esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 5 Experimental Setup Parameters and Performance Metrics
### Experimental Setup Parameters  
Each D2D route consists of two intermediate nodes. The intermediate nodes use different channels with different amount of white spaces. There are three available routes, namely route 1 (\(fc_{s}\)-\(fc_{1}\)-\(fc_{4}\)-\(fc_{d}\)), route 2 (\(fc_{s}\)-\(fc_{2}\)-\(fc_{5}\)-\(fc_{d}\)), and route 3 (\(fc_{s}\)-\(fc_{3}\)-\(fc_{6}\)-\(fc_{d}\)). Each PU has a fixed ON time of 150 s, and different OFF time of either 150 s, 300 s, or 450 s. The nodes set the signal parameters, including the carrier frequency 850 MHz and the sample rate 1.1 MB. The routes are prioritized based on the PUs' activity levels. Specifically, the source node \(fc_{s}\) prioritizes and selects the D2D route with the highest number of white spaces (e.g., 450 s and 150 s OFF time have the highest and the least priorities, respectively)at all times, and so it switches to another route with the second highest number of white spaces when PUs' activities reappear in the current route. Hence, the backbone route is not selected if one of the D2D routes does not have PUs' activities. The least priority is given to the backbone route to promote traffic offload. This means that the backbone route is selected when PUs' activities appear in the three D2D routes. Further description about route selection is provided in Section 4.2.  
Figure 6 shows the experimental setup between source and destination node through three D2D routes (i.e., route 1, route 2 and route 3) and backbone route through MC BS.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Category** & **Parameter** & **PCU** & **RPU** \\ \hline Experiment & Duration & 600 s & 600 s \\ \hline USRP & Number of channels & 2 & 9 \\  & Number of nodes & 1 & 8 \\  & Transport layer & UDP & UDP \\  & Bandwidth & 40 MHz & 40 MHz \\  & Sample rate & 1.1 MB & 1.1 MB \\  & Transmission power & 20 dBm & 10 dBm \\ \hline Antenna & Carrier frequency & 850 MHz & 850 MHz \\ \hline Computer & Operating system & Ubuntu & - \\ \hline Switch & Number of units & 1 & - \\  & Number of inputs & 1 & - \\ \hline RP3 & Operating system & - & Ubuntu-Mate \\ \hline PoE & Number of units & None & 8 \\  & Number of inputs & None & 1 \\ \hline PU activity & PU ON time & None & 150 s \\  & PU OFF time & None & 150 s, 300 s, 450 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental setup parameters.  
Figure 6: Experimental setup for three D2D routes with the use of RP3 and a backbone route with the use of a PC.
## 2 Materials and Methods
### Isolation of Halotolerant Rhizsphere and Endophytic Root Bacteria  
Microorganisms were isolated from plant root tissues (endosphere) and from sediments directly contacting the roots (rhizosphere).  
For the isolation of rhizosphere bacteria, freshly harvested _S. ramossissima_ specimens were manually shaken to remove loose sediment particles (bulk sediments). A pool of plant roots with attached sediments (10 g) was separated from the aboveground plant biomass, transferred to 50 mL falcon tubes containing 35 mL of sterile phosphate-buffered saline buffer (PBS) supplemented with 0.01% Tween 80 and vortexed for 5 min [34]. The roots were removed, and the suspension was serially diluted in ringer solution and pour-platted in Trypic Soy Agar (TSA; Liofilchem, Roseto degli Abruzzi) supplemented with 2.5% NaCl (0.4 mM) in order to select for halotolerant bacteria [35]. For the isolation of endophytic bacteria, roots were washed with sterile PBS and surface sterilized by immersion in 15% hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) for 5 min, under shaking [36]. Roots were rinsed three times with sterile PBS, and 100 \(\mu\)L aliquots of the last rinse were pour-platted to confirm the efficiency of sterilization. Disinfected root samples were aseptically dried with sterile filter paper under flow chamber and weighed. Approximately 0.3-0.5 g of roots were cut into fragments (\(\sim\)30 cm) and homogenized with mortar and pestle with 10 mL sterile PBS. One gram of the homogenate was used as a starting sample for the preparation of serial tenfold dilutions in PBS [4], which were pour-plated in TSA supplemented with 2.5% NaCl.  
Cultures were incubated at 30 \(\pm\) 2 \({}^{\circ}\)C for 7 days. Isolated colonies with different macroscopic characteristics were selected for purification in saline TSA. The purity of the cultures was assessed by visual inspection and Gram stain. Stock cultures in Tryptic Soy Broth (TSB; Liofilchem, Roseto degli Abruzzi) were amended with 20% glycerol (\(\
icefrac{{v}}{{v}}\)) and stored at \(-80\)\({}^{\circ}\)C.
###### Abstract  
Agricultural supply chains face growing scrutiny due to rising concerns over food authenticity, safety, and sustainability. These challenges stem from issues such as contamination risks, fraudulent labelling, and the absence of reliable, real-time tracking systems. Existing systems often rely on centralised databases and fragmented data flows, limiting traceability, data integrity, and end-to-end visibility. While blockchain technology offers potential, most research focuses narrowly on traceability, overlooking its role in establishing secure product identity and its integration with emerging tools. This review investigates how Decentralised Identifiers (DIDs), digital twins, and smart contracts--in conjunction with blockchain--can create verifiable digital representations of agricultural products and automate trust mechanisms. Through an analysis of over sixty recent sources and leading standards (e.g., W3C DIDs, Hyperledger Aries), the study identifies key gaps in interoperability, governance, and system maturity. A layered system architecture is proposed, and its application is demonstrated in a cold-chain case scenario. The paper concludes with a roadmap for empirical validation and policy alignment, contributing a practical and scalable framework for researchers, practitioners, and regulators advancing blockchain-enabled traceability systems.  
Keywords:blockchain technology; agricultural supply chain; product identity; traceability systems; smart contracts; food safety and transparency +
Footnote †: journal: Applied sciences
## 5 Conclusions  
This paper proposes a cost-effective method for correcting vibration-induced BFP ellipsometric spectral errors. The method first calibrates the defocused incident angle distortion using the Brewster angle of reference material. Then, an ellipsometric compensation amount is inserted into the system model as a Jones matrix to correct the error of the ellipsometric parameters at different defocus positions. Moreover, the defocus position of an actual BFP image is determined by analyzing its frequency domain and energy characteristics. Once the defocus position of a BFP image is calculated, the corresponding incident angle distortion can be corrected, and the ellipsometric errors can be compensated using the previous calibration results. Our experiments showed that the absolute error of the measurement reduced from over 30 nm down to less than 1 nm, within a defocused range of \(\pm\)85 um. As the compensation completes every single frame of the BFP image, the method is expected to be applied for on-line BFP ellipsometry under vibration conditions.  
In the future, the defocus measurement method can be improved from three aspects. Firstly, the method needs to be demonstrated in real-time vibrational environments. Secondly, a monotonic defocus position determination method with better resolution and sensitivity needs to be developed. Third, antivibrational methods for general spectral ellipsometry, such as wavelength scanning BFP ellipsometry, need to be developed.  
Conceptualization, methods, comments, verification, J.W., L.P. and D.T.; investigation and issuing letters of credit, F.G., R.C. and L.Z.; writing--first draft preparation, J.Y. and J.W. All authors have read and agreed to the published version of the manuscript.  
Figure 9: SiO\({}_{2}\) film thickness measurement experiments using the proposed method: (**a**) results of a film with nominal thickness of 25 nm; (**b**) results of a nominal thickness of 45 nm; (**c**) results of a nominal thickness of 100 nm; and (**d**) absolute thickness errors of the three thin films.  
**Funding:** This research was supported by the National Natural Science Foundation of China (No. 52075206, 51835005) and the Key R&D Program of Hubei Province (2021 BAA056).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Application
### Non-Clinical Application
#### 4.2.7 Emotion/Stress Monitoring  
Stress is one of the major issues for various diseases such as cardiovascular diseases, diabetes, and cerebrovascular diseases. To understand and control personal stress, it is necessary to monitor an individual's emotional state or stress level continuously. Physiological signals are more reliable than other factors such as facial expression, gesture or voice to measure emotional state. Several researchers have used iPPG to monitor stress. For instance, [PERSON] et al. [138] detected and classified emotional states exploiting HR extracted from videos captured by webcam and using machine learning algorithms. Another study by [PERSON] et al. [139] also used a webcam to detect emotional state by measuring changes in HR during sitting and standing conditions. By using a five band digital camera, [PERSON] et al. [140] measured cognitive stress by measuring HRV, where subjects were requested to do a mental arithmetic task. [PERSON] et al. [141], [PERSON] et al. [142] and [PERSON] et al. [143] have shown that iPPG can be effectively used to monitor emotional states of human beings.
## 5 Experimental Results
### Evaluation Metrics  
We quantitatively assess performance based on the following metrics:  
* \(RMSE_{a}\): a standard metric for directly assessing the accuracy of the analysis state generated by the DA system.
* \(RMSE_{f}\): continuous predictions initialized from the analysis state are performed over 72 h, 7 days, and 15 days without additional data assimilation. The resulting forecast performance is used to directly evaluate the dynamical consistency and adaptability of the initial conditions provided by the DA system.
## 3 Nonlinear Identification Method Based on Broad Learning System and Particle Swarm Optimization
### BLS Network Optimized by PSO
The position boundaries are defined as shown in Equation (36):  
\[x_{i}^{(t+1)}= \begin{cases}x_{\text{min}},&\text{ if }x_{i}^{(t+1)}<x_{\text{ min}}\\ x_{\text{max}},&\text{ if }x_{i}^{(t+1)}>x_{\text{max}}\\ x_{i}^{(t+1)},&\text{ otherwise}\end{cases}  \tag{36}\]  
A time-decay-based inertia weight annealing mechanism is introduced to enable global exploration in the early stage and smooth convergence in the later stage, as defined in Equation (37):  
\[\omega(t)=\omega_{\text{max}}-\frac{(\omega_{\text{max}}-\omega_{\text{min}}) \cdot t}{T_{\text{max}}} \tag{37}\]  
The final convergence criterion of the PSO algorithm is defined as:  
\[|f_{t}-f_{t-1}|<\epsilon \tag{38}\]Equation (38) indicates that when the change in fitness between two consecutive iterations is less than threshold \(\epsilon\), the algorithm is considered to have reached a stable solution. Through continuous iterative updates of the equations, each particle converges toward the optimal region in the global search space, thereby enhancing the nonstructural identification capability of the constructed BLS network. The objective of particle swarm optimization is to minimize the loss function defined based on identification error. Let \(N\) be the number of training samples, \(J\) the model output, and \(\hat{\tau}_{i}\) the true torque values. The fitness function is then defined as shown in Equation (39):  
\[J=\frac{1}{N}\sum\
olimits_{i=1}^{N}{\|\hat{\tau}_{i}-\tau_{i}\|^{2}} \tag{39}\]  
This equation quantifies the mean squared error between the estimated torque output and the true torque under the current BLS network structure parameters. The optimization objective is to find the optimal combination of hyperparameters that minimizes this error.
# Effects of Different Pretreatments on the GABA Content of
## 3 Results and Discussion
### Effect of Pretreatments on the Quality of Germinated Brown Rice
\begin{table}
\begin{tabular}{c c c c c} \hline
**Germination Treatment** & **L* & **a* & **b* & \(\triangle\)E** \\ \hline Ungermination & 55.33 \(\pm\) 1.34 bed & 2.19 \(\pm\) 0.21 d & 16.92 \(\pm\) 1.15 d & 0 \\ Water & 55.65 \(\pm\) 0.13 bed & 2.58 \(\pm\) 0.45 cd & 18.83 \(\pm\) 2.15 bc & 1.98 \\ \hline \multicolumn{5}{c}{Soaking} \\
0.1\% CaCl\({}_{2}\) & 56.16 \(\pm\) 0.73 bed & 3.01 \(\pm\) 0.32 abcd & 17.62 \(\pm\) 1.22 cd & 1.36 \\
0.1\% Glu & 54.55 \(\pm\) 1.97 cd & 2.93 \(\pm\) 0.34 abcd & 16.65 \(\pm\) 0.42 d & 1.11 \\
0.2\% CaCl\({}_{2}\) & 54.62 \(\pm\) 1.16 cd & 2.86 \(\pm\) 0.45 bed & 17.35 \(\pm\) 0.67 cd & 1.07 \\
0.2\% Glu & 54.21 \(\pm\) 0.79 d & 3.22 \(\pm\) 0.32 abcd & 17.34 \(\pm\) 0.52 cd & 1.58 \\ \hline \multicolumn{5}{c}{Soaking/HPP (100 MPa, 10 min)} \\ Water & 56.77 \(\pm\) 1.21 abc & 3.69 \(\pm\) 1.22 ab & 18.20 \(\pm\) 1.22 bed & 2.44 \\
0.1\% CaCl\({}_{2}\) & 58.92 \(\pm\) 1.70 a & 2.47 \(\pm\) 0.33 cd & 17.72 \(\pm\) 1.26 cd & 3.69 \\
0.1\% Glu & 56.18 \(\pm\) 0.94 bed & 3.91 \(\pm\) 0.30 a & 19.53 \(\pm\) 0.56 ab & 3.24 \\
0.2\% CaCl\({}_{2}\) & 55.88 \(\pm\) 1.04 bed & 3.70 \(\pm\) 0.66 ab & 20.66 \(\pm\) 0.77 a & 4.07 \\
0.2\% Glu & 57.12 \(\pm\) 0.79 ab & 3.71 \(\pm\) 0.79 ab & 19.79 \(\pm\) 0.63 ab & 3.71 \\ \hline \multicolumn{5}{c}{Soaking/ultrasonic treatment (400 W, 20 min)} \\ Water & 55.81 \(\pm\) 1.43 bed & 3.12 \(\pm\) 0.58 abcd & 17.67 \(\pm\) 0.24 cd & 1.29 \\
0.1\% CaCl\({}_{2}\) & 56.12 \(\pm\) 1.45 bed & 3.06 \(\pm\) 0.55 abcd & 19.00 \(\pm\) 0.37 abcd & 2.39 \\
0.1\% Glu & 55.92 \(\pm\) 0.16 bed & 3.22 \(\pm\) 0.48 abc & 18.85 \(\pm\) 0.66 bc & 2.27 \\
0.2\% CaCl\({}_{2}\) & 57.31 \(\pm\) 2.17 ab & 3.21 \(\pm\) 0.17 abc & 19.59 \(\pm\) 1.06 ab & 3.48 \\
0.2\% Glu & 55.96 \(\pm\) 0.76 bed & 2.60 \(\pm\) 0.05 cd & 18.97 \(\pm\) 0.12 abcd & 2.18 \\ \hline \end{tabular} Data were expressed as mean and error bars of \(\pm\) S.D. (\(n\) = 6). Means with different superscript letters in the same column are significantly different (\(p<0.05\)).  
\end{table} 
Table 6: Color of germinated brown rice by different pretreatments.
## 3 Results and Discussion
### Linearity of the Calibration Curves  
Figure 7 illustrates calibration curves of red, yellow and blue samples batch dyed in water and reverse micellar D5 medium. It is a plot of the \(K/S\) sum value versus dye concentration (percentage on weight of fabrics, % owf). The \(K/S\) sum value is the summation of \(K/S\) values of the samples at wavelengths of 400-700 nm. The results of Figure 7 show that the \(K/S\) sum values of red, yellow and blue colors of the samples dyed in the reverse micellar D5 system are higher than samples dyed in water, indicating that the use of the reverse micellar D5 dyeing system can achieve better color yield than the conventional dyeing system.  
The linearity of the calibration curves was calculated in terms of \(R\)-square (\(R^{2}\)), a method widely applied for measuring the goodness-of-fit of a regression [42]. The value of \(R\)-square ranged from  
Figure 5: Reflectance curves of the blue color of batch samples dyed in water and D5 medium.  
Figure 6: Reflectance curves of the color mixture of standard samples dyed in water and D5 medium.  
0 to 1, where zero meant non-linear and one meant linear in structure. As presented by Figure 7 and Table 5, the values of \(R\)-square of water-dyed fabrics ranged from 0.9605 (water yellow) to 0.9937 (water red), whereas the \(R\)-square of D5-dyed fabrics ranged from 0.9630 (D5 blue) to 0.9933 (D5 red). This indicates that the calibration curves of samples dyed using the conventional and the D5 reverse micellar system are almost linear in structure and, thus, suitable for computer color matching.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 5 Conclusions  
In this paper, a large capacity histogram-based watermark algorithm is proposed. The watermark design of the proposed method has satisfactory functionalities in terms of robustness to geometric attacks and common image processing operations. In the embedding range selection, possible bad bins will be eliminated successfully with the assistance of a predefined threshold. In the watermark embedding design, by taking the approximately equal number of adjacent histogram bins into account, the proposed method makes every three consecutive bins carry a 3-bit watermark; or rather, it realizes the maximum embedding rate (1 bpb) for the first time. The experiment results demonstrate that the proposed watermark algorithm can provide a preferable tradeoff between embedding capacity and robustness, especially suitable for applications where robustness and embedding capacity are essential, such as copyright protection requirement for enough watermark information and higher robustness. However, a possible limitation for the proposed histogram-based watermarking is the situation where certain operations may make the histogram shape distorted too much. In this case, the watermark extraction will be affected directly. In our future research, one consideration is to improve the newly-designed appropriately equal condition so that when the histogram shape is distorted seriously, the accuracy of watermark extraction is still acceptable.  
All authors contributed equally to this work.  
We would like to take the opportunity to thank the reviewers for their thoughtful and meaningful comments. This work is supported by the Projects of the National Natural Science Foundation (61370188); the Scientific Research Common Program of the Beijing Municipal Commission of Education (KM201610015002, _KM_201510015009); the Beijing City Board of Education Science and the technology key project (_KZ_201510015015, _KZ_201710015010); the Project of Beijing Municipal College Improvement Plan (_PXM_2017_014223_00063); the cooperative education project between the Ministry of Education and Qualcomm Corp (201602034017).  
The authors declare no conflict of interest.
## 3 Results and Analysis
### Relationship of Compressive Strength and Thermal Conductivity  
As shown in Figure 13 at an earlier age, when the proportion of mineral admixtures replacing cement amounts for 30% had slower hydration reactions, higher porosity ratios, lower unit weights, and smaller thermal conductivities. However, as the samples aged, mineral admixtures gradually produced pozzolan reactions and reacted with the cement hydration product CH to form a C-S-H gel; thus, the compressive strength increased significantly.  
As shown in Figure 6, as the samples aged, the Thermal Conductivity/Compressive Strength (TC/CS) ratio decreased. After 120 d, the TC/CS ratios per unit compressive strength of most mixture ratios were higher than that of the controlled sample (MA0). After 7 d, only the TC/CS ratios of FA10 and BFS10 were slightly higher than those of MA0. Overall, GLP10 presents the highest TC/CS ratio: its TC/CS ratio at 7 d was 29.8% higher than that of MA0, and it increased to 41.3% higher at a later age. The TC/CS ratio of DLS30 at an earlier age was 18% higher than that of MA0; at 120 d, it was already slightly lower than that of MA0. As shown in Figure 14a, among samples with an addition of 10% mineral admixtures, GLP10 and DLS10 showed the highest TC/CS ratios. As shown in Figure 14b, among samples with an addition of 30% mineral admixtures, except for DLS30, whose TC/CS ratio was slightly higher than the controlled sample, the TC/CS ratios at a later age were higher than that of MA0.  
Figure 13: The relationship between compressive strength and thermal conductivity.  
Figure 14: Thermal conductivity/compressive of MLAC with (**a**) 10% (**b**) 30% mineral admixtures.
# Assessment of Mechanical Properties of Corroded Prestressing Strands
## 3 Tensile Test of Corroded Strands
### Residual Mechanical Properties from Test Result
Furthermore, there were specimens that showed similar strengths and ultimate strains despite having highly varying degrees of corrosion. Such deviations in the test result distribution may have been due to highly localized corrosion pits. Errors occurred in the process of measuring the section loss due to corrosion, and specifically in deciding the location of the maximum depth pit by inspection and in measuring due to the intercept of the near wire and curvature of the spiral wire. The stress was concentrated in the damaged part due to the irregular surface condition of the corrosion pits [22]. This is the factor that caused the most difficulty in quantifying the corrosion property. The most corroded wire can determine the behavior of the corresponding strand. This was indicated by the terraced behavior shown in Figure 10a, and it seemed more valid to measure the wire-unit section loss rather than the strand-unit section loss. Figure 14c,d shows the section losses converted from the strand-unit to the wire-unit with the largest section loss within the strand. The explanatory power of the regression analysis increased from R\({}^{2}=-0.015\) to R\({}^{2}=0.3472\). Figure 13c,d shows the lower limit curves with a 95% reliability and \(\pm 2\sigma\) of the performance distribution, depicting the relations of the wire-unit section losses with the ultimate strength and strain. It was assumed that the test result followed a normal distribution with the regression analysis curve as the average. According to lower limit curve of 95% reliability, the ultimate strength proportionally decreased in relation to the maximum corroded wire section loss. The ultimate strength values decreased by 20% and 38% when the section losses were 20% and 40%, respectively. Furthermore, the ultimate strain of the corroded strand at the lower limit curve of 95% reliability could not meet the requirement of the minimum tensile strain (3.5%) from KS D 7002 [21] if the section loss exceeded approximately 4%. The ultimate strain was evaluated as 0 when the section loss was approximately 17%. The equations for the lower limit curve with a 95% reliability, shown in Figure 14c,d, are as follows. \[\frac{F_{cpu}}{F_{pu}}=-0.6642\eta+0.913, \tag{5}\]  
\[\varepsilon_{cpu}=\varepsilon_{pu}\mathrm{e}^{-6.03\eta}-0.0278 \tag{6}\]  
where \(F_{pu}\) and \(F_{cpu}\) are the ultimate loads of the non-corroded and corroded strands in kN, respectively. \(\varepsilon_{pu}\) and \(\varepsilon_{cpu}\) are the ultimate strains of the non-corroded and corroded strands, respectively. \(\eta\) is the section loss according to Equation (4).