esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 5 Design Procedure and Application Example  
This section provides a summary of the design procedure for both the charger/discharger circuit and SMC parameters. Moreover, the design procedure is illustrated using an application example based on realistic conditions.  
The first step of the design process is to define the design requirements. Table 1 reports the requirements adopted for illustrating the design process, where a standard 12 [V] volt battery must be connected to a 48 [V] bus, which is used in DC microgrids [10; 11; 12]. The battery charger/discharger must be designed to provide or absorb a maximum of 1 [A], with a maximum slew-rate (current derivative) of 50 [A/ms], which could completely charge (SOC = 100%) a EnerSys NP0.8-12-ND [41] lead-acid battery (12 V-0.8 Ah) in 48 min, or charge/discharge the 10% of the SOC in 4.8 min.  
The sources and loads require a maximum voltage ripple of 0.5 [%] for a correct operation, and those devices could be damaged for voltage perturbations higher than 3.5 [%], which could be triggered by the highest current perturbation possible, i.e., a step from the maximum current (1 [A]) to the minimum current (\(-\)1 [A]) or vice versa, thus \(\pm\)2 [A]; moreover, the bus voltage must exhibit a settling time lower than 1 [ms]. Since the ripple in the magnetizing current defines the peak values of the currents at both primary and secondary sides of the HFT, that peak ripple was limited to 5 [A]. Finally, the maximum switching frequency achievable with the adopted Mosfets is 30 [kHz], but any switching frequency below that value is acceptable.
## 4 Materials and Methods
### Satellite Spectral Analysis
#### 4.3.2 Spectral Analysis of Iron Oxide  
Unfired mudbrick was the most common building material in ancient Egypt, with Hermopolis being no exception (Figure 3). These mudbricks were utilized for the construction of various domestic installations, in addition to some administrative and palatial complexes [47]. Sun-dried mudbricks comprise large amounts of clay, of which \(\sim\)5 to 6% is the chemical compound iron oxide (Fe\({}_{2}\)O\({}_{3}\)) [48]. For this reason, studying the patterns of presence/absence of iron oxide via satellite imagery could assist in the identification of possible archaeological remnants. To conduct this study, the WorldView New Iron Index (WV-II) was utilized. The WV-II is a geological index, which pinpoints pixels rich in iron oxide, and can thus be indicative of subsurface mudbrick features. By using the visible light portion of the electromagnetic spectrum, specifically the green, yellow, and blue wavelengths, pixels with spectral signatures corresponding to that of iron oxide are identified.  
\[\sum\frac{\rho\text{Green}\;\times\;\rho\text{Yellow}}{\rho\text{Blue}\;\times \;\rho\text{Red}}=\text{WV}\;\text{II} \tag{4}\]In the resulting WV-II processed image, the areas with higher levels of iron oxide appear darker in their response than the rest of the background. Unlike the vegetation indices, the spectral indices testing for certain geological substances are very rarely applied to archaeological case studies. Therefore, this research strives to apply this often-unused technique to delineate archaeological remains by exploring a certain geological component, in this case, iron oxide. Similar to the spectral analysis of vegetation (4.3.1), a 3 \(\times\) 3 ENVI directional filter mask at 45 degrees was applied to the processed WV-II image in order to enhance any linear features.
## Results
### Air Sampling and Analysis  
During the monitoring of the atmospheric air in Almaty using the optimized method, all analytes were detected except methyl ethyl ketone and 1,2-dichloroethane. Mean concentrations of analytes ranged from 0.2 to 83, from 0.1 to 70 and from 0.1 to 74 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) on 30 March, 2 April and 4 April, respectively. The highest concentrations (0.7-89 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\)) for 16 of the 23 analytes were detected on the 3 rd day of sampling (Table 6). It can be caused by higher temperatures (14-16 \(\mathrm{\SIUnitSymbolCelsius}\)) than on previous sampling days (6-11 \(\mathrm{\SIUnitSymbolCelsius}\)). The highest concentrations for the rest of the VOCs, such as benzene, propylbenzene, benzaldehyde and hexadecane, were detected on March 30, which made up 56, 0.3, 1.8 and 123 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\), respectively. The 2 nd sampling day showed the highest concentrations of naphthalene, phenol and fluorene--2.4, 3.9 and 0.8 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\), respectively. The lowest concentrations of the greatest number of analytes (16) were detected in sampling point P2 (Table S5 in Supplementary Materials) located in the upper part of the city close to mountains. The highest concentrations of the greatest number of analytes (12) were detected in sampling point P3 located in the central part of the city.  
The relative standard deviations of three replicate analyses of the air samples did not exceed 15.6%. From 108 measurements, the greatest numbers of outliers (one out of three replicate measurements according to Grubbs' test) were identified for \(n\)-hexadecane (19), methylene chloride (14), \(n\)-undecane (13) and acenaphthene (12). No outliers were identified for ethylbenzene and \(m\)-xylene.  
Toluene-to-benzene (T/B) concentration ratios during the sampling period varied from 0.46 to 1.69. During the first two days of sampling, T/B ratios were below 1 indicating that the main source of BTEX was not transport. During these two days, the temperatures were 3-7 \(\mathrm{\SIUnitSymbolCelsius}\) lower than during the third day of sampling, and the central and domestic heating systems were more active. During the third day of sampling, T/B ratios were 1.69 and 1.06, and the main BTEX emissions originated from transport. The same trend was reported for the similar period in 2015 [25]. However, T/B were lower than 1 mostly in days with negative temperatures. In 2019, such ratios were observed at temperatures between 6 and 11 \(\mathrm{\SIUnitSymbolCelsius}\), which could mean that the fraction of BTEX emissions from transport-related sources decreased since 2015.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Sampling Date \(\boldsymbol{>>}\)**} & \multicolumn{1}{c}{**Concentration \(\boldsymbol{\pm}\) Standard Deviation (\(\mathbf{ug\,m^{-3}}\))**} & \multicolumn{1}{c}{**Outliers**} \\ \cline{2-13} \multicolumn{1}{c}{**Sampling Date \(\boldsymbol{>>}\)**} & \multicolumn{3}{c}{**Saturday, 30 March**} & \multicolumn{3
## 3 TRNSYS Modelling
### Auxiliary Unit  
To raise the temperature of HTF up to 109 \({}^{\circ}\)C for generator operation of the chiller, a boiler is used as an auxiliary heating unit named type700 in TRNSYS library. This component gives the amount of energy consumed to raise the temperature of the water up to the desired temperature by considering input combustion and boiler efficiencies. When the heat transfer fluid enters the unit with a temperature less than the designed temperature, auxiliary heating unit turns on by the controller  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type of Controller** & **ETC** & **FPC** \\ \hline \(a_{0}\) & 0.804 & 0.800 \\ \(a_{1}\) (k/h\(\cdot\)m\({}^{2}\cdot\)K) & 1.56 & 13 \\ \(a_{2}\) (k/h\(\cdot\)m\({}^{2}\cdot\)K) & 0.0054 & 40 \\ Testing flow rate (kg/h\(\cdot\)m\({}^{2}\)) & 100 & 40 \\ IAM longitudinal at 50\({}^{\circ}\) & 0.93 & \(1-0.2(1/cos\theta-1)\) \\ IAM Transversal at 50\({}^{\circ}\) & 1 & \(1-0.2(1/cos\theta-1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of collectors.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Designed Parameters** & **Values** \\ \hline
**Type 700 (Auxiliary Boiler)** & \\ \hline Time step (minutes) & 7.5 \\ Boiler Efficiency (\%) & 85 \\ Combustion Efficiency (\%) & 78 \\ \hline
**Type 107 (Single effect hot water fired Absorption Chiller)** & \\ \hline Specific heat of heat transfer fluid (kJ/kg\(\cdot\)K) & 4.19 \\ Chiller’s rate COP (default) & 0.53 \\ Hot water’s flow rate from tank to Chiller (kg/h) & 17,507 \\ Chilled Water’s flow rate from Chiller to Building (kg/h) & 16,710 \\ Cooling Water’s flow rate for condenser and absorber heat rejection (kg/h) & 54,527 \\ Minimum Temperature for chiller operation (\({}^{\circ}\)C) & 109 \\ Cooling water inlet temperature (\({}^{\circ}\)C) & 29.4 \\ Chiller Water Setpoint (\({}^{\circ}\)C) & 6.667 \\ Peak Cooling Demand (kW) & 108 \\ Auxiliary electrical energy for solution pumps etc. (KW) & 5.55 \\ \hline
**Type 4a (Thermal Storage Tank)** & \\ \hline Heat transfer coefficient for tank (W/m\({}^{2}\cdot\)K) & 0.83 \\ Number of total nodes & 10 \\ Density of water stored in tank (kg/m\({}^{3}\)) & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters for solar absorption cooling system.  
signal and heat up the fluid to the designed temperature. The boiler part load ratio is given by the following equation [21]:  
\[PLR=\frac{Q_{need}}{Q_{max}}. \tag{2}\]  
In this equation, \(Q_{max}\) is the maximum capacity of the auxiliary unit and \(Q_{need}\) is the amount of energy consumed by the auxiliary heating unit to keep the hot water temperature at the designed point. In order to raise the temperature of the incoming fluid, the value of \(Q_{max}\) is selected in such a way that the part load ratio of the boiler should remain equal or less than one, for raising the temperature of incoming fluid.
## 3 Results
### Accuracy Test
#### 3.1.3 Conventional Object Measurement  
In the above two experiments, the measurement accuracy is quite different, so the third object is tested. The third measured object, as a conventional object, is irregular, opaque and less reflective, with a size of 4000 mm \(\times\) 1000 mm \(\times\) 2000 mm. 10 feature distances are selected as shown in Figure 11. The measurementsareprocessedin the same way as that in Section 3.1.2, and the result is shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Standard Value**} & \multicolumn{3}{c}{**Measurement Value**} & \multicolumn{3}{c}{**Error**} \\ \cline{2-13}  & \(d_{x}\) & \(d_{y}\) & \(d_{z}\) & \(d\) & \(d^{\prime}_{x}\) & \(d^{\prime}_{y}\) & \(d^{\prime}_{z}\) & \(d^{\prime}\) & \(\Delta d_{x}\) & \(\Delta d_{y}\) & \(\Delta d_{z}\) & \(\Delta d\) \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
**1** & 394.526 & 285.011 & 105.542 & 498.017 & 393.696 & 285.502 & 106.424 & 497.829 & -0.830 & 0.491 & 0.882 & -0.188 \\
**2** & 402.012 & 153.214 & 22.123 & 430.787 & 401.328 & 153.858 & 215.959 & 430.352 & \(-0.684\) & 0.644 & \(-0.524\) & \(-0.435\) \\
**3** & 408.995 & 179.562 & 34.856 & 448.034 & 409.543 & 179.190 & 34.130 & 448.330 & 0.548 & \(-0.372\) & \(-0.726\) & 0.296 \\
**4** & 333.785 & 389.215 & 486.671 & 706.930 & 334.598 & 390.123 & 486.156 & 707.460 & 0.813 & 0.908 & \(-0.515\) & 0.530 \\
**5** & 494.594 & 390.996 & 190.462 & 658.617 & 495.294 & 391.669 & 190.962 & 599.687 & 0.700 & 0.673 & 0.500 & 1.070 \\
**6** & 265.123 & 314.014 & 1405.102 & 1463.969 & 265.510 & 313.717 & 1466.461 & 1465.280 & 0.387 & \(-0.297\) & 1.359 & 1.311 \\
**7** & 164.920 & 97.126 & 534.857 & 568.070 & 165.572 & 97.549 & 534.124 & 567.643 & 0.652 & 0.423 & \(-0.733\) & \(-0.427\) \\
**8** & 158.152 & 299.102 & 1135.216 & 1184.563 & 175.859 & 298.376 & 1136.121 & 1185.208 & \(-0.293\) & \(-0.726\) & 0.905 & 0.645 \\
**9** & 852.156 & 256.983 & 2668.247 & 2812.784 & 583.678 & 257.492 & 2669.656 & 2814.628 & 1.522 & 0.509 & 1.409 & 1.844 \\
**10** & 225.562 & 1899.215 & 389.529 & 1951.827 & 225.187 & 1900.248 & 390.191 & 1952.921 & \(-0.375\) & 1.033 & 0.662 & 1.094 \\
**Mean error (mm)** & & & & & & & & & & & & & \\
**RMS errors (mm)** & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: The result of snow sculpture measurement. Figure 11: Conventional object measurement; **(a)** 10 feature distances selected on the conventional object; **(b)** 10 feature distances on the 3D point cloud.
## 3 Results and Discussion
### Primary Metabolite Profiling of Roasted/Unroasted Mahlab Seeds via GC-MS Analysis (Post-Silylation)
#### 3.1.1.1. Sugars  
Sugars, mostly represented by di-saccharides, were the most dominant primary metabolite class at levels ranging from 57.4 to 94.1%, comprising 30 peaks for 24 different sugars. Sucrose (peaks 51 and 52) was the main sugar detected in all samples, except for unroasted white mahlab from Egypt \"WE\" and Greece \"WG\", as turnose (peaks 54 and 56) was the predominant sugar. White mahlab and red mahlab from Sudan, \"WS\" and \"RS\", along with their roasted seeds, \"RWS\" and \"RRS\", were characterized by an abundance of sucrose at 52.3-71.9%, compared to 15-23.7% in other samples. Turanose, a non-cariogenic isomer of sucrose, is present naturally in honey and exhibits a 50% sweetness level compared to that of sucrose [13]. Turanose abundance presents added value, being of low-glycemic response, and it has potential as a promising functional sweetener for controlling obesity [14]. Turanose was detected in all white mahlab seeds (11.4-32.6%) at much higher levels than in the red ones (0.1-0.6%), suggesting that it can function as a marker to distinguish between cultivars. Trehalose (peak 59) and cellobiose (peaks 63 and 64) content showed variation according to cultivar type or seed color. Monosaccharides in  
Figure 1. Representative GC-MS chromatograms of TMS derivatives of primary metabolites, collected from the extracts of (**a**) white mahlab from Greece “WG”, (**b**) white mahlab from Egypt “WE”, (**c**) red mahlab from Egypt “RE” and (**d**) roasted red mahlab from Egypt “RRE”.  
unroasted red mahlab from Egypt, \"RE\", were the major sugars detected, at 29.7%, while the lowest level of monosaccharides was found in unroasted WS (0.8%). A significant decrease in monosaccharide levels was observed in both roasted RRE samples from Egypt and RRS from Sudan, dropping to 9.7% and 2.5%, respectively, likely due to the sugars' utilization to generate Maillard products, which greatly influence seed color and flavor [15]. Major monosaccharides were fructose (peak 38), sorbose (peak 39) and glucose (peak 45) reaching 9.7, 7.8 and 7.5%, respectively, in RE.  
Having higher thermal stability, sugar alcohols do not take part in any Maillard reaction and are regarded as low-glycemic-index sweeteners compared to free sugars [16]. Roasting increased sugar alcohols, with RRE appearing to be the most enriched, at 18.9%, followed by 14.4% for WG. Glucitol (peak 43) represented the major sugar alcohol in all specimens, except for WE, WS and RWS samples, which encompassed galactinol (peaks 55, 57, 60) as the most abundant sugar alcohol. Myo-Inositol (peaks 46 and 49), the most abundant inositol isomer in cereals, nuts, beans and fruits, improves insulin sensitivity [17]. Whether mahlab seed could represent a potential functional food for diabetic patients needs to be now investigated based on these results. Unroasted WE from Egypt has the best sugar profile owing to the presence of reduced levels of sucrose (15%).
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
### The Proposal of SC-SA
Software-based cipher algorithms exhibit better adaptability to this heterogeneity. By inheriting the design principles of LFSR and Feedback with Carry Shift Register (FC-SRS), cipher algorithms can be designed for software platforms, leveraging mature cipher algorithm concepts. While most lightweight ciphers are initially designed for hardware implementation, the escalating trend of heterogeneity in IoT devices poses a significant challenge. These devices utilize diverse processor platforms, architectures, and models, making it difficult to adopt a unified solution that incorporates built-in cryptographic acceleration modules. Moreover, the security of IoT devices is a paramount concern, especially for those lacking built-in cryptographic modules or relying on outdated ones. The inflexibility in deploying lightweight ciphers impedes their ability to fully exploit the advantages of cryptographic design. Designing a lightweight cryptographic algorithm with easy deployability and emphasizing software implementation emerges as an effective solution to tackle security issues such as data information leakage in heterogeneous and resource-constrained IoT devices. The objective of this paper is to design a sequence cipher algorithm (SC-SA) specifically for resource-constrained IoT devices and heterogeneous devices. SC-SA demonstrates superior software implementation performance. Moreover, the cipher algorithm incorporates non-linear operations and transformations, such as addition and S-box substitution, to enhance resistance against stronger cryptographic attacks. Additionally, SC-SA involves only one round of iteration in the key-stream generation process after the key obfuscation phase, aligning with the requirements of real-time applications. SC-SA utilizes an 80-bit key length, which strikes a balance between computational complexity and security in cipher design. By default, the user needs to input 80 binary values. If the user wishes to use \"characters\" as the key, they can be converted using a custom Hash algorithm. This key length can meet the security requirements of the majority of resource-constrained devices. In the key distribution and padding phase of SC-SA, the initial key is used to fill the 8-byte Queue T (bytes in Queue T follow the properties of a queue, where elements enter at the tail and exit at the head), the 1-byte Index Data Group A, and the State Data Group V. The overall logical structure is roughly illustrated in Figure 1.
## 3 Results and Discussion
### Adsorption Experiments  
The molecular structure and characteristics of the anionic dyes Acid Yellow 42 (AY 42) and Acid Red 213 (AR 213) used as adsorbate are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dye** & **Molecular Structure** & **Molecular Weight  
The synthesized PMs were tested as adsorbents for the mentioned anionic dyes. The studies were performed at an optimum solution pH (2.1 for both dyes) and a temperature of 25 \({}^{\circ}\)C, using 1 g/L PM and a dye concentration of 50 mg/L. Preliminary adsorption results are presented in Table 5.  
The results show that the removal efficiency/adsorption capacity of the magnetic nanocomposites increased from PM1 to PM4, which can be explained by the increase in the specific surface and total pore volume (Table 3). The PM4 sample has been selected as the adsorbent in adsorption studies due to its large specific surface area, high removal efficiency/adsorption capacity, and also because it can achieve good magnetic separation from the aqueous solution.
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 1 Introduction
* The potential to have all the kinematic chains below the mobile platform, which is essential in gait and balance rehabilitation devices [24] to avoid collisions with the patients. In addition, we propose a methodology that considers singularities, collisions and kinematic joint range limitations to obtain the workspace of this new PM. This methodology helps us to detect the most critical factors that limit the workspace and, therefore, provides practical information to deal with the design of the PM depending on its final application. To have a comprehensive characterization of the kinematic behavior of the PM, we applied the methodology to all its working modes. The rest of the paper is organized as follows: Section 2 describes the novel 2T2R PM. In Section 3, the kinematic analysis of the manipulator is carried out, in which the inverse position problem is solved and the velocity problem is set up to study the singularities. Section 4 explains the method used to obtain the useful workspace for different working modes of the manipulator. Finally, some relevant conclusions are addressed in Section 5.
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications
### Interaction in VR Applications
#### 4.3.3 Holding Objects in Virtual Reality  
In some of our Virtual Reality applications, such as Crvena stijena VR and Roman Heritage in the Balkans, we enabled the user to touch and explore reconstructed 3D objects. We used the grip buttons of the controller to pick up exhibit objects. The user needed to hover their hand over the exhibit object, and then press and hold the grip button of the corresponding controller so that the item would appear in the user's hand. While the grip button was pressed, the item could not be dropped and would keep following the hand. In Crvena stijena VR, when the user releases the grip button, the item falls to the ground, and in Roman Heritage in the Balkans, we decided to return the object to its original position when it is released. This method of interacting with the exhibits mimics a  
Figure 3: Interacting with the user interface via laser pointers on the Crvena stijena VR Application.  
natural feeling of holding objects in your hand, which is much more realistic than simply pressing a button to pick up an object or picking the object with the laser pointer. A problem with this approach can occur when the VR setup is in a smaller room, so the user is not able to reach some of the objects. This could be solved by allowing the user to teleport between different points in the scene.
## 3 Outage Probability of RIS-ONOMA
### Probability of Outage of RIS-AP-ONOMA  
Figure 2 illustrates the RIS-aided AP-ONOMA system. In AP configuration, RIS is located near to transmitter. \(N\) passive reflectors are placed nearer to AP, hence the fading effect between AP and RIS is less effective. This scenario represents one-hop communication.  
The received signals \(y_{1}\) at user 1 and \(y_{2}\) at user 2 are given by  
\[y_{1}=\Big{[}\sum_{i=1}^{\frac{N}{2}}v_{i}\Big{]}x_{s}+n_{1} \tag{33}\]  
\[y_{2}=\Big{[}\sum_{i=\frac{N}{2}+1}^{N}v_{i}\Big{]}x_{s}+n_{2} \tag{34}\]  
Let \(P=\sum_{i=1}^{\frac{N}{2}}v_{i}\) and \(Q=\sum_{i=\frac{N}{2}+1}^{N}v_{i}\) are the overall gains of the one-hop channel. As per the CLT, \(P\) and \(Q\) follow complex Gaussian distribution where \(P\in\ \mathbb{C}\!\!N(0,\ N_{1})\) and \(Q\in\ \mathbb{C}\!\!N(0,\ N_{2})\)[27].  
Following the same procedure as in RIS-SR-NOMA, we can derive the PDF and probability of outage expressions for RIS-AP-NOMA.