esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Models
### APF Theory and Flocking Principles  
In APF methods, the analysis is based on potential functions \(P\big{(}d_{i,j}(t)\big{)}\), where \(d_{i,j}(t)=\big{\|}\mathbf{p}_{i}(t)-\mathbf{p}_{j}(t)\big{\|}\) is the Euclidean distance between agents \(i\) and \(j\). The agent \(i\) uses a gradient descent algorithm based on \(P\big{(}d_{i,j}(t)\big{)}\) to move. In flocking algorithms, we are interested in a particular potential function \(P\big{(}d_{i,j}(t)\big{)}\), described in Definition 1.  
The potential function \(P\big{(}d_{i,j}(t)\big{)}=P^{a}\big{(}d_{i,j}(t)\big{)}-P^{r}\big{(}d_{i,j}(t) \big{)}\) is a flocking potential field if:  
* \(P\big{(}d_{i,j}(t)\big{)}\) is a non-negative function of the distance \(d_{i,j}(t)\) between agents \(i\) and \(j\),
* \(P^{r}\big{(}d_{i,j}(t)\big{)}\) is monotonically increasing in \([0,+\infty[\) and its gradient is the highest when \(d_{i,j}(t)\to 0\),
* \(P^{a}\big{(}d_{i,j}(t)\big{)}\) is monotonically increasing in \(]0,+\infty[\) and its gradient is the highest when \(d_{i,j}(t)\rightarrow+\infty\),
* \(P\big{(}d_{i,j}(t)\big{)}\) is convex and even,
* \(P\big{(}d_{i,j}(t)\big{)}\) attains its unique minimum when \(i\) and \(j\) are located at a desired distance \(r_{eq}\).  
Where \(P^{a}\big{(}d_{i,j}(t)\big{)}\) and \(P^{r}\big{(}d_{i,j}(t)\big{)}\) are, respectively, attractive and repulsive potential fields. An example of such potential fields are shown in Figure 1. It is a reactive approach to guidance where the trajectory is not planned. Since \(P\big{(}d_{i,j}(t)\big{)}\) is convex, we can directly use the sign of its gradient to describe the behavior of agents \(i\) and \(j\). If the gradient is positive, then agents \(i\) and \(j\) will repel each other. If the gradient is negative, then agents \(i\) and \(j\) will attract each other. The agent will stop when it attains an equilibrium distance \(r_{eq}\) between agents \(i\) and \(j\), where the potential function is null and stable in the sense of Lyapunov [12; 35; 38]. Collisions between neighbors in the swarm are thus avoided and the inter-agent distance is controlled owing to near-equilibrium distance parameter \(r_{eq}\). Hence, the swarm will attain a stable formation, when individually each agent minimizes its potential function, whatever the initial position of the agents. When the swarm is stable, there is a _velocity matching_ between the agents [12; 46]. Ref. [47] use the consensus metric \(V(t)\) as follows :  
\[V(t)=\frac{\sum_{i=1}^{N}\sum_{j=1,j\
eq 1}^{N}\big{\|}\mathbf{v}_{i}(t)- \mathbf{v}_{j}(t)\big{\|}}{N(N-1)}. \tag{4}\]  
Hence, if \(\lim_{t\rightarrow+\infty}V(t)=0\), we are sure to attain a consensus in the swarm speed. We define as follows the swarm stability.  
The swarm is stable if there is a consensus as follows:  
\[\forall\epsilon>0,\exists t_{0}/\forall t\geq t_{0},V(t)<\epsilon.\]  
Figure 1: Example of potential field \(P(d_{i,j})\) with respect to Definition 1, \(r_{eq}=1\) m.
## 4 Technologies for Smart Grid
### Energy Storage  
Energy storage is a key component which contribute to different services such as energy maintenance, energy management, load shifting, frequency regulation, peak shaving and energy arbitrage in SG system. Energy storage systems are the game-changing technology in grid-connected systems, determining factors pertaining to generating, storing and selling back to the grid by customers. Some of the available energy storage means are electrochemical (batteries and fuel cells), electromechanical (flywheels, pump hydro, and compressed air), electrostatic (ultra-capacitors) and electromagnetic type (superconducting magnetic storage) [51; 52]. Electrochemical batteries, however, has a growing demand in the market for their downward price and upward performance [53]. Their use in advanced automotive technology, such as plug-in EVs [54] are gar  
Figure 4: Operational block diagram of an advanced distribution automation system. It can monitor the system in real time and can respond automatically to the demand [45].  
Figure 5: Block diagram of V2G and G2V for SG system. V2G and G2V together are one instance of bi-directional power flow in SG system. V2G can help in balancing the loads during peak-shaving [49].  
retired EV batteries can be replenished for further use as second life batteries in energy storage systems (SLBESS) [55]. Energy management through such energy storage units in SG domain will reinforce grid stability, reliability and efficiency. In addition, energy storage can mitigate the intermittency and fluctuation of renewable energy sources such as wind and solar. It also increases the penetration level of solar and wind power in SG system. It has been reported that energy storages have already been using for load-shifting, frequency support and power quality improvement in power system network [51; 52; 56]. For real-time energy storage arbitrage (storage of energy during cheap price for future use during high price), several optimization algorithms are utilized to determine the arbitrage policy [57]. In load shifting, the energy stores during off-peak is utilized to supply power for the peak demand by the load. The frequency regulation is performed both in transmission and in distribution stages. However, the system power qualities can be improved by smoothing the voltage and frequency fluctuations in the distribution side. The output of renewable energy sources with and without energy storage system is shown in Figure 6.
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks
## 3 Proposed Technique for Generating Attitude Reference Profiles
Therefore, to calculate the quaternion and angular rate error at the end of the profile for optimization, the quaternion and rate values at \(t_{7}\) generated up to that point was compared using the quaternion and rate information at the start of the mission, and it was backpropagated for a duration of \(t_{8}\) as follows:  
\[\theta(t)_{back}=\omega_{mission}(t-t_{8}), \tag{36}\]  
where \(\theta(t)_{back}\) is the backpropagated angle from the start of target pointing and \(\omega_{mission}\) is the angular rate at the start of target pointing. The cost function used for generating an optimized profile for the maneuver segment in the second step is given by  
\[\textit{Cost function for optimization}=(\textit{Quaternion error})_{ target\textit{ pointing start time}}+(\textit{Rate error})_{target\textit{ pointing start time}}. \tag{37}\]  
When applying the generated reference maneuver profile to an actual satellite's attitude control logic, the additional errors that may arise during the restoration process must be considered. Thus, we included a margin in determining the convergence range of the cost function used for optimization, taking into account the potential additional errors. A quaternion error of \(10^{-4}\) and a rate error of \(10^{-30}\)/s were used for optimization. The process for generating the maneuver reference profile for LEO satellites is shown in Figure 5.  
Figure 4: Maneuver reference profile generation network for LEO satellites.
## 4 Architecture Design
### Emulation Environment
#### 4.2.2 Wireless Mesh Network Emulation Environment  
The emulation mesh network environment is based on earlier in-house development of the millimeter-wave wireless mesh network (WMN) emulation environment [27, 28] that was modified by adding support for omnidirectional APs and moving nodes. The emulator contains emulation nodes, which act as mobile routers (V1 and V2), access points (AP1, AP2, and AP3), and a gateway. Emulation nodes are connected to each other with emulated wireless RF links that are implemented over optical fiber via their 10 gigabit Ethernet (Gbe) ports. The nodes additionally have two 1 Gbe ports for outside communication. One 1 Gbe port gives its control level (Linux) access to programs running on a FreeBSD server. Mobile routers send signal data to InfluxDB, and the gateway node communicates with the centralized network control program WMN centralized controller (WCC, that manages WMN resources, such as routing and link scheduling). WCC calculates the mesh topology configuration and sends it via the gateway to the nodes. The other 1 Gbe node port is connected to mesh user computers, which send and receive payload traffic via the mesh network.  
The deployment diagrams in Figures 11 and 12 illustrate the emulation node and emulation mesh network environment. The components of emulation nodes (Figure 11) are described as follows:  
Wireless mesh network data plane:  
* Handles packet routing between 10 Gbe mesh ports and between mesh ports and 1 Gbe user data ports. In access points, it manipulates packet flow according to emulation rules received from the wireless mesh network controller. Wireless mesh network controller:
* The mobile router sends emulated AP signal quality data to the machine learning module, receives delay predictions from it, and makes a routing decision, which it sends to the data plane, and via Telegraf server agent, to InfluxDB.
* The access point node reads emulated packet flow rules from the received files and sends them to the data plane.
* The gateway node receives a network topology configuration from WCC and sends it to the data plane to be broadcasted to other nodes. Telegraf server agent:
* Receives routing data from the controller and sends it to the InfluxDB data base. Machine learning module:
* Communicates received AP signal quality data to RedisAI, receives predictions, and returns them to the caller (wireless mesh network controller).  
Figure 11: Deployment diagram of an emulation node.
## 4 Results
### Experiment Setup
#### 4.1.1 Two-Class Inter-Session and Inter-Subject Classification Scenario  
In this experiment, the EMC-MMFT, ROD, DTE, and LSA-MMFT frameworks are implemented based on a two-class inter-session and inter-subject classification scenario. As such, the performance of each of the four domain selection algorithms is evaluated and compared based on a two-class problem, using nine of our own recorded SSMVEP sessions, acquired from a single subject over different days, and our own dataset recorded using nine SSMVEP subjects, respectively. Moreover, the data of nine motor imagery (MI) subjects acquired from a publicly available EEG dataset and our own recorded (MI) dataset acquired from nine subjects are also utilized for experimentation. In this case, when EMC-MMFT is applied on both the data from the nine sessions and subjects, respectively, EMC-MMFT first iterates through the sessions (domains) and then assigns one session/subject the role of TD. However, each of the nine sessions/subjects takes a turn being the TD in each iteration, while the best combinations of highly beneficial SDs are selected from the remaining eight sessions/subjects, whereby the best selected combinations of SDs are utilized for TL [12].  
Moreover, in our experiment, LSA-MMFT, ROD, and DTE are all further enhanced and modified for performance evaluation. In this case, when both ROD and the DTE algorithm are applied on the nine sessions/subjects, a single domain is selected to be the TD, and each of the nine sessions/subjects takes a turn being the TD. However, each time a TD is assigned, the algorithms iterate through the remaining eight domains, removing a single less-beneficial domain in each iteration, using mathematical similarity calculations to evaluate the similarities between the domains. The similarities between the domains are evaluated based on the single-to-single (STS) transfer method, then followed by multi-source-to-single-target (MTS) transfer [10; 13]. As such, whenever a less-beneficial domain is removed in each iteration, the other remaining domains are assigned the roles of SDs and are then utilized for TL.  
Furthermore, when LSA-MMFT is employed on both nine sessions/subjects, respectively, each of the nine domains takes a turn being the TD. Therefore, each time a single session/subject is assigned the role of TD, LSA-MMFT iterates through eight SDs, removing a single less-beneficial domain in each iteration based on a label similarities analysis, mainly to identify highly beneficial domains using the STS approach [11]. Once a single less-beneficial domain has been removed, the remaining SDs and the assigned TD are utilized to perform TL based on the MTS approach, in each iteration.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 5 Discussion  
The lower the frequency, the higher the maximum coverage of the signal with the same TX power (considering devices with similar sensitivity). Consequently, the use of a frequency band below 2.4 GHz increases the range of the radio signals. In comparison with a 2.4 GHz solution, sub-GHz enables networks with fewer hops, allowing simpler deployments but with the performance and flexibility that mesh networks provide.  
The adoption of the PHYs described in the \"g\" amendment of IEEE802.15.4 can be beneficial to any type of WSN due to the following reasons:  
* They can be used in sub-GHz bands, increasing the range of the radio links.
* Higher data rates, up to 800 kbps, reduce the transmission/reception time for a packet; this lowers the electric charge consumption per byte exchanged and increases the maximum amount of packets that can be exchanged considering duty cycle regulations.
* They provide long range as a Low-Power Wide Area Network (LPWAN) combined with the flexibility of a mesh network. This enables networks to be built with simple architectures (few-hop deep networks) bringing good trade-off between performance and simplicity.
* The diverse characteristics of each modulation enable further optimization to the low-power networks solutions. Networks are now able to trade-off data rate, robustness, electric charge consumption, range and duty cycle. Choices on the PHY to be used can be made according to the current conditions.  
Regulations on sub-GHz bands are in constant revision, which poses an additional challenge to keep up with all the evolutions.  
This work is meant to provide the reader a first reference of what can be achieved by the IEEE802.15.4g standard in terms of PDR and range on different scenarios where WSN are likely to be deployed. We acknowledge that the ideal approach would be to run the experiments several times, well separated in time in order to provide results richer in statistical relevance. By doing this, we can reduce the impact that transient effects may have in the results. In addition, experiments should be repeated with the RX nodes located half wavelength away in several directions from the selected measurement point. This would be useful in order to discard positions where multi-path fading can heavily attenuate the radio signal.  
Nonetheless, these empirical results are taken in real-world scenarios. This provides a snapshot of the performance of IEEE802.15.4g at the precise moment of the experiment, including few inconsistencies in some measurements due to external factors, as a real deployment would encounter. To the best of our knowledge, these are the first experiments exploring the entire IEEE802.15.4g standard.
## 4 Experiments and Results Discussion
### Models Evaluation
#### 4.2.4 Grad-CAM Explanation of MDISE Model  
In order to more intuitively understand the gas concentration inversion mechanism of the MDISE model, the gradient-weighted class activation mapping (Grad-CAM) approach [35] was used to visualize the feature weights generated by the model. The absorbance spectrum collected by the 1 st filter channel is more complex than that from the 2 nd filter channel. Therefore, the mean absorbance spectrum of CO\({}_{2}\) at a concentration of 90% and a blackbody temperature of 383 K from the 1 st filter channel in the test set was used as input. The feature weights generated by the Base, 1D Inception, MD-1D Inception, and MDISE models were visualized, as shown in Figure 14.  
In Figure 14, the black solid line represents the spectral data input into the model, while the different colored backgrounds represent the features extracted by the model with varying weights. The colors transition from deep blue to deep red, indicating that the feature weights range from 0 to 1. The higher the feature weight, the more important the corresponding wavelength is for the gas concentration inversion. From Figure 14, it can be observed that the Base model, which only uses 3 \(\times\) 1 standard convolution kernels in each convolutional layer, focuses too much on wavelengths near the CO\({}_{2}\) absorption valleys due to the limited receptive field, neglecting other important wavelengths. In contrast, the 1D Inception model, which uses the 1D Inception module, has a broader receptive field and the features with a weight greater than 0.3 cover the main CO\({}_{2}\) absorption bands. The MD-1D Inception model, by adding the MD module, retains both local and global shallow features of the spectrum, further enhancing the model's attention to wavelengths near the main and secondary CO\({}_{2}\) absorption bands compared to the 1D Inception model. The MDISE model, with the addition of the SE module, recalibrates the feature weights, reducing the emphasis on wavelengths near the CO\({}_{2}\) absorption valleys, and better highlighting the importance of wavelengths near the CO\({}_{2}\) absorption peaks. Thus, the sequential combination of the MD module, 1D Inception module, and SE module maximizes the performance of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **1 st Filter Channel** & **2 nd Filter Channel** \\ \cline{2-3}  & _RMSE_**/**\%** & _RMSE_**/**\%** \\ \hline Base & 2.1049 & 2.7659 \\ MD & 1.7675 & 2.3676 \\ \(1\)D Inception & 1.6965 & 2.1256 \\ SE & 1.5079 & 1.9661 \\ MD-1D Inception & 1.3688 & 1.3824 \\ MD-SE & 1.2726 & 1.1477 \\ \(1\)D Inception-SE & 0.6599 & 0.8107 \\ MDISE & 0.4524 & 0.5759 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the ablation experiments for modules.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 4 Results and Experimentation
### Alive Node Analysis  
The number of active nodes in the network may be gleaned by examining the active nodes per transmission round. Nodes that are still alive can be used to estimate the network's lifespan. Alive node analysis is shown in Figure 8 and Table 4, where the first 6000 cycles of the network are analyzed. An initial node density of 60 is used for this. This shows that PSO-HSA is more efficient than LEACH and LEACH-C node life expectancy. Comparison findings are presented in tabular form in Table 4.  
Figure 8: Lifetime of nodes.  
Figure 7: Clustering life time.
## 3 Theoretical Context
### Queuing Theory, [PERSON]'s Notation  
Queuing theory is the study of a technique based on operations research to solve problems that arise in situations where waiting for shifts or queues are formed for the provision of a service or execution of a job [21; 22; 23].  
Among the most important terms that comprise the queuing theory [24; 25; 26] are \"Customers,\" which refer to the entity that arrives at the system, such as: Cars waiting at a traffic light, machines waiting to be repaired, airplanes waiting to land, among others. \"Arrivals\" refers to the number of customers arriving at the service facility. \"Service Rate\" is used to designate the service capacity, which can be provided by one server or by multiple servers. \"The Arrival Rate\" describes in units of time the feeding of the system. \"TheServer\" oversees providing the respective service to the client. \"The Queue Capacity\" can be infinite or finite. Given the above, different queue models can be presented, with corresponding efficiency measures that characterize the system [27; 28; 29].  
[PERSON] suggested a valuable notation for classifying the vast diversity of different wait-line models that have been developed [30]. [PERSON]'s notation of three symbols is as follows: A/B/K, where A indicates the probability distribution of arrivals, B indicates the probability distribution of service times, and K indicates the number of channels. Various waiting line systems can be described depending on which letter appears in the A or B position.  
Commonly used letters are M, which designates a Poisson probability distribution for arrivals or exponential probability distribution for service time; D, which designates the fact that arrivals or service time is deterministic or constant, and G, which indicates that arrivals or service time have a general probability distribution, with known mean and variance [31; 32].
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 2 Data and Instrument
### Classification of Sky Conditions  
In order to forecast solar irradiance using sky images during a variety of sky conditions, the 25 days from May to August in July 2020 were selected. These days provided all-sky conditions based on the clear-sky index and variability. Thereafter, days have been grouped according to the mean of the clear-sky index and clear-sky index variability, which is calculated using the following equation:  
\[\mu_{kt}=\frac{1}{n}\sum_{i=1}^{n}\frac{GHI_{m}}{GHI_{c}} \tag{1}\]  
\[\delta_{kt}=Std\{kt_{t-1}-kt_{t}\} \tag{2}\]  
where \(kt\) is the clear-sky index, \(\mu_{kt}\) is the mean of the clear-sky index, \(GHI_{m}\) is the GHI measurement, \(GHI_{c}\) is the clear-sky GHI, and \(\delta_{kt}\) is the clear sky index variability. Statistics from the clear-sky index can be used to determine the sky conditions. The clear-sky index estimates the atmospheric attenuation due to the clouds by measuring the ratio of the surface solar radiation to the solar radiation received under a clear sky. In this study, the days have been grouped into three categories [12] as shown in Figure 3: clear-sky day (\(\mu_{kt}>0.65\)) and (\(\delta_{kt}<0.03\)), partly cloudy day (\(0.3\leq\mu_{kt}\leq 0.65\)) and (\(\delta_{kt}>0.10\)), and overcast day (\(\mu_{kt}<0.3\)) and (\(\delta_{kt}<0.03\)). Figure 4 compares the GHI measurement variability in all sky conditions for three representative days. It illustrates the small variation in GHI for clear and overcast days from 9:00 a.m. to 5:00 p.m. On the other hand, the partly cloudy day presents the high GHI variability with the maximum and the minimum values of GHI are 956.0 Wm\({}^{-2}\) and 184.0 Wm\({}^{-2}\), respectively.  
Figure 2: Solar irradiance measurement equipment installed on the rooftop of Kookmin University. (a) MS-80 pyranometer to measure the global horizontal irradiance; (b) measurement station location.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 3 Results and Discussion
### Mixing Performance Characterization of Mixer
In Figure 3d, we could extract the input third-order intercept point (IIP3) for our fabricated mixer, and the intermediate frequency signal (IF) power increased with the dependence of 10 dB/dec, while the third signal of intermodulation (IM) power followed the dependence of 30 dB/dec. By extrapolating IF and IM lines to intersect each other, we extracted 18 dBm of the input third-order intercept point (IIP3). The statistical distribution of CNT-based mixers is also shown in Figure S5 of the Supplementary Materials.