esa-sceva/satcom-chunk-collection huggingface dataset:
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 4 Towards Software-Defined Heterogeneous Vehicular Networks
### Architectural Challenges
#### 4.2.1. Seamless, Ubiquitous and Undifferentiated Network Connectivity  
The prime intent of heterogeneous vehicular networking is to guarantee a seamless, ubiquitous and undifferentiated network connectivity to ensure the stringent quality-of-service (QoS) and QoE of diverse vehicular safety and non-safety applications and vehicular users, respectively. Undoubtedly, both vehicles and vehicular users should always be connected to the optimal radio access technologies, which could ensure ultra-low end-to-end delay (latency), higher bandwidth, enhanced data rates and that also in a cost-efficacious manner. However, the heterogeneity of diverse radio access technologies is an arduous task to tackle, and therefore, intelligent vertical handover decisions are required to ensure seamless mobility. In a heterogeneous vehicular networking environment, vehicles either traverse along the geographical regions of overlapping radio access technologies or different geographical regions with distinct radio access technologies. Moreover, owing to the highly dynamic behaviour of vehicles, they are expected to perform frequent handovers. Nevertheless, too frequent handovers result in the wastage of precious network resources and need to be mitigated. Furthermore, for any vertical handover to transpire successfully, three vertical handover decision mechanisms should be executed with a minimal possible delay: (a) _handover necessity estimation_--determining whether a particular handover is essential to an available network (or networks); (b) _handover target selection_--opting for the best network out of the available networks; and (c) _handover-triggering condition estimation_--determining the precise time instance to trigger handover to the selected network.  
Since the centralized SDN controller in the heterogeneous network possesses a bird's eye view of the entire underlying architecture, it could help to meet the seamless, ubiquitous and undifferentiated network connectivity. This is illustrated in Figure 4, wherein the abstraction of physical radio resources of diverse radio access technologies in terms of their _bandwidth, time_ and _location_ is suggested.  
Figure 5: The logical architecture of SDHVNet.  
This abstraction refers to the bandwidth resources that are available for a vehicular application at a particular time and location via each of the radio access technologies in the current and anticipated trajectory of the vehicles. Since the SDN controller has global knowledge of the available physical radio resources of each radio access technology's BS/AP along the vehicle's anticipated travelling direction, intelligent vertical handover schemes can be accordingly employed for switching purposes.
## Abstract  
Short-term load forecasting is the guarantee for the safe, stable, and economical operation of power systems. Deep learning methods have been proven effective in obtaining accurate forecasting results. However, in recent years, the large-scale integration of distributed photovoltaic systems (DPVS) has caused changes in load curve fluctuations. Current deep learning models generally train with historical load series and load-related meteorological data series as input features, which limits the model's ability to recognize the load fluctuations caused by DPVS. In order to further improve the accuracy of load forecasting models, this paper proposes an input feature reconstruction method based on the maximum information coefficient (MIC). Firstly, the load curves with DPVS are classified by Gaussian mixture model (GMM) clustering. Then, considering the coupling relationship between the load and input features at different times, the load data and input features are reordered. Finally, the MIC between different features and loads at different times is calculated to select the relevant features at those different times and construct new input features. The case analysis shows that the feature reconstruction strategy proposed in this paper effectively improves the prediction performance of deep neural networks.  
short-term load forecasting; maximum information coefficient; feature reconstruction; bidirectional LSTM +
Footnote †: journal: Applied Sciences
## 3 Demonstration of the Proposed Approach
### Parametric Studies
The evaluation is extended to a single-antenna system with an IFA mounted beside a conducting wall whose height and width are \(H_{wall}\) and \(W_{wall}\), as illustrated in Figure 5. The given environment produces strong platform effects, and unaffected patterns are separately calculated for each distance to the wall. It is assumed that \(H_{wall}\) is scaled as \(W_{wall}/2\), and \(\mu_{\theta}\) is 40\({}^{\circ}\), which indicates the angle of the maximum gain of the stand-alone IFA. Figure 6a shows the variations in \(\rho_{PDC}\) for various \(W_{wall}\) at 20 mm, while Figure 6b presents those for different distances with \(W_{wall}=200\) mm. As can be seen, when \(W_{wall}=40\) mm, \(\rho_{PDC}\) is close to unity, which implies that the pattern is almost identical to the case without the conducting wall. However, \(\rho_{PDC}\) is drastically reduced to 0.27 when \(W_{wall}\) becomes larger; thus, \(W_{wall}\) should be smaller than 80 mm for \(\rho_{PDC}\geq 0.9\). A similar trend can be observed  
Figure 4: Simulated normalized patterns of the patch antenna in the \(xz\)-plane according to (**a**) distance at 6 GHz and (**b**) frequency (distance = 20 mm). Figure 3: Simulated variation in \(\rho_{PDC}\) and \(1\text{S}_{21}\) for patch antenna according to (**a**) the distance at 6 GHz and (**b**) frequency (distance = 20 mm). in the case of the distance variations, and the antenna should be placed 80 mm away from the wall to guarantee \(\rho_{PDC}\geq 0.8\). Figure 7a,b show the distorted radiation patterns of the IFA at 1.5 GHz according to the size of the conducting wall and the distance, respectively. It is obvious that the patterns are more distorted for larger platform sizes and closer distances, which agrees well with the trend of \(\rho_{PDC}\) presented in Figure 6. Figure 8 presents the variations in \(\left|\,S_{11}\,\right|\) for the IFA mounted beside the wall with different design parameters. As can be seen in Figure 8a, the conducting wall size affects the level of \(\left|\,S_{11}\,\right|\), while the distance to the wall causes unexpected frequency shifts with distortions of impedance matching properties, as illustrated in Figure 8b. Figure 5: Illustration of the antenna system where a metallic wall is situated beside the IFA. Figure 6: Simulated variation in \(\rho_{PDC}\) for IFA according to (**a**) the metallic wall size (distance = 20 mm) and (**b**) distance (\(W_{wall}\) = 200 mm).
## 3 Results
### Meta-Analyses  
Initially, none of the included 17 RCTs [76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92] showed the required SD changes from baseline to allow a meta-analysis to be performed. Additionally, in-text information provided by the included studies was not enough to calculate these missing SDs from baseline using the RevMan calculator [55]. Therefore, corresponding authors of the eligible studies were contacted to obtain the required data. Missing outcome data of seven RCTs [76; 77; 79; 83; 84; 89; 90] were collected in this way. As a second step, if the corresponding authors did not share their data with this systematic review, and the SDbaseline and SDfinal values were known, the SDchange value was calculated by assigning a value of 0.7 to the r in the formula [64; 65], to provide a conservative estimate [66] as used by previous systematic reviews [67; 68; 69; 70]. Using this method, four additional RCTs [78; 82; 88; 91] were included in quantitative analyses. In total, eleven RCTs [76; 77; 78; 82; 84; 88; 89; 90; 91] were included in the meta-analyses of this systematic review. Due to the insufficient outcome data, six RCTs [80; 81; 85; 86; 87; 92] were not included in a meta-analysis. However, participants and intervention characteristics and the results of these studies are presented in Supplementary Tables S1 and S2.
## 3 Design of the Algorithm
### Preliminaries  
The images used in this study are matrices in which each element corresponds to a position in the image (pixel). The value of each matrix element is determined by the color of the corresponding pixel (Figures 2 and 3). Since the images being used are in grayscale, each image is determined by a single matrix. Once the images are uploaded, an automatic check is performed to determine if the image has borders, and if so, they are not considered in the execution of the algorithm. This way, unnecessary calculations are avoided without preprocessing the images in the dataset.  
Images 1-12 in Figures 2 and 3 correspond to different axial planes of samples 2 and 5, respectively. The portions corresponding to the pulp chamber are highlighted in blue, while the pulp chamber pixels detected by the algorithm are marked in red.  
Figure 1: CB-CT and micro-CT images of the molar identified as sample 2 (corresponding to a female aged 62 years). CB-CT reconstruction (**a**,**b**) and sagittal (**c**) and axial (**d**) images. Micro-CT sagittal images (**e**,**f**) and pulp reconstructions (**g**,**h**). (**i**) corresponds to an isolated and enlarged detail of the pupar chamber shown in (**h**).  
The application d is defined as follows:  
\[\begin{split}\text{d}\ :\ \mathbf{Z}^{2}\rightarrow\mathbf{R}\\ \mathbf{x},\mathbf{y}\ \rightarrow\ d(\mathbf{x},\mathbf{y})&=|x_{1}-y_{1}| +|x_{2}-y_{2}|\end{split} \tag{1}\]  
where \(\mathbf{x}=(x_{1},x_{2})\), \(\mathbf{y}=(y_{1},y)\in\mathbf{Z}^{2}\), and \((\mathbf{Z}^{2},\text{d})\) is therefore a metric space.  
Given \(\alpha\in\mathbf{N}\), on a set \(A\in M_{2\times N}(\mathbf{Z})\), the equivalence relation \(\sim_{\alpha}\) is defined as follows:  
\[\begin{split}\mathbf{x},\mathbf{z}\in A\ \Rightarrow\ \mathbf{x}\sim_{\alpha}\ \mathbf{z}\ \Leftrightarrow\ \exists\ \{\mathbf{y}_{k}\}_{k=1,\ldots,N}\in A\ /\ \left\{\begin{array}{l}d(\mathbf{x},\mathbf{y}_{1})\leq \alpha\\ d(\mathbf{y}_{k},\mathbf{y}_{k-1})\leq\alpha,\ \ k=1,\ldots,N-1\\ d(\mathbf{y}_{N},\mathbf{z})\leq\alpha\end{array}\right.\end{split} \tag{2}\]  
Figure 3: CB-CT images corresponding to the molar identified as sample 5. Axial images and area occupied by the pulp chamber (highlighted with red dots). In this sample, the algorithm values were very different from those of micro-CT.  
Figure 2: CB-CT images corresponding to the molar identified as sample 2. Axial images and area occupied by the pulp chamber (highlighted with red dots). In this sample, the algorithm values match those of micro-CT.  
Given two elements \(C_{1}\), \(C_{2}\in M_{2\times N}\) (**Z**), the distance \(d_{c}\) between them is defined as follows:  
\[\begin{split} d_{c}:\ M_{2\times N}(\textbf{Z})\times M_{2\times N }(\textbf{Z})\rightarrow\textbf{R}\\ C_{1},C_{2}\ \rightarrow\ d_{c}(C_{1},C_{2})=min(|x_{1}-y_{1}|+|x_{2}-y_{ 2}|)\end{split} \tag{3}\]  
where (\(x_{1},x_{2}\))\(\in C_{1}\) and (\(y_{1},y_{2}\)) \(\in C_{2}\).
## 2 Materials and Methods
### Data Pre-Processing  
AIS provides automatic reporting between vessels and between vessels and land, and is used for vessel traffic analysis by providing navigation data of vessels. Further, it also contributes to navigation safety and facilitates vessel traffic management by autonomously and continuously exchanging information such as vessel identity, location, time, route, and speed [19]. The AIS receiver station on land stores all AIS data received in the VTS area as well as the entire AIS coverage of the vessel. In this study, the following data pre-processing is required because the experimental area is limited to the VTS area by specifying the target area. First, only the data for the target area were extracted from the entire dataset, and the data of vessels that did not move, such as anchoring, mooring, and drifting vessels, were removed. Next, the data were converted into position data according to the reference time by moving the position according to the received time, as shown in Figure 3. The AIS data set different reporting intervals, as shown in Table 1, according to the speed and status of the vessel, and transmit the information of the own ship in a manner such that the unit time is divided and the time slot is occupied, as shown in Figure 4 [19]. Simultaneously, it reserves another time slot for the next position message. The same procedure was repeated for all other AIS-equipped ships. Each minute of time for each designated frequency was divided into 2250 slots, yielding a total of 4500 slots for the two frequencies. In addition, each frame of 2250 slots was repeated every minute. Therefore, the navigation data, according to the time transmitted by the vessel, should be converted into a reference time unit and used as a reference for a specific unit time. Data classified according to unit time were stored as a dataset that had been pre-processed for data training and used in the next process.  
Figure 2: Data processing sequence including data pre-processing, location-based clustering, priority extraction using fuzzy logic, and overall final priority extraction.
### Carbon Nanofluids in FSCs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nanofluids** & **Metal Oxide** & **Carbon** & **Metal–Oxide–Metal** & **Metal–Oxide–Carbon** & **Reference** \\ \hline Fe\({}_{3}\)O\({}_{4}\) & x & & & [8; 17] \\ Al\({}_{2}\)O\({}_{3}\) & x & & & [5; 8; 10; 16; 17; 18] \\ MgO & x & & & [5; 8; 11; 13] \\ CuO & x & & & [5; 9; 12; 17; 18] \\ TiO\({}_{2}\) & x & & & [5; 8; 9; 18] \\ SiO\({}_{2}\) & x & & & [5; 9; 16; 18] \\ ZrO\({}_{2}\) & x & & & [5; 9] \\ CeO\({}_{2}\) & x & & & [8] \\ ZnO & x & & & [8] \\ MWCNT & x & & & [5; 13; 17] \\ SWCNT & x & & & [5] \\ Graphene & x & & & [15; 16] \\ Al\({}_{2}\)O\({}_{3}\)-Cu & & & x & [6] \\ CuO-MWCNT & & & x & [13] \\ MgO-MWCNT & & & x & [13] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simple and hybrid nanofluids in FSCs in the literature. carbon nanofluids. This work aims to develop a mathematical model to simulate the behavior of FSCs using metal oxide and carbon nanoparticles, along with hybrid metal-oxide-carbon nanofluids. The study evaluates the effect of nanoparticle concentration and compares the performance of solar collectors using simple and hybrid nanofluids. A parametric analysis is conducted to examine the impact of key variables on the performance of the solar collectors. The results will assess the potential of hybrid carbon nanofluids in enhancing the efficiency of FSCs. Additionally, this research may serve as a foundation for future studies on the application of nanofluids in other thermal energy systems.
## 4 Implementation and Discussion
### Blockchain Performance Results
The block gas limit sets a cap on the total gas within a block, which indirectly determines data storage capacity. Increasing the gas limit allows for more data to be included in a block [46]. Our investigation focuses on the storage of up to 200 vehicles, each identified by one authentication message. The memory needed to execute the transactions and to store in a block with the varying number of authentication messages is illustrated in Figure 12. This illustration demonstrates the relationship between authentication messages and memory storage in a block. Our experiment used the typical Ethereum block size of 1-2 MB [47]. When the transaction in blockchain is successfully completed, storing an authentication message in the blockchain network needed approximately 10 kilobytes of memory. The maximum memory required to store 200 authentication messages (for 200 vehicles) was found to be approximately 30 kilobytes, which is significantly lower than the maximum block size (1-2 MB) of the Ethereum network. This evaluation underscores the practicality of the system in terms of memory demands by the system. Figure 11: Number of vehicles vs. gas values.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Recovery Module
#### 3.2.1 Adaptive LSB Extraction  
The bits corresponding to the secret message are extracted in this block. Due to the fact that the number of bits that are concealed in the hiding process may vary for each sample of the host signal, it is necessary to extract all the hidden bits first, and then separate and convert them into samples.  
The procedure within this block is explained as follows:  
1. Reading the stego signal and the \(BH\) value.
2. Representing the samples of the stego signal in a 16-bit format. The resulting signal will be referred to as \(S\).
3. Extracting the least significant bits from every sample (Equation (5)). \[T=S-\left\{2^{(MSB-BH)}*\left\lfloor\frac{S}{2^{(MSB-BH)}}\right\rfloor\right\}\] (5) where \(T\) corresponds to the decimal value of extracted bits, which is later converted into a binary value.  
Figure 2: Block diagram of the recovery module.  
Equation (5) is applied on all the samples of the stego signal, creating a bit sequence.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Considered Intensity Standardisation Techniques
#### 2.3.2 Fuzzy c-Means-Based Standardisation  
The fuzzy c-means-based standardisation leverages fuzzy clustering of the white matter. The process is as follows. First, fuzzy c-means clustering takes place [25]. Given a set of voxels \(x_{i}\in I\), fuzzy c-means finds a set of \(K\) clusters, \(C=\{c_{1},...,c_{K}\}\), minimising the following loss function:  
\[\operatorname*{arg\,min}_{C}\sum_{x_{i}\in I}\sum_{c_{k}\in C}w_{ik}\cdot||x_{ i}-c_{k}||_{2}^{2}, \tag{2}\]  
where \(c_{k}\) is the centroid of the cluster \(k\) and \(w_{ik}\in[0,1]\) is the membership of \(x_{i}\) to the cluster \(k\). The membership of a data point to a certain cluster is given by  
\[w_{ik}=\frac{1}{\sum_{j=1}^{K}\frac{||x_{i}-c_{k}||_{2}^{2}}{||x_{i}-c_{j}||_{ 2}^{2}}}. \tag{3}\]  
The process is carried out iteratively until convergence. Second, the method divides all voxel intensities by the mean intensity of the detected white matter [18]. The selection of \(K\) may depend on the regions of interest to be segmented or on the presence of abnormal tissue (e.g., white matter hyperintensities, stroke lesions or tumours) or extra-cerebral regions. As in the original implementation, we set \(K=3\) (ideally white matter, grey matter and cerebrospinal fluid).
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 3 System Design
### Protocol Design
In the returned ACK, it checks whether the free bytes in the ACK option field have been assigned. If no value is assigned, the bandwidth allocation information is written to the ACK directly. If the value is less than or equal to the value carried in ACK, the switch replaces the value carried in ACK. If the value is greater than the value carried in ACK, the switch directly forwards it without modifying the ACK. Assume that there are n routers on the link, and each router allocates BWn bandwidth to a TCP stream. The link bandwidth BWbottleneck that the sender receives from an ACK is expressed as follows:  
\[\text{BWbottleneck}=\min(\text{BW}_{1},\text{BW}_{2},\ldots,\text{BW}_{1}) \tag{7}\]
# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Chaos Theory
In their model CIT2-FNON was integrated with 2048 trading day time series financial data and top-10 major financial signals for the real-time prediction of 129 worldwide financial products. Four papers were based on chaos theory. By analyzing those papers, it was found that many two and three-stage hybrids based on the chaos theory were implemented in recent years. Among the applied methods, three-stage hybrid chaos + multilayer perceptron (MLP) + non-dominated sorting genetic algorithm (NSGA-II) outperformed all other three-stage hybrids. Research proves chaos's wide applicability as it can be used in a broader way. Reference [70] shows that chaos theory can successfully be used as both a financial time series predictor and as a trading strategy optimizer. One of the most important concerns is choosing the input parameters. The methods are chosen based on the dynamics underlying the chosen data and what kind of analysis is intended for the system. That makes the system highly complex and not always accurate.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### Amount of Datagram Sent  
This section verifies that the number of datagrams necessary to transport the file corresponds with the number of datagrams sent by OPNET. The size of the transferred file in the OPNET simulation is 10,000,000 bytes. The standard Ethernet maximum transmission unit (MTU) is 1500 bytes. When the IP header without any options (20 bytes) and the TCP header without any options are removed (20 bytes), the TCP maximum segment size (\(MSS\)) is 1460 bytes. The number of datagrams n required to transport the file is given by  
\[n=\frac{s}{MSS}=\frac{10,000,000}{1460}=6849.32 \tag{5}\]  
Thus, about 6850 datagrams are required to transfer the file from _FTP_ server to the _FTP_ client. The Riverbed Modeler simulator recorded 6854 frames sent by the _FTP_ server for the scenario \(p_{drop}=0\%\) (Table 6).  
The difference between the four packets can be explained as follows. The first packet is related to the TCP handshake. It is the answer (TCP SYN/ACK) to the TCP connection request of the FTP client. The next two frames are related to the TCP teardown. One is the TCP FIN and the other one is the TCP ACK. Both are sent from the server to the client. The last frame is related to the link-layer address resolution. The server needs to determine the link-layer address of the next-hop and sends, therefore, an Address Resolution Protocol  
Figure 8: Figure 7 is redrawn for the first 80 s of simulation time.  
(ARP) request. This adds up to the four frames, the difference between the calculated and the recorded value. Thus, the number of datagrams sent during the simulation are the same as the calculated expected number of datagrams necessary to transfer the file of size \(s\) = 10,000,000 bytes with \(MSS\) = 1460 bytes.  
To conclude, the simulation matches the amount of datagram (packets) expected.