esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### System Parameters
The value of \(T_{s}\) was selected to be approximately equal to seven multiples of the time constant created by the thermal capacitance and resistance of the heat sink, ensuring the device reaches thermal steady state. The value for \(c_{c}\) was chosen through a survey of similarly rated power electronic converters on the market. The values for \(T_{ref}\), \(L_{c}\), and \(E_{a}\) were derived from examples from the AEC-Q101 Rev. E documentation [43] as these values would have to be defined from a variety of experimental lifetime tests for the specific power electronic device in each system. Mean water velocity and water temperature data from a river near the central California coast with tidal influence were collected from the United States Geological Survey (USGS) online database [44]. The raw flow velocity data from the USGS database were normalized and scaled such that they had a mean flow velocity of 1.5 m/s, a middle ground in terms of the regions of control. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{5}{*}{SARSA Parameters} & Initial Step-Size \(\alpha_{0}\) & 0.05 \\  & Discount Factor \(\gamma\) & 0.995 \\  & Initial Exploration Rate \(\varepsilon\) & 0.7 \\  & Exploration Rate Decay \(\varepsilon_{d}\) & 0.6 \\ \hline \multirow{5}{*}{RBF Parameters} & RBF Centers per Dimension \(n_{c}\) & 5 \\  & RBF Width \(\sigma\) & 0.125 \\  & Base Feedback Coefficient \(k_{b}\) & 1.445 Nm/s \\  & Base Flow Velocity \(u_{b}\) & 3.5 m/s \\  & Base Flow Temperature \(T_{b}\) & 26 \({}^{\circ}\)C \\  & Base Energy Cost \(c_{b}\) & USD 0.55/kWh \\ \hline \multirow{5}{*}{Reward Parameters} & Sample time \(T_{s}\) & 350 s \\  & Estimated Converter Cost \(c_{c}\) & USD 5000 \\ \cline{1-1}  & Reference Temperature \(T_{ref}\) & 353.15 K \\ \cline{1-1}  & Reference Converter Lifetime \(L_{c}\) & 12,000 h \\ \cline{1-1}  & Activation Energy \(E_{a}\) & 0.9 eV \\ \cline{1-1}  & Boltzmann’s Constant \(k_{B}\) & \(8.617\cdot 10^{-5}\) eV/K \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reinforcement learning parameters. Lastly, as the reward function requires energy price data, historical data on the locational marginal price (LMP) from the day-ahead market for Pacific Gas and Electric were collected from [45]. Data could only be found from March 2019 to October 2024, which limited the available training data for the agent. The LMP data were also normalized and scaled so the LMP profiles for each year reached a maximum of USD 0.52/kWh, the absolute maximum price over the defined period from 2019 to 2024. This was conduct3 ed to ensure ample exploration of the LMP state space, considering this region's lack of LMP data. These profiles of LMP data are then used for the values of \(c_{e}\) in (23).
## 4 Experimental Design
### ARMA Configuration  
A time series is considered stationary if its mean and variance are constant over time. Since stationarity is an underlying assumption for the ARMA model, the coefficients outputted from ERGM must be adjusted to ensure stationarity. This is especially advisable when a small sample size is used, in which any significant amount of noise or outliers could cause the time series to appear to violate stationarity. Therefore, to ensure stationarity on the ERGM outputs, a technique of rolling z-score subtraction was used, where the average of the previous 7-h z-score was subtracted from the present term to a produce more stationary time series data. In order to confirm the stationarity of the transformed time series, our ARMA model contains an optional stationarity report method, which runs an Augmented Dickey Fuller unit root test on the transformed data. Although, the details of unit roots and the Augmented Dickey Fuller unit root test are beyond the scope of this paper, in simple terms, unit roots are trends in stochastic processes that indicate the process is unpredictable. Therefore, if such trends exist in data then the data should not be used for time series analysis, as it may cause spurious regressions and erratic behavior in t-scores and z-scores. Unit root tests, such as the Augmented Dickey Fuller, are one such tool that is used to detect such unit roots.  
Once stationarity for our data was confirmed, we used the pmdarima package in our ARMA model to perform hyperparameter tuning and to determine the optimal order \(p\) and \(q\) for ARMA. As a side note, pmdarima is usually used to fit ARMA(\(p\),\(q\)).  
The train-test splitting is an important factor to consider with ARMA. The test set size should be kept small, as ARMA's predictive ability decreases in accuracy as timegoes on from the start of the prediction period. For our experiments, we decided to train on the initial 66 h and to predict on the terminal 6 h. Figure 2 shows a sample ARMA output trained on ERGM coefficients for 66 h of data and a prediction on a test set of 6 h. Predictions come in the form of windows which are based off the standard deviation of the ERGM coefficients. The size of the windows can be varied in our proposed solution, allowing for a higher threshold (e.g., one standard deviation) for high traffic days, and a lower threshold (e.g., 1/4 standard deviation) for low traffic days. For our experiments, we chose window sizes of 1/4, 1/2, and 1 standard deviations. If an observed data point falls outside its prediction window, it is labeled as anomalous.
## 3 Results
### Change of Direction Test
#### 3.2.1 Right Side  
There are significant differences in the time factor (F (1,32) = 9.184, \(p\) = 0.005, \(\eta_{\rm p}^{2}\) = 0.223, 1 - \(\beta\) = 0.836). That is, there are significant changes between pre- and post-test regardless of group. Furthermore, there are differences in the time x group interaction (F (1,32) = 13.216, \(p\) = 0.001, \(\eta_{\rm p}^{2}\) = 0.292, 1 - \(\beta\) = 0.94). There are differences in the pre between both groups (\(p<0.001\)) and in the post (\(p\) = 0.038). In addition, there are differences between pre and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **F-Value** & **Df** & \(p\)**-Value** & \(\mathbf{n_{p}^{2}}\) & \(\mathbf{1-\beta}\) \\ \hline Time & 3.788 & 2,31 & 0.034 & 0.196 & 0.65 \\ Time \(\times\) Group & 0.179 & 2,31 & 0.837 & 0.011 & 0.07 \\ Muscle & 4.151 & 2,31 & 0.025 & 0.21 & 0.69 \\ Muscle \(\times\) Group & 1.178 & 2,31 & 0.321 & 0.071 & 0.24 \\ Time \(\times\) muscle & 4.054 & 2,31 & 0.027 & 0.207 & 0.68 \\ Time \(\times\) muscle \(\times\) group & 1.546 & 2,31 & 0.229 & 0.091 & 0.30 \\ \hline \hline \end{tabular} Note: Df = Degree of freedom, \(\eta_{\rm p}^{2}\) = Eta partial Square, \(1-\beta\) = power statistic.  
\end{table}
Table 4: Multivariate analysis values of left leg.  
Figure 3: Changes in COD 90\({}^{\circ}\) values to the left side and the right side. Note: Pre, first assessment moment; Post, second assessment moment; CG, control group; EG, experimental group.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{\(p\)**-Value**} & \multicolumn{4}{c}{\(\eta_{\rm p}^{2}\)} \\
**Leg** & **Var** & **Ti** & **MixG** & **M** & **MixG** & **MixM** & **MixMxG** & **Ti** & **MixG** & **MixG** & **MixM** & **MixMxG** \\ \hline Right & Tc (ms) & \(<\)0.001 & 0.005 & \(<\)0.001 & 0.089 & \(<\)0.001 & 0.007 & 0.471 & 0.296 & 0.952 & 0.12 & 0.476 & 0.278 \\  & Dm (mm) & 0.003 & 0.363 & \(<\)0.001 & 0.41 & 0.001 & 0.413 & 0.33 & 0.036 & 0.866 & 0.03 & 0.365 & 0.029 \\ Left & Tc (ms) & 0.002 & 0.171 & 0.066 & 0.525 & 0.627 & 0.461 & 0.332 & 0.076 & 0.134 & 0.017 & 0.01 & 0.023 \\  & Dm (mm) & 0.071 & 0.864 & 0.024 & 0.665 & 0.135 & 0.543 & 0.13 & 0.001 & 0.194 & 0.008 & 0.09 & 0.016 \\ \hline \hline \end{tabular} Note: Ti, time; TaxG, time-group interaction; M, muscle; MxG, muscle-group interaction; TaxMx, time-muscle-group interaction; \(\eta_{\rm p}^{2}\) = partial eta squared; Tc, time contraction; Dm, maximal radial muscle-belly displacement.  
\end{table}
Table 5: Factors and interaction analysis in both legs.  
post depending on the group. The control group has significant differences between pre and post (\(p<0.001\)), while the experimental group does not (\(p=0.652\)).
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 1 Introduction
[14] presented a novel allocation method for the electricity produced in an ISCC based on the exergy loss approach by implementing internal exergy balances. They showed that this method is reliable and as good as the conventional Separate Production Reference method. [PERSON] et al. [15] constructed a model using ASPEN HYSYS simulation software and MATLAB code to exergetically analyze an ISCC with a high-temperature energy storage system. It was found that the largest exergy losses were at the solar collector, the energy storage system, and the combustor. [PERSON] and [PERSON] [16] carried out energy and exergy analyses for an ISCC in Yazd, Iran using the design data of the power plant. The results showed that the energy and exergy efficiencies of this power plant are higher than those for a simple CCPP without a solar contribution and those for steam power plants with PTC technology. [PERSON] et al. [17] simulated the performance of the first ISCC in Algeria, under HassiR'mel climate conditions. The results showed that the output power and the thermal efficiency increased at daytime than at night by 17% and 16.5%, respectively. [PERSON] et al. [18] analyzed the performance variation of the solar field and overall ISCC using advanced exergy analysis methods and hourly analysis within a typical day. The results showed that increasing the solar energy input to the ISCC system decreases the exergy destruction of the Brayton cycle and increases the exergy destruction of the Rankine cycle. In the literature, one can find several papers regarding the investigation of ISCC power plants applied to different atmospheric conditions. The originality of this work is the parametric study of the energy and exergy analyses regarding an existing ISCC power plant in Egypt, under Kureimat climate conditions, as a whole and for the main components in the ISCC power plants. This work aims to identify the sites of major exergy destruction, clarify the reasons for exergy destruction in these sites, and attempt to clarify how to decrease the exergy destruction in this type of power plant. Besidesthis, given the challenges for the electricity market with the continuing expansion of intermittent renewables, in this work, we investigate the operational flexibility of ISCC power plants. In this paper, we start with a description of the ISCC power plant under investigation, and we provide the method and the equation for the calculation of energy and exergy parameters. Then, we show the influence of ambient temperature and solar heat input on the plant performance. Finally, on the basis of these results, we investigate the sources of exergy destruction in the solar field and the combustion chamber to identify the possibility to enhance the performance of these components.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Phase 1: Establishing Quantum-Resistant Communications  
The foundation of our security model rests on CRYSTALS-Kyber, chosen not merely for its NIST standardization but for its practical performance characteristics. Unlike many post-quantum schemes that impose significant computational or bandwidth penalties, Kyber achieves quantum resistance with minimal overhead, a crucial factor for interactive applications.  
The key establishment protocol, detailed in Algorithms 1 and 2, deliberately separates key generation from session establishment. This separation enables clients to pre-generate key pairs during idle periods, reducing connection latency. Additionally, the use of the HKDF (HMAC-based Key Derivation Function) with client identifiers ensures that the derived symmetric keys are cryptographically independent, even if the same shared secret is generated across different sessions.  
```
1:Input: Client identifier \(\mathcal{C}_{ID}\)
2:Output: Shared AES key \(K_{AES}\), IV base \(IV_{base}\)
3:\((pk_{c},sk_{c})\)\(\leftarrow\) Kyber.KeyGen()
4:Transmit \(pk_{c}\) to server
5:Receive \(pk_{s}\) from server
6:\((ss_{c},ct_{KEM})\)\(\leftarrow\) Kyber.Encapsulate(\(pk_{s}\))
7:Transmit \(ct_{KEM}\) to server
8:\((K_{AES},IV_{base})\)\(\leftarrow\) HKDF(\(ss_{c},\mathcal{C}_{ID}\))
9:return\((K_{AES},IV_{base})\)
```  
**Algorithm 1** Client-Side Quantum-Resistant Key Establishment
## 4 Results Analysis
### Comparison between Measured and Simulated Ultrasonic Wave Velocities  
Figure 5a,b show the differences between simulated and measured wave velocities. For the simulated wave velocity, two types of dynamic modulus were utilized as inputs, namely the NDT-measured modulus in Figure 5a and the laboratory-measured modulus (at 10 Hz and 21.1 \({}^{\circ}\)C) in Figure 5b. All other input parameters in the models remained constant. Figure 5a shows no difference (less than 10%) based on engineering judgement between the simulated and experimental ultrasonic wave velocities. In contrast, Figure 5b shows an obvious difference (more than 70%) based on engineering judgment between the two measurements. A statistical t-test confirmed the above results. This difference is due to the fact that the models used in simulation process were different from real asphalt concrete samples used in NDT testing. In real AC samples, fine aggregates, coarse aggregates, and asphalt binder interact with each other and this heterogeneity of real AC samples caused large energy attenuation and absorption during the propagation of the ultrasonic wave in the NDT tests. In contrast, the simulated model was assumed to be homogenous, which induces less energy attenuation and absorption, resulting in a more stable wave propagation. In addition, ultrasonic wave velocity increases with the rise of the dynamic modulus. As the dynamic moduli of AC samples measured in the laboratory were much lower than those calculated by ultrasonic test results, the simulated results of wave velocity were definitely smaller in this case.
# Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations
## 5 Conclusions  
This paper proposes an adaptive amplitude panning algorithm designed to accurately reproduce the virtual sound source at the desired azimuth angle in irregular loudspeaker configurations and then implements it within a stereo system. The method leverages measured RIRs and BRIRs to effectively incorporate environmental information. Inverse filters are applied to the loudspeakers to improve their similarity. Subsequently, ITD and IACC are employed to optimize gain and time delay parameters. The method was validated through both objective and subjective experiments. The objective evaluation confirmed the accurate reproduction of the virtual sound source with the target ITD, achieving an error margin within 0.02 ms. Correspondingly, the subjective results demonstrated that the proposed method can reproduce virtual sound sources with a maximum angular deviation of 5\({}^{\circ}\).  
Conceptualization, S.Z. and Y.Z.; resources, Z.L.; writing--original draft preparation, S.Z. and Y.Z.; writing--review and editing, Z.L.; supervision, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Grant No.12274221.  
Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We would like to thank [PERSON] for her assistance with the experimental design.  
Figure 9: Perceived azimuth angles of virtual sources reproduced by three methods of (**a**) pink noise low-pass filtered at 1.5 kHz, (**b**) pink noise low-pass filtered at 9 kHz, (**c**) male and female conversation, and (**d**) song clip.
## 4 Experimental Results and Discussion
### HSI Datasets  
Indian Pines Dataset: This hyperspectral image is a scene of northwest Indiana collected by an airborne imaging spectrometer sensor in 1992. It consists of \(145\times 145\) pixels and \(220\) spectral bands that cover the wavelength from \(400\) to \(2450\) nm. After removing the noise and water absorption bands, \(200\) spectral bands remained for use in the experiments. This image contains total \(16\) land cover types. The dataset in false color and its corresponding ground truth map are shown in Figure 4.  
University of Pavia (PaviaU) Dataset: This hyperspectral image covering the area of Pavia University in northern Italy was collected by ROSIS sensor in 2002. The spatial size of this image is \(640\times 340\) pixels and the number of spectral band is \(115\). Due to the atmospheric affection, \(12\) bands were discarded and the remaining \(103\) spectral bands were used for experiment. The false color and ground truth map for this scene are shown in Figure 5.
# Calculation of 1/f Fluctuation from Sound Signal and Comfort Evaluation
## 2 Calculation Method for Fluctuation Coefficient of the Sound Signal
### Fluctuation Coefficient of the Frequency  
The instantaneous frequency of the sound signal was calculated using the zero-cross method for the time history of the sound signal data. The zero-cross method calculates the frequency from the number of times the amplitude value of the sound signal intersects the zero point at a particular time. In this study, the time interval between the zero-cross instances of the sound signal was 25 ms to detect frequencies of more than 20 Hz, which is the absolute threshold of human hearing. Figure 2 shows the result of cutting out the sound signal shown in Figure 1a from 21.5 to 25 ms. The x plots are zero-cross points in the figure. The average frequency in each interval was obtained using the following equation:  
\[f_{zc}=\frac{1}{T_{zc}}\frac{n_{zc}}{2}, \tag{5}\]  
where \(f_{zc}\) is the average frequency [Hz], \(T_{zc}\) is the sampling period, and \(n_{zc}\) is the number of zero-crosses at a particular time. Figure 3a shows the time history of the average frequency of sound signal every 25 ms. The spectrum was calculated using the discrete Fourier transform, as defined in Equations (1)-(4). The time history of the average frequency in each interval was used to analyze the fluctuation as the frequency changes value. The fluctuation coefficient \(\lambda\) was defined by calculating the spectral slope from 0.05 Hz to 0.5 Hz. In this study, we defined the fluctuation coefficient of the frequency as \(\lambda_{t}\), which was calculated using the above method.  
Figure 2: Result of cutting out the sound signal from 21.5 to 25 ms.  
Figure 3b shows the result of the spectral slope obtained by the equations. The frequency fluctuation coefficient \(\lambda_{f}\) was \(-2.148\).
# Image Watermarking Scheme Using LSB and Image Gradient
## 3 Methodology
### Watermarking Embedding  
Figure 3 shows the watermark embedding process, consisting of seven steps. Moreover, Figure 4 represents the flow diagram for extraction of watermark.  
Figure 3: Watermark insertion flow diagram.  
**Step 1**. Divide the host image into 16 \(\times\) 16 nonoverlapping blocks. The nonoverlapping blocks prevent data loss.  
**Step 2**. Calculate the gradient of each block, i.e., gradient magnitude and direction.  
**Step 3**. Choose the central pixel of each block, and separate them into LSB and MSB.  
**Step 4**. Watermark signals are embedded in the LSB according to the following cases:  
For magnitude M = \(\sqrt{g_{y}^{2}+g_{x}{}^{2}}\),  
For direction \(\lambda\) = tan\({}^{-1}\Big{(}\frac{g_{x}}{g_{x}}\Big{)}\)  
**Case 1:** M \(\geq\) (max (magnitude)/2) && \(\lambda\) > 0; in this case, one-bit watermark is implanted into the LSB.  
**Case 2:** M < (max (magnitude)/2) && \(\lambda\) < 0; in this case, two watermark bits are implanted into the LSB.  
**Step 4**. Select the watermark image and scramble it using the chaotic substitution box.  
**Step 5**. Split the scrambled watermark into LSB and MSB. XOR the LSB of the watermark with the one- or two-bit LSB of the host image.  
**Step 6**. Watermarked image can be constructed with the combination of LSB and MSB.  
**Step 7**. To extract the watermark image, conduct all steps in reverse.
## 2 Materialsand Methods
### Enzymatic Activity Assays
#### 2.6.3 Identification of Microorganisms Using NGS  
The genomic DNA of the community samples was extracted directly from compost and purified using the PureLink Microbiome DNA Purification Kit, according to the instructions of the manufacturer. NGS sequencing of the DNA from sample B was performed by Macrogen (Republic of Korea). The 16S rRNA gene targeting the V3-V4 region was amplified from 16S rRNA primers specific to bacteria:  
(Forward 5\({}^{\prime}\)-TCGTCGGCAGCGTCAGATGTGATAAGAGACAGCCTACGGGNGCCWGCAG and Reverse:5\({}^{\prime}\)-GTCTCGTGGCCTCGGATGTGATAAGAGACAGGACTACHVGGGATCTAATCC). For the target amplicon of fungi, the internal transcribed spacer ITS2 region was amplified using the following primers: forward primer ITS3 (5\({}^{\prime}\)-GCATCGATGAAAGCAGC-3\({}^{\prime}\)) and reverse primer ITS4 (5\({}^{\prime}\)-TCTCCGTTATTGATATGC-3\({}^{\prime}\)).  
Bioinformatics analysis and annotation of the data were carried out using Mothur to analyze the 16S rRNA gene fragments for bacteria and DADA2 to analyze the ITS region for fungi. Mothur is an open source bioinformatics tool for performing the analysis of microbial DNA from raw sequencing data generated on Illumina or other sequencing platforms [24]. DADA2 is an open source pipeline that uses different algorithms for sequence clustering, which is considered to be more accurate and faster than Operational Taxonomic Unit (OTU )clustering [25].