esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation of Applying Large Language Models to Spoken Language Learning
## 3 Experiments and Results
### Zero-Shot and Few-Shot Benchmarks
As we examine the two data subsets more closely (as depicted in Figure 4), we can draw similar conclusions as above. Notably, regarding the Knowledge and Concepts subset, models with parameter sizes exceeding 20B displayed reduced performance variation, signifying heightened stability in their performance. **Concept Memorization and Knowledge Reasoning:** The LLMs excelled in concept memorization but exhibited a weaker ability to apply knowledge for reasoning. Even on a relatively small model (\(\mathcal{T}\) B), the accuracy of concept memorization reached nearly 80%, and a model with a size of around 11 B reached the level of GPT-3.5. At around 20 B, the LLMs reached performance saturation in the knowledge and concept-related questions. However, in reasoning the application questions, even the most powerful LLMaA2 models with sizes of 70B, as well as the GPT series models, performed relatively poorly, achieving low accuracy (42.6% and 64.3%, on average, respectively). **Knowledge Preference:** We analyzed whether these models exhibited any specific preferences for certain types of knowledge. Figure 5 demonstrates that these models exhibited no significant differences in terms of accuracy among the different types of questions. Figure 4: The distribution of performance on two subsets across different model sizes. Figure 5: The accuracy distribution of all tested LLMs for different question types in the Application Questions subset. **Answer Bias:** We selected several models and conducted a corresponding distribution analysis of the generated answers, as shown in Table 4. It can be observed that apart from GPT-3.5 and GPT-4, the other models exhibited apparent answer bias. It is well accepted that LLMs are likely to generate toxic, biased, or even harmful content for humans because their training procedures mainly aim to capture the characteristics of pre-training datasets. Among the LLMs used in this study, GPT-3.5 was fine-tuned with RLHF, enabling it to follow humans' expectations. Therefore, the answers of GPT-3.5 were more consistent with human values.
# Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter
## 5 Assimilation Results
### Results with SHPB Observations  
The EnKF assimilation estimates the most likely mean and covariance of a normal distribution for each parameter that produces the HOSS SHPB simulation that best matches the data. The assimilation experiment setup is the same as in Section 5.1, where we are estimating \(\sigma_{t}^{max}\) and \(E_{t}\).  
From our twin experiments shown in Section 5.2, three EnKF assimilation iterations are needed to estimate a fairly accurate parameter value for \(\sigma_{t}^{max}\) and \(E_{t}\). The observations are composed of a series of stress versus time points obtained from a SHPB laboratory experiment. The results show a good convergence for \(\sigma_{t}^{max}\), where the assimilation provides a parameter value of around 0.15 (see Figure 6). For \(\sigma_{t}^{max}\), the assimilation is able to hone in to the most likely value. This might not be the case for \(E_{t}\), where the assimilation  
Figure 4: Ensemble average parameter value (blue line) for the initial sample, generated from LHS using Table 2, and three EnKF assimilation iterations, as well as the reference parameter value (black line) also taken from Table 2. As can be seen, with each iteration, the EnKF provides an estimated parameter value (blue line) that is closer to the reference parameter value (black line). From these experiments, three iterations of the EnKF seem to be sufficient to estimate the correct value for both parameters \(\sigma_{t}^{max}\) and \(E_{t}\).  
Figure 5: Time–stress curves of the reference simulation (black curve) and for each EnKF iteration, where each plot shows the evolution of the EnKF ensemble, starting with the initial ensemble of 25 members without assimilation (top left, blue curves), ensemble of the first EnKF iteration (top right, green curves), second iteration (bottom left, yellow curves), and third iteration (bottom right, magenta curves). At each iteration it is clearly seen how the EnKF iteration provides an ensemble that is getting closer or tighter around the reference solution.  
provides a monotonically decreasing parameter value. Given that no strong suggestion of convergence is seen in this parameter value, it is difficult to conclude that the final value provided in the third iteration of the assimilation is the most likely parameter value for this SHPB observations.  
To provide a clearer assessment of the convergence of the assimilation, the time-stress curve for all ensemble members, from the initial estimation to the second iteration, is plotted against the observed SHPB stress versus time curve. Figure 7 shows the time-stress curve for the initial ensemble with uniform distribution (blue lines), the curves for the first EnKF assimilation iteration (green curves), and the second assimilation iteration (red lines). As can be seen, the curves are converging towards the observed SHPB curves, but with the risk of ensemble collapse, it is more prudent to avoid over-iterating the EnKF.
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
## 2 Code Construction
The total length of the resulting code, \(N_{T}\) after the mapping is dependent on the number of mapping, \(M\), and can be attained by \(N_{T}=M\times N\), where \(N\) is the length of the matrix before mapping. \(M\) can be attained by \(X_{T}/W\). Here \(X_{T}\) is the total number of users.
## 1 Introduction
Thus, the optimal values for the inclination angle and plate length can minimize wave reflections from the walls of the wave basin while simultaneously reducing the spatial footprint. The MLP regression algorithm of the ANN model is used to determine the optimal design parameters. This approach significantly reduces the need for complex and time-consuming numerical calculations while efficiently identifying the best optimal design parameters. This supervised learning method derives insights through a dataset used in training. In the present study, the inclination angle and non-dimensional plate length have been adopted as input features, and the averaged reflection coefficient as an output feature. The training dataset of the given input features and the corresponding output feature was generated using the DBEM. The trained ANN model was subsequently used to predict the averaged reflection coefficient using a larger dataset comprising a combination of design parameters. During the optimization process to minimize both reflected waves and spatial footprint, the weighting factors are assigned based on their relative importance to each other, employing the weighted sum model (WSM) within the multi-criteria decision-making approach.
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 4 Cooperative Crossing Cache Placement Optimization  
In this section, we study the offloading probability of a D2D-aided cellular network with cooperative crossing between BSs and devices. The overall problem to obtain the optimal cache placement that maximizes the offloading probability can be expressed as follows:  
\[\max_{q}\ P_{off}=P_{1}+P_{2}+P_{3} \tag{19}\]  
\[\text{s.t.}\ \sum\limits_{k=1}^{K}\ q_{k}\leq M \tag{20}\]  
\[0\leq q_{k}\leq 1\ \text{for}k=1,\ldots,K. \tag{21}\]  
Constraint (20) dictates that the desired content in each device cannot exceed the cache storage size constraint.
## 4 Experiment of Geometry Processing Accuracy Evaluation
### Introduction of Experimental Data  
The experiments were carried out on the Chinese Remote Sensing Satellite, but the method is also applicable to other high-resolution SAR satellites such as QL-1. Eighteen sliding-spot scenes of the China Xinjiang calibration field were collected as calibration images. It should be noted that both the range sampling frequency (RSF) and the pulse-width of the SAR signal will affect the range measurement accuracy [12]. The higher the RSF, the higher the ranging accuracy, and the RSF and the range bandwidth are completely correlated. The pulse-width also affects the ranging accuracy because different reference points for recording the system delay results in different system delays. Therefore, for a certain satellite, the geometric calibration and positioning process need to be executed according to different combinations of the pulse-width and bandwidth parameters of the SAR system. Here the 18 images are divided into 6 calibration groups B1-B6 according to their pulse-width and bandwidth parameter combinations. Their imaging information is shown in Table 2. We use several corner reflectors as high-precision ground control points for the calibration process. Their geographic coordinates are obtained by GPS measurement whose accuracy is better than 0.1 m. The image coordinates of these reflectors are obtained by manual selection whose accuracy is better than 1 pixel.  
Fourteen sliding-spot images of Chinese Inner Mongolia calibration field from the same SAR satellite were collected as verification data, and the geometric positioning was processed to verify the uncontrolled positioning accuracy after geometric calibration based on the above calibration images. In this area, several corner reflectors are also laid out as high-precision check points. The coordinate measurement methods of these points are the same as the calibration control points. Corresponding to pulse-width and bandwidth combinations of the calibration groups, the verification images are also divided into five verification groups B1-B5 whose image information is shown in Table 3.
## 4 SMC Parameters Tuning Based on PACSA
### Optimal Tuning Process of SMC based on PACSA
#### 4.2.1 Brief Introduction of PACSA
The mutation rate gradually decreases with the progress of iteration, making the antibody infinitely close to the global optimal solution, thereby enhancing the local search ability. \[p_{r}=\left\{ \begin{array}{ll}p_{r,\max}-x_{n}\big{(}\frac{p_{r,\max}-p_{r, \min}}{x_{\max}}\big{)},A>A_{avg}\\ p_{r,\min}\hskip 56.905512 pt,A\leq A_{avg}\end{array} \right. \tag{30}\]  
where \(A\) expresses the antibody-antigen affinity; \(A_{avg}\) is average value of \(A\); \(p_{r}\) indicates the high frequency mutation rate of the antibody to be mutated; \(p_{r,\max}\) and \(p_{r,\min}\) are the maximum value and minimum value of the mutation rate, respectively; \(x_{n}\) represents the current evolutional algebra; \(x_{\max}\) is the maximum evolutional algebra. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Weight Coefficients** & \(\tau_{1}\) & \(\tau_{2}\) & \(\tau_{3}\) & \(\tau_{4}\) & \(\tau_{5}\) & \(\tau_{6}\) \\ \hline
**Handling Stability Control** & 0.053 & 0.8623 & 0.2362 & 0.1764 & 2.142 & 1.661 \\
**Ride Comfort Control** & 0.48 & 0.06 & 131.3 & 210000 & 9777 & 485.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Weight coefficients of the controllers. **Step 7. ** Judgment of the iteration termination condition. Whether the number of iterations meets the requirements, or whether the antibody-antigen affinity of the optimal antibody in the current population meets the requirements, and if it is satisfied, the procedure is terminated, and the result is the output. Otherwise, return to step (4).
###### Abstract  
Cyclodext (CDs) are macrocycles that are well suited for forming inclusion complexes for surface-enhanced Raman scattering (SERS) detection of analytes due to their low Raman activity, which minimizes background SERS signals and enhances the detection of target molecules without interference. In this paper, we systematically investigated the synthesis of both silver (Ag) and gold (Au) nanoparticles (NPs) using CDs as reducing and capping agents in a basic environment using microwave (MW), ultrasound (US), and room temperature (RT) synthesis. We found that replacing NaOH with K\({}_{2}\)CO\({}_{3}\) as an alkaline environment almost doubles the size (from \(<\)10 nm to around 20 nm) of AgNPs in the MW-assisted synthesis. Synthesis using the US produces less stable particles due to the sample evolution after US irradiation. Ag (20 nm) and Au (11 nm) were successfully obtained at room temperature in the presence of CDs and K\({}_{2}\)CO\({}_{3}\). All synthesized particles present SERS activity. CD capping allowed us to detect hydrophobic molecules like naphthalene and melamine. In the case of methylene blue, the CD capping prevents the changes induced in the SERS by a basic pH. We also demonstrate that the newly synthesized NPs can discriminate by SERS the propranolol enantiomers. Moreover, propranolol inclusion in CDs leads to a dramatic change in its SERS spectrum.  
gold nanoparticles; silver nanoparticle; cyclodextrin; inclusion complex; SERS +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Series** & \(\mathbf{p_{\text{stab}}}\) & \(\mathbf{w_{\text{stab}}}\) & \(\mathbf{\sigma_{\text{stab}}}\) \\ \hline  & & +50 mL & & \\ FEM/EXP & \(-11.7\%\) & & 19.0\% & 30.1\% \\ ANA/EXP & \(-1.7\%\) & & 20.0\% & 34.6\% \\ ANA/FEM & 11.3\% & & 0.8\% & 3.4\% \\ \hline  & & \(-50\) mL & & \\ FEM/EXP & \(-10.9\%\) & & 35.2\% & 40.7\% \\ ANA/EXP & \(-1.3\%\) & & 36.4\% & 32.8\% \\ ANA/FEM & \(10.8\%\) & & 0.8\% & \(-5.6\%\) \\ \hline  & & +100 mL & & \\ FEM/EXP & \(-9.1\%\) & & 7.7\% & 30.4\% \\ ANA/EXP & \(0.5\%\) & & 8.6\% & 33.7\% \\ ANA/FEM & \(10.5\%\) & & 0.8\% & 2.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results from experimental research, numerical and analytical calculations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Series** & \(\mathbf{p_{\text{max}}}\) & \(\mathbf{p_{\text{stab}}}\) & \(\mathbf{w_{\text{max}}}\) & \(\mathbf{w_{\text{stab}}}\) & \(\mathbf{\sigma_{\text{max}}}\) & \(\mathbf{\sigma_{\text{stab}}}\) \\  & [kPa] & [kPa] & [mm] & [mm] & [MPa] & [MPa] \\ \hline  & & +150 mL & & & \\ EXP & \(2.171\pm 0.008\) & \(2.021\pm 0.008\) & \(0.329\pm 0.008\) & \(0.232\pm 0.008\) & \(2.867\pm 0.060\) & \(2.702\pm 0.060\) \\  & (\(-6.91\%\)) & (\(-1.82\%\)) & (\(-1.82\%\)) & & (\(-5.76\%\)) \\ FEM & - & 1.855 & - & 0.355 & - & 3.518 \\ ANA & - & 2.054 & - & 0.360 & - & 3.610 \\ \hline  & & & \(-150\) mL & & & \\ EXP & \(-2.153\pm 0.011\) & \(-1.998\pm 0.011\) & \(-0.315\pm 0.011\) & \(0.315\pm 0.008\) & \(-2.732\pm 0.052\) & \(-2.643\pm 0.052\) \\  & (\(-7.20\%\)) & (\(-0.03\%\)) & (\(+0.00\%\)) & & (\(-3.26\%\)) \\ FEM & - & \(-1.890\) & - & \(-0.361\) & - & \(-3.514\) \\ ANA & - & \(-2.052\) & - & \(-0.360\) & - & \(-3.608\) \\ \hline  & & +200 mL & & & \\ EXP & \(2.791\pm 0.008\) & \(2.653\pm 0.008\) & \(0.437\pm 0.009\) & \(0.432\pm 0.009\) & \(3.766\pm 0.066\) & \(3.555\pm 0.066\) \\  & (\(-4.94\%\)) & & \((-1.14\%)\) & & (\(-5.60\%\)) \\ FEM & - & 2.471 & - & 0.472 & - & 4.699 \\ ANA & - & 2.729 & - & 0.480 & - & 4.800 \\ \hline  & & \(-200\) mL & & & \\ EXP & \(-2.784\pm 0.023\) & \(-2.618\pm 0.023\) & \(-0.432\pm 0.013\) & \(-0.433\pm 0.013\) & \(-3.567\pm 0.058\) & \(-3.497\pm 0.058\) \\  & (\(-5.96\%\)) & (\(+0.23\%\)) & & (\(-1.96\%\)) \\ FEM & - & \(-2.536\) & - & \(-0.485\) & - & \(-4.696\) \\ ANA & - & \(-2.726\) & - & \(-0.480\) & - & \(-4.790\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 3 Approach
### Multi-Modal SCD Model
#### 3.2.3 Feature Fusion and Classification  
To integrate audio and text modalities, a simple concatenation is performed between the two. The resulting audio and text embeddings are then concatenated:  
\[\mathbf{h}^{\text{fusion}}=[\mathbf{h}^{\text{audio}};\mathbf{h}^{\text{text }}], \tag{5}\]  
where \(\mathbf{h}^{\text{fusion}}\) is the fusion embedding obtained by concatenating the embeddings from the text and audio extraction modules.  
The final audio representation serves as input to a fully connected neural network for classification. The classifier consists of three linear layers with decreasing dimensions. Each linear transformation is followed by layer normalization to stabilize training and improve generalization. Non-linearity is introduced using the ReLU activation function, and dropout regularization is applied after each hidden layer to mitigate overfitting. The final output is passed through a sigmoid activation function to produce a probability score for the classification task.  
To optimize the model, we employ focal loss, which is designed to address class imbalance by down-weighting well-classified examples and focusing more on hard-to-classify instances. Given the predicted probability and the ground-truth label, the focal loss is computed as follows:  
\[\mathcal{L}_{\text{focal}}=\alpha(1-p_{t})^{\gamma}\mathcal{L}_{\text{BCE}} \tag{6}\]  
where \(\mathcal{L}_{\text{BCE}}\) is the binary cross-entropy loss, \(p_{t}\) represents the predicted probability for the true class, \(\alpha\) is a weighting factor that balances the contribution of positive and negative samples, and \(\gamma\) controls the down-weighting of easy examples.  
By combining structured feature extraction with a classification strategy, the model effectively learns meaningful audio representations while addressing class imbalance in the dataset.
# A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution
## 2 Exposure Data Management in Support of Disaster Risk Management
### Distributed Version Control  
With respect to non-distributed version control systems, where clients have to connect to a central server to coordinate the versioning, a distributed version control system takes a peer-to-peer approach. In a distributed version control system, each client's copy of a repository is a repository on its own, able to synchronize with any other peer to exchange patches and further software development [10]. Recently, a new class of distributed version control systems, which have evolved in the domain of software development, can be considered mature enough to be used for handling geospatial information. The most popular and widely used among these systems is Git, which was originally developed by [PERSON] for managing Linux source-code. With the focus on source-code versioning, it can only be applied to version information that is stored in text-based formats such as GeoJSON or Comma Separated Value (CSV) files. GeoGig is a system that builds up on the general principals of Git and explicitly supports the versioning of geospatial information. GeoGig is developed and maintained by Boundless on a free and open-source basis. The founders of GeoGig support a shift away from treating geospatial information as strict data (stores of rows and columns optimized to be sliced in different ways, copied extensively, and occasionally updated), towards treating it as software developers treat source code; as a material living in collaborative infrastructure that constantly tracks its origin and evolution, even when copied and edited by disparate users [10]. With distributed version control systems like Git or GeoGig major problems that have affected users of geospatial information can be addressed, including multi-organization/multi-user collaboration, crowd-sourced vs. authoritative data collection, or meta-data sharing and updating. The capabilities of such distributed version control systems in disaster risk management have recently been demonstrated during Typhoon Yolanda where crowd-sourced damage mapping activities have been version controlled by GeoGig [10]. In this study, we use both the Git and GeoGig workflows to test the distributed version control of database release states.
## 3 System Model
The system operates on \(K\) orthogonal channels, with each node selecting a channel during the CP and using it for communication during the DTP. The hybrid MAC protocol combines CSMA/CA and TDMA to manage channel access efficiently. CSMA/CA is used in low-load conditions to avoid unnecessary transmission collisions, while TDMA is used for guaranteed collision-free transmission when the load is high. This dual approach optimizes the network's throughput and reduces interference. The adaptive power control is another aspect that is worthy of discussing here. The adaptive ability is achieved using reinforcement learning, where each node dynamically adjusts its wake-up schedules and transmission power based on environmental conditions (such as path loss, depth, and temperature). Actor-Critic RL is employed to optimize network performance, ensuring efficient energy use and avoiding collisions in varying sensor node locations.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 3 Fast Acquisition Method
### Search Space Reduction Method
If there is no CPPI available, the code phase search space size is expressed as  
\[\mathrm{S_{c}}=\frac{\mathrm{N_{p}}}{\mathrm{\tau_{c}}} \tag{19}\]  
After the CPPI is obtained, the code phase search space size becomes  
\[\mathrm{S_{rc}}=\frac{2\mathrm{c_{dmax}}}{\tau} \tag{20}\]  
Figure 7: Schematic diagram of adjacent FSBS. Search space number and code phase search space size is reduced using ATI and CPPI. Small frequency search step is needed to obtain the required Doppler estimates. However, smaller search step causes the increase of frequency search space size. Therefore, a double dwell (DD) coarse-to-fine structure is used here to solve the problem. Coarse frequency estimation is acquired with big frequency search step. Frequency fine estimation stage with small frequency search step is then carried out to refine the Doppler estimation in second dwell to get the required accuracy before it is passed to the tracking stage.
## 2 3D Measurement Point Cloud Acquisition and Preprocessing
### Point Cloud Data Preprocessing  
#### 2.3.1 Point Cloud Denoising  
In the process of obtaining massive 3D point cloud data, other redundant points or noise points will inevitably be obtained. These noises not only increase the volume of point cloud data, but also affect the precision of point clouds fitting and registration [25], so they must be removed.  
At present, there are two main methods for dealing with noise, manual denoising and automatic denoising [26, 27]. For automatic denoising of point clouds, algorithms such as least squares filtering (for ordered point clouds), median filtering (for ordered point clouds), and bilateral filtering algorithms (for cluttered point clouds) are commonly used [28, 29]. Manual denoising means that for some isolated points and redundant points, these points can be manually removed. Due to the advanced production process of the track slab, the surface of the track slab is smooth and flat, and the measured track slab point cloud data quality is better, and the noise points are mostly isolated points and redundant points, which can be directly denoised manually.  
Figure 2: Track slab solid modeling process.