esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electrified Power Unit Design
#### 2.1.3 Battery Pack and BMS  
The battery pack consists of four 24 V modules connected to form a pair of two modules in parallel and two pairs in series, as shown in Figure 5.  
Each module is composed of 420 battery cells (Samsung 2900 mAh INR18650-29E), arranged as seven groups in series; each group is composed of 60 cells in parallel. The nominal capacity of a single battery module is 3.8 kWh and the nominal capacity of the entire battery pack is 15.2 kWh.  
The BMS has the purpose of checking and controlling the state of charge (SOC) and the state of health (SOH) of the battery pack during its charge and discharge cycles; the model used in this study was the Emus G1. Each battery module is controlled by the corresponding BMS slave unit, which acquires the voltage values of the seven groups within the corresponding battery module. The four slave units are managed by a single master unit that can communicate with the other devices through the Can-Bus channel. The master unit also receives information on the current flowing into and out of the battery pack, through current sensors.
## 2 Materials and Methods
### Equipment, Software, and Calibration  
The emission probe system used had all the capabilities necessary to reliably measure DPOAEs up to 16 kHz. Prior to DPOAE measurements, a cavity with acoustic characteristics similar to the human ear canal (B&K 4157 ear canal simulator) was used to estimate distortion from the transducers and microphones. Distortion was generally 90 dB lower than the stimulus levels. DPOAEs were recorded in the ear canal in response to two stimulus frequencies (f\({}_{1}\) and f\({}_{2}\)) which were first converted from digital-to-analog (D/A) utilizing a MOTU 96 kHz Audio Firewire Interface (Cambridge, MA, USA) then delivered to the ear canal using modified Audax TW010F1 tweeters. The DPOAE was recorded using an ER 10B+ probe microphone. Emissions were amplified 20 dB using a pre-amplifier and then converted from analog-to-digital (A/D) by the MOTU interface. The emission was then collected using EMAV (Otoacoustic Emission Averager) software [38] which recorded 46 ms time windows of the ear canal sound pressure. These recordings were gathered into two interleaved buffers, and each buffer was averaged in the time domain. Fast Fourier transform (FFT) was used to calculate the difference between the two sub-averages (A - B) to estimate the noise floor. The level and phase of stimulus tones and distortion products in the ear canal were calculated using a grand average ((A + B)/2). Recordings were averaged in total for at least 4 s until one of two automatic stopping criteria were met: the noise floor at the frequency that corresponded to the DPOAE frequency was less than \(-\)20 dB SPL, or until 4 s of recording were averaged with no artifact. Forward pressure level (FPL) calibration was used for more control over stimulus levels to reduce variability and the effect of standing waves in DPOAE measurements, which increased sensitivity and reliability when compared to traditional sound-pressure level (SPL) calibration [18; 19; 21]. The reduction in variability observed across sessions allows for smaller changes due to ototoxicity to be detected in DPOAE measurements. Additionally, calibration files were used to extract reflectance data and compared across sessions to ensure middle ear function was unchanged between sessions. Additional details are provided in [PERSON] et al. [16].
###### Abstract  
The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist-edge-cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines.  
ML-RASPF; smart healthcare; Internet of Things (IoT); edge computing; rate-adaptive provisioning; latency-aware services; machine learning; reinforcement learning; mist-edge-cloud architecture +
Footnote †: journal:
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 2 Background and Preliminaries
### Stages of ML Implementation in Building Energy Management Systems  
As shown in Figure 3, the implementation of ML in energy management systems typically progresses through the following three distinct stages:  
1. Conceptual stage: This initial phase focuses on evaluating the technical feasibility and strategic necessity of deploying ML technology to optimize building energy efficiency. It involves a critical process of identifying significant problems that ML can effectively resolve and subsequently selecting the most suitable data formats and algorithms. During this stage, model designers must possess interdisciplinary competence, combining expertise in computer science with a deep understanding of energy management principles to formulate effective energy management tactics. The conceptual stage is crucial for determining the appropriate quantity and nature of data required, the specific ML methodology to be adopted, and ultimately, whether the overall modeling initiative will genuinely enhance building energy efficiency [1, 3].
2. Experimental stage: The experimental phase is dedicated to evaluating the effectiveness of ML technology in predicting outcomes and assessing the suitability of proposed algorithms for handling building energy data. This stage encompasses model development and meticulous data preparation, followed by post-collection data processing aimed at enhancing data quality and ensuring a sufficient volume of data for robust ML model construction. A notable challenge in this phase, despite the availability of data collection and processing methodologies, is the persistent lack of effective quantitative assessments for data adequacy, which often leads to variations in data amounts across different research works [1, 3].  
Figure 2: IEMS architecture and models examples.  
Application stage: The final application stage involves the seamless integration of a verified ML model into a building's existing energy management system. The primary goal is to enhance decision-making processes and improve overall system performance. This integration can manifest in several ways: it might involve transitioning from a traditional physical model to an innovative ML model, which effectively replaces the current prediction model but necessitates additional demands for data collection and organization. Alternatively, novel management content and decision-making logic could be generated based on new data patterns discerned by the ML models, which would require the thorough optimization and reconfiguration of the current system. Contemporary research predominantly focuses on refining predictive models within established systems [1].
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 3 PoAIR Algorithm
### Reconstructing a Rainstorm Process by Linking Rainstorm Sequences
#### 3.3.2 Reconstructing Rainstorm Processes  
As rainstorm sequences overlapping in space and time belong to the same rainstorm process, this stage uses their intersection to reshape and construct the rainstorm processes. Additionally, during the above linkages, one rainstorm sequence may be linked with two or more rainstorm objects or sequences, that is, the generated rainstorm sequence intersects with two and more others. Thus, PoAIR designs a recursive loop to link all the related sequences to reconstruct a new rainstorm sequence, until the new sequence has no changes. The final rainstorm sequence is a rainstorm process. The recursive algorithm is as follows:  
Step 1: Extract the \(i^{th}\) sequence from the rainstorm sequence datasets, denoted as \(i^{th}\)ss;  
Step 2: Extract the \(j^{th}\) sequence, \(j^{th}\)ss, from the rainstorm sequence datasets, where \(j\) is greater than \(i\);  
Step 3: Calculate an intersection between the \(i^{th}\)ss and \(j^{th}\)ss. If the intersection is null, \(j=j+1\), and go to Step 2; otherwise, obtain a union of the \(i^{th}\)ss and \(j^{th}\)ss, and use the union to replace the \(i^{th}\)ss, update the rainstorm sequences, and go to Step 1;  
Step 4: \(i=i+1\), repeat Steps 1-3 until all the rainstorm sequences have no changes.  
Figure 6: Expressions of linkages between rainstorm sequences. \(O_{S}^{i}\) represents an object occurring at time \(t\) within a rainstorm sequence \(S\), i.e., _preS_, _postS_, \(S1\), or \(S2\).  
**Example 1**: There are four rainstorm sequences, i.e., \(ss1\), \(ss2\), \(ss3\), and \(ss4\). Since \(ss1\) and \(ss2\) intersect, \(ss1\) and \(ss2\) are linked into a new rainstorm sequence which replaces \(ss1\), and datasets are updated. Similarly, \(ss1\) links \(ss4\) into a new sequence. After this is done two times, the datasets are updated, and \(ss1\) and \(ss3\) have no changes and are independent of each other. Thus, \(ss1\) and \(ss3\) are output as rainstorm processes. Figure 7 gives the rainstorm reconstruction process of the example.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy
### Development Procedure  
The development of photoresists, the recording mediums in SXCM, has received considerable attention owing to their use in lithographic techniques. The development procedure is a critical step in SXCM because it affects image contrast and resolution. Irradiation of photoresists with soft X-rays results in bond breakage in the polymer structure, changing the molecular weights of resists locally and enabling these areas to be dissolved away in a chemical bath by solvents, such as methyl isobutyl ketone (MIBK), with the addition of isopropanol (IPA). Generally, each photoresist requires chemical solvents to dilute the irradiated area of the resist. The developer concentration and time exposure during the development process must be optimized experimentally [37]. Usually, a mixture of MIBK and IPA 1:1 (_v:v_) up to 1:3 (_v:v_) is used. A 1:1 (_v/v_) solution [38; 41] means that the developer is chemically aggressive. A high concentration of methyl isobutyl ketone in the isopropanol increases the developing sensitivity but might blur some delicate structures in the photoresist. A more chemically gentle mixture of MIBK:IPA 1:3 (_v:v_) was recommended [42; 43], as it provides sufficient sensitivity while preserving the nanometer height in delicate structures and features.  
The resist is usually spin-coated on top of a silicon wafer, forming a uniform layer, typically 200-500 nm thick. The thickness is then tested by scratching the layer and measuring the height step using the AFM microscope. Moreover, after the resist is deposited, it must be calibrated for various SXR doses. The dose is changed by adjusting the number of SXR pulses used to expose the resist. For each dose, the depth of the relief is measured under the same developing conditions. It is necessary to optimize the SXR dose delivered to the resist to adjust its response and, finally, the dynamic range of the image contrast [38; 41; 44; 45].
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 2 Materials and Methods  
### Synthesis of ZnO/Ag Nanocomposites  
ZnO/Ag nanocomposites were prepared by the sol-gel method. The solutions were prepared using metallic precursors of zinc acetate dihydrate (C\({}_{4}\)H\({}_{6}\)O\({}_{4}\)Zn\(\cdot\)H\({}_{2}\)O, Sigma Aldrich, Steinheim, Germany, >99%) and silver acetate (C\({}_{2}\)H\({}_{3}\)AgO\({}_{2}\), Sigma-Aldrich). These ZnO/Ag nanocomposites were prepared with concentrations of 0, 1, 3, 5, 7, and 10 at% of silver, labeled Zn1, Zn2, Zn3, Zn4, Zn5, and Zn6. The concentration of the final solutions was 0.4 M.  
The first step of sol-gel synthesis is based on dissolving the two metallic precursors in 20 mL of ethanol (EtOH, Scharlau, Sentmenat, Spain) using a magnetic stirrer at room temperature (25 \({}^{\circ}\)C). After preparation, triethanolamine (C\({}_{6}\)H\({}_{15}\)NO\({}_{3}\), Acros Organics, Spain, Barcelona, 99%) was added to the solution as a reducing/precipitation agent. After approximately 30 min stirring at room temperature, a dense white gel was formed. We let it dry for 30 min at room temperature before the gel was calcined at 600 \({}^{\circ}\)C for 1 h in a muffle furnace to obtain white-colored nanopowders as the final product (Scheme 1).
# A Survey on Visual Mamba
## 3 Mamba for Vision
### Comparison of Mamba Models and Other State-of-the-Art Models
#### 3.4.1 Analysis and Comparison in Image Classification Tasks  
As shown in Table 3, the ImageNet-1K dataset is designed for image classification tasks, where accuracy is the key measure of performance. Therefore, we used the Top-1 Accuracy metric to compare the classification capabilities of different models. The comparison shows that CNN models have moderate parameters and computational complexity but relatively low Top-1 accuracy, mostly below 80%. The highest Top-1 accuracy among CNN models was 81.7% for RegNetY-8G [94]. In contrast, most Transformer models have more parameters and greater computational complexity. For example, the ViT-L/16 [95] model has 307M parameters and 190.7G FLOPs, making it suitable for high-performance computing environments. In such environments, Transformer models generally achieve higher Top-1 accuracy, such as 83.7% for ViL-Base-RPB [96] and 83.8% for Focal-Base [97]. Mamba models exhibit various parameters and computational complexities, accommodating various application needs. They include lightweight models like EfficientV Mamba-T [30] and more complex models like V Mamba-B [27]. Many Mamba models achieved Top-1 accuracy above 83%, such as LocalV Mamba-S [29] and SiMBA-B [39].  
From Table 3, it is evident that CNN models do not perform as well in terms of accuracy compared to Transformer and Mamba models. Let us focus on comparing the Transformer and Mamba models. Among the Transformer models, Swin-B [91] performed the best, with a Top-1 accuracy of 84.5%. Of the Mamba models, SiMBA-B (MLP) [39] performed the best, with a Top-1 accuracy of 84.7%. Notably, Swin-B used an image size of 3842, whereas SiMBA-B (MLP) used an image size of 2242. Swin-B can provide more detailed information, but SiMBA-B (MLP) still outperformed Swin-B. Additionally, because Swin-B uses a larger image size, its parameters and computational complexity are significantly higher than SiMBA-B's (MLP).  
Based on the above analysis, we can summarize the advantages and disadvantages of these three types of models in image classification tasks. CNN models have relatively low parameters and computational complexity, making them suitable for environments with limited resources, but perform worse for accuracy. Transformer models have relatively high accuracy, higher parameters, and computational complexity, requiring substantial resources, thus fitting high-performance computing environments. Mamba models are more diverse, fitting different computational resources and application requirements, with many models being competitive in accuracy. However, researchers must select the appropriate model based on specific application scenarios.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 2 Related Works  
Fingerprint classification is the most common approach to reduce the database penetration rate of a fingerprint identification system. It is well-known, fingerprints can be classified into five major categories: arch, tented arch, left loop, right loop, and whorl, as shown in Figure 1. On this regard, several approaches have been proposed, and two main tendencies are identified to address the fingerprint classification problem (refer to Table 1):  
1. Via feature extractors that obtain the most important characteristics of the fingerprint image, by reducing the original size severally. In this context, the feature extractor models with the best-reported results in the literature are [3,5]: Capelli02 [22], Hong08 [23], and Liu10 [24], which are based on global level characteristics of the image such as orientation maps, ridge structure, and singular points, respectively. Afterward, the classification problem is performed trough a supervised learning technique, e.g., support vector machines, or artificial neural networks based on the gradient operation. 2. By employing only a CNN directly on the input images, where the feature extractors are discarded. In practice, CNNs are complex networks that combine different types of neuron layers (convolutional, pooling, and fully connected) with diverse activation functions (e.g., Rectified linear unit (RELU), softmax, RELU plus dropout). Besides, it can be accompanied by a Bayesian framework. However, CNN-based approaches require very time-consuming training process with millions of parameters to be optimized. Table 1 summarizes state-of-the-art approaches on the fingerprint classification problem during the last decade. It contains the following information: author(s), year, feature extractor, classifier, database, classification accuracy, and evaluation time of the artificial neural machine. Whereas the first group (feature extractor along with classifier) allows exceeding 90% as classification performance without increasing the complexity time, the CNN-based approaches are near to 100% accuracy but with large learning time, in the order of hours. It should be noticed that this drawn-back comes from even in the presence of high-performance computing methods [1,3,4,13]. Furthermore, it can be seen than the most of works consider some version of fingerprint databases from the National Institute of Standard and Technologies (NIST) [27] or the FVC [28], which obey to uniform and natural class distribution, respectively. Furthermore, an specific version of both databases is composed by small images of fingerprints (from 1000 to 3000 samples approximately), which limits overall observations (a real fingerprint identification system deals with extremely large databases) due to training/validation/testing results can be optimistic and/or wrong. In other words, the origin (database) of these studies, their solutions (feature extractors and/or classifiers), and conclusions can not directly implement a fingerprint identification scheme.
## 3 Materials and Methods
### Dataset Preprocessing  
In terms of data preprocessing, a series of measures were taken to ensure the quality of both textual and image data, as shown in Figure 1. Textual data were cleaned to remove HTML tags, special symbols, and irrelevant content, which was followed by tokenization and normalization using NLP tools (v.0.4.0). Sentiment tagging was applied to texts from social media and forums to indicate their sentiment orientation. Image data were standardized by resizing to uniform dimensions and resolutions, and image augmentation techniques such as rotation, scaling, and color adjustment were employed. Additionally, feature extraction methods like edge detection and texture analysis were applied to support subsequent model training. These meticulously collected and preprocessed data serve multiple research purposes. These include building market prediction models to analyze key factors affecting financial markets, conducting market sentiment analysis using textual data, and studying the effects of different marketing strategies using financial advertisement image data. Through these steps, the quality and diversity of the data were ensured, laying a solid foundation for subsequent analysis and model development.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 5 Experimental Results  
The defense performance of the USDAA algorithm, USDAA-F algorithm and DAED algorithm in [12] is analyzed by simulation. The defense models of USDAA and USDAA-F are obtained by unsupervised pre-training of adversarial samples and clean samples, and fine-tuned by adversarial samples. The adversarial samples with perturbations (_per_) range per \(\in[0.06,0.1]\) with a step size of 0.01 are used for pre-training and fine-tuning, and PGD attacks 10 times iteratively, and the value of \(\gamma\) is 0.007.  
The algorithm hyperparameter settings are shown in Table 2, the batch size of the three algorithms is 64, the learning rate is set to 0.0001, and the Adam optimizer is used to train the network. The USDAA algorithm and the USDAA-F algorithm have 30 rounds of adversarial pre-training and 10 rounds of fine-tuning, and the hyperparameters, as well as feature extraction network structure used in DAED algorithm, are the same as those of USDAA. The USDAA algorithm uses a few labeled samples in the fine-tuning stage and the DAED algorithm uses large quantities of labeled samples to train the sensing classifier.
## 3 Results and Discussion
### Experimental Performance of NF
#### 3.1.3 Multi Response Optimization  
The optimization of all input variables was done using the desirability function approach to arrive at the best response values of the factors Y1, Y2, Y3, Y4. As depicted in Table 4, at the optimal condition, the predicted response values of factors (Y1, Y2, Y3, Y4) were observed as 36.9 (L/m2-h), 94.77%, 88.67%, 95.89%, respectively. The average values of factor responses after three runs, were found to be 41.93 (L/m2-h) for Y1, 98.64% for Y2, 90.54% for Y3, and 99.79% for Y4. All the experimentally derived values are close to the predicted response values, showing a good correlation (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimum Input Variables (Feed Solution)** & **Predicted Responses by RSM** & **Experimental Validation** & **Difference\%** \\ \hline Concentration (ppm) & 14.24 & Flux (L/m2-h) & 36.92 & 41.93 & 13.58 \\ \hline Temperature (°C) & 25 & Zn Removal\% & 94.77 & 98.64 & 4.08 \\ \hline pH & 7.84 & Ni Removal\% & 88.67 & 90.54 & 2.11 \\ \hline Pressure (Bar) & 22.79 & Cu Removal\% & 95.89 & 99.79 & 4.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NF Optimization of response through RSM.  
Figure 3: NF membrane RSM plots for pH and pressure effects on metals ions rejection% efficiency— (**a**) Zn ions, (**b**) Ni ions, and (**c**) Cu ions; and effect of temperature and concentration on metal ions rejection% efficiency—(**d**) Zn ions, (**e**) Ni ions, and (**f**) Cu ions.