esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Compared to the Tier 2a method, the Tier 2b method showed the same formula but, dissimilarly, it considered the wafer size of 200 mm or less or 300 mm size. As a result, the method used for estimating GHG emissions based on wafer size is the most noticeable change in the semiconductor and display sectors of the '19 IPCC Refinement guidelines. The Tier 2c method was used for the sub-sector calculation of electronics, and both emission factors (\(U_{i,\_p}\) and \(B_{k,\_i,\_p}\)) were provided as (\(p\)) for each type of process [15; 16]. The semiconductor process types were divided into six types: etching and wafer cleaning (EWC), remote plasma cleaning (RPC), in situ plasma cleaning (IPC), in situ thermal cleaning (ITC), N\({}_{2}\)O TFD, and N\({}_{2}\)O 'Other', etc., and display process types could be classified into four types: etching, remote plasma cleaning (RPC), in situ plasma cleaning (IPC), and N\({}_{2}\)O TFD [17]. In the case of the semiconductor process, the emission was obtained for each wafer size, but in the case of the display, the wafer size was separately ignored. When the default emission factors were not applicable, a (1-\(U_{i}\)) value of 0.8 was used, and the by-product factor value applied 0.15 for CF\({}_{4}\) and 0.05 for C\({}_{2}\)F\({}_{6}\)[18]. In the case of the Tier 3a methodology, which is the focus of this study, the same calculation formula as Tier 2c was applied, but all coefficients (U\({}_{i,\_p}\), D\({}_{k,\_p}\), and B\({}_{k,\_l,\_p}\)) required for calculation used values that were estimated by measuring each manufacturing process. Since the coefficient was calculated through direct measurement, it was also possible to estimate the emissions of the manufacturing process, equipped with a wafer size of 450 mm, and research on accurate measurement methods for the use rate of gas (\(U_{l}\)), destruction removal efficiency (DRE), and by-product emission factor (\(B_{by\_product,\ i}\)) was essential in order to accurately estimate GHG emissions. Furthermore, the Tier 3b method was added to Tier 3b to calculate emissions using the stack system, which measures the GHGs emitted by each process at the end-of-pipe (stack) process. Since this methodology also estimated GHG emissions by adopting the direct measurement method, it was also possible to measure emissions on the 450 mm wafer size and calculate GHG emissions by applying more variables [15]. In this paper, by offering accurate measurement methods for the destruction removal efficiency (DRE), use rate of gas (\(U_{l}\)), and by-product emission factor (\(B_{by\_product,\ i}\)), which are characteristic parameters for calculating GHG emissions of the Tier 3a method, we aimed to increase the accuracy of GHG emissions by improving emission factors that are unique to the semiconductor and display industries of the Republic of Korea.
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
## 3 Performance Analysis
### Outage Probability Floor Analysis  
In a CR system, the OP floor implies that even if the average SNR keeps rising, the OP cannot drop below a specific bound. In real-world networks, PNs are arranged in one of two ways: \((i)\) farthest from the SNs (\(P_{th}\gg\bar{P}_{S}\)) and \((ii)\) closest to the SNs (\(\bar{P}_{S}\gg P_{th}\)). Consequently, more information about the system architecture may be obtained from the performance floor analysis assessed for a user's OP.  
**Theorem 2**.: _The asymptotic expression for the OP floor of the m-th SU when \(P_{th}\gg\bar{P}_{S}\) and \(\bar{P}_{S}\gg P_{th}\), respectively, are given as_  
\[\mathcal{P}_{m}^{\infty}\approx\sum_{a=0}^{M-m}\frac{\Delta_{M}(-1)^{a}}{m+a} \bigg{[}\frac{1}{\Gamma(k_{m})}\gamma\bigg{(}k_{m},\frac{\theta_{m}^{*}}{\eta _{S}w_{m}}\bigg{)}\bigg{]}^{m+a} \tag{27}\]  
_and_  
\[\begin{split}\mathcal{P}_{m}^{\infty}&\approx \frac{K\pi^{2}}{\Gamma_{S}\mathcal{D}4I}\sum_{l=0}^{K-1}\sum_{a=0}^{M-m}\left( \begin{array}{c}K-1\\ l\end{array}\right)\frac{\Delta_{M}(-1)^{l+a}}{(m+a)}\\ &\times\sum_{c=1}^{l}\sqrt{1-\varphi_{c}^{2}}\sec^{2}\bigg{(}\frac{( \varphi_{c}+1)\pi}{4}\bigg{)}\bigg{[}\frac{1}{\Gamma(k_{m})}\gamma\bigg{(}k_{ m},\frac{\theta_{m}^{*}\bar{\omega}_{c}}{w_{m}\eta_{th}}\bigg{)}\bigg{]}^{m+a}e^{- \frac{l+1}{\Gamma_{S}\mathcal{D}_{c}}\bar{\omega}_{c}}\\ \end{split} \tag{28}\]  
_where \(\bar{\omega}_{c}=\tan\Big{(}\frac{(\varphi_{c}+1)\pi}{4}\Big{)}\)._  
**Proof of Theorem 2.** To calculate the asymptotic expression for the OP of \(m\)-th users. The term \(\mathcal{P}_{m}^{\infty}\) in (21) can be calculated as  
\[\mathcal{P}_{m}^{\infty}=\begin{cases}F_{\mathcal{G}_{m}^{2}}\left(\frac{ \theta_{m}^{*}}{\eta_{S}}\right)&,P_{th}\gg\bar{P}_{S}\\ \int\limits_{0}^{\infty}f_{Z_{S}D}(y)F_{\mathcal{G}_{m}^{2}}\left(\frac{ \theta_{m}^{*}y}{\eta_{th}}\right)dy&,P_{th}\ll\bar{P}_{S}\\ \end{cases} \tag{29}\]  
With the help (6), (22), and applying similarly the Gaussian-Chebyshev quadrature method, (28) can be obtained. The proof is completed. \(\square\)
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 3 Results
### Penetration Test Results  
At least two CPTu probes were realized at every tested pile, as close as possible to the pile foundation. After data analysis, a representative CPTu record was assigned to a particular pile. The record of cone penetration resistance \(qc\), shaft friction \(fs\), and pore pressure \(u_{2}\) is plotted in Figure 7. According to the evaluation of boreholes and pile manufacturing reports, a geological profile was added to the CPTu charts together with the actual vertical position of the corresponding pile.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of Pile**} & \multirow{2}{*}{
\begin{tabular}{c} **Bridge** \\ **Designation** \\ \end{tabular} } & \multirow{2}{*}{**CPTu Probe**} & \multicolumn{2}{c}{**Pile Diameter**} & \multirow{2}{*}{**Pile Length (m)**} & \multicolumn{2}{c}{**Bearing Capacity \(Q_{m}\)**} \\  & & & **(m)** & & **(kN)** \\ \hline
1 & SO—201 & CPT—1 & 1.0 & 8.6 & 2467 \\
2 & SO—201 & CPT—2 & 1.0 & 8.6 & 2132 \\
3 & SO—201 & CPT—3 & 1.0 & 8.6 & 2228 \\
4 & SO—201 & CPT—4 & 1.0 & 8.6 & 2439 \\
5 & SO—202 & CPT—5 & 1.0 & 8.8 & 2900 \\
6 & SO—202 & CPT—6 & 1.0 & 8.8 & 1935 \\
7 & SO—203 & CPT—7 & 1.0 & 6.2 & 1900 \\
8 & SO—203 & CPT—8 & 1.0 & 5.4 & 1807 \\
9 & SO—204 & CPT—9 & 1.0 & 6.2 & 1800 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric characteristics of tested piles and measured piles bearing capacity \(Q_{m}\).  
Figure 6: Loading curves for tested piles.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 4 Experimental Results
### Results  
#### 4.2.1 Image Quality Assessment  
To evaluate the image quality of the watermarked frame, we inserted 16 bits of data into the original video clips under various scale factors \(\alpha\). The watermarked frames were generated to yield an average peak signal-to-noise ratio (PSNR) of more than 36 dB. Here, the PSNR was defined using the mean squared error between the \(m\times n\) image \(I\) and watermarked image \(W\) as follows. \[PSNR=10\cdot log_{10}(\frac{255^{2}}{MSE}) \tag{6}\]  
where \(MSE=\frac{1}{min}\sum_{i}^{m}\sum_{j}^{n}[I(i,j)-W(i,j)]^{2}\). Figure 6 shows an example of the compressed original and watermarked frames. To compensate for the limitations of the PSNR metric, regarding the watermark invisibility evaluation, we also evaluated the imperceptibility based on the SSIM metric [30]. After the watermark embedding, the video clips generated were compressed using the MPEG-4 codec (part 10 H.264), with quality factors of between 56 and 59, as depicted in Table 1.  
The compressed video clips with quality factors of between 56 and 59 satisfied the bit rate of 0.01 better than the uncompressed video frame. In addition, using the same quality factor, the unwatermarked video clips were encoded. Both the watermarked video frames and the original video frames were decoded, and the PSNR was computed for each pair of luminance values as follows:  
\[Y=0.7152G+0.0722B+0.2126R. \tag{7}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**ITE/ARIB Test Number** & **Quality Factor** \\ \hline \hline ITE/ARIB sequence \#2 & 58 \\ ITE/ARIB sequence \#8 & 59 \\ ITE/ARIB sequence \#20 & 58 \\ ITE/ARIB sequence \#23 & 58 \\ ITE/ARIB sequence \#46 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quality factor to generate less than \(1/100\) of the original bit rate. Figure 5: Converters for DA-AD conversion. (**a**) Fosmon HDMI to component video converter box, (**b**) skyHD captureX HDMI, and (**c**) DA-AD conversion testing environment. Table 2 and Figure 7 present the perceptual quality assessment results for various watermark strengths \(\alpha\), i.e., the bit error rate (BER), PNSR, and SSIM. No errors occurred in our assessment at \(\alpha\geq 0.4\) according to the results. The resulting clip #2 exhibited the best detection performance with the highest perceptual score. However, the evaluation of video clip #23 showed the worst detection score because it contained a large number of motion objects throughout the entire playback. In addition, the PSNR and SSIM values of the uncompressed original and watermarked frames are shown in Table 3. These results are shown in Table 4.  
Figure 6: (**Left**) Compressed original frames based on H.264, and (**Right**) watermarked frames from evaluation Set-4 (\(\alpha=0.4\), SSIM 0.9945, PSNR 44.03 dB). metric scores demonstrate a visual degradation of the video sequences when applying MPEG compression.
## 2 Materials and Methods
### Receiver Operating Characteristic Curves  
To validate the quality of models built on different features and segment lengths, we plotted receiver operating characteristic (ROC) curves. It was carried out using samples of all subjects concatenated after both linear transformations had been applied to the data of each subject independently. The intersubject manner was chosen in order to reduce the number of resulting graphs. First, a logistic regression-based model with parameters described in _D. Classification_ was optimized using the very same approach. The model with the best hyperparameters found was then re-fitted on half of the sample, and the other half was used to obtain the values of the decision function to further construct the ROC curves using the corresponding Scikit-Learn function [34]. The best score of hyperparameter optimization during cross-validation was used as the accuracy of the intersubject classification by the model considered.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 5 Results
### Domain Knowledge Validation  
The HCM provides methods for computing relevant traffic parameters to estimate the capacity and the level of service for different road types [45]. Level of service is defined using a relative traffic flow speed on the observed road segments, labeled with letters from A to F, where A represents the best traffic conditions, with vehicle speeds larger than 80% of the free-flow speed, and F represents the most extreme congestion, where vehicle speeds are less than 30% of the free-flow speed. The 2000 STMs were labeled using the HCM data for the level of service. STMs were labeled as anomalous only if the transition contains a significant change in the level of service, i.e., from A to F, or from F to A. With this setup, the recurrent congestion was not detected as an anomaly. In other cases, STMs were labeled as normal. With this setting, only most extreme anomalies were labeled as abnormal. Firstly, 500 anomalous and 500 STMs without the anomaly were selected randomly from the labeled data as a training dataset. Then the results of our approach were compared to the HCM classification as the ground truth. We report the precision calculated as \(trupositive/(truepositive+falsepositive)\), recall as \(truepositive/(truepositive+falsenegative)\), and F-1 score in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Anomalous STMs** & **Normal STMs** & **Precision** & **Recall** & **F-1** \\ \hline
500 & 500 & 92.88\% & 87.55\% & 90.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Validation results of the proposed method by using the domain knowledge data.  
Figure 9: Positions of the anomalous cells on the map (**a**–**h**) represent most anomalous parts of the traffic network in the City of Zagreb.
## 3 DL Models for Ocean Studies
### Gan  
Generative Adversarial Networks (GANs) comprise two neural networks: a generator, which generates synthetic samples resembling real data, and a discriminator, which distinguishes between real and synthetic inputs. These two networks are trained simultaneously through an adversarial strategy, where the generator strives to create increasingly realistic data, and the discriminator aims to improve its ability to differentiate between real and fake samples. This dynamic competition prompts both networks to enhance their performance, generating highly realistic synthetic data. In ocean studies, GANs have been successfully employed to enhance training sample diversity by data augmentation, generate synthetic imagery to fill gaps caused by cloud cover or sensor malfunctions [87], and improve the spatial resolution of satellite data for refined observation [88,89].
# Archaeoacoustics around the World: A Literature Review (2016-2022)
## 4 Discussion
### Recent Advances
#### 4.1.2 Rock Art  
In rock art studies, researchers generally aimed to determine whether the locations chosen for the paintings corresponded to areas with long reverberation times [21; 54; 57]. They also investigated whether the inhabitants had any prior knowledge of the acoustic properties of the location [51; 52; 55]. For example, [PERSON] explored five caves in Spain and found that murals were painted in areas with moderate reverberation and low frequency resonances [21]. [PERSON] analyzed murals painted in shelter areas in France and Italy and used Ambisonics techniques to record sounds. They found that the shelters had a long reverberation time [53]. [PERSON] studied three rock cliffs in northern Finland, where large-scale murals with various shapes, such as people playing drums, were found. Those rock cliffs presented a high level of reverberant sound, as well. One of the cliffs even generated a phantom sound source, simulating that the sound came from the paintings [54]. [PERSON] investigated murals in Spain, Italy, and France and proposed that the Neolithic artists who painted them had some understanding of acoustics [51]. [PERSON] hypothesized that some ancient paintings served ritual purposes and were painted in caves with strong echoes, simulating that the paintings spoke. They tested this hypothesis by examining several caves, including Horseshoe Canyon, the Cave of Niaux, and the Cave of Cougnac [52]. [PERSON] analyzed the Lascaux cave, where large bull paintings were found. They found significant echoes in the areas where the paintings were made [55]. A method for measuring the acoustic properties of the Sierra de San Servan area in Extremadura (Spain) was proposed in [57]. The authors used transmission loss (TL) analysis to determine the audibility of distant sounds and concluded that prehistoric artists believed that sounds went beyond their paintings. In Mexico, [PERSON] showed that ancient artists selected the best sonic landscapes to paint their murals in Baja California Sur [56]. In [46], the authors acoustically analyzed three caves out of the 12 Kanheri Caves located in India and found that one of them had high levels of reverberant sound, around 5.145 s. In [58], the authors reported that murals in the Lower Chuya River were located in places where sound is amplified, and music and speech are clearly diffused.  
To carry out the acoustic characterization, some works, including [21; 46; 53; 58], followed the ISO 3382-1:2009 standard. Some authors such as [21; 54] used frequency sweeps, while others, such as [46; 53; 55; 56; 58], generated impulsive noise. Works including [46; 53; 56] used air balloons, and in [55], concussion idiophones were preferred. The most relevant findings of the selected literature are described in Table 3.
## 2 Materials and Methods
### Services and Performance Indicators
#### 2.3.2 Service 2: Reduction in Electricity Carbon-Footprint (CO\({}_{2}\))  
The CO\({}_{2}\) content of the electricity being consumed by the users of the SB is also a common optimization objective searched when performing EM in MGs. Its added value can be understood as the ability of the SB to provide electricity with less CO\({}_{2}\) content than the electricity coming from the utility grid. The seemingly obvious indicator, which is used to quantify this service, is the equivalent amount of grams of CO\({}_{2}\) per unit of energy consumed in the MG. This performance indicator can be computed in a similar way, as was presented at Equation (5) for the EC indicator. Its units are gCO\({}_{2}\)e/kWh and will be designated hereafter as CO\({}_{2}\). The equivalent mathematical expression to compute its value is presented in Equation (6).  
\[\text{CO}_{2}=\frac{1}{E_{load}^{T}}\cdot\sum_{h=1}^{H}\left(E_{grid}^{h}\cdot \text{CO}_{2 grid}^{h}+E_{butt}^{h}\cdot\text{CO}_{2 butt}^{h}+E_{pv}^{h}\cdot \text{CO}_{2 pv}^{h}\right) \tag{6}\]  
where \(E_{load}^{T}\) represents the total energy consumed by the load during the test period, \(E_{grid}^{h}\) is the energy bought from the utility grid, \(E_{butt}^{h}\) is the energy delivered by the battery (battery discharging), while CO\({}_{2}^{h}\) represents the CO\({}_{2}\) content, in gCO\({}_{2}\)e/kWh, of the energy coming from the corresponding DER \(X\). It can be either the nominal value (see Equation (A7) for battery and Equation (A8) for PV), or the corrected value (see Equation (A9) for battery and Equation (A10) for PV). The carbon footprint of the utility-grid energy (CO\({}_{2 grid}^{h}\)) is considered known beforehand [33].  
The calculations of the nominal and adjusted values of CO\({}_{2}\) (accounting for the battery cycling-life reduction and PV curtailment) of the energy coming from the battery and PV panels are presented in Appendix A.2.
## 5 Discussion
### Implementation Framework and System Integration  
Our research demonstrates that AI-driven fixture optimization achieves a 15% reduction in total fixtures while maintaining service quality. At the Singtel Serangoon TEPL building, this optimization resulted in SGD 280,000 construction cost savings and SGD 42,000 annual maintenance savings. The physical space optimization yielded an additional 45 m\({}^{2}\) of usable area.  
The parallel implementation methodology validates these results while addressing practical adoption barriers. During the 12-month validation period, the AI system ran alongside traditional planning methods, enabling direct performance comparison. The model maintained compliance with Singapore Building and Construction Authority standards while identifying optimization opportunities through data-driven analysis.  
Integration with existing building systems utilizes a standardized API framework compatible with common BIM platforms. The developed BIM plugin enables automated fixture planning updates based on predicted usage patterns. Testing at the Singtel building demonstrated successful integration with Autodesk Revit, allowing dynamic visualization of occupancy predictions and automated regulatory compliance reporting.  
The IoT sensor framework enhances prediction accuracy through real-time data collection. Environmental and occupancy sensors deployed throughout the test site achieved 99.7% data reliability using standard MQTT and REST protocols. This sensor integration improved prediction accuracy by 12% compared to the base model through enhanced contextual awareness and real-time pattern recognition.  
While these implementation results demonstrate significant potential, several practical considerations warrant attention. The requirement for continuous data collection and system monitoring represents a shift in facility management practices. Additionally, privacy considerations emerged as an important factor, though our experience shows that transparent communication about data collection and its benefits effectively addresses user concerns.
## 5 Results and Discussions
### Evaluation of the Grid Frequency and the Voltage Magnitude  
The same load variations are considered in this sub-section. Employing various values, for the virtual inertia and VMPE in the proposed controller, can impact on both steady stated and transient responses of the grid frequency and voltage magnitude under high renewable energy sources penetration.  
The grid frequency responses, along with the virtual inertia and VMPE changes, are illustrated in Figure 11. Figure 11a shows the best response of the proposed controller for the grid frequency. According to Figure 11b, both very high and low values of the virtual inertia will lead to the worst response for the grid frequency. The grid frequency in the case of the high VMPE is making effort to remain at desired value as depicted in Figure 11c, which is in contrast to the very high values of the VMPE that leads to unstable reaction for the grid frequency. The responses of grid voltage magnitude are shown in Figure 12. Based on Figure 12a, for the appropriate values of virtual inertia and VMPE, the proposed controller is able to force the grid voltage following its reference value, with a very good transient response. The grid voltage magnitude is collapsed for both very high and low values of virtual inertia. The same scenario happens for very high VMPE and inversely the high value of VMPE can somewhat provide stable response for the grid voltage magnitude as illustrated in Figure 12c.  
Figure 11: The grid frequency under (**a**) appropriate virtual inertia and VMPE, (**b**) very high and low virtual inertia, and (**c**) high and very high values of VMPE.