esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 3 Materials and Methods
### Hardware Components for Data Collection  
A connection was established between the DCC railway control system and the industrial PLC to collect data from the model railway. Signal conditioning units were created to convert raw data from the model railway into usable measurements for the SIMATIC PLC [11, 12]. This conversion was essential for obtaining information on track occupancy, switch positions in train stations, and the status of level crossings.  
Track occupancy data are gathered using the LB 101 module from Lenz (see Figure 4), which operates on an antiparallel diode configuration to detect current flow when a train occupies an isolated track section.  
The interlocking system must continuously know the position of all switches within the train station. For this purpose, the model railway employs Tillig switch machines (see Figure 5). Two relays with normally open contacts connected to the SIMATIC S7-1200 PLC inputs provide feedback for the control system. The motor's DC power supply is cut off when the switch reaches its end position, and the end feedback relay contact closes.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 3 Proposed Architecture  
This Section presents the proposed architecture, along with the software modules and hardware used to create an aquatic monitoring platform, capable of collecting environmental and quality data from the sensors and send it to a remote server. Section 3.1 overviews the proposed network architecture along with its requirements, and Section 3.2 presents the network elements along with its hardware and software characterization.
## 2 Two-Stage Time-Domain Equalization Method
### THz Wireless Communication System  
In the THz wireless communication system, a pseudo-random bit sequence is first encoded using low-density parity-check (LDPC) coding and modulated using quadrature phase-shift keying (QPSK). The resulting symbols are then mapped to baseband In-phase and Quadrature (I/Q) signals via a field-programmable gate array (FPGA) and a digital-to-analog converter (DAC). Subsequently, the baseband signal is upconverted to the target carrier frequency through a radio frequency (RF) chain for transmission. At the receiver side, the signal is first downconverted to an intermediate frequency (IF) using a down-conversion module, amplified by an intermediate-frequency low-noise amplifier (LNA), and further downconverted to baseband. The baseband signal is then digitized using an analog-to-digital converter (ADC) and processed by an FPGA. Frame synchronization is performed to extract valid segments for DL. After synchronization, the signal is fed into the proposed two-stage DL-based equalization module. Due to the highly directional nature and narrow beamwidth of THz signals, and given that the experiments are conducted under short-range line-of-sight (LOS) conditions, the effect of multipath interference is considered negligible [29; 30; 31; 32]. It is also important to note that the selected channel is far from molecular resonance absorption frequencies, and the communication distance is relatively short [33; 34; 35]; thus, in this study, the dominant source of nonlinearity is attributed to the THz hardware itself.
## 3 Experiment and Results
### Experimental Platform  
The experimental setup depicted in Figure 13 was used to evaluate the proposed sensor. Particles were generated by burning the sample in the chamber while using a fan to mix the exhausted gas with air thoroughly, then pumped into the airbags. For the sample, we chose leaf fragments whose combustion could produce particles of various diameter from 0.5 \(\upmu\)m to larger than 10 \(\upmu\)m, with a median of about 1 \(\upmu\)m, which met our needs [26]. We set the air pump (AirChek TOUCH, SKC Inc., PA, USA) to pump particle-containing gas at a constant flow rate of 2 LPM. Before each use, we pumped in nitrogen to clean the airflow pipes to prevent interference of the particles in the remaining airflow. The dual ports of the surface acoustic wave resonator were connected to RF connectors on  
Figure 11: Fabrication process of SAWR’s electrodes.  
Figure 12: The photograph of the cascade impactor assembled with the surface acoustic wave resonator: (**a**) sensor; (**b**) SAWR placed on the impaction circuit board; (**c**) 311 MHz SAWR.  
the impaction plate. The phase response change caused by the particle attached to the sensitive surface of the SAWR was discriminated by the phase discriminating circuit. The result was showed in voltage (mV) on the PC in real-time. A network analyzer (E5061B, Agilent Technologies, CA, USA) was used to observe the chip's amplitude-frequency and phase-frequency response curve.  
The SAWRs placed on impaction plates will have different phase shifts due to the different particles loaded on the surface; therefore, the voltage signal change rate (mV/s) represents the particulate mass change per second. We linear fitted the voltage signal when processing response data, and its slope was the change rate of the voltage.
## 7 Heuristic Security Analysis
### Perfect Forward Security  
Even with access to keys like \(s_{j}\) and \(k\), adversaries cannot calculate the session key \(SK_{ij}\) due to the inclusion of \(Q_{ij}\) in the initial authentication phase. Since the necessary random numbers \(b_{i}\) and \(b_{j}\) for the computation of \(Q_{ij}\) are beyond the reach of adversaries, determining \(SK_{ij}\) is infeasible. This unavailability also extends to the \(TID_{i}\) required for subsequent handover authentication, ensuring our scheme's provision of perfect forward security.
## 3 Results  
### Comparative Experiment  
To evaluate the registration performance of MCANet and other advanced registration methods on both the Mindboggle101 and Neurite OASIS datasets, we conducted comprehensive comparative experiments. The results, listed in Table 1, include average Dice scores, the degree of folding of the deformation field (measured by the number and percentage of folded voxels), and other registration performance indicators. The data clearly demonstrate that MCANet achieves the highest registration accuracy across both datasets. On the Mindboggle101 dataset, MCANet significantly outperforms traditional methods, improving the Dice score by 0.252 compared to Affine (\(p<0.001\)), 0.129 compared to SyN (\(p<0.01\)), and 0.145 compared to LDDMM (\(p<0.001\)). In terms of registration speed, MCANet is markedly faster than SyN and LDDMM (\(p<0.001\) for both comparisons). On the Neurite OASIS dataset, MCANet also shows substantial improvements, with a Dice score increase of 0.250 over Affine, 0.119 over SyN, and 0.198 over LDDMM, all with \(p\)-values less than 0.001. Compared to deep-learning-based methods, MCANet achieves a Dice score improvement of 0.018 over FAIM (\(p<0.05\)), 0.056 over VoxelMorph-1 (\(p<0.001\)), and 0.034 over VoxelMorph-2 (\(p<0.01\)) on Mindboggle101. On Neurite OASIS, MCANet attains a Dice score of 0.782 \(\pm\) 0.023, slightly lower than Transmorph (0.784 \(\pm\) 0.054) but significantly better than other deep-learning methods. Figure 4 clearly shows the remarkable effect of MCAnet. In the box graph of Dice score in the upper left corner, MCAnet outperforms or is equivalent to traditional algorithms on both datasets, which indicates that MCAnet has advantages in maintaining high registration accuracy. The centerline map of SDlogl box shows that the deformation field of MCANet is smoother, which is a significant advantage compared with the traditional algorithm, because, although the traditional algorithm performs well in the smoothness of the deformation field, it needs to carefully adjust the parameters, and with the increase of parameters the folding rate of the deformation field increases linearly, which may lead to complex and difficult to explain deformation results. In the box graph at the lower left corner and the lower right corner, MCAnet shows excellent performance, and its deformation field folding rate is low, which further proves the ability of MCAnet in generating natural deformation results. MCANet can provide accurate registration results
## Results and Discussion
### Autoaggregation and Coaggregation  
In most cases, aggregation ability is associated with cell adhesion properties. Bacterial aggregation between microorganisms of the same strain (autoaggregation) or genetically different strains (coaggregation) is of great importance in the human gut [26].  
MKHJ 1-1 showed autoaggregation ability with 91.32 \(\pm\) 0.74% after 24 h (Table 5). The autoaggregation percentages of _B. clausii_ ATCC 700160 and _B. subtilis_ P223 were reported to be 93.42 \(\pm\) 0.86% and 86.03 \(\pm\) 2.46% after 24 h incubation, respectively [27]. MKHJ 1-1 was found to have outstanding autoaggregation ability compared to previously reported _Bacillus_ strains. A strain with high autoaggregation ability is known to have high cell adhesion [35]. Thus, MKHJ 1-1 is expected to have high cell adhesion.  
Coaggregation ability was tested using _E. coli_ KCTC 1682 and _S. aureus_ KCTC 3881. After 24 h, coaggregation of MKHJ 1-1 (61.72%) was similar to the results of [PERSON]'s study (2017) [27], in which the coaggregation percentages of _B. subtilis_ P223 with _S. aureus_ ATCC 6538 and _E. coli_ ATCC 25922 were 68.96% and 70.19%, respectively. Coaggregation with a potential pathogen has demonstrated that probiotic strain can inhibit the growth of pathogens in the gastrointestinal and urogenital tracts by producing antimicrobial substances [37].  
Overall, MKHJ 1-1 exhibited desirable autoaggregation and coaggregation ability as a potential probiotic strain.
## 3 Study Area and Data  
This section outlines the study area, the sources of the datasets, and their processing. The study uses multi-source datasets, including bikeshare data and metro station data in Shenzhen, which were all collected in 2018 to ensure that the multi-source data could be integrated.  
Study area: Shenzhen is located on the southeast coast of China, with 10 administrative regions under its jurisdiction. It is a national economic center city and an international comprehensive transportation hub city, with strong transportation impact both domestically and internationally. This paper uses Shenzhen as the study city. To be consistent with the time of the study data, the study area of this paper is the six administrative districts (Futian District, Luohu District, Nanshan District, Baoan District, Longgang District, and Longhua District) that operated the metro in the same period in Shenzhen. The study area is shown in Figure 1a.  
Metro station data: The metro stations opened for operation in Shenzhen in 2018 can be found at [[https://www.szmc.net](https://www.szmc.net)]([https://www.szmc.net](https://www.szmc.net)) (accessed on 10 November 2023). The metro station data are obtained through the API of AMAP (a map application, [[https://www.amap.com/](https://www.amap.com/)]([https://www.amap.com/](https://www.amap.com/)),(accessed on 10 November 2023)), and the spatial distribution of metro stations is shown in Figure 1b.  
Bikeshare data: This paper uses bikeshare order data from October 24 (workday) and 28 (non-workday) in 2018, including OFO, MOBIKE, One Step, U-bicycle, and 99 bicycle. The raw data contain the sequence number, bike ID, date, timestamp, longitude, latitude, and status. Historical weather information from [[http://lishi.tianqi.com](http://lishi.tianqi.com)]([http://lishi.tianqi.com](http://lishi.tianqi.com)) (accessed on 8 November 2023) shows that the weather during this period was sunny or cloudy, with suitable temperatures and low wind levels having less impact on the demand for bikeshare trips. Using the Transbigdata package [32] in Python, the travel chains containing the latitude, longitude, and timestamps of the origin and destination of bikeshare trips were extracted, and those with a travel time of less than 1 min or more than 30 min and a travel distance of less than 100 m or more than 5000 m were eliminated [18; 33]. The spatial distribution of the origin of bikeshare on the workday and non-workday is shown in Figure 1c,d, while the spatial distribution of the destination is shown in Figure 1e,f.  
In addition, to ensure the accuracy of the results, the multi-source data were converted to the same coordinate system, and the WGS84 coordinate system was used in this study.
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Standards of NOMA  
The integration of NOMA with enhanced mobile broadband (eMBB) improves the data rate fairness, capacity, and QoS between users in ultra-dense networks as discussed in [44; 45; 46]. NOMA, on the other hand, resolves the huge connectivity and vast coverage area requirements for URLLC and massive machine type communication (mMTC). If the transmission is grant-free, it delivers better quality of link with minimal latency. Furthermore, under LTE Release-13, the 3 GPP introduced multi-user superimposed transmission (MUST) as an initial standard for downlink (DL) applications. The 3 GPP classifies MUST system into three main groups based on DL scenarios: MUST 1, MUST 2, and MUST 3.  
Figure 3: Road map of the paper.  
In MUST 1, the constellation symbols are constructed without dependence on the grey mapping by mapping two users bits separately. Grey mapping is used in MUST 2 to combine the user's bits to generate constellation symbols. MUST 3 uses the grey mapping, but the power ratio of signal is not applicable in this standard.
## 5 Other Applications  
### Stand-Up Time  
In subsurface openings, time-dependent loss of the rock mass is normal, causing either accelerated damage or slow damage [122]. A practical consequence of time dependency in tunneling is the amount of time the excavated rock mass can remain unsupported before instabilities occur, resulting in failure and collapse. This period of time to collapse is known as the stand-up time. The stand-up time was established as an empirical classification system [13]. According to this classification, the rock mass was classified into seven classes and a relation between the rock-mass classes, stand-up time and active span was established. The stand-up time classification was one of the three classification systems that were considered during the development of the \(RMR\) system and a chart was developed to correlate the predicted stand-up time for the unsupported span of the tunnel and the \(RMR\) score [8]. Along with the update of the \(RMR\) system for tunneling, the stand-up time chart was also updated for drill and blast tunnel [22] and was also revised for \(TBM\) tunnel [123].  
In the \(Q\) system, instead of providing the stand-up time, the system suggests the span of the unsupported tunnel based on the tunnel-quality index value, that is, \(Q\). The unsupported span can be calculated using Equation (64) [9].  
\[D_{e}=2\times Q^{0.4} \tag{64}\]  
The general condition requirement for the unsupported span was expressed for different ESR values [85]. Based on the correlation between the \(RMR\) and \(Q\) systems, the stand-up time for an unsupported span was extended for the \(Q\) system [53], as shown in Figure 1.  
Figure 1: Stand-up time versus unsupported span of the tunnel for different \(RMR\) and \(Q\) values.
## 3 Experiments and Results  
### Experiment 1  
This experiment was based on Set 2. The accuracy of the improved vertical measurement model (Equations (16) and (17)) was compared with the original method [24] using 10-fold cross-validation. The original method used the value of \(30^{\circ}\) for the standard angle. At this angle, the grid becomes a square that is parallel to the x- and y- axes, which facilitates the labeling of the laser points. The same standard angle was also used for the proposed improved version. Recordings from Set 2 were split into training and testing sets. Both models were trained using the training set. The trained models were then applied to the testing set and measurement errors were computed. First, the effect of different laser points on the error was investigated. MAE was computed for each laser point averaged over all working distances. Figure 8A shows the result. Based on this figure we see different laser points exhibit different performances in the original method, where the top-row laser points (refer to Figure 7A) produce inferior results. Conversely, all laser points exhibit comparable performances in the improved PCA method. A second analysis was conducted to test the effect of working distance on the accuracy of both methods. For this analysis, the magnitude (i.e., absolute value) of error from laser points at different working distances were computed. Additionally, laser points from the top row were discarded from the original method and only the remaining 42 laser points were used. However, all 49 laser points were used for the analysis of the improved PCA method. Figure 8B shows the results. The lines represent the linear model fitted on the individual data points. We can use the slope of regression lines to compare the dependence of the magnitude of error of different methods on the working distance. Slopes of original and PCA methods were 0.008 and 0.001 mm/mm, respectively. Therefore, we may conclude that the performance of the improved PCA method is less dependent on the working distance.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 1 Introduction
This work addresses the challenge of effectively extracting discriminative features for breast cancer detection from mammograms using lightweight and computationally efficient learning models, without relying on data augmentation or large training datasets. This challenge is particularly relevant in clinical scenarios where data availability is limited and computational resources are constrained. Initially, the mammograms are preprocessed to remove annotations and suppress noise. Afterwards, an ROI is extracted from each mammography, containing the potential cancer lesion. Each extracted ROI is then transformed into a three-channel image consisting of the local phase, local orientation, and phase congruency features in each channel, respectively. These components are obtained with the help of the first-order Riesz transform. Next, a data vector of 1280 elements is obtained from the ROI, employing two variants of EfficientNetV2 trained on an image dataset composed of the Mammogram Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM), and An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer (CSAW-M) datasets. Afterwards, a compact five-element feature vector is obtained by computing different statistics from the resultant 1280-element data vector. Finally, four classifier ensembles (voting, stacking, boosting, and bagging) are considered for breast cancer classification. The main contributions of this work are as follows: (1) a simple preprocessing strategy is introduced to eliminate undesired artifacts and enhance the quality of regions of interest (ROIs) in mammography images; (2) a novel image representation method based on phase information is proposed, transforming ROIs into three-channel images that capture important structural details that are not easily distinguishable by conventional techniques; (3) an efficient CNN-based feature extractor is developed by adapting EfficientNetV2 variants for well-known mammography datasets; and (4) an effective approach is suggested to generate compact feature vectors using basic statistical techniques, thereby reducing overfitting and simplifying classification tasks. These contributions allowed the proposed method to achieve high classification performance without relying on data augmentation or extensive hyperparameter tuning, reducing complexity and contributing to the feasibility of practical implementation in clinical settings. The paper is organized as follows. Section 2 presents a review of existing methods for breast cancer detection in mammograms. Section 3 explains in detail the proposed methodology. Section 4 presents the results obtained with the proposed methodology for breast cancer detection in terms of objective measures. These results are discussed and compared with respect to existing state-of-the-art methods. Finally, Section 5 presents the conclusions.
# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 6 Discussion
### Real-Life Applications and Limitations  
There are many use-cases for the presented datasets and models to enable improvement of meaningful life outcomes. [PERSON] et al. [81] presented an overview of the psychophysiological measures that can be utilized to assess cognitive states while driving. The psychophysiological measures included EEG, optical imaging, heart rate and HRV, blood pressure, GSR, ECG, thermal imaging, and pupillometry. Another use-case includes measuring workload of pilots. For example, [PERSON] et al. [82] analyzed HRV features for measuring the cognitive workload of 20 fighter aircraft pilots in a flight simulator environment. The statistical analysis in their study revealed a strong significant difference between workload with respect to HRV parameters. [PERSON] et al. [83] analyzed cognitive load in five physician team leaders during trauma resuscitation. Eye-tracking, GSR, and heart rate measures were captured during trauma resuscitations in a real-world setting. [PERSON] et al. [84] used psycho-physiological measures to assess task difficulty in software development. They conducted a study with 15 professional programmers to see how well an eye-tracker, a GSR, and an EEG sensor could be used to predict whether developers would find a task to be difficult. [PERSON] et al. [85] explored PPG, EEG, temperature, and pupil dilation sensors to assess the mental workload of 61 participants during web browsing. They evaluated Multinomial Logistic Regression, SVM, and MLP models using 70%:30% train-test split. The best signal modality was EEG with an accuracy of 70%, while the rest of the modalities achieved an accuracy around 35%.  
The size of the datasets used in our study is comparable to the related studies on cognitive load [82; 83; 84; 86; 87; 88]. However, the findings should be confirmed in a larger study with more participants, in order to draw general conclusions.  
Finally, the secondary task used in the CogLoad dataset may be problematic for participants with vision problems. Any individual differences here could have skewed results. In future similar studies, vision should be taken into account.