esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Based on the problem of electromagnetic interference in the darkroom of connected vehicle communication systems, a research method is proposed to evaluate the wireless communication quality between antennas using gain coupling. The electromagnetic compatibility of the far-field radiation pattern, radiation coupling degree, and near-field radiation pattern of the vehicle antenna and the vehicle antenna model were analyzed by establishing a 3D simulation model of the vehicle antenna and the external transmitting antenna. Based on the obtained simulation data, the arrangement scheme of the transmitting antenna in the darkroom can be determined. The performance index of the automotive OTA antenna can then be tested using the determined arrangement scheme, which can effectively reduce costs and provide reliable data.  
automotive OTA antenna; antenna gain coupling; antenna radiation simulation; electromagnetic compatibility 1
## 2 Single-Camera Trilateration Algorithm
### Notation and Assumptions
The pinhole model is established on the basis of the calibrated camera parameters and corrected image. In the following derivation, we assume that the image distortions have been corrected according  
Figure 1: Pinhole camera model. This figure is adapted from Figure 1 in [58]. (c) (2007) IEEE. Reprinted, with permission, from [[PERSON]; [PERSON] Recovering the position and orientation of a mobile robot from a single image of identified landmarks. In Proceedings of 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007; pp. 1065–1070.1). to the calibrated distortion coefficients. We also assume that at least three landmarks have been captured, segmented, identified and located at \({}^{m}\mathbf{q}_{i}\) in UV. Relevant image processing techniques can be found in existing literature [59; 63; 64]. Moreover, we assume that the positions of the involved landmarks in the global frame \(\mathrm{X_{g}Y_{g}Z_{g}}\), \({}^{s}\mathbf{p}_{i}\), have been retrieved from a map of known landmarks using existing methods [53; 54; 55; 56; 57]. The camera position is defined by the position of its optical center \(\mathbf{p}_{0}\) in \(\mathrm{X_{g}Y_{g}Z_{g}}\), \({}^{s}\mathbf{p}_{0}\), and the camera orientation is defined by the rotation matrix of \(\mathrm{X_{c}Y_{c}Z_{c}}\) with respect to \(\mathrm{X_{g}Y_{g}Z_{g}}\), \({}^{s}\mathbf{R}_{c}\). The proposed algorithm will estimate \({}^{s}\mathbf{p}_{0}\) and \({}^{s}\mathbf{R}_{c}\), which will be discussed in the following two subsections respectively. The following relationships obtained from the pinhole model will be used in the derivation:  
\[{}^{s}\mathbf{p}_{i}={}^{s}\mathbf{R}_{c}{}^{c}\mathbf{P}_{i}+{}^{s}\mathbf{P }_{0}, \tag{2}\]  
\[{}^{c}\mathbf{P}_{i}={}^{c}\mathbf{q}_{i}\frac{c_{z}}{f}, \tag{3}\]  
\[{}^{c}\mathbf{q}_{i}=\left[ \begin{array}{cc}\frac{u_{i}-u_{0}}{D_{u}u_{0}}& \frac{v_{i}-v_{0}}{D_{v}}&f\end{array} \right]^{T}, \tag{4}\]  
where \(u_{i}\) and \(v_{i}\) denote the U and V components of \({}^{m}\mathbf{q}_{i}\) respectively.
## 3 Results
### Predictive Analysis of SEU Incidence in Space Environment  
We conducted a predictive analysis of SEU incidence in a space environment based on the irradiation test results using Cosmic Ray Effects on Micro Electronics (CREME) [23], which is an analysis software package developed by the Naval Research Laboratory for predicting the error occurrence probability in a space environment. Specifically, the SEU incidence for an LEO with a 400 km altitude was calculated by inputting the test data into this software package. First, the Weibull-curve characteristic parameters were obtained to input to CREME by constructing LET-\(\sigma\) graphs based on the test data. The equation used for the Weibull curve was as follows [21]:  
\[\sigma=\sigma_{0}\times\left\{1-\exp\left[\left(-\frac{L-L_{0}}{W}\right)^{S} \right]\right\} \tag{2}\]  
where \(\sigma_{0}\) is the saturation upset cross-section [cm\({}^{2}\)/bit]; \(L\) is the LET [MeV/(mg/cm\({}^{2}\))]; \(L_{0}\) is the threshold LET [MeV/(mg/cm\({}^{2}\))]; \(W\) is the scale parameter [MeV/(mg/cm\({}^{2}\))]; and \(S\) is the shape parameter. Figure 9 shows a graph of the LET--\(\sigma\) characteristic curve fitted with Equation (2) and Table 7 lists the SEU curve parameters calculated from Figure 9.  
Next, we evaluated parameters, such as \(\sigma_{0}\) and \(L_{0}\), listed in Table 7, using CREME [23]. As the input to CREME for the environmental conditions relating to space radiation, we used conditions 1-4 in Table 8 for the solar activity and ge  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weibull Curve Parameter** & **Value** \\ \hline Saturated inverted cross-section [cm\({}^{2}\)/bit] & \(2.5\times 10^{-7}\) \\ Threshold LET [MeV/(mg/cm\({}^{2}\))] & 5.0 \\ Scale parameter [MeV/(mg/cm\({}^{2}\))] & 480 \\ Shape parameter & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SEU Weibull curve parameters.  
Figure 9: LET-SEU upset cross-section characteristic curve (wireless module: CC1120).  
incidence prediction results are given in the same table. Condition 1 corresponded to the strongest radiation in space environment; i.e., the least favorable condition. From Table 8, the probability of SEU incidence in the wireless communication module was \(2.64\times 10^{-9}\) [SEU/bit/day] for Condition 1, according to CREME. The test result for the wireless communication module was \(1.08\times 10^{-5}\) [SEU/device/day], suggesting that SEU incidence for a single device was approximately once in 3000 months. From the above results, the module radiation tolerance is considered sufficient for practical use in space environment, if the SEU occurrence frequency is at approximately this level.
# Adaptability of Buildings: A Critical Review on the Concept Evolution
## 4 Discussion and Open Questions
process flexibility and multi nature enablers along the whole value chain to facilitate implementation of design enablers. 2. What is the role of modern-day technology and intelligent systems in promoting existing design practices, assessing performance, and managing information throughout the whole lifecycle of a building? In this regard, multiple authors emphasized the role of BIM in facilitating the future adaptability of buildings, allowing access to more efficient and accurate information regarding the construction process, materials properties, repair and maintenance, and deconstruction planning [80]. However, some studies still perceive that the use of BIM for such purposes is quite challenging and requires further improvements to achieve further efficiency [67]. Modeling of various scenarios relates to different variables to choose the most efficient adaptability solution, e.g., in [115]. 3. To what extent can adaptability be implemented into existing buildings, bearing in mind they constitute a large part of future stock? Extending the life of the existing stock as a hub of anthropogenic materials and embodied energy through more efficient reuse is a global concern derived by increasing demand volatility together with sustainable development agenda [120]. 4. Potential future research may also address the temporary adaptations of spaces posed by the actual challenges due to the current COVID-19 emergency. The current challenge of COVID-19 further highlights the necessity of creating buildings of high response capacity to changeable circumstances, environments, and demography [41; 121], which should be examined in consideration of other aspects that might affect the new normality of the built environment in post-COVID-19 society.
## 1 Introduction
For flexible, long-span structures, structural and economic considerations were involved for the configuration of dampers. The optimization of dampers parameters was essential to ensure the limited vibration amplitude below the specified threshold [18; 19]. Besides, though galloping was always observed in cable structures, due to its complexity, galloping in a long-span CRS will be discussed in a further study. Figure 1: Long-span cross-rope suspension overhead transmission line. The CRS transmission line system was composed mainly of conductors, suspension cables and insulators. The suspension cable of CRS, which was fixed in a rigid foundation, was the main bearing structure of CRS. In order to cross the valley, the suspension cable was made of high-strength-stranded steel wire, and it spanned beyond thousands of meters. The conductors were connected to the cable by wire fittings. For the multiphase conductor, tension insulators were adopted to separate conductors from each other. A Diagram of the CRS transmission line system is shown in Figure 2.  
In this paper, the recommended design parameters of long-span CRS are shown in Table 1.  
For the aim of structural assessment of applying a CRS transmission line system in a mountainous area, structural responses induced by wind load were discussed in this paper. Based on the assembly modeling approach, all components of CRS were separately modeled, analyzed and finally assembled with their deformed shape and loading states. With static wind load, lateral displacement of conductor and tension of suspension cable was calculated. Considering ice-covered conductors in cold climates, by the same method, the results of bare and ice-covered cases were compared. Moreover, based on the dynamic properties of CRS, wind-induced vibration and its controlling of CRS were simulated. The effect of a Stockbridge damper on the overall vibration of CRS was studied by theoretical approach and FEM. For the needs of practical application, the factors, including configuration and mass of the Stockbridge damper, span length of the suspension cable and conductor and the number of spans of the conductor, were assessed for further discussion on vortex-induced vibration controlling of CRS.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 1 Introduction
Massive MIMO (m-MIMO) [17] deploys a massive number of transmitter antennas on the BS and serves a cell with a large number of terminals in the same time-frequency resource, separated in the spatial domain by receiving very directive signals [18]. In addition, it explores multipath propagation in order to boost overall data rates. Therefore, it represents one of the key technologies deployed in 5G wireless communication standards, providing high diversity and beamforming gains and spatial multiplexing of users and, hence, granting an increase on the throughput, reliability, and spectral and energy efficiency with simple signal processing, while reducing the inter-cell interference [19; 20]. In [21] are presented in more detail the five disruptive technologies that can lead to both architectural and component design changes in 5G systems. From the physical layer (PHY) perspective, modulation and waveform design is one of the most important aspects that plays a major role in fulfilling the 5G and beyond systems requirements. Because the current 4G long term evolution (LTE) communication systems are limited and cannot fully meet the previously mentioned objectives, an highly flexible air interface design must be identified, being capable of supporting mixed services with different waveform parameters [22; 23]. Therefore, tremendous research and development of new suitable candidate waveforms for future cellular networks have been performed. The goal of this paper is to provide a fair comparison between several candidates waveforms that are expected to be implemented in the next generation of wireless communications, 6G. After a brief introduction to the waveforms, the evaluation is performed by measuring some KPIs in a similar downlink transmission scenario. The main contributions of this paper are then:  
* To provide a detailed overview of some promising multi-carrier waveforms. * To perform an analysis between these waveforms regarding the peak-to-average power ratio (PAPR), power spectral density (PSD) and computation complexity. * To derive a performance comparison between the waveforms in typical channel models. The outline of this paper is as follows. Section 2 lists and explains the 5G and beyond network requirements that must be fulfilled by candidate waveforms. Section 3 presents a detailed description of the transceiver architecture of the main candidate waveforms being proposed for beyond 5G, while in Section 4 these are compared. Section 5 concludes the paper, drawing the main conclusions. Throughout this paper the following notation will be employed: capital bold lettering (e.g., \(\mathbf{S}_{k}\)) is used to refer a block/vector of samples at the frequency domain, and lower-case bold lettering (e.g., \(\mathbf{s}_{n}\)) to denote a block/vector of samples at the time domain, while non-bold capital (e.g., \(\mathbf{\zeta}_{k}\)) or lower-case lettering (e.g., \(\mathbf{s}_{n}\)) are used to denoted the symbols/samples of each of those block/vectors, respectively.
# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Proposed Fusion Network for Change Detection with Panchromatic Imagery
### Training of the Proposed Fusion Network for Change Detection  
During the training stage, this paper introduced a loss function (\(L\)) by combining the contrastive loss (\(E_{c}\)) [36] and weighted binary cross entropy loss (\(E_{B}\)) as defined by:  
\[L=\alpha E_{c}+(1-\alpha)E_{B} \tag{3}\]  
where \(\alpha\) is a weight loss. Given a training set consisting of \(40\ \times\ 40\) image pairs and a binary label of the ground truth (\(\Upsilon\)), the proposed network was trained using backpropagation. Here, \(E_{c}\) was applied to optimize the parameters of \(N^{1}\) and \(N^{2}\), and is as computed as follows [36]:  
\[E_{c}=\sum_{i}(1-y_{i})L_{S}(D_{i})+(y_{i})L_{D}(D_{i}) \tag{4}\]  
Figure 3: The proposed fusion network architecture for change detection.  
where \(y=1\) is a changed pixel and \(y=0\) is an unchanged pixel. In addition, \(Ls\) is a partial loss function for a pair of similar pixels, and \(L_{D}\) is a partial loss function for a pair of dissimilar pixels, as defined by [36]:  
\[Ls=\frac{1}{2}(D_{i})^{2} \tag{5}\]  
\[L_{D}=\frac{1}{2}(max\{0,m-D_{i}\})^{2} \tag{6}\]  
The value of \(m\) is set to 1 as the margin value. In addition, \(E_{B}\) was used to optimize the parameters of \(N^{3}\), as defined by:  
\[E_{B}=-\sum_{i}W_{i}(y_{i}\log(O_{i})+(1-y_{i})\log(1-O_{i})) \tag{7}\]  
where \(W\) is the proposed weighted function used to penalize the false-positive and false-negative errors. Thus, \(W\) is computed by:  
\[W_{i}=y_{i}\bigg{(}\beta_{c}\bigg{(}1-\frac{C}{N}\bigg{)}\bigg{)}+(1-y_{i}) \bigg{(}\beta_{u}\bigg{(}1-\frac{U}{N}\bigg{)}\bigg{)} \tag{8}\]  
where \(\beta_{c}\) and \(\beta_{u}\) are penalization weights for false-negative and false-positive errors, respectively. Moreover, \(C\) and \(U\) are the changed and unchanged numbers of pixels in the full dataset (\(N\)), respectively.  
The proposed network was trained using a stochastic gradient descent (SGD) with the training parameters, including 0.001, \(1\times 10^{-6}\), and 0.9 as the learning rate, decay, and momentum, respectively. In addition, the epoch number was set to 30. The value of \(\alpha\) was set to 0.7 to further penalize \(E_{c}\). It was to prevent false positives, which are possible in a back-end network. The goal of prediction through the front-end was to obtain better true-positive rates regardless of the number of false positives. Thus, the false negatives were penalized ten times more than false positives, namely, \(\beta_{c}=10\) and \(\beta_{u}=1\).
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 4 The Proposed NPIVGAE
### The Influence of the Noninformative Prior on the Posterior  
In Section 3.1, we mentioned that our purpose of designing the noninformative prior was to reduce the influence of the prior on the posterior. We take a single Gaussian variable \(s\sim\mathcal{N}(\alpha,\beta)\) as an example to analyze this effect. Assuming that the variance \(\beta^{2}\) is known, we desire to get the solution of the mean \(\alpha\) based on a set of \(t\) sample data \(\mathbf{S}=\{\mathbf{s}_{1},\mathbf{s}_{2},\ldots\mathbf{s}_{t}\}\) and set the conjugate prior distribution \(p(\alpha)=\mathcal{N}(\alpha_{0},\beta_{0}^{2})\). According to [20], the posterior probability of \(\alpha\) is \(p(\alpha|\mathbf{S})=\mathcal{N}(\alpha|\alpha_{t},\beta_{t}^{2})\), of which \(\alpha_{t}=\frac{\beta^{2}}{t\beta_{0}^{2}+\beta^{2}}\alpha_{0}+\frac{t\beta_ {0}^{2}}{t\beta_{0}^{2}+\beta^{2}}\alpha_{L}\) and \(\frac{1}{\beta_{t}^{2}}=\frac{1}{\beta_{0}^{2}}+\frac{t}{\beta^{2}}\). The \(\alpha_{L}\) represents the maximum likelihood estimate of \(\alpha\), and \(\alpha_{L}=\frac{1}{t}\sum_{k=1}^{t}s_{k}\) is given by the sample data.  
When \(\beta_{0}^{2}\) in the prior distribution tends to infinity, the prior distribution \(p(\alpha)\) becomes a uniform distribution, also regarded as a noninformative prior. At this time, the posterior mean is given by the maximum likelihood \(\alpha_{t}\approx\alpha_{L}\), and the variance is \(\beta_{t}^{2}\)=\(\frac{\beta^{2}}{t}\), i.e., \(\alpha_{t}\) and \(\beta_{t}\) have nothing to do with the prior parameters \(\alpha_{0}\) and \(\beta_{0}\). Thus, we can conclude that when the prior of the Gaussian distribution is selected as the noninformative prior, the prior can have the smallest impact on the posterior distribution, and the posterior parameters can be learned almost all from the sample data.
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 4 Sensors
### Remotely Piloted Aircrafts (RPAs)
For this reason, thorough research in this domain is recommended to ensure a reliable application of RS. A recent study undertaken on this aspect of technology offers the implementation of blockchain and smart contracts to safeguard data, which are then archived through IoT-enabled RPAs and sensors, with their respective deployment considerations [143]. Alternative and economical solutions are urgently required to address these issues.
## 3 System Model
### System Overview  
In a traditional centralized network, any node in any network environment can directly access the centralized node and interact with data because the centralized node is always reachable, but in a decentralized network this is changed because there are no centralized nodes.  
In a decentralized network, there are connectivity problems for unreachable nodes as shown in Figure 3. If simple name-to-IP conversion naming system is used only, incoming packets will be blocked due to the presence of NAT devices.  
Figure 3: Unreachable nodes are discarded directly from the packet.  
Figure 2: Overlay networks on top of physical carrier networks.  
This paper combines the physical network properties with the decentralized design philosophy and gives an overview of the system organisation as shown in Figure 4. The red and black points represent reachable and unreachable nodes respectively, and their definitions will be given below.  
* Reachable node: A node directly connected in the public network environment or statically mapped from the public network environment to the internet, which can passively receive packets from other nodes through the node IP address and the specified port.
* Unreachable node: A node that is behind one or more levels of NAT or firewall and cannot be actively accessed by other nodes through any port.  
It is divided into the following parts: node identity, virtual networking, handling of unreachable nodes, routing messaging and data transmission both direct and relay connections.  
Firstly, in order to enable the nodes in the decentralized network to be uniquely addressed, regardless of their geographical location and physical network. The address takes into account both the logical address of the P2P overlay network and the basis for ensuring the security of data transmission in the P2P network.  
Secondly, in order to enable nodes in the network to be connected on a decentralized basis, a virtual overlay network plane is created, and special treatment is given to unreachable nodes so that unreachable nodes can be routed to each other via node addresses.  
Thirdly, the data transmission mechanism of direct connections and third-node relays ensures the efficiency and availability of data transmission.  
In the rest of this section we will describe the threat model and requirements in the system.
# A Systematic Review on Low-Level Laser Therapy in the Management of Shoulder Impingement Syndrome
###### Abstract
In recent years, many systematic reviews about shoulder impingement syndrome treatment have been published; several studies shows that conservative management in the case of SIS is a resolutive solution in 70-90% of subjects. These studies correlated the effectiveness of interventions on different aspects such as pain, range of motion, functional limitations, and return to work. A systematic review by [PERSON] et al. demonstrated that exercise programs and exercise-based treatments such as kinesiotaping and specific exercise and acupuncture are effective therapies for people at an early stage of SIS. [PERSON] et al. also demonstrated that physiotherapy exercises are effective in the management of SIS. Many other studies have shown that the elective treatment for SIS is therapeutic exercise and stretching. The primary objective in the treatment of patients with SIS is to reduce pain and increase range of motion [10]. Other treatments mentioned in the literature include the low-level laser therapy (LLLT) technique. It uses the light energy of a laser to reduce pain and inflammation, accelerates healing of damaged tissue, relaxes muscle, and stimulates nerve regeneration. LLLT has been classified as a safe, non-invasive treatment modality; it constitutes a phototherapy or photobiomodulation, using photons at a non-thermal irradiance to stimulate biological processes. Cells or tissues are exposed to low levels of red and near infrared (NIR) light, and it is defined as \"low level\" because the energy densities of its light are low compared to other types of laser therapy that are used for other conditions. Power densities used in LLLT are lower than those needed to produce the heating of tissue, in fact it is also referred to as 'cold laser' therapy [2]. Recent studies describe LLLT as an additional treatment in the conservative management of individuals with shoulder pain. Nonetheless, the effectiveness of LLLT in reducing pain and improving function in individuals with shoulder pain, and specifically in Shoulder Impingement Syndrome is unclear. The aim of this systematic review was to demonstrate the effectiveness of LLLT in SIS treatment.