esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure ECDSA SRAM-PUF Based on Universal Single/Double Scalar Multiplication Architecture
## 3 The Proposed Secure SRAM-PUF Scheme Based on Custom ECDSA  
### Parameter Selection for RM Code  
Since the RM error correction code is used in the soft-decision algorithm, selecting the order \(r\) and the number \(m\) of the RM code is essential. Theoretically, the soft-decision algorithm can choose any RM code. However, various factors need careful consideration before the soft-decision of the RM code in PUF is made.  
The order \(r\) dictates the recursion complexity and error correction ability of the RM code. The error correction capability of the RM code should align between the intra-chip and inter-chip error rates of the SRAM-PUF. If the error correction capability is lower than the intra-chip rate, the error correction will fail. Conversely, suppose the error correction capability surpasses the inter-chip rate. In that case, the attacker can correct the response value of the attacked chip using the response value of another chip with identical parameters, completing PUF replication. We choose \(r=2\) so that the error correction capability of the RM code is about 20%.  
The parameter \(m\) affects the information entropy of the RM code. Four segments of 256-bit SRAM initial values are chosen for \(RM(2,8)\) soft-decision, resulting in four responses with 37-bit information entropy each. Repeated RM code soft-decision necessitates the reuse of the same RM code soft-decision hardware multiple times, and ROM also needs to store the error probability of each segment. For different RM codes, the larger the value of \(m\), the greater the information entropy of the message, but this may lead to a higher likelihood of entropy leakage caused by the code word. A value of \(m=8\) strikes the trade-off between entropy and security. Therefore, this paper chooses \(RM(2,8)\) for generating PUF.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 4 Experiment Results
### On/OFF model with QoS Features  
We then set the following parameters to the ON/OFF model: (i) the server remains in states ON and OFF for time periods \(T_{\text{ON}}=T_{\text{OFF}}=20\) s (exponentially distributed); (ii) the mean service demand for message processing is \(D=0.125\) s; and (iii) message arrival rate \(\lambda\) varies from 0.05 to the maximal value of 3.9 messages/s. On the above basis, we set up four experiments (which correspond to different ON/OFF models) by considering the  
Figure 5: Response time for various \(T_{\text{ON}}\), \(T_{\text{OFF}}\) parameters.  
following QoS features: (1) \(\zeta=1\), lifetime = \(\infty\) and \(K=\infty\); (2) \(\zeta=1\), lifetime = 30 s, \(K=\infty\); (3) \(\zeta=0.75\), lifetime = \(\infty\), \(K=\infty\); and (4) \(\zeta=1\), lifetime = \(\infty\), \(K=100\).  
Response times: For each of the four experiments above, we ran the corresponding ON/OFF model 10 times and derived the curves of Figure 6 on the basis of the average values of these runs. According to Figure 6, the highest values of mean response times (\(\sim\)10-360 s) were observed in the first experiment where messages were buffered and served (i.e., no message losses occurred). On the other hand, the lowest values of mean response times (\(\sim\)5-10 s) were observed in the fourth experiment, where we considered the application of lifetime periods in messages. In the third experiment, where a certain buffer capacity was considered, mean response time varied from 10 to 16 s. Lastly, the fourth experiment provided low mean response times (\(\sim\)8-14 s) for \(\lambda=0.05\)-2 and higher response times (\(\sim\)17-49 s) for \(\lambda=2.5\)-3.9.  
Success rate vs. response time: In order to investigate the trade-off between success rates and response times, the rates of successful message service are presented in Figure 7 for the same set of experiments. In the first experiment, there was a 100% message success rate since no messages were lost. Lowest rates were observed in the case of lifetime periods and increased arrival rates. In the third experiment, low arrival rates led to high message success rates (in the range of \(\sim\)90-100%), while higher arrival rates led to a decrease (\(\sim\)87-77%). Lastly, the fourth experiment led to a stable message success rate (75%).
# Multi-Objective Optimization Study on Production of AlSi10 Mg Alloy by Laser Powder Bed Fusion
###### Abstract  
In additive manufacturing, production parameters play a critical role in the microstructure, mechanical properties, and surface quality of a product. The correct selection of these parameters is of great importance for the success of the production process. In this study, the aim was to improve product quality in the additive manufacturing of an AlSi10 Mg alloy. The experiments were conducted using a full factorial design, with a constant layer thickness of 0.04 mm. The production parameters included two laser powers (200 and 275 W), two scanning speeds (800 and 1400 mm/s), and two hatch distances (0.08 and 0.14 mm). The performance properties of the produced parts were evaluated according to the relative density and surface roughness criteria. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to optimize both relative density and surface roughness performances simultaneously. The results revealed that the most suitable production parameters for the additive manufacturing of the AlSi10 Mg alloy were 275 W laser power, 0.14 mm hatch distance, and 800 mm/s scan speed.  
AlSi10 Mg laser powder bed fusion; surface roughness; relative density; TOPSIS; selective laser melting; additive manufacturing +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# The Effects of Train Passes on Dwell Time Delays in Sweden
## 4 Discussion
### Regression Results  
The results from the regression models are found in Table 2 and show essentially the same trend as Figure 4: delays are much less likely when passes are cancelled and much more likely when they are unscheduled, compared to when they happen as scheduled. The odds ratios were about 0.1 and 2.6, respectively, in both models--where a value lower than 1 indicates a reduced risk and above 1 indicates an increase.  
To be more specific, the estimated odds of a dwell time delay was 2.6 times higher if a pass was unscheduled rather than scheduled. When a scheduled pass was cancelled, the estimated odds of there not being a dwell time delay was instead \(1/0.102=9.8\). The reduced risk of delays when scheduled passes were cancelled highlights the possibility to improve timetables going forward, and of trying to reduce the need for passes. The results also suggest that the cancellation of a train pass can be used as a tool to avoid severe delays during disturbances, and this is often done in practice. In other cases, however, passes cannot easily be cancelled but must be shifted from one station to another, where it appears as an unscheduled pass and then greatly increases the probability of delay.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Predictor**} & \multirow{2}{*}{**Estimate**} & \multirow{2}{*}{**Odds Ratio (OR)**} & \multicolumn{2}{c}{**Confidence Interval**} \\ \cline{3-4}  & & & **Lower** & **Upper** \\ \hline (Intercept) & \(-0.152\) *** & & & \\ \hline Train passes: & & & & \\ Cancelled pass & \(-2.283\) *** & 0.102 & 0.098 & 0.107 \\ Unscheduled & & 0.952 *** & 2.592 & 2.479 & 2.710 \\ pass & & & & \\ \hline R2 & 0.310 & & & \\ \hline \hline \end{tabular}  
*** indicates statistically significant with \(p\)-value \(<0.0005\).  
\end{table}
Table 2: Summary of logistic regression models.  
Figure 4: Probability of dwell time delays for different types of train passes.
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 3 Simulation and Analysis
### Result Simulation
#### 3.2.3 Coefficient Quantification and Q-Penalty  
As we observed in the previous chapter, the Q value decreases and becomes more stable as the quantization stage M increases. Therefore, we assume that Q deteriorates to 50% as the threshold to verify the required quantization stage M. the modulation formats are DP-QPSK and DP-16 QAM. The transmission distance is 1000 km and 700 km, and the OSNR is 15 dB and 21 dB, respectively. The rest of the simulation conditions remain unchanged, and the relationship curve between the quality factor Q and the quantization stage M is shown in Figure 7.  
It can be seen that as the number of quantization stage M increases, the quality factor Q also gradually decreases. Whether it is in DP-QPSK or DP-16 QAM modulation, when M is greater than 10, the Q value drops below 50%. When M exceeds 18, the Q becomes a negative number, and it can be seen from Equation (24) that the BER of the CQWO-TD-CDE is lower than that of the TD-CDE. The higher the quantization stage M, the closer the performance of the CQWO-TD-CDE is to that of the WO-TD-CDE, which means that under this condition, the performance of CQWO-TD-CDE exceeds TD-CDE, and Q tends to stabilize at about \(-10\%\).
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 5 Performance Measurements
### Anycast Group Size  
Although it would depend on the layout of the network topology and the group member that are chosen, the general expectation is that larger anycast groups would involve shorter delivery times. To verify this observation, a set of experiments was designed with different group sizes. Starting from the two-member case of the prior sections, new group members were added in the following order: g29, g26, g28, g22, g24, g23, and g25, yielding group sizes from 2 to 9. To define this sequence, the farthest node from the source was selected to join the anycast group for the next experiment.  
Figure 5 depicts the average response time as a function of the anycast group size. Four cases are shown, which correspond to traffic levels of 10, 20, 50, and 100 bundles. The expected response times become larger with higher traffic levels, but smaller with larger groups. A significant change can be observed in the response time after increasing the group size to 6. One of the reasons for this outcome is that all of the first five gateways in the group belong the ground section of the network and so all feasible paths must cross one of the long-haul links. The first gateway located at the space section side, where the flow source is also located, is g22, which was added to make a group of size 6.  
Another reason for the large change in the response time after adding node g22 is because of the network topology, whose structure allows a significant reduction in the path length once the group size becomes at least 6 (see Figure 6). Additionally, note that the differences on the average path length tend to diminish as the anycast group becomes larger due to the reduced path diversity.  
Figure 4: Scenario 3: Network with regular link disruptions. (**a**) Average response time; (**b**) average path length; (**c**) bundle throughput; (**d**) traffic split.  
The observations regarding the average flow throughput follow a similar logic with one difference (see Figure 7). For light traffic levels (i.e., 10 or 20 bundles) the throughput remained almost constant regardless of the group size. This happened because no saturation was reached with those traffic levels and thus the observed throughput was basically identical to the flow rate. With large traffic levels, congestion is created in parts of the network and the throughput is less than the flow rate. For those cases, the addition of space-section gateways to the group (size 6 onwards) allowed boosting the flow throughput significantly, thus removing all congestion at least for the case of \(\lambda=50\).  
Figure 5: Effect of the anycast group size on the average response time of the flow. Given the vector M = [g29, g26, g28, g22, g24, g23, g25], the anycast group \(A\) of size \(n\), \(n=1,\ldots,|M|\) was defined as \(A=\{M_{i}\}\), \(i=1,\ldots\)\(n\).  
Figure 6: Smaller path lengths were observed on average with larger anycast group sizes.
## 3 Technical Overview  
The primary functionality of the proposed dynamic ARA mixing process is based on a real-time auditory model that defines the binaural loudness of the virtual sources in the final ARA signal. This definition is performed using a target loudness difference value with the reference being the measured loudness of the real acoustic world. Figure 3 illustrates the overall architecture of the dynamic ARA mix model. The real and virtual environment signals are both fed as inputs. The real component is pre-filtered to reduce the closed-ear audio effects due to the use of headphones, and then the input signals are set for calibration. The calibration procedure is applied to the input signals to assign each with a desired level of dB SPL. An FFT analysis algorithm analyzes each calibrated signal and an operation sorts the analyzing signal into specific frequency bands, thus, creating the excitation pattern of input signals. The energy information of the signal constitutes the first part of the _BL_ adaptation input. The second input consists of the results of the analysis performed by the filter bank on the virtual source. The analysis filter bank deconstructs the virtual signal in the same frequency bands that have been determined by the _BL_ model. The _BL_ adaptation block defines the coefficients, as well as combines the binaural loudness model, the virtual source, and the real environment. A synthesis filter bank constructs the _BL_ adapted virtual signal. As a result, the signal is exported for spatialization.  
The whole procedure is described in seven subsections: the first is the construction of the _BL_ conversion matrix, the second is the signal calibration of the real and the virtual audio components, the third is the auditory excitation calculations,  
Figure 3: Technical overview of the adaptive ARA mix model.  
bank construction, the fifth is the \(BL\) prediction algorithm, the sixth is the \(BL\) adaptation algorithm, and the seventh contains the implementation overview.
## 2 Study Area and Data
### Conditioning Factors
#### 2.2.1 Geological and Tectonic Factors  
The geological and tectonic factors that caused the formation of sinkholes were chosen as proximity to faults (PTF) and the lithology of the study area (LTG), and these factors were evaluated in the same group within the scope of this study. As it is known, sinkholes are formed as a result of the erosion of lithological units containing permeable and soluble rocks such as limestone, dolomite, and traverting, due to the penetration of water [49; 50]. Lithology, which examines the composition, structural features, origin, and distribution of rocks in the earth's crust, is seen as the main factor of sinkhole formations because it contains information about the properties of underground rocks, such as dissolution potential, porosity, and permeability [21; 25]. As mentioned above, a high density of faults and cracks in carbonate rocks can create permeability conditions suitable for the  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Factors** & **Abbreviation** & **Data Source** & **Explanation** \\ \hline Meteorological & \begin{tabular}{c} Monthly average precipitation \\ Monthly average temperature \\ Monthly average water \\ vapor pressure \\ \end{tabular} & \begin{tabular}{c} MAP \\ MAT \\ MAW \\ \end{tabular} & \
\begin{tabular}{c} Observations of 32 meteorological \\ stations obtained from the Turkish \\ General Directorate \\ of Meteorology \\ \end{tabular} &
\begin{tabular}{c} It was produced by \\ performing density analysis \\ (14,317 water wells). \\ It is produced from \\ vector data. \\ It is produced from \\ vector data.  
formation of sinkholes by causing the formation of cavities for the movement of groundwater. Therefore, areas close to fault zones are considered favorable regions for sinkhole formation [44; 51]. Lithology and fault data were digitized from 1/25,000 scale geological maps and revised by field studies (Figure 2).
## 2 Materials and Methods
### The Database  
The database is the training set of the Tumor Detection, Segmentation and Classification Challenge on ABUS 2023 (TDSC-ABUS2023). It contains 100 ABUS volume images. Currently, the database is the only ABUS database available online to the community, paving the way for improved data availability and 3D ultrasound image analysis [15].  
In the database, the matrix size of volumetric images ranges between [843; 546; 270] and [865; 682; 354], the physical in-plane spacing is [0.200 mm, 0.073 mm], and the between-slice spacing is around 0.476 mm. The volumes are acquired by using an Invenia ABUS system (GE Healthcare) at Harbin Medical University Cancer Hospital, Harbin, China. The data are stored in rrrd format, and the pixel intensity ranges from 0 to 255. An experienced radiologist checked the data cases and annotated the tumor regions.  
Figure 1 shows the distribution of case numbers in terms of the voxel numbers of tumor regions. The horizontal axis presents the values of the base-10 logarithm of the voxel numbers \(v\) (\(\log_{10}v\)). The values are equally divided into seven bins. The vertical axis displays the number of ABUS cases. It shows that most cases (90 out of 100 cases) contain around \(10^{4}\) to \(10^{6}\) voxels in the annotated tumor regions.  
Moreover, the database provides the volume images and corresponding biopsy labels (42 benign and 58 malignant). The voxel number of tumor regions is (1.06 \(\pm\) 1.36) \(\times\)\(10^{5}\) (benign) and (3.91 \(\pm\) 9.38) \(\times\)\(10^{5}\) (malignant), and the voxel intensity is 67.10 \(\pm\) 11.96 (benign) and 70.19 \(\pm\) 10.58 (malignant). In addition, the smallest tumor contains 3539 voxels, and the largest tumor contains 6,863,915 voxels. The uniform voxel intensity and varying tumor shapes and sizes present significant challenges for accurate lesion classification.
## 3 Results and Discussion
### Relating Samples to VOCs by Univariate Analysis of Variance (ANOVA)
#### 3.3.1 Identification of VOCs Associated with a Single Spoilage  
The VOCs which could characterise the butyric spoilage (high contribution of both F282 and F283 to their VOCs' models) were (see Supplementary Table S2): methyl butanoate (V13), ethyl butanoate (V18), butanoic acid (V62), and methyl salicylate (V69). Besides, several VOCs were linked to only one of the butyric samples. Methyl propanoate (V8),  
Figure 2: Example of the samples’ contribution to dimethyl sulfide (**A**) and methyl butanoate (**B**) (as _clr coefficients_), based on the standardised ANOVA coefficients. Symbols of samples correspond to industry A (F282 and F283, affected by butyric spoilage; F284, F285, F286, and F309, normal fermentations) and industry B (F508 and F562, affected by sulfidic and putrid spoilages, respectively; F502 and F592, normal fermentations).  
pentanoic acid (V68), and octanoic acid (V85) were related to F282 (see Supplementary Table S2). Conversely, propanoic acid (V58) was related to F282 (mainly) and F283, but the simultaneous contribution of other fermentation vessels from industry A prevents its univocal association with the spoilage.  
Regarding sulfidic spoilage, F508 had significant-highly-relevant ANOVA coefficients for the following VOCs: dimethyl sulfide (V2), methanol (V6), 2-methylbutanal (V9), Ethanol (V10), 2-methyl-2-butenal (V24), 1-pentanol (V37), 1-hexanol (V45), and 2-propyl-1-pentanol (V53) (see Table S2). Then, they characterise this alteration.  
F562 sample, affected by putrid spoilage, significantly contributed to the models of D-limonene (V32), 4-ethylphenol (V88), and 2,3-dihydropyobenzofuran (V91). The reduced number of VOCs related to this alteration could be due to its incipient stage of development.
###### Abstract
Nonetheless, with regulatory shifts favouring the adoption of alternative fuels and technologies to decrease CO\({}_{2}\) emissions, H\({}_{2}\) has emerged as a critical element in transitioning towards more sustainable transportation [23]. This shift includes strategies involving H\({}_{2}\) combustion [24] and dual-fuel strategies [25]. Specifically, the dual-fuel H\({}_{2}\) combustion approach has demonstrated substantial gains in fuel efficiency and a general decrease in raw emissions [26]. This enhancement is attributed to the formation of reaction intermediates favoured by the H\({}_{2}\) promotion of hydroxyl radicals (-OH) on the catalyst surface [27]. However, introducing H\({}_{2}\) as a fuel alters the composition of exhaust gases, impacting the operating conditions for ATS. Consequently, a comprehensive assessment of ATS performance is essential to ensuring its effectiveness across various conditions, notably when its efficiency can be affected. From the previous results obtained in studies on oxidation catalysts under CDC (high altitude in a cold environment) [16] and dual-fuel strategies with H\({}_{2}\) (sea level, room temperature) [28], this study combines both approaches to examine the impact of H\({}_{2}\) on an OC under various environmental conditions in order to study more unfavourable conditions for the catalyst. A simplified reaction mechanism including the primary conversion paths for CO, HC, and H\({}_{2}\) was implemented into a one-dimensional model for multi-layer washcoat honeycomb catalytic converters [29]. The model was calibrated with data from two sets of experimental tests. First, data from a Euro 6d-Temp engine coupled with a MEDAS altitude simulator were used, operating at two distinct conditions: sea level (0 m) and 20 \({}^{\circ}\)C, and an altitude of 2500 m at \(-\)7 \({}^{\circ}\)C in ambient temperature. These data were used as the basis for calibrating the model in the absence of H\({}_{2}\). To assess the impact of H\({}_{2}\), the calibration process integrated selectivities for various reaction pathways derived from temperature ramp tests conducted on diesel exhaust gases with varying concentrations of H\({}_{2}\). The calibrated model was then used to evaluate how H\({}_{2}\) affects the conversion efficiency of CO and HC under the specified environmental conditions, covering a broad spectrum of H\({}_{2}\) concentrations. Furthermore, the study investigated the reactivity of CO and HC along the catalyst monolith, focusing on the selectivity towards different oxidation pathways. The objective was to understand how H\({}_{2}\) variably influences these reaction pathways in the environmental scenarios studied and to identify the mechanisms behind these differences.