esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methodology Framework
#### 3.2.2 Models' Architecture  
LSTM; BiLSTM; CNN_LSTM; Transformer Multi-head self-attention layers, positional encoding for temporal sequence order, and feed-forward layers for feature extraction; ConvTransformer that combines CNN layers (for spatial pattern extraction) with Transformer attention layers to effectively integrate spatial-temporal dependencies; and Informer that employs probabilistic sparse attention mechanisms, significantly reducing computational complexity for long-sequence predictions. All of these methods are employed in this pipeline for time series prediction. LSTM and BiLSTM are effective in capturing long-term dependencies in sequential data by using memory cells and bidirectional processing [66]. CNN-LSTM integrates convolutional layers to extract spatial features, followed by LSTM to capture temporal dependencies, thus enhancing spatial-temporal learning [67]. Transformers utilize self-attention mechanisms to effectively handle long-range dependencies, making them suitable for complex time series prediction [68]. ConvTransformer combines CNN for spatial learning with Transformer for sequence processing, while Informer optimizes the Transformer architecture by focusing on key information, thus reducing computational complexity. Each model is specifically described in Table 3, along with their hyperparameter variations to enhance the accuracy of our results and have an effective training process.
## 2 Methods
### Workflow for the Method  
The separation process is shown in Figure 2. The black arrows represent the events of the target wavefield, which are not parallel in the original seismic data (a). In the first step, (b) is obtained from the original seismic data (a) by the dynamic time shift. The events of the target wavefield are not parallel in (a), but the events are parallel in (b) after the dynamic time shift. In the second step, the aligned target wavefield is obtained by the median filtering method, from (b) to (c). In the third step, the target wavefield is obtained by reverse alignment using the time shift of the first step, from (c) to (d). In the fourth step, the target wavefield (d) is subtracted from the original data (a), and the remaining wavefield (e) is obtained.  
The complete steps of this method are shown in Figure 3. The original VSP common-shot gathers are transformed to the modified \(\tau\)-\(p\) domain, and the slowness points corresponding to the optimal energy spectrum values in the target wavefield are fitted linearly. Then, according to the fitting results, the original wavefield is separated according to the steps of Figure 2, and as a result, the target wavefield and the remaining wavefield are obtained. One then repeats the above steps with the remaining wavefield as the original wavefield until the last residual wavefield no longer contains the regulate wavefield. We separated the target wavefields in turn according to the strength of the energy spectrum. In Figure 3, only the adopted median filtering is a routine VSP data processing approach. In general, the downgoing wave energy spectrum is stronger than that of the upgoing wave [21], and the P-wave energy spectrum is stronger than that of the S-wave. Hence, the downgoing P-wave energy spectrum is stronger than that of the upgoing S-wave. The energy spectrum difference between the P-wave and S-wave in field data is larger than that in synthetic data because of the attenuation. So, the upgoing P-wave energy spectrum is stronger than that of the downgoing S-wave in field data but not in synthetic data. Therefore, the synthetic records are separated in this order: downgoingP-wave, downgoing S-wave, upgoing P-wave, and upgoing S-wave; meanwhile, for the field data the order is as follows: downgoing P-wave, upgoing P-wave, downgoing S-wave, and upgoing S-wave.  
Figure 3: Flowchart of separation for the vertical seismic profile (VSP) wavefield.  
Figure 2: The process of separating a single wavefield using the new method. Black arrows denote downgoing P-wave events, red arrows denote downgoing S-wave events, green arrows denote upgoing S-wave events, and blue arrows denote upgoing S-wave events. (**a**) Original seismic data; (**b**) Dynamic aligned data; (**c**) Target wavefield after median filtering; (**d**) Target wavefield; (**e**) The remaining wavefield.
## 2 Materials and Methods
### Sampling Method  
Two studies were carried out to investigate the presence of organic contaminants in raw and treated Aquarium waters using POCIS devices.  
The initial monitoring was performed between March 2017 and January 2018. POCIS were used to monitor shark tank water, treated water and supply water.  
In particular, the following samplings of two weeks were performed:  
* Sampling I (spring): Two POCIS deployed in the shark tank (P1-P2) and two POCIS in a laboratory beaker filled with 4 L of Aquarium treated water (T1-T2);
* Sampling II (winter): Two POCIS deployed in the shark tank (P3-P4), two POCIS in a laboratory beaker filled with 4 L of Aquarium treated water (T3-T4) and two POCIS in a beaker filled with 4 L of supply water (S1-S2).  
POCIS deployed in the shark tank were protected by stainless-steel grids, a Zn tablet was fixed on the protective system to avoid corrosion, and the devices were positioned at the bottom of the tank. Treated and supply waters, sampled in laboratory beakers, were subjected to magnetic agitation (Figure S1), and water was replaced as frequently as the shark tank water. This action allowed the minimization of differences between the two water samplings, although agitation conditions may still have an influence on the sampling rate of analytes [18].  
The second study, carried out in January 2019, involved a sampling of two weeks:  
* Sampling III (winter): Three POCIS deployed in a tank of 150 L (Figure S2) filled with supply water (S3, S4 and S5) or treated water (T5, T6 and T7).  
POCISs were placed parallel to the water flow to allow the correct diffusion of analytes into the sampler [19]. Based on the results of the first study, no POCISs were deployed in fish tanks. Indeed, the focus of this last monitoring (sampling III) was to confirm the good performance of the filtration system and to evaluate the presence of emerging contaminants. To take into account the blank signals, for each study two POCISs were submerged in beakers filled with 1.5 L of milli-Q water for 7 days.  
The average temperature and salinity of the sampled waters were 23 \({}^{\circ}\)C and 36 ng kg\({}^{-1}\), respectively.
###### Abstract  
A smart city is an environment where a pervasive, multi-service network is employed to provide citizens improved living conditions as well as better public safety and security. Advanced communication technologies are essential to achieve this goal. In particular, an efficient and reliable communication network plays a crucial role in providing continue, ubiquitous, and reliable interconnections among users, smart devices, and applications. As a consequence, wireless networking appears as the principal enabling communication technology despite the necessity to face severe challenges to satisfy the needs arising from a smart environment, such as explosive data volume, heterogeneous data traffic, and support of quality of service constraints. An interesting approach for meeting the growing data demand due to smart city applications is to adopt suitable methodologies to improve the usage of all potential spectrum resources. Towards this goal, a very promising solution is represented by the Cognitive Radio technology that enables context-aware capability in order to pursue an efficient use of the available communication resources according to the surrounding environment conditions. In this paper we provide a review of the characteristics, challenges, and solutions of a smart city communication architecture, based on the Cognitive Radio technology, by focusing on two new network paradigms--namely, Heterogeneous Network and Machines-to-Machines communications--that are of special interest to efficiently support smart city applications and services.  
smart communications; cognitive radio; Heterogeneous Networks; machine-to-machine communications; spectrum sharing; spectrum sensing +
Footnote †: journal:  
_Future Internet_**2016**, \(8\), 23; doi:10.3390/f8020023
## 4 Results and Analysis
### Heatmaps as a Result of the Experiment  
When using 3D and VR tools, there is not only the added value of the ergonomics of the interfaces, especially for non-designers, but the additional benefit of the availability of 3D data on different stakeholders' opinions in 3D co-creation and co-design. At the end of the experiment, participants were shown a VR implementation of their paper design from Test 1, which was created through the intervention of an operator (Figure 8a). By combining the designs of different participants in overlay, a heat map for trees or benches was created, which was also displayed in 3D (Figure 8b,c). By asking for the opinion of residents and users of the public realm at the beginning of the design process, who were considered user experts, subjective information could be gathered and delivered to the officially appointed designer to increase the co-creative level of the process (Figure 1). This new information could inspire the design process, improve the final design quality, and augment citizen involvement.
## 8 Discussion
### Challenges
#### 8.1.9 Qualified Service Provision End-to-End  
Computation Oriented Communications (COC), Con-textually Agile eMBB Communications (CAeC), and Event Defined uRLLC (EDuRLLC) [393] are three types of newly supported 6G services, similar to 5G. Despite the fact that the exact service types enabled by 6G have yet to be determined, the standards will undoubtedly be more strict. For starters, future 6G services would require end-to-end assurances, which means the metric should be measured from the transceiver to the receiver rather than just the main network element in the 5G system. Second, in addition to typical measurements like link bandwidth, latency, and security, other KPIs should be considered. Future services will be evaluated in terms of situational awareness, learning capability, storage cost, and computing capacity. Furthermore, the heterogeneity of communication technologies and infrastructure hardware, as well as the complex requirements on numerous metrics, would hasten the adoption of machine learning in networking.
## 5 Discussion
### Added Value of the ESTARFM Framework for Large-Scale Analyses  
Previous studies on the ESTARFM algorithm indicate its great potential for the generation of high spatial and temporal resolution time series, but they also pointed out issues like the slow processingspeed or the way how similar pixels are selected that hinder its application on large scales [3; 10; 31]. The ESTARFM framework developed here significantly decreases processing time by parallelization and automating the algorithm. This feature, in addition to avoiding repeated manual input, is crucial for its application on large scales. Moreover, the way similar pixels are selected for the regression between Landsat and MODIS was changed in the framework. The approach in the original ESTARFM with a global threshold for the area of interest does not account for spatial differences in the heterogeneity of the study area. This means that if a study area comprises several Landsat tiles covering different landscape types, a single threshold is not sufficient for the selection of similar pixels. With the new approach presented here, regional differences are considered independently of the size of the study area.  
When working on large scales, several Landsat paths have to be used and, due to the different acquisition dates of the paths, errors might occur in the overlapping areas of the predicted output. In order to improve the consistency of ESTARFM predictions for certain periods of the time series (compare Section 4.2), a matching of the different paths with suitable algorithms might be useful. This, however, depends on the further application of the ESTARFM time series and is beyond the scope of this study.  
The fusion algorithm has as of yet only been applied to small study areas of less than 6000 km\({}^{2}\), which is far less than one Landsat scene (\(\sim\)33,300 km\({}^{2}\)). However, many relevant research questions in the field of vegetation mapping and monitoring require consistent vegetation information and related analyses at the national level or in larger geographic regions [39; 40]. It is expected that ESTARFM time series can be of great use for improved mapping and monitoring of agricultural areas in West Africa. This has been a challenging task due to the patchy small-scale farming practices in the region, where areas of agriculture can be adequately separated from rangeland only during short periods of the year coinciding with prevailing cloud cover [1; 41; 42]. The phenological signatures produced by ESTARFM in the presented study show distinct characteristics of the small-scale rain-fed and irrigated agriculture and savanna types which could be helpful for an improved discrimination. If, however, these results cannot be produced efficiently at the national to regional levels, their relevance for decision makers would remain limited.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 1 Introduction
### Related Work
Some of the problems in the commonly used path planning algorithms, path smoothing techniques and any-angle path planning techniques for mobile robots are shown in Table 1.  
Based on the above problems, an AP-JPS algorithm is proposed in this paper based on the JPS algorithm. The AP-JPS algorithm uses the any-angle path planning rule in AP-Theta* to improve the drawback that the original JPS algorithm cannot be multi-angle pathfinding. Meanwhile, this paper uses a seven-segment polynomial optimization method based on minimum snap, incorporating flight corridor inequality constraints, so that the smoothness and continuity of the resultant path generated by the AP-JPS algorithm can be guaranteed while satisfying safety. For the overall structure of the article, first, this paper proposes a vision triangle judgment to optimize the generation path of JPS by selecting inherited search points. Second, the LOS reachability detection between two nodes of the generated path is modified using the viewable angle in the AP Theta* algorithm. Finally, the paths are optimized for smoothness using a seventh-order polynomial based on minimum snap. Figure 2: Limitations of search angle.
# A Review of Deep Learning Based Speech Synthesis
## 3 Statistical Parametric Speech Synthesis
### Parameter Prediction
#### 3.2.2 DNN-Based Parameter Prediction  
It is well known that the acoustic features of a particular phoneme will be affected by the context information associated with the phoneme [30]. It means that the context information plays a significant role in the prediction of the acoustic features. Researchers show that the human speech generation process usually uses a hierarchical structure to convert the context information into a speech waveform [31]. Inspired by this idea, the deep structure models have been introduced in predicting acoustic feature parameters for speech synthesis [32]. The framework of the DNN-based parameter prediction progress can be seen in [20].  
To compare with the HMM-based parameter prediction methods, the DNN-based methods can not only map complex linguistic features into acoustic feature parameters, but also use long short-term context information to model the correlation between frames which improves the quality of speech synthesis. In addition, for the HMM-based methods, the principal of MLE is used to maximize the output probability which makes the parameter sequence a mean vector sequence, resulting in a step-wise function. The jumps cause discontinuities in the synthesized speech. To address this problem, the maximum likelihood parameter generation (MLPG) algorithm is used to smooth the trajectory by taking the dynamic features including the delta and delta-delta coefficients into account. However, the DNN-based methods cannot suffer from this problem.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O\({}_{2}\)-A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.  
solar-induced chlorophyll fluorescence (SIF); gross primary production (GPP); automated; in situ; measurement system +
Footnote †: journal: Sensors
## 3 Methods and Results
### Relationship between Net Slip and 3D Volume Strain  
The volume strain generated by a fault can be geometrically related to the net slip of the fault, which is a vector with direction, magnitude, and sense of movement. So, we can find a relationship between the maximum volume strain and fissure structures across the fault zone (or fault-propagation folding). Four faults were observed in the fissure structure canals along Sections 5, 6, 7, and 8 (Figures 9 and 10a-d). The net slip was calculated using the measured horizontal and vertical displacements on these faults (Table 5). Figure 11 depicts the obtained relationship between net slip and volume strain as a suitable linear equation.
## 3 Results
### Magnetic Targeting and Migration of SPION-Labeled D1 MSCs  
To investigate the magnetic targeting efficiency, SPION-labeled D1 MSCs were further labeled with a 675-nm fluorescent marker using \(1\times 10^{4}\)-\(2\times 10^{6}\) cells. Fluorescence signals were detected for \(2\times 10^{6}\) cells, revealing high migration at the 1.5 T magnetic field. The commercial SPIONs prior to cell labeling only resulted in 50% migration even after 72 h in the magnetic field (Figure S4), while our cells showed a migration value of over 95% at 30 min (Figure 5). Fluorescence values increased consistently over time for different numbers of cells, demonstrating that SPIONs were uniformly distributed in each cell. We also confirmed that SPIONs were not lost after differentiation (Figure 6).  
Figure 5: FOBI of SPION-labeled D1 MSCs. (**A**,**B**) Fluorescence value of \(1\times 10^{4}\)-\(2\times 10^{6}\) SPION-labeled cell number. (**C**,**D**) Migration change by magnetic field up to 30 min.  
Figure 6: Confocal fluorescence microscopy analysis of SPION-labeled D1 MSCs. (**A**) The final concentration of 50 \(\upmu\)g/mL SPION-labeled D1 MSCs after incubation for 24 h. (**B**) SPIONs were retained after neurogenic differentiation.
## 5 End-to-End Reliable Transmission Mechanisms for HCWSNs  
There are two common approaches suggested in the literature to improve end-to-end reliability in HCWSNs. One approach explores the use of reliable transport protocols [32] and the other explores the use of redundant transmissions and coding techniques [33] to allow data to be reconstructed at the receiver. In the following subsections, we explore both options in further detail.
## 3 Materials and Methods
### Relation Filtering Decoding Module  
The entities and relations extracted by the global pointer network are redundant relations, which shows that the number of relations identified in a sentence is much lower than the expected number. To address such issues, this research integrates a relation filtering decoding module into the GPLinker model. This module allows for the filtering and selection of predefined relations, which can improve the accuracy of entity relations identified by the global pointer network. The specific operational process is illustrated in the structural diagram.  
Given a predefined set of relations \(\mathrm{R}=\{r_{1},r_{2},\ldots,r_{k}\}\) (with \(\mathrm{k}\) denoting the size of set \(\mathrm{R}\)), the embedding vectors \(\mathrm{hi}\) corresponding to each character obtained from the output \(\mathrm{V}=(\mathrm{h}_{1},\mathrm{h}_{2},\ldots,\mathrm{h}_{n})\) at the encoding layer are subjected to pooling:  
\[\mathrm{h}_{i}^{r}=\mathrm{Pooling}(\mathrm{h}_{i}) \tag{8}\]  
where \(\mathrm{h}_{i}\) represents the corresponding embedding vector of each character. \(\mathrm{h}_{i}^{r}\) represents the result after pooling of vector \(\mathrm{hi}\), \(\mathrm{h}_{i}^{r}\in\mathrm{R}^{d\times k}\), \(\mathrm{d}\) represents the embedding dimension.  
Subsequently, the pooled results are passed through a linear layer and a non-linear activation function to obtain relation probabilities:  
\[\mathrm{P}^{r}=\mathrm{Sigmoid}(\mathrm{W}^{r}\mathrm{h}_{i}^{r}+\mathrm{b}^{r}) \tag{9}\]  
where \(\mathrm{P}^{r}\in\mathrm{R}^{k}\) denotes the probability of the text regarding relations, Sigmoid represents the non-linear activation function with a range of (0, 1), \(\mathrm{W}^{r}\in\mathrm{R}^{d\times k}\) denotes the trainable weights, and \(\mathrm{b}^{r}\in\mathrm{R}^{k}\) represents the bias term.  
Finally, a threshold \(\upmu\) is set to filter out redundant relations, and it can be taken as a multi-label binary classification task. If the relation probability \(\mathrm{P}^{r}\) is greater than \(\upmu\), it is considered a relation to be retained; if it is less than \(\upmu\), it is deemed a redundant relation.