esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Briquetting Process of Walnut Shells for Pyrolysis and Combustion
## 3 Results and Discussion
### Factors Affecting the Quality of Biomass Briquettes
#### 3.3.3 Natural Binders  
Lignin, proteins, lipids, and other compounds present in biomass help to produce good-quality densified materials, because these natural binders can generate solid bridges between particles. Natural binders are excreted from biomass cells at compression pressure and/or temperature; therefore, their adhesive effect helps obtain high-durability densified materials [34; 38].  
Table 5 shows the major components of walnut shells [39]. In the same table, the major components of another lignocellulosic material, furniture waste, are also shown for comparison [18]. It can be observed that lignin content is lower in walnut shells; however, the protein content is higher.  
Furthermore, it must be taken into account that walnuts are not lignocellulosic materials and their major components are different, as can be seen in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Walnut Shells [39]** & **Furniture Wood Waste [18]** \\ \hline
**Hemicellulose (wt.\%)** & 13.2 & 18.0 \(\pm\) 3.6 \\
**Cellulose (wt.\%)** & 60.2 & 44.7 \(\pm\) 2.5 \\ \hline
**Lignin (wt.\%)** & 18.6 & 33.0 \(\pm\) 1.1 \\ \hline
**Protein (wt.\%)** & 1.3 & negligible \\ \hline \hline \end{tabular}
\end{table}
Table 5: Organic composition of lignocellulosic materials.  
Figure 9: Relationship between the surface tension of water in contact with air and temperature based on data from Lide [37].  
As mentioned in the Introduction section, lignin, lipids, proteins, and carbohydrates act as natural binders in densification processes. From Table 6, it can be concluded that briquettes from walnut shells with 8% of walnuts showed higher contents of natural binders than those without walnuts (only shells). Therefore, it could be expected that the durability of the briquettes of walnut shells with 8% of walnuts was higher and, effectively, this is what happened, as can be observed in Table 3.
## 1 Introduction
The results showed that a large amount of CaCO\({}_{3}\) was generated in the cracks, and the cracks were completely covered by CaCO\({}_{3}\) film. Four months after repair, there was no rainwater seepage in the cracks. A few studies have confirmed the feasibility of repairing high-temperature concrete damage using MICP. [PERSON] [16] carried out experimental research on repairing high-temperature concrete damage using MICP. The results showed that after repair, the surface cracks on the damaged specimens were all covered, the water absorption rate was reduced to its pre-damage state, the chloride ion migration coefficient decreased up to 60.6%, the compressive strength increased by 42% and the porosity decreased by 12.9%. At present, research on MICP for passive repair has mainly focused on windbreak, sand fixation, heavy-metal-contaminated soil treatment and the repair of cultural relics. However, there are very little research on MICP in repairing high-temperature concrete damage. The influences of calcium source types and calcium source addition methods on MICP are rarely reported. There are almost no analyses on the regulatory mechanism of calcium source types and calcium source addition methods on the mineralization products of MICP. This study has guiding significance for the application of MICP in repairing high-temperature concrete damage. The essence of MICP is that microbes continuously secrete urease from their metabolic processes; urease decomposes urea in the surrounding environment and CO\({}_{3}\)\({}^{2-}\) combines with the surrounding Ca\({}^{2+}\) to form CaCO\({}_{3}\) deposits. For MICP, the Ca\({}^{2+}\) source in the mineralizing environment is a very important influencing factor, and the type of Ca\({}^{2+}\) source and method of addition will greatly affect the production, phase, morphology, structure, etc., of the mineralized products, which will, in turn, affect the quality of the repair. In this study, whether _Sporosarcina pasteurii_ could mineralize and deposit CaCO\({}_{3}\) under conditions where urea and Ca\({}^{2+}\) sources exist in the surrounding environment was explored; then, a qualitative analysis of the mineralized products was carried out. By changing the type of Ca\({}^{2+}\) source and the method of Ca\({}^{2+}\) addition, the productivity, material, phase, morphology, and structure of the mineralized products were determined using weighing, multi-parameter analyzer, X-ray diffraction (XRD), energy dispersive spectrometry (EDS), scanning electron microscopy (SEM) and other testing methods. In this way, the appropriate type of Ca\({}^{2+}\) source and method of Ca\({}^{2+}\) addition was determined. Finally, the repair effect on high-temperature concrete damage was verified.
## 4 Simulation and Results
### Dynamic Simulation  
The trace of dynamic simulation is from (1, 1, 22) to (99, 99, 22) and back to (1, 1, 22) with 5 m/s velocity as shown in Figure 11a (red triangle dots). The dynamic simulation results, displayed in Figure 11, show the proposed system also has high dynamic positioning accuracy although the error is a little larger than that under static conditions. The average error is \(-0.13\) m in the X axis and 0.09 m in the Y axis. The STD is 1.24 m in the X axis and 1.15 m in the Y axis.  
More dynamic tests are conducted using back-and-forth trace between (1, 1, 22) and (99, 99, 22) with different velocities of 1.5 m/s (normal human walking speed), 2.5 m/s (jogging speed), 10 m/s, 15 m/s and 20 m/s. The Root Mean Square (RMS) of the X-axis and Y-axis errors are summarized in Figure 12. The simulation results show the positioning accuracy in the X-axis and Y-axis generally degrades with increasing velocity. In the case of velocity = 20 m/s, the X-axis error is 1.81 m, 0.56 m larger than that in the case of velocity = 5 m/s. The Y-axis error increases from 1.15 m at velocity = 5 m/s to 1.63 m at velocity = 20 m/s. The increase reaches 0.56 m. As the human normal walking and jogging speeds are  
Figure 10: Distribution of TA for 3-D positioning test and its positioning error: (**a**) TA distribution; and (**b**) positioning error.  
less than 5 m/s, the proposed system is able to provide position estimation with accuracy of about 1.2 m in the X-axis and Y-axis for persons.
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 2 Design of the Combined Measurement System  
### Overall Architecture  
The overall hardware architecture is shown in Figure 2, and mainly includes the power supply layer, signal acquisition layer, and digital circuit layer. The power supply layer (shown in purple in Figure 2) provides high and low voltage power to the entire system, including high-voltage power supply, a high-voltage bridge, low-voltage power supply, and other circuits. The signal acquisition layer (shown in yellow in Figure 2) controls the switching of channels and collects voltage and current information, including sensors, input/output control, preamplification, filters, and acquisition circuits. The digital circuit layer (shown in green in Figure 2) is mainly responsible for controlling the instrument's workflow, including the system on a programmable chip (SoPC), data storage, communication, and other circuits. Communication between the SoPC control circuit and the computer is established via Wi-Fi or an Ethernet cable. The computer issues instructions to the SoPC control circuit, which controls all channel inputs or high-voltage outputs, adjusts the preamplifier gain, and manages the AD converter to convert analog signals into digital signals. The SoPC control circuit uses an field programmable gate array (FPGA) + advanced RISC machine (ARM) structure, where the FPGA is primarily responsible for data acquisition, and the ARM is mainly responsible for data storage and communication with the host computer.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **16 SG24-N** & **KMS-820** & \begin{tabular}{c} **The Instrument** \\ **Developed in** \\ **This Paper** \\ \end{tabular} \\ \hline \begin{tabular}{c} Number of \\ channels \\ \end{tabular} & up to 24 & up to 6 & up to 120 \\ \hline \begin{tabular}{c} Sampling rate \\ \end{tabular} & \begin{tabular}{c} 500 sps to \\ 31.25 ksps \\ \end{tabular} & up to 80 ksps & up to 100 ksps \\ \hline \begin{tabular}{c} Geophysical \\ methods \\ \end{tabular} & \begin{tabular}{c} seismic method, \\ high-density \\ electrical method \\ \end{tabular} & \begin{tabular}{c} seismic methods, \\ electromagnetic \\ methods \\ \end{tabular} & \begin{tabular}{c} seismic method, \\ high-density \\ electrical method \\ \end{tabular} \\ \hline \begin{tabular}{c} Analog-to-digital \\ conversion \\ \end{tabular} & 24-bit & 24-bit and 32-bit & 24-bit \\ \hline \
\begin{tabular}{c} Acquisition time \\ \end{tabular} & up to 65,536 ms & unlimited & unlimited \\ \hline
\begin{tabular}{c} Cable type \\ \end{tabular} & centralized cable & single-core cable & coverage cable \\ \hline \end{tabular}
\end{table}
Table 1: Seismic-electrical combined acquisition system parameters.
## 3 Results and Discussion
### Pile Failure Mode  
To further analyze the failure mode of HSCM piles under uplift loading, the deformation calculation results of HSCM-1-1 and SDCM-1-2 were extracted, as shown in Figure 8a,b.  
Figure 6: Load–displacement curve of HSCM-1-1.  
Figure 7: Load–displacement curve of SDCM-1-2.  
When \(C_{ref}/s_{u}=80\), the deformation of HSCM-1-1 and SDCM-1-2 was cylindrical, the relative slip of the soil-cement column and soil interface occurred, and the failure mode was both pile and soil interface failure. As \(D_{sc}\) was equal for both piles, the model had the same pile and soil contact area, and therefore, \(P_{1-1}\) was approximated by \(P_{1-2}\). When \(C_{ref}/s_{u}\) was reduced from 80 to 20, the deformation of HSCM-1-1 increased, but the failure mode was still a pile and soil interface failure. Meanwhile, the steel pipe and soil-cement interface of SDCM-1-2 produced cohesive slip, and the failure mode changed from pile and soil interface failure to steel pipe and soil-cement interface failure. The failure mode of the HSCM-1-1 was transformed when \(C_{ref}/s_{u}=10\). The cohesive slip between the steel pipe and soil-cement occurred at the location of the larger helix spacing, resulting in an individual helix bearing failure. In comparison, the pile and soil interface failure still occurred at the location of the smaller helix spacing. When \(C_{ref}/s_{u}=5\), furthermore, the steel pipe and soil-cement interface failure occurred around the steel pipe but remained a pile and soil interface failure in the dense area of the helix plate. The above results showed that different soil-cement strengths changed in the failure mode of the HSCM pile. Pile and soil interface failure occurred when the cohesive force \(q_{u}\) at the interface of the steel pipe and soil-cement exceeded the skin resistance \(f_{s}\) of the pile, and steel pipe and soil-cement interface failure occurred when \(q_{u}<f_{s}\). The presence of helix plates improved the \(q_{u}\) of the steel pipe and soil-cement interface. According to [PERSON]'s [PERSON] (1977) research, the failure mode of the SDCM piles changed with different cement soil strengths. Pile soil damage occurred at higher cement soil strengths, and as the soil-cement strength decreased, damage occurred at the end of the pile core. The results of this study reached similar conclusions, but the failure interface only occurred at the position without helix plates during the lower soil-cement strength, which was due to the helical plates enhancing the interaction between the pile core and the soil-cement.  
Figure 8: Calculated deformation results with different \(C_{ref}/s_{u}\) conditions.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 5 Simulation and Analysis
### Performance Evaluation  
Figure 8 presents a demonstration of the time-varying network topology, where Figure 8a,b denote two constructed topologies at two different times with an interval of \(\Delta t=0.1\) s. The scatters plotted in one color belong to the same cluster and they are connected to a CH via edges with the same color. Figure 8c,d plot the corresponding graph matrices based on Equation (20). On this basis, the changed edge number can be easily calculated based on Equation (21). In Figure 8, we can find that the majority of the changed edges stem from the newly selected CH as a result of node movement, and no cross-cluster changed edges appear during this process.  
Figure 7: The Kalman-filter-assisted prediction results of node speed and position, (**a**) x position, (**b**) x speed, (**c**) y position, (**d**) y speed, (**e**) z position, (**f**) z speed.  
Figure 9 plots the average stability metric results during the whole simulation, whereas Figure 9a plots the average stability metric values of all clusters at different time. In this figure, the black solid curve denotes the results based on the measured positions and velocities, whereas the dashed blue curve refers to that of the Kalman-filter-assisted estimated results. Obviously, the whole values of these two cases fluctuate around a value of 1. Figure 9b draws the corresponding cumulative distribution function (CDF) curves of these two data. It can be observed that the predicted results present a similar trend to the measured data, proving that our proposal can be used to assist the highly mobile FANET networking.  
Figure 10 presents the ratio of changed edge number compared to the sensor measurement-based data over different group movement velocities, including 10 m/s, 30 m/s, 60 m/s, and 100 m/s. Moreover, we assume the maximal random speed \(v_{r,\max}\)  
Figure 8: The demonstration of the time-varying graph, (**a**) network topology at time 0 s, (**b**) network topology at time 0.1 s, (**c**) connection matrix at time 0 s, (**d**) connection matrix at time 0.1 s.  
Figure 9: The stability metric results, (**a**) stability metric values at different time, (**b**) CDF results.  
is proportional to the group speed \(v^{C}\), implying that a higher \(v^{C}\) is accompanied by more severe interferences. We can observe that the proposed algorithm can yield an overall ratio of 2-3% regardless of different group velocities, proving a good option to reduce communication overheads.
## 4 Results
### Hypothesis Testing
#### 4.3.2 Testing Specific Hypotheses  
The analysis of the first specific hypothesis examined whether a direct and significant relationship exists between customer data management and satisfaction levels within the service environment under study. Using [PERSON]'s rho as the appropriate non-parametric test, the results yielded a \(p\)-value of 0.000, which is well below the conventional significance threshold (\(p<0.05\)). As a result, the null hypothesis (H\({}_{0}\))--stating that no relationship exists--was rejected, while the alternative hypothesis (H\({}_{1}\))--suggesting a direct and significant association between the quality of data management and customer satisfaction--was accepted. This outcome underscores the critical role that effective, personalized, and integrated data practices play in enhancing users' perceptions of service quality and overall experience (Table 12, Figure 3).  
The second specific hypothesis assessed the link between the availability and performance of digital payment options and customer satisfaction. Applying the [PERSON]'s rho test, the analysis produced a \(p\)-value of 0.000, indicating a statistically significant relationship between the two variables. Accordingly, the null hypothesis (H\({}_{0}\)) was rejected, and the alternative hypothesis (H\({}_{1}\)) was confirmed, validating the existence of a direct and significant association between digital payment flexibility and customer satisfaction. This finding highlights the importance of providing diverse and accessible payment modalities--such as mobile wallets and online card transactions--in order to meet user expectations and to enhance the perceived ease and trustworthiness of the service (Table 13, Figure 4).  
The third specific hypothesis explored the association between omnichannel logistics management and customer satisfaction. Based on the results of the [PERSON]'s rho test, a \(p\)-value of 0.000 was obtained, indicating a statistically significant relationship (\(p<0.05\)). Consequently, the null hypothesis (H\({}_{0}\)) was rejected, and the alternative hypothesis (H\({}_{1}\))--asserting a direct and significant connection between the quality of logistical coordination and customer satisfaction--was accepted. This result emphasizes that flexible and reliable logistics solutions, including options for delivery and in-store pickup, are essential components for reinforcing customer trust, convenience, and overall perceived service value in omnichannel systems (Table 14, Figure 5).  
\begin{table}
\begin{tabular}{l l l r} \hline \hline  & & & **Satisfaction** \\ \hline \multirow{3}{*}{**Spearman’s Rho**} & \multirow{3}{*}{**Data**} & Correlation & 0.531 \\  & & coefficient & 0.000 \\  & & Sig. (bilateral) & 0.000 \\  & & N & 108 \\ \hline \hline \end{tabular} Note: From SPSS 26 software.  
\end{table}
Table 12: Specific hypothesis test 1.  
Figure 4: Scatter plot of omnichannel payment and satisfaction.
## 5 Simulation Results and Discussion
### Effects of the Node Density  
In this section, we compare different protocols' performances with 40, 60, 80, 100, and 150 nodes in the test phase, separately. The movement speed of nodes is 10 m/s. The packet send rate is 1 Hz and the initial energy is 1000 J. The simulation results are shown in Figures 8-10.  
Figure 8 shows the effects of the different network densities on the average E2E delay. As can be seen, with the increase in node density, the average E2E delays of DSEGR,  
Figure 6: Performance comparison of the PDR among GPSR, QNGPSR, DSEGR, and FEQSAI with different nodes’ movement speeds.  
Figure 7: Performance comparison of the time of the first dead node among GPSR, QNGPSR, DSEGR, and FEQSAI with different numbers of nodes’ movement speeds.  
QNGPSR, and GPSR increase firstly and then decline. That is because the nodes can achieve more long paths which lead to an initial increase in delays, but the routing void area decreases as the node density increases, which causes an increase in delay. However, FEQSAI needs to relearn online when the number of nodes increases. the average E2E delay of DSEGR increases since Q-Tbale learning complexity increases. So the FEQSAI has the worst delay performance. The average E2E delay of DSEGR is similar to QNGPSR at the same node density. However, GPSR chooses the perimeter forward when it encounters a void area, which causes its average E2E delay to be larger than DSEGR and QNGPSR.  
Figure 9 depicts the relationship between PDR and different node densities. It can be seen that, with the total number of nodes increasing in the UANET, each node has more neighbor nodes. Even if some neighbor nodes' energy is exhausted, there are other neighbor nodes providing assistance to forward packets. Thus, there is an increasing trend in the packet delivery ratio of each protocol. DSEGR significantly outperforms QNGPSR, GPSR, and FEQSAI since DSEGR considers link stability and residual energy prediction. Although FEQSAI considers E2E transmission energy, it is an online learning algorithm and cannot converge in our scenario, so the PDR is the worst.  
Figure 8: Performance comparison of the average E2E delay among GPSR, QNGPSR, DSEGR, and FEQSAI with different node densities.  
Figure 9: Performance comparison of the PDR among GPSR, QNGPSR, DSEGR, and FEQSAI with different node densities.  
Figure 10 illustrates the time of the first dead node versus the different node densities. When there are 40 nodes in the network, there are few feasible links. Thus, few packets are effectively and successfully delivered to the destination, which results in network nodes consuming less energy. So there is no dead node. As node density increases, the DSEGR can predict the residual energy of a neighbor node, which can avoid forwarding packets by a low-energy neighbor node. Thus, the time of the first dead node is postponed, and the energy consumption of nodes is more balanced. Finally, it can reach a stable state. Hence, the time of the first dead node is almost same when node density is high for DSEGR, GPSR and QNGPSR.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 2 Model Formulation
### Problem Formulation  
In the conventional STFT-domain dereverberation problem, the CTF approximation is employed, i.e., the contribution of the crossband filters is neglected, and the observed signal \(y_{f,t}\) is approximated as  
\[y^{(m)}_{f,t}\approx\sum_{l=0}^{L-1}x_{f,t-l}h^{(m)}_{f;f,l} \tag{3}\]  
where we assume a length \(L\) band-to-band filter (or CTF) \(h^{(m)}_{f;f,t}\). In the context of this paper, the goal of the dereverberation is to predict the late reflection component and subtract it from the observed signal, resulting in an enhanced signal \(z^{(m)}_{f,t}\) that consists of the direct sound and the early reflections:  
\[z^{(m)}_{f,t}=y^{(m)}_{f,t}-\sum_{l=D}^{L-1}x_{f,t-l}h^{(m)}_{f;f,l}\approx \sum_{l=0}^{D-1}x_{f,t-l}h^{(m)}_{f;f,l} \tag{4}\]  
where \(0<D\ll L\) is a predefined parameter that enables separation between early and late reflections.  
Extension to Crossband Filters  
The model described in (4) can be extended by employing the accurate model in (2). The enhanced signal is then given by  
\[z^{(m)}_{f,t}=y^{(m)}_{f,t}-\sum_{f^{\prime}=0}^{F-1}\sum_{l=D}^{L_{f^{\prime} }-1}x_{f,t-l}h^{(m)}_{f;f^{\prime},t}, \tag{5}\]  
where \(L_{f^{\prime}}\) is the length of the crossband filter corresponding to frequency bin \(f^{\prime}\). In terms of computational complexity, the accurate model in (5) is expensive since it increases the complexity by a factor of \(F\) compared to the CTF approximation in (4). The analysis in [28] shows that in terms of energy, the band-to-band filter is more significant compared to the crossband filters, and the energy of \(h_{f;f^{\prime},t}\) decreases when \(|f-f^{\prime}|\) increases. Based on this observation, and to improve the accuracy of the CTF model in (3) with a relatively small price of increasing model complexity, we consider the contribution of the two nearest crossband filters, i.e., we approximate the observed and the enhanced signals as  
\[y^{(m)}_{f,t}\approx\sum_{f^{\prime}=f-1}^{f+1}\sum_{l=0}^{L_{[f^{\prime}-f^ {\prime}]}-1}x_{f^{\prime},t-l}h^{(m)}_{f;f^{\prime},t}, \tag{6}\]  
\[z^{(m)}_{f,t}=y^{(m)}_{f,t}-\sum_{f^{\prime}=f-1}^{f+1}\sum_{l=D}^{L_{[f^{ \prime}-f^{\prime}]}-1}x_{f,t-l}h^{(m)}_{f;f^{\prime},t}, \tag{7}\]  
where \(L_{0}:=L_{\text{bb}}\) and \(L_{1}:=L_{\text{cb}}\) are the lengths of the band-to-band filter and the crossband filter, respectively.