esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 3 Results
### Antarctica Data

Table 21: SSIM and CC between the maps shown in Figure 17.  
Figure 18: Consecutive 5 min means over 20 min at SNAOP station on 13 October 2016. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**13 October 2016** & \multicolumn{2}{c}{**18:50:00**} & \multicolumn{2}{c}{**18:550:00**} & \multicolumn{2}{c}{**19:00:00**} & \multicolumn{2}{c}{**19:05:00**} \\ \hline
**Criteria** & **SSIM** & \begin{tabular}{c} **CC** \\ **(p-Value)** \\ \end{tabular} & **SSIM** & \begin{tabular}{c} **CC** \\ **(p-Value)** \\ \end{tabular} & **SSIM** & \
\begin{tabular}{c} **CC** \\ **(p-Value)** \\ \end{tabular} & **SSIM** &
\begin{tabular}{c} **CC** \\ **(p-Value)** \\ \end{tabular} \\ \hline MP vs. \(\sigma_{\varphi}\) & 0.07 & \(-\)0.00 (0.99) & 0.36 & 0.32 (0.48) & 0.14 & 0.08 (0.88) & 0.31 & 0.25 (0.58) \\ \hline ROTI vs. \(\sigma_{\varphi}\) & 0.52 & 0.47 (0.29) & 0.80 & 0.79 (0.03) & 0.30 & 0.79 (0.06) & 0.38 & 0.31 (0.50) \\ \hline \hline \end{tabular}
\end{table} 
Table 22: SSIM and CC between the maps shown in Figure 18.  
uncertainty in the estimation of CC and SSIM. As such we can only make qualitative observations on these plots and cannot back those observations up with reliable CC or SSIM results.
# A BIM-Based Value for Money Assessment in Public-Private Partnership: An Overall Review
## 4 A Statistical Review of BIM Development
### BIM and VFM
Since this stage occurs close to the procurement, rich data should provide the required scope to commence VfM. The information exchange from the BIM environment could support parties to source relevant data for the employers' information requirement (EIR) and provide the initial design or existing models for NPV. A 5D representation, which includes the digital design, should have sufficiently detailed asset information. The current cost-related tools align with BIM and, thus, could provide favourable measurements to make it possible to structure value more holistically. At this point, the quantitative assessment will not rely on historical data, because BIM provides a real-time information flow [8]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Stages** & **VfM Assessment Contents** & **BIM Functions** & **Tools/Carriers** \\ \hline
**Screen** & VfM methodology & Information formatting & dPOW; OIR(Organization information requirement) \\ \hline
**Structure and Appraise** & **VfM qualitative** & Compliance checking; Semantic BIM approach; (Whole project lifecycle integration; Operation flexibility & Projetwise InfraWorks 360 \\  & Risk management; Contract and assets Duration; Incentives and Monitoring; Market interest; Efficient procurement & Projetwise InfraWorks 360 \\ \hline
**VfM Quantitative assessment (PSC)** & Cost analysis; Quantities take-off Facility management costs, Construction costs, Operation costs, Transportation costs, Resource costs, Risk-related costs, and Other costs & Five Dimension BIM tools; PEP Solibri; CostX®; The semantic approach aligns with BIM costs, and Other costs & Five Dimension BIM tools; PEP Solibri; CostX®; The semantic approach aligns with BIM \\ \hline \hline \end{tabular}
\end{table} 
Table 7: VfM indicators and corresponding BIM function resources could potentially maximise the benefits along the entire project lifecycle. PEP: project execution plan, dPOW: a digital plan of work, and PSC: public-sector comparator.
# UAV Based Relay for Wireless Sensor Networks in 5G Systems
## 4 Simulation  
In this section, we explore the performance of the LU algorithm in UAV based relay in WSN in the 5G scenario with the corresponding parameters in Table 1. Unless otherwise specified, the parameters are defined in Table 1. In this paper, we compare the performance of LU with the traditional wireless transmission without relay; i.e., direct transmission. The simulation is based on Matlab 2013b.  
In Figure 3, we show the relationship between sight distance \(T\) and the system power consumption \(P_{\text{max}}\). As shown in Figure 3, the LU algorithm can largely cut down the power consumption due to the relay via UAV. As \(T\) increases, the gap between the two algorithms enlarges, which suggests that the effectiveness of the UAV based relay in WSN.  
In Figure 4, we study the impact of \(C\) on system power consumption. Obviously, as \(C\) increases, the power consumption increases due to the Shannon capacity. The performance of LU still outperforms the direct transmission method.  
In Figure 5, we study the impact of \(B_{U}\) on the system power consumption. Since direct transmission method does not have a UAV, the power consumption remains. As shown in Figure 5, LU algorithm can largely cut down the power consumption as \(B_{U}\) increases, because the increased wireless bandwidth of UAV can decrease the demanded power for transmission in WSN based on Shannon capacity.  
In Figure 6, we define \(M=\frac{x}{T}\) as the proportion of UAV position to \(T\). As Figure 6 shows, as \(B_{U}\) increases, the UAV will move towards the concentrator. This can cut down the power of the concentrator, meanwhile the increase of UAV power can be controlled due to the increased wireless bandwidth of UAV.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(B_{S}\) & 50 MHz \\ \(B_{U}\) & 50 MHz \\ \(T\) & 200 m \\ \(C\) & 500 Mbps \\ Noise power spectral density & \(-174\) dBm/Hz \\ The omnidirectional path model & \(86.6+24.5\,\log_{10}\,d\) \\ Link & Uplink \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameter for Location of UAV (LU) algorithm.  
Figure 4: The impact of \(C\) on system power consumption (the total power consumed in system under different configurations of wireless transmission capacity (bps)).  
Figure 3: The impact of \(T\) on system power consumption (the amount of total power needed in system under different configurations of sight distance).
## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Model 1** & **Model 2** & **Model 3** \\ \hline MLR & 7.33 & 7.5 \\ ANN & 6.86 & 6.77 \\ SVR & 7.22 & 7.01 \\ RF & 7.52 & 7.16 \\ ADB & 7.64 & 7.43 \\ XGB & 6.9 & 7.28 \\ GBM & 6.85 & 7.27 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline
**\# of Clusters** & **MLR** & **ANN** & **SVR** & **RF** & **ADB** & **GBM** & **XGB** \\ \hline
2 & 7.5 & 6.77 & 7.01 & 7.68 & 7.64 & 7.27 & 7.28 \\
3 & 8.06 & 6.94 & 7.68 & 7.16 & 7.43 & 7.27 & 7.42 \\
4 & 10.25 & 6.95 & 7.84 & 7.71 & 7.53 & 7.37 & 7.52 \\
5 & 10.85 & 7.03 & 8.09 & 7.93 & 7.58 & 7.49 & 7.66 \\
6 & 14.15 & 7.21 & 8.14 & 8.04 & 7.74 & 7.65 & 7.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Resulting MAPE of Model 1, Model 2, and Model 3 for various machine learning techniques. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**\# of Clusters** & **MLR** & **ANN** & **SVR** & **RF** & **ADB** & **GBM** & **XGB** \\ \hline
2 & 7.5 & 6.77 & 7.01 & 7.68 & 7.64 & 7.27 & 7.28 \\
3 & 8.06 & 6.94 & 7.68 & 7.16 & 7.43 & 7.27 & 7.42 \\
4 & 10.25 & 6.95 & 7.84 & 7.71 & 7.53 & 7.37 & 7.52 \\
5 & 10.85 & 7.03 & 8.09 & 7.93 & 7.58 & 7.49 As can be seen from the third column in Table 2, the effect of overfitting successfully reduced by using the SELM, which enables us to select a group of models that more effectively complement each other's weaknesses when combined. In the experiments, MLR, the GBM, and the ANN were selected as the best group that produced the best performance, with the resulting MAPE being lower than that for all other cases. Considering that we performed building scale load forecasting, which exhibits more fluctuation and is noisier compared to larger-scale systems, and that we even used a small-size dataset (covering less than two years) with the assumption that smart metering equipment such as AMI was only recently installed in the building resulting in a lack of data, our results are fairly good given the conditions imposed. As presented in Table 4, the ANN and the GBM were always included in the best group that performed the best, even though the GBM failed to outperform the other techniques alone. This means that these two techniques complement each other's weaknesses well, resulting in a more effective forecasting model. Therefore, when applying the SELM, it is reasonable to figure out which predictions of certain techniques are not highly correlated and select the least correlated ones. On the other hand, as shown in Figure 7, performance tends to decrease as the number of clusters increases. This occurs because, as the number of clusters increases, the number of data samples in each cluster decreases, which negatively affects model performance. This limitation can be overcome if a larger amount of data is available. Consequently, in this study, the number of clusters that produces the best forecasting performance is two. Examples of load forecasts for a certain week produced by the SELM model are presented in Figure 8.  
Figure 6: MAPE over time for the ANN, the GBM, and ADB.
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 5 Experimental Results
### Speed/Acceleration Prediction
#### 5.1.2 Performance with Different Models  
First, to verify that the LSTM in TA-LSTM is the preferred encoding layer, we compared LSTM and other RNN variants on four road segments of the dataset. RNN and GRU are commonly used for time series forecasting, and all three models follow the five-fold CV approach introduced in the previous section to identify the best combination of hyperparameters. All three models obtained the minimum average RMSE at \(\{T=5,m=32\}\). The first seven driving cycles of each road segment were used as the training set and the remaining four driving cycles were used as the test set, and each road segment was trained and tested separately. In addition, 20% of the data in the training set were randomly selected as the validation set during the training process, and the best-performing model in the validation set was saved for testing. The best test results for each model after training multiple times are shown in Table 4, as can be seen in Table 4, LSTM performs the best in all four road sections. Therefore, we choose LSTM as the coding layer in this task.  
Then, to verify the effectiveness of temporal attention, we compared the LSTM with TA-LSTM. The training process and the allocation of training and test sets are the same as in the previous experiment. The results of the comparison are depicted in Figure 6. The comparison shows that the addition of temporal attention yields better results.  
Since temporal attention can be combined with various variants of RNN, we compared the performance of different encoding layers combined with temporal attention. From Tables 4 and 5, it can be seen that adding temporal attention to all three different encoding layers can improve the prediction accuracy. TA-LSTM has the best performance. Therefore, we chose TA-LSTM for the task of predicting speed. The comparison between the predicted and true values of each driving cycle for each road section is shown in Figures 7-10. As can be observed from these figures, our model can fit the real data very well in most cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **AtoC** & **CtoD** & **DtoE** & **EtoF** \\ \hline RNN & 0.4485 & 0.3702 & 0.4549 & 0.4924 \\ GRU & 0.4369 & 0.3689 & 0.4572 & 0.4873 \\ \hline LSTM & 0.4240 & 0.3556 & 0.4524 & 0.4864 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE results of different models.  
Figure 6: The effect of temporal attention on RMSE results.
# Research on Blockchain Data Availability and Storage Scalability
## 2 Related Work
### Data Availability in P2P Networks  
Improving the scalability of blockchain storage while also considering data availability requires research on data availability. Within the currently collected literature, there has been no research measuring the data availability in blockchain. Blockchain is a distributed database that relies on P2P networks. This section discusses data availability in P2P networks and identifies research ideas for blockchain data availability.  
The definition of data availability in P2P networks can be divided into three aspects:  
1. The ratio of the online time of a node or system to the total time [14; 15].
2. A combination of time-varying functions that reflect the activity characteristics of nodes [16]. Since data is stored on nodes, and the opening and closing of nodes change over time, the activity characteristics of nodes can be represented by a series of functions that change over time.
3. The probability of data being accessed within a specified time limit [17; 18; 19; 20].  
The diverse application scenarios of P2P networks result in different application scenarios for the three definitions. Definition 1 is applicable to non-real-time scenarios and is suitable for measuring the performance of nodes or systems at coarse granularity, such as when examining the online status of nodes or systems during a certain period of time. Definition 2 is applicable to real-time scenarios, such as real-time video streaming transmission, wherein the availability of nodes is related to factors such as bandwidth, delay, and packet loss rate. These factors can be expressed as bandwidth functions, delay functions, and packet loss rate functions over time. The availability of nodes is expressed as a combination of these time-varying functions, but the complexity of time-varying functions is high. Definition 3, which describes data availability as the probability that data can be obtained, applies to data storage in P2P networks and is applicable to most application scenarios in P2P networks.  
Blockchain relies on P2P networks, and in order to ensure the characteristics of decentralization and transparency, consensus mechanisms are introduced, with higher requirements for data redundancy. Therefore, it is possible to learn from the research methods used to measure data availability in P2P networks, integrate the characteristics of blockchain, and propose a definition and measurement model for blockchain data availability.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 2 Related Work
To build a V-Cell, sophisticated network metrics, as well as additional factors such as signal intensity, distance, and speed, are used to choose the optimal cells. Based on Q-learning frameworks and subtractive clustering approaches, article [26] offers an intelligent handover triggering system for UE. Subtractive clustering is used to transform the input measurements to state vectors, which can increase the training process's efficiency and efficacy. Following that, the Q-learning framework learns from the environment the best handover triggering policy. To start the handover procedure, the trained Q table is deployed to UE. The authors of [27] paper has discussed about the mobility management in 5G Networks. In 5G technology, the use of millimeter waves [28; 29] is a major factor affecting mobility. This is due to high path loss when millimeter wave frequency bands are used, which reduces cell coverage. This greatly increases the probability of transfer and increases mobility issues such as high transfer failures, transfer ping-pong effects, and wireless link failures. In addition, new types of mobile connectivity systems are expected to be implemented in future networks. The implementation of these systems also contributes to increasing mobility problems. These issues will lead to a future increase in mobile data traffic. This rapid growth also contributes to the high probability of transfer rates.
# Anti-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks
## 4 Proposed Solution
### The Proposed ALL Protocol
To guarantee rendezvous with other SUs, a period consists of \(NR_{a}-1\) channel hopping cycles (line 1). A repetitive pattern of frames, \(TTRR\), makes up the first column (line 2). Each SU \(a\) takes the last \((\lfloor\log_{n_{e}+1}N_{u}\rfloor+1)\) bits of its MAC address as \(ID_{a}\) (line 3). The unfixed \(R\) frame symbol for each column are selected according to the positions of all fixed \(R\) frame symbols of the receivers (line 4-10), while the channels are assigned for all \(T\) and \(R\) frames (line 11). The channel hopping sequence is generated by picking elements of the channel hopping matrix in row-major order (line 12). ```
0:\(n_{e},N_{u},\theta\)
0:\(CHS_{a}\)
1:for\(k\in[1,|NR_{a}-1|]\)do
2: Assign frames by the repeated \(TTRR\) pattern for the first column of the hopping matrix;
3: Let the last \((\lfloor\log_{(n+1)}N_{u}\rfloor+1)\) bits of MAC address be \(ID_{a}\);
4:for\(m\in[1,|ID_{a}|]\)do
5:for\(n\in[1,|R_{a}|]\)do
6: Find \(R_{a}^{n},a\) and \(\delta\) of \(SU_{a}\). 7:endfor;
8: Assign the \(NR_{a}-1\) (mod \((n_{e}+1)\)) symbols next to symbol \(w\) as \(uFR_{a}^{m}\) where \(w=FR_{a}^{m}+k\times(NR_{a}-1)\) (mod \((n_{e}+1)\). 9: Set the symbols not being assigned to \(FR_{a}^{m}\) or \(uFR_{a}^{m}\) as \(T\) frame symbols. 10:endfor;
11: Apply OLAA_T to allocate channels for \(T\) and \(R\) frames;
12: Each node hops by row order of the channel hopping matrix;
13:endfor;
```  
**Algorithm 1** ALL  
Note that a period for SU \(a\) is equal to \((NR_{a}-1)\times L_{c}\times(|ID_{a}|+1)\) time slots, where \(L_{c}=k_{a}\times 4N_{c}\). An example is utilized below to illustrate the operation of the ALL protocol. The following assumptions are made in this example: Assume that the sender, SU \(a\), and the receiver, SU \(b\), are load balanced with \(C=C_{a}=C_{b}=1,2\). SUs \(a\) and \(b\) with \(ID_{a}=(01)_{4}\) and \(ID_{b}=(22)_{4}\) use the ELP sequence (0, 0, 3, 1, 2, 1, 3, 2) to build their channel hopping matrixes while \(e_{a}^{1}=(0,0,3,1,2,1,3,2),e_{a}^{2}=(2,0,0,3,1,2,1,3),e_{b}^{1}=e_{b}^{2}=( 3,2,0,0,3,1,2,1)\). SU \(a\) joins the network one slot earlier than the receiver SU \(b\). Since SUs \(a\) and \(b\) are load balanced, we have \(NT_{a}=NR_{a}=NT_{b}=NR_{b}=2\). Note that \(FR_{b}^{1}=FR_{b}^{2}=\{2\}\), which means that the fixed \(R\) frames of the second and third columns of \(M_{b}\) are frames 1 and 6, as shown in Figure 2(a). When \(\theta=1\), the \(M_{a}^{3}\) and \(M_{b}^{2}\) have a rendezvous and thus SU \(a\) try not to assign an unfixed \(R\) frame in \(M_{a}^{3}\) in frames 1 and 6 in each sub-column. Assume that SU \(a\) assigns frames 0 and 5 as unfixed \(R\) frames, as shown in Figure 2(b). Because \(M_{a}^{2}\) and \(M_{b}^{1}\) have a rendezvous and \(M_{b}^{1}\) is built from the repeated (TTRR) pattern, SU \(a\) will assign the consecutive \(NR_{a}-1(=1)\) symbol next to \(FR_{a}^{2}(=\{0\})\) as \(uFR_{a}^{2}=\{1\}\), as shown in Figure 3c. The complete \(M_{a}\) is shown in Figure 3d.
## 3 Performance Analysis  
### Preliminary Results  
#### 3.1.1 Terrestrial Channel Model  
In this paper, we suppose that all of the terrestrial links undergo independent and identically distribution (i.i.d) Rayleigh fading.  
Next, according to [21], the PDF for \(\gamma_{RP}\) and the CDF of \(\gamma_{RD}\) are given, respectively, by:  
\[f_{\gamma_{RP}}\left(x\right)=\sum_{i=1}^{\rho\left(\mathrm{A}_{ RP}\right)}\sum_{j=1}^{\tau_{i}\left(\mathrm{A}_{RP}\right)}\chi_{i,j}\left( \mathrm{A}_{RP}\right)\frac{\mu_{\left\langle i\right\rangle}^{-j}}{(j-1)!}x^ {j-1}e^{-x/\mu_{\left\langle i\right\rangle}}, \tag{12}\]  
\[F_{\gamma_{RD}}\left(x\right)=1-e^{-\frac{x}{\gamma_{RD}}}, \tag{13}\]  
where \(\mathrm{A}_{RP}=\mathrm{diag}\left(\mu_{1},\mu_{1},\ldots,\mu_{M}\right)\), \(\rho\left(\mathrm{A}_{RP}\right)\) is the number of distinct diagonal elements of \(\mathrm{A}_{RP}\), \(\mu_{\left\langle 1\right\rangle}>\mu_{\left\langle 2\right\rangle}>\ldots>\mu_{ \left\langle\rho\left(\mathrm{A}_{RP}\right)\right\rangle}\) are the ascending order of \(\mu_{\left\langle i\right\rangle}\), \(\tau_{i}\left(\mathrm{A}_{RP}\right)\) is the multiplicity of \(\mu_{\left\langle i\right\rangle}\) and \(\chi_{i,j}\left(\mathrm{A}_{RP}\right)\) is the \(\left(i,j\right)\)-th characteristic coefficient of \(\mathrm{A}_{RP}\)[21].
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 4 Analysis of Numerical and Experimental Results
Note that in Equation (6), the boundary of the free surface is actually the calm waterplane; therefore, the physical instantaneous wetted surface obtained by the first step should be mapped to ensure that the clam waterplane is the boundary of the transformed instantaneous wetted surface [33]. Therefore, the second step is the mapping of the physical instantaneous wetted surface, as shown in the lower part of Figure 7, and the mapped instantaneous wetted surface will be adopted. Different from [\(\mathbf{F}_{I}\)] and [\(\mathbf{F}_{H}\)], [\(\mathbf{F}_{D}\)] and [\(\mathbf{F}_{R}\)] are actually linear and computed based on the mean wetted ship hull surface. In principle, [\(\mathbf{F}_{D}\)] and [\(\mathbf{F}_{R}\)] are also nonlinear, since [\(\mathbf{F}_{I}\)] and [\(\mathbf{F}_{H}\)] are nonlinear for the case of ship hydroelastic responses in large waves, and therefore, should also be calculated on the instantaneous wetted surface. However, the re-panelization of the deformed ship hull at each time step, as introduced in the last paragraph, requires the corresponding re-computation of Green function and the subsequent radiation and diffraction wave potentials as determined in Equation (6), thus considerably increasing the computing time, and significantly restricting the application of the presented method in shipbuilding industry. In the present research, we do not consider the nonlinearities of [\(\mathbf{F}_{D}\)] and [\(\mathbf{F}_{R}\)] associated with the change of the actual wetted ship hull geometry. Before we proceed to present the experimental and numerical results in the following sections, we briefly discuss the effect of the tank side walls. Compared with the real ship sailing in open seas, the ship model may be affected by the reflected waves from both tank side walls during the experiments. The International Towing Tank Conference (ITTC) released guidelines [34] to avoid such interference from the tank side walls in head waves. Based on the guidelines, the two regular wave conditions are free from the tank side wall effects. However, the effect of the side walls is unavoidable for irregular wave conditions because the irregular wave inevitably contains those regular wave components which will induce the side wall effect. To minimize the side wall effect, the first irregular wave condition in the present research has a rather large significant wave height, resulting in severe slamming events and subsequent wave breaking and large eddies that will greatly dissipate the energy of scattered waves. For the second irregular wave condition, only very few regular wave components may cause the side wall effect due to the relatively high vessel speed. In addition, the relatively high ship speed will also give rise to frequent slamming events that will significantly reduce the side wall effect by the energy dissipation mechanism. Therefore, the tank side wall effect in the present study is significantly reduced, and the experimental results are trustable.