esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation
### Prototype  
A prototype was machined and assembled to conduct the experiment, as shown in Figure 7. The manipulator consisted of seven middle parts, one distal part, and one proximal part (SUS 304), and the flat springs were placed at each joint. A circular indentation and a protrusion were present at the top and bottom of the middle part; the radius of curvature (\(r_{p}=0.95\) mm) of the protrusion was fixed, but the radius of curvature (\(r_{d}=0.98\), 1.0, 1.1, 1.2 mm) of the indentation was machined by adjusting the length according to the flat spring used. The angle \(C\) of the inclined surface was \(10.8^{\circ}\), which indicated a bending of \(21.6^{\circ}\) per joint (the actual bending was slightly less than this value owing to the resistance of the flat spring). The outer diameter \(w\) of the segment was 4.5 mm; the segment was hollow (\(ch=2\) mm), with four side holes (\(sh=0.6\) mm). The cable for driving the manipulator passed through the side hole. The center hole could be used by the operator either for transmitting force, such as forceps or scissors, or for passing energy to the site in the form of electrodes or lasers. The proximal part had a small groove for coupling with another part, and taking this into account, the total length of the manipulator was approximately 28 mm. Sophistication in the manufacturing process is the best approach to reduce the error between actual bending experimental and simulation results. Even a slight distortion, when fixing the segments and flat springs, can lead to a significant error. Hence, the assembly should be given a careful consideration. When assembling this prototype, the flat spring was applied with adhesive and turned upside down to maintain the balance, and the segment was then placed on it.
## 3 Laboratory Study  
In this laboratory study, different amounts of a single type of lignin were added to a single type of bitumen with two different blending protocols. The lignin-modified bitumens produced were then tested to evaluate their viscosity and their viscoelastic properties before testing their chemical and thermal properties. The description of the materials, the blending protocols, and the different tests used are shown hereafter.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mixer** & **Additive** & **Bitumen** & **Blending** & **Blending** & **Shear** & **Ref.** \\
**(Type)** & **(Type)** & **(Type)** & **Temp. (\({}^{\circ}\)C)** & **Time (min)** & **Rate (rpm)** & **(number)** \\ \hline \multirow{8}{*}{Lignin} & \multirow{8}{*}{Lignin} & Grade 60/70 & 155 & 30 & 5000 & [43; 44] \\ \cline{3-8}  & & & 163 & 30 & 3000 & [23] \\ \cline{3-8}  & & Grade 70/100 & 160 & 120 & 3000 & [45] \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & Grades 50/70 and & 150 & 60 & 6000 & [46] \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & PG 58–28 & 160 & 40 & 6000 & [16] \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & Grade 50/70 & 160 & 60 & 5000 & [49] \\ \cline{3-8}  & & & PG 67–22 & 180 & 30 + 30 & 4000 + 8000 & [50] \\ \cline{3-8}  & & & PG 64–22 & 160 & 60 & 5000 & [51] \\ \hline \multirow{3}{*}{Wood lignin} & \multirow{3}{*}{Wood} & Grade 70 & 160–168 & 45 & 1200 & [15] \\ \cline{3-8}  & & Grade 70/100 & 163 & 30 & 3000 & [22] \\ \cline{3-8}  & & Grade 70/100 & 163 & 30 & 3000 & [22] \\ \hline \multirow{3}{*}{Ogranosolv lignin} & \multirow{3}{*}{Grade 60/70} & \multirow{3}{*}{155} & \multirow{3}{*}{10 + 50} & \multirow{3}{*}{4000 + 8000} & [52] \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & Grade 60/70 & 160 & 60 & 4000 & [54] \\ \cline{3-8}  & & & & & & \\ \hline Alkali lignin and & \multirow{3}{*}{Kraft lignin} & \multirow{3}{*}{PG 64–22 and \multirow{3}{*}{PG 76–22}} & \multirow{3}{*}{170} & \multirow{3}{*}{45} & \multirow{3}{*}{4500} & [55] \\ \cline{3-8}  & & & & & & \\ \cline{3-8}  & & Grade 50/70 & 160 & 30 & 2000 & [56] \\ \cline{3-8}  & & & PG 585–28 and \multirow{3}{*}{170} & \multirow{3}{*}{15} & \multirow{3}{*}{1000} & [20] \\ \cline{3-8}  & & & PG 525–34 & & & \\ \cline{3-8}  & & & PG 64–22 and \multirow{3}{*}{PG 76–22} & \multirow{3}{*}{163} & \multirow{3}{*}{30} & \multirow{3}{*}{1500} & [17] \\ \cline{3-8}  & & & PG 76–22 & & & \\ \cline{3-8}  & & Lignin & - & & & \\ \hline \end{tabular}
\end{table}
Table 1: Various blending protocols of lignin and bitumen in the literature.
## 3 Study Area and Data Sources  
### Study Area Overview  
The Jinchuan Cu-Ni mining area, located in Jinchang, Gansu Province, is the largest nickel and cobalt production and reserve base in China and represents a super-large underground mining operation. Situated in the eastern section of the Longshou Mountains along the Hexi Corridor, this region is characterized by an erosional hilly and low-mountain landform, with elevations ranging from 1700 to 2700 m. The area experiences an arid climate, with an average annual precipitation of only 170 mm and an evaporation rate of approximately 1700 mm. Tectionically, the mining zone lies within a high-stress region dominated by horizontal tectonic stress, and both the orebody and surrounding country rock have undergone extensive fracturing due to multiple geological events. This has led to pronounced deformation in the surrounding underground rock; typically, this is rapid and persistent and exhibits significant temporal and spatial heterogeneity.  
As illustrated in Figure 2, the Jinchuan deposit consists of four mining areas--IV, II, I, and III, from east to west. Specifically, Areas I and III are managed by Longshou Mine, Area II is jointly managed by Mine 2 and Mine 3, and Area IV is under the jurisdiction of Mine 3 [29]. The region's stratigraphy has undergone multiple phases of metamorphism and magmatic intrusion, resulting in a lithological assemblage dominated by migmatite, gneiss, and marble. It has also been subject to repeated tectonic movements, which have further compromised the stability of the rock mass [30]. In recent years, deep-mining engineering stability has become a growing concern due to mining activities, geological faults, and complex rock formation structures. This has led to severe surface subsidence, roof falls, rib failures, and fractures in the Jinchuan mining area, posing a significant threat to safe and  
Figure 1: InSAR data processing flowchart. The left section illustrates the time-series processing and corner reflector InSAR processing workflow, while the right section presents the detailed analysis method for the Jinchuan mine area.  
efficient production. Effective surface deformation monitoring is thus crucial in ensuring safe mining operations [31].
###### Abstract  
Active distribution networks (ADNs) are a typical cyber-physical system (CPS), which consist of two kinds of interdependent sub-networks: power networks (PNs) and communication networks (CNs). The combination of typical characteristics of the ADN includes (1) a large number of distributed generators contained in the PN, (2) load redistribution in both the PN and CN, and (3) strong interdependence between the PN and CN, which makes ADNs vulnerable to cross-domain cascading failures (CCFs). In this paper, we focus on the robustness analysis of the ADN against the CCF. Rather than via the rate of the clusters with size greater than a predefined threshold, we evaluate the robustness of the ADN using the rate of the clusters containing generators after the CCF. Firstly, a synchronous probabilistic model is derived to calculate the proportions of remaining normal operational nodes after the CCF. With this model, the propagation of the CCF in the ADN can be described as recursive equations. Secondly, we analyze the relationship between the proportions of remaining normal operational nodes after the CCF and the distribution of distributed generators, unintentional random initial failure rate, the interdependence between the sub-networks, network topology, and tolerance parameters. Some results are revealed which include (1) the more distributed generators the PN contains, the higher ADN robustness is, (2) the robustness of the ADN is negatively correlated with the unintentional random initial failure rate, (3) the robustness of the ADN can be improved by increasing the average control fan in of each node in the PN and the average power fan in of each node in the CN, (4) the robustness of the ADN with Erdos-Renyi (ER) network topological structure is greater than that with Barabasi-Albert (BA) network topological structure under the same average node degree, and (5) the robustness of the ADN is greater, when the tolerance parameters increase. Lastly, some simulation experiments are conducted and experimental results also demonstrate that the conclusions above are effective to improve the robustness of the ADN against the CCF.  
active distribution networks; cyber-physical system; interdependent network; cross-domain cascading failures; dynamical robustness +
Footnote †: journal: Applied Soft 2019, 9, 5021; doi:10.3390/app9235021
# Augmented Reality Applications in Industry 4.0 Environment
## 4 Results  
During the first step of the research, publications on AR and I4.0 in online databases were searched. A large number of papers have been collected and categorized and the most important data from them are presented later in this manuscript.  
A total of 201 manuscripts were collected, which contain the searched strings in their titles, keywords or abstracts. The collected papers are divided into four groups (Figure 3):  
1. There are 19 papers referring to the application of AR in educational processes;
2. Out of the total number of manuscripts, 115 are related to the implementation of AR in I4.0 applications;
3. Out of the total number of manuscripts, 45 describes AR in general or user guidelines for implementing AR solutions in companies;
4. The remaining 22 papers are excluded due to irrelevance.  
For further analysis within this paper, only a group of papers describing the implementation of AR applications in an industrial environment is relevant (AR for I4.0). This group of relevant publications was analyzed based on the following criteria:  
1. Origin of the studies;
2. Areas of applications in which AR have been applied or tested;
3. Types of devices used to work with developed AR applications;
4. Tracking methods implemented in the AR applications.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design  
In this section, we present the design and test methods of SRMCP. We make the following assumptions in order to demonstrate SRMCP:  
1. Both the client and server have their own private x509 key and certificates assigned by the same trusted certificate authority. Key and certificate creation are beyond the scope of SRMCP.
2. Both the client and server are capable of TCP/IP stack and opening IP sockets with Transport Layer Security (TLS). The implementation of TCP/IP stack and sockets is beyond the scope of SRMCP.
3. Proper routing configuration exists to allow the client and server to reach each other. Routing and switching are beyond the scope of SRMCP.
# Transfer Learning Based on Clustering Difference for Dynamic Multi-Objective Optimization
## 4 Experiments
### Running Speed
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Functions** & **Settings** & **DNSGA-II-A** & **DNSGA-II-B** & **TCD-DMOEA** & **PPS** & **T-DMOEA** & **KF-DMOEA** & **Winner** \\ \hline \multirow{3}{*}{P9} & S1 & 0.87298 & 0.83399 & **0.99596** & 0.65263 & 0.9203 & 0.7995 & TCD-DMOEA \\  & S2 & 0.87466 & 0.87180 & **0.95608** & 0.64542 & 0.84163 & 0.7807 & TCD-DMOEA \\  & S3 & 0.92019 & 0.92503 & **0.99328** & 0.85485 & 0.88981 & 0.8143 & TCD-DMOEA \\ \hline \multirow{3}{*}{DF2} & S1 & 0.91976 & 0.90297 & **0.99738** & 0.52831 & 0.9473 & 0.8020 & TCD-DMOEA \\  & S2 & 0.91425 & 0.91246 & **0.99786** & 0.77191 & 0.91661 & 0.8029 & TCD-DMOEA \\  & S3 & 0.94907 & 0.94420 & **0.9863** & 0.74677 & 0.93941 & 0.803 & TCD-DMOEA \\ \hline \multirow{3}{*}{DF3} & S1 & 0.34843 & 0.38187 & **0.75188** & 0.44202 & 0.4701 & 0.223 & TCD-DMOEA \\  & S2 & 0.54510 & 0.54590 & 0.54075 & **0.61212** & 0.44961 & 0.2828 & PPS \\  & S3 & 0.31963 & 0.30727 & **0.96457** & 0.42035 & 0.61989 & 0.2195 & TCD-DMOEA \\ \hline \multirow{3}{*}{DF4} & S1 & 0.23576 & 0.24019 & 0.37769 & 0.20971 & **0.4383** & 0.2705 & T-DMOEA \\  & S2 & 0.33726 & 0.33918 & 0.25217 & **0.37772** & 0.24422 & 0.2985 & PPS \\  & S3 & 0.23191 & 0.23489 & **0.56408** & 0.28755 & 0.31761 & 0.308 & TCD-DMOEA \\ \hline \multirow{3}{*}{DF5} & S1 & 0.99550 & 0.99626 & 0.99991 & 0.99233 & 1 & 0.9426 & T-DMOEA \\  & S2 & 0.99769 & 0.99685 & **0.99988** & 0.99959 & 0.99865 & 0.956 & TCD-DMOEA \\  & S3 & 0.99790 & 0.99836 & 0.99993 & **0.99998** & 0.9990 & 0.9903 & PPS \\ \hline \multirow{3}{*}{DF6} & S1 & 0.89098 & 0.94927 & 0.99909 & 0.93195 & 1 & 0.7099 & T-DMOEA \\  & S2 & 0.99325 & 0.99724 & **0.99999** & 0.989478 & 0.632845 & 0.8682 & TCD-DMOEA \\ \cline{1-1}  & S3 & 0.96554 & 0.98884 & **0.99962** & 0.966298 & 0.61565 & 0.75 & TCD-DMOEA \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The MS values of six comparative algorithms with different dynamic test settings.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 1 Introduction
Consequently, there is a strong requirement for a suitable mesh protocol that can make hospital communication effective and robust. Therefore, as per the literature review, during the last few years, researchers have presented different strategies (flooding-based and routing-based) to enable the mesh topology for BLE. Flooding is a simple solution because it does not involve the formation of a connection or the routing protocol [9]. Due to the lack of a routing protocol, it avoids route formation delays and consumes less memory. However, flooding is inefficient because there would be a flood of messages for successful end-to-end communication, which can cause network congestion and overheads and, hence, consumes more power resources. To address the concerns, the routing strategy is best suited for large networks that require efficiency. According to the literature review, researchers have made certain recommendations for the implementation of the BLE mesh routing protocol. Furthermore, regarding the routing-based mesh solution, a single BLE-based protocol is available from [6] presenting an AODV-based solution for BLE mesh networks. However, there are still issues that have to be resolved as stated in the aforementioned research objectives. Moreover, we concentrated on stationary IoT devices for the hospital situation, as well as for O-AODV protocol experimentation. The organization of the rest of the paper is as follows: Section 2 discusses the BLE mesh architecture and message routing protocols, while Section 3 discusses related works based on the BLE mesh. Section 4 talks about the Optimized-AODV (O-AODV) protocol topologies and testbed design. Section 5 is devoted to the proposed optimized-AODV protocol optimizations achieved with the experimental results, the discussion, and research contributions, while Section 6 concludes the discussions for the paper.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 2 Generation of Sensors Data by AERMOD
### Preprocessing of Data  
Several widely used preprocessing methods are discussed in this paper. After receiving the raw data, the preprocessing steps are as follows:  
1. Data normalization is widely used in related work. The min-max scale is adopted to reduce the numerical differences between the data. The formula is as below: \[C_{NORMALIZED}=\frac{C_{SENSOR}-C_{MIN}}{C_{MAX}-C_{MIN}}\] (3) where \(C_{NORM}\) represents the normalized concentration, \(C_{SENSOR}\) represents the concentration received by the sensors, and \(C_{MIN}\) and \(C_{MAX}\) are the minimum and maximum values of the concentration among the raw data.
2. After random shuffling, the dataset is randomly divided into two parts, a training set and test set, in a ratio of 5:1. The training set is used to train the fully connected model. Then, the model is evaluated by the test set to verify its effectiveness.
3. Since the source leaking speed is fixed during the forward modeling procedure, data augmentation is adopted to make the dataset larger and more robust. The data are mixed up and down, ranging from \(-20\) to \(20\%\), with intervals of \(1\%\). This method is not applied to the testing process.  
Figure 2: An example of a diffusion result. The warmer the region’s color, the higher the gas concentration.  
To ensure the robustness of the forward model, data perturbation is applied to the test set. A random perturbation from \(-1\) to 1% is added to simulate the inaccurate measurement of the sensors. This method is only applied to the model testing process.
# Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
## 4 Experiment and Analysis
### Experimental Results of Dimensionality Reduction Algorithm
The network traffic dataset needs to retain the structure of the reduced data as much as possible after dimensionality reduction, so principal component analysis (PCA) cannot be applied to the study in this paper. Compared with the running time of 695.31s of T-SNE algorithm, the running time of UMAP algorithm is only 93.82s, which is much smaller than that of T-SNE algorithm, and UMAP algorithm can better retain the data structure after dimensionality reduction. In this paper, the UMAP algorithm is introduced for the first time to reduce the dimensionality of network traffic data, which can not only preserve the data structure to the maximum extent for visualization and research but can be processed very fast when facing massive network traffic data.
# Characterization and Reactivity of Natural Pozzolans from Guatema
## 3 Results and Discussion
### Pozzolanic Activity
#### 3.2.2 Pozzolanicity Test (Frattini)  
Pozzolanicity can be determined by comparing the hydroxyl ion and calcium ion concentrations in an aqueous solution in contact with hydrated cement after a stated period of time. The calcium hydroxide standard solubility curve was used according to UNE-EN 196-5 [26]. Cement is considered to have pozzolanicity when the experimental points are below the saturation curve [26].  
In the present study, cement was replaced at different ratios (0%, 5%, 25%, 35%, 45%, and 55%) with both pozzolans (T and R). The pozzolanicity after 8 and 15 days is shown in Figure 8.  
The samples that accomplished the pozzolanicity test were the cements with the 45% and 55% replacements for 8 test days (Samples T and R). For 15 testing days, the cements with 25%R and with the 35%, 45%, and 55% replacements (Samples T and R) met the pozzolanicity test. Given the reactivity obtained in the electrical conductivity test, not all of the percentages fulfilled the test at 8 days. Similar results at the same testing time have been obtained by other authors when testing various types of pozzolans of volcanic origin [30]. These results confirm that these natural pozzolans present sufficient reactivity to prepare pozzolanic CEM IV cements, although sample R may be slightly more reactive than sample T.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Line:Pozzolan** & **0.5:9.5** & **1.0:9.0** & **1.5:8.5** & **2.0:8.0** & **2.5:7.5** & **3.0:7.0** \\ \hline T & 94.04 & 79.54 & 63.46 & 47.96 & 16.29 & 8.74 \\ R & 93.01 & 79.75 & 61.50 & 32.78 & 21.77 & 10.77 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Electrical conductivity loss (%) at 60 \({}^{\circ}\) C and 7 days of testing.
# Design and Functionality of a Prototype for Cold Needle Perforation of Wheat
## 1 Introduction  
Wheat is one of the most important cereal crops for human nutrition [1, 2] and makes up approximately 20% of the total global calorie intake [3]. According to the Food and Agriculture Organization (FAO), 750 million tonnes are harvested globally and 25% of the harvested wheat is affected by moulds and mycotoxins [4, 5]. Mycotoxins are secondary metabolites of moulds and have an adverse effect on human health, also referred to as mycotoxicosis [6]. Possible health effects range from acute gastrointestinal issues to the development of cancer. Intoxication with mycotoxins can occur through renal absorption or through the inhalation of spores [7]. Thus, mycotoxins form a substantial public health concern.  
The toxins are unevenly distributed in the wheat kernel, with the highest concentration present in the bran layer. Thus, a cost-effective method of reducing the toxic load and conforming with legal limits is to physically separate the endosperm from the bran layers. This results, however, in a loss of up to 20% of total mass and at the same time reduces the dietary fibre content, micronutrient and mineral content, and other valuable components [8].  
Recently, biological detoxification strategies have been developed in wheat kernels using functional microorganisms and enzymes [9, 10]. To reduce cross contamination during the milling of mycotaxion-containing wheat, the detoxification of whole wheat kernels is a promising approach.  
To achieve this, the hydrophobic aleurone layer must be disrupted first to enable the diffusion of active substances throughout the layers. This disruption has been shown to be best achieved through cold needle perforation (CNP), as previously shown by [PERSON] et al. [11].  
The authors further demonstrated that fluorescent particles in the size range of enzymes (5 nm) and microorganisms (10 \(\upmu\)m) were able to pass into the kernel through fluorescence image analysis. This work reports on the scale-up of cold needle perforation for wheat kernel perforation and the effectiveness of the resulting CNP prototype.
# Tire Defect Detection via 3D Laser Scanning Technology
## 2 Materials and Methods
### Normal-Based 3D Rendering
By mapping the point-wise depth (i.e., the value of the \(z\)-axis) to the corresponding pixel, a projected depth image can be obtained, which significantly reduces the computational  
Figure 3: Projection process from raw 3D data to 2D feature maps. By extracting pointwise depth and normal values, our method projects input 3D point clouds onto 2D planes, which can capture depth and surface orientation information. Red circles indicates the defects. cost during detection. Moreover, as our scanner adopts fixed resolutions across the planer area, there is no information loss in depth channels between the 2D features and original 3D data. * **Rendered images** Similarly, we project point normals into pixel values arranged in a regular 2D feature map. Before projection, the global normal directions are reoriented toward the same direction by defining a virtual camera position outside the tire surface. The normal of each point possesses spatial coordinates represented as a 3D vector \((n_{x},n_{y},n_{z})\). We normalize each channel and project the normalized values into RGB values to reduce information loss, which leads to a rendered image representing the tire surface directions in the 2D domain (as shown in Figure 3). To facilitate annotation, we label defects with bounding boxes in the rendered images. Because of the projection property, the boxes in the rendered images and depth images are in one-to-one correspondence.