esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Damage Pattern of Fiber Shotcrete Laminated Beams Based on the Thickness Ratio
### Thickness Ratio-Based Damage Mechanism of Fiber Shotcrete Stacked Beams  
Combined with the macroscopic failure mode of fiber shotcrete beams and DIC cloud diagrams, the crack expansion of fiber shotcrete beams mainly goes through three stages: the initial generation of cracks, crossing the interfacial interface, and running through the whole structure. The damage process of fiber shotcrete beams with different thickness ratios behaved differently, as shown in Figure 11.  
Figure 10: Main strain field.  
The thickness ratio \(\lambda_{h}=0\) (unstratified) specimen is a mixed bending-shear failure in the four-point bending test, and the crack does not undergo a stage of crossing the interface due to the absence of the interface. The presence of internal fibers in the specimen causes the crack not to develop vertically upwards but to show some inclination during the whole process from the generation of the crack at the crack to its penetration through the specimen. Compared with the thickness ratio \(\lambda_{h}=0\) (unstratified) specimens, for the thickness ratio \(\lambda_{h}=1\) specimens, the cracks developed across the interfaces with a sudden change because the interfaces are the weak surfaces of the structure, the bond is not enough, the shear capacity is weak, and the cracks changed transversely when they developed to the interfaces. For the thickness ratio \(\lambda_{h}=2\), \(3\), \(4\) specimen, at the beginning of the crack for the vertical upward flexural failure, the  
the concrete shear span ratio is large, and the bending moment dominates the damage. The crack then undergoes the stage of crossing the interfacial interface, with a transverse abrupt change, and then the crack runs through the whole specimen. Mixed bending and shear damage was observed at the time of cracking across the second layer of concrete. In the thickness ratio \(\lambda_{h}=1\) to the thickness ratio \(\lambda_{h}=5\), when the crack undergoes the stage of crossing the interface, the distance of transverse mutation of the crack increases with the increase in the thickness ratio. This is due to the fact that under the premise that the neutral axis is the centroid axis of the cross-section, the increase in the thickness ratio leads to the downward shift of the position of the interface, and the cross-section is located in the position where it is subjected to a greater shear stress, which leads to the greater transverse displacement of the crack during the damage.
## 3 Proposed Structure  
PUFs are widely used to protect sensitive information in a data transmission environment. However, if attackers access a PUF in an untrusted environment, they will eventually obtain CRP information from the PUF. Our proposed architecture improves the security of a pm-PUF by removing the PUF access of the attacker with Bloom filter querying. In this paper, we propose a structure to improve security between authentication servers and devices by applying two different types of Bloom filters to a PUF. The proposed structure applies the Bloom filter to a pm-PUF. The pm-PUF is based on a memristive PUF with its wide resistance variation under various pulses. The proposed structure reduces unnecessary CRP transmission between the device and the authentication server by applying a Bloom filter and avoids reuse by verifying the duplicates of the CRP. Figure 1 shows the proposed PUF database Bloom filter (PDBF) and challenge-response pair Bloom filter (CRPBF).  
The PDBF, located in front of the pm-PUF, stores all the challenge information in the CRP table of the authentication server to prevent a challenge access of the attacker to the pm-PUF. Additionally, the PDBF improves the security of pm-PUF by removing the challenge information of the CRP used. The CRPBF behind the pm-PUF stores all CRP information in the CRP table of the authentication server to prevent transmission of responses that do not exist in the authentication server.  
Figure 1: Proposed physical unclonable function database Bloom filter (PDBF) and challenge–response pair Bloom filter (CRPBF).
## 3 Results and Discussion
### Adsorbent Characterisation
#### 3.1.6 Characterisation by Methylene Blue Adsorption  
Figure 6 shows the Langmuir isotherms for the two activated carbons. The evaluation of the different results obtained indicates that the activation with KOH presented a high adsorption capacity of methylene blue (645.5 mg/g), slightly lower than that of the CAC (734.9 mg/g). These results corroborate those obtained by [PERSON] et al. [72], who found that activation of carbon with alkaline salts favours the appearance of mesopores. Thus, the high adsorption of methylene blue can be justified by its basic character, which has a greater affinity for basic carbon than for acidic carbon, as shown by [PERSON] et al. [73]. Similarly, [PERSON] et al. [74] confirmed that the acidic surface functions significantly decrease the electron density on the base layer of the carbon, which limits the adsorption of basic dye.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Carboxylic** & **Lactones** & **Hydroxyls** & **Acidity** & **Basicity** \\  & **(mmol/g)** & **(mmol/g)** & **(mmol/g)** & **(mmol/g)** & **(mmol/g)** \\ \hline SCGs & 0.82 & 0.09 & 0.15 & 1.06 & 0.74 \\ \hline ACSCGs & 0.12 & 0.03 & 0.08 & 0.23 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface functions of SCGs and ACSCGs.  
Figure 6: Adsorption isotherm of methylene blue on ACSCGs and on CAC.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 3 Common Machine Learning Algorithms in IDS
### Machine Learning Models
#### 3.1.3 Shallow Models Compared to Deep Models  
Deep learning is a branch of machine learning, and the effects of deep learning models are obviously superior to those of the traditional machine learning (or shallow model) methods in most application scenarios. The differences between shallow models and deep models are mainly reflected in the following aspects.  
**(1) Running time**. The running time includes both training and test time. Due to the high complexity of deep models, both their training and test times are much longer than those of shallow models.  
**(2) Number of parameters**. There are two types of parameters: learnable parameters and hyperparameters. The learnable parameters are calculated during the training phase, and the hyperparameters are set manually before training begins. The learnable parameters and hyperparameters in deep models far outnumber those in shallow models; consequently, training and optimizing deep models takes longer.  
**(3) Feature representation**. The input to traditional machine learning models is a feature vector, and feature engineering is an essential step. In contrast, deep learning models are able to learn feature representations from raw data and are not reliant on feature engineering. The deep learning methods can execute in an end-to-end manner, giving them an outstanding advantage over traditional machine learning methods.  
**(4) Learning capacity**. The structures of deep learning models are complex and they contain huge numbers of parameters (generally millions or more). Therefore, the deep learning models have stronger fitting ability than do shallow models. However, deep learning models also face a higher risk of overfitting, require a much larger volume of data for training. However, the effect of deep learning models is better.  
**(5) Interpretability**. Deep learning models are black boxes [32; 33; 34; 35]; the results are almost uninterpretable, which is a critical point in deep learning. However, some traditional deep learning algorithms, such as the decision tree and naive Bayes, have strong interpretability.  
Figure 8: The structure of an RNN.
# Geophysical Monitoring of Leachate Injection in Pretreated Waste Landfill
## 3 Results
### Leachate Recirculation and Biogas Generation  
The tests to verify the effects of leachate recirculation on biogas generation were done on two wells, monitoring their biogas composition and flow rate. The data allowed to calculate the ratio volume of methane (CH4) to the volume of carbon dioxide (CO2). This parameter reflects the presence of anaerobic biodegradation with methane generation: in common landfills, usually, it is lower than 1.4, which corresponds to a methane concentration equal to 58-59% by volume in the biogas.  
The data obtained for the monitored wells during the tests with and without leachate recirculation are shown in Figure 7a,b, respectively.  
The influence of leachate recirculation on methane generation is evident in both cases, and very similar results were achieved.  
In both wells, the ratio CH4/CO2 had values around 2 (or higher) during the 2 nd period of leachate recirculation. This can be due to different moisture of the waste during the recirculation periods:  
* during the 1 st period, the leachate could have increased the water content in the waste, that is to say, part of the leachate could be absorbed by the waste itself since the landfill was filled with pre-treated waste (rather dry);
* during the 2 nd recirculation period, the operation could have further increased the water content to the optimal values for biodegradation, influencing the methane generation around the well.  
Unfortunately, the increase in the ratio CH4/CO2 does not correspond to an evident growth of the biogas flow rate (data not shown). One reason could be the partial filling of landfill porosity by leachate itself, which would constitute a barrier to biogas extraction.  
Looking at the leachate properties, we can note that they do not influence the concentration change of methane.  
Figure 8 shows the monitoring of temperature and pH, where it is evident that  
* the seasonal trend of leachate temperature seems to not influence the biological process since the methane concentration grows anyway during the cold season; the disturbance given by the injection of cold leachate is not evident since the leachate amount is small compared to the hot mass of waste (around 46-48 \({}^{\circ}\)C);
* leachate pH is almost constant along the monitoring time and around neutral values suitable for biogas/methane generation.  
Figure 7: Influence of leachate recirculation on ratio CH4/CO2: (**a**) well A; (**b**) well C.  
Figure 9 reports the main chemical properties of the recirculated leachate, such as COD, chlorides concentration and ammonium one. These parameters are rather constant during the season and, as for pH and temperature, they do not show influence on the changes of methane concentration in biogas.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 2 Principles and Methods
### Simulation Scenario
For a certain number of satellites and a certain constellation pattern, the flow chart to reach the optimal constellation configuration is presented in Figure 2. From this figure, it can be seen that the optimal constellation configuration which corresponds to the maximum value of the fitness function shown by Equation (6) is found with a GA and PSO, respectively, and the final optimal constellation configuration is the one with the highest fitness value resulted from the two optimal algorithms. It must be noted that, to ensure the fairness of the comparison between the two algorithms, the same termination condition (maximum iteration of 100 times), is set for both of the two evolutionary algorithms. Figure 1: The simulated 24 h Global Navigation Satellite System (GNSS) radio occultation events (ROEs), observed by COSMIC using the real orbits (**a**) and the simulated orbits (**b**).
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 3 Experimental Setup and Details  
The ASVspoof database consists of three partitions for evaluating logical access (LA), physical access (PA), and deep-fake (DF), with the DF partition being newly added in ASVspoof 2021. The model experiments were based on the ASVspoof partitions.
## 3 Results and Analysis  
### Time-Varying Characteristics of AE Parameter  
AE parameters effectively reflect the rock failure process, providing essential information about the rock's condition and facilitating the study and understanding of rock failure processes. The stress and AE parameter evolution curves during uniaxial compression of basalt and granite rocks can be observed in Figure 3.  
Clearly, basalt rocks exhibit higher uniaxial compressive strength due to having fewer pores in the samples. In contrast, granite rocks, characterized by a relatively higher number of pores, exhibit lower uniaxial compressive strength and produce many AE signals.  
We can observe that each stress drop is accompanied by high-amplitude AE signals. Before the initial stress drop, the sample absorbs energy continuously without undergoing failure, resulting in fewer AE signals. During the stress drop, the sample releases some of its energy, including dissipative and emitted energy, leading to higher AE amplitudes and energy. After the stress drop, internal cracks within the sample begin to merge and propagate, eventually releasing all the stored energy, causing catastrophic collapse. This stage generates numerous and densely packed AE signals, with the highest AE energy at the time of collapse. Therefore, AE parameters are closely linked to crack development and can simultaneously characterize energy variations in the rock instability process.  
Figure 4 presents images after experiments on two types of rocks, with the red line indicating the main fracture direction of the specimen. It is worth noting that, as evident from Figure 4, fracture occurred after the specimen reached strength, with the primary mode of failure being cleavage. During the testing process, significant cleavage fractures occurred along the direction of the red line, revealing the brittle characteristics of the rock.  
Figure 2: AE parameters diagram.
## 3 Methodology
### Location and Flight Test Scenarios  
There were two locations used in our flight tests, as shown in Figure 8. The first one was the UAV flying zone near the Yunlin high-speed rail (HSR) station (23.730495 N, 120.418773 E), which is popularly used by the Huwei UAV community. It is a suburban location where there are some residential and commercial buildings mixed with parks and empty space. The second place was the National Formosa University (NFU) Agriculture campus (23.728259 N, 120.375310 E), which is considered a rural area with a majority area of farmland.  
There were two conflict scenarios of flight tests conducted in our research. The first scenario involved a stationary intruder represented by a Remote ID staying on the ground. The second scenario involved a flying intruder represented by both the Remote ID modules flying in UAVs. However, in both scenarios, only one UAV was instructed to conduct collision avoidance maneuvers. This scenario avoided both UAVs execute avoidance maneuvers that led to another conflict. In addition, due to safety considerations, the second scenario flight tests were only conducted in the rural flight test location. For both scenarios, the UTM monitoring application was in the same location where the flight test was conducted.
# Core Competency Quantitative Evaluation of Air Traffic Controller in Multi-Post Mode
## 1 Introduction
Focusing on the current ATM production and operation practice, based on the ICAO competency framework, this paper proposes an improved adaptive multi-dimensional core competency model for the multi-post mode's diversity and convergence characteristics. Moreover, it establishes a general classification and grading model for various professional posts, which can assist in enhancing the universality and practicability of the model and apply it to the decision-making of post-adaptation. The core competency quantitative evaluation mainly focuses on model construction, index weighting, and model application. This paper carries out quantitative evaluation and verification according to the technical route of \"analysis of core competency characteristic elements, construction of core competency model of multi-post mode, determination of dimension index weight, competency measurement of benchmark population, and evaluation of competency-post fit degree\". It shows the research framework in Figure 1, and the specific research content includes the following three parts. Figure 1: Research framework. 1. The first part is constructing an improved adaptive multi-dimensional core competency model. Based on literature analysis, expert interview, and work analysis, this paper summarizes ATCOs' core competency index library according to the characteristics, operation environment, and operation rules of posts. It carries out the ATCOs' core competency questionnaire and data analysis to analyze the effective indexes by verifying the structural model. Then it establishes the content and structure of localized multi-dimensional core competency evaluation index according to the local organizational context and constructs the multi-dimensional core competency model of general and professional posts by classification. 2. The second part proposes a combination weighting method of the ATCO core competency index. Aiming to combine subjective and objective weighting methods based on AHP, FA, and EWM, it proposes the AHP-FA-EWM (AFE) method to determine the weight coefficient of each dimension and secondary indexes of the core competency model. 3. The third part proposes a calculation method of competency-post fit degree based on the AFE weighting method and the distance method of superior and inferior solution (AFE-TOPSIS). According to combined index weights, it puts ATCOs' core competency evaluation results into the four types of models constructed above and then calculates the fit degree between the core competency index value of each controller and the post requirements based on AFE-TOPSIS. This paper opens up a new perspective of core competency classification research, expands the application scope of core competency theory in the field of ATM, and puts forward an ATCO core competency evaluation and analysis method based on multi-post mode, which can provide a new auxiliary decision-making basis for ATCO ability evaluation, competency-post fit and fine management of human resources.
## 3 System Design
### Member Event Handling  
If a multicast receiver is interested in the multicast service identified by the MSEID, it sends the join message which contains the MSEID. After receiving the join message, the edge router queries the NRS for the MTNs' NAs of the multicast service. The NRS returns all the MTNs' NAs it maintains to the edge router. The edge router selects an MTN based on the path state-aware multicast tree node selection mechanism from the returned MTNs. Then the edge router sends the join message to the selected MTN hop by hop. Each router that the join message passes through instantiates the MNFE and registers the mapping between the MSEID and its NA with the NRS. The reverse path of the join message becomes a branch of the multicast tree. Then, the multicast flow is forwarded to the receiver along the reverse path of the join message.  
When a receiver is no longer interested in the multicast service identified by MSEID, it sends a leave message. After receiving the leave message, the edge router updates the MNFE corresponding to the MSEID. If there is no receiver corresponding to the multicast service, the edge router first deletes the MNFE, then deregisters the mapping between the MSEID and its NA from the NRS, and finally sends a prune message to the upstream node. After receiving the prune message, the upstream node deletes the interface that received the prune message from the out-interface list corresponding to the MSEID. If the out-interface list is empty, it will first delete the MNFE corresponding to the MSEID, then deregister the mapping between the MSEID and its NA from the NRS and continue to forward the prune messages to the upstream node. In this way, the multicast flow is no longer forwarded to the receiver.
## 3 Materials and Methods
### Model Deployment, Fine-Tuning, and Predictive Evaluation  
In this study, the purpose is to assess which of our four models most effectively identifies fake news content in the provided article, specifically for classification tasks. By pinpointing the model that best captures the context between words, we aim to create a robust tool for automatically extracting insights from news articles. This tool would empower individuals and organizations to make well-informed decisions, enhance business strategies, and drive improved results and overall success.  
To accomplish this, each of the three models was tasked with generating predictions on the test set, both prior to and after fine-tuning, through few-shot learning. To ensure fair training conditions, we maintained consistent hyperparameters across all models, as follows: a learning rate of \(2\times 10^{-5}\), a batch size of 6, and three training epochs, using the Adam optimizer for fine-tuning.  
The BERT and CNN models are typically designed to process inputs up to a 512-token limit, roughly equivalent to 2560 characters. For articles or texts exceeding this length, these models must truncate content to fit within the token constraint, which can lead to the loss of valuable context or critical information from longer texts. While techniques such as chunking (dividing texts into manageable segments) or hierarchical processing (analyzing segments in separate parts and then synthesizing results) are potential solutions for handling longer inputs, these methods add considerable complexity to the processing pipeline [34]. Specifically, these approaches can require additional layers of interpretation and synthesis to combine segmented outputs accurately, potentially affecting both the efficiency and precision of the results.  
In contrast, models like GPT-4o--especially those configured with extended context windows--can handle significantly longer inputs than 512 tokens. Some versions of GPT-4 can process inputs up to 128,000 tokens [35], enabling the model to accommodate entire articles, books, or other complex documents in a single pass. This large token capacity allows GPT-4 to capture and integrate information across a much broader context, resulting in a more comprehensive understanding of lengthy texts without requiring chunking or hierarchical processing.  
However, these significant differences in token capacity between BERT/CNN models and LLMs such as GPT-4 present challenges when trying to compare their performance directly. To enable a fair comparison, longer articles were removed from the training, validation, and test sets, as described in Section 3.1.1, ensuring that only articles with a maximum length of 2560 characters remained. This selection process helped standardize input sizes across models, limiting them to a shared input length, which allowed for a balanced assessment without the need for complex chunking methods or truncation inconsistencies.  
Below, we present the deployment strategy for each model, outlining our tailored approach to applying them for fake news detection and classification tasks.