esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Introduction to the Model Structure  
To address the issue that the feature extraction of the convolutional layer is disturbed by the sandstorm environment and the traditional pooling operation fails to meet the requirement of retaining key features, we made improvements based on the AlexNet model, specifically introducing BN layers after each convolutional layer. The BN layer helps to reduce the perturbation of input features, ensuring that before feature extraction by the convolutional layers, the image characteristics are already normalized [21]. This mitigates the impact of uneven illumination distribution, accelerates model convergence, and improves training stability. Additionally, we utilized max pooling operations to further enhance the model's resistance to interference, as max pooling retains the most prominent features when noise levels are high [22], thereby ensuring the effective extraction of key information from the license plates.  
Furthermore, considering the sensitivity of fully connected layers to noise, lighting, and rotation in complex environments, we retained only one fully connected layer in the model to reduce the number of parameters and avoid overfitting [23]. Ultimately, the license plate images are processed through five convolutional layers, each followed by a BN layer, and then further refined through pooling layers, before being integrated by the fully connected layer, and finally, character recognition is achieved via the SoftMax layer [24], and the model structure is shown in Figure 1.  
This improved AlexNetBN architecture effectively handles license plate recognition tasks under extreme conditions such as sandstorms, enhancing the model's robustness and training efficiency.
# Experimental and Theoretical Study of One-Way Cyclic Lateral Responses of Piles in Sloping Ground
## 2 Experimental Setup and Procedure  
### Similarity Criterion and Similarity Constant  
The engineering prototype of the model experiments is a bridge pile foundation at the First Si Xi River Bridge on the Zhangjiajie-Huayuan Highway in Hunan Province in China [6; 7]. The prototype pile (25 m in length and 2 m in diameter) is made of C25 concrete and steel reinforcement and installed in the sloping ground with a slope angle of 40\({}^{\circ}\) to 60\({}^{\circ}\).  
The similarity criterion of model experiments and similarity constants of pile and soil parameters are given as follows:  
The governing differential equations of pile deflection under inclined loading can be expressed as Equations (1) and (2) for the prototype and model pile, respectively. The subscripts 'p' and'm' of physicochemical parameters (i.e., pile flexural rigidity \(El\), pile deflection \(y\), depth \(z\), axial load \(P\), pile calculation width \(b\), proportional coefficient of subgrade reaction coefficient \(m\)) correspond to the prototype and model experiments, respectively.  
\[E_{\rm P}\,P_{\rm p}\frac{\rm d^{4}y_{\rm P}}{\rm d^{2}_{\rm P}}+P_{\rm p}\frac {\rm d^{2}y_{\rm P}}{\rm d^{2}_{\rm P}}+b_{\rm p}m_{\rm p}z_{\rm p}y_{\rm p}=0 \tag{1}\]\[E_{\rm m}I_{\rm m}\frac{\rm d^{4}y_{\rm m}}{\rm dz_{\rm m}^{4}}+P_{\rm m}\frac{\rm d ^{2}y_{\rm m}}{\rm dz_{\rm m}^{2}}+b_{\rm m}m_{\rm m}z_{\rm m}y_{\rm m}=0 \tag{2}\]  
Substituting the similarity constants of the aforementioned physicochemical parameters (i.e., \(\lambda_{b}=b_{\rm p}/b_{\rm m}\), \(\lambda_{z}=z_{\rm p}/z_{\rm m}\), \(\lambda_{EI}=E_{\rm p}I_{\rm p}/E_{\rm m}I_{\rm m}\), \(\lambda_{m}=m_{\rm p}/m_{\rm m}\), and \(\lambda_{P}=P_{\rm p}/P_{\rm m}\)) into Equation (1), Equation (1) can thus be rewritten as Equation (3).  
\[\frac{\lambda_{EI}\lambda_{y}}{\lambda_{z}^{4}}\cdot E_{\rm m}I_{\rm m}\frac{ \rm d^{4}y_{\rm m}}{\rm dz_{\rm m}^{4}}+\frac{\lambda_{P}\lambda_{y}}{\lambda_ {z}^{2}}\cdot\frac{\rm d^{2}y_{\rm m}}{\rm dz_{\rm m}^{2}}+\lambda_{b}\lambda_ {m}\lambda_{z}\lambda_{y}\cdot bmzy=0 \tag{3}\]  
The similarity criterion, Equation (4), can be obtained by comparing Equation (2) with Equation (3).  
\[\frac{\lambda_{EI}\lambda_{y}}{\lambda_{z}^{4}}=\frac{\lambda_{P}\lambda_{y}}{ \lambda_{z}^{2}}=\lambda_{b}\lambda_{m}\lambda_{z}\lambda_{y} \tag{4}\]  
By rearranging Equation (4), the similarity index \(\kappa_{1}\) and \(\kappa_{2}\) are given as Equation (5) and Equation (6), respectively.  
\[\kappa_{1}=\frac{\lambda_{b}\lambda_{m}\lambda_{z}^{5}}{\lambda_{EI}} \tag{5}\]  
\[\kappa_{2}=\frac{\lambda_{b}\lambda_{m}\lambda_{z}^{3}}{\lambda_{P}} \tag{6}\]  
Following the similarity criterion, the physicochemical parameters of the prototype and model experiments are summarized in Table 1, and the similarity index is given as \(\kappa_{1}=0.97\), proving that these parameters satisfy the similarity criterion.
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 3 Methodology
### CICFlowmeter Preprocessing  
CICFlowmeter preprocessing is another method for traffic metering and flow exportation. However, this tool lacks an integrated nDPI library function, leading to uninspected flow samples that may include a large number of flows generated by other software applications. To address this, it is necessary to use the Wireshark network analyzer tool. Wireshark's statistics conversations window displays all mutual traffic between two specific endpoints, including information from the link layer, IP layer, and transport layer. Typically, link layer traffic consists of Ethernet or IEEE 802.11 standard traffic [29], the IP layer covers both IPv4 and IPv6 traffic, and the transport layer includes TCP and UDP traffic.  
Since the MAC address of the link layer is uniquely determined before packet sniffing, the next step is to sort all conversations in the IP layer by the number of packets, from largest to smallest. Video traffic is generally characterized by a high packet count and large byte size, and web videos delivered via CDNs may involve multiple IP addresses. By comparing the start time and duration of each conversation in seconds, these features can help identify the most prominent one to three conversations.  
After selecting the most relevant one to three conversations from the Wireshark IP layer conversation window, these selected packets are then exported as a new PCAP file with the same video attribute category. Using CICFlowmeter, flow samples for each new video attribute category are exported, and a column is labeled with the corresponding name. All flow samples with zero-second durations, which have no practical meaning, are deleted, and the remaining flow samples for each video attribute category are saved. Finally, all video-category-attribute flow samples are merged into a single dataset, and this process is repeated for each of the three web video platform datasets, as outlined in the CICFlowmeter Video Quality Category (CICQC) Algorithm 3.  
```
1:Input: Each PCAP file from the video attribute category PCAP files
2:Output: TVQC, TVQC, FVQC dataset
3:for Each PCAP file in the video attribute category PCAP files do
4: Filter the most prominent one to three conversations in the Wireshark conversion module
5: Save filtered packets as a new same video attribute category PCAP file
6:endfor
7: Save all updated PCAP files
8:for Each PCAP file in the updated PCAP files do
9: CICFlowmeter exports flow samples from each PCAP file
10: Create a new column and label with the same video attribute category name for each flow sample
11: Save all flow samples as a dataset
12:endfor
13: Save three datasets according to video attribute categories
14:for Each dataset in datasets do
15:for Each flow sample in the dataset do
16:if Flow duration = 0 then
17: Delete the flow sample
18:endif
19:endfor
20: Save all remaining samples
21:endfor
22: Update all three datasets
23:Output TVQC, YVQC, FVQC dataset
```  
**Algorithm 3** Pseudo-code for CICFlowmeter video Quality Category dataset generation (CICQC)
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 3 Results
### Basic Functionality Test  
The goal of this basic functionality test was to make sure that the power at the transmitting end can propagate along a plastic/seawater interface and reach multiple loads at the receiving end. During this test, the experimental setup as proposed in Figure 2 was used throughout the experiment. The spark gap at the transmitting end was fed with a 100 kV pulse chain at 44 MHz. The water-filled plastic duct was heavily bent during the process of measurement. A seawater with a conductivity of 4 S was used as the ma terial for medium 2. At the receiving end, three modified live-wire detectors were used simultaneously to capture the transmitted power.  
The current at the 1 ohm resistor attached to the top of a randomly selected live-wire detectors was measured. This current represents the load current. This load current was found to be powerful enough to cause an electric shock to humans. It was also found that the load current was further increased by approximately 10-20% if the water-filled plastic duct was straightened.  
It was also observed that the success of a wireless power delivery was subject to fulfillment of the following conditions:  
1. The voltage of input feeding the spark gap was sufficiently high, typically above 20 kV.
2. The top of the monopole antenna in the transmitting end reached an altitude of at least 1 m above the experimental setup.
3. The top of each of the live-wire detectors was connected to a vertically mounted monopole antenna as stated in (2).  
In each of the live-wire detectors, the voltage across the 1 ohm resistor represents the current flowing through the live-wire detector circuit. The measured current waveform and frequency spectrum are respectively included in Figure 10a. According to the measured results, the waveform of the current at the transmitting end was tainted with periodical vertical spikes induced by the high-voltage sparks from the spark gap.  
Figure 10b shows the harmonic components detected using an endfire antenna pointing to the transmitting end. Using the peak search function of the spectrum analyzer, we managed to locate the maximum peak at around 44 MHz. The resonant frequencies of these harmonic peaks very close to 44 MHz dramatically moved from low to high periodically.  
For the purpose of comparison, the waveforms of the detected currents at the transmitting end and the receiving end were plotted together in Figure 3a. As Figure 3a shows, the peak-to-peak currents for the transmitting end and the receiving end were respectively 2.04 A and 1.62 A, corresponding to an estimated transmission efficiency of 79.42%.  
Figure 3b shows how multiple live-wire detectors were simultaneously lit at the receiving end. It should be noted that the top of each of these wire detectors was connected to vertically mounted monopole antenna.
## 6 Unbalanced PSI-CA Protocol Based on Single-Cloud Assistance
### Security Analysis  
In the design of this protocol, the primary security objective is to ensure that, even in a partially trusted cloud environment, neither the small health app developer's data nor the large medical institution's data can be accessed or inferred by unauthorized entities. Overall, this protocol, compared to the previous one, merely outsources the tasks, thus inheriting the security of the previous protocol. Specifically, since other participating parties are unaware of the large medical institution's private key \(\alpha\), they cannot deduce the data held by the large medical institution. Similarly, since other parties do not know the small health app developer's private random number \(r_{1}\), they cannot deduce the small health app developer's data.  
However, this scheme has inherent security risks, primarily because it does not withstand collusion attacks. If the large medical institution and the cloud server collude, they can jointly deduce the small health app developer's data. This is possible because the cloud server possesses the blinded data \(h_{j}\times r_{1}\), and if the large medical institution leaks the private key \(r_{1}\) to the cloud server, then both the cloud server and the large medical institution could deduce the small health app developer's original data \(h_{j}\). Collusion attacks are a security threat where two or more distinct entities (for example, users, systems, or service providers) secretly cooperate to undermine or circumvent security mechanisms and privacy measures. In cloud computing environments, cloud service providers and cloud users may collude to steal or infer other users' sensitive data stored on the cloud. In the medical scenario of this article, the intersection represents the patient's sensitive data, and leaking this information will cause very serious damage.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 4 Simulation Experiment
### Scenario 2
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **The Mean RMSE (Position/(m))** & **The Mean RMSE (Velocity/(m/s))** \\ \hline Improved AFKF & 5.9081 & 0.0938 \\  & 7.3566 & 0.1070 \\  & 5.6831 & 0.0334 \\  & 2.5781 & 0.0272 \\ Classic FKF & 3.3287 & 0.0299 \\  & 3.2131 & 0.0239 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mean RMSE of 50 simulation experiments. Figure 6: Comparison chart of the running time for 50 simulation experiments. speed of 0.26 m/s. The improved AFKF algorithm proposed in this paper was used to locate the movement of the unmanned vehicle, and the reference trajectory is shown in Figure 8.  
From Figure 9, it can be observed that in the simulated environment, the unmanned vehicle's GNSS measurement errors increased due to occlusion. Compared to the classic FKF algorithm, the improved AFKF algorithm reduced the position error by 35.1% and the velocity error by 61.0%. Figure 8: Reference trajectory. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Reference Value** & **Simulated Value** \\ \hline The maximum line speed & 0.26 m/s & 0.26 m/s \\ The maximum angular velocity & 1.82 rad/s & 1.0 rad/s \\ Linear acceleration & 0.3 m/s\({}^{2}\) & 0.3 m/s\({}^{2}\) \\ Angular acceleration & 2.5 rad/s\({}^{2}\) & 2.5 rad/s\({}^{2}\) \\ \hline \end{tabular}
\end{table} 
Table 5: The main model parameters in simulation. Figure 7: Simulation experiments on the Gazebo platform: (**a**) Unmanned vehicle model TurtleBot3-Waffle-Pi. (**b**) Environment for unmanned vehicle travel.
# Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments
## 3 Event-Driven Approach Based on an Ontology Model
### Event Representation
#### 3.2.3 Location Ontology  
The places where events can have occurred are related to the physical spaces for an ambient. They are necessary for the operation of the academic organization. Regarding the physical space context for events, we depicted a customized location ontology, which is shown in Figure 3.  
In our customized location of events, there are two location types as follows. Indoor spaces are heavily academic context based and bounded by physical divisions; for instance, cubicles, laboratories, classrooms, or auditoriums. Outdoor spaces are open places without boundaries. Both physical spaces can be located next to each other, which made us consider an object property (isBesideOf) for this purpose. Moreover, it is possible that physical space is part of another or is contained within it, originating the isLocatedIn object property.  
The Location ontology has 13 data properties to compose the location context for event modeling, reasoning, and enhancing. They help us to know in detail the attributes depicted in a physical space, such as hasAirConditioner, hasArea, hasCarCapacity, hasDoorState, hasFan, hasLampsNumber, hasLevel, hasNamePhysicalSpace, hasPeopleCapacity, hasProjectionScreen, hasProjector, hasService, and hasWindow.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 5 Opportunities and Cross-Cutting Themes
### Industry 4.0 and Predictive Maintenance  
The concept of Industry 4.0 emphasizes automation, data exchange, and interconnectivity in manufacturing systems. Factories increasingly employ sensors for condition-based monitoring, quality control, and predictive maintenance, aiming to predict machine failures before they occur [78]. Embedding AI into these sensors or edge gateways can enable real-time anomaly detection, reducing unplanned downtime and increasing overall equipment effectiveness [74]. Instead of collecting vast amounts of raw vibration or acoustic signals from each machine, local AI can parse relevant features, identify outliers, and send only crucial insights to a centralized platform, thus lowering bandwidth demands [21].  
In practice, these environments are often harsh, with vibration, dust, and potential interference from other machinery. Resource-constrained AI hardware must be ruggedized and shielded against electromagnetic disturbances [75]. Moreover, the diversity of industrial protocols and hardware calls for flexible solutions capable of interfacing with legacy systems while delivering advanced AI. Intermittent connectivity or strict real-time control loops amplify the challenges. As with other domains, solutions for Industry 4.0 are gravitating toward compressed and efficient models, specialized accelerators, and co-design strategies that integrate the entire data pipeline, from sensor to analytics, under a unified optimization framework [23].
# User Modelling Validation over the Security Awareness of Digital Natives
## 4 Analysis of Results and Discussion
### Use of Mobile Devices  
#### 4.1.1 Question 1  
We asked the security experts to identify how many out of ten digital natives across the three SCG groups restore the factory settings of their mobile device prior to selling or donating it. The results presented in Figure 1 show that the security experts underestimated the responses of the GSCG group (representative of the general digital natives population), with a difference of \(-3.27\) and only \(29.4\%\) of them approximating it correctly (\(8\pm 1\)). Additionally, the distribution of responses in respect to the GSCG group is not concentrated around a central value, with a skewness of \(0.0446\) and a median deviation equal to 2. However, the results in respect to the MSCG/HSCG groups are improved in comparison, with \(44.1\%\) and \(58.8\%\) of the experts approximating the results correctly, while the values of difference and skewness are also improved.
# An Intelligent Real-Time Object Detection System on Drones
## 3 Materials and Methods
### FPGA Design  
To perform real-time object detection using different NN models, we take advantage of an FPGA and design a reconfigurable accelerator architecture with it. The FPGA that we adopt is Kintex-7 410T, as shown in Figure 1. It contains 406,720 logic cells, 1540 DSP slices, 16 high-speed serial transceivers, 12.5 Gb/s transceiver aggregate bandwidth, 28 Mb block RAM, 500 I/O pins, and 800 Gb/s bandwidth. The Kintex-7 FPGA is ideal for applications including 3G and 4G wireless, flat panel displays, and video over IP solutions. We design an NN engine to accelerate NN models on it.  
The architecture of the NN engine on the FPGA is displayed in Figure 2. Data are first transferred to the FPGA. Then the NN engine assembles the data into the data stream, which are used for the following processing. Specifically, data streams are sent to multiple fetch and jump pointers. Note that we adopt a multi-threading strategy such that parallel computation can be achieved using multiple weights and buffers.  
A fetch and jump (F&J) mechanism is used to independently obtain required activation data for each thread from the data stream. Fetch pointers--which are programmed in accordance with the kernel structure--access the data stream in parallel, and fetch the activation data into specific L1 buffers. Together with a low-cost jump logic, a fetch pointer catches the required data individually. Then, we filter the activation data with sliding windows and use a dynamic thread-allocation module to mark thread buffer and thread status. At the same time, neural network weights are put into weight buffers. The activation data and weights are used to process image data using multiple processing elements (PEs) for NN acceleration, whose outputs are written to the out-of-order commit buffer. The out-of-order data are reconstructed by the in-order construction module and they are sent to the external data sink.
## 3 Methodology
### Data Processing
#### 3.2.5 Data Reconstruction  
In this study, the initial data consisted of two-dimensional tensors, where each tensor represented a frame of trajectory data for two vehicles involved in a merging scenario. Each  
Figure 5: Scenario visualization classification.  
frame included the x and y coordinates of both the MSV and the NRV. This structure is suitable for analysis but not directly compatible with the TimeGAN model, which requires a three-dimensional input tensor format.  
The three-dimensional tensor format needed by TimeGAN consists of samples, time steps, and features. Each sample represents an individual merging scenario, containing a sequence of frames, and each frame includes the coordinates of the two vehicles.  
To convert our two-dimensional data into the required three-dimensional format, we performed a data reconstruction process. This process involved splitting the original files into individual samples: since the data collection frequency was 25 Hz, the total time step of the scenario was composed of key frames, which included 50 frames from the first 2 s and 25 frames from the subsequent 1 s, summing up to 76 frames. Each frame featured four features, which included the coordinates of the two vehicles. As shown in Figure 6, data reorganization of the original 79,116*4 dataset was carried out and converted into three-dimensional tensors (samples, time step, and features) = (1041, 76, and 4).