esa-sceva/satcom-chunk-collection huggingface dataset:
# Tabular Data Models for Predicting Art Auction Results
## Appendix A Data Preprocessing
### Ensuring Data Structure  
The first stage in the data processing pipeline involves structuring of raw data to remove inconsistencies and prepare the dataset for analysis. The specific actions taken during this stage are as follows:  
1. Column Elimination: Non-essential columns are identified and removed based on predefined criteria outlined in columns structure. This step reduces noise and ensures that only relevant variables are retained.
2. Date Formatting and Validation: The AUCTION DATE column is standardized to the datetime format. Rows containing invalid or missing dates are excluded from the dataset to maintain temporal accuracy (invalid 'AUCTION DATE' rows removed: 4)
3. Object Type Filtering: The dataset is filtered to include only entries where the OBJECT is classified as \"Print.\" This restriction narrows the scope of the analysis to graphic, non-unique works (non-print rows removed: 1016).
4. Artist Name Standardization: 1. Accent Removal: Accented characters in artist names are normalized using the unidecode function. 2. Replacements and Normalization: The script applies a series of regular expression-based replacements to unify various forms of artist names. This includesremoving year ranges of their life span, special characters, and standardizing prefixes such as \"after\".
* Name Sorting: Artist names are further normalized by sorting the characters alphabetically, thereby making the order of names and surnames insignificant.
* Artist Filtering: Rows that are empty or contain erroneous classifications (e.g., \"print\" in the artist name) are systematically removed. This step enhances the integrity of the artist data (rows removed due to empty 'ARTIST' or erroneous classifications: 10,454).
* Handling Missing Values:
* Years are extracted from the YEAR or PERIOD columns using regular expressions, and entries with unresolved or invalid year values are removed. This ensures chronological accuracy within the dataset (rows removed due to invalid 'YEAR': 52).
* Technique Standardization: The TECHNIQUE column is cleaned and standardized to match a predefined list of accepted techniques. Special emphasis was to not include reproductions but only original art works. Entries not meeting these criteria are removed (rows removed due to unwanted 'TECHNIQUE': 17,966).
* Poster Exclusion: Descriptions containing terms related to posters are excluded to avoid irrelevant data.
* Dimensional and Price Validation: The TOTAL DIMENSIONS column is standardized to a consistent unit (centimeters), and outliers or erroneous entries (e.g., dimensions equal to zero) are excluded. The PRICE column is converted to a numeric format, with non-numeric values discarded (rows removed due to invalid 'TOTAL DIMENSIONS': 1069).
# Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
###### Abstract  
In today's digital landscape, content delivery networks (CDNs) play a pivotal role in ensuring rapid and seamless access to online content across the globe. By strategically deploying a network of edge servers in close proximity to users, CDNs optimize the delivery of digital content. One key mechanism involves caching frequently requested content at these edge servers, which not only alleviates the load on the source CDN server but also enhances the overall user experience. However, the exponential growth in user demands has led to increased network congestion, subsequently reducing the cache hit ratio within CDNs. To address this reduction, this paper presents an innovative approach for efficient cache replacement in a dynamic caching environment while maximizing the cache hit ratio via a cooperative cache replacement policy based on reinforcement learning. This paper presents an innovative approach to enhance the performance of CDNs through an advanced cache replacement policy based on reinforcement learning. The proposed system model depicts a mesh network of CDNs, with edge servers catering to user requests, and a main source CDN server. The cache replacement problem is initially modeled as a Markov decision process, and it is extended to a multi-agent reinforcement learning problem. We propose a cooperative cache replacement algorithm based on a multi-agent deep-Q network (MADQN), where the edge servers cooperatively learn to efficiently replace the cached content to maximize the cache hit ratio. Experimental results are presented to validate the performance of our proposed approach. Notably, our MADQN policy exhibits superior cache hit ratios and lower average delays compared to traditional caching policies.  
Keywords:content delivery networks; cooperative cache replacement; reinforcement learning +
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# A Differential Measurement System for Surface Topography Based on a Modular Design
###### Abstract  
In this paper, a novel design of a surface topography measurement system is proposed, to address the challenge of accurate measurement in a relatively large area. This system was able to achieve nanometer-scale accuracy in a measurement range of 100 mm \(\times\) 100 mm. The high accuracy in a relatively large area was achieved by implementing two concepts: (1) A static coordinate system was configured to minimize the Abbe errors. (2) A differential measurement configuration was developed by setting up a confocal sensor and a film interferometry module to separate the motion error. In order to accommodate the differential measurement probes from both sides of the central stage and ensure the system rigidity with balanced supports, separate linear guides were introduced in this system. Therefore, the motion Degree of Freedom (DoF) was analyzed in order to address the challenge of an over-constrained mechanism due to multiple kinematic pairs. An optimal configuration and a quick assembly process were proposed accordingly. The experimental results presented in this paper showed that the proposed modular measurement system was able to achieve 10 nm accuracy in measuring the surface roughness and 100 nm accuracy in measuring the step height in the range of 100 mm \(\times\) 100 mm. In summary, the novel concept of this study is the build of a high-accuracy system with conventional mechanical components.  
topography measurement; differential measurement system; modular design; confocal sensor; film interferometry; over-constrained mechanism Article
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
###### Abstract  
Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems.  
active STAR-RIS; SWIPT; semicontinuous relaxation; penalty algorithm; alternate iterative optimization +
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Footnote †: journal:  
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Footnote 1: School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221116, China; [EMAIL_ADDRESS] (C.G.): [EMAIL_ADDRESS] (S.D.): [EMAIL_ADDRESS] (M.W.): 1
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 3 Module Design
### Training Pipeline
#### 3.2.2 Creation of the Grid and Binary Search Tree  
The present work's algorithm proposal is based on a leap rate grid, where the leaping rate between two adjacent grid rectangles is learned for each vessel. In the present work, we consider a leap to be defined as a transition from a specific grid rectangle to any of its adjacent grid rectangles. The grid must be small enough to account for small vessels that  
Figure 5: Number of AIS messages per found trip in the month of June 2018, along the southern coast of Portugal.  
Figure 6: Registered reported locations of vessels in trips lasting under 10 min, coloured per vessel category (June 2018).  
perform near-shore fishing and wide enough for large vessels that perform intercontinental journeys. The inherently different characteristics of vessels, even when analysing vessels from the same category, presents themselves as challenges for inferring the appropriate width and height of the grid. In Figure 7, we can observe an example of a very wide grid, juxtaposed with the map of the southern coast of Portugal.  
In order to adjust the grid rectangle width and length hyperparameters for each vessel, we propose a simple approach based on the average vessel speed. Considering the array of vessels V, where each element \(v_{i}\in V\) has an array of trips \(T_{i}\), where \(t_{j}^{v_{i}}\) represents the trip j of the vessel \(v_{i}\), the average speed is defined as the sum of all recorded speeds for every received AIS message k, for every trip \(t_{j}^{v_{j}}\) over the total number of elements across all trips in the array \(v_{i}\). For each vessel, we iterate its trip vector and calculate the average speed of all registered speed values that are over \(1\) knot. Formally, the sum of all speed values for vessel \(v_{i}\  
All vessel trip vectors are then discretized by using the computed binary search tree. For each AIS message present in the trip data, we apply the binary search function, as shown in Algorithm 1. Depicted in Figure 9 is an example of a discretized vessel trip, using the learned grid for the vessel. A faint line is shown, corresponding to the original trip performed by the vessel. Overlayed are the grid squares that have been traversed on this trip.  
Figure 8: Example of a generated binary search tree.  
Figure 9: Discretization example from a vessel trip, from the inferred binary-search tree grid representation.
# Electromagnetic Field Variation of ELF Near-Region Excited by HED in a Homogeneous Half-Space Model
## 1 Introduction
[PERSON] [17] proposed an approximate quasistatic approximation method for solving the \"sea-rock\" half-space, but the ELF near-field distribution in the deep-sea half-space was obtained using a simplified Sommerfeld integral in the paper, which was not applicable to the \"air-seawater\" half-space environment. In addition, with the rapid development of computer technology and computational EM methods, efficient numerical methods have been developed for solving far-field problems with arbitrary source distribution. However, the calculation of the low-frequency near-field has always been a difficult problem to solve. For example, the method of moment (MOM) [18; 19; 20] has a singularity in the calculation of the near-field, and it is difficult to deal with it. The finite element method (FEM) [21; 22; 23] and finite difference time domain (FDTD) [24; 25; 26] have errors caused by boundary truncation; the former may have pseudo-solutions, and the latter is troubled by the difficulty of accurately fitting the surface boundary. In addition, numerical methods may even have problems such as  
Figure 1: Half-space model of EM wave propagation in two-layer media. \"low-frequency crash\" when the frequency is in the very low ranges. Therefore, in view of the above problems, we still have to use the regression analytic method to calculate the EM field propagation in the near-field region of ELF. For ELF, the wavelength can be compared with the circumference of the Earth. For example, when the working frequency of ELF is 10 Hz, the wavelength is close to three-quarters of the circumference of the Earth. Thus, when the field and source points are in close proximity, it can be regarded as a near-field propagation problem. The propagation environment can be assumed as an idealized model, i.e., a homogeneous half-space, and the interface as a homogeneous plane. In this study, the EM field distribution in the ELF near-field region excited by an HED in homogeneous half-space seawater is studied based on the Sommerfeld integral. The expression is then simplified using an approximate method under the condition that the propagation distance \(k_{0}\rho<<1\) (\(k_{0}\) is the wave number in air and \(\rho\) is the propagation distance) in the near-field region. Then, the analytical approximate expression and spatial distribution of the EM field components in the near-field region are derived via simulation. Finally, we compare the quasistatic approximate calculation results with [PERSON]'s approximation method and [PERSON]'s exact expression approximation method to verify the effectiveness of the proposed method.
# A Review of Sustainable pavement Aggregates
## 6 Analysis and Discussion  
One of the most relevant aspects of the use of substitute aggregates is the replacement percentage in pavement mixes. Reviewed studies have shown that it is possible to replace a certain percentage of traditional aggregates with recycled materials without compromising the mechanical performance of the mixes. However, it is important to consider that the optimal replacement percentage may vary depending on the specific characteristics of each recycled material and the pavement mix in question. For example, in the case of RAP, it has been observed that it is possible to replace up to 30-40% of traditional aggregates in asphalt mixes without significantly compromising the strength and durability of the pavements. On the other hand, recycled tire rubber has proven to be an effective substitute aggregate in asphalt mixes, allowing for the replacement of up to 10-20% of conventional aggregates. In the case of foundry sand, mining waste, PET, glass waste, and recycled hydraulic concrete, the optimal replacement percentages may vary depending on the specific application and the characteristics of the recycled materials. Additionally, the review compiles studies with promising results in terms of compression, bending, and fatigue strength, as well as wear and erosion resistance. However, it is important to note that the mechanical performance of the mixes may vary depending on the amount and type of recycled material used, as well as the application and service conditions they are exposed to.  
It is necessary to understand that each recycled material has unique properties that can influence the mechanical performance of pavement mixtures. RAP may have higher stiffness and fatigue resistance than recycled hydraulic concrete, while recycled tire rubber may have greater flexibility and abrasion resistance. Therefore, the combination of these materials in different percentages can lead to significant variations in the mechanical performance of the mixtures. Additionally, the lack of comparability of results is due to the absence of standardized and unified methodologies for evaluating the performance of pavement mixtures with recycled materials. Each research or study may use different evaluation methods and acceptance criteria, making direct comparisons of results between studies difficult. Furthermore, the lack of compatibility of results refers to the interaction between different recycled materials and their effect on the mechanical properties of the mixtures. For example, the addition of recycled tire rubber can affect the adhesion between aggregates and asphalt binder, which may result in a decrease in the mix's skid resistance. Similarly, the presence of mining residues can alter the particle size distribution in the mixture, which may affect compaction resistance and durability.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.1 Random Forest in Network Measurement  
Regression and classification issues are solved using RF, an integrated learning strategy. A machine learning technique called ensemble learning increases accuracy by combining various models to address the same issue [35; 36]. The fundamental concept is to translate problems, such as delay prediction, into many classification labels by using multiple decision trees to merge numerous weak classifiers to get better results than simple addition. To ensure that the sample features and output outcomes of each tree are distinct, choose a portion of the samples from the training data and randomly choose some of the features for training. Random forest, as an integrated technique, fits several decision tree classifiers concurrently on various dataset subsamples and employs majority voting or averaging for the outcome. As a result, the overfitting issue can be reduced, and prediction accuracy, control, and training speed can all be improved [37]. It applies to scenes with low real-time performance requirements.  
A classifier with a fair amount of accuracy, random forest can be used in a variety of situations. [PERSON] et al. [38] built a simple machine learning technique based on random forest regression that can identify the minimal set of the path and host measurements needed to produce reliable and accurate predictions. The program requires very little training time and can estimate packet retransmission accurately for any quantity of data transmission. However, the issue of abnormal packet loss remains unresolved, and the random forest approach also places restrictions on the flexibility of the input parameters. As a result, changes in the input characteristics have a major impact on the predictor's accuracy. Random forests are ensemble models that are learner-based and have low variance and bias. They can be used for classification and regression. However, when there are too many data dimensions, each value is impurely calculated, which slows down the efficiency of avoiding basic decision trees and prevents continuous output.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Comparison with Various SOTA Losses  
In these experiments, we apply our method to four types of APLs on the UCMD dataset with 20% noise rate (results in Table 5) and compare them with seven state-of-the-art robust losses on the NWPU dataset with 20% noise rate (results in Table 6). These methods are described below. 1. MAE [13]: It is a passive and symmetric loss, as described in Section 1. Although it can maintain gradient stability for different input values, its training is limited due to slow convergence. 2. GCE [14]: It is also a passive and symmetric loss, as described in Section 1. Its robustness is achieved by combining the MAE with the CE, and it is only robust when reduced to the MAE loss. 3. RCE [15]: It can be seen as the reverse version of the CE, as it exchanges the positions of the predicted probability and the one-hot coding in the formula of the cross-entropy loss function. However, it also converges slowly. 4. SCE [15]: It combines the CE loss with the RCE. Its robustness and convergence stability are guaranteed by RCE and CE, respectively. However, it requires the adjustment of two hyperparameters. 5. ACE [16]: It uses the predicted probabilities \(p_{t}\) of the true labels of the samples to adaptively de-termine the two weights in SCE. As the \(p_{t}\) of the sample tends toward zero, it gradually transforms into RCE. 6. AUL [31]: It is a noise robust function that is an asymmetric version of the unhinged loss [27]. 7. AEL [31]: It is an asymmetric noise-robust function, which assumes that the noise distribution in the data satisfies the clean label domination assumption. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Loss**} & \multicolumn{4}{c}{**Noise Rates**} \\ \cline{2-5}  & **Clean (0\%)** & **5.0\%** & **10.0\%** & **20.0\%** & **30\%** \\ \hline NCE + RCE & 98.05 \(\pm\) 0.16 & 97.58 \(\pm\) 0.44 & 96.58 \(\pm\) 0.24 & 95.92 \(\pm\) 0.52 & 95.27 \(\pm\) 0.31 \\ A-NCE + RCE (ours) & 98.57 \(\pm\) 0.40 & 98.70 \(\pm\) 0.49 * & 97.72 \(\pm\) 0.09 * & 96.97 \(\pm\) 0.68 * & 96.68 \(\pm\) 0.42 * \\ \hline \hline \end{tabular}  
* * represents the highest retrieval precision.  
\end{table} 
Table 4: Comparison of image classification accuracy between manual and automatic weighting methods on the UCMD dataset with 20% noise.
## 4 Conclusions  
The combination of MIMO transmission techniques with the OFDM waveform has brought an emerging technology to the wireless telecommunication field. In the last decade, MIMO-OFDM has drawn distinguished advances in terms of hardware and software innovations. Universities and telecommunication leaders have made prominent massive MIMO architectures operational and accessible for research proposals to further encourage new additions to the current literature.  
We presented an extensive review of MIMO-OFDM, tackling several difficulties to regroup all the important research axes. The most important and recent references were included to point out the necessary information for the readers without redundancy. Nonetheless, some recent papers could have been missed as we mainly limited the resources to three databases: ISI Web of Science, IEEE Xplore, and Google Scholar. Hence, some papers were automatically excluded from the search, but the papers indexed on those databases have more impact on and relevance to the addressed topic. The reported state of the art concerned three essential points: MIMO hardware/software technology, MIMO waveforms, and MIMO-OFDM channel estimation and equalization.  
From the above extensive search, some points can be noticed:  
* The part on implementation is more limited to research testbeds that apply traditional channel estimation algorithms.
* The most deployed channel estimation algorithms are complex and have a lower level of mitigation of ICI.
* The equalization algorithms are quite limited in terms of performance.  
In the next section, some challenges and research opportunities are highlighted.