esa-sceva/satcom-chunk-collection huggingface dataset:
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
###### Abstract  
The HYPSO-1 satellite, a 6U CubeSat carrying a hyperspectral imager, was launched on 13 January 2022, with the Goal of imaging ocean color in support of marine research. This article describes the development and current status of the mission and payload operations, including examples of agile planning, captures with low revisit time and time series acquired during a campaign. The in-orbit performance of the hyperspectral instrument is also characterized. The usable spectral range of the instrument is in the range of 430 nm to 800 nm over 120 bands after binning during nominal captures. The spatial resolvability is found empirically to be below 2.2 pixels in terms of Full-Width at Half-Maximum (FWHM) at 565 nm. This measure corresponds to an inherent ground resolvable resolution of 142 m across-track for close to nadir capture. In the across-track direction, there are 1216 pixels available, which gives a swath width of 70 km. However, the 684 center pixels are used for nominal captures. With the nominal pixels used in the across-track direction, the nadir swath-width is 40 km. The spectral resolution in terms of FWHM is estimated to be close to 5 nm at the center wavelength of 600 nm, and the Signal-to-Noise Ratio (SNR) is evaluated to be greater than 300 at 450 nm to 500 nm for Top-of-Atmosphere (ToA) signals. Examples of images from the first months of operations are also shown.  
hyperspectral imaging; remote sensing; imaging spectroscopy; earth observation; ocean color; CubeSat; push-broom +
Footnote †: journal: remote sensing  
0203
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology  
### Hierarchical Control Framework  
In the active tracking task, quadrotors use only the raw images captured by the onboard camera to execute proper subsequent actions. Since the raw images are very high-dimensional observations, designing an effective controller for quadrotors manually is challenging. Instead, we build a neural network controller and train it via deep RL. In theory, this learning method can map raw images to quadrotor motor commands directly. However, the deep RL method suffers from the problems of exploration and local optimality. Training such a complicated policy is not easy, even with sufficient interaction data between the quadrotor and the environment.  
We propose a hierarchical controller framework, as shown in Figure 1, to solve this dilemma. This framework consists of two-level policy layers. In the high-level policy layer, the neural RL controller outputs the desired speed commands for the quadrotor, given three sequential raw images \([\mathbf{O}_{t-2},\mathbf{O}_{t-1},\mathbf{O}_{t}]\) as inputs. This high-level RL controller perceives the features of the dynamic target and makes high-level decisions to follow it. The high-level decisions consist of the desired linear speed along the head direction \(V_{fb}\) and the desired yaw speed \(V_{yww}\). In the low-level policy layer, the PID controller is used to track high-level decisions under the current altitude and speed and then outputs the desired motor commands \([u_{1},u_{2},u_{3},u_{4}]\) for the quadrotor. This hierarchical framework enables us to focus on the learning of a high-level RL controller without concern for the low-level dynamic control of the quadrotor.  
Figure 1: Hierarchical control framework for the quadrotor active tracking task.
###### Abstract  
Due to expansive soils and high slopes, the deep excavated channel section of the China South-North Water-Diversion Middle-Route Project has a certain risk of landslide disaster. Therefore, examining the deformation law and mechanism of the channel slope in the middle-route section of the project is an extreme necessity for safe operation. However, the outdated monitoring method limits research on the surface deformation law and mechanism of the entire deep excavation channel section. For these reasons, we introduced a novel approach that combines SBAS-InSAR and GNSS, enabling the surface domain monitoring of the study area at a regional scale as well as real-time monitoring of specific target regions. By using SBAS-InSAR technology and leveraging 11-view high-resolution TerraSAR-X data, we revealed the spatiotemporal evolution law of surface deformations in the channel slopes within the study area. The results demonstrate that the predominant deformation in the study area was uplifted, with limited evidence of subsidence deformation. Moreover, there is a distinct region of significant uplift deformation, with the highest annual uplift rate reaching 19 mm/y. Incorporating GNSS and soil-moisture-monitoring timeseries data, we conducted a study on the correlation between soil moisture and the three-dimensional deformation of the ground surface, revealing a positive correlation between the soil moisture content and vertical displacement of the channel slope. Furthermore, combining field investigations on surface uplift deformation characteristics, we identified that the main cause of surface deformation in the study area was attributed to the expansion of the soil due to water absorption in expansive soils. The research results not only revealed the spatiotemporal evolution law and mechanism of the channel slope deformation in the studied section of the deep excavation channel but also provide successful guidance for the prevention and control of channel slope-deformation disasters in the study area. Furthermore, they offer effective technical means for the safe monitoring of the entire South-North Water-Diversion Middle-Route Project and similar long-distance water-conveyance canal projects.  
InSAR deformation monitoring; channel slope deformation; expansive soils; soil moisture; deformation mechanism +
Footnote †: journal: remote sensing  
Article  
TerraSAR-X and GNSS Data for Deformation Detection and Mechanism Analysis of a Deep Excavation Channel Section of the China South-North Water-Diversion Project
## 2 Materials and Methods
### Connectivity Models
#### 2.2.4 TRGC--Time-Reversed Granger Causality  
As our baseline method, Time-Reversed Granger Causality (TRGC), as implemented (by means of the Matlab function \"tr_gc_test\", embedded in \"simulation_source_connectivity\"), and evaluated by [1], was used. As stated before, the difference with \"traditional\" GC is the type of significance procedure. Instead of the classical way to determine significance (a likelihood ratio test), which cannot distinguish between actual versus spurious correlations due to source mixing, it determines whether the \"standard\" GC scores for non-reversed and reversed data have opposing directions and are both significant. In other words, direction-flipping must occur when data are time-reversed. This is referred to as conjunction-based TRGC [7]. A drawback of GC (and hence, TRGC) is that one needs to define the model order, which is feasible when the ground truth is known, such as in simulations, but in \"real\" EEG data, this quickly becomes a tricky problem. An advantage, on the other hand, is the fact that with TRGC, one model for all sources is constructed, after which one threshold is applied to all obtained GC scores.  
Configuration. Function tr_gc_test takes as input an NxL matrix H', the model order, the number of time steps in the time series, alpha, the type of significance test (\"conservative\", requiring significant GC scores with original as well as reversed data; versus a significance test based on difference scores between GC scores in normal and reversed order) and finally, the type of VAR model estimation regression mode to calculate pairwise-conditional time-domain Granger Causality scores. In this work, the model order of TRGC was set to two, we opted for \"conservative\" significance testing, and ordinary least squares (OLS) was used as Vector-Autoregression (VAR) estimator. We used an alpha level of 0.05, FDR corrected [31]. The corresponding p-value was taken as a threshold to binarize connectivity scores.
## 3 Results
### The Impact of Drought on NPP
#### 3.3.3 Relationship between NPP and Drought  
We ascertained the decrease in NPP to different levels during the drought events. Further, we focused on the relationship between NPP and the droughts expressed as NDVI anomalies. Figure 7 shows the spatial correlation between the growing season mean NDVI anomaly and total NPP in Mongolia during the period 2003-2018. The majority of areas in Mongolia suffered NPP loss due to drought (indicated through positive correlations), accounting for 92.95% of the vegetation. Negative correlations were mainly distributed throughout the forest and high mountain regions (covering 7.05% of the total area).  
The temporal variations of NPP and droughts were also explored by Pearson's correlation analysis between NPP and NDVI anomaly concerning accumulated and average values in the growing season for 2003-2018 (Figure 8). A slightly increasing trend in both NDVI anomaly and BEPS-derived NPP was observed across Mongolia. The calculated correlation coefficient between the growing season total NPP and mean NDVI anomaly was 0.61 (\(p\) < 0.001, \(n\) = 16). Meanwhile, the Pearson's correlation coefficient of the NPP and the drought-affected area was \(-0.55\) (\(r\) = \(-0.55,\)\(p\) < 0.001, \(n\) = 16), suggesting that drought was one of the factors for the decline in terrestrial NPP in Mongolia. We also quantified the contribution of drought to NPP in different vegetation types. Figure 8 illustrates the influence of droughts on vegetation productivity and accumulated NPP over different vegetation types during 2003-2018. The droughts reduced the grassland NPP in Mongolia (\(r\) = 0.64, \(p\) < 0.001) in the growing seasons of 2007, 2009, 2010, 2015, and 2017. For the forest, the correlation between NPP and NDVI anomaly was lowest (\(r\) = 0.39, \(p\) < 0.001) because trees have deeper roots and have access to groundwater. However, the droughts in the growing seasons of 2003, 2009, 2010, 2015, 2016, and 2017 reduced the forest NPP (Figure 8). The droughts reduced the growing seasonal NPP for the wetland (\(r\) = 0.70, \(p\) < 0.001) in 2008, 2009, and 2012; for sparse vegetation (\(r\) = 0.74, \(p\) < 0.001) in 2004, 2005, 2007, 2009, 2015, and 2017; and for shrubland vegetation (\(r\) = 0.52, \(p\) < 0.001) across Mongolia in 2009, 2015, and 2017 (Figure 8).  
Figure 6: The spatial pattern of percentage change in NPP across Mongolia during periods experiencing the most drought: (**a**) May–September 2004, (**b**) May–September 2005, (**c**) May–September 2006, (**d**) May–September 2007, (**e**) May–September 2009, and (**f**) May–September 2017. The percentage change refer to the NPP variation during drought events relative to the long-term average growing seasonal value of NPP corresponding to the period. The negative/positive value represents the decrease/increase in NPP during the drought events.  
Figure 7: Spatial distribution of correlation coefficient between growing season total NPP and mean NDVI anomaly across Mongolia during 2003–2018.
## 3 Methods
### [PERSON] Correlation  
[PERSON] predicted the instantaneous heat transfer coefficient using Nusselt and Reynolds non-dimensional numbers [58]. His correlation considers both convection and radiative heat transfer coefficients. As for this engine application, radiative heat transfer is not used. Therefore, the correlation only considers the convective heat transfer coefficient and is given in Equation (11):  
\[h_{g}=\frac{dk_{gas}Re^{0.7}}{B}, \tag{11}\]  
where \(Re\) is the Reynolds number, \(B\) is the bore diameter, and \(k_{gas}\) is the thermal conductivity of the gas, which can be deduced from experimental polynomial curve fitting using the working temperature of the fluid. The coefficient d is the correction coefficient given by [PERSON] using Sieder-Tate given in Equation (12) [59]:  
\[d=0.023 Pr^{0.4}, \tag{12}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase** & \(C_{1}\)**[-]** & \(C_{2}\)**[\(\frac{m}{SR}\)]** \\ \hline Intake & 6.18 & 0 \\ Compression & 2.28 & 0 \\ Combustion & 2.28 & \(3.24\times 10^{-3}\) \\ Expansion & 2.28 & \(3.24\times 10^{-3}\) \\ Exhaust & 6.18 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Woschni correlation constants.  
where \(Pr\) is the Prandtl number. Furthermore, the flow velocity used in the Reynolds number calculation is the mean piston velocity.  
The correlations mentioned above are established based on four-stroke diesel engine experiments. Due to the differences in the combustion principles between SI and CI engines, practically, these models cannot provide a viable solution but can be considered for SI engines as well. As mentioned earlier, several researchers have theoretically and practically shown that, with minor adjustments, the above correlations can also be used for SI engines.
## 7 Evaluation and Results  
To assess the efficiency of the proposed model, we studied the models considering the following aspects, discussed below: accuracy, precision, and localization ratio.
## 6 On Control Messages Protection
### SeFLOOD: The Secure FLOOD
(1) \(u\to*:\texttt{HELLQ},u,\
u_{u}\)  
(2) \(v\to u:\left\langle\texttt{CLUSTER\_KEY},v,c_{v},\
u_{u}\right\rangle_{\ell_{uv}}\)  
The protocol is composed of two rounds, the _discovery round_ and the _key distribution round_. In the discovery round, a node \(u\) generates a fresh quantity \(\
u_{u}\) and broadcasts it together with its identifier \(u\) (message M6.3). Upon receiving message M6.3, a node \(v\) replies \(u\) with its identifier \(v\), its cluster key \(c_{v}\), and the fresh quantity \(\
u_{u}\) (message M6.3). The message is protected by means of the link key \(\ell_{uv}\). Upon successfully receiving message M6.3 from \(v\) (otherwise, the message is discarded), node \(u\) stores the received cluster key \(c_{v}\) into its CKT. From a security point of view, we can argue that the encipherment by \(\ell_{uv}\) makes node \(u\) to believe that \(c_{v}\) actually comes from \(v\). Furthermore, the presence of the fresh quantity \(\
u_{u}\) guarantees that message M6.3 is not a replay of an older message. The encipherment also guarantees the secrecy of \(c_{v}\). Figure 7 shows an execution instance of CKDP. The network is composed of three nodes, where GW corresponds to \(u_{1}\). The broadcast domain of \(u_{1}\) covers only node \(u_{2}\), whereas node \(u_{2}\) is able to reach both \(u_{1}\) and \(u_{3}\). Node \(u_{3}\) is able to reach only \(u_{2}\). At the end of the protocol three clusters are built, \(\mathcal{C}_{1}=\{u_{1},u_{2}\}\), \(\mathcal{C}_{2}=\{u_{1},u_{2},u_{3}\}\), and, finally, \(\mathcal{C}_{3}=\{u_{2},u_{3}\}\) with cluster keys \(c_{u_{1}}\), \(c_{u_{2}}\), and \(c_{u_{3}}\), respectively. Notice that the attack described in Section 6.2 is not possible anymore. Actually, in order to play such an attack, the adversary would need to either know the cluster keys of neighbours or disseminate its own. However, none of these alternatives is possible because the adversary does not know the link keys of nodes. Figure 7: The CKDP applied to FLOOD.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Description of the Protocol Phases
#### 3.3.4 Phase 3--Verification of the Signature and Application of the Instructions  
Figure 8 shows phase 3 of the protocol, where the functions performed by each entity and the messages sent are shown. This phase has as inputs \(K_{s},S_{KA},C_{m}\), and an instruction message \(M1\) which is formed at the discretion of the physician's interpretation. The decryption function \(D_{k}(K_{s},C_{m})\to M_{MP}\) allows [PERSON] to know the original message generated by C. Upon learning \(M_{MP}\), A proceeds to generate and sign \(M1\) using a function \(S_{k}\) of the digital signature scheme. The function \(S_{k}(M1,S_{kA})\to s\) delivers a value \(s\) which is the digital signature. The actuator identifier \(ID_{D}\), the message \(M1\), the message \(M_{MP}\), and the signature \(s\) enter the hash function, such that \(H(ID_{D},s,M1,M_{MP})\to h_{M1m}\). The resulting value is sent together with the value \(M1\) and the signature \(s\) to entity B. Finally, when these values reach B, it must verify these received values utilizing the hash function. If they are complete, they must be sent to the corresponding entity D, considering the \(ID_{D}\) to identify the target actuator. When entity D receives the signature and instructions, phase 3 ends, and phase 4 begins.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 3 Soft Demapping Algorithm
For 1D-NUC and 2D-NUC with modulation order \(M\), when demapping each received symbol, the number of one-dimensional EDs required to be calculated are \(2\sqrt{M}\) and \(2M\), and the number of EDs required to be compared are \(\sqrt{M}\log_{2}M\) and \(M\log_{2}M\). To reduce the number of EDs that need to be calculated and compared, especially the number of EDs that need to be calculated, this paper proposes an improved algorithm named SCSR. The details of the SCSR algorithm are described in the next section.
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
###### Abstract  
In the scenario of high dynamics and low C/N\({}_{0}\), the discriminator output of a GNSS tracking loop is noisy and nonlinear. The traditional method uses a fixed-gain loop filter for error estimation, which is prone to lose lock and causes inaccurate navigation and positioning. This paper proposes a cascaded adaptive vector tracking method based on the KF+EKF architecture through the GNSS Software defined receiver in the signal tracking module and the navigation solution module. The linear relationships between the pseudo-range error and the code phase error, the pseudo-range rate error and the carrier frequency error are obtained as the measurement, and the navigation filter estimation is performed. The signal C/N\({}_{0}\) ratio and innovation sequence are used to adjust the measurement noise covariance matrix and the process noise covariance matrix, respectively. Then, the estimated error value is used to correct the navigation parameters and fed back to the local code/carrier NCO. The field vehicle test results show that, in the case of sufficient satellite signals, the positioning error of the proposed method has a slight advantage compared with the traditional method. When there is signal occlusion or interference, the traditional method cannot achieve accurate positioning. However, the proposed method can maintain the same accuracy for the positioning results.  
vector tracking; Kalman filter; innovation sequence; noise covariance matrix; GNSS software receiver +
Footnote †: journal: remote sensing
###### Abstract
For example, we take the resource potential and environment constraint into account. It is known that bilateral relationship between China and the targeted country also plays a vital role when it comes to the investment, since a friendly and stable relation will definitely smooth the mutual cooperation. Therefore, this paper also takes the Chinese factor as a vital index. In addition to the common macroscopic factors of political risk, investment environment, and economic foundation, the above six factors build the index system for foreign energy investment of China along the \"Belt & Road\". Moreover, this paper further splits the above 6 indexes into 36 sub-indexes, trying to cover all necessary points that are worthwhile to be involved for evaluation. Naturally there comes a question that how to evaluate the above index system? This paper proposes a sequence to sequence (seq2 seq) framework inspired by the recent development of deep learning technique. This work first transforms the numerical statistics into embeddings so that the deep learning technique could be applied. Afterwards, to handle the historical statistics, the hierarchical convolution neural network (CNN) is applied as an encoder on the generated embeddings as such hierarchical architecture enables the model to deal with the long historical sequence. After processing the input embeddings via the CNN encoder, a self-attention layer is then adopted to compute the weights of each sub-index. Finally, the conditional random field (CRF) layer is utilized to calculate the final assessment results of each country. This paper has three main contributions:  
* Considering both micro-level and macro-level factors, as well as the energy-related factors, this paper builds an index system having six indicators to comprehensively assess China's oversea energy investment risk on \"Belt & Road\" countries. * This paper adopts a deep learning method to evaluate the above indicator system. Firstly the hierarchical CNN is leveraged to handle the historical numerical statistics, following the indicator weights assigning by the self-attention layer. Finally, the CRF layer is employed for calculating the final evaluation results. * This work assigns the risk ratings of 50 \"Belt & Road\" countries so that a policy recommendation could be made in this paper for Chinese investors when it comes to the national energy investment. The reminder of the paper is organized as follows. Firstly, Section 2 introduces the related work, and Section 3 justifies the detailed information of index system and deep learning framework. Section 4 presents the assessment results of 50 \"Belt & Road\" countries, along with the policy recommendation. Finally, Section 5 briefly concludes the findings of this work.
## Materials and Methods  
### Plane-Wave Compound Imaging  
Unlike the conventional method of ultrasound imaging where a line-by-line scanning method utilizing a number of focused ultrasound beams is applied, several tilted plane-waves are used in plane-wave compound imaging. The frames acquired using plane-waves of various steering angles are summed to achieve synthetic transmit focusing as shown in Figure 2.  
Using this technique, a very high frame rate can be achieved due to the reduced number of transmissions per frame. In addition, uniform resolution can be achieved throughout the ROI as shown in Figure 3.  
Figure 3: Simulated B-mode images reconstructed using (**a**) 128 focused beams (focal depth of 25 mm) and (**b**) 16 plane beams (steering angles of \(\pm 9^{\circ}\)) with L7-4 linear array (a 128-element transducer with a pitch of 298 \(\upmu\)m and a center frequency of 5.2 MHz). The simulation was conducted using a Field II ultrasound simulator [22].  
Figure 2: Synthetic focusing based on the plane-wave compounding method with three plane-wave angles.  
Although plane-waves can be easily generated using a linear array transducer due to the straight arrangement of the elements, since a linear shape is not suitable for scanning wide and deep areas, a curved array transducer should be chosen for a broader application of plane-wave imaging. To generate a plane beam from a curved array, the time delay for each array element must be calculated properly by considering the shape of the array, which is an arc.