esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Channel Model
#### 2.2.2 Probabilistic Path Loss Model  
We consider massive MIMO networks with the mmWave band, and therefore we used a realistic 28 GHz channel assessment described in [31; 32; 33; 34; 35; 36; 37; 38; 39]. The mmWave frequencies are much sensitive to any line-of-sight (LOS) or non-LOS (NLOS) condition that exists in the channel link. Furthermore, there is  
Figure 1: Example of the beam pattern in relation to \(L=3\) case. (**a**) beam pattern configuration in Cartesian (left plot) and polar (right plot) coordinate systems with \(G_{t}(0)\), (**b**) beam pattern configuration in Cartesian (left plot) and polar (right plot) coordinate systems with \(G_{t}(1)\), (**c**) beam pattern configuration in Cartesian (left plot) and polar (right plot) coordinate systems with \(G_{t}(2)\).  
a possibility that there is no available link between Tx and Rx, and such a channel link is said to be in outage. We can use the probability function to find a channel condition as shown below [31, 32]:  
\[p_{\rm OUT}(d) = \max\left(0,1-e^{-a_{\rm out}d+b_{\rm out}}\right), \tag{9}\] \[p_{\rm LOS}(d) = \left(1-p_{\rm out}(d)\right)e^{-a_{\rm LOS}d},\] (10) \[p_{\rm NLOS}(d) = 1-p_{\rm out}(d)-p_{\rm LOS}(d), \tag{11}\]  
where \(a_{\rm out}\), \(b_{\rm out}\), and \(a_{\rm LOS}\) are outage and LOS coefficients, respectively, and the values are obtained by measured data in [31]. With this probabilistic model, we define the distance pathloss model based on an alpha-beta-gamma model as [31, 37]:  
\[L_{\rm LOS}(d) = \alpha_{\rm LOS}+10\beta_{\rm LOS}{\rm log}_{10}(d)+\xi_{\rm LOS}, \tag{12}\] \[L_{\rm NLOS}(d) = \alpha_{\rm NLOS}+10\beta_{\rm NLOS}{\rm log}_{10}(d)+\xi_{\rm NLOS}, \tag{13}\]  
where \(L_{\rm LOS}(d)\), \(L_{\rm NLOS}(d)\) are dB scale values in relation to \(d\) in meters. Furthermore, \(\alpha_{\rm LOS}\), \(\alpha_{\rm NLOS}\), \(\beta_{\rm LOS}\), and \(\beta_{\rm NLOS}\) represent measured data in [31]. \(\xi_{\rm LOS}\) and \(\xi_{\rm NLOS}\) are shadow-fading variables.
# Gaussian Process Modeling of Specular Multipath Components
## 2 System and Signal Models
### Amplitude Estimation  
For UWB transmission, it is reasonable to assume that the SMC signals \(s_{k}(t)=s(t-\tau_{k}(\mathbf{p}))\) are well separated in delay domain and thus approximately orthogonal. The SMC amplitude can be estimated by projecting the received signal onto the corresponding shifted pulse [29], that is,  
\[\hat{a}_{k}(\mathbf{p}) =\int_{0}^{T}r(t;\mathbf{p})s_{k}^{*}(t)dt \tag{5}\] \[=\alpha_{k}(\mathbf{p})+\int_{0}^{T}\left[s(t)*\
u(t;\mathbf{p})+w(t) \right]s_{k}^{*}(t)dt\] \[=\alpha_{k}(\mathbf{p})+\
u_{k}(\mathbf{p})+w_{k}\,. \tag{6}\]  
The variance of the estimated amplitude as derived in Appendix A is given as  
\[\mathrm{var}[\hat{a}_{k}(\mathbf{p})]=\mathbb{E}\big{[}|\
u_{k}(\mathbf{p})+w_{k}|^{2} \big{]}=S_{\
u}(\tau_{k};\mathbf{p})T_{\mathrm{P}}+N_{0}\,, \tag{7}\]  
where \(T_{\mathrm{P}}=\int_{-\infty}^{\infty}|S(f)|^{4}df\) is the effective pulse duration (which is being discussed in Appendix A). The first term on the right-hand side is a Gaussian process corresponding to the DMC and the second term is measurement noise. The estimated amplitude is thus distributed according to  
\[\hat{a}_{k}(\mathbf{p})\sim\mathcal{CN}\left(\alpha_{k}(\mathbf{p}),S_{\
u}(\tau_{k}; \mathbf{p})T_{\mathrm{P}}+N_{0}\right)\,. \tag{8}\]  
This equation indicates that the mean SMC amplitudes \(\alpha_{k}(\mathbf{p})\) depend deterministically on the agent position \(\mathbf{p}\). The same holds for the variance of the SMC amplitudes, being a result of the interfering diffuse multipath that is characterized by the PDP \(S_{\
u}(\tau_{k};\mathbf{p})\), cf. Equation (4).
## 3 System Model
### Caching Model
\tag{8}\]  
The video caching state vector is defined as \(\chi_{\mathcal{F}}\Big{(}t_{y}^{x}\Big{)}=\Big{\{}\chi_{f_{1}}\Big{(}t_{y}^{x }\Big{)},\chi_{f_{2}}\Big{(}t_{y}^{x}\Big{)},\dots,\chi_{f_{F}}\Big{(}t_{y}^{x }\Big{)}\Big{\}}\), \(\chi_{f_{1}}\big{(}t_{y}^{x}\big{)}\) is the number of video copy of \(f_{1}\) in MECS cached at the time slot \(t_{y}^{x}\). \[\chi_{f_{i}}\Big{(}t_{y}^{x}\Big{)}=\sum_{j\in L}x_{f_{i},\ell_{j}}\Big{(}t_{y }^{x}\Big{)}\leq 1. \tag{9}\]  
The Formula (9) is the constraint that the MECS can only cache one video copy of the bitrate for the same video simultaneously. Additionally, existing research shows that low bitrate video copies can be obtained from high bitrate video transcoding by MECS. Hence, multiple video copies which are cached simultaneously will cause heavy waste of caching resources [34]. Therefore, the constraint condition of the caching capacity of MECS is expressed as  
\[\sum_{i\in F}\sum_{j\in L}g_{i}x_{f_{i},\ell_{j}}\Big{(}t_{y}^{x}\Big{)}\leq \varphi_{MEC}. \tag{10}\]  
Based on Zipf's law, the video's requested probability of \(\mathcal{F}\) can be denoted as \(P_{\mathcal{F}}^{x}(f)=\big{\{}P_{\mathcal{F}}^{x}(f_{1}),P_{\mathcal{F}}^{x }(f_{2}),\dots,P_{\mathcal{F}}^{x}(f_{F})\big{\}}\). Then video \(f_{i}\) is the requested probability by the vehicle user at the time slot \(t^{x}\) and can be expressed as  
\[P_  
\[\mathcal{H}\Big{(}t_{y}^{x}\Big{)}=\sum_{i=1}^{F}P_{\mathcal{F}}^{x}(f_{i})\chi_{f_ {i}}\Big{(}t_{y}^{x}\Big{)}=\frac{\sum_{i=1}^{F}f_{i}^{-\mu(t^{x})}\chi_{f_{i}} \Big{(}t_{y}^{x}\Big{)}}{\sum_{k=1}^{F}f_{k}^{-\mu(t^{x})}}. \tag{15}\]  
Then the caching loss rate at the time slot \(t_{y}^{x}\) is calculated as  
\[\overline{\mathcal{H}}\Big{(}t_{y}^{x}\Big{)}=1-\mathcal{H}(t_{y}^{x})=\frac{ \sum_{i=1}^{F}f_{i}^{-\mu(t^{x})}\Big{(}1-\chi_{f_{i}}\Big{(}t_{y}^{x}\Big{)} \Big{)}}{\sum_{k=1}^{F}f_{k}^{-\mu(t^{x})}}. \tag{16}\]
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## Methods  
### Pleiades Stereo-imagery Acquisition and DEM Generation  
In the present experiment, stereo images were acquired when the angles between the line-of-sight of the satellite camera and the horizontal plane of the ground were 72\({}^{\circ}\) and 76\({}^{\circ}\), for the first and second images respectively. The ground projection of the Pleiades position during the stereo-pair collection was 218 and 174 km from the coast respectively. Pleiades was overlooking the coastal area of interest from the sea side. This satellite setup indicates that both images were collected close to nadir angle (90\({}^{\circ}\) from the ground), with an azimuth of 19\({}^{\circ}\) for the first and \(-8^{\circ}\) for the second image, resulting in an azimuth angle difference of 27\({}^{\circ}\) between the two images.  
The Pleiades panchromatic band of the stereo pair was processed using the Ames Stereo Pipeline, ASP [24] to generate a DEM and ortho-images at 2 and 0.5 m resolution, respectively. The ASP uses the rational polynomial coefficient (RPC) camera model format for the DEM generation. The RPC model is provided in the imagery metadata (by AIRBUS) and gives a relationship between the image coordinates and the ground coordinates. No ground control points were initially used in the DEM generation. The planimetric coordinates were referenced to the WGS84 UTM 30N coordinate system and the heights were computed above the WGS84 ellipsoid. The Pleiades DEM and ortho-images were a posteriori coregistered by applying a first order polynomial transformation defined by 37 concomitant points manually identified in the Pleiades-ortho and DEM and in the IGN/BRGM orthophoto map (used as the reference). This process was performed with QGIS software and Georeferencer GDAL plugin, with an average planimetric error of 0.5 m. To convert Pleiades altimetric data from WGS1984 to the French NGF-IGN vertical datum, the Pleiades elevations were corrected from the average difference with the LiDAR elevations (Pleiades elevations 12.2 m higher) determined at the same point locations and then used for the planimetric correction.
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 4 The Proposed Scheduling
### Simulations
The normalized bandwidth is calculated by the ratio between the number of allocated resources and total resources in a hyperframe. We also select the successfully scheduled hyperframe with 100 nodes and the configuration of \(p_{m}=0.5\) s, \(b=1\). As can be seen, the average normalized bandwidths of Tp4 of M-LLF and M-RM are 0.918 and 0.906, respectively, which are similar to each other, while CEM-RM only requires 0.627. The same conclusion also applies to other types of topologies. This is because the number of transmissions is determined by a graph in M-LLR and M-RM. However, CEM-RM adopts CES to solve the problem of overlapping paths and to reduce the phenomenon of DL-ACK loss, which can decrease the number of dedicated slots additionally. The remaining unscheduled resources can be allocated for network management to improve network stability. Figure 8: Comparison of average normalized bandwidth. Figure 7: Comparison of scheduling time of the three algorithms.
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 1 Introduction
As demonstrated by the above literature review, the vibration characteristics of internal beveloid gear transmission systems have not been extensively studied, with most research focusing on the machining process of these gears. Additionally, understanding the nonlinear behavior of gear systems is crucial for accurately predicting their performance and optimizing their design, but there is a dearth of research on the nonlinear properties of gear systems, particularly when considering multiple degrees of freedom and parameter coupling. Therefore, this paper investigates the nonlinear vibration characteristics of internal beveloid gears under various external and internal factors. It explores the influence of these factors on the gear system's vibration behavior, aiming to optimize its design and reduce vibration and noise levels.
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 3 The Proposed Method
### Feature Extraction Backbone Network  
In alignment with the transformer method [6; 8], we utilize features extracted from the template image and search region in initial frames with annotations as inputs to the feature extraction network, commonly referred to as the backbone network. In the initial frame, a template patch, \(T\in R^{H_{0}\sim\hat{W}_{20}\times 3}\) centered around the object's coordinates and with dimensions twice the length of the object's surroundings, captures both the object's appearance and surrounding features. Simultaneously, the search region, \(S\in R^{H_{0}\sim\hat{W}_{20}\times 3}\)  
Figure 2: The pipeline of proposed GFETrack tracker. It contains three basic components: the feature extractor, the attention enhancement fusion network, and the prediction head.  
extends from the previous frame's object center coordinates, with dimensions four times the length of the potential object movement range. These patches are reshaped into a square format and fed into the backbone network for feature extraction. The ultimate feature map is derived from the fourth stage of the ResNet50 network [21]. Notably, we removed the last stage of ResNet50 and adopted the outputs of the fourth stage as the final outputs. By changing the convolution stride of the downsampling unit in the fourth stage from 2 to 1, we aimed to achieve a larger feature resolution. Additionally, to increase the receptive field, we replaced the \(3\times 3\) convolution in the fourth stage with a dilation convolution with a stride of 2, inspired by [9]. In general, the backbone network could extract the feature maps of the template, \(F_{T}\in R^{H_{T}\times W_{T}\times C}\), and search region, \(F_{S}\in R^{H_{S}\times W_{S}\times C}\). While, \(H_{T}=\frac{H_{T0}}{8}\), \(W_{T}=\frac{W_{T0}}{8}\), \(H_{S}=\frac{H_{T0}}{8}\), \(W_{S}=\frac{W_{S0}}{8}\), and \(C=1024\).
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 2 Problem Description
### Graph Clustering Model
These original tasks \(\{t_{1},t_{2},\cdots,t_{n}\}\) can be described by a set of vertexes \(V=\{v_{1},v_{2},\cdots,v_{n}\}\) in the graph theory. Consequently, the graph clustering model could be represented by the adjacency matrix \(A_{n\times n}\). If \(t_{u}\) and \(t_{v}\) meet the clustering constraint conditions, the relationship between two tasks can be described as \((v_{u},v_{v})\in E(G)\). Correspondingly, the element in the matrix \(A_{uv}=1\), otherwise \(A_{uv}=0\). Finally, the adjacency matrix \(A_{n\times n}\) consisting of 0 and 1 forms the graph clustering model \(E(G_{i})\) in the \(ith\) orbit.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 5 Results and Analysis
### Packet Delivery Ratio and Data Loss  
Figure 5a illustrates the packet delivery ratio by calculating the number of packets sent and received during data exchange. Results show that packet delivery ratios were 66.38%, 71.27%, 73.91%, 77.18% and 80.23% for PEDAP, LEACH, PEGASIS, DADCNS and L-VH, respectively. Our proposed EMRP dominated by achieving 85.16%. The main reason for receiving fewer packets was due to the selection of lengthy paths where the message exchange may traverse in the opposite direction to BS. It may lead to excessive communication and fewer chances for packet delivery. Figure 5b depicts the amount of communication lost in kilobytes during message exchange using different routing protocols. The data packet's size was set to 256 bytes as per the values exchanged in a message. Results show that the amount of communication lost due to packet drops was 258.13 KB, 281.20 KB, 200.35 KB, 175.23 KB and 134.33 KB, in the case of PEDAP, LEACH, PEGASIS, DADCNS and L-VH, respectively. EMRP achieved a minimum loss of 113.93 KB, where the loss ratio was 0.1483 when 3000 packets were exchanged for EMRP.  
Figure 4: Average data collection time is presented in (**a**), average lifetime in (**b**), first node expiry time in (**c**), and average residual energy after routing operations in (**d**).
## 2 Geodetic Data Sets and Processing
### InSAR: ERS-2 Interferometric Processing Chain  
ERS images were interferometrically processed using the ESA (European Space Agency) open-software SNAP (Sentinel Application Platform). Their parameters are presented in Table 1 and their coverage shown in Figure 1A. Only the footwall of the fault could be analyzed on the interferograms. The hanging-wall, which experienced the main deformation during this event, is densely vegetated resulting in low coherence. Coseismic interferograms show about 10-11 fringes in the footwall which is equivalent to a surface displacement of up to 30 cm (Figure 4).  
Figure 3: Levelling deformation profiles of 2 lines: (**A**) WU007-AF27, (**B**) T001-T045. See Figure 1D for profile locations.  
Figure 2: (**A**) Levelling and (**B**) geodetic benchmark in Taichung city, Taiwan and (**C**) a GPS antenna in Puli City, Taiwan. [Photo credit: [PERSON]].  
Three interferograms were processed using SNAP and SNAPHU (Statistical-cost, Network-flow Algorithm for Phase Unwrapping [19; 20; 21]) for unwrapping the phase (the interferograms are consistent to each other with a regression coefficient of 0.7-0.8). After the coregistration of the primary and secondary images using Delft orbits and a 30 m SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), the interferograms were formed and multilooked by 4 \(\times\) 20 pixels to reduce the speckle effect, which gives a ground resolution of about 80 m. Finally, the interferograms were corrected for the effects of topography using the 1 arc-second (30 m) resolution SRTM DEM and filtered using Goldstein filtering (an adaptative power spectrum filter [22]) before unwrapping the phase and geocoding. Only pixels with a coherence higher than 0.2 were kept to be used in the joint inversion. The low coherence was the main challenge of the InSAR processing especially for the unwrapping of the phase. Furthermore, the hanging-wall of the fault, which is covered by dense vegetation, presents the main deformation as shown by GPS data. Thus, the main area of the deformation is not visible on the interferograms. Due to this reason, we decided to use optical images in addition to the radar interferometry. They have a better spatial resolution (10 m) than pixel offsets from InSAR (about 20 m) and will give us information about the deformation on both sides of the fault.
## 5 Materials and Methods
### Greenhouse Experiment  
This experiment was conducted at the greenhouse of the Department of Horticultural Sciences, Cape Peninsula University of Technology (CPUT), South Africa. The experiment was carried out under the following conditions: An average day temperature of 25 \(\pm\) 5 \({}^{\circ}\)C and average RH of 65 \(\pm\) 5% between March and April 2018. _V. vinifera_ plants were transplanted separately into 40 glass bottles containing a plant growth medium consisting of a mixture of inert substrate materials, vermiculite and perlite in a ratio of 1:1. The bottles were covered with black cloth to prevent algal growth. Twenty spotted plants were drenched with 200 mL suspension of _B. bassiana_ at 1 \(\times\) 10\({}^{8}\) conidia mL\({}^{-1}\). Twenty control plants were drenched with the 200 mL of sterile distilled water containing 0.01% Tween 80 only. Throughout the experiment, the plants were fertigated weekly with a hydroponic fertilizer, Nutrifeed (Starke Ayres, Cape Town, South Africa), containing 65 g/kg N, 27 g/kg P, 130 g/kg K, 70 mg/kg Ca, 20 mg/kg Cu, 1500 mg/kg Fe, 10 mg/kg Mo, 22 mg/kg Mg, 240 mg/kg Mn, 75 mg/kg S, 240 mg/kg B, and mg/kg Zn. The nutrient solution was prepared by dissolving 60 g of the fertilizer in 60 L reservoir with tap water, and each plant was hand-fed with 500 mL every week. After two weeks, all experimental plants were infested with female adult mealybugs (30 per plant) by transferring insects onto plants using a soft brush [51]. Plants in test and control treatments were inoculated for a second time by drenching with a suspension of 200 mL _B. bassiana_ at 1 \(\times\) 10\({}^{8}\) conidial mL\({}^{-1}\) in 0.01% Tween 80 and 0.01% Tween, respectively. On the third week, fungal colonization was assessed as described below (see Section 5.4). The experiment ran for four weeks, after which plant growth parameters (shoot height, number leaves, number of shoots, root length, and shoot and root fresh and dry weights) were recorded. Only plants that showed evidence of successful fungal colonization were selected to represent the fungus treatment thereafter. Mealybug infestation levels were assessed by counting numbers of adult females and larvae on control and fungus-exposed plants. Six representative potted live plants; i.e., three from each treatment, were taken to the central analytical facilities, GC-MS Unit, Stellenbosch University for GC-MS analysis. Fresh leaf samples representing six control samples and six test samples were sent to Bemlab, a commercial testing laboratory in Somerset West, Western Cape, for tissue analysis of nutrient content. The remaining plants were oven-dried separately, at 25 \({}^{\circ}\)C for seven days, and then ground with a Jankel and Kunkel Model A 10 mill into fine powder. One hundred milligrams (100 mg) of powdered material from each of 10 samples in control and 10 in the test groups was analyzed for secondary metabolites (total alkaloid, polyphenol, and flavonoid) at the Oxidative Stress Centre at CPUT.
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 4 Examples of Gas Jet Density Measurements
### Piezo Gas Jet for Free Electron Laser Beam Instrumentation  
Gas-based monitors are attractive for beam instrumentation, as they can be used to build quasi-noninvasive diagnostics in free electron lasers. The low density of the active medium allows the electrons and photons, respectively, to pass the monitor with only minimal impact on the beam. The gas atoms are ionized by the beam, and the photoelectrons and/or ions can be characterized by appropriate detectors. Piezo gas jets can deliver these atoms directly into the beam line vacuum. When combined with a turbo-molecular vacuum pump and used with microsecond opening times [22], these systems can be operated without the need for beam windows.  
SwissFEL is equipped with a Terahertz streak camera for measurements of the X-ray pulse length and arrival time [23]. In this device, the X-ray pulses ionize xenon atoms, and the photoelectrons are streaked by an externally-applied terahertz field [24]. For the X-ray pulse length measurement of SwissFEL, the knowledge of the gas jet diameter determines the maximum interaction length, which is used to assess the Guoy phase shift of the terahertz wave [25].  
The interaction point of the X-ray pulse and the exit nozzle of the gas jet is given by the size of the ion or electron spectrometers, respectively. We have thus set up a measurement of a piezo gas jet (Amsterdam Piezovalve) with an opening time of 7 \(\upmu\)s. We present here measurements performed 30 mm away from the nozzle. In order to measure the gas density up to the relevant distance, the piezo valve is mounted on a linear vacuum stage. The opening time of the valve is set to 55 \(\upmu\)s to measure the static gas flow without opening and closing effects of the valve (exposure time of CCD: 30 \(\upmu\)s). Figure 13 shows the radial density distribution, at distances of 5 mm-29 mm from the piezo valve, at 6 bar backing pressure. In the same figure, the full width at half maximum of the radial distribution is shown. The gas expands linearly with a half opening angle of 9\({}^{\circ}\) in the considered region. Figure 14 depicts the central density of the gas flow. After expanding over a distance of almost 30 mm, the density is reduced by a factor of 20 down to 1.5 \(\times\) 10\({}^{15}\) cm\({}^{-3}\).  
Figure 12: Reconstruction times and speed-up using CPU, multi-core CPU and GPU.  
Similar methods may be used to study the electron beam that is used to generate the X-rays in a free electron laser. The tunnel ionization rate of the gas atoms depends on the peak current in the beam, which is a key parameter for the amplification in the FEL. The knowledge of the gas density, and its distribution, are important for the understanding the data.