esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Consumption Model
### Model Design  
Each component of the behavior monitoring device was represented using a function that kept track of the components power state, used the current time as input, and delivered the amount of energy consumed since the last time step. Software modeling of the selected behavior monitor was implemented using MATLAB. At each time step, current of each components' current consumption was summated and then converted to milliampere-hours using the amount of time between samples. After all of the component's energy usage were summated for the time step, any excess current available from the PVC was then directed to the battery model to simulate charging. The components included for power usage modeling included the microcontroller, GPS, the cellular modem, the photovoltaic cell, and the battery. Components such as the flash memory and real-time clock were included in the microcontroller model.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 6 Simulation of 5G RAN Deployment and Operation Approaches
### Types of 5G RAN Deployment Approaches  
As previously emphasized, a rapid increase in mobile users and the high-throughput requirements of future applications will contribute to the constant increase of the DVs in 5G HetNets during the upcoming decade. Therefore, the analyzed BS deployment approachessimulate this growth of DVs for the 10-year period starting with the year 2021 in every device density class (Table 1). In Table 3, the main characteristics of the analyzed network deployment and operation approaches are presented. The analysis takes into account broadly accepted MNOs practice based on the initial deployment of the fixed number of macro 5G BSs for each deployment approach in every device density class (Figure 1). The number of installed macro BSs corresponds to the number of BSs required to provide a minimal level of signal coverage and capacity requirements for the transfer of expected DVs in a specific device density class (Figure 1).  
The number of installed small BSs and corresponding RRM principles differs among deployment approaches (Figure 1), and this constitutes the main difference among the analyzed deployment approaches (Table 3). In order to model various BSs deployment and RRM approaches and compare their impact on the standardized EE metrics, five different types of deployment and operation approaches were selected for analysis.
## 2 Materials and Methods
### Experimental Equipment and Procedure
#### 2.7.3 Rheometer  
An Anton Paar MCR 51 rotational rheometer with 50 mm diameter roughened parallel plates and a gap of 0.6 mm was used to determine the rheological parameters (yield stress and viscosity) of the fresh cement paste. A shear rate ramp was applied to the paste sample and the rheological properties were extrapolated from applying the Bingham model to the downward curve of the hysteresis loop measurement. The hysteresis loop consists of two branches of measurements which starts at a shear rate of 0.1 s\({}^{-1}\) and ends at 100 s\({}^{-1}\), and then the shear rate decreases from 100 s\({}^{-1}\) back to 0.1 s\({}^{-1}\). The rheometer recorded measurements at 10 intervals going up, and at 10 intervals coming down with a test duration of 300 s per test (15 s per measuring point). Before the start of each test, the cement sample was first pre-sheared at a shear rate of 50 s\({}^{-1}\) for 10 s to break down any network of agglomerates that could have formed at the start of the measurement. The Bingham model was fitted to the downward curve data to obtain the yield stress (y-intercept) and viscosity (slope) [17; 18]. The Bingham model is as follows:  
\[\tau_{y}=\tau_{0}+\mu_{p}\dot{\gamma} \tag{4}\]  
where \(\tau_{y}\) is the yield stress (Pa), \(\mu_{p}\) is the Bingham plastic viscosity (Pa\(\cdot\)s) and \(\dot{\gamma}\) is the shear rate (1/s).  
The 100 g cement powder was weighed on a digital scale, and 44.75 g of distilled water was weighed to accommodate the percentage of SP used. The SP was added to the distilled water by using a syringe at a percentage w.r.t the weight of the cement. The distilled water (with SP) was added to the cement powder and was immediately mixed for two minutes before moving the sample to the rheometer for measurement. The sample was trimmed and covered with a sample protection cell to control the test temperature and to prevent the sample from losing water through evaporation during the shearing process. The temperature controlling bath attached to the rheometer was used to maintain the temperature at 25 \({}^{\circ}\)C [17; 19]. After each test, the parallel plates of the rheometer were wiped clean in preparation for the next sample. This procedure was followed for all the measurements at different SP dosages.
## 4 Experiments  
### Datasets and Metrics  
In this study, we evaluated the proposed method using the publicly available datasets KITTI [27], MaiCity [28], and Newer College [29] as well as a custom dataset called ROI. The KITTI dataset was used to assess the system's performance in highly dynamic outdoor scenes. MaiCity, a LiDAR dataset, simulates an urban outdoor environment, while Newer College is a real-world LiDAR dataset collected on a university campus. The ROI datasetis designed specifically for LiDAR SLAM research, and was collected using an MID360 LiDAR (Manufacturer: MID360 Technologies, City: Shanghai, Country: China) and Intel RealSense D435 RGB-D camera (Manufacturer: Intel Corporation, City: Santa Clara, State: CA, Country: USA). It includes two typical scenarios: real outdoor scenes (ROI-OD) and industrial manufacturing scenes (ROI-IND). Each scenario consists of six independent sequences, capturing the diversity and complexity of both outdoor and industrial environments. The scenes in the dataset are highly challenging, featuring characteristics such as lighting variations, dynamic occlusions, multi-scale object distributions, and complex textured backgrounds. As such, this dataset provides a comprehensive benchmark for the development and evaluation of LiDAR SLAM algorithms.  
To ensure a fair comparison, we use the Root Mean Square Error (RMSE) of the Absolute Trajectory Error (ATE) in meters for tracking evaluation, where lower values indicate higher estimation accuracy. For mapping evaluation, we adopt commonly used metrics from existing methods, including accuracy (cm), completion (cm), Chamfer-L1 distance (cm), and F-score (%). These metrics are computed by comparing the predicted results with the ground truth.
# Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications
###### Abstract  
Fifth Generation (5G) wireless systems are expected to connect an avalanche of \"smart\" objects disseminated from the largest \"Smart City\" to the smallest \"Smart Home\". In this vision, Long Term Evolution-Advanced (LTE-A) is deemed to play a fundamental role in the Internet of Things (IoT) arena providing a large coherent infrastructure and a wide wireless connectivity to the devices. However, since LTE-A was originally designed to support high data rates and large data size, novel solutions are required to enable an efficient use of radio resources to convey small data packets typically exchanged by IoT applications in \"smart\" environments. On the other hand, the typically high energy consumption required by cellular communications is a serious obstacle to large scale IoT deployments under cellular connectivity as in the case of Smart City scenarios. Network-assisted Device-to-Device (D2D) communications are considered as a viable solution to reduce the energy consumption for the devices. The particular approach presented in this paper consists in appointing one of the IoT smart devices as a collector of all data from a cluster of objects using D2D links, thus acting as an _aggregator_ toward the eNodeB. By smartly adapting the Modulation and Coding Scheme (MCS) on the communication links, we will show it is possible to maximize the radio resource utilization as a function of the total amount of data to be sent. A further benefit that we will highlight is the possibility to reduce the transmission power when a more robust MCS is adopted. A comprehensive performance evaluation in a wide set of scenarios will testify the achievable gains in terms of energy efficiency and resource utilization in the envisaged D2D-based IoT data collection.  
s amart city; internet of things; LTE-A; Device-to-Device communications; power control; small data transmission +
Footnote †: journal: Sensors
# Semiconductor Laser Linewidth Theory Revisited
###### Abstract  
More and more applications require semiconductor lasers distinguished not only by large modulation bandwidths or high output powers, but also by small spectral linewidths. The theoretical understanding of the root causes limiting the linewidth is therefore of great practical relevance. In this paper, we derive a general expression for the calculation of the spectral linewidth step by step in a self-contained manner. We build on the linewidth theory developed in the 1980s and 1990s but look from a modern perspective, in the sense that we choose as our starting points the time-dependent coupled-wave equations for the forward and backward propagating fields and an expansion of the fields in terms of the stationary longitudinal modes of the open cavity. As a result, we obtain rather general expressions for the longitudinal excess factor of spontaneous emission (\(K\)-factor) and the effective \(\alpha\)-factor including the effects of nonlinear gain (gain compression) and refractive index (Kerr effect), gain dispersion, and longitudinal spatial hole burning in multi-section cavity structures. The effect of linewidth narrowing due to feedback from an external cavity often described by the so-called chirp reduction factor is also automatically included. We propose a new analytical formula for the dependence of the spontaneous emission on the carrier density avoiding the use of the population inversion factor. The presented theoretical framework is applied to a numerical study of a two-section distributed Bragg reflector laser.  
s 2021 28 June 2021  
Semiconductor laser; spectral linewidth; coupled-wave equations; traveling wave model; longitudinal modes; noise; Langevin equations; Henry factor; Petermann factor; chirp reduction factor; population inversion factor
# Imputation Methods for scRNA Sequencing Data
## 2 Imputation Methods
The cold deck imputation include netSmooth, netNMF-sc and ADImpute [80]. The method of regression imputation include CIDR, SAVER, scImpute, VIPER, ZINB-WaVE, MISC, scTSSR, RIA, I-Impute [81] and EinImpute. The main method of machine learning is clustering imputation, include BISCUIT, BUSseq, DEWASS, kNN-smoothing, LSImpute, PRIME, DrImpute, RESCUE, 2 DImpute, AutoImpute, SAUCIE, ENHANCE, McImpute, SAVER-X, GraphSCI, SAUCIE, SISUA, TRANSLATE, ZINBAE [82], GNNImpute and ccImpute. The third way can be categorized into local or global methods according to how the imputation information is used from the observed data (Figure 3). The fourth classification (Figure 4) can be divided into Bayesian and non-Bayesian method. For detailed division, see Figures 3 and 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method Type** & **Dropout Type** & **Advantage** & **Disadvantage** & **Scope of Application** \\ \hline Mean Completer & Complete random deletion & The operation process is simple & Only the observed information is used, which is subjective, unstable and error & The data scale is small, the missing proportion is small, and the distribution is concentrated \\ \hline Expectation Maximization & Complete random deletion or random deletion & Good stability and small error & It is not suitable for high-dimensional data & It is applicable to data sets with Normal distribution or approximate Normal distribution \\ \hline Hot Deck Imputation & Random deletion & It has a better effect on maintaining the empirical distribution of variables & The mean square error formula is not clear, and the filling value is easily affected by auxiliary variables & Between data sets collected in the same batch \\ \hline Cold Deck Imputation & Random deletion & The operation process is simple & The filling effect depends on the quality of previous data, and there is estimation & Between data sets collected in different batches \\ \hline Regression Imputation & Random deletion & The operation process is simple and makes full use of the relationship between variables & Without considering the uncertainty of data, it is not suitable for high-dimensional data & It is applicable to data sets with Normal distribution or approximate Normal distribution and multiple auxiliary variables \\ \hline Clustering Imputation & Random deletion & Low variable type requirements, good fitting effect, high stability and small error, suitable for high-dimensional data & The operation process is complex and the time cost is high & It is suitable for any missing pattern and various distribution types of data sets \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dropout types, advantage and disadvantages and scope of application of “dropout” values of statistical methods and machine learning methods. Figure 3: The third classification of imputation method for the detailed division of global methods and local methods.
## 4 Experiments and Results
### Analysis of Generated Images Using the Modified DCGAN  
In order to analyze the data distribution generated by the modified DCGAN more intuitively, the t-distributed stochastic neighbor embedding (t-SNE) [26] dimensionality reduction method is used in this paper. t-SNE is an algorithm for visualizing high-dimensional datasets by measuring the distance between each data point and other data points to calculate their correlation [27]. We randomly select 200 images from each category of the original data and the modified DCGAN-generated data for testing (as shown in Figure 8), where different colors represent different labels.  
The results of t-SNE dimensionality reduction highlight the effectiveness of the modified DCGAN in generating synthetic remote sensing images that closely resemble real-world images while introducing valuable diversity. The key takeaways from this research include:  
1. Realism and Diversity: The modified DCGAN has demonstrated its capability to produce synthetic remote sensing images that exhibit realism, making them highly similar to authentic remote sensing data. Additionally, the diversity observed within the generated images is a significant advantage. The ability to generate diverse samples within each class contributes to a more comprehensive and representative dataset.  
2. Matching Feature Distribution: The use of t-SNE for dimensionality reduction and visualization has substantiated that the feature distribution within the generated images closely aligns with that of real images. This alignment suggests that the modified DCGAN has successfully captured and retained essential features present in real remote sensing data, enhancing the data's quality.  
3. Enhanced Data for Training: By effectively generating realistic and diverse data samples, the modified DCGAN provides a valuable resource for training neural network  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Equipment** & **Software Environment** \\ \hline CPU: Intel(R) Xeon(R) Gold 5218R 2.10 GHz & Rocky Linux 8 \\ RAM:32 GB & CUDA 11.4 \\ GPU: NVIDIA RTX 3090 & Pytorch 1.12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of experimental conditions.  
models. This expanded dataset can be instrumental in improving the generalization capabilities of recognition models, as it exposes them to a wider range of scenarios and variations present in the real world.
# Two-Way Shape Memory Effect of a Shape Memory Composite Strip
## 2 Thermomechanical Properties of the SMA Composite Strip  
### Materials and Fabrication Method  
The silicon rubber was supplied by Hongyejie Technology Company (Shenzhen, China), the SMA wire was purchased from Shape Memory Material Company (Shanghai, China) [35; 36], and the styrene-based SMP solution was obtained from Harbin Institute of Technology (Harbin, China) [37]. The silicon rubber/SMP was used as the matrix material and the SMA wire was used as a reinforcement material to fabricate the composite strip.  
The fabrication process of rubber-based SMA composite strips (length 120 mm \(\times\) width 10 mm \(\times\) thickness 10 mm) was similar to that described in [35]. The detailed process (Figure 1) mainly included: (1) Designing and fabricating a rectangular Teflon mold to cure the rubber-based composite strip; (2) Placing a SMA wire eccentrically with a 1 mm thick protective layer on one side and pouring rubber solution into mold to maximize the bending angle; (3) Curing the composite strip for 24 h at room temperature 30 \({}^{\circ}\)C and removing from mold; (4) Heating composite strip to 150 \({}^{\circ}\)C and deforming into \"U-shaped\" state under external force effect to obtain the final state at high temperature; (5) Cooling to room temperature and releasing external force to obtain the temporary shape at room temperature.  
On the other hand, the rubber was replaced with SMP to improve the load-bearing ability of composite strip at room temperature. The detailed process (Figure 2) mainly included: (1) Heating-treated SMA wire from straight state into \"L-shape\" at 150 \({}^{\circ}\)C for 10 min; (3) Deforming the heated-treated SMA wire into straight state; (3) Placing the straight SMA wire into mold and pouring SMP solution into mold; (4) Curing the SMP-based composite strip for 24 h at 60 \({}^{\circ}\)C; (5) Cooling to room temperature and removing mold to obtain the temporary shape of SMP-based strip at room temperature. When the temperature was heated to high temperature, the composite strip would produce bending deformation.
## 3 Results
### Literature Classification
#### 3.6.3 Studying the Train/Test Mechanism, Evaluation Metrics, and Segmentation Approaches
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Authors** & **TT Mechanism** & **Evaluation Metrics** & **Segmentation Approach** \\ \hline
18 & [PERSON] et al., 2021 [33] & Split 8-0-20, cross-validation method & Dice score, C-index, SSIM & CNN \\ \hline
19 & [PERSON] et al., 2022 [34] & [PERSON] optimization & Hausdorff 95 distance, mean Intersection over union (MIOU) & CNN, transformer \\ \hline
20 & [PERSON] et al., 2016 [35] & SVM & Accuracy & K-means clustering, median filtering, and skull masking \\ \hline
21 & [PERSON] et al., 2021 [36] & Split 8-0-20, cross-validation method & DICE score, accuracy, sensitivity, specificity, F1 score, precision & CNN \\ \hline
22 & [PERSON] et al., 2022 [37] & Augmentation, Z-score & DICE score & Neuroxai framework \\ \hline
23 & [PERSON] et al., 2021 [38] & Split 80-20, cross-validation method, augmentation & DICE score, sensitivity, specificity, Hausdorff95 distance & U-Net \\ \hline
24 & [PERSON] et al., 2023 [39] & Split 70-15-15, augmentation & Accuracy, sensitivity, specificity, and recall & Canny Mayfly algorithm (ACMA), spatial gray level dependence matrix (SGLDM) \\ \hline
25 & [PERSON] et al., 2021 [40] & & Accuracy, precision, and recall & CNN \\ \hline
26 & [PERSON] et al., 2021 [41] & Cross-validation method & DICE score, Jaccard coefficient & HPU-Net, fuzzy c-means clustering \\ \hline
27 & [PERSON] et al., 2021 [42] & Split 64-20-16, cross-validation method & DICE score & U-Net \\ \hline
28 & [PERSON] et al., 2022 [43] & Split 87-13, cross-validation method & Accuracy, error rate & CNN \\ \hline
29 & [PERSON] \& [PERSON], 2023 [44] & Split 80-20 & DICE score, accuracy, F1 score, precision, recall & U-Net \\ \hline
30 & [PERSON] et al., 2020 [45] & Split 75-0-25, cross-validation, augmentation & Hausdorff95 distance & Context residual module, inter-slice context information \\ \hline
31 & [PERSON] et al., 2021 [46] & Split 80-0-20, cross-validation & DICE score, peak signal-to-noise ratio & CNN, Chan–Vese algorithm \\ \hline
32 & [PERSON] et al., 2024 [47] & SVM & Accuracy, sensitivity, specificity, peak signal-to-noise ratio, Jaccard coefficient (JC) & Fuzzy c-means clustering, Adaptive Contrast Enhancement Algorithm (ACEA), and a median filter \\ \hline
33 & [PERSON] et al., 2021 [48] & Split 80-0-20, cross-validation method & False positive rate (FPR), true positive rate, positive predictive value (PPV) & Teacher model with adversarial learning, signed distance maps (SDM) \\ \hline
34 & [PERSON] \& [PERSON], 2020 [50] & SVM & Accuracy, sensitivity, specificity & FAHS-SVM, skull stripping, morphological operations, and wavelet transformation \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Applying Automatic Translation for Optical Music Recognition's Encoding Step
## 2 Methodology
### Neural Approaches
#### 2.3.1 Sequence to Sequence with Attention Mechanisms  
The first neural approach used in this work is based on the sequence to sequence (Seq2 Seq) system proposed in [22]. This network's architecture tries to maximize the conditional probability of a given token, which in our case is a \"_kern_\"word, based on its inputs and the previous predicted tokens. The proposed model to solve this problem was designed with a recurrent neural network (RNN), which output a prediction based on the received inputs and the previous states of the neurons. The structure of the Seq2 Seq model is composed of two parts: an encoder that processes the input sequence element by element and embeds its relevant information into an internal state, often referred to as a context vector; and a decoder, which combines the internal state of the encoder and the previous predicted token to generate its equivalent embedding in the target encoding that is then used to predict the next token. Since its inception, this model has evolved in the context of machine translation. In particular, this evolution has been directed towards the attention mechanisms, which are masking matrix operations whose goal is to assist with the model's predictions by enhancing the values of the most relevant information in an input. In particular, we resort to the \"Global Attention\" strategy proposed by [PERSON] et al. [23], with a scoring method given by the scalar product between the encoder and the decoder outputs. The attention matrix produced in this method acts as a weighting element in order to select which tokens are relevant to produce the next token in the target sequence. A representation of the implemented model in this work is depicted in Figure 3.  
Figure 2: An example of an agnostic encoding sequence including context information (see the full example in Appendix A Figure A1). Symbols representing notes are extended to include information about the respective clefs and key signatures. The string token @clef.G:L2@3b inserted in these symbols represents a G clef an a three flat signature (as found at the beginning of the sequence).