esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Frequency-Domain Third-Order Spherical Harmonics (FD-SP\({}_{3}\)) Light Propagation Model  
The frequency-domain simplified spherical harmonics (FD-SP\({}_{\text{N}}\)) model can be obtained from the time-domain simplified spherical harmonics (TD-SP\({}_{\text{N}}\)) model, assuming that the refractive index in the medium is spatially invariant and that the discrete ordinates (\(\hat{\varOmega}\)) are independent from spatial position (i.e., \(\
abla_{r}\cdot\hat{\varOmega}=0\)) [39]. The SP\({}_{3}\) model for \(N=3\), along with appropriate boundary conditions, is presented below,  
\[-\
abla\cdot D_{1}\
abla\phi_{1}+\bigg{(}\mu_{a}+\frac{i\omega}{v}\bigg{)}\phi _{1}=\dot{q}+\frac{2}{3}\bigg{(}\mu_{a}+\frac{i\omega}{v}\bigg{)}\phi_{2} \tag{1}\]  
\[-\
abla\cdot D_{2}\
abla\phi_{2}+\bigg{(}\frac{4}{9}\mu_{a}+\frac{5}{9}\mu_{a 2}+\frac{1}{3}\frac{i\omega}{v}\bigg{)}\phi_{2}=-\frac{2}{3}\dot{q}+\frac{2}{ 3}\mu_{a}\phi_{1} \tag{2}\]  
where \(\phi_{1}\) and \(\phi_{2}\) denote composite moments of the fluence in units of Wcm\({}^{-2}\), and \(D_{1}=\left(3\mu_{a,1}\right)^{-1}\) and \(D_{2}=\left(7\mu_{a,3}\right)^{-1}\). The \(n\)-th order absorption coefficient is given by \(\mu_{a,n}=\mu_{a}+(1-g^{n})\mu_{s}\), where \(\mu_{a}\) is the absorption coefficient, \(\mu_{s}\) is the scattering coefficient, and \(g\) is the anisotropy factor. The modulation frequency of the source laser is denoted by \(\omega\), \(v\) is the speed of light in the medium, and \(\dot{q}\) denotes an internal source of light.  
Following the node based control volume finite element (CVFEM) discretization [33], Equations (1) and (2) can be rewritten into a system of linear equations: \(Au=b\), where \(A\) is the discretized SP\({}_{3}\) model, \(u=\left(\phi_{1}^{T},\phi_{2}^{T}\right)^{T}\) is the composite moments of the fluence, and \(b\) is the source vector that accounts for boundary or/and interior light sources. This linear system \(Au=b\) was solved using the restarted generalized minimal residual (GMRES) algorithm [40]. The transillumination measurement \(M\) on CCD pixels can be expressed as the following linear function:  
\[M=Qu \tag{3}\]  
where \(Q\) is the measurement operator that projects the composite moments of the fluence from the mesh surface space onto the ICCD image space.
# Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning
## 2 Materials and Methods
### Cervical Cancer Classification
On the other hand, [PERSON] et al. evaluated pre-trained DenseNet-201 models for Pap smear image classification, underscoring the value of transfer learning in leveraging large-scale data sets for specific medical applications [25]. Further improvements showed very good classification results when combining 3D convolutional neural networks with Vision Transformer modules to achieve a remarkable 98.6% accuracy [26]. In the literature, we can also see a variety of studies exploring the application of feature-based approaches but focusing mainly on texture descriptors [27]. These studies have shown that LBP enhances the efficiency of deep learning architectures, especially in the context of content-based image retrieval (CBIR) systems [28]. The development of new feature extraction techniques, including the square symmetric LBP texture descriptor(SSLBP) and the merging two-class linear discriminant analysis (M2 CLDA) approach, have improved the performance of image retrieval and classification systems [29]. These studies collectively emphasize the capabilities of LBP and additional feature extraction techniques to improve the performance of deep learning models. From the above results, we can conclude that the introduction of liquid-based cytology considerably improved the cancer diagnosis process. This process extended features taken into account, and therefore, the morphometric analysis of the LBC features is important. To our knowledge, from the works described above only the attempts of [PERSON] et al. [21] and [PERSON] et al. [30] provide the calculation of cervical cancer features from Pap smears. The initial and most recent method employs convolutional neural networks to evaluate features. This study proposes an alternative approach that integrates handcrafted feature-based methods with feature selection techniques to address these limitations. By employing stepwise and significant feature selection, the dimensionality of feature vectors is reduced, retaining critical diagnostic information while enabling efficient and interpretable classification. The approach demonstrates competitive accuracy with reduced computational complexity, offering a cost-effective solution particularly suited for low-data environments. In Section 2.4, we propose a set of features resembling the traditional LBC features.
## 4 Model Validation and Analysis
### Experimental Setup  
All models in this experiment were implemented using the PyTorch framework (version 2.0.1). Training and testing were carried out on the high-performance computing platform of China University of Geosciences (Beijing), leveraging NVIDIA A800 GPUs (80 GB) with CUDA 11.8 for acceleration. A weighted combination of Cross-Entropy Loss and Dice Loss was adopted as the loss function and optimized with Adam. The learning rate was dynamically updated on the basis of the F1-score, with the patience parameter set to 15. The initial learning rate was set to \(1\times 10^{-4}\). A ReduceLROnPlateau scheduler monitored the validation-set F1-score after each epoch; if no improvement was observed for 15 epochs (patience = 15), the learning rate was multiplied by a factor of 0.5. To stabilize early optimization, a linear warm-up from 0 to the base learning rate was applied over the first five epochs. Training continued until either the maximum epoch count (200) was reached or the learning rate decayed below \(1\times 10^{-6}\), whichever came first. These settings were selected empirically to balance convergence speed and generalization. Each input patch was resized to 512 \(\times\) 512 pixels, the maximum number of training epochs was 200, and the batch size was 20. Details of the experimental dataset are provided in Section 2.3.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Datasets for Analysis  
Two distinct sets of data were examined. The initial two objectives of the study focused on analyzing the first dataset, which comprised 2700 stimulus-based samples (15 subjects \(\times\) 12 sessions \(\times\) 15 stimuli). This dataset aimed to investigate changes in EEG and eye-related variables during tests of varying difficulty levels and to develop machine learning algorithms for predicting mental workload levels. The reason for considering the dataset on a stimulus basis was that the mental processes of visual perception, recall, matching, and reaction occurred within a 2500-millisecond timeframe after each stimulus was presented on the screen. Statistical analysis was conducted using SPSS 21.0, while Python 3.8 was utilized to establish classification models.  
The second dataset aimed to explore changes in band power across different brain regions under various task conditions and to visualize the statistical findings. It consisted of 180 set extension files of raw EEG data obtained through EEG device software (version 2.6.4). These files were converted into set extension files using MATLAB for each participant's session and underwent data pre-processing with EEGLAB. The dimensions of the files for the 0-back task were 4800 \(\times\) 14; for the 1-back task, 5120 \(\times\) 14; for the 2-back task, 5440 \(\times\) 14; and for the 3-back task, 5760 \(\times\) 14. The number of observations varied based on the number of stimuli in each task. As the EEG device had 14 channels, the columns in the dataset corresponded to the electrodes.
## 9 Conclusions
* In this work, the used oscillator is the Citizen CMR200T, which has a cost of US$ \(0.25@2K\), and also, the basic thermistor (US$ \(0.06@2K\)) is needed to measure the temperature, which makes a total of US$ \(0.31\). On the other hand, a self-compensated oscillator with temperature (TCXO) can be used to adjust the frequency of the oscillator, for example, the KT3225T oscillator, which is a TCXO model with the same characteristics of CMR200T, but whose cost is about US$ \(3.10@3K\). With the proposed methods, the hardware cost of the motes can be reduced by simply using software mechanism to compensate for the drift of the frequency. * The basic mechanism (AT) is suitable for environments where the temperature of the reference node is not available (e.g., with faulty temperature sensors). It requires low computational power. Besides, it uses less bandwidth, as no temperature is sent in the synchronization packets. * The advanced adjustment mechanism (A2T) takes into account both the local temperature and the reference node temperature. It provides better accuracy than the AT method, although it has higher computational requirements and a higher bandwidth usage.
## 2 Data and Methods  
### Data  
#### 2.1.1 Eddy Covariance Data  
The eddy covariance method is a standard method to directly measure fluxes between ecosystems and the atmosphere by computing the covariance between the vertical wind velocity and the entity of interest [21; 25; 26]. Such measurements are made routinely on all continents [27; 28; 29], with many sites linked across a global network called FLUXNET, dispersed across the world's climate zones and representative biomes [29; 30; 31; 32; 33]. FLUXNET teams standardize the data format, perform uniform quality checks, and use highly vetted gap-filling and flux partitioning, with the newest product version being the FLUXNET2015 dataset (we used data for 2000-2014), which includes over 212 sites [32] (see Figure 1).  
We used data from 171 Tier 1 sites with a data policy consistent with the Attribution 4.0 International (CC-BY-4.0) data usage license. Daily net ecosystem exchange (NEE) was filtered using a variable friction velocity threshold for each year and quality flags indicating the measured and good quality percentage (data higher than 0.8 were used, indicating percentage of measured and good quality gap-fill data). The following land-cover classes based on the International Geosphere-Biosphere Programme (IGBP) classification were used: cropland (CRO); grassland (GRA), which includes grasslands, closed shrublands (CSH), and open shrublands (OSH) [34]; evergreen needleleaf forest (ENF); deciduous broadleaf forest (DBF); evergreen broadleaf forest (EBF); mixed forest (MF); wetland (WET); and savannas and woody savannas (SAV).
## 3 Simulation Model for Surveillance Systems
### The Overview of Simulator Structure
* Initialization and Configuration: To ensure consistent randomness, it is imperative to set the random seed. This should be done prior to configuring the surveillance cameras, which involves determining their positions and properties. Additionally, parameters for pedestrians, including their initial positions and behavior, should be set up. Finally, the road layout should be configured, defining lanes, intersections, and dimensions. * Main Simulation: The execution of the simulation loop is the core of the simulator, and as such, it must be executed seamlessly. Continuously updating the positions of moving pedestrians based on their behavior is necessary. It is also imperative to monitor the pedestrians' interactions with the environment and cameras. The system's performance should be measured by tracking detection successes, failures, and spatial resolution. * Visualization: Visualizing the surveillance cameras' coverage areas and positions is crucial, as is displaying the movements of pedestrians within the simulation environment. The road layout should be rendered, showing the lanes and intersections. * Post-Processing: Saving and storing the simulation results, which include data on pedestrian paths, camera detections, and system performance, are essential. Furthermore, options to print or export the simulation outcomes for further analysis and reporting should be provided.
# Bundle Enrichment Method for Nonsmooth Difference of Convex Programming Problems
## 6 Numerical Experiments
### Solvers and Parameters  
We consider the following nonsmooth DC optimization solvers in our comparison:  
* Augmented subgradient method for nonsmooth DC optimization (ASM-DC) [36];
* Aggregate subgradient method for nonsmooth DC optimization (AggSub) [35];
* Proximal bundle method for nonsmooth DC optimization (PBDC) [29];
* DC Algorithm (DCA) [8; 20];
* Proximal bundle method for nonsmooth DC programming (PBMDC) [30];
* Sequential DC programming with cutting-plane models (SDCA-LP) [25].  
The parameters in algorithm BEM-DC are chosen as follows: \(\eta=10^{-7}\), \(\delta_{min}=10^{-5}\), \(\theta=0.5\), \(\mu=0.2\), \(\sigma_{1}=0.2\), and \(\sigma_{2}=0.4\). The same set of parameters is used with all the test problems. The initial value of the proximity parameter is computed according to the recommendation given in [46]; that is, \(\delta_{1}=\|\xi_{1,1}-\xi_{2,1}\|\). We select the parameters of other algorithms considering the recommendations given in their references.  
The algorithms BEM-DC, ASM-DC, AggSub, PBDC, and DCA are implemented in Fortran 95 and compiled using the gfortran compiler. For PBMDC and SDCA-LP, we use their MATLAB implementations, available at [[http://www.oliveira.mat.br/solvers](http://www.oliveira.mat.br/solvers)]([http://www.oliveira.mat.br/solvers](http://www.oliveira.mat.br/solvers)) (accessed on 1 August 2023). Since the SDCA-LP algorithm requires the feasible set to be compact, to apply this method to unconstrained problems, we define a large \(n\)-dimensional box around the starting point and consider only those points generated by the SDCA-LP that belong to this box.  
We set a time limit for all algorithms. For solvers in MATLAB, we consider the limit to be three hours, and for those in Fortran, the limit is half an hour. The source code of algorithm BEM-DC is freely available on GitHub: [[https://github.com/SnTa2019/Nonsmooth-Optimization](https://github.com/SnTa2019/Nonsmooth-Optimization)]([https://github.com/SnTa2019/Nonsmooth-Optimization](https://github.com/SnTa2019/Nonsmooth-Optimization)) (accessed on 1 August 2023) and at [[http://napsu.karmitsa.fi/bemdc](http://napsu.karmitsa.fi/bemdc)]([http://napsu.karmitsa.fi/bemdc](http://napsu.karmitsa.fi/bemdc)) (accessed on 1 August 2023). We carry out our numerical experiments on a computer with Intel(R) Core (TM), i7-9750H, CPU @ 2.60 GHz, 32 GB (RAM), under Windows 10, 64 Bits. Since the solvers PBMDC and SDCA-LP are implemented in MATLAB, we do not report their CPU time in our comparison.
## 4 Measurement Methods and Data Analysis
### Data Analysis
#### 4.2.1 Analysis of the Data Collected at Different Distances with Equal Spacing
For example, when the measurement distance is 1 m and the equal spacing of the laser tracker is set to 5 mm, 10 mm, and 20 mm, the dynamic indication error is \(-0.0253\) mm, \(-0.0273\) mm, and \(-0.0191\) mm, respectively. When the measurement distance is 2 m and the equal spacing of the laser tracker is set to 5 mm, 10 mm, and 20 mm, the dynamic indication error is \(-0.0146\) mm, \(-0.0194\) mm, and \(-0.0143\) mm, respectively. When the measurement distance is 3 m, the dynamic indication error is \(-0.0117\) mm, \(-0.0026\) mm, and \(0.0020\) mm, respectively. The value of the dynamic indication variable increases with the increase of the measurement spacing. Figure 8 shows the relationship between measured distance and dynamic indicating value error variate. Under the same distance, the dynamic indication variable is irregular when the measurement spacing set by the laser tracker increases. For instance, when the measurement distance is 1 m, and the equal spacing of the laser tracker is set to 5 mm, the dynamic indication variable is 0.00409 mm, and when the measurement distance is 2 m, the dynamic indication variable is 0.00462 mm. When the measurement distance  
Figure 6: The relation between dynamic limit speed and actual measured interval. Figure 7: Measurement distance and dynamic display error diagram—(equally spaced). is set to 3 m and the equal spacing of the laser tracker is 5 mm, 10 mm, and 20 mm, the dynamic indication variable is 0.01240 mm, 0.00615 mm, and 0.00995 mm, respectively.
## 4 Conclusions  
In summary, the chemical states of PPyNPs are investigated at various pH levels for application in hazardous-gas sensors. The structure of the polymer chain changes from the expanded to contracted state, with an increase in pH. The nanoparticles at pH 1 show higher electrical conductivity (128 S cm\({}^{-1}\)) and larger diameters than those at other pH levels, due to an increase in the amount of charge carriers (hole) in the chain structure and the osmotic effect of the counter ions. As an NH\({}_{3}\) sensing transducer material, the nanoparticles at pH 1 have short response and recovery times of less than 20 s and 50 s, respectively, with high sensitivity (down to 1 ppb) at 25 \({}^{\circ}\)C. Thus, the performance of the conductive nanostructures is optimized through chemical-state control, and the proposed method can be effectively applied for the development of electrical devices and sensor systems.  
W.K. performed the experiments; [PERSON] conceived and designed the experiments; [PERSON] analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by Korea government (NRF-2018R1C1B5041562).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Sensing performance histogram of the PPyNPs treated at pH 1 toward different oxidizing and reducing volatile gases. The concentrations of the gases are 1 ppm for NH\({}_{3}\) and MeOH and 100 ppm for other gases.  
Figure 9: Normalized resistance changes of the electrodes with periodic exposure to 0.1 ppm of NH\({}_{3}\) for 15 days (black: pH 1; red: pH 7; blue: pH 13).
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ2: Parameter Influence  
In this section, we answer the research question RQ2 about the influence of hyperparameters. Specifically, we investigate the two hyperparameters \(\lambda\) and \(\tau\) (see Section 4.1). The trade-off parameter \(\lambda\) controlled the importance of calibration during the optimization stage of our model. The parameter \(\tau\) reshaped the predicted scores of all items, which can affect the computation of the calibration loss function.
## 4 Results  
### The VTS Systems' Locations  
VTS systems located in Poland operate in designated areas (Figure 1).  
The VTS Zatoka Gdanska system supports all vessels operating in the Gulf of Gdarsk area after crossing the so-called \"reporting line\". All ships in this area should use TSS Zatoka Gdanska [2; 7; 52; 53].  
Figure 2: Research methodology.  
The VTS Lawica Shupska system services all ships sailing in the central part of the Polish coast and the Slupsk Bank. Ships are obliged to use TSS Lawica Shupska. This VTS system is located entirely within the area of Polish territorial waters [8].  
The VTS Swinoujscie-Szczecin system services ships over 20 m long entering this VTS system's area, as designated by reporting lines. Vessel traffic in the area covered by the VTS Swinoujscie-Szczecin system takes place mainly on a section of the \"natural\" fairway delineated in the Pomeranian Bay and an \"artificial\" fairway, dredged in the Oder riverbed [9; 11; 54].  
A comparison of the VTS systems taking into account their location is presented in Table 2.  
To sum up, it should be mentioned that considering the location of each VTS system, the vessel traffic served is different. It consists of ships involved in international shipping, so-called regular SOLAS vessels, as well as port traffic ships, such as passenger vessels, inland barges, etc. The types of served vessels impact the VTS systems services and decision-making processes performed by the operators of these systems.
## 3 Results
### Findings  
Based on the analysis of participant data (Table 2), four main themes were identified:  
1. Therapeutic effects of roobtherapy;
2. Preferences for the type of activities during robotic therapy sessions;
3. Interaction with the robot cat;
4. Technological characteristics of the robot cat.  
The overall results revealed that older women with mild dementia experienced positive therapeutic effects from roobtherapy, including relaxation and the evocation of positive emotions. However, they also mentioned negative effects, such as feeling infantilized when using animal robots that resemble stuffed animals.  
When it comes to preferences for activities during roobtherapy, participants ranked social interaction activities and individual activities with the robot as their top priorities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Median (Interquartile Range)/** \\  & **Frequency (\%)** \\ \hline Age, years & 80 (4) \\ Gender & Women, 13 (100) \\ Mini-Mental State Examination score, points & 20 (2) \\ Dementia type & Alzheimer’s (61.53) \\ Unspecified (38.46) \\ \multirow{2}{*}{Pet preference} & Dog (53.38) \\  & Cat (46.15) \\ \multirow{2}{*}{With or without pets at home?} & (76.92) \\  & No (23.07) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the participants (n = 13).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Themes** & **Subthemes** & **Codes** \\ \hline \hline \multirow{6}{*}{Therapeutic effects of roobtherapy} & \multirow{3}{*}{Positive effects} & \(-\) Relaxation \\  & & \(-\) Mood improvement \\ \cline{1-1}  & & \(-\) Cognitive training \\ \cline{1-1}  & & \(-\) Increased social interaction \\ \cline{1-1} \cline{2-3}  & & Negative effects \\ \hline \multirow{6}{*}{Preferences for the type of activities during robotic therapy sessions} & Social interaction group activities & \(-\) Creative and playful sessions with the robot cat \\ \cline{1-1}  & \multirow{2}{*}{Individual activities with the robot} & \(-\) Personalized sessions \\ \cline{1-1} \cline{2-3}  & & Positive attitude with the robot cat \\ \cline{1-1} \cline{2-3}  & & \(-\) Appearance of the robot cat \\ \cline{1-1} \cline{2-3}  & & \(-\) Interactive behavior with the robot cat \\ \hline \multirow{2}{*}{Technological characteristics of the robot cat robot cat} & \(-\) Functionals such as walking \\ \cline{1-1}  & & \(-\) Robot cat tail movement \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main themes, subthemes, and codes.  
The study also found that older people with dementia had a positive attitude towards using the JFA robot cat, particularly in terms of its appearance and interactive behaviors. However, participants expressed that they expected the robot to have more advanced technological features and offer additional functions, such as walking. Each theme is explained in more detail below, and all quotes have been translated from Spanish to English.