esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis
### Two-Factor ANOVA on Controller Situational Awareness Related Indicators
#### 3.2.4 ATC Information Processing and Decision-Making Analysis  
The author conducted interviews with frontline controllers and pertinent individuals to evaluate their communication and coordination skills in remote tower operations. This resulted in the creation of an assessment scale for communication and coordination abilities tailored for remote tower controllers, which measures their information processing and decision-making phases during the construction of situational awareness.  
Figure 5: Comparison of Two Factor ANOVA Results of ATC Working Memory Scale.  
On-duty supervisors used the scale throughout job execution to ensure the precision and promptness of the assessment, evaluating the remote controllers' performance in real time. The normality tests conducted on the data revealed that all groups exhibited \(p\)-values over 0.05, indicating a normal distribution and hence validating the use of a two-way ANOVA. This investigation mainly examined the impact of controller qualifications and tiredness levels on several eye movement characteristics. The results showed that the main effect on the information processing and decision-making phases was the controller's tiredness (F = 98.97, \(p\) < 0.0001), while the main effect on the phases of qualifications (F = 2.489, \(p\) = 0.1342) was not significant. Furthermore, there was no notable interaction between the tiredness state and qualifications for the information comprehension phase (F = 0.09265, \(p\) = 0.7648). The controllers' weariness significantly affected their information processing and decision-making capabilities.  
Figure 6 illustrates that when the tiredness levels of remote tower controllers varied, both seasoned and novice controllers showed a decline in communication and coordination skills. The deterioration in the information processing and decision-making ability of controllers was associated with their degrees of weariness, with trainee controllers exhibiting lower average scores than their seasoned counterparts.
## 3 Results
### Experimental Results  
In experimental tests, we used the platform installed in the MIS Laboratory of the University of Picardie Jules Verne in France, as Figure 14 presents.  
Figure 13: Transient simulation results: (**a**) solar panel radiation; (**b**) output power of the conventional and proposed converter; (**c**) power conversion efficiency.  
The platform is made up by one SANYO, HIP-215 NKHE5 PV panel and a radiation sensor. The PVG is connected to the SEMIKRON shopper module as a DC/DC boost converter that is considered as an adaptation block between the load and PV generator. The system is controlled using a dsPACE controller board. The MPPT algorithm is implemented in Simulink for implementation in real time.  
Several experimental tests have been carried out to validate the proposed topology. In particular, two systems have been tested. The first one adopts a standard boost as presented in Figure 3 while the second uses a coupled inductor as shown in Figure 4. Two similar PV panels installed close to each other in the MIS platform are used to compare the performance of the two systems. Two SMIKRON devices are used: one for the Boost with the coupled inductor circuit and the other for the standard Boost circuit. The two inductors are made by the same magnetic core (ETD49). A P&O algorithm is used to track the MPP of the PVG, and the same kind of the resistive load is used. The detailed schematic of the two systems is shown in Figures 15 and 16, respectively. In both installations we used an 1104 card of dSPACE to implement our algorithm in real time.  
The Fuzzy controller is used to control the inductor current \(I_{2}\); this current must track the reference current evaluated on PV current measurement, \(I_{1}\). The buck topology has been chosen because the output current of a buck is always greater than its input current. In this case we ensure that the second windings draws lower current even when \(I_{2}\) has greater value.  
To verify the improvement of the power efficiency of the proposed topology, the power efficiencies of the two systems are evaluated under the same operating conditions. Figure 17 shows the measured radiation during the experimental test of the two circuits.  
Figure 16: (**a**) Schematic of the experimental setup for the proposed boost DC/DC converter. (**b**) Schematic of the small Buck converter.  
The input and output voltages for the proposed coupled inductor topology are shown in Figure 18 whereas the coil and load current are presented in Figure 19.  
Figure 17: Measured solar radiation.  
Figure 18: (a) Input voltage, (b) output voltages.  
In Figure 20 the measured power transferred to the load for both circuits is presented.  
Figure 19: (a) Current through \(L_{1}\), (b) load current.  
Figure 20: Measured power delivered to the load for the conventional and proposed converter.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 2 Sensor Technologies
### Flow Rate Measurement
#### 2.2.4 Tracer Dilution Method  
Tracer dilution is used to measure the enthalpy of geothermal fluid in addition to the flow rate. To determine the well performance, two different chemical tracers are injected into the fluid stream in the pipe and mixed with water. Fluid tracers disperse into a liquid phase; gas tracers mainly disperse into a gaseous phase, but also partially into a liquid phase. Samples of vapor and water are then collected from the pipeline and tested to identify the dilution of the tracer in both phases and the background concentration. From this information, the flow rate and enthalpy can be estimated [1]. The advantages of this method are the ability to keep the well online during measurement and the lack of an effect on the flow; they are outweighed, however, by the large costs for lab testing [1], the poor precision of the results [2] and the lack of real-time measurement capability [43].
## 2 Materials and Methods
### Construction of Peptide-Linked Magnetic Bead  
The Accu BeadTM COOH Magnetic Bead (diameter = 4.56 \(\upmu\)m) was selected to be bonded with peptide. Initially, the bead was washed twice with 25 mM MES buffer. Then, the peptide was covalently bonded onto the bead surface via carbodiimide-mediated amide bond formation between N-terminus of peptides and the carboxylic acid groups on the bead surface, according to a slight modification of the Thermo Fisher Scientific protocol [27]. First, 3 mg of bead was mixed with 50 \(\upmu\)L of EDC (50 g/L in cold 25 mM MES) and NHS (50 g/L in 25 mM MES), under slow tilt rotation, at room temperature, for 30 min. Then, the activated bead was collected, using magnets, before being washed with 25 mM MES. Next, 100 \(\upmu\)L of peptide (100 mg/L) was incubated with the EDC/NHS-activated bead, at room temperature, for 1 h. After the peptide-linked bead was collected magnetically, the peptide concentration remaining in the supernatant was measured to determine the amount of peptide covalently bonded onto the bead. For comparison, the EDC/NHS-activated bead without further coating and the activated bead coated with albumin protein were prepared via the same protocol. Then, both beads lacking the peptide were incubated in 60 mg/L Pb(NO\({}_{3}\))\({}_{2}\) for 1 h, and the lead concentration in the supernatant after collecting the bead with a magnet was measured to determine the amount of lead adsorbed on the bead.
## 4 Results and Discussion
### Objective 1: Results in Terms of Displacements
For the case of \(R_{M}\), unlike the \(R_{A}\) parameter, the mean values may be much smaller than unity, indicating that the bending moments are greater for the models with deep columns. The reason for this is that, as for the case of \(R_{V}\), the bending stiffness is greater for deep columns; the design flexural strength (\(\phi_{b}M_{n}\)) is 1830 kip-ft and 3200 kip-ft for the W14 \(\times\) 257 and W27 \(\times\) 258 columns, respectively. It is also observed that the values, in general, tend to slightly decrease as the seismic intensity increases but no clear variation is observed with the model height. The values range from 0.54 to 0.73, from 0.68 to 0.85, and from 0.64 to 0.74 for the three-, nine-, and twenty-story models, respectively. Objective 3: Results in Terms of Normalized Single Overall and Normalized Combined Local Resultant Stresses  
In Sections 4.1 and 4.2 of the paper, the seismic demands in terms of displacements and resultant stresses, respectively, were calculated for the models with medium columns and compared to those of the models with deep columns. It is worth mentioning that drifts and top displacements are commonly used to represent the overall behavior of steel buildings; however, interstory shears, axial loads, and bending moments are not. In addition, the comparison between the responses of the buildings with the two columns sizes in terms of these three response parameters may not be representative of the structural response since the shear, the axial load, and the bending capacities are not considered. In this sense, the comparison in terms of the seismic responses normalized with respect to the design strengths is more appropriate.
## 3 Proposed Hybrid JADE-DO Algorithm
### Mathematical Formulation of the Hybrid JADE-DO Method
#### 3.2.1 Initialization  
The iteration counter is initially set to \(l=0\). The JADE-related parameters are also defined: the mean crossover probability is \(uCR=0.5\), and the mean scaling factor is \(uF=0.5\). The percentage of top individuals in the population is \(p0=0.05\), and the number of these top individuals is \(\text{top}=\left\lceil p0\times\text{pop}\right\rceil\), where pop denotes the population size. The JADE archive is initialized as \(A=\emptyset\) with an archive size counter \(t=1\). Two arrays, curve and exploitation_curve, record the convergence of the best fitness values and the exploitation metric, respectively, over time.  
The population \(P\) is then randomly initialized within the bounds \([\text{lb},\text{ub}]\). Each row \(P(i,\cdot)\) represents a candidate solution, and lb and ub are the lower and upper bounds of the search space.
# Quantum Privacy Query Protocol Based on GHZ-like States
## 4 Functional Comparison and Security Analysis
### Security Analysis
#### 4.2.1 Security against an Outside Eavesdropper  
As shown in Figure 3, we assume that an external attacker can intercept the particles [PERSON] distributes to [PERSON] and [PERSON] and then entangle the particles [PERSON] distributes  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Protocols** & **Using Quantum States** & **Quantum Resource** & **Authentication** & **Number of Keys for the Querier** \\ \hline Ref. [2] & No & No & No & No \\ Ref. [19] & Yes & EPR & No & \(1/6\) of the total key number \\ Ref. [20] & Yes & W states & Unsafe & \(1\) \\ ours & Yes & GHZ-like states & Yes & \(1\) \\ \hline \end{tabular}
\end{table}
Table 4: Protocol comparisons.  
to [PERSON] and [PERSON] with \(|E\rangle=\{|E_{1}\rangle,|E_{2}\rangle,|E_{3}\rangle,\ldots,|E_{i}\rangle,\ldots\}\). We assume an attack on the particles as follows:  
\[U|0\rangle|E_{i}\rangle=\chi_{1}|0\rangle|e_{00}\rangle+\chi_{2}|1\rangle|e_{01}\rangle \tag{10}\]  
\[U|1\rangle|E_{i}\rangle=\chi_{3}|0\rangle|e_{10}\rangle+\chi_{4}|1\rangle|e_{11}\rangle \tag{11}\]  
\[U|+\rangle|E_{i}\rangle=\frac{1}{2}[|+\rangle(\chi_{1}|e_{00}\rangle+\chi_{2} |e_{01}\rangle+\chi_{3}|e_{10}\rangle+\chi_{4}|e_{11}\rangle)+|-\rangle(\chi_{1 }|e_{00}\rangle-\chi_{2}|e_{01}\rangle+\chi_{3}|e_{10}\rangle-\chi_{4}|e_{11} \rangle)] \tag{12}\]  
\[U|-\rangle|E_{i}\rangle=\frac{1}{2}[|+\rangle(\chi_{1}|e_{00}\rangle+\chi_{2} |e_{01}\rangle-\chi_{3}|e_{10}\rangle-\chi_{4}|e_{11}\rangle)+|-\rangle(\chi_{ 1}|e_{00}\rangle-\chi_{2}|e_{01}\rangle-\chi_{3}|e_{10}\rangle+\chi_{4}|e_{11} \rangle)] \tag{13}\]  
where \(a,b,c,d\in[0,1]\) and \(\chi_{1}^{2}+\chi_{2}^{2}=\chi_{3}^{2}+\chi_{4}^{2}=1\), \(|e_{00}\rangle,|e_{01}\rangle,|e_{10}\rangle,|e_{11}\rangle\) are the states that the attacker has to distinguish. If the attacker wants to avoid the security check in step 3, then it must satisfy Equations (14)-(16).  
\[\chi_{2}=\chi_{3}=0 \tag{14}\]  
\[\chi_{1}|e_{00}\rangle-\chi_{2}|e_{01}\rangle+\chi_{3}|e_{10}\rangle-\chi_{4} |e_{11}\rangle=0 \tag{15}\]  
\[\chi_{1}|e_{00}\rangle+\chi_{2}|e_{01}\rangle-\chi_{3}|e_{10}\rangle-\chi_{4} |e_{11}\rangle=0 \tag{16}\]  
From the above equation, we can conclude that \(\chi_{1}|e_{00}\rangle=\chi_{4}|e_{11}\rangle\); if the attacker wants to avoid the security check in step 3 completely, he cannot distinguish between the two states, \(|e_{00}\rangle\) and \(|e_{11}\rangle\). So, our protocol can still detect the attack through auxiliary particles.
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 1 Introduction
The HTCC technology enables high integration density, high-reliability hermetic self-packaging, and high RF performance. The distinctive double-sided layout architecture with the combination of the multi-layer routing and the BGA interface enables seamless integration with systems, ensuring optimal input-output port matching and supporting array-scale expansion. The proposed design method also shows the advantages of high consistency and low cost, making it ideal for mass production and use for large-scale phased array systems.
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 2 Literature Review
### Cost and Schedule Integration Models  
The Cost-schedule integration is required for systematic management of quantity, payment, productivity, material, and labor. The unified system for cost and schedule data can be used as a basis for (1) securing consistent, accurate, and objective information management, (2) improving systematic and well-informed construction management, and (3) establishing a database for major projects. Much research has contributed to this area of study and proposed various models based on WBS, faceted classification, or work-packaging model, such as Percent Allocation Model [9], Work Element Model [10], Design Object Model [11], Work-package Model [3], Earned Value Management System (EVMS) [12], Faceted System [13], and Flexible WBS System [14].  
Although there are differences in detailed procedures and methods, the above models show common features. They utilize hierarchical forms to represent data in a top-down approach, ranging from projects to organization (project, space, system/element, task, organization). In addition, they link WBS, CBS, and organization breakdown structure (OBS) by incorporating numbering systems in standard construction information classifications. The connection represents data in a 2-way matrix with rows and columns. The matrixes establish a complicated relational database that ties various  
Figure 1: Current Practice of Data Control in Construction Management.  
planned or progress data for quantity, schedule, and cost. These models show limitations in achieving data consistency and sufficient flexibility in the data structure and produce a vast amount of data whenever low-level information units are added.  
The closest model to this paper is the Construction Information Database Framework (CIDF) proposed by [PERSON] et al. [4]. It develops 5W1H facets (Who, Where, What, Why, When, and How dimensions) to cost, schedule, and performance data. It incorporates operation, zone, element, task, date, organization at five levels for integrating cost, schedule, and performance data. Each facet acts as metadata in a spreadsheet-based application and allows for the various transformation and integration of data. Compared to the above-discussed models, it achieves flexibility and full connectivity of cost and schedule data; however, it fails to discuss how to extract and build data from various project documents, which is essential for securing practical application in the construction project. Yet, there is a dearth of cost-schedule integration theory that is practical and applicable in construction management.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework  
In this section, we propose the real-time elbow and T-junction detection and estimation (RT-ETDE) framework consisting of four blocks, as shown in Figure 4. The first block estimates the camera pose in SCS and the straight pipe model. It enables the transformation of the camera point cloud to SCS and the identification of points on the straight pipe. The second block detects the presence of the elbow and the T-junction. Upon detection, the framework activates either the elbow or T-junction estimation accordingly. When the elbow is detected, the elbow estimation estimates the elbow direction, radius, distance from the 3D ToF camera, and length. Similarly, for a T-junction, the branch direction, diameter, distance from the 3D ToF camera, and length are estimated.  
Figure 4: Block diagram of the real-time elbow and T-junction detection and estimation framework.  
Figure 3: T-junction model. (**a**) Smooth T-junction model (3D view). (**b**) Smooth T-junction model (Top view). (**c**) Shaft T-junction model (Top view).
## 3 Case Study
### Performance Criterion  
To evaluate the performance of solar irradiance forecasting models, we employ three effective error indexes that are Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). The smaller RMSE and MAE, together with the higher R denote the good performance  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Forecasting Models** & **Structure** \\ \hline \multirow{3}{*}{CNN} & Convolutional layer (64 filters + 3 filter size) + maxpooling \\  & (3 pooling size) + convolutional layer (64 filters + 3 filter size) \\  & MaxPooling (3 pooling size) + Fully connected layer (100 neurons) \\ LSTM & 2 LSTM layers (100 neurons) \\ ANN & 2 fully connected layers (100 neurons) \\ ARIMA & Determined by the minimum AIC of each input sample \\ \hline \hline \end{tabular}
\end{table}
Table 4: The structure of the other forecasting models used as benchmarks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Option** & **Parameter Setting** \\ \hline Training Method & Adam Optimizer \\ Learning rate & [0.001] \\ Batch size & [24] \\ Epoch & [200] \\ Training stop strategy & \{early stopping\} \\ Loss Function & MSE \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter setting detail of DWT-CNN-LSTM model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Output Shape** \\ \hline Input layer & (180,1) \\ Conv-1D layer & (180,64) \\ Max-Pooling layer & (60,64) \\ Conv-1D layer & (60,64) \\ Max-Pooling layer & (20,64) \\ LSTM layer & (20,100) \\ LSTM layer & (100) \\ Fully connected Layer & (100) \\ Output layer & (60,1) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The output data format of the input layer, each intermediate layer, and the output layer in DWT-CNN-LSTM model.  
of a forecasting model. The mathematical calculation methods of these three error indexes are shown in the following equations in turn.  
\[RMSE=\sqrt{\frac{\sum\limits_{t=1}^{N}{(y_{t}-\hat{y}_{t})}^{2}}{N}} \tag{18}\]  
\[MAE=\frac{\sum\limits_{t=1}^{N}{|y_{t}-\hat{y}_{t}|}}{N} \tag{19}\]  
\[R=\frac{Cov(y,\hat{y})}{\sqrt{V(y)}\sqrt{V(\hat{y})}} \tag{20}\]  
in which \(\hat{y}_{t},y_{t}\) are, respectively, the forecasting value and actual value at time \(t\). \(\overline{y}\) refers to the mean value of the whole \(y_{t}\), and \(N\) is the sample size of the test set.
## 2 Background
### Nelder-Mead Algorithm
Compute the inside contraction vertex \(\mathbf{x}_{ic}\) using Equation (8), where \(\gamma\) is the coefficient of contraction. \[\mathbf{x}_{ic}=(1-\gamma)\overline{\mathbf{x}}+\gamma\mathbf{x}_{n+1}\] (8) ii. If \(f(\mathbf{x}_{ic})<f(\mathbf{x}_{n+1})\), accept \(\mathbf{x}_{ic}\) and terminate the iteration; otherwise, go to step 4. 3. **Shrinkage:** If none of the above operations result in a better vertex, the algorithm performs a shrinkage operation. It shrinks the simplex toward the best vertex by updating each vertex, except the best vertex, to move closer to the best vertex by a certain fraction. This contraction of the simplex assists in refining the search around the current best solution. The shrinkage phase can be described as follows: 1. [label=()]
4. Update the vertices \(\mathbf{x}_{2},\dots,\mathbf{x}_{n},\mathbf{x}_{n+1}\) using Equation (9), where \(\sigma\) is the coefficient of shrinkage. \[\mathbf{x}_{i}=\mathbf{x}_{1}+\sigma(\mathbf{x}_{i}-\mathbf{x}_{1})\,\ i\in\{2,\dots,n,n+1\}\] (9) 2. The new vertices calculated using Equation (9) are to be considered for the next iteration. The iterations continue until certain convergence criteria are met, such as reaching a maximum number of iterations, achieving a small improvement in the function value, or obtaining a small change in the size of the simplex. The Nelder-Mead algorithm is widely used in various domains, including engineering, computer science, and mathematical optimization. It is particularly suitable for problems with non-smooth or non-convex objective functions. Although it does not guarantee finding the global optimum, it often converges to good local optima [57]. The Nelder-Mead algorithm is a derivative-free optimization algorithm commonly used to solve unconstrained optimization problems. Here are some advantages and disadvantages of the Nelder-Mead algorithm [64]: