esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Strength and Elastic Weakening Evidenced through Standard Mechanical Testing
#### 3.1.4 Yielding Envelopes
The best lines tangent to the Mohr circles were obtained by a least-squares method. Following [PERSON]'s theory, the slope of the straight line is the internal friction coefficient \(\mu=\tan(\varphi)\) with \(\varphi\) the friction angle, and the intercept is \(\mathrm{S_{o}}\), the cohesion of the tested material. For the dry Obourg Chalk we found \(\mu^{\mathrm{(dry)}}=0.59\) (\(\varphi^{\mathrm{(dry)}}=30.8^{\circ}\)) and \(\mathrm{S_{o}}^{\mathrm{(dry)}}=2.13\) MPa, and for the water-saturated Obourg Chalk we found \(\mu^{\mathrm{(sat)}}=0.45\) (\(\varphi^{\mathrm{(sat)}}=24.3^{\circ}\)) and \(\mathrm{S_{o}}^{\mathrm{(sat)}}=1.35\) MPa. These values provide wet-to-dry ratios WDR(\(\mu\)) = 0.76 for the internal friction coefficient and WDR(\(\mathrm{S_{o}}\)) = 0.63 for the cohesion. Knowing the friction coefficient and the cohesion, one can calculate the unconfined compressive strength [11] using the following relationship \(\mathrm{UCS}=2\mathrm{S_{o}}\left(\mu+\sqrt{1+\mu^{2}}\right)\): based on the above Mohr-Coulomb parameters, we get \(\mathrm{UCS}^{\mathrm{(dry)}}=7.5\) MPa and \(\mathrm{UCS}^{\mathrm{(sat)}}=4.2\) MPa, in reasonably good agreement with the measured values given in Section 3.1.1. (7.0 +/ - 1.3 MPa and 3.6 +/ - 0.6 MPa, respectively)
## 2 Materials and Methods
### Isolation and Analysis of Volatile Compounds  
Saffron dried stigmas were ground and transferred in a 15 mL screw top (22.7 \(\times\) 86 mm) vial with PTFE/silicone septa, adding the corresponding volume of distilled water (pH 7.0). Then, a portion of 7 \(\upmu\)L of cyclohexanone (Alfa Aesar, Kandal, Germany) was added as an internal standard for the semi-quantification of volatile compounds. Safranal was semiquantified by dividing the peak area of the compounds by the peak area of the internal standard, multiplying this ratio by the initial concentration of the internal standard. The samples were equilibrated for 30 min in a water bath (\(\pm\)1 \({}^{\circ}\)C accuracy). Isolation of the volatile fraction of saffron was performed based on the RCCD runs reported at Table 1 and the SPME solution proposed by the model.  
For SPME experiments, a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m fiber of 1 cm length (Supelco, Bellefonte, PA, USA) was used. Conditioning of fiber was performed at 270 \({}^{\circ}\)C. GC-MS analysis was achieved using a Trace Ultra Gas Chromatograph (Thermo Scientific Inc., Waltham, MA, USA), coupled to a Mass Spectrometer (DSQII, Thermo Scientific Inc., Waltham, MA, USA). A Restek Rtx-5 MS (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) column was used. Helium was the carrier gas with a flow rate of 1 mL-min\({}^{-1}\). Desorption was performed at an inlet temperature of 260 \({}^{\circ}\)C for 3 min, operated in the splitless mode, using a 0.8 mm injector liner (SGE International Pty Ltd., Ringwood, Australia). Chromatographic analysis was performed with the following conditions: initial temperature 40 \({}^{\circ}\)C, held for 6 min, heated to 120 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, heated to 160 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C min, heated to 250 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min, and held to 250 \({}^{\circ}\)C for 1 min. The MS source and quadrupole temperatures were 240 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The transfer line temperature was 290 \({}^{\circ}\)C. The acquisition mode was electron impact 70 eV with a mass range of \(m/z\) 35-650. GC-EI-MS data have been recorded in full scan mode. Peak identification was performed by utilizing the Wiley 275 mass spectra library, and the arithmetic index provided by [PERSON] [31]. Retention Index  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & **A: Sample Weight** & **B: Water Volume** & **C: Temperature** & **D: Exposure Time** & **Safranal** \\  & **(mg)** & **(mL)** & **(\({}^{\circ}\)C)** & **(min)** & **(\% Area)** \\ \hline
1 & 15 & 4 & 45 & 40 & 95.3 \\
2 & 35 & 4 & 45 & 20 & 95.5 \\
3 & 35  
(RI) values were calculated based on the observed retention times of n-alkanes (C\({}_{8}\)-C\({}_{20}\)) standard (Supelco, Bellefonte, PA, USA).
[25] reported that fluorite-type Zr\({}_{1-x}\)Pr\({}_{x}\)O\({}_{2-5}\) (\(x\) = 0.25, 0.50, 0.75) prepared by sol-gel processing retains a single-phase fluorite structure at temperatures \(\leq\) 900 \({}^{\circ}\)C in air. [PERSON] et al. [26] claimed the formation of the entire range of nanocrystalline Zr\({}_{1-x}\)Pr\({}_{x}\)O\({}_{2-8}\) solid solutions with a cubic fluorite structure by combustion synthesis. In both cases, temperature-programmed reduction studies confirmed that the oxygen storage capacity of solid solutions increases with increasing Pr content. Available data on ternary ZrO\({}_{2}\)-Y\({}_{2}\)O\({}_{3}\)-PrO\({}_{y}\) solid solutions and praseodymium substitutions into YSZ are limited. Hereafter, the conventional abbreviation \(n\)YSZ is used to refer to \(n\) mol.% yttria-stabilized zirconia, e.g., 8 YSZ corresponds to (ZrO\({}_{2}\))\({}_{0.92}\)(Y\({}_{2}\)O\({}_{3}\))\({}_{0.08}\). The formation of a solid solution with a cubic fluorite-type structure at 1600 \({}^{\circ}\)C was confirmed for (ZrO\({}_{2}\))\({}_{0.82}\)(Y\({}_{2}\)O\({}_{3}\))\({}_{0.04}\)(Pr\({}_{2}\)O\({}_{3}\))\({}_{0.05}\)[27]. The solubility of praseodymia was reported to be up to at least \(x\) \(\sim\) 0.22 in (6 YSZ)\({}_{1-x}\)(PrO\({}_{y}\))\({}_{x}\) and to be limited to \(x\) \(\sim\) 0.20 for (8.7 YSZ)\({}_{1-x}\)(PrO\({}_{y}\))at 1650 \({}^{\circ}\)C [28; 29]. As for binary solid solutions, doping by praseodymium into YSZ was found to introduce \(p\)-type electronic conductivity [28]. The present work is focused on the assessment of thermal treatment conditions and praseodymia contents on the properties of ZrO\({}_{2}\)-Y\({}_{2}\)O\({}_{3}\)-PrO\({}_{y}\) mixed oxides for prospective applicability as redox-active inclusions or buffer layers at the electrolyte/oxygen electrode interface of solid electrolyte cells. The preparation method relied on the direct firing of simple powder mixtures of 5 mol.% yttria partially stabilized zirconia (SYSZ) and praseodymium oxide to assess the conditions for the facile processing of solid oxide cell components. As the excessive concentration of lower-valence dopants typically results in a decline in ionic conductivity in fluorite-type zirconia-based solutions due to point defect association [30], 5 YSZ was selected as a starting material with a somewhat lower yttria content compared to conventional 8 YSZ. The emphasis in this work was on the effect of PrO\({}_{y}\) content and direct firing conditions (temperature and time) on properties relevant for the application including phase composition, ionic and electronic transport properties, Pr oxidation state and associated redox behavior, and thermomechanical compatibility with 8 YSZ in terms of thermal expansion.
## 3 Research Method
The mentioned paper reviews some of the most common algorithms applied to EOL systems, thus answering Q1 and Q2, where the obtained answers suggest that some reviewed applications show that it is possible to identify these failures by exploiting data from quality control procedures, selecting the most critical characteristics of the products being manufactured, and identifying patterns related to failures or anomalies. Automatic data processing can be an asset to the test system since the processing of these data ensures that when anomalous samples are encountered, their impact will be reduced. Based on this information, and as a way to answer question Q1, it can be concluded that the mentioned techniques affect the EOL production/test lines through predictive maintenance, product quality assurance, demand forecasting, safety-issues forecasting, and increased resources. In a specific way, predictive maintenance allows the EOL system to avoid critical equipment downtime with a proactive monitoring of the production line. Furthermore, product quality control tools ensure that production-related problems can be identified early in the production process. Finally, predicting demand for a specific product on EOL systems reduces costs and increases production profits. Answering question Q2, the most common applications of the mentioned technologies are anomaly detection, isolation and correction, proactive maintenance, resource scalability, and reduced production time. When applied to the manufacturing environment, these advantages allow the EOL system to be more productive, reliable, and with a lower time expenditure.
# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
###### Abstract  
Accurate glaucoma diagnosis relies on precise segmentation of the optic disc (OD) and optic cup (OC) in retinal images. However, despite the development of numerous automatic segmentation models, the lack of annotations in the target domain and domain shift among datasets continue to limit their segmentation performance. To address these issues, we propose a Causal Self-Supervised Network (CSSN) that leverages self-supervised learning to enhance model performance. First, we construct a Structural Causal Model (SCM) and employ backdoor adjustment to convert the conventional conditional distribution into an interventional distribution, effectively severing the influence of style information on feature extraction and pseudo-label generation. Subsequently, the low-frequency components of source and target domain images are exchanged via Fourier transform to simulate cross-domain style transfer. The original target images and their style-transferred counterparts are then processed by a dual-path segmentation network to extract their respective features, and a confidence-based pseudo-label fusion strategy is employed to generate more reliable pseudo-labels for self-supervised learning. In addition, we employ adversarial training and cross-domain contrastive learning to further reduce style discrepancies between domains. The former aligns feature distributions across domains using a feature discriminator, effectively mitigating the adverse effects of style inconsistency, while the latter minimizes the feature distance between original and style-transferred images, thereby ensuring structural consistency. Experimental results demonstrate that our method achieves more accurate OD and OC segmentation in the target domain during testing, thereby confirming its efficacy in cross-domain adaptation tasks.  
causal inference; cross-domain adaptation; self-supervised learning; fundus image segmentation; glaucoma
## 4 Results
### Deep Learning Models for Evaluating Data Augmentation Methods  
Three representative SOTA models are selected for the evaluation of the proposed data augmentation algorithm. These three models are DAE [27], SE-MSFN [29], and LSTM2 [28]. The reasons for selecting them for the algorithm evaluation are as follows: First, the selected models represent three different types of network structures: DAE, LSTM2, and SE-MSFN are auto-encoder, RNN, and CNN structures, respectively; second, they are superior in performance; DAE is a lightweight neural network for computationally constrained platforms, while LSTM2 and SE-MSFN, as representatives of high-accuracy models, achieved SOTA performance on several modulation recognition benchmark datasets. A detailed description of the selected models is given below.  
The Denoising Auto-Encoder (DAE) [27] is a lightweight network structure that is based on the denoising auto-encoder and RNNs. The DAE's inputs are L2-normalized amplitudes, and the classifier's encoder is a two-layer LSTM, while the decoder is a shared, fully connected layer. Since the network output includes both the decoder and the classification output, the loss function includes both the reconstruction and classification losses.  
LSTM2 [28] uses amplitudes and phases (APs) as model inputs, as does DAE, and the backbone is stacked with two layers of LSTM. LSTM2 is a simple structure with reasonable classification performance that is widely used in existing studies as a benchmark model.  
SE-MSFN [29] is a CNN classifier that uses large kernel convolution, multi-scale structure, and a combined attention mechanism to extract features from original IQ samples. It has SOTA performance on several benchmark datasets and has the advantages of fast convergence and high recognition accuracy. At the same time, its large number of parameters and model complexity make it suitable for use on high-capacity platforms.
## 3 Measuring the Effectiveness of Implementation
The packet rate and CPU usage, as well as the average call setup time are recorded. Additionally, the system is tested under varying scales of DoS attack, until maximum CPU capacity is reached and the calls begin to timeout. This demonstration of performance is much more informative than the static rate of attack used by [PERSON] et al. [4]. However, [PERSON] [19] only provides data on two types of attack compared to the four demonstrated by [PERSON] et al. Improving on these metrics, the method in this paper will be tested using variations in the scale and strength of the DoS attack. This will provide a comprehensive analysis of the effectiveness and limitations of the DoS mitigation strategy, from which comparisons can be drawn to other proposed strategies.
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
## 3 Results
### Assessment of Fatty Acid Content  
Following CoQ10 treatment, the fatty acid profiles exhibited notable changes in both early and late passage cells. In early passage cells (P5), CoQ10 induced a dose-dependent increase in the majority of fatty acids, particularly lauric, myristic, pentadecanoic, and palmitic acids, but elaidic acid levels decreased with higher concentrations. In late -passage cells (P16), CoQ10 exhibited variable effects. Palmitic acid levels consistently increased with treatment, mirroring the trend observed in P5 cells, though a slight decline from the peak was noted at higher CoQ10 doses. Stearic acid and elaidic acid levels showed a dose-dependent increase in response to CoQ10 supplementation. Pentadecanoic acid levels exhibited a modest increase in both P5 and P16 cells. In contrast, lauric acid, myristic acid, and oleic acid levels decreased following CoQ10 treatment. These findings are summarized in Figure 6, while representative chromatograms highlighting the primary methylated fatty acids are presented in Figure S3.  
Figure 5: Sirtuin gene expression response to CoQ10 across passages 5 and 16 at 10 and 50 \(\upmu\)M treatment (* \(p\) < 0.05, ***_p_ < 0.0001).
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 4 Method
### Architecture and Working
#### 4.2.3 Surrogate Face Generation  
The third stage is to generate synthetic surrogate face images that achieve anonymity in the cluster, i.e., any surrogate face image cannot be linked to the input picture among images in the original cluster in an unambiguous manner on the premise of maintaining the facial features. Given the face image \(x_{i}\) and its corresponding centroid \(x_{\mu}(x_{i},x_{\mu}\in X)\), to obtain the surrogate face image \(f_{x_{i}}\), we first utilized Dlib [71] to detect key facial feature landmarks on both \(x_{i}\) and \(x_{\mu}\), producing two key point sets \(S_{m}\) and \(S_{n}\), correspondingly. For every key coordinate (e.g., \((a_{i},b_{i})\)) in \(S_{m}\) and the corresponding pixel \(S_{n}\) (e.g., \((a_{j},b_{j})\)), calculating the location of the new key point (e.g., \((a_{m},b_{m})\)) by Equation (12), we obtained a set of points \(S_{i}\), which are the key facial feature landmarks of the surrogate face image \(f_{i}\).  
\[a_{m}=(1-k)a_{i}+ka_{j},b_{m}=(1-k)b_{i}+kb_{j} \tag{12}\]  
where \(k\) is used to assign the proportional weight of the image coordinates and the corresponding centroid image coordinate. The value of \(k\) ranges from 0 to 1.  
Then, we performed Delaunay Triangulation for all the sets \(S_{m}S_{n}S_{i}\), producing a one-to-one correspondence between triangles in the images \(x_{i}\), \(x_{\mu}\), and \(f_{i}\). Selecting each triangle \(T\) from the image \(x_{i}\) and its corresponding triangle \(Y\) in the image \(f_{i}\), we calculated the affine transform that converts the triangle \(T\) to \(Y\). Similarly, we calculated the transformation matrix of the corresponding triangle \(W\) in the image \(x_{\mu}\) to the triangle \(Y\). Applying the transformations above to images \(x_{i}\) and \(x_{\mu}\) correspondingly, we obtained warped images \(x_{i}^{\prime}\) and \(x_{\mu}^{\prime}\). Then, \(f_{i}\) was calculated by using the equation below with warped images.  
\[f_{i}=(1-t)x_{i}^{\prime}+tx_{\mu}^{\prime} \tag{13}\]  
where \(t\) is used to assign the proportional weight and it ranges from 0 to 1.
## 3 Results
### Spectrum  
Furthermore, sound frequency characteristics were analyzed for each block based on typical recordings, as shown in Figure 5. The sound of each block had a distinct characteristic in the middle-to-high frequency range (\(>\)500 Hz). The sound of BLK-I dropped to 0 dB at the frequency of 20 kHz, while that of BLK-III dropped to 0 dB at 10 kHz. In the other two blocks, the sound dropped to 0 dB at 15 kHz. Unlike the other blocks, BLK-IV contained dominant sounds between 2 kHz and 5 kHz, which are typically bird sounds [53; 54], consistent with the in situ observations. In terms of soundscape identification, the place with a natural soundscape, BLK-IV, contained some high-frequency sounds; the place with an artificial soundscape, BLK-I, contained sounds with a broad frequency range; and the place with a neutral-to-natural soundscape, BLK-II, had sounds in a wider frequency range than the place with a neutral-to-artificial soundscape, BLK-III.  
The above results showed the consistency of the sound spectrum and in situ sound observations. They also verified the accuracy of soundscape identification, which is based on the sound source and its bonding landscape observations.  
Figure 5: Typical sound spectra in the four blocks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & **BLK-I** & **BLK-II** & **BLK-III** & **BLK-IV** & **ANOVA** \\ \hline
**Identified Soundscape** & **Artificial** & **Neutral–Natural** & **Neutral–Artificial** & **Natural** & **F** & \(p\) \\ \hline \multirow{4}{*}{Sound levels} & L\({}_{\text{Aeq}}\) (dBA) & 66.8 & 64.3 & 64.0 & 64.2 & 3.28 & 0.02 \\  & L\({}_{50}\) (dBA) & 63.6 & 57.7 & 57.7 & 58.1 & 16.95 & 0.00 \\  & L\({}_{90}\) (dBA) & 59.0 & 53.4 & 53.3 & 53.2 & 25.22 & 0.00 \\  & L\({}_{10}\) (dBA) & 69.4 & 66.5 & 67.0 & 66.7 & 2.78 & 0.04 \\ \hline \multirow{2}{*}{Psychoacoustic parameters} & Loudness & 14.89 & 10.86 & 10.43 & 11.6 & 14.17 & 0.00 \\  & Sharpness & 2.30 & 2.26 & 2.09 & 2.6 & 14.96 & 0.00 \\  & Roughness & 1.88 & 1.67 & 1.70 & 1.6 & 4.45 & 0.00 \\ \hline \multicolumn{2}{c}{Spatial Function} & Exercise & Relaxation & Communication & Restoration & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Acoustic environments of the four blocks.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 5 Hardware Architecture of the FPGA-Based System: An Application Example
### The Proposed FPGA-Based Architecture  
The design purpose is the achievement of a hardware implementation for real-time applications. A solution characterized by low power consumption and minimum hardware resources requirements is also developed.  
Fixed-point arithmetic is adopted for data processing. The implemented procedure operates in real time. The FPGA has a time resolution of \(1 ps\) and the chosen clock is \(1 ns\) with a duty cycle of 0.5.  
The block diagram of our system is indicated in Figure 6. It is composed of three modules: the timing block, the ROM block and, the sum/multiplier block.
# Advanced Technologies for Geosite Visualization and Valorization: A Review
###### Abstract  
This review attempts to summarize contributions by authors who, in the last decade, have dedicated their efforts to making geoheritage accessible to the public. Geoheritage is composed of geosites, which are, nowadays, real milestones on which field-based geological education can be conducted. However, the COVID-19 pandemic in particular has made it clear that a new paradigm is needed; a series of tools must be introduced and increasingly used to make it possible for potential users, be they academics, students, or the lay public, to experience geosites from locations that can be thousands of kilometers away. All these have been achieved over time by a wide range of evolving techniques and advanced technologies such as GIS tools, virtual reality applications and further innovative technologies such as WebGIS platforms accompanied by appropriate navigation tools (VR headsets and thumbsticks). The viewers, in this way, are provided with a complete view of a virtual geosite, which enables visualizing its characteristics at different scales. VR technologies, especially, have revealed a high degree of satisfaction, based on feedback collected from VR geosite visualization events, both by scientists, students and the general public, and could be the forefront of geosite visualization and valorization in the near future.  
geosites; geoheritage; technologies; virtual reality; questionnaires +
Footnote †: journal:  
kind have used the scientific value [15; 16; 52], which consists of four sub-criteria, namely representativeness, integrity, rarity [15; 16; 53; 54] as well as the degree to which a geosite has been the focus of scientific publications. In regard to representativeness, this reflects how exemplary a geosite is in terms of the natural events that have taken place there. Parity reflects to which degree a geosite is uncommon [54].  
Besides the scientific value, other, so-called additional values [15; 16; 55; 56] can be determined and become subject to assessment. Such additional values are of the aesthetic, ecological, economic, educational, and cultural type. The latter consists of religious, artistic, literary, historical, and geohistorical sub-criteria [54]. The educational value [14; 57] expresses the combination of the didactic potential of the geosite (which is related to how easily the general public can understand its characteristics), its accessibility, safety, as well as its possible \"use\" for educational goals--for instance in the form of guided tours.