esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform
## 3 WT-Based Sparse Channel Estimation  
### HFM Signal  
HFM signal is a kind of non-linear frequency modulation signal whose instantaneous frequency is a hyperbolic function of time, which can be expressed as [16]:  
\[s(t)=\begin{cases}\cos(-2\pi k\ln(1-\frac{t}{t_{0}}))&-\frac{T}{2}\leq t\leq \frac{T}{2}\\ 0&\text{otherwise}\end{cases} \tag{4}\]  
where \(t_{0}=\frac{T(f_{2}+f_{1})}{2(f_{2}-f_{1})}\), \(k=\frac{Tf_{2}f_{1}}{f_{2}-f_{1}}\). In addition, \(T\) is the duration of the HFM signal, \(f_{1}\) is the starting frequency and \(f_{2}\) is the end frequency. If \(f_{1}<f_{2}\) then \(s(t)\) is HFM up sweep signal, otherwise represents HFM down sweep signal. The signal instantaneous frequency is the derivation of the phase term of the cosine function:  
\[f(t)=\frac{1}{2\pi}\frac{d[-2\pi k\ln(1-\frac{t}{t_{0}})]}{dt}=\frac{k}{t_{0} -t} \tag{5}\]  
We consider to apply HFM signal as the channel training signal for its good Doppler-invariability. Introducing a Doppler scale factor \(a\) to (4), the phase term \(\varphi(t)\) becomes  
\[\varphi_{a}(t)=-2\pi k\ln(1-\frac{at}{t_{0}}) \tag{6}\]Correspondingly, the instantaneous frequency is  
\[f_{a}(t)=\frac{1}{2\pi}\frac{d\varphi_{a}(t)}{dt}=\frac{ak}{t_{0}-at} \tag{7}\]  
In addition, from [17], there is a constant time shift \(\Delta t=\frac{(a-1)t_{0}}{a}\) to satisfy the following equation.  
\[f_{a}(t-\Delta t)=f(t) \tag{8}\]  
Thus, the HFM signal is Doppler invariant.
## 5 Controller Optimization Procedure  
Initially, the shipboard MG system was considered for the frequency regulation problem with constant variations in the renewable sources and a constant step load variation as a disturbance. The values for the solar source and the sea wave source were obtained from the average values of real data, which will be presented in the following section. Thus, for the solar source, the value of 0.06 p.u. was used, while for the sea wave source, the value of 0.075 p.u. was used, and the variation in the load was given the value of 0.1 p.u. At first, the LFC problem was implemented with the PIDF controller and its gains were adjusted with the different algorithms: ALO, GOA, GWO, HHO, JSO, and WOA. The same procedure was followed for FOPID and 2 DOF-PIDF. For the reliability of the results, each simulation experiment was performed 10 times, with 100 iterations and a population number of 50, where the ranges of the gains are taken as shown in Table 1. Also, for clarity reasons, the parameters of the shipboard microgrid are given in Table 1.  
Figure 6: Flowchart of the jellyfish search algorithm. The simulations have yielded significant results, which are summarized in Figure 7a-c and Table 2. With regard to the PIDF controller, Table 2 shows that the ITAE for the different algorithms are (in descending order) the GWO (0.2867), ALO (0.2867), HHO (0.2228), GOA (0.2146), WOA (0.2081), and JSO (0.1683). Figure 7a provides a visual representation of the frequency response of the PIDF controller exhibited by the different optimization algorithms. The figure clearly shows that the GWO (6.8512 mHz) and ALO (6.6323 mHz) produced the highest overshoot, while the JSO and WOA did not show any overshoot. Notably, all the optimization algorithms began with an initial undershoot value of \(-\)59.761 mHz. It was observed that GWO (14.5088 s) and ALO (14.5059 s) exhibited the longest settling times, while the JSO (12.4524 s) and GOA (12.3677 s) had the shortest settling times. From the convergence curve, it is clear that the GWO and ALO converged in the first iterations (they were trapped in a local minimum), while the value of the JSO objective function continuously decreased as the iterations passed and managed to give the minimum ITAE. In summary, the results demonstrated that the JSO algorithm is the most-effective optimization algorithm for the first PIDF controller, producing the lowest ITAE value, the second-shortest settling time, and no overshoot. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Lower Limit** & **Upper Limit** & **Parameter** & **Lower Limit** & **Upper Limit** \\ \hline \(K_{\text{p}}\) & \(-\)3.0 & 3.0 & \(\lambda\) & 0.0 & 2.0 \\ \(K_{\text{i}}\) & \(-\)3.0 & 3.0 & \(\mu\) & 0.0 & 2.0 \\ \(K_{\text{d}}\) & \(-\)3.0 & 3.0 & \(b\) & 0.0 & 5.0 \\ \(N\) & 0.0 & 5.0 & \(c\) & 0.0 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Range of controllers’ parameters (search space).
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Energy Efficiency
Simulation results demonstrated that MILP optimized the assignment of SF and CF pairs with over six percent increase in DER compared to the standard LoRaWAN ADR and number of collisions thirteen times smaller. The network energy consumption became almost three times lower than the equal-distribution and random dynamic allocation strategies. The strength of their approach is that it has backward compatibility with the standard ADR scheme, implying they are implementable in off-the-shelf LoRa devices. Future work could entail extending the optimization approach to longer transmission distances and a bigger number of gateways. The authors in [51] present a performance improvement technique through SF distributions for LoRa networks. They formulate the optimization problem for the SF distribution to maximize the packet reception probability (PRP) under a constraint for the mean energy utilization of each end node. This makes provision for the network performance to improve under the constraint for each end node by solving this optimization problem. The authors develop a technique to solve the formulated problem based on a distributed genetic algorithm, which is a metaheuristics technique. The technique improves the network performance by assigning the SFs to end nodes under the constraint for the average energy consumption of each end node. The assumption is that the end nodes are static and their number in the network does not change. The PRP is derived while considering the imperfect orthogonality of the SF in LoRa networks. Their results showed that the PRP performance of their proposed technique is superior and uses less average energy utilisation for all end nodes compared to the existing schemes. Future work could involve energy consumption per node rather than an average for all the end nodes.
## 2 Materials and Methods
### Retrieval Methodology
#### 2.2.3 The State Vectors of Fs in Forward Model  
In this study, the Fs state vector is generated using the Soil Canopy Observation, Photochemistry, and Energy fluxes (SCOPE v2.1) model. SCOPE is a vertical (1D) integrated radiative transfer and energy balance model [25]. The model calculates SIF spectra at the scale of chlorophyll molecules, leaves, and canopies. Here, we use the SCOPE to generate SIF spectra set at the canopy scale. Principal Component Analysis (PCA) is applied to extract the far-red spectral bands from these spectra. Given the simplicity and stability of the SIF spectral shape within the specified retrieval window [26], this study employs the first principal component, accounting for over 99% of the corresponding variance, as the state vector of SIF to represent the SIF spectra.
## 5 Conclusions  
This paper proposes a new method for identifying submarine hydrodynamic coefficients based on CFD simulations of free navigation data using Bayesian ridge regression models. The developed method utilizes overset grid techniques to simulate the submarine's multi-body movement, enabling specific sinusoidal movements of the horizontal and vertical rudders. It combines the URANS equations with a six-degree-of-freedom motion model and a body force propeller model to achieve six-degree-of-freedom free navigation of the underwater submarine. The velocity and angular velocity data are collected during the free navigation process, and the Bayesian ridge regression model is employed to identify the motion data and obtain all hydrodynamic coefficients.  
This method was compared against the traditional CFD-constrained model test method, and most of the errors in the hydrodynamic coefficients were within 10%, with a maximum error of \(-43.26\%\). Furthermore, this method provides more comprehensive hydrodynamic coefficients while offering the advantages of cost-effectiveness and time efficiency.  
Using the identified hydrodynamic coefficients allows the inversion of the submarine's spatial motion, resulting in a maximum trajectory error of 0.22 m. The velocity and angular velocity profiles of the submarine's body closely match the expected values, except for the roll angular velocity profile, which has a maximum error of 0.089 rad/s.  
Currently, the submarine data are obtained through CFD simulations. Future work will conduct self-propulsion model tests using real data to identify the hydrodynamic coefficients and validate this method's accuracy.  
Figure 18: Comparison of time history curves for velocity and angular velocity. (**a**) X-direction velocity, \(u\); (**b**) Y-direction velocity, \(v\); (**c**) Z-direction velocity, \(w\); (**d**) roll rate, \(p\); (**e**) pitch rate, \(q\); (**f**) yaw rate, \(r\).  
**Author Contributions:** Conceptualization, G.X. and Y.O.; methodology, G.X. and J.C.; validation, H.W., W.W. and Y.O.; writing--original draft preparation, G.X.; writing--review and editing, Y.O. and H.W.; supervision, W.W.; project administration, Y.O.; funding acquisition, Y.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Pre-Research on Equipment (Shared Technology) of China under grant no. 41407020602.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 2 Network Model  
### Wi-Fi Architecture  
The most common technology with which to deploy wireless local area networks (WLAN) is, without any doubt, based on the family protocols IEEE 802.11, commercially known as Wi-Fi. In this work, we focus our attention in the most widely deployed Wi-Fi operating mode, called infrastructure mode. Under this mode, there two different types of wireless devices: stations (STAs), which are the final devices that make use of the network, and access points (APs), which connect STAs to the rest of the network (wired and/or wireless). For that reason, each STA will be wirelessly associated with a single AP to be able to communicate with the rest of the network. The generic term STA can represent a wide range of wireless devices--smartphones, tablets, laptops, TVs, etc. It is important to highlight that in infrastructure mode, direct communications occurs only between STAs and APs. Direct communication between STAs is not permitted, as those communications must go through at least one AP. This is the general environment which frames our research, and our focus will be to assign a channel to each AP in the network, as the STAs will use the same channel that is chosen for their associated AP.  
In this work, without loss of generality, we focus our attention on the 2.4 GHz frequency band, as it is the most congested one and it is very widely used not only in Wi-Fi 4, but also in the recent Wi-Fi 6, so its future use is guaranteed. It is important to note that, as the frequency bands where Wi-Fi can operate are unlicensed, the interferences in some channels can be unacceptable, which makes proper channel assignment even more necessary. The 2.4 GHz frequency band has from 11 to 14 frequency channels that APs can choose. The choice between 11, 13 or 14 will depend on the region of the world we are in. For example, in Europe there are 13 channels available but in the USA there are only 11. In this work we use the most restrictive choice of having only 11 available channels at hand.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Centralized** & **Distributed** & **Heuristic** & **Optimization** & **STAs** \\ \hline
[2] & X & & X & & \\  
CB-AIIM & X & & X & & X \\ \hline \end{tabular}
\end{table}
Table 1: Summary of channel assignment techniques in Wi-Fi.  
Although the set of channels that can be chosen is 11, the main peculiarity of IEEE 802.11 networks is that those channels are partially overlapped. However, as the use of the three non-overlapping channels is commonly accepted and widely deployed, in this paper we focus on those channels only. In an 11-channel setting, only channels 1, 6 and 11 are used, so that only APs operating in the same channel will interfere.
# Probabilistic Analysis of Low-Emission Hydrogen Production from a Photovoltaic Carport
## 3 Components of the Low-Emission Hydrogen Generation System
### Energy Storage  
Generating electricity from renewable energy sources is not only a global trend but also an important element of the decarbonization of entire countries [85]. The percentage of energy from the photovoltaic systems supplied for the system owner's self-consumption and to the power grid increases significantly every year. However, photovoltaic systems are characterized by periodicity (due to day and night periods) and seasonality (due to the changing seasons) in energy production [86]. The electricity demand profile of individual and institutional customers may differ significantly from the energy production profile of photovoltaic systems. The power distribution network is usually used to balance such  
Figure 7: Electrolyzer with energy storage in a container building.  
differences; however, its possibilities in this area are sometimes limited. The use of energy storage can be helpful in this regard [87]. Energy storage is an important component in the energy supply system of residential and institutional buildings [88]. It is able to store significant amounts of energy, which constitute excess production in relation to the momentary demand for power and its release at any programmed time.  
Due to the storage of electricity in the energy storage facilities in the form of direct current, these facilities can be charged directly with the direct current generated by the photovoltaic system. The combination of a DC/DC converter and a DC/AC converter in one housing is called a hybrid energy inverter. The effective selection of a photovoltaic system, hybrid inverter and energy storage allows one to configure the operation of the energy system in on/off-grid mode and in the form of an emergency UPS power supply [89]. The autonomy and self-consumption of such solutions are much greater than those of the traditional solutions.  
There is currently a substantial offer of hybrid inverters on the market. The smaller ones work with single-phase power networks; the larger ones work in a three-phase system. The offered hybrid inverters work with various types of energy storage units (LFP and NMC) with different voltage levels from 24 to 500 V. The energy capacity of the energy storage units can also be freely configured from 2 to over 100 kWh [90].  
For the production and storage of the energy obtained from renewable energy sources to generate green hydrogen, it is proposed to use a three-phase hybrid inverter with a capacity of 6 kW and an energy storage facility in the LFP [91; 92] or NMC [93; 94] technology with an energy capacity of 20 kWh. One can consider purchasing a secondhand traction battery from an electric vehicle to use it as a stationary energy storage device [95].
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis  
To validate the effectiveness of the proposed approximation treating SWIPT AF relays as their non-SWIPT counterparts, we extend the analysis to systems employing multiple SWIPT AF relays and perform a comprehensive performance evaluation.  
For a system with \(R\) SWIPT relays, the Equations from (1) to (11) can be generalized to the \(r\)th relay by adding the subscript \(r\in\{1,2,\cdots,R\}\). Specifically, for the \(r\)th relay, the SR link SNR (denoted as \(\gamma_{r}=\gamma_{\rm sr_{r}}\)), the RD link SNR, and the normalized RD link SNR are, respectively, presented as  
\[\begin{split}\gamma_{r}&=\frac{(1-\rho_{r})P_{\rm s }\abs{h_{\rm sr_{r}}}^{2}}{\sigma_{r}^{2}}=\gamma_{\rm sr_{r}}\\ \gamma_{\rm rdr_{r}}&=\frac{\eta\rho_{r}P_{\rm s} \abs{h_{\rm rd_{r}}}^{2}}{\sigma^{2}}\\ \beta_{r}&=\gamma_{\rm rdr_{r}}\frac{\Omega_{\rm sr_ {r}}}{\gamma_{\rm sr_{r}}}=\beta_{\rm sr_{r}}\end{split} \tag{25}\]  
where  
\[\begin{split}\gamma_{r}&=(1-\rho_{r})P_{\rm s} \Omega_{\rm sr_{r}}/\sigma_{r}^{2}=\tilde{\gamma}_{\rm sr_{r}}\\ \tilde{\gamma}_{\rm rd_{r}}&=\eta\rho_{r}P_{\rm s} \Omega_{\rm rd_{r}}/\sigma^{2}\\ \tilde{\beta}_{r}&=\gamma_{\rm rdr_{r}}\Omega_{\rm sr _{r}}/\gamma_{\rm sr_{r}},\end{split} \tag{26}\]  
\(\Omega_{\rm sr_{r}}=E\Big{[}\abs{h_{\rm sr_{r}}}^{2}\Big{]}\), and \(\Omega_{\rm rd_{r}}=E\Big{[}\abs{h_{\rm rd_{r}}}^{2}\Big{]}\).
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix A Two-Stage Detection Algorithm
### Step # 2: Calculation of the Detection Threshold Level  
Following the computation of the noisy-only set of PDP samples, in Step #1 of the algorithm, the next step of the algorithm involves computing the synchronization detection threshold level, \(\alpha\). The synchronization detection threshold level, \(\alpha\), is computed following the decision method known in the literature as the Cell Averaging (CA) method [30].  
The synchronization detection threshold level, \(\alpha\), is computed assuming that there is no synchronization sequence present within the set of PDP reference samples for a given probability of false alarm, \(P_{FA}\). The probability of false alarm, \(P_{FA}\), is given by:  
\[P_{FA}=P\left\{\frac{\mathrm{PDP}(i)}{Z_{\mathrm{REF}}}\geq\alpha\mid H_{0} \right\}.\] (A17)  
Next, we consider that the noise-power reference, \(Z_{\mathrm{REF}}\), and the PDP samples both follow scaled central Chi-squared distributions, given by \(\frac{N_{\mathrm{Synch}}\sigma^{2}}{2}\chi^{2}(2k)\) and \(\frac{N_{\mathrm{Synch}}\sigma^{2}}{2}\chi^{2}(2)\) with \(2k\) and \(2\) degrees of freedom, respectively. Moreover, it is known from the theory of random variables that the ratio of two central Chi-squared random variables results in a random variable that exhibits a Fisher distribution [30; 33], whose CDF is given by:  
\[\mathrm{FCDF}(\alpha)=1-P\left\{\frac{\mathrm{PDP}(i)/2}{Z_{\mathrm{REF}}/2k} \geq k\alpha\right\},\] (A18)  
consequently, the synchronization detection threshold level, \(\alpha\), is computed as:  
\[\alpha=\mathrm{FCDF}^{-1}(1-P_{FA},2,2k)/k.\] (A19)  
where FCDF represents the CDF of the Fisher distribution. It is important to notice that the scaling factor given by \(\frac{N_{\mathrm{Synch}}\sigma^{2}}{2}\) is the same for \(Z_{\mathrm{REF}}\) and \(\mathrm{PDP}(.)\), and consequently, it vanishes when the ratio of the two central Chi-squared random variables is taken.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 2 Relative Work
### Solution to Class Imbalance  
The sample number of new class is always more than each old class. The incremental Classifier and Representation Learning (iCaRL) [8] uses old sample preservation and knowledge distillation to avoid catastrophic forgetting, and uses cross-entropy loss to classify targets. This method introduces a nearest class mean classifier by computing the class mean of the samples in the sample feature representation. Because this process is independent of the weights and biases of the last layer, the class imbalance problem is well solved. A helpful solution strategy is also proposed in the End-to-End Incremental Learning (EEIL) [12] method, where it includes a phase called \"balanced training\" at the end of each training session. In this phase, the same number of samples from all visible classes are used to perform a small batch of fine-tuning. However, when these samples are not fully representing the class distribution, balancing training is likely to over-fitting the stored samples. [PERSON] et al. [28] propose to learn a unified classifier incrementally via rebalancing (United), which uses a cosine normalization layer to replace the standard Softmax layer, and combines the fine-tuning technique to improve classification performance. [PERSON] et al. [29] use a two-phases training approach to train a new task using distillation loss and cross-entropy loss in the first phase and correct the imbalance problem using a validation set selected in advance in the second phase. [PERSON] et al. [31] propose the class Incremental Learning with dual Memory (IL2M) method to modify network predictions. The imbalance is corrected using the deterministic statistics of class predictions saved from previous tasks. However, most of the methods mentioned above require a long offline training time, and each incremental phase takes a lot of time to obtain good performance.  
Based on the above research, class imbalance will cause bias, namely the last fully connected layer of the model has bias in target recognition. The classification logits of the new targets are larger than the old targets, and the model is easier to recognize the sample as the new targets. To solve this problem, a bias correction method for class imbalance is introduced in this paper. A bias correction layer is added after the last fully connected layer of the model, and a linear function with two parameters is used to correct the bias. It solves the problem of bias in the fully connected layer due to class imbalance and achieves superior performance in incremental learning.
## 2 System Model
### Communication Model
#### 2.3.1 Rician Fading Model and Path Loss  
To accurately represent the maritime wireless environment, we adopt a Rician fading model, which captures the presence of both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) components due to sea surface reflections and ship movements. The received signal is modeled as  
\[h=\sqrt{\frac{K}{K+1}}h_{\text{LoS}}+\sqrt{\frac{1}{K+1}}h_{\text{NLoS}}, \tag{12}\]  
where  
\(h_{\text{LoS}}\) represents the direct wave component, \(h_{\text{NLoS}}\) denotes the Rayleigh-distributed scattered component, \(K\) is the Rician K-factor, representing the ratio of LoS to NLoS power, defined as  
Figure 2: MIMO communication system utilizing hybrid beamforming.  
\[K=\frac{A^{2}}{2\sigma^{2}}. \tag{13}\]  
Here, \(A\) is the LoS amplitude, and \(\sigma^{2}\) represents the average power of the scattered multipath components.  
The large-scale path loss is modeled using the log-distance path loss model:  
\[L(d)=\bigg{(}\frac{4\pi df_{c}}{c}\bigg{)}^{2}, \tag{14}\]  
where \(d\) is the transmission distance, \(f_{c}\) is the carrier frequency, and \(c\) is the speed of light. This formulation accounts for the attenuation of signals over a distance, a key consideration for maritime links.