esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Experimental Validation of Down-Conversion Double-Balanced Mixer Prototype
#### 4.5.1 Gain, Isolation, and Return Loss Measurement  
The gain, isolation (LO-RF, LO-IF, and RF-IF), and return loss of the RF port, LO port, and IF port were measured on the Rohde & Schwarz ZNB 20 vector network analyzer mixer mode test setup. The setup was calibrated using the Rohde & Schwarz (Germany) ZV-Z52 calibration unit and Rohde & Schwarz thermal power sensor NRP-Z55 (PUOSM calibration) [37; 38].  
Figure 23 shows the measurement setup where RF and LO frequencies are sweeping from 300 MHz to 2500 MHz with the fixed IF difference of 145 MHz. This test setup is performing down conversion. Since the NRP-Z55 can only measure precise power levels between \(-30\) dBm and \(+20\) dBm, the power level of the RF port was set to \(-25\) dBm; CAD tools, however, do not have that limitation.  
The measured conversion gain of the developed down-conversion mixer is illustrated in Figure 24. Since conversion gain is directly correlated with trans  
Figure 23: Mixer mode measurement setup. DUT measures conversion gain and port-to-port isolation.  
Figure 22: Developed prototype of novel down-conversion double-balanced mixer for validation purposes: (**a**) top view of the PCB and (**b**) bottom view of the PCB.  
variation in gain as the transconductance decreases with increasing frequency. It shows that conversion gain varies 12 dB \(\pm\) 1 dB from 600 MHz to 1.8 GHz. On the other hand, gain falls off above 1.8 GHz as a result of \(C\pi\), \(C\mu\), and substrate capacitance. The variance in simulated performance can be attributed to the accuracy in determining the parasitic capacitance of the substrate (bond pad) during the simulation. Furthermore, due to the perfect symmetry in the layout, BJT-based Gilbert-cell mixers provide outstanding LO-IF isolation of up to 60 dB compared to conventional active mixers [39].  
Figure 25 shows the measured return loss of the down-conversion double-balanced mixer. The tuned circuit exhibits the ideal match to the system impedance at around 145.5 MHz because of the component's standard values and tolerances.  
Figure 24: Measured conversion gain and RF–IF (RF isol) isolation of the down-conversion mixer prototype as a function of RF frequency. LO–IF (Lo thru) and LO–RF (Lo leak) isolation are measured as a function of LO frequency. Test conditions: RF frequency: 300 MHz to 2.5 GHz at \(-\)25 dBm. LO frequency: 155 MHz to 2.355 GHz at 0 dBm and fixed IF 145 MHz. (Supply voltage of 3.6 V and total current of 19 mA).  
Figure 25: Measured return losses of the down-conversion mixer prototype.
## 3 Results and Discussion
### Historical Storm Surge Event, January 2019  
The primary objective of this study was to assess the feasibility of using the HEC-RAS 2D (version 6.6) model to simulate wind surges in the Vistula Lagoon. To validate the model, the January 2019 storm event was analyzed. The validation was made possible primarily by hydrometeorological data obtained from field measurements conducted by IMGW-PIB, which are described and analyzed in detail in Sections 3.1.1-3.1.3. Subsequently, a numerical simulation of the storm event was carried out, as outlined in Section 3.1.4. The validation of the computational model was based on a comparison of the field measurement data with both current and previous [11] simulation results, as presented in Section 3.1.5.
# An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
## 4 Results of Overview
### Virtual Reality (VR) Therapies  
Virtual reality interventions have been found to assist individuals in managing anxiety and depression by offering them secure settings to acquire coping mechanisms for circumstances, ideas, or memories that trigger anxiety or dread. Virtual reality simulations enhance skill development exercises and provide experience learning opportunities, allowing individuals to practice and improve their communication, problem-solving, and coping abilities in virtual settings [75]. These approaches encompass several techniques, including exposure therapy, mindfulness-based therapies, cognitive behavioral therapy, biofeedback mechanisms, social support networks, and skills development exercises [76]. Virtual reality exposure treatment enables patients to confront challenging experiences in a regulated setting, promoting emotional resilience. Mindfulness-based therapies offer intensive opportunities to practice relaxation methods and guided meditation, which facilitate stress reduction and emotional regulation [77].  
Furthermore, VR algorithms have the capability to replicate situations in order to trigger maladaptive beliefs and actions, hence aiding in the process of cognitive restructuring [78]. By incorporating biofeedback sensors into virtual reality (VR) systems, individuals are able to see and control their physiological responses, therefore improving their self-awareness and ability to manage stress [79]. Conversely, virtual support groups and their guided treatments (social support networks) in virtual reality (VR) settings provide chances for social bonding, shared encounters, and reciprocal assistance, therefore decreasing the sensations of seclusion and social disgrace linked to mental disorders [80]. VR simulations offer secure and regulated settings for practicing coping skills, problem-solving approaches, and effective communication tactics (skill-building exercises).
## 2 Materials and Methods
### Spatial Analysis  
#### 2.3.1. Symbology Analysis  
Theoretically estimated data were joined to the administrative shapefiles (district and sub-district boundaries) and projected to the universal data of WGS 1984 longitude and latitude using the projection and monitoring tools of GIS software. Then, all the parameters, such as biogas potential, compost potential, electricity and fertilizer generation, etc., were analyzed using symbology analysis of the software. The districts and sub-districts were classified into five categories, from very low to very high, with an equal interval range based on the maximum and minimum value of each indicator and identified by distinct colors.
## 2 Mobile Networking Background
### Incorporating Flying Base Station  
Sixth-generation networking introduces the UAV flying base stations to improve the base station coverage and the channel link qualities to mobile users. As is typical with new technologies in mobile networking, the incorporation of the flying base station to mobile networking should support backward-compatibility to the 5G networks described in Section 2.1. For example, a flying base station can provide the connectivity service even if the mobile user only supports 5G or even lower-generation telecommunications, e.g., 2G for emergency applications.  
Figure 2: Control communication protocol interactions between the user, base station, and core network to set up the data channel.  
Figure 1 therefore builds on the traditional cellular/5G architecture (lower row) and introduces the flying base station (upper row). In Figure 1, the flying base station connects to the mobile user and to the stationary ground base station (to access the backend and the internet), and these communications are in wireless/RF communications, as drawn in dotted arrow lines. While mobile, the flying base station connects to the stationary ground base station to access the rest of the network. In addition, the flying base station can sometimes connect directly to the cellular provider infrastructure via a switch, e.g., when it is recharging its battery, requiring physical connections as drawn in solid arrow lines.  
The flying base station can be multiple physical entities (multiple flying base stations), although drawn as one logical entity in Figure 1. These flying base stations can network with each other (forming their own network) for coordination and connectivity-provision control (involving ad hoc networking capabilities, e.g., flying ad hoc network or FANET), while maintaining the connections with the rest of the connectivity provider infrastructure including the ground base station. The collaborative network of flying base stations can extend the connectivity range by forming a relay network.  
The emerging flying base station has the purpose of serving connectivity to mobile users, requiring connectivity in an on-demand basis or dynamically entering/exiting the flying base station's cellular range. To serve the public's mobile devices, the flying base station will be publicly accessible and broadcast and advertise RRC messages, similarly to the stationary terrestrial base stations described in Section 2.1. This makes the flying base station distinct from some other flying/UAV applications requiring privacy and/or having dedicated communication targets.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 3 Motivation/Desired Outcome of Transaction-Based Energy
### Cost Optimization and Economics  
According to [35], one of the promises of power grid modernization is the possibility to optimally deploy DERs for the benefit of asset owners. From the perspective of prosumers, cost optimization is widely reported as a major motivation for bilateral TE among peers [36; 37; 38; 39; 40; 41; 42]. Cost optimization can be achieved through reductions in generation costs, transport costs, energy demand or through profit maximization. In addition, minimized losses and energy cost in distributed micro-grid (MG) have been considered as a motivation for prosumers' participation in MG and TE [43]. The optimization mechanism is further discussed in Section 5.3.
## 3 Method
### Parameter Estimation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**Urban Roadways**} & \multicolumn{2}{c}{**Rural Roadways**} \\ \cline{2-5}  & & **Mean** & **S.D.** & **Mean** & **S.D.** \\ \hline ROUGH & 1 if rough road surface, 0 good road surface & 0.013 & 0.112 & 0.032 & 0.177 \\ \hline RAINING & 1 if under rainy weather, 0 otherwise & 0.028 & 0.164 & 0.031 & 0.174 \\ \hline SMOKE\_DUST\_FOG} & 1 if under dust/foggy weather, 0 otherwise & 0.018 & 0.135 & 0.034 & 0.182 \\ \hline WET & 1 if wet road surface, 0 otherwise & 0.037 & 0.190 & 0.037 & 0.189 \\ \hline AFTERNOON & 1 if 12:01 a.m.–4:00 p.m., 0 otherwise & 0.223 & 0.416 & 0.254 & 0.435 \\ \hline MORNING & 1 if 8:01 a.m.–12:00 a.m., 0 otherwise & 0.129 & 0.335 & 0.147 & 0.354 \\ \hline NIGHT\_LIGHT & 1 if nighttime and lit road, 0 otherwise & 0.313 & 0.464 & 0.174 & 0.379 \\ \hline DARK & 1 if nighttime and unlit road, 0 otherwise & 0.130 & 0.336 & 0.225 & 0.417 \\ \hline \hline \end{tabular} Note: The frequency of motor vehicle accidents involving young riders, stratified by roadway type, demonstrates a pronounced contrast between urban and rural environments. Specifically, the data reveals that there were 5950 reported instances of young rider crashes on urban roadways and 11,863 reported instances on rural roadways. Moreover, when examining the temporal dynamics of these incidents, it is apparent that the annual frequency of such accidents experienced fluctuations over the course of three consecutive years. In 2018, there were 7537 reported accidents involving young riders, which decreased to 6169 in 2019 and further decreased to 4053 in 2020. It is worth noting that the 2020 dataset encompasses the influence of the COVID-19 lockdown measures, which may have contributed to the observed decline in accident rates.  
\end{table} 
Table 2: _Cont._and consistent with empirical data, according to previous studies [11]. The selection of the best model was based on how closely it approximated the empirical data, using the \(\rho^{2}\) value as follows [18]:  
\[\rho^{2}=1-\frac{LL(\beta)}{LL(0)} \tag{4}\]  
where \(LL(\beta)\) represents the log-likelihood of the model at convergence after estimating its parameters, while \(LL(0)\) is the log-likelihood of the model with only a constant parameter estimated [30]. The selection of the appropriate value of \(\rho^{2}\) does not have a fixed value, but a value greater than 0.1 is generally considered acceptable for the road crash model, according to previous studies [17]. Moreover, the average Marginal Effect (ME) was utilized to assess the impact of factors on crash severity. This value represents how modifying a single variable (such as changing from male to female) would affect the probability of a YAM motorcycle crash resulting in death on local roads [28].
## 3 Results and Discussion  
### Effect of Treated Sludge on Soil Fertility, and Chemical and Microbiological Properties  
In agreement with other researchers' findings [2, 7], soil application of treated or untreated sludge significantly increased the pH of the acid soil (Table 2). This could be beneficial for white clover's growth since legume productivity is higher nearer to neutral pH values [36]. The highest pH value (8.4) was observed upon lined sludge addition because of the Ca(OH)\({}_{2}\) content. The pH of alkaline soil treatments ranged at levels similar to the control because of the soil's buffering capacity originating from its CaCO\({}_{3}\) initial content (Table 2). The same trend has been reported upon the application of lined sludge to alkaline soils [37]. Although in all cases pH remained at levels below the critical value of 8.5 that stands for alkalinity problems [21], a pH value of 8.4 was rather undesirable since such high pH values represent a potential environmental hazard [38]. The CEC of alkaline soil, which was considerable, was not affected by treatment in any case, whereas it was significantly increased in all treatments with sludge on the acid soil. The latter was attributed to the pH increase, which resulted in an increase of the
# Predicting Runoff from the Weigan River under Climate Change
## 4 Discussion
### Evaluation of Models' Simulation Capability
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time Period**} & \multicolumn{2}{c}{**SSP1–2.6**} & \multicolumn{2}{c}{**SSP2–4.5**} & \multicolumn{2}{c}{**SSP3–7.0**} & \multicolumn{2}{c}{**SSP5–8.5**} \\ \cline{2-7}  & **Runoff/m\({}^{3}\)\(\cdot\)s\({}^{-1}\)** & **Rate/\%** & **Runoff/m\({}^{3}\)\(\cdot\)s\({}^{-1}\)** & **Rate/\%** & **Runoff/m\({}^{3}\)\(\cdot\)s\({}^{-1}\)** & **Rate/\%** \\ \hline
1965–2014 & 80.63 & - & 80.63 & - & 80.63 & - & 80.63 & - \\
2021–2030 & 81.92 & 1.61\% & 80.73 & 0.12\% & 83.25 & 3.26\% & 81.03 & 0.50\% \\
2031–2040 & 84.58 & 4.90\% & 86.48 & 7.26\% & 86.65 & 7.48\% & 90.69 & 12.48\% \\
2041–2050 & 87.98 & 9.12\% & 89.83 & 11.42\% & 92.52 & 14.75\% & 99.44 & 23.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Interdecadal variation in future runoff from the Weigan River. consistent with the results of the studies by [PERSON] et al. [17], [PERSON] [45] and others. However, there is also a single model with the best simulation effect for predicting precipitation, whose accuracy is closer to the observed values. In summary, there is a certain degree of variability between the simulation effects of different climate models for different meteorological elements, and the simulation effects reflected by the same model data in different regions are not the same as the actual observation series.
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 4 Conclusions  
In this study, a technical program for the detection of UA events was proposed via step-wise regression with pilot survey data, and its relative factors were quantitatively explored. Moreover, ESDA methods, namely a temporal heatmap and spatial distribution map, were utilized for spatio-temporal distribution analysis. The relationships between the triggering frequency of UA events and the relative meteorological and topographical factors were studied with both global and local  
Figure 6: Geographically-weighted correlation coefficients (GWCCs) between UA triggering frequency and wind levels 5–6.  
Figure 7: GWCCs between UA triggering frequency and elevation.  
correlation analysis methods. The results demonstrate that the spatio-temporal characteristics of UA events and relative factors including wind level, bad weather, and elevation reflect diverse correlations.  
In addition, as compared with the traditional Pearson's correlation coefficient, the GWCC can reflect the correlative relationships between UA events and relative factors from a local perspective. The results yielded by this study provide an important research basis and guidance for future quantitative causal analysis, as well as for the effective avoidance of such risks and the improvement of prediction accuracy.  
Notably, this study makes a preliminary attempt at understanding the underlying factors of UA events, and much more work is needed in the future. For instance, the period of data collection is limited to January, which corresponds to weather conditions in winter time, but complex conditions may also appear in summer time. In this sense, long time series analysis should be conducted. Moreover, some other potential factors, such as pilot workload [27], level of automation (LOA) [28], and airmanship [29], could be also incorporated.  
Conceptualization, H.S. and B.L.; methodology, B.L.; software, J.X.; validation, Y.J., B.L. and J.X.; formal analysis, J.X.; investigation, B.L.; resources, H.S.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, B.L.; visualization, J.X.; supervision, R.H.; project administration, R.H.; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. U1833201.  
The authors declare no conflict of interest.
## Nomenclature  
The majority of symbols and notations used throughout the paper are defined below for quick reference. Others are clarified with their appearance in case of need.  
\(\alpha_{h}^{g}\)Constant coefficient related to the maximum thermal output of CHP unit.  
\(\alpha_{0}\sim a_{5}\)Constant coefficients in the cost function of CHP unit.  
\(\beta_{t}^{g}/\beta_{m}^{g}\)Constant coefficient indicating the minimum and maximum electric storage level.  
\(\beta_{t}^{g}/\beta_{m}^{g}\)Constant coefficient indicating the minimum and maximum thermal storage level.  
\(\beta_{t}^{cd}/\beta_{hm}^{cd}\)Energy to power ratio of electric/thermal energy storage, constant coefficient  
\(C_{g}\)Capacity of CHP unit, parameter.  
\(E_{S}/H_{S}\)Capacity of the electric/thermal storage unit, parameter.  
\(E_{0}/H_{0}\)Initial state-of-charge of the electric/thermal storage unit, depending on \(E_{S}/H_{S}\).  
\(h_{t}^{d}\)Heat load curve, problem data.  
\(h_{t}^{g}\)Thermal output of CHP unit, decision variable.  
\(h_{t}^{s}\)Generation profile of solar thermal collector, problem data.  
\(h_{t}^{sc}/h_{t}^{sd}\)Charging/discharging power of thermal energy storage in period t.  
\(p_{t}^{d}\)Electric load curve, problem data  
\(p_{t}^{s}\)Electric output of CHP unit, decision variable.  
\(p_{t}^{s}\)Generation profile of solar panel, problem data.  
\(p_{t}^{sc}/p_{t}^{sd}\)Charging/discharging power of electric energy storage in period t.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(c_{PV}\) & \(c_{EES}\) & **PV Panel** & **STC** & **EES** & **TES** & **Payback** & **Net Profit** \\
**CNY/MW** & **CNY/MWh** & **(MW)** & **(MW)** & **(MWh)** & **(MWh)** & **Time (days)** & **CNY** \\ \hline \(5.6\times 10^{6}\) & \(1.2\times 10^{6}\) & 240.6 & 133.9 & 0 & 92.6 & 3207 & \(2.07\times 10^{8}\) \\ \(5.2\times 10^{6}\) & \(1.1\times 10^{6}\) & 250.9 & 156.5 & 0 & 194.7 & 3080 & \(2.78\times 10^{8}\) \\ \(4.8\times 10^{6}\) & \(1.0\times 10^{6}\) & 264.9 & 174.9 & 0 & 267.5 & 3033 & \(3.05\times 10^{8}\) \\ \(4.4\times 10^{6}\) & \(0.9\times 10^{6}\) & 271.1 & 205.8 & 23.4 & 401.1 & 3030 & \(3.07\times 10^{8}\) \\ \(4.0\times 10^{6}\) & \(0.8\times 10^{6}\) & 280.6 & 133.9 & 275.4 & 117.1 & 3028 & \(3.08\times 10^{8}\) \\ \hline \end{tabular}
\end{table}
Table 4: Optimal sizing strategies with different costs of solar panel and EES.  
\(t_{p}^{e}/t_{q}^{n}\) Charging/dispatching efficiency of electric energy storage, constant coefficient.  
\(t_{c}^{h}/t_{q}^{h}\) Charging/dispatching efficiency of the thermal energy storage, constant coefficient.  
\(t_{p}^{g}/t_{h}^{g}\) Efficiency of electricity/heat conversion of CHP unit, constant coefficient.  
\(\Delta t\) Duration of period t.
## 4 Discussion
When the gas concentration exceeds the threshold value, new gas needs to be replenished to reach the target concentration as outlined in the control process in Figure 3.  
Here, we assume the gas concentration deviation threshold value is \(\pm\)0.5, and the gas system needs to adjust once every two hours because the gas concentration exceeds the threshold value. According to the calculation method, the gas consumption is 17 mL of CO2 and 150 mL of N2 per adjustment, totaling 204 mL of CO2 and 1800 mL of N2 daily. A hypothetical gas concentration scenario for week-long cultivation is outlined: (1) Day 1 starts with air, transitions to 5% CO2 and 5% O2 for one day; (2) Day 2 maintains 5% CO2 and increases O2 to 8%; (3) Day 3 increases O2 to 13%; (4) Day 4 increases O2 to 18%; (5) Day 5 increases O2 to 23%; (6) Day 6 increases O2 to 28%; (7) Day 7 increases O2 to 33%. Throughout the cultivation, the lid is opened once daily for medium change and observation, and the other time the breezing state is kept with a little flow for circulation. The average daily consumption during cultivation is calculated to be 3570 mL of N2, 330 mL of CO2, and 115 mL of O2. Comparatively, the ESCO medical Miri TL, which also utilizes a recirculating gas supply method, consumes \(<\) 2 L/h of N2 and \(<\)12 L/h of CO2 under normal conditions without gas concentration changes or lid openings. The recirculating system's gas consumption is significantly lower than that of the ESCO MIRI, let alone traditional incubator systems with open gas supply methods.