esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 5 Simulation and Experiment  
### Simulation  
Figure 21 shows the experimental platform. The left panel is the physical platform, and the right panel is the 3D online simulation environment, which has the function of collision detection. The robot motion planning results can be verified in a simulation environment.  
To evaluate the performance of the algorithm, we randomly set 20 groups of cable poses for path planning using different algorithms. The planning space is a 6D joint space, and the range of motion of each joint is 360\({}^{\circ}\). The step size of both algorithms is 2\({}^{\circ}\), and the greedy strategy coefficient \(\alpha=0.1\). The rewire radius of the RRT* and the offline method is 5\({}^{\circ}\). The same smoothing method is used to smooth the paths. The test results are shown in Table 2.  
In terms of computational efficiency, the algorithm is evaluated with the number of samples and the number of collisions detected. Compared with the other two algorithms, RRT*-offline can complete trajectory planning with a small amount of sampling. In the experiment, FCL [29] was used for collision detection, and the environmental model was simplified to improve the detection efficiency. RRT-connect and RRT*-offline take similar amounts of time, but in the absence of dynamic obstacles, RRT*-offline is faster.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Index** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline Sampling method & a & b & c & ab & ac & bc & ba & ca & cb & abc & acb & bca & bac & cab & cba \\ \hline \hline \end{tabular}  
* Maximum distance sampling is used; 2 Minimum distance sampling is used; 3 Sequential sampling is adopted; 4. Use maximum distance sampling first, then sequential sampling, etc., are adopted.  
\end{table}
Table 1: Effect of the sampling method on smoothing results.  
Figure 21: Test platform and simulation verification environment.  
The path length is the sum of the modules of each path section in the joint space. The planned paths of each algorithm are smoothed using the same smoothing method. In comparison, the length and segment number of the RRT*-offline planning path are less than those of the other two algorithms.  
In 20 rounds of planning, both RRT-connect and RRT*-offline completed the planning, and only RRT* did not complete 3 planning tasks after 4000 rounds of sampling. Path availability refers to whether the planned trajectory meets expectations. Most of the results of RRT*-connect meet the preset preferences. The planning results of RRT* and RRT-connect are highly uncertain, and some of the results do not meet the preferences.
###### Abstract  
It is most effective to reduce floor impact noise as close to the sound source as possible. In apartments, there are multiple layers in the floor system, from floor finishing to the structural concrete slab. Apart from the floor finishing, mortar lies at the top layer of the floor system, followed by autoclaved lightweight concrete, insulation, and the concrete slab. The present study aims to identify the reduction characteristics of light and heavy floor impact noises by changing the glass transition temperature of an SBR (styrene-butadiene rubber) latex mortar. To achieve this, structural tests were undertaken to find the appropriate mix proportions of SBR latex in the mortar, meeting the glass transition temperature based on the physical test results regarding the latex mortar. As seen in the study method and process, because this study aimed to both increase and decrease the strength compared to general mortar, a 7% mixture ratio of Tg 4 \({}^{\circ}\)C SBR latex was decided upon for the strength increase, while a 5% mixture ratio of Tg \(-\)16 \({}^{\circ}\)C SBR latex was chosen for the strength reduction. A mock-up specimen was created using the SBR latex-modified mortar according to the identified mix proportions, and the characteristics of light- and heavy-weight floor impact noises of the SBR latex-modified mortar were then examined. Comprehensive analysis of the reduction performance of the floor impact noise revealed that the Tg \(-\)16 \({}^{\circ}\)C SBR latex-mixed mortar showed a reduction effect of about 2-5 dB for light-weight impact noise and about 7-10 dB for heavy-weight impact noise.  
floor impact noise; SBR latex-modified mortar; glass transition temperature; compressive and tensile strength; light- and heavy-weight floor impact noises +
Footnote †: journal: Medical Engineering  
14 June 2024  
Revised: 5 July 2024  
Accepted: 7 July 2024  
Published: 18 July 2024
## 3 Experimental Results and Analysis
### Data Processing  
The dataset used in the paper is a dataset of foreign objects in a power station within the power grid, containing four types: bird nest, kite, balloon, trash bag. This dataset contains a total of 1111 images of four categories of foreign objects in the transmission tower line, including 103 balloons, 284 kites, 331 trash bags, and 642 bird nests. Due to the insufficient number of samples of the dataset of this study, the imbalance in the number of each category of foreign objects exists. Through the data enhancement methods such as mirroring, cropping, scaling, panning and rotating, the spatial geometric transformations were used to change the spatial position of the pixels in the images without changing the content of the images, increasing the number of samples and avoiding the occurrence of overfitting [35]. Field-captured images of transmission lines often suffer from external interferences, leading to issues such as unclear visuals, motion-induced distortions, and weather-related blurriness. To address these real-world complexities, this study implements image preprocessing techniques, including Gaussian noise addition, random luminance adjustment, and motion blur simulation. These methods enrich the training dataset, enabling the model to learn and recognize foreign object features more effectively in varied environmental conditions. This approach simulates actual field scenarios, thereby enhancing the model's validation accuracy. The preprocessing resulted in a final dataset of 5084 images, categorized by different types of foreign objects as follows Table 1:  
In this paper, the dataset is divided into training set, validation set, and test set according to the ratio of 8:1:1. The training set and validation set are used for model training, and the test set is used for testing the accuracy of the model. The dataset annotation tool used in this paper is LabelImg, and after the annotation is completed, corresponding txt file will be generated for each image after labeling.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 4 Materials and Methods
### Case Study
#### 4.2.3 Application Architectures  
Independent task-based microservices were containerized with Docker and orchestrated with Kubernetes, allowing dynamic scaling and fault isolation, and enabling the scaling of individual services according to load demands. By contrast, a monolithic integrated Flask application manages all tasks (data ingestion, processing, and storage) within one instance. Although simpler to deploy, it lacks fault isolation and scalability, proving that it is more resource-intensive under high loads. The comparison metrics include the response time for request processing, throughput, and scalability.
## 3 QoS Self-Provisioning and Interference Management Concept  
The high level diagram and algorithm flow of the proposed quality of service self-provisioning and interference management (QoS-SP-IM) concept is presented in Figure 3.  
The overall solution design includes four execution steps in order to configure the HNB. First of all, the P-CPICH is calculated with respect to the coverage requirements and the surrounding macrocell radio frequency (RF) environment in _Step 1_. In _Step 2_, the HUE power calculations are performed. All UL traffic in the overlaying macrocell is assumed at the femtocell edge and the corresponding interference towards the HNB can be computed. From this it is possible to configure the HUE power to meet the UL required throughput target. Moreover, it is checked if the NR from HUE exceeds the limit. If the NR limit is exceeded, the femtocell coverage area is reduced and the CPICH calculations in _Step 1_ are reiterated. If uplink verification is passed, the HS-DSCH transmit power is configured in _Step 3_. The HS-DSCH is configured such that end-users get the targeted downlink data rate, without exceeding it at the femtocell edge. Thus, the leakage to other base stations is effectively controlled. Finally, downlink requirement verification is performed in _Step 4_. Both the HUE coverage area and the  
Figure 3: High level overview of the QoS self-provisioning and interference managing algorithm.  
HUE downlink data rates are checked. If not passed the HNB coverage area must be reduced and the CPICH power calculations in Step 1 are reiterated.  
If the target requirements cannot be fulfilled in a real life deployment (e.g., the femtocell coverage is smaller than desired or target throughput rate not met), a warning report is generated and transferred to the operation and the maintenance system. Further development of the algorithm include dynamic power control based on UE feedback, however, the aim of this paper is the initialization of HNB power parameters.
# Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization  
[PERSON]  
1 SHU-USTS SILC Business School, Shanghai University, Shanghai 201899, China;  
important to optimize control strategies while balancing both robustness and dynamic requirements and to implement a control strategy with strong stability and adaptability to the dynamic variation of traffic flow. Based on the above considerations, a robust dynamic nondominated sorting multi-objective genetic algorithm, DFCM-RDNSGA-III, based on densities and the fuzzy C-means clustering method is proposed in this paper. A robust control strategy for the multi-on-ramp expressway network can then be provided; this strategy can help solve the congestion situation in the peak h of the road network and make a positive contribution to environmental protection.  
Specifically, DFCM-RDNSGA-III is designed to optimize the variable speed limit strategy and ramp control scheme of urban expressway networks. Considering the three performance indicators of travel time, ramp queue and traffic emissions, it combines the improved multi-class freeway macroscopic traffic flow model Multi class METANET with the multi-class emission and fuel consumption model Multi-class VT-macro. The variable speed limit and ramp metering strategy of the expressway system with multi-on-ramps and multi-off-ramps are optimized, and finally, the effect of the algorithm is verified through building road network under the VISSIM simulation environment.  
The contributions of this paper are as follows:  
1. A new robust dynamic multi-objective optimization algorithm is designed to simultaneously optimize the travel time, ramp queue and traffic emissions of expressway networks. In the proposed robust dynamic multi-objective optimization algorithm, the density fuzzy c-means clustering algorithm DFCM is adopted to explore the irregular Pareto fronts in the multi-objective optimization algorithm. This can lead to more reasonable reference point setting, therefore improving the diversity of Pareto solutions and accelerating the convergence speed of the optimization algorithm;  
2. Based on the improved multi-class freeway macroscopic traffic flow model, multi-class METANET and the multi-class emission and fuel consumption model uti-class VT-macro, the algorithm can optimize the variable speed limit and ramp metering rate of the expressway system with multiple on-ramps and off-ramps at the same time. Moreover, the ramp inflow rate and main road variable speed limit control scheme can be obtained with strong robustness and adaptability to the dynamic variation of traffic flow. Therefore, the main road and ramp traffic congestion can be improved at the same time and energy conservation as well as emission reduction can be achieved;  
3. By building the actual road network in the VISSIM simulation environment, the algorithm proposed in this paper is compared with the existing algorithms. It is verified that the proposed algorithm, DFCM-RDNSGA-III, can provide better traffic control strategies, and the freeway traffic congestion and environmental pollution can be improved effectively.
# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 2 Materials and Methods
### Auditory-Perceptual Evaluation  
A panel of three native Lithuanian-speaking otolaryngologists with at least 10 years of experience in the field of laryngeal pathology and phoniatrics were recruited for the auditory perceptual evaluation of the speech samples. Over the course of three auditory-perceptual evaluation sessions, they were asked to rate the same group of recordings that were used for training and testing the current algorithm using the Impression, Intelligibility, Noise, Fluency, and Voicing perceptual rating scale (IINFVo), which is aimed specifically at quantifying the perception of SV and speech after laryngeal oncosurgery [9]. The IINFVo scale rates the impression (first I) of voice quality; intelligibility (second I), the amount of effort required to understand a given segment of speech; unintended additive noise (N); fluency (F), hesitations between successive sounds and within continuous sounds; and quality of voicing (Vo), unintentionally voiced or unvoiced speech fragments. Each parameter of the IINFVo scale is scored on a 10.0-cm long visual analog scale (VAS) from left (worst/absent) to right (optimal) [6]. The total IINFVo score can range from 0 to 50 points, with a higher score indicating better speech quality.  
The purpose of the study, SV and speech evaluation standards, and retesting were all explained to the panel ahead of time. Before each rating, a training session was held to standardize the criteria and achieve a better level of unanimity. An independent training set of five speech samples was supplied (two samples of normal speech and three samples of SV from distinct SV subgroups, i.e., cordectomy, partial and total laryngectomy). The primary rating set does not include these speech recordings.  
The SV rating processes were conducted in a quiet area with little ambient noise. Speech samples were presented at a suitable hearing level using MacBook Pro model A1211 stereo speakers (Apple Inc., Cupertino, CA, USA). Each voice sample was played in random order. Before a choice, each sample was repeated as many times as necessary. Following the training session, no conversation among panel members was permitted.
## 3 Study and Results on Messy Broadcasting
### Messy Broadcasting in Grid
#### 3.2.2 Model \(M_{3}\)
To summarize all the findings:  
**Theorem 3** (Two-dimensional grid, messy model \(M_{3}\)). : _Consider a two-dimensional grid \(G[n+1,\leavevmode\
obreak\ m+1]\), where \(n\leq m\). With \(0\) as the originator, under the messy model \(M_{3}\),_\[t_{3}(0)= \begin{cases}3m+3n-2&\text{if $n\leq 7$}\\ 3m+3n-4+\left(4n-3\left\lceil\frac{n+6/7}{8}\right\rceil-4\left\lfloor\frac{7n-2} {8}\right\rfloor\right)&\text{if $n\geq 8$ and $n\bmod 8=6$}\\ 3m+3n-5+\left(4n-3\left\lceil\frac{n+6/7}{8}\right\rceil-4\left\lfloor\frac{7n- 2}{8}\right\rfloor\right)&\text{otherwise}\end{cases} \]  
Moreover, an optimum scheme similar to the border-evading scheme is known, in which the sole last-informed vertex is  
\[ \begin{cases}\left(\left\lfloor\frac{7n-2}{8}\right\rfloor+1,\ m\right)&\text {if $n\leq 7$}\\ \left(\left\lfloor\frac{7n-2}{8}\right\rfloor,\ m-\left\lceil\frac{n+6/7}{8} \right\rceil\right)&\text{if $n\geq 8$ and $n\bmod 8=6$}\\ \left(\left\lfloor\frac{7n-2}{8}\right\rfloor+1,\ m-\left\lceil\frac{n+6/7}{8} \right\rceil+1\right)&\text{otherwise}\end{cases} \]  
Corollary 1: _Consider a two-dimensional grid \(G[n+1,\ m+1]\), where \(n\leq m\). Under the messy model \(M_{3}\), for \(n\geq 8\)_  
\[3m+3\frac{1}{8}n-7.322\leq t_{3}(0)\leq 3m+3\frac{1}{8}n+0.679\]  
Proof: By the definitions of the floor and ceiling functions, \(\frac{n+6/7}{8}\leq\left\lceil\frac{n+6/7}{8}\right\rceil\leq\frac{n+6/7}{8}+1\) and \(\frac{7n-2}{8}-1\leq\left\lfloor\frac{7n-2}{8}\right\rfloor\leq\frac{7n-2}{8}\) are obtained. Using the second case for the upper bound and the third case for the lower bound, it is deduced that  
\[3m+3\frac{1}{8}n-7.322\leq 3m+3\frac{1}{8}n-\frac{205}{28}\leq t_{3}(0)\leq 3m+3 \frac{1}{8}n+\frac{19}{28}\leq 3m+3\frac{1}{8}n+0.679\]
## 3 Primary Controller Design  
The primary controller has a cascade control, where the inner loop is a sliding mode control (SMC) with a switching surface based on the converter inductor current. Each ESS module has its own switching surface \(S_{j}(i_{j})\), which is valid for both the voltage step-up and step-down modes and is given by Equation (3).  
\[S_{j}(i_{j})=i_{ej}-i_{j} \tag{3}\]  
The sub-index \(j\) corresponds to sub-indexes \(b\) and \(s\) for the battery pack inductor current and supercapacitor module inductor current, respectively. The variable \(i_{ej}\) is the current reference given by the outer control loop, as illustrated in Figure 4.  
The choice of the current inner loop allows for the use of a modular design structure that is implementable in each ESS module with the possibility of connecting similar modules to expand the capacity of the HESS. In addition, the converter current is related to all the other state variables present in the module, ensuring a complete control over the voltages and currents at the converter ports.
## 3 Method of Prediction and Optimization
### Long Short-Term Memory (LSTM)
#### 3.1.2 Model Training  
Curves of uncertain variables which should be learnt and predicted include wind power, solar power and three types of load (electrical load, heat load and cooling load). Each of them needs a specific LSTM prediction model, we take the wind prediction as an example in this paper. Other uncertain variables could be predicted by the same method.  
Without loss of generality, we take the LSTM model for wind prediction for instance. As in Figure 3, the total wind power at the time step \(t\) is denoted as \(x(t)\). The sequence of the coordinates \(x(t-n),\cdots,x(t)\) taken from \(n+1\) hours can be labeled as the training data. Then, \(x(t-n+1),\cdots,x(t+1)\) can be labeled as next sequence. A plenty of sequences involve several years form into training set to fit the LSTM model, where the last data in every sequence are the learning targets of the training data. In this paper, we orderly set input fully-connected layers, two LSTM cells followed by dropout layers respectively, and the output fully-connected layers. \"Adam\" optimizer and \"mean absolute error\" loss standards are used to compile the model.  
Figure 3: Structure of LSTM for wind prediction.  
Figure 2: Basic structure of long short-term memory (LSTM).
## 3 Experiment Results and Analysis
### Effects of Controlling Factors on Seepage-Erosion Process
#### 3.2.1 Effects of Soil Compactness on Seepage-Erosion Process
The ultimate water-outflow rate decreases to 6.49 m\({}^{3}\)/h from 8.14 m\({}^{3}\)/h as the relative density up to 0.75 from 0.25, and meanwhile, the mass loss rate significantly drops to 55.90% from 90.16%. This phenomenon reflects the growth of erosion-resistance ability of soil sample along with an increasing relative density.
# Antibiofilm Potential of Natural Essential Oils
## 3 New Natural AM Strategies  
The decreasing efficacy of antibiotics in the face of increased microbial resistance has led to the search for alternative AM therapies, including antiviral drugs, i.e., molecules that can affect virulence factors and not cell growth. BF inhibition would boost the efficacy of antibiotics and would make pathogens more sensitive to the immune response of the host. Novel therapeutic approaches are focused on preventing the synthesis or breaking down constituents of the BF matrix, thereby making the bacteria more susceptible to antibiotic therapy [23; 24]. Various natural products, like lantibiotics (nisin, subtilisin, epi-dermin), AM peptides (LL-37, Burford-II, PR-39), plant-derived active substances (tannins,flavonoids, flavones, flavonols), bacteriophages and enzymes (DNases, depolymerases, lactonases, and bacteriophage-based endolysins), have been extensively investigated for their BF-forming capability [18]. Natural products derived from plants, bacteria, fungi, and marine organisms have been shown to be effective as anti-BF (ABF) agents by inhibiting the formation of a polymeric matrix, cell adhesion and attachment, and the production of virulence factors, acting as QS inhibitors. More than 90 ABF compounds from the groups of alkaloids, polyphenols, terpenes, and EOs have been identified in different morphological parts of plants [25; 26; 27]. Some studies have reported, among others, the capability of eugenol (EUG), garlic, and phenolic extract from _Rubus rosifolius_ [PERSON] for attenuating BF formation by _P. aeruginosa_ and _Serratia marcescens_[15]. Sulfated polysaccharides (fuoidal), carotenoids (zeaxanthin and lutein), lipids, fatty acids (\(\gamma\)-linolenic acid and linoleic acid), and phlorotannins can inhibit cell attachment, reduce cell growth, impede QS by blocking associated enzymes, and disrupt extracellular polymeric substances [27]. [PERSON] et al. [28] have indicated the potential modulation of bacterial cell-to-cell communication, BF formation by _E. coli_ O157:H7, and virulence of _Vibrio harveyi_ by flavonoids, including in particular naringenin, quercetin, sinensisetin, and apigenin. Among the flavonoids tested, naringenin turned out to be a strong and, probably, non-specific inhibitor of autoinducer-mediated cell-to-cell signaling. [PERSON] et al. [19] demonstrated that salicaldehyde, vanillin, \(\alpha\)-methyl-trans-cinnamic aldehyde, and trans-4-nitrocinnamic acid were potent (15-92%) inhibitors/reducers of BF of drug-resistant S. aureus isolates at a concentration of 1-10 mg/mL. They also emphasized the significant suppression of gene expression by phenylropenes and phenolic aldehyde, which can inactivate bacterial adhesion and reduce BF-formation by _S. aureus_. These molecules may attenuate virulence factors and serve as potential AM and ABF drugs [19].
## 2 Background
### The Design Goal  
Reverse engineering of VM-based code protection usually follows many steps. The attacker first reverse-engineers the virtual interpreter to understand the semantics of the various bytecode instructions, such as how the bytecode is translated to native code. Then, he/she translates the bytecode back to native machine instructions or even high-level programming language to understand the program logic. Among these steps, understanding the semantics of a single bytecode instruction is often the most time-consuming process, which involves analyzing the handlers used to interpret each bytecode instruction. Many methods have been proposed to protect the VM handlers from reverse engineering. Most of them aim to increase the diversity of program behavior by obfuscating a handler or using multiple interpretation techniques to transform a single program iteratively multiple times. However, all previous work employs a fixed strategy where each bytecode is deterministically translated to a fixed set of native code. For programs that use the same obfuscation protection, these techniques are vulnerable. In particular, an attacker can reuse the knowledge of the handler implementation obtained from one program (termed analytical knowledge in this paper) to attack another program. This paper presents _VMGuards_, an enhanced VM-based code obfuscation system to address the issue of attacking the VM. We use two types of security instructions, ADI and TPL, to monitor the dynamic changes of VM in memory.