esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Influence of SPI Addition on the Thermo-Mechanical Properties of the Gluten-Free Mixtures
[38] reported a decrease in intermolecular and anti-parallel \(\beta\)-sheets and an increase in \(\beta\)-turns upon the addition of soy protein hydrolysate, stating that the intermolecular \(\beta\)-sheets are an indicator of protein polymerization. The same authors noted that during fermentation, the content of \(\alpha\)-helices and \(\beta\)-turns decreased, but the content of anti-parallel \(\beta\)-sheets increased. When compared with the sample supplemented with SPI, the addition of sSPI delayed starch gelatinization, causing an important reduction in the gelatinization maximum and the decrease in starch retrogradation; rSPI facilitated the faster achievement of the gelatinization maximum by approximately 120 s while increasing starch retrogradation, whereas hSPI addition resulted in delaying the maximum gelatinization by about 60-120 s. The maximum dough consistency and starch retrogradation varied with the nature of the enzyme used for soy protein hydrolysis and consequently with the degree of hydrolysis. Thus, flour supplementation with hSPI-T, which presented the lowest degree of hydrolysis among all investigated samples, resulted in a gelatinization maximum (C3 of 1.55 Nm after 1500 s of Chopin+ protocol) comparable to the dough with SPI (C3 of 1.58 Nm after 1560 s) (Table 3). The samples prepared with hSPI-B and hSPI-N exhibited significantly lower gelatinization maxima, a C3 of 1.44-1.45 Nm being recorded at 1620 s (Table 3). The slight delay in starch gelatinization could be attributed to the effect of soluble peptides present in the hydrolysate, which produced important changes in the behavior of the dough in which SPIH was incorporated. The dough consistency continued to increase throughout Ph III, IV and V (Figure 3, Table 2). The starch retrogradation of the dough samples with hSPI-B and hSPI-N was significantly lower compared to the control (\(p<0.05\)), whereas the starch retrogradation behavior of the dough supplemented with hSPI-T was similar to the sample with SPI (Table 3). Taking into account the overall behavior of the dough supplemented with soy protein hydrolysates, over the five phases of the Mixolab curves (Figure 3), it can be concluded that hSPI-B and hSPI-N ensured the most important improvements to the dough, compared to the sample with SPI. In agreement with our results, [PERSON] et al. [35] reported a decrease in C3, C4 and C5 upon the addition of soy protein hydrolysate to wheat flour. However, the authors noted that the presence of protein hydrolysate accelerated starch gelatinization, compared to the control sample and reduced (C5-C4). As in the case of the dough samples prepared with hSPI-B and hSPI-N in the present study, [PERSON] et al. [35] mentioned the anti-retrogradation effect exerted by protein hydrolysates on starch, a very important aspect for preventing the staling of bakery products.
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 4 Stray Light Suppression and Simulation Analysis  
Stray light is one of the crucial indicators for assessing the performance of optical systems. Factors such as internal reflections on optical surfaces within the spectrometer, defects in the grating, and surface irregularities of optical components can lead to unnecessary distortion in spectral measurements. This becomes a significant source of uncertainty in high-spectral radiometer measurements. Particularly in the context of ocean color measurements, such undesired optical disturbances often contribute significantly to errors in radiance and irradiance measurements, especially in the case of high instrument sensitivity or low signal-to-noise ratios, such as deep-sea and near-infrared spectra. Therefore, to ensure the reliable performance of underwater solar spectrometer systems, it is imperative to conduct stray light analysis and suppression.
## 2 Materials and Methods  
### Material and Preparation  
Two commercial membranes were investigated in this study: the composite Nafion\({}^{\text{\tiny TM}}\) XL and the non-reinforced Nafion\({}^{\text{\tiny TM}}\) NR211 membranes. They have similar chemical compositions based on perfluorosulfonic acid (PFSA), similar ion-exchange capacities (IEC)--0.92 meq/g for XL and 0.98 meq/g for NR211--as well as similar thicknesses of about 25-30 \(\upmu\)m. Nevertheless, unlike NR211, the XL membrane contains an additional PTFE-rich reinforcement and cerium-based radical scavengers for improved mechanical and chemical endurance. These two membranes were compared to study the contribution of such mitigation strategies with coupled mechanical and chemical degradations. Finally, the size of the membrane samples used for the various chemical-mechanical aging tests was approximately 40-45 mm wide and 70-75 mm long.  
Prior to aging tests or measurements, the commercial membrane samples were pretreated according to a standard procedure close to those already been established by [PERSON] et al. [27].
# Numerical Calculation of Slosh Dissipation
## 5 Results
### Finite Volume Method Results with Elemental\({}^{\circledR}\)
#### 5.2.4 Oil 3D Results  
Three-dimensional Elemental(r) runs were completed at resolutions of \(N33\) and \(N50\), as per Table 2. Figure 27 shows the oil-air interface in light brown, represented by an iso-surface of the VOF at the 0.5 level. The developing Rayleigh-Taylor instability is shown at \(t=1.11T\), just before the first roof impact. The interface is very three-dimensional in nature, which explains the difference in computed energy dissipation as compared to the 2D simulations (see below). Different from the 2D simulations, no initial perturbations were imposed on the interface resulting in a symmetrical interface topology. After the first roof impact at \(t\approx 1.17T\), the flow becomes very chaotic with large mixing of the two phases.  
Figure 28 shows the energy distribution for the 3D oil simulation at the \(N50\) resolution, with similar information as contained in the 2D results in Figure 19. Similar to the 2D simulations, the viscous component of boundary work \(W_{NF}^{V}\) is negligible. For the dissipation  
Figure 26: Water average energy dissipation components. The **(top)** shows the impact loss and the **(bottom)** viscous dissipation.  
Figure 27: Three-dimensional oil Elemental® results on the \(N50\) resolution at various time instants prior to and shortly after the first roof impact.  
in this simulation, the impact loss component is much more significant than the viscous component, which is different from the much finer \(N200\) resolution in 2D. A comparison of impact loss and viscous dissipation is presented in Figure 29, including results from 2D. Comparing between 2D and 3D simulations at the \(N50\) resolution level, both the laminar and turbulent components are larger in 3D than in 2D, with the turbulent (LES) component significantly so. However, a finer resolution will be required when considering the \(N200\) resolution in 2D, which still has a much more significant laminar viscous component. Impact losses are also larger in 3D than in 2D, as shown in the middle plot. The result is that the total dissipation at the \(N50\) resolution in 3D is more than the finest resolution (\(N200\)) in 2D. This indicates that the problem is truly three-dimensional in nature.  
Figure 28: Energy distribution for oil 3D simulation at N50 resolution.  
Figure 29: Comparison of energy viscous dissipation (**top**), impact losses (**middle**) and total dissipation (**bottom**) for oil.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 4 Performance Analysis
### Multipath Error Envelope
#### 4.1.3 Boc(2,1)  
Figure 36 shows the multipath error envelope for the BOC(2,1) signal of infinite bandwidth. The LS non-coherent discriminator and W2 have a comparable multipath error envelope because their S-curve is flat in the left side to the linear region. The HEU S-curve has a smaller MEE, because of the narrower operation range. However, the proposed LS S-curve here appears to be much better at mitigating multipath effects where there are medium and long multipath delays, as a result of the narrower negative pull-in region.  
The area of each multipath error envelope is shown in Table 8. In all, the W2 discriminator has a minimum envelope area, which is 68.4% of the coherent LS discriminator envelope, 68.4% of the non-coherent LS discriminator envelope, 33.1% of the double-delta correlator envelope and 86.7% of the HEU discriminator envelope.  
Figure 34: Multipath error envelopes for the S-curves of the BOC(1,1) signal (infinite bandwidth).  
Figure 35: Multipath error envelopes for the S-curves of the BOC(1,1) signal (6.138-MHz bandwidth).  
A similar conclusion can be drawn from the 8.184-MHz bandwidth case in Figure 37. The CCRW W2 discriminator is shifted in the zero-crossing point about 0.005 chips, but it still has a minimum envelope. The LS coherent and non-coherent discriminators have a shift about \(-0.005\) chips. In all, the CCRW W2 discriminator has a minimum envelope area, which is 32.4% of the coherent LS discriminator envelope, 57.6% of the non-coherent LS discriminator envelope, 34.6% of the double-delta correlator envelope and 60.7% of the HEU discriminator envelope.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bandwidth** & **Coherent** & **Non-Coherent** & **W2** & **\(\Delta\Delta\)** & **HEU** \\ \hline Infinite (chip\({}^{2}\)) & 0.0057 & 0.0057 & 0.0039 & 0.0118 & 0.0045 \\
16.368 MHz (chip\({}^{2}\)) & 0.0143 & 0.0120 & 0.0047 & 0.0144 & 0.0039 \\
8.184 MHz (chip\({}^{2}\)) & 0.0105 & 0.0059 & 0.0034 & 0.0103 & 0.0056 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The multipath error envelope of the BOC(2,1) signal.  
Figure 37: Multipath error envelopes for the S-curves of the BOC(2,1) signal (8.184-MHz bandwidth).  
Figure 36: Multipath error envelopes for the S-curves of the BOC(2,1) signal (infinite bandwidth).
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 4 Proposed System
### Animal Intrusion Detection  
In view of the serious problems caused by animal intrusion to farmers, our goal is to automatically detect animal intrusions, identify animals, repel animals with automatic actuations like beep sounds and laser lights, and inform the farmer(s) in a timely manner about the intrusion. This work is performed using two types of sensors: a PIR sensor for detecting any motion in its field of view and an all-day camera sensor attached to the Raspberry Pi for capturing images that will be processed to identify animals. To meet the low-latency requirement, the scheduling mechanism and the prediction algorithm are implemented in the fog layer, while the object detection is carried out on the Raspberry Pi. This is because the low bandwidth of LoRa cannot support the transmission of large-sized images. To achieve a highly flexible architecture that accommodates future additions, we have designed our system to deploy each microservice in its own container, ensuring complete separation and independence. As shown in Figure 2, there are three microservices:  
1. The Security module passes the sensor data it receives from authenticated sensors to the appropriate Prediction container.
2. The Prediction container runs localization and prediction algorithms on these data as well as recorded data, and then sends the predicted position at a future time to the camera.
3. The Notification module notifies the farmer via messages once animals are detected. Corresponding to the process marked with capital letters in Figure 2, the steps are described as follows:  
1. Animal movement is detected by PIR sensors and the data are transmitted to the server using LoRa.
2. A container on the edge server predicts the location of the animal at a future time based on input from multiple sensors and sends this location to the Raspberry Pi that operates a camera on the field.
3. The edge server sends a \"possible animal invasion\" alert to the farmer.
4. The Raspberry Pi instructs the camera to rotate in the direction of the predicted position and take a picture. The Raspberry Pi then runs an animal detection algorithm on this image and sends the results to the edge server.
5. If an animal was identified, actuations are activated immediately to repel it and the edge server sends a reliable alert to the farmer.  
Figure 1: Proposed system architecture.
## 3 Simulation Results and Discussions
### Neural Network Performance Evaluation  
The performance evaluation of the neural networks was carried out by using statistical measures such as Mean Absolute Error (MAE) for the electric dipole position and for the corresponding electric moments along with the Relative Error (RE) for each vector of the electric field (E\(x\), Ey, E\(z\)) at the 14 measurement points across all of the training and testing data samples for all scenarios, as follows:  
\[\text{MAE}=\frac{1}{\text{N}}\sum_{n=1}^{\text{N}}\left|s_{n}-g_{n}\right| \tag{6}\]  
\[\text{RE}=\frac{\sqrt{\sum_{n=1}^{\text{N}}\left(\text{E}_{n,\text{pr}}-\text {E}_{n,\text{act}}\right)^{2}}}{\sqrt{\sum_{n=1}^{\text{N}}\text{E}_{n,\text{ act}}^{2}}} \tag{7}\]  
Regarding \(MAE\) loss function, \(n=1,2,\ldots,N\) stands for the number of the test or training data samples, \(s_{n}\) is the EDMnet's predicted values of the electric dipole parameters, and \(g_{n}\) is the corresponding artificially generated ones. Moreover, as a second metric for better understanding the effect of the deviations of the dipole parameter values on the loss function (\(MAE\)), the \(RE\) metric for the field values was also calculated for each measurement point. Thus, for \(k=1,2,\ldots,14\) and for each sample, the electric field vector \(E_{n,pr}\) was calculated from the EDMnet's predicted values \((x,y,z,p_{x},p_{y},p_{z})\) at the 14 measurement points and \(E_{n,act}\) the corresponding electric field vector from the artificially generated datasets.
# Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning
###### Abstract  
In industrial defect detection tasks, the low probability of occurrence of severe industrial defects under normal production conditions has brought a great challenge for data-driven deep learning models that have just a few samples. Contrastive learning based on a sample pair makes it possible to obtain a great number of training samples and learn effective features quickly. In the field of industrial defect detection, the features of some defect instances have small category variance, and the scale of some defect instances has a great diversity. We propose a few-shot object detection network based on contrastive learning and multi-scale feature fusion. Aligned contrastive loss is adopted to increase the instance-level intra-class compactness and inter-class variance, and the misalignment problem is alleviated to a certain extent. A multi-scale fusion module is designed to recognize multi-scale defects by adaptively fusing features from different resolutions with the idea of exploiting the support branch's information. The robustness and efficiency of the proposed method were evaluated on an industrial wear debris defect dataset and the MS COCO dataset.  
few-shot learning; contrastive learning; multi-scale fusion; defect detection +
Footnote †: journal: Applied  
doi: 10.3390/
## 3 Speed Sensor Faults Classifier Based on Neural Networks  
The damage classification mechanism is based on the classic multilayer perceptron. The perceptron is a feedforward neural network consisting of an input layer, n- hidden layers, and an output layer. Each neuron in each layer is connected to a neuron in the next layer; there are no connections between the neurons of the same layer. The operation of such a neural network can be written in a simple way by the equation [17]:  
\[y_{k}=f_{3}\left(f_{2}\left(\sum_{i=1}^{M}w_{ki}^{(2)}\times f_{1}\left(\sum_{j =1}^{N}w_{ij}^{(1)}\times x_{j}+w_{0}^{(1)}\right)+w_{0}^{(2)}\right)+w_{0}^{(3 )}\right) \tag{15}\]  
where  
\(y_{k}\)--th output of the network;  
\(x_{j}\)--j-th input of the network;  
\(w_{ij}^{(1)},w_{ki}^{(2)}\)--weights of the first and second hidden layers, respectively;  
\(w_{0}^{(1)},w_{0}^{(2)},w_{0}^{(3)}\)--biases in the first and second hidden layers, and output layer, respectively;  
\(f_{1},f_{2},f_{3}\)--activation functions of first hidden layer, second hidden layer, and output layer, respectively.  
The paper presents two types of speed sensor fault neural classifiers based on two different speed observers. Classifiers are presented as two separate neural structures. Apart from the use of two different types of observers, the classification schemes are the same. In both cases, the Levenberg-Marquardt method was used to train the network.  
Moreover, 23-15-1 was chosen as the network structure according to the theory with the highest efficiency with the number of neurons in the first hidden layer \(2\text{N}+1\).  
The basic element while designing this type of damage classifier is the selection of appropriate diagnostic signals. The input vector of the neural network is based on the measured and estimated speed value, as well as the measured and estimated \(q\)-axis current value.  
The difference between the current and previous samples is given to the input of the neural network. A simple comparative detector is primarily responsible for damage detection. The value of the measured and estimated speed and the value of the current in the \(q\) axis are compared. The output value of this detector activates the sensorless mode. This signal is also fed to the input of the neural network. The full input vector is presented below:  
\[\begin{array}{llll}[\omega_{ref}(k),&\omega_{err}(k),&\omega_{err}(k-1),& \omega_{err}(k-3),\\ \omega_{err}(k-5),&\omega_{err}(k-7),&i_{sqcr}(k),&i_{sqcr}(k-1),\\ i_{sqcr}(k-3),&i_{sqcr}(k-5),&i_{sqcr}(k-7)]^{T}\end{array} \tag{16}\]  
The description of individual input signals is presented in Table 2.  
Figure 7: Transients of measured and estimated speed error with the use of MRAS (**a**) and SMO (**b**) in no load conditions.  
The classifier consists of one output, which indicates faults and is defined as follows: 0--no fault; 1--signal interference; 2--scaling error; 3--signal loss.  
In addition to the listed failures, the system can also detect the disappearance of individual pulses of the detector. Training the network for a signal loss is sufficient for the detector to consider this type of failure as well.
## 4 Results
### The Behavior of Fresh Geopolymer Pastes
#### 4.1.9 Permeability Test Results  
From the hydraulic conductivity tests, it was found that both mixtures had very low permeability. The results are shown in Figure 19. In the case of the geopolymer activated with 100% NaOH, a permeability coefficient of 2.78 \(\times\) 10\({}^{-7}\) cm/s was obtained, which is a very low value and represents a practically impermeable material. For the case of the geopolymer activated with 100% SS, this presented a permeability coefficient of 1.17 \(\times\) 10\({}^{-7}\) cm/s, which also relates to a relatively low permeability value, but is slightly higher than the case of the geopolymer activated with 100% NaOH, but both cases in the same order of magnitude.  
Figure 18. Stress–strain curves of the created geopolymers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & **Maximum Allowable Value** & **Sample 100\% NaOH** & **Sample 100\% SS** \\  & **(D.S. N\({}^{\circ}\) 148) (mg/L)** & **(mg/L)** & **(mg/L)** \\ \hline Ag & 5.0 & \textless{}0.1 & \textless{}0.1 \\ As & 5.0 & \textless{}0.1 & \textless{}0.1 \\ Ba & 100.0 & 4 & \textless{}2 \\ Cd & 1.0 & \textless{}0.02 & \textless{}0.02 \\ Cr & 5.0 & 0.3 & \textless{}0.1 \\ Hg & 0.2 & \textless{}0.05 & \textless{}0.05 \\ Pb & 5.0 & \textless{}0.1 & \textless{}0.1 \\ Se & 1.0 & \textless{}0.1 & \textless{}0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: TCLP test results to geopolymers.  
As both geopolymers had low permeability, they can be considered suitable for use as backfill in mining shafts, construction material, stability in tailings dams, and other multiple industrial uses in large-scale mining, at least because of this characteristic, since they practically prevent the transport of fluids through their structure.
# Assessing Water Infiltration and Soil Water Repellency in Brazilian Atlantic Forest Soils
## 2 Materials and Methods  
### Field Sites and Soil Sampling  
The study area (\(22^{\circ}53^{\prime}\) S, \(46^{\circ}54^{\prime}\) W) is located in the county of Campinas, Sao Paulo State, Southeast Brazil. The area is located inside the sub-basin of Atibaia River (2800 km\({}^{2}\)), which belongs to the Piracicaba River basin. The vegetation is classified as seasonal semideciduous forest. The zone is characterized by a complex geology located at the transition between the Atlantic Plateau and the Peripheral Depression geomorphological provinces, with Ultisols and Entisols as main soils [25].  
The elevation varies from 600-900 m a.s.l. The climate is classified as Cwa according to the Koppen classification, with annual rainfalls of 1700 mm and mean annual temperature of 20 \({}^{\circ}\)C [26].  
The three investigated land covers (pasture, P, restored forest, R, and remnant forest, F) correspond with those of [PERSON] et al. [14], with the use of the same 18 plots (\(7\times 7\) m in size). These plots represent two pasture (P1 and P2), two restored forest (R3 and R4), and two remnant forest (F5 and F6). Each of these sites is further divided into three blocks (i.e., upslope, U, midslope, M, and downslope, D). For a detailed description of the field sites, the reader may refer to our previous work [13]. In brief, for a given plot, three undisturbed soil cores (5 cm in height and 5 cm in diameter) were collected at the 0-5 cm depth. With these samples we determined in the laboratory the initial volumetric soil water content, \(\theta_{\text{i}}\) (cm\({}^{3}\) cm\({}^{-3}\)), and the soil bulk density, \(\rho_{\text{b}}\) (g cm\({}^{-3}\)). Three disturbed soil samples (0-10 cm depth) were also collected to determine the soil texture and the soil organic carbon content (OC). The soil texture was determined by the hydrometer method [27] and the OC was determined by the Walkley-Black method.
## 2 The Proposed Methodology  
Knowledge graph technology has established a standardized construction pipeline, which can generally be categorized into top-down and bottom-up approaches. The former initiates with defining a data schema (ontology model), followed by progressive refinement, storing knowledge into the graph while linking it to concepts. The latter begins with entity connections and gradually abstracts to form a data schema. Following conventional practices, domain-specific knowledge graph construction typically adopts a top-down approach. Considering the established knowledge system of loess region slope geological hazards and the emphasis on knowledge association and hierarchical structure in knowledge graphs, this study employed a top-down methodology for knowledge graph construction. The overall workflow was illustrated in Figure 1.  
This research first developed an ontology model for loess slope stability factors, using this model to establish the framework for the entire knowledge graph and define its schema. This included taxonomic classification of concepts; essential and optional attributes, with their data types and subordinate items; entity relationships; and class membership relations. To address content modeling and object extraction from unstructured data, knowledge extraction paradigms for slope stability factors were systematically investigated. Leveraging deep learning-based knowledge extraction methods and knowledge fusion techniques, a novel approach was proposed, combining knowledge graphs with tree-structured graph neural networks to address the multi-factor evaluation challenge of loess slope stability under complex influencing conditions.