esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Optimization
### Quantization  
Quantization is a model optimization technique that has the objective of reducing the size of a DL model by converting weight values to a lower-precision bit representation, which is usually from 32 bits to 16 bits or 8 bits. Furthermore, it is possible to represent floating-point numbers in binary [42]. Quantization can be divided into two main categories: post-training quantization (PTQ) [43] and quantization-aware training (QAT) [44].  
The PTQ technique is applied to a trained model with default values of representation--commonly 32-bit floating-point values. These values can be quantized, e.g., from 32-bit floating points (FP32) to 16-bit floating points (FP16) or 8-bit integers (INT8), without the need to retrain the model [45].  
Figure 2 shows the FP32, FP16, FP8, and INT8 value representations on a computer system. It is observed that different representations use different numbers of bits. The precision is related to the number of bits used to represent a value. The more precise the representation is, the greater the amount of memory that has to be allocated to store the model.  
Figure 1: Example of pruning.  
The floating-point representation consists of three parts: the sign, exponent, and mantissa. The sign, represented in red, is the most significant bit and can either represent a positive or negative value (0 means positive and 1 means negative). The set of binary digits represented in green in the image is the exponent; it is responsible for defining the power to which the base 2 is raised. Occasionally, the exponent is called the signed or unbiased exponent. The other bits, represented in blue, are the mantissa and define the fractional part of the number [46]. For 8-bit integer representations, there is no exponent and the values can be represented from \(-128\) to \(127\). Figure 3 shows dynamic range quantization (DRQ) from FP32 to INT8 representations. The values after conversion are less precise, but this results in less information to be stored in a system.  
The QAT strategy requires more computational effort since it is used while training the model [47].
## 6 Conclusions  
To conclude, we have developed a sensitive and compact Raman laser spectrometer suitable for analyzing gas mixtures containing relevant greenhouse gases such as carbon dioxide and methane. In particular, it is capable of identifying the contributions of carbon isotopologues, such as \({}^{12}\)CO\({}_{2}\)/\({}^{13}\)CO\({}_{2}\) and \({}^{12}\)CH\({}_{4}\)/\({}^{13}\)CH\({}_{4}\). The features of the developed spectrometer result in a specially designed gas cell with the following advantages: (i) it can withstand high gas pressures (up to 100 atm.), (ii) it has a relatively small internal volume of \(\sim\)0.6 cm\({}^{3}\), relaxing the requirements for sample preparation, and (iii) together with the optical scheme of the spectrometer, it ensures good collection efficiency for the scattered light and, thus low limits of detection (LODs) for the studied gases. By studying isotopically-enriched \({}^{12}\)CO\({}_{2}\)/\({}^{13}\)CO\({}_{2}\) and \({}^{12}\)CH\({}_{4}\)/\({}^{13}\)CH\({}_{4}\) gases, we estimated easily accessible LODs of less than 100 ppm for the isotopologues of carbon dioxide and less than 25 ppm for those of methane. We also show the proof-of-the-concept of the suitability of the developed spectrometer for studying human exhalation.  
Further work will focus on developing a gas analyzer using calibrated gas mixtures. Such Raman gas spectrometers and gas analyzers are promising for studies of sources of pollution in the atmosphere, combustion processes and human exhalation.  
Potentially, the developed Raman gas spectrometer can be used for analyzing virus structures in human exhalation.  
Conceptualization and methodology, E.P., I.C., A.K. ([PERSON]), P.C. and P.L.; validation, A.P. and A.D.; formal analysis, V.K.; investigation, V.V., I.C., E.P., A.P., A.D. and V.K.; data curation, A.K. ([PERSON]); writing--original draft preparation, I.C., K.G.; writing--review and editing, P.L., M.A. and F.D.; visualization, K.G. and V.K.; supervision, V.V.; project administration, V.V. and X.M.; funding acquisition, V.V. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Raman spectrum of the human breath (exhalation): (**a**) an overview spectrum; (**b**) a close view of the \({}^{12}\)CO\({}_{2}\)/\({}^{13}\)CO\({}_{2}\) lines. The excitation wavelength \(\lambda_{\mathrm{exc}}\) = 532.123 nm, the resolution is \(\sim\)1 cm\({}^{-1}\). Gas pressure: 1 atm. Exposure time: (**a**) 30 s, (**b**) 600 s.  
**Funding:** This research was funded by Ministry of Education and Science of Russian Federation, grant number RFMEFI57518X0180.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Evaluation and Results
### User Experience Evaluation of a Tele-Rehabilitation Tool  
The User Experience Questionnaire (UEQ), a validated subjective user experience (UX) measuring instrument [102], was used to interview 8 physicians and 11 potential end users (people doing exercises for our dataset, 11 responses were received from the original group of 31 participants). UEQ assesses the appeal of a tool and includes goal-directed characteristics (i.e., dependability, efficiency, perspicacity) and not goal-directed quality (i.e., stimulation, novelty).  
Responses are presented in Figure 21 and Table 6. The quality levels were the following: exceptional (top 10%), good (10-25%), above average (25-50%), below average (50-75%), and terrible (bottom 25%). The results discussed above indicate that our approach may be able to satisfy the expectations of its intended user base. The survey participants noted the dependability and novelty aspects.  
The majority of them were enthusiastic about the efficacy of the product and eager to test it. Except for Question 9, which asks whether 3D body analysis was quick or sluggish  
Figure 21: The results of UEQ survey.  
Figure 20: Processing timeto interact, all questions got a mean score of 5 or above, suggesting a somewhat favorable experience (4 is neutral). The findings obtained for the subscales indicate a satisfactory experience with the system. These reported results from the ongoing investigation may vary when the project is completed. Following the end of the actual prototype development, a full analysis of the various outcome measures and subjective input from the various participants will be performed. The UEQ survey allowed us to get feedback on the practical validity of such a system. Researchers delivered a brief demonstration of our technique at the beginning of the investigation. Users were advised to spend some time at the start of a session getting acquainted with the program environment.
# Optimized Isolation and Characterization of the Major Polysaccharide from Grape Pomace
## 3 Results and Discussion  
### Effect of Single Parameter on the Extraction Yield of GPP  
The effect of the material to solvent ratio on the extraction yield of GPP was first investigated (Figure 1A). The extraction yield of GPP increased significantly when the material to solvent ratio increased from 1:20 to 1:30 and reached the highest extraction yield of 15.3%, but slightly decreased when the material to solvent ratio was higher than 1:30. These changes might be due to the increased dissolution rate of polysaccharides with increasing solvent volume [20]. Differences in solution concentration outside and inside plant cells can influence the diffusion rate of solute particles and prompt more polysaccharide molecules to enter into the solution [21]. In view of the fact that GPP extraction yield increased little when the ratio of raw material to solvent surpassed 1:30, and the economical industry objective of reducing the waste of raw materials, the material to solvent ratio of 1:30 was chosen for further experiments.  
Figure 1: Effect of single parameter on the extraction yield of grape pomace polysaccharide (GPP). (**A**) Material to solvent ratio; (**B**) extraction time; (**C**) extraction temperature. Data are means \(\pm\) SD (\(n\) = 3). The error bars represent the standard deviation. Values marked by the same letter are not significantly different (\(p\) < 0.05).  
Extraction time was another factor that significantly impacted the extraction yield. As shown in Figure 1B, in the extraction system with a material to solvent ratio of 1:25 and incubation temperature of 90 \({}^{\circ}\)C, the extraction yield increased rapidly from 10.9% to 16.0% under the change in the extraction time from 20 to 30 min. As the extraction time continued to extend, the extraction yield changed indistinctively. This may be related to the fact that polysaccharides have been sufficiently extracted [22, 23]. Considering that a longer extraction time accompanies with lengthening production cycle, an extraction time of 30 min was identified as the optimal extraction time.  
As shown in Figure 1C, the extraction yield increased and gradually reached the maximal value with the increase in extraction temperature, and then decreased lightly during the extraction temperature ranging from 90 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. This phenomenon can be attributed to polysaccharide hydrolysis under high temperature and long extraction time [24]. Regarding energy saving, 70 \({}^{\circ}\)C was an appropriate extraction time for crude GPP.
## 2 System Modelling
### Optical Transmitter  
The optical transmitter is responsible for converting the information signal from the electrical to the optical domain. First, a 16-QAM Nyquist signal with a roll-off factor of 5% and symbol rate of 64 Gbaud is generated using distinct random sequences for both components (in-phase and quadrature) of the signal. The modulator is a dual parallel Mach-Zehnder modulator (DPMZM) with the ability to modulate the \(I\) and \(Q\) components of the electrical field. This is achieved by biasing the inner MZMs in the null bias point and the outer MZM in the quadrature bias point.  
Figure 2 shows the spectrum of the signal at the output of the transmitter. In Figure 2a, an illustrative spectrum is presented. The optical tone is added to the signal to fulfill the minimum phase condition for the KK receiver [10]. Figure 2b shows the power spectral density (PSD) of the signal at the transmitter output obtained by simulation. The spacing between the carrier and the signal is 5% of the signal bandwidth. This spacing was chosen to maximize the spectral efficiency without adding distortion to the received signal.  
Figure 1: Schematic diagram of the short-reach MCF based system employing KK receivers.  
Figure 2: (a) Illustrative spectrum of the signal at the transmitter output. (b) Power spectral density obtained by simulation of the signal at the transmitter output.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 4 Discussion
### Countermeasures
#### 4.2.5 Higher Orbits  
Future QKD satellites are likely to go to higher orbits (MEO or GEO) to increase the QKD transmission window, coverage area, and common-view range. This will also have the effect of increasing the propagation range and attenuation of the laser from the laser terminal. While the QKD system itself will also suffer additional attenuation, in the case where light scattered off the satellite is picked up by the QKD ground station, the light from the laser terminal suffers greater losses because it must make a round trip to the satellite and back to earth, unlike the one-way propagation of the QKD signal. Higher laser power will maintain the disruption, but the power of the laser from the mobile terminal will have to increase at a greater rate than the power of QKD transmission.
## 4 Experiments
### Results
#### 4.2.3 Error Analysis  
To provide a deeper analysis of model performance, we examined errors in the generated ontologies to understand recurring limitations and potential challenged. Errors were primarily observed in three areas: (1) generating invalid axioms, where models produced statements that were either syntactically incorrect or logically inconsistent; (2) mismatched relationships, where object properties were incorrectly assigned, leading to incorrect domain and range specifications; and (3) poorly structured hierarchies, where models misclassified subclasses under incorrect parent concepts, affecting the overall ontology structure.  
One notable example of invalid axiom generation was observed in Mistral 7B's handling of disjoint class constraints. It classified Firefighter as a subclass of FirstResponder, correctly indicating that all firefighters are first responders. However, it then correctly classified Firefighter as a subclass of \"not FirstResponder\", implying that firefighters are explicitly not first responders. This contradiction creates an inconsistency in the ontology, as an entity cannot belong to both class and its negation. Such errors disrupt logical reasoning and can cause failures in automated inference systems. A correct ontology should either maintain Firefighter as a subclass of FirstResponder without contradiction or correctly define disjointness when necessary. Mistral 7B also incorrectly assigned the isAssignedTo relationship between firetrucks and firefighters. The model stated that a firefurch is assigned to a firefighter, which is incorrect. In reality, it is firefighters who are assigned to firetrucks, not the other way around. This error misrepresents the logical structure of the ontology and could lead to incorrect reasoning in applications. The correct relationship should define firefighters as the subject of the assignment, with firetrucks as the object. Poorly structured hierarchies were frequently observed in cases where models had to infer subclass relationships. For example, Mistral 7B placed \"EmergencyResponseTeam\" as a subclass of \"FirstAidKit\", which is a category error. GPT-4, on the other hand, successfully maintained the integrity of subclass structures but tended to create overly granular subclass divisions when unnecessary, leading to ontology bloat.  
These observations highlight the different strengths and weaknesses of each model and reinforce the necessity of structured fine-tuning with domain-specific datasets to improve the logical coherence of ontology outputs.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 3 Situation-Based Contextual Model: Definitions and Formalizations
### Modeling Multidimensional Recommendation Space  
In this section, we present a new hybrid recommendation model based on three dimensions categories as input: (1) user context dimension including location, time, role and preferences, (2) device capability dimension including CPU speed, memory size and network bandwidth and (3) rule item dimension as rule's content. These input dimensions are the base to develop the recommendation system intended to suggest useful and relevant situations rules to the user based on his context (i.e., user location and user roles) and his device capabilities. The dimension of user context reflects different spatiotemporal information related to prior usage rules as well as the assigned rules scores, information related to possible roles that a user can play in the smart space (driver, student, etc.), and information related to user's explicit and implicit preferences. The rule item dimension consists of several projections on different context attributes. Each projection is a combination of location, time, role, etc. The device dimension defines device's features and capabilities. The score of rule item is defined as an output. Since the score determines the user's interest level in the suggested situation rule item, it is given from a lower value (0) of the score (least preferred) to a higher value (1) of the score (most preferred) (see Figure 1). The score is calculated as presented in the following relation:  
\[R(UserContext,\ RuleItem,\ DeviceCapability)=\mathit{score}\]  
We consider as inputs:  
* \(UserPreference\ (i)=\{(\mathit{security};\ 0.65),\ (\mathit{health};\ 0.41),\ (\mathit{home};\ 0.58),\ (\mathit{work};\ 0.41)\}\).
* \(RuleItem\ (j)=\{(\mathit{domain};\mathit{home}^{\prime}),\ (\mathit{day};\ \mathit{``weekday\"}),\ (\mathit{(field};\ \mathit{``security\"})\}\).
* \(DeviceCapability(k)=\)  
\(\{(\mathit{CPU};\ 4.5\ \mathrm{GHz}),\ (\mathit{RAM};\ 4\ \mathrm{GB}),\ (\mathit{Modality};\ [\mathit{text},\mathit{ audio},\ \mathit{image},\ \mathit{video}])\}\)  
We obtain as output: **Score = 0.88**.
## 2 Materials and Methods
### Cell Line Measurement
#### 2.3.4 Permeability Measurements  
For the permeability experiments, the inserts were transferred to 12-well plates containing 1.5 mL Ringer-HEPES buffer in the lower (acceptor) compartments. In the apical (donor) compartments 0.5 mL buffer was pipetted with suspensions of KTP raw or DPI samples. To avoid the unstirred water layer effect, the plates were kept on a horizontal shaker (120 rpm) during the assay. The assays lasted for 60 min. Samples from both compartments were collected and the KTP concentration was measured by UV spectrophotometry. The apparent permeability coefficient (P\({}_{\text{app}}\)) was calculated as previously described [18]. The cleared volume was determined based on the concentration of the tracer in the acceptor compartment (\(\Delta[\text{C}]_{\text{A}}\)) after 60 min (\(\Delta\)t), the concentration in the donor compartment at time zero ([C]\({}_{\text{D}}\)), the volume of the acceptor compartment (V\({}_{\text{A}}\); 1.5 mL), and the surface area available for permeability (A; 1.12 cm\({}^{2}\)), using Equation (1):  
\[\text{P}_{\text{app}}\ (\text{cm}/\text{s})=\frac{\Delta[\text{C}]_{\text{A}} \times\text{V}_{\text{A}}}{\text{A}\times[\text{C}]\times\Delta\text{t}} \tag{1}\]
# Removal of Metal Ions in Spin-on Hardmask Using Functionalized Porous Silica Adsorbents
The metal ion removal efficiency of the functionalized adsorbents was evaluated for various metal ions (Al\({}^{3+}\), Cr\({}^{3+}\), Cu\({}^{2+}\), Fe\({}^{3+}\), Ni\({}^{2+}\), and Ti\({}^{4+}\)) that are considered critical ionic contaminants in SOH and PR processes. These residual metal ions, even in trace amounts, can have a significant negative impact on semiconductor performance. For instance, [PERSON] et al. showed that transition metal contaminants degrade device performance by introducing recombination centers or by affecting etch selectivity. Therefore, efficient removal of such trace metal ions is critical to ensuring high process reliability and product quality in semiconductor fabrication [13]. The adsorption performance was tested under three different solution conditions: a solvent-only solution, a monomer-dispersed solution, and a polymer-dispersed solution that represents the actual PR formulation condition. In addition, adsorption isotherm modeling was employed to analyze the adsorption behavior and underlying mechanisms. The study also systematically investigated how adsorption characteristics varied depending on the type of metal ion and the surface functional group, thereby evaluating the applicability of functionalized mesoporous silica for effective ion removal in SOH-related semiconductor processes. These findings are expected to contribute to the development of a more effective and chemically compatible ion-removal strategy for advanced lithographic applications. While previous studies have explored mesoporous silica materials for environmental applications such as wastewater treatment, few have focused on their use in removing residual metal ions from SOH solutions--a source of contamination in semiconductor manufacturing. This study uniquely investigates the performance of functionalized mesoporous silica (MCM-41 and SBA-15) in the presence of actual process-relevant solvents, monomers, and polymers, offering insights into real-world applicability. The novelty of this work lies in its systematic comparison of multiple functional groups (-NH\({}_{2}\), -SH, -CH\({}_{3}\), and -OH) under realistic industrial conditions, which has not been reported in the previous literature.
## 5 Results and Discussions
### Simulation Study
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(p\)} & \multirow{2}{*}{**\%Out**} & \multicolumn{4}{c}{**T\({}^{2}\)-FMCD**} \\ \cline{3-6}  & & **Accuracy** & **AUC** & **FP Rate** & **FN Rate** \\ \hline \multirow{6}{*}{20} & 5 & 0.994 & 0.997 & 0.006 & 0.000 \\ \cline{2-6}  & 10 & 0.994 & 0.997 & 0.007 & 0.000 \\ \cline{2-6}  & 20 & 0.994 & 0.996 & 0.007 & 0.000 \\ \cline{2-6}  & 30 & 0.799 & 0.670 & 0.007 & 0.653 \\ \cline{2-6}  & 40 & 0.599 & 0.500 & 0.008 & 0.992 \\ \cline{2-6}  & 50 & 0.500 & 0.500 & 0.007 & 0.991 \\ \hline \multirow{6}{*}{30} & 5 & 0.994 & 0.997 & 0.007 & 0.000 \\ \cline{2-6}  & 10 & 0.994 & 0.997 & 0.007 & 0.000 \\ \cline{2-6}  & 20 & 0.985 & 0.972 & 0.007 & 0.049 \\ \cline{2-6}  & 30 & 0.702 & 0.508 & 0.007 & 0.978 \\ \cline{2-6}  & 40 & 0.599 & 0.501 & 0.007 & 0.991 \\ \cline{2-6}  & 50 & 0.500 & 0.500 & 0.007 & 0.992 \\ \hline \multirow{6}{*}{40} & 5 & 0.994 & 0.997 & 0.007 & 0.000 \\ \cline{2-6}  & 10 & 0.994 & 0.997 & 0.007 & 0.000 \\ \cline{2-6}  & 20 & 0.886 & 0.726 & 0.007 & 0.541 \\ \cline{2-6}  & 30 & 0.698 & 0.502 & 0.007 & 0.989 \\ \cline{2-6}  & 40 & 0.600 & 0.502 & 0.007 & 0.989 \\ \cline{2-6}  & 50 & 0.501 & 0.501 & 0.007 & 0.990 \\ \hline \multirow{6}{*}{50} & 5 & 0.994 & 0.997 & 0.006 & 0.000 \\ \cline{2-6}  & 10 & 0.994 & 0.997 & 0.007 & 0.000 \\ \cline{1-1} \cline{2-6}  & 20 & 0.805 & 0.524 & 0.007 & 0.946 \\ \cline{1-1} \cline{2-6}  & 30 & 0.699 & 0.503 & 0.007 & 0.988 \\ \cline{1-1} \cline{2-6}  & 40 & 0.599 & 0.501 & 0.007 & 0.991 \\ \cline{1-1} \cline{2-6}  & 50 & 0.500 & 0.500 & 0.008 & 0.991 \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._with an outlier percentage of 40%. Besides \(p=5\) and \(p=40\), at 40%, the outlier percentage still has a pretty good AUC in the 70-90%. The FN Rate is not too high, namely 0.3-0.5. The absence of a decrease in performance when the quality characteristics increase indicates that robust MRCD-based Hotelling's \(T^{2}\) control charts can show consistent performance when used on high-dimensional data. The performance results of each diagram in detecting outliers were shown and explained. These results can be compared to find out which diagram has the best performance in detecting outliers in various conditions. The following is a visualization of the chart's performance comparison.