esa-sceva/satcom-chunk-collection huggingface dataset:
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 3 Implementation Results and Analysis
### Electrical Quality Metric  
The critical charge (Qc) is an important metric for evaluating the soft-error tolerance of SRAM cells. However, the overall performance of SRAM cells is determined not only by Qc but also by various other parameters such as RAT, WAT, WWTV, RSNM, HSNM, and hold power. In this work, an overall evaluation of SRAM cell performance is conducted by calculating the EQM, which comprehensively considers these major design parameters [20]. The EQM is defined as follows.  
\[\text{EQM}=\frac{\text{Q}_{\text{c}}\times\text{RSNM}\times\text{HSNM}\times \text{WWTV}}{\text{RAT}\times\text{WAT}\times\text{HoldPower}} \tag{3}\]  
A higher EQM value indicates superior overall performance of the SRAM cell. The EQM values of all designs were directly compared using the absolute scores obtained from the defined performance equation, without normalization. Experimental results show that the proposed EWS-16T cell achieves the highest EQM among all evaluated designs, demonstrating excellent overall performance in terms of write characteristics, soft-error resilience, and power efficiency. Table 1 summarizes the key performance parameters used for EQM calculation, including Qc, RAT, WAT, WWTV, RSNM, HSNM, and hold power, for all compared SRAM cells. Figure 13a presents the comparison of EQM values for all cells. Additionally, Figure 13b shows the comparison of weighted EQM values,  
Figure 12: Waveforms presenting soft-error recovery when SEU affects (**a**) node Q and (**b**) node S0 for the proposed SRAM cells.  
where the EWS-16T cell demonstrates superior performance compared to the other cells in both cases. To reflect application-specific priorities, a sensitivity analysis was performed by applying a weight factor of 1.1 to Qc and 0.9 to hold power. Figure 13b presents the resulting EQM values based on this weighted configuration. These two parameters are particularly important in space exploration and low-power applications. Even with the applied weights, the EWS-16T cell maintained the highest EQM, demonstrating robust performance under varied design conditions.
## 2 Materials and Methods
### C-ICT Framework  
As a hybrid model based on ICA and IVA-G, C-ICT factors and fuses multimodal data. By assuming that each underlying SCV has a multivariate Gaussian distribution, IVA-G takes into account only second-order statistics and thus maximizes correlation across different datasets. Due to its strong identifiability condition, IVA-G has been demonstrated to have superior performance to mCCA at identifying the correlation structure [21; 24; 30; 55]. C-ICT exploits the advantages of ICA and IVA-G by independently decomposing each dataset and then fusing the subject covariations together to identify the associations among the modalities. Let \(\mathbf{X}^{[k]}\in\mathbb{R}^{M\times V_{k}}\), \(k=1,2,\ldots,K\) be feature datasets from \(K\) modalities, where \(M\) is the number of subjects, common across datasets, \(V_{k}\) is the number of voxels from the \(k\)th dataset, and the \(m\)th row of each feature dataset \(\mathbf{X}^{[k]}\) represents the feature of the \(m\)th subject. A generative framework of C-ICT is shown in Figure 1. Briefly, C-ICT consists of the following steps: (1) ICA is used to estimate ICs and corresponding subject covariations for each dataset; (2) artifact components and corresponding subject covariations are eliminated; (3) IVA-G is used to explore the associations between subject covariations between modalities; and (4) associated subject covariations are used to identify associated ICs.
## 3 Results
### Use-Model, Drug Exposure, and CYP3A4 Comparisons between _u_Fluidic Systems and Static Cultures
#### 3.5.1 Data Generation  
Day 1 signifies one day of culture after a single drug dose (Figure 7(a1)). Days 3 and 5 after three and five doses (Figure 7(b1,c1)). \(\bullet\)At Day 1, the DMSO vehicle shows baseline VOCs (cell counts) range between 5000-6300; Figure 7(a1). Similarly, VOCs are quantified for APAP (4400-6300) and NEF (4300-6500). \(\bullet\)At Day 3, the DMSO control has cell counts between 4000-5800; Figure 7(b1). APAP shows fewer cell numbers in upstream wells 3-5 (1200-2100), increasing cell numbers in midstream wells 6-8 (3100-4000), and equivalent DMSO control cell numbers in downstream wells 9-11 (5000-5100). NEF has DMSO  
Figure 7: High content imaging to evaluate the variance of cell counts to indicate direct-compound or indirect-cell-byproduct toxicity locations; nuclei count (DAPI). Initially, HepaRGs were cultured for 7 days in static conditions prior to activating flow and dosing 1\(\times\) daily (well 1). **(a1,a2)** After 1 day and a single bolus dose in well 1. The DMSO control is baseline culture while APAP and NEF are study compounds. **(b1,b2)** After 3 days and three bolus doses into well 1. **(c1,c2)** After 5 days and five bolus doses into well 1. Data are expressed as means \(\pm\) SEM (n = 9). Applied statistics **(a2,b2,c2)** using six-sigma control charts with DMSO vehicle control signifying process average (mean) and \(\pm\)1\(\sigma\) (68%), \(\pm\)2\(\sigma\) (95%), and \(\pm\)3\(\sigma\) (99.73%) significance offsets. If data fall outside of significant offsets, UCL or LCL, variations in the data imply special or assignable causes, **(b2,c2)**.  
equivalent numbers in upstream wells 3-4 (4400-4700), a rapid count decline in midstream wells 5, 6, and 7 (400-2000) with increasing cells numbers in downstream wells 8-11 (3100-5800). \(\blacklozenge\)At Day 5, the DMSO control has sustained cell counts between 3900-4100; Figure 7(c1). APAP displays much lower cell numbers across the entire channel, wells 3-11 (300-2900). NEF has DMSO equivalent numbers in upstream wells 3-7 (4100-5000) and a rapid count decline in midstream wells 8-9 (0-100), with slightly increased cells numbers in downstream wells 10-11 (800-2100).
## 3 Results
### Microbiological Analyses  
The microbiological analysis results obtained at the four time points (T0, T1, T2, and T3) are reported in the table below (Table 3), illustrating the temporal evolutions of the microbial loads across the four tested products (three experimental formulations (MTS1, MTS2, and MTS3) and one control (MTC)). Specifically, the total aerobic mesophilic bacteria (TAB at 30 \({}^{\circ}\)C), well known as a key factor in assessing the microbiological quality and safety of foods, exhibited constant increases over time in MTS1 and MTC (albeit reaching different concentrations) and fluctuating trends in MTS2 and MTS3 (Table 3). However, none of the samples exceeded the acceptability limit set at \(>\)6 Log (CFU/g) by regulation EC No. 2073/2005 [53] on microbiological criteria for foodstuffs. The artisanal mortadella produced with mandarin (_Citrus reticulata_ L.) peel powder (MTS1), exhibited the highest value of TAB 30 \({}^{\circ}\)C, reaching 6.46 Log (CFU/g) at 30 days (T3) after vacuum packaging, approaching the regulatory threshold but not exceeding it. In contrast, lower mesophilic counts and lower growth rates, closely resembling those of the control sample (MTC), were observed in MTS2 (5.94 Log (CFU/g)) and MTS3 (5.65 Log (CFU/g)), likely because of the inclusion of herbs, particularly oregano, which have already been shown in other studies to have strong antimicrobial properties when used as natural additives in meat and meat products [19,24]. Similar results were obtained in the present study, where their use appeared to effectively contribute  
Figure 4: Trends of TBAR values in mortadella formulations during storage. MTS1 = mandarin peel, MTS2 = common lambsquarters, MTS3 = oregano, and MTC = sodium nitrite. to extending the shelf lives of the artisanal mortadellas compared to that of the control sample made with a chemical additive. Regarding the other hygiene indicator microorganisms, the detection of enterobacteria and _Escherichia coli_ at concentrations below 1 Log (CFU/g) over time indicates that operators adopted proper hygienic practices during the production process. These low microbial loads reflect effective sanitation protocols and contamination control implemented during the early stages of production. Conversely, the consistent presence of coagulase-positive staphylococci at all the time points (T0, T1, T2, and T3) suggests contamination occurred during the processing chain. Notably, considering that, except for the control mortadella MTC (likely due to the presence of the chemical additive), the concentration trends were non-linear for any of the three experimental samples, with a consistently increasing until T2 followed by a decreased at T3 in MTS1 and MTS2 and a more fluctuating pattern in MTS3 with con-centrations initially decreasing, then increasing and subsequently decreasing
# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 2 Related Work
### Time Adaptive EWMA with Personal Evaluation Criteria
Otherwise, it is computed using the same method as \(PECavg\) to keep track of changes on evaluation values. **Normalized Evaluation (NE):**\(NE\) is an evaluation given by a user, normalized with sigmoid function based on her grading criteria \(PEC\) using Equation (4). After the normalization process, original rating values are converted into a range of 0 to 1. However, there are two exception cases to consider; If it is the first time that the user is giving a rating, \(NE\) is initialized with \(e(t)/10\) to fit in the range of 0 to 1 as there is no previous transaction. When there are previously evaluated transactions but their values are all the same, \(PECdev\) will be zero, resulting in a situation in which normalization is not possible. In this case, a neutral value of 0.5 is used for \(NE\), implying that \"multiple ratings all with same value\" is regarded as an indeterminable rating. After the normalization process, each \(NE\) value per transaction becomes the input for deriving \(ARNE\), reputation of an individual, and trust of one to another. **Average Received Normalized Evaluation (ARNE):**\(ARNE_{B}\) is an average of all \(NE\) that user B has received from others, averaged using the time-adaptive EWMA parameter for transactions made by user B. As stated in Equation (5), \(ARNE_{B}\) is initialized with \(NE_{A.tx}(t)\) when user B is evaluated for the first time by user A. Thereafter, \(ARNE\) accumulates received \(NE\)s while the incoming value is weighted by time difference weight \(\alpha\left(td(t)\right)\).
# Laser Technology in Photonic Applications for Space
## 3 Lasers in Space
### Integrated Solid State Gyroscopes  
Laser gyroscope devices based on the Sagnac effect are widely used in the aerospace sector to monitor orientation and angular velocity for rockets or satellites with a normal required resolution between 0.1 \({}^{\circ}\)/h and 10 \({}^{\circ}\)/h and a bias drift between 0.001 \({}^{\circ}\)/h and 0.1 \({}^{\circ}\)/h [127]. Previously, most of the instruments used were based on moving mechanical parts and thus could suffer deterioration and failures due to the harsh environmental conditions required for the missions. However, optical devices based on laser technologies can provide the expected device resolution with the additional advantages of compactness and robustness. The first optical gyroscope launched to space was sent by NASA in 1995 on board of the X-Ray Timing Explorer Spacecraft. In this initial mission, a fiber optical gyroscope (FOG) was mounted together with a mechanical gyroscope for comparison reasons [128]. Further space missions used optical gyroscopes normally based in two different configurations [63]:the Ring Laser Gyroscopes (RLG) or the previously mentioned FOG, also implemented in the ASTRIX product-family design of IXSPACE, which comprises a 980 nm laser diode, a low noise photodetector, a lithium niobate phase modulator, an erbium doped fiber, a Bragg grating, a Sagnac fiber loop and and isolator. The instrument design has been selected for several space missions, including Galileo, Planck, and Pleiades [129]. However, as described by [PERSON] et al. [127], new advances in miniaturized and integrated laser chips gyroscopes could enhance the current devices.
## 3 Materials and Methods
### Analysis of the Forecasting Methods
#### 3.1.2 Simple Average Method  
The Simple Average Method is a forecasting method that uses the mean value of data for the \(n\) most recent time periods to forecast demand/traffic for the next time period (Figure 3), i.e., forecast = average of all data for the past months. The evaluation criteria RMSE for this model is equal to 42,956.
## 4 Dynamic Response of the System
### The Influence of Pier Height on System Dynamic Response  
This paper only analyzes the influence of unequal pier heights on the dynamic response and running safety of the high-speed train-track-bridge system under seismic excitation when the train passes over the bridge at a speed of 300 km/h. The first- to fourth-order natural frequencies of the bridge as shown in Table 6.  
The working conditions of the bridge piers are shown in Table 4 in Section 3.2. From Figure 12, it can be seen that compared to equal-height piers, the peak lateral dynamic response of bridges with unequal-height piers (gradually increasing) decreases, while the peak vertical dynamic response of bridges increases. The peak lateral dynamic response of bridges with unequal-height piers (Sharply increasing) increases sharply, while the peak vertical dynamic response of bridges decreases. From Figure 13, it can be seen that compared to equal-height piers, the peak lateral dynamic response of trains with unequal-height piers (gradually increasing) decreases, which is beneficial for stabilizing the vehicle body. The peak lateral dynamic response of trains with unequal-height piers (sharply increasing) increases sharply, seriously reducing passenger comfort. Moreover, the vertical dynamic response of trains under both unequal-height pier conditions increases, and the safety indicators of equal-height piers are significantly better than those under the two unequal-height pier conditions. From the data in the above figures, it can be concluded that considering the comfort and safety of the train, the optimal choice is to design equal-height bridge piers, followed by gradually increasing pier heights, and avoiding steep increases in pier heights.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working Condition Type** & **1 st Modulus** & **2 nd Modulus** & **3 rd Modulus** & **4 th Modulus** \\ \hline Equal-height pier & 13.187 & 13.302 & 15.806 & 21.443 \\ Gradually increasing & 10.452 & 10.518 & 12.998 & 15.538 \\ Sharply increasing & 9.630 & 10.832 & 13.964 & 15.595 \\ \hline \hline \end{tabular}
\end{table}
Table 6: First- to fourth-order natural frequencies of the bridge.  
Figure 11: Train safety index.
# Classroom Expression Recognition Based on Deep Learning
## 4 Experiment and Analysis
### Expression Recognition Effect Display
#### 4.2.1 Expression Recognition Results  
This section uses the expression recognition network algorithm model that integrates multiple attention mechanisms as the initial recognition module. First, face recognition is performed on the input data, and the detected face is cropped into an image of size 48 \(\times\) 48. Then, the expression recognition model is called to complete the recognition of the expression.  
When this system is used in actual classrooms, it automatically identifies students' facial expressions and generates emotional feedback reports. For example, in a math class, when the system detects that students are becoming more confused, the teacher can re-stimulate students' interest through examples, questions, or interactive exercises. In addition, the system can identify students' long-term performance and help teachers provide personalized coaching and attention. Data-driven teaching adjustments can effectively improve students' learning experience and classroom participation, thereby optimizing the overall teaching quality. The recognition results are shown in Figure 14.  
Figure 14: MAF-ER recognition results.  
Figure 13: Expression recognition workflow.  
For a more intuitive description, Table 7 describes the confidence of expression recognition using Figure 14 as an example. The numbers in the first column of the table are sorted from front to back and from left to right according to the position of the students in the picture. This table only uses the four students in the front row of Figure 14 as an example.  
As shown in Table 7, the MAF-ER algorithm successfully performs the task of student expression recognition. In the classroom setting, the majority of detected expressions are categorized as Neutral, indicating that most students maintain a relatively serious demeanor during class.
## 2 Data and Methods
### Electre I  
ELECTRE I was developed to complement effective and realistic decision-making approaches, particularly when dealing with complex and contradictory criteria. The method aimed to surpass traditional techniques that relied on simple data weighting and calculation. This alternative offered a way to effectively grasp the complexity of the real world [38].  
ELECTRE I incorporates the concept of incomparability and the principle of dominance. The technique does not require all alternatives to be comparable, offering a realistic view of the decision-making process. The dominance principle helps identify alternatives that significantly outplay others across most or all criteria, while discarding inferior options [67]. ELECTRE I involves several key steps [68; 69]:  
1. Decision matrix construction: A matrix is created, with values assigned to each alternative according to specific criteria;
2. Criteria weighting: A weight is assigned to each criterion, reflecting its importance in the decision-making process;
3. Concordance and discordance calculation: Concordance and discordance indexes are calculated for each pair of alternatives. The concordance index measures how much one alternative exceeds another, while the discordance index indicates whether there are any important criteria showing that one variable is significantly worse;
4. Threshold values: Concordance and discordance threshold values are set to determine the minimum level at which a variable is considered \"acceptable\";
5. Dominance graph: The results from the concordance and discordance analysis are represented in a graph depicting dominant and subordinate alternatives.  
ELECTRE I covers areas with complex decision-making processes that require a realistic assessment of the variables. The method considers the incomparability of alternatives, avoiding unrealistic calculations of pros and cons. This makes ELECTE I one of the most effective decision-making techniques in situations where traditional approaches fail. The decision-making process follows a structured procedure of the ELECTRE I method to ensure consistency and fairness in evaluating alternatives (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Maximum** & **Load-Carrying** & **Redcharging** & **Purchase** & **Load-Bearing** & **Consumption** \\  & **Mileage [km]** & **Capacity [m3]** & **Speed [min]** & **Price [in \&]** & **Capacity [t]** & **[kWh/100 km]** \\ \hline Manager & 7 & 7 & 9 & 10 & 8 & 9 \\ Logistics director & 10 & 8 & 6 & 2 & 9 & 8 \\ Inbound logistics & 8 & 10 & 5 & 2 & 9 & 6 \\ External logistic & 9 & 8 & 6 & 4 & 10 & 5 \\ Electrotechnician & 6 & 10 & 5 & 2 & 8 & 7 \\ \hline \end{tabular}
\end{table}
Table 2: Preferences on a scale from 0–10. Source: authors based on preferences of the workers of K-plyn Ltd.  
The diagram above outlines the sequence of steps involved, from assigning weights to criteria to developing the final preference matrix. This approach ensures that the analysis remains objective and transparent.
## 3 Results and Discussion
### Evolution of the Dynamic Young's Modulus over Time
The results derived from the shrinkage test carried out on specimens with a size of \(25\times 25\times 287\) mm\({}^{3}\) following the recommendations of the UNE 80-112-89 standard are shown in Figures 11 and 12. This test is particularly relevant in mortars made with recycled aggregate, where this typology of materials is sometimes not capable of withstanding the stresses produced by the rapid evaporation of water, giving rise to cracks and imperfections during curing [59]. Figure 11: Evolution of shrinkage (mm/m) versus time (days). Cement mortars with a cement/aggregate ratio of 1:3. Figure 12: Evolution of shrinkage (mm/m) versus time (days). Cement mortars with a cement/aggregate ratio of 1:4. As can be seen in Figures 11 and 12, following the greater absorption of recycled aggregates shown in Table 3, the values of shrinkage during setting were higher in mortars made with aggregates from CDW than in those made with natural aggregate. The specimens of recycled ceramic aggregate presented higher shrinkage values than those made with aggregates from concrete waste. These values agree with those obtained in previous investigations and show the negative influence of the content of fine material and residues adhered to the recycled aggregates [60]. Finally, in Figure 13, as an example of the correct setting of the mixes made with recycled aggregate, the images collected with the help of a scanning electron microscope (SEM) are reflected for the blend made with recycled concrete aggregate and cement/aggregate at a ratio of 1:3 at 28 days. This test was carried out using a JEOL JSM-820 scanning electron microscope, applying a gold surface coating to the dried samples to improve electronic conduction. The software used by the equipment for the acquisition, treatment and evaluation of the analysis was the EDX Oxford ISIS-Link (c). The type of test shown in Figure 13 allows the textural, morphological and chemical characterizations of the mortar samples to be determined. The analysis was carried out on one of the two fractured parts that separated after carrying out the flexural strength test. Figure 13a shows the uniformity in the distribution of the mortar matrix as well as the good compactness and adherence of the binder to the aggregate. This good compactness is related to the increase in density, which increases the ease of transmitting the ultrasound waves through the mortar and allows higher values of the dynamic Young's modulus to be obtained. Finally, in Figure 13b, we present the formation of ettrinigite crystals in the hardened mortar at the age of 28 days. A chemical compound of high relevance in this type of material is generated from the reaction of tricalcium aluminates with calcium sulfate [61]. These results are complementary to other studies carried out by various authors on cement pastes with additives, where it has been shown that the dynamic modulus of elasticity of pastes increased when the water-cement ratio decreased [62].
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Thermodynamic Consistency
#### 3.12 Implementation Guidelines  
* For passive systems: \(\zeta_{p}=0\), \(\zeta_{q}>0\)
* For active systems: \(0<\zeta_{p}<\zeta_{q}\)
* Numerical stability requires: \[\Delta t<\frac{2m(1+\zeta_{p})}{\zeta_{q}}\] (32)  
Relation to Liouville's Theorem in Hamiltonian Systems  
While [PERSON]'s theorem strictly holds for Hamiltonian dynamics, our modification (Equations (22) and (23)) applies to systems where non-conservative forces (e.g., entropy gradients, dissipative cooling) break phase-space incompressibility. This does not contradict [PERSON]. [25]'s defense of the theorem but rather extends its applicability to regimes where thermodynamic and mechanical effects coexist, as demonstrated in stochastic cooling experiments [26].