esa-sceva/satcom-chunk-collection huggingface dataset:
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 4 Proposed Schemes
### A-Dbr  
In this section, the proposed scheme is discussed in detail. We have proposed the A-DBR scheme to cater to the problems of the void hole discussed in Section 2.1. In this scheme, when a void hole occurs, it adaptively adjusts its transmission range and forwards the data packet towards the sink node, as shown in Figure 2. With a view to lessen neighborhood of requests, every node collects the records on neighbor nodes upon receiving packets: \(neighbor_{requests}\) or \(ack\). In this way, every node can reap newer statistics about neighbor nodes dynamically. As shown in Figure 2, in the A-DBR scheme, when node \(S\) senses data within its vicinity, it gathers the data packet and forwards it to nodes \(n1\) and \(n2\). Moreover, when a void node occurs, this scheme adjusts its transmission range as illustrated in Figure 2 to find a forwarder node and continues forwarding of information without dropping the data packet. In this scheme, when a node receives a packet, it handles the data packet as follows:  
* If the node is not within the effective forwarding range of the preceding hop, it solely updates its neighbor table. In any other case, it enqueues information if it finds no report about the packets in queue.
* If the node is not within the forwarding area, it will wait for the subsequent packet, this means that the packet is inside the suppression area. In any other case, the node searches for nodes in the neighbor table.
* If the table is empty, instead of dropping a packet, the source node adjusts its transmission range and updates the neighbor table to avoid the void hole.
* After updating the neighbor table, it is going to send the packet if no different transmission of the data packet is heard towards the destination. It then updates the packet queue.
## 3 Results and Discussion
### Mechanical Characterization of TPMS Structures
#### 3.1.3 Energy Absorption Evaluation Indexes Comparison  
The cumulative absorbed energy of the porous TPMS structure in the effective compression stroke and the measurement indicators mentioned above were counted, and the obtained energy absorption indicators are shown in Table 5. According to Equation (3), the energy absorption efficiency curve of different porous TPMS structures is drawn as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TPMS** & _f (\%)_ & _m (g)_ & _S\({}_{\textit{eff}}\)_ **(mm)** & _ESR (\%)_ & _F\({}_{\textit{int}}\)_ **(kN)** & _SEA (J/g)_ \\ \hline Gyroid & 11.27 & 7.013 & 11.88 & 59.40 & 18.38 & 31.14 \\ Diamond & 11.10 & 7.643 & 11.61 & 58.05 & 17.58 & 26.84 \\ I-WP & 10.08 & 7.460 & 10.56 & 52.80 & 17.51 & 24.79 \\ Neovius & 9.19 & 8.687 & 10.03 & 50.15 & 17.03 & 19.66 \\ Primitive & 11.68 & 6.767 & 12.28 & 61.40 & 16.09 & 29.20 \\ Fischer-Koch S & 9.61 & 8.520 & 9.99 & 49.95 & 13.58 & 15.92 \\ F-RD & 7.16 & 9.257 & 3.38 & 16.90 & 22.89 & 8.36 \\ PMY & 8.40 & 10.213 & 2.82 & 14.10 & 24.59 & 6.79 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of compression energy absorption indexes of porous triply periodic minimal surface (TPMS) structure.  
Figure 11: Energy absorption efficiency curves of different porous triply periodic minimal surface (TPMS) structures.  
The comprehensive analysis of the energy absorption index of the porous TPMS structure shows that the greater the stiffness of the porous TPMS structure, the greater the peak load and average load, and the unloading buffer effect is not good, but the _SEA_ value is often higher, and the unit structure mass can absorb relatively more energy. Combined with the compression performance of various porous TPMS structures, as shown in Figure 9, Gyroid and Primitive porous structures with protracted effective compression displacement, flat platform stage, and ideal specific energy absorption value are selected to fill the internal structure of the automobile energy-absorbing box, which is expected to realize the lightweight design of the automobile and meet the requirements for passive energy-absorbing structures.
# A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller
## 3 Background
### Renyi Entropy  
In real-time networks, attack traffic does not always exist, but tends to be stable from scratch. Renyi entropy is \(\alpha\) order generalized entropy that is proposed in phase space and it is a common generalized entropy [26]. Before the attack begins, the high probability events in the data center network are normal traffic accesses, when attack occurs, high probability decreases. With the increase of attack rate, the probability of L-DDoS attack increases, and the high probability event that eventually transits to the data center network is the L-DDoS attack. In this transition process, there is a certain order of Renyi entropy which makes the difference between normal traffic and attack traffic maximum. The following is the definition of Renyi entropy:  
**Definition 1**.: _Renyi entropy is defined as: This is an example of an equation:_  
\[H_{\alpha}(x)=\frac{1}{1-\alpha}\log\Biggl{(}\sum\limits_{i=1}^{n}p_{i}^{ \alpha}\Biggr{)} \tag{1}\]  
_where \(p_{i}\) is the probability of the random variable \(x_{i}\), and \(\sum\limits_{i=1}^{n}p_{i}=1\)._  
Renyi entropy has several entropy forms that are used frequently, they are maximum entropy, minimum entropy, and Shannon entropy.  
* Maximum entropy: when \(\alpha=0\) or \(p_{x_{1}}=p_{x_{2}}=\cdots=p_{x_{n}}\), a maximum entropy is obtained. The maximum entropy model is to use the weakest randomness of maximum entropy to obtain the model parameters at the highest time. Then the model parameters are obtained at the highest uncertain time. \[\max(H_{\alpha}(x)~{})=\log(n)\] (2)
* Minimum entropy: when \(\alpha\rightarrow\infty\), \(H_{\alpha}(x)\) converges to the minimum entropy. When the possible number of all events is \(b\), the probability of all events is shown to be \(1/2^{-b}\) in the minimum entropy. Minimum entropy is the smallest of the entropy family and is the most lightweight method to measure unpredictability, it pays an important role in theoretical computer science. \[H_{\infty}(\alpha)~{}=\min(-\log(p_{i}))=\max(\log(p_{i}))=-\log(\max(p_{i}))\] (3)
* Shannon entropy: when \(\alpha\to 1\), \(H_{\alpha}(x)\) converges to Shannon entropy. \[\underset{\alpha\to 1}{\lim}H_{x}(\alpha)=\underset{\alpha\to 1}{\lim}\frac{1}{1- \alpha}\log_{2}\Biggl{(}\sum\limits_{i=1}^{n}p_{i}^{\alpha}\Biggr{)}= \underset{\alpha\to 1}{\lim}\frac{\sum\limits_{i=1}^{n}p_{i}^{\alpha}\ln(p_{i})}{ \sum\limits_{i=1}^{n}p_{i}^{\alpha}\ln(2)}\] \[\underset{i=1}{\lim}p_{i}=1\text{, so:}\] \[\underset{\alpha\to 1}{\lim}H_{x}(\alpha)=-\sum\limits_{i=1}^{n}p_{i}* \log_{2}(p_{i})\] (4)  
When a random variable obeys the uniform distribution, the entropy of all kinds of Renyi is the same, and the detection effect has few differences. However, in the case of uneven distribution, the entropy of all kinds of Renyi is quite different, which can reflect the unpredictability in the process of detection.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Edge Addition and Edge Deletion  
#### 3.2.1 Edge Deletion  
Let \(G\) be a graph and \(v,w\in V(G)\) be two vertices. For \(\{v,w\}\in E(G)\), we define by \(G-\{v,w\}\) the graph we obtain from \(G\) by deleting the edge \(\{v,w\}\), i.e.,  
\[G-\{v,w\}=(V(G),E(G)\setminus\{\{v,w\}\}).\]  
With _edge deletion_ defined, the following theorem shows that removing an edge from a graph decreases the width of the graph by at most one.  
Theorem 4: _For a graph \(G\) and two different vertices \(v,w\in V(G)\), it holds that_  
\[tw(G)-1 \leq tw(G-\{v,w\})\leq tw(G)\quad\text{and}\] \[pw(G)-1 \leq pw(G-\{v,w\})\leq pw(G).\]  
Proof: The upper bound follows immediately because a tree-decomposition (path-decomposition) for \(G\) is also a tree-decomposition (path-decomposition) for \(G-\{v,w\}\).  
As \(G\) can be obtained from \(G-\{v,w\}\) by adding edge \(\{v,w\}\), the lower bound follows from the upper bound of Theorem 5.
## 3 Results and Discussion  
### Cytotoxic Activity of EO against Lung A549 and Colon HCT-116 Cells  
The cytotoxic activity of the eight EOs was assessed by exposing A549 and HCT-116 cancer cells to increasing EO concentrations (0-500 \(\mu\)L/L) and calculating their IC\({}_{50}\) values (Table S1). _L. alba_ (chemotype dihydrocarvone) essential oil (LaDEO) and CnEO were the two most active EO in both cell lines, inhibiting cell growth in a concentration-dependent manner (IC\({}_{50}\) LaDEO = 275 and 145 \(\mu\)L/L; IC\({}_{50}\)CnEO = 205 and 200 \(\mu\)L/L in A549 and HCT-116 cells, respectively). Except for _L. alba_ chemotypes linalool (IC\({}_{50}\) = 400 \(\mu\)L/L) and _globulus_ (IC\({}_{50}\) = 500 \(\mu\)L/L) in HCT-116 cells, IC\({}_{50}\) values were >500 \(\mu\)L/L in all cases. Based on these results, LaDEO and CnEO were selected for further studies.
# Assessing Voided Reinforced Concrete by Numerical Modelling of Impact-Generated Rayleigh Waves
## 5 Feasibility of Modelling Framework  
The testing method ensures a non-destructive approach, allowing multiple inspections of concrete structures without causing physical harm. A key advantage of this method is its ability to safely evaluate concrete structures in place. The examination reveals a correlation between void characteristics and Rayleigh wave property measurements at the 5 kHz velocity index. These correlations indicate that the method can both identify voids in concrete structures and determine their fundamental characteristics. The experimental system utilises basic equipment, enhancing its accessibility for field assessments compared to more complex testing methods. Additionally, the procedure relies on surface-based measurements, enabling rapid inspection of large concrete surfaces. The use of two-dimensional modelling improves computational efficiency, particularly during initial assessments or variable examination processes, compared to the complexity of full three-dimensional modelling.
## 3 Pose Optimization Based on Point-Line Features
### IMU Data Fusion Mechanism  
To enhance the robustness of the system in complex mining environments, this paper improves the visual-inertial odometry (VIO) framework of the ORB-SLAM3, focusing on optimizing the fusion mechanism between IMU data and visual data. Specific improvements include an adaptive weight-allocation strategy, enhancements to the visual-inertial optimization framework, and a time synchronization compensation mechanism.  
In the visual-inertial fusion process, the weight allocation of IMU data significantly impacts system performance. This paper proposes an adaptive weight allocation method that dynamically adjusts the weight ratio between visual and inertial information based  
Figure 3: Line feature depth map.  
on the current camera motion state. During rapid motion, such as severe vibration or high-speed movement, the weight of IMU data increases, with the weight coefficient raised to 0.7-0.8, to compensate for the feature loss caused by motion blur in visual data. Conversely, during stationary or low-speed motion, the weight of IMU data reduces, with the weight coefficient lowered to 0.3-0.4, to minimize the impact of the IMU noise on the system's accuracy.  
This paper also makes targeted improvements to the visual-inertial optimization framework of the ORB-SLAM3. In the marginalization process, information matrix analysis is introduced to retain critical visual-inertial constraints, avoiding constraint loss due to marginalization. A sliding window strategy is used to dynamically adjust the optimization range, ensuring high accuracy during long-term operation. For global bundle adjustment (BA) optimization, IMU pre-integration residuals are integrated into the global bundle adjustment process, enabling multi-objective joint optimization of visual reprojection errors and inertial residuals.  
The joint objective function is minimized using nonlinear optimization methods, as follows:  
\[E_{total}\ =E_{visual}\ +w_{IMU}\cdot E_{IMU} \tag{11}\]  
where \(E_{visual}\) is the visual reprojection error, \(E_{IMU}\) is the IMU residual, and \(w_{IMU}\) is the adaptive weight coefficient.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 7 Future Directions
### Drone Antennas  
Drones can travel in three dimensions at various speeds. There is an urgent need to develop a new tracking antenna that can adapt to drone mobility and enable an ultra-high data rate transfer between drones and BSs. Since the accelerometer, gyro, and GPS data are used to track BSs, the antenna is tilted [144]. Another challenge is the limited area available for antennae on drones, particularly for small drones. Additional research and development must be prioritized in this regard.
## 3 Case Study: Structural Factor Analysis for Public Transportation Bus
### Data Description  
This study utilizes maintenance records collected from bus depots and seeks consultation from professional staffs of the public transportation operator.  
In practice, the public transportation operator has regular inspections and corrective maintenance to ensure the good conditions of vehicle operations. The maintenance database comprises aggregated repair information, with each maintenance activity documented in a structured format. The original record for each entry includes information columns. The most essential columns are occurrence date, replaced parts, and performed operations. Although the maintenance database is initially designed for other purposes, such as warranty analysis and tracking warehouse finances, it is also useful for data mining to find the most likely root failure causes. This study utilizes the maintenance data generated from January 2022 to March 2023. This study focuses specifically on the vehicle type with the most considerable quantity in operation. The raw data used for analysis consist of 1971 maintenance records. However, it is worth noting that the quality of the collected records limits their usability. One significant limitation is the absence of standardized documenting codes. Additionally, since much information is entered manually, there is a higher risk of human errors, such as typographical mistakes and missing values. These factors introduce potential inconsistencies and inaccuracies in the data. Therefore, the maintenance data source underwent several preprocessing steps to enhance its quality and usability.  
In addition to utilizing maintenance data as a data source, DMs are invited for interview and consultation. The invited DM group consists of five domain experts with extensive experience in fleet operation and maintenance, including one head of the depot, two senior engineers, and two technical engineers. The detail information of the DMs is presented in Table 3. Due to their extensive working experience, these experts are considered to have sufficient expertise to contribute meaningfully to this research. To gather insights from the DMs, this study employs questionnaire-based surveys to gather their perceptions and conducts three brainstorming meetings to achieve consensus for factor selection, evaluation, and result reasonability. The survey was conducted during the period from January 2023 to May 2023.
# Study on Shielding Effect of the Pile Group in a Soft-Soil Foundation
## 1 Introduction
Although several theories and models have been proposed, current research mainly focuses on describing the shielding phenomenon, with a limited exploration of the mechanisms behind the shielding effect. This limitation restricts its applicability in more complex construction environments and technologies. For the further enhancement and validation of the theoretical framework, it is essential to conduct experiments and numerical simulations. As a result, this paper presents a numerical study on the disturbance effects and shielding mechanism with a typical pile group, a soft soil system, using the case of a high-speed railway project in China. The finite difference program FLAC\({}^{\text{3D}}\) is utilized to simulate the piling process of a pile group, employing the cylindrical-spherical expansion method. Multiple response characteristics, such as displacements, stresses, and strains, were analyzed to systematically investigate the mechanisms underlying the shielding effects induced by pile group construction in the soft soil foundation. The research findings may provide valuable guidance for minimizing disturbances during pile group construction in soft-soil regions.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 6 Product Label Analysis
The method could detect regular and irregular texts with a complex background, non-uniform spacing and different texts in a single image. The more important contributions to the robustness of the model are the use of ResNet50 + FPN, the use of an algorithm to select the centring point in the text centre line instead of picking a random point, and finally, the introduction of a post-processing technique named Width-Height-based Bounding Box Reconstruction (WHBBR) to enclose the starting and the ending characters. The detected text is cropped and then sent to Selective Context Attentional Text Recognizer (SCATTER) [108], a text recognition model. The framework is computationally expensive during training, but can detect and recognise objects in real-time and efficiently during testing. Given the lack of adequate databases for this problem, the performance of each module was tested separately in different benchmark datasets for each task. Text detection and recognition tasks were evaluated in scene text datasets. The application of label detection and recognition techniques in the grocery context is relatively recent. In this sense, the evaluated methods are often based on approaches used in the detection and recognition of text in natural images. Therefore, with the intention of contributing to the development of package label analysis, we briefly review deep learning methods applied to scene text images. Traditional methods are excluded from this survey due to their limitations in more complex scenes and low-quality images. Similar to the previous section, the methods are organised into text detection, text recognition and end-to-end STR.
# A Coarse-Grained Molecular Model for Simulating Self-Healing of Bitumen
## 3 Results and Discussion
### Diffusion Coefficient  
From the microscopic perspective, the speed of molecular motion can be represented by the diffusion coefficient, and there is a certain relationship between the mean square displacement (MSD) and the diffusion coefficient [34]. Theoretically, if the time is long enough, the slope of the function of lg (MSD) and lg (Time) should be 1, at which time Einstein diffusion occurs, and the self-diffusion coefficient of bitumen component can be calculated only when this condition is met [35]. However, due to the short simulation time of molecular dynamics, as long as the function of MSD and time is linear, the diffusion coefficient can be calculated by the slope.  
The healing time is about 300 ps. For each component, we can calculate the slope of the MSD versus time function in the range of 150-295 ps and 300-410 ps (in these two time periods, the function of MSD and time is almost linear, which is shown in Figure 8) and convert them into the diffusion coefficient of each component molecule before and after healing. The diffusion rate of each component is asphaltenes < polar aromatics < saturates < aromatics. The diffusion rate of saturates is relatively close to that of aromatics, which is much greater than that of asphaltenes and polar aromatics. This phenomenon is consistent with colloidal theory [33]. According to this theory, asphaltene, which is at the core of the colloidal bitumen system, should have the lowest diffusivity. Saturates and aromatics, on the other hand, are in the shell and should have higher diffusivity.  
The diffusion coefficients of the four components decrease during healing. As shown in Figure 9, asphaltenes decreased by about 3.1%, polar aromatics by roughly 49.0%, saturates decreased by about 38.3%, and aromatics decreased by about 40.8%. The reason why the healing affects the bitumen molecular diffusion coefficient is that, as bitumen heals, the free movement space of bitumen molecules becomes less and less. This effect is more significant on polar aromatics, saturates, and aromatics. This indicates that the self-healing behavior of bitumen is the main reason for the molecular diffusion of the polar aromatics, saturates  
Figure 7: The density of the model in the process of self-healing.  
fraction, and aromatics fraction, while the self-healing behavior of bitumen hardly affects the molecular diffusion of asphaltenes. Saturates and aromatics appear to be the primary contributors to the self-healing behavior of bitumen since their diffusion coefficients are substantially greater than those of asphaltenes and polar aromatics.
## Materials and Methods
### Characterization of GR-SO\({}_{4}\) and GR-PEDOT  
The crystal structure of GR-SO\({}_{4}\) and GR-PEDOT was evaluated with a powder X-ray diffractometer (XRD; Bruker AXS D2 Phaser with LYNXEYETM detector; Bruker AXS GmbH, Karlsruhe, Germany). The XRD patterns were measured with scanning range from 3\({}^{\circ}\) to 70\({}^{\circ}\) as following condition (1 mm air-scattering slit, a 0.1 mm equatorial slit, and time step increments of 0.02\({}^{\circ}\) and 0.5 s per step). The crystallite size was calculated using [PERSON]'s equation (\(\tau\) = K\(\lambda\)/\(\beta\)cos\(\theta\), \(\tau\): crystallite size (A), K: dimensionless shape factor (0.9), \(\lambda\): X-ray wavelength (1.5406 A), \(\beta\): full-width at half-maximum, \(\theta\): Bragg angle). The surface charge of GR-SO\({}_{4}\) and GR-PEDOT was investigated using an ELSZ-1000 (Otsuka, Kyoto, Japan). For zeta-potential measurement, each sample powder was dispersed in deionized water (1 mg/mL) and then the prepared suspension transferred to a quartz flow cell with platinum electrode. The particle morphology of GR-SO\({}_{4}\) and GR-PEDOT was evaluated by scanning electron microscopy (FE-SEM; FEG Quanta250, FEI, USA). In order to obtain SEM images of samples, both powder samples were attached on carbon tape, and then sample surface was coated through Pt/Pd sputtering for 60 s. The SEM images were obtained under a 30 kV electron beam acceleration voltage with 10 mm working distance. The X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectra were obtained using X-ray absorption spectroscopy (XAS) in the 8C beam line at the Pohang Accelerator Laboratory (Pohang, Korea). To obtain XAS spectra, a powder sample was attached between kapton tapes, and then X-ray absorption was measured around Fe K-edge energy (7112 eV). The gained EXAFS spectra was analyzed with XAFSVIEW package in order to examine the local structure and coordination state around Fe.