esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Method
### Brief System Overview and Integration  
Medical imaging has made great progress in last few decades. Over the years, different types of medical imaging have been developed such as X-ray, computed tomography (CT), molecular imaging, and magnetic resonance imaging (MRI). HIMS use these files for reporting, data transfer, diagnostic, and treatment purposes. These images, which doctors can use to help better understand a patient's condition, are the most important part of diagnosis and treatment. In this study, client application refers to a health information management system which is running on a web-server with a static IP.  
There are major theoretical and conceptual frameworks that were used in this study and can best be summarized under three headings: service-routines (client and server side), security issues (for secure connection), and developing distributed file system architecture (the main part of our system). Service routines have been implemented by using client-server socket tools to accomplish secure communication between client-side and server-side, client-service routine (CSR), and data-node service routine (DNSR). These services communicate with each other using transmission blocks that are shown in Figure 1, in JSON data structure over TCP/IP. Library files (DLL files) must be installed on the client application for the client application to integrate into the system. The CSR is responsible for sending the medical image files regardless of its size and type to DNSR and reading them on demand. The CSR sends the client application's requests (HIMS) to DNSR with a secure connection. Also, one smaller Windows service has been implemented for replica nodes to search the files. Moreover, this service provides encrypted data transfer with the other nodes.  
The server side is the important part of this work, it is a comprehensive kernel service responsible for (a) listening to the specified port number for any client application request; (b) the authentication process includes checking CSR IP value; and (c) processing reading, writing, and deleting operations according requests.  
Another important key part of the study is achieving a strong security level which includes: (a) primarily static IP value of CSR; (b) JSON and BSON data structure for data transfer; (c) symmetric encryption algorithm.  
Thanks to security measures, the security of the developed middle layer platform has been increased dramatically.
# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 5 Hybrid CNN-LSTM Model for PdM
### Feature Engineering  
To construct features based on the properties of individual data sources, we use a lag feature engineering method [39]. The purpose of this feature creation is to identify variable inputs and dynamic lags that aid in capturing regular trends in time-series data and to create new features from the input variables that explicitly represent the time-series components. The first data source telemetry data includes timestamps, used to calculate lagging features. We choose a 3-h lag window size for the lag features to be constructed, then compute the mean, standard deviation, minimum, and maximum values to represent the time-series data's short-term history over the lag window. The extracted features are merged to create a final dataset for telemetry. Similarly, for error data source we count each type of error in a lagging window and reformat the data to have one entry for each machine with the timestamp at which the error occurred. Finally, we calculate the total number of errors of each type that occurred over a period of 24 h, at a sampling rate of 3 h. Maintenance records are an important data source, as they have the number of replacements for each component in the last three months. We calculate the duration since the last component was replaced, which gives us the information about the component failure. The days since the last component was replaced are calculated for each type of component as features from the maintenance data source. The machine features are used without any modification. All the features are merged together to create a final dataset. Finally, the labels are generated to estimate the probability of a machine failing due to a certain component. We create a categorical failure feature label for each row. The final dataset consists of 34 features for 100 individual machines collected for a period of one complete year with a sampling rate of 3 h. Therefore, for a total of 100 machines, we have 291,641 observations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Set** & **Validation** & **Test Set** \\ \hline
70\% & 10\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Train, validation, and test data splits.
###### Abstract  
A series of microbial-induced carbonate precipitation (MICP) experiments were conducted using _Sporosarcina pasteuri_ to reinforce coastal soft clay in Zhejiang. By analyzing the physical and mechanical parameters of samples of varying ages, specifically focusing on each sample's unconfined compressive strength, triaxial shear strength, and permeability coefficient, it was revealed that MICP technology can be used effectively to reinforce coastal clay. The unconfined compressive strength of treated soil increased by 23% compared to untreated soil, while the permeability coefficient decreased by 75%. The internal friction angle of the clay remained almost constant, whereas cohesion significantly increased by approximately 53%. One-dimensional compression experiments were also performed, yielding consolidation parameters such as the compression coefficient, compression index, and consolidation coefficient. The results indicated a notable decrease in the soil compression index. Furthermore, microscopic analysis revealed that clay particles were cemented by calcium carbonate, whose precipitation was induced by the bacteria. Our XRD results also indicated that the bacteria facilitated the conversion of Ca\({}^{2+}\) present in the soil into calcium carbonate.  
MICP; soft clay; experimental research; physico-mechanical indexes; microanalysis +
Footnote †: journal: Applied
## 2 Methods
### Feature Selection
#### 2.3.1 Filter Method  
To optimize the ML model published by [PERSON] and colleagues [14; 15], the team used a filter approach as a benchmark method to select the best possible feature subset. This method aims to establish a relevance metric based on selected correlation (or dependency) criteria between individual features and the output, ranking them from weak to strong [23; 24; 25]. In this case, the relevance metric was established based on the computed mutual information (MI) value for each feature [26; 27; 28]. Based on a user-specified relevance threshold of the established MI-metric, the best possible feature subset was selected and passed on to the ML algorithm.  
Generally, as shown in Figure 2a, the user-specified threshold value is adjusted until the best possible ML training performance is achieved. It should be noted that the filter step is independent of the learning algorithm since it is established before the ML training start; thus, as shown in the results section, the same rank of features was used with four different types of ML algorithms: (a) Simple Tree with optimal pruning of 13 parents; (b) Radial Basis Function kernel Support Vector Machine (RBF SVM) with a standard \(\gamma\) calculated from the number of features used; (c) Linear SVM with automatically optimized hyper-parameters; (d) \(k\)-Nearest Neighbor (\(k\)NN) with euclidean distance, no distance weighting and 4 nearest neighbors.
# Pipeline Damage Identification Based on Additional Virtual Masses
## 1 Introduction
Using the basic concept of VDM, this paper proposes the addition of virtual masses to pipeline structures and accurately identifying damage degree with combined sensitivity and frequency. We posit that doing so evades the difficulty of attaching real masses. The method divides a pipeline structure into several substructures and increases the sensitivity of the frequency to damage by adding virtual masses to each substructure. Constructing a response after adding virtual mass is key to this method, and frequency response by adding random mass can be obtained according to the measured force and acceleration frequency responses. First, this paper introduces the basic principle of damage identification based on additional VMD. Then, numerical simulation and experimental verification are carried out on the pipeline structure, which expands the applicability of the method in different structures. In this experiment, to quantify the degree of damage in the pipeline, a certain size of the hole is made to simulate the damage. The damage represents the longitudinal split of the pipe in real life, but in order to achieve the experimental results, the simulated damage is relatively large.
## 2 Materials and Methods
### Data Source Layer
#### 2.1.1 Existing Terrain Information  
Unmanned aerial vehicles (UAV/drone) have quickly become one of the most versatile tools on the earthwork construction site. Drones have become the job site's aerial eye, speeding up data collection for earthwork projects, helping construction professionals work better and more productively. An extensive amount of research has been conducted in UAV photogrammetry for 3D mapping and modeling [33]. UAV application for terrain information gathering has become more reliable with the invention of accurate GPS and gyroscope technology.  
Images are collected during the UAV flight equipped with a high-resolution camera and GPS (Figure 2). The developed flight planning software uses different parameters to compute a full flight plan consisting of the waypoints where the UAV takes the photos along its flight path automatically.
## 6 Conclusions  
This paper presents a dual-band fully integrated high linearity CMOS power amplifier (PA). To deliver high output power, a design methodology for a dual-band matching network is proposed. Based on the proposed methodology, reconfigurable dual-band matching networks are implemented. Furthermore, the proposed input dual-band matching network employs a reconfigurable transformer to achieve a low reflection coefficient across both bands, and the principle of the reconfigurable transformer is thoroughly studied. The simulation results validate the PA's dual-band operation. For continuous-wave operation, the PA achieves saturated powers (\(P_{sat}\)) of 28.03 dBm and 27.5-28.2 dBm, with power added efficiencies (PAE) of 33.2% and 24.6-31.1%, in the2.4 GHz and 5 GHz WLAN bands, respectively. Compared with other dual-band or multi-band PAs, the PA demonstrates a superior performance in output power. According to the simulation results, the proposed PA shows great potential for WLAN applications.  
Conceptualization, H.S. and B.W.; data curation, H.S.; formal analysis, H.S.; investigation, H.S.; methodology, H.S. and B.W.; resources, B.W.; supervision, B.W.; validation, H.S. and B.W.; writing--original draft, H.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Hangzhou Science and Technology Development Program. (No: 2024 SZD1A40).  
Not applicable.  
The data in this paper are all from simulation and test results of real circuits, and there is no data plagiarism or falsification. Due to project confidentiality requirements, we regret that we cannot release more details of the data at this time.  
[PERSON] was employed by Zhejiang CASEMIC Electronics Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial re-lationships that could be construed as a potential conflict of interest.
# SoK: Delegated Security in the Internet of Things
## 5 Security Evaluation of Existing Protocols
### Process and Results of Security Evaluation
#### 5.2.1 Access Control and/or Authorisation Delegation  
In this domain, the group-oriented access control security mechanism based on DTLS presented by Park and Park (see Section 2.1, Figure 3) is examined. According to the Definition 3, \(\mathcal{A}_{weak}\) initially receives as an input list \(F\) identified parties which are remotely reachable by \(\mathcal{A}_{weak}\). \(\mathcal{A}_{middle}\) and \(\mathcal{A}_{strong}\) receive as input the access matrix \(M\); hence, they are aware of all participants of the system, and their legitimate access.  
In this application domain, ThP is not available; therefore, it is assumed that \(\mathcal{A}_{weak}\) can query only Del and interact directly with \(\mathcal{D}\). In order to violate the confidentiality and integrity of any system participant \(\mathcal{TP}\), \(\mathcal{A}_{weak}\) has to obtain its secret s and perform authorisation pretending to be \(\mathcal{TP}\). However, as \(\mathcal{A}_{weak}\) does not have the corruption capability, they cannot defraud the target client acting as \(\mathcal{TP}\) to obtain its s and alter the associated resources. Hence, the given system can be proven secure against \(\mathcal{A}_{weak}\) in terms of confidentiality and integrity. Considering the availability of the system, \(\mathcal{A}_{weak}\) is able to disrupt system functionality by exploiting the single point of failure (SPoF) vulnerability via performing a successful denial of service (DoS) attack against the SP acting as \(\mathcal{D}\), sending multiple access requests.  
In addition to Del, \(\mathcal{A}_{middle}\) can query Obs. This allows \(\mathcal{A}_{middle}\) to eavesdrop on the communication; however, due to using DTLS, confidentiality remains secure. However, having \(Ex^{+}\) position relating to the system, \(\mathcal{A}_{middle}\) is potentially able to modify data from sensor devices using allies illegally. Also, by performing DoS attacks against SP, \(\mathcal{A}_{middle}\) can violate system availability. Therefore, it can be concluded that this system is not secure against \(\mathcal{A}_{middle}\) regarding confidentiality and availability properties, but secure concerning integrity.  
\(\mathcal{A}_{strong}\) is an insider with access to all available oracles and unlimited time. Thus, they potentially can retrieve secrets s of any system participant (e.g., security proxy, clients), alter any data from sensor devices, and disrupt the availability of these parties. Therefore, the given system is not considered secure against this type of adversary in terms of confidentiality, integrity, and availability.
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 4 Discussion
This has been done in order to characterize the WBAN channels under their real use conditions, where potential algorithms and strategies are supposed to function. In the future, it would be interesting to characterize the performance of these same three bands but using different hardware or different antenna configurations, in order to further study and isolate the influence of the frequency.
## 4 Simulation Study on Disturbance Transient Control
### Transient Stability Control Experiment for Uncertainty Disturbances  
Using Equation (7), system uncertainty disturbances were introduced into the simulation environment for each controller. Under identical conditions, each controller's bus voltage-tracking performance was compared. Simulation experiments were conducted with input bus voltages of 20 V, 30 V, 40 V and 50 V. The experimental curves are shown in Figures 15-18.  
Figure 16: The 30 V bus tracking voltage disturbance rejection performance.  
Figure 17: The 40 V bus tracking voltage disturbance rejection performance.  
Figure 18: The 50 V bus-tracking voltage disturbance rejection performance.  
Based on the curves from Figures 15-18, it can be observed that compared to other controllers, the BP-ADRC controller achieves the smallest fluctuations and the most stable tracking performance under uncertain disturbances.
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## Appendix A Birth-Death Diagrams on User-Item Graphs  
The dynamic graphs studied in this paper can be classified into three distinct types:  
* Unipartite undirected: For instance, in dynamic graphs of face-to-face interaction such as from the HighSchool dataset, there is only one type of nodes (students), and an interaction between nodes \(u\) and \(v\) does not have a direction. Contact, SocialEvo, UNvote, and USLegis are examples of such graphs.
* Unipartite directed: For example, in the Enron email dataset, there is only one type of nodes (users involved in mail exchanges). However, the interactions (emails) have an orientation: a given user \(u\) sends an email _to_ user \(v\). The datasets UCI, UNtrade, Flights, and Enron are examples of such graphs.
* Bipartite: in this case, there is a clear separation of the nodes into two node types. Typically, in _User-Item_ graphs such as Wikipedia, Lastfm, MOOC, and Reddit, users are always involved in interactions as senders, while items always receive an interaction (a click, a like, a subscription, etc.).  
As mentioned in the main body of the paper, Birth-Death diagrams allow us to visualize when nodes and edges start and stop interacting for the first time in a history of events. However, for Bipartite datasets, there is a clear separation between the nodes that will be involved in events as _source_ nodes and those that will appear as _destination_ nodes. For these specific datasets, plotting the Birth and Death time of users and items separately yields extra information. In Figure 11, it can be observed, for instance, that in all datasets, most of the _items_ are overlap nodes in the sense that they are observed in both the train and test sets. In the MOOC dataset, all the items (courses that can be followed by students) are actually observed at least once during the test period; thus, there are no historical destination nodes in that case. For the Lastfm dataset, This is important since for the nodes in the lower right, utilizing the prediction method results in the chance to accumulate a lot of information about them over time. This is in contrast with user nodes, where it can be seen that users more commonly appear and disappear during the training period.
## 3 Various System Configurations Studied
### Configuration Type III: A Single DCB Source with an FC
Therefore, the selection of the most appropriate configuration should be guided by the specific requirements of the application, including cost constraints, control complexity, and desired output voltage quality.
## 5 Energy  
A wireless sensor network is a set of devices distributed in such a way that the sensors implemented in such network are capable of transmitting the parameters to which they are exposed, for example, temperature, pressure, humidity, etc.; in this way, a network of sensors is composed of the following elements:  
* Sensors
* Radio transmitter/receiver
* CPU/memory
* Power source, usually a battery.  
Regarding the energy problem, there are some aspects that directly affect node battery consumption. The term unbalanced energy depletion, shown in [42], describes a situation where the nodes that are closer to the coordinator node carry more traffic, and so they consume more energy than those nodes further away from the root node. This imbalance causes the overall energy to be distributed non-uniformly in the network, making some nodes to run out of power faster than others [43].
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## SEMS Method
### SEMS Assumptions and Model
#### 3.4.2 SEMS Model  
The SEMS model consists of the following fairness function and constraints:  
\[f_{1}=\max\sum_{k=1}^{K}\left(\frac{\alpha_{jk}^{p}}{\sum\limits_{j=1}^{I}\alpha_ {jk}^{p}}\ln(\frac{\alpha_{jk}^{p}}{\sum\limits_{j=1}^{I}\alpha_{jk}^{p}}/\frac {\sum\limits_{i=1}^{I}\sum\limits_{r=1}^{R}x_{ijrk}^{p}/d_{jk}^{p}}{\sum \limits_{j=1}^{I}\left(\sum\limits_{i=1}^{I}\sum\limits_{r=1}^{R}x_{ijrk}^{p} /d_{jk}^{p}\right)})\right) \tag{5}\]  
\[f_{2}=\min(\sum_{i=1}^{I}\sum_{j=1}^{I}\sum_{r=1}^{R}\sum_{k=1}^{K}\alpha_{jk}^{ p}k_{ijrk}^{p}k_{ijr}^{p}) \tag{6}\]  
s.t.  
\[\sum_{i=1}^{I}\sum_{j=1}^{I}\sum_{r=1}^{R}\sum_{k=1}^{K}c_{rk}^{p}l_{ijr}x_{ijrk }^{p}\leq C^{p} \tag{7}\]  
\[\sum_{j=1}^{I}\sum_{r=1}^{R}l_{ijk}^{p}x_{ijrk}^{p}=q_{ik}^{p} \tag{8}\]  
\[q_{ik}^{p}=q_{ik}^{p-1}-\sum_{j=1}^{I}\sum_{r=1}^{R}x_{ijrk}^{p-1}+q_{k}^{new} \tag{9}\]  
\[d_{jk}^{p}=d_{jk}^{p-1}-\sum_{i=1}^{I}\sum_{r=1}^{R}x_{ijrk}^{p-1}+d_{k}^{new} \tag{10}\]  
\[h_{ijk}^{p}=\left\{\begin{array}{l}0,x_{ijk}^{p}=0\\ 1,x_{ijk}^{p}\
eq 0\end{array}\right. \tag{11}\]  
Equation (5) maximizes the SEMS fairness of material scheduling, whereas Function (6) minimizes the SEMS transportation time. Constraint (7) ensures that the total transportation cost does not exceed the available budget. Constraint (8) indicates that the sum of all supply depots is equal to their storage. Constraint (9) indicates that the supply in stage \(p\) is the sum of the remaining in stage \(p-1\) and the supply generated in stage \(p\). Similarly, Constraint (10) states that the demand in stage \(p\) is the sum of a new demand generated in stage \(p\) and the demand not met in stage \(p-1\).
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 2 Geological Structure of Strike-Slip Faults  
### Basic Characteristics  
The Tarim Basin is a typical superimposed basin that encompasses an area of 40 \(\times\) 10\({}^{4}\) km\({}^{2}\), and it is the largest petroliferous basin in China (Figure 1a) [16]. The central part of the Tarim Basin has a long history of uplift. The research area is in the north-centralTarim Basin, which is controlled by faults (Figure 1b) [18]. Fault systems can be divided into two sets based on their fault scales, time of breakage, and faulted horizons: the Tazhong I tectonic belt, which developed primarily during the Caledonian, trends NW-SE and is associated with secondary thrust faults, and the strike-slip fault systems, which developed primarily during the Hercynian and trend NE-SW [19; 20].  
The strike-slip faults found in the Tarim Basin are geographically distant from active plate boundaries and result from the reactivity of pre-existing structures under stress concentrations within the craton [21; 22]. While the Anderson model provides a reasonable explanation for the mechanics of shallow crustal strike-slip faults, the complex nature of deep cratonic basins introduces several factors that influence fault formation and development [23]. These factors encompass regional and local stress fields, pre-existing structures, pre-existing faults, and rock physics, and they result in non-Anderson mechanisms such as fault end expansion, sectional friction, and dynamic rupture [24; 25]. The strike-slip faults within cratons exhibit a high degree of structural complexity and are often associated with folds, secondary faults, and fault blocks. Within fault zones, secondary faults and extensional faults develop heterogeneously, typically initiating in regions experiencing the highest tensile stress [26]. These faults serve as crucial connectors, bridging otherwise discontinuous fault segments and playing a key role in fault development [27; 28]. Minor  
Figure 1: (**a**) The location of the Tarim Basin and its subdivisions. The location of the research area is marked by a red rectangle. The location of the seismic section is marked by a pink line. (**b**) The seismic section in the north-central Tarim Basin. This seismic section shows that the deep structure in Tarim is very complicated and controlled by faults.  
and meso-scale faults are generally not planar but manifest as trapezoidal fault zones interconnected at various scales [18]. The weaker portions of pre-existing structures facilitate fault nucleation and connection growth, not only leading to constraints on the development and distribution of subsequent faults but also influencing the underlying mechanism of fault occurrence [29]. Additionally, fluid flow within different stress tectonic zones along the fault may display heterogeneity, resulting in the formation of channels within highly faulted rock zones [30].