esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 4 Experimental Results and Comparative Analyses
### Total Transferred Pages  
As shown in Figure 4, the number of transferred pages is associated with the \(\alpha\) value. In the original hybrid-copy algorithm, all memory pages are transferred only once in the pre-copy phase.  
Figure 3: The total migration time of the experiment. **(a)** SPEC VIRT_SC 2013; **(b)** Linux Kernel Compile.  
We found that a total 297,564 pages are transferred in SPEC VIRT_SC 2013 workload case with the original hybrid-copy algorithm. In our experimental platform, every VM has 262,144 memory pages. This means that there are 35,420 invalid transfers in the migration process.  
From Figure 4a,b, we know that if the \(\alpha\) is large enough, the novel hybrid-copy algorithm has less transferred pages than original hybrid-copy algorithm because the Markov model avoided parts of the invalid transfers. In our experiments, if the \(\alpha\) is set to 0.7, our novel hybrid-copy algorithm transfers a similar number of memory pages to the original hybrid-copy algorithm. If the \(\alpha\) is set to 1.0, it always has less total transferred memory pages. If the \(\alpha\) is small, we may transfer many more pages than the original hybrid-copy algorithm because more iterative copies are used in the pre-copy phase. The number of transferred pages is positively correlated with the consumption of network resources. Therefore, if the network condition is poor, we need set a large value for \(\alpha\) to save more bandwidth resources.
## 4 Simulation and Analysis
### Impact of URA and \(P_{\text{sat}}\)
On the other hand, \(P_{\text{sat}}\) changes had a relatively smaller impact on the global average integrity risk and availability but posed a more significant threat to areas with poor satellite geometry. The RAIM integrity risk was more sensitive to _URA_ than \(P_{\text{sat}}\). Therefore, more attention should be paid to determining _URA_. Figure 3: Global worst-case integrity risk error with different _URA_ and \(P_{\text{sat}}\). (**a**) Global worst-case integrity risk error with different _URA_; (**b**) Global worst-case integrity risk error with different _P_sat.
## 2 Literature Survey
### Analytical Capacity Estimation  
Analytical capacity estimation methods are efficient and cost-effective approaches for the initial feasibility assessment of storage projects. There are numerous studies utilizing the concept of material balance in estimating CO\({}_{2}\) storage capacity in geologic formations. [PERSON] et al. [15] conducted a material balance study to evaluate CO\({}_{2}\) sequestration in depleted gas fields considering a weak aquifer influx through various injection scenarios that alter the number of wells and injection schemes. [PERSON] et al. [16] adapted the established p/z plot method for estimating CO\({}_{2}\) storage capacity with different sensitivities of the gas compressibility factor according to different production scenarios. [PERSON] et al. [17] derived a set of material balance equations to estimate CO\({}_{2}\) storage capacity in a depleted gas reservoir with and without a water drive and compared the results with numerical simulation from a commercial compositional reservoir simulator. [PERSON] et al. [18] discussed the impact of water drive on CO\({}_{2}\) storage capacity estimations and demonstrated the difficulty it imposes. They also conducted a comparative analysis between different analytical methodologies [17; 19; 20; 21]. [PERSON] et al. [21] presented a material balance method for monitoring formation pressure during CO\({}_{2}\) storage and quantifying the maximum injection capacity in depleted gas reservoirs with encroached water.  
Since hydrogen storage is still an emerging field, there are relatively fewer published analytical methods compared to those for CO\({}_{2}\) storage. [PERSON] et al. [22] proposed a material balance approach for evaluating underground hydrogen storage capacity in depleted gas reservoirs using CO\({}_{2}\) as a cushion gas. [PERSON] [23] assessed hydrogen storage capacity in porous media across Europe through a volumetric approach considering density,viscosity, flow efficiency, and capillary trapping. [PERSON] and [PERSON] [24] evaluated the storage efficiency of hydrogen in saline aquifers in comparison with carbon dioxide using a compositional reservoir simulator. Based on their results, they proposed several analytical equations to estimate hydrogen storage capacity by applying a correction factor to CO\({}_{2}\) storage efficiency. Lastly, [PERSON] et al. [25] proposed a simple volumetric method for characterizing hydrogen storage potential in U.S. underground gas storage facilities based on the density ratio of H\({}_{2}\) to CH\({}_{4}\). Overall, these studies showcase the importance of material balance and analytical methods in estimating gas storage capacities in various geological formations.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 7 Analysis of Solutions
More details can be found in Section 5.2), including the Key Back-Up/ Recovery/ Revocation Scheme (KBURRS) and Backward Compatibility Scheme (BCS) to handle the key during Back-Up/ Recovery/ Revocation stages, and tells the end device what to do based on the requirement of the device-security, in such guidance, the security of the keys can be further protected. By providing more key management solutions/guidance that we can ensure the keys are more secured. Hence, the security of the LoRaWAN v1.1 can be greatly protected. In conclusion, our proposed complete key management scheme covered the areas such as: 1. The session key generation algorithm which has proved to be better than other algorithms listed in the experimental section of Section 6. 2. We provided guidance on the Key Updating Scheme (KUS), Key Back-Up/ Recovery/ Revocation Scheme (KBURRS), and Backward Compatibility Scheme (BCS) to further protect the secret keys where some of the papers in Section 2 did not cover. Therefore, our complete key management scheme can ensure the security of the LoRaWAN v1.1.
## 2 Related Works and the Key Contribution of This Paper
### Layered Video Coding Techniques  
This paper refers to works that have addressed layered video coding challenges, which involve creating multiple layers of varying importance to progressively enhance video quality. Prior studies, such as those in [13; 14], have examined methods for improving the robustness of video transmission by encoding videos in hierarchical layers. These techniques aim to preserve critical data and enhance the overall quality of the transmitted video by prioritizing different information layers based on their importance.
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout
### Best Suitable 5G Antenna Design for Fiji Geographical Terrain
Antenna arrays at both connection ends, propagation paths, and MIMO channel elements can be used in all circumstances, including indoor, urban, and rural ones in Oceania's geographical terrain. The \(U\times S\) MIMO channel's time-variant impulse response matrix is given by  
\[H(t;\tau)=\sum_{n=1}^{N}H_{n}(t;\tau) \tag{4}\]  
where \(t\) represents time, \(\tau\) represents delay, \(N\) represents number of paths, and \(n\) represents path index. The channel matrix of dual polarization is \(h_{n}\) for cluster \(n\) and the antenna array response matrices \(F_{tx}\) and \(F_{rx}\) for the transmitter (\(T_{X}\)) and receiver (\(R_{X}\)), respectively. The channel response matrix of dual polarization channel matrix is as follows:  
\[H_{n}(t;\tau)=\iint F_{rx}(\varphi)h_{n}(t;\tau,\phi,\varphi)\;F_{tx}^{T}( \varphi)d\phi\;d\varphi \tag{5}\]  
The channel for cluster \(n\) from \(T_{X}\) antenna element \(s\) to \(R_{X}\) element \(u\) is written as follows:  
\[ \begin{array}{c}H_{u,s,n}(t;\tau)=\sum\limits_{m=1}^{M}\begin{bmatrix}F_{ rx,u,V}(\varphi_{n,m})\\ F_{rx,u,V}(\varphi_{n,m})\end{bmatrix}^{T}\begin{bmatrix}\alpha_{n,m,VV}& \alpha_{n,m,VH}\\ \alpha_{n,m,HV}&\alpha_{n,m,HH}\end{bmatrix}\begin{bmatrix}F_{rx,\;s,\;V}( \varphi_{n,m})\\ F_{rx,\;s,\;H}(\varphi_{n,m})\end{bmatrix}\cdots\\ \times\exp\left(j2\pi\lambda_{0}^{-1}\left(\overleftarrow{\varphi}_{n,m}. \vec{r}_{rx,u}\right)\right)\exp\left(j2\pi\lambda_{0}^{-1}\left(\overleftarrow {\varphi}_{n,m}.\vec{r}_{tx,u}\right)\right)\cdots\\ \times\exp(j2\pi\upsilon_{n,m}t)\delta(\tau-\tau_{n,m})\end{array}  \tag{6}\]  
here,  
Figure 6: The three sectors of a hexagonal cell and the planned antenna bearing direction for the simulations. The patterns of the \(u\) fields for the vertical and horizontal polarizations of antenna elements are represented as \(F_{rx,u,V}\) and \(F_{rx,u,V}\). The complex gains of ray \(n,m\) vertical-to-vertical and horizontal-to-vertical polarizations are represented as \(a_{n,m,VV}\) and \(a_{n,m,HV}\)  
The carrier frequency's wavelength is represented as \(\lambda_{0}\)  
The unit vector for angle of departure is represented as \(\overline{\phi}_{n,m}\)  
The unit vector for angle of arrival is represented as \(\overline{\phi}_{n,m}\)  
The \(s\) and \(u\) respective elemental location vectors represented as \(\overline{r}_{rx,u}\) and \(\overline{r}_{rx,u}\). The \(n.m\) ray's doppler frequency components represented as \(v_{n,m}\). All the small-scale parameters described above are time-variant, or functions of \(t\), when the radio channel is modeled as dynamic.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 4 Performance Evaluation  
To demonstrate that RPL-CGA can quantitatively improve LLN performance, we conducted extensive simulation experiments through OPNET to demonstrate that RPL-CGA is superior to existing related algorithms such as ETXOF (a representative algorithm of RPL), 0.8 \(\times\) ETX + 0.2 \(\times\) RER and 0.6 \(\times\) HC + 0.4 \(\times\) RER (0.8 \(\times\) ETX + 0.2 \(\times\) RER and 0.6 \(\times\) HC + 0.4 \(\times\) RER, the famous additive manners, can get optimal performance [15]).
# A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum
## 3 Simulation and Analysis
### Sinusoidal Trajectory Tracking Experiment
#### 4.2.2 Motor High-Velocity Experiment  
The desired high-velocity sinusoidal position trajectory was 30 \(\pi\)sin (1.6\(\pi\)t), and its first derivative was the desired velocity. Compared with the low-velocity case above, the observed total friction torque mapped to the motor side at high velocities was slightly larger than that in Figure 11a and ranged from \(-6.4\times 10^{-3}\) N-m to \(2.2\times 10^{-3}\) N-m, as shown in Figure 12a. In addition, the relationship between the actual motor velocity and estimated friction torque in Figure 12b was the same as in the low-velocity case. Moreover, the position tracking performance was improved less than that of the velocity by compensating for the observed friction torque, as shown in Table 5 and Figure 12c,d. The reason for this has been explained in the motor low-velocity experiment above. In addition, the velocity tracking performance at a high velocity was decreased compared with that under low-velocity conditions  
Figure 11: Motor low-velocity without payload experiment results. (**a**) Observed total friction torque mapped to the motor side at low velocities. (**b**) Normalized actual motor velocity (NV) and normalized friction torque (NF). (**c**) Motor position tracking errors at low velocities. (**d**) Motor velocity tracking errors at low velocities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Errors** & **No Compensation** & **Compensation with \(K_{\mu}\) = 50** & **Performance Improvement** \\ \hline RMSE\_SpLeV[rad] & 0.982 & 0.981 & 0.1\% \\ RMSE\_SvLeV[rad/s] & 4.207 & 2.813 & 33.1\% \\ RMSE\_SPeHV[rad] & 3.685 & 3.650 & 1.0\% \\ RMSE\_SvHV[rad/s] & 19.823 & 20.068 & \(-1.2\%\) \\ \hline \hline \end{tabular}  
* \({}^{2}\) RMSE\_SpLeV and RMSE\_SPeHV denote the root mean squared static position error at low velocities and high velocities from 0.1 s to the end, respectively. RMSE\_SvLeV and RMSE\_SvFeIV denote the root mean squared static velocity error at low velocities and high velocities from 0.1 s to the end, respectively.  
\end{table}
Table 5: Position and velocity tracking error comparison without payload \({}^{2}\).  
because more uncertainties, such as model uncertainties and noise, affected the performance of the observer and the estimated and compensated friction torque was then not accurate enough. Therefore, the velocity performance of the controller is improved more at low velocities than at high velocities.
## 2 Materials and Methods
### Methods
#### 2.2.4 CA Model Construction  
The CA model is comprised of five fundamental components: tuple and tuple space, tuple state, neighborhood, time, and transformation rules. The acquisition of transformation rules is the central aspect of CA model construction. A comprehensive probabilistic model based on CA typically consists of four elements: land development suitability, neighborhood constraint, restrictive constraint, and random perturbation, as depicted in Equation (5):  
\[P=d  
constraint of the current raster, which is set to watershed without conversion to urban in this study; \(t_{ij}\) is the random perturbation, which is set to a random number from 0 to 1 in this study. In this model \(d_{ij}\) and \(\Omega_{ij}\) are the keys to learning the conversion rules.  
Previous studies have demonstrated that RF stochastic classifiers outperform other machine learning classifiers and regression methods in terms of classification and fitting accuracy. Therefore, in the CA iterative model presented in this paper, RF is employed as the development suitability extractor to calculate the suitability of land for urban development.  
In addition, the SNSCNN model is introduced to calculate the neighborhood effect under multi-scale regional characteristics, and the overall conversion probability of all current non-urban rasters is obtained by combining the random perturbation and restriction constraints in the CA model. During the simulation, the simulation is performed based on the base period year land-use data, and the total number of urban rasters at the end of the land use year is used as the demand constraint to convert sites from high to low based on the overall probability until the total demand is met. After multi-period CA iterative simulation, the result of urban land expansion in the target year is obtained. The flow chart is shown in Figure 7.
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 2 Related Work
### Deep Learning-Based Alzheimer's Disease Classification Methods  
#### 2.1.1 CNN-Based Methods  
CNN-based methods for AD classification usually preprocess fMRI data, then use point multiplication of image data and convolution kernels in convolution layer calculations to extract image data features and obtain classification prediction results. For example, [PERSON] et al. [7] used the LeNet-5 model to extract shift and scale-invariant features before performing deep learning classification for AD, achieving an accuracy rate of 96.85% on the dataset. This method opened new avenues for medical image analysis, but it may not be well-equipped to handle more complex problems in the future. [PERSON] et al. [16] employed the convolutional neural network architecture AlexNet to extract and learn features from low to high levels, improving AD classification performance. Compared to previous studies, this method achieved classification for all stages of Alzheimer's disease, reaching an accuracy rate of 94.97%. [PERSON] et al. [17] converted and scaled 3D images to 2D images before extracting features and used the VGGNet architecture to classify fMRIimages. [PERSON] et al. [18] proposed a three-dimensional CNN to combine and analyze rs-fMRIs, clinical assessment results, and demographic information to predict the conversion from MCI to AD within an average of 5 years. The method successfully predicted the prognosis of MCI to AD with high accuracy, demonstrating the potential of CNN methods in solving this problem. In comparison to other experiments based on CNN models, this study improved the classification average accuracy to 99.95% by performing some preprocessing on the image dataset before sending it to the CNN architecture for feature extraction. However, since adjacency matrices are non-Euclidean data, CNNs cannot effectively handle them and capture all their properties.
## 2 Improved Deep Learning Model for Day-Ahead Solar Irradiance Forecasting
### Long Short Term Memory Based Sequence Forecasting Model (from RNN to LSTM)  
In the previous works, some sequence models (e.g., Markov models, Kalman filters and conditional random fields) are commonly used tools to address the raw sequential input data. However, the biggest drawback of these traditional sequential models is that they are unable to adequately capture long-range dependencies. In the application of day-ahead solar irradiance, many indiscriminative or even noisy signals that exist in the sequential input during a long time period may bury informative and discriminative signals. This can lead to the failure of these above sequences models. Recently, RNN has emerged as one effective model for sequence learning, which has already been successfully applied in the various fields, including image captioning, speech recognition, genomic analysis and natural language processing [52].  
In our proposed DWT-CNN-LSTM model, LSTM that overcomes the problems of gradient exploding or vanishing in RNN, is adopted to take the output of CNN based local feature extractor to further predict the targeted subsequences. As mentioned in Section 2.1, these subsequences are decomposed from solar irradiance data. In the following two subsections, the principle of RNN is simply introduced and the construction of its improved variant (i.e., LSTM) is then illustrated in detail.
## 1 Introduction  
During the last two decades, liquid crystals (LC) have become a promising approach for the design of tunable devices at microwave frequencies. The anisotropy of the molecules of these materials allows LC electrical properties to depend on their molecules orientation, which can be changed by applying an external electric field. Concretely, the LC permittivity can be varied between two extreme values, \(\varepsilon_{\mathrm{rL}}\) and \(\varepsilon_{\mathrm{rI}}\), as a function of the applied voltage. This is the reason which permits LC-based devices to be voltage-controlled. Some examples of tunable devices based on LC technology at microwave frequencies in the bibliography are capacitors [1], antennas [2], filters [3], _etc_.  
Moreover, the advantages of using LC in size, cost or power consumption [4] compared to other studied technologies for designing tunable microwave devices, make these materials very suitable for this purpose. For example, varactor diodes need high voltages, up to 30 V, in order to achieve large tuning ranges for frequencies higher than 1 GHz [5], while devices based on ferrite technology [6] require a magnetic field for tuning, which leads to problems in terms of size and power consumption. Therefore, the improvement of the existing LC-based prototypes and the development of new LC tunable devices at these frequencies are interesting challenges to be faced in the immediate future.  
Filters are very important devices at microwave telecommunication systems, for example, in satellite communications, due to their ability of selecting (bandpass filters) or rejecting (bandstop or notch filters) bands of frequencies. Furthermore, the demand on the development of tunable filters has increased during the last years. In this work, a tunable LC-based band-pass filter with microstrip geometry is presented. A reshaping of a dual-mode square patch geometry reported in previous works has been proposed as the microstrip filter. This new patch structure is expected to improve the filter performance, increasing the pass-band return loss. A high return loss is desirable in a filter frequency response because it involves a lower pass-band filter insertion loss variation. The design, manufacture and experimental results of the device are detailed in this paper.
## 5 Simulation Parameters, Data Description and Case Study  
The simulation system parameters for BESS, WT and PV are as summarized in Table 1. The hourly historical meteorological for the considered site was obtained from meteoblue [31] and NASA database for the year 2017 to 2019. The proposed approach in this study adopted an isolated microgrid in the Marsabit county of Kenya as a case study to evaluate and demonstrate the techno-economic gains of the proposed system flexibility management techniques. In order to demonstrate the efficiency of the proposed model, two simulation scenarios were considered as outlined below:  
* Case 1: Capacity optimization without DSM strategy.
* Case 2: Capacity optimization considering FDR scheduling as DSM strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Component and Economic Specification**} \\ \hline \multicolumn{3}{c}{Economics} \\ Discount rate, \(r\) & 5\% \\ Inflation rate & 6\% \\ \hline \multicolumn{3}{c}{**PV Module**[31]} \\ \hline Investment cost & 1690 & US \$/kW \\ Maintenance & 26 & US \$/kW/yr \\ (PV) reduction factor & 84\% \\ lifetime & 25 & years \\ \hline \multicolumn{3}{c}{**Wind Turbine**[31]} \\ \hline Investment costs & 2030 & US \$/kW \\ Operation and maintenance & 76 & US \$/kW/year \\ lifetime & 25 & years \\ Cut-in wind speed: & 4 & m/s \\ Rated wind speed: & 14.5 & m/s \\ Cut-out wind speed: & 25 & m/s \\ Shear Coefficient & 0.14 & \\ Turbine hub height: & 50 & m \\ \hline \multicolumn{3}{c}{**Lead-Acid BESS**[3]} \\ \hline Investment cost & 300 & US \$/kWh \\ Replacement costs & 300 & US \$/kWh \\ Self-discharge rate & 0.01\% & \\ Efficiency & 85\% & \\ lifetime & 5 & years \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for the proposed system.