esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
## 3 Results
### The Morphology of Porous Chitosan Micospheres
#### 3.2.2 Effect of the Concentration of Chitosan Solution  
In order to determine the optimal concentration of the chitosan solution for the porous microspheres, the morphological structures of the microspheres fabricated using various concentrations of chitosan solution (1%, 2% and 3%, \(w/v\)) with the freezing temperature kept at \(-20\)\({}^{\circ}\)C for 24 h followed by lyophilization are shown in Figure 3. It can be seen from Figure 3a that a stack of sheet-like structures, distributed throughout the aspherical architecture, was formed when the concentration of the chitosan solution was 1%. When the concentration was increased to 2%, a shriveled sphere with fibrous spines on the surface with irregular pores in the interior was prepared, as shown in Figure 3b. When the chitosan concentration was increased to 3%, on the one hand, porous structures with a good spherical shape were formed and dense and smooth microporous membranes appeared on the surface of the microsphere (Figure 3c). On the other hand, abundant round pores with a high degree of interconnectivity were distributed in the interior of microspheres, as shown in the cross section of the morphological structure (Figure 3c); the sizes of the inner pores were notably smaller than those of the chitosan microspheres prepared with 2% chitosan solution.  
Figure 3: Scanning electron micrographs of porous chitosan microspheres prepared using various concentrations of the chitosan solution: (**a**) 1%, (**b**) 2% and (**c**) 3% (\(w/v\)), with the freezing temperature kept at \(-20\)\({}^{\circ}\)C for 24 h.  
Obviously, chitosan concentration played an important role in the structure and morphology of the porous microspheres. It was noticed that a higher chitosan concentration resulted in a more compact structure and smaller pore sizes, both on the surface and the inside of the chitosan microspheres, which was in accordance with the fact that the increased difficulty for ice crystal formation was due to an increase in impurities and, in this case, the impurity was chitosan [28]. This phenomenon could be explained by the balance of the adsorption and desorption of a solute on the surface of an ice crystal significantly affected ice crystal growth [27]. At the same time, with an increase in chitosan concentration, more impurities tended to accumulate and adsorb on the surface of ice crystals, which hindered the growth of ice crystals. As a result, more ice crystals were formed, and the ice crystals became smaller and produced more dense pores. However, the low concentration of the chitosan solution formed ice crystals with a large size though in small amounts, which were connected with each other and grew into flakes and blocks, which increased the preparation difficulty of spherical ice crystals. Consequently, chitosan solution with a high concentration of 3% was chosen for further analysis.
## 4 Influence of Axial Joint Transfixion Degree on Energy Dissipation
### Analysis of the Relationship between Energy Absorption and Porosity Change  
The reflected waves elapsed time of the 0-100% transfixion joint specimens were extracted, and the energy density per unit time (\(E_{\rm VT}\)) of the specimens with different joint transfixion degrees was calculated by Formula (6). The results are shown in Table 2. The right side of Table 2 shows the porosity and porosity change rate of specimens with different joint transfixion degrees. Because the porosity of the specimen decreases in the compacted state or increases in the crack initiation, development, and expansion state, the change of the porosity is related to the dissipated energy, so the absolute value of the porosity change rate is calculated when calculating the porosity change rate. Figure 7 shows the change trend relationship of the energy absorption and porosity change rate of the specimen with the joint transfixion degree. Figure 6: Energy proportion of different transfixion degree joint specimens. Figure 7: The relationship curve between energy density per unit time and pore change of the specimen. It can be seen from Table 2 and Figure 7 that the \(E_{\rm VT}\) of the 0-100% transfixion degree joint specimens generally showed a trend of first decreasing and then increasing with the increase of transfixion degree. The \(E_{\rm VT}\) of the 80% transfixion joint specimen was the lowest, indicating that the specimen had the worst absorption of incident energy. The reason is that the transfixion degree is large, the load-bearing capacity of the specimen is weak, a small number of cracks in the specimen are initiated and expanded under the impact, and the time to complete the formation of surface cracks is short, and the number of tests is small. Therefore, this point can be excluded from the trend analysis of energy absorption. In the range of 0-40%, the \(E_{\rm VT}\) value decreased from 0.9060 J\(\cdot\)cm\({}^{-3}\cdot\)ms\({}^{-1}\) to 0.7135 J\(\cdot\)cm\({}^{-3}\cdot\)ms\({}^{-1}\), and the energy absorbed by the specimen kept decreasing in this range. When the transfixion degree is 40%, the energy absorption reaches the lowest level. Within the range of 40-100% (except 80%), the \(E_{\rm VT}\) value decreased from 0.7135 J\(\cdot\)cm\({}^{-3}\cdot\)ms\({}^{-1}\) to 0.9268 J\(\cdot\)cm\({}^{-3}\cdot\)ms\({}^{-1}\). Within this range, the energy absorbed by the specimen continued to rise. When the transfixion degree is 100%, the energy absorption reaches the highest level. Under the combined action of water-saturated freezing and impact load, more crack initiation and expansion consume more energy inside the specimen. It is extended to engineering practice, and it is aimed at cement mortar materials in alpine and long
## 3 Materials and Methods  
Mapless navigation in the domain of robotics refers to navigation in an unknown environment without the use of a predefined model of the environment. Mapless navigation can include object avoidance and a goal state. The former serves to navigate an agent in an environment while avoiding collision with obstacles in the environment. Mapless navigation with the inclusion of a goal state aims to navigate an agent in an environment from an arbitrary start state to the goal state. Traditional supervised learning methods have been applied to solve the problem but require large amounts of training data to generalize unseen environments. Recently, few-shot learning method has been proposed to solve the problem of data deficiency. Few-shot learning allows for function approximation from fewer examples, in this work we propose the use of a Siamese convolutional neural network that makes use of two identical neural networks to learn feature embedding. Reinforcement-learning-based approaches to mapless navigation have relied on the distance to the goal state being known a priori, or that the distance to the goal can be obtained at each timestep. In simulated environments, obtaining the distance to the goal is a trivial task but when applied to the real-world the distance to the goal must be obtained through complex localization techniques. The use of localization techniques increases the complexity of the agent's design. For agents navigating in unknown environments, using information about the goal to either form part of the state representation or act as the reward mechanism is expensive in terms of robot design and computing cost. In this work, we make use of a pre-trained Siamese convolutional neural network to navigate our agent from a start to goal state. We demonstrate that the Siamese network is able to learn the 'distance' between the agent and its goal using relatively few samples for training, allowing for mapless navigation using only visual state information thus reducing the need for complex localization techniques.
## 3 Example Test and Result Analysis
### Test Results and Analysis
Based on the monitoring environment shown in Figure 5, the method and comparison method proposed in this article were used to monitor the uplift deformation of the soft rock tunnel invert at a grouting depth of 5 m in the K13 + 060\(-\)K13 + 170 section, and the monitoring results of the uplift deformation were compared. The comparison results are shown in Figure 7.  
Figure 6: Deformation monitoring results of uplift at different grouting depths. Figure 7: Monitoring results of uplift deformation using different methods at a grouting depth of 5 m [6; 7]. According to Figure 7, the monitoring results of inverted arch uplift deformation obtained by different comparison methods at a grouting depth of 5 m are significantly different from those obtained by this method; these methods cannot effectively monitor the effect of arch bottom grouting reinforcements. From this, it can be seen that the method proposed in this article has good applicability to the monitoring of inverted arch uplift deformations.
## 4 Experimental Results
### Impact of the Number of Fixation Points
Pearson (PCC) and Spearman (SROCC) values obtained with and without the saliency-based patch selection as well as with a random patch selection for each viewing distance of the CID:IQ database. Highest values are in bold. Figure 6: Correlation values when changing the number of fixation points. Figure 8 shows the patch scores predicted for a given image and the corresponding subjective scores for two viewing distances. As can be seen, there is a gap between the predicted patch scores for the distance 50 cm (blue curve) and those predicted for the distance 100 cm (green curve). This gap reflects well the gap between the corresponding subjective scores (black and red dotted lines). Therefore, the integration of the viewing distance as input to our model allowed to well shift the predicted score according to the viewing distance considered. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**50 cm (2.5*H)**} & \multicolumn{2}{c}{**100 cm (5*H)**} & \multicolumn{2}{c}{**ALL**} \\ \hline
**Selection** & **PCC** & **SROCC** & **PCC** & **SROCC** & **PCC** & **SROCC** \\ \hline \multicolumn{6}{c}{**With integration of the viewing distance (proposed model)**} \\ \hline
**Random** & 0.757 & 0.750 & 0.764 & 0.718 & 0.750 & 0.729 \\ \hline
**No** & 0.819 & 0.815 & 0.819 & 0.775 & 0.813 & 0.797 \\ \hline
**Saliency** & **0.870** & **0.867** & **0.870** & **0.846** & **0.876** & **0.865** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 7: Loss in training and validation sets across the number of epochs for one splitting of the CID:IQ dataset.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 2 Materials and Methods
### Algorithm Design
#### 2.4.1 Stage 1: Combined Acceleration Interval Counting Method  
This study analyzed the collected acceleration data related to horse activity levels and identified the relationship between activity levels and acceleration (Figure 6). Statistical findings revealed that the combined acceleration ranges (units m/s\({}^{2}\)) for low activity are [9.0, 10.5], for medium activity are [7.0, 15.0], and for high activity are [2.0, 40.0]. These specific combined acceleration ranges constitute the boundary values for the interval counting method.  
Specific analysis methods:  
Equation (2) is the counting function, where \(A=[a_{1},a_{2},a_{3},\ldots,a_{n}]\) represents a collection of combined acceleration values, and \(a_{n}\) represents the \(n\)th combined acceleration. For each element \(A_{i}\) in the array \(A\), if it satisfies \(x\leq A_{i}\leq Y\), then the value of \(I(x\leq A_{i}\leq Y)\) is 1; otherwise, its value is 0. By assessing all these combined acceleration values, the total count of elements within the range \([x,y]\) is obtained.  
\[count(A,x,y)=\sum_{i=1}^{n}I(x\leq A_{i}\leq Y) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activity Levels** & **X-Axis** & **Y-Axis** & **Z-Axis** & **ACC** \\ \hline \multirow{4}{*}{high activity} & \(-23.8\) & \(-19.83\) & \(10.23\) & \(32.62\) \\ \cline{2-5}  & \(-22.64\) & \(16.25\) & \(12.59\) & \(30.58\) \\ \cline{2-5}  & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \cline{2-5}  & \(3.6\) & \(-13.36\) & \(-2.86\) & \(14.12\) \\ \cline{2-5}  & \(-3.3\) & \(0.68\) & \(2.09\) & \(3.96\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont.\)  
Figure 6: Signal characteristics of combined acceleration for low-activity, medium-activity, and high-activity behaviors.  
\(N\) represents the size of the data collection window, which is determined by the specific experimental setup, and \(f(A)\) represents motion behaviors. Equation (3) individually computes the number of combined acceleration values within specific intervals for low-activity, medium-activity, and high-activity behaviors. If this count exceeds \(\frac{2N}{3}\) (see Table 3) of the total array count, it is considered indicative of the corresponding activity level occurring.  
\[f(A)=\left\{\begin{array}{ll}\textit{``low activity''}&if\ count(A,9.0,10.5) \geq\frac{2N}{3}\\ \textit{``medium activity''}&if\ count(A,7.0,15.0)\geq\frac{2N}{3}\\ \textit{``high activity''}&if\ count(A,2.0,40.0)\geq\frac{2N}{3}\\ \end{array}\right\}. \tag{3}\]
## 2 Methods
### Comparative Study
#### 2.3.2 Topology Optimization in Ansys  
Topology Optimization without AM Constraints:  
In Phase 1, the objective functions (i.e., compliance, stress, and stress and compliance) and the volume constraint were only used to find the optimal geometry. In the first phase of optimization, the variables of compliance and stress are minimized both together and individually relative to the volume fraction constraint (i.e., \(V\leq V_{\text{max}}\)). In this study, \(V_{\text{max}}\) is set to be 30% of the initial design domain volume.  
Topology Optimization with AM Constraints:  
In the second phase, the AM constraints (overhang and member size) are employed along with the volume constraint to perform the topology optimization. In order to use the overhang constraint, the build orientation is set in ANSYS. The build orientation may have a significant impact on the final optimized design, so in the presence of overhang constraints, topology optimization is performed for all three cases to study orientation effects. Six different build orientations (\(\pm X\), \(\pm Y\), and \(\pm Z\)) are selected for additive manufacturing, and the best build orientation with the lowest objective function value is also used for further analysis in Sections 3.2 and 3.3. As part of the final analysis, three types of objective functions with volume and AM constraints are defined for topology optimization. Compliance, Stress, or Compliance + Stress is minimized subject to the volume fraction constraint and AM constraints, including (1) overhang (the maximum allowable overhang angle = 45\({}^{\circ}\)) and member size (the minimum member size = 25 mm).
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 6 Biological Activity of _A. aculeata_ Extract
### Anti-Inflammatory and Diuretics Effects  
The oil from A. aculeata pulp (MPO) exhibited anti-inflammatory and antimutagenic activities, possibly related to its profile of fatty acids and antioxidant compounds, such as carotenoids [12]. Recent studies highlight the antioxidant and medicinal potential of Acrocomia aculeata, including its anti-inflammatory, diuretic, antidiabetic, and antimutagenic properties [14; 30]. These properties may be related to the presence of bioactive compounds, such as fatty acids, phenolic compounds, flavonoids, and carotenoids. The exploitation of such properties may contribute to the development of new products based on natural products, such as alternative food supplements and treatments for diseases related to oxidative stress [17]. In addition, bocaiuva is rich in galactoglucamannan, a polysaccharide that also acts in anti-inflammatory processes [30].  
Studies using microencapsulated oil from Acrocomia aculeata have shown significant effects of the fruit as a diuretic and anti-inflammatory in tests conducted on rats [30]. The authors evaluated the diuretic action of macauba oil by analyzing diuresis (urine production) and the sodium content in the rats' urine. The results indicated a significant increase in urine production and sodium excretion, indicating a diuretic effect of _Acrocomia aculeata_ oil.
# Quantum Key Distribution with Post-Processing Driven by Physical Unclonable Functions
## 2 Results and Discussion
### Security Considerations
In particular, the \(j\)th entry in the databases of CRPs for two different users, say A and B, pertains to the joint keys \(K_{1}:=k_{j}^{\text{(MA)}}\oplus k_{j}^{\text{(A)}}\) and \(K_{2}:=k_{j}^{\text{(MB)}}\oplus k_{j}^{\text{(B)}}\), where all of the individual keys refer to the same challenge but to different independent PUFs, namely, \(\text{PUF}_{\text{MA}}\), \(\text{PUF}_{\text{A}}\), \(\text{PUF}_{\text{MB}}\) and \(\text{PUF}_{\text{B}}\). By virtue of the OTP encryption, even if user A is malicious, they cannot deduce either \(k_{j}^{\text{(MB)}}\) or \(k_{j}^{\text{(B)}}\) from \(K_{1}\) and \(\text{PUF}_{\text{A}}\), provided that conditions (C1) and (C2) are satisfied for the PUFs under consideration. Perhaps one way to avoid the use of different PUFs for each user is to consider the use of PUF duplicates in the spirit of the recent work by [PERSON] et al. [45]. In this preprint, the authors demonstrate for a particular type of PUF, that the original PUF manufacturer (which is a trusted authority in our case), can fabricate multiple identical structures (tokens) that possess essentially the same optical scattering behaviour (i.e., the same challenge-response pairs), but at the same time, they remain unclonable for external adversaries who do not know their internal features and inner construction plan. The use of PUF duplicates deserves a thorough analysis, which goes beyond the scope of the present work because it may open up new security loopholes which are not present at the moment.
# Coherent Image Source Modeling of Sound Fields in Long Spaces with a Sound-Absorbing Ceiling +
## 1 Introduction  
Sound prediction is very important for design of practical long spaces such as traffic tunnels and subway stations to evaluate acoustical qualities such as speech intelligibility of public address systems [1]. For noise control in such long spaces, it is often the case to apply acoustical liners to the space ceiling for larger noise attenuation. Regarding sound prediction in such spaces, classic room acoustics formulas are unsatisfactory [2, 3] because the sound field is not diffused due to the extreme dimensions. The commonly used incoherent geometrical acoustics models [4, 5, 6, 7, 8] cannot account for the interference between multiple sound reflections on impedance boundaries, which were experimentally observed to be distinct and can notably affect the sound prediction accuracy in this situation [3, 9, 10], especially at lower frequencies and in early parts of the impulse response [10].  
For the coherent geometrical acoustics models, [PERSON] et al. developed a numerical model for coherent sound prediction inside long spaces [3, 11] and afterwards applied this prediction model into full-scale tunnels [9] and a long space with impedance discontinuities [12]. It was shown that their coherent prediction model provides much better prediction accuracy than the usual incoherent ones for long spaces with reflective boundaries. However, for applications with sound-absorbing boundaries, the applicability of their model may be limited. The numerical model of [PERSON] et al. [3] originated from a coherent image source method by [PERSON] and [PERSON] [13], in which it is implicitly assumed that the wave front shapes remain spherical during each successive reflection of the initial spherical wave radiation [13]. This assumption may hardly hold for reflections on sound-absorbing boundaries.  
Recently, [PERSON] et al. [14] proposed a coherent image source method for fast yet accurate sound prediction in flat spaces with absorbent boundaries. They proposed different refection coefficients to evaluate the reflections on the absorbent and reflective boundaries, which avoids the prediction difficulties with absorbent boundaries in the method of [PERSON]. Unfortunately, their model is currently limited to spaces with two parallel infinite boundaries in theory.  
Upon reviewing the studies above, there is still the problem of sound prediction in long spaces with sound-absorbing boundaries for practical noise control. In this paper, a coherent image source model is extended and examined theoretically and experimentally for fast yet accurate sound prediction in long spaces with sound-absorbing boundaries.
# System-Level Statistical Eye Diagram for Signal Integrity
## 2 Statistical Eye Diagram
The DER approach obtains a statistical eye diagram with the rising and falling responses [22]. The difference between the SBR and the DER approaches is the consideration of the asymmetry between the N/P-type metal-oxide-semiconductors (N/PMOSs). The N/PMOS have independent electrical characteristics. Thus, the rising and falling responses are always asymmetric in the waveform. Therefore, the DER approach constructs an eye diagram with the separate rising and falling responses to consider the above asymmetry. However, the purpose of the DER approach is to handle the bit transitions in an eye diagram calculation. To reflect the effect of the encodings onto an eye diagram, the SBR method has to be converted in terms of the bit transition. The conversion may limit the probability calculation. Therefore, the DER method was used in [21]. The assumption in the conventional estimation method is that ONE and ZERO have the same probability. However, the assumption is no longer valid for the encoded channels. As a result of the encodings, the probability of the bit transitions might be lower or higher than 0.5. The stochastic model has the resultant bit probabilities. The stochastic model for 8B/10B encoding was obtained by counting the number of ONEs in the LUTs. The calculated probabilities were defined as the stochastic model for 8B/10B encoding. Likewise, the stochastic model for TMDS encoding was obtained by calculating the bit transition probabilities using the TMDS algorithm. Hence, the TMDS algorithm is replaced by the corresponding stochastic model. Therefore, a statistical eye diagram for the TMDS-encoded channel is obtained by the above DER method and the stochastic model. Figure 5 shows the statistical eye diagrams depending on the encoders. In an eye diagram, the transition area corresponds to the rising or falling region. And the non-transition area corresponds to the top and bottom regions. The statistical eye diagram without any encoding had similar probabilities for the transition and non-transition areas. However, the non-transition area had a lower probability compared to that of the transition area in the statistical eye diagram. Likewise, the TMDS-encoded eye diagram shows that the transition area has a lower probability compared to that of the non-transition area. Figure 4: The above figures show the results of (**a**) 8B/10B and (**b**) TMDS encoding, respectively. Their purposes are opposite. Thus, the number of bit transitions is increased by 8B/10B encoding and decreased by TMDS encoding [21]. Therefore, the proposed method with the stochastic model for the encoder successfully shows how the encoder was interpreted in the statistical eye diagram.