esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Effect of Operation Parameters on AC-CoPcS Catalytic Performance
#### 3.2.4 The Effect of Reaction Temperature (\(T_{\text{re}}\))  
Commonly, the reaction temperature has a great influence on the reaction rate. The effect of \(T_{\text{re}}\) on the \(CPP_{\text{SMM}}\), \(Yield_{\text{DMDS}}\), and \(Purity_{\text{DMDS}}\) was investigated when other operation parameters kept constant, and the result is shown in Figure 9. The \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) increase gradually when \(T_{\text{re}}\) increases from 35 \({}^{\circ}\)C to 65 \({}^{\circ}\)C, but it decreases slightly when \(T_{\text{re}}\) continues to increase from 65 \({}^{\circ}\)C to 75 \({}^{\circ}\)C, so the optimum \(T_{\text{re}}\) is 65 \({}^{\circ}\)C where the best \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\) are achieved. The \(T_{\text{re}}\) seems to have little effect on \(Purity_{\text{DMDS}}\) when it is lower than 65 \({}^{\circ}\)C, however, when \(T_{\text{re}}\) increases from 65 \({}^{\circ}\)C to 75 \({}^{\circ}\)C the \(Purity_{\text{DMDS}}\) decreases sharply from 99.8% to 98.0%. The reason for these results is that the reaction of the SMM and oxygen is endothermal, and the reagents are easier to activate, which accelerates the reaction rate when \(T_{\text{re}}\) increases, leading to the increase of the \(CPP_{\text{SMM}}\) and \(Yield_{\text{DMDS}}\). However, extremely higher \(T_{\text{re}}\) of 75 \({}^{\circ}\)C brings not only the byproduct formation problems, which will seriously affect the purity of DMDS product, but also the decrease of \(Yield_{\text{DMDS}}\) because of the volatilization of DMDS into the gaseous state [6]. When \(T_{\text{re}}\) is at 75 \({}^{\circ}\)C, a white cotton-like byproduct in the organic layer was observed in the experiment.
## 2 Predictive Model
### Regression-Based Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Regression Methods**} & \multicolumn{2}{c}{**Equipment Under Test**} & \multicolumn{2}{c}{**Type of Fault**} & \multicolumn{2}{c}{**Effectiveness**} \\  & & & & & **Percentage** \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Multilayer SVM [57] & \multirow{2}{*}{\(\blacksquare\)} & Transformer & \multirow{2}{*}{\(\blacksquare\)} & Partial discharge and arcing & \multirow{2}{*}{100} \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Multilayer SVM [58] & \multirow{2}{*}{\(\blacksquare\)} & Transformer & \multirow{2}{*}{\(\blacksquare\)} & Discharge and thermal faults & \multirow{2}{*}{100} \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Multilayer SVM [59] & \multirow{2}{*}{\(\blacksquare\)} & Transformer & \multirow{2}{*}{\(\blacksquare\)} & Discharge and thermal faults & \multirow{2}{*}{90} \\ \hline \multirow{2}{*}{\(\blacksquare\)} & SVM [60] & \multirow{2}{*}{\(\blacksquare\)} & High-voltage, direct current & \multirow{2}{*}{\(\blacksquare\)} & Pole-1 to ground, pole-2 to ground and pole-1 & \multirow{2}{*}{100} \\  & & & transmission lines & to pole-2 & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & SVM [61] & \multirow{2}{*}{\(\blacksquare\)} & Series & \multirow{2}{*}{\(\blacksquare\)} & Line-to-ground, line-to-line fault involving ground and line-to-line & \multirow{2}{*}{98.703} \\  & & & transmission line & & and line-to-line & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & LSTM and SVM [62] & \multirow{2}{*}{\(\blacksquare\)} & Power systems & \multirow{2}{*}{\(\blacksquare\)} & Data-based line trip fault & \multirow{2}{*}{97.7} \\  & & & & & \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Multiclass SVM [63] & \multirow{2}{*}{\(\blacksquare\)} & Induction motor & \multirow{2}{*}{\(\blacksquare\)} & Mechanical fault & \multirow{2}{*}{97.48} \\ \hline \multirow{2}{*}{\(\blacksquare\)} & Robust locally linear embedding algorithm and SVM [64] & Motor, torque transducer/encoder and dynamometer & \multirow{2}{*}{\(\blacksquare\)} & Gear fault & \multirow{2}{*}{90–100} \\  & & and dynamometer & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Regression-based methods and their applications.
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
## 4 Conclusions  
In this paper, different concepts for the distribution of the calibration and local oscillator signals to distributed sub-arrays of a dual-band multi-antenna GNSS receiver were compared in terms of the resulting system performance and practical implementation issues. RF performance as well as hardware and software efforts and costs were reflected in our design considerations. The distributed arrangement of sub-arrays necessitates layout cable connections, which challenge the phase coherence required for the array signal processing. Despite the similar performance in terms of phase imbalance between the signals at both sub-arrays, the REF signal distribution and the CAL signal distribution at IF were eventually selected. This decision is based on the most important specification parameters of the intended application, installation space and cost, while considering the targeted system performance. For other applications, the required application parameters could lead to a different conclusion. Accordingly, the criteria compared in Tables 1 and 2 provide guidance for the selection process. The distributed system approach facilitates the integration of robust GNSS receivers into the limited installation spaces available in series cars and thus opens the market for an improved level of navigation safety.  
Conceptualization, U.S., B.B., M.B., S.N.H. and M.A.H.; methodology, U.S.; software, M.B.; investigation, U.S. and B.B.; writing--original draft preparation, U.S.; writing--review and editing, S.N.H., B.B., M.B. and M.A.H.; validation, supervision, project administration, funding acquisition, M.A.H. and M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the German Aerospace Center (DLR) Space Administration on behalf of the German Federal Ministry for Economic Affairs and Climate Action under the grant numbers 50 NA1901 and 50 NA1902.  
Not applicable.  
Figure 6: (a) Receiver sub-array consisting of antenna and frontend board (the module height is 2 cm); (b) antenna board (**top**) and frontend board with dedicated GNSS receiver ASIC (**bottom**) with CAL and LO signal input connectors.  
Data Availability Statement: The data that support the findings of this study are available upon reasonable request from the authors.  
The authors wish to thank all colleagues of the ROSATNA project teams at TU Ilmenau, RWTH Aachen University and DLR Institute of Communication and Navigation, and IMMS GmbH for their valuable contributions and useful discussions.  
Author [PERSON] is with IMMS GmbH, a non-profit limited liability company. The authors declare no conflict of interest.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 3 Comparative Analysis of Extraction and Analytical Methods for Determining Antioxidant Activity
### Extraction Methods
The efficiency of this unconventional extraction method mainly depends on operating conditions and parameters like treatment time (100-800 us), pulse duration, strength (10-80 kV/cm), and frequency (1-1500 Hz). In addition, the extraction efficiency varies with the food matrix because of differences in membrane polarity and conductivity [47, 48, 55]. This technique was successfully used for extracting the phenolic compounds, anthocyanins, and flavonoids, from various berries such as barberries, blueberries, and strawberries [48; 49; 50; 55]. Enzyme-assisted extraction (EAE) involves the use of various enzymes responsible for cell wall hydrolysis, followed by the release of intracellular compounds. In order to ensure efficient disintegration of the cell wall, the use of a mixture of enzymes is recommended [51]. Enzyme specificity and concentration, pH, temperature, extraction time, and properties of the plant material, such as particle size and moisture, are all factors influencing the extraction yield [8; 44; 52].
# Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services
## 5 Case Studies
### Case Study 2: Cost Comparison Using Clustering
#### 5.3.2 Method Selection and Integration  
Within the _cost comparison_ module of the framework, multiple clustering algorithms were evaluated, including K-Means, DBSCAN, and Hierarchical Clustering. The evaluation phase involved running these algorithms on a sample subset of the dataset and comparing their performance based on metrics such as silhouette score and inertia.  
K-Means clustering was selected as the optimal method due to its superior performance, particularly in creating distinct, interpretable clusters based on CPU and RAM  
Figure 6: Comparison of coefficients across IaaS, CaaS, and PaaS datasets.  
metrics. This choice is supported by our previous detailed study [31], where K-Means demonstrated the best balance between accuracy and computational efficiency for cloud service categorization. The framework's extensibility allowed for the seamless integration of this algorithm, showcasing its ability to accommodate the best-suited method for the problem at hand.  
Using the same datasets as in the first case study (Section 5.2), the cost comparison module was configured to focus on the most impactful attributes identified earlier: CPU, RAM, and storage. The modular design of the framework facilitated a smooth transition from price policy analysis to clustering, enabling a focused examination of performance-cost relationships [31].  
The selected K-Means algorithm was applied to categorize the service bundles based on their CPU-RAM and storage-disk type combinations. The analysis generated clear clusters, providing a structured view of how different service configurations compare in terms of performance and cost.
## 2 Results and Discussion
### Volatile Composition
A total of six ethyl esters were detected and quantified in three wine samples. The esters of this group make a positive contribution to the general quality of wine. Most of them have the typical fruity aroma of young wines [27]. Among these ethyl esters, the most abundant compounds were ethyl hexanoate, ethyl octanoate and ethyl lactate, which all exhibited higher concentration in slope land wines than those of the F-Land wine. As compared with the upper three ethyl ester compounds, the contents of ethyl dodecanoate was lower in three terrain wines, especially in F-Land wine, but the ethyl dodecanoate can cause more abundant and complex wine aromas [28]. Furthermore, this study and previous research shows that the composition and concentration of the wine aroma could be regulated by the position of the vineyard [29]. The production of fatty acids has been reported to be dependent on the composition of the must and fermentation conditions [30]. Five fatty acids were detected in all wine samples, and the content of each fatty acid detected from the F-Land wine showed the lowest than those from two slope land vineyards. Octanoic acid, hexanoic acid and decanoic acid were the major fatty acids found, however, the contents of isobutyric acid and heptanoic acid were very low in all wine samples (and existed in at least one of the wines studied), especially for heptanoic acid, it is well known that both of them are not associated with wine quality but play an important role in the complexity of the aroma [31]. Specifically, they are important for the aromatic equilibrium in wines because they are opposed to the hydrolysis of the corresponding esters [32]. Appropriate content of fatty acids was necessary for higher contents of aroma esters in wines. These C\({}_{6}\) to C\({}_{10}\) fatty acids at concentrations of 4 to 10 mg/L impart mild and pleasant aromas to wine; however, at levels beyond 20 mg/L, their impact on wine becomes negative [31]. The C\({}_{6}\) to C\({}_{10}\) fatty acids might have a positive impact on the aroma of the three wines examined in the current study since their levels were all far below 10 mg/L. The composition and concentration of aldehydes and ketones varied among the different terrain wines. Acetoin, benzaldehyde, benzylethylaldehyde, nonanal and geranylactone were found in these wine samples. The concentration of aldehydes and ketones class from the LS-Land Cabernet Franc wine was higher than in other terrain wines. The identification of volatile phenols in wine (phenol) can have an influence on the aroma of the wine. Those yeast strains that are naturally present on the grapes and in the winery such as _Brettanomyces_ yeasts can also contribute to the production of volatile phenols [33]. In addition to the metabolic activity of yeasts, other factors such as oak maturation can also increase the amounts of volatile phenols in wine [34]. In present study, the phenol was found in the all wine samples, but it was only present at very low amount.
## 6 Methodology
### System Overview
#### 6.1.1 Platform  
To implement an end-to-end ML attestation solution, the STM32H573l-DK [37] development platform (Figure 4) is utilized. This platform features an STM32H573 ARM Cortex-M33 core with TrustZone enabled, 2 MB of internal flash, 640 kB of SRAM, a hardware security module with dual AES coprocessors, a hash accelerator, a True Random Number Generator (TRNG), secure data storage, and tamper detection capabilities. Additionally,it incorporates an immutable root of trust (ST-iRoT) that functions as the first boot stage. An advantage of this platform is its integration into the STM32 Trust Ecosystem, which enhances security and simplifies implementation. The STM32 Trust Ecosystem provides a TEE Secure Manager (SM), streamlining the setup and management of secure services.  
The SM is designed to reduce the development complexity of secure applications. However, one drawback is that the SM image is provided in an encrypted format, meaning developers do not have full control over how security services are implemented. A more customizable solution would be TF-M [13], offered by ARM Community, but this comes at the cost of increased complexity, particularly when integrating TF-M with platform-specific hardware features.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 4 Experiments
### Ablation Experiment
#### 4.4.2 Depth Map Stitching  
Finally, we combined the background and foreground depth maps into final depth maps. According to our experimental results, we chose to use the SE and AM attention mechanisms with our background and foreground depth prediction models to predict the background and foreground depth maps, respectively. Then, we compared the performance of two depth map stitching strategies: direct stitching and CNN stitching. The direct stitching strategy extracted the foreground depth regions from the foreground depth maps and overlaid them on the background depth maps. The CNN stitching strategy processed the directly stitched depth maps using convolutional layers with a filter size of \(3*3\) for stitching. Table 3 shows that the direct stitching strategy outperformed the CNN stitching strategy for stitching together the foreground and background depth maps. The CNN stitching strategy performed poorly as the convolutional layers blurred the depth maps. Figure 7 shows the depth prediction results from the different stitching strategies. Finally, we chose the direct stitching strategy to stitch together the foreground and background depth maps in the subsequent experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Attention** & \(\delta\) **\textless{} 1.25** & \(\delta\) **\textless{} 1.25\({}^{2}\)** & \(\delta\) **\textless{} 1.25\({}^{3}\)** & **Abs Rel** & **Sq Rel** & **RMSE** & **RMSE log** \\ \hline No Attention & & & & & & \\ Mechanism & 0.947 & 0.991 & **0.996** & 0.069 & 0.105 & 0.860 & 0.097 \\ \hline AM & **0.969** & **0.993** & **0.996** & **0.057** & **0.080** & **0.797** & **0.086** \\ \hline SE & 0.967 & 0.993 & **0.996** & 0.059 & 0.088 & 0.810 & **0.086** \\ \hline CBAM & 0.965 & 0.993 & **0.996** & 0.058 & 0.087 & 0.808 & **0.086** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The evaluation of our foreground depth prediction model using the different attention mechanisms.  
Figure 5: A visualization of the results from our background depth prediction model using the different attention mechanisms: AM, SE, and CBAM.  
Figure 6: A visualization of the results from our foreground depth prediction model using the different attention mechanisms: AM, SE, and CBAM.
## 8 5G Potentials and Promises for Robot Applications
### RF Measurements with More Resolution  
The 3 GPP 5G New Radio (NR) is envisioned to pave the way towards achieving higher accuracy and robustness of localisation in GPS-denied environments such as indoor settings, and towards improving localisation outdoors combined with GPS. 5G NR provides improved measurements for localisation, such as time-based, angular-based, and energy-based measurements. The measurements include TOA, TDOA, AOA/AOD, and multi-cell round-trip time. For robot localisation, 5G in the downlink defines a new reference signal called a positioning reference signal (PRS), based on which these measurements can be extracted. (Readers are referred to [15] for further information).  
Owing to the high carrier frequency, high bandwidth, and MIMO technology, accurate measurements will be delivered. 5G NR operates at high-frequency bands: Frequency Range 1 (FR1) (450 MHz to 6 GHz) and Frequency Range 2 (FR2) ( frequency bands from 24.25 GHz to 52.6 GHz). Relying on the Cramer-Rao Lower Bound (CRLB), the lower bound for variance of TOA is obtained by [102]:  
\[var(\text{TOA})\geq\frac{1}{8\pi^{2}BT_{s}F_{c}^{2}\text{ SNR}}\,,\]  
with \(B\) being the bandwidth, \(F_{c}\) is the central frequency, \(T_{s}\) is the duration of the signal, and SNR is signal to noise ratio. This inequality indicates that higher frequency and bandwidth contribute to higher TOA estimation accuracy.  
The probability of LOS increases due to the strong path loss in higher frequencies (mmWave). This feature and the large transmission bandwidth make distinguishing between LOS and NLOS measurements in the multipath effect feasible by applying proper analysis over the received signals, resulting in highly precise time-based and angular-based localisation.  
The Massive MIMO technology is one of the most noticeable enhancements and relevant features for localisation offered by 5G NR. It allows the implementation of ultra-massive antenna arrays consisting of hundreds or thousands of antennas in a single base station, leading to finer angular resolution (azimuth and elevation of the beam) of even less than one degree, which can contribute to accurate localisation.
## 4 Results
### Computational Effort of the Methods  
As it was already described in Section 3.1, our bubble parser does not need any training phase. The bubble parser needs roughly 0.1 s to parse a middle-length sentence (about 15 words without punctuation) on a single core of an up-to-date standard PC (12 th Gen Intel(R) Core(TM) i5-1235U). If the sentence is more extended and exhibits many ambiguities, the parsing time on a single core increases to roughly one second because the parser has to handle hundreds of interpretations. To parse a whole corpus of sentences, we parallelize the parsing process between ten cores of the same CPU. One million sentences of the \"Wortschatz Leipzig\" [21] can be parsed in roughly 32 h (see Table 4).  
The literature lacks a comprehensive analysis of the computational costs associated with the dependency parser from SpaCy and the constituent parser from Stanford. However, approximate estimations can be made regarding the training costs for the treebanks and the parsing costs for individual sentences post-training, based on various online sources.  
The dependency parser and constituent parser are trained on the German Universal Dependencies (UD) Treebank [24] and the German TuBa-D/Z Treebank [25], respectively, containing 15,000 and 25,000 annotated sentences. Both models are trained on a modern GPU optimized for artificial intelligence workloads. The computational costs for training differ depending on whether the model undergoes initial training with randomly initialized weights or fine-tuning of pre-existing weights. The initial training for both parsers takes approximately 3-7 days, whereas fine-tuning significantly reduces this time to several hours. Sentence parsing is a single-threaded process, executable on a standard PC single core. The time required for parsing a sentence is comparable to that of our bubble parser: the dependency parser typically requires 0.01 to 0.3 s, while the constituent parser requires approximately 0.1 to 1.0 s. The longer processing time of the constituent parser is due to the handling of hierarchical constituent structures (see Table 4).  
In contrast to the SpaCy and Stanford parsers, LLMs like GPT-4 and GPT-3.5 were not specifically designed for parsing but are general-purpose statistical models. That is why the computational costs of LLMs are substantially higher than those of the aforementioned parsers. Nevertheless, for the sake of completeness, we will give here all the computational costs of GPT-4 based on the information taken from the Internet source THE DECODER [26]. Thus, training GPT-4 on a modern supercomputer takes several months and incurs approximately EUR 63 millionin costs. With 1.8 trillion parameters and being trained on 13 trillion tokens, GPT-4 requires a cluster of 128 GPUs for inference. A typical response from GPT-4 consumes approximately 30 TFLOPs of computational resources.
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 3 Silk Fibroin Membrane
### Silk Fibroin Membrane with Other Components
#### 3.2.2 Tetracycline  
Tetracycline (TC) is a broad-spectrum antibiotic that is effective against gram-positive and negative bacteria [77]. It inhibits the protein synthesis of bacteria and has bacteriostatic activity [77]. TC has an affinity to the mineralizing tissue of bone and tooth and causes the proliferation of osteoblastic cell [78]. It decreases the degradation of the collagen matrix and bone resorption and has been used for the treatment of periodontal disease [79; 80]. Using its antimicrobial and therapeutic activity, TC has been used as a component of the SF membrane for new bone regeneration [81].  
TC has different osteogenesis effects depending on the concentration [80]. In vitro, the 1% and 5% TC-loaded SF membranes showed more proliferation and osteogenic potential in a gingiva-derived mesenchymal cell compared with that of 10% TC. The 1%, 5%, and 10% TC-loaded SF membranes in a rat calvarial defect showed more bone formation compared with the SF-only membrane, and significantly higher bone formation was observed in the 5% TC-loaded membrane (Figure 6 and Table 1) [81]. In another study, the 1% TC-loaded SF membrane showed increased new bone formation compared with the SF-only membrane [82]. The antibiotic activity of TC helps to prevent bacterial infection, and the osteogenic property of TC leads to the increase of new bone formation in the defect area [82]. However, the TC-loaded SF membranes are brittle and weak in their dry states and are difficult to handle, such as in the casting of the SF membrane [82]. Weak mechanical properties should be improved for clinical use as GBR membranes. In high concentrations, TC can inhibit the proliferation of osteoblasts [83]. The optimal concentration should be established for the use of TC as a component of the GBR membrane.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **SFM** & **TC1** & **TC5** & **TC10** \\ \hline Bone volume (mm\({}^{3}\)) & 2.00 \(\pm\) 1.81 & 3.65 \(\pm\) 2.86 & 9.87 \(\pm\) 3.80 & 4.86 \(\pm\) 1.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Micro-computed tomographic analysis.  
Figure 6: Micro-computed tomography at 4 weeks after operation. Tetracycline (TC)-loaded SF membrane was applied on a rabbit calvarial defect [81]. (**a**) SF membrane; (**b**) 1% TC-loaded SF membrane; (**c**) 5% TC-loaded SF membrane; (**d**) 10% TC-loaded SF membrane.