esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### The Description of the Beating Phenomenon in Vibration Displacement by Numerical Integration
This approach assumed that in the result of numerical integration of vibration velocity depicted in Figure 5b, a supplementary low frequency component was generated and should be removed. For the time being we unfortunately do not have a consistent explanation for the appearance of this low frequency component. This low frequency component (depicted in Figure 5b in white) was detected by low-pass numerical filtering of the vibration displacement signal (the evolution emphasised in blue). A computer-generated moving average filter [43] was used, with the first notch frequency equal to the combination frequency \(f_{c}=(f_{1}+f_{2})/2\) (assuming that this definition from Equation (6) is accurate), in order to completely remove the variable component from Figure 5b having the resultant vibration frequency, and in order to obtain the low frequency component. The number of points in the average of the filter is defined as integer of the ratio \(1/(f_{c}\Delta t)\). The removal of this low frequency component from the result of numerical integration (the vibration displacement signal) depicted in Figure 5b is shown in Figure 7. It is obvious that this evolution properly describes the resultant vibration displacement during the beating phenomenon, previously described in Figure 3, by the velocity of the resultant vibration. Figure 6: The result of the numerical derivative of the evolution from Figure 5b (vibration velocity, practically similar with Figure 3). Figure 7: The headstock vibration displacement evolution during the beating phenomenon, deduced by numerical integration and correction of the signal depicted in Figure 3.  
There is a supplementary confirmation of this result: the numerical differentiation of the vibration displacement signal from Figure 7 (using Equation (7)) fits very well with the vibration velocity signal from Figure 3, as a very short detail (15 ms duration) of both evolutions (given in Figure 8a) from the area labelled as A (Figures 3 and 7) indicates. Thus, the absolute velocity vibration sensor together with the proposed numerical signal integration method acts as an absolute displacement vibration sensor. Figure 8b presents a short detail of the vibration displacement evolution in the area labelled with A in Figure 7. This figure has the same size on the abscissa as Figure 4. By comparison with Figure 4, the evolution is much smoother here, as a consequence of numerical integration, which drastically reduces the amplitudes of high frequency components. The integration acts as a low-pass filter. In Figure 7 two relationships between the vibrations amplitudes \(A_{1}\) and \(A_{2}\) are available (from Equations (1) and (2)) due to the constructive interference in anti-nodes (\(A_{1}+A_{2}=118~{}\upmu\)m) and destructive interference in nodes (\(A_{1}-A_{2}=~{}52~{}\upmu\)m), so \(A_{1}=85~{}\upmu\)m and \(A_{2}=33~{}\upmu\)m. For the time being \(A_{1}\) does not necessarily refer to vibration amplitude generated by the main spindle or shaft 1.
## Appendix C Deriving the Rate Equation for the Intensity and Phase  
To further inspect the laser dynamics, the rate equations for the intensity and phase must be studied. Considering Equation (15), and a slowly varying electric field given by:  
\[\mathcal{E}(t)=\mathcal{A}(t)e^{-i\omega_{\text{FB}}t}, \tag{123}\]  
and following the approach from [PERSON] and [PERSON] [39], the laser field equation which considers EOF from both sides of the laser cavity can be written as:  
\[\dot{\mathcal{E}}=\left(-i\omega_{\text{ref}}+\Delta G\frac{1-i\alpha_{\text {H}}}{2}\right)\mathcal{E}(t)+\kappa_{2}\mathcal{E}(t-t_{2})+\kappa_{1} \mathcal{E}(t+t_{1})\]  
\[\overset{\eqref{eq:2.1}}{\Leftrightarrow}\frac{d\Big{[}\mathcal{A}(t)e^{-i \omega_{\text{FB}}t}\Big{]}}{dt}\overset{\eqref{eq:2.1}}{=}e^{-i\omega_{\text{ FB}}t}\bigg{[}\left(-i\omega_{\text{ref}}+\Delta G\frac{1-i\alpha_{\text{H}}}{2} \right)\mathcal{A}(t)+\kappa_{2}\mathcal{A}(t-t_{2})e^{i\phi_{2}}+\kappa_{1} \mathcal{A}(t+t_{2})e^{-i\phi_{1}}\bigg{]} \tag{124}\]  
\[\Leftrightarrow\dot{\mathcal{A}}(t)\overset{\eqref{eq:2.1}}{=}\left(i \delta\omega+\Delta G\frac{1-i\alpha_{\text{H}}}{2}\right)\mathcal{A}(t)+ \kappa_{2}\mathcal{A}(t-t_{2})e^{i\phi_{2}}+\kappa_{1}\mathcal{A}(t+t_{2})e^ {-i\phi_{1}}.\]  
The last two right-hand terms appear as a result of the imposed feedback conditions, each term to account for feedback on each side of the cavity. In the case without feedback the lasing frequency become \(\omega_{\text{FB}}=\omega_{\text{ref}}\) and \(\kappa_{j}=0\), thus recovering the no-feedback field Equation [42]. The slowly varying field amplitude \(\mathcal{A}\) can be modeled as Equation (15a), and thus the rate equations for the photon number \(S\) and phase \(\phi\) can be found using:  
\[\dot{S}=\frac{d[\mathcal{AA}^{*}]}{dt}=\mathcal{AA}^{*}+\mathcal{A}^{*}\dot{ \mathcal{A}} \tag{125a}\] \[\dot{\phi}=-\frac{1}{S}\Im\big{(}\mathcal{A}^{*}\dot{\mathcal{A}}\big{)}. \tag{125b}\]  
Replacing Equations (15a) and (124) into Equation (125a) the photon rate equation reads:  
\[\dot{S}\overset{\eqref{eq:2.1}}{=}\overset{\eqref{eq:2.1}}{=}S\Delta G+2 \mathcal{S}_{2}^{-}\sqrt{S}\cos(\Delta\Phi_{2}^{-})+2\mathcal{S}_{1}^{+}\sqrt {S}\cos(\Delta\Phi_{1}^{+}). \tag{126}\]  
In the case of the phase, its rate equation comes from replacing Equations (15a) and (124) into Equation (125b):  
\[\dot{\phi}\overset{\eqref{eq:2.1}}{=}\frac{\Delta G}{2}\alpha_{\text{H}}- \delta\omega-\frac{\mathcal{S}_{2}^{-}}{\sqrt{S}}\sin\left(\Delta\Phi_{2}^{-} \right)-\frac{\mathcal{S}_{1}^{+}}{\sqrt{S}}\sin\left(\Delta\Phi_{1}^{+}\right). \tag{127}\]  
Equations (126) and (127) are the amplitude and phase rate equations for the laser system proposed in this work. These are the starting point to compute the frequency noise PSD, and extract the intrinsic linewidth.
# _Cannabis sativa_ L. as a Natural Drug Meeting the Criteria of a Multitarget Approach to Treatment
## 3 Chemical Composition
### 3.2. Tepenes
The environmental conditions and plant maturity influence the amount of terpen produced by the plant and its distribution. Terpenes found in _Cannabis_ have a wide variety of biological properties [76]. Nerolidol, constituting 0.09% of _Cannabis_ terpen, is active against malaria and leishmaniasis [42]. D-limonene has cytotoxic in vitro, immunostimulatory, and anxiolytic properties. \(\beta\)-amyrin has been recognized as a compound with analgesic, anti-inflammatory, and anxiolytic properties. \(\alpha\)-Pinene shows an improvement in memory capacity as it acts antagonistically to memory deficits caused by \(\Delta^{9}\)-tetrahydrocannabinol, inhibiting acetylcholinesteraseolaseterase. _Cannabis_ containslinalool, also present in _Lavandula angustifolia_, showing anxiolytic, analgesic, anticonvulsant, and anti-inflammatory effects. \(\beta\)-Caryophyllene, also found in copaiba balm and black pepper, has anti-inflammatory and cytoprotective properties for stomach cells. It also binds to the CB\({}_{2}\) receptor. Pentacyclic triterpenes, such as \(\beta\)-amyrin and cycloartenol, show antibacterial, antifungal, antitumor, and anti-inflammatory properties [42]. T triterpenes might play an essential role in _Cannabis_ biological activity and change the properties of the cannabinoids [60]. Products containing cannabinoids, such as Sativex(r), Cannador(r), and Bedrocan(r), may contain terpen as support. As mentioned above, due to the \"entourage effect\" hypothesis, cannabinoids are more pharmacologically effective with terpenes than themselves [78]. Terpenes may alter the pharmacokinetics of THC by increasing the permeability of the blood-brain barrier [79]. Moreover, they may also  
Figure 6: Diagram of the biosynthesis of monoterpenes (**A**), sesquiterpenes, and triterpenes (**B**) [42]. MEP: methylerythribtol phosphate, IPP: isopentenyl diphosphate, DMAPP: dimethylallyl diphosphate, GPP: geranyl diphosphate, MVA: mexulonic acid, and FPP: farnesyl diphosphate. affect their affinity to CB1 receptors and influence the analgesic and psychotic effects of cannabinoids related to their interaction with neurotransmitter receptors [80]. Thus, the effects of _Cannabis_ are likely to be the sum of the interactions between cannabinoids, terpenoids, and flavonoids [79]. However, no clinical trials confirming these effects have been conducted [78]. Combinations of cannabinoids and terpen are already used to treat certain conditions. Some of them are presented in Table 3.
## 4 Discussion
### Biocompatibility  
#### 4.2.1 Live/Dead Assay  
All three cell types (MG-63, MLO-Y4 and MSC) were able to adhere well to the scaffolds and proliferate. The proportion of dead cells was at all times significantly lower than the proportion of live cells. On closer inspection, it was noticeable in the MG-63 cells that the proportion of dead cells was highest at 17% on day 3 of the experiment. The days after that, the percentage decreased to about 6% (day 10) and the cells proliferated by leaps and bounds (day 3: 263 cells/mm\({}^{2}\), day 10: 1518 cells/mm\({}^{2}\)). One reason for this could be that the switch (culture flask to scaffold) initially caused a negative reaction for the MG-63 cells [23]. However, after a few days, the cells adapted to the new environment and were thus able to proliferate strongly. The MLO-Y4 cells showed an opposite development. Here, the percentage of dead cells increased from 10% on day 3 to 23% on day 10. The number of MLO-Y4 first increased sharply from day 3 (354 cells/mm\({}^{2}\)) to day 7 (1174 cells/mm\({}^{2}\)), but then decreased to 984 cells on day 10. One possible explanation for this could be images of the MLO-Y4 displayed on the ESEM. Moreover, since the MLO-Y4 are able to migrate under the collagen, the area of the scaffold is limited and already completely colonized after 7 d. [PERSON] et al. [24] also reported MLO-Y4 cells in collagen scaffolds. They could not demonstrate a negative effect of collagen on MLO-Y4 cell number, but an influence of the initial cell number. Overall, the MSC show a similar cell growth behavior to the MLO-Y4. The number of MSC also increases from day 3 (398 cells/mm\({}^{2}\)) to day 7 (744 cells/mm\({}^{2}\)) and subsequently decreases to 630 cells/mm\({}^{2}\) on day 10. Compared with MLO-Y4, the number of MSC does not increase as much from day 3 to day 7. The proportion of dead cells is very low in MSCs compared to the other two cell types. At day 3, the percentage of dead cells was only 7%, at day 7, 8%, and at day 10, 10%. The decreasing number of MSC may be based on the same cause as for MLO-Y4. [PERSON] et al. [25] also report an increase in the MSC cell numbers on collagen coatings.
## 3 Results and Discussion  
### Optimization of Preparation Conditions for Adsorption Substrates of La-EPRC Particles  
#### 3.1.1 Optimal Lanthanum-to-Substrate Mass Ratio  
Figures 2 and 3 present the experimental findings, highlighting the discernible impact of varying w(La)/w(SS) and w(La)/w(FA) on the adsorption and phosphorus removal capabilities of the materials. Initially, an increase in the lanthanum-to-substrate mass ratio led to greater binding sites, enhancing adsorption capacity and removal rate. However, at later stages, the interplay of factors, including w(La)/w(SS) and w(La)/w(FA), resulted in a decline in material adsorption capacity and removal rate. Notably, lanthanum-modified steel slag exhibited optimal phosphate removal at a mass ratio of 0.4, achieving a removal rate of 96%. Lanthanum-modified fly ash demonstrated superior phosphate removal at a mass ratio of 0.3, yielding an 84% removal rate. Consequently, w(La)/w(SS) = 0.4 and w(La)/w(FA) = 0.3 were identified as the optimal mass ratios for adsorption substrate modification in this study.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Summary  
The experimental results presented above indicate that the rearrange method introduced in this paper provides considerable advantages over the natural and traditional approaches. Moreover, the sample-adaptive encoder outperforms the block-adaptive encoder in terms of compression performance. Nevertheless, the predictor parameters appear to have much more impact on the compression effectiveness than the entropy coder parameters, particularly for the prediction mode, local sum type, number of prediction bands, and weight adaptation parameters. On the basis of these results, the adequate compression settings of the CCSDS-123 for the rearranged LASIS data are listed in Table 1.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 3 Proposed Multi-Color Channels Based GSR Model
### Construction of the Proposed Model
#### 3.1.3 Adaptive Dictionaries  
An important problem of the proposed multi-color-channels-based GSR model is the selection of the dictionary. The dictionary is usually learned from natural images. Compared with the dictionaries of traditional analysis and design [33], such as wavelet, curvelet curve and discrete cosine transform, the dictionary directly learned from images can better adapt to the local structure of images, enhance the sparsity, and hence obtain higher performance. For example, the well-known K-SVD over-complete dictionary not only preserves the local structure of images, but also provides excellent denoising results [20]. However, the sparse representation on the over-complete dictionary is very computationally expensive and easily leads to artifacts in the image restoration [34]. In order to better adapt to the local structure of images, an adaptive dictionary \(D_{i}^{C}\) is learned for each group \(Y_{i}^{C}\). The learning procedures of the adaptive dictionary \(D_{i}^{C}\) are similar to those depicted in Section 2.3. Firstly, the bilinear interpolation algorithm is used to initialize the degraded image \(y\), and the estimation group \(r_{i}^{C}\) as in Formula (12) is obtained from the initialized image. Then singular value decomposition is performed on the estimation group, and the adaptive dictionary \(D_{i}^{C}\) of each group is learned through the estimation group \(r_{i}^{C}\).  
\[r_{i}^{C}=\left[\begin{array}{ccc}r_{i,1}^{R}&r_{i,2}^{R}&\ldots&r_{i,m}^{R} \\ r_{i,1}^{C}&r_{i,2}^{C}&\ldots&r_{i,m}^{C}\\ r_{i,1}^{B}&r_{i,2}^{B}&\ldots&r_{i,m}^{B}\end{array}\right]\in R^{3b\times m} \tag{12}\]
# Special Issue \"Body Sensors Networks for E-Health Applications\"
## 2 Articles Related to Sensor Technologies in the Context of BSN and e-Health
The proposal is complemented by a first aid provision system based on an unmanned aerial vehicle (UAV) that carries a package of first aid supplies from the CEC to the person who has suffered a fall. For the route planning, an intelligent autopilot program that uses the subject's geolocation as a reference point is used. The first aid packages are adapted according to the state of the subject who has suffered the fall. The experimental results showed good performance of the system in the measurement of heart rate, the detection of falls, the accuracy of the GPS location and the improvements in saving time in the emergency assistance to the user. The system described in Reference [3] proposes a sensor technology based on sensors integrated in insoles to be placed on shoes for the development of a pedometer adapted for use in elderly people. Pedometers are commonly used for the evaluation of energy expenditure and physical activity in rehabilitation activities, patient monitoring and training. However, the precision of these systems, normally based on accelerometers, has been limited in studies conducted in elderly people or patients with pathologies related to brain damage. To address this problem, the authors of Reference [3] propose an insole with five force-sensitive resistances to measure the foot pressure. Based on the information provided by the sensors, a new method to accounting for the number of steps is proposed. Common methods typically employ averaging or summation of pressure signals, which can result in dual signal peaks resulting from delayed heel and forefoot support, and therefore overestimation of the step count. Instead, the proposed method is based on the cumulative sum of the pressure data obtained in the foot support phase in the gait cycle, which generates a single pressure peak. The results confirmed the adequacy of the system and the method to be used in elderly people and considering different walking speeds.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 3 The Proposed Method
### YOLOv8n-CBAM-EfficientNetV2 Modules
#### 3.1.2 Cbam  
The CBAM represents a module designed to enhance the performance of convolutional neural networks. It improves the perceptual ability of the model by introducing channel attention and spatial attention in the model to improve the performance without increasing the complexity of the network. The structure of the CBAM is illustrated in Figure 5, which contains the channel attention and spatial attention submodules.  
Figure 4: Structures of Fused-MBConv and MBConv modules. H, W, and C, respectively, represent the height, width, and channels of the extracted feature map. (**a**) MBConv module; (**b**) Fused-MBConv module.  
Figure 5: CBAM structure. \(F\), \(F^{\prime}\), and \(F^{\prime\prime}\), respectively, represent the feature map characteristics in CBAM. \(M_{C}(F)\) and \(M_{S}(F^{\prime})\), respectively, represent the mapping functions of the channel attention submodule and the spatial attention submodule. AvgPool and MaxPool, respectively, represent average pooling and max pooling. MLP stands for Multi-Layer Perceptron.  
In the channel attention submodule, the input feature map \(F\) was subjected to global maximum pooling and global average pooling, thereby resulting in the generation of two one-dimensional feature maps. The two feature maps were then passed through a fully connected neural network MLP (Multi-Layer Perceptron) and summed to generate the channel attention feature \(M_{C}(F)\) through a sigmoid activation operation. This value, \(M_{C}\), was then used to describe the channel attention. Finally, the channel attention feature map was multiplied by the input feature map \(F\), thereby generating the channel attention feature map \(F^{\prime}\).  
The spatial attention submodule, denoted by \(M_{S}\), accepts the feature map \(F^{\prime}\) as the input feature map. Two feature maps with channel 1 were obtained by applying the average pooling and maximum pooling operations to the feature maps, which were then spliced together. Subsequently, a spatial attention feature, denoted by \(M_{S}(F^{\prime})\), was generated by a \(7\times 7\) convolution and sigmoid activation, with the objective of characterising the spatial attention. Ultimately, this \(M_{S}(F^{\prime\prime})\) was multiplied with the input \(F^{\prime}\) of this spatial attention submodule, thereby resulting in the spatial attention feature map \(F^{\prime\prime}\).
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 4 The Proposed NPIVGAE
### Framework  
Figure 1 shows the overall framework of NPIVGAE, which mainly includes two parts: encoder and decoder. Firstly, we input the adjacency matrix \(\mathbf{A}\) and the attribute matrix \(\mathbf{X}\) to the encoder to obtain the mean and variance of the latent variables' posterior and then sample to get \(\mathbf{Z}\). Unlike VGAE, we introduce the noninformative prior probability as the prior of \(\mathbf{Z}\) and leverage node embeddings as well as block-block correlation matrix to reconstruct the adjacency matrix. Finally, the loss function is calculated to update the parameters in reverse. Details of each section are described below.
## 2 Space Segment
### Satellite Constellations
Despite being theorized a few decades ago, the needed technology to make these massive constellations economically viable has only been developed recently, with the evolution in digital payload and the rise of the new space philosophy causing a revamp in these mega-constellation projects. These constellations operate mainly in Ku- and Ka-bands to maximize the throughput provided and can use even higher frequencies such as V- and Q-bands for their feeder link to the ground station network. With respect to a GEO communication satellite, an LEO or MEO constellation has some advantages, mainly related to the physical position of the satellites in space, substantially closer to Earth than a geostationary satellite. The reduced distance from the Earth surface is responsible for lower path losses, reducing power requirements and costs of single satellite and Earth user terminals, as well as latency. The reduction in latency enables mission critical communications and high demand applications that are more challenging with GEO and therefore are not yet commonly associated with satellite communications: real-time communications, video chat and videoconferencing, interactive social media, on-line gaming, and some high-end enterprise applications such as remote control (UAVs, terrestrial vehicles, boats), telemedicine, and trading. Another advantage that an LEO constellation has over higher-altitude systems with fewer satellites is that the limited licensed communication frequencies can be reused across the Earth's surface within each satellite's coverage footprint. This reuse leads to far higher simultaneous transmission and, therefore, system capacity. The available capacity achievable with the scarce bandwidth available is key in defining the metrics of the constellation and its economic advantage and feasibility, as it plays a major role in lowering the cost per bit of the network. Compared to a GEO satellite whose orbit is synchronized with the Earth rotation and therefore appears static in the sky for an Earth-bounded observer, LEO and MEO satellites in constellations are constantly moving in the sky. Therefore, the terminal antenna always has to track the satellites in its trajectory across the sky. This means that some of the complexity saved in the space segment is transferred across to the user terminal that has to manage handovers between satellites without dropping the link. For example, depending on the steering approach and complexity of the terminals, the system may  
Figure 2: Constellation pattern of OneWeb system: 648 satellites distributed across 12 circular orbital planes at an altitude of 1200 Km; each plane inclined at 87\({}^{\circ}\). -independent make-before-break or break-before-make handover. Conversely, LEO and MEO constellation user terminals have the advantage of a better look angle to the satellites, which makes flat panel antennas more suitable for this kind of applications with respect to GEO networks. This topic will be described more in detail in the user terminal section (see Section 4.1).