esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 4 Experimental Study on Semi-Active Vibration Control
### SMVS Vibration Control Experiment  
When the sinusoidal excitation frequencies are 1 Hz, 5 Hz, and 10 Hz, the vibration response test results of the pipeline under the SMVS vibration control are shown in Figure 16. When the vibration control of the pipeline is controlled by the SMVS vibration control, the statistical results of the vibration response of the pipeline under different excitation frequencies are shown in Table 4.  
Figure 15: Pipeline vibration response under PID control and excitation frequency of 1, 5, 10 Hz: (**a**) Displacement-time curve (1 Hz); (**b**) Acceleration-time curve (1 Hz); (**c**) Displacement-time curve (5 Hz); (**d**) Acceleration-time curve (5 Hz); (**e**) Displacement-time curve (10 Hz); (**f**) Acceleration-time curve (10 Hz).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Displacement**} & \multicolumn{3}{c}{**Acceleration**} \\ \cline{2-7}  & **RMS (mm)** & **Difference (dB)** & **Attenuation** & **RMS (m/s\({}^{2}\))** & **Difference (dB)** & **Attenuation** \\ \hline
1 & 0.2196 & 15.4975 & 83.21\% & 0.0992 & 4.8257 & 42.63\% \\
2 & 0.3308 & 12.9361 & 77.45\% & 0.3401 & 4.6154 & 41.22\% \\
3 & 0.1205 & 22.1555 & 92.20\% & 0.2952 & 11.4318 & 73.18\% \\
4 & 0.1372 & 21.2880 & 91.38\% & 0.3913 & 12.8376 & 77.19\% \\
5 & 0.3485 & 13.5973 & 79.10\% & 1.0443 & 7.6931 & 58.76\% \\
6 & 0.2034 & 18.3840 & 87.96\% & 0.9785 & 10.5796 & 70.42\% \\
7 & 0.1994 & 18.9202 & 88.68\% & 1.1215 & 11.6560 & 73.87\% \\
8 & 0.1678 & 19.7356 & 89.69\% & 0.9826 & 13.7677 & 79.51\% \\
9 & 0.1392 & 20.8341 & 90.92\% & 1.1065 & 13.9681 & 79.97\% \\
10 & 0.1172 & 22.3107 & 92.34\% & 0.8842 & 16.3444 & 84.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vibration response of pipeline under PID pipeline vibration control.  
As shown in Table 4, when the vibration of the pipeline is controlled by the SMVS control, the displacement and acceleration of the pipeline are significantly reduced. As shown in Table 4, the displacement drop of the pipeline vibration at different excitation frequencies ranges from 10.2215-17.9017 dB, the acceleration drop range is 1.2689-14.4325 dB, the displacement attenuation range is 69.17-87.27%, and the acceleration attenuation range is 13.59-81.02%, and the vibration isolation rate ranges from 84.80-96.87%.  
Figure 16: Pipeline vibration response under SMVS control and excitation frequency of 1, 5, 10 Hz: **(a)** Displacement-time curve (1 Hz); **(b)** Acceleration-time curve (1 Hz); **(c)** Displacement-time curve (5 Hz); **(d)** Acceleration-time curve (5 Hz); **(e)** Displacement-time curve (10 Hz); **(f)** Acceleration-time curve (10 Hz).
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 4 Implementation and Evaluation  
### Performance Evaluation  
We have implemented the DID-related operations of our system in Python3 using the coor2 library ([[https://pypi.org/project/cbor2/](https://pypi.org/project/cbor2/)]([https://pypi.org/project/cbor2/](https://pypi.org/project/cbor2/))) and the coset library ([[https://pypi.org/project/cose/](https://pypi.org/project/cose/)]([https://pypi.org/project/cose/](https://pypi.org/project/cose/))).  
The size of a DID document that includes a public key is 242 bytes, and the size of the corresponding proof is 124 bytes. The size of a membership VC is 127 bytes, and the size of a corresponding proof is 112 bytes.  
In our system, the following cryptographic operations are needed. For DID creation, an Ed22519 key pair, a DID document, and the corresponding proof have to be generated. Additionally, a group administrator has to generate a membership VC, and an IoT device has to sign the \"attestation\" CoAP header. Finally, for the CoAP response verification, the CoAP client has to verify the DID document, the membership VC and the attestation signature included in the corresponding CoAP headers. Table 1 shows the time required (in ms) to perform the cryptographic operations of our system, as measured in a Raspberry Pi 4 model B with 2 GB of RAM, as well as in a Espressif ESP32 WROOM-32 IoT device (240 MHz dual-core Xtensa LX7 CPU).  
It should be noted, however, that IoT devices are expected to perform only the attestation generation. For a memory-efficient implementation of the required signing algorithms, as well as for a discussion related to their energy consumptions, interested readers are referred to [20].
# A Study of High-Efficiency Laser Headlight Design Using Gradient-Index Lens and Liquid Lens
###### Abstract  
In the field of vehicle lighting, due to the diode laser, its small size and high energy conversion efficiency, it can be effectively used as the headlight source of high beam. In recent years, it was adopted by European advanced car manufacturers as a new generation of automotive headlight lighting products. The current mature technology on the market is to extend the laser beam by means of reflection and to use a single high-power laser as the light source to meet the needs of surface lighting. In this research, we propose a new integrated optical design for an automotive headlight system with the rod lens, gradient-index lens (GRIN lens) and freeform lens to expand the laser beam. With regard to the diffusion of the beam by reflection and refraction, the liquid lens is used as a switch for the high beam and low beam lights to meet the needs of vehicle lighting functions and to use low-power diode lasers to synthesize the array light source. Compared with the 24-W LED headlight module available in the current market, the energy saved by this proposed optical design can increase efficiency by an average of 33%. The maximum illuminance is 56.6 lux in the high-beam mode, which is 18% higher than the standard value. Let the laser light meet the lighting requirements of regulatory standard values even beyond.  
laser automotive headlight; optical design; laser beam expansion; non-image optics; freeform surface; GRIN lens; optimization Article
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 6 Experiments and Results
### Evaluation Results
#### 6.3.4 Interpretability Capability  
As shown in Figure 7, the proposed model can provide detailed classification causes. These causes include the name of the defence line (red), the description of the normal pattern (blue) and the observed operation (purple). For example, the second report indicates that this operation violates the execution time defence line (fourth line) because the observed execution time is 94 ms, while the normal execution time range is \(\mathcal{N}(58.66,2.53^{2})\), 94 greater than \(58.66+2.53*3\). This is necessary for security experts to understand and quickly respond to ongoing attacks.
## 6 Future Research Directions
### Technology
The literature lacks solutions that provide algorithms that preprocess collected raw data and use manual data cleaning and wrangling, which are time-consuming when it comes to providing reliable and authenticated datasets since the synchronization of these sensors is an issue. The sensors' types might be chosen according to the current sensors' capability to record and sense entities in real-time traffic conditions. The used sensors must assess the quality of water (Ph, temp, Oxygen, etc. ), and the status of the monitoring system (active, idle, or sleepy) ensuring a reliable communication architecture. Such metrics might offer a whole picture regarding the pattern of water quality and identifying the critical water quality conditions. Moreover, an approach to selecting a combination of adequate sensors was required to detect and record all features/factors. As a result, more research is required to evaluate the efficiency of sensors in determining water quality. To determine which equipment, whether it be a sensor or a DAS in various circumstances and at various times, best captures water characteristics, a generic framework to define the procedure of selection and benchmarking is required. A trade-off between complexity, cost-efficiency, and scalability criteria should be considered by the framework.
## 3 The VTP-GCN Model
### Spatial-Temporal Interaction Feature Extraction  
The graph convolutional neural network has proved its effectiveness in extracting information from graph data in previous research [23; 24; 25]. Therefore, this paper builds a spatial interaction feature extraction module and a temporal interaction feature extraction module based on GCN. The difference between the 2D convolution and graph convolution is shown in Figure 3. Both are aggregation operations of neighbor information, but the 2D convolution processes data with a fixed 2D grid structure, while the graph convolution processes more general unstructured graph data.  
Figure 3: An illustration of the difference between 2D-CNN (**a**) and GCN (**b**).  
To obtain the spatial-temporal interaction features, \(V_{spa}\), \(A_{spa}\), and \(V_{temp}\), \(A_{tmp}\) were fed into the spatial feature extraction module and temporal feature extraction module, respectively. To speed up the operation of GCN, we symmetrically normalized the weighted adjacency matrices.  
\[\widetilde{A}_{spa}=A_{spa}+I_{spa} \tag{10}\]  
\[\widetilde{A}_{tmp}=A_{tmp}+I_{tmp} \tag{11}\]  
\[\hat{A}_{spa}=\Lambda_{spa}^{-\frac{1}{2}}\widetilde{A}_{spa}\Lambda_{spa}^{- \frac{1}{2}} \tag{12}\]  
\[\hat{A}_{tmp}=\Lambda_{tmp}^{-\frac{1}{2}}\widetilde{A}_{tmp}\Lambda_{tmp} ^{-\frac{1}{2}} \tag{13}\]  
where \(I_{spa}\in\mathbb{R}^{N\times N}\) and \(I_{tmp}\in\mathbb{R}^{t_{h}\times t_{h}}\) are identity matrices, and \(\Lambda_{spa}\) and \(\Lambda_{tmp}\) are diagonal node degree matrices. Then, spatial and temporal interaction features can be obtained through the following graph convolution operation:  
\[h_{spa}^{(l)}=\sigma(\hat{A}_{spa}h_{spa}^{(l-1)}W_{spa}^{(l)}) \tag{14}\]  
\[h_{tmp}^{(l)}=\sigma(\hat{A}_{tmp}h_{tmp}^{(l-1)}W_{tmp}^{(l)}) \tag{15}\]  
where \(W_{spa}^{(l)}\) and \(W_{tmp}^{(l)}\) are the matrices of trainable parameters at layer \(l\), \(\sigma\) is an activation function, and \(h_{spa}^{(l)}\) and \(h_{tmp}^{(l)}\) are the features matrices of vertices at layer \(l\). The term \(h_{spa}^{(0)}\) is initialized as \(V_{spa}\), and \(h_{tmp}^{(0)}\) is initialized as \(V_{tmp}\). We denoted the spatial-temporal interaction feature \(h_{s-t}\in\mathbb{R}^{t_{h}\times N\times C^{\prime}}\) as follows:  
\[h_{s-t}=h_{spa}+h_{tmp} \tag{16}\]
## 2 Direct \(\alpha\)-Halogenation of Aryl Ketones
### Metal Halides (MX)
#### Polymeric Halogenation Agents  
Poly(4-vinyl-N,\(N\)-dichlorobenzenesulfonamide) was first used for the halogenation of one aryl ketone, yielding 2-chloro-1-phenylethanolamine in a moderate 40% yield (Figure 7) [106]. The straightforward reaction conditions and recyclability of the chlorination agent are the main advantages of the protocol. However, prior to halogenation, the ketone deprotonation had to be performed separately using NaH.  
Figure 6: [Hmim][NO3], as co-solvent and promotor of the direct halogenation.  
In 2019, a simple and green bromination method was developed by [PERSON] and co-workers [107], whereby poly(vinylphenyltrimethylammonium tribromide) (PVBMATB) was synthesised by treating the Amberlite 717 resin with HBr and KBrO3 (Scheme 41). The resin proved to be an effective brominating agent as it accommodated the halogenation of eleven aryl ketones in moderate to excellent yields (60-96%). _Ortho_-substituted ketones were less efficiently brominated, highlighting this route's limitations with sterically hindered substrates. PVBMATB provided a visual feedback system for the reaction progress, as its colour changed from red to gold when the reaction is completed. The authors claimed that the resin could be recycled and reused up to three consecutive runs with no significant loss in activity. This route is highly valuable from both economic and environmental points of view, associated with the low cost and recyclability of PVBMATB.  
[PERSON] and others showed that a resin, obtained upon treatment of diphenylphosphine-functionalised polystyrene with CH3 Br and Br2, exhibited an excellent bromination activity (Figure 8) [108]. The bromination of a variety of alkenes, alkynes and ketones was carried out in DCM, a swelling solvent for the polymeric agent. Only one aromatic ketone was halogenated via this protocol, as 2-bromo-1-phenylethanone was obtained in 98% yield. The \(\alpha\),\(\alpha\)-dibromination was always observed, regardless of the nature of the substrate. For comparison, the authors designed a homogenous methyl-triphenylphosphonium bromide-catalysed halogenation in DCM which yielded 2-bromo-1-phenylethanone in 80% yield, which is lower than the heterogeneous conditions used above. This route's main features are its simple work-up and product isolation, in conjunction with the recyclability of the bromination agent.  
Figure 8: Methyl-triphenylphosphonium bromide supported on polystyrene.  
Figure 7: Poly(4-vinyl-\(N\),\(N\)-dichlorobenzenesulfonamide) as an effective chlorination agent.  
[PERSON] and co-workers reported a polymer-supported halogenation agent which mediated the selective \(\alpha\)-iodination of four aromatic ketones in excellent yields (Scheme 42) [109]. The polystyrene-bound selenium bromide was regenerated upon treatment with Br\({}_{2}\). However, lithium diisopropylamide (LDA) had to be employed prior to halogenation to generate the nucleophilic enolate, which is not ideal, especially when other straightforward one-step options are available.
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
## 5 Conclusions  
Four ground-connected UWB-MIMO radiators were designed and fabricated on FR-4 dielectric substrate material with relative permittivity of \(\varepsilon_{r}\) = 4.5, a loss tangent of 0.025, and a height of \(h\) = 1.5 mm. The UWB radiator element was proposed to operate in the FCC-UWB network. It has a bandwidth of 13.01 GHz extending from 2.03 to 15.04 GHz with triple band notches. The triple bands are the WiMAX band (3.28-3.8 GHz), WLAN band (5.05-5.9 GHz), and X-band (7.78-8.51 GHz). In addition to this, it supports the Bluetooth band (2.4-2.483 GHz). An alpha-shaped coupled line connected with a stub-loaded resonator (SLR) was proposed to achieve a band-stop characteristic at the WiMAX band. Two similar U-shaped slots etched on the radiator are utilized to obtain a band-stop characteristic WLAN band. Two unit cells of an octagon-shaped EBG structure are used to achieve a band-stop characteristic at X-band satellite communication. The three notch bands have excellent selectivity or sharpness at both the start and the end of each notch band. The UWB radiator is used in the MIMO configuration as an element. The ground planes of the four UWB radiators are connected together. Four DGS band-stop filters were placed between each ground plane and the center H-shaped ground connection to improve the matching characteristics at the Bluetooth band. In addition, high isolation characteristics were obtained at the operating band.  
Conceptualization, M.S.E.-G. and M.M.M.A.; Data curation, E.B.T.; Funding acquisition, E.B.T.; Investigation, M.M.M.A. and I.A.; Methodology, M.M.M.A.; Resources, E.B.T.; Software, M.S.E.-G.; Validation, I.A.; Writing--original draft, M.S.E.-G.; Writing--review and editing, I.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the European University of Atlantics.  
Not applicable.  
The authors would like to thank the Electronics Research Institute (ERI), Cairo, Egypt for the support provided.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Hyperspectral Imaging (HSI)
#### 4.3.2 Image Processing  
Before analysing an image, an optional program can be employed to improve the image quality using appropriate methods like Fourier transform (FT) and wavelet transform (WT) [217]. Through the process of image pre-processing, the region of interest (ROI) is identified by excluding redundant background in the image [218]. This step is crucial in both spectral and image processing, as the ROI is fundamental to all the subsequent analysis. After image pre-processing, several image-processing strategies can be employed to extract valuable information from hyperspectral images [219]. One approach is to utilise a series of images taken at specific wavelengths to identify surface defects or properties that may only be visible in certain bands [220]. Another method involves applying principal component analysis (PCA) or the independent component correlation algorithm (ICA) to generate PCA or ICA images, respectively [221]. Using PCA or ICA images, surface defects can be identified effectively, but textural features can be quantified by applying texture algorithms [220].
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 3 Biochemical Effects of EGCG
### Influence on SAPK/JNK Pathway  
The family of SAPKs plays a crucial role as intracellular signalling components to transduce the various signals of agonists [56]. SAPKs are induced by protein kinase cascades that have an association with sensors or receptors on the cell surface. This leads to the dual phosphorylation of SAPKs on adjacent tyrosine and threonine residues [130]. Meanwhile, _c_-Jun N-terminal kinases (JNKs) can be induced mainly by pro-inflammatory cytokines or exposure to environmental stress. The JNK subfamily consists of three kinase types: JNK1, JNK2, and JNK3. These three isoforms are encoded by the _Jnk1_, _J2_, and _Jnk3_ genes, respectively. The first two kinases are expressed in various tissues, whereas the third one is mainly found in the brain, heart, and testes [131].  
In the human body, JNK regulates various cell processes such as cell differentiation, proliferation, survival, and metabolic reprogramming, best known for its role in programmed cell death [132; 133; 131]. According to [PERSON] and [PERSON], these kinases respond to various cellular stress signals triggered by cytokines, free fatty acids, as well as hyperglycemia [134]. It also stands as a key mediator in the shift from obesity to type 2 diabetes. Various disorders in humans, such as neurological conditions like neurodegenerative disease, chronic inflammatory conditions (rheumatoid arthritis and inflammatory bowel disease), infectious disorders, and cancer, have connections with dysregulated JNK signalling [131].  
Several studies on the influence of EGCG on the SAPK/JNK pathway were performed on cloned osteoblast-like MC3T3-E1 cells derived from newborn mouse calvaria. [PERSON] et al. noted that enhanced SAPK/JNK activation in osteoblast-like MC3T3-E1 cells promoted PGF2\(\alpha\)-stimulated VEGF synthesis by enhancing SAPK/JNK activation in osteoblast-like MC3T3-E1 cells and thus regulating capillary endothelial cell proliferation [56]. [PERSON] et al. studied the inhibition of the PDGF-BB-induced phosphorylation of SAPK/JNK and found that the inhibition of PDGF-BB stimulated IL-6 synthesis via the suppression of the SAPK/JNK pathway in osteoblasts [57]. [PERSON] et al. noted that the reduction in the TGF-\(\beta\)-stimulated induction of HSP27 in osteoblasts prevents the suppression of bone loss [34].
# Galileo Receiver Performance Analysis with New I/NAV Improvements Live Data +
## 3 Results
### Reed-Solomon and Reduced CED Results  
The results in this section present the statistical value of Data Demodulation by computing the needed time for each satellite to retrieve the necessary CED words. The values of Time to Data (TTD) were grouped per scenario case (Urban or Open Sky) for the first four satellites retrieved per reset point for all reset points within each scenario. For each satellite, the legacy configuration computes CED 1-4, while the RS configuration computes any four different words from CED 1-4 + CED 17-20, and the RedCED + RS configuration computes either RedCED W16 or the same words as RS configuration. In order to fully benefit from RS CED, the receiver is not waiting for W05, which does contain Signal-In-Space (SIS) health status information, Broadcast Group Delay (BGD), Galileo System Time (GST), and Ionospheric correction data:  
* Confirmation of healthy SIS status can be derived from the presence of W16. In case the SIS is flagged as unhealthy or marginal, W16 is not broadcast via the Galileo satellite and is replaced with W05.
* BGD information is generally stable over a long time and can be reused from information retrieved earlier and stored in the receiver.
* Galileo System Time can be recovered from SSP, assuming the receiver has a residual time uncertainty of +/\(-\) 3 s or can be retrieved from W05 decoded from any other satellite.
* Ionospheric correction data can be retrieved from W05 decoded from any other satellite or can be omitted at the cost of higher ranging error.  
Figure 2: Data Demodulation and TTFF visual scheme.  
Figure 3a shows the results in the Open Sky environment. Looking at the 95 percentile of the data demodulation in Figure 3a, we see an improvement of 10 s (33.3%) using RS and 16 s (53.3%) using RS + RedCED. The Open Sky results are deterministic since, in ideal conditions, the retrieval of the CED words is a function of the I/NAV sub-frame layout. Therefore, the obtained results are useful to confirm the obtained theoretical values.  
Figure 3b shows the results in an Urban environment. Compared to the Open Sky results, the increase in the data demodulation time in the Urban case is caused by the loss of words due to the loss of satellite visibility. Looking at the 95 percentile of the data demodulation in Figure 3b, we see an improvement of 69.3 s (38.2%) using RS and 108.4 (59.8%) using RS + RedCED.  
As a complement to the displayed results, we have included the maximum and average data demodulation values for both Open Sky and Urban scenarios. It is important to note that the results for the Urban scenario are highly dependent on the specific dataset used and should be considered illustrative examples:  
* Open sky: \(\bigcirc\)  Legacy: max = 40 s, average = 24.5 s. \(\bigcirc\)  RS: max = 26 s, average = 17.5 s. \(\bigcirc\)  RS + RCED: max = 14 s, average = 8 s.
* Urban \(\bigcirc\)  Legacy: max = 448 s, average = 48.1 s. \(\bigcirc\)  RS: max = 404 s, average = 35.5 s. \(\bigcirc\)  RS + RCED: max = 400 s, average = 18.9 s.