esa-sceva/satcom-chunk-collection huggingface dataset:
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 1 Introduction  
The concept of pavement management systems in foreign countries integrates pavement design with maintenance and rehabilitation activities, primarily evaluating pavement conditions based on the extent of road surface deterioration. These systems assess pavement condition indices and categorize common types of distress, assigning deductions based on severity levels--minor, moderate, and severe--to facilitate analysis. In comparison, this study references relevant international cases to develop an assessment mechanism tailored to Taiwan's specific conditions.  
In Taiwan, maintenance is a key part of road engineering, with county and city governments integrating inspections into routine maintenance contracts. In New Taipei City, road opening contracts are structured into four phases: planning/design, procurement/construction, completion/acceptance, and maintenance. While basic road quality checks exist, there is no mechanism for long-term performance tracking. Inspections of asphalt concrete focus on compliance during acceptance, with penalties for non-conformance, but do not assess long-term pavement outcomes.  
Traditional road inspections involve manual or vehicle-based photo recording, which office staff organize and summarize. These methods are subjective, non-quantitative, labor-intensive, and inconsistent. Modern practices now use vehicle-mounted electronic systems for automated damage recording, which saves time and ensures consistency. Intelligent inspections objectively assess damage data without bias and automatically transmit large volumes of data to management systems, aiding long-term pavement performance tracking [1,2].  
Road maintenance projects stem from public reports, official assignments, annual agency renewals, and contractor reports. Many governments are shifting to intelligent inspections for faster, objective maintenance case documentation. Various report sources can be combined with a mobile app for manual input and intelligent inspection data to build complete life cycle records, enabling more effective fund allocation to necessary road sections.
## 3 Results and Discussion
### Circumferential Test  
The circumferential test is a procedure developed by [PERSON] et al. in 2018 [PERSON] et al. (2018) to determine the degree of homogeneity in the distribution of damage within intact specimens and specimens tested to failure. This method can also be applied to pores in order to know their distribution inside the specimen.  
In a simplified manner, in the circumferential test, the pore position is replaced by a numerical value \(L\) corresponding to the distance between its center of gravity and an axis parallel to the vertical axis \(Z\) passing through the center of gravity of the specimen. In order to be able to compare specimens of different sizes, the coefficient \(L\) is made dimensionless by defining the coefficient \(l\), which is obtained according to the following expression (Equation (2)):  
\[l=\sqrt{\left(\frac{x-x_{\text{\tiny{cog}}}}{a}\right)^{2}+\left(\frac{y-y_{ \text{\tiny{cog}}}}{b}\right)^{2}} \tag{2}\]  
where \(a\) and \(b\) are the real dimensions of the specimen cross section, and \(x_{\text{\tiny{cog}}}\) and \(y_{\text{\tiny{cog}}}\) are the values of the specimen center of gravity. The coefficient \(l\) can take values between \(0\) and \(\sqrt{2}\).  
In the case that the specimen has all its pores uniformly distributed, the histogram of \(l\) distribution presents a specific geometry (Figure 7).  
In real specimens, the histogram of \(l\) distribution shows a different shape (Figures 8 and 9). Depending on the shape of the histogram and how much it differs from the theoretical histogram, we can obtain an idea of what the pore distribution inside the specimen is like.  
Figure 8 shows how there are hardly any differences in pore distribution between specimens. The small differences are due to the intrinsic dispersion of the concrete, which results in a non-identical distribution of pores within the matrix.  
However, the average distribution histogram is almost coincident with the theoretical value corresponding to a uniform distribution. This means that, at a larger scale, this intrinsic dispersion disappears. The fact that the specimens have been cut from the inside of a prism means that the edge effect does not exist.  
Figure 6: Identification of \(\theta\) (theta) and \(\phi\) (phi) angles in Spherical coordinates (**a**), and of \(\alpha\) (alpha), \(\beta\) (beta) and \(\gamma\) (gamma) angles in Cartesian coordinates (**b**).  
In this example, all the pores of the specimens have been analyzed. In certain situations, it may be interesting to evaluate a fraction of the pores (for example, those above or below a certain critical size). It is also possible to evaluate only the pores located in a region of the specimen (to study, for example, if there is segregation). In all these cases, the process for obtaining these partial histograms is simple, since it is enough to filter out the pores that meet the required conditions.  
Figure 8: Circumferential test. Real histogram of empty voxels.  
Figure 7: Circumferential test. Theoretical histogram of empty voxels according to a random distribution.
# Trust-Aware Fog-Based IoT Environments: Artificial Reasoning Approach
## 4 Subjective Logic Trust Model
### Subjective Logic
```
0: Node \(i\) as source, Node \(j\) as target, \(S_{i}\) as service, \(\lambda\), Threshold
0: Trust Level of \(j\)
1: Retrieve list of \(j\)s that provide \(S_{i}\)
2:for each \(j\)do
3:for each \(r\in RTT_{i}\)do
4: Acquiring recommendations for \(j\)
5: Apply discounting operator
6: Apply consensus operator
7: Apply discounting and consensus operators using Equation (8)
8:\(\Psi_{i}^{j}=\lambda\times T_{i,j}^{t}+(1-\lambda)\times R_{i,j}^{t}\triangleright\) calculate trust level \(\Psi_{i}^{j}\) by combining the direct trust and reputation
9:if\(\Psi_{i}^{j}\geqslant Threshold\)then
10: Do transaction with \(j\)
11: Go to line 12
12: Compute direct trust \(T_{i,j}^{t+1}\) using Equation (9)
13:if\(T_{i,j}^{t+1}\geqslant Threshold\)then
14:\(pos_{i,j}=1\)
15:else
16:\(neg_{i,j}=1\)
17:if\(j\
otin FCD_{i}\)then
18: Send feedback to \(FC_{j}\)
19: Update \(ITT\) by \(FC_{j}\)
20:\(T_{i,j}^{t}=\lambda\times T_{i,j}^{t}+(1-\lambda)\times T_{i,j}^{t+1}\)\(\triangleright\) update \( DTT_{i}\)
21:for each \(r\in RTT_{i}\)do\(\triangleright\) update \(RTT_{i}\)
22: Update trust level for \(r\)
```  
**Algorithm 1** Computing and Updating Trust-related Tables. Algorithm 1 starts with the member node that wants a specific service by contacting its coordinator and asking for service providers that provide the required service. The coordinator utilizing the inter-ring, sends a lookup query and gets a list of all service providers. The coordinator selects a trustworthy provider by consulting its DTT and the recommendations from its recommenders. Next, the coordinator receives the recommendations in the form of opinions. These recommendations are weighted and combined to get the final reputation of the coordinator of the service provider by applying discounting and consensus operators together. Then, the coordinator computes the trust level \(\Psi_{i}^{j}\) based on the reputation and the direct trust with the coordinator of the service provider. Based on the aggregated results, the coordinator decides to go with the transaction or not if \(\Psi_{i}^{j}\geqslant Threshold\). After doing the transaction, the \(T_{i,j}^{t+1}\) of the service provider is computed using Equation (9). If the \(T_{i,j}^{t+1}\geqslant Threshold\), it assigns positive feedback; otherwise, it assigns negative feedback. Then, the feedback is sent to the Fog coordinator of the service provider to update its internal trust table. Finally, the coordinator of \(i\) updates its \(DTT_{i}\) and \(RTT_{i}\).
## 4 TRSA: Trust-Aware Evidence Reasoning and Spatiotemporal Feature Aggregation Model
### Evidence Representation Based on Spatiotemporal Structure  
As shown in Figure 2, the propagation process of source information can be expressed by a spatiotemporal structure graph. The temporal sequence can reflect the dynamic evolution of comments' (evidence) content over time, while the spatial structure can reflect the real semantic interactions between evidence items. To alleviate the semantic sparsity issue, we enriched the semantic representation of evidence by aggregating the temporal neighborhood and spatial neighborhood information of evidence based upon the information propagating spatiotemporal structure. Additionally, considering the difference in evidence quality, evidence credibility should be integrated into the evidence representation module to enhance the reliability of the evidence's semantic representation. Specifically, we considered three types of units: a temporal sequence representation unit, a spatial structure representation unit, and a fusion gate unit.
## 4 Sentinel-3A SRAL and MWR Cal/Val Results
### Summary of Transponder, Sea-Surface, and Crossover Cal/Val results  
Table 3 summarizes the Cal/Val results for Sentinel-3A Pass No.14 and Pass No.335 as estimated by three diverse and independent calibration methodologies. The last column presents the standardized uncertainty values as determined by the concept of Fiducial Reference measurements. The bias for the Sentinel-3A comes close to zero based on the observations carried out by the transponder and the sea-surface calibrations.  
In summary, the results out of this investigation can be succinctly described as follows: (1) both the transponder and sea-surface Cal/Val methodologies agree that the Sentinel-3A SRAL altimeter bias was less than 1 cm; (2) no significant drift in SRAL performance was monitored, although some trend has been observed in its radiometer that needs to be verified; (3) at present, no significant directional errors can be reported, with confidence, based on the ascending Pass No.14 and the descending Pass No.335; (4) the difference between the transponder and the sea-surface biases for Sentinel-3A Pass No.14 was quite small and within their uncertainty bounds; (5) the Sentinel-3A altimeter seemed to measure sea-surface height +4 cm higher than the Jason-3 reference altimeter, based on crossover  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error Constituent** & **Type** & **Variance Estimate** & **Standard Uncertainty** \\ \hline Cal/Val Site Coordinates & & & \\ GNSS height & A & 0.10 mm & \(\pm\)0.10 mm \\ GNSS receiver & B & 6.00 mm & \(\pm\)3.50 mm \\ Antenna reference point & B & 2.00 mm & \(\pm\)2.00 mm \\ SSH@Cal/Val site & & & \\ Water level & A & 1.30 mm & \(\pm\)1.30 mm \\ Tide gauge zero point & A & 0.15 mm & \(\pm\)0.15 mm \\ Tide gauge vertical alignment & B & 2.40 mm & \(\pm\)1.40 mm \\ Tide gauge certificate & B & 5.50 mm & \(\pm\)5.50 mm \\ Leveling repeatability & A & 0.12 mm & \(\pm\)0.12 mm \\ Monumentation & B & 1.10 mm & \(\pm\)0.64 mm \\ Vertical misalignment & B & 1.00 mm & \(\pm\)0.60 mm \\ Leveling observer & B & 1.00 mm & \(\pm\)0.60 mm \\ Leveling instrument/method & B & 1.00 mm & \(\pm\)0.60 mm \\ Tide pole reading & B & 1.00 mm & \(\pm\)0.60 mm \\ MSS/MDT/Geoid & & & \\ MSS & B & 33.00 mm & \(\pm\)33.00 mm \\ MDT & B & 85.00 mm & \(\pm\)85.00 mm \\ Geoid & B & 80.00 mm & \(\pm\)46.20 mm \\ Processing Errors & & & \\ Processing & B & 0.50 mm & \(\pm\)0.30 mm \\ Geoid slope/offshore transfer & B & 10.00 mm & \(\pm\)5.80 mm \\ Unaccounted & & & \\ Unaccounted effects & B & 20.00 mm & \(\pm\)11.55 mm \\
**Root-sum-Squared Uncertainty** & & & \(\pm\)**36.1 mm** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uncertainty budget for the sea-surface calibration following the analysis proposed in the International Standards Organization “Guide to the Expression of Uncertainty in Measurement”.  
analysis; (6) the MWR measurements along the descending Pass No.335 were land contaminated by Gavdos in a distance about 15-20 km from the land mass.
# Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)
## 6 Guidance Navigation and Control (GNC)
### Control  
Control is the action of determining the necessary forces and moments produced by thrusters so that USV moves following the path. Proportional Integral Differential (PID) control is employed to control USV speed, and that integrated with Feed Forward (FF) control is implemented to control steering rate. Feed Forward control is included for enhancing response performance (to minimize the error between the current state of the system and the desired state). This combination is commonly benefited for autonomous mission purposes due to its significant contribution to eliminating disturbance and enhancing the controller's performance for an autonomous system [27; 28]. The control block diagram of the USV is shown on the block diagram in Figure 9.  
The motion of USV is controlled by PID compensator, where the output of the PID controller is in the form F\({}_{0}\) and \(\delta\). The applicable equation to determine the F\({}_{0}\) and \(\delta\) values  
Figure 8: Path generation.  
are written in Equations (31) and (32). Where \(\mathrm{e_{1}(t)}\) is the longitudinal error position of USV, and \(\mathrm{e_{2}(t)}\) is the Heading Error of USV.  
\[\mathrm{F_{0}}\mathrm{=K_{P2}e_{2}(t)+K_{I2}\int e_{2}(t)d(t)+K_{D2}\frac{d_{e2}( t)}{dt}} \tag{31}\]  
\[\mathrm{\delta=K_{P1}e_{1}(t)+K_{I1}\int e_{1}(t)d(t)+K_{D1}\frac{d_{e1}(t)}{dt} +K_{FF}f(\mathrm{V_{d}})} \tag{32}\]  
In this work, the PID Gains were obtained experimentally to control the Throttle (\(\mathrm{K_{P2}}\), \(\mathrm{K_{I2}}\), \(\mathrm{K_{D2}}\)), which is 0.5 for the \(p\)-value and 0.01 for the value of I. While the PID and FF Gain obtained to control Steering Rate (\(\mathrm{K_{P2}}\), \(\mathrm{K_{I2}}\), \(\mathrm{K_{D2}}\), \(\mathrm{K_{FF}}\)) is 1.5 for the FF value, 0.5 for the \(p\)-value, and 0.01 for the value of I, as shown in Figure 10.  
The GNC process is managed and monitored by the Ground Control Station (GCS). Moreover, it is software operated to monitor USV status when in operation under FPV (First-person View). The GCS Software selected and adopted in this study is Mission Planner. The full-featured GCS can be used as a GCS from various platforms, for instance, an aircraft, helicopter, drone, rover, and boat. [PERSON] developed this software, and it is open-source. This software is only compatible with Windows. With Mission Planner, sailing data mined from GPS, IMU, and other sensors are exposed in the screen of GCS. For example, Figure 11 presents the information data from the IMU sensor, such as heading, ground speed, and position data from GPS, battery level, and communication signal strength. Moreover, as a GCS, Mission Planner is also able to set up, config., and tune a USV for optimum performance.  
Figure 10: PID control tuning parameter.  
Figure 9: Control scheme employed on the proposed USV.
## 5 Experiments and Results
### Cross-Validation of SHAP and LIME Results  
The results from LIME and SHAP will be compared in this part, with an emphasis on the lessons learned from two scenarios: Sample 1 (benign traffic) and Sample 2 (attack traffic). This comparison study aims to assess the dependability and consistency of the insights produced by SHAP and LIME in various traffic situations. In order to improve decision-making and model interpretability, we want to make sure that the model's predictions are clear, reliable, and applicable to a variety of real-world scenarios by looking at both benign and attack traffic. The comparison of LIME and SHAP for both benign and attack traffic is highlighted in the analysis below, showcasing each protocol's unique insights and consistency across various traffic types.  
* Both LIME and SHAP recognize a number of crucial characteristics that help with the classification of benign traffic. Regular web and TCP behavior is reflected in \"Rate\", \"HTTPS\", \"HTTP\", \"fint_count\", and \"syn_flag_number\", which LIME highlights as favorable signs of benign traffic. In contrast, SHAP shows a wider alignment with typical benign patterns by emphasizing \"rst_count\", \"HTTPS\", \"urg_count\", \"Radius\", \"ack_flag_number\", \"flow_duration\", and \"Header_Length\" as important for benign traffic.
* LIME identifies \"syn_flag_number\", \"rst_flag_number\", \"IAT\", \"ARP\", and \"rst_count\" as positive indications for attack traffic, indicating the existence of attack patterns. Indicators that indicate deviations from attack characteristics are also identified as negative: \"Duration\", \"flow_duration\", \"Variance\", \"ICMP\", and \"fin_count\". Similar to this, SHAP employs \"IAT\", \"rst_count\", \"Header_Length\", and \"HTTPS\" as negative features that support the attack prediction, whereas \"Variance\", \"ack_flag_number\", \"urg_count\", and \"flow_duration\" are the indicative features that are less likely to be attacked.  
In conclusion, both approaches identify \"rst_count\", \"HTTPS\", and \"flow_duration\" as influential for benign traffic and highlight important characteristics like \"rst_count\" and \"IAT\" for distinguishing between benign and attack traffic. Both offer a complementary perspective on attack traffic, with SHAP emphasizing elements like \"IAT\", \"rst_count\", \"Header_Length\", and \"HTTPS\", while LIME identifies \"syn_flag_number\", \"rst_flag_number\", \"IAT\", and \"ARP\" as important. It is also noteworthy that the predictions produced by the SHAP and LIME explanations consistently provide the same class type for a given sample. By forecasting the same class type for a given sample, SHAP and LIME align, demonstrating the consistency and dependability of the model's behavior and bolstering confidence in its interpretability and decision-making process.
## 3 Compact magnetometer and GPS integration  
Figure 1 shows the compact integration of the GSM-19 magnetometer and the geodetic high precision GPS antenna. Only a screw to connect the staff with the antenna is needed.  
Figure 1: Magnetometer and geodetic GPS integration.  
No magnetic signal distortion is observed in any of the observations carried out with these coupled devices. That is, measurement time and signal amplitude numbers were under optimal conditions in the observation data files.  
GSM-19 is equipped with a low precision GPS antenna. The static and kinematic precision of this antenna can be seen in Table 1, where static observations on control points (absolute error) and kinematic comparison with VRS results are presented. These control points can be seen in Figure 3.  
As can be seen in Table 1, the magnetometer GPS does not guarantee high levels of accuracy, the quadratic component of absolute error introduces an error of 1.84 meters and the quadratic component of kinematic error of 0.86 meters (these are the expected results for this kind of GPS device). The data collected by the magnetometer is taken in Universal Coordinated Time (UTC) if the GPS option is activated on the control unit of the magnetometer. In this respect, correlation with the precise coordinates obtained with the geodetic antenna and VRS approach is made easier in post-process (the magnetic data and precise GPS coordinates share the same reference time system).
## 2 Materials and Methods
### SHAP Analysis  
We used the SHAP method to assign a value to each lexical feature; doing so illuminated each feature's importance in the optimal regression model. The SHAP method is based on game theory and a local explanation. Suppose that a domain concept, \(C\), is represented as a collection of lexical features, \((x_{1},\ x_{2},\ x_{3},\cdots,\ x_{N})\), where \(N\) is the number of lexical features. The explanation model \(G(X^{\prime})\) with simplified features, \(X^{\prime}\)=\((x_{1}\)\({}^{\prime}\), \(x_{2}\)\({}^{\prime}\), \(x_{3}\)\({}^{\prime}\), \(\cdots\), \(x_{N}\)\({}^{\prime}\)), for a prediction model, \(H(X)\), can be written as Equation (8), where \(\phi_{0}\) represents a constant value when all lexical features are absent; \(\phi_{i}\) is the Shapley value of the lexical feature \(x_{i}\); and \(x_{i}\)\({}^{\prime}\) is feature that is simplified by assigning its value to 1 (indicating that the variable \(x_{i}\) is present) or 0 (indicating that the variable \(x_{i}\) is absent). The Shapley value, \(\phi_{i}\), can be calculated as Equation (9), where \(Z^{\prime}\subseteq X^{\prime}\) represents all \(Z^{\prime}\) vectors where the non-zero entries are a subset of the non-zero entries in \(X^{\prime}\); \(|Z^{\prime}|\) is the number of non-zero entries in \(Z^{\prime}\); \(f_{x}(Z^{\prime})\) is a prediction model trained with the \(i\)-th lexical feature; and \(f_{x}(Z^{\prime}\backslash i)\) is another prediction model trained without it. A solution to Equation (8) proposed by [PERSON] and [PERSON] [24] is known as SHAP values, where \(f_{x}(Z^{\prime})=E[f_{x}(X)|X_{S}]\) and \(S\) represent the set of non-zero indices in \(Z^{\prime}\).  
\[H(X)=G\big{(}X^{\prime}\big{)}=\phi_{0}+\sum_{i=1}^{N}\phi_{i}{x_{i}}^{\prime} \tag{8}\]  
\[\phi_{i}=\sum_{Z^{\prime}\subseteq X^{\prime}}\frac{|Z^{\prime}|!(N-|Z^{ \prime}|-1)!}{N!}\big{[}f_{x}\big{(}Z^{\prime}\big{)}-f_{x}\big{(}Z^{\prime} \backslash i\big{)}\big{]} \tag{9}\]  
Consequently, the SHAP method was devised to enhance model interpretability, which affirms users' trust in model predictions. SHAP also allows for a firmer understanding of features' roles in improving model performance. SHAP values signify features' respective contributions to prediction outcomes. Put simply, a feature's SHAP value indicates the expected prediction change from the base value. Figure 2 presents an example from our data set. Compared with the base value (5.253), the lexical features in red show the degree to which the model's performance improved; the lexical features in blue show how much the model's performance declined. Overall, using this method, features' importance can be ranked by taking the mean SHAP values of each instance in a data set. For implementation, we built a Python program with the SciPy package for statistical analysis and the PyCaret package for interpretable machine learning.
###### Abstract  
South African agriculture accounts for 62% of the national water demand. Almost 45% of the water is wasted. Therefore, irrigation systems need to improve their water-use efficiency (WUE). However, the WUE of smallholder irrigation schemes in the country, including Agri-Parks, is not precisely known. A study was performed at four Agri-Parks (Rooiwal, Soshanguive, Tarlton, and Westonaria) in Gauteng province to assess the condition and performance of the irrigation systems, as part of a project that aimed to develop a WUE model for smallholder irrigation systems. The Agri-Parks were equipped with efficient irrigation systems in forms of drip and microjets. The assessments were performed at the system component level in February-March 2021. A Condition Assessment Model (CAM), developed by ARC-NRE/AE, was used for the condition assessment. Enumerators observed the system components visually and assigned conditions, which they uploaded into the model to generate condition indices (CIs). Water conveyance efficiency (_CE_) and distribution uniformity (_DU_) were assessed on delivery and infield systems, respectively. The CI values ranged 4-6, implying significant deterioration had occurred. The _CE_ was 61-78%, while the _DU_ was 60-95%. The infield system CI correlated positively with the _DU_, suggesting the CI could predict the _DU_ in drip systems, which was encouraging for the proposed WUE model. However, further research covering a longer period and more Agri-Parks is recommended.  
Agri-Parks; condition index; performance evaluation; irrigation water management Article