esa-sceva/satcom-chunk-collection huggingface dataset:
# EXPRESS: Exploiting Energy-Accuracy Tradeoffs in 3D NAND Flash Memory for Energy-Efficient Storage
## 4 Experimental Evaluation
### Evaluation of the Proposed Technique on MLC Memory
Similar to the SLC current profiles, we observe the periodic program pulses and verify the phases in the current waveform. For example, the LSB page, analyzed in Figure 8b, requires nine ISSP cycles, with the total  
Figure 8: (**a**) percentage of programmed bits for a page in MLC mode as a function of the partial-page program time, \(t_{pp}\). Measured current drawn during regular-page program operations in MLC mode for the (**b**) LSB page and (**c**) MSB page. program time, \(t_{prog}^{LSB}\approx 1000\)\(\upmu\)s. However, the bit accuracy reaches above 99% with only seven program pulses (\(t_{pp}^{LSB}\approx 750\)\(\upmu\)s), indicating a 25% energy saving with a <1% bit accuracy loss. Programming MSB pages generally requires more time than programming LSB pages. For example, the MSB page analyzed in Figure 8c requires \(t_{prog}^{MSB}\approx 1500\)\(\upmu\)s, or 11 ISPF cycles. In addition, the verification phases in the case of MSB program operations take more time than those that take place during LSB program operations. Still, we find that partial program operations can be utilized on MSB pages, offering more than 20% in energy savings, with a negligible (<1%) bit-accuracy loss. The optimal partial program time for MSB pages is \(t_{pp}^{MSB}\approx 1150\)\(\upmu\)s.  
We observe considerable page-to-page variability in the bit accuracy (error bar in Figure 8a), even though the \(t_{pp}\) was fixed. Such page-to-page variability may arise in the NAND memory because of the inherent process variations, physical organization, and the presence of program and read noise. In the next section, we elaborate further on the page-to-page timing variability and the possible countermeasures.
## 3 Analysis Results  
### The Impacts of Ionospheric Background Environment  
Based on the findings of statistical research on ionospheric plasma parameters associated with seismic disturbances [44; 45; 46; 47], it has been found that anomalies predominantly arise within a two-week timeframe prior to earthquakes, with the highest frequency of anomalies occurring within the final week before the seismic event. As a consequence, our primary focus was directed towards the investigation of ionospheric disturbances 15 days before the Jiuzhaigou earthquake. Initially, several indices are employed to establish a comprehensive understanding of the pre-earthquake ionospheric spatial environment before engaging in data analysis, aiming to discern it from the effects of space weather activity on the ionosphere. Figure 1 presents the relevant indices describing the spatial weather activity from 24 July to 8 August 2017, including the solar flux at 10.7 cm (F10.7), Dst, and Kp indices. It can be seen that the solar activity was relatively calm during the study period. According to some Kp indices greater than 3 and Dst indices close to \(-\)30 nT on 24-26 July, it was indicated that there was a weak magnetic storm event. On 3-6 August, the Kp indices increased significantly, and the Dst indices showed that there was a clear onset phase of magnetic storms on 4 August with a minimum of \(-\)37 nT, and then the magnetic storm entered the recovery phase. Overall, the ionospheric spatial environment was relatively calm from 27 July to 2 August and on the days immediately before the earthquake on 7 and 8 August.
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Generalized Mathematical Model for Criticality Assessment
#### 3.3.1 Definition of Variables  
1. Failure Frequency (FF): (FF\({}_{i}\)) is the frequency with which an asset (\(i\)) fails in a given period of time (e.g., failures per year). This frequency can be derived from historical maintenance data or predictions based on the type of asset and its operating environment.
2. Consequences of Failure (C): (C\({}_{i}\)) is the impact that the failure of an asset would have on different (\(i\)) in different dimensions. It can be modeled as a vector of consequences: \[\text{C}_{i}=[\text{C}_{\text{safety}},\,\text{C}_{\text{environment}},\,\text{C}_{ \text{service}},\,\text{C}_{\text{costs}}])\] where each component reflects the consequence on a specific dimension (safety, environment, quality of service, operational cost overruns).
3. Asset Criticality (CR): (CR\({}_{i}\)) is the total criticality of an asset (\(i\)), which combines the frequency of failure with the consequences of those failures. The result is the key value that will be used to prioritize the most critical assets in maintenance.
4. Operational Characteristics of the Asset (A): (A\({}_{i}=[a_{1},a_{2},\ldots,a_{n}]\)) is a set of attributes describing the asset (\(i\)) such as its location (path or station), type of network, traffic speed, whether it is in a curve or tunnel, etc. These attributes affect both the frequency of failure and the consequences of failure.
## 3 Proposed Approach
### Progressive Rate System  
The energy cost calculation follows a tiered pricing model, where the unit price increases with higher consumption levels. The model is divided into three tiers:  
* \(E_{1}\): Price per kWh for consumption up to 200 kWh,
* \(E_{2}\): Price per kWh for consumption between 201 and 400 kWh,
* \(E_{3}\): Price per kWh for consumption above 400 kWh.  
The cost for a given demand \(D\) is calculated as follows:  
\[C(D)=\begin{cases}D\times E_{1}&\text{if }D\leq 200,\\ 200\times E_{1}+(D-200)\times E_{2}&\text{if }200<D\leq 400,\\ 200\times E_{1}+200\times E_{2}+(D-400)\times E_{3}&\text{if }D>400,\end{cases} \tag{6}\]  
where \(C(D)\) is the total cost of energy consumption for the household, \(D\) represents the total energy demand (kWh) consumed by the household, \(E_{1}\) is the price per kWh for consumption up to 200 kWh, \(E_{2}\) is the price per kWh for consumption between 201 and 400 kWh, and \(E_{3}\) is the price per kWh for consumption exceeding 400 kWh.  
This tiered system reflects the actual pricing model used in South Korea and considers both summer and non-summer seasons, with distinct rates for each.
## 3 Results and Discussion
### Synchronous Fluorescence Spectra Analysis  
Figure 4A-H observed the fluorescence of MPs-fermentation-stinky compound complexes by \(\Delta\lambda=15\) and \(\Delta\lambda=60\), respectively. The fluorescence quenching abilities decreased as the concentration of the additional fermentation-stinky compounds increased. Furthermore, blue shift presented in all fluorescence spectra figures. The binding sites could be located at around Trp and Tyr residues. In brief, Trp and Tyr residues were exposed to a more hydropic environment, the polarity of complexes was reduced, and the hydropic interaction was enhanced. Due to the flexible structure of MP, a conjugate system might exist around the binding sites.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & **T (K)** & \begin{tabular}{c} \(K_{\text{sv}}\) \\ **(10\({}^{3}\)M\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} \(K_{\text{sv}}\) \\ **(10\({}^{11}\)M\({}^{-1}\)s\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} \(K_{\text{sv}}\) **(10\({}^{4}\)M\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} \(n\) \\ **(10\({}^{4}\)M\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} \(\Delta H\) \\ **(1\({}^{3}\)J\({}^{-1}\)M\({}^{-1}\) K\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} \(\Delta S\) \\ **(1\({}^{3}\)J\({}^{-1}\)M\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} \(\Delta G\) \\ **(1\({}^{3}\)J\({}^{-1}\)M\({}^{-1}\) K\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} \(\Delta G\) \\ **(1\({}^{3}\)J\({}^{-1}\)M\({}^{-1}\))** \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{Indole} & 293 & 9.6 & 9.6 & \(2.57\times 10^{3}\) & \(1.1077\) & \(-512.42\) & \(-1607.01\) & \(-41.57\) \\  & 303 & 1.8 & 1.8 & 3.51 & 0.5650 & & \(-25.50\) \\  & 313 & 2.2 & 2.2 & 2.05 & 0.4609 & & & \(-24.18\) \\ \hline \hline \multirow{2}{*}{Isovaleric acid} & 293 & 1.1 & 1.1 & 6.18 & 0.6915 & \(-79.89\) & \(-257.51\) & \(-4.44\) \\  & 303 & 1.4 & 1.4 & 2.15 & 0.5010 & & & \(-1.86\) \\  & 313 & 0.4 & 0.4 & \(3.06\times 10^{8}\) & 2.4453 & & & \(-47.60\) \\ \hline \hline \multirow{2}{*}{Dimethyl disulfide} & 293 & 0.9 & 0.9 & 6.30 & 0.9650 & \(-275.50\) & \(-905.81\) & \(-10.10\) \\  & 303 & 1.2 & 1.2 & 1.53 & 0.4711 & & & \(-1.04\) \\  & 313 & 1.2 & 1.2 & 1.60 & 0.4555 & & & \(-1.15\) \\ \hline \hline \multirow{2}{*}{Dibutyl phthalate} & 293 & 3.8 & 3.8 & \(1.02\times 10^{5}\) & 1.2105 & \(-390.19\) & \(-1235.80\) & \(-28.10\) \\  & 303 & 9.9 & 9.9 & \(6.40\times 10^{2}\) & 0.9438 & & & \(-15.74\) \\  & 313 & 16.1 & 16.1 & \(1.36\times 10^{4}\) & 5.1350 & & & \(-23.19\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The equilibrium constant \(K_{a}\) and thermodynamic parameters of the complexes between myofibrillar proteins and fermentation-stinky compounds calculated by the Stern–Volmer equation.
## 3 Effect of Boundary Conditions on Dynamic Characteristics of Cable
### Effect of Changing Boundary Conditions on Frequency
According to the calculation and analysis of relevant data and the limit value in the design code [20], the upper and lower limits of the material influence coefficient \(\lambda_{AB}\) can be roughly determined, i.e., \(0.3\leq\lambda_{AB}\leq 2.4\). \(\lambda_{AB}\) starting from 0.3 to vary in steps of 0.3, when \(0.3\leq\lambda_{AB}\leq 1\),\(\upsilon_{B}\) is taken as 0, 0.2, 0.4, 0.6, and 0.8, respectively; when \(1\leq\lambda_{AB}\leq 2.4\), \(\upsilon_{B}\) is taken as 0, 0.2, 0.4, 0.6, and 0.8, respectively. Using the calculation method in this paper, the frequency variation graphs of each order of #1 cable and #4 cable under different stiffness ratios in Table 2 above are obtained, as shown in Figures 3 and 4. Figure 3: The #1 cable frequency variation of each order: (**a**) to (**f**) are the variations of the first to sixth order frequencies of #1 cable under different boundary condition coefficients for each material influence factor, respectively. From Figures 3 and 4, it can be seen that no matter how the material influence coefficient changes, when the boundary condition coefficient \(v_{A}\) or \(v_{B}\) reaches 0.4, boundaries at both ends can reach the solid support boundary, and under different material influence coefficients, the change trend of each order frequency of the cable with the change of boundary conditions tends to be the same. In the variation diagram of each order frequency of the cable, with the increase in the material influence coefficient \(\lambda_{AB}\), each order frequency of the cable increases slightly; however, with the increase in the tension-bending ratio \(\zeta\) of the cable, this change trend is close to zero, that is, the influence of the material influence coefficient on the frequency of the cable can be ignored. At the same time, when the material influence coefficient \(\lambda_{AB}=1\) is the ideal equal boundary condition \({k_{A}}^{\theta}={k_{B}}^{\theta}\), the relevant conclusions obtained are consistent with this analysis, that is, in actual engineering, it is not necessary to consider the stiffness ratio of the upper and lower boundaries of the cable, and the two ends of the cable can be treated as equal boundaries.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Calculation of Threshold
#### 4.3.2 Preprocessing of Data  
Previously, we discussed the data structure of the CAN bus. For our current needs, we only require the ID, Data1, Data2, Data3, Data4, Data5, Data6, Data7, and Data8 fields. In the case of the normal dataset, we divide the data into windows and store the values of \(N\) windows of size \(K\) as our benchmark. These windows consist of the aforementioned fields from the CAN messages, allowing for us to establish a reference for normal behavior  
Figure 3: Overview.  
To ensure consistency in the data structure, for messages that do not have a length of 8 bytes, we append \"00\" at the end of the frame content. By doing this, we make all frames the same length, allowing for easier processing and analysis. Each window in our approach consists of \(K\) frames. These windows, derived from normal data, serve as the basis for calculating the threshold. The threshold range represents the expected variation in the vehicle's behavior under normal conditions. It provides a measure of the difference between the normal state and potentially anomalous states. It is important to note that the magnitude of the threshold range can vary depending on the injected data by an attacker.
# ORC Optimal Design through Clusterization for Waste Heat Recovery in Anaerobic Digestion Plants
## 4 Results  
### Annual Production  
The ORC hourly operation for the year may be estimated from the historical hourly trend of the exhaust gases' mass flow rate and outlet temperature, and by considering the digesers' thermal energy requirement for each hour. Data used for the analysis were the same shown in Section 2.1. ORC net power output histogram is presented in Figure 8 by considering the design derived from the most populated cluster (Cluster 3). ORC operates most of the time in off-design conditions. For around 700 h (less than 1/10 of the year), it operates at nominal power. Furthermore, for over 200 h/y, the ORC cannot operate due to the load being too low (\(P\leq\) 0.2 \(P_{rom}\)). However, the ORC positively affects the plant operation, as it substantially increases the total power output in comparison to the reference configuration. The net power output from the plant is reported in Figure 9. The whole plant net power output is roughly shifted upward by a value equal to the ORC average power output. This value may be calculated by considering ORC off-design operation (Table 4).  
Table 4 shows that the fuel utilization efficiency of the whole plant \(\eta_{tot}\) (mGT + ORC) was increased by a \(\Delta\eta\) = 10%, in comparison to the case with no recovery. Furthermore, ORC had a quite high value of equivalent operating hours \(h_{eq,orc}\), which implies a reasonably good use of the installed ORC capacity, despite the off-design and downtime periods. The difference between the mGT exhaust gases' energy content and the biomass digester heat requirement is the gross thermal energy available for the ORC. By dividing this amount for the net ORC production, the annual ORC efficiency \(\eta_{year,orc}\) may be calculated. For the considered design, the average yearly efficiency of the ORC \(\eta_{year,orc}\) was equal to 8%, as reported in Table 4.  
Figure 8: Off-design ORC net power output for the whole year operation.
# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 2 Materials and Methods
### Statistical Analysis  
A Kolmogorov-Smirnov test of normality was performed, which indicated a normal distribution of all the kinematic measures. To analyze the main effects of the order and cutoff on gait analysis in different types of gait trials, a 3 (order) \(\times\) 9 (cutoff) ANOVA was conducted to compare the calculated gait characteristics for each trial type (regular gait, gait-slip, and gait-trip). If a significant effect was detected, post-hoc analyses were conducted to compare the gait characteristics across filters with different parameters using [PERSON]'s Honest Significant Difference (HSD) method. The intra-class correlation coefficient (ICC) was used to compare the difference between the standard filter and other filters. To evaluate the main effects of the order and cutoff on model performance, a 3 (order) \(\times\) 9 (cutoff) ANOVA was conducted to compare the AUC and overall accuracy for each type of model (LR, SVC, and RFC). Post-hoc analysis using the HSD method was conducted if a significant effect was detected. All statistical analyses were performed using Python 3.11.9.
## 2 Dispersion Curve Calculation
### Energy and Power Considerations  
The total energy flow and time-averaged power associated with wave propagation in a structure provide important insight into the properties of wave modes such as group velocity and the fundamental principle of energy conservation. The time-averaged total energy of a particular wave mode is the sum of kinetic (\(E_{k}\)) and potential (\(E_{p}\)) energies [31]. The relations in terms of the wave basis are shown below:  
\[\begin{split} E_{k}&=-\frac{1}{4}\omega^{2}\mathbf{ Re}\bigg{\{}\Big{(}\boldsymbol{\phi}_{q,x}\Big{)}^{\mathrm{H}}\mathbf{M}_{ \mathbf{red}}\Big{(}\boldsymbol{\phi}_{q,x}\Big{)}\bigg{\}},\quad\mathbf{M}_{ \mathbf{red}}=\mathbf{T}^{\mathrm{H}}\mathbf{M}\mathbf{T},\\ E_{p}&=\qquad\frac{1}{4}\mathbf{Re}\bigg{\{}\Big{(} \boldsymbol{\phi}_{q,x}\Big{)}^{\mathrm{H}}\mathbf{K}_{\mathbf{red}}\Big{(} \boldsymbol{\phi}_{q,x}\Big{)}\bigg{\}},\quad\mathbf{K}_{\mathbf{red}}=\mathbf{ T}^{\mathrm{H}}\mathbf{K}\mathbf{T}.\end{split} \tag{6}\]  
Here, \((.)^{\mathrm{H}}\) denotes a Hermitian conjugate and \(\boldsymbol{\phi}_{q,x}\) is the displacement mode shape for a particular wave mode. For an element length \(\Delta l\) in the propagation direction, the total time-averaged energy per unit length is given as:  
\[\bar{E}=\frac{E_{k}+E_{p}}{\Delta l}. \tag{7}\]  
The time-averaged power (\(P\)) associated with a propagating wave is obtained as follows [30]:  
\[P=\frac{\omega}{2}\mathbf{Im}\bigg{\{}\Big{(}\boldsymbol{\phi}_{f,x}\Big{)}^{ \mathrm{H}}\Big{(}\boldsymbol{\phi}_{q,x}\Big{)}\bigg{\}}. \tag{8}\]  
The group velocity \(c_{g}\) for a guided wave is defined as the speed at which energy propagates in a particular wave. It has a number of implications for setting up a guided wave simulation, such as the maximum element size in the wave propagation direction and the maximum time step increment allowed. Normally, there should be at least 10 elements per wavelength and the wave should not propagate more than one element distance in a single time step [25]. The knowledge of \(c_{g}\) as a function of frequency for a wave mode helps with fixing these parameters a priori with sufficient degree of confidence. It is obtained from the ratio of power and energy as follows:  
\[c_{g,x}=\frac{P}{\bar{E}}. \tag{9}\]  
where \(c_{g,x}\) denotes the \(x\)-component of group velocity of a particular wave mode. Notably, group velocity is only defined for a propagating wave as evanescent modes do not transfer any energy.
## 5 Conclusions  
In this paper, the ODMT algorithm was used to collect data on the state of high-voltage transmission lines in smart grids, and detect them through state monitoring sensors. This research also utilized an intelligent system design to monitor the state of high-voltage transmission lines. This approach was analyzed through several simulation studies, and its efficiency was compared to similar systems. The results showed that the proposed method can achieve a higher acceptance rate compared to previous methods, and, at the same time, obtain a longer network survival time. According to the results, ODMT demonstrates a higher resistance of acceptance rate (varying about 1%) compared to previous works. This means the proposed algorithm can accurately represent the network acceptance capacity of different sensors. In addition, when the current exceeds 20% of the rated current, the ratio difference and phase difference change values are small; the overall measurement error is approx. 3%. Voltage sensing monitoring showed an input voltage to output voltage difference ratio of approx. 5% at a height of 4 m. The error range of temperature sensing monitoring is within \(\pm\)1 \({}^{\circ}\)C. In summary, the measurement performance of each state quantity is good. Testing communications showed that, when the communication distance is within 200 m, the communication reliability is large, and the error rate is zero. When the communication distance is more than 200 m, the communication error rate will be increased and kept at approx. 7%, which is within the acceptable range of error. These results confirm the desired performance of the model proposed in this paper in simulated real-world applications.  
Although this research tried to evaluate various functional aspects of the proposed method, this research still has limitations in terms of tested operational scenarios that can be resolved in future research. Additionally, the combination of optimization techniques for improving the configuration of the model is one of the aspects that can be improved in this research, which will be investigated in future works.  
Conceptualization, M.H. and N.K.; methodology, M.H.; software, N.K. and C.Z.; validation, M.H., N.K. and C.Z.; formal analysis, M.H.; investigation, N.K.; resources, N.K. and C.Z.; writing--original draft preparation, M.H. and C.Z.; writing--review and editing, N.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available on request from the corresponding author.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Distance (m)** & **Sent Successfully** & **Send Failure** & **Error Rate (\%)** \\ \hline \(<\)100 & 30 & 0 & 0 \\
100–200 & 30 & 0 & 0 \\
200–300 & 29 & 1 & 3.3 \\ \(>\)300 & 28 & 2 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Communication test results.
## 4 Discussion
Based on the overall spectral analysis demonstrated in this study, it is not possible to predict or quantify individual pollen constituents based on spectral data due to numerous overlapping spectral effects related to the functional group vibrations of proteins, lipids and sugars that were observed in the FTIR spectra of various unifloral and multifocal bee pollen samples. However, integral spectral profiles of each bee pollen IR spectrum provide information on its overall composition and prevalence of individual constituents. This enables rapid preliminary screening of the relative amounts of certain compounds in bee pollen. A total of 16 unifloral bee pollen types have been characterized in this study by means of physico-chemical, chromatographic and spectroscopic analytical tools. Compared to previous studies, 10 new unifloral bee pollen types were described (_P. spinosa, Viburnum_ spp., _A. hippocampus_, _Q. pubescens_, _J. regia_, _F. vulgaris_, _M. sativa_, _P. tanacetifolia_, _C. biennis_, and _P. mahaleb_) given that previous reports covered research (using various analytical methods) on _Quercus_ spp. [11; 53], _Salix_ spp. [56], _T. officinal_[12; 53], _Prunus_ spp. [11; 12], _C. sanguinea_[25], and _P. rhoas_ bee pollen [25; 52; 53].