esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
Table 1 displays the results of this experiment. The ViT L/16 model outperformed all other models with an accuracy of 90.47%. For ResNet, the best performing model was the ResNet-50 model with an accuracy of 84%, which was about 6% lower than the ViT L/16 model. The performance disparity between the best ViT model and the worst ViT model was only at 5%. The same holds true for ResNet. The worst performing ViT models were the ones which use 32 \(\times\) 32 input patches. This indicates that they were too complex for the simpler task at hand. For ResNet, the worst performing model was ResNet-18. This indicates that the model was not complex enough to capture the intricate patterns in the data. Next, the precision, recall, and F1 score of the best performing ViT and ResNet models were compared. The accuracy and recall rate of both models were of the same value, which indicates that both models were performing well albeit one was performing better than the other. For a model to be practically useful, both values must be high. In terms of species accuracy, the most misidentified species was _Armillaria mellea_. It was mostly misidentified as _Pleurotus ostreatus_. The outcome was the same across all the models. Figure 5 shows an example on why the mushrooms can potentially be misidentified. They both have similarly wavy shapes when reaching maturity. The species _Phallus indusiatus_ was identified with the highest accuracy, 97% and above, across all models. This could be due to its unusual and unique shape compared to the other mushroom species. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline ViT-B/16 & 90.12 & 90.20 & 90.12 & 90.10 \\ ViT-B/32 & 85.87 & 85.98 & 85.87 & 85.88 \\ ViT-L/16 & 90.47 & 90.52 & 90.47 & 90.46 \\ ViT-L/32 & 88.40 & 88.52 & 88.40 & 88.42 \\ ResNet-18 & 79.77 & 80.60 & 79.77 & 79.82 \\ ResNet-34 & 81.67 & 82.08 & 81.67 & 81.63 \\ Resnet-50 & 84.00 & 84.00 & 84.00 & 83.95 \\ ResNet-101 & 82.82 & 83.07 & 82.82 & 82.85 \\ ResNet-152 & 83.25 & 83.30 & 83.25 & 83.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the experiment. Figure 5: (**a**) An example of _Armillaria mellea_; (**b**) an example of _Pleurotus ostreatus_. Tables 2 and 3 show the accuracy in the identification of each species for ViT and ResNet models, respectively. For ViT-L/16, the most misidentified inedible mushroom was _Panus conchatus_, with an accuracy of 78%. It was mistakenly identified as _Panus lecomtei_, which is an edible species. However, for the ResNet-50 model, the most misidentified inedible mushroom was _Mycena pura_. It was often mistaken for _Amantia vaginata_, also an inedible species. The misidentification of inedible mushrooms as edible is of the utmost concern and should be reviewed in future research.
## 1 Introduction  
The investigations of destructions of monumental masonry vaults due to strong kinematic seismic loading have uncovered that the protection of these types of structures is a difficult technical task [1; 2; 3; 4]. In particular, for historical structures located on seismic activity regions, the kinematic loading generated by seismic events are one of the most destructive factors [4; 5]. Hence, monumental structures of this type, which are memorable heritage landmarks, have to be secured and, if necessary, strengthened against seismic loadings [6; 7; 8; 9]. Taking into consideration the assessment methods which were proposed by different researchers [4; 5; 6; 7; 8; 9], the numerical evaluation of damages in structures subjected to seismic events is significant stage in designing strengthening. Calculations with nonlinear plastic material models provide good quality data for strengthening the structural parts that are at risk of being destroyed by strong seismic events [10; 11; 12]. It must be emphasized, that the majority of seismic mainshocks are usually preceded by foreshocks and succeeded by aftershocks within a short period of time [13; 14; 15; 16; 17]. Regarding the periodic mechanism of earthquake loadings, it is rational to consider the full sequence of the seismic eventsthat may load the structure. Regarding the safety of the masonry vaults that are exposed to seismic influences, it is reasonable to consider the set of issues that were introduced by researchers. The protection of these types of monumental structures, the applications of non-linear materials for their assembling, mechanism of earthquake loadings, and types of the strengthening system are issues that are significant for the prevention of potential destruction.  
In this work, the numerical assessment of damages to a monumental masonry vault exposed to kinematic loading generated by a foreshock-mainshock-aftershock seismic sequence is presented. This case study was based on a vault which is a structural part of the basilica erected in Southern Poland in the 12 th century. In the numerical simulations, the vault was exposed to a foreshock-mainshock-aftershock earthquakes sequence which was recorded in Central Italy, i.e., Mascioni, in the Province of L'Aquila, in 2017 [18]. The main objective of the analysis is the comparison of the dynamic performance of two variants of the structure under the investigated seismic sequence. The first variant of the masonry vault was the structure without any strengthening, whereas the second variant was with a strengthening system realized by the application of composite materials, i.e., a carbon fiber reinforced cementitious matrix (C-FRCM). In order to evaluate the impact of the studied seismic events on the investigated vault, the Barcelona Model (BM) was applied as a constitutive elastoplastic model of the masonry material [19]. The research was divided into four stages. The general framework of the investigation is shown in Figure 1.  
Figure 1: General concept of the investigation.
## 3 Results and Discussion  
### Gold Nanoparticles Capped with Citrate Ions  
Gold nanoparticles were synthesized by a modified protocol based on the Turkevich method [12]. A dispersion of citrate-capped spherical gold nanoparticles with a distibution of values of a diameter of 9 \(\pm\) 2 nm and a polydispersity index (PDI) of 1.51 was obtained, see Figure 1. The polydispersity of the obtained gold nanoparticles was slightly higher than expected from a typical synthesis based on the Turkevich method. We ascribe the higher polydispersity to the presence of impurities in the reaction mixture during the synthesis of the gold nanoparticles. Compared to physical deposition methods, this value of polydispersity is still good. On the other hand, physical deposition methods usually do not suffer from such sensitivity to impurities [13; 14]. Note that our study focuses on the plasmonic features of the gold nanoparticle and its activation through incoming resonant light. According to the UV-vis spectra, the plasmon band of the gold nanoparticles is well defined, see Figure 2. Therefore, we assume that the polydispersity of the gold nanoparticle does not play a role in the obtained results of photothermal heating.  
During the synthesis of the nanoparticles, the citrate ions act both as reducing and capping agents. Therefore, the surface of the Au nanoparticles is coated by citrate ions attached by electrostatic and non-specific interactions. The gold nanoparticles display a net negative surface charge at the value of pH of the solution used herein. The gold nanoparticles are thus stabilized by charge repulsion [15]. The UV-vis spectrum shows the characteristic peak of small spherical gold nanoparticles at 524 nm, see Figure 2. However, this wavelength value for the plasmon peak is obtained when the gold nanoparticles are dispersed in ultrapure water, a situation significantly different from biological media, which is typically enriched in ions and biological molecules [10]. Therefore, we herein study the plasmonic photothermal features of the gold nanoparticles under different values of ionic strength by adding sodium chloride to the dispersion of gold nanoparticles. We note that the effect of the so-called \"protein corona\" formation on the gold nanoparticles will not be taken into account [16]. Rather, weseek a fundamental observation on the effect of aggregation on the photothermal capability of the gold nanoparticles against ionic strength.
# A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography
## 2 Tomography Theory and Method
### Water Vapor Tomography Modeling
The horizontal constraint equation, which stabilizes the tomographic result, is constructed by the Gaussian inverse distance weighted function [29]:  
\[w_{1}x_{1}+w_{2}x_{2}+\cdots+w_{i-1}x_{i-1}-x_{i}+w_{i+1}x_{i+1}+\cdots w_{m}x _{m}=0 \tag{5}\]  
where \(w_{i-1}=d_{i-1,i}/\sum\limits_{j=1}^{m}d_{i-1,j}\) is the horizontal weighted coefficient, \(d_{i-1,i}\) represents the distance between voxel \(i-1\) and \(i\), and \(m\) denotes the total number of voxels in the same layer. To reasonably describe the vertical profile information of the water vapor, vertical constraints must be added. Otherwise, the tomographic results cannot be distinguished before and after the exchange of two arbitration layers of the tomography grid. The analysis of meteorological data for many years reveals that the vertical constraint equation is established by the form of exponential function [30; 31]:  
\[x_{j}-e^{(h_{j+m}-h_{j})/H}\times x_{j+m}=0 \tag{6}\]  
where \(x_{j}\) and \(h_{j}\) denote the water vapor density and corresponding height within voxel \(j\), respectively. \(x_{j+m}\) and \(h_{j+m}\) represent that of voxel \(j+m\). \(H\) is the water vapor scale height, which ranges from 1 to 2 km.
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 3 Methodology  
Electronic health records have completely revolutionized how medical data are recorded, accessed, and shared in the modern digital health environment. They enable more efficient data management, which enhances the information that healthcare professionals have access to and supports the integration of patient care in different healthcare settings. However, there are significant concerns regarding patient data security and privacy as EHRs expand. Deep learning, a field of artificial intelligence, has emerged as a effective technique for strengthening the security of sensitive EHRs. Bi-LSTM is a novel deep learning paradigm that builds upon the traditional LSTM architecture to make it highly effective in sequence modeling tasks. Bi-LSTM analyzes sequences bidirectionally, which allows it to identify contextual dependencies from the past and future more efficiently than regular LSTM, which processes information in a single direction [24]. This bidirectional feature leverages the complete context that is included in the data to enable the creation of more accurate predictions. The structure consists of interconnected layers of neurons, or cells, with the ability to store, change, and reset internal states [25]. The network acquires knowledge sequence patterns and correlations as data flow through these cells, making Bi-LSTM especially appropriate for time-series data, such EHRs. The advantages of this technology include enhanced contextual sensitivity, real-time monitoring, and adaptability to changing patterns, among other attributes that raise its significance in enhancing security standards in healthcare systems [26]. Healthcare institutions can effectively protect patient data from new threats by incorporating Bi-LSTM into security frameworks. This ensures patient privacy and preserves confidentiality in digital healthcare infrastructures.  
Our proposed methodology integrates CRF and Bi-LSTM, as illustrated in Figure 1, to accurately identify categories of Protected Health Information (PHI). CRF, a statistical model renowned for its ability to predict label sequences, is leveraged to enhance the performance of PHI identification. Central to our contribution is the careful design, optimization, and hyperparameter tuning of both models, which forms the backbone of our deep learning approach. Unlike conventional methods, we optimize each model individually, fine-tuning the hyperparameters to achieve the best possible performance. By combining these models and emphasizing rigorous model design and hyperparameter optimization, our approach aims to surpass traditional methods in accuracy, recall, precision, and overall effectiveness, offering a more robust solution for PHI categorization.
## 2 Materials and Methods
### Assessment of Physiological and Phenotypic Indices for Leaf Stress Resistance  
Leaf samples (0.5 g) were collected and homogenized with 5 mL of phosphate-buffered saline (PBS) and quartz sand in a pre-chilled mortar. The homogenate was then centrifuged at 10,000 r/min for 15 min at 4 \({}^{\circ}\)C, and the supernatant was collected for enzyme assays. The activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) were determined using the guaiacol-H\({}_{2}\)O\({}_{2}\) reaction, nitroblue tetrazolium (NBT) assay, and H\({}_{2}\)O\({}_{2}\) scavenging method, respectively. Oxygen free radical (OFR) content was measured using the hydroxylamine hydrochloride method.  
To quantify hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), 2 g of leaf tissue was homogenized with 2 mL of cold acetone and quartz sand, and then centrifuged at 3000 r/min for 10 min at 4 \({}^{\circ}\)C. The supernatant was used to measure H\({}_{2}\)O\({}_{2}\) content using the titanium sulfate colorimetric method. Proline (Pro) content was determined using the acid ninhydrin method, after boiling 0.5 g of leaf tissue with 5 mL of 3% sulfosalicylic acid for 10 min in a water bath.  
Malondialdehydedehyde (MDA) content was measured using the thiobarbituric acid (TBA) method. Briefly, 0.5 g of leaf sample was homogenized with 10 mL of 10% trichloroacetic acid and quartz sand, followed by centrifugation at 4000 r/min for 10 min. The MDA content was then determined by measuring absorbance at 532 nm.  
Soluble sugar (SS) content was determined using the anthrone colorimetric method. Briefly, 0.1 g of leaf tissue was ground with 1 mL distilled water, then heated at 95 \({}^{\circ}\)C for 10 min. After cooling, the mixture was centrifuged at 8000\(\times\)\(g\) for 10 min, and the supernatant was collected. The volume was adjusted with distilled water, and the solution was incubated at 95 \({}^{\circ}\)C for another 10 min. Absorbance at 620 nm was measured to determine soluble sugar content.  
Soluble protein (SP) content was determined using the Coomassieic Brilliant Blue G-250 method. Approximately 0.2 g of leaf tissue was ground with 8 mL distilled water, then centrifuged at 4000\(\times\)\(g\) for 10 min. The supernatant was mixed with 5 mL Coomassieic Brilliant Blue reagent, shaken, and left to stand for 2 min. Absorbance at 595 nm was measured to calculate the soluble protein content.  
Plant height and root length were measured using a tape measure, root diameter was measured using a vernier caliper, and fresh weight was measured using a balance.  
The experimental procedures for measuring SOD, POD, CAT, MDA, Pro, H\({}_{2}\)O\({}_{2}\), and OFR, as well as for SS and SP determinations, followed the methods described by [PERSON] et al. [24] and [PERSON] et al. [27], respectively
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 3 CNN Model Construction and Training
### Model Architectural Design  
In this study, a lightweight convolutional neural network is proposed for the spatial properties of ERA5 reanalysis data, with model training and testing conducted on the Colab T4 GPU (Google Colab, Google, Mountain View, CA, USA).  
The model input is a 41 \(\times\) 41 temperature matrix which represents the study area of 17-27\({}^{\circ}\) N and 109-119\({}^{\circ}\) E. Our model utilizes two layers of convolution and pooling to extract features (as shown in Figure 3), after which the grid data values of Macau are extracted by fully connected layers. During training under mean square error loss function, the regularization strategies of batch normalization (BN) and a 0.2 dropout are used to prevent overfitting of high-dimensional features.  
Dropout is more widely used in image tasks, but it is less used in regression tasks. This study is a regression task based on image-like two-dimensional grid data, which combines the characteristics of image task and regression task, so BN (batch normalization) and dropout are still effective in preventing overfitting in this regression task model. In the image task, the pooling layer is used to extract features and prevent overfitting. In this study, pooling can extract features from 2D grid data, reduce the number of parameters,  
Figure 3: CNN model structure.  
Figure 2: Conceptual diagram of bilinear interpolation, with the four orange points indicating data points and the blue Ps being the interpolated points of demand.  
and prevent overfitting in subsequent regression tasks. Overall, the addition of dropout and pooling leads to better model performance in this type of regression task with image input.
## 3 Results and Discussion
### Moment-Curvature Behavior
The height of the PP-ECC portion does not show obvious influence on the moment-curvature hysteretic curves of PP-ECC pixels, from the comparisons between PP-ECC-1 and PP-ECC-2, or, PP-ECC-3 and PP-ECC-4. When the height of the PP-ECC region increases from 250 to 500 mm, the area of the hysteretic curve of PP-ECC specimens changes slightly. Basically, it is hard to tell which height of the PP-ECC portion has better seismic resistance. Figure 11: Moment-curvature skeleton curves of piers.
## 4 Implementation of Smart City Services
### Controlling and Reading Status of IoT Devices  
Users have the capability to interact with the urban elements by means of the connected IoT devices. For example, they can adjust the brightness level of individual streetlights by interacting with a slider integrated into the virtual interface. Alternatively, they can switch between different predefined values by pressing the corresponding dimming button. When a virtual streetlight is selected, the VR environment sends a REST PUT message to the IoT platform to update its current dimming value, ranging from 0% (fully off) to 100% (fully on). Upon confirmation, the VR environment is updated to reflect the new status, and a synchronization message confirms the action along with a timestamp. Correspondingly, the physical streetlight's LEDs adjust accordingly. In the event of communication failure, an error message is displayed, and the dimming value reverts to its last known valid state. This service proves invaluable for targeted maintenance tasks, such as connectivity testing, hardware diagnostics, or selective lighting control. Figure 7 illustrates a user controlling a streetlight's dimming rate, with the real-world lamp's status displayed in the inset of the upper left corner.  
Users may also consult the status of the urban elements by accessing real time data from connected IoT sensors. Figure 8 shows the interaction with a garbage bin (left) and a parking lot (right), with the real-world status displayed in the inset of the lower left corner. When accessing the IoT waste sensor, the filling level (in both percentage and volumetric amount) can be checked. For the parking lot, the DT tool shows the real status by locating a vehicle in the position and shows the occupancy patterns during the last days.  
Figure 7: Interacting with the dimming controller to switch on the streetlight directly from the VR environment. The corresponding streetlight in the campus receives the command and automatically updates its status. This interaction highlights the potential of the VR-based Digital Twin in controlling/testing real world services (e.g., public lighting) in a complex urban environment.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 5 Results
### Communication Cost Considerations  
Unlike many federated learning works that target extreme bandwidth constraints, our study assumes a reliable network setting in which the communication overhead of transmitting model updates (five clients, 100 rounds, and MLPs/CNNs under 1 MB each) is modest and does not impact the core algorithmic contributions. Consequently, we deliberately omit communication-compression techniques and focus this work solely on the statistical and optimization benefits of entropy regularization under non-IID data.  
Figure 3: Test accuracy (%) on CIFAR-10 as a function of the learning rate \(\eta\) (vertical axis) and entropy weight \(\lambda\) (horizontal axis).
## 4 Methodology
### Sentence Encoder  
In this section, we will provide a detailed overview of the structure and functionality of the sentence encoder. By introducing the sentence encoder, we encode the input instances and relationship information into low-dimensional vector representations, laying the foundation for subsequent processing and analysis. The encoder layer comprises two primary components: (1) Sentence Representation: Utilizing a pre-trained language model to encode each word in the input instances, we obtain the sentence representation. (2) Relationship Representation: By employing the encoder to represent relationship information, we obtain two vector representations for the relationship. This process involves encoding the relationship information to acquire a relationship-level representation and directly concatenating them to form the final relationship representation.
## 2 Experimental Soil Sample and Research Plan
### Research Plan  
Since the bearing capacity of soil is significantly influenced by the degree of compaction (\(K\)), and the mechanical properties of permafrost are closely related to temperature (\(T\)) and moisture content (\(\omega\)), this experiment investigated the effects of these three factors on indoor CBR and CPT test indicators. Considering the high ice content in actual permafrost, the \(\omega\) levels were designed to range from 10% to 100%. Specific test designs are shown in Table 2, with the experimental scheme as follows:  
1. Study the relationship between CBR values and \(T\), \(\omega\), and \(K\). Using the method of controlling variables, \(K\) was set to 80%, 85%, and 90% in three groups, each group maintaining a fixed \(\omega\) of 40%, with various temperatures (\(T\)), and conducting indoor CBR tests at different moisture contents (\(\omega\)) and a temperature of \(-2.1~{}^{\circ}\)C.
2. Study the relationship between \(q_{\rm c}\) and \(T\), \(\omega\), and \(K\). Indoor CPT tests revealed arching and cracking when the probe penetrated the soil body, affecting the accuracy of the side friction (\(f_{\rm s}\)). This experiment analyzed the variation in \(q_{\rm c}\) under different temperatures (\(T\)), moisture contents (\(\omega\)), and compaction degrees (\(K\)).
3. Discuss the correlation between CPT and indoor CBR tests and propose CPT technical evaluation standards for frozen sandy soil. By using Pearson correlation coefficients, the relationship between \(q_{\rm c}\) and CBR values is discussed and a linear regression model between the two is established. This builds the association between CPT and indoor CBR tests, and based on the specifications for CBR values, proposes evaluation standards for \(q_{\rm c}\).