esa-sceva/satcom-chunk-collection huggingface dataset:
# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
###### Abstract
By creating a single entity out of several motion sequences, MHI can effectively capture temporal information. The Lite General Network and MagFace CNN (LGNMNet) model is a general low-dimensional feature embedding loss model built by [6]. Thus, inspired by [6], MobileNet V3 CNN backbone with the MagFace loss function [7] method, commonly used in facial recognition tasks, is proposed. This paper also combines the proposed model with the Random Forest machine learning classifier to improve the ME detection accuracy using MHI rather than optical flow as input features; this method is given the name LGNMNet-RF. To the best of our knowledge, this is the first application of a CNN-ML combination technique in the field of ME and MaE detection study. Additionally, the feature learning process is enhanced by incorporating pseudo-labelling during training and predicting the apex frame in each video using peak detection, following a smoothing process. The framework architecture is also illustrated in Figure 1.  
The combination of the Lite General Network (LGN), MagFace CNN, and a Random Forest (RF) classifier was chosen for its unique balance of efficiency, discriminative power, and interpretability, which makes it particularly well-suited to the task of spotting micro-expressions. LGN offers a lightweight, computationally efficient architecture, while MagFace enhances feature regularisation, improving intra-class compactness and inter-class separability, which is crucial for distinguishing subtle micro-expressions. The Random Forest adds robustness and interpretability to the classification, making the overall system not only accurate but also resilient to small, imbalanced datasets. This combination outperforms other techniques by addressing both the computational and task-specific demands effectively. The main contributions of this study are as follows:  
1. This study proposes MHI for the ME and MaE detection using LGNMNet-RF architecture. 2. This study implements pseudo-labelling with temporal extensions to generate more accurate labels for deep learning training according to the characteristics of the MHI features. 3. This study introduces an LGNMNet-RF architecture that integrates MobileNet V3 with the MagFace loss function and utilises a Random Forest classifier to enhance interval detection accuracy and mitigate noise. This article is organised as follows: Section 2 presents Related Works, Section 3 presents the Methodology, Section 4 deals the Experiments, Section 5 presents the Results and Discussions, and a Conclusion is provided in Section 6.
## 3 Performance Analysis
### Spectral Efficiency Computation  
#### 3.2.1 Spectral Efficiency of FU  
Recall that FU is not directly connected to the BS. It needs the assistance of D\({}_{1}\) to communicate with the BS. Therefore, both the first hop (BS\(\rightarrow\) D\({}_{1}\)) and second hop (D\({}_{1}\)\(\rightarrow\) FU) links need to be considered to compute the SE of FU. Now, using [54] (Equation (22)) and (5), the e2e overall SE of the FU in S\({}_{1}\) is obtained as  
\[\begin{array}{ll}R_{\text{SE,FU}}^{\text{S}_{1}}=&\frac{1}{2}N_{1}\bigg{\{}1+ P_{\text{e2e,FU}}^{\text{S}_{1}}\log_{2}(P_{\text{e2e,FU}}^{\text{S}_{1}})\\ &+(1-P_{\text{e2e,FU}}^{\text{S}_{1}})\log_{2}(1-P_{\text{e2e,FU}}^{\text{S}_ {1}})\bigg{\}}.\\ \end{array} \tag{7}\]  
where \(N_{1}=\log_{2}(M_{1})\) in S\({}_{1}\) and \(N_{1}=\log_{2}(A_{\text{BS}})\) in S\({}_{2}\).  
Again, using [54] (Equation (22)) and (6), the e2e overall SE of the FU in S\({}_{2}\) is obtained as  
\[\begin{array}{ll}R_{\text{SE,FU}}^{\text{S}_{2}}=&\frac{1}{2}N_{1}\bigg{\{}1+ P_{\text{e2e,FU}}^{\text{S}_{2}}\log_{2}(P_{\text{e2e,FU}}^{\text{S}_{2}})\\ &+(1-P_{\text{e2e,FU}}^{\text{S}_{2}})\log_{2}(1-P_{\text{e2e,FU}}^{\text{S}_ {2}})\bigg{\}}.\\ \end{array} \tag{8}\]Please note that the data transmission policy for \(S_{1}\) and \(S_{2}\) is different in \(P_{1}\) but identical in \(P_{2}\). In (7) and (8), the scaling factor \(\frac{1}{2}\) is used to denote that the e2e data transmission is divided into two cooperative phases and each phase requires one time slot.
## 3 Pose Estimation Based on Visual-IMU Fusion
### Pre-Integration Error Based on IMU  
We collect the angular velocity \(\widetilde{\omega}\) and acceleration \(\widetilde{a}\) of the IMU sensor on the roll, pitch, and yaw, but they are affected by the gyroscope bias \(b_{\omega}\) and noise \(n_{\omega}\), and the acceleration bias \(b_{a}\) and noise \(n_{a}\). We assume that the derivatives of biases \(b_{\omega}\) and \(b_{a}\) and noises \(n_{\omega}\) and \(n_{a}\) are Gaussian; the real angular velocity \(\omega\) and acceleration \(a\) where the system is located are given by  
\[\omega=\widetilde{\omega}-b_{\omega}-n_{\omega},a=\widetilde{a}-b_{a}-n_{a} \tag{3}\]  
Several IMU measurements are sampled between the \(k\)th and \(k+1\)th frames of the camera, and the position \(p_{k+1}\), speed \(v_{k+1}\), and attitude \(q_{k+1}\) at the \(k+1\)th frame can be obtained by integrating the IMU measurement values between the two frames. However, the system needs to recalculate the integral between the two frames every time afterthe pose of the \(k\)th frame is optimized, which is time consuming. Therefore, this paper adopts the pre-integration method in VINS-Mono [33] to transform the integration term corresponding to the world coordinate system between the \(k\)th and \(k+1\)th frames into the pre-integration term corresponding to the \(k\)th frame. At the same time, due to the bias and noise of the IMU pre-integration term, the IMU sensor usually estimates the motion by minimizing the pre-integration error. This paper uses the pre-integration error \(r_{l_{k}}\) of the IMU term. We view the pre-integration component between two frames as the measured value, and all state variables of two frames are subtracted as the observed value:  
\[\left[\begin{array}{c}r_{p}\\ r_{v}\\ r_{q}\\ r_{ba}\\ r_{ba}\end{array}\right]=\left[\begin{array}{c}R_{w}^{k}\Big{(}p_{k+1}^{w}-p _{k}^{w}-v_{k}^{w}\Delta t+\frac{1}{2}g^{w}\Delta t^{2}\Big{)}-\alpha_{k+1}^{k }\\ R_{w}^{k}\Big{(}p_{k+1}^{w}-p_{k}^{w}+g^{w}\Delta t\Big{)}-\beta_{k+1}^{k}\\ 2\Big{[}q_{k}^{k+1}\otimes\Big{(}q_{w}^{k}\otimes q_{k+1}^{w}\Big{)}\Big{]}_{xyz }\\ b_{k+1}^{a}-b_{k}^{a}\\ b_{k+1}^{w}-b_{k}^{w}\end{array}\right] \tag{4}\]  
where \(r\) represents the error of the state quantity; \(g\) represents the acceleration of gravity, which is \(9.8\) m/s\({}^{2}\); \(\Delta t\) represents the time interval between two adjacent frames \(b_{k}\) and \(b_{k+1}\); \(\alpha\) and \(\beta\) represent the pre-integration term of \(p\) and \(v\), respectively; and \([\cdot]_{xyz}\) represents the three-dimensional vector consisting of the imaginary part of the quaternion \((x,y,z)\). Therefore, through the above preprocessing, the IMU information is converted into the IMU pre-integration error, which provides IMU constraints for the pose estimation of visual-IMU fusion.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 1 Introduction  
Rainfall is a critical factor of water cycle dynamics as it is a vector for energy exchange between atmosphere, sea and land. Moreover, it has a key role for water supply; rainfall monitoring is an important tool to be used for water management, especially in areas characterized by water scarcity. Some other important practical applications also concern agriculture and human welfare. Finally,the measurement, the monitoring and the forecasting of rainfall are different phases of meteorological studies substantial for the management of alerts and the assessment of environmental risks due to floods and landslides. They also play a key role in climatological studies, as the rainfall amount is an important climate index. The knowledge of the instantaneous rainfall intensity during precipitation events is very useful in urban areas as it allows a better understanding of hydrological dynamics for the correct interpretation of the complex infiltration and runoff processes. Urban hydrological flows are constrained by the drainage system and highly dependent on the rainfall intensity. In order to produce current estimates of the system conditions, the real-time rainfall data are very useful; they are also able to highlight short and very intense events.  
Several methods for measuring precipitation exist, based on different physical principles, direct and indirect methods, and ground and space-based systems. A complete up-to-date review regarding the fundamental and technical aspects of rainfall estimation is offered by [1], together with the description of the physical operating principle, an overview of the scientific knowledge and the description of existing methods and future emerging technologies. Although indirect methods based on remote measurements (weather radars and weather satellites) are widespread, they unfortunately do not allow the providing of a high accuracy of measurements while providing a correct view of the spatial distribution of precipitation. Conversely, point observations (rain gauges and disdometers) provide the most direct measurements of precipitation, but they are referred only to the specific point of installation and in most cases (rain gauges) always produce time-accumulated values. The use of point gauges for monitoring large areas requires the deployment of a large number of sensors, leading to a cost rise.  
The combination of remote sensing and in situ observations for precipitation estimation is particularly suitable for now-casting purposes [2], as the indirect measurements of radar and satellites are related to the presence and vertical development of clouds, atmosphere microphysics as well as the precipitation; this information is very useful to interpret the weather phenomena properly.
## 3 System Architecture
### Add Start of Frame and SN  
The preamble is the first part of the symbol frame. It is applied to synchronize and detect the start of frame and helps the receiver side to decode data easily. If the preamble  
Figure 1: Reference architecture of OCC for an IoT-system-based 2D OOK–MIMO technique using an LED array.  
part is redundant (i.e., if it does not carry the desired data), then the length of the preamble should be carefully considered. In this paper, we proposed a preamble length of 6 bits (SF = \"011100\"). With the preamble, we can easily detect the start of frame in each image.  
The sequence number (SN), which helps the receiver side combat the frame rate variation effect, is inserted into each packet after the preamble. It helps to remove the redundant packets in case of oversampling. Next, the large package will be merged from adjacent images. The SN also enables the cameras in the receiver sides to detect the missing packet in case of undersampling. The advantages of the SN are presented in Section 4.
## 3 Experimental Results  
For the clinical trial, Institutional Review Board approval and informed consent were obtained before beginning any of the experiments (IRB No. RERI-IRB-191230). The hand can perform four hand gestures used as a means of communication as well as four grip motions used commonly in the activities of daily life. Figure 9 shows the experimental results verifying that the hand can perform four hand gesture motions using the control algorithm.  
We confirmed the performance of the algorithm developed for normal subjects in an earlier study [31]. The results indicated that a normal subject could easily and effectively perform various hand motions using the multi-DOF myoelectric hand prosthesis with only two EMG sensors. Thus, the EMG signals of an upper limb amputee were used in this experiment. The subject who participated in the experiment is a bilateral upper limb amputee who has been without the upper limbs for over 15 years. The residual amputation site is 80 mm above the elbow. The subject currently uses a three-finger type myoelectric hand prosthesis with one DOF, and the number of EMG signals generated from the residual muscles is two. In this study, two EMG sensors were installed on the extensor carpi radialis on the lateral side and on the flexor carpi uln  
Figure 8: Eight hand gestures and grip motions performable by the hand.  
Figure 7: The developed multifunctional myoelectric hand prosthesis system.  
Figure 9: Experimental results obtained using the developed easy and efficient control algorithm: (**a**) EMG signals of an upper limb amputee and the hall sensor value according thumb position; (**b**) Results of the various hand motions performed using developed hand.  
Figure 9(1-12) shows that upon receiving an EMG CC signal, the hand changed from the grip motion state to the gesture motion state. At this time, because HS1 was higher than HS2, the hand was in the abduction state. In this state, upon applying an OS signal, the hand performed the thumb up gesture (see Figure 9(3)), and upon applying a CS signal, the hand performed the point gesture (see Figure 9(5)). Upon applying a CC signal, the hand returned to the gesture motion state and changed to the grip motion state (see Figure 9(6)). At this time, when the thumb was rotated manually to make HS2 higher than HS1, the hand entered the adduction state (see Figure 9(8)). In this state, upon applying a CS signal, the hand performed a precision grip motion (see Figure 9(9)), and upon applying an OS signal, the hand performed a power grip motion (see Figure 9(11)). These experimental results demonstrate that the developed hand can perform various hand gesture motions and grip motions through the control algorithm while using only two EMG signals.
###### Abstract  
In the experiment, crude proteins from spotless smoothhound (_Mustelus griseus_), cartilages were isolated by HCl-Guanidine buffer, and its hydrolysate was prepared using trypsin at pH 8.0, 40 \({}^{\circ}\)C with a total enzyme dose of 2.5%. Subsequently, three antioxidant peptides were purified from the hydrolysate using membrane ultrafiltration, anion-exchange chromatography, gel filtration chromatography, and reverse phase high-performance liquid chromatography. The amino acid sequences of isolated peptides were identified as Gly-Ala-Glu-Arg-Pro (MCPE-A); Gly-Glu-Arg-Glu-Ala-Asn-Val-Met (MCPE-B); and Ala-Glu-Val-Gly (MCPE-C) with molecular weights of 528.57, 905.00, and 374.40 Da, respectively, using protein amino acid sequence analyzer and mass spectrum. MCPE-A, MCPE-B and MCPE-C exhibited good scavenging activities on 2,2-diphenyl-1-picrylhydrazyl radicals (DPPH\(\bullet\)) (EC\({}_{50}\) 3.73, 1.87, and 2.30 mg/mL, respectively), hydroxyl radicals (HO\(\bullet\)) (EC\({}_{50}\) 0.25, 0.34, and 0.06 mg/mL, respectively), 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothiazazoline-6-sulfonic acid radicals (ABTS\({}^{\star}\)\(\bullet\)) (EC\({}_{50}\) 0.10, 0.05, and 0.07 mg/mL, respectively) and superoxide anion radicals (O\({}_{2}^{\circ}\)\(\bullet\)) (EC\({}_{50}\) 0.09, 0.33, and 0.18 mg/mL, respectively). MCPE-B showed similar inhibiting ability on lipid peroxidation with butylated hydroxoluphene (BHT) in a linoleic acid model system. Furthermore, MCPE-A, MCPE-B, and MCPE-C could protect H\({}_{2}\)O\({}_{2}\)-induced HepG2 cells from oxidative stress by decreasing the content of malonaldehyde (MDA) and increasing the levels of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and glutathione reductase (GSH-Rx). Glu, Gly, Met, and Pro in their sequences and low molecular weight could be attributed to the antioxidant activities of three isolated peptides. These results suggested that GAERP (MCPE-A), GEREANVM (MCPE-B), and AEVG (MCPE-C) from cartilage protein hydrolysate of spotless smoothhound might serve as potential antioxidants and be used in the pharmaceutical and health food industries.  
s pacs: spotless smoothhound (_Mustelus griseus_); cartilage; protein hydrolysate; peptide; antioxidant activity; antioxidant enzyme
## 2 Wellbore Attitude Measurement Method and Error Analysis
### Wellbore Attitude Calculating Method
#### 2.1.2 Magnetometer-Based Azimuth Calculation  
A magnetometer is sensitive to the weak magnetic field of the Earth. The geomagnetic field has a fixed magnetic azimuth at a fixed place and time. Therefore, the angle between the direction of the instrument and the magnetic north can be calculated from the tri-axial magnetometers. The geomagnetic field vector in the \(G\) frame is denoted as \(\mathbf{m}^{G}=\left[m_{E}\quad m_{N}\quad m_{U}\right]^{T}\). The spatial relation of the geomagnetic field vector and the magnetic declination and magnetic dip is shown in Figure 2. Noting that the magnetic north is regarded as the northward reference, the eastward component of the magnetic field is 0 [15]. The detailed form is written as  
\[\left\{\begin{array}{c}m_{E}=0\\ m_{N}=m\ cos(dip)\\ m_{U}=m\ sin(dip)\end{array}\right.. \tag{4}\]  
The matrix expression of the magnetometers in the \(D\) frame is written as \(\mathbf{m}^{D}=\left[m_{x}\quad m_{y}\quad m_{z}\right]^{T}\), which can be expressed
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
###### Abstract  
In this study, a new method for the optimal design of multimode shunt-damping circuits is presented. A modification of the \"current-flowing\" shunt circuit is employed to control multiple vibration modes of a piezoelectric laminate beam. In addition to the resistor damping components, the method considers the capacitances and the shunting branch inductors as new design variables. The \(H_{\infty}\) norm of the damped system is minimized using the particle swarm optimization (PSO) method in the suggested optimization strategy. Two additional numerical models are addressed in order to compare the proposed method with other methods from the literature and to thoroughly examine the effect of the design variables on damping performance. To simulate the dynamic behavior of the piezoelectric composite beam, a finite-element model is created which provides more accurate modeling of thick beam structures. Results show that the suggested method may improve damping efficiency when compared to other models, since it generates a highest peak amplitude reduction of 39.61 dB for the second mode and 55.92 dB for the third mode. Finally, another benefit provided by the suggested optimal design is the reduction of the required shunt inductance values.  
m _Acticle_  
New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control. _Signals_  
202238308563390/signals3040050
## 3 Results and Discussion
### Bubble Distribution Pattern on the Anode--Experiment 1
The bubble concentrated areas in Figure 3 overlap with the areas with a low bubble generation rate in Figure 4. Additionally, the areas with few bubbles in Figure 3 respond to areas with a high bubble generation rate in Figure 4. It is easy to understand that the large bubbles could form a bubble layer covering some local areas on the anode surface, inhibiting effective contact between the anode and electrolyte. Therefore, these local areas showed low bubble generation rates, while for the 'active regions' on the anode surface, where high bubble generation rates are observed, the appearance of a large number of small bubbles can also be found at the same location in Figure 3.  
It is inevitable that the anode surface behaves inhomogeneously in material properties since the carbon anode consists of carbon particles and binder, for both the graphite anode and industrial prebaked anode. As is illustrated in the bubble patterns (Figure 3), the 'active regions' on the anode are not uniformly distributed as well (Figure 4). Anyway, the location of the 'active regions' are probably to change in the long term with the consumption of the anode. However, significant variation was not observed within 60 s under the apparent current density of 0.9-1.3 A\(\cdot\)cm\({}^{-2}\) in this investigation. Figure 4: The bubble generation patterns on the flat and slotted anodes from statistical analysis within 60 s, the gradient bars indicate the number of newly generated bubbles on the specific anode surface within 60 s. (**a**) Apparent current density 0.9 A\(\cdot\)cm\({}^{-2}\), flat anode (**b**) 1.1 A\(\cdot\)cm\({}^{-2}\), flat anode (**c**) 1.3 A\(\cdot\)cm\({}^{-2}\), flat anode **(d**)** 0.9 A\(\cdot\)cm\({}^{-2}\), slotted anode **(e)** 1.1 A\(\cdot\)cm\({}^{-2}\), slotted anode **(f)** 1.3 A\(\cdot\)cm\({}^{-2}\), slotted anode. The bubble evolution behavior on several carbon materials has been reported in other investigations [18; 19; 20]. Combined research should be carried out in the future to understand the relationship between the bubble behavior and the material properties, with the aim of improving the performance of industrial anode. In the commercial aluminum reduction cell, the carbon anode behaves as self-adaptive to the change of anode cathode distance (ACD) during electrolysis. In commercial cells, the ACD is maintained at a relative constant of 3-5 cm. With the consumption of carbon material at the 'active regions', the 'active regions' appear as concave shapes on the surface; consequently, the 'less active regions' protrude from the surface. Due to the change in the local ACD, the convex regions gain extra over potential and are activated to take a larger proportion of current than they used to [21].
# Inland Water Level Monitoring from Satellite Observations:
## 2 Methodological Framework
### Search and Selection Strategy
Then, the final dataset was confirmed, ascertaining the corpus selection efficiency through the full-text screening procedure.