esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[24] constructed the graphic evaluation, reviewed technique (GERT) reliability model for the arrival time of emergency rescue and proposed the GERTS simulation algorithm based on the analysis of post-earthquake roadblock risk probability, travel time fluctuation and path choice preference. [PERSON] et al. [25] studied the reliability of traffic travel time after an earthquake, used Monte Carlo simulation method to simulate the connectivity of road network after an earthquake. This method could predict the reliability of traffic travel time after an earthquake on this basis, emphasized the impact of building collapse on the reliability of road travel time. [PERSON] et al. [26] constructed a weighted network model of urban bus based on Space L method considering trip time reliability and took network connectivity rate and network trip time reliability as metrics to study the survivability of urban bus network. [PERSON] et al. [27] analyzed the relationship among path, OD pair, road network trip and road network trip time reliability under emergency conditions and established a VISSIM simulation model. The results showed that with the decrease in emergency events, road network trip time reliability would also decline. Until now, the research on floating vehicle data processing and probability distribution estimation of travel time under normal traffic remains immature, the research on post-earthquake emergency rescue stage is still basically blank. Hence, combining the estimation method of the probability distribution of travel time with the existing research results of earthquake road vulnerability, and gives the estimation method of the probability distribution of post-earthquake travel time, which is great significance to improve the ability of disaster prevention and mitigation of transportation system. This research gives an index to quantify the reliability of road travel time, namely degree of travel time reliability. In combination with road earthquake damage factors, the authors use Bayesian Adaptive Kernel Density Estimation method to improve the weighting coefficient of the probability density function of road travel time after an earthquake. To obtain a formula for calculating degree of travel time reliability which can be used for relevant departments to judge the reliability of road networks, this paper also provides effective basis for improving the speed of material transportation and emergency rescue.
## 5 Experimental Results
### Verification of VIT Performance for Different Numbers of Encoder Layers  
The dataset encompassed video recordings from six distinct finger-spelling videos, with an equal division of three videos, each contributed by male and female participants. Each video was analyzed to extract 54 sequential data points, resulting in a total of 324 feature vectors, arranged in a two-dimensional matrix for each Japanese character. Across the dataset, 46 unique characters were represented, culminating in an aggregate of 14,904 feature vectors.  
Of these, 75% were used as training data and 25% as validation data. The training was conducted over 45 epochs, with the loss and accuracy values recorded for each epoch. The dataset used in this process was Dataset 2. The ViT's performance results as a function of the number of layers in the encoder are shown in Figure 5. As shown in Figure 5, the highest accuracy was achieved with four layers, and a decline in accuracy was observed with each additional layer beyond five. The model was trained for 45 epochs, achieving high accuracy by epoch 15. No significant improvement was observed beyond epoch 15, suggesting overfitting. However, there was an increase in accuracy up to about the 20 th epoch. In terms of processing time, the ViT model took 100 s per epoch with four layers, and 317 s per epoch with ten layers.  
Figure 5: The results of ViT’s performance as a function of the number of layers in the encoder.
## 7 Results and Analysis
### Impact of Limitations on the Results  
These dataset and emulation limitations may introduce biases that could skew experimental outcomes. Specifically, the narrow attack taxonomy employed in the DAWN Dataset might lead to an overestimation of the proposed framework's robustness by excluding adversarial strategies that exploit cross-layer dependencies, such as falsified beamforming metrics that could trigger cascading resource allocation anomalies. Furthermore, OpenRAN Gym's idealized latency profiles, which typically assume deterministic processing delays, may understate the true impact of micro-segmentation on Near-Real-Time control loops. In real-world deployments, stochastic queuing delays and unpredictable network fluctuations can exacerbate latency penalties, potentially undermining the effectiveness of critical control functions. These gaps risk misrepresenting the framework's efficacy, particularly in operational settings that incorporate legacy infrastructure, proprietary protocols, or non-standard configurations, where system behaviors often deviate significantly from idealized models.
## 4 Experimental Testing
### Manufacturing Workshop Data Transmission Test  
In order to test the throughput of the system, the OPC UA server customized in Python was used to simulate the data changes of equipment in a real production workshop. The production and consumption of 10,000, 30,000, and 50,000 data were tested, respectively. Iz4 format was used to compress the messages. The six groups of test results are shown in Figure 15. The results show that 99.9% of the message sending delays are within 1 s, and the average system throughput is 51.96 MB/s.  
In order to evaluate the performance of the entire framework in real-time data collection and processing, study paper uses Python to design an OPC UA server to simulate the generation of real workshop big data; change the value of the node at a frequency of 100 ms; and conduct actual tests on data collection, processing, and transmission based on the Kafka-based distributed processing framework. Given that Kafka uses batches to send and consume data, this study conducted seven sets of data sending and receiving tests, each containing about 50 messages. Figure 16 shows the delay from the sender to the receiver of each set of data.  
Figure 16: Data latency analysis after Kafka is introduced.  
Figure 15: System throughput test.  
When the PDC program detects that the data of the specified node has changed, the system will specify the current time as update_time. When the database side pulls data from the Kafka Cluster and writes it to the database, the system will also specify the current time as insert_time. This article uses two different times of the same data as two time nodes for delay comparison. The results show that all data are completed from data collection to data storage within 7 ms, and the introduction of Kafka improves the throughput and horizontal expansion capabilities of the entire system and has a wider range of applications.
### Challenges  
Despite the benefits of CAV interconnectivity, it introduces vulnerabilities. Critical ECUs managing vehicle operations are prime targets for attacks like frame injection, sniffing, falsification, and Denial of Service (DoS), posing risks to vehicle safety and occupant well-being. Wireless V2X communication channels are also vulnerable to signal jamming, spoofing, and data interception, enabling unauthorized access and control over vehicle systems.  
Furthermore, the sensory layer comprising sensors and cameras is susceptible to manipulation and false data injection, potentially leading to erroneous decision-making by vehicle control systems. Addressing these formidable security challenges necessitates robust, adaptive solutions capable of safeguarding both in-vehicle networks and wireless communication interfaces.
# A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
## 1 Introduction
Although these methods employ a kind of pilot with a circulated cluster format to promote the performance of channel estimation, the efficiency is quite low. It has been concluded that, for a frequency-selective channel, the optimal pilot sequence should have the same interval and the same power. In addition, the sequence phase should be mutually orthogonal each other, which can be designed by some specific methods that guarantee phase orthogonality for the reason that phase orthogonality is more superior than position orthogonality. Although there are many hotspots in the research on the non-blindness channel estimation for MIMO-OFDM systems, the research is still focused on increasing the accuracy and stability while decreasing the complexity. Therefore, this paper is dedicated to the study of an improved method for non-blindness channel estimation.
## Results and Discussion
### PCA of Spectroscopic Data  
The PCA was performed on the FT MIR, FT NIR and FT Raman spectra of the _P. ostreatus_ basidiocarps to detect clusters according to the protein and total glucan contents. Three main principal components, PC1, PC2 and PC3, explaining \(\sim\)70-86% of the variance (\(\sim\)49-51% for PC1, \(\sim\)19-30% for PC2 and \(\sim\)8-15% for PC3) were used for the discrimination of basidiocarp samples. Unfortunately, none of these principal components or their pairs gave an unambiguous separation of the samples in accordance with their composition. So, we had to use all three components together in 3D to visualize the discrimination of the samples. In the case of FT MIR, and especially FT NIR, the use of the first derivative gave better discrimination results in comparison with the spectra themselves that could be explained by the leveling out of the background effects.
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 3 Results
### Internal Heating Source  
In the winter, hives should be designed to retain as much internal warmth as possible. To study this, we also conducted a supplementary experiment by placing a heat source inside the hives and monitoring their internal temperatures over time. Figure 7 shows the humidity and temperature graphs obtained in internal heating tests with the control hive (without insulation) and a hive with black agglomerated cork insulation.  
The hive without an insulating layer exhibits a higher humidity than its counterpart equipped with black agglomerated cork. During the cooling phase, the humidity in the non-insulated hive approaches 30% after 30 min, while in the hive with black agglomerated cork, it remains at about 20%.  
The temperature data reveals a 10 \({}^{\circ}\)C discrepancy between the control hive and the hive insulated with black agglomerated cork after a span of 30 min. Hives with this insulation achieve a temperature of 30 \({}^{\circ}\)C more swiftly than the control hive and manage to sustain this warmth throughout the cooling phase. A similar observation emerges from the external thermal images. As depicted in Figure 8, 30 min postexperiment, the control hive, with its lighter hues, records an average temperature of 25 \({}^{\circ}\)C in its warmest zones. In contrast, the hive insulated with black cork showcases darker shades, indicative of a cooler exterior surface temperature, averaging around 18 \({}^{\circ}\)C.  
Figure 8: Images of the thermal camera test of the left control hives and right hives with black cork insulation: (**a**) at the beginning of the test and (**b**) after 30 min of heating.  
Figure 7: Plots of relative humidity (%) and temperature (\({}^{\circ}\)C) inside the control hive and with black agglomerated cork in the 30 m of exposure to internal heating by the infrared lamp and another 30 m of cooling.
# Collaborative Analysis of Learners' Emotional States Based on Cross-Modal Higher-Order Reasoning
## 3 Methods  
### Overall Architecture  
The overall architecture of the collaborative perception model based on attention mechanism is illustrated in Figure 5.  
This model mainly consists of four core components:  
The first part is the modality feature extraction layer, which extracts key information from different input modalities such as text, video, and audio, laying the foundation for subsequent processing;  
The second part is the modality attention extraction module, which utilizes multi-head attention mechanisms to delve into the complex relationships within each modality, enhancing the model's understanding of specific modality features;  
The third part is the cross-modal attention collaborative interaction layer, which achieves complementary and integrated information exchange between different modalities through cross-modal collaborative analysis, facilitating the model to capture richer cross-modal interaction information;  
The fourth part is the emotion prediction layer, which uses multilayer perceptrons to comprehensively judge and make decisions based on the information processed in the previous three parts, outputting the final model prediction results. This module not only integrates features from various modalities but also optimizes the flow and processing of information, ensuring high-quality and accurate model outputs.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Depth Prediction
#### 2.1.2 Loss Function  
In order to obtain better model precision, we combine different loss functions to conduct experiments. For in depth prediction tasks, researchers usually apply the L1 loss function and the L2 loss function. However, training with a sole reconstruction loss function can cause the model to tend to generate an average value, which can lead to ambiguous outputs. In the work of [PERSON]. et al. [17], structural similarity (SSIM) error as a loss function was also mentioned. As mentioned in that work, using structural similarity as a loss function is a good loss term for depth estimating CNNs. We also tried this loss in this article. The formulas of the loss functions were as follows: y represents the ground truth of the depth value and \(\hat{y}\) represents the prediction of the depth value:  
\[L_{1 loss}=\frac{1}{n}{\sum_{i=1}^{n}}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{1}\]  
\[L_{2 loss}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left({y_{i}-\hat{y}_{i}}\right)^{ 2}}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Total Datasets** & **Training Sets** & **Test Sets** \\ \hline
29,608 & 21,429 & 8179 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset composition of deep prediction model.  
Figure 2: Dataset acquisition process. (**a**) shows CT images and the segmentation of key anatomical sites; (**b**) represents the reconstructed 3D model of the kidney and its center path; (**c**) denotes a pair of SI-DM images.  
\[L_{SSIMloss}=\frac{1-SSIM(y,\hat{y})}{2} \tag{3}\]  
We quantitatively evaluated the performance of the model. The quantitative evaluation generally uses threshold accuracy, root mean square error (RMSE), root mean square log error RMSE (log), average log10 error, and relative error (REL) to judge model performance. The formula of evaluation functions are as follows:  
\[\delta=\max\left(\frac{y_{i}}{\hat{y}_{i}},\frac{\hat{y}_{i}}{y_{i}}\right)< \textit{threshold} \tag{4}\]  
\[L_{\textit{rmse}}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})}^{2}} \tag{5}\]  
\[L_{\textit{rmse}(log)}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(\log(y_{i})-\log(\hat{ y}_{i}))^{2}}} \tag{6}\]  
\[L_{\log 10}=\frac{1}{n}{\sum_{i=1}^{n}}|log_{10}(y_{i})-log_{10}(\hat{y}_{i})| \tag{7}\]  
\[L_{\text{rel}}=\frac{1}{n}{\sum_{i=1}^{n}}\frac{|\hat{y}_{i}-y_{i}|}{y_{i}} \tag{8}\]  
Usually, in formula (4), the value of the threshold is set as 1.25, 1.25\({}^{2}\), or 1.25\({}^{3}\).
# Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN
## 3 IoT/5G System Structure
### Core Network (CN)  
The core network, CN, deploys the SDN technology, where the control plane and the data plane are separated. The proposed system deploys multiple SDN controllers since a physical SDN controller handles only a limited number of requests at a time, and as previously introduced, an SDN network with a single controller is efficient only for small-scale networks. SDN controllers communicate and interact via OpenFlow protocol [37]. Distributed OpenFlow switches connect to the appropriated SDN controller and receive their flow tables over the appropriate OpenFlow version deployed for the networks. A core networks cloud with powerful computing capabilities is deployed at the core networks and connected to the control plane of SDN networks. Our previously proposed controller placement algorithm, introduced in [38], is deployed to dynamically define the optimum number of SDN controllers required for the networks based on the current status of net work traffic. Furthermore, the algorithm provides the optimum connections of OpenFlow switches with SDN controllers in a way that achieves the highest latency performance.
## 3 Methodology
### MDDA Augmented GNSS/INS Integration
#### 3.6.4 Error Model  
Inertial sensor error is one of the main causes of process noise error.  
The error model is defined as in (33):  
\[u_{measured}(t)=u(t)(1+s(t))+b(t)+w(t) \tag{33}\]  
where \(u(t)\) is the sensor input, \(s(t)\) is the scale factor error, \(b(t)\) is the bias, \(w(t)\) is random walk noise, and the measured value of the sensor is \(u_{measured}(t)\). The stochastic sensor's errors like scale factor and bias are frequently modeled using the first-order Gauss-Markov (GM) process as follows:  
\[\dot{n}(t)=-\beta n(t)+\sqrt{(2\beta\alpha^{2})}w_{u}(t) \tag{34}\]  
where \(n(t)\) is a random process, \(w_{u}(t)\) a unity variance zero-mean Gaussian noise, \(\beta\) is correlation time inverse, and \(a^{2}\) is the white noise variance. By analyzing the sensor measurements over a fixed length of time, these parameters can be roughly determined [50; 53].
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 5 NRA Crossing in Parametric Paths
### The Case of Points on the Border, Again!  
In Figure 9 specific situations arise if the testing point is exactly a NRA crossing point or, which is to say, the testing point is over the border. Once again, this situation forbids the possibility of seamlessly applying a unique algorithm for points on the border. For example, still in Figure 9, if the testing point occurs on the first crossing point (the one on the left of \(a_{1}\)), since it has no other crossing on its left, this implies being outside the region; so, its own intersection contribution (\(I_{C}\)) should be counted to consider it inside. But, if this reasoning is applied to the \(4^{\text{th}}\) intersection (between \(a_{3}\) and \(a_{4}\)), then the overall contribution at that point (\(+1-0.5+0.5-1\)) would result in \(0\) and that point would be considered outside the region, contrarily to the intention of considering it as inside. The conclusion is that there is no unique solution to distinguish with the same operation whether a point on the border is always considered inside or outside the region: this is unavoidable, as supported by the Jordan curve theorem presented earlier, where the sense of crossing (circulation) affects the winding contribution, and therefore the conclusion about inclusion. The solution, as already mentioned, is to check those situations before further calculation.
## 4 Soft-Collision M* (SC-M*)
### Completeness and Cost-Suboptimality
#### 4.3.1 Completeness  
**Theorem 1**.: _SC-M* is a complete algorithm._  
Proof of Theorem 1.: SC-M* inherits the sub-dimensional expansion from M* (i.e., it changes the \(G^{sch}\) only when one of the soft-collision sets \(C_{p}\) changes). The algorithm applies A* in the updated search graph. Due to the merging operation applied to collision set \(C_{p}\), as shown in Equation (3), \(C_{p}\) for each vertex will change finite times (at most \(m\) times, which is the number of agents). Because A* is complete, applying A* to a given \(G^{sch}\) takes finite time to return a result. Therefore, SC-M* is complete.
## 2 Materials and Methods
### Studies on Antimicrobial Activity of Hydrogel Loaded with Antibiotics  
The antimicrobial activity of hydrogels loaded with antibiotics was tested in a disc-diffusion assay. A slightly soaked sheet of pure hydrogel was cut into discs with a diameter of 8 mm. Antibiotics were introduced into the discs, and we applied to the centre of each hydrogel disc 200 uL of suspension of chloramphenicol (30 ug) dropwise in Ringer's solution with a pipette, and it was left to be absorbed. Paper discs containing chloramphenicol (30 ug in each) and pure hydrogel discs were used as controls. The antimicrobial activity of hydrogel discs containing antibiotics was tested against _Staphylococcus aureus_ and _Escherichia coli_. Overnight cultures of both pathogens were seeded on the surface of TSA plates. Hydrogel discs containing chloramphenicol, paper disc with chloramphenicol, and pure hydrogel discs were aseptically placed on the TSA plates and incubated at 37 \({}^{\circ}\)C for 24 h. Then, the diameter of the zones of inhibition was measured, and the discs of the selected hydrogels were transferred onto freshly seeded TSA plates and further incubated at 37 \({}^{\circ}\)C. The diameter of the zones of inhibition was measured again after 24 and 96 h. The tests of antimicrobial activity of each hydrogel were carried out in triplicate.  
Figure 1: Obtained sample 1H NMR spectra as result of hydrolysis of (**A**) \(\gamma\)-PGA–PEG1000 hydrogel, (**B**) DMAP, and (**C**) EDC.
## 4 Conclusions  
Based on the results presented in this paper, conclusions can be drawn with respect to the possible use of waste products, coming from the production of styrene butadiene rubber (SBR) and polybutadiene rubber (PBR), as bitumen modifiers.  
The outcomes of the advanced performance-related rheological tests indicate that the waste SBR product considered in the investigation is equivalent to the corresponding premium quality polymer. In particular, in the range of dosages that were considered in the study (3-5% by weight of neat bitumen), such a product led to the preparation of PMBs characterized by a highly elastic response in a wide range of temperatures and loading frequencies, and by a satisfactory behavior in terms of their resistance to permanent deformation, fatigue damage, and ageing.  
Different results were obtained when considering the PBR polymers. Although it was found that such products enhanced the elasticity and stiffness of neat bitumen, it was also observed that they had a limited effect on the control of ageing and that they produced no benefits in terms of fatigue resistance. Overall, no significant differences were highlighted when comparing the waste polymer to the corresponding premium quality product.  
Results of rheological analyses were consistent with those coming from chemical characterization tests. In particular, it was observed that the two types of polymer exhibited totally different interactions with neat bitumen. In the case of the SBR polymers, obtained results suggest the formation of dispersed elastic domains, whereas a high level of dissolution was hypothesized for the PBR polymers.  
Further investigations are needed in order to validate the findings synthesized above. Assessment of the full potential of both types of polymer will require consideration of component materials coming from different sources, evaluation of plant-produced PMBs, and analysis of performance-related properties of corresponding bituminous mixtures. Furthermore, the rheological and failure behavior at low service temperatures deserve to be addressed by means of specific tests.  
Conceptualization, S.Y. and M.A.; methodology, M.A., D.D. and E.S.; investigation, S.Y.; resources, M.A. and E.S.; data analysis, S.Y. and D.D.; writing--original draft preparation, S.Y.; writing--review and editing, D.D. and E.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.