esa-sceva/satcom-chunk-collection huggingface dataset:
# Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid
## 4 Conclusions  
A series of triaxial compression tests were performed on coarse soils reinforced with geogrids to study the effect of soil type and geogrid influence on the mechanical properties of the soils.  
Based on the test results, it was found that:  
* The choice of cell pressure in the triaxial device is crucial, as it significantly affects the reinforcement effect (the influence of the geogrid). Higher cell pressure corresponds to a reduced reinforcement effect.
* At a cell pressure of 20 kPa, two peak values of deviatoric stress were observed, and this phenomenon was consistent across a few soils reinforced with geogrids.
* Gravelly soils exhibited higher vertical strains at which the maximum deviatoric stress was reached compared to sandy soils.
* The vertical strains at which the maximum deviatoric stress was reached were higher for gravelly soils compared to sandy soils.
* The introduction of geogrids resulted in a slight reduction in the angle of internal friction but notably increased cohesion values in both sandy and gravelly soils. This increase in apparent cohesion was more pronounced in gravelly soils.
* A strong dependence of deviatoric strength on the initial specimen density was observed, with deviatoric strength increasing as density increased. This influence of density was more noticeable in gravelly soils than in sandy soils.
* Deviatoric strength was found to correlate with the coefficient of uniformity, with higher values corresponding to higher coefficients of uniformity. Gravelly soils consistently exhibited higher deviatoric strength values compared to sandy soils.  
Conceptualization, S.S.; methodology, N.D.; validation, M.Z., S.S. and N.D.; formal analysis, M.Z.; investigation, M.Z.; resources, M.Z.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, S.S.; visualization, M.Z.; supervision, S.S.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Vilnius Gediminas Technical University Excellency center project \"Civil Engineering Research Centre\", project No: 10.6-64-10.2-12545.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are cointained within the article.  
The equipment and infrastructure of the Civil Engineering Scientific Research Center of Vilnius Gediminas Technical University were employed for investigations. The authors sincerely thank HUESKER Synthetic GmbH for supporting the research with geogrids and for allowing the publication of this research work, and anonymous reviewers for their constructive comments and suggestions for improving the quality of this article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### Our Contributions  
This paper present a practical framework for enhancing coffee supply chain transparency using blockchain and simulated IoT integration. This framework represents a simulation-based prototype in which realistic sensor outputs were modeled to test blockchain integration and smart contract functionality. Our key contributions are as follows:  
* We propose a blockchain and IoT-based system architecture that enables secure and traceable transactions across all stages of the coffee supply chain.
* We design and implement Ethereum smart contract to handle ownership transfers, quality assurance, and environmental compliance, demonstrating how real-world data can trigger on-chain actions.
* We provide a unified role-based interaction model that aligns farmers, cooperatives, exporters, roasters, and retailers through transparent smart contract interactions.
* We simulate IoT sensor data, including RFID, GPS, weighing systems, environmental sensors, and mobile validation, then integrate these into blockchain-based smart contract workflows for end-to-end traceability testing.
* We evaluate performance using 1000 transactions and provide transaction fee, time distribution, and administrative cost insights, demonstrating low overhead and smart contract efficiency.
* We outline a future-ready system architecture by identifying the IoT hardware and platforms (e.g., Raspberry Pi, Node-RED) suitable for transitioning from simulation to real-world deployment.
## 4 Experiments and Results
### ICA as Fragmentation-Wave Enhancement Tool
(**c**) Cumulative ratio of fragmentation wave enhancement, this ratio is computed by joining the results of isolated independent components. Figure 13: Different measurements for the lateral region of the heart with ICA model. (**a**) Distribution of \(\rho_{RMSE}\) coefficient for each independent component, values lower than zero represents cases where the RMSE measured over the ECG is greater than RMSE measured over the independent component. (**b**) Ratio of fragmentation wave enhancement for each independent component, this ratio is computed as cases where the \(\rho_{RMSE}\) value is greater than zero. (**c**) Cumulative ratio of fragmentation wave enhancement, this ratio is computed by joining the results of isolated independent components. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Frag 1** & **Frag 2** & **Frag 3** & **Frag 4** & **Frag 5** & **Frag 6** & **Frag 7** & **Frag 8** \\ \hline Ctrl 1 & 0.59 & 0.26 & 0.12 & 0.02 & 0.01 & 0.00 & 0.00 & 0.00 \\ Ctrl 2 & 0.18 & 0.46 & 0.24 & 0.11 & 0.01 & 0.01 & 0.00 & 0.00 \\ Ctrl 3 & 0.10 & 0.11 & 0.46 & 0.25 & 0.07 & 0.01 & 0.00 & 0.01 \\ Ctrl 4 & 0.05 & 0.05 & 0.07 & 0.46 & 0.30 & 0.05 & 0.01 & 0.01 \\ Ctrl 5 & 0.03 & 0.03 & 0.04 & 0.07 & 0.51 & 0.26 & 0.05 & 0.02 \\ Ctrl 6 & 0.02 & 0.02 & 0.01 & 0.04 & 0.05 & 0.54 & 0.28 & 0.04 \\ Ctrl 7 & 0.01 & 0.03 & 0.02 & 0.02 & 0.02 & 0.07 & 0.59 & 0.23 \\ Ctrl 8 & 0.02 & 0.04 & 0.03 & 0.03 & 0.04 & 0.07 & 0.08 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Normalized confusion matrix of ICA over 8 independent lead model. The _ij_ value of the table represent the ratio of _i_-th component of control patient that is paired with the _j_-th component of synthetically fragmented patient according to the value of PCC. Figure 15: Different measurements for ICA over independent leads model. (**a**) Distribution of \(\rho_{RMSE}\) coefficient for each independent component, values lower than zero represents cases where the RMSE measured over the ECG is greater than RMSE measured over the independent component. (**b**) ratio of fragmentation wave enhancement for each independent component, this ratio is computed as cases where the \(\rho_{RMSE}\) value is greater than zero. (**c**) Cumulative ratio of fragmentation wave enhancement, this ratio is computed by joining the results of isolated independent components.
## 5 Experimental Evaluation
### Experimental Results  
The comprehensive results for _Dataset1_, consisting of sparse graphs, and for _Dataset2_, consisting of dense graphs, in terms of sum-span and execution time are reported in Figure 3. DLMinTC+ outperforms FastMinTC+ in terms of sum-span on both sparse and dense instances, despite it taking a longer execution time. Indeed, on _Dataset1_, it achieves an average reduction of 9.3% in coverage size compared to FastMinTC+, demonstrating the model's better ability to learn temporal patterns that reduce the overall interval count, despite the 29% increase in execution time compared to FastMinTC+, due to the added computational complexity required by the DL model. Similarly, on _Dataset2_, DLMinTC+ continues to outperform FastMinTC+ in terms of coverage size, with an average reduction of 7.5%. This highlights the model's effectiveness in capturing complex structures and interactions also in dense temporal graphs. However, the execution time shows a more noticeable increase, averaging 38% due to the greater complexity of the graphs, which requires more iterations and calculations to produce the activity intervals. As we can see in Table 3, the increase in execution time comes with a corresponding increase in memory consumption on both _Dataset1_ and _Dataset2_. This is due to the com \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **Support** \\ \hline
72.13\% & 75.01\% & 69.22\% & \textgreater{}500\(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Neural model evaluation metrics (accuracy, precision and recall) computed over the synthetically generated dataset composed of a total of 500 graphs. Support represents the total number of samples over which these metrics have been computed, i.e., the total number of nodes  
Figure 3: Comparison of sum-span (**top**) and execution time (**bottom**) by dataset and size. In the top figure, we can see the improvement in terms of percentage reduction of sum-span introduced by DLMinTC+ over FastMinTC+; in the bottom figure, we can see the percentage increment in execution time of DLMinTC+ expressed as percentage reduction in FastMinTC+. plexity of the trained neural model, which, even if it does not require memory to compute backpropagation at the inference time, needs memory to load the entire graph. Experiments conducted on publicly available graphs corroborate the findings observed in the synthetic test instances too. Detailed results for these real-world instances are presented in Tables 4-6. For each graph considered, Table 4 provides the results in terms of sum-span, Table 5 provides the results in terms of execution time, and Table 6 provides the results in terms of memory consumption. In this scenario, DLMinTC+ shows variable behavior depending on the graph structure. Figure 4 shows the details of the comparison with respect to sum-span percentage reduction and execution time percentage increment. The model achieves an average reduc
## 3 Optical Error Analysis
### Simulation Experiment Results
#### 4.2.3 The Combined Error of FPI and FTIS
The MJI-HI super-resolution interferogram \(I_{sup}\) calculation results for tilt direction angle differences of \(\varphi_{FTIS}\) - \(\varphi_{FPI}\) = \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\) were calculated separately, as shown in Figure 18. The results demonstrate that under these error conditions, the reconstructed \(I_{sup}\) remains largely consistent with \(I_{0}\), with minor deviations, indicating a limited impact on MJI-HI performance. Converting \(I_{sup}\) to super-resolved spectra and calculating the error relative to \(B_{0}\) using Equation (36), the errors for \(\varphi_{FTIS}\) - \(\varphi_{FPI}\) = \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\) are \(-17.61\) dB, \(-17.63\) dB, \(-17.65\) dB, and \(-17.61\) dB, respectively. These values closely align with the super-resolved spectral error (\(-17.85\) dB) observed in Section 4.2.2 under FTIS tilt error alone (\(FWHM_{tilt}\) = 0.125 \(\mu\)m). This similarity arises because the interferogram \(I_{1}\), also affected by FTIS tilt errors, becomes \(I_{1-tilt}\), leading to final results closer to \(I_{0-tilt}\). The slight precision degradation stems from reduced interference intensity due to FTIS tilt errors, which lowers the spectral energy. These findings confirm that when the FPI and FTIS broadening widths are better than \(I_{0-tilt}\), the super-resolved spectra maintain high accuracy regardless of the angular offset between tilt directions. Figure 17: (**a**) When \(B\)(v) = 1 is used as the input spectrum, the overall situation of the interferogram \(I_{2}\); (**b–e**). When the tilt direction angle differences are \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\), the response function convolution broadening of \(I_{2}\) occurs at \(\pm m\Delta_{FPI}\); (**b1–b4**). When the tilt direction angle difference is \(\varphi_{FTIS}\) - \(\varphi_{FPI}\) = \(0^{\circ}\), the convolutional broadening of the response functions of the \(m\) = 0, \(\pm\)1, \(\pm\)2, \(\pm\)3 order caused by the combined errors; (**c1–c4**). When \(\varphi_{FTIS}\) - \(\varphi_{FPI}\) = \(30^{\circ}\), the convolutional broadening of the response functions of the \(m\) = 0, \(\pm\)1, \(\pm\)2, \(\pm\)3 order; (**d1–d4**). When \(\varphi_{FTIS}\) - \(\varphi_{FPI}\) = \(60^{\circ}\), the convolutional broadening of the response functions of the \(m\) = 0, \(\pm\)1, \(\pm\)2, \(\pm\)3 order; (**e1–e4**). When \(\varphi_{FTIS}\) - \(\varphi_{FPI}\) = \(90^{\circ}\), the convolutional broadening of the response functions of the \(m\) = 0, \(\pm\)1, \(\pm\)2, \(\pm\)3 order.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis
#### 4.2.2 Simulation Results for Different Link Capacity  
Figure 3 depicts the simulation results of scenarios when the bandwidth \(C_{1}=2000\), \(3000\), \(4000\) packets/s respectively. To show the details, the enlarged images are superimposed in Figure 3a,c. The following subsections use the same method to show the details. Figure 3a shows that the queue length can converge within 15 s regardless of the link capacity. As shown in Figure 3c, although the bandwidth is different, the queue packet loss ratios are under \(6\times 10^{-4}\) (under \(3\times 10^{-5}\) in stable state). Figure 3b shows that the window sizes become stable with different bandwidths. In addition, a larger bandwidth corresponds to lower packet loss ratio and larger window size. That is to say, we can improve the effectiveness (lager window size) and reliability (lower packet loss ratio) of the networks by increasing the bandwidth.
###### Abstract  
Senodotropin-releasing hormone (GnRH)-loaded chitosan nanoparticles (GnRH-ChNPs) were used at different doses and routes of administration to induce ovulation in rabbits as an attempt to improve artificial insemination (AI) procedures and outcomes. In this study, the characteristics (size, polydispersity, loading efficiency, and zeta-potential) of GnRH-ChNPs and the GnRH release pattern were determined in an in vitro study. A first in vivo study assessed the pituitary and ovarian response to different GnRH-ChNPs doses and routes of administration (two i.m. doses, Group HM = 0.4 \(\mu\)g and Group QM = 0.2 \(\mu\)g, and two intravaginal doses, Group HV = 4 \(\mu\)g and Group QV = 2 \(\mu\)g) against a control group (C) receiving bare GnRH (0.8 \(\mu\)g). The HM, QM, and HV treatments induced an earlier LH-surge (90 min) than that observed in group C (120 min), whilst the QV treatment failed to induce such LH surge. The number of ovulation points was similar among treatments, except for the QV treatment (no ovulation points). A second in vivo study was consequently developed to determine the hormonal (progesterone, P4, and estradiol, E2) profile and pregnancy outcomes of both HM and HV treatments against group C. The treatment HM, but not the treatment HV, showed adequate P4 and E2 concentrations, conception and parturition rates, litter size, litter weight, and viability rate at birth. Overall, the use of GnRH-ChNPs allows for a reduction in the conventional intramuscular GnRH dose to half without compromising fertility. However, the addition of GnRH-ChNPs to semen extenders, although successfully inducing ovulation, has negative impacts on fertility. Thus, more studies are needed to explore this point and allow further adjustments.  
artificial-insemination; GnRH; nanotechnology; ovulation; rabbit 1
# Innovation and Application of the Type III Secretion System Inhibitors in Plant Pathogenic Bacteria
## 2 Regulation of T3 SS in _D. dadantii_
### Bacterial Second Messengers Regulate T3 SS
performed a phenotypical study on twelve _gcp_ deletion mutants and found that a DGC GcpA synthesizes c-di-GMP to repress T3 SS and Pel via negatively regulating _rsmB_ at the post-transcriptional level and positively regulate RsmA protein levels [53]. H-NS, a nucleoid-associated protein known to activate Pel [54; 55; 56], is negatively regulated by GcpA at the level of post-transcription, which leads to the repression of _rsmB_[53]. Interestingly, H-NS is not involved in the regulation of T3 SS by GcpA, possibly due to its role in modulating the DNA topology in _D. adandtii_[56]. Together, c-di-GMP has been shown to control the expression of T3 SS in _D. adandtii_ via two pathways: The RpoN-HrpL pathway and the RsmA/RsmB-HrpL pathway. The expression of T3 SS in plant pathogenic bacteria is known to be repressed in nutrient-rich media and induced in minimal media, which may correspond to the nutrient-deficient and low pH environment in plant apoplast [57; 32; 58]. To study the role of carbon source in T3 SS regulation, the _D. adandtii_ strain containing a transcriptional fusion of the _hrpN_ gene to the _E. coli lacZ_ gene was used as a reporter system [59]. The expression of the _hrpN:lacZ_ fusion was under the control of the _hrpN_ promoter and determined by the \(\beta\)-galactosidase assay. [PERSON] and colleagues reported that the expression of _hrpN:lacZ_ fusion in _D. adandtii_ was induced 5-fold in a minimal medium when supplemented with sucrose compared with glycerol [59], suggesting that the carbon source is involved in T3 SS regulation. [PERSON] et al. recently showed that citrate, one of the major intermediates in the tricarboxylic acid (TCA) cycle, represses Pel production [60] and the T3 SS gene expression ([PERSON], unpublished data) in a c-di-GMP-dependent manner in _D. adandtii_. Deletions of TCA cycle enzymes reduce intracellular c-di-GMP levels in _D. adandtii_[60]; application of exogenous citrate induces intracellular c-di-GMP via promoting the expression of _gcpA_ while repressing the expression of _egcpB_[60]. As the production of c-di-GMP has been reported to be induced by citrate in _P. fluorescens_[61], these data suggest that c-di-GMP might generally serve as an intracellular signal that regulates T3 SSs in response to various environmental signals. The nucleotide second messengers guanosine tetraphosphate (ppGpp) and guanosine pentaphosphate (pppGpp) are stringent response regulators that globally reprogram bacterial transcription via interacting with RNA polymerase and its binding protein DksA [62; 63]. Several recent studies highlighted the role of (p)ppGpp in regulating T3 SS in _E. amylovora_ and _P. syringae_, showing that the (p)ppGpp-triggered T3 SS gene expression might rely on the RpoN-HrpL cascade in _E. amylovora_[64; 65; 66]. However, whether this regulation occurs in _D. adandtii_ has not yet been determined.
# Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations
## 3 Results
### Security of Keys  
Original keys are acquired with the security QKD protocols in this scheme. Therefore, it is impossible for an attacker to crack the keys which are shared between the signer and the verifier. In fact, two situations that make encryption with CPCO have no influence on the message \(|M\rangle\) need to be considered. One is that the bits of the original key are all 0 (1), i.e., \(K_{original}=\{00\ldots 0\}\) or \(K_{original}=\{11\ldots 1\}\). The other is that only the last bit of the original key is 1, i.e., \(K_{original}=\{00\ldots 001\}\). For example, if \(n=5\), \(|M\rangle=|\alpha\rangle_{1}|-i\alpha\rangle_{2}|\alpha\rangle_{3}|-\alpha \rangle_{4}|i\alpha\rangle_{5}\) and \(K_{original}=\{00000\}\) or \(K_{original}=\{00001\}\), then \(K_{final}=\{1,2,3,4,5\}\). According to the negotiated rules and Equation (2), the phase-shift operation \(P(0)\) is performed. Hence the signature \(|S\rangle\) finally equals to the original message \(|M\rangle\) referred to Equation (13).  
\[\begin{split}|S\rangle&=E_{K_{AT}}(|M\rangle_{P}^{ \prime})\\ &=PCO(|M\rangle_{5},|M\rangle_{5})PCO(|M\rangle_{4},|M\rangle_{4} )\ldots PCO(|M\rangle_{1},|M\rangle_{1})\\ &(|\alpha\rangle_{1}|-i\alpha\rangle_{2}|\alpha\rangle_{3}|- \alpha\rangle_{4}|i\alpha\rangle_{5})\\ &=|\alpha\rangle_{1}|-i\alpha\rangle_{2}|\alpha\rangle_{3}|- \alpha\rangle_{4}|i\alpha\rangle_{5}=|M\rangle.\end{split} \tag{13}\]  
However, the probability of the two situations above is very small. Here, denote the probability which corresponds to the first (second) situation as \(P\) (\(P^{\prime}\)). \(P\) and \(P^{\prime}\) can be calculated as Equation (14).  
\[P=(\frac{1}{2})^{n},P^{\prime}=(\frac{1}{2})^{n-1}. \tag{14}\]  
As shown in Figure 2, the probability \(P\) becomes smaller with the increase of \(n\), if the length of original key can remain greater than 10-bit, \(P\) tends to zero, thus the final key is extremely efficient. No doubt that the same analyses can be applied to the second situation. Moreover, CPCO can involve a huge difference between plain and cipher even the number of swap in \(K_{final}\) is just one, which can ensure the security of the quantum dual signature scheme.
## 2 Trans-Scale Formulation for Fatigue Crack Growth of Steel Wire and Cable
### Trans-Scale Formulation for Fatigue Crack Growth of Steel Wire  
Previous studies [23; 29; 30] showed that the fatigue crack in steel wires initially has a circular front and then gradually changes to a straight line crack front and finally fractures without necking effect, exhibiting the brittle characteristics. In spite of the surface effect, a simplified crack model with an equivalent straight front in instead of clam-shell configuration shown in Figure 2 is adopted, according to the assumption that the crack depths in the direction of crack propagation are the same. The equivalent edge crack depth \(a_{c}\) will remain the same as the crack size \(a\). Hence, according to the  
Figure 1: Macro/micro dual scale crack model. Adapted with permission from [22], Copyright 2006, Elsevier Ltd.  
dual scale fatigue edge crack model, the fatigue crack growth rate \(da/dN\) from micro to macro can be described through the following Equation [24]:  
\[\frac{da}{dN}=B\left(\Delta S^{macro}_{micro}\right)^{m}, \tag{4}\]  
where \(N\) stands for the number of load cycle. \(B\) and \(m\) are two material fatigue parameters, which can be obtained from laboratory tests. Figure 3 shows the relation between \(da/dN\) and \(\Delta K\) (the amplitude of stress intensity factor) for high strength galvanized steel wires in logarithmic coordinate system, ignoring the mean stress effect, where trans-dimension effect is represented through a straight line [29]. Based on the method in [30], the crack growth rate \(da/dN\) can be substituted into the logarithmic form of Equation (4), and \(B\) can be determined according to the slope in Figure 3. For the high strength steel wire, \(m\) is approximately equal to \(1\)[30].
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
###### Abstract  
In this paper, a variable structure PID controller with a good convergence rate and robustness for satellite attitude is proposed. In order to improve the system convergence rate, the variable structure for the proportional and differential term was designed, and an angular velocity curve with a better convergence rate was achieved by this variable structure. In addition, an integral partitioning algorithm was designed, and the system robustness to disturbance torque was improved; meanwhile, the negative effect of the integral term was avoided during the converging process. The actuator failure condition was also considered, and a fault tolerant control algorithm was designed. System stability was analyzed by the Lyapunov method, and its performance was demonstrated by numerical simulation.  
satellite attitude control; PID control; fault tolerant; actuator failure