esa-sceva/satcom-chunk-collection huggingface dataset:
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 4 System Design
### NMDS-Based Cooperative Algorithm  
If we let the characteristic vector of the \(m\)th tag be \(\xi^{(m)}=[d_{0}^{(m)},\cdots,d_{i}^{(m)},\theta_{0}^{(m)},\cdots,\theta_{i}^{ (m)}]\), and the characteristic vector of the \(n\)th tag be \(\xi^{(n)}=[d_{0}^{(n)},\cdots,d_{i}^{(n)},\theta_{0}^{(n)},\cdots,\theta_{i}^{ (n)}]\), then the dissimilarity between the \(m\)th tag and the \(n\)th tag can be expressed as  
\[\mathbf{E}_{mn}=\left\|\xi^{(m)}-\xi^{(n)}\right\|_{2} \tag{18}\]  
Assume that the dissimilarity matrix between the characteristic vectors of different tags is positively related to the actual distance from the \(m\)th tag to the \(n\)th tag, i.e., the closer the two tags are, the smaller the dissimilarity between them. Assuming that the dissimilarity \(\mathbf{E}_{mn}\) is the Euclidean distance between the \(m\)th tag and the \(n\)th tag characteristic vector. The target tags, i.e., the tags to be located, have unknown location coordinates.  
Figure 5: Linear model of DOA parameters.  
The Euclidean distance between the tags is used as a matrix element to construct the dissimilarity matrix, which is used as the input of the NMDS algorithm.  
\[\mathbf{P}=\begin{bmatrix}0&\mathbf{E}_{12}&\mathbf{E}_{13}&\cdots&\cdots&\cdots& \mathbf{E}_{1k}\\ \mathbf{E}_{21}&0&\mathbf{E}_{23}&\cdots&\cdots&\cdots&\mathbf{E}_{2k}\\ \mathbf{E}_{31}&\mathbf{E}_{32}&0&\vdots&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&0&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&0&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&0&\mathbf{E}_{(k-1)k}\\ \mathbf{E}_{k1}&\mathbf{E}_{k2}&\cdots&\cdots&\cdots&\mathbf{E}_{k(k-1)}&0 \end{bmatrix} \tag{19}\]  
Using the phase dissimilarity matrix, the relative coordinates of the tags can then be obtained according to the NMDS algorithm. Combined with the known coordinates of the reference tags, the absolute coordinates of the tags can be calculated using the coordinate system registration algorithm [41].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Feature** & **Best Model** & **Hyperparameters** & **Important Features** & **Metrics Values** \\ \hline \multirow{4}{*}{BOD\({}_{5}\) (effluent)} & & learning\_rate 0.05 & Temperature & SMAPE 19.2 \\  & & iterations 150 & Substrate & RMSE 0.962 \\  & & depth 7 & Inlet point & MAE 0.670 \\  & & random\_strength 2 & DO & R\({}^{2}\) 0.78 \\ \hline \multirow{4}{*}{COD (effluent)} & & Temperature & & \\  & & learning\_rate 0.35 & Substrate & SMAPE 19.1 \\  & & iterations 180 & Inlet point & RMSE 1.211 \\  & & depth 6 & DO & MAE 0.854 \\  & & random\_strength 2 & BOD influentent & R\({}^{2}\) 0.85 \\  & & COD influent & & \\ \hline \multirow{4}{*}{Ammonium (effluent)} & & learning\_rate 0.25 & DO & SMAPE 10.6 \\  & & iterations 210 & Temperature & RMSE 0.145 \\  & & depth 6 & C/N ratio & MAE 0.111 \\  & & random\_strength 2 & R\({}^{2}\) 0.79 \\ \hline \multirow{4}{*}{Phosphorus (effluent)} & & DO & & \\  & & learning\_rate 0.12 & Temperature & SMAPE 9.1 \\  & & iterations 160 & NH\({}_{4}\) influent & RMSE 0.054 \\  & & depth 8 & C/N ratio & MAE 0.036 \\  & & random\_strength 2 & Substrate & R\({}^{2}\) 0.81 \\  & & Inlet point & & \\ \hline \multirow{4}{*}{Nitrate (effluent)} & & DO & & \\  & & learning\_rate 0.08 & Temperature & SMAPE 9.1 \\  & & iterations 130 & NH\({}_{4}\) influent & RMSE 0.765 \\  & & depth 7 & C/N ratio & MAE 0.484 \\  & & random\_strength 2 & Substrate & R\({}^{2}\) 0.82 \\  & & Inlet point & & \\ \hline \multirow{4}{*}{Nitrite (effluent)} & & learning\_rate 0.05 & & SMAPE 12.4 \\  & & iterations 140 & DO & RMSE 0.006 \\  & & depth 7 & Temperature & MAE 0.003 \\  & & random\_strength 2 & NH\({}_{4}\) influent & R\({}^{2}\) 0.88 \\ \hline \multirow{4}{*}{Filamentous bulking} & & learning\_rate 0.8 & DO & \\  & & iterations 150 & Temperature & ROC-AUC \\  & & depth 6 & NH\({}_{4}\) influent & 1.0 \\  & & random\_strength 2 & C/N ratio & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary table of modeling results within the framework of the tasks set. For the regression task results, residual analysis was performed for each target feature. A graphical representation of the analysis for some of the target features is shown in Figure 10.  
The residual analysis demonstrated favorable results. Residuals were randomly distributed around zero, indicating no systematic bias in the model predictions. Slight biases in the median were observed, which can be addressed by incorporating a larger dataset of historical data [15].
# Novel Internet of Things Platform for In-Building Power Quality Submetering
## 2 IoT Power Quality Sensor
### Calibration  
Considering design conditions in the steady state, the conditioning circuit systems for voltage and current measurements were calibrated in the PQ sensor prototype. The completed measurement system and conditioning circuits were calibrated for the verification of voltage and current algorithm processing. The equipment used for these calibration tests was composed of an AC programmable power system, a programmable active load, and the PQ sensor connected to the output of the power system. The AC programmable power system, California Instruments 3001 iX [45], integrates a transient network analyzer and can also run several standardized PQ tests as voltage sags. The programmable active load, California Instruments 3091 LD [46], provides precisely controlled, nonlinear loads for testing AC power generation equipment. Therefore, using different voltage and current levels, the quality of the measurement system over a wide dynamic range was determined as well as the stability of different signal generation conditions. A program locally executed on the IoT PQ meter device was specifically designed to this aim. Table 1 shows a calibration test of the voltage measurement system where the electronic load is configured as a resistive load (constant current) and connected to a pure sine wave, achieving the unity total Power Factor (tPF) required by the tested load.  
Typical tolerance figures for components used in the meter are 5%, which means the assembled meter has an inherent error of the same magnitude. Hence, each sensor must be calibrated before accurate results can be measured. In this case, the voltage and current calibration has been done using  
Figure 4: Signal conditioning circuit to current ADC.  
Figure 3: Signal conditioning circuit to voltage ADC.  
two calibration points for each parameter as recommended in the IC datasheet [41]. Results are shown in Tables 1 and 2. A maximum deviation of 0.11% for voltage and 0.26% for current measurements were reached after the calibration tests.
## 3 Results
### Detection  
[PERSON] et al. [60] presented an ML-based framework for detecting DFUs using thermogram images. They utilized the Plantar Thermogram Database, which [PERSON] et al. [61] originally published, which contains 167 foot-pair thermograms. They compared the traditional ML classifier, AdaBoost, with deep learning models, including MobileNetV2, DenseNet201, ResNet50, and InceptionV3. These deep learning models leverage transfer learning with ImageNet weights to enhance performance. The study's key findings indicate that the AdaBoost classifier achieved the highest F1 score using optimized feature selection, while MobileNetV2 demonstrated strong performance when analyzing dual-foot thermograms. They suggested that AdaBoost is a viable option for deployment on mobile applications due to its low computational cost. [PERSON] et al. [62] introduced DFINET, a CNN designed to enhance the automated detection of infections in DFU images. The dataset utilized is a DFU-Part (B) dataset [31]. The DFINET architecture features ten convolutional layers, five max-pooling layers, and five batch normalization layers, with ReLU and two fully connected layers, using parallel convolution filters to extract diverse features. The model uses the adaptive moment estimation (Adam) optimizer for weight updates and the SoftMax layer for binary classification. DFINET has promising results in infection recognition compared with other models such as GoogLeNet, VGG16, and AlexNet. [PERSON] et al. [22] presented a method for detecting DFUs using the EfficientNet deep learning model. They utilized a dataset from the Kaggle repository [36]. The researchers applied data augmentation techniques such as rotation, flipping, and scaling to improve model training. EfficientNet is a CNN architecture designed to optimize feature extraction by balancing three key parameters: network width, depth, and image resolution. Unlike traditional CNNs, which typically scale these dimensions independently, EfficientNet employs a compound scaling method that adjusts all three dimensions simultaneously to achieve higher efficiency and performance. The comparative analysis demonstrated that EfficientNet significantly outperformed AlexNet, VGG16, DFUNet, and GoogleNet, which focus on individual dimensions like depth or width without comprehensive optimization. [PERSON] et al. [63] presented a deep learning system designed to enhance the detection and localization of DFUs. The system leverages two object detection models, YOLOv8m and Faster R-CNN ResNet101, combined using weighted bounding box fusion (WBF) for improved accuracy. YOLOv8m features a C2f backbone for efficient feature extraction. Faster R-CNN ResNet101 employs a two-stage detection framework, using a region proposal network for generating candidate bounding boxes and a classifier for refined
# Rate of Penetration Estimation with Parameter Correction
## 5 Case Study
### Case 2: ROP Estimation  
In this case study, we simulate the estimation of ROP using a Kalman filter, which allows for real-time correction of noisy ROP measurements by integrating a dynamic model of the drilling process. The Kalman filter is used to estimate ROP based on MD and dynamic elongation. The Kalman filter combines these noisy measurements with a dynamic model of the drill string to provide more accurate ROP estimates. This simulation includes the modeling of both static and dynamic elongation corrections and applies a Kalman filter to  
Figure 2: MD correction.  
filter out noise from real-time ROP measurements. The values of the parameters are the same as the ones in Case 1. The Kalman filter was applied to estimate ROP over a time period of 5500 s, with a time step of 0.01 s. Here the matrix Q and matrix R are given as  
\[Q=\begin{bmatrix}10^{-8}&0&0&0\\ 0&10^{-7.5}&0&0\\ 0&0&10^{-1}&0\\ 0&0&0&10^{-9}\end{bmatrix},\ R=\begin{bmatrix}10^{-4}&0\\ 0&10^{-3}\end{bmatrix}.\]  
The simulation results are displayed in Figure 3, comparing the true ROP (calculated from the bit depth measurements) to the estimated ROP. As shown, the estimated ROP closely follows the true ROP, with reduced noise. Additionally, the model effectively estimates the tripping speed when the bit is off-bottom, providing valuable insights into the overall drilling dynamics. The Kalman filter successfully smooths out fluctuations, providing a more accurate estimate of ROP over time. The dynamic elongation correction is plotted over time in Figure 4, showing how the Kalman filter adjusts for the fluctuating elongation of the drill string. The correction fluctuates due to transient forces affecting the drill string, and the Kalman filter helps separate these effects from the true bit penetration. Figure 5 compares the corrected MD (accounting for both static and dynamic elongation) with the true MD. Without correction, the MD would overestimate the true depth, especially during periods of high dynamic elongation.  
The Kalman filter effectively reduces noise in ROP estimates, providing more reliable data for real-time decision-making. This noise reduction is critical in ensuring accurate ROP measurements under fluctuating operational conditions. The dynamic elongation correction is crucial in adjusting for transient elongation due to drill string vibrations and bit-rock interactions. The Kalman filter's ability to capture these dynamic effects improves the overall accuracy of both MD and ROP estimates. With accurate real-time ROP estimates, operators can optimize drilling parameters such as WOB, rotational speed, and mud flow, leading to enhanced operational efficiency and reduced drilling costs.  
Figure 3: Comparison of raw ROP and estimated ROP using the Kalman filter.
## 3 Data Bias
### Common Mitigation Techniques for Data Bias  
According to the above-mentioned findings, there exist some common issues with regard to data bias, as well as corresponding techniques that can be applied to mitigate it. More specifically, as stated in the beginning of this section, common techniques that can be applied to address data bias, regardless of the use case, are under-sampling and over-sampling. As for use-case-specific approaches, major issues regarding data bias are mostly common in NLP, computer vision and the health domain. The relatively common domains that are affected from data bias and the corresponding solutions that have been proposed in the literature and presented above are depicted in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Year** & **Ref.** & **Domain** & **Data Category** & **Type** \\ \hline RB1 & 2022 & [59] & Not specific & Text & Identification and Mitigation \\ \hline RB2 & 2023 & [60] & Computer Vision & Text and Images & Identification and Mitigation \\ \hline RB3 & 2021 & [61] & Computer Vision & Images & Identification and Mitigation \\ \hline RB4 & 2016 & [62] & Computer Vision & Images & Identification and Mitigation \\ \hline RB5 & 2021 & [63] & Facial Recognition & Images & Identification and Mitigation \\ \hline RB6 & 2018 & [64] & Security & Numeric & Identification and Mitigation \\ \hline RB7 & 2019 & [65] & Security (Malware Detection) & Numeric & Identification and Mitigation \\ \hline RB8 & 2023 & [66] & Health & Numeric & Identification and Mitigation \\ \hline RB9 & 2023 & [67] & Health & Numeric & Identification and Mitigation \\ \hline RB10 & 2020 & [68] & Health & Numeric & Identification and Mitigation \\ \hline RB11 & 2023 & [69] & Education & Numeric & Identification and Mitigation \\ \hline RB12 & 2020 & [70] & Education & Numeric & Identification and Mitigation \\ \hline RB13 & 2019 & [71] & Aerospace & Prognostic Data & Identification and Mitigation \\ \hline RB14 & 2017 & [72] & Biology & Mixed & Identification and Mitigation \\ \hline RB15 & 2023 & [73] & Environment & Time series data & Identification and Mitigation \\ \hline RB16 & 2023 & [74] & Generic & Mixed & Identification and Mitigation \\ \hline RB17 & 2021 & [75] & Finance & Numeric & Identification and Mitigation \\ \hline RB18 & 2018 & [76] & Social media & Text & Identification \\ \hline \hline \end{tabular}
\end{table}
Table 5: Approaches to identify and/or mitigate reporting bias.  
By comparing the above-mentioned techniques, in terms of the domain that they are applied to, it is noticeable that methods like under-sampling, over-sampling, DIR, AD and ROC are interesting candidates that can be used for mitigating data bias, regardless of the domain. Moreover, IPW is also a useful technique when it comes to text data and can be applied to several domains as well, given the fact that the corresponding data are text data. With regard to image data, there exist specific techniques that are appropriate for this kind of data which can also be applied to several domains, like health, considering that the corresponding data are image data.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### EANN Identification Model  
Indoor anomalous sound events are aperiodic, random and the background environment is uncertain [32]. The commonly used 1-D CNN model has been unable to meet the recognition requirements. In this paper, the recognition model is an improved 2-D Audio Neural Network (EANN) model; we added an Embedding layer before the full connection layer of the ANN [9]. It can further compress the feature vector dimension and ensure sensitivity to abnormal indoor sounds, and additional embedding layers of ANN recognition models are effective when training a small amount of data. The core components of the EANN recognition model include convolutional block, pooling layers, an Embedding layer, and fully connected layers. Therefore, in order to verify the effect of data augmentation on indoor anomalous sound detection, we used this model as a benchmark.  
Each convolution block of EANN consists of two convolution layers, and the core size is 3 \(\times\) 3. Batch normalization was applied between convolution layers, and the ReLU nonlinearity was used to accelerate and stabilize training. We applied an average pooling of size 2 \(\times\) 2 to each convolutional layer for down sampling. Convolutional block were primarily used to extract local features from the data while pooling layers served to reduce the dimensionality and complexity of the data. The Embedding layer [35] maps content from one space to another, typically used in deep learning to transform high-dimensional sparse feature vectors into low-dimensional dense feature vectors. The low-dimensional dense feature vector space often offers better interpretability and generalization, capturing more details and features in the data, thereby improving the performance of the recognition model. In addition to convolutional layers, pooling layers, and the Embedding layer, the EANN model also includes a fully connected layer responsible for mapping feature vectors to class probabilities. The specific structure of the EANN recognition model is illustrated in Figure 5.  
Figure 4: MFCC of different DA methods (**a**) No Aug (Augmentation), (**b**) Time Stretching, (**c**) Pitch Shifting, (**d**) Background Noise, (**e**) Spectral Masking Aug, and (**f**) Time Stretching and Spectral Aug.
# Data Analysis for Information Discovery
## 7 Pca  
Principal component analysis (PCA) is a well-known method often used to reduce the dimensionality of large databases. Reducing the dimensions of a dataset introduces a loss in accuracy, but on the other hand, it improves the efficiency of algorithms of data analysis,such as data exploration, data visualization, machine learning, etc. From this perspective, when using PCA, it is required to find a trade-off between performance improvement and accuracy loss.  
In [22], there is an in-depth analysis of the PCA method, while in this section, a brief operative description is proposed.  
Essentially, PCA can be seen as a five-step process:  
1. Standardization: In this step, the range of each initial variable (each column of the dataset is a variable) is standardized so that each variable contributes equally to the overall analysis;
2. Covariance matrix computation: The goal of this step is to compute the degree of the relationship among the variables of the dataset;
3. Identification of the principal components: These are new variables obtained as a linear combination or mixture of the initial variables. These new variables have the following properties: they are uncorrelated and most of the information contained in the initial variables is compressed in the first components. This allows for the reduction in dimensionality without losing too much information, discarding the components with low information and considering the remaining components as the new variables. To find these principal components, eigenvectors and eigenvalues of the covariance matrix are computed because they are the directions of the axes where there is the most variance (most information);
4. Feature vector selection: In this step, the eigenvectors are disposed in descending order by their eigenvalues, allowing it to find the principal components in order of significance. In this way, it is possible to choose the number of components to keep for further analysis, discarding those of lesser significance (of low eigenvalues);
5. Recast the data along the principal component axes: Once the principal components have been chosen, it is necessary to recast on them the original dataset.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
###### Abstract  
Energy efficiency is the key requirement to maximize sensor node lifetime. Sensor nodes are typically powered by a battery source that has finite lifetime. Most Internet of Thing (IoT) applications require sensor nodes to operate reliably for an extended period of time. To design an autonomous sensor node, it is important to model its energy consumption for different tasks. Each task consumes a power consumption amount for a period of time. To optimize the consumed energy of the sensor node and have long communication range, Low Power Wide Area Network technology is considered. This paper describes an energy consumption model based on LoRa and LoRaWAN, which allows estimating the consumed power of each sensor node element. The definition of the different node units is first introduced. Then, a full energy model for communicating sensors is proposed. This model can be used to compare different LoRaWAN modes to find the best sensor node design to achieve its energy autonomy.  
communication sensors; energy consumption; autonomy constraint; LoRa; LoRaWAN; energy optimization +
Footnote †: journal: Sensors
## 4 Experiments and Analysis of Results
### Evaluation Metrics  
In this subsection, precision (\(P\)), recall (\(R\)), and the mean average precision (\(mAP\)) were adopted as the evaluation metrics of the model.  
Precision (\(P\)) represents the proportion of predicted positive samples that are actually positive and is calculated as follows:  
\[P=\frac{TP}{TP+FP}\times 100\%, \tag{15}\]  
where \(TP\) denotes the number of actual positive samples correctly predicted as positive, and \(FP\) represents the number of actual negative samples incorrectly predicted as positive.  
Recall (\(R\)) represents the proportion of all actual positive samples predicted to be positive. Its calculation is given by the following:  
\[R=\frac{TP}{TP+FN}\times 100\%, \tag{16}\]  
where \(FN\) is the number of actual positive samples incorrectly predicted as negative.  
Figure 6: The dataset processing images. (**a**) Original image; (**b**) cropped image; (**c**) rotated image; (**d**) noise-added image; (**e**) brightness-adjusted image.  
Average precision (\(AP\)) quantifies the recognition performance for a specific category by computing the area enclosed by the precision-recall (PR) curve. The calculation is defined as follows:  
\[AP=\int_{0}^{1}P(R)\mathrm{d}R, \tag{17}\]  
where \(P(R)\) denotes precision as a function of recall. The mean average precision (\(mAP\)) is an important evaluation metric for object detection models, which is computed as the mean of \(AP\) values across all categories. It is defined as follows:  
\[mAP=\frac{\sum_{i=1}^{N}AP_{i}}{N}, \tag{18}\]  
where \(N\) denotes the total number of categories, and \(AP_{i}\) represents the \(AP\) for the \(i\)-th category.