esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Communications from remote areas that may be of interest is still a problem. Many innovative projects applied to remote sites face communications difficulties. The GOLDFISH project was an EU-funded project for river pollution monitoring in developing countries. It had several sensor clusters, with floating WiFi antennas, deployed along a downstream river's course. Sensor clusters sent messages to a Gateway installed on the riverbank. This gateway sent the messages, through a backhaul technology, to an Internet server where data was aggregated over a map. The communication challenge in this scenario was produced by the antennas' movement and network backhaul availability. Since the antennas were floating on the river, communications could be disrupted at any time. Also, 2G/3G availability near the river was not constant. For non-real-time applications, we propose a Delay/Disruption Tolerant Network (DTN)-based solution where all nodes have persistent storage capabilities and DTN protocols to be able to wait minutes or hours to transmit. A mechanical backhaul will periodically visit the river bank where the gateway is installed and it will automatically collect sensor data to be carried to an Internet-covered spot. The proposed forwarding protocol delivers around 98% of the messages for this scenario, performing better than other well-known DTN routing protocols.  
Keywords: disruption-tolerant; delay-tolerant; WSN; delivery probability; opportunistic forwarding; pollution monitoring; rural telecommunications +
Footnote †: journal: Sensors
_Article_  
**Towards New Mappings between Emotion Representation Models**  
**[PERSON]**  
Department of Software Engineering, Faculty of Electronics, Telecommunications and Informatics,  
Gdansk University of Technology, 80-233 Gdansk, Poland; [EMAIL_ADDRESS]; Tel.: +48-58-347-2989  
Received: 24 November 2017; Accepted: 9 February 2018; Published: 12 February 2018  
**Featured Application: (1) When you need emotions described in one representation model and get results in another form from an affect recognition system; (2) In a late fusion of hypotheses on affect from diverse algorithms (in multimodal emotion recognition); (3) In an evaluation of mappings between emotion representation models.**  
**Abstract:** There are several models for representing emotions in affect-aware applications, and available emotion recognition solutions provide results using diverse emotion models. As multimodal fusion is beneficial in terms of both accuracy and reliability of emotion recognition, one of the challenges is mapping between the models of affect representation. This paper addresses this issue by: proposing a procedure to elaborate new mappings, recommending a set of metrics for evaluation of the mapping accuracy, and delivering new mapping matrices for estimating the dimensions of a Pleasure-Arousal-Dominance model from [PERSON]'s six basic emotions. The results are based on an analysis using three datasets that were constructed based on affect-annotated lexicons. The new mappings were obtained with linear regression learning methods. The proposed mappings showed better results on the datasets in comparison with the state-of-the-art matrix. The procedure, as well as the proposed metrics, might be used, not only in evaluation of the mappings between representation models, but also in comparison of emotion recognition and annotation results. Moreover, the datasets are published along with the paper and new mappings might be created and evaluated using the proposed methods. The study results might be interesting for both researchers and developers, who aim to extend their software solutions with affect recognition techniques.  
**Keywords:** affective computing; emotion recognition; emotion representation models; emotion mapping; [PERSON]'s six basic emotions; Pleasure-Arousal-Dominance model
## 5 Specific Application of Topic Identification Models and Algorithms
### Application of Machine Learning and Deep Learning Models and Algorithms
#### 5.2.3 TF-IDF Algorithm  
TF-IDF is a statistical method used to evaluate the importance of a word to a document set or one document in a corpus. In the research on topic recognition, TF-IDF is often accompanied by a K-means clustering algorithm. [PERSON] used the TF-IDF algorithm and the K-means clustering method to construct a technical theme identification and analysis framework, enabling them to identify industrial wastewater treatment technology topics [57] and the IPC classification number. [PERSON] et al. applied the TF-IDF method to vectorize each document, and used the K-means++ algorithm for clustering to identify emerging technology topics in the field of unmanned combat platforms [58].
## 3 Modeling and Algorithms
### Class Adjusted Spatial Distance  
For the purpose of incorporating spatial information into PCSSR, we propose using CASD to replace distance matrix \(M\) required by SR process in the original PCSSR. We first provide a brief introduction to th planar Euclidean distance (PED). Consider two points \((a_{1},b_{1})\), \((a_{2},b_{2})\) in a plane. The PED between these two points is defined as:  
\[d=\sqrt{\left(a_{1}-a_{2}\right)^{2}+\left(b_{1}-b_{2}\right)^{2}} \tag{3}\]  
As we have discussed, to improve the performance of the PCSSR algorithm, a proper distance measurement between each two samples is needed. The distance matrix should reflect the similarity or difference among samples. Since each sample is just an area on the ground, the simplest way to measure the distance between each two samples is by calculating the spatial distance, i.e., the PED between them. The distribution of land covers is usually in a continuous way, so if a sample belongs to some class \(c_{i}\in\{c_{1},c_{2},c_{3},\ldots\}\), the samples in its spatial neighborhood are likely to belong to the same class as it. Thus, we can use the PED between two samples to represent their similarity.  
However, PED has its limitation for measuring the distance information that PCSSR needs. It is possible that two samples distant from each other belong to the same class, which is not unusual in the land cover classification. In this case, PCSSR using PED would fail to classify such samples. To overcome this limitation, we introduce the class adjusted spatial distance (CASD) to replace the naive planar Euclidean distance. Generally speaking, the CASD is a distance measurement which considers not only the Euclidean distance between two samples but also their class difference. We mainly use the Euclidean distance algorithm and the shortest path algorithm to solve the CASD.  
We first generate a complete undirected graph \(G(V,E)\) where \(V\) represents all the \(n\) samples and \(E\) is valued with the Euclidean distances between every two samples. The distance from a sample point to itself is defined as 0. Then, we check all the labeled samples (vertices) in the complete graph \(G\). If two labeled samples belong to the same class, we change the edge weight between them to 0. In this way, we make the samples with the same class \"closer\" to each other. At the last step, we apply the shortest path algorithm (for example Dijkstra algorithm Dijkstra (1996)) between every two vertices in the graph \(G\) and revalue the edge weight between them with the length of the computed shortest path. We define this new edge weight as \"the class adjusted spatial distance\". The above process is illustrated in Figure 2, and the Algorithm 1 is described below.  
Figure 1: The general flow of the proposed class adjusted spatial distance (CASD)-assisted hyperspectral image (HSI)_classification method.  
The element value \(M_{ij}\) in the output adjacency matrix \(M\) represents the calculated CASD between the i-th sample and j-th sample.
# Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size
## 2 Materials and Methods
### Used Datasets  
#### 2.3.1 Simulation Settings and Block Definition  
Rockfall source areas were in the case of both rockfall areas determined by comparing the point clouds before (national LiDAR point cloud from year 2014 and survey with unmanned aerial vehicle after the event) and after the rockfall event. In the case of RS1, the photogrammetric point cloud was obtained from the UAV survey in July 2018, while in the case of RS2, the survey was done in March 2020. The area of source area at RS1 encompasses approximately 4000 m\({}^{2}\), and at RS2, it encompasses approximately 330 m\({}^{2}\). The rock density of both source areas was set to 2500 kg/m\({}^{3}\) (limestone) [47], and based on the height difference between the source and runout areas, it was calculated using the LiDAR DTM1 data, and the initial fall height was set to 50 m in both cases. Block dimensions and block sizes are based on the field measurements of rocks at the maximum runout of the rockfall. At RS1, the block dimensions that were used for modelling were 1.4 m (d1), 0.9 m (d2), and 0.8 m (d3), and at RS2, they were 1.9 m (d1), 1.5 m (d2), and 1.1 m (d3). The prevailing block shape at both sites was set to rectangular. Variation in block volume was set to \(\pm\) 0% in order to specifically focus on studying the effect of grid cell size and effect of forest on rockfall modelling, and neglecting variations in shape and size for a given block diameter. Throughout the paper, we use the term \"block\" for defining the simulation of single, individual pieces of falling rocks within the model, since the same term has been used by the author of the model [21]. This term does not necessarily reflect sedimentological nomenclature where rock blocks would, based on their dimensions, be treated i.e., in planetary and space research as large clasts, divided to boulders (<1 m) and megaclasts (blocks 1-10 m; megablocks 10-100 m; superblocks > 100 m) [48; 49; 50; 51; 52].  
The input data that were of interest in this study were the DTM. In the Rockyfor3D guidelines, it is stated that both the spatial precision of the simulated maps and the accuracy of the simulated kinematics decrease with increasing cell size [21]. In order to test the preferred grid cell size of the data, we used the following set of DTM grid cell sizes: 1 \(\times\) 1 m (DTM1), 2 \(\times\) 2 m (DTM2), 5 \(\times\) 5 m (DTM5), and 10 \(\times\) 10 m (DTM10). Different grid cell sizes of DTM were created based on the LiDAR 1 \(\times\) 1 m point cloud [53] obtained in 2014, using the binning interpolation type with average elevation values in ArcGIS Pro [54].
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 1 Introduction
Together with the impact analysis, we present a set of methods to mitigate these effects and calibrate the dataset. First, in Section 2 the capabilities of SMAP-R are defined as compared to CYGNSS and TDS-1. Following this comparison, a general method for mapping the variable spatial resolution of each individual GNSS-R measurement is developed. Section 3 details the calibration methodology which accounts for the GPS transmitter variability and the filtering effect of the SMAP high gain antenna. In Section 4 we analyze the impact of this calibration in a SMAP-R dataset characterized by coherent scattering over the Chukchi-Beaufort Sea area covered by mostly old ice in February 2018, with minimal presence of first year ice towards the Alaskan coast. The selected data reduces the variability external to our calibration, allowing us to clearly see the effect of our corrections on the data. In Section 5 we discuss the capabilities and the potential of SMAP-R. Finally, in Section 6 we discuss our conclusions.
## 4 Discussion  
### Technological Innovation  
Our findings demonstrated that all patent assignees developed the electrolytic solution using the mixed lithium salts, mixed solvents, and at least one additive compound. LiPF\({}_{6}\), LiBF\({}_{4}\), LiClO\({}_{4}\), etc. were widely used as inorganic salts, and LiN(CF\({}_{3}\)SO\({}_{2}\))\({}_{2}\), LiN(C\({}_{2}\)F\({}_{5}\)SO\({}_{2}\))\({}_{2}\), LiCF\({}_{3}\)SO\({}_{3}\), etc. were organic salts. For the mixed solvents system, EC, PC, VC, and other carbonate are the most common examples. In addition to carbonates, there have been some other solvents used by leading companies  
Figure 5: Multi-dimensional scaling analysis (MDS): (**a**) H01M10/0568 (lithium salts) and (**b**) H01M10/0567 and 0569 (organic solvents and additives).  
in the preparation of liquid electrolyte for LIBs such as the lactone compound, e.g., \(\gamma\)-butyrolactone, \(\gamma\)-valerolactone, \(\alpha\)-acetyl-\(\gamma\)-butyrolactone. It is interesting to mention that patent assignees used most of the same lithium salts and organic solvents. We believed that various compositions of electrolytes with additives, in addition to the combinations of lithium salts and solvents, were developed from these companies with emphasis on the battery cycle life, as the technology-function matrix analysis suggests where HJ-AJ05 (specific numerical limitation, size, or scope in combination with cycle life) was also the bigger bubble in the matrix.  
Additive compounds were much more different and complicated in all considered patents. Vinylene carbonate, vinyl ethylene carbonate, and other carbonate compounds were the most commonly used. Besides these carbonate compounds, for example, Mitsubishi Chem. (US7416813B2 and US6942948B2) used sulfonic compounds, such as 1, 2, 3-propanetriol trimethanesulfonate or with sulfone and acid anhydride, to provide a battery of high capacity that is excellent in storage, load, cycle, and charging and discharging characteristics and that reduces gas generation. Panasonic (US6958198B2) mixed carbonate with the fluorine-containing compound, e.g., fluoroalkyl (C2-C20) carboxylic acids and sulfur compound, e.g., ethylene sulfite to uniformly \"wet\" the electrode material, to provide LIBs with excellent cycle life, storage characteristics, and high rate discharge performance. Samsung SDI (US7223500B2) developed an additive system that includes a carbonate-based compound, sulfone-based compound, and anisole-based compound, to inhibit the generation of gas inside the battery. We thought that additives technology is vital in the electrolyte composition.  
From the above findings, we believed that using various combinations of typical lithium salts with organic solvents, and developing novel additive compounds were the developing trend of the LIBs industry.
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 3 A Three Step MUSIC-Taylor-Based Localization Approach
### Step 1, MUSIC-Based Coarse JAEDE
2:Get mesh grid \([\mathbf{\hat{\theta}},\mathbf{\hat{\phi}},\mathbf{\tilde{\tau}}]\). 3:Construct \(S(\theta,\phi,\tau)\) following (5) and (6). 4:Get received sequence \(R(\theta,\phi,\tau)\). 5:\(C(\theta,\phi,\tau)\leftarrow\mathbb{E}\{R(\theta,\phi,\tau)R(\theta,\phi,\tau)^ {\mathrm{H}}\}\). 6:\(\mathbf{V}\leftarrow\mathrm{EVD}\{C(\theta,\phi,\tau)\}\). 7:\(\mathbf{p}_{0}=[\mathbf{\hat{\theta}},\mathbf{\hat{\phi}},\mathbf{\tilde{\tau}}]\leftarrow \underset{\theta,\phi,\tau}{\arg\max}\mathbf{\hat{f}}(\theta,\phi,\tau)\). 8:Initialize \(\mathbf{p}_{\mathrm{t}}\leftarrow\mathbf{p}_{0},\mathbf{t}\gets 0\), \(\mathbf{\Delta}=0\). 9:while\(\mathbf{t}\leq N_{\mathrm{T}}\)do
10:\(\Delta\theta_{\mathrm{t}}\leftarrow\) (20). 11:\(\Delta\phi_{\mathrm{t}}\leftarrow\) (21). 12:\(\Delta\tau_{\mathrm{t}}\leftarrow\) (22). 13:\(\mathbf{\tilde{\theta}}_{\texttt{t}+1}\leftarrow\mathbf{\tilde{\theta}}_{\texttt{t}}+ \Delta\theta_{\mathrm{t}}\). 14:\(\mathbf{\tilde{\phi}}_{\texttt{t}+1}\leftarrow\mathbf{\tilde{\phi}}_{\texttt{t}}+ \Delta\phi_{\mathrm{t}}\). 15:\(\mathbf{\tilde{\tau}}_{\texttt{t}+1}\leftarrow\mathbf{\tilde{\tau}}_{\texttt{t}}+ \Delta\tau_{\mathrm{t}}\). 16:if\(\Delta\theta_{\texttt{t}+1}<\theta_{\texttt{th}}\) and \(\Delta\phi_{\texttt{t}+1}<\phi_{\texttt{th}}\) and \(\Delta\tau_{\texttt{t}+1}<\tau_{\texttt{th}}\)then
17: Break. 18:endif
19:\(\texttt{t}\leftarrow\texttt{t}+1\)
20:endwhile
21:\((\tilde{\vartheta}_{t},\tilde{\vartheta}_{t},\tilde{\tau}_{t})\leftarrow\mathrm{ LOS}(\mathbf{\tilde{\theta}}_{\texttt{t}+1},\mathbf{\tilde{\phi}}_{\texttt{t}+1},\mathbf{\tilde{\tau}}_{ \texttt{t}+1})\). 22:\([\mathbf{\tilde{\tau}}_{\texttt{m}},\mathbf{\tilde{\tau}}_{\texttt{m}},\mathbf{\tilde{ \tau}}_{\texttt{m}}]\leftarrow\) (24). ```  
**Algorithm 1** MUSIC-Taylor-based Single BS Localization
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## Appendix A The Convergence Proof Process for PFPS-LWC  
**Assumption A1**. : _Lipschitz Smoothness. The gradients of client i's local complete heterogeneous model \(w_{i}\) are \(L1-\)Lipschitzsmooth:_  
\[ \begin{split}\|\
abla\,\mathcal{L}_{i}^{t_{1}}\Big{(}w_{i}^{t_{ 1}};x,y\Big{)}-\
abla\,\mathcal{L}_{i}^{t_{2}}\Big{(}w_{i}^{t_{2}};x,y\Big{)} \|\leq L_{1}\|w_{i}^{t_{1}}-w_{i}^{t_{2}}\|,\\ \forall t_{1},t_{2}>0,i\in\{0,1,...,N-1\}.(x,y)\in D_{i}\end{split}  \tag{1}\]  
_The above formulation can be further derived as_  
\[\mathcal{L}_{i}^{t_{1}}-\mathcal{L}_{i}^{t_{2}}\leqslant\Big{\langle}\
abla \,\mathcal{L}_{i}^{t_{2}},\Big{(}w_{i}^{t_{1}}-w_{i}^{t_{2}}\Big{)}\Big{\rangle} +\frac{L_{1}}{2}\|w_{i}^{t_{1}}-w_{i}^{t_{2}}\|_{2}^{2} \tag{2}\]  
**Assumption A2**. : _Unbiased Gradient and Bounded Variance. Client is random gradient \(g_{w,i}^{t}=\
abla\mathcal{L}_{i}^{t}(w_{i}^{t},\mathcal{B}_{i}^{t})(\mathcal{B}\) is a batch of local data) is unbiased:_  
\[\mathbb{E}_{\mathcal{B}_{i}^{t}\subseteq D_{i}}[g_{w,i}^{t}]=\
abla\mathcal{L} _{i}^{t}(w_{i}^{t})\] (A3)  
_and the variance of the random gradient \(g_{w,i}^{t}\) is bounded by_  
\[\mathbb{E}_{\mathcal{B}_{i}^{t}\subseteq D_{i}}\Big{[}\big{\|}\
abla \mathcal{L}_{i}^{t}(w_{i}^{t};\mathcal{B}_{i}^{t})-\
abla\mathcal{L}_{i}^{t}( w_{i}^{t})\big{\|}_{2}^{2}\Big{]}\leq\sigma^{2}\] (A4)  
**Assumption A3**. : _Bounded Parameter Variation. The parameter variations of the homogeneous small feature extractor \(\theta_{i}^{t}\) and \(\theta^{t}\) before and after aggregation are bounded as_  
\[\|\theta^{t}-\theta_{i}^{t}\|\leq\delta^{2}\] (A5)  
**Lemma A1**. : _There is an upper bound on the loss range of any client's local model, \(w\), in the \(t\) local training round._  
\[\mathbb{E}[\mathcal{L}_{t+1}^{E+0}]\leq\mathcal{L}_{t}^{E+0}+(\frac{L_{1}\eta ^{2}}{2}-\eta)\sum_{e=1}^{E}\|\
abla\mathcal{L}_{t}^{E+e}\|_{2}^{2}+\frac{L_{ 1}E\eta^{2}\sigma^{2}}{2}\] (A6)  
**Lemma A2**. : _For the \((t+1)\) local training round, the loss of any client before and after aggregating local homogeneous small feature extractors on the server is bounded by_  
\[\mathbb{E}[\mathcal{L}_{t+1}^{E+0}]\leq\mathbb{E}[\mathcal{L}_{t}^{E+1}]+\eta \delta^{2}\] (A7)  
Based on the above assumptions, we can perform a further derivation.
## 2 Materials and Methods
### Precipitation Data  
In order to assess the daily precipitation estimate of the satellite product, a set of rainfall data observed at 18 surface gauge stations was used as a reference. These stations are distributed throughout the SFB and are managed by the National Meteorological Institute (INMET), through the Meteorological Database Project (BDMEP).  
Table 1 presents information about the surface gauges, such as the municipalities where the gauges is installed, latitude, longitude, elevation (m) and percentage of missing data for each gauge within the period from June 2000 to December 2019. The data series  
Figure 1: Location and topography of the São Francisco River basin region and rain gauge stations.  
observed by each surface gauge does not exceed 10% of missing data (Table 1). Condition imposed by several researchers as [18; 42; 43]. To assess the IMERG estimates, the dates with missing data were excluded from the two databases, Gauge and IMERG.
## 5 Conclusions  
In this paper, active control of flow-induced cavity resonance noise is experimentally investigated with the usage of a narrowband ANC system. An experimental duct system with side-wall cavities is built up at low Mach numbers. First, a hybrid numerical method combining LES and the FW-H equation is applied to predict the frequency of the flow-induced resonance noise. Then, a narrowband ANC system is further established to suppress this resonance noise, where the secondary loudspeaker and the acoustic sensor are placed at the bottom and near the leading edge of the cavity, respectively. The FxLMS algorithm is used as the control algorithm, within which an internally generated sinusoidal signal is used as the reference.  
Experiments are carried out, whose results show that the resonance frequency could be predicted with 1.5% errors by the hybrid numerical method, and more than 10 dB attenuations can be obtained for both the upstream and the downstream resonance noise, which indicates a global control performance. Compared with methods of active flow  
Figure 10: Experimental results with 261 mm cavity: the noise spectrum at the test (**a**) microphone I and (**b**) microphone II with a 331 Hz (resonance frequency) reference; the noise spectrum at the test (**c**) microphone I and (**d**) microphone II with a 337 Hz reference (best); the noise spectrum at the test (**e**) microphone I and (**f**) microphone II with a 343 Hz reference.  
control, resonance noise is suppressed without enhancements at other frequencies. Further, it was also found that the noise energy for both the fundamental resonance frequency and its first harmonic can be suppressed at the same time, although only the former is under control inside the ANC system. Furthermore, the optimal reference frequency could differ significantly from the original resonance frequency and a relatively high tolerance for the reference FM could be observed, which is rather different from the cases in linear systems. All these results indicate a nonlinear control mechanism for the control of flow-induced resonance noise with a narrowband ANC system proposed in this paper.  
Although the effectiveness of the proposed method has been confirmed experimentally, the explicit nonlinear control mechanism is still not clear. Future work would focus on the joint simulations of the physical and the control systems, through which guidelines for the choices of the best reference frequencies might be obtained.  
Conceptualization, F.A. and H.L.; methodology, data curation and investigation, H.L. and C.S.; validation and software, F.A. and X.Z.; resources and supervision, B.L.; writing--original draft preparation, H.L.; writing--review and editing, F.A. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (grant number 11874034), and the Taishan Scholar Program of Shandong (No. ts201712054).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.