esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### The Influence of PCM or SIM Layer Thickness
As a result, the PCM layer did not have the capability of modulating the temperature change inside the roof with latent heat and its function was similar to the sensible insulation material with a low thermal conductivity. As the thermal conductivities of paraffin (PCM) and perlite (SIM) used in this work were similar, the energy saving amounts of a PCM roof and SIM roof were almost the same when the layer thickness, \(D\), was less than 35 as shown in Figure 11. However, the specific heat, \(c_{p}\), of perlite is much larger than that of paraffin. When the thicknesses of a PCM roof and a SIM roof were large, the interior surface temperature rise inside the SIM perlite roof was much slower than that inside the PCM paraffin roof during the daytime under the influence of solar radiation. Hence, as displayed in Figure 11, it costs more energy for heating the room of a SIM roof using the air conditioner than that of a PCM roof. Figure 10: The PCM layer average liquid fraction during the summer cooling season with different layer thicknesses. Figure 9: The effect of layer thickness on the energy saving amount of PCM or SIM roofs during the summer cooling season. From the above discussions, due to the effect of latent heat, the PCM layer could modulate the temperature inside the building roof and improve the thermal comfort in the room and its energy saving capability was also better than the SIM roof if the appropriate PCM melting temperature and PCM layer thickness were chosen. However, besides the energy saving capability of green building roofs, its incremental cost in the construction period and the incremental benefit during the operational period are significant in practical engineering. The comprehensive incremental benefits of a paraffin PCM roof and a perlite SIM roof during life cycle with different PCM or SIM layer thicknesses are shown in Figure 12. The data indicate that the perlite SIM roof has a positive comprehensive incremental benefit for all the different SIM layer thicknesses. When the SIM layer with a thickness of \(D=60\) mm was used, the SIM roof achieved the maximum comprehensive incremental benefit of 3110 yuan (\(6\) m \(\times\) 6 m roof). On the contrary, the comprehensive incremental benefit of a paraffin PCM roof was always negative, which indicates that the paraffin PCM roof could not have an economic benefit so that it is not proper for the practical engineering application at the current stage. The reason for the huge difference between the comprehensive incremental benefit of a paraffin PCM roof and a perlite SIM roof was that the material price of paraffin is 24.14 times more than that of perlite as shown in Table 1. In this case, developing cheap PCMs with stable chemical properties is a key scientific bottleneck for wide engineering applications of PCM envelope building in recent future.
## 3 Validations of the Two-Phase and Porous Media Model
### Numerical Results by Porous Media Model in a Large-Scale Tower  
A mesh with number of grid points 1.2 \(\times\) 10\({}^{6}\) is required for a small-scale tower that has 1200 holes at those four sieve trays. As for the large-scale tower, the number of holes becomes 600,000, and it could require a mesh of six hundred million grid points if their resolutions remain the same. The previous subsection validated the feasibility of the porous media model in a small-scale tower, which has also been utilized for the large-scale tower in this study to save computational costs. The number of grid points for the large-scale tower is 8 \(\times\) 10\({}^{5}\) with the porous media model near the sieve trays.  
The size and flow conditions of the large-scale tower were introduced in Figure 4, Sections 3.1 and 3.3. The setup of the boundary conditions is the same as that for the small-scale tower in Section 3.4. The characteristics of perforated sieve trays used in the large-scale tower are \(f_{h}\) = 27.7%, \(t_{h}\)/\(D_{h}\) = 0.75, \(Re_{h,g}\) = 5500, and \(Re_{h,  
Figure 11 shows the instantaneous variation of mass fraction of SO\({}_{2}\) (Y\({}_{\text{SO2}}\)) at outlet for the full-scale tower. The value of \(Y_{\text{SO2}}\) varies between 4 and 11 ppm, corresponding to SO\({}_{2}\) removal efficiency of 97-98.9%. The average efficiency is 98.3%. The experiment of this large-scale tower by China Steel Cooperation reports the efficiency as 96%. Therefore, this subsection further validates the porous media model in the full-scale tower.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 1 Introduction
The specific objectives of this paper were as follows: (1) to select the characteristic wavelengths for bruise detection of Korla pears by the method of combining ROC analysis with PCA; and (2) to compare the performance of the PLS-DA model based on selected wavelengths with that of the PLS-DA model based on all of the wavelength variables.
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 2 Related Works
### Improved Density Peak Clustering Algorithm  
In pursuit of this goal, the initial step involves the selection of a substantial number of centers to establish sub-clusters, which are subsequently merged using heuristic techniques based on pattern similarity. However, this approach exclusively takes into account the inter-cluster distance during merging, neglecting other cluster attributes, which diminishes its effectiveness when dealing with intricate data structures. [PERSON] and [PERSON] (2017) [14] introduced a semi-supervised density peak clustering (DPC) algorithm known as SSDPC. This algorithm leverages labeled data to enhance clustering performance both on labeled and unlabeled data. Nevertheless, when dealing with extensive datasets, substantial volumes of labeled data are essential to furnish adequate supervisory information, and the precision of the labeled data significantly influences the outcomes. [PERSON] and [PERSON] (2016) [15] sought to enhance the DPC method through the implementation of a divide-and-conquer strategy. This method autonomously identifies the number of clusters without relying on expert knowledge. However, it primarily takes into account the global data structure and, in comparison to the original DPC, leads to a loss of numerous clusters, resulting in reduced performance. In a different approach, [PERSON] et al. (2016) [16] proposed a hierarchical density peak method (DenPEHC) by introducing a grid granularity framework. DenPEHC demonstrates the ability to cluster high-dimensional and large-scale data effectively. It automatically identifies all potential centers and constructs a hierarchical representation if the dataset inherently exhibits a hierarchical structure.  
One notable drawback shared among these methods lies in their ineffective label allocation strategy. When computing density values, they exclusively consider the global data structure, overlooking the local structure, which can lead to the omission of certain clusters.  
In pursuit of improving the automatic determination of cluster centers, our approach adopts a hybrid clustering methodology, which combines various clustering techniques or models to enhance clustering performance on intricate datasets. This approach leverages the principles of diverse clustering algorithms to bolster the DPC algorithm's capability to autonomously determine unstable cluster centers.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions
### Spaceborne LiDARs for Terrestrial Applications
#### 5.1.6 GEDI--The Global Ecosystem Dynamics Investigation  
The Global Ecosystem Dynamics Investigation (GEDI) is a joint NASA and the University of Maryland mission launched to the International Space Station (ISS) on 5 December 2018. Mounted on the Kibo Exposed Facility of the ISS, GEDI collects data primarily to assess forest structure and above-ground biomass worldwide. The initial duration of the mission was 48 months, starting 25 March 2019; after an extension until March 2023, it resumed for a second phase on 22 April 2024 [107; 136]. Its primary goal is to generate high-resolution laser-based measurements to map forest biomass in temperate, subtropical, and tropical ecosystems. During the original lifetime of the mission, the system was expected to produce approximately 10 billion cloud-free observations [145; 146]. During the original lifetime of the mission, the system was expected to produce approximately 10 billion cloud-free observations [146].
## 3 Results
### Knee Osteoarthritis Detection
#### 3.3.1 Deep Learning Models
The method yielded a quadratic kappa coefficient of 0.83 and an average precision of 66.71%. [PERSON] et al. [11] have developed an automatic diagnostic model of KOA that can be used in a portable device named RefineDet. This model is composed of two connected models: the anchor refinement module (ARM), which is used for ROI localization, and the object detection module (ODM), which is used for refinement and classification of the KOA severity. To facilitate the sharing of information between the proposed models, a transfer connection block (TCB) was incorporated. A dataset of 2579 posterior-anterior (PA) view radiographic images of knees collected from the General Hospital of the People's Liberation Army in China was utilized to train the proposed approach. In this study, the images were captured using an iPhone fixed at a distance of 40 cm. Furthermore, the pre-trained model was constructed using 2499 images of the train, 263 images of the validation set, and 941 images of the test set. The model was trained using a batch size of 128 images, a momentum of 0.9, a weight decay of \(5\times 10^{-4}\) with the Adam optimizer, and a learning rate of \(2\times 10^{-2}\). The automated diagnostic model for knee osteoarthritis achieved an accuracy of 95.7%. In 2020, [PERSON] et al. [23] applied ResNet34 with a convolutional block attention model (CBAM) for the automatic classification of knee osteoarthritis. The model was trained using a large dataset acquired and augmented from the OAI dataset. This comprised 38,232 training dataset images, 5422 validation dataset images, and 10,986 test dataset images. The kernel size of the final average pooling layer of ResNet34 was modified from 7 to 28, and the linear layer was altered to 512 \(\times\) 5. While CBAM was implemented at the end of each residual block of ResNet34, cross-entropy was applied as a loss function to train the model. The accuracy of ResNet34 was enhanced by the incorporation of CBAM, resulting in an improvement from 73.75% to 74.81%. Concurrently, the mean-squared error (MSE) was reduced from 0.39 to 0.36.
## 3 Results and Discussions
### User Study  
Five chest doctors were invited to experience and evaluate our indication system. After the test, all the experimenters were asked to complete the questionnaire as the raw material for the system evaluation, in which there were five key questions: (1) the accuracy of thoracic tissue contour segmentation; (2) the accuracy of cutting curve indication; (3) system smoothness; (4) the cutting guidance under obstruction of appliances and tissues; (5) the utility of online guiding. All the questions are scored between 0 and 10, where the value is positively correlated with satisfaction degree. Table 6 records the average scores for the five questions, and the limits to be improved are also illustrated. As depicted in Table 6, the accuracy of the thoracic tissue contour segmentation and the correctness of the cutting curve indication have received better feedback (8.5 higher on average). The cutting guidance under view occlusion is also acceptable. For the system smoothness, corresponding scores are rather general. This is due to the current system's computational efficiency; clinical surgery indication requires timely responses, and changes in patient states caused by the surgical procedure could range between a few seconds. Therefore, we need to concentrate more on real-time performance, by optimizing the algorithm efficiency with TensorRT or reducing network parameters. Moreover, it is also a feasible choice to process surgery data parallelly using multi-thread processing.
# Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors
## 4 Experimental Set-Up
### Baseline Method: EvoColor
The second difference between EvoColor and EvoStick is how the output of the neural network is mapped to the velocity of the e-pucks. In EvoColor, we introduce a velocity mapping based on tuples to facilitate the evolution of standstill behaviors--as we expect robots need them to perform stopp and aggregation. In EvoStick, the control software maps directly a single output node of the neural network into velocity commands \((v=[-12,12])\) for each wheel \((v_{k}\in\{l_{r}\})\)--a robot can stand still only if the velocity of the two wheels is set exactly to 0. A standstill behavior is then difficult to achieve since only one pair of values in the output nodes maps exactly to \(v_{l}=0\) and \(v_{r}=0\); Moreover, the output nodes can not maintain a steady value because they are subject to the injection of sensory noise. In EvoColor, the control software maps the sum of elements of a tuple \((v^{\prime})\) into velocity commands for each wheel \(v_{k\in\{l_{r}\}}\)--each tuple is defined by two output nodes and provides a negative and a  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Input Node** & **Description** \\ \hline \(in_{a\in\{1,\ldots,8\}}\) & readings of proximity sensors \(prox_{i\in\{1,\ldots,8\}}\) \\ \(in_{a\in\{9,\ldots,11\}}\) & readings of ground sensors \(gnd_{j\in\{1,\ldots,3\}}\) \\ \(in_{a\in\{12\}}\) & value of the density function \(z^{\prime}(n)\) \\ \(in_{a\in\{13,\ldots,16\}}\) & scalar projections of \(V_{n}\) \\ \hline \(in_{a\in\{17,\ldots,80\}}\) & scalar projections of \(V_{c\in\{R,G,B,C,M,Y\}}\) \\ \(in_{a\in\{41\}}\) & bias input \\ \hline
**Output Node** & **Description** \\ \hline \(out_{b\in\{1,\ldots,A\}}\) & tuples \(v^{\prime}\) to map each velocity in the set \(v_{k\in\{l_{r}\}}\) \\ \(out_{b\in\{5,\ldots,8\}}\) & activation of each color in the set \(\{\varnothing,C,M,Y\}\) \\ \hline
**Connection** & **Description** \\ \hline \(\langlepositive component to compute the velocity. We expect that this mapping facilitates the evolution of standstill behaviors: first, robots can stand still if the elements of each tuple \((v^{\prime})\) are any pair of values of equal magnitude--steady values are not required provided that the output nodes that encode the same tuple vary proportionally; second, the sum of the positive and negative components can cancel out the sensory noise injected in the output nodes that encode a tuple--given a proper tuning of the synaptic weights. If one compares EvoColor with EvoStick, the first has more freedom to tune neural networks that lead to standstill behaviors.
## 5 Summary and Future Study  
A CNN-based supervised learning method for classifying inter-floor noise types and positions is proposed in this paper. An inter-floor noise dataset was used to evaluate the proposed method. The dataset was built based on a report by the Floor Management Center. State-of-the-art CNNs for image classification (AlexNet, VGG16, and ResNet V1 50) were adapted and used for noise classification. The inter-floor noises were converted to log-scaled Mel-spectrograms and used as the input to the CNNs. The weights of each CNN were initialized with weights that were pre-trained on ImageNet. The hyperparameters in each CNN were optimized using random search. The CNN with the optimal hyperparameters was evaluated using 5-fold cross validation. VGG16 shows the best type classification performance with 99.5% accuracy and the best position classification performance with 95.3% accuracy for 9 positions included in the dataset.  
To evaluate the robustness of the VGG16-based inter-floor noise type/position classification, the trained models were tested with the newly gathered dataset. The newly gathered dataset is composed of inter-floor noises generated at the unlearned positions near the learned positions. The VGG16-based model showed robust type classification against position change with 98.5% accuracy. The newly gathered dataset was classified into the learned position near their actual positions.  
In summary, we presented a feasibility study of a convolutional neural network-based classifier for inter-floor noise type and position in a single building. Future study should focus on the generalizability of our results through the evaluation against new inter-floor noise datasets including diverse noise types. For example, HH can be generated with a new hammer. Also, inter-floor noises in other buildings can be gathered to evaluate the generality of the type/position classification. These can be improved in our future study and these improvements can contribute to other single sensor-based approaches for their evaluations.  
Conceptualization, H.Y. and W.S.; methodology, H.C., H.Y., S.L. and W.S.; software, H.C.; validation, H.Y., S.L. and W.S.; writing, H.C., H.Y. and W.S.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1E1A2A01078766) and (No. NRF-2019R1F1A1058794).  
The authors declare no conflict of interest.  
Figure 7: Position classification of the new inter-floor noise dataset into the learned 9 positions (%).
## 4 Simulation Results
### Discussion  
The proposed method allows us to understand what would be the expected from a BER and thus the preferred medium based on the consideration on the environment and the value of an SNR. Based on the simulation results, the following considerations can be summarized:  
* Rural Environment: Simulation results suggest that the RF link demonstrates better performance in rural areas. Since they typically have lower population density and less electromagnetic interference, the use of the RF link can be more feasible and provide reliable communication with better performance. Interestingly, PLC can be a valid option even in low-density rural areas if repeaters can be installed. In this viewpoint, the protocol choice is crucial. The G3 or PRIME protocol has a mechanism to promote simple nodes to relay nodes and automatically build networks. This kind of protocol can be exploited in order to obtain a strong interconnected and reliable PLC network.
* Industrial Environment: Simulation results indicate that NB-PLC can be more suitable for industrial areas, likely due to the advantages mentioned earlier, such as the presence of abundant power lines, signal penetration, interference immunity, and noise rejection.
* Urban Environment: The choice between the RF link and NB-PLC for communication between the SM and DC in an AMI system is more complicated and it depends on  
Figure 14: RF versus NB-PLC performances in an industrial environment, and a measurement point (MP (X)).  
the density of the area. The decision between them in this scenario depends on other factors, such as infrastructure availability, cost considerations, and specific application requirements. The communication between the SM and DC is affected by multipath propagation and external interferences. This limits network performances in terms of reliability and availability. Therefore, additional schemes, such as repeaters or error correcting codes, are needed to improve the link reliability requirements.
## 3 High-Dynamic Atmospheric Turbulence Compensation Algorithm  
In this section, we introduce our Zernike polynomials based method by which we interpret image information acquired from FSM and SHWS to wavefront phase screen so as to evaluate the quality of the incident and emergent laser beam. Secondly, a coupled second-order modeling strategy for FSM and a complete second-order modeling scheme for DM are demonstrated. Both modeling plans map controlling voltages on electrodes to offsets of spots. Subsequently, in view of the calibrated models, we apply an iterative method to solve the models obtaining optimal controlling voltages for electrodes on wavefront correctors. Finally, the workload distribution is illustrated which is the core of the high-dynamic atmospheric turbulence compensation algorithm.
### The Contributions of this Paper  
This paper proposes a robust low-cost SLAM frame work, with the combination of low-cost LiDAR and camera. By combining the laser points cloud data and image feature points data as constraints, a graph optimization method is used to optimize the pose of the robot. At the same time, the BoW based on visual features is used for loop closure detection, and then the grid map built by laser is further optimized. Finally, a 2.5D map presenting both obstacles occupy and vision feature is built, which is ready for fast re-localization.  
The main contribution/advantages of this paper are:  
* Proposes a new low-cost laser-vision slam system. For graph optimization, a new cost-function considering both laser and feature constraints. We also added image feature-based loop-closure to the system to remove accumulative errors.
* Proposes a 2.5D map, it is fast in re-localization and loop-closing, compared with feature map, it is ready for path-planning.  
The remaining parts of this paper are organized as follows: Section 2 introduces the slam framework based on graph optimization; Section 3 introduces the back-end optimization and loop closing method; Section 4 introduces the 2.5D map and fast relocation algorithm; Section 5 is the experiment; and Section 6 is the conclusion part.
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
## 1 Introduction
Additionally, by exploring the relationship between geomagnetic storms and thermosphere atmospheric density, this study can have practical applications in space weather forecasting and satellite operations.