esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 1 Introduction
### Crack Detection  
The majority of newly constructed and rehabilitated dams have structural health monitoring (SHM) systems that provide real-time screening and monitoring of the safetyand health condition of the structures [8; 9]. An adequate evaluation of the aging of dams provides a better understanding of their present safety as well as enabling better planning of rehabilitation and reconstruction investments. Therefore, the monitoring of dams plays a key role in dam safety operations and has become indispensable for the identification of behavioral changes that may develop over the lifetime of the dams.  
The prediction of measurements and the interpretation of the behavior of dams, using the data obtained from the measurements, is a standard practice nowadays for dam engineers [10; 11; 12]. In this regard, the use of different forms of behavioral models (digital twins) makes a major contribution in capturing the performance of a dam, making such behavioral models a key component of dam safety systems.  
Nowadays, engineers can use predictive models to evaluate dam behavior, estimate dam response to actual loading conditions, and establish warning levels. Both data-driven models and numerical models based on the finite element method (FEM) have been extensively applied to predict dam displacements, stresses, and strains and, thereby, to estimate the response of the dam [10; 11; 13]. As computing power has increased, both data-driven and numerical models have improved in terms of their level of detail and precision, as well as their complexity.  
In this work, a methodology using FEM models was developed based on the displacements measured in the monitoring devices of an arch dam. This methodology aims to detect if a crack has been formed in the dam and, as a second goal, to estimate the severity of this crack. For this purpose, the results of the monitoring devices were compared with the simulated movements based on certain reference cracks.  
As a first step, it was necessary to create a 3D model of the dam to be analyzed; in this sense, the tests of the methodology were carried out on a real dam by adding several reference cracks. Later, this 3D model will be used to calculate the displacements through FEM simulations, as if they were measured in the monitoring devices.
## 4 Conclusions  
The aim of this study was to investigate resistivity and its anisotropy characterization of 3D-printed conductive composites. A conductive, CB-filled ABS filament for a fused filament modeling printer was created. Comparing the resistivity of the filaments and cubic samples made by 3D-printing, it can be concluded that the resistivity of the printed samples increased remarkably. Specifically, the resistivity increased from 29.06 \(\pm\) 0.77 \(\Omega\cdot\)m for the filaments to 42.59 \(\pm\) 1.94 \(\Omega\cdot\)m and 101.60 \(\pm\) 6.85 \(\Omega\cdot\)m for the cubic samples in the horizontal direction and the vertical direction, respectively.  
The influence of process parameters, i.e., layer thickness, raster width, and air gap, on the resistivity of the 3D-printed parts was also studied. Raster width and air gap significantly affected the resistivity in the vertical direction, and layer thickness and air gap significantly affected the resistivity in the horizontal direction. It was evident that process parameters affected the internal structure of printed parts and influenced the resistivity. It also appeared that a wider raster width produced a greater bonding area, which decreased the resistivity in the vertical direction; a greater air gap produced a larger void, which increased the resistivity; increasing either the layer thickness or the air gap produced larger voids and then increased the resistivity in the horizontal direction. Hence, the resistivity of printed parts can be adjusted by carefully changing the process parameters.  
The resistivity of the printed parts was anisotropic as the internal structure of the parts was anisotropic. The process parameters alter the internal structure and, thus, modify the resistivity anisotropy ratio simultaneously. This study indicates that the resistivity anisotropy ratio can be adjusted from 1.01 to 3.59 by adjusting the raster width. This feature offers an efficient and reliable method to fabricate objects with controllable resistivity anisotropy.  
The following are available online at www.mdpi.com/2076-3417/7/1/20/s1. Figure S1: The fabrication of filaments from pellets by the single screw extruder, Figure S2: The fabrication of samples by 3D printer, Figure S3: Resistivity measurement system, Figure S4: The relationship between the resistivity in the vertical direction and the contact resistivity, Table S1: Experimental design showing the three-level, three-factor factorial design and experimental results.  
This work was supported by the National Natural Science Foundation of China under Grant; 51477176 and 31600799.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments. [PERSON], [PERSON], M.D., [PERSON], and [PERSON] carried out the experiments. [PERSON], [PERSON], M.D., [PERSON], and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the paper. All authors reviewed the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (WSNs) are a soaring frontier research hotspot in multiple subject areas [1]. A WSN can collaboratively monitor, sense, and collect the various information of environments or monitored objects in real time through various integrated microsensors, and transmits the perceived information to the user terminal by multihop relay through a random self-organized wireless communication network. WSNs can realize the idea of \"ubiquitous computing\", and have become an integral part of the C4 ISRT system (command, control, communication, computing, intelligence, surveillance, reconnaissance, and targeting). Today, it is widely used in various areas of IoT/AI, such as healthcare [2], energy management [3], smart transportation [4, 5, 6], thermal comfort [7], and energy load identification [8].  
The nodes of a WSN are usually a miniature embedded system. Its advantages are small size, low cost, and easy to deploy, while the disadvantages are that its energy, storage space, computing power, and bandwidth are greatly limited. Most WSNs are deployed insparsely populated or human-inaccessible areas, and it is difficult to replace the batteries for the nodes. Therefore, the limited power energy is one of the most important constraints in the design of the whole WSN, which directly determines the life cycle and application life of the network. To extend the life cycle of WSN, there are two ways: power supply and energy saving. For this reason, many researchers at home and abroad have carried out a lot of work, such as investigating self-power supply technology for harvesting energy from nature [9], wireless power technology [10], and the node energy consumption problem [11]. However, their proposed schemes lack comprehensiveness and exhibit poor performance. Additionally, certain schemes may be too computationally demanding to be implemented on sensor nodes that have limited resources. To prolong the life cycle of WSNs, we focused on three key areas for improvement: (1) Energy harvesting technology. We analyzed the current mechanisms and methods of energy harvesting technology, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and examined the voltage output characteristics of a bistable piezoelectric cantilever in order to gain further insights. (2) Low-duty-cycle working mode. We proposed a neighbor discovery mechanism that utilizes a separation beacon, based on reply to ACK, to facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reducing idle listening time and resulting in substantial energy savings. (3) Data fusion technology. We introduced a data fusion mechanism founded on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatiotemporal correlation and can reduce energy consumption associated with data transmission by the nodes.
## 5 Conclusions  
Marine vehicles use ad hoc network technology, and sensor networks have been implemented both on the surface of the water and in underwater environments. These networks can suffer from energy consumption, path loss, delay, etc. Introducing a cooperation and relay optimization scheme can avoid such problems and deliver an energy-efficient, path-loss-aware routing approach. In this article, a path loss avoidance scheme was proposed, focusing on energy consumption. For use in such cases, the cooperation and relay optimization scheme was introduced. The proposed CR-NBEER protocol was tested under different simulation settings. Additionally, the performance of the protocol was evaluated in comparison with the existing Co-UWSN, CEER, and NBEER approaches for routing in the MUSN. The simulation were carried out in MATLAB, where the performance of the proposed method was evaluated in comparison with existing approaches. Four performance evaluation parameters, EED, PDR, EC, and PL, were considered. From the results and discussion, it was found that the proposed CR-NBEER protocol performed well due to the advanced relay optimization with fair possibilities in the network. The proposed protocol was demonstrated to be efficient for use in future marine communication systems in which the ad hoc network is composed of sensor nodes.  
The proposed method was developed in consideration of the following points.  
* The proposed method was focused on the cooperation scheme. Furthermore, a relay optimization scheme was developed.
* In terms of effective operation, the proposed CR-NBEER protocol achieved better results and performance than the previous protocols.
* The neighbor node identification approach was implemented.
* The presented protocol was tested while employing different simulation parameters.
* The results and evaluations show that the proposed method had a dramatic impact on cooperation and relay optimization with respect to energy consumption.  
The marine sensor network is still in its infancy, and requires robust and efficient routing and other localization and relay approaches. However, the proposed method can effectively be deployed for marine communications. The work performed in this article will have a dramatic impact on sensor network performance in underwater regions. This could be further improved by effectively implementing quality-of-service, energy, void hole avoidance, node scalability, geographic scalability, and localization approaches.  
Conceptualization, A.H.; Data curation, M.Z.K.; Formal analysis, I.U.; Funding acquisition, I.U. and A.G.; Investigation, B.M.; Methodology, A.H.; Project administration, I.U., O.A. and A.G.; Resources, B.M. and A.G.; Software, A.H. and T.H.; Supervision, I.U.; Validation, T.H.; Visualization, O.A.; Writing--original draft, A.H.; Writing--review and editing, I.U. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Researchers Supporting Project Number (RSP2023R102), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
All authors declare no conflict of interest.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 2 Methodology  
This article firstly performs a series of preprocessing on the VPN-encrypted traffic data to obtain a new encrypted traffic data collection. Next, the features of the VPN-encrypted traffic dataset can represent each other, i.e., the correlation between features and features is high. This results in redundancy in encrypted traffic data features. The first-order incremental search method is used to analyze the correlation between Time-Related VPN-encrypted traffic features, and calculate the correlation coefficient between features and their importance ranking. Then, mRMR feature selection is performed on VPN-encrypted traffic to eliminate irrelevant or redundant features and obtain the optimal Time-Related VPN-encrypted traffic feature subset. Then, it is necessary to focus on unbalanced VPN traffic samples that are difficult to classify. The Focal Loss function is designed based on the cross-entropy loss function to improve the Xgboost model, and the calculation weight of VPN traffic samples is changed. It is necessary to obtain the VPN-encrypted traffic identification model based on FL_XGB, which realizes the processing of data imbalance between data classes. Eventually, the objective function of a given optimization is combined with Bayesian optimization to implement a global parameter search strategy. the posterior distribution of the objective function is updated by continuously adding sample points to find the optimal value of the objective function of the combined parameters. The optimized global optimal parameter combinations are imported into the FL-XGB model for training. It can solve the problem of the low identification rate of the VPN-encrypted traffic identification model, and finally output the identification results.  
The general framework of the FL_XGB-VPN-encrypted traffic identification model is shown in Figure 1.
### Problem Statement  
In the present study, we considered a peg-in-hole assembly task that required the mating of two components. One of the components was grasped and manipulated by the robot manipulator, while the second component had a fixed position either via fixtures to an environment surface or by being held by a second robot manipulator. Figure 1 provides a 2D representation of the considered insertion tasks and the components assumed to be available to solve the task. The proposed method was designed for a position-controlled robot manipulator with a force/torque sensor at its wrist. Typically, these insertion tasks can be broadly divided into two main phases [27], search and insertion. During the search phase, the robot aligns the peg within the clearance region of the hole. In the beginning, the peg is located at a distance from the center of the hole in a random direction. The distance from the hole is assumed to be the \"positional error\". During the insertion phase, the robot adjusts the orientation of the peg with respect to the hole orientation, and pushes the peg to the desired position. We focused on both phases of the assembly task with the following assumptions:  
* The manipulated object was already firmly grasped. However, slight changes of object orientation within the gripper were possible during manipulation.
* There was access to imperfect prediction of the target end-effector pose (as shown in Figure 1) or a reference trajectory and its degree of uncertainty.
* The manipulated object was inserted in a direction parallel to the gripper's orientation.  
We considered the second assumption fair given the advances in vision-recognition techniques, wherein the 6D poses of objects can be estimated from single RGB images [28; 29] or RGB images with depth maps (RGB-D) [30; 31]. The high accuracy of the predictions is in many cases enough for robot manipulation. Moreover, this second assumption included the specific case of using an assembly planner [32; 33], where even if the initial position of the objects is known, the inevitable error throughout the manipulation (e.g. pick-and-place, grasping, and regrasping) that makes the positions/orientations of the manipulated objects uncertain during the insertion phase. A reference trajectory could be similarly obtained from demonstrations [34; 35; 36] when a complex motion is required to achieve the insertion. The last assumption allowed for defining a desired insertion force that may vary for different insertion tasks without loss of generalization.  
Figure 1: Insertion task with uncertain goal position.
# V2X-Based Highly Reliable Warning System for Emergency Vehicles
## 6 Conclusions  
Our proposed work will help EVs to overcome the time delay in reaching the destination by finding the fastest route based on the traffic density of every ITS that is continuously updated to a centralized server and updates the interworking of DSRC and cellular network technologies. When the ITS is within a close range, DSRC can be used in long-range 4G-LTE to overcome the limitations of both DSRC and 4G-LTE. Traffic congestion is overcome by customizing each traffic signal using J2H and by updating it to the next traffic signal. Path blockage can be cleared by V2V communication. Thus, the EV can maneuver smoothly and reach the destination without any delay, thereby saving lives. The results obtained show that the proposed J2H approach performed well in terms of transmitting the alert messages in high traffic density with a delay of 0.013 s, and with a 0.003 s delay in the low traffic density scenarios when compared with the existing systems. In terms of throughput, J2H has 32% higher throughput than the existing solutions. Thus, the proposed solution moves from a jitter to a highly reliable solution for the EVs in the ITS.  
Conceptualization, K.S.A.; methodology, R.S.M. and S.B.P; writing--original draft preparation, K.S.A.; validation, S.B. and M.R.; formal analysis, A.D. and R.S.M.; writing--review and editing, S.B. and M.R; supervision, S.B.P.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R195), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
Data Availability Statement: Data in this research paper will be shared upon request to the corresponding author.  
The authors declare no conflict of interest.  
Figure 10: Analysis of throughput when time changes.
## 2 Results and Discussion
### Antifungal Activities  
The _in vivo_ fungicidal results of title compounds against _Pythium ultimum_ Trow, _Phtyophthora infestans_ ([PERSON], _Corynespora cassicola_, _Botrytis cinerea_ and _Rhizoctonia solani_ were listed in Table 2, Zhongshengmycin, Dimethomorph, Procymidone, Chlorothalonil and Validamycin were used as controls. As shown in Table 2, some of the title compoundsshowed good control efficacy against _Pythium ultimum_ Trow at a concentration of 100 \(\mathrm{\SIUnitSymbolMicro m}\)/mL, such as compound **5a** (66.7%), **5b** (55.6%), **5h** (55.6%) and **5i** (55.6%), which is better than that of control. While some of them exhibited low activity  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Method** & **Time** & **Temperature/\(\mathrm{\SIUnitSymbolOhm}\)C** & **Yield/\%** \\ \hline
**5b** & No-MW & 24 h & r.t. & 78 \\
**5b** & No-MW & 10 min & 90 & 42 \\
**5b** & MW & 10 min & 90 & 79 \\
**5b** & MW & 15 min & 90 & 81 \\
**5b** & MW & 20 min & 90 & 81 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of yields of **5b** through methods with or without microwave irradiation.  
against _Pythium_ thrown. For example, compound **5c** (\(-\)88.9%), **5d** (\(-\)111.1%), **5e** (\(-\)55.6%), **5j** (\(-\)33.3%), **5k** (\(-\)122.2%) held no inhibitory against _Pythium_ thrown. On the opposite, these compounds can increase the fungal growth. For this fungal _Pythium_ _ultimum_ _Trow, the control Zhongshengmycin also cannot inhibit it. For the fungal _Corynespora cassiicola_, the compound **5c** (46.2%), **5d** (46.2%), **5e** (61.3%) and **5f** (50.9%) displayed excellent inhibition respectively. They exhibited the same inhibitory as that of the controlchlorotaholanil (45.9%).All the title compounds exhibited no inhibition effect against _Rhizoctonia solani_ and _Phytophthora infestans_ (Mont.[PERSON]. Only compound **5a** (3.9%), **5c** (6.7%) and **5k** (10.6%) displayed weak inhibitory against _Rhizoctonia solani_. The control procymidone also exhibited no activity against _Rhizoctonia solani_. Surprisingly, most of these compounds showed stimulate growth activity against _Botrytis cinerea_, except compound **5c** (23.8%), **5d** (19.9%) and **5e** (19.9%).
## 3 The n-Dimensional Phase Portraits by Coordinates' Transformations
### Foundation  
To illustrate this idea, consider the system:  
\[\frac{dx}{dt}=f(x) \tag{5}\]  
and a transformation \(T\) such that \(\overline{x}=Tx\), \(\overline{x}=\left[\widehat{x}\,\widehat{x}\right]^{T}\), \(\widehat{x}\in\mathbb{R}^{k}\), \(T=\left[W\ T_{2}\right]^{T},T^{-1}=\left[V\ T_{1}\right],\) such that \(VW^{T}\) is a projection along the kernel of \(W\) spanned by \(V\):  
\[\overline{x}=\left[\begin{array}{c}\widehat{x}\\ \widetilde{x}\end{array}\right]=\left[\begin{array}{c}W\\ T_{2}\end{array}\right]\left[\begin{array}{cc}V&T_{1}\end{array}\right] \left[\begin{array}{c}\widehat{x}\\ \widetilde{x}\end{array}\right]=\left[\begin{array}{c}W\left(V\widehat{x}+ T_{1}\widehat{x}\right)\\ T_{2}\left(V\widehat{x}+T_{1}\widetilde{x}\right)\end{array}\right] \tag{6}\]  
If the new state vector is replaced in (5) one has:  
\[\frac{d\widehat{x}}{dt}=W\widehat{f}\left(V\widehat{x}+T_{1}\widehat{x}\right) \tag{7}\]  
If the term \(T_{1}\widetilde{x}\) is small enough, it can be neglected and a reduction of order that preserves the most important dynamics is obtained:  
\[\frac{d\widehat{x}}{dt}=W\widehat{f}\left(V\widehat{x}\right) \tag{8}\]  
The construction of such projection is not a trivial task for a nonlinear system, and it depends on its particular characteristics. Mostly, linearization methods are used to ensure a local proper reduction [19; 20; 21]. For linear systems, criteria about the eigenvalues [22], passivity [23; 24], Pade approximations, Arnoldi/Krylov methods [25; 26; 27], among others are regularly used.  
In this paper, linearization/multi-linearization and largest-eigenvalues view reduction is proposed because of its simplicity, enough to plot phase portraits with univocally determined representative views instead of using the original \(\
u\)-views phase portraits as in the previous section. That is, the other reduction methods are not a trivial task and they are not always possible.  
Figure 9: Phase portrait view \(x_{3}-x_{4}\) for the system (4).  
Consider the linearization of (5) in some initial condition:  
\[\frac{dx}{dt}=Ax \tag{9}\]  
The state space transformation \((\overline{x}=Tx)\) may be selected as \(AT=T\Lambda\) where \(T\) is an invertible matrix and \(T^{-1}AT\) is a diagonal matrix consisting of the eigenvalues of \(A\) from which dominant eigenvalues can be selected to truncate the rest. This process is called modal truncation [28]. If such a transformation can not be achieved, it is possible to use another truncation method as singular decomposition [29], controllability and observability Gramians [18], among others [28]. Alternatively, one can try to select the dominant eigenvalues of \(A\) as those of maximum absolute value.