esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Basic Principle of Safety Optical Fiber Time Transfer Equipment
The remote site calculates and compensates \(\Delta T\) according to Formula (3), so as to realize the synchronization between the remote and the local site. At present, the data are plaintext measured via the TIC unit in some optical fiber time transfer systems. Thus, there is a risk that an attacker can tamper with it. The remote site will receive the wrong data if the information is tampered with. An error will occur in the time transfer if compensation for the time difference is implemented. For example, if the comparison data at the local site are added with a time offset of 10 ns, the time signal output at the remote side will have a leading offset of 5 ns. The error is difficult to find and prevent independently through the remote site, which will bring security risks to the related applications of time service. The plaintext is usually used for information exchange in optical fiber time transfer technology. In this paper, security protection encryption was carried out for data to avoid  
Figure 1: Schematic diagram of a safety optical fiber time transfer. the situation in which plaintext is tampered with. Firstly, an experiment using single optical fiber safety time transfer equipment was completed. Then, a network system of time synchronization was built by using three safety time synchronization devices and a ring timing scheme. The three safe time synchronization nodes were linked through an optical fiber link. Lastly, the local and remote sites were configured with encryption and decryption units to form a safe optical fiber safe time transfer system for realizing the safe networking of the whole optical fiber time synchronization network. There was no significant difference in time and frequency transfer accuracy compared with those of the unencrypted case.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Framework  
The framework of the proposed **M**ulti-**s**ource Data **D**eep Fusion **N**etwork (MsDFN) for vehicle maintenance project prediction is presented in Figure 2. The framework comprises two key modules:  
(1) Maintenance project correlation representation: Recognizing the existence of correlations among different maintenance projects, we propose a correlation representation function to derive correlation representation results for each project based on the co-occurrence matrix.  
(2) Multi-source data fusion network: Acknowledging the significant impact of vehicle mileage and vehicle base information on the development of maintenance projects, a multi-source data fusion network based on attention mechanism is proposed to effectively incorporate the project correlation representation results with the vehicle mileage and vehicle base information.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Description** \\ \hline \(B=\{B_{1},B_{2},\ldots,B_{n}\}\) & Base information about each vehicle \\ \(P=\{p_{1},p_{2},\ldots,p_{|P|}\}\) & Set of all maintenance project codes \\ \(\{V_{t}\}_{t=1,2,\ldots,T}\) & Set of all maintenance projects for a vehicle \\ \(V_{t}\in\{0,1\}^{|P|}\) & The \(t\)-th maintenance projects for a vehicle \\ \(F_{1}(V_{t},A)\) & Correlation representation function of \(V_{t}\) \\ \(\{M_{t}\}_{t=1,2,\ldots,T}\) & Set of all mileage for a vehicle \\ \(F_{2}(M_{t})\) & Representation function of \(M_{t}\) \\ \(N_{t}\) & Representation results of \(M_{t}\) \\ \(A\in\mathbb{R}^{|P|\times|P|}\) & Co-occurrence matrix for all maintenance projects \\ \(R_{t}\) & Representation results of \(V_{t}\) \\ \(O_{t}\) & Attention output results for \(R_{t}\) and \(N_{t}\) \\ \(E\) & Fusion representation results for \(\{O_{t}\}_{t=1,2,\ldots,T}\) \\ \(Em_{1}(B_{i}),Em_{2}(B_{j})\) & Representation functions for base information \(B_{i}\) and \(B_{j}\) \\ \(S_{i}\) & Representation results of \(B_{i}\) \\ \(U\) & Fusion representation results for \(\{S_{i}\}_{i=1,2,\ldots,n}\) \\ \(L\) & Attention results for \(E\) and \(U\) \\ \(H\) & Results of the fusion representation of vehicle \\ \(V_{T+1}\) & Maintenance projects for the (\(T+1\))-th time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and descriptions.  
Figure 2: An overview of the MsDFN model. It is mainly composed of two modules: (1) Maintenance Project Correlation Representation; (2) Multi-source Data Deep Fusion Network.
## 3 Results and Discussion
### Wind Field Characteristics of the Vertical Direction  
#### 3.2.1 Mean Wind Speed  
The vertical wind component ratio (VWCR) \(S_{w}\) is used to represent the vertical mean wind speed, which can be defined as  
\[S_{w}(Z_{i})=\frac{V_{w}(Z_{i})}{V_{0}(z)} \tag{5}\]  
where \(V_{w}\) is the vertical wind speed at the height of \(Z_{i}\), and \(V_{0}\) is the wind speed at the height of \(z\) in the undisturbed ABL. Its definition is similar to the steamwise speed-up \(S_{u}\).  
Figure 10 shows the VWCRs measured on the along-wind center line of the hill (\(y=0\)). In the upstream side of the hill, the VWCR is positive, which indicates that the wind blows up. In contrast, on the downstream side of the hill, the VWCR is negative, which indicates that the wind blows down. There is almost no vertical wind at \(x/L=-1.00\) (the foot of the upstream side of the hill) and \(x/L=0\) (the  
Figure 9: The streamwise turbulence intensity at different heights.  
hill crest where the separation usually occurs). The largest positive VWCR appears at the height of 30 m above the surface of the hill where \(x/L=-0.5\), and the largest negative vertical wind component ratio appears at the height of 150 m above the surface of the hill where \(x/L=0.75\). These findings are mainly attributed to the separation and attachment of the flow on the lee side. Although a large VWCR appears at a higher position with \(x/L=0.75\), the VWCR is close to 0.0 provided \(Z_{i}\) is less than 60 m. The VWCRs measured at different heights are almost the same, in case of \(x/L=-0.75\).  
Figure 11 shows the VWCR measured on the crosswind center line of the hill (\(x=0\)). The VWCR at each measuring point is very small, which indicates that the vertical wind speed predominately functions for the structure near the along-wind center line. Moreover, at the same location, there is no significant difference between the VWCRs along the height.  
Figure 12 shows the contour plots of the VWCR in case of \(Z_{i}=30\) m, 60 m, 90 m, 120 m, and 150 m, respectively. The maximum positive VWCR at all heights occurs on the upstream side of the hill and the maximum positive value is 0.28, which indicates that the climbing effect is significant at this location. Meanwhile, the maximum negative VWCR among those measured at all heights occurs at the mid of the downstream side of the hill, and the maximum negative value is \(-0.26\). It implicates that this position has a significant downhill effect. As the \(Z_{i}\) increases, the absolute values of the upward and downward VWCR decrease. It means that the hill-climbing effect and the downhill effect decrease as the height increases, due to the decrease of the disturbance caused by the hill. The area where the VWCR is close to zero is mainly located on the upstream side of the hill near the center line of the hill (\(x=0\)).  
Figure 11: The vertical wind component ratio \(S_{w}\) on the crosswind center line of the hill (\(x=0\)).  
Figure 10: The vertical wind component ratio \(S_{w}\) on the along-wind center line of the hill (\(y=0\)).
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 5 Conclusions  
* Closed-form solutions for the common and differential mode currents excited on a pair of parallel wires by a voltage source on one wire have been derived. Spectral solutions using a continuous spectrum of currents and the modified Sommerfeld-Goubau (mSG) and quasi-TEM modes in the common and differential mode cases, respectively, are given.
* It is shown that the mSG mode is not the primary reason why microwave communication using power line conductors for distances on the order of 100 m has relatively small losses.
* A simple formula for the total current on the driven wire may often be used to approximate the total induced current in the two-wire case.
* For distances comparable to microwave power line communication system repeater spacing, using power line conductors as a waveguide has significant advantages over free space communications. Further, a second conductor can enhance system performance.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author would like to thank [PERSON] of CRIEPI in Japan for reading and commenting on the manuscript.  
Figure 6: Comparison of the individual wire currents in the two-wire case and the single-wire case. f = 1 GHz. All other parameters are given in Table 1.  
Figure 7: Comparison of the individual wire currents in the two-wire case and the single-wire case. f = 10 GHz. All other parameters are given in Table 1.
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design
### Software Design
#### 3.3.2 Application Software and Payload Control  
The payload control software running on the HYPSO-1 OPU has been described in [66]. In short, at the end of booting, the main application software is started by a startup script. The main application software consists of multiple parallel running threads that implement services. A complete list of service threads is shown in Table 2. The majority of the threads listen for incoming commands via the Cubesat Space Protocol (CSP) [67], where the commands are predefined in code and may contain parameters. The HYPSO team has developed a utility to send these messages from a ground station to the application software using a human readable Command Line Interface (CLI). The CLI and the main application software use a modified version 1.5 of CSP. These messages trigger various actions such as initiation of HSI data capture, file transfer and data buffering. The satellite bus, specifically the Flight Computer (FC) and Payload Controller (PC) subsystems, feature script engines, with which CSP packets with arbitrary data can be scheduled to be sent to any satellite subsystem at specified points in time without ground station contact. In addition to sending arbitrary CSP packets to the OPU, the scripts on the FC control satellite pointing or slewing, and power control to the OPU and HSI. After capture, either immediately or with some delay, HSI images are buffered to the PC outside Ground Station (GS) passes via the slow CAN interface. By doing so, downlink speed improves during a GS pass due to a fast direct communication link between the PC and the S-band radio.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Loss Function  
In order to realize the matching of points and lines, the point-line extraction loss, descriptor extraction loss, and point-line matching loss were used as the loss functions during model training.
# Inelastic X-ray Scattering as a Probe of Terahertz Phonon Propagation in Nanoparticle Suspensions
## 4 Experimental Details
### The Line Shape Model  
The model used to describe the structure factor is the sum of a finite number of excitations yielding the following analytical profile:  
Figure 3: A schematic representation of what one would expect to observe by performing an IXS measurement on an NP suspension for increasing \(Q\) values. The figure is taken from Ref. [22].  
\[S(Q,E) = A_{e}(Q)\delta(E)+[n(E)+1]\frac{E}{k_{B}T}\Bigg{\{}L_{A_{0},z_{0}}(Q,E )+ \tag{4}\] \[\sum_{j=1}^{k}\frac{2}{\pi}A_{j}(Q)DHO_{j}(Q,E)\Bigg{\}}.\]  
Here, the term \(\delta(E)\) is the Dirac delta function, which defines the elastic component of the spectrum having amplitude \(A_{e}(Q)\). The energy-dependent term \(n(E)=(e^{E/k_{B}T}-1)^{-1}\) is the Bose factor, which ensures the fulfillment of the detailed balance condition. The term in curly brackets is the sum of a Lorentzian central peak having half-width at half-maximum \(z_{0}\) and amplitude \(A_{0}\). This term accounts for the spectral contribution of non-propagating dynamical processes in the sample, such as diffusion and relaxation phenomena. The \(k\) inelastic contributions are described by Damped Harmonic Oscillator \((DHO_{j}(Q,E))\) terms having amplitude \(A_{j}(Q)\). The parameters defining the shape of the DHO excitations, \(\Omega_{j}(Q)\) and \(\Gamma_{j}(Q)\), represent the undamped energies and damping coefficients, respectively, they determine the analytical profile of the \(i\)th excitation through the following:  
\[DHO_{j}(Q,E)=\frac{\Omega_{j}^{2}(Q)*\Gamma_{j}(Q)}{(E^{2}-\Omega_{j}^{2}(Q))^ {2}+4[E\Gamma_{j}(Q)]^{2}} \tag{5}\]  
It is worth noticing that the number, \(k\), of \(DHO_{j}(Q,E)\) excitations and their shape coefficients are equally treated as adjustable model parameters.  
Finally, to provide an accurate approximation of the measured line shape, the model function in Equation (4) should be convoluted with the instrument resolution function \(R(E)\) and the result summed to the spectral background. Explicitly:  
\[\hat{S}(Q,E)=R(E)\otimes S(Q,E)+B(E) \tag{6}\]  
where \(B(E)\) is a mildly \(E\)-dependent background intensity.
## 1 Introduction
### Equifinality of the Combinations of Conditions Leading to Technological Innovation  
In the context of our research, equifinality means that there are several paths or combinations of conditions that may lead to the same outcome; in our case, the innovation performance of the clustered firms and, more precisely, the technological innovation outcome, as we are focusing on adoption of technologies.  
Far to consider a cluster as a homogenous group of companies, it is normally composed of a wide variety of firms in terms of competitive positioning, knowledge bases or innovation strategies. Some contributions attempted to analyze, under a core-periphery network perspective, how larger and peripheral companies (having high amount of internal resources devoted to internal research) share the location with smaller companies, which, having a limited amount of R&D resources, are highly interconnected with other similar companies in the core of the cluster [59; 60]. Cluster categorization, following [PERSON] and [PERSON]'s analysis [53], divides cluster networks into two different areas. On the one hand, the network core, where the companies benefit from the intensive exchange of knowledge and information, and, on the other hand, the network periphery, where relationships are more distant and the flow of knowledge among companies is less intensive.  
Previous studies have demonstrated how these two different network levels use different paths to explore and exploit the stocks of knowledge and technologies [61]. Consequently, we conclude that there is not a unique path for firms in a cluster to improve their innovative performance, but these paths are created according to the predominant ways they are using to explore and exploit new sources of innovation. More precisely, to the purpose of the present research, we focus attention on the way the internal cluster diversity prevents a unique path for reaching the major firms' technological innovation outcomes; companies can alternatively develop internal or external factors to obtain a higher level of technological innovation. Therefore, we highlight that diverse combinations of factors, including high values of internal attributes related to absorptive capacity, are likely to lead to a higher level of technological innovation. Thus, we state the following proposition.  
**Proposition 1**.: _There are different configurations of attributes (paths) leading to strong technological innovation in clustered firms._
## 2 Theoretical Modeling  
### Geometry and Material Properties  
Figure 1 depicts a thin solid rectangular FGM plate of size \(a\times b\times h\) made of ceramic and metal mixture constituents. As the material properties of the FGM plate vary along with the thickness as a function of the power-law function, the FGM plate is termed the P-FGM plate. The P-FGM plate's top surface is made of ceramic, while the bottom surface is made of metal.  
The volume fraction of the metal and ceramic constituents varies with the power-law index \(k\). The volume fraction is expressed as  
\[V_{c}=(z/h+1/2)^{k};\ V_{C}+V_{m}=1 \tag{1}\]  
where \(V_{c}\) denotes the volume fraction of ceramic constituent in the FGM plate, while \(V_{m}\) denotes the volume fraction of metallic constituent in the FGM. \(z\) represents the thickness variation of each layer, \(h\) represents the half-ply thickness from the middle surface, and \(k\) represents the power-law index ranging from \(0\ \leq\ k\ \leq\ \infty\).  
The rule of the mixture does not appear to work with multi-layered models because it does not account for the interaction of the intermittent layered constituents [44]. Therefore, the Mori-Tanaka scheme [45; 46; 47; 48; 49] is used to compute the effective modulus of the P-FGM plate. Hence, the effective density and effective Young's modulus of the FGM plate are respectively given by  
\[\rho(z)=\rho_{c}V_{c}+\rho_{m}V_{m} \tag{2}\]  
and  
\[E(z)=E_{m}+(E_{c}-E_{m})\frac{V_{c}}{1+V_{m}\Big{(}\frac{E_{c}}{E_{m}}-1\Big{)} (1+\
u)3(1-\
u)} \tag{3}\]  
where \(c\) and \(m\) denote ceramic and metal, respectively, rho denotes density, and \(E\) denotes the FGM plate's Young's modulus of elasticity. Because the plate is thin, the variation in Poisson's ratio (\(v\)) along the thickness direction is considered negligible [50; 51; 52; 53]. Material properties of different P-FGM plates can be deduced from [54; 55; 28; 56]. Table 1 summarizes the different FGM material constituents.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sr. No.** & **Materials** & **Young’s Modulus (\(E\), Pa)** & **Poisson’s Ratio (\(\
u\))** & **Density (\(\rho\),kg/m\({}^{3}\))** \\ \hline
1 & Aluminum (Al) & \(70\times 10^{9}\) & 0.3 & 2700 \\ \hline
2 & Aluminum Oxide (Al2O3) & \(380\times 10^{9}\) & 0.3 & 3800 \\ \hline
3 & Zirconia (ZrO2) & \(151\times 10^{9}\) & 0.3 & 3000 \\ \hline
4 & Stainless Steel (SUS304) & \(208\times 10^{9}\) & 0.32 & 8166 \\ \hline
5 & Silicon Nitride (Si3N4) & \(322\times 10^{9}\) & 0.24 & 2370 \\ \hline
6 & Ti-6 Al-4V & \(106\times 10^{11}\) & 0.298 & 4429 \\ \hline \end{tabular}
\end{table}
Table 1: Material properties of different FGM plates [28; 55].  
Figure 1: P-FGM plate with physical neutral surface.
## 2 Materials and Methods
### Hardware Development and Layer Description
#### 2.2.2 Layer 2: Gas Sensor Driver  
We design the driver in a universal configuration mode, which is compatible with wide range of hazardous/toxic gas sensors (2-3 lead gas sensors). The driver can be used for both, oxidizing and reducing gas sensors (Figure 2).  
It includes a 24-pin board-to-board connector, a gain adjuster, a front-end amplifier (LMP91000, Texas Instruments, Dallas, TX, USA), and a signal filter. To improve signal quality during calibration, the voltage reference is strictly held. Thus, in addition to the microcontroller voltage reference, we use a voltage reference chip (Intersil SL60002 DIH320Z-TK). We implement a low-pass filter to eliminate ripples. To tackle the possible mismatch impedance between the output signal of the gas sensor and the ADC micro-controller, a gain unity buffer (MicroChip MCP6041T-I/OT) is used (Figure 2).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Sensor/Component** & **Company** & **Layer** & **Category** & **Parameter** & **T. Int. (ms)** & **Interface** & **Pow. Supply (V)** & **Pow. Cons. (\#W)** & **Resolution** \\ \hline Microphone & Panasonic & & & & 500 & & 2-10.0 & 1500 (Max) & \(1\,\mathrm{dB}\) \\ \hline LM385 & TI & & & & & 500 & & 2-032 & 1.65 & \\ \hline GUVA-512 SD & Eocinc & HW interface & ambient-phys. & & & 15,000 & ADC & 2-0.5 & & \(1\,\mathrm{index}\) \\ \hline MCP6001 & Microchip & & & UV & NA & & 18.6 & & \\ \hline MAX30205 & Maxim & & & skin temp. & 15,000 & IIC & 2.7-3.3 & & \(1\,^{\circ}\mathrm{C}\) \\ \hline AM2520 ZCC09 & Ringbright & physiological & vital sign & heart rate & 15,000 & ADC & 2.7-6 & 130 & \\ \hline APD59008 & Broadcom & & & & 15,000 & ADC & 1.6-5.5 & 1900 & \\ \hline KXG03 & Kionix & & & & ac/gyr & 20 & & 1.8-3.6 & \\ \hline KX062-1031 & Kionix & & & mag. & 20 & & 1.7-3.6 & & \\ \hline KX122-1037 & Kionix & & & acel. & 20 & & 1.7-3.6 & & \\ \hline LP8254 HB & STMicr. & & & barometer & 1000 & & 1.7-3.6 & & 1 hPa \\ \hline SHT31- DIS-B & Sersition & & ambient-phys. & hum./temp. & 1000 & & 2.4-5.5 & & 1?sRH/\(1\,^{\circ}\mathrm{C}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 2: Layer 1: Gas sensor (**left**); Layer 2: Gas sensor driver’ bottom (**middle**) and top (**right**).
## 5 DRRF Extensions (for Non Real-time Traffic)
### Minimum Reserved Traffic Rate Extension  
As described so far, the scheduling scheme ensures that all users will achieve fair throughput with the channel conditions' considerations. However, by defining _minimum reserved traffic rate_, or _rmin_, some users may need to be favored over others. Note that users without minimum reserved traffic rates are treated as users with a zero guaranteed rate.  
We modified DRRF by updating the deficit counter to achieve a minimum guaranteed rate criterion. First, similar to what we described in Section 3, we derive the number of requested slots, _requested_slots_, from the queue length and its corresponding MCS. Then, we calculate the number of slots required to guarantee the minimum reserved traffic rate, _rmin_slots_. The minimum of these two numbers, called _pre_deficit_, is used to update the deficit counter. Then, the number of free slots used to calculate the proper quantum is also updated accordingly.  
\[pre\_deficit(i)=Min[requested\_slots(i),rmin\_slots(i)] \tag{1}\] \[rmin\_slots(i)=\lceil\frac{rmin(i)\times t\_frame}{MCS\_size(i)}\rceil \tag{2}\]  
Equations 1 and 2 show how to derive these numbers. Here, _t_frame_ is Mobile WiMAX frame size (5 ms). _MCS_size(i)_ is the number of bytes per slot for the given MCS level of mobile station \(i\). Note that after these derivations, we also apply the max-min fairness algorithm (See Figure 2) to calculate the proper quantum size to distribute the left-over slots fairly for both two categories: (1) MSs without minimum reserved traffic rate and (2) MSs with minimum reserved traffic rate but still need more slots than the total guarantee.