esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 3 Experimental Analysis
### Analysis of Gearbox Fault Signal Characteristics  
Based on the analysis of the vibration signal characteristics of the gearbox fault, it is known that in the actual rotation process, when the gear produces a vibration signal, the amplitude and frequency will affect one other [12]. The author summarized the gear failure operation and vibration signal modulation phenomenon, and detailed information can be found in Tables 1 and 2.  
From the above two tables, it can be seen that the time-domain and frequency-domain characteristics of the vibration signals of the gear in the fault state show a variable state. Due to the interference of wind power size, the load of the mechanical transmission system cannot maintain a stable state, especially in the mountainous and hilly areas of China, where the complex terrain will have different degrees of impact on the airflow changes, resulting in wind power instability.  
Therefore, when diagnosing the gearbox fault, the unstable data of the signal of the faulty gearbox should be removed, to maximize the accuracy of the fault diagnosis.  
Figure 4 show that six subsequences \(\{\mathbf{IMF}_{i}\}_{i=1}^{6}\) and one residual component \(\mathbf{r}\) are obtained by the EMD decomposition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault Types** & **Time-Domain Phenomenon** & **Frequency-Domain Phenomenon** \\ \hline Normal & Sine wave appearing & Frequency and harmonics \\ Off-centering & Amplitude modulation & Frequency is the same as the rotation \\ Abrasion & Waveform damage & Bigger amplitude \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vibration signal representation of typical gearbox faults.  
Figure 4: Empirical Mode Decomposition (EMD).  
Based on the above analysis, this paper further studied the distribution of gear vibration signals, summarized the causes and forms of wind turbine bearing faults, collected vibration acceleration signals through professional methods, and uploaded them to sensors. Finally, important parameters used in the vibration signal of the gearbox were used as fault feature indicators for gearbox fault diagnosis.
## 5 Results and Discussion
### Classical Computer Vision Method vs. Neural Network Approach  
When providing the comparison of the results of the classical computer vision and neural network methods for the number and total area of bubbles we get the NN method as the one showing more stable results in terms of the number of bubbles (Figure 32a). The quantity of the detected bubbles also prevails for NN by 20%. However, the numbers are still in the 95% confidence intervals of each other; therefore, it cannot be claimed that statistically an improvement was achieved. However, as for the estimated areas--they differ much more (Figure 32b), namely the NN method provided twice greater estimation. It is odd since it means that 20% of bubbles undetected by the classical method have 100% of total area of detected bubbles. The reason may lie in different approaches for the area calculation: the NN method uses points of the mask, while the CCV method calculates the elliptical area.  
Thus, we also compared the methods on videos (Figure 33a). The ratios between the number of bubbles and frequency for both methods is shown to be similar. Although the NN provides relatively better results for the F3 video (50 Hz), the ratio did not change. As a result, both methods seem to be able to detect and distinguish various bioreactor modes. In addition, there is a space for the CCV method to obtain more stable results: its parameters might be tuned; however, it seems to be unusable since the CCV method can already detect the differences and tuning might result in over-fitting. A comparison of the total area estimations obtained by both of the methods shows that the gap between estimates remains (Figure 33b). However, the NN method results have a greater variance now: it might be explained by the fact that the CCV method uses the ellipse as an approximation model which adds the information to a method and might result in more stable estimates. However, more data with smaller steps between bioreactor modes are needed to investigate the limits of the methods' detection and sensitivity. The current limit is defined as the 30 Hz mode since it produces too high bubble density values.
###### Abstract
The GGO can be associated with \"Crazy Paving\" areas or other signs of organizing pneumonia. The \"undetermined\" pattern is characterized by the absence of the \"typical\" pattern findings, with diffuse GGO areas with a perililar or unilateral distribution, with or without consolidated areas. The \"atypical\" pattern is characterized by either the absence of the \"typical\" or \"undetermined\" signs and the presence of lobar consolidations, \"tree in bud,\" smooth thickening of the septa and pleural effusion; in this presentation, no GGO are detectable. The \"negative\" pattern is characterized by the absence of pathological findings. The \"typical\" and \"negative\" patterns have proven to be very accurate in identifying the disease in patients with suspected COVID-19 infection [3]. Many authors have already proved an occasional discordance between HRCT and rt-PCR. There have been observations of patients with a high clinical suspect of COVID-19 supported by epidemiological criteria and imaging, but with negative rt-PCR [39; 40]. On the other hand, there was evidence of patients with a positive rt-PCR and suggestive clinical findings, that did not present pathological findings on HRCT [41]. The clinico-radiological dissociation in asymptomatic individuals requires reconsidering the role of radiological findings in the clinical management of these patients [42]. To improve the reliability of radiological examination, several authors presented Texture Analysis of the CT scans as a valuable tool to aid the diagnosis [43; 44; 45] and to identify clinically severe patients [46]. Texture Analysis can identify putative features that are not part of the RSNA criteria, such as enlargement of pulmonary vessels [47; 48; 49; 50; 51], and that could be overlooked during the human visual inspection, such as fine characteristics of the GGO areas [52]. Currently, only a few methods exist to automatically process HRCT scans and quantify the extent of the pulmonary involvement [32; 53]. From the clinical point of view, the disease can be assessed with the newly developed PREDI-CO (prediction of severe respiratory failure in hospitalized patients with SARS-COV2 infection), which considers clinical parameters predictive of severe respiratory failure in hospitalized patients, and is defined as the sum of the following conditions:  
* age \(\geq\) 70 years
* obesity BMI \(\geq\) 30 kg/m\({}^{2}\)
* fever at hospitalization \(\geq\) 38 \({}^{\circ}\)C
* respiratory rate \(\geq\) 22 breaths/minute* lymphocyte count \(\leq\) 900 cells/mm\({}^{3}\)
* creatinine \(\geq\) 1 mg/dl
* C-reactive protein \(\geq\) 10 mg/dl
* lactate dehydrogenase \(\geq\) 350 IU/l. This score outperforms in the stratification of patients the well-established qSOFA, SOFA, CURB-65, and MEWS scores; this is due to the fact that the PREDI-CO score was designed and validated ad-hoc for this purpose [54]. In the current work, we aim to automatize the evaluation of the PREDI-CO score and several radiomic features using a novel, non-supervised image processing pipeline.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
### Background
#### 1.2.2 QLoRA  
The Quantized Low-Rank Adapters (QLoRA) technique [15] is an efficient fine-tuning approach that can reduce memory usage to an extent so that a model having as many as 65 B parameters can be stored on a single GPU with 48 GB memory and still preserve the full 16-bit fine-tuning task performance. Quantization is the process of discretizing or approximating an input from a representation that holds more information to a representation with less information. It often means converting a data type with more bits to one with fewer bits, such as from 32-bit floats to 8-bit integers. QLoRA quantizes the pretrained weights to 4-bit precision, reducing memory usage during backpropagation/fine tuning. It then adds a small set of learnable low-rank adapter weights tuned by backpropagating gradients through the quantized weights.  
QLoRA backpropagates gradients to low-rank adapters (LoRAs) through a frozen pretrained 4-bit quantized language model. Whenever a QLoRA weight tensor is used, it is dequantized to BFloat16, and then the matrix is multiplied by 16 bits.  
Therefore, to work around the common GPU memory bottleneck issue encountered in full fine tuning and, to some extent, in LoRA, QLoRA becomes a feasible alternative to train the model. It offers 33% GPU memory savings but at the cost of a 39% increase in the runtime. In QLoRA, the quantization and dequantization steps of the pretrained model weights incur additional processing times. QLoRA presents or offers a memory and time tradeoff that might be worthwhile if constrained by the GPU memory. Overall, the benefits obtained using LoRA and QLoRA PEFT techniques enabled us to perform an in-depth study of the classification task performance of the LLaMA3-8B model, which would have been impossible using regular or full fine tuning because of the memory bottleneck. Figure 1 illustrates and compares these three techniques.
## 4 Experiments  
### Dataset and Implementation  
Two publicly available datasets are used to evaluate the method, as detailed below: DIOR [12], a large optical remote sensing dataset with 5862 training, 5863 validation, and 11,738 test images, totaling 23,463 images and 192,472 instances. The images are 800 x 800 pixels with resolutions from 0.5 m to 30 m and cover 20 classes. We bifurcate it into base and novel class subsets for few-shot scenario model assessments, detailed in Table 1.  
RSOD [49], a four-class geospatial dataset for object detection, comprises aircraft, oil tanks, overpasses, and playgrounds. We designate the overpass as the novel class and the rest as the base class.  
The training process of our method consists of two distinct stages. In the first stage, Branch 1 is trained using examples from all the base classes, which includes the Faster R-CNN and FEM components. In the second stage, the parameters obtained from the initial phase are utilized to initialize the RPN, FEM, and original BBH of Branch 1 in the subsequent STPPGN and STCPSGN of Branch 2. Additionally, the parameters for Branch 3 are shared with the corresponding components of Branch 1. PSGFE undergoes fine-tuning with a balanced set containing only a few annotated instances of novel classes, activates the pseudo-sample generation module, and ceases gradient computation for Branch 3.  
Our method is built based on the PyTorch 1.7 deep learning framework and is trained and evaluated on a single NVIDIA Tesla V100 GPU manufactured by NVIDIA Corporation, Santa Clara, CA, USA. The method utilizes a pre-trained ResNet101 on ImageNet as the feature extractor. The optimizer uses stochastic gradient descent (SGD) with a momentum of 0.9 and a weight decay coefficient of \(1\times 10^{4}\). In the first phase, the initial learning rate is set to 0.01 with a batch size of 16, and the model is trained for 80,000 iterations on the DIOR dataset and 10,000 iterations on the RSOD dataset. In the second phase, the initial learning rate is reduced to 0.001, and the model undergoes 21,000 iterations of training on the DIOR dataset and 3000 iterations on the RSOD dataset.
## 4 Joint Recommendation Selection and Visiting Time Management Problem  
### Framework Design  
As mentioned before in this paper, the problem of joint Recommendation Selection and Visiting Time Management (RSVTM) in a museum environment (or, in general, in an exhibition area) is addressed. Museum visitors arrive at the museum and, before starting their museum tour, they select a type of recommendation that will drive their visit based on a reinforcement learning framework. Specifically, the museum visitors act as learning automata, gaining knowledge and experience from their past potential actions. They are able to intelligently sense the environment while keeping a history of their decisions in order to make more advantageous actions in the future. As time evolves, their decisions converge and they select the type of recommendation that will improve their perceived QoE. The necessary information in order to take their decision is their visiting time \(\mathbf{t}=\{t_{1},\ldots,t_{i},\ldots,t_{N}\}\) and the corresponding combined QoE values \(\mathbf{Q}=\{Q_{1},\ldots,Q_{i},\ldots,Q_{N}\}\) at the previous time slot \(\tau\) of the reinforcement learning framework. Furthermore, apart from these parameters, museum visitors consider the penetration i.e., \(p_{x}(\tau)\), of each recommendation \(R_{x},x=A,B,C\).  
Given the actions \(a(\tau)\) of museum visitors in terms of recommendation selection, a distributed non-cooperative visiting time management game among them is performed every time slot (of the external loop of the reinforcement learning framework) in order to determine their optimal visiting time \(t_{i}^{*},\forall i\in\aleph\) and their corresponding combined QoE values. Therefore, an overall cycle of joint recommendation selection and visiting time management is realized. The overall procedure described above is performed iteratively in time before museum visitors start their touring, as presented in Figure 1, and it is explained in detail in the following Sections 4.2 and 4.3. Moreover, it is highlighted that the RSVTM framework is executed every time a new museum visitor arrives at the museum while the decisions of the previously arrived visitors remain constant if they have started their museum tour. Lastly, the proposed RSVTM framework is executed before the museum visitors start their touring toward determining both the recommendation selection and the optimal visiting time. The actual running time of the RSVTM framework is quite short (order of few seconds for practical purposes) since it will be presented in the detailed numerical results and performance evaluation in Section 6.
# Phtyromediation of Toxic Metals: A Sustainable Green Solution for Clean Environment
## 6 Molecular Adaptation Mechanisms of Toxic Metals in Higher Plants
### Accumulation and Translocation of Toxic Metals  
Compared to typical plants, hyperaccumulator plants have several special properties, such as large amounts of metal uptake and a rapid and effective translocation rate of toxic metals from roots to shoots. Furthermore, they have remarkable effectiveness in binding or generating toxic metals into various chemical compositions, resulting in a decreased concentration of those toxic metals in the free ionic form. These characteristics resulted in hyper-accumulator species with enhanced ion transport tissue. For example, [170] found that _A. halleri_ and _T. caerulescens_ have genes linked to the ZIP family that encode the plasma membrane located transporters such as ZIP6 and ZIP9 in _A. halleri_ and ZTN1 and ZTN2 in _T. caerulescens_ and make additional uptake of Zn compared to non-hyperaccumulators. Though hyper-accumulators efficiently transport toxic metals from roots to shoots via xylem tissue, sensitive plant species must first detoxify metals in the cytoplasm of root cells, or vacuoles, before translocating and accumulating in shoots [171]. Compared to metals-sensitive plants, a representative hyper-accumulator plant such as _T. caerulescens_ had a nearly two-fold faster translocation rate for Zn from roots to shoots and a near 50-70 percent lower concentration of Zn in roots [71; 172]. If hyper-accumulators want to control the accumulation of metals and metalloids, they must maintain a balanced state in their plant tissues. Hyper-accumulators use a variety of transporters to maintain this equilibrium, including ATPases, ATP-binding cassettes (ABC), cation diffusion facilitators (CDF), cation exchangers (CAXs), copper transporters (COPTs), and ZIPs, among others [173; 174]. Hyper-accumulators, on the other hand, use non-selective channels or membranes to transport non-essential toxic metals such as Cd. Proces transporters primarily aid in the movement of important plant nutrients such as Zn [175]. The P1B-ATPase subgroup of the HMA transporter family detoxifies metals and is involved in ATP-dependent transmembrane transport of essential and toxic metals [176]. The HMA4 and HMA5 transporters are members of the HMA family and are thought to be involved in long-distance root to shoot metal translocation [177].
# Scalable Photonic Digital-to-Analog Converters
## 7 Conclusions  
This work highlights the importance of photonic digital-to-analog converters in bridging the gap between digital and analog domains, enabling efficient signal processing in optical communication, analog computing, and optical machine learning applications. A novel parallel architecture with symmetric multimode interference couplers for weighting and combining schemes, utilizing coherent summation of modulated optical signals, is presented. Microring modulators are deployed to enable a compact and scalable design. The experimental results of a 2-bit PDAC are reported along with a four GBaud PAM-4 eye diagram supporting a PDAC conversion (sampling) rate of 4 GSample/sec. Stationary voltage biases to the four arms of the 4-bit PDAC demonstrate 16 distinct intensity levels, which would generate PAM-16 with RF driving signals.  
[PERSON] conducted and analyzed the major experiments and wrote the paper, [PERSON] made significant contributions to the design and device characterization and wrote the state-of-the-art literature survey, [PERSON] contributed to the design and simulation, [PERSON] helped with the experiments, [PERSON] provided valuable help and guidance on the entire project, [PERSON] managed and supervised the work done at 3E8 and contributed to the innovative design idea, and [PERSON] managed the project, supervised the co-authors affiliated to McGill University, and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 3E8 Inc. Montreal, Canada, and the Mitacs Accelerate Program (fund number IT30251).  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
We would like to acknowledge CMC Microsystems for the provision of products and services that facilitated this research, including the subsidized multi-project wafer fabrication through Advanced Micro Foundry (AMF). The authors acknowledge the help from [PERSON], full professor at Institut National de la Recherche Scientifique (INRS), for providing the Optical Vector Analyzer (Luna, OVA5100). This work was supported by Mitacs through the Mitacs Accelerate program.
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 3 TMS Measures
### Motor Threshold  
The motor threshold (MT) is defined as the minimum stimulus intensity capable of evoking MEPs with a given peak-to-peak response in at least 50% of trials [7]. In the clinical use of TMS, the MT must be determined as a foundational step, given that the stimulus intensity must be adjusted to the MT for both the individual patient and session to elicit reliable MEPs for analysis [12; 13]. Thresholds for both resting and active muscles can be determined using single-pulse TMS in resting and contracted (at approximately 20% of maximal muscle strength) muscles, respectively, with contracted muscles demanding a lower threshold. Additionally, the intensity of stimulus required to reach the MT will also depend on the location of the target muscle, as lower thresholds are reported for upper limb muscles and higher thresholds are reported for lower limb muscles [13]. In the literature, the MT has been described as a representation for the excitability of the corticospinal pathway [12; 14; 15], and previous studies have reported abnormal MTs in patients with MS [16]. However, the clinical utility of the MT on its own may be somewhat limited due to wide variation between and even within individuals. Factors such as age, wakefulness, technical setup, pharmacological influence, and posture can all affect the MT [7; 12].
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 3 Interference Alignment in Cognitive Radio: First Paradigm
### Techniques For IA in CR Networks
#### 3.2.4 Offma IA  
Turning attention to classical OFDMA transmission due to its popularity and simple signal model, the self-sustainability of block transmission systems is analysed in ref. [98]. In this work, the cyclic prefix that is usually discarded at the Rx of legacy OFDMA systems is used to scavenge useful energy as a novel energy-harvesting Rx, thus enabling the received signal to carry both energy and data. Most importantly though is the fact that an enhanced Tx strategy where the Tx signal consists of an OFDMA signal and a cognitive IA signal, is also proposed to increase flexibility and generality of the whole scheme. Particularly interesting directions of this work is the practical design and implementation of an energy harvesting OFDMA receiver. The allocation and utilization of resources has also been analysed for OFDMA systems in ref. [98] based on IA in order to increase the spectral efficiency of CR systems without having a detrimental effect on the quality of service (QoS) of the primary system. Based on a frequency-clustering algorithm for multiple antenna scenarios IA plays a role in the proposed algorithm, which considers the channel qualities, power budget limitations as well as the induced interference to the PU band. The work in ref. [99] achieves a significant sum rate gain in comparison with the work in ref. [100], which leads to a considerable increase in the spectral efficiency.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Network Architecture
#### 3.2.1 Feature Extraction and Alignment Module
This network consists of three parts, namely a feature extraction and alignment module, temporal feature fusion module, and decoding module.