esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The coherence bandwidth is defined as the range of frequencies over which two frequency components are likely to experience similar amplitude fading [26]. Hence, two bands with a maximum frequency gap smaller than the channel's CBW will be strongly correlated (in terms of the propagation phenomena experienced). The final aim of this work is thus, to empower the process of choosing the working frequency band for SG-oriented IoT networks with enough quantitative metrics (such as battery consumption or network delay) to shift it from a premade industry decision to a requirements-based decision. The rest of the paper is organized as follows. Section 2 reviews previous related work. Section 3 details the propagation model, the methodology followed and the improvements made on the TOSSIM simulator. In Section 4 we present and discuss the results obtained for different configurations of interest. Finally, Section 5 summarizes the main conclusions.
## 3 PBCS Structure
### Transmitter Structure and Constructing the Glossary
Similarly, when the frequency change is chosen to be between 2410 MHz and 2414 MHz, 2414 MHz and 2410 MHz are assigned to \(+\)L2 and -L2 respectively. Thus, basic signal glossary space \(G\) is formulated in Equation (2):  
\[G\!=\!\!\left[ \begin{array}{c}G_{1}\\ G_{2}\\ \vdots\\ G_{m}\end{array} \right]\!=\!\!\left[ \begin{array}{c}S_{1,1}\!\left(t\right) &S_{1,2}\!\left(t\right)&...&...&S_{1,n_{(3)}}\!\left(t\right)\\ S_{2,1}\!\left(t\right)&S_{2,2}\!\left(t\right)&...&...&S_{2,n_{(2)}}\!\left(t \right)\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ S_{m,1}\!\left(t\right)&S_{m,2}\!\left(t\right)&...&...&S_{m,n_{(m)}}\!\left(t \right)\end{array} \right] \tag{2}\]  
Each row represents a group of \(n_{(\alpha)}\) signal pattern inside the respective SNR and LSE limitations. Although the chosen glossary set \(G_{i}\) can be changed due to channel conditions, the signal pattern sequence is constructed from the same glossary for a certain data frame length. This is required for synchronization between the transmitters and the receivers besides satisfying the continuation of the communication signal. Each communication pattern is the consequence of signal patterns from one of the \"m\" glossaries. The number of signal pattern \(n_{(\alpha)}\) is determined by encoding of the binary data to be transferred. For instance, if \(G_{x}\) is 4-bit glossary, \(n_{(\alpha)}\) is set to 16. \(j^{th}\) signal pattern from the \(i^{th}\) glossary is defined as:  
\[S_{i,j}\!\left(t\right)\!=f\!\left(\!A_{i,j}\!\left(t\right),f_{i,j}\!\left(t \right)\!,\Phi_{i,j}\!\left(t\right)\!,t\right)\ \ \text{for}\ \ i=1...m;\ j=1...n \tag{3}\]  
\[S_{i,j}\!\left(t\right)\!=\!\!\left[\left(A_{0}+\left(A_{i,j}\!\left(t\right) \!\times\!\Delta t\right)\right)\right]\!\sin\!\left[2\pi\left(f_{0}+\left(f_{ i,j}\!\left(t\right)\!\times\!\Delta f\right)\right)\!\right]\!\times\!t+\!\left( \Phi_{i,j}\!\left(t\right)\!\times\!\Delta\Phi\right)\right] \tag{4}\]  
where \(A_{i,j}\!\left(t\right)\), \(f_{i,j}\!\left(t\right)\), and \(\Phi_{i,j}\!\left(t\right)\) varies between \(-1\) (minus quarter sinus change) and \(+1\) (plus quarter sinus change) with respect to one of the envelope combinations shown in Figure 3 and they satisfy SNR and LSE conditions assigned for the \(i^{th}\) glossary. \(f_{0}\) is the central frequency, \(A_{0}\) is the mean value of amplitude interval, \(\Delta\)A and \(\Delta\)f is the allowed limitation on amplitude and frequency change for each level. In the initial condition of t = 0 (at L0), which corresponds to (0, 0, 0) coordinate on the SPEC space, the formula the basic signal pattern is simplified into the following:  
\[S_{0}\!\left(t\right)\!=A_{0}\times\!\sin\!\left(2\pi\times f_{0}\times t\right) \tag{5}\]
## 2 Developed Methodology
### Computed Error Metrics
#### 2.3.1 PPF Error Metrics  
A review of the literature [4; 6; 10; 18; 26; 39; 46] suggests that the root mean square error (RMSE) and R-squared (R\({}^{2}\)) error metrics are commonly calculated not only to analyze the PPFs' prediction accuracy, but also to compare them against other studies' results. Therefore, in this study both error metrics are calculated through the following equations,  
\[\text{RMSE}=\ \sqrt{\frac{1}{N}\ \sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}} \tag{15}\]  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N}(y_ {i}-\overline{y})^{2}} \tag{16}\]  
where \(N\) represent the number of predicted values during a time lapse, \(y_{i}\) refers to the actual parameter's value for \(i\) sample, \(\hat{y}_{i}\) is the PPF's predicted value for sample \(i\), and \(\overline{y}\) is \(\hat{y}_{i}\) predictions' averaged value. Due to the database resolution, 15 min, the number of predicted values during a day is, \(N=96\).
# An Equivalent Non-Uniform Beam-Like Model for Dynamic Analysis of Multi-Storey Irregular Buildings
## 3 Applications
### Type 2: Building with Planar Symmetry and Vertical Non-Uniform Distribution of Mass and Stiffness  
The second benchmark regards Type 2, an eight-storey frame characterized by a reduction of the cross sections of the columns along the height. The planar distribution of the cross section of the columns is reported for each floor in Figure 10(a), while Figure 10(b) shows a scheme of the FEM model.  
For this building the minimization of the objective function provided the following values for the stiffness correction coefficients: \(k_{x}=0.3865;\quad k_{y}=0.3669;\quad k_{c}=0.4192\).  
The first three modal properties of Type 2 evaluated by means of the proposed approach were compared to those obtained by the conventional FEM model and are reported in Table 3 and Figure 12.  
Figure 10: Four-storey benchmark. Maxima floor displacements in the (**a**) Santa Venerina and (**b**) L’Aquila earthquakes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Type 2 & Mode 1 & Mode 2 & Mode 3 \\ \hline FEM & 0.941 & 0.917 & 0.812 \\ Proposed beam-like model & 0.941 & 0.917 & 0.812 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modal Period comparison.  
Figure 9: Four-storey benchmark. L’Aquila earthquake. Displacements time history along the (**a**) x and (**b**) y direction.  
For this building the matrix MAC_Tp2 assume the following expression:  
\[\text{MAC\_Tp2}=\begin{array}{ll|ll}\varphi_{1,\text{FEM}}&\varphi_{2,\text{FEM}} &\varphi_{3,\text{FEM}}\\ \varphi_{1,\text{BL}}&0.9743&0&0\\ \varphi_{2,\text{BL}}&0&0.9738&0.0001\\ \varphi_{3,\text{BL}}&0&0.0006&0.6650\\ \end{array}\end{array}\, \tag{25}\]  
Figure 11: Eight storey building benchmark (Type 2): (a) the column cross section plan, and (b) the FEM model.  
Figure 12: Eight -storey benchmark. Modal shapes comparison between the (a) FEM and (b) beam-like models.  
The observation of Figure 12 shows that the first and second mode of vibration are translational in the two directions while the third one is torsional. A good correspondence can be observed between the results obtained by means of the two approaches, as the MAC_Tp2 matrix confirms.  
Figures 13 and 14 report the displacement time histories in the \(x\) and \(y\) directions of the considered control point during the Santa Venerina and L'Aquila earthquake, respectively. Minor differences can be noticed between the responses obtained by means of the two considered models, but they do not sensibly affect the global response.  
Figure 15 reports the maxima displacements of the same corner located at each floor level. Some small differences can be noticed in this case with reference to the intermediate floors but, globally, the proposed beam-like model reproduces the dynamic behaviour of this benchmark with satisfactory agreement.
## 2 FMCW LiDAR
### FMCW LiDAR Principle of Operation
The received reflected signal is mixed with the emitted signal, and the frequency difference can be used to measure the distance of the target, while the Doppler shift in the signal can be used to measure the speed of the target. In the case of a static target, the signal received by the FMCW LiDAR system is simply a time-delayed version of the transmitted signal. By mixing the transmitted and received signals on an optical heterodyne receiver, the frequency difference between the transmitted  
Figure 2: The basic ranging principle diagram of FMCW LiDAR. and received signal can be extracted. The frequency difference is proportional to the round-trip time:  
\[R=\frac{c\cdot\tau}{2}, \tag{4}\]  
where \(R\) is the target distance, \(c\) is the speed of light, and \(\tau\) is the time delay. When the target object is moving, the received signal will have an additional frequency shift, \(\Delta f_{D}\), due to the Doppler effect. This frequency shift is proportional to the speed \(v\) of the target object:  
\[\Delta f_{D}\approx\frac{2v}{\lambda}\, \tag{5}\]  
where \(\lambda\) represents the wavelength, \(\Delta f_{D}\) represents the additional frequency shift due to the Doppler effect, and \(v\) is the velocity of the target object  
The performance of FMCW LiDAR is also affected by the modulation waveform. The modulation waveform frequency used varies depending on the measurement purpose [6]. Commonly used waveforms include triangle wave and sawtooth wave.
## 4 Corpus Augmentation by Sentence Segmentation
### Use of Sentences Not Divided into Partial Sentences  
Some sentence pairs could not be divided into partial sentence pairs through the procedure in Section 4.3, even though they included punctuation marks. We retried generating pseudo-parallel sentences using only the target sentences of those undivided sentence pairs as follows.  
1. Extract target sentences from the undivided sentence pairs that can be split into multiple segments.
2. Back-translate the extracted target language sentences into the source language.
3. Split the target language sentence and its back-translation result into segments in the manner used for Step 2 in Section 4.1.
4. Let \(t\) and \(\overline{t}\) be respectively a target sentence and its back-translation result; let \(n\) and \(m\) be respectively the numbers of the segments of \(t\) and \(\overline{t}\); i.e., \(t=(s_{1},s_{2},\ldots,s_{n})\) and \(\overline{t}=(s^{\prime}_{1},s^{\prime}_{2},\ldots,s^{\prime}_{n})\). Extract sentence pairs \((t,\overline{t})\) such that \((n=m)\) and \((n\geq 2)\). (4-1) Back-translate each segment \(s_{i}\) of \(t\) to \(\overline{s_{i}}\). (4-2) For \(i\) (\(1\leq i\leq n\)), replace \(s^{\prime}_{i}\) in \(\overline{t}\) with \(\overline{s_{i}}\) to generate \(n\) pseudo-source sentences: \(\overline{t}^{(1)}=(\overline{s_{1}},\overline{s^{\prime}_{2}},\ldots,s^{ \prime}_{n})\), \(\overline{t}^{(2)}=(s^{\prime}_{1},\overline{s_{2}},\ldots,s^{\prime}_{n})\), \(\ldots\), \(\overline{t}^{(n)}=(s^{\prime}_{1},s^{\prime}_{2},\ldots,\overline{s_{n}})\). (4-3) Make \(n\) pseudo-parallel sentence pairs \((\overline{t}^{(1)},t),\ldots,(\overline{t}^{(n)},t)\) from each obtained pseudo-source sentence \(\overline{t}^{(i)}\) and the target sentence \(t\).  
We also supply code (see Supplementary Materials) that can reproduce our proposed method.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 1 Introduction
The novelty of this work is the openness and collaboration of the platform that \"crowdsourcing\" the searching operation to a large group of people who share information and contribute to finding possible survivors in a large disaster such as wildfires. To the best of our knowledge and based on our reviews of current research, we have not found a similar concept that allows anyone using a drone to collaborate with SAR operations. The contributions of this paper are as follows: (1) We introduce the **Open Collaborative Platform** for multiple drones to assist SAR operations; (2) we propose three different searching paths (_Centralized_, _Distributed_, and _Hybrid_ approaches) for drones to collaborate with each other; (3) we extend the capability of the Krypto module further with a _Dynamic Searching Path_ that dynamically adjusted drone's flight path based on received Wi-Fi signal, return-to-home distance, and its current power level; (4) we demonstrate that the proposed platform can reduce the searching time for mission-critical tasks such as SAR operations. The remainder of this paper is organized as follows. In Section 2, the related works are discussed and our previous work on Krypto is presented to make the paper more self-contained. The proposed Open Collaborative Platform for multiple drones is introduced in Section 3. Our experimental and simulation results are presented in Section 4. Finally, we drew our conclusions in Section 5.
# MedChain: Efficient Healthcare Data Sharing via Blockchain
## 5 Evaluations
### Discussion  
Without compromising security, privacy, and scalability, MedChain shows higher efficiency than the existing designs in the previously discussed communication overhead and storage overhead. Furthermore, another advantage is flexibility. By separating the mutable information of healthcare data from the immutable information, MedChain allows healthcare providers to readily update the description of data with minimal overhead. For example, a hospital may change its domain name, which results in the change of all the data locations. If they are stored on blockchain, either a description update is not allowed, or new events and blocks have to be generated for new descriptions, increasing both computation and storage overhead. Such an advantage is intuitive. Therefore, it is not shown in the experiment.  
Figure 11: System responsiveness with the number of directory nodes.  
Figure 12: Transaction rates with a different number of blockchain nodes and directory nodes.  
The scalability from the patient point of view is a common issue of all patient-centric solutions, since patients have to respond to each data-sharing request and add each requested data into a session for access authorization. This is a tradeoff between patient controllability and overhead. By controlling session creation and revocation, MedChain is no worse than other solutions. If a patient wants to reduce the overhead from the same requester, he/she can leave the session open forever. Then the data sharing overhead in MedChain is similar to Reference [32]. A more elegant solution could be constructed by introducing attribute-based data sharing and medical history-based data sharing to further reduce overhead, which is complementary to the session-based scheme. Attribute-based data sharing [46] can allow a patient to share his/her healthcare data to a group of requesters tagged with the same set of attributes, e.g., physician and biomedical laboratory. Medical history-based data sharing [47] can enable a patient to grant access to all his/her medical data related to a specific disease/symptom. We will study them in future work.  
In summary, Table 4 compares MedChain with other solutions. The result shows the advantage of the proposed scheme in many aspects.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 4 Preliminary Design of an IoT-Aided Building Fire Evacuation Control System
### Information Sources and Data Transmission
#### 4.2.2 People Counting
The major disadvantage for infrared beam counters is that when two or more persons walk side by side into the area of PIR sensor detection, the infrared beam counter may count only one, so it is not suitable for rooms with large front entrances. Fortunately, the width of the doors for most individual rooms in buildings only allows one or two persons to pass at a time. At the same time, the test by [PERSON] [60] also showed that fire conditions have no direct influence on infrared sensor uses. Therefore, it is applicable to use the PIR sensor for counting occupants at room level. Since the infrared beam counter transmits only the counted number of occupants, narrowband wireless networks in low power consumption can be applied for data communication between the sensors and the IoT platform, the same as those sensors and devices for fire hazard monitoring. To better understand the solutions for people counting in building fire evacuation control, the major proposed people counting solutions are summarized in Table 3.
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 2 Numerical Method
### Mesh of the Computational Domain  
The whole model adopts the combination of structured grid and unstructured grid for grid generation. When analyzing the wind field with obvious viscous effect near the ground, it is necessary to strictly control the y+ value of the calculation grid. However, for the numerical simulation of wind field in a large area, the value of y+ has little influence on  
Figure 1: Satellite topographic map of the island.  
Figure 2: Computational domain with boundary conditions and schematic diagram of measuring points.  
the calculation results. In order to ensure the calculation accuracy and calculation resources, the height of the first layer of grid is set to 0.0038 m and the growth rate is 1.05. It can be seen that the grid in the center of the island is small and gradually sparse outward. In order to accurately simulate the wind field on the island, some areas are encrypted. Figure 3 shows the relevant meshing. After grid independence verification, the total number of grids is 5.3 million.  
In the simulation, the N-S equation is solved by simple algorithm, the pressure adopts the second-order interpolation scheme, and the momentum adopts the second-order upwind scheme. In order to meet the conditions of CFL, the time step is set to 0.0005 s and the total number of time steps is 30,000.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 3 System Model
### Fault Model  
Communication systems are subject to perturbations caused by multiple sources, such as Electro-Magnetic Interference, high energy particles, and loose connectors. From the system point of view, these perturbations can induce faults that can lead to message transmission errors.  
In this work, we consider that fault arrivals follow a Poisson process (as in [10]), which possesses a random exponential time between arrivals with an average of \(\lambda\) faults per second (\(\lambda\) is also referred as process intensity). Equation (5) shows the probability of having \(k\) faults in a time interval \(\tau\)  
Figure 2: Insert idle time in the Synchronous Window (SW).  
as a function of \(\lambda\), \(k\) and \(\tau\). According to the Poisson process definition, this probability is independent of past events.  
\[P_{\lambda}(k;\tau)=e^{-\lambda\tau}\frac{(\lambda\tau)^{k}}{k!} \tag{5}\]  
The average fault arrival rate \(\lambda\) can be obtained from experimental data available in the literature. We will rely on the numbers presented in [18], which used an experimental setup to measure the BER in three types of operational environments, denoted Benign, Normal, and Aggressive. For instance, a factory plant with several welding machines near the nodes was used as representative of an Aggressive environment. These experiences were conducted using a CAN bit-rate of 1 Mbps, and, for reference and future use, the measured values are the ones presented in Table 1.  
Knowing the bit-rate of the CAN network, the length of the elementary cycle, the length of the synchronous window, and the type of environment where the system is deployed, Equation (5) can be used to compute the probability of error occurrence. These values will be used in the remainder of this paper to compute diverse parameters of the error recovery mechanisms.  
In the following, we assume that the automatic retransmission of messages by the CAN controllers is disabled, to avoid jeopardizing the schedule defined in the TM, and that the Master can detect, in a consistent way, the errors occurring in the communication channel. Potential inconsistent error detection is addressed in Section 7. Bus partitions are not considered. The fault model is basically the same as the one presented in [12] concerning the arrival of faults, but without the restriction of a maximum of one fault per EC, resulting on a more realistic scenario.
## 1 Introduction
By using the instrument to obtain the displacement data, the data can be identified using fast Fourier transform (FFT) analysis and compared with the known input values to analyze the accuracy of the wMPS while measuring a rigid structure. This article is organized as follows: in Section 2, the working principle and the mathematical model of the wMPS are studied and deduced. In Section 3, the experiment design based on the reverse-engineering method is presented, and a coordinate transformation method suitable for this experiment is proposed. This method uses the least-squares method to fit a straight line to define the coordinate transformation method of the X-coordinate axes. In Section 4, the time-series data are analyzed using the FFT method, and the feasibility of the intrinsic parameters of the wMPS measurement structure is evaluated. In Section 5, the characteristics of the main sources of the wMPS dynamic measurement errors are described and analyzed. In Section 6, the respective advantages of wMPS, GPS, and RTS for monitoring the intrinsic parameters of engineering structures are discussed.
###### Abstract  
The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 \(\times\) 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. The model assumed a linear relationship that varied with both time and space between soil moisture and backscattering coefficient (SM-\(\sigma^{0}\)). Results showed that three LME models developed with different polarized \(\sigma^{0}\) images all meet the European Space Agency (ESA) accuracy requirement for GMES soil moisture product (\(\leq\)5% in volume), with the vertical transmit and vertical receive (VV) polarized model achieving the best performance. However, the SM-\(\sigma^{0}\) relationship was found to depend strongly on space, making it difficult to predict absolute soil moisture for each grid. Therefore, a relative soil moisture index was then proposed to correct for site effect. When compared with those of the linear fixed effects model, the soil moisture indices predicted by the LME model captured the temporal dynamics of measured soil moisture better, with the overall R\({}^{2}\) and cross-validated R\({}^{2}\) being 0.68 and 0.64, respectively. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones.  
Sentinel-1; linear mixed effects model; soil moisture index; Poyang Lake ungauged zone