esa-sceva/satcom-chunk-collection huggingface dataset:
# Increasing the Reliability of Flood Embarments with Neural Imaging Method
## 1 Introduction
Among the methods used for modeling and prediction of phenomena occurring inside the flood embankments are: deterministic methods [5], statistical methods [6], probabilistic methods [7], the Hasofer-Lind reliability index [8], harmonic analysis [9], heuristic methods based on fuzzy logic [10; 11], genetic algorithms [12]. Other methods used in maintenance support systems for flood protection are: Support Vector Machine [13], Non-linear Shear Strength Criterion [14], as well as artificial neural networks (ANN) [15; 16]. Currently, the main application area of ANNs are predictive issues, improving operational processes by identification [17] or classification of faults [18]. The most widely used algorithms for tomographic image reconstruction include deterministic methods [5], stochastic methods, artificial neural networks [19], hybrid optimization methods and topological methods (level-set methods). Deterministic methods are characterized by the fact that they look for a local minimum of the objective function that is continuous in a given variability range. Stochastic methods make use of the objective function only in test points of the examined area, without imposing additional requirements on continuity and differentiability of the function. In addition, the global minimum of the objective function is searched for in a defined set of allowable values of design variables. Artificial neural networks are used to achieve the desired solution in a very short time, practically on-line. This method consists in selecting a suitable neural network and collecting training data for the network. ANNs behave like approximation systems and have a certain capacity to generalize training data. Hybrid optimization methods combine features of deterministic and stochastic methods. In the first stage of calculations, the stochastic algorithm usually finds the global minimum of the objective function. Once this algorithm's efficiency starts to decrease, a deterministic algorithm is introduced as it can find a solution in a small number of iterations. In hybrid methods, genetic algorithms and the simulated annealing method are often used as stochastic algorithms. This paper presents a new technique for monitoring the state and performance of flood embankments by neural imaging. It is an advanced tomographic reconstruction method for flood embankments, walls and dams. The neural architecture of the proposed innovative intelligent imaging system ensures higher-resolution images of the cross-section of the scanned object. The novelty of the proposed flood embankment monitoring system that distinguishes it from previous solutions lies in the use of a unique system of interconnected neural networks. The research used the original system consisting of 2012 separately trained neural networks. By generating a reconstructed image, each of the ANNs has the same input vector, consisting of 208 measurements coming from the electrode system.
# Review of Remote Sensing Methods to Map Coffee Production Systems
## 3 Results
### Texture-Based Approaches  
Classifying imagery based on the texture of a surface, i.e., surface roughness, is commonly applied to high-resolution imagery and with SAR imagery backscatter. For this paper, we reviewed three studies that exclusively employed a texture-based analysis [74; 75; 76]. [PERSON] and [PERSON] [74] used Ikonos and Quickbird panchromatic images to classify various tropical crops, including two coffee classes. To avoid the issues associated with distinguishing the spectral signatures of different shade systems from the surrounding forest, they calculated height textural indices (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) on the two images for windows from 3 to 9 pixels and using a maximum likelihood classifier. The authors did not report an accuracy assessment with this study, however, so the efficacy of this method remains uncertain. [PERSON] et al. [75] used a combination of the VV and VH (vertical and horizontal) polarization backscatter from Sentinel-1 to classify forests. They performed a forest classification using both a thresholding method based on the Otsu algorithm (an automated thresholding method for image segmentation presented in Otsu [77]) and k-means clustering (a statistical method to identify data cluster centers presented in [PERSON] and [PERSON] [78]) to obtain overall accuracies of 92% and 88%, respectively. [PERSON] et al. [76] used SAR data to specifically map coffee. They used single polarization, combinations of two and three polarizations, and fully polarimetric data to classify imagery in Brazil collected by an airborne SAR-R99B sensor. Accuracy for this classification varied widely depending on the polarization used: single polarization achieved 38.4-56.4% overall accuracy, while the fully polarimetric data achieved 96.6-97.6% overall accuracy. Given the success of the fully polarimetric data in [PERSON] et al. [76], researchers should continue to explore the potential utility of SAR data to map coffee.
# Securing SDN-Based IoT Group Communication
## 2 Background
### CoAP and CoAP Group Communication  
CoAP [8] is a lightweight protocol, designed to be the \"HTTP of the IoT.\" CoAP resources are identified by a URI scheme, similar to HTTP URIs, and the CoAP interaction model is similar to the client-server model of HTTP. Therefore, IoT endpoints act as CoAP \"servers\", exposing one or more CoAP URIs that can be accessed by CoAP \"clients\" using a suitable CoAP \"method\".  
CoAP group communication is a CoAP extension that allows CoAP clients to retrieve (or set) resources from a group of CoAP servers, e.g., retrieve the temperature measurements from all sensors of a building, turn on and off all the lights of a smart city, etc. An  
Figure 1: An overview of the entities of the proposed solution.  
approach for realizing CoAP group communication is using IP multicast (Section 2 of [2]). With this approach, CoAP servers belonging to the same group join an IP multicast address, and CoAP clients learn the IP multicast address of a group using DNS resolution. Then, CoAP clients can send CoAP requests to an IP multicast address and receive the corresponding response(s) using unicast. Nevertheless IP multicast is not the only option; other underlay networking architectures can be used instead. For example, as as we discuss in the following section, our solution relies on SDN to implement one-to-many communication.
# Adaptive Ransomware Detection Using Similarity-Preserving Hashing
## 2 Literature Review
### Windows API-Based Methods  
[PERSON] et al. [20] introduced an approach to ransomware detection based on a dynamic analysis of Windows API calls. To begin, the system captures a log of all Windows API calls made via processes. Following that, feature engineering using n-grams takes place to transform the data into a valid format for further processing. Before running the data through the ML models, relevant features are extracted to reduce complexity. While this can be done manually by qualified experts in the field, it is time-consuming and obstructs the automation of the procedure and its scalability. Thus, the authors proposed a particle swarm optimization (PSO)-based method for automatic feature selection. Next, the refined features are passed to an ML model to detect ransomware behavior. The authors tested the following ML models: regularized logistic regression (RLR), support vector machine (SVM), KNN, decision trees (DTs), and random forest (RF). The proposed method was able to achieve results close to and occasionally higher than those of manually selected features.  
Moreover, [PERSON] et al. [21] put forward a similar framework that analyzes Windows API calls. However, they proposed a novel approach to feature extraction using enhanced min-max relevance (EmRmR), which filters out irrelevant logs, reducing the workload in the subsequent steps. The new approach was tested on the following ML models: SVM, KNN, DT, and RF. RF consistently outperformed all other methods, reaching a peak accuracy of 98.7% using a 3-gram feature length.  
Another paper by [PERSON] et al. [22] combines static signature analysis with Windows API analysis, using ML models to develop a two-stage ransomware detection system. First, the system checks for known malware samples, using traditional signature matching with the SHA-256 hashing algorithm. In the second stage, the system uses ML models to analyze the Windows API calls made via the programs to provide an accurate prediction of whether the software is malicious or not. While the system achieves high detection accuracy (>99%) on the testing dataset, this method, like other Windows-API-based methods, cannot generalize to other operating systems.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 8 Validation  
The full validation of the proposed UAS design is a comprehensive task. Therefore, this section will present some selected examples of validation of the UAS. The included examples of validations and their rationale are as follows:  
* Formation flying validates the swarm operation from the coordinated motion path of two drones during an inspection;
* Messaging services with ROS communication characterize the D2D communication needed for coordination within the swarm;
* Long-Range Radio Communication validates the use of open industrial radio standard for D2G with LoRa;
* Automated Fault Detection with Deep Learning validates the application of deep learning for object/fault detection;
* Cloud Services support drone swarm inspection. It is validated by demonstration with inspection at a powerline test facility.  
In addition, the paper offers three demonstration videos as supplementary materials. Video S1 presents the drone hardware and validates the cable detection and identification algorithm. Video S2 validates the formation flying during a powerline inspection. Video S3 demonstrates the operational aspects of the UAS including the integration of the drone with cloud services.
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 2 Materials and Methods
### Determination of DPPH (2,2-Diphenyl-1-picrylhydrazyl) Radical scavenging activity  
_DPPH Radical scavenging_ activity of the extracts was determined using [PERSON] et al.'s (2018) method, with minor modifications. To determine the _Radical Scavenging_ activity of phenolic compounds derived from olive waste, a stock solution of 60 \(\mu\)M _DPPPH_ was prepared daily in methanol, and the solution was kept in the dark at 4 \({}^{\circ}\)C. An amountof 50 \(\mu\)L of the phenolic concentrated stock solution was added into the well, and it was further diluted down to the lowest concentration by performing a serial two-fold dilution in a 96-well microtiter plate. A row of negative _DPPH_ controls was added in the same 96-well microtiter plates by adding 50 \(\mu\)L of methanol into each well. An amount of 150 \(\mu\)L of methanolic _DPPH_ was added into each well, and the reaction was allowed to proceed for 30 min in the dark. The absorbance was measured at 560 nm using a microtiter plate reader.  
Determination of ABTS (2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-sulfonic Acid) Radical Cation Stabilisation  
_ABTS Radical Scavenging_ activity of the extracts was determined using [PERSON] and [PERSON]'s [31] method. The _ABTS_ radical cation was produced by mixing a 7 mM stock solution of _ABTS_ with 2.45 mM of potassium persulfate. This mixture was left to stand in the dark at room temperature for 12 h before use. The concentration of the blue-green _ABTS_ radical solution was adjusted with methanol to an absorbance of 0.700 at 734 nm. An amount of 50 \(\mu\)L of the phenolic concentrated stock solution was added into the well, and it was further diluted down to the lowest concentration by performing a serial two-fold dilution in a 96-well microtiter plate. A row of negative _ABTS_ controls was added in the same 96-well microtiter plate by adding 50 \(\mu\)L of methanol into each well. An amount of 280 \(\mu\)L of _ABTS_ radical solution was added into each well, and the reaction was incubated for 5 min at 30 \({}^{\circ}\)C. The absorbance was measured at 450 nm using a microtiter plate reader.
## 1 Introduction
With these data, obtained from the texture analysis of an image, a common pattern can be detected in patients with a specific genomic mutation, such as the KRAS mutation in patients with colorectal cancer. Trying to describe different textures of an object is extremely difficult for the human eye, even more so if that description is intended to be done on a CT image. However, by using computer programs it is possible to assign a quantitative value to a certain texture of a CT image, taking into account many characteristics that are invaluable to the human eye. If this quantitative analysis of the texture of a tumour with a certain pathophysiological singularity (mutation in the KRAS oncogene) is able to obtain a characteristic pattern, it will be possible to check whether other tumours have thispattern (and therefore, the KRAS oncogene mutation). This is of utmost importance because the radiological data derive from the entire tumour, while the pathological data only derives from the sample obtained invasively. Therefore, radiomics can provide valuable information regarding the genomics of the tumour process, and this may have an important role in the diagnosis, prognosis, treatment, and monitoring of these patients [18]. To identify a pattern in the images of patients with a mutation in the KRAS gene, it is necessary to extract from CT images characteristics of the texture which cannot be extracted by the visual analysis of the radiologists. Subsequently, using machine learning algorithms, e.g., Support Vector Machine (SVM), Grading Boosting Machine (GBM), Neural Networks (NNET), or Random Forest (RF), the molecular signature of colon cancers could be automatically classified into KRAS + or KRAS- based on those previously extracted texture characteristics. In this way, the existence of common texture patterns that are present in patients with colorectal cancer with mutated KRAS, but not in patients with native KRAS, will be demonstrated, which will allow determination of the presence of the mutation from the analysis of the CT image. The Materials and Methods section details the selection of the patients, the description of the texture features and machine learning models, and the performance measures. The Results and Discussion section summarizes the results achieved. Finally, the main conclusions and future work are reported in the Conclusions section.
## 2 Related Works
The centroid of these four sub-squares creates a path in the form of the letter Z. The paths between the squares are linked using a letter Z Hamilton path as the trajectory for the mobile anchor. Many similar methods have been proposed in which the deployment area is divided into small areas at the beginning and then different linking strategies are used in the four sub-squares, for example, the M-curves [33], H-curve [32], and NHexCurves [34] methods. [PERSON] et al. proposed an algorithm that integrates the SCAN trajectory into a structure with multiple squares, referred to as SCAN-ET [31]. SCAN_ET divides the deployment area into several small zones. The mobile anchor uses the SCAN method to travel through every zone. If the mobile anchor detects any unknown nodes, it moves along the edges of the triangle in this zone and broadcasts messages at each of the vertices of the triangle. Some studies have scheduled the trajectory of the mobile anchor as a curve in order to solve the problem of three-point collinearity [36; 37]. The curve path has a minimal number of straight-line segments, resulting in much less collinearity occurring during the localization process. For example, the CIRCLES trajectory, proposed in [27], consists of a sequence of concentric circles with their center in the deployment area. One potential problem with the use of CIRCLES is coverage of corners. If the deployment area is a square,CIRCLES is not able to effectively cover the four corners. Thus, the S-CURVES trajectory schedules an 'S' curve to mitigate the problem of corner coverage. [PERSON] et al. proposed the mobile anchor centroid localization (MACL) method [37]. MACL places the mobile anchor in the center of the deployment area and drives it in a spiral trajectory. MACL also has the problem of corner coverage. [PERSON] et al. proposed a DeteRministic bEAcon Mobility Scheduling (DREAMS) algorithm [39]. The mobile anchor first locates a sensor by random movement. Subsequently, depth-first traversal (DFT) is performed. The mobile anchor uses the track of the DFT graph to locate unknown nodes. The sensors are used to create the DFT in the DREAMS algorithm. However, this method does not take into consideration the communication range of the sensors. Therefore, it is unsuitable in scenarios where the communication range is limited. Considering that unknown nodes may be located anywhere in the deployment area, most of the above methods schedule the trajectory for the mobile anchor in accordance with the information on the size of the deployment area in which positioning is to be performed and the communication range of sensors. In this paper, a trajectory scheduling method is proposed in which the nodes whose locations have already been determined are used to help the mobile anchor dynamically change the default scheduled trajectory used in the SCAN method, thus reducing the traveling distance.
# Method to Predict Performances of PCB Silicone Conformal Coating under Thermal Aging
## 2 Materials and Methods
### Conformal Coating Sample Preparation  
Silicone conformal coating films A and B are coated using a Hohsen PI-1210 auto film applicator of 10 \(\upmu\)m accuracy on a PET release liner (Figure 1). Meanwhile, both conformal coatings are spray-coated on a PCB using a PVA 350 benchtop coating system. Conformalcoating thickness after drying is controlled to range from 100-120 \(\upmu\)m. PCB used in this paper is from Hytera with XY-direction CTE equal to 18 ppm/\({}^{\circ}\)C.
# Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing
## 4 Materials and Methods  
### Proposed NLP-Based Command-Creation Model  
This study covers two main areas. These are the field of robotics and the field of NLP. Therefore, the study is conducted on two levels. The NLP system detects what the user says or writes, translates it into an appropriate format, and transfers it to the robot system. The robotic part is the structure that implements these commands on a robot and realizes them in the simulation. The stages required to realize these processes are divided into four parts. A block diagram of these stages is shown in Figure 2.  
First, a morphological analysis is performed on the commands, which are received as text from the user or as audio and converted into text. Here, the morphology module in the Zemberek application is used [10]. The types of words in the given sentence, such as adjectives, nouns, and verbs, are obtained via the morphological analysis. For example, if  
Figure 2: A block diagram of the proposed approach.  
we analyze the sentence \"Yesil bardagi kutuya koy (English: \"Put the green glass in the box\"),\" the following information will be obtained (hereafter, English translations of the Turkish command sentences will be given in parentheses after the statement for a better understanding of the commands): yesil (\"green\") \(\rightarrow\) adjective bardak (\"glass\") \(\rightarrow\) noun kutu (\"box\") \(\rightarrow\) noun koy (\"put\") \(\rightarrow\) verb As can be seen from the result, we start with a morphological analysis, but the output we obtain at this stage is not enough. In sentence structure, the component that performs the action and is affected by the action must be obtained. To understand the task in a task sentence, it is necessary to infer the purpose for which the words are used in the sentence rather than their types. For this reason, a syntactic inference must be made. The elements of the task must be identified by correctly separating the sentence into its components.
## 3 Case Study
### Case Study Description  
Before the reliability analysis, the structure and working principle of the case should be mastered, which will be described as follows. The wellhead connector fixed on the subsea wellhead is crucial connection of subsea wellhead and subsea X-mas tree. The subsea connector is suitable for deep water that can be quickly connected or disconnected in the subsea environment. When the subsea connector is docked, the locking structure is driven by the driving structure (pressure ring) to apply a pre-tightening force to the flanges and the seal ring to seal the connector and form a complete closed cavity, thereby realizing the transportation of subsea oil/gas. However, the wellhead connector is subjected to unexpected external loads/forces, such as corrosion, wear and the accumulated fatigue caused by dynamic loadings, which significantly affect the reliability of the wellhead connector of subsea X-max tree. The wellhead connector of the subsea tree is as shown in Figure 6, the connector is mainly made up of two components: the top hub and the bottom hub. The top hub is mainly composed of the top flange, the collet, the driving structure and the seal ring. The bottom hub mainly includes the bottom flange, the upper and lower cylinder wall and the collet supporting plate.  
The following sections contain a detailed description of the top hub and the bottom hub of the wellhead connector of subsea X-max tree.