esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aeroelasticity and Aerodynamics of Cable-Supported Bridges
### Rain-Wind-Induced Vibration--Aeroelasticity of the Cable System  
#### 3.3.1 Field Studies of Rain-Wind-Induced Vibration  
As the spans of cable-stayed bridges become longer, so too do the cables, and as a result, cable vibration is becoming more of a concern. Of interest is the rain-wind-induced vibration (RWIV) of cables, which can accelerate cable fatigue and endanger vehicle and pedestrian use of the bridge. The first in-depth study of this phenomenon was by [PERSON] and [PERSON] [85] who conducted a five-month full-scale measurement of cable oscillations and wind velocities on the Meikonishi Bridge at Nagoya Harbour, Japan. During this time, vibrations of cables with diameters of 140 mm and lengths varying from 65 to 200 m were recorded to have a peak amplitude of 0.55 m under a wind velocity of 14 m/s. A wind tunnel test was also conducted which showed that the RWIV was caused by a change in apparent cable cross-section during rainfall where the rainwater formed rivulets along the upper windward surface of the cable [85].  
Several years later, [PERSON] [86] collected and analyzed nine months' worth of weather and vibration data on the Aratsu bridge and found that vibrations of stay cables on this bridge were always accompanied with light rain and moderate wind, and thus confirmed these instabilities were induced by the combination of rain and wind effects. In the same year, [PERSON] et al. [87] concluded from previous cases that RWIVs are stimulated by two factors: an axial wind flow in the near wake region of the cable and the formation of upper water rivulets. Similarly, a report by FHWA [88] concluded from field measurement data of different bridges that the critical wind velocity for RWIV ranges from 8 to 15 m/s, yet no critical rainfall values are provided. [PERSON] et al. [89] performed a 45-day continuous observation of RWIV on the Dongting Lake bridge, in which long-term data of mean wind velocities and cable motions were collected simultaneously. It was found that the critical mean wind velocity ranges from 6 to 14 m/s and the critical rainfall was less than 8 mm/h. However, one of the main challenges of field measurements is that findings are limited to a specific bridge. Therefore, further research is required to find the relationship between the critical wind velocity, rainfall and features of the cables. Another challenge with field measurements of this kind is the length of time needed to gather the data. Additionally, these long-term field observations might face the problem associated with the degradation of field measurement devices and sensors due to their exposure to complex weather conditions. [PERSON] and [PERSON] [90] found that sometimes instruments installed on the Fred Hartman Bridge failed to record data at specific locations, and as a result, a statistical approach had to be employed to extract the missing data from other positions. It was also reported that, in specific conditions, anemometers would be in the wake of the deck, which prevented these devices accurately measuring the wind velocity on the bridge deck itself.
# Fast One-Step Synthesis of Anisotropic Silver Nanoparticles
## 1 Introduction  
Metal nanoparticles (NPs) have generated a great deal of interest in a range of fields including sensing, photonics, biology, and catalysis, owing to the unique interaction of their conduction electrons with electric fields [1, 2, 3, 4]. Such an interaction is commonly known as Surface plasmon resonance (SPR) and is strongly related to the size and shape of the NPs, as well as the surrounding medium, which makes them particularly suitable for sensing applications [5]. While spherical NPs are easily obtained by chemical or physical methods, significant effort has been devoted to the synthesis of anisotropic metal NPs, with gold nanorods and silver nanoplates (NPTs) being among the most popular [6, 7], due to their ability to better confine light below the diffraction limit, which enhances their sensitivity. Silver NPTs in particular show the highest sensitivity to the refractive index of the surrounding medium among metal NPs [8, 9, 10, 11, 7].  
Ag NPT synthesis often occurs in two steps, since the first step is required to produce spherical NPs, while the second step transforms them into flat triangular nanoplates. This is necessary in order to separate the nucleation of new particles from their growth and shape transformation. The most widely used process so far is the so-called seed-mediated growth, where spherical particles are first produced by chemical reduction of Ag ions by NaBH\({}_{4}\), then transformed chemically by hydrazine and citrate [12, 7, 13]. Other methods were also proposed in place of the first or second step of the seed-mediated growth or both. For example, chemically produced NPs can be transformed into NPTs through the use of light irradiation or H\({}_{2}\)O\({}_{2}\) or a combination of both [14, 15, 16, 17, 18, 19, 20, 21, 22, 23]. More recently, Ag NPTs were produced from laser-ablated Ag NPs using light irradiation and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), fully avoiding the chemistry involved in the seed-mediated growth [10, 23, 24, 25]. A few examples of a single-step synthesis of anisotropic Ag NPs have been reported [26, 27, 28, 29, 30]. However, most of the processes reported are very time consuming (from 12 h to 90 h). In this paper, we report on the synthesis of Ag NPTs in a single step, combining the laser ablation process with light irradiation and H\({}_{2}\)O\({}_{2}\), a process that is completed within lessthan one hour. To our knowledge, this is the first time Ag NPTs have been obtained by a single laser ablation step. We also investigated the plasmon sensitivity of such nanoparticles under a varying refractive index.
## 3 Analysis and Discussion of Test Results
### Analysis of Unconfined Compressive Strength Results
Compared to recycled fine powder (RFP), F1 ion-curing agent, wood calcium-source EICP solution, and composite modified water glass, geopolymer-solidified loess offers several advantages. Firstly, the material is easily accessible and cost-effective, meeting the requirements of green emission reduction. Moreover, the solidification effect of geopolymers on loess is remarkably significant. This study found that when the geopolymer addition is 20%, the consolidated soil's unconfined compressive strength reaches its peak value at 570.86 kPa. This strength is 1.59 times, 2.03 times, and 1.98 times higher than that of loess solidified by F1 ion-curing agent, wood calcium source EICP solution, and composite modified water glass, respectively.
# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 2 Materials and Methods  
### Landslide Hazard Estimation  
Rotational and translational landslide hazards can be evaluated by analyzing landslide inventories by means of a statistical spatial and temporal probability model [27; 28; 29; 30]. These models estimate the term _P(Mi)_ of the landslide hazard equation. When the landslide is spatially well defined and its velocity is slow, the term _P(Mi)_ can be considered as being equivalent to the landslide hazard [2]. These models combine the spatial and temporal probability of landslides by considering them as two separate conditional probabilities [27; 28; 29; 30]. The hazard is estimated by combining susceptibility (calculated using a statistical model), the probability of the magnitude (calculated using a power law), and the temporal probability (determined using a landslide inventory) [27; 28; 29; 30]. However, this approach has some limitations. The power law is not based on empirical data and the methodology produces different maps for each landslide magnitude (area) per return period, which is impractical for risk communication [29]. Additionally, the spatial and temporal probabilities are estimated using the inventory as an input, potentially affecting the independence between the two probabilities--a precondition of this method [29]. The present study proposes some modifications to this conceptual model to overcome these limitations, including simplifying the formula by eliminating the landslide magnitude probability term. This is feasible as the combination of landslide intensity and vulnerability is characterized by a damage function. Furthermore, the use of a physical model to estimate the spatial probability contributes to the independence between spatial and temporal probabilities. The formula proposed to integrate these probabilities is as follows:  
\[H=P_{t}\ \cdot P_{s} \tag{3}\]  
where \(H\) is the landslide hazard, \(P_{t}\) is the temporal probability, and \(P_{s}\) is the spatial probability.  
Figure 2: Lithological map of the study area.  
Considering that a previous study showed that translational and rotational landslides were concentrated in the colluvium and residual soil layer, the landslide risk analysis was limited to this layer (Figure 3), which covers 39 km\({}^{2}\) (or 15%) of the study area.
## 1 Introduction  
Natural language processing often requires grapheme-to-phoneme (G2P) conversion of an orthographic text [1]. G2P converts strings of graphemes to corresponding sequences of phonetic transcription characters, directly from orthographic representations and it is crucial for many applications in various areas of speech and language processing [2]. The most frequent applications of grapheme-to-phoneme conversion occur in text-to-speech systems, which require high quality phonetic transcriptions to function well [3]. Tools for converting graphemes to phonemes are also used in theoretical and applied linguistics. Such tools are useful in many areas of linguistic research (e.g., phonetics, phonology, dialectology, and language acquisition), in order to obtain preliminary phonetic transcriptions of large language corpora [4].  
The main goal of research on the conversion of graphemes to phonemes is improving speech recognition for the Polish language [5, 6]. In addition, research studies have been conducted on the phonetic properties of Polish phonemes [7, 8], speech recognition based on such analyses [9], speaker recognition [10, 11, 12, 13, 14] and new applications of speech and language processing (e.g., speech translation) [15]. Particularly good results in speech recognition are achieved through the use of statistical language models [16, 17, 18, 19]. However, currently the field of language modelling is shifting from statistical methods to neural networks and deep learning methods [20, 21]. The development of statistical and deep learning methods for speech recognition and language modelling requires access to large language corpora, including phonemic corpora [18, 19, 20, 21, 22, 23, 24, 25]. The main motivation for undertaking this research on automatic grapheme-to-phoneme conversion and its application, was the development of effective methods of creating a phonemic language corpus for Polish, comprised of phonemic transcriptions derived from an orthographic language corpus through grapheme-to-phoneme conversion.
# Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems +
## 3 HULK: Hyper-Heuristic Based on ACO and Local Search Operators
### Pheromone Trail Update  
One of the most important steps on ACO algorithms is how to adjust the pheromone trail to guide the search to promising solutions in the search space as well to forget misleading paths. The pheromone trail plays an important role for HULK since it maintains relevant information about previous dynamic environments, accelerating the solution search in the new dynamic environment or when applying a different LSO. The pheromone adjustments in \(\mathcal{MMAS}\) are made in the same way as in [8], i.e., the evaporation is commanded by Equation (13).  
\[\tau_{ij}\leftarrow(1-\rho)\tau_{ij},\forall(i,j)\in A, \tag{13}\]  
where \(\rho\) is the evaporation rate, satisfying \(0<\rho\leq 1\), and \(\tau_{ij}\) is the current pheromone value. After evaporation, the best ant updates the pheromone trail by adding pheromone as described in Equation (14).  
\[\tau_{ij}\leftarrow\tau_{ij}+\Delta\tau_{ij}^{best},\forall(i,j)\in\pi^{best}, \tag{14}\]  
where \(\Delta\tau_{ij}^{best}=1/\phi(\pi^{best},t)\) is the amount of pheromone deposited by the best ant. The pheromone can be deposited by the best-so-far ant (i.e., a special ant that may not necessarily belong to the current constructing colony), in this case, \(\pi^{best}=\pi^{bs}\), or by the iteration-best ant, in this case, \(\pi^{best}=\pi^{ib}\). These ants allow a transition from early stronger exploration to later stronger exploitation of the search space. To better explain this process, consider \(f^{bs}\) the frequency with which the best-so-far ant deposits pheromone on its trail. Consider \(f^{bs}\) to be the number of iterations performed by the algorithm between two best-so-far ant pheromone updates. This frequency is adjusted as the search progress [29] starting with a value that allows the best-so-far ant deposits pheromone at each iteration. After the first 25 iterations, we reduce \(f^{bs}\) to 5, 3, 2 and 1 at every 25 additional iterations, retaining the last value until the end of the ACO. This scheme allows that, as the search evolves, the influence of the iteration-best ant on the pheromone trail decreases while the impact of the best-so-far ant grows. This process refreshes at the start of each dynamic change. Observe that the pheromone trail is updated after the end of the local search, so the improvements obtained by the local search can be explored by the proposed hyper-heuristic in the sequence.
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 3 Cloud Based E-Government Framework
### Cloud Based G2G E-Government Framework Stages and Assimilation Process
#### 5: Identify the cloud deployment model  
Once the appropriate cloud deployment strategies have been developed and cloud service model has been selected the government need to decide what cloud deployment model they need to adopt. The framework supports the government to adopt appropriate cloud deployment model. Selection of the certain types of deployment model depends on several factors. Considering the cost and infrastructural factors we have found that the hybrid cloud deployment model would be appropriate for developing countries. The hybrid model would allow the government to keep their critical data on-premises and less critical data in the cloud platform.
# Leveraging Language Models for Inpatient Diagnosis Coding
## 4 Discussion
### Study Limitations  
The results of our study are subject to several limitations. First, although the dataset used for training and evaluation was relatively large, there are few examples of some class labels. This may have limited the model's ability to generalise and capture the full complexity of the coding task for inpatient diagnoses. Second, although we used dynamic class weights, dealing with class imbalance is still poses a challenge as certain diagnosis codes have limited representation in the dataset, which affects the performance of the model for these specific diagnosis codes. Thirdly, the lack of use of historical data prevents the model from incorporating temporal trends and longitudinal information from patients that could provide valuable context for accurate diagnosis coding. Next, our models have shown strong performance with ICD-10-TM in Thai context and mixed English-Thai clinical notes, however its applicability in different settings remains uncertain due to language differences. Our models can certainly useful in different hospitals and regions of Thailand, but their effectiveness in other countries needs to be further evaluated through extensive testing. Finally, the lack of qualitative assessments to evaluate the performance of the model when used by clinicians or professional coders. This may hinder understanding of usability and user experience in real-world scenarios.
## 1 Introduction
Note: In this paper, matrices are denoted by bold uppercase letters and vectors are denoted by bold lowercase letters. The Euclidean norm, transpose, conjugate transpose, conjugate transpose operation and mutual information are denoted by \(\|\), \(\|\), \([\cdot]^{T},[\cdot]^{H}\), \([\cdot]^{\dagger}\) and \(I(\cdot,\cdot)\), respectively. Without the loss of generality, \(x\) represents the set of optimization variables.
# Development of Practical Design Approaches for Water Distribution Systems
## 4 Application and Results
### Hanoi Network  
Optimal cost design is performed considering pipe size continuity in the Hanoi network. As a result, the design cost using the proposed pipe size continuity approach is $6,093,181, which is the same optimal cost design from the existing study. However, the average computation time for the optimal cost design is lowered by approximately 83%. The results of comparing the computation time depending on the iteration number between the existing pipe continuity approach and the proposed algorithm of this study are shown in Table 4.  
The existing approach [10] requires a relatively long time to derive the design considering pipe size continuity. The reason is that the approach searched all of the flow paths in WDSs and determined whether each path satisfied continuity. However, since the proposed technique in this study is repeatedly performed to examine the pipe size continuity of two adjacent pipes around one node, it takes a short time compared to the existing approach. In other words, the time spent for the continuity search is improved, as it substantially reduced the redundant time to examine the pipe size continuity in the existing algorithm. As the Hanoi network consisted of one water source, 31 nodes, and 34 pipes, it can be categorized to relatively small-scale distribution networks. Since the existing pipe continuity approach finds all the flow paths and checks the continuity, when the scale of distribution networks is enlarged with an increasing number of nodes and pipes, the time for calculation is exponentially increased. However, the proposed technique reduced the redundant research process and considered the pipe continuity at each joint simultaneously. Therefore, the optimal design solutions after applying each of the continuity approaches showed the same, but the average computation time spent to design the Hanoi network was lower by 83% compared to the existing approach (when the number of iterations was 500,000, approximately 13 h were reduced). Therefore, it is expected to substantially shorten the search time with the even larger scale of WDSs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **Existing Pipe Size Continuity Search** & **Continuity Search Approach** \\  & **Approach [10]** & **Proposed in This Study** \\ \hline Design cost (\$) & 6,093,181 & 6,093,181 \\ \hline \multirow{3}{*}{Mean calculation time} & 21 min 20 s (Iterations = 10,000) & 4 min 49 s (Iterations = 10,000) \\ \cline{2-3}  & 1 h 46 min 39 s (Iterations = 50,000) & 24 min 6 s (Iterations = 50,000) \\ \cline{2-3}  & 17 h 47 min 33 s (Iterations = 500,000) & 4 h 1 min 13 s (Iterations = 500,000) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization results and computational time for the Hanoi network.
## 1 Introduction
### Interference Monitoring Method Based on Characteristic Parameter Threshold  
The method based on characteristic parameter threshold compares the actually measured indicators or monitoring quantities with a preset threshold in order to determine whether there are interferences. The monitoring indicators include the quantization weight gain of the A/D converter [4], power spectral, AGC (automatic gain control) [5] carrier to noise ratio [6], the correlator output [7], _etc._ This method requires a monitoring threshold, which is usually derived from the corresponding characteristic of the GNSS signals without interference. However, the GNSS signal environment is always complex, and especially during transfer, the GNSS signal inevitably suffers from intentional or unintentional interferences. Therefore, utilizing the characteristic without interference as the threshold will increase the false alarm rate and missing report rate.
## 2 Materials and Methods
### Statistical Analysis  
Quantitative variables are represented as the mean \(\pm\) standard deviation. For all analyses, only consistent features are eligible. Statistical analysis was conducted by categorizing patients based on the confirmed diagnosis of PVE.  
The differences in quantitative values were evaluated by means of the Mann-Whitney U test and Benjamini Hochberg correction procedure for multiple comparison (only \(p\) values \(<\) 0.05 after Benjamini Hochberg correction were considered statistically significant). SPSS software version 19.00 (IBM Corp., Armonk, NY, USA) was employed to perform all the statistical procedures. The Synthetic Minority Over-sampling Technique (SMOTE) [44] was used to balance the dataset with respect to the diagnostic groups, increasing the effective sample size (\(n\) = 22) and addressing class imbalance issues. To ensure further reliability, non-parametric Spearman correlations were calculated between the remaining radiomic features post-univariate and reproducibility analyses, with highly correlated features (\(p\geq 0.9\)) discarded to mitigate multicollinearity issues (correlation coefficients are detailed in Supplementary Materials Figure S1).  
Eight machine learning (ML) classification models were evaluated in Orange 3.31 software (Bioinformatics Laboratory at the University of Ljubljana, Slovenia; [[https://orangedatamining.com/](https://orangedatamining.com/)]([https://orangedatamining.com/](https://orangedatamining.com/)), accessed on 23 February 2024) for Confirmed PVE vs. Discarded PVE classification based on the following remaining standardized radiomic features: logistic regression, neural network, k-nearest neighbor (kNN), naive bayes, support vector machine (SVM), random forest, gradient boosting, and decision tree. The model hyperparameters are summarized in Supplementary Materials Table S2. A 5-fold cross validation was performed to validate the ML models. The ML models' performance was assessed in terms of the area under the curve (AUC), accuracy, F1-score, precision, recall, and specificity. A value greater than 0.75 was considered as an indicator of acceptable model performance.