esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Framework and Results
### Comparative Analysis of RAG and CARAG Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & (**a**) COVID Theme & & & \\ \hline
**Index** & **RAG (PCA)** & **CARAG (PCA)** & **Diff (PCA)** & **RAG (t-SNE)** & **CARAG (t-SNE)** & **Diff (t-SNE)** \\ \hline
0 & 0.1133 & 0.2263 & 0.1130 & 31.5851 & 31.5608 & \(-\)0.0242 \\ \hline
1 & 0.1423 & 0.2506 & 0.1083 & 31.4120 & 31.4665 & 0.0544 \\ \hline
2 & 0.3000 & 0.2720 & \(-\)0.0280 & 31.4419 & 31.4444 & 0.0025 \\ \hline
3 & 0.1913 & 0.1995 & 0.0083 & 31.4158 & 31.4030 & \(-\)0.0128 \\ \hline
4 & 0.0645 & 0.1354 & 0.0709 & 31.5440 & 31.4492 & \(-\)0.0948 \\ \hline
5 & 0.1779 & 0.1736 & -0.0043 & 31.4712 & 31.4843 & 0.0132 \\ \hline
6 & 0.1809 & 0.1767 & \(-\)0.0042 & 31.6878 & 31.5558 & \(-\)0.1320 \\ \hline
7 & 0.6405 & 0.1687 & \(-\)0.4717 & 32.1598 & 31.6895 & \(-\)0.4704 \\ \hline
8 & 0.3688 & 0.1994 & \(-\)0.1693 & 31.9011 & 31.7242 & \(-\)0.1769 \\ \hline
9 & 0.5524 & 0.3178 & \(-\)0.2346 & 32.0992 & 31.8005 & \(-\)0.2987 \\ \hline _Average_ & **0.2732** & **0.2120** & **\(-\)0.0612** & **31.6718** & **31.5578** & \(-\)0.1140 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparison of RAG and CARAG embedding distributions across themes. Each sub-table shows distances to centroids in PCA and t-SNE spaces, with differences highlighted in the Diff (PCA) and Diff (t-SNE) columns. Per-theme averages are included as the last row in each sub-table.
## 3 Methodology
### Notification Experience Comparison  
To answer the second research question, the characteristics of the previously analyzed were compared in terms of user experience, frequency, and importance of the received notifications. The goal was to supplement the previous qualitative findings to understand better their overall notification experience and how the characteristics' experiences differ regarding the aforementioned scales.  
Firstly, the averages of experience, frequency, and importance were mapped out for each characteristic. Such mapping was expected to have two purposes: firstly, to understand the levels of each scale within the characteristics, and secondly, to reveal if and where the differences in notification experiences between the characteristics are. A bubble chart was chosen to visualize the positioning of each characteristic on the three scales as this allows easy comparison across characteristics and scales, highlighting key patterns and potential discrepancies.
###### Abstract  
Emergency situation awareness during sudden natural disasters presents significant challenges. Traditional methods, characterized by low spatial and temporal resolution as well as coarse granularity, often fail to comprehensively capture disaster situations. However, social media platforms, as a vital source of social sensing, offer significant potential to supplement disaster situational awareness. This paper proposes an innovative framework for disaster situation awareness based on multimodal data from social media to identify social media content related to typhoon disasters. Integrating text and image data from social media facilitates near real-time monitoring of disasters from the public perspective. In this study, Typhoon Haikui (Strong Typhoon No. 11 of 2023) was chosen as a case study to validate the effectiveness of the proposed method. We employed the ERNIE natural language processing model to complement the Deeplab v3+ deep learning image semantic segmentation model for extracting disaster damage information from social media. A spatial visualization analysis of the disaster-affected areas was performed by categorizing the damage types. Additionally, the Geodetector was used to investigate spatial heterogeneity and its underlying factors. This approach allowed us to analyze the spatiotemporal patterns of disaster evolution, enabling rapid disaster damage assessment and facilitating emergency response efforts. The results show that the proposed method significantly enhances situational awareness by effectively identifying different types of damage information from social sensing data.  
disaster informatics; social sensing; computer vision; ernie; spatiotemporal awareness +
Footnote †: journal: Applied sciences  
1
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 1 Introduction
### Research Objectives  
The objective of this study is to explore in depth the potential of digital twin technologies in building energy management, especially in the context of Building Information Modelling (BIM) and Digital Twins (DT), to improve energy efficiency and achieve sustainability in building energy management by monitoring, optimizing, and predicting building energy consumption in real time. The study explores in depth the application of Building Information Modelling (BIM) and Digital Twins (DT) technologies in energy management, understands their role and impact in the energy sector, predicts their future trends and challenges, and explores their implications and guidance for future research and practice. Overall, this study expects to drive energy efficiency improvements and building sustainability through an in-depth study of the potential of digital twin technologies to improve energy management in the building sector. With advances in building energy analytics and machine learning technologies, the expectation is to improve prediction accuracy, optimize energy efficiency and improve management processes to achieve sustainability goals.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 4 Latent Heat Measurements of PR Data  
The latent heat (LH) produced during the rainfall process accounts for three-fourths of all heat energy for the atmosphere and has a significant impact on water cycles of the Earth. Studies on structures of the LH could provide references for the diabatic control and the feedback of rainfall, the large scale circulation and its forecasting, and cloud parameterization schemes. It is meaningful to analyze the features of the LH measured by PR for a long time period and from a global view. Previous representative studies are listed in Table 7.
## 3 Results
### Classification Results  
The classification of harmonized data using the deep neural network described in Section 2.4 gave an AUC = \(0.71\pm 0.01\). The classification of non-harmonized data with the ad-DNN yields similar results: AUC = \(0.70\pm 0.03\). These results are in line with other results reported in the literature on the same topic.
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
###### Abstract  
The nonlinearity and complexity of coastal ecosystems often cause difficulties when analyzing spatial and temporal patterns of ecological traits. Environmental DNA (eDNA) monitoring has provided an alternative to overcoming the aforementioned issues associated with classical monitoring. We determined aquatic community taxonomic composition using eDNA based on a meta-barcoding approach that characterizes the general ecological features in the Gwangyang Bay coastal ecosystem. We selected the V9 region of the 188 rDNA gene (185 V9), primarily because of its broad range among eukaryotes. Our results produced more detailed spatial patterns in the study area previously categorized (inner bay, main channel of the bay and outer bay) by [PERSON] et al. (2019). Specifically, the outer bay zone was clearly identified by CCA using genus-level identification of aquatic organisms based on meta-barcoding data. We also found significant relationships between environmental factors. Therefore, eDNA monitoring based on meta-barcoding approach holds great potential as a complemental monitoring tool to identify spatial taxonomic distribution patterns in coastal areas.  
Keywords:coastal ecosystem; eDNA; spatial patterns; complemental monitoring tool +
Footnote †: journal: Applied Sciences
## 2 Theory and Methodology
### Apparent Resistivity Calculated by the Magnetic Field Gradient
The resulting resistivity value at this point represents the optimal apparent resistivity. Figure 3 is the apparent resistivity solved from magnetic field gradient of uniform half-space models with various resistivity values. Model parameters are \(\rho=10\), 100, and 1000 \(\Omega\cdot\)m, respectively; the transmitting frequency range is set from 16 Hz to 8192 Hz. Figure 2: Relationship between the magnetic field gradients and resistivity. Figure 3a-c shows the calculated apparent resistivity from \(H_{z}^{\Delta x}\) by Equation (2), Figure 3d-f shows the calculated apparent resistivity from \(H_{z}^{\Delta y}\) by Equation (3). Globally, the calculated apparent resistivity distributes near the true model, but there are varying calculation errors for different models. Specifically, in Figure 3a,d, when the uniform earth resistivity is 10 \(\Omega\cdot\)m, the apparent resistivity in the high-frequency band significantly deviates from the true model, and when the frequency is 8192 Hz, the deviation reaches a maximum of 26 \(\Omega\cdot\)m. In Figure 3b,e, when the earth resistivity is 100 \(\Omega\cdot\)m, there exists a larger error in calculating apparent resistivity using the \(x\)-direction gradient \(H_{z}^{\Delta x}\) in the high-frequency band, the deviation reaches a maximum of 87 \(\Omega\cdot\)m when \(f\) = 8192 Hz; while the \(y\)-direction gradient \(H_{z}^{\Delta y}\) yields a larger error in the low-frequency band, the deviation reaches a maximum of 24 \(\Omega\cdot\)m when \(f\) = 64 Hz. In Figure 3c,f, when the earth resistivity is 1000 \(\Omega\cdot\)m, there is a distinct difference between the calculated apparent resistivity and real model in the low-frequency band, when the frequency is 16 Hz, the deviation reaches a maximum of 732 \(\Omega\cdot\)m. The irregular calculation errors of the uniform model may result in random artificial anomalies in apparent resistivity imaging of anomaly models. Therefore, this deviation cannot be ignored. In order to mitigate the presence of random artificial anomalies, for the anomaly models, it is necessary to eliminate the non-uniform apparent resistivity derived from the uniform earth model. The difference between the apparent resistivity calculated from the anomaly model and that from the background uniform model is defined as the apparent resistivity variation \(\Delta\rho\). Generally, the background resistivity remains unknown during detection. Therefore, this study employs LSSVM to obtain the apparent resistivity variation eliminating the background resistivity of the uniform model when its value is unknown. The inputs include the geometric position of measurement points, the transmitting frequency, and magnetic field gradient values. For correcting background apparent resistivity, we utilize the apparent resistivity variation as output to train our network.
## 1 Introduction  
Actebular fractures are relatively uncommon, with an incidence rate ranging from 3 to 8.1 cases/100,000 person/year. These fractures have a high impact on patient quality life and are mostly seen in younger patients and persons aged over 50 years old [1, 2]. Traditionally, acetabular fractures are classified according to Letournel classification into ten major fracture patterns, which consist of five simple patterns and five complex patterns [3, 4]. Due to the complexity of the pelvic anatomic structure, acetabular fractures represent a challenging procedure for orthopedic surgeons.  
At present, a well-established surgical procedure in the treatment of acetabular fractures is represented by the anterior intrapelvic approach (AIP) [5, 6, 7]; this technique allows for the minimization of operation times and risks related to blood loss during surgical intervention [5; 6]. At any rate, the purpose of the surgical treatment is the achievement of anatomical reduction and stable fixation. In transverse and T-shaped fractures there is no bony structure able to oppose to medial subluxation of the femoral head since the entire medial wall is detached from the superior aspect of the dome. Therefore, systems able to fix both columns are traditionally used in these fracture patterns. Recently, a new pre-contoured anatomic plate design has been developed aiming at fixation of both column and quadrilateral plate [8]. Its anatomic shape was designed to be implanted with its native curvature, but this latter could not be always adequate to perfectly match to the bone shape. Thus, further in-situ bending is often indispensable [6], with a consequent increase in surgical times and modification of the performance of the native device.  
In this framework, a lack of biomechanical data regarding these plates in operative conditions emerges. Many examples are present in the literature about transforming the qualitative assessment of clinical problems to quantitative by exploiting numerical modeling [9; 10; 11; 12; 13; 14; 15]. Here, finite element modeling was used to biomechanically examine different bone-plates configurations.  
The aim of this study is therefore to compare the stability guaranteed by two different supraprecineal plates: (1) a standard pelvic plate (SPP) and (2) a supraprecineal quadrilateral surface buttressing plate (SQBP).
## 2 Materials and Methods
### Nuclei Segmentation Methods  
Two state-of-the-art nuclei instance segmentation methods, Hover-Net [36] and STARDIST [37], were employed to assess the impact of developed methods on the segmentation quality.  
The Hover-Net network received an input RGB patch of 270 \(\times\) 270, with values normalized between 0 and 1. Since we wanted to verify whether removing compression artifacts would cause segmentation improvement, reference models were trained for each dataset separately using original images without any modification for training and validation. These models were marked as no correction method. For reference models, in online augmentation, identical techniques were used as in the original paper [36], that is, flip, rotation, Gaussian blur, median blur, additive Gaussian noise, modification of contrast, hue, and saturation.  
Next, we trained models for each selected artifact removal method for each dataset. First, compression artifacts were removed from images from the training and test sets. The EPIIC and EPIIC Sobel methods used a Gaussian filter with a window size of \(3\times 3\) and a standard deviation \(\sigma=0.7\), a parameter \(T_{m}=1.25\), and an averaging filter of size \(5\times 5\). For the Gaussian filtering, the same \(\sigma\) and kernel size were used as in the previous methods. For the Guided filter, we set the degree of smoothing to 0.001 and the neighborhood size to 3. During model training, the augmentation was limited to flip, rotation, contrast modification, hue, and saturation.  
All models described above were initialized with pre-trained weights on the ImageNet dataset [38]. Only decoders were trained for the first 50 epochs, and then all layers were fine-tuned for 75 epochs. The Adam optimizer was used with the following learning schedule: for each stage, the initial value of the learning rate was \(10^{-4}\), and after every 25 epochs, it was reduced ten times. Batch size was set to 10 and 2 for the first and second stages, respectively. An early stopping method with patience equal to 50 was used by selecting the model that achieved the best \(DICE\) score on the validation dataset.  
The STARDIST model with 64 rays and a U-Net [39] backbone of depth 4 was used. The dropout equal to 0.7 was added to the network to avoid overfitting, and loss functionsspecified by the authors were used. The weights of the network were initialized randomly. Each model was trained for 400 epochs with a batch size equal to 2 using the Adam optimizer, starting with a learning rate of 0.0003. If no progress was made for 80 epochs, the learning rate was reduced by half. The weights for the final model were selected based on the smallest validation loss during training. The network received an RGB input patch with a size of \(256\times 256\) normalized to the range 0-1. The same online augmentation procedure as for the Hover-Net model was applied.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments
### Underwater Acoustic Channels  
Figure 7 shows the two impulse responses of underwater channels at (a) Fuxian Lake and (b) Wuyuan Bay. The environmental characteristics of these locations are not similar and can represent different underwater environments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline DFT Point & 1024 \\ Number of Sub-carrier & 512 \\ Number of Data Sub-carrier & 324 \\ Number of Pilot Sub-carrier & 103 \\ Sampling Rate (kHz) & 144 \\ Bandwidth (kHz) & 3.6 \\ Band (kHz) & 29.1\(\sim\)32.7 \\ Guard Interval & 0.125 \(\times\) Symbol Length \\ Sub-carrier Spacing (Hz) & 3.516 \\ Refinement Factor D & [2 3 4] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter of communication model.  
Figure 6: The map of the outfield in the experiment.  
Figure 7: Time evolution of the magnitude impulse response at two different distances.
# Polarimetry for Photonic Integrated Circuits
## 2 Measurement Methodology
### Setup Calibration
Then, the PC Muller matrix is written as a composite as follows:  
\[\underline{M}_{\text{PC}}\Big{(}\theta_{LP},\theta_{\frac{3}{4}},\theta_{ \frac{3}{4}}\Big{)}=\underline{M}_{\frac{3}{4}}\Big{(}\theta_{\frac{3}{4}} \Big{)}\underline{M}_{\frac{3}{4}}\Big{(}\theta_{\frac{3}{4}}\Big{)} \underline{M}_{LP}(\theta_{LP}) \tag{2}\]  
With that, we get  
\[\tensor{s}{s}_{\textit{in}}=\underline{M}_{\textit{fiber,in}}\underline{M}_{ \text{PC}}\tensor{s}{t}_{\textit{Is}} \tag{3}\]  
Since the absolute orientation of \(\tensor{s}{t}_{\textit{Is}}\) is arbitrary, we find that \(\Delta\psi_{\frac{3}{4}}\) can be absorbed in it. Now, all open parameters of the setup model are given. They are summarized in Table 1. In total, the model has 18 degrees of freedom. Figure 1: Schematic overview of the measurement setup. The first step of the setup calibration procedure was to replace the DUT, PM, and the interconnecting fiber by a free-space path, as shown in (1) in Figure 2. A lens collimates the beam, which is then filtered by an LP that is set to transmit only TE. A photodetector (PD) measures the transmitted optical power \(P_{FS}\). The three angles \(\theta_{LP},\ \theta_{\lambda/4},\ \theta_{\lambda/2}\) in the PC are swept in the ranges \([-45^{\circ},45^{\circ}]\), \([-90^{\circ},90^{\circ}]\), and \([-45^{\circ},45^{\circ}]\) with 5, 5, and 3 equidistant steps, respectively, yielding \(5\times 5\times 3=75\) measurement steps in total. For each step, \(i\), the free space power, \(P_{FS,i}\), is recorded and stored. The second step is to replace the free-space path by the actual SMF + PM link that is intended for device characterization, see (2) in Figure 2. The same PC angles are swept in the same 75 steps as in (1). Now, however, the Stokes vectors \(\tensor{s}{s}{m}{s}{s}\) are recorded for each measurement. The measurements are used as input for a numerical solver that is implemented in python using the SciPy package [10]. The solver fits the model to the measurement with  
\[\mathrm{P_{FS,i}}=\alpha_{FS}\Big{(} \begin{array}{ccc}1&0&0&0\\ \end{array} \ \  
With these equations, the \(2\times 75\) measurements give rise to \(5\times 75\) equations. i.e., \(1\times 75\) via Equation (4) and \(4\times 75\) via Equation (5). With that, the 18 open parameters are fitted and the overdetermination of the equation system is used to reduce the sensitivity to measurement noise via least squares fitting. Note that the equation system is nonlinear in the open parameters due to the occurring trigonometric functions. All of the above is performed on a per wavelength basis. The TLS in the setup can be swept over an optical bandwidth of 100 nm spanning from 1.47 \(\upmu\)m to 1.57 \(\upmu\)m. The calibration, as above, is performed every 5 nm in this wavelength range. For intermediate wavelengths, the open parameters are interpolated using cubic spline fits.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 3 Materials and Methods
### Feature Extraction
#### 3.3.1. Term Frequency-Inverse Document Frequency  
We applied TF-IDF to create a vector representation of preprocessed textual Reddit data. In the large body of text, the terms that appear very frequently (e.g., \"is\", \"are\", \"the\", and \"a\") have low semantic importance to the content of the document. TF-IDF considers how frequently the term appears in the corpus and penalizes the most frequent terms, giving rarer terms higher importance. Concerning the Reddit dataset, a document refers to an individual post. The TF-IDF value of a term \(t\) in a given document \(d\) is the product of term frequency (_tf_) and inverse document frequency (_idf_) and is calculated as  
\[tf-idf(t,d)=tf(t,d)\times idf(t) \tag{1}\]  
where _tf_(_t_,_d_) is the number of times a term \(t\) appears in a given document \(d\). The _idf_(_t_) component is calculated as  
\[idf(t)=\log_{e}\frac{1+n}{1+df(t)}+1 \tag{2}\]  
where \(n\) is the total number of documents in the corpus (i.e., posts in the dataset), and _df_(_t_) is the number of documents \(d\) that contains the specific term \(t\). The value is calculated for all terms in the corpus. The output of this process is a sparse matrix, where each row is a vector of floating point values that numerically represents a single post. Each resulting vector is then normalized by applying the Euclidian norm. Each column of the matrix represents a single term that serves as a feature. Figure 1 shows an example of the resulting matrix. The values in this example are arbitrary. To generate TF-IDF features, we utilized the TfidfVectorizer package from the scikit-learn (1.5.1) library [32]. The resulting matrix contained, on average, 5902 features.
## 2 Dynamic Modeling
### Analysis of the Interference Mathematical Model  
In this section, the interference of wind, waves, and currents is reasonably modeled, and the proposed interference mathematical model is used to simulate the external environment, which makes the simulation experiment closer to the actual situation.