esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Prompt gamma imaging is an important technique to locate the Bragg's peak position and energy. The readout circuits used for prompt gamma detection and processing are in close proximity to the radiation source. Secondary neutrons generated during proton treatment can also negatively affect the performance of the electronic devices present in the treatment room that renders their reliability important. GaN HEMT is more rad-hard compared to the silicon counterpart, and GaN-based operational amplifier is designed in this work.  
Simulation results show that the designed OPA has open loop gain of 70 dB and unity gain frequency of 34 MHz. The slew rate of the OPA is 20 V/\(\upmu\)s and common mode rejection ratio is 84.2 dB. The proposed OPA is configured for different applications such as transimpedance amplifier, integrator, and the adder which are needed in the prompt gamma readout system. Simulation results show successful operation for these applications. When these different applications are put together, a complete GaN-based prompt gamma readout circuit is implemented, and the result shows successful processing of the prompt gamma signal where its energy and position can be accurately provided for subsequent digital conversion and information extraction.  
Conceptualization, C.T. and V.K.P.; circuit design and simulation, V.K.P.; Writing--original draft preparation, V.K.P.; project administration, C.T.; writing--review and editing, C.T. and V.S.; All authors have read and agreed to the published version of the manuscript.  
This work was funded by Chang Gung University, Grant number CIRPD2F0024.  
All authors have no conflict of interest to the organizations mentioned in the paper.
### Dietary Interventions as a Key Strategy for Managing IR  
Given the limitations of pharmacotherapy in reversing IR, dietary intervention has emerged as a cornerstone strategy for prevention and management. Extensive evidence from epidemiological and clinical studies demonstrates the critical role of dietary patterns in modulating IR and metabolic syndrome (MetS).  
In a Chinese cohort, participants in higher quartiles of the modern dietary pattern had significantly higher odds of MetS, mediated by IR [6]. Similarly, adherence to a Western dietary pattern was associated with increased risks of MetS (RR: 2.32; 95% CI: 1.34, 4.00), elevated IR (RR: 1.69; 95% CI: 1.07, 2.65), and reduced insulin sensitivity (RR: 0.57; 95% CI: 0.39, 0.84), while a prudent dietary pattern was associated with more favorable outcomes [7]. In light of these findings, plant-based dietary patterns have garnered growing attention for their preventive and therapeutic potential in IR. Diets rich in whole grains, legumes, fruits, vegetables, nuts, and other minimally processed plant-derived foods have been associated with improved metabolic outcomes and a reduced risk of type 2 diabetes mellitus (T2 DM) [8].  
Among children and adolescents, a higher Healthy Eating Index (HEI) 2015 score was inversely associated with IR risk, particularly in girls [9]. Specific dietary interventions, such as high-protein diets, have also shown greater efficacy than Mediterranean diets in improving IR and glycemic variability among morbidly obese women [10]. In addition, nutrient-specific strategies such as walnut supplementation have demonstrated modest benefits on Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and body weight, although other metabolic markers remained largely unchanged [11].
## 1 Introduction
The fact that this approach examines color across an image's complete surface rather than one point is an advantage, as are the method's low cost and suitability for use in universities [19]. Spectrophotometers have frequently been used as a reference device in numerous recent studies about color measurement [20; 21]. Depending on the measuring geometry and illuminant used, color measurements taken with spectrophotometers may show variations. As a result, when using such instruments to measure color, the resulting color values are highly dependent on the method used [22]. Instead of focusing on a single location, this approach's advantages include color analysis for all sections of a specimen [23]. Another study assessing SR Nexco composite's color stability reported that turmeric solution causes more discoloration, followed by Cola-Cola and coffee solution [24]. Addressing color stability concerns remains a priority in dental research, and continued efforts are necessary to enhance our understanding of the interplay between composite materials, patient habits, and measurement techniques. Furthermore, conducting comprehensive studies with larger specimen sizes and a variety of measurement methods will be essential to further validate color stability assessments and contribute to the advancement of dental shade matching. The objectives of this study were (1) to assess color stability following staining solutions of microhybrid and nanohybrid composite resins; (2) to evaluate which solution causes more composite staining after immersion of the composite in the discoloration media; and (3) to compare color measurement results between the IOS 3 Shape Trios, Easyshade spectrophotometer, and Vita classical shade guide.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods
### Ensemble Patch Transformation (EPT)
#### 2.1.2 Ensemble Process  
The \"ensemble process\" is an \"ensemble\" obtained by shifting the time point \(t\) of the patch to represent the temporal transformation of the data by enhancing the temporal resolution of the data. Once the patching process is introduced, ensemble patch transformations can be defined by moving patches by a point in time \(t\). The ensemble patch transformation is defined as follows.  
For any fixed period \(\tau\), the \(\ell-\)th shifted patch at time point \(t\) is defined as \(P_{t+\ell}^{\tau}(X_{t})\), \(\ell\in[-\tau/2,\tau/2]\).  
Thus, a collection of all possible shifted patches at time point \(t\) is defined as an ensemble patch:  
\[EP_{t}^{\tau}(X_{t}):=\left\{P_{t+\ell}^{\tau}(X_{t}):\ell\in[-\tau/2,\tau/2]\right\} \tag{5}\]  
After that, we can obtain the high-frequency mode \(HF_{t}^{\tau}(X_{t})\) and the low-frequency mode \(EM_{t}^{\tau}(X_{t})\):  
\[HF_{t}^{\tau}(X_{t})=X_{t}-EM_{t}^{\tau}(X_{t}) \tag{6}\]  
\[EM_{t}^{\tau}(X_{t})=average\big{(}M_{t+\ell}^{\tau}(X_{t})\big{)}over\ell^{ \prime}s \tag{7}\]  
In the EPT framework, the period \(\tau\) is chosen very flexibly, so that different temporal features in the signal can be extracted using the EPT method. In load forecasting, the load sequence has a certain daily period, monthly period, etc. Since the load dataset used in this paper is relatively dense with 96 sampling points a day sampled every 15 min, the period \(\tau\) is fixed to a daily length.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 4 Delay Analysis of All Back-Off AIFS Channel Access Scheme
\right)_{up}^{k}\cdot(1-i_{up})^{N_{up}-k} \tag{13}\]  
and \(I_{up}^{col}=1-I_{up}(0)-I_{up}(1)\). Since a backoff counter decreases only when both channels are in the idle state in the _all backoff AIFS_ scheme, the interference probability by a certain station transmitting a 20-MHz frame, \(i_{up}\), can be written as  
\[i_{up}=p_{sslite}(AIFS+aSlotTime)\cdot\tau_{up}, \tag{14}\]  
where \(p_{sslite}(AIFS+aSlotTime)\) is the channel idle probability of the secondary channel for _AIFS_ + _aSlotTime_, and is approximated as  
\[p_{sslite}(AIFS+aSlotTime)\approx(\frac{d_{s}-aSlotTime}{d_{s}})^{\frac{(AIFS+aS SlTime}{aSSlTime}\backslash N_{s}}. \tag{15}\]  
Similarly to Equation (11), the mean access delay of frames carried in the primary 10-MHz channel, _ds_, can be converted to  
\[d_{s}=AIFS+E\text{ [length of a slot time in secondary 10 MHz]}/\tau_{s}, \tag{16}\]  
where \(E\) [length of a slot time in secondary 10 MHz] is calculated as  
\[ \begin{array}{c}E[\text{length of a slot time in secondary 10 MHz]}=P_{s}(0)\cdot I_{us}(0)\cdot aSlotTime\\ +P_{s}(1)\cdot I_{us}(0)\cdot(T_{s}+AIFS+aSlotTime)\\ +P_{s}(0)\cdot I_{us}(1)\cdot(T_{w}+AIFS+aSlotTime)\\ +P_{s}^{col}\cdot I_{us}(0)\cdot(T_{s}+EIFS+aSlotTime)\\ +P_{s}(0)\cdot I_{us}^{col}\cdot(T_{w}+EIFS+aSlotTime)\\ +\{(1-P_{s}(0))\cdot(1-I_{us}(0))\}\cdot(\max(T_{s},T_{w})+EIFS+aSlotTime) \end{array}  \tag{17}\]  
where \(I_{us}(k)\) is the interference probability to the secondary channel by the 20-MHz frame sent by \(k\) stations. From the interference probability to secondary channel by 20-MHz frame transmission, \(i_{us}\), \(I_{us}(k)\) can be derived as  
\[I_{us}(k)=\left( \begin{array}{c}N_{w}\\ k\end{array} \right)_{us}^{k}\cdot(1-i_{us})^{N_{w}-k} \tag{18}\]  
and \(I_{us}^{col}=1-I_{us}(0)-I_{us}(1)\), where \(i_{us}\) is derived as  
\[i_{us}=p_{pslite}(AIFS+aSlotTime)\cdot\tau_{up}, \tag{19}\]  
where \(p_{pslite}(AIFS+aSlotTime)\) is the channel idle probability of the primary channel for _AIFS_ + _aSlotTime_, and is approximated as  
\[p_{pslite}(AIFS+aSlotTime)\approx(\frac{d_{p}-aSlotTime}{d_{p}})^{\frac{(AIFS+aS SlTime}{aSSlTime}\backslash N_{p}}. \tag{20}\]  
In order to show that the analysis is accurate, a brief simulation is performed under full-buffered traffic and simulation result is compared with analysis values. As a main consideration point of the proposed method is the fairness with 10-MHz transmissions on the secondary channel, the analysis result of the mean access delay of 10-MHz transmissions in the secondary channel is compared with the simulation result. As shown in Figure 8, the analysis can be verified as accurate by comparing it with the simulation result in terms of the mean access delay of 10-MHz transmissions in the secondary channel. However, the main assumption in the analysis is full-buffered environment, where each STA always has data to transmit. Since the full buffer assumption causes too heavy load in each channel, the simulation is performed with reduced number of STAs.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 4 Threat Model
### Speaker Recognition (SR) Systems  
To investigate the potential threat of PF attacks on machines, we selected three state-of-the-art SR systems: an open-source tool, a cloud service, and a mobile app.  
**Resemblyzer:** Resemblyzer is an open-source tool that provides three major tasks, namely, speech detection, speech segmentation, and embedding extraction [63]. It is a side project of [64] that can extract a detailed representation of a voice by using a deep learning model. It creates a summary vector of 256 values that capture the unique characteristics of the voice when given an audio file of speech. This tool analyses and compares the similarities between potential fake speech and real speech to determine whether it is real. Resemblyzer speech detection requires a minimum of 30 s of recording speech for training.  
**AWS Cloud:** The Amazon Cloud's voice ID service [65] is designed to authenticate customers during phone interactions. To enrol in the system, an approximate 30 s sample of the caller's speech is required to create a voice print. During subsequent calls, the caller's voice is compared to the registered template, and the voice ID system takes around 10 s to authenticate and confirm a match.  
**Amazon Alexa:** Alexa is a virtual assistant that can perform various tasks and provide information in response to user voice commands using natural language processing and voice recognition. It is mainly available on Echo devices and as an app on mobile devices. One of Alexa's features is voice ID, which allows users to create a voice profile by reading six sentences aloud. This feature adds different lanes for specific users without the need for account switching. Figure 2 illustrates various attacks performed in this study.
## 4 Adaptive OFDM-IM Method
### Fuzzy Logic Optimization
#### 4.4.1 Calculation of Fuzzy Factors
* Calculation of the BER Factor \[BER=\frac{m_{e}}{m_{t}}=\frac{\sum\limits_{g=1}^{C}m_{e,g}}{m_{t}},\] (26) where \(m_{e}\) represents the number of bits erroneously received in the OFDM-IM index modulation system during transmission. The total number of bits transmitted is \(m_{t}\), and the number of bits erroneously received in each subblock is \(m_{e,g}\). Therefore, the system's BER can be calculated using the formula provided above. According to this formula, the smaller the BER factor, the better, as a lower BER signifies higher transmission accuracy, indicating that the system has better energy efficiency. In the IoV, an appropriate BER is crucial. An excessive BER can lead to packet loss and retransmissions, reducing the transmission speed, increasing the latency, and potentially causing system instability, which in turn raises the energy consumption. Conversely, an overly low BER may result in the waste of system resources through excessive protection and unnecessary data verification, reducing the system's performance and increasing the costs. Therefore, IoV system design and optimization must carefully consider the impact of the BER to achieve a balance between the BER and communication efficiency, thereby enhancing the overall user experience.
## 3 Results and Discussion
### Microstructure  
Figure 12 shows the SEM test results of the 28-day sample. In Figure 12a, common hydration products such as needle-shaped AFt, lamellar CH, and granular C-S-H can be observed. These hydration products fill the pores inside the cement, promoting the development of strength. Comparing Figure 12a,b, it is evident that the OPC group is denser than the QZ group. This is because in the late hydration stage, the hydration degree of QZ is low, and using QZ to replace cement will reduce the cement clinker, resulting in a decrease in hydration products, which makes the QZ group have more pores. This explains why the late-stage strength and UPV values of the QZ group are lower than those of the control group.  
Figure 12c shows the group with FA content of 20% by weight of cement, and Figure 12e shows the original appearance of FA. The spherical shape of FA acts as a ball bearing in cement, which can increase the fluidity of mortar. It can also be observed that its surface is smooth, which may result in the hydration products not being easily attached. Figure 12d is a partially enlarged view of Figure 12c. As the curing age increases to 28 days, the secondary hydration reaction of FA proceeds, and hydration products precipitate on the surface of FA particles. This increases the amount of hydration products and roughens the FA surface, improving the interface between FA and cement matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **C100** & **20 FA** & **20 QZ** & **10 QZ10 FA** \\ \hline CH (\%) & 16.62 & 15.08 & 15.22 & 15.16 \\ Wt (\%) & 18.38 & 17.68 & 16.24 & 17.15 \\ \hline \end{tabular}
\end{table}
Table 6: CH content and bound water content.
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 2 Polarization Detection System of Space Targets  
In this paper, the polarization imaging system based on the focal plane detector is used to carry out the experiment; the structure is shown in Figure 1. The [PERSON] telescope (Meade, 203/BC, Meade Instruments, Watsonville, CA, USA) is responsible for collecting the reflected light from targets, a polarization camera (FLIR, BFS-U3-51S5P-C, FLIR Integrated Imaging Solutions, Inc., Wilsonville, OR, USA) is used to acquire the polarization image of targets, and the polarization image is transmitted to the image processing unit to extract the target features.  
The polarization camera contains an imaging unit with a Micro-Polarizer Array (MPA), which is composed of superpixels; each superpixel contains a grating in the four polarization directions of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\). Stokes vector \(S\) is a four-element vector applied to characterize the polarization state of light. According to the definition, the solution method of \(S\) is shown in the following formula.  
\[\left\{\begin{array}{l}S_{0}=I_{0}+I_{90}\\ S_{1}=I_{0}-I_{90}\\ S_{2}=I_{45}-I_{135}\\ S_{3}=I_{RCP}-I_{LCP}\end{array}\right. \tag{1}\]  
\(I_{0}\), \(I_{45}\), \(I_{90}\), and \(I_{135}\) represent the light intensity of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) in four different polarization directions; \(I_{RCP}\) and \(I_{LCP}\) represent right-handed polarized light and left-handed polarized light, respectively.  
The Degree of Polarization (DoP) [13] is used to express the portion of fully polarized light intensity out of its total intensity. It can be calculated using the following equation:  
\[DoP=\frac{\sqrt{S_{1}^{2}+S_{2}^{2}}}{S_{0}} \tag{2}\]  
Figure 1: The diagram of the polarization detection system of space targets.  
The angle of polarization (AoP) [14] is used to indicate the angle between the vibration direction of polarized light and the reference direction. AoP can be obtained as follows.  
\[AoP=\frac{1}{2}\text{arctan}(\frac{S_{2}}{S_{1}}) \tag{3}\]
## 7 The Experimental Results and Discussion
### The Reconfigurability-Reliability Dependency Based on the Improved Fault Coverage
During this stage, emergency repairs and replacements are conducted to offset the device's degradation levels, which, if unsuccessful, may result in the functional failure point (F), as determined by the G3 polynomial function, highlighting the system's breakdown. At this point, the solar tracker's reliability reaches a critical value (37.67%), showing that the system may be unable to continue its normal routine functions. Reactive maintenance is often considered the least desirable maintenance strategy because it can be costly, inefficient, and disruptive to the solar tracker's energy harvesting operations. Therefore, this work advances a new approach to improving the predictive maintenance domain by integrating fault diagnosis systems, as well as software solutions for active health monitoring of solar tracking equipment. By combining the previously formulated fault coverage-aware metrics with the proposed maintenance-oriented metric system, we aim to develop an efficient dependability assessment tool for evaluating the reliability, availability, and maintainability of modern solar trackers.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
###### Abstract  
The Environmental Trace Gases Monitoring Instrument (EMI) series are second-generation Chinese spectrometers on board the GaoFen-5 (GF-5) and DaQi-1 (DQ-1) satellites. In this study, a comparative analysis of EMI series data was conducted to determine the daily trend of ozone concentration changes owing to different transit times and to improve the overall quality and reliability of EMI series datasets. The daily EMI total ozone column (TOC) obtained using the Differential Optical Absorption Spectroscopy (DOAS) method were compared to vertical column density (VCD) gathered by the TROPOspheric Monitoring Instrument (TROPOMI). The results from October to November 2023 indicated a fine correlation (R = 0.98) between the daily EMI series data and a fine correlation (R \(\geq\) 0.95) and spatial distribution closely resembling that of the TROPOMI TOCs. Furthermore, the EMI series data fusion results were highly correlated with TROPOMI TOCs (R = 0.99). Since the EMI series instruments had two different overpass times and the volume of available data at same pixel was increased by approximately three-fold, the temporal and spatial resolution was improved a lot. The results indicated that, compared to a single sensor, the EMI series DOAS TOCs generated more accurate and stable global TOC results and also enabled looking at the changes in the intraday TOCs. These outcomes highlight the potential of the EMI instruments for reliably monitoring the ozone variations in polar regions.  
EMI series; total ozone columns; DOAS; ozone hole; multi-satellite inter-comparison; weighted fusion correction
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## IVv6 Routing Protocol for Low Power and Lossy Networks (RPL)
### DODAG Construction
In addition, the RPL protocol also supports traffic from one node to another node in the DODAG network, which is referred to as point-to-point (P2P) communication. For more detail, refer to [5]. Figure 2 depicts the details of constructing upward and downward routing. The function of one or more DODAGs that provide a specific objective is known as RPL Instance. A single network can use multiple RPL Instances concurrently to meet different application criteria, and each RPL Instance can provide different and potentially antagonistic criteria. Within a network, a unique identifier, called Instance_ID, identifies each RPL Instance. To distinguish DODAGs in an RPL Instance, each DODAG is recognized by a unique identifier called DODAG_ID. Each node can only join a particular DODAG within an RPL Instance, but it can be associated with the several RPL Instances simultaneously. Figure 3 shows the DAG structure that enables two RPL instances. Figure 2: Route building operation; (**a**) DIOs multicast initiated by destination oriented directed acyclic graph (DODAG) root (LBR) and rank calculation. New node intend to join the network; (**b**) propagation of DAOs to build the downward routes; (**c**) complete DODAG with both upward and downward routes.
## 2 Methodology
### Mahalanobis-Taguchi System
The SNR\({}_{\rm LB}\) gain value of the feature variable \(x_{k}\) is calculated using the following equation:  
\[Gain=\overline{\eta_{k}^{+}}-\overline{\eta_{k}^{-}} \tag{6}\]  
where \(\overline{\eta_{k}^{+}}\) indicates the mean value of the SNR\({}_{\rm LB}\) of the feature variable that the orthogonal experiment uses, and \(\overline{\eta_{k}^{-}}\) indicates that the orthogonal experiment does not use this. When the gain value of the SNR\({}_{\rm LB}\) is positive, then the feature variable is retained; otherwise, the feature variable is eliminated. Step 4: Optimize the MS  
The selected feature variables are used to reconstruct the MS. According to the optimized MS, _MD_s of normal and abnormal samples are calculated. When the MS is optimized, the differences in the _MD_s of the normal and abnormal samples are greater. Step 5: Calculate threshold value and predict classification  
In the classic MTS, the Japanese quality management expert [PERSON] used a quadratic loss function to obtain the threshold value. In its actual application, this method requires the subjective evaluation of experts from relevant fields, resulting in this method's limitations and operational difficulties. Thus, this study introduces the probabilistic thresholding model (PTM) to solve the threshold value problem [27]. The method uses the probability distribution of _MD_, is objective, and is easily obtainable. The calculation method is as follows:  
\[\lambda=\overline{x}_{MD}+\sqrt{\frac{1}{1+\delta-\theta}}*s_{MD} \tag{7}\]  
where \(\delta\) is a small parameter typically set as 0.05; \(\theta\) is the ratio of the number of normal samples with an _MD_ less than the minimum value of the _MD_ of the abnormal samples in the normal samples; and \(s_{MD}\) is the standard deviation of the _MD_ of the normal samples. Then, the _MD_ of the samples to be classified regarding the optimized MS is calculated. When the _MD_ of the samples to be classified is smaller than or equal to the threshold value \(\lambda\), then the sample is identified as a normal sample; otherwise, the sample is abnormal.