esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Out-of-Plane Test
#### 3.1.3 Comparison of Quasi-Static and Dynamic Behavior  
To avoid uncertainties related to size effects [17], the results of specimens of the same size under quasi-static and dynamic tests are compared in Figure 17. With regard to the evolution features, the dynamic stress-strain curves were different from the quasi-static stress-strain curve. During quasi-static loading, the stress increased as the strain increased, and finally the specimen failed at the fracture point. On the other hand, during dynamic loading, there were two different features after the peak point. One feature is that, when the strain rate was 2153 or 2452 s\({}^{-1}\), there was a spring-back and the slope of the spring-back curve was similar. Another feature is that higher strain rates led to the specimens being damaged completely with the spring-back phenomenon disappearing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**D-ls/L-W-ls** & **Area** & **Volume** & **Modulus** & **Mean** & **Absolute Difference** & **Relative Difference** \\  & **A** & **V** & **E** & **M** & \(|\mathbf{E-M}|\) & \(|\mathbf{E-M}|\)/[M] \\
**mm** & **(mm2)** & **(mm3)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(\%)** \\ \hline \(\Phi 10\times 5\) & 78.5398 & 392.6991 & 5237.9036 & & 251.8655 & 5.0514 \\ \(\Phi 10\times 4\) & 78.5398 & 314.1593 & 5362.2738 & & 376.2358 & 7.5458 \\ \(\Phi 10\times 3\) & 78.5398 & 235.6195 & 4641.9097 & 4986.0380 \(\pm\) & 344.1284 & 6.9018 \\ \(\Phi 9\times 4\) & 63.6172 & 254.4690 & 4866.7752 & 283.5900 & 119.2628 & 2.3919 \\ \(\Phi 8\times 4\) & 50.2655 & 201.0619 & 5063.2357 & & 77.1977 & 1.5483 \\ \(8\times 8\times 4\) & 64.0000 & 256.0000 & 4744.1302 & & 241.9078 & 4.8517 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The modulus of specimens of different sizes.  
Figure 16: (**a**) The fitted modulus of the \(\Phi 9\times 4\) specimens; (**b**) the modulus of each size of specimen.  
With regard to the damaged features, the dynamic features were different from the quasi-static features. It can be seen from Figure 17 that the stress-strain curve of the quasi-static test dropped like a cliff, while the stress-strain curve of the dynamic test dropped relatively slowly. For the quasi-static test, this was possibly because the loading speed was slow, and the duration from the fracture moment to the specimen becoming compact again was long. In this duration, the specimen could not hold any loading; thus, the stress was nearly 0. In contrast, the loading speed of the dynamic test was fast, and the duration from the fracture moment to the specimen becoming compact again was short. Thus, the specimen could hold loading to some extent during the fracture process. Therefore, the dynamic stress-strain curve dropped relatively slowly. Furthermore, as seen in a previous study [36], a second loading curve was also observed in the damaged test, which can be seen in Figure 11 for the \(\Phi 9\times 4\) specimens.
## 6 Conclusions  
This study conducted cyclic loading-unloading experiments on coal under quasi-static and dynamic loading conditions and analyzed the mechanical property disparities under the two strain rate regimes. The failure processes and modes were investigated using AE localization and crack characterization methods. The findings provide theoretical foundations for hazard prevention in underground engineering. The main conclusions can be summarized as follows:  
1. The stress-strain curves of coal under quasi-static loading can be divided into four stages: compaction, linear elasticity, nonlinear crack propagation, and post-peak softening. In contrast, dynamic loading induces bifurcation of the post-peak phase into first and second unloading stages due to rebound effects.
2. Under quasi-static loading, peak strain increases linearly with cycle number N, while dynamic loading exhibits nonlinear strain growth. Post-peak stress drops abruptly after quasi-static failure but declines gradually under dynamic loading due to rapid energy dissipation.
3. The AE counts during quasi-static loading transition from gradual to abrupt increases, corresponding to oblique shear failure. Under dynamic loading, lateral tensile cracks initiate first, followed by multiple axial compressive fractures.
4. The crack closure stress \(\sigma_{\text{cc}}\) under quasi-static loading initially decreases and then increases with N, whereas it follows an inverse trend (increase then decrease) under dynamic loading. Crack damage stress \(\sigma_{\text{cd}}\) generally increases with N for both regimes. However, dynamic loading produces a slight \(\sigma_{\text{cd}}\) reduction in the final cycle due to severe cumulative damage prior to failure.  
L.H.: writing--review and editing; F.G.: methodology; Y.X.: investigation and data curation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Nos. 2022 YFC3004602 and 2022 YFC3004600) and National Natural Science Foundation of China (Nos. 51934007, 42030810, 52004268, and 52174091).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM
## 2 Relevant Network Models  
### YOLOv4 Algorithm  
YOLO is the first target detection system based on a single neural network proposed in 2015. YOLOv1 is fast in its detection but not accurate enough in localization and has a low recall rate. YOLOv2 often improves on YOLOv1, increasing both accuracy and speed. YOLOv3 uses the residual model and FPN architecture to deepen the network structure and realize multi-scale detection. YOLOv4 uses many optimization strategies based on the original YOLO target detection architecture, with different degrees of optimization in data processing, backbone, network training, activation function, and loss function. Yolov4 and yolov5 are worth mentioning. Besides the improvement of the backbone, they also made innovations to data enhancement, such as the introduction of mosaic, mixup, and other methods. YOLOX uses YOLOv3 as the basic network to improve, adding three Decoupled heads to the output layer and converting the anchor-based detector into the anchor-free mode. Recently, the follow-up work of the YOLOv4 team YOLOv7 was put forward. It is superior to most existing target detectors in speed and accuracy by introducing dynamic label allocation and the re-parameterization of the model structure. We finally chose the YOLOv4 algorithm as the basis of improvement.  
The YOLOv4 algorithm is a single target detection algorithm that does not generate candidate regions for direct classification and regression, and its network structure is shown in Figure 1. YOLOv4 consists of four parts: the input, backbone network, neck network, and prediction. The network model structure is shown below: the input was processed by mosaic data enhancement; the Backbone was selected with the CSPDarknet53 backbone network to extract features, and three effective feature layers were generated for prediction and regression analysis. The CSPDarknet53 network structure was used to reduce the computational bottleneck and memory cost, ensure its model accuracy, and improve CNN's learning ability for model training. The feature maps extracted by the backbone network were input to the neck network neck. The SPP module and FPN [34] + PAN feature pyramid were integrated. The network Prediction was performed on the three effective feature layers to generate the final prediction frame.
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## Appendix A Derivation of Outage Probability  
\[P_{2}=Pr\left(\gamma_{b\tau s}<\Gamma^{th}\right)=Pr\left(\frac{aX^{2}Z}{\overline {\rho}bXZ+bXZ+cX+\overline{\rho}+1}<\Gamma^{th}\right), \tag{10}\]  
\[\begin{array}{l}\frac{aX^{2}Z}{\overline{\rho}bXZ+bXZ+cX+\overline{\rho}+1}< \Gamma^{th}\\ \longrightarrow aX^{2}Z<(Z(\overline{\rho}bX+bX)+cX+\overline{\rho}+1)\Gamma^{ th}\\ \longrightarrow\left[aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})X\right]Z <(cX+\overline{\rho}+1)\Gamma^{th}\\ \\ P_{2}=\int_{0}^{\infty}Pr\left[aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{ th})X\right]Z<(cX+\overline{\rho}+1)\Gamma^{th}f(X)dX\end{array} \tag{11}\]  
(a) The case when \(aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})X<0\)  
\[\begin{array}{l}aX<(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})\\ X<\mu\\ \mu=\frac{(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})}{a}=\frac{\Gamma^{th}(2- \rho)}{\overline{\gamma}(1-\rho)}\end{array}\]  
In this case, the inequality \(\left[aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})X\right]Z<(cX+\overline {\rho}+1)\Gamma^{th}\) holds for any \(X\) and \(Z\) therefore  
\[Pr\left[aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})X\right]Z<(cX+ \overline{\rho}+1)\Gamma^{th}=1. \tag{12}\]  
(b) The case when \(aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})X>0\)  
\[\begin{array}{l}aX>(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})\\ X>\mu\end{array}\]  
Then  
\[\begin{array}{l}Pr\left[aX^{2}-(b\Gamma^{th}\overline{\rho}-b\Gamma^{th})X \right]Z<(cX+\overline{\rho}+1)\Gamma^{th}\\ =Pr\left[Z<\frac{(cX+\overline{\rho}+1)\Gamma^{th}}{[aX^{2}-(b\Gamma^{th} \overline{\rho}-b\Gamma^{th})X]}\right]=Pr\left[Z<\Theta\right]\end{array} \tag{13}\]  
where  
\[\Theta=\frac{(cX+\overline{\rho}+1)\Gamma^{th}}{\left[aX^{2}-(b\Gamma^{th} \overline{\rho}-b\Gamma^{th})X\right]}\approx\frac{\xi}{X-\mu} \tag{14}\]  
\[\xi=\frac{(c+\overline{\rho}+1)\Gamma^{th}}{a}, \tag{15}\]  
\[P_{2}=\int\limits_{0}^{\mu}f\left(X\right)dX+\int\limits_{\mu}^{\infty}Pr\left( Z<\Theta\right)f\left(X\right)dX, \tag{16}\]\[\begin{array}{c}P_{2}=\int\limits_{0}^{\mu}f\left(X\right)dX+\int\limits_{\mu}^{ \infty}\left(1-e^{-\Theta}\sum\limits_{k=0}^{N-1}\frac{\Theta^{k}}{k!}\right)f \left(X\right)dX=\\ \quad\quad\quad\quad=\int\limits_{0}^{\mu}f\left(X\right)dX-\int\limits_{\mu}^ {\infty}e^{-\Theta}\sum\limits_{k=0}^{N-1}\frac{\Theta^{k}}{k!}f\left(X \right)dX=\\ \quad\quad\quad\quad=1-\int\limits_{\mu}^{\infty}e^{-X}e^{-\Theta}\sum\limits_ {k=0}^{N-1}\frac{
## 4 Conclusions  
To establish a robust procedure for evaluating the performance of Brazed Plate Heat Exchangers based on dimpled corrugation, a framework combining initial forming simulations and subsequent computational fluid dynamics analyses was developed and validated against experiments on pressure drops, using water as the working fluid. The focus of this preliminary work was to achieve consistent comparisons of quantities associated with flow within the channels while excluding uncertainties arising from external flow effects in the full device. The key findings can be summarized as follows:  
* The study successfully extended the analysis of heat plates with a dimple pattern to full-channel configurations, including regions near the ports, addressing a significant gap in existing research.
* The plate distance was found to have a significant impact on the results, with a value of twice the profile height identified as optimal in balancing accuracy and repeatability.
* The \(k-\varepsilon\) realizable turbulence model with enhanced wall treatment provided good results in matching both the experimental pressure drops and their trend with the Reynolds number, outperforming the \(k-\omega\) SST model in accuracy and stability.
* Leveraging the CFD outcomes, a strategy for estimating pressure drops in different channel regions was proposed as a foundation to significantly reduce reliance on computationally expensive CFD simulations. It includes a revised definition of the hydraulic diameter for capturing the continuous and periodic change in the cross-sectional area and wetted perimeter, which characterizes the dimpled geometry.
* The methodology implemented provides a workflow for enabling further investigations on heat transfer phenomena and optimizing the BPHE design.  
Future work should focus on improving the modeling of outer regions to predict flow rate distribution around closed ports and explore conjugate heat transfer CFD simulations. A specific approach will be necessary to overcome the preprocessing limitations in representing the entire object while reliably estimating thermal performance.  
Conceptualization, L.G. and F.G.; methodology, L.G. and F.G.; software, L.G.; validation, L.G.; formal analysis, L.G.; investigation, L.G. and F.G.; resources, F.G.; data curation, L.G.; writing--original draft preparation, L.G.; writing--review and editing, F.G., G.V., U.M., A.M. and L.T.; visualization, L.G.; supervision, F.G., A.M. and L.T.; project administration, F.G. and L.T.; funding acquisition, F.G. and L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data unavailable due to privacy.  
Author [PERSON] and [PERSON] were employed by the company Danfoss S.r.l. Author [PERSON] was employed by the company Danfoss Tata d.o.o. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 A Novel Protection Mechanism against the SIP-DRDoS Attack
### Proposed Defense Mechanism in Detail: Components and Workflow
#### 5.1.1 Statistics Module:
The dynamic rate-limiting rule engine submodule creates dynamic rate-limiting rules that may trigger the action module by checking the following conditions:  
* **Condition 1**: SIP connection requests coming from a single IP address are counted per second. All SIP INVITE and SIP REGISTER messages are considered incoming connections. The statistics module's rate limits are utilized to decide if the action module needs to be triggered. * **Condition 2**: SIP connection requests directed towards a single IP address are counted per second. All SIP INVITE and SIP REGISTER messages are considered incoming connections. The statistics module's rate limits are utilized to decide if the action module needs to be triggered. * **Condition 3**: SIP connection requests from a single IP address are counted per second, including reflected requests. The statistics module's rate limits are utilized to decide if the action module needs to be triggered. * **Condition 4**: SIP connection requests towards a single IP address are counted per second, including reflected requests. The statistics module's rate limits are utilized to decide if the action module needs to be triggered. **Action Module:**  
The action module's actions comprise one passive and two active responses. When an attack is presumed, the action module puts the SIP server in one of the following modes: detect mode, drop mode, and block mode. It can also be set up to perform IP verification, before it drops/blocks packets, by rejecting incoming UDP packets and requesting them again. Each action in this module is logged and sent to the admin as an alert, as given in Figure 5. Responses by the action module are given as follows:* _Detect mode_: If the present rate of SIP traffic is larger than _Suspect Limit_, the system notifies the operator of a threshold violation. * _Drop mode_: If the present rate of the SIP traffic is larger than _Attack Limit_, SIP packets are dropped for 5 min. * _Block mode_: After Drop Mode is activated, if the present rate of SIP traffic is above 95% of the inbound packet rate limit, then SIP packets are blocked for 5 min.
# Editorial on Wireless Power Transfer (WPT): Present Advancements, Applications, and Future Outlooks
## 4 Future Directions in WPT  
**Advancements in Materials and Circuit Design**: The next wave of innovations in WPT is expected to come from advancements in materials science [10, 11, 12, 13, 14]. Researchers are exploring the use of metamaterials--artificially engineered materials with properties not found in nature--that can focus and guide electromagnetic waves with higher precision. These materials have the potential to significantly improve the efficiency of WPT systems, particularly over longer distances. In parallel, breakthroughs in semiconductor technologies are expected to lead to more efficient power management circuits that minimize energy loss and maximize power transfer efficiency.  
**WPT for Renewable Energy Systems**: Integrating WPT with renewable energy systems such as solar and wind power offers exciting possibilities for off-grid power generation. For instance, solar-powered drones equipped with WPT systems could be used to transmit power to remote locations where traditional power infrastructures are lacking. Similarly, WPT could be used in conjunction with floating solar farms to wirelessly deliver power to nearby shore facilities. As renewable energy technologies continue to evolve, WPT may become a key component in creating decentralized, sustainable energy networks.  
**Artificial Intelligence and WPT Systems**: Artificial intelligence (AI) is poised to play a transformative role in optimizing WPT systems [1; 2; 3]. AI algorithms can be used to dynamically adjust the transmission parameters of WPT systems in real time, ensuring that power is delivered efficiently even in changing environments. For example, AI-driven WPT systems in smart homes could automatically adjust power delivery based on the presence of devices and their energy requirements, thereby reducing waste and enhancing energy efficiency.
## 4 Security Threats and Attacks
### Attacks on Network Layer  
The network layer takes information from the physical layer and transmits it to the application layer in real time. This layer is responsible for the reliable exchange of information in layers. In this layer, attackers destabilize the system by disturbing data transmission in real time. There are three types of CPS attacks at the network layer. The  
Figure 2: Real CPS attacks in recent years.  
first is the denial of service (DoS) attack that drops data packets and specifically prevents the transmission of control data. These attacks interrupt the data transmission and cause a communication jam. In a wireless network, it is easy to interrupt the transmission, but, in wireless networks, jamming signals are generated by the specialized antenna for signal interference. Man-in-the-middle is a DoS attack that creates vulnerabilities in the network and stops packet forwarding and destabilizes the system by diverting the state. In a communication-based train control system, communication failure occurs if an attacker launches a jamming attack.  
The second is the flooding attack that causes a delay in data transmission by exhausting resources, such as network bandwidth or device memory, intentionally. In this attack, a massive amount of data traffic is generated to block a network, and the memory of devices is also filled. A flooding attack can be launched without having enough information about the network and make CPS unstable. During a flooding attack, the physical system switches to fail-safe mode and reduces the operation for safety purposes.  
The third is the packet manipulation attack which consists of two phases; the first is packet stealing and the second is packet manipulation. In the first phase, the attacker accesses the network nodes and devices using vulnerabilities of the network; then, the network packet is forwarded to the attacker. However, in the second phase, the attacker modifies that packet and sends it back to the destination. If the hacker manipulates by adhering to network protocols then the destination node deceives by packet manipulation. If the control information is modified, then it has an equal effect on the network as attacks on the physical layer. For example, in a communication-based train control system, packet manipulation can cause train accidents by manipulating the route direction. However, a Sybil attack deceives different nodes on the traffic road in a vehicular ad-hoc network and causes traffic congestion. Sending incorrect messages results in inconvenience for vehicle traffic.
# GazeRayHand: Combining Gaze Ray and Hand Interaction for Distant Object Manipulation
## 4 User Study
### Conditions  
#### 4.1.1 Gaze&Pinch  
Gaze&Pinch [19] exclusively employs gaze interaction for object selection. To select an object, a user looks at the object to place the gaze pointer at it and makes a pinch gesture with a hand. Same as our GazeRayHand technique, the selected object changes its color to yellow, indicating the confirmation of selection.  
The translation of the pinched hand triggers the translation of the selected object in the direction in which that hand is moving until the pinch is released (see Figure 3a). The distance of the translation is controlled using an equation: \(Movement_{Object}=Movement_{Hand}*Distance_{Between\_object\_and\_user}\). According to this equation, the amplification of object translation increases as the distance between the user and selected object grows. The translation stops when the pinch is released, and the object turns back to its original color.  
Similar to our GazeRayHand technique, the Gaze&Pinch has a different input mode for object rotation. For the rotation mode, selection should be performed by a two-hand pinching gesture while the user is looking at an object. After selection, rotation is performed with a two-hand interaction which decides the rotating movement (see Figure 3b). Similar to translation, rotation stops when the pinch is released.  
Figure 2: Object translation with the GazeRayHand2 technique. If a user’s gaze point moves out from the sphere, the selected object is repositioned to the gaze point (**a**). If a user’s gaze point is within the sphere, GazeRayHand2 works in the same manner as the GazeRayHand (**b**).
# A Novel Wideband Splitter for a Four-Element Antenna Array
###### Abstract  
In the paper, a novel design of a wideband power splitter for a four-element antenna array using two RF antiphase segments is proposed. Based on a detailed analysis of the power splitter circuit, an analytical model was set up in the MATLAB environment. The derived analytical model allows the development of a design of the described structure for any operating frequency and estimates the properties of the designed structure. In addition to the RF electrical part, the copper cover is also considered in this study. The copper cover serves as both a support and shielding part of the proposed structure. The electrical part consists of two sections of transmission lines. The first transmission line is symmetrical, while the second transmission line is asymmetrical. The given transmission lines can be realized using any technology (microstrip, coaxial, etc.). A prototype of the proposed wideband splitter operating at 650 MHz with a fractional bandwidth of 84.3% was designed and tested in real-world conditions to prove the concept. The board of the manufactured prototype has dimensions of 25 \(\times\) 152 mm. A double-sided FR4 material with a substrate height of 1.48 mm, copper thickness of 50 \(\upmu\)m, and \(\epsilon_{r}\)\(\equiv\) 4.3, with a dielectric loss tangent of 0.021 was used to manufacture the prototype. The prototype was tested and its parameters were verified in practical conditions as a part of the current radio communication system for the 5G band. Under these conditions, verification measurements of the proposed splitter with a four-element antenna array were carried out.  
array antennas; bandpass filters; feeding networks; four-element antenna; power divider +
Footnote †: journal: Applied  
**Citation**: [PERSON]; [PERSON]; [PERSON] A Novel Wideband Splitter for a Four-Element Antenna Array  
_Appl. Sci._**2024,** 14, 1593. [[https://doi.org/10.3390/app14041593](https://doi.org/10.3390/app14041593)]([https://doi.org/10.3390/app14041593](https://doi.org/10.3390/app14041593))