esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.3 Roaming Services  
Roaming services have become a critical requirement in deploying mobile communication services. If a roaming agreement is signed between the home country and the mobile operators of a foreign country, mobile users can enjoy mobile services while visiting that country. Based on roaming agreements, a visitor's use of services and usage records are submitted to a clearing house--that is, an intermediary between the operators. In this process, the clearing house applies processing and settlement fees, resulting in higher costs for the end users. Figure 3 illustrates international roaming between two mobile operators in existing mobile networks.  
Decentralized interoperable blockchain platforms can optimize roaming services through providing transparent, intermediary-free, and real-time settlement mechanisms between telecom operators. A candidate blockchain solution can use smart contracts as preset roaming exchange rules and automatically apply the process if a roaming service is triggered in the visiting country. Finally, it calculates the roaming fees without an intermediary, such as a roaming clearing house. [PERSON] et al. [29] discussed roaming as an opportunity in 5G, which can be addressed using blockchain. They discussed international and national roaming, in which blockchain can improve the quality and experience of roaming services. According to [PERSON] et al., blockchains help to reduce costs and fraud,  
Figure 2: Sixth-generation (6G) space–air–ground integrated network (SAGIN) architecture.  
improve billing accuracy, and enhance the overall user experience of international and national customers. The Global System for Mobile Communications Association (GSMA) [30] has already developed the \"GSMA eBusiness Network\" [31] using a private, permissioned, industry-wide blockchain network. This blockchain solution focuses on wholesale roaming, clearing, and settlement as the industry's first application. With the introduction of the 6G SAGIN architecture, roaming services are expected to become more complicated and subject to privacy leakage if privacy and identity requirements are not considered. According to [24], roaming fraud is expected in 6G networks due to their increased complexity and interoperability requirements.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 3 The Hybrid EMD-SARIMA Forecasting Framework
### Stage 2: SARIMA Modeling  
The SARIMA model has been used successfully in modeling and forecasting time series with seasonality [40]. If there is seasonality in the time series, the time series can be modeled as  
\[X_{t}=s_{t}+Y_{t}, \tag{5}\]  
where \(Y_{t}\) is a stationary process. The seasonal component is \(s_{t}=s_{t-h}\) where \(h\) is the length of the series and  
\[\sum_{k=1}^{h}s_{k}=0. \tag{6}\]If the series is differenced at lag \(h\), the seasonal effect can be removed by  
\[\
abla_{h}X_{t}=X_{t}-X_{t-h}=X_{t}-B^{h}X_{t}=(1-B^{h})X_{t}, \tag{7}\]  
which gives  
\[\
abla_{h}X_{t}=s_{t}+Y_{t}-s_{t-h}-Y_{t-h}=\
abla_{h}Y_{t}, \tag{8}\]  
where \(\
abla_{h}\) is the lag operator. Based on this fact, the SARIMA model can be written as  
\[\
abla_{h}X_{t}=s_{t}+Y_{t}-s_{t-h}-Y_{t-h}=\
abla_{h}Y_{t}, \tag{9}\]  
where  
\[\Phi(B^{h})=1-\Phi_{1}B^{h}-\Phi_{2}B^{2h}-\ldots-\Phi_{P}B^{Ph},\]  
and  
\[\Theta(B^{h})=1+\Theta_{1}B^{h}+\Theta_{2}B^{2h}+\ldots+\Theta_{Q}B^{Qh}.\]  
The seasonal difference is defined as the difference between a value and a value with lag that is a multiple of \(h\). For monthly data, the seasonal difference is  
\[(1-B^{12})X_{t}=X_{t}-X_{t-12}.\]  
To generalize, there are four steps for identifying a SARIMA model:  
* Test the stationarity of the time series through a unit root test and examine its power spectrum for trend and seasonality.
* Do necessary differencing. If there is seasonality and no trend, take a difference of lag \(h\); if there is both a trend and seasonality, do a seasonal difference to the time series and evaluate the trend. If a trend still exists, take the first difference.
* Examine the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the differenced time series.
* Estimate the model and examine the residuals, and compare the Akaike information criterion (AIC) and Bayesian information criterion (BIC) to identify a best model if multiple models are tried.
## 3 Results and Discussion  
In total, 124 chemical compounds were found. Peaks with a low correlation of their mass spectra were not identified, but were rendered \"unknown\" and their respective fragments were described, whereas 7 compounds were identified as isomers. The percentage participation (%) of compounds (average and standard deviation), their retention time (R.T.), retention indices (RIs), and mass fractions (m/z) (the underlined fractions are the main for each compound) are given in Table 1, whereas the representative chromatogram for each kind is presented in Figure 2.  
The percentage participation of different categories of organic compounds is shown in Figure 3, where the presence of hydrocarbons, aldehydes, and alcohols is highlighted. Indeed, the group of hydrocarbons stood out (40.3%), followed by ketones, alcohols, and aldehydes with 15.3%, 14.5%, and 12.9%, respectively. To a lesser extent, esters (2.4%) and sulfur compounds (1.7%) were found, whereas a small percentage of organic compounds was not identified, named as \"unknown\" (5.6%). Another group, which accounted for 7.3%, included volatile compounds such as heterocyclics, which were rarely detectable in honey samples, without justifying their grouping into a separate class of compounds.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Dynamic Detection Module
#### 3.3.2 Dynamic Region Detection  
The dense pyramidal optical flow method is applied to detect dynamic regions in images. By building a dense two-dimensional optical flow field on the image, richer motion information in the image is obtained. In motion scenes, if the speed of dynamic objects is too fast, the difference between the two images is more obvious, and the single-layer optical flow can easily reach the local optimum and cannot be tracked correctly. The same image is scaled by image pyramids to obtain images at different resolutions, as shown in Figure 4. When calculating the optical flow, the result of the optical flow calculated in the previous layer is used as the initial value of the optical flow in the next layer. By calculating the optical flow in this coarse-to-fine way, the effect of object motion uncertainty on the optical flow can be effectively solved, and the results are shown in Figure 4(b). After obtaining the optical flow field, the Munsell color system [34] optical flow is used to interpolate Figure 4(b), and the results are shown in Figure 4(c). To determine the dynamic regions in the image, a dynamic optical flow threshold \(\theta\) is defined. The region where the optical flow amplitude is higher than \(\theta\) is considered as a moving region. On the contrary, the optical flow amplitude in the static region is lower than \(\theta\). The optical flow fields are binarized using \(\theta\), as shown in Figure 4(d). The optical flow amplitude is defined according to Formula (1).  
\[f=\sqrt{f_{u}^{2}+f_{v}^{2}} \tag{1}\]  
where \(f\) represents the optical flow amplitude, \(f_{u}\) represents the horizontal optical flow value, and \(f_{v}\) represents the vertical optical flow value.  
Figure 3: Dynamic feature points elimination.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 3 Detection Model
### Statistical Detection Model
The threshold factor is used to adjust the detection rate; \(\Delta E\) denotes the fluctuation of abnormal flow similarity and benign flow similarity. That is the possibility of being attacked. The \(\Delta E\) between \(P\) and \(U\) is given by:  
\[\Delta E=E_{\text{incoming}}-\overline{E}_{\text{benign}} \tag{9}\]  
\[P=(p_{1},p_{2},\ldots,p_{n})(0\leq n\leq K) \tag{1}\]0  
In Equation (10), \(\text{e}_{\text{Benign}}^{2}\) is the similarity variance of the benign traffic. \(\delta\text{e}_{\text{Benign}}^{2}\) denotes benign traffic fluctuations. When the difference between benign traffic and incoming traffic is higher than the threshold, the incoming traffic is abnormal. We discuss the similarity threshold \(\delta\) in Section 5.1. The MFPK precoder algorithm is given in Algorithm 1. ```
1:Input: Flow \(X\), Flow \(Y\), \(M\), \(L\), \(K\);
2:Output: True: abnormal, False: benign;
3:for\(m=0\); \(m<M\); \(m++\)do
4:for\(n=0\); \(n\leq k\); \(n++\)do
5:\(x[a_{m}^{n}]\leftarrow\text{Count}(a_{m}^{n})\);
6:\(P_{n}\gets x[a_{m}^{n}]/M\);
7:endfor
8:endfor
9:for\(l=0\); \(l<L\); \(l++\)do
10:for\(n=0\); \(n\leq k\); \(n++\)do
11:\(y[b_{l}^{n}]\leftarrow\text{Count}(b_{l}^{n})\);
12:\(U_{n}\gets y[b_{l}^{n}]/L\);
13:endfor
14:endfor
15:\(S_{\text{incoming}}\); \(E_{\text{incoming}}\leftarrow\text{CalculateSimilarity}(p_{n},u_{n})\)
16:if \(E_{\text{incoming}}-\overline{E}_{\text{benign}}>\delta\text{e}_{\text{ Benign}}^{2}\)then
17:return True;
18:endif
19:return False;
```  
**Algorithm 1** MFPK Precoder Algorithm.
# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
###### Abstract  
The research domain of device-free localization (DFL) is centered on the study of localization techniques which do not require targets to wear any kind of device. Passive radio mapping or passive fingerprinting is an example of a training-based DFL technique which uses the impact of a human target on radio frequency (RF) communication between stationary nodes to perform localization. We describe a set of experiments performed in a 42 m\({}^{2}\) empty office environment in which we installed a RF network with nodes communicating on the 433 MHz and 868 MHz bands. We attempted to locate a single stationary human target based solely on signal strength measurements and did so for six different participants using two different fingerprinting methods. One method was based on Euclidean distance minimization while the other made use of a naive Bayesian classifier. We investigated the impact of frequency band, number of nodes and target body type on localization accuracy. Results indicated that a root mean square error of 48 cm could be obtained with only four nodes, provided that the data from both frequency bands was combined. Additionally, we investigated the potential of these fingerprinting approaches to distinguish between targets based on body type and perform a rudimentary form of passive identification. Accuracy rates for identification could vary significantly depending on target location, with results ranging from 0.07 to 0.75 in the exact same environment. However, the experiment participant with the lowest height and weight could be distinguished from the other participants in over 90% of cases.  
device-free; tagless; radio frequency; RF; passive sensing; sensorless sensing; fingerprinting; passive identification; passive localization
# The Extreme Function Theory for Damage Detection: An Application to Civil and Aerospace Structures
## 4 Experimental Validation
It is necessary to remark that the specific experimental setup (single-input multi-output acquisitions with a random noise as input) only allows linear system identification [45]. The experimental identification of nonlinear normal modes would require more complex procedures which are beyond the objective of this study. Therefore, the extracted mode shapes should be considered as the ones of the underlying linear system for these eight nonlinear states [45]. Figure 12: The frequency shifts induced in the averaged frequency response function (FRF). (**a**) Due to the mass added at the 1 st floor (State 3); (**b**) due to the stiffness reduction inserted in two columns of the 1 st inter-storey (State 5) (**c**) due to the column-bumper with 0.20 mm gap (State 10, weakly nonlinear system); (**d**) due to the column-bumper with 0.05 mm gap (State 13, strongly nonlinear system). The Data Augmentation procedure described in Section 2 was applied to increase the number of training and validation data. For illustrative purposes, Figure 13 shows the resulting model obtained by applying the GP Regression over the three mode shapes identified from the experimental data. The results of the experimental validation are shown in Figure 14. As for all the previous examples, no damage was to be identified in the first state, therefore there are no false alarms nor successful damage identifications. The EFT-based procedure correctly identifies all the structural changes except for the configurations with the bumper-column gap larger than 10 mm and no added mass (i.e., states 10 to 12). However, as it can be seen from Figure 12c, the nonlinear distortions are minimal for this larger gap and the response of the structure is almost indistinguishable from the pristine baseline. The algorithm struggled to recognise the last two states (16 and 17) as well when fed with the first mode shape; this issue did not arise either with the second or the third eigenmode. On the other hand, due to the larger confidence intervals (Figure 13c), the fitting of the third mode shape returned a relatively larger number of false positives (almost constantly 17% for any damage scenario). Figure 13: An example of Gaussian Process (GP) Regression over the experimental data for the first (**a**), second (**b**), and third (**c**) mode shapes.
## 3 The Proposed Method
### AMED Prediction and Recoding
#### 3.1.3 Recoding  
Initially, 3 bits are allocated to record the chunk size \(B\). In the experiments outlined in this paper, \(B\) takes on values of 512, 256, 128, 64, and 32, which will be thoroughly elaborated on in the experimental section. Following this, \(32\times BN\times BM\) bits are utilized to document the adaptive parameter _ARP_ for all pixel blocks within the image, 32 bits are utilized to to record the Huffman coding rule (HCR). We convert \(W\) into a binary sequence _WS_, allocating an additional 22 bits to record the length of _WS_ as \(WL\). Finally, we concatenate \(WL\), \(B\), _ARP_, HCR, and _WS_ in sequence with the pixel values of the first row and the first column of the original image to generate an edge information object identified as \(O\).  
Moving forward, we now proceed to integrate the edge information. Initially, we replace the pixel values of the first row and first column of the encrypted image with the first \(8(M\times N-1)\) bits of the edge information \(O\). Subsequently, leveraging the pixel label as a reference, we embed the remaining edge information into the encrypted image through bit substitution. This embedding takes place within the highest \((w+1)\) MSB of the unreplaced pixels in the encrypted image. The embedding formula is detailed as follows:  
\[{x_{e}}^{\prime}(i,j)=\left\{\begin{array}{l}x_{e}(i,j)\bmod 2^{7-w}+\sum \limits_{l=0}^{w}(s_{l}\times 2^{7-l}),0\leq w\leq 6\\ \sum\limits_{l=1}^{8}(s_{l}\times 2^{8-l})\end{array}\right.,7\leq w\leq 8 \tag{9}\]  
The pixel value post-information embedding is represented as \(x_{e}^{\prime}(i,j)\), where \(s_{l}\) denotes the auxiliary information that can be embedded into the current pixel, and \(l\) signifies the load capacity of a single pixel. Following the embedding of all edge information, random noise is generated and embedded into the remaining positions post-edge information, as described in Equation (9), resulting in the transitional image denoted as _IR_.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 3 Multiple Relay UAVs Selection Scheme Based on Fuzzy Optimum Selection
### UAV Control Command Optimization Based on PSO with Elite Mechanism  
In a basic particle swarm optimization system, a population of individuals is randomly initialized in the search space. Each individual is considered as a particle, and each particle is a potential solution. The position and velocity of the \(i\)th particle can be respectively described as \(\mathbf{X}_{i}=(x_{i,1},x_{i,2},\ldots,x_{i,d})\) and \(\mathbf{V}_{i}=(v_{i,1},v_{i,2},\ldots,v_{i,d})\) in the \(d\)-dimensional space. The personal best position \(\mathbf{P}_{i}=(p_{i,1},p_{i,2},\ldots,p_{i,d})\) represents the best fitness value obtained so far, namely pbest, and the global particle gbest is denoted by \(\mathbf{P}_{g}\), which represents the best particle found so far [29]. In each iteration cycle, the velocity and position of particle can be updated as follow:  
\[\begin{array}{l}\mathbf{v}_{i}^{k+1}=\omega\cdot\mathbf{v}_{id}^{k}+c_{1}\tau_{1}( \mathbf{p}_{i}-\mathbf{z}_{i}^{k})+c_{2}\tau_{2}(\mathbf{p}_{g}-\mathbf{z}_{i}^{k})\\ \mathbf{z}_{i}^{k+1}=\mathbf{z}_{i}^{k}+\mathbf{v}_{i}^{k+1}\end{array} \tag{31}\]  
where \(\tau_{1},\tau_{2}\) are random parameters ranging from \([0,1]\), and \(c_{1},c_{2}\) are acceleration coefficients. Besides, \(\omega\) is an inertia factor.  
In this work, inspired by [25], elite mechanism is employed to improve the effectiveness of PSO. It is obvious that some high ranking particles will produce more diversity particles than just one single particle, which is useful to improve optimization performance. Correspondingly, the elite mechanism consists of the elite group of each individual and elite group of the global best particle, which increases the diversity of particles and effectively overcomes the problem of premature convergence associated with conventional PSO. Moreover, it is noted that the introduced elite mechanism should not increase the complexity in relevant calculation process. Accordingly, the modified velocity can be expressed as follow:  
\[\mathbf{v}_{i}^{k+1}=\omega\cdot\mathbf{v}_{id}^{k}+\sum_{m=1}^{E_{p}}c_{1,m}r_{1}( \textit{Pbest}_{i,m}^{k}-\mathbf{z}_{i}^{k})+\sum_{n=1}^{E_{g}}c_{2,m}r_{2}(\textit {Gbest}_{n}^{k}-\mathbf{z}_{i}^{k}) \tag{32}\]  
where \(E_{p},E_{g}\) are the numbers of personal elite group and global elite group respectively. \(\textit{Pbest}_{i}\) is the elite group set of \(i\)th particle so far, and \(\textit{Gbest}\) is the elite group set of the global swarm.  
In multi UAV cooperative surveillance, each UAV can optimize its control command sequence just according local information, namely the motion plan of each UAV can be implemented in parallel. Therefore, each particle represents one possible control sequence of UAV, and the corresponding UAV motion plan optimization can be solved by PSO with elite mechanism.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 6 Hydraulic Fracturing in Croatia  
The first treatment of hydraulic fracturing in the former Yugoslavia was carried out in Slovenia in 1956 in the Pt-58 well (Lendava oil field) by using 30 m\({}^{3}\) of oil-based gel and 5000 kg of proppant (quartz sand), but without success. At the same year, eight more wells were fractured at Lendava field and oil production was increased up to 250% [73]. The first successful hydraulic fracturing in Croatia was carried out in 1958 in the Klo-14 well (Klostar oil field) by using 32 m\({}^{3}\) of oil-based gel and 5500 kg of proppant [3]. From that year until the 1980s, all the performed fracturing treatments were considered to be unsuccessful because they were made with insufficient fluid volume and low proppant concentration, which resulted in very small fractures.  
The year 1985 could be considered as a turning point, when the gas condensate Kal-5 alfa well was fractured and, despite the extremely difficult reservoir conditions, an excellent result was obtained. HF in this well has been followed by the implementation of many successful fracturing treatments. After that, hydraulic fracturing became a standard process for increasing well productivity [6]. Since 1958, approximately 200 operations of hydraulic fracturing have been performed in Croatia.  
The high formation temperature of 179 \({}^{\circ}\)C posed problems for most fracturing fluids [74]. On the Stari Gradac-2 well, in 1989, the Croatian company INA and Schlumberger injected 2569 m\({}^{3}\) of gel and 530,000 kg of proppant. It was the biggest hydraulic fracturing job in Europe at that time [6]. In order to complete many other fracturing treatments, equipment for fracturing was improved and the monitoring of production data, before and after fracturing, began. In Croatia, up to ten (10) HF operations are performed annually, primarily in sandstone formations.  
Figure 1: Example of the composition of water-based fracturing fluid used in Croatia [3].
# Facile Method by Bentonite Treated with Heat and Acid to Enhance Pesticide Adsorption
## 3 Results and Discussion
### Adsorption Isotherms
The values of \(\Delta\)H\({}^{\circ}\) and \(\Delta\)S\({}^{\circ}\) can be calculated from the slope and intercept of the linear variation of \(\ln\) K\({}_{L}\) with reciprocal of temperature (1/T) as in the following equation:  
\[\mathrm{lnK_{L}}=-\frac{\Delta\mathrm{H}^{\circ}}{\mathrm{RT}}+\frac{\Delta \mathrm{S}^{\circ}}{\mathrm{R}} \tag{5}\]  
The thermodynamic parameters of the system: Gibbs free energy change (\(\Delta\)G\({}^{\circ}\)), enthalpy change (\(\Delta\)H\({}^{\circ}\)), and entropy change (\(\Delta\)S\({}^{\circ}\)), are shown in the supplementary information in Tables S2-S5; the free energy values at temperatures 303 K, 313 K and 323 K in the adsorption of pesticides are negative in all cases, demonstrating the spontaneous nature of the process. \(\Delta\)H\({}^{\circ}\) values are positive for all of the pesticides, showing the endothermic nature of the adsorption process. \(\Delta\)S\({}^{\circ}\) values are also reported; positive values suggest that the organisation of the adsorbate in the solid-solution interface becomes more random. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{4}{c}{**Langmuir Isotherm**} \\ \cline{3-5}  & & **q\({}_{m}\) (mg g\({}^{-1}\))** & **K\({}_{L}\) (M\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline Bentonite & 303 & 5.12 & 0.83 & 0.9580 \\  & 313 & 5.10 & 1.06 & 0.9920 \\  & 323 & 5.45 & 1.30 & 0.9608 \\ \hline BC\({}_{500}\) & 303 & 10.34 & 11.85 & 0.8725 \\  & 313 & 11.27 & 13.87 & 0.8531 \\  & 323 & 11.95 & 14.86 & 0.8935 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parameters for adsorption of diuron on bentonite and BC\({}_{500}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{4}{c}{**Langmuir Isotherm**} \\ \cline{3-5}  & & **q\({}_{m}\) (mg g\({}^{-1}\))** & **K\({}_{L}\) (M\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline Bentonite & 303 & 94.34 & 480.83 & 0.9966 \\  & 313 & 92.59 & 563.90 & 0.9970 \\  & 323 & 87.72 & 4023.82 & 0.9697 \\ \hline BC\({}_{500}\) & 303 & 92.59 & 328.58 & 0.9959 \\  & 313 & 84.03 & 1446.36 & 0.9671 \\  & 323 & 84.03 & 1446.36 & 0.9671 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Parameters for adsorption of paraquat on bentonite and BC\({}_{500}\).