esa-sceva/satcom-chunk-collection huggingface dataset:
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 4 Diamond PPSS Devices
These structures included an insulating high-pressure high-temperature (HPHT) type Ib substrate (highly N-doped), a chemical vapor deposited (CVD) type IIa substrate (unintentionally doped), and a CVD-grown semiconducting B-doped epilayer on a type IIa substrate, as well as B-implanted type Ib and IIa substrates. Significant extrinsic photoconductivity, characterized by a large on/off current ratio, was demonstrated in both highly N-doped type Ib diamond and unintentionally doped type IIa single-crystalline diamond using below-bandgap visible excitation at 532 nm. In contrast, photoconductivity in response to lower-energy 1064 nm excitation was only observed in B-doped diamond. The photoresponse in insulating Ib and IIa diamond substrates was believed to be facilitated by carrier photogeneration at the contact edges, combined with the photoionization of substitutional nitrogen, which reduced the resistivity of the diamond. The on/off current ratio of the type Ib device reached values on the order of 10\({}^{11}\). Furthermore, the effects of dopant compensation between B and N in diamond were observed in three distinct ways: (1) reduced dark currents in devices on B-implanted Ib versus IIa substrates, (2) diminished photoresponses in devices on B-implanted versus unimplanted Ib substrates, and (3) the presence of a photoresponse to 1064 nm in devices on B-implanted IIa substrates, which was absent in those on B-implanted Ib substrates. A recent study demonstrated a 130 mA/W intrinsic responsivity (for wavelengths below 226 nm) using a buried, metallic heavily doped (\(p^{+}\)) current channel within an HPSI diamond as the active absorption layer. The lateral PCSS devices exhibited high on/off ratios (>10\({}^{11}\)), fast rise and fall times (\(\sim\)2 ns), and impressive current densities of up to 44 A/cm\({}^{2}\). Additionally, these devices showed linear current-voltage characteristics up to a DC bias of \(\pm\)60 V, suggesting that even higher current densities could be achieved before reaching carrier velocity saturation [42]. Future studies could focus on optimizing the switch design to enhance photon absorption. The development of transparent or buried contacts could provide solutions to reduce both contact resistance and diamond resistivity under photoexcitation, without contributing to an increase in dark current.
## 2 Model Constructions and Hypothesis
### Critical Analysis of Theoretical Constructs  
This study explores the interplay between key constructs from the UTAUT framework to provide a holistic understanding of the factors influencing robot acceptance in office environments.  
Performance Expectancy and Effort Expectancy: These constructs are deeply interconnected. Performance expectancy measures the perceived utility of robots in enhancing workplace efficiency, while effort expectancy assesses the ease with which individuals can use these technologies. The findings demonstrate that higher ease of use significantly enhances perceptions of utility, thereby positively influencing behavioral intention.  
Social Influence and Perceived Sociability: Social influence evaluates how external factors, such as managerial support and peer perceptions, affect an individual's acceptance of robots. On the other hand, perceived sociability measures the extent to which robots can engage in meaningful social interactions. Together, these constructs address both cultural and interpersonal dimensions of robot acceptance.  
Integration with the UTAUT Framework: By analyzing these constructs collectively, this study demonstrates the robustness of the UTAUT framework in capturing the multifaceted nature of robot acceptance. Performance expectancy and effort expectancy address the functional and usability aspects, while social influence and perceived sociability provide insights into social and cultural dimensions. This comprehensive approach enables a deeper understanding of the factors driving behavioral intention, offering actionable insights for both theory and practice.  
Before presenting the hypotheses related to performance expectancy, we contextualize its relevance to robot acceptance. Performance expectancy evaluates the extent to which individuals believe that interacting with robots will enhance workplace efficiency and productivity. In office environments, this construct is particularly important as robots are expected to augment employee capabilities, streamline tasks, and contribute to overall organizational effectiveness. By examining performance expectancy, this study seeks to understand how perceived utility influences behavioral intentions to use robots.  
For hypotheses concerning social influence and perceived sociability, we aim to address the cultural and interpersonal dimensions of robot acceptance. Social influence captures the effect of workplace norms, managerial endorsements, and peer opinions on an individuals' willingness to use robots. Perceived sociability, on the other hand, assesses robots' ability to engage in meaningful social interactions, a critical factor in environments where collaboration and communication are valued. These constructs are particularly vital in cross-cultural contexts, where differences in societal norms and interpersonal expectations can significantly shape attitudes toward robot adoption.
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results  
The obtained results are organized into four stages, corresponding to those described in Section 4. These stages aim to evaluate and compare the performance of the proposed neural network against classical machine learning methods, which serve as reference points for validating performance improvements. Subsequently, the architecture of the neural network is optimized and analyzed in terms of depth and layer width. The optimal configuration is further investigated by isolating and evaluating specific architectural components. Finally, a resilience analysis is conducted to assess the sensitivity of classification accuracy to variations in input data quality.
## 4 Numerical Modeling
### Grid Independence Study
In order to investigate the influence of the grid on the local conditions of fluid flow, Figure 8a,b show the mean radial and tangential velocity profiles, respectively, obtained in the impeller region (z = 0.08 m) with the three different grids employed here, i.e., the velocity in two different directions of the domain for a specific placement of the domain. The results are calculated when the fluid flow reaches the steady state. Then, the last time interval of \(t=1.5\) s is used to obtain the mean velocity profiles. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mesh 1** & **Mesh 2** & **Mesh 3** & \(r_{32}\) & \(r_{21}\) & \(p\) & **GCI\%** \\ \hline
5,484,258 cells & 1,878,417 cells & 610,767 cells & 1.45 & 1.42 & 2.82 & 0.1027 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the application of the GCI method. account initiation values of 0.01 m\({}^{2}\)/s\({}^{2}\) and 0.001 m\({}^{2}\)/s\({}^{3}\) for turbulence kinetic energy and dissipation rate, respectively. Moreover, Figure 9 shows the fields of turbulent kinetic energy in a vertical plane (\(r\)-\(z\)) for the baffle region predicted with the three investigated meshes comprising 610,767 cells (Figure 9a), 1,878,417 cells (Figure 9b) and 5,484,258 cells (Figure 9c). The results in Figures 8 and 9 show that there are no significant local differences when Mesh 1 and Mesh 2 are compared. However, some localized differences in the velocity profiles, mainly for the tangential one, and for the fields of turbulent kinetic energy were noticed for the coarser mesh. As a consequence, the mesh with 1,878,417 elements was chosen since it had fewer deviations than the coarse mesh (Mesh 3) and required a lower computational effort than Mesh 1. The independent mesh used here can be seen in the Figure 10.  
Figure 8: Results of mean velocity profiles obtained with the present code at \(z\) = 0.08 m for three different grids: (**a**) radial velocity, (**b**) tangential velocity. Figure 9: Turbulent kinetic energy fields obtained for three different mesh refinements: (**a**) 610,767 cells, (**b**) 1,878,417 cells, (**c**) 5,484,258 cells.
## 4 Simulation Verification  
In this section, simulation results in Matlab/Simulink are shown to validate the proposed control strategy. The changes in bus voltage, inductor current, and output current of the DC microgrid before and after the application of the control strategy are shown in Figure 8. Initially, the traditional droop control was applied. Due to the influence of CPLs, the system suffers oscillation. At t = 0.5s, the control strategy is switched from the traditional droop control to the proposed control strategy. Obviously, the DC microgrid bus voltage can quickly be restored to a stable state. Compared with the virtual  
Figure 6: Improved droop controller.  
Figure 7: Virtual negative inductive reactance control strategy.  
capacitance control strategy based on droop control (the gray curve in the figure) in [18], the proposed control strategy in this paper can reduce the adjustment time and make the system reach the balance faster. In addition, the control strategy can reduce the bus voltage fluctuation during the regulation process. Overall, this strategy will have more advantages in improving stability. To further verify the effectiveness of the proposed strategy, the following tests are carried out with the change of droop coefficients, line lengths, and operating points.
# Optimization of Additive and Current Conditions for Void-Free Filled Through-Silicon Via
###### Abstract  
Studies of through-silicon vias (TSVs) have become important owing to the increasing demand for 3D packaging. To obtain high-performance devices, it is important to fill the holes inside TSVs without voids. In this study, poly(ethylene glycol), bis-(3-sodiumsulfopropyl disulfide), and Janus Green B are used as a suppressor, accelerator, and leveler, respectively, to achieve void-free filling of a TSV. The optimum conditions for the additives were studied, and electrochemical analysis was performed to confirm their effects. Different current conditions, such as pulse, pulse-reverse, and periodic pulse-reverse, were also employed to enhance the filling properties of copper (Cu) for a TSV with a hole diameter of 60 \(\upmu\)m and depth/hole aspect ratios of 2, 2.5, and 3. The behavior of Cu filling was observed through a cross-sectional analysis of the TSV after Cu plating under various conditions.  
through silicon vias; void free filling; additive; current condition; electro deposition +
Footnote †: journal: Applied Sciences
## 1 Introduction
AGB dynamics were obtained by incorporating simulations derived from two ecological models (the MODIS MOD17 (MOD_17) GPP and the Biome-BioGeochemical Cycles (Biome-BGC) models) using the 2009 forest basis map generated using Landsat Thematic Mapper 5 (TM) data [52]. Using Global LAnd Surface Satellite (GLASS) LAI products, vertical parameters were applied in order to parameterize \(d\), \(z_{\rm 0m}\), and, then, \(z_{\rm 0h}\) using the outperformed roughness model determined from our previous study [47] and the \(kB^{-1}\) model. Afterward, original MODIS products (NDVI and land LST), refined GLASS Albedo products, and downscaled weather research and forecasting (WRF) estimates were applied for driving the SEBS over the mountainous forests using the forest dynamics parameterization scheme. Based on this procedure, estimates were obtained for forest ET over the QMs from 2000 to 2012. Finally, two-year (2010-2011) comparisons were performed amongst MODIS MOD16 ET products, SEBS ET outputs, and EC measurements to investigate the applicability and feasibility of the SEBS over heterogeneous forests.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis
### Ablation Study
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **DSC** & **RV** & **MYO** & **LV** \\ \hline U-Net [2] & 87.84 \(\pm\) 1.38 & 86.51 \(\pm\) 1.32 & 84.66 \(\pm\) 5.65 & 92.36 \(\pm\) 3.92 \\ Att-UNet [19] & 88.04 \(\pm\) 2.02 & 86.70 \(\pm\) 2.33 & 84.59 \(\pm\) 8.96 & 92.83 \(\pm\) 1.57 \\ TransUNet [43] & 89.45 \(\pm\) 1.13 & 87.85 \(\pm\) 2.22 & 86.09 \(\pm\) 2.23 & 94.39 \(\pm\) 1.43 \\ nnUNet [22] & 90.14 \(\pm\) 1.83 & 88.58 \(\pm\) 1.44 & 89.72 \(\pm\) 1.18 & 92.13 \(\pm\) 2.24 \\ Swin-UNet [26] & 90.20 \(\pm\) 0.78 & 87.44 \(\pm\) 2.46 & 88.20 \(\pm\) 3.40 & **94.97 \(\pm\) 1.26** \\ LeVIT-UNet [46] & 90.42 \(\pm\) 1.15 & 88.85 \(\pm\) 1.52 & 88.29 \(\pm\) 2.04 & 94.12 \(\pm\) 1.88 \\ HiFormer [47] & 90.69 \(\pm\) 0.85 & 90.06 \(\pm\) 1.30 & 89.00 \(\pm\) 1.29 & 93.00 \(\pm\) 1.07 \\ VM-UNet [12] & 90.72 \(\pm\) 0.77 & 90.61 \(\pm\) 1.67 & 89.40 \(\pm\) 1.88 & 92.14 \(\pm\) 0.76 \\ EMCAD [48] & 91.30 \(\pm\) 0.35 & 90.17 \(\pm\) 1.88 & 89.16 \(\pm\) 1.24 & 94.55 \(\pm\) 1.59 \\ TransCASCADE [49] & 91.59 \(\pm\) 0.21 & 90.12 \(\pm\) 2.66 & 90.14 \(\pm\) 0.68 & 94.51 \(\pm\) 2.55 \\ HCMUNet (Ours) & **92.11 \(\pm\) 0.26** & **91.50 \(\pm\) 0.78** & **90.20 \(\pm\) 0.56** & 94.61 \(\pm\) 1.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results on the ACDC dataset. the model is capable of leveraging additional training data effectively, offering a promising path for future extensions to larger-scale datasets.
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 2 Modeling of the Proposed System
### Bi-Directional Converter Model  
A bidirectional transducer is adopted to maintain power flow between DC and AC components. There are two kinds of power conversion devices in a power system, the inverter which converts DC current to AC current and the rectifier which converts AC current to DC current. The technical characteristics of the inverter model are presented in Table 5. The hourly input power of the inverter \(\mathrm{P_{inv}(t)}\) can be expressed as below [16];  
\[\mathrm{P_{inv}(t)}=\mathrm{P_{L}}\ (t)/\mathrm{\eta_{inv}} \tag{12}\]  
In which, \(\mathrm{\eta_{inv}}\) represents the inverter efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Model & & RS lead acid battery \\ Nominal battery voltage [39] & 5 & V \\ Nominal battery capacity [39] & 360 & Ah \\ Storage capacity of battery [7] & 4.8 & kWh \\ \(\mathrm{\eta_{CH}}\)[30] & 90 & \% \\ \(\mathrm{\eta_{DIS}}\)[30] & 85 & \% \\ \(\sigma\)[30] & 0.005 & - \\ Battery cost (\(\mathrm{C_{Bat}}\)) [30] & 3880 & \$ \\ Battery lifetime (\(\mathrm{Bat_{S}}\)) [30] & 25 & year \\ \hline \hline \end{tabular}
\end{table}
Table 4: The main parameters characteristics of the batter bank.
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Vulnerability
#### 2.2.2 Building Age  
The building age serves as a fundamental measure of structural resilience in urban flood conditions. By examining local building records, one can determine how old these structures are and whether they align with current safety standards. Typically, older buildings pose a higher risk, indicating that reinforcement or renovations may be needed to bolster their durability. This assessment aids in ensuring that the built environment meets modern urban flood preparedness requirements, ultimately contributing to the community's safety.
# Brief Review of Recent Developments in Fiber Lasers
## 3 Rare Earth-Doped Fiber Lasers  
The wide range of industrial and defense applications has fueled the development of RE-doped fiber lasers. Fibers used for data transmission are known as passive fibers, since they do not change the power of the propagating signal. RE-doped fibers can amplify the propagating optical signal. They are called active fibers. The successful development of low-loss optical fibers doped with various RE elements, such as ytterbium (Yb), erbium (Er), thulium (Tm), holmium (Ho), praseodymium (Pr), or dysprosium (Dy), opened the door to creating fiber lasers. Each RE-doped fiber laser has a specific emission window defined by its dopant. For example, Yb\({}^{3+}\)-doped fiber lasers have a window of emission wavelengths ranging from \(\sim\)1050 nm to \(\sim\)1120 nm; Er\({}^{3+}\)-doped fiber lasers have a window of emission wavelengths ranging from \(\sim\)1530 nm to \(\sim\)1590 nm.  
A laser beam can be characterized by its brightness:  
\[B=\frac{P}{A\Omega}=\frac{P}{\left(M^{2}\right)^{2}\lambda^{2}} \tag{10}\]where \(P\) is the source power divided by the product of the mode area, \(A=\pi w_{0}^{2}\), and the solid angle, \(\Omega=\pi\theta_{0}^{2}\). Here \(w_{0}\) and \(\theta_{0}\) are the beam waist and half-angle divergence, respectively. \(\lambda\) is the wavelenght of the laser beam.  
\[M^{2}=\frac{\pi w_{0}\theta_{0}}{\lambda} \tag{11}\]  
Equation (11) is used to calculate the beam quality factor.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Research Gaps and Future Directions
#### 4.2.1 Advanced Analytical Methods  
A large portion of the studies reviewed in this paper employ conventional data analytics techniques such as regression and clustering for either modeling or predicting demand flexibility. These techniques are very effective in lower-dimensional settings with less complex dynamics. We saw a few examples of neural networks used in studies within this field [45; 52; 66]. However, as the diversity and breadth of the available data increases, more advanced data-driven models might be needed. For instance, non-intrusive load monitoring used for indirect occupancy detection in a building may require the adoption of deep learning techniques to detect and distinguish nuanced usage patterns and signatures of various appliances in the household. This is especially true if those patterns are investigated across multiple buildings and users. Another complexity arises if the dimensions of a demand flexibility model increase by considering not just building characteristics and outdoor environmental factors but also indoor variables such as temperature, airflow, occupancy, body heat, and/or user convenience models. This can turn the problem into a complex multilayered model in which conventional techniques and algorithms may fail to fully identify and represent the complexity of demand flexibility. Deep learning models such as autoencoders or recurrent neural networks may prove to be more efficient in those situations. However, when adopting more advanced machine learning models, their superior modeling capabilities must be balanced against their interpretability (or lack thereof).
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 2 Related Work  
Since the proposal of RSMA, numerous researchers have investigated its application in various communication systems, including Cloud Radio Access Networks (C-RAN) [13], multibeam satellite systems [14], overloaded cellular Internet of Things (IoT) [15], and UAV communication systems [16]. The core concept of RSMA is to split messages into common and private parts, which are then decoded using Successive Interference Cancellation (SIC) [17].  
[PERSON] [18] designed a full-duplex cooperative multicast mechanism based on RS. The authors developed specific beamforming methods for cooperative rate splitting and multicast beams, proposing an optimization scheme. They introduced a low-complexityiterative algorithm based on Successive Convex Approximation (SCA) to solve the problem. This approach effectively mitigates inter-group interference, improves resource utilization, and reduces system energy consumption. In reference [19], RS was introduced in an uplink NOMA system and its application in high-throughput satellite systems was examined. By modeling the channel, the authors derived a closed-form expression for the average system throughput. Numerical simulations demonstrated that the proposed scheme outperforms NOMA and SDMA in terms of throughput.  
Further, [PERSON] [20] explored rate optimization in uplink communication systems using RSMA, focusing on optimizing user transmission power and the decoding order at the base station to maximize system throughput. In reference [21], RSMA was applied to an uplink cognitive radio system comprising a primary and secondary user, where the authors proposed an RSMA-based protocol and a SIC-based protocol for message decoding. Meanwhile, [PERSON] [22] investigated RSMA in downlink multi-antenna cognitive radio systems, focusing on joint communication and interference cancellation for multicarrier waveforms. By employing RSMA, the authors aimed to communicate with secondary users while simultaneously mitigating interference to primary users.  
Recent literature on RS/RSMA can be broadly categorized into studies of its applications in uplink systems [19; 20; 21] and downlink systems [22]. It is evident that most research focuses on RS/RSMA in uplink communication systems. However, RS/RSMA also holds significant potential for downlink systems. Therefore, further investigation into its application and performance in downlink systems is essential.
## 3 Results and Discussion
### Influence of Aromatic Amines on the Surface Tension Data, Formation of cac, and CMC  
The obtained experimental data were interpreted for both systems, i.e., aqueous tyramine (TYR) and taurine (TAU) with D-glucose. In contrary to taurine behavior with glucose, tyramine has a breaking point in the curve at a critical aggregation concentration (cac), suggesting that the behavior of mixed D-glucose with the aqueous tyramine (TYR) system is equivalent to the system of a surfactant in aqueous media. Further, it was demonstrated in Figure 4 that the observed first curve break at concentration 0.02998 mol kg\({}^{-1}\) at 298.15 K was emphasized as the critical aggregation concentration (cac). Further, as the concentration (C)  
Figure 3: (**a**) Surface tension versus concentration plot of aqueous taurine with D-glucose at different temperatures, (**b**) Surface tension versus temperature plot of aqueous taurine with D-glucose at different concentrations.  
increases, there is a sharp decrease in the curve obtained, and after a point, it becomes constant. This point is considered as a critical micelle concentration (CMC). However, the results we attained for D-glucose in the aqueous tyramine (TYR) system in terms of CMC were treated similarly to the CMC of a surfactant molecule, as illustrated in Figure 4a. Meanwhile, the attained and observed values of CMC at different temperatures are believed to be observed after the hydrophobicity, surface energy, and bulkiness of the interacting mono-amino acid tyramine (TYR) with D-glucose in aqueous media. In addition, the surface tension versus log C profile was depicted in Figure 4b to deduce the effect of tyramine aggregation in aqueous media with D-glucose molecules.