esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Hysteretic Model of a Prototype Seismic Isolation System Made of Polymeric Bearings
## 4 Mathematical Model  
In order to accurately simulate the complex mechanical behaviour of PBs under lateral dynamic excitations in numerical analysis, an advanced hysteretic model was firstly developed. The general form of this mathematical model is presented in Equations (3)-(5). It defines the lateral force, \(\mathrm{F_{b}}\), as a nonlinear function of the shear displacement, \(\mathrm{u_{b}(t)}\), and the deformation velocity, \(\mathrm{\dot{u}_{b}(t)}\). In the proposed mathematical approach \(\mathrm{k_{b}}\), and \(\mathrm{c_{b}}\) represent the stiffness and damping of the PBs, respectively. Function parameter \(\mathrm{a_{1}}\) defines the initial stiffness of PBs, parameter \(\mathrm{a_{2}}\) depicts stiffness degradation for higher strain rates (i.e., deformation velocity), parameters \(\mathrm{a_{3}}\) and \(\mathrm{a_{4}}\) are responsible for broadening and rounding hysteresis loops. Finally, parameter \(\mathrm{a_{5}}\) defines damping properties of PBs. The function  
\begin{table}
\begin{tabular}{c c} \hline
**Excitation Frequency** & **Damping Ratio, \(\xi\)** \\ \hline
1 Hz & 49.5\% \\
2 Hz & 30.5\% \\
5 Hz & 20.4\% \\ \hline \end{tabular}
\end{table}
Table 1: Average equivalent damping ratios.  
Figure 3: Experimentally obtained hysteresis loops at excitation frequencies of 1 Hz (**a**); 2 Hz (**b**); and 5 Hz (**c**).  
parameters \(\mathrm{a_{1}}\)-\(\mathrm{a_{5}}\) can be estimated by curve fitting the general form of the mathematical model to the experimentally obtained hysteresis loops using the least squares optimization method.  
\[\mathrm{F_{b}}(\mathrm{u_{b}}(\mathrm{t}),\dot{\mathrm{u}_{b}}( \mathrm{t})) =\mathrm{k_{b}}(\mathrm{u_{b}}(\mathrm{t}),\dot{\mathrm{u}_{b}}( \mathrm{t}))\cdot\mathrm{u_{b}}(\mathrm{t})+\mathrm{c_{b}}\cdot\dot{\mathrm{u_{b }}}(\mathrm{t}) \tag{3}\] \[\mathrm{k_{b}}(\mathrm{u_{b}}(\mathrm{t}),\dot{\mathrm{u}_{b}}( \mathrm{t})) =\mathrm{a_{1}}+\frac{\mathrm{a_{2}}}{\mathrm{cosh[\mathrm{a_{3} }\dot{\mathrm{u_{b}}}(\mathrm{t})]\cdot\mathrm{cosh[\mathrm{a_{4}}\dot{ \mathrm{u_{b}}}(\mathrm{t})]}}}\] (4) \[\mathrm{c_{b}} =\mathrm{a_{5}} \tag{5}\]  
In the present study, the curve fitting procedure was conducted for hysteresis loops with different excitation frequencies and the exemplary results for the frequency of 2 Hz are shown below. The evaluated set of function parameters is summarized in Table 2. Comparison between experimentally and numerically obtained hysteresis loops is presented in Figure 4. It should be underlined that the normalized mean square error calculated for the fit is equal to 4.44%.
## 2 Materials and Methods
### Evaluation of the Image Data
#### 2.4.3 Displacement Field and Strain  
The results of the registration were given back to MATLAB where the transformation \(T(\mathbf{x})\) was converted to the displacement field. The displacement field gave information about the spatial relationship of every material point between two frames. It consisted of the displacement vectors for all material points.  
The displacement vector \(\mathbf{u}(\mathbf{x},t)\) gave the relation between every voxel in the first frame to the same voxel in the following frame, which was estimated by the image registration.  
\[\mathbf{u}(\mathbf{x},t)=[u_{x},u_{y}]^{T} \tag{6}\]  
Furthermore, strain was calculated. It was the change of length per unit length, which could be also expressed as the gradient of the displacement field [38].  
The estimated displacement field was contaminated with noise. If the strain was directly calculated from the noisy displacement field, the noise was amplified. We used a Savitzky-Golay filter to reduce the noise, without losing local information [43]. The filter was applied in two ways: temporal and spatial. The temporal filter smoothed the displacement over a range of frames at the same location. The underlying assumption was that the displacement had no sudden changes over a minor time range. Secondly, the spatial filter smoothed the displacement field in one frame. Again, the assumption was that in adjacent regions, no sudden changes occurred, whereas continuous changes were mapped by the filter.  
The strain in the tissue was calculated according to [PERSON] et al. [44]. The displacement gradient for a two-dimensional displacement field is:  
\[\
abla\mathbf{u}(x,t)=\left(\begin{array}{cc}\frac{\delta u_{x}}{\delta x}&\frac{ \delta u_{x}}{\delta y}\\ \frac{\delta u_{y}}{\delta x}&\frac{\delta u_{y}}{\delta y}\end{array}\right) \tag{7}\]  
Further, the deformation gradient \(\mathbf{F}\) was calculated:  
\[\mathbf{F}(\mathbf{x},t)=(\mathbf{I}-\
abla\mathbf{u}(\mathbf{x},t))^{-1} \tag{8}\]  
\(\mathbf{I}\) denotes the identity matrix. Subsequently, the 3D Eulerian strain tensor \(\epsilon\) was determined by:  
\[\epsilon(\mathbf{x},t)=\frac{1}{2}[\mathbf{I}-(\mathbf{F}^{-1}(\mathbf{x},t))^{T}\mathbf{F}^{-1}( \mathbf{x},t)]. \tag{9}\]
# Developing Tungsten-Filled Metal Matrix Composite Materials Using Laser Powder Bed Fusion
## 2 Materials and Methods  
The experiments of this study were conducted on the L-PBF system EOS M 100 by EOS GmbH (Germany) equipped with a 200 W laser unit (YLR-series, CW-laser, wavelength 1070 nm, Gaussian TEM\({}_{00}\) mode). The maraging steel MS1 (1.2709/X3 NiCoMoTi 18-9-5, particle size distribution D\({}_{10}\): 17 \(\mu\)m, D\({}_{50}\): 31 \(\mu\)m, D\({}_{90}\): 54 \(\mu\)m) was mechanically mixed with spherical tungsten powder (particle size distribution D\({}_{10}\): 20.8 \(\mu\)m, D\({}_{50}\): 29.0 \(\mu\)m, D\({}_{90}\): 43.4 \(\mu\)m). The mixed powder was processed in 20 \(\mu\)m layers. During the process, the steel particles were molten and solidified around the tungsten particles. A series of powder mixtures with tungsten contents of 40-70 vol% was used to build test cubes of 1 cm\({}^{3}\). An argon-based inert gas atmosphere of O\({}_{2}\) < 0.1% was applied in order to avoid oxidation.  
The densities of the resulting test cubes were measured by the Archimedean principle according to DIN-EN-ISO-3369 at a Kern ABT 220-5 DM (with the readability d = 0.01 mg and the calibration value e = 1 mg) and considered as a first quality criterion. This method was chosen as it is known to give reliable and repeatable results [10]. The principle consists of measuring the sample mass, m\({}_{\text{a}}\) in air and m\({}_{\text{l}}\) in liquid (distilled water), and calculating the density of the sample \(\rho_{\text{s}}\) by using the known density of the liquid \(\rho_{\text{l}}\):  
\[\rho_{\text{s}}=\text{m}_{\text{a}}/(\text{m}_{\text{a}}-\text{m}_{\text{l}}) \cdot\rho_{\text{l}}. \tag{1}\]  
Subsequently, the cubes were prepared by standard metallographic means and analyzed using light microscopy regarding aspects such as the distribution and shape of the tungsten particles as well as the porosity of the composite material. For the microscopic investigations, a Zeiss Axio Imager.Z2m system was used.  
For generating the SEM images shown in this article, a Zeiss EVO 15 system was used. In addition to a secondary electron detector for topographical investigation, a backscattered electron detector was applied in order to identify material contrasts. The intensity of the detected signal represented in greyscale values is dependent on the mass of the element. Heavy elements enhancing the number of backscattered electrons appear brighter in the image than lighter elements. This enables the differentiation between heavy tungsten particles and the lighter steel matrix. A coupled Octane Plus detector by EDAX Inc. was employed for energy-dispersive X-ray spectroscopy (EDS), which gives information on the local elemental composition due to specific X-rays emitted by the elements.  
Quasi-static tensile tests were carried out according to DIN EN ISO 6892 on an Instron 8033. The strain was measured by a tactile extensometer.  
Heat treatments were executed in a Nabertherm LHT 04/17 oven. The MMC samples were placed inside the heating chamber at room temperature. After the heat treatment the samples were water quenched.
## 2 Methodology
### Data Collection, Labeling, and Preparation
#### 2.1.1 Data Collection and Labeling  
We have created a comprehensive dataset from dashcam videos taken from YouTube, containing both collision events and normal driving scenarios. We collected a diverse set of more than 120 dashcam videos involving car collisions with a duration between 5 s and 8 s. Each video was further processed to extract the frames containing collision or no collision. Each extracted frame was labeled manually. All extracted frames were initially labeled by a single annotator to maintain consistency across the dataset, and the final labels were subsequently verified by another annotator to ensure accuracy and reliability. During the labeling process, the frames exhibiting visible collision cues and frames closer to the collision were labeled as 'collision', while those without such events were labeled as 'no-collision'. After the labeling, the dataset was partitioned using stratified sampling with an 80/20 split (training and test sets), and the training set was further split into training and validation subsets (80/20 split). Figure 2 shows the train and test data distribution in our experiments. Due to the inherent imbalance between collision and non-collision frames, the training data were balanced using the RandomOverSampler from the imblearn library. This step ensured that minority collision classes were adequately represented, thereby reducing bias during model learning [25]. While RandomOverSampler was selected to balance the collision and non-collision classes due to its simplicity and effectiveness, we also  
Figure 1: The basic flow of the proposed car collision detection system.  
considered the Synthetic Minority Oversampling Technique (SMOTE) and class-weighted loss functions. However, RandomOverSampler was chosen as it preserved original data integrity, whereas SMOTE's synthetic samples risked introducing noise in the dashcam dataset, and class-weighted losses required extensive tuning.
# A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph
## 5 Lightweight V2V Identity Authentication and Key Agreement Scheme
### Equipment Registration and Access Process  
The user reports the equipment information (include equipment manufacturer, type, model, serial number, and so on) to the ERC and registers the equipment in ERC when the equipment need access to the C-V2X system. Then, the ERC generates the equipment VID with the equipment information and saves the equipment VID and information in the internal database. The equipment saves its VID in the internal secure storage area. The equipment registration process is completed.  
The equipment sends its VID to the KMC to apply for its private key when accessing the C-V2X system. The KMC uses the VID as the public key of the equipment, generates the equipment private key with the identity-based cryptographic algorithm, and sends the private key to the equipment through an internal secure channel which will be discussed in future research. The equipment saves its private key in an internal secure storage area. The equipment registration and private key generation process is shown in Figure 2.
## 3 Results and Discussion
### Smoke Point  
The fuel flow rate, flame length and residence time of smoke point under different pressures are compared, as the results show in Figure 6. As the pressure decreases from 97.75 to 60 kPa, more air is drawn into the flame region, leading to an increase in the \(\alpha\) of the flame region under lower pressure. Hence, when the flame begins to emit smoke, the fuel flow rate of the smoke point also increases with decreasing pressure. As aforementioned, the flame length decreases with decreasing pressure under fuel-lean combustion, as the results show in Figure 4. However, it is observed that the smoke point flame length increases with decreasing pressure in Figure 6b. The soot formation increases with increasing pressure, which will reduce the smoke point flame length at a higher pressure. In a word, the increase of the smoke point flame length with decreasing pressure may be attributed to the decrease in oxygen mass per unit volume of air. In our work, the residence time is calculated here by dividing the flame height at the smoke point by the volumetric fuel flow rate [16], as shown in Figure 6c. Similarly, the residence time at the smoke point increases with decreasing pressure under sub-atmospheric pressure conditions. But other researchers observed the opposite conclusion at elevated pressures. This indicates that under sub-atmospheric pressure, the pressure-sensitive reaction pathways, which are governing the overall balance between smoke production and oxidation, are different with elevated pressure. In Figure 6, it is observed that the smoke point flame height, fuel flow rate, and residence time are found to vary with pressure to power laws, which is similar to the change patterns of ethylene, n-heptane and wood flames obtained by other researchers in an open environment [19; 28]. Meanwhile, the fuel flow rates, flame lengths and residence time of the smoke point can be increased by increasing air velocity, and the effect still exists under sub-atmospheric pressures. Additionally, experimental results also show that the effect of air to the fuel velocity ratio on the smoke point is important. Further tests of the smoke point at different pressures and nozzle structures need to be conducted in the future.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 6 Experimental Results and Analysis
### The Result of Combining Posture Detection with Physiological Data  
The MAX3012 module monitors the heart rate of the target in real time, and the data obtained are sent to Arduino. When the neural network models detect that the target state is fall, they send data to Arduino through the serial port. Arduino combines the received posture information with the judgment statement if (heart rate > 90&&flag==1) for further detection. If the heart rate is >90 at this time, that is, the heart rate is in the abnormal range, it is judged that the target's posture is fall. On the contrary, if the heart rate is <90 at this  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Determination Coefficient (R2)** \\ \hline Original LSTM & 0.65575 \\ CNN & 0.58604 \\ NN & 0.49748 \\ Improved LSTM & 0.75810 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Determination coefficients of the models.  
Figure 23: Posture prediction performance of the models on testing data set: (**a**) original LSTM model; (**b**) improved LSTM model; (**c**) CNN model; (**d**) NN model.  
time, that is, the heart rate is in the normal range, it is judged that the target's posture is not fall, as shown in Table 7.
## 3 QoS Aware MAC Protocols for WMSNs
### [PERSON] and [PERSON] proposed a hybrid energy efficient and delay-aware MAC protocol named EQ-MAC [39]. In EQ-MAC, sensors are grouped into clusters where the cluster head takes care of scheduling TDMA based slots for data transmissions. Communication in EQ-MAC is organized into frames. Each frame begins with a synchronization message broadcast by the cluster head. After synchronization, cluster members enter a CSMA/CA based medium access phase to transmit their control messages to the cluster head and those with data to transmit will also send requests for TDMA slots. Upon receiving all requests, the cluster head generates a schedule of TDMA slots with consideration of traffic priorities and the schedule is broadcast to all cluster members for the subsequent data transmission phase. Cluster members transmit data during their giving slot in a contention-free manner while nodes with no data to send go to sleep to save energy. Delay guarantee is achieved by classifying packets according to their priority levels assigned in the application layer, and real-time packets are stored in an instant queue in which packets will be served immediately.  
Pros and Cons. By allowing nodes without data to transmit to go to sleep, EQ-MAC achieves energy efficiency and at the same time improves channel utilization under light traffic conditions. Meanwhile, by giving definite priority to high QoS required instant traffic, EQ-MAC is good for delivering multimedia data that requires minimum delay. However, this strategy can lead to starvation of low priority traffic such as best effort traffic. Also the necessity of global synchronization makes the protocol not scale well.
## 2 Materials and Methods
### Analysis  
Interannual variations and seasonal cycles of all aforementioned variables were calculated by taking the average over sugarcane-planting areas, and were also calculated at each grid over sugarcane-planting areas in Chongzuo city during 2001-2020 (2001-2018 for SPEI, precipitation, and air temperature, and 2001-2018 for evaporation). Monthly or 16-day anomalies of regional means and each grid for all variables were computed as the departures from the corresponding multiyear means. The 16-day relative changes in LST, ET, EVI, SIF, PAR, fPAR, SIFpar, and SIFyield were defined as the anomalies divided by their corresponding multiyear means. We also used the monthly normalized anomalies of LST, ET, EVI, SIF, PAR, fPAR, SIFpar, and SIFyield, which were calculated as the monthly anomalies divided by the standard deviation of the monthly anomalies during the periods, to better compare their temporal and spatial dynamics.  
To further analyze the different responses of sugarcane EVI and SIF to drought, we used fPAR, SIFpar and SIFyield to interpret SIF. The SIF, SIFpar, and SIFyield can be described as follows:  
\[SIF=PAR*fPAR*SIFyield*\eta \tag{1}\]  
\[SIFpar=\frac{SIF}{\eta*PAR} \tag{2}\]  
\[SIFyield=\frac{SIF}{\eta*APAR} \tag{3}\]  
\[APAR=PAR*fPAR \tag{4}\]  
where PAR is the photosynthetically active radiation, APAR is the absorbed PAR, fPAR is the fraction of APAR, and \(\eta\) is the proportion of fluorescence escaping from the canopy to the space. Considering the low absorption and relatively simple plant structure, when infrared SIF is applied to grasses and crops, it can be assumed that \(\eta\approx 1\)[25; 26; 75]. SIFpar is PAR-normalized fluorescence, and contains comprehensive information on vegetation structure (such as chlorophyll content) and SIFyield [22; 51]. SIFyield is the APRA-normalized fluorescence that quantifies the proportion of energy used for fluorescence emission in each absorbed PAR and is the light utilization efficiency (LUE) of SIF [25].  
Due to the linear relationship between crop yield and GPP, sugarcane yield loss can be directly compared to GPP loss [25; 76]. In this work, we analyzed the correlation of sugarcane EVI, SIF, and yield during 2004-2020, and also compared the correlations of sugarcane EVI, SIF, and GPP during 2001-2020. Correlation analysis was quantified by using the Pearson correlation coefficient, and statistical significance was the coefficient of determination R\({}^{2}\)[27].  
Note that most of the datasets were resampled to a spatial resolution of 0.05\({}^{\circ}\) by using the nearest interpolation method, and further recalculated to 16-day and monthly datasets by averaging data from corresponding periods.
## 4 Discussion
### TGFB Signaling, Immunosuppressive Effects, and AOID  
IFN-\(\gamma\) is an important cytokine produced by activated T-cells (Th1), natural killer cells, and group 1 innate lymphoid cells [3]. IFN-\(\gamma\) has a crucial and non-redundant role in defending against mycobacterium infection [3]. The IFN-\(\gamma\)/IL-12 pathway signaling, which predominantly activates monocytes, is important to prevent invasion of mycobacteria and other intracellular pathogens and reactivation of latent varicella-zoster virus [5]. This pathway is disrupted in patients affected with AIDS and AOID [19], which makes individuals susceptible to mycobacterial and viral infection.  
TGF\(\beta\) signaling normally promotes the differentiation of naive T cells into Th17 cells [20]. Aberrant TGF\(\beta\) signaling in our patients might therefore disrupt the differentiation of naive T cells into Th17 effector cells. The clinical features of patients 3 and 4 and other patients with AOID are similar to those with genetic defects of the Th1 response in which patients present with disseminated infection with mycobacteria of low virulence [19]. The presence of pustular reaction in a number of patients with AOID suggests a possible association between anti-IFN-\(\gamma\) autoantibody production and the recruitment of neutrophils (Figure 8). Recruitment of neutrophils into the psoriatic skin aggravates the symptoms because of the overproduction of pro-inflammatory cytokines, as observed by the significantly increased expression of IL1B, IL6, IL17, IL23, KRT17, and IFNG throughout the epidermis and dermis of patient 3 compared to normal skin tissue. It is noteworthy that depletion of neutrophils significantly relieves the symptoms [21]. The minor differences in protein staining found in the tissues from patients 1(GPP) and 3 (AOID) might have been due to the difference of the disease processes or the genetic backgrounds of the patients.  
The pathogenic mechanisms of anti- IFN-\(\gamma\) autoantibody production as a result of _TGFBR2_ mutation is uncertain. However, marked increased expression of IFN-\(\gamma\) was observed in the lesions of patients 1 and 3. It is hypothesized that the _TGFBR2_ variants in the patients caused dysregulation of T cell homeostasis, B-cell tolerance defects, and subsequent failure to counter-select developing autoreactive B cells, which promote the development of autoimmunity (AOID) through the presentation of self-antigen to T cells [22].