esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Research Material  
The research material consisted of four extracts (two water-ethanol extracts--crude (EC) and purified (EP)--and two water-acetone extracts: crude (AC) and purified (AP)) made of frozen _Rosa rugosa_ Thunb. pseudo-fruit pomace. The exact preparation of the extracts was described by [PERSON] et al. [39]. For the frozen pomace of _Rosa rugosa_ Thunb. pseudo-fruit, two portions of 2 kg (after the production of rose juice produced on the same day) (Polska Roza, Falenty New) were subjected to 0.5 h of water extraction at a ratio of 1:2 (\(v/v\)). Next, the pomace was extracted twice statically for 24 h with a 60% percent acetone solution or 60% ethanol at a ratio of 1:3 (\(v/v\)). The received ethanol and acetone extracts were concentrated in a vacuum evaporator (Hei-VAP Precision, Heidolph, Schwabach, Germany). The concentrated acetone or ethanol extracts were then divided into two parts. One of them was purified by column chromatography on Amberlite XAD 1600 N (divinylbenzene copolymer) (30.0 \(\times\) 3.7 cm). The concentrated crude extracts were applied to the column (flow rate: 3-5 mL/min). Then, the column was washed with water, and the adsorbed polyphenols were eluted with 10% and 68% ethanol. The alcohol fractions were combined and concentrated on a Heidolph 24/7 automatic evaporator. The crude and purified extracts were freeze-dried using a freeze dryer ([PERSON], Alpha 1-2 LD plus, Osterode am Harz, Germany). In the obtained freeze-dried extracts, the content of the selected groups of polyphenols was determined by using HPLC chromatographic methods. The concentrations of ellagitannins, ellagic acid, and flavonoids were determined in the extracts by dissolving 100 mg in 80% methanol in 10-mL volumetric flask, diluted, and analyzed as described by [PERSON] et al. [40; 41] and [PERSON] et al. [42]. Procyanidins and free catechin were determined as described by [PERSON] et al. [16].  
The tested extracts had the following composition. EC: total of measured polyphenolic compounds = 8.8 (g/100 g DM/dm), 4714.3 (mg/100 g DM/dm) including ellagitannins; free ellagic acid = 168.1 (mg/100 g DM/dm), flavonoids = 357.5 (mg/100 g DM/dm), procyanidins = 3377.5 (mg/100 g DM/dm), free catechins = 196.2 (mg/100 g DM/dm). EP: total of measured polyphenolic compounds = 29.2 (g/100 g DM/dm), 15,379.7 (mg/100 g DM/dm) including ellagitannins; free ellagic acid = 496.6 (mg/100 g DM/dm); flavonoids = 993.7 (mg/100 g DM/dm); procyanidins = 10,750 (mg/100 g DM/dm), free catechins = 1529.6 (mg/100 g DM/dm). AC: total of measured polyphenolic compounds = 14.9 (g/100 g DM/dm), of which ellagitannins = 8649.1 (mg/100 g DM/dm); free ellagic acid = 278.7 (mg/100 g DM/dm), flavonoids = 603.2 (mg/100 g DM/dm); procyanidins = 5144.3 (mg/100 g DM/dm); free catechins = 205.5 (mg/100 g DM/dm). AP: total of measured polyphenolic compounds = 33.2 (g/100 g DM/dm), 17,680.9 (mg/100 g DM/dm) including ellagitannins; free ellagic acid = 645.8 (mg/100 g DM/dm); flavonoids = 978.4 (mg/100 g DM/dm), procyanidins = 12,475.5 (mg/100 g DM/dm); free catechins = 1399.9 (mg/100 g DM/dm) [39].
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 3 Improved Adaptive Federated Kalman Filter (IAFKF)
### Adaptive Information Sharing Factor  
In a classic FKF [14], the information sharing factor is evenly allocated among all the sub-filters. However, this equal distribution method neglects the unique characteristics of each sub-filter, which can result in errors from some sub-filters impacting the positioning accuracy of the main filter. Improper allocation of the information sharing factor can also decrease the accuracy of the integrated navigation system. Therefore, an adaptive allocation of the information sharing factor based on the characteristics of each sub-filter is crucial for improving their performance. To address this issue, this paper proposes an adaptive information sharing factor that can be dynamically allocated based on the state covariance matrix of each sub-filter, reducing the impact of low-precision sub-filters on the positioning accuracy of the main filter. In practical filtering, the state covariance matrix \(\mathbf{P}\) reflects the precision of the filter, where a smaller \(\mathbf{P}\) corresponds to a higher accuracy of the filter.  
The relationship between the precision \(AC_{i}(k)\) of the sub-filtering system in this paper and the state covariance \(\mathbf{P}_{i,k}\) is as follows:  
\[AC_{i}(k)=\sqrt{tr(\mathbf{P}_{i,k}\cdot\mathbf{P}_{i,k}^{\mathrm{T}})} \tag{29}\]  
where \(AC_{i}(k)\) represents the filtering accuracy of the \(i\)-th sub-filter at time \(k\), and \(tr\) represents the trace of the matrix. The adaptive information sharing coefficient can be expressed by the precision of the sub-filtering system \(AC_{i}(k)\) as follows:  
\[\beta_{i}(k)=\frac{\left(AC_{i}(k)\right)^{-1}}{\sum\limits_{i=1}^{N}\left(AC _{i}(k)\right)^{-1}},\quad i=1,2\ldots,N \tag{30}\]  
where \(\beta_{i}(k)\) represents the information sharing factor, and \(N\) represents the number of sub-filters. According to Equation (16), the information fusion state of the main filter is as follows:  
\[\hat{\mathbf{x}}_{g,k}=\sum\limits_{i=1}^{N}\frac{\left(AC_{i}(k)\right)^{-1}}{ \sum\limits_{i=1}^{N}\left(AC_{i}(k)\right)^{-1}}\hat{\mathbf{x}}_{i,k},\quad i=1, 2\ldots,N \tag{31}\]
# Incorporating Transverse Normal Strain in the Homogenization of Corrugated Cardboards
## 2 Plate Element Incorporating Transverse Normal Strain
### Element Formulation  
Use the finite element method to interpolate the unknown functions:  
\[\mathbf{u}\mathbf{=}\mathbf{N}\mathbf{q} \tag{5}\]  
where \(\mathbf{u}=\begin{bmatrix}u_{0}&v_{0}&w_{0}&\varphi_{x}&\varphi_{y}&\psi&\zeta \end{bmatrix}^{T}\), \(\mathbf{q}\) is the nodal displacement vector, and \(\mathbf{N}\) is the shape function matrix.  
\[\mathbf{N}_{(7\times 56)}=\begin{bmatrix}diag(N_{1})&diag(N_{2})&\ldots&diag(N_{8}) \end{bmatrix} \tag{6}\]  
The potential energy of the element can be derived by:  
\[\Pi^{e}=\frac{1}{2}\mathbf{q}^{eT}\mathbf{K}^{e}\mathbf{q}^{e}-\mathbf{F}^{eT}\mathbf{q}^{e} \tag{7}\]  
with  
\[\mathbf{K}^{e}=\int_{A}\mathbf{B}^{T}\mathbf{Q}_{\mathbf{0}}\mathbf{B}dA \tag{8}\]  
where the superscript \(e\) means element, \(\mathbf{K}^{e}\) is the element stiffness matrix, \(\mathbf{F}^{e}\) is the nodal force vector of nodes, and \(\mathbf{B}=\mathbf{L}\mathbf{\cdot}\mathbf{N}\), is the strain-displacement matrix (see Appendix A for \(\mathbf{L}\)). Using the minimum potential energy theorem, the equilibrium relation of the element is obtained:  
\[\mathbf{K}^{e}\mathbf{q}^{e}=\mathbf{F}^{e} \tag{9}\]  
In the present work, an eight-node serendipity element, as shown in Figure 2, is used. The shape function is:  
\[\begin{split} N_{1}&=-\tfrac{1}{4}(1-\xi)(1-\eta)(1+ \xi+\eta);N_{2}=-\tfrac{1}{4}(1+\xi)(1-\eta)(1-\xi+\eta);\\ N_{3}&=-\tfrac{1}{4}(1+\xi)(1+\eta)(1-\xi-\eta);N_{4}=- \tfrac{1}{4}(1-\xi)(1+\eta)(1+\xi-\eta);\\ N_{5}&=\tfrac{1}{2}(1-\xi)(1+\xi)(1-\eta);N_{6}=\tfrac{1}{2}(1- \eta)(1+\eta)(1+\xi);\\ N_{7}&=\tfrac{1}{2}(1-\xi)(1+\xi)(1+\eta);N_{8}=\tfrac{1}{2}(1- \eta)(1+\eta)(1-\xi)\end{split} \tag{10}\]  
The integral of Equation (8) is calculated using Gaussian quadrature in a parametric element. Four Gauss points are used in the present element.
## 4 Our Proposed GCI-IGGM-PMBM Scheme  
Multitarget tracking for aquaculture sensor networks allows obtaining the state density and cardinality density of multiple culture objects adopting an appropriate highly efficient tracking method. According to the standard multitarget PMBM model in Section 3.2, some pending problems should be considered.  
First of all, detection probability is of critical importance in a multitarget tracking scheme, and the standard PMBM filter assumes that the detection probability is known and time-invariant. In actual multitarget tracking, targets can move in uncertain environments at different times, and sensors collecting target state information can also be located in an uncertain area of tracking position. Thus, the detection probability has much uncertainty and may be time-variant, which brings about a decrease in the accuracy of target state or cardinality depending on the detection measurements.  
Secondly, the standard PMBM filter tracks multiple targets with the assumption that newborn target birth density is known and time-invariant, or the prior density is known [19], which only takes the known observation area into target density estimation without considering the unknown observation area. However, newborn targets (including spontaneous newborns and spawned newborns) may be randomly located in the coverage range with adaptive newborn density in actual applications, leading to primitive PMBM with further missed detection and inaccurate estimation.  
Lastly, large-scale sensors located in the tracking coverage range can collect and exchange location information originating from multiple targets or other tracking information for target tracking. For such an energy-limited, computation-limited, and processing-limited sensor network, it is impossible for each sensor, even if it is an anchor with better hardware equipment, to execute and process so much tracking information. In addition to increased energy consumption for tracking multiple targets, extensive delays are also taken into account for exchanging and processing tracking data by the respective sensors.  
In order to solve these existing problems of the standard PMBM filter, an improved method based on a GCI-IGGM-PMBM filter is proposed for our aquaculture tracking sensor network. Firstly, an adaptive target detection probability, modeled as a gamma distribution, is taken into account to estimate multiple target states and the cardinality density. Secondly, an adaptive newborn density originating from measurements based on the time-variant detection probability is proposed, with IG distribution. Lastly, a GCI fusion strategy, consisting of an associative distributed fusion of target states measured by multiple sensors according to their respective weights, is applied to a large-scale tracking network.  
This improved GCI-IGGM-PMBM scheme estimates the target state density and cardinality of multiple targets, as elaborately presented below.
###### Abstract  
In the mechanized mining of metal mines, identifying and optimizing vulnerabilities within the production system is essential for enhancing operational efficiency and ensuring sustainable development. By leveraging data from 88 slopes at Guangxi Tongkeng Mine over a decade, we constructed a comprehensive dataset encompassing drilling, charging, blasting, ventilation, support, ore drawing, and maintenance. The XGBoost algorithm was employed to model factors influencing stope production capacity (PC), with its parameters optimized using the Marine Predator Algorithm (MPA). The MPA-XGBoost model demonstrates a high predictive accuracy for PC (\(R^{2}\) = 0.958, \(V\!A\)F = 95.981%, \(MAE\) = 4.844, \(RMSE\) = 7.033). A Shapley Additive Explanations (SHAP) analysis reveals that drilling efficiency (DE) contributes most positively (35.6%), while ventilation time (VT) and equipment maintenance time (EMT) negatively impact PC. SHAP dependence plots indicate that increasing DE significantly enhances PC, whereas excessive VT or EMT leads to a substantial decline in PC. These findings offer valuable insights and a robust foundation for optimizing design and improving production management in mechanized mining operations.  
underground metal mines; mechanized mining; machine learning; SHAP analysis; production capacity optimization Article
# A Low Torque Ripple Direct Torque Control Method for Interior Permanent Magnet Motor
## 2 Torque Analysis and Proposed DTC
### Proposed DTC for PMSM  
As previously discussed, it is considered that the error in flux linkage angle would influence the accuracy of the voltage vector command and consequently affect the torque ripple. In order to improve this problem, the _f-t_ frame was used to develop the new algorithm to generate a proper voltage vector command for the VSI. The solution proposed in this paper is to decouple the mutual effect between the voltage magnitude and flux linkage angle so that the voltage command will not be affected by the error of flux linkage angle. For the vectors shown in Figure 2, \(\Psi_{s}\) is the flux linkage at \(t_{k}\), \(\Psi^{\prime}_{s}\) is the flux linkage at \(t_{k+1}\) (i.e., \(\Psi^{\prime\prime}_{s}=\Psi_{s(k+1)}\)), the angle \(d\delta\) between \(\Psi_{s}\) and \(\Psi^{\prime}_{s}\) is the variation of the load angle, \(d\Psi_{s}\) is the variation of flux linkage, and \(\eta\) is angle of flux linkage variation referring to _f_-axis.  
From Figure 2, the magnitude of motor voltage \(V_{s}\) can be derived by applying the law of cosines, as given by [18]  
\[|V_{s}|=\frac{\sqrt{\psi_{s}^{2}+\psi_{s}^{\prime\prime 2}-2\Psi_{s}\Psi^{ \prime}_{s}cos(d\delta)}}{T_{PWM}} \tag{20}\]  
where \(T_{PWM}\) is the period of pulse-width modulation (PWM). Additionally, the phase of the voltage \(V_{s}\) is the same as \(d\Psi_{s}\)[18] and can be formulated with the law of sines as follows:  
\[\eta=sin^{-1}\frac{\Psi^{\prime}_{s}sin(d\delta)}{\sqrt{\Psi^{2}_{s}+\Psi^{ \prime 2}_{s}-2\Psi_{s}\Psi^{\prime}_{s}cos(d\delta)}} \tag{21}\]  
where \(\eta\) is the angle between voltage vector and _f_-axis. In Figure 3, the angle between voltage vector and \(\alpha\)-axis can thus be obtained as  
\[\theta_{v}=\theta_{s}+\eta \tag{22}\]  
where \(\theta_{s}\) is phase of flux linkage vector in \(\alpha\)-\(\beta\) frame.  
Figure 3: The flux linkage vector in \(\alpha\)-\(\beta\) frame.  
Figure 2: The flux linkage vector in the _f-t_ frame.  
Based on the conventional SVM-DTC scheme shown in Figure 4[19], the proposed method in this paper, the rSVM-DTC as previously mentioned, replaces the dashed area by the diagram shown in Figure 5. It should be noted that the flux linkage \(\Psi_{s}\) in Figure 5 can be simply obtained by \(\Psi_{\alpha}\) and \(\Psi_{\beta}\) in Figure 4. Note that \(\Psi_{s,cmd}\) in Figure 5 is \(\Psi_{s}^{\prime}\) in Figure 3. As shown in Figure 5, there is no mutual influence between the magnitude of voltage vector and flux linkage angle. The angle of voltage vector referring to \(\alpha\)-axis, \(\theta_{v}\), is the sum of the angle of flux linkage, \(\theta_{s}\) and \(\eta\). The voltage command vector is calculated by the proposed architecture shown in Figure 5 to improve the torque ripple of the PMSM.
## 3 Results  
### Military Communication Institute  
The tests were carried out for different sizes of font characters. For the tests, besides the secure fonts, traditional fonts Arial and Times New Roman, were used, such that one could observe more easily the differences between secure fonts and traditional fonts in the protection of text information against electromagnetic eavesdropping.  
Additionally visual analyses and correlation analyses were carried out for each secure font. The analyses were based on values of character error rate (CER) [20].  
\[CER=\frac{c+k}{g}=\frac{c+(r-e)}{g} \tag{1}\]  
Figure 3: Telecommunications Services test bed.  
where:  
* \(r\)--number of wanted characters in the analyzed image,
* \(c\)--number of incorrectly recognized characters,
* \(e\)--number of correctly recognized characters,
* \(k\)--number of unrecognized and wanted characters (\(k=r-e\)),
* \(g\)--the number of all characters in the image.  
For secure fonts, this parameter has high values in contrast to traditional fonts. This made it impossible to recognize each character of the secure fonts in reconstructed images. To confirm the results and the efficiency of using the secure fonts for electromagnetic protection of processed information against electromagnetic penetration, additional tests were conducted in another laboratory. The results are shown in the next chapter of this paper.  
During the first stage of the tests, selected characters of the fonts were displayed on the monitor. The characters were separated by one space each (Figure 4). The solution is not typical for the processing of text data. However, we can observe the shape of each character on the output of Side Channel Attack (SCA) better. For the Arial and Times New Roman fonts, the reading of the characters is not hard.  
For the special fonts, the situation is totally different. The characters in particular sets ('a', 'c', 'e', 'n', 'o','s', 'u', 'z'), ('g', 'y') and ('b', 'h', 'k') look similar. We have to remember that the size of the characters is 24 points. The observed phenomenon will be more beneficial for smaller sizes of the primary characters.  
In the second step of tests, the texts were displayed on the monitor. The tests included sequences of characters (size 20 points) without additional spaces. This situation was shown in Figure 5. For Arial and Times New Roman fonts, each character could be selected. The characters of secure fonts remain illegible. Only upper and lower elongations of the characters (e.g., 'b', 'd', 'g', 'h', 'k') could be recognized.  
Figure 4: Reconstruction of the displayed image based on sensitive emissions measurement on frequency 840 MHz (\(BW\) = 50 MHz, the size of characters-36 points): (**a**) Arial font, (**b**) Times New Roman font, (**c**) secure asymmetrical font, (**d**) secure symmetrical font.
# Multimodal Model for Automated Pain Assessment: Leveraging Video and fNIRS
## 4 Experiment and Results
### Implementation Details  
Our model, illustrated in Figure 1, was implemented using the TensorFlow-Keras framework. We employed the Adam optimizer with a learning rate of 0.0001 and trained the model with a batch size of 64. The cross-entropy loss function was used for training. Although we initially set the maximum number of epochs to 300, as the validation loss increased while training loss decreased, indicating overfitting, we applied early stopping based on the validation loss, halting training when no improvement was observed. Our experiments on the AI4 Pain dataset involved two types of dataset treatments. In the first treatment (denoted as B-1), we used the original class ratio without additional preprocessing. In the second treatment (denoted as B-6), we divided the baseline \"No Pain\" samples into six parts to investigate the impact of class imbalance on the model's performance. Additionally, we validated our model using the original class ratio to ensure the robustness of our findings. Due to the relatively small dataset size, a dropout rate of 0.25 was applied to the multi-head attention modules. For data sampling, we extracted the first \(8\times 32\) frames from each video and the corresponding first 8 s of fNIRS signals, using a stride of 5. Our experiments showed that these sampling parameters had minimal impact on the overall model performance.
## 2 Materials and Methods
### CIELAB Color Leaf Measurement  
Color coordinates were recorded using a Minolta Chroma Meter CR-300 (Minolta Camera Co. Ltd., Tokyo, Japan). At each harvest date, ten measurements were taken on the upper surface of young expanded leaves of ten plants per experimental unit. As described by the International Commission of Illumination (CIE), the color was expressed with degree of lightness (L\"), greenness (a*), and yellowish (b*) values, chroma, and hue angle. Chroma is the color saturation quantitative attribute representing the degree of visual difference from neutral grey of the same lightness. Higher chroma values indicate a higher color intensity perceived by humans. The hue angle describes the qualitative color attribute with respect to the red/green (+a*/\(-\)a*) and yellow/blue (+b*/\(-\)b*) axes.
## 3 Results  
### Semi-Automated Mapping Results  
Figure 4 shows the OBIA landslide mapping results for each Landsat image. In addition to the Butangbunasi landslide area, we detected a temporary landslide-dammed lake at the confluence of the Butangbunasi tributary with the Laonong River on six images.  
Each selected typhoon event was linked to the respective Landsat image date when the OBIA mapping of the landslide area was performed. Figure 5 shows the time series where landslide area evolution was tracked along with the preceding typhoon events. After typhoon Morakot in 2009, an abrupt increase in landslide area was identified, keeping a steady or even slightly decreasing trend for the following years. Section 3.3 provides additional information about the evolution of the Butangbunasi landslide and the temporary landslide-dammed lake in the Laonong river bed with respect to the heavy rainfall events.  
Figure 4: Object-based image analysis (OBIA) landslide mapping results for Butangbunasi. The evolution of the landslide area from 1984 to 2018 is shown per Landsat image. Additionally, a landslide-dammed lake was detected on six of the images (2016, 2010, August 2008, 2000, 1992 and 1990).
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 1 Introduction
How can reduced data transmission speeds affect the UWSN when using the Telnet and Superframe protocols? How do Telnet and Superframe influence the dependability of the UWSN in comparison to other protocols? This research attempts to provide answers to these questions in order to shed light on the strengths and drawbacks of the Telnet and Superframe protocols as they relate to UWSNs. This information can be used to improve underwater sensing and monitoring systems by informing the development of more effective and reliable UWSN communication protocols. Key contributions made by this manuscript are as follows:  
To evaluate the performance metrics of AODV, LAR1, OLSR, Fisheye, and STAR-LORA routing protocols in terms of energy in different modes, such as transmit, idle, and receive modes;  
To evaluate the energy efficiency of all the routing protocols as the number of underwater wireless sensor nodes increases;  
To evaluate the energy trade-off between receiver and transmitter modes;  
To propose a suitable routing protocol for an underwater wireless sensor network, taking into account the desired levels of transmitted and received power. The remainder of this manuscript is organized as follows. Section 2 describes the related works. Section 3 briefly describes the background on communication protocols. Section 4 briefly discusses the proposed underwater network topology. Section 5 introduces the proposed CB-UWSN design parameters. Section 6 shows the obtained results and describes the discussion. Section 7 provides an overview of the entire manuscript and also the future directions.