esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CS Simulation Framework Based on LUCC  
#### 2.3.1 GeoSOS-FLUS Model  
An artificial neural network is an advanced algorithm designed to simulate the structure of biological neurons, primarily used for estimating nonlinear functions with multiple input variables. By incorporating additional driving factors, the accuracy of the results is enhanced, enabling a more detailed representation of the interactions and competition among different land types [25].  
In the GeoSOS-FLUS model, the likelihood of land conversion is influenced not only by the probabilities derived from the neural network's output based on the driving factors, but also on the influence of neighborhood density (Formula (1)), inertia coefficient (Formula (2)), conversion cost, and competition among lands, indicating the strength of expansion ability. Finally, the total probability of land type conversion was determined (Formula (3)).  
\[\Omega^{t}_{p,k}=\frac{\sum_{M\times N}con(c_{p}^{t-1}=\text{k})}{N\times N-1} \times w_{k}, \tag{1}\]  
where \(\Omega^{t}_{p,k}\) signifies the field density, while \(\sum_{M\times N}con(c_{p}^{t-1}=\text{k})\) is an overview of the pixels. Additionally, \(w_{k}\) reflects the weight assigned to the influence of different LU areas.  
\[\left\{\begin{array}{l}A^{t-1}_{k}(\left|D^{t-2}_{k}\right|\leq\left|D^{t-1 }_{k}\right|)\\ A^{t}_{k}\times\frac{D^{t-2}_{k}}{D^{t-1}_{k}}(0>D^{t-2}_{k}>D^{t-1}_{k})\\ A^{t-1}_{k}\times\frac{D^{t-1}_{k}}{D^{t-2}_{k}}(D^{t-1}_{k}>D^{t-2}_{k}>0)\\ \end{array}\right. \tag{2}\]  
In this case, \(A^{t}_{k}\) represents the inertia coefficient for the \(k\)th land type at iteration time \(t\). On the other hand, \(D^{t-1}_{k}\) and \(D^{t-2}_{k}\) indicate the differences between the pixel count of the Kth land type and the desired number in the previous and the second-to-last iterations, respectively.  
\[TProb^{t}_{p,k}=sp_{p,k}\times\Omega^{t}_{p,k}\times A^{t}_{k}\times(1-sc_{c-k}) \tag{3}\]  
where \(TProb^{t}_{p,k}\) is the total probability of pixel p transforming into land-type \(k\) at the iteration number \(t\), \(sp_{p,k}\) represents the adaptive probability of the neural network, \(sc_{c-k}\) represents the cost of converting LU-type c to type \(k\), and \(1-sc_{c-k}\) represents the difficulty of conversion.
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 1 Introduction
preliminary analyses of specific hotspots or localized regions, with no comprehensive quantitative assessment of the DME beacons across the entire Chinese mainland, and it was also found that signal propagation loss factors need to be more thoroughly incorporated into existing models. The research related to investigating the DME effects on B2a, compared with GPS and Galileo, is still relatively limited. The general aim of this paper was, thus, to develop an enhanced evaluation model for quantifying the impact of DME beacons on B2a signal reception performance across the whole area of the Chinese mainland. Quantitative analysis of the multi-parameter performance degradation for airborne users caused by multiple DME interference in high-altitude scenarios was conducted with determination of the worst-case interference values. In this study, a mathematical model was first constructed based on the principle of pulse blanker to quantify the B2a signal \(C/N_{0}\) degradation under DME interference scenarios. Taking into account the signal processing process of BeiDou receivers, an evaluation model was established to assess the degradation of B2a signal reception performance. Subsequently, the degradation of B2a signal reception performance and the distribution of interference hotspots were evaluated using empirical data from the DME beacon network in the Chinese mainland and the interference budget model. The findings establish a scientific foundation for developing Minimum Operational Performance Standards for B2a signal receivers and strategically optimizing DME beacon deployment across the Chinese mainland.
## 3 Proposed TFR Approach
### Concepts  
There are several new concepts defined in the proposed TFR approach, as follows:  
* Link-Alive timer: a timer for counting the number of Sync-Intervals that have to pass without receiving Sync messages from the MC. Specifically, the timeout duration is equal to 3 \(\times\) Sync-Interval.
* Announce timer: a timer for counting the number of Announce-Intervals that have to pass without receiving Announce messages from the MC. Specifically, the timeout duration is equal to 3 \(\times\) Announce-Interval.
* Link-Alive message: a message that has been used to recover a failure in the network by using specific information inside the message. This message is used to identify a failure in the network of an SC.  
Figure 3: Delay request–response mechanism of the PTP timing exchange protocol.  
Sync-Request message: a message that has been used to acknowledge the recovery mechanism by the slave nodes after detecting a failure in the network using specific information inside this message.
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
###### Abstract  
Stochastic parallel gradient descent (SPGD) algorithm is one of the most promising methods for effective coherent beam combination. However, the algorithm also has some disadvantages, such as slow convergence speed and local extremum. This paper proposes an AdaDelSPGD algorithm, which combines an AdaDelta algorithm with a SPGD algorithm, and improves the traditional AdaDelta algorithm with adaptive gain coefficient. It is worth noting that the adaptive gain coefficient can be adjusted in real time to improve the convergence rate. The effectiveness of the proposed algorithm is verified by relevant simulation experiments, and the results show that the proposed algorithm can significantly improve the convergence speed. Following the experiments with the fiber laser-phased array multiplex communication system, we can draw the conclusion that the addition of communication modulation reduces the beam quality, and the higher the modulation frequency, the worse the beam quality. However, adding the SPGD algorithm can improve the beam quality. The AdaDelSPGD algorithm proposed in this paper can further improve the beam quality, and the bit error rate of communication is also decreased after testing. This provides a foundation for further research on the fiber laser-phased array multiplex communication system.  
SPGD algorithm; laser communication; phase lock; laser coherent synthesis +
Footnote †: journal:  
Article
## 1 Introduction
This is because the change in refractive index from water to air (\(n=1.33\) to \(n=1\)) is both much larger than the fluctuations in atmospheric turbulence and discontinuous rather than smoothly varying. While the mechanics of the underwater bubbles and atmospheric turbulence are different, the resulting intensity distributions of transmitted light appear to be similar. By passing light through underwater turbulence, we are not attempting to recreate exact conditions found in the atmosphere, but rather create conditions in which the index of refraction changes even more rapidly than in turbulent air. As such, the assumption that the depolarizing term in Equation (2) is negligible is not valid. This point can be illustrated by considering a set of light rays passing through a region of water with air bubbles. When a ray collides with a bubble, some portion of it will be reflected while the rest will be refracted according to [PERSON]'s law. Both reflection and refraction are known to change the SOP and DOP of light depending on the initial polarization and incident angle. For example, the reflected portion of unpolarized light hitting a bubble at the Brewster angle (\(\sim\)37\({}^{\circ}\) for the water to air interface) will be completely polarized perpendicular to the plane of incidence while the refracted portion becomes slightly polarized. It's clear that, in general, the SOP and DOP of a ray that encounters a bubble will not be preserved and thus the depolarizing term in Equation (2) is significant. However, it is possible that not all of the rays will hit a bubble. Thus, it may be possible that the average SOP and DOP of the rays remains well-preserved even though the polarization of individual rays is not. In this article, we demonstrate experimentally and theoretically that the polarization of light is still well-preserved after passing through turbulent media. Section 2.1 describes the details of our experimental setup, along with out polarization tomography technique. The use of the underwater air bubble setups may speed up the design-test cycle for FSO communication methods. In Section 2.2 we describe our simulations. We also propose the use of DOP as a viable degree of freedom for FSO communication given that we find it is well preserved for even weakly polarized states. This may lead to improvements for polarization based modulation schemes and methods for communication.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 7 Zigbee Network Traffic Dataset and Its Characteristics
### Network Traffic Characteristics
#### 7.2.2 Packet Length Analysis  
Packet length is the amount of information carried by each data packet on a network and is usually measured in bytes. We divided packet length into three groups: short, medium, and long packets. The lengths of short, medium, and long packets were defined as between 0 and 19 bytes, 20 and 79 bytes, and 80 and 159 bytes, respectively. There are 9,987,365 short packets in the dataset. Short packets are generally acknowledgment (ACK), data requests, or control messages. There are 13,572,619 medium packets in the dataset, and medium-length packets generally indicate standard data exchanges, such as sensor readings and data transmission. Long packets can contain more complex data in addition to image/sensor data. These may be bulk data or device software updates. In the dataset, there are 1,119,839 packets. Long packages constitute 4.53% of the total number of packages.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
###### Abstract  
The growing needs for high-speed and secure communications create an increasing challenge to the contemporary framework of signal processing. The coexistence of multiple high-speed wireless communication systems generates wideband interference. To protect the security and especially the privacy of users' communications requires stealth communication that hides and recovers private information against eavesdropping attacks. The major problem in interference management and stealth information recovery is to separate the signal of interest from wideband interference/noise. However, the increasing signal bandwidth presents a real challenge to existing capabilities in separating the mixed signal and results in unacceptable latency. The photonic circuit processes a signal in an analog way with a unanimous frequency response over GHz bandwidth. The digital processor measures the statistical patterns of the signals with sampling rate orders of magnitude smaller than the Nyquist frequency. Under-sampling the signals significantly reduces the workload of the digital processor while providing accurate control of the photonic circuit to perform the real-time signal separations. The wideband mixed signal separation, based on photonic signal processing is scalable to multiple stages with the performance of each stage accrued.  
photonic signal processing; interference management; stealth communication; blind source separation; hybrid analog and photonic systems +
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## 3 Results
### Accuracy of Control-Free Nap-of-the-Object Photogrammetry Images
#### 3.2.2 Measurement Error of DSMs  
The data collected via image control nap-of-the-object photogrammetry were used as a reference to determine the measurement accuracy of DSM data obtained through control-free-image-based nap-of-the-object photogrammetry. The image data collected in different sessions (22 June and 15 July 2022) were individually processed, generating mean absolute error and standard deviation information for the DSM data of the two periods, as depicted in Figure 14. From the figure, we can see a gradual increase in the mean absolute error and standard error of DSM data for the two periods, before and after rainfall. However, the maximum mean absolute error in the DSM with or without image control points, before and after rainfall, is only 0.031 m (i.e., relatively small).  
Figure 14: Difference diagrams of DSMs with and without image control points.  
Figure 13: Error kernel density plots. (**a**) 22 June; (**b**) 15 July.  
The DSM was utilized to generate profiles of the erosion gully along the X- and Y-directions. Line 1 represents the cross-section along the X-direction, and Line 2 is the cross-section along the Y-direction of the erosion gully (Figure 15). Data from the two periods before and after rainfall were used to extract profiles of the erosion gully for both periods, as depicted in Figure 16.  
Figure 16: Profile of DSM with and without image control points. (**a**) 22 June Line 1 DSM profile; (**b**) 15 July Line 1 DSM profile; (**c**) 22 June Line 2 DSM profile; (**d**) 15 July Line 2 DSM profile.  
Figure 15: Profile line bitmap of DSM with and without image control points.  
The profile analysis of Line 1 reveals a consistent trend in the profiles obtained with or without image control points before and after rainfall. A similar trend is observable in the profile of Line 2, indicating a high degree of coincidence. However, there is a significant fluctuation in the section distance from 7.5 mcm to 8.5 m. The average height error for 22 June is approximately 6.94 cm, with an offset along the X-axis of approximately 11.78 cm. For 15 July, the average height error is around 5.02 cm, with an offset along the X-axis of about 6.41 cm. However, the deviation in elevation and X-direction observations for both periods remains at the subcentimeter level.
## 1 Introduction  
Orbital friction vibration actuators (OFVA) have become a core part of orbital friction welding technology whose applications fall into many industries [1; 2; 3; 4]. As the force-supply unit of the orbital friction welding machine, the OFVA should generate higher thrust force when it operates in relatively high frequency. A permanent magnet (PM) OFVA composed of four identical PM LOAs and springs is proposed, as shown in Figure 1, for orbital friction welding machine. The performance of the OFVA is mostly determined by the thrust force characteristics of the LOAs. The parameters of LOA have a significant influence on the thrust force and its fluctuation. In this paper, the stator configurations of PM LOAs are thoroughly investigated to increase the force and suppress the fluctuation.  
Figure 1: Proposed a permanent magnet (PM) orbital friction vibration actuators (OFVA).  
There are two LOAs distributed in the x direction and two more in the y direction of the OFVA to control motion in x and y directions, separately. Due to the decoupled relationship between x and y direction movement, only one LOA was here analyzed.  
The thrust force ripple is an important parameter of LOA, key to ensuring the smooth operation. The thrust force ripple is affected by the end effect common existed in linear motors, and it is also affected by the magnetic circuit structure of LOA. The thrust force ripple needs to be modeled and reduced.  
The thrust force density is another important parameter of LOA. At present, the thrust force per area of LOA can be 48,360 N/m\({}^{2}\)[5], the linear motor can reach 83,000 N/m\({}^{2}\)[6; 7] by optimization. A design method was proposed for unit LOA to maximize thrust force density in [8], the quasi-Halbach magnetized unit LOA can reach 112,700 N/mm\({}^{2}\), while it focus on the comparative design of the PM mover, and the stator is still needed to design and analysis.  
To improve the performance of OFVA, the design principles and the structural characteristics of the E-cored PM LOA are illustrated in this paper. The influences of the end effects associated with the finite length armature and stator's design parameters are also investigated. The main design parameters of the actuator and the shape of the teeth and slot were designed to maximize thrust force density while minimizing thrust force ripple to improve the electromagnetic performance of OFVA. Finally, the predicted thrust force characteristics of the LOA were validated by measurements on a prototype actuator.
## 3 Logging Response Characteristics
### Acoustic and Density Logging  
Compressional wave slowness (AC) and bulk density (DEN) responses reflect the dual effects of organic matter's low density (\(\sim\)1.3 g/cm\({}^{3}\)) and elevated porosity in hydrocarbon-rich zones. Acoustic slowness shows a moderate positive correlation with TOC (r = 0.58, \(p\) < 0.01), though its diagnostic value diminishes in overpressured intervals where compaction effects dominate (Figure 2b). Conversely, bulk density exhibits a robust negative correlation (r = \(-\)0.76, \(p\) < 0.01), with TOC-driven density reductions reaching up to 0.25 g/cm\({}^{3}\) in high-maturity shales (Figure 2c). The complementary nature of AC and DEN responses--where AC compensates for DEN anomalies caused by pyrite cementation--provides a synergistic approach for TOC evaluation in mineralogically complex sections [17; 18; 19].
## 4 High Capacity 2 \(\times\) 2 OPS Node with Contention Resolution
### Experimental Results and Discussion  
In the optical switch, optical packets with label B were dropped and packet contentions between packets with label A of TX1 and TX2 were resolved. In this designed packet contentions, four packets with label A of TX1 and TX2 were given a delay of {0, 0, 0, 100} ns and {100, 0, 100, 200} ns, respectively. All packets with label A were output successfully without overlapping. Figure 13 shows the measured BER for the entire 64 wavelength channels after the OPS node, for every individual packet (P1 to P8) and averaged over 10 packets from different experimental realizations. The observed saw-tooth shape is due to uneven biasing of the DPMZMs between odd and even channels. These plot show that the payload BER is below the FEC limit of 3 \(\times\) 10\({}^{-3}\) after the OPS node and demonstrates the successful operation of the 2 \(\times\) 2 OPS node including packet buffering with a capacity of 12.24 Tb/s/port.  
Figure 12: (**a**) experimental setup of the 2 \(\times\) 2 optical packet switch one drop line and three fiber sheet delay lines; (**b**) schematic of the optical packet trains at the output of each optical switch and the aggregated packet train to be processed by the receiver.
## 3 Motivating Scenario
#### Challenge of Using AmI Context for WSN Optimizations  
The purpose of the WSN in the above AmI scenario is to collect sensor data of the monitored environments and deliver them to the intelligent agents for the inference of possible events related to particular users. A WSN node can only generate raw sensor data, which may not present any meaningful information if the data is not processed further. Therefore, communicating the sensor data from the WSN to the intelligent agents is an important part of the AmI system in order to successfully deduce and notify the user-relevant events on time.  
In this scenario, the pub/sub based communication is used, in which data from the same sensor can be subscribed by different intelligent agents at different rates, _i.e._, frequency of receiving an update of the data, under different situations. For instance, when [PERSON] is detected to be approaching the street, the intelligent agent of his smart device takes the sensor data about vehicles on the street very seriously, and changes its subscription to receive an update of this sensor data more frequently. On the other hand, the same sensor data would be less important to [PERSON]'s agent if the ball had not rolled onto the street, and his agent would have just monitor this sensor data at a normal rate.  
Therefore, the importance of a piece of sensor data may not be determined by individual sensor nodes, but by an AmI system, such as [PERSON]'s intelligent agent in the above scenario. In addition, different agents may perceive the importance of a piece of sensor data differently even at the same time. For instance, [PERSON]'s agent will regard the sensor data from the street as highly important, while those of his playmates who remain in the backyard may not. Therefore, there is a need for a mechanism for the sensor nodes to optimize the communication of their data to the AmI systems in different situations.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.12. External Contract Referencing  
Smart contracts need to re-use other contracts' codes sometimes by referencing their addresses. For example, a developer could initiate an outside library in his contract's constructor. The library's address will be provided during deployment. This seems legitimate and safe from code audit. However, if given a wrong address in the deployment, a security hole is created because a malicious contract (honey pot) may be called from that wrong address.  
_Countermeasures_. One could hard code the external addresses into contract's codes if they are public or use new keyword to create contracts instead of deployment input.
## 3 Results and Discussion
### Simulations
The starting block total deformation under a 2500 N centre vertical load was 0.00030553 m. The gravity at sea level (9.81 m/s\({}^{2}\)) tested over the underwater structure and two force plates vertically fixed on it revealed a maximal deformation of 0.00012322 m. Moreover, using the same standard gravity at sea level, the underwater structure with force plates has showed a maximum of 0.00000813 Pa and 0.0000409 m/m, considering equivalent von-Mises stress and equivalent von-Mises elastic strain, respectively, indicating short stress gradients in the underwater structure regions. An antero-posterior 2000 N load applied both on the lowest and on the highest horizontal and vertical handgrips revealed 200, 165 and 115 maximal \(\mu\varepsilon\). A vertical 2000 N load applied on the lowest and on the highest horizontal and vertical handgrips indicated 591, 585 and 205 maximal \(\mu\varepsilon\).