esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Channel Model  
A waveguide model with multimode is applied to the channel model of an underground tunnel. The attenuation and phase shift coefficient of each mode can be described as [25]:  
\[\alpha_{mn}=\frac{1}{a}\bigg{(}\frac{m\pi}{2 ak}\bigg{)}^{2}\operatorname{Re} \Bigg{(}\frac{\overline{k_{v}}}{\sqrt{\overline{k_{v}}}-1}\Bigg{)}+\frac{1}{b} \bigg{(}\frac{n\pi}{2 bk}\bigg{)}^{2}\operatorname{Re}\Bigg{(}\frac{1}{\sqrt{ \overline{k_{h}}-1}}\Bigg{)} \tag{1}\]  
\[\beta_{mn}=\sqrt{k^{2}-\bigg{(}\frac{m\pi}{2a}\bigg{)}^{2}-\bigg{(}\frac{n\pi} {2b}\bigg{)}^{2}} \tag{2}\]  
where \(\operatorname{Re}\big{(}\mathbf{\cdot}\big{)}\) represents the real part of the results. \(\big{(}m,n\big{)}\) is the mode order, which indicates the reflection number of electromagnetic wave from vertical and horizontal walls of mine tunnel. The more reflection number \(\big{(}m,n\big{)}\) is, the higher the mode order gets. \(\overline{k_{h}}\) and \(\overline{k_{v}}\) are the electrical parameters of vertical and horizontal walls respectively: \(\overline{k_{h}}=\frac{\varepsilon_{0}\varepsilon_{h}+\frac{\sigma_{h}}{j2\pi f _{n_{v}}}}{\varepsilon_{0}\varepsilon_{a}+\frac{\sigma_{a}}{j2\pi f_{n_{v}}}}\), \(\overline{k_{v}}=\frac{\varepsilon_{0}\varepsilon_{v}+\frac{\sigma_{v}}{j2\pi f _{n_{v}}}}{\varepsilon_{0}\varepsilon_{v}+\frac{\sigma_{a}}{j2\pi f_{n_{v}}}}\). The wave number for the considered WSN is defined as \(k=2\pi f_{n_{v}}\sqrt{\mu_{0}\varepsilon_{0}\varepsilon_{a}}\), where \(\varepsilon_{0}\) is the permittivity of vacuum space; \(\mu_{0}\) is the permeability which is same for the air, vertical and horizontal walls. \(\varepsilon_{v}\), \(\varepsilon_{h}\), \(\varepsilon_{a}\) and \(\sigma_{v}\), \(\sigma_{h}\), \(\sigma_{a}\) are the relative permittivity and conductivity of vertical, horizontal  
Figure 3: The block diagram of signal transmission in two phases: \(t=1\): Phase 1; \(t=2\): Phase 2.  
walls and the air in underground tunnel respectively. \(f_{n_{c}}\) is the central frequency of \(n_{c}\)-th subcarrier of wireless sensor nodes based on MC-CDMA. Assume that the coordinates of a wireless sensor in the considered WSN are (\(x\), \(y\), \(z\)) as shown in Figure 1. Therefore, the field intensity of the sensor with distribution mode \(\left(m,n\right)\) is given by  
\[E_{m,n}^{\text{\tiny eign}}(x,y)\text{$\succeq$}\sin(\frac{m\pi}{2a}x+\phi_{x} )\cdot\cos(\frac{n\pi}{2b}y+\phi_{y}) \tag{3}\]  
where \(\varphi_{x}=0\) if \(m\) is even; \(\varphi_{x}=\pi/2\) if \(m\) is odd; \(\varphi_{y}=\pi/2\) if \(n\) is even; \(\varphi_{y}=0\) if \(n\) is odd. The channel gain of \(n_{c}\)th subcarrier between transmitting sensor \(i(x_{0}\), \(y_{0}\), \(z_{0})\) and receiving sensor \(j(x,y,z)\) in the considered WSN is expressed as \(h_{i,j}^{n_{c}}\). The channel gain of each subcarrier is obtained by adding up the front 50 modes which are stronger.
# Numerical and Experimental Analysis of the Oil Flow in a Planetary Gearbox
## 3 Results and Discussion
### Comparison of Numerical and Experimental Results
At the highest velocity (SIM-id2v3, Figure 11), similarly to SIMI-id1-v3, the centrifugal forces create an evident oil ring around the planet carrier (red dashed arc), in accordance with the observations by [PERSON] et al. [8]. The oil is splashed all around the gearbox (blue dashed dot rectangle), resulting in a completely different behavior with respect to the other investigated velocities, in which the oil is also present in the front region. The absence of the oil in front of the planet carrier and its splashing around the gearbox is well predicted by the model. However, differently from the experimental recordings, in the CFD model of SIM-id2v1 and SIM-id2v2 more oil is sticking to the planet carrier and less oil is falling into the sump. A possible explanation for this difference could be related to the modeling of wall adhesion at the contact point between the wall surface and the interface, as explained in Section 2.4.4. For SIM-id2v3, a larger amount of oil in the red dashed arc is predicted by the simulation if compared with the experimental recordings, but the shape and the flow path of the oil have been captured. In this section, the numerical results were compared with the experiments. The transparent housing allowed to observe the flow that originates in the front part of the gearbox. In this regard, the oil rings that originate from the planet carrier rotation are already evident from the shown side-by-side comparison. Additional oil rings originate inside the gearbox around the planets, as will be presented from the CFD results in the following paragraph.
## 3 Numerical Case Study  
The subspace method is adopted from [26] and the weight matrices are introduced. The state-space equation of dynamic systems is extracted by using different weight matrices for each numerical and experimental case-study. Furthermore, separate evaluation algorithms are implemented for analysis of time-efficiency, noise-robustness, and accuracy. The algorithms include but are not limited to:  
1. Identification of the system parameters using all three weight matrices;
2. Calculating pole values by solving the system's characteristic equation;
3. Predicting the estimation value and comparison with the real signal response by calculation of the error residual, variance, and the associated variance accounted for (VAF);
4. Implementing fairly large number of identification cycles and measuring the elapsed time for every step; and
5. Plotting the obtained results for each algorithm.  
A 6-degree of freedom, lumped mass model is used as the numerical example to evaluate different subspace algorithms. The configuration of the simulation model is shown in Figure 3.  
The main parameters of the numerical example are as follows: \(C,\ K,\) and \(M\) are damping stiffness and mass matrices, respectively. The stiffness equals to \(k=2000\) N/m for each mass to mass interlink. \(M\) is an identity matrix of size six. Rayleigh damping coefficients are as: \(C=0.6798M+1.7431\times 10^{-4}\ K\) equal to:  
\[C=\left[\begin{array}{ccccc}1.377&-0.3486&0&0&0&0\\ -0.3486&1.377&-0.3486&0&0&0\\ 0&-0.3486&1.377&-0.3486&0\\ 0&0&-0.3486&1.377&-0.3486\\ 0&0&0&-0.3486&1.377&-0.3486\\ 0&0&0&0&-0.3486&1.377\\ \end{array}\right] \tag{25}\]  
Figure 3: The lumped mass model of the numerical case study.  
An impulsive load of 1 kN is applied to the dynamic model. The sampling frequency for obtained acceleration response is 500 Hz. Random noise with different ratios are induced to the response signal to simulate the applied ambient uncertainty. The ratio of the applied noise is demonstrated in each evaluation process. System order can be determined by inspecting convergence of the principal angles toward 90 degrees. In Figure 4, the CVA principal angles are plotted versus the block rows. The convergence of the principal angles is achieved by increasing the number of block rows. The first iteration starts from nearly 10 degrees. Second and third iterations are around 20 and 50 degrees. It can be seen that the principal angles approach to 90 degrees after 10 iterations that means the optimum system order of 10 or above, can be selected to reach the optimum estimating of the simulation model.  
Poles of the simulation system are extracted from the system parameters of the SSI algorithm. The first natural frequencies and mode shapes of the simulation model with the assumption of zero damping are shown in Figure 5. However, the eigensolution of the damped structures are in the form of complex dynamic parameters.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.7 CoAR  
CoAR proposed by [PERSON] et al. (2018) is another congestion-aware RPL variant which considers ETX, queue utilization, and residual energy as routing metrics. In CoAR, a node is deemed congested when its buffer occupancy exceeds its threshold, which is adaptive according to the current buffer occupancy and the ratio of incoming traffic to outgoing traffic. To rank the candidate parents, CoAR adopts TOPSIS, which is a multicriteria decision-making approach. TOPSIS selects the best alternative based on the shortest distance from the positive ideal solution and the furthest distance from the negative ideal solution. For tiebreaking, CoAR uses NI, which indicates the ratio of the number of downstream nodes to the number of all neighbor nodes. By selecting a candidate parent with the smallest NI, CoAR tries to avoid using nodes with many potential children and few parents.  
The authors compared performance of CoAR to that of the basic RPL and ECRM ([PERSON] et al., 2017) via Cooja simulation. At the data link layer, CSMA/ContikiMAC(RDC)/802.15.4 was used. Simulation results show that CoAR outperformed the basic RPL and ECRM in terms of PRR (packet receiving ratio), end-to-end delay, packet loss ratio, throughput, and energy consumption.
## 1 Introduction
In many studies, vegetation indices based on optical sensors serve as proxies to examine the spatial and temporal patterns of grassland productivity [40; 41; 42; 43; 44; 45; 46]. Remote sensing is not actively used enough when studying and monitoring mountain natural grasslands in Armenia. Some studies on the inventory of grasslands via visual image processing and mapping were performed within the framework of the CARMAC project funded by the World Bank [47]. Afterwards, [PERSON] et al. developed and evaluated a method of assessing the degradation of grasslands based on spectral analysis of high-resolution Quick Bird imagery [48]. Also, some attempts on multidecadal trend analysis of Armenian grasslands for some study areas in the context of climate change were implemented as well [27; 49]. All these studies were performed using single-source data, and there has been no approach to compare the data obtained from different remote sensing sources with different spatial and temporal resolutions. However, combined application of satellite sensors and drones, will make it possible for more detailed and efficient studies of these areas and will be more credible and reliable than single-source data [50; 51; 52]. Hence, we aimed to assess the feasibility of multisource remote sensing for (i) productivity analysis of the mountainous natural grasslands in Armenia and (ii) exploring the spatial-temporal changes via trend analysis of the biomass of these grasslands. For this purpose, research questions were set up as follow:  
1. A spatial correlation analysis of the space-born and UAV-born spectral vegetation indices best reflecting the state of the biomass of grasslands. 2. A trend analysis of grassland biomass based on remote sensing data.
## 3 Thin Film Cathodes
### ABO\({}_{3}\) Oxide Thin Films  
ABO\({}_{3}\) perovskite oxides are conventional materials for electrochemical applications, in particular, SOFCs owing to their good reactivity, flexibility in terms of oxygen stoichiometry, and viability of A- and B-site cation substitutions. In most cases, the A-site cation is a combination of rare and alkaline earths for SOFC cathodes. However, the B-site cation is generally a transition metal, such as Mn, Fe, and Co. The atomic structure of perovskites consists in a 3D network of BO\({}_{6}\) octahedra connected by apex, which are maintained by a cubic lattice of A atoms of coordination 12, as shown in Figure 2. The symmetries of ABO\({}_{3}\) perovskites can theoretically be directly determined from the ionic radii of A and B cations, which govern strains inside the materials, since the size mismatch is responsible for the distinct stacking.  
In the early stage, LaCoO\({}_{3}\) (LCO\({}_{113}\)) [36; 73] and LaMnO\({}_{3}\) (LMO\({}_{113}\)) [36; 74] based ABO\({}_{3}\) perovskite oxides were utilized as a cathode material for SOFCs. These stoichiometric compositions, however, were found unsuitable in terms of electrochemical performance for a cathode. Usually, the increase of ionic conductivities is more influenced by Sr concentration at the A-site while the increase of the electronic conductivities is more influenced by Fe and Co concentration at the B-site. The substitution of Sr\({}^{2+}\) for La\({}^{3+}\) can enhance the oxygen ion transport kinetics due to an increase in the oxygen vacancy concentration in the perovskite structure [17]. Therefore, high electrochemical performance at the cathode could be achieved relative to undoped ABO\({}_{3}\) perovskite oxides. In this sense,  
Figure 2: Schematic crystal structure of ABO\({}_{3}\) perovskite oxides, where A and B are a rare earth and transition metal atom.  
LSC113[17, 21, 27, 28, 46, 64, 73, 75, 76] and La1-xSrxCo1-yFyO3-8 (LSCF113)[22, 45, 47, 66, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86] perovskite oxides are commonly used as these oxides are known to show oxygen deficiency and the oxygen nonstoichiometry (\(\delta\)) affects the electrochemical properties, conductivity and lattice expansion of the materials.
# Energy Prediction of Access Points in Wi-Fi Networks According to Users' Behaviour
## 4 Experimental Environment
### Access Point Technology  
The AP devices considered for the experiments were Alcatel-Lucent IAP-215 from Aruba Networks. They have dual radio \(3\times 3\) MIMO technology and support standards 802.11 ac at 1.3 Gbps in 5 GHz band and 802.11n at 450 Mbps in 2.4 Ghz band. The APs are focused on high-performance Wi-Fi environments with medium users' density (up to 256 simultaneous users and 16 BSSIDs or Virtual APsper radio). The devices are configured as hive in Instant AP mode under InstantOS operating system from Aruba. At the same time, the hive is monitored by Nodowifi, a management tool from UEX.  
The APs have 6 integrated omnidirectional downtilt antennae (3 per radio) with a maximum gain per antenna of 5 dBi. Nevertheless, the maximum gain of the antenna operating in the same band is 6.9 dBi at 2.4 Ghz and 8.8 dBi at 5 GHz. The power and channel assignment in the installed APs is driven by Adaptive Radio Management (ARM) technology from Aruba, which analyses the frequency spectrum in order to select the optimal power and channel, according to the regional configuration and the interferences and signals received from other devices. In any case, the power is selected in the range from 13 dBm to 18 dBm in both radios.  
The IAP-215 device has a CPU Freescale P1010 800 MHz, 256 MB SDRAM and 32 MB flash memories. The processor has a 45 nm, low power single core e500, with 36-bit physical addressing, double-precision floating-point support, 32 KB L1 separated caches and 256 KB L2 cache, 3 Ethernet 10/100/1000 Mb/s enhanced ports, TCP/IP acceleration and classification capabilities, and integrated security engine supporting several crypto algorithms.  
Lastly, the power of the APs is supplied by PoE (Power Over Ethernet) of 48 VDC with 802.3 af compliance. The maximum operational energy consumption is 14.9 W, the minimum with radius is 4.5 W, and the power base emitting both radios at 18 dBm without connected users is 7 W. The efficiency of the PoE energy conversion to 12 VDC is around 88%.
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 3 Methods
### State-Exchanging Long Short-Term Memory (SE-LSTM)
#### 3.1.2 State-Exchanging Package  
The LSTM memory cell usually refers to one of the two shares of an LSTM layer's hidden state [45]. This portion of the hidden state has the ability to modify by addition, subtraction, and scaling. Therefore, it tends to preserve information for a relatively long time. In this study, we consider the cell as a variable, not as a function, and this variable takes a piece of additional information at each time step rather than information learned from the previous time steps. Assuming the advantage of updating the cell state with additional learned information, we exchange the extracted information within each time step of both RGB and optical flow streams by calculating the average value as in Equation (7) and sharing it between the next time steps of bot streams. The resulting information from this cell information exchanging process we define as the \"State-exchanging Package\" \((P_{t})\), which is represented in Equation (8).  
\[P_{t}=\Big{(}Cell_{rgb}+Cell_{opt}/2\Big{)}, \tag{7}\]  
\[P_{t}=(C_{t-1}+C^{\prime}_{t-1})/2, \tag{8}\]  
where \(C_{t-1}\) and \(C^{\prime}_{t-1}\) are the previous cell states of RGB and optical flow streams, respectively.  
For the RGB stream, the modified cell state using proposed SE-LSTM with the \(P_{t}\) is formulated using Equations (9)-(11). Here \(C_{t}\), \(Z_{t}\), \(I_{t}\) and \(F_{t}\) are the cell state, cell gate, input gate, and forget gate of the modified LSTM, respectively. According to the SE-LSTM process, the previous cell state \(c_{t-1}\) in Equation (9) needed to be updated with the averaged newly learned information of both streams' cell states \(\big{(}\big{(}c_{t-1}+c^{\prime}_{t-1}\big{)}/2\big{)}\). Therefore, the modified cell state can be formulated as in Equation (10). Based on Equation (8), the new cell state \((C_{t})\), represented in Equation (10), can be replaced with \(P_{t}\) as in Equation (11). Then we updated the general objective function \((H_{t})\) in Equation (12) using \(P_{t}\) as in Equation (13); this is one of our main contributions in this study, i.e., modifying the objective function of general LSTM unit using our proposed SE-LSTM process. By the same token, we defined and modified the cell state and the objective function of the optical flow stream with \(P_{t}\). The state-exchanging internal structure of both LSTM sequences are shown in Figure 2.  
\[c_{t}\ =\ z_{t}\otimes i_{t}+c_{t-1}\otimes f_{t}\, \tag{9}\]  
\[C_{t}\ =\ Z_{t}\otimes I_{t}+\Big{[}c_{t-1}+\big{(}c_{t-1}+c^{\prime}_{t-1} \big{)}/2\Big{]}\otimes F_{t} \tag{10}\]  
\[C_{t}\ =\ Z_{t}\otimes I_{t}+[c_{t-1}\ +\ \mathbf{P_{t}}]\otimes F_{t} \tag{11}\]  
\[H_{t}\ =\ O_{t}\otimes\tanh(C_{t})\, \tag{12}\]  
\[H_{t}\ =\ O_{t}\otimes\tanh(Z_{t}\otimes I_{t}+[c_{t-1}\ +\ \mathbf{P_{t}}]\otimes F_{t})\, \tag{13}\]
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 3 Methodology
### Washer Modeling  
To precisely simulate and analyze the physical characteristics of the washer shape, we leveraged the NumPy library in Python for mathematical modeling. We defined the essential dimensional parameters of the washer, particularly its inner and outer diameters, serving as the foundation for constructing the model. Utilizing the built-in code provided by the SuperScreen 0.10.4 software package, we seamlessly generated the model of the washer shape.  
Simulating Complex Washer  
However, the function of this software package is limited, and it is difficult to model complex patterns. In order to simulate and analyze the performance of complex washer shapes, we adopted a method that combines layout GDS files and the NumPy library.  
This method involves two primary steps: Firstly, develop a program to convert the GDS file of the layout into a two-dimensional array format compatible with NumPy. This initial step enables the extraction of detailed geometric information from complex shapes. Subsequently, these shapes are accurately modeled using NumPy. Figure 4 illustrates the successful modeling of complex washer shapes achieved through this method. The graphic displays two octagonal washers connected in series, demonstrating the method's capability to precisely reconstruct complex washers.  
This approach not only simplifies the modeling process of complex shapes but also provides an accurate graphical foundation for our subsequent simulation analysis. Additionally, it offers reliable model support for optimized design and performance evaluation.  
Figure 4: The figure depicts two octagonal washers connected in series. The layout’s GDS file is converted into a two-dimensional array manageable by NumPy, followed by modeling of the graphical representation using NumPy within PyCharm.  
Figure 3: This provides a visual comparison of the area of square, octagonal, and circular washers as a function of width (ranging from 50 microns to 100 microns) and diameter (varying from 175 microns to 225 microns). There are three surfaces in the figure, each representing a washer shape.
## 5 Positioning and Orbit Determination of Space Targets  
In this section, a joint position method using multiple star trackers is proposed to achieve the target position of multiple optical observations using a Gaussian minimum mean square error difference correction algorithm, fusing the spatial target position information of multiple frames of images and completing the solution of the orbital equations. The schematic diagram of multiple star trackers observing debris simultaneously is shown in Figure 11:  
Figure 10: The simulating orbital trajectory of space debris in 100 different motion velocities.  
From Equations (10) and (11), it can be seen that the right ascension and declination information of the observed debris through the star tracker has a high order nonlinear relationship with the debris orbital position information, which cannot be obtained with high accuracy by using the traditional geometric solution. From the observation equation, a high-precision static equation solving algorithm is introduced to solve the problem to filter out the noise to the maximum extent and achieve the optimal estimation result.
## 3 Methodology  
### The Horizontal-to-Vertical Ratio Technique  
The HVSR technique (also known as H/V) was initiated by [PERSON] and [PERSON] [26], and developed by [PERSON] [25]. It is one of the most commonly used techniques that allows for the retrieval of one of the most important parameters of site response, i.e., the resonance frequency at a given site. This technique has proven its reliability in estimating the soil resonance frequency [37], although several authors have reported that this technique is not reliable in estimating the amplification factors, especially at lower frequencies [38; 39; 40; 41]. This inability is due to the contribution of body and surface waves, and it is the latter ones that control the HVSR curve. The low contribution of body waves is sufficient for estimating the fundamental frequency, but not enough for estimating the amplification factor [38; 39; 41]. Recently, [PERSON] [42] has also demonstrated how the peak amplitude of the HVSR of the seismic noise may vary considerably with time.  
Figure 3: Zonation map of the study area. Red dots correspond to single-station measurements. Red polygons correspond to the limits of the different cities.  
This method consists of estimating the ratio between the Fourier amplitude spectrum of the horizontal and vertical components of the ambient noise, which provides the HVSR curve as a result. The resulting HVSR frequency peak is very well-correlated with the resonance frequency of the soil [43]. This peak corresponds to an impedance contrast in the soil column.  
The low cost, the simplicity of acquisition and processing, and the reliability in estimating the soil resonance frequencies, makes this technique one of the most used for site characterizations, especially for urban areas.
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 3 Results
### Accuracy of the Photogrammetric Survey  
The mean of the RMSE, GCPs, and ChPs, was calculated for the four blocks studied in the four scenarios (Figure 9).  
The accuracy factor in the three \(\mu_{\text{xyz}}\) components depends on the scenario selected for the UAV flight planning, the GSD, and the posterior sigma naught of the bundle block adjustment (\(\sigma_{\text{o}}\)); in case this latter value is unknown by the user, it can be set equal to 1.