esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Solving Methodology
```
1:Data: List of tasks \(T\), and matrix of expected delays \(D\)
2:Result: Permutation of completed tasks \(P\)
3:\(P\leftarrow\emptyset\)
4:\(t_{c}\gets 0\)
5:while\(C\
eq\emptyset\)do
6:\(T\leftarrow\text{estimateDelays}(T,D,t_{c})\)
7:\(T\leftarrow\text{sort}(T)\)
8:\(t\leftarrow\text{selectTask}(T)\)
9:\(t\leftarrow\text{executeTask}(t)\)
10:\(P\gets P\cup\{t\}\)
11:\(t_{c}\leftarrow\text{updateSystem}(t_{c},t)\)
12:endwhile
13:return\(P\)
```  
**Algorithm 2** Reactive Heuristic for Dynamic Delays  
The reactive heuristic approach, which receives the same input parameters as the non-reactive heuristic for dynamic delays, works as follows. First, an empty permutation of tasks \(P\) is constructed (line 3). Additionally, the system conditions track the current time \(t_{c}\), which is initialized to the starting time \(t=0\). Next, the list of tasks to be completed \(T\) is iterated until no more tasks are in the list (lines 5-11). In each iteration, the delays of each circuit are estimated based on the system conditions, and the list of tasks is then sorted from the lowest to the highest estimated delay (lines 6 and 7). The first task from the new permutation of tasks \(t\) is extracted, and the task is executed by the AGV (lines 8 and 9). The expected total travel time is then updated with the actual time the AGV takes to travel throughout the circuit. In other words, we compute the real total time needed by the AGV to complete task \(t\), taking into account the actual delays encountered along the way. Next, the task is added to the end of the permutation of completed tasks (line 10). Finally, the system conditions are updated, which adds the actual time needed to complete the task to the system variable that tracks the current time (line 10), not the estimated time needed to complete the task. Once the list of assigned tasks to be completed is empty, the constructed permutation of completed tasks is returned (line 12). Notice that the reactive heuristic proposed here selects the task with the lowest estimated delay at each iteration, just as the non-reactive heuristic. However, the reactive heuristic updates the system conditions with the real total time the AGV needs to complete the circuit. Also, it recomputes the permutation of remaining tasks according to the current system conditions in subsequent iterations. This gives the reactive heuristic the ability to adapt to potential disruptions.
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 3 Experimental Setup  
The combined EKF is validated in a test campaign, conducted at the Mugello Circuit proving ground (Italy). The test vehicle is an Alfa Romeo Giulia, shown in Figure 4. All relevant vehicle parameters are listed in Table 1. The case study instrumentation featured on the vehicle is (Figure 4) as follows:  
1. **Kistler Correvit S-motion.** It is an optical sensor for measuring the overall vehicle velocity \(V\) and the sideslip angle \(\beta\). It is herein used to provide a reliable ground-truth comparison.
2. **dSPACE MicroAutoBox II.** It serves as a signal logger, ensuring the synchronization of all recorded signals. Signals logged from the vehicle CAN include accelerations \(a_{x}\), \(a_{y}\), yaw rate \(r\), wheel velocities \(v_{\mathit{wheel},i}\) and the steering angle, \(\delta\). Additionally, the dSPACE MicroAutoBox is a powerful real-time system with a high-performance multi-core processor, adept at handling complex control tasks with low latency, making it an excellent candidate for an ECU validation platform.  
Double lane change (DLC) and slalom manoeuvres are performed. The first is carried out at a velocity of \(50\,\mathrm{km}/\mathrm{h}\). Except for the forward velocity, the test parameters are set  
Figure 4: Vehicle testing setup.  
Figure 3: Combined dynamic–kinematic EKF scheme. Subscript (\(-\)) and (\(+\)) refer to a priori and a posteriori estimates, respectively.  
accordingly to the ISO 3888-2:2011 standard [21]. Figure 5 presents the geometry of the double lane change.  
The slalom test is performed at 60 km/h. The manoeuvre consists of going around ten cones at a distance of 20 m between each other [22]. The geometry of the slalom test is outlined in Figure 6. To mark the beginning and end of the manoeuvre, the vehicle must pass through two cones at a distance of 10 m from the first and last cones of the slalom. The manoeuvre is performed at constant speed.
# Spherical Planting Inversion of GRAIL Data
## 3 Results
### Data  
The gravity model GRGM1200A, applied in Bouguer correction by NASA, expanded to degree and order 1200 with sensitivity down to \(<\)5 km resolution from the GRAIL mission, is used in this paper [54]. Deliberating the very high correlation between gravity and topography at L = 7-700 [55], following [PERSON] et al. [56], the spatial resolution of the gravity field is related to the spherical harmonic coefficient and defined by the half wavelength in spherical inversion [57]. When the grid size of the interpretation model is 0.8\({}^{\circ}\)\(\times\) 0.8\({}^{\circ}\) on a spherical earth, according to the relation between half wavelength lambda and the suitable degree \(N\) of the gravity model in [PERSON] et al. [12], the suitable degree \(N\) = 450 [58]. Therefore, in Figure 7, the residual Bouguer gravity is expanded between spherical degrees 7 and 450 [57] to suppress the influence of noise from the high degrees and filter out signals from the deep interior (such as the crust-mantle boundary or Moho) from the low degrees for highlighting lunar mascons [59; 60; 61]. The topography of the Crisium mascon is derived from the spherical harmonic topography model MoonTopo2600p, as shown in Figure 8. Then, 20 \(\times\) 20 = 400 observation data points are calculated at 10 km height relative to the reference radius of 1738 km using the gravity field model mentioned above, as shown in Figure 9.  
Figure 6: Density-contrast distribution maps. (**a**) Horizontal slice of the reconstruction model along with a radial length of 1698 km, and (**b**) vertical profile of the composite model along a latitude of 35\({}^{\circ}\).  
Figure 8: The topography of the Crisium mascon.  
Figure 7: A filtered Bouguer map (l = 7\(-\)450) highlights the lunar mascons.  
In Figure 8, Mare Crisium is surrounded by an upland massif [53], with much of its interior and a small part of its marginal upland filled with basaltic rocks and a ring of folded ridges on its outer boundary [62; 63]. A folded ridge is a low, linear, or arcuate broad ridge, usually tens of kilometers long. It is placed symmetrically/asymmetrically on an arch close to the circular Maria edge [64; 65].
## 4 Results
### Experimental Comparison with Existing Techniques
#### 4.2.1 Experiments on the DrivFace Dataset  
DrivFace is a challenging dataset because it contains images acquired in real driving scenarios with different head pose variations and various facial features, including glasses and beards.  
Table 2 shows that our proposed approach outperforms the existing approaches in all the performance measures. One of the leading reasons is that the Boosting framework, which tweaks its weak learners due to its adaptive nature, ultimately results in performance improvement. Moreover, J48 gives better performance on datasets with missing values, such as the DrivFace dataset.
# Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder
## 2 Materials and Methods
### Autonomous Navigation and \"Range Finder\" Sensors
Among the several rangefinder devices commercially available and suitable to be used with the NAO robot, we chose the following sensor that belongs to the LRF family based on phase displacement measurements: the LRF model URG-04 LX-UG01 (Hokuyo Automatic Co., Ltd., Osaka, Japan), a device widely employed in both industrial and research fields and devoted to indoor applications only [46]. This device, as depicted in Figure 2, can acquire up to a maximum angle of 240\({}^{\circ}\), ranging between 20 and 5600 mm. The device shows an angular resolution equal to 0.0063 rad (i.e., 0.36\({}^{\circ}\)), which means about three scans for each sexagesimal degree. A maximum number of 683 scans can be performed in the range between \(-120^{\circ}\) and \(120^{\circ}\) and each of them are separated from the next by an angle of 0.36\({}^{\circ}\). The sensor acquisition rate for the entire viewing angle (\(-120^{\circ}\) and \(120^{\circ}\)) is equal to 100 ms, and it shows an accuracy that is dependent on the measured distance, which is as follows: \(\pm 30\) mm (in the range of 60 mm to 1 m) and \(\pm 3\%\) of the measurement (between 1 and 4.095 m). The device communication system is based on the standard Secure Communications Interoperability Protocol (SCIP2.0). The module can be connected with the host via the URG-SCIP protocol over a native Universal Serial Bus (USB) link or using the same port configured as a serial link. From a mechanical point of view, the Hokuyo Automatic's rangefinder size is \(50\times 50\times 70\) mm, and its weight (without cable) is 139 g.  
A few years ago, the French company that designed and built the NAO robot put a modified NAO head on the market, equipped with a rangefinder sensor [47]. This customization, for several reasons, is no longer available today, despite its undeniable effectiveness [48; 49; 50]. This work aims to restore this accessory by making it available again to the community of robot NAO users. It required both mechanical and electric design to overcome the issues of its suitable power supply and the relative mechanical hooking on the robot's head. Therefore, the study aims to design and manufacture, by 3D printing, a helmet that is wearable by the robot and suitable for more applications [51; 52]; the idea is to create a scaffold that is mountable on the robot's head and that could host different typologies of payload. For example, this could consist of additional sensors or digital data memory to integrate the already available data obtained by the robot or a secondary battery to extend operational autonomy. Finally, the payload could also consist of an additional high-performance computing unit to implement those algorithms with high computational costs that are otherwise not directly executable by the robot. This section describes the mechanical and electric project of a helmet that is customized to host the previously described rangefinder laser sensor.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 2 Materials and Method
### ANNs Analysis
#### 2.6.1 Data Used by the Network  
Laboratory experiments on stabilized and unstabilized soils provided the information used to train and validate the artificial neural network (ANN). The primary objective of this study was to investigate the effect of water content, curing time, and percentage of WMP on UCS/UPV. These factors were selected as inputs for the artificial neural network (ANN) model based on their observed importance in influencing the experimental results. This finding is consistent with other research that has also identified these variables as significant factors influencing the strength and stability of stabilized soils [12]. Previous studies have included additional factors, e.g., compaction energy, soil plasticity, additive fineness, and others in modeling stabilized soils [30]. However, the additional parameters were omitted in this study because the primary objective was to closely examine the effects of mix design and the curing process.  
By limiting the inputs, it was possible to reduce the complexity of the model while maintaining a high level of accuracy. Correlation coefficients for both UCS and UPV predictions were found to be approaching 1. This finding is consistent with a previous study [25], which showed that using a smaller number of highly influential inputs can yield strong artificial neural network (ANN) models for stabilized soils. However, it is worth noting that the inclusion of factors such as clay mineralogy, organic content, temperature, and other relevant parameters may have the ability to improve the accuracy of model predictions, as shown in previous studies [16]. The existing model provides a robust basic framework; however, the incorporation of additional input parameters represents a promising avenue for further development.
## 4 System Modeling
### Actuation Systems
#### 4.4.3 Weight Distribution Control System  
In the same manner as the previous system, the flow rates jetted from the outlets are all equal. Moreover, the nozzles do not rotate. The maneuverability of the system is controlled by the horizontal position of a weight block moving on its top, i.e., \(x_{w}\) and \(y_{w}\). The reaction force and torque act on the head part due to the movement of the weight block are obtained from its motion dynamics as follows:  
\[\begin{split}\mathbf{F}_{wb}=M_{w}\big{(}\mathbf{g}_{b}-\dot{\mathbf{v}}_{wb} -\mathbf{\omega}_{b}\times\mathbf{\omega}_{wb}\big{)},\\ \mathbf{\tau}_{wb}=\mathbf{r}_{wb}\times\mathbf{F}_{w}-J_{w}\dot{\mathbf{\omega}}_{b}- \mathbf{\omega}_{b}\times J_{w}\mathbf{\omega}_{b}\end{split} \tag{15}\]where:  
\[\mathbf{v}_{wb}=\mathbf{v}_{b}+\mathbf{\omega}_{b}\times\mathbf{r}_{wb}+\dot{\mathbf{r}}_{wb},\ \ \mathbf{J}_{w}=\mathbf{J}_{w0}-M_{w}[\mathbf{r}_{wb}]^{2} \tag{16}\]  
\(M_{w}\) is the mass of the block, and \(\mathbf{J}_{w}\) and \(\mathbf{J}_{w0}\) are the inertia tensor of the weight about the rotation center of the head and the mass center of the block, respectively. \(\mathbf{r}_{wb}=\begin{bmatrix}\mathbf{x}_{w}&\mathbf{y}_{w}&z_{w}\end{bmatrix}^{T}\) is the position vector of the weight block, in which the vertical distance \(z_{w}\) is a constant value and horizontal positions \(x_{w}\) and \(y_{w}\) vary by the 2-degree linear motion mechanism.  
**Remark 3**.: _One can see the presence of the weight block's acceleration, i.e., \(\tilde{\mathbf{r}}_{wb}\), in the motion equation of the head part. This acceleration introduces a non-minimum phase phenomenon to the system. That is, when the weight block moves to tilt the head part in one direction, the inertia force of the block makes the head initially moves backward before diving and moving in that same direction._  
**Remark 4**.: _A person can fully control his/her flyboard, especially the yaw rotation, by leaning back and forth, and side to side, thanks to the inextensible condition of the hose. However, with a long hose conveying water in the firefighting task, the weight distribution control approach is insufficient to control the yaw. Hence, an additional actuator is needed._  
Now consider the use of an impeller fan, as in Figure 2c. Since its mass and inertia are small so being neglected. When the fan rotates, it generates thrust force and drag moment as follows [37]:  
\[\mathbf{F}_{wb}^{f}=\begin{bmatrix}0&0&-C_{T}\rho_{a}A_{f}\left(R_{f} \omega_{f}\right)^{2}\end{bmatrix}^{T}, \tag{17}\] \[\mathbf{\tau}_{wb}^{f}=\begin{bmatrix}0&0&-C_{Q}\rho_{a}A_{f}R_{f}^{3} \omega_{f}^{2}sign\left(\omega_{f}\right)\end{bmatrix}^{T},\]  
with \(C_{T}\) the thrust coefficient, \(C_{Q}\) the drag coefficient, \(\rho_{a}\) the air density, \(A_{f}\) the fan disk area, \(R_{f}\) the rotor radius, and \(\omega_{f}\) the fan angular velocity.  
**Remark 5**.: _By substituting Equations (13), or (14), or (15), (16) and (17) into Equations (11) and (12), the motion equations of each design are completely obtained._
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 2 Materials and Methods  
### Direct Pose Estimation  
Recent studies [20; 21; 22; 23] have focused on directly predicting rotation and translation for pose estimation. DenseFusion [20] introduced a dense fusion module that fuses RGB and depth features at the pixel level to predict object poses, selecting the final pose based on the pixel with the highest confidence. ES6D [21] proposed XYZNet to reduce the demand for random memory access in dense fusion modules, enhancing time efficiency. It also introduced the A(M)GPD loss to address challenges posed by symmetric objects, improving upon the previous ADD-S loss. Experimental results demonstrated that the A(M)GPD loss is more effective for symmetric objects. Unit6D [22] and Unit6 Dv2 [23] improved pose estimation by incorporating additional UV data as input to resolve projection factorization issues, enabling more accurate object pose estimation with a unified backbone.  
While direct pose prediction (or regression) methods are time-efficient, they generally exhibit reduced performance compared to keypoint-based methods, particularly due to sensor noise.
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 1 Introduction
Additionally, it augments the model's capacity to extract temporal information about the action through an action (ACTION) attention module, resulting in superior feature extraction results. The main work of this paper can be summarized in the following three points:  
1. Implementing the CA attention module and presenting the enhanced SlowFast network with improved slow branching to enhance the accuracy of video recognition for human labor actions. 2. Introducing the ACTION attention module, devising the enhanced SlowFast network with improved fast branching, and integrating the two attention modules to further enhance the accuracy of video recognition for human labor actions. 3. Collecting agricultural labor data from diverse perspectives in the field and producing the Field Work Behavior Dataset (FWBD). Through the aforementioned efforts, the enhanced SlowFast network, as proposed in this study, attains superior outcomes in agricultural behavior recognition, demonstrating enhanced precision in the video recognition of agricultural labor actions. The primary tasks of this paper are delineated as follows. Section 1 provides an overview of the current research landscape in the domain of human behavior recognition, encompassing existing methodologies and techniques, along with an elucidation of the research approaches and contributions of this paper. Section 2 reviews pertinent works related to the methodology employed in this paper, encompassing existing research and associated techniques. Section 3 meticulously provides a detailed exposition of the research methodology and data sources employed in this study, encompassing the creation of the dataset, model architecture, feature extraction methods, and other pertinent details. Section 4 undertakes experimental endeavors, encompassing trials with varied parameter configurations, alongside a comprehensive analysis and discussion of the experimental outcomes. Section 5 synthesizes the research findings of this paper and outlines prospects for future research.
## 2 Literature Review  
### Trajectory Data Used for Conflict Risk Analysis  
Conflict data can more comprehensively represent the safety status so that it gradually replaces historical crash data for traffic safety assessment. Compared to historical crash data, traffic conflict data have four advantages [13; 20; 21]. First, compared with crash data, conflict data can better evaluate traffic safety from a micro perspective. Second, the quality of vehicle trajectory data is typically better than that of historical crash data, because traffic conflict data may contain more details such as various vehicle-following patterns. Third, traffic conflict data are abundant and inexpensive to acquire in comparison to historical crash data. Finally, many crashes may be not reported due to a variety of non-random factors (such as the willingness to report the crashes to police), leading to selection bias in the reported crashes [22]. [PERSON] et al. [23] pointed out that the use of actual crash data may result in the overrepresentation of risky drivers in crash data, whereas conflict-based measures are much less likely to suffer such potential data problems.  
High-resolution trajectory data can be used to capture the movement characteristics of vehicles from a microscopic perspective that has been widely used for microscopic conflict-based analysis. For example, [PERSON] et al. [24] investigated the factors affecting the potential conflict risks during the lane change process and analyzed its unobserved heterogeneity based on the NGSIM trajectory dataset. Based on the HighD trajectory dataset, [PERSON] et al. [25] proposed a real-time evaluation method for vehicle conflicts at a lane level. [PERSON] et al. [26] evaluated the transition durations of rear-end collision risks based on real trajectory data. [PERSON] et al. [2] used time-varying mixed logit models to explore the traffic conflict risks in toll plaza diverging areas based on the trajectory data collected by UAV. Nevertheless, the contributing factors to vehicle conflict risk in complex road nodes have not been fully explored from the perspective of different vehicle-following patterns.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 4 Proposed Scheme
### Preamble Codeword Set Selection Algorithm
#### 4.1.4 The Proposed Preamble Codeword Set Selection Algorithm  
The BS performs the preamble codeword set selection algorithm as shown in Algorithm 1. The proposed preamble codeword set selection Algorithm 1 selects the optimal preamble codeword set that maximizes the RA success rate based on the number of active device estimations. In lines 3, 4, and 5 of Algorithm 1, the BS performs the preamble transmission success rate analysis, the preamble codeword utilization rate analysis, and the PUSCH timeout rate analysis for each candidate scale of the preamble codeword sets. In Figure 4, the preamble transmission success rate indicates the success rate up to the preamble transmission in phase 2. Preamble codeword utilization represents the proportion of actual transmission preamble codewords among the inferred preamble codewords. The PUSCH timeout rate indicates the proportion of the number of PUSCH allocations among the successfully transmitted preamble codewords in phase 4. Because all the aforementioned conditions should be satisfied for the entire RA to succeed, the RA success rate can be defined as a product of the three aforementioned ratios and is calculated in line 6. Finally, Algorithm 1 selects and returns the \(N_{fp}\) value that maximizes the RA success rate in lines 7, 8, and 9. The BS can calculate the selectable preamble in the RA cycle based on the last preamble index of the first RA slot and \(N_{fp}\) value. After the determination of the preamble sequences in the first RA slot, the BS broadcasts the selectable preamble sequences in the first RA slot through the SIB-2 message. Algorithm 1's computational complexity is \(O(N_{fp})\). Since the complexity of the proposed algorithm is low, it is possible for the BS to perform Algorithm 1 in each RA cycle. Algorithm 1 is performed in an online manner and the optimal preamble codeword set is selected based on the number of active device estimation values in every RA cycle. This means that the proposed CeRA-eSP technique can operate efficiently without complex computing operations and state information collection phases.  
```
0:\(\
u\)
0:\(N_{fp}\)
1:for\(i=2\) to \(N_{fp,max}\)do
2:\(\mathcal{R}_{max}\gets 0\)
3:\(\mathcal{S}=\frac{w^{\frac{w}{M\cdot i}}}{M\cdot i}\)
4:\(\mathcal{E}=\frac{M\cdot i\big{(}1-\big{(}1-\frac{1}{M}\big{)}^{v}\big{)}}{M \cdot i\big{(}1-\big{(}1-\frac{1}{M}\big{)}^{v}\big{)}\big{(}1-\big{(}1-\frac {1}{M}\big{)}^{v}\big{)}}\)
5:\(\mathcal{T}=\frac{N_{to}(t)}{N_{de}(t)}=\frac{max(0,max(0,N_{de}(t+W)+G(t+W)-N _{ST}-N_{to}(t))+N_{de}(t)-N_{ST}\cdot W)}{M\cdot N_{fp}\big{(}1-\big{(}1- \frac{1}{M}\big{)}^{v}\big{)}\Big{(}1-\Big{(}1-\frac{1}{N_{fp}}\Big{)}^{v} \Big{)}}\)
6:\(\mathcal{R}=\mathcal{S}\cdot\mathcal{E}\cdot\mathcal{T}\)
7:if(\(\mathcal{R}_{max}<\mathcal{R}\))then
8:\(\mathcal{R}_{max}\leftarrow\mathcal{R}\)
9:\(N_{fp}\gets i\)
10:endif
11:endfor
```  
**Algorithm 1** Preamble codeword set selection in the base station