esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Pilot Plant Analyses
### Methodology  
Different experiments comprising charging and discharging processes at a thermal range of 48 \({}^{\circ}\)C and 68 \({}^{\circ}\)C and at different constant HTF mass flow rates were performed. A narrow temperature range around the PCM melting temperature is selected to study the PCM behavior, mostly in the latent phase. The three different tested mass flow rates were 500 kg/h, considered as low flow, 1500 kg/h, considered as mid flow and 2500 kg/h, considered as high flow. Despite having a maximum mass flow rate of 2500 kg/h, only a laminar regime could be achieved because of the high viscosity of the HTF and the limitations of the pilot plant facility.  
Before starting the charging process, a warming process was required to make the PCM be uniform and homogeneous at the initial temperature of charging. Once the PCM reached the initial temperature conditions for charging (48 \({}^{\circ}\)C), the HTF was heated up outside the tank up to the inlet HTF temperature (68 \({}^{\circ}\)C) and afterwards the charging process started. The charging process was considered to be finished once the difference between the inlet and outlet HTF temperature was less than 2 \({}^{\circ}\)C and the weighted average temperature of the middle temperature sensors reached 68 \({}^{\circ}\)C.  
Figure 2: Storage system: (**a**) temperature sensors location at three different levels, highlighted in grey (lower), black (middle) and dotted (upper); (**b**) distribution of the phase change material (PCM) inside the storage tank colored in blue (corner part), red (main part) and green (central part).  
Then, a reverse process took place to carry out the discharging process (PCM initial temperature of 68 \({}^{\circ}\)C and HTF inlet temperature of 48 \({}^{\circ}\)C).
## 4 Experiment and Analysis
### Results and Analysis  
#### 4.2.1 Calculation of the Entropy of Special Terrain Types  
The Euclidean-distance-based method was used to calculate the entropy of five special terrain types shown in Figure 2. The results showed that the entropy values of (a-d) were 0, 0.727 bit, 0.992 bit, 1.517 bit, and 2.105 bit, respectively. By comparing the entropy results calculated via the Euclidean-distance-based method with those obtained by the four currently available methods, it was found that the four conventional methods fail to accurately calculate the entropy of the five special terrain types. However, the Euclidean-distance-based method achieved accurate calculation of the entropy of these terrain types, and the calculated entropy values revealed an increasing trend in terrain complexity.  
Figure 6: Twelve simulated DEMs (30 \(\times\) 30). (**a**) a flat terrain. (**b**) a simple slope. (**c**) a single peak. (**d–l**) terrains with increasing complexity.
## 2 Results
### Antibiofilm Activity Measured by Different Methods
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Peptides (uMol/L)**} & \multicolumn{6}{c}{**PAO1 (BM2)**} \\ \cline{2-9}  & **MIC at 600 nm** & \multicolumn{2}{c}{**MBIC (CV at 595 nm)**} & \multicolumn{2}{c}{**MBRC (CV at 595 nm)**} & \multicolumn{2}{c}{**MBEC from Calgary Biofilm Device (CBD)**} \\ \cline{2-9}  & **LD50** & **LD90** & **LD50** & **LD90** & **LD50** & **LD90** & **LD50** & **LD90** \\ \hline Tritrp-Arg & 4 & 8 & 4 & 8 & 32 & 32 & 16 & 32 \\ \hline Tritrp-Lys & 32 & 32 & 32 & 32 & 64 & 64 & 128 & 128 \\ \hline Tritrp-P59A & 2 & 4 & 4 & 4 & 32 & 32 & 16 & 32 \\ \hline Tritrp-P59A-Lys & 4 & 8 & 4 & 8 & 64 & 64 & 128 \\ \hline Tritrp-W678Y & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & 128 & \textgreater{}128 & \textgreater{}128 & \textgreater{}128 \\ \hline Tritrp-W678F & 32 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & 128 & \textgreater{}128 & 128 & 128 \\ \hline Tritrp-P5A & 4 & 8 & 8 & 8 & 32 & 32 & 32 & 128 \\ \hline Tritrp-P9A & 2 & 2 & 1 & 2 & 16 & 32 & 8 & 16 \\ \hline Tritrp-P9A-Lys & 4 & 8 & 8 & 16 & 32 & 64 & 64 & 64 \\ \hline Tritrp-W678 hW & \textgreater{}64 & \textgreater{}64 & 32 & \textgreater{}64 & \textgreater{}128 & \textgreater{}128 & \textgreater{}128 \\ \hline Indolicidin & 16 & 16 & 32 & 32 & 64 & 64 & 128 & 128 \\ \hline PuroA-Arg & 8 & 16 & 16 & 16 & 32 & 32 & 16 & 64 \\ \hline PuroA-Lys & 8 & 16 & 8 & 16 & 32 & 32 & 32 & 64 \\ \hline \hline \end{tabular} Note: MIC, minimum inhibition concentration; MBIC, minimum biofilm inhibitory concentration; MBRC: minimum biofilm reduction concentration; the MBEC (Calgary biofilm device (CBD)) was defined as the lowest concentration of peptide with no bacterial growth seen on the spot agar plate.  
\end{table} 
Table 5: The activities measured with the crystal violet method for tritrpticin analogs against _P. aeruginosa_ PAO1.
# Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model
## 1 Introduction
The existing versions of ELECTRE II method are effectively used and applied to solve multi-criteria decision making problems having exact data or fuzzy values. There are many problems in which the considered actions have bipolar uncertainties and can be described properly by two-sided information. These problems cannot be solved or ranked by using fuzzy versions of ELECTRE II method. To overcome this difficulty, in this research article, ELECTRE II method is designed within the context of the bipolar fuzzy environment to solve problems having bipolar uncertainties, and named as bipolar fuzzy ELECTRE II method (BF-ELECTRE II). The proposed method is established by defining the concept of bipolar fuzzy strong, median and weak concordance as well as discordance sets and indifferent set to define the two types outranking relations, namely strong outranking relation and weak outranking relation.The normalized weights of criteria, which may be partly or completely unknown for decision makers, are calculated by using an optimization technique, which is based on the maximizing deviation method. A systematic iterative procedure is applied to strongly outrank as well as weakly outrank graphs to rank the alternatives or to choose the best possible solution. The implementation of the proposed method is presented by numerical examples such as the selection of business location and supplier. A comparative analysis of proposed ELECTRE II method is also presented with already existing multi-attribute decision-making methods including TOPSIS and ELECTRE I under bipolar fuzzy environment by solving the problem of business location.
## 4 Discussion
Future research will focus on integrating mechanical, thermal, and material effects into analytical and AI-driven models to enhance their validity and practical applicability in the energy industry. Moreover, it will aim to eliminate the research gaps related to the limited integration of actual boundary conditions and consider key physical phenomena, such as friction-induced nonlinearities, variable lubricant viscosity, asymmetric loads, and temperature effects.
## 3 Inertial Aiding Method Based on a Nonlinear Tracking Differentiator
### Jerk Aided Kalman-PLL Based on Nonlinear Tracking Differentiator  
If the input signal with noise is \(y(t)\), its differential \(x(t)\) can be deduced by a time differential method. Generally the time differential method is as follows:  
\[x(t)=\frac{y(t)-y(t-\tau)}{\tau} \tag{15}\]  
The above equation matches Equation (14), and this differential operation will increase the noise. Using the nonlinear tracking differentiator, the smoothed output \(\vec{y}(t)\) and its differential \(x(t)\) can be obtained through the input \(y(t)\), and the equation is as the following:  
\[\begin{split}\left\{\begin{aligned} \vec{y}(t+\tau)&=\vec{y}(t)+ \tau\cdot\vec{x}(t)\\ \vec{x}(t+\tau)&=\vec{x}(t)+\tau\cdot f(e,\vec{x}(t)) \\ e&=\vec{y}(t)-y(t)\end{aligned}\right.\end{split} \tag{16}\]  
where \(\tau\) is sampling interval, \(f(\cdot)\) is nonlinear function. The function \(f(\cdot)\) used herein is [26]:  
\[f(e,\vec{x}(t))=\mathrm{R}^{2}\left[-\mathrm{a}_{0}e-\mathrm{a}_{1}\,\mathrm{ fall}(e,\alpha,\delta)-\mathrm{a}_{2}\,\mathrm{fall}(\frac{\vec{x}(t)}{ \mathrm{R}},\alpha,\delta)\right] \tag{17}\]  
where the function \(\mathrm{fall}(\cdot)\) is expressed as [26]:  
\[\mathrm{fall}(x,\alpha,\delta)=\left\{\begin{aligned} \left.x^{a}sign(x), \left|x\right|\geq\delta\\ x\,/\,\delta^{|-\alpha}\,,\left|x\right|\leq\delta\end{aligned}\right. \tag{18}\]  
If R is larger, the tracking is faster, but the noise will be accordingly increased. The nearer \(\alpha\) approaches 1, the more the system approaches linearity. The setting of threshold value \(\delta\) is in order to prevent the system from vibrating in the origin, and \(\delta\) should be far less than 1. Here, we select the parameters as follows: set T as per sampling frequency and R = 2, \(\mathrm{a}_{0}\) = 60, \(\mathrm{a}_{1}\) = 60, \(\mathrm{a}_{2}\) = 60, \(\alpha\) = 6/9.
# A Survey on Blockchain-Based Federated Learning
## 5 Challenges and Future Directions
### Security Concerns  
A BFL system may encounter issues related to the abuse of decentralized authority. While blockchain technology offers reliable solutions for protecting all parties involved in the federated learning system and parameter exchange during predictive analysis, the entire blockchain system is still vulnerable to network attacks like the 51% attack [142]. Furthermore, the consensus mechanism, depending on the mining power, may also be compromised, leading to a concentration of the originally decentralized platform around mining fields that control consensus and settlement. This security issue is more pronounced in public blockchains like Ethereum and Bitcoin, whereas private blockchain platforms are less affected because consensus protocols are predefined among the parties.  
While BFL frameworks can offer some resistance against poisoning attacks through well-designed consensus mechanisms, many blockchain consensus algorithms themselves face security risks. For instance, in the most common Proof of Work (PoW) consensus algorithm, miners may experience delays in receiving blocks, which can lead to forking issues. In a recent study [80], the introduction of ACK (ACKnowledge character) was proposed to determine, within a waiting period, whether a fork has occurred. If a fork is detected, the mining process is restarted, mitigating the problem. Some recent research has put forward new consensus algorithms; however, the security of these algorithms often lacks theoretical proof and practical validation. Consequently, the challenge of developing provably secure BFL consensus algorithms remains an urgent issue to address.
## 2 Materials and Methods
### Image Evaluation  
All images were assessed by 12 examiners (eight oral surgeons, four maxillofacial surgeons) with at least two years of training in oral radiology and diagnosing CBCT images. All evaluators were calibrated by receiving instructions (by the primary investigator QD) during the examination of five test mandibles. Assessment was performed in three steps: the marking of a lesion with a cursor, the indication of the lesion type, and the qualitative visibility grading of the lesion: from 1, very low to 10, very high.  
All scans were assessed in Sidexis 4 using the same workstation (Supermicro, Windows 10 Professional Edition 64 Bit; Intel(r)Core(tm) i7-6700 CPU (c) 3.40 GHz Intel64 Family 6 Model 94 Stepping 3, 3400 MHz/x64) and display (HP Z23n 58, 4 cm, 23 Zoll, IPS LED Backlight).  
Each mark was registered in a digital coordinate system within Sidexis 4, and the coordinates were saved in an Excel sheet (Microsoft Excel 2016, Microsoft Corporation, Redmond, WA, USA) along with the lesion type and the qualitative visibility grading information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & **FOV (cm)** & **kV/mA** & **Radiation Time (s)** & **Voxel Size (\(\mu\)m)** & **Effective Dose (\(\mu\)Sv)** \\ \hline LD & \(11\times 10\) & 85/13 & 2.2 & 160 & 20 \\ SD & \(11\times 10\) & 85/13 & 4.4 & 160 & 145 \\ \hline \hline \end{tabular} Abbreviations: FOV, field of view.  
\end{table}
Table 1: Low-dose and standard-dose mode settings of the Orthophos SL (Dentsply Sirona, York, PA, USA) [20].  
Figure 1: Screenshots of the four lesion types imaged with low-dose (upper row) and standard-dose mode (lower row) of cone-beam computed tomography (CBCT).
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 3 Design of 5G QC-LDPC Encoder
### QC-LDPC Encoder Architecture
The execution in 22 parallel CN sub-modules use four clock cycles to get the intermediate variables \(\lambda_{i}(i=1,2,3,4)\) and to store in memory. In order to speed up the calculation of (6), Part II may use more CN sub-modules. In each calculation cycle, the same as the execution in each code block of \(K_{b}\), a codeword is delivered to \(\text{CN}_{23-64}\), and a column elements of \(C\) matrix is read as the cyclic shift coefficient inputs each CN. When the cyclic shift is completed, the results are kept in registers. After 22 calculation cycles, 22 column of \(C\) matrix is used by 42 CN sub-modules. The 42 CN process the cyclic shift of the information code block in parallel. Finally, the outputs of 42 XOR blocks give the last result. After the \(CS^{T}\) calculation is completed, the 4 column elements of \(D\) matrix in ROM are sent into 42 CN in turn, and 4 sets of \(P_{1}\) vectors are also delivered to \(\text{CN}_{23-64}\) in parallel; a group of results of \(DP_{1}^{T}\) are obtained in each cycle. After four times of cyclic shifts and XOR operations, the execution of (12) is completed, and the second group parity \(P_{2}\) is obtained. When two groups of parities are obtained, the filler invalid bits are removed by the codeword output processing module. Meanwhile, the first two columns of the information code blocks with \(Z\)-length are punched out, and the parity codes are punched according to the code rate, then the encoded blocks give output towards the rate matching section.
## Materials and Methods
### Application of PPG-nZVI Beads in Simulated Stormwater Infiltration Facility  
To examine the removal efficiency of PPG-nZVI beads in remediation system, PPG-nZVI beads (particle size 0.6-1 cm, thickness 3 cm) were paved as one layer in the simulated stormwater infiltration facility (named as pool 1) to treat the heavy metal wastewater. Besides the beads layer, the traditional filling materials including sand (\(\leq\)2.5 cm, thickness 5 cm), sawdust (thickness 3 cm), pumice (particle size 1.5-2.5 cm, thickness 15 cm) and fine gravel (particle size 1-2 cm, thickness 15 cm) as other layers were set under the beads layer. The pool without beads layer (named as pool 2) was also set up to verify the effect PPG-nZVI beads layer on treating heavy metals. Multi-heavy metal solution with the concentration of 10 mg-L\({}^{-1}\) for Pb\({}^{2+}\), 5 mg-L\({}^{-1}\) for Cu\({}^{2+}\), 30 mg-L\({}^{-1}\) for Zn\({}^{2+}\), 5 mg-L\({}^{-1}\) for Cr\({}^{6+}\), and 5 mg-L\({}^{-1}\) for Cd\({}^{2+}\) was flowed from the inlet. The devices run in a continuous feed mode and were set to 100 L-h\({}^{-1}\) by the constant current pump for 3 h. Water samples in the outlet were taken out every 15 min and analyzed. The infiltration pool and water intake diagram were shown in Figure 1. The length, width and height of the devices were all settled to be 50 cm.
## 3 Methodology
### Stage 3: ANOVA Test
A similar representation is observed in crack depth prediction, which is shown in Table 15. In Table 16, it is noted that only MLM influences the performance of the prediction of crack width. Figure 4: Factorial and Interaction Plots for Performance of ML Models for Crack Length Prediction. (**a**) R\({}^{2}\) Factorial Plot for Training Dataset—L. (**b**) R\({}^{2}\) Factorial Plot for Testing Dataset—L. (**c**) RMSEFactorial Plot for Training Dataset—L. (**d**) RMSEFactorial Plot for Testing Dataset—L. (**e**) R\({}^{2}\) Interaction Plot for Training Dataset—L. (**f**) R\({}^{2}\) Interaction Plot for Testing Dataset—L. (**g**) RMSE Interaction Plot for Training Dataset—L. (**h**) RMSE Interaction Plot for Testing Dataset—L. Table 6 represents the best ML model, the feature selection method, and the number of features for each crack dimension. It is understood that out of the six ML models, XGB performed well for all the crack dimensions. For comparison purposes, the optimum parameters obtained by the desirability function method using Minitab R19 Software are given in Table 7.  
Table 8 represents the list of hyper parameters [15] considered for tuning the performance of the XGB ML model. 'RandomizedSearchCV' function is used in Python to get the best hyper parameters for crack dimensions, and the same is presented in Table 9. \begin{table}
\begin{tabular}{l l l r r r r r r r} \hline \hline
**Characteristic** & **FSM** & **MLM** & **NoF** & **R2 Tr** & **R2 Tt** & **RMSETr** & **RMSETt** & **Deng’s Value** \\ \hline Length & MIS & XGB & 17 & 0.8757 & 0.8705 & 0.4055 & 0.6869 & 0.6444 \\ Depth & FS & XGB & 19 & 1.0000 & 0.5153 & 0.0006 & 3.9049 & 0.6836 \\ Width & MIS & XGB & 17 & 1.0000 & 0.4196 & 0.0006 & 0.2440 & 0.6444 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Best ML Model and Number of Features for Crack Dimension Prediction in Taguchi Method (TM). \begin{table}
\begin{tabular}{l l r r r r r r r r} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**DF**} & \multicolumn{6}{c}{**R2 Tr**} & \multicolumn{6}{c}{**R2 Tr**} \\ \cline{3-10}  & & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** \\ \hline Regression & 13 & 1.0433 & 0.0803 & 31.9400 & 0.0310 & 1.0445 & 0.0803 & 2780.6000 & 0.0000 \\ NoF & 1 & 0.0008 & 0.0008 & 0.3300 & 0.6250 & 0.0033 & 0.0033 & 113.4200 & 0.0090 \\ FSM & 1 & 0.0001 & 0.0001 & 0.0500 & 0.8480 & 0.0000 & 0.0000 & 0.4700 & 0.5640 \\ MLM & 3 & 0.0051 & 0.0017 & 0.6700 & 0.6450 & 0.0057 & 0.0019 & 65.8000 & 0.0150 \\ NoFNoFNoF & 1 & 0.0020 & 0.0020 & 0.8100 & 0.4640 & 0.0021 & 0.0021 & 72.5800 & 0.0130 \\ NoF*FSM & 1 & 0.0037 & 0.0037 & 1.4900 & 0.3470 & 0.0013 & 0.0013 & 44.0600 & 0.0220 \\ NoF*MLM & 3 & 0.0053 & 0.0018 & 0.7000 & 0.6320 & 0.0025 & 0.0008 & 28.7400 & 0.0340 \\ FSM*MLM & 3 & 0.0414 & 0.0138 & 5.4900 & 0.1580 & 0.0394 & 0.0131 & 454.0900 & 0.0020 \\ Error & 2 & 0.0050 & 0.0025 & & & 0.0001 & 0.0000 & & & \\ Total & 15 & 1.0484 & & & & 1.0446 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA for Crack Length Prediction.
## 3 Results
### Data Collection and Correction Results  
#### 3.2.1 Data Collection  
To minimize the influence of wind on pollutant dispersion and focus on the relationship between microenvironment characteristics and air quality, data collection was conducted during periods of relatively calm, low-wind conditions. This ensured that the variations in air quality indices observed in the study primarily reflected the effects of local geographical features and structures, rather than external meteorological factors.  
* Training Data Collection  
To construct the neural network correction model, data necessary for training were first collected. The UAV dynamically measured a series of data points in various flight attitudes, while simultaneously statistically measuring using sensors at the same location to obtain reference values. The difference (i.e., correction values) between these data sets served as the dependent variable for model training, while UAV flight attitudes, speed, and other parameters served as independent variables. By training the BP neural network, the aim was to reveal the relationships between these parameters and the correction values.  
* Testing Data Collection  
Testing data collection involved two methods: vertical collection and fill-in collection. Vertical collection consisted of the UAV ascending uniformly from ground level to 60 m, continuously measuring air quality. Fill-in collection involved the UAV randomly flying in three-dimensional space to ensure the even distribution of sampling points.  
All data were collected in the afternoon (16:00-18:00) from May to June 2024, primarily under stable weather conditions with a higher humidity, often cloudy or rainy.
###### Abstract  
Sparse Bayesian learning (SBL) is widely used for sound field reconstruction (SFR). Among various SBL approaches, pattern-coupled SBL has been demonstrated to achieve superior performance. Building on the pattern-coupled SBL framework, this study replaces matrix multiplication with tensor and matrix cross-correlation operations, significantly reducing the algorithm's spatial and temporal complexity. Furthermore, we compare the performance of different coupling structures within the pattern-coupled SBL method for reconstructing room impulse responses (RIRs) in the time domain. Specifically, we analyze nine coupling structures that incorporate both temporal and spatial coupling terms and validate their performance via experiments using two datasets. The results indicate that the coupling structure known as CPC-ST (Centered Pattern-Coupled with Spatio-Temporal Coupling) achieves the best performance, especially in the extrapolation of the sound field. For lightweight systems, where a slight performance trade-off is acceptable, the coupling structure known as CPC-S (Centered Pattern-Coupled with Spatial Coupling) is also recommended due to its balance between effectiveness and simplicity.  
sound field reconstruction; pattern-coupled hierarchical model; coupling structures; variational inference Article
## 3 Results
### Frequency Spectrum  
In addition to temporal variations, the power distribution of electrons in the frequency domain exhibits significant changes. From an energy perspective, small parameter combinations result in minimal energy generation by electrons. By observing Figure 10, when \(\mathbf{a_{0}}\) is relatively small, the influence of the waist radius on the energy can be negligible. As \(\mathbf{a_{0}}\) increases, variations in the beam waist radius demonstrate amplify the effects on maximum radiation intensity. Notably, at \(\mathbf{a_{0}}\) = 9, the beam waist radius enhances the maximum radiation intensity by two orders of magnitude, followed by a gradual energy decay toward zero after reaching the peak. Overall, both increased light intensity and an enlarged beam waist radius contribute to spectral broadening. Under constant light intensity, the controlled adjustment of the beam waist radius can expand the spectral width tenfold. However, when the beam waist radius exceeds a critical threshold, the spectral range tends to saturate without further expansion. Comparative analysis indicates distinct spectral characteristics: prominent supercontinuum features emerge when \(\mathbf{\omega_{0}}\) < 5, while enhanced monochromaticity dominates when \(\mathbf{\omega_{0}}\) > 5.  
Figure 9: The influence of light intensity and waist radius on the full width at half maximum.