esa-sceva/satcom-chunk-collection huggingface dataset:
# Content Adaptation and Depth Perception
## 6 Potential Applications
### Retro-Gaming Platform  
We have modified an open-source MegaDrive (Genesis) emulator to render each 2D layer separately. The chosen emulator is Helios, coded in Java.  
The original MegaDrive employs five layers in which games draw their tiles and sprites; these layers are Background, Plane B, Plane A, Sprites, and Window. We exploit this feature and instead of combining all the layers in the screen, we render each layer at different slices and depths. The resulting parallax effect is appropriate for most retro games and provides a novel experience of classic games.  
Many Megadrive games can be played in SliceView, but some were not completely adaptable. Some games employ a sprite priority mechanism that switches the rendering order of specific parts of the layers. On some occasions, layers contain glitchy graphics because they are not meant to be seen, since they are totally occluded by a layer on top. Sometimes, background elements are much brighter than foreground elements, making foreground colors and details difficult to perceive. As light is perceived cumulatively, brighter colors can severely bleed into darker ones. To mitigate these problems, we implemented two different approaches. Firstly, background dimming and foreground brightening can be applied to enhance the important game elements. Secondly, we added an option to mask in black, in a layer, the silhouette of the layers on top of it. This option solves the background layers with glitchy graphics and improves the final image quality, but leaves gaps when the display is not directly observed from the front (see Figure 13).  
Videogames that do not use the pixel priority system have a horizontal-scroll gameplay, dark backgrounds and bright foregrounds, and implement some parallax effect between layers working better in SliceView. Ghosts'N GoblinsTM is an example that has all these characteristics (Figure 14).  
Figure 12: User painting in SlicePainter at different layers; depending on the point of view, the layers align differently and provide different interpretations.
# Performance Analysis of Hard-Switching Based Hybrid FSO/RF System over Turbulence Channels
## 2 System Model
### Outage Probability
The outage probability can be estimated using the cumulative distribution function (CDF) of the FSO link SNR. The total outage probability is given as [19]:  
\[F_{out}(\gamma_{th-FSO},\ \gamma_{th-RF})=F^{\mathrm{FSO}}(\gamma_{th-FSO})F^{RF}( \gamma_{th-RF}) \tag{11}\]  
where the CDF for the Gamma-Gamma FSO link is given as, [20]:  
\[F^{\mathrm{FSO}}(\gamma_{th-FSO})=\frac{(ab)^{\frac{\alpha+b}{2}}}{\Gamma( \alpha)\Gamma(b)}\Bigg{(}\frac{\gamma_{th-FSO}}{\overline{\gamma}_{FSO}} \Bigg{)}^{\frac{\alpha+b}{4}}G_{1,3}^{2,1}\Bigg{(}ab\sqrt{\frac{\gamma_{th- FSO}}{\overline{\gamma}_{FSO}}}\Bigg{|} \begin{array}{c}1-\frac{\alpha+b}{2}\\ \frac{a-b}{2},\frac{b-a}{2},-\frac{a+b}{2}\end{array} \Bigg{)} \tag{12}\]  
where \(G_{p,q}^{m,n}[\cdot]\) stands for the Meijer G-function, [21]. Furthermore, the CDF for the RF link is obtained by integrating (9), [22]:  
\[\mathrm{F}^{\mathrm{RF}}(\gamma_{\mathrm{th-RF}})=\int_{0}^{\gamma_{th}}f_{ RF}(\gamma_{RF})=\ 1-e^{-\gamma_{th-RF}/\overline{\gamma_{RF}}} \tag{13}\]  
By substituting (12) and (13) into (11) we obtain:  
\[F_{out}(\gamma_{th-FSO},\ \gamma_{th-RF})=\frac{(ab)^{\frac{\alpha+b}{2}}}{\Gamma(a) \Gamma(b)}\Bigg{(}\frac{\gamma_{th-FSO}}{\overline{\gamma}_{FSO}}\Bigg{)}^{ \frac{\alpha+b}{4}}G_{1,3}^{2,1}\Bigg{(}ab\sqrt{\frac{\gamma_{th-FSO}}{ \overline{\gamma}_{FSO}}}\Bigg{|} \begin{array}{c}1-\frac{\alpha+b}{2}\\ \frac{a-b}{2},\frac{b-a}{2},-\frac{a+b}{2}\end{array} \Bigg{)}(1-e^{-\gamma_{ th-RF}/\overline{\gamma_{RF}}}) \tag{14}\]  
The above equation is a closed form expression for the outage probability of the hybrid FSO/RF system which is governed by the factors of gamma-gamma and Rayleigh distribution, respectively.
## 5 Discussion: The Difficult Transition to an Equitable and Sustainable Future
Although low-emission tropical countries are likely to suffer most under further climate change [38], as discussed above, extreme weather events are increasingly now experienced by middle- and high-income countries. As recognized 50 years ago by the authors of 'Limits to Growth' [85], an end of GDP growth is probably inevitable if we are serious about an equitable and ecologically sustainable planet. The concept of 'degrowth'--the planned reduction in national economic output--is increasingly popular; the database Scopus shows 181 papers published in 2021 with the term degrowth in the title, abstract or keywords, up from 11 in 2010. Rich country GDP reductions do not necessarily mean a loss of quality of life. When an alternative measure which more closely human welfare is used, the Genuine Progress Indicator (GPI) (which takes account of inequality), it is found that while GDP per capita has risen in OECD countries, the GPI, and especially 'life satisfaction' has in many cases fallen. [PERSON] and colleagues [93] found that globally 'While global Gross Domestic Product (GDP) has increased more than three-fold since 1950, economic welfare, as estimated by the Genuine Progress Indicator (GPI), has actually decreased since 1978'. This is perhaps not so surprising when we learn from Zenithmedia [94] the money that advertisers found it necessary to spend: 'Global adspend is forecast to increase by USD 58.0 billion in 2022, rising to USD 781 billion from USD 723 billion in 2021'. The outlay is increasingly used to get consumers to buy things they do not really need. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Units** & **1950** & **1990** & **2020** \\ \hline Atmospheric CO\({}_{2}\) & ppm & 310 & 354 & 416 \\ Energy-related CO\({}_{2}\) & Gt/year & 6.0 & 21.3 & 32.1 1 \\ Fossil fuel use & EJ/year & 72.5 & 278.5 & 463.7 1 \\ Global car fleet & Million & 51 & 452 & 1186 \\ Ecol. footprint (EF) & No. of Earths & 0.73 2 & 1.29 & 1.75 \\ Anthropogenic mass & Gt & 103 & 492 & 1154 \\ GDP & 10\({}^{9}\) 2017 USD & 10.0 & 51.2 & 126.3 1 \\ Population & Million & 1563 & 5327 & 7795 \\ Cement production & Mt/year & 140 & 1100 & 4370 \\ Plastics production & Mt/year & 2.0 & 120 & 367 \\ \hline \hline \end{tabular} \({}^{1}\) 2019 values are larger, because of COVID-19 economic downturn; 2 1961 EF value, the earliest available. Sources: [32; 78; 86; 87; 88; 89; 90; 91; 92].  
\end{table} 
Table 2: Global values for various parameters, 1950, 1990, and 2020. Although OECD countries presently produce most of the global solar, wind and geothermal energy, any serious and rapid reductions in GHGs will inevitably require deep cuts in FF use in the OECD and other high-energy use countries. Such deep cuts will produce surplus FF capacity in electricity generation, dampening incentives for new RE construction. In presently low-energy countries, any energy capacity expansion will need to be RE, and given the many ecological damages from tropical hydro construction, should mainly be from wind, solar and geothermal.
# Mechanical Behavior and Fracture Evolution of Artificial Rock Specimens Within 3D-Printed Fractures
###### Abstract  
Coal rock is characterized by numerous cracks, which significantly impact its mechanical properties, such as fracture evolution and strength. In this study, various fracture network models were created using three-dimensional (3D) printing technology. Employing rigid adhesive and different proportions of coal powder, coal-like samples with intricate fracture networks were successfully fabricated. To replicate the mechanical properties of natural coal rocks, uniaxial compression tests were conducted to investigate the mechanical characteristics and failure modes of samples with different coal powder ratios. Additionally, the mechanical response of samples with discrete fracture network (DFN) models was evaluated after freezing treatment. Findings revealed that increasing the coal powder content enhanced the strength of the samples, whereas the introduction of the DFN model reduced their compressive strength. Samples containing the DFN model predominantly exhibited longitudinal fractures as their failure mode, contrasting with the shear fractures observed in the solid model samples. Furthermore, under low-temperature conditions, the artificial specimens exhibited a distinct trend, where brittleness increased proportionally with coal powder content, a phenomenon attributed to the influence of AB adhesive. After applying freezing treatment to DFN model coal-like samples, stress-strain curves resembling those of actual coal rocks were observed, along with a slightly reduced compressive strength and a brittle failure mode characterized by oblique shear failure.  
Keywords:coal specimen; 3D printing; discrete fractures network; uniaxial compression; experimental study +
Footnote †: journal: Applied sciences
# Synchronizing Many Filesystems in Near Linear Time
## 5 Theory  
This section contains supporting theoretical results from the algebraic theory of filesystems. Some of the results were used to justify the correctness of algorithms presented in Section 4. Algorithm 6, that checks whether some canonical sets are refluent is presented in this section as it uses the specific characterization given in Theorem 3. First, we look at conditions which guarantee that a canonical set is applicable to a filesystem. Then, these conditions will be used to characterize refluent canonical sets.  
**Claim 1**: _The canonical set \(A\) is applicable to the filesystem \(\Phi\) if and only if the following conditions hold for every command \(\sigma=\langle n,x,y\rangle\in A\): (a) \(\Phi(n)=x\); (b) If \(\sigma\) is a destructor, then \(\Phi(n^{\prime})=\mathbb{O}\) at every node \(n^{\prime}\) below \(n\) not mentioned in \(A\); (c) If \(\sigma\) is a constructor, then \(\Phi(n^{\prime})=\mathbb{D}\) at every node \(n^{\prime}\) above \(n\) not mentioned in \(A\)._  
Proof.: The conditions are necessary. Condition (a) is clear. For (b) and (c), note that no command in \(A\) changes the filesystem value at \(n^{\prime}\), and after executing \(\sigma\), the value at \(n^{\prime}\) must be empty (or directory in case c), respectively. To show that the conditions are sufficient, let \(\sigma\in A\), for which there is no \(\tau\in A\) where \(\tau\ll\sigma\). Then \(\sigma\) can be executed on \(\Phi\) as \(\Phi(n)=x\) by condition (a), and because if \(\sigma\) is a constructor, then no commands on nodes above \(n\) are in \(A\), and thus the values at those nodes are \(\mathbb{D}\); if \(\sigma\) is a destructor, no command below \(n\) is in \(A\), and thus all nodes there contain the empty value. Furthermore, conditions (a)-(c) are clearly inherited in the filesystem \(\alpha\Phi\) and the command set \(A\smallsetminus\alpha\).
## 4 Discussion
The Cepstrum analysis not only displays the degree of harmonics as a single peak on the Spectrum through log Fourier transformation, but also visualizes the harmonic structure of the speech signal more prominently than the Spectrum through the inverse Fourier transformation of the Spectral measurement. In particular, among Cepstral measures, CPP is an index that can check the amount of harmonics in the voice signal through the difference between the maximum Cepstral value and the regression line [45]. Speakers with prominent voice problems tend to show a small CPP value [46; 47], and CPP tends to increase as quality improves or loudness increases [48]. In a number of previous studies, CPP has shown good accuracy and effectiveness in detecting dysarthria (e.g., accurately predicting the severity of dysarthria) and a high correlation with auditory-perceptual evaluation results [25; 45; 46].
###### Abstract  
Vehicles experience various frequency excitations from road surfaces. Recent research has focused on active dampers that adapt their damping forces according to these conditions. However, traditional magnetorheological (MR) dampers face a \"block-up phenomenon\" that limits their effectiveness. To address this, additional flow-type MR dampers have been proposed, although revised designs are required to accommodate changes in damping force characteristics. This study investigates the damping performance of MR dampers with an additional flow path to enhance the vehicle ride quality. An optimization model was developed based on fluid dynamics equations and analyzed using electromagnetic simulations in ANSYS Maxwell software. Vibration analysis was conducted using AMESim by applying a sinusoidal road surface model with various frequencies. Results show that the optimized diameter of the additional flow path obtained from the analysis was 1.1 mm, and it was shown that the total damping force variation at low piston velocities decreased by approximately 56% compared to conventional MR dampers. Additionally, vibration analysis of the MR damper with the optimized additional flow path diameter revealed that at 30 km/h, 37.9% acceleration control was achievable, at 60 km/h, 18.7%, and at 90 km/h, 7.73%. This demonstrated the resolution of the block-up phenomenon through the additional flow path and confirmed that the vehicle with the applied damper could control a wider range of vehicle upper displacement, velocity, and acceleration compared to conventional vehicles.  
magnetroheological (MR) damper; additional flow energy path; quota vehicle model; vibration analysis; electromagnetic analysis; AMESim +
Footnote †: journal: applied sciences
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 5 Results and Discussions
### Resiliency in Winter
When the load shedding reoccurs at hour 8-9, the PV and ESU start supplying at this interval. However, since the demand increases, the generator has to be utilized. The duration of the generator in operation is determined by the present SOC (SOC\({}_{c}\)), as shown in plot a. If SOC\({}_{L}\) is reached, Gen1 is turned on. At noon, i.e., hour 12-13, PV and ESU coordinate together to sustain the demand. At hours 16-18, load shedding is imposed again. ESU supplies during the first hour (16-17), but in the next hour (17-18) Gen2 (Gen2_Mode, pink) is needed because Gen1 cannot meet the load requirements during this period. Using this EMS strategy, the HES system has maintained the resiliency, as shown in plot c.  
Figure 16: The global tilted irradiance and the cloud opacity (on 7 November 2019). Figure 17: Resiliency of HES during a moderate winter season (on 7 November 2019). **(a)** The ESU charging and discharging operation with SOC variations. **(b)** Grid, PV, ESU, and Gen modes. **(c)** The resiliency of HES vs. variable load. Figure 18: The global tilted irradiance and the cloud opacity (on 2 January 2020). The successful outcomes of the proposed approach can be observed by the consistency of the power supply during all the considered scenarios. Despite the heavy load shedding from the grid, varying load demands are satisfied even during the abnormal weather conditions. In addition, the continuous exploitation of PV and surplus grid power indicates the optimized utilization of the available energy. Furthermore, the scheme also results in the lowest possible burden on the grid. This is because of PV support to grid operations during the daytime. For further assessment, simulations are performed for a one-year period logged from August 2019 to August 2020. The annual results are illustrated in Figure 20. In particular, both generator's (Gen1 and Gen2) operation can be clearly seen in the zoomed plot. This shows the emergency conditions in terms of the cloudy sky scenario. The Gen_Mode is activated only when the demand exceeds the PV and ESU power during load shedding. Over the entire year (366 days, 8784 h), such situation occurs for 80 h at different time intervals. The Gen1 operates for 49 h, while the Gen2 is triggered for 31 h. It is evident that Gen1 (being smaller) is utilized more than Gen2 in order to minimize the fuel consumption. The results prove the optimized outcome of the proposed EMS. The annual contribution from different energy sources is described in Table 5. The annual net energy demand is 119,519 kWh. Accordingly, the seasonal contribution from these sources is plotted in Figure 21. Clearly, the contribution of HES is greater in the summer season due to the higher availability of PV power. Figure 15:0 Resiliency of HES during an extreme winter season (on 2 January 2020). (**a**) ESU charging and discharging operation with SOC variations. (**b**) Grid, PV, ESU, Gen modes. (**c**) The resiliency can be seen.
###### Abstract  
A new design of the MOSFET dosimeter is being developed in a different study to measure the dose delivered to the tissue layers. Development of zinc oxide-Lead (ZnO-Pb) of different thicknesses fabricated by chemical bath deposition were investigated to study their sensitivity following irradiation using a low absorbed dose that can be used in diagnostic and interventional radiology (9, 36.5, and 70 mGy) and high absorbed dose (1, 5, and 10 Gy) of X-ray. The morphology and structure of the as-prepared films were analysed using FESEM and XRD measurements. The device relies on sensing the changes in the local electric field arising from radiation interactions in the absorber, coupled with the semiconductor materials used in this work--ZnO-Pb as the EGFET. Then the sensitivity of all devices was examined. Generally, thin-film devices showed less sensitivity to X-ray than the disk type. The sensitivity of the thin film dropped from 6.66 mV/to 1.42 mV/Gy, while the sensitivity of the ZnO-Pb disk type was 23.3 mV/Gy, which then dropped to 6.30 6.42 mV/Gy. Furthermore, the disk type ZnO-Pb was exposed to a high absorbed dose and obtained a sensitivity value of 0.08 mV/Gy, while the ZnO-Pb thin film obtained 0.01 mV/Gy. This can be related to the influence of thickness on the sensitivity of the dosimeter. However, the device's performance characteristics, like sensitivity to radiation exposure and operating dose area, were discovered to be strongly dependent on the materials employed, effective atomic number, and thickness of the materials. Based on the results shown above, these devices might be considered a low-cost candidate for real-time -radiation dosimetry at room temperature. Furthermore, the thickest sample of 1 mm showed better sensitivity to radiation, compared to the thinner samples.  
X-ray; radiation detector; EGFET; MOSFET; gamma ray; personal dosimeter; thin film +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effects of Injection Pressure
#### 3.1.3 Emission Characteristics  
A high injection pressure promoted the formation of NOx. As depicted in Figure 7, at the injection pressure of 300 bar, D100 and P50D50 generated 499 and 519 ppm NOx, respectively, which were maintained at a relatively low level. However, when the injection pressure was gradually increased to 450 bar, the emitted NOx also increased to 726 and 735 ppm, with a maximum increase rate of 45.5%. The formation of NOx depends on oxygen concentration, peak temperature, and residence time in the cylinder [30]. Although a high injection pressure limits the penetration range of the fuel, it greatly refines the spray particle size, which is conducive to rapid fuel evaporation and heat release, resulting in higher cylinder temperature, thereby promoting the production of NOx [31; 32; 33]. Regarding the influence of fuel, D100 released less NOx at 300 and 450 bar injection pressures, but its NOx emission performance was inferior to that measured at medium injection pressure.  
Figure 8 shows the smoke released by the combustion of various fuels under different injection pressures. Contrary to the behavior of NOx, smoke opacity improved as the injection pressure increased. Under lower injection pressures, the atomization condition was not good, and the ejected fuel droplets had a larger diameter, easily becoming the precursor core of particulate matter; however, under higher injection pressures, atomization improved, and the fuel particle size became smaller. The fuel could be easily mixed with air  
Figure 6: EGT under various injection pressures.  
Figure 7: NOx emissions under various injection pressures.  
and burned better, contributing fewer smoke emissions [23; 25; 31]. The very high viscosity of palm oil deteriorated the spray performance of P50D50 and caused a larger mist particle diameter, which facilitated the formation of particles. Therefore, the burning of P50D50 released more smoke, and only at 400 bar did it showed less emissions.
# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 4 Blockchain-Based Architecture and System Design
### Block Data Structure  
The block data structure [7; 8; 9] of the proposed blockchain-integrated IIoT system is meticulously designed to ensure secure, scalable, and efficient operations, with each block consisting of two main components: the block header and the block body. The block header contains essential metadata that ensures integrity, security, and traceability. The block header includes the previous block hash, which cryptographically links blocks together, ensuring immutability; the Merkle root hash, which represents all transactions in the block via a Merkle tree for efficient verification; the edge root hash, which integrates IoT device data from edge devices or edge nodes; the timestamp, providing chronological context; the nonce, used in mining processes; and the block hash, a unique identifier generated by hashing the header. The block data structure of the proposed blockchain-integrated IIoT system is depicted in Figure 5. For instance, a mined block might have a hash like _000026d2118c4a35 be647d1f3040839 bef66 ead8096556d63d6 cd6278b78 bff_, with fields such as a previous hash of _00000000..._ (for the genesis block) and a timestamp of 1743053969.0619528.  
The block body contains the actual data stored in the blockchain, including transactions (e.g., sensor readings, such as Tx1: 25.4 \({}^{\circ}\)C) and edge device data (e.g., light node or edge node data), both of which are hashed and incorporated into the Merkle and edge root hashes. Table 1 provides an example of the block header and body data. This structure ensures immutability and integrity through cryptographic hashing, efficient Merkle tree verification, and the seamless integration of IoT and edge computing data. The system achieves scalability, real-time performance, and robust protection against cyber threats, such as DDoS (Distributed Denial of Service) attacks, by leveraging edge computing for distributed processing and smart contracts for automated threat responses. These features align with the proposed IIoT architecture, emphasizing edge computing for latency reduction, smart contracts for tamper-proof actions, and collaborative mechanisms involving deep learning, edge nodes, and blockchain for transparent and accountable DDoS mitigation.  
\begin{table}
\begin{tabular}{c c} \hline Block Hash & 000026d2118c4a35 be647d1f3040839 bef66 ead8096556d63d6 cd6278b78 bff \\ \hline Block Data & Index: 0, Previous Hash: 00000000..., Merkle Root: 9d89 ed10..., Edge Root: 7f19b63e..., Timestamp: 1743053969.0619528, Nonce: 329640, Block Hash: 000026d2... \\ \hline \end{tabular}
\end{table}
Table 1: Example block data.  
Figure 5: Data structure of blocks in the proposed blockchain-integrated industrial internet of things (IIoT) system.