esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring the Capillary Jet Breakage by Vibration Using a Fast-Video Camera
## 5 Droplet Diameter versus Jet Diameter  
The droplet diameter has been measured for three nozzle sizes and different frequencies while adapting the flow to remain in optimum breakage conditions (Figure 6A). The size shows an asymptotic decay with the frequency (Figure 6B). The theory, however, predicts that the frequency would not affect the droplet size.  
The deviation between the experimental data and Equation (9), based on the nozzle diameter, may be explained by the jet expansion at the nozzle exit (Equation (5)). Figure 7A shows that the droplets' size is mainly proportional to the jet diameter (Equation (5)).  
Data from Figure 7A was converted to an optimal wavelength (Equation (9)) and plotted in Figure 7B. The theoretical value of the optimal wavelength (Equation (1)) was also plotted. A good correlation between the experimental and the theoretical data is observed (correlation coefficient 98%).  
Figure 6: (**A**) Selected flow rate in function of the frequency to maintain optimum breakage. (**B**) Effect of the frequency on the droplet diameter for different nozzle sizes.
# Towards Explainable Pedestrian Behavior Prediction:
## 5 Experimental Results and Discussion
### Experiment 5--Incorporation of Diverse Datasets  
The primary objective of this experiment was to determine _whether the performance of the explainable predictor improves when training data from multiple datasets is combined_. To investigate this, three training configurations were designed, integrating data from both JAAD and PIE during the fuzzy rule mining process. These configurations are defined as follows:  
Figure 8: Features correlation.  
* **J8K-P8K**: Consists of 8000 records from each dataset.
* **J10K-P10K**: Includes 10,000 records from each dataset.
* **J14K-P80K**: Incorporates 14,000 records from JAAD and 80,000 records from PIE.  
These configurations were selected to analyze the impact of different dataset proportions on the model's ability to generalize across diverse pedestrian behavior scenarios.  
As shown in Table 7, combining datasets to generate the training input results in only a marginal improvement in performance during cross-testing. In the JAAD dataset there is not a clear improvement neither in the accuracy nor F1 score, while in the PIE dataset, there are improvements. This outcome suggests that the limited performance gain is likely due to the datasets containing similar scenarios, reducing the potential benefits of data fusion.
# A Low-Overhead Message Authentication and Secure Message Dissemination Scheme for VANETs
## 3 Proposed Approach
### Proposed Method
Technol._**2013**, _62_, 3339-3348. * (12) [PERSON]; [PERSON] An improved RSU-based authentication scheme for VANET. _J. Internet Technol._**2020**, _21_, 1137-1150. * (13) [PERSON]; [PERSON]; [PERSON]; [PERSON] RAISE: An efficient RSU-aided message authentication scheme in vehicular communication networks. In Proceedings of the IEEE International Conference on Communications (ICC), Beijing, China, 19-23 May 2008. * (14) [PERSON]; [PERSON]; [PERSON]; [PERSON] A cluster-based vehicular cloud architecture with learning-based resource management. _J. Supercomput._**2015**, _71_, 1401-1426. [CrossRef]
* (15) [PERSON]; [PERSON]; [PERSON]; [PERSON] Secure message communication protocol among vehicles in smart city. _IEEE Trans. Veh. Technol._**2018**, _67_, 4359-4373. [CrossRef]
* (16) [PERSON]; [PERSON]; [PERSON]; [PERSON] Vehicular cloud data collection for intelligent transportation systems. In Proceedings of the IEEE Smart Cloud Networks and Systems, Dubai, United Arab Emirates, 19-21 December 2016. * (17) [PERSON]; [PERSON]; [PERSON] EAAP: Efficient anonymous authentication with conditional privacy-preserving scheme for vehicular adhoc networks. _IEEE Trans. Intell. Transp. Syst._**2017**, _18_, 2467-2476. [CrossRef]
* (18) [PERSON]; [PERSON]; [PERSON] Efficient privacy-preserving dual authentication and key agreement scheme for secure V2V communications in an IoV paradigm. _IEEE Trans. Intell. Transp. Syst._**2017**, _18_, 2740-2749. [CrossRef]
* (19) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Privacy-preserving aggregation-authentication scheme for safety warning system in Fog-Cloud based VANET. _IEEE Trans. Inf. Forensics Secur._**2022**, _17_, 317-331. [CrossRef]* [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON] A scalable robust authentication protocol for secure vehicular communications. _IEEE Trans. Veh. Technol._**2010**, _59_, 1606-1617. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A secure and distributed architecture for vehicular cloud. _Internet Things_**2021**. [CrossRef]
* (22) 1609.2-2016; IEEE Standard for Wireless Access in Vehicular Environments-Security Services for Applications and Management Messages; IEEE. Piscataway, NJ, USA, 2016; pp. 1-240. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Enhancing misbehavior detection in 5 g vehicle-to-vehicle communications. _IEEE Trans. Veh. Technol._**2020**, _69_, 9417-9430. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] A privacy-preserving misbehavior detection system in vehicular communication networks. _IEEE Trans. Veh. Technol._**2021**, _70_, 6147-6158. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] Pseudonym changes scheme based on candidate-location-list in vehicular networks. In Proceedings of the IEEE International Conference on Communications (ICC), London, UK, 8-12 June 2015. * [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] SEMA: secure and efficient message authentication protocol for VANETs. _IEEE Syst. J._**2021**, _15_, 846-855. [CrossRef]
# Region-Based Approach for Machining Time Improvement in Robot Surface Finishing
## 3 Materials and Methods
### Optimal Machining Path Direction  
During the machining of the workpiece, the tooltip must follow a specific path embedded in the workpiece surface. Both position and orientation constrain this toolpath, assuming the tooltip must maintain a perpendicular orientation relative to the curved workpiece surface. In similar articles, the robot surface finishing trajectory is usually predetermined, like in [18], where only the workpiece position is optimized based on the existing toolpath. In our scenario, we take into account the robot-constrained velocity kinematics featured by (13), and simultaneously determine both the optimal robot direction and speed using the modified DTF method [19] to find the machining paths with the maximum feasible end-effector linear speed \(V_{\text{max}}\) (17) for the workpiece surface point under consideration.  
First, for each point \(P_{i}\), the searched two-dimensional Cartesian direction vector \(u_{2}=\left[\cos(\varphi),\sin(\varphi)\right]^{T}\) is mapped to obtain a three-dimensional Cartesian direction vector for machining on a workpiece surface tangent plane:  
\[u_{T}=\frac{S_{\text{v}}u_{2}}{\left\|S_{\text{v}}u_{2}\right\|} \tag{16}\]  
Then, \(u_{T}\) is used to determine the maximum surface-constrained feasible speed at which the robot surface finishing can occur at each point. The concept of the DTF method [19] is utilized here, which yields  
\[V_{\text{max}}=\frac{1}{\left\|\frac{\mu}{C}\cdot u_{T}\right\|}_{\infty} \tag{17}\]  
where \(J_{\text{C}}^{\#}\) is the constrained inverse Jacobian matrix defined in (13), and \(u_{T}\) is the direction vector (16).  
To determine the optimal machining direction and speed, the following optimization problem is set:  
\[V_{\text{max}}^{opt}=\underset{u_{2}}{\text{max}}\bigg{(}\underset{i=1,\dots,N }{\text{min}}\left(V_{\text{max}}(P_{i})\right)\bigg{)} \tag{18}\]  
where \(V_{\text{max}}^{opt}\) represents the optimal maximum feasible linear speed on the surface at the optimal direction \(u_{2}\), and \(N\) is the number of the surface points under consideration. Additionally, Equation (18) can be described as* Inner optimization procedure with the expression \(\min_{i=1,\ldots,N}(V_{\max}(P_{i}))\): This step identifies the maximum feasible speed across all points \(P_{i}\,,i=1,\ldots,N\) under consideration within a selected surface path. This speed represents the limiting factor for the machining process since the robot cannot exceed this speed in the specified direction.
* Outer maximization: After computing the maximum feasible speed for each direction, this step searches for the optimal direction \(u_{2}\) that maximizes the machining speed on the surface patch. Maximizing this value ensures that the robot can achieve the highest feasible speed across the workpiece points in the optimal direction.  
The optimal machining directions enable the forming of a velocity vector field (VVF) used for the path planning process.
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 6 Discussion  
Compared with land-based landing, the glide landing of CBA is extremely complex and difficult because of the limited dimensions of the carrier deck, the impacts of the marine environment and the characteristics of the inverse manoeuvring zone. Consequently, it is the stage with the highest accident rate in CBA operational missions. To enhance the safety of CBA landings, scholars have conducted extensive research and achieved remarkable results [1; 2; 3; 4; 45; 46]. However, there are some shortcomings in the related studies, primarily focused on the following three aspects.  
Firstly, current researchers commonly use linear small disturbance models to design automatic landing systems for CBA [14; 18; 47; 48]. In fact, there are significant disparities between linear model and the nonlinear model. Additionally, the landing environment is complex and highly variable, making it challenging to apply linear models effectively. Secondly, most studies were based on the hypothesis that CBA remains in their normal states without any faults [15; 17; 21; 22]. whereas in reality, they are susceptible to combat damage and actuator faults due to complex flight environments and large variations in dynamic pressure in operational missions, leading to serious degradation of system performance and posing direct threats to landing safety. Thirdly, there is currently limited literature on fault-tolerant landing control for CBA, and the existing literature generally were focused on passive FTC, which can reduce the sensitivity of the controlled system to faults by enhancing the robustness of the controller [27; 28]. Although neither fault diagnosis nor control reconstruction is needed, all types of possible faults must be known beforehand, which makes the controller relatively conservative and limited in its fault-tolerant function.  
To address the aforementioned issues, this study proposes an active FTC strategy based on AFSMFTC. The strategy involves using an adaptive fuzzy network to conduct fuzzy approximation for the sliding-mode switching terms, and it effectively weakens the impacts of unknown disturbances on the control system without estimating the upper limit of perturbation. Adaptive laws are used to counter the actuator efficiency loss fault, automatically adjust the controller parameters in the light of actuator faults and complete FTC under fault conditions, and a sliding-mode controller is used to ensure CBA tracking along the expected flight path. Finally, the effectiveness of the proposed method is validated through multiple sets of comparative simulation experiments.  
Currently, various advanced control theories such as fuzzy control and sliding-mode control have been gradually applied in the industrial field, playing a crucial role in driving technological development. In the future, with the improvement of the intelligent capabilities of naval forces worldwide, relevant carrier landing control strategies will also be widely promoted and applied.
## 2 Literature Review
A study conducted in Phnom Penh, Cambodia, found that the commuters perceive comfort and availability of public transport to be more important [31]. The implications of a study conducted in Calgary, Canada, were that increasing transit service reliability and convenience can significantly improve transit ridership [32; 33] investigated the valuation of time in public transport compared with car travel time, walk time, wait time, and headway time and recommended that frequent, more accessible, and faster bus service is imperative to reduce and compensate for the high value of time attached to British bus system. Another study by [34] suggested that necessity, time saving, and low-cost transport services can be used as a behavioral change strategy in modal shift from personalized vehicles to public transport in neighborhoods where most travelers are students. [35] argued that on-timeperformance and security are important latent variables for female travelers of public transport in Indonesia. In a study conducted in Dhaka city, [36] claimed that punctuality, reliability, and service features are an important dimension for the success of public transport in the developing countries. [37] reported that safety, comfort, capacity, responsiveness, reliability, and tangible are six important dimensions of concern for public transport users in Bali, Indonesia. [38] found waiting time at a stop, trip purpose, profession, income, and vehicle ownership to be significant predictors of users' attitudes and preferences with app-based DRT services in Lahore, Pakistan. A study conducted on 100 bus lines in Beijing referred that the lowest satisfactory scores are pertinent to timeliness, which might be influenced by travel purpose and travel time. A study conducted by [39] in Lahore, Pakistan, highlighted that symbolic information, time, and cost are important variables for Daewoo public bus service riders. In another study, conducted on acceptance of metro-bus service in Lahore, [40] proposed that reliability, friendliness, and instrumental dimensions of service quality played an important role for passenger's positive preference towards this particular public transport service. As it is evident from the literature mentioned above, user satisfaction surveys are an important and commonly used tool for evaluating passenger satisfaction levels regarding transport services. Reliability, frequency, comfort, on-time performance, travel speed, cleanliness, and service frequency are the key elements affecting passenger satisfaction levels. Most of the studies conducted so far discussed the performance of traditional bus and DRT services. As per authors' best knowledge, there are no past studies on user satisfaction about app-based DRT in Pakistan. The current study addresses this gap in the body of literature.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 6 Conclusions  
In this work, an analytical solution for multiple ground target point acquisitions has been presented. In general, the attitude generator planned (\(AGP\)) must generate the guidance that first brings the satellite to the right position and then leads it in the right pointing path. Hence, the proposed technique consists of two different parts: the attitude target pointing generator (\(ATPG\)) and the attitude point-to-point generator (\(APPG\)). The first one finds a geometrical solution to generate the pointing trajectory in order to acquire the ground targets, while the second phase generates the target-tracking trajectory during the acquisition time interval. An analytical solution is found by approximating the angular velocity and acceleration with polynomials. This strategy gives the possibility to satisfy both boundary conditions and physical constraints. The results reported in this work demonstrate the effectiveness of the proposed approach, as the provided solution can rapidly point the satellite to successive targets and track them for the time needed for the acquisition. It is worth noting that all the constraints and boundary conditions are satisfied as they are directly imposed into the solving equation. Finally, the algorithm is suitable for possible future implementation in real on-board flight hardware given the fact that a closed-form solution with simple analytical calculations has been attained.  
Conceptualization: A.C.; methodology: A.C., D.S. and F.C.; software: A.C.; validation: A.C.; formal analysis: A.C.; investigation: A.C.; resources: A.C. and F.C.; writing--original draft preparation: A.C.; writing--review and editing: A.C. and F.C.; visualization: A.C.; supervision: A.C. and F.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### Message Format
#### 3.4.2 Message Header  
The second row of the Figure 2 is the header of each one of the protocol message:  
1. _Message Type_: It has 1 byte of size.
2. _S (Security)_: Reserved for security implementation (1 bit).
3. _Reserved_: Reserved for functionality future extensions (7 bits).
4. _Message Size_: It consists of 2 bytes.  
Next the format of each kind of message include in the MESSAGE field is shown.  
Figure 3 shows the SERVER_DISCOVERY message format.  
Figure 3: SERVER_DISCOVERY message format.  
Figure 2: Packet from the D2 HCP protocol.  
_ID_: Node Identification (6 bytes).The node has to choose the MAC address from one of its interfaces, if it has more than one. This identity field has the same value for every SERVER_DISCOVERY and SERVER_POLL message emitted by the node, although it was doing from different interfaces.  
1. _NIPs_: Amount of IP addresses solicited by the node. It will be equal to the interface number of client node (1 byte).
2. _Count_: Number of times that the SERVER_DISCOVERY petition has been tried (1 byte).
* _SERVER_OFFER_  
Figure 4 shows the SERVER_OFFER message format.  
1. _Range_: Number of IP addresses offered (30 bits).
2. _Ready_ (R): It indicates whether the node that offers the IP addresses is ready to assign them or only communicates their existence but at this point cannot assign them (1 bit).
3. _Local_ (L): It indicates whether the range offered is from the sending node, or else be asked to turn to a third node (1 bit).
* _SERVER_POLL_  
Figure 5 shows the SERVER_POLL message format.  
1. _ID_: Node identification (6 bytes). It is the same identification as that elected in the SERVER_DISCOVERY message.
2. _Reserved_: Reserved for future implementations (2 bytes).
* _IP_ASSIGNED_  
Figure 6 shows the IP_ASSIGNED message format.  
Figure 4: SERVER_OFFER message format.  
Figure 5: SERVER_POLL message format.  
* _First IP_: IP address start of a free address block (4 bytes).
* _Last IP_: IP address end of free address block (4 bytes).
* _Network Mask_: It consists of 4 bytes.
* _IP Node n, First IP Node n, Last IP Node n_: This represents an entry on the free block table for all the nodes in the network. Each node is represented by these fields of 4 bytes, each one being: the node IP address, the initial IP address and the final from its free address block, respectively.
* _IP RANGE REQUEST_  
Figure 7 shows the IP RANGE REQUEST message format.  
1. _Server IP_: Server address that requests the range for client (4 bytes). It can be different from the IP address of the sender message, by treating multi-hop networks.
2. _Client ID_: Client node identification. It has the same value as the ID field of the SERVER DISCOVERY and SERVER_POLL message (6 bytes).
3. _NIPs_: Requested IP Address Number (2 bytes).  
Figure 6: IP_ASSIGNED message format.  
Figure 7: IP_RANGE_REQUEST message format.  
* _IP_RANGE_RETURN_  
Figure 8 shows the IP_RANGE_RETURN message format.
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 3 Related Works
### Watermarking Applied to Data
#### 3.1.1 Without ML Methods  
The work exposed in [12] proposed a watermarking algorithm for colored images, combining the discrete wavelet transform (DWT), the discrete cosine transform (DCT), singular value decomposition (SVD), and scrambling. More investigations into watermarking, based on DWT and SVD, using colored images were discussed in [13; 14]. Ref. [13] proposed a semi-blind image watermark scheme that used the finite ridgelet transform (FRT), particle swarm optimization (PSO), and the Arnold transform, in addition to the conversion of RGB images into YCbCr color space [13]. Ref. [14] presented a robust image watermarking approach, considering the image as a third-order tensor and transforming it using tensor-SVD [14]. In medical imaging, SVD was applied combined with the fast curvelet transform (FCT), achieving WMd medical data [15]. Further, SVD has been employed for watermarking authentication and self-recovery in [16], and images were divided into four groups and fed into the SVD algorithm, with the results replacing the least significant bit (LSB) of each block. Another choice introduced was a watermarking scheme based on a block mapping algorithm and dual matrix, generating authentication and recovery data from the original image [17].  
Ref. [18] employed watermarking systems for colored images based on the fusion of spatial domain and LU factorization. LU factorization is a method used to decompose a matrix as the product of a lower triangular matrix and an upper triangular matrix [19]. The watermark was embedded by altering elements that exhibited strong correlations with the L matrix (lower diagonal matrix). Another application of the watermarking technique utilizing LSB substitution was by [20], and a blind fragile watermark was applied for colored images to detect manipulations and enable self-recovery; this method employed a pseudo-random binary sequence based on a secret key as the watermark, with recovery information stored accordingly.