esa-sceva/satcom-chunk-collection huggingface dataset:
# Mid-State Kalman Filter for Nonlinear Problems
## 5 Simulation
### Simulations Results
As shown in Table 2, the MSKF proposed in this paper has better filtering performance and higher target tracking accuracy than the CKF when the uncertainty region of measurement is CL distribution. Meanwhile, since the MSKF performs linear updates in the measurement space and is more affected by measures, the convergence speed of the MSKF is faster under the same two-point starting mode. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filter** & **Range RMSE/m** & **Angle RMSE/\({}^{\circ}\)** & **ASETC/s** \\ \hline EKF & 1.6700 & 17.7700 & 0.03155 \\ \hline UKF & 0.0956 & 1.2730 & 0.04779 \\ \hline CKF & 0.1139 & 0.1977 & 0.04578 \\ \hline MSKF & 0.0926 & 0.0781 & 0.07162 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different filtering accuracy and time consumption in scenario (2). Figure 8: Range estimation accuracy of CL distribution. Figure 9: Angle estimation accuracy of CL distribution. The state component estimation RMSE and theoretical performance lower bound CRLB is given in Figure 10. Due to the non-Gaussian measurement distribution, the initial RMSE of the four filters and CRLB are all large. As the iteration progresses, the UKF, CKF, and MSKF proposed in this paper gradually converge to steady-state, while the range accuracy of the EKF gradually diverges. The steady-state value indicates the approximation performance of the algorithm to nonlinear filtering. The convergence rate  
Figure 11: Comparison of ANEES of CL distribution. Figure 10: Comparison of state component RMSE and CRLB of CL distribution: (**a**) RMSE in X position; (**b**) RMSE in Y position; (**c**) RMSE in X speed; and (**d**) RMSE in Y speed. of RMSE represents the speed at which the algorithm finds the center of the non-Gaussian distribution. Figure 10a,b clearly show that the CKF and MSKF gradually converge to CRLB, and the MSKF has a faster convergence rate, a smaller steady-state value, and is closer to the theoretical boundary. Figure 11 shows the ANEES distribution of the four filters. Since the consistency metric ANEES measures the difference between the real error and the estimated covariance matrix, the smaller the value, the better the consistency and the higher the robustness. It can be seen that the ANEES of EKF increases gradually, which means that the covariance matrix of the state vector increases gradually in the estimation process, eventually leading to the divergence. The ANEES of UKF gradually converges to a constant value, but its robustness is poor which leads to a significant estimation error. However, the ANEES of the CKF and MSKF gradually converge to achieve high-precision and stable target tracking. The MSKF has better state covariance estimation and higher robustness.
## 3 Analysis of Moire THz Printed FSS
From our observations, three factors are critical  
Figure 4: Numerically obtained electric field distribution in near-field in the case of: (**a**) 0\({}^{\circ}\) rotation; (**b**) 10\({}^{\circ}\) rotation; (**c**) 20\({}^{\circ}\) rotation; (**d**) 30\({}^{\circ}\) rotation. in this experiment based on PE-based samples: (1) a presence of air gap spacing between the two samples, (2) a hole-to-hole misalignment during the superposition of the two samples, and (3) a reproducibility error of the identical fabricated samples (although in the case of printable electronics-based samples, this error is insignificant). These three factors could explain the difference between the experimental results and a perfect situation (simulation). Despite the differences in values between the simulated and the experimental transmission and modulation depths, the general behavior was the same in both cases, and we obtained a maximum modulation depth for the 20\({}^{\circ}\) rotation (as shown in Figure 6e), in agreement with the simulation. One of the major advantages of our analyzed printed Moire reconfigurable THz FSS is that no external stimulus, such as a bias voltage or photoexcitation, needs to be applied in order to achieve the tunability of the transmitted wave. To achieve reconfigurability, two samples could simply be superposed over one another, with only one layer needing to be rotated relative to the other. Furthermore, this rotation can be done and controlled remotely with a motorized translation stage to make THz FSS become active. Additionally, we used two FSSs which were 3D printed, as shown in Figure 1b, to characterize the Moire interference effect by varying the rotation angle between the samples, just as we did with samples made of PE. However, the high malleability and non-flat surface of the 3D-printed samples made it difficult to superimpose them on each other, and this surface variation resulted in a significant misalignment error, i.e., the holes did not form the desired interference patterns. Because of their non-flat surface, the analyzed 3D printed Moire FSS was not performed effectively in terms of modulation depth (figure not shown here) when compared to PE-based Moire FSS. Moreover, the aforementioned problems could be minimized by increasing the printing layers to make it flat surface in the case of 3D-printed FSSs in order to achieve the desired modulation depth. Figure 5: **(a)** Simulated normalized transmission for different rotation angles as 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\); (b) normalized transmission (blue line) and modulation depth (orange line) as a function of rotation angles at operating frequency of 0.25 THz; (c) simulated modulation depth as a function of rotation angles from 0\({}^{\circ}\) to 120\({}^{\circ}\) at 0.25 THz.
## 4 Results
### GPR Surface and Facies  
According to radar stratigraphy concepts, we detected two types of radar surfaces in the 2D radar profiles (Figure 4) based on reflection terminations, facies differences and the existence or not of a coherent reflection systematically truncating the other reflections. These radar surfaces delimit eight types of radar facies differentiated based on shape, dip, mutual relation and continuity of the reflectors that compose them (Figure 5).  
Facies with a seaward inclination predominated, especially in the deeper portions of the GPR profiles. The inclination, length and locally concave/convex shapes allowed us to group the seaward inclined reflectors into two facies, i.e., SI1 (4-6\({}^{\circ}\), 1\(\times\)) and SI2 (6-8\({}^{\circ}\), 1\(\times\)). Landward inclined reflectors characterize infrequent and limitedly extensive facies. Two facies were distinguished according to the inclination and reflection amplitude, i.e., LI1 and LI2 (Figure 4).  
Figure 4: Radar surfaces. GPR image, reflections tracing and interpretation.  
Figure 3: Radar profile with some of the diffraction hyperbolas used for the determination of GPR waves’ velocities. From the velocity dataset, an average velocity of 10 cm/ns was adopted.  
Figure 5: GPR image, reflections tracing and interpretation of the radar facies.  
Between the two upward curved facies, the one characterized by medium amplitude, short lengths and moderate continuity (CU1) was interbedded between SI1 facies (Figure 5). The downward curved facies (CU2) was composed of more continuous and longer reflectors, with seaward and landward inclinations greater than those of CU1 (5-7\({}^{\circ}\) and 7-9\({}^{\circ}\), respectively; 1\(\times\)). The facies composed of downward curved reflectors (CD) is rarely present in the GPR profiles (Figure 5). Interference phenomena of diffraction hyperbolas explain the presence (even rare) of radar facies with chaotic reflectors, i.e., lacking a coherent geometry (CH).  
The reflective framework is completed by a radar facies exhibiting very weak or absent reflections and sporadic phenomena of diffraction (TR) (Figure 5). This facies exclusively characterizes the shallower parts of the subsurface.
## 3 Implementation
### Implementation of Performance Tests
#### 3.2.1 Building Certification Tool  
The Danish building energy regulation BR10 was introduced in January 2011, comprising a 25% improvement on the overall energy performance of buildings compared to previous energy classes (14). Two additional future energy classes were added: the low energy class 2015 and the 2020 building energy class, which set strict requirements on the building annual overall energy demand for ventilation, heating, hot water, and lighting. To assess if a building is complying with the BR10 regulations and energy classes, the Danish Building Research Institute (SBi) has developed an energy performance modeling and simulation tool, Be10 [25], which is the official energy tool for Danish buildings certification based on BR10 regulations. In terms of the modeling and simulation approach, the tool uses a simplified static approach, treating the whole building as one large thermal zone. The tool enables defining static systems parameters in addition to various building constructions with their corresponding area and orientation. However, major assumptions are adopted regarding the loads, schedules, and set points, in addition to neglecting the effect of the weather conditions and the occupancy behavior. Despite this, we consider the outcomes of the model to be valid, as the BE10 modeling tool is a nationally accepted certification tool.  
Figure 6: Query for collecting the sets of lights being fed by each meter in SPARQL on the left. The sets are constructed from individual pairs in post processing. An illustration of the query question and example results on the right.  
Based on the simulation with the BE10 model, the overall yearly energy consumption of the building is around 41300 Wh/m\({}^{2}\). Regarding lighting energy consumption, the tool assumes a standby light intensity of 0.1 W/m\({}^{2}\) of the zone area in addition to a nominal light intensity of 3.5 W/m\({}^{2}\). Using the assumption of a normal usage for a teaching/office building of operation 9 h per day, 5 days in the week, the BE10 model yields a value of 324 Wh/m\({}^{2}\) for lighting in two weeks of normal operation.
# Current Progress Regarding _Cordyceps militaris_, Its Metabolite Function, and Its Production
## 2 Biological Compounds of _C. militaris_
### Cordycepin
This natural compound reduces serum levels of histamine, immunoglobulin E (IgE), and inflammatory cytokines; suppresses the infiltration of mast cells and eosinophils; and downregulates expressions of thymic stromal-derived lymphopoieitin (TSLP), macrophage inflammatory protein (MIP)-2, intracellular cell adhesion molecule (ICAM)-1, thymus and activation-regulated chemokine (TARC), and C-C chemokine receptor (CCR)-3 [89]. Regarding the antiviral effect of cordycepin, it was found that cordycepin significantly inhibited the transfer of Epstein-Barr virus (EBV) from lymphoblastoid cell line (LCL)-EBV-green fluorescent protein (GFP) cells to AGS cells (a human gastric adenocarcinoma cell line), indicating a significant inhibition of EBV infection in gastric epithelial cells [24]. In terms of its antitumor effects, cordycepin significantly increased expressions of DNA methyltransferases (DNMTs), particularly DNMT3, leading to hypermethylation of the BCL7A tumor-suppressor gene in response to cordycepin treatment [24]. Cordycepin inhibits LPS-induced inflammation by suppressing the activation of nuclear factor (NF)-\(\kappa\)B, Akt, and p38 phosphorylation, resulting in the downregulation of inducible nitric oxide synthase (_iNOS_) and cyclooxygenase (_COX_)-2 gene expressions and subsequent inhibition of NO production [57]. It was shown to prevent hyperlipidemia by activating adenosine monophosphate-activated protein kinase (AMPK), demonstrating its potential as a functional food ingredient for managing lipid metabolism disorders [78]. In a dose-dependent manner, cordycepin inhibited platelet aggregation induced by thapsigargin and significantly decreased levels of ([Ca+]i), which had been increased by thapsigargin (1 mM) or U46619 (a stable analog of the endoperoxide prostaglandin, H2) (3 mM). Furthermore, cordycepin increased cyclic guanosine monophosphate (cGMP) levels that had been reduced by thapsigargin [79]. The 50% inhibitory concentration (IC\({}_{50}\)) values of cordycepin for human neuroblastoma SK-N-BE(2)-C and human melanoma SK-MEL-2 cells were 120 and 80 mM, respectively, indicating significant inhibition of their proliferation [80].
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 3 The Proposed Model
### Lung Segmentation  
The primary objective of segmentation in medical imaging is to produce precise lung outlines that closely mirror the reference or original outline. These outlines are pivotal for many medical uses and further examinations by accurately defining the edges of the lungs. To achieve this, we utilize a duo of neural networks: a generator (G) and a discriminator (D). [PERSON] is crucial in crafting lung outlines, taking black-and-white CT scans as input. Through its complex structure, [PERSON] is adept at identifying significant features and patterns within the images, creating outlines that suggest the areas of the lungs. These outlines endeavor to accurately capture the complex shapes and perimeters of the lungs, which is crucial for detailed segmentation. Conversely, [PERSON] collaborates with [PERSON] to boost the precision and quality of these outlines. Acting as a rigorous assessor, [PERSON] examines how closely the created outlines match the reference or original outlines. Utilizing sophisticated methods like the Earth Mover's (EM) distance, [PERSON] measures the variance between the predicted and the reference outlines, enhancing [PERSON]'s output. This motivates [PERSON] to produce outlines that align with the original, improving the segmentation outcomes. During the training phase, [PERSON] is fed a lung CT scan image, referred to as \(I_{i}\), and generates a corresponding outline \(M_{i}\) that highlights the areas of the lungs. [PERSON] then scrutinizes the created outline \(M_{i}\) for its fidelity and quality, offering critical feedback that aids [PERSON]'s learning and advancement. This dynamic interaction between [PERSON] and [PERSON], coupled with reference outlines, aids in continuously enhancing the lung outlines generated through the training. In the forthcoming sections, we will explore the intricacies of [PERSON] and [PERSON]'s designs, elucidating their structure and functionalities. We will also discuss the loss function utilized during training, which is significant in steering the networks toward yielding more precise and dependable lung segmentation.
## 4 Numerical Applications
### Sensitive Analysis
#### 4.2.5 Effect of the Maximum Energy Stored in Energy Storage  
The desired installed \(P_{ddchmax}\) as a function of state of charge (SOC) under certain system reliability is shown in Figure 12 for one of the test systems. SOC and \(P_{ddmax}\) of energy storage are the attributes of the energy storage system. In Figure 12, the desired installed \(P_{ddmax}\) of energy storage decreases with the increasing of SOC. SOC and \(P_{ddmax}\) are the attributes of the energy storage, and the ability of peak load shifting of energy storage will be improved by the increase in \(P_{ddmax}\). Therefore, to maintain the system reliability, the desired installed \(P_{ddmax}\) decreases with increasing SOC. Although the differences are small, a high SOC value can be chosen for a study that is intended to show small differences of different energy storage system installation locations, whereas a low SOC can be used to emphasize the difference. When the SOC of energy storage system is determined, the desired installed \(P_{ddmax}\) of energy storage differs at the various locations. The desired installed \(P_{ddmax}\) increases at LP45, LP30, DG and LP40, respectively. The desired \(P_{ddmax}\) is close when the energy storage is installed at DG and LP30, and is higher than that when the installation occurs at LP45 lower than LP40. The desired installed \(P_{ddmax}\) goes from <170 for the cases, to values >0.7 of SOC. The relation between the installed locations and \(P_{ddmax}\) is irrelevant to the SOC of energy storage. Such arbitrariness is not good for the credibility of the results, and it seems more preferable to select the installation location.
## 3 FTN Signal Processing Based on the UEP Algorithm
### UEP Setting Algorithm for the HSS-Based LDPC Decoding Method
For example, the 26, 27, 30, 36, 74, 90, and 179 th bit node groups, linked to the first check node group, should have a high priority. The priority algorithm set the priority of the bit node group, linked with the 1 st to 90 th check node group. Hence, as shown in the right part of Figure 6, it is necessary to rearrange the bit nodes to be transmitted firstly according to the HSS-based LDPC decoding process of the priority algorithm. For instance, with no UEP setting, signals are transmitted in the order from the 1 st bit node group to the 180 th bit node group. If the priority algorithm configures and sends the UEP setting, the 26 th bit node group, connected to the 1 st check node group, is transmitted instead of the 1 st bit node group, as shown in the right part of Figure 4.1 Thus, the priority algorithm for the UEP setting determines the priority of groups and arranges them according to the positions of the bit node groups connected to the check node groups in the H matrix of the code rates to be transmitted.
## 4 Experiments
### Evaluation Metrics  
#### 4.2.1 Dice  
In the context of medical imaging competitions, the Dice coefficient is the most frequently utilized metric. It is an ensemble similarity metric, typically employed to quantify the similarity between two samples, with a value domain of \([0,1]\). Its applications extend to image segmentation in medical imaging, where the optimal outcome is a segmentation value of 1, and the least favorable result is a segmentation value of 0. The Dice coefficient is calculated according to the following Equation (15): _pred_ represents the set of points predicted by the network, and _true_ represents the set of true values.  
\[Dice=\frac{2*(pred\cap true)}{pred\cup true} \tag{15}\]
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 5 Methodology  
Figure 3 shows the block diagram of a coded wireless communication system using BCH and RS codes with the existing multipath propagation.  
On the transmitter side, binary sequences \(K\)-bits long are generated by a pseudo-random generator block and input into the BCH/RS encoder block. The encoder block maps k bits of the sequence into \(N\) bits of the sequence. The \(N\) bits are passed on to the BPSK modulator. The Gaussian-like (AWGN and Rayleigh) channel block is designed to introduce a fading effect and add noise to the modulated signal. The channel block helps to study and analyse the effect of the actual signal properties.  
On the receiver side, \(L\) signals are chosen (LoS and (\(L-1\)) from NLoS). The threshold which is used to choose \(L\) paths was selected randomly for each transmission. It represents the minimum signal-to-noise ratio (SNR) that can be accepted to consider the received signal as a copy of the original transmitted signal. If the received signal has a SNR less than the threshold, the receiver will ignore it. The threshold ranges between 40% and 75% from the SNR of the LoS signal. After selecting \(L\) signals (copies), the signals are first demodulated by the demodulation block to obtain the coded signals. After that, the demodulated signals pass to the combiner block to combine them into one good signal. The BCH/RS decoder block takes the result of the combiner block as an input and performs the appropriate decoding operation to recover the transmitted signal. The final step in the simulation is to compute the BER to analyse and study the performance of the coded communication system with and without the existing multipath propagation. The BER calculator block is designed for this purpose.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chip Rate (Mcps)** & **Delay \(\tau\) (\(\mu\)s)** & **Path Length at Less \(TL\) (m)** \\ \hline
1 & 1 & 1 \\
3.84 & 0.26 & 78.125 \\
10 & 0.1 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different chip rates and the time delay needed to obtain more than one copy.  
Figure 3: Coded wireless communication system with multipath propagation.
## 2 System Model
### Error Correction  
Several works (e.g., [15; 40]) have shown that the FER of a finite length code can be well approximated by  
\[P_{e}(R,\text{SNR})=Q\left(\frac{\mu-R}{\sigma}\right), \tag{1}\]  
where \(R\) represents the code rate (i.e., number of information bits divided by codeword length) and \(\mu\) and \(\sigma\) are code-specific parameters that depend on the SNR. We model such dependency as  
\[\mu =a_{\mu}\cdot\text{SNR}^{-c_{\mu}}+b_{\mu}, \tag{2}\] \[\sigma =a_{\sigma}\cdot\text{SNR}^{-c_{\sigma}}+b_{\sigma}. \tag{3}\]  
The techniques proposed in this paper could be applied to any code by adjusting the parameters (\(a_{\mu},b_{\mu},c_{\mu},a_{\sigma},b_{\sigma},c_{\sigma}\)), but the numerical simulations in this paper will focus on the binary QC-LDPC  
Figure 1: Relay system model.  
code of length \(n=648\) and \(k=432\) (rate \(2/3\)) proposed in the 3 GPP standard for 802.11n [41], for illustrative purposes. Our prior work [42; 43] showed that  
\[a_{\mu} =-0.2 b_{\mu} =0.86 c_{\mu} =1.74 \tag{4}\] \[a_{\sigma} =0.12 b_{\sigma} =-0.08 c_{\sigma} =0.42 \tag{5}\]  
provide a good fit to this code when \(\textsl{SNR}\in[0.5,2]\).  
Binary QC-LDPC codes offer very efficient encoding and decoding using parallel shift registers [44; 45]. This has made them the preferred option in 5G NR, over turbo codes such as the ones proposed in the LTE standard. Additionally, QC-LDPC codes can be flexibly punctured and extended for nearly continuous rate adaptation. A QC-LDPC code is uniquely defined by a sparse parity check matrix \(H\in\{0,1\}^{(n-k)\times n}\), such that \(Hx=0\) for all codewords \(x\). Received channel values (i.e., matched filter outputs) are processed to obtain a log-likelihood ratio (LLR) for each individual bit \(b\) as  
\[\ell=\text{LLR}(b|r)=\log\frac{p(b=0|r)}{p(b=1|r)}, \tag{6}\]  
where \(p(0|r)\) and \(p(1|r)\) represent the conditional probability of \(b=0\) and \(b=1\), respectively, given the received value \(r\). It is not hard to prove that for an AWGN channel with equiprobable and symmetric inputs, the LLR values are given by  
\[\text{LLR}(b|r)=2\cdot\textsl{SNR}\cdot r. \tag{7}\]  
The decoding of LDPC codes is typically done through message-passing algorithms, which refine these LLR values iteratively until convergence or until a prefixed maximum number of iterations is reached. When the algorithm does converge, it is almost always to the right codeword. We thus assume that a codeword error occurs if and only if the LDPC decoder fails to converge.