esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigations of Working Characteristics of Transferred Arc Plasma Torch Volume Reactor
## 2 The Experimental Setup  
A transferred arc torch-type volumetric plasma chemical reactor was used for waste neutralization and process research. A prototype of this device was developed at the Plasma Processing Laboratory of the Lithuanian Energy Institute. Both the plasma torch unit and the volumetric reactor walls were cooled with water and air at flow rates of 1.24 kg/s and 6.8 g/s, respectively. The bottom of the plasma chemical vector was made of a conductive graphite layer of material, which also played the role of an anode. The electric arc in the volume reactor was moving chaotically and was formed using air with a flow rate of 0.8 g/s. The recycled material consisting of a mixture of dry clay and hydroquinone (composition of 25% hydroquinone and 75% clay) was dosed onto a conductive graphite surface layer. Hydroquinone was selected as the less active substance. Clay was chosen as the carrier, and hydroquinone was chosen as neutralized waste. With a hydroquinone content of more than 25%, there are technical problems associated with dosing of the mixture into the reactor chamber as the mixture becomes sticky. The distance between the plasma torc and the top layer of conductive material can be varied in the range of 10 cm. The transferred arc torch power is up to 40 kW, and the discharge current is 160 A. Based on the literature, the plasma flow temperature at central part is about 13,000 \({}^{\circ}\)C [5]. A schematic diagram of the transferred arc torch-type plasma chemical reactor is shown in Figure 1.  
The investigation of the formation of multiphase flow parameters has been performed also experimentally. A high-speed RedLake MotionPro video camera was used for instantaneous imaging of the plasma arc. A fast, 12-bit CMOS camera (MotionPro from Redlake) equipped with a zoom lens and a neutral density filter was used to visualize the plasma. The camera exposure time is 2-43 \(\upmu\)s.  
A platinum-rhodium thermocouple was used to record the distributions of plasma flow temperatures and plasma current potential. The Agilent 34972A digital multimeter, which has a data transfer interface with a personal computer, was used to gather thermocouple data.  
Figure 1: The schematic diagram of a transferred arc torch-type plasma chemical reactor.  
During the plasma chemical process, when the materials were recycled inside the volume reactor, a gas analyzer SWG 300-1 was used to analyze the composition of the emitted gas, to which the gas was fed using a water-cooled probe. The probe is structurally made of a double metal wall, making it possible to cool the entire probe (the entire surface of the probe). The morphology of the materials used for neutralization was evaluated using an electronic scanning microscope Hitachi S-3400N with magnification ranging from 20 to 100,000 times and 5 nm resolution.
## 3 Experimental Setup
### Software Implementation  
#### 3.2.1 Data Input and Real-Time Acquisition  
The input data consist of real-time timestamps and weather information, sourced via OpenAPI from the nearest meteorological station to the target building. This integration ensures that the data remain accurate and relevant, which is essential for generating reliable power consumption predictions. Key weather parameters--including temperature, humidity, wind speed, solar radiation, and precipitation--are incorporated as features to account for environmental influences on energy consumption patterns.
## 5 Conclusions  
In summary, while acknowledging the limitations of our 3D-printed cartilage, such as the challenges in replicating the exact biochemical composition and water content of natural cartilage, our primary focus was on assessing the potential of Digital Anatomy Printer (DAP) technology to emulate the mechanical behavior of knee cartilage. We explored the capability of DAP technology to modify overall mechanical responses through adjustments in polymer compounds and the manipulation of stiffness and elasticity. Our efforts also aimed at precisely tuning compressive and extension responses by varying fiber incorporation within the polymer compound, allowing the replication of specific load-bearing characteristics observed in natural knee cartilage.  
To examine the knee mechanical behavior under osteoarthritis conditions, this study integrated 3D printing, advanced image-processing techniques, and computational simulations. The importance of considering the global morphology of cartilage tissue in designing knee osteoarthritis treatment plans was underscored in the first two parts of the study. Future studies and medical advancements may benefit from employing a synthetic knee joint model and computational simulation to comprehensively investigate the cartilage morphology and mechanical behavior in parallel, providing a more holistic understanding of osteoarthritis pathology.  
In the final phase, the study assessed the feasibility of mimicking cartilage tissue's internal structure and components, utilizing fiber-reinforced material printed with PolyJet technology. The subsequent plan involves designing a multi-layer model that incorporates the native cartilage's zone micro-structure division, optimizing parameters through a more accurate mathematical model. This approach aims to enhance the understanding of Short Fiber-Reinforced Polymer Composites' mechanical performance, optimizing the fibers' volume fraction, and evaluating the impact of the fiber dimensions, thereby saving cost and time.  
In conclusion, this work demonstrated the potential to replicate the patient-specific morphology and mechanical properties of biological tissues with 3D-printable polymers. The envisioned future application involves the use of 3D-printed osteoarthritis cartilage as a scaffold for bioinjection, offering promising prospects for advancements in medical treatment.  
Conceptualization, R.F. and P.G.; methodology, D.C., G.D. and R.F.; tests, R.F., G.B., D.C. and G.D.; formal analysis, D.C. and G.D.; investigation, D.C. and G.D.; writing--original draft preparation, D.C. and G.D.; writing--review and editing, G.B., F.K.C., R.F. and P.G.; supervision and funding acquisition, P.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EU Horizon Projects RESTORE grant ID: 814558.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 4 Evaluation
### Throughput
#### 4.2.2 IOPS Latency  
Latency is the time between a user issuing a request and of the system receiving a response. From the perspective of a file system, latency can be used to measure the time it takes to complete a single I/O request. In this section, we evaluate I/O latencies for reading or writing 64 KB transfer sizes. Since the local file system XFS does not require a network connection to transfer data, it naturally has the lowest latency performance. From the results shown in Figure 5, it is proven that F3 BFS has a lower and more stable I/O latency than Ceph.
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 2 Related Research Work
### Application of Game Theory in the Field of Security  
Game theory is a decision analysis theory based on advance. Due to its value in understanding and modeling conflict, game theory has been recently applied to information warfare and computer cyber security. [PERSON] [49] proposed the idea of rational analysis of normal and malicious nodes in a cyber network by using a random game. [PERSON] [50] proposed to use repeated games with incomplete information to model the behavior of attackers and defenders in information warfare. [PERSON] and [PERSON] [51] analyzed the Nash equilibrium of the defender and the attacker and their respective optimal strategies using the form of stochastic game. [PERSON] [52] designed and analyzed the DDoS defense system based on the static game with complete information and optimized the performance of the system. [PERSON] [53] proposed intrusion intent and strategy reasoning models based on game theory, which contributed to the further development of the security field.  
This paper draws on the related research results above, but it is also different from the research work above. The details are as follows: First, this paper presents a defense graph model for the risk assessment of UAV cyber range attack and defense, and calculates the attack and defense benefits. Secondly, an offensive and defensive game model with incomplete information is established to calculate the mixed strategy Bayesian-Nash equilibrium. Finally, according to the defender's benefit and the attacker's optimal strategy, the probability distribution is selected as the basis for the UAV range risk assessment calculation.
## 3 Results
### Adhesive Remnant Index (ARI)  
A high ARI score of 2 was observed for the CB and MB groups. Thus, more than 50% of the adhesive remained on the enamel surface. In contrast, in the 3 DB groups, no residual adhesive or less than 50% of the adhesive remained on the enamel surface (ARI scores 0 and 1). A significant difference was determined amongst the ARIs across the three types of brackets (\(p<0.05\)) (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name of the Bracket**} & \multicolumn{4}{c}{**ARI of the Tooth \({}^{*}\)**} \\ \cline{2-5}  & **0** & **1** & **2** & **3** \\ \hline
3 DBC & 6 & 13 & 3 & 0 \\ \hline
3 DBCA & 4 & 15 & 2 & 1 \\ \hline
3 DBF & 13 & 8 & 1 & 0 \\ \hline
3 DBFA & 1 & 17 & 1 & 3 \\ \hline CBC & 0 & 6 & 15 & 1 \\ \hline MBC & 4 & 4 & 14 & 0 \\ \hline \hline \end{tabular}  
* ARI 0: no composite left on the enamel surface; ARI 1: less than 50% of the composite left on the enamel surface; ARI 2: more than 50% of the composite left on the enamel surface; and ARI 3: all composites left on the enamel surface. Significant difference amongst ARIs at \(p<0.05\).  
\end{table}
Table 4: Frequency distribution and the result of [PERSON]’s exact test of the adhesive remnant index.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name of the Bracket**} & \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Shear Bond Strength**} \\ \cline{3-6}  & & **Mean** & **SD** & **SE** & **Median** & **IQR** \\ \hline
3 DBC & 22 & 12.3 \({}^{\text{a}}\) & 1.53 & 0.326 & 11.9 & 2.54 \\ \hline
3 DBCA & 22 & 12.6 \({}^{\text{a}}\) & 1.85 & 0.395 & 12.6 & 2.43 \\ \hline
3 DBF & 22 & 12.3 \({}^{\text{a}}\) & 1.47 & 0.314 & 12.1 & 2.08 \\ \hline
3 DBFA & 22 & 11.0 \({}^{\text{a}}\) & 1.12 & 0.238 & 10.7 & 1.54 \\ \hline CBC & 22 & 19.3 \({}^{\text{b}}\) & 5.54 & 1.180 & 20.0 & 10.00 \\ \hline MBC & 22 & 16.9 \({}^{\text{b}}\) & 3.29 & 0.701 & 15.9 & 4.94 \\ \hline \hline \end{tabular}  
* The standard group’s mean values with the same superscript letters (\({}^{\text{a}}\) or \({}^{\text{b}}\)) were not significantly different (\(p\geq 0.05\)). SD, standard deviation; SE, standard error; IQR, interquartile range.  
\end{table}
Table 3: Descriptive statistics and statistical comparisons for SBS (MPa) amongst six groups.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 2 Sparse Planar Array Optimization Design Model
### Optimization Model Based on Antenna Pattern Reconstruction  
The antenna pattern can be reconstructed to meet the specific demands of satellite communication networks, space remote sensing, and large-angle scanning phased arrays, and it has a wide range of application prospects. In satellite communication scenarios, there are certain special requirements for beam width, main lobes, sidelobes, etc. The goal of the optimization model based on antenna pattern reconstruction is to optimize the excitation and element layout by using fewer elements and to change the antenna pattern according to actual requirements so that the reconstructed antenna pattern is closest to the expected antenna pattern.  
Assuming that the number of elements of the sparse planar array is \(Q\), and \(\tau\) is the upper limit of reconstruction error set by the user, i.e., the acceptable deviation between the reconstruction pattern and the target pattern, the basic optimization model can be expressed as  
\[\underset{w_{mn}}{\text{min}}Q \tag{10}\] \[s.t.|F_{REF}(\theta,\varphi)-F(\theta,\varphi)|\leq\tau\]  
The objective of the model presented in Equation (10) is to minimize the number of elements of a sparse planar array when the difference between the reconstructed antenna pattern and the expected antenna pattern is less than the upper limit of reconstruction error set by the user. Here, \(F_{REF}(\theta,\varphi)\) represents the expected antenna pattern, and \(\theta\) and \(\varphi\) are the pitch and azimuth angles of either observation direction in the plane relative to the origin, respectively.
## 3 Materials and Methods
### Methodology
#### 3.2.3 Data Conversion  
As the obtained data varied in terms of their temporal resolutions, data standardization was very much necessary for any kind of further analysis. \"Naive\" method of conversion [22] was used to convert the NDVI, EVI, LAI, and fAPAR data into their average monthly values that facilitated comparability with the monthly PDSI information.
###### Abstract
SAR satellites can acquire ground information around the clock and in all-weather due to their wavelength characteristics [20]. More and more methods are based on SAR images to monitor the changes in water bodies, which has become an indispensable data source in flood emergency monitoring [21]. However, most current studies mainly focus on improving the accuracy of water body extent extraction, and relatively little research has been conducted on the damage to road networks in flooded areas. The urban road network is dense in form and complex in structure, carrying various types of traffic flows, which is the key to maintaining the city's regular operation. Therefore, it is essential to identify the inundated location and damage to the road network under extreme weather conditions for post-disaster relief. In the event of flooding, due to the number of satellite constellations and revisit cycles, a specific type of satellite often does not meet the longtime series monitoring requirements for flooding, which also becomes an important problem for flood rescue and post-disaster assessment. To quantitatively monitor the road damage in the \"7-20\" rainstorm in Zhengzhou, this paper designs a scheme to extract the road network damage in the flood disaster by combining the heterogeneous SAR data and the road network data before, during, and after the rainstorm. At the same time, a time-series monitoring of the disaster process of the \"7-20\" mega rainstorm is carried out. The length of flooded roads was extracted by the SAR image threshold segmentation method and GIS spatial analysis method to provide data support for quantitative road damage assessment.
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## 3 Experiments
### Verification of Real Subpixel Imaging System  
We have realized a subpixel camera according to Figure 2 using IL-E2 TDI-CCDs (Figure 12). The Table 2 gives the main indexes of camera and TDI-CCD. TDI-CCD line scanning speed and the target speed of motion is synchronized in this subpixel imaging.  
We used WT1005-62 transmitted resolution board to examine the MTF of this subpixel imaging system. Figure 13b reveals the reconstruction result of the image pair which has an inexact offset of half a pixel based on projections onto convex sets (POCS) algorithm. Figure 13c shows the same processing result of the image pair with the exact offset of half a pixel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Index** \\ \hline CCD type & IL-E2 TDI-CCD \\ Number of pixels & 2048 \\ Dynamic range & 1600:1 \\ Pixel size & 13 \(\upmu\)m \\ TDI stages & 24 \\ Speed of valid data & 30 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main indexes of camera and TDI-CCD.  
Figure 11: MTF differences with different sampling positions. (**a**) MTF difference between Figure 10b,c; (**b**) MTF difference between Figure 10b,d.  
Figure 14 demonstrates more details with the 1:1 view. We found that the brightness contrast of Figure 14b is still better than Figure 14a, although the contour information is still indistinct. The resolution of Figure 14c is clearly improved, especially in the left second and fourth pairs of lines in the first row. There are many gray and indistinct regions in Figure 14d, with Figure 14e being clearer than the origin image despite the continuous presence of vague edges. In Figure 14f, each pair of lines can be clearly distinguished, especially those in the red box and in 45\({}^{\circ}\) direction. The experimental results show that the image quality of two images when taken in the ideal position is better than in the non-ideal position. A half pixel is the best imaging offset between two images. At the same time, this result is supported by the simulation results from the comparison of MTF (Figure 15).  
Figure 13: Experimental results from subpixel imaging. (**a**) Origin image; (**b**) Results from super-resolution in the non-ideal position; (**c**) Results from the super-resolution in the ideal position.
# Optimised Twin Fluid Atomiser Design for High-Viscosity, Shear-Thinning Fluids
## Nomenclature  
### Abbreviations  
CWS  
Coal-water slurry  
DS  
Dataset  
MP  
Measurement point  
NC  
Nozzle configuration  
ONC  
Optimal nozzle configuration  
PDMS  
Polydimethylsiloxane  
**Subscripts**  
\(A\)  
Ambient  
\(G\)  
Gas  
\(L\)  
Liquid  
**Variables**  
\(\Delta m\)  
Change in mass, kg  
\(\Delta t\)  
Change in time, s  
\(\Delta U\)  
Velocity difference between gas jet and liquid stream, m s\({}^{-1}\)  
\(\dot{m}\)  
Mass flow rate, kg s\({}^{-1}\)  
\(\dot{V}_{\rm G}/\dot{V}_{\rm L}\)  
ALR = air-liquid ratio, -  
\(\dot{V}_{\rm L}\)  
Volumetric flow rate of liquid, m\({}^{3}\) s\({}^{-1}\)  
\(\dot{V}_{\rm G}\)  
Standard volumetric flow rate of gas, std m\({}^{3}\)/s  
\(\eta^{\rm T}\)  
Viscosity at specified shear rate, Pa s  
\(\gamma\)  
Shear rate, s\({}^{-1}\)  
\(\rho\)  
Density, kg m\({}^{-3}\)  
\(\sigma\)  
Surface tension, N m\({}^{-1}\)  
\(\theta\)  
Geometrical apex angle of atomising agent, \({}^{\circ}\)  
\(A_{G}/A_{L}\)  
Area ratio of gas nozzle holes to liquid tube cross-section, -  
\(AX\)  
Axial offset between atomiser and image acquisition location, m  
\(D\)  
Characteristic droplet length scale, m  
\(d_{G}\)  
Diameter of gas nozzle orifices, m
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 4 Uplink Transmission
### Full Zero Forcing  
Several investigations have explored Zero Forcing (ZF) processing in Cloud-Fog Massive MIMO (CF-MMIMO) systems, as outlined in [40]. Centralized ZF methods, as described in [40, 41], involve transmitting real-time Channel State Information (CSI) from all Access Points (APs) to a central processing unit (CPU) for designing ZF precoding or combining vectors. However, this centralized approach can result in substantial fronthaul traffic, making it less feasible as the number of User Equipments (UEs) increases. To mitigate this challenge, our study investigates distributed schemes where APs manage the processing independently.  
Various distributed ZF precoding schemes, such as full-pilot ZF (FZF), partial FZF (PFZF), and protective PFZF (PPFZF), are presented in [7]. These approaches either fully distribute interference suppression or implement it in a coordinated and scalable manner, showing improved performance compared to the Maximum Ratio (MR) scheme. Additionally, local regularized ZF (LRZF) is evaluated as an upper benchmark in [7], despite its limitations in scalability.  
The performance of FZF and an adapted LRZF precoder is further analyzed in [42] within NOMA-aided CF-MMIMO systems. However, the uplink scenario is not addressed in [42, 43].  
In the following sections, we focus on FZF and PFZF as distributed algorithms. For FZF, the local combining vector chosen by an AP for UE \(k\), denoted as \(v_{i,l}^{\text{FZF}}\in\mathbb{C}^{N\times 1}\), is given by  
\[v_{i,l}^{\text{FZF}}=c_{i_{k}}\theta_{i_{k}}\bar{H}_{l}(H_{l}^{H}\bar{H}_{l})^{ -1}, \tag{90}\]  
where \(\theta_{ikl}=E\Big{\{}|\bar{H}_{leik}|^{2}\Big{\}}=\frac{\gamma_{il}}{c_{il}^{ \text{FZF}}}\).  
In our research, we apply Equation (91):  
\[v_{i,l}^{\text{FZF,DMD}}=c_{i_{k}}\theta_{i_{k}}\bar{H}^{\text{DMD}}(H_{l}^{ \text{DMD}})^{H}(\bar{H}_{l}^{\text{DMD}})^{-1}, \tag{91}\]  
where \(H^{\text{DMD}}\) is determined using the proposed algorithms: DMD/PiDMD and DMD/mpeDMD.  
Matrix inversion is greatly simplified using Equation (92):  
\[\begin{split}\tilde{A}&=\tilde{U}^{*}A\tilde{U}= \tilde{U}^{*}X_{0}\tilde{V}\tilde{\Sigma}^{-1},\\ (\hat{A})^{-1}&=\left(\hat{U}^{*}X_{0}\hat{V}\hat{ \Sigma}^{-1}\hat{U}\right)^{-1}=\hat{U}^{*}X_{0}^{-1}\hat{V}^{-1}\hat{\Sigma} \hat{U}.\end{split} \tag{92}\]  
The inversion of matrix \(A\) is considerably more efficient with DMD due to the dimensionality reduction of matrix \(R\) and the use of Singular Value Decomposition (SVD), which simplifies the process. Channel prediction, taking these factors into account, is described by Equation (93):  
\[H_{k+1}^{-1}=H_{k}^{-1}A^{-1}. \tag{93}\]
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 2 The Demucs-Denoiser Model
This is evidence that the Demucs-Denoiser model is a prime representative of the current state of the art in online speech enhancement. However, in the same evaluation [13], it was shown that all the evaluated techniques, including Demucs-Denoiser, suffered from considerable enhancement degradation when there was more than one speech source present in the input signal. Output signal-to-interference ratios dropped between 10 and 20 dB in the majority of the evaluated situations. To be fair, this is not surprising, since speech enhancement techniques assume only one speech source to be present [1] and, empirically, it seems that Demucs-Denoiser tends to enhance the loudest speech source [13], which may not always be the target speech. Thus, it is of interest to improve the enhancing performance of Demucs-Denoiser when there is more than one speech source present. This can be carried out by the use of target selection strategies, as described in the following section.