esa-sceva/satcom-chunk-collection huggingface dataset:
Because FSO and RF do not interfere, FSO technology has also been explored to complement the existing RF systems [29]. This feature is critical for applications where RF interference must be avoided, such as hospitals and personal entertainment systems aboard commercial airplanes, to avoid interfering with RF-sensitive navigation and avionics electronic equipment [30]. Furthermore, along with RF technology, the next generations of wireless communication systems (e.g., 5G, fifth-generation (6G)) contain numerous complimentary access technologies, including FSO [31; 32]. To overcome the disadvantages of FSO technology, hybrid FSO/RF communication is utilized as a substitutional nonstop method to use when the primary FSO link is under bad weather conditions. As shown in Figure 1, the sender switches between the links based on the feedback received from the receiver using a hybrid FSO/RF system which gives excellent advantages to the communication system. The system becomes highly reliable and always available for wireless communication between sender and receiver, even under extreme weather conditions. A hybrid structure of FSO with RF backup combines the advantages of both the technologies, such as low cost, non-line-of-sight applications, high data rate, and low latency [16]. The hybrid system provides dual transceivers RF and FSO by switching between them based on the weather conditions and electromagnetic interference levels. Mixing the FSO link with the RF link is a solution to provide a high-speed data rate for backhaul wirelessly. Rayleigh scattering occurs when particle size is much smaller than the wavelength, such as in snow [33]. Mie scattering applies to particles that have a comparable size to the wavelength, such as water droplets in clouds and fog. Non-selective scattering can be used for particle sizes much greater than the wavelength, such as raindrops [34; 35]. In long-range RF connections, some atmospheric conditions can easily disturb the connection. Using an access point to amplify the signals through a short-range RF link to a long-range FSO link solves this problem without additional power consumption [36]. In addition, the FSO communication system solves the last mile access problem. Therefore, many researchers are interested in its field [37]. The last mile mainly characterizes the final networking part that connects the end-user to the internet. The last mile problem occurs due to funneling all the network traffic in this link, i.e., bottlenecking of the network throughput. Consequently, the bandwidth available in this link will limit the amount of data transmitted to the respective internet service provided [38]. To sum up, Table 1 summarizes the main differences between FSO and RF communication systems [39; 40; 41]. Most terrestrial FSO links are between 300 m and 5 km, although depending on the data rate and availability needed, greater lengths, such as 8-11 km, may be implemented [42]. On the contrary, with a power of 1 W, the typical RF link ranges with a frequency range of 400 MHz are up to 30 km, and with a power of 10 W, up to 80 km [43].
## 5 Conclusions  
To compensate for the non-negligible phase of periodic modulation induced by high-frequency vibration error of a radar platform, a novel vibration error compensation method based on STFT with the combination of the optimal window width and RANSAC was proposed in this paper. The method not only reduced the influence of the STFT window widths but also avoided the singular values of IF estimation in the LS regression. Therefore, regardless of whether the SNR is high or low, the proposed method can fulfill the focusing requirements of the image. Finally, our simulation results have verified its validity. However, at present, the low power of terahertz radiation sources means that such a radar must operate at a close range, and there are few THz-SAR systems installed on aircraft for the time being. Therefore, we will use the measured SAR data to further test the effectiveness of the algorithm in the future. Meanwhile, all the existing high-frequency vibration error estimation methods ignore the effect of low-frequency motion errors. Thus, research on the synchronous estimation of low-frequency motion error and high-frequency vibration error in THZ-SAR imaging is the key to work advancing this technology in the future.  
Conceptualization, Y.L.; Funding acquisition, X.D. and Y.Z.; Methodology, Y.L.; Project administration, X.D. and Y.Z.; Resources, Q.Z.; Software, Q.W.; Validation, J.J. and Q.Z.; Writing--original draft, Q.W. and J.J.; Writing--review and editing, Y.L., Q.Z. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of Shanghai (Grant No. 21 ZR1444300), in part by the National key R and D Project of China (Grant No. 2018 YFF01013003), in part by the National Natural Science Foundation of China (Grant No. 61722111, 61731020, 42005110), the 111 Project (Grant No. D18014), the International Joint Lab Project of Science and Technology Commission Shanghai Municipality (Grant No. 17590750300), the Key Project of Science and Technology Commission Shanghai Municipality (Grant No. YDZX20193100004960).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### UHPLC-MS/MS Analysis  
NEOs were analyzed using UHPLC-MS/MS (8045, Shimadzu, Kyoto, Japan) coupled to a triple quadrupole mass spectrometry instrument with an electrospray ionization source. The analytical column used was a Syncronis C8 column (50 mm \(\times\) 2.1 mm, 1.7 \(\upmu\)m, Thermo) at a flow rate of 0.2 mL/min. Mobile phase A was H\({}_{2}\)O, and mobile phase B was ACN. The applied elution gradient was as follows: 1% B in 0.5 min, 1-65% B in 4.5 min, and 65-100% B in 2 min. Subsequently, the system was maintained at 100% B for 2 min and re-equilibrated at 1% B for 5.5 min. The total run time and injection volume was 14.5 min and 10 \(\upmu\)L, respectively. Ions were monitored in the positive multiple reaction monitoring (MRM) mode. Table 1 and Figure A1 present MS parameters and ion transitions for quantification and qualification of each compound. Figure 1 presents the chromatograms for a spiked milk sample (16 \(\upmu\)g/kg of each analyte and 8 \(\upmu\)g/kg of IS).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytes** & **RT (Min)** & **Transition 1 (Quantifier)** & **CE 1(V)** & **Transition 2 (Qualifier)** & **CE 2(V)** \\ \hline Dinotefuran & 4.74 & 203\(\rightarrow\)129.05 & 13 & 203\(\rightarrow\)113.10 & 10 \\  & & & & 203\(\rightarrow\)87.10 & \\ Nitenpyram & 5.34 & 271\(\rightarrow\)225.00 & 20 & 271\(\rightarrow\)189.00 & 21 \\  & & & & 271\(\rightarrow\)99.00 & \\ Thiamethoxam & 5.96 & 292\(\rightarrow\)211.05 & 14 & 292\(\rightarrow\)181.05 & 24 \\ d\({}_{3}\)-Thiamethoxam & 5.93 & 295\(\rightarrow\)214.10 & 12 & 295\(\rightarrow\)132.00 & 25 \\ Clothianidin & 6.34 & 250\(\rightarrow\)169.10 & 12 & 250\(\rightarrow\)132.00 & 21 \\ d\({}_{3}\)-Clothianidin & 6.26 & 253\(\rightarrow\)172.10 & 30 & 253\(\rightarrow\)132.00 & 33 \\ Imidadoprid & 6.43 & 256\(\rightarrow\)209.05 & 16 & 256\(\rightarrow\)175.05 & 18 \\ d\({}_{4}\)-Imidadoprid & 6.45 & 260\(\rightarrow\)213.05 & 14 & 260\(\rightarrow\)179.10 & 21 \\ Acetamiprid & 6.66 & 223\(\rightarrow\)126.05 & 18 & 223\(\rightarrow\)56.10 & 42 \\ d\({}_{3}\)-Acetamiprid & 6.61 & 226\(\rightarrow\)126.05 & 19 & 226\(\rightarrow\)59.00 & 16 \\ Thiacloprid & 7.16 & 253\(\rightarrow\)126.00 & 22 & 253\(\rightarrow\)90.00 & 45 \\ \hline \hline \end{tabular} RT: Retention time; CE: Collision energy.  
\end{table}
Table 1: Mass spectrometric parameters for determining neonicotinoid levels and their isotope labeled forms.  
Figure 1: _Cont._  
Figure 1: Representative chromatograms of a milk sample spiked seven neonicotinoids standards at 16 \(\upmu\)g/kg and 8 \(\upmu\)g/kg of IS.
## 6 Evaluation and Discussion
### Security Analysis
#### 6.1.1 Impersonation Attack  
In this attack scenario, an adversary \(adv\) attempts to impersonate another producer by sending an \(Interest\) packet with a fake or stolen producer CID and VID: \(adv(PV)\hookrightarrow MS:si(/NDN-BDA/MS/sign/{VID/<key>}K_{MS})\). If \(adv\) sends an \(Interest\) with the keyword join (\(stepI\)), it will be discarded by the \(VRE\) as the CID included in the name does not match the key \(K_{adv}^{-1}\) that signed the \(Interest\). Suppose that the \(adv\) fakes an \(Interest\) with the keyword sign(\(step4\)), willing to request a fraudulent certificate. This attempt will also fail, as the \(Interest\) packet will be rejected by the \(MS\) since it does not have the certificate that signed the \(Interest\).
## 3 Results
### Cavity of Length \(L=16\lambda\)
#### 3.2.2 Several Source Amplitudes  
To determinate which medium is more convenient to obtain more harmonic amplitudes for a given \(f\), we change both \(N_{g}\) and \(v_{0g}\) (thus also \(R_{0g}\) and \(f_{0}\)) by keeping \(v_{v}\) constant at \(v_{v}=3.2725\times 10^{-5}\). Figure 6 shows the maximum amplitude of the 2 nd, 3 rd, and 4 th harmonics vs. ratio \(f_{0}/f\) for \(p_{s}=20\) kPa, \(p_{s}=22.5\) kPa, and \(p_{s}=25\) kPa. A maximum for each 2 nd, 3 rd, and 4 th harmonics exists. If we focus our attention on the generation of the second harmonic, Figure 6a, the maximum amplitude takes place when the ratio  
Figure 4: Maximum amplitude of \(2f\) (**a**), \(3f\) (**b**), and \(4f\) (**c**) in the cavity of length \(L=\lambda/4\) vs. \(f_{0}/f\), for three different source frequencies \(f=200\) kHz (blue line), \(f=300\) kHz (green line), and \(f=400\) kHz (red line).  
Figure 5: Amplitude of \(f\) (black line), \(2f\) (blue line), \(3f\) (green line), and \(4f\) (red line) in the cavity of length \(L=16\lambda/4\) vs. for \(p_{s}=20\) kPa.  
\(f_{0}/f\approx 5\), with amplitudes of the order of 50% (of \(p_{s}\)) for the three source amplitudes studied. The third harmonic has the maximum amplitude when \(f_{0}/f\approx 7\), with amplitudes of the order of 33% (of \(p_{s}\)) for the three source amplitudes studied. The fourth harmonic has the maximum amplitude when \(f_{0}/f\approx 9\), with amplitudes of the order of 23% (of \(p_{s}\)) for the three source amplitudes studied. The three harmonics have a similar behavior with \(f_{0}/f\), the harmonic amplitude grows quickly until it reaches a maximum, and then it decreases quickly until reaching a constant value. This behavior is similar for the three \(p_{s}\) studied here, although, as expected, the harmonic generation is more intense for larger \(p_{s}\).
# Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine
## 3 Optimization for TMD Parameters
### Method of Exhaustion (ME) for Lower Stiffness TMD  
The optimal mass for TMD is approximately 1-5% of the structural mass based on previous studies on vibration control of tall buildings [24]. Therefore, to accomplish a comprehensive investigation on the effects of TMD mass, its values are chosen from 6000 kg to 20,000 kg with a 2000 kg interval in this study. With respect to spring and damping coefficients, the most direct but computationally expensive method is to perform exhaustive search by the established model. Thus, in the consideration of time spent and accuracy, the searching range of spring and damping coefficients is within 0-12,000 N/m and 0-12,000 N/(m/s), respectively, for each TMD mass, and the searching interval is 100 for both variables. The optimal results for eight TMD masses are listed in Table 4. The reduction ratio \(\eta\) defined in Equation (19) is adopted to reflect the performance of TMD.  
\[\eta_{\sigma_{\text{TID}}}=\left(\sigma_{\text{TID}}^{\text{Baseline}}-\sigma_ {\text{TID}}^{\text{TMD}}\right)/\sigma_{\text{TID}}^{\text{Baseline}} \tag{19}\]  
Figure 5: Comparison of 5\({}^{\circ}\) pitch free decay responses between the simplified model and FAST simulation with active TMD: (**a**) without TMD stops; and (**b**) with TMD stops.  
We can find that \(\sigma_{\text{TTD}}\) decreases monotonously with increasing the TMD mass. The minimum value occurs when the TMD mass is 20,000 kg, and the reduction ratio is 14.23%. As the \(\theta_{\text{p}}\) and \(x_{\text{TTD}}\) curves shown in Figures 4 and 5, the natural frequency of pitch for OC4-DeepCwind system is about 0.0378 Hz. For these lower stiffness TMD devices, their frequencies \(f_{n}\) are close to the pitch motion frequency and the vibration energy dissipation is focused in this band. However, all the damping ratios are larger than one for an overdamped state.  
To illustrate the entire variation tendency of TMD parameters, the 3D surfaces of \(\sigma_{\text{TTD}}\) for 10,000 kg and 20,000 kg TMD mass are shown in Figure 6. It is interesting to note that the 3D surfaces are divided into two regions, i.e., the rough area (lower stiffness) and the smooth area (higher stiffness). The irregular variations in rough area may be induced by the stop limitation of TMD movement. In the lower range of stiffness, the TMD mass block is supposed to collide on the nacelle wall, and the collision may complicate the system dynamics and response. In the higher range of stiffness, the TMD mass block can operate within the limitation without collision. Moreover, it can be deduced that a minimum \(\sigma_{\text{TTD}}\) value shall lie in the smooth area. Therefore, another approach to search for the optimum in this infinite region is needed.
# Compact 3 \(\times\) 1 Matrix Converter Module Based on the SiC Devices with Easy Expandability
###### Abstract  
This paper discusses a new approach for building a compact all-in-one matrix converter module based on SiC semiconductors arranged in a common source connection. The used transistors are in the D2 PAK package. The design of the module is divided into two parts, namely a power module designed at one-layer aluminum substrate printed circuit board (PCB) to ensure good thermal performance and voltage isolation between the module and heatsink. The second board is responsible for the SiC driving and is mounted at the top of the power PCB and consists of metal-oxide semiconductor field effect transistor (MOSFET) drivers, isolated power supplies, a current direction detection circuit, and current value sensors. In the paper, the proper function of the SiC MOSFET drivers, current direction detection, and current measurement sensors were evaluated. Finally, 3D design together with the final prototype is presented. The modules contain three bidirectional cells for interconnection three input voltage sources and one output phase. The uniqueness and novelty of the presented module are the compactness and easy expandability of the module to achieve higher power outputs and multiphase applications such as five phase machines.  
matrix converter; silicon carbide; matrix module; driver +
Footnote †: journal: Applied Sciences
###### Abstract
For example, [PERSON] et al. [10] showed that dominant species in Uttarakhand Himalaya, India, and the overlying soils are important contributors to the total carbon stocks. These factors play a crucial role in carbon sequestration, which will further help mitigate GHG emissions. Another study recommends that in addition to conserving forests for climate change mitigation, carbon sequestration potential extends to trees outside of forests as well, hence non-forested lands should also be protected [11]. Furthermore, estimation of stem volume and tree biomass is very important for planning sustainable forest resource use, and for research on the energy and nutrient flow in the ecosystems [12]. Estimates of forest ecosystem productivity are often expressed as gross or net primary productivity (GPP and NPP, respectively). GPP is defined as a balance between the total amount of carbon fixed by the ecosystem through photosynthesis and carbon loss through photosprespiration [13]; NPP is the share of GPP used for new biomass growth [14]. Thus, NPP is theoretically defined as the difference between GPP and autotrophic respiration (Ra) [15]. Studies of ground-based NPP are often based on litterfall and AGB accumulation measurements, and are therefore not closely related to the concept of NPP as a balance between GPP and Ra[15; 16]. In practice, NPP is often estimated as the sum of biomass production components [16; 17], which can be done using literature or site-specific allometric equations. To estimate tree biomass, it is recommended to use allometric relationships based on tree parameters such as diameter at the breast height (DBH) and tree height [14]. Many allometric equations can be found in the literature, for example in the study by [PERSON] et al. [12]. These are also often derived for individual species [18]. One important indicator of ecosystem function that can be estimated with known NPP and GPP is carbon use efficiency (CUE = GPP/NPP). CUE is commonly defined as the ratio of carbon sequestered by plants from the atmosphere and carbon allocated to growth [19], which also indirectly informs about the share of carbon respired by plants due to Ra[7]. Earlier studies have used various methods to estimate biomass increment, carbon storage and CUE [14; 19; 20; 21; 22], for example biometric in situ measurements and eddy covariance observations for NPP estimation of Pedunculate Oak Forest in Croatia [23]. Similarly, temporal variability of the NPP-GPP ratio in a temperate beech forest has been estimated at both seasonal and interannual time scales [24]. Further concepts and methods of measuring NPP in forests have been described earlier [16]. The main aim of this study was to present the differences in NPP between the two years that varied in terms of water availability: a dry year (2019) that followed extreme droughts at the study area in 2018, and a moderate year (2020). It was hypothesized that water conditions have a significant impact on the amount of carbon absorbed by trees, and thus biomass production. The relationship between changes in GPP and NPP over the two studied years was also explored.
## 2 Related Works
### IoT Applied in Waste Classification
The findings emphasize the potential of ML models, especially CNNs, in waste classification, with transfer learning techniques showing promise for enhancing model performance. Future research directions include addressing real-life complexities and improving the system through sensor integration and low-cost solutions. Additionally, image sensor analyses based on color identification technologies capture visual data. The process classifies waste types using the analyzed images [24]. The implementation of IoT technologies increases the capabilities of waste identification systems. IoT-enabled smart bins monitor waste levels in real time and provide data transmitted to a central system for analysis and action [25; 26]. For example, a smart waste bin with ultrasonic sensors measures the fill level and sends alerts when it approaches capacity. Next, it optimizes collection routes and schedules [27; 28]. In conjunction with the IoT, DL algorithms allow for advanced waste classification based on visual data [29; 30]. In addition to traditional sensors, advanced technologies, such as Radio Frequency Identification (RFID) and X-ray imaging, are being explored for their potential in waste management. RFID tags are attached to waste items, while X-ray technology details the composition of waste materials [31; 32]. Combining these technologies contributes to sustainability efforts by promoting recycling. Reducing landfill waste is another objective of combining AI technologies. Integrating the IoT into waste management allows for real-time monitoring processes and making decisions based on data. For example, the IoT system presented by [PERSON] et al. [33] has several hardware components, including GPS/GPRS modules for precise geolocation, IoT buttons for reporting green waste, and sensors for monitoring waste levels. Additionally, sweeping carts are equipped with devices that ensure data transmission and secure operation in challenging environments [34; 35]. Implementing such smart waste systems has improved waste collection management, as shown by various case studies [36; 37]. IoT platforms allow municipalities to analyze data for better operations management, leading to further improvements. Smart bins with sensors automatically sort waste into various categories, reducing contamination in recyclable materials. [PERSON] et al. [38] introduce a smart waste management system for urban areas, employing IoT technologies. The system uses sensors to monitor waste levels in bins and segregate waste into categories such as wet, dry, and metallic. The proposed prototypes have ultrasonic sensors to track waste levels. At the same time, these devices send real-time data to a cloud server. When bins are full or if a fire is detected, email alerts are sent to the authorities. This capability supports environmental goals in urban waste management [39]. The IoT in waste management also presents challenges, such as high upfront costs, strong infrastructure, and concerns about data privacy and security [40; 41].
# MZM Optimization of PAM-4 Transmission in Data Center Interconnect
## 3 Analog Optimization Method  
It is well known that the input to output transfer function of optical modulators is usually non-linear. This non-linearity of the optical modulator can be used in a beneficial way to optimize the power spacing discussed above. Figure 2a presents a typical MZM L/V curve (light vs. applied voltage curve) which was achieved using the modulator DC bias sweep. The MZM DC bias point of the linear regime (quadrature point) in this example is 5 V, as shown in Figure 2a. Using a lower voltage bias point, of 4.1 V as an example, leads to operation within the non-linear regime. The eye diagrams of two different transmission schemes using two different bias points of 5 V and 4.1 V, in this example, are shown in Figure 2b,c, respectively. In Figure 2c, reducing the bias point to 4.1 V increases the spacing between level 3 and level 2 on account of reducing the spacing between level 1 and level 0, and increases the extinction ratio (ER). As will be shown in the following experimental section, the level spacing optimization leads to performance improvement by means of optical signal-to-noise ratio (OSNR) sensitivity and BER, in systems which are dominated by signal dependent noise. It should be pointed out that operating at the lower bias points leads to operation in the MZM non-linear regime which, in turn, introduces distortions by the MZM L/V curve, as shown in Figure 2c. These distortions are mostly compensated by the MLSE at the post-detection stage, as demonstrated in the following section. The other mechanism of improvement is enabled in a transmission system which uses a booster optical amplifier with a fixed optical output power, which is the common practice in a long reach fiber optic application. The reduction of the MZM bias reduces the average optical power at the optical amplifier input which increases the optical amplifier gain and in turn, increases the outer OMA (OMAouter) at the optical amplifier output. Consequently, the power level spacing between all levels is uniformly increased.