esa-sceva/satcom-chunk-collection huggingface dataset:
# TGA-GS: Thermal Geometrically Accurate Gaussian Splatting
## 2 Related Work
### Three-Dimensional Reconstruction and Novel View Synthesis  
Three-dimensional reconstruction technology, which retrieves the geometric information of a 3D scene from 2D images, holds significant value in the fields of visualization, measurement, and analysis. Traditional methods are mainly divided into two categories: point cloud reconstruction methods based on Structure from Motion (SfM) [76; 77] and Multi-View Stereo (MVS) approaches [78] and volume modeling methods based on implicit representations such as radiance fields or signed distance fields. SfM algorithms like COLMAP recover camera poses and sparse point clouds by matching feature points across multiple views, followed by dense reconstruction using MVS. However, these methods are prone to a decrease in reconstruction quality due to feature matching failures in low-texture scenes or with low-resolution inputs [79], and they rely on rigid scene assumptions [80; 81].  
The advent of Neural Radiance Fields (NeRFs) [1] marks a breakthrough in implicit 3D representation, using Multi-Layer Perceptrons (MLPs) to model scene radiance fields and density fields, enabling the generation of high-quality novel views from sparse RGB images. Subsequent research focus on various directions of improvement. RawNeRF [82] and Aleth-NeRF [83] enhance reconstruction capabilities in low-light environments, W-NeRF [84] handles dynamic lighting, and NeuS [85] achieves high-fidelity surface reconstruction. [PERSON] et al. [86] propose a method for reconstructing power transmission lines (PTLs) based on images, and some works combine LiDAR point clouds [87] or near-infrared images [53] to enhance representation capabilities, while others use depth information to enhance effects [88].  
The emergence of 3D Gaussian Splatting (3 DGS) [2] revolutionizes the explicit 3D reconstruction paradigm. Due to its real-time rendering capabilities and impressive performance, 3 DGS garners increasing attention. Subsequent works further explore rendering effects [89, 90, 91], compact representations [92], and reconstruction capabilities [93, 94, 95]. Additionally, [PERSON] et al. [96] discuss the influence of different camera settings on the quality of 3D reconstruction, and [PERSON] et al. [97] design a camera response module to compensate for inconsistencies in multiple views. However, the current GS reconstruction method is not ideal for 3D reconstruction of RGB or thermal images obtained in low-light environments and cannot fully utilize the complementary information of the two modalities.
## 3 Urban Water System Issues and Potential Smart Solutions
### Drought  
Drought is currently a resiliency challenge in many areas and climate change may increase the magnitude, frequency, and locations of impact [2; 43]. A typical strategy that municipalities employ to deal with short-term drought is to impose blanket, outdoor watering restrictions on residential customers [43]. There are several downsides to this strategy. First, it requires the type of enforcement that involves patrolling streets to look at people's lawns, which is resource intensive, and neighbors reporting each other, which is socially negative. Second, it may result in a loss of a household's landscing. This type of loss would affect residents unequally, putting a heavier financial burden on lower income people. Lastly, it may not be an adequate strategy in times of extreme or long-term drought.  
Long-term drought planning is a complex and is data and modeling intensive. The drought plan for the State of Arizona cites a lack of sufficient data and instruments to predict drought and mitigate its impacts. Two of the mitigation strategies that Arizona has chosen include increasing water storage and conservation [44].  
The smart water grid enables more creative solutions for dealing with drought. For example, water restrictions could be managed better with a smart water grid system. Rather than using a simple lawn watering restriction that requires field enforcement and the potential loss of landscapes, a more flexible approach can be taken. If users are able to monitor their own water use on a disaggregated and real-time basis through the use of smart meters and end-use sensing devices,they can make decisions about how to save water during a drought. At the same time, utilities will be able to monitor on a real-time basis and from a remote location, what end-users are saving water during drought and what end-users are not.  
Alternatively, smart water grids could support temporary drought pricing of water. In the same way that conservation-promoting tiered pricing and smart water meters can help people save money and conserve water on a daily basis through online tools and real-time data, drought pricing can also be supported by smart water grids. Consumers will be able to track their water rates and consumption more easily when drought prices and in use. If water utilities want to change the drought pricing in the middle of a billing period, consumers will easily be able to see this when using their online tools and can adjust their use patterns accordingly.  
On the broader level of long-term drought and regional water planning, water saved from the reduction of water losses could be banked through water storage projects, creating more water availability during times of drought. Additionally, real-time data from smart water grids can feed into water resource planning and modeling, making drought and long-term water planning a more accurate and dynamic process.
## 2 Materials and Methods
### Proposed Methodology
#### 2.3.1 Buildings Identification through DBSCAN Algorithm
In particular, \(k_{inf}\) has been obtained by multiplying the number of PSs in a m\({}^{2}\) for the minimum unity surface. The parameter \(k_{sup}\), instead, has been obtained by multiplying the number of PSs in a m\({}^{2}\) for a mean area of the buildings in the area. Then, for each value of \(k\) included in the interval \([k_{inf},k_{sup}]\), a curve can be drawn, and the point of maximum curvature can be found. Finally, the mean of the ordinates of the retrieved points gives the optimal value for _eps_. Anyway, the value of _eps_ shall be greater than the minimum distance between the buildings of the area (known or estimated). With respect to the _minPoints_, instead, according to [PERSON] et al. [34], the DBSCAN default value is 4 for 2-dimensional  
Figure 3: Flowchart of the building identification method through the DBSCAN algorithm. data. For data with more than 2 dimensions, according to [PERSON] et al. [45], _minPoints_ is computed as: _minPoints_ = 2*dim_, where _dim_ is the dimension of the dataset. At the end of the clustering operations, a plot of the cluster results with building identification is created (red dotted box of the flowchart in Figure 3). As additional check to remove unexpected noise, the users can choose to reject the clusters with less than a minimum area (for example, the minimum unity surface of the building according to local regulations). Each cluster is framed by a rectangle, to delimit the boundary between one cluster and another.
# Simulation of the Fracturing Process of Inclusions Embedded in Rock Matrix under Compression
## 2 Fracturing Behaviors of Single Inclusion Embedded in Rock Matrix
### Effect of Material Properties on the Fracturing Behaviors of Inclusion
#### 2.4.2 Effect of Stiffness on Fracturing Behavior  
Figure 11 shows the fractures development process of inclusion with different \(E_{i}\). The stiffness of matrix (\(E_{r}\) = 5 GPa) and other parameters remain the same, for a relatively soft inclusion, \(E_{i}\) = 5 GPa, the fractures occur firstly inside the inclusion, then the fractures are kept expanding to reach the inclusion interface along the direction of maximum principal stress. For a relatively hard inclusion embedded in a soft matrix, \(E_{i}\) = 50 GPa, most of the flaws are initiated in the interface at an earlier stage. There are several sub-parallel fractures in the inclusion extending from the interface. Finally, too many non-consecutive fractures are formed in the inclusion, because there are too many flaws initiated from the interface.  
Figure 11: _Cont._  
Figure 10: Fracturing behavior of inclusion with different homogeneity index of inclusion.  
Figure 12 shows the different fracturing modes of inclusion with different matrix stiffness \(E_{r}\). In the case with a relatively soft surrounding matrix, \(E_{i}=100\) GPa, \(E_{r}=2\) GPa, the debonding fractures firstly formed at an early stage. Then some fractures initiated from the interface are kept propagating to connect each other to be non-consecutive and/or consecutive fractures. While for the inclusion embedded in a relatively hard matrix, the debonding fractures along the interface are not easier to form. Most of the fractures are initiated from the central vicinity of the inclusion, then are kept extending along the direction of maximum principal stress. The number of final macroscopic fractures is relatively lower.  
Figure 11: The effect of inclusion stiffness on the fracturing behavior inside inclusion. It is assumed the value of homogeneity index, \(m=3.0\) for the inclusion representing relatively heterogeneous materials and \(m=20\) for the matrix representing relatively homogeneous materials. \(E_{i}\) and \(E_{r}\) are the elastic modulus of the inclusion and the matrix, respectively. In the case, \(E_{r}=5\) GPa.  
Figure 12: The effect of matrix stiffness on the fracturing behavior inside inclusion. In the case, \(E_{i}=100\) GPa.
## 4 Result Analysis and Discussion
### Training and Verification of the Proposed LSTM Neural Network
Figure 15 illustrates the training and analysis of the proposed LSTM network using the Sgdm optimizer. Additionally, Table 4 presents the initial learning rate and associated losses, along with the iteration-wise mini-batch accuracy for both training and validation phases. By the conclusion of the training epochs, the model achieved an RMSE accuracy of 0.039 and a loss accuracy of 1.2 \(\times\) 10\({}^{-4}\). This comprehensive approach ensures the model's robustness and accuracy, highlighting the effectiveness of the LSTM network in handling time-series data for fault diagnosis. In this regard, it should be noted that in Figure 15, the dark colors typically correspond to metrics on the training data, while the light colors correspond to metrics on the validation data. Figure 15: (**a,b**): RMSE and loss function of the predicted values along with the proportion of predictions at each training iteration of the LSTM network. Figure 17: RMSE analysis of a noisy signal test on a power transformer during an external fault under CT saturation condition. Figure 18: RMSE analysis of a noisy signal test on a power transformer during an internal fault without CT saturation effect.
# Dynamic RACH Partition for Massive Access of Differentiated M2M Services
## 3 System Model
### System Architecture  
The network architecture of M2M communications in 3 GPP is depicted in Figure 1, in which an MTC user can control various MTC devices via the MTC server. In the M2M network, various MTC devices access the eNB over wireless links. The eNB is responsible for collecting the data from MTC devices and forwards these data to the backhaul network and/or the Internet via a gateway for diverse applications. Notably, the M2M gateway ensures that M2M devices can interconnect with access networks. Clearly, the massive accesses of MTC devices to the eNB over wireless links form the major bottleneck and/or congestion in M2M networks, which, therefore, are the main focus of the work conducted in this paper. The work mainly concentrates on the multiple accesses by a large amount of MTC devices to the eNB. In order to help characterize the system architecture of the proposed scheme, here the motivation scenario in the case of the co-existence of delay-sensitive and delay-tolerant devices is specifically illustrated. In the scenario, delay-sensitive and delay-tolerant devices respectively correspond to one MTC application. The urban London scenario [31] is considered, and the delay-tolerant devices are from the application of smart meters. The delay-sensitive devices from the hospital e-care application are from the hospice beds from one of the largest hospitals in London. Obviously, the number of smart meters is quite huge, while the hospital e-care is very rare. When two types of services simultaneously attempt to access the network, the RAN overload issue appears. Meanwhile, the design of the resource allocation to realize the access control becomes very important, which is the work conducted in this paper.  
Figure 1: M2M communications in LTE-Advanced cellular networks towards IoT.
## 3 Inferring an Agent's Desires
### Reward Function Discrimination
#### 3.1.2 Probabilistic Methods  
Probabilistic methods cast the IRL problem as a (Bayesian) inference problem, aiming to find an estimate for \(R\) that best explains the given demonstrations (interpreted as noisy observations of the expert's policy) [55]. The agent's action choice probabilities \(\Pr(a|s,R_{E})\) are modelled as a Boltzmann distribution (Section 2.3) (or alternatively made deterministic as \(\arg\max_{a\in\mathcal{A}}Q^{*}(s,a;R_{E})\)). This allows us to define the likelihood of a pair \((s,a)\in\mathcal{S}\times\mathcal{A}\) under any given \(R\) as  
\[\Pr((s,a)|R)=\frac{\exp(\alpha Q^{*}(s,a;R))}{\sum_{a\in\mathcal{A}}\exp(\alpha Q ^{*}(s,a;R))}. \tag{26}\]  
Under an assumption of independence between state-action pairs in a given demonstration (based on a stationarity assumption for the agent's policy), the likelihood of the demonstration is  
\[\Pr(\mathcal{D}|R)=\prod_{(s,a_{t})\in\mathcal{D}}\Pr((s_{t},a_{t})|R). \tag{27}\]  
Combining the likelihood from the demonstrations with a given prior over rewards \(P(R)\), we can obtain a posteriori  
\[\Pr(R|\mathcal{D})=\Pr(\mathcal{D}|R)\Pr(R). \tag{28}\]  
Two solution methods are used to find this posterior in the literature: gradient-based methods are used to directly find an (approximate) maximum a posteriori estimate for \(R\), and Markov chain Monte Carlo (MCMC) methods to approximate the entire posterior distribution of \(R\)[55]; more recently, variational inference-based methods have been proposed to this end, e.g., [56; 57; 58].
## 1 Introduction
Other indices, such as the Floating Algae Index (FAI), the Virtual Baseline Floating macroAlgae Height (VB-FAH), and the Difference of Vegetation Index (DVI), were found to be less sensitive to perturbations from sun glint and possibly from aerosols, but they were suitable for high-resolution images [21]. With the accelerating development of deep learning (DL), more and more DL-based methods are being applied to RS images. For example, an Elegant End-to-End Fully Convolutional Network (E3 FCN) was proposed by [PERSON] et al. [22] for green-tide coverage detection. The distribution and coverage area statistics indicate the general situation of _U. prolifera_ growth, which is beneficial for evaluating the severity of green tide events, while the migration of the barycenter plays an important part in prediction models of _U. prolifera_. In addition to the conventional aspects described above, this paper has concentrated on the spatial distribution of the occurrence probability of _U. prolifera_ and the morphological characteristics of its distribution range by analyzing the growth situation in detail during 2016 to 2018. According to probabilities, the grade of severity of each event was classified in the study area, which generated a reliable basis for defining the safe area for offshore operations. Furthermore, two morphological evaluation targets, directionality and regularity, were considered. The exploration of directional laws provides a new method for discovering the factors that cause directional mutations in the distribution of _U. prolifera_, whereas regularity reflects the difficulty of governing such a distribution. By synthesizing the analysis of all the above indicators, the current situation and development trend of _U. prolifera_ can be better characterized, which would be conducive to long-term monitoring, prevention and control, early warning, and the prediction of green tide events.
# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
### Description of the Tests
#### 2.4.2 eBee RTK Survey Accuracy Assessment  
To find out whether or under which conditions GNSS-AT delivers the same accuracy as InSO, three experiments were performed varying the block control configuration. In the first one, referred to as **12 GCP** in the following, only GCP were used, fixing the coordinates of 12 points distributed in the survey area as in a standard InSO (see Figure 1). In the second one (**RTK** for short hereinafter), only the camera positions from the navigation data (without any GCP) were used. In the last one (**RTK+1 GCP**), besides the camera positions, the coordinates of one GCP (GCP No. 13 in the middle of the block, see Figure 1) have also been fixed. This minimal, and practically quite acceptable, block control configuration has also been tested as remarks made in Sections 1.3 and 1.4 suggest that camera self-calibration might be inaccurate, with bias affecting the ground coordinates, unless GCP are used.  
All BBA have been executed assigning a standard deviation of 1 pixel to the tie points image coordinates, as mentioned in Section 2.4.1. As far as GCP are concerned, the empirical precision found from repeated measurements of GCP is in the order of 1 cm. However, a much lower standard deviation of 1 mm has been assigned to the coordinates of the GCPs as it has been found that, in PhotoScan, with 1 cm standard deviations, all GCP coordinates would otherwise show residuals much larger than expected (up to 10 cm). That is probably due to the overwhelming influence of the collinearity equations: since SfM extract huge number of tie points, the weight of the GCP coordinates needs strengthening to be effective. The BBAs for **RTK** and **RTK+1 GCP** have been executed considering, in all three packages, camera position standard deviations between 1-2 cm in XY and 3-5 cm in elevation. This choice has been made based on the available information from the EXIF and the remarks in Section 2.4.1 (see also the discussion in Section 3.1).  
In all of these experiments, since the reference solution is **12 GCP**, the coordinates of the remaining 14 known points have been used as CP in the computation of the RMSE.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 7 Conclusions and Future Work  
In conclusion, our study systematically evaluated six embedding techniques across eight classifiers using a telecommunication company's database. The results demonstrated these embedding techniques effectively generate input vectors for classifiers, significantly influencing model performance. Among the tested combinations, the integration of OpenAI's text embeddings with logistic regression achieved the highest accuracy of 89%, outperforming other methods. These findings suggest that leveraging advanced embedding techniques, particularly OpenAI's, can enhance model performance in classification tasks.  
Companies aim for a churn rate as close to 0% as possible. Predicting customers likely to leave allows them to implement plans and strategies to keep them engaged with the brand or service. We contributed to this field of research by building a robust predictive model that is capable of accurately predicting customer churn. This model serves as a decision-support tool for telecommunication companies, enabling them to effectively focus their marketing efforts on retaining existing customers. Moreover, our proposed model can be generalized to other industries.  
In future research, we will investigate the scalability of this approach across different datasets and domains, including the fashion retail industry. We will plan to evaluate the effectiveness of exploiting OpenAI embedding techniques and deep learning models for customer churn classification tasks. Additionally, we will include emotion extraction into our model to make decisions based on both objective and subjective factors.  
**Author Contributions:** Conceptualization, M.C. and E.H.N.; methodology, M.C. and E.H.N.; software, M.C.; validation, E.H.N.; formal analysis, M.C. and E.H.N.; investigation, E.H.N.; resources, M.C.; data curation, M.C.; writing--original draft preparation, M.C.; writing--review and editing, M.C. and E.H.N.; visualization, M.C.; E.H.N.; supervision, E.H.N.; project administration, E.H.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Publicly available datasets were used in this study. These data can be found in the following URLs: [[https://www.kaggle.com/datasets/underscore/predict-the-churn-risk-rate](https://www.kaggle.com/datasets/underscore/predict-the-churn-risk-rate)]([https://www.kaggle.com/datasets/underscore/predict-the-churn-risk-rate](https://www.kaggle.com/datasets/underscore/predict-the-churn-risk-rate)) (accessed on 5 June 2024).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Fuzzy Logic Based Collaborative Task Offloading  
In this study, we propose a Fuzzy-based collaboration scheme among SBS-MEC servers to improve the efficiency of resources utilization and reduce the task execution latency. Without this collaboration, if there are the small number of service requests from the user devices to connected SBS-MEC servers, these servers are not utilized properly and the resources are wasted. In contrast, the server will be overloaded when handling a large number of service requests and applications. According to Figure 4, the fuzzy edge orchestrator module has complete information resource capacities of the SBS-MEC server including task size, delay sensitivity of task, neighboring SBS-MEC, and local SBS-MEC virtual machine utilization as well as network delay. We assume that each mobile device is represented by a single virtual machine which has the computing capacity of 2 giga instructions per second (GIPS), while each MEC server consists of eight virtual machines with the capacity of 10 GIPS. The three main components of the proposed fuzzy-logic system (FLS) are fuzzification, fuzzy inference engine, and defuzzification.