esa-sceva/satcom-chunk-collection huggingface dataset:
# Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification
## 4 Experimental Setup  
### Data  
We chose the Bitcoin data set, as it is distinct from other cryptocurrency data sets. Bitcoin was created in 2008 and was the first cryptocurrency. It boasts the largest market share and has a large data set. Moreover, its high frequency and volatility [48] made it an ideal fit for our research.  
The data used in this research were Bitcoin [49] data from 13 July 2013 to 22 October 2023. We selected this timeframe to address the issue of data set imbalance after splitting the data into different classes. Prior to 2013 (and even a few timesteps after), closing prices remained relatively stable. Consequently, we ended up with more STABLE images compared to INCREASE and DECREASE images. Figure 5 shows a plot of the time-series data.  
The data set contains 3745 data rows, each representing a specific time point in the data. We opted for a 70:30 ratio for training and testing. This resulted in 2554 days for training and 1095 days for testing. Our study utilized these historical Bitcoin price data, which were loaded from a CSV file. The 'date' column was converted into the DateTime format and set as the index of the DataFrame.  
After generating and categorizing the images for each time-series data point (either 'INCREASE', 'DECREASE', or 'STABLE'), it was observed that the image data set was highly imbalanced, favoring the 'STABLE' category. A class weight was introduced in the classification layer to address this imbalance. This adjustment compelled the neural network to focus more on the under-represented categories.
###### Abstract  
Due to the rising costs of transportation and the more advanced treatment needed to meet the water demands of the urban population, the costs of potable water tend to increase as cities expand. For some water uses, potable water is not required, such as concrete mixing industries. Previous studies have proven the viability of using reused water in concrete production. In Caju, Rio de Janeiro, four large concrete mixing plants (CMPs) are located close to the Alegria Wastewater Treatment Plant. This study focused on assessing the technical and financial feasibility of reusing these effluents as an alternative source of water for the four CMPs. The alternatives were potable and reused water via two supply methods, water tank trucks and pipelines. All costs were analyzed and projected into the future with the market cost of capital. After comparing the costs, the use of reclaimed water proved to be highly feasible, especially when a using pipeline to supply the CMPs. The present costs for the reclaimed water supply were calculated to be USD 0.99 per m\({}^{3}\) for the pipeline alternative and the other alternatives varied in being 5 to 7 times more costly. This water reuse project using pipelines to transport reclaimed water to concrete mixing plants provides a feasible, sustainable alternative water supply in water stressed regions such as Rio de Janeiro.  
industrial reuse of wastewater; reclaimed water; water for concrete production; water security; water supply; wastewater treatment plant effluents +
Footnote †: journal: Applied  
1 Civil, Environmental, and Construction Engineering Department, Texas Tech University,  
Lubbock, TX 79409, USA 1
## 2 Materials and Method
### RGB Photoelastic Image Analysis  
Each photoelastic image was used to gather information about the red, green, and blue color components. The additive RGB color model merges these three primary colors in various combinations to produce a vast spectrum of colors. In this model, every light source (red, green, and blue) is represented by an intensity ranging between 0 and 255. For instance, \"RGB (255, 0, 0)\" appears red since red has the maximum value, while green and blue are set to 0. Conversely, \"RGB (0, 255, 0)\" shows up as green because green has the top value, while red and blue are at 0. There can be as many as 16,777,216 (equal to 255 \(\times\) 255 \(\times\) 255) unique colors resulting from various RGB code combinations.  
The analysis of the RGB code-based photoelastic image requires a three-step data processing procedure. Initially, the captured image is trimmed to fit a circular section of the model particle and adjusted to a resolution of 100 \(\times\) 100 pixels. Subsequently, a circular region of interest is defined within the radius of 0.8 R, where R is the radius of the cylindrical section of the model particle, covering approximately 5000 pixels (refer to Figure 6). Following this, the average value of the red, green, and blue color intensities is computed within this designated region of interest. For each testing condition, ten average values are obtained from a set of ten identical specimens. Finally, variations in these average values are represented at each load level using a box plot. The interquartile range (IQR), which demonstrates the data spread in a box plot, is employed to statistically ascertain the consistency of measurements for each test condition. A smaller IQR implies a lower spread of data, indicating more consistent results.
# CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
## 2 Materials and Methods
### Details of the Analyzed Geometric Variants and the Adopted Assumptions of CFD Study  
The performance of all the cyclone geometries considered in the present work is analysed using numerical simulations. The governing (differential) equations, which are in conservation form, are first integrated over each control volume. Next, these equations are converted into a set of linear algebraic equations, which are further solved iteratively with user-defined solver tolerance (residuals) and under-relaxation factors. Since there are no moving bodies or boundaries in the present case, the Eulerian grids are generated. However, since the secondary (particulate) phase is tracked throughout the flow field, this demands implementing the Lagrangian approach. Hence, collectively, the approach is termed the Euler-Lagrangian approach [40].  
Since cyclonic flows constitute high streamlined curvatures with rapid changes in strain, and steep pressure gradients--which makes it highly anisotropic--a one equation model (viz. Spallart-Allmaras), and two-equation models (such as \(k\)-\(\varepsilon\) and \(k\)-\(\omega\) closures) are not suitable here due to isotropic treatment to the turbulence. Hence, a more advanced (a seven equation) closure model to RANS viz. Reynolds stress model (RSM) is preferred, which solves transport equations for the Reynolds stresses together with an equation for the dissipation rate. RSM is capable to account for the anisotropy associated with the swirling flows [9; 13; 36; 40].
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 3 Software Architecture
### Simulation  
Simulators are increasingly being used in the development of autonomous vehicles as a result of their capability to ease data acquisition and for the design and testing of software features.  
In the proposed platform, the CARLA simulator is used to create a digital twin of the real platform inside a simulated environment to achieve fast prototyping and testing, with the latter being vital as, in Spain, it is not legal to test an autonomous vehicle on ordinary roads or under normal traffic conditions.  
The CARLA simulator is an open-source autonomous car simulator specialized in urban environments [24]. It is designed on top of Unreal Engine 4--a cutting-edge video game engine--which allows visual information and graphics, physics and environments to be as realistic as the technology allows. In addition, CARLA supports all the common autonomous vehicle sensors with a high level of customization, allowing the replication and simulation of any real sensor, such as those described in Section 2, if parametrized correctly. Furthermore, CARLA is fully compatible with ROS thanks to an official and supported CARLA-ROS bridge, making it an appropriate choice for the proposed architecture.  
The complete platform's software architecture described above, except for the communication module, is replicated inside CARLA using ROS as a bridge, which acts as an interface. As the proposed platform's sensor information is presented in a standardized ROS message format, it is possible to substitute the sensor drivers with the artificial sensor data generated by the simulator, consequently allowing the digital twin to perceive the simulated environments as if they were real. Consequently, the cameras, LiDARs, the GNSS and the IMU have been mimicked inside the simulator using their datasheets and CARLA's parametrization feature to achieve the maximum correlation between reality and simulation. Once added, their simulated readings are automatically published through ROS, making them readable by all the platform's software architecture modules. Likewise, the control commands output by the Stanley controller can be directly interfaced with the simulated car through ROS, closing the information loop and transferring the proposed architecture to the simulated environment. In consequence, it is possible to extract information from the digital environment with the several simulated sensors and to drive and act upon the simulated car within the real proposed software architecture.  
It should be stressed that no software module is modified in this process; thus, the simulated platform is identical to the physical one, ensuring maximum reliability and correlation and maintaining the crucial platform's modularity and flexibility.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 3 Experimental Settings
### Baseline NABFCN System  
We compared our network with a baseline system, named NABFCN, to demonstrate the potential of the complex network. NABFCN is an end-to-end system, whose structure is similar to that of CNABCFCN, except that it accepts real inputs. All network structures of NABFCN consist of common LSTM and convolutions, and the frame structure of NABFCN is illustrated in Figure 7. The input noisy speech is still \(L=\) 16,000 long, like the CNAB's. The NAB model is also composed of three LSTM layers and two linear layers, whose input and output parameters are consistent with the CNAB and which are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Name** & **Input Size** & **Hyperparameters** \\ \hline complex input & (B, 2, 16,000) & — \\ \hline
1D Conv & (B, 16,000) \(\times\) 2 & (1, 256), 40, 20 \\ \hline LayerNorm & (B, 2, 256, L) & BatchNorm2d \\ \hline complex 1 \(\times\) 1 Conv & (B, 2, 256, L) & (1, 1), (5, 2), (2, 1) \\ \hline partial complex 1D Conv & \(\ldots\) & \(\ldots\) \\ \hline \(1\times 1\) Conv & (B, 256, L) & (256, 512), 1, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CFCN configuration, where B is the batch size and L is the length of the feature map. The format of hyperparameters for the convolutional network is the dimensions of the input and output, the size of the convolutional kernel, and the step size of stride.  
Figure 6: Frame structure of 1D Conv. The configuration in 1 \(\times\) 1 Conv is kernel size and stride. The internal configurations of 1D dilated Conv are kernel size, stride, padding, and dilation.  
To be consistent with the length of the filter, the size of the linear layer was chosen as \(N=26\).  
The linear layer of NAB produces beamforming filter coefficients that are convolved with the 160 time-domain sampling points to generate the NAB output. The FCN model used in NABFCN has a structure similar to that of the CFCN, but it lacks complex 1D Convs. As the output of NABFCN is a real signal, its loss function only utilizes real information to calculate the loss value as follows:  
\[\text{SI-SDR}=10\log_{10}\frac{\|\beta_{\text{S}}\|^{2}}{\|\beta_{\text{S}}-s \|^{2}},\]  
\[\beta=\frac{\hat{s}^{T}s}{\|s\|^{2}}=\arg\min_{\beta}\|\beta_{\text{S}}-s\|^{2}, \tag{17}\]  
where \(s\in\mathbb{R}^{1\times T}\) and \(\hat{s}\in\mathbb{R}^{1\times T}\) denote clean speech and enhanced speech, respectively.  
To evaluate the benefits of the complex time-domain features and complex neural networks developed in this work, we kept the network parameters of NABFCN and CNABCFCN consistent.
## 2 System Model and Channel Model
### System Model
#### 2.1.3 Global Model Aggregation and Broadcast  
At this step, BS receives the local models from all UDs and aggregate them to obtain the global model. Then, the global model is broadcast to each UD for the next iteration. Due to the powerful computing capability and transmission power of the BS, the delay in this step is neglected compared with other steps. Furthermore, because of the stable energy supply in BS, the energy consumed is also ignored [20].
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems  
The integration of AI in PV systems has gained significant attention due to its potential to enhance efficiency, reliability, and predictability. This part explores the recent advancements and applications of AI techniques in PV systems, focusing on fault detection and diagnosis, predictive maintenance, and energy forecasting and optimization.
# Non-Local Effects in Brillouin Optical Time-Domain Analysis Sensors
## 3 Mitigation of Non-Local Effects
### Probe Dithering in Pulse-Coded BOTDA Sensors for Reducing Pump Pulse Depletion  
Different pump pulse coding techniques [26] have been used in recent years to improve the SNR of BOTDA sensors so that measurements can be made with longer fiber lengths [4; 27]. In a BOTDA sensor with coding, instead of having a pump signal with a single pulse, the pump wave is composed of multiple pulses, with the total number given by the code length. Therefore, the amount of pump pulse depletion suffered by each single-pulse of a sequence, instead of being just related to the power of the probe wave injected into the fiber, also depends on the amplification of the probe wavefronts by previous pulses in the coded sequence. In this way, the successive pulses of a sequence experience an increased depletion factor, since the \(n\)-th pulse in the sequence meets a probe wavefront that has previously been amplified by \(n-1\) pulses [4]. Hence, non-local effects will appear earlier in BOTDA setups that make use of coding techniques than in conventional single-pulse BOTDA sensors [4].  
Furthermore, even for a dual-probe wave BOTDA configuration, which mitigates the appearance of non-local effects, the presence of multiple pulses in the fiber when using coding makes pump pulse depletion become significant at lower input probe wave power than for single-pulse dual-probe systems [4]. Furthermore, pulse-coded BOTDA sensors are less tolerant to a given level of pump depletion with big BFS measurement error even for a pulse depletion factor smaller than \(d=0.2\). This is due to the fact that in coding schemes, it is mandatory to precisely know the power of all the pulses in the sequence in order to properly weigh the decoding matrix. However, the differential depletion of the pulses in the sequence alters those weights and leads to decoding errors [4].  
Figure 7: Depletion spectrum of the pulse for different ER pulses of 20 dBm counter-propagating to a \(-3\) dBm probe wave power per sideband in a 25-km length single-mode fiber.  
The use of our probe dithering method provides a reduction of pump depletion and enables the use of higher probe powers in pulse-coded BOTDA sensors. For instance, Figure 8 depicts the depletion factor of each pulse in a large code length sequence when a FM modulated dual-probe BOTDA configuration is used. This depletion is significantly smaller than without probe dithering, as we observed experimentally. In fact, we have demonstrated up to 164-km fiber-loop measurement distance, with 1-m spatial resolution and 3-MHz BFS precision using a BOTDA sensor with probe dithering and deploying a sequence length of 79 pulses [4]. To the best of our knowledge, this is the longest sensing distance achieved with a BOTDA sensor using mono-color cyclic coding.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 3 Results  
In this study, features extracted from RADARSAT-2 quad-pol SAR data were analyzed. The pseudo RGB image of the Radarsat-2 data on the Pauli basis are provided in Figure 2. It was acquired during the 2011 Norwegian oil-on-water experiment, in which three verified slicks were present; from left to right, they were: biogenic film, emulsions and mineral oil [34]. The biogenic film was simulated by Radiagreen ebo plant oil. Emulsions were made of Oseberg blend crude oil mixed with 5% IFO380 (Intermediate Fuel Oil), released 5 h before the radar acquisition. Additionally, the Balder crude oil was released 9 h before the radar acquisition [34].  
In this study, the effect of feature numbers on the final classification result is analyzed, by considering three major supervised classifiers, namely, SVM, ANN and ML. Based on the quad-pol SAR data, dual-pol and compact polarimetric SAR data were also simulated, then features were extracted based on uniform feature extraction algorithms. Before the process of supervised classification, all of the features were normalized to the range of 0-1. Finally, the performance of features extracted from different polarimetric SAR modes in oil spill classification are compared and analyzed.  
In the supervised classification experiment, 5393 and 5467 pixels of mineral oil covered and non-covered (including clean sea surface and biogenic films) training samples were picked within the study area respectively. Then, 5550 and 5535 testing samples of these two types are picked as the ground truth. The training and testing samples do not include each other. Both the training and testing sample include comparable numbers of pixels that are visually identified (based on ground truth) as clean sea surface, mineral oil and biogenic films (weak-damping surfactants).
# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 2 Proposed Methodology
### Feature Extraction Using Off-the-Shelf CNN: Resnet-50  
For accurately inferring emotion tags from different image categories based on object analysis, the selection of appropriate features is crucial. However, it is hard to know what features can produce good results, particularly in this case where heterogeneous images are present with a single emotion class. Each object category, tagged with a single emotion label, entails various types of objects with different appearances and shapes. Hence, it is not effective to utilize object-related features for emotion recognition as multiple object types may have diverse features with in the same category. Similarly, visual features like color and texture of an image are generally extracted based on the domain knowledge, i.e., human observations and assumptions. Hence, they do not encounter all the visual details [42]. Consequently, a major gap arises between what humans perceive and what we get from these hand-crafted features. Therefore, it is required to extract deep high-level features from different object images for better realization and representation of emotion. For our purposed scheme, we preferred CNN-based automatically extracted features for inferring emotion tags from object images. In this regard, we utilized a CNN model that is pre-trained on the benchmark dataset, i.e., ImageNet [28; 31], entailing 1000 object categories with 1.2 million training images. From this large collection of images, CNN can learn rich feature representations for a wide range of images, which can outperform the hand-rafted features. One such type of pre-trained state-of-the-art CNN architecture is ResNet-50 that consists of 152 layers [43]. This residual network makes the training phase easier as compared to other CNN models due to its substantially deeper network layers [44]. The first layer of the ResNet-50 network utilizes filters for capturing blob and edge features. These primitive features are then processed by deeper network layers to combine the early features to form higher-level image features, as shown in Figure 4. These higher-level features are better suited for recognition tasks as they combine all the primitive features into a richer image representation [45]. Thus, selecting the rightest deep layer, just before the classification layer, is an effective design choice as it incorporates more rich information is a good place to start. Therefore, in our proposed model, we used the last fully-connected layer, i.e., 'fc1000', of ResNet-50 to automatically learn features from the emotion-tagged data. We obtained \(1\times 1000\) feature vector per sample. The main reason for adopting ResNet-50 architecture is that it has a minimal error rate (i.e., 3.57% on the ImageNet test set) as compared to other models.
# Wireless Epidermal Six-Axis Inertial Measurement Units for Real-Time Joint Angle Estimation
###### Abstract
To minimize data errors caused by movement of the human body, and to perform accurate measurements, it is essential that the wearable device is brought close to the skin. Additionally, in order to improve the fidelity between the system and the skin, the implementation of robust interconnecting parts is fundamental in the development of system-level-based epidermal electronic devices. Furthermore, the integration of wireless communication units enables continuous data logging with mobility, which can be used in ambulatory monitoring. This paper presents an epidermal, patch-type, six-axis IMU for real-time joint angle estimation, which can be used in gait analysis, alleviating the effects of musculoskeletal disorders, or in the monitoring of daily routines. In the previous report [14], a comparison of the performance between an inertial sensor and an optoelectronic system was presented. In this paper, a total of 17 IMUs were installed for whole-body joint kinetic analysis. The inertial sensors were fixed to the head, trunk, pelvis, and upper/lower extremities of the subject. Wearable inertial sensors were used to measure joint forces and moments in the whole-body joints of the human subject, with reports indicating that they were suitable for normal movements. The proposed system is implemented using silicon-based ICs soldered onto bonding pads, with the metal interconnects between the ICs having serpentine structures. Typically, it has been reported that human skin can endure an ultimate failure strain of approximately 27% [22]. For commercialized wearable IMUs, it is recommended to wear straps for the purpose of preventing the devices from moving as a result of joint movement and breathing. To resolve this issue, the whole device is positioned on a soft adhesive substrate with stretchable serpentine-structured interconnects which is to be attached to the skin on any part of the human body with high conformity. The proposed system is configured by a commercialized six-axis accelerometer, a Bluetooth Low Energy (BLE) system-on-a-chip (SoC), several passive components, and a chip antenna which can obtain biometric information continuously and easily. The detailed design schematic, manufacturing process, and performance evaluations are discussed in the following sections. The results show that the system, and the method proposed in this paper, can be extended to the development of a high-performance, multiparametric sensor module platform.