esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## Materials and Methods
### Lap Shear Strength  
Bondline shear strength tests were done according to EN 205 [38]. Beech (_Fagus sylvatica_) wood of density 700 \(\pm\) 50 kg/m\({}^{3}\) and 5% moisture content was used as the substrate. Adhesives were applied at 200 g/m\({}^{2}\) rate and bonded at 80, 110, and 130 \({}^{\circ}\)C for 7 min under 0.8 MPa pressure.  
Bonded specimens (Figure 1) were conditioned at normal conditions (20 \(\pm\) 2 \({}^{\circ}\)C and 65 \(\pm\) 5% relative humidity) for 7 days before testing. Twelve specimens were tested in each series.  
Shear strength (\(R_{t}\)) was calculated using the Equation (2):  
\[R_{t}=\frac{F_{\text{max}}}{S} \tag{2}\]  
where: \(F_{\text{max}}\)--the maximum force in Newtons, \(S\)--lap area in mm\({}^{2}\).
## 4 Discussion
### Enhancing Resilience  
While our focus has been on transparency and sustainability, the resilience dimension is also worth discussing. Resilience in supply chains refers to the ability to withstand and recover from disruptions. How do blockchain traceability and circular integration contribute to that? We argue that agility is a function of visibility. When every component and material is traceable, if a certain component is found defective or a certain supplier has an issue, companies can quickly identify which products or batches are affected and isolate the problem. For example, if a dye used in a batch of fabric was later found toxic, the ledger can pinpoint all garments containing that batch, enabling a targeted recall (thus minimizing disruption and maintaining customer trust). Another aspect is alternative sourcing: in a disruption (say a raw material shortage), having a record of recycled material availability could provide an alternate supply. If the virgin cotton supply is cut, but the company has a store of recycled cotton (and knows exactly how much and where it came from via ledger), it can use that to keep production running. Blockchain's decentralized nature also means the system is not reliant on one central IT infrastructure; each participant has a copy, so data integrity persists even if one node (organization) is offline or compromised. This distributed robustness is a subtle but important resilience benefit. Finally, documentation of episodes during which a disruption and recovery occurred may be valuable in situations where enhanced testing or special scrutiny of materials that entered the chain during the period of disruption could later be required to protect against tampering during the disruptive event.  
Our findings that performance holds under added load imply that the system can handle surges in activity, which might occur during a disruption (when many queries are made, or when a flurry of transactions happens to adjust orders). Blockchain technology has sometimes been critiqued for scalability, but permissioned systems like Fabric are proving capable for moderate scales relevant to individual supply chains. Of course, extreme spikes might need specialized strategies like scaling out orderers or using partitioned channels per product line.
## 2 Materials and Methods
### HPLC-DAD-MS Analysis of Phenolic Compounds  
A Shimadzu Nexera HPLC-DAD-ESI-MS system (Kyoto, Japan) operating using Lab Solutions LC-MS software (version 5.97.1, Shimadzu, Kyoto, Japan) was used for the identification and quantification of the phenolic compounds [11]. The free and bound phenolic extracts were filtered through a 0.22 \(\mu\)m PTFE syringe filter and, after the appropriate dilution, were put into the autosampler with an injection volume of 10 \(\mu\)L. The separation was performed in a Poroshell 120 EC-C\({}_{18}\) analytical column (4.6 \(\times\) 150 mm, 4 \(\mu\)m) (Agilent Technologies, Inc., Santa Clara, CA, USA) with a flow rate of 0.5 mL/min. The mobile phase A was water containing 0.1% formic acid, and B was acetonitrile: 0 min, 15% B; 3 min, 25% B; 12 min, 35% B; 24 min, 65% B; 24.5 min, 100% B; 30 min, 15%, and kept at 15% B for another 15 min. For MS analysis, an electrospray interface was used with +4.5 kV and 20 V interface and curved desolvation line (CDL) voltages, respectively. The mass spectrometer was operated using negative ionization mode with a full scan \(m/z\) 100-1000.  
Phenolic compounds were tentatively identified in comparison with the retention profile, UV-Vis spectra, and mass spectra of unknown peaks with those of authentic standardsor with literature data. The majority of phenolic compounds were quantified by selecting ion monitoring mode (SIM) of standard solutions, where the quantification calibration curves of the standard were created using an internal standard method using salicylic acid (1 \(\upmu\)g/mL), while the quantification calibration curves of carvacrol created at a monitoring wavelength of 280 nm using the DAD system. When standards were unavailable, compounds with similar structures were used instead to perform quantification of the phenolic compounds. Specifically, lithospermic acid A, as well as other salvianic acid isomers, were quantified as salvianic acid B equivalents.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 8 Evaluation Results
### Micro Benchmarks
#### 8.2.5 Runtime Breakdown  
In Section 7.2, we pointed out that computing the FSS keys, specifically the fixed-key AES computation, is the performance bottleneck of our scheme. From Figures 11-14, we provide a runtime breakdown of \(\text{Sillcom}_{3R}\) and \(\text{Sillcom}_{trd}\) for both single thread and 8 threads. In the single-thread execution of \(\text{Sillcom}_{3R}\), the server computation time is predominantly spent on the calculation of FSS keys. In the 8-thread \(\text{Sillcom}_{trd}\), which achieves optimal acceleration, the runtime of other modules significantly increases; however, it still accounts for less than 10% of the total server computation time. This provides clear guidance for future optimization efforts: further accelerating the computation of FSS keys, which is still the bottleneck module.
## 2 Nanotechnology and Nanomedicine  
Over the past few decades, nanotechnology has gained tremendous attention with good future prospects focusing more on nanoparticles, which is basically the bedrock of nanotechnology [1; 13; 17; 23; 24; 25; 26]. Nanotechnology is an important aspect of science that focuses on the continuous designing and manipulation of materials (atoms and molecules) to produce structures at the nanometre scale size ranging from smaller nanometre to 100 nm with unaltered initial unique features of the material used, with broad nanoscale schemes in clinical applications for therapeutic, protective as well as diagnostic purposes [8; 27]. The entire application of nanotechnology from the synthesis processes, control release profiling, monitoring of biological processes and diagnosis is referred to as \"Nano-medicine\" [8]. This comprehensively means a process of transformation and encapsulation of a drug's molecules using nanostructures with or without the help of a carrier materials, masking some inherent drawbacks and limitations of free drugs such as poor bioavailability, hydrophobicity, high dosages, rapid assimilation, short half-life of photo degradation, poor selectivity as well as off targeted effects [28]. Conventional utilization of free drugs portrays poor bioavailability, low efficacy, non-selectivity and undesirable side effects [8; 29]. For the past few years, a considerably large number of poorly soluble drug candidates has gradually increased as a result of the use of high-throughput screening and combinatory chemistry in drug discovery [30; 31; 32]. Approximately 70% of marketed drugs and many new chemical drug candidates, medicinal herbs as well as food supplements sometimes fails to be absorbed in the gastrointestinal tract (GIT) and often possess poor intravenous circulation and muscular tissue absorption after administration [33]. The low solubility nature of drugs limits their dissolution rate leading to a variety of issues which consequently results in low bioavailability as well as an erratic absorption pattern of drugs in biological systems [34]. However, an alternative way of overcoming such problems can be achieved by dose escalation although, this could result in undesirable effects associated with increased toxicity leading to patient's non-compliance [32]. Delivering therapeutic compound to the desirable localised site is quite challenging for the treatment of many ailments [29]. However, the application of nanomedicine have currently helped to overcome some of the problems of free drugs for therapeutic purpose [35]. This could be due to the uniqueness of the physicochemical properties presented by nanoparticles such as; (a) increased solubility; (b) increased drug pharmacokinetics; (c) co-delivery of multiple drugs to the same specific location at the same time (synergistic treatment); (d) enhanced bio distribution and bioavailability of drug to the targeted area; (e) improved drug permeability and retention effects; (f) increased specificity of drugs to targeted site of interest; (g) increased surface area to volume ratio and (h) decreased patient-to-patient variability [2; 36].
## 4 Proposed Right-Hand Sine Wave Smooth Edge Transmission Line-Based LWA  
### Dispersion Analysis  
The transmission line periodically loaded with a smooth edge sine wave unit cell exhibited unidirectional-scanning characteristics, i.e., only right-hand radiation. The unit cell has been extracted to perform the dispersion analysis. The geometry of the single unit element is plotted in Figure 12. Figure 12 also shows the field distribution pattern of the unit element where most of the radiation leaks out of the smooth bent corner. CST simulation has been done for the smooth-edge sine wave modulated unit element. The dimensions of the element were as follows: the period of the unit element/cell \"d\" was equal to 6.0 mm in the x-direction, and its height was equal to 5.0 mm in the y-direction. The angle of each bend in the unit cell was 56 degrees.  
Figure 13 shows the dispersion diagram of the proposed unit element. As before, the dispersion curve presents three modes, i.e., dominant mode, the first higher order mode, and the second higher order mode. The slow wave modes and fast wave modes have been separated by the air line, which served as the reference. The slow wave mode was the quasi-TEM mode, which was below the air line and had a phase velocity less than the speed of light. The fast wave modes are shown in the graph above the air line, which had a phase velocity higher than the speed of light. The fast wave modes were in general responsible for the desired scanning feature. Furthermore, the upper and lower  
Figure 11: 3D radiation patterns showing forward and backward scan.  
Figure 12: (a) Unit cell of the sine wave LWA and (b) field distribution.  
frequency limits were defined by this dispersion diagram. This analysis can be further verified with the help of the S-parameter graph, which will be discussed in a later section of this paper. Figure 14 shows the normalized propagation constant and normalized attenuation constant curve. The curve plotted is intended to show the inverse normalized propagation constant, i.e., Vp/c as a function of frequency. This graph has verified the frequency of operation of the proposed unit element in the fast wave region, i.e., the right-hand quadrant. This unit element had smooth bends with the same angle, i.e., 56 degrees. By comparing both shapes, it can be concluded that the sharpness of the bends had a great impact on the antenna behavior, especially in terms of the radiation characteristics.  
Figure 14: Phase velocity of the sine wave LWA (inverse propagation constant) and attenuation constant (leakage).  
Figure 13: Dispersion curve of the sine wave LWA: the slow wave and fast wave regions separated by the air line.
## 2 Comparison of Experimental Devices
### Udl-GREiA III
#### 2.2.3 Type C--Tests on Phase Change Slurries (PCS)  
ISE PCS  
The device \"ISE PCS\" (Figure 27) is a phase change slurry cycling test rig to determine the stability of PCS while cycling through a hydraulic system in which the dispersed PCM is exposed to repeated phase transitions and shear stress caused by a pump and other hydraulic components. It consists of three hydraulic circuits: the PCS test circuit, a heating circuit, and a cooling circuit; the last two are connected to thermostats (Figure 28). The heat flux in these two circuits is recorded by measuring the volume flows and the temperature difference over the heat exchangers. In the PCS circuit, the volume flow and the temperatures over the heat exchangers are measured as well, so it is possible to monitor the heat flux into and out of the PCS. Additionally, the pressure difference over the cooling heat exchanger on the PCS side is recorded. Samples of PCS for analysis can be taken while the test facility is in operation.  
To cycle PCS, the following temperature profile is typically applied: constant temperature at the output of the heating and cooling heat exchanger in the secondary circuit with temperature levels depending on the phase transition temperature range of the dispersed PCM. The cycle rate is determined by the volume of PCS filled into the secondary circuit and the volume flow applied. At 200 l-h\({}^{-1}\) volume flow, approximately 1300 cycles per day are carried out. During the experiments, the following data are measured: temperatures at the inlets and outlets of both heat exchangers in the primary and secondary circuits; volume flows in all three circuits; and pressure loss in the secondary circuit of the cooling heat exchanger. The detection of degradation is done on five different parameters: (a) heat flux into and out of the PCS, (b) pressure drop over the cooling heat exchanger, (c) particle size distribution, (d) enthalpy determination via DSC, and (e) viscosity measurement of samples taken.  
Figure 27: Picture of stability testing device \"ISE PCS\".  
Summary of Type C Tests  
For this survey, one device for the investigation of the stability of PCS has been considered. Comparing the PCS test rig with the other stability testing devices of type A and type B, it can be observed that the PCS test rig applies the highest heating and cooling rates and the highest number of thermal cycles that are carried out per day to its samples. Such a fast melting and solidification of the sample is possible because PCS consist of PCM in comparably small volumes (emulsified or encapsulated) leading to a large heat transfer area and a relatively fast phase change between solid and liquid. Due to the special properties of PCS, the methods for investigating possible degradation processes also differ from those used for PCM and PCM with stable supercooling.
## 2 Numerical Analysis Model
### Analysis of Effect of Tube Skid Charging Time  
The maximum charging rate for a tube skid is primarily governed by the compressor's capacity to supply high-pressure hydrogen, whereas the minimum charging speed can be adjusted based on environmental conditions through a regulator. Given that tube skids are configured by interconnecting multiple tanks in parallel, hydrogen can be charged faster than the designated rate when only a few tanks are being filled. Therefore, examining the temperature distribution within the tube skid relative to charging duration is crucial for defining operational standards. This study conducted numerical analyses across four scenarios, ranging from 1 to 4 h, with a benchmark charging period of 2 h.  
Figure 5 shows the temperature distribution across the central section of the tube skid following charging. Notably, the upper region exhibited higher temperatures compared to the lower region. This temperature discrepancy arose as the cooler hydrogen, introduced at the tube skid's base, warmed up during compression and ascended due to buoyancy. The incoming hydrogen from the inlet ascended a certain distance propelled by its momentum  
Figure 4: Validation of numerical model with literature data.  
before descending again due to density variations, resulting in unstable flow patterns near the skid's bottom and a pronounced temperature gradient in the section where mixing occurred. Moreover, [PERSON] et al. (2013) indicated that a temperature gradient could also be present along the axis of a horizontal tank if the ratio between its axial length and diameter was substantial.  
Accordingly, the position within the tube skid significantly influences the disparity between the average temperature (mass-weighted average) and the maximum temperature (vertex maximum) of the hydrogen. Specifically, shorter charging intervals increase the temperature difference between the upper and lower sections due to a rapid pressure rise and diminished heat transfer duration. The difference between the peak and average temperatures was 20.7 \({}^{\circ}\)C for case 1 (the shortest charging duration) and 8.3 \({}^{\circ}\)C for case 4 (the longest charging duration).  
Figure 6 illustrates the peak temperature within each region of the tube skid as a function of charging time, including the hydrogen's maximum temperature inside the tube skid, the solid region's (tank shell) peak temperature, and the outer surface's maximum temperature. Overall, a prolonged charging period corresponded with a narrowing gap between the average internal hydrogen temperature and the outer surface's temperature, decreasing from 19.9 to 8.5 \({}^{\circ}\)C, attributable to an extended heat transfer duration. The extended period also allowed for more effective heat dissipation to the external air, establishing an inverse relationship between the tube skid's maximum temperature and the charging duration. Table 3 presents numerical data detailing the maximum temperatures upon charging completion.  
Figure 5: Temperature distribution of central section at completion of charging by change in charging time.  
Figure 6: _Cont._
## 4 Security and Performance Analysis
### Security Analysis
#### 4.1.2 Security against Replay Attacks  
In each communication session, including communication from the sensor node to the cluster node or communication from the cluster node to the base station, \"two-way\" mutual authentication is used to prevent the replay attack. We use the nonces \(N_{1}\), \(N_{2}\) and \(N_{3}\) to check each communication message. Any communication can be determined to be legal or illegal by checking the correctness of the nonces. The related descriptions are given in steps 4.2, 5.2 and 6.2 in Figure 12. Our scheme is able to prevent replay attacks.
## 1 Introduction
In order to accomplish our goals, we explore the following research questions concerning the impact of the S2 NeTM approach on enhancing semantic interoperability, improving social IoT service quality, and addressing limitations and challenges:  
RQ1. How do the S2 NeTM components enable social relationships among IoT devices, and facilitate communication and data sharing within a network? RQ2. What are the benefits of using the S2 NeTM in IoT environments, and how does it compare to other middleware solutions in terms of challenges addressed and limitations? RQ3. How can the S2 NeTM be customized and adapted to support specific use cases and applications in different domains? The remainder of this paper is organized as follows. Section 2 provides a review of the related work in ontology-based social IoT solutions. Section 3 describes the proposed S2 NeTM in detail, including its general context and components. It also discusses the S2 NeTM ontology, which is used in the semantic annotation and reasoning modules of the proposed solution. Section 4 describes the Green Route use case and demonstrates how the S2 NeTM is applied in this use case. Section 5 conveys the results of this study, centered on the research questions that directed the inquiry, and discusses prospective paths for advancing future research in this field. Finally, Section 6 concludes the paper.
# Topdressing Nitrogen Demand Prediction in Rice Crop Using Machine Learning Systems
## 2 Study Area  
Currently, an extent of 20,000 ha of rice is grown in Thessaloniki Plain. The study area comprises a rice farm in the Plain of Thessaloniki, Greece, within the agricultural area of Chalastra village. A long and a medium grain variety (cv. Thai Bonnet and Ronaldo, respectively) were water-seeded for four consecutive years: 2016, 2017, 2018, and 2019. During the growing season, the grower tried to maintain a 10 cm flood depth. However, fields were drained for a relatively long period of seven days, which is the standard practice for rice growing in Greece, for application of foliar herbicides.  
The total extent of the study farm ranged from 99.9 to 128 ha over the monitoring years, averaging to 112.2 ha and shared between 30 rice fields (minimum, in 2019) and 36 rice fields (maximum, in 2016). Differences in farm size and field allocation was due to rotation (mainly with maize), occasional field merging and renting of new land or abandonment of other. However, a major part of the cultivated land was spatially constant over the period (about 90.7 ha) (Figure 1).  
The whole management was based on dividing the fields into management zones according to the precision agriculture principles. The management zones were delineated first in 2016 and remained geometrically constant for 2017. In 2018, most of the zones were further divided and remained constant in 2019, too (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Total Extent (ha)**} & \multirow{2}{*}{**Number of Fields**} &
\begin{tabular}{c} **Average Field** \\ **Extent (ha)** \\ \end{tabular} & \multirow{2}{*}{**Number of Management**} & \multirow{2}{*}{**Average Extent of**} \\  & & & & **Zones** & **Zone (ha)** \\ \hline
2016 & 99.9 & 36 & 2.77 & 55 & 1.81 \\
2017 & 107.8 & 35 & 3.08 & 58 & 1.85 \\
2018 & 113.0 & 36 & 3.13 & 118 & 0.95 \\
2019 & 128.0 & 30 & 4.26 & 118 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the study rice fields and their management zones over the years.  
Figure 1: The study rice fields in the Plain of Thessaloniki, Greece (spatial union of all years).