esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 5 Evaluation
### Energy Results--Key Generation
#### 5.1.1 Energy Results--Commentary  
The Post-Quantum Cryptography techniques were broadly equivalent in terms of their energy utilisation when compared with the Elliptic Curve Cryptography methods at  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**NIST ‘Sec’ Level** & **Protocol** & **Category** & **J/1000 Keygens** \\ \hline Level 1 & RSA-1024 & Classic Technology & 218.64 \\ \hline \multirow{2}{*}{Level 2} & EC secp160r1 & Elliptic Curve Cryptography & 8.69 \\  & RSA-1536 & Classic Technology & 828.84 \\ \hline \multirow{4}{*}{Level 3} & EC secp224r1 & Elliptic Curve Cryptography & 10.16 \\  & EC P-256 (secp256r1, FIPS 186-4) & Elliptic Curve Cryptography & 7.33 \\  & ML-DNA-44 & Post-quantum Technology & 8.36 \\  & ML-KK-512 (Kyber-512, FIPS 203) & Post-quantum Technology & 7.61 \\  & RSA-2048 & Classic Technology & 1093.08 \\ \hline \multirow{4}{*}{Level 4} & EC P-384 (secp384r1, FIPS 186-4) & Elliptic Curve Cryptography & 17.05 \\  & ML-DNA-65 & Post-quantum Technology & 8.97 \\  & ML-KEM-768 (Kyber-768, FIPS 203) & Post-quantum Technology & 7.76 \\  & RSA-3072 & Classic Technology & 4014.84 \\ \hline \multirow{4}{*}{Level 5} & EC P-521 (secp521r1, FIPS 186-4) & Elliptic Curve Cryptography & 33.76 \\  & ML-DNA-87 & Post-quantum Technology & 9.82 \\ \cline{1-1}  & ML-KEM-1024 (Kyber-1024, FIPS 203) & Post-quantum Technology & 7.89 \\ \cline{1-1}  & RSA-4096 & Classic Technology & 11,952.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equivalent to Figure 4 with numerics to compare instead of logarithmic bar heights. RSA was particularly resource-hungry at larger key sizes.  
Figure 4: Plot of energy rate in Joules/1000 key generations for each algorithm, categorised by NIST security level and type of algorithm. Experiment performed over a typical 500,000 key generation count for each algorithm. (A tabulated version is provided in Table 2).  
NIST Security Level 3. At NIST levels 4 and 5, the post-quantum techniques were more energy efficient for the same security level than the tested current ECC methods. Turning to RSA, as expected, the energy efficiency became exponentially worse as the key size increased. Compared with ECC, RSA was around two or more orders of magnitude less efficient at key generation as the elliptic curve equivalent, as well as generating larger keys. Comparing RSA to the PQC techniques shows an even greater offset, as the PQC was much flatter across the resources it required as the security level increased.
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 4 Results  
The key to further improving the smartphone positioning performance is to understand the smartphone GNSS measurement characteristics. Therefore, analysis regarding range errors on different frequencies and the relationship between signal-to-noise ratio (SNR) values or elevation angles with range errors is first carried out, followed by the assessment of correlations between range errors and pre-fit residuals, range errors under  
Figure 3: Photos of experimental setup. (a) experimental vehicle and setup, (b) experimental smartphones.  
Figure 2: (a) aerial and street views of vehicle trajectory with (b) open-sky parking lots, (c) suburban road, and (d) short underpass.  
different environments, and comparison of range errors when the smartphone is mounted on the car dashboard and roof. The smartphone data processing strategies have the assumption that the measurement errors should follow a similar distribution as geodetic receivers; therefore, the zero baseline ZIM2-ZIM3 MGEX stations are processed as a reference to provide a clear view of the characteristics of smartphone range errors compared with geodetic receivers. It is worth mentioning that the first (L1 for GPS and GLONASS, E1 for Galileo, and B1 for BDS) and third (L5 for GPS, and E5a for Galileo) frequencies are used in this analysis for both smartphone and geodetic receiver assessments. Hereafter, P1 and P5 are used to denote the first and third frequencies, respectively. In the result analysis, dataset 1 is used in Sections 4.1-4.4, and datasets 2 and 3 are used in Section 4.5.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 10 Test Quality Metrics for SDDs and HDDs
### Statistical Delay Quality Level (SDQL)  
The \(SDQL\) is calculated using the delay defect distribution of the particular technology node. This value gives information about the probability of the test escapes of SDDs [16]. Two slacks are selected, say \(Tmgn\) and \(Tdet\), for the metric calculation. \(Tmgn\) is the tested path slack with respect to the test clock. \(Tdet\) is the longest functional path slack with respect to the system clock. These slacks divide the delay defect distribution into three regions, as shown in Figure 13. The area under the curve between the two slack values gives the metric value. This means that the detectable defects of sizes within the range of two slack values escape the SDD test. The value must be as small as possible, i.e., it denotes the fewer test escapes. The Equation (5) calculates the \(SDQL\) value. The first disadvantage of the metric is that, the delay defect distribution of the required process is not always available for the particular technology and process. The second is the lack of normalization. This is because the metric value increases when the number of fault sites increases, even though the test escapes are less. Hence, this metric cannot be used to compare two designs that have a different number of fault sites [82]. The third disadvantage arises while testing different designs with different test clock frequencies. When the test clock frequency is low, the timing margin increases, and the ability to tolerate the SDDs also increases. Hence, the \(SDQL\) value will be low. But in actual cases, the SDD test escapes are masked by a high timing margin. Hence, this metric is not accurate for validating the SDD coverage. In FAST, sometimes the \(Tdet\) is less than the \(Tmgn\). The metric calculation in such a case is not evident.  
\[SDQL=\sum_{i=1}^{N}\int_{Tmgn}^{Tdet}F(s)\,ds \tag{5}\]  
Figure 12: Comparison of different test quality metric at Slow (S), At-speed (A), Fast (F) test clock [84].
## 3 Results and Discussion
### Simulation Results and Analysis of S-U-S
#### 3.1.2 Forces Affected by the Different Arrangement Spacing of the Optimal Structure
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Spacing.**} & \multicolumn{2}{c}{**Draft Force/N**} \\  & & & **S-U-S-I7h3.5** & **O-P-S** \\ \hline
**Test NO.** & \(S_{1}\)/mm & \(S_{2}\)/mm & & \\ \hline
1 & 1 & 1 & \(-\)1728.91 & \\
2 & 2 & 2 & \(-\)4021.37 & \\
3 & 2 & 1 & \(-\)3284.78 & \\
4 & 1 & 2 & \(-\)2971.81 & \\
5 & 3 & 3 & \(-\)2130.69 & \\
6 & 2 & 3 & \(-\)2081.68 & \\
7 & 3 & 2 & \(-\)2293.92 & \\
8 & 1 & 3 & \(-\)2590.09 & \(-\)2275.92 \\
9 & 3 & 1 & \(-\)4017.78 & \\
10 & 1 & 4 & \(-\)3584.43 & \\
11 & 4 & 1 & \(-\)2507.11 & \\
12 & 2 & 4 & \(-\)2053.15 & \\
13 & 4 & 2 & \(-\)4447.47 & \\
14 & 4 & 4 & \(-\)2156.3 & \\
15 & 1 & 5 & \(-\)3405.93 & \\
16 & 5 & 1 & \(-\)4282.04 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Draft force affected by different arranged spacings.
## 3 Parametric Investigation
### Varying the TE Noise Source Location and/or Frequency
At this stage, however, it is worth noting that it is custom to locate microphones downstream the airfoil (\(x/c>0\)), if only for avoiding the multiple backscattering to possibly occur between the mock-up and each wall. In conclusion, whatever the frequency and (positive) incidence is, the microphone should ideally be located on the _top_ wall, at a position such that \(x/c\in[0,\ 2.5]\) (please note that a negative incidence would lead to opposite trends, i.e., the microphone should rather be positioned on the _bottom_ wall, still such that \(x/c\in[0,\ 2.5]\).) Further assessing this observation is the matter of the next subsection. Figure 10: Moderate to high frequency (\(St\) = 0.882, 1.47, 2.94) noise radiation from the trailing edge of an airfoil set at low to high incidences (\(\alpha\) = 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), 15\({}^{\circ}\), 20\({}^{\circ}\), 25\({}^{\circ}\)). From top to bottom: \(St\) = 0.882 (**a**,**b**), \(St\) = 1.47 (**c**,**d**), \(St\) = 2.94 (**e**,**f**). Deltas (in dB) between the incident and total RMS pressure fields, as recorded along the top (left: **a**,**c**,**e**) and bottom (right: **b**,**d**,**f**) walls. Figure 11: Low to high frequency (\(St\) = [0.0147; 2.94]) noise radiation from the trailing edge of an airfoil set at low to high incidences (from top to bottom): \(\alpha\) = 0\({}^{\circ}\) (**a,b**), \(\alpha\) = 5\({}^{\circ}\) (**c,d**), \(\alpha\) = 10\({}^{\circ}\) (**e,f**), \(\alpha\) = 15\({}^{\circ}\) (**g,h**), \(\alpha\) = 20\({}^{\circ}\) (**i,j**), \(\alpha\) = 25\({}^{\circ}\) (**k,l**). Deltas (in dB) between the incident and total RMS pressure fields, as recorded along the top (left: **a,c,e,g,i,k**) and bottom (right: **b,d,f,h,j,l**) walls. Characterizing the Installation Effects on Flush-Mounted Microphones Located Right Downstream the Trailing Edge  
To further assess what would be the ideal location(s) where to position a flush-mounted microphone, the present section focuses on these top and wall regions such that \(x/c\in[0,\ 2.5]\).
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 4 Proposed Architecture
### Proposed Algorithm
#### 4.2.3 Combination  
As discussed in Section 3.1.2, only variants of the average function (average, pruned average, damped average) are suitable for our algorithm. Pruned average requires a pruning threshold to be set and damped average requires a dampening function. For neither of these is it obvious how to choose these parameters in an unsupervised setting. Therefore, simple average as our combination function was used, consistent with much of the prior state-of-the-art outlier ensembles.
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
###### Abstract  
Due to the escalating network throughput and security risks, the exploration of intrusion detection systems (IDSs) has garnered significant attention within the computer science field. The majority of modern IDS are constructed using deep learning techniques. Nevertheless, these IDSs still have shortcomings where most datasets used for IDS lies in their high imbalance, where the volume of samples representing normal traffic significantly outweighs those representing attack traffic. This imbalance issue restricts the performance of deep learning classifiers for minority classes, as it can bias the classifier in favor of the majority class. To address this challenge, many solutions are proposed in the literature. TDCGAN is an innovative Generative Adversarial Network (GAN) based on a model-driven approach used to address imbalanced data in the IDS dataset. This paper investigates the performance of TDCGAN by employing it to balance data across four benchmark IDS datasets which are CIC-IDS2017, CSE-CIC-IDS2018, KDD-cup 99, and BOT-IOT. Next, four machine learning methods are employed to classify the data, both on the imbalanced dataset and on the balanced dataset. A comparison is then conducted between the results obtained from each to identify the impact of having an imbalanced dataset on classification accuracy. The results demonstrated a notable enhancement in the classification accuracy for each classifier after the implementation of the TDCGAN model for data balancing.  
Keywords:data balancing; deep learning; generative adversarial network; intrusion detection systems; security; TDCGAN +
Footnote †: journal: _Acticle_
## 4 Results and Discussion
### Result Comparison
Table 13 provides a comparison of previous studies and the proposed work. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Model** & **Features** & **Results** \\ \hline [PERSON] et al. [15] & SVM, k-NN, CNN & Tempogram, spectrogram, MFCC, ZCR & Acc: 88.84; Sens: NA \\ \hline [PERSON] et al. [16] & Capsule Neural Network (CapsNet) & MFCC & Acc: 91.67; Sens: NA \\ \hline [PERSON] et al. [17] & Artificial Neural Networks (ANNs) & Statistical parameters, amplitude, fundamental frequency, MFCC & Acc: 91.86; Sens: 88.1; \\ \hline [PERSON] et al. [18] & CNN & MFCC, Log-MFCC & Acc: 91.74 \(\pm\) 3.72; Sens: 73.86; \\ \hline [PERSON] et al. [19] & CNN + BiGRU with Attention Mechanism & MFCC & Acc: 90; Sens: 91; Spec: NA \\ \hline [PERSON] et al. [20] & SVM & LBP, ALBP, RLBP & Acc: 92.12; Sens: 97.53; \\ \hline [PERSON] et al. [21] & RBPNN & Gram polynomials and FFT & Acc: 94; Sens: 93; Spec: 91 \\ \hline [PERSON] et al. [22] & Feed-forward ANN & Mean energy spectrum, Shannon entropy, MFCC & Acc: 82.63; Sens: 76.96; \\  & & Shannon entropy, MFCC & Spec: 88.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance comparison of model reported in the literature and proposed model for PCG classification (acc: accuracy; sens: sensitivity; spec: specificity; NA: not available). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Features Set (Count)** & \multicolumn{3}{c}{**Harmonic**} & \multicolumn{3}{c}{**Percussive**} \\ \hline  & **Acc.** & **Sen.** & **Spec.** & **Acc.** & **Sen.** & **Spec.** \\ \hline All Features (82) & 93.80\% & 82.50\% & 82.40\% & 90.60\% & 85.60\% & 79.30\% \\ Chroma (12) & 93.40\% & 82.40\% & 80.60\% & 80.50\% & 99.30\% & 35.10\% \\ Chroma CENS (12) & 80.30\% & 98.40\% & 39.50\% & 80.30\% & 97.70\% & 42.00\% \\ MFCC (40) & 92.60\% & 83.80\% & 81.50\% & 90.70\% & 85.10\% & 78.10\% \\ Statistical (18) & 92.70\% & 82.60\% & 78.80\% & 84.60\% & 91.80\% & 67.80\% \\ Chroma and Statistical (30) & 92.90\% & 83.10\% & 80.60\% & 85.80\% & 90.60\% & 71.70\% \\ Chroma CENS and Statistical (30) & 92.40\% & 83.30\% & 79.40\% & 80.30\% & 97.70\% & 42.00\% \\ MFCC and Statistical (58) & 93.50\% & 82.80\% & 81.80\% & 91.00\% & 86.10\% & 82.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results: harmonic vs. percussive.
# Research Progress and Future Prospects of Key Technologies for Dryland Transplanters
## 2 Classification of Transplanters
### Handheld Transplanter  
To reduce the labor intensity of small farmers during seedling transplantation, scholars have researched handheld transplanters [20; 50; 51]. As shown in Figure 2a, the handheld transplanter consists of a seedling feeding port, a handle, upper and lower cables, a seedling guide tube, a foot pedal, and a jaw-type planting mechanism. During operation, the operator manually holds the handle to move the transplanter, vertically inserts the planting mechanism into the soil, and then places seedlings into the seedling guide tube through the feeding port, allowing them to drop into the planting mechanism by gravity. Squeezing the handle opens the jaws of the planting mechanism, releasing the seedlings into the soil. Finally, lifting the transplanter causes the soil to backfill, completing the planting process. Figure 2b illustrates its workflow.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Transplanters** & **Advantage** & **Disadvantage** & **Application Scenarios** & **Reference** \\ \hline Handheld Transplanter & & Simple structure, low cost & Low efficiency, poor transplanting quality & Suitable for small-scale farmers and small plots & [PERSON] et al. [20] \\  & & High uprightness, good transplanting effect, transplanting effect, transplanting effect is strongly applicable & Plant spacing. The forward speed & Plug-seedling transplanting operation & [PERSON] et al. [45] \\  & Chain-Clip & & Low cost, good uprightness of plug seedlings in planting two spacing
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 3 Overall Framework
### REASPP Module  
The quality of feature extraction can affect the segmentation performance of the network. RFB [28] simulates the characteristics of receptive fields (RFs) of the human visual system. RFB uses convolutional layers with convolution kernels of different sizes to construct multibranch and dilated convolutional structures of different sizes to simulate the relationship between the dimensions of RFs and the eccentricity rate of RFs. Meanwhile, the reference [28] also points out that different pixels in the RFs have different contributions to the neural nodes, and the center of the RFs contributes the most. Thus, keeping the target object as close as possible to the center of the RFs will enhance the accuracy of the model for segmenting small parts in mechanical assemblies.  
In feature extraction, the size of the RFs is important to the extraction ability. When the RF is too small, the local information is overutilized, and the corresponding global information is not obtained. This leads to a correct segmentation of local areas of this category in the image and disorderly segmentation of other areas, thereby reducing the final extraction ability. When the RF is too large, small objects are directly ignored as the background, thereby affecting the segmentation accuracy.  
In this study, deeplabv2 is used as the segmentation network of the AdvSemiSeg network, and it adopts the ASPP structure. At the cost of a small amount of calculation, the RF of the convolution kernel is increased, and then more deep features are extracted. To make the target domain close to the center of the RF, this study fuses the RFB and ASPP structures, retains their dominant structures, and proposes a new structure called atrous spatial pyramid pooling based on receptive fields block (RFASPP) (Figure 3). RFASPP removes the shortcut operation in the RFB structure, adds two dilated convolutions with an expansion rate of 18, and adds global pooling. It splices in the channel direction through the concat operation, and finally adjusts the number of channels through 1 \(\times\) 1 convolution. The RFASPP structure proposed in this study enlarges the RF while ensuring that the target  
Figure 2: The adaptive spatial feature fusion module.  
object is close to the center of the RF. This can improve the ability of the network to extract deep features.
## 3 Results and Discussion
### Characterization of MWCNTs Layer
In Figure 10, it can be seen that the DTA curves, near the peak around 446 \({}^{\circ}\)C, characteristic of the RB5 dye, tended to have more intense peaks for the upper layers, confirming a greater adsorption than in the lower layers. This nonhomogeneity in the nanotube filters, in which the higher layers had a greater weight loss and therefore a greater adsorption, can be attributed to the fact that the solution, as it arrived in the deeper layers of the filter, flowed outwards in a faster way by having an increasingly smaller layer in front that opposed its passage. On the other hand, in the higher layers of the filter, the descent of the solution was hindered by the lower layers. All this determined, within the filter, different contact times of the solution, which were greater in the higher layers and less in the lower ones. This gradient of contact times within the filter generated a stratification of the dye, which was more evident at greater thicknesses of the layer. Figure 10: DTA curves of different columns at different heights. In a recent review, it was reported that carbon nanotubes appear to be the best and most appropriate adsorbent materials for the removal of Reactive Black 5 dye, which has a rapid adsorption rate as its main feature [81]. By comparing our data with those reported in the literature [74; 75; 76; 77; 78; 79; 80; 82], where adsorption processes in static conditions are studied in the latter, it was possible to deduce that they were consistent, and to confirm a great propensity of carbon nanotubes to treat of water contaminated by Reactive Black 5. The semicontinuous adsorption process, studied in particular in this work, has shown that the rapid removal rate and efficiency of carbon nanotubes was maintained even after several consecutive adsorption cycles, and that, for example, a column containing only 2.5 g of nanotubes was able to treat 3.8 L of Reactive Black 5 solution, with a concentration equal to 37 mg/L. We are not aware of any research that has used columns of carbon nanotubes in semicontinuous processes for the treatment of water specifically contaminated by Reactive Black 5. However, studies reported in the literature have used similar experimental conditions, although for the removal of other substances, such as NOMs (natural organic matter) [83], glimepiride [84], or heavy metals [85], and achieved results consistent with those reported by us, in which the quantity of carbon nanotubes, and therefore the height of the read in the column, played the fundamental role capable of amplifying the adsorption capacity. The data obtained in this research are encouraging for a potential implementation of a continuous plant characterized by several columns that alternate during the regeneration phases of the nanotubes.
## 7 Conclusions  
The findings from this study can be summarized as follows:  
1. The BoNTs produced by _C. botulinum_ are of a protein nature targeting the nervous system (Neurotoxic).
2. _C. botulinum_ has seven types of toxins in terms of antigenicity; A, B, C, D, E, F, G, and subtypes.  
* When food is contaminated with toxins, it can cause botulism disease, which can lead to craniocaudal paralysis with respiratory failure.
* Differential diagnosis is important because to counter its lethal effects antidote (antiotoxin, antibodies or fragments that bind the antibody's antigen that can block BoNTs), must be administered in the shortest possible time.
* Researchers have been successfully converting the BONTs into a safe and effective drug that can be used to treat several diseases and for aesthetic purposes.
* Two types of BoNTs are currently available in patient treatment and cosmetic use: Type A (onabotulinumtoxinA, abobotulinumtoxinA, and incobotulinumtoxinA) and Type B (rimaboutulinumtoxinB).
* BONT injections are effective and expected to provide 50-90% improvement for at least two to three months (if the injections do not seem effective, then the possibility of changing it dose or injection site should be evaluated).
* BoNTs have been recognized as potential risk agents for use in bioterrorism (CBRN events).  
Conceptualization: I.A.C., M.A.P., M.M., L.S., F.I. and G.D.; methodology: G.D., F.I., A.D.I, F.L., A.E.W. and M.C.; validation: F.I., L.S., A.E.W., F.L., M.C., I.A.C. and A.D.I; formal analysis: A.S.; M.M., F.L. and F.I.; investigation: G.D., F.L., A.D.I., L.S., F.I., M.M., M.A.P., G.D., M.C. and I.A.C.; data curation, L.S., M.M. and F.L.; writing--original draft preparation: M.C., F.L., I.A.C., L.S. and F.I.; writing--review and editing: F.I., F.L., A.D.I, G.D., I.A.C., A.E.W., M.A.P. and M.M.; All authors have read and agreed to the published version of the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.  
The authors declared no external funding for the present research.  
The authors declare no conflict of interest.