esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of an Acoustic Modern
## 3 Preliminary Results and Analysis
### Experimental Results and Analysis  
The time-varying response of the sea trial channel in the first experiment is shown in Figure 8. It can be seen that the channel had obvious multipath as well as Doppler shifts caused by the relative motion of the transceiver platform.  
In order to evaluate system performance, a total of ten frames of communication signals were collected for analysis between the software and hardware demodulation. For the software receiver, Matlab software was adopted for offline demodulation, while the proposed STM32 microprocessor hardware was used for real-time hardware demodulation with Doppler compensation implemented  
Figure 8: Response of the experimental channel for the first trial.  
Figure 7: Experiment configuration and sound velocity profile of the 1 st trial. (**a**) Configuration of the first trial. (**b**) Sound velocity profile of the first trial.  
by tuning the resampling rate. The values of Doppler estimated by software and hardware are shown in Figure 9a, respectively, which reveals that the estimated results of both approaches were almost the same.  
Shown in Figure 9b is the bit error rate curve corresponding to software and hardware receivers, wherein the solid line is a hardware demodulation error rate curve, and the dashed line is a software demodulation bit error rate curve. From the bit error rate (BER) results, it is evident that, with a higher calculation complexity, the performance of software demodulation is slightly better than that of hardware demodulation. The reason is that the precision of the hardware sampling chip, system clock, etc., is not as good as that of the software implementation.  
In addition, Figure 9b also indicates that the BER after Doppler compensation is less than 0.06, and the communication performance is obviously better than that before compensation. Moreover, the adoption of channel coding further improves the BER to the level of under 0.01. In conclusion, by incorporating DSSS modulation and channel coding, the proposed Doppler compensation method based on an AD variable sampling rate is capable of achieving a good communication performance for a moving vehicle.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 5 Evaluation  
### Environment  
In this section, we evaluate the performance impact of our optimizations. Table 6 summarizes our experiment environments. We used two hardware environments. The first environment is an APU heterogeneous system (denoted as _APU_) on which a CPU and GPU share the main memory. The second system is a discrete GPU system (denoted as _d-GPU_) on which a GPU has its private memory separated from the main memory. The detailed specification of the system, as well as the GPU configuration can be found on the table. The software environments are Ubuntu version 14.04, OpenCV version 3.1, and OpenCL version 2.0 for the two hardware environments.  
We applied the optimization schemes presented in Section 4 to the object recognition algorithms analyzed in Section 3. The baseline of each scheme is denoted as _baseline_, which indicates the original implementation of each algorithm. The proposed optimization techniques are denoted as follows:  
* _occup_: The occupancy increase technique by moving variables from the VGPR to LDS memory (Section 4.1.1).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Kernel Name** & **Global Work Size** & **Local Work Size** & **Kernel Merge** \\ \hline gaussianBlur & 768/480/1 & 256/1/1 \\ resizeLN & 256/64/1 & NULL \\ polynomiaExpansion & 256/60/1 & 256/1/1 \\ gaussianBlur & 768/480/1 & 256/1/1 \\ resizeLN & 256/64/1 & NULL \\ polynomiaExpansion & 256/60/1 & 256/1/1 \\ updateMatrices & 96/64/1 & 32/8/1 \\ boxFilter5 & 256/60/1 & 256/1/1 \\
**updateFlow** & **96/64/1** & **32/8/1** \\
**updateMatrices** & **96/64/1** & **32/8/1** \\ boxFilter5 & 256/60/1 & 256/1/1 \\
**updateFlow** & **96/64/1** & **32/8/1** \\
**updateMatrices** & **96/64/1** & **32/8/1** \\ boxFilter5 & 256/60/1 & 256/1/1 \\ updateFlow & 96/64/1 & 32/8/1 \\
**resizeLN** & **256/120/1** & **NULL** \\
**KF** & **256/120/1** & **NULL** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Work size information for Farneback optical flow kernels.  
Figure 9: Flowchart of kernel merging.  
* _wave_: The occupancy increase technique by increasing the global work size to secure a sufficient number of active wavefronts (Section 4.1.2).
* _merge_: The optimization technique that merges kernels with identical global work sizes to reduce kernel call overheads (Section 4.2).
## 3 Results and Discussion
### Initial Temperature of Self Healing
This indicates that the flowability of the asphalt mastic decreases as a result of the addition of fibers, and higher temperatures are needed to reach the same flow condition. The plain asphalt mastic's initial self-healing temperature was discovered to be 46 \({}^{\circ}\)C at the 0.9 level. When basalt fibers were added to the asphalt mastic at 3%, 6%, and 9% fiber contents, the initial self-healing temperatures were found to be 46 \({}^{\circ}\)C, 46 \({}^{\circ}\)C, and 47 \({}^{\circ}\)C, respectively. In contrast, the bamboo-fiber-modified asphalt mastics with the same fiber contents displayed initial self-healing temperatures of 48 \({}^{\circ}\)C, 49 \({}^{\circ}\)C, and 47 \({}^{\circ}\)C, respectively. The findings imply an ideal fiber content for equally distributed fibers in asphalt mastic, and that increasing the fiber concentration reduces the asphalt's flowability. Maintaining the same flow state requires raising the temperature. When the fiber content exceeds the optimum value, clustering of fibers is likely to occur. This phenomenon may lead to an  
Figure 10: Viscosity results of modified asphalt mastic: (**a**) 3% basalt fiber; (**b**) 6% basalt fiber; (**c**) 9% basalt fiber; (**d**) 3% bamboo fiber; (**e**) 6% bamboo fiber; (**f**) 9% bamboo fiber. increase in the self-healing capacity and flowability of the asphalt [36; 37]. Additionally, it reduces the temperature required to maintain the desired flow state. Consequently, it is crucial to establish an appropriate crack self-healing temperature based on the type and quantity of fibers incorporated into the asphalt mastic. This approach ensures the achievement of the desired self-healing effect and minimizes material consumption during actual construction. The research findings indicate that an increase in fiber concentration leads to a rise in the initial self-healing temperature of the asphalt mastic, which suggests that fiber-fiber interaction is the main factor influencing the flowability of asphalt mastic with high fiber content. Consequently, in practical applications, the crack self-healing temperature should be adjusted according to the type and quantity of fibers used to achieve the desired self-healing effect while minimizing material consumption.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 7 Proposed Scheme
### Medium Selection Mechanism  
In this section, multi-media techniques in the adaptation layer will be discussed. Multi-media techniques are used to select the appropriate medium from the various media in underwater communications, such as acoustic, VLC, or IR. Figure 6 shows a means of communication scheme from the physical to the routing layer, and the placement of the medium selection mechanism inside the MAC layer.  
Multi-media techniques consist of two phases 1. Distance calculation 2. Medium selection and data transfer
## 6 Conclusions  
In this study, the Born approximation method was used to derive the LSRTM process first. Next, the gradient of the true model and the perturbation model was calculated utilizing a method similar to the RTM, and the Lagrange multiplier method was used to update the reflection coefficient model. Finally, the methodology was integrated into the data processing of the GPR application. To further improve the imaging resolution, we then introduced MTV regularization into the LSRTM process as an optimization scheme to reduce artifacts.  
The numerical experiment results demonstrated that the imaging result of LSRTM-MTV outperforms the LSRTM in terms of noise suppression and artifact elimination. Via LSRTM-MTV processing, the undulation interface and the location of the irregular anomalies were sufficiently characterized with a high convergence rate. Compared with LSRTM, the computation speed of LSRTM-MTV is higher by 30.4%, and the MSE is reduced by 21.1%. This implies that the proposed LSRTM-MTV has considerable potential for GPR applications for refined delineation of the undulation interface and irregular anomalies. The laboratory data experimental results demonstrated the efficacy and robustness of the proposed method in practical applications.  
Conceptualization, Q.D. and S.W.; methodology, S.W. and Y.L.; software, S.W. and Y.L.; formal analysis, Q.D., S.W. and Y.L.; investigation, Y.L.; data curation, S.W. and Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, S.W. and Y.L.; supervision, Q.D.; funding acquisition, Q.D. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant Nos. 41874148 and 42174178), National Key Research and Development Program of China (grant Nos. 2018 YFC0603903), and in part by the Postdoctoral Science Foundation of Central South University under grant 22021133.  
Not applicable.  
Data Availability Statement:Data sharing is not applicable to this article.  
We thank [PERSON] from Guangzhou University for the field data.  
The authors declare no conflict of interest.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 3 Multichannel SA-CNN-BiLSTM Network Model
### Data Preprocessing  
#### 3.2.1 Data Pre-Description  
The experimental data come from the degradation dataset of NASA's C-MAPSS turbofan engine, a description of which is given in Table 1[18; 19]. The data include four sub-datasets, from FD001 to FD004, each of which contains three files: the training set, the test set, and the true value of RUL.  
The training and test sets consist of three operational parameters (altitude, Mach number, and sea level temperature) and 21 sensor parameters.  
We evaluated the proposed method using the FD001 and FD003 datasets. It is worth noting that these two subsets correspond to specific operational conditions. For instance, the FD001 subset might cover data under a particular combination of altitude and Mach number, while the FD003 subset has its own distinct set of operating conditions and is unable to cover all operational scenarios. Nevertheless, their significance cannot be underestimated.  
On the one hand, they offer a foundation for verifying the core algorithm of the prediction method and can assess the effectiveness of the model within specific scenarios. On the other hand, these specific scenarios are associated with high-frequency or critical operation phases in real life. The resultant model can provide direct support for the formulation of maintenance plans and the early warning of failures under the corresponding scenarios. Moreover, it can also serve as a crucial module for the construction of a comprehensive prediction system.
# Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms
###### Abstract  
Anchor-bolt support operations are lengthy and conducted under harsh conditions, restricting the efficiency and safety of roadway excavation. To address these challenges, we developed an integrated solution combining mechanical structure optimization with control algorithms. Specifically, we designed a novel automated drilling system equipped with a robotic manipulator and an anchor-bolt magazine to handle modular hollow self-drilling anchor bolts, enabling automated support operations. To achieve precise docking in unmanned conditions, we employed an inner-loop fractional-order proportional-integral-derivative (FOPID) controller optimized by an improved particle swarm optimization (ILPSO) algorithm. Additionally, robust control based on Ho control theory was introduced to ensure reliable system performance under disturbances and model uncertainties. Simulation results indicate that the ILPSO-tuned FOPID controller significantly outperforms conventional controllers in dynamic response accuracy; frequency-domain analysis further confirms that the Ho control approach enhances system stability. Collectively, these results provide a theoretical basis for advancing automated mining technologies.  
anchor bolts; automated drilling rig; fractional-order PID control; improved particle swarm optimization; Ho robust control +
Footnote †: journal: Applied sciences  
1
Footnote 1: Key Laboratory of In-Situ Property-Improving for Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China; {jingkai000126.com (J.L.); [EMAIL_ADDRESS] (J.Z.)
###### Abstract  
Monitoring of fire-related changes is essential to understand vegetation dynamics in the medium and long term. Remote sensing time series allows estimating biophysical variables of terrestrial vegetation and interference by extreme fires. This research evaluated fire recurrence in the Amazon and Cerrado regions, using Moderate Resolution Imaging Spectroradiometer (MODIS) albedo time series, enhanced vegetation index (EVI), gross primary productivity (GPP), and surface temperature. The annual aggregated time series (AAT) method recognized each pixel's slope trend in the 2001-2016 period and its statistical significance. A comparison of time trends of EVI, GPP, and surface temperature with total fire recurrence indicates that time trends in vegetation are highly affected by high fire recurrence scenarios (R\({}^{2}\) between 0.52 and 0.90). The fire recurrence and the albedo's persistent changes do not have a consistent relationship. Areas with the biggest evaluated changes may increase up to 0.25 Kelvin/Year at surface temperature and decrease up to \(-0.012\) EVI/year in vegetation index. Although savannas are resistant to low severity fires, fire regime and forest structure changes tend to make vegetation more vulnerable to wildfires, reducing their regeneration capacity. In the Amazon area, protection of forests in conservation units and indigenous lands helped in the low occurrence of fires in these sensitive areas, resulting in positive vegetation index trends.  
fire recurrence; Amazon; Cerrado; vegetation biophysics; MODIS; Landsat +
Footnote †: journal: Geo-Inform
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 4 Discussion
### Advantages and Limitations of UAV in River Feature Recognition  
The advancement in UAV has paved the way for vertical dimension formation density and position referencing [42]. UAVs are also reliable for fluvial geomorphic study and reduce the error in observation arising in field study [43]. UAVs ensure less time consumption in data acquisition. They also have an upper hand in capturing real-time series data  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Feature** & **DSC** & **Jaccard Index** \\ \hline \multirow{2}{*}{S1} & Flow Area & 97.86 & 94.36 \\  & Vegetation & 94.91 & 92.20 \\ \hline \multirow{2}{*}{S2} & Flow Area & 96.22 & 93.58 \\  & Vegetation & 92.51 & 91.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative evaluation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Feature** & **White Pixels\% (ROI)** & **Area (m\({}^{2}\))** \\ \hline \multirow{2}{*}{S1} & Flow Area & 39.441 & 4437.1 \\  & Vegetation & 19.513 & 2195.2 \\ \hline \multirow{2}{*}{S2} & Flow Area & 39.531 & 4447.2 \\  & Vegetation & 25.314 & 2847.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Image segmentation results.  
that we use in capturing flood events and landslides. Also, UAV technology compiles different data models produced in a single flight. Apart from being less expensive, UAV mapping provides equivalent spatial information and better operational features than airborne LIDAR [44]. However, the optical data acquisition of UAVs cannot pierce through vegetation cover and terrain features [45]. Another limitation of UAV technology is that the region with extensively low vegetation is unclassified in removal from the surface [46], but point cloud acquisition from the ground under the vegetation is possible using UAV LIDAR [47]. Another disadvantage of using small UAV platforms is that it limits the payload, while implementing drone-based imaging to capture temporal images of the same cross-sections is a tedious job.
## 5 Results and Discussion
### Emission Characteristics
#### 5.3.2 NOx and HC Emissions  
The formation of NOx depends largely on the temperature inside the cylinder, the concentration of oxygen in the fuel, and the residence time of the reaction. Figure 11a shows the emissions of nitrogen oxides (NOx) for all test fuels. Higher NOx formations were produced by increasing the percentage of biodiesel in the fuel. Blends with sunflower oil PBSSB5 and PB10 SB5 had the highest NOx emission rates. On average, it was observed that biodiesel PB5, PB10, PB5 SB5, and PB5 SB10 produce 5.46%, 9.81%, 14.32%, and 18.3% more NOx emissions compared to diesel, respectively. The above results can be explained by considering the greater presence of oxygen in the biodiesel blends, mainly in the PB5 SB5 and PB10 SB5, and the higher temperatures reached by the biodiesel, as shown in Figure 8.  
Figure 10: (a) CO and (b) CO\({}_{2}\) emissions for fuels tested.  
Figure 11b shows the variation of HC emissions with respect to engine operating modes. HC emissions decrease with the increase in the percentage of biodiesel in the fuel. Hydrocarbons are the product of incomplete combustion of fuel. Therefore, more efficient combustion allows for reducing this type of emissions. In this case, the oxygen content of the biodiesel blends allows the combustion process to be improved. Therefore, it results in a lower amount of hydrocarbon emissions. On average, the HC emissions for PB5, PB10, PB5 SB5, and PB10 SB5 were 4.8%, 9.72%, 15.58% and 19.5% lower compared to diesel, respectively.
# \(HM^{3}a1D\): Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model
## 3 Background
### Hidden Markov Model  
Markov models [29] are state machines in which the current state depends on previous states statistically. In a first order Markov model, the next state depends on only the current state. A hidden Markov model (HMM) is a statistical Markov model in which the states are not observable directly so they are called hidden states. In summary and formally, HMM includes the following elements, the set of \(N\) distinct states: \(S=\{s_{1},s_{2},\ldots,s_{N}\}\); the set of \(M\) distinct observations in each state: \(V=\{v_{1},v_{2},\ldots,v_{M}\}\); \(A\): state transition probabilities, \(A=[a_{rj}]\) where \(a_{rj}=P(q_{t}+1=S_{j}|q_{t}=S_{r})\); \(B\): observation probabilities, \(B=[b_{j}(m)]\) where \(b_{j}(m)=P(O_{t}=v_{m}|q_{t}=S_{j})\); \(\pi\): initial state probabilities; and \(\pi=[\pi_{r}]\) where \(\pi_{r}=P(q_{1}=S_{r})\). Recall that \(q_{t}\) denotes the state at time \(t\) where \(t=1,2,...,N\). Thus, an HMM, \(\lambda=(A,B,\pi)\), is defined by \(A,B\), and \(\pi\) (and implicitly by \(M\) and \(N\) dimensions). For a set of observation sequences \(\mathcal{X}=\{O^{l}\}_{l}\), an HMM \(\lambda\) is trained such that \(P(\mathcal{X}|\lambda)\), the probability \(\mathcal{X}\) generated from \(\lambda\) is maximized. Then, for any given observation sequence \(O^{l}\), and the learned model \(\lambda\), HMM by applying a forward algorithm finds the corresponding state sequence \(Q=\{q_{1},q_{2},\ldots,q_{T}\}\) of \(O^{l}\), such that it maximizes \(P(O^{l}|\lambda)\).  
The last issue is HMM topology, which is defined by the number of the states and their connections. Three kinds of general topologies can be found: \"Bakis topology\", \"left-right topology\" and \"fully connected topology\" [30]. In Bakis topology, the rule is: \(a_{rj}>0\) only for \(j=r\) or \(j=r+1\). In the left-right topology, the rule is: \(a_{rj}>0\) for \(j\geq r\); and in the fully connected topology the rule is: \(a_{rj}>0\) for any \(r,j\). The third topology is also called the ergodic model. Note that the HMM topology can be serial or parallel. In a parallel mode, a sequence of states is parallelized with another sequence of states.
## 4 Discussion  
### Temporal Evolutions of Polarimetric Observables  
For crop growth monitoring, temporal evolutions of all 27 polarimetric observables extracted from the multi-temporal RADARSAT-2 data for different crop types were analyzed. The backscattering coefficients in cross-pol (HV), Pauli second channel (HH-VV), and HV/VV ratio were found to be the most sensitive observables to crop growth. The reason can be attributed to the sensitivity of these parameters to crop structure and its associated scattering mechanisms. The results are in line with previous studies [10; 26; 27]. The crop structural features were also well characterized by their unique scattering patterns using parameters derived from polarimetric decompositions (i.e., FD, CP, and ND). The difference in temporal evolution patterns of scattering mechanisms among crops provided useful information for crop classification, which have been frequently used in crop growth monitoring and classification in previous reports [7; 17; 27; 38; 39; 41]. Among the polarimetric complex correlation parameters, the correlation coefficient \(\mathrm{p}_{\mathrm{HHVV}}\) showed the highest sensitivity to crop growth, which has been proven useful for identification of growth stages [31; 36]. The RVI also varied dramatically following the course of crop development, which has been successfully introduced for crop growth monitoring with full, dual and compact polarimetric SAR data [21; 51; 59].
## 4 Intra-Layer Contact Plan Design
### Intra-Layer Contact Plan for HAP Layer  
In the mesh network constructed by HAPs, the contact can only be established continuously between neighboring nodes. To improve the transfer efficiency within HAP layer as much as possible, a modified Dijkstra algorithm is utilized to minimize the number of hops in data transfer. Due to the limited number of transponders, the sequence of the contacts established within the layer may affect transfer efficiency. For example, if the lifetime of the data is short, and if the system prioritizes the establishment of contacts far from the destination node, the closer contacts cannot be established because of the occupation of the transponder, which may make the data unable to reach the destination node on time. Therefore, in the modified Dijkstra algorithm, the contacts are established in the order of hop count from small to large. In other words, the algorithm preferentially delivers the data that close to the destination node.  
In addition, in the case of a limited number of transponders, if data is delivered by its own unique shortest path, this may break the constraints and further increase the congestion within the layer. In fact, since the model evaluates the distance from the destination node based on the hop count, each node may have more than one shortest path. Under this consideration, multiple shortest paths are reserved in the modified Dijkstra algorithm. When the data transmission is congested in HAP layer, the algorithm will traverse the next hop nodes of all the shortest paths of the congested node, select an available node and then deliver the data. If all the next nodes are occupied, the data will not be delivered at current state.