esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Competency Assessment Evaluation
#### 4.2.3 Confidence  
Confidence was not a focus many articles have undertaken in combination with eye-tracking analysis, yet three articles considered a relationship between professional confidence and level of expertise [19; 22; 23]. All three articles surmised that a greater level of experience related to a greater level of confidence in interpreting radiographic materials.  
In regard to the study design, both [PERSON] et al. [22] and [PERSON] et al. [23] used a scale from zero (not confident) to ten (very confident) as a measure after each radiograph diagnosis. [PERSON] et al. [19] used a similar method, with a four-point Likert scale including the options \"Guess, Maybe, Probably and Definitely\" to grade their own confidence in their diagnosis. Both of these studies used an oral recording to capture responses, whilst [PERSON] et al. [19] did not explicitly mention the method of collection. Whilst there may be differences in data collection, there appears to be no history of gathering methods influencing confidence results [39]. All articles utilised strong study designs, large samples, appropriate analysis and reflective conclusions.  
A disadvantage to the comparative nature of these studies is that they all utilised different methods of statistical analysis. [PERSON] et al. [19] used a standard signal detection statistical method, whilst [PERSON] et al. [23] and [PERSON] et al. [22] used Kruskal-Wallis and the proportional odds model, respectively. Whilst these are all adequate statistical analysis methods, it does pose challenges when comparing the significant figures of the articles.  
Despite differences in statistical analysis and data collection methods, the literature points to a common theme which suggests a relationship between a higher level of education and participants' greater confidence level in radiographic interpretation.
## 5 Engineering Application  
The horizontal well gas control technology for coal seam roofs has been successfully applied in several soft- and low-permeability coal mining areas in China, such as the Huainan and Huaibei mining areas in Anhui Province, and the Baode mining area in Shanxi Province. This technology provides strong technical support for achieving large-scale gas extraction and utilization of fragmented and low-permeability coal seams, as well as the goal of comprehensive gas extraction coverage in coal mines. The following is an explanation using the typical examples of the Huainan and Huaibei mining areas in Anhui Province.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods
### Microdialysis  
Microdialysis is one of the well studied neurotransmitter detection techniques and its in vivo applications have been used for over three decades. For instance, it has been used for the measurement of neurotransmitters such as acetylcholine, neuropeptide, amino acids and amines in human brain [206]. It is usually used in conjunction with other detection and separation techniques such as liquid chromatography and mass spectrometry [207]. The technique consists of inserting into the brain a probe formed by a semi-permeable membrane of 1-4 mm length and 0.2-0.4 mm diameter, perfused at a constant flow between 0.1-5 \(\mu\)L/min as shown in Figure 7[206; 208; 209]. Artificial cerebrospinal fluid is used to perfuse the probe to prevent ionic diffusion [208; 209]. The membrane is semi-permeable so that molecules in the extracellular fluid will diffuse according to their gradient [206; 208]. This principle can also be used to inject molecules into the extracellular fluid for reverse dialysis or retro-dialysis [208; 209]. The micro-dialysis probe is placed outside the synaptic gap. Therefore, the neurotransmitter concentration measured by micro-dialysis is a balance between the molecules released into and removed from the extracellular space [208].  
Figure 6: HPLC analysis of some neurotransmitters; chromatogram of neurotransmitters separated by the microbore HPLC column. (**A**): standard DOPAC (50 pg), dopamine (150 pg), serotonin (100 pg) and 5-HIAA (50 pg); (**B**): DOPAC, dopamine, serotonin and 5-HIAA separated from zebrafish brain lysate [205].
## 3 Results and Discussion
### Load Flow Analysis Results for the IEEE 34 Bus Test System  
In this section, it is aimed to comparatively evaluate the results of the load flow analysis achieved with the Developed model and SCP model [12] for the IEEE 34 bus radial distribution test system, prepared by IEEE [20] and modified by [PERSON] et al. [27]. The modification of the test system was carried out by adding two generators with two service transformers to its two buses in [PERSON] et al. [27].  
The single-line diagram of this test system is given in Figure 8. Here, it should be noted that the bus 800 refers to the main feeder in Figure 8. The wind turbines are placed at the buses 848 and 890 with the proper service transformers, and their parameters are given in Table 12. Wind turbines with induction generators have been planned as having 660 kW rated output power, and all of their parameters are included in Table 13.  
Figure 7: Variation of the reactive power demand (**a**), and injected active power (**b**) of the generator at the 2% _VUF_ value for different rotor slip values.  
For the test system detailed above, load flow analysis is performed using two models and thus phase voltages of each buses are obtained to compare with the phase voltages provided by [PERSON] et al. [27]. The tolerance value (\(\varepsilon\)) of the machine iterations at each sweep in power flow analyses has been determined as \(10^{-9}\) for both models. In addition, the tolerance value (\(\varepsilon\)) for the power flow analyses has been determined as \(10^{-4}\).  
The percentage difference of the phase voltages and angle differences for the each buses are given in Figures 9 and 10 respectively. In Figure 9, the overall differences of bus voltage do not exceed 0.23%, 0.43% and 0.28%, respectively, for phases A, B and C. Additionally, in Figure 10, the overall differences of bus voltage angle values do not exceed \(-0.6^{\circ}\), \(0.5^{\circ}\) and \(-0.5^{\circ}\), respectively, for phases A, B and C.  
The highest magnitude difference values of Developed Model and SCP Model [12] for phase A is 0.23% and 0.14%, for phase B is 0.43% and 0.09%, and for phase C is 0.28% and 0.08%. In addition, the highest angle difference values of the Developed Model and the SCP Model [12] for phase A is \(-0.6^{\circ}\) and \(-0.4^{\circ}\), for phase B is \(0.5^{\circ}\) and \(0.4^{\circ}\), and for phase C is \(-0.3^{\circ}\) and \(-0.5^{\circ}\). It is seen from Figures 9 and 10 that the Developed model is in close agreement with the SCP Model [12].  
Figure 8: 34 bus radial test system with the placement of wind turbines (redrawed from [27]).  
Figure 9: Phase voltage differences (%) of each bus with models for (**a**) Phase A; (**b**) Phase B; and (**c**) Phase C.
## 4 Conclusions  
In this study, a circular diffusion model is first deduced by considering the diffusion coefficient as a time-dependent variable. The comparison between the circular diffusion and slab diffusion models in term of chloride diffusion, estimating the pre-corrosion initiation time and durability design are analyzed. Replacing the complicated form of circular diffusion model, a modified slab one for circular section is proposed. The ensuing conclusions are as follows:  
Figure 14: The difference between the minimum concrete cover thickness designed by the modified slab diffusion model and the original slab one.  
Figure 15: The effect of design reliability index (\(\beta_{\text{d}}\)) on \(\Delta x_{\text{d}}\).  
1. The use of a constant diffusion coefficient causes the overestimation of the chloride concentration distribution, shortening the service life of the structure.
2. The shape of the circular section element accelerates chloride diffusion compared with the slab element. The error caused by adopting the slab diffusion model shows close relationships with the radius and the diffusion depth. The decrease of the radius of column and the increase of the diffusion depth enlarge this error. In general, the modified slab diffusion model is preferable for a radius below 60 cm.
3. The pre-corrosion initiation time of the RC circular column is underestimated with the slab diffusion model. Each model parameter shows sensitivity for the difference value between the time estimated with slab diffusion model and that estimated for the modified slab one. The shape of the circular section affects minimally the estimation of the time to corrosion initiation compared with other model parameters.
4. The service life of RC circular column is overestimated when the circular section is viewed as a slab element. The modified slab diffusion model should be used when the radius of the column below 50 cm.
5. The RC circular section simplified as a slab element slightly affects the durability design against chloride corrosion. The minimum concrete cover thickness increases by 5 mm for the RC circular column based on the design result of the slab element.  
Conceptualization, G.Y.; methodology, L.P.; software, L.P.; validation, G.Y. and L.P.; formal analysis, L.P.; curation, G.Y.; writing and revising, L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51578157.  
The authors declared no potential conflicts of interest concerning the research, authorship, and the publication of this article.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Evaluation and Discussion  
#### 3.4.1 Performance of Models  
To quantitatively analyze the testing result, several evaluation factors commonly used in the binary classification task, which have been discussed in detail in [25], are chosen to compare the performance of models. According to the evaluate results in Table 7, SSENets is superior to other models in accuracy, precision, specificity and \(F_{1}\) score.
## 4 Experimental Verification
### Analysis of Experimental Results
#### 4.2.2 Fitting Plane of Station Coordinates and Coordinates of Optimized Stations  
The plane vector corresponding to the minimum singular value in the SVD transform is the normal vector of the fitting plane equation. The coefficients of the plane equation \((aX+bY+cZ+e=0)\) are \(a=1.9405\times 10^{-5}\), \(b=1.5739\times 10^{-5}\), \(c=0.9999\), and \(e=-122.5119\), respectively. According to the results in Section 3.2, the SVD transform of the covariance matrix was used for plane fitting, and the fitting plane was obtained (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Coordinates** & \(\mathbf{X_{j}}\) & \(\mathbf{Y_{j}}\) & \(\mathbf{Z_{j}}\) & \(\mathbf{d_{j}}\) \\ \hline
1 & \(-64.9938\) & \(-6.6256\) & 122.5146 & 1128.5363 \\
2 & \(-53.0600\) & \(-38.1824\) & 122.5137 & 1128.5372 \\
3 & \(-26.9493\) & \(-59.5448\) & 122.5127 & 1128.5363 \\
4 & \(6.3444\) & \(-64.9883\) & 122.5125 & 1128.5375 \\
5 & \(37.8969\) & \(-53.0580\) & 122.5123 & 1128.5373 \\
6 & \(59.2589\) & \(-26.9510\) & 122.5112 & 1128.5378 \\
7 & \(64.7067\) & \(6.3400\) & 122.5104 & 1128.5377 \\
8 & \(52.7784\) & \(37.8971\) & 122.5107 & 1128.5373 \\
9 & \(26.6701\) & \(59.2624\) & 122.5109 & 1128.5382 \\
10 & \(-6.6235\) & \(64.7100\) & 122.5107 & 1128.5383 \\
11 & \(-38.1803\) & \(52.7804\) & 122.5108 & 1128.5379 \\ \hline \end{tabular}
\end{table}
Table 1: Coordinates of laser tracer stations and their distance to the initial measured point (unit: mm).  
Figure 6: Path planning of sampling points in the measurement space.  
According to Equation (21) and the obtained values of the coefficients of the plane equation (\(a\), \(b\), \(c\), \(e\)), \(\lambda=-0.0013\) was calculated. The optimized coordinates of laser tracer stations are shown in Table 2.  
According to Equations (28)-(31), the coordinate of the center of the fitting circle (\(x_{c}\), \(y_{c}\), \(z_{c}\)) is obtained as (\(-0.1434\), \(-0.1396\), \(122.5119\)).
## 2 Mitochondrial (mtDNA) and Nuclear DNA Analysis
### Microsatellite Analysis  
Microsatellites are also known as simple sequence repeats (SSRs) or short tandem repeats (STRs). Microsatellites are distributed throughout the genome, with a greater presence in noncoding regions. Microsatellites are mainly neutral and unaffected by selection [33]. Regions flanking repeated sequences tend to be highly conserved. The advantages of using microsatellites are their high reproducibility and high resolution. The most common repeats are di-, tri- and tetra-nucleotides, and the maximum length is approximately 200 bp. Microsatellite loci with a dinucleotide motif are the most widely used due to their higher density (on average, one dinucleotide per 30-50 kb) [34]. Microsatellite markers are very useful in analyses of population structures and gene flow.  
The main limitations of the use of SSRs derive from possible errors during PCR amplification [35] and the occurrence of homoplasy products (equal types and numbers of different alleles) [36]. These phenomena lead to underestimations of the true level of divergence between two populations. Genotyping errors can also occur due to the preferential amplification of small alleles (while larger alleles are not amplified) [37] or replication slippage, which can produce additional elements (in the form of allelic prepeaks) [38]. Most of these problems can be solved by using markers from different sources, e.g., nuclear and mitochondrial genes. Microsatellites can be easily identified in new species by de novo techniques [39]. Numerous authors have used microsatellite markers for genetic analysis of brown trout [32; 40; 41].
## 4 Proposed Data Collection Algorithms
### Grid and Probabilistic Neighborhood-Based Data Collection Algorithm with Layered-Scan(GPN-LSCAN)
#### 4.1.3. Data Collection Phase  
In this phase, the AUV travels along the specified path and moves around the specified locations to collect data from nodes within each probabilistic neighborhood. The residence time varies with the number of nodes within each probabilistic neighborhood and the value of transmission rounds  
Figure 4. Layered-scan of the AUV.  
Figure 3. Network partition in GPN-LSCAN.  
adaptively. Then, a TDMA-based multiple access control protocol as shown in Figure 5 is utilized. The protocol mainly contains three phase illustrated as follow:  
(1) _Initiation:_ All the functional nodes which are randomly distributed in the water start in an inactive state. When the AUV approaches a predefined location, it broadcasts a wake-up control packet including initial schedules for the nodes within the probabilistic neighborhood. The high power wake-up control packet can trigger the nodes to enter an active state.  
(2) _Scheduling:_ Using the initial schedules, the nodes that received the wake-up packet send a small acknowledgement packet in response to the AUV. Then, the AUV transmits scheduling information for the next round of transmission to these nodes.  
(3) _Data Transfer:_ Using the updated scheduling information, the functional nodes within the current collect area transfer their data packets to the AUV. Once all transmissions in one round have occurred, the AUV re-schedules the nodes for the next round of data transmission. The total round of transmission is predefined. Finally, the AUV broadcasts a short packet to announce the end of data collection in current probabilistic neighborhood. Then the nodes reenter the inactive state.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection  
To ensure the accuracy and reliability of AIS data and to fulfill the needs of subsequent analysis and application, it is crucial to preprocess the AIS data and to detect and deal with sparse and abnormal missing points. Therefore, this study first preprocesses the AIS data to remove invalid and irrelevant data, finely divides the trajectory segments based on sailing time, then develops the trajectory sparse and anomalous evaluation method to identify and mark the trajectory points to be restored, and finally utilizes the cubic spline interpolation method for the initial sparse trajectory restoration; the overall process is shown in Figure 1.  
Figure 1: AIS trajectory data preprocessing and preliminary restoration framework.
## 4 Methods Used
### Validation Methods  
In this study, some statistical criteria were applied; sensitivity (SEN) [61], Specificity (SPC), Accuracy (ACC), MAE, RMSE [83], and area under the ROC curve (AUC) [43; 84] were used to validate the applied prediction models. In general, higher SEN, SPC, ACC, and AUC values and lower MAE and RMSE errors show better performance of the models and vice versa [43]. The SEN, SPC, ACC, and AUC are computed using four metrics including true positive (TP) (landslide correctly classified as landslide), true negative (TN) (landslide correctly classified as non-landslide), false positive (FP) (landslide incorrectly classified as landslide), and false negative (FN) (landslide incorrectly classified as non-landslide) [85; 86]. According to the definition, the SEN and SPC are denoted as the fraction of landslide and non-landslide pixels that correctly and incorrectly classified [19; 44]. The validation metrics can be formulated as follows:  
\[SEN=\frac{TP}{TP+FN} \tag{5}\]  
\[SPC=\frac{TN}{TN+FP} \tag{6}\]  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|X_{pre.}-X_{act.}\right| \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(X_{pre.}-X_{act.}\right)^{2}} \tag{9}\]  
where \(X_{est.}\) and \(X_{obs.}\) are defined as predicted values obtained from modeling and actual values obtained from real observation, respectively, \(n\) is defined as the total number of samples used in the datasets.  
Another standard statistical metric to validate the models is the ROC curve analyses [87; 88]. The ROC curve is plotted based on the sensitivity and 100-specificity on the \(x\)- and \(y\)-axis, respectively. The AUC is used to judge the performance of a model in which a value of 1 indicates an accurate model, while an AUC equal to 0.5 is an inaccurate model [41]. The AUC is calulated based on the following equation:  
\[AUC=\sum TP+\sum TN/P+N \tag{10}\]  
where, \(P\) and \(N\) are defined as the total number of landslide and non-landslide samples, respectively.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### Overview  
As shown in Figure 5, we divide the advanced saturation attack into three phases: the inferring phase, the triggering phase, and the maintaining phase. Firstly, the attacker infers which type of timeout strategy is used, and acquires the range of possible timeout values of inserted flow rules. Based on this, the attacker can select a target timeout value (\(T_{f}\)) large enough to launch the attack. Then, in the triggering phase, the attacker starts to generate malicious flow rule installation. Unlike existing attack methods, we use multiple rounds of probes to trigger the flow rule reinstallation and force the controller into assigning large timeouts for them. This phase generates attack flows and pollutes the flow table with malicious flow rules. Finally, in the maintaining phase, the attacker can craft packets and send them with a specific inter-arrival time to keep the corresponding flow entries permanently active.
# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
## 3 Traffic Estimation Algorithm
### Position Estimation Algorithm  
Due to the absence of destination and planned route data in ADS-B transmissions, a simplified estimation algorithm is necessary to infer short-term aircraft intent. Although the integration of flight plan data could improve prediction accuracy, maintaining independence from such data enhances the algorithm's ability across diverse use cases. This is particularly valuable where access to flight plans is restricted or inconsistent. The estimation algorithm is initially based on a uniform linear motion model, which can be readily extended or refined due to the modular design of the simulation environment. Notably, in many applications--such as airspace sectorization--key performance metrics depend more on aggregate traffic characteristics than on precise aircraft positions. Furthermore, the area-based upgrade mechanism described in Section 3.2 enhances the effectiveness of the estimation process.  
The algorithm estimates the three-dimensional position of an aircraft over a specified prediction interval. The position of aircraft \(j\) at time \(t_{i}\) in the scope of this work is defined by the authors in Equation (1):  
\[D_{j}=\begin{bmatrix}lat_{j,t_{i}}\\ lon_{j,t_{i}}\\ alt_{j,t_{i}}\\ hdg_{j,t_{i}}\\ v_{j,t_{i}}\\ vrr_{j,t_{i}}\end{bmatrix} \tag{1}\]  
The following definitions are used above:  
* \(lat_{j,t_{i}}\), \(lon_{j,t_{i}}\) is the latitude and longitude coordinate of aircraft \(j\) in time \(t_{i}\) in WGS84 decimal degrees;
* \(alt_{j,t_{i}}\) is the altitude of aircraft \(j\) in time \(t_{i}\) in meters;
* \(hdg_{j,t_{i}}\) is the heading of aircraft \(j\) at time \(t_{i}\), in decimal degrees clockwise from true north (north = 0\({}^{\circ}\));
* \(v_{j,t_{i}}\) is the ground speed of aircraft \(j\) in m/s;
* \(rrr_{j,t_{i}}\) is the vertical rate in m/s, where a positive value indicates climbing, a negative value indicates descent.  
For estimating horizontal motion, the model assumes uniform linear motion. Latitude and longitude updates depend on heading and velocity vector, with adjustments made for Earth's curvature according to Equations (2) and (3). In the current realization MATLAB's _deg2 km_ and _km2 deg_ functions (part of the Mapping Toolbox) are responsible for this task. In Equations (2) and (3) a km2 deg function is used as a notation of a function that converts the distance in kilometers to degrees. Note that the source code of the simulation also employs conversion between distance units to align with those presented in Equation (1) and Table 2.  
\[lat_{j,t_{i}+t_{E}}=\begin{cases}lat_{j,t_{i}}+km2 deg\left(\sin(hdg_{j,t_{i}}) \cdot t_{E}\cdot v_{j,t_{i}}\right),\ if\ 0\leq hdg_{j,t_{i}}<90,\\ lat_{j,t_{i}}+km2 deg\left  
For vertical motion, a similar linear assumption is made using the aircraft's vertical rate. The altitude is updated as Equation (4) and then converted to flight level.  
\[alt_{j_{t};t+t_{E}}=alt_{j_{t};t_{i}}+vrr_{j_{t};t_{i}}\cdot t_{E} \tag{4}\]