esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Evaluation of Flexibility in Terms of Cost  
The flexibility KPI, proposed by [PERSON] et al., 2015, is used to assess flexibility in terms of cost [PERSON] et al. (2017). The Flexibility index (Equation (3)), which ranges from 0% to 100%, is maximized  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(C_{ADR}\) & \(\eta_{ADR}\) \\ \hline Unit & kWh & \% \\ Reference & 115.6 & 43.9 \\ Case A & 121.9 & 53.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermal mass flexibility evaluation.  
when all the energy needed is consumed at a time of day when the energy prices are at the lowest levels.  
\[Flexibility_{Cost}=\frac{p_{el,max}-p_{el,arg}}{p_{el,max}-p_{el,min}} \tag{3}\]  
Figure 9 depicts the results for each building (reference, case A) and each control scenario. We noticed that the reference control scenario presents a cost flexibility of about 47%, both for the reference and the case A building. The flexibility increased in the CO\({}_{2}\) PRBC scenario to about 48% for both building scenarios. As expected, the Cost PRBC led to the highest cost flexibility index, with values around 51%, with the case A building producing a little higher index value. The small increase between scenarios shows the difficulty in shifting high amounts of energy from high prices periods to low price periods and, at the same time, maintaining adequate thermal comfort levels. Still, even this small increase of about 4% during the Cost PRBC implementation shows that this method works as expected and has the potential of increased cost-efficient operation of heating under DR.  
In their research, [PERSON] et al. evaluated the flexibility in terms of cost of a residential building with a heat pump for heating, and various levels of building envelope insulation and heat emission systems [11]. The results showed a flexibility index of approximately 78% for the case of hot-water radiators. We noted that the differences with respect to our results are significant. This difference is mainly due to the use of the building, which in our case is a building with limited hours of operation, while the building in [PERSON] et al. had a continuous operation schedule. This fact limits the ability of the building under study to achieve a high value of flexibility since the limited operating hours have an impact both on the ability to charge the thermal mass of the building, but also on the energy cost, which, during the morning hours, showed little variability.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.1 Confusion Matrix  
The confusion matrix [53] is not properly a metric to estimate the performance of a classification in ML. However, it does bring together the factors used in the performance metrics used in this work. In a stress classification and detection problem, as is the case at hand, the aim is to identify, based on an input \((p,q)\)-plane, whether or not an individual is stressed. The variable that identifies the status or class of a subject is called the objective variable. If the individual is stressed, the target variable is assigned a value of '1'; otherwise, it is assigned a value of '0'. The confusion matrix contains a two-dimensional table, as reflected in Table 2. In its columns, the classes are labeled according to the current state according to the actual training data. Its rows represent the states predicted after the application of the model once trained and validated.  
The terminology associated with the confusion matrix is as follows:  
* True positives (_True positives_ or TP): number of samples whose true and predicted class is '1'.
* True negatives (_True negatives_ or TN): number of samples whose true and predicted class is '0'.
* False positives (_False positives_ or FP): number of samples whose real class is '0' and the predicted class is '1'.
* False negatives (_False negatives_ or FN): number of samples whose real class is '1' and the predicted class is '0'.  
Ideally, there are always correct predictions, with no FPs or FNs.
## 4 Evaluation of One-Hop Neighbor Wireless Link Status Based on Fuzzy Logic Model
### Defuzzification  
Defuzzification is the process of generating a crisp numerical result based on fuzzy membership functions. For the corresponding output membership function, we consider the center-of-gravity (COG) method, since it is the most common defuzzification method that is widely used in real applications to  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Rules** & **ATE** & **LQ** & **DP** & **TP** & **Fuzzy Weight** \\ \hline Rule1 & High & High & VeryClose & Long & Perfect \\ Rule2 & High & High & Close & Short & Good \\ Rule3 & High & High & Far & Long & Good \\ Rule4 & High & Medium & VeryClose & Short & Acceptable \\ Rule5 & High & Medium & Close & Long & Good \\ Rule6 & High & Medium & Far & Short & Not Preferred \\ Rule7 & High & Low & VeryClose & Long & Good \\ Rule8 & High & Low & Close & Long & Good \\ Rule9 & High & Low & Far & Short & Not Preferred \\ Rule10 & Medium & High & VeryClose & Long & Good \\ Rule11 & Medium & High & Close & Short & Acceptable \\ Rule12 & Medium & High & Far & Short & Acceptable \\ Rule13 & Medium & Medium & VeryClose & Long & Acceptable \\ Rule14 & Medium & Medium & Close & Short & Not Preferred \\ Rule15 & Medium & Medium & Far & Long & Acceptable \\ Rule16 & Medium & Low & VeryClose & Short & Bad \\ Rule17 & Medium & Low & Close & Long & Not Preferred \\ Rule18 & Medium & Low & Far & Short & Bad \\ Rule19 & Low & High & VeryClose &  
produce the fuzzy output (Figure 6). It ranges 0-1, where the highest value represents the best candidate node in \(C_{n}\). In the situation where multiple candidate nodes have the same rank value, the candidate with the highest transmission range is selected as the next-hop forwarder.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 2 Channel Sounding Using IEEE 802.11n Wireless LANs
### Developed System  
The bandwidth of IEEE 802.11n is approximately 20 MHz with an optional 40 MHz mode. Therefore, the delay time resolution is theoretically limited to 50 ns (=1/20 MHz), which is not sufficient to apply WiFi to sensing with subtle variation of the propagation path. Bandwidth expansion with channel bonding has been proposed [22], and the toolkit has been published as open-source [23]. Utilizing this toolkit, the channel bandwidth could be extended to approximately 67.8125 MHz (delay resolution: approximately 15 ns, distance resolution: approximately 4.5 m) by concatenating the CSIs taken at six consecutive WiFi channels, as shown in Figure 4.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 2 The Principle of the Fuzzy-FTA Analysis
### Basic Principles of Fuzzy Mathematics  
#### 2.2.1 Fuzzy Set and Membership Degree  
Definition: Set on the universe \(U\), specify a mapping for any \(x\ \in\ U,\ \mu_{\underline{A}}\rightarrow[0,1]\), existence:  
\[\mu\big{|}\rightarrow\mu_{\underline{A}}(\mu)\rightarrow[0,1] \tag{1}\]  
where \(\mu_{\underline{A}}(\mu)\) is the membership degree \(\underline{A}\) of a set (or the membership degree of \(\underline{A}\)), and the mapping is a membership function of a set \(\underline{A}\). When the membership degree can only assume the values 0 or 1, the fuzzy set degenerates into a classical set.
## 2 Assumptions and Methods
### Building Detection
This approach produces a resulting map at the spatial resolution of the SAR sensor because it identifies bright pixels using TAI\({}_{\text{VV}}\) and TAI\({}_{\text{VH}}\), whose resolution is that of the SAR; TAC\({}_{\text{VV}}\) has a worse spatial resolution, equal to the size of the moving window used for computing the coherence, but it is only used to reduce the false alarm rate. Then, for urban areas where an important amount of vegetation is present, the vegetation could produce a decrease of coherence, leading to the removal of buildings from the final classification. Consequently, some under-detection can occur, but the resolution of the building map remains that of the TAIs images. The algorithm in step (3) is extensively described in [PERSON] et al. [16]. It is a combination of histogram thresholding and region-growing and requires the calibration of the probability density function (PDF) attributed to the double-bounce class. The parametric binarization of an image to separate the double-bounce class from its background becomes challenging when the class of interest occupies only a small fraction of the whole image. In the algorithm, a hierarchical tiling approach based on statistical modeling has been introduced to identify regions of the image where the PDFs from the double-bounce and background classes can be parameterized more reliably and accurately. The size of the tiles depends on the possibility of parameterizing the PDFs attributed to two different classes. The final objective of the method is to define PDFs for two classes (i.e., the double-bounce and its background).
###### Abstract  
This work focuses on the evaluation of third-order simplified spherical harmonics (SP\({}_{3}\)) model-based image reconstruction with respect to its clinical utility to diagnose rheumatoid arthritis (RA). The existing clinical data of 219 fingers was reconstructed for both absorption and scattering maps in fingers by using the reduced-Hessian sequential quadratic programming (rSQP) algorithm that employs the SP\({}_{3}\) model of light propagation. The \(k\)-fold cross validation method was used for feature extraction and classification of SP\({}_{3}\)-based tomographic images. The performance of the SP\({}_{3}\) model was compared to the DE and ERT models with respect to diagnostic accuracy and computational efficiency. The results presented here show that the SP\({}_{3}\) model achieves clinically relevant sensitivity (88%) and specificity (93%) that compare favorably to the ERT while maintaining significant computational advantage over the ERT (i.e., the SP\({}_{3}\) model is 100 times faster than the ERT). Furthermore, it is also shown that the SP\({}_{3}\) is similar in speed but superior in diagnostic accuracy to the DE. Therefore, it is expected that the method presented here can greatly aid in the early diagnosis of RA with clinically relevant accuracy in near real-time at a clinical setting.  
rheumatoid arthritis; simplified spherical harmonics; optical tomographic imaging; feature extraction; classification; \(k\)-fold cross validation; sensitivity; specificity Article
# Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
## 3 Results and Discussion
### Influence of Flexure Beam Thickness on Bi-Stability Performance
#### 3.2.2 Snap-Through Points as a Function of Thickness  
From the experiments, we obtained STPs within a range of 18.78 mm to 32.6 mm. Figure 6b shows the mean of the snap-through points of the three tested samples of each caliber as a function of thickness. It demonstrates that larger thicknesses snap at longer distances, which indicates that thinner flexure beams snap-through faster, requiring less energy input to reach bi-stability. The STP-thickness relationship shown in Figure 6b is non-linear and tends to flatten at the end, suggesting that the STP of even thicker beams would uphold or even not be reached, making the flexure beams fail to snap as they gain thickness.  
The snap-through points also present fluctuations within samples of the same thickness. As observed in the force-distance plots in Figure 5a-e, the curves converge at the end, indicating that samples of the same caliber travel a similar linear distance before snapping. However, snap-through points happen at slightly different distances in flexure beams of the same caliber, except for the c10 samples that present a more considerable range of variability. This behavior is better illustrated in the error bars in Figure 6b. These findings demonstrate that, regarding discrepancies in the accuracy of the setup because of manual assembly, the snap-through distances stay within a limited range, especially in larger widths, which can be suitable for concrete applications.  
In accordance with the critical forces, the distances at which the mechanism snaps (snap-through points) also increase with thickness. Figure 6c compares the critical force to the snap-through points by standardizing their different values within a range between 0 and 1. The graph shows an increment in both CF and STP as the flexure beam becomes thicker but exhibits contrasting behavior: the CF curve increases sharply while the slope of the STP curve decreases gradually at the end of the curve. This behavior suggests that flexure beams with even larger thickness would continue to require greater input forces and would snap-through at a distance close to the limit exhibited in c40 or even fail to transit to the second stable equilibrium position.  
Figure 6: (**a**) The force-thickness graph exhibits a non-linear relationship (**b**) The snap-through point-thickness plot demonstrates that thicker flexure beams snap at longer distances. (**c**) Plot that exhibits the opposite growth of the critical force and snap-through point curves as a function of thickness.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 4 Experimental Results
### Discussions  
Section 4.1 shows the experimental results of memory patterns generated when images are gradually input without generating prior memories, and Section 4.2 presents the experimental results of memory patterns generated when images are gradually input after pre-memory generation.  
There are two main differences between results of Experiment 1 and Experiment 2. The first difference is that the number of memory patterns in Experiment 1 is greater than those in Experiment 2. In Experiment 1, a small difference in the image is recognized as another memory pattern and the number of memory patterns increased, when a memory pattern is created only with gradual image input without prior memory. In particular, the number of memory patterns is increased rapidly when high SDR overlap is applied based on similarity criteria. However, if memory patterns are created after pre-memory formation, fragmentation is reduced, and if SDR overlap is applied at 20%, the same number of memory patterns as the number of ground-truth labels are created.  
The second difference is that it is much more difficult to find a dominant ground-truth label in Experiment 1. There are various ground-truth labels in a formed memory pattern when it is made only with gradual image input without prior memory. However, when a memory pattern is created after pre-memory formation, a dominant label appears in each memory pattern.  
Figure 25 illustrates the comparison results with and without the pre-memory process when the 20% SDR overlap is used as the mutual similarity criterion. In this graph, the comparison values are the proportion of labels that appear dominant in each memory pattern. Meanwhile, the number of memory patterns differs when using and not using a pre-memory process. When the pre-memory process is used, 10 memory patterns are obtained, whereas 16 memory patterns are obtained when the pre-memory is not used. In order to compare the results under the same number of memory pattern conditions, 10 memory patterns containing many images out of 16 memory patterns were used.  
Figure 26 illustrates the proportion of dominant memory pattern per ground-truth label according to the presence or absence of pre-memory when SDR 20% overlap is used as a similarity criterion. We can find that the proportion of the dominant memory pattern is very high in the result of the case where the pre-memory generation process is not performed compared to the result in the case where the pre-memory generation process is not performed.  
Figure 25: Comparison of the proportion of dominant labels with and without the pre-memory process. The similarity criterion is 20% SDR overlap.
## 3 Simulation and Analysis
### Experimental Simulation and Analysis  
#### 3.3.1 Map 1  
Figure 7 displays the outcomes of the three RRT algorithms applied in Map 1. The path marked in red represents the ultimate route, while the green signifies the starting planning path and node. The diagram illustrates that within a winding road environment, the conventional RRT algorithm, the RRT* algorithm, and the combined PRM and enhanced RRT algorithm discussed in this document are capable of converging near the intended point. However, due to the randomness of its sampling, the traditional RRT and EP-RRT* algorithms will cause the random tree to extend in all directions, sometimes even in the opposite direction to the goal point, resulting in the goal point not being able to be reached in the limited number of iterations. The degree of twist and the length of the path are higher than the enhanced algorithm proposed in this document. This paper's enhanced algorithm adopts fan-shaped target orientation sampling, so that the nodes sampled each time are more likely to approach the location of the target point, and adopts an adaptive step size expansion strategy, which greatly improves the random tree expansion rate. Therefore, under the same number of iterations, compared with the traditional RRT algorithm, the search efficiency and accuracy are improved.  
Figure 6: The average time to generate the initial path. (**a**) The average time of generating the initial path with different steps in Map 1; (**b**) the average time of generating the initial path with different steps in Map 2; (**c**) the average time of generating the initial path with different steps in Map 3.  
Table 3 is the experimental comparison data. The table illustrates that the enhanced algorithm presented in this study exhibits improvements in path length, path tortuosity, and the rate of planning success when contrasted with the conventional RRT algorithm and EP-RRT* algorithm. Regarding the length of paths, this paper's enhanced algorithm shows a decrease of 15.96% and 5.31%, respectively, compared with the other two algorithms. Regarding the count of inflection points, this paper's enhanced algorithm shows a decrease of 80.36% and 72.17% in comparison to the initial two algorithms. The reason for the optimization of the path length and the number of inflection points lies in the integration with the PRM algorithm. For the initial generated feasible path, the points on the redundant path are connected in a straight line under the premise of avoiding obstacles, and the path is optimized. In terms of planning success rate, fan-shaped target-oriented sampling is used to make each sampling node more likely to be close to the target point, which improves the search efficiency. The adaptive step size strategy is used to increase the step size of the random tree in the narrow roadway, which can quickly explore the area. Therefore, it can be seen from Table 3 that the success rate of the improved algorithm in this paper is 32.97% and 17.02% higher than that of the first two algorithms. This demonstrates the efficacy and dominance of the enhanced algorithm in the first map.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 3 Basic Predicted Model  
### Preprocessing of Satellite Clock Offsets  
In the case of satellite clock prediction, the precision of clock offset observations directly determines the accuracy of the prediction model. Therefore, the abnormal values and blunders in the observations should be eliminated before the model has been established [27; 28; 29; 30; 31; 32; 33]. In this paper, the median absolute deviation (MAD) method was used to detect the phase and frequency jumps which exist in the clock offset sequence. The MAD method can be expressed as [16; 24]:  
\[M=\mathrm{Median}\{|b_{u}-m|/0.6745\}, \tag{1}\]  
where \(M\) is the difference value which is the threshold value in this model. \(u\) is the frequency epoch number. \(m\) is the median of the clock frequency sequences, and \(m=\mathrm{Median}\{b_{u}\}\). \(b_{u}\) is the clock frequency sequence. It is considered as a blunder if \(|b_{u}|>(m+n\cdot M)\) (in this paper n was set as 3) [33; 34; 35]. In order to verify the MAD method, the C04 clock offsets results of 4 January 2017 were chosen as shown in Figure 2.  
As shown in Figure 2a, there are more blunders existing in the data. It is not smooth in the clock frequency sequences. However, in Figure 2b, the clock offset sequence is smooth and has no significant fluctuation by using the MAD method. This method is not sensitive to the magnitude of the blunders. Usually the blunders detected just applied to the clock frequency data, and for raw clock phase data it should be converted to the original frequency data through the single difference method.  
Figure 2: The MAD method gross error detection. (**a**) The black line is the original phase data and the blue line is the original frequency data. It is a sequence that has not been subjected to gross error detection; and (**b**) the black line and blue line are phase data and frequency data by using the MAD method.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
###### Abstract
First, the light absorption of 2D materials is low, although they have a large absorption coefficient due to the ultrathin thickness. The bulk Si significantly enhances the light absorption in 2D material/Si heterojunctions. Second, 2D materials exhibit diverse electrical properties, enabling the fabrication of Schottky diodes, p-n diodes, n-n or p-p diodes, as well as single barrier heterojunction photodetectors. Third, the unique properties of both the 2D material and Si are manifested in the heterojunction, enabling new functionalities and a broadband spectral response. Fourth, the difference in Fermi levels between the 2D material and Si results in the formation of an internal electrical field that promotes the separation of photogenerated electron-hole pairs and the efficient transport of photocarriers, thereby increasing the response speed. Fifth, when operated at zero or reverse bias, the 2D material/Si heterojunction photodetector exhibits low dark current, leading to improved specific detectivity. These intriguing advancements in 2D material/Si heterojunction-based photodetectors offer promising applications in future optoelectronic systems. In this review, we aim to highlight recent advancements in the study of photodetectors based on 2D material/Si heterojunctions, with a specific focus on diode-like detectors. We will begin by introducing the figures of merit of photodetectors. Following this, we will delve into the recent progress made in the field of 2D material/Si heterojunction-based photodetectors. Notably, we will discuss the extensive research conducted on graphene/Si heterojunction photodetectors. In addition, we will explore the topic of transition metal dichalcogenide (TMD)/Si heterojunction photodetectors. It is worth mentioning that noble metal dichalcogenides (NMDs) are a subset of TMDs that exhibit distinct electrical and optoelectrical properties. Therefore, we will discuss NMD/Si heterojunction photodetectors separately from TMD/Si heterojunction photodetectors. Then, we will review other types of 2D material/Si heterojunction photodetectors. Finally, we will provide an outlook on the future prospects and challenges associated with 2D material/Si heterojunction-based photodetectors.