esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Inulin Addition on Rice Dough and Bread Characteristics
###### Abstract  
Inulin may be widely used in in the food industry due to its many health benefits. It has the potential to increase the insufficient nutritional quality of gluten-free bread. Therefore, the aim of this study was to test the applicability of inulin in rice baking. The impact of added inulin (5%, 10%, 20%, 30%, and 40%) on the dough's rheological, bread's textural and sensory characteristics was evaluated. The extensibility of rice dough during uniaxial deformation tests (8.5 mm) was improved by the added inulin (10.2-12.3 mm). The presence of inulin softened the dough and shifted the gelatinization temperature toward higher values. The added inulin also increased the loaf's specific volume (1.16-1.48 mL/g), tenderized the breadcrumbs, increased the crumb porosity (36-58%), and generally improved the crumb structure. The panelists favored the sensory characteristics of breads with inulin. However, baking losses were increased in these breads as well (15.1-18.5%). The effect of the added inulin on the dough and bread characteristics generally rose with an increasing addition of inulin, reaching the maximum in samples with 30% inulin. The presence of 40% inulin deteriorated some characteristics of the bread. Therefore, the addition of up to 30% of inulin seemed to be optimal for rice bread.  
dough extensibility; bread texture; bread volume; baking losses; sensory characteristics; oligofructose
## 3 Results
### Ablations Study  
We conducted ablation studies to demonstrate the effectiveness of each module in our proposed method. We evaluate the performance of optical flow tracking, BRIEF descriptor, RANSAC, preserve feature points and feature point homogenization. Because the algorithm is based on sparse optical flow, detecting and tracking too many feature points will consume more time. So the number of tracked feature points is limited to 260. In this experiment, the root mean square error (RMSE) of the absolute trajectory error (APE) of positioning is used to evaluate the effect of mismatch removal. The EVO evaluation tool is used to evaluate the positioning results. The quantitative comparison results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **MH01** & **MH02** & **MH03** & **MH04** & **MH05** & **V101** & **V102** & **V103** & **V201** & **V202** & **V203** & **avg.** & **std.** \\  & **Easy** & **Easy** & **M id** & **Hard** & **Hard** & **Easy** & **Mid** & **Hard** & **Easy** & **Mid** & **Hard** & **avg.** & **std.** \\ \hline a & 0.074 & 0.068 & 0.065 & 0.128 & 0.125 & 3.333 & 0.063 & 0.145 & 0.048 & 0.088 & 0.137 & 0.535 & 0.976 \\ b & 0.089 & 0.064 & 0.092 & 0.114 & 0.130 & 0.052 & 0.051 & 0.094 & 0.055 & 0.073 & 0.095 & 0.021 & 0.026 \\ c & 0.075 & 0.072 & 0.066 & 0.110 & 0.105 & 0.052 & 0.042 & 0.116 & 0.057 & 0.076 & 0.126 & 0.023 & 0.028 \\ d & 0.078 & 0.065 & 0.065 & 0.108 & 0.109 & 0.057 & 0.048 & 0.101 & 0.055 & 0.068 & 0.103 & 0.019 & 0.023 \\ e & 0.080 & 0.053 & 0.071 & 0.099 & 0.102 & 0.063 & 0.054 & 0.077 & 0.063 & 0.053 & 0.087 & 0.014 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Location error (RMSE/m) of each step on EuRoC dataset, ave. and std. are the average and the standard deviation of location error. a is only the module of optical flow tracking; b is the module of optical flow tracking and BRIEF descriptor; c is the module of optical flow tracking, BRIEF descriptor and RANSAC; d is the module of optical flow tracking, BRIEF descriptor, RANSAC and preserve feature points; e is the module of optical flow tracking, BRIEF descriptor, RANSAC, preserve feature points and feature point homogenization.  
Table 1 shows that when only the optical flow module is used, the average and standard deviation of position error are the worst, with values of 0.2 and 0.5, respectively. Although the positioning error of combining the module of optical flow tracking, BRIEF descriptor, RANSAC, preserve feature points and feature point homogenization is not the lowest in easy scenarios, its performance is the best in hard scenarios. As the module increases, the average and standard deviation of position error decreases significantly. When combining the module of optical flow tracking, BRIEF descriptor, RANSAC, preserve feature points and feature point homogenization, the average error and standard deviation of position error are 0.014 and 0.017, respectively.
# Plant Iridoids Affect Intraocular Pressure and Vascular Flow in the Rabbit Eye
###### Abstract  
For plant-derived raw materials, there are very few studies regarding the effect of intraocular administration on intraocular pressure (IOP) and associated blood flow. Traditional folk medicine uses many natural resources for eye disorders. However, in the main, these exhibit an anti-inflammatory and moisturizing effect. The intraocular pressure reduction and neuroprotective effects are known, but only for orally administered products. In the work presented here, the effect of eight natural iridoids in concentrations of 0.1 and 0.5% in saline on IOP and blood flow in iris vessels was studied in _white New Zealand_ rabbits. No ocular adverse effects were observed during the whole experiment. We demonstrated, for the first time, significant reductions in IOP for five of the eight iridoids tested at a concentration of 0.5%. These were verbenalin, aucubin, oleuropein, gentiopicroside, and secologanin. The highest effect of IOP lowering, a nearly 1.5 mmHg difference from baseline, was observed for verbenalin 2 h after administration. An increase in vascular inflow was observed only with the administration of aucubin, catalpol, and gentiopicroside at 2 and 3 h after administration of the 0.5% solution. This effect was contrary to the result for the reference--timolol--which significantly reduced flow by more than 100 flux during the first hours of the experiment. In summary, selected iridoids could be considered, after further investigation, as natural components for ophthalmic formulation in the prevention of eye diseases.  
Keywords:iridoids; natural products; glaucoma; eye; intraocular pressure +
Footnote †: journal: applied sciences
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 3 Results
### Use the Observations from Days of the Previous Orbit Repeat Period for Testing
At the same time, the positioning accuracy after model correction with MHGM was slightly better than that of SF, and the results showed that the performance of MHGM and SF was comparable.
## 7 Engineering Plant-Microbe Interactions for Sustainable Agriculture
### SynCom Studies on Physiological Parameters  
A SynCom derived from the rhizosphere of the xerophyte _Haloxylon ammodendron_ significantly improved maize drought tolerance in both greenhouse and field trials by increasing stomatal conductance, photosynthetic rate, and proline content, while reducing transpiration and modulating stomatal density and xylem structure, with the authors reporting up to a 700% increase in water use efficiency under their test conditions [151]. Similarly, a four-species SynCom (SPMX), _Stentroophomonas rhizulia, Xanthomonas retrovirus, Microbacterium oxydans_, and _Paenibacillus amyloidicus_, in Arabidopsis demonstrated that emergent community properties, such as synergistic biofilm formation, were essential for enhanced drought survival, sustained chlorophyll content, and the activation of abscisic acid (ABA) signaling, effects not observed with individual strains alone [152]. In maize, SynCom inoculation of naturally occurring, highly abundant bacteria from the sugarcane root and stalk core microbiomes reduced yield loss, lowered leaf temperature, improved turgor maintenance, and accelerated recovery after rehydration, likely through improved sap flow and water usage, while also recruiting additional beneficial microbes from the soil and seed microbiome [153].  
In a study, a SynCom of four PGP microbial species was constructed and applied either as a seed dressing (T1) or to the soil (T2) across five different cotton cultivars (_Gosspium hirsutum_). The results showed that the seed application of SynCom had a significant impact on plant fitness, with a significantly higher germination rate (14.3%) in addition to an increase in plant height (7.4%) and shoot biomass (5.4%). A significant increase was observed in the number of flowers (10.4%) and yield (8.5%) in T1. The soil nitrate availability was enhanced by 28% and 55% under T1 and T2, respectively. The SynComapplications also enriched the members of bacterial phyla _Actinobacteria_, _Firmicutes_, and _Cyanobacteria_ in the rhizosphere. Additionally, a significant increase was observed in the relative abundance of fungi from the _Chytridionyota_ and _Basidiomycota_ phyla in SynCom treatments [154].
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 3 Module Design
### Training Pipeline
#### 3.2.1 Data Preparation  
This Section deals with the data preparation pipeline, as shown in Figure 4, part of the high-level training pipeline explained in Section 3.2.  
As observed in the data analysis, all messages with repeat fields that are not equal to zero have been sent more than once, oftentimes reaching the coast in repeat. For this reason, we first removed all repeated messages, for which the field repeat is not equal to zero and which are preceding a message with the same content less than 10 s prior to the reception of the said repeated message.  
An important concept to infer is the definition of a trip. We define a trip as a sequence of chronologically ordered points bound to space and time for the same vessel, represented by received AIS messages, and with a time window between two consecutive points no larger than two hours. Based on this definition, we first group the dataset according to the existing MMSIs and order each group by the received date. Each vessel dataset is now comprised of all messages received for a specific vessel, ordered chronologically. We then iterate the dataset and, if two consecutive messages have been received with an interval of over two hours, we split the dataset into two. This routine is done recursively until we end  
Figure 4: Data preparation pipeline.  
Figure 3: Training pipeline.  
Figure 2: Univariate dataset distribution analysis for the month of June 2018.  
up with an array of trips for each vessel. The cleaned data has the format of an array of vessels with size J \(V=[v_{0},\ldots,v_{j-1}]\), where each vessel \(v_{i}\) includes an array of trips of size K \(v_{i}=[v_{i}^{0},\ldots,v_{i}^{K-1}]\). Each trip is a group of AIS messages of size L, where each message includes the parameters explained in Section 3.1, \(v_{i}^{j}=[ais_{0}^{0},\ldots,ais_{L-1}^{v_{j}^{j}}]\).  
By analysing the size of each trip vector in Figure 5, we conclude that many of the found trips have less than 50 AIS messages. In Figure 6, we can analyse the location of these small trips, grouped by vessel type, which indicates most of these correspond to small trips inside ports, rivers, and vessels that immediately left the area of surveillance. All of these trips were discarded from the training dataset.
## 4 Experimental Results and Performance Analysis
### Comprehensive Performance Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**SNR Range (dB)**} & \multicolumn{2}{c}{**DAR (\%)**} & \multirow{2}{*}{**Gain (\%)**} \\ \cline{2-3} \cline{5-5}  & & **PSSD** & & **EPD** \\ \hline \(-20\)\(\sim\)\(-15\) & 47.10 & 37.39 & 9.71 \\ \(-15\)\(\sim\)\(-10\) & 60.52 & 50.19 & 10.33 \\ \(-10\)\(\sim\)\(-5\) & 70.67 & 62.19 & 8.48 \\ \(-5\)\(-0\) & 74.23 & 69.24 & 4.99 \\ \(0\)–5 & 75.42 & 72.31 & 3.11 \\ \(5\)–10 & 76.99 & 74.51 & 2.48 \\ \hline \end{tabular}
\end{table} 
Table 6: Average DAR (%) statistics of PSSD and EPD algorithm with 4 CWIs. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**SNR Range (dB)**} & \multicolumn{2}{c}{**DAR (\%)**} & \multirow{2}{*}{**Gain (\%)**} \\ \cline{2-3} \cline{5-5}  & **PSSD** & & **EPD** \\ \hline \(-20\)\(\sim\)\(-15\) & 50.04 & 37.88 & 12.16 \\ \(-15\)\(\sim\)\(-10\) & 67.33 & 52.51 & 14.82 \\ \(-10\)\(\sim\)\(-5\) & 79.59 & 69.48 & 10.11 \\ \(-5\)\(-0\) & 85.77 & 80.36 & 5.41 \\ \(0\)–5 & 87.72 & 83.88 & 3.84 \\ \(5\)–10 & 88.67 & 85.46 superiority in eLoran signal demodulation and strongly demonstrates its great potential value in practical applications.
# Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
## 3 Implementation
### Evaluation Index  
The loss, which can be considered as the error between the predicted result and the true result, is a vital index to judge whether convergence is achieved during the training process. Four loss functions were adopted during the model training process, namely, the RPNLogLoss, RCNNLogLoss, RPNL1 Loss, and RCNNL1 Loss. The RPNLogLoss is used to calculate the classification loss for the RPN network and is expressed as follows:  
\[L_{cls}(p_{i},p_{i}^{*})=-\text{log}[p_{i}^{*}p_{i}+(1-p_{i}^{*})(1-p_{i})] \tag{4}\]  
\[p_{i}^{*}=\left\{\begin{array}{ll}0&\text{negative label}\\ 1&\text{positive label}\end{array}\right. \tag{5}\]  
where \(p_{i}\) is the predicted probability of being an object in the \(i^{\text{th}}\) anchor, and \(p_{i}^{*}\) is 0 or 1 for the negative label and positive label, respectively.  
RCNNLogLoss is used to calculate the classification loss of the detection network, which can be calculated as:  
\[L_{cls}(p,u)=-\log p_{u} \tag{6}\]  
where \(p\) and \(u\) are the predicted class scores and true class scores, respectively.  
RPNL1 Loss is used to calculate the regression loss of the RPN network, which can be expressed as:  
\[L_{reg}(t_{i},t_{i}^{*})=\sum_{i\in[x,y,w,h]}\text{smooth}_{L1}(t_{i}-t_{i}^{ *}) \tag{7}\]  
where \(t_{i}=\left\{t_{x},\ t_{y},\ t_{w},\ t_{h}\right\}\) is a vector with four parameters determining the sizes \((w,h)\) and the position \((x,y)\) of the predicted bounding box, and \(t_{i}^{*}\) is also a vector with four parameters determining the sizes and the position of the ground truth.  
RCNNL1 Loss is used to calculate the regression loss of the detection network, which can be expressed by:  
\[L_{loc}(t^{u},v)=\sum_{i\in[x,y,w,h]}\text{smooth}_{L1}(t_{i}^{u}-v_{i}) \tag{8}\]  
where \(t^{u}\) and \(v\) also represent the coordinates and the sizes of the ground truth box and predicted bounding box, respectively, and the smooth function in (7) and (8) are both defined as:  
\[\text{smooth}_{L1}(x)=\left\{\begin{array}{ll}0.5x^{2}&\text{if}|x|<1\\ |x|-0.5&\text{otherwise}\end{array}\right. \tag{9}\]  
The average precision (AP) is used to evaluate the detection accuracy of each category and the mean average precision (mAP) is the average of the APs of all categories under consideration. Details about calculating the APs and mAP can be found in PASCAL VOC Challenge 2007 [44].
# The Current Development Status of Agricultural Machinery
## 5 Summary and Future Perspectives
### Integrated Intelligent Perception Systems  
Integrated intelligent perception systems amalgamate multi-source sensors, edge computing, and artificial intelligence algorithms to establish autonomous decision-making capabilities within smart systems. In the context of agricultural machinery designed for hilly and mountainous regions, these systems are predominantly applied in three key areas:  
1. Adaptive Leveling Systems: Employing model predictive control (MPC) algorithms to facilitate rapid responses and enhance leveling precision during operations on slopes;
2. Intelligent Navigation Systems: Integrating three-dimensional path planning to achieve centimeter-level operational accuracy on sloped terrains;
3. Load-Power Matching Systems: Optimizing fuel efficiency in hybrid machinery through adaptive adjustments.  
Currently, critical technologies such as LiDAR-based navigation have reached a stage of industrialization, while advanced methodologies like swarm intelligence coordination are still under exploration. This technological framework is propelling the transition of agricultural machinery in hilly regions from traditional mechanization towards intelligent and interconnected operations, thereby providing essential solutions to address agricultural challenges posed by complex terrains.
## 2 Underwater Glider Modeling
### Underwater Glider Kinematic Modeling
\begin{bmatrix}\cos\psi\cos\theta&\cos\psi\sin\theta\sin\phi-\sin\psi\cos \phi&\cos\psi\sin\theta\cos\phi+\sin\psi\sin\phi\\ \sin\psi\cos\theta&\sin\psi\sin\theta\sin\phi+\cos\psi\cos\phi&\cos\psi\sin \theta\cos\phi-\cos\psi\sin\phi\\ -\sin\theta&\cos\theta\sin\phi&\cos\theta\cos\phi\end{bmatrix} \]  
Figure 3: Relationship diagram of the inertial, body, and velocity frames. The rotation matrix from the body to the velocity coordinate system is denoted as \(T_{V}^{B}\). The specific expression of \(T_{V}^{B}\) is as follows:  
\[T_{V}^{B}= \begin{bmatrix}\cos\alpha\cos\beta&\cos\alpha\sin\beta&-\sin\alpha\\ -\sin\beta&\cos\beta&0\\ \sin\alpha\cos\beta&\sin\alpha\sin\beta&\cos\alpha\end{bmatrix}  \tag{2}\]  
Studying the kinematic equations of UGs is equivalent to studying their motion in an inertial coordinate system. Therefore, it is necessary to use the rotation matrix \(T_{B}^{V}\) to transform the velocity \(V\) in the frame of the UG, and then use the rotation matrix \(T_{V}^{B}\) to transform the velocity in the frame of the UG to \(V_{E}\). The transformation expression for \(V_{E}\) is as follows:  
\[V= \begin{bmatrix}u\\ v\\ w\end{bmatrix} =T_{B}^{V} \begin{bmatrix}V\\ 0\\ 0\end{bmatrix} = \begin{bmatrix}V\cos\alpha\cdot\cos\beta\\ -V\sin\beta\\ V\sin\alpha\cdot\cos\beta\end{bmatrix}  \tag{3}\]  
\[V_{E}= \begin{bmatrix}V_{1}\\ V_{2}\\ V_{3}\end{bmatrix} = \begin{bmatrix}\hat{X}\\ \hat{Y}\\ \hat{Z}\end{bmatrix} =T_{E}^{B}V=T_{E}^{B}R \begin{bmatrix}V\cos\alpha\cdot\cos \beta\\ -V\sin\beta\\ V\sin\alpha\cdot\cos\beta\end{bmatrix}  \tag{4}\]  
In addition to velocity, the state variables of an underwater glider in the inertial coordinate system also include the angular variables \(\Phi=[\phi,\theta,\psi]^{T}\). The expression, using the angular velocity in the body coordinate system through the rotation matrix \(T_{V}^{B}\) as \(\Omega=[p,q,r]^{T}\), is as follows:  
\[\mathbf{\theta}= \begin{bmatrix}1&\sin\phi\tan\theta&\tan\theta\cos\phi\\ 0&\cos\phi&-\sin\phi\\ 0&\sec\phi\sin\phi&\cos\phi\sec\theta\end{bmatrix} \mathbf{\Omega} \tag{5}\]  
The expressions for obtaining the magnitude of the velocity vector, the angle of attack, and sideslip angle are as follows:  
\[\left\{ \begin{aligned} V&=\sqrt{u^{2}+v^{2}+w^{2}}\\ \alpha&=\arctan\Bigl{(}\frac{w}{u}\Bigr{)}\\ \beta&=\arcsin\Bigl{(}\frac{v}{V}\Bigr{)}\end{aligned} \right. \tag{6}\]  
By substituting the expression of the rotation matrix into Equations (5) and (6), we obtain the kinematic equations of the underwater navigation robot as follows:  
\[\left\{ \begin{aligned} \hat{X}&=u\cos\theta\cos\phi+v(\cos\psi\sin \theta\sin\psi-\sin\psi\cos\phi)\\ &\quad+w(\cos\psi\sin\theta\sin\phi+\sin\psi\sin\phi)\\ \hat{Y}&=u\sin\psi\cos\theta+v(\sin\psi\sin\theta\sin\phi+\cos\psi\cos \phi)\\ &\quad+w(\sin\psi\sin\theta\cos\phi-\cos\psi\sin\phi)\\ \hat{Z}&=-u\sin\theta+v\cos\theta\sin\phi+w\cos\phi\cos\theta\\ \hat{\phi}&=p+q\tan\theta\sin\phi+r\tan\theta\cos\phi\\ \hat{\theta}&=q\cos\phi-r\sin\phi\\ \hat{\psi}&=\frac{1}{\cos\phi}(q\sin\phi+r\cos\phi)\\ \end{aligned} \right. \tag{7}\]
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 4 Simulation  
In this section, we designed three simulation examples to discuss the effectiveness and the robustness of the proposed guidance law. First, various desired terminal angles are taken into consideration to demonstrate its nice performance. Next, in order to test the robustness, a Monte Carlo method with uncertain input parameters is utilized. Finally, the proposed guidance law is compared with other guidance laws under two different cases. The whole simulations are supported by the MATLAB platform, and a fourth-order Runge-Kutta solver is used throughout the simulations.
## 5 Model Compilation Using Deep Learning Methods
### Results and Discussion  
#### 5.4.1 Impact of Different Models  
As explained above, we tested four different deep learning models, namely CNN, LSTM, BiLSTM, and GRU across two datasets. Our experiments used some of the most prevalent word-embedding techniques (i.e., random embedding, pre-trained Word2 vec, and FastText). Tables 6 and 7 show the performance of the tested models according to precision, recall, and the F-score as performance metrics for both Hajj and Brexit datasets; while the accuracy of the tested models is presented in Tables 8 and 9. In all tables, we marked the best results in bold.  
For the Hajj dataset, the results showed that, in general, CNN performed the best among the tested models when using random embedding or FastText, with an F-score of 0.91 and 0.92, respectively. However, using the pre-trained Word2 vec, we noticed that BiLSTM yielded the best results, with an accuracy of up to 86.6%. Moreover, it was observed that CNN and GRU achieved their lowest precision, recall, and F1 using pre-trained Word2 vec with approximately 11% and 9% lower accuracy, respectively. This may have been because, among the 50,814 words in the trained corpus, 18,107 words in the data were not found in the Word2 vec vocabulary.  
Additionally, BiLSTM achieved the same performance for both Word2 vec and FastText, which was 2% lower than the random embeddings. Finally, LSTM achieved its highest accuracy using Word2 vec.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{4}{c}{**LSTM**} & \multicolumn{4}{c}{**BiLSTM**} & \multicolumn{4}{c}{**GRU**} \\ \cline{2-13}  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & F1 & **P** & **R** & **F1** \\ \hline Random & 0.92 & 0.92 & **0.91** & 0.79 & 0.82 & 0.80 & 0.89 & 0.89 & 0.89 & 0.84 & 0.83 & 0.83 \\ Word2 vec & 0.81 & 0.81 & 0.81 & 0.81 & 0.81 & 0.87 & 0.87 & **0.87** & 0.74 & 0.73 & 0.74 \\ FastText & 0.92 & 0.92 & **0.92** & 0.80 & 0.76 & 0.76 & 0.87 & 0.87 & 0.87 & 0.84 & 0.83 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of the basic tested deep learning models using different embedding techniques for the Hajj articles dataset.  
Figure 11: The CNN+GRU network architecture.  
The performance scores achieved by the models for the Brexit dataset presented in Table 7 confirmed the previous results. CNN outperformed the other models using random embedding and FastText with F-scores of 0.75 and 0.77 compared to 0.58 using the pre-trained Word2 vec embedding. whereas, BiLSTM had the best scores using Word2 vec. Additionally, both CNN and GRU maintained their worst scores using pre-trained Word2 vec, with approximately 9% and 18% lower accuracy, respectively.