esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 1 Introduction
Although, such configurations usually attain high classification accuracy, the overall computational cost deems them inapplicable for real-world application (e.g., brain-computer interfaces), since minimal computational resources and prediction time are essential in order to achieve real-time functionality. Another important factor that presents a limitation in the current DL architectures is their explainability. DL entails the need for enormous, multi-layered networks that are hard to train and even harder to explain. The models are perceived as black boxes with little understanding of the actual system usability [13]. Explainability is crucial, since it increases the trustworthiness of the system. Understanding the reason behind a model's predictions can provide confidence in the model's results, while providing indications of the flow of reasoning behind the results produced. Taking the above into consideration, a DL architecture was employed in this study, which utilized EEG data from a working memory task triggering visual memory processes. The overall procedures included two recordings (before and after on-call shifts) of medical and nursing staff eliciting high mental fatigue due to the influence of sleep deprivation. In this regard, a fairly shallow neural network architecture (with three layers of cascading CNNs and a fully connected layer) was applied, based on non-invasive EEG recordings and an appropriate data (pre-)analysis. Our results indicate the efficiency of the proposed methodology towards both classification performance and computational cost. In detail, our workflow achieved an accuracy of 97% (97% F-score) within a particularly cost-effective setup, bearing notable implementation prospects for real-time applications. Moreover, the application of an explainability design was able to provide indication of the input brain activity features utilized by the DL algorithm to assess mental fatigue. The main contributions of this study are: (a) to effectively classify fatigue states with high overall accuracy while employing an explainable and trustworthy AI architecture; (b) to emphasize the importance of hand-engineered features in the performance of deep learning models.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 4 Discussion
### Comparison with NOMA  
As is known, unlike OMA, the NOMA method uses non-orthogonal detection means such as POWER detection, which can enable multi-access without codecs, and usually the number of access users can be higher than OMA with lower detection complexity [12]. The MED method used in this paper uses a non-orthogonal coherent-state signal for detection and thus is essentially a NOMA method [34; 35].  
The bigger difference is USD; although USD also uses non-orthogonal-state detection means and has many advantages over NOMA, it uses zero-error resolution measurements to achieve lower detection errors than traditional NOMA. In Figure 8, we compare the BER-SNR performance of USD and NOMA. The noise power of NOMA is set to 0.1 [12], and the noise of USD is set to 0.1 and 0.2, accordingly. From the results, we can see that USD has a better BER performance than NOMA, and unlike intuition, when the noise power increases, the BER of USD also decreases significantly. This is because its error probability, according to Equation (28), is not only determined by SNR, but also by the signal power (under a certain SNR, the noise power determines the signal power). In other words, when the signal power is large enough, a high BER performance can be guaranteed even with a small  
Figure 7: The maximum channel capacity of USD in the background of thermal noise (noise average photon number from 0.1 to 0.5).  
SNR. This characteristic of USD essentially comes from its ability to perform zero-error discrimination, extracting as much correct information as possible during detection, which is not available in traditional NOMA.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 3 Methodology
### Pre-Processing  
Data pre-processing is a crucial step in the machine learning process, as the quality and accuracy of the final model heavily depend on the quality of the input data. This process involves techniques such as data cleaning, normalization, transformation, and feature extraction, with the goal of transforming raw data into a more structured and clean format that is easier to process and understand by machine learning models.  
1. The first step involves loading the data from CSV files into Spark DataFrames using the spark.read.csv function. The data consist of multiple columns representing the pixel values of the images and one column representing the label of the image.
2. The next phase is to convert the pixel columns into a single feature vector for each image. This is done using the VectorAssembler tool in PySpark, which consolidates the pixel values into a unified vector for easier processing and analysis by machine learning models.
3. Following this, the pixel values are normalized to a range between 0 and 1 using the MinMaxScaler. This normalization improves the model's convergence during training.
4. The final stage of pre-processing involves transforming sparse vectors (SparseVectors) into dense vectors (DenseVectors). This transformation is necessary because working with sparse tensors in TensorFlow Federated (TFF) is more complex than using dense tensors.  
In summary, the pre-processing workflow in PySpark for the MNIST, Fashion MNIST, and CIFAR-10 datasets includes creating a unified feature vector, normalizing the values, and converting sparse vectors to dense vectors, optimizing data preparation for federated learning.
## 3 Materials and Methods
### Underlying Hardware Components
#### 3.1.3 Network Connectivity and Protocols  
The main tasks of a node were split into sensing/actuation, computation, storage, and communication, among the other nodes [31]. The node architecture was a bundle of multiple sensors which gathered environmental parameters to be sent to the cloud. The controller was the part of the node that fetched data from the sensor, exchanged them with the communication device, and controlled the behavior of the actuators. The microcontroller added the ability to interact with it by programming it. It had memory, enabling it to control the behavior of the node [32].  
The implementation used the RPi board, which allowed LAN internet connectivity. The communication used two protocols: MQTT and HTTP. MQTT operated through data messaging architecture that used the publish/subscribe paradigm to ensure that the data was sent smoothly if the sensor node was subscribed to the broker and the database. At the end-user data access, the web services used HTTP so the user could access the information through a browser application.
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 3 Proposed Method
### Flow Features and QoS Metrics Extraction  
The network flow features, as shown in Table 2, are extracted as the input of machine learning model, and further labeled to train or validate the model, which can be summarized as the following steps.  
1. [label=(0)]
2. Connect users' terminal devices (smart phones and tablets) to the wireless network server; here, we use the Wi-Fi hotspot.
3. Run the first target APP (application) on the users' terminal devices, and start to capture the network packet data.
4. After a period, e.g., 3 min, restore the captured packet data of the first kind of APP, let users stop running the current APP and open another, collect packet data of the next APP, and repeat such process on each APP one by one until the packet data collection and storage are completed.
5. Extract the data features of each kind of APP from its captured data flow.  
Figure 1: System work flow of our proposed method.  
* 5. Combine the features data of each APP together, as a feature dataset, and divide it into two sub-datasets: training dataset and test dataset.
* 6. Label the training dataset.
* 7. Train the machine learning model with training dataset, and then verify its accuracy with test dataset.
## 3 Case Studies
### Transmission Clutch
#### 3.1.3 Integration of CAD Models with Knowledge Stored in Spreadsheets  
Once the main design problem has been addressed by the previous layout, the clutch DA delivers a CAD model, the parameters of which are linked together by the physical laws complying with mechanical and thermal constraints. The designer must enter the following parameters in a GUI window, linked to a datasheet (e.g., Microsoft Excel):  
* Dynamic and thermal requirements;  
Figure 5: Variation of the torque capacity by increasing the number of friction discs.  
Figure 6: Torque capacity requirement addressed by the clutch DA.  
* Lubrication oil properties and friction material;
* Reused components as constraints;
* Safety factor;
* Main dimensions.  
And it returns:  
* Dimensions of the parts;
* Clutch torque capacity;
* Maximum transmissible torque;
* Maximum disc pressure;
* Specific dissipated energy to transmit the maximum torque;
* Verification of the fatigue life.  
A design concept is quickly generated from the datasheet so that the designer can evaluate performances and feasibility. If the concept is rejected, a new concept is generated with slight parameter variations. The datasheet is used to produce the CAD models of the clutch assembly and parts. Thus, the characteristic dimensions are automatically updated, as shown in Figure 7.
# The Synthesis and Biological Evaluation of 2-(1H-Indol-3-yl)quinazolin-4(3H)-One Derivatives
## 3 Materials and Methods
### Synthesis
#### 3.3.11. Synthesis of 4-[2-(1\(H\)-indol-3-yl)quinazoline-4-yl]morpholine (**3 an**)  
To a solution of 4-chloro-2-(1\(H\)-indol-3-yl)quinazoline (**3 am**) (1.0 mmol, 279 mg) and Et\({}_{3}\)N (1.5 mmol, 202 \(\mu\)L) in MeCN (5 mL), morpholine (1.2 mmol, 103 \(\mu\)L) was added. The reaction mixture was refluxed for 1.5 h (TLC control). Then, the reaction mixture was poured into H\({}_{2}\)O (50 mL) and the product was extracted with dichloromethane (3 \(\times\) 20 mL). The combined organic layers were washed with brine (3 \(\times\) 20 mL), dried with anhydrous Na\({}_{2}\)SO\({}_{4}\), and concentrated to dryness under reduced pressure. The product was purified using column chromatography on silica gel using the mixture of petroleum ether/EtOAc (10:1) as an eluent. A yield of 0.261 g (79%), beige oil. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 11.58 (br.s, 1H), 8.72-8.68 (m, 1H), 8.26 (d, \(J\) = 2.8 Hz, 1H), 7.97 (br.d, \(J\) = 7.6 Hz, 1H), 7.84 (dd, \(J\) = 8.4, 0.8 Hz, 1H), 7.78-7.74 (m, 1H), 7.50-7.46 (m, 1H), 7.44-7.39 (m, 1H), 7.21-7.16 (m, 2H), 3.89-3.87 (m, 4H), and 3.77-3.75 (m, 4H) ppm; \({}^{13}\)C[\({}^{1}\)H] NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) = 164.0, 158.4, 152.2, 137.1, 132.5, 129.5, 127.6, 125.8, 125.0, 123.9, 122.1, 121.6, 120.2, 115.3, 114.1, 111.8, 66.0 (2C), and 50.0 (2C) ppm. HRMS (ESI) Calcd for C\({}_{20}\)H\({}_{19}\)N\({}_{4}\)O [M + H]\({}^{+}\) 331.1553; Found 331.1559.
# Capability of Advanced Ultrasonic Inspection Technologies for Hydraulic Turbine Runners
## 2 Experimental Procedure
### Flaw Size and Distribution  
For technical manufacturing constraints imposed by the small size of the flaws examined by the current study (e.g., size of flaws between 1 mm and 5 mm), the scope is limited to inclusion flaws. Inclusions designate the incorporation, in the weld bead, of a foreign compound. Besides, the case of metal inclusions is studied to control the size of the flaws and the repeatability that are defined by the imprisonment of a foreign particle in the metal's mass. These flaws were relatively present in welded joints in laboratory samples that made inclusion a representative flaw for the current study. Spherical beads of a specific size are integrated into the weld fusion to simulate an inclusion. The choice of spherical shape is justified by limiting the external error factors that are linked to the defects' positioning and orientation.  
To conduct the study, the requirement for precision on the defects' size, the obligation to respect an acceptable level of repeatability, and the flaws spherical shape guided in choosing ball bearings as the defect to be included. The extreme thermal conditions and the electric arc passage, during welding, close to the balls, forced the researchers to choose ceramic balls that were more resistant to extreme temperatures. This is because they interfeed less with the heat-welding arc because of their non-conductive electrical properties.  
The study considered three types of ceramic balls; alumina oxide balls (Al\({}_{2}\)O\({}_{3}\)), zirconia balls (ZrO\({}_{2}\)), and silicon nitride balls (Si\({}_{3}\)N\({}_{4}\)). In this regard, all three types of balls were tried to determine the best ones for the study. After a series of tests, the study found that the alumina oxide (Al\({}_{2}\)O\({}_{3}\)) balls were to be avoided. This is because, despite the fact they offer a higher operating temperature compared to silicon nitride (Si\({}_{3}\)N\({}_{4}\)), they did not resist the electric arc passage and exploded [17]. Therefore, the study chose the silicon nitride balls because they were stronger and kept their original shape.  
During welding, holes have been drilled in the weld pass to implement artificial flaws at known locations, as shown in Figure 1. Ceramic beads are inserted into these holes, and the holes are filled by welding. Thirty ceramic beads are distributed in the weld of the T-joint sample. They were of either 1, 2, 3, 4, or 5 mm in diameter (6 beads of each size). This range of discontinuity sizes reflects the critical range to initiate a fatigue crack. A three-millimeter diameter flaw has the critical size to develop a fatigue crack under cyclic loading from stress calculation. Beads are located within 75 mm from the edges of the sample in order to mimic the situation observed on in-service runners.
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 6 Numerical Experiments
### Experiment Set 2: Rate Demand Traffic  
In the second set of experiments, we add rate demand traffic (i.e., background traffic) as well as the periodic traffic. This reflects use cases where there is additional data to send on top of the control traffic. An example of such a case could be a robot navigating through a factory and performing tasks. Here, periodic data are needed to control the robot, while there may also be streaming data from cameras or other sensors to aid in navigation. This latter type of data is still a high priority as it may be safety-critical, and so, it should be scheduled in the same network slice; however, it is not subject to the same real-time deadlines as for control traffic, and in the case of video, may even be able to suffer some limited packet loss without loss of overall performance, depending on the encoding used. As such, we aim to provide a guaranteed bit rate (specified by the node's demand rates \(\hat{h}(k)\) and \(\hat{h}(k)\)) for this traffic, but do not need to ensure regular transmissions within a given period as for control traffic.  
As for the periodic traffic experiments, we define two traffic profiles, with high and low rate demands. In order to investigate the effect of the type of traffic on the performance of the optimization problem, we set the traffic rate demands to be of equal magnitude to the periodic demands. The high traffic demands are set to between 0.1 and 0.5 transmissions per slot, thus matching the short periodic demands by simply taking the reciprocal since the traffic rates represent a frequency rather than a period. The low traffic demands are similarly set to between 0.05 and 0.1 to match the long-period traffic. In our experiments, we did not distinguish between uplink and downlink demands, so for each node \(k\in\mathcal{K}\), \(\hat{h}(k)=\hat{h}(k)\), and in the following we will refer to the rate demand as simply \(h(k)\).  
Once again, we conducted three experiments for each number of nodes. Experiment 2A considered traffic where the periodic demands are dominant, with all nodes having short periods and low rate demands. Conversely, Experiment 2B considered the case where the rate demands are dominant, with long periods and high rate demands. Experiment 2C takes a mixed case, with half the nodes having short periods and low rates, and the other half having long periods and high rates. As for Experiment Set 1, node rates and periods were generated uniformly and randomly within the ranges for their assigned trafficprofiles, and we generated 10 problem instances for each experiment and number of nodes, once again taking the average and 95% confidence interval across the instances.
# Physicochemical and Antioxidant Properties of Selected Polish and Slovak Honeys
## 2 Materials and Methods
### Methods
#### 2.2.8 Electron Paramagnetic Resonance Measurements of Antioxidant Activities  
The EPR experiments were performed in duplicates, using a portable X-band EPR spectrometer e-scan ([PERSON], Karlsruhe, Germany) with accessory. As the reference, 80% methanol (\(v/v\)) was used.  
Exactly 300 \(\mu\)L of sample (prepared in the same way as in case of HMF analysis) was mixed with 700 \(\mu\)L of ABTS\({}^{\star+}\) (2,2\({}^{\prime}\)-azino-bis(3-ethylbenzthiaazoline-6-sulphonic acid) (initial concentration \(c_{0}\)(ABTS) = 0.1 mmol/L). Mixture was purged with 2 mL of air and immediately transferred into the EPR flat cell. EPR measurements started exactly 3 min. after the ABTS\({}^{\star+}\) addition and a set of 10 EPR spectra was recorded in time domain during approx. 15 min.  
The ABTS\({}^{\star+}\) radical-scavenging activities of honey samples were expressed as Trolox equivalent antioxidant capacity (TEAC\({}_{\text{ABTS}}\)\({}^{\star+}\), mmol/kg) [23].
## 4 Experiments and Analysis
### Comparative Experiments and Analysis
To further verify the effectiveness of the ARDU model on the noise reduction in actual seismic signals, comparison experiments were conducted between the ARDU model and the DnRDB model, the U-Net model, and the RDBU model. The average signal-to-noise ratio was used to evaluate the noise reduction performance of the model, which was obtained using a blind estimation algorithm of the signal-to-noise ratio. Experimental results are shown in Table 4 and Figure 14. Among them, the picture shows the denoising results of four noise reduction methods on the mixed-frequency noise in the same frequency band. Here, (a) and (b) are the actual seismic signals in the time domain and the time-frequency domain, and (c) and (d), (e) and (f), (g) and (h), and (i) and (j) are denoising signals in the time domain and time-frequency domain of the DnRDB model, the U-Net model, the RDBU model, and the ARDU model, respectively.
## 5 Conclusions  
In this study, we propose a PCA+LR two-way 2, a Pearson correlation-based score, an RF R-square-based score, an RF Pearson correlation-based score, and a CF scheme for the RF R-square-based score for two-way cooperative CF for binary market basket data. The experimental results show that the proposed two-way cooperative CF approaches work better than the existing PCA+LR two-way 1. For the Grocery dataset, the PCA+LR two-way 1 does not even provide an appropriate predicted value, which demonstrates that it is clearly outperformed by the Pearson correlation-based score and RF Pearson correlation-based score. For the non-high-dimensional EachMovie dataset, the Pearson correlation-based score as well as the RF R-square-based score clearly improve the accuracy of the one-way approaches, whereas the PCA+LR two-way 1 does not. For the extreme high-dimensional EachMovie dataset, only the RF Pearson correlation-based score and the proposed CF scheme clearly improve the performance of the one-way approaches.  
Most significantly, for the first time, we apply the proposed two-way cooperative CF approaches to the Grocery transaction dataset and obtain promising results. Two-way cooperative CF is crucial for binary market basket data; therefore, the proposed two-way cooperative CF approaches would be useful for marketing practitioners. However, the two proposed CF approaches cannot always improve the performance of the one-way CF approaches because the prediction performance depends on the datasets. In our future research, we plan to apply the proposed two-way CF approaches to other domains and employ other supervised learning approaches.  
Conceptualization, W.-Y.H. and J.-S.L.; methodology, W.-Y.H.; software, W.-Y.H.; data curation, W.-Y.H.; writing--original draft preparation, W.-Y.H.; writing--review and editing, J.-S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dong-A University research fund.  
Not applicable.  
EachMovie dataset ([[https://grouplens.org/datasets/eachmovie/](https://grouplens.org/datasets/eachmovie/)]([https://grouplens.org/datasets/eachmovie/](https://grouplens.org/datasets/eachmovie/)), accessed on 5 September 2004), Grocery dataset (arules R package).  
The authors declare no conflict of interest.
## 1 Introduction
They exclusively concentrate on creating protocols that can accommodate the UAV network's primary requirements, which include high mobility, dynamic topology, intermittent connectivity, power limits, and variable link quality. Second, many studies concentrated on UAV applications that either tracked or targeted ground equipment, but the UAVs lacked cooperation since they employed sensors to detect their target ground user equipment before tracking it. However, no consideration is given to UAVs and target equipment communicating. Researchers in the third category concentrated on the ground or aerial base station that serves the user equipment that is located on the ground. The base station connects to and manages UAVs. The UAVs' ability to communicate with one another is not taken into account. For controlling and deploying UAVs in disaster or afflicted areas, multiple controllers are also deployed at once. A controller can connect the UAVs' network to other networks and communicate with at least one UAV to transfer control packets. In this research, we proposed a communication protocol called intelligent cluster-based multi-unmanned aerial vehicle (ICBM-UAV) to get beyond the aforementioned restrictions. The ICBM-UAV protocol has three different modes of communication; base station (BS) comes first, followed by user equipment (UE), then drone (UAV). For an effective survey of the ground and detection of the user equipment, a protocol scenario where there is a significant number of user equipment in the ground was taken into consideration. The suggested work is separated into two sections: first, the scanning part, in which a UAV scans the area and marks or identifies the precise location of the smart devices; second, the part in which base station information is provided; the last section offers communication through UAVs throughout the entire damage area. One CH (cluster head) is made up of an average of every five drones in the proposed protocol, which allows for effective communication. The remainder of this paper is organized as follows. Section 2 introduces the literature review of a different model of the UAV-based search and rescue model in various situations. In Section 3, the proposed algorithm and the hovering positions for the UAVs based on different creation of cluster searching strategies are described. Section 4 presented the simulation results, and Section 5 concludes the paper.