esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Major Contributions  
The key contributions of this work are summarized as follows:  
1. The analytical expressions for outage probability in multi-user IOS-aided C-HNOMA systems are derived, considering various user pairing schemes.
2. A novel optimization framework is proposed to allocate power fractions dynamically within each time slot, maximizing the overall SSE while maintaining computational efficiency.
3. The complexity of different user pairing strategies is analyzed, and their effectiveness in enhancing system performance is systematically evaluated.
4. The analytical expressions for throughput and diversity gain are derived as a function of \(L\), providing key insights into the system's scalability and performance trade-offs.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Multiple** \\ **UE** \\ \end{tabular} } & \multirow{2}{*}{**HNOMA**} & \multirow{2}{*}{\begin{tabular}{c} **C-** \\ **NOMA** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Outage** \\ **DOMA** \\ \end{tabular} } & \multirow{2}{*}{**Optimization**} & \multirow{2}{*}{**Throughput**} & \multirow{2}{*}{\
\begin{tabular}{c} **Throughput** \\ **Under iSIC** \\ **and HWDs** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **SSE Under** \\ **iSIC** \\ **and HWDs** \\ \end{tabular} } \\ \hline
[3] & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) \\
[22] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ & \(\times\) \\
[6] & ✓ & ✓ & \(\times\) & \(\times\) & ✓ & \(\timesThe effects of iSIC and HWDs on outage probability, throughput, and SSE are thoroughly examined, providing a comprehensive understanding of system limitations and possible enhancements.
## 2 Materials and Methods  
### Plasmids and Strains  
Nucleotide sequences respectively encoding UGT76G1 (Q6 VAB4), _A_tSUS1 (P49040), _S_tSUS1 (P10691), _A_tSUS3 (Q9M111), and _A_tSuSy (A0A059 ZV61) were codon-optimized for expression in _Escherichia coli_ and synthesized by Genscript Biotechnology Co., Ltd. (Nanjing, China). pRSFDuel-1 (Novagen) was used as the backbone for constructing recombinant plasmids. The double mutant of _A_tSuSy (L637M-T640V) was made using a Mut Express(r) II Fast Mutagenesis Kit V2 (Vazyme Biotech Co., Ltd., Nanjing, China), and the resultant clone was named as _A_tSuSym. More details regarding the plasmid and strain constructs used in this study are shown in Table 1. The linkers adopted for fusion constructs are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strains** & **Plasmids** & **Plasmid Description** \\ \hline At1-76G1 & pRSF-A4 SUS1-76G1 & Genes encoding _A_tSUS1 and UGT76G1 inserted in _Ncol_/_E_o_RI and _N_el_/_N_el of pRSFDuel-1, respectively \\ \hline St1-76G1 & pRSF-64 SUS1-76G1 & The coding region of _A_tSUS1 was replaced by that of _S_tSUS1 in pRSF-A4 SUS1-76G1 \\ \hline Ac-76G1 & pRSF-A4 SUS1-76G1 & The coding region of _A_tSUS1 was replaced by that of _A_tSuSy in pRSF-A4 SUS1-76G1 \\ \hline A3-76G1 & pRSF-A4 SUS1-76G1 & The coding region of _A_tSUS1 was replaced by that of _A_tSUS1 in pRSF-A4 SUS1-76G1 \\ \hline
76G1-At1 & pRSF-76G1-A4 SUS1-76G1 & Genes encoding UGT76G1 and _A_tSUS1 inserted in _Ncol_/_E_o_RI and _N_el_/_N_el_ of pRSFDuel-1, respectively \\ \hline At1-79-76G1 & pRSF-A4 SUS1-79-76G1 & The coding regions of _A_tSUS1 and UGT76G1 are fused with a linker of PPP in pRSF-A4 SUS1-76G1 \\ \hline
76G1-P3-At1 & pRSF-76G1-P3-A4 SUS1 & The coding regions of UGT76G1 and _A_tSUS1 are fused with a linker of PPP in pRSF-76G1-A4 SUS1 \\ \hline R1 & pRSF-A4 SUS1-R1-76G1 & The coding regions of _A_tSUS1 and UGT76G1 are fused with an R1 linker in pRSF-A4 SUS1-76G1 \\ \hline R2 & pRSF-A4 SUS1-R2-76G1 & The coding regions of _A_tSUS1 and UGT76G1 are fused with an R2 linker in pRSF-A4 SUS1-76G1 \\ \hline R3 & pRSF-A4 SUS1-R3-76G1 & The coding regions of _A_tSUS1 and UGT76G1 are fused with an R3 linker in pRSF-A4 SUS1-76G1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strains and plasmids used in this study.
# Numerical Analysis of Laser Pattern Effects to Residual Stress on Metal 3D Printing
## 3 FEM Analysis Result
### Experimental Verification  
To verify the experimental results, a specimen was printed using a metal 3D printer. The equipment used for printing was the 'Orlas creator' equipment of the 'OR laser' company. The processing variables were set to be the same as that used in the simulation, and the other values used are summarized in Table 2[13]. Specim printing was performed such that the same pattern as that in the analysis was applied, and computed tomography scans were performed to verify the shape and printing quality of the specimen. Except for the 180\({}^{\circ}\) specimen, some pores appeared inside the specimen; however, they were not sufficiently many to affect the overall quality.  
Figure 9 shows the result of photographing at approximately 1 mm high for the specimen. The reason for this, however, is to ignore the cracks that occur when the lower support is separated from the base plate. Unlike other specimens, the 180\({}^{\circ}\) specimen observed had cracks that occurred in the corners. The initial laser path angle was set at 45\({}^{\circ}\) and was printed by applying the same heat input path to each layer. Consequently, cracks occurred in the corners. Furthermore, as confirmed by numerical analysis, cracks occurred owing to the concentration of deformation stress and thermal deformation at the end of the laser path. Based on the numerical analysis results, it was confirmed that stress was generated in the direction where the specimen was lifted from the corner and that stacking was repeated in the height direction of the specimen, resulting in cracks in the lower region.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Property Name** & **Value** & **Units** \\ \hline \multirow{4}{*}{Laser condition} & Laser type & Yb fiber & \\  & Wavelength & 1070 & nm \\  & Max power & 250 & W \\  & Apply power & 82 & W \\ \hline \multirow{2}{*}{Powder property} & Material name & \multicolumn{2}{c}{Ti-6 Al-4V Grade 23} \\  & Manufacturer & \multicolumn{2}{c}{Advanced Powders \& Coatings} \\  & Size distribution & D10 & D50 & D90 \\ \cline{2-4}  & & 18.0 & 31.6 & 43.5 \\ \hline \multirow{2}{*}{Production property} & Initial angle & \multicolumn{2}{c}{45} \\  & Specim size & \multicolumn{2}{c}{\(5\times 5\times 5\) (W \(\times\) D \(\times\) H)} & mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Process parameters for specimen printing [13].
## 7 Challenges and Limitations
### Ethical and Regulatory Issues
#### 7.4.2 Real-World Applications and Practical Constraints  
To illustrate how these strategies translate into practical applications, I present two real-world scenarios:  
1. Anomaly Detection in Critical Networks: A deep learning-based intrusion detection system was deployed in a 5G network to identify malicious traffic patterns. To comply with GDPR, the system employed federated learning, ensuring that raw user data never left local devices. Differential privacy techniques further protected sensitive information by introducing statistical noise to training datasets, making the model compliant without affecting its accuracy (95%).
2. Traffic Management in Smart Cities: An AI-driven traffic prediction system was implemented to optimize vehicle flow during peak hours. To address trustworthiness, the model incorporated XAI methods, providing city planners with interpretable insights into how predictions were made. By adhering to data minimization principles, the system processed only anonymized location data, ensuring compliance with regulatory standards.  
Table 13 compares regulatory compliance costs across different regions, showing the economic implications of deploying AI in communication networks worldwide:  
These examples highlight the importance of balancing regulatory compliance with model performance and usability. By weaving ethical principles into AI development, systems can achieve high accuracy while fostering trust among stakeholders.  
The application of AI in communication networks is an exciting and rapidly advancing field, yet it faces several challenges and limitations. Addressing these challenges requires a combination of technological innovation and policy development. Ensuring data privacy, optimizing AI models for scalability, improving model interpretability, and navigating the ethical and regulatory landscapes will be key to the successful deployment of AI in communication systems. As the technology continues to evolve, solutions to these challenges will be critical for enabling the full potential of AI-powered communication networks.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 4 Comparative Analysis and Technical Difficulties
### Technical Difficulties and Solutions for Linewidth Characterization
#### 4.2.2 Self-Coherent Detection Based on a Strong Coherent Envelope  
When the delay time is equivalent to (or shorter than) the coherence time, due to the influence of delta function modulation and AOM frequency shift stability, there is a significant spike in the center frequency of the spectral line, and its amplitude is not conducive to linewidth characterization. We analyze the contrast difference between the second peak and second valley of the envelope spectrum (CDSPST) and the relationship between the linewidth and the length of the delay fiber based on the strong coherent envelope self-coherent detection method. The linewidth is calculated as shown in Figure 12d.  
The delay time will be much shorter than the coherence time when using short-delay fiber-optic measurements. At this point, the beat frequency photocurrent power spectrum can be represented as a Lorentz function \(S_{1}\) and a quasiperiodic function \(S_{2}\). The envelope period of the beat frequency power spectrum is related to \(S_{2}\), and the period of \(S_{2}\) depends on the product of the delay fiber length and linewidth. The functions containing delta and Taylor series expansions were applied to the exp term, ignoring high-order terms:  
\[\mathsf{S}(f)=\triangle v\frac{P^{2}}{4 TI(f-f_{0})^{2}}\Bigg{\{}1-\left(1- \frac{2 TI_{s}n_{s}}{c}\triangle v\right)\Bigg{[}cos\frac{2 TI_{s}n_{s}(f-f_{0} )}{c}+\frac{\sin(f-f_{0})\frac{2 TI_{s}n_{s}}{c}}{f-f_{0}}\Bigg{]}\Bigg{\}} \tag{32}\]  
where \(\Delta v\) is the Lorentz linewidth,\(f_{0}\) is the center frequency, and the frequency difference between the minimum points of each order and the center frequency of the power spectrum \(\Delta f_{m}\) can be expressed as:  
\[\triangle f_{k}=\frac{(k+1)c}{l_{s}n_{s}} \tag{33}\]  
where \(f_{AOM}\) is the frequency shift of the AOM and \(k\) is the order of the minimum point. The maximum and minimum points of the power spectrum curve are determined through the maximum and minimum points of \(S_{2}\):  
\[\triangle S(\triangle v)= 10 log_{10}^{S_{peak}}-10 log_{10}^{S_{bright}}=10 log_{10}^{(S( \triangle f-\frac{(2)-1}{2\square t},\triangle v)/S(\triangle f-\frac{m\epsilon }{tL},\triangle v))} \tag{34}\] \[=10 log_{10}^{([1+(2c/\triangle vL)^{2}][1+exp(-2\pi\triangle\frac {bL}{c})]/[1+(3c/2\triangle vL)^{2}][1+exp(-2\pi\triangle\frac{bL}{c})])(m=2,L =2)}\]  
The delay length and linewidth have a corresponding relationship. When \(\Delta S\) and \(L\) are known, the linewidth can be determined. The measurement and calculation method using a coherent envelope can avoid the influence of \(1/f\) noise on the measurement range and accuracy of the linewidth and achieve accurate measurement of the linewidth.
## 2 Materials and Methods
### Pseudo-CT Synthesis  
In this work, we propose a Deep Learning architecture based on a U-net architecture that has been used in various approaches before [18; 21; 22]. The U-net architectures have been implemented in different ways, nevertheless it is always based on a progressive down-sampling of the feature maps followed by a step of up-sampling to generate the final output. Generally, during the down-sampling phase, several convolution filters and sub-sampling operations, such as max-pooling or convolutions with stride, are employed. On the other hand, during the up-sampling phase, the feature maps are up-sampled using operations, such as unpooling or transposed convolutions. Additionally, before each sub-sampling operation in the down-sampling phase, the feature maps are usually connected to their counterpart in the up-sampling phase with the same size to improve the quality of the output.  
Considering this, we have redesigned the architecture of the U-net to incorporate residual operations and convolutions with dilation. These operations have been successfully employed for image classification [24] and image segmentation [30]. Figure 4 depicts an overview of our proposed U-net architecture incorporating these strategies.  
The residual operation consists in a shortcut that adds the feature maps generated by a previous layer to the result of a layer ahead. Figure 5 depicts the residual operation used inside each blue box in our implementation depicted in Figure 4. The residual operation has demonstrated to be very effective to increase the depth of neural networks without a degradation of the results [24]. Therefore, we performed a residual shortcut after every two convolutions during the down-sampling and the up-sampling phase. The convolutions  
Figure 4: Schematic representation of the Franken-net architecture. Our implementation in TensorFlow processes 3D cubes of the MR T1-weighted input image to generate a pseudo-CT cube with the same shape as the input.  
with dilation conform the convolution called \"Atrous\" [30], which entails the use of kernels that are not applied directly on neighborhoods in the feature maps but on values that are separated by a few neighbors. Figure 6 gives a visual explanation of the Atrous convolution. We started with a filter with dilation 1, which means a regular convolution filter, but after every two convolutions (i.e., after each residual block) we increased the dilatation parameter by 1 and reset it after every sub-sampling or up-sampling operation.  
As for our concrete implementation of the sub-sampling and up-sampling operations during the U-net phases, we decided to use convolutions with stride 2 as sub-sampling and transposed convolutions as up-sampling operations. Depending on the stage of the sub-sampling and the up-sampling, a different number of residual blocks are performed, increasing them as the feature maps are down-sampled, and reducing the amount of blocks when the feature maps are up-sampled. Finally, after every convolution a batch normalization is performed and then a ReLU activation function is applied to generate the feature maps.
## 3 Theoretical Analysis
### Singular Value Decomposition (SVD) Denoising  
Consider a sampled electric energy signal \(\mathrm{X}(i)=[x(1),x(2),\cdots,x(N)]\), which can be written as  
\[\mathrm{X}(i)=\mathrm{S}(i)+\mathrm{E}(i),i=1,2,\cdots,\mathrm{N}, \tag{1}\]  
where \(\mathrm{S}(i)\) denotes the noiseless signal and \(\mathrm{E}(i)\) denotes the pure noise signal. SVD denoising aims to remove \(\mathrm{E}(i)\) while retaining \(\mathrm{S}(i)\).  
\[\mathbf{H}=\begin{bmatrix}x(1)&x(2)&\dots&x(n)\\ x(2)&x(3)&&x(n+1)\\ \vdots&\vdots&\ddots&\vdots\\ x(m)&x(m+1)&\cdots&x(\mathrm{N})\end{bmatrix}, \tag{2}\]  
where N is the length of the one-dimensional signal \(\mathrm{X}(i)\) and \(m=\mathrm{N}+1-n\).  
Hence, for the real matrix \(\mathbf{H}_{m\times n}\in R^{m\times m}\), there must be an orthogonal matrix \(\mathbf{U}_{m\times m}\in R^{m\times m}\) of order \(m\) and an orthogonal matrix \(\mathbf{V}_{n\times n}\in R^{n\times n}\) of order \(n\) which satisfy the following relationship:  
\[\mathbf{H}_{m\times n}=\mathbf{U}_{m\times m}\mathbf{U}_{m\times n}V_{n\times n}^{T}, \tag{3}\]  
Figure 1: Process flowchart of the proposed method.  
where \(\mathbf{\Sigma}_{m\times n}\) is a real diagonal matrix of \(m\times n\): \(\mathbf{\Sigma}_{m\times n}=\text{diag}\big{(}\lambda_{1},\lambda_{2},\cdots,\lambda _{q}\big{)}\) in which \(q=\text{min}(m,n)\) and \(\lambda_{q}\) denotes all the non-zero singular values of matrix \(\mathbf{H}_{m\times n}(\lambda_{1}\geq\lambda_{2}\geq\cdots\geq\lambda_{q}>0)\). The first \(k\) singular values represent the noiseless signal information in the \(\mathbf{H}_{m\times n}\) matrix, and the last \((q-k)\) singular values represent the noise information (\(q>k>0\)).  
The last \((q-k)\) singular values are set to zero, after which signal reconstruction is carried out to obtain the signal after denoising. Hence, the determination of effective order \(k\) is crucial to the denoising effect of SVD. To this end, an \"adaptive order determination\" algorithm is proposed in this paper to deduce the optimal effective order for enhanced denoising performance of SVD.
# A Perspective on Information Optimality in a Neural Circuit and Other Biological Systems
## 1 Introduction
### Optimality of a Neuronal System  
This question of optimality in a neural system has been examined at the level of structure and function in a neural network [13, 24, 25, 26, 27, 28]. In the case of the nematode worm _Caenorhabditis elegans_, one type of optimization is observed in the neuronal wiring length and minimization of the overall path length for efficient information communication [25].  
Furthermore, it is expected that the neural system is optimized for its roles across the development of an animal. In support of this hypothesis is that evolutionary adaptation typically leads to a reasonably optimal design in the case of anatomical and physiological features. A similar argument is from the role of natural selection that tends to disfavor individuals with inefficient use of energy, such as in evolving a large brain size, along with its higher energy requirements, in the case where it offers no benefit over a smaller ancestral form. This example shows costly energy use and would result in individuals weakening their chances of survival during the \"struggle for existence\" against conspecifics.  
Other structural properties of the C. _elegans_ neural network have been described, such as an observation of a small-world architecture [10, 29, 30], sparsity of neuronal connectiveness [16], and commonly observed motifs [8, 31]. The neural network may also be described at the informational level.  
A simpler neural system, or neural circuit, is more tractable for measuring information flow efficiency. Past studies have observed the coding of animal behavior in these neural circuits of the nervous system [9, 12, 14, 25, 32, 33, 34, 35]. It is also of interest to relate these systems with the informational processes [10, 36]. The concept of information flow, efficiency, and the presence of neural circuits suggest an applicability of information flow theory, and use of a logic gate model for estimating the rate of information flow in a system [8, 37]. Other applications of models for explaining neural system function include studies by [PERSON] and others [27], [PERSON] [16], and [PERSON] and [PERSON] [38]. In particular, [PERSON] and others [27] proposed a model based on control theory to explain and predict the structure of a neural circuit involved in _C. elegans_ locomotion. Their predictions were further verified by empirical work. The prospect of building models at the small and large scales of life is also present in the study of ecological systems, another area involving nonlinear dynamical processes, where the efficiency and flow of energy and matter are measured without parameterizing the properties of the living organisms themselves [39].
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 4 Simulation Experiments
### Feasibility Verification Experimental Methodology  
In order to validate the feasibility of the proposed method and evaluate the impact of the quantification error of the search scope on the estimation accuracy, the range and Doppler values of the scatterers on the parabolic antenna edge extracted from the ISAR image sequence are error-free in the feasibility verification experiment.  
The flowchart of the feasibility verification experiment is shown in Figure 9. Step 1: Set the ISAR imaging parameters. Set the search scope of the look angle to 15-55\({}^{\circ}\). Set the actual look angle of the spaceborne SAR antenna to 35\({}^{\circ}\). Step 2: Obtain all the visible arcs when the ground-based radar observes spaceborne SAR, within 24 h from the spaceborne SAR orbit epoch time, and select the LowEleArc and HighEleArc as experimental arcs. Step 3: According to the sliding window shown in Figure 10, obtain an experimental arc segment. Step 4: Generate the ISAR image of spaceborne SAR during the experimental arc segment. Step 5: Extract the range and Doppler values of the scatterers on the parabolic antenna edge. Step 6: Obtain the position vectors of scatterers on the parabolic antenna edge using (27). Step 7: Randomly generate the search step between 0.02\({}^{\circ}\) and 0.029\({}^{\circ}\) and quantify the search scope according to the search step length to obtain a search set. Step 8: Calculate the values of the objective function (30) in the search set. Step 9: Find the minimum value of the the objective function (30) to obtain the estimated look angle. Step 10: If the sliding window process has not ended, go to Step 3; otherwise, the experiment ends and all the estimated look angles are obtained.  
Figure 8: Three-dimensional model of the spaceborne SAR parabolic antenna.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 1 Introduction
* Investigation and analysis of the lifetime for LPWAN powered end-device with reference to the battery capacity. Figure 1: LPWAN characteristics that makes it an excellent choice for the IoT applications. Real-time case study for energy consumption in an IoT application. The remainder of this paper is structured as follows. Section 2, describes the state-of-the-art for energy consumption in LPWAN and, the evolution of energy-efficient approaches and its integration with LPWAN wireless technology. Section 3, demonstrates the generic LPWAN energy consumption states. Section 4 explains the approach for energy consumption along with in-depth energy consumption analysis for each communication technology. In Section 5, we discuss the important challenges impacting energy consumption and compared the battery life in accordance with different LPWAN technologies in Section 6. Section 7, provides the energy performance for a real-time use case. Finally, we discuss in Sections 8 and 9 draws conclusions and outlines ideas for future work.