esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 4 Conclusions  
This paper focuses on the research of defect A-scan signals based on ultrasonic phased array detection. By combining experimental detection and theoretical analysis methods, specific research on the acquisition of ultrasonic phased array defect A-scan data and signal denoising was conducted. Based on a customized weld containing five types of typical defects in the flat plate butt joint test block, an ultrasonic phased array detection experiment was carried out, the process flow of detection was introduced in detail, and the waveform characteristics of the five types of defect signals were analyzed and studied. A total of 150 defect signals were collected, including 30 defects in each category. A joint wavelet packet analysis, variational modal decomposition of, and sparrow optimization algorithm, a method that can be used for noise reduction of ultrasonic phased array defect echo signals, is developed. The research findings are summarized as follows:  
1. Through noise reduction analysis of the experimentally acquired defect echo signals, the extraction of effective information to extract defects from the detection signal was realized, and the method proposed in this paper could effectively retain the detailed features of the ultrasonic phased array defect A-scan signals, with a better noise reduction effect.
2. The noise-containing signal was simulated by adding Gaussian noise to the simulated signal, which was processed using the proposed SSA-VMD-WPD noise reduction method. A comparative study was carried out by combining four noise reduction methods, namely, SSA-VMD, WPD, SSA-VMD-WAVE, and wavelet analysis, and the results showed that the effect of the proposed method in this paper was better.  
In addition, the experimental data in this paper were derived from customized test blocks, which were somewhat different from the signals obtained from actual testing, and the data in the experiments all originate from the same type of material; therefore, in future research, the actual testing data in the field should be introduced, and the effect of the type of material should be further considered.  
Conceptualization, X.C. and W.P.; methodology, X.C.; software, W.P.; validation, W.P. and S.G.; formal analysis, W.P. and S.G.; investigation, W.P. and W.G.; resources, X.C. and W.P.; data curation, W.P.; writing--original draft preparation, W.P.; writing--review and editing, Y.K. and W.G.; visualization, W.P.; supervision, Y.K.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Administration for Market Regulation, grant number 2022 MK090.  
Not applicable.  
The data supporting this study's findings are included and will be available from the corresponding author upon reasonable request.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 6 Conclusions and Future Works  
A novel path planning algorithm that applies UAVs as an efficient data collector from WSNs in emergency environments is proposed herein. Considering the advantage of the PRM algorithm in obtaining the shortest collision-free map and that of the ABC in operating and converging quickly, we have addressed the issues of slow convergence and easy entrapment in the locally optimal solutions observed in other algorithms. The proposed HPP algorithm has an optimization process for different flying parameters such as altitude, path length, flying time, and hovering time. It can effectively obtain the shortest and safest path, as well as managing different dynamic environment constraints. The proposed algorithm performs better than the conventional ABC and PRM algorithms and requires a shorter convergence time. The path coordinates that are obtained through the HPP algorithm can be effectively used for real-world applications. The simulation results demonstrate that the proposed HPP reduces flight time and energy consumption in UWSNs. The HPP algorithm manages fixed and dynamic obstacles, avoids restricted areas effectively, and shortens its planning time. Therefore, it can also be used for real-time path planning.  
However, our current work has some limitations. We can consider more realistic and complex simulation scenarios and varying sizes and speeds of dynamic obstacles. Additionally, we can use intelligent computing methods to lessen the computation time, as discussed in the problem formulation. Using a swarm of UAVs with better coordination among UAVs can help improve network coverage in large UWSNs. The precise synchronization of sleep and wake-up schedules with UAV's arrival time is quite challenging during actual implementation. We can work towards a better synchronization to reduce energy and time in power-constrained sensor devices. We will exploit these in our future work. We will also consider some advanced methods such as mobile edge computing and software-defined network techniques in our upcoming work, because they help minimize the computation costs of delay-sensitive networks.  
[PERSON] developed and simulated the HPP protocol; [PERSON] directed the research and contributed to refining the protocol and interpreting the simulation results; the paper was drafted by [PERSON] and subsequently revised and approved by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1F1A1060501).  
**Institutional Review Board Statement:** Not applicable.  
Figure 11: Convergence time.
## 6 Results and Discussions
### Study System
#### 6.2.2 Analysis of Demand Response Based on Electricity Price  
The results of the demand response of each MG to the price set by the DNO were obtained. Figure 5 shows the optimized SOCs and the charging and discharging power of the energy storage system in three MGs. In time slots 0-1 and 3-5, the DNO set a low selling price so the energy storage could be charged by cheap electricity to prepare for the daytime load demand. In time slots 6-9 and 16-21, the PV power generation was not sufficient, and the energy storage could choose to discharge. In time slots 8-9, the energy storage in the MG1 discharged too much in the previous hour and reached the boundary value of the capacity, so it was necessary for it to be charged. In the period of 9-15, the PV power generation was excessive, and the energy storage was charged to prepare for the peak of load demand at night. At time slot 24, the energy storage capacity returned to 60 kWh to satisfy the time coupling characteristics of the energy storage.  
Figure 6 shows the optimization results of the transferable load of three MGs. In time slots 0-6, the DNO set a low selling price to shift more load demand into this period. In time slots 7 and 8, the PV power generation and energy storage discharge could satisfy the load demand, so the transferable load was reduced. In time slots 9-15, the PV power generation was high, so the DNO set a low selling price to encourage more of the load to consume PV power. In time slots 16-22, the electricity consumption was in the peak period and the PV system did not generate electricity, so the transferable load was reduced. In time slots 23-24, the DNO set a low selling price to encourage the MGO to increase the transferable load to meet the global constraint. The optimization of the transferable load achieved the effect of cutting the peak and filling the valley.  
Figure 5: Optimized state of charge (SOCs)and the charging and discharging power of the energy storage system.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 2 Research Methodology and EWMA-KAN-Transformer Model
### Structure of KAN-Transformer Prediction Model Based on EWMA Data Processing
#### 2.2.4 Encoder Layer  
The flow of the Encoder layer is illustrated in Figure 8. The output of the new Embedding layer is fed into three linear layers to obtain \(Q\), \(K\), and \(V\). Multi-head attention mechanism calculations are performed using Formula (4) to establish temporal correlations and extract relevant features through weighting. The output then passes through an Add & Norm layer, followed by a feedforward neural network composed of CNN-Gelu-CNN for nonlinear transformation, enhancing the model's ability to capture nonlinear relationships in the data. Finally, another Add & Norm layer processes the data to produce the output of the Encoder layer. It is important to note that the last dimension of the Encoder layer's input and output remains unchanged at 512.  
The multi-head attention mechanism algorithm is described using Formula (4):  
\[\left\{\begin{array}{l}Score_{(w)}=\text{softmax}\Big{(}\frac{Q\times K^{T} }{\sqrt{d_{k}}}\Big{)}\\ C=Score_{(w)}\times V\end{array}\right., \tag{4}\]  
where \(Q\) represents the query matrix, \(K\) represents the key matrix, \(V\) represents the value matrix, and \(\sqrt{d_{k}}\) represents the scaling factor for the attention weight range.
## 4 Conclusions  
The conclusions which could be drawn from the current work reveal that a successful matrix acidizing processes for carbonate reservoir core samples recovered from the Qamchuqa Formation in the Kirkuk oilfield, northern Iraq, could be performed using a new combined acid formulation based on (HCI: Oxalic acid). The samples show optimum performance using 12% HCl:3.2% Oxalic acid based on analyzing the rock samples treated through different experiments designed by RSM. The dissolution of the samples and the % increase in acid density after acidizing seemed to increase with increasing acidizing temperature. A newly adopted laboratory technique (in-situ micro-model) was employed to study the features of matrix acidizing, as well as identification and evolution of pore spaces based on optical microscopy and petrographic observations from thin sections of the carbonate core samples, which are in-situ injected by the combined acids. The adopted procedure can provide high-resolution and rapid information on porosity enhancement detected by visual observations, and later using ImageJ software to observe the microscale-sized alterations on the surface of thirty-micrometer-thick targeted core samples, and calculations of the enhanced pore area after acidizing at different temperatures which simulate the real temperature of the field environment.  
Author Contributions: Writing and preparation the original draft, N.S., I.K. and A.A.; Review and editing, N.S., I.K. and A.P., Methodology and software, N.S., I.K. and A.A.; Supervision, N.S., I.K. and A.P.; Project administration, N.S. and I.K.; Funding acquisition, A.P. All authors have read and agreed to the published version of the manuscript.  
The study benefited from research funds of the Universite Libre de Bruxelles (ULB)-Belgium.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: The increase in pore area (%) as a function of acidizing temperature.
## 2 LiCSAR System Architecture
### Processing and Storage Facility  
The LiCSAR processing and storage system runs on CEDA's data analysis infrastructure JASMIN [57]. JASMIN is a computing facility designed for environmental data analysis supercomputing. The processing is carried out mainly using a Load Sharing Facility (LSF) for distributed computer clusters named LOTUS. Since September 2018, JASMIN offers over 40 PB of storage and over 10,000 computing cores distributed between the LOTUS computing cluster and a community cloud [57; 58]. LOTUS is managed through a queuing system, which allows splitting of large processing jobs to run on a requested number of computing cores reserved from LOTUS computing nodes. As we use a dedicated processing queue with a limit of a maximum number of 128 reserved computing cores, we do not use parallel processing algorithms but rather send larger numbers of processing jobs by reserving one computing core per job.  
A community cloud service at CEDA offers managed cloud instances, which we use to run a MySQL/MariaDB database system dedicated to the LiCSInfo database.  
Apart from a 350 TB disk area for permanent internal, publicly shared and temporary LiCSAR output files, the JASMIN infrastructure offers direct access to a CEDA Sentinel Mirror Archive (SMA), a service mirroring data from the Copernicus Sentinel programme [59]. Currently, SMA contains more than 2 PB of data or ~1.6 million individual Copernicus Sentinel data products, over 60% of which is Sentinel-1 SLC data [57]. To cater to the increasing amount of Sentinel data, CEDA has developed the Near Line Archive (NLA) system. The NLA is used to archive older data onto a modern tape storage system. This brings about the limitation of having only the newest data (acquisitions younger than 3 months) available on disk via instant access. Archived data can still be requested--it should take no longer than 24 h to retrieve data from the NLA into its original location in the SMA directory structure. The restored data can be accessed for a limited period of time (typically 3 weeks). Therefore, the Sentinel-1 data does not have to be downloaded before processing, greatly reducing the time necessary to obtain results.  
In cases where the requested Sentinel-1 data are not available at SMA, we use one of the optimised high speed transfer servers at the CEDA JASMIN facility to download the required data from either NASA's ASF DAAC or the Copernicus SciHub server. The necessary SLC datasets are normally available on Copernicus SciHub within a few hours of the satellite acquisition.  
Finally, we have established a dedicated computing node at the University of Leeds supercomputer (ARC4, [[http://arc.leeds.ac.uk](http://arc.leeds.ac.uk)]([http://arc.leeds.ac.uk](http://arc.leeds.ac.uk))) that serves as a stable extension of the LiCSAR system, primarily running at CEDA facility. We use the node for running the LiCSAR EIDP service.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
###### Abstract
The next step after finding the target is the preparation of the threat and payload so that **Malware is delivered** to the user. Malware can be delivered either by a direct approach (user responsible), including E-Mail, SMS, or a malicious Link on malicious websites, or an indirect approach (attacker's experience), including an encrypted/encoded link, an active directory attack, privilege escalation, the directory access method, adware, etc. After the malware is transmitted, the following phase is **Exploitation**, or payload, which refers to the activities that malware performs. At this stage, it may participate in an active attack or passive attack. Active attacks are masquerade attacks such as login access or a bypass, session hijacking/reply attack, message modification using a change in header by taking advantage of weak cryptography algorithms, a Denial of Service/Distributed DoS (DoS/DDoS) attack causing flooding, and malformed data. Possible passive attacks are the monitoring of network traffic, scanning of open ports and vulnerabilities, eavesdropping, footprinting, spying, and dumpster driving (information collected from discarded devices and the recycle bin). After this second phase is started, the attacker decides whether to quit or continue, but in both cases, the threat continues. They install malware and use a backdoor to obtain  
Figure 1: Malware exploitation stage. access to more information, as well as steal credentials that allow them to maintain a long life. They may install new malware, and the next stage is **Command & Control (C&C)** in which a persistent connection is granted to an attacker, and this may infect other connected systems, constituted as **Data Exfiltration** (the violation of data integrity and availability) and **Spread laterally** (mapped drives and connected systems/devices). The structure of this paper is as follows: Section 2 highlights work related to Malware, the most famous attack on digital systems. Section 3 focuses on the architecture of the Internet of Things (IoT), industrial IoT (IIoT), different attacks, and mitigation proposed by several researchers, i.e., physical, network, data attacks, and software attacks. Section 4 explains details of the Android architecture, vulnerabilities in the Android system, and different malware detection techniques, and Section 5 focuses on application-hardening techniques applicable for any kind of digital system, but especially in the context of IoT and Android. Section 6 concerns the novelty and limitations of the article, and finally, Section 7 presents the conclusion and future work. The taxonomy of this presentation is shown in Figure 2.
# Enabling Role-Based Orchestration for Cloud Applications
## 3 TOSCA Model-Based Approach to Cloud Application Orchestration
### Architectural Design of a Topology Model-Based Orchestration System
#### 3.1.3 Extension to OAM Kubernetes Runtime and Workflow Engine  
To fulfill OAM configuration manifests of an application in a Kubernetes cluster, we have developed an extension to OAM Kubernetes runtime [18]. Basing our orchestration system on OAM Kubernetes, we integrated several in-house extensions to it. One of them is a set of Kubernetes controllers fulfilling the capabilities' functions, such as auto-scaling and rollout. Following Kubernetes operator pattern, these controllers can create underlying platform resources and organize them to work according to the configuration  
Figure 8: Conversion of Workloads_Connection Relationship from TOSCA to OAM.  
Figure 7: Conversion of AttachesTo Relationship.  
defined by corresponding TOSCA capability Node types [19]. They take care of low-level configuration details intended to be hidden from the capability layer, allowing application builders to remain concentrated on their orchestration goals without being distracted by low-layer details.  
Another extension is the workflow engine interacting with the runtime to synchronize the state of application components and execute workflow directives defined in TOSCA Service Templates. TOSCA provides an imperative workflow definition that can be used to deploy, manage, or terminate a TOSCA Service Template. It allows the specification of the cases that has not been planned in the definition of Node and Relationship types ahead of time.  
In contrast, workflow is not supported by OAM runtime because OAM is positioned to be a purely declarative standard model. Therefore, we designed a workflow engine to parse and execute workflow definitions from the TOSCA part. With regard to the schema of TOSCA workflow, our workflow engine faces two challenges; one is to observe the state of components at runtime, and the other is to interpret and execute workflow directives, when a certain state is reached. Kubernetes API dictates a design convention that a resource should record the current state in its \"_status_\" sub-resource. Our workflow engine makes use of it to keep track of the state changes. To address other challenges, we defined a series of custom directives that help the workflow engine to resolve operations defined by a TOSCA model into those of modified OAM configurations. An operation of a Node type will result in certain modifications to generated OAM entities. For instance, the _set_new_image_ operation of _dcc.workload.containerized_ Node type can accept an image tag as an input parameter to update the node's image tag property. If this operation is invoked, the workflow engine asks for inputs to set the image tag field of the OAM Component. Once a Component's configuration is updated, OAM Kubernetes runtime automatically applies the changes to the live workloads.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Oil Spill (OOS)
#### 2.7.1 SAR
The clustering algorithms have the feasibility in the detection of dark regions. For example, [PERSON] et al. (1995) [255] applied a fuzzy c-mean algorithm. A combination of opening and closing as two mathematical operations was also adopted by [PERSON] et al. [256] to detect suspicious spills. In the feature extraction step, several discriminative features are extracted from the dark regions obtained from the first step. Feature extraction is the most crucial and challenging step in OOS detection. A series of features describing OOS and lookalikes are extracted in this step. The extracted features must be adequately discriminative to facilitate the following classification step (i.e., the third step). The proposed features can be summarized into four main groups [224]. 1. Geometrical features: OOS generally has a regular shape. For instance, sailing tankers that illegally discharge oil waste form a linear spill. The geometrical features provide useful information about the shape of the identified dark regions. The following geometrical features are the most well-known features for OOS detection using RS data: Spill area (\(A\)), Spill perimeter (\(P\)), Spill width, Spill complexity (\(\frac{P^{2}}{A}\)), and Hu moments [257; 258]. 2. Radiometric features: radiometric features provide information about the physical property of the segmented areas and their surrounding based on the backscattering coefficient (\(\sigma\)0). The well-known radiometric features are the average and standard de viation of \(\sigma 0\) inside and outside dark regions [239; 258], maximum and mean contrast between dark area and background [258], maximum, mean, and standard deviation gradient of dark region's border [258], local area contrast ratio [239], power-to- mean ratio of background [239], and the homogeneity of background [239; 259]. 2. Texture features: these features provide information about the spatial correlation between neighboring pixels [224]. The most commonly used texture features are those obtained from the GLCM (e.g., contrast, dissimilarity, homogeneity, angular second moment, and energy and correlation) [259], statistical features after applying a discrete wavelet transformation (e.g., the logarithm of energy, Shannon's index, angular second moment, and entropy) [260], and fractal [261; 262]. 3. Ancillary information: in addition to the above-mentioned features, several features were proposed to facilitate discriminating between OOS and lookalikes. These features are wind speed, the number of ships in the image [238], distance to ships and lands [263], and weather information [264]. In the final step, a classification algorithm is trained based on the extracted features to discriminant the OOS from lookalikes. Various classifiers have been applied in the literature, including ANNs [258], Mahalanobis classifier [257], SVM, fuzzy classifier, an algorithm based on a statistical model and rule-based approach [239].