esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Compressive Strength and Drying Shrinkage of AASC
#### 3.1.2 Effect of the Sand Ratio  
Figure 7 depicts the change in compressive strength and drying shrinkage with curing time for AASC specimens considering the effect of the sand ratio. It can be seen from Figure 7a that there were different compressive strength development trends at the early and late ages. After three days of curing, the compressive strength of the AASC increased with higher sand ratios from 0.54 to 0.64, then decreased from a sand ratio of 0.64 to 0.68; the greatest compressive strength occurred at a sand  
Figure 6: Effect of water to slag ratio on (**a**) compressive strength of alkali-activated slag concrete (AASC) specimens; (**b**) drying shrinkage of AASC specimens.  
ratio of 0.64. After seven days of curing, the compressive strength increased with the increase in sand ratio, increasing most dramatically from a sand ratio of 0.54 to 0.59, then more gradually from a sand ratio of 0.59 to 0.68. After seven days of curing, there was little difference between the development trends of compressive strength for different sand ratios. [PERSON] et al. [PERSON] et al. (2016) investigated the effect of the paste-to-aggregate ratio on the compressive strength of AASC, indicating that the compressive strength decreased once the paste-to-aggregate ratio was above 0.36. This was explained as possibly due to part of the activating solution being absorbed by the large quantity of aggregate, reducing the effective water-to-geopolymer solid ratio as well as the paste content in the mixture. Consequently, the optimum sand ratio to provide both the highest early and highest late compressive strengths was determined in this research to be 0.64.  
The drying shrinkage was in the range of 0.018-0.034% after 28 days for the different sand ratios, as shown in Figure 7b. The drying shrinkage of the AASC could be observed to decrease as the sand ratio increased from 0.54 to 0.68. Drying shrinkage is approximately proportional to water loss [PERSON] et al. (2016), and because aggregates are dimensionally stable, shrinkage is dominant within the binder paste [PERSON] et al. (2016). Therefore, an increase in binder content in the concrete leads to an increase in the paste volume, naturally resulting in an increase in drying shrinkage [PERSON] et al. (2016); [PERSON] et al. (2016). Moreover, the ceramsite can provide internal curing to AASC due to its water absorption and desorption capabilities, which will directly affect the shrinkage of AASC [PERSON] et al. (2016); [PERSON] et al. (2016).
###### Abstract  
Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters \(C\) and \(g\) by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters.  
ionosphere; total electron content; prediction; support vector regression; international reference ionosphere; statistical machine learning 202416202024162701[[https://doi.org/10.3390/rs16152701](https://doi.org/10.3390/rs16152701)]([https://doi.org/10.3390/rs16152701](https://doi.org/10.3390/rs16152701))  
12 June 2024162 June 2024162024162701 [[https://doi.org/10.3390/rs16152701](https://doi.org/10.3390/rs16152701)]([https://doi.org/10.3390/rs16152701](https://doi.org/10.3390/rs16152701))
## 3 Technical Background on SCHC and Cellular IoT
### SCHC Framework
It is also important that the rule specify the direction (uplink, downlink or bidirectional). For more detailed information on SCHC compression, please refer to RFC 8724 [37]. Figure 1: SCHC architecture. Figure 2: Operations carried out at the transmitter. On the other side of communication, the receiver uses the received RuleID to identify the rule used in the compression. By using this rule and the compression residue, the original packet is retrieved. Note that the compressor and decompresser can share several rules to take into consideration several types of packets. SCHC can be used to compress the UDP and IP protocols as well as upper-layer protocols, such as CoAP [38] and DLMS [39]. The compression of upper layers can be performed along with other layers, i.e., with the same rule, or independently. Compressing the upper layer independently becomes essential when encryption algorithms are used to secure upper-layer payloads. In this case, several SCHC layers can be used.
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## Parabolic Sliding Mode Filters  
This section provides an overview of parabolic sliding mode filters for understanding of the presented analysis in the following sections.  
In Reference [15], [PERSON] et al. presented a sliding mode filter that employs a parabolic-shaped sliding surface (PSMF), of which continuous-time representation is given as follows:  
\[\dot{x}_{1} =x_{2} \tag{1a}\] \[\dot{x}_{2} \in-\frac{F(H+1)}{2}\text{sgn}(\sigma(F,u,x_{1},x_{2}))-\frac{F(H- 1)}{2}\text{sgn}(x_{2}), \tag{1b}\]  
where  
\[\sigma(F,u,x_{1},x_{2})\triangleq 2F(x_{1}-u)+|x_{2}|x_{2}, \tag{2}\]  
\(u\in\mathbb{R}\) is the input, \(x_{1}\in\mathbb{R}\) is the output, \(x_{2}\in\mathbb{R}\) is the derivative of \(x_{1}\), and \(F>0\) and \(H>1\) are constants. In addition, \(\text{sgn}()\) is the following set-valued signum function:  
\[\text{sgn}(z)\triangleq\begin{cases}1&\text{if }z>0\\ \@@LTX@noalign{\vskip 6.0 pt plus 2.0 pt minus 2.0 pt}\omit\cr[-1,1]&\text{if }z=0\\ \@@LTX@noalign{\vskip 6.0 pt plus 2.0 pt minus 2.0 pt}\omit\cr-1&\text{if }z<0,\end{cases} \tag{3}\]  
where \(z\in\mathbb{R}\) is a scalar. Figure 1 shows the sliding surface and state trajectories of PSMF in \(x_{1}\)-\(x_{2}\) space. In PSMF, \(\dot{x}_{2}\) can be seen as the _acceleration_ of the output \(x_{1}\), and \(F\) can be considered as the gain of the _acceleration_.  
The paper [15] also presented the following discrete-time algorithm of PSMF, which is derived by using the backward Euler discretization (i.e., by replacing \(x_{2}\) by \((x_{1}(k)-x_{1}(k-1))/T\)):  
Figure 1: The parabolic-shaped sliding surface (thick curve) and trajectories of the state of the parabolic sliding mode filter (PMSF) (thin curve).  
In AG-PSMF, i.e., Algorithm 2, steps 1-8 check whether all previous outputs are inside the area determined by the two end-outputs \(x_{1}(k-1)\) and \(x_{1}(k-n-1)\) and the constant R, as shown in Figure 2. If it is the case, the window size \(n\) is further increased. The increase of \(n\) is continued until at least one previous output lies outside the area or \(n\) reaches its maximum value \(N_{\max}\). Then, in step 9, gain \(F^{*}(k)\) is obtained by applying the adaptively determined window size for balancing the tradeoff the output smoothness and the suppression of delay.
# Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network
## 1 Introduction
Therefore, achieving control of boundaries along the advancing direction and outer boundaries under the hyperbolic mesh generation algorithm is a subject worthy of further research. In 2019, [PERSON] et al. [24] proposed physics-informed neural networks (PINNs) to handle forward and inverse problems involving nonlinear partial differential equations. In PINNs, physical information is incorporated into the loss function to ensure the physical consistency of the obtained results across the entire domain [25; 26; 27]. Driven by the discretized data within the region, PINNs could approximate the solution of differential equations more easily. After extensive development by numerous researchers, PINNs have been successfully used in different equation solving, such as Navier-Stokes equations [28; 29; 30], Poisson equations [31; 32; 33], Wave equations [25; 34; 35], et al. It implies that PINNs can also be employed to solve the governing equations of hyperbolic mesh generation. In practical application, [PERSON] et al. [36; 37] achieved hyperbolic mesh generation by solving the transformed governing equation using PINNs. In their method, the aforementioned boundary control issue remains unsolved due to the same governing equation as the traditional solving strategy. Moreover, PINNs need the data driven from the interior region, which makes the extra training data preparation procedure unavoidable [38; 39]. Considering that PINNs essentially restrict the feasible solution space of the neural network by incorporating physical information, this allows the network to more effectively approximate the solution of the equation. Based on this theory, this paper introduces output range constraints on PINNs to further limit the feasible solution space of the neural network in solving the governing equations for three-dimensional hyperbolic mesh generation. This enables PINNs to approximate the solution of the governing equations within a specific physical domain driven solely by boundary data, avoiding the training data preparation. Additionally, the governing equations for mesh generation were transformed into a form that is defined on the physical domain, where the orthogonality control of the mesh and boundary conditions can be directly incorporated into the loss function, thereby enabling effective control of the outer boundary and boundaries along the advancing direction during the mesh generation process. Finally, by utilizing neural networks that are n-timesdifferentiable to solve the governing equations, issues such as boundary discontinuities being propagated into the interior domain and instability in the solution process, which are commonly encountered in traditional solution schemes, are effectively mitigated.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 4 Parameter Training
### Model Training  
We use the AlexNet model pre-trained by the ImageNet dataset on Caffe [33], and set the parameters of the first five convolutional layers as the initial values of the parameters in the feature extraction layer. Meanwhile, we use the Matterport3D dataset to fine tune the model. The Matterport3D  
Figure 4: Parameter training model for feature extraction layer.  
dataset is the world's largest public 3D dataset from 3D scanning solution provider Matterport. It is a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. The dataset was acquired by a Pro 3D camera. The camera rotates around the center of gravity at each sampling point, and samples the images at six rotation positions, each of which corresponds to 18 sets of pictures. This special way of data acquisition makes the camera cover a wider range of angles, and it provides many loop closure data. Figure 5a-f are some examples of RGB images in the MatterPort3D dataset.  
We selected 30,000 pairs of positive samples (loop closure) and 30,000 pairs of negative samples (non-loop closure) in the Matterport3D data set. Then by some data enhancement means such as rotating and translating, the positive and negative samples were each doubled to 60,000. Of these, 10,000 pairs of positive samples and 10,000 pairs of negative samples were selected as test sets, and the other 50,000 pairs of positive samples and 50,000 pairs of negative samples were used for training. Some researchers have found that multi-scale training can improve the accuracy of the network containing SPP layers in the tasks of image classification and object detection. In view of this, this paper uses three different scales of data to train the model: 1280 \(\times\) 1024, 227 \(\times\) 227 and 180 \(\times\) 180. The latter two data are cropped from the original image. In this way, the number of our training set is 300,000, including 150,000 pairs of positive samples and 150,000 pairs of negative samples.  
The deep learning framework Caffe requires a fixed size of input images; therefore, we used a combination of single-scale and multi-scale to train the model. In one epoch of model training, single-scale images are input, while in each different epoch of model training, the input batches are of different scales.  
In addition, in order to analyze the influence of different SPP layers in the feature fusion layer on the performance of the algorithm, three different SPP are used: a one-layer SPP with a scale of \(1\times 1\), a two-layer SPP with a scale of \(1\times 1\), \(2\times 2\), and a three-layer SPP with a scale of \(1\times 1\), \(2\times 2\), \(4\times 4\). For these three cases, we build three models and train them separately.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 3 Simulations
### Network Performance
#### 3.1.4. Network Throughput Comparison between the Data Ferry and Multi-Hop Method  
Since DaaG selects the data transfer method for a given environment, we measured the performance of each data transfer method. The result is shown in Figure 5. When the connection is built, the throughput of the multi-hop network method is higher than the data ferry method.  
Figure 4: Normalized cumulative priority service throughput. (**a**) Data ferry method; (**b**) multi-hop network method.  
The multi-hop network method could transmit data at the maximum throughput continuously. However, the data ferry method only transmits data at the maximum throughput when it communicates with nearby sensors or actuators. However, to construct a multi-hop network, drones have to fly to the points and hold their position. Therefore, the network construction time of the multi-hop method is higher than the time of the data ferry method. Furthermore, sensors and actuators have to continuously transmit/receive the data.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 5 Review of the Technologies for Smart Homes
### Smart Home Micro-Computers  
Smart Home Micro-Computers (SHMC) are small-sized computers that are connected to other devices in order to automatize and control the whole smart home system. They consist in a microcontroller with complementary components that facilitate programming and incorporation into other circuits. An important aspect is their standard connectors, which lets users be connected to a central processing unit (CPU) board to a variety of interchangeable add-on modules known as shields. They allow the users make interactive projects and applications with the environment by using multiple extensible connectors and by receiving inputs from many sensors and affect its surrounding by controlling lights or other actuators.  
In literature, there are some examples of applications where SHMCs have been combined with wireless sensors to create home automation systems to monitor and control home appliances [81].  
The strengths and weaknesses of each SHMC have been summarized in Table 5.
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 4 Experimental Results and Analysis  
We conducted experimental validation of the proposed system using the public datasets KITTI and TUM. In our experiments, the proposed method was compared and evaluated against the mainstream ORB-SLAM3 system, with the Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) selected as performance metrics. The ATE reflects the overall deviation between the system's estimated trajectory and the actual trajectory, intuitively demonstrating the cumulative error caused by mismatches in dynamic features. The RPE measures the accuracy of relative pose estimation between adjacent frames but is more susceptible to instantaneous dynamic disturbances; as a result, it is primarily used to assess a system's local stability. For error quantification, the ATE is assessed using the Root Mean Square Error (RMSE) to reflect the degree of overall trajectory deviation, while the RPE is measured using the mean in order to reflect the system's stability performance in inter-frame pose estimation. The above evaluation methods enable a comprehensive verification of the system's accuracy and robustness in different dynamic environments.  
To reduce interference from external factors, each test sequence was subjected to ten repeated experiments, with the median taken as the final experimental outcome. All experiments were conducted on a desktop computer equipped with an Intel i5-14600 KF processor, NVIDIA GeForce RTX 40705 graphics card, and 32 GB of memory.
## 3 Proposed Method
### Spectro-Temporal Descriptors
#### 3.2.2 Local Binary Patterns (LBP)  
Local binary patterns (LBP) are effective local descriptors which have been applied for textures classification [21] and sound classification [12], etc. The LBP convert local structures into binary patterns by comparing values to the central pixel, which is briefly introduced by Figure 4. In this study, we adopt LBP as spectro-temporal feature extractor. The general formulation for LBP can be written as:  
\[D_{\text{LBP}}(\mathcal{L}_{\mathbf{c}};\tau_{\mathbf{c}})=\sum_{j=1}^{I}2^{j -1}\llbracket I(\mathbf{r}_{j})>\tau_{c}\rrbracket \tag{3}\]  
where \(I\) is gray-level pixel value at spatial position \(\mathbf{r}_{j}\), and \(\llbracket\cdot\rrbracket\) generates 1 only if bracketed condition is met and 0 otherwise. \(\mathcal{L}_{\mathbf{c}}=\left\{\mathbf{r}\right\}_{j=1}^{J}\) indicates local 2D structure surrounded \(\mathbf{c}\in\mathcal{R}^{2}\), including \(J\) spatial positions \(\mathbf{r}_{j}\) close to \(\mathbf{c}\) and \(\tau_{c}\) is the gray-level value of the central pixel. For ordinary LBP, \(J\) is set to 8 and hence local patch is limited to \(3\times 3\). In this study, we use prototype version.
## 3 Related Work
In [8], the authors conducted a performance test of these conversational systems on DDC and LCC classification systems, analyzing error typologies and highlighting both potential efficiency gains and critical accuracy concerns in classification number assignment. A study on using ChatGPT for metadata [5] demonstrated that while LLMs may produce inaccurate results for licensed classification systems like DDC, they perform reliably for subject-heading suggestions and keyword generation, provided that prompts are carefully engineered and results systematically vetted. A study using Claude [13] similarly showed that conversational agents can reduce the mental workload of librarians by proposing call numbers and subject terms, albeit with variability across sessions and models. Beyond classification, broader surveys underscore the fast-evolving capabilities of LLM-based conversational systems across sectors, including information retrieval and digital library services [14]. Moreover, retrieval-augmented generation (RAG) LLM deployments [15] illustrate how retrieval augmentation can ground chatbot outputs in authoritative sources [16; 17], thus mitigating hallucination risks in compliance-sensitive domains [18; 19; 20]. User-centered design principles have been applied to recommender interfaces for classification systems [21], revealing that librarian involvement in feature selection and result presentation is key to adoption and trust. While custom LLM pipelines fine-tuned on curated library metadata such as specialized UDC classifiers and ontology-driven recommenders have achieved impressive accuracy in controlled settings [22], most libraries lack the technical infrastructure, licensed training corpora, and in-house expertise required to develop and maintain such models [23]. Instead, many librarians turn to publicly available, off-the-shelf conversational systems like ChatGPT, Gemini, and Claude, which nearly half of surveyed cataloguing professionals report using for classification and subject-heading tasks [24]. This study, therefore, addresses the practical needs of libraries by systematically evaluating the performance of unmodified proprietary LLMs for UDC classification, providing actionable insights for librarians working with readily available tools.
## 3 Results and Discussion
### Accumulation Characteristics in the Coking Chamber after Coal Charging
#### 3.4.2 Bulk Density  
It showed the influence of coal characteristics obtained by the three accumulating methods on the bulk density in Figure 14. The three methods were tamping, the new charging method, and the traditional method, respectively. For different coal characteristics, the bulk density of damping was different from that of the new charging method and the traditional method, but it indicated a consistent trend for the three accumulating methods of coking coal bulk density. In Figure 14a, with an increase in particle size, the coal bulk density obtained by the three methods decreased first and then increased, the accumulating method had a significant effect on the bulk density of small particles. When the particle size distribution range was less than 0.6 mm, the bulk density obtained by the new charging method was greater than that obtained by the traditional. Whereas, with the increase of the distribution of large particles, the bulk density obtained by the traditional charging method was greater than the new charging method. This was mainly due to the traditional coal charging process, the small particles during the fall through the action of air resistance, in the settlement process, resulted in loose coal density. For large particles, gravity played a leading role, and the falling process would have an impact on the coal that had accumulated at the bottom, leading to a more dense coal accumulation. In Figure 14b, for moisture within 3-11%, the lower the moisture content, the higher the coking coal bulk density. With a decrease of moisture content, the bulk density increased faster. The moisture content of coal was less than 6% and the bulk density obtained by the new charging method was higher than that by the traditional. This was mainly because the low moisture coal was more likely to cause particles to escape during the gravity fall, making the accumulating coal loose. However, the new coal charging method can control this problem, so it is more suitable for low moisture coal charged into furnace.  
Figure 13: Influence of coking coal characteristics on repose angle. (**a**) The effect of particle size distribution on the repose angle; (**b**) The effect of moisture content on the repose angle.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion
### Datasets
#### 4.2.1 A Dataset of Neonatal EEG Recordings with Seizure Annotations  
The EEG recordings in this database were acquired between 2010 and 2014 at the neonatal intensive care unit (NICU) of Helsinki University Hospital [37]. The recordings were obtained from 79 newborn patients aged 32 to 45 weeks. The recordings have durations ranging from 64 to 96 min and utilize 19 channels.