esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 5 Conclusions  
In this paper, we developed a method for reducing the fluctuation in the amplitude of the RF signal applied to ion traps using an FPGA-based feedback controller. This controller allows us to freely change the control method and feedback configuration. In particular, the voltage adders and subtractors were very effective in increasing the resolution. When the FPGA-based PI controller and lag compensator were used, the fluctuations in the amplitude of the RF signal were 94.510% and 94.359% lower, respectively, than the fluctuation when the commercial universal PI controller was used. If the helical resonator is mounted on a vacuum chamber, the fluctuation will be further reduced.  
Conceptualization, Y.P., D.C. and T.K.; methodology, Y.P., D.C. and T.K.; software, Y.P., H.O. and S.Y.; validation, Y.P. and D.C.; formal analysis, Y.P. and D.C.; writing--original draft preparation, Y.P.; writing--review and editing, Y.P. and D.C.; visualization, Y.P.; supervision, D.C.; project administration, D.C. and T.K.; funding acquisition, T.K. and D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Samsung Research Funding & Incubation Center of Samsung Electronics.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of data** & **Commercial UniversalPI** & \multicolumn{2}{c}{**FPGA-Based Controller**} \\  & **Controller** & **PI Controller** & **Lag Compensator** \\ \hline Standard deviation (\(\upmu\)V) & 1121.7 & 61.58 & 63.28 \\ Percentage of the fluctuation in the amplitude of the RF signal (\%) & 1.806 & 0.099 & 0.102 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The fluctuations in the amplitude of the RF signal using a commercial universal PI controller and the FPGA-based feedback controller.  
Figure 7: The amplitudes of the RF signal using a commercial universal PI controller and the FPGA-based feedback controllers.  
**Acknowledgments:** This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1901-09. Part of the validation of performance of the voltage divider was taken with the help from [PERSON] at Seoul National University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 PID Control Design: Problem Statement
It is the difference between the particle \(i\) individual best position, \(b_{i}\), and the particle current position, \(x_{i}\), multiplied by a cognitive constant \(c_{1}\) and a disturbing random value, \(\phi_{1}\). The third part (\(c_{2}\phi_{2}[g(t)-x_{i}(t)]\)) represents the particle social knowledge update: The difference between the swarm global best position, \(g\), and the particle current position, \(x_{i}\), multiplied by a social constant \(c_{2}\) and a disturbing random value, \(\phi_{2}\). The same relevance is usually given to the cognitive and social knowledge, and thus, \(c_{1}\) and \(c_{2}\) take the same value (e.g., 2 or 1.49445). The two random numbers, \(\phi_{1}\) and \(\phi_{2}\), are uniformly generated in the interval \([0,1]\). Algorithm 1 is represented by the following pseudo-code:  
```
1.\(t=0\)
2.Initiate m,\(\omega\)
3.Initialize swarm X(\(t\))
4.Evaluate X(\(t\))
5 while(not (termination criterion))
6.determine personal and global bests for\(i=1\) to m
7.Update \(v_{i}(t+1)\)
8.Update \(x_{i}(t+1)\) endfor
9.Update \(\omega\)
10.t = t + 1 endwhile
```  
**Algorithm 1** PSO algorithm  
As it can be observed from the PSO pseudo-code, this algorithm is conceptually quite simple. Regarding the PSO algorithm parametrization, there are three adjustable parameters: The swarm size, \(m\), the termination criterion and the inertia weight limits. While these parameters are problem dependent, they can easily be selected for most PID control design cases. The swarm size should be large enough to guarantee a diverse solution representation across the search space. The termination criterion is often a pre-defined number of iterations and is adopted in this study. Regarding the inertia weight, it is commonly accepted that it should assume a higher value at the beginning of the search (e.g., \(\omega_{i}=0.7\)) and then gradually decreased over the search evolution until it reaches a minimum value (e.g., \(\omega_{i}=0.4\)). This inertia weight variation is to establish an important trade-off between space exploration in an early search stage and gradually transition to a global minimum consensus in a final search stage. The inertia weight is linearly decayed between the maximum and minimum values over the search iterations. However, other approaches can also be used (e.g., see [42]).
###### Abstract  
This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12-0.23 to 0.40-0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model).  
regression modeling; electric power industry; satellite images; attribute selection; local pollution modeling; stochastic variable; model refinement
## 2 Materials and Methods
Based on the transverse size of the bright area in the camera image (see the top part of Figure 1b and the bottom parts of all the subfigures of Figure 2 for examples of instantaneous thickness profiles), the local film thickness can be measured at each downstream position. This method would underestimate the amplitude of three-dimensional waves; however, the initial waves under study were proven to be essentially two-dimensional [29]. Combining instantaneous spatial records of film thickness, \(h(x)\), one receives a spatiotemporal matrix, \(h(x,t)\). An example of such a matrix in graphical representation is shown in Figure 1b. The brightness of each pixel of this image is directly proportional to the film thickness value in a corresponding cell of \(h(x,t)\) matrix; white corresponds to h = 1 mm. The spatial resolution of the technique in the longitudinal direction is 40 \(\upmu\)m per pixel; in the radial direction, spatial resolution is 30 \(\upmu\)m/pixel due to magnification of the image at the outer wall of the pipe. The sampling frequency is 10 kHz; for each set of conditions, at least one record with duration of 1 \(\mathbf{s}\) was obtained.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 4 Reliability Assessment Hierarchy
### Navigation System  
This system is the most important part of the vehicle; it is characterized by a high failure rate compared to other systems. Nevertheless, it has the highest number of hot redundant subparts/subsystems. Therefore, due to the high level of electronic miniaturization, it is possible to replicate a large number of its subsystems [57] making it intrinsically reliable. The Hot (standby) redundancy is a method in which one system runs simultaneously with an identical primary system. Upon failure of the primary system, the hot standby system immediately takes over, replacing the primary system.  
Moreover, due to the strong integration derived from the experience of the development of automotive applications, the aerospace world enjoys highly-reliable electronic components for navigation (as Inertial Navigation System--INS and Global Positioning System--GPS receivers). A second benefit comes from their high computing capacity that allows a parallel computing architecture, greatly increasing the overall reliability [58].
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 4 Simulation Results and Discussion
### Simulation 1: The MSE of All NANC System
Furthermore, the proposed system shows the best convergent performance in Figure 9b,c, while the system with MLMS cannot achieve similar results, but it is slightly better than conventional NANC system.
## 4 Discussion  
### Exceptional Fire Outbreak  
From December 2015 to February 2016, a drought is observed in the Republic of Congo, leading to an unprecedented fire outbreak in the Marantaceae forests in the north of the country. A review of the meteorological data (Figure 3) clearly shows the arrival of this anomaly, and with hindsight could have been used to predict a potential outbreak of fire in the forest ecosystem.  
More than 36,000 ha of forest have burnt in less than three months. The most important fires are observed in January and February 2016. Some active fires were observed at the end of December 2015 and in March-April 2016. The end of the fire outbreaks can be linked to the arrival of rains in mid-March. The magnitude of the anomaly is demonstrated by the exceptional number of active fires that have been detected for the region compared to the average of fires observed during the 2000-2015 period. It is the only time that fires have been detected in these formations since November 2000 when the MODIS sensor became operational on the Terra satellite.  
While observations of fire in tropical forests are frequent in Amazonia or Indonesia, few records of fire are found for Central Africa. To our knowledge, it is the first time that type of event is describe and documented in Central Africa. [PERSON] [35] reports an outbreak of fire in 1970 in the southern part of the Marantaceae forest within the Ngombe logging concession, supposedly following an El Nino event. This event is reported by the timber company now operating in the concession, but no satellite image or maps are available to corroborate this.
## 2 Formation of the Model of Radiation Thermometry  
Real objects with a thermodynamic temperature emit thermal emission, part of which is transmitted to the measuring instrument and detected by the detector.  
A model of the physical process, named the direct model, is constructed to correspond to the physical laws in the process of radiation thermometry (depicted in Figure 1), governing emission, travel and absorption of thermal radiation. As the goal of radiation thermometry is to obtain a true temperature reading, the direct model will be inverted to obtain the inverse model, corresponding to the mathematical inverse of the physical process. The resulting function converts from radiance to true temperature. A model of radiation thermometry is a combination of both, the direct and the inverse model, permitting simulation of non-contact temperature detection.  
Figure 1: Schematic representation of thermal radiation in non-contact temperature measurement.
# JITScanner: Just-in-Time Executable Page Check in the Linux Operating System +
## 5 Usage Models and Potential Extensions
### Flexibility and Performance Trade-Offs  
It is crucial to highlight that JITScanner does not enforce any specific binary code checking methodology. The current Proof-of-Concept (PoC) implementation showcases the use of Yara rules for asynchronous checks and the meterpreter shellcode signature search for synchronous checks merely as examples. However, in principle, JITScanner remains flexible to integrate any alternative technique capable of operating on a selected section of code, specifically the executable page accessed during instruction fetching.  
In scenarios where a particular analysis method is resource-intensive or time-consuming, JITScanner has the capability to delegate its operation entirely to an external user-space thread. This delegation facilitates asynchronous analysis without impeding the execution of the code under examination. Additionally, for less critical services, JITScanner allows the bypassing of synchronous checks. This prioritizes application performance and accommodates potential delays in identifying malicious signatures, especially when the asynchronous check is activated. This adaptability ensures that JITScanner can accommodate various analysis methods while catering to diverse performance and security needs based on specific application contexts.
## 4 Proposed System
### Selective Forwarding Detection (SFD) Using Multi-Layers  
Rule-based IDS, also known as signature-based IDS, is one of the mechanisms for protecting a network from security threats. The network layer in WSNs is threatened with many types of attacks, including wormhole and sinkhole attacks. Our proposal focuses on the selective forwarding attack. We design a multi-layer approach to detection that includes the three security layers shown in Figure 2. Figure 3 shows the details of the algorithm. The first layer is a pool of MAC IDs. In this layer, the important information is filtered and stored. The information includes message fields (e.g., packet, destination, and source IDs) that are useful for rule-based processing. The second layer is the rule-based processing layer. In this layer, there are some rules that must be applied to the stored data. Incoming traffic is either accepted or rejected. In addition, no rules are applied to a message that fails. The third layer is the anomaly detection layer, which detects the false negative anomalies that comprise unknown attacks. The second layer (rule-based processing) and the third layer (anomaly detection-based IDS) can identify and control selective forwarding attacks in all phases. The three layers are supported by three algorithms. These algorithms are used to resolve the attacks on the network. The detection approach saves energy by using little time and memory. It chooses a secure route along which to transfer data between the source and base station. Furthermore, the SFD approach will be reliable, energy efficient, and scalable. All of these factors are important for sensor node networks. Additionally, this approach has a high accuracy rate. We compared our approach with other approaches and found SFD has a 98.3% accuracy rate so it is higher than others.  
Figure 2: Multi-Layers in Rules-Based IDS.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 2 Detection System in the Peruvian Navy  
The Peruvian Navy has several surface search radars, such as RAN-11X, RAN-105 and Sperry Marine. The radar detection system comprises two critical processes: (1) extracting plots (a plot is the term used to identify an object, called a contact or target in the military context; in the survey, it represents a marine vessel), and (2) tracking and prediction. Figure 1 shows the diagram of the detection system processes.
## 4 Problem Definition and Modeling
### Fairness Function  
In this subsection, we design a fairness function. We believe that in any given area of the urban road network, the average service that each electric vehicle (EV) receives should be approximately balanced. Therefore, we define the average number of charging stations matched at each intersection as the evaluation benchmark, and employ the Mean Square Error (MSE) to quantify fairness. The average number of charging stations matched at a single vertex is represented as:  
\[scope_{ave}(V)=\frac{1}{|V|}\sum_{v\in V}scope(v) \tag{23}\]  
Furthermore, the fairness of the plan \(p\) is measured as follows:  
\[fairness(p)=\frac{1}{|V|}\sum_{v\in V}(scope(v)-scope_{ave}(V))^{2} \tag{24}\]  
Finally, we obtain the objective function \(gain(p)\):  
\[gain(p)=c_{1}profit(p)-c_{2}cost(p)+c_{3}fairness(p) \tag{25}\]  
where \(c_{1}\), \(c_{2}\), and \(c_{3}\) represent the weight coefficients assigned to different terms, satisfying \(c_{1}\geq 0\), \(c_{2}\geq 0\), \(c_{3}\geq 0\), and \(c_{1}+c_{2}+c_{3}=1\).
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 2 Preliminaries and Background
### Trusted Execution Environments  
Trusted Execution Environments (TEEs) are expected to provide hardware-enforcement mechanisms, such as sealed storage, memory encryption, and hardware secrets, to protect private computing from untrusted users and processes. They also must enable secure interchange communication through local and remote authentication assertion, also known as attestation. This effectively protects against malicious parties with root privileges and creates a reverse sandbox that protects enclaves from remote attacks, operating systems, hypervisors, firmwares and drivers. As the adoption of TEEs is becoming increasingly popular in the industry, many more flavors of the technology are being commercialized. Yet, the most widespread solutions remain Intel's Software Guard Extentions (SGX) [21], AMD's Secure Encrypted Virtualization (SEV) [35], and ARM's TrustZone [36].  
**SGX-based TEEs.** In our study, we focus on Intel SGX as the most promising solution available today for general-purpose computing. The SGX threat model assumes all privileged software is potentially malicious and provides integrity and confidentiality guarantees by isolating the enclave's code and associated data from the operating system, hypervisor and other hardware attached to the system, effectively reducing the attack surface of an application as illustrated in Figure 1.  
It encrypts the enclave memory with a 128-bit key that randomly changes every power-cycle. Intel's industry partners such as Fortanix, Anjuna and Scone have offered runtime security frameworks that promise the capability of porting existing applications to an SGX Enclave without the need for code modifications. These services, although successfully curbing development overheads, do not offer proper application-specific optimizations and end-up suffering from the high performance penalties imposed by SGX.
## 2 Preliminaries  
### Hierarchical Topology of IWSNs  
Industrial wireless sensor networks can be structurally divided into planar and hierarchical structures. In the planar structure, each sensor node has the same functional attributes. Flooding is used in data collection by the sink node to send query commands to all sensor nodes in the region [37]. Due to the universality of this structure, existing wireless sensor cascading failure models are mainly studied for planar structures. Table 1 shows some planar structural networks for cascading-failure studies. However, for more complex industrial wireless sensor-network environments, the hierarchical topology is more practical, because the cluster head nodes in the hierarchical structure can fuse the raw data and improve the efficiency of the network [38]. In this structure, the sensor network consists of multiple clusters, and the nodes are divided into cluster head nodes in the upper-level network and general nodes in the lower-level network. The role of the general nodes in the lower layer is to collect and detect the collected environmental data and then transmit the data to the cluster head node where they are located. The cluster head node aggregates and processes the data in this cluster and transmits the processed data to the sink node by multi-hop relaying. Figure 1 shows the hierarchical topology of industrial wireless sensor networks; this type of hierarchical topology approach dramatically improves the efficiency of information processing and transmission.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Related Literature** & **Network Structure** \\ \hline
[20,22,30] & scale-free network \\
[21,28] & Barabási–Albert (BA) network \\
[15,26,39] & reality network \\
[24,39,40] & coupled network \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some planar structural networks for cascading failure studies.
###### Abstract
However, RNN struggles to learn interdependency between input and output data when the sequence span gets longer [49]. In order to overcome the limitation of this DL technique, a special kind of RNN, long short-term memory (LSTM) is proposed by [50]. With the LSTM, information from a sequence can be carried along the consecutive sequences, and the model can learn the relationship between sequential data and output data. The study conducted by [51] applied LSTM architecture for the first time in SM studies by using the SMAP L3_SM_P product with climate and soil texture data to improve the design accuracy of SMAP SM data. In 2018, ref. [52] presented a model for the long-term SM forecast on both surface and different depths over the continental US, aiming to exploit the SMAP data together with the land surface models. The model can predict long-term SM values in the same region by using the SMAP SM time series data. In [53], the LSTM model trained with the same data classes used in [51] to nowcast the SM data, when the SMAP L3_SM_P product became available. Another study [54] downscaled the SMAP SM data in (\(\sim\)1 km) with the help of climate, soil texture, and topography data by implementing LSTM. This research aims to improve short-term SM prediction by combining the high temporal resolution SMAP SM product and high spatial resolution S1 backscatter coefficients integrated with the auxiliary data to assist the agricultural activities in the field scale. In this context, we used the SM data of the ground stations from ISMN, distributed around the world, to train an LSTM model with two microwave radar data products (SMAP and S1) together with soil texture, climate, and topographical data that are considered as the predictors of SM. The short-term forecast of SM on a field scale was successfully achieved by utilizing an approach dependent on microwave remote sensing, satellite-based observations. The model used in this study predict accurate SM values of the next day with high spatial resolution in regions with different geophysical properties and climate classes. The manuscript is structured as follows: Section 2 explains the materials and methods. Section 4 describes the experimental research with data processing, model optimization, and our findings by focusing on the accuracy assessments of utilized methods. Section 5 presents the interpretation of the results and focuses on the effects of land cover, especially in the presence of vegetation, soil texture, and climate, on SM estimation. We finalized the paper by highlighting the important outcomes of this study in Section 6.