esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Results and Discussion
### Experimental Setup
#### 4.1.2 Network Topology Generation  
The physical network illustrated in Figure 5 is not encumbered by vendor-specific features or other proprietary specifications, since the proposed framework operates seamlessly across applied SN technologies. This neutrality enables the reuse of the deployed SN to serve multiple scenarios of the same scale and node connection density. Consequently, the first examined scenario contemplates the deployment of emergency communications over a UAV-supported first responders network, while the latter suggests the deployment of XR activities over an industrial environment. To achieve a result closer to the reality of both cases [83; 18], the SN topology was designed to obtain moderately connected nodes and to avoid long edges between remote vertices.  
To materialize the experimental setup, the Waxman network topology generator provided by ALEVIN is employed. In the premises of the VNE study, the Waxman algorithm is one of the most commonly utilized topology creation mechanisms [2; 3; 7; 50; 84; 85; 86; 87]. Consequently, the SN and VNR constructs of these trials were generated by following the established practices. By adjusting the \(\alpha\) and \(\beta\) parameters that Waxman supports, the ability to proportionately control the probability of connections between vertices and the ratio of long to short edges interchangeably is gained.  
Since both examined scenarios specify the need for moderately connected nodes and the absence of long edges, the ALEVIN parameters were handled appropriately. The \(\alpha\) and \(\beta\) variables of the Waxman algorithm were set to the values of 0.5 and 0.1, respectively, to achieve the desired result. Afterwards, the uniform distribution in a \(1\times 1\) square area of the SN nodes took place. The output SN topology and the incoming VNRs are depicted in Figures 5 and 6.
# Methods Improving Energy Efficiency of Photovoltaic Systems Operating under Partial Shading
## 4 Discussion  
Figure 11 shows the summary graph of power losses of a PV battery consisting of parallel-connected arrays of solar cells formed from 10 series-connected PS-250 PV modules with partial sequential shading of modules of one of the arrays, obtained as a result of mathematical modeling. Figure 12 shows the conducted experimental work on APE prototype estimation on the Sevastopol solar plant.  
Figure 10: Block diagram of a method for taking maximum power from arrays of PV modules.  
It can be seen from the graph that the installation of additional power cells significantly reduces the energy losses of the PV battery with partial shading of solar cells, which significantly increases the energy output of the battery operating in such conditions. A particularly significant effect from the use of this method is achieved when it is used in solar PV installations consisting of a large number of parallel arrays of solar cells, or when using MPPT with search algorithms for MPP that establish the mode of taking electrical energy outside the AMP. In such cases, partial shading of more than one panel in a parallel group consisting of ten panels practically does not allow the generation of electrical energy of such a group without voltage matching with other parallel groups.  
The obtained estimates of the effectiveness of various methods for increasing the output power of a partially shaded array of PV modules are shown in Table 2.  
Figure 11: Graph of the dependence of the power losses of a PV battery on the partial shading of PV modules of one of the solar cell arrays (1—with APE; 2—consisting of three arrays of solar cells and installation of MPPT in AMP; 3—consisting of 10 arrays of solar cells and the installation of MPPT in AMP; 4—installing MPPT in LMP).  
Figure 12: The conducted experimental work on APE prototype estimation on the Sevastopol solar plant.  
The presented results demonstrate the effectiveness of the proposed method (APE) based on voltage matching of parallel-connected arrays.
## 3 Study Area and Data Description
### Data Description  
To start the research process, the first and most important stage is data collection and compilation. At this stage, all required secondary data including spatial and non-spatial were provided by the city of Kigali. Those data are shapefiles of routes, slope, land use, urban villages, and a boundary map along with the needed attribute data such as population. The National Institute Statistics of Rwanda (NISR) provided documents and the population data from the fourth Population and Housing Census of 2012.  
Currently, the existing evacuation planning in the city of Kigali does not show a specific place of evacuation. The local authorities are in charge of providing the facilities and locations of safe areas for evacuation and sheltering when a disaster occurs. Thus, the safe areas were selected based on the international standards of evacuation planning for flood and landslide hazards [50]. This includes open spaces, schools, and churches that meet the suitability criteria of being located out of the disaster-prone zones, on gentle slopes and having access to resources, including water sanitation, food, electricity, and toilets.  
GIS was used for the preparation and analysis of spatial data. A densely populated region covering an area of 6.9 km\({}^{2}\), with a population of 176,741, was selected as a study area (Figure 1b). The population data from NISR was aggregated to the level of small blocks. So, there were 1525 small blocks considered as residential/commercial communities and only one population value was considered for each block. A table was created to store each block, its coordinates, the number of evacuees (population size), and the distance to each shelter following the shortest path. Ten shelters were selected and their capacities were calculated based on the crowd density standard considering 3.5 m\({}^{2}\) per person [51]. The table was created to store each candidate shelter, its coordinates, and its capacity to host evacuees. In total, the selected ten shelters have the capacity to host 134,462 evacuated persons. Each shelter might be overloaded due to a large number of evacuees.  
A matrix of the shortest distance from each block to each shelter was generated using network analysis in ArcGIS. The origin-destination cost matrix tool was used to compute the minimum distance.
## 1 Introduction  
The QCA technology, with its unique features such as minimal dimensions, high speed, very low latency, low power consumption, and high operating frequency [1], has attracted the attention of many researchers and scientists as a new method of communication and computation. It has introduced significant novelties in the field of computer science and logic circuits. Adders are one of the most fundamental computational circuits of digital logic and have attracted researchers' attention. Adders are one of the main building blocks of many VLSI systems, such as various microprocessors and processors. Are the new designs aiming at optimizing the relevant blocks compatible with the development of this technology? A complete Adder/Subtractor design with a simple structure and low power consumption can significantly simplify digital circuits. A Full Adder/Subtractor design should include a composite computations circuit and allow performing both addition and subtraction processes. One of the problems in creating hybrid courses is the appropriate composition of wires crossover to reduce costs.  
Due to the high price and increasing circuit complexity, a multilayer crossovers design in the implementation of QCA circuits is not desirable (favorable) [2, 3]. To achieve coplanar crossovers, it was suggested to rotate the QCA cells, but due to the coexistence of two types of QCA cells, this caused some problems, such as low stability and high implementation cost. Therefore, a design including this type of cells is not desirable [2, 4]. The best method for designing QCA circuits is based on the use of 90-degree cells with non-adjacent clock phases (four clock phases) to develop the crossover wires in a single layer [2; 5]. Therefore, we used these two types of crossover for this research.  
For this reason, The proposed designs, due to their coplanar structure and to the fact that they do not require other layers, have a reduced number of cells, occupied area, and delay. The remainder of this article is organized as follows. Section 2 (Background) provides an overview of QCA and previous literature. Section 3 (Proposed Circuits) presents the proposed architecture of Full Adder/Subtractor circuits. In Section 4 (Guidelines Performance Evaluation), we compare the proposed designs with previous architectures. In Section 5 (Conclusion), we discuss our conclusions.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
###### Abstract  
In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.  
underwater acoustic communication; underwater channel; OFDM; FBMC; spatial modulation 40/1.
## Theory
### Calculation of Radiated Sound Power
The equations are integrated into Equation (5), and then the total radiated sound power can be presented as  
\[\Pi=\iint\limits_{s}\frac{1}{2}\text{Re}\left(\sum\limits_{n,m}^{\infty} \sum\limits_{q}^{\infty}W_{nq}Z_{nmq}\phi_{nm}\cdot\left(j\omega\sum\limits_{ g,t}^{\infty}W_{gt}\phi_{gt}\right)^{*}\right)ds=\sum\limits_{n,m}^{\infty} \frac{1}{2}\text{Re}\left(-j\omega\varepsilon_{nm}W_{nm}^{*}\sum\limits_{q}^{ \infty}W_{nq}Z_{nmq}\right) \tag{7}\]In the low frequency range, modal coupling can be neglected, which does not influence the radiated sound power considerably [23; 27]. If the mutual radiation impedance (off-diagonal case) is ignored, then Equation (7) can be simplified to  
\[\Pi=\sum_{n,m}^{\infty}\frac{1}{2}\text{Re}\Big{(}-j\omega\varepsilon_{nm}|W_{ nm}|^{2}Z_{mmm}\Big{)}=\sum_{n,m}^{\infty}\Pi_{nm} \tag{8}\]  
If the mutual radiation impedance is ignored, then the radiated sound power of a cylindrical shell can be expressed as the superposition of the radiated sound powers of each mode as shown in Equation (8). **3. Simulation Analysis of Vibration and Acoustic Radiation of Cylindrical Shell**  
To analyze the relationship between modal radiated sound power and total sound power at low frequencies, a simulation of modal analysis and sound radiation analysis of a cylindrical shell with fixed ends is presented. The low frequency band mentioned in the article refers to the frequency band with the first few sound radiation peaks. The cylindrical shell model is shown in Figure 1. The length of the selected cylindrical shell model is 1 m, the radius is 0.2 m, and the thickness is 0.005 m. The material of the model is steel, with a density of 7800 kg/m\({}^{3}\), a Young's modulus of 2.06 \(\times\) 1011 pa, a Poisson's ratio of 0.3, and a loss factor of 0.01. COMSOL Multiphysics (a commercial FEM software) is used to calculate the cylindrical shell's modes, the mode, and the radiated sound power.
## 4 Applications for IoT Technology in Smart Buildings
### IoT-Based Security System  
A Raspberry Pi is a single board computer that supports multiple functionalities such as home automation, security, etc. It is an embedded controller that controls the operations of sensors and other various devices connected to it. It combines all the devices through GPIO pins [107]. The researchers reveal that Raspberry Pi can be utilized as the controller. It furnishes more significant similarities with the most recent gadgets and sensors since it is the latest innovation. Especially the passive infrared sensors can be connected to the Raspberry Pi and can be utilized for home automated security systems. Also, it provides more space for future upgrades, for example, taking advantage of a tremendous quantity of the Raspberry Pi's usefulness in areas of productive power utilization via computerized light control for a much more proficient force on the board. The framework is for checking the differentiation in the energy level using IoT. Arduino has a micro-controller to peruse the sensor observations. The Arduino is connected to the current sensor and voltage divider. A USB connection connects the Arduino to the Raspberry Pi. The Raspberry Pi (RPi) serves as a server. Through RPi, the information from the Arduino will be available on the web page. The observed data is sent to the cloud through the Raspberry Pi. An open-source operating system (Raspbian, a Linux distribution) and high-level programming language (Python) can be used to capture video and photos of any moving object. Detects motion and captures visual data recorded on a local server in our prototype project and may be accessed through a dedicated website. IoT-based home security has been built using RaspberryPi 3 and a camera from Pi-camera [108].
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 4 Results
### Variation of Attitude Angle
The corrected water level height can then be determined using Equation (1). Figure 13 shows the change in water level after correction. The root-mean-square error between the corrected water level and the water level obtained through field water gauge  
Figure 12: The position of the virtual antenna is determined from the pitch angle and its position in the ideal state. data is 2.46 cm. This is a reduction of 0.03 cm compared to before the correction, indicating that the pitch angle plays a role in improving the accuracy of water level monitoring. Although the improvement in accuracy is small, it provides direction for future research. In the future, we will study the relationship between pitch angle and water level in depth to further improve the accuracy of water level monitoring.
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 2 Method
### Monte Carlo Simulation
#### 2.1.2 Types of Photon Interactions  
After sampling the photon transmission distance \(r\) using the above random number method, the same method was used to determine whether the interaction type between photons and the water medium is absorption or scattering. Generate a random number \(\zeta\) uniformly distributed from 0 to 1, and then compare it with the single scattering probability \(\omega_{0}\). If \(\zeta>~{}\omega_{0}\), it is an absorption interaction; If \(\zeta\leq~{}\omega_{0}\), it is a scattering interaction, and therefore \(~{}\omega_{0}\) also refers to the survival rate of photons. Assuming that the weight of the photon is equal to 1 at the beginning, the weight of the photon changes to \(\omega_{0}{}^{n}\) after \(n\) times interactions with the medium. Additionally, boundary absorption and reflection were taken into account. To avoid invalid tracing and high computing costs, the tracking of the photon was interrupted when the photon weight was less than the threshold.
## 4 Results
### Ablation Study
#### 4.4.2 Effect of Data Augmentation Consistency  
It is always necessary for unsupervised training to perform data augmentation, which improves the results of the self-supervised depth estimation network without affecting training time. As shown in Table 5, all indicators are better than the basic model. At the same time, data augmentation improves the generalization ability of the model and facilitates the migration of the model.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Strategy** & **sq\_rel \(\downarrow\)** & **abs\_rel \(\downarrow\)** & **rmse \(\downarrow\)** & **rmse \(\downarrow\)** \\ \hline Train set mean & supervised & 15.517 & 0.893 & 11.542 & 0.223 \\ Karsch [39] & supervised & 4.894 & 0.417 & 8.172 & 0.144 \\ Laina berHu [8] & supervised & 1.665 & 0.198 & 5.461 & 0.082 \\ Monodepth & unsupervised & 9.515 & 0.521 & 10.733 & 0.238 \\ Monodepth + pp & unsupervised & 5.808 & 0.437 & 8.966 & 0.216 \\ Ours & unsupervised & 8.497 & 0.491 & 10.525 & 0.233 \\ Ours + pp & unsupervised & **5.054** & **0.418** & **8.671** & **0.212** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on the Make3D dataset [38]. Losses are calculated on the cropped central images and the depth is lower than 70 m.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \(\mathcal{L}_{\text{per}}\) & \(\mathcal{L}_{\text{aug}}\) & pp & **abs\_rel \(\downarrow\)** & **sq\_rel \(\downarrow\)** & **rmse \(\downarrow\)** & **d1\_all \(\downarrow\)** & **1.25 \(\uparrow\)** & **1.25\({}^{2}\)** & **1.25\({}^{3}\)**\(\uparrow\)** \\ \hline  & & & 0.1016 & 1.7372 & 5.581 & 17.200 & 0.905 & 0.964 & 0.983 \\  & & ✓ & 0.0920 & 1.2444 & 5.127 & 17.382 & 0.909 & 0.969 & 0.987 \\  & ✓ & & 0.1013 & 1.7183 & 5.539 & 17.154 & 0.905 & 0.964 & 0.983 \\  & ✓ & ✓ & 0.0921 & 1.2345 & 4.996 & 17.890 & 0.912 & 0.970 & 0.987 \\ ✓ & & & 0.0948 & 1.3093 & 5.264 & 17.089 & 0.908 & 0.965 & 0.985 \\ ✓ & & ✓ & 0.0855 & 1.0402 & 4.797 & **16.514** & 0.915 & **0.971** & **0.988** \\ ✓ & ✓ & & 0.0916 & 1.1597 & 5.150 & 16.909 & 0.910 & 0.966 & 0.985 \\ ✓ & ✓ & ✓ & **0.0846** & **0.9613** & **4.701** & 16.683 & **0.916** & 0.970 & **0.988** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of different components in PDANet.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Cross-Site Scripting (XSS) Attacks: Characteristics and Analysis
#### 1.1.4 Impact and Consequences  
XSS attacks can lead to the following:  
* Cookie theft and session hijacking;
* Credential harvesting through fake login forms;
* Sensitive data exfiltration;
* Website defacement;
* Browser history and clipboard stealing;
* Installation of keyloggers and trojans.
## 6 ECMS with Transient Correction Factor  
As discussed above, in transient, the fuel consumption is corrected with the TCF of Equation (4). The introduction of the TCF in the objective function of ECMS penalizes the torque changing rate for ICE, which leads to the ICE operating more steadily than the ECMS based on steady-state fuel consumption only, as in the previous case.  
Then the objective function with TCF can be introduced to Equation (8):  
\[J_{ecms}=TCF\cdot\dot{m}_{ice}(P_{ice})+PF_{soc}\cdot\dot{m}_{app}(P_{bat}) \tag{12}\]  
Figure 8 reports the values of the torques when TCF is considered and the charge sustaining condition is satisfied. The overall fuel consumption was reduced to 616.3 g, which corresponds to an additional 4.4 g of fuel savings with respect to ECMS with a steady-state map.  
Figure 9 shows the torque values of the ICE and EM, and fuel consumption rate values for the two ECMS cases: with and without TCF. The zoom-in to a specific region is shown on the right plot. It can be seen that the engine torque \(T_{ice}\) is smoother for the case with TCF (blue solid line). Due to the influence of TCF in the time interval of 55-57 s, the controller chooses a hybrid mode that minimizes the engine's high transient operation. Instead, in the case when TCF is not considered (dashed orange line) the pure ICE traction mode is chosen by the controller in the same time range. On the last subplot (Figure 9c), the fuel consumption rate shown highlights the reduction of the fuel consumption rate in the considered time interval. Hence, as the charge sustaining constraint is satisfied, the cumulative fuel consumption is also reduced to the values stated before.  
Figure 8: (**a**) Requested torque, (**b**) ICE torque, (**c**) EM torque at the gearbox output level, (**d**) SOC profile under ECMS controller with TCF, checking for demand fulfillment by the power sources and charge sustaining operating condition, and (**e**) vehicle velocity.  
The values of total fuel consumed on three driving cycles using ECMS controller with and without TCF are summarized in Table 5. Because US06 is the most aggressive driving cycle in terms of transients, the largest benefit of 5.1 g fuel saving is achieved with it. In the HW driving cycle, which is characterized by steady driving scenarios instead, the fuel-saving is reduced to 3.2 g. In UDDS, the reduction is 4.4 g. This improvement can be increased further to 8.9 g by doubling battery capacity (14s24p). The increased utilization of electric traction leads to needs on a larger battery.
## 2 Principles and Methods
### Attention Block U-Net Deep Learning Network
#### 2.3.3 Loss Function  
In order to map low-quality PA images to high-resolution space, we need to train our network to learn whether each pixel belongs to bone trabecula structure or background. Consequently, it can be regarded as a pixel-wise classification task.  
In this paper, we choose dice coefficient loss function \(L_{dice}\), as in Equation (4) [71]. It is a commonly used binary optimization function in medical image processing, evaluating the overlap of two objects.  
\[L_{dice}=1-\frac{2\sum_{i}^{N}p_{(i)}g_{(i)}}{\sum_{i}^{N}p_{(i)}^{2}+\sum_{i} ^{N}g_{(i)}^{2}} \tag{4}\]  
where \(N\) is the pixel number, \(p_{(i)}\) and \(g_{(i)}\) denote predicted value of each pixel and ground truth label, respectively.  
Additionally, the L2 regularization loss \(L_{reg}\) (also called weight decay) was added to prevent network overfitting [72]. The final loss function can be written as:  
\[L_{loss}=L_{dice}+L_{reg} \tag{5}\]
## 1 Introduction
### Connectivity  
Connectivity is still an issue in many developing countries. Nearly nine out of ten young individuals with no access to Internet live in Africa, Asia or the Pacific according to the latest ITU  
Figure 2: LoRaWAN Secure Architecture.  
statistics [5]. Connecting in a stable way to a cloud service such as the one offered by TTN is not feasible in many developing countries rural or isolated areas.  
The collected data at [[https://www.ripe.net](https://www.ripe.net)]([https://www.ripe.net](https://www.ripe.net)) on the round trip time (RTT) for packets originating from some African countries to the Ripe servers in Europe and back to the countries, show significant high Internet access latencies, which hinders many real time applications (see Figure 3).  
In Figure 3, very few green dots indicates quick connection to Internet and most of the dots range from light brown to dark brown showing higher latency connections from most African countries. The analysis performed on the Ripe data also reveals that the round trip time can exceed 6 seconds, which is not tolerable for real-time applications.