esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Models
### Deployment and Documentation  
The entire implementation code was uploaded to a GitHub repository [30], making it accessible for public use and further research. The repository includes the following:  
* Source Code Directory: This includes the Python scripts for data preprocessing, model definition, training, forecasting, optimization, SHAP analysis, and strategic planning. * Models Directory: This includes the trained model, scalers, and SHAP explainer. * Outputs Directory: This contains generated outputs, such as prediction and forecasting results, SHap analysis results, as well as the strategic plan. * README.md: This provides an overview of the project, instructions for use, and a summary of the key findings. *.gitignore: This ensures that unnecessary files, such as large datasets and environment-specific files, are excluded from the repository. * Requirements.txt: This lists all of the dependencies required to run the project, ensuring that others can replicate the environment easily. In Table 2 the pseudocode that was utilized for the implementation of GREENIA is presented. \begin{table}
\begin{tabular}{l} \hline \hline
**GREENIA PSEUDOCODE** \\ \hline \multicolumn{3}{l}{**Data Pre-process**} \\ \# _Load and preprocess meteorological data from various locations_ \\ for each file in raw\_data\_files: \\ df = read\_excel(file, skiprows=9) \\ rename\_columns(df, location\_name) \\ append\_to\_main\_dataframe(df) \\ \# _Merge all dataframes on common columns (Date, Year, Month, Day, Hour)_ \\ merged\_df = merge\_all\_dataframes\_on\_date(main\_dataframe) \\ \# _Create ’Date’ column and set it as the index_ \\ merged\_df[Date’] = concatenate\_year\_month\_day\_hour(merged\_df) \\ merged\_df.set\_index(’Date’) \\ \# _Resample data to daily frequency_ \\ resampled\_df = resample\_dataframe(merged\_df, ’D’) \\ \# _Handle missing values_ \\ for each column in resampled\_df: \\ if column has more than 65 NaN values: \\ drop\_column(column) \\ else: \\ impute\_nan\_with\_mean\_of\_neighbors(column) \\ \# _Prepare data for model training_ \\ X, y = split\_features\_and\_target(resampled\_df, target\_column=”Energy (MWh)’) \\ X\_scaled, y\_scaled = scale\_features(X, y) \\ X\_padded = pad\_sequences\_to\_length(X\_scaled, target\_length=50) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GREENIA full implementation pseudocode.
## 4 Discussion
### 18F-Fluorodeoxyglucose Positron-Emission Tomography (18F-FDG PET)-Based Radiomic Features (Table 3)
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **Year** & **Imaging** & **No. of** & **HPV** & **Enrollment** & **Prospective** & **Multicenter** & **Predictive** & **Main Findings** \\  & & **Modality** & **Patients** & **Status** & **Period** & & & & \\ \hline \multirow{4}{*}{_[PERSON] et al._[14] & 2020 & 18F-FDG & 120 & HPV+: 70 & January & & & & AUC 0.83 (deep & Deep learning diagnostic & model with FDC-PET \\  & PET/CT & 120 & HPV–: 50 & 2019 & & & & & & imaging data can be \\  & & & & & & & & & & useful for determining the HPV status in \\ \hline \multirow{4}{*}{_[PERSON] et al._[50] & 2020 & 18F-FDG & 435 & HPV+: 315 & & & & & & & AUC 0.78 \\  & PET/CT & 435 & HPV–: 120 & & & & & & (cross-validation); & & from combining PET- and \\  & & & & & & & & & (independent) & & CT-based radiomics for \\ \hline \multirow{2}{*}{_[PERSON] et al._[51] & 2022 & 18F-FDG & 806 & HPV+: 115 & & & & & & & \\  & PET/CT & 86 & HPV–: 86 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Retrospective studies of HPV status prediction according to 18F-FDG PET/CT-radiomic features. In the context of metabolic imaging, 18F-FDG PET/MRI scan is a new hybrid technology that combines functional uptake information from PET and anatomical and soft tissue details provided by MRI with the advantages of more accurate diagnosis and therefore treatment options, reduced radiation exposure and convenience of two scans in one. In terms of head and neck cancer, 18F-FDG PET/MRI could be useful for evaluating cancer stages in OPSCC patients based on the new American Joint Committee on Cancer (AJCC) staging system due to the fusion of high-resolution and multiplanar MRI images with 18F-FDG avidity [53]. In regard to the prediction of HPV status, [PERSON] et al. [54] retrospectively analyzed the feasibility of PET/MRI parameters in assessing HPV status in OPSCC patients and found that primary tumor pre-treatment ADC could discriminate HPV status with 76.9% sensitivity and 72.2% specificity. Both ADC and metabolic PET parameters could predict tumor response to treatment. In [PERSON]'s work [55] investigating the role of PET/MRI in pre-operative stages of head and neck cancer, a correlation could not be demonstrated between maximal SUV and HPV status, and maximal tumor diameter determined with CT or PET/MRI did not correlate with the presence of p16 or HPV. Combined PET/MRI parameters (metabolo-volumetric parameters corrected by tumor cellularity on simultaneous 18F-FDG PET/MRI) have shown the capability of predicting treatment failure in surgically resected head and neck cancer [56]. Combined PET/MRI parameters could then be exploited as a prognostic imaging modality for the assessment of HPV status as well. Further studies are warranted in this regard.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Aquatic Vegetation  
Four genera of aquatic vegetation grow in excess in MIA irrigation channels impeding flow and water delivery [19]. Three of the genera are native. One is cumbungi, _Typha_ spp., an emergent flat leafed cylindrically stalked (2 cm diameter) reed that grows up to 4 m tall. Both _T. domingensis_ and _T. orientalis_ grow in the MIA region. They have clonal growth, with upright shots emerging each year from extensive underground rhizome beds. The other two genera of weedy vegetation are also attached to bottom of the channels, but their leaves float on the water surface. The ribbonweed _Valliseneria_ spp. grows straplike leaves, 3 m long and 3 cm wide, from stolons in the sediment. In high nutrient waters, the green leaves appear brown as they are covered in algae. Both _V. australis_ and _V. nanna_ grow in the MIA, with _V. nanna_ having a slightly narrower leave width. The third native genera of weedy plant in the MIA is pondweed _Potamogeton_ spp. It has both submerged and floating leaves off stems up to 4 m long which grow out of rhizomes in the sediment. The floating leaves tend to be more robust, 10 cm long and 7 cm wide, than the translucent submerged leaves which are 20 cm long and 1 cm wide. Both _P. tricarinatus_ and _P. ochreatus_ grow in the MIA.  
Figure 1: **(a)** a portion of the Murrumbidgee Irrigation Area with imagery from the Sentinel-2 constellation from 1 Jan 2018. The supply channels are shown in blue. The areas mapped by the UAV and satellite are shown; **(b)** one of the WorldView-3 satellite images; **(c)** UAV image of the untreated (U) channel.  
The introduced weedy vegetation is parrot's feather _Myriphyllum aquaticum_, a native of South America. Stems of up to 2 m grow from rooted stolons and pale green, whorled and feathery leaves grow off the stems. Submerged leaves rot, leaving only the emergent leaves at the end of a long, bare submerged stem.  
_Potamogeton_ spp. grow rapidly in spring (September to November), followed by summer (December to February) growth of _Valliseneria_ spp. _Typha_ shoots can grow at any time of year, but the majority emerge in summer and autumn (March to May). Control of the biomass of the four genera in the irrigation channels is by different strategies and include both physical removal and chemical treatment. Photographs of the weed types is shown in Figure 2.
## 2 Related Work
[13], which aims to minimize the overall energy consumption. We distinguish our work from the above-mentioned ones as follows. Existing studies dealt with either energy consumption or timeliness of data forwarding in stationary topology, or simply considered energy consumption in dynamic topologies. None of these studies jointly considered all of them. Therefore, we propose a data forwarding method in joint consideration of timeliness and energy efficiency in UWSNs with passive mobility.
## 3 Research Methodology
### Outer Model Assessment  
The assessment of the outer model aims to omit the factors unrelated to the group; this can be achieved by assessing the construct and reliability validity (Cronbach's alpha, composite reliability, and the average variance extracted) and discriminant validity (Fronell-Larcker criteria and cross-loading). The construct and reliability validity are used to measure Cronbach's alpha (\(\alpha\)), the composite reliability (CMR), and the average variance extracted (AVE) of the group with acceptable-level values of 0.70, 0.70, and 0.50, respectively [43]. They can be computed based on the outer loading of the group's indicators (\(\lambda_{i}\)), as shown in Equations (4)-(6) [42], respectively.  
\[\alpha=\frac{K}{K-1}\left(1-\frac{\sum\limits_{i=1}^{K}S_{i}^{2}}{S_{t}^{2}}\right) \tag{4}\]  
\[CR=\frac{\left(\sum\limits_{i=1}^{K}\lambda_{i}\right)^{2}}{\left(\sum\limits _{i=1}^{K}\lambda_{i}\right)^{2}+\text{var}(e_{i})} \tag{5}\]  
\[AVE=\frac{\sum\limits_{i=1}^{K}\lambda_{i}^{2}}{\sum\limits_{i=1}^{K}\lambda_ {i}^{2}+\text{var}(e_{i})} \tag{6}\]  
where \(K\) is the number of the factors, \(s_{t}^{2}\) is the variance of the \(i^{\text{th}}\) factor, \(s_{t}^{2}\) is the total variance, \(\lambda_{i}\) is the standardized loading value of the \(i^{\text{th}}\) factor, and \(Var(e_{i})\) is the error variance of the ith factor. The values of \(\alpha\) and \(CR\) should exceed 0.7. In addition, the \(AVE\) should be more than 0.5. When one of the three-construct coefficient (\(\alpha\), \(CR\), and \(AVE\)) values does not satisfy the threshold value, the impact of the elimination of each factor on the unsatisfied coefficient is studied. For example, if eliminating the \(i^{\text{th}}\) factor leads to an increase in the value of the unsatisfied factor, this factor should be deleted unless the factor should remain. The discriminant validity can be measured using the Fronell-Larcker criteria among the group and cross-loading for the factors and groups [43]. Due to its simplified usage, the SmartPLS software was utilized in this study to carry out the contract and discriminant  
Figure 2: The outer and inner models of a PLS-SEM with their components.  
validity assessments. The outer (the relationship of groups with their factors) and inner models (the relationships among groups) are shown in Figure 2.
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 4 Steady-State ORC Performance  
### Operating Parameters Optimisation  
The optimal values of the operating- or control-parameters (that are the resistive load, \(R_{\rm exp}\), connected to the expander generator and the pump speed, \(\omega_{\rm p}\)) vary with the external conditions, that is, with the cooling-water and heat-source conditions. They are determined for various heat-source temperatures, ranging from 100 to 140 \({}^{\circ}\)C, through a parametric optimisation that allows to determine the optimum-load and to investigate the part-load performance of the ORC engine, as further explained in Section 4.2.  
The pump RPM and expander load are varied through the available ranges and the steady-state ORC performance is recorded systematically. Figure 3 presents an example of a map thus obtained, where the measured gross power output is presented as a function of the decision variables, for a heat-source temperature fixed at 140 \({}^{\circ}\)C. The value of the optimum expander load does not vary significantly with the imposed pump speed (as it remains around 30 \(\Omega\)), while a maximum expander power output of 670 \(\pm\) 2 W is obtained for a pump rotating velocity of 3000 RPM, as seen clearly from the contours plotted at the base of Figure 3. Although the quantitative description of the optimal performance and associated control parameters is of interest to experimentalists running the testing facility, it does not provide useful data for comparison to model predictions.
## 2 System Model and Problem Formulation  
A UAV-enabled full-duplex relaying system is considered to aid an uplink mmWave NOMA cellular system consisting in a BS, with \(N\) users and \(K\) UAVs, as shown in Figure 1. The \(N\) users are randomly distributed in the cell of radius \(R\), and the BS is located at the center of the cell.  
We assume that the BS is unable to deliver the superimposed signals to the NOMA users in a far subarea of the cell because the link between users and BS is negligible due to severe blockage.  
Each UAV acts as a DF relay to help data transmission between the BS and users and operates in FD mode.  
We suppose that \(K\) UAVs are flying at height \(H_{k}\) in such a way that all links from UAVs to BS are LOS channels. The elevation angles between the BS and the UAVs are denoted as \((\theta_{1},\theta_{2},\...,\ \theta_{k})\), and \(\varphi_{n,k}\) is the elevation angle between the \(n\)-th user (named as user equipment UE\({}_{n}\)) and UAV\({}_{k}\), as shown in Figure 1.
## 3 Takeover Experiment Results and Discussion
### Single Modality Takeover Request  
As described in Figure 8 and Section 2.4, single modality takeover experiments were conducted in the following order: predrive questionnaire, sensor attachment, practice manual driving, and partially automated driving experiment. Twelve event scenarios occurred while driving the course in Figure 3, and one of three single notifications was provided. Figure 10 shows the drivers' response to 264 takeover scenarios using a single modality notification method of visual, auditory, or haptic notification. Since we analyzed driver reaction after the notification was provided, the case wherein the driver was looking ahead for more than 1 s before the notification was excluded from the analysis.  
None of the six variables showed distinct differences depending on the notification method as assessed with one-way analysis of variance (ANOVA) (tTOR, \(p\) = 0.14; tEyeIn, \(p\) = 0.556; tEyeMv, \(p\) = 0.903; Ap, \(p\) = 0.58; Bp, \(p\) = 0.73; Steer, \(p\) = 0.479). Analysis of variance is a hypothesis testing method used when comparing three or more groups. One-way ANOVA tests for significant differences between groups under the conditions of one independent variable and one dependent variable. This experiment evaluated whether the \"type of notification\" caused a difference in each independent variable. As shown in the results of Table 2, the single modality notification method took about 2.8 s of takeover time and about 2 s of gaze road fixation time on average. Recipients of all three notification methods tended to press the brake pedal before the steering operation (70% applied the brake first, and the overall average difference was 0.24 s), and the time difference between the two inputs in the visual notification was the shortest at 0.17 s.  
Contrary to the results of the shortest time to takeover after a single visual notification, the questionnaire evaluation showed that it had the most negative results among the three methods (Figure 11, average scores were 3.4, 4.9, 5.4). Most drivers with negative responses indicated that they could not freely focus on their secondary tasks in the autonomous driving mode as they had to constantly keep track of the cluster monitor to avoid missing the visual notification (76% opted for single visual notifications as an inefficient method). The haptic notification delivered through the vibrotactile sheet was the best in terms of improving concentration. However, there were cases of surprise or discomfort, as shown in Figure 12.  
Figure 10: Driver reactions of single modality TOR (\(n_{visual}=103\), \(n_{ auditory}=69\), \(n_{ haptic}=92\)).
## 3 Case Study
### Update Model Parameters through the PSO Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**f-Simulation** & **f-Measurement** & **Error (\%)** & **MAC** & **Type** \\  & (Hz) & (Hz) & & & \\ \hline
1 & 7.45 & 7.47 & 0.27\(\downarrow\) & 0.96\(\uparrow\) & 1 st torsion \\
2 & 8.61 & 8.62 & 0.12\(\downarrow\) & 0.92\(\uparrow\) & 1 st vertical bending \\
3 & 24.91 & 24.99 & 0.32\(\downarrow\) & 0.95\(\uparrow\) & 2 nd torsion \\
4 & 36.43 & 36.16 & 0.74\(\downarrow\) & 0.94\(\uparrow\) & Bending \\
5 & 48.13 & 48.81 & 1.39\(\downarrow\) & 0.91\(\uparrow\) & 3 rd torsion \\ \hline \hline \end{tabular}  
* Error = \(|f_{simulation}-f_{measurement}|\times 100/f_{measurement}|\).  
\end{table} 
Table 6: Analysis results of the plate after updated. Figure 14: Fitness function convergence curve using PSO. The FE model's MAC values before and after updating are displayed in Figure 15. After being updated, the accuracy of the model has increased a lot. These MAC values show good agreement between the FE and the actual models. Figure 15: MAC values of mode shapes: (**a**) before model updating; (**b**) after model updating. The first model was established based on the characteristics that are easy to investigate, and the analytical experience had a relatively high error rate compared to the experimental measurements. After updating the model, the natural frequency of errors between calculation and measurement is significantly reduced, and the model has high accuracy and reliability (Figure 16). The calculation results show that, after about 30 iterations performed by PSO, the model parameters converge and give good results, minimizing the process of testing parameters in the modeling. More than just updating the model with material specifications, other uncertain parameters can be changed if the exact parameters are unknown. This work can be of great significance for structural safety assessments. For example, when there is an updated model of a structure, when the structure fails, zoning and damage assessment will be calculated by selecting the uncertainty parameters for the structure within the damaged area. The calculation results can evaluate the deterioration of structural function and propose reasonable repair and upgrade measures. The updated model becomes a temporary digital twin of the structure at a specified time. Based on this model, other techniques will be further researched and developed to create an interactive data stream between the real and virtual models to create a complete digital twin.
## 2 Material and Methods
### Hardware Design
#### 2.1.1 Air Conditioning  
The temperature of the growing chamber in which subjects grow influences various metabolic processes as well as triggers certain biological events. In the case of _Lentinula edodes_ fungi, fruiting is triggered by a temperature drop [15]. Similarly, in the case of plants, biological processes, such as photosynthesis and even onset of anesthesia is affected by temperature [16]. Humidity also influences the metabolic activities of both the subject and unwanted fungus. Humid environment with sufficiently warm temperature provides suitable platform for organisms such as unwanted fungus to thrive and possibly suppress or destroy the growth of the subject [17]. Therefore, it is important to condition the air inside the chamber.  
Application of compact ceramic heating elements for heating air can be seen widely in industrial and home appliances [18; 19]. However, cooling the air requires sophisticated mechanical parts and increases size and cost. Some research works [20; 21] showcase the application of thermoelectric cooler (TEC) module for heating, cooling, and de-humidification.  
Figure 1: Process model of the controlled environment consisting of central chamber unit, air conditioning unit, liquid and gas circulation pumps (M1–M5), LED light panel, sensors (S1–S5), and central embedded controller.  
Humidification on the contrary can be achieved by accelerating the evaporation of water. Despite the transpiration contributed by metabolic activities of the subject growing in the chamber, it requires long duration to reach desired humidity and is uncontrolled. To compensate for this shortcoming, the humidification process is controllably accelerated using an ultrasonic atomizer that breaks water into fine particles [22]. These technologies were combined together to implement the air conditioning system.
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 3 Selection Methods of Bistatic T/R Pairs
### Convex Hull Optimization Method
The proposed method is also suitable for the calculation of convex hull of arbitrary dimensions.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 3 SR Method for CLLC Resonant Converters Based on State-Trajectory Models
### Simulation Verification  
Compare the error between the simulation results and the calculated results, and contrast the synchronous rectification scheme under other modeling methods with the proposed method in this paper to verify the feasibility and accuracy of the proposed approach.  
As shown in Figure 11, the delay duty cycle calculation results of the proposed synchronous rectification method are compared with the simulation results. It can be observed that the proposed calculation results exhibit high accuracy, with only a minor error between the calculated and simulated values.  
Figure 10: Closed-loop control block diagram.  
Figure 9: Flowchart of the proposed synchronous rectification scheme.  
As shown in Figure 12, the duty cycle calculation results of the proposed synchronous rectification method are compared with the simulation results. It can be observed from the figure that the proposed method maintains high accuracy over a wide range of current and frequency variations. Although the error in the over-resonant region is slightly larger, with a maximum error of 1.7%, the impact on the simulation results is minimal due to the relatively large value of the duty cycle itself. This demonstrates the high accuracy of the proposed method.  
The small error between the calculated and simulated results in Figure 12 arises from the simplified geometric approximations used in the state-trajectory model, particularly in the below-resonance region. While these approximations reduce the computational complexity, they introduce minor deviations from the exact nonlinear behavior of the resonant converter. The error remains within an acceptable range (max 1.7%) and has a negligible impact on the overall performance.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix A Compendium of Mappings and Compilations
### Bit-Flip (X) Mixers
#### a.1.2 Max-\(\ell\)-Sat  
**Problem:** Given \(m\) disjunctive clauses over \(n\) Boolean variables \(\mathbf{x}\), where each clause contains at most \(\ell\geq 2\) literals, find a variable assignment that maximizes the number of satisfied clauses. Let \(\mathbf{x}_{\text{\tiny R}}\) be the variables involved in clause \(\alpha\) and \(C_{\alpha}(\mathbf{x}_{\text{\tiny R}})=1\) (\(0\)) if the state of \(\mathbf{x}_{\text{\tiny R}}\) (resp., does not) satisfies the clause.  
**Prior QAOA work:** Considered in Reference [20].  
**Approximability:** APX-complete [51]. The best classical approximation ratio for Max-2-Sat is 0.940 [57], which cannot be improved beyond 0.943 under the unique games conjecture [55], or beyond 0.954 unless \(P=NP\)[53]. For Max-3-Sat, an efficient \(7/8\)-approximation [58] is known, which is optimal unless \(P=NP\)[53]. Remains APX-complete when any literal appears in at most 3 clauses [50].  
**Objective:**\(\max\sum_{j=\alpha}^{m}C_{\alpha}(\mathbf{x}_{\text{\tiny R}})\).  
Phase separator:\(H_{\mathrm{P}}=\sum_{\kappa=1}^{m}H_{\mathrm{C}_{\kappa}}(\mathbf{x}_{\kappa})\).  
Resource count for phase separator:Can be implemented with \(\binom{\ell}{k}\) many \(k\)-local \(ZZ\cdots Z\) gates, for \(k=1,\ldots,\ell\), overall requiring at most \(O(m2^{\ell})\) two-qubit gates.