coils, measured in case 1, for no misalignment between the two coils: \((\Delta x)\), \((\Delta y)\), and \((\theta)\) are zero. Measurements shown are taken from the transmitter side of the WPT–NFC system after the intervention of the AIMN for the impedance adaptation, which tried to adapt the impedance to 60 Ohm. In summary, the experimental dataset covers 3 configurations at 57 distances, while the simulation dataset consists of 229 configurations at 57 distances. The main goal of this article is to predict the distance and the best capacitance values of the system based on either the measured \(S\) parameters or the input impedance \(Z_{in}\).
## 1 Introduction
For this reason, preparation of chiral PMOs with highly ordered mesostructures, which can exhibit high catalytic activity and chiral induction abilities, is still a challenging research topic. Recently, some reports described the possibility of preparing chiral PMOs constructed only from single enantiopure chiral organosilane precursors. These 100% chiral PMOs represent a unique class of hybrid mesoporous systems, although no catalytic applications have been reported yet [20; 22; 97; 98; 99; 100; 101; 102]. One of the most elegant strategies for preparing chiral catalytic nanosystems is based on supporting metal nanoparticles onto these solid supports. Magnetic nanoparticles (MNPs) have emerged as a promising class of nanomaterials that have resulted in massive advancements in many biological [103], biomedical [104; 105], and catalytic applications [106; 107; 108; 109; 110; 111]. Recent studies show that magnetic nanoparticles serve as excellent supports for catalysts [112; 113]. The supported catalysts have proved to be effective and easily separated from the reaction media by applying an external magnetic field. Herein we report a successful method for the preparation of chiral periodic mesoporous organosilica nanoparticles in a sol-gel process under mild conditions. Their preparation was initiated by the synthesis of chiral bis-organosilane precursors that served as chiral building blocks for the formation of chiral PMO NPs. In our project, we succeeded to synthesize and characterize different chiral bridged-silane ligands that were utilized for the preparation of the synthesis attempts were focused on the preparation of pure chiral PMO NPs that were composed merely from a chiral bridged organo-alkoxysilane monomer. The preparation of these systems was accomplished by applying different surfactants and ligands who could finally afford a monodispersed chiral PMO NPs consisted of a 100% bridged-silane precursor. Additionally, for the first time, a successful preparation of magnetic chiral PMO NPs was also accomplished. The PMO nanoparticles were synthesized by a co-polymerization of 1,1'-((1R,2R)-1,2-diphenylethane-1,2-diyl)bis(3-(3-(triethoxysil)propyl)urea) chiral monomer by an o/w emulsion process, to give magnetic chiral PMO NPs with magnetite NPs in their cores.
## 2 Mathematical Model of PEM and Numerical Method
### Compression of PEM-FC's GDL  
It is believed that only the pore volume compresses throughout GDL compression, while its solid volume continues to be fixed. The GDL's porosity is then decreased and can be estimated as [3]:  
\[\epsilon=\frac{\epsilon_{0}-1+e^{vs}}{e^{2s}} \tag{5}\]  
where \(\epsilon_{0}\) represents the initial porosity and the superscript \(vs\) shows volumetric strain at each point.  
The friction between the gas diffusion layer and the bipolar plate is the primary factor in power loss in PEM-FCs, especially when titanium, molded graphite, or stainless steel are used as the BPP material. Through material properties, stabilization pressure, surface topology, and operating conditions, friction is determined, and as the contact area between the GDL and the dipole plate increases due to the high stabilization compression, friction is reduced. However, increasing the GDL pressure does not increase the flow resistance due to large compression. Furthermore, significant pressure will distort the MEA, resulting in cell leakage and an internal short. As a result, the interaction behavior of the bipolar plate and GDL must be investigated.  
The contact resistance within the GDL and bipolar plate, _Rcontact_ (m\(\Omega\) cm\({}^{2}\)), is determined by [3]:  
\[R_{contact}=2.2163+\frac{3.5306}{P_{contact}} \tag{6}\]  
where \(P_{contact}\) is the contact pressure (MPa).
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
## 4 Experimental Results on Communication and Data Detection  
In this section, communication results of one actuator transmitting one data stream are presented first. These will serve as benchmarks. Then, we present the communication results of two actuators transmit ting two data streams simultaneously in order to double the transmission rate over the same bandwidth.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
###### Abstract  
Fabric defect detection is a crucial step in ensuring product quality within the textile industry. However, current detection methods face challenges in processing efficiency for high-resolution images, detail recovery during upsampling, and the adaptability of loss functions for low-quality samples, which limit detection accuracy and real-time performance. To overcome these limitations, this paper introduces an improved YOLOv8-based model that optimizes both aspects for fabric defect detection. First, we introduce an efficient RG-C2f module to improve processing speed for high-resolution images. Second, the DySample upsampling operator is adopted to enhance edge and detail preservation, improving detail recovery within defect regions. Finally, an adaptive inner-WIoU loss function is designed to dynamically adjust focus on low-quality samples, thereby strengthening the model's generalization capability. Experimental results validated on the TILDA and Tianchi datasets show that, compared with YOLOv8, the proposed model achieves mAP improvements of 6.4% and 1.5%, respectively, demonstrating significant enhancements in detection accuracy and speed. This advancement provides strong support for fabric defect detection.  
fabric defect detection; object detection; YOLOv8; deep learning +
Footnote †: journal: applied sciences  
Article
## 3 Methods
### Feature Generation
#### 3.3.2 Frequency-Domain Measurements  
In the frequency domain, we calculated the features that were derived from the frequency range bands of each segment, which reflected various ranges: very low frequency (vLF), low frequency (LF), and high frequency (HF). For instance, LF is derived from the spectral power density with a frequency range band of 0.04-0.15 Hz. The HF feature is derived from the range band of 0.15-0.40 Hz. Interestingly, it has been shown that LF is related to the sympathetic nervous system, whereas HF reflects the parasympathetic nervous system [6]. As a result, the LF/HF ratio can be calculated, which provides insight into the amount of sympathetic and parasympathetic activity.
## 4 Functional Modules of the Proposed System
### Service Matching Module  
Figure 6 shows the detailed transaction flow of the service matching process. First, an SR posts his or her service inquiry onto the blockchain. If the posting process is successful, the service inquiry's state changes to the state \"NewPost.\" Second, as long as that service inquiry is alive, the client app will automatically invoke the match function at a fixed frequency. And the state of the service inquiry will be searchable by other services when its state is denoted as a \"Candidate.\" After posting the service inquiry, our system will upload the service-related data to the blockchain. The target SR will find the candidate SP's service inquiry when the matching function is invoked next time. Third, after the service inquiries of the SP and the SR fulfill a matching strategy, the target SR sets the \"serviceOwner\" of the matched SP to himself or herself and vice versa. Finally, the SP can choose to accept or not accept this service match. If not, both parties will return to their candidate state and continue to look for other compatible service inquiries.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CG (\(n\) = 88)**} & \multicolumn{2}{c}{**CS (\(n\) = 117)**} & \multicolumn{4}{c}{**CG vs. CS**} \\ \cline{2-9}
**Polymorphism** & \(n\) & \% & \(n\) & \% & \(p\)\({}^{\text{a}}\) & \begin{tabular}{c} **Compared** \\ **Genotypes or** \\ **Allees** \\ \end{tabular} & \(p\)\({}^{\text{b}}\) \\ \hline \multicolumn{9}{c}{Th1111I genotype} \\ \hline CC & 40 & 45.45\% & 54 & 46.15\% & CC + CT vs. TT & \begin{tabular}{c} 0.85 \\ (0.258–2.875) \\ \end{tabular} & 0.792 \\ \hline CT & 41 & 46.59\% & 55 & 47.01\% & CC vs. CT + TT & \begin{tabular}{c} 0.972 \\ (0.537–1.758) \\ \end{tabular} & 1 \\ \hline TT & 7 & 7.95\% & 8 & 6.84\% & CC vs. TT & \begin{tabular}{c} 0.848 \\ (0.246–2.994) \\ \end{tabular} & 0.786 \\ \hline \multicolumn{9}{c}{Th1111I allele} \\ \hline C & 121 & 68.75\% & 163 & 69.66\% & C vs. T & \begin{tabular}{c} 0.958 \\ (0.614–1.499) \\ \end{tabular} & 0.914 \\ \hline T & 55 & 31.25\% & 71 & 30.34\% & & & \\ \hline \multicolumn{9}{c}{N363S genotype} \\ \hline AA & 82 & 93.18\% & 110 & 94.02\% & \begin{tabular}{c} AA vs. AG + \\ GG \\ \end{tabular} & 0.87 & \begin{tabular}{c} 0.87 \\ (0.24–3.261) \\ \end{tabular} & 1 \\ \hline AG & 6 & 6.82\% & 6 & 5.13\% & & & \\ \hline \multicolumn{9}{c}{N363S allele} \\ \hline A & 170 & 96.59\% & 226 & 96.58\% & A vs. G & \begin{tabular}{c} 1.003 \\ (0.299–3.577) \\ \end{tabular} & 1 \\ \hline G & 6 & 3.41\% & 8 & 3.42\% & & & \\ \hline ER22/ER23 EK genotype & & & & & & \\ \hline AG & 2 & 2.27\% & 1 & 0.85\% & AG vs. GG & \begin{tabular}{c} 2.685 (0.138– \\ 160.345) \\ \end{tabular} & 0.578 \\ \hline GG & 86 & 97.73\% & 116 & 99.15\% & & & \\ \hline ER22/ER23 EK allele & & & & & & \\ \hline A & 2 & 1.14\% & 1 & 0.43\% & A vs. G & \begin{tabular}{c} 2.672 (0.138– \\ 138.519) \\ \end{tabular} & 0.579 \\ \hline G & 174 & 98.86\% & 233 & 99.57\% & & & \\ \hline \multicolumn{9}{c}{Th1111/N363S/ ER22/23 EK haplotype} \\ \hline CAG & 114 & 64.77\% & 156 & 66.67\% & CAG vs. other & \begin{tabular}{c} 0.92 \\ (0.597–1.419) \\ \end{tabular} & 0.752 \\ \hline TAG & 54 & 30.68\% & 69 & 29.49\% & TAG vs. other & \begin{tabular}{c} 1.058 \\ (0.675–1.656) \\ \end{tabular} & 0.828 \\ \hline CGG & 6 & 3.41\% & 7 & 2.99\% & CGG vs. other & \
\begin{tabular}{c} 1.144 \\ (0.312–4.056) \\ \end{tabular} & 1 \\ \hline TAA & 1 & 0.57\% & 1 & 0.43\% & TAA vs. other &
\begin{tabular}{c} 1.33 (0.017– \\ 104.872) \\ \end{tabular} & 1 \\ \hline TCA & 1 & 0.57\% & & & & \\ \hline TGG & & & 1 & 0.43\% & & \\ \hline \hline \end{tabular}  
* CS—patients with CA stenosis; CG—control group. \({}^{\text{a}}\)\(p\)-values were calculated with \(\chi^{2}\) test. \({}^{\text{b}}\)\(p\)-values were calculated with [PERSON]’s exact test.  
\end{table} 
Table 2: Th111I, N363S, and ER22/23 EK genotypes in CG and CS groups. \begin{table}
## 4 Factors Influencing the Phytochemical Compounds of _Brassica_ Vegetables
### Genetic Factors
#### 4.1.2 Brassica rapa  
Species and Other Cruciferous Crops  
_Brassica rapa_ species include turnip tops and leaves (\"cima di rapa\" and \"friariello\"), turnip, pak choi, Chinese cabbage, choy-sum and mizuna, evidencing a wide variability among close species [99] and varieties of the same species [100]. Phenolic compounds are mainly affected by the interaction between environment and genotype; this means that their variability strictly depends on the environmental conditions, hence they possess low heritability.  
Choy sum, a _Brassica rapa_ variety, showed the highest antioxidant potential compared to broccoli, cabbage, and cauliflower [101]. Some studies found that watercress showed a higher antioxidant potential in comparison to salad rocket; however, these two varieties, together with wild rocket and mizuna, are good sources of antioxidants [102; 103]. In _B. rapa_, the aliphatic glucosinolates (GLS) is the predominant form, with gluconapin as the most abundant, followed by glucobrassicanapin [104]. _B. rapa_ varieties have shown a high concentration of isorhamnetin, irrespective of the plant organs considered [105].
# Application of Image Recognition Methods to Determine Land Use Classes
## 4 Results
### Satellite Data Acquisition Results  
During this phase, approximately 300 GB of Sentinel-2 (S2) satellite data was successfully acquired in the archived packages. These packages contained JPEG2000 (.jp2) file format satellite images. Each Sentinel-2 band was downloaded individually and then stored securely on a local storage system. After the download process was completed successfully, all archives were extracted, and unnecessary files were subsequently removed.  
The dataset encompasses observations from 2022 to 2024, effectively capturing seasonal variations across the Lithuanian territory. The analyzed Sentinel-2 tiles were systematically categorized into ethnographic regions of Lithuania, facilitating easier territorial traceability. The ones that were analyzed are presented in Table 2.