esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Lessening Fluctuation Method
### Fluctuations in Cascade Development
However, since there are several particles in a cascade, fluctuations of individual interactions can partially compensate each other. When there are many particles in a cascade, the property of universality of the cascade development is realized. All cascades initiated by particles of the same energy and mass develop in the same way [27; 28]. Thus, the cascades differ greatly in the depth of the first interaction. They also differ greatly in the parameters of the first interaction (multiplicity, peripherality, etc.). This leads to major fluctuations at the beginning of the cascade curve. However, the cascade fluctuates weakly if it contains many secondary particles. Thus, instead of the penetration depth, it is necessary to find another parameter that does not depend on these fluctuations in the development of the cascade process. LFM is based on the use of so-called correlation curves--the dependence of the cascade size (\(S=\log\,q\)) on the cascade development rate \(R=(S_{1}-S_{2})/(d_{1}-d_{2})\). As unit absorber (\(d_{1}-d_{2}\)), a thickness equal to three layers of the PAMELA calorimeter was chosen. Therefore, the rate of shower development was calculated as the difference between the cascade size on the \(L\)-th and \(L+3\) measuring layers, \(R=S_{L}-S_{L+3}\). When choosing the thickness of the unit absorber, we took into account two main factors. The first factor was fluctuations from layer to layer. The thinner the unit absorber, the higher the relative fluctuations of \(R\) due to fluctuations from layer to layer. Therefore, it was preferable to choose a thicker unit absorber. The second factor was the calorimeter thickness. The thinner the unit absorber used in the LFM, the more points on the correlation curve could be obtained. Looking to the future, a thin unit absorber makes it possible to use an ultra-thin calorimeter to measure primary energy. Figure 2b shows the correlation curves for the same three showers that were presented in Figure 2a. As can be seen in Figure 2b, in contrast to the cascade curves (Figure 2a), the correlation curves almost coincide for fast, medium, and slow showers. This greatly simplifies the task of determining the primary energy. Regardless of fluctuations in the development of a shower, all proton showers of the same energy are located on the same curve. Therefore, the energy of these showers will also be defined as the same.
## 2 Materials and Methods
### Data Correction and Calibration
#### 2.3.1 Scattering Volume Correction and Angular Calibration  
Scattering volume is defined as the volume illuminated by the incident beam, and it must be constant over all scattering angles for VSF measurements. However, it varies steadily with the scattering angle in a complex way. Scattering volume correction should thus be implemented. Scattering volume correction is performed following the method proposed by [PERSON] et al. [23], who reported that the variation of scattering volume relative to the scattering volume at 90\({}^{\circ}\) (about 0.02 cm\({}^{3}\) in this work) can be expressed as the inverse of a sine function.  
Specifically, the consistency of the angular measurements was checked. When the rotating device is activated, acceleration occurs. To ensure a uniform rotation in the scattering angle range of 0\({}^{\circ}\)-180\({}^{\circ}\) and a uniformly spaced angle interval, we set the instrument so that it starts sampling before the scattering angle reaches 0\({}^{\circ}\). Thus, the aim of angular calibration is to find the first and last sampling points and calibrate the angular discrepancy. Angular calibration was realized through VSF measurement on standard beads with a mean diameter of 2.0 \(\upmu\)m suspended in water. The results for 2.0 \(\upmu\)m beads showed a distinct pattern with several scattering maxima and minima due to the constructive and destructive interferences of the scattered light from a nearly monodisperse population of beads that are large relative to the light wavelength. Angular calibration can be achieved by assigning the detected VSF maxima or minima using a second-order polynomial curve fitting. Furthermore, 3, 5, and 11 \(\upmu\)m beads are used for angular validation.
# Home Automation System Based on Intelligent Transducer Enablers
## 4 Home Automation System Based on Intelligent Transducer Enablers
### Hardware Architecture  
Figure 2 illustrates the main elements present in HASITE's hardware architecture, which can be separated into two groups:  
* HASITE-independent infrastructure: elements already available in most homes, but needed by HASITE to achieve full functionality. Such elements are also used for other purposes:
* Gateway: it provides Internet access.
* Network access point: it connects the nodes to the home automation gateway.
* User interface devices: smartphones, tablets or personal computers that allow for the interaction with HASITE.
* HASITE-dependent infrastructure: This includes the hardware elements directly used by HASITE:
* Home automation gateway: It registers and provides access to the nodes. It also provides external access from the Internet through the gateway to facilitate remote control and monitoring. It is connected to a configuration access point to support the self-configuration mechanism. It also supports and grants access to the API.
* Configuration AP: This is a special access point used for the auto-configuration protocol. It can be a traditional AP or a transducer node of the network with AP capabilities.
* Intelligent Transducer Node (ITN): This is a node consisting of a number of transducers, a processing system and wired or wireless communication modules that allow for exchanging data with the home automation gateway. Figure 3 shows a block diagram of an ITN. Note that  
Figure 2: HASITE’s architecture.  
ITNs within a home automation network can control sensors (that send environmental information) and actuators (that perform specific tasks). For instance, temperature, humidity, light, presence and electrical current sensors were the basis of different ITNs built during the development for HASITE. Moreover, actuator-based ITNs included relays, which switch power sockets on and off, and light intensity controllers.  
It is important to note that the home automation gateway functionalities can be carried out by any regular PC [26; 41; 42], but such an element requires dedicated hardware with high dimensions and consumption. For the experiments detailed in Section 8, we opted for a Single Board Computer (SBC), like other authors chose in recent publications [27; 28]. An SBC is a small board with one or several microprocessors, memory, input/output (I/O) pins and other features typically found in a traditional computer. The use of an SBC is very attractive for home automation because its low cost, small in size and has low consumption.  
In contrast to this approach, a decentralized system [29] could yield better power consumption (a central processing element is not required) and lower response times for automatic decision making. However, such a system would increase the complexity of HASITE, mainly the plug-and-play protocols and the API access. Thus, using a central control element was preferred instead.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 4 Discussions and Conclusions  
In this paper, we examined the impact of the atmosphere on FSO uplink/downlink channels between satellites and ground stations. We present mathematical equations on atmospheric factors such as fog attenuation coefficient, refractive index parameter, coherence length, turbulence model, and angle-of-arrival fluctuation to consider various atmospheric effects. Different channel conditions were simulated, and the effects on channel performance were investigated to aid in the design of FSO links.  
By adjusting channel parameters, we illustrated the impacts of optical channel parameters and channel conditions on FSO channel performance. Our primary contributions of this paper are as follows.  
* We present the correlation between various distinctive parameters that impact the near-Earth FSO channel, such as coherence length (Fried parameter), zenith angle, refractive index structure parameter, scintillation index, Rytov variance, and angle-of-arrival fluctuations.
* We confirm the appropriate wavelength to be considered while designing uplink and downlink satellite-ground communications in the presence of atmospheric turbulence. The 1550 nm wavelength is best suited for both uplink and downlink because atmospheric turbulence has less impact, and the coherence length is longer, compared to shorter wavelengths.
* We suggest that the location and altitude of an optical ground station should help ensure a constant wind speed, and we demonstrate that the value of the refractive index parameter, which characterizes the strength of turbulence in the atmosphere, is less with increases in altitude above sea level.  
Figure 11: Variation in angle-of-arrival fluctuations (diameter = 5 m).  
Overall, our results provide a broad perspective on how a channel behaves and on what measures need to be taken to improve the communications system for FSO links. The findings of this study can be used to build satellite-ground FSO links and determine the best link parameter values.  
Conceptualization, N.M., N.D., and B.W.K.; methodology, N.M., N.D., and B.W.K.; formal analysis, N.M., N.D., and B.W.K.; investigation, N.M., N.D., and B.W.K.; writing--original draft preparation, N.M., N.D., and B.W.K.; writing--review and editing, N.M., N.D., and B.W.K.; supervision, B.W.K.; project administration, B.W.K.; funding acquisition, B.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2022R1A2B5B01001543).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Discussion
### Mean Entropy and Entropy Standard Deviation of the Dataset  
Entropy is calculated based on the grayscale histogram of the image, which reflects the distribution of pixel intensity levels in the image. The entropy formula is given as follows:  
\[H(X)=-{\sum_{i=1}^{n}{p(x_{i})}}{\log_{2}{p(x_{i})}} \tag{1}\]  
In this context, \(H(X)\) represents the entropy of the image, \(x_{i}\) denotes the \(i\)-th intensity level (pixel value) in the image, \(p(x_{i})\) refers to the probability distribution of the intensity level \(x_{i}\), and \(n\) represents the total number of distinct intensity levels in the image. For coseismic InSAR observations, the dataset-level mean entropy and standard deviation of entropy are calculated using the following equations to quantitatively evaluate the overall informational diversity and statistical consistency of the dataset.  
\[\overline{H}(D)=\frac{1}{N}{\sum_{j=1}^{N}{H(I_{j})}} \tag{2}\]  
\[\sigma_{H}(D)=\sqrt{\frac{1}{N}{\sum_{j=1}^{N}{\left({H(I_{j})-\overline{H}(D) }\right)^{2}}}} \tag{3}\]  
Here, \(\overline{H}(D)\) denotes the average entropy of dataset \(D\), \(N\) represents the total number of interferograms in the dataset, \(H(I_{j})\) is the entropy of the \(j\)-th interferogram, and \(\sigma_{H}(D)\) refers to the standard deviation of entropy across the dataset.  
As shown in Table 2, the mean entropy of Dataset A is 7.58, higher than the 7.55 observed in Dataset B. This suggests that Dataset A exhibits greater diversity in coseismic deformation features. The standard deviation of entropy is 0.25 for Dataset A, compared to 0.42 for Dataset B. The notably lower standard deviation in Dataset A indicates greater consistency in image quality and structural complexity. Such stability is essential for deep learning-based InSAR modeling and seismic deformation detection, as it facilitates more effective learning of key features and reduces the risk of poor convergence and generalization caused by large variations in data quality. In contrast, the higher entropy standard deviation in Dataset B reflects greater variability in information complexity, which may introduce instability during training and ultimately compromise the model's accuracy and reliability in practical applications.
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 2 Methodology
### Heat-Fluid Coupling
#### 2.1.2 Heat Transfer  
The heat transfer process in the cooling plate involves thermal conductivity in the solid structure and fluid flow, respectively. According to Fourier transform theory, the governing equation for the thermal conductivity in a solid structure can be constructed as follows:  
\[k_{s}\
abla^{2}T+Q=0 \tag{3}\]  
where \(k_{s}\) is the thermal conductivity in the solid phase, \(T\) is the temperature, and \(Q\) is the internal thermal source.  
The governing equation for the thermal conductivity in the fluid phase is given as follows:  
\[\rho_{f}C_{pf}u\cdot\
abla T=k_{f}\
abla^{2}T+Q \tag{4}\]  
where \(C_{pf}\) and \(k_{f}\) are the fluid-specific heat capacity and the conduction coefficient in fluid. Combining Equations (3) and (4), we can obtain a unified governing equation for the thermal conductivity, which can be formulated as follows:  
\[\tilde{\phi}\rho_{f}C_{p}(\tilde{\phi})u\cdot\
abla T=k(\tilde{\rho})\
abla^{ 2}T+Q \tag{5}\]  
where \(\tilde{\phi}\) is the physical density of the cooling plate, in which 0 and 1 indicate the solid and fluid phases, respectively.
###### Abstract  
The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption and ensuring a more comfortable driving experience, this study investigates a flexible and real-time control method to navigate the CAVs at signalized intersections utilizing reinforcement learning (RL). Initially, control of CAVs at intersections is formulated as a Markov Decision Process (MDP) based on the vehicles' motion state and the intersection environment. Subsequently, a comprehensive reward function is formulated considering energy consumption, efficiency, comfort, and safety. Then, based on the established environment and the twin delayed deep deterministic policy gradient (TDS) algorithm, a control algorithm for CAVs is designed. Finally, a simulation study is conducted using SUMO, with Lankershim Boulevard as the research scenario. Results indicate that the proposed methods yield a 13.77% reduction in energy consumption and a notable 18.26% decrease in travel time. Vehicles controlled by the proposed method also exhibit smoother driving trajectories.  
connected and automated vehicles; Markov decision process; travel time
## 4 Calculation of the Load Rise Space  
In the REG cluster, the factors that affect the power of the delivery section of the REG cluster are the output of the REG stations and the load in the cluster [28].  
When the transmission power of the delivery section of the REG cluster reaches the upper limit and the output of each REG station remain unchanged, the load within the cluster increases. This growth triggers a corresponding decrease in the transmission power of the section, and the power cannot reach the upper limit. Before the margin distribution in the next period, the dispatch center of the REG cluster should, according to the actual situation, give timely instructions to increase the power output of the capable REG stations so that the delivery section can be fully utilized and the consumption of new energy can be improved.  
Considering the impact of load growth in the REG cluster on the power transmission of the delivery section, a load rise space is introduced to describe the average range of load growth in the REG cluster. The clustering analysis used to study the operation modes of the REG cluster tries to ensure that the load rise space under each type of operation mode is considered when allocating the section margin. This is to make sure that before the dispatch center allocates the margin for the next period, in order to make full use of the capacity of the delivery section, the probability of each new energy station meeting the output requirements is still at its maximum when the dispatch center asks each REG station to increase its output. This ensures that the transmission power of the delivery section meets the upper limit as far as possible and improves renewable energy consumption.
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Robustness Capabilities  
Communication technologies will provide robustness to signal interference and/or loss of network operation. When deployed in locations with other tactical networks (i.e., vehicular deployment), proper measures to avoid interference from adjacent users will be needed. For mesh or Point-to-Multi-Point (PMP) modes, the network will provide redundancy and be robust to a single point of failure. Systems should be robust to jamming, supporting techniques to actively track jamming signals and applying automatic jamming avoidance measures. It should include cognitive radio and dynamic spectrum management techniques to automatically overcome bad conditions in the communications environment.  
The operational requirements for robustness also include the physical attributes of the device. Generally, this is addressed by the target platform requirements, which, in turn, is dependent on the deployment scenario. Equipment should be also physically robust to environmental damage, i.e., shock- and water-proof. The IoT system should provide mechanisms to allow for fast switching between the technology chosen and back-up/legacy communications in the event of failure. Although there are many metrics available to assess the performance of IoT devices, evaluating their performance is a challenge since it depends on many components as well as the behavior of the underlying technologies. The evaluation of routing protocols, information processing [102], application layer protocols, and QoS have been reported in literature, but there is a lack of a thorough performance evaluation for IoT services.
# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 2 Materials and Methods
### Three-Dimensional Point Cloud Momentum Contrast Learning Framework
#### 2.1.5 Jaccard Contrast Loss  
The equivariance of the capsule network makes the output feature pay more attention to the transformation operation of the sample [21], and a certain dimension in the feature vector may represent a certain attribute of the sample. As shown in Figure 2, the feature vector obtained by inputting a positive sample that has undergone rotation transformation to the capsule network is roughly the same, and only the value of the first dimension representing the rotation attribute may be different. In this paper, the Jaccard coefficient is used to measure the similarity of two capsules. The Jaccard coefficient is used to compare the similarity and difference between a limited sample set. The larger the value of the Jaccard coefficient, the higher the similarity of the sample set. Compared with the traditional two vector dot product representing similarity, it can better reflect the characteristics of the equivariance of the capsule network.  
Given the two n-dimensional capsule vectors \(A\) and \(B\), the Jaccard coefficient is as set out in Formula (4):  
\[J(A,B)=\frac{\sum_{i}\min(x_{i},y_{i})}{\sum_{i}\max(x_{i},y_{i})} \tag{4}\]  
where \(x_{i}\) and \(y_{i}\) are the \(i\)-th digit of the capsule vectors \(A\) and \(B\), respectively. According to the Jaccard coefficient, this paper proposes the Jaccard contrast loss as the loss function of the network, and the Jaccard contrast loss is as set out in Formula (5):  
\[L=-\log\frac{\exp(J(q,k_{+})/\tau)}{\sum_{i=0}^{K}\exp(J(q,k_{i})/\tau)} \tag{5}\]  
where \(\tau\) is the temperature hyperparameter that controls the local separation and global uniformity of the feature distribution [25]. The number of features stored in the queue is \(K\), \(\boldsymbol{\xi}\)\(q\) is the feature of the network output in each iteration, \(k_{+}\) is the feature of the positive sample of \(q\) in the queue, and \(k_{i}\) is the \(i\)-th feature in the queue.  
Using Jaccard contrast loss as an unsupervised objective function, the distance between the feature \(P_{i}\) obtained by \(f(\cdot)\) and all the negative sample features in the feature queue is increased, thereby training the backbone networks \(f(\cdot)\) and \(g(\cdot)\). Compared with traditional contrast loss, Jaccard contrast loss pays more attention to the similarity and difference of features, so it is more conducive to the network to distinguish between positive and negative samples.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 3 Results  
### Accuracy Assessments  
Table 3 shows the confusion matrix for the eight forest datasets. The FROM-GLC10 dataset in 2017 has the best accuracy, with an overall accuracy (OA) of 84.48% and a Kappa of 0.65. Followed by FROM-GLC2017 with 30 m resolution and the GlobeLand30, they  
Figure 3: Spatial distribution of forests in Myanmar extracted from eight global land cover datasets. (**a**) Hansen2010; (**b**) CCI-LC-2015; (**c**) FROM-GLC-2015; (**d**) GLC-FCS-2015; (**e**) FROM-GLC-2017; (**f**) FROM-GLC10; (**g**) GLC-FCS-2020; (**h**) GlobeLand30-2020.  
both have an overall accuracy of 83.51%. The OA of the Hansen2010 and the FROM-GLC2015 both exceed 80%. In contrast, CCI-LC and GLC-FCS have lower accuracy for the forest. Specifically, the GLC-FCS has a high misclassification rate on non-forest. In both years of GLC-FCS, the user precision of non-forest is 60.92% and 60.96%, respectively. Meanwhile, it has a relatively high percentage of omission of 31.81% for forests in 2015. This percentage improves in 2020, which decreases to 25.74%. The CCI-LC has a high percentage of misclassification of 34.32% for non-forest and an omission of 26.71% for the forest.  
Although the OA and Kappa of FROM-GLC10 are the highest among the eight datasets, it has a lower mapping accuracy of 69.16% in non-forest. It means that there is a higher possibility of omission in non-forest, i.e., there is a large amount of overestimation in forests. In comparison, the OA and Kappa of the FROM-GLC2017 with 30 m resolution and GlobeLand30 are slightly lower, but their user precision and mapping accuracy are more stable. In the classification of forests, the misclassification and omission of GlobeLand30 are slightly better than the FROM-GLC. Therefore, GlobeLand30 is the best dataset in terms of the production of forest classification.