esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Analysis and Field Distribution
### The Simulation of the Influential Factors  
With the variation in the parameters of the coil, the electromagnetic field distribution can also be changed. Therefore, it is necessary to analyze the influence of the typical parameters for further optimizations. As shown in Figure 15, at the location 8 cm beyond the coil plane, with the increase in the diameter of the little coil, the FWHM of the electric field decreases and reaches a peak of 10 cm. When the diameter of the little coil increases by more than about 10 cm, the FWHM increases again with the diameter. Therefore, the lowest FWHM is obtained for a coil diameter of approximately 10 cm.  
Besides, other distances beyond the coil plane, such as 7 cm and 9 cm, are also simulated. As shown in Figures 16 and 17. The trend is similar to the previous case where the distance is 8 cm. When the distance approaches 2 cm beyond the coil plane, the simulations show that the diameter of 9 cm has the lowest FWHM. However, the distance of 2 cm from the head to the coil is too close. Therefore, in this research, from a practical standpoint, the little coil of diameter 10 cm has the lowest FWHM.  
Figure 16: Simulation of FWHM at 7 cm beyond the coil plane.  
Figure 15: Comparison of FHWM between the different diameters of the little coil.  
Moreover, the FWHM of the electric field is also influenced by the intersection angle between the big and little coils. As shown in Figure 18, the shape of the curve is similar to the curve in Figure 14. Thus, at the location of 8 cm beyond the coil plane, the FWHM of the electric field is lowest when the intersection angle is around 53 degrees.  
The simulations show that the locality of the coils is sensitive to the diameter of the little coils and the intersection angle of the coil planes. The stimulation strength can still be enhanced by increasing the diameter of the big coils, but the trade-off of its inductance increases and the degradation of the focality should not be neglected.
## Results
### Comparison of Antioxidant Effects of Shuidouchi Fermented in Different Containers  
The results that are shown in Figure 10 and Table 9 show that, under otherwise identical fermentation conditions, the fermentation container affects Shuidouchi fermentation. The antioxidant effect of Shuidouchi fermented in the glass container exceeded that of Shuidouchi that was fermented in ceramic and metal containers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Group** & **Linear Regression Equation** & **IC\({}_{\text{50}}\) (mg/mL)** \\ \hline DPPH free radical & 1 Fold-FSDC & Y = 25.32X – 98.66 R\({}^{2}\) = 0.9807 & 2.32 \(\pm\) 0.02 a \\ scavenging capacity & 2 Fold-FSDC & Y = 31.937X – 94.64 R\({}^{2}\) = 0.9719 & 1.71 \(\pm\) 0.02 c \\  & 3 Fold-FSDC & Y = 30.909X – 95.2733 R\({}^{2}\) = 0.9816 & 1.79 \(\pm\) 0.02 b \\ \hline ABTS\({}^{+}\) free radical & 1 Fold-FSDC & Y = 9.2571X – 91.3 R\({}^{2}\) = 0.998 & 5.54 \(\pm\) 0.01 a \\ scavenging capacity & 2 Fold-FSDC & Y = 20.24X – 94.12 R\({}^{2}\) = 0.9767 & 2.67 \(\pm\) 0.02 c \\  & 3 Fold-FSDC & Y = 18.589X – 93.98 R\({}^{2}\) = 0.9704 & 2.90 \(\pm\) 0.03 b \\ \hline Hydroxyl free radical & 1 Fold-FSDC & Y = 9.2971X – 91.32 R\({}^{2}\) = 0.9732 & 5.52 \(\pm\) 0.03 a \\ scavenging capacity & 2 Fold-FSDC & Y = 13.417X – 90.28 R\({}^{2}\) = 0.9869 & 3.75 \(\pm\) 0.02 c \\  & 3 Fold-FSDC & Y = 11.983X + 0.68 R\({}^{2}\) = 0.9939 & 4.23 \(\pm\) 0.03 b \\ \hline \hline \end{tabular} Values present the mean \(\pm\) standard deviation. a–c Mean values with different letters in the same row are significantly different (\(p<0.05\)). 1 Fold-FSDC: 1-fold soaking water for Shuidouchi fermentation; 2 Fold-FSDC: 2-fold soaking water for Shuidouchi fermentation; 3 Fold-PSDC: 3-fold soaking water for Shuidouchi fermentation.  
\end{table}
Table 8: DPPH (A), ABTS\({}^{+}\) (B), and hydroxyl (C) free radical scavenging capacity of Shuidouchi soaked in different amounts of water.  
Figure 9: Soybean isoflavone contents of Shuidouchi fermented in different containers. a–c Mean values with different letters in the bar are significantly different (\(p<0.05\)). US: unfermented soybean; C-FSDC: Ceramic container used for Shuidouchi fermentation; G-FSDC: Glass container used for Shuidouchi fermentation; M-FSDC: Metal container used for Shuidouchi fermentation.  
Due to the different materials of fermentation containers, the temperature and water content of fermentation products can differ, thus resulting in different growth of fermentation microorganisms, which in turn affects the fermentation products [19]. After analyzing various fermentation factors, it was found that Shuidouchi fermented under specific condition (twice as much soaking water in glass container at 35 \({}^{\circ}\)C for 48 h) contained the most soybean isoflavenes. This particular fermented Shuidouchi showed the highest DPPH, ABTS+, and hydroxyl radical scavenging capacities of all the different tested conditions. These results indicate that soybean isoflavenes is an important active component that is produced by the antioxidant effect of Shuidouchi and exhibits a direct relationship with the antioxidant effect.
## 3 Methodology  
Figure 3 illustrates the complete workflow of the proposed human activity recognition system in the context of public transportation. The process consists of four main stages:  
1. Data Capture: RGB video recordings are collected at urban bus stops using a fixed camera. These videos capture real-world passenger behaviors, including standing, walking, boarding, and alighting.
2. Pose Estimation: Each video frame is processed with a pose estimation algorithm, which detects and maps 17 key body joints per person. The output is a structured JSON file containing the coordinates (X, Y) and confidence scores for each joint.
3. Feature Extraction: The raw JSON data are parsed and transformed into CSV format. For each detected individual, 51 features are computed (17 joints \(\times\) 3 values per joint: X, Y, Score), forming the feature vector used in the prediction stage.
4. Activity Prediction: The feature vector is input into a pre-trained machine learning model, which classifies the activity for each person in every frame. The model distinguishes between activities such as standing, sitting, walking, and falling.  
This structured pipeline enables the system to operate in complex, real-world environments with multiple people and varying conditions, offering insights into passenger behavior and potential improvements in public transport accessibility and safety. The methodology used is explained in detail below.  
Figure 2: Diagram of the Random Forest algorithm.
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 2 Principles of NMR Core Experiments
### T\({}_{2}\) Measurement  
Transverse relaxation time T\({}_{2}\) is typically measured using the CPMG spin echo method. Initially, a 90\({}^{\circ}\) pulse is transmitted, followed by a series of 180\({}^{\circ}\) pulses at specified intervals to create a pulse sequence for measuring T\({}_{2}\). The echo amplitude measured using the CPMG pulse sequence exhibits multi-exponential decay over time, as represented by Equation (1):  
\[b_{i}=\sum\
olimits_{j=1}^{m}f\big{(}T_{2j}\big{)}exp\left(-\frac{t_{i}}{T_{ 2j}}\right), \tag{1}\]  
where \(b_{i}\) is the echo amplitude at time \(t_{i}\); T\({}_{2j}\) is the \(j\)-th transverse relaxation time; and \(f\big{(}T_{2j}\big{)}\) is the amplitude corresponding to the j-th transverse relaxation time. By inverting Equation (1), we obtain \(f\big{(}T_{2j}\big{)}\), the T\({}_{2}\) distribution. For samples saturated with a single fluid, the T\({}_{2}\) distribution represents the pore size distribution: the longer the T\({}_{2}\) relaxation time, the larger the corresponding pore size. Measuring the transverse relaxation time T\({}_{2}\) of core samples, in conjunction with the difference in T\({}_{2}\) distribution, and enhanced diffusion methods, facilitates the identification of fluid types within the samples [19].  
In porous media, the transverse relaxation time of the fluid in the pores consists of three relaxation processes, represented by the following formula:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2B}}+\frac{1}{T_{2S}}+\frac{1}{T_{2D}}, \tag{2}\]  
where \(T_{2B}\) is the bulk relaxation time, \(T_{2S}\) is the surface relaxation time, and \(T_{2D}\) is the diffusion relaxation time.  
Bulk relaxation (\(T_{2B}\)) refers to the inherent relaxation mechanism of fluids within the pores of porous media, where the relaxation rate depends solely on the physical properties of the fluid. Surface relaxation (\(T_{2S}\)) is governed by the pore size of the porous media. When fluid molecules approach the pore surface, they interact with the solid surface, accelerating the relaxation of the fluid's magnetization vector. Surface relaxation significantly influences the distribution of T2 relaxation times in NMR measurements. It is expressed as:  
\[\frac{1}{T_{2S}}=\rho\frac{S}{V}, \tag{3}\]  
where \(\rho\) represents the surface relaxation rate of the rock [20], \(S\) denotes the total surface area of the rock pores, and \(V\) represents the pore volume.  
Diffusion relaxation primarily depends on magnetic field non-uniformity and the diffusion behavior of fluid molecules [21] and is further influenced by factors such as pore size, pore shape, pore connectivity, and fluid properties. For small pore structures or complex pore networks, the diffusion relaxation effect is particularly pronounced and is expressed as:  
\[\frac{1}{T_{2D}}=\frac{D(\gamma GT_{E})^{2}}{12}, \tag{4}\]  
where \(D\) represents the fluid diffusion coefficient, \(\gamma\) denotes the spin magnetic ratio, \(T_{E}\) is the echo interval, and \(G\) is the magnetic field gradient.
## 4 Simulation Results
### Industrial Environment  
In order to supervise physical equipment, the industrial network represents the most suitable system since it works on studying different interlinked equipment. Figure 14 shows the performance of RF and NB-PLC in terms of the BER. For the RF link, the path number is equal to 40 and the noise is GMM and uses Equation (9), where \(\pi_{1}\) = 0.99, \(\pi_{2}\) = 0.01, \(\sigma_{1}^{2}\) = 0.001, and \(\sigma_{2}^{2}\) = 100. For the NB-PLC link, the length line is equal to 10 m and the noise is cyclostationary impulsive. Dually to the rural case, for the industrial environment to have the same BER value, a lower SNR value is required with NB-PLC if compared to RF.  
Figure 13: RF vs. NB-PLC performances in an urban environment: 2 nd scenario for a moderately dense urban environment, with an example point that gives a data rate equal to 195 kbps.  
The authors demonstrate that despite the high connection density, the presence of light loads, non-linear loads, and various distribution types in the industrial area, the NB-PLC is more performant than the RF link. In fact, NB-PLC systems are designed to handle and mitigate such noise, ensuring reliable and robust communication even in the presence of these disturbances. Furthermore, NB-PLC signals can penetrate through various obstacles and materials commonly found in industrial areas, such as walls, metal structures, and machinery. This allows for better signal propagation and coverage compared to RF.  
The authors selected another measurement point (MP) from [51]. Its parameters are in accordance with the conditions of the modelized industrial environment in this paper as shown in Figure 14. At this MP, the PER is equal to 95% and the SNR is equal to 20 dB. Applying (9) returns a BER equal to \(3.10^{-2}\), which corresponds to an SNR equal to 23 dB, according to Figure 14. A difference of 3 dB is noticed between simulation results and experimental measurements confirming the reliability of the industrial environment model and the simulation results.
## 2 Application of the Space Syntax Analysis to an Urban Context
### Observation Campaign of Pedestrian and Motorised Traffic  
To validate and confirm the results of space syntax analysis, an observation campaign of pedestrian and motorised traffic has been performed on 40 roads (see Figure 6), representative of the roads in the urban context of Pontedera and corresponding to the 13% of all Pontedera roads. The 40 roads have been selected with the help of space syntax analysis. The 305 public roads in Pontedera have been divided in six groups: A--main roads; B--roads of the historical centre; C--secondary roads; D--roads connecting districts; E--access roads to neighbourhoods; and F--neighbourhoods roads. Each group is obtained considering a constant increment, \(\Delta\), of the \(I\) index (from \(I_{min}\) to \(I_{max}\) with increments of \(\Delta=0.32\)), see Table 1. The 40 roads were selected choosing a variable number of roads by each group, so as to reproduce the percentage distribution of the Pontedera public roads in the six groups shown in Table 1. For example, in the \"A group\" (i.e., Pontedera main roads) there are 37 roads (12.2% of 305); consequently, within the 40 roads, 5 roads have been selected from the \"A group\" (12.2% of 40).  
The observation campaign of pedestrian and motorised traffic has been conducted with the gate method: for each road a significant section has been chosen (see Figure 6), far from any perturbative element (e.g., intersections, stops of public transport, entries of companies or shops, construction sites, etc.). For each road section, the number of vehicles and passers-by in 10 min has been observed, in the period of maximum crowding (from 5:30 p.m. to 7:30 p.m.), corresponding to the most difficult  
Figure 6: Location of the 40 roads in which the traffic volumes have been observed (the sections adopted for the traffic volumes observations campaign are shown with red lines).  
condition required by the visual task due to the high traffic volumes. The period of maximum crowding has been determined after an observation of the traffic conditions during night-time from sunset (when the Pontedera street lighting is turned on to sunrise when it is turned off). The traffic observation campaign has been conducted in the month of June 2017 and in Table 2 the results are shown.
## 3 Analytical Solution for Anchor Load Transfer
### Elastic State Solutions  
Bringing Equation (5) into Equation (4) yields the elastic stage differential equation:  
\[\frac{d^{2}s_{t}(x)}{dx^{2}}=\frac{K\text{U}}{EA}s_{t} \tag{6}\]  
Figure 3: Stages of interaction between anchorage body and rock mass: (**a**) elastic state; (**b**) plastic state; (**c**) debonding state.  
Figure 2: Linear–nonlinear bond-slip model.  
The generalized solution of the differential Equation (6) is  
\[s_{t}(x)=C_{1}e^{ax}+C_{2}e^{-\alpha x} \tag{7}\]  
where \(C_{1}\) and \(C_{2}\) are coefficients to be determined, and \(\alpha=\sqrt{\frac{UK}{EA}}\). Substituting Equation (7) into Equations (3) and (5) yields the distribution of axial and interfacial shear stresses, respectively, in the anchorage body in the elastic phase:  
\[p_{t}(x)=\alpha EA(C_{2}e^{-\alpha x}-C_{1}e^{xx}) \tag{8}\]  
\[\tau_{t}(x)=K(C_{1}e^{ax}+C_{2}e^{-ax}) \tag{9}\]  
For the elastic deformation stage, the boundary conditions of the problem are \(p_{t}(x)|_{x=0}=P\), \(p_{t}(x)|_{x=L}=0\), where \(L\) is the length of the anchoring section, from which the coefficients \(C_{1}\) and \(C_{2}\) corresponding to the elastic phase can be obtained as follows:  
\[\left\{\begin{array}{l}C_{1}=\frac{p}{\alpha EA(e^{2 xL}-1)}\\ C_{2}=\frac{p}{\alpha EA(e^{2 xL}-1)}\end{array}\right. \tag{10}\]  
Further, the expressions for the anchorage body's displacement, axial force, and interfacial shear stress in the elastic phase can be obtained as follows:  
\[s_{t}(x)=\frac{P}{\alpha EA(e^{2 xL}-1)}(e^{ax}+e^{2 xL-ax}) \tag{11}\]  
\[p_{t}(x)=\frac{P}{(e^{2 xL}-1)}(e^{2 xL-ax}-e^{ax}) \tag{12}\]  
\[\tau_{t}(x)=\frac{PK}{\alpha EA(e^{2 xL}-1)}(e^{ax}+e^{2 xL-ax}) \tag{13}\]  
When \(\tau_{t}|_{x=0}=\tau_{1}\), for the pulling end shear stress, the part of the anchor near the pulling end will enter the plastic stage, from which the elastic ultimate load of the anchor can be obtained as follows:  
\[P_{tk}=\frac{\tau_{1}\alpha EA(e^{2 xL}-1)}{K(e^{2 xL}+1)} \tag{14}\]  
From Equation (14), it can be seen that when \(2\alpha L=6\), \((e^{2 xL}-1)/(e^{2 xL}+1)\approx 1\), the limiting length of the elastic stage can be obtained as \(L_{t}=3/\alpha\); it is also difficult to increase the elastic ultimate load, when the length of the anchoring section exceeds this value, which can be simplified as Equation (15).  
\[P_{tk}=\frac{\tau_{1}\alpha EA}{K} \tag{15}\]
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
## 3 Results and Discussion  
### Morphology and Structure of PPyNT  
PPyNT is oxidatively polymerized through the reactive self-degraded template method, as shown in Figure 1a. With the addition of FeCl\({}_{3}\) into the solution of MO and pyrrole, fibrillar MO-Fe\({}^{3+}\) complexes are formed in the reaction system [19]. These MO-Fe\({}^{3+}\) complexes induce the oxidation of pyrrole monomer on their surface and then degrade automatically with polymerization proceeding due to the reduction of Fe\({}^{3+}\) by pyrrole monomer [19]. Eventually, the obtained polymer assembles into nanotubes. The morphological characteristics of as-synthesized PPyNT are investigated by SEM and TEM. Uniform tubular nanostructures with very high yield (Figure 1b) and rough surfaces (Figure 1c) are observed in SEM images owing to the adhesion of some granules. These tubular nanostructures are several micrometers in length with an average diameter of 104.2 \(\pm\) 18.4 nm (Figure S1a, see Supplementary Materials). The hollow feature of the tubular nanostructures is confirmed by TEM images (Figure 1d,e), where the average thickness of the wall is statistically determined as around 31.4 \(\pm\) 3.4 nm (Figure S1b, see Supplementary Materials). XRD and IR investigate the structure of as-synthesized PPyNT. A broad peak assigned to the \(\pi\)-\(\pi\)interaction of the polypyrrole chain is observed at 24.1\({}^{\circ}\) in the XRD pattern (Figure 1f) [20] and numbers of typical bands of PPy [21], including the ring skeletal vibration of PPy at 1533 cm\({}^{-1}\) and 1453 cm\({}^{-1}\), the =C-H in-plane vibration at 1292 cm\({}^{-1}\), the C-N stretching vibration at 1160 cm\({}^{-1}\), the C-H/N-H in-plane vibration at 1027 cm\({}^{-1}\), the C-C out-plane deformation vibration at 962 cm\({}^{-1}\) and the C-H wagging at 768 cm\({}^{-1}\), are observed in IR spectrum (Figure 1g). The conductivity of PPyNT measured by a four-probe meter is about 0.35 S/cm. The hollow structure can enrich multi reflection to facilitate microwave attenuation, and large aspect ratios can promote fair filler connection in rubber matrix [22]. All these features imply that the obtained PPyNT may serve as a suitable absorbing filler to develop Rubber-Based microwave absorbers.
## 2 Materials and Methods
### Commercial Trial with an Evaluation of Worker Safety  
A commercial-scale trial of EF fumigation of agricultural machinery was conducted using a polyvinyl chloride (PVC) tarpaulin fumigation chamber (30 m\({}^{3}\), 6.0 \(\times\) 2.0 \(\times\) 2.5 m) in a warehouse located at Busan port (Busan, Republic of Korea) (Figure 2a). The agricultural machinery used in the commercial trial was a drilling machine weighing 4980 kg imported from Hong Kong. Then, 35 g/m\({}^{3}\) EF was applied for 4 h at 23 \({}^{\circ}\)C. Liquid EF was vaporized with SFM (Safefume Co., Deagu, Republic of Korea) with N\({}_{2}\) used as a carrier gas, and a fan was placed at the bottom of the PVC tarpaulin fumigation chamber for efficient gas circulation. A more-than-average amount of 180 _H. ludys_ adults were placed in three different locations (front, middle, and rear; \(>\)180 adults/location) inside the PVC tarpaulin fumigation chamber (Figure 2b). Gas samples were collected at specific time intervals (0, 1, 2, and 4 h), and EF concentration was measured using GC-FID. As it is difficult to perform GC-FID directly on the field, the EF concentration was checked in advance on the field using a gas analyzer (IBRID MX6; Industrial Scientific, Pittsburgh, PA, USA). After the completion of fumigation, _H. ludys_ adults were transferred to the insect rearing room. The mortality of _H. ludys_ adults was recorded 3 days after fumigation.  
For evaluating worker safety, after the completion of fumigation, the EF concentration in the PVC tarpaulin fumigation chamber was measured at time intervals for worker safety during ventilation using a portable gas analyzer (Minirae 3000, RAE Systems, San Jose, CA, USA).
## 3 Results and Discussion
### Chemical Characteristics  
A test HMR product was prepared by thawing frozen AMPS and CSM using HFD, followed by superheated steam roasting at a particular value for both AMPS and CSM. The test product consisted of 27.5% AMPS, 27.5% CSM, and 45% sauce. Chemical properties, such as pH, VBN, and TBARS, were used to evaluate the quality of the product (Table 6). The pH, VBN, and TBARS of the test HMR were 6.30 \(\pm\) 0.13, 9.45 \(\pm\) 1.09 mg\(\%\), and 1.13 \(\pm\) 0.27 mg MDA/kg, respectively. Consistent with the results of [PERSON] et al. [11] and [PERSON] et al. [10], who reported near-neutral pH in seafood products (squid meat and AMPS) following superheated steam roasting, the pH of the test HMR was close to neutral (7). Moreover, for fresh fish and fishery products, an ideal pH level is almost neutral. However, post-mortem period-associated nitrogenous compounds increase the pH, leading to decreased quality [41].  
A product's VBN value indicates the amount of nitrogen derived from proteolytic bacteria and endogenous enzymes [29], with the VBN test examining the level of protein breakdown and the presence of non-protein nitrogenous compounds. Furthermore, unpleasant smells generally correlate with high VBN values. Superheated steam roasting has been observed to lead to low VBN values. Similar results were reported by [PERSON] et al. [11], who found that the VBN of roasted squid meat was lower when using superheated steam roasting compared to other methods. Furthermore, [PERSON] et al. [10] also reported a lower VBN for superheated steam roasted AMPS.  
The low TBARS value of a product labels it as a perfect product according to [PERSON] [41], who categorized seafood products into the following three TBARS value-based groups: perfect product (less than 3 mg MDA/kg), good product (3-5 mg MDA/kg), and consumable limit (7-8 mg MDA/kg). Using superheated steam roasting resulted in a low TBARS in squid meat [11] and AMPS [10]. This could be attributed to the reduced oxygen availability during this process resulting in higher inhibition of lipid oxidation and decreased product peroxide (POV) and TBARS values [42].  
Collectively, the results of chemical characteristics of the test HMR indicated the quality of the product. According to VBN and TBARS values, test seafood HMR products can be classified as very good.