esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
#### 4.1.1 Strong Versus Weak PUFs  
The security and performance characteristics of edge devices in a distributed and uncontrolled environment with limited resources vary significantly with the choice of PUF types. Choosing between weak and strong PUFs in an EC ecosystem depends on numerous factors such as resource requirements, security against threat types, authentication capabilities, reliability, and robustness against physical attacks. Environmental factors like temperature and voltage variations are detrimental to both types of PUFs. The simple and efficient weak PUFs, e.g., SRAM PUFs, are suitable for key generation in secure boot or communication. In contrast, strong PUFs like APUFS are used in devices that require frequent authentication or cryptographic security. Table 6 compares various tradeoff factors of weak and strong PUFs.  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline
**PUF Types** & **Merits** & **Demerits** \\ \hline \multirow{7}{*}{Weak PUFs} & _Low Resource Consumption_: Weak PUFs are simple in implementation, requiring limited hardware resources. For example, SRAM and RO PUFs exploit on-chip memory blocks, thus making them a natural choice for resource-limited edge devices. & Environmental variations such as the ambient temperature or the supply voltage can have a detrimental effect on the performance of weak PUFs [228]. \\ \cline{2-3}  & _Low Power Usage_: SRAM’s minimal static power requirements and quicker access times, or energy-efficient comparison of oscillator frequencies in RO PUFs, make them ideal for edge devices [229]. & _Physical Attack Vulnerability_: Weak PUFs are susceptible to probing or cloning attacks; however, exposure of their CRPs is minimal due to their internal operation. Thus, if an attacker gains physical access to the device, they might probe the PUF to recover its response or the keys [230]. \\ \cline{2-3}  & _Fast Response Time_: The simple architecture and limited set of CRPs of weak CRP suits applications like secure boot due to their quick response, supporting real-time experience in EC. & The responses of weak PUFs are processed internally as a secret key, thus requiring an error correction on-chip and storage of error-correcting helper data. This adds some overhead [231]. \\ \hline \end{tabular}
\end{table}
Table 6: PUFs trade-offs in the context of EC security.  
Figure 18: Structure diagram of n-stage APUF.
## 2 Materials and Methods  
### Materials  
Graphene oxide (GO) in an aqueous dispersion of 4 mg/mL with a monolayer content >95% was purchased from Graphene (Spain). The elemental analysis of GO showed C: 49-56%, H: 0-1%, N: 0-1%, S: 2-4%, and O: 41-50%. To reduce the GO, L-ascorbic acid (AsA) or vitamin C (>99.0%, Sigma-Aldrich) was used. Nanoceria was purchased from Evonik Degussa GmbH (Essen, Germany) and used without purification. For the polymer brushes, the reagents used were purchased all from Aldrich: acrylic acid (AA) 99%, [2-(Methacryloyloxy)ethyl] trimethylammonium chloride (METAC) solution (75 wt% in H\({}_{2}\)O), N-Vinylcaprolactam 98% (NVCL), (3-Aminopropyl)triethoxysilane (APTES) 99%, 4,4\({}^{\prime}\)-Azobis(4-cyanovaleric acid) >98.0% (CVA) (initiator), and Bis(carboxymethyl)trifluor carbonate 98% (change transfer agent (CTA)).
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Pulsed Thermography (PT)  
In the case of PT, thermographic sequences are processed as depicted in Figure 3 using the MATLAB programming platform. Sample S45, as inspected by PT in reflection mode from the back face, is used as an example to illustrate the processing procedure. Figure 3a presents a thermogram at a given time arbitrarily chosen (0.35 s after the flash pulse in this case), in which a suspect area can be visually identified. From this thermogram, a region-of-interest (ROI) is manually selected around the identified anomaly (defect 1 in this case, see Figure 1b) and the whole sequence is cropped to contain only this ROI as shown in Figure 3b.  
Figure 3: Pulsed thermography processing (sample S45, defect 1): (**a**) thermogram at \(t\) = 0.35 s showing a suspect area (defect 1); (**b**) thermogram sequence corresponding to the ROI identified in (**a**); (**c**) sound area and defect temporal profiles for the complete sequence; and (**d**) profiles after cold image subtraction.  
The next step is to manually select the defect area (red square inside the defect) and sound area (blue area around the defect) from one thermogram of the defect as shown in Figure 3c. Alternatively, semi-automatic identification of sound and defect areas (by locating the position of a point inside the defect) would also be possible as reported previously [15].  
The temperature profiles are calculated as the average temperature value inside these areas (defect vs. sound) at each frame of the sequence. These profiles are shown in Figure 3c for the entire sequence, which includes images acquired before activating the flashes, i.e., cold images. The cold images are then averaged and subtracted from the whole sequence. This procedure allows the reduction of different types of noise, such as environmental reflections and fixed pattern noise. Cold image subtraction is also a requirement prior to the application of thermographic signal reconstruction. The cold images are then removed from the sequence resulting in the profiles shown in Figure 3d that will be subjected to further processing.  
All identified ROIs (containing defects) are treated using the following advanced signal processing techniques [16]: thermographic signal reconstruction (TSR) [17; 18], including first and second time derivatives; principal component thermography (PCT) [19]; and pulsed phase thermography [13; 20].  
In addition, two approaches were investigated to assess the impact of noise. First, PCT and PPT were applied to both: raw (unprocessed sequences) and TSR data in order to evaluate the denoising inherent to TSR. Second, the use of spatial filters was also examined through the use of Gaussian filters of two degrees with variances \(\sigma^{2}\) = 1 and 3.
## 3 Results and Discussion
### Mushroom Intake and Its Risk to Human  
The European Food Safety Authority (EFSA) sets a tolerable weekly intake (TWI) for Al as 1 mg kg\({}^{-1}\) of body weight (BW) for all groups of people. For an individual with a bodyweight of 65 kg, the value is 9.29 mg day\({}^{-1}\)[37]. At the same time, the Institute of Medicine Food and Nutrition Board sets a tolerable upper intake level (UL), which is the maximum daily intake that poses no health risk. The UL value of Cu, Fe, Ni, and Zn are 10 mg day\({}^{-1}\), 45 mg day\({}^{-1}\), 1 mg day\({}^{-1}\), and 40 mg day\({}^{-1}\), respectively. However, the UL was not established for Cr [38]. The World Health Organization (WHO) suggested that supplemental intake of chromium should not exceed 0.250 mg day\({}^{-1}\)[39].  
According to the trace elements concentration in each sample that was examined in this study, an attempt to calculate the total maximum daily intake (kg) was taken, and the limiting factor was its lowest value (Table 4). Due to results obtained, the minimum mushroom consumption, which could cause adverse effects varied from 0.02 to 0.38 kg day\({}^{-1}\). Cu content was a limiting factor for total maximum daily intake for samples collected and picked individually (Samples 1, 2). In turn, Al content was a limiting factor in Samples 3-6, which were purchased on the market from local producers (offering packaging between 20 and 30 g). According to this, by consuming a single portion (20-30 g) of Sample 5, the maximum daily intake could be exceeded. Presumably, different limiting factors for these groups of samples may result from different picking or drying techniques. In consequence, total maximum daily intake was significantly different for Samples 1, 2 (0.28-0.38 kg) and Samples 3-6 (0.02-0.21 kg).  
Generally, a single mushroom portion (20-30 g) could not pose any health issue in accordance with the Tolerable Upper Intake Level (UL). Since Sample 5, which was collected near one of the industrial centers, became an outlier, a potential health risk might be caused in this case by consuming 20 g of mushrooms. Although this value was relatively lower than the average mushroom consumption per capita globally (13 g day\({}^{-1}\)) [40]. Therefore, 20 g daily is still considered as low toxicity, aligning with the result from the MARA assay. Compared to the rest, Samples 4 and 5 (collected near Krakow by the same company) contained less Zn and Cu while also containing more Al, Cr, and Fe. Moreover, the daily consumption of Samples 4 and 5 should not exceed 0.09 and 0.02 kg, respectively. In Samples 1-3 and 6, consuming 100 g mushrooms could not pose any health issue, which corresponded to 4-5 portions (20-30 g) purchased on the market from local producers.  
Figure 6: Dendrogram representing clustered toxic fingerprint based on heavy metal content of the six mushroom samples: In (**a**) dried samples and (**b**) water extracts.
# Interaction Design Based on Big Data Community Home Care Service Demand Levels
## 2 Big Data Community Home Care Service Demand Hierarchy  
### Analysis of Demand for Home Care Services  
If the sampling is done in actual, equal proportions of the target population, the sample will be concentrated in female elderly and lower-aged elderly, which is not reasonable [18]. Therefore, a multi-stage, unequal proportion of CLHLS target sampling method was used to balance the sample size and gender proportion of different age groups. The questionnaire content of the CLHLS sampling method was designed based on the content of community-based home care services. Using the CLHLS sampling method, the results of the survey of the elderly at home group were collected to form the community big data, and they were used as the data basis for the hierarchy of elderly service needs. After sampling by CLHLS method, the formed community big data is shown in Figure 1.  
According to the community's big data, the needs of home care services can be divided into four categories: living care, medical care, spiritual comfort, and legal assistance. From Figure 1, it was illustrated that 57.84% of the sample population had living needs and 59.28% had shopping needs. The percentage of the sample population without living needs was 40.54%, and the percentage of the sample population without shopping needs was 44.03%. The percentage of people who needed home services was 83.14%, and the percentage of people who needed to learn about health care was 78.83%. The percentage of the sample who did not need home services was 16.37%, and the percentage of the sample who did not need to learn about health care was 21.25%. Regarding the demand for spiritual comfort services, which include chatting and organizing social entertainment activities, 66.54% of the sample population needed to chat to relieve boredom and 70.05% wanted to organize and participate in social entertainment activities. The percentages of those who did not need to chat to relieve boredom and did not want to organize or participate in social recreational activities were 34.67% and 35.26%, respectively. Regarding the demand for legal aid services, 77.2% and 62.31% of the sample population wanted the community to provide rights protection services and help with disputes, respectively. The percentage of those who did not want the community to provide rights protection services was 38.13%, and the percentage of those who did not need the community's help in handling disputes was 35.49%.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
###### Abstract  
Vehicular Ad hoc NETworks (VANET) has been well studied for a long time as a means to exchange information among moving vehicles. As vehicular networks do not always have connected paths, vehicular networks can be regarded as a kind of delay-tolerant networks (DTNs) when the density of vehicles is not high enough. In this case, packet delivery ratio degrades significantly so that reliability of networks as an information infrastructure is hardly held. Past studies such as SADV (Static-node Assisted Data dissemination protocol for Vehicular networks) and RDV (Reliable Distance-Vector routing) showed that the assistance of low-cost unwired static nodes located at intersections, which work as routers to provide distance-vector or link-state routing functions, significantly improves the communication performance. However, they still have problems: SADV does not provide high-enough delivery ratio and RDV suffers from traffic concentration on the shortest paths. In this paper, we propose MP-RDV (Multi-Path RDV) by extending RDV with multiple paths utilization to improve performance against both of those problems. In addition, we apply a delay routing metric, which is one of the major metrics in this field, to RDV to compare performance with the traffic-volume metric, which is a built-in metric of RDV. Evaluation results show that MP-RDV achieves high load-balancing performance, larger network capacity, lower delivery delay, and higher fault tolerance against topology changes compared to RDV. As for routing metrics, we showed that the traffic-volume metric is better than the delay one in RDV because delay measurement is less stable against traffic fluctuation.  
Keywords:vehicular networks; delay tolerant networks; distance-vector routing +
Footnote †: journal:
## Abbreviations  
9-octadecenoic acid (C18:1); \(\alpha\)-linolenic acid (C18:3 \(\alpha\)); analysis of variance (ANOVA); biogenic amines (BA); brightness/darkness (L*/ -L*); cadaverine (CAD); cis-11-eicosenic acid (C20:1); colony-forming units (CFU); de Man-Rogosa-Sharpe (MRS) agar; diode array detector (DAD); dry matter (d.m.); eicosanoic acid (C20:1); fatty acid methyl esters (FAME); fatty acids (FAs); folic acid (vitamin B9); Food and Agriculture Organization (FAO); Food and Drug Administration (FDA); free amino acids (FAA); \(\gamma\)-aminobutyric acid (GABA); gas chromatographer(ry)/mass spectrometer, gas chromatography(/ph) mass spectrometry(/ter) (GC-MS); high-performance liquid chromatography (HPLC); histamine (HIS); inductively coupled plasma mass spectrometry (ICP-MS); internal diameter (ID); internal standard (ISTD); lactic acid bacteria (LAB); lentils grown in pure stands (L); lentils grown in relay intercropped with winter rye (LR); linoleic acid (C18:2); monounsaturated fatty acids (MUFA); National Bureau of Standards (NBS); niacin (vitamin B3); no observed adverse effect level (NOAEI); non-fermented (NF); palmitic acid (C16:0); pantothenic acid (vitamin B5); phenylethylamine (PHE); polyunsaturated fatty acids (PUFA); putrescine (PUT); pyridoxine (vitamin B6); redness/greenness (a*/\(-\)a*); riboflavin (vitamin B2); saturated fatty acids (SFA); solid-phase microextraction (SPME); solid-state (SS); solid-state fermentation (SSF); spermidine (SPRMD); spermine (SPRM); standard error (SE); stearic acid (C18:0); submerged (SM); submerged fermentation (SMF); tetradecanoic acid (C14:0); thiamine (vitamin B1); tryptamine (TRIP); tyramine (TYR); United Nations (UN); United States of America (USA); volatile compounds (VCs); volume by volume (\(v/v\)); wavelength (\(\lambda\)); weight by weight (\(w/w\)); World Health Organization (WHO); yellownress/blueness (b*/\(-\)b*).
# Classification of Bugs in Cloud Computing Applications Using Machine Learning Techniques
## 5 Comparative Analysis  
All studies showed that balanced datasets with optimal model hyper-parameters produced good results. The four classifiers, Naive Bayes, decision tree, random forest, and logistic regression, are built on balanced and imbalanced datasets. Table 7 lists all of the models' final highest achieved accuracies [28].  
The random forest classifier achieved the maximum training and test accuracy in the following hyper-parameters. You may see a diagrammatic depiction of the accuracy in Figure 24.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SR\#** & **Classifier** & **Training Accuracy** & **Test Accuracy** \\ \hline
1 & Naive Bayes & 93.84\% & 87.01\% \\
2 & Decision tree & 100\% & 88.45\% \\
3 & Random forest & 100\% & 91.73\% \\
4 & Logistic regression & 93.37\% & 88.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The accuracy of various classifiers.  
Figure 23: Prioritization of bugs in SaaS applications.  
With the training and test accuracy, it may, therefore, be said that the random forest outperformed other classifiers. Additionally, the recall, accuracy, and F1 score outperformed those of other classifiers.  
The limitation of this approach for the classification of bugs can evolve over time as cloud computing applications are updated or new features are added. This can result in models that become outdated and less accurate over time. To maintain accuracy, models must constantly be updated and retrained with new data.
# Donning/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control
## Materials and Methods
### Study Design
#### 2.3.1 Training Phase  
During the training, the machine and the user collaborated to achieve a common goal: minimize the error between estimation and target. The proposed co-adaptive system set both agents (machine and user) as active learners. The machine learning algorithm obtained the model coefficients grouped in the \(\beta_{i}(t)\) vector for each direction, while the user adapted his behavior to the estimated machine output. As feedback, a visual interface was prompted to represent the machine's performance in a natural and understandable environment for the user.  
The displayed interface had several elements (Figure 2a). In the background, there was a coordinate system centered on a resting position with two axis (mapping the movements as mentioned above). These two axis represented the DoFs controlled by the algorithm. As moving elements, there was a green circle indicating the target position \(\mathbf{d}(t)\), three small circles indicating upcoming target direction, a red cross (cursor) for the current estimated position \(\mathbf{y}(t)\), and a orange arrow indicating the current direction from the cursor to the target, i.e., the error. The UI was updated every 40 ms (25 Hz), same rate as the adaptive filter (feature vector rate). Before the experiment, the participants tested the feedback system until they got familiarized with the information given by each element.  
The training process consisted in 4 laps of the same trajectory. Each lap was composed by 4 movements. Starting in an initial position, the target moved until reaching the up-top position in 4 s, rebounded and came back to the center in another 4 s. Once the movement was completed, the same was repeated for the other three directions: left, down and right. A lap was completed when the 4 direction movements were conducted. One lap was 32 s long, 8 s each movement. Therefore, the complete training (4 laps) lasted 128 s. The training phase was equal for every participant independently of the experiment. The purpose of the training was to generate the model coefficients for each participant. They were requested to try to follow the target with the cursor controlled by their wrist movements.  
Since we were using a velocity control model, what was being learned was the direction of the movement, with large deviations between the target and the cursor position. Therefore, we explained the subjects to keep contracting the muscles in the correct way even if the algorithm was not learning properly or there was some offset between the target and the estimated user position.
## 3 Materials and Methods
### Ground-Penetrating Radar Method
On concrete dams, GPR proved to be useful in detecting hidden metallic objects (e.g., pipes) in the dams affected by earthquakes in Greece [12] and provide good imaging of the position of other structures found inside the dam's body, like basements, for example, or galleries. In general, it could be said that, along with other geophysical methods, the GPR method manages to point to areas which require attention [10], as this method still represents one of only methods which could actually scan through the walls or pavements of the dams to provide a subsurface image of any shallow inhomogeneity that might occur inside the dam's body. On both concrete and embankment dams which have public roads running on top of the crest, GPR can be applied to assess cracks affecting the roads or the quality of the rebars, for example [15; 16; 17]. The non-invasiveness and short amount of time required for the GPR surveys to take place represent another advantage [35] for this method to be used in dam-monitoring surveys.
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
###### Abstract  
In order to develop wireless sensor networks, which are defined by the IEEE 802.15.4 specification, researchers are considering low-power wide-area networks (LPWAN) due to their advantages of being long range, low power, low cost, and highly mobile. The issue of mobility is covered in the IEEE 802.15.4g standard for supporting a smart utility network (SUN), which is mainly controlled by orthogonal frequency-division multiplexing (OFDM) modulation. In a high mobility scenario, inter-carrier interference is a primary factor in reducing the performance of OFDM transmissions due to the destruction of the subcarrier component's orthogonality. This paper analyzes the mobility effect in multi-rate multi-regional orthogonal frequency-division multiplexing (MR-OFDM) for low-power wide-area networks in general, and the SUN MR-OFDM system in particular. As mentioned in standard 802.15.4 2015, IEEE 802.15.4g MR-OFDM is one of the low-power wide-area (LPWA) technologies in which energy optimization problems are of first priority. We are especially interested in simple technologies that provide high efficiency. Therefore, we propose a highly adaptive method that uses the cyclic prefix to mitigate the mobility effect in real time. At a symbol frames interval of 120 us, the Doppler shift effect from the mobility of the MR-OFDM system adapted smoothly. This is not the best method to mitigate Doppler shift but it is a simple method that suits the LPWA network. The proposed scheme clearly simulated the mobility of the MR-OFDM system, and had the advantage of using a cyclic-prefix with a bit error rate performance through Additive White Gaussian Noise (AWGN) and the Rician channel of Matlab.  
MR-OFDM; SUN OFDM; mobility effect; carrier frequency offset; CFO; FCFO; fractional carrier frequency offset; doppler Article Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM