esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Process Parameters  
As mentioned above, the centrifugal force is an excellent option as an assisted technique in the BCC technology, since it improves the separation process. Figure 5 shows the efficiency (Eff), solute yield (Y), and percentage of concentrate (PC) at each CBCC cycle.  
Initially, the Eff presented an 82%, 77%, and 73% from the first to the third CBCC cycle, respectively. The results were better, in each cycle, than previous studies in our laboratory (with same conditions), with values close to 58%, 70%, 72%, and 79% in the first cycle to pineapple juice [28], sucrose solution [13], blueberry juice [17], and orange juice [19], respectively. The decrease in Eff is due to the increase in solutes (C\({}_{\text{s}}\)), that increase the viscosity and prevents a better extraction of solutes from the ice phase [53].  
The PC increased significantly (\(p\leq 0.05\)) at each cycle, with values of 74% (1 st cycle), 80% (2 nd cycle) and 85% (3 rd cycle). The results were significantly higher than the PC values obtained in our laboratory with different liquid samples such as blueberry juice [17], orange juice [19], and pineapple juice [28]. The different values are due to the apple has fewer components such as wastes, shells and/or seeds in the juice than other juices, which facilitates the separation of fractions.  
The solute recovery was calculated as solute yield (Y), and in the first cycle, it reached a value close to 0.79 kg solute per 1 kg initial (kg/kg). Later, Y showed a linear and significant increase, with values of \(\approx\)0.83 kg/kg and \(\approx\)0.86 kg/kg for the second and third CBCC cycle, respectively. The results had a similar trend in previous investigations in liquid samples [13; 14; 40]. This behavior could be associated with the solute mass (m\({}_{\text{s}}\)) at each centrifugation step, since the solid-liquid interface accumulates more mass due to the increase in the solute content [54].
## 4 Testing and Validation
Table 2 presents the measured sets of values (\(t\), \(\Delta l\)) and Figure 16 shows the curves that describe the variation of the axial contraction in time. The dots represent the measured values of the axial contractions, and the continuous lines show the polynomial regression curves suggested by Graph v. 4.4.2 software. The dependencies of \(\Delta l=f(t)\) type proposed by Graph software for the two curves represented above are:  
\[\Delta l(t)=-0.00256\cdot t^{3}+0.1915\cdot t^{2}-4.7083\cdot t \qquad t\in[0,\ 30) \tag{3}\]  
\[\Delta l(t)=-0.00083\cdot t^{3}+0.0895\cdot t^{2}-1.5716\cdot t-49.1 \qquad t\in[30,\ 60] \tag{4}\]  
The variation in time of the rotation angle of lever AB, respectively of the wrist, was measured by means of a E6A2-CS5C Omron rotary encoder mounted on the axis of joint A. Table 3 presents the measured values, while the corresponding curves are shown \begin{table}
\begin{tabular}{c c c c c c c c} \hline \(t\) [s] & 0 & 10 & 20 & 30 & 40 & 50 & 60 \\ \hline \(\Delta l\) [mm] & 0 & \(-30.5\) & \(-38.1\) & \(-38.2\) & \(-22.1\) & \(-8.1\) & \(-1.2\) \\ \hline \end{tabular}
\end{table} 
Table 2: Variation in time of the pneumatic muscle axial contraction. Figure 16: Variation versus time of the pneumatic muscle axial contraction. in Figure 17. The dots represent the measured values of the angles, being connected by polynomial regression curves proposed by Graph software. The estimating functions of the variation in time of the wrist rotation angle proposed by Graph are the following:  
\[\theta(t)=0.0133\cdot t^{3}-0.907\cdot t^{2}+20.195\cdot t-80\qquad\qquad\qquad t \in[0,\;30) \tag{5}\]  
\[\theta(t)=0.0072\cdot t^{3}-0.835\cdot t^{2}+25.265\cdot t-132.2\qquad\qquad t \in[30,\;60] \tag{6}\]  
The obtained experimental results materialized in the curves of Figures 16 and 17 confirm the validity of the proposed functional model generated and simulated by means of MATLAB(r). This is proved by the two graphs being very close as to their form and values to the modelled and simulated ones represented in Figures 10 and 11, the differences being minimal.
## 3 Results and Discussion
### Creation of the Geometrical Model of the Elliptical Gear Set
In the case of the created model of the elliptical gear set with eccentrically placed centers, the dedendum is determined by the pitch ellipse offset in the distance of 1.25 times of the module value in the normal plane (Figure 13). The addendum does not equal the module, because the involute and the non-involute portion of the tooth side profile would be in mesh. The addendum has to be reduced so that the correct mesh of the intermeshing gears occurs. Based on the tooth contact analysis, it was necessary to adjust the height of the addendum. Three-dimensional contact analysis in SolidWords was used. The reduction in height of addendum is shown in Figure 6:1 The design of parameters for gear dimensions was based on the modulus value \(\mathrm{m_{n}}=3.75\) mm. This module was specified by the customer. The tooth dedendum is \(\mathrm{h_{f}}=1.25\mathrm{m_{n}}\). The addendum of the tooth was modified, its value was reduced from value \(\mathrm{h_{a}}=\mathrm{m_{n}}=3.75\) mm to value \(\mathrm{h_{a}}=3\) mm. Figure 6:2 Resulting shape of the gear toothing. The difference between the tooth profile of the designed elliptical gear set and the tooth profile of the standard spur gear is shown in Figure 6:3 The profile of spur gear tooth with the number of teeth z = 24 and the module m\({}_{\text{n}}\) = m\({}_{\text{t}}\) = m = 3.75 mm is drawn using black color. The profile of the tooth denoted by the number 6 in the central point of mesh (Figure 5) is drawn using red color. At the mesh of the tooth with the intermeshing gear, the gear ratio u = 1 of the designed elliptical gear set is achieved. Standard spur teeth are teeth with a symmetrical profile. Left and right active--involute curve has the same shape. The teeth of the designed non-circular gear have an asymmetrical tooth profile. The left involute curve of the tooth profile is different from the right. In Figure 16 is an example of the difference for the left and right side of tooth  
Figure 6:4 Dedendum and addendum of teeth. Figure 6:5 Tooth profile comparison for tooth No. 6.  
number 6 of the proposed gear. It is the result of different evolutes of involute tooth curves (Figure 10). The gears for the given transmission with the time-variable gear ratio were designed as elliptical gears which were eccentrically placed so that the conditions of correct mesh were met (Figure 17).
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 5 The Performance Analysis of Dual-Hop Multi-Relay System
### Optimal Relay Selection Scheme: S-R-D Link  
The ORS scheme maximizes the instantaneous SNR at the receiver by selecting the best relay based on CSI of S-\(R_{l}\)-\(D\) links. The best relay (\(k\)) is selected by  
\[k=\arg\max_{l}\{\gamma_{l}\}, \tag{25}\]  
where \(\gamma_{l}\) is defined in (10). The e2e SNR, \(\gamma_{op}\), under the ORS scheme is given by  
\[\gamma_{op}=\max_{l}\{\gamma_{l}\}. \tag{26}\]  
Since all hops follow \(\mathcal{F}\) distribution, the CDF of \(\gamma_{op}\) is written as a product of CDFs of an individual branch as follows:  
\[F_{\gamma_{op}}(\gamma) =\prod_{l=1}^{L}F_{\gamma_{l}}(\gamma)=\prod_{l=1}^{L}\bigl{(}1- \overline{F}_{\gamma_{l}}(\gamma)\bigr{)}\] \[=1-\sum_{i_{1}=1}^{L}\overline{F}_{\gamma_{i_{1}}}(\gamma)+\sum _{i_{1}=1}^{L-1}\sum_{i_{2}=i_{1}+1}^{L}\overline{F}_{\gamma_{i_{1}}}(\gamma )\overline{F}_{\gamma_{i_{2}}}(\gamma)+\cdots+\] \[(-1)^{L-1}\sum_{i_{1}=1}^{L-(L-2)}\sum_{i_{2}=i_{1}+1}^{L-(L-3)} \cdots\sum_{i_{L-1}=i_{L-2}+1}^{L}\overline{F}_{\gamma_{i_{1}}}(\gamma) \overline{F}_{\gamma_{i_{2}}}(\gamma)\cdots\overline{F}_{\gamma_{i_{L-1}}}( \gamma)+\] \[(-1)^{L}\sum_{i_{1}=1}^{L-(L-1)}\sum_{i_{2}=i_{1}+1}^{L-(L-2)} \cdots\sum_{i_{L}=i_{L-1}+1}^{L}\overline{F}_{\gamma_{i_{1}}}(\gamma) \overline{F}_{\gamma_{i_{2}}}(\gamma)\cdots\overline{F}_{\gamma_{i_{L}}}( \gamma). \tag{27}\]  
The CDF can be represented in a short form as follows:  
\[F_{\gamma_{op}}(\gamma)=1+\sum_{l=1}^{L}(-1)^{l}\sum_{l}\overline{F}_{\gamma_ {l_{l}}}(\gamma), \tag{28}\]  
where  
\[\sum_{l}=\sum_{i_{1}=1}^{L-(l-1)}\sum_{i_{2}=i_{1}+1}^{L-(l-2)}\cdots\sum_{i_ {l-1}=i_{l-2}+1}^{L-1}\sum_{i_{l}=i_{l-1}+1}^{L}, \tag{29}\]  
and \(\overline{F}_{\gamma_{l_{l}}}(\gamma)=\prod_{n=1}^{l}\overline{F}_{\gamma_{ l_{n}}}(\gamma)\). In the case of identical branches, the CDF of \(\gamma_{op}\) is given by  
\[F_{\gamma_{op}}(\gamma)=\bigl{(}1-\overline{F}_{\gamma_{l}}(\gamma)\bigr{)}^{ L}=\sum_{l=0}^{L}(-1)^{l}\binom{L}{l}\overline{F}_{\gamma_{l}}^{l}( \gamma). \tag{30}\]
## 3 Results
### Comparison of TED-IM Group and TED-NM Group  
These results showed that RFV was greater in TED-NM than in TED-IM subjects (\(p<0.05\)) (Figure 3C). RFV/OV was higher in the TED-NM group than in the TED-IM group (Figure 3D). There was a significant increase in EMV in TED-IM subjects compared to the TED-NM group (\(p<0.05\)) (Figure 3A). EMV/OV and EMV-OA/OAV in the TED-IM group were significantly higher than that in TED-NM subjects (\(p<0.05\) and \(p<0.01\)) (Figure 3B,E). The difference in orbital volume, orbital apex volume, and EMV-OA between the orbits of TED-NM and TED-IM subjects was not significant (Table 1). To explain the ability of the EMV/OV to differentiate between cases with and without DON, ROC curves were used. The highest performance in identifying TED patients with motility impairment was achieved at EMV/OV = 15.24% with 81.6 percent sensitivity and 93.3 percent specificity (Figure 4A).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **TED-IM** & **TED-NM** & \(p\)**Value** \\ \hline Number of orbits & 62 & 30 & \\ Age (years) & 47.83 \(\pm\) 11.49 & 39.11 \(\pm\) 12.86 & \textless{}0.05(0.012) \\ Sex (male: female) & 31:31 & 8:22 & 0.309 \\ Parameters of orbits & & & \\ OV (cm\({}^{3}\)) & 22.31 \(\pm\) 0.38 & 23.06 \(\pm\) 0.65 & 0.357 \\ EMV (cm\({}^{3}\)) & 4.59 \(\pm\) 0.25 & 3.23 \(\pm\) 0.43 & \textless{}0.05(0.014) \\ RFV (cm\({}^{3}\)) & 7.60 \(\pm\) 0.46 & 9.86 \(\pm\) 0.64 & \textless{}0.05(0.012) \\ EMV/OV (\%) & 20.44 \(\pm\) 1.06 & 13.68 \(\pm\) 1.82 & \textless{}0.05(0.004) \\ RFV/OV (\%) & 38.89 \(\pm\) 20.36 & 42.95 \(\pm\) 2.84 & \textless{}0.05(0.023) \\ OAV (cm\({}^{3}\)) & 1.79 \(\pm\) 0.91 & 1.70 \(\pm\) 0.15 & 0.813 \\ EMV-OA (cm\({}^{3}\)) & 0.80 \(\pm\) 0.58 & 0.34 \(\pm\) 0.08 & 0.064 \\ EMV-OA/OAV (\%) & 42.43 \(\pm\) 15.37 & 20.46 \(\pm\) 5.41 & \textless{}0.05(0.001) \\ \hline \hline \end{tabular} OV: orbital volume, EMV: extraocular muscle volume, RFV: retroorbital fat volume, OAV: orbital apex volume, EMV-OA: extraocular muscle volume in orbital apex.  
\end{table}
Table 1: Clinical characteristics of TED patients with impaired motility (TED-IM) and normal motility (TED-NM).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **DON** & **Non-DON** & \(p\)**Value** \\ \hline Number of orbits & 30 & 32 & \\ Age (years) & 48.65 \(\pm\) 11.79 & 47.09 \(\pm\) 11.43 & 0.666 \\ Sex (male: female) & 16:14 & 15:17 & 0.775 \\ Parameters of orbits & & & \\ OV (cm\({}^{3}\)) & 23.02 \(\pm\) 2.51 & 21.97 \(\pm\) 2.04 & 0.172 \\ EMV (cm\({}^{3}\)) & 4.93 \(\pm\) 1.15 & 4.44 \(\pm\) 2.05 & 0.386 \\ RFV (cm\({}^{3}\)) & 8.38 \(\pm\) 2.07 & 7.57 \(\pm\) 3.00 & 0.466 \\ EMV/OV (\%) & 21.45 \(\pm\) 4.65 & 19.93 \(\pm\) 8.26 & 0.504 \\ RFV/OV (\%) & 36.22 \(\pm\) 9.80 & 34.47 \(\pm\) 14.10 & 0.711 \\ OAV (cm\({}^{3}\)) & 1.68 \(\pm\) 0.79 & 1.94 \(\pm\) 1.06 & 0.391 \\ EMV-OA (cm\({}^{3}\)) & 0.83 \(\pm\) 0.49 & 0.77 \(\pm\) 0.70 & 0.759 \\ EMV-OA/OAV (\%) & 48.81 \(\pm\) 12.79 & 34.07 \(\pm\) 14.30 & \textless{}0.05(0.003) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clinical characteristics of DON and non-DON groups.
# Are Serious Games an Effective Teaching Tool in Anatomy Education? A Systematic Review
## 3 Results
### Game Setting and Anatomy Assessment and Branches  
Nine studies were conducted in-person either in computer labs or classrooms [11; 12; 13; 16; 21; 22; 23; 24]. Two studies utilized the online option [15; 19]. One study made its game publicly available online, reaching over 16,000 users in more than a year collected through online written reviews [12]. Four studies focused on describing the technical and developmental part of the game without specifying the setting in which the study was implemented [1; 3; 10; 20].  
Nearly all studies (93.3%, 14/15) assessed the students' anatomy knowledge gained via serious games, with some also exploring perception, engagement, or skills enhancement. The developed games in terms of their focus varied between general anatomy topics such as musculoskeletal and vascular anatomy, and certain anatomical branches including neuroanatomy [10; 24] and histology [21; 23]. The serious games that are developed in  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & Selection & & Comparability & Outcomes & & & & & \\ \cline{3-10} S.N. & **Front Author** & **Representations of fine** & **Selection of** & **Selection of** & **Duration of** & **Consurability** & **Visu** & **Front Author**  
relation to different branches of anatomy are mentioned in Figure 2. These different games from the included studies are distributed across the different anatomy branches, highlighting the variety between studies.  
Intervention Evaluation  
Post-intervention evaluations were conducted in 13 of 15 studies. These evaluations assessed learning outcomes and, in some cases, gathered feedback through surveys [10; 11] or interviews [21]. Notably, two studies repeated the post-game tests on different intervals after a few days [23; 24]. Another study conducted a surprise test after 4-6 weeks [1]. The remaining two studies did not specify post-intervention assessments [19; 20].
## 3 Laboratory Model Tests
### Characteristics of Materials Used in the Experimental Procedure  
#### 3.2.1 Backfill  
Dry silicon sand was selected as the backfill soil in the experimental stand. A sieve analysis was performed in order to determine the particle size distribution curve. Based on the results obtained, the grain size distribution curve of the material used as backfill was plotted (Figure 4). The coefficient of uniformity was 2.0 and the average particle size was 0.15 mm. The unit weight was \(\gamma\) = 18.5 \(\times\) 10\({}^{3}\) N/m\({}^{3}\), the relative density = 0.73, and the void ratio = 0.47 (Table 3). The friction angle of the sand obtained from standard triaxial compression tests was \(\phi\) = 34.5\({}^{\circ}\) and the cohesion of the soil was \(c\) = 0 kPa.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline  & Soil: sand & \\ \hline Unit weight \(\gamma\) & kN/m\({}^{3}\) & 18.5 \\ Friction Angle \(\phi\) & degrees & 34.5 \\ Cohesion \(c\) & kN/m\({}^{2}\) & 0 \\ Relative density & - & 0.73 \\ Void ratio & - & 0.47 \\ Average particle size & mm & 0.15 \\ Uniformity coefficient & - & 2.0 \\ \hline  & Reinforcement: aluminum foil & \\ \hline thickness & m & 18 \(\times\) 10\({}^{-6}\) \\ ultimate tensile strength R & N/m\({}^{2}\) & 61 \(\times\) 10\({}^{6}\) \\ stiffness \(E\) & N/m\({}^{2}\) & 3533 \(\times\) 10\({}^{6}\) \\ elongation at failure & \% & 3.7 \\ coefficient of friction between & - & 0.05 \\ the soil and the reinforcement & - & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of the main materials used for construction of the RS wall.  
Figure 3: Cross-section of the experimental stand showing the positions of transducers.
## 2 Related Work and Research Gap
[22] explored design principles for cognitively accessible cybersecurity training, indicating the need for security practices that are understandable and usable by all user groups. However, a gap still exists in architectural models that effectively integrate accessibility and cybersecurity from the outset. Traditional web application architectures, such as the three-layered model comprising the Data Access Layer, Application Logic Layer, and Presentation Layer [23], have been widely adopted due to their simplicity and clear separation of concerns. These models focus on enhancing modularity and maintainability; however, they often treat security and accessibility as add-ons rather than integral components of the architecture. [PERSON] et al. [24] examined an architecture-based reliability analysis of multi-layered web applications but did not focus on integrating accessibility or security comprehensively. [PERSON] et al. [25] compared monolithic and microservice architectures, discussing scalability and performance but not addressing the integration of accessibility and cybersecurity. The lack of integration between accessibility and cybersecurity in existing models highlights the need for a holistic architectural approach. [PERSON] and [PERSON] [26] emphasized the importance of comprehensive strategies in digital transformation initiatives. By embedding accessibility and security considerations into each layer of the architecture, developers can create applications that are both inclusive and secure. In summary, while significant progress has been made in advancing accessibility and cybersecurity individually, there is a paucity of research on their integration within web application architectures. By reviewing the existing literature, we establish the necessity and relevance of a holistic approach, setting the foundation for our proposed solution.
## 3 Results
### Unimodal Data Approaches
#### 3.2.3 Unimodal Text Data Approaches  
Text analytics for emotion recognition has experienced significant growth in recent years, driven by a wide range of readily available and easily accessible data sources such as social media comments on Twitter, Facebook, Instagram, and blogs. Other researchers have used stock market data and tourism-related reviews; these sources offer valuable insight into the perceptions and emotions of investors in the financial markets, as well as the experiences and opinions of travelers in the tourism industry. These data have enabled the training and validation of text analytics models. Among the most prominent trends are deep learning models, such as LSTM, CNNs, bidirectional long short-term memory (Bi-LSTM), and gated recurrent units (GRUs), which are a variant of RNNs but designed to address the problem of gradient fading often found in standard RNNs. This is addressed by introducing \"gates\" that regulate the flow of information within the unit. These gates allow the GRU to retain long-term information and discard irrelevant information. Theseapproaches also include their hybrid variants. In addition, transformer-based models, such as bidirectional encoder representations from transformers (BERT), have emerged as one of the latest trends. These models were developed by Google and are known for their capability to understand the context of a word in a sentence from two directions (bidirectional), which is a significant improvement over previous models that read text sequentially (either left-to-right or right-to-left). Moreover, the BERT model architecture uses attention mechanisms to weight the influence of different parts of the input on the output, which allows the model to learn complex contexts and relationships between words and sentences, significantly improving performance on a wide range of natural language processing tasks, such as text comprehension, machine translation, and question answering. A good example of the application of these models is provided by the study reported by [PERSON] et al. [54], which presents a new model for sentiment analysis that combines the BERT-based transformer model RoBERTa and the recurrent neural network model GRU. RoBERTa helps understand the text using attention mechanisms, while GRU handles long-term information and avoids common issues like vanishing gradients. To address imbalanced datasets, the model uses data augmentation to increase the representation of minority classes. The RoBERTa-GRU model was tested on IMDD, Sentiment140, and Twitter US Airline Sentiment datasets, achieving high accuracy rates, demonstrating its strong performance in sentiment analysis.
# When Social Networks Meet D2D Communications: A Survey
## 3 How Social Ties Can Improve D2D Communications
### Communication Mode Selection
In conclusion, the communication mode selection can include various social ties to be used for managing channel condition, intercell interference and network load, based on the following social concepts: similar community interests, community bridges awareness, social knowledge, social selfishness, social-communication graph and community structure information.
## 2 Antenna Pointing Determination in Elevation for Spaceborne SAR Systems  
Homogeneous distributed targets are recognized as valuable references for retrieving the SAR antenna pattern in elevation. After compensating for deterministic behavior of these targets, like a known incidence angle dependency, variations of SAR data can be attributed to changes in the antenna gain. Dedicated antenna patterns like the one from a mono-pulse or notch beams provide a characteristic steep gain drop into a certain (predictable) radiation direction which can be detected and analyzed in a reliable way [17] to determine a potential discrepancy between predicted and actual antenna pointing.  
There are several homogeneous distributed targets conceivable for SAR antenna pointing estimation in elevation: The rainforest in the Amazon region is the most widely used reference target. Other rainforest areas (e.g., Cameroon) are less commonly used, mainly due to their smaller extent. Similarly, certain deserts and ice regions could be used, but they often suffer either from low backscatter or from long-term availability.  
The conventional technique uses a notch pattern to extract the antenna elevation pointing from the level 1 SAR data. Antenna pattern correction is not applied to these data. The method is conducted according to the following steps:  
1. masking of known inhomogeneities like rivers or deforested areas,
2. converting the radar brightness \(\beta\) given in the single-look (SLC) complex SAR data to normalized \(\gamma\)-values by \[\gamma=\beta\cdot\cos(\theta),\] (1) where \(\theta\) is the local incidence angle,
3. averaging a sufficiently large among of range lines along azimuth to compensate for fluctuations in the backscatter of the scene,
4. and comparing the retrieved \(\gamma\)-profile to the expected elevation profile extracted from the antenna model. The shift between both profiles equals the expected pointing offset.  
Step (1) accounts for the variation in the incidence angle and, hence, the illuminated area. In step (2) the antenna pattern estimation is improved by removing a-priori known areas which are not rainforest. These areas would increase the variability of the scene and would hence deteriorate the pattern estimate. The averaging done in step (3) is performed along azimuth direction. It assumes that the shape of the pattern is constant in azimuth direction in the focused SAR data, which is fulfilled as along as the SAR antenna pattern is stable over the integration time.  
The last operation (step 4) can be conducted in several ways: The position of the notch can be evaluated by curve fitting, e.g., using a polynomial to reliably estimate the notch position instead of just finding the smallest power value over elevation [6; 17]. Uncertainties of less than 8 mdeg have been reported using this approach [17]. As an alternative, the offset between predicted (from antenna model) and actual (measured) notch position can be retrieved using a correlation technique. This method is described in more detail in section 3 as it has several advantages when used in combination with the hereafter proposed technique.