esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 3 Proposed Protocol (AOMDV-ER)
### Optimal Route Discovery Algorithm  
The algorithm is based on residual energy and geographical location which uses the recoil technique. The link cost of each route is evaluated on the basis of node distance from SD line. The least value is assigned to the node nearer to the SD line. This optimal route discovery algorithm is depicted in Algorithm 1.  
```
begin  R\(\leftarrow\)set of routes  N\(\leftarrow\)set of nodes  RE\(\leftarrow\)residual energy (of set ofroutes) greater than threshold value  MD\(\leftarrow\)single optimal route (from the set of RE) having minimum distance from SD-line.  R:= {r1,r2,r3\(\ldots\) rn}  RE\(\leftarrow\)threshold_energy_path(R)  MD\(\leftarrow\)argmin(f(RE))  flag\(\leftarrow\)transmit(MD,pkt)  if flag = 0 then  Coordinated Recoiled Node()  endif  end
```  
**Algorithm 1**Optimal Route Discovery  
Given S: source and D: (Figure 1(a,b)) destination. \((\mathrm{S}\to\mathrm{B}\to\mathrm{D})(\mathrm{S}\to\mathrm{C}\to\mathrm{D})\)and\((\mathrm{S}\to\mathrm{A}\to\mathrm{D})\) are probable routes from S to D. The minimum consumes energy or minimum route cost is \((\mathrm{S}\to\mathrm{B}\to\mathrm{D})\). However, the optimized path is based on the max-min metric, which is \((\mathrm{S}\to\mathrm{A}\to\mathrm{D})\). Figure 3, describes the optimal route on the basis of distance from the line joining S and D. The minimum summation of perpendicular distance carrying path would be the optimal path.  
_Electronics_**2018**, 7, 56  
Figure 3: Minimum distance from SD line.  
Figure 2: (a) Min-power route; (b) Max-Min Energy Distance Path.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
###### Abstract  
The rapid growth of the Internet of Things (IoT) and the massive propagation of wireless technologies has revealed recent opportunities for development in various domains of real life, such as smart cities and E-Health applications. A slight defense against different forms of attack is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed in transmission during data packet routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. In this research, we propose a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. This mainly detects, mitigates, and isolates attacks in RPL networks. The detection is based on a comparison of the rank strategy. The mitigation uses threshold and attack status tables, and the isolation adds them to a blacklist table and alerts nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies, such as Standard RPL with Attacks, Sink-Based Intrusion Detection Systems (SBIDS), and RPL+Shield. The analysis results showed that the SRPL-RP achieved significant improvements with a Packet Delivery Ratio (PDR) of 98.48%, a control message value of 991 packets/s, and an average energy consumption of 1231.75 joules. SRPL-RP provided a better accuracy rate of 98.30% under the attacks.  
IoT; security; RPL; rank attack; version number attack
# Research on Defect Detection of Bare Film in Landfills Based on a Temperature Spectrum Model
## 3 Defect Detection Experiment  
### Experimental Materials and Performance Parameters  
An infrared thermal imager (Fluke Ts10 9 Hz Thermal Imager, Fluke, Everett, WA, USA) was used; accuracy is \(\pm\)2 \({}^{\circ}\)C, the working temperature is \(-\)10 to +50 \({}^{\circ}\)C, resolution is 80 \(\times\) 60, and the infrared thermal imager working band is 7.5-14 \(\upmu\)m to meet accuracy claim. HDPE film thickness is 0.15 cm and 0.20 cm, with an emissivity of 0.93 and a transmittance of less than 5%, puncture strength is 249 N, fracture strength is 53 N, thermal conductivity is 0.527 W/(m\(\cdot\)k), and the average thermal conductivity of the wet soil layer is 1.052 W/(m\(\cdot\)k).
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline
**Inherited Disorders** & **Ciliary Abnormalities** \\ \hline Primary ciliary dyskinesia (PCD) & Dynein arm defects [5] \\  & Reduced ciliary beat frequency [11] \\  & Uncoordinated motion [12] \\  & Immotile cilia [13] \\  & Windscreen wiper/stiff planar/rotational motion [12; 13] \\ Cystic fibrosis (CF) & Regions of missing cilia [10] \\  & Cilia with missing inner dynein arms [10] \\  & Abnormal numbers and location of microtubule doublets [10] \\ \hline
**Acquired Disorders** & \\ \hline Cigarette Smoking & Missing cilia [14] \\  & Shorter cilia [15; 16] \\  & Reduced ciliary beat frequency [10] \\ Environmental Pollutants & Shorter cilia [17] \\  & Regions of cilia loss [17] \\  & Disruption of trachea cilia structure [18] \\  & Reduced ciliary beat frequency [19] \\ Asthma & Reduced ciliary beat frequency [9] \\  & Abnormal ciliary beating in bronchial epithelial [20] \\ Chronic obstructive pulmonary disease (COPD) & Shorter cilia [21] \\  & Impaired ciliary beating [22] \\ \hline \end{tabular}
\end{table} 
Table 1: Ciliary abnormalities due to inherited and acquired disorders. experimental findings [30] argued that if PCL was stationary, inhaled hydrophilic toxicants might remain in the lung for extended periods of time. Hence, axial PCL transport is needed in order to efficiently clear the lung from this class of toxic substances. Overall, by analysing the transport behaviour of PCL, the present 3D model not only could provide valuable insights into muco-ciliary clearance, the results of this parametric study could be useful for designing lab-on-a-chip microfluidic devices which are used for pumping and/or mixing purposes [31; 32; 33]. The paper is organized as follows: Section 2--details of the numerical method are given; Section 3--the results and relevant discussions are presented in terms of PLC flow rate, velocity changes above the tips of cilia, and alterations in shear stress at the epithelium; and Section 4--conclusions for the results are provided, and directions for future research are suggested.
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
## 2 Three Types of Fiber Mode  
### Cylindrical Vector Modes  
Cylindrical vector (CV) modes, whose polarization varies based on the spatial location, show many unique properties and applications compared with the conditional plane light waves. As the eigenmodes in fiber, any mode field in fiber can be regarded as the superposition of CV modes, with different amplitudes and phase differences. CV modes are divided into different azimuthal orders. Each azimuthal order mode is composed of two or four degenerated modes, whose propagation constants are almost the same. For general step index fibers, the zeroth azimuthal order mode, which is only composed of two degenerated modes, namely, \(HE_{1,m}^{even}\) and \(HE_{1,m}^{odd}\) mode. The first order mode is composed of \(TM_{0,m}\), \(TE_{0,m}\), \(HE_{2,m}^{even}\), and \(HE_{2,m}^{odd}\) mode. The \(l\)th (\(l>1\)) order mode is composed of  
Figure 1: A summary of typical OAM generation techniques based on spatial components (spiral phase plate [18], spatial light modulator [16], metamaterial [24], micro-ring resonator [30], and computer-generated holograms [22]) [16; 17; 18; 19; 20]. Reprinted with permission from [18], copyright 2010 Springer Nature; [16], copyright 2004 The Optical Society; [24], copyright 2011 AAAS; [30], copyright 2012 AAAS; [22], copyright 2011 AAAS.  
\(EH_{i-1,m}^{even}\), \(EH_{i-1,m}^{odd}\), \(HE_{i+1,m}^{even}\), and \(HE_{i+1,m}^{odd}\). Here, \(m\) is the radial order, denoting the number of noaxial radial nodes of the mode. In general, researches are simply conducted on the properties of radial order \(m=1\). We use the default \(m=1\) in this paper unless indicated. Figure 2a,b give the propagating properties and time average intensity pattern of two typical CV modes, \(TE_{01}\) and \(TM_{01}\). As shown in Figure 2a,b, the polarization state of each point is linearly polarized, and the polarized direction of each point is related to the spatial angle. In the center of CV mode, there is a so-called \"polarized singularity\", where the intensity vanishes in the area. This is because the radial distribution is defined by the cylindrical function with higher azimuthal order (\(l>0\)), such as the Bessel function, Laguerre Gaussian (LG) function, and Hermite-Gaussian (HG) with non-zeroth orders. These functions with non-zeroth orders are zero at the center point. The last column are the time average patterns during an integer-number period. Because the response frequency of a detective device is much slower than the frequency of light, what we detect using the detective devices are the time average intensity patterns during countless periods of light, which is close to the integer-period time average patterns. The integer-period time average intensity patterns of CV modes are doughnut shaped for a higher azimuthal order mode (and biscuit shaped for zeroth order mode).
## 3 Technologies for Treating Landfill Leachate
### Biological Treatment Processes
#### 3.3.2 Anaerobic Treatment Processes  
Anaerobic biotechnologies have emerged as cornerstone processes in advanced landfill leachate treatment, with two predominant pathways demonstrating exceptional nitrogen removal capabilities: anaerobic ammonium oxidation (Anammox) and heterotrophic denitrification. [PERSON] (2014) engineered a continuous plug-flow multistage A/O system combining first-stage partial nitrification (DO 0.5-1.0 mg/L, pH 7.8-8.2) with anaerobic ammonium oxidation (PNA) and fixed-film activated sludge. During long-term operation (HRT = 32 h, 35 \(\pm\) 1 \({}^{\circ}\)C), the system achieved 98.1% inorganic nitrogen removal and 52.9% COD reduction over 400 days (days 301-405), with PNA contributing 94.3-95.0% nitrogen elimination through selective enrichment of anammox bacteria (Candidatus Brocadia abundance: 15-18%, VSS/TSS ratio \(>\) 0.85). [PERSON] (2014) investigated the partial nitrification (PN), carbon oxidation, denitrification, and anaerobic ammonia oxidation performance of a continuous tower biofiliter (TBFR) with a large height-to-diameter ratio of 13 for treatment of real landfill leachate. The average removal of TN and COD by the TBFR reached 87.47% and 88.45% at the maximum nitrogen loading rate and organic loading rate (OLR) of 1.31 kg-N/(m\({}^{3}\)-d) and 4.76 kg/(m\({}^{3}\)-d), respectively.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 4 Discussion
### Comparison with Previous Models  
Our findings extend--and partly refine--the trends reported in earlier gear dynamic studies. [PERSON] et al. (2014) [5] reproduced shaft order tones by injecting analytically prescribed indexing errors into an otherwise ideal spur gear model; our simulations confirm this mechanism but show that the amplitude of the 16.66 Hz component nearly doubles once real flank topologies are added. [PERSON] et al. (2017) [11] and later [PERSON] et al. (2024) [17] predicted that pitch error produces mesh frequency side-bands; the present model recreates the same \(\pm\)16.66 Hz skirts around 383 Hz, demonstrating that the measured pitch table is correctly mapped into the multibody solver. Unlike those studies, which relied on nominal tooth geometry or average micro-modifications, the current work imports a full, tooth-resolved surface scan, revealing a non-linear reinforcement between microgeometry and indexing error that was not captured previously. This added fidelity explains why our peak-to-peak TE rises more sharply than in models that treat the two error families in isolation, and it underscores the need to include both effects when setting NVH-critical tolerances for high-speed e-drive gears.
## 2 Literature Review
### Remaining Challenges and Research Gaps  
Despite these advances, several challenges remain. First, there is still a lack of sufficiently large mixed-language datasets, limiting the ability of ASR models to generalize effectively to real-world applications. Second, traditional ASR systems require significant manual effort, including the creation of language-specific components, which reduces scalability and efficiency. Third, current models are not optimized to handle the dynamic mixed-language patterns found in spontaneous speech, which is common in multilingual communities. Furthermore, while E2E models have shown potential, there is still a need for more comprehensive solutions that fully automate mixed-language ASR without relying on manual intervention.  
Our study aims to address these gaps in mixed-language, particularly in bilingual ASR systems, and further optimize them for real-world applications. Compared to the SEB-ASR system, existing bilingual speech recognition systems exhibit several differences and similarities. SEB-ASR is specifically designed for Mandarin-English mixed-language scenarios with a self-evaluation mechanism that enhances its adaptability and robustness. Traditional systems often lack such self-assessment features, relying heavily on fixed evaluation pipelines. While most systems use end-to-end models or hybrid hidden Markov model-deep neural network (HMM-DNN) frameworks, SEB-ASR incorporates tailored optimization for bilingual and mixed-language environments.
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 3 Basic Concepts for Virtual Belt Algorithm
### Loop Add and Loop Subtract  
The mathematic functions loop add (represented by \(+_{L}\)) and loop subtract (represented by \(-_{L}\)) are defined for the calculation of integrators. Based on these functions, the collisions among VGs can be discussed in a simple way, which will be demonstrated in the following section. Consider two integrators \(i\) and \(j\) (\(1\leq i,j\leq L\), \(L\) is an integrator and \(2\leq L\)). The function of loop add is defined as  
Figure 6: Motion of virtual grid.  
Figure 7: Concept and motion of virtual belt.  
\[i+_{L}j=\begin{cases}i+j,&(i+j)\leq L\\ i+j-L,&L<(i+j)\end{cases}. \tag{1}\]  
For example, one has \(3+_{3}2=3+2-3=2\).  
Similarly, the function of loop subtract is defined as  
\[i-_{L}j=\begin{cases}i-j,&(i-j)\geq 1\\ i-j+L,&(i-j)<1\end{cases}. \tag{2}\]  
For instance, one has \(1-_{3}2=1-2+3=2\).
## 2 Materials and Methods
### Materials
#### 2.1.3 GLASS LAI Data  
FPAR is a critical component of the MOD17A2H GPP algorithm. In this study, we introduced the GLASS LAI product to assess the influences of FPAR on the effectiveness of MO17A2H GPP. In previous studies [37; 38; 39], it had been proven that the GLASS LAI product could provide better estimates of LAI than existing MODIS and CYCLOPES LAI datasets. For each EC flux tower site, the GLASS LAI subsets (eight-day interval at a spatial resolution of 0.05\({}^{\circ}\)\(\times\)0.05\({}^{\circ}\)) during 2001-2014 were downloaded from the Generation and Applications of Global Products of Essential Land Variables website [40]. We interpolated the GLASS LAI product down to the 500-m MOD15A2H pixel spatial resolution using a spatial nonlinear interpolation method (the same as the GMAO data). Based on the Beer's law approach, we converted GLASS LAI (m\({}^{2}\)\(\cdot\)m\({}^{-2}\)) to GLASS FPAR by following equation:  
\[\rm{FPAR}=1-e^{(-K)\times LAI} \tag{4}\]  
where K is the light extinction coefficient of canopy, which is set to 0.5 [41].
## 2 Related Work  
MonoSLAM [21], the first real-time visual SLAM system, was proposed by [PERSON] et al. It is based on a monocular camera; it takes the camera pose and the coordinates of three-dimensional points as state variables, uses an extended Kalman filter to update its state estimation, reduces the uncertainty of state variables through multiple observations, and converges them. This method is able to meet the requirement of real-time operation when the dimension of the state variable is small. Parallel tracking and mapping (PTAM) [22] was the first monocular SLAM system based on the concept of the keyframe, and it was applied in augmented reality. It abandons the filtering method and uses nonlinear optimization to estimate the state variables. It uses bundle adjustment (BA) on the keyframe to reduce the error accumulation. In addition, PTAM creatively divides camera tracking and map optimization into two parallel threads at the same time, which lays the foundation for the establishment of the classic SLAM framework. On the basis of PTAM, [PERSON] et al. proposed ORB-SLAM [23] and then extended it to stereo and RGB-D cameras, namely ORB-SLAM2 [24]. ORB-SLAM2 is mainly divided into three threads: tracking, local mapping, and loop closure. It uses ORB feature descriptors for data association and achieves a good balance between computing complexity and feature robustness to rotations, illumination changes, and certain scale changes. Its optimization is divided into local and global optimization. Local optimization optimizes the pose and feature points in a series of keyframe sets related to the current keyframe. When the loop is detected, it uses the essential graph to optimize the keyframe pose. All the optimization steps are realized by the g2 oframework [25].  
Large scale direct monocular SLAM (LSD-SLAM) [26] uses a direct image alignment method and a filter based semi-dense depth map estimation method to obtain high precision pose estimation and reconstruct a large scale 3D map of the environment in real time, including a keyframe pose map and semi-dense depth map. It can run in real time on a regular CPU. The main contribution of LSD-SLAM was the construction of a large scale direct monocular SLAM framework, the proposal of a new perceptual scale image alignment algorithm to directly estimate the pose translation between keyframes, and consideration of the uncertainty of depth information in the process of pose tracking. The FAB-MAP [27] method was used to detect loop closure. In addition, [PERSON] and others extended the monocular camera to the stereo camera and constructed stereo LSD-SLAM [28]. Direct sparse odometry (DSO) [29; 30] is a direct method of visual odometry based on sparse points. The main innovation of this method is that it considers the camera's photometric calibration model [31] and optimizes the camera's internal and external parameters, the inverse depth of keypoints, and photometric parameters as state variables simultaneously with improved estimation accuracy. Because it only uses sparse feature points, it has high performance computing efficiency.
## 3 Methodology and Data Description
### Traffic Flow Models for Congestion and Travel Time Analysis  
Traffic flow models simulate the movement of vehicles on the road network, assessing congestion levels and travel times to ensure timely delivery of goods.  
Formulation: A macroscopic traffic flow model is employed, using variables such as vehicle density, flow, and speed to model congestion on major transport routes. The Fundamental Diagram of Traffic Flow is used to relate these variables and predict traffic conditions under different scenarios.  
Steps: Real-time traffic data are collected to estimate current vehicle flow and congestion levels. The model is then used to simulate different traffic patterns, enabling predictions of travel times and the identification of bottlenecks that could delay cargo movement.
###### Abstract  
The aim of this study was to identify the influence of reservoir depth on reservoir rock mass breakdown pressure and the influence of reservoir depth and injecting fluid pressure on the flow ability of reservoirs before and after the hydraulic fracturing process. A series of fracturing tests was conducted under a range of confining pressures (1, 3, 5 and 7 MPa) to simulate various depths. In addition, permeability tests were conducted on intact and fractured samples under 1 and 7 MPa confining pressures to determine the flow characteristic variations upon fracturing of the reservoir, depending on the reservoir depth and injecting fluid pressure. N\({}_{2}\) permeability was tested under a series of confining pressures (5, 10, 15, 20 and 25 MPa) and injection pressures (1-10 MPa). According to the results, shale reservoir flow ability for gas movement may reduce with increasing injection pressure and reservoir depth, due to the Klinkenberg phenomenon and pore structure shrinkage, respectively. The breakdown pressure of the reservoir rock linearly increases with increasing reservoir depth (confining pressure). Interestingly, 81% permeability reduction was observed in the fractured rock mass due to high (25 MPa) confinement, which shows the importance of proppants in the fracturing process.  
hydraulic fracturing; siltstone; breakdown pressure; fracture permeability; shale gas; unconventional oil and gas +
Footnote †: journal: Applied Physics Letters  
1
## 4 Analysis of the Properties of LCPSO  
### Metrics and Hypothesis  
While we already illustrated the behavior of the LCPSO algorithm earlier through simulation [40], these properties were only shown intuitively. We now wish to give some mathematical basis to this intuition. We make the following assumptions that will be valid throughout the mathematical analysis of this section:  
* Communication range is unlimited. As a result, the local-best attractor \(\mathbf{p}_{i}^{l}(t)\) is the best tracker position of the swarm \(\mathbf{p}^{\delta}(t)\).
* We focus our efforts on the APF analysis, and to ease the analysis we set \(c_{0}\) to \(0\). So the speed vector \(\mathbf{v}_{i}(t+\Delta t)\) is updated only with the gradient descent of the potential field equation.
* The target's behavior is not known from the swarm's point of view and can be dynamic. Tracker \(i\) measures \(f(\mathbf{p}_{i},t)\) and adjusts its local-best position \(\mathbf{p}_{i}^{l}(t)\) as a function of maximum measurement of the neighborhood. Since the communication range is small, we make the hypothesis that information exchange is instantaneous between the trackers and is limited to their position in absolute coordinates and their measurements, without noise.  
To illustrate the inter-agent distance between nearest neighbors in the swarm, we introduce a new function, the _swarm spacing_\(\rho(N)\). We normalize this spacing by \(r_{eq}\), a parameter allowing us to control the inter-agent equilibrium distance when the swarm is stable, and the number of agents \(N\) :  
\[\rho(N)=\frac{1}{r_{eq}\cdot N}\sum_{i=1}^{N}\min_{i\
eq j}d_{i,j}. \tag{13}\]  
Another important parameter is the surface area taken up by the swarm. For some cases, this parameter is critical to have a good tracking of the source. As we will see in Section 4.2, swarm formations have a convex hull inside a ball, and we will thus be able to represent this surface with only one parameter, the radius of this ball \(r_{max}\). Whatever the dimension, we have:  
\[r_{max}=\max_{i\in[1,\cdots,N]}\frac{\|\bar{\mathbf{p}}-\mathbf{p}_{i}\|}{r_{eq}}. \tag{14}\]  
With \(\bar{\mathbf{p}}\) the center of gravity of our swarm, represented by the following equation:  
\[\bar{\mathbf{p}}(t)=\frac{1}{N}\sum_{i=1}^{N}\mathbf{p}_{i}(t). \tag{15}\]  
Our swarm model has a lot of mathematical similarities with the models of [39]. So, for conciseness, the demonstrations too close from [39] are not given, and the other ones are in the Appendix A. Moreover, all the theorems presented in this paper are true regardless of the repulsive potential \(P^{r}(d_{i,j})\), as long as it respects Definition 1. Thus, we could imagine that, if the repellent potential is not suitable, we could test others that exist in the state of the art [38; 39].