esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[32] measured sprinkling uniformity coefficients of 80% and 90% for RSPSs and FSPSs, respectively. Other researchers obtained sprinkling uniformity coefficients of 80%-90% and 90%-95% for RSPSs and FSPSs [33], respectively, under conditions of a real center pivot. [PERSON] et al. [34] reported that a higher proportion of small drops was produced with FSPS than with RSPS. Some research studied droplet size distributions in RSPSs [35; 36] and FSPSs [20]. However, practical applications need dozens to hundreds of RSPSs or FSPSs to be used on a center pivot, and all of the installed sprinklers need to have nozzles with diameters differing over a wide range. The distances between sprinklers (sprinkler intervals) must vary according to the configurations of center pivot. The differences in performance between RSPSs and FSPSs have not been fully compared under different combinations of nozzle diameters and sprinkler intervals. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sprinkler**} & **Nozzle Diameter** & **Pressure Range** & **Flow Range** & **Wetted Radius** \\  & **(mm)** & **(kPa)** & **(m\({}^{3}\).h\({}^{-1}\))** & **1 (m)** \\ \hline RSPS (Nelson R3000) 2 & 2.78–9.92 mm & 98–196 & 0.293–5.470 & 5–9 \\ FSPS (Nelson D3000) 2 & 1.79–9.92 mm & 40–274 & 0.077–5.470 & 4–6 \\ \hline \hline \end{tabular}  
* for the case when the height of sprinkler is 80 cm; 2 R3000 and D3000 are types of RSPSs and FSPSs respectively.  
\end{table} 
Table 1: Basic performance parameters of rotating and fixed spray plate sprinklers (RSPSs and FSPSs). Previous studies tested the application performance of RSPSs and FSPSs, however, those did not study the different performance between them under a wide range of nozzle sizes, different sprinkler intervals and different configurations of the center pivot. These aspects are actual and common situations for center pivot managers, which should to be considered in the cases of evaluating different types of sprinklers. Thus, the present study aims to focus on comparisons of sprinkling performance between RSPSs and FSPSs through a combination of results from field measurements and numerical simulations under conditions with differing nozzle diameters, sprinkler intervals and nozzle configurations in center pivots. The detailed objectives of this study were, under outdoor conditions, (1) to measure and analyze the water distribution characteristics of individual FSPSs and RSPSs with nozzles in different diameters under field conditions, (2) based on the water distribution of individual sprinkler, and following a model for nozzle configuration of a center pivot, to simulate water distributions of pipe sections consisting of multiple RSPSs and multiple FSPSs with differing nozzle diameters and sprinkler intervals, and (3) to simulate the water distribution of the full circular irrigated area of a center pivot using RSPSs and FSPSs at different intervals. These comparative results may provide insights for understanding the different sprinkling performances of RSPSs and FSPSs.
## 4 Results  
To evaluate the effectiveness of the proposed BiCA-LI model, we conducted comprehensive experiments on two key tasks: (1) time series prediction of critical operational variables (regression task), and (2) anomaly detection (classification task). Model performance was assessed using standard evaluation metrics for each task, and results were benchmarked against established baseline models, including single-encoder variants based on LSTM and Informer architectures.
## 3 Parameterization Schemes for Fiber Orientation
### Discrete Parameterization  
The counter scheme restricts the orientation design space through a discrete orientation optimization formulation to avoid multiple local optima issues. Initially, it was solved using a genetic algorithm at the cost of a computational burden [101; 102; 103]. Thus, [PERSON] [76] relaxes the combinatorial problem to a continuous optimization problem. The discrete material candidates are chosen a priori through transforming the base anisotropic stiffness tensor for given fiber orientations. Finally, the effective anisotropy elasticity tensor is calculated as a convex combination of material candidates and satisfies the following conditions, as shown below:  
\[\mathbf{C}_{eff}=\sum_{i=1}^{n_{c}}w_{i}\overline{\mathbf{C}}_{i},\quad\sum_{i= 1}^{n_{c}}w_{i}=1 \tag{13}\]  
Thus, the scheme shares some similarities with the multi-material optimization problem in [104; 105]. The suggested scheme assigns weighting functions to different candidates and employs gradient-based optimization with a penalization coefficient, forcing the weighting functions to seek a binary design and fiber convergence, i.e., one discrete material at each design point. This method is known as Discrete Material optimization (DMO). DMO laid the foundation for shape function with penalization (SFP) [106] and bi-value coding parameterization (BCP) [107] to perform discrete fiber orientation optimization. Later, DMO was extended for laminated composite structures to determine the material distribution and thickness variation, known as discrete material and thickness optimization (DMTO) [108]. Some recent works [109; 110; 111] further improved the applicability of DMTO. A comparison of these methodologies using various numerical examples is contained in [111; 112]. Another work proposed a self-penalization interpolation model for fiber orientation (SPIMFO) based on convergent Taylor series for sine and cosine functions to optimize composite hyperelastic material [113] and the dynamic design of laminated piezo-composite actuators [114].  
DMO does not incorporate design problems for continuously varying orientation distributions. First, it is an imperative design consideration to circumvent stress constraints and degradation in the strength by order of magnitude compared to that for continuous fiber paths due to fiber discontinuity. Secondly, these methods fail to address the fiber convergence, even against the significant penalization factor; hence, their benefit relies on an optimization algorithm to circumvent impractical mixtures of fiber orientations. Third, the discrete parameterization schemes should further minimize the number of design candidates for efficient optimization.
# IoT-Enabled Smart Cities: Evolution and Outlook
## 2 IoT-Enabled Smart Cities Landscape
### Shenzen  
The smart city in Shenzhen, China, aims to strengthen the existing manufacturing industry while connecting it to the advantages offered by the digital industry. The smart city plan established by the city in 2018 includes public services (healthcare, education, community, and weather), city management services (safety, transportation, operation, and water quality), and economic development services (industry, digital economy, and industrial complexes) [24].  
Shenzhen is using IoT technology to encourage multiple institutions to share various public services and city management service data. To this end, the city installed surveillance cameras, smart street lights, water quality sensors, and other devices to be connected to a cloud-based IoT platform. Such data are being collected and managed on a city-wide level.  
The following describes some of the smart city services operated in Shenzhen.  
* **Water quality management:** An IoT platform called Smart Sponge collects and manages water quality-related data on a real-time basis to provide city-wide water management services (water volume, quality information, and location information of water pipe networks).
* **Smart industrial complexes:** This service connects a series of smart factories to share various technologies and provide effective smart factory services. To achieve this, the city established ultra-high-speed broadband Internet, next-generation wireless network, and free Wi-Fi infrastructure, in addition to connecting and managing various IoT devices in industrial complexes via a common cloud platform.
* **Public safety:** The smart public safety service in Shenzhen deals with public safety, production safety, food and pharmaceutical safety, and geological risks in the city. This is achieved through a surveillance camera infrastructure and a network that covers the entire city. This IoT public safety platform was designed to be used all-year-round, and it is used to provide a variety of services including social safety, anti-terrorism and crime prevention efforts.  
Key smart city technologies used in Shenzhen include IoT, AI, and cloud computing. That is, IoT technologies help manage various sensors and infrastructure installed in the city. Data are transferred to the cloud-based IoT platform to be collected and shared in real-time. Such shared data are processed using AI technology to provide various intelligent smart city services.  
The following are the smart city technologies used in Shenzhen.  
* IoT: Video cameras with network features, smart street lights, smart traffic lights, smart water quality management, utility management, environmental pollution management, smart meter, smart factory, etc.  
* Big data and AI: Social credit platform, intelligent transportation system, fire incident analysis system, smart surveillance camera, intelligent parking service, and smart maintenance service.
* Cloud computing: Cloud-based IoT platform and city data management centre
# Fatigue Crack Calculation of Steel Structure Based on the Improved McEvily Model
## 2 Improved McEvily Model
### The Effectiveness of Improved McEvily Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**The Thickness of Specim**} & \multirow{2}{*}{**Stress Ratio**} & \multicolumn{3}{c}{**Material Constants of the Paris Model**} & \multicolumn{3}{c}{**Material Constants of the Improved McEvily Model**} \\ \cline{3-6}  & & \(\overline{m}\) & \(\overline{\mathbf{lg}C}\) & \(\overline{\mathbf{B}}\) & \(\overline{\mathbf{lg}A}\) \\ \hline \multirow{3}{*}{6.1 mm} & 0.1 & 2.63 & \(-\)10.78 & & \\  & 0.2 & 2.51 & \(-\)10.39 & 2.53 & \(-\)10.65 \\ \cline{1-1}  & 0.5 & 2.69 & \(-\)10.56 & & \\ \hline \multirow{3}{*}{10.0 mm} & 0.1 & 2.68 & \(-\)10.88 & & \\  & 0.2 & 3.04 & \(-\)11.41 & 2.58 & \(-\)10.79 \\ \cline{1-1}  & 0.5 & 2.84 & \(-\)11.07 & & \\ \hline \multirow{3}{*}{23.5 mm} & 0.1 & 2.40 & \(-\)10.61 & & \\  & 0.2 & 3.32 & \(-\)12.23 & 2.61 & \(-\)11.11 \\ \cline{1-1}  & 0.5 & 3.02 & \(-\)11.45 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of material constants under the two models [29]. Figure 3: Comparison of fatigue crack growth models of 6.1 mm specimens. (**a**) R is 0.1; (**b**) R is 0.2; (**c**) R is 0.5. Fatigue fracture growth rate curves of improved McEvily models are highly similar to results obtained from experiments and the Paris models, as shown in Figures 3-5. It proves that for the same sample thickness, the improved McEvily model requires only a set of stress ratio experimental to determine the material constant, which overcomes the influence of the weld. Further demonstrates that the improved McEvily model has good robustness and applicability. In order to be able to apply the improved McEvily model to the practical engineering, according to the fitting results of the material constants of the above Q345 qD steel, the material constants \(\lg A\), and \(B\) used in this paper are \(-10.65\) and \(2.53\), respectively. The improved McEvily model is as follows:  
\[\frac{da}{dN}=2.2387\times 10^{-11}M^{2.53}. \tag{15}\]
# Modeling and Analysis of High-Power Ti:sapphire Laser Amplifiers-A Review
## 1 Introduction  
Owing to features such as excellent thermal conductivity, high mechanical durability, and wide amplification bandwidth, Ti:sapphire is widely used as a medium for high-power chirped pulse amplification (CPA) technology [1; 2]. Some researchers from RAL, SIOM, PEARL, Apollon Project, and APRI, ELI, J-KAREN, and Vulcan have previously generated several petawatt (PW) laser pulses and are targeting 10 PW or more [3; 4; 5; 6; 7; 8; 9; 10; 11]. A high-power laser can be used in applications such as laser-material interaction, particle acceleration, basic research about materials under extreme environments, and medical applications [12; 13; 14]. In addition, the realization of high-power laser applications will enable the pioneering of new scientific fields [8; 15]. However, for using high-power lasers in scientific research and industrial applications, it is essential to have high-power pulses with both a high repetition rate and high spatio-temporal stability for practical applications [16; 17]. The simulation model and analysis of the high-power laser amplifier introduced in this review will be useful for the design and development of laser systems.  
In this review, the amplification model constructed by [PERSON] and [PERSON] has further expanded to model the depletion of the pump energy and to provide an amplified pulse shape simulation [18; 19]. In addition, we show how to apply the model to a CPA system and describe the analysis method of the square pump condition, which has attracted the interest of many research institutes in recent years [20]. Furthermore, an interpretation of the phase distortion occurring in the active medium, which has a significant impact on the energy and spatio-temporal shape of the pulse, is also included in this review [21; 22]. The phase retardation generated inside a high-power laser medium, particularly in the case of Ti:sapphire, is relatively inadequately researched and is now essentially gaining attention for its effect [23]. We provide a model of the atomic phase shift (APS) andthe improved form of the thermal distortion effect. The models introduced in this review are aimed at representing a laser system in a suitable form for the design and maintenance of high-power laser systems. Most of the considerations introduced in this review will be applicable to PW-class laser generators in general, regardless of PW pulses with an ultrashort pulse width (J/fs) or PW pulses with high-pulse-energy (kJ/ps). However, it should be noted that most of the examples used in this review relate to the ultrashort PW pulse generation using Ti:sapphire as the CPA medium.
## 3 Operational Mechanisms and Traffic Flow Characteristics of Non-Conventional Lanes
### Chapter Summary  
This chapter systematically analyzed the operational mechanisms and traffic flow characteristics of non-conventional lanes from micro, meso, and macro levels. Multi-scale integrated analysis represents a fundamental requirement for comprehensive non-conventional lane behavior simulation, addressing the complex interactions that span individual driver decisions, platoon-level dynamics, and system-wide performance characteristics. Microscopic analysis captures vehicle trajectory modifications, lane-changing behaviors, and driver adaptation processes essential for safety assessment and operational understanding. Mesoscopic models aggregate these behaviors into queue formation patterns, shockwave propagation mechanisms, and traffic flow stability characteristics that determine intermediate-scale performance. Macroscopic analysis provides capacity measurements, delay calculations, and network-level impact assessments that guide policy decisions and implementation strategies. Methodological challenges in data integration arise from disparate temporal scales (second-level microscopic movements, minute-level mesoscopic patterns, hourly macroscopic performance) and spatial scales (individual vehicles, platoon groups, intersection systems) that require sophisticated calibration procedures ensuring parameter coherence and behavioral consistency. [PERSON] et al. [50] demonstrate how dynamic lane assignment effects propagate across multiple scales, while [PERSON] et al. [49] illustrate trajectory reconstruction complexity bridging microscopic positioning with mesoscopic flow analysis. Model consistency challenges extend to behavioral representation transferability and boundary condition specification, requiring advanced computational frameworks that maintain accuracy across scale transitions. The cellular automata approaches by [PERSON] et al. [12] and [PERSON] et al. [13] provide microscopic foundations, but scaling to network analysis demands careful parameter validation and interaction effect consideration, highlighting the sophisticated integration requirements for effective multi-scale non-conventional lane analysis.  
Current operational research exhibits systematic limitations. Microscopic studies [49; 50; 51; 57] rely heavily on simulation with limited real-world validation, potentially overestimating performance. [PERSON] et al. [52] demonstrated CLL system convergence, while [PERSON] et al. [53] reported persistent oscillations under similar conditions, suggesting analytical frameworks require refinement. Macroscopic studies show scale-dependent results, with benefits varying significantly across intersection sizes and traffic volumes [58], indicating threshold effects not adequately captured in current models.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.8. Frequency Jump Method (FJ)  
There are certain dead zones present in the perturbing current signal in this approach, which is similar to AFD. The dead zones are injected once every three cycles of the DGs output current waveform. The frequency of voltage will not change under normal circumstances, but it will alter if the main grid is lost. This approach is regarded as an improved version of the active frequency drift approach. However, if the system includes numerous inverters linked in parallel, FJ may not be able to identify the islanding [24].
## 4 Results
### Quantitative Evaluation  
#### 4.3.1 Evaluation Metrics  
Considering the potential impact of subjective factors such as individual variances and preferences, the qualitative evaluation of experimental outcomes may inherently exhibit a degree of bias. To attain a more objective and accurate assessment of the generated image quality, this study incorporated two evaluation metrics: inception score (IS) [46] and Frechet inception distance (FID) [46].  
The IS represents a metric employed to assess the diversity and quality of generated images. This metric involves categorizing the images generated by a classification model, typically employing the Inception V3 model. The IS value is derived by computing the entropy of the softmax output distribution for the generated images, combined with the  
Figure 7: Comparison of the existing image generation models and the model proposed in this paper (the numbers below the images correspond to the model’s iterations).  
exponent of the average class probability. The higher the value of IS, the better the diversity and quality of the generated images. The IS can be calculated by  
\[\text{IS}(G)=exp(\mathbb{E}_{x\sim p_{g}}D_{KL}(p(y|x)||p(y))), \tag{9}\]  
where \(x\sim p_{g}\) denotes the generated image, and \(p(y|x)\) indicates that the generated image \(x\) is input into the initial model to obtain a 1000-dimensional vector \(y\), i.e., the probability distribution of the image represents various types. Moreover, \(p(y)\) means the average corresponding probability distribution vector obtained by \(N\) generated images input into the initial model, i.e., the marginal distribution of the image generated by the generator in all categories.  
The FID is utilized to quantify the dissimilarity between generated and real images. This assessment involves passing both generic and authentic data through a pre-trained Inception V3 model [47] originally trained on the ImageNet dataset [48] to extract visually relevant features. The mean and covariance of the authentic and generated features are represented by \((M_{t},C_{t})\) and \((M_{g},C_{g})\), respectively, and \(T_{r}\) denotes the trace of the covariance matrix of the feature vectors of the real data. This metric can be calculated by  
\[\text{FID}=||M_{t}-M_{g}||_{2}^{2}+T_{r}(C_{t}+C_{g}-2(C_{t}C_{g})^{\frac{1}{2} }). \tag{10}\]  
A lower FID value indicates that the generated images are closer to real images in terms of visual quality, diversity, and distribution of features.
# Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation
## 4 Discussion
### Possible Clinical Applications  
The conducted analysis indicates that the unique physical characteristics and the individual anatomy of a model undeniably differentiates the induced EF distribution among subjects. To highlight those differences, two high-resolution models of the well established virtual population (ViP) family [29; 43], were used for comparison: [PERSON], a 35-year-old male, and [PERSON], a 26-year-old female. When placed at the frontal position, the H7 coil stimulated 12.0% of [PERSON]'s frontal lobe, compared to 9.4% of a male subject with similar age and weight (Vol06), and 15.9% of a younger male subject (Vol18). At the same stimulation position for female models, the volume, where EF had an amplitude higher than 100 V/m, was 7.2% of [PERSON]'s frontal lobe, compared to 14.7% for subject Vol05, a model of similar age and weight, and 20.3% for an older model (Vol19). A detailed analysis of regional stimulation volume parameters on the frontal lobe of anatomical and homogeneous models can be found in Appendix B. It is clear from the above that specific anatomical geometries, even widely used in the literature computational models, can only provide rough information about a clinical TMS application. It is preferable to engage personalized computational models, in order to correctly interpret biological responses and clinical outcomes.
## 3 Results and Discussion
### Microwave Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Frequency**} \\ \cline{3-8}
**Group of** **Samples** & \multirow{2}{*}{**Code of** **Sample**} & \multicolumn{3}{c}{**12 GHz**} & \multicolumn{3}{c}{**18 GHz**} \\ \cline{3-8}  & & \multicolumn{3}{c}{**Components of Shielding Effectiveness \%**} \\ \cline{3-8}  & & \(\Gamma\) & **A** & **T** & \(\Gamma\) & **A** & **T** \\ \hline \multirow{2}{*}{**I**} & S1C & 75.0 & 21.8 & 3.2 & 70.0 & 26.8 & 3.2 \\  & S1 PC & 75.0 & 24.0 & 1.0 & 55.0 & 44.0 & 1.0 \\ \hline \multirow{2}{*}{**II**} & S2C & 60.0 & 37.5 & 2.5 & 45.0 & 52.5 & 2.5 \\  & S2 PC & 50.0 & 46.8 & 3.2 & 25.0 & 74.8 & 2.0 \\ \hline \multirow{2}{*}{**III**} & S3C & 90.0 & 6.8 & 3.2 & 70.0 & 26.8 & 3.2 \\  & S3 PC & 75.0 & 21.8 & 3.2 & 70.0 & 26.8 & 3.2 \\ \hline \multirow{2}{*}{**IV**} & S4C & 65.0 & 34.0 & 1.0 & 50.0 & 48.4 & 1.6 \\  & S4 PC & 65.0 & 33.0 & 2.0 & 60.0 & 39.0 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Microwave properties of tested coated fabrics. Figure 8: Reflectance of tested fabrics coated with PEDOT:PSS formulation: (**a**)—I group of samples; (**b**)—II group of samples; (**c**)—III group of samples; (**d**)—IV group of samples.