esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DE-KELM-Based Gearbox Fault Classification
### Basic Principle of KELM  
Kernel extreme learning machines are single-hidden layer feedforward networks developed by [PERSON] [44]. When compared with other common supervised learning algorithms, such as backpropagation neural network and SVM, KELM has faster training speed and better generalization ability [45]. Therefore, KELM was adopted as a fault classification method in this paper.  
Assume an ELM network has \(n\) inputs (representing \(n\)-dimensional fault feature vector inputs), \(m\) outputs (representing \(m\)-dimensional fault type vector outputs), and \(L\) nodes in the hidden layer. Suppose \(N\) distinct training sample pairs (\(\mathbf{x}_{i}\), \(\mathbf{t}_{i}\)) are available, where \(i=1\), 2, \(\ldots\), \(N\), \(\mathbf{x}_{i}\) (= \(x_{iL}\), \(x_{i2}\), \(\ldots\), \(x_{in}\))\(T\in R^{n}\) is the input vector of the \(i^{\text{th}}\) sample, and \(\mathbf{t}_{i}\) (= (\(t_{iL}\), \(t_{i2}\), \(\ldots\), \(t_{in}\))\(T\in R^{m}\) is the target output of \(\mathbf{x}_{i}\). According to Reference [44], the output vector of KELM is  
\[Y=H(\omega,\ b,\ x)\cdot\beta \tag{14}\]  
where \(\mathbf{Y}\) is the \(N\times m\) output matrix of KELM, \(\mathbf{H}(\omega,\mathbf{b},\mathbf{x})\) is the \(N\times L\) output matrix of the hidden layer and is a function of input weight \(\omega\), bias vector \(\mathbf{b}\) of the hidden layer, and sample input matrix \(\mathbf{x}\), while \(\mathbf{\beta}\) is the \(L\times m\) output weight matrix between hidden and output layers.  
The training process of KELM is to minimize the training error and the norm of \(\mathbf{\beta}\) at the same time, and it can be formalized as the following optimization problem.  
\[\min\ \frac{1}{2}\|\ \beta\ \  
If an arbitrary kernel function \(k(x_{1},x_{2})\) is applied to the above ELM, the inputs can be mapped from a low dimensional space to a higher one to find a linear separation solution. In this case, the output matrix, denoted as \(f\), of KELM is calculated with  
\[f=\left(\begin{array}{c}k(x,x_{1})\\ k(x,x_{2})\\ \vdots\\ k(x,x_{N})\end{array}\right)^{T}(H^{T}H+\frac{I}{C})^{-1}Y \tag{18}\]  
where \(k(x_{1},x_{2})\) represents a kernel function. Common kernel functions include linear kernel, polynomial kernel, wavelet kernel, Radial Basis Function (RBF) kernel, etc. In this paper, RBF kernel was adopted with the following expression  
\[k(x_{1},x_{2})=\exp(-\frac{\|x_{1}-x_{2}\|^{2}}{g}) \tag{19}\]  
where \(g\) is an adjustable kernel parameter.
## 2 WBANs for Life and Safety Critical Applications
### Applications of WBANs  
WBANs span a wide range of target applications such as the rescue and emergency management in disaster areas [10; 11; 12; 13; 14], the monitoring of mobile workforces operating in harsh or unhealthy environments [15; 16; 17; 18; 19], the monitoring and tracking for personal healthcare [20; 21], and many other applications. This section focuses on safety and life critical applications and describes their main characteristics and requirements.
# Analysis of Hybrid Ship Machinery System with Proton Exchange Membrane Fuel Cells and Battery Pack
## 4 Developed Numerical Model
The propeller speed demand was determined by load profile, which simulates an average cyclical navigation route of 13 nautical miles with the load profile presented by the values in Table 1, which are graphically shown in Figure 7.  
The actual propeller speed is regulated with a PID controller, which maintains the desired propeller speed with adjustments of the EM load based on the comparison between the actual and desired speeds. In terms of energy storage and management, the system includes a battery pack that acts as energy storage, supplying the excess power demand that cannot be met by the fuel cell to the EM and other components that require electricity. The PEMFC stack is the only source of electricity for battery charging with conversion of chemical energy from hydrogen to electrical energy. Significant aspects of the system include power demand and voltage regulation. The controller function calculates the total power required for the EM, compressor, steering pump, and battery charging. Based on this power demand and the battery's SoC, the controller decides the PEMFC stack's power output. If the power demand is high and the battery's SoC low, PEMFC operates at its nominal power, utilizing the hysteresis mechanism to avoid frequent on-off cycling under certain conditions. The hysteresis mechanism has a boundary condition to limit propeller speed to 110 rpm if the SoC drops below 20%, and it holds that limit until the SoC reaches 30%. The controller also determines the fuel cell's current demand considering the voltage limits and whether the fuel cell is on or off. It includes a delay feature to simulate the gradual ramping up of current when the fuel cell  
Figure 6: Torque distribution of electric engine per number of revolutions in one minute. activates, and a low-pass filter is applied to smooth out any spikes in the output current. Finally, the necessary current from the PEMFC was determined by dividing the calculated power demand by the system voltage. Additionally, stable voltage across the system is maintained by two DC/DC controllers, ensuring efficient and consistent power supply.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 5 HiZ-Gundam
### Expected Performance  
In Figure 8, we show schematic views of the HiZ-GUNDAM satellite. The size of the satellite bus is about 1 m\({}^{3}\), except for the solar paddles and mission payloads. For orbital placement, we have chosen a Sun-synchronous polar orbit along the twilight line. This choice is based on the favorable thermal conditions for the NIRT, which is cooled down to \(<\)200 K. However, X-ray observation may face disturbances from both the South Atlantic Anomaly and aurora belt at the high-latitude polar region. The nominal operation of HiZ-GUNDAM follows the sequence outlined below:  
1. Set the satellite attitude to a solar separation angle of 120 degrees and a forward movement of 50 degrees.
2. Maintain the inertial pointing direction for approximately 560 s, during which HiZ-GUNDAM monitors X-ray transients.
3. After the monitoring time, the satellite slews to the same attitude configuration as described in (1), but with a different pointing direction to prevent thermal radiation from the Earth exposing the NIRT. This nominal sequence is repeated, and the observation of eight different fields of view is performed in every orbit. The satellite continuously monitors the X-ray sky, except during the maneuver. This optimized sequence facilitates follow-up observations with the NIR telescope. HiZ-GUNDAM can execute this operation for 97% of the GRBs discovered by itself, ensuring more than 10 min of follow-up time in each case.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Specification** \\ \hline Optics & Lobster-Eye \\ Focal Length & 300 mm \\ Focal Detector & pnCCD \\ Size of Focal Detector & \(55\times 55\) mm\({}^{2}\) \\ Energy Band & 0.5–4.0 keV \\ Field of View & \(>\)0.5 str (in total) \\ Sensitivity (100 s) & \(\sim\)10\({}^{-10}\) erg cm\({}^{-2}\) s\({}^{-1}\) \\ Localization Accuracy & \(\sim\)3 arcmin \\ Time Resolution & \(\sim\)0.1 s \\ \hline \end{tabular}
\end{table}
Table 1: HiZ-GUNDAM wide-field X-ray monitor.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Specification** \\ \hline Telescope Type & Offset/Athermal \\ Aperture Size & 30 cm \\ Telescope Temp. & \(<\)200 K \\ Focal Detector & HyViSi (Optical) \\  & HgCdTe (NIR) \\ Field of View & \(15\times 15\) arcmin\({}^{2}\) \\ \hline Band & Sensitivity (2 min \(\times\) 5) \\
0.5–0.9 μm & 21.4 mag (AB) \\
0.9–1.3 μm & 21.3 mag (AB) \\
1.3–1.7 μm & 21.4 mag (AB) \\
1.7–2.1 μm & 20.8 mag (AB) \\
2.1–2.5 μm & 20.7 mag (AB) \\ \hline \end{tabular}
\end{table}
Table 2: HiZ-GUNDAM near-infrared telescope.
# Novel Microfluidic Septum to Optimize Energy Recovery in Single-Chamber Microbial Fuel Cells
## 3 Results
### Inoculation Phase and Start-Up  
During the inoculation period, proper biofilm growth on the anode electrode represents a crucial aspect in determining electrical output performance. The results obtained in this period allowed us to confirm that the presence of the IMS as a physical barrier allowed correct biofilm formation and, at the same time, ensured a shorter start-up time when compared with control cells.  
Indeed, Figure 2 demonstrates how the output potential provided by the IMS was immediately comparable with that of the control cells, thus leading to confirmation of the correct proliferation of biofilm on the anode electrodes. Moreover, it was possible to appreciate that the IMS achieved reproducible voltage peaks within the first electrolyte replenishment. In comparison, control cells steadily reached the value of the first peak, but not until the third electrolyte replenishment (see Figure 2).  
These results allowed us to demonstrate how, under normal operative conditions, the intermediate spacing resulted in being pivotal in ensuring proper electrolyte replenishment by developing the microfluidic flow field inside the cell. At the same time, such an IMS acted as a separator material, able to reduce oxygen crossover without modifying electrodes' spacing. Indeed, the electrodes' spacing was close to 1.8 cm, which, according to [PERSON] et al. (2015), ensured an optimal reduction in the dissolved oxygen amount close to the anode surface for membrane-less SCMFCs. Moreover, these results were in line with  
Figure 2: Cell output potential measured during the inoculation phase (from day 1 to day 15) and the beginning of the working phase (start-up period). Each arrow represents one electrolyte replenishment with inoculum medium, while triangles on the horizontal axis correspond to refills with 12 mM of sodium acetate electrolyte.  
the hypothesis that optimizing the drop-shape electrolyte flow distribution by the IMS promotes biofilm formation by maximizing the interaction between the biofilm and the electrolyte [31,32].  
Later, after raising the external load from 470 \(\Omega\) to 1 k\(\Omega\), both the IMS and control cells reached a reproducible stable output within three refills (Figure 2). In conclusion, the presence of the IMS sped up the formation of a stable biofilm on the anode electrode, while also accelerating the acclimation process.
# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 3 Results
### Comparison Algorithms  
1. To demonstrate the superiority of MAC-SSL, performance comparison experiments were conducted with the following seven state-of-the-art semi-supervised algorithms:
2. CoMatch[34] combines pseudo-based, contrast-loss-based, and graph-based models to improve model performance with limited labeled data. It jointly learns class probabilities and low-dimensional embeddings, enhancing the quality of pseudo-labels by imposing a smoothness constraint on the class probabilities.
3. MixMatch[33] optimizes both supervised and unsupervised losses. It utilizes cross-entropy for supervised losses and mean square errors (MSEs) between predictions and generated pseudo-labels for unsupervised losses. MixMatch constructs pseudo-labels through data augmentation and improves their quality using the Sharpen function. MixUP[38] interpolation is also employed to create virtual samples.
4. Mean-Teacher[32] employs a student-teacher approach for SSL. The teacher model is based on the average weights of a student model in each update step. Mean-Teacher utilizes MSE loss as the consistency loss between two predictions and updates the model using exponential moving average (EMA) to control the update speed.
5. ICT[45] extends MixUP by interpolating unlabeled data, generating diverse mixed samples. It enforces consistency across different interpolation ratios using regularization. ICT trains a model by constraining predictions of mixed data to align with mixed predictions of original data. It effectively utilizes unlabeled data, particularly in scenarios with limited labeled data, resulting in improved generalization capabilities.
6. VAT[46] replaces data augmentation with adversarial transformations. It perturbs input data through adversarial transformations, leading to lower classification errors.
7. Temporal-ensembling[30] is a method based on temporal ensembling that improves model consistency and robustness by using an exponential moving average of historical prediction results during training. It trains the model by minimizing the consistency loss between the predictions of unlabeled data and the true labels of labeled data.
8. Pimodel[30] is a method based on data augmentation and consistency regularization. It generates virtual samples using data augmentation and trains the model by applying consistency constraints between labeled and unlabeled data.
## 3 System Model and Security Goals
### Security Goals
#### 3.3.3 Data Privacy  
Sensitive and private data such as user identity, passwords, secret keys, patient records, etc., are exchanged in the hospital network. If the data are leaked, then this may affect the reputation of the hospital. So, data must be exchanged confidentially and no one should have access to the data. For data privacy, cryptography and hashing techniques are used with secret keys.
# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
## 3 Experimental Setup and Dataset Description  
### Main Architecture and Pre-Trained Models  
The Wav2 Vec 2.0 architecture, illustrated in Figure 1, constitutes a robust framework for ASR tasks. It comprises three core components: a CNN-based encoder network, a transformer-based context network, and a vector quantization module. These components work in tandem to transform raw audio samples, denoted as \(x_{i}\in X\), into latent speech representations (\(z_{1},z_{2},\ldots,z_{T}\)) [26]. First, the encoder network, denoted as \(f:X\to Z\), plays a pivotal role in this architecture. It consists of seven sequential blocks of temporal convolution layers, each equipped with 512 channels, strategic strides (5,2,2,2,2,2), and kernel sizes (10,3,3,3,3,2,2). This configuration allows the encoder to compress approximately 25 milliseconds of 16 kHz audio data into latent representations every 20 milliseconds. Following each convolution layer, layer normalization and Gaussian error linear unit (GELU) activation are applied to enhance feature extraction. The context network, represented as \(g:Z\to C\), takes these latent representations (\(z_{i},\ldots,z_{T}\)) and builds context representations (\(c_{i}\)) that encapsulate the contextual information across the entire sequence of latent speech representations. Here, we leverage two different model sizes: one with 300 million (300 M) parameters and the other with 1 billion (1 B) parameters. The context network comprises 24 blocks (48 for the bigger model), each with a model dimension of 1024, an inner dimension of 4096, and 16 attention heads. This configuration enables it to capture intricate temporal and contextual dependencies within the audio data [26].  
In the fine-tuning phase of our study, we evaluated two versions of the XLS-R (cross-lingual speech recognition) model framework: XLS-R-300M and XLS-R-1B [63]. The numbers refer to the quantity of trainable parameters that each model possesses. These models were initially pre-trained on a vast dataset consisting of 436,000 h of publicly available multilingual speech data, in 128 languages, sourced from various repositories, including CommonVoice [64], Babel [65], Multilingual Librispeech (MLS) [66], VoxPopuli [67], and the VoxLingua107 [68] datasets. During the fine-tuning phase, we assessed the performance of the XLS-R-300M and XLS-R-1B models for each Indigenous language separately, utilizing the available training data for each language.
# Production Plant and Warehouse Automation with IoT and Industry 5.0
## 6 IoT Contribution in Performance Indication
### RFID for Identification and Tracking  
RFID flags are classified into two types: passive RFID flags, which retro modulate the incoming wave from the interrogator to transmit information, and active RFID flags, which do not have any RF transmitters and instead rely on electromagnetic waves to power the on-board circuitry. This tag typically includes a power transmission source as well as humidity and temperature indicators/sensors. However, there are some limitations to this because the manufacturing costs are quite high, limiting the use of this technology, and companies still prefer to label each of their products to improve traceability [54]. RFID usually provides solutions to the problems in which the demand distortion that travels throughout the supply chain is larger than that of the sales; therefore, its benefits in the supply chain can reduce error rates, customer relationship management, and security.
## 1 Introduction
_Data Bias and Generalization:_ Incomplete or biased data can lead to flawed analyses and generalizations, potentially impacting treatment decisions. For example, if certain demographics are underrepresented in datasets, medical algorithms trained on such data may not be equally effective for all patient groups. * 9. _Data Ownership and Intellectual Property Concerns:_ Healthcare data generated during patient care are often considered the property of healthcare institutions. This ownership dilemma can hinder data sharing efforts, especially when research collaborations involve multiple stakeholders who may have conflicting interests in retaining ownership or intellectual property rights [13]. **Impact on Healthcare Delivery:**  
1. Limited Personalized Medicine: With fragmented data and limited sharing, healthcare providers may lack access to comprehensive patient histories, leading to suboptimal treatment decisions. Personalized medicine, which relies on detailed patient data, may not reach its full potential under traditional data sharing constraints. 2. Slow Medical Research: Traditional data sharing approaches slow down medical research by limiting the pool of available data for analysis. Researchers often struggle to access diverse datasets, slowing the pace of discoveries and medical breakthroughs [14]. 3. Inefficient Healthcare Systems: The lack of interoperability and data fragmentation can lead to the duplication of tests and procedures, increasing healthcare costs and straining medical resources [15]. 4. Delayed Public Health Response: During public health emergencies or disease outbreaks, timely access to comprehensive healthcare data is crucial for an effective response. Traditional approaches can delay data sharing, hindering the ability to track, manage, and contain the spread of diseases. Missed Opportunities for Precision Medicine: Precision medicine, which tailors treatments based on a patient's genetic makeup, requires vast amounts of genetic data. The limited data sharing hampers the progress of precision medicine initiatives. In conclusion, traditional approaches to data sharing and analysis in healthcare face numerous challenges, particularly related to data privacy and security. These challenges impact healthcare delivery by limiting personalized medicine, slowing medical research, and hindering public health responses. Addressing these challenges requires a collaborative effort from healthcare organizations, policymakers, and technology experts to implement robust data privacy measures, improve data governance, and develop secure, interoperable data-sharing platforms that prioritize patient privacy and data security.
## 4 System Model  
The network design, node deployment pattern, AUV movement design, functioning principles, and restrictions are all covered in detail. The classifications of nodes are based on a number of attributes.  
1. The Member nodes (MN).
2. The Gateways nodes (GN).
3. An autonomous underwater vehicle. Objective: The main objective of this research is to enhance the network lifetime and network throughput (minimize data loss). The network settings are as follows:  
1. The GNs and MNs are randomly deployed in the network area and have four types of nodes; MNs, GNs, AUVs, and sink nodes. The AUV moves in a predefined trajectory, and the sink nodes reside on the water surface.
2. MNs collect data from their neighbor nodes and submit them to the GNs in the specified period. The GNs hand over the data to the AUV as it moves in a predefined trajectory. Finally, the AUV transmits the data packets to sink nodes.
3. Nodes update other nodes with their residual information via a packet header when sending information to each other. There are some limitations of the proposed system model:  
1. It does not perform well where the network area is irregular.
2. The AUV cannot move in a backward direction.
3. Without path association, member nodes cannot send data to gateway nodes.
4. It does not support clustering in the network.
5. It does not support the routing procedure of more than 350 sensor nodes.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 2 Materials and Methods  
### Synthesis  
General proceduresReagents and solvents were purchased from Sigma-Aldrich (St. Louis, MO, USA) and used without further purification or processing. TLC plates were used to check the reaction course and purity of the compounds (Silica gel with F254 nm, Merck KGaA, Darmstadt, Germany). All melting points were measured with a B-540 melting point analyzer (Buchi Corporation, New Castle, DE, USA) and were uncorrected. \({}^{1}\)H and \({}^{13}\)C NMR spectra were determined in DMSO-\(d6\) (the \(\delta\) for \({}^{1}\)H NMR is 2.50 ppm, and the \(\delta\) for \({}^{13}\)C NMR is 39.52 ppm) on a Brucker Avance III (400, 101 MHz) spectrometer (Bruker BioSpin AG, Fallanden, Switzerland) at 25 \({}^{\circ}\)C. Chemical shifts were expressed in (\(\delta\)) ppm. The spectral data are incorporated as follows: chemical shift, multiplicity, integration, coupling constant (Hz), and assignment. IR spectra (v, cm\({}^{-1}\)) were obtained on a Perkin-Elmer Spectrum BX FT-IR spectrometer (Perkin-Elmer Inc., Waltham, MA, USA) (KBr pellets). For microanalysis (C, H, and N), the Elemental Analyzer CE-440 was  
Figure 1: Some benzoxazole-2-ones with therapy efficacy.  
used, and the results were found within an acceptable range (\(\pm\)0.3%) in comparison with the calculated values. Hydrazine-containing compounds were stored in tightly closed dark glass containers below 25 \({}^{\circ}\)C, avoiding direct sunlight, heat, sparks, flames, or contact with air.  
Methyl 3-(2-oxobenzo[d]oxazol-3(2\(H\))-yl)propanoate (**2**): A mixture of acid **1** (11.28 g, 54 mmol), methanol (300 mL), and conc. H\({}_{2}\)SO\({}_{4}\) (1 mL) was heated under reflux for 8 h. After the solvent evaporation, the residue was neutralized with 5% aqueous Na\({}_{2}\)CO\({}_{3}\) solution to pH 8, and the mixture was stirred and extracted with ethyl ether (3 \(\times\) 100 mL). The combined ether layers were dried with sodium carbonate, and the solvent was evaporated under reduced pressure.  
Yield 9.67 g (81%), mp 58-60 \({}^{\circ}\)C (from hexane);  
\({}^{1}\)H-NMR (400 MHz): \(\delta\) 2.80 (t, 2H, \(J\) = 6.8 Hz, CH\({}_{2}\)CO), 4.06 (t, 2H, \(J\) = 6.8 Hz, CH\({}_{2}\)N), 3.56 (s, 3H, OCH\({}_{3}\)), 7.12 (t, 1H, \(J\) = 7.8 Hz, H\({}_{\rm{Ar}}\)), 7.21 (t, 1H, \(J\) = 7.8 Hz, H\({}_{\rm{Ar}}\)), 7.32 (t, 2H, \(J\) = 7.0 Hz, H\({}_{\rm{Ar}}\));  
\({}^{13}\)C-NMR (101 MHz): \(\delta\) 31.51, 37.79 (CH\({}_{2}\)CO, CH\({}_{2}\)N), 51.58 (OCH\({}_{3}\)), 109.44, 109.59, 122.19, 123.83, 130.79, 141.96, 153.55, 171.06 (Car, CO);  
IR (KBr): \(\
u_{\rm{max}}\) = 1757, 1718 (C=O) cm\({}^{-1}\).  
Anal. Calcd for C\({}_{11}\)H\({}_{11}\)NO\({}_{4}\) (221.21), %: C, 59.73; H, 5.01; N, 6.33. Found: C, 59.57; H, 4.85; N, 6.20.