esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dataset Used for the Model Development
### Dataset Limitations  
Conducting subjective QoE studies is inherently resource-intensive and logistically complex, so every publicly available database reflects design trade-offs. LIVE-YT-HFR was selected because it offers a large open-access corpus that pairs high-frame-rate video with collected MOS. Nevertheless, it focuses on a single compression format (VP9) and was generated under impairment-free network conditions; consequently, it does not capture the influence of alternative codecs (e.g., H.264/AVC, H.265/HEVC) or packet loss, rebuffering, and jitter. The accompanying MOS was obtained from 85 university students, which, despite high inter- and intra-subject consistency reported by the database authors, may not fully represent the broader viewing population. These factors could limit the direct generalization of results presented in this work to other delivery scenarios. Future work will therefore retrain and validate the developed VQA model on additional datasets that include multiple codecs, network impairments, and more heterogeneous subject pools, providing a wider assessment of the model's robustness.
## 3 Results and Discussion
### Adsorption Isotherms  
The sorption isotherms play an important role in the design of adsorption systems. Three popular isotherm models, i.e., Langmuir (1955), Freundlich (1955), and Temkin (1955) isotherms, were applied for the equilibrium modelling of Pb(II) adsorption on the resins.  
The Langmuir isotherm is commonly used to describe monolayer sorption at equilibrium, and the linear form is given by the following equation:  
\[\frac{C_{e}}{q_{e}}=\frac{1}{Q^{0}b}+\frac{C_{0}}{Q^{0}} \tag{3}\]  
where \(q_{e}\) is the amount of metal adsorbed at equilibrium (mg/g), \(C_{e}\) the equilibrium concentration of the adsorbate (mg/L), and \(Q^{0}\) (mg/g) and \(b\) (L/mg) are the Langmuir constants related to the adsorption capacity and energy of adsorption, respectively.  
The Freundlich model assumes adsorption on heterogeneous surfaces. The linear form of the Freundlich isotherm equation is represented as follows:  
\[logq_{e}=logK_{F}+\frac{1}{n}logC_{e} \tag{4}\]  
where \(K_{F}\) is the adsorption equilibrium constant relating to the adsorbent capacity, and \(1/n\) is the parameter relating the adsorption intensity.  
The Temkin isotherm is based on the interaction between adsorbate and adsorbent, which causes heat of adsorption due to repulsions forces. The linear form of the Temkin equation is given by the following equation:  
\[\begin{array}{rl}q_{e}&=\text{B}\ \ln\left(K_{T}\ \right)+\text{B}\ \ln\left(C_{e}\ \right)\\ &\ \  
where \(K_{T}\) (L/g) and B (J/mol) are the Temkin and heat constants of sorption, respectively. \(R\) = universal gas constant (8.314 J/mol/K), and \(T\) = Temperature at 298 K.  
The Langmuir, Freundlich, and Temkin isotherm parameters for the adsorption of lead (II) onto G-26 and MTS9570 resins were calculated by using isotherms and their correlation coefficients and listed in Table 2. Comparison of the correlation coefficients values shows that the adsorption of Pb(II) onto G-26 and MTS9570 resins is fitted better by Langmuir model (\(R^{2}\) = 0.9973-0.9906) and Freundlich than Temkin models (\(R^{2}\) = 0.9806-0.9943 and \(R^{2}\) = 0.9681-0.9557). The Langmuir, Freundlich, and Temkin isotherms of the adsorption of Pb (II) on G-26 and MTS9570 are presented in Figures 8-10, respectively.  
The maximum adsorption capacities of G-26 and MTS 9570 resins are excellent compared with some other adsorbents for the adsorption of Pb(II) ( Table 3).  
Figure 10: Temkin isotherm of Pb(II) adsorption on G-26 and MTS9570.  
Figure 9: Freundlich isotherm of Pb(II) adsorption on G-26 and MTS9570.
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 4 Analytical Procedure  
### CNN Model  
For the motions measured at the bedrock, velocity histories were calculated by integrating the acceleration histories over time, and displacement histories were calculated by integrating the velocity histories over time. They were assigned to input data, and the acceleration histories measured at the surface were to output data. Every motion applied to the model was tailored to have 12,000 acceleration values measured at time increments of 0.01 s, having 120 s duration. Among the 100 datasets, 80 datasets were used for the train (train dataset), and 20 datasets were for the test (test dataset). Two different CNN models, A and B, were established and tried with the earthquake datasets for site FKSH18. Between them, a model which outperformed the other was selected as a main model.
## 1 Introduction  
In a pulse-echo ultrasound, ultrasonic waves are reflected at the boundaries of materials with different densities. The reflected echo signals can then be used to visualize structural information of an imaging object by reconstructing the spatial geometry inside the object. Volumetric ultrasound imaging, to acquire additional structural information, has grown rapidly since two-dimensional (2-D) arrays have come into use [1, 2]. In particular, volumetric ultrasound imaging has been developed primarily for medical applications. Cardiac ultrasound imaging has been one of the clinical diagnostic applications to fully access cardiac anatomy and function as well as to improve quantitative measurements on physiological conditions of the heart [3, 4]. However, the main technical challenge faced by the volumetric imaging is that an ultrasound system needs thousands of the channels to operate a large number of elements in a 2-D array. To overcome the channel limitation in utilizing the 2-D arrays, various approaches were proposed such as the microbeamformer, the reconfigurable array, and the sparse array [5, 6, 7, 8, 9, 10]. A sparse array has been suggested by picking array elements in a random or regular manner to minimize the number of required channels [7, 8, 9, 10, 11]. Once the sparse array set is fixed, the elements in a 2-D array can be directly controlled by an ultrasound system, which can avoid the complicated hardware designs. In addition, the volumetric ultrasound imaging suffers from low frame rates due to the increase in the imaging area and the computational load of the data.  
However, a higher frame rate is essential for the volumetric cardiac ultrasound imaging in order to provide potentially important diagnostic information on cardiac dysfunction such as valvular and wall motion abnormalities [3; 4]. Hence, ultrasound imaging using unfocused plane waves was introduced to increase the frame rate by applying a reduced number of beams [12; 13; 14; 15]. The previous studies showed that the sparse array in a 2-D array could be used with focused transmits [7; 8]. However, the performance of the sparse array with unfocused transmits has not yet been investigated explicitly. The hardware design for the receive array can be far more complex than that for the transmit array since the formation of three-dimensional (3-D) volumetric images needs to consider time delays from various reconstruction locations in the image. In this paper, a cross-shaped array (hereinafter referred to as the X-shaped array) was selected to simplify the receive configuration by applying the sparse array concept using a smaller number of the elements within the receive aperture. To investigate the feasibility and the limitation of the X-shaped sparse array, we evaluated the applicability of the X-shaped sparse array for high-frame-rate volumetric ultrasound imaging when used with the unfocused plane waves.
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 2 Sun Position
### Anomaly \(\rightarrow\) Coordinates  
A single anomaly variable fully describes the Sun's position, but for practical work, it is preferable to split this information into two separate coordinates, a longitude and a latitude (step 7).  
In general (see Figure 3), ecliptic longitude \(\lambda\) measures the angle between the prime meridian (defined by the north ecliptic pole, the south ecliptic pole, and the First Point of Aries) and the meridian passing through the target. The ecliptic latitude \(\beta\) measures the angle from the ecliptic plane to the target, along the target meridian.  
The Sun is always in the ecliptic plane, so the ecliptic latitude \(\beta\) is always \(0^{\circ}\) (for the purposes of this work, tiny fluctuations, around \(\pm 12\) arcseconds, can be ignored [22]). The ecliptic longitude \(\lambda\) is just the true anomaly \(\
u\) (measured from Perigee) plus the angle correction \(\varpi_{g}\) (from the First Point of Aries to Perigee) [22]: \(\lambda=\
u+\varpi_{g}\).
## 3 EVs Charging Station Load Modeling  
The power consumption or generation of an EVs charging station depends on several factors such as capacity of batteries; type of vehicle's connection to the smart MG; SoC, which depends entirely on the vehicle's driving patterns; and charging power level. There are two type of connection for EVs. Some vehicles only have charging capability and are unsuitable for vehicle-to-grid (V2G). Some vehicles have both charging and discharging capabilities and are suitable for V2G [10]. In addition, there are two rates of chargers named level 2 chargers with a low charge rate and level 3 chargers, which are known as fast chargers [27]. Furthermore, the shape of the charging station load profile can be affected by the changes in the charge and discharge programs of EVs in response to the electricity price and business hours of the charging station. In this section, all the mentioned factors are described below.  
Figure 2 shows the possible states of the type and the mode of EVs in charging station. According to this figure, the EVs (which can include all types such as PEVs and PHEV, etc.) are divided into two types--suitable for V2G and unsuitable for V2G. Each EV's owner can choose to charge or discharge their vehicle based on the electricity price at the time of use. Moreover, each EV's owner uses a suitable charger according to its power level and the vehicle charging technology. Some EVs are not able to use a level 3 charger due to their lack of direct current charging ports. In this paper, it is assumed that the smart charger in the charging station, by connecting the vehicles, has the ability to receive the information divided in Figure 2 and also some other information like SoC and battery capacity [28]. This information is sent to the manager via the telecommunication network, as shown in Figure 3. By registering this information, a database is created in which the specifications of the vehicles used from the charging station are available on each day of the week. Therefore, by having this information for a long time (e.g., one year), the probability distributions can be fitted.  
Figure 1: Configuration of the proposed model.  
Therefore, the manager of smart MGs can use these probability distributions and estimate the amount of EVs charging station load before day of operation. This estimation for EVs helps the decision maker to operate the system in optimal points in presences of severe uncertainty of EVs behaviors. The considered distributions are described in Section 3.1 and the proposed algorithm based on MCS for modeling the EVs load is described in Section 3.2.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range Free Technique
#### 3.2.2 Hop Count-Based  
Hop count-based methods are most popular in range-free localization techniques. The number of anchor nodes required in this technique are comparatively less. Distance vector hop (DV-Hop) plays a vital role in several localization algorithms by providing information about the initial distance among the sensor node and anchor node. Initially, anchor nodes broadcast hop count to the adjacent nodes, then every node collects the message updates the count. Nodes possessing higher counts are generally ignored. At the end of this stage, every node in the network must have a minimal number of hops through the anchor node then the anchor nodes estimate the mean size of every hop. The Hop count-based localization scenario is presented in Figure 7. Lastly, every sensor node in the network multiplies this mean size with the hop count to determine its distance from the anchor node.  
Hop size between any two anchor nodes \(\left(u_{l},v_{l}\right)\)_and_\(\left(u_{m},v_{m}\right)\) is determined by using  
\[Hopsize_{l}=\frac{\sum_{m\
eq 1}\sqrt{\left(u_{l}-u_{m}\right)^{2}+\left(v_{l}-v_{ m}\right)^{2}}}{\sum_{m\
eq 1}N_{lm}} \tag{2}\]  
where \(N_{lm}\) represents the number of hops between the anchor node \(l\) and anchor node \(m\). DV-hop is a useful and straightforward localization technique. However, the drawback is that it needs homogeneously positioned IoT networks and the uniform amplitude reduction of signal strength in every direction. To overcome this drawback, improvements have been proposed in the literature [33; 34; 35]. Minimal mean square error measure for modifying mean hop distance [33] works well for isotropic topologies and improves location precision. However, for non-isotropic and anisotropic environments, this modification generates more errors while estimating the distance. Four closest anchor nodes assume that the shortest paths to the closest anchor node are less influenced by irregularities in the topology [36]. It produces better results for certain scenarios, but there is the probability of incorrectly discarding few virtuous anchor nodes to improve the location precision.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### AOA and ADOA  
AOA is a technique of determining the position of objects by taking the angular data of that object with respect to the orientation of the receivers. A simple AOA calculation is to work on an antenna array on one sensor node. The angle-based method needs a minimum of three reference nodes coordinated to determine the position of the object by using a triangulation method as shown in Figure 6[67]. In general, the AOA method can obtain angle data using radio array techniques and can estimate by using directional or multiple antennas. In multiple antennas, this acts by analysing the phase or time variation between the signals at different array items that have seen locations regarding the centre element.  
In directional antennas, it acts by computing the RSSI ratio between many directional antennas that are carefully located to have a similarity between their major beams [61; 68]. AOA determinations with the support of exact antenna design or hardware apparatus are utilized for inferencing the location of the receiver. The improved complexity and the hardware necessity are the major interferences for the extensive success of AOA-based location systems [69]. AOA is also disturbed by noise, NLoS and the multipath. Moreover, the defects of LoS can be more serious than those of TDOA- or RSS-based techniques [70]. AOA needs additional space to offer spatial diversity and extra hardware that is a real waste of power, but it does not require time synchronization between nodes [71]. Both TOA and AOA parameters require reference units that can decide the arrival time and angle of the received signal which is unattainable to common WLAN devices. Thus, the RSSI technique is most extensively used in an indoor localization, positioning and tracking system. ADOA does not need the information on angles as it can be ignored in the variance between two AOA values. This means that the receivers are to be located towards a definite angle. AOA-based optical indoor positioning systems are more challenging due to the necessity to identify the orientation of the receiver. The optical receiver is either limited to certain orientations or it must be combined with gyroscopes and accelerometers to define its exact orientation. To solve this problem, ADOA is used for an optical indoor positioning system [72]. Hence, the ADOA does not require extra sensors like gyroscopes [69; 73].
## 5 Numerical Results
### Performance Evaluation in a Single-EHD Scenario  
In this subsection, we evaluate our proposed method in a single-EHD scenario. For the Rician fading channel, the EHD's deterministic LoS component \(\mathbf{a}_{U}\) is obtained from (4) with \(\frac{d_{a}}{\lambda_{c}}=0.5\) and an AoD of \(\phi_{U}=\frac{\pi}{3}\).  
Figure 2 compares the value of the objective function in problem \(\mathbf{P}_{3}^{h}\) with respect to the UAV's altitude (i.e., variable \(h\)) for different horizontal distances of an EHD, i.e., \(r_{U}=\{50,100,250\}\) m. As shown in Figure 2, the objective function depends on the UAV's altitude as well as the horizontal distance of an EHD. Specifically, when \(r_{U}=50\) m and \(r_{U}=250\) m, the objective function is monotonically decreasing and increasing, respectively, as the UAV's altitude increases. On the other hand, it increases and then decreases when \(r_{U}=100\) m.  
Figure 3 shows the optimal UAV altitude according to the horizontal distance of the EHD, obtained with the exhaustive 1D line search method (Algorithm 1) and the proposed low-complexity method (Algorithm 2). Moreover, Table 2 presents the corresponding average execution time to obtain the optimal value, where we set the same value for the sample grid accuracy \(\Delta\) and the error tolerance \(\epsilon_{\text{GS}}\), respectively, i.e., \(\Delta=\epsilon_{\text{GS}}=10^{-5}\), in Algorithms 1 and 2 for performance comparison. As expected, the proposed method yields almost identical results to the 1D exhaustive line search method with a considerably reduced execution time. Specifically, it was verified that the optimal UAV altitude is the minimum altitude for horizontal distances of up to 50 m, and then it increases from 50 m to 250 m for horizontal distances up to 235 m. Beyond that range, it becomes the maximum  
\begin{table}
\begin{tabular}{l c c} \hline
**Description** & **Symbol** & **Value** \\ \hline The transmit sum power & P & 1 W \\ \hline The path loss at the reference distance & \(\beta_{0}\) & 1 \\ \hline The path loss exponents of ground links & \(\alpha_{0}\) & 3.5 \\ \hline The path loss exponents of aerial links & \(\alpha_{\frac{\pi}{2}}\) & 2 \\ \hline The Rician factor-related parameters & \(\mathcal{K}_{\tau}^{\text{GB}}\) & 5 dB \\  & \(\mathcal{K}_{\tau}^{\text{GB}}\) & 15 dB \\ \hline The minimum altitude of a UAV & \(H_{\text{min}}\) & 50 m \\ \hline The maximum altitude of a UAV & \(H_{\text{max}}\) & 250 m \\ \hline The number of antennas of a UAV & \(N\) & 16 \\ \hline \end{tabular}
\end{table}
Table 1: Environment for evaluation.  
Figure 2: Comparison of the objective function for different horizontal distances of the EHD.  
altitude. This is because the minimum and maximum altitudes of the UAV are 50 m and 250 m, respectively.
# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
\begin{table}
\begin{tabular}{c c c c c} \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[103] & 2012 & NIDE-IN & Niching differential evolution with index-based neighbourhoods combines differential evolution with an index-based neighbourhood strategy to maintain diverse solutions and improve performance in multimodal optimisation. & Speciation \\ \hline
[104] & 2013 & NCPSO & Niching-based cooperative particle swarm optimisation enhances neural network learning by maintaining diverse particle positions, leveraging cooperative and competitive interactions among particles to prevent premature convergence and improve solution quality. & Speciation \\ \hline
[105] & 2014 & LIS-DE & Local information sharing differential evolution induces niching behaviour by sharing information locally among individuals to find multiple optima. & Speciation \\ \hline
[106] & 2014 & LCM-PGA & Local competition model in island model PGA uses local information to achieve global diversification, maintaining multiple optima by allocating subpopulations to distinct search areas. & Speciation \\ \hline
[107] & 2014 & TN-CMA & Topological niching covariance matrix adaptation employs topological niching techniques to maintain diversity in the population, enhancing convergence by adapting covariance matrices. & Speciation \\ \hline
[108] & 2014 & PMP-DE & Pseudo multi-population differential evolution simulates multiple populations within a single run to maintain diversity and improve convergence. & Speciation \\ \hline
[109] & 2014 & QIEA-LL & Quantum-inspired evolutionary algorithm with linkage learning integrates quantum computing principles with linkage learning to enhance performance in multimodal optimisation, maintaining diverse solutions and improving convergence through the use of quantum-inspired operators and adaptive linkage models. & Speciation \\ \hline
[110] & 2014 & NCGA & Niching cumulative genetic algorithm uses evaluated probability to maintain diverse solutions in multimodal optimisation, leveraging cumulative selection pressure and niching mechanisms. & Speciation \\ \hline
[72] & 2015 & EPSDE & Speciation-based differential evolution with m neighbourhood members and arithmetic recombination crossover. & Speciation \\ \hline
[111] & 2015 & FFA & Firefly algorithm uses attractiveness and distance-based movement to optimise electromagnetic device shapes, incorporating niching to find multiple optima. & Speciation \\ \hline
[112] & 2015 & NPSO & Niching particle swarm optimisation with real-time object detection based on niching techniques. & Speciation \\ \hline
[113] & 2015 & IDE-CTHV & Improved differential evolution algorithm combines chaotic theory and a novel hill-valley method to enhance performance in large-scale multimodal optimisation problems. & Speciation \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 5 Securing CPS  
Over the last few years, an alarming increase has been witnessed in the rate of cyber-attacks on CPS with disastrous results. Due to the heterogeneity and complex nature of CPS, it is highly prone to malicious attacks. These attacks can collapse the whole system as presented in Figure 2. A detailed description of these attacks can be found in [35]. Different types of attacks on CPS include eavesdropping (obtaining sensitive information from network traffic), password cracking, phishing emails, DoS/DDoS, and different types of malware. However, securing a CPS is not an easy task due to the increase of challenges and limitations in existing solutions. IoT and CPSs rely on privacy, security, reliability, and consistency, and these characteristics are combined to develop a trustworthy system. A secure environment for CPS can be maintained by fulfilling security goals. Security goals for CPS are presented in Figure 3.  
To maintain the confidentiality of CPS, cryptographic techniques have been adopted, while the integrity of CPS can be maintained by avoiding any modification (physical or logical) in data. Availability of different devices is the main goal of CPS that can be achieved by encryption techniques to avoid DoS/DDoS attacks. Authentication is the first step of defense, and it should be well designed and maintained. User authentication can be ensured by using biometric parameters. However, preserving the privacy of CPS while dealing with big data are not easy. Different techniques are designed to avoid any possible malicious event or cyber-attack.  
For securing CPS, non-cryptographic solutions have also been designed. Any solution against malicious events in a network is designed by considering different factors such as cost, configuration, and placement in a network.