esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Experiment Descriptions and Instrumentation  
The field experiment covered 2 km of a relatively straight channel reach downstream from the pontoon (Figure 1) for a 21 h period during both ebb and flood conditions (27-28 April 2015). Key environmental forcings include wind, tide, and discharge (Table 1). To obtain the flow velocity at the near-surface, both GPS-tracked floating drifters and fixed ADV were used.  
Drifters utilised in this study were low-cost, low-resolution, cylindrical PVC pipes: 4 cm diameter and 50 cm long with similar physical configuration as the high-resolution drifters described in [PERSON], [PERSON] [9]. The drifters were ballasted to achieve a ~47 cm submerged height. These drifters have an absolute horizontal position accuracy between 2 and 3 m and were sampled at 1 Hz. A fleet of 18 drifters was launched in Currimundi  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Inlet** \\ **Condition** \\ \end{tabular} } & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Tide**} & \multirow{2}{*}{**Range (m)**} & \multirow{2}{*}{**Water Elevation**} & \multirow{2}{*}{**Range Range (m\({}^{3}\)/s)**} & \multirow{2}{*}{**Speed Range (m/s)**} & \multirow{2}{*}{**Instrument**} & \multirow{2}{*}{**Frequency (Hz)**} \\  & & & & & & & & \\ \hline \multirow{2}{*}{**Open**} & 27 April & \multirow{2}{*}{Ebb} & \multirow{2}{*}{0.4} & \multirow{2}{*}{0.2} & \multirow{2}{*}{0.6–17} & \multirow{2}{*}{0–4.0} & ADV- Sontek-2D & \\  & 2015 & & & & & & & side-looking (16 MHz) & \\ \hline \multirow{2}{*}{**Open**} & 28 April & \multirow{2}{*}{Flood/Ebb} & \multirow{2}{*}{0.6} & \multirow{2}{*}{0.7} & \multirow{2}{*}{0.15–30} & \multirow{2}{*}{0–4.0} & \multirow{2}{*}{LR, low cost drifter} & \multirow{2}{*}{1} \\  & 2015 & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of environmental conditions and instrumentation.  
Figure 1: (a) Aerial view of Currimundi Lake main channel including instruments and observation locations. Drifters were deployed at point A and removed at point B. (b) Aerial view of Currimundi Lake catchment including Lake Kawana and the weir separating Currimundi Lake and Lake Kawana (Maps created using ArcGIS software (Esri, 2020)).  
Lake on 28 April 2015 in clusters of four; they floated in the main channel for three hours from 13:00 to 16:00. Four drifters were either lost or experienced logging errors, thus data from 14 drifters were used in this study. The distance travelled by drifters between the bridge (point A) and pontoon (point B) is shown (Figure 1). The ADV was mounted 0.5 m below the water surface at the pontoon (B) and sampled continuously at 50 Hz during the entire experiment period from 19:00 on 27 April to 16:00 on 28 April (Figure 1). Further details about environmental conditions, instrumentation and quality control for this experiment are explained in [28].
## 4 Simulations and Experiments
### Calibration Experiments  
According to the identification simulation results, 42 different poses obtained using the D-optimal method were measured by a coordinate measuring machine, and the corresponding stroke lengths of the hydraulic cylinders were obtained from the controller of the parallel robot. The experimental system built in Harbin Institute of Technology involving a parallel robot, a coordinate measuring machine, and three feature spheres for pose measurement is shown in Figure 14 and described in [59]. The kinematic parameter identification of the parallel robot was implemented by using the improved PSO algorithm and nonlinear least squares (NLS) algorithm. After the actual kinematic parameters of the parallel robot were identified, 100 poses were randomly selected in the workspace and independently used to demonstrate the positioning and orientation accuracy of the parallel robot after kinematic calibration. The 100 poses were remeasured, and the corresponding positioning and orientation errors were recalculated. The absolute positioning and orientation errors of the parallel robot before and after kinematic calibration are shown in Figures 15 and 16. The maximum value (Max), average value (Mean), standard deviation value (STD), and root mean square value (RMS) of the positioning and orientation errors of the parallel robot before and after kinematic calibration of the 100 poses are shown in Table 6.  
Figure 12: Correlation between position error and measurement noise for parameter error set 3.  
Figure 13: Correlation between orientation error and measurement noise for parameter error set 3.  
Figure 16: Absolute orientation errors before and after kinematic calibration.  
Figure 14: The experimental system built in HIT.  
Figure 15: Absolute position errors before and after kinematic calibration.  
The maximum positioning error after kinematic calibration based on the NLS algorithm is 1.4676 mm, and the maximum based on the improved PSO algorithm is 1.1581 mm. The maximums of positioning error are reduced by 78.79% and 83.26%, respectively. The maximums of orientation error are 0.1502 and 0.1105 deg, respectively. Accordingly, the maximums of orientation error are reduced by 60.73% and 71.11%. The mean of positioning error decreased from 4.3269 to 0.9895 and 0.7692 mm, respectively. The mean of orientation error is decreased from 0.2221 to 0.1003 and 0.0674 deg, respectively. Although the two kinematic parameter identification methods can enhance the position and orientation accuracy of the parallel robot, the kinematic parameter identification method based on the improved PSO algorithm is more precise and effective than that based on the NLS algorithm.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 7 Malware Detection Tools
### Comparison of Malware Detection Tools  
In Table 5, a comparison of some well-known malware detection tools is presented. Each tool has its unique strengths and focus areas. Google Rapid Response (GRR) excels in incident response and live forensics, making it ideal for organizations prioritizing rapid detection and response to security incidents. Wireshark stands out as a powerful network protocol analyzer, suitable for those requiring in-depth analysis of network traffic.  
VirusTotal serves as a convenient web-based service for file and URL analysis, making it accessible to a broad audience and effective for post-event analysis. Suricata and Snort specialize in network intrusion detection and prevention, with high-performance engines and rule-based detection, making them well-suited for network-focused security. Sophos Intercept X provides robust endpoint protection, leveraging a combination of signature-based detection, machine learning, and behavioral analysis, making it a comprehensive solution for protecting individual devices. The choice among these tools depends on specific needs, with GRR and Wireshark excelling in incident and network analysis, respectively, whereas VirusTotal and Sophos Intercept X offer accessible and comprehensive solutions for broader audiences and endpoint protection. Organizations with a network-centric focus may find Suricata and Snort particularly valuable.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 3 State of the Art of Practice and Research
### Current Practice and Research
#### 3.1.5 Urban Planning of Future Transportation Infrastructures  
Due to the flexibility in data acquisition and sensor integration, in the domain of urban planning, UAVs can collect the spatial extent and setting of urban areas, the spatial distribution of the varied land use and land cover types, census related data, and land change detection over a period of time [79]. The Department of Transportation (DoT) can employ these data to study and plan the expansion of the current transportation infrastructures or networks.  
[PERSON] et al. demonstrated a methodology to design a 3-D city model using the information provided by UAV imagery [48]. Their research concluded that urban planners could effectively use UAV data for 3-D city modeling in terms of boundary mapping, change monitoring, and topographical surveying applications. A similar study by [PERSON] et al. presented a technical procedure for 3-D urban mapping using UAVs, considering site area, built-up area, building dimensions, building setbacks, and building height as the primary measurement parameters [49].  
A research study by [PERSON] et al. demonstrated that urban drainage models with a high degree of spatial detail could also be obtained via UAV imagery [50]. [PERSON] and [PERSON] proposed a 3-D city map reconstruction method using radio measurements made from UAVs flying at low altitudes [51]. The optimal altitude for UAVs was predicted analytically based on the reconstruction quality of the map. [PERSON] et al. presented an assessment of ortho-images based on UAV imagery to upgrade basic maps [52]. The experimental study concluded that the accuracy of UAV-derived ortho-images was sufficient, and the update of basic maps using these ortho-images reduced the processing time by 40%.
## 3 Consistent Path Congestion Model
### Path Swap Indicator  
The _Path Swap Indicator (PSI)_\(I_{\Delta t_{n}}\) is defined in Table 1 as the indicator for differential path congestion consistency. In the following context, the parameters of CDPCCI are omitted where no ambiguity occurs.  
Table 1 and the continuation of this section present in detail the derivation logic of the PSI \(\Delta t_{n}\), elaborating at each time \(t\), how \(I_{\Delta t_{n}}\) is determined from CDPCSI \(S_{W,+/-}\). When \(I_{\Delta t_{n}}=1\), it implies that it is necessary to switch the route to the backup path \(j\), while \(I_{\Delta t_{n}}=0\) means the route should stay  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(S_{W,+}(\theta_{s+},T_{s+},\Delta t_{n})\) & \(S_{W,-}(\theta_{s-},T_{s-},\Delta t_{n})\) & \(I_{\Delta t_{n-1}}\) & \(I_{\Delta t_{n}}\) \\ \hline
1 & 0 & 1 & 1 \\
1 &with the current path \(i\). From Table 1 and Equation (6) and (7), we can see that when \(S_{W,+}\) is 1 and \(S_{W,-}\) is 0, it implies that the latency on the current channel has exceeded the backup one and is still steeply increasing. This works as a sign that the current path's situation will keep deteriorating in the near future, and thus it is worth to switch to the backup one. On the contrary, when \(S_{W,+}\) is 0 and \(S_{W,-}\) is 1, it indicates that the backup channel's situation has worsened and will keep deteriorating in the near future, meaning we should stay with the current route. There is also a relatively rare case when both \(S_{W,+}\) and \(S_{W,-}\) are 1; it happens when both channels are experiencing serious turbulences. In this case, we chose to have indicator stay unchanged until there is an obvious and steady difference between the channels.
# Remote Speckle-Based Measurements of Backward Brillouin Acoustic Vibrations in Optical Fibers
###### Abstract  
We propose a novel technique for measurements of Brillouin acoustic vibrations based on temporal tracking of back-reflected speckle patterns. The proposed method holds the potential to enhance some of the limiting factors in Brillouin frequency measurements while yielding increased spatial resolution and shorter scanning times of the inspected fiber. Experimental results show the capabilities of the proposed method are presented, using a two pump-waves configuration.  
Keywords:brillouin sensing; speckle analysis; fiber sensors +
Footnote †: journal: Applied Physics Letters
# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
## 3 Proposed Graph Convolutional Network with High-Order Features
### High-Order Spatial Features  
For spatial features in a single frame, we propose combining the bone feature with the relative distance feature of 3D joint. From the Figure 2b,d we can directly get the information contained by these two features.  
Figure 3: An AGCN block consists of spatial GCN(AGC), temporal GCN(T-CN), and other operations: batch normalization (BN), Relu, dropout, and the residual block. A, M, and S in AGC represent the adjacency matrix, edge importance matrix, and similarity matrix, respectively.  
**Bone feature:** [PERSON] et al. [7] argued that the coordinate information of the joints could not represent the action of the human body well. Therefore, they proposed the second-order information, which is referred to bone feature, as a feature to enhance the performance on action recognition. The bone feature is extracted from bone data, which includes the length and the direction. Each bone is a human physical connection between joints; [PERSON] defined the person's center of gravity as the target joint; and all directions of the bone are centripetal. Each bone is connected to two joints. The distance from joint \(j_{1}(x_{1},y_{1},z_{1})\) to center of gravity is farther than \(j_{2}(x_{2},y_{2},z_{2})\). The vector representation of bone between \(j_{1}\) and \(j_{2}\) is \(e_{j_{1},j_{2}}=(x_{1}-x_{2},y_{1}-y_{2},z_{1}-z_{2})\). The direction is from \(j_{1}\) to \(j_{2}\).  
The number of bones is always one less than the number of joints because each bone is connected to two joints. In order to keep the quantity consistent, we set the empty bone at the center of gravity. The input dimension of the bone network thereby can be the same as the joint network.  
**Relative distance feature of 3D Joints:** We find that the feature extracted from relative distance between 3D joints is useful for skeleton data. For example, nodding requires only a head movement. The acceleration/velocity values of all vertices are zero, except for those of head-related joints. However, the relative distance from the head to the other joints must be changing at all frames and it can not be zero. In addition, we set the distance between the vertex and itself as zero, so the relative distance information of one vertex is 25-dimensional. For a single frame skeleton, we can use a 25 \(\times\) 25 matrix to represent it. This matrix is a diagonal matrix, and the principal diagonal elements are zeros. The shape of relative distance information is \((N,25,T,25,2)\), while the shape of other information is \((N,3,T,25,2)\), where \(N\) denotes the batch-size we set and \(T\) denotes the length of one action sequence.
## 4 Future Directions
### Data-Driven Design Optimization Based on Cloud Services  
Considering the characteristics and benefits of the technologies mentioned above, a data-driven design optimization platform can be developed based on industrial big data (material data and manufacturing data) and available cloud services (cloud computing [14; 161] and manufacturing). In the future, the optimal design of an electromagnetic device should include the best topology, shape, dimension, and material, and the most appropriate manufacturing process. Reliability-based design and analysis results should be available by evaluating a multidisciplinary analysis model and digital twin technology. Machine learning, especially deep learning, will play an important role in this process.
###### Abstract  
Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth's surface. Unlike monostatic radar, the illuminated areas are elliptical regions centered on specular reflection points. Evaluation of the spatiotemporal resolution of the reflections is necessary at the GNSS-R mission design stage for various applications. However, not all specular reflection signals can be received because the size and location of the GNSS-R antenna's available reflecting ground coverage depends on parameters including the on-board receiver antenna gain, the signal frequency and power, the antenna face direction, and the LEO's altitude. Additionally, the number of available reflections is strongly related to the number of GNSS-R LEO and GNSS satellites. By 2020, the Galileo and BeiDou Navigation Satellite System (BDS) constellations are scheduled to be fully operational at global scale and nearly 120 multi-GNSS satellites, including Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) satellites, will be available for use as illuminators. In this paper, to evaluate the future capacity for repetitive GNSS-R observations, we propose a GNSS satellite selection method and simulate the orbit of eight-satellite LEO and partial multi-GNSS constellations. We then analyze the spatiotemporal distribution characteristics of the reflections in two cases: (1) When only GPS satellites are available; (2) when multi-GNSS satellites are available separately. Simulation and analysis results show that the multi-GNSS-R system has major advantages in terms of available satellite numbers and revisit times over the GPS-R system. Additionally, the spatial density of the specular reflections on the Earth's surface is related to the LEO inclination and constellation construction.  
multi-GNSS; GNSS reflectometry; revisit time; L-band reflected signals +
Footnote †: journal: Remote Sensing, 10, 67
## 2 Theoretical Background, Related Work, and Hypotheses
### Trust-Driven Reliance Behavior in Operational Contexts  
Automation reliance, the primary behavioral outcome of automation trust, refers to the level of dependence on automation, and it is measured through users' interactions with automated systems [44]. The relationship between trust and reliance behavior is complex, as trust and its influencing factors can have varied impacts on reliance [45]. Research hasshown that reliance patterns vary significantly depending on the type of automation error. For instance, a high frequency of false alarms results in users that under-rely on automation during alarm states while over-relying on it during non-alarm states. Conversely, frequent automation failures result in over-reliance on alarms and under-reliance on automation in non-alarm states [46]. This phenomenon is further supported by a simulated seafloor sonar detection study, where participants' trust and reliance decreased as the false alarm rate increased [41]. A similar correlation was observed in the context of anti-phishing tools, where user-calibrated trust significantly and positively influenced both current and future reliance on detection systems [47]. Given the complexity of scheduling tasks [48], the effect of trust on reliance behavior may differ across operational contexts. This study proposes that (4a) dispatchers' trust in the ATS system affects their reliance behavior under routine monitoring, (4b) their trust in the ATS system affects their reliance behavior during emergency response, and (4c) their reliance behavior under routine monitoring further influences their reliance behavior during emergency response.  
While trust is a critical factor in automation reliance, [PERSON] and [PERSON] emphasize that it is not the sole determinant; reliance is also influenced by a combination of other factors [23]. For example, even when operators distrust an automation system, they may still rely on it under conditions of high workload and time pressure to complete tasks. Conversely, in situations where trust is high, operators might reduce their reliance on automation and opt for manual control due to concerns about economic costs or safety risks [49]. In such cases, trust acts as an intermediate variable influencing behavior. Additionally, a disconnect can occur between subjective trust evaluations and objective reliance behaviors. Operators may perceive their trust levels as unchanged, while their actual reliance behaviors shift significantly [50]. For subway dispatchers who rely on the ATS system as their primary information channel, reliance on the system is further influenced by its capability redundancy status. This study suggests that (5a) the ATS's capability redundancy affects dispatchers' reliance behavior under routine monitoring, and (5b) the ATS's capability redundancy also affects their reliance behavior during emergency response.
## 5 Convergence of the Learning Procedure
Proposed Algorithm**  
Let us define \((x(r),r)\) as a generic par that for fixed \(r\) has p.d.f \(f(x;r)\). We use semicolon ; in order to indicate that \(r\) is a non-random variable. Observations:  
Figure 1:2  
we admit that \(X_{i}(r_{i})\) and \(X_{j}(r_{j})\) are independent random variables even when \(r_{i}=r_{j}\) for \(i\
eq j\). In general, r.v. 's in (39) are assumed to be mutually independent. A family of p.d.f. 's is defined on \([\kappa_{1},\kappa_{2}]\times[R_{1},R_{2}]\) where \(\kappa_{1}<\kappa_{2}\), \(R_{1}<R_{2}\) are real numbers. For each \(r\in[R_{1},R_{2}]\,f(x;r)\) is a p.d.f of a random variable \(X(r)\). Consider two orthonormal sequences \(v_{k}(x),x\in[\kappa_{1},\kappa_{2}]\,k=1,2,\ldots\) and \(T_{j}(r),r\in[R_{1},R_{2}]\), \(j=1,2,\ldots\) that are complete in \(L_{2}([\kappa_{1},\kappa_{2}])\) and \(L_{2}([R_{1},R_{2}])\), respectively. Then, \(f(x;r)\) can be represented by the series (convergent in \(L_{2}[\kappa_{1},\kappa_{2}]\)  
Figure 1:3  
Notice that \(a_{k}(r)\)'s can be interpreted as  
Figure 1:4  
where \(E_{r}\) stands for the expectations with respect to random variable \(X(r)\) with p.d.f \(f(x;r)\). Furthermore, each \(a_{k}(r)\) can be represented by the series:  
Figure 1:5  
that is convergent in \(L_{2}([R_{1},R_{2}])\) for constant coefficents \(\alpha_{kj}\) defined as  
Figure 1:6By the back substitution, we obtain the following series representation (in \(L_{2}([\kappa_{1},\kappa_{2}]\times[R_{1},R_{2}])\))  
Figure 1:7  
The simplest from the computational point of view, estimator \(\hat{f}(x;r)\)or the family \(f(x;r)\), \(r\in[R_{1},R_{2}]\) we obtain, when we additionally assume that the observations of \(X(r)\)'s are made on an equidistant grid \(\rho_{1}<\rho_{2}<\ldots<\rho_{M}\) that splits \([R_{1},R_{2}]\) into nonoverlapping intervals of the length \(\Delta>0\). At each of \(\rho_{m}\), \(m=1,2,\ldots M\), \(n_{m}\) observations \((X_{k}(\rho_{m}),\rho_{m})\), \(l=1,2,\ldots,n_{m}\) are made, keeping the above-mentioned assumptions on mutual independence in force. Then, one can estimate \(\alpha_{kj}\)'s by  
Figure 1:8  
where  
Figure 1:9  
Estimators (46) are justified by (42). Notice that in (46) the simplest quadrature formula is used for approximating the integral \(\int_{R_{1}}^{R_{2}}\). When an active experiment is applicable, then one can select \(\rho_{m}\)'s at nodes of more advanced quadratures with weights, in the same spirit as in [17] for nonparametric regression estimators. Truncating series (44) at \(K\)-th and \(J\)-t terms and substituting \(\hat{\alpha}_{kj}\) instead of \(\alpha_{kj}\), we obtain the following estimator if the family \(f(x;r)\), \(r\in[R_{1},R_{2}]\)  
\[\hat{a}_{k}(r)=\frac{1}{n}\sum_{i=1}^{n}v_{k}(X_{i}(r)). \tag{21}\]0  
If as \(T_{j}(r)\) a trigonometric series is used on \([R_{1},R_{2}]\), then \(\hat{\alpha}_{kj}\) can be calculated using FFT. In addition, \(v_{k}\) can be trigonometric too.