esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Average Total Execution and Save Time by File Size  
While in the previous section we considered the average number of fragments with respect to the file size, here we put the focus on the execution time and the save time. Figure 7 illustrates how the total execution time and save time increase with the file size.  
This metric is crucial for evaluating the performance of the storage system. A longer execution time for larger files might indicate the need to optimize the writing process or to improve the underlying hardware. Specifically, the \(y\)-axis represents the execution time, while the \(x\)-axis corresponds to the different file sizes. As in Figure 6, the different colors indicate the six distinct hardware solutions used for testing. During this test, we observed that most of the time was primarily influenced by the underlying network. Therefore, the temporal impact of the infrastructure is not particularly significant compared to the cloud solutions presented in Section 2. This will be clearer and more evident in the following test. In the meantime, the result is highly encouraging, both for future developments and for immediate deployment.  
Figure 6: This metric represents the average number of fragments into which a file is divided before being saved to storage per system.
## 2 Materials and Methods
### Long Short-Term Memory--LSTM  
For comparison, the results from using LSTM [31] for link quality prediction are used as the baseline. The training part is performed using a dataset that has Signal-to-Noise (SNR) values generated during simulation trials, enabling the model to decrypt the underlying patterns and links. The input layer of the LSTM network takes the SNR value sequence, followed by a hidden layer, and then an output layer. The LSTM cells in the model function in a unidirectional manner, aiding in the sequential processing of data. The training of the model utilizes back-propagation through time, which efficiently propagates error gradients through the LSTM cells over temporal spans. The results highlight the effectiveness and assurance of LSTM models in tasks associated with the prediction of new SNR values as the signal propagates.
## 3 Results and Discussions
### Raman Shift Spectroscopy  
The results of the measurements are depicted graphically as Raman spectra recorded for \(\alpha\)-MnO\({}_{2}\) (black and brown) to identify the main groups of its chemical composition (Figure 3). Minerals with the hollandite structure are made up of double chains of Mn-O octahedra with chains linking corners to produce a structure with tunnels that are two octahedra on a side [38]. In general, the Raman active bands for MnO\({}_{2}\) are at 500-510, 575-585, and 625-650 cm\({}^{-1}\), which represent the Mn-O stretching vibration of the MnO\({}_{6}\) octahedra [39], Mn-O stretching vibration of the basal plane of the MnO\({}_{6}\), and the symmetric stretching vibration of Mn-O of the MnO\({}_{6}\) group, respectively [37; 40], and are indicative of  
Figure 2: The FT-IR spectrum of the \(\alpha\)-MnO\({}_{2}\) type manganese oxide was synthesized using the hydrothermal method.  
a well-developed tetragonal structure with an interstitial space consisting of (2 \(\times\) 2) tunnels and are assigned to the Ag mode [41].  
The fingerprint for \(\alpha\)-MnO\({}_{2}\) type at 576 cm\({}^{-1}\) and 632 cm\({}^{-1}\) belongs to Ag species resulting from breathing oscillations of MnO\({}_{6}\) octahedral units. The relative intensity of these two bands is assigned to tunnel-type materials [37; 42; 43] indicating the crystalline nature of \(\alpha\)-MnO\({}_{2}\) that is in agreement with structural results. The relative intensity of these two bands is assigned to tunnel-type materials [38] indicating the crystalline nature of \(\alpha\)-MnO\({}_{2}\) that agrees with structural results. Therefore, \(\alpha\)-MnO\({}_{2}\) always has Raman spectra similar to the ones from Figure 3.  
The data are in good agreement with both the number and the position of the bands (e \(\sim\) 573 and 640 cm\({}^{-1}\)) reported by [PERSON] et al. [44] and [PERSON] et al. [45]. These authors reported that the presence of heavy tunnel cations dampens the vibrational components perpendicular to the octahedral chains.
## 2 Materials and Methods
### Reactive Oxygen Species (ROS) Direct Assay  
HepG2 cells were seeded and grown in 24-multiwell plates (2 \(\times\) 10\({}^{5}\) cells/well) with FBS containing solution (500 uL). The medium was washed and replaced until the cell confluency reached 70%. The medium was changed to DMEM-F12 without FBS and incubated for 24 h in a CO\({}_{2}\) incubator at 37 \({}^{\circ}\)C. Then, 10 uL of 2\({}^{\prime}\),7\({}^{\prime}\)-dichlorodithoorescein diameterate (5 uM) reagent was added into the wells and incubated in a CO\({}_{2}\) incubator for 45 min. The medium was washed out using one mL of 1\(\times\) PBS solution twice. The cells were treated with 500 uL of tert-butyl hydroperoxide (400 uM) as a positive control, and with test samples at different concentrations (0.1-10 ug/mL). Absorbance was obtained immediately at an excitation of 485 nm\(/\pm\) 20 and emission of 528 nm\(/\pm\) 20, and repeated every 30 min for 2 h [9].  
Figure 1: Flow chart of bioassay-guided fractionation and the yield of _Chrysanthemum morifolium_ fractions and sub-fractions. PE, petroleum ether; EtOAc, ethyl acetate; RS, residual; PO, potassium oxonate; XO, xanthine oxidase; * Moderate XO inhibitory activity; ** > 75% XO inhibitory activity; *** > 90% XO inhibitory activity; *** High cellular antioxidant activity and pro-proliferative rate.
## 2 Numerical Study
### Numerical Simulation Results of PIPR with Different End Faces
#### 2.4.1 Numerical Simulation Results of PIPR with Hemispherical-Shaped End Face  
The pressure distribution of the PIPR with a hemispherical-shaped end face is shown in Figure 10. It can be seen that the upstream fluid pressure after the plugging process is smaller than that of PIPR with the original footstep-shaped end face. Therefore, the hemispherical structure of the PIPR plays a significant role in the redistribution of pressure during the plugging operation. During the plugging operation, although the pressure  
Figure 8: The relationship between the force, plugging process, and pipeline operating parameters. (**a**) The plugging process and inlet pressure; (**b**) The plugging process and inlet velocity.  
Figure 9: The PIPR’s end faces with a hemispherical shape and parabolic shape. (**a**) Hemispherical-shaped end face; (**b**) Parabolic-shaped end face.  
distribution trend on the hemispherical surface is roughly the same as that of the footstep-shaped end face, the pressure distribution near the downstream low-pressure area is different before the plugging process. The pressure gradient area is relatively small. From the perspective of the PIPR's force, the PIPR with a hemispherical end face is not greatly influenced by the low-pressure area.  
The velocity distribution of the flow field around the PIPR with a hemispherical-shaped end face is shown in Figure 11. Compared with the PIPR with a footstep-shaped end face, the velocity of the flow field around the PIPR with a hemispherical-shaped end face is not uniformly distributed before plugging. A thicker boundary layer area appears upstream of the PIPR, and the stagnation area near the front-end face of the PIPR is smaller. The distribution of fluid particles with high velocity is roughly the same as that of the PIPR with a footstep-shaped end face. Two stagnation regions extend on the rear end face, and the downstream flow field is not symmetrically distributed.  
It can be seen from Figure 12 that the distribution of fluid particles on the entire center plane was relatively stable before the plugging process, and the overall level of in-pipe flow velocity is lower than that of the original model. In the later stage of the plugging process, the flow velocity is almost zero, and only fluid particles that pass through the narrow gap between the PIPR and pipeline inner wall have high velocity. There is a significant difference in the flow field of the downstream compared to the footstep-shaped end face. The separation points of the boundary layer move backward due to the hemispherical surface, which can result in weaker separation intensity and a reduction in the area affected by downstream vortices. The stable flow field only exerts force on the PIPR from the static pressure, and it does not generate fluctuations to interfere with the plugging operation.  
Figure 10: The fluid pressure distribution of the PIPR with hemispherical-shaped end face. (**a**) Before the plugging process; (**b**) After the plugging process.
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 2 Literature Review
### Cooperative Energy-Efficient Protocol for Underwater WSNs (Co-UWSN)
Each node inside the region receives the message and activates itself. When the sink is ready to move to another region and leave the specific zone, it broadcasts another message to warn the sensor nodes of leaving, allowing them to return to sleep mode. During the phase of initialization, sensor and sink nodes are deployed. Random sensor nodes are placed in aquatic environments. The nodes have restricted energy, while the sink nodes have no such restrictions. Initially, depth and position information are shared among regionally based neighbors. Each sink traverses three vertical and one horizontal region; more specifically, each sink traverses three square zones out of 12. There are two types of data transmission in the data transmission stage: (1) Direct transmission, a sensor node usually scans its transmission range for a mobile sink. After a predetermined amount of time, the transmission range of each sensor is computed for this purpose. The time interval for calculating the transmission range is set to 100 s.  
(2) Because EERBCR is also a cooperative routing protocol, the forwarder node will route the packets if the mobile sink is not within the communication range of a sensor node. Essentially, the relay node is selected from the sensor node's neighbors. The criteria for relay node selection are as follows. (I) The relay node must originate in the same region as the sensor node. (II) The relay node has the shortest distance to sink and the highest remaining energy.
## 3 Mobility-Aware Duty Cycling  
### Beacon Messaging  
The related work we have reviewed demonstrates a propensity in studies to determine, by various different parameters, an optimal path for the MSN, which has merit in the results of each particular work. However, a common theme to be found is in the approach taken to keep track of the MSN. The regular exchange of beacon messages can have a significant negative effect on the energy consumption of a network. Our new mobility-aware duty cycling approach utilises a pre-defined mobility pattern. Our hypothesis is that given network parameters such as sink start position, speed, and time, each static node is capable of independently calculating the current sink position. This can therefore be achieved without the expensive exchange of messages, with the result being a lightweight algorithm with no network overhead.
## 3 Results
### Quantitative Accuracy  
#### 3.4.1 Distance between Target Structure and Inner Circumference  
The results are depicted in Figure 7. In all three planes, the graph peaks at around 200 \(\upmu\)m. Close to 90% of slides had a deviation below 500 \(\upmu\)m (Figure 7B). Figure 7C shows the distribution of the accuracy in the different sectional planes. In the plane S1: 53.8%, 93.2% and 96.2% of all measured deviations were below 200 \(\upmu\)m, 400 \(\upmu\)m, and 500 \(\upmu\)m, respectively.  
Differences between S1 and S3 (\(p=0.047\)) were statistically significant, with respect to the deviation from set annotation. Between S2 and S3 (\(p=0.080\)) no statistically significant difference was present.
## 3 Relevant Research in Wind Farm Design and Optimization
### Sub-Modeling
#### 3.2.1 Wind Turbine Wakes and Turbulence Modeling
New semi-empirical approximations that complement the Jensen-Katic wake model have been proposed to describe: (1) the wake expansion and recovery as a function of the net TI (composed of both the ambient and the WT mechanically added turbulence intensities); (2) the effects of yaw misalignment and unsteady conditions in wake development; and (3) the effects of operating WTs under partial wake conditions [282].
## 2 Related Work  
### SRCNN((Super-Resolution Convolutional Neural Network)  
The SRCNN [6] is a pioneering work of applying CNN to image super-resolution reconstruction. It mainly implements end-to-end mapping of LR images to HR images.  
The specific algorithm principle is as follows.  
(1) Feature extraction:  
The main purpose of feature extraction is to extract image blocks from low-resolution images. At this time, the dimensions of feature vectors are still in low-dimensional space. This is the process of the first convolution. The specific formula is as follows.  
\[F_{1}(Y)=f(\omega_{1}*Y+B_{1}), \tag{1}\]  
In the formula, \(Y\) is a low-resolution input image. \(\omega_{1}\) is a convolution kernel, and \(B_{1}\) is a bias, where the number of convolution kernels is \(n_{1}\). The size is \(c\times f_{1}\times f_{1}\), and \(c\) is the number of channels, which is set to 1. \(f\) is the activation function. The activation function used in this case is Rectified Linear Unit (ReLU), that is \(f(x)=\max(0,x)\).  
(2) Non-linear mapping:  
The purpose of non-linear mapping is to map the extracted low-dimensional features to high-dimensional features. This process is a second convolution. The specific formula is as follows.  
\[F_{2}(Y)=f(\omega_{2}*F_{1}(Y)+B_{2}), \tag{2}\]  
Based on the formula, the non-linear mapping is performed on the basis of feature extraction, and \(\omega_{2}\) is the convolution kernel of the non-linear mapping. Since the number of convolution kernels represents the feature vector dimension,\(n_{2}>n_{1}\), and \(B_{2}\) represents the bias.  
(3) Image reconstruction:  
The image reconstruction process is equivalent to convolution using the mean convolution kernel. This is the third layer of convolution. The specific formula is as follows.  
\[F(Y)=\omega_{3}*F_{2}(Y)+B_{3}, \tag{3}\]  
\(F(Y)\) is the final reconstructed image, \(\omega_{3}\) is the convolution kernel used in the reconstruction process, and the size is \(f_{3}\times f_{3}\times n_{2}\), and \(B_{3}\) is the bias.  
Experiments show that SRCNN has a better reconstruction effect than other traditional methods. However, it is still not ideal for restoration of details.
## 4 Modeling and Analysis
### Preliminary Analysis
#### 4.2.2 Contention Latency \(T_{\text{con}}\)  
With the exponential back-off timer applied, the maximum width of the back-off timer doubles after each collision. Since the contention happens in the nonempty segment, following the similar analysis in [14], the single probabilities of the two cases (success, collision) in the \(c\)-th contention of the \(i\)-th final segment can be derived as  
\[p^{i,c}_{\text{suc\_con}}(\hat{\lambda},R_{\text{p}})=\frac{\mu_{i}(\hat{ \lambda},R_{\text{p}})p_{c}e^{-\mu_{i}(\hat{\lambda},R_{\text{p}})p_{c}}}{1-e^ {-\mu_{i}(\hat{\lambda},R_{\text{p}})p_{c}}} \tag{9}\]  
\[p^{i,c}_{\text{col\_con}}(\hat{\lambda},R_{\text{p}})=1-\frac{\mu_{i}(\hat{ \lambda},R_{\text{p}})p_{c}e^{-\mu_{i}(\hat{\lambda},R_{\text{p}})p_{c}}}{1-e^ {-\mu_{i}(\hat{\lambda},R_{\text{p}})the success probability of the whole contention process when the \(c\)-th contention succeeds in the \(k\)-th final segment is computed as  
\[p_{\text{suc}}^{i,c}\left(\hat{\lambda},R_{\text{p}}\right)=p_{\text{suc}\_{ \text{con}}}^{i,c}(\hat{\lambda},R_{\text{p}})\prod_{l=0}^{c-1}p_{\text{col}\_ {\text{con}}}^{i,l}(\hat{\lambda},R_{\text{p}}) \tag{11}\]  
where \(p_{\text{col}\_{\text{con}}}^{k,0}(\hat{\lambda},R_{\text{p}})=1\) and \(c=1,2,...\).  
For the similar reasons, the whole contention latency \(T_{\text{con}\_{\text{seg}}}^{i}(\hat{\lambda},R_{\text{p}})\) of the \(i\)-th segment can be derived as  
\[T_{\text{con}\_{\text{seg}}}^{i}(\hat{\lambda},R_{\text{p}})=\sum_{c=1}^{ \infty}p_{\text{suc}}^{i,c}(\hat{\lambda},R_{\text{p}})\left(\sum_{l=0}^{c-1} T_{\text{col}\_{\text{si}}}(l)+T_{\text{suc}\_{\text{si}}}(c)\right) \tag{12}\]  
where \(T_{\text{con}\_{\text{si}}}(c)\) and \(T_{\text{suc}\_{\text{si}}}(c)\) are the durations spent in the collision case and the success case in the \(c\)-th contention, respectively.  
It is noteworthy to mention that after some collisions in the case of high vehicle densities, the last success contention cost a little time since multiple nodes contend, and it can be approximated by the success contention duration in the first contention. Finally, the average contention latency in one-hop can be computed as  
\[T_{\text{con}}(\hat{\lambda},R_{\text{p}})=\sum_{i=1}^{N_{\text{seg}}(N_{ \text{iter}})}T_{\text{con}\_{\text{seg}}}^{i}(\hat{\lambda},R_{\text{p}})P_{ \text{seg}\_{\text{sel}}}^{N_{\text{iter}},i}(\hat{\lambda},R_{\text{p}}) \tag{13}\]
## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods
#### Method Based on Link Quality and Node Reliability  
[PERSON] et al. [33] calculated the weight of each recommender based on link quality and node reliability, as shown in Equation (22):  
\[w_{r}=\frac{1}{(1+(\alpha R_{r}+\beta LQ_{r}))} \tag{22}\]  
where \(R_{r}\) is the node reliability of recommender \(r\) and \(LQ_{r}\) is the link quality between recommender \(r\) and the evaluator. Link quality is evaluated based on the signal-to-noise ratio, distance, and received signal strength. Node reliability is evaluated based on the success rate of interactions between nodes.
## 4 Conclusions  
For the simple and accurate prediction of roadways broken rock zone (BRZ), a Gaussian process model optimized by PSO based on a 181 datasets database was proposed. During the training process, 10-k-fold cross-validation was utilized for evaluating the generalization performance and guide the model training, three validation metrics including \(VAF\), \(R^{2}\) and \(RMSE\) were used to verify the predictive performance of developed intelligence models. After comparing with the prediction performance of MLR (_VAF_ of 76.2155, \(R^{2}\) of 0.7639 and _RMSE_ of 0.2973 for training datasets and _VAF_ of 88.6696, \(R^{2}\) of 0.8899 and _RMSE_ of 0.1678 for testing datasets), ANN (_VAF_ of 90.7759, \(R^{2}\) of 0.9086 and _RMSE_ of 0.1779 for training datasets and _VAF_ of 88.4466, \(R^{2}\) of 0.8993 and _RMSE_ of 0.1630 for testing datasets) and _GP_ (_VAF_ of 90.4209, \(R^{2}\) of 0.9051 and _RMSE_ of 0.1798 for training datasets and _VAF_ of 86.7895, \(R^{2}\) of 0.8900 and _RMSE_ of 0.1733 for testing datasets), the obtained results show that the PSO-GP shows the best performance with _VAF_ of 94.3008, \(R^{2}\) of 0.9438 and _RMSE_ of 0.1387 for training datasets and the _VAF_ of 89.2192, \(R^{2}\) of 0.9153 and _RMSE_ of 0.1591 for testing datasets. Meanwhile, the obvious optimization performance of the PSO algorithm was also verified. Last, the sensitivity analysis shows that there is no big difference between the effect of \(ED\), RMS and \(JI\) on _BRZT_, and \(JI\) was found to be the most sensitive factor. The developed predictive model in this paper provides a simple and accurate method for broken rock zone thickness (_BRZT_) prediction, and can be referenced when solving similar complex engineering problems.  
Conceptualization, R.H.; methodology, Z.Y. and J.Z.; software, Z.Y.; validation, X.S. and J.Z.; formal analysis, Z.Y. and Y.G.; investigation, Z.Y. and Y.G.; resources, Z.Y.; data curation, Z.Y.; writing-original draft preparation, Z.Y. and R.H.; writing-review and editing, Z.Y. and J.Z.; visualization, Z.Y. and Y.G.; supervision, R.H. and Y.G.; project administration, Z.Y. and X.S.; funding acquisition, Z.Y., X.S., J.Z. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Natural Science Foundation Project of China (Grant Nos. 51874350 and 41807259), the National Key R&D Program of China (2017 YFC0602902), the Innovation-Driven Project of Central South University (2020 CX040) and the Fundamental Research Funds for the Central Universities of Central South University (20182 zts217).  
The authors declare no conflict of interest.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
This approach aims to minimize phasor data transmission delay in the backup path. (4) Six IEEE benchmark test power system communication networks are utilized, with varying link load conditions set as experimental network topologies. It has been verified that BPDM-GCN can effectively reduce fault link recovery delay, while also improving the success rate of fault link recovery.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### Excellent RF Performance
#### 3.2.1 Improving Equivalent Inductance  
The design and structure of the capacitive MEMS switch beam membrane can be equivalent to a series combination of capacitors, inductors, and resistors [138; 139; 140]. The equivalent inductance of the switch significantly impacts its RF performance. Therefore, optimizing the equivalent inductance can effectively improve the switch's RF performance.  
For instance, improving the equivalent inductance of the beam membrane primarily involves adjusting its physical dimensions, shape, materials, and layout. These changes can directly impact the electromagnetic parameters of the switch, including inductance [141], thereby optimizing its RF performance. [PERSON] et al. [142] proposed a capacitive MEMS switch based on two different shapes of non-uniform cantilevers, as shown in Figure 9a. The different structural designs of the cantilever beams can achieve different inductance values when the device is closed, thus adjusting the electrical resonance frequency. [PERSON] et al. [141] achieved less than a 2 dB low insertion loss in the 240 to 280 GHz range by reducing the equivalent parallel inductance of the switch.  
Figure 9b shows an RF MEMS switch with short high-impedance transmission line (T-line) sections added to both sides of the beam membrane [143]. These parts act as series inductors to compensate for the capacitance when the switch is in the upstate. This design provides good matching at the desired frequency, thus improving the isolation by up to 24.05%, at the cost of increased insertion loss. The independence of device impedance from input RF signal power, which indicates linearity, is typically characterized by the third-order intercept point (IIP3) in dual-tone RF intermodulation measurements [144]. For instance, to prevent intermodulation between GSM at 1800 MHz and LTE at 2400 MHz, the IIP3 of the antenna switch must be as high as possible. [PERSON] et al. [145] designed a novel single-pole twelve-throw RF MEMS switch structure, as shown in Figure 9c. This design uses specially designed parallel double cantilever beams, aimed at improving RF matching and achieving smaller size and more compact devices. The switch also uses the least squares method to reduce the uncertainty of the IIP3 measurement. In this design, the insertion loss and isolation of each channel of the switch are better than 0.94 dB and 30 dB, respectively.