esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Void Distribution and Fractal Dimension Analysis  
Figure 10a presents the radial void distribution patterns of CPAM specimens. The analysis shows a consistent trend of increasing void ratio with radial distance from the specimen center. CPAM1 and CPAM2 display superior radial uniformity, resulting from their optimized asphalt content (4.5-4.7%). Conversely, CPAM4 and CPAM5 exhibit significant radial variations, with void ratio fluctuations reaching 6%. This non-uniformity mainly results from insufficient 1.18-2.36 mm aggregate content, leading to inadequate void filling between coarse aggregates.  
The longitudinal void distribution characteristics, shown in Figure 10b, reveal three distinct zones: (1) an upper transition zone (0-15 mm) with rapidly decreasing void ratio, (2) a stable middle zone (15-55 mm) showing consistent void distribution, and (3) a lower transition zone (>55 mm) with accelerating void ratio increase. Comparative analysis indicates CPAM4 maintains a 4.6-12.5% higher void ratio than CPAM2 and CPAM3 at corresponding heights. This difference arises from CPAM4's conc  
Figure 10: The distribution of voids in the specimen: (**a**) radial void fraction distribution; (**b**) longitudinal void fraction distribution.  
Figure 9: Cumulative distribution of equivalent diameter of CPAM specimens.  
distribution (primarily 13.2 mm and 4.75 mm fractions), which lacks adequate 2.36 mm aggregates for effective inter-particle void filling.  
Fractal dimension analysis (Figure 11) enables quantitative evaluation of void distribution uniformity. CPAM4 shows the highest fractal dimension (1.38), indicating non-uniform void distribution and greater potential for large void formation. This directly correlates with its unbalanced aggregate gradation, specifically the excessive 4.75 mm aggregate concentration. In contrast, CPAM1 and CPAM5 exhibit lower fractal dimensions (1.08 and 1.12, respectively), representing more homogeneous void distributions and decreased large void occurrence. These results verify that proper inclusion of small-sized aggregates (particularly 2.36 mm fraction) effectively fills inter-particle voids, improving overall void distribution uniformity.
# An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
## 3 Validation with Tests for SHM Use
On the other hand, the link \(7\to 1\) the ETX values are much better, making it a good candidate for the transmission of node 12 data towards the sink. Figure 6: Number of messages transmitted nodes \(12,7,1\) and nodes \(9,1,13\). The individual ETX values of the links \(9\to 1\), \(9\to 13\), and \(13\to 1\) have also been investigated. The direct link exhibits good ETX; thus, it is used as the best link by the CTP as we can see in Figure 7b. Furthermore, the relay node with the sink exhibits good performance. The most significant finding, though, is the fact that the potential relay node 9 is not utilised as the best neighbour, since the ETX performance of the direct link is mostly preferred. Hence, an example is observed that the direct link to the sink is used mostly.
## 3 Results
### Object Oriented Classification with Random Forest
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Land Cover Category} & \multicolumn{4}{c}{Orthophoto} & \multicolumn{4}{c}{PlanetScope} & \multicolumn{4}{c}{Fused Image (Sentinel+ 2 + PlanetScope)} \\ \cline{2-13}  & Elevation & Slope & B1 & B3 & B4 & B2 & B3 & B4 & B5 & B8 & B8A & B11 & B12 & NDVI & NDEE & SI \\ \hline
**Bare Ground** & 27.46 & 36.66 & 25.08 & 21.58 & 23.80 & 20.17 & 21.26 & 19.42 & 14.69 & 23.93 & 30.40 & 17.45 & 19.39 & 19.42 & 19.15 & 14.16 \\ Built Environment & 32.04 & 23.31 & 35.90 & 27.64 & 24.26 & 25.32 & 23.94 & 27.44 & 24.45 & 22.64 & 23.27 & 24.61 & 18.71 & 21.00 & 17.35 & 28.92 \\ Cedrelas-Cedar & 13.26 & 4.52 & 9.92 & 10.31 & 11.21 & 12.00 & 6.56 & 13.34 & 5.08 & 10.55 & 11.23 & 9.32 & 8.27 & 28.43 & 18.14 & 7.16 \\ _Cinchone-Quanine_ & 28.42 & 14.58 & 20.36 & 12.66 & 14.24 & 17.51 & 15.95 & 10.92 & 8.34 & 12.48 & 11.95 & 7.97 & 8.79 & 13.64 & 13.11 & 10.59 \\ _Coffea-Coffee_ & 99.06 & 34.49 & 44.41 & 36.42 & 38.45 & 30.14 & 30.70 & 31.74 & 29.96 & 34.39 & 37.87 & 38.61 & 34.58 & 29.81 & 27.31 & 23.29 \\ Cultivated Grass & 49.33 & 31.79 & 42.60 & 34.04 & 33.42 & 29.21 & 29.11 & 24.67 & 23.29 & 31.27 & 32.87 & 25.83 & 24.78 & 26.34 & 27.86 & 25.62 \\ Dedicious Forest & 42.88 & 27.26 & 41.86 & 25.52 & 29.42 & 22.30 & 27.33 & 21.49 & 25.02 & 25.66 & 29.03 & 30.57 & 28.87 & 18.93 & 21.58 & 22.05 \\ Evergreen Forest and & & & & & & & & & & & & & & & & & & \\ Strubalal & 75.88 & 15.75 & 25.48 & 21.26 & 32.71 & 20.19 & 24.77 & 19.29 & 14.96 & 24.31 & 23.62 & 15.99 & 16.76 & 18.38 & 17.98 & 16.73 \\ Synchronal & & & & & & & & & & & & & & & & & \\ Evgengengen Seasonal Forest & 40.31 & 33.18 & 39.28 & 34.25 & 32.52 & 27.11 & 30.16 & 26.59 & 24.61 & 36.30 & 34.87 & 30.92 & 30.09 & 28.36 & 31.79 & 23.99 \\ Freshwater & & & & & & & & & & & & & & & & & & \\ Frubal Tallgrass & 29.24 & 23.24 & 22.29 & 20.09 & 22.44 & 15.18 & 16.27 & 15.36 & 13.52 & 19.43 & 20.70 & 29.39 & 21.17 & 17.35 & 15.54 & 15.06 \\ Hundi Tallgrass & & & & & & & & & & & & & & & & & & \\ Mixed Forest & & & & & & & & & & & & & & & & & & & & \\ Persistent-Elephant Grass & & & & & & & & & & & & & & & & & & & \\ Permanent Crops & 50.89 & 29.67 & 33.65 & 30.74 & 31.07 & 25.96 & 34.12 & 22.64 & 21.70 & 29.65 & 30.31 & 20.38 & 27.28 & 25.95 & 26.10 & 28.21 \\ Pioneer & & & & & & & & & & & & & & & & & & & & \\ Psiamine-Guxux & 45.78 & 19.65 & 31.98 & 28.17 & 28.77 & 20.42 & 21.76 & 25.12 & 15.19 & 26.04 & 28.70 & 21.66 & 25.99 & 22.11 & 21.64 & 24.99 \\ Rubus-BlackRerry & & & & & & & & & & & & & & & & & & & & \\ Siluposture & 39.14 & 20.19 & 27.40 & 23.05 & 25.29 & 25.41 & 31.43 & 23.79 & 24.40 & 25.21 & 26.80 & 28.18 & 24.70 & 20.86 & 23.25 & 20.64 \\ Syzygian-Pomaroza & & & & & & & & & & & & & & & & & & & & \\ Transitory Crops & 50.13 & 27.64 & 47.92 & 35.27 & 29.66 & 29.35 & 32.07 & 28.65 & 31.47 & 31.31 & 33.28 & 31.46 & 26.11 & 23.56 & 24.19 & 27.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Variable Importance for Each Land Cover Category.
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 2 Materials and Methods
### Parameters for the Different Scenarios  
The average characteristics of the thickened sludge obtained by gravity and dissolved air flotation thickeners were used in the calculations. The evaluation period covers the years between 2011 and 2016. Mean values were weighted arithmetic average values based on the ratio of primary and WAS production. The mean flow of primary sludge was 19.804 \(\pm\) 3530 kg/d, with a total solid (TS) content of 4.7 \(\pm\) 0.5% and a VS content of 72.3 \(\pm\) 3.4%. The mean flow of WAS was 16.167 \(\pm\) 2417 kg/d, with 6.7 \(\pm\) 0.8% of TS and 81.0 \(\pm\) 1.4% of VS. Table 3 shows the thermal hydrolysis parameters assumed for calculating mass and energy balances. Appendix B contains Table A1, with assumptions for the different parameters considered for the WWTP. The specific parameters of Scenario 1 are those of the operating conditions of the WWTP of Burgos. Table 4 shows the specific parameters used for evaluating the different scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thermal Hydrolysis Parameters** & **Units** & **References** \\ \hline Nominal treatment capacity of THP-B6.2 (t TS/d) & 35 & \\ Maximum treatment capacity of THP-B6.2 (t TS/d) & 42 & \\ Outlet sludge stream from the hydrolysis reactor & 6300 & \\ (kg/batch) & 30 & \\ Outlet flash steam from the hydrolysis reactor (kg/batch) & 30 & \\ Outlet process gases from the hydrolysis reactor & 8 & \\ (kg/batch) & & \\ TS concentration in inlet sludge stream in the hydrolysis reactor (\%) & 12.7 & \\ Relative density of dry sludge (w.u) & 1.4 & [37] \\ Specific heat capacity (k/kg \({}^{\circ}\)C) & 1.5 & [38] \\ Volume of pulper (m\({}^{3}\)) & 1 \(\times\) 15.2 & \\ Volume of reactor (m\({}^{3}\)) & 2 \(\times\) 6.9 & \\ Volume of flash-tank (m\({}^{3}\)) & 1 \(\times\) 15.2 & \\ Emptying time of reactor to the flash-tank (min) & 10 & \\ Reaction time (min) & 20–30 & \\ Reactor temperature (\({}^{\circ}\)C) & 165 & \\ Reactor pressure (kPa) & 700 & \\ Flash-tank temperature (\({}^{\circ}\)C) & 107 & \\ Flash-tank pressure (kPa) & 128 & \\ TS concentration of inlet sludge stream to the digester (\%) & 10 & [39] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the modern wastewater treatment plant (WWTP). Technical characteristics of units operating in the different WWTP sections that are common to all scenarios evaluated.
## 1 Introduction
The maximum adsorption capacity was 0.19 mmol Pb per gram of adsorbent at 90 min contact time with initial Pb(II) concentration 200 mg/L and temperature 30 \({}^{\circ}\)C. The adsorption efficiency of the lead-reloaded sample can reach 80% after five cycles of adsorption-elution compared with the fresh ones. Removal of lead using Purolite S-930 resin with imoniacetic functional group in aqueous solution was studied by [PERSON] et al. [30]. The column adsorption results concluded that 91.12% of mean lead removal ratio from drinking water containing up to 22 mg/L Pb can be achieved using Purolite S-930 during 21-day service at pH 6.5, which demonstrates an economic and technically feasible lead-removal process. [PERSON] et al. [31] reported a comparison study of Pb(II) adsorption on XC-72 carbon black and multi-walled carbon nanotubes (MWCNTs). The load of Pb(II) was 125.0 mg/g and 17.5 mg/g on XC-72 and MWCNTs, respectively, at pH 6.0 and 293 K temperature. The performance of G-26 and MTS9570 resins have been examined by us for removing Cd(II) ions successfully from acidic solutions. Maximum cadmium-removal rates of 99.2% and 98.9% were achieved for G-26 and MTS9570, respectively, at 30 min of adsorption, 5.0 of initial solution pH, 1000 mg/L of initial metal concentration, and 0.01 g/mL of resin dosage for G-26 and 0.025 g/mL of resin dosage for MTS9570 [32; 33]. As lead and cadmium are both persistent bio-accumulative toxic (PBT) metals, and they often coexist in nature, comparative study of G-26 and MTS9570 for Pb(II) removal from aqueous solution was conducted in this work for a co-removal process in the future. The research involves exploratory experiments using twenty-three resins with different functional groups. The adsorption process using G-26 and MTS9570 was evaluated with adsorption isotherms, kinetic, and thermodynamic studies at different operation conditions.
# Thermal Threat Monitoring Using Thermal Image Analysis and Convolutional Neural Networks
## 1 Introduction
The system will analyze thermal images autonomously based on deep learning methods using a CNN, which will enable high effectiveness and additional classification of threats when the user's arms or hands appear in the monitored area. The use of CNNs should significantly increase the effectiveness of the system relative to similar solutions.
# Inactivation of the Plant Pathogen _Pythium ultimum_ by Plasma-Processed Air (PPA)
## 4 Discussion
[45], who applied theoretical model calculation, proposed charge accumulation on the outer surface of the bacterial cell envelop and by that provided an important aspect of the inactivation mechanism. This was further underpinned by experimental data using a resistive barrier discharge (RBD) to treat bacterial cells [46]. Besides the efficient inactivation of pathogens, unimpaired seed germination has to be guaranteed after PPA exposure to ensure unaffected field emergence. Therefore, future investigations have to be performed with infected seeds (natural or artificial) of the relevant crop species to ensure both inactivation and seed germination after the PPA treatment. The effect of PPA exposure on the maximum germination of lupine, barley, wheat and rapeusing similar process parameter as in this study was published in an earlier study [35], showing marginal effects on maximum germination in cereals and lupine.
## 1 Introduction
Secondly, the uses of strategies for optimization include in-network processing, data aggregation, and cross-layer related optimization methods. In-network processing in wireless sensor networks typically involves operations such as data compression and fusion. Despite the cost of increased latency (more time is spent in data processing), the impacts of this approach on energy reduction in [13] are always found to be significant. The idea of data aggregation is to gather the data from different source nodes to be forwarded onto further destination nodes, and is considered as an essential part in routing protocols of wireless sensor networks as it directly aims at reducing the amount of data to be transported. A data aggregation based method derived from compressed sensing (CS) is proposed in [14] to minimize the total energy consumption in the sensory data collecting process. Overall, the aggregation process helps eliminate redundancy from packet overhead, minimize the number of transmissions, and thus save total energy [15]. By exploiting the interactions between different layers of WSNs, cross-layer optimization techniques are able to improve energy conservation. In [16], taking both MAC layer and routing layer into consideration, researchers extended Dynamic Source Routing (DSR) to improve its routing energy efficiency by minimizing the frequency of recomputed routes (routing overheads). For the final method, by exploring different parameter configurations of a beaconless IEEE 802.15.4 network [17] via simulation, the optimal parameter settings of unslotted CSMA/CA algorithm [17] under different traffic loads are suggested in [1]. With an effective management mechanism for backoff counter/scheme, a semi-random backoff (SRB) method is proposed in [18] aiming to achieve resource reservation for channel access in contention-based wireless LANs. However, when considering increasingly complex system designs, the above-mentioned optimizations cannot be considered to be comprehensive, since they only focus on optimizing a specific objective. Even when several objectives are under evaluation, their optimization procedures are separate. Thus, such optimizations are inefficient to provide a quick overview of problem solutions. In the meantime, they cannot satisfy the requirements of application-specific wireless sensor networks, in which many design criteria need to be optimized simultaneously. Thus, in the following section we give an overview of genetic algorithms (GAs) as an intelligent and efficient optimization technique, and present some of GA based optimizations in wireless sensor networks.
## Materials and Methods
### Test Methods  
In comparison with the testing of hardened concrete, [PERSON] et al. [12] applied the splitting tensile testing, conforming to British standard BS EN 12390-6:2009 [26], to the half-spacer and half-concrete composite specimen to estimate the interface bond strength between the spacer and the concrete. The same rationale can be extended to other existing test methods for hardened concrete.  
The present study has applied the compressive strength testing, conforming to the China code GB/T 50081-2019 [27], to the 28 day and 56 day cubic composite specimens to determine the compressive strength, particularly at the spacer location. In GB/T 50081-2019 [27], the cubic specimen of 150 mm \(\times\) 150 mm was considered as the standard specimen and the compressive strength of the non-standard specimen should consider the size effect. In this study, the non-standard cubic specimen of 100 mm \(\times\) 100 mm \(\times\) 100 mm was utilised and a size-effect factor of 0.95 was applied according to the code. As the spacer locations in a real construction project are not specified by the structural designers, it is important to ensure that any spacer that replaces a portion of the concrete does not significantly impair the overall compressive strength.  
This study evaluated the resistance of the composite specimen to chloride ion penetration by examining the diffusion coefficients and the coulomb electric flux from non-steady-state migration experiments conforming to the China code GB/T 50082-2009 [28]. The test methods in the China code for the evaluation of the rapid chloride migration coefficient \(D_{RCM}\) and the coulomb electric flux follow the same principles as the European standard NT BUILD 492 [29] and the American standard ASTM C1202 [30], respectively. However, to identify the effect of spacers on the chloride resistance of the composite specimen, some modifications were made to the test methods. First, a slice \(50\pm 1\) mm thick and 100 mm in diameter was cut from the bottom portion of the composite specimen and the surface that was nearer to the bottom surface (i.e., the opposite surface to the as-cast surface) is the surface exposed to the chloride solution. This modification on the preparation of the test specimen is intended to reproduce the actual chloride ion erosion process that begins from the exposed surface where the spacers are placed. Moreover, to estimate the long-term collective durability of the spacer and the concrete, the chloride ion diffusion testing was also performed on the 56 day composite specimen. The modified testing on the chloride resistance of the composite specimen may provide a straightforward evaluation of the composite specimen performance, particularly at the spacer-concrete interface.
## 3 Experimental Results
### Single-Axis Robotic Arm Controller Experiment and Study
When comparing the tracing results of the previous three controllers, it can be seen in Figure 15a that the PID controller is not very effective in the tracing experiment due to parameter uncertainty, and in Figure 15b, using the LMS-PID controller in the tracing experiment improves the tracing effectiveness of the PID controller, but it still has phase lag. In Figure 15c, the ILC was introduced to improve the phase lag problem of the previous PID control, and the accuracy of the tracing control was improved. In the single-axis robotic arm controller experiment, firstly, based on the PID controller, three parameters were simulated to give the robotic arm joints a basic tracing function, but it is not an easy task to select the appropriate parameters in the PID control. In self-adaptive PID controller experiments, a set of PID parameters with good control performance is obtained in a more efficient way using an LMS algorithm through multiple tracing cycles of the target. Then, an iterative learning control is introduced. From the above experiments, it can be observed that the application of the iterative learning controller can reduce the error within the required range within a limited number of learning times, and improve the phase lag phenomenon of the PID controller in transient response, thus significantly improving its control accuracy. Figure 14: Convergence curves of RMSE with different learning rates under tracking experiment. Figure 13: RMSE convergence curves for different convergence factors in the tracking experiment. In order to test the fastest speed of each rotary axis of the robotic arm within the limiting angle, the P of the PID controller is used to give a larger proportional gain to ensure that the control of the pneumatic valve is the maximum speed output when each rotary axis is operating within the limiting angle, and the PAM has a hysteresis phenomenon. Therefore, there should be a difference between the speed of angle increase and decrease. The position versus the time curve is shown in Figures 16 and 17. Figure 16: Four axes—curve of position rise versus time. Figure 17: Four axes—curve of position drop versus time. Figure 15: (a) Curve of the relationship between desired trajectory and PID controller tracking results. (b) Curve of the relationship between the desired trajectory and the tracking result of LMS-PID controller. (c) Curve of the relationship between expected trajectory and ILC tracing results.
# Journey Planning Algorithms for Massive Delay-Prone Transit Networks +
## 7 Experimental Study  
In this section, we present our experimental study to assess the performance of d-ptl. In particular, we implemented, in C++, both PTL and d-ptl, and developed a simulation environment to evaluate the two algorithms on given input transit networks. Our entire framework is based on NetworkKit [30], a widely adopted open-source toolkit for graph algorithms and interactive large-scale network analysis. Our code has been compiled with gnu g++ v.4.8.5 (O3 opt. level) under Linux (Kernel 4.4.0-148) and all tests have been executed on a workstation equipped with an Intel Xeon(r) CPU and 128 GB of main memory.
## 2 Mathematical Model Analysis  
The manipulator is defined as:  
\[M\ddot{q}+V+G=\tau \tag{1}\]  
To enhance the fidelity of the model and improve its realism, it is necessary to incorporate uncertainty \(D\in\mathbf{R}^{n}\) into the model. Consequently, the model is described as follows:  
\[M_{\theta}\ddot{q}+V_{\theta}+G_{\theta}+D=\tau \tag{2}\]Item \(\mathbf{D}\) encapsulates multiple uncertainty elements, item \(\Delta\mathbf{M}\) associates with the inertial matrix, item \(\Delta\mathbf{V}\) pertains to the Coriolis and centrifugal force terms. The uncertainty of the gravitational term is labeled as item \(\Delta\mathbf{G}\), and the external disturbance is denoted as item \(\mathbf{\tau}_{d}\). Thus, \(\mathbf{D}\) can be expressed as follows:  
\[\mathbf{D}=\Delta\mathbf{M}\mathbf{\tilde{q}}+\Delta\mathbf{V}+\Delta\mathbf{G}+\mathbf{\tau_{d}} \tag{3}\]  
Substituting the value from Equation (3) into Equation (2) yields the following expression:  
\[(\mathbf{M_{0}}+\Delta\mathbf{M})\mathbf{\tilde{q}}+(\mathbf{V_{0}}+\Delta\mathbf{V})+(\mathbf{G_{0}}+ \Delta\mathbf{G})+\mathbf{\tau_{d}}=\mathbf{\tau} \tag{4}\]  
The model is formulated under the following assumptions:  
**Assumption 1**.: _The uncertainty item \(\mathbf{D}\) is bounded._  
**Assumption 2**.: _The reference output signal \(\mathbf{q}_{r}(t)\) and its derivatives \(\dot{\mathbf{q}}_{r}(t)\) and \(\mathbf{\tilde{q}}_{r}(t)\) are bounded [30]._