esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Soaked Unconfined Compressive Strength  
During the soaked UCS test, all unstabilized samples lost their integrity during immersion in water, which was consistent with observations reported in the literature [10; 14; 42]. This loss of integrity was attributed to the limited ability of both the clayey soil and the sludge to maintain the cohesion of the sample. Figure 4 shows sample M3C0 losing its cohesion during water immersion.  
M1C2.5 and M2C5 also lost their integrity when submerged in water, whereas M2C2.5, M2C5, M3C2.5 and M3C5 maintained their structural integrity. This difference was due to the fact that in mixtures containing M1, which consisted solely of clayey soil, CEM was unable to establish strong bonds between the soil particles, leading to a loss of integrity when exposed to water. In contrast, the inclusion of gravel- and sand-sized by-products in M2 and M3 enabled the CEM to bind these coarser particles together, creating a more durable matrix that could withstand water exposure and maintain its integrity.  
Figure 4: M3C0 sample losing its cohesion during water immersion.  
Table 6 shows the results obtained for the M2C2.5, M2C5, M3C2.5 and M3C5 combinations in the soaked UCS test. The results for combinations M1C0, M1C2.5, M1C5, M2C0 and M3C0 are not presented, as these combinations lost their integrity and no data could be recorded.  
M2C2.5 and M2C5 achieved 0.9 MPa and 1.8 MPa in soaked UCS values at 28 days. The soaked UCS/dry UCS ratios obtained for these two combinations were 0.21 and 0.20, demonstrating a significant reduction in UCS after water immersion. However, the values obtained were better than those reported by [PERSON] et al. [10], who observed a complete loss of integrity in specimens stabilized with 2% of CEM. They also noted an improvement in the ratio when increasing the CEM content to 4% and 6%, achieving ratios of 0.24 and 0.47 respectively. Nevertheless, the ratios obtained for M2C2.5 and M2C5 remained below the 0.33 threshold suggested by [PERSON] [43], meaning these combinations would not meet the desired requirements. However, an improvement in M2 compared to M1 was observed, attributed to the use of by-products that allowed CEM to develop cementitious gels that bound these aggregates, forming stronger specimens.  
Combinations M3C2.5 and M3C5 achieved ratios of 0.26 and 0.40, respectively. While combination M3C2.5 did not meet [PERSON]'s requirements, combination M3C5 did. Moreover, both combinations achieved higher ratios than M2 with the same CEM dosages. This demonstrated that a mix composed entirely of by-products performs better against water exposure than a clayey soil or a clayey soil mix improved with by-products.
## 3 Theoretical Background
### Overview of Transformer Architecture  
The development of today's popular large language models like ChatGPT [15; 17] was preceded by the Transformer architecture, which has become state-of-the-art for NLP tasks. [PERSON] et al. introduced this revolutionary architecture in their paper \"Attention Is All You Need\" [5] while working at Google Research and Google Brain [4]. The Transformer architecture replaced RNN and LSTM networks, which faced challenges in understanding context, and became the foundation for advanced NLP models such as BERT and GPT. The key innovation lies in its attention mechanism, especially the \"self-attention\" method, which allows the model to efficiently connect information from different parts of the text [18].  
The Transformer model utilizes an encoder-decoder structure, as shown in Figure 1. The encoder transforms the input sequence into continuous vector representations using a series of identical layers containing a \"multi-head\" and \"self-attention\" mechanism and a positional feed-forward neural network. The decoder generates the output using these representations and an additional \"attention\" mechanism that refers to the encoder's outputs [5; 18]. The central part of this architecture is the \"self-attention\" mechanism, which allows each word in the sequence to dynamically attend to other words regardless of their relative position, enabling the model to understand contextual relationships between words in a way that was impossible with previous models. Another key feature is \"multi-head attention\". The Transformer uses multiple parallel heads to analyze the input from different perspectives, allowing the model to gain deeper insights into the data [18]. Positional encoding is used to provide the model with information about the position of each word within the sequence, compensating for the Transformer's lack of inherent word order information.  
Different models in this research study utilize specific parts of the Transformer architecture to solve particular NLP tasks. BERT [6] uses only the encoder part to analyze input sequences in both directions, gaining rich contextual insights. GPT models [19] use only the decoder part to generate text by predicting the next word based on previous words. Models like BART [20] combine encoder and decoder to achieve bidirectional understanding and generative capabilities, enabling tasks such as text correction and summarization. The Transformer architecture has become the foundation for modern NLP models due to its ability to efficiently process language, understand context, and adapt to various tasks, making it revolutionary in machine learning. The following sections will delve deeper into the most famous representatives of this architecture.
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 4 Experiments
### Comparison Experiment  
To evaluate the performance of the proposed algorithm (ICFTU), the traditional user-based collaborative filtering algorithm (UCF), the collaborative filtering algorithm with user characteristics (CFUC), the collaborative filtering algorithm based on clustering (K-MCF), and the algorithm based on optimizing similarity calculation (ICFOS) [34] are selected for comparison. These four algorithms used for comparison are classical and often used in the recommender systems. The above five algorithms are trained in the training set of two data sets, respectively, and the ratings prediction is carried out in the test set to compare the MAE and RMSE value of different algorithms. The nearest neighbor \(K\) is set as 35 for all of the algorithms used in this experiment, the comparison of recommendation accuracy is show in Table 2 and Figure 5.  
It can be seen from the figure that the ICFTU proposed in this paper has a better recommendation effect on both datasets. Among them, the traditional user-based collaborative filtering UCF algorithm has the largest error and the lowest prediction accuracy. The CFUC method (a collaborative filtering algorithm with user characteristics) combines the user's characteristic information, which makes up for some defects of the traditional algorithms and improves the accuracy of recommendation. The K-MCF algorithm based on clustering and ICFOS algorithm based on optimizing similarity calculation both improve the accuracy of recommendation to a certain extent. The ICFTU algorithm combines the TF-IDF method and user characteristics, reduces the impact of popular items on user similarity, improves the calculation of user characteristics similarity, improves the accuracy of recommendation, and still has some advantages in large-scale data sets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**MAE**} & \multicolumn{5}{c}{**RMSE**} \\ \cline{2-11}  & **UCF** & **CFUC** & **ICFOS** & **K-MCF** & **ICFTU** & **UCF** & **CFUC** & **ICFOS** & **K-MCF** & **ICFTU** \\ \hline ML-100k & 0.976 & 0.839 & 0.806 & 0.792 & 0.771 & 1.156 & 1.011 & 1.05 & 0.982 & 0.941 \\ ML-1M & 0.992 & 0.852 & 0.836 & 0.822 & 0.801 & 1.219 & 1.101 & 1.102 & 1.042 & 0.976 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the comparison experiments.  
Figure 5: The results of the comparison experiment: (**a**) of MAE; (**b**) of RMSE.
## 4 Conclusions  
In this study, we explored the use of CoFeB thin films in high-temperature applications, such as in the automotive industry, by investigating the variations in their magnetic properties at high temperatures. We examined the temperature dependence of the magnetization properties (\(M_{\rm s}\), \(\alpha\), \(H_{\rm k}\), \(K_{\rm u}\), and \(\Delta\)) of the Ta/CoFeB/MgO/Ta and W/CoFeB/MgO/W structures at temperatures exceeding room temperature. Both static and dynamic properties were analyzed to compare the temperature-dependent characteristics of the samples with Ta buffer and capping layers and samples with W buffer and capping layers. Our results revealed that the values of \(M_{\rm s}\), \(H_{\rm k}\), \(K_{\rm u}\), and \(\Delta\) decreased with an increase in temperature for both samples, whereas the values of \(\alpha\) increased. We evaluated the thermal stability of the two samples by combining the results of VSM and FMR spectroscopy obtained at high temperatures. The \(\alpha\) value of the sample with Ta buffer and capping layers increased by 69.4% at 443 K compared with that at 293 K, whereas that of the sample with W buffer and capping layers increased by 13%. The \(\Delta\) value of the sample with Ta buffer and capping layers decreased by 74.2% at 550 K compared with that at 293 K, whereas that of the sample with W buffer and capping layers decreased by 69.4%. These results underscored that the sample with W buffer and capping layers exhibited less variation in \(\alpha\) and \(\Delta\) values with temperature than the sample with Ta buffer and capping layers. The findings of this study provide significant insights for the development of thermally robust spintronic devices that can operate at high temperatures.  
Conceptualization, B.K. and Y.H.H.; methodology, B.K., Y.J.K., J.L. and O.L.; formal analysis, B.K. and J.S.L.; investigation, B.K., S.L. and S.H.S.; resources, S.-Y.P. and B.-K.J.; data curation, Y.H.H.; writing--original draft preparation, B.K.; writing--review and editing, B.K. and Y.H.H.; supervision, S.-Y.P. and B.-K.J.; funding acquisition, B.-K.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
This work was supported by the Brain Korea 21 Plus Project in 2023, Samsung Electronics Co., Ltd. (IO201214-08159-01), KIST Institutional Program and Institute for information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2022-0-01026), Commercialization Promotion Agency for R&D Outcomes(COMPA) funded by the Ministry of Science and ICT (MSIT) (1711198544, Development of analytical instrumentation for electromagnetics/optics/thermal characteristics under extreme environment), and Korea Institute for Advancement of Technology (KIAT) grant funded by the South Korean government (MOTIE) (P0020967, Advanced Training Program for Smart Sensor Engineers).  
The authors declare no conflicts of interest.
## 3 Results
### Albert Rift Regional Tectonostratigraphy
#### 3.1.2 Tectonostrangtragraphy of the Central Lake Albert Basin  
The Kaiso-Tonya area, part of the Central Lake Albert Basin, exhibits a tectonoststratigraphic evolution influenced by extensional tectonics, sedimentary infill, and fluctuating lake levels. The basin's development is characterized by three rifting phases, each marked by specific tectonost stratigraphic units (Figure 4). This tectonostrigraphy is primarily based on the integration of lithost stratigraphic interpretation (Table 2) and tectonic events.  
Late Miocene to Early Pliocene Rift Initiation Phase  
This phase involved the regional northward propagation of extensional faulting, leading to the development of the Kaiso-Tonya trough. The Nkondo succession records this phase, beginning with conglomerates indicative of high-energy deposition near fault scarps. It transitions to sandstones, siltstones, and deep lacustrine shales, reflecting the shift from fluvial and deltaic environments to a deep lacustrine setting, with a maximum flooding surface marking the transgressive-highstand phase.  
Early to Mid-Pliocene Syn-Rift Base Level Fall  
Intense extensional faulting during this phase led to base-level fall and erosion, with notable environmental stress, including a molluscan extinction event. The Warwire succession captures the geological record, and is characterized by alternating siltstones, sandstones, claystones, and coquina beds. These reflect shallow lacustrine, deltaic, and fluvial environments. Depositional shifts indicate active faulting, base-level changes, and sediment supply during fluctuating lake levels.  
Late Pliocene to Recent Rifting Phase  
The final phase, marked by regional uplift of the Rwenzori massif in the south and rift shoulders, together with rift-floor subsidence, is represented by the Kaiso package, dominated by claystones and siltstones. These sediments reflect deposition in fluvial and floodplain environments near the shrinking lake shoreline, along with reduced accommodation space. The phase marks the transition from lacustrine systems to a primarily fluvial landscape.
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 2 Preliminaries
### Voice Activity Detection  
Voice activity detection (VAD) is a technique for detecting the presence of speech signal in speech data [22]. It has been widely used to enhance the speech contents such as speech classification [23], speaker recognition [24], and speech enhancement [25; 26]. Figure 4 shows three processing steps for VAD: (1) noise reduction, (2) segmentation, and (3) elimination [27]. As depicted in Figure 4, the length of the original signal becomes shorter after applying VAD by eliminating the non-VAD segments. For the efficient denoising, we aim to exclude only the segments that definitely do not contain speech. To this purpose, we use segmentation and elimination steps of the overall VAD process, which we call the speech segment detection, for the preprocessing of denoising process.  
Figure 2: The concept of the proposed preprocessing strategy for denoising.  
We classify existing methods for the speech segment detection in VAD into three categories: (1) filtering methods, (2) unsupervised methods, and (3) deep learning-based methods. Filtering methods detect the speech segment based on statistical features of the signal such as LPC parameters, energy levels, and ZCR [28,29]. For the unsupervised methods, [PERSON] et al. have proposed fuzzy C-means based clustering [30]. [PERSON] et al. have presented multiple observation likelihood ratio test (MO-LRT) [31], and [PERSON] et al. have devised the method that improves MO-LRT [32]. [PERSON] et al. have proposed an unsupervised method that can calculate spectral flatness efficiently for the robust  
Figure 4: The overall process of the voice activity detection.  
Figure 3: The architecture of Wavenet-based denoising [13].  
VAD [33]. For deep learning-based methods, [PERSON] et al. have designed a fully connected deep neural network to classify speech and non-speech segments [22]. [PERSON] et al. have formalized VAD problem as a binary classification and have classified speech and non-speech segments using DNN-based model [34].  
Although it has been known that the deep learning-based methods generally outperform the other methods, they require significant computational costs. In this paper, because the speech segment detection will be used as pre-processing to improve the efficiency of denoising, we need a simple and fast method for detecting speech segments. To this purpose, we investigate filtering and unsupervised methods that have fast inference time due to the simple mechanism [35].
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 7 Selected Properties of HCFs
### Fiber Dimensions  
The cladding of solid-core optical fiber performs two functions: prevents the escape of light from the core and provides the tensile strength and rigidity of the fiber. To perform the first function in single-mode fiber operating at 1550 nm, cladding must have a minimum thickness (measured from the border with core) of 24-35 \(\upmu\)m, depending on the presence of a \"trench\" structure around the core; otherwise, an increase in fiber attenuation resulting from light leakage, and rising exponentially with wavelength, occurs [80].  
In hollow-core fibers, \"cladding\" means a photonic structure performing function (a). Its mechanical strength is marginal due to the small cross-section of glass parts. Mechanical strength and hermeticity are provided by a separate layer of solid glass, called \"jacket\" (Figure 17). In NANF or DNANF, the core diameter is the diameter of the largest circle fitting in the space between the tubes (Figure 17b).  
The diameters of cladding in HCFs designed for transmission in the 1550 nm band are between 55 \(\upmu\)m and 100 \(\upmu\)m. If the HCF is to have tensile strength equal to solid 125 \(\upmu\)m telecom fiber made of the same material, the cross-section of its jacket shall equal the total cross-section of telecom fiber: 12,272 \(\upmu\)m\({}^{2}\).  
Estimated cladding diameters of OFS HC-PBGF fibers presented in Section 4.2 and Section 4.3 and Lumenisity NANF and DNANF fibers presented in Section 5.3 are 82 \(\upmu\)m and 90 \(\upmu\)m, respectively. For tensile strength equivalent to that of a 125 \(\upmu\)m solid-core fiber, jacket diameters of 150 \(\upmu\)m and 154 \(\upmu\)m are required, but the diameters estimated from photographs and drawings are larger: 220 \(\upmu\)m and 200 \(\upmu\)m. The authors suspect that a thick jacket of preform reduces the deformations of the cladding, especially its outer parts, caused by surface tension in softened glass during drawing.
# Optimization Method of Square Hole Measurement Based on Generalized Point Photogrammetry
## 5 Experiments  
### Simulation Experiment  
In order to verify the reliability of the square hole measurement method based on constraint conditions, the simulation data are used for theoretical verification, and the external orientation elements are set, as seen in Table 1. The simulation experiment completed several groups of experiments with Gaussian error, camera focal length and square hole side length as variables. The coordinates extracted by image processing are considered as the true values of coordinates, and the coordinates calculated by traditional photogrammetric methods are considered as the initial values of the coordinates. The schematic diagram is shown in Figure 4.  
Figure 4: Projection of square holes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & \(\varphi\) & \(\omega\) & \(\kappa\) & \(X_{S}\) & \(Y_{S}\) & \(Z_{S}\) \\ \hline First image & 0 & 0 & 1813 & 0 & 0 & 0.2 \\ Second image & 200 & 200 & 1818 & 0 & 0 & 0.1 \\ \hline \hline \end{tabular}  
There are eight groups in the tests, and the parameters set are shown in Table 2.  
\end{table}
Table 1: Camera extrinsic parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **(\(X_{1},Y_{1},Z_{1}\))** & **(\(X_{2},Y_{2},Z_{2}\))** & **(\(X_{3},Y_{3},Z_{3}\))** & **(\(X_{4},Y_{4},Z_{4}\))** \\ \hline
1 & (0, 100, 100) & (100, 100, 100) & (100, 0, 100) & (0, 0, 100) \\
2 & (0, 150, 100) & (150, 150, 100) & (150, 0, 100) & (0, 0, 100) \\
3 & (0, 100, 100) & (100, 100, 100) & (100, 0, 100) & (0, 0, 100) \\
4 & (0, 150, 100) & (150, 150, 100) & (150, 0, 100) & (0, 0, 100) \\
5 & (0, 100, 100) & (100, 100, 100) & (100, 0, 100) & (0, 0, 100) \\
6 & (0, 150, 100) & (150, 150, 100) & (150, 0, 100) & (0, 0, 100) \\
7 & (0, 100, 100) & (100, 100, 100) & (100, 0, 100) & (0, 0, 100) \\
8 & (0, 150, 100) & (150, 150, 100) & (150, 0, 100) & (0, 0, 100) \\ \hline \hline \end{tabular}  
* Corresponding to the Gaussian error in the above table, the initial coordinates of spatial points of eight experiments are shown as Table 4.  
\end{table}
Table 2: Experimental parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **(\(X_{1},Y_{1},Z_{1}\))** & **(\(X_{2},Y_{2},Z_{2}\))** & **(\(X_{3},Y_{3},Z_{3}\))** & **(\(X_{4},Y_{4},Z_{4}\))** \\ \hline
1 & (0, 100, 100) & (100, 100, 100) & (100, 0, 100) & (0, 0, 100) \\
2 & (0, 150, 100) & (150, 150, 100) & (150, 0, 100) & (0, 0, 100) \\
3 & (0, 100, 100)It can be seen from Table that when there are different degrees of errors between the initial value and the true value of coordinates, the eight groups of experiments can quickly converge to an accurate value with fewer iterative steps. Whether the focal length is changed or the side length of the square hole is changed, the convergence results are not affected.
## 6 Case Data and Simulation Results
### Optimal Scheduling Result Analysis
#### 6.2.2 The Summer Cooling Scenario Simulation Analysis
The BIPV microgrid costs $152.46 a day when only ACVES is considered, which is 5.67% less than the operating cost of the microgrid without VES. The charging/discharging of ACVES and indoor/outdoor temperature variation are shown in Figure 9.  
AS can be seen in Figure 9, at 10:00 and 16:00 with higher electricity price, the ACVES is charged and the indoor temperature drops to 22 Celsius, next moment, the ACVES is discharged and the indoor temperature rises to 26 Celsius. The ACVES is charged when the electricity price is low, and discharged when the electricity price is high, playing a similar role to the battery. In order to illustrate that ACVES  
Figure 8: The optimal scheduling results when only air conditioner-based VES (ACVES) is considered.  
Figure 7: The EWHVES and battery charging/discharging power and their state of charge.  
can supersede battery, reduce the certain capacity of battery to simulation, the result shows that when reducing 61 kWh capacity of the battery, the system has the same cost as the scene that the VES is not considered.  
Compared with the EWH, the AC only need to adjust the indoor temperature to achieve VES, additional equipment is not required. However, ACVES is affected by weather, indoor temperature zone, building parameters and other factors, which cannot be quantified its capacity specifically. Moreover, the ACVES can only play its role in winter and summer, cannot provide stable VES utility. The WMVES is less affected by the external environment, and the energy storage utility is stable, but in the early stage of system construction, the tank equipment needs to be invested.  
In order to maximize the effectiveness of VES, the two types of VES are integrated into the BIPV microgrid for optimal scheduling. The results are shown in Figure 10.  
The system costs $144.69 to operating for a day, which is 12.34% less than the operating cost of the microgrid without VES. We reduced the capacity of the battery and set the system to have the same daily operating cost as the system without VES. The simulation results show that VES has the effect of 115 kWh of battery capacity. The operating cost of the system under four scenarios is shown in Table 4.  
Table 4 summarizes the cost of the system under four scenarios. It is clear that VES can reduce the operating cost of the system. When two kinds of VES are integrated into the system, the cost saved is not the linear superposition of the effect when they participate in the system separately. Figure 10: The optimal scheduling results when EWHVES and ACVES are considered. Figure 9: The ACVES charging/discharging power and indoor/outdoor temperature.
## 3 Results
### Phenological Characterisations
The results based on the average value of all pixels are presented in Table 2.  
The difference in SOS and EOS generated from both the satellites were less than ten days for both growing seasons and were not statistically significant following one-way ANOVA (\(p\) = 0.967). This indicates that the phenological metrics generated from MODIS data are comparable to the those extracted from Sentinel-2 data.