esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparing the Characteristics WFSM and PMa-WFSM  
The comparison analysis provides valuable insights into the performance disparities between the basic WFSM and the optimized PMa-WFSM, shedding light on key improvements achieved through design enhancements. Notably, the reduction in rotor current by 1.5 \(\mathrm{A_{dc}}\) translates to a substantial decrease of 63.08 W in rotor copper loss, underscoring the efficiency gains realized with the PMa-WFSM. Furthermore, the notable decrease of 42% in rotor current density signifies enhanced heat dissipation capabilities, contributing to improved overall system reliability. Despite a marginal decrease in torque compared with the baseline, the significant reduction of 5.43% in torque ripple demonstrates the enhanced stability and smoother operation of the PMa-WFSM, with the load counter-electromotive force waveform remaining at a similar level, as shown in Figure 12. The average torque remains nearly identical, resulting in the peak and waveform of the EMF being almost the same. However, the torque ripple significantly decreases due to the increased magnetomotive force (MMF) provided by the permanent magnets. The overall output performance is enhanced with the incorporation of assisted permanent magnets compared with that of the basic WFSM shown in Table 4. The addition of permanent magnets to the basic WFSM will also increase manufacturing costs. Although it is challenging to predict the exact cost due to the unstable price of neodymium, it is estimated that the manufacturing costs will increase by approximately 10% compared with those of the basic WFSM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Torque** & **Torque Ripple** \\ \hline Surrogate model & 31.91 Nm & 5.73\% \\ FEM model & 31.61 Nm & 5.72\% \\ Error & 0.94\% & 0.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results from optimal approximation model and FEM results.
# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 7 Comparative Analysis  
Table 1 is converted to crisp scale as Table 16. Table 17 gives the crisp evaluation of the decision-makers, obtained by converting Table 3 from fuzzy evaluations to crisp evaluations. The remaining steps are followed using crisp values instead of fuzzy numbers. The criteria obtained and the alternative weights are given.  
Table 18 shows minor weight variations assigned to each criterion (see Table 10 for alternative comparison). C1 experiences a slight weight decrease from 0.25 to 0.24, indicating a marginally lower priority. Likewise, C2 undergoes a more significant reduction from 0.18 to 0.15, signaling a change in its relative significance. Conversely, both C3 and C4 reflect small increases in their weights, rising by 0.02 and 0.01, respectively. Interestingly, C5 demonstrates the most pronounced change, increasing from 0.23 to 0.26, indicating that its importance has risen in the second table. Lastly, C6 undergoes a minor decline in weight, falling from 0.12 to 0.11. In summary, these adjustments reveal a slight reallocation of priorities, with C5 becoming the most prioritized criterion in Table 2, while C2 sees a decrease in emphasis.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Linguistic Terms for Criteria** & **Linguistic Terms for Alternatives** & **Scale** \\ \hline Absolutely less important than (ALI) & Absolutely Low Value (ALV) & 1 \\ Very strongly less important than (VSLI) & Very Low Value (VLV) & 2 \\ Strongly less important than (StLI) & Low Value (LV) & 3 \\ Slightly less important than (SILI) & Slightly Low Value (MLV) & 4 \\ Exactly equal importance (EEI) & Medium Value (AV) & 5 \\ Slightly more important than (SIMI) & Slightly High Value (MHV) & 6 \\ Strongly more important than (StMI) & High Value (HV) & 7 \\ Very strongly more important than (V SMI) & Very High Value (VHV) & 8 \\ Absolutely more important than (AMI) & Absolutely High Value (AHV) & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Linguistic scale to weight criteria.  
Figure 2: Sensitivity results concerning C1.  
Analyzing the rankings between the two approaches (CINFU AHP&CODAS and AHP&CODAS) showcases some variations in the perceived significance of the alternatives (see Table 15 for alternative comparison). A1 maintains its top position as the most favored alternative across both methods, indicating a strong agreement on its priority. A2 experiences a shift in its ranking, changing from 2 nd place in CINFU AHP&CODAS to 3 rd place in AHP&CODAS, which suggests a minor decrease in its relative significance. A3 consistently ranks 4 th in both methods, indicating consensus regarding its lower preference than the other options. A4 experiences the most significant change, advancing from 3 rd place in CINFU AHP&CODAS to 2 nd place in AHP&CODAS, reflecting a rise in its perceived importance.
## 4 Discussion
### Relationship of Predicted Stream Burial to Impervious Surface Area (ISA) across Multiple Scales  
In general, it appears that streams are buried in proportion to ISA when ISA is below 30%. Above this threshold, it is common to find areas where streams are buried at a higher rate per unit ISA. This phenomenon is most likely due to the lack of remaining developable land that is not directly adjacent to streams, with continued development (\"infilling\") leading to stream impacts. For example, the ratio of predicted burial to ISA for any given analysis unit (county, watershed, or regularly-spaced grid cell) did not exhibit a dependence on the spatial area of the analysis unit. Initial observations at the county-level (Figure 6) suggested an approximate 1:1 relationship between predicted burial and ISA, until an ISA level of roughly 30% is achieved (Figure 7A). County units with greater than 30% ISA exhibited greater ratios of burial/ISA, and included 5 independent cities in Virginia, Arlington County, VA, and Washington, DC We hypothesize that this is due principally to the long time since development in these areas (since the mid-late-18 th century), and their long term roles as commercial and government centers, leading to a greater degree of predicted stream burial through sustained and dense development during a period when there were few governmental policies constraining development.  
Observations suggest areas with elevated burial/ISA ratios tend to be older developments, either in the DC core, or near-suburbs that have been developing and redeveloping for decades, though there are a couple of newer-developing areas with elevated ratios (e.g., Hagerstown, MD). Areas above 30% ISA that fall along the 1:1 line include a mixture of both old and new development, and tend to be areas that include significant non-urban cover areas, such as parks or agriculture, or are centered on large bodies of water such as the tidal portions of the Potomac River (Figure 8). Areas with lower burial/ISA ratios tend to be new suburban and commercial centers, including those surrounding important transportation hubs and corridors. Whether this is due to policies more effective at preventing stream burial, or whether they have simply not developed to their full density, is as yet unclear.  
A larger fraction of small landscape units generally exhibit ISA values above the 30% ISA threshold (Table 5). The simple explanation is that because of the larger number and smaller size of the analysis units, the units are more efficient in capturing portions of the study area with a higher percentage of ISA, and therefore potential stream burial. Nevertheless, a value of 30% ISA, which corresponds with an elevated burial/ISA ratio at all analysis scales, appears to be a useful threshold value for separating areas that seem to have slightly different behavior.
# New Subclass Framework and Concrete Examples of Strongly Asymmetric Public Key Agreement
## 2 Methods and Abstract of This Study
### Explicit Definition of SAPKA  
We present the definition of SAPKA [18]. The SAPKA algorithms have the following common ingredients:  
* a multiplicative semi-group \(S\) with \(1\);
* a set \(\widehat{M}_{\mathcal{S}}\) of easily invertible maps \(:\mathcal{S}\to\mathcal{S}\), known as _noise space_; and
* a set \(M_{\mathcal{S}}\) of maps \(:\mathcal{S}\to\mathcal{S}\). In the above, \(S\) is public, and \(\widehat{M}_{\mathcal{S}}\) and \(M_{\mathcal{S}}\) belong to [PERSON]'s secret. From the key space, \[\mathcal{K}_{B}:=M_{\mathcal{S}}\times M_{\mathcal{S}}\times M_{\mathcal{S}} \times M_{\mathcal{S}}\times\widehat{M_{\mathcal{S}}}.\]  
[PERSON] prepares the quintuple \((x_{1},x_{2},x_{3},x_{4},N_{1})\) as their secret key, and \(x_{1},x_{2},x_{3},x_{4}\) must satisfy the following condition, which allows [PERSON] and [PERSON] to obtain the SSK.  
**Definition 2**.: _Let \(\mathcal{S}\) be a multiplicative semi-group with \(1\). If the functions \(x_{1},x_{2},x_{3},x_{4}:\mathcal{S}\to\mathcal{S}\) satisfy the following condition for all \(y\in\mathcal{S}\):_  
\[x_{1}\circ x_{2}(y)=x_{3}\circ x_{4}(y), \tag{13}\]  
_the maps \(x_{1},x_{2},x_{3},x_{4}\) are said to be compatible, where \(\circ\) denotes a map composition._  
**Definition 3**.: _For a multiplicative semi-group \(\mathcal{S}\) with \(1\) and the quintuple \(C:=(x_{1},x_{2},x_{3},x_{4},N_{1})\)\(\in\mathcal{K}_{B}\), if the maps \(x_{1},x_{2},x_{3},x_{4}\) satisfy (13), we state that \(C\) is a member of the SAPKA class. We express this relation as:_  
\[C\in SAPKA.\]  
The key agreement process of SAPKA is described as follows:  
* [PERSON] prepares the maps \((x_{1},x_{2},x_{3},x_{4},N_{1})\in\mathcal{K}_{B}\), of which \(x_{1},x_{2},x_{3},x_{4}\) satisfy (13). In this case, each of \(x_{1},x_{2},x_{3},x_{4},N_{1}\) is their secret key.
* [PERSON] constructs their public keys \(y_{B,1},y_{B,2}\) as a map for each: \[y_{B,1}:=N_{1}^{-1}\circ x_{4}\] \[y_{B,2}:=x_{1}\circ x_{2}\] and sends \((y_{B,1},y_{B,2})\) to [PERSON].
* [PERSON] selects her secret key \(x_{A}\) from \(\mathcal{S}\).
* [PERSON] calculates her public key \(y_{A}\) as follows: \[y_{A}:=y_{B,1}(x_{A})=N_{1}^{-1}\circ x_{4}(x_{A})\]and sends it to [PERSON].  
* [PERSON] calculates the SSK denoted by \(\kappa_{A}\) as follows: \[\kappa_{A}:=y_{B,2}(x_{A})=x_{1}\circ x_{2}(x_{A}).\]
* [PERSON] calculates the SSK denoted by \(\kappa_{B}\) as follows: \[\kappa_{B}:=x_{3}\circ N_{1}(y_{A})=x_{3}\circ N_{1}\circ N_{1}^{-1}\circ x_{4} (x_{A})=x_{3}\circ x_{4}(x_{A}).\]
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## Appendix A Average Distortion of Canonical Signed Digit Form  
For the CSD form, the set of reconstruction values is \(\mathcal{R}=\{\pm 2^{k}:k\in\mathbb{Z}\}\). Let the target variable \(t\) be uniformly distributed within \([0,1]\). Thus, only reconstruction values for \(k\leq 0\) are used.  
Consider the interval \([2^{k-1},2^{k}]\) with \(k\leq 0\). The probability \(p_{k}\) that the variable \(t\) falls within that interval is equal to its width \(w_{k}=2^{k-1}\). The average mean-squared quantization error within that interval is well known to be \(w_{k}^{2}/12\). Averaging over all intervals, the average mean-squared distortion for quantization with a single digit is given by  
\[\frac{1}{12}\sum_{k=-\infty}^{0}p_{k}w_{k}^{2} =\frac{1}{12}\sum_{k=-\infty}^{0}w_{k}^{3}=\frac{1}{12}\sum_{k=- \infty}^{0}2^{3k-3} \tag{1}\] \[=\frac{1}{96}\sum_{k=0}^{\infty}8^{-k}=\frac{1}{96}\times\frac{8 }{7}=\frac{1}{3\times 28}. \tag{2}\]  
By symmetry, the same considerations hold true, if \(t\) is uniformly distributed within \([-1,0]\). Due to the signed digit representation, these considerations even extend to \(t\) uniformly distributed within \([-1,+1]\).  
For representation by zero digits, \(t\in[-1,+1]\) is quantized to \(0\) with average mean-squared distortion equal to \(\frac{1}{3}\). This is \(28\) times larger than for quantization with one digit.  
The CSD representation is invariant to any scaling by a power of two. Thus, any further digit of representation also reduces the average mean-squared distortion by a factor of \(28\).
## 1 Introduction
### Liquid Insulation  
Liquid insulation plays a key role in the operation, safety, performance, and life span of transformers as it performs various functions such as providing electrical insulation between energized components and enhancing the dissipation of the heat generated by the transformer cores and windings. Liquid insulation also serves as a diagnosis agent that indicates the transformer's health through testing of oil samples that are collected and analyzed to assess the chemical degradation of the oil caused by the energy dissipated through the oil [5]. A variety of insulating liquids are used in the industry, with mineral oil being the most commonly used insulation material. However, other insulating liquids are employed due to their superior tolerance in biodegradability and flash points when compared to the commonly used mineral oils [2; 3; 4]. Considering the various properties that each insulation liquid possesses which may be superior or inferior to other insulation liquids, carefully thought compromises are made when selecting a suitable liquid based on factors such as application, desired operating temperature, environmental impact, and associated costs.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Intrusion Detection
With two real-world tests, a water distribution system and a water treatment plant, it was proven that this framework can successfully derive broader meaningful invariant rules than those defined manually and perform better in anomaly detection. [PERSON] et al. [71] proposed a new method for detecting interrule vulnerabilities in trigger-action platforms. First, they leveraged an NLP-aided technique to infer interrule information flows from the manual of triggers and actions. Then, they introduced an analysis framework, called iRuler, which utilizes satisfiability modulo theory (SMT) solving and model checking to analyze information flow graphs and discover interrule vulnerabilities. They evaluated 315,393 IFTTT applets, and 66% of the rulesets were linked to inter-rule vulnerabilities (e.g., condition bypassing or blocking, action looping, action conflicts, and action duplication). To prevent power grid attacks or reduce their damaging consequences, researchers have developed effective intrusion detection systems (IDSs) based on supervisory control and data acquisition (SCADA) networks. However, the SCADA system is still vulnerable to complicated attacks. To solve this problem, [PERSON] et al. [10] presented a radio frequency-based distributed intrusion detection system (RFDIDS). This system leverages radio frequency (RF) emissions to monitor power grid substation activities as a side-channel signal, which cannot be spoofed or played back. The simulation and experimental results verified that four types of extracted diagnostic information can be effectively leveraged to detect specific power grid attacks.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.9 Fabrication  
Multi-objective optimization formulation has been used in designing various parameters during fabrication of biosensor to increase the detection sensitivity. In [77], a design guide for extremely sensitive photonic crystal biosensor has been proposed. The scheme facilitates the selection of grating pitch and duty based on the constraints of lithography and measurement system. Photonic sensors have the potential to replace the traditional electrical sensors due to their peculiar properties namely, small size and weight, enhanced sensitivity and immunity from electromagnetic interference [78]. A multi-objective optimization scheme has been proposed in [79] to design a wavelength division multiplexing fiber Bragg grating sensor network to simultaneously minimize the bandwidth of the optical source and the overlapping spectra.
## 6 Network Layer for WVSN
### Dropping Scheme
#### 6.2.2 Random Early Dropping  
Some packets are dropped before arriving at _energy aware dropping_ so that the quality of video is still satisfactory. Applying random early dropping to data packets which are not dropped in energy aware dropping also helps increase the lifetime of the network. Early dropping of the data packets occur on the basis of node energy and hierarchical level with considering the priority and influence of these packets on video quality. Packet selection for early dropping consists of two phases: _energy level_ and _hierarchical level_. All packets selected in both _energy level_ and _hierarchical level_ selection phases are early dropped. In the following, two packet selection policies are described.
## 2 Results and Discussion
### Principal Component Analysis (PCA)  
PCA is a powerful tool to reduce the dimensions of multivariate data set. In this study, PCA was applied to determine in what respect one sample is different from another and which variables contribute most to this difference. Similar to the DA, 26 compounds were used as the variables. Figure 6 shows that all the three mango varieties were distinctively separated. The first two factors at 31.99% and 12.56% of variation were selected to provide the highest variation of data for convenient visualization and differentiation. The result suggested that each mango variety had their own unique chemical compositions [20]. The results presented by the PCA also indicated that there was a greater dispersion among the samples of Harumanis mango, which was consistent with the dendrogram in CA and can be explained by the higher quantity of compounds detected in smaller concentrations. The samples of the unauthentic Harumanis presented a higher clustering tendency \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**\% Correct**} & \multicolumn{3}{c}{**Source Assigned by DA**} \\ \cline{3-5}  & & **Harumanis** & **Susu** & **Tong Dam** \\ \hline Standard DA mode & & & & \\ Harumanis & 100 & 10 & 0 & 0 \\ Susu & 100 & 0 & 6 & 0 \\ Tong Dam & 100 & 0 & 0 & 7 \\ Total & 100 & 10 & 6 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification matrix of VOCs in different variety of mangoes. Figure 5: Dendrogram of the authentic (CI) and unauthentic (CII) Harumanis mango. and were located closely and in the same vertical axis. In addition, the agreement between CA and DA is consistent with this idea. Table 3 tabulated PCA loadings for mango samples having a total variance of 77.148% where seven factors were extracted with contributions of 31.994%, 12.558%, 8.733%, 7.151%, 6.479%, 5.1935%, and 5.0408%, respectively. The strong loadings of all the ocimene compounds (_beta_-ocimene, _trans-beta_-ocimen, and _allo_-ocimene) and factors 1 and 3 confirmed that these compounds can be the chemical markers for Harumanis authentication. In addition, _beta_-myr  
Figure 6: PCA plots of the three mangoes varieties; Susu (CI), Harumanis (CII), and Tong Dam (CIII).
# Traceability of the Micro Scale Pipe Viscometer for Traceable Calibration of Dynamic Viscosity
## 2 Materials and Methods
### Measurement Uncertainty of the Pipe Viscometer  
Now that the extended uncertainty of the inner diameter of the microtube is determined, the uncertainty contributions of the pipe viscometer are known and are listed in Table 5. Three main contributions can be identified. The largest contribution is the measurement of the pressure drop in the microtube with an uncertainty contribution of 0.28% for pressure drops larger than 0.4 bar(g). Other important contributions are the uncertainty of the inner diameter of the microtube being 0.20% and the uncertainty of single-point analysis for the determination of the dynamic viscosity being 0.20%. The latter was determined empirically by analyzing several sets of data, where the linear fit method and the single point analysis were used for the determination of dynamic viscosity and the additional contribution to it. Therefore, the expanded uncertainty was set to 0.90% for the pipe viscometer build with the glass micro tube.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Contribution** & **Uncertainty (k = 2)** & **Coefficient** \\ \hline Piston prover for the generation of flow & 0.10\% & 0.25 \\ Length measurement of the microtube & 0.01\% & 0.25 \\ Pressure difference measured using 2 sensors of maximum & 0.01\% & 0.25 \\ Pressure difference measured using 2 sensors of maximum & 0.28\% & 0.25 \\ pressure at 10 bar in the range of 0.4 bar(g) to 10.0 bar(g). & 0.28\% & 0.25 \\ Dynamic viscosity of water calculated according to [19] by & & \\ measuring the temperature and pressure. Uncertainty of & & \\ temperature measurement is 0.2 \({}^{\circ}\)C, leading to an uncertainty & 0.71\% & 0.25 \\ contribution of 0.50\% of the dynamic viscosity. Uncertainty & & \\ from the formula is 0.50\% [20,21]. & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Uncertainty contributions for the determination of the inner diameter of the microtube with water as reference liquid. All these values are either direct uncertainties from calibration certificates and/or empiric estimations.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Contribution** & **Uncertainty (k = 2)** & **Coefficient** \\ \hline Piston prover for the generation of flow & 0.10\% & 1.0 \\ Length measurement of the microtube & 0.01\% & 1.0 \\ Pressure difference measured using 2 sensors of maximum & 0.28\% & 1.0 \\ Pressure at 10 bar in the range of 0.4 bar(g) to 10.0 bar(g). & & \\ Inner diameter of microtubes determined with the pipe & 0.20\% & 4.0 \\ viscometer and reference liquid (water). & & \\ Single-point analysis at each flowrate for a given pressure drop vs. linear regression over the flowrate range. & 0.20\% & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Uncertainty contributions of the pipe viscometer for the determination of the dynamic viscosity. All values are either direct uncertainties from calibration certificates and/or empirical estimations.