esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Mastic Asphalt Waterproofing Layer in High-Speed Railway Track in Cold Regions
## 1 Introduction
developed impermeable asphalt concrete for application on the subgrade surface of the slab track, and it was tested in the Wuguang high-speed railway. [PERSON] et al. designed Railway Asphalt Concrete (RCA) and its good dynamic performance was proved through full-scale dynamic models in the laboratory. Nevertheless, both of the asphalt concrete types were designed for slab tracks in warm regions but not in cold regions [25]. Based on the experiences mentioned above, the use of asphalt layer in railway tracks helps to reduce train-induced vibration, improve waterproof performance, and increase stability of the subgrade, however, due to poor workability and insufficient cracking resistance, the asphalt mixture used in sub-ballast layer cannot meet the requirements of the waterproofing layer in the slab track. Mastic asphalt is a type of asphalt mixture extensively used as waterproofing on flat roofs. As the mastic asphalt is reasonably fluid at the processing temperature, it can be applied to form a dense and continuous layer covering flat, sloped or curved surfaces without compacting. In addition, due to great deformation capacity, mastic asphalt mixture shows strong cracking resistance at low temperatures. Therefore, mastic asphalt mixture could potentially be applied as a waterproofing layer on the subgrade of railway track. In this paper, mastic asphalt mixture was designed and applied in the waterproofing layer, which is paved on the shoulder and the middle area between track slabs. Firstly, the mastic asphalt mixture was designed and evaluated in the laboratory, then a 200-m test section was constructed as part of a real high-speed railway line in northeastern China, and finally, field investigation results and monitoring data over the two years of operation were analyzed and compared with conventional concrete waterproofing layers.
## 6 Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Peak Control**} & \multicolumn{3}{c}{**Amplitude**} \\ \cline{2-4}  & **Open-Loop** & **With LMS** & **With SM-LMS** \\ \hline
**1 st Peak** (15 Hz) & 0.0037290 & 0.0008709 (12.6 dB\(\downarrow\)) & 0.0005123 (17.2 dB\(\downarrow\)) \\
**2 nd Peak** (33 Hz) & 0.0011960 & 0.0008719 (2.7 dB\(\downarrow\)) & 0.0005129 (7.4 dB\(\downarrow\)) \\
**3 rd Peak** (117 Hz) & 0.0017240 & 0.0048300 (8.9 dB\(\uparrow\)) & 0.0028410 (4.3 dB\(\uparrow\)) \\
**4 th Peak** (312 Hz) & 0.0027970 & 0.0041200 (3.4 dB\(\uparrow\)) & 0.0024240 (1.2 dB\(\downarrow\)) \\
**Overall RMS** & \(3.3386\times 10^{-4}\) & \(3.6430\times 10^{-4}\) (0.8 dB\(\uparrow\)) & \(2.1429\times 10^{-4}\) (3.9 dB\(\downarrow\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vibrational amplitudes of each case for the original beam structure. Figure 19: Experimental result for the baseline structure: (**a**) time domain; and (**b**) frequency domain. After 0.5 s, the vibration amplitudes for both control algorithms are very similar, but LMS shows worse vibration isolation performance than SM-LMS. The frequency response shows a more obvious performance difference between the two methods, as indicated in Table 5. LMS shows excellent attenuation of mode 1 but has very little effect on higher modes. SM-LMS shows excellent attenuation of mode 1 and slight attenuation of modes 2 and 3, while mode 4 shows very slight amplification. At every peak, the difference in attenuated amplitude between the two methods is about 2 to 3 dBs. The amplitudes of the 1 st, 2 nd, and 3 rd modal frequencies are reduced, and the 4 th modal frequency has some spill-over with SM-LMS. However, with LMS, the 2 nd and 4 th peaks have spillovers, and the amounts are higher than in the case with SM-LMS. When observing the vibration level in broadband, the SM-LMS shows better vibration isolation performance, thus showing a reduced overall RMS value, although it is 1.1 dB lower than the baseline case. As shown in the simulation results, the time-domain responses caused by impulsive disturbances are significantly reduced, and the amplitude of each resonance peak is nearly flattened. These results verify that the SM-LMS method can reduce the vibration of the cantilevered beam considerably, even though the structural dynamics is dramatically changed with the additional impedance.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 8 Conclusions  
In conclusion, the proposed framework for integrating IoT systems in precision agriculture applications is a promising solution for managing agricultural and livestock farms. The simulation results obtained using the COOJA simulator demonstrate the effectiveness of the proposed framework in both fixed and mobile scenarios. The results also indicate that network connectivity and average hop count play crucial roles in the performance of RPL in precision agriculture applications. Additionally, the best end-to-end packet delivery probability was achieved for network scenarios of 75 \(\times\) 75 and 100 \(\times\) 100 for both the olive tree and horse stable scenarios. The results also demonstrate that the average end-to-end delay is directly proportional to the average hop count. As the network becomes denser with a decrease in the network area, there is an increase in network connectivity and a decrease in hop count. This research can serve as a foundation for future studies to enhance the performance and scalability of the proposed framework.  
One limitation of this methodology is that the simulation results may not fully represent the real-world performance of the proposed framework in precision agriculture applications. This is because the simulation does not take into account various real-world factors that may affect the performance of the system, such as environmental conditions, human factors, and unexpected failures. Therefore, further validation of the proposed framework in real-world settings is necessary to ensure its reliability and effectiveness in precision agriculture applications.  
In terms of future improvements, incorporating advanced technologies such as machine learning algorithms and edge computing can enhance the performance and scalability of the proposed framework. Additionally, integrating more sophisticated sensor technologies such as drones and cameras can provide a more comprehensive understanding of the farm environment. Furthermore, incorporating a more dynamic mobility model for the mobile scenario can provide a more realistic representation of the movement of animals in precision agriculture applications.  
Conceptualization, S.A., K.F.B.H. and H.M.; methodology, S.T. and A.G.; software, M.D. ([PERSON]) and Y.H.; validation, M.D. ([PERSON]) and S.A.; formal analysis, A.G.; investigation, A.G.; writing--original draft preparation, M.D. ([PERSON]); writing--review and editing, M.D. ([PERSON]) and Y.H.; supervision, W.M.; project administration, M.D. ([PERSON]), W.M. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Enzymatic Hydrolysis of CR  
CR obtained from green bamboo after the sequential hydrothermal-DES pretreatment was employed as feedstock for enzymatic hydrolysis. As shown in Figure S4 (I), the glucose concentration increased with increasing solid loading, whereas the saccharification ratio reached a maximum value of 22.7% at the solid loading of 5%. It is seen from Figure S4 (II) and (III) that the maximum saccharification ratios were 36.2% (increase of 59.7%) with adding Tween-20 at 5 mg/mL, and 37.4% (increase of 64.8%) with adding BSA at 17.5 mg/mL, which probably results from Tween-20 or BSA binding to the residual lignin instead of enzyme and thus reducing the nonproductive adsorption of the enzyme. Moreover, without adding chemical additives, cellulase complexes composed of 60% Cellic CTC3 and 40% PH9018 exhibited a maximum saccharification ratio of 26.0% shown in Figure S4 (IV). From Figure S4 (V-VII), the saccharification ratio was not proportional to cellulase dosage, which is probably due to the limited reaction sites (\(\sim\)1 m\({}^{2}\)/g) or the inherent recalcitrance of the substrate. Consequently, considering the cost of the enzymatic saccharification process, the optimum enzymatic conditions are 5% (\(w/v\)) of solid loading, Tween-20 at 5 mg/mL, a cellulase dosage of 30 FPU/g\({}_{\text{glucan}}\), and a Cellic CTec3 percentage of 60% (40% of PH9018), and the corresponding saccharification ratio is 37.2% (marked as dotted line in Figure S4).  
Notably, it is observed in Figure 8 that although the enzymatic digestibility crude green bamboo is low, with a saccharification ratio of 6.5% owing to its inherent recalcitrance, the saccharification ratio of CR (37.2%) is nearly twice as high as that of HTB (20.9%), demonstrating a high efficiency of the integrated hydrothermal-DES pretreatment upon destroying the recalcitrance of green bamboo.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Lignocellulosic Source** & **Pretreatment Method** & **IC\({}_{50}\) (mg/mL)** & **Ref.** \\ \hline Bamboo (_hambusa rigida_ sp.) & Dimethyl sulfoxide/N-methylimidazole-dissolved lignin & 0.06–0.11 & [65] \\ Bamboo (_Phylostachys pubescen_) & Steam-exploded lignin & 0.18–0.50 & [66] \\ Pine wood & Organosolv ethanol lignin & nearly 0.10 & [34] \\ _Pentisteum_ & FeCl\({}_{3}\)-catalyzed ChCl/glycerol DES pretreatment & 0.055–0.115 & [20] \\ Bamboo (_Dendrocalamopsis oldham_) & Sequential hydrothermal–ChCl/LA DES pretreatment & \textless{0.05} & In this work \\ \hline \end{tabular}
\end{table}
Table 1: Comparison in IC\({}_{50}\) among lignins from different lignocellulosicoses using different pretreatment methods.
###### Abstract  
This work presents a comparative study of a step-down converter controlled through the algorithm MPPT Perturb and Observe (P&O) with or without a fuzzy logic controller supplied by a photovoltaic system. This study aimed at increasing the quantity of electric energy taken over from the photovoltaic systems by the load through the DC-DC convertor. To follow up the maximum power point where the transfer is performed from the photovoltaic system to the load at maximum power, the Perturb and Observe (P&O) method was used. Two programs were elaborated in MATLAB-Simulink R2018a to control the buck convertor commanded through the P&O algorithm with or without a fuzzy logic controller. The analysis of the results showed that a higher quantity of energy is transferred from the source to the receptor circuit in the case of the buck convertor controlled through the P&O algorithm with a fuzzy logic controller. The P&O algorithm was implemented on an experimental stand at the Laboratory of Electrical Machinery and Drives of the Engineering Faculty in Bacau, with the help of a program issued in the Arduino IDE programming environment. The analysis of the results showed that for an increase in the power conveyed to the receptor circuit, there will also be an increase in the filling factor of the PWM signal that controls the power transistor in the structure of the DC-DC convertor. The P&O algorithm with a fuzzy logic controller may also be implemented in the DC-DC converters in the structure of the driving systems of electric vehicles.  
DC-DC convertor; Perturb and Observe; fuzzy logic controller
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 2 Materials and Methods
### Factor Graph Model of INS/GPS/VO Integrated Navigation System
#### 2.1.2 Prior Factor  
Prior information \(p(x_{0})\) signifies that the navigation system possesses a predetermined understanding of the initial variables prior \(x_{0}\) to real-time navigation. For instance, INS can determine the initial state accuracy of the carrier through initial alignment. The available prior information \(p(x_{0})\) can be subjected to additional factorization, resulting in discrete priors pertaining to relevant variables. Each of these priors is encapsulated by an individual prior factor. Termed as a unary factor, the prior factor is expressed as  
\[f^{Prior}(x_{0})\doteq d(x_{0}) \tag{7}\]  
In the case of a Gaussian distribution, prior information is typically specified through a mean \(\mu_{0}\) and a covariance matrix \(\Sigma_{0}\).
## 3 Results and Discussion
Figure 4b shows an example of a large pore in Sample 2, which can be assumed to have formed due to keyhole collapse and shrinkage during L-PBF processing. The higher energy density used for printing Sample 2 increases the chance for keyhole formation [48]. Figure 4b indicates a prolate spheroid shape pore located close to the sample edge. As discussed earlier, higher energy densities at the end of a laser track lead to keyhole pore formation. Considering the physical limitations of the laser scanning system optics, stabilizing the energy density by varying the laser power is a solution to solve this problem [43; 49]. This emphasizes the fact that modification of process parameters is a practical approach to control defect formation (pores, cracks, etc.) in the final printed samples [50; 51]. Figure 3: 3D (**a,b**) and X-Y (**c,d**) plane projections of visualized pores (10–50 micrometers) with illustrated laser scanning strategies. Most of the pores are located at the end of the laser tracks, where laser off events occur. Panels (**e,f**) represent the distribution of the pores at the surfaces of the target shape-printed Samples 1 and 2, respectively (The pores are in white). Irregular pores located in the vicinity of the part surface are due to partially melted/sintered/entrapped metallic powder particles, which stick to and agglomerate on the outer edge of the melt pool. These residual powders are associated with the presence of complex voids, which can be distinguished from other types of defects [52; 53]. The specific process parameters of Sample 2 (i.e., higher power, lower speed, smaller hatching distance) lead to higher energy density, which leads to the accumulation of more heat during the printing process. This heat input diffuses up to hundreds of micrometers from the melt pool and increases the temperature of the surrounding powder. Some of the powder grains will thus sinter or partially melt, leading to their agglomeration to the solidified surfaces [54], as visualized in Figure 4c. Due to their sub-10-micrometers size, the induced porosities cannot be analyzed in detail using 3D neutron microtomography. However, the partially melted zones exhibit significantly lower LAC than the well-processed zones and can, therefore, be clearly distinguished (Figure 4c).
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 2 Related Work
### Approaches to Enhancing DETR: Performance vs. Efficiency
#### 2.2.2 Efficiency-Oriented Enhancements  
To alleviate DETR's computational burden, various studies have focused on reducing redundant computations and optimizing query processing. Deformable-DETR [8] introduced a sparse attention mechanism [23; 24] that significantly reduces the computational complexity of self-attention. Unlike standard DETR, where each query attends to all spatial locations in the image, Deformable-DETR restricts attention to a small set of relevant key points. This modification not only lowers memory and computation costs but also improves scalability for high-resolution images. Building on this direction, RT-DETR [5] further optimized DETR's efficiency by refining the encoder-decoder interaction. It introduced a hybrid encoder that combines CNN [25; 26] and Transformer [20] architectures, effectively leveraging the strengths of both paradigms. By incorporating CNN-based feature extraction, RT-DETR enhances computational efficiency while retaining the global context modeling capability of Transformers, making it more suitable for real-time applications.  
Other methods have focused on reducing the number of object queries processed in the decoder. Rank-DETR [6] proposed a query sorting mechanism, where queries are ranked at each decoder layer to help the model prioritize the most relevant ones. While this method effectively limits the initial number of queries, it does not dynamically adjust the query count during inference, restricting its potential for computational savings. [PERSON] [7] introduced a different strategy by applying NMS at each decoder layer to eliminate redundant queries, successfully reducing computational overhead and improving small object detection. However, because DDQ reintroduces NMS--a step that DETR originally sought to eliminate--it deviates from the model's end-to-end learning paradigm and adds additional computational complexity.  
Although these efficiency-oriented approaches achieve some level of computational reduction, they primarily focus on selecting more informative queries or modifying attention mechanisms rather than dynamically reducing the total number of queries throughout the decoding process. As a result, they do not fully resolve the inefficiencies arising from DETR's excessive reliance on object queries, underscoring the need for a more direct and adaptive query reduction strategy.
## 3 Diagnostic Reasoning for the EPS  
The simulations run for both healthy and faulty conditions in the EPS digital twin show that faults induced in the EPS affect the LRUs of other systems, thus propagating their effects to these interacting systems. In order to detect and isolate the faults propagating to other aircraft systems from the EPS, a diagnostic reasoning engine is developed through a series of steps, as discussed below.  
Data is collected by running the simulation at both healthy and all known fault scenarios. A set of parameters was chosen to monitor the health of the EPS digital twin during these simulations, and the raw data was collected for these parameters through sampling.  
Preprocessing of the sampled raw data is undertaken by calculating the root mean square (rms) values of the monitored parameters followed by computing the mean values of the steady-state data.  
Factor analysis is carried out using the computed mean values to understand the effects of injected faults on the monitored parameters. This step helps in shortlisting the factors that need to be considered for building the diagnostic reasoning engine and ignoring the redundant parameters.  
Thresholds are determined to define the diagnostic rules for the reasoning engine. If crisp thresholds are established between the parameters for all the simulated scenarios, these threshold values can be used to define the diagnostic rules for the reasoning engine. However, from Figure 5, it is observed that for certain faults like F7 or F1, the apparent power does not vary much from the 100% healthy case results. In such scenarios, defining a crisp boundary of the parameter in order to differentiate a healthy case from the fault cases and to differentiate a fault case from another, would require a large number of very specific rules. To avoid this, the thresholds will hence be defined by fuzzy boundaries, and diagnostic rules will be framed using fuzzy reasoning.  
This step involves developing fuzzy diagnostic reasoning. In order to avoid specificity in the threshold values, the membership functions of the fuzzy system are determined using artificial neural networks. The following subsections present the fundamentals of ANFIS and how it is implemented for the EPS diagnostics.
## 3 Materials and Methods
### Semantic Trajectory Analytics  
The Semantic Trajectory analytics module provides an analysis of the trajectories resulting from the Semantic Annotation module. To support this functionality in ReconTraj4 Drones, the Owlready2 library was used. ReconTraj4 Drones integrates the native SPARQL engine instead of the RDFlib ([[https://rdflib.readthedocs.io/en/stable/](https://rdflib.readthedocs.io/en/stable/)]([https://rdflib.readthedocs.io/en/stable/](https://rdflib.readthedocs.io/en/stable/)), accessed on 30 December 2022). The first is used since it is 60 times faster than the latter. Moreover, it automatically creates prefixes from the last part of the ontology's IRI. For example, the ontology ([[http://myontology.org/onto.owl](http://myontology.org/onto.owl)]([http://myontology.org/onto.owl](http://myontology.org/onto.owl))) will be automatically associated with the \"onto:\" prefix. The following prefixes are automatically pre-defined, so there is no need for them to be defined by the user:  
* rdf: [[http://www.w3.org/1999/02/22-rdf-syntax-ns#](http://www.w3.org/1999/02/22-rdf-syntax-ns#)]([http://www.w3.org/1999/02/22-rdf-syntax-ns#](http://www.w3.org/1999/02/22-rdf-syntax-ns#))
* rdfs: [[http://www.w3.org/2000/01/rdf-schema#](http://www.w3.org/2000/01/rdf-schema#)]([http://www.w3.org/2000/01/rdf-schema#](http://www.w3.org/2000/01/rdf-schema#))
* owl: [[http://www.w3.org/2002/07/owl#](http://www.w3.org/2002/07/owl#)]([http://www.w3.org/2002/07/owl#](http://www.w3.org/2002/07/owl#))
* xsd: [[http://www.w3.org/2001/XMLSchema#](http://www.w3.org/2001/XMLSchema#)]([http://www.w3.org/2001/XMLSchema#](http://www.w3.org/2001/XMLSchema#))  
Figure 7: Basic concepts and relations of the Onto4 drone ontology.  
* obo: [[http://purl.obolibrary.org/obo/](http://purl.obolibrary.org/obo/)]([http://purl.obolibrary.org/obo/](http://purl.obolibrary.org/obo/))  
The execution of SPARQL queries is feasible only after the execution of the semantic annotation process. Indicative examples of SPARQL queries concern the number of recording points and the number of POIs in a single trajectory. The corresponding queries are the following:  
_Query 1:_  
list(default_world.sparql)(\"\"\"  
SELECT (COUNT(?recording_points) AS?rp) WHERE [  
?tr Onto4 drone_v1.0.1:encloses?rs.  
?rs TopDataCronOnto_SSN_FM:comprises?recording_points.  
)(!==))[0][0].  
_Query 2:_  
list(default_world.sparql)(\"\"\"  
SELECT?point_of_interest WHERE {  
?tr Onto4 drone_v1.0.1:encloses?rs.  
?rs TopDataCronOnto_SSN_FM:comprises?rp.  
?rp Onto4 drone_v1.0.1:hasOccurredEvent?pse.  
?pse Onto4 drone_v1.0.1:records?point_of_interest.  
)(!==)).  
The _sparql()_ method is used to execute a SPARQL query and obtain the returned results. Because this method returns a generator, the _list()_ function is used to present the results. Finally, even if the implemented framework contains a couple of predefined SPARQL queries, users can execute their own in the Jupyter notebook interface.