esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications with J-KAREN Laser System
### Hard X-ray Generation with Laser-Cluster Interaction  
The laser-cluster interaction for the X-ray generation is attractive because of no debris production and a high X-ray conversion efficiency. Since the X-ray pulse duration, which is shorter than 1 picosecond, can be generated by the interaction [88], it may be applied for the real-time measurement of a phase transition in the crystal. In particular, Xenon (Xe) K-shell X-rays have a high energy close to 30 keV, which is suitable for the application.  
However, there is a problem for the X-ray production. When the amplitude of laser pre-pulse is large, the clusters are broken by the prepulse before the main pulse arrival. Therefore, the high-contrast laser, J-KAREN laser system is utilized for the Xe K-shell X-ray generation. The laser is focused on the Xe clusters with 18 \(\upmu\)m \(\times\) 36 \(\upmu\)m spot diameter (1/e\({}^{2}\)) at the peak intensity of 10\({}^{19}\) W/cm\({}^{2}\)[24]. The clusters are produced by adiabatic expansion of the Xe gas inside a specialized nozzle.  
Figure 17 indicates the X-ray energy spectrum using Xe clusters measured with a hard X-ray charged coupled device. Besides the broad energy spectrum from 10 to 40 keV, there is a clear peak, the energy of which is around 30 keV. This peak corresponds to the Xe K-shell X-ray emission, which is due to the excited Xe K-shell electrons fall down to the ground state. The high peak laser intensity with the high contrast ratio enables the Xe K-shell X-ray emission efficiently.  
Figure 16: Spectrum of the radiation reflected by the relativistic flying mirror.  
Although the laser-cluster interaction is studied vigorously, little is known about the underlying mechanism of Xe K-shell X-ray generation. This result gives us a hint for understanding the mechanism of the Xe X-ray generation.
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 2 MIMO System Design with CoI Cancellation
### Projection Based Channel Estimation
It is interesting to see that  
\[\mathbf{P}_{k}\mathbf{B}_{k}=\mathbf{B}_{k}-\mathbf{B}_{k}(\mathbf{B}_{k}^{H} \mathbf{B}_{k})^{-1}\mathbf{B}_{k}^{H}\mathbf{B}_{k}=\mathbf{0}. \tag{11}\]  
Equation (11) is the key principle of the orthogonal projection method. After all interference is canceled, the remaining signal related to \(\mathbf{h}_{m,n}\) is  
\[(\prod_{i=1}^{M}\mathbf{P}_{i})\mathbf{y}_{n}=(\prod_{i=1}^{M}\mathbf{P}_{i}) \mathbf{A}_{m}\mathbf{h}_{m,n}+(\prod_{i=1}^{M}\mathbf{P}_{i})\mathbf{w}_{n}. \tag{12}\]  
From (12), one can observe that all the interference is canceled. In Ref. [11], the CoI is sequentially measured and then subtracted from the received signal. If the measured CoI is not accurate, the error will propagate to the next iteration; thus, this results in performance degradation. The proposed channel model does not need to directly measure the CoI, so the performance will be significantly improved. Equation (12) can be directly solved by LS algorithm. When the number of transmitters is \(M=2\), the proposed channel model reduces to the case in Ref. [29].
## 1 Introduction
In [14], the first-order Markov process was used to model noise in the factory environment, and noise in the factory was compared with additive white Gaussian noise. The analysis results showed that factory noise was not a conventional additive white Gaussian noise, and the properties between them were significantly different. However, the difference between noise in factories and additive white Gaussian noise was only discussed theoretically in this paper, and the actual measurement was not considered. In [15], the time domain measurement of noise was carried out in paper mills and iron and steel mills by using a spectrum analyzer, and the amplitude probability distribution (APD) information of noise at 439 MHz, 440 MHz, 570 MHz, and 2450 MHz was provided. However, the time-varying characteristics of noise were not discussed in this paper. In [16], the researchers measured the impulse noise generated by an automotive ignition system and an automotive electronic parts manufacturing system at 2.4 GHz frequency point, but the typical frequency bands of the IIoT system such as 315 MHz, 779 MHz, and other possible frequencies were not considered. In this paper, log-period antennas and spectrum analyzers were used as measurement and analysis equipment. Based on the measurement results in the frequency domain, the frequency occupation in the factory environment was analyzed. According to the time domain measurement results at 315 MHZ, 779 MHz, and 916 MHZ, the amplitude probability distribution (APD), noise amplitude distribution (NAD), pulse duration distribution (PDD), and pulse separation distribution (PSD) of noise were extracted, and the characteristics of electromagnetic noise were investigated. Finally, the continuous hidden Markov models (CHMM) were used to model the time-varying characteristics of electromagnetic noise. The rest of this article is organized as follows. The descriptions of the measurement environment and methods are provided in Section 2. In Section 3, we use four types of parameter extraction methods, APD, NAD, PDD, and PSD, to analyze the statistical properties of the gathered noises. The results of noise measurement in the frequency domain and time domain are given in Sections 4 and 5, respectively. In Section 6, a brief conclusion is given.
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 3 Improved Decoding Algorithm  
### Quantization-Based Correction Factors  
When implementing LDPC decoding algorithms on FPGA hardware, it is necessary to convert floating-point data to fixed-point data using quantization. Let the quantization module have a quantization range of \([\theta_{\text{min}},\theta_{\text{max}}]\), a quantization bit-width of q, and a step size of \(\Delta\). The relationship among these parameters can be expressed by the following formula:  
\[\theta_{\text{max}}-\theta_{\text{min}}=2^{q}\times\Delta \tag{15}\]  
After processing through the quantization module, the quantized output sequence can be represented as follows:  
\[y^{\prime}_{i}=\text{sgn}(y_{i})\Delta\bigg{[}\frac{|y_{i}|}{\Delta}+\frac{1}{ 2}\bigg{]} \tag{16}\]  
Here, \(y^{\prime}_{i}\) denotes the quantized output sequence, \(\lfloor x\rfloor\) represents the floor operation, \(y_{i}\) indicates the output sequence before quantization, and \(\text{sgn}(y_{i})\) represents the sign of \(y_{i}\).  
Under the influence of AWGN channel noise, the signal range at the input of the decoder is \([-4,4]\). With 8-bit quantization, the step size is \(1/32\) according to Formula (15), resulting in an amplification factor of 32. In this 8-bit quantization, 1 bit is for the sign, 2 bits represent the integer value, and 5 bits represent the fraction.  
FPGA excels at performing parallel additions due to its parallel architecture, but more complex operations like look-up tables or successive approximations require significant logic resources. For this reason, in the NMS algorithm's check node information update (Formula (10)), the multiplication by the correction factor \(\alpha\) can be approximated using shift-add operations for hardware implementation.  
Here is an illustrative example:  
Assume the smallest value \(L^{(l)}(c_{ji})\) processed by a check node has a binary representation of 0001010. The first bit is the sign bit, while the remaining bits 0010110 represent the absolute value 22. If the bits are cyclically right-shifted by one position, the result is 0001011, representing 11. Thus, right-shifting by one position is equivalent to \(L^{(l)}(c_{ji})/2\). Similarly, right-shifting by two positions approximates \(L^{(l)}(c_{ji})/4\), and so forth. By using shift-add operations, the multiplication by the correction factor \(\alpha\) can be approximated.  
For example, to achieve \(L^{(l)}(c_{ji})^{\prime}=L^{(l)}(c_{ji})\times 0.8\), use \(L^{(l)}(c_{ji})^{\prime}=L^{(l)}(c_{ji})\times(1/2+1/4+1/32+1/64)\) as an approximation by right-shifting the data bits by one, two, five, and six positions before summing them up. Due to the limited number of quantization bits, each multiplication results in some loss of precision.
## 4 Calibration Comparison of Landsat 8 and Sentinel 2
### Pseudo Invariant Calibration Sites (PICS) Absolute Calibration Based on Hyperspectral Sensor Models  
Two different models are being used to predict the TOA reflectance of the PICS regions using hyperspectral satellite data as the source of the \"field\" data. The models consider the atmospheric conditions, BRDF, and solar and viewing geometries in converting hyperspectral TOA reflectances to multispectral TOA reflectances (either OLI or MSI bandpasses). The South Dakota State University Absolute PICS (APICS) model uses Hyperion data [23], and the ESA Database for Imaging Multi-spectral Instruments and Tools for Radiometric Intercomparison (DIMITRI)-PICS model uses Medium Resolution Imaging Spectrometer (MERIS) data [16]. Results using these models are shown in Figure 9 and indicate that the two instruments are calibrated to within 2% of each other, except for possibly the CA band.  
Figure 8: The average lifetime ratio between top-of-atmosphere (TOA) reflectances for coincident acquisitions of Landsat-8 and Sentinel-2A for the common spectral bands with 1-sigma error bars. There were seven cloud-free coincident acquisitions for Algeria-3 since the Sentinel-2A launch and five of Libya-4.  
Figure 9: Absolute PICS model results for calibration of Landsat-8 OLI and Sentinel-2A MSI for the Libya-4 region. The top figure shows the Database for Imaging Multi-spectral Instruments and Tools for Radiometric Intercomparison (DIMITRI) results for the visible and near-infrared (VNIR) bands (DIMITRI does not include data for the shortwave infrared (SWIR) bands) and the bottom figure shows the APICS results for all the common spectral bands.
# A Development and Validation of an AI Model for Cardiomegaly Detection in Chest X-rays
###### Abstract  
In this study, the development of a deep learning approach for distinguishing cardiomegaly in chest X-ray images and its validation process are presented. Typically, radiologists diagnose cardiomegaly by examining X-ray images. However, their interpretations can vary owing to subjective judgments, and mild cardiomegaly can be missed. For this reason, there is ongoing research into the use of AI-based deep learning algorithms as an adjunct to X-ray interpretation. In this study, radiologists collected 10,000 public images, from which 718 useful images were selected to create a dataset. A DenseNet21 algorithm was then used to develop an AI model for cardiomegaly detection. The results demonstrate an accuracy of 0.95, a recall of 0.91, and an F1 score of 0.94. Additional validation was performed to ensure the accuracy of the cardiomegaly detection model. The validation methods included saliency maps and guided backpropagation, which indicated the significance of the model. In conclusion, this study demonstrates the potential to develop a high-quality AI model with fewer data than previous studies, suggesting its applicability in the medical field.  
cardiomegaly; radiologists; AI; machine learning; validation methods Article
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Azimuth Thruster
#### 2.1.2 Monitored Components  
As was mentioned in Section 1, the commonly damaged components include bearings and gears. In this study, the monitoring is focused on the pinion shaft support bearing and the gear system consisting of pinion and wheel gears, which are located near the selected sensor, as presented in Figure 1. In order to identify the response from different components in the multicomponent system, it is important to identify the frequency components in the signals. The mathematical relationships between the kinematic properties of rotating components and frequency content are well documented, in bearings, for example in [20]. The frequencies of interest in this study are therefore summarized in Table 1 and they include:  
* Ball Pass Frequency, Inner race (BPFI),
* Ball Pass Frequency, Outer race (BPFO),
* Ball (roller) Spin Frequency (BSF),
* Fundamental Train Frequency\(-\)cage speed (FTF), and
* Gear Mesh Frequency (GMF).
# Selected Useful Properties of Polylactide Films Containing Nisaplin and Natamax
## 3 Results and Discussion
### Films Analysis by SEM  
Images of the film surface are shown in Figure 3, which were carried out using SEM analysis. The first image, Figure 3a, shows a control sample of polylactide (L). The second (Figure 3b) and third (Figure 3c) photos show the surface of the foil (LN1.0, LX1.0) containing the highest concentration of the additive, i.e., 1% by weight, nisaplin and natamax, respectively. The similarity of Figure 3b,c may result from the same nature of the biocidal substances added to the polymer, i.e., bacteriocins (nisaplin, natamax). Figure 3a has a different surface from the others, the difference may be due to the fact that it is a control sample containing polylactide, without other additives, so another surface is possible.  
Thanks to the results obtained through these analyses, it is possible to critically look at other results, such as the biocidal effect or the homogeneity of the obtained films. The biocidal substance affects the morphology and surface of the polymer. If the compound is uniformly distributed and dispersed in the polymer matrix, a smooth surface is obtained.  
Figure 3: SEM images of the studied materials: (**a**) L, (**b**) LN1.0, (**c**) LX1.0).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name of Samples** & **Light Transmission [\%]** & **Hazing [\%]** & **Gloss [gU]** \\ \hline L & 86.34 \(\pm\) 0.40 & 42.56 \(\pm\) 1.21 & 86.90 \(\pm\) 0.78 \\ LN0.2 & 87.30 \(\pm\) 1.09 & 56.52 \(\pm\) 1.58 & 85.70 \(\pm\) 0.57 \\ LN0.6 & 85.98 \(\pm\) 1.10 & 63.89 \(\pm\) 0.61 & 85.00 \(\pm\) 0.64 \\ LN1.0 & 85.04 \(\pm\) 0.90 & 65.34 \(\pm\) 0.73 & 84.02 \(\pm\) 0.87 \\ LX0.2 & 85.32 \(\pm\) 1.20 & 44.52 \(\pm\) 1.23 & 86.10 \(\pm\) 1.13 \\ LX0.6 & 85.89 \(\pm\) 0.98 & 49.50 \(\pm\) 1.22 & 85.50 \(\pm\) 0.43 \\ LX1.0 & 86.50 \(\pm\) 0.73 & 55.20 \(\pm\) 1.11 & 83.42 \(\pm\) 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of transmittance and gloss determination.  
Minor changes or even defects may also appear due to sample drying or solvent evaporation. The films were produced using the solvent method, so some changes may result from the evaporation of the chloroform that was used to produce the films. A certain hypothesis can be made regarding the type of interaction between the additive and the polymer. Namely, regardless of the additive used, it may affect the formation of hydrogen bonds. Depending on the nature of the additive used, additional chemical bonds may also be formed, e.g., ionic bonds or cross-linking bonds, ester bonds. Analyzing the SEM results, it can be concluded that the type of additive, its concentration, the nature of the polymer matrix, and the method of obtaining the foil have a direct impact on the surface structure [24].
# Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
## 2 Materials and Methods
### Preliminaries
#### 2.1.2 Maximum Flows  
A flow network \(\mathcal{N}=(V,A,S,T,c)\) is a simple symmetric directed graph \((V,A)\) with two disjoint non-empty _terminal_ node sets \(S,T\subsetneq V\), also called the source and target node set, as well as a capacity function \(c:A\to\mathbb{R}_{\geq 0}\). A flow in \(\mathcal{N}\) is a function \(f:A\to\mathbb{R}\) subject to the _capacity constraint_\(f(a)\leq c(a)\) for all arcs \(a\), _flow conservation_\(\sum_{(u,v)\in A}f((u,v))=0\) for all nonterminal nodes \(v\), and _skew symmetry_\(f((u,v))=-f((v,u))\) for all arcs \((u,v)\). In this paper, we consider only unit flows and unit capacities, i.e., \(f:A\to\{-1,0,1\}\), \(c:A\to\{0,1\}\). The _value_ of a flow \(|f|:=\sum_{s\in S,(s,u)\in A}f((s,u))\) is the amount of flow leaving \(S\). The _residual capacity_\(r_{f}(a):=c(a)-f(a)\) is the additional amount of flow that can pass through \(a\) without violating the capacity constraint. The residual network with respect to \(f\) is the directed graph \(\mathcal{N}_{f}=(V,A_{f})\), where \(A_{f}:=\{a\in A|r_{f}(a)>0\}\). An _augmenting path_ is an \(S\)-\(T\) path in \(\mathcal{N}_{f}\). A node \(v\) is called _source-reachable_ if there is a path from \(S\) to \(v\) in \(\mathcal{N}_{f}\). We denote the set of source-reachable nodes by \(S_{r}\), and define the set of _target-reachable_ nodes \(T_{r}\) analogously. The flow \(f\) is a _maximum flow_ if \(|f|\) is maximal among all possible flows in \(\mathcal{N}\). This is the case if and only if there is no augmenting path in \(\mathcal{N}_{f}\). The well-known max-flow-min-cut theorem [20] states that the value of a maximum flow equals the capacity of a minimum \(S\)-\(T\) edge cut. \((S_{r},V\setminus S_{r})\) is the source-side cut, and \((V\setminus T_{r},T_{r})\) is the target-side cut of a maximum flow.
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.6. Lean Construction (1994)  
The first indexed reference to lean construction (LC) dates back to 1994, when [PERSON] published the proceedings paper _Lean Construction_[70], which placed lean production in the particular context of a product (a building) that cannot be moved in a continuous flow.  
It was not until 2002 when research attention returned to the topic [71], and in 2006 [PERSON] et al. [72] proposed a practical view toward implementing lean tools.  
In 2019, [PERSON] presented an epistemological perspective not only on LC but also on lean and its Japanese origins [73]. In 2020, [PERSON] et al. [74] proposed \"Construction 4.0\" as the link between LC and \"Industry 4.0\" with the aim to go further in construction operations efficiency.  
The last review [75] was published in 2020, and it explored the barriers to implementing LC.  
As a conclusion, lean construction targeted this specific sector in which the product cannot be moved in a continuous flow. It adapts lean principles and tools to this particular production process. It was quite ignored until 2008, but the topic has generated moderately increasing interest in the past decade, recently linked with 14.0 (Figure 10).
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 2 Vehicle Dynamics Modeling
### Tire Model  
Instead of relying on theoretical frameworks, tire semi-empirical models incorporate realistic data and provide advantages such as streamlined calculations, enhanced real-time performance, and increased accuracy [32]. The Magic Formula and Duggoff tire models are among the most commonly used. Given the need for real-time computation and computational efficiency, this study utilizes the Duggoff tire model. The following calculation formula is obtained by disregarding the camber angle and concentrating just on the tire's longitudinal and lateral forces:  
\[F_{xij}=\mu F_{z}^{0}\cdot C_{xij}\frac{\lambda_{ij}}{1-\lambda_{ij}}\cdot f(U) \tag{4}\]  
\[F_{yij}=\mu F_{z}^{0}\cdot C_{yij}\frac{\tan(\alpha_{ij})}{1-\lambda_{ij}} \cdot f(U) \tag{5}\]  
\[f(U)=\left\{\begin{array}{l}U(2-U),\;U<1\\ 1,\;U\geq 1\end{array}\right. \tag{6}\]  
\[L=\frac{(1-\lambda_{ij})}{2\sqrt{C_{xij}^{2}\lambda_{ij}^{2}+C_{yij}^{2}\left( \tan(\alpha_{ij})\right)^{2}}} \tag{7}\]  
where \(\mu\) stands for the road adhesion coefficient. The boundary condition U is used to determine the current state of each tire and \(v_{cij}\) represents the wheel center velocity. \(C_{x}\) indicates the lateral stiffness of the tire. \(C_{y}\) indicates the longitudinal stiffness of the tire. \(\lambda_{ij}\) represents the longitudinal slip coefficient of the vehicle. \(\alpha_{ij}\) determines the tire slip angle.  
Figure 1: The 3-DOF model represents the dynamics of a vehicle.  
The formula for calculating the wheel center velocity for each wheel is as follows:  
\[v_{cf,cfr}=(v_{x}\mp\frac{B_{f}}{2}r)\cdot\cos\delta+(v_{y}+ar)\cdot\sin\delta \tag{8}\]  
\[v_{crl,crr}=v_{x}\mp\frac{B_{r}}{2}r \tag{9}\]  
where \(B_{f}\), \(B_{r}\) respectively, represent the wheelbases of the front and rear wheels of the vehicle.  
By replacing the calculated wheel center velocity for each wheel, it is possible to determine the longitudinal slip ratio for each wheel.  
\[\lambda_{ij}=\left\{\begin{array}{ll}\frac{v_{ij}-v_{ij}R_{e}}{v_{ij}},& \text{{Braking}}\\ \frac{v_{ii}R_{e}-v_{ij}}{v_{ij}},&\text{{Drive}}\end{array}\right. \tag{10}\]  
The slip angle of the tires can be determined using the following formulas.  
\[\alpha_{fl,fr}=\delta-\arctan\left(\frac{v_{y}+ar}{v_{x}\mp\frac{B_{r}}{2}r}\right) \tag{11}\]  
\[\alpha_{rl,rr}=\delta-\arctan\left(\frac{v_{y}-br}{v_{x}\mp\frac{B_{r}}{2}r}\right) \tag{12}\]
## 4 Results
### Results of Monitoring Corrosion Using UGWL
Figure 6: Comparison of select data through 175 days, Specim A. On the 175 th day, the percent change in the amplitude for the 38 cm (15 in) point is approximately 154%, while for the 53 cm (21 in) point it is 105%. When the general trend is studied, regardless of the day-to-day fluctuations, a permanent change in amplitude occurs after roughly a 50% change is established. This happens at day 18 for Point 1 (38 cm or 15 in from the edge) and day 83 for Point 2 (53 cm or 21 in from the edge). Consequently, this change in amplitude (50%) is suggested as the indicator of \"significant change in the condition of the reinforced concrete slab\". Anything lower than 50% can be considered \"uncertain\" or \"mild deterioration potential\", similar to the HCP uncertainty considerations. To further evaluate this conclusion, the second specimen is discussed below. Later, chloride levels corresponding to the day 175 UGWL readings will be established to further correlate the UGWL amplitude readings to corrosion progression. Figure 8 shows the UGWL data for Specim B for all the measurement points through 132 days of observation, while Figure 9 presents a daily plot showing the change in the amplitudes for three specific points. The generally lower amplitudes in Specim B compared to Specim A is due to the fact that the transmitter is attached to the 33-degree cut angle in this case, decreasing the energy propagating through the waveguide. The three points selected for further investigation of this specimen are points at 23, 69, and 84 cm (9, 27, and 33 in, respectively) away from the transmitter end, because these areas showed the most significant changes in UGWL amplitudes (17, 105, and 151 percent, respectively, over the 132 days). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Day 0** & **Day 9** & **Day 30** & **Day 101** & **Day 175** \\ \cline{2-10}
**UGWL Data Points** & **Amp.** & **Amp.** & **\% change** & **Amp.** & **\% change** & **Amp.** & **\% change** & **Amp.** & **\% Change** \\
**(v)** & **(v)** & **(v)** & **vs. Day 0** & **(v)** & **vs. Day 0** & **(v)** & **vs. Day 0** & **(v)** & **vs. Day 0** \\ \hline
38 cm (15 in) & 0.039 & 0.061 & 56 & 0.074 & 90 & 0.092 & 136 & 0.099 & 154 \\
53 cm (21 in) & 0.038 & 0.055 & 45 & 0.051 & 34 & 0.065 & 71 & 0.078 & 105 \\ \hline \end{tabular}
\end{table} 
Table 3: Change of amplitude for various points for Specim A.  
Figure 7: Specim A: UGWL amplitude change for two sensor points over 175 days. Figure 8: Specim B—UGWL readings for select days between day 0 and day 132. Figure 9: Specim B—Progression of the UGWL readings from three points over time. Finally, to establish an intermediary data verification, after monitoring Specim B for 132 days using the UGWL method, an autopsy was performed, as shown in Figure 10. Some early/mild corrosion build-up can be observed on the rebar that was embedded in this specimen, which correlates to the minor changes in the amplitudes. This experiment confirms that very small changes (such as 17%) in amplitude can be an indication of the onset of the corrosion process.