esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[21] introduced the adaptive maximum correlation entropy Unscented Kalman Filter algorithm, which improved localization accuracy by incorporating the maximum correlation entropy criterion and recursively updating the noise covariance. However, its high computational complexity imposes significant challenges for real-time applications. To address the issue of particle degradation in PF and the limitations of EKF in handling highly nonlinear distributions, a novel algorithm called EKF-GAPF (Extended Kalman Filter-Genetic Algorithm Particle Filter) is proposed in this paper, which integrates EKF, PF, and the adaptive genetic algorithm (AGA). First, to enhance the accuracy of state prediction and estimation, the state prediction and updating mechanism of EKF is utilized to provide more accurate state estimation and transfer models for particles in PF, which combines the local linearization capability of EKF and the nonparametric advantages of PF. Second, to mitigate the issue of particle degradation, AGA is introduced to improve the importance sampling process. By dynamically adjusting the importance weights and state update strategies of particles, the global search capabilities of EKF-GAPF are enhanced and the possibility of particles falling into local optima is reduced.
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 4 Simulation Results and Discussion
### Verification of the Low Rank Property  
To apply the matrix completion technique to recover the EDM matrix from observed entries, the matrix \(\hat{\mathbf{X}}\) should have low rank \(r\) to ensure that the generated distances have a strong correlation. We can approximate unknown entries from known ones because they are dependant. To check whether the data matrix \(\hat{\mathbf{X}}\) has a good low-rank approximation, we apply the singular value decomposition [41]. An \(n\times n\) matrix \(\hat{\mathbf{X}}\), can be decomposed as:  
\[\hat{\mathbf{X}}=\mathbf{OFQ}^{T}, \tag{37}\]  
where \(\mathbf{O}\) is an \(n\times n\) unitary matrix, \(\mathbf{Q}\) is a \(n\times n\) unitary matrix and \(\mathbf{F}\) is a \(n\times n\) diagonal matrix. The diagonal elements of \(\mathbf{F}\) are the singular values of \(\hat{\mathbf{X}}\), they are organized in a decreasing order (i.e., \(\mathbf{F}=diag(\gamma_{1},\gamma_{2},...,\gamma_{r},0...,0)\)). \(r\) is the rank of the matrix \(\hat{\mathbf{X}}\), it is equal to the number of its non zero singular values. If \(\hat{\mathbf{X}}\) is a low rank matrix, its top \(l\) singular values occupy the total or nearly the total energy (i.e., \(\sum_{i=1}^{l}\gamma_{i}^{2}=\sum_{i=1}^{n}\gamma_{i}^{2}\)) [42]. The metric used to verify the property of low rank, is the fraction of the nuclear norm caught by the top \(l\) singular values. This fraction is defined as:  
\[g(l)=\frac{\sum_{i=1}^{l}\gamma_{i}^{2}}{||\hat{\mathbf{X}}||_{*}}=\frac{\sum_ {i=1}^{l}\gamma_{i}^{2}}{\sum_{i=1}^{n}\gamma_{i}^{2}}, \tag{38}\]  
Figure 5 illustrates the fraction of the nuclear norm captured by the top \(l\) singular values. We find that the top 3 singular values capture 90% of the nuclear norm. This indicates that the matrix \(\hat{\mathbf{X}}\) has a low rank approximation. So, we are able to apply matrix completion.
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 2 Hardware Virtualization
Figure 3 depicts yet another use case example regarding dynamic and flexible spectrum access. In the current wireless communications networks, channels have fixed central frequencies and bandwidths. Such static channel allocation causes spectrum fragmentation, lowering the spectrum use efficiency. Figure 3 shows a common coexistence scenario in industrial, scientific and medical (ISM) bands. In these bands it is common to have the coexistence of several different technologies, such as Wi-Fi, IEEE 802.15.4 (e.g., ZigBee), LTE-U, etc. [17]. In the figure, two IEEE 802.15.4 networks occupy two channels that overlap with a 20 MHz-wide Wi-Fi channel. As can be seen, the two IEEE 802.15.4 networks may render the channel unusable for Wi-Fi communications due to the narrow-band  
Figure 2: High-level view of a hardware hypervisor in 4G or 5G-like networks. interference caused by the two IEEE 802.15.4 networks. However, the remaining fragmented spectrum in that channel could be used by a flexible and dynamic system, as depicted by the red-dashed lines in Figure 3. As it is shown, the spectrum gaps created by the IEEE 802.15.4 transmissions could be used to establish LTE-like networks (e.g., LTE-U, 5G-NR, NB-IoT, etc.) or any other kind of OFDM-based network. This flexible and dynamic spectrum usage completely removes the concept of predefined and static channel allocations. Based on this approach, frequencies and bandwidths can be dynamically allocated based on the availability of spectrum and the requirements of the applications.
## 2 System and Signal Model
### Mode of Relay Selection
#### 2.4.2 Case 2 (Source-Relay-Select)  
In this mode, the relay selection is based on the \(SR_{i}\) channel quality, and the UAV node that achieves the highest quality of the first hop is chosen to maximize the instantaneous SNR between \(S\) and \(R_{i}\) as  
\[I_{2}=\arg\max_{i=1,2\cdots,N}\bigl{\{}\gamma_{SR_{i}}\bigr{\}}, \tag{8}\]  
where \(I_{2}\in\{1,2\cdots,N\}\) denotes the best relay selected in case 2, and the selected relay \(I_{2}\) is utilized for communication in the second hop. In contrast to case 1, only the first hop needs a feedback channel in case 2.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 3 Performance Analysis
### BER Performance Analysis with Change in the Shape of the Pulse  
In pulsed systems, one problem is that the emission of pulsed signals through real band-limited antenna systems leads to a change in the shape of the pulse. It stretches out in time and interference appears between pulses, which leads to synchronization inaccuracies and reduced reception noise immunity. In this part, a brief analysis of the BER performance with a change in the shape of the pulse is proposed. Due to the operation of the time-hopping sequence, the TH-PPM pulses are quite sparse. So, the shape stretches out in time and will not lead to interference between pulses. However, it will reduce the SNR of the receiver. For convenience, we assume that the pulse is stretched out in time uniformly. Without loss of generality, we define \(\delta\) as the factor of change in the shape of the pulse by (25),  
\[\delta=\frac{\tau-\Delta T}{\Delta T} \tag{25}\]  
where \(\Delta T\) is the width of the TH-PPM pulse, and \(\tau\) is the width of the pulse which is stretched out in time. According to the analysis in Section 3.3, we can have the BER performance of conventional TH-PPM as (26), and the BER performance of new TH-PPM as (27).  
\[P_{eb}=\frac{Q/2}{Q-1}\cdot\sum_{k=1}^{Q-1}\binom{Q-1}{k}\frac{(-1)^{k+1}}{k+1 }\exp\biggl{(}-\frac{k}{k+1}\cdot(1-\delta)\cdot\log_{2}^{Q}\frac{E_{b}}{N_{0} }\biggr{)},\ 0\leq\delta\leq 1 \tag{26}\]  
\[P_{eb}=\frac{Q/2}{Q-1}\cdot\sum_{k=1}^{Q-1}\binom{Q-1}{k}\frac{(-1)^{k+1}}{k+1 }\exp\biggl{(}-\frac{k}{k+1}\cdot\frac{1}{(1+\delta)}\cdot\log_{2}^{Q}\cdot \frac{E_{b}}{N_{0}}\biggr{)} \tag{27}\]  
From (22) and (26), it is shown that, for conventional TH-PPM, the BER performance with change in the shape of the pulse is the same as the BER performance with jitter when \(\delta=\varepsilon\).  
From (24) and (27), it is shown that, for new TH-PPM, the BER performance with change in the shape of the pulse is the same as the BER performance with jitter when \(\delta=\varepsilon\).  
The analysis above, which proposes the BER performance of TH-PPM with change in the shape of the pulse, is semi-quantitative, for the assumption that the pulse is stretched out in time uniformly. However, it is sufficient for trend analysis. The weighted receivers can be used to improve the BER performance of TH-PPM with a change in the shape of the pulse. Because there is much literature about weighted receivers [26; 27; 28], no more expatiation in our work.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Device-to-Device (D2D)
#### 4.2.4 Interference Control Level  
Generally, the strategies of interference management can be categorized as centralized, semi-distributed, and distributed according to the scenario operation. Figure 10 depicts the classification of the interference control level.  
1. Centralized  
In the centralized method, the interference between D2D and cellular users is completely managed by BS. This central entity combines information about each user in the network, such as the quality of the channel, CSI, and interference level. Moreover, it selects the channels that must be allocated to each user in the network with the appropriate format and power level. The central entity assigns the resources to each CUE or DUE depending on the collected information. The major issue of centralized methods is the massive amount of signaling necessary to exchange CSI and feedback. Furthermore, because the process is conducted by a single entity, which must handle massive amounts of data, the complexity of interference management increases significantly with the users' number in the network. Therefore, centralized methods are appropriate only for limited-scale D2D networks.  
Figure 10: Control level of interference in D2D communication.  
Figure 9: The interference in D2D HetNet.  
Distributed  
In a distributed method, the interference management process does not need a central entity and is conducted independently by DUEs. Due to finite CSI and feedback, the distributed method minimizes control and computational cost. Thus, due to the difficulty of interference coordination, this method is better suited for large-scale D2D networks.  
Semi-distributed  
Although both centralized and distributed methods have benefits and drawbacks, trade-offs can be made between them. Interference management strategies of this type are referred to as semi-distributed or hybrid. Various levels of participation can be established in the strategies of semi-distributed interference management. Such strategies can be appropriate for relatively massive networks [88; 97].
# Collision Avoidance Resource Allocation for LoRaWAN
## 8 Conclusions and Future Work  
In this paper, a collision avoidance resource allocation algorithm, named CARA, has been designed with the objective of increasing the capacity of LoRaWAN networks. CARA divides the wireless medium capacity into resource blocks which are defined by one channel and one spreading factor. Thanks to the orthogonality of the spreading factors in LoRa, transmissions in different resource blocks will not cause collisions. In addition, CARA leverages the existing joining procedure for parameters exchange and synchronization, not requiring communication between the end-devices and the network afterwards. CARA has been implemented in a MATLAB-based dynamic simulator, using a cyclical assignment which ensures that nodes with a different initial resource block will not collide. Simulation results show that our solution outperforms standard LoRaWAN networks achieving a 95.2% of capacity gain when radio link conditions are ideal. In realistic propagation environments, CARA increases the capacity by 40%. The coexistence between each type of devices has been also verified. Finally, a proof-of-concept has been developed using commercial equipment, which has allowed us to check the feasibility and the proper operation of the proposed solution.  
Future work will include an experimental deployment in real urban and rural scenarios, analysing the performance of CARA in these environments. Additionally, the definition of a smart resource assignment algorithm that considers the RBs utilization in dense LoRaWAN networks is also left for further work.  
Conceptualization, N.C.-R., J.N.-O.; methodology, N.C.-R., J.N.-O.; validation, N.C.-R., J.N.-O., P.M.; formal analysis, N.C.-R., J.N.-O., P.A.; investigation, N.C.-R., J.N.-O., P.M., P.A.; simulations: N.C.-R., J.N.-O., P.A.; proof-of-concept: N.C.-R., J.N.-O., P.M.; writing--original draft preparation, N.C.-R., J.N.-O., P.M., P.A.; writing--review and editing, N.C.-R., J.N.-O., P.M.. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Andalusian Knowledge Agency (project A-TIC-241-UGR18), the Spanish Ministry of Economy and Competitiveness (project TEC2016-76795-C6-4-R) and the H2020 research and innovation project 5G-CLARITY (Grant No. 871428).  
Figure 15: Resource blocks usage in the proof-of-concept (only 5 nodes shown).
## 2 Basic Analysis of Prediction Models and Model Verification
### Prediction Models of Saturated Vapor Pressure and Saturated Density
#### 3.2.2 Criterion of Iterative Computations for Comparison of Different Prediction Models  
Taking the common electronic fluoride liquids (FC-72, Novec 649 and HFE-7100) used in two-phase liquid immersion cooling systems as examples, the saturated vapor pressures and saturated liquid densities are calculated by the nine prediction models summarized in Table 3. The condition of vapor-liquid equilibrium is regarded as the criterion of iterative computations. According to the condition of vapor-liquid equilibrium, the fugacity coefficients of the saturated vapor and liquid are equal under the same temperature, i.e., \(\varphi_{s,V}=\varphi_{s,L}\). Thus, when \(\mid\ln(\varphi_{s,V}/\varphi_{s,L})\mid\)\(<10^{-5}\), it can be regarded that the fluids are in vapor-liquid equilibrium. The equations of \(\ln(\varphi_{s,V}/\varphi_{s,L})\) are shown in Table 4.
## 3 Results and Discussion  
### Dried Tree Tomatoes Specification  
#### 3.1.1 Color  
In the present research, L (lightness), a (redness/greenness), and b (yellowness/blueness), \(\Delta\)E, Hue Angle and Chroma values were analyzed to characterize the color and the results are presented in Table 3. Delta-E (\(\Delta\)E) representing the distance between two colors; whereas in this study the comparison was between the fresh tree tomato fruit to the dried ones. Considering the obtained \(\Delta\)E values, there is a big difference between the fresh tree tomato and the dried ones as when the \(\Delta\)E value is greater than 5, it indicates a large color difference [59].  
According to the results of the present study, the color parameters lightness (L value), redness-greenness (a value) and yellowness-blueness (b value) changed when we compared the dried tree tomatoes with the fresh tree tomato fruit samples. Accordingly, the highest lightness L was observed in the tree tomatoes dried with the freeze dryer with 27.71 \(\pm\) 0.47. The change in lightness was not significant (\(p>0.05\)) between the samples dried with microwaves at different powers. The literature pointed out the degradation of color pigment due to non-enzymatic browning as a result of Maillard and caramelization of sugar components of the fruit [60; 61; 62]. The freeze-drying method affected less the tree tomatoes color as indicated by the lowest \(\Delta\)E value (13.49 \(\pm\) 0.27). Previous studies pointed out the same thoughts and support the present study stating the change in color of food material because of enzymatic and non-enzymatic reactions taking place during drying process [13; 63].
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 4 System Analysis and Control
### Filter Inductor Calculation  
The filter inductor is an essential component of the grid-tied inverter, with significant implications for the size and efficiency of the inverter, therefore we briefly present the selection of the inductance for the H6 topology under the proposed HMM. Assuming the switching frequency is \(f_{s}\), the conduction duty cycle of switches is \(d\), according to reference [17], it is clear to conclude that  
\[\Delta i_{L}=\frac{[-(d-0.5)^{2}+0.25]E}{Lf_{s}} \tag{5}\]  
when \(d=0.5\), the ripple current \(\Delta i_{L}\) will reach the maximum  
\[\Delta i_{L\max}=\frac{E}{4 Lf_{s}} \tag{6}\]  
and this must be less than the allowable the current ripple \(\Delta i_{LN}\).  
As shown in [10], in order to connect to the grid with grid voltage \(V_{ac}\), the DC voltage \(E\) needs to meet the following conditions  
\[E\geq\sqrt{2}\sqrt{V_{ac}{}^{2}+\left(\omega LI_{LN}\right)^{2}} \tag{7}\]  
where, \(I_{LN}\) is the RMS rated current. Combining Equations (6) and (7), we can attain the range of inductance for the H6 grid-tied inverter with the new HMM as  
\[\frac{E}{4\Delta i_{LN}f_{s}}\leq L\leq\frac{\sqrt{E^{2}-\left(\sqrt{2}V_{ac} \right)^{2}}}{\sqrt{2\omega I_{LN}}} \tag{8}\]As indicated in Reference [11], the inductance range for the H4 grid-tied inverter with bipolar modulation is  
\[\frac{E}{2\Delta i_{LN}f_{s}}\leq L\leq\frac{\sqrt{E^{2}-\left(\sqrt{2V_{ac}} \right)^{2}}}{\sqrt{2\omega I_{LN}}} \tag{9}\]  
Therefore, comparing Equation (8) with Equation (9), we can clearly observe that under the same current ripple conditions the H6 topology using HMM only requires half of the H4 inductance value, effectively reducing the overall size and weight of the system.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 4 An SK-Type Scheme for the MISO Fading Relay Channel with Noisy Feedback and Its Security Analysis
### Security Analysis of the Proposed Extended Scheme
_where_  
\[\kappa_{1} =\log\Biggl{(}1+\frac{\left\|\mathbf{h}_{\text{sd}}\cdot\mathbf{g}_{ \text{se}}^{\text{tr}}\right\|^{2}P}{\left\|\mathbf{h}_{\text{sd}}\right\|^{2} \sigma_{e}^{2}}\Biggr{)}, \tag{67}\] \[\kappa_{2} =(N-1)\log\Biggl{(}1+\frac{\left\|\mathbf{g}_{\text{de}}\right\|^{ 2}P_{f}}{\sigma_{fe}^{2}}\Biggr{)},\]  
_Note that \(\kappa_{1}\) and \(\kappa_{2}\), respectively, represent the information leaked in Source at time \(1\) and in the feedback channel from time \(1\) to \(N-1\)._  
Proof. : The detailed proof is given in Appendix C.
## 3 Materials and Methods
### Overview of Dataset Used  
The DLRSD was introduced as a resource for research related to solving semantic segmentation challenges, which involve pixel-level labeling in RSIR. This dataset serves as an extension of the multi-label UC Merced dataset wherein multiple labels are assigned to each image. The DLRSD provides imagery with a one-foot pixel resolution and labels each pixel in the images with one of seventeen distinct class labels. These class labels encompass a range of objects and land cover types, including fields, tanks, chaparral, grass, airplanes, ships, pavement, water, cars, bare soil, buildings, docks, seas, mobile homes, sand, courts, and trees. The structure of the UC Merced dataset is mirrored by the 100-image limit for each class. Each image in the UC Merced dataset was semantically segmented using the eCognition 9.0 program to provide these labels. Each image was divided into areas, and depending on its features and content, each region was given one of the seventeen pre-defined class names. The densely labeled dataset known as DLRSD was produced as a result of this labeling process, and it is an important tool for research on semantic segmentation in the field of remote sensing.  
Labeling masks and related pictures from the DLRSD are shown in Figure 3. The ground truth photographs were tagged into two main categories in order to use this dataset for our research: the image backdrop, which is represented by the color black, and the aircraft, which is represented by the color white. The dataset consists of a total of 100 images, each with a pixel size of 256 \(\times\) 256. These images were partitioned into different sets for training, validation, and testing purposes. Specifically, 80 images were designated for the training set, 16 for the validation set, and 4 for the testing set. During the training stage, several data augmentation techniques were applied to enhance the diversity of the training dataset and prevent model overfitting. These augmentation methods are outlined in Table 2 within this study.  
Figure 3: Example images and corresponding labeling masks from DLRSD [4].
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### OFDM Setup  
The OFDM setup for the UWA communication system is configured with critical parameters to optimize performance under challenging acoustic channel conditions as depicted in Table 4. The system employs OFDM modulation, leveraging 1024 sub-carriers. Comb-type pilot insertion with an N/4 spacing facilitates accurate channel estimation and synchronization, crucial for maintaining transmission integrity in dynamic environments. A CP is used as a guard interval to mitigate ISI caused by multipath propagation, typical in underwater environments. The system operates at a sampling frequency in the range of 48 kHz to 96 kHz, depending on the specific configuration. A 14 kHz carrier frequency is usually used in UWA while the system is modeled under AWGN with an SNR range of \(-10\) dB to 15 dB, representative of typical underwater noise environments. BPSK modulation is employed for its robustness under low SNR conditions, ensuring reliable communication.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Swarm Exchange Particle Swarm Optimization Algorithm
#### 3.2.3 Swarm Exchange Particle Swarm Optimization Algorithm Operation Procedure  
In this paper, the swarm exchange particle swarm optimization algorithm uses the particle swarm divided into two subgroups: one for the main search group and one for the auxiliary search group. The operation process is as follows:  
1. Initialize the corresponding parameters such as the number of particles of the particle swarm, the number of dimensions, and the position.
2. Calculate the initial fitness value of each particle of the main and auxiliary swarms, respectively, record the individual particles' historical initial fitness value and best initial individual position, and save the global historical best as well as the local historical best position and its fitness value.
3. Update the velocity and position of the particles according to the iterative equations for the main and auxiliary search groups.
4. Update the historical best positions of the main and auxiliary populations of particles and the corresponding fitness values.
5. Sort the main and auxiliary groups of particles in descending order according to the fitness value calculated in step (4), and exchange the first \(0.382*N\) particles of the auxiliary search particle group with the penultimate same number of particles in the main search group.
6. Iteration termination: in this paper, the iteration termination condition is to reach the set maximum number of iterations and output most particles of the main search group as well as their fitness values.  
The flowchart is shown in Figure 6:  
Figure 6: Flowchart of swarm exchange particle swarm optimization algorithm.