esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 3 From the Synthesis of a 3D Scene to Its Safe Reception: Process Description
### Scene and CGH Synthesis  
To illustrate the encryption algorithm used, a 3D scene has been designed with well-known objects in the field of computer graphics: a dragon and a teapot contained within a Cornell Box (Figure 2a).  
Figure 1: Flowchart of the method for generating a CGH from a virtual scene, encryption, decryption, and visualization: (**a**) generation of the synthetic image using path tracing techniques; (**b**) CGH synthesis; (**c**) CGH encryption in two steps: pixel shuffling and random phase modification; (**d**) decryption; and (**e**) visualization, both through simulation and in the laboratory.  
Using the ray tracing technique \"path tracing\" [39], a CGH is computed and stored in a matrix of \(N\times M=1920\times 1080\) elements. The phase information can be converted into a PNG-format image, which can then be displayed on an SLM in our case, the Pluto Holoeye [40], with a pixel size of 8 \(\upmu\)m.  
The CGH calculation already inherently includes the first phase mask described in the DRPE algorithm, and the presented procedure allows for encoding both 2D and 3D scenes. This phase corresponds to Figure 1b.  
Figure 2a shows the proposed scene, and Figure 2b displays the phase of the computed CGH in PNG format. The scene window matches that of the SLM (8 mm \(\times\) 15 mm). The detailed process of scene synthesis and CGH generation can be found in [41].
## 2 Mechanisms for Generating Shaft Frequency Signals of Vessels
### Adaptive Spectral Energy Detection  
To comprehensively analyze the signal features and to further assess the effectiveness of the four different decomposition methods for signal feature extraction, the adaptive line spectrum energy detection method is adopted [30]. As analyzed in Section 3.2, signals decomposed by different methods are separated into multiple IMFs. After calculating the spectrum through the Fourier transform and comparing, it is observed that, apart from background noise, the signal exhibits distinct frequencies in IMF3 and IMF4. Therefore, based on the spectral detection method, a fixed threshold value is applied to detect signals in the decomposed signals. The experimental results are shown in Figure 7.  
Figure 6: Vessel shaft frequency electric field signals and Fourier transforms using different decomposition methods: (**a**) based on EWT and signal Fourier transform; (**b**) based on EMD and signal Fourier transform; (**c**) based on EEMD and signal Fourier transform; and (**d**) based on EEMD and signal Fourier transform.  
In Figure 7, the left side shows the time-domain signals obtained after applying four different decomposition algorithms, and the right side displays the time-frequency plots after spectral detection. The figure shows that the time-domain signal after EWT decomposition has distinct shaft frequency signal features in the IMF8 component, lasting approximately from 25 s to 60 s. In Figure 7b, the frequency duration spans about 15 s to 60 s. For the time-domain signal after EMD, as shown in Figure 7c, the target signal persists from around 30 s to 60 s, corresponding to the spectral plot of the target's duration in Figure 7d. As for the EEMD and ICCEMDAN algorithms, the time-domain target signal is prominent, but the corresponding frequency spectra are mostly noise signals. In summary, the EMD signal algorithm more effectively extracts target signals from random signals in the time and frequency domains. It exhibits strong anti-interference capabilities and retains as much feature information from the target source as possible.
## 3 Results and Discussion
### Four-Point Bending Fatigue Test  
#### 3.3.1 Four-Point Bending Fatigue Test Results  
The failure forms of the bending fatigue specimens were all flexural and tensile failure of CNFPUC under fatigue load. The fatigue life of CNFPUC tested at different ambient temperatures and different stress levels is shown in Table 9. Six effective test blocks were at different ambient temperatures per stress level. The fatigue life was arranged from small to large. It could be seen that, under the same stress level, the fatigue life decreased with the increase of the temperature, and the fatigue life decreased with the increase of the stress level at the same ambient temperature.  
The fatigue life of CNFPUC was sensitive to temperature. With the increase of temperature, the hydrogen bond in the elastomer was gradually dissociated, which led to the decrease of physical cross-linking density. Then, the effective cross-linking density of elastomer decreased, and the gelation effect weakened, resulting in a reduction in fatigue life. Compared with temperature, stress level had great influence on fatigue life.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 2 Materials and Methods
### CCC Retrieval Model  
Three distinct methods were used to estimate CCC to retrieve CCC using VIs based on simulated canopy spectrum data from the PROSAIL-D model. Figure 3 summarizes the steps in retrieving canopy chlorophyll. The published VIs calculated from the simulated canopy reflectance were used based on the PROSAIL-D model presented in this study. First, linear regression was used to assess the relationships between CCC and the VIs. A random forest regression approach trained with VIs was then employed to estimate the CCC. Finally, field measurements and MERIS satellite data were used to validate the three types of constructed model.  
Figure 3: Flowchart of the VI combination approach for CCC retrieval.  
Figure 2: Soil reflectance values used for simulation in the PROSAIL-D model.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 4 Method Implementation and Validation  
This section shows the results of the validation process of the proposed method. The results are analysed in terms of stego signal imperceptibility, recovered message quality, and influence of the \(BH\) value on the stego signal imperceptibility and quality.
## 2 Research Methods
### Two-Stage Dynamic Network Cross-Efficiency Model
\begin{array}{l}\max\quad W\\ s.t.\\ \sum\limits_{t=1}^{T}\left(\begin{array}{c}\sum\limits_{a=1}^{A}\delta_{ao}z_ {ad}^{t}+\sum\limits_{r=1}^{R}\psi_{ro}^{1}k_{rd}^{1t+}\\ \sum\limits_{m=1}^{M}\zeta_{mo}y_{md}^{t}+\sum\limits_{s=1}^{S}\psi_{so}^{2}k_ {sd}^{2t}\end{array}\right)-\theta_{dd}^{MT*}\cdot\sum\limits_{t=1}^{T}\left( \begin{array}{c}\sum\limits_{i=1}^{I}\mu_{i\alpha}x_{id}^{t}+\sum\limits_{r =1}^{R}\psi_{ro}^{1}k_{rd}^{1(t-1)}+\sum\limits_{a=1}^{A}\delta_{ao}z_{ad}^{t}\\ +\sum\limits_{b=1}^{R}\zeta_{bo}f_{bd}^{t}+\sum\limits_{s=1}^{S}\psi_{so}^{2}k_ {sd}^{2(t-1)}\end{array}\right)=0\\ \sum\limits_{t=1}^{T}\left(\sum\limits_{i=1}^{T}\mu_{i\alpha}x_{io}^{t}+\sum \limits_{r=1}^{R}\psi_{ro}^{1}k_{ro}^{1(t-1)}+\sum\limits_{a=1}^{A}\delta_{ao}z_{ ao}^{t}+\sum\limits_{b=1}^{B}\zeta_{bo}f_{bo}^{t}+\sum\limits_{s=1}^{S}\psi_{so}^{2}k_ {sd}^{2(t-1)}\right)=1\\ \sum\limits_{t=1}^{T}\left(\begin{array}{c}\sum\limits_{a=1}^{A}\delta_{ao}z_ {aj}^{t}+\sum\limits_{r=1}^{R}\psi_{ro}^{1}k_{rj}^{1t}+\\ \sum\limits_{m=1}^{M}\zeta_{mo}y_{mj}^{t}+\sum\limits_{s=1}^{S}\psi_{so}^{2}k_ {sj}^{2(t)}\end{array}\right)-\sum\limits_{t=1}^{T}\left(\begin{array}{c} \sum\limits_{i=1}^{I}\mu_{i\alpha}x_{ij}^{t}+\sum\limits_{r=1}^{R}\psi_{ro}^{1}k _{rj}^{1(t-1)}+\sum\limits_{a=1}^{A}\delta_{ao}z_{aj}^{t}\\ +\sum\limits_{b=1}^{B}\zeta_{bo}f_{bj}^{t}+\sum\limits_{s=1}^{S}\psi_{so}^{2}k_ {sj}^{2(t-1)}\end{array}\right)\leqslant 0\\ (j=1,\ldots,n)\\ \sum\limits_{a=1}^{A}\delta_{ao}z_{aj}^{t}+\sum\limits_{r=1}^{R}\psi_{ro}^{1}k_ {rj}^{1t}-\left(\sum\limits_{i=1}^{I}t_{io}^{t}x_{ij}^{t}+\sum\limits_{r=1}^{R} \psi_{ro}^{1}k_{rj}^{1(t-1)}\right)\leqslant 0\ (j=1,\ldots,n;t=1,\ldots,T)\\ \sum\limits_{m=1}^{M}\xi_{mo}y_{mj}^{t}+\sum\limits_{s=1}^{S}y_{so}^{2}k_{sj}^{ 2t}-\left(\begin{array}{c}\sum\limits_{i=1}^{I}(\mu_{i\alpha}-\tau_{i\alpha}^{t })x_{ij}^{t}+\sum\limits_{a=1}^{A}\delta_{ao}z_{aj}^{t}\\ +\sum\limits_{b=1}^{B}\zeta_{bo}f_{bj}^{t}+\sum\limits_{s=1}^{S}\psi_{so}^{2}k_ {sj}^{2(t-1)}\end{array}\right)\leqslant 0\ (j=1,\ldots,n;t=1,\ldots,T)\\ \sum\limits_{t=1}^{T}\left(\varphi_{ao}z_{ao}^{t}+\phi_{ro}^{1}k_{ro}^{1}+v_{ mo}y_{mo}^{t}+\phi_{so}^{2}k_{so}^{2t}\right)-W\geq 0\\ L_{i}^{t}u_{id}\leqslant\tau_{id}^{t}\leqslant H_{i}^{t}u_{id}\ (i=1,\ldots,I;t=1,\ldots,T)\\ u_{id}\geqslant 0,\delta_{ad}\geqslant 0,\psi_{rd}^{1}\geqslant 0,\psi_{sd}^{1}\geqslant 0,\psi_{ sd}^{2}\geqslant 0,\zeta_{bd}\geqslant 0,\zeta_{md}\geqslant 0\\ u_{io}\geqslant 0,\delta_{ao}\geqslant 0,\psi_{rd}^{1}\geqslant 0,\psi_{so}^{2}\geqslant 0, \zeta_{bo}\geqslant 0,\zeta_{mo}\geqslant 0,W\geq 0\\ (i=1,\ldots,I;a=1,\ldots,A;r=1,\ldots,R;s=1,\ldots,S;b=1,\ldots,B;m=1, \ldots,M)\end{array} \]The current model has complex situations, such as external resource input for each sub-stage, shared input, and shared output, and the network structure it constructs is also more complex. Therefore, according to the endogenous relationship among efficiencies and the efficiency decomposition model based on the addition principle, the overall efficiency is decomposed into the weighted sum of the efficiencies of each link [27].
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
## 4 Discussion
In the pH range of the solutions after filtration (i.e., pH = 12-12.5), all the ions are captured by the geopolymeric filter, due to the presence of insoluble oxides and hydroxides. It can be hypothesized that ions are captured in neutral form in view of the strong basicity of the monolith surface. Indeed, ions are precipitated in their hydroxides or oxide forms by the monolith surface, and then are trapped into their pores. Such a mechanism, very efficient in terms of metal removal, could be not so effective in terms of strength of the metal-sorbent interaction. This point deserves further verification and study, being of paramount importance in case of disposal of the exhausted monoliths to landfill, to avoid release with time. \begin{table}
\begin{tabular}{c c c c} \hline
**Ion** & **pH 2–4** & **pH 4–10** & **pH 10–14** \\ \hline Cu & Cu\({}^{2+}\) & Cu\({}^{2+}\), CuOH\({}^{+}\), Cu\({}_{2}\)(OH)\({}_{2}\)\({}^{2+}\), CuO & CuO, Cu(OH)\({}_{3}\)–, Cu(OH)\({}_{4}\)\({}^{2-}\), Cu(OH)\({}_{2}\) \\ Fe & Fe\({}^{3+}\), Fe(OH)\({}_{2}\)\({}^{+}\), FeOH\({}^{2+}\) & Fe\({}_{2}\)O\({}_{3}\) & Fe\({}_{2}\)O\({}_{3}\), Fe(OH)\({}_{4}\)\({}^{-}\) \\ Ni & Ni\({}^{2+}\) & NiOH\({}^{+}\), Ni(OH)\({}_{3}\)\({}^{-}\) & Ni(OH)\({}_{2}\), Ni(OH)\({}_{3}\)–, Ni(OH)\({}_{4}\)\({}^{2-}\) \\ Mn & Mn\({}^{2+}\) & Mn\({}^{2+}\), MnOH\({}^{-}\), Mn\({}_{2}\)(OH)\({}_{3}\)\({}^{+}\), Mn(OH)\({}_{2}\) & Mn(OH)\({}_{2}\), Mn(OH)\({}_{3}\)\({}^{-}\), Mn(OH)\({}_{4}\)\({}^{2-}\) \\ \hline \end{tabular}
\end{table} 
Table 5: pH ranges and corresponding species (by MEDUSA calculation).
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 3 Theoretical Background
### Dataset  
Real ship data are not open-access data. Thus, we collected data from a real ship. The data in this study were those of a 13,000 TEU container ship used in actual operations. It is propelled by one MAN-Burmeister and Wain diesel engine and has four 3480 kW generators. Table 1 lists the specifications of the target ship.  
The power load of the ship continuously fluctuates according to the operating state of the ship [30]. Furthermore, the steering equipment installed on the ship is powered by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ship Type** & **Container Ship** \\ \hline Length & 365 m \\ Width & 48 m \\ Draft & 10.8 m \\ Engine Output & 79,106 BHP \\ Generator Output & 3480 kW \(\times\) 4 \\ Maximum Speed & 23.0 knots \\ TEU & 13,154 TEU \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of a container ship.  
Figure 2: SAE.  
an electric motor, and the power load consumed by the electric motor is affected by the external resistance of the ship's hull [31]. Therefore, this study collected data on the electric load, which is the total electric load of the ship, as well as data that indicate the external resistance of the ship, such as its heading, rudder angle, water depth, water speed, wind angle, wind speed, and ship speed. Table 2 shows the types of data measured in this study.  
The data were measured in 10 min intervals, and a total of 30,340 data values were collected. Figure 3 shows the collected ship power load data. Changes in the power load occurred as the ship was operated.  
Table 3 shows the number of data points according to the ship's state. The ship is most often operated At Sea, and the least amount of time is spent in Stand By. In addition, by the number of data points measured, there are twice as many instances of the In Port state compared to the Stand By state.
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
###### Abstract  
In this study, a kind of sustainable ultra-high-performance concrete (UHPC) was designed by using coal gangue ceramics (CGC) and a modified Andreasen-Andersen model. However, when CGC lightweight aggregate with high water absorption is used in UHPC with a low water-cement ratio, CGC has an adverse effect on the working performance of UHPC and may lead to the decrease of mechanical properties. This study found that a 5% silane coupling agent KH560 can make CGC hydrophobic, and cause its contact angle to increase from 0\({}^{\circ}\) to 111.32\({}^{\circ}\). Adding 100% hydrophobic modified CGC into UHPC will significantly improve its working performance, with the highest increase of 38.51%. At the same time, the addition of 20% modified CGC can further improve the compressive strength of UHPC (28 days reached 150.1 MPa), reduce the internal porosity by 21.4%, and make the interface bond more compact. In addition, the hydration degree of UHPC has also been improved, a result caused by the cement obtaining more free water for a more complete hydration reaction. This study can provide a new scheme for solving the problem of the solid waste of coal gangue.  
coal gangue ceramics; UHPC; AAA model; silane coupling agent Article
## 2 Materials and Methods  
### Sampling Area  
Charsadda is a district of Khyber Pakhtunkhwa (KPK) province in Pakistan. Charsadda lies between 34\({}^{\circ}\)03\({}^{\prime}\) and 34\({}^{\circ}\)38\({}^{\prime}\) North latitudes to 71\({}^{\circ}\)28\({}^{\prime}\) and 71\({}^{\circ}\)53\({}^{\prime}\) East longitudes. Charsadda is located in the west of the Khyber Pakhtunkhwa and is bounded by Malakand district to the North, Mardan district to the East, Nowshera and Peshawar districts to the South and the Mohmand Agency to the West. The total area of the district is approximately 996 square kilometers (243,753 acres). The total cultivated area is 210,255 acres (61%) and the irrigated area is 180,339 acres, i.e., 86% of the total cultivated area. Three rivers pass through Charsadda: the River Jindi, the Swat River and the Kabul River. These are the main sources of irrigation for Charsadda. The three rivers then merge and join the Indus River. The population of Charsadda was 1.7 million in the year 2000. Charsadda has a density of 1081 people/sq.km. The main crops in Charsadda are tobacco, sugarcane, sugar beet, wheat and maize, etc. The major vegetables of Charsadda are potato, tomato, cabbage, okra and spinach, while the main fruits are apricot, citrus, plum, strawberry and pears. The Charsadda district is a purely agricultural area and is very fertile. Farmers use extensive amounts of pesticides on crops, fruits and vegetables for the control of various pests. Therefore, in this study to improve public awareness, we selected Charsadda district for the analysis of contamination from 20 organochlorine pesticides in surface water samples. Various surface water sample sites in the Charsadda area given in Figure 1.
## 5 Implementation
### Technologies Used  
The main technologies used are shown in the Figure 9. They are described in detail below, specifying the technologies used and the relevant version implemented:  
* **Python, v3.6**: the language used to implement the motion capture system.
* **Unity 2022.3.19f1 and C#**: Unity was used as the main framework for the design of the three different architectures. Furthermore, specific scripts were created in C# for the more complex tasks.
* **OpenCV v3.2**: a fundamental Python library for implementing the first architecture, as it allows the use of pre-trained machine learning models [(19)].
* **Flusk v1.1**: a framework that allows the easy implementation of multiplayer software, particularly optimized for Unity.
* **Docker v4.30**: mainly used to create volumes capable of storing pre-loaded 3D assets [(20)].
* **Android Studio and Xcode**: used to convert applications created in Unity for the iOS and Android operating systems, respectively [(21)].
* **Firebase**: mainly used as online storage through the Firestore and Storage services [(22)].
* **Normcore**: a cloud service mainly used to share real-time data. After implementing the different architectures, each was validated through different case studies to verify their functioning and performance [(23)].  
The following subsections present several case studies we implemented to evaluate the effectiveness and performance of the reference architectures described in Section 4. These case studies focus on different interaction paradigms, using both virtual reality (VR) and augmented reality (AR) technologies to create collaborative environments.
# Analysis of Technologies for Carbon Dioxide Capture from the Air
## 2 DAC Technologies
### Additional Technologies for CO\({}_{2}\) Capture from Air
The photocatalytic process uses an engineered photocatalyst to artificially mimic photosynthesis and reduce CO2 into solar fuels, increasing overall process efficiency. Photocatalysts are semiconductors with a relatively low bandgap energy. By providing electromagnetic radiations, such as solar energy, with an energy equal or higher than that of the bandgap, valence electrons are excited into the conduction band, creating positively charged holes in the valence band. The promoted valence electrons are able to reduce atmospheric CO2 to valuable fuels. On the other hand, holes in the valence band promote the oxidation of water, generating H2 [18]. A deep description of the photocatalytic process has been provided in [PERSON] et al. [26]. Limitations of this route include the following: low yield, low selectivity, the use of batch photoreceptors with low surface area-volume ratio and poor mixing systems, as well as present economic non-feasibility [18]. Cryogenic separation is a physical process that separates CO2 under extremely low temperatures (about \(-160\)\({}^{\circ}\)C at 400 ppm). A scheme for cryogenic separation is shown in Figure 13[25]. In this process, ambient air enters the unit (point 1) and is cooled in a precooler stage (point 2). The cooled stream (point 3) enters a CO2 deposition chamber (point 4), where additional cooling takes place. This additional cooling is performed by a cryogenic refrigeration cycle. After CO2 deposition, the bulk of the air (point 5) is cycled back through the precooler (point 6), where it absorbs thermal energy from the incoming ambient air before being exhausted (point 7). The precooler is composed of two functional components: a heat exchanger and a radiative cooler to further remove heat from the ingested air. A disadvantage is that the cryogenic separation is expensive due to the very low temperature needed for refrigeration, requiring about 10-100 GJ/tonCO2--a value higher than other methods [45]. However, this technology can achieve percent of capture (about 99.99%) and purity (99.99%) levels higher than those of other processes without requiring additional chemicals, and the obtained CO2 is suitable for geological storage [39]. Figure 13: CO2 cryogenic separation process scheme. Adapted from [25].
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
###### Abstract  
Hydrogen safety remains a paramount concern in pipeline transportation. Once hydrogen leaks and ignites, it quickly escalates into a jet fire incident. The substantial thermal radiation released poses significant risks of fire and explosion. Therefore, studying the thermal radiation characteristics of hydrogen jet fires and developing accurate prediction models are crucial for establishing relevant safety standards. To address the oversimplified consideration of weighted coefficients in thermal radiation prediction models, this study investigated the thermal radiation characteristics of hydrogen jet fire by carrying out experiments and numerical simulations. The results reveal the significant impacts of the leakage diameter and pressure on thermal radiation. Increases in both the leakage diameter and pressure lead to a rapid escalation in the thermal radiation release, highlighting their critical importance in establishing safety standards for hydrogen pipeline transportation. Additionally, this study optimized the weight coefficients in the multi-point source prediction model based on temperature distribution along the flame axis. The optimized model was validated through comparison with experimental data. After optimization, the prediction error of the multi-point source radiation model was reduced from 19.5% to 13.9%. This model provides significant support for accurately evaluating the risk of hydrogen jet fire.  
hydrogen jet fire; flame size; flame thermal radiation; weighted multi-point source model +
Footnote †: journal: Applied sciences
# On Beamforming with the Single-Sideband Transform
## 4 Comparisons and Simulations
### Comparison for Different Numbers of Samples per Frame  
We now compare the beamformers in a scenario with the MTF model and change the number of samples per window (Figure 3). This circumvents the problems addressed in Section 2.4 for both transforms since we do not consider a convolutive filter. By increasing the number of samples per window, we minimize the frequency aliasing effects caused by the windowing process of the time-frequency transform. This allows us to capture more of the desired signal on each window.  
The first effect is that the desired signal's distortion for the SSBT-E decreases as more samples are considered. Also, for all other metrics, the SSBT-E performance approaches that of the SSBT-A as we increase \(N\). The same happens for the STFT filters; however, the difference between the STFT-A and STFT-E is negligible with far fewer samples per frame. It is also observed that for a high \(N\), the SSBT results are only slightly worse than the STFT ones for both the accurate and estimated RFR cases. This supports the previous theoretical claims, where we state that increasing the number of samples per frame reduces the RFR estimation errors.