esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Low-Cost (Clonic) Device  
Main advantages and disadvantages of the commercial equipment were considered as a reference to select the main components of the clonic device. In this regard, two elements were fundamental to create it: a sensor to monitor the concentration of CO\({}_{2}\) and an electronic motherboard that allows the processing of atmospheric data. In order to select the most suitable components, a series of criteria matrices were used to score the characteristics considered, which are detailed below.  
Firstly, a CO\({}_{2}\) sensor was chosen, for which its main characteristics evaluated were:  
* Measurement range: it should be high (0-5000 ppm).
* Accuracy: it should be less than \(\pm 0.1\%\)
* Capability of monitoring: it should measure one IAQ gas. In this case, CO\({}_{2}\).
* Sensitivity: it must provide high sensitivity and speed of response (in minutes).
* Heating time.
* Price: it should be less than EUR 30.  
Four different sensors were considered: Amphenol Advanced Sensors Telaire T6713, Sensirion SCD30, DFRobot SEN0219, and Amphenol SGX Sensortech MiCS-VZ-89 TE. The result of the criteria matrix is shown in Appendix A. After performing the criteria matrix (see Table A1), the most suitable sensor was the SEN0219. This is an analog sensor capable of monitoring CO\({}_{2}\) in an exclusive way by using non-dispersive infrared (NDIR) principle to detect the existence of CO\({}_{2}\) in the air. It features a long life, high resolution, accuracy and sensitivity, as well as a medium response speed. In addition, it is easy to install on any microcontroller and its price is relatively low (between EUR 30 and EUR 60).  
Secondly, the selected motherboards included the Raspberry Pi Model B+ microcomputer and the Arduino MEGA 2560 R3 and ELEGOO MEGA 2560 R3 microcontrollers. Due to the similarities between the Arduino and ELEGOO microcontrollers, ELEGOO was considered because of its lower price. Their main factors evaluated were:  
* General characteristics.
* Software and hardware.
* Innovation.
* Interconnection.
* Price.  
The result of the criteria matrix is shown in Appendix A. Although both boards were suitable and had the right features for mounting the programmable device (see Table A2), ELEGOO MEGA 2560 Rev3 had a more intuitive and user-friendly software interface. The C++-based programming language allows fast programming of the SEN0219 sensor, as it is designed for implementation on microcontrollers. Although Raspberry Pi has many advantages, this project could not make full use of them. Additionally, the programming language (mainly Python, which is one of the base programming languages of RaspberryPi) is complex and designed for expert programmers. After performing the criteria matrix (see Table 11), the most suitable motherboard was the ELEGOO MEGA 2560 Rev3.
## 3 Example Building Structures with Semi-Active Control System
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**26-Story Model w/SMIS**} & \multicolumn{4}{c}{**11-Story Model w/STMD**} \\ \hline
**Story** & **Mass (kg)** & **Stiffness (k/N)** & **Story** & **Mass (kg)** & **Stiffness (k/N)** & **Story** & **Mass (kg)** & **Stiffness (k/N)** \\ \hline
1 & 3,080,514 & \(3.18\times 10^{6}\) & 14 & 3,186,563 & \(2.32\times 10^{6}\) & 1 & 215,370 & \(4.68\times 10^{5}\) \\
2 & 2,582,900 & \(2.68\times 10^{6}\) & 15 & 3,140,676 & \(2.63\times 10^{6}\) & 2 & 201,750 & \(4.76\times 10^{5}\) \\
3 & 1,726,352 & \(6.34\times 10^{6}\) & 16 & 3,132,518 & \(2.65\times 10^{6}\) & 3 & 201,750 & \(4.68\times 10^{5}\) \\
4 & 1,733,490 & \(5.92\times 10^{6}\) & 17 & 3,125,381 & \(2.59\times 10^{6}\) & 4 & 200,930 & \(4.50\times 10^{5}\) \\
5 & 1,716,155 & \(5.71\times 10^{6}\) & 18 & 3,170,247 & \(2.59\times 10^{6}\) & 5 & 200,930 & \(4.50\times 10^{5}\) \\
6 & 1,715,135 & \(5.36\times 10^{6}\) & 19 & 3,168,208 & \(2.48\times 10^{6}\) & 6 & 200,930 & \(4.50\times 10^{5}\) \\
7 & 1,718,195 & \(5.20\times 10^{6}\) & 20 & 3,117,223 & \(2.49\times 10^{6}\) & 7 & 203,180 & \(4.50\times 10^{5}\) \\
8 & 1,697,801 & \(4.95\times 10^{6}\) & 21 & 3,094,790 & \(2.34\times 10^{6}\) & 8 & 202,910 & \(4.37\times 10^{5}\) \\
9 & 1,721,254 & \(4.79\times 10^{6}\) & 22 & 3,084,593 & \(2.24\times 10^{6}\) & 9 & 202,910 & \(4.37\times 10^{5}\) \\
10 & 3,127,A20 & \(4.45\times 10^{6}\) & 23 & 3,076,435 & \(2.17\times 10^{6}\) & 10 & 176,100 & \(4.37\times 10^{5}\) \\
11 & 3,128,440 & \(1.08\times 10^{6}\) & 24 & 3,447,606 & \(2.11\times 10^{6}\) & 11 & 66,230 & \(3.12\times 10^{5}\) \\
12 & 4,030,874 & \(8.07\times 10^{5}\) & 25 & 3,461,882 & \(1.73\times 10^{6}\) & - & - & - \\
13 & 3,567,930 & \(3.11\times 10^{6}\) & 26 & 5,769,463 & \(1.51\times 10^{6}\) & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Story mass and stiffness of the example structures. Figure 4: Analytical models of example structures: (**a**) Model with a semi-active tuned mass damper (Example 1); (**b**) Model with a semi-active mid-story isolation system (Example 2). 2, respectively. Ten MR dampers were used for the optimal seismic control of Example 2. The voltage sent to the MR damper varied within the range 0-10 V.
# Electromagnetic Soil Characterization for Undergrounded RFID System Implementation
## 4 Practical Verification of the Link Budget Model Using a Buried Metal-Backed RFID Tag Antenna
where \(R\) is the reading distance from the sand tank surface to the reading antenna (in m), and \(f\) is the frequency in Mhz. Factor 2 in Equation (3) is inserted to compensate the round trip of the electromagnetic wave (from the reading antenna to the tag then from the tag to the reading antenna). Now for the yellow sand case, according to Equation (2) an attenuation of 32 dB can be evaluated for a 40 cm thickness of the soil. When the distance \(R\) is set to be 0.5 m above the sand box, tag localization and its stored data can be fetched successfully. The expected received power from the buried tag is expected to be \(-\)71.3 dB, however the actual received power is found to be \(-\)73 dB. The discrepancy between the expected and the actual received power (3.85% error percent) can be attributed to the multipath effect form of the surrounding objects and from the edges of the container. These undesired reflections act as new illumination sources for the tag antenna and result in higher received signals than what is expected from the total path loss calculations. Fortunately, this situation cannot occur over wide areas of land, and the accuracy is still within an acceptable margin.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 3 Receiver Design Considerations for LIS: Proposed Improvements in Channel Estimation  
We propose several methods to address the challenges associated with pilot-based channel estimation and improve it in LIS systems, which can enhance the accuracy of the estimation process and, consequently, the system's performance. To overcome the limitations of conventional LS channel estimation in LIS systems, this section introduces four enhancements: (1) optimized pilot design with orthogonal pilot sequences; (2) increased channel estimation accuracy through pilot power optimization; (3) an increased number of pilots and selective LIS antenna configurations; and (4) decreased error with increasing iteration and the impact of LDPC codes on channel estimation. These improvements aim to reduce pilot contamination, enhance estimation accuracy, and strike a balance between performance and computational efficiency.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Advanced Malware Detection Techniques
#### 3.6.7 Federated Learning for Anomaly Detection in IoT Networks  
The article titled Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks by [PERSON] et al., published in _Computers & Security_, addresses the issue of cyberattacks on Internet of Things (IoT) devices and proposes a novel architecture for intrusion detection based on federated learning (FL). This approach seeks to mitigate the limitations of traditional intrusion detection methods and model training architectures in the cloud or at the edge.  
The growing adoption of IoT devices brings multiple benefits, such as increased productivity, automation, cost reduction, and minimized production errors. However, it also increases the risk of cybersecurity breaches due to high connectivity and an expanded attack surface. Traditional intrusion detection and prevention methods, based on signatures, face challenges in integrating into the IoT environment due to hardware and software diversity, as well as the rapid evolution of threats.  
Machine learning (ML) has shown promising results in developing intrusion detection systems (IDS). However, cloud-based training architectures present significant issues in IoT environments, such as high bandwidth consumption, network resource congestion, and privacy concerns due to centralized data. As an alternative, edge computing approaches have been proposed, but they also face data isolation issues.  
FL is an approach that allows training a global model using distributed data across multiple devices without centralizing the data. Each device trains a local model and sends updates to a central server, which aggregates these updates to train the global model. This reduces network overload and addresses privacy concerns by keeping data locally.  
The proposed architecture in this study integrates an unsupervised clustering algorithm in the FL pipeline to address heterogeneity issues in IoT networks. The methodology includes the following steps:  
(1). Environment Setup and Data Capture: A testbed is used, which includes several emulated IoT/IIoT devices and attackers interacting in a complex network topology. (2). Training of Local Models: Each device trains a local model using benign network traffic data. (3). Clustering of Local Models: Before model aggregation in each FL round, partially trained local models are clustered based on model parameter similarities. (4). Clustered Federated Learning Process: For each identified cluster, an independent FL process is initiated.  
The architecture was evaluated using a testbed that included various IoT/IIoT devices and threat actors performing real-world attacks, such as the complete lifecycle of the Mirai malware. The experimental results show that the proposed architecture can effectively detect network anomalies [30].
# Projection-Based Augmented Reality Assistance for Manual Electronic Component Assembly Processes
## 3 Proposed Method
### Board Tracking  
As the proposed system uses the image captured by the camera to recognize components, it is essential to avoid distortions in the image due to the camera lens. It is therefore necessary to calibrate the camera, i.e., to know the intrinsic camera parameters, before or prior to using the system. In our system, we propose to use the well known [PERSON]'s camera calibration algorithm [24]. This calibration process only needs to be done once, and it allows us to calculate the compensation that has to be applied to each image captured by the camera to avoid distortions.  
During the component assembly phase, the boards have a non-fixed position, having one degree of freedom for horizontal displacement. They have also different sizes, shapes and colors due to the mounting boards and used materials. Owing to a component's position being referred to via the bottom-left corner of the board, the use of some markers is proposed with the final purpose of tracking the board position. In this system, the ArUco markers are used [25].  
Two ArUco markers are placed to locate the vertical board position, an other two ArUco markers are placed to locate the horizontal board position. During the assembly, the operator might occlude the horizontal markers, but if it happens, the system assumes the previously captured horizontal marks positions as current positions (temporal coherence assumption). The corner of the board is calculated by intersecting the vertical line and the horizontal line referenced to the markers; see Figure 3. This corner is necessary to obtain the reference system of the PCB, and therefore, to locate component positions. If vertical line calculation is not possible, the component inspection stops. Thus, visible vertical markers are necessary to track correctly the board.
## 5 Pseudo-Experimental Results  
The entire data were categorized into training, validation, and testing parts as shown in Table 8. Once the data set was generated, the features set was calculated as mentioned in Section 2. It is pertinent to mention that to train the network, undamaged cases (NUL) and two damaged cases (NBL1 and NBL2) are considered for training and validation. As with experimental studies, damage data are obtained from the locations near to damage. As an example, for NLC-1, data obtained from SG2, SG3, and SG7 are considered damaged data while the remaining data obtained from SG1, SG4-SG6, SG8 to SG24 are considered undamaged data. The feature sets were calculated as estimated for the experimental study and given as input of the SVM-based ML framework. The training and testing accuracy after carrying out a 10-fold test was found to be 91.50% and 85.40% as shown in Table 9. Table 10 depicts average testing accuracy as 77%. The results show the damaged joint with significant accuracy represents the probable locations of damage (all springs at that joint). One important observation is that with this algorithm, the probable locations for multiple damage scenarios can also be identified. As an example, NBL5 can be placed here in which S7 and S23 are assigned fixity factors 0.7. The confusion matrix as shown in Table 9 depicts that all the sensors' data near damaged and undamaged locations are classified successfully.
## 3 Problem Statement and Schedule-Based Network
### Schedule-Based Network Representation
#### 3.2.1 Waiting Arc  
Passengers will make a choice when there are different kinds of trains operating on the line. When the train they want to take is not the current arrival train, the waiting time will be extended. Let \(a(vo_{r}^{l},vd_{r}^{l^{\prime}})\) denote the waiting arc from virtual origin node \(vo_{r}^{l}\) to the departure node \(vd_{r}^{l^{\prime}}\). This represents passenger waits for train \(l^{\prime}\) whereas the current arrival train is \(l\) at origin station \(r\). Assuming that the passengers arrive at the station following the uniform distribution, the corresponding passenger average arrival time is the intermediate value of the departure time \(t_{d}(r,l)\) and \(t_{d}(r,l-1)\), as shown in Figure 4. The waiting time of the waiting arc can be calculated by Equation (1). If the current train is \(l\) and passenger also choose to take train \(l\), then \(l^{\prime}=l\),  
\[cw(a)=t(vd_{r}^{l^{\prime}})-t\big{(}vo_{r}^{l}\big{)}=t_{d}\big{(}r,l^{\prime }\big{)}-t_{o}(r,l)\ \ \ \ \ l,l^{\prime}\in L \tag{1}\]  
\[t_{o}(r,l)=\frac{1}{2}[t_{d}(r,l)-t_{d}(r,l-1)]\ \ \ \ \ l\in L\ . \tag{2}\]  
Additionally, the situation at the junction station is different from other stations. There are three types of waiting passengers: the passengers of the suburban line itself, the state passengers on the cross-line train, and state passengers on the non-cross train, as shown in Figure 5. Passengers on the non-cross state train need to transfer from the state railway hub station platform to the suburban railway platform at the junction station, and an extra time cost \(T_{h}\) should be considered in the travel time of the waiting arc. For passengers on the cross-line train, the waiting time of the waiting arc is 0.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(L\) & Set of train list. \\ \hline \(va_{r}^{l}\) & Arrival node of train \(l\) at station \(r\). \\ \hline \(vd_{r}^{l}\) & Depart node of train \(l\) at station \(r\). \\ \hline \(vo_{r}^{l}\) & Virtual origin node denotes passenger travel from station \(r\) and current arrival train is \(l\). \\ \hline \(ve_{s}\) & Virtual destination node of station \(s\). \\ \hline \(a(i,j)\) & Arc from node \(i\) to \(j\). \\ \hline \(t(i)\) & Time of node \(i\). \\ \hline \(t_{d}(r,l)\) & Time of depart node \(vd_{r}^{l}\), namely depart time of train \(l\) at station \(r\). \\ \hline \(t_{a}(r,l)\) & Time of arrival node \(va_{r}^{l}\), namely arrival time of train \(l\) at station \(r\). \\ \hline \(t_{o}(r,l)\) & Time of virtual origin node \(vo_{r}^{l}\). \\ \hline \(T_{h}\) & Transfer time for passengers transfer from the state railway platform to suburban railway platform at hub station. \\ \hline \(cw(a)\) & Time of waiting arc \(a\). \\ \hline \(cr(a)\) & Time of section running arc \(a\). \\ \hline \(cs(a)\) & Time of station-stop arc \(a\). \\ \hline \(ct(a)\) & Time of transfer arc \(a\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Schedule-based network related notations.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerability Causes  
Based on previous work [11, 13, 22, 25, 85], we summarize several causes for smart-contract vulnerabilities.  
* **Visibility:** Ethereum records all of its validated historical transactions in _world state_ (refer to Section 2), which is visible to the whole network. This visibility may cause some problems when the smart-contract owner wants to keep it from the public due to privacy concerns. Prior studies have shown that inspecting accumulated transactions' statistic characteristics and leveraging analysis graph structures could reveal valuable and practical information, given users are granted anonymity with pseudonymous public keys [86, 87].
* **Opacity:** On the other hand, the opacity of smart contracts poses another concern. Live contracts need rigid inspection and source-code audits since they manage millions of USD. However, 77% of running smart contracts are opaque and hold USD 3 billion [88].
* **Immutability:** Smart contracts cannot be modified once deployed on Ethereum. This immutability perpetuates problematic contracts on the chain. Workarounds such as updating contracts or addresses at the user side could solve the problem to a certain degree, however, they introduce other security concerns.
* **Automated Execution:** A control flow transfer (CFT) between two instructions \(A\) and \(B\) means that after \(A\)'s execution, \(B\) is excuted immediately. When smart contracts interact with each other, those CFTs are not always under control. In the uncontrolled-function call-permission cases, the self-elevation of a malicious attacker could happen by triggering some high-privilege functions [85].
* **Mining:** Which transactions to be processed depends on the miner's choice. The _deterministic_ result of a single transaction's execution no longer exists when multiple transactions are involved. Their execution order may vary based on the miner's choice. This uncertainty severely affects _order-sensitive transactions_. The final result of a series of transactions might reach vastly different outcomes.
* **EVM:** EVM structure provides a stand-alone, isolated running-time environment (sandbox) for smart contracts. EVM utilizes an oracle to handle the incoming real-world inputs. An oracle is a concept that bridges blockchain and the real world and acts as APIs on chain, which can be accessed by the smart contracts to receive feeds from the real world [89]. Centralized oracles introduce several attack vectors and issues to smart contract such as _integrity_, _accuracy_, and _consistency_[90]. However, the decentralized designs of oracle such as Chainlink [91] are trying to solve this problem.
* **Immutative Programming Language:** Solidity is an evolving programming language and its widely known vulnerability is the _re-entrancy problem_, which is caused by the fallback mechanism in Solidity. Mishandled exceptions could be a severe problem for smart contracts too.
## 4 Gaussian Smoothing  
The extracted features from each participant are the weighted mean intensity value of each ROI, which must be more homogeneous to their class and heterogeneous to others. However, upon careful observation as in Figure 8, the original data (blue) contains some highly unusual spikes at some point and can be considered abnormal. These spikes (x = 70, 110, 120, 144) were considered abnormal for the graph due to its extreme shifting from mean positioning (however this doesn't mean they are a less useful feature, each feature values depends on its cluster size, only in regard to the distribution we consider it as abnormal, which were averaged using Gaussian smoothing (GS) for accurate classification using BR-NN). In general, the Gaussian distribution for one-dimensional vector operates under the following equation:  
\[G(x_{i,w})=\frac{1}{\sqrt{2\pi\sigma_{i,w}}}e^{\frac{-x_{i}^{2}}{2\sigma_{i,w }}} \tag{3}\]  
where \(x_{i}\) represents the input value for the \(i\)th feature of each image for each ROI, and \(\sigma_{i,w}\) represents the standard deviation for each group of ROI, \(w\) is the size of the window over which the Gaussian weighted moving average is calculated. This window slides over the specified length, and provides the average Gaussian value over that window, as shown in Figure 8. With a Gaussian weighted moving window, data is transformed to its normal distribution after the completion of its window operation. Please note the window is an averaging moving function hence \(\sigma_{i,w}\) also keeps on changing however we can approximate the final distribution to be a normal distribution for higher window size. In our case, we have chosen \(w\) = 9 from our experiment. In Figure 8, the right part separated from \(x\) = 1 to 74 represents the mean value for the AD patients and \(x\) = 75 to \(x\) = 148 represents the mean value of CN patients from one cluster of ROI_CL. Here, GS transforms the data from unknown haphazard distribution to the normal distribution, so that it can be classified with less computational burden using the Bayesian regularized neural network. It shifted the characteristics of each vector toward the mean value of its target class and bring homogeneity in its cohort. This can be considered as a data cleaning step similar to the normalization process. Furthermore, it can work as a replacement for the feature selection step where the features are transformed for better classification results.
## 3 Results and Discussion
### Cold Chain Management of HP Treated Meat Products
Even for the worst case scenario t-T profile, the HP treated products would still have a very long SLR and would be very unlikely to fail. Considering all the above, three months is a satisfactory shelf life of commercially available HP-treated sliced meat products, and although technically it is achievable to have longer times, the meat industry would not be necessary to claim for it. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Days**} & \multicolumn{3}{c}{**Sliced Cooked Ham**} & \multicolumn{3}{c}{**Sliced Cooked Turkey**} \\ \cline{2-7}  & **Control** & **HP 500 MPa** & **HP 600 MPa** & **Control** & **HP 600 MPa** \\ \hline Recommended handling conditions throughout the & & & & & \\ whole chain (cold chain at 4 \({}^{\circ}\)C) & 2.0 & 50 & 192 & 1.5 & 168 \\ \hline Representative profile (Figure 8) & 3.5 & 52 & 201 & 2.0 & 176 \\ \hline Best case t–T profile scenario (Figure 8) & 6.0 & 55 & 212 & 3.5 & 180 \\ \hline Worst case t–T profile scenario (Figure 8) & 1.5 & 49 & 189 & \(-\)1.0 & 162 \\ \hline \end{tabular}
\end{table} 
Table 6: The remaining shelf life, SLR (days), of sliced cooked meat products (ham and turkey) if stored in consumers’ refrigerators with a constant temperature of 4 \({}^{\circ}\)C, after 19 days in the cold chain. Figure 7: Representative time–temperature profile, best case scenario t–T profile, and worst case scenario t–T profile, all generated by the CCP software based on the records of contributed actual profiles.