esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Hempeseed Screenings Bioprocessing
#### 3.2.1 Hempseed Screenings Bioprocessing Treatments Increased the Solubility of Dietary Fibre (NSP)
Moreover, for p-coumaric acid, protocatechuialdehyde, protocatechuic acid, vanillin, and syringaldehyde (3, 5-dimethoxy-4-hydroxybenzaldehyde), and syringe acid (3, 5-dimethoxy-4-hydroxybenzoic acid), which were abundant mostly in bound forms (Table S1), their in vitro early GI bioavailability was low, and they were resistant to the impact of the bioprocessing treatments. Gentisic acid was undetectable in the raw material and after EM_1 treatment, this reflected in a low in vitro early GI bioavailability, but was significantly released in the free form (solubilised) after EC_2 treatment, this directly contributing to its higher in vitro early GI bioavailability. However, for homoprotocatechuric acid, phloretic acid, and caffe acid, with only trace amounts being detected in the raw material and first bioprocessing, their in vitro early GI bioavailability was similar between raw, EM_1 and EC_2, despite being significantly released in the free form after the second bioprocessing. Hence, for several plant metabolites, the increase in free form content by bioprocessing treatments was not always equivalent to the high in vitro early GI bioavailability, probably due to the food matrix effect. 2.4 The Bioprocessing Treatments and In Vitro Digestion Treatments Showed no Impact on the Microbial Metabolism, and Both Raw and Bioprocessed Screenings were Rapidly Metabolised within 24 h with Little Further Transformation up to 72 h  
The in vitro colonic fermentation using human mixed microbiota obtained from faecal samples from three donors was used to explore the production and potential bioavailability of plant metabolites in the human colon. The freeze-dried digesta pellet obtained from the in vitro digestion was used as the fermentation substrate, using the raw product, and the fractions treated with EM_1, and EC_2 without simulated digestion as the controls. The PCA of microbial metabolites (Figure 3c) shows a scattering of baseline samples (0 h in red color), with the raw material (with no processing) observed as an outlier in the PCA. This reinforces the significant influence of bioprocessing treatments on raw hemp seed screenings as presented and discussed at point 3.1.3. The PCA diagram depicts a grouping of all the blanks (0 and 72 h in pink color) in one quadrant indicating no significant microbial transformation in time on the blank sample within 72 h. Furthermore, a clear segregation and grouping of microbial metabolites were measured at 24 h (bright green color), and in close proximity, two more groupings for the microbial metabolites were measured at 48 (blue color) and respectively 72 h (yellow color). The bioprocessed and in vitro digestion treated samples showed no difference in microbial metabolites, and both raw and bioprocessed screenings were rapidly metabolised with a clear change
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 2 Data Review  
### The Origin Data  
In order to ensure the efficient and healthy excavation of TBM, it was necessary to monitor and coordinate various subsystems. The TBM carries a variety of sensors, which collect 228 kinds of excavation parameters, and record them every minute. These parameters include the control parameters set by TBM operators according to the current geological environment and the response state under the control parameters. In order to ensure that TBM could tunnel according to the designed tunnel axis, the VMT automatic guidance system installed in the system was used for real-time monitoring of TBMAP during tunneling. This measurement system is composed of the total station, laser targets, rear-view prisms, industrial computer, and other modules, which can measure the position deviation of TBM head and tail center from the designed tunnel axis, including horizontal deviation of the head (HDTH), vertical deviation of the head (VDTH), horizontal deviation of the tail (HDTT), and vertical deviation of the tail (VDTT). Based on these data, the deviation of the tunneling axis from the designed axis could be calculated, namely TBM dip angle (TDA) and TBM flip angle (TFA), which represent the direction deviation of the tunneling axis from the designed axis in the up and down directions and the left and right directions, respectively. These deviation data are symmetrical of positive and negative, representing the two opposite directions.  
The GEC is a kind of engineering geological classification standard for the geological environment in the \"Code for Geological Survey of Water Conservancy and Hydropower Engineering (GB50487-2008)\" [23]. This standard is based on the rock mass feature parameters, such as rock strength, rock integrity, and rock mass structure type, and divides the stability of the geological environment into five categories. This index is widely used to measure the engineering stability of the geological environment in actual engineering and provides a basis information for setting the control parameters of TBM tunneling. Before construction, geological researchers conduct a rough exploration of the geological environment by sampling and analyzing its category. After excavation, the category of tunneled geological environment was re-analyzed to correct the previous judgment, and the final accurate GEC data was obtained.
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Quantitative Segmentation Results  
We first examine the segmentation performance quantitatively using only a few labels for downstream training.  
**Results on PASTIS-R:** Table 3 reports the mIoU on the PASTIS-R test set using both 100% and 10% of the labeled dataset. S4 outperforms all competing baselines across the board for both optical and radar inference experiments. We observe less relative improvement in the radar inference experiments due to radar being a low-resolution modality that provides less information than nonoccluded optical images. We also observe greater improvement when the amount of labeled data provided is lower. Finally, although the self-supervised fusion technique leverages multimodal, temporal data and largely outperforms all other baselines, S4 provides greater performance gain through its sophisticated cross-modal contrastive and reconstruction framework.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Dataset** & \multicolumn{4}{c}{**PASTIS-R**} & \multicolumn{4}{c}{**Africa Crop Type**} \\ \hline
**Method** & **Radar** & **Optical** & **Radar** & **Optical** & **Radar** & **Optical** & **Radar** & **Optical** \\  & **100\%** & **100\%** & **10\%** & **10\%** & **100\%** & **100\%** & **10\%** & **10\%** \\ \hline SatMAE & 37.9 & 36.2 & 11.1 & 28.2 & 12.4 & 11.9 & 3.30 & 7.06 \\ SeCo & 43.3 & 23.9 & 27.2 & 12.4 & 9.05 & 9.05 & 2.85 & 2.58 \\ GSSL & 41.8 & 21.9 & 30.9 & 13.6 & 18.8 & 4.36 & 5.67 & 8.04 \\ CaCO & 42.7 & 23.4 & 24.9 & 16.4 & 9.05 & 9.05 & 2.78 & 2.82 \\ MMF 1  & 53.2 & 48.3 & 36.0 & 27.4 & 9.08 & 18.1 & 9.02 & 18.5 \\ Sup. 2  & 51.6 & 52.9 & 22.8 & 17.1 & 7.08 & 6.94 & 2.58 & 2.69 \\ \hline \hline
**S4 (Ours)** & **54.6** & **53.4** & **36.5** & **33.7** & **21.2** & **31.2** & **10.2** & **24.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation results on the PASTIS-R and Africa Crop Type Datasets (mIoU).  
Results on Africa Crop Type Mapping:In Table 3, we report segmentation results on the Africa Crop Type Mapping test set. This dataset is more challenging due to multiple reasons. First, the pretrain dataset contains less temporal information due to sparse image collection by the Sentinel satellites. Therefore, we see lower mIoU values for this dataset. However, S4 continues to significantly outperform all baselines for different modalities. Without S4's self-supervised multimodal approach, the mIoU drops for both single and multimodel baselines.  
We also provide quantitative results on a strong supervised baseline [62] specifically designed for SITS semantic segmentation and crop type mapping. Since the baseline is supervised, it is only trained on labeled data from the fine-tuned set in our experiments. Naturally, the supervised model performs well when given lots of labeled data but performs very poorly in limited labeled scenarios. We observe, however, that S4 still manages to outperform the supervised baseline in all cases, demonstrating its efficacy in extracting useful information from the pretraining dataset.
## 4 Discussion
However, based on mainly dormant frequencies extracted from vowel /u/, the XGBoost model estimated the change in FOG-Q with 13% error. Generally, the significant impact of formants in this specific task is in line with our previous study [56]. The second correlation analysis (\(\Delta\) of the baseline features vs. \(\Delta\)FOG-Q) revealed some relations between changes in FOG and changes in aperiodicity, tremor of jaw/tongue, and acoustic noise. Although most of the studies dealing with the acoustic analysis of phonation in PD patients focus on sustained vowel /a/, it is not sufficiently explained why this corner vowel is more important than the other two, i.e., /i/ or /u/. Looking at the Hellwag (vowel) triangle [34], we can see that, during phonation of vowel /a/, the tongue is in its lowest position from a vertical point of view, and in its central position from a horizontal one. In other words, a speaker does not have to make an effort to keep the tongue in a limit position (the tongue is almost relaxed). Therefore, some phonatory disorders could not be accepted. This limitation is not present in vowels /i/ or /o/, where the speaker has to exert a force in both directions. On the other hand, the lowest limit position of jaw is reached during the phonation of vowel /a/. In summary, although some research teams employed a more complex set of vowels in their experiments [19; 20; 21; 23; 24; 35; 36; 37], the vowel /a/ is still the most frequently used one. However, this choice should be supported by a complex, robust, and multilingual study (theoretically, the effect of culture and language plays no role here, but this should be proven as well). Based on these assumptions, we have decided to explore significance of all five Czech vowels. In addition, the results suggest that the progress of PD is reflected in each vowel differently. Moreover, each vowel differently correlates with changes in scores of clinical scales. Finally, in our case, the best prediction of the change in the clinical rating scales under the focus have never been based on phonatory parameters of the vowel /a/. If we have to choose one optimal candidate for considered clinical scores changes prediction (see Table 5), it would be the corner vowel /i/, where the tongue is in limit position in both directions. In our previous works, we proved that HD shares some pathophysiological mechanisms with other motor/non-motor features of PD. For instance, based on a combination of acoustic analysis and machine learning approaches, it is possible to predict cognitive deficits or gait disorders [44; 56]. Although in the frame of this research we explored only the field of phonation, our results confirm the ability of acoustic HD analysis to predict the progress of PD. These findings and conclusions could have practical applications in eHealth, mHealth and generally Health 4.0 systems that could be used to remotely monitor and assess motor/non-motor deficits in PD patients.
## 5 Experimental Process and Results Analysis
### Detection Experiment of the Flange End Face Image  
A group of flange end face images was used for our experiments, as shown in Figure 11. This image set contains five different flange images, which are, respectively, Figure 11a-e. Figure 11a represents the detection condition that is closest to the real working condition. Figure 11b represents the detection condition when the flange deviation angle is large. Figure 11c represents the detection condition with similar chromatic aberrations between the target flange and the background. Figure 11d represents the optimal working conditions--that is, the detection condition with an obvious chromatic aberration, pose position, and orientation facing the camera with no interference from ambient light. Figure 11e represents the detection of interference from ambient light to the flange surface. In the experiment, the same images were tested several times to fit the spatial circle and confirm the circle center position.  
Figure 8: Camera calibration.  
Figure 10: The circle center absolute error distribution curve along the X and Y axes.  
Figure 9: Detection experiment results of the center of the target end face.  
Table 1 shows the operation data of the flange end face positioning detection using the above algorithm under different working conditions, including the image resolution, the approximate ratio between the ellipses and the regular circles identified in different views, and the running time of the procedure. The approximate ratio is determined by the size of area S and perimeter L, and the approximate ratio formula is \(M=\frac{4\pi\cdot S}{L^{2}}\). As can be seen from Table 1, this algorithm is different from standard circle recognition for an incomplete, non-vertical angle of a spatial circle but can still accurately locate the flange end surface and center coordinates in a running time superior to the traditional HOG + SVM algorithm [20]. The data results of the HOG + SVM algorithm that are presented in Table 1 were compiled by extracting the HOG features of the single test flange. With the trained SVM classification model, the result of the single flange identification has reference values for comparison. Experimental results show that this method meets the actual requirements of the project and can locate the target flange effectively.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 6 Experimental Results
### Discussion  
In the experiments, the feasibility of direct visual servoing of a UAV was demonstrated by a position hold task and two target tracking tasks with different moving speeds. We used the PnP algorithm to obtain the relative pose of the target, and we assumed that the 3D positions of the features on the target coordinate system were known. However, in actual applications, detection of feature points and estimation of their 3D positions are required at the moment of entering the field of view. This is usually difficult with a monocular camera alone. Nevertheless, tracking and avoidance are often possible even if the relative position is not exactly accurate. In this case, a rough estimate is sufficient.  
The measurement errors \(\Delta\mathfrak{p}\) of the vision-based position estimator can be described by the relationship of propagation of uncertainty:  
\[\Delta\mathfrak{p}=\mathfrak{f}(\Delta\mathfrak{u}_{1},\Delta\mathfrak{u}_{2}, \Delta\mathfrak{u}_{3},\Delta\mathfrak{u}_{4}), \tag{16}\]  
where \(\Delta\mathfrak{u}_{n}\) (\(n\in 1,2,3,4\)) are errors of four selected features on the image. (16) can be expanded as  
\[\Delta\mathfrak{p}\approx(\frac{\partial\mathfrak{f}}{\partial\mathfrak{u}_{1 }})\Delta\mathfrak{u}_{1}+(\frac{\partial\mathfrak{f}}{\partial\mathfrak{u}_{2 }})\Delta\mathfrak{u}_{2}+(\frac{\partial\mathfrak{f}}{\partial\mathfrak{u}_{ 3}})\Delta\mathfrak{u}_{3}+(\frac{\partial\mathfrak{f}}{\partial\mathfrak{u}_{ 4}})\Delta\mathfrak{u}_{4} \tag{17}\]  
From (17), we can calculate the errors of the estimated relative position \(\Delta\mathfrak{p}\) by propagating errors from \(\Delta\mathfrak{u}_{1}\), \(\Delta\mathfrak{u}_{2}\), \(\Delta\mathfrak{u}_{3}\), \(\Delta\mathfrak{u}_{4}\). In the experiment, we obtained estimation errors \(\Delta\mathfrak{p}\) of relative positions of about \([57.3,125.9,142.7]^{T}\)[mm] in the \(x,y\), and \(z\) directions, respectively. In addition, we found that the velocity estimation results of the EKF state estimator diverged as the sample rates of the estimated relative position became higher than that of the IMU (250 Hz) with such estimation errors. Therefore, though we were able to recognize a target at about 350 Hz, its position information was sent to a flight controller operating at about 120 Hz to ensure a safe flight. To improve the accuracy and decrease the computational load of the state estimator, we will adopt an unscented Kalman filter (UKF) for velocity estimation in the future.  
Figure 18: Autonomous target tracking (Moving speed: v = 0.5 [m/s], a = 1.0 [m/s\({}^{2}\)].
# Consumer Acceptance of Cultured Meat: An Updated Review (2018-2020)
## 7 Key Uncertainties
### Sensory Experience  
In our previous review, we found some evidence that consumers tended to expect cultured meat to be worse tasting than conventional meat, and to have an inferior texture and appearance [12; 46; 53]. More recent evidence suggests that consumers have relatively low expectations for the taste of cultured meat, tending to view it as not fresh, lacking in taste, and worse than conventional meat [34; 35]. Moreover, [PERSON] and [PERSON] [28] show that expected taste strongly predicted purchase intent, demonstrating a need to reassure consumers of the sensory experience of cultured meat as a central feature of messaging.  
However, some findings can reframe these low consumer expectations as an opportunity. [PERSON] et al. [19] found that consumers who tried two pieces of the same meat rated a piece marked 'cultured meat' as being tastier than a piece marked 'conventional meat'. It is likely that this occurred due to their prior expectations of the cultured meat tasting worse, and pleasant surprise on finding it did not. Taste was amongst the most common themes in the positive remarks participants made in this study.  
It is important to highlight sensory attributes other than taste: indeed, flavor may be less important of an important factor for meat consumers than textural characteristics such as tenderness and juiciness [68]. This is an area where consumers anticipate a particular deficit, often expressing that cultured meat is likely to be mushy or have an otherwise unappealing texture [12].
## 5 Proposed Method
### Description
#### 5.1.2 Confirmation of Jamming Attack on a Node Through '2-Means Clustering' of Node Neighborhood  
After each node has been assigned a crisp jamming index (JI) as per its SNR and PDPT values by the base station through the aforesaid method, the base station now confirms whether a node can be declared jammed or not jammed by looking at the jamming indices of neighboring nodes. This is done by the base station as follows:  
1. Depending upon the information war conditions, it decides the lower cut-off value of \(JI\), \(LC\) for declaring all nodes with \(JI\geq LC\), as jammed nodes, _i.e._, jamming detected at these nodes. 2. It makes a list of all jammed nodes, _i.e._, of nodes having \(JI\geq LC\) and finds the number, \(t\) of such nodes. 3. For each of the \(t\) jammed nodes, it does the following: 1. Identifies and counts the number of one-hop neighbors, \(n\). 2. Out of the \(n\) neighbors, it identifies those neighbors who are in the list of jammed nodes and counts their number, \(nj\) and names the group of these nodes as _jammed neighbors cluster_. 3. Out of the \(n\) neighbors, it identifies those neighbors who are not in the list of jammed nodes and counts their number (\(n\)- \(nj\)) and names the group of these nodes as _non-jammed neighbors cluster_. We thus have a total of \(n\) nodes divided into 2 clusters in neighborhood of a node under consideration. Therefore, the deciding figure is n/2. If the number of nodes (_nj_) in the _jammed neighbors cluster_ is more than n/2 then majority of the neighbors are jammed and hence it is confirmed that the node under consideration is also jammed. If _nj is less than or equal to n/2_, further examination is required for taking any decision. The subsequent steps of the algorithm proceed accordingly. 4. If _nj_ > _n/2_, then it confirms that the node is jammed. 5.
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 3 Mathematical Formulation
### Wave-Generating Method  
For wave generation, a fifth-order wave was modeled with a fifth-order approximation to the [PERSON] theory of waves with the given wave height and wave period. The relevant equations of the Stokes fifth-order wave are as follows:  
Velocity potential function:  
\[\Phi=\frac{c}{k}\sum_{n=1}^{5}\lambda_{n}\mathrm{cosh}\;nk(z+d)\mathrm{sin}\;n (kx-\omega t) \tag{4}\]  
Wave surface equation:  
\[\eta=\frac{1}{k}\sum_{n=1}^{5}\lambda_{n}\mathrm{cos}\;n(kx-\omega t) \tag{5}\]  
The velocity of the water mass point in the horizontal and vertical directions:  
\[u_{x}=\frac{\partial\Phi}{\partial x}=c\sum_{n=1}^{5}n\lambda_{n}\mathrm{cosh} \;nk(z+d)\mathrm{cos}\;n(kx-\omega t) \tag{6}\]  
\[u_{z}=\frac{\partial\Phi}{\partial z}=c\sum_{n=1}^{5}n\lambda_{n}\mathrm{sinh} \;nk(z+d)\mathrm{sin}\;n(kx-\omega t) \tag{7}\]  
The acceleration of the water mass point in the horizontal and vertical directions:  
\[a_{x}=\frac{\partial u_{x}}{\partial t}=\omega c\sum_{n=1}^{5}n^{2}\lambda_{n }\mathrm{cosh}\;nk(z+d)\mathrm{cos}\;n(kx-\omega t) \tag{8}\]  
\[a_{z}=\frac{\partial u_{z}}{\partial t}=-\omega c\sum_{n=1}^{5}n^{2}\lambda_{n }\mathrm{sinh}\;nk(z+d)\mathrm{sin}\;n(kx-\omega t) \tag{9}\]  
where \(c\) refers to the wave velocity, \(k\) refers to the wave number, \(\omega\) refers to the angular frequency, \(d\) refers to the water depth, and \(\lambda\) is a constant for each wave.  
Figure 4: The schematic diagram of the overset mesh.  
To avoid the reflection from the exit boundary, a damping method was used for the wave generation [47]. The expression of the momentum equation after damping modification is defined as  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{\partial u}{ \partial y}=g_{x}-\frac{1}{\rho}\frac{\partial p}{\partial x}+v\left[\frac{ \partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{\partial y^{2}}\right]-\mu( x)u \tag{10}\]  
\[\frac{\partial v}{\partial t}+u\frac{\partial v}{\partial x}+v\frac{\partial v }{\partial y}=g_{y}-\frac{1}{\rho}\frac{\partial p}{\partial y}+v\left[\frac{ \partial^{2}v}{\partial x^{2}}+\frac{\partial^{2}v}{\partial y^{2}}\right]- \mu(x)v \tag{11}\]  
where \(\mu(x)\) is the damping coefficient varying with the \(x\)-position as follows:  
\[\mu(x)=\left\{\begin{array}{cl}a_{s}(x-x_{0})\,/\,L_{s}&x>x_{0}\\ 0&x\leq x_{0}\end{array}\right\} \tag{12}\]  
where \(x>x_{0}\) is defined as the damping region; \(L_{s}\) refers to the length of damping region; and \(a_{s}\) refers to the wave-absorbing coefficient.