esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 D-MSR Protocol Stack Architecture
### Routing Layer and Transport Layer  
We use RPL in the routing layer. RPL is designed for Low power and Lossy Networks (LLNs), which consist of nodes with limited capabilities, such as processing power, memory, and battery power. RPL is defined for a network, in which nodes interconnections are lossy and the traffic rate is low [13]. These characteristics make RPL suitable for use in wireless industrial networks.  
RPL is a distributed routing protocol that supports the up, down, and point-to-point traffic model by forwarding the packet to its selected parent from the parent list, based on the objective function (for example, by selecting the parent with the best Expected Transmissions values in the up direction) or by selecting a neighbor form the routing table as a next hop (in the down direction). The parent list and route table in the network layer, and their relationship to the neighbor table in the data link layer are shown in Figure 5(b). In the point-to-point traffic model, when a node (e.g., a sensor) needs to reach another node (e.g., a actuator), its packet travels in the \"up\" direction toward a common ancestor and is then forwarded down toward the final destination. For example, as is shown in Figure 7, node 8 needs to communicate with node 11. The packet first travels \"up\" toward node 0. However, in the \"up\" route toward the root, the packet reaches node 2, which is a common ancestor between node 8 and 11. Node 2, which contains the destination address of the packet in its routing table, then forwards the packet toward node 11 through node 5.  
In the transport layer, the _end-to-end connection manager_ establishes the management connection (between new devices and the gateway) as well as an end-to-end connection (between sensors and gateway/actuators) through the D-SAR protocol. In the case of node or edge failure, the connection manager releases the previously allocated resources along the old path, and re-establishes a new connection by allocating new resources along the new path.
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 2 Materials and Methodology
### Bamboo Plant
#### 2.1.1 Bamboo Fibers  
A new processing technology was developed to process bamboo culms into fibers that were suitable for use in a novel engineered bamboo composite materials [33]. Accordingly, the fibers were obtained by processing entire bamboo culms. They were then added to epoxy resin and fabricated into high-tensile-strength bamboo composite materials by using a hot-press fabrication method. The process yielded an engineered bamboo composite material, which was then cut into different sizes to be used in concrete as reinforcement in place of steel bars in previous studies [39; 40].  
Throughout this process, some portions of the fibers are not used and remain as the waste by-products of the fabrication process. This study adopts the use of these waste portions of these fibers to reinforce the mortar matrix. In an earlier study [37], the mechanical properties of _Dendrocalamus asper_ bamboo from Indonesia were investigated with respect to culm physical properties including culm diameter, wall thickness, height, moisture content and specific density. Correlations were drawn between the culm's physical properties and the resulting mechanical properties, including tensile strength, modulus of rupture and modulus of elasticity in flexure and tension.  
Bamboo fibers were ground and sieved with a sieve tower using sieve sizes of 1 mm and 500, 300 and 125 \(\upmu\)m. The final bamboo fibers used in this study were those remained in the 500 \(\upmu\)m and 300 \(\upmu\)m sieves (Figure 1) and are referred to as \"500 \(\upmu\)m\" and \"300 \(\upmu\)m\" fibers throughout this study, respectively. Bigger and smaller sieves were used for a better separation of the fiber sizes. This ensured more constant fiber sizes and avoided clogging of the 500-micron sieves with bigger fibers. Trail mixes with 212 \(\upmu\)m fibers showed no promising results and were therefore not considered in this article.
## 1 Introduction
This leads to the formation of multi-molecular associations, which can greatly enhance the stability. Due to their stability in salinity compared to unmodified polymers, in addition to their enhanced viscosity, HAWPs have great potential for use in high-temperature and high-salinity reservoirs [32]. At present, HAWPs also have several problems, including:  
(1) Poor solubility. With the increase in the polymer molecular weight and hydrophobic group content, the solubility becomes lower and lower. Some researchers enhance solubility by adding surfactants;  
(2) Poor injectivity. Although the resistance coefficient (RF) and residual resistance coefficient (RRF) of hydrophobically associated polymers are improved during displacement, their injectivity is not as good as that of HPAMs [33];  
(3) The temperature and salt resistance of hydrophobically associating polymers can be further improved. At present, the commonly used monomers are AMPS, NVP and sodium p-styrene sulfonate (SSS). In order to solve the above problems, we have carried out a series of studies [34; 35]. Block A of Lukeqin oilfield (Xinjiang, China) is a typical deep heavy oil reservoir with high temperature (85-100 \({}^{\circ}\)C), high salinity (8.3-16.02 \(\times\) 10\({}^{4}\) mg/L, CaCl\({}_{2}\)) and deep burial depth (2300 m\(\sim\)3100 m). It has a thick pay zone of 20\({}^{+}\) m, an average porosity of 27.31%, a live oil viscosity of 526 mPa\(\cdot\)s and a saturation pressure of 4.1 MPa. The reservoir heterogeneity is strong. With the deepening of the oilfield, the formation energy has rapidly decreased, production in Lu block A has decreased rapidly, the number of low-yield and low-efficiency wells has continued to increase, and the water cut (65%) of some wells has increased rapidly. SAGD technology is usually used for high-temperature and -salinity heavy oil reservoirs, but Lukeqin oilfield has a deep burial depth and extensive wellbore heat loss, making it not suitable for SAGD technology [36; 37; 38]. According to predictions,the ultimate recovery from water flooding in Lu block A is less than 15%, so there is an urgent need for a new method to improve the development effect. In this study, we conducted a series of experiments to analyze the feasibility of polymer flooding in Lukegin oilfield. A new type of temperature-resistant and salinity-tolerant polymer was designed and synthesized, and its structure and properties were tested. The effects of temperature, salinity and aging stability on the polymer were evaluated, and its injectivity and oil displacement ability were also evaluated through laboratory experiments. This study has great significance for the application and popularization of polymer flooding technology in high-temperature and high-salinity reservoirs.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 4 Deceptive Jamming Extraction  
In this part, deceptive jamming extraction based on the interferometric process is further discussed. The basic steps of the proposed method are shown in Figure 4.  
To differentiate the deceptive targets from real ones, co-registration of corresponding SAR images is first applied and the interferogram with deceptive jamming is derived. Secondly, phase filtering is carried out to effectively improve the detection accuracy. Then, frequency detection in the range direction is employed to extract the false phase in the interferogram. The false scene in the SAR image is obtained ultimately according to the deduced 0-1 valued mask matrix.
## 3 Results
### Economic Optimum Systems of the Five Cases
#### 3.1.1 Components of the Economic Optimum Systems
On the other hand, as shown in Figure 6a, in the PV optimum systems with Li-ion battery, the PV generator had a higher size than that of the optimum with lead-acid, in four of the five cases (+8% to +87%). The only case in which it decreased is the one (single-family home) in which the battery reduction was the least. It must be taken into account that the DOD of 90% allowed in the simulations of Li-ion batteries made their useful capacity greater than in lead-acid batteries whose allowed DOD was 80%. Moreover, the higher cycling efficiency of the Li-ion battery (95%) compared to the lead-acid (85%) played in favor of the lower need for PV production. Both characteristics are shown in Table 4. In consequence, the decrease in useful storage capacity was less than nominal. As a result, the reduction in nominal capacity by 6.5% had not required an increase in PV generation, but had even allowed its reduction. Regarding the optimal hybrid systems, Figure 6b shows that the diesel genes assumed the change in production, while the PV generators barely changed (0% to \(-\)11%). Thus, in four of the five cases, the fuel consumption increased (+15% to +108%). In the same way that in the optimal PV systems, in the case where the reduction in the nominal capacity of the battery is the smallest (this time, the second home) there was no increase in production, i.e., its PV generation did not vary, and fuel consumption decreased (\(-\)6%). In summary, the effects of changing the battery type from lead-acid to Li-ion on the size of the optimal system components were similar in PV and hybrid systems, despite the latter requiring less storage capacity. This included a reduction in the nominal capacity of the battery. If the higher DOD and the higher cycling efficiency of Li-ion batteries were not enough to compensate for the lower storage, the production of energy increased from  
Figure 5: Lifetime of batteries of the economic optimum systems: (**a**) PV systems; (**b**) hybrid systems. Figure 6: Differences in size of battery, PV generator and fuel consumption in the economic optimum systems with Li-ion in relation to lead-acid battery: (**a**) PV systems; (**b**) hybrid systems. PV or diesel origin. It was also observed that the five case studies presented quantitatively different behaviors in front of the change of type of battery. However, each case presented a similar trend in optimal PV and hybrid system.
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 4 Viscosity
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Researcher** & **Derived Model** & **Discussion** \\ \hline \multirow{4}{*}{[PERSON] et al. [66]} & \(\mu_{nf}=A\varphi^{2}T^{c}P_{nf}^{d}\) & Presented correlation considered the effect of volume fraction, temperature, and viscosity of the base fluid. Where \(A\), \(B\), \(C\), and \(D\) represented the correlation coefficients used to predict the viscosity. \\  & \(\mu_{nf}=\left[\frac{1}{1-3A\varphi\left(\frac{\varphi}{d}\right)^{2}\varphi^{ \mu}}\right]\mu_{bf}\) & This model provides the results with a 1.84% of the standard deviation of error and appropriate within the following boundaries for temperature (293-232 K), nanoparticle diameter (25-200 nm) and volume fraction (0.0001-0.071). \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [101]} & \(\mu_{nf}=\left(a+b\varphi+c\varphi^{2}\right)\mu_{bf}\) & The suggested model provided reasonable agreement with experimental results for TiO\({}_{2}\) \\  & 15 & 1.0225 & 0.0427 & -0.0112 \\  & 25 & 1.013 & 0.0022 & -0.015 \\  & 38 & 1.018 & 0.0112 & -0.0277 \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [115]} & \(\mu_{nf}=\left(13.47\,e^{25.96\varphi}\right)\mu_{bf}\) & The developed model used for TiO\({}_{2}\) nanofluid when the solution is prepared by suspending the nanoparticles in diluted water with concentration (0.05-0.12% and correlation factor \(R^{2}=0.98\). \\ \hline \multirow{4}{*}{[PERSON] et al. [116]} & \(\mu_{nf}=\mu_{bf}\left(1-\frac{\varphi}{\mu_{bf}}\left(\frac{\varphi}{\mu} \right)^{12}\right)^{-2}\) & The presented model used in case of spherically shaped dispersants, nanofluids with high shear rate and showed Newtonian behaviour. \\  & \(\mu_{nf}=\mu_{bf}\left(1-\frac{\varphi}{\mu_{bf}}\left(\frac{\varphi}{\mu} \right)^{12}\right)^{-2}\) & The presented model used in case of spherically shaped dispersants, nanofluids with high shear aggregates; \(\varphi_{m}=\) crowding factor (\(\varphi_{m}\approx 0.65\)) in case of random packing of spheres; \(d=\) diameter of suspended particles \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Viscosity models for TiO\({}_{2}\) nanofluids. The studies on viscosity confirm the following points regarding viscosity variation:  
* The viscosity of nanofluids shows an inverse relationship with the temperature. * Particle concentration directly influences the viscosity of the nanofluid and shows a direct relationship with it.
# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 1 Introduction
first systematically analyzed the group delay of single-rate systems and proposed a multi-rate signal-processing approach [24] that uses asymmetric FIR filters. However, this method requires a determined model to design the reconstruction filter and anti-aliasing filter under the assumption that the spectral structure of the primary sound source is known. As audio processing in ANC systems is an important application and the basic requirement of the sampling rate for audio processing is higher than that for active noise control, a multi-rate system has also been used to balance the computational complexity and functional requirements [25]. As field-programmable gate array (FPGA) processors develop, their better parallel calculation properties can significantly enhance the calculation speed; therefore, some researchers have tried to use FPGAs for room noise control [26] and head-mounted feedforward ANC systems [27]. The use of FPGAs is an effective way to enhance the processing speed, but the associated programming process is often difficult and inflexible, and the debugging work in such systems is tedious. Some other meaningful works have also been conducted [2], and another effective method is the use of parallel analog and digital adaptive feedforward controllers in a system [28]; however, accurately modifying the analog filter coefficient is often not easy. As the new proposed methods are often restricted by system flexibility and adjustment accuracy, [PERSON]'s method is still very attractive. According to the available literature, whether it is a single-rate system or a multi-rate system, the design of reconstruction and anti-aliasing filters is completed before the control starts, and the filter parameters remain fixed and unchanged in the control process [29; 30; 31; 32]. In practical applications of adaptive ANC systems, the characteristics of the primary source may change over time, and the requirements for reconstruction and anti-aliasing filters also change. However, there is currently no in-depth research on the methods and effects of the dynamic matching of reconstruction and anti-aliasing filters. In order for an adaptive ANC system to match the characteristics of a primary sound source in the control process, a dynamic matching method is proposed for the design of reconstruction and anti-aliasing filters. The core idea of this method is to replace the fixed parameters of model-based reconstruction and anti-aliasing filters with dynamic variations in these parameters driven by the data features. The remainder of this article is organized as follows. Section 2 outlines the traditional ANC systems and the challenges confronting them. Section 3 demonstrates the proposed dynamic matching method for reconstruction and anti-aliasing filters in adaptive noise control. Section 4 introduces the experimental setup and the results of the proposed method. Section 5 summarizes some important conclusions.
# Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
## 3 Experimental Materials and Testing  
In this study, dynamic modulus tests were conducted on four asphalt mixtures with varying RA contents (0%, 30%, 40%, and 50%). The test data were used to determine the parameters of both models--sigmoid and Generalized Huet-Sayegh (2S2P1D)--and the accuracy of these models was evaluated.  
The used asphalt mixtures were graded with a nominal maximum aggregate size of 16 mm, as shown in Figure 5, and were described as shown in Table 1. All mixtures had a targeted binder content of 4.5% by weight, distributed between binder reactivated from reclaimed asphalt and added virgin binder. The added asphalt binder was 50/70 paving-grade asphalt binder, which is the most commonly used asphalt binder in the Czech Republic (see Table 2).  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **0\%** & **30\%** & **40\%** & **50\%** & **EN Standard** \\ \hline Bulk density [g.cm\({}^{-3}\)] & 2.392 & 2.368 & 2.400 & 2.390 & EN 12697-6 [20] \\ Maximum density [g.cm\({}^{-3}\)] & 2.538 & 2.520 & 2.524 & 2.553 & EN 12697-5 [21] \\ Voids content [\%-vol.] & 5.7 & 6.0 & 4.9 & 6.4 & \\ Moisture susceptibility, ITSR [\%] & 94.0 & 77.4 & 71.4 & 79.6 & EN 12697-12 [22] \\ Stiffness @15 \({}^{\circ}\)C [MPa] & 7654 & 10.760 & 11.159 & 17.497 & EN 12697-26, annex C [3] \\ Fracture toughness [N/mm\({}^{3/2}\)] & 35 & 39 & 38 & 39 & EN 12697-44 [23] \\ Added virgin asphalt binder [\% mass] & 0 & 2.7 & 2.0 & 1.6 & \\ Total asphalt binders in the mix [\% mass] & 4.5 & 4.5 & 4.5 & 4.5 & \\ \hline \end{tabular}
\end{table}
Table 1: Basic used mixture’s characteristics.  
The reclaimed asphalt was collected from a cooperating asphalt mixing plant and its grading was characterized according to EN 13 108-1 [27] and CSN 73 6121 [26] standards (see Table 3). RA was then fractionated and stored in the laboratory in two fractions: fine/medium fraction (0/8 mm) and coarse fraction (8/16 mm) and characterized as follows:  
A steel segment roller was used to compact a slab measuring 405 mm in length, 300 mm in width, and 50 mm, in accordance with EN 12697-33 [28]. This slab was then cut into 5 prismatic samples, each measuring 405 mm in length, 50 mm in width, and 50 mm in height.  
The dynamic modulus test was conducted under a controlled strain with a fixed value of 50 micro-strains according to the European Standard EN 12697-26 at four temperatures, 0 \({}^{\circ}\)C, 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 30 \({}^{\circ}\)C, and at eleven selected frequencies, 50 Hz, 30 Hz, 20 Hz, 15 Hz, 10 Hz, 8 Hz, 5 Hz, 3 Hz, 2 Hz, 1 Hz, and 0.1 Hz, for each temperature, using a Cooper research technology setup. During this test, the asphalt mixture sample was fixed at two endpoints, and sinusoidal loading was applied to two central points. The required force of deformation was measured as a function of time and phase lag. The dynamic modulus was then calculated:  
\[|E^{*}|=\sqrt{E_{1}^{2}+E_{2}^{2}}, \tag{7}\]  
The phase angle was calculated as follows:  
\[\Phi=\tan^{-1}\left(\frac{E_{2}}{E_{1}}\right), \tag{8}\]
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 6 Conclusions  
This paper presents a point cloud-based modeling method tailored to coal mine excavation tunnels, successfully addressing the challenges faced by existing point cloud modeling algorithms in such environments. Through steps including point cloud preprocessing, centerline extraction, normal vector optimization, and Poisson surface reconstruction, the proposed method significantly enhances the authenticity and completeness of tunnel models. Experimental results demonstrate that this method effectively filters equipment point clouds and repairs occluded areas, producing detailed 3D tunnel models. This modeling approach not only supports safety monitoring and production in coal mines, but also provides precise support for remote control, path planning, and other applications. In the future, this algorithm holds great potential for handling tunnels of different shapes and complex geometries.  
Conceptualization, H.Z., S.M. and M.L.; methodology, H.Z.; software, H.Z.; validation, H.Z.; formal analysis, H.Z., S.M. and M.L.; investigation, H.Z., S.M. and M.L.; resources, H.Z.; data curation, H.Z.; writing--review and editing,H.Z., S.M. and M.L.; visualization, H.Z., S.M. and M.L.; supervision, S.M.; project administration, S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R&D Program of China (2022 YFC3004701).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to confidentiality concerns regarding the high-precision mine data.  
**Acknowledgments:** The authors express their appreciation to the editor and anonymous reviewers for their insightful recommendations, which significantly contributed to enhancing the initial manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results and Discussions
### Effect of Operating Pressure on the Amount of Condensation  
Figure 13 shows the effect of the inlet total pressure (given in Table 1) on the condensation observed over the center-line axial profile. The plot shows the amounts of condensation, as in Figure 11. From case 1 (the furthest from the CO\({}_{2}\) critical point) to Case 5 (the closest to the CO\({}_{2}\) critical point), an acceleration of condensation is observed, as the initial slope of the gas volume fraction increases near the throat. Furthermore, the condensation front moves before the nozzle throat (position at X = 0) and a sharper two-phase gradient is also observed as the inlet pressure is increased from Case 1 to Case 5. These numerical results are qualitatively corroborated by the experimental and numerical images shown in Figure 14. It is also worth noting in Figure 13 that, in Case 4 and especially Case 5, some of the liquid condensed near the throat evaporated before reaching the nozzle outlet section. This demonstrates the ability of the RFM model used in this work to simulate both condensation and evaporation.  
Figure 14: Qualitative comparison of the condensation as observed in the experimental images adapted from [PERSON] [11] (**left**) and numerical results (**right**).  
Figure 12: Illustration of the low condensation of pure CO\({}_{2}\) in case 1 conditions.  
Figure 13: Gas volume fraction profile plots of 5 cases with 95% CO\({}_{2}\) and 5% N\({}_{2}\) as an impurity. Case 5 shows the highest amount of condensation observed as it has the lowest gas volume fraction.  
Finally, Figure 15 gathers all the calculated pressure axial profiles along the nozzle and compares them with the measured pressure profiles obtained from the [PERSON] experiments [10; 11]. The instantaneous local center-line pressure obtained at the fully supersonic regime are plotted. It is normalized by the inlet total pressure. The RFM model results are in fairly good agreement with the data. The onset of condensation for the highest pressure case 5 occurs well before the throat (upstream of the nozzle throat) and represents the challenge to the CFD calculations due to its proximity to the critical point and the rapid and sharp condensation front. [PERSON] et al. [10; 11] also comment on the experimental results for this condition, noting that additional high pressure experiments need to be performed to resolve the issue. From a CFD point of view, this work provides the validation in terms of capturing the main characteristics as observed in the experiments.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 3 Experimental Results
### Datasets  
Two datasets were used in the experiment. The first is the Cityscapes dataset, a widely used segmentation dataset, which includes 2975 images for the training/validation set and the testing set contains 500 images. The second, vertical image, dataset for the road area was obtained by attaching an SVM camera to an actual vehicle from HMG. It consists of 863 images in the training/validation set and 216 in the testing set. To check the network robustness, we applied our method to rain/haze images and real-world road images, which are treated as different domains.