esa-sceva/satcom-chunk-collection huggingface dataset:
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### The Probabilistic Routing Table Set-Up Phase
#### 4.1.4 Initialization of the Probabilistic Routing Table  
The probabilistic routing table is a set of elements: \(\widetilde{\varphi}_{i,j}\), for each possible link \(n_{i},n_{j},\forall n_{i}\in\mathbb{N},\forall n_{j}\in\mathbb{N}_{i}\), such that \(\varphi\)\({}_{i,j}\) is calculated by using \(\tilde{\varphi}_{i,j}\), as explained in Equations (11) and (12).  
**Remark 1**.: _The value of \(\widetilde{\varphi}_{i,j}=0\), if \(n_{j}=n_{i}\) or \(n_{j}\
ot\in\mathbb{N}_{i}\);_  
\[\tilde{\varphi}_{i,j}=\overline{h}\Big{(}n_{i,j}\Big{)}*\widetilde{d}_{i,j}* \overline{E}_{i,j}=\frac{\left(1-\tilde{h}\Big{(}n_{i,j}\Big{)}\right)^{ \alpha}\ \left(1-\tilde{d}_{i,j}\right)^{\beta}\overline{E_{i,j}}^{\gamma}}{\sum_{l=1} ^{m_{i}}\left(1-\tilde{h}\Big{(}n_{i,l}\Big{)}\right)^{\alpha}\ \sum_{l=1}^{m_{i}}\left(1-\tilde{d}_{i,l}\right)^{\beta}\ \sum_{l=1}^{m_{i}}\overline{E_{i,l}}^{\gamma}} \tag{11}\]  
Hence,  
\[\tilde{\varphi}_{i,j}=\frac{\tilde{\varphi}_{i,j}}{\sum_{n_{j}\in\mathbb{N}_{ i}}\tilde{\varphi}_{i,j}}=\ \frac{\left(1-\tilde{h}\Big{(}n_{i,j}\Big{)}\right)^{\alpha}\ \left(1-\tilde{d}_{i,j}\right)^{\beta}\overline{E_{i,j}}^{\gamma}}{\sum_{l=1} ^{m_{i}}\left(1-\tilde{h}\Big{(}n_{i,l}\Big{)}\right)^{\alpha}\left(1-\tilde{ d}_{i,l}\right)^{\beta}\overline{E_{i,l}}^{\gamma}}. \tag{12}\]  
For initialization, since the initial energy is the same for all nodes in the network (except for the sink node), we can initialize the probabilistic routing table by assuming at the beginning that:  
\[\tilde{\varphi}_{i,j}{}^{*}=\overline{h}\Big{(}n_{i,j}\Big{)}^{*}*\widetilde {d}_{i,j}{}^{*} \tag{13}\]  
We note that the hop-count will be defined as depth in a tree structure (since all nodes have the same residual energy distribution). From Equation (13) the initial probabilistic routing table is the set of elements \(\widetilde{\varphi}_{i,j}{}^{*}\), for each possible link \(n_{i},n_{j},\ \forall n_{i}\in N,\forall n_{j}\in\mathbb{N}_{i}\). These elements are calculated by Equation (14).  
\[\widetilde{\varphi}_{i,j}{}^{*}=\frac{\tilde{\varphi}_{i,j}{}^{*}}{\sum_{n_{j} \in\mathbb{N}_{i}}\ \tilde{\varphi}_{i,j}{}^{*}}=\frac{\left(1-\tilde{h}\Big{(}n_{i,j}\Big{)} \right)^{\alpha}\!\!\left(1-\tilde{d}_{i,j}\right)^{*\beta}}{\sum_{l=1}^{m_{i}} \left[\left(1-\tilde{h}\Big{(}n_{i,l}\Big{)}\right)^{\alpha}\!\!\left(1-\tilde {d}_{i,l}\right)^{*\beta}\right]} \tag{14}\]
## 2 Materials and Methods
### Database Generated from Digital Geological Maps and SPOT Satellite Images: Material Factors  
Distance from faults and type of stratum are the two key components of the database the researchers created based on a digital geological map. Identifications of the catchment areas with the strongest potential for debris flows were statistically analyzed following [PERSON] and [PERSON]'s [40] grading criteria.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Topographic Factors** & **Minimum** & **Maximum Value** & **Average** & **Standard Deviation** \\ \hline Total area of catchment (m\({}^{2}\)) & 2.3 & 3949.2 & 317.2 & 708.3 \\ Average slope of the catchment (degrees) & 20.3 & 38.8 & 30.8 & 4.2 \\ Length of stream (m) & 127.0 & 9163.0 & 1859.0 & 1991.9 \\ Average slope of stream bed (degrees) & 8.4 & 35.3 & 18.1 & 6.9 \\ Shape coefficient & 0.596 & 0.918 & 0.773 & 0.588 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values and standard deviations of topographic factors for all 33 potential debris-flow areas.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Total Area of** & **Average Slope of** & **Length of** & **Average Slope of** & **Shape** \\  & **Catchment** & **Catchment** & **Stream** & **Stream Bed** & **Coefficient** \\ \hline Total area of catchment & 1 & 0.086 & 0.858 & \(-\)0.363 & \(-\)0.137 \\ Average slope of catchment & 0.086 & 1 & 0.153 & 0.386 & 0.137 \\ Length of stream & 0.858 & 0.153 & 1 & \(-\)0.525 & \(-\)0.382 \\ Average slope of stream bed & \(-\)0.363 & \(-\)0.525 & 0.386 & 1 & 0.357 \\ Shape coefficient & \(-\)0.137 & \(-\)0.382 & 0.137 & 0.3577 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pearson correlation coefficients for the topographic factors.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 6 Modeling of the Breakdown (Arc) Stage
The computed resistance values were necessary to design circuit models that allow to determine operational characteristics of discharge model equipped with power source. \[R=\frac{P}{I^{2}}=\frac{\sum\limits_{i=1}^{N}p_{Vi}\cdot V_{i}}{\left[\sum \limits_{i=1}^{N}\left(J_{i}\cdot F_{i}\right)\right]^{2}}, \tag{13}\]  
where: \(P\)--active power value, \(p_{Vi}\)--Joule heat of \(i\)-th finite element, \(V_{i}\)--volume of \(i\)-th finite element, \(J_{i}\)--current density in \(i\)-th finite element, \(F_{i}\)--external area if \(i\)-th finite element  
Thermal calculations were performed as transient, using previously computed heat sources and boundary conditions characteristic for conduction and convection heat transfer. In the example, the temperature distributions in time 0-0.001 s are shown in Figure 17.  
After thermal calculations in any time step, it is necessary to change the material parameters (primarily the plasma conductivity) and the supply voltage in the breakdown stage. In current algorithm, voltage source was modeled as the RC circuit. Resistance values determined based on the field modeling (13) and the capacitance (the value assumed as equal to the power source capacitor--Figure 4) were used to compute the time constant of RC circuit and source voltage values in any time step of the analysis (14). Figure 18 shows the voltage over time characteristic, obtained by the conducted analysis. \[V(\tau)=V_{0}e^{-\frac{\tau}{RC}} \tag{14}\]  
where: \(V_{0}\)--initial voltage, \(\tau\)--time  
Figure 17: Temperature distribution in the sample in subsequent moments. (**a**) 0.0001 s; (**b**) 0.0005 s; (**c**) 0.001s. Figure 14:0 The voltage course during the simulated discharge. The voltage value on the electrodes, after thermal calculations, was introduced as the initial condition in the next cycle of static electromagnetic calculations. Presented algorithm can be realized iteratively until whole energy stored in the capacitor before discharge initiation is dissipated. From the moment of capacitor discharge, no current will flow in the arc channel. During the break time between next discharges, temperature distribution problem was analyzed.
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 3 Improvements of the KCFS-YOLOv5
### Optimize the Feature Fusion Framework  
In the conventional YOLOv5 framework, PAN+FPN framework is used in the feature fusion framework of neck network, this framework can make the information fusion more effective and realize information communication between the shallow layer and deep layer of the network. Compared with the ordinary optical image, the aerial remote sensing image has more tiny objects scattered randomly and the complex background. Therefore, we hope to fuse more multi-size object features in different dimensions. However, the framework of PAN+FPN emphasizes strict Up-sampling and Down-sampling in the process of feature fusion, and does not focus on the fusion of multi-size object features in aerial remote sensing images. The Bidirectional Feature Pyramid Network (BiFPN) [43] framework can effectively solve the above problems. In this paper, we embed the BiFPN framework into the neck network of the YOLOv5. The framework of the BiFPN is shown in Figure 6. Compared with the framework of PAN+FPN, instead of emphasizing strict stepwise sampling, BiFPN uses a short-circuit link method to better integrate information from different layers. Meanwhile, the conventional YOLOv5 uses Concat fusion method, which cannot extract features effectively. BiFPN introduces a weighted feature fusion method, which can adjust the importance of different features by training weights, emphasizing the object features and ignoring the background information. BiFPN normalizes the weight of each feature to a value between 0 and 1, which indicates the importance of the feature. Furthermore, the process is as follows:  
\[O=\sum_{i}\frac{\omega_{i}}{\epsilon+\sum_{j}\omega_{j}}I_{i} \tag{10}\]  
where, the feature map is represented as the \(O\). the \(\omega_{i}\) and \(\omega_{j}\) are the weights of the feature map. The value of \(\epsilon\) is 0.0001, which ensures the stability of weight update. In the improved neck network, short-circuit links are added to P3 and P4 layers for weighted feature fusion of different dimensions. Furthermore, the fusion process of layer P4 is shown as follows:  
\[P_{4}^{td}=Conv\Bigg{(}\frac{\omega_{1}P_{4}^{in}+\omega_{2}Sample\big{(}P_{5} ^{in}\big{)}}{\omega_{1}+\omega_{2}+\epsilon}\Bigg{)} \tag{11}\]  
\[P_{4}^{out}=Conv\Bigg{(}\frac{\omega_{1}^{in}P_{4}^{in}+\omega_{2}^{in}P_{4}^{ in}+\omega_{3}Sample\big{(}P_{3}^{out}\big{)}}{\omega_{1}^{in}+\omega_{2}^{in}+ \omega_{3}^{in}+\epsilon}\Bigg{)} \tag{12}\]  
where \(Conv\) is the process of convolution. Furthermore, \(Sample\) represents the process of Up-sampling and Down-sampling.  
Figure 5: The feature map comparison of YOLOv5 and YOLOv5 with Coordinate Attention module. Where, (**a**) is the original remote sensing image; (**b**) is the feature map without the Coordinate Attention module; (**c**) is the feature map with the Coordinate Attention module.
## 1 Introduction  
A disc brake is a braking system composed of a brake rotor, brake pads, and calipers. The brake rotor rotates with the wheel, and the brake pads are mounted on the brake calipers, which clamp the rotor to stop or slow the wheel. The brake pads generate frictional heat by converting kinetic energy into thermal energy to reduce the kinetic energy of the vehicle. Consequently, due to the thermal energy generated during the braking process, the disc temperature rises on the friction part and fatigue accumulates, causing cracks that decrease the life of the disc.  
There is a trade-off relationship between the cooling performance and structural stability of a brake disc [1,2]. Vertilated discs are generally used to improve the heat dissipation performance. A ventilated disc has the form of a general disc with added ventilation blades that help dissipate heat. As it is cooled with air circulation by the ventilation blades, when it rotates at a higher speed, high centrifugal force facilitates heat dissipation. In addition, the temperature rise can be suppressed even at medium and low speeds with small centrifugal force through holes on the outer surface, and the cooling performance can be maximized by interlocking with the ventilation blades.  
The holes and slits have advantages of improving the braking performance by improving the frictional force, heat dissipation, dust and gas emission, disc deformation and fading, and weight reduction. However, thermal stress is concentrated around the holes [3]. Previous studies have shown that concentrated stress is related to the starting points of crack growth [4]. Therefore, it can be estimated that the possibility of crack growth due to concentrated stress increases around the holes and slits.  
Studies in structural and thermal characteristics and their correlations with stability and lifespan using finite element analysis have been conducted [5; 6; 7; 8]. There have also been studies on the shape of the disc, but the number of disc samples that were tested or analyzed was limited [9; 10; 11; 12]. In this study, in order to understand structural stability and thermal characteristics when designing a brake disc, ANSYS and PIAnO software were utilized to optimally design a brake disc. Stress and temperature were set as objective functions, and hole shapes of the disc, arrangement patterns, and ventilation blades were set as design variables.  
Through optimal design formulation, results were derived using the design of experiments (DOE). In addition, factors that have the largest effect on the objective functions were used to carry out the second optimal design. Through this study, a brake disc shape model with improved structural stability and cooling performance compared to conventional discs is presented.
# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 1 Introduction
### Main Contributions  
The main contributions of this paper can be summarized as follows:  
* A web-based software solution designed for MNOs to monitor/solve network problems related to planning, operation, and optimization for multiple Radio Access Technologys (RATs).
* The development of a modular 3-tier architecture for handling the access to multiple MNO data sources providing rich and diversified visualizations, on top of data managing operations, and allowing the integration of future AI procedures.
* The incorporation of multiple RAN algorithms into the software solution, that combined with data managing operations allows the development of dynamic and customized use cases for mobile network OAM.
* The users can explore network simulations, resorting to the RAN algorithms, of how the network will behave under certain conditions, and visualize the outcome of those simulations.
## 5 Conclusions  
In this study, we presented an optimized 120 Gbps three-channel free-space optical (FSO) system utilizing the space-division multiplexing (MDM) approach, incorporating various duobinary modulation formats. We demonstrated the optimization of power-sharing among transmission modes, employing different duobinary (DB) modulation formats to enhance system performance. Notably, the mitigation of the power penalty was shown to significantly improve the link's overall performance. Additionally, the impact of SPM over 40 Gbps per channel was explored across multiple modified duobinary (MDB) formats.  
Our analysis further included the optimization of optical low-pass filter bandwidths in response to variations in the Q-factor, providing critical insights for MDM-FSO links. The results reveal that the MDB MDM-FSO link, based on NRZ data modulation, outperforms other formats, exhibiting greater resilience to impairments such as SPM and modal dispersion. Moreover, optimized filter bandwidth values were achieved for MDB-modulated MDM-FSO formats, with ASK and NRZ-modified duobinary designs showing similar performance trends.  
Further investigation into optical received power sensitivity across different formats is recommended to determine the most suitable format for practical deployment. Our findings also indicate that MDB MDM-FSO RZ signals demonstrate superior received power sensitivity when compared to MDB MDM-FSO NRZ signals, making them a potentially more robust choice for high-capacity optical systems.  
The numerical analyses conducted in this study can serve as a foundation for future research aimed at optimizing filter parameters for MDM-based FSO systems utilizing duobinary and modified duobinary modulation. By identifying the best system configuration to mitigate SPM and modal dispersion-induced impairments at specified input launch powers, we can significantly improve the overall performance and reliability of MDM-based FSO systems.  
Writing--original draft, R.I.S. and A.M.; Writing--review & editing, A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 4 Fundamentals of Zigbee
### Zigbee Data and Security  
Data in Zigbee networks are transmitted in the form of frames, with a layered structure that supports efficient and secure communication. A Zigbee frame consists of several key components: the header, which includes addressing information; the payload, which contains the actual data; and the footer, which carries error-checking information such as the frame check sequence (FCS). The payload is the core of the data frame, containing application-specific data, sensor readings, or commands. To maintain efficiency and low power consumption, Zigbee frames are compact, and their payloads are designed to carry only essential data.  
Security is a major concern in Zigbee networks, particularly given the increasing deployment of these systems in critical applications such as smart grids and home automation. Zigbee incorporates several security features to protect data transmission and prevent unauthorized access. The most fundamental layer of security is encryption, which is applied to the data payload using the Advanced Encryption Standard (AES) with 128-bit keys. Encryption ensures that the contents of the payload remain confidential, even if the data frame is intercepted by malicious entities.  
A crucial aspect of Zigbee's security model is the use of network and link keys. The network key is shared by all devices in the network and is used to secure communication between them. The link key, on the other hand, is used for securing communication between specific pairs of devices and provides an additional layer of security. When a device joins the network, the coordinator distributes the network key in an encrypted form, ensuring that only authenticated devices can participate in the network. This key exchange process is fundamental to ensuring that unauthorized devices cannot intercept or manipulate the data being transmitted.  
In addition to encryption, Zigbee employs message integrity codes (MICs) to ensure the integrity of the data. The MIC is appended to the data frame to verify that the payload has not been tampered with during transmission. Furthermore, Zigbee supports replay protection mechanisms, which prevent attackers from resending captured data frames to disrupt the network. These combined security features make Zigbee a robust and secure protocol for wireless communication, even in environments where security is a primary concern.  
In summary, the Zigbee protocol's structure, communication process, and security mechanisms work together to provide a reliable and efficient solution for IoT applications. Its use of flexible topologies, energy-efficient communication, and strong security measures make it an ideal choice for applications requiring low-power wireless connectivity across large, scalable networks.
## 3 Results and Discussion
### Effect of Media Type  
Figure 3 presents the callus induction rates (%) observed with different culture media, and Figure 4 illustrates selected calluses induced from meristem and stipe explants of _E. cava_. The highest callus induction (31.73 \(\pm\) 4.41% in stipe tissue and 38.28 \(\pm\) 8.37% in meristem tissue) was observed on PESI medium, leading to its selection for subsequent experiments. The addition of potassium iodide in PESI medium [60] may have enhanced callus formation, aligning with the findings of [PERSON] and [PERSON] [26]. No callus growth was observed on ASP2 medium, possibly due to the presence of nitrilotriacetic acid and mannitol, acting as potential toxins [26; 65] and increasing osmotic pressure of media [66], respectively.
## 3 Results and Discussion
### Sensor Performance
#### 3.5.2 OTFT Chloride Ion Sensor
Figure 8: Hydrogenated graphene-based OTFT Sensor Performance: (**a**) Output characteristic at fixed V\({}_{\text{GS}}\) of 1 V at different chloride ion concentration, (**b**) logarithmic sensor response vs. chloride ion concentration at V\({}_{\text{GS}}\) 1 V and V\({}_{\text{DS}}\) 13 V. (**c**) Sensor’s temperature drift. Regardless of variation in the transistor dimensions, measurements of % change in output current in response to Cl\({}^{-}\) concentration are consistent and repeatable. OTFT sensors experience drift and decreasing drain current with repeated measurements but remain operational over several weeks using the same device. Despite these variations, the OTFT was used to fabricate chloride ion and humidity sensors with robust response to minute concentrations. Currently, the fabricated sensors are at the development stage and were tested in a laboratory environment without encapsulation and packaging. Further improvements are currently in progress and it is the aim to implement drift correction measures by limiting exposure to contaminants during fabrication, implementing temperature drift compensation, and designing proper encapsulation and packaging.
## 2 Definitions, Representation and Assumptions
### Definitions  
SDSE estimates the security level of each sensor data that arrives at the base station (BS) of the WSN. Generally, sensor data is sent to the BS in response to data queries generated by WSN users. Whenever the base station receives sensor data, SDSE delivers that data, along with its estimated security level, to the requesting user.  
The security level estimated by SDSE represents the probability of sensor data being secure even in the presence of attacks. This security level results from the combination of a few security metrics. In turn, these security metrics are defined by SDSE for each security mechanism present in the network to indicate the likelihood of these mechanisms resisting attacks (details are in Section 3.2). In other words, a security metric indicates how likely it is that a mechanism will continue to keep network data secure, even when the sensor nodes are being attacked.  
More specifically, the data security level estimated by SDSE corresponds to the security level calculated for the sensor nodes that forwarded that data to the BS, including the sensor that generated the data. Thus, to estimate the security level of the given sensor data, SDSE calculates the security metric for each security mechanism in relation to each node of the route traveled by the data and combines these metrics for obtaining the security level of the data (details are in Section 3.3). Therefore, the data security level varies not only according to the current state of the network and the configuration of security mechanisms, but also according to the route traveled by the data.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Applications of DDPM in Image Processing
#### 3.3.7 Uncertainty Estimation  
In addition to its generative capabilities, DDPM has been explored for uncertainty estimation in various tasks. Bayesian neural networks based on DDPM can provide valuable uncertainty estimates, aiding in decision-making processes and improving the reliability of models [43; 53].