esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Voltage-Based Realignment Cell Balance
### Proposed New Cell Balance Method to Prevent Overvoltage Due to Aging  
In previous studies, a cell balancing method was proposed to make the SOC of all cells 100% when charging was complete [7]. However, this method can put a heavy burden on the BMS, as it requires the individual calculation of the SOC of all series-connected batteries. Therefore, in this study, a method using only the voltage is proposed to solve the problem of the maximum voltage rising due to aging. Table 5 shows that Bat #3 with the minimum capacity determines the charge-discharge current, and it can be seen that it quickly approaches overvoltage as the aging relatively quickly progresses. In addition, it is confirmed that the voltage rise of the average capacity battery suppresses the maximum voltage rise of a relatively small-capacity battery. Figure 9 shows the cell balance flow chart of the new method. Initially, the full charge and full discharge are slow-charging and without cell balancing. Measuring the highest voltage of each battery and setting the measured voltage as the reference for the cell balance voltage allows for the highest voltage of a battery to have a relatively small capacity, which is applied as the cell balance reference voltage of the remaining batteries. It is applied as the cell balance reference voltage of a relatively small-capacity battery. The reference voltage for the cell balancing of the remaining batteries is set to the highest voltage. The proposed new cell balancing method suggests low-voltage charging for batteries with a relatively low capacity. This slows down the aging process of low-capacity batteries, resulting in a slower increase in the maximum voltage. Additionally, starting from a lower maximum voltage point also slows down the time it takes to reach overvoltage. Cell balancing is performed only at voltages higher than the reference voltage, so the remaining batteries, excluding those with a relatively low capacity, have a relatively high SOC value at the end of charging, which balances the aging rate.
## 4 Experimental Results and Analysis
### Evaluation Metrics  
For model performance evaluation, we use Classification Accuracy (ACC) and Factor of Merit (FOM) [21] as the primary evaluation metrics. Among these, FOM is a widely used metric in the field of particle identification, assessing the separability of feature vectors between two particle classes. The specific calculation formula of FOM is as follows:  
\[FOM=\frac{\left\|\overrightarrow{\mu_{1}}-\overrightarrow{\mu_{2}}\right\|}{ (\sigma_{1}+\sigma_{2})\cdot 2.35} \tag{3}\]  
where \(\overrightarrow{\mu_{1}},\overrightarrow{\mu_{2}}\) and \(\sigma_{1}\), \(\sigma_{1}\) represent the corresponding two-dimensional centers and one-dimensional standard deviations of the classes, respectively. Values of FOM greater than 0.75 are typically associated with a good classification performance.  
Considering that our experiments focus on multi-class classification for various types of particles rather than binary classification, we use the Average Factor of Merit (Avg_FOM) as the evaluation metric, which is calculated as follows:  
\[Avg\_FOM=\frac{1}{C_{num\_classes}^{2}}\sum_{1\leq i\leq j\leq num\_classes} FOM(i,j) \tag{4}\]  
where \(C_{num\_classes}^{2}\) represents the total number of pairwise combinations among all classes, and \(FOM(i,j)\) denotes the Factor of Merit between the \(i\)-th and \(j\)-th classes. Higher values of ACC and Avg_FOM indicate stronger particle identification capabilities. Using these evaluation metrics, we are able to comprehensively assess the performance of our proposed network in the particle identification task.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 2 Background and Problem Statement
### Multipath Diversity
#### 2.3.1 Diversity of Underlay Network  
Generally, end-to-end packets are forwarded via one or a set of autonomous systems. i.e., packets may pass via border devices, named exchange points, between autonomous systems. We decompose underlay networks in two dimensions--intra-AS and inter-AS--as shown in Figure 3a; vertical layers signify three autonomous systems, and in each layer locate forwarding nodes (solid circles) and dotted rectangle boxes denote six geographic locations. The aggregated networks are depicted in Figure 3b and the integrated topology in Figure 3c. We discuss end-to-end multipath transmission between hosts C1, C2 and C3. For C1 and C2, there exist two types of end-to-end multiple paths, i.e., completely intra-AS paths in AS1, and inter-AS paths via AS2 or AS3. For C1 and C3, only the second type exists. Notations are listed in Table 1.  
Figure 3: The underlay bearing networks consisting of multiple autonomous systems in different locations. (**a**) shows the underlay networks with three different autonomous systems (represented by three layers), and in each autonomous systems, forwarding nodes are deployed in several locations (represented by dotted rectangles); (**b**) shows the independent topology view of each autonomous systems and (**c**) shows the merged topology view of three independent views.
# A Deep Attention Model for Environmental Sound Classification from Multi-Feature Data
## 4 Experiment and Analysis
### Model Training  
The experimental framework was TensorFlow and Keras. The entire network was constructed with TensorFlow as the bottom layer and Keras as the backend. The models and algorithms were implemented with the programming language Python3.6. Optimizing iterative model parameters was performed on a GeForce RTX 2080 Ti GPU. The initial learning rate was set to be 0.001, and learning rate WarmRestart was performed with the Cosine Annealing method [24]. The learning rate was dynamically adjusted. The learning rate was initialized when 3, 7, 15, 31, 63, 127, 255, 511, or 1022 iterations were completed. The loss function was expressed in binary cross entropy. The optimization function was based on SGD. Each batch was trained on 64 data, and there were a total of 1024 rounds of training. In the training phase, the following data augmentation methods were applied: time-domain Random Cropping and Mixup [25]. The former effectively enhanced the expression ability of individual samples, while the latter enhanced the expression ability between multiple samples.
## 4 III-V-on-Silicon Devices for Optical Communication Applications
### III-V-on-Silicon Laser Sources for Optical Communication Applications
#### 4.1.3 1550 nm Tunable and Multi-Wavelength Lasers Integrated on Silicon PICs
These examples show the strength of the hybrid III-V on silicon platform in building complex laser devices fully integrated on chip. The use of on-chip DBR-reflectors to form the laser cavities allow  
Figure 9: Spectral response of hybrid III-V silicon lasers. (**a**) tunable laser (from [8]) (**b**) AWG-based multi-frequency laser (from [27]). straightforward integration with further functionality offered by the silicon photonics platform such as silicon modulators and Ge-detectors, as demonstrated in [30].
# Comparing the Measured and Thermodynamically Predicted AFm Phases in a Hydrating Cement
## 5 Analysis Output  
### Comparison between Measured and Predicted Portlandite and Calcite Contents  
As shown in Table 2, the percentages of portlandite and calcite were measured using TGA to be 1.44 and 4.75%, respectively, in the unhydrated clinker, although some pre-hydration is thought to have occurred. Figure 7a-c and Table 4 compare the measured and predicted Portlandite and calcite contents over the initial 28 days for the three w/c ratios. There is reasonably good agreement between both for these minerals. Calcite is predicted to dissolve quicker than measured. This may lead to higher amounts of Ca-rich hydrates being formed than maybe would be seen experimentally.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{2}{c}{**Mineral Name**} & \multicolumn{8}{c}{**Dissolution Reaction**} \\
**[Composition]** & & & & & & & & \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} SO\({}_{3}\) \\ [SO\({}_{3}\)] \\ \end{tabular} } & vi & Component & \(\Delta_{\rm f}\)C\({}^{\rm o}\)\({}_{\rm m}\) & \(\Delta_{\rm f}\)H\({}^{\rm o}\)\({}_{\rm m}\) & \(S^{\rm o}\)\({}_{\rm m}\) & \(C_{\rm p}\)\({}^{\rm o}\)\({}_{\rm m}\) & RMM & \multirow{2}{*}{
\begin{tabular}{c} Z (-) \\ (J/mol) \\ \end{tabular} } \\  & \(-\)1 & SO\({}_{3}\) & \(-\)37,141 & \(-\)451,663 & 79.496 & No \(\it data\) & 80.0642 & 0 \\  & \(-\)1 & H\({}_{2}\)O & \(-\)237,181.38 & \(-\)285,837.3 &  
However, the thermodynamic model only predicts the formation of monocarbonate. This can be attributed to the drawbacks in modelling carbonate-AFm as its formation is strongly dependent on kinetics, whereas thermodynamic modelling is usually only carried out with a thermodynamic stable state.
## 2 Asymmetric Turbo Code (ATC)
### Asymmetric Turbo-Coded Spatial Modulation (ATC-SM) Scheme with CMI
We have assumed that the perfect knowledge of \(\mathbf{H}\) is always known at the receiver throughout this paper. At the destination end, the SM demapper first performs soft detection on the received signals. The \"soft\" signifies the log-likelihood ratio (LLR). In this paper, we employed the soft output maximum likelihood detector (SOMLD) that was used in [19]. The SM demapper produces LLRs \(\gamma(\mathrm{I}(\mathrm{j}))\) and \(\gamma(\mathrm{v}_{\mathrm{s}}(\mathrm{j}))\) for active antenna index I(\(\mathrm{j}\)) and modulated symbol \(\mathrm{v}_{\mathrm{s}}(\mathrm{j})\), respectively. After passing through bit combiner, the demodulated LLRs \(\gamma(\mathrm{I}(\mathrm{j}))\) and \(\gamma(\mathrm{v}_{\mathrm{s}}(\mathrm{j}))\) are combined into \(\gamma(\mathbf{v}(\mathrm{j}))\), which is further fed into bit ungrouping block that outputs the coded bit sequence \(\gamma(\mathbf{v})\). Finally, the ATC decoder takes \(\gamma(\mathbf{v})\) to generate the estimated information sequence \(\mathbf{\hat{a}}_{1}\). Figure 4: Non-cooperative asymmetric turbo-coded spatial modulation (ATC-SM) scheme with the code matched interleaver (CMI).
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 5 Field Campaign  
This section discusses the initial experimentation results on the maximum coverage distance and receiver orientation impact, when using the HOP-5G mmWave positioning testbed in a field campaign with LoS conditions.
## 1 Introduction
However, its effectiveness heavily relies on the choice of embedding dimension and time delay, and it can be sensitive to high noise levels, potentially affecting classification accuracy. In reference [16], the authors combine PSR with convolutional neural networks (CNN) to enhance the classification performance of power quality disturbance signals. First, PSR extracts dynamic features from the signal, capturing its nonlinear and time-varying characteristics. These features are then fed into the CNN, which leverages its deep learning capabilities to further refine and extract effective classification features. The combination of PSR and CNN improves classification accuracy, particularly in noisy and complex scenarios, demonstrating enhanced robustness. The results of PQDs signal detection and analysis cannot be directly classified, so the feature set related to signal processing results needs to be extracted. At present, the artificial intelligence methods for screening and optimizing feature sets mainly include the genetic algorithm, particle swarm optimization, simulated annealing, wolf pack algorithm, random forest, and so on. Through the artificial intelligence algorithms listed above, the optimal feature vector combination can be found [17; 18]. An ML-DT algorithm has been proposed based on single-label decision tree extension to support multi-label data sets in [17]. With this algorithm, a random forest classifier, ML-RF, was constructed. By applying the ML-RF algorithm, PQDs can be classified. In addition, common methods include support vector machine, artificial neural network, deep learning [19; 20], and so on. Considering the limitations of the traditional time-frequency domain method and the complexity of the optimization algorithm in extracting features, a detection and classification method combining the unscented Kalman filter (UKF) and the kernel extreme learning machine (KELM) is proposed for the classification of PQDs in this paper. The UKF is used to detect and process the original disturbances, and the optimal feature vector set is selected. The classification of single or composite PQD types is completed by the kernel extreme learning machine. The UKF-KELM classification method is used to track the disturbances in the experimental platform of the PQD classification, and the classification of PQDs is completed. The experimental results show that the classification method in this paper has a high degree of fit, strong effectiveness, and high accuracy. This paper is structured as follows: Section 2 presents the unscented Kalman filter (UKF) for detecting power quality disturbances (PQDs), with Section 2.1 describing the methodology of the UKF and Section 2.2 analyzing its anti-noise detection performance. Section 3 focuses on the feature extraction techniques used for PQDs. Section 4 introduces the kernel extreme learning machine (KELM) method applied to PQDs classification. Section 5 provides experimental verification of the proposed approach, and Section 6 concludes the paper with a summary of the findings and potential future work.
## 4 Experiments and Results
### Experiment Results
Therefore, the remaining value after differencing can be treated as the error caused by the environment of our receiver location. The double-difference-labelled pseudorange error of the \(i\)-th satellite, \(\
abla\Delta\rho^{i}\), can be calculated by,  
\[ \begin{split}&\
abla\Delta\rho^{i}=\rho^{i}_{rcv}-\rho^{m}_{rcv}- \left(\rho^{i}_{ref}-\rho^{m}_{ref}\right)-\
abla\Delta D^{i}\\ &\text{where }\
abla\Delta D^{i}=D^{i}_{rcv}-D^{m}_{rcv}-\left(D^{i}_{ ref}-D^{m}_{ref}\right)\end{split}  \tag{12}\]  
where \(*_{rcv}\) stands for receiver data while \(*_{ref}\) stands for reference station data. \(*^{m}\) stands for the master satellite's data. It is selected in a system-specific pivot satellite manner with the highest elevation angle. \(\rho\) and \(D\) stand for pseudorange measurement and geometric distance, respectively. Reference station data was retrieved from NYS Spatial Reference Network (NYSNet) for data evaluation in New York. For Hong Kong dataset evaluation, reference station data was retrieved from Hong Kong Satellite Positioning Reference Station Network (SatRef). The HK dataset reveals a better pseudorange quality, and it is reasonable to expect better positioning performance. A similar conclusion can be made in this vehicle-mounted experiment based on the results. 3 DMA GNSS and LC-FGO obtain similar performance in this data, but comparing the positioning error shown in Figure 9b, LC-FGO can reduce the positioning better than the 3 DMA, resulting in a smaller standard deviation on the positioning error. In other words, LC-FGO can provide a smoother and more robust trajectory, which applies to LC-FGO-PP. However, the error of velocity can degrade the integration performance. Figure 11 shows the epoch around 1300 s. Although the 3 DMA GNSS performs well, the wrong velocity estimated by Doppler measurements with WLS results in wrong integrated results. As a result, error mitigation or a correction for Doppler measurements have to be explored in the future. Therefore, tightly coupling these approaches with Doppler measurements can potentially address the problem. In doing so, wrong Doppler measurements will be identified and isolated from the state estimation separately. A sophisticated model may be developed to model the Doppler errors [57] so that inaccurate measurements are used in the future. Meanwhile, 3 DMA GNSS can be integrated with Doppler measurements more tightly in future work by expressing discrete sampled locations with a continuous mathematical model. Figure 10: Probability density function plot on pseudorange error labelled by double differencing technique. Note that the master satellite is excluded from pseudorange error labelling, e.g., \(i\
eq m\) in Equation (12).
## 4 Simulation and Experiment Results
### Experimental Results in a Realistic Environment
#### 4.2.3 Experiment Results
By comparing the robot's real movement trajectory with the trajectory estimated by the EKF-SLAM algorithm regarding the microphones as landmarks, it was found that the two are basically coincident. At the same time, comparing the actual location of the feature landmarks in the environment with the estimated location, it can also be found that the two are basically coincident. However, there was a small difference between the estimated location and the actual location of robots and landmarks. Figure 18b,c show the error analysis of the experimental results. Tables 3 and 4 show the actual and estimated position for the robot and landmarks, and by comparing them, the standard deviation of the robot's position and the landmarks' position are calculated. A very important feature of sound signals are that they are easily affected by environmental noise. The result of this experiment is based on taking into account the actual environmental noise and reverberation. Since this paper uses the GCC method to estimate the TDOA, both environmental noise and reverberation were well suppressed. From the results, it can be seen that the feasibility and effectiveness of the proposed method are well verified. Figure 17: Positional relationship of the sound source array, robot, and landmarks for the experiment. Figure 18: Experimental result of acoustic EKF-SLAM based on microphone landmark observations: (**a**) EKF-SLAM for a mobile robot based on microphone landmark observations; (**b**) robot’s position errors in the \(x\)- and \(y\)-directions; and (**c**) landmark position errors in the \(x\)- and \(y\)-directions.
# Coregistration and Spatial Compounding of
## 2 Materials and Methods
### Spatial Compounding and Coregistration  
Coregistration and spatial compounding was performed based on three-dimensional phase mapping for the fundamental frequency of the heart. The phase map for a given position of the array and/or excitation wavelength was first calculated and taken as reference. Specifically, the image for the central position of the raster scan was taken as a reference for spatial compounding multiple positions and the image for 800 nm wavelength was taken as a reference for coregistration of multi-spectral data. The phase difference with respect to other images sequences was estimated via least square minimization with respect to the reference frame. For this, an area of \(5\times 5\times 5\) mm\({}^{3}\) was considered in the case of multi-spectral data, while an area of \(2\times 2\times 2\) mm\({}^{3}\) corresponding to the overlapping region was taken for different scanning positions. Spatial compounding of multiple scanning positions was then performed with a rigid body transformation consisting of a translation matrix. All data processing procedures were implemented in MATLAB (MathWorks Inc., Natick, MA, USA).
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 2 Related Work
A more recent work that adaptively considers redeployment of gateways and takes into consideration the dynamics of the underwater water environment is presented in [35]. However, this work assumes a heterogeneous acoustic/optical underwater wireless sensor network and its main objective is reducing energy. As seen above, most of the redeployment strategies for underwater wireless sensor networks are concerned with redeploying underwater nodes which is practically hard due to the remote deployment locations of underwater nodes below the sea surface [15]. Therefore, gateway deployment strategies to enhance various performance metric presents a better solution. However almost all gateway deployment strategies found in the literature assume either a static network environment or deals with the dynamic case by periodically running the static deployment algorithm that negatively affects the connectivity, coverage, and energy consumption.
# Efficient QC-LDPC Encoder for 5G New Radio
## 1 Introduction
In addition, the proposed algorithm does not require the inverse of the component matrix, which presents a primary advantage over the RU method. Moreover, block-memories are not required to store the generator matrix \(G\), and the number of required components is reduced. The ROM size of the proposed method is 98.2% and 88.9% lower than those of the \(G\) matrix method and RU method, respectively. To assess the benefits of the proposed encoding approach, we further implement and synthesize several QC-LDPC encoder architectures with different submatrix sizes \(Z=30\), 64, 96, 144, and 352. The application specific integrated circuit (ASIC) post synthesis implementation results on TSMC 65-nm complementary metal-oxide semiconductor (CMOS) technology revealed an area efficiency up to 597 Gbps/mm\({}^{2}\) when the proposed encoding method was implemented. Hence, it can be concluded that a promising encoding architecture design for 5G NR LDPC codes was developed in this study. The remainder of this paper is organized as follows. Section 2 gives a brief overview of the characteristics of 5G NR QC-LDPC codes. In Section 3, two conventional LDPC encoding algorithms from the literature are outlined. A novel 5G NR QC-LDPC encoding approach and a low-complexity high-throughput QC-LDPC encoder architecture are described in Section 4. Section 5 presents the implementation and comparison results, followed by the conclusions in Section 6.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 3 Channel Access Mechanisms
### License Assisted Access with Listen before Talk (LAA-LBT)
The carrier selection, discontinuous transmission and transmit power control for LAA-LBT have been considered in [26] with an enhanced Q-learning technique for carrier sensing. In [27], the optimal sizes of the contention window are set to \(100\) and \(100\). The optimal contention window size of LTE-U SCs is \(100\) and \(100\). window have been derived, and a novel resource scheduling approach employing linear programming has been proposed to maximize the spectrum efficiency. A joint optimization problem considering transmit power, channel access time, and contention window size of the LAA-LBT based SC users that guarantees them a minimum data rate while minimizing the interference to the nearby Wi-Fi users have been presented in [28]. Channel access algorithms utilizing the solution of the joint optimization problem have been proposed in [28]. Furthermore, an algorithm for admission control of LAA-LBT SC users based on the available unlicensed bandwidth from Wi-Fi networks is also discussed Alternatively, a Markov chain based approach has been used to derive the optimal contention window size for LAA based systems in [29]. Furthermore, this optimal contention window size has been used to perform user scheduling with the objective of minimizing the waiting time of the packets in the queue. An Interior Point Algorithm based solution has also been proposed in [27] for user scheduling. In [29], the LTE-U interference has been modeled using a hidden Markov process. The problem of jointly adapting Wi-Fi rates/modes using a framework of partially observable Markov decision process has been solved in [23]. Furthermore, a detection approach based on the sliding window correlation is analyzed for the transmit and sense mode. In [30], resource allocation of dynamically configurable channel occupancy duration and flexible allocation of subcarriers has been considered for an LTE-U system. In some geographical regions like India, USA, and China, the LAA-LBT is not mandatory [3]. For such regions, the following channel access mechanism has been proposed for the LTE's operation in the unlicensed band.