esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The threshold method simply determines the SOS and EOS (start and end of growing season) using a fixed value (e.g., NDVI) or a fixed percentage of the annual maximum value. The second method captures not only the SOS and EOS when the derivative of the smoothed growth curve is at the local maxima or minima, but also retrieves the time when rats are increasing (decreasing) rate (tangent slope) during green up (senescence) stages [10; 20; 21]. Thus, the skewness of plant growth patterns can be depicted using the asymmetric features of growing rates. Differences in growing season length and cumulative greenness (or production) during vegetation green up and senescence phases may supply more information to portray phenological asymmetry. These variables can be seen as comprehensive-phenomenological parameters, and their changes can differentially alter plant functions, affecting carbon, water, and energy fluxes between the terrestrial biosphere and the atmosphere. With extensive studies based on field data, satellite images, and ecosystem process models, it has become evident that the response of plant phenology to climate variability and change is both location- and species-specific [22; 23; 24; 25]. Thus, it would be more meaningful to focus the investigation on vegetation growth changes at the regional scale than at the global scale. Northeast China (hereafter NEC), a typical temperate zone in the mid-latitudes, has a variety of vegetation types, including forests (evergreen needleleaf forest, deciduous needleleaf forest, deciduous broadleaf forest, and mixed forests), croplands, and grasslands [26]. It has proven to be a suitable area for studying shifts in vegetation phenology and concomitant climatic feedbacks [26; 27; 28]. Furthermore, the availability of numerous satellite datasets, such as GIMMS NDVI3g, Modis NDVI and Spot VGT, enable us to explore vegetation phenology changes and climatic impacts across a long time period for Northeast China [29; 30; 31]. In this study, long-term vegetation greenness data from GIMMS NDVI3g was adopted to derive key vegetation phenological metrics (start, end, peak, growth rate, and duration of the growing season), to generate asymmetrical growth indices across the NEC. Next, the strength and spatial pattern of asymmetries in regional location, plant type and temporal were investigated. Thereafter, detailed patterns of vegetation asymmetry responses to changes in climatic forces (temperature and precipitation) were examined using correlation analysis. Finally, the utility of these detailed asymmetrical indices depicting vegetation growth cycles and their climatic implications were discussed.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Constant Bandwidth
#### 5.1.3 Bandwidth Utilization  
We evaluate bandwidth utilization using the total network throughput as an indicator. As shown in Figure 12, when compared to CCLF, the bandwidth of the congested link is not fully utilized in PCON. This phenomenon becomes evident after the 12 th second.  
Figure 11: Network throughput under constant bandwidth using the PCON method.  
Figure 10: Network throughput under constant bandwidth using the CCLF method.  
This occurs due to the congestion feedback mechanism of PCON, which causes all consumers to slow down the sending rate of Interests, resulting in insufficient bandwidth utilization. In contrast, as shown in Figure 12, when using the CCLF method, the total throughput of the congested link remains close to 10 Mbps, with bandwidth utilization approaching 100%. The CCLF method completes all flow transmissions in 15.51 s, whereas the PCON method requires 18.43 s. Compared to PCON, the CCLF method reduces the total flow completion time by 16%.
## 3 Results
### Effect of MLE on Plant Biomass
#### 3.2.4 Root Dry Weight  
Figure 2D shows the effect of MLE application on root dry weight of wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) significantly reduced the dry weight of root by 17.4% and 72.4% as compared to the control, while, on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions increased the root dry weight by 39% and 33% compared to control.  
More interestingly, 3% MLE with both cadmium treatments under shade resulted in higher root dry weight compared to MLE 3% alone treatment, while under light reduction in root dry weight was observed.
## 5 Conclusions  
It was discovered that, despite the availability of commercially viable Quantum Key Distribution (QKD) solutions, there remains significant room for improvement in the areas of system monitoring and maintenance. Addressing this gap is crucial for accelerating the widespread adoption of QKD technologies. Enhanced monitoring and maintenance protocols would ensure the stability and reliability of these systems and build confidence in their deployment by providing a track record of stability and zero downtime.  
Achieving these improvements requires a multidisciplinary approach to identify and establish appropriate thresholds for monitoring across different applications and environments. This involves collaboration between experts in quantum physics, cybersecurity, network engineering, and systems maintenance, among other fields. The goal is to develop comprehensive monitoring frameworks that can preemptively identify and mitigate potential issues, thereby enhancing the overall resilience of QKD systems.  
Further studies should focus on tailoring QKD solutions to various use cases, ranging from secure communications in government and military applications to protecting sensitive data in the financial and healthcare sectors. This research would involve identifying the common variables that are critical to system reliability, such as environmental factors, hardware performance, and network conditions. By understanding these variables, it becomes possible to develop standardized metrics and protocols that ensure the consistent performance and security of QKD systems across different scenarios.  
Ultimately, this approach aims to establish QKD as a trustworthy and practical solution for secure communications, paving the way for broader integration into existing and future infrastructure. This will not only enhance data security, but also contribute to the development of quantum-safe encryption standards essential for protecting information in the coming quantum era.  
This monitoring approach can be used to assess various optical connections as they are used for QKD solutions and create a matrix of thresholds that must be met for a stable communication channel. This requires further experiments in different fields to see how environmental changes affect the QKD stability.  
The APC was funded by Riga Technical University, Faculty of Computer Science, Information Technology and Energy.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Electrophysiological Processing
#### 2.6.2 Time-Frequency Analyses  
In order to measure target-related alpha attenuation, EEG data were reprocessed to generate time-frequency estimates of alpha activity. EEGs were filtered and downsampled, similar to the ERP analyses, with a single change that the high-pass filter was raised from 0.1 Hz to 1.0 Hz. Filtered data were segmented into 2.5 s test epochs ranging from \(-\)1250 to +1250 ms, relative to the onset of each letter stimulus (\(t\) = 0 ms), and then separated according to target class. Individual epochs were transformed into scaleograms via continuous wavelet transform (CWT) using a complex Morlet mother wavelet (\(c\) = 5), with frequencies ranging 4-16 Hz in 48 linear steps. CWT output was normalized to uniform scale power (unit energy normalization), such that different frequency layers all possessed an energy value of 1, and complex-valued output was transformed to real-valued voltage. Lastly, mean and standard deviation information were extracted from a 500 ms baseline window of \(-\)600 to \(-\)100 ms and used to transform all time-frequency samples within each of the test epochs into Z-scores.  
To more pointedly examine the target-related alpha attenuation response derived from time-frequency decomposition, we defined \"alpha activity\" as the average of all Z-sored samples within the response window of 300-800 ms post-stimulus onset [19]. In turn, the alpha attenuation response was defined as the difference in alpha activity responses following target versus non-target stimuli. Rather than look at the entire alpha band (\(-\)8-13 Hz), values were taken on a per-participant basis from the wavelet layer nearest to that participant's individual alpha frequency (IAF) [39]. To identify each participant's IAF, a fast Fourier transformation (FFT) was performed on all available 2.5 s inquiry epochs for the pooled occipitoparietal signal and averaged within each of the calibration recordings. The FFT was parameterized in Brain Vision to utilize a 20% Hanning window, periodic variance correction, and produce non-complex voltage output normalized relative to a frequency range of 4-20 Hz. Local amplitude maxima were identified 7-13 Hz and manually reviewed to correct cases of obvious peak capture by noise or a harmonic of the SSVEP signal. There was no apparent IAF in 19/96 calibration recordings, so in these cases a default IAF value of 10 Hz was established to broadly approximate alpha activity. Importantly, the resolution of the FFT was limited computationally to 0.293 Hz, so estimates of IAF were interpreted accordingly as approximations. The result of this IAF process was that each participant had four estimates of IAF: one per each of the calibration recordings.
## 4 Results and Discussions  
### Case Study  
To validate the effectiveness of the proposed CSES optimization framework for AC/DC distribution systems with dual-time-scale coordination, comprehensive case studies are conducted on the AC/DC distribution system illustrated in Figure 1. The network comprises three IEEE 33-node AC subnetworks interconnected through a medium-voltage DC ring network. The simulation is implemented in MATLAB R2020a, with Gurobi 10.0.2 solver employed to solve the optimal scheduling problems.  
For the comparative analysis, two BESS configurations with equivalent total capacities (12 MWh) and power ratings (1 MW per subnetwork) are implemented. The CSES connects to node 15 in three AC subnetworks, while the individual ESS deploys two units at nodes 15 and 32 in three AC subnetworks.  
Figure 4 demonstrates the forecasted RES and load power profile in each subnetwork at different time scales, with forecast curves derived from [42] and appropriately adjusted for the application to AC/DC distribution systems.  
To comprehensively evaluate the proposed methodology, three distinct cases are established:  
Case 1: Independent ESS Operation (IE)  
In this case, each AC subnetwork operates with its individual ESS independently. The energy storage units are optimized to minimize individual subnetwork operational costs without coordination across the system.  
Case 2: Capacity Leasing CSES Operation (CL-CSES)  
This case implements CSES with fixed capacity leasing among subnetworks. Each subnetwork is allocated a predetermined shared energy storage capacity, and the complementary characteristics across subnetworks are not considered during the optimization process.  
Case 3: Coordinated Optimization CSES Operation (CO-CSES)  
The proposed methodology is implemented in this case, featuring coordinated optimization of CSES. This approach leverages the temporal-spatial complementarity of RESs and loads across subnetworks to optimize system-wide performance.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 1 Introduction
In Figure 1, some of the key cellular markets with the corresponding channel access mechanism for LTE's operation in the unlicensed band are presented [5; 10]. It is observed from Figure 1 that a significant proportion of the cellular network regulators across the globe are planning/conducting trials with both the channel access mechanisms for LTE's operation in the unlicensed band. Furthermore, a sizable proportion of the global markets have not started experimenting with either of the technologies. Hence, this article is even more relevant, as the presented results can be used by the regulators and network operators to make suitable decisions in the context of LTE's operation in the unlicensed band. Most regulations put limits on transmission powers in the unlicensed bands. For instance, for the lower 5 GHz band, the maximum transmission power in Europe is 23 dBm EIRP. As a result of the transmission power limits, LAA will generally be more suited for small cell deployments. The organization of this paper is as follows. The introduction and related works are covered in Sections 1 and 2, respectively. In Section 3, we present the channel access mechanisms for LTE-U. The channel selection techniques for LTE-U are discussed in Section 4. In Section 5, the fairness of these mechanisms and techniques in terms of the scaling with the users, operators, and access points is evaluated. The coverage, capacity, and coordination in the context of LTE-U are analyzed in Section 6. The LTE-'s performance evaluation in diverse traffic type, environments, and scenarios is studied in Section 7. In Section 8, latency and handover scenarios for LTE-U are analyzed. The upcoming 5G New Radio and MulteFire technologies are discussed in Sections 9 and 10, respectively. The open issues in LTE-U are discussed in Section 11, followed by some concluding remarks in Section 12.
## 2 Comparative Method
### Scaling Approach  
The portfolio of studied OIs (Table 1) is associated with a wide variability of modeling approaches to estimate the climate mitigation potential as well as different definitions of the underlying reference cases (see Appendix A). Hence, a direct comparison between the individual study results is limited so that a comparative assessment method is required. For this purpose, we differentiate into the scaling of in-flight and on-ground effects (Figure 1), which is required due to substantial differences in the related effects: In-flight aviation emissions comprise CO\({}_{2}\) and non-CO\({}_{2}\) species such as H\({}_{2}\)O, NO\({}_{x}\) or aerosols [3]. The resulting climate effect is, among others, strongly dependent on the emission altitude. For instance, H\({}_{2}\)O-induced effects (either directly or indirectly through the formation of CiC) are significant around the tropopause and comparably negligible at lower altitudes [14,63]. The same holds true for NO\({}_{x}\)-induced climate effects as the concentration of NO\({}_{x}\) emissions in the upper troposphere is significantly lower than in urban regions. Hence, relatively small amounts of aircraft emissions have notable effects in higher altitudes [3,63,64]. In other words, near-ground NO\({}_{x}\)-induced climate effects are negligible compared to effects from in-flight emissions. Moreover, non-CO\({}_{2}\) gases and particles are associated with relatively short atmospheric residence times, leading to a more heterogeneous distribution in the atmosphere compared to CO\({}_{2}\) emissions, whose climate effect can be considered independent of emission location and time of release [3,65]. Consequently, scaling of climate mitigation potentials for in-flight measures requires to account for both different traffic sample sizes, directly related to CO\({}_{2}\) climate effects, and non-CO\({}_{2}\)-induced effects. Traffic sample size scaling and associated fuel consumption are approximated with covered ASK per flight plan, while the location- and time-dependent non-CO\({}_{2}\) effect is estimated via route-specific CO\({}_{2}\) equivalents (CO\({}_{2}\)-Eq), as for instance introduced by [PERSON] et al. [66]. CO\({}_{2}\)-Eqs deal as a proxy for the non-CO\({}_{2}\) climate effects as they represent the total climate impact along a route relative to the CO\({}_{2}\) climate impact. Conversely, mitigation potentials of on-ground measures at the airport can be limited to an analysis of CO\({}_{2}\) emissions and effects, hence do not necessarily require the inclusion of emission location. The consideration of local air quality is excluded from this study. The climate impact is scaled according to the traffic volume at an airport indicated by the number of movements.
## Appendix B Hyperparameters
Lastly, absolute_error determines the mean absolute error and minimizes the L1 loss by using the median of each terminal node. * The maximum depth of the tree (max_depth) specifies how deep the tree can grow. If set to _None_, the tree will continue to expand until all leaves are pure or until each leaf has fewer than the specified min_samples_split samples (see below). Alternately, the tree depth can be explicitly set to values such as 2, 4, 6, 8, or 10. * The Max_features hyperparameter determines the number of features to evaluate when identifying the optimal split. If set to None, all features are considered. If set to sqrt, then max_features is the square root of the total number of features. * The Min_samples_leaf hyperparameter specifies the minimum number of samples that must be present in a leaf node. The possible values were 1, 2, or 4. * The Min_samples_split hyperparameter defines the minimum number of samples necessary to divide an internal node. The options for this setting were 2, 5, or 10. The hyperparameters adjusted for the RandomForestRegressor included the number of trees in the forest (n_estimators) as well as those previously specified for the DecisionTreeRegressor. The available options for n_estimators were 100, 200, or 500. The hyperparameters tuned for the SVR algorithm included:  
* The kernel type, which transforms the input data into higher-dimensional spaces. The polynomial kernel, denoted as poly, raises the input features to a specified degree, allowing the SVM to learn complex, non-linear decision boundaries. The Radial Basis Function (RBF) kernel, or rbf, measures the distance between samples in a Gaussian-distributed space, effectively managing non-linear separations even when the data have no clear boundaries. Lastly, the sigmoid kernel mimics the behavior of a neural network's activation function, transforming the feature space according to a logistic function. * The kernel coefficient, known as gamma, which has possible values of 1, 0.1, 0.01, and 0.001
* The regularization parameter, referred to as C, with choices of 0.1, 1, 10, and 100.
## 2 Literature Review
### Advancements in End-to-End (E2E), Hybrid, and Semi-Supervised Models for ASR  
Recent advancements in attention-based end-to-end (E2E) models, as well as hybrid and semi-supervised approaches, offer a promising solution for mixed-language ASR. These models had demonstrated success across various speech tasks, including ASR [12; 39], keyword recognition [40], speech emotion classification [41], and speaker verification [42; 43]. These models eliminate the need for manually produced language-specific resources, as demonstrated by [PERSON] et al. [15], who introduced an E2E system for Mandarin-English mixed-language speech using the connected timing classification (CTC) loss function. By creating a simulated dataset and incorporating language identification techniques, their system reduced the error rate by 5%, achieving an overall error rate of 24.61%. Other CTC-based approaches had also been proposed to improve code-switching ASR. [PERSON] et al. [44] developed a hybrid CTC-Attention end-to-end system, achieving an MER of 34.24% on the SEAME dataset. [PERSON] et al. [45; 46] further enhanced CTC-based performance by integrating a Transformer architecture with a language identification module, reducing the MER to 30.95%. Similarly, [PERSON] et al. [8] applied semi-supervised learning techniques to enhance the pronunciation lexicon, acoustic model, and language model within their Mandarin-English mixed-language speech recognition system. Their research reported error rates of 20.5% and 29.56% on two different datasets. Building on this line of work, [PERSON] et al. [47] introduced a mutual learning-based semi-supervised speech (MLSS) recognition approach and achieved an MER of 17.6% on the SEAME dataset. [PERSON] et al. [48] explored multi-task learning using deep neural networks, reporting an MER of 32.2% on a combination of LDC and in-house data. A summary of related work on Mandarin-English ASR systems is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Related Work** & **Model/Method** & **Dataset** & **MER (\%)** \\ \hline \multirow{3}{*}{[PERSON] et al. [8]} & Lattice-Free Maximum Mutual Information & \multirow{3}{*}{SEAME} & \multirow{3}{*}{20.54–29.56} \\  & (LF-MMI)-based semi-supervised training under & & \\ \cline{1-1}  & code-switching conditions & & \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & End-to-end system using connected timing & \multirow{2}{*}{SEAME} & \multirow{2}{*}{24.61} \\  & classification (CTC) & & \\ \hline \multirow{3}{*}{[PERSON] et al. [32; 33]} & Two-pass system with IPA and Bhattacharyya distance, & \multirow{3}{*}{SEAME} & \multirow{3}{*}{36.60} \\  & discriminative training, statistical machine translation & & \\ \cline{1-1}  & (SMT)-based text generation, and language identification & & \\ \cline{1-1}  & (LID) system integration & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Mandarin–English ASR systems.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 2 Materials and Methods
### Data Pre-Processing
#### 2.4.6 Classification Model  
After determining the relevant features, each observation will be characterized by a set of features instead of the CSI values. These feature vectors will be used to build the classification models responsible for determining the activity being performed. To perform the activity recognition phase, in this work, we are using the SVM classifier. The used SVM classifier has a Gaussian radial basis function (RBF) kernel with uniform prior for the different classes. We are using a one-vs.-one (OvO) approach to convert the multi-class classification problem into multiple binary classification problems. For each of the developed experiments, to optimize the SVM parameters, a coarse grid search followed by fine grid search is used. The decision behind using the SVM classifier in this work over the other classification techniques was due to the following reasons: First, the SVM classifier is the most commonly used classification technique in many classification domains, one of which is the human activity recognition domain [63; 64; 65]. Second, the SVM classifier is considered stable in the sense that it is not affected by noise compared to other classification algorithms such as decision trees. Furthermore, SVM classifier with a Gaussian RBF kernel has few hyper-parameters, namely the regularization parameter (C), and the RBF width (\(\sigma\)), to be optimized, which effectively reduces the fine-tuning time.