esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technology Modules  
The technology modules needed for the realization of the presented device include the fabrication of the silicon nano-ridge with appropriate geometrical cross-section and high-quality Si/SiO\({}_{2}\) interfaces, the realization of side-gates with a damascene process, and the fabrication of an array of multiple nanoscale top-gate electrodes suitable for electron localization. To probe the quality of the nano-ridge sidewalls, we perform capacitance voltage measurements and determine the defect densities at the Si/SiO\({}_{2}\) interface and within the oxide. Chemical-mechanical polishing is used to prepare appropriate TiN side-gates, and for the top-gate fabrication, we develop a self-aligned spacer process that can be combined with a damascene process.  
Figure 3: Fabrication, polishing, and topology of the silicon nano-ridge. (**a**) Schematic drawing (not to scale) of the silicon nano-ridge realized with anisotropic wet-etching of \(<\)100\(>\)-oriented silicon and dry oxidation. The inset depicts a close-up of the tip-area of the nano-ridge. (**b**) Top-view scanning electron micrograph (SEM) image of the trapezoidal structure formed after TMAH-etching. A small undercut of the thin silicon nitride hard mask is visible at the edge of the line structure, which reduces the impact of line-edge roughness of the initial lithography process on the resulting [111] facets. (**c**) Atomic force microscopy image of the etched (111) plane from a different sample with larger feature size. The root-mean-squared value 0.3992 nm is measured in a scanning area of 800 nm \(\times\) 250 nm. A line-cut along the [110] direction shows a peak-to-peak value of \(<\) 2 nm. (**d**) SEM cross-section of the fabricated silicon nano-ridge. An additional cobalt layer is deposited to achieve better contrast during SEM imaging. The nitride hard mask is not visible due to its low thickness. (**e**) Electron micrograph of the planarized TiN side-gate electrodes. Reactively-sputtered TiN shows the typical columnar growth behavior.
## 3 Results and Discussion
### Effects of AM Constraints and Objective Functions
#### 3.2.2 Stress Minimization  
The stress minimization process was performed for all three designs in the presence and absence of AM constraints. Figure 11 shows that there is an increase in stress for all designs when AM constraints are included. On average, the stress increases by 10.16% due to the influence of AM constraints. The design validation result also shows that the equivalent stress slightly increases slightly when AM constraints are considered. However, it is important to note that despite this increase, the average support volume required under AM constraints decreased significantly by 84.81%.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 3 Evaluation Criteria
### Anti-Jamming Capability  
The effective methodology to predict and access the anti-jamming capability is based on the effective carrier to noise density ratio (\((C/N_{0})_{eff}\)) [31]. The quantity \((C/N_{0})_{eff}\), indicates the level of interference on characteristics at the input of the receiver. According to the representative of the GNSS receiver functions and the interference characteristics, the following merits factors are adopt to describe the anti-jamming performance [32; 33]:  
The Demodulation & anti-jamming of narrowband (Dem&AJNB) merit factor is given by:  
\[Q_{DemAJNB}=10\lg\left(\frac{1}{R_{d}\cdot\max\left[G_{s}\left(f\right)\right] }\right)\left(\mathrm{dB}\right) \tag{10}\]  
where, \(R_{d}\) is the message rate, \(\max\)[. ] represents the maximum operation.  
The Code Tracking & anti-jamming of narrowband (CT&AJNB) merit factor is given by:  
\[Q_{CTAJNB}=10\lg\left(\frac{\int_{-\beta_{r}/2}^{\beta_{r}/2}f^{2}G_{s}\left( f\right)df}{\max\left[f^{2}G_{s}\left(f\right)\right]}\right)\left(\mathrm{dB}\right) \tag{11}\]The Demodulation & anti-jamming of matched spectrum (Dem&A)JMS) merit factor is given by:  
\[Q_{DemAJMS}=10\lg\left(\frac{1}{R_{d}\cdot\int_{-\beta_{r}/2}^{\beta_{r}/2}G_{g} ^{2}\left(f\right)df}\right)\left(\text{dB}\right) \tag{12}\]  
The Code Tracking & anti-jamming of matched spectrum (CT&A)JMS) merit factor is given by:  
\[Q_{CTAJMS}=10\lg\left(\frac{\int_{-\beta_{r}/2}^{\beta_{r}/2}f^{2}G_{s}\left(f \right)df}{\int_{-\beta_{r}/2}^{\beta_{r}/2}f^{2}G_{s}^{2}\left(f\right)df} \right)\left(\text{dB}\right) \tag{13}\]  
The greater the quality factors, the better the anti-jamming capability will be. It should be noted that the performance of the anti-jamming of the narrow band interference largely depends on the maximum of the normalized PSD of a signal, that is, the intentional narrow band interference aims at the maximum power of the signal spectrum to achieve the interference of utility-maximization. The performance of the anti-jamming of the matched spectrum interference depends greatly on the degree of overlap of the signals.
## 1 Introduction
### Aims and Main Contributions  
The main purpose of this article is to determine the optimum ESS capacity by analyzing the electrical performance of a PV power plant and the demand energy of the load and to make an economic analysis of the system in line with the measured data. For this purpose, meteorological data such as radiation and temperature were acquired every 5 min for a year. Electrical performance analysis was performed using these real data for each hour. The hourly demand energy of the load was provided through the automatic meter reading system. To determine the optimum ESS capacity, the amounts of energy transferred from the grid to the load, from PV to the load, and from PV to the grid were analyzed on an hourly basis. In addition, grid-to-load and PV-to-load energy price tariffs were taken into consideration when determining the optimum capacity of the ESS. The innovative aspect of this study is not only the separate consideration of gaps in literature but also the comprehensive and dynamic approach to techno-economic analysis. Many studies in the literature perform analyses using static load profiles and general meteorological data. However, in this study, real-time measurement data for PV production, load demand, and meteorological conditions were used. In addition, economic parameters such as real-time electricity price tariffs and self-sufficiency rates were integrated into the optimization process and evaluated. It is very rare to see these parameters considered together in similar studies in the literature. In existing studies, energy management for photovoltaic and storage systems is typically handled using predefined static rules, fixed threshold values, or conventional optimization methods. However, these approaches often struggle to adapt to fluctuations in grid conditions, varying load profiles, and dynamic electricity prices. The algorithm proposed in this study introduces an innovative framework that optimizes energy flow and decision-making dynamically.  
The battery capacity that is most suitable for PV capacity was determined under electricity price tariffs, time of use, and feed-in tariffs using technical indicators such as the self-sufficiency rate, self-consumption rate, and economic indicators such as LCOE, NPV, and PBP. The structure of the article is as follows: first, a detailed literature review was conducted, and related papers were presented in the introduction by the subject compliance and the date of presentation. We organized the rest of this paper in the following way. The experimental setup and method are described in Section 2. The results are provided in Section 3, and the conclusion is in Section 4.
## 2 Materials and Methods
### Multi-Software Approach for Network Modeling
#### 2.3.4 EPS Analysis and Leakage Estimation  
Following calibration and validation, an EPS was conducted to analyze the system's behavior over time and identify critical zones prone to excessive pressure and leakage risks. The EPS considered daily and hourly demand variations, interactions between pumps, reservoirs, and demand nodes, and pressure variability across different zones. The pressures inside the pipelines for a water supply network are of greater importance. Specifically, they are the number one cause of real water losses and the wear and tear of the water supply network itself, mainly due to hydraulic shock. Hence, the bigger and more -frequent problems in a network are mainly due to high pressures. Leakage estimation was based on pressure-dependent orifice modeling to refine predictive accuracy [36]. Leakage rates are often modeled using the orifice equation combined with a power law relationship, where the leakage rate \(Q_{l}\) is related to pressure \(P\) as follows:  
\[Q_{l}=c\cdot P^{N} \tag{1}\]  
where \(Q_{l}\)--leakage flow rate, \(c\)--leakage coefficient, \(P\)--pressure (often in meters or kPa), and \(N\)--pressure exponent (typically between 0.5 and 2.5, with 0.5 for fixed area leaks and higher values for pressure-dependent areas). The leakage coefficient in WaterGEMS (or in general hydraulic modeling) varies depending on the pipe material, age, diameter, pressure, and condition. It is typically used in modeling background leakage from distribution systems, especially for real loss analysis.
## 3 Results and Discussion
### PLS-DA Analysis of Volatile Components  
Compared to PCA, PLS-DA is a supervised pattern recognition method used to describe the separation trend between different parts of _A. tsaoko_ and adulterant plants.  
Figure 4: PCA score plot and PLS–DA score plot of three species of the genus _Amomum_. ((**A**) PCA score plot of _A. tsao-ko_, _A. paratsaoko_, and _A. koenigii_ from different parts; (**B**) PLS–DA score plot of _A. tsao-ko_, _A. paratsaoko_, and _A. koenigii_ from different parts).  
The PLS-DA score plot showed that the three groups of samples were distributed on both sides of PC1, and PC1 and PC2 explained 16.6% and 15.6% of the total variance, respectively (Figure 4B). The leading component regression coefficient Q\({}^{2}\) of the model was 0.798 \(>\) 0.5, indicating the model's predictive solid ability. At the same time, R\({}^{2}\) was 0.971, indicating that the model is an excellent fit. Considering that the supervised classification model is prone to overfitting, 1000 permutation tests were conducted to verify its accuracy (Figure S2); the results indicate that the model did not overfit and the evaluation model is reliable and effective. Therefore, it can be used to analyze the volatile components of different genus _Anomum_ plants and for metabolite screening.
## 2 Results and Discussion
### Ailanstigols A and B Lead to a Reduction in Bacterial Viability  
To investigate whether compounds **1** and **2** could reduce the viability of bacteria, _P. syringae_ pv. _maculicola_ and _Xanthomonas oryzae_ pv. _oryzae_ were cultured in the presence of **1** or **2**, and resazurin was then added to the culture mix. The resazurin was reduced by metabolic activities in live bacterial cells to the pink fluorescent substance resorufin, which allows for the assessment of bacterial metabolic activity and viability via changes in color and fluorescence. Our results showed that compound **1** reduced the bacterial cell viability of ES4326, PXO 71A, and PXO 86A strains. After the ES4326 strain was cultivated in the presence of compound **1** at a concentration of 4 times IC\({}_{50}\), cell viability was reduced by 29.28 \(\pm\) 3.38% compared with the solvent control group (Figure 6A). At a concentration of 4 times IC\({}_{50}\), compound **1** exhibited the most apparent inhibitory effect on the cell viability of the PXO 71A strain, with an inhibition rate of 51.65 \(\pm\) 4.14%. Furthermore, as the concentration increased, the bacterial cell viability decreased progressively (Figure 6B). However, compound **1** had only a slight inhibitory effect on the cell viability of the PXO 71A strain (Figure 6C). Compound **2** exhibited a certain inhibitory effect on the cell viability of the ES4326, PXO 71A, and PXO 86A strains. After the ES4326, PXO 71A, or PXO 86A strain was cultured for 6 h in the presence of compound **2** at a concentration of 4 times IC\({}_{50}\), the cell viability inhibition rates were 33.85 \(\pm\) 2.92%, 55.77 \(\pm\) 6.97%, and 46.26 \(\pm\) 2.14%, respectively (Figure 6D-F).  
Figure 6: Determination of the effects of compounds **1** and **2** on the cell viability of _Xanthomonas oryzae_ pv. _oryzae_ PXO 71A, PXO 86A, and _Pseudomonas syringae_ pv. _maculicola_ ES4326. (**A–C**) The results of the cell viability assay on the ES4326, PXO 71A, or PXO 86A strains treated with compound **1** for 6 h. (**D–F**) The results of the viability assay of the ES4326, PXO 71A, or PXO 86A strain after treatment with compound **2** for 6 h. IC\({}_{50}\): Half maximal inhibitory concentration. The IC\({}_{50}\) values of compound **1** against the PXO 71A, PXO 86A, and ES4326 strains were determined to be 485.85 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{mL}\), 314.16 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{mL}\), and 289.23 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{mL}\), respectively. The IC\({}_{50}\) values of compound **2** against the PXO 71A, PXO 86A, and ES4326 strains were determined to be 354.09 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{mL}\), 274.12 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{mL}\), and 476.18 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{mL}\), respectively. One-way ANOVA and Tukey’s tests were used to compare mean differences, and different lowercase letters (a, b, and c) represent significant differences (\(p<0.05\)).
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 4 Security Scheme
### Multicast Communication Phase  
Since all members of the multicast group now share the group key \(k_{g}\), confidentiality is easily realized. Authentication of the messages from the publisher is obtained by using the one-way key chain on the side of the publisher and the stored end value \(h_{w}\) on the side of the subscriber. Consequently, the publisher can now securely submit the message \(M\) in an authenticated way to its subscribers as follows:  
\[T_{c},E_{k_{g}}(M,h_{w-1}),H(T_{c}\|M\|h_{w-1}).\]  
Here, \(T_{c}\) denotes the current timestamp and \(h_{w-1}\) the hash value prior to \(h(w)\). Upon receiving this message, the subscribers can decrypt the ciphertext and verify whether \(h_{w}=H(h_{w-1})\). If so, the message is authenticated and \(h_{w-1}\) is stored instead of \(h_{w}\).
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 4 Materials and Methods
### Evaluation Metrics  
The evaluation metrics employed during the algorithm testing phase align with those utilized in previous research, particularly in the study [3]. These established metrics encompass key performance indicators, including accuracy, precision, recall, and F1-score, essential for a comprehensive assessment of the model's effectiveness and reliability [30].  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1-Score=2X\frac{PXR}{P+R} \tag{10}\]  
Within this context, the following variables are defined:  
* \(TP\) (True Positive): Represents the count of correctly classified instances of healthy water quality.
* \(TN\) (True Negative): Denotes the count of accurately classified instances of non water quality.
* \(FP\) (False Positive): Signifies the count of misclassified instances as water quality when they are, in fact, non water quality.
* \(FN\) (False Negative): Indicates the count of misclassified instances as non water quality when they are, in reality, water quality.  
These evaluation metrics provide a comprehensive framework for assessing the model's capacity to accurately classify water quality. The application of these well-established measures allows for an objective and rigorous evaluation of the ensemble model's performance and dependability, ensuring its alignment with the desired criteria for practical applications in water quality assessment and monitoring.
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
###### Abstract  
The wireless channel is volatile in nature, due to various signal attenuation factors including path-loss, shadowing, and multipath fading. Existing media access control (MAC) protocols, such as the widely adopted 802.11 wireless fidelity (Wi-Fi) family, advocate masking harsh channel conditions with persistent retransmission and backoff, in order to provide a packet-level best-effort service. However, the asymmetry of the network environment of client nodes in space is not fully considered in the method, which leads to the decline of the transmission efficiency of the good ones. In this paper, we propose CoFi, a coding-assisted file distribution protocol for 802.11 based wireless local area networks (LANs). CoFi groups data into batches and transmits a random linear combination of packets within each batch, thereby reducing redundant packet and acknowledgement (ACK) retransmissions when the channel is lossy. In addition, CoFi adopts a MAC layer caching scheme that allows clients to store the overheard coded packets and use such cached packets to assist nearby peers. With this measure, it further improves the effective throughput and shortens the buffering delay when running applications such as bulk data transmission and video streaming. Our trace based simulation demonstrates that CoFi can maintain a similar level of packet delay to 802.11, but increases the throughput performance by a significant margin in a lossy wireless LAN. Furthermore, we perform a reverse-engineering on CoFi and 802.11 using a simple analytical framework, proving that they asymptotically approach different fairness measures, thus resulting in a disparate performance.  
CoFi distribution protocol; delay; harsh channel condition; hybrid coding scheme; IEEE802.11 standard; internet of things; media access control protocol; throughput; Wi-Fi +
Footnote †: journal:  
Article
###### Abstract  
We compared the results of absorbed dose measurements made using the TRS-398, TG-51, and DIN protocols and their associated uncertainties to reduce discrepancies in measurement results made using the three protocols. This experiment was carried out on two Varian Medical linear accelerators with 4, 6, 10, and 20 MV photon energies using FC65-G and CC15 (cylindrical) and NACP-02-type (plane-parallel) ion chambers in water phantoms. The radiation beam quality index (Q) was determined from the measurement of percentage depth dose. It was used to determine the photon beam quality factor required with the ionization chamber calibration factor to convert the ion chamber reading into the absorbed dose to water. For the same beam quality, the TRS-398/TG-51 varied from 0.01% to 1.8%, whereas the ratio for TRS-398/DIN 6800-2 varied from 0.1% to 0.88%. The chamber-to-chamber variation was 0.09% in TRS-398/TG-51, 0.03% in TRS-398/DIN, and 0.02% in TG-51/DIN 6800-2. The expanded uncertainties (k = 1) were 1.24 and 1.25 when using TRS-398 and DIN 6800-2, respectively. Using the aforementioned three protocols, the results showed little chamber-to-chamber variation and uncertainty in absorbed dose measurements. The estimated uncertainties when using cylindrical ion chambers were slightly lower than those measured using plane-parallel chambers. The results are important in facilitating comparisons of absorbed dose measurements when using the three protocols.  
radiotherapy; radiation dosimetry; ionization chamber; Linac; absorbed dose to water; dosimetry protocols +
Footnote †: journal: _sciences_
## 3 Quality Factor Analysis of Air-Core Inductors  
In PSiPs, the power loss of air-core inductors can be increased due to high-frequency effects. For megahertz PoL applications, power losses of air-core inductors are related to the inductance current, geometric parameters of coil-wire, coil material, skin effect, eddy effect, proximity effect, fringe effect, etc. Among many factors, the geometric parameters and skin effect are highly related to the switching frequency. To calculate the power loss of the air-core inductors, the inductance current is expanded into sinusoidal waves of various frequencies. Their frequencies and amplitudes are \(f_{s}\), \(2f_{s}\), \(3f_{s}\),... and \(I_{L1}\), \(I_{L2}\), \(I_{L3}\),... respectively. Upon consideration of the geometric parameters and skin effect, in [40], the power loss of air-core inductors can be estimated by  
\[\left\{\begin{array}{l}P_{ind}=P_{ind,DC}+P_{ind,AC}\\ P_{ind,DC}={I_{L0}}^{2}R_{DC}={I_{L0}}^{2}\rho\frac{l}{\Lambda}\\ P_{ind,AC}=\frac{1}{2}{\sum{I_{Ln}}^{2}R_{AC,n}}=\frac{1}{2}{\sum{I_{Ln}}^{2 }\rho\frac{l}{\rho\rho_{in}}}\end{array}\right.. \tag{11}\]  
In (11), the inductance power loss consists of the DC loss (\(P_{ind,DC}\)) and AC loss (\(P_{ind,AC}\)). Smaller \(R_{AC}\) at the same inductance value is more beneficial for efficiency, especially in reducing high-frequency power loss. In order to simplify the analysis, the quality factor, \(Q\), is optimized to decrease the AC power loss, as given by  
\[Q=\frac{2\pi f_{s}L}{R_{AC,n}}, \tag{12}\]  
where higher \(Q\) means a lower \(AC\) resistance value at the same inductance value.  
According to the profile, air-core inductors are classified into planar spiral inductors and solenoid inductors, as shown in Figure 7, which are discussed in the following sections.  
Figure 6: Power loss of the integrated Si LDMOSFET with drain-source current when \(M\
eq 1\).