esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Mechanical Properties of Chitosan/CaCO3 Composites  
The growth process of CaCO3 crystals in the chitosan hydrogel has been largely determined through the aforementioned experiments. However, as a promising direction in the fields of biomedicine and materials science, this ceramic/polymer composite material should possess certain mechanical properties for practical applications. Based on the previous results, it is evident that the number and size of crystals in the hydrogel vary with different mineralization times. Therefore, the next step involves studying the mechanical properties of the hydrogel at various mineralization times. Uniaxial tensile tests were performed on the different samples. The results, including stress-strain curves with computational errors, are presented in Figure 8 and Table 1. The findings indicate that as mineralization time increases, the Young's modulus and tensile strength of the composite hydrogel initially increase and then decrease, while the ultimate strain decreases. The hydrogel sample mineralized for 1 h exhibited the best mechanical properties, with a 110% increase in Young's modulus and a 90% increase in tensile strength compared to the pre-mineralized state. The experimental results indicate a 17% increase in tensile strength compared to the microfiber chitosan hydrogel network prepared by [PERSON] et al. [28] using 3D printing technology. Additionally, the strength increased by 260% compared to the alginate/polyacrylamideamide and glass fabric composite hydrogel material prepared by [PERSON] et al. [42]. These findings suggest that the hydrogels produced through this process and method demonstrate superior mechanical strength.  
The increase in Young's modulus and tensile strength can be attributed to the effective distribution of calcium carbonate nanocrystals within the three-dimensional fiber network of chitosan during the early stages of mineralization. This distribution forms a strong interface, and the higher stiffness of CaCO crystals provides structural support, thereby enhancing the overall strength of the hydrogel. However, as the crystals grow and aggregate, over-mineralization occurs, leading to the accumulation of CaCO crystals on the fiber network. This aggregation weakens interfacial bonding and causes stress concentration, resulting in reduced elasticity, increased brittleness, and diminished mechanical properties. The decrease in ultimate strain after mineralization is likely due to the restriction of chitosan network movement and deformation by the introduced CaCO during the tensile process, which reduces the ductility and toughness of the hydrogel.
## 3 Results and Case Study: UAV-Assisted Blast Design in a Basalt Quarry  
### Site Description and Context  
The methodology for designing a blashhole pattern using photogrammetric techniques was implemented in one of the basalt quarries located in the Lower Silesian region of Poland. The exploited deposit is characterized by a high degree of fracturing, which significantly complicates rock fragmentation using blasting methods. Accurate determination of the actual and optimal burden values on the working face is critical to ensuring the safety of blasting operations. This must account for the potential risk of excessive flyrock and the necessity to control seismic impacts on the surroundings of the mining site.  
An increase in the geometric parameters of the burden directly correlates with a rise in the resistance of the rock mass, which in turn affects both the intensity of seismic vibrations and the degree of rock fragmentation. Precise determination of key parameters, such as blasthole length, angle and direction of inclination, and subdrill depth, is an essential step in blast design. Errors introduced at this stage are difficult if not impossible to correct in subsequent phases of the process, such as selecting the type and mass of explosives, determining the initiation sequence, and setting millisecond delays.  
Therefore, it is essential to use measurement and analytical tools that enable continuous verification of the design assumptions throughout all stages of blasting operations from planning to execution.  
The presented case study aims to illustrate the scope of challenges involved in designing blasting operations with a focus on controlling the extent of flyrock. Work on implementing and optimizing the use of UAV-based photogrammetry for blasting applications began in late 2023. Since then, each blast series has been designed using actual 3D data obtained from the excavation site. The 3D models obtained from UAV flightswere validated using reference measurements conducted at fixed control points within the quarry area, utilizing precise geodetic instruments, such as a total station. This verification confirmed the high geometric accuracy and reliability of the generated models.
## 6 Lipid-Based Nanocarriers
### Quatosomes  
Quatosomes are a type of nanostructured lipid carrier that is formed by the self-assembly of cationic bilayers. They are composed of a mixture of lipids and surfactants, where the surfactant is a quaternary ammonium compound (quat) such as cetyltrimethylammonium bromide (CTAB). Quatosomes have a unique structure where the lipid bilayer surrounds an aqueous core, and they can encapsulate both hydrophilic and hydrophobic molecules. These nanostructures have gained attention as potential drug delivery systems due to their biocompatibility, stability, and ability to efficiently deliver therapeutic agents to target sites. Quatosomes are stable structures that maintain their integrity over extended periods of storage, exhibiting uniformity in size, lamellarity, and membrane organization; therefore, these innovative systems offer a promising solution to address the stability limitations often associated with liposomes and similar structures [102]. Recent studies claim that quatosomes can be an important alternative to fight biofilm-related infections [103; 104].
# LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
## 1 Introduction
Therefore, we are studying LBTM performance through a simulated environment developed in Netlogo [8]. All experiments and code developed in this work are publicly available for reproducibility purposes at the Github authors' public repository ([[https://github.com/plopezmp/LBTM](https://github.com/plopezmp/LBTM)]([https://github.com/plopezmp/LBTM](https://github.com/plopezmp/LBTM))). Our experiments show that, with a proper selection of the number of listening periods (the \(\Delta\)'s), it is possible to accommodate each traffic to its specific requirements. For example, for the grocery shop example, it is easy to achieve a faster checkout process for customers keeping a similar accuracy ratio in the inventory process. For the case of the hospital, it is possible to achieve a balance point where the number of interrogation cycles per second is suitable for the selected initial goals. Additionally, we also address the problem of deciding online which is the best \(\Delta\) (priority class) that readers have to use to achieve a required QoS, which is modeled by using a utility function. The algorithm runs independently in each reader and has a greedy operation, increasing the \(\Delta\) whenever the utility is below a given threshold. Results show that this online mechanism achieves close to optimal performance for the example of the hospital (comparing it to the optimal one obtained offline). The rest of this work is organized as follows: Section 2 reviews the related literature on multiclass traffic control in the context of RFID networks. Section 3 describes the LBTM protocol, while Section 4 describes the simulator framework used. The grocery shop example is then analyzed in Section 5,while the hospital example is described in Section 6. Section 7 discusses the greedy mechanism for online network setup. Section 8 provides a comparison between LBTM+DIM and DIM standalone mode. Finally, Section 9 summarizes our main findings and future work.
## 2 Background and Related Work
### Deep-Learning Background
#### 2.1.1 Ann  
ANNs, also known as feedforward neural networks, are computing systems inspired by the biological human brain and consist of input, hidden, and output layers with connected neurons, wherein connections between neurons do not form a cycle. An ANN is capable of learning nonlinear functions and processing information in parallel [14]. Each neuron computes the weighted sum of all of its inputs, and a nonlinear activation function is applied to this sum to produce the output result of each neuron. The weights are adjusted to minimize a metric of the difference between the actual and predicted values of the data using the back-propagation algorithm [15].
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 2 State of the Art
### Sensor Modelling. The Semantic Web  
Beyond identifying things, different applications must be able to access and use the information coming from things. This goal is difficult to achieve due to the heterogeneity of data representation formats. There are a number of initiatives and standardisation bodies in the field of WSN. IEEE 1451 [23] is a set of smart transducer interface standards. The Open Geospatial Consortium Sensor Web Enablement (OGC SWE) [24] has created three different sensor modelling standards and observations: Sensor Model Language (SensorML), Transducer Markup Language (TransducerML) and Observations & Measurements (O&M). Standardization of OGC SWE is a revolutionary approach to the operation of Web-enabled sensors. The aim of SWE is to create web-based sensor networks (_Web of Things_, WoT) making all sensors and sensor data repositories _discoverable_, _accessible_ and, where applicable, _controllable_ through the _World Wide Web_.  
Another obstacle standing in the way to interoperability is the lack of interconnection standards between wireless networks (e.g., between _Bluetooth_ and _Zigbee_).To meet that challenge, the OGC and the _World Wide Web Consortium_ (W3C) [25] have been conducting research and doing standardisation work following a data-centric approach [26]. The term Semantic Web refers to W3C's vision of the Web of linked data. Semantic Web technologies allow developers to create data stores on the Web, build vocabularies, and write rules for handling data. The Semantic Sensor Web (SSW) [27] is an approach in which semantic metadata is registered along with sensor data to provide context awareness [28]. Specifically, OGC SWE-based spatial, temporal and thematic metadata is registered.
## 2 Numerical Simulations and Wind Tunnel Tests
### Grid Division and Working Condition Setting  
As shown in Figure 3, this study employs a structured grid method. The near-wall region exhibits a low Reynolds number flow. To accurately capture the complex turbulent characteristics of the flow field around the structure, the grid in the near-wall region of the hill model is refined. In regions with high wind velocity gradients, such as near-wall and wake regions of the hill model, adjacent grid sizes should not differ significantly. The grid growth rate is set at 1.05. Different turbulence models correspond to different grid numbers, and the grid settings for different working conditions are shown in Table 2. \(Y^{+}\) is a dimensionless value commonly used in boundary layer theory and is defined by the following equation:  
\[Y^{+}=\frac{u^{*}y}{v} \tag{11}\]  
where \(u^{*}\) is the wall shear velocity, \(y\) is the height of the first grid from the wall, and \(v\) is the kinematic viscosity of the fluid. The size of \(Y^{+}\) reflects the height of the first grid from the wall. A large \(Y^{+}\) indicates that the first grid node is further from the wall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Turbulence Model** & **Number of Grids (\(\times\)10\({}^{6}\))** & **First Layer Grid (m)** & \(\mathbf{Y^{+}}\) \\ \hline SST & 5.6 & 0.002 & 40 \\ RNG & 5.6 & 0.002 & 40 \\ REA & 5.6 & 0.002 & 40 \\ LES & 8.3 & 0.0005 & 10 \\ DES-SA & 8.3 & 0.0005 & 10 \\ DES-RK & 8.3 & 0.0005 & 10 \\ DES-SST & 8.3 & 0.0005 & 10 \\ DDES-SST & 8.3 & 0.0005 & 10 \\ IDDES-SST & 8.3 & 0.0005 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions settings.  
Figure 3: (**a**) Grid division in the numerical simulation; (**b**) grid division on the horizontal plane of the hill; (**c**) grid division in the radial direction; (**d**) grid division on the vertical plane of the hill.
## 4 Results
### Model Evaluation
#### 4.3.2 Analysis of Evaluation Results  
To validate the accuracy of the prediction model, RNN, LSTM, CNN-LSTM, and LSTM-Attention network prediction models were developed using the same SCADA data, and these models were compared with the CNN-LSTM-Attention network prediction model. After training, each model was used to predict the validation set. The prediction results of the different models are shown in Figure 17. According to a comparative analysis of the prediction performance, it is evident that the hybrid CNN-LSTM-Attention neural network exhibits the best performance in predicting the gearbox oil sump temperature. This model not only accurately tracks the actual temperature trend more but also significantly outperforms the other comparative models in terms of fitting precision.  
Based on Figure 17 and the three evaluation metrics for the different models in Table 5, it can be concluded that the _RMSE_ and _MAPE_ values from the CNN-LSTM-Attention model proposed in this paper are, on average, 0.3945 and 0.546 lower, respectively, and the \(r^{2}\) value is 0.061 higher on average compared to those from the other four models. This demonstrates that the CNN-LSTM-Attention prediction model has higher prediction accuracy and a better fit than the other four prediction models, underscoring the effectiveness and superiority of the model proposed in this paper.  
Figure 17: Comparison of actual values, CNN-LSTM-Attention, and other models.
## 4 Case Studies: Part I: Simulation Analysis
### Performance Comparison with Advanced PQD Classification Methods  
#### 4.4.1 Performance Comparison with Advanced Image Recognition Models  
To validate the advantages of the L-DenseNet-CBAM model in the PQD classification task, three advanced image classification networks that have been applied to PQD classification tasks, namely, CNN [11], ResNet-18 [12], and ResNet-50 [13], are selected as benchmark models for comparative analysis under the same LT dataset and experimental conditions. To accommodate the classification task in this paper, the number of neurons in the fully connected layers of the benchmark models is fine-tuned. The classification accuracy curves of each model on the validation set during training are shown in Figure 7, while the overall classification performance comparison on the test set is presented in Table 5. Note that, before being input into the network, the LT images need to be upsampled to 224 \(\times\) 224 to meet the input size requirements of ResNet 18 and ResNet 50. Additionally, the images are resized to 200 \(\times\) 200 to match the input specifications of the CNN model.  
It can be seen in Figure 7 and Table 5 that CNN exhibits significantly lower classification performance during the training stage and on the test set compared to the other three models. This discrepancy is primarily due to the relatively simplified network architecture of CNN, which limits its ability to extract deep image feature information effectively. On the other hand, compared to the two ResNet models, the proposed fusion model demonstrates obvious advantages in learning efficiency during the early training stage and convergence speed, ultimately stabilizing at approximately 98.8%. In terms of performance on the test set, L-DenseNet-CBAM achieves higher overall classification accuracy than ResNet-18 and ResNet-50 under SNR levels of 0, 20, 40, and 60 dB, with improvements of 2.31%/1.58%, 2.20%/1.37%, 1.67%/1.31%, and 2.64%/1.33%, respectively. These results validate the superiority of the proposed fusion model in executing PQD classification tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Overall Classification Accuracy**} \\ \cline{2-5}  & **0 dB** & **60 dB** & **40 dB** & **20 dB** \\ \hline CNN [11] & 87.45 & 86.22 & 84.96 & 81.17 \\ ResNet-18 [12] & 96.52 & 95.85 & 94.51 & 91.22 \\ ResNet-50 [13] & 97.25 & 96.68 & 94.87 & 92.53 \\ L-DenseNet-CBAM & 98.83 & 98.05 & 96.18 & 93.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of L-DenseNet-CBAM and existing advanced recognition networks on the test set.  
Figure 7: Accuracy performance comparison of L-DenseNet-CBAM and advanced image recognition networks on the validation set.
applied sciences  
The Dynamic Failure Behaviour of High-Pressure Zones during Medium-Scale Ice Indentation Tests  
[PERSON] *\({}^{\copyright}\) and [PERSON]  
Faculty of Engineering and Applied Sciences, Memorial University, St. John's, NL. AB 3X5, Canada; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
**Featured Application: The work presented here is valuable for estimating design loads for offshore structures operating in ice-prone regions, especially the ones susceptible to ice-induced vibrations. By exploring the effects of interaction speed, ice temperature, indenter size and structural compliance on failure behaviour, the work provides valuable insights into the dynamics of high-pressure zones (_hpz_s) during ice-structure interaction, which is essential for calculating dynamic ice load on offshore structures.**  
Results from medium-scale ice-crushing dynamic tests are presented in this paper based on a series of indentation experiments on confined ice samples using spherical indenters to simulate high-pressure zones (_hpz_s) with areas on the order of 10\({}^{3}\)-10\({}^{4}\) mm\({}^{2}\). The effects of ice temperature, interaction speed, indenter size and structural compliance on failure behaviour and associated structural dynamics have been studied. Observed failure behaviour consisted of a combination of continuous crushing extrusion and intermittent spalling, both of which were highly dependent on test conditions. Overall, the effects of the studied conditions on ice failure behaviour and associated interaction dynamics were found to be similar to the results reported from previous small-scale experiments, suggesting scale independence of the mechanisms that dominate ice failure behaviour. In general, warmer ice and smaller contact areas are associated with continuous extrusion with intermittent spalling, resulting in smoother peak pressures, while colder ice and larger contact areas tend to result in fracture-dominated behaviour with sharp peaks and substantial load drops. Ice temperature was also found to significantly influence interaction dynamics, with colder ice showing larger amplitude and longer duration dynamic activity, and higher peak pressures. Interaction speed was observed to primarily affect dynamic aspects of ice-structure interactions, with faster tests leading to higher failure frequencies. Similarly, structural compliance was found to mainly impact failure frequency, as well as the extent of load drops, with compliant structures tending to produce more significant load drops following failure. Overall, these experiments have helped enhance our understanding of compressive ice failure and contribute to improved models for dynamic ice-structure interactions.  
dynamic ice-structure interaction; high-pressure zones; ice compressive failure; ice mechanics
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 1 Introduction
By comparing the dwell time with the threshold time, the horizontal-vertical cooperative handover algorithm is developed, which improves the communication stability of the user receiving equipment and reduces the power consumption of the networking system. The remainder of this article is presented as follows: Section 2 discusses the basic theory of visible light communication and the basic framework model of ACO-OFDM. Section 3 shows the visible light communication network light source layout design, ACO-OFDM channel processing, and the flow of the horizontal-vertical collaborative handover algorithm based on communication blind area residence time. Section 4 evaluates the performance of the horizontal-vertical collaborative handover algorithm based on the communication blind dwell time, which assesses the average network throughput, the average number of handovers, and the handover failure probability of the networking system. Section 5 concludes the article.
# ER Morphology in the Pathogenesis of Hereditary
## 6 Other Proteins Implicated in ER Morphogenesis and Function  
Other SPG proteins have been proposed to localize to ER membranes and play a role in ER shaping. Although the precise cellular function of these proteins is not completely clear, the possibility that other HSP proteins, directly or indirectly, may affect ER morphogenesis further supports the idea that ER maintenance is essential for axonal wellness.  
ARL6 IP1 (ADP-ribosylation factor-like 6 interacting protein 1, _SPG61_) contains short hairpin transmembrane domains and its overexpression induced ER tubulation with exclusion luminal protein in mammalian cells. For this reason, ARL6 IP1 has been proposed to be involved in shaping ER tubules in a Reticulon-like fashion [167]. However, depletion of the protein in _Drosophila_ causes ER fragmentation [168] thus questioning the validity of the proposed hypothesis.  
Rab3 GAP2 (Rab3 GTPase-activating protein non-catalytic subunit, _SPG69_) is a component of Rab3 GAP complex required for the recruitment and activation of Rab18 to the ER membrane [13]. Depletion of Rab3 GAP2 resulted in loss of Rab18 at the ER and reorganization of ER sheets, which extend to the cell periphery [169]. The precise mechanism underlying this modification, however, remains unexplained.  
The ER coordinates a variety of cellular processes fundamental for the life of the cell, including protein synthesis, folding and modification, translocation of secretory proteins, synthesis of phospholipids and steroids, storage of calcium. Known ER shaping proteins as well other HSP-causing proteins not directly implicated in the regulation of ER morphology, have been reported to affect additional ER-related processes, such as ER-phagy and ER stress response [33; 170; 171; 172; 173]. However, there is no evidence showing a direct role for ER-shaping proteins in the ER stress response that is likely a secondary consequence of morphology alteration.