esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
## 2 Materials and Methods
### The HVAC Model  
The HVAC system consists of four main subsystems: the evaporator, the compressor, the condenser and the expansion valve. Each subsystem is connected to the previous and subsequent subsystems through the main physical properties of the refrigerant fluid: the enthalpy and pressure of the refrigerant fluid and its mass flow rate. The schematic of the connections between the four devices and the heat transfer processes in the evaporator and condenser is shown in Figure 2. In this work, we consider the steady rotational speed of the compressor. Therefore, the volumetric flow rate of the refrigerant is constant inside the single loop, and the mass flow rate depends only on the physical properties of the refrigerant at the compressor inlet. The control system is of an off type, controlled by the vehicle cabin temperature set point \(T_{sp}\). Defining the deadband [\(T_{sp}-\delta T;T_{sp}+\delta T\)], the following rule is adopted:  
\[\left\{\begin{array}{ll}T_{cab}<T_{sp}-\delta T&\quad\text{HVAC is off}\\ T_{cab}>T_{sp}+\delta T&\quad\text{HVAC is on}\end{array}\right. \tag{3}\]  
The energy consumption of HVAC can be calculated as the product between the electrical power consumption (\(P_{el}\)) and its working time (\(\Delta t\)):  
\[E_{el}=P_{el}\cdot\Delta t \tag{4}\]  
The energy balances within the four main components of the HVAC system are described below.
# Intent to Adopt Location Sharing for Logging Safety Applications
###### Abstract  
Logging entails work in remote areas with multiple hazards and consistently ranks among the most fatal occupations in the United States. Location-sharing (LS) devices that enable users to communicate geographic positions to others have been suggested as a technological approach to improving workplace safety on logging operations. This study investigated logger intent to adopt LS-based safety practices. Employing concepts from the Theory of Planned Behavior, including intent, attitude, norms, and perceived behavioral control, we surveyed Idaho loggers at three logger training programs. We evaluated their likelihood of using LS devices on logging operations and examined factors associated with LS adoption. The results showed that Idaho loggers are likely to use (a) automatic position updates for hand fallers, (b) LS devices on all ground workers and heavy equipment, and (c) LS technology for general situational awareness. Participants also recognized specific safety benefits to LS, particularly for emergency situations, such as communicating the need for help or expediting the discovery of injured coworkers. Our findings support further development of LS technology for logging safety, particularly devices and applications that facilitate injury response for isolated workers, such as hand fallers.  
location-based services; situational awareness; Theory of Planned Behavior (TPB); survey; occupational safety; Idaho; cable logging; Global Navigation Satellite System (GNSS) Article
## 2 Numerical Methods
### Infinitely Long Phase Screen  
A turbulent atmosphere is equivalent to placing a series of thin random phase screens in a vacuum. The light field diffracts to the phase screens, and each phase screen introduces a random contribution to the phase. This diffraction and phase modulation process continues until the beam is transmitted to the receiving end. The multilayer random phase screen is a common random phase screen method used in laser propagation simulations [19]. The main random phase screen generation methods are the FFT power spectrum inversion method [20], fractal method [21], and Zernike polynomial method [22], where the FFT method with added subharmonics is the most common method [23]. However, the random phase screen is established on the fact that the laser pulse width is significantly narrower than the time scale width of random refractive index fluctuations [24]. Therefore, the dynamical process of incident light propagating through atmospheric turbulence cannot be investigated using this method. Moreover, the static assumption will lose its usefulness when the influence of the wind velocity on ISAL imaging is studied.  
The infinitely long phase screen method can overcome this barrier to simulate very long exposure times (without using the whole memory space) as well as the time evolution of the turbulence parameters [17]. First, we generate an initially random phase screen with the FFT power spectrum inversion method and take the last few columns (rows) of the last-generated phase screen to construct a matrix \(Z\). The matrix \(Z\) is combined with the turbulence power spectral density function to generate a new column (row) \(X\), which is added to the last generated one, and the excess part of the oversized phase screen is discarded. In the simulation process, these new columns (rows) are generated in each iteration according to the wind velocity \(v\), wind direction \(\theta\), iteration time, grid spacing, and other parameters. The relationship between the new column (row) \(X\) and the partial data \(Z\) is given by [17]  
\[X=AZ+B\beta \tag{1}\]  
where \(\beta\) is a Gaussian random vector with zero mean and its covariance is equal to unity [25; 26; 27]. Matrices \(A\) and \(B\) can be calculated from the covariance of the \(X\) and \(Z\) vectors, including the matrices \(\langle ZZ^{T}\rangle\), \(\langle XZ^{T}\rangle\), \(\langle ZX^{T}\rangle\), and \(\langle XX^{T}\rangle\). All the covariance matrices can be acquired from constructing the distance matrix \(r_{i,j}\) and acting on the phase  
Figure 1: Diagram of ISAL system in atmospheric turbulence.  
covariance function \(C(\varphi_{r})\). In the von Karman spectrum, the covariance can be expressed as [28]  
\[C_{\varphi}(r)=\left(\frac{2\pi r}{L_{0}}\right)^{\frac{5}{6}}\left(\frac{L_{0}} {r_{0}}\right)\frac{\Gamma(11/6)}{2^{5/6}r^{3/8}}\left[\frac{24}{5}\Gamma \left(\frac{6}{5}\right)\right]^{\frac{5}{6}}K_{5/6}\left(\frac{2\pi r}{L_{0}}\right) \tag{2}\]  
where \(K_{5/6}(\cdot)\) represents the McDonald function, \(\Gamma(\cdot)\) represents the gamma function, \(L_{0}\) is the outer scale, and \(r_{0}\) is the Fried parameter.
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
###### Abstract  
We present a novel stereo visual odometry (VO) model that utilizes both optical flow and depth information. While some existing monocular VO methods demonstrate superior performance, they require extra frames or information to initialize the model in order to obtain absolute scale, and they do not take into account moving objects. To address these issues, we have combined optical flow and depth information to estimate ego-motion and proposed a framework for stereo VO using deep neural networks. The model simultaneously generates optical flow and depth information outputs from sequential stereo RGB image pairs, which are then fed into the pose estimation network to achieve final motion estimation. Our experiments have demonstrated that our combination of optical flow and depth information improves the accuracy of camera pose estimation. Our method outperforms existing learning-based and monocular geometry-based methods on the KITTI odometry dataset. Furthermore, we have achieved real-time performance, making our method both effective and efficient.  
stereo visual odometry; optical flow; depth map; cost volume +
Footnote †: journal: Applied Sciences
## 2 Material and Methods
### Flux and Ancillary Data  
As a CarboEurope and Fluxnet site, eddy covariance fluxes of CO\({}_{2}\), sensible heat, latent heat and momentum have been measured continuously since 2001 from the top of a 12 m high tower that is approximately 6 m above the canopy. Our eddy covariance facility included a three-dimensional sonic anemometer (Solent R3, Gill Instruments, Lymington, England) and a closed path infrared gas analyzer (IRGA, model LI 6262, Li-Cor Inc., Lincoln, NE, USA), both sampling at a rate of 21 Hz. Flux data were processed with protocols in accordance with the CarboEurope network [17]. Processing schemes of Fluxnet have been used for filling data gaps and partitioning net ecosystem productivity into GPP and ecosystem respiration [18,19]. Half-hour GPP fluxes were summed at a daily time step for further comparison with MODIS GPP and called EC GPP. Half-hourly data of air temperature, VPD, incoming flux of photosynthetically active radiation PAR\({}_{\mathrm{top}}\) were recorded at the top of the flux tower. The fraction of PAR absorbed by the canopy FPAR was derived from 14 PAR-sensors randomly set up in understory locations measuring PAR\({}_{\mathrm{below}}\) as:  
\[FPAR=1-(PAR_{{}_{\mathrm{below}}}/PAR_{{}_{\mathrm{top}}}) \tag{1}\]
## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
### Improving Energy Efficiency in the RAN Part of 5G Network Slices
#### 5.4.1 Possibilities for Energy Savings in Sliced 5G RAN in the Time Domain  
In the time domain, possible energy savings are at the level of OFDM symbol shutdown. In the case of BSs serving a single NS, the activity of power amplifiers (PAs) when there is no transmission of OFDM data symbols can be significantly reduced, as well as the frequency of transmitting control signals.  
As shown in Figure 13, a basic principle of energy saving is shutting down PA during periods with no data OFDM symbol transmission. This concept has already been implemented for new generations of 5G BSs in unsliced 5G networks, and it is shown that such a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Macro-Sleep Mode** & **Micro-Sleep Mode** \\ \hline Remote radio head/unit (RRH/U) & Frequency blocks \\ Active antenna element (AAE) & Subcarriers \\ Power amplifier (PA) & Wireless channels \\ Baseband unit (BBU) & OFDM symbols \\ Complete BS & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Categorization of 5G BS components as candidates for operation in sleep mode.  
concept can contribute to the reduction in BS energy consumption. It is worth emphasizing that this type of energy saving can be implemented when the BS is moderately loaded, which means that switching the PA on and off can only be allowed in the periods when a lower amount of traffic is transferred over the NS. In the case of intensive data traffic being transferred in NS, every symbol's time slot will be occupied, and this reduces the possibility of shutting down PAs [45].  
Additionally, in the 5G sliced network with BSs simultaneously serving multiple NSs, to obtain further energy savings, the concept of scheduling PAs' activity can be extended to the distribution of PAs among different NSs, as presented in Figure 14.  
One concept is based on an approach where a specific PA is permanently allocated to specific NSs, as presented in Figure 14, while another approach is based on the time scheduling of NSs traffic transfer among different PAs (Figure 14). In both concepts, energy savings are obtained for specific NSs in periods when there is no data traffic for transfer in the NS. The implementation of such a concept will require the development of techniques that will enable the dynamic scheduling of PAs among NSs on the same BS or will require the possibility of allocating the specific PA to the specific NSs through BS configuration.
## 3 A Prototype System for Pedestrian Positioning
### Particle Filtering  
#### 3.9.1 Introduction  
The particle filter is a sequential version of the Monte Carlo method [37] which enables to find solutions of a multidimensional problem by generating a large number of possible solutions, so-called particles, and then verifying these solutions by a given criterion, _i.e._, an error function. The particle filter can be looked at from the statistical point of view, whereby the solution is represented by a probability density function. The particle filter improves upon Kalman filter in case of multi-modal distributions and non-linear dynamics at the cost of high computation burden. Let \(i\) denote the number of a particle and \(L\) the total number of particles, thus \(i\in<1,L>\). A particle at a given time instant \(k\) is represented by vector \(\mathbf{c}_{i}(k)\) given by Equation (48).  
\[\mathbf{c}_{i}(k)=\left[\begin{array}{c}\mathbf{x}_{i}(k)\\ w_{i}(k)\end{array}\right] \tag{48}\]  
where \(\mathbf{x}_{i}(k)\) is a possible system state at time instant \(k\) and \(w_{i}(k)\) is the weight associated with \(i\)-th particle. A possible system state is understood as a possible solution of the problem in question. In our case \(\mathbf{x}_{i}(k)\) represents a hypothetical user location and orientation, \(\mathbf{x}_{i}(k)=[x_{i},y_{i},\phi_{i}]^{T}\), \(x_{i},y_{i}\) denote coordinates in the local coordinate system defined in Section 3.1.2. \(\phi_{i}\) is the azimuth orientation. The aim of this simulation is to to find the most probable user location. Given the series  
Figure 14: **(a)** An example of the \(f_{ Lj}\) function from Equation (45) where \(k_{j}>k_{\rm thr}\). Example values of \(a=0.5\) and \(\sigma_{L}\) = 6 \(\rm pix\) were used (to account for the units, \(f\) = 279\(\rm pix\) was substituted). **(b)** A 3D plot of \(f_{ Lj}\) function where \(k_{j}>k_{\rm thr}\) and \(\Delta z=d_{Sj}-d_{ Lj}\). The base of the function widens as the compared distances increase.  
of measurements \(\mathbf{y}(1)\ldots\mathbf{y}(k)\) the pedestrian's location is given by the probability density function that can be approximated by the probability mass function (49).  
\[p(\mathbf{x}(k)|\mathbf{y}(1:k))\approx\sum\limits_{i=1}^{L}\delta(\mathbf{x}- \mathbf{x_{i}}(k))\cdot w_{i}(k) \tag{49}\]  
where \(\delta(\cdot)\) is the Dirac delta function. Weights \(w_{i}(k)\) of all generated particles should sum up to unity.
## 4 Experiments and Evaluation
### Experimental Results and Analysis
Because a new item often has no rating records, the IBCF uses JAC measure to compute similarity by comparing attribute values between items, however it cannot explore the hidden information from the given attributes. By extending binary relations between different attributes, the proposed approach constructs interrelated attributes for each item so that more information can be utilized in evaluating the similarity computation. Further, items in a new item's neighborhood will be more similar to this new item, which shows that the accuracy of the recommendation has improved. Moreover, items with more common attributes will have higher similarity in IBCF, thereby making items in a neighborhood to concentrate on a few types. The proposed approach can be used to extract characteristic from different attributes, which can enhance distinction among different items. Thus, in IM-IBCF, items types in the neighborhood are more than in IBCF, and more types of items can be recommended to the target user. Therefore, the diversity of recommendations has also been enhanced.
# Low-Frequency Magnetic Field Exposure System for Cells Electromagnetic Biocompatibility Studies
## 4 Construction of Exposure System
### Measurement of Magnetic Flux Density  
The magnetic flux density measurement is performed using the Narda NBM-550 EMF analyzer with the EPH-50D probe in the center of each coil for each sample position in the incubator with the door closed. The incubator contains the control coil, the exposure coil, and shielding at the time of measurement. The exposure coil is powered by an arbitrary wave generator RIGOL DG4162; the signal is amplified by a HUBERT KEYSIGHT Infnii Vision MSO-X 3012A linear amplifier while the driving current is controlled by a 6 \({}^{1}\)/2 local digital multimeter Aligent 34401A. The exposure coil is powered by \(I\) = 0.7 A (time course of sine shape, RMS), \(f\) = 50 Hz. The conductors are attached to the coil through a drain channel at the rear of the incubator, which is used to drain excess water when the incubator is used for cooling so that the integrity of the incubator is not compromised.  
The measured data are compared with the results obtained from the numerical simulation at the evaluation curve corresponding to the size of the probe.  
The measured data of magnetic flux density are compared with the values obtained from the simulations, Figure 8. The percentage difference between the measured and simulated values does not exceed 11%, Figure 9.  
The maximum of measured B-field RMS values in the control coil is 18.47 \(\mu\)T, while the exposure coil is powered by \(I\) = 0.7 A (time course of sine shape, RMS), \(f\) = 50 Hz, with the incubator's door closed. The measured magnetic flux density inside the control coil is 90 times lower compared to the value measured in the same position in the exposure coil.  
For further using the exposure system, the values obtained by measurement are considered the reference values. A calibration curve is constructed to correctly determine the value of current required to obtain the desired value of magnetic flux density.  
Figure 7: Exposure and control coils with supporting structure.
## 2 Related Work
### Ad-Hoc/_Sensor Routing_  
The AODV (_Ad hoc_ On Demand Distance Vector) protocol [11] is one of the most representative routing protocols designed for mobile _ad-hoc_ networks, and also can be used on ubiquitous sensor networks. It is a reactive algorithm, meaning that it builds routes between nodes only as desired by source nodes. It maintains these routes as long as they are needed by the sources. However, after a route is set up, the route is not instantly adapted for faults or attacks on middle nodes. Such results will be shown in Section 4. [PERSON] _et al._[12] designed a routing protocol in mobile _ad hoc_ networks in order to reduce control message overhead and maintain route paths, even where nodes move continuously at a high speed. It maintains continuously the path with low overhead through tracking the destination's location, but the route is not quickly prepared for jamming on the middle nodes, like AODV.  
Usually, most routings on ubiquitous sensor networks focus on energy efficiency in normal cases [6, 7, 13]. All these protocols provide the dynamics for changes of the network condition, but do not cope speedily with jamming conditions, because the new routes are regenerated between a sink and the sources. Specially, FAP [13] provides a long-lived sensor network through energy-aware routing protocol, but intermittent jamming induces instead considerable energy consumption for electing new cluster heads and configuring newly the network. In order to enhance the reliability of path and decrease the end-to-end delay, [PERSON] _et al._[14] designed a routing protocol providing the reliable path through hotlines between gateway nodes, but this scheme also cannot defeat speedily the jamming around sensors.  
On the other hand, multipath routings might become an effective countermeasure for the avoidance of jamming. However, mechanisms providing multiple paths simply focus on the energy efficiency as data centric routings [6, 7] and data aggregation and in-network processing on hierarchical sensor networks [15], and moreover they do not provide the immediate avoidance of dynamic jamming areas.
# Image Magnification Based on Bicubic Approximation with Edge as Constraint
## 4 Constructing the Whole Sampling Surface
### Constructing Correcting Surface  
The magnified image obtained from the initial whole sampling surface \(F(x,y)\) (Equation (19)) is inevitably subject to errors. This section discusses constructing a correcting surface to improve the accuracy of \(F(x,y)\) (Equation (19)). Firstly, the error image is computed, and the correcting surface is constructed by the error image.  
The error image is calculated as follows. First, the initial whole sampling surface \(F(x,y)\) is sampled to obtain the doubled image \(p\), and the pixels in \(p\) are denoted as \(p_{i,j},i,j=1,2,3\cdots,2n\). Let  
\[\overline{P}_{i,j}=(p_{2i-1,2j-1}+p_{2i-1,2j}+p_{2i,2j-1}+p_{2i,2j})/4.\]  
Ideally, for a given pixels \(P_{i,j},i,j=1,2,3,\cdots,n\), \(\overline{P}_{i,j}\) and given pixels \(P_{i,j}\) should satisfy the relationship \(\overline{P}_{i,j}=P_{i,j}\). Therefore, the error pixels are defined by the difference between \(\overline{P}_{i,j}\) and \(P_{i,j}\) as follows:  
\[E_{i,j}=P_{i,j}-\overline{P}_{i,j},i,j=1,2,3,\cdots,n. \tag{22}\]  
From the error image \(E_{i,j},i,j=1,2,3,\cdots,n\), a quadratic correcting sampling patch \(e_{i,j}(x,y)\) similar to Equation (4) can be constructed in the neighborhood of each pixel \(P_{i,j},i,j=2,3,4,\cdots,n-1\), using the method in Section 3 above:  
\[e_{i,j}(x,y)=e_{0}(u^{2}+h^{2}/3)+e_{1}uv+e_{2}(v^{2}+h^{2}/3)+e_{3}u+e_{4}v+e5. \tag{23}\]  
Then, the two quadratic polynomials defined by Equations (4) and (23), respectively, are merged; that is, \(a_{k}+e_{k}\to a_{k},k=0,1,2,3,4,5\). The new quadric sampling surface is still denoted as Equation (4). We still denote the new whole sampling surface as \(F(x,y)\). The new error image can be obtained by sampling the new \(F(x,y)\), and a new correcting surface patch of the form in Equation (23) can be obtained. Then, Equations (4) and (23) are merged to get a new \(F(x,y)\) again. In this way, the surface \(F(x,y)\) (Equation (19)) includes bicubic sampling surface patches and correcting surface patches. Experimental results show that, if Equation (4) corrects more than four times, the correction effect will not be improved much and the amount of calculation will be increased. Therefore, we only correct the quadratic polynomial in Equation (4) four times under comprehensive consideration.
# FANet: Improving 3D Object Detection with Position Adaptation
## 1 Introduction  
Point cloud data constitute a significant modality of three-dimensional (3D) scene information, encompassing a richer set of attributes, including but not limited to dimensions (length, width, and height), velocity, and reflection angles, when compared to images. The utilization of point cloud detection holds immense potential across diverse domains, including autonomous driving, robot navigation, and virtual reality [1, 2]. Presently, the development of three-dimensional object detection algorithms that leverage point cloud data has emerged as a focal point within the research community. Nevertheless, the accurate identification of three-dimensional objects continues to pose a substantial challenge owing to the inherent irregularity and disorderliness inherent in point cloud data.  
Researchers have proposed a number of solutions for 3D object detection, which can be broadly classified into two categories [3], namely voxel-based [4] approaches and point-based [5] approaches. The voxel-based approach converts irregular point cloud data into a regular voxel network, encodes the data points falling within the voxel and extracts features through deep learning, and uses 3D convolutional neural networks or voxel-based deep learning to detect objects. This approach can achieve excellent detection accuracy, but inevitably results in causing a loss of information and reduced fine-grained localization accuracy, which leads to reduced accuracy in object recognition. Point-based methods can easily achieve larger perceptual fields through point set abstraction, but are computationally more expensive. In addition, some researchers have recently designed convolutional kernels that deal directly with point clouds, which can be used for feature extraction and help in the subsequent generation of 3D proposal boxes. However, in practice, the lack of flexibility of the convolution kernel has led to a lack of accuracy.  
In this paper, we present FANet, a highly accurate 3D object detector that incorporates key elements from PAConv and PV-RCNN for enhanced performance. The convolutionoperation of our network constructs kernels based on a basic weight matrix, with adaptive combination coefficients learned by the LearnNet from relative points. This enables FANet to capture multi-scale feature information including voxel semantics and original point locations. Consequently, our network excels in handling irregular and disordered point clouds, boasting robust feature extraction capabilities and the ability to accurately generate 3D proposal boxes.