esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
## 4 Discussion
In operational forestry, there are many situations in which positioning transponders may receive a GNSS signal ([PERSON], 2015; [PERSON], 2015), but radio propagation of coordinates to other devices elsewhere on the jobsite is blocked topographically by dense vegetation or interference from other radio systems ([PERSON], 2015; [PERSON], 2015). If a portion of data packets are missing due to radio signal interference, the classification of machine elements may be affected in more complex ways not evident in our controlled experiment. With the quantities of data collected in this way, post-hoc manual and visual inspections of outlier points is almost impossible, so automated data quality control procedures are especially critical in such an environment. In order to further develop real-time modeling of machine movements on active logging operations, further studies exploring the impacts of forest canopy on GNSS-RF accuracy in particular applications should be explored. For example, in stands with high mortality due to beetle-kill with many trees that have dropped most of their foliage, this may not be a problem, but that remains to be evaluated. Past studies exploring canopy impact in GNSS accuracy have shown large decreases in accuracy depending on canopy cover ([PERSON], 2015; [PERSON], 2015; [PERSON], 2015; [PERSON], 2015; [PERSON], 2015). However, these studies were for GNSS only and did not study dual effects of canopy on GNSS and RF, or the interaction of these two signals. Because canopy density tends to be low in beetle impacted stands due to high levels of mortality, for example, canopy impacts on GNSS multipath error may be less of a concern than in healthy stands. Additionally, developing a methodology for similar element and cycle classification when the machine is traversing the landscape on steep slopes in beetle impacted stands in biomass utilization operations as opposed to sitting in a fixed location on flat ground will be necessary. Introducing machine movements on slopes into the analysis will add an additional level of complexity but is important for development of subsequent applications to improve the efficiency of harvesting in beetle impacted and unaffected forests alike.
## 4 Problem Formulation
### Definition of Consensus Tracking Errors  
We first define the dynamics of the target vehicle, which can be seen as the leader of the UAVs.  
\[\left\{\begin{array}{l}\mathbf{\dot{\xi}}_{0}=\mathbf{\zeta}_{0}\\ \mathbf{\dot{\xi}}_{0}=\mathbf{f}_{0}(t)\end{array}\right. \tag{16}\]  
where \(\mathbf{\zeta}_{0}=(x_{0},y_{0})^{\top}\), \(\mathbf{\zeta}_{0}=(\dot{x}_{0},\dot{y}_{0})^{\top}\), \(\mathbf{f}_{0}(t)\) are the position, velocity and acceleration of the target vehicle, respectively.  
We define the tracking error between the target and \(i\)-th UAV to be  
\[\mathbf{e}_{i}=\mathbf{\xi}_{i}-\mathbf{\zeta}_{0}-\mathbf{\xi}_{i0}, \tag{17}\]  
where \(\mathbf{\xi}_{i0}=(x_{i0},y_{i0})^{\top}\) is the predefined position from UAV \(i\) to the target. Then the error dynamic of \(i\)-th UAV is obtained as follows  
\[\left\{\begin{array}{l}\mathbf{\dot{e}}_{i}=\mathbf{\zeta}_{i}-\mathbf{\zeta}_{0}\\ \mathbf{\ddot{e}}_{i}=\mathbf{G}_{i}(\mathbf{u}_{i}+\mathbf{f}_{i}+\mathbf{d}_{i})-\mathbf{f}_{0}\end{array}\right. \tag{18}\]  
To achieve the formation of UAVs with the consensus protocol, we define the consensus tracking error for each UAV to be  
\[\begin{split}\mathbf{\varepsilon}_{i}&=\sum_{j=1}^{n}a_{ij} \bigl{(}\mathbf{\xi}_{i}-\mathbf{\xi}_{j}-\mathbf{\xi}_{ij}\bigr{)}+b_{ii}(\mathbf{\xi}_{i}- \mathbf{\xi}_{0}-\mathbf{\xi}_{i0})\\ &=\sum_{j=1}^{n}a_{ij}\bigl{(}\mathbf{e}_{i}-\mathbf{e}_{j}\bigr{)}+b_{ii} \mathbf{e}_{i},\end{split} \tag{19}\]  
where \(a_{ij}\) is the \(ij\)-th entry of the adjacency matrix \(\mathbf{A}\) of the communication graph among the UAVs, \(b_{ii}\) is the \(ii\)-th entry of the diagonal matrix \(\mathbf{B}\), and \(\mathbf{\xi}_{ij}\) is the predefined position from UAV \(i\) to UAV \(j\), which can be calculated by \(\mathbf{\xi}_{ij}=\mathbf{\xi}_{i0}-\mathbf{\xi}_{j0}\).  
Control Problem: Consider a group of n UAVs with dynamics Equation (14) and the target vehicle Equation (16), a distributed control law is to be designed such that the consensus tracking error for each UAV converges to finite bounds with probability.
## 4 Discussion
### Impact of Irrigation on Cropland CWUE  
The spatiotemporal trends of the cropland WUE\({}_{\rm GPP}\), GPP, and ET across Henan Province from 2001 to 2019 largely correspond with the global patterns of these variables in cropland from 2000 to 2014, as reported by [PERSON] et al. [58]. Human activities, such as changes in irrigation management, significantly influence the ecosystem GPP, NPP, ET, and WUE [20; 35]. In Henan Province, the WUE\({}_{\rm GPP}\) and WUE\({}_{\rm NPP}\) of non-irrigated cropland are higher than those of irrigated cropland, which is consistent with previous findings indicating that when differences in the NPP, GPP, and ET between irrigated and non-irrigated cropland are minimal, WUE tends to be lower in irrigated fields [59; 60]. This pattern can be explained by water management during vegetation growth: while moderate supplemental irrigation can quickly enhance WUE in the short term, excessive irrigation leads to decreased WUE as ET rises sharply and the marginal gains in carbon assimilation diminish [61]. Additionally, ET values are higher in irrigated cropland because irrigation increases soil moisture, which promotes the crop LAI and photosynthetic activity, thereby enhancing water ET [43; 62].
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
###### Abstract  
In this paper, a Reconfigurable Mesh-Ring (RMR) algorithm is proposed for Bluetooth sensor networks. The algorithm is designed in three stages to determine the optimal configuration of the mesh-ring network. Firstly, a designated root advertises and discovers its neighboring nodes. Secondly, a scatternet criterion is built to compute the minimum number of piconets and distributes the connection information for piconet and scatternet. Finally, a peak-search method is designed to determine the optimal mesh-ring configuration for various sizes of networks. To maximize the network capacity, the research problem is formulated by determining the best connectivity of available mesh links. During the formation and maintenance phases, three possible configurations (including piconet, scatternet, and hybrid) are examined to determine the optimal placement of mesh links. The peak-search method is a systematic approach, and is implemented by three functional blocks: the topology formation block generates the mesh-ring topology, the routing efficiency block computes the routing performance, and the optimum decision block introduces a decision-making criterion to determine the optimum number of mesh links. Simulation results demonstrate that the optimal mesh-ring configuration can be determined and that the scatternet case achieves better overall performance than the other two configurations. The RMR topology also outperforms the conventional ring-based and cluster-based mesh methods in terms of throughput performance for Bluetooth configurable networks.  
Keywords:bluetooth networks; scatternet formation; topology configuration; routing protocol +
Footnote †: journal:
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 2 Background  
### Ambisonics  
Ambisonics is a 3D spatial audio format which allows sound sources to be placed above, below and behind the listener in addition to the horizontal plane supported by 2D audio formats. The format uses spherical harmonics to encode the audio such that the sources can be mapped to any location on the inner surface of a sphere, the center of which is the listener position. More specifically,Ambisonics B-format is independent of loudspeaker layout and the number of loudspeakers used. As a result, Ambisonics is a popular audio format for VR and AR use cases given that it allows full rotation of the soundfield in three dimensions [6]. Most VR headset applications require audio to be presented binaurally and respond to head movement in real time, making Ambisonics an ideal choice.  
An infinite number of spherical harmonics would be required to achieve perfect localization within the Ambisonics soundfield but this is not practical due to the amount of data required. As a result, a smaller number of spherical harmonics is used. The most popular format today, known as first-order Ambisonics (FOA), uses 4 spherical harmonics denoted as W, X, Y and Z. The W channel represents an omnidirectional gain and channels X, Y and Z respectively represent front-back, left-right, and up-down directions within a sphere. The B-format representation can be extended from FOA to second and third-order Ambisonics (SOA and 3 OA) to get higher localization accuracy. Second-order and third-order Ambisonics contain 9 and 16 channels, respectively. It was found in [7] that third-order Ambisonics (3 OA) gives a significantly better Quality of Experience (QoE). This is further supported in [3]. In [8], subjective testing shows that localization accuracy decreases monotonically as a function of both order number and codec bit rate. Figure 1 shows the spherical harmonics associated with first, second and third-order Ambisonics. As illustrated, the second order encapsulates the 4 harmonics comprising the first order. Similarly, the third order encapsulates the 9 harmonics comprising the first and second orders combined. As a result, it should be noted that the number of channels increases according to \((n+1)^{2}\) where \(n\) is the order.  
Using higher-order Ambisonics (HOA) results in better localization accuracy and better listening quality [3] but the large amount of data to be processed requires higher transmission bandwidth and processing power. To facilitate this, efficient codecs are required to compress spatial audio without compromising QoE.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 3 Prediction of RUL Based on the Multi-Feature Fusion Method
### Fusion of Multisensor Data  
Data processing is a critical step in life prediction, and in order to eliminate, as much as possible, the reliance on expert knowledge in this part of the process, Multiple sensors provide the capability to gather vibration data from various positions on the bearing, in contrast to a single sensor, so that feature information from different viewpoints and locations can be obtained. By fusing these diverse characteristics, the condition of the bearing system may be described more comprehensively, providing a richer set of input features. Therefore, this paper employs a multi-sensor information fusion strategy to enhance the quantity of feature information included in the model inputs. The fusion process is illustrated in Figure 6.  
Assuming the existence of \(M\) rolling bearings and their performance degradation data, the performance degradation data of \(C\) sensors will be gathered for each bearing, and each sensor will have the same sample period (\(t\)). Moreover, the \(t\)th data sample collected by the Cth sensor may be represented as \(X_{t}^{c}\), \(X_{t}^{c}\in\mathbb{R}^{H\times 1}\), where each sample's length is represented by H, and each sensor's data may be seen as a separate data channel. To lessen the impact of variations in the data distribution across several bearings, each sensor's raw data was normalized in the manner described below:  
Figure 5: The proposed method’s RUL prediction mechanism.  
Figure 6: Fusion of data from multiple sensors via channel fusion.  
\[\widetilde{x}_{t,i}^{c}=\frac{x_{t,i}^{c}-\min(x_{t,i}^{c})}{\max(x_{t,i}^{c})- \min(x_{t,i}^{c})} \tag{15}\]  
where \(x_{t,i}^{c}\) is the original data sample's full value. The normalized data sample may be represented by the symbol \(\widetilde{x}_{t,i}^{c}\), which stands for the \(i\)th value of the normalized data sample. Each sensor's data is then spliced according to channel to obtain the multichannel fusion data \(x_{t}^{m},x_{t}^{m}\) which can be expressed as:  
\[x_{t}^{m}=\left\{\widetilde{x}_{t}^{1},\widetilde{x}_{t}^{2},\cdots,\widetilde {x}_{t}^{c}\right\} \tag{16}\]  
where after multichannel fusion, the \(t\)th sample of the \(m\)th bearing is represented by \(x_{t}^{m}\in\mathbb{R}^{H\times C}\), \(x_{t}^{m}\), and all of the samples of the \(m\)th bearing are designated by \(\{x_{t}^{m}\}_{t=1}^{N}\), where \(N\) is the total amount of time spent sampling. Finally, the label of the \(t\)th sample of the \(m\)th bearing can be denoted as \(y_{t}^{m}\) and the labels of all the samples of the \(m\)th bearing are represented as \(\{y_{t}^{m}\}_{t=1}^{N}\). Based on the above samples and labels, the \(m\)th bearing's multi-sensor fusion data can be expressed as \(\{x_{t}^{m},y_{t}^{m}\}_{t=1}^{N}\). For \(M\) bearings, the multi-sensor data of each bearing after channel fusion will be used as a deep learning model's training set, and the RUL of additional bearings will be predicted using the learned model.
## 4 Initial and Boundary Conditions  
At the moment of time \(t=0\), the computational space is uniformly filled with a stationary gas medium with known properties and initial thermal dynamic parameters (temperature \(T_{0}=300\) K and pressure \(p_{0}=10^{3}\div 10^{5}\) Pa). The electrical breakdown of the gas gap between the electrodes is modeled as the instantaneous appearance at the moment of time adjacent to the surface of the end face of the MPC layer (2 counting cells thick) of ionized gas with parameters \(T\approx 15\) kK and pressure \(p_{0}\). At \(t=0\), the discharge current \(J(0)=0\), the voltage across the capacitor \(U_{k}(0)=U_{0}\), and the magnetic field is zero, there is no radiation.  
The formulation of the boundary conditions requires setting the values of the gas dynamic, radiative, and electromagnetic parameters at the boundaries \(\Gamma\) of the computational domain \(G\). The rectangular boundary \(\Gamma\) in the \(rz\) plane (Figure 1) consists of the surface of the dielectric insert and end electrodes with coordinates (\(z=0,\ \mathtt{r}\in[0,\ \mathtt{r}_{\mathrm{x}}]\)), the \(0z\) axis of symmetry for \(z\in[0,\ \dot{\uptau}_{\mathrm{x}}]\), and lines (\(r=r_{\mathrm{k}},\ z\in[0,\ \dot{\uptau}_{\mathrm{k}}]\)), (\(z=\dot{\uptau},\ r\in[0,r_{\mathrm{k}}]\)).  
Gasdynamic parameters: velocity \(\overset{\rightarrow}{V}(0,\ \mathtt{r},\ \mathtt{t})\), density \(p(0,\ \mathtt{r},\ \mathtt{t})\), pressure \(p(0,\ \mathtt{r},\ \mathtt{t})\), internal energy \(e(0,\ \mathtt{r},\ \mathtt{t})\), from the IDI and electrodes (\(z=0,\ \mathtt{r}\leq r_{3}\)) are determined by the no-flow conditions in the absence of evaporation, i.e., when the saturated vapor pressure \(p_{s}(0,\ \mathtt{r},\ \mathtt{t})<p(0,\ \mathtt{r},\ \mathtt{t})\) or, according to Equation (4), in the case when the dielectric insert and the electrodes evaporate. On all other boundaries of the computational domain, the impermeability conditions were set, and on the \(0Z\) symmetry axis--the symmetry conditions.  
When solving the equations of radiation transfer at the boundaries of the computational domain, the conditions were set for the absence of radiation incident from the outside, and on the symmetry axis--the condition for the symmetry of the radiation fluxes.  
To determine the magnetic field strength \(B_{\varphi}(r,\ \mathtt{z},\ \mathtt{t})\) on the left (\(z=0\)) boundary of the computational domain, we used the relations:  
\[B_{\varphi}=\frac{2 Ir}{\sigma_{1}^{2}}\ \mathrm{for}\ \mathtt{r}\leq \mathtt{r}_{1};\ \mathrm{B}_{\varphi}=\frac{2I}{\sigma r}\ \mathrm{for}\ \mathtt{r}\in[\mathtt{r}_{1},\ \mathtt{r}_{2}];\] \[B_{\varphi}=\frac{2I}{\sigma}\frac{r_{3}^{2}-r}{r_{3}^{2}-r}\ \mathrm{for}\ \mathtt{r}\in[\mathtt{r}_{2},\ \mathtt{r}_{3}];\ \mathrm{B}_{\varphi}=0\ \mathrm{for}\ \mathtt{r}\in[\mathtt{r}_{3},\ \mathtt{r}_{ \mathrm{k}}].\]  
\(B_{\varphi}\equiv 0\) on all other boundaries of the computational domain.
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 2 Geometry and Simulation Conditions  
### Geometry of the Model  
The pentamaran used in this paper is based on a shuttle SV-O monohull with a simple modification of the shape line to accommodate the installation of the front and rear side hulls of the pentamaran. An X-shaped symmetrical shape was selected, and the Wigley hull was used as the front and rear side hulls [11]. The main particulars and the geometry of the full-scale monohull are shown in Table 1 and Figure 2, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particulars** & **Symbols/Unit** & **Value** \\ \hline Overall length & \(L_{OA}\)/m & 22.022 \\ Length at the water line & \(L_{wl}\)/m & 21.484 \\ Breadth & \(B_{wl}\)/m & 3.742 \\ Molded depth & \(D\)/m & 1.927 \\ Draft & \(d\)/m & 1.000 \\ Displacement & \(\
abla\)/m\({}^{3}\) & 26.877 \\ Wetter area & \(S\)/m\({}^{2}\) & 72.250 \\ Waterline coefficient & \(C_{w}\) & 0.687 \\ Block coefficient & \(C_{b}\) & 0.342 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main particulars of the monohull.  
The main hull of the pentamaran is the same as the monohull. The design of the front and rear hulls usually meets three conditions: first, the length of the rear hull generally does not exceed 1/3 of the waterline length of the main hull; second, the displacement of the rear hull is controlled within 15% of the total displacement; third, the front hull is usually higher in the horizontal position than the rear hull and can be lifted out of the water surface when sailing at high speed. On the basis of the above conditions, this paper designed a side hull shape line with a thinner front than the rear (shown in Figure 3). The main particulars of the front hull and the rear hull are shown in Tables 2 and 3, respectively.  
The inlet flow velocity is set according to the Froude number \(F_{n}\) based on hull length, where the \(F_{n}\) is defined as follows:  
\[F_{n}=\frac{V}{\sqrt{gL}} \tag{1}\]  
In the wave simulation, the speed is fixed at \(F_{n}\) = 0.527 and two head wave environments are selected with different wave periods. The simulation conditions in waves are shown in Table 5. At the initial moment of calculation, the hull remains upright, and the degree of freedom of the hull is released after 0.5 s. The calculation time is 20 s.
# Towards a Catalogue of Self-Sovereign Identity Design Patterns
## 2 Background and Related Work
### Self-Sovereign Identity
#### 2.1.1 SSI Actors and Process Flow  
Within the SSI approach, users do not need to create an account with each service provider (SP), nor is there a need for an identity provider (IdP) to provide the user with an identity and its management, as identity data are stored in the user's domain in digital wallets that are under the user's full control. Similarly to other identity models, we can  
Figure 1: Identity models [6], representing the shift of control (e.g., data, process).  
differentiate between three actors, namely, the _credential issuer_, the _identity holder_, and the _credential verifier_, as depicted in Figure 2. Each entity can act in a different role, depending on the context. The issuer attests to particular attributes of the holder by issuing and signing a verifiable credential (VC) digitally, containing one or multiple attributes or claims related to the holder responsible for retrieving, storing, managing, and presenting the acquired identity data with the verifier, who usually requires proof of identity to identify, verify, and ultimately allow access to the desired services. Therefore, the holder is at the centre of digital interactions, deciding when, with who, and what data he/she is willing to share. Thereby the verification process does not require the involvement of the issuer, as trust can be established by the utilisation of _decentralised identifiers_ (_DIDs_) and _DID documents_ holding the public keys of the holder, cryptographically signed _verifiable credentials_ (_VCs_) and _verifiable presentations_ (_VPs_), _verifiable data registry_ (VDR), and _identity wallets_, which are essential components of SSI architectures.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 3 Stability of Planets in Alpha Centauri
### Stability of Test Particles in Alpha Centauri  
We note \(a\) and \(e\) as the particle semimajor axis and eccentricity, respectively. The inclusion of test particles on S-type orbits in the \(a\)-\(e\) parameter space constitutes a preliminary estimate of the stability within the binary system. We therefore placed massless particles around each of the stars in the Alpha Cen binary. The orbital initialisation in REBOUND was the same as the one described in Section 2.1. For this simulation, we restricted the semimajor axis of the test particles to range from 0.1 to 5 au, which was based on the estimated extension of the circumstellar discs after their interaction (see Figure 2). Furthermore, we did not consider eccentricities over 0.5, since this would hamper planet stability and also prevent additional planets in the system [30]. We set the maximum integration time to 10,000 years, which corresponds to about 127 orbits of the inner binary system (\(P_{\mathrm{b}}\approx 80\) yr). For these simulations, we used the symplectic integrator 'whfast', with a time step of \(1\times 10^{-5}\) yr, which allowed us to map the orbit closest to the star with at least 100 points.  
Figure 5 presents the results of such simulations. The regions coloured in blue correspond to zones in the \(a\)-\(e\) parameter space, whose stability is assured by the algorithm, while the red zones are regions of high instability. For scale purposes, we have added the rocky planets of the Solar System to each graph. The blue areas in the left (right) panel correspond to the stability regions for test particles around star A (B). We identified two distinct regions in terms of stability around A: one spanning from 0.1 to \(\sim\)2.5 au and the other from \(\sim\)2.5 to 5 au. The former is narrower and contains all the stable orbits for \(e\) ranging from 0 to 0.5. Remarkably, individual massless particles with the same orbital parameters as those of the inner planets in the Solar System would maintain regular orbits. A similar scenario was observed around star B, where the parameter space was segmented again into two zones according to stability: one from 0.1 to 2.5 au and another beyond 2.5 au. Here, the latter region was highly unstable and inhospitable for test particles. In the case of star C (shown in Appendix A), owing to its considerable distance from the binary system, the entire range of the parameter space remained perfectly stable for the particles under consideration. Hence, individual massless particles with the same orbital parameters as the planets of the inner Solar System should be stable around Alpha Cen A, B, and C.