esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Based on these findings, the \"super learning\" model emerged as the better option, as it consistently demonstrated high performance across various parameters with low variability. Figure 2 provides a visual representation of the AUC in the test set (n = 26,368) for the super learning model. The super learning model exhibited the highest AUC among all models, boasting an AUC of 0.817, a score that is typically considered a strong performance for prediction models. Other matrices for the super learning model include accuracy (0.751), precision (0.743), recall (0.926), and F1 score (0.825). This outcome underscores the model's strong ability to distinguish women with co-occurring mental and substance use disorders from those without. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **AUC** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Random Forest & 0.807 & 0.742 & 0.734 & 0.923 & 0.818 \\ Gradient Boosted Trees & 0.799 & 0.733 & 0.726 & 0.921 & 0.812 \\ XGBoost & 0.809 & 0.745 & 0.739 & 0.918 & 0.819 \\ Extra Trees & 0.803 & 0.738 & 0.733 & 0.916 & 0.814 \\ SGD & 0.778 & 0.725 & 0.728 & 0.898 & 0.804 \\ Deep Neural Network & 0.631 & 0.628 & 0.628 & 1 & 0.771 \\ Single Layer Perceptron & 0.776 & 0.721 & 0.718 & 0.916 & 0.805 \\ K Nearest Neighbors (grid) & 0.670 & 0.661 & 0.655 & 0.971 & 0.782 \\ Super Learning & 0.817 & 0.751 & 0.743 & 0.926 & 0.825 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance matrices for each algorithm applied for binary classification in the test set (N = 26,368). \begin{table}
\begin{tabular}{l c c} \hline
**Metric** & **Mean** & **Standard Deviation (SD)** \\ \hline AUC & 0.772 & 0.059 \\ Accuracy & 0.713 & 0.037 \\ Precision & 0.711 & 0.037 \\ Recall & 0.935 & 0.032 \\ F1 Score & 0.804 & 0.020 \\ \hline \end{tabular}
\end{table} 
Table 3: Mean and standard deviation of key metrics across the evaluated models.
## 4 Materials and Methods
### Reagents and Materials  
Commercial reagents included: Plant RNA Purification Reagent (Promega, Madison, WI, USA, Cat# A7280); PrimeScript RT Reagent Kit with gDNA Eraser and SYBR Premix Ex Taq (Takara Bio, Shiga, Otsu, Japan, Cat# RR047A/RR420A); KOD-Plus-Neo DNA Polymerase (TOYOBO, Osaka, Japan, Cat# KOD-401); KpnI/Xhol restriction enzymes (New England Biolabs, Ipswich, MA, USA, Cat# R3142/R0146); In-Fusion HD Cloning Kit (Takara Bio, Cat# 639649); Ni Sepharose 6 Fast Flow (GE Healthcare, Chicago, IL, USA, Cat# 17531801); isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG, Sigma-Aldrich, St. Louis, MO, USA, Cat# 367-93-1); farnesyl diphosphate (FPP, Cat# 44270) and geranyl diphosphate (GPP, Cat# G6772) from Sigma-Aldrich. Instrumentation comprised: CFX96(tm) Real-Time PCR system (Bio-Rad Laboratories, Hercules, CA, USA); Leica TCS SP8 confocal microscope (Leica Microsystems, Wetzlar, Germany); Agilent 7890A/5975C GC-MS system with HB-5 MS column (5% phenyl methyl silox, Agilent J&W Scientific, Folsom, CA, USA). Oligonucleotide primers were synthesized by Sunbiotech Co., Ltd. (Beijing, China). General chemical reagents were purchased from Sangon Biotech (Shanghai, China).
## 3 Results and Discussion
### Impact on the Growth and Fermentation Kinetics
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Initial Thiamine Concentration (\(\upmu\)g/L)**} \\ \cline{2-4}  & **0** & **50** & **250** \\ \hline \(\upmu_{\max}\) (h\({}^{-1}\))\({}^{1}\) & 0.16 \(\pm\) 0.07 \({}^{\text{a}}\) & 0.15 \(\pm\) 0.06 \({}^{\text{a}}\) & 0.13 \(\pm\) 0.07 \({}^{\text{a}}\) \\ G (h)\({}^{2}\) & 4.8 \(\pm\) 0.1 \({}^{\text{a}}\) & 4.9 \(\pm\) 0.1 \({}^{\text{a}}\) & 5.3 \(\pm\) 0.1 \({}^{\text{a}}\) \\ \(\gamma_{\max}\) (cells/mL)\({}^{3}\) & 1.5 \(\times\) 10\({}^{\text{b}}\) \(\pm\) 6.0 \({}^{\text{a}}\) & 1.4 \(\times\) 10\({}^{\text{b}}\) \(\pm\) 8.3 \(\times\) 10\({}^{\text{c}}\) & 1.1 \(\times\) 10\({}^{\text{b}}\) \(\pm\) 3.5 \(\times\) 10\({}^{\text{c}}\) \\ \(\text{t}_{\text{AF}}\) (h)\({}^{4}\) & 136 \(\pm\) 22 \({}^{\text{a}}\) & 134 \(\pm\) 17 \({}^{\text{a}}\) & 112 \(\pm\) 19 \({}^{\text{b}}\) \\ \(\text{F}_{\text{CO22 max}}\) (g\({}_{\text{CO2200}}\)/200 \(\text{L}\))\({}^{5}\) & 1.1 \(\pm\) 0.9 \({}^{\text{ab}}\) & 0.7 \(\pm\) 0.1 \({}^{\text{a}}\) & 0.9 \(\pm\) 0.2 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Note: different letters in the same line indicate significant differences between conditions (Scherher–Ray–Hare and Dunn tests, \(p<0.05\)). \({}^{\text{a}}\) [h\({}_{\text{max}}\): maximal specific growth rate; \({}^{\text{a}}\) [2] G: generation time; \({}^{\text{b}}\) [3]\({}_{\text{max}}\): maximal population; \({}^{\text{a}}\) [4] t\({}_{\text{AF}}\): duration of the alcoholic fermentation; \({}^{\text{c}}\) [5]\({}_{\text{CO2 max}}\): maximal CO2 production rate.  
\end{table} 
Table 3: Initial thiamine impact on the growth and fermentation kinetics. replicate common yeasting conditions that may be found in winemaking contexts. Such differences in the yeasts' physiological states might have impacted their behavior in regard to thiamine and led to the observed differences. Plus, the constant agitation applied on the fermentation broth during the assays led by [PERSON] and colleagues, opposingly to the static mode selected here, might have notably impacted thiamine assimilation by the fermenting yeasts, as it does other nutrients [39; 40]. All in all, those results highlight the essential character held by biotin on yeast growth, allowing for it to reach sufficient population and rates for proper course of the biological processes. Similarly, thiamine's significance in regard to fermentation [15] has been further assessed here, although a complete thiamine deprivation has been associated with maintained fermentation rates, possibly through the establishment of a survival strategy by the yeast, or the possibility that _S. cerevisiae_ might have had some leftover inner thiamine resources that have allowed it to cope with thiamine deprivation.
## 6 Model Result  
In this section, we present the single- and multi-hop network scenarios and present and discuss the multi-hop LoRaWAN analytical model result.  
To determine the performance of single-hop and multi-hop LoRaWAN networks, the analytical model was considered. In addition, it was also used to assess the performance of several multi-hop LoRaWAN network scenarios.  
Consequently, two experiments were conducted, one to compare single-hop and multi-hop in terms of capacity, throughput, and energy consumption. The second experiment was to compare three different multi-hop scenarios using the same performance metrics. To conduct the experiments, we needed to determine the parameters of LoRaWAN specifications.
# A Simulator for Investigation of Breakdown Characteristics of SiC MOSFETs
## 4 Conclusions  
In order to improve the convergence in the numerical simulation of 4H-SiC devices, in this paper, we use a novel numerical processing method to develop a device simulator for 4H-SiC with high efficiency, high numerical accuracy and convergence. The simulator incorporates various physical models to investigate breakdown characteristics and device performance. To ensure its accuracy, we validated the simulator by comparing its results with those obtained from both experiments and a commercial TCAD for a VDMOSFET. The obtained results exhibit excellent agreement across various quantities, affirming the fidelity of our simulator. Our simulator also shows good convergence in these simulations. Subsequently, leveraging the capabilities of our simulator, we conduct an in-depth analysis of the breakdown voltage (BV) dependence on lattice temperature, device structure and doping profile. Our findings reveal that the doping profile emerges as the dominant factor influencing the decreases in BV, followed by the device structure, while the impact of lattice temperature on BV is minimal. Overall, by providing valuable insights into the intricate interplay between these factors, our simulator emerges as a powerful tool for the evaluation and optimization of 4H-SiC device performance and behavior.  
Conceptualization, Y.H. and X.L.; methodology, X.L. and Y.H.; software, Y.H.; validation, Y.H., X.L. and F.L.; formal analysis, Y.H. and X.L.; writing--original draft preparation, Y.H.; writing--review and editing, X.L., Y.H. and F.L.; supervision, X.L. and F.L.; funding acquisition, X.L. and F.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant no. 61974003, grant no. T2293700 and grant no. T2293703.  
Not applicable.  
All the data used in this paper can be obtained by contacting the authors of this study.  
The authors declare no conflict of interest.
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
###### Abstract
The wireless power transfer technology can enhance battery-operated devices and exterminate complications due to wired networking [10]. WPT has become a significant and promising substitute for addressing the deliverance of energy for sensor nodes. However, wireless charging technology has ubiquitous potential research area for both industry and academia. When WRSN system uses efficient energy usage, it will become a crucial task. Particularly, optimal energy management has a basic rule to ensure the lifetime of low-powered devices like the IoT and sensor networks. The most promising solutions based on duty cycles arise during the use of WRSN. The development of clustering algorithms for improving the energy efficiency and the energy supply to the sensors has become an essential task for enhancing their life as well as the functional life of the Internet of Things (IoT)'s network [11]. The WRSN has come forth as a promising substitute for the traditional method of battery-powered operation and the energy harvesting techniques. RF can harvest and stably store energy from the corresponding devices such as cluster-head during WET cycle. Further, the stored energy utilizes by adopting energy efficient transmission techniques. Various research works have been proposed concerning the use of energy efficiency through using the WRSN technique and utilizing intelligently [12]. In the current study, an energy efficient firework algorithm has proposed for the wireless charging vehicle. The charging scenario in this study is similar to multi-hop provisioning, and the dynamic charger is replaced by wireless charging vehicles (WCV) which consist of Ni-MH batteries. Additionally, wireless charging vehicles will move from one sensor node to another throughout the entire network and recharge the batteries of sensor nodes wirelessly via multi-hop power transfer. Our approach uses the firework algorithm with the adaptive transfer function (FWA-ATF) to optimize the cluster head and chooses the cluster head nodes within each cluster. Firstly, firework algorithm divides the network into many regions. Further, it will choose the nearby cluster head based on the requirement of the remaining energy of the sensor node with position and center of the distance given in the region of gravity.
## 2 Theoretical Foundation and Hypotheses Development
### Legal Factors
#### 2.3.2 Democracy Level  
Various studies have indicated that technological breakthroughs result from humanity's search for freedom, democracy, and equality. [PERSON] and [PERSON] [49] asserted that countries in which the military or other authorities exercise effective executive power adopt technologies more slowly. Compared to authoritarian regimes, democratic governments have promoted widespread Internet access to bridge society's digital divide [50]. In addition, an administration headed by democracy is inclined to advocate for a wider trade openness [51]. [PERSON] [51] argues that as trade liberalization expands, more sophisticated technology is used to generate goods and services. A distinguishing feature of blockchain-based solutions is decentralization, which allows for user autonomy by minimizing information discrepancies. Conversely, the adoption of a central bank digital currency by countries can be successful in systems in which institutions and citizens may function freely. As a result, we hypothesize that the issuance of central bank digital currencies is dependent on countries' democratic practices and that in countries with authoritarian tendencies, the issuance of central bank digital currencies is low.  
**H5.**_Democracy level is positively associated with the adoption of central bank digital currency._
## 3 Results and Discussion
### Discussion  
The development of low-power, large (\(>=32\) channels), complex readout ASICs with on-chip fast ADC in each channel has only recently become possible. Examples of such ASICs include the 32-channel SAMPA [10] ASIC for gaseous detectors in the ALICE experiment at LHC or the 128-channel SMX2 ASIC for silicon and gaseous tracking detectors for the future CBM experiment at FAIR [41]. Other prototype ASICs, presented so far only at conferences, are the 64-channel VMM [11] for tracking detectors at the upgraded ATLAS experiment at the LHC, the 32-channel FLAME [9] for the luminosity calorimeter in the future linear collider, or the 72-channel HGCROC [12] chip being developed for a future high granularity calorimeter in the CMS experiment at LHC. Table 3 compares some of the main parameters of the SALT chip to those of SAMPA, and SMX2 ASICs.  
The table shows the state-of-the-art for readout ASICs with on-chip fast ADCs in HEP experiments, and is not intended for direct comparison as each ASIC was designed according to different detector specifications and requirements. In the case of the SAMPA a higher ADC resolution was required, while for the SMX2, in addition to the amplitude, a time measurement was required. As can be seen from the table, the advantage of the SALT chip is the high sampling rate and fast shaping, allowing it to cope with 100% channel occupancy.  
Figure 18: Pulse shapes for 128 channels obtained from the ADCs outputs after trim procedure applied, with a biased UT sensor connected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chip Name** & **SALT** & **SAMPA** & **SMX2** \\  & **(This Work)** & [10] & [8] \\ \hline Detector type & SST & TPC/MCH & SST/MCH \\ Technology node & 130 & 130 & 180 \\
[nm] & 128 & 32 & 128 \\ No of channels & 1.6–12 & 18.5, 40–80 & \(\leq\)50 \\ Sensor capacitance & 1.6–12 & 18.5, 40–80 & \(\leq\)50 \\
[PF] & both & both & both \\ Signal polarity & bomb & both & both \\ Shaping & complex poles\&zeros & CR–RC\({}^{4}\) & CR–RC \\ Peaking time [ns] & 25 & 160, 300 & 80–270 \\ Sampling rate [MHz] & 40 & 5–10 & 0.5 \\ ADC architecture & SAR & SAR & Flash \\ ADC resolution [bit] & 6 & 10 & 5 \\ Power/channel [mW] & 3.5 & 8.3 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: State of the art readout ASICs for HEP experiments with on-chip per channel ADCs (SST–Silicon Strip Tracker, TPC—Time Projection Chamber, MCH—Muon Chamber).
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results
### Experimental Setup and Results Analysis
#### 4.1.2 Model Training  
The operating environment of this experiment was as follows: the GPU was a GeForce RTX 2080 ti, the CPU was an Intel i9 9900K, the video memory size was 12 GB and the operating system was Windows 10; library files such as CUDA10.0 and OpenCV were installed, and the development language was Python, using the PyTorch framework. In this experiment, the method of stochastic gradient descent was used to train the improved YOLOv5s UAV grassland object detection model end-to-end. The parameters for training the improved model were set as follows: the batch-size parameter was set to 8, the initial learning rate was set to 0.001, the regular coefficient of heavy attenuation was set to 0.0005, the dynamic factor was set to 0.937, the coefficients of hue, saturation and brightness were set to 0.014, 0.65 and 0.45, the learning rate was set to 0.0032, and the number of training sessions was set to 300 times. After the training was completed, the optimal detection model file was saved, and the verification dataset was used to test the performance of the network model. The output of the network model was the probability of the target category being detected and its position in the image, which was selected by a rectangular frame.
## 3 Results
### The Relationship between NEP and LUCC in the Three Northern Protection Forest Project Area  
Figure 6 illustrates significant land-use and cover changes (LUCC) within the Three-North Shelter Forest Program area from 2000 to 2010. During this period, the largest reduction in land area was in grasslands, totaling 16,681.6 km\({}^{2}\), while the smallest decrease was in urban, industrial, and residential lands, amounting to only 481.4 km\({}^{2}\). Conversely, the largest increase was in cropland, reaching 16,807.1 km\({}^{2}\), and the smallest was in forestland, totaling 2222 km\({}^{2}\).  
Moving into the decade from 2010 to 2020, the scale of land conversion significantly expanded. The most notable decrease was in grasslands, totaling 277,886.8 km\({}^{2}\), while the smallest decrease was in urban, industrial, and residential lands, at 16,866.1 km\({}^{2}\). During this stage, the largest increase was in grasslands, amounting to 283,127.9 km\({}^{2}\), and the smallest increase was in water bodies, totaling 27,965.1 km\({}^{2}\).  
Viewing the period from 2000 to 2020 as a whole, over these 20 years, a total of 430,274.3 km\({}^{2}\) of forests, grasslands, and farmlands were converted to rural and urban industrial areas, mining areas, residential areas, and other land uses. Conversely, 453,468.1 km\({}^{2}\) of various land types were converted back to forests, grasslands, and farmlands. During this time, the net ecosystem productivity (NEP) of the study area generally showed an increasing trend, despite some fluctuations. The average carbon sink value rose from 56.916367 gCm\({}^{-2}\)a\({}^{-1}\) in 2000 to 101.0944 gCm\({}^{-2}\)a\({}^{-1}\) in 2005, slightly increased to 103.133156 gCm\({}^{-2}\)a\({}^{-1}\) in 2010, and eventually reached 132.78133 gCm\({}^{-2}\)a\({}^{-1}\) in 2020. Meanwhile, the average carbon source value also showed a decreasing trend, dropping from \(-\)42.685974 gCm\({}^{-2}\)a\({}^{-1}\) in 2000 to \(-\)22.612026 gCm\({}^{-2}\)a\({}^{-1}\) in 2010, and further to \(-\)8.049237 gCm\({}^{-2}\)a\({}^{-1}\) in 2020.  
These changes indicate that land reclamation and changes in land use led to the exposure of soil organic matter and an increase in soil carbon emissions, while the restoration of forest ecosystems increased aboveground biomass and soil organic carbon content, becoming an important carbon sink process. Converting farmland back to forests and grasslands is not only a key step in enhancing carbon sink capacity but the effect of reforestation surpasses that of grassland restoration. These land-use changes positively impacted the regional NEP, emphasizing the importance of implementing reasonable land-management strategies within the Three-North Shelter Forest Program area to promote ecological restoration and enhance carbon sink capacity, further fostering ecological balance and sustainable development.
## 2 Observation Systems and their Integration
### VGI and other Types of User-Generated Content  
User-Generated Content (UGC) comes in many facets. To arrive at a simple typology, we make two distinctions. First, whether the UGC was explicitly or implicitly volunteered, _i.e._, whether it was contributed for a specific purpose, or is \"just\" publicly available. Second, whether the UGC is explicitly or implicitly geographic, _i.e._, whether it is about a geographic location, or \"just\" has coordinates or a location as associated meta-data. Thus, if UGC is about a specific geographic place, it is explicitly geographic, while if it is merely geo-coded but not about a place, it is implicitly geographic. If an author contributes UGC as participation in a public effort or initiative, it is volunteered explicitly, while any UGC that is available to everyone but not contributed to a public effort or initiative is implicitly volunteered. This gives us a matrix of four types of UGC, with examples using the most common UGC (Table 1).  
The typology also impacts the sensing of UGC. We propose to differentiate between active and passive sensing, which corresponds to explicitly volunteered and implicitly volunteered information. Other possible terms are \"participatory\" sensing and \"opportunistic\" sensing [15]. The former provides a framework for the citizen participation and includes examples such as counting birds. The latter approach provides no a priori guidelines, and aims to tap into the abundance of UGC offered without any institutional or organizational framework. We acknowledge that the categories presented above only represent the poles of a spectrum of possible cases. For example, in the case of Tweets about natural disasters, the authors may not send their tweets to a particular institution or within a particular framework, yet they hope that their information is read and acted upon, thereby actively (explicitly) volunteering it. Initiatives like \"Tweek the Tweet\" [16] aim to strengthen the participatory character by offering a framework to structure the UGC. While our examples on natural disasters deal mostly with VGI, it is important to keep in mind that the UGC sensor we propose in the following sections is able to sense all types of UGC in an opportunistic or participatory manner.
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## Appendix A  
**Proof of Theorem 1.** The problem of relaxing \(rank(W_{i})=1\) is jointly convex with respect to the optimization variables, and it satisfies Slater constraints; therefore, strong duality holds. To reveal the structure of \(W_{i}\), the Lagrange function is given by the following equation:  
\[\begin{array}{l}L=\sum\limits_{i\in K_{I}}\text{TR}(W_{i})-\sum\limits_{i\in K _{I}}\text{TR}(Y_{i}W_{i})+\mu_{i}(\overline{\gamma}_{i}\text{TR}(H_{i,i}W_{ i}^{*})-\text{TR}(H_{i,i}W_{i}))\\ -\lambda_{k}(\sum\limits_{k\in K_{I}}\text{TR}(H_{rj}W_{k}))+\Delta\end{array} \tag{1}\]  
where \(\Delta\) is the set of all items that do not depend on \(\{W_{i}\}\); \(\mu_{i}\), \(\lambda_{k}\), and \(Y_{i}\) are Lagrange multipliers related to constraints (10), (11), and (13), respectively. Based on the Karush-Kuhn-Tucker (KKT) condition regarding \(W_{i}\), the optimal structure of \(W_{i}^{*}\) can be characterized as follows:  
\[\begin{array}{l}K1:\lambda_{k}^{*},\mu_{i}^{*}\geq 0,Y_{i}^{*}\succeq 0\\ K2:Y_{i}^{*}W_{i}^{*}=0_{N}\\ K3:\
abla_{W_{i}^{*}}L=0\end{array} \tag{2}\]  
Among them, \(\lambda_{k}^{*}\), \(\mu_{i}^{*}\), and \(Y_{i}^{*}\) represent the optimal Lagrange multiplier; \(0_{N}\) represents the \(N\)-th order zero matrix; and \(\
abla_{W_{i}^{*}}L\) is the gradient of \(L\) with respect to \(W_{i}^{*}\). \(\
abla_{W_{i}^{*}}L\) can be represented as follows:  
\[Y_{i}^{*}=I_{N}+\mu_{i}^{*}(\overline{\gamma}_{i}H_{rj}-H_{rj})-\lambda_{k}^{* }H_{rj} \tag{3}\]Using the results in Appendix A of reference [34], it can be proven that \(rank\big{(}Y_{i}^{s}\big{)}=N-1\). Furthermore, equation K2 means \(rank\big{(}Y_{i}^{s}\big{)}+rank\big{(}W_{i}^{s}\big{)}\leq N\); therefore, \(rank\big{(}W_{i}^{s}\big{)}\leq 1\) is established. Due to the service quality constraint in (10), the proof is complete for the optimal solution \(rank\big{(}W_{i}^{s}\big{)}=1\). Relaxing the rank constraint of \(rank\big{(}V_{j}\big{)}=\)1 proves the same principle. \(\square\)