esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Phospholipid fatty acids, the main component of the cell membrane of all microbes were separated from fresh groundwater samples and analyzed to estimate biomass and reveal the signature and relative population of bacteria, such as _Firmicutes_, _Proteobacteria_, metal reducers, and sulfur-reducing bacteria (SRB), as well as the presence of eukaryotes in microbial communities [6]. Quantitative polymerase chain reaction (qPCR) was used to detect the presence and concentration of bacterial taxa such as _Dehalococcus_ (DHC) and _Dehalobacter_, and genes for important functions harbored by these taxa, the reductive dehalogenases (RDases) [7; 8; 9]. The MMMs implemented for the pilot test were performed by Microbial Insights, including their Census(r) qPCR analysis, which quantitates these microbes and genes in groundwater samples. When normalized with biomass calculation, qPCR has a detection limit of 100 cells per sample. The benefit of the MMMs implemented for the pilot test is that the analyses provide a snapshot that can be compared to the current knowledge base. This is the case with the services provided Microbial Insights, which, for example, were used to study bioremediation potential at a 1,4-dioxane- and TCE-contaminated site [10]. Metagenomic analysis using shotgun sequencing of DNA does not require prior amplification. Sequences are identified by comparison to microbial genomic databases and the increased throughput provides a more detailed picture of the consortia diversity. With adequate sampling, it is even possible to detect and assemble genomes of unculturable, and hence undiscovered, microbes. The benefit of applying high-throughput DNA sequencing techniques is exemplified by an extensive project focused on a groundwater site undergoing in situ injections [11]. As a result of this research, 47 new candidate phyla are proposed and extensive insights to changes in biogeochemical cycles are revealed by gene annotations. The primary objective of the pilot testing was to obtain the necessary information to select a treatment formulation for use during the full-scale treatment operations. The tested treatment formulations were electron donors and nutrients. Bio-augmentation was not tested because the site conditions did not indicate it would be necessary (e.g., detectable VCin portions of the NRAP groundwater plume overlapped with petroleum contamination from a nearby leaking retail fuel business). Secondary objectives for the pilot test were to inform designers on the dosage and frequency of the selected bio-amendment and to obtain more representative information on the subsurface hydrogeology and as-built well and piping hydraulics for use in optimizing the full-scale operations. The field tests results consisted of biorecirculation tests to select amendments for full-scale treatment and the development of site-specific understanding of PCE contaminant breakdown pathways and rates.
# Fast Format-Aware Fuzzing for Structured Input Applications
Utilizing these, it will construct test cases that fit the input format, improve the code coverage, and expose the hidden vulnerabilities of the target software. To address I2S misses and enhance the effectiveness and efficiency of discovering I2S dependencies, _I2S recognition based on de Bruijn matching_ is proposed and is applicable to scenarios without seeds that meet the input format. Assisted by the unique feature of the _de Bruijn-based_ test case, the I2S dependencies are obtained directly by analyzing the runtime logs about the target's comparison instructions. Unlike looking up the changes of key states, observing the I2S dependencies via matching the value of _cmp_ operands to the fragments is its novelty. This approach can recognize more I2S dependencies than previous works [12; 13]. Aimed at indiscriminate dependency recognition, we present an intelligent _Selective Flip_ strategy to obtain indirect dependencies. We lightly identify and exclude those fragments that cannot affect any comparison instruction operands. Performing lightweight dependency existence analysis before recognizing to avoid ineffective identification is its novelty. In this way, we obtain indirect dependencies faster and more specifically from fragments containing dependencies. We implemented a fast format-aware fuzzing prototype, FFAFuzz, and evaluated it compared with Redqueen and WEIZZ, using 11 real-world applications as benchmarks. The result showed that, in terms of effectiveness, FFAFuzz outperforms Redqueen significantly and is on par with WEIZZ. FFAFuzz reduces the time cost by up to 89.10% on average. Compared with WEIZZ, FFAFuzz can discover more than 14.53% of paths. **Contribution. ** In summary, we make the following contributions:  
* To address I2S misses, we firstly present _I2S recognition based on de Bruijn matching_ to obtain more I2S dependencies directly and promote more comprehensive identification of the input format. In addition, the _de Bruijn-based_ test case generation algorithm is proposed for scenarios without seeds that meet the input format. * Tackling indiscriminate dependency recognition, we firstly present the _Selective Flip_ strategy to recognize indirect dependencies faster and more purposefully, while fragments without any dependencies will be omitted. This enables the input format recognition to be more efficient. * We implemented a fast format-aware fuzzing prototype, FFAFuzz. Our evaluation results showed that FFAFuzz can recognize more dependencies faster than the state-of-the-art and can achieve higher code coverage regardless of whether the input satisfies the target format. The remainder of this paper is organized as follows. Section 2 introduces the two common types of fields within the structured input, existing fuzzing approaches, and limitations. We describe the overview and the key stages of our method in Section 3. Section 4 present the evaluation. We discuss the limitations of FFAFuzz in Section 5 and conclude the paper in Section 6.
# The Impact of Digital Safety Competence on Cognitive Competence, AI Self-Efficacy, and Character
### AI Self-Efficacy  
Self-efficacy refers to one's beliefs about his/her ability to complete a specific task [57]. AI self-efficacy is defined as individuals' confidence in using AI tools, their understanding of these tools' capabilities, and their willingness to engage in learning about them [58; 59]. Studies show that AI self-efficacy is a significant predictor of students' positive learning experiences, academic achievement, and learning satisfaction [60; 61]. For example, [PERSON] et al. [59] found that students with high AI literacy perceived AI positively, expressed interest in AI, and reported a higher level of AI self-efficacy. Similarly, based on structural equation modeling, [PERSON] et al. [62] observed that AI self-efficacy is negatively related to AI-related anxiety, which in turn leads to increased actual AI usage among university students.  
With regard to digital safety, task-specific self-efficacy is tightly related to digital competence. For example, [PERSON] et al. [8] found that adolescents with better digital competence were more likely to regulate their social media usage, as they were aware of personal privacy and the data implications of social media. Similarly, a study by [PERSON] et al. [63] suggested that students who spend more time on digital devices and social media reported great digital competence in safety. This relationship is more salient among boys. Research has shown that fear of data breaches and algorithmic discrimination hinders exploratory learning, especially among marginalized groups [64]. On the contrary, individuals who learn about encryption, privacy settings, and ethical hacking are more willing to experiment with AI tools, perceiving their risks as manageable rather than prohibitive. University students who completed AI courses exhibited greater confidence in troubleshooting algorithms and engaging in ethical discussions [65]. The reciprocal relationship between safety literacy and confidence is evident in AI-powered learning platforms. Adaptive systems like Duelingo enhance self-efficacy through personalized feedback, but their effectiveness depends on users' trust in data security [66]. University students are future leaders equipped with essential digital safety skills for their professional careers. Educators and policymakers have acknowledged the impact of digital technology, specifically with the arrival of AI, which is characterized with greater autonomy [67]. Assessing the impact of digital competence in the area of safety can shape our beliefs and perceptions on the use of AI, thereby enhancing employability and achieving success in careers [68]. The present study attempts to test the relationship between DSC and AI self-efficacy in university contexts. We hypothesized that digital safety competence is positively associated with AI self-efficacy (Hypothesis 2).
## 5 Case Studies
### Validation with Person Trip Survey Data
#### 5.1.2 Elapsed Time Threshold Estimation with SC and GPS Data  
To validate the effectiveness of the proposed method, the SC data and GPS data at 7:00-9:00 on 11-15 March and 18:00-20:00 on 16-17 March was assessed. The metro-to-bus transaction pairs at 7:00-9:00 on 11-15 March and 18:00-20:00 on 16-17 March at these four metro stations were filtered  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Day of Week** & **Time Period** & **Time of Day** \\ \hline S1 & & AM-peak hours & 07:00–09:00 \\ S2 & Weekdays & PM-peak hours & 18:00–20:00 \\ S3 & & Off-peak hours & 09:00–18:00 \\ \hline S4 & & AM-peak hours & 07:00–09:00 \\ S5 & Weekends & PM-peak hours & 18:00–20:00 \\ S6 & & Off-peak hours & 09:00–18:00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the six investigated scenarios.  
by Rules 1 and 2, and the CDF curves are drawn as shown in Figures 6 and 7. The estimated results show that the 95 th quantile of the CDF curves for the specific stations during different time periods is basically the same, which proves that the method has better robustness. Further, the average of the 95 th quantile of different time periods is estimated as the elapsed time threshold for each scenario in Table 2, which is used to identify passengers' transfer trips.
## 2 Emerging Algorithms Description
### Whale Optimization Algorithm
#### 2.2.3 WOA Algorithm  
The WOA mathematically models the process of humpback whales for searching and encircling the prey as well as for applying the bubble-net feeding technique. The WOA assumes that the best candidate solution is the desired prey and it updates the position vectors of the whales with respect to this optimal solution (prey encirclement). At each iteration, the population member with the best position vector is also updated if it scores a better solution. Extending the hunting behavior of humpback whales, WOA models the bubble-net feeding technique (exploitation phase) by combining simultaneously two different sub-processes: a shrinking radius-based prey encircling sub-process, where the members of the group are swimming towards the prey on a shrinking radius and an updating position vector sub-process based on a spiral trajectory, where the whales are swimming upwards in a spiral path to feed the trapped prey. It is worth mentioning that the bubble-net feeding of humpback whales is a cooperative technique. The members of the group have to learn the technique to be able to use it. As for the exploration phase, the WOA utilizes the same approach as in the prey encirclement to search for prey. To this end,  
Figure 3: Bubble-net feeding technique of humpback whales.  
the algorithm is adjusted by a set of randomly distributed coefficient vectors, suitable to balance the search mechanism between the exploration and the exploitation phase.  
Algorithm A2 (see Appendix A.2) depicts the pseudo-code of the Whale Optimization Algorithm. After the initialization and at each iteration, the position vector \(\vec{V}_{whale}\) of each member of the whales' group is computed,  
\[\vec{V}_{whale}=\left\{\begin{array}{l}\vec{V}_{whale}^{rand}-\vec{R}^{l}\cdot \vec{W}\\ \vec{V}_{leader}-\vec{R}^{l}\cdot\vec{W}\\ \vec{W}\cdot e^{p_{1}p_{2}}\cdot cos(2\pi p_{2})+\vec{V}_{whale}^{leader}\end{array}\right. \tag{5}\]  
where \(p\), \(p_{1}\), and \(p_{2}\) are given by \(p=rnd\), \(p_{1}=1\), \(p_{2}=(c_{2}-1)\cdot rnd+1\) (\(rnd\) is a random number \(\in[0,1]\)), \(c_{1}\) and \(c_{2}\) are coefficients given by \(c_{1}=2-i\cdot\frac{2}{MaxH}\), \(c_{2}=-1+i\cdot\frac{-1}{MaxH}\), \(\vec{W}\) is  
\[\vec{W}=\left\{\begin{array}{l}|\vec{R}^{l1}\cdot\vec{V}_{whale}^{rand}- \vec{V}_{whale}|\\ |\vec{R}^{l1}\cdot\vec{V}_{whale}^{leader}-\vec{V}_{whale}|\\ |\vec{V}_{whale}^{leader}-\vec{V}_{whale}|\end{array}\right. \tag{6}\]  
In (5) and (6), the first branch of the formula is applied in the exploration phase of the algorithm, the second branch in the prey encirclement, and the third branch in the bubble-net feeding technique based on the absolute value of the coefficient vector \(\vec{R}^{l}\) and the value of the parameter \(p\). These two values trigger one of these mechanisms at each \(i\)-th iteration of the WOA.
## 5 RT POD Processing and Analysis
### Availability Analysis of Onboard GNSS Data  
High-quality GNSS onboard observations are required for the accurate RT POD of LEO satellites. In this subsection, the availability of onboard GNSS observations made on 31 January 2022 is analyzed as an example. The onboard GNSS receiver on the TJU-01 satellite tracks all GPS satellites and 33 BDS-2/3 satellites. The receiver channels are assigned to 5 GEO satellites including C01-C05, 7 IGSO satellites including C06-C16, and 22 MEO satellites including C11, C12, C14, C19-C30, and C32-C37. The sampling interval is 1 s. Approximately 8 GPS satellites and 12 BDS satellites only are tracked in the same epoch owing to the limited number of navigation channels. The undifferenced ionosphere-free pseudo-range and carrier-phase combination model used in this study requires GPS/BDS dual-frequency phase and code measurements at each epoch. Here, the data availability of the satellite is defined as the ratio of the number of epochs with complete dual-frequency observations to the total number of epochs for that satellite. The data availability of the observations of the TJU-01 satellite is analyzed as follows. Figure 10 shows that the availability of GPS observations is clearly lower than that of the BDS observations, which may lead to the RT POD having a lower accuracy when using the GPS than when using the BDS.
## 4 Discussion
### One, Two, or Three Inter-Rows... How Many Inter-Rows Do We Need to Measure?  
We measured the soil lowering during 25 years in 1, 2, and 3 inter-row areas. This information gave us the insight to understand the soil mobilization rates since the time of plantation using 2016, 3960, and 5904 sampling points. We covered a total area from 302.4 m\({}^{2}\) in each row to 907.2 m\({}^{2}\) in the whole study site. The total average estimations of soil mobilization did not show great differences among plot sizes. Values slightly higher than 70 Mg ha\({}^{-1}\) yr\({}^{-1}\) were recorded. The measurements with the second and third-row did not find different soil erosion rates. Then, we propose that only one inter-row should be measured and represents a homogeneous area.  
Another key issue is to determine how representative the data recorded for an entire vineyard is. We affirm the potential predictability of ISUM in order to give an overall overview of the soil erosion process attending the obtained results for vineyards that follow the same soil management system. When ISUM is applied, an important decision must be made in order to determine the representative of the results: paying attention to the importance of measuring more separated areas in the same vineyard or, on the contrary, measuring continuous and parallel inter-row areas. This question should be answered in the future too.  
Tillage is able to homogenize soil properties, and subsequently, soil erosion processes. Similar results were found among inter-rows in Eastern Spanish vineyards [89]. It would be interesting to test if there is a similar representativity of these results in ecological vineyards where no-tillage is applied.  
ISUM has been seen as an easy-to-apply methodology as the measurements in the paired-graft union and performing three extra-measurements in the inter-row areas can supply key information for mapping soil erosion estimations in only one day. In this case, if we pass from measuring oneinter-row area to testing three, increasing the number of sampling points and plot area, the time and efforts invested will be more elevated. Therefore, solving this issue will represent a new advance in the standardization of the method in order to be applied by other authors following the same procedure in the future.
## 3 Results and Discussion  
A real-world experiment was employed to study the effectiveness of the proposed FAWS algorithm.  
The experiment was carried out on a station of the metro system for the city of Shenyang, Liaoning province, China. Olympic Metro Station is a transfer station of Shenyang metro line 9 and line 2 in the downtown of Shenyang city. The property of the super shallow buried soil layer should be tested on site to make sure the load-carrying capacity of the underground construction. The transfer station is oriented in a west-east direction as shown in Figure 4a. For metro line 2, the station was built in 2016, thus, a tunnel is constructed to connect the previous station (line 2) to the new one of metro line 9. To estimate the mechanical properties of the soil layer, a linear array of 50 geophones with the spacing of 0.5 m was used to acquire the site response of ground under the impact. The testing site is flat, free of obstacles, and of one-dimensional geometry. The total length of 25 m test array ran through the west-east direction of the metro line 9 before the construction of the new metro station, as shown in Figure 4a. The in-site testing were conducted in summer with daily temperature from 20 to 30 \({}^{\circ}\)C.  
A sledgehammer with a 4 kg weight was employed to excite the vertical ground motion. The impact source caused by the hammer blow is located at the offset distance of 0.5 m. The locations of the first and last geophones are 0.5 m and 25 m, respectively. The data were recorded by multiple geophones with 5 Hz resonant frequency and a digital geometric seismography to save as a.dat file with a sampling frequency of 1000 Hz. The record length of the multiple channel data is 600 ms. A schematic of the experimental setup is shown in Figure 4b.  
Figure 3: Flow diagram of the proposed fast analysis of wave speed (FAWS) strategy.  
Figure 2: Magnitude of the proposed bandpass filter bank: (**a**) second-order bandpass transfer function; and (**b**) bandpass filter bank.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
## 4 Experimental Results

Table 5: Comparison of LRP relevance scores between normal data and data with anomalies injected from sample 1100 to 2000 for different features. Scores with asterisk (*) indicate the feature where an anomaly was introduced, showing a significant increase in relevance scores. The percentage in parentheses indicates the growth rate compared to normal conditions. These experimental results demonstrate several key capabilities of our approach. We now examine their broader implications for GNSS applications and explore potential limitations and future directions.
## 3 System Model and Problem Formulation
### GDRL-SFCR Intelligent Routing Framework
#### 4.2.1 Embedding of LEO Satellite Network  
Graph data employ a data structure that represents entities and relationships by nodes and edges [45]. The connections in a satellite network are modeled as weighted undirected graphs, where nodes (satellites, ground stations, UEs, remote servers) and edges (inter-satellite/ground links) define complex spatial-temporal relationships. Most traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), cannot be directly applied to graph data due to the lack of capacity to capture geometric features from a graph. This is because traditional models are designed based on the translation invariance or local connectedness of data in Euclidean space. However, graph data do not have the same regular spatial structure as  
Figure 2: Model architecture of the proposed method.  
image and text data. The proposal of using a GNN extends the existing neural network mechanism, which can achieve end-to-end processing of graph data [46]. GNNs [47] consist of multi-layer graph convolution, and their essential purpose is to use graph convolutions to extract the spatial features of graph data. They have strong generalization and reasoning capabilities in resource management, end-to-end communication, MIMO detection, and other applications, and they have achieved good results [48; 49].  
In order to extract the characteristics of the LEO network topology, we utilized a GNN to process the network topology data. It passes messages between the nodes of the graph through an iterative message passing algorithm so that each node can acquire the characteristics of its surrounding neighbors, expanding the node's perceptual domain, which can help to make more appropriate routing decisions. At each time step \(t\), the current state of the LEO satellite network \(S_{t}=(A;X)\) is fed into the GNN to learn a new matrix \(H_{t}\).  
The forward propagation of the graph convolution is given as follows:  
\[H_{t}=\sigma\left(\bar{D}^{-\frac{1}{2}}\bar{A}\bar{D}^{-\frac{1}{2}}XW\right) \tag{20}\]  
where \(\sigma(\bullet)\) represents the activation function, and \(W\) is the trainable parameters. \(\bar{D}^{-\frac{1}{2}}\bar{A}\bar{D}^{-\frac{1}{2}}\) is the approximated graph convolution filter that is similar to the Convolutional Neural Network (CNN). \(\bar{D}\) represents the degree matrix, and \(\bar{D}_{ii}=\sum_{j}\bar{A}_{ij}\); \(\bar{A}=A+\wedge\) is the adjacency matrix of the LEO satellite network with added self-connections using a renormalization trick, where \(\wedge\) is the identity matrix.
## 4 Results and Discussions
### Species Distribution  
#### 4.1.1 The Behavior of Components H\({}_{2}\), CO  
Firstly, the aforementioned hydrodynamic parameters are implemented in the computer model then the equations related to small bubbles of gas species and the liquid phase are coupled to obtain the concentration behavior of all FT key components from the beginning to the steady state conditions through the length of the reactor. As Figure 1 shows, at \(\tau\) = 0 there is no carbon monoxide and hydrogen in either of the two phases. At normal operation (\(\tau\) > 0), these species enter the reactor with their own inlet values (which are already calculated based on the boundary conditions in Table 3; C\({}_{\text{g,in}}\)), initiating the reaction. At the reactor inlet a maximum value is seen and liquid is supposed to be saturated with gas phase. The CO and H\({}_{2}\) are consumed due to the chemical reaction and their concentration decreases across the reactor length to the equilibrium values in two phases. In this respect, \"wall effects\" cause small bubbles to dissolve faster into the liquid phase before reaching steady state values. The concentration behavior of species (mol/m\({}^{3}\)gas for gaseous components and mol/m\({}^{3}\)liquid for liquid components) shows that the steady state values are reached at 10 min, as expected from the experimental data [12]. The CO conversion in this base load condition equals 58%, which is slightly overestimated compared to the experimental data. The run is performed for T = 503 K, P = 20 bar, U\({}_{\text{g}}\) = 0.17 m/s, \(\varepsilon_{\text{cat}}\)= 0.34.