esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in HetNets  
To support capacity scaling and improved coverage, HetNets composed of macro-, micro-, pico-, and femto-cells have become essential in B5G. However, their uncoordinated and multi-tier deployment introduces severe co-tier and cross-tier interference, which 3 GPP addresses with the following strategies:
## 3 Network Developed for Desktop and Server Environments  
A fully convolutional neural network architecture is trained using a synthetic dataset containing images of a wide variation. It was tested on a dataset of real images to ensure that the learned features are robust to different image capturing conditions [14]. Torch computing framework was used to construct and train this architecture for a license plate recognition system. Two different networks were used for detection and localization of Italian plates and characters. Both networks (for plates and characters) are able to simultaneously perform the task of detection and localization using sharedconnections of convolutional layers, as shown in Figures 1 and 2. By training with the new dataset and minor modifications, the same classifiers can be used for multi-style license plate detection [15].  
Instead of fully connected layers, these networks use weights in a convolution pattern together with max-pooling to decrease the spatial resolution and relevance, keeping only the strongest feature responses. By restructuring the fully connected layers into convolutional layers, an image of any size can be classified. The output of this network becomes a matrix where each element is a classification of a corresponding window in the original image.  
Figure 3 shows the flow of a neural network-based Italian ALPR system developed for computationally powerful environments like a desktop workstation or GPU server.  
Figure 1: Italian plate detector and localizer.  
Figure 2: Character detector and localizer.  
This system has an accuracy of 98% on real photos. This highly precise license plate recognition system comprises multiple image processing blocks that perform different operations, including multiscale classification (detection and localization) of a single frame for the plates and then characters. This complete architecture and its components has high computational requirements. An embedded device such as a mobile platform has limited resources in terms of computational power and memory storage; it is very difficult to realize a computationally intensive neural architecture on the mobile platform. By simplifying the flow of the trained architecture and using an optimized framework for the neural networks, architectures like these can be realized on a mobile platform. However, there would be a tradeoff between the accuracy and the execution time for the simplified architecture.
## 2 Background and Related Literature
### Univariate Anomaly Detection  
To model normal/anomaly event patterns, traditional techniques such as wavelet analysis, hypothesis testing, Singular Value Decomposition (SVD), and Autoregressive Integrated Moving Average (ARIMA) often use handmade features [PERSON] et al. [18]. Recently, Netflix published a comprehensive principal component analysis-based scalable anomaly detection system that has been effective in various real-world circumstances. Additionally, Twitter has made a seasonality-based anomaly identification technique available that employs the seasonal hybrid extreme study deviation test (S-H-ESD). Time-series anomaly detection has a solid foundation thanks to recent developments in neural networks. The spectral residual convolutional neural network (SR-CNN) [PERSON] et al. [19] combines the advantages of Spectral Residual (SR) and convolutional neural network to provide state-of-the-art performance on univariate time series anomaly identification. DONUT is an unsupervised anomaly detection approach based on Variational Auto-Encoder (VAE).
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Data Imputation
#### 2.3.4 Data Imputation  
The KNN imputation technique is a method to identify \(k\) samples in the used dataset by its similarity or closeness in the space [27]. The \(k\) samples are used to estimate the value of missing points. Generally, the value is imputed with the mean value of the \(k\) samples that are neighbors in the dataset.  
Once the data were segmented, the KNN imputation technique was applied to extrapolate the missing samples. In the KNN imputation technique, we first found k-closest neighbors to the missing samples, and then these missing samples were imputed based upon the known k-closest neighbors. The data points having the shortest distance based on Euclidean distances were considered as the closest neighbors. The value of every missing sample was interpolated using the mean value of the k-closest neighbors. The missing samples count every time was noticed before applying the KNN imputation. If the missing samples count was less than 10, then the missing samples were filled in the first iteration. However, if the missing samples count was more than 10, then the window was moved 10 steps forward to make another chunk of data and apply the KNN imputation technique to extrapolate the missing samples. As shown in Figure 1, this process continued until all the missing samples were extrapolated.  
In short, each recorded activity of the given dataset was analyzed, and the missing samples count was identified. Based upon the missing sample count, the comparison of the missing sample count with the sample frequency rate was performed. If the missing sample count is greater than the frequency rate of given excerpt, then that particular excerpt was ignored, and the next excerpt was analyzed. However, if the missing sample count is less than or equal to frequency rate, then the NULL values are inserted after a fixed time interval until all the missing sample are filled with the NULL values. Once the NULL values are inserted, then the samples were segmented into a window of 100 samples. Finally, the KNN imputation technique was applied for extrapolating the missing samples values based upon the known samples and this process was repeated as all the unknown values were extrapolated.
# Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention
## 1 Introduction
#### Contribution  
This paper proposes a schedulability test for fixed priorities in hard real-time multicore systems. We assume the temporal model defined in [4], which considers the interference generated by shared hardware resources. We extend the work presented in [9] to consider fixed priorities in hard real-time systems. In particular, we propose a schedulability test based on WCRT. The novelty of the contribution is that both the task model and worst-case response time equation incorporate interference due to shared hardware resources.  
The rest of the paper is organized as follows: Section 2 presents the main contributions in the related research area. In Section 3, we present the system model for constrained-deadline systems that is used throughout this paper. Section 4 reviews the classical schedulability analysis for multicore systems with fixed priorities. Then, we propose an upper bound for the classical worst-case response time analysis, considering contention due to shared hardware resources. We also propose the corresponding schedulability test. The experimental evaluation in Section 5 exposes the compliance of our schedulability test. It also assesses different allocation techniques by comparing how closely the suggested algorithm matches the real value. These results are confirmed by a real case. Finally, Section 6 presents the main conclusions and further lines of research.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 4 Experiment 2: Listening Effort for High-Intelligibility Oesophageal Speech
### Analysis and Results
#### 4.2.3 LE, WER and Cognitive Tasks  
The Flanker effect was calculated as shown in Equation (2). RT_incong and RT_neutral are the average reaction times taken to respond to an incongruent trial (\"\(>><<<<\)\") and a neutral trial (\"\(--<--\)\"), respectively. These reactions times are calculated for correct trials only.  
\[Flanker\ Effect=\frac{log(RT\_incong)-log(RT\_neutral)}{log(RT\_neutral)}. \tag{2}\]  
The mean Flanker effect score was 0.413 \(\pm\) 0.019 and the mean digit span score was 4.125 \(\pm\) 1.258.  
Correlations between Flanker effect and mean LE ratings were not significant ([PERSON]'s rho = \(-\)0.432, \(p\) = 0.096). Significant negative correlation ([PERSON]'s r = \(-\)0.554, \(p\) = 0.049) was found between digit span scores and mean WERs (average of OS and HS).
## 3 Mixed FSO and Optical Network Presented Model  
The block description of the presented mixed FSO and optical network is depicted in Figure 1. The technologies are converged into a single model, which uses FSO and optical network links to integrate the fronthaul. The 3 GPP released 15, 5G frequency standard is used for the presented FSO model, aiming to transmit RF signals. The proposed 5G transceiver generates an F-OFDM signal with 790 MHz. The combiner (3 dB insertion losses) is applied to integrate the 790 MHz and 3.5 GHz signals. The received electrical signals are then joined by using a diplexer with M-QAM. The power range is set for two conditions. For normal conditions, the power level for the RF signal is set to 0 dBm, whereas the 5 dBm power range is used for M-QAM at 25 GHz because of the RF cable, photodetector (PD), and Mach-Zehnder modulator (MZM). The MZM takes the integrated signals and modulates the laser 10 dBm carrier at 1550 nm. Whereafter these signals are transformed over 15 km single mode fiber (SMF), and, at the collimator, the data are gained for injection to the FSO system. To decrease optical losses and provide a safe environment for FSO, the erbium-doped fiber amplifier (EDFA) is applied. The five-fold magnified optical laser is installed at the receiver, purposing to collimate the optical beam and coupled with an optical cord. The outputs of EDFA are associated with optical attenuation for monitoring the power level. The installed PD converts the optical received signal into electrical form, where the signals are further amplified by an electrical amplifier (EA) with a 20 dB gain. The presented 5G transceiver is displayed in Figure 2, which explains that the data from the MAC layer are sent to the physical layer with the help of the L1 and L2 interface. All the received control information is included for modulation coding and transmitting. The polar code has high correction error capabilities with low complexity. Therefore, it is used as forwarding error correction (FEC) here. The transceiver is designed based on F-OFDM waveform transmission; however, it can also transmit other waveforms such as OFDM. The fundamental goal of the transmitter is to manage the fiber nonlinearities and enhance system accuracy.  
Figure 1: Mixed FSO and optical network based framework for addressing the FSO pointing and co-channel interference.
## 3 Preliminaries
### Notation  
This paper defines a user set \(U=\left\{u_{1},\cdots,u_{i}\right\}\), an item set \(V=\left\{v_{1},\cdots,v_{j}\right\}\), a review set \(C=\left\{c_{1,1},\cdots,c_{i,j},\cdots,c_{n,m}\right\}\) and a rating set \(R=\left\{r_{1,1},\cdots,r_{i,j},\cdots,r_{n,m}\right\}\), \(u_{i}\), \(v_{j}\), \(c_{i,j}\) and \(r_{i,j}\) denote the \(i\)-th user, the \(j\)-th item, the \(i\)-th user's review of the \(j\)-th item and the \(i\)-th user's rating of the \(j\)-th item. Let \(\hat{r}_{i,j}\) be the predicted rating of the \(i\)-th user for the \(j\)-th item. \(n\) represents the number of users, and \(m\) represents the number of items. \(d\) represents the embedding size. In addition, we defined some key symbols used in the intermediate process of the model, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definitions** \\ \hline \(r_{i,j}\) & The \(i\)-th user’s rating of the \(j\)-th item \\ \(c_{i,j}\) & The \(i\)-th user’s review of the \(j\)-th item \\ \(\hat{r}_{i,j}\) & The predicted rating of the \(i\)-th user for the \(j\)-th item \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and definitions.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 3 Our Approach
### Classifier  
#### 3.1.1 Construction of Knowledge Graph  
In our study, the nodes of the knowledge graph are disease-related topics. Edges are semantic associations between concepts. Our knowledge graph consists of two parts. The first part is defined by the CheXpert [32] tagger, a rule-based system that extracts and classifies medical reports into 14 common diseases. The label of each disease has four states, namely positive, negative, uncertain, or unmentioned. The MIMIC-CXR dataset has been annotated by the CheXpert tagger. The second part consists of supplementary concepts and their interrelationships, mined from radiology reports in a data-driven manner. Specifically, we count nouns in radiology reports using the SentencePiece tool, which is an unsupervised text tagger and de-tagger, and then we select nouns with top-k occurrences as additional disease labels, if they are not included in the fourteen disease labels defined by the CheXpert tagger. We establish the knowledge graph according to the co-occurrence of labels in radiology reports (Figure 2), and then construct the incidence matrix and binarize the matrix to form the adjacency matrix of the knowledge graph [33]. Specifically, we build a \(n\times n\) matrix, where each row or column represents a label, and then calculate the values in the matrix based on the number of co-occurrences of the labels in the radiology report. If the number of co-occurrences between two labels is greater than the average number of co-occurrences, the two labels are considered to be associated, and the corresponding value of the matrix is assigned a value of 1. On the contrary, if the number of co-occurrences between two labels is less than the average number of co-occurrences, the two labels are  
Figure 1: Our model mainly includes three parts. The classifier is used to read chest X-ray images and clinical history to extract visual features and text features, and then the image–text hybrid features and the adjacency matrix of the knowledge graph are passed into the graph convolutional network to obtain the features containing prior knowledge. The obtained features are combined with topic features and state features and fed into the generator based on the visual self-attention model to generate the radiology report. The generated report is then passed into the interpreter to adjust the generated radiology report.  
regarded as not associated, and the corresponding value of the matrix is assigned a value of 0. This matrix is then regarded as the adjacency matrix of the knowledge graph.
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Practical Challenges  
It is evident that the IRS technology leads to enhancements of the wireless system performance and that is why this topic has received special attention in the literature.  
Figure 5: IRS applications.  
However, there are still some challenges for the practical implementation of IRS-assisted systems which also require attention.
# An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
## 4 Pim Parameter Back Analysis Based on Line B Data.  
### Ga Method of PIM Parameter Back Analysis  
It is necessary to obtain reliable mining subsidence parameters to predict the shaft pillar mining subsidence. A GA method is used for PIM parameter back analysis [24; 25]. This method uses a GA and the minimum mean square error criterion based on the subsidence inversion to obtain the PIM parameters. The main process flow is shown in Figure 7 [24].  
Figure 6: Subsidence procedure of B10  
Figure 7: PIM parameter inversion flowchart based on GA.  
The GA method with its crossover, mutation mechanism, and global optimization superiority can invert the PIM parameters and obtain reliable results.
## 5 Case Study and Results: An Assessment of Earthquake Hazards in Portugal
### Analysis Using the Entropy Weighting Algorithm  
The weighting method centres around the entropy weighting approach, realized through a specially designed Python 3.11.7 algorithm [102; 103]. This algorithm comprises several essential steps:  
* Normalization: The initial phase involves normalizing the dataset values to ensure uniformity, scaling them to a range between 0 and 1. The normalization technique differs based on whether the data are positive (suggesting a beneficial outcome) or negative (indicating a detrimental outcome). \[\text{For positive data : }y_{ij}=\frac{x_{ij}-min\ x_{j}}{max\ x_{j}-min\ x_{j}}\] (1) For negative data (inversion of the scaling) : \[y_{ij}=\frac{x_{ij}-min\ x_{j}}{max\ x_{j}-min\ x_{j}}\] (2)  
Here, \(x_{ij}\) represents the value for the \(i\)th munici ality for the \(j\)th variable, and \(min\ x_{j}\) and \(max\ x_{j}\) denote the minimum and maximum values for the \(j\)th variable.  
* Calculation of \(P_{ij}\): After normalization, the \(P_{ij}\) matrix comes into the fold. This matrix represents the proportionate weight of a variable relative to the total weights in its row, which is very important for gauging the importance of each variable. \[P_{ij}=\frac{y_{ij}}{\sum_{i=1}^{n}\ y_{ij}(n=1,2\ldots)}\] (3)
* Entropy (\(E_{i}\)) Calculation: We calculate the entropy value for each variable to measure its information content. Entropy, a marker of data randomness, is key in this analysis. \[E_{j}=-\frac{I}{ln\ n_{i}}\sum_{i=1}^{n}\ P_{ij}ln\ P_{ij}\] (4) and \[d_{j}=1-e_{j}\] (5)
* Weight (\(W_{i}\)) Calculation: Next, we determine the weight (\(W_{i}\)) for each variable by normalizing these entropy values, indicating each variable's significance in the broader context. \[w_{i}=d_{ij}/\sum_{j=1}^{m}\ d_{ij}(m=1,2,\ldots)\] (6)
* Final Score of entropy using of \(W_{i}\): The weights are further normalized for comparability across all variables. \[U=\sum_{i=1}^{m}\ y_{ij}w_{j}\] (7)  
The Python script presented below, intricately designed for this analysis, enables the execution of these steps with accuracy and consistency, ensuring the reliability of the entropy weighting process.  
import pandas as pd import numpy as np def calculate_urban_resilience(input_csv):  \"\"\"  Calculate urban resilience from the provided CSV file.  Args:  input_csv (str): Path to the input CSV file.  Returns:  None: Saves the results to output CSV files and prints the file names.  \"\"\"  # Load data from CSV  df = pd.read_csv(input_csv + \".csv\", sep=',', header=0)  # Normalize data based on positive or negative values  df_norm = pd.DataFrame()  for col in df.columns:  if col!= 'HASC_2': # Exclude non-numeric column  normalization_type = df[col][0]  col_data = pd.to_numeric(df[col][1:], errors='coerce').dropna()  # Normalize based on the type specified in the first row  if normalization_type == 'pos':  df_norm[col] = (col_data - col_data.min()) / (col_data.max() -  col_data.min())  elif normalization_type == 'neg':  df_norm[col] = (col_data.max() - col_data) / (col_data.max() -  col_data.min())
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 3 The Proposed Multi-Population Parallel Wolf Pack Algorithm (MPPWPA)
### Pretreatment of the Population  
In order to enhance the exploration of the search space, the population is pretreated before population division. The basis of the pretreatment is to double the population size by adding variants of some individuals in the original population and to generate new individuals randomly. Individuals with the same solution are called redundant individuals. If an individual cannot obtain a better solution after multiple iterations and it does not contribute to the optimization, we define this individual as aging. In the optimization process, too many redundant individuals aging will lead to premature convergence. During the pretreatment, the redundant individuals of the population are removed periodically to prevent premature convergence. The original population is expressed as \(Pop=\{wolf_{i}|i=1,2,\cdots,N\}\), where \(N\) is the size of population. The steps of pretreatment are as follows:  
Step 1: Sort the \(Pop\) in ascending order;  
Step 2: The first \(\tau\cdot N\) individuals from the \(Pop\) form a mutant population expressed as \(Pop_{m}=\{wolf_{i}|i=1,2,\cdots,N_{m}\}\), where \(N_{m}=\tau\cdot N\) is the size of mutant population, and \(\tau\in[0,1]\) is the mutation ratio. Individuals in \(Pop_{m}\) undergo minor variation, which is similar to the walking behavior of WPA.  
Step 3: Build a temporary population \(Pop_{t}=Pop+Pop_{m}\), whose population size is \(N_{t}=N+N_{m}\). If the time interval (defined as \(\Delta I\)) for redundancy removal is met, remove the redundant individuals of \(Pop_{t}\) and the population size decreases to \(N_{t}^{\prime}\);  
Step 4: Randomly generate a new population \(Pop_{r}\) with the size of \((2\cdot N-N_{t})\) or \((2\cdot N-N_{t}^{\prime})\);  
Step 5 Coalesce all populations \(Pop_{total}=Pop+Pop_{m}+Pop_{r}\). Sort the \(Pop_{total}\) in ascending order and the first \(N\) individuals form the new \(Pop\) that replaces the original \(Pop\)
## 4 Experimental Setup
### Evaluation Metrics and Hyperparameters  
To evaluate the performance of each method, we employed two commonly used metrics AUC (area under ROC curve) [40] and ACC (accuracy). AUC is a widely used metric for evaluating classification problems. Reference [25] validated AUC as a good measurement in CTR estimation. Formally, the AUC of a classifier C is the probability that C ranks a randomly drawn positive sample \(x^{+}\) higher than a randomly drawn negative sample \(x^{-}\):  
\[\text{AUC}(C)=P\big{[}C(x^{+})>C(x^{-})\big{]}. \tag{23}\]  
The second metric, ACC calculates the fraction of correctly classified samples:  
\[\text{ACC}=\frac{\text{\#\;True\;Positives}+\text{\#\;True\;Negatives}}{\text {\#\;Samples}}. \tag{24}\]  
For hyperparameters, we experimented with different ones to find the best configurations of our method for each dataset. The configurations of our model are summarized in Table 5.  
\begin{table}
\begin{tabular}{l l l c} \hline \hline
**Attribute Type (\# Fields)** & **Field Name** & **Field Type** & **\# Categories** \\ \hline \multirow{8}{*}{User Attributes (8)} & cms\_segid & Categorical & 95 \\  & cms\_group\_id & Categorical & 13 \\  & final\_gender\_code & Categorical & 2 \\  & age\_level & Categorical & 7 \\  & pvalue\_level & Categorical & 3 \\  & shopping\_level & Categorical & 3 \\  & occupation & Categorical & 2 \\  & new\_user\_class\_level & Categorical & 4 \\ \hline \multirow{4}{*}{Item Attributes (5)} & cate\_id & Categorical & 5273 \\  & campaign\_id & Categorical & 233,398 \\  & customer & Categorical & 155,979 \\ \cline{1-1}  & brand & Categorical & 66,215 \\ \cline{1-1}  & price & Numerical & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attribute statistics for the Taobao dataset.