esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison of CARS and CC Feature Band Extraction  
Two methods were employed for selecting sensitive spectral bands: the CC method and CARS method. The CC method, while straightforward, selects bands based solely on their individual correlation with the target variable and may miss complex interactions. In contrast, CARS iteratively refines band selection through Monte Carlo sampling and adaptive reweighting [25]. This process effectively eliminates redundant bands and enhances model performance. Our results showed that the CARS+PLSR model outperformed the CC+PLSR model in both prediction accuracy and computational efficiency. For example, the AA model using CARS+PLSR achieved a test R\({}^{2}\) of 0.80 compared to 0.63 with the CC method, and the model building time was reduced significantly. PLSR excels at handling highly collinear spectral data by projecting predictors into a small set of orthogonal latent variables. CARS then selects the most informative wavelengths, removing redundant or noisy bands. Together, PLSR and CARS reduce overfitting and improve model stability, which explains their better performance compared to simpler algorithms.
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 3 Digital AI Models for Energy Management in Greenhouses
### Energy Demand in Smart Greenhouses and Energy Optimization Algorithms
However, the use of bio-inspired algorithms to reduce energy demand in isolation would not suffice, considering energy expenditure in greenhouses was a complex non-linear, multi-input and output process; this may explain why CFD (computational fluid dynamics) modeling coupled with mathematical transformations and systematization of greenhouse design parameters were appealing alternatives [144; 145; 146]. [PERSON] et al. employed mathematical models in the design of adjustable solar greenhouse structures with optimal height for better regulation of heating and cooling. In contrast to [PERSON] et al. [142] and [PERSON] et al. [143], [PERSON] et al. [144] employed computational fluid dynamics to regulate surplus air thermal energy in greenhouses. The data were employed in the operation of the fan-pad evaporative cooling system. Even though the latter system did not rely on bio-inspired algorithms, it demonstrated the effectiveness of modeling and simulations in regulating greenhouse cooling. In line with [PERSON] et al. [144], [PERSON] et al. demonstrated the effectiveness of mathematical simulations using the Monte Carlo model. However, the effectiveness of the modeling system was largely dependent on the level of uncertainty. The ACOA proposed by [PERSON] et al. [143] regulated energy demand in smart sensor networks. However, it remained unclear whether the optimal performance of the bio-inspired algorithms in controlled environments would yield satisfactory performance in real-world scenarios. Such concerns were premised on the fact that the performance of the bio-inspired algorithm was influenced by the dynamic relationship between biological interactions and physical conditions (atmospheric, weather, soil, and humidity). The concerns raised by [PERSON] et al. [143] were partly in agreement with the assessment by [PERSON] et al. [147] of system optimization under non-deterministic weather conditions. The unpredictable weather patterns impaired the performance of sensors and actuators for heating, lighting, and CO\({}_{2}\) dosing and the development of a climate and irrigation strategy.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Biometric Authentication  
Each individual has unique biological traits, and because biometrics cannot be easily falsified over an extended period of time, they can be utilized as a trustworthy method of identity authentication [15]. The emergence of biometric modalities also sparked a realization that this issue warrants further exploration and discussion. However, there is no clear baseline concerning the criteria and specifications required for security testing, specifically for biometric products, systems, etc. that the national government uses in  
Figure 1: Static password authentication.  
Figure 2: Dynamic password authentication.  
adopting biometric technologies [14]. Therefore, some biometric authentication technologies have been accompanied by an optimization algorithm. Authors in [16] proposed a multimodal system based on an evolutionary algorithm for the security of the biometric system. However, some of the recent optimization algorithms can be adopted to overcome the challenge of the evolutionary algorithm used for the security of the biometric system, such as the algorithms suggested by [17] and [18].  
Each legitimate user will capture their biometric information for this type of authentication. This is easier nowadays, because most smartphones are equipped with a built-in camera to capture face or iris, a voice recorder to capture voice, and a fingerprint reader to capture thumbprints. The biometric information will be preprocessed and bound with user information during registration. Each login will require the user to present their biometrics for authentication.  
The risks of using biometrics are quite huge. Once breached, they are not available for recovery. The data are not renewable and hence, cannot be used once stolen. Some disadvantages of using biometric authentication are the high computational cost, required accuracy and usability during the unlocked state, plus hardware sensor devices that are not practical for frequent logins/authentications [12]. Another issue with biometric authentication is that it might slightly change over time. Table 1 lists the impact of having biometric changes [14].
## 2 Data Materials and Preprocessing
### Laguna Lake of the Philippines  
Laguna Lake, with an area of 900 km\({}^{2}\) and an average depth of 2.5 m, is the largest lake in the Philippines. There are more than 20 million people living in the surrounding areas of Laguna Lake, indicating the importance of the lake in providing freshwater for local daily needs [35]. However, around 17% of the lake water body (\(\sim\)150 km\({}^{2}\)) is occupied by aquaculture cages, where the nutrients and hazardous substances from industrial activity may pollute Laguna Lake, in addition to the issues of rapid population growth, industrialization, and urbanization [36; 37]. In this study, field measurements of Chla concentrations were conducted during five different campaigns in 2019, as shown in Figure 1. Infinity-CLW ACLW2-USB, an optical-based data logger used to measure Chla concentrations, was installed on a boat at a depth of 0.5 m below the water's surface. The data logger recorded the Chla concentrations once per second during a 5-hour field survey, collecting more than 15,000 records at each campaign. Outlier removal and data downsampling were conducted to remove noise and to match the Chla concentration sampling resolution with the spatial resolution of the Sentinel-3 images, respectively. After the data pre-processing, 257 in situ Chla samples were obtained from the five field campaigns, as shown in Table 2, and the resulting samples were utilized to train the ANN model and related models for comparison and evaluation.  
Figure 1: Laguna Lake and field campaigns for Chla concentration collection. The routes of the field campaigns and the locations of the collected samples are visualized by colors.
## 4 Results
### TinyML Battery Life  
Considering the calculated amount of energy required for one face mask operation, the battery life can be estimated. The 18650 battery with a typical capacity of 2500 mAh and nominal voltage of 3.7 V will be used for calculations. As there are high-efficiency  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario** & **Accuracy** \\ \hline Correct detection of the correctly masked face & 79.51\% \\ Correct detection of the incorrectly masked face & 79.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overall system accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Window Size** & **Total** & **Total** & **Total** \\  & **Downscaling Time** & **K-Means Time** & **Detection Time** \\ \hline
320 \(\times\) 320 & 3360.00 ms & 273.0 ms & 3633.00 ms \\
352 \(\times\) 352 & 3036.60 ms & 204.8 ms & **3241.35 ms** \\
384 \(\times\) 384 & 3088.80 ms & 175.5 ms & **3264.30 ms** \\
416 \(\times\) 416 & 2008.00 ms & 97.5 ms & **2105.50 ms** \\
448 \(\times\) 448 & 1162.00 ms & 48.8 ms & **1210.75 ms** \\
480 \(\times\) 480 & 1064.80 ms & 39.0 ms & **1103.80 ms** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Face detection time calculation results.  
power supply chips available with several \(\mu\)A of quiescent current and over 90% efficiency, for example, for TPS63806, the energy loss can be pessimistically modeled as 10%. The singular face mask operation energy consumption results are shown in Table 12.  
Considering the power consumption of the microcontroller, the system could continuously operate for 225 h. With the example of shops visited by 5000 customers every day, the proposed system uptime without recharging the battery is shown in Table 13. This is especially important, as our low-power design makes the mounting of the proposed system possible wherever it is needed, regardless of the presence of mains power. In the example with a shop, it would be inconvenient to install the mains power cables on market shelves, where the proposed system could be mounted.
## 3 Results  
### Spectral Channel FWHM  
According to the observations made with GLAMR, the FWHM of FIREFLY spectral channels varied between \(\sim\)0.12 nm to \(\sim\)0.2 nm, and a negative relationship was observed between FWHM and wavelength (Figure 4). Further investigation in the spatial dimension of the sensor showed the FWHM is also spatially dependent (Figure 5). The combined FIREFLY FWHM was fitted as an exponential relationship with spatial pixel and wavelength:  
\[FWHM=2\times 10^{-6}D-7\times 10^{-4}D-7\times 10^{-4}W+0.76405 \tag{3}\]  
where \(D\) is the spatial pixel number of FIREFLY (1-266) and \(W\) is the wavelength in nanometers of the spectral band of FIREFLY. The standard deviation of the residual of this fit was \(7.32\times 10^{-3}\) nm.
# Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance
## 4 Example Evaluation and Results  
To conduct an illustrative evaluation, a series of test recordings were conducted on the Wels-Passau railroad line, specifically on the section between Wels (route kilometer 0) and Riedau (route kilometer 42.2). This section corresponds to route number \(\mathsf{\ddot{O}}\)BB 205 01 and route number DB 5831. The evaluation employed a dataset comprising 11,800 images capturing rolling marks. These images were captured using a measuring vehicle provided by the company Plasser & Theurer. A new image was captured every 250 mm, depicting a track section of approximately 340 mm.
## 3 BAT-Transformer: Bus Arrival Time with Transformer Encoder  
### Overall Architecture  
This study proposes a novel architecture for predicting bus arrival times. The proposed architecture leverages various data resampling and preprocessing techniques, and enhances the performance of the transformer-encoder model by incorporating linear projection, a learnable positional encoding technique, and a fully connected layer. The system was structured into data collection, data preprocessing, model training, and prediction stages. The overall architecture is shown in Figure 2.  
Data from 55 buses in the Gangnam area were collected using the OPEN API of the public data portal. These include real-time bus locations, bus operations, and route information. As shown in the upper part of Figure 2, the collected data underwent a filtering stage to extract the necessary information based on which key features were selected. These features provide essential information for model training and play a crucial role in enhancing the data quality and prediction performance in subsequent stages.  
The selected features are used to generate a balanced dataset using resampling techniques. Resampling alleviates data imbalance and helps the model operate stably in various situations. The generated data were normalized using a standard-scaler. Normalization ensures that the data scales are aligned, facilitating efficient model training, and mitigating convergence issues that may arise during the learning process. Categorical variables were processed using one-hot encoding. One-hot encoding transforms categorical data into binary vectors, allowing the model to learn effectively. The transformed data were linearly projected onto a high-dimensional space, making them suitable for model training. Through this process, the data were ready for training and used as input for the model.  
The preprocessed data are then trained using the transformer-encoder model. This flow is shown in in the lower architecture of Figure 2. In this model, the input data were projected into a d_model dimensional space, and learnable positional encoding was added to capture the sequential order of the time-series data. Positional encoding helps the model effectively process time-series data by incorporating temporal information. The model employs a multi-head attention mechanism and dropout to learn significant patterns while preventing overfitting. Multi-head attention allows the model to analyze data from multiple perspectives, whereas dropout enhances the generalization performance of the model by mitigating overfitting during training. Finally, a fully connected layer is added to convert the final output of the transformer into a single prediction value.
## 5 Simulations and Analysis
### Performance of the Observer for the Compromised LFC System  
Based on the method studied in Section 3 and the parameters of the system, the observer gains for the tie-line power of the compromised area \(i\) can be calculated as follows:  
\[W_{i}=\begin{bmatrix}-6.6667&0&0.0031&0\\ 3.2258&-3.2258&0.0003&0\\ 0&0&-9.053&0\\ 0&0&0.0001&0\end{bmatrix},N_{i}=\begin{bmatrix}0\\ 0\\ 1\\ 0\end{bmatrix}\]  
\[Y_{i}=\begin{bmatrix}-6.6635&-0.0003&9.053&0.1884\end{bmatrix}^{T}\]  
In this case, we make \(\tau=1\) s. The signal-to-noise ratio (SNR) of the white Gaussian noise is chosen as 30 dB. Systems compromised of different attack templates are simulated, and the simulation results are shown in Figures 4-6.  
Figure 4: Different data sources of tie-line power and observation error under exogenous attack. (**a**) different data sources of tie-line power; (**b**) observation error.  
Figure 5: Different data sources of tie-line power and observation error under scaling attack. (**a**) different data sources of tie-line power; (**b**) observation error.
## 4 Results and Discussion
### Experimental Results  
A good correspondence between the numerical prediction and the experimental results was obtained; however, only three of the four stages were clearly visible in the experimental results.  
In the first stage, both the faces and the core behaved linearly elastically. After reaching the matrix cracking stress in the area of the constant moment, the bottom face started to crack, indicating the start of stage II in Figure (a)a. Figure 13 shows the longitudinal strains in the sandwich beams measured by both DIC systems, and therefore shows the appearance of the first crack after reaching the ultimate matrix cracking strength. These strain plots must be interpreted carefully. Strain results of the DIC technique were calculated from the average displacements, meaning that the displacements in the neighbourhood of cracks were responsible for apparent high strains at the location of the cracks. In reality, however, the strain in a crack is zero, so strain colormaps, as in Figure 13, can only be used to identify crack patterns; no significance should be attributed to the value of the strain in the vicinity of a crack. Simultaneously, the core showed a linear elastic shear strain, as shown in Figure 14.  
Figure 14: Shear strain of the core in stage II, at a bending load of 5 kN.  
Figure 12: (**a**) Vertical displacement (U, in mm) of 91 mm, and (**b**) shear stress in the core at a displacement of 91 mm.  
Figure 13: Longitudinal strain at the start of stage II, when the first crack appears at a bending load of 5 kN.  
During stage III, cracking and propagation of the cracks occurred as shown in Figure 15. Since the highest tensile stress occurred in the area with the constant moment, most of the developed cracks are located between the loading beams.  
At a load of 26 kN (stage IV), the tensile face showed multiple cracking and the saturation of cracks between the loading beams (see Section 2.1.1), while the core reached a shear strain of 0.019, which is equal to the plastic shear strain of XPS. Both the tensile strain of the tensile face and the shear strain of the core are shown in Figures 16 and 17. The numerical model, however, predicted the plastic shear deformation of the core at a load of 25.5 kN. The observed phenomenon, plastic shear deformation of the core, was the same for the experiments and the numerical prediction. Also, the degradation of the stiffness corresponded well. The core continued deforming plastically, until its ultimate shear stress was reached and failure of the core occurred.
# Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller +
## 3 Materials and Methods
### Measuring Latency  
Separately, latency was measured between a single peripheral and central node (distance of 0.5 m) to determine the time required for ADC data to pass through the wireless system. The measure of latency is confounded, because a full data block is collected on the peripheral node before transmission to the central node. Transmission of an ADC sample captured at the beginning of the sampling block is delayed until the end of the sampling block, resulting in a delay of up to one full connection interval. Conversely, an ADC sample captured at the end of the sampling block experiences little to no delay prior to transmission to the central node. In addition, once a data block is queued for transmission  
Figure 3: Flow diagram detailing the steps used for each trial measuring the maximum number of nodes and the maximum and average power consumption.  
in the peripheral node, it must wait until scheduled to do so by the central node. This queuing-to-transmit delay is unsynchronized between the peripheral and central nodes.  
To measure latency, a step input, generated using one of the general purpose output pins of a nRF52840-DK (a standalone device), was applied to the ADC input of a single peripheral node. The step input was manually triggered for each trial to avoid synchronization with the ADC sampling process. A logic analyzer [Analog Devices, Inc. Active Learning Module 2000 (ADALM2000), Wilmington, MA, USA] measured the time between critical actions in the data pathway [32]. For each trial, the rising edge of the step input was used to trigger the beginning of the measurement. Software edge detection on both the peripheral and central nodes was implemented to set a general purpose digital output high (on each respective node) when the step input edge was detected first on the peripheral in the full block of recently captured ADC samples and then on the central node (after transmission) in the incoming BLE packet. Detection on the peripheral node measured the time between when the step input was applied and when it was available for BLE transmission following an ADC sampling block. Detection on the central node indicates that the step input has been received from the peripheral node. For each combination of event length and connection interval tested, 10 trials were completed. Latency from step input to detection on each of the peripheral and central nodes was manually measured on the logic analyzer for each individual trial. Figure 4 illustrates the timing associated with and GPIO flags set for a single trial starting with the initial trigger of the logic analyzer when the step input signal is applied, followed by the change in GPIOs on the peripheral and central, indicating that the software edge detection found the rising edge of the step input at each respective stage of the data flow.
## 2 Materials and Methods
### Multilineage Differentiation of HFMSCs  
In this study, a specific differentiation medium was used to induce human HFMSCs differentiation potential. For adipogenic differentiation, the HFMSCs with density \(3\times 10^{3}\) cells/cm\({}^{2}\) were grown in adipogenic medium including DMEM containing 10% FBS, 1% P/S antibiotics, 1 \(\upmu\)M dexamethasone, 0.5 mM isobutyl-methylxanthine, 10 \(\upmu\)M insulin, and 200 \(\upmu\)M indomethacin. For chondrogenesis, the cell differentiation was induced by culturing \(1\times 10^{3}\) HFMSCs/cm\({}^{2}\) in the presence of DMEM-high glucose (DMEM-hg) containing 10% FBS, 1% P/S antibiotics, 100 \(\upmu\)M dexamethasone, 0.9% sodium pyruvate, 50 \(\upmu\)mL ascorbic acid, 10 ng/mL transforming growth factor beta 1, 5 ng/mL basic fibroblast growth factor, 1% insulin-transferrin-selenium, and 40 \(\upmu\)g/mL L-proline. For osteogenic differentiation potential, the HFMSCs were induced with an osteogenic medium at a density of \(6\times 10^{3}\) cells/cm\({}^{2}\) per 35-mm dish and incubated in the DMEM-hg containing 1% FBS, 1% P/S antibiotics, 0.1 uM dexamethasone, 50 uM of ascorbic acid, and 10 uM \(\beta\)-glycerophosphate. The cells were cultured for 28 days (the medium was changed every 3 days). After the end of the cell culture period, the cells were stained with Oil Red O Kit (adipogenic), Alcian Blue Kit (chondrogenesis), and Alkaline Phosphatase Kit (osteogenic), respectively. A model FV1000/IX81 inverted fluorescence microscope (Olympus, Tokyo, Japan) was employed to observe the morphology of the stained cells under different magnifications.