esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
#### 3.1.2 Charging and Cool-Down Phase II  
Figure 3 and Table 2 show the cell behavior during charging (C) and cool-down phase II (D). All of the materials start at the charging start temperature of 46 \({}^{\circ}\)C (the onset temperature of the charging unit at cool down) with a 6 A charger. During charging, the cells heat up until the upper safety temperature limit (the stop temperature) of 50 \({}^{\circ}\)C is reached. The charging pauses at this temperature to let the battery pack cool down until it has reached the starting temperature (46 \({}^{\circ}\)C) and charging starts again. After the charging  
is finished, the pack cools down in cool-down phase II, and room temperature is reached after another 304-428 min.  
When using non-convective air (reference), the charging is paused twice for 21 min each. This significantly prolongs the total charging time. Due to the low heat capacity, the system heats up quickly, and therefore the cells can only partly be charged in each charging step. The cells are fully charged after 225 min (a charging time of 91.6 min).  
Heat-conductive polymer I, heat-conductive polymer II, and the latent heat storage material are characterized by comparable behaviors during charging. For all configurations, the charging was interrupted once for 21 min. The cells were fully charged after comparable time spans of 70.8, 70.9, and 71.2 min of charging time. However, due to the different starting times, the total charged state is reached after different times (160, 164, and 143 min). The latent heat storage effect has no influence on the charging process, as the melting point (phase change temperature) is lower than the start temperature.
# CATSWoTS: Context Aware Trustworthy Social Web of Things System
## 1 Introduction
Various QoS constraints are considered in the state of the art, but, in this research, three constraints like reliability, availability, and latency (because of their significance) are considered for the evaluation. For context evaluation; location, time and capabilities of a service provider are considered. In short, the main idea is to propose a system that helps in identification of quality IoT services by exploiting the concept of Social Web of Things and then maintains the trust level of such services. The trustworthiness is evaluated through direct experience of a service consumer and indirect experience (also called recommendation). The contributions of this research are described below:  
1. The CATSWoTS is an encouraging step towards the settlement of trustworthy recommendations of IoT services provided either by a social circle or by a third party. The system also considers the self-experience and the recommendation of a centralized level.The authentication and authorization mechanism makes the system security as it limits the access to trusted contacts only. 2. To evaluate and measure the trustworthiness of service, a rule-based collaborative filtering technique is proposed by identifying and integrating the various criteria of context and QoS in Social Web of Things. After recommending a service, feedback is also collected from a service consumer to update the trustworthy level of the consumed service which is then stored to help other service seekers. 3. The experiments are conducted on a real dataset of 2507 services to evaluate the performance of the CATSWoTS. The results show that the proposed technique recommends trustworthy services by fulfilling the criteria of QoS and context awareness of a service seeker with great accuracy. The remaining of the paper is organized into four sections. Section 2 describes the foundational concepts and state of the art in the related areas. The CATSWoTS is presented in Section 3. In Section 4, the effectiveness of CATSWoTS is demonstrated, whereas Section 5 concludes the research and describes future work.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 5 Fair Editions of Ensemble Models
### FairXGBoost  
The authors of [26] proposed FairXGBoost, a bias-mitigation framework that seamlessly integrates fairness constraints into XGBoost's training process through a fairness regularizer, without requiring significant modifications to the model's core framework. The fairness regularizer is designed to eliminate correlations between the sensitive attribute and the target variable, thereby promoting fairness. Additionally, a hyperparameter called \(\mu\) regulates the strength of fairness enforcement, with higher values imposing stricter fairness constraints. By fine-tuning \(\mu\), practitioners can effectively balance predictive accuracy and bias mitigation, ensuring both performance and fairness in model outcomes. Experiments conducted on the Adult Dataset, COMPAS Dataset, Default Dataset, and Bank Marketing Dataset demonstrated minimal performance degradation while successfully achieving regulatory compliance by maintaining a Disparate Impact (DI) score of at least 0.8 (four-fifths rule).
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 3 Implementation and Simulation Results  
### Practical Implementation  
One of the advantages in the proposed method is that there are several options to implement it. It can be implemented by PMU with high measurement accuracy or by the protection relays without any extra hardware investment. In this section, the latter option is selected for demonstration. In addition, the proposed method estimates the short-circuit current before it really happens. The online procedure of the estimation guarantees the time-varying factors incurred by DGs are considered. The practical implementation of fault level estimation in the relays is shown in Figure 5, where the abbreviation \"SCCE\" represents the module of short-circuit current estimation. The phasor values of the positive voltage and current at the studied breaker are analyzed on the basis of the recorded data. Certainly, PMUs can alternatively be applied to provide more-accurate data and more-rapid data processing, if necessary. The values of \(Dir\), \(\Delta\hat{U}\) and \(\Delta\hat{I}\) are calculated for every non-trivial change. A series of consecutive disturbance downstream is employed. The confidence level of the data is derived to weight the results. If there happens a significant disturbance on the grid side, representing the change of the equivalent circuit topology upstream, the initial operation states will be updated to start a new estimation process. After the downstream disturbances occur consecutively for \(n\) times, the confidence level of the recorded voltage and current through the fuzzy logic identifier are assigned to be \(w_{i}\in\left\{0,1,2,\ldots 13\right\}\)\(\left(i=1,2,\ldots n\right).\) The short-circuit current is then estimated with Equation (2), and the corresponding confidence level of the final result is defined as follows:  
\[CL=\frac{\sum\limits_{i=1}^{n}\left(w_{i}\right)}{13n}\times 100\% \tag{12}\]  
This parameter can be used to evaluate the creditability of the final result.  
If the fault really occurs, the module of SCCE will be blocked as soon as the tripping signal is triggered, because the fault might lead to severe errors due to the saturation of PT and CT. The SCCE also has a low voltage/current block function to prevent the equipment from outputting the results with large errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\Delta\hat{\Omega}\) & \(\Delta\hat{I}\) & **O** & **PS** & **PM** & **PL** \\ \hline O & **O** & PVS & PS & PLM \\ PS & **PVS** & PS & PLM & PM \\ PM & PS & PLM & PM & **PML** \\ PL & PLM & PM & PML & **PL** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The fuzzy logic rules for forming the CL.  
Figure 4: Membership function of the input–output variables: (**a**) membership function of the input; and (**b**) membership function of the output.
# Thermal Characterization of Phase Change Materials by Differential Scanning Calorimetry: A Review
## 3 Calorimetric Characterization
### Calorimetric Methods
Note that in the following, small samples contain typically tens of milligrams while large samples are of the order of tens of grams;
* Due to the fast heating up/cooling down process, less time is consumed in the range where little heat is stored;
* The obtained signal is not dependent on the heating rate, so the signal/noise ratio is always high. The three layer calorimeter (3 LC) is an instrument specially built to measure heat capacity, melting enthalpy and phase transition of PCMs [6]. In this method, 100 g of a PCM can be tested in a box-shaped container. Thus, the measurement condition is close to the actual application [26]. In DSC, T-history and 3 LC, heat is supplied by a thermal resistance. In this review, according to the widespread use of heat-flux DSC (Figure 3), we focus more on the research carried out by the mentioned DSC. The other calorimetric method relies on the supply of heat \(\dot{Q}\) (W) to the sample by electrical heating:  
\[\dot{Q}=\Delta h=P_{el}\cdot\Delta t=U\cdot I\cdot\Delta t \tag{2}\]  
where \(P_{el}\) is the electric power (W), \(t\) the time (s), \(U\) the voltage difference (V) and \(I\) electrical current (A). Figure 2: Schematic of T-history set-up adapted from [18]: (**a**) early T-history set-up from 1999; (**b**) the modified set-up from 2006 fabricated at the Bavarian Center for Applied Energy Research (ZAE Bayern). In this method, there is no heat exchange between the sample and the ambient. Thus, the calorimeters that measure based on this principle are known as Adiabatic Calorimeters (AC), and the heat flow calibration is not needed [6]. In this method, the finite heat pulse is applied to give finite \(\Delta\)Q. It appears highly time-consuming and requires preparation, but it is essential for measuring the heat capacity of reference materials such as sapphire or copper. Thanks to the near-equilibrium condition, the adiabatic calorimetry is mostly used for exact measurement of reference materials [27]. In 1970, the Adiabatic Scanning Calorimetry (ASC) was introduced and the heat was supplied using a constant electrical power (constant rate) instead of a finite pulse. This constant rate leads to continuous curves of enthalpy and heat capacity. Both AC and ASC require careful training to operate. The modern type of adiabatic calorimeter is equipped with a sensitive temperature detector that establishes a temperature equality between sample and adiabatic shield and is known as Peltier-element-based ASC (pASC) [6]. Finally, thermal conductivity measurement methods were updated for calorimetry of large samples, PCM composite and encapsulated PCM [6]. A summary of calorimetric methods is proposed in Table 2.
# QoE Modeling on Split Features with Distributed Deep Learning
## 6 Results
### Distributed Ensemble Learning (DEL)
#### 6.1.1 Content-Based Split  
We experimented on G0 test set, with a content-based split between G0 and G1. The results are given with or without _meanBitrate_ feature in Figures 8 and 9, respectively. For the scenario when _meanBitrate_ is included, as given in Figure 8, weighting factors \(W_{0}=0.6\) and 0.5 yielded the best accuracy when the split index is set to 2 and 4, as shown in Figure 8a,b, respectively. This indicates that involvement of target and source domain models approximately equally increases the model performance to the greatest extent. When the split index is set to 6, weighting factors \(W_{0}=0.1\) yielded the best F1-score, 0.9. We think the reason for this is that the G1 model might be more generic and robust with less features where one of those features is a _strong_ QoE indicator such as _meanBitrate_.  
The results of the scenarios without the _meanBitrate_ are given in Figure 9. The benefit of _DEL_ is not clear in all split index settings. When the split index is set to 4 and 6, G0 works best alone by using its own model. Only when split index is set to 2, a slight increase in model performance is observed when the model is ensembled using \(W_{0}=0.6\). We test the statistical significance of this scenario in Section 6.1.3.  
Figure 8: \(DEL\) on group 0 when all features (with meanBitrate) are used and split into two workers. G0 and G1 are split with respect to the content-based criteria.  
Figure 9: \(DEL\) on group 0 when all features (but without meanBitrate) are used and split into two workers. G0 and G1 are split with respect to the content-based criteria.  
Figure 11: \(DEL\) on group 0 when all features (but without meanBitrate) are used and randomly split into two workers. G0 has 3 times less data as compared to G1.  
Figure 10: \(DEL\) on group 0 when all features (with meanBitrate) are used and randomly split into two workers. G0 has 3 times less data as compared to G1.
## 2 Preliminaries  
### Fractional Order Calculus  
Recently, many systems are modeled as fractional-order systems utilizing fractional-order calculus. The fractional-calculus is found in several applications [34] in areas including electromagnetic waves [38], chemical engineering [39], signal processing [40], polymer science [41], electro-chemistry [42], neural networks [43], fluid mechanics [44], bio-engineering and bio sciences [45], control of power electronics [46], nonlinear control [47], etc.  
The generalization of the integration and differentiation operators in fractional calculus [48] of \(k^{th}\) order is defined as  
\[{}_{p}D_{q}^{k}=\left\{\begin{array}{ll}\frac{d^{k}}{dt^{k}}&\Re\left(k \right)>0\\ 1&\Re\left(k\right)=0\\ \int\limits_{p}^{q}\left(d\tau\right)^{-k}&\Re\left(k\right)<0\end{array}\right. \tag{1}\]  
where \(p\) and \(q\) are the limits of the operation. The integration and differentiation of any real order can be performed. The Riemann-Liouville formula [49] for differentiation is given as  
\[{}_{p}D_{q}^{k}f(t)=\frac{1}{\Gamma(n-k)}\frac{\dd^{n}}{\dd^{n}}\int_{p}^{q} \frac{f(\tau)}{(t-\tau)^{k-n+1}}\dd\tau \tag{2}\]  
where \(k\) can be any value in-between \((n-1)\) and \(n\), and \(\Gamma(.)\) is a Gamma function.  
The [PERSON]'s definition of the fractional-order derivative [34] is expressed as  
\[{}_{p}D_{q}^{k}f(t)=\lim_{h\to 0}h^{-k}\sum_{a=0}^{\frac{(q-p)}{\pi_{h}}}(-1)^{a} \left(\begin{array}{c}k\\ a\end{array}\right)f(t-ah) \tag{3}\]  
where \((-1)^{a}\left(\begin{array}{c}k\\ a\end{array}\right)\) are the binomial coefficients \(C_{a}^{(k)}\), \((a=0,1,\ldots)\); and \([.]\) denotes the integer part. The formula given in [48] can be utilized to calculate the binomial coefficients.  
The other commonly used formula for differentiation is known as Caputo expression [48]. The Caputo expression is defined as  
\[{}_{p}D_{q}^{k}f(t)=\frac{1}{\Gamma(n-k)}\int_{p}^{q}\frac{f^{n}(\tau)}{(t-\tau )^{k-n+1}}\,\mathrm{d}\tau \tag{4}\]  
where \((n-1)<k<n\).
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Input Feature Extraction Based on Trajectory Features Correlation
#### 4.2.2 Select the Step Size of the Input Feature  
The step size of input data is a key parameter in neural network models, which has a significant impact on the performance and accuracy of the model. Smaller step sizes can result in more repetitive samples being used for training, which in turn makes the model overfit to local features. Larger step sizes may cause the model to fail to capture fine patterns and key features in the data, resulting in a loss of information and model underfitting. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are commonly used concepts in time series analyses. Of the two, the ACF measures the degree of correlation between the time series and the lagged version of the series at different lags by calculating the autocorrelation coefficients at different lag orders. The PACF measures the direct correlation between the current lagged value and the series after further removing the effect of other lagged terms, reflecting the effect of this lag order on the series, thus providing guidance for determining the appropriate step size of the input data.  
This study considers the longitude and latitude series as time series and calculates the ACF and PACF parameters for the longitude and dimension series, respectively. The AIC and BIC evaluation models are utilized to select the optimal lag order as the step size of the input features. The AIC and BIC information criteria are common measures of the evaluation value of the time series model and the smaller value represents the higher quality of the model. Therefore, selecting the model with the smallest AIC and BIC values is used to determine the optimal lag order, and this is used as the step size of the input features. The calculation Equation (10) is as follows:  
\[\left\{\begin{array}{l}\text{AIC}=-2\ln(\text{L})+2k\\ \text{BIC}=-2\ln(\text{L})+k\ln(n)\end{array}\right. \tag{10}\]  
where L is the parameter maximum likelihood estimate, \(k\) is the number of model variables, and \(n\) is the length of the series.
## 3 Results and Discussion
### Production of eLpp-OmpA-AppA and Display of AppA
#### 3.1.3 Analysis of OM-Linked AppA Activities  
Although the above findings revealed that MWCB-phyrase met expectations, one specific result caught our attention: bacteria harboring pESE, expressing only the carrier,  
Figure 3: Analysis of protein expression in _E. coli_ Lemo21(DE3) cells. (**a**) Electrophoretic separation of bacterial proteins by SDS-PAGE (12% gel stained with Coomassieie Brilliant Blue). Lanes loaded with total extracts of cells harboring expression plasmids: 1: pESE; 2: pESE-AppA. Lane M, Protein MW standards (kDa). The colored arrows indicate the relative mobility of eLpp-OmpA (red, lane 1) and eLpp-OmpA-AppA (purple, lane 2). (**b**) Immunodetection of surface-displayed (Myc-tagged) proteins in non-permeabilized engineered bacteria using IFM (top-right label: expression plasmid). Note: Cells harboring the pET2B(+) plasmid served as controls to normalize the background (lack of fluorescence). Micrographs at 630\(\times\) magnification. (**c**) Quantitative analysis of the WC-linked acid phosphatase activity. Substrate (pNPP) hydrolysis by engineered bacteria (bar label: expression plasmid) under standard conditions.  
showed a considerable degree of acid phosphatase activity (Figure 3c). This observation suggests that eLpp-OmpA overexpression affects outer membrane permeability, favoring both the influx of pNPP (substrate) into the periplasm and its availability for hydrolysis by native AppA, resulting in a detectable increase in the concentration of the leaving group (p-nitrophenolate, pNP). This notion is supported by the fact that OmpA acts as a low-permeability porin, allowing slow entry of small solutes [60]. Moreover, a pioneering study demonstrated that displaying \(\beta\)-lactamase (using Lpp-OmpA as a carrier) causes increased permeability, whereas no significant leakage of periplasmic proteins occurs [40]. Therefore, it is reasonable to assume that these changes in permeability are inevitable and represent a consequence of the large proteins displayed in _E. coli_.  
Supplementary analysis of the catalytic function of cell surface-displayed AppA by assaying acid phosphatase and phytase on OM fractions (i.e., free of activity associated with the periplasmic enzyme) corroborated our hypothesis. The OM fractions isolated from cells expressing eLpp-OmpA-AppA showed significant acid phosphatase and phytase activity (\(p<0.001\)), whereas the equivalent fractions from cells expressing eLpp-OmpA showed no activity (Figure 4).  
Although this result supports our goal, it is worth noting that the difference between the background and surface-linked activities (Figure 3c) can be improved because the Lemo21(DE3) cell system allows rhamnose-induced modulation of T7 Lys, which in turn inhibits T7 RNAP [56,57]. Therefore, using this approach, it is possible to regulate eLpp-OmpA-AppA expression, thus reducing OM permeability and likely resulting in a low phytate influx.
## 4 Analysis of the CRB for Short Observation Intervals
### CRB when \(\Delta\rho>\rho_{chip}\)
#### 4.1.2 Reflection Coefficient Estimation  
Analogously, we compute now \(\text{CRB}_{r}\) for \(|\Gamma|\) and \(\phi_{\Gamma}\) (subindex \(r\)), but in this case, we proceed by removing the row and the column associated with \(h\), as shown, once again, in Appendix A. We obtain the following expressions:  
\[\text{CRB}_{r}(|\Gamma|) = \text{CRB}\left(|\Gamma|\right), \tag{42}\] \[\text{CRB}_{r}\left(\phi_{\Gamma}\right) = \frac{1}{2\,\gamma\,\text{SNR}_{0}\,R\left(0\right)}. \tag{43}\]  
As expected, the \(\text{CRB}_{r}(|\Gamma|)\) is the same as in the joint estimation of \(\mathbf{\
abla}\), while \(\text{CRB}_{r}(\phi_{\Gamma})\) is now orders of magnitude smaller when compared to Equation (39) and well bellow \(2\pi\), thus implying that in this case, it is possible to obtain useful phase estimates.
## 5 Compensation for Frictional Disturbance Observer
### Establishment of the System Friction Model  
The Stribeck friction model is the most suitable description of friction behavior in the low-speed range, particularly for analyzing the friction phenomenon in servo systems during low-speed operation. Since this paper mainly investigates the impact of friction on the low-speed performance of servo systems and its compensation methods, the Stribeck friction model is selected as the research subject. This model effectively reflects the trend of friction force changing with speed as an object transitions from rest to motion. Specifically, in the low-speed region, the friction force gradually transitions from static friction to Coulomb friction as speed increases and decreases exponentially. The relationship curve between friction force and speed is shown in Figure 4.  
The Stribeck friction model can be represented as follows [29]:  
\[T_{\text{f}}(v)=\Big{[}T_{\text{c}}+(T_{\text{s}}-T_{\text{c}})\text{e}^{-(v/ v_{\text{v}})}\Big{]}\text{sgn}(v)+B_{v}v \tag{35}\]  
where \(v\) represents the rotational angular velocity; \(T_{s}\) is the maximum static friction torque; \(T_{c}\) is the Coulomb friction torque; \(B_{V}\) is the viscous friction coefficient; and \(V_{S}\) is the Stribeck velocity.  
In the experimental system presented in Section 6, the selected friction parameters are shown in Table 1.  
Therefore, the applied friction disturbance is as follows, shown in Equation (36):  
\[T_{\text{f}}(v)=\Big{[}0.459+(\ 0.4879-0.459)\text{e}^{-(v/0.3236)}\Big{]} \text{sgn}(v)+0.005735v \tag{36}\]
## 3 Results and Discussion
### Influence of Working Pressure around the Cooling Surface on Frost Crystal Morphology  
The frost crystal morphologies are photographed at 10 min, as shown in Figure 5, when \(T_{\rm a}\) = 20 \({}^{\circ}\)C, \(T_{\rm w}\) = \(-\)38 \({}^{\circ}\)C, \(P_{\rm v}\) = 350 Pa, and \(P_{\rm a}\) = 0.25 \(\times\) 10\({}^{5}\) Pa, 0.75 \(\times\) 10\({}^{5}\) Pa and 2.00 \(\times\) 10\({}^{5}\) Pa, respectively. The frost crystal morphologies shift from feather to needle shape when the working pressure around the cooling surface increases, as shown in Figure 5b. The frost crystals at 0.25 \(\times\) 10\({}^{5}\) Pa are thicker than those at higher pressure conditions. It can be attributed to the water vapor needing to cross a nucleation barrier when forming droplets or ice nuclei on cooling surfaces. Then the new frost nuclei can be created and continue to grow. The nucleation barrier is related positively to the overall air pressure, which affects the diffusion coefficient of water vapor directly [17]. Therefore, higher working pressure raises the nucleation barrier for forming frost nuclei, resulting in a slower growth rate of frost crystal. Moreover, the distribution of frost crystals in the lateral direction gradually becomes thinner with the increasing working pressure around the cooling surface.  
Figure 4: Frost thickness during the repeatability tests.  
Figure 5: (**a**) Morphologies and (**b**) local details of frost crystals under different working pressures around the cooling surface.