esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 4 Experimental Results
### Test Sequence Classification  
All sequences were filmed in a rural area on a mostly cloudy day. Every sequence captured a different scene with various spatio-temporal characteristics. As mentioned in [28], scene complexity plays an important role in determining the compression quality. As spatial and temporal complexity increases, a higher bitrate is required to achieve satisfactory quality. Spatial information (or complexity) is commonly determined using an edge energy metric. This means that images with lots of smooth areas contain a small amount of spatial information (\(SI\)), while images with lots of spatially high-frequency areas contain a lot of spatial information. Temporal information (\(TI\)) is approximated from the difference between two consecutive frames. The higher the difference, the more temporal information. \(SI\) and \(TI\) can be calculated using mathematical models; for example, ITU-T P910 [29] defines \(SI\) as in Equation ((2)) and \(TI\) as in Equation ((3)):  
\[SI=max_{time}\{SI_{std}\}, \tag{2}\]  
\[TI=max_{time}\{std[M_{p}^{n}]\}, \tag{3}\]  
\[M_{p}^{n}=F_{p}^{n}-F_{p}^{n-1}, \tag{4}\]  
where \(SI_{std}\) is computed for each frame in a sequence, and the maximum among all frames is the final \(SI\); and \(M_{p}^{n}\) is the pixel intensity difference between the current frame (\(F_{p}^{n}\)) and the previous frame (\(F_{p}^{n-1}\)). Again, the standard deviation of \(M_{p}^{n}\) is computed for each frame in a sequence, and the maximum is the final \(TI\). The objective \(TI\) and \(SI\) approximation methods described were created in 2008, so to obtain the most credible results, we have approximated \(SI\) and \(TI\), both subjectively and objectively, for every sequence. We classified sequences into four classes, from A to D (Figure 2), which are described as follows.  
* A: low spatial and low temporal complexity;
* B: low spatial and high temporal complexity;
* C: high spatial and low temporal complexity;
* D: high spatial and high temporal complexity.  
For example, a scene capturing a slow-moving panoramic view of crops would represent class A, while a scene capturing dynamic flight close to various obstacles would represent class D. We captured 16 sequences (Figure 3) in total and subjectively classified exactly four sequences into each class. When using objective approximations, each class contained at least two sequences.  
Figure 3: Class A, B, C, and D frames.  
Figure 2: Sequence classification.
# Optimization-Based Input-Shaping Swing Control of Overhead Cranes
## 2 Model Description of Overhead Crane  
A two-dimensional schematic diagram of the motion of an overhead crane is shown in Figure 1. Before modeling, the following assumptions need to be made: (1) the mass of the rope is ignored; (2) the rope is regarded as rigid; and (3) the friction between the actuator and the rope connection is ignored. Based on [20], the dynamic model of the overhead crane is established using Lagrange equations as shown in (1).  
\[\left\{\begin{array}{l}(M+m)\ddot{x}+ml\ddot{\theta}\cos\theta-ml\dot{\theta }^{2}\sin\theta+f=F\\ ml\ddot{\theta}+m\ddot{x}\cos\theta+mg\sin\theta=-lb\dot{\theta}\end{array}\right. \tag{1}\]  
where \(M\) and \(m\) are the mass of the trolley and the payload, respectively. \(\theta\) is the angle between the payload and the vertical direction, \(l\) is the length of the rope, \(F\) is the thrust of the trolley, and \(x\) is the displacement of the trolley in the horizontal direction. Additionally, the sliding friction force \(f=\mu\dot{x}\), and \(\mu\) is the sliding friction coefficient. \(b\) is the equivalent damping coefficient acting on the payload. Usually, the payload swing angle \(\theta\) is not large within the safe operating range, which makes \(\sin\theta\approx\theta\), and \(\cos\theta\approx 1\). At the same time, there is also an approximate equivalent transformation \(\ddot{\theta}\cos\theta-\dot{\theta}^{2}\sin\theta=\frac{d}{dt}\left(\dot{ \theta}\cos\theta\right)\approx\frac{d}{dt}\left(\dot{\theta}\right)=\ddot{ \theta}\). Hence, (1) can be simplified as:  
\[\left\{\begin{array}{l}(M+m)\ddot{x}+ml\ddot{\theta}+\mu\dot{x}=F\\ l\ddot{\theta}+\frac{lb}{m}\dot{\theta}+g\theta+\ddot{x}=0\end{array}\right. \tag{2}\]  
Under zero initial conditions, with \(F\) as the input and \(x\) and \(\theta\) as the output, the transfer function of system (2) can be expressed as:  
\[\left\{\begin{array}{l}\frac{x(s)}{F(s)}=\frac{ls^{2}+\frac{m}{m}s+g}{Mls^{4} +\left(\frac{M+m}{m}b+\mu\right)ls^{3}+(Mg+mg+\frac{ml}{m})s^{2}+\mu gs}\\ \frac{\theta(s)}{F(s)}=\frac{-g^{2}}{Mls^{4}+\left(\frac{M+m}{m}b+\mu\right)ls^ {3}+(Mg+mg+\frac{ml}{m})s^{2}+\mu gs}\end{array}\right. \tag{3}\]  
It can be seen in (2) that \(x\) and \(\theta\) are highly coupled, and the acceleration \(\ddot{x}\) acts directly on the swing angle system. Therefore, how to design a suitable controller based on the simplified system (3) so that the trolley can reach the desired position quickly, and the swing angle of the payload is as small as possible, is an important objective of overhead crane control. This work focuses on the swing control of the payload using input shaping and optimization techniques.  
Figure 1: Schematic diagram of motion of overhead crane.
## Permeate
### Anaerobic-Anoxic-Oxic (AO/O) Integrated with MFC  
An MFC was also integrated into an anaerobic-anoxic-oxic (AO/O) system, resulting in a hybrid system with a total volume of 1 m\({}^{3}\) (Figure 8) for treatment of domestic wastewater and power generation at the same time [54]. During its 1-year operation, a maximum current density of 3.6 mW/m\({}^{3}\) and a COD removal of 95% were reached under an HRT of 18 h, a temperature of 8-23 \({}^{\circ}\)C, and a recirculation ratio of 200%. [PERSON] et al. [53] combined an MFC with a constructed wetland (CW), resulting in an energy recovery of 0.448 W/m\({}^{3}\) and a COD reduction of 92% while treating dewatered alum sludge.  
Notably, [PERSON] et al. [55] established the feasibility and long-term viability of a linked septic-tank-MFC-disinfection system for residential wastewater treatment. As shown in Figure 8, raw influent from a five-person residence flowed by gravity to a 1300 L septic tank, then to a 700 L Aquox(r) MFC comprising two stacks of 9 MFC units, and lastly to a sodium hypochlorite disinfection system. An ultralow power consumption unit made up of capacitors and microcontrollers gathered and stored energy from the MFC. Because no external energy was required, the system was declared sustainable and viable, and the treated effluent met the local discharge standard.  
Figure 8: (**A**) Integrated MFC-AA/O system for wastewater treatment; (**B**) integrated MFC septic tank for wastewater treatment.
## 9 Evaluation Methods and Results
#### Time Complexity  
The time complexity of algorithms in terms of average execution time (training and testing time) is illustrated in Table 8. Results in Table 8 indicate that LR was the fastest algorithm to execute in overall experimentation and has achieved the best training and prediction time of 0.06 s, closely followed by multinomial naive Bayes and Adaboost with 0.156 and 1.63 s, respectively. Whereas SVM consumed the worst time of 35.1 s for training and prediction. However, SVM also outperforms in terms of accuracy, compared on all the models implemented and experimented in this research. The ensemble method XGboost took the second-longest execution time, followed by the Bagging classifier.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & \multicolumn{2}{l}{**BoW Approach (Evaluation Metric \(\pm\) Standard Deviation)**} \\ \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SVM & 0.80 \(\pm\) 0.02 & 0.78 \(\pm\) 0.01 & 0.74 \(\pm\) 0.2 & 0.76 \(\pm\) 0.01 \\ Multinomial NB & 0.80 \(\pm\) 0.03 & 0.79 \(\pm\) 0.02 & 0.76 \(\pm\) 0.03 & 0.77 \(\pm\) 0.02 \\ LogisticRegression & **0.82**\(\pm\) 0.04 & 0.79 \(\pm\) 0.03 & 0.78 \(\pm\) 0.04 & 0.79 \(\pm\)0.03 \\ Decision Tree & 0.76 \(\pm\) 0.03 & 0.75 \(\pm\) 0.03 & 0.69 \(\pm\) 0.02 & 0.72 \(\pm\) 0.03 \\ Ada boost & 0.71 \(\pm\) 0.02 & 0.66 \(\pm\) 0.04 & 0.74 \(\pm\) 0.02 & 0.69 \(\pm\) 0.02 \\ XGBoost & 0.75 \(\pm\) 0.06 & 0.71 \(\pm\) 0.04 & 0.73 \(\pm\) 0.03 & 0.72 \(\pm\) 0.03 \\ Bagging classifier & **0.78**\(\pm\) 0.02 & 0.80 \(\pm\) 0.04 & 0.66 \(\pm\) 0.01 & 0.72 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation metric comparison of proposed models using BoW Approach.  
Figure 14: Proposed approach for detecting cyberbullying patterns in colloquial Roman Urdu.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 5 Simulation Evaluations
### Performance Comparison
#### 5.3.2 Impact of Charger's Traveling Distance Constraint  
Figure 6 shows the performance comparisons of NJNP, TSP, WCI, CI and BC with varying charger's traveling distance constraint from 400 to 800 meters. The advantage of WCI can be clearly observed. For example, when \(L=600\) m, WCI's total disjointed time, inactive time and data loss rate is 72%, 70%, and 69% of that delivered by the TSP, respectively. When compared with NJNP, the three ratios are reduced to 42%, 34%, and 34%, respectively. The two durations and data loss rate delivered by the NJNP decrease as \(L\) increases, this is because the charger can visit more nodes and accomplish more charging tasks in charging tour if \(L\) increases. In contrast, the TSP and WCI don't have any improvement on the results as the charger's traveling distance increases. When \(L\) increases, the charger is able to charge more nodes but only once in a tour using the TSP and WCI, which makes some high-energy-consumption nodes charged in the early time of tour exhausted again and have to be waited for charging until the next tour. Meanwhile, the CI's total inactive time and data loss rate are approaching to the results of TSP and WCI. This is because that the number of charged nodes in the tour increases and the difference of selected nodes between these algorithms is diminishing. Even so, the WCI still outperforms CI in most cases.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 2 Background
### NDP in IPv6 Communications  
NDP enables key IPv6 communication functions, including address resolution, router discovery, redirects, and auto-configuration, as explained below:  
* Address Resolution  
To send a packet, the source must determine the destination's link-layer address. Table 2 outlines the NDP address resolution process:
## 1 Introduction  
The Lyapunov theorem can be categorized into local theorem and global theorem. The local theorem can be used to prove whether an equilibrium point is stable or asymptotically stable [1; 2; 3; 4; 5; 6], whereas the global theorem can be used to prove if an equilibrium point is globally asymptotically stable and if it has been used in several applications [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The [PERSON] theorem can also be categorized into a local version and a global version, and they are collectively called invariant set theorem [18; 19]. The purpose of using the [PERSON] global invariant set theorem is to further prove that an equilibrium point is globally asymptotically stable, given the fact that the [PERSON] theorem is limited somehow to proving the global stability of an equilibrium point [20; 21; 22; 23]. For example, for the mass-spring-damper system, we usually choose the total energy (i.e., the kinetic and potential energy together) of the system as our Lyapunov candidate function, and it is known that if the total energy keeps dissipating, the system will approach an equilibrium point and stop there [24]. So, by looking at the time derivative of the chosen Lyapunov function, we can judge if the energy increases or decreases as time goes on. In order to have a stable equilibrium point, we want to make sure that the time derivative of the chosen Lyapunov function at is least negative semi-definite so that the energy decreases as time goes on. If we use the Lyapunov theorem, it can only prove that the mass-spring-damper system has a stable equilibrium point because the time derivative of the Lyapunov function is negative semi-definite but not negative definite. However, the fact of the matter is that the mass-spring-damper system not only has a stable equilibrium point, but also that equilibrium point is asymptotically stable [25]. By further using the [PERSON] global invariant set theorem, we can then prove that the equilibrium point is asymptotically stable. The [PERSON] local invariant set theorem is usually used to prove whether the limit cycle of a dynamical system is stable or not, assuming that there exists a limit cycle.  
The Lyapunov and LaSalle theorems are not the only way to prove stability of an equilibrium point [26; 27; 28; 29] and are not the best approaches to use in some cases [30; 31; 32; 33] in order to prove stability of an equilibrium point, given the amount of calculations required and
# Generative Adversarial CT Volume Extrapolation for Robust Small-to-Large Field of View Registration
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 Optimization Strategy  
We trained the critic to distinguish between fake (\(\tilde{\epsilon}\)) and real samples (\(f\)), thus maximizing the Wasserstein distance between the real (\(P_{r}\)) and fake (\(P_{g}\)) data distribution [28]:  
\[L_{\textit{critic}}=E_{\tilde{\epsilon}\sim P_{g}}[D(\tilde{\epsilon},g)]-E_{f \sim P_{r}}[D(f,g)]+\lambda E_{\tilde{\epsilon}\sim P_{g}}[(||\bigtriangledown \hat{\epsilon}\left(D(\tilde{\epsilon},g)\right)||_{2}-1)^{2}] \tag{1}\]  
Equation (1) displays the objective function used to train the critic, where the third term is a gradient penalty term used to improve the training stability [29].  
Secondly, we trained the generator to produce images which are indistinguishable from the real ones, thus minimizing \(L_{\textit{critic}}\) by optimizing:  
\[L_{\textit{adv}}=-E_{\tilde{\epsilon}\sim P_{g}}[D(\tilde{\epsilon},g)] \tag{2}\]  
To further stimulate the generation of image details and consistent internal structures, in addition to the adversarial component, we also used a feature loss [30] penalty. This component aims at minimizing the \(L_{1}\) distance between features \(F\) extracted from real and fake samples, respectively. The feature maps are provided by the third convolution layer of a 3D network trained in brain tumor segmentation [31].  
\[L_{\textit{feat}}=E_{\tilde{\epsilon},f}[||F(\tilde{\epsilon})-F(f)||_{1}] \tag{3}\]  
As depicted in Figure 5, the grid information (volume \(g\)) was only used at training time by the critic to constrain the generator to find the right position of the thin slab within the target field of view.  
The objective of the generator represents a weighted combination of the two terms of Equations (2) and (3). The weights have been empirically chosen such that the components take values in the same range: \(\lambda_{\textit{adv}}=1\) and \(\lambda_{\textit{feat}}=1\), which has been shown to lead to a better performance of the model. When using a larger weight for the supervision signal, as suggested in [27], the adversarial loss became unstable in the early stages of the training, hindering an improvement of the generated images over time.  
\[L_{\textit{gen}}=\lambda_{\textit{adv}}L_{\textit{adv}}+\lambda_{\textit{feat }}L_{\textit{feat}} \tag{4}\]  
Since the cost function used to train GANs stems from another neural network trained jointly, the loss alone can be misleading when trying to identify the best performing model. Therefore, for the current experiment, model selection was performed through a visual inspection of the samples produced by the generator over time.  
Figure 4: Network architectures; **left—**U-net like generator; **right—**patch discriminator. The first layer of the generator performs a 4\(\times\) repetition of the thin input to define the extrapolation extent.
## 2 Study Area and Data  
### Study Area  
The Hopi and Navajo Nation stretch from the Four Corners Monument landmark across the Colorado Plateau into Arizona, Utah, and New Mexico. The area is known for its cold winters and very hot summers (Figure 3) and its north-south precipitation contrasts. The total annual precipitation is almost equally divided between summer monsoons and winter snow storms. The annual freeze-free period varies between 130 and 180 days. Annual precipitation varies between 127 mm in the southern lower elevations and 381 mm in the higher northern Pinyon-juniper woodland areas atop Black Mesa plateau. Rainfall is low and moderate in the early winter, increases in February and March, and then decreases quickly in April. May through June is a very dry period [22]. Vegetation gradients are further controlled by the bi-seasonal and north-south climatic gradients. Three major cover types can be found in the study area: (1) grass-shrub at altitudes below 1650 m mainly dominated by sparse grassland-browse types of vegetation; (2) Pinyon-juniper between 1650 m and 2300 m dominated by woodland-browse species; and (3) pine forest above 2300 m.  
Figure 3: Climograph showing average annual temperature and precipitation, derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) data. The precipitation has a distinct seasonality where it is moderate in the winter, and then decreasing quickly in April, with a large monsoonal pulse (July–September).  
Figure 2: Time series of annual precipitation anomalies (brown) and annual average Palmer Drought Severity Index (red) in the Hopi and Navajo Nation region from 1989 through 2013 (data from NOAA NCDC).
## 2 FM-CSK and QCPSK Communication Systems
### Hardware Implementation  
Figure 5a,b show the block diagrams of the QCPSK and FM-CSK hardware prototypes. The figures demonstrate that the chaos oscillators of the transmitter and receiver, modulators, and demodulators are implemented in hardware for both systems. The information signal generation and detection are done in software on a host computer in MATLAB, while a dedicated Python script provides an interface for controlling the Analog Discovery 2 multifunction oscilloscopes. The device passes the generated information signal \(b(t)\) to the transmitter and records the chaos signals used then in MATLAB to detect the message signal \(b^{\prime}(t)\).  
The \"Chaos board TX\" includes a Vilnius chaos oscillator, a chaos shift keying circuit based on DG419 integrated circuit (IC). The board also includes circuitry that pre-processes the chaos signals for the modulators. For the QCPSK system, the quadrature modulator and demodulator are based on AD8346 and AD8347 ICs. The local oscillator signal for the quadrature modulator and demodulator is generated with the Rohde&Schwarz SMC100A signal generator, setting the carrier frequency to 879 MHz. For the FM-CSK system, the FM modulator and demodulator are proprietary FM modules with a 100 MHz carrier frequency. The \"Chaos board RX\" includes a Vilnius chaos oscillator and the circuitry to post-process the output signals of the demodulators. The works [56; 57] elaborate more on the design of the implementation of QCPSK and FM-CSK.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 3 Description of UBSI Resource Allocation Protocol
### Queue Model and Backhaul Capacity of UBSs  
#### 3.1.1 Queue Model  
A UBS can be modelled as a queuing system as illustrated in Figure 2, where the arrival rate and the service rate represent the input traffic from UTs located in its cell and the backhaul capacity of the UBS allocated by the UBSC, respectively. This queue model helps a UBSC to allocate the total backhaul capacity to all the UBSs. Basically, the backhaul capacity of each UBS will be proportional to its input traffic. However, note that there is the maximum backhaul capacity of a UBS in the proposed UBSI resource allocation protocol.  
Figure 1: An underwater cellular wireless network consisting of UBS controller (UBSC), underwater base stations (UBSs) and underwater terminals (UTs).  
Figure 2: A queue model of underwater cellular wireless networks (UCWNs).  
In the following, we explain several parameters in Figure 2.
* \(\lambda_{i}(n)\) is the arrival rate of a UBS \(i\) during the \(n\)-th time interval. In other words, this is the number of packets of a UBS \(i\) received from all the UTs residing in its cell during the \(n\)-th time interval.
* \(\mu_{i}(n)\) is the service rate a UBS \(i\) during the \(n\)-th time interval. That is, this implies the number of packets served by UBS \(i\) during the \(n\)-th time interval. Once the backhaul capacity of a UBS is determined, it holds until the next reallocation. And a UBS transmits its uplink traffic with the data rate up to its backhaul capacity. \(Q_{i}(n)\) is the queue length of a UBS \(i\) at the end of the \(n\)-th time interval. Specifically, this is the number of packets remaining at the queue of UBS \(i\) at the end of the \(n\)-th time interval. The queue length of a UBS is expressed as a function of its previous queue length at time interval \(n-1\), the current input traffic (i.e., packet arrival) rate and the backhaul capacity during time interval \(n\). Besides, the queue length has its maximum value denoted by \(K\). Thus, it is expressed as the following equation. \[Q_{i}(n)=\left\{\begin{array}{cc}max[0,Q_{i}(n-1)+\lambda_{i}(n)-\mu_{i}(n) ],&Q_{i}(n-1)+\lambda_{i}(n)-\mu_{i}(n)<K\\ K&,&Q_{i}(n-1)+\lambda_{i}(n)-\mu_{i}(n)\geq K\end{array}\right..\] (1)
* \(P_{i}(n)\) is the number of packet drops of a UBS \(i\) during the \(n\)-th time interval. In other words, this is the number of packets dropped from the queue of a UBS \(i\) during the \(n\)-th time interval. Similar to the \(Q_{i}(n)\), \(P_{i}(n)\) is a function of its previous queue length at time \(n-1\), the current input traffic (i.e., packet arrival) rate and backhaul capacity during time interval \(n\), and represented as \[P_{i}(n)=\left\{\begin{array}{cc}0&,&Q_{i}(n-1)+\lambda_{i}(n)-\mu_{i}(n)<K \\ Q_{i}(n-1)+\lambda_{i}(n)-\mu_{i}(n)-K,&Q_{i}(n-1)+\lambda_{i}(n)-\mu_{i}(n) \geq K\end{array}\right..\] (2) The parameters used in the UBSI protocol are summarized in Table 1.