esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 2 Related Work
### Pdr  
PDR positioning using the inertial measurement units (IMU) is commonly used in indoor positioning since accelerometers, gyroscopes, and magnetometers have been integrated into smartphones, and it does not need an extra infrastructure investment [9; 26]. However, PDR using IMU sensors has an acceptable positioning accuracy only for a short distance since it suffers from cumulative error in heading estimation over time. Extensive studies have been conducted recently to reduce cumulative error. First, to achieve accurate and robust step detection, various algorithms are proposed, such as peak-detection [27], zero-crossing [28], wavelet transform [29], dynamic time warp (DTW) [30], and a combination of the above methods [31]. Second, to acquire accurate step length, several step-length estimation technologies have been proposed. In some technologies, various parameters are required, such as step frequency, user's leg length or height, and even angle between legs [32], which are inconvenient to obtain. Empirical models based on acceleration, and features extracted from acceleration, are the most frequently exploited [33; 34]. Third, the most commonly used heading direction estimation scheme is fusing data from accelerometer and magnetometer measurements to compensate for gyro drift [35]. [PERSON] et al. [36] propose a fusion algorithm based on the gradient descent algorithm combining a linear Kalman filter and the least square solution based on aided vector measurements to improve the accuracy and convergence. Recently, some data-driven technologies leveraging deep learning methods have been proposed for step detection [37] and step length estimation [38; 39; 40]. In addition, data-driven technologies are also adopted to directly regress the speed and the heading [41; 42], achieving 3-D inertial navigation. However, applying deep learning methods requires the collection of massive datasets to train the neural network and additional equipment to obtain the ground-truth information beforehand, which is labor-intensive to execute. Moreover, for real-time PDR positioning, it is better to deploy the trained neural network on the smartphone for local computing, which may bring a heavy computation load to the end-device.
# Comprehensive Genetic Exploration of Fused Teeth by Whole Exome Sequencing
## 3 Results
### Genetic Analysis  
WES was performed on 15 individuals: two each from Families A-D, three from Family E, and four from Family F. The average coverage depth of the analysed data was 152.9\(\times\), with 96.01% of target bases covered by at least 15 reads; the quality score (Q30) was 91.16%-94.08%. This finding supports a high level of confidence in the variant calling.  
Figure 2: Intraoral and panoramic radiographs, respectively, of: (**a,b**) patient A-1 (Family A); (**c,d**) patient B-1 (Family B); (**e,f**) patient C-1 (Family C); (**g,h**) patient D-1 (Family D); (**i,j**) patient E-1 (Family E); (**k,l**) patient F-1 (Family F); (**m**) intraoral photograph of patient F-2 (Family F). White arrows indicate fused teeth.  
We used the general method of extracting variants from WES [19]. Based on the genome database, we set the minor allele frequency (MAF) of each variant at 1% and extracted rare variants that were considered to be related to the disease. As a noncoding region filtering, variants not in exonic or splicing regions were excluded. Next, variants present in the subjects with and without fused teeth were extracted and excluded, respectively. The CADD score evaluates the deleteriousness of SNVs. A CADD score of \(\geq\)20 implies that the top 1% of SNVs are deleterious variants [20]. Variants with a CADD scaled score of \(\geq\)20 were then extracted.  
We extracted the following: solute carrier family 27 member 3 (_SLC27A3_) variants (c.1385G>A (\(n=1\))) from Family A; _SLC27A3_ variants (c.1036C>T (\(n=1\))) from Family B; kinesin family member 25 (_KIF25_) variants (c.208G>A (\(n\)=1)) from Family C; and ERCC excision repair 6, chromatin remodeling factor (_ERCC6_) variants (c.2204G>T (\(n=2\))) and obscurin, cytoskeletal calmodulin, and titin-interacting RhoGEF (_OBSCN_) variants (c.5867C>G (\(n=2\))) from Family F (Table 2). No candidate variants were identified in Family D.  
The WES results for all 15 individuals from the six families showed that the genes with variants were _ERCC6_, _OBSCN_, _SLC27A3_, _KIF25_.  
Table 3 summarises the scores of candidate gene variants and pathogenicity prediction tool results. _ERCC6_ (c.2204G>T), and _SLC27A3_ (c.1385G>A and c.1036C>T) showed high frequency in East Asians. CADD and PolyPhen2 were used to predict and evaluate the pathogenicity of the variants. Scaled CADD scores were particularly high in _ERCC6_ (c.2204G>T) and _SLC27A3_ (c.1036C>T and c.1385G>A), with scores above 25. Moreover, Polyphen2 scores also showed 1. _ERCC6_ (c.2204G>T) showed the highest value, with a scaled CADD score of 35 and a PolyPhen2 score of 1, indicating that it is more confidently predicted to be damaging.
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 3 The Proposed Hybrid Deep Learning Approach
### BERT-Based Encoder for Image Segmentation Dataset
#### 3.2.6 Final Classification Output
```  
**Algorithm 3**Feature Mapping Using ResNet  
Algorithm 4 Algorithm 3 uses Particle Swarm Optimization (PSO) to optimize ResNet parameters for the classification of cotton leaf diseases. A swarm is formed where each particle represents a set of parameters for ResNet, and PSO changes these parameters in an iterative manner by updating particle positions and particle velocities to minimize classification error. After finding the optimal parameters, multiple ResNet models are trained and combined with an ensemble. The final classification performance in terms of accuracy, precision, recall and F1-Score is more robust compared to the individual networks of the ensemble, achieved by majority voting or weighted averaging. Objective:  
To classify the disease in the cotton leaf using features extracted by \(ResNet\) and optimized through PSO, with final predictions enhanced by ensemble learning. Step 1: Initialize PSO for \(ResNet\) Optimization  
Define a swarm of particles, each representing a set of ResNet parameters  
Initialize the position \(x_{i}\) and velocity \(v_{i}\) for each particle. \(P_{i}(0)=P_{min}+r_{i}\times(P_{max}-P_{min})\)  
Step 2: PSO-Based Optimization  
For each particle:  
Update Velocity:  
\(v_{i}(t+1)=\omega v_{i}(t)+c_{1}r_{1}(p_{i}(t)-x_{i}(t))+c_{2}r_{2}(g(t)-x_{i}( t))\)  
Update Position:  
\(x_{i}(t+1)=x_{i}(t)+v_{i}(t+1)\)  
Evaluate Fitness: Calculate the fitness of each particle using the classification error. \(Fitness(P_{i})=\frac{1}{Error(\frac{P_{i}}{P_{i}})}\)  
Repeat until convergence or maximum iterations. Step 3: Model Training and Ensemble Formation  
Train multiple ResNet models using different PSO-optimized parameter sets. Combine the models into an ensemble using majority voting or weighted averaging. \(\hat{y}=model\ (\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{k})\)  
Output: Final disease classification for each cotton leaf image, evaluated with accuracy, precision, recall, and F1-Score. **Algorithm 3** Classification Using PSO-Optimized ResNet and Ensemble Learning
# Yeast Fermentation for Production of Neutral Distilled Spirits
## 2 Overview of Neutral Distilled Spirits Production
### Yeast Handling/Management  
As indicated above, distilleries can choose between freshly propagated and commercially supplied pure-cultured strains of _S. cerevisiae_. The latter may be supplied in compressed (cake) form, liquid (cream) form, or in dried form (Table 6 and [21,24]).  
Figure 3: Effect of yeast strain and temperature on ester formation during corn-whisky fermentation. Congener concentrations were measured in distillates from single distillation with a 30 L capacity hybrid still and are expressed in grams per hectolitre of absolute alcohol (AA, 100% vol. alcohol). EA: ethyl acetate. Esters analysed: isoamyl acetate, ethyl caprylate, ethyl caprate. Analysis courtesy of Lallemand Biofuels & Distilled Spirits R&D, Montreal, QC, Canada.  
The pitching rate (inoculum) is generally around 10\({}^{7}\) cells/mL. Distilled spirit producers, as compared to brewers and winemakers, do not recycle yeast, and instead, the fermented wash, including the yeast, is frequently distilled. This results in the concomitant destruction of yeast cells, thus necessitating supplies of freshly propagated or freshly supplied yeast for subsequent fermentation. The distillation of yeast-containing fermented wash also contributes flavour to the resultant spirit, which is undesirable for neutral alcohol production.  
Typically, distilled spirit fermentations are allowed to proceed for a few days without precise temperature control. The pH is also not commonly controlled in a cereal-mash fermentation. The pH prior to fermentation is typically 5-5.5 pH and can fall to a pH of 4.2-4.5 by the end of fermentation. Yeast viability declines at the end of fermentation due to the combination of a low pH, temperatures > 30 \({}^{\circ}\)C, and a high final ethanol concentration. These additional reasons are why yeast is not recycled by distilleries.  
For all types of industrial fermentation processes that use yeast, it is important to ensure the yeast strain employed has good viability and vitality and is a pure culture. The latter means that the strain of yeast is genetically pure and that the culture population is free from contaminating micro-organisms, including other yeast strains. Some (larger) distilleries have suitable equipment to propagate enough pure yeast aerobically to pitch (inoculate) into the distillery's fermenters. Note, that yeast propagation is different from fermentation. The former relies on the yeast's respiratory capabilities to generate sufficient energy (in the form of ATP) from the aerobic metabolism of sugar, whilst the latter produces ethanol and CO\({}_{2}\) under anaerobic conditions with lower energy generation.  
Yeast strain purity, viability, and vitality are key analytical parameters to ensure consistent fermentation [25]. Therefore, a pitching yeast contaminated by lactic-acid bacteria is considered to have poor viability; if it were to be used to initiate a distillery fermentation, a sluggish fermentation with low alcohol yield would be expected.
# Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence
## 2 Materials and Methods
### Parameter Learning by Least Squares and Least Absolute Deviation
#### 2.2.1 Least Absolute Deviation  
The optimal \(\theta\) can be learned by minimizing either the absolute error/deviation or the squared loss between the true and estimated relative ratios. The absolute error is defined as  
\[J_{L_{1}}(x)=\int\left|w_{\alpha}(x)-\hat{w}(x;\theta)\right|p_{te,\alpha}(x)dx, \tag{7}\]  
which can be expressed as  
\[J_{L_{1}}(x)=\left|1-\alpha\int\hat{w}(x)p_{tr}(x)dx-(1-\alpha)\int\hat{w}(x) p_{te}(x)dx\right|. \tag{8}\]  
Minimizing the absolute error loss is equivalent to solving the following convex optimization problem:  
\[\hat{\theta}=\underset{\theta\in\mathbb{R}^{n}}{\operatorname{argmin}}\left|1 -\alpha\widehat{\mathbf{h}}_{tr}^{\top}\theta-(1-\alpha)\widehat{\mathbf{h}} _{te}^{\top}\theta\right|, \tag{9}\]  
where \(\widehat{\mathbf{h}}_{tr}\) and \(\widehat{\mathbf{h}}_{te}\) are \(n\)-dimensional vectors with the \(l\)-th element given by  
\[(\widehat{\mathbf{h}}_{tr})_{l}=\frac{1}{n_{tr}}\sum_{i=1}^{n_{tr}}K_{il}^{tr };\quad(\widehat{\mathbf{h}}_{te})_{l}=\frac{1}{n_{te}}\sum_{j=1}^{n_{te}}K_{ jl}^{te}; \tag{10}\]  
and \(K_{il}^{tr/te}=K(Y_{i}^{tr/te},Y_{l};\sigma)\) represents the Gaussian kernel evaluated for the training or test samples.  
The global optimal solution \(\hat{\theta}\) for the least absolute deviation problem can be obtained analytically as  
\[\hat{\theta}=\Big{[}\alpha\widehat{\mathbf{h}}_{tr}+(1-\alpha)\widehat{ \mathbf{h}}_{te}\Big{]}^{-1}. \tag{11}\]
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
###### Abstract  
A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.  
5G and beyond systems; machine learning; radio access networks; reinforcement learning; supervised learning; unmanned aerial vehicles (UAVs) +
Footnote †: journal: Electronics Electronics
# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
## 4 Result and Analysis  
### Simulation Results and Analysis  
In the simulation, we trained 1500 epochs on the classic YOLOv5s, YOLOv5m, YOLOv5I, and the YOLOv5_Tel. The loss function convergence curves of the four network models on the training set are shown in Figure 9.  
Figure 8: The teleoperated robot experiment platform for distance estimation.  
Figure 9: Convergence of loss functions for different models on the training set.  
To assess the effectiveness of the YOLOv5_Tel model, the simulation results of the four network models were compared and analyzed. The evaluation metrics for model detection accuracy are precision, recall, mAP, and F1-score [12]. The model's degree of lightweight and detection speed was characterized using the model size and inference time. The simulation results of the four different models are shown in Table 3.  
The comparison and analysis of the YOLOv5_Tel model and the classic model was done in light of the results of the simulation.  
(1) In the training stage, the loss function value of the YOLOv5_Tel model tends to stabilize after 1100 iterations, whereas the classic YOLOv5s, YOLOv5m, and YOLOv5l models tend to stabilize after 1400 iterations, indicating that the YOLOv5_Tel model converges faster, which can reduce the training cost.  
(2) The YOLOv5_Tel model offers better object detection accuracy, as demonstrated by higher precision, recall, mAP value, and F1-score of 92.6%, 99.4%, 99.4%, and 95.9%, respectively.  
(3) The YOLOv5_Tel model is 12.8 MB in model size, which is smaller than the classic YOLOv5s, YOLOv5m, and YOLOv5l network models by 11.1%, 70.0%, and 86.4%, respectively, indicating that the YOLOv5_Tel is lighter and better suited for embedded devices with insufficient computing power.  
(4) The YOLOv5_Tel model outperforms the classic YOLOv5s, YOLOv5m, and YOLOv5l network models in terms of inference time by 9.1%, 42.9%, and 58.3%, respectively, indicating that the YOLOv5_Tel has a faster detection speed and is more suitable for the teleoperated robot.
## 4 Proposed Solution
### Trajectory Optimization Algorithm Based on DDPG
#### 4.3.2 Action Space \(\mathcal{A}\)  
Action space represents the set of all possible actions. For each UAV at time slot \(t\), its action space consists of three parts, namely the direction \(\theta_{f}\) of the UAV's horizontal movement, the distance \(d^{f}_{v}\) of the UAV's horizontal movement, and the distance \(d^{f}_{h}\) of the UAV's vertical movement. The specific description is shown in Table 2.  
Therefore, action space can be defined as:  
\[\mathcal{A}\triangleq\left\{a_{t}\right\}=\left\{\theta_{f},\,d^{f}_{v},d^{f}_ {h}\right\} \tag{19}\]
## 3 State-of-the-Art Review on Energy Optimization Techniques in UIoT  
This section describes the state-of-the-art concerning various energy optimization techniques of UIoT networks such as underwater wireless power transfer, auto-recharge or battery-free systems, solar charging systems, and battery swapping approaches, etc. The description of each energy optimization technique is described underneath:
## 4 Dataset and Data Augmentation
### Dataset  
To train our method, we used a benchmark dataset of EEG recordings at a resting state, which is publicly available [32] and has been used in recent works for schizophrenia detection [11; 14; 11; 15]. This dataset contains EEG signals of 84 subjects. A total of 45 of them are adolescents with symptoms of schizophrenia, and 39 are normal control adolescents. EEG were recorded using 16 channels (i.e., F7, F3, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2). For each subject, an EEG signal was recorded with a one-minute duration, and the sampling rate was 128 Hz. However, this number of samples is insufficient to build a deep-learning model. Therefore, a data augmentation scheme is essential to tackle this problem. The subjects consist of adolescents who were screened by a psychiatrist and received either a positive or negative diagnosis for schizophrenia neuropathology.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline Conv B6 & Conv B5 & Conv B6 & Conv B6 \\ \(1\times 3/1/1/8\) & \(1\times 3/1/1/12\) & \(1\times 3/1/1/8\) & \(1\times 3/1/1/8\) \\ \hline Conv B7 & Max-Pooling & Max-Pooling & Max-Pooling \\ \(1\times 3/1/1/8\) & \(1\times 2/2\) & \(1\times 2/2\) & \(1\times 2/2\) \\ \hline Max-Pooling & Conv B8 & Conv B7 & Conv B7 \\ \(1\times 2/2\) & \(1\times 3/1/1/4\) & \(1\times 3/1/1/4\) & \(1\times 3/1/1/4\) \\ \hline Conv B8 & Conv B9 & Conv B8 & Conv B8 \\ \(1\times 3/1/1/4\) & \(1\times 3/1/1/4\) & \(1\times 3/1/1/4\) & \(1\times 3/1/1/4\) \\ \hline Conv B9 & Max-Pooling & Max-Pooling & Max-Pooling \\ \(1\times 3/1/1/4\) & \(1\times 2/2\) & \(1\times 2/2\) & \(1\times 2/2\) \\ \hline Max-Pooling & FC1 & FC1 & FC1 \\ \(1\times 2/2\) & 4 & 4 & 4 \\ \hline FC1 & FC2 & FC2 & FC2 \\ \(4\) & 2 & 2 & 2 \\ \hline FC2 & - & - & - \\ \hline
**Avg-acc** & 99.88\% \(\pm\) 0.13 & 98.45\% \(\pm\) 0.58 & 98.45\% \(\pm\) 1.71 & 97.98\% \(\pm\) 2.28 \\ \hline
**Avg-sens** & 100\% & 98.67\% & 99.11\% & 97.56\% \\ \hline
**Avg-spe** & 99.74\% & 98.21\% & 97.69\% & 98.46\% \\ \hline
**\#parameters** & 7358 & 21,718 & 6542 & 14,734 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Deep Neural Network Equalization for Optical Short Reach Communication
## 3 Experimental Verification and Discussion
### Measurement Results over an Optical Channel with 600 Gb/s/\(\lambda\)  
For the ultimate goal of reaching 600 Gb/s net data rate with the lowest possible BER over a wide range of OSNR levels, an optimal combination of modulation scheme and baud rate needs to be chosen in a first step, before improving its performance further with nonlinear equalization. Figure 5 shows the hard-decision preFEC BER for different modulation schemes with varying noise loading without applying NLE compensation.  
The horizontal red dashed line indicates a typical FEC limit of \(1\times 10^{-2}\). It can be observed that for the given setup, smaller constellation sizes improve performance much more than symbol rate reductions. DP-64 QAM/58 GBd does not even reach the FEC threshold within the observed OSNR range without NLE. DP-16 QAM/88 GBd proves to be the most performant scheme and thus, it is chosen for NLE architecture comparisons upon its offline data.  
For fair comparisons, all options apply 4 memory taps. Per option, calibration is done exactly once on the first of four frames of the capture taken at 29.7 dB OSNR (calibration point). This ensures strict separation of calibration and measurement data, respectively training and testing data.  
Figure 6 depicts the training and evaluation process. Each capture from the oscilloscope includes up to four frames, represented by the squares. For each capture the DAC is reloaded with pseudo random data. The blue square indicates the training data, while the green squares indicate the testing data. The gray colored frames are not used. For each OSNR value the overall BER is evaluated over four frames.  
Figure 5: Back to back 600 Gb/s (+15% FEC) measurements of different modulations without nonlinear equalizers (NLE).
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 3 Methodology and Data  
We can summarize the fundamental concept of Bayesian theory into three keywords: _prior_, _likelihood_, and _posterior_. The prior is an initial belief to begin with, based on the current/historical information, and can be updated when new information arrives. The likelihood is the joint distribution of the data, given the model parameters \(\beta\), and \(\sigma\). Model parameters can be determined after updating the prior. Lastly, the posterior is the conditional probability distribution of our dependent variable \(\theta\), which depends on the data and the prior. The posterior is computed as the product of the prior and likelihood [17].  
Generally speaking, the Bayesian inference can be performed as follows: (1) define the prior empirical probability distribution or the assumptions for the hypothesis; (2) compute the marginal likelihood probability of the data using a sampler (e.g., MCMC) to generate random samples from the probability distribution [51]. In each sample, calculate the marginal likelihood probability, which contains all the relevant information to evaluate the evidence. However, estimating the marginal likelihood typically is a difficult task because we have to integrate all model parameters; and (3) determine the posterior, which is the probability distribution of a particular value of the parameter after having seen the whole data set [16]. The Bayesian inference theory is formally expressed as follows:  
\[posterior\propto prior\ \times likelihood\]