esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 2 The Channel Model
### Directional Derivative with Respect to the Direction of Motion of the Moving Terminal  
On the other hand, the directional derivative describes how the value of a multivariable function changes as movement occurs in a specific direction. Let \(P_{Rx}=f(\theta,R)\) be a function of two variables, \(\theta\) and \(R\), with existing partial derivatives \(f_{\theta}\) and \(f_{R}\). Then, the directional derivative of \(f\) in the unit direction \(\mathbf{u}=\cos\gamma\,\mathbf{i}+\sin\gamma\,\mathbf{j}\) is given by Equation (18) [12]:  
\[D_{\mathbf{u}}f(\theta,R)=f_{\theta}(\theta,R)\cos\gamma+f_{R}(\theta,R)\sin \gamma. \tag{18}\]
## 3 Architecture of the Satellite-Enabled IoRT Network
### Data Management System  
The data management system [44; 45], as presented in Figure 4, was implemented to receive, manage, and publish the monitoring data obtained on the TP via the satellite-enabled IoRT network. In this research, the National Tibetan Plateau/Third Pole Environment Data Center (TPDC) functioned as the data management system of the proposed satellite-based IoRT network. The TPDC is one of the first 20 national data centers approved by the Ministry of Science and Technology and the Ministry of Finance of the People's Republic of China in 2019. The TPDC is the only data center in China that stores comprehensive scientific data pertaining to the TP and surrounding regions. As of May 2021, the TPDC  
Figure 3: Interaction of diagram of the satellite-enabled IoRT system.  
Figure 2: Prototype of the ground IoT terminal device for the Xingyun satellite IoT. The key components of the terminal device include a LEOBIT satellite transceiver, transmitting and receiving antennas, and RR1016 datalogger.  
hosts more than 3500 datasets. A cloud service platform was developed at the TPDC to facilitate the extensive integration of data, methods, models, and services with the goal of enabling big data analysis for TP research. The TPDC was certified as a recommended repository by Scientific Data and Springer Nature in July 2020, making it the first certified data repository in China.
# Par3 DNet: Using 3 DCNNs for Object Recognition on Tridimensional Partial Views
## 4 Experimentation
### Results on Noisy ModelNet10 Dataset  
The point clouds returned by the method explained in Section 3.2, makes the tridimensional representations too perfect. This is understandable as the models are synthetic. Nonetheless, the data  
Figure 7: Receiver operating characteristic (ROC) curves and area under the curve for each category of the ModelNet10 dataset.  
provided by actual sensors yield some noise. To benchmark the accuracy of our proposal in presence of noise, we artificially disturbed the point clouds at different levels. To do so, we artificially added Gaussian noise. The mean is the position of each point and the variance was modified for each experiment. Resultant samples for different noise levels can be seen in Figure 8. It is worth noting that the noise levels are arbitrary values as the point clouds come from synthetic models and do not have an actual metric scale. Nonetheless, the samples were resized so they all share the same size and are affected in the same way for each noise level.  
The results are as shown in Table 3. The accuracy in this experiment decreases as the noise level is augmented. This is expected as if the noise is too high, the geometric features of the objects are lost and the object becomes unrecognizable. Nonetheless, our method is able to deal with some level of noise until 0.30, where the accuracy drastically decreased. The noise tolerance is provided by the binary voxelized representation. As long as the noisy points are near the optimal position, it will be in the same voxel or in the adjacent ones. If the noise is too high, the points would be very far from the optimal location. It is important to remark that the model was not retrained with noisy samples, so the noise tolerance is provided by our method and not by the data.  
Table 3 shows the performance achieved by PointNet too. In this case, PointNet suffers an accuracy drop of around a 15% just by adding a slight noise level of 0.01 (compared with the performance provided by the same architecture in presence of no noise at all). The accuracy is more or less kept along the range of tested noise levels. It is worth noting that, as the authors of PointNet suggest, a live data augmentation method is carried out by randomly jittering the points. Thus, in this case, the eventual robustness to noise is provided by the data and not by the method itself.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches
#### 2.1.8 Neural Network-Based Formalisms  
Neural network-based formalisms, and other similar deep connectionist formalisms, employ interconnected layers of artificial neurons to learn patterns and relationships from data. They excel at handling complex, high-dimensional data and are capable of automatic feature extraction, making them suitable for tasks such as classification, regression, and pattern recognition [20, 21]. These formalisms correspond mainly to approaches that are realized by artificial neural networks (ANN) approaches, including Deep Neural Networks (DNN), Deep Q-learning Networks (DQN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Deep Reinforcement Learning (DRL) [22].
## 5 Literature Review by Domain Area
### Other Applications  
There are more applications found in smaller sectors, or at least, not as developed as those previously mentioned in this section. This doesn't mean these applications are less developed than others found in more prolific sectors, it is important to understand that one thing is the sector development and another different one the level of application development due to the used technology. A very clear example is Pokemon Go [22], popularity was not only thanks to the originality of the idea at that time but also to how well implemented the technology in order to simulate the capture of Pokemons. Following are sectors/areas in development with important AR technologies:  
* **Video Games:** There are more games that implement AR into the gameplay aside from Pokemon Go [22], like \"AR Quake\", which is the first game to implement AR [93]. AR games can also be educational. For instance, learning history by scanning a historical marker with your phone to see events unfold on screen [94].
* **Retail and Marketing:** Nowadays, a lot of retail stores use AR technology not just to inform customers about products but also to enhance customer experience, for example, customers can scan products with their smartphones to get instant access to reviews, tutorials, and other content. Also, stores offer \"virtual fitting rooms\" where customers can see how clothes, glasses, or makeup would look on them without physically trying them on [12].
* **Real State:** Instead of physically visiting a property, you can take a virtual tour where AR overlays can provide information about the property, like dimensions, furniture arrangement, etc. Another interesting thing is to see how a property will look after specific changes, such as a different paint color or added furniture, via AR. Real estate developers can use AR to visualize how a construction project will look upon completion [13].
* **Sports:** Athletes can use AR to analyze and improve their performance by overlaying data like speed, trajectory, and form [95]. In stadiums or at home, fans can use AR through their phones or AR glasses to get real-time statistics, player profiles, and other interactive content. Also, sports brands can use AR to offer virtual try-ons of equipment or apparel [96].
###### Abstract  
In order to detect the abrasion of rails, a new point cloud alignment method combining 4-points congruent sets (4 PCS) coarse alignment based on internal shape signature (ISS) and K-dimensional iterative closest points (KD-ICP) fine alignment is proposed, and for the first time, the combined algorithm is applied to the detection of rail wear. Due to the large amount of 3D rail point cloud data collected by the 3D line laser sensor, the original data are first downsampled by voxel filtering. Then, ISS feature points are extracted from the processed point cloud data for 4 PCS coarse alignment, and the feature points are quantitatively analyzed, which in turn provides good alignment conditions for fine alignment. Then, the K-dimensional tree structure is used for the near-neighbor search to improve the alignment efficiency of the ICP algorithm. Finally, the total rail wear is calculated by combining the fine alignment results with the wear calculation formula. The experimental results show that when the number of ISS feature points extracted is 4496, the 4 PCS coarse alignment algorithm based on ISS feature points is higher than the original 4 PCS algorithm as well as the other algorithms in terms of alignment accuracy; the ICP fine alignment algorithm based on the kd-tree is less than the original ICP algorithm as well as the other algorithms in terms of the time consumed. Further, the proposed new ISS-4 PCS + KD-ICP two-stage point cloud alignment method is superior to the original 4 PCS + ICP algorithm both in terms of alignment accuracy and runtime. The combined algorithm is applied to the detection of rail wear for the first time, which provides a reference for the non-contact rail wear detection method. The high accuracy and low time consumption of the proposed algorithm lays a good foundation for the calculation of rail wear in the next step.  
ISS feature points; 4 PCS coarse alignment; KD-ICP fine alignment; point cloud alignment; 3D line laser sensor; rail wear
# Dynamic Stall Characteristics of the Bionic Airfoil with Different Waviness Ratios
## 4 Results and Analysis
### Dynamic stall control mechanism of the wavy leading Edge
When R = 0.6, the effect of waviness on the \(C_{p}\) distribution became evident and it propagated over the whole airfoil surface, leading to the formation of a \(C_{p}\) wave profile. However, the average values of high and low \(C_{p}\) in the crest and trough regions were similar to the baseline airfoil. Therefore, the \(C_{\rm I}\) and \(C_{d}\) values were almost the same as those of the baseline airfoil. When R increased to 0.8 and 1.0, the peak pressure coefficient on the upper surface of the bionic airfoil increased and consequently the lift coefficients for the R = 0.8 and 1.0 airfoils were different from those of the baseline airfoil. In particular, the drag coefficients of the R = 0.8 and 1.0 airfoils decrease significantly at this angle of attack. However, when the angle of attack increased to \(18^{\circ}\), the waviness completely changed the pressure distribution on the airfoil surface. The pressure coefficient on the baseline airfoil suction surface was symmetrically distributed but that on the bionic airfoil surface was irregularly distributed. Consistent with the conclusions obtained in Section 4, the peak pressure coefficient of the bionic airfoil appeared at the trough. Among all the bionic airfoils, the peak value of the pressure coefficient at the trough was the lowest (\(-\)6.5) for the R = 0.4 airfoil. Thus, the \(C_{\rm I}\) of the bionic airfoil with R = 0.4 was the largest due to the greater pressure difference at the leading edge, as shown in Figure 12.  
After the airfoil reached the maximum angle of attack, it began to move downward. It can be seen from Figure 16 that during pitching from \(20^{\circ}\) to \(17^{\circ}\), the pressure coefficient of the baseline airfoil changed dramatically, resulting in sharp oscillations of the lift coefficient of the airfoil. Compared with the baseline airfoil, the pressure coefficient on the suction surface of all the bionic airfoils changed smoothly during pitching from \(20^{\circ}\) to \(17^{\circ}\) and thus the lift coefficient of the bionic airfoils changed smoothly at the maximum angle of attack. In summary, when R = 0.2 and 0.4, the control effect of waviness on the dynamic stall is not apparent, especially during upward pitching from \(0^{\circ}\) to \(18^{\circ}\), because the number of functional factors is low. For R = 0.6, with a greater number of functional factors, the inhibitory effect of waviness on the dynamic stall was moderately enhanced. When R = 0.8 and 1.0, the bionic wavy leading edge significantly improved the dynamic stall characteristics. The wavy leading edge restrains the occurrence of dynamic stall, reduces the dynamic aerodynamic hysteresis of the airfoil, and improves the stability of the dynamic aerodynamic force.
## 1 Introduction  
It is well known that there is an urgent need to reduce the energy consumption in paving activities and utilize sustainable methods that benefit the environment, users and industry. In most cases, recycling byproduct materials saves money and reduces the need for virgin materials. However, the use of alternative materials is effective provided that the pavement's performance properties are not compromised [1; 2; 3; 4; 5]. Asphalt mixes cover approximately 90% of the road pavements worldwide. The long-term viability of these roadways is critical, and the recycling of materials has been proven to be a cost-effective and environmentally responsible solution. Materials such as reclaimed asphalt pavement (RAP), recycled concrete aggregates, waste plastic, steel slag, discarded tire crumb rubber, fly and bottom ash and recycled asphalt shingles have all been used to prepare asphalt mixtures. Indeed, the utilization of waste products in the production of hot-mix asphalt (HMA) can lead to (1) a decrease in the virgin materials that are used; (2) a reduction in emissions, energy consumption and by-product materials that are disposed of in landfills; (3) a reduction in public anxiety regarding emissions; and (4) increased paving construction competitiveness [6; 7; 8; 9].  
In this regard, RAS addition to HMA is gaining increasing interest. As an example, nearly 11 million tons of asphalt shingles are produced yearly in the United States [10; 11; 12]. This recycled asphalt shingle waste is produced from two primary sources, i.e., post-consumer and post-industrial roofing membranes. Over ten million tons of recycled asphalt shingles were obtained from post-consumer shingles (tear-off scrap shingles--TOSS). The remaining 1 million tons of waste consist of shingles produced during the installation of roofing membranes, commonly known as manufactured waste scrap shingles (MWSS) [13; 14; 15; 16]. Hence, TOSS have been mostly used to improve the performance properties of modified asphalt mixtures due to the large quantity of such scrap waste available, although MWSS-modified asphalt mixtures have also been investigated.  
In this regard, this paper provides a critical overview of the existing literature to synthesize the diverse information provided in several research studies addressing different materials and methodologies.
## 4 Experiments and Analysis
### Experimental Results and Analysis  
This section describes the experimental results of the proposed meta-learning-based model selection framework, focusing on its effectiveness in selecting appropriate forecasting models for LPG usage in constrained computing environments. The experiments aim to assess whether the framework can provide accurate forecasting results while minimizing the overall service time, including both model selection and inference.  
The experiments were conducted with five different levels of CPU availability (20%, 40%, 60%, 80%, and 100%) and with varying numbers of concurrent model requests (1, 10, 20, 40, 60, 80, and 100). The results are analyzed in terms of forecasting accuracy, model selection time, inference time, total service time, and performance gap compared to an exhaustive search.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 5 Framework Design for 5G Mm-Wave Backhaul
### MAC Layer Procedure  
Currently, there is no specific industrial standard for 5G mm-wave small cells. However, existing mm-wave standards, e.g., IEEE 802.11 ad, could be considered as useful references, in order to define  
Figure 3: System framework for 5G mm-wave backhauling.  
MAC layer operations of backhaul routing and scheduling. In this paper, we borrow the idea of the cluster mechanism in IEEE 802.11 ad, where several nearby small cells form a cluster. One SBS acts as the synchronization SBS (S-SBS), which provides synchronization to other SBSs, and S-SBS is able to exchange information by transmitting and receiving Beacon frames and Announce frames to and from other SBSs. After beamforming training between SBSs in a cluster, the transmitter SBS sends directional channel quality measurement request to the receiver SBS and then receives the measured signal to noise ratio (SNR) or signal to interference-plus-noise ratio (SINR) value via the directional channel quality measurement report for each backhaul link. Thus, the achievable rate for every backhaul link could be estimated and stored in a channel capacity matrix. Since the positions of SBSs are almost stationary, it is unnecessary to update the capacity matrix frequently unless a big drop occurs in system performance.  
In the mm-wave backhaul system, the network time is partitioned into consecutive non-overlapping time periods, termed frames. Each frame is divided into two parts: frame header and data transmission interval. In the frame header, routing and scheduling schemes are performed for the backhaul required flows accumulated at the S-SBS in the previous frame. The S-SBS sends the result of path selection and scheduling to all other SBSs, and backhaul flows are transmitted according to this result in the data transmission interval.  
In addition, [PERSON] _et al._ [21] has proposed a software-defined cross-layer design for the mm-wave network. A logically-centralized controller, which usually resides on the gateway, is introduced to make rules and control the behaviours of SBSs globally over the system from the network layer to the physical layer. Optimized routing path selection and spatial reuse scheduling for backhaul links could be achieved through the software-defined and cross-layer controlling if the proposed architecture becomes applicable in future 5G mm-wave communications.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results
### Effects of Human Activities and Climate Factors on WUE  
There is great spatial heterogeneity in the impact of climate factors and human activities on the spatial distribution of WUE across the Three-North region of China, as shown in Figure 10. For the Three-North region, climate factors mainly have almost no effect (54.97%) or a slight promotion (24.31%) on the WUE of this region. Less than 20% of the total area showed inhibitory effects of climate factors. For human activities, the result was obviously different from that of climate factors. First, the area with almost no effect was reduced to 20.18%, while the areas with slight promotion, moderate promotion, and significant promotion increased to 24.39%, 12.53%, and 7.34%, respectively, suggesting that, in this region, human activities have a great impact on the vegetation conditions, similar to the results obtained from [PERSON] et al. (2019). For example, the significant promotion region of human activities was mainly distributed around Shaanxi and Shanxi in the southeastern parts of the Three-North region. The significant promotion region was consistent with those regions with large scales of afforestation program, as there are plenty of precipitation and irrigation conditions in these regions, suggesting that human activities have greatly promoted vegetation conditions here.  
Figure 9: Annual WUE values of different land cover types in the Three-North region of China.
# Contextual Explanations for Decision Support in Predictive Maintenance
## 4 Experiments
### Experimental Settings  
In this study, it was assumed that, due to the insufficient number of failure examples, the identification of the machine's fault condition would be performed using an outlier identification algorithm. Following the approach validated in [11], the outlier identification method based on the HDBSCAN algorithm [37] and implemented in the hdbscan ([[https://hdbscan.readthedocs.io/en/latest](https://hdbscan.readthedocs.io/en/latest)]([https://hdbscan.readthedocs.io/en/latest](https://hdbscan.readthedocs.io/en/latest))) (accessed on 1 August 2023) Python library was chosen. HDBSCAN is a hierarchical density-based clustering algorithm that can be used to identify anomalies [38] by determining how much each data instance stands out. The initial 30 days of measurements were used as the initialisation period for the method. During this period the monitored machine was assumed to operate under normal conditions. After this period, the model was used to identify measurements that were anomalies, potentially indicating an impending failure. The model was used statically, with no adaptation of model parameters, until the failure occurred. After the failure, the model was generated again on data collected up to a week before the failure. The only parameter set for the HDBSCAN algorithm was min_cluster_size. The value of this parameter was assumed to be 0.01, as the share of outliers in the monitored data was assumed to be 1%.  
For the purpose of generating explanations, the SHAP method [21] was selected. This well-established and frequently used method aligns with the assumptions set forth in this study. It generates local explanations in the form of an attribute ranking that illustrates attribute importance in the model's decision-making. In this way, a user who needs to decide whether a reported alarm is indicating an impending failure can refer to the sensors (and their locations) represented by individual attributes. The generation of model decision explanations was performed in the shap ([[https://shap.readthedocs.io/en/latest/index.html](https://shap.readthedocs.io/en/latest/index.html)]([https://shap.readthedocs.io/en/latest/index.html](https://shap.readthedocs.io/en/latest/index.html))) (accessed on 1 August 2023) Python library.
## 2 Materials and Methods
### Pressure Drop Characterization Tests  
The FWMU was originally manufactured to test different manufacturing techniques, and then it was repurposed for the LOFA experimental campaign [4]. To evaluate the manufacturing quality, and to get an understanding of the flow behavior through each channel, the FWMU was subjected to several pressure drop characterization tests [10]. The tests were carried out injecting air at different mass flow rates into each one of the ten channels of the FWMU. The air at the inlet was kept at constant temperature (20 \({}^{\circ}\)C) and at nearly constant pressure (\(\sim\)6 bar), whilst the mass flow rate was decreased, step by step. Different mass flow rate profiles were employed for each channel and for each entrance direction (horizontal or vertical). The boundary conditions adopted are aimed at reproducing the Reynolds number foreseen in the LOFA experimental campaign.  
These tests resulted in a large amount of experimental data that showed an overall good flow behavior, except for Channels 1 and 2. These two channels resulted affected by high pressure losses due to an imperfect construction. This behavior made them unsuitable for the LOFA tests and pushed for a peculiar experimental setup (see next section).  
Figure 2: Arrangement of the pipes feeding the FWMU.