esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 6 Discussion  
We briefly present some trade-off of our proposed approach. Since we focused on sensed data and the prediction of the data using correlations and regression, ACxSIMv2 is recommended only for monitoring type application, i.e., all sensor nodes periodically gather information by monitoring phenomena on a specific geographic area. Other types of applications would require adjusts mainly in the system model. In _Target-detection_ (or Classification) applications [46], for instance, the signal power would be an interesting decision variable to incorporate to the model.  
ACxSIMv2 tries to capture the dynamics of data correlation by considering a regression predictor on each sensor node. As soon as one sensor node detects that the error between the sensed data and the predicted one is beyond a threshold, it warns the middleware to reconstruct the clusters again. However, in a highly dynamic environment, regarding changes in phenomena conditions or the position of the physical nodes (mobile nodes), it would be required the execution of ACxSIMv2 consecutive times in a short period of time. In this case, the energy consumption of the proposed approach would not be efficient, since each sensor node could predict only a few measurements between the executions of ACxSIMv2.  
We also have not considered priority of applications/queries. To provide priority, i.e., applications that must be execute before orders, the system model, given by equation 6 should be adapted.  
In our problem definition (Section 3), we have defined 2D spatial in accordance with the dataset used in our simulations. We have chosen to use Intel Lab and Green orbs dataset since they are popular dataset in WSN literature. Both dataset provides only the x and y coordinates of the sensor nodes (in meters relative to the upper right of the map); however, it is important to emphasize that the whole approach is not dependent on the dimensionality of the spatial cells adopted.
## 6 Applying Anomaly Detection in Water Systems
### Data Integrity Attack  
The authors in [67] attack the fieldbus communications of the Secure Water Treatment (SWaT) testbed using a Man-in-the-middle (MitM) attack to illustrate the devastating consequences such attacks could have in industrial control systems (ICS). The SWaT testbed is a scaled down version of a water treatment plant that has six main processes which emulate the functionality their full-scale counterparts. The industrial control network for the SWAT testbed is shown in Figure 4. In the network, the SCADA system communicates with the PLC which in turn has a ring topology connection with the RIO (remote input/out) device. The ring topology consists of two PLCs (one main device and a backup) to ensure reliable communication within the fieldbus network. This network redundancy is what makes this type of attack challenging because cutting the communication link of the main PLC will activate the backup until the attacker is able to close the loop again. The RIO allows communication between the PLC and the actuators and sensors by converting the digital signals required by the former and analog signals required by the latter before transmitting to the respective devices.  
In the attack described in [67], the authors show that MitM attackers would be able insert themselves between the PLC and the actuators and sensors and consequently manipulate sensor reading and/or control commands sent to the actuators. This could lead to, among other things, tank overflows and the manipulation of the treatment process to contaminate the water and potentially damage tanks and pipes. The attackers could also launch a series of stealthy attacks which emulate  
Figure 4: SWaT Industrial Control Network.  
the physical processes of the system in order to remain undetected by the built-in fault detection mechanisms of the control system. These types of attacks are not easy to carry out because an attacker first needs to have an intimate knowledge of how the system operates which he can gain by first launching passive attacks. These attacks are also more sophisticated than network traffic-based attacks (e.g., DoS attacks), which may not require prior knowledge of the system, but they are far more difficult to detect and have substantially more devastating consequences when successful [68].
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
## 2 Analysis Method for Electromagnetic Actuators and Design Process
### Design and Analysis Framework  
Figure 3 shows the main steps and procedures in the actuator design and evaluation process performed in the present study. As shown, the framework consists of three parts. In the first part, an experimental model of the jet valve is constructed to measure the total resistance force acting on the needle, \(F_{TRF}\), and evaluate the minimum value of the thrust force which each actuator must therefore produce (referred to henceforth as the necessary thrust force, \(F_{NTF}\)). In the second part, numerical models of three simplified 2-D SA, MCA, and MMA models are constructed based on the dimensions proposed. FEA simulations are performed to determine the thrust force, \(F_{TF}\), produced by each actuator when excited by a fixed electric current (referred to as initial excitation current, \(I\)). The geometric dimensions of each model are then modified in such a way as to generate a maximum thrust force corresponding to effective SA, MCA and MMA models. In the third part, the minimum excitation current, \(I_{j_{i}}\) found to satisfy the thrust force, \(F_{TF}\), generated by the modified models will be greater than or equal to \(F_{NTF}\). Finally, the actuation performance and thrust force magnitude of the three modified actuators are evaluated and compared and preliminary designs for the corresponding actuator-driven jet dispensers also prepared.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 4 Methodology
### Dynamic Sampling Strategy
GenerateExtraSample: The function verifies if a node with the slowest expansion speed exists (perhaps in an insufficiently explored area), if there is such a node, extra sampling points \(N\) are produced at this node. This step achieves adding more sampling points in unexplored areas, thereby improving the depth and breadth of the search. isAnyObstacleInPath: these extra sampling points are processed individually. For each sample point, an initial check determines if obstacles exist in the path from the slowest expanding node to this sample point. If the path is clear (i.e., there are no obstacles), this sample point is added to the search tree. This step ensures that the extra sampling points generated have relevance, meaning they can be directly reached from the current node. (Algorithm 1. 5-9)  
If there is no node with the slowest expansion speed, the current iteration is skipped, moving to the subsequent iteration. This means that, without a clear determination of unexplored areas, no additional sampling will be carried out, but regular search steps will continue to be performed. This step is carried out to ensure the robustness of the algorithm, which can function normally even in special circumstances. (Algorithm 1. 11-15)  
While there is a need to spend time calculating the expansion speed of existing nodes in the tree to find the node with the slowest expansion speed, this also allows us to concentrate our resources on the most promising nodes. With this strategy, our tree expansion strategy can effectively avoid falling into local optimal solutions, and, to some extent, this also accelerates the search process. In addition, this adaptive optimization method aims to dynamically adjust the sampling strategy according to the information obtained during the search process and the current environment, to improve the efficiency of the path planning algorithm in complex environments and improve the quality of the generated path. This is an optimized allocation of computational resources, aiming to maximize the possibility of global optimization, reduce search time, and provide strong support for robot navigation in complex environments. It helps to improve the efficiency of the path planning algorithm in complex environments and the quality of the generated path. This strategy can adaptively adjust the sampling distribution, making the search process more focused on difficult-to-expand areas, thereby improving the efficiency and quality of path planning given the updated node expansion speeds in each iteration, and ensuring that the search process always focuses on the most challenging area currently.
## 3 Related Work
### Business Process Analysis Based on PM  
In general, traditional BPA methods focus on the process model analysis. In contrast, the BPA-based on PM is not confined to process modeling analysis. In the domain of PM, the process model analysis is the control-flow perspective, but other perspectives (such as case perspective, performance perspective, and process comparison perspective) can also help us understand the process in depth.  
PM was applied to identify human operators' information navigation characteristics in main control room by [PERSON] et al. mainly from control-flow perspective [23]. [PERSON] and [PERSON] applied PM to the domain of manufacturing and logistics, which would conductive to the comprehensive understanding of business processes from control-flow as well [4]. To discover the healthcare process model and analyze the process, the algorithms of heuristic miner, fuzzy miner, and Comp miner were used [24]. [PERSON] et al. proposed a framework to analyze the financial processes based on PM from several perspectives, such as organizational perspective, control-flow perspective, case perspective, performance perspective, and so on [7]. [PERSON] et al. mainly focused on workload and delay analysis in manufacturing process based on PM [25]. [PERSON] et al. used the process modeling algorithm of fuzzy miner to explore the study law of students based on PM [26]. To gain insights into internal transaction fraud mitigation, [PERSON] et al. proposed a PM framework which covered the stages of event log preparation, event log inspection, control-flow analysis, role analysis, case analysis, and so on [27]. [PERSON] et al. focused on the issues of data quality for PM in the domain of customer service [28]. [PERSON] et al. analyzed the production plan process based on dotted chart technique and heuristic miner under the guidance of PM framework [29]. [PERSON] applied PM to service mining [30].  
Although the industries may vary when applying PM, we can conclude five common points: (1) the business experts are necessary in the process of PM practice; (2) control-flow is the most important perspective for PM, but other perspectives can deepen the process analysis; (3) the theory of PM mainly focuses on [PERSON]'s viewpoint, which means that PM convers the contents of process discovery, conformance checking and enhancement; (4) PM may involve several organizations; and (5) the business process analysis based on PM focused on historical event logs.  
Meanwhile, there are two problems in the process of BPA-based on PM: (1) although most of the BPA cases based on PM can involve several perspectives, how to integrate the PM results from different perspectives is a big challenge, and (2) most of the BPA cases based on PM focus on operational level and how to analyze the PM results across the organizational levels is another big challenge.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 3 Materials and Methods  
### Data Collection  
We collected a crop image dataset for model training from AI-hub, a Korean platform that discloses open-source data needed for developing AI-based technology, products, and services [22]. As for crop image data, the \"Facility Crop Disease Diagnostic Image\" dataset [23], which is the main field crop (10 types) disease image data, and the \"Outdoor Crop Disease Diagnostic Image\" dataset [24], which is the main facility horticultural crop disease image data (12 types), were used for the disease diagnosis of crops. Both datasets contain images of normal and disease-infected crops. We selected five crops; \"peper\", \"pumpkin\", \"tomato\", \"bean\", and \"spring onion\", which are common in Korean households.  
Using the annotation of metadata contained in AI-hub, the crop image data was used to detect normal crops and nine diseases with \"Pepper Anthracnose\", \"Pepper White Powder\", \"Pumpkin Old Disease\", \"Pumpkin White Powder Disease\", \"Tomato Leaf Mold Disease\", \"Soybean Spot Disease\", and \"Black Bottle\". As shown in Figure 2a, crop images appropriate for model training could be well collected.  
Meanwhile, to prevent disease from being detected in non-crop objects, we had to secure non-crop images for model training so that the model could learn there are no diseases in the non-crop image. We collected non-crop image datasets by randomly crawling non-crop images on websites such as Coupang, Danawa, Lotte Home Shopping, Monthly Airlines, JoongAng Ilbo, Dong-A Ilbo, and Chosun Ilbo. We also took pictures of objects, such as cups and pencils, which can be commonly encountered in everyday life. As shown in Figure 2b, non-crop images could be well secured.  
Figure 2: (a) Red Pepper with Anthracnose; (b) Non-Crop Image.
## 4 Results and Discussion
### Effect of Low Temperature on Fatigue Properties of DH36 Welded Joints  
Low temperature has a great influence on fatigue [51]. Here is an example of cruciform welded joints to discuss the effect of low temperature on the fatigue properties of welded DH36. The fatigue test results of cruciform welded joints of DH36 steel studied by [PERSON] [47] were cited and compared with the results at \(-60\)\({}^{\circ}\)C in this paper, as shown in Figure 16.  
At the same logarithmic stress range, the logarithmic fatigue life of cruciform welded joints at room temperature (RT) and \(-60\)\({}^{\circ}\)C were different. For example, at the logarithmic stress range of 2.50 (316 MPa), the logarithmic fatigue life (lg\(N_{5}\)) of cruciform welded joints at \(-60\)\({}^{\circ}\)C was 5.55 (354,813 cycles), while the logarithmic fatigue life of cruciform welded joints (lg\(N_{6}\)) at RT was 5.181 (151,808 cycles) which was almost three times that of room temperature. This means the fatigue properties of cruciform welded joints at \(-60\)\({}^{\circ}\)C were better than at room temperature.  
The effect of low temperature on fatigue properties can still be analyzed from the aspect of Vickers hardness. The Vickers hardness of cruciform welded joints at room temperature and \(-60\)\({}^{\circ}\)C was put in one figure, as shown in Figure 17. It could be found that Vickers hardness profiles at \(-60\)\({}^{\circ}\)C was higher than that of room temperature. In particular, the Vickers hardness at \(-60\)\({}^{\circ}\)C was 12.17 HV higher than that of room temperature in the failure zone.  
Figure 16: The logarithmic S-N curves of cruciform welded joints at room temperature and \(-60\)\({}^{\circ}\)C.  
Figure 17: The Vickers hardness of cruciform welded joints at room temperature and \(-60\)\({}^{\circ}\)C.  
The low temperature raises the Vickers hardness of the welded joints, and the increase of hardness enhances the fatigue properties of the welded joints of DH36 steel. In this paper, the fatigue properties at low temperature were investigated from the aspect of hardness, and a more comprehensive and detailed study on the mechanism of fatigue at low temperature still needs to be carried out.
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
## 3 Damage Diagnose Strategy
### Normalization and Classification Tools
#### 3.3.1 \(k\) Nearest Neighbor  
The \(k\) nearest neighbor (\(k\)NN) algorithm has been used since 1970. It is a classification algorithm that is used to make a prediction of a new observation based on the category of the \(k\) nearest neighbors. Two elements are key to this approach:  
1. the one and only parameter \(k\); and
2. the distance measure [38].  
The most commonly used distance measures in machine general are, in general, the Hamming distance, Euclidean distance, the Manhattan distance and the Minkowski distance. In this paper, the Euclidean distance is used.
## Materials and Methods
### Chemicals and Standard Solutions  
Nonadecanoic acid (Internal Standard, IS), cholesterol (IS), myristic acid, palmitic acid, palmitoleic acid, stearic acid, oleic acid, linoleic acid, linoleic acid, \(\beta\)-sitosterol, ergosterol, squalene, chloroform, hexane, methanol, pyridine, NO-bis(trimethylsilyl)trifluoroacetamide (BSTFA), with trimethylchlorosilane (TMCS) were purchased from Sigma (Western Cape, South Africa).  
A 100 mg/L lipid stock mixture was prepared (using the above-mentioned lipids) in degassed anhydrous chloroform and stored at 4 \({}^{\circ}\)C. This stock mixture was used to prepare a standard curve with lipid mixtures at the following concentrations: 0.5, 1, 2, 5, 10, 20 and 50 mg/L. Following this, the lipid mixtures were stored at \(-20\)\({}^{\circ}\)C until they were used. The lipid mixtures used in this study are based on the concentration of free fatty acids and sterols found in grape juice [26; 43]. Lipids were extracted in SGM which was comprised of 100 g/L glucose, 100 g/L fructose, 2.5 g/L tartaric acid, 3 g/L malic acid, 0.2 g/L citric acid, 1.14 g/L potassium phosphate dibasic, 1.23 g/L magnesium sulfate heptahydrate, 0.44 g/L calcium chloride dihydrate, supplemented with vitamin, trace element and nitrogen stocks [49; 50]. Furthermore, lipids were extracted from a YNB  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Aim** & **Sample Type** & **Matrix/Solvent** & **Parameters Tested** & **Observations** \\ \hline \multirow{3}{*}{1} & GC-MS & \multirow{3}{*}{Standard mixtures} & \multirow{3}{*}{Chloroform} & Separation & Derivatization well \\  & optimization & & & MS parameters & documented in the \\  & & & & MS dynamic range & literature \\ \hline \multirow{3}{*}{2} & Extraction & \multirow{3}{*}{Standard mixtures} & YNB medium & Volume (sample, solvent, & GC-MS optimized in \\  & optimization & & Yeast (one strain) & Pellet size, solvent & stage 1 \\  & & & & volume, aliquot & Any additional \\  & & & SGM & Volume (sample, solvent, & Any additional \\  & & & & aliquot) & compounds of interest? \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Method testing} & \multirow{3}{*}{Standard mixtures} & YNB medium & \multirow{3}{*}{Performance parameters} & Reference, \\  & & & Yeast (various strains) & & non-interfering matrix \\ \cline{1-1}  & & & SGM & & - \\ \hline
4 & Applications & \multicolumn{3}{c}{Real samples from fermentation experiments} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Method optimization strategy.  
medium (Difco, Le Pont-de-Claix, France) supplemented with 20 g/L sugars and nitrogen stock used for the preparation of SGM.  
An SGM medium was autoclaved prior to performing lipid extractions, whereas the YNB medium was filter-sterilized. Next, lipid stock mixtures were added to the SGM and YNB media at the aforementioned concentrations.
## 2 Dynamic Compression Test at the High Strain Rate  
### Test Specim  
The regular hexagon aluminum honeycomb structure is adopted. The hexagonal aluminum honeycomb structure is commonly made of 0.02-0.1 mm thick aluminum foil through bonding. There are two manufacturing methods, forming and stretching. Stretch method is suitable for industrial production due to the high efficiency, so it is widely used. The process flow of the stretch method to manufacture aluminum honeycomb structure include aluminum foil cleaning, node glue, solidification, slitting, stretch. The cleaning process mainly contain alkali wash, rinsing, phosphoric anodization, spray and drying. Then J-70 adhesive based on the epoxy resin is applied, which can inhibit corrosion. The gluing process is completed by a special gluing machine. After that, the coated aluminum foil needs to be folded to form a panel and then curing. The curing parameters are related to the selected adhesive, generally speaking, the pressure is 0.5 Mpa, the time is 3-5 min. After slitting and stretching forming, the honeycomb panel is formed.  
The basis material of the aluminum honeycomb structure is 5052 aluminum alloy. The chemical composition is shown in Table 1. The cell characteristics are as follows: the cell wall thickness \(t\) is 0.06 mm with the length \(l\) of 1.732 mm; cell structure characteristic size \(d\) is 3 mm, as shown in Figure 1. The wall thickness of the opposite cells in each cell structure is 0.12 mm due to the manufacturing method. The thickness of the aluminum honeycomb structure sample is 5 mm, and 3 samples were prepared for each test. The coordinate system of the aluminum honeycomb structure is shown in Figure 2. The direction \(x_{3}\) is the direction of the out-of-plane loading, and \(x_{1}\) and \(x_{2}\) are the directions of the in-plane loading.
## 2 RF Features
### Time of Arrival  
The Time-Of-Arrival (TOA) technique measures the time it takes for an RF signal to travel from transmitter to receiver, multiplying by the signal's speed in the medium, which is usually the propagation speed of light. Thus, the range can be inferred. As the obstruction does not significantly impact the speed of the wave, TOA delivers higher accuracy compared to RSS provided that tight clock synchronisation among receiver and transmitters is carried out, and there is a Line-of-Sight (LOS) path. The existence of LOS and clock synchronisation, however, are the two challenges in TOA estimation. When no LOS path exists, the multi-path components are received while each travels further than the distance between the receiver and transmitter. Stringent clock synchronisation also calls for complex hardware.
## 2 Literature Review
By leveraging this mechanism, we enhance the overall security of V2I and V2V communications. * Our protocols establish reliable and secure communication channels for V2I and V2V interactions. They enable efficient and seamless communication between vehicles and infrastructure units, facilitating the exchange of critical data and messages. * We propose a novel resource-sharing approach based on graph theory principles. By modeling the VANET as a graph, we optimize the allocation of resources, such as bandwidth and power, among vehicles and infrastructure units. This ensures fair and efficient resource utilization, enhancing the overall performance of the communication network. * By addressing these essential aspects, our research advances the security, reliability, and efficiency of V2I and V2V communications in vehicular networks. The proposed protocols and resource-sharing mechanism pave the way for more robust and secure communication systems, fostering the development and deployment of intelligent transportation solutions.
## Abbreviations/Nomenclature  
**APS**: atmospheric phase screen
**CPT**: coherent pixel technique
**CS**: coherent scatterers
**\(D_{A}\)**: amplitude dispersion
**DDS**: digital direct synthesizer
**DEM**: digital elevation model
**DInSAR**: differential SAR interferometry
**FMCW**: frequency modulated continuous wave
**GB-SAR**: ground-based SAR
**GCP**: ground control point
**LFM-CW**: linear frequency modulated continuous wave
**LTP**: long-term processing  
**LOS**: line-of-sight
**MAF**: model adjustment function
**ML**: multi-look
**PS**: permanent scatterer
**PSI**: persistent scatterer interferometry
**RSLab**: remote sensing laboratory
**SAR**: synthetic aperture radar
**SCS**: stable coherent scatterers
**SLC**: single look complex
**SNR**: signal-to-noise ratio
**STP**: short-term processing
**SVA**: spatial variant apodization
**TSC**: temporal sublook coherence
**UPC**: Universitat Politecnica de Catalunya