esa-sceva/satcom-chunk-collection huggingface dataset:
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 3 Properties of NSPs
### Modifications of NSPs  
Modifications of polysaccharides include changing their structure using chemical, physical and biotechnological methods in order to obtain compounds with desired properties. The most commonly used modifications are cross-linking and hydrolysis.  
Cross-linking of AX results in an increase in molar mass, an almost 100-fold increase in the viscosity of their solutions compared to unmodified ones, and an increase in the water-binding capacity of these polysaccharides [151]. AX molecules are subjected to the crosslinking process due to the presence of fertile acid molecules in their structure, with the help of which the AX molecules are linked [9,83,86,110]. The addition of cross-linked AX to bread dough causes an increase in the water absorption of flour, reduces the hardness of the bread crumb, and improves organoleptic properties [83,154,155]. Information on cross-linking of other non-starch polysaccharides from cereal grains is scarce.  
Another frequently used method of modifying NSPs is their partial enzymatic hydrolysis, for which enzymes naturally present in the grain and/or seeds can be used. Enzymatic hydrolysis of AX is most often carried out using the endo-1,4-xylanase enzyme. As a result of hydrolysis, the molecules are divided into smaller fragments [156]. Hydrolyzed AX are characterized by lower molar mass and higher solubility, and their aqueous solutions have lower viscosity compared to native or cross-linked AX [9,83]. Hydrolysis of \(\beta\)-glucan chains leads to changes in the structure of the molecules; as a consequence of such modifications, the molar mass of \(\beta\)-glucan and the viscosity of their solutions are reduced [157]. Enzymatic hydrolysis of oat grain \(\beta\)-glucans using a commercial cellulase resulted in a decrease in the molar mass of \(\beta\)-glucans. As a result of reducing the molar mass of \(\beta\)-glucans, their solubility increased by 78-100% compared to non-hydrolyzed \(\beta\)-glucans. In contrast, the water-holding capacity of hydrolyzed \(\beta\)-glucans decreased by 60-76% compared to non-hydrolyzed \(\beta\)-glucans [158]. \(\beta\)-glucans from bran and oat flakes were also modified using lichenase. As a result of this modification, oligosaccharides with a degree of polymerization (DP) \(\geq\) 3 (but lower than DP \(<\) 10) were obtained. The hydrolysis of \(\beta\)-glucans with lichenase positively influenced the yield of these polysaccharides from the soluble fraction of dietary fiber [159].  
It is worth noting that above mentioned modifications of NSPs may also occur naturally in dough during the breadmaking process because the modifying agents are naturally present in dough ingredients. Observing the strong impact of such modifications on the molecular properties of NSPs, it may be expected that they may have a high impact on the process and properties of the final product. However, further research is needed to reveal and characterize the modifications, their mechanism, and their effect on the whole breadmaking process.
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 1 Introduction  
The secondary mirror must maintain good face accuracy against ambient temperature changes, manufacturing limitations, installation, gravity, heat transfer, and material properties. These factors can cause a relative attitude shift between the primary and secondary mirrors, leading to an optical axis tilt, thus affecting the system's pointing accuracy and imaging quality. Therefore, the relative positions between the primary and secondary mirrors must be corrected. Due to the large mass, it is difficult to move the primary mirror. In contrast, due to its small size, the secondary mirror can be adjusted in multiple degrees of freedom (DOFs). As an essential part of the telescope system [1], the secondary mirror supports and changes its position. It must maintain a close range of position and attitude, thus ensuring good imaging quality of the telescope system [2]. The adjustment mechanism includes piston and tip/tilt, which eliminates third-order coma aberrations, and enables the elimination of third-order comet aberrations caused by deviations in the position of the primary and secondary mirrors. It is essential to consider the chopping of the rapid oscillation of the secondary mirror in IR modulation technology. Therefore, the adjustment mechanism of the secondary mirror must be designed as a system with at least three DOFs. The BLAST-TNG telescope uses three linear actuators to adjust the three DOFs of the secondary mirror piston/tip/tilt [3, 4]. The William Herschel Telescope uses four Focus Translation Units to correct the primary focus for the three DOFs of the piston tip/tilt [5, 6]. The Stewart stage is employed in the secondary mirror adjustment mechanism (SMAM) of large aperture telescopes, such as VST telescopes [7, 8, 9] and LSST telescopes [10, 11]. It offers many advantages, such as high stiffness, six degrees of freedom of adjustment, and non-accumulation of position errors [12]. In addition to five DOFs of adjustment, the SOFIA sub-mirror has a fast chopper with frequencies up to 20 Hz [13, 14].  
This paper deals with the design of the secondary mirror adjustment in the context of a large vehicle-mounted telescope. It is an optical telescope with an aperture greater than 2 m and is used for applications with significant aberrations. First, the kinematic modeling of the mechanism is conducted. The designed dynamic secondary mirror truss adjustment mechanism is analyzed through static and modal analyses in finite element analysis software. Then, kinematic performance tests on the test prototype are performed to verify the movement accuracy of the designed mechanism requirements. The presented study provides a guideline for SMAMs and large aperture telescopes.
## 3 Results and Discussion
### Potential for Beach Sand Solidification by MICP Treatment
#### 3.3.1 Effect of Bacteria Population and Reactant Concentration
In contrast to that, the nucleation of new crystals is more dominant than the growth of existing crystals when the carbonate concentration is lower [24]. The reduced shear stresses and availability of nutrients at particle contact points cause bacteria to prefer smaller surface features like grain contacts [62] whereas cells will also be distributed over grain surfaces depending the surface properties of grains [63]. Therefore, in this case, numerous smaller crystals will be first formed which will eventually be developed to a dense crystal layer with the repeated injection of cementation solution. From these results, it is apparent that uniformly distributed smaller crystals precipitated on grain surfaces and gaps between sand particles provide better strength and homogeneity rather than continuous heterogeneous precipitation of larger crystals. This is in line with the previous findings reported in literature. [PERSON] and [PERSON] [41] stated that concentrations above 0.5 M of urea and CaCl\({}_{2}\) reduce the efficiency of MICP. [PERSON] et al. [56] and [PERSON] et al. [12] also demonstrated that low concentrations of cementation solution yields higher specimen strengths. Moreover, the flow rate of solutions through the column also has a crucial effect on the precipitation process and thus should be  
Figure 9: SEM images of showing variation of crystal morphology for the samples treated under different bacteria and reactant concentrations: (**a–c**) 0.5 M cementation solution with bacteria injected twice; (**d–f**) 1.0 M cementation solution with bacteria injected only in the beginning. maintained at a suitable rate for homogeneous strength improvement [64]. On the other hand, utilizing a different kind of bacteria or a different bacterial cell concentration would result in a precipitation pattern and crystal morphology different to what has been obtained in this study.
# Relative Pose Estimation between Image Object and ShapeNet CAD Model for Automatic 4-DoF Annotation
## 2 Literature Review  
### Retrieval of CAD Models  
With regard to one of the SHREC tracks, [PERSON] et al. [4] introduced various techniques for retrieving CAD models and estimating the pose of an object using the 2D information and 3D depth information of the object from an RGB-D image. In their study, a 3D CAD model dataset called ObjectNN was provided to track participants, and the performances of various algorithms were compared. ObjectNN was composed of 1667 RGB-D images obtained from the SceneNN [5] dataset and 3308 CAD models of 20 categories obtained from ShapeNet [6]. Their study compared the performances of six deep-learning-based algorithms and three general feature-based algorithms, and the results of the experiment showed that the model retrieval performance of the deep-learning-based algorithms was superior to that of the feature-based algorithms.
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 3 Pet Behavior Prediction via S2 GAN-based Data Synthesis
### Data Collection  
Data collection focused on behaviors occurring in indoor environments such as homes. To ensure smooth data collection, the setup was configured to enable data gathering from a webcam for CCTV and wearable devices around the data collection device through communication. During data collection, the pet may or may not be within the camera's capturing radius. However, it is assumed that the sensor data are collected without missing values. Herein, data related to the natural behaviors of pets were collected to predict pet behavior. Therefore, behaviors such as petting or touching the dog during filming are allowed unless it significantly disturbs the collar. However, in the case of data bias and shortage of data for specific behaviors, actions that can be induced through training (such as sitting and lying down) are permitted.  
Pets exhibit various behaviors depending on their surroundings and interactions. Representative actions must be selected for monitoring pet behaviors to mitigate the individual differences in common behaviors among pets. [PERSON] et al. investigated the impact of factors such as breed, gender, and neutering status on pet behavior when left alone at home [29]. Their analysis of 77 pets revealed passive and low-activity levels in the absence of their owners. Therefore, pet behaviors in an indoor environment are monitored herein, emphasizing low-activity postures and behaviors such as walking and sniffing.  
This paper selects and defines nine representative behaviors based on previous studies, as shown in Table 2. The duration of each behavior can vary significantly. Some behaviors occur instantaneously and conclude quickly, while others persist for relatively longer periods. To enable the model to effectively learn and reflect this diversity of behaviors, it is necessary to establish a uniform sequence length that can encompass all types of actions. Accordingly, by considering the minimum and maximum durations of behaviors with short durations (e.g., shaking, scratching, fluffing, and barking) and those with long durations (e.g., walking, sniffing, and eating/chewing), the sequence length was set to 3 s.  
Figure 2: Overall process of pet behavior prediction via S2 GAN-based data synthesis.  
All video and sensor data were stored and managed based on the starting time of the behavior. Video data were saved with the name of the pet targeted for prediction, video file name, start time, and end time of the video. Sensor data were saved with the name of the pet targeted for prediction and the MAC (Media Access Control) address of the wearable device and collected three-axis accelerometer data, three-axis gyroscope data, three-axis magnetometer data, and time. Because sensor data were collected at 50 Hz, all data were synchronized based on microseconds.
## 3 Model Design
### Cooperation Probability and Energy Decomposition
#### 3.3.3 Cooperation and Energy Decomposition  
We consider the cooperation probability matrix. Suppose we have n nodes. The probability of forwarding messages from n nodes to different nodes is different. In other words, the selfishness of nodes for different nodes is different. We use the above method to calculate the cooperation value to form a cooperative forwarding probability matrix. Considering that the cooperation relationship between nodes is related to the ratio of the remaining energy of nodes, we believe that the greater the residual energy ratio of nodes, the more stable the cooperative relationship of nodes. It is significant to analyze the probability of node cooperation through the residual energy ratio of nodes. From the above analysis, we can define this as:  
\[\widetilde{R}=E^{T}\circ R \tag{19}\]  
where \(E\) is a vector with a residual energy ratio of \(n\) nodes, and the \(\circ\) symbol is defined as each element corresponding to the \(i\)-th column element multiplied by the \(i\)th column of \(R\). The new matrix we get is still represented by \(R\), which is \(R=\widetilde{R}\).  
Therefore, we let \(r_{ik}\) represent the probability of the message node, \(n_{k}\), forwarding sent by node, \(n_{i}\). We set \(N\in R^{l\times n}\) and \(Y\in R^{l\times n}\) as the feature matrix of potential nodes and cooperation factors, and the column vectors, \(N_{i}\) and \(Y_{k}\), respectively represent potential feature vectors of the node and cooperation factor. We define the conditional distribution of the observed cooperative probabilities as:  
\[p(R|N,Y,\sigma_{R}^{2})=\prod\limits_{i=1}^{n}\prod\limits_{k=1}^{n}\mathcal{N }\left[(r_{ik}|g(\sum\limits_{z\in\kappa(i)}S_{iz}N_{z}^{T}X_{jk}),\sigma_{R}^ {2})\right]^{I_{ij}^{R}} \tag{20}\]  
where \(I_{ij}^{R}\) is an exponential function. If node \(n_{i}\) has the probability of forwarding the message sent by node \(n_{k}\), it is equal to 1, otherwise it is equal to 0. We also place zero-mean spherical Gaussian priors [24; 25] on the node and cooperation factor feature vectors:  
\[p(N,\sigma_{N}^{2})=\prod\limits_{i}^{n}\mathcal{N}(N_{i}|0,\sigma_{N}^{2} \mathrm{I})\,p(Y,\sigma_{Y}^{2})=\prod\limits_{k}^{n}\mathcal{N}(Y_{k}|0, \sigma_{Y}^{2}\mathrm{I}) \tag{21}\]  
Therefore, through the node and cooperation factors features with a simple Bayesian inference [26] in Formulas (20) and (21), we have:  
\[\begin{array}{l}p(N,Y|R,\sigma_{R}^{2},\sigma_{N}^{2},\sigma_{Y}^{2})\\ \propto p(R|N,Y,\sigma_{R}^{2})p(N|\sigma_{N}^{2})p(Y|\sigma_{Y}^{2})\\ =\prod\limits_{i=1}^{n}\prod\limits_{k=1}^{n}\mathcal{N}\left[(r_{ik}|g(N_{i} ^{T}Y_{j}),\sigma_{R}^{2})\right]^{I_{ik}^{R}}\times\prod\limits_{i=1}^{n} \mathcal{N}(N_{i}|0,\sigma_{N}^{2}\mathrm{I})\times\prod\limits_{k=1}^{n} \mathcal{N}(Y_{k}|0,\sigma_{Y}^{2}\mathrm{I})\end{array} \tag{22}\]
## 3 Design of the DAB Converter Prototype
### Output Capacitor Selection  
Output capacitors are commonly used to regulate and filter output voltage output. Equivalent series resistance (ESR) inside the capacitor affects the output voltage ripple. Increasing the RMS value of the capacitor current leads to more loss dissipated across the ESR of capacitance. Taking these factors into account, the output capacitor was selected to keep the ripple voltage below 5% as a design requirement. This paper calculates the capacitance of capacitors \(C_{1}\) and \(C_{2}\), respectively, using Formula 6 and Formula 7 [16], where the ripple of the \(V_{Dchus}\) (\(\Delta V_{Dchus}\)) is 6 V, and the ripple of \(V_{bat}\) (\(\Delta V_{bat}\)) is 0.75 V.  
\[C_{1}=\frac{P_{Dchus}}{2\times f_{sw}\times V_{Dchus}\times\Delta V_{Dchus}} \cong 2.0833\ \upmu\text{F} \tag{6}\]  
\[C_{2}=\frac{P_{bat}}{2\times f_{sw}\times V_{bat}\times\Delta V_{bat}}\cong 133.3333\ \upmu\text{F} \tag{7}\]  
Considering the non-ideal case and the requirement for bidirectional power conversion, one 100 \(\upmu\)F/450 V capacitor was chosen for the high-voltage side, and three 120 \(\upmu\)F/63 V capacitors in parallel were selected for the low-voltage side.
## 5 Results and Discussion
### Synthetic Attributes  
The second set of experiments ignore the real attributes of the graph, replacing them with a set of randomly generated synthetic attributes. The purpose is to evaluate the relative contribution of the discrete vertex labels and numeric attributes to the graph structure. By reducing the number of numeric attributes to zero, we can evaluate the contribution of the numeric attributes in isolation.  
We replaced the real labels in the input graph with a synthetic vertex attribute taking \(2^{0}\dots 2^{9}\) values allocated uniformly at random, then learned the edge weight distributions using VI as normal. We compare our results with an alternate interpretation of the MAG model, which ignores the true attribute values from the input graph and represents attributes as latent variables, which are learned using a VI EM approach [14]. We also show Agwan with one real attribute for comparison.  
Vertex Strength (Figure 9): Agwan with synthetic attributes significantly outperforms MAG for the accuracy of the vertex strength distribution, and has similar accuracy to Agwan-R1. Varying the number of synthetic attributes has a small effect on the accuracy. We conclude that vertex strength is dependent on both edge weight and vertex label distribution, but the edge weights play a more important role.  
Spectral Properties (Figure 10): Agwan has a very close fit to the spectral properties of the input graphs. Varying the number of attributes has a moderate effect on the closeness of fit. On the undirected graph, MAG-L9 matches the singular vector very closely but performs poorly with the singular values; in general, MAG is a poor fit as the edge weight distribution is not taken into account. These results confirm that the spectral properties are dependent on both the vertex label distribution and the edge weight distribution, and the degrees of freedom of the vertex labels are important.  
Clustering Coefficients (Figure 11): Agwan and MAG are both significantly more accurate using synthetic attributes than with real attributes. This (somewhat surprising) result implies that clustering is not closely related to the real attribute values. While real labels appear to be influenced by the (unobserved) process which gives rise to clustering, synthetic labels with more degrees of freedom can model it more accurately. We note that Agwan performs better where there are more degrees of freedom, while MAG performs better with few degrees of freedom (due to the independence assumption mentioned in Section 3.3). As before, clustering appears to be independent of the edge weight distribution.  
Triad Participation (Figure 12): Similarly to clustering, Agwan and MAG are both more accurate using synthetic attributes than they were with real attributes. The effects of degrees of freedom of the synthetic attributes is even more pronounced than it was for clustering: Agwan-L9 has a good fit whereas Agwan-L1 is not so good. Edge weights appear to have little influence.
# Cascade or Direct Speech Translation? A Case Study +
## 8 Conclusions
Nonetheless, the cascade models still obtained better results overall in terms of reference-based metrics and manual evaluations, in line with the findings reported in [25], where cascade models achieved the best results overall on the IWSLT 2021 shared task datasets. It is worth noting that this was the case even under the controlled conditions of our study, where additional audio and translation data were considered to some extent, but with volumes of data largely under what might be exploited in this language pair [16; 69]. The gap between cascade and direct speech translation models is thus likely to be larger under unrestricted conditions, despite the significant progress achieved with direct models in recent years. In future work, a more detailed examination of the characteristics of the Basque-Spanish language pair, in terms of syntactic and morphological variation in particular, along with further manual examination of the corpus in terms of literality and audio variation, could shed more light on the respective strengths and limitations of cascade and direct approaches. **Author Contributions:** Conceptualisation, T.E., A.A., H.A. and H.G. ; methodology, T.E., A.A., H.A., H.G., A.G.-D., I.G.T., J.M.M.-D. and E.B.F. ; validation, T.E. and A.A.; formal analysis, T.E. and H.G. ; investigation, T.E., A.A., H.A., H.G, A.G-D., I.G.T., J.M.M.-D. and E.B.F. ; resources, T.E., H.A. and H.G. ; data curation, T.E., H.A., H.G. and A.G.-D.; writing--original draft preparation, T.E. ; writing--review and editing, T.E., A.A., H.A., H.G., A.G.-D., I.G.T., J.M.M.-D. and E.B.F. ; visualisation, T.E. and H.G. ; supervision, T.E. and A.A.; project administration, T.E. ; funding acquisition, T.E. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Department of Economic Development and Competitiveness of the Basque Government (Spri) Grant Number KK-2019/00065. **Data Availability Statement:** Publicly available datasets were analysed in this study. These data can be found here: [[https://github.com/vicomtech/mintzai-st](https://github.com/vicomtech/mintzai-st)]([https://github.com/vicomtech/mintzai-st](https://github.com/vicomtech/mintzai-st)), accessed on 11 March 2021. **Conflicts of Interest:** The authors declare no conflict of interest.