esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunities for Early Detection and Prediction of
## 2 Related Works
### Research Related to Ransomware Countermeasures
#### 2.3.2 Ransomware Prediction and Detection
The deep learning represented by long short-term memory (LSTM) was utilized by [50] to provide an early prediction of malware attacks in Android devices. The model helps to protect Android devices from malware attacks by the early prediction of suspicious behavior. It captures the implicit contextual relations between various data generated bythe running application. However, the model predicts the suspicious behavior of a running process based on the data captured in the early phases of the attack during the same session. Such an approach lacks the sufficient data needed for accurate prediction and is unable to predict the future behavior of the malware. Table 1 summarizes the studies related to ransomware prediction. It gives a brief description of the problems those studies have tried to address, with the solutions they proposed, methods, tools, and limitations. Predictive modeling can help to mitigate and prevent future attacks by tracing the attack development over time. It can be developed using a combination of operational data and managerial data. The operational data are collected from the running process of the ransomware. The data consist of the process-tracing activities as well as the situational data representing the context surrounding the running environment. The situational data consolidate the operational data by adding context to the operational data. This is imperative for ransomware attack prediction as many of those malware applications can adaptively change their behavior according to the context. Furthermore, situational data can also be collected from many sources related to the system of interest using threat intelligence. Together with contextual data, situational data can enrich the knowledge base of the prediction model. Nonetheless, this type of data fusion was overlooked by existing studies. Table 1 summarizes the studies related to ransomware behavior prediction. It can be concluded that current studies rely solely on operational data extracted from the running process of the ransomware on the system. These data contain information about the interaction between the malicious process and the resources in the target system and are used to train machine-learning models for detecting and predicting attacks. However, these studies rely on historical data that do not capture how ransomware behavior evolves over time, and they ignore environmental factors that affect the behavior of ransomware when running. As ransomware can change its behavior based on several factors, including the running environment, current security posture, and the situation on the targeted system, predictive models for ransomware attacks on SCADA must take these factors into account. Researchers and security professionals can address these limitations when designing predictive models for ransomware attacks.
## 2 Materials and Methods
### Model Development
#### 2.5.1 ANN Model 1
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**NET** & \multicolumn{4}{c}{**INPUTS**} & \multicolumn{4}{c}{**TRAINING**} & **VALIDATION** & \multicolumn{4}{c}{**TESTING**} \\ \hline  & **Tpanel** & **Iglo** & **RH** & **Tamb** & **P** & \(\mathbf{\alpha}\) & **MSE** & **R** & **MSE** & **R** & **MSE** & **R** & **Duration** & **Iterations** \\ \hline
1 & x & x & x & x & x & x & 2.86 & 0.75 & 2.96 & 0.74 & 2.79 & 0.75 & 0.00:12 & 173 \\
2 & x & x & x & x & x & 3.29 & 0.7 & 3.37 & 0.69 & 3.19 & 0.71 & 0.00:05 & 85 \\
**3** & x & x & x & x & **x** & **3.03** & **0.73** & **3.14** & **0.73** & **3.09** & **0.72** & **0.00:06** & **98** \\
4 & x & x & x & x & **x** & **x** & **4.07** & 0.61 & 3.88 & 0.61 & 4.24 & 0.62 & 0.00:07 & 117 \\
5 & x & x & x & & x & x & 3.12 & 0.72 & 2. In this scenario, the working mechanics are the same as in the previous one, adding one more possible entry. To find the variables, 19 tests were made, shown in Table 4, and divided into three blocks. The first block was comprised of network 1 to 8, the second block from 9 to 14, and the third from 15 to the last test. In the first block, the pressure was again dispensed with, since in its absence, better results were obtained. In the second block, in addition to the pressure, the ambient temperature was eliminated, another of the stable variables, as it presented values higher than 0.9 in several of the training phases. Network 16 of the third block was the most suitable for this sector, presenting the smallest mean square errors and the regression coefficients closer to 1, eliminating solar altitude as an input variable. Comparing the three best grids in each block, it can be seen that the most suitable was grid 11. They were better results with respect to grid 16, and with respect to grid 4 there was no noticeable difference, reducing the iterations. Once the input variables to the network were selected, the work was done with more than 10 neurons, reaching 40 although finally opting for 20 (Table 6). Working with 30 neurons resulted in a greater error, and the network of 40 neurons has no longer duration without a notorious improvement. Therefore, the selected network provided MSE values close to 1% and regression coefficients higher than 0.9. Figure 6 shows the initial network structure. Finally, the variables chosen for this Model 2 were the panel temperature, irradiance, relative humidity, solar altitude, and the short-circuit current.
# Semantic Traffic Sensor Data:
###### Abstract  
Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city's future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.  
Keywords:data management; semantics; sensor data; data integration; data annotation; traffic in smart cities +
Footnote †: journal: Applied sciences
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 3 System Model
### Relay Assisted Models  
Among the various possible strategies available for user-based relay assisted cooperation [19; 20], in this paper we have adopted: the AF and DF schemes. In this case, the source transmits a packet (or symbol) in one time slot and the RN re-transmits it in the next time slot, which are then combined at the destination prior to decision making. The scheme like in [18] consists of two phases. At first, the Tx sends data to both the relay and the Rx. In the relaying phase, the Tx remains silent and the relay terminal forwards the data to the Rx.
# A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay
## 2 Optimization Model for Relay Device Setting
### Determination of Parameters for Overcurrent Protection Devices  
In addressing faults in individual phases and interphase faults on power lines, the common choice is to employ overcurrent relay devices for overcurrent protection. The tripping time of the relay must adhere to the prescribed relationship outlined by the current-delay curve, and its delay duration is primarily influenced by the current traversing through it. Consequently, the operating time of the overcurrent protection device is intricately linked to three key variables: time dial setting (_TDS_), plug setting (_PS_), and fault current (\(I_{fault}\)) at the primary port fault of the current transformer.  
TDS signifies the time interval required before the relay protection device initiates operation when the fault current equals or surpasses the PS rating. PS represents the ratio of fault current to contact current. The relay device's operation time not only hinges on the relay type employed and the contact current but is also contingent upon the fault current, adhering to a corresponding nonlinear numerical characteristic curve.  
Given the aforementioned attributes, we formulate the mathematical analytical expression for the total protective action time of the overcurrent relay device as follows:  
\[T=\frac{\lambda\cdot TDS}{\left(\frac{I_{fault}}{\text{PS-CT}_{ration}} \right)^{v}-\mu} \tag{1}\]  
where \(\lambda,\ \
u,\ \mu\) are the known constants, which are always set to 0.14, 0.02, 1.00 according to the IEEE standard [34]. \(I_{fault}\) is the fault current is in the primary port of the current transformer. \(CT_{ration}\) is the primary setting value of the current transformer.  
Simultaneously, we can ascertain the current \(I_{rd}\) of the relay device to be  
\[I_{rd}=\frac{I_{fault}}{CT_{ration}} \tag{2}\]
# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 4 Data-Driven Approaches to Building Energy Retrofitting  
Data-driven building energy retrofit refers to the utilization of data analytics, machine learning, and advanced computational methods to guide, optimize, and implement energy efficiency measures in existing buildings. These approaches leverage extensive datasets and sophisticated algorithms to pinpoint effective retrofit strategies, forecast energy savings, and continuously monitor and enhance building performance. The scope of data-driven retrofitting includes residential, commercial, and public buildings, aiming to cut energy consumption, reduce greenhouse gas emissions, and improve occupant comfort and productivity.
## 5 Deposition Methods
### Chemical Vapor Deposition (CVD)  
One of the critical challenges for creating PCM devices is the continued downscaling and the reduction of the set/reset current. The most promising approach is to move towards into 3-D architectures, where holes are first etched then filled with Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\). Unfortunately, conventional physical vapor deposition (PVD) methods, such as sputtering and evaporation, are not suitable for high-aspect-ratio filling due to the poor step coverage. Therefore, it is useful to review the deposition method that can provide better conformal coverage. Chemical vapor deposition (CVD) is based on a reaction that takes place directly on the surfaces, which leads to a good conformal step coverage on most structures. In CVD methods, alkyl and alkyl-amino compounds of Ge, Sb, and Te are used as precursors in the fabrication of GST [124; 125; 126]. The schematic of the CVD system is shown in Figure 21a. This method can provide a very high growth rate by using the hot-wire CVD set-up system [127], see Figure 21b. However, the atoms or the ligands introduced by the precursors can behave as contaminants in the GST-based memory cell. One drawback lies in this fabrication method is unlike the co-sputtering technique, it is a challenge for doping the GST materials in the chemical vapor deposition methods.
## 5 Simulation Results
### Optimal BS Location and BS Mobility in a Large Area Network  
In this Section, the effectiveness of the suggested algorithm in a large-area network regarding network lifetime and residual energy is discussed. In the simulations, 400 SNs are scattered in a 400 m \(\times\) 400 m sensor network field. When the proposed algorithm is applied on the SEP protocol utilizing two and three energy levels (SEPWMBS and SEPWMBS-3L) in a large-area network, their resulting network lifetimes as well as the network lifetimes of the conventional SEP protocol utilizing two and three energy levels (SEP and SEP-3L) are shown in Figure 8. It can be observed that SEPWMBS improves the lifetime of SEP and SEPWMBS-3L improves the lifetime of SEP3 significantly.  
In Figure 9, the total remaining network energy is presented for the above-mentioned protocols. The network's energy is completely depleted under SEPWMBS and SEPWMBS-3L at rounds 2585 and 2701, respectively. Whereas underneath SEP and SEP-3L, the network's energy is completely depleted at rounds 2342 and 2452, respectively. Therefore, the usage of mobile BS improves the lifetime of SEP by 243 rounds and SEP-3L by 249 rounds. These results demonstrate that the proposed algorithm can reduce the energy consumption of the network better when compared with SEP.  
Figure 8: Comparison of network lifetimes in a large area network.  
Figure 7: An illustration of a 100 m \(\times\) 100 m sensor network field with BS virtual locations.
# Parameter Determination of a Minimal Model for Brake Squeal
## 1 Introduction
The CEA of the finite element method can simultaneously obtain several unstable modes, but it is limited by underestimate or overestimate error due to a linearization assumption [23]. Compared with eigenvalue computation, the TA of the finite element method can achieve the dynamic response of components with nonlinear effects included, such as nonlinearity of friction, contact pressure, and lifting off of the pad (e.g., contact separation between the two sliding surfaces), which are referred to as deterministic chaos by [PERSON] et al. [24]. So additional unstable modes may be detected while CEA fails. However, it is not conducive to extensive parameter designs because of excessive time consumption [25], so its engineering application is limited. In contrast with a full-scale brake finite element model, the 2-DoF minimal model is of high efficiency during stability analysis owing to its fewer DoFs, although it is a simplification, and can consider only a certain order mode. It is helpful to analyze the effect of key parameters on brake squeal, and to reveal the squeal mechanism [19]. Nevertheless, the parameters in the model are difficult to determine. Therefore, this paper aims to explore a more accurate method for parameter determination of the 2-DoF minimal model, so as to make up for the shortcomings of existing literatures. The response surface method (RSM) is applied in the parameter determination. Then, TA is performed for the parameter-optimized minimal model to discuss the effect of braking velocity on squeal stability. The results demonstrate that the squeal is more prone to occur under relatively low velocity.
###### Abstract  
The application of connected and automated vehicles (CAVs) technology has changed the operation characteristics of vehicles. Investigating the traffic capacity of bus stops under a CAVs environment can allocate traffic flow more reasonably, which is effective in alleviating traffic congestion. Therefore, this paper proposes a method that can be used to evaluate the traffic capacity of bus stops under a CAVs environment. First, two evaluation indexes, failure duration time (FD) and forced lane-changing rate (FLR) are proposed. Second, the simulation scheme with ten scenarios is determined, and simulation experiments are conducted. Then, the relationships between FD, FLR, and traffic flow under different penetration rates of CAVs are analyzed. Finally, the relationship models between FD, FLR, and traffic capacity are fitted to verify their validity for traffic capacity analysis. Additionally, a predictive model is proposed for estimating capacity under a CAVs environment using indicators from HV traffic flow. Results indicate that: (i) FD and FLR both positively correlate with capacity, and perform well in capacity evaluation of bus stops; (ii) FD and FLR can be utilized to predict the capacity under a CAVs environment; (iii) the higher the penetration rate of CAVs, the smaller the impact of the bus failure phenomenon and forced lane change on traffic flow.  
connected and automated vehicles; bus stops; failure duration; forced lane-changing rate; capacity +
Footnote †: journal: Applied  
Article  
Simulation Analysis of Capacity Evaluation of Bus Stops under Connected and Automated Vehicles Environment. _Appl. Sci._**2023**, 13, 9186. [[https://doi.org/10.3390/appl3169186](https://doi.org/10.3390/appl3169186)]([https://doi.org/10.3390/appl3169186](https://doi.org/10.3390/appl3169186))  
Academic Editors [PERSON] and [PERSON]
## 3 Results and Discussion
### FTIR Spectroscopy
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Absorption Frequencies (cm\({}^{-1}\))**} \\ \hline
**4137** & **4251** & **4264** & **4009** & **4169** & **4222** & **9487** & **9445** & **9454** & **Vat** & **Assignments** \\ \hline
**C1/2034** & **C2/2039** & **C1/2038** & **C2/2039** & **C2/2039** & **C1/2038** & **A/1** & **A/3** & **A/3** & **V** & \\ \hline
1631 & 1627 & 1630 & 1625 & 1620 & 1630 & 1631 & 1634 & 1630 & 1635 & FeO(OH) 1620–1635 \\ \hline
1400 & 1394 & 1394 sh & 1396 & 1398 & 1401 & 1394 & & 1400 w & 1399 sh & CO\({}_{3}\) 1390–1410 \\ \hline  & & 1167 sh & 1166 sh & & 1165 sh & 1167 sh & & 1165 sh & & SO\({}_{2}\) 1165–1170 \\ \hline
1090 sh & 1094 sh & 1076 sh & 1087 sh & 1084 sh & 1080 sh & 1087 sh & 1091 sh & 1087 & &
\begin{tabular}{} \end{tabular} \\ \hline
1026 & 1025 & 1045 & 1030 & 1029 & 1023 & 1032 & 1041 sh & 1030 & &
\begin{tabular}{} \end{tabular} \\ \hline
908 & 906 sh & 913 & 908 & & 913 sh & 913 & & 910 sh & SO\({}_{2}\) 910–915 \\ \hline  & & 876 & 875 & 882 & & 890 & 883 & 871 sh & CO\({}_{3}\) 890–870 \\ \hline  & & & & & & & & 824 m & FeO(OH) 824 \\ \hline
\begin{tabular}{} \end{tabular} \\ \hline
606 sh & & & & & & 608 & 606 sh & &
\begin{tabular}{} \end{tabular} \\ \hline
565 sh & & & & & & 563 sh & & 560 & FeO(OH) 560–565 \\ \hline  & 524 sh & 524 & 524 & 526 & 526 sh & 519 & & &
\begin{tabular}{} \end{tabular} \\ \hline  & & & & & & & & 508 sh & Fe\({}_{3}\)O\({}_{4}\) 508 \\ \hline
\begin{tabular}{} \end{tabular} \\ \hline
424 sh & & 421 & & 421 & 418 & 421 & &
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The main FTIR peaks assignment of selected slag sample (bulky matter).
###### Abstract
Additionally, it plays a role in impeding secondary caries, promoting the remineralization of hypomineralized teeth, and treating infected root canals [68; 69; 70]. Furthermore, SDF is employed to desensize hypersensitive teeth [71]. Some practitioners have explored the potential of using SDF in the management of dental erosion, while others have suggested its use as a diagnostic dye to identify carious tissues, aiding in the guidance of caries excavation [71; 72; 73; 74; 75]. In the realm of research, the combination of laser irradiation with SDF therapy has been studied for its efficacy in preventing tooth fracture post-endodontic treatment [76; 77; 78; 79]. Moreover, researchers have investigated its ability to prevent the development of root caries by enhancing fluoride uptake in dentine [90; 91]. SDF stands out as an alternative treatment for managing dental caries in situations where other approaches are not feasible. It represents a minimally invasive, cost-effective, and straightforward method capable of alleviating fear and anxiety, especially in young children. Furthermore, it holds potential for application in community settings [60; 92; 93; 94; 95; 96; 97]. Figure 1: Decayed second deciduous molar. Its ease of application, low cost, and the potential to address caries in non-cooperative pediatric patients have positioned SDF as a compelling intervention (Figure 2) [59; 98; 99; 100]. By synthesizing the existing literature and analyzing clinical trials, observational studies, and other relevant research, this review aims to provide evidence-based insights into the effectiveness of SDF applications in arresting various lesions, preventing the progression of caries, and its potential adverse effects, thereby informing clinical practice and guiding future research in the field of dentistry.
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Dentistry Robot Systems  
Table 2 provides a comprehensive overview of the dental robots used in hospital settings, comparing them in terms of characteristics, advantages and disadvantages, and stage of development. This comparison includes various robots, like YOMI, Yakebot, and SDI, highlighting their unique features and their impact on patient care [30; 31].  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Robot System** & **YOMI—Dental Implant Robotic System**[30] & **Yakebot—Dental Implant Robotic System**[31] & **Smart Dental Implant (SDI) Robot System**[32] \\ \hline \multirow{4}{*}{**Description**} & Used to aid dental implantation surgery in both the planning and surgical phases (cost \(\approx\) EUR 2.8 M) [30]. & Used to perform dental implant surgery (cost \(\approx\) EUR 183 k) [31]. & Used for dental implants powered by continuous human oral movement and as a modality of outpatient photo-biomodulation therapy (PBM) [32]. \\ \hline \multirow{4}{*}{**Stage**} & Research stage [30]. & Clinical trial stage [31]. & Research and development stage [32]. \\ \hline \multirow{4}{*}{**Features**} & Provides physical guidance of the depth, orientation and position of the drill, thus avoiding the customized manufacture of surgical guides and the operator’s hand. & Enables the acquisition of intraoperative feedback information (including tactile and visual information), different surgical methods (automatic drilling and manual drilling), monitoring the position of the patient, and simulating the surgeons’ tactile sensations. & It allows light to be supplied in situ, which is made possible by collecting energy from dynamic human oral movements (chewing and brushing) through a designed piezoelectric dental crown, an associated circuit, and light-emitting microdiodes (LEDs). \\ \hline \multirow{4}{*}{**Advantages**} & The navigation system provides high predictability and precision in the preparation of dental implant osteotomies using vibrational feedback. & It can perform dental implant surgery under general anesthesia; it can also be performed under local anesthesia, with patients awake but unable to remain completely still throughout the procedure. & It provides a high degree of spatial and temporal control of light emission and adequate mechanical strength like a dental crown. It integrates an energy management circuit that allows energy to be collected. \\ \hline \multirow{4}{*}{**Disadvantages**} & Relatively expensive and works under supervision [30]. & Complex operation and sharp learning curve [31]. & The current method of applying right, such as using an LED probe or optical fiber, usually requires high power or a qualified doctor to establish the interface between the junctional epithelium and the tissue adjacent to the implant abutment [32]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary table of key dentistry robots in hospital settings.