esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Machine Learning of MobileNet, Efficient, and Inception Models
## 4 Results--Verification of Tree CNN Models
### Three Models: MobileNet, EfficientNetB0, and InceptionV3  
Third Training Database--Database with Different Objects  
Previous results have shown that MobileNet models have a high accuracy of 98% and even up to 100% under certain favourable conditions. Therefore, due to the scope of the tasks to be performed, we decided to select 2 more models, out of 38 of them, proposed in the Keras library [33]. In addition to MobileNet, these were the EfficientNetB0 and InceptionV3 models based on the TensorFlow and Keras library functions, such as Convolution2D, Batch Normalization, ReLU, and MaxPooling2D. The choice of models depended on the hardware parameters of the PC and virtual disk. The PC parameters were Intel(R) Core(TM) i5-9400F CPU @2.90 GHz \(\times\)64 processor; 16.0 GB installed RAM; and 64-bit Operating System. Virtual disk limitations: 10 GB RAM and 4 of 6 CPU cores used. We decided to verify the third database, built from 100 images, on 3 models across 10 epochs, as see in Table 4:  
* MobileNet;
* EfficieNetB0;
* InceptionV3.  
The graph in Figure 6 shows the accuracies of the three models carried out for learning on the third dataset, namely the images of the electrical elements mixed with other objects.  
Due to the interesting results obtained for the InceptionV3 model, the learning times for all three models were compared, as see in Figure 7.  
It can be seen that InceptionV3 had the best accuracy of the three models, but this was at the cost of the longest learning time. While both MobileNet and EfficientNetB0 showed comparable efficiency, the former achieved this in a significantly shorter learning time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Epoch**} & **Accuracy of** & **Accuracy of** & **Accuracy of** \\  & **MobileNet Model** & **EfficientNetB0 Model** & **InceptionV3 Model** \\ \hline
1 & 0.4916 & 0.5166 & 0.4791 \\ \hline
2 & 0.5041 & 0.5083 & 0.5833 \\ \hline
3 & 0.5041 & 0.4791 & 0.5166 \\ \hline
4 & 0.5000 & 0.4541 & 0.5875 \\ \hline
5 & 0.5333 & 0.5083 & 0.6083 \\ \hline
6 & 0.4500 & 0.5083 & 0.7000 \\ \hline
## 4 Deconvolution
### Maximum Correlated Kurtosis Deconvolution
#### 4.2.1 Definition  
MED has the limitation of only deconvolving a single pulse or partial pulse, so [PERSON] et al. Maximum correlated kurtosis deconvolution (MCKD) is proposed, which considers the periodicity of the fault and aims to extract periodic pulse faults from machine vibration signals. It uses the correlation kurtosis as the objective function of deconvolution, and it maximizes the correlation kurtosis value by finding the optimal filter parameters. By deconvolving the continuous pulse buried in noise, the correlation kurtosis value of the original signal is enhanced, making it suitable for extracting weak fault signals from continuous transient shocks.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
###### Abstract  
A 3D finite element model (FEM) for predicting the distribution of lateral pressure in a square flexible walled steel silo during the filling phase was analyzed in this study. The numerical approach, developed using Abaqus software, predicts the stress state in bulk solids, as well as the pressures exerted on the silo walls. An elasto-plastic model using the Drucker-Prager criterion was employed to simulate the behavior of the granular materials. The FEM simulates the behavior of the bulk solid and its interaction with the silo's wall and base using a surface-to-surface discretization model. The model's predictions were validated by previous experimental measurements. The results revealed good agreement between the FEM predictions and the experimental measurements. The research confirms that the lateral pressure distribution is not uniform at any silo level. This highlights the fact that many available theories and current design codes are not accurate for flexible steel walls. As a result of the wall's deformability, pressure regimes on the silo wall change significantly in the horizontal direction at any level. The results showed that the horizontal variations of lateral pressure change drastically with regard to wall stiffness. The FEM has been used to investigate the effect of critical parameters on wall pressure distribution, such as properties of bulk solids, wall thickness, and silo type, whether deep or flat.  
finite element modeling; three-dimensional steel silos; square silos; silo filling; wall pressure; granular material; wall deformability; wall stiffness +
Footnote †: journal: Applied
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 3 Proposed Framework and Methodology
### Proposed UAV IoT Architecture  
As shown in Figure 4, the introduced architecture supports multiple solutions for security, safety, and privacy for mobile IoT toward the provision of services to different users through a set of distributed systems. In this way, end users can deploy and query their \"flying\" things, such as drones, in a secure and safe ecosystem respecting existing privacy regulations. In the proposed architecture, we have three main entities: the control center, the distributed systems, and the embedded solutions on the UAVs. The system is based on a drone-to-drone communication utilizing rooting algorithms, while communication with the control center takes place through the Internet. The Control Center (CC) is responsible for the management and orchestration of the proposed ecosystem. For that purpose, the CC shown in Figure 4 is responsible for end-to-end communication with the \"things\". It integrates all traditional IoT management elements and novel functional blocks to realize searching, information retrieval, and group instruction administration. Distributed Systems (DSs) are focused mainly on three areas: monitoring, management, and reputation. In more detail, the monitoring system is responsible for the registration processes and information authentication. In the case of the reputation system, we consider ranking functionalities for all drones, whereas the last component provides fleet-management solutions. The third entity represents all components integrated in the \"flying\" things.  
In the proposed architecture, we focused mainly on the security and privacy components supported by vision-based systems. One component targets on security, protecting the drone from attackers aiming to hijack it and take control using network and wireless channel exploitation techniques. A second component is related to the privacy of the payload that incorporates advanced encryption and anonymity solutions. A third computer-vision component is included that provides support and solutions both for security and privacy. This component introduces mechanisms for destination and path verification for security, as well as behavior analysis and scene understanding for privacy and safety, respectively. Finally, components for monitoring and drone-to-dronecommunication supporting registration and routing algorithms are part of the embedded system on the mobile IoT devices.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 1 Introduction
This points towards the presented research, which is to incorporate these technologies to the assembling systems, i.e., in the supervision of the assembling systems. The paper aims at contributing to fill in a gap in literature by introducing the necessary methodology and elements, which will result in a successful supervision of the assembling processes, by using wireless technology. Therefore, the main objective of the paper is to develop an overall customized supervision model that fits the requirements of the assembling systems, and to incorporate wireless sensor network (WSN) technology for assembling process management, i.e., for solving localization problems during the assembling process. The main objective points towards the definition of several sub-objectives:  
* Define the necessary technology and equipment for the supervision process;
* Define the usability, i.e., the limitations of wireless technology, by taking into account the requirements for controlling the assembly processes;
* Define the supervision algorithms;
* Adapt the general supervision model structure to be able to respond adequately to the specific requirements of managing the process of assembling by using wireless sensor network (WSN) technology;
* Define the characteristics of the identified elements of the adapted general control system;
* Establish the connection between the system elements. From a methodological standpoint, the proposed solution is based on knowledge gained from the literature that deals with supervising the assembling process, the literature that deals with implementing WSN technology, the analysis of existing solutions, and the authors' experience in incorporating WSN technology in production processes [22].
## 2 Materials and Methods
### Equipment of Experimental Research
Since the system was designed to be used in lightly obstructed (no more than one solid concrete wall) and medium distance open (straight corridor up to 100 m in length) propagation environments, connection quality's effect on synchronization quality was not researched. A disadvantage of the whole system being synchronized is that the data-acquisition speed of slave boards is limited to the speed of the master board, whereas the master's speed is limited to the speed of the system to which data are getting exported (in this case, the allowed amount of incoming data of the cloud storage). This drawback could be circumvented by separating the data-acquisition and data-export actions into distinct subroutines and temporarily storing the collected data inside the master board. An alternative method of synchronization (Figure 4) involved slave and master boards working asynchronously. The synchronization of slave boards would be ensured, but not the synchronization of the master board with slave boards. The synchronization message is sent by the master board once at the beginning of the data-logging session instead of at the beginning of each data-acquisition cycle. The synchronization message commands both slave boards to reset their internal timers, the values of which are then used to generate timestamps for the collected data. Figure 3: Developed local network operation in the data-acquisition process diagram. In this synchronization method, slave boards constantly collect and send data independently from the master board. The master board's duties are reduced to just processing the incoming data. However, the data processing now also involves the timestamp-based pairing of incoming data, unlike in the proposed synchronization method, where data were paired based on messages arriving during the same data-acquisition cycle. The increased workload of the master does not outweigh the advantage of having slave and master boards operate asynchronously, allowing a more rapid data collection. A comparison of the proposed and the alternative methods of synchronization is provided in Table 2.
## 5 Results
### Comparing Computation Effort
Therefore, it can be concluded that either too many randomized solutions \((ICR<0.4)\) or too many copied solutions \((ICR>0.6)\) do not sufficiently represent the correlation between deterministic and stochastic solution and that, therefore, for a diverse initial population, an equal ratio of copied and randomized solutions is most promising. The relative saving \(Best_{rel}\) between the copied population (\(ICR=0.2,0.4,0.6,0.8\) and \(1.0\)) compared to the randomized population \((ICR=0.0)\) is used to evaluate the performance of the initial copy rate \(ICR\). A positive value \(Best_{rel}\) means a saving; a negative value \(Best_{rel}\) is a loss. \(Best_{rel}\%\) is calculated by the following:  
\[Best_{rel}(\%)=\left(1-\frac{Best(ICR=0.2;\ 0.4;\ 0.6;\ 0.8;\ 1.0)}{Best(ICR=0.0)} \right)\cdot 100 \tag{12}\]  
Figure 6: Example of how the initial copy rate works; worst value: \(Best=1.00\). With increasing initial copy rate \(ICR\), the range of the relative saving potential \(Best_{rel}\) increases (Table 2). From this, it can be concluded that, with initial copy rate \(ICR\), the saving potential \(Best_{rel}\) fluctuates significantly more and can be predicted less reliably. Thus, the initial copy rate \(ICR\) should be selected in such a way that the savings are maximized on average \(\bar{x}(\%)\) with a high probability \(ki_{0.95}(\%)\). When using the initial copy rate of \(ICR=0.2\)-1.0, better results \(Best_{rel}\) are obtained for 58.3% (\(ICR=1.0\)) to 69.7% (\(ICR=0.4\)) of the considered individual experiments than with an initial copy rate of \(ICR=0.0\). If the best initial copy rate \(ICR_{Best}\) was always selected, the results \(Best_{rel}\) will be better in 79.17% of the individual experiments compared to randomized solutions (\(ICR=1.0\)). In contrast, the results \(Best_{rel}\) will be worse in 55.2% of the individual experiments if the worst initial copy rate \(ICR_{Worst}\) was selected. The average \(\bar{x}\) best saving \(Best_{rel}\) is achieved with an initial copy rate \(ICR=0.6\) (\(\bar{x}=0.42\%\)), and the median \(\widetilde{x}\) best saving \(Best_{rel}\) is achieved with an initial copy rate \(ICR=0.4\) (\(\widetilde{x}=0.46\%\)). These values should not be regarded as too low, since, as will be examined in Section 5.4, the overall optimization potential is approximately 4%. With both \(ICR=0.4\) and \(ICR=0.6\), the confidence interval is \(ki_{0.95}=\pm 0.49\%\). Thus, there is no clear recommendation for the selection of an initial copy rate \(ICR\), but \(ICR=0.4\) or \(ICR=0.6\) should be preferred. The following experiments uses \(ICR=0.6\).
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 2 Data and ANN Model
### Data
Using AOD data from 1 January 2010, we studied the variation of relative error with R and an upper limit of the radius was set up as 500 km. Note that the AOD used to find R was from a MODIS level 2 product, which has a spatial resolution of \(10\times 10\) km. Note that MODIS level 2 products have a higher spatial resolution than level 3 products, and that can help to better study the effect of interpolation changing with R. The flowchart to find the best R is given (Figure 2), and the code for this is shown in the supplementary documents.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 5 Discussion
However, the classification accuracy wasnot uniform among gluten-free flours, with chickpea, amaranth, maize, teff, and lentil flours showing the lowest accuracy rates. Several factors may explain the low performance of these flours. First, these flour types may not have been well represented during the training phase, limiting the model's generalization ability. Second, certain flours, such as maize flour, may share visual similarities with gluten-containing bread, especially when milled at smaller particle sizes, which enhance dough viscoelastic properties and potentially blur distinctions [57]. Finally, a notable number of misclassified images were of poor quality, both gluten-free and gluten-containing images, including low-resolution images that obscured key details necessary for accurate classification. The findings achieved by RGB have practical implications for both individuals with celiac disease and the food industry. For individuals with celiac disease, integrating this method into mobile applications could provide real-time gluten detection, offering a non-invasive and cost-effective alternative to traditional testing methods. This could improve confidence when dining out or purchasing food products. For the food industry, AI-driven gluten detection could enhance quality control and certification processes, helping manufacturers to ensure gluten-free compliance and reduce cross-contamination risks. Automating gluten detection could also streamline ingredient verification and improve labeling accuracy, benefiting consumers who rely on precise gluten-free information.
## 3 DTN Solutions for IoT
### Literature Review
#### 3.3.1 BP-Based  
These solutions target DTN architecture and BP implementations. In [22], the authors proposed an implementation of the BP for WSNs. They developed a BP convergence layer for IEEE 802.15.4-based networks as a replacement for layers 3 and 4 of the IP stack. For evaluation, they used an elevator as a data mule to transfer measurements from the roof of a 15-storey building to a computer on the third floor. The results show that the overhead is reduced to a minimum and the applicability of BP even for low-power 8-bit WSN nodes. The authors of [21] proposed direct opportunistic interaction between smart objects and mobile users through features of Bluetooth Low Energy (BLE), enabling objects to define their interaction models and interface, without need for Internet connectivity and preinstalled user interfaces. The authors of [19] implemented BP binding in place of UDP as an underlying transport for Constrained Application Protocol (CoAP) to enable Delay Tolerant IoT. They chose CoAP as a solution to limited capacity and energy constraints of small objects and BP as a solution to intermittent and delayed connectivity. Besides, we can find many BP implementations in the literature such as IBR-DTN [23], \(\mu\)DTN [24] and nanoDTN [25]. nanoDTN is a lightweight BP implementation based on \(\mu\)DTN and compliant with hardware constrained IoT devices. \"IoT-cum-DTN\" is a framework proposed in [32] for communication between IoT and DTN, in which both typical publish/subscribe semantics of IoT communications and DTN-based communications are preserved; however, this work still only focuses on improving delivery performance and does not consider resource constraints. To evolve data collection in IoT, the authors of [33] proposed an architecture relying on DTN approach and MQTT messaging protocol. They analyzed MQTT-SN (MQTT for Sensor Networks) behavior, combined with the IBR-DTN implementation of DTN. The performance evaluation showed good results in terms of delivery in a disrupted environment but it is limited in terms of network scalability.
## 7 Conclusions  
In this paper, we investigated on-demand access and roaming authentication protocols in a multi-operator heterogeneous scenario in a satellite-ground integrated network. We have proposed a scheme that includes anonymous unlinkable and batch authentication protocols, as well as fast roaming authentication. Specifically, users with higher privacy requirements are suitable for the unlinkable authentication scenario, where the scheme can provide anonymity and unlinkability; a large number of users with higher efficiency requirements are suitable for the batch authentication scenario, where the scheme provides traceable anonymity. In addition, for users who need to switch between multi-operator networks, this scheme provides a cross-domain fast roaming authentication solution. The proposed protocol delegates the authentication task to the satellite, which significantly reduces the transmission delay in the SGIN. We performed a formal analysis using ProVerif and an informal analysis to prove the security of the scheme. In addition, we evaluated the performance of the scheme and the results showed that our scheme is effective.  
To enhance our research, we intend to study short group signature algorithms in more depth in our future work and propose more secure and more efficient protocols for unlinkability authentication scenarios based on zero-knowledge proofs. In addition, the application scenario of 6G will be more complex than 5G, and it is a challenge to cope with the fair billing of multiple operators and balance their interests. Especially, the introduction of eSIMs brings novel security risks and conflicts of interest. Thus, in the future, we will further consider the interests of multi-operator scenarios and try to find a suitable solution to authentication.  
Conceptualization, Y.T. and J.X.; methodology, Y.T. and J.X.; software, Y.T.; validation, B.C.; formal analysis, Y.T.; investigation, Y.T.; resources, B.C.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, J.X. and B.C.; visualization, Y.T.; supervision, B.C., L.S. and H.D.; project administration, B.C. and H.D.; funding acquisition, L.S. and H.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center.  
Not applicable.  
The authors declare no conflict of interest.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 2 Related Work
### Precomputed Approaches  
In these approaches, closed mathematical models (geometric and/or kinematic) are used to compute, in advance and for each limb, a sequential set of joint configurations that, when performed, will propel the robot body forward during the support phase, while during the swing phase, the limb tip describes a given parametric trajectory to the next support point, using most commonly sine, Bezier, and triangular trajectories [6; 14; 15; 16; 17]. During locomotion, these trajectories are repeated in a logical sequence; in some cases, the trajectory during the swing phase can be adapted to increase or decrease the horizontal travel distance and/or clearance of the gait (see Figure 1b). Furthermore, depending on the chosen mathematical model, actions such as jumping, main body orientation and clearance control may be considered [7]. Another option to a mathematical model are Probabilistic Graphical Models (PGMs) that can be trained and sampled to infer a walking gait [18].  
A different paradigm consists of a footstep planning before the actual locomotion. For example, the robot ATHLETE, designed at the Jet Propulsion Laboratory (Pasadena, CA, USA), computes in advance the most useful support points across the terrain before performing any movement [19]. This planning implies that the robot must be able to accurately build a model of its surroundings, using exteroceptive sensors such as rangefinders, increasing the complexity and the total computational cost required for the robot's locomotion. In contrast, other approaches embrace the uncertainty of the terrain, not focusing on planning the position of all support points beforehand, and instead propelling low-dexterity hexapods with a fixed gait and focusing their efforts on correct state estimation under high-uncertainty circumstances [12].  
These approaches allow us to obtain a precise estimation of energy consumption during the locomotion through, for example, a two-layer hierarchical cooperative control scheme [20]. A top-level controller determines the forces and torques that every limb should exert on the body of the robot, so that the robot can follow a given trajectory, while low-level controllers independently command every leg of the robot to exert such forces. Because energy consumption may vary depending on the type of surface the robot is walking on, the travel speed can be adapted by performing slow, medium, or fast gaits, depending on the energy consumed by the actuators driving every limb [15]. Another approach to hierarchical control is to use a top-level exteroceptive methodology to observe and evaluate the terrain and command a low-level routine to switch among precomputed gait patterns, with the objective of maximizing the stability of the robot when traversing uneven terrain [21].