esa-sceva/satcom-chunk-collection huggingface dataset:
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 6 SemKoRe Implementation
### Anonymization Service  
Privacy protection is a very important concern for our customers. They do not want to share any machine and process-related data in a way that could potentially expose sensitive business information.  
To address this concern, we implemented a simple service (described below), in the SemKoRe Server in the cloud, to anonymize the collected data before sharing it with other sites or locations.  
When a failure occurs, the gateway creates an instance of \"Failure Occurrence Class\", containing information about the failure, e.g., symptoms, impact, root causes if known, and the failure context, which includes the machine ID, its location, timestamp when failure occurred, and a snapshot of the current parameters. The whole process consists of three steps:  
1. The SemKoRe Server removes the machine ID, location, and owner-related information and does not share this information.
2. A human expert reviews and validates all of the failure information before integrating it into the SemKoRe Knowledge Graph. This additional check helps to protect sensitive business information.
3. Finally, all the validated failure information is aggregated and then shared with the connected gateways. This process ensures that no one can deduce the origin of the data, the failure location or the ownership details.  
This service is a subject for future SemKoRe versions. The goal is to automate this process so that little to no human involvement is required.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 2 Background
### Document Representation Models
#### 2.1.1 Bag-of-Words Model  
The bag-of-words (BOW) model represents each document as an unordered set (bag) of features that correspond to the terms in a vocabulary for a given document collection. The vocabulary can include words, a sequence of words (token n-grams) or sequences of letters of length \(n\) (character n-grams) [16; 24; 25]. Each vocabulary term is represented with one numerical value in a feature vector of a document. The feature value can be calculated in different ways. The simplest is to observe the occurrence of a term in a document, leading to a Boolean feature value. More commonly used feature values measure the frequency of a term in a given document. Another commonly used measure is \"term frequency, inverse document frequency\" (TFIDF), where the term frequency (TF) is multiplied by the reciprocal frequency of the term in the entire document collection (idf). In this way, TFIDFreduces the importance of the terms that appear in many documents and increases the importance of rare terms.  
Traditionally, the BOW model is used as the state-of-the-art document representation model in many natural language processing applications. Its success emerges from the simplicity of implementation and the fact that it often leads to high accuracy document representation. Still, it is well known that BOW is characterized by many drawbacks such as high dimensionality, sparsity, and the inability to capture semantics or simple dependencies between words, such as word ordering in the document [4; 5]. Therefore new representation models in the forms of distributed word embeddings (word2 vec and doc2 vec) and graph-of-words (GOW) have been proposed and tested to challenge the open issues in document classification.
###### Abstract  
In the non-free-field, with the effect of reflection sounds from the reflection boundary, the vibration character of a submerged structure often changes, which may have significant influences on the measurement system configurations. To reduce the engineering cost in low-frequency sound prediction of a submerged structure with finite depth, two methods based on the theory of acoustic radiation mode (ARM) are proposed. One is called the vibration reconstruction equivalent source method (VR-ESM), which utilizes the ARM to reconstruct the total vibration of the structure, and the sound prediction is completed with the equivalent source method (ESM); the other is called the compressed modal equivalent source method (CMESM), which utilizes the theory of compressive sensing (CS) and the ARM to reinforce the sparsity of source strengths. The sound field separation (SFS) technology is combined with the above two methods for constructing the ARMs accurately in the non-free field. Simulations show that both methods are efficient. Compared with the traditional method based on the structural modal analysis, the methods based on the ARM could efficiently reduce the scale of the measurement system. However, the measurement point arrangement should be optimized to keep the prediction results accurate. In this paper, the optimization process is completed with the efficient independence (EFI) method. In addition, some factors that may affect the prediction accuracy are also analyzed in this paper. When the submerge depth is large enough, the process of contrasting ARMs could be further simplified. The results of the paper could help in saving engineering costs to predict the low-frequency sound radiation of submerged structures in the future.  
structure with finite submerge depth; sound prediction; ARM; SFS; EFI +
Footnote †: journal: Applied Sciences
## 2 Structure Design of MRE Vibration Isolator
### Materials Selection  
The structure of the MRE isolator contains magnetic-conducting materials and non-magnetic-conducting materials. The magnetic conductive material is mainly used to construct the magnetic circuit, and the magnetic field is concentrated in the MRE material area, which is related to the magnetic control performance of the vibration isolator. The role of non-magnetic materials is to prevent the leakage of magnetic fields, but also to avoid electromagnetic interference. The requirements for the magnetic material of the MRE isolator are as follows:  
1. High permeability: higher permeability ensures higher magnetic induction intensity under the same input;
2. Low coercivity: Due to the existence of the coercivity of the magnetic material, the remanence phenomenon will occur after the vibration isolator stops working. The remanence changes the initial state of the vibration isolator and increases the control difficulty of the vibration isolator;
3. High magnetic induction saturation intensity: After the magnetic material reaches magnetic saturation, the magnetic induction intensity will no longer increase with the current, which will affect the magnetic control range of the device;
4. High thermal conductivity and specific heat capacity: The heat generated by the MRE isolator during operation increases the working temperature, reduces the performance of the elastomer, and increases the coil resistance to increase the load power of the  
Figure 5: Basic configuration of MRE isolator (two-dimensional).  
current driver. It is necessary to have good heat dissipation performance of the material to reduce the temperature of the isolator during stable operation.  
Based on the above requirements, the internal and external magnetic conductivity parts of the MRE isolator are made of 20# steel with high strength and high permeability, while the upper cover plate, lower bottom plate, and winding shaft are made of aluminum alloy with non-magnetic conductivity, light weight, and certain strength. The excitation coil is made of copper enameled wire with a diameter of 0.6 mm, and epoxy resin is coated on the outer surface of the coil to ensure insulation performance.
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 3 Results
### California Bearing Ratio Test (CBR Test)  
The CBR tests are performed on samples with and without additives for 7 and 28 day curing periods; the results are presented in Figure 5. The pure sand sample exhibits a CBR value of 19% at both time points. Adding only 3% cement (S3C) without fly ash results in a CBR value of 87% after 7 days of curing, indicating a 4.6-fold increase compared to  
Figure 4: Change in cohesion for mixtures.  
Figure 3: Change in internal friction angles for mixtures.  
the pure sand. As it can be seen from the figure, the CBR values of the samples continue to increase with higher fly ash ratios. After 7 days of curing, the CBR values for 5%, 10%, 15%, 20%, and 25% fly ash additions, along with fixed 3% cement, are 87%, 99%, 113%, 115%, and 124%, respectively. The S3C25 FA sample exhibits the highest CBR value at 124%, corresponding to a 6.5-fold increase compared to the pure sand sample.  
In Figure 5, the S3C sample's CBR value after 28 days of curing is 125%, indicating that the addition of 3% cement increases the CBR value of fine sand by 6.58 times. Adding 5%, 10%, 15%, 20%, and 25% fly ash to sand with a fixed 3% cement increases the CBR values of the samples to 179%, 195%, 216%, 230%, and 241%, respectively, after 28 days of curing. The highest CBR value is still observed in the S3C25 FA sample, and the increment is 12.7 times that of the pure sand sample. This may be because of the fly ash which binds the particles and therefore increases the soil stiffness and strength which was emphasized in the study of [PERSON] [30].  
The experiment results show that cement increases the CBR value of fine sand soil, while the CBR value of samples continues to increase with an increasing fly ash ratio. [PERSON] et al. [29] suggested that the increase in the CBR value of fly ash added soil was due to the interlocking phenomenon of soil and fly ash. It is seen that the curing time is significantly affected by the CBR values of test samples. [PERSON] et al. [14] also emphasized that the curing time had a positive effect on the mechanical properties of treated sand soils. The increase in the CBR value with curing time was attributed to the hydration reaction of cement and the slow pozzolanic reaction of fly ash, which caused self-hardening of the generated bond [31].
# Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments
## 3 Results
### Lane-Exchanging Scenario  
Lane-exchanging is more complex than overtaking and is in urban traffic environments. Assume that two vehicles exchange information through V2V and then change lanes, and the initial times of changing lanes are the same.  
In this section, the longitudinal speed of the subject vehicle is 100 km/h, the speed of the Lead vehicle is 72 km/h, the longitudinal distance between the two vehicles in the initial position is 18 m, and the road adhesion coefficient is 0.7; the vehicle model parameters are shown in Table 1. The minimum vehicle lane change time, as shown in Figure 6, is 2.013 s, while maximum lane change time is generally defined as 9 s. Matlab and CarSim were applied for the simulation, and the results are shown below:  
As shown in Figure 11a, lane change trajectories for both VM and VLe are trajectory clusters. As shown in Figure 11b, the safe lane-changing time for the Subject vehicle ranged from 2.013 s to 6.4 s. As shown in Figure 11c, the multi-objective optimization results showed that the best lane change end time was 5.6 s. The results of the Matlab and CraSim simulation are shown in Figure 11d. It can be seen that the two vehicles did not collide and both vehicles followed the expected trajectories without skidding or overturning.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Meaning** & **Numerical Value** \\ \hline \(m\) & Vehicle mass & 1370 kg \\ \(w_{H}\) & Vehicle width & 2131 mm \\ \(H\) & Center of mass height & 520 mm \\ \(a\) & Distance from center of mass to front axle & 1110 mm \\ \(b\) & Distance from center of mass to rear axle & 1670 mm \\ \(R_{m}\) & Main gear ratio & 4.1 \\ \(P_{E}\) & Power of the engine & 150 kw \\ \(I_{z}\) & Rotational inertia of the pendulum & 2315.3 kg/m\({}^{2}\) \\ \(d\) & Wheelbase & 1550 \\ - & Tire model & 215/55 R17 \\ \hline \end{tabular}
\end{table}
Table 1: Selected parameters of the simulation vehicle.  
Figure 10: (**a**) Lane change trajectory cluster and lead vehicle trajectory; (**b**) Results of the safe collision avoidance algorithm; (**c**) Results based on Topsis algorithm scores; (**d**) Optimal lane change trajectory for the subject vehicle.
## 2 Mathematical Model of the Above-Knee Prosthesis
### Stiffness Analysis  
When the manipulator executes a motion, the end-effector exerts force/moment onto the target. This force/moment causes deflection, which is related to the applied force and the stiffness of manipulator. The stiffness of the serial manipulator directly affects positional accuracy [21]. There are three types of stiffness sources for serial robotic manipulators, namely base, joint and link stiffness. As stated in study [22], the primary source of stiffness in serial manipulators is the joint stiffness, which is in the axial direction of the actuation torque [22]. The following equation can be written to illustrate the relation between forces and torques.  
\[\tau=J^{T}F \tag{17}\]  
where \(F(f_{x},f_{y}f_{z})\) and \(\tau(\tau_{1},\tau_{2},\tau_{3})\) are the 3 \(\times\) 1 vectors of external forces acting on the end-effector of manipulator and actuator forces/torques required to balance these external forces, respectively. The relation between small deflection and joint torques can be stated as follows.  
\[\tau=K_{\theta}\Delta\theta \tag{18}\]  
where \(K_{\theta}=diag\begin{bmatrix}k_{\theta_{1}}&k_{\theta_{2}}&\cdots k_{\theta_ {n}}\end{bmatrix}\) is the joint stiffness matrix and \(\Delta\theta=\begin{bmatrix}\Delta\theta_{1}&\Delta\theta_{2}&\cdots\Delta \theta_{n}\end{bmatrix}\) is the small change which occurs in the joint positions. The relation between small change in the end effector \(\begin{pmatrix}\delta\chi=\begin{bmatrix}\delta x&\delta y&\delta z\end{bmatrix} \end{pmatrix}\) and joint positions can be written as follows.  
\[\delta\chi=J\Delta\theta \tag{19}\]  
The following equation is obtained by using Equations (17)-(19)  
\[\delta\chi=C_{x}F \tag{20}\]  
where \(C_{x}\) is called as compliance matrix and stated as  
\[C_{x}=JK_{\theta}^{-1}J^{T} \tag{21}\]
## 4 Complexity and Performance
### Performance Comparison
This parallel processing strategy not only significantly reduces ANLPU but also ensures high reliability (as shown in Figure 5). These improvements significantly reduce ANLPU, verifying the effectiveness of the proposed optimization strategy in shortening the average time required to decode a frame of data and increasing processing speed. Therefore, even in scenarios where the processor must execute serially, the carefully designed algorithms can effectively reduce decoding latency. In parallel execution scenarios, these algorithms not only accelerate decoding but also maintain high decoding accuracy. Moreover, we have evaluated the performance indicators for the LSB page, including ANLPUPTTER, ANITER, ANLPU, FER, and BER, as shown in Figure 6a,b; Figure 7; and Figure 8, respectively. The performance trends for the LSB page are consistent with those observed for the MSB page, with the S-EFB-LNMS and P-EFB-LNMS algorithms demonstrating similar  
Figure 5: BER/FER results for MSB pages using LNMS, S-EFB-LNMS, and P-EFB-LNMS LDPC decoding algorithms: \(T=5000\), with PE cycles ranging from 17,000 to 26,000. (**a**) FER. (**b**) BER/RBER. Figure 6: Performance comparison of LNMS, S-EFB-LNMS, and P-EFB-LNMS LDPC decoding algorithms for LSB pages: \(T=5000\), with PE cycles ranging from 22,000 to 26,000. (**a**) Average number of layered processing units executed per iteration (ANLPUPTTER). (**b**) Average number of iterations during decoding (ANITER). advantages in reducing decoding latency and improving efficiency. Overall, the proposed algorithms maintain their efficiency across both MSB and LSB pages. Consequently, the proposed S-EFB-LNMS and P-EFB-LNMS LDPC decoding algorithms significantly reduce the average number of LPUs executed during the decoding process, thereby reducing decoding latency. These algorithms are particularly suitable for NAND flash memory applications that are sensitive to latency.
## 1 Introduction
In complex industrial environments, ensuring robustness and accuracy is particularly difficult when dealing with worn bolts or deformed weld joints, where current algorithms often struggle with efficiency and precision, especially in real-time applications [22; 23]. While multimodal data fusion holds promise for complex scene recognition, improving real-time processing and reducing latency are critical challenges that still need to be addressed. Striking a balance between computational efficiency and accuracy is a key focus of ongoing research. Furthermore, given the diversity of power equipment models and specifications, developing adaptable automated disassembly systems that can accommodate various equipment types is becoming increasingly important [10; 11]. To address the limitations of current technologies, this study aims to improve the efficiency and safety of transformer disassembly through the use of multimodal technology, proposing an intelligent system that integrates multimodal data processing, deep learning, and automated control. The system starts by accurately identifying the transformer cover type using 2D images and 3D point cloud data from an RGB-D camera, then selects the most suitable disassembly strategy based on real-time conditions. For screw-fastened covers, the system assesses the screw conditions and performs automated disassembly. For welded covers, it employs precise edge detection and cutting path planning. The system extracts 2D image features of the internal transformer structure using a convolutional neural network (CNN), mapping these features into 3D space. It then utilizes the K-Nearest Neighbor (KNN) algorithm and positional coding for 3D feature fusion, accurately locating cutting components (e.g., screws, copper plates, studs, silicon steel sheets) through target detection and executing the cutting process based on the planned path. This approach enables the efficient disassembly of waste transformers. Additionally, the developed wire extraction line effectively separates wire insulation from metal cores, enhancing both the integrity and efficiency of wire recovery.
## 5 Denoising Experiments  
### ECG Database  
The algorithm in this paper is mainly verified by the currently popular MIT-BIH ECG database. In order to better simulate the patient's ECG signals in the real state, we artificially add noises of different amplitudes. The MIT-BIH noise stress test database provides three common ECG noise signals: baseline drift, power-line interference, and motion artifacts. The frequency range of the first baseline drift is between 0.02 Hz and 2 Hz. The first baseline drift as the noise source is more representative [47]. Superimposing different signal-to-noise ratio (SNR) Gaussian white noise on ECG signals simulates the ECG with noises in the real state. We add different SNR noises during the experiment.
## 3 OLGA Modeling
### Boundary Conditions  
Table 3 illustrates the default settings of the OLGA simulation model. The TEMPERATURE model relies on 'WALL' selection for temperature calculation, whereas the STEADYSTATE preprocessor necessitates temperature calculation (ON). MASSEQCHEME mass equation numerical discretization employs '1 STORDER'. The COMPONENTAL component tracking model invokes PVT tables for calculation and does not track during simulation. Minimization of simulation errors is enabled by setting NOSLIP to 'OFF' to account for sliding between air and water in the pipeline. OLGA restricts fluid phases in the tube to single-phase or three-phase only, with this model set to three-phase and a zero-volume fraction for the third phase (with no impact on simulation). Due to slug flow in the pipe, the simulation adopts 'ON' for HYDSLUG. The bubble porosity phase relationship model is set to 'AIR', whilst FLASHMODE provides a mass transfer model for air and water, with 'WATER' selection indicating mass transfer between the two. TABLE-TOLERANCE is set to 'ON' during PVT table operations to prevent exceeding temperature and pressure limits established within the simulation's PVT file.  
The present simulation incorporates variable time steps, ranging from 0.001 to 0.5 s, determined by assigning the minimum value to secure actual simulation time and the maximum value to prohibit probable errors attributable to PVT tables. Initialization data is acquired to secure computational convergence during the simulation's preliminary stages. The simulation runs for a total period of 30 min.  
The flowchart of this study is shown in Figure 5, from which the structure and method of this article can be clearly understood.  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Mode Fraction (\%)** & **Density (kg.m\({}^{-3}\))** \\ \hline N\({}_{2}\) & 78.126 & \\ \hline O\({}_{2}\) & 20.907 & \\ \hline Ar & 0.934 & \\ \hline CO\({}_{2}\) & 0.033 & \\ \hline \end{tabular}
\end{table}
Table 2: PVTsim physical parameters of gas.  
\begin{table}
\begin{tabular}{c c} \hline
**Flow Model** & **Setting** \\ \hline TEMPERATURE & WALL \\ STEADYSTATE & ON \\ MASSEQCHEME & 1 STORDER \\ COMPOSITIONAL & OFF \\ NCSLIP & OFF \\ PHASE & THREE \\ HYDSLUG & ON \\ SLUGVOID & AIR \\ TABLETOLERANCE & ON \\ FLASHMODEL & WATER \\ \hline \multirow{4}{*}{Integration} & Simulation start time & 0 s \\ Simulation stop time & 30 min \\ Minimum time step & 0.001 s \\ Maximum time step & 0.5 s \\ \hline \end{tabular}
\end{table}
Table 3: The default value of simulation in OLGA.