esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Horizontal scaling involves the replication of DT modules across multiple assets or units (e.g., composting beds, fields, tanks), while vertical scaling allows for integration into enterprise-level analytics platforms or AI-based supervisory control systems. The use of containerization technologies (e.g., Docker) and standardized message schemas (e.g., JSON, XML, OPC-UA nodesets) facilitates deployment in hybrid environments (on-premise + cloud). This interoperability approach ensures not only the extensibility of the methodology across industrial sectors but also its resilience and adaptability to evolving technological ecosystems. It enables organizations to gradually adopt DT technologies without requiring complete system overhauls, aligning with the principles of sustainable digital transformation. To ensure that the proposed methodology can be successfully verified and transferred to other domains, its implementation should follow a structured validation process. This involves (i) adapting the system configuration to local operational and environmental characteristics, (ii) defining measurable key performance indicators (KPIs), and (iii) conducting case study trials to monitor short-term and long-term impacts. For this purpose, the following minimum requirements are necessary:  
* Logistical support: Routine access to physical infrastructure, operational continuity, and availability of basic tools for system monitoring and adjustment. * Technological infrastructure: At least one sensor or data acquisition device per critical variable; a local gateway or cloud platform for processing and storage; and basic connectivity (WiFi, 4G, LoRa). * Human resources: One or more personnel trained in system operation, familiar with basic process variables, and capable of following operational procedures and responding to DT suggestions. * Integration capability: The ability to connect with existing systems, such as PLCs, spreadsheets, SCADA, or ERP platforms, directly or via APIs, depending on the digital maturity of the target environment. Meeting these requirements allows the methodology to maintain its modular and adaptable nature while ensuring operational feasibility. In the proposed architecture, the integration of the proposed model with other existing technologies and platforms different from those discussed in this document can be validated. This strategic approach facilitates deployment even in environments with limited resources and lays the foundation for scaling the methodology to more complex industrial or urban contexts. The robustness of the methodology is also reflected in its modular architecture, which allows for continuous iterations and facilitates adaptation to various contexts. In conclusion, the proposed methodology represents a significant advancement in the application of DTs for the improvement of production and service systems. However, future research should focus on addressing the identified limitations, improving interoperability, and exploring strategies to ensure sustainable and scalable implementation across different industrial sectors.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
###### Abstract
It should be stressed that, although UAV blocks with high overlaps and large tie point multiplicity are more rigid compared to past aerial blocks with film cameras, control information should be included as a constraint in the bundle block adjustment (BBA), as non-linear model deformations are dealt with more effectively. The accuracy and precision of photogrammetrically-generated DSMs depend on geometric and physical parameters, such as: the image scale, ground sampling density (GSD), stereo base-length to object distance ratio, camera network geometry (nadiral, cross, oblique strips), percentages of strip overlap (forward and sidelap), the accuracy and distribution of ground control points (GCPs), camera calibration, surface texture and albedo, lighting conditions, air refraction index, sensor quality (signal-to-noise ratio and dynamic range), image sharpness (no blur), as well as on processing: SfM, BBA, image matching, point cloud noise, and outlier removal algorithms. Measuring DSM quality is therefore a complex task, as the number of variables involved is very large, and no single experimental study can encompass all of the relevant aspects. Many studies addressed the accuracy of UAV-generated DSM in different environments; a valuable review of image-based surface reconstruction in geomorphometry can be found in [PERSON] et al. [20]. The theoretical elevation error in the normal and convergent cases of stereo restitution [21; 22], though in principle a key element in block design, is seldom mentioned ([PERSON] et al. [20] being an exception). The influence of accuracy, precision, number and GCP distribution on DSM quality was extensively investigated by empirical studies [23; 24; 25] and Monte Carlo simulations [19] due to its relevance on survey costs and block deformation control. Attention was paid [26; 27] to elevations errors due to residual errors in interior orientation parameters (the so-called dome effect), while limits of self-calibration effectiveness were discussed in [PERSON] [28]. The differences between DSMs produced by different software packages [29; 30] and the influence of processing parameter settings in SfM and dense matching on DSM accuracy [14] were systematically investigated. Methods for the co-registration of multi-temporal DSMs are presented in the context of morphology changes monitoring, erosion, and snow volume change estimates [4; 31; 32].
# Enhanced Antifungal Activity of Engineered Proteins via Swapping between Thiodexin H2 and H3
## 3 Results and Discussion
### Membrane-Permeable Effects of AtTrx Proteins in Fungal Cells  
Next, we tested the membrane-permeable activities of H2, H3, 2N3C, and 3N2C proteins using the SYTOX Green uptake assay. SYTOX Green, a membrane-impermeant dye, emits green fluorescence upon binding to nucleic acids. Moreover, this dye can penetrate the cell membrane upon damage by antifungal agents. We used florescence microscopy and flow cytometry analysis to measure membrane permeability in the presence of H2, H3, 2N3C, and 3N2C proteins. As shown in Figure 4A, SYTOX Green uptake in a large population of _C. albicans_ and _C. oleosoperiodides_ cells treated with 3N2C, similarly to melittin, a membrananolytic peptide, shifted to the right in flow cytometry. The percentage of 3N2C-positive cells was higher than that of 2N3C-positive cells, suggesting that the degrees of SYTOX green uptake correspond to their antifungal activities. In contrast, no cellular green fluorescence was detected in the presence of H2 and H3 because these proteins did not induce membrane damage. These results correspond to those observed under the fluorescence microscope (Figure 4B). Green fluorescence in fungal cells treated with H2 and H3 was not detected, whereas 3N2C-treated cells facilitated SYTOX Green uptake into the cytosol, indicating that this protein destabilizes the fungal cell membrane before internalization into the cytosol.  
Figure 4: _Cont._  
Figure 3: Reactive oxygen species (ROS) generation of Trx-H proteins and their swapped proteins in _C. albicans_. Cells were treated with the proteins at their MIC concentrations in pH 5.2 for 8 h, after which they were stained with 100 uM DCF-DA for 1 h. Cells with ROS generation were visualized by fluorescence microscope. (**a**): Trx-H2, (**b**): Trx-H3, (**c**): Trx-2N3C, (**d**): Trx-3N2C. Scale bar: 10 \(\upmu\)m.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 2 Methodology  
### Database Construction  
This paper uses the image database provided by the Hong Kong Observatory and the images were collected at Central Pier Automatic Weather Station. The image database composes of the digital images collected by the webcam at the weather station from 6 a.m. to 6 p.m. with fixed viewing angle, December 2019 to January 2020. The visibilities provided by Hong Kong Observatory are based on hourly visual observations by a trained meteorological observer and measurement by the visibility meter. The database consists of a total number of 4841 images.
## 4 Results
### Classification into Multiple Venue Types  
The following four machine-learning methods have been proposed in this study based on the generalized linear model, deep learning, logistic regression, and gradient-boosted trees for venue classification in order to improve the classification of LBSN data that have been done manually for decades in many fields by a variety of researchers. The previously studied 10 venue classes have been used for supervised learning, namely \"Ed-ucational,\" \"Entertainment,\" \"Food,\" \"General Location,\" \"Hotel,\" \"Professional,\" \"Residential,\" \"Shopping & Services,\" \"Sports,\" and \"Travel.\" Figure 2 shows the overall performance of these methods for classification problems.  
The results show the high accuracy of deep learning for our classification problem of LBSN data into prespecified categories. The other models, including the generalized linear model, logistic regression, and gradient-boosted trees, also performed very well in the classification of LBSN data. The deep-learning model has a high accuracy, which suggests that it is able to accurately predict the location category of a given user based on the input variables. The gradient-boosted tree model also has a high accuracy, which indicates that it is able to make accurate predictions. The linear regression models are simple, easy to interpret, and fast to train. They are best suited for linear problems, where the relationship between the predictors and the target is approximately linear. In these cases, linear regression models can provide accurate predictions and good interpretability. The gradient-boosted trees, on the other hand, are more flexible and powerful and can handle non-linear relationships between the predictors and the target. They are based on decision trees, which are decision-making models that can capture complex relationships in the data. Gradient-boosted trees can also handle missing or noisy data, and they can learn interactions between predictors. In general, gradient-boosted trees tend to outperform linear regression models when the relationship between the predictors and the target is non-linear, and when the data contains noise or missing values. However, gradient-boosted trees can be more difficult to interpret and can be slower to train than linear regression models. It is important to note that the choice of the best model depends on the specific requirements of the task, the nature of the data, and the desired performance characteristics.  
Figure 2: Venue Classification into 10 classes using Machine Learning.  
In the following section, we provide the confusion matrix for each implemented model to show how significantly they performed on the testing data. The performance of each individual model is provided in the following sections.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.3 Postequalization FSDNN  
The frequency-slicing deep neural network (FSDNN) is a variant application of DNN that could be used in a high-speed VLC system [55]. It has the characteristics of processing high and low frequency respectively to decrease computation complexity by 11.15% compared to the traditional MLP when it comes to the equalization performance in VLC system.  
In order to solve the nonlinear frequency spectrum fading issue of the received signal after going through the VLC channel, DNN is introduced as an outstanding postequalizerto equalize linear and nonlinear distortion. However, the DNN structure must be complex enough, which means that more layers and nodes are needed and computation complexity improves to handle complicated linear and nonlinear distortions. For the expectation to release the pressure of DNN, it is worth noticing that high and low domain frequency suffer different degrees of fading. The high-frequency spectrum suffers more serious amplitude attenuation, while the low-frequency spectrum suffers less fading in the received signal in VLC system, so complex MLP structure is unnecessary for the low-frequency domain. Therefore, the received signal can be separated into high-frequency and low-frequency domains and processed, respectively, using a DNN equalizer with different complexity.  
The received wide-band signal is split into two narrow-band parts in the frequency domain. Its frequency spectrum is separated into two sub-bands using a low-pass filter and a high-pass filter. Then, the two sub-band signals are respectively fed into two MLPs to train individually. The main factors of the two-MLP network should be tested artificially and adjusted to optimal values, including the number of layers, nodes in every layer, taps, and epochs. Once the MLP is finished training and the weight values are fixed, the sum of the output signal from two MLPs is the equalized and recovered signals.
## 3 Methodology
### Water Mask Extension to Include Across-Track Measurements  
Water masks are delineated using the nominal track coordinates and the water occurrence dataset provided by GSW [33]. The algorithm presented incorporates waveforms focused on nadir surfaces with water and waveforms focused on nadir surfaces but without _nadir_ water, that is, with water in the across-track direction, and thus what we call _across-track_ measurements, as introduced earlier. While including across-track measurements can significantly augment the number of independent measurements obtained from each satellite pass, their accuracy is compromised due to the diagonal propagation of the signal between the water surface and the satellite, as opposed to the vertical propagation with nadir water measurements. The expected bias introduced by across-track measurements can be computed by determining the hypotenuse of a triangle, where the vertical side represents the nominal altitude of the satellite (1336 km for S6 [9]), and the horizontal side signifies the distance across-track between the nadir location above land and the reflecting water surface. Beyond a distance of 250 m, the bias would exceed 2.3 cm. Considering that the accuracy of FFSAR measurements has been observed to be below 5 cm [36], a distance of 250 m is deemed to be the threshold for accepting across-track measurements within the water mask. Beyond this distance, across-track measurements would require correction, which is not trivially computed for complex topographies.  
The algorithm for constructing the extended water masks involves several steps. First, coarse limiting latitudes are defined for the area of interest through manual and visual map inspection. In this definition, the following aspects are taken into account: the entire historical extent of the targeted water surface, the nominal satellite track, and the potential satellite drift of up to 2 km from the nominal track. Then, the satellite nominal track coordinates within these latitude limits are identified and interpolated to a resolution of 30 m, which corresponds to the approximate resolution provided by the GSW dataset. The algorithm then iterates through the interpolated locations, considering a potential satellite drift of up to 2 km from the nominal track. For each location, possible satellite positions are determined perpendicularly to the along-track direction at a 30 m resolution. The algorithm further iterates through these across-track locations by constructing a rectangle centered at each location, with dimensions of 30 m in the along-track direction and 500 m in the across-track direction. It checks if the GSW dataset contains water pixels within this rectangle with a water occurrence of 25% or higher. If this condition is met, a square of 30 m in size is built around the current location. Finally, all neighboring squares are joined into polygons, and the relevant polygons corresponding to the water target are selected, ensuring that unwanted water sources in the vicinity are excluded.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 4 Proposed Relay Selection Scheme
### Singular Value Decomposition (SVD)-Based Relay Selection in 2-Dimensional (2D) Channel Model
The detected signal at the \(j\)-th UE is as follows:  
\[\hat{\mathbf{x}}_{UE,j} =\mathbf{G}_{UE,j}\mathbf{y}_{UE,j}=\left(\left[\mathbf{H}_{R,UE \;j}\mathbf{W}_{j}\right]^{H}\left[\mathbf{H}_{R,UE\;j}\mathbf{W}_{j}\right] \right)^{-1}\left[\mathbf{H}_{R,UE\;j}\mathbf{W}_{j}\right]^{H}\left(\mathbf{ H}_{R,UE\;j}\mathbf{W}_{j}\hat{\mathbf{x}}_{R,UE\;j}+\mathbf{N}_{j}\right) \tag{15}\] \[=\left(\left[\mathbf{H}_{R,UE\;j}\mathbf{W}_{j}\right]^{H}\left[ \mathbf{H}_{R,UE\;j}\mathbf{W}_{j}\right]\right)^{-1}\left(\left[\mathbf{H}_ {R,UE\;j}\mathbf{W}_{j}\right]^{H}\left[\mathbf{H}_{R,UE\;j}\mathbf{W}_{j} \right]\right)\hat{\mathbf{x}}_{R,UE\;j}+\mathbf{G}_{UE,j}\mathbf{N}_{j}\] \[\sum_{tr,i}=\text{tr}\Big{(}\underset{i}{\sum}\Big{)}=\sum_{k=1}^{N_{t}}\sigma _{i,k}, \tag{9}\]0  
The proposed relay selection scheme uses the singular values of the channel matrix between the BS and relays. After calculating the SVD of the channel matrix between the BS and relays, the singular values are obtained from the matrix having the singular values. Then, the sum of the singular values is obtained, and the relay having the largest value is selected. As a result, the proposed relay selection scheme provides the method of selecting the appropriate relay in the MU-MIMO multi-hop transmission system only using the SVD of the channel matrix between the BS and relays.
## 4 Results and Discussion
### Building Energy End Use Analysis
#### 4.2.1 Putrajaya  
The breakdown of end-use energy consumption of the base model was compared between NWC and HST0.75 + PP0.1. The highest energy consumption was in July, while the lowest was in February. Figure 6 illustrates the percentage breakdown of energy consumption by the end-use base model. In Malaysia, ventilation (26%) and space cooling (24%) constituted about 50% of the energy consumption, followed by the miscellaneous equipment (21%), lighting (15%), hot water (13%) and external use (1%). Cooling loads represented 24% of the total energy consumption use. [PERSON] and [PERSON] [58] also reported that 21% of electricity was used as a cooling load in the residential sector of Malaysia.  
Cooling energy and ventilation needs were both higher by 6.2% for NWC as compared with HST0.75 + PP0.1. The other energy needs showed no significant variations.
## 3 Experiments
### Experiments on EEI-100 Dataset
#### 3.3.1 Parametric Analysis Experiment  
By following the approach outlined in Section 3.2.2, the values of parameters \(\alpha_{1}\) and \(\alpha_{2}\) are determined to optimize the performance of the model on EEI-100. Experimental results show that \(\alpha_{1}\) has the optimal value of 0.6 within the range of [0.1, 1], as illustrated in Figure 4a. Similarly, \(\alpha_{2}\) has the optimal value of 0.1 within the range of [0.01, 0.1], as illustrated in Figure 4b.
# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
###### Abstract
For instance, \"_10-Ethyl-5-methyl-5,10-diadeazaaminopterin_\" and \"_12-o-tetradanoylphorol 13-acetate\"_ contain alphanumeric and special characters. Different tokenizers handle these special characters differently. Therefore, applying different tokenizers may produce diverse outputs for the same entity. Descriptive entities, e.g., \"_Pigment epithelium-derived factor_\", \"_Medullary thymic epithelial cells_\", etc. make it challenging for entity boundary identification. Biomedical entities may also comprise nested entities, e.g., \"_CIITA mRNA_\" symbolizes an RNA mention; however, \"_CIIT_\" refers to DNA [7]. Additionally, a practice common in biomedical text writing is the use of acronyms for entities, where an acronym may refer to different entities. For instance, \"_TCF_\" can refer to \"_Tissue Culture Fluid_\" or the same acronym may apply to \"_T cell factor_\" [8]. Similarly, \"_EGFR_\" can stand for \"_estimated glomerular filtration rate_\" or \"_epidermal growth factor receptor_\". Identifying an acronym for the specific entity depends on the context of the sentence; therefore, the BioNER system must learn how to distinguish them from each other. Additionally, the capitalization feature of the entities in biomedical literature does not provide valuable information about the entity. In view of the limitations above, as mentioned earlier, the BioNER task is more challenging compared to the standard NER task. The early BioNER methods (e.g., dictionary-based and rule-based approaches) are effective, but their performance is still limited against open and growing biomedical literature. As compared to the dictionary-based and rule-based methods, the classical machine learning algorithms have shown improved results. The machine learning algorithms require an extensive handcrafted feature engineering phase that has a direct impact on the performance of the models. The performance  
Figure 1: An illustration of the biomedical named entity recognition task. enhances with more discriminating features, while redundant and irrelevant ones may degrade the performance. The state-of-the-art techniques are based on deep learning methods, which somehow eliminate the need for handcrafted features, while still producing the desired results. Deep learning (DL) architectures consist of many layers, through which these systems learn the features and complex structure of the data layer by layer. The implicit feature learning ability of the DL models has been successful in different fields, e.g., computer vision [9], speech recognition [10], and drug discovery [11], among others.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### Calculating the Node Honesty
#### 4.3.1 Communication Confidence  
The communication confidence can be calculated based on the number of transmitted packets. Communication confidence is calculated using Equation (4), presented in [51]:  
\[\left\{\begin{array}{l}{C_{com}=\frac{2C_{rd}+C_{ft}}{2}}\\ {C_{rd}=\frac{2s_{p}+1}{2\gamma}}\\ {C_{fct}=\frac{1}{\gamma}}\\ {\gamma=s_{p}+f_{p}+1}\end{array}\right. \tag{4}\]  
where \(s_{p}\) is the number of successfully delivered packets, \(f_{p}\) is the number of unsuccessfully delivered packets, \(C_{com}\) is communication confidence, \(C_{rd}\) is forward packet confidence, and \(C_{fct}\) is factors of uncertainty confidence.