esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 2 Theoretical Background of Cognitive Radio Networks  
### Detection Performance of a Local Sensing  
The availability of the detected channel can be modeled as the following binary hypotheses:  
\[\begin{array}{llll}H_{0}:&x=w&(idle\ channel),\\ H_{1}:&x=y+w&(busy\ channel),\end{array}\]  
where \(x\) is the detected signal, \(w\) is additive white Gaussian noise and \(y\) is the received PU signal, while \(H_{i=0,1}\) represent the binary hypotheses of absence and presence of a PU, respectively.  
The criteria assessment of a CR's detection performance are the probability of false alarm and the probability of detection. The probability of false alarm, \(P_{f}\), is defined as a probability of falsely detecting the presence of the PU when the channel is idle. Minimizing \(P_{f}\) is a goal in order to maximize spectrum efficiency. In other words, increasing \(P_{f}\) results in losing available spectrum opportunities. The \(P_{f}\) can computed as:  
\[P_{f}=\frac{\Gamma\left(\frac{M}{2},\frac{\lambda}{2}\right)}{\Gamma\left( \frac{M}{2}\right)}, \tag{1}\]  
where \(M\) is the time-bandwidth product (i.e., \(\frac{M}{2}\) is the number of samples), \(\lambda\) is the detection threshold, \(\Gamma(.)\) is the Gamma function and \(\Gamma(.,.)\) is the incomplete Gamma function.  
The probability of detection, \(P_{d}\), is defined as a probability of sensing the existence of the PU correctly when the channel is busy. In other words, \(P_{d}\) reflects the accuracy of detection and maximizing \(P_{d}\) is an ultimate goal. The \(P_{d}\) can be computed as  
\[P_{d}=\frac{\Gamma\left(\frac{M}{2},\frac{\lambda}{2(1+\gamma)}\right)}{ \Gamma\left(\frac{M}{2}\right)}, \tag{2}\]  
where \(\gamma\) is the instantaneous SNR.  
Equation (2) shows that \(P_{d}\) is dependent on \(\gamma\) which varies according to the channel conditions. For instance, if a CR experiences Rayleigh fading channels, the \(P_{d}\) is determined by averaging Equation (2) over the probability density function (PDF) of \(\gamma\) provided by  
\[f\left(\gamma\right)=\left(\frac{1}{\gamma}\right)exp\left(\frac{-\gamma}{ \gamma}\right),\ \gamma\geq 0 \tag{3}\]  
where \(\gamma\) is the average SNR.  
In general, the expression of the average \(P_{d}\) in case of Rayleigh fading, \(P_{d,Ray}\) can be as [22]  
\[P_{d,Ray}=\int_{0}^{\infty}\frac{\Gamma\left(\frac{M}{2},\frac{\lambda}{2(1+ \gamma)}\right)}{\Gamma\left(\frac{M}{2}\right)}\left(\frac{1}{\gamma} \right)exp\left(\frac{-\gamma}{\gamma}\right)d\gamma, \tag{4}\]
## 4 Results and Discussion
### Qualitative Comparison  
#### 4.4.1 Comparison of the Segmentation Results with Other Models  
To verify the effectiveness of the proposed method, we compared the segmentation results with other classic models, such as LBF [29; 30], LGIF [31], LIF [27], and RMPCM [3], respectively. The comparison results are shown in Figure 5.  
It can be seen from Figure 5 that the LBF model is limited by the initial contour curve and cannot completely extract the target contour. The LGIF model is minimally affected by local background noise due to the fusion of the global intensity fitting, but it still cannot accurately extract the target contour. The LIF and RMPCM models can completely extract the target contour, but they are greatly affected by background noise and local target features. Our model introduces semantic information to filter out background noise very well. Moreover, because of the global-local intensity fitting, our method can handle local inhomogeneous regions without interfering in the local target features. In addition, the target contour of the original image perfectly complements the missing semantic information.  
Furthermore, we compared the segmentation results with [PERSON]'s method [15] and [PERSON]'s method [20], which also introduced saliency images as semantic information. Since we could not obtain the source codes of [PERSON]'s method [15] and [PERSON]'s method [20], to ensure the fairness of the comparison results, we adapted the segmentation images from  
Figure 5: Comparison of our method with LBF, LGIF, LIF, RMPCM. (**a**) Results of the LBF model. (**b**) Results of the LGIF model. (**c**) Results of the LIF model. (**d**) Results of the RMPCM model. (**e**) Results of our method.  
[PERSON]'s method [15] (2017,IEEE) and [PERSON]'s method [20] (2019,IEEE) as the comparison images. The comparison results are shown in Figure 6.  
As can be seen in Figure 6, even though our method, [PERSON]'s method [15], and [PERSON]'s method [20] all introduce semantic information, our method can extract the target contour more accurately than [PERSON]'s method [15] and [PERSON]'s method [20]. As shown in the blue circle region of Figure 6a, our method extracted the target contour in the detail region more accurately. This is because we added the local-global fitting term to better extract the contours of local inhomogeneous regions, and the original image correction module can correct the errors in semantic information. As shown in the green circle region of Figure 6b, our method can filter out background noise better than [PERSON]'s method [20] and is more robust.
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 4 Research Status of the Task-Scheduling Algorithm
### Heuristic Scheduling Algorithm  
Heuristic scheduling algorithms rely on empirical rules or simplified problem models and aim to determine approximate solutions quickly. They seek the approximately optimal scheduling scheme by simulating natural processes or applying statistical principles, which is suitable for solving the NP-difficult problem of scheduling. Due to the effective solutions of different processing units in the heterogeneous environment, heuristic and meta-heuristic algorithms can explore several feasible scheduling methods in a reasonable time and find the best balance between performance and cost. The heuristic scheduling algorithm aims to discover a solution to the problem at hand at the right time. Heuristic methods are usually more flexible and adaptable to better respond to real-time changing environments and incomplete information; common heuristic algorithms can be roughly divided into three  
Figure 6: Illustration of the HEFT list scheduling process [3].  
categories: the table scheduling algorithm, the cluster-based scheduling algorithm, and the task replication-based scheduling algorithm [25].  
[PERSON] et al. used list-scheduling techniques to rank nodes in the DAG and then group these nodes so that tasks in each group can be scheduled independently [26]. [PERSON] also proposed a new weight calculation method, aiming to reduce the difference in scheduling results caused by different weight calculation methods. [PERSON] et al. combined a genetic algorithm and a list-based approach [27]. The convergence rate of the algorithm is accelerated by introducing two guiding chromosomes to improve the initial genetic algorithm population. [PERSON] et al. used deep learning for task ordering, which uses the real-time heterogeneous chip scheduling simulator design process and two novel neural network design features to improve the performance of task execution: hierarchical operation and task graph embedding [28]. The heuristic scheduling algorithm rules of heterogeneous chips cannot fully cover all possible situations, resulting in severe performance degradation in the face of some dynamic changes.
## 5 Performance Analysis and Optimization
### Average BEP/SEP
#### 5.3.3. ASEP for MQAM
By inserting (36) into (79) and adopting a similar approach as in (76), then finding a root of a quadratic equation \(cQ^{2}\bigg{(}\sqrt{2\text{g}_{QAM}\gamma_{\text{T}}}\bigg{)}-Q\bigg{(}\sqrt{2 \text{g}_{QAM}\gamma_{\text{T}}}\bigg{)}+\Delta=0\), we can get a closed-form expression of the optimum switching threshold of MQAM for SSC over Fisher-Snedecor \(\mathcal{F}\) composite fading as:  
\[\gamma_{\text{T}}^{*}=\frac{1}{2\text{$g$}_{\text{QAM}}}\bigg{[}Q^{-1}\bigg{(} \frac{1-\sqrt{1-4c\Delta}}{2c}\bigg{)}\bigg{]}^{2}, \tag{83}\]  
where: \(\Delta=\frac{(2\sqrt{\pi})^{-1}}{\Gamma(m)\Gamma(n)}\Big{(}\text{$g$}_{\text{ \ref{Q}}}^{3,1}[\frac{\text{$g$}_{\text{QAM}}}{\Lambda}\big{|}_{0,n,0}^{1-m,1}]-\frac{c}{\sqrt{2\pi}}H_{1,1;2;1,1}^{0,1;2;1,1,1}[\frac{2\text{$g$}_{ \text{QAM}}}{\Lambda},-0.5]\big{|}_{(-0.5-1,1),\big{|}}_{(0.5-1,1),\big{|}} \big{|}_{(0.1)}^{(1-m;1)}\big{|}_{(0.1)}^{(0.51)}\big{|}_{(0.1)}^{(0.51)}\big{|} \Big{)}\).
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 3 Results and Discussion
### Prediction Model Performance  
Table 2 presents the prediction model performance when only clean test samples are available, as well as when the noisy samples are included. As can be seen, in the \"random split\" scenario, the system based on MFCCs outperformed the two other systems and achieved an MAE of 0.86 for the clean samples. When environmenta; factors were included, the DWT-based system was the most affected. Overall, MFCC-based systems achieved lower MAE under urban sound and heavy rain conditions, while the spectrogram-based system achieved lower MAE when speech was present. For the \"hive-independent\" setting, the three feature sets achieved similar results under clean conditions. The errors, however, are substantially larger than those achieved in the random-split case, suggesting great variability between hives. As mentioned previously, noise severely degraded performance. In this case, the spectrogram features achieved lower MAE for urban sounds and speech, whereas MFCCs performed better for heavy rain. In this setting, speech artifacts caused the most harm to monitoring systems. Taken together, these results highlight the potential of acoustics used in a machine learning framework and also the importance of context awareness for beehive monitoring to ensure that such environmental factors are taken into account during remote monitoring.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
###### Abstract  
The impact of reservoirs on downstream river hydrological characteristics is always a focal point in relevant studies exploring the relationship between rivers and dams. Anticipating river runoff patterns following the construction of new dams is crucial for the design of riverine engineering projects, particularly during dry periods. This paper presents a semi-theoretical estimation method based on the correlation between hydrological alterations and reservoir operation. The method incorporates differences in runoff increment distribution and the discrepancy between theoretical and practical results. It was validated and applied in the sub-basins of the upper reaches of the Yangtze River, namely the Jinsha River and Min River. The runoff increment during the driest month for the Jinsha River and the Min River is 817 m\({}^{3}\)/s and 434 m\({}^{3}\)/s, respectively. The estimated prediction biases were within 30% of the practical runoff increments observed in the Jinsha River and Min River, which is an acceptable range considering the inherent variability in such studies. Since the construction of the Wudongde and Baihetan dams in 2021, the average runoff during the driest month and the navigation assurance runoff at a 95% probability were predicted to be 2866 m\({}^{3}\)/s and 2174 m\({}^{3}\)/s, respectively. Therefore, the method developed in this paper provides a reasonable and straightforward tool for researchers, which can help prevent future engineering invalidation and minimize resource costs. Moreover, in the application process, this method requires careful consideration of the characteristics of the studied river section and the operation of the reservoir group. It relies on measured data to determine the differences between theoretical and actual runoff rather than simply generalizing to all watersheds.  
r 2024 14 560. [[https://doi.org/10.3390/app14020560](https://doi.org/10.3390/app14020560)]([https://doi.org/10.3390/app14020560](https://doi.org/10.3390/app14020560))  
c 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Unboxing Buildings: Engaging with Occupants during Design, Testing and Use
## 3 Three Examples from Interdisciplinary Norwegian Building Research
### Handling over to Occupants
The occupants interviewed were often interested in learning more about the specific technical standard of their new homes and whether it had implications for everyday use. According to housing developers, the technical standard was not in focus during the buying process, buyers placed as higher value on aspects such as comfort and location. After purchasing the house, buyers asked for information about the technical system. Interviews revealed that expectations towards an energy efficient home were high, especially in terms of thermal comfort. Interviewees also described the energy saving potential as a \"bonus\" for the environment and for the household economy, they expected reduced heating bills. The houses did not always achieve their expectations and the use and understanding of the technical systems was part of this. For example, the functions and importance of the ventilation system in highly insulated dwellings was often not clearly understood by occupants in the eight cases. An occupant stated,  
\"If we had received more information, I believe we could have used the ventilation system of the dwelling in a better way. We open the windows a lot to ventilate but I guess we could use the ventilation system more instead. It is a pity we did not learn more about the system possibilities.\"  
Problems, errors, and adjustments were common reasons why occupants switched off systems or asked for professional help. Several interviewees believe they could have used their house and the systems more efficiently if they had been given better instructions both written and verbal. Practices adopted by occupants vary from not changing anything to daily adjustments to the heating and ventilation systems. In situations where people make fundamental changes to their lives, such as moving to a new dwelling, they may be more receptive to adapting their routines. Early introduction of information about the use and operation is essential, taking advantage of a rare opportunity because people only move to a new house four to five times during their lives [28] (p. 51, 56). Few occupants were motivated to find detailed information later. Much of the responsibility for information production and exchange lies in the hands of the experts, system designers and housing developers. The segregation of construction and operation phases is common and has negative effects on use and operation of new buildings [29]. It can be understood as a black boxing of system choices, we propose that keeping the box open whilst developing relevant information material for occupants in collaboration with them has the potential to reduce the number of problems, errors and adjustments. Addressing use and operation already in design and planning processes establishes continuity and responsibility, all the way through to the hand-over to occupants. Occupant representation and engagement avoids simplifications about who potential buyers are and their motivations.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### The Preprocessing of the Iterative Positioning Algorithm
#### 3.2.3 The Triangular Centroid Positioning Algorithm  
The basic principle of the triangle centroid positioning algorithm is as follows: the three circles are determined by treating the three anchor nodes as their respective circle centers by addressing the distances between the given anchor nodes and unknown target node as their respective radiuses. The intersection of the three circles can obtain six intersection points and a triangle is constructed by treating the three closer intersection points as vertexes, with the centroid of a triangle taken as the coordinates of the node to be positioned. The schematic diagram of triangular centroid positioning is shown in Figure 2.  
In Figure 2, \(O_{1}\), \(O_{2}\) and \(O_{3}\) are defined as the positions of three anchor nodes with coordinates of \(O_{1}(x_{11},y_{11})\), \(O_{2}(x_{22},y_{22})\), and \(O_{3}(x_{33},y_{33})\) and whose radiuses are \(d_{1}\), \(d_{2}\), and \(d_{3}\), respectively. Points \(S_{1}(x_{1},y_{1})\), \(S_{2}(x_{2},y_{2})\), and \(S_{3}(x_{3},y_{3})\) are the three closer intersections points, that is, the three vertices of the triangle centroid positioning algorithm.  
The intersection point coordinates of the circles \(O_{1}\) and \(O_{2}\) can be obtained by Equation (11).  
Figure 2: Schematic diagram of triangular centroid positioning.  
\[\left\{\begin{array}{l}(x-x_{11})^{2}+(y-y_{11})^{2}=d_{1}^{2}\\ (x-x_{22})^{2}+(y-y_{22})^{2}=d_{2}^{2}\end{array}\right., \tag{11}\]  
In two sets of coordinates solved by Equation (11), the intersection S\({}_{3}\) (\(x_{3}\), \(y_{3}\)) is closer to the center of the positioning triangle. The solution of the remaining points \(S_{1}(x_{1},y_{1})\) and \(S_{2}(x_{2},y_{2})\) is similar to that of point \(S_{3}(x_{3},y_{3})\).  
Thus, the initial coordinates \(O(x_{g},y_{g})\) of the target node can be calculated by Equation (12).  
\[\left\{\begin{array}{l}x_{g}=\frac{1}{m}\sum\limits_{i=1}^{m}x_{i}\\ y_{g}=\frac{1}{m}\sum\limits_{i=1}^{m}y_{i}\end{array}\right.\left(m=3\right), \tag{12}\]  
where \(m=3\) and \(i=1,2,3\).
###### Abstract
In [16], the authors proposed electrolyzer- and fuel-cell-based MG to meet both hydrogen and power energy demands. Due to the low efficiency of the electrolyzer, its input power is fed by a photovoltaic system [17]. Fuel-cell-based electric vehicles (FCBEVs) are also used as transportation vehicles [18]. There are several kinds of optimization algorithms to solve short-term and long-term issues. To solve the optimization scheduling problem, the authors proposed an ant colony algorithm [19]. In [20], the authors applied a combination of Tabu search (TS) and the particle swarm optimization (PSO) algorithm to solve a planning problem. In [21], the authors used general algebraic modeling system (GAMS) software to model the stochastic problem and, by implementing a mixed-integer linear program, achieved all of the optimal solutions. In [22], the authors presented a comprehensive study evaluating the impacts of battery storage in reducing the operational costs of MG. The positive effects of considering demand response and storage systems in the optimal management of a smart home were evaluated in [23]. In [24], the authors presented an effective strategy to reduce power and energy losses by using storage systems in the distribution network. As can be seen from the latest literature, comprehensive studies that include effective predictive techniques to model system uncertainties and utilize simultaneous generation of power, hydrogen, and thermal energy are lacking in previous works. In this study, the main objective is to optimally design an SPL-CHHP-based MG in the distribution system. The proposed station is equipped with a wind turbine, photovoltaic system, and a battery. The novelties of this research are classified as follows:  
* Optimal planning of CHHP in the active distribution network;
* Employing the LSTM model to model wind and sun irradiation;
* Obtaining the CHHP's converged power by the LSTM;
* Employing TOPSIS as a multicriteria decision analysis method. The rest of the paper is organized as follows. In Section 2, the problem definition is addressed. The genetic algorithm (GA) as an optimization algorithm is explained in Section 3. In Section 4, all of the obtained results are analyzed in detail. Section 5 concludes the paper.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 3 Experimental Results
### Antenna Pattern Demodulation  
In ST mode, GF-3 always illuminates the same scene on the ground, so the brightness of images will be modulated by the antenna pattern, as Figure 6a shows. The inverse of the curve in Figure 4c can compensate for this modulation in the image level, and the result can be found in Figure 10.  
In order to verify the accuracy of the phase information, we conducted a repeat-pass SAR interferometry experiment based on the ST imagery above and a normal SL imagery on the same scene. The interferogram of the spot marked with white box in Figure 10 can be found in Figure 11a. Only when the data processing presented in this paper keeps the phase fidelity very well, can the ST focused image conjugate multiplied by the image obtained in regular SL mode effectively cancel the common backscatter to form an interferogram. More details of this interferogram experiment will be published in the future.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Resolution (m)** & **PSLR (dB)** & **ISLR (dB)** \\ \hline Range & 0.734 & \(-\)23.085 & \(-\)18.838 \\ Azimuth & 0.543 & \(-\)18.679 & \(-\)15.995 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance of compression after stop-and-go correction.  
Figure 11: Some results of repeat-pass SAR interferometry experiment. (**a**) The interferogram of the spot marked with the white box in Figure 10; (**b**) The flattened interferogram of (**a**).  
Figure 10: The final product of GF-3 in ST mode.