esa-sceva/satcom-chunk-collection huggingface dataset:
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 3 Study of the Receiver Criticalities and Workflow for the Upgrading Feasibility Study
### Mitigation of the RFIs Derived from the Surrounding Area
On the other hand, with regard to the L-band, data collected by SRT using the L-band receiver and its BPF 1250-1820 MHz (model 5B120-1540/T520-O/O from K&L), and the Rohde and Schwarz FSV40 spectrum analyzer as back-end, are plotted in Figure 10. The highest RFI signals, into the portion of the L-band covered by the receiver, are due to:  
* Italian army radars (1310-1370 MHz);
* Global Positioning System (GPS) bands (the GPS L1 band 1575.42 MHz with a bandwidth of 15.345 MHz, the GPS L2 band 1227.6 MHz with a bandwidth of 11 MHz and the GPS L5 band 1176.45 MHz with a bandwidth of 12.5 MHz);
* Self-produced signal coming from the focus selector of SRT (1499 MHz) [20];
* Radio communication links (1620 MHz);
* Mobile communications (1810-1880 MHz) [48; 49]. Figure 9: Spectra with data collected by SRT using the P-band receiver, with the selection of the BPF centered at 357.5 MHz with a bandwidth of about 120 MHz (model 5B340-357.5/T120-O/O from K&L), and using the Rohde and Schwarz FSV40 spectrum analyzer as back-end. Analyzing Figure 10, the L-band receiver bandwidth is also busy with disturbing signals. In addition, because the BPF is a five-pole filter and its S21 curve does not present an edge band with very steep drop, several out-of-band signals are detected by SRT and turn out a trouble for the scientific research. Based on these considerations, in this feasibility study we present the features of a possible new filter that can replace the old filter of the receiver filter selector block, in order to improve the whole system. This part is addressed in Section 4 (results and discussion section).
## 3 Results and Discussion
### Potential Differences in VOCs in 'hongjiang' Obtained by Different Factors  
This study used S plots to identify chemical differences between two groups of samples and identify metabolites of statistical and potential biochemical significance, with the points at the ends of the \"S\" indicating the variables with the largest contributions to the model. In contrast, the variables that contributed less were clustered around the origin [25]. To more deeply explore the differential VOCs in the samples obtained from the three factors regarding the results of the OLPS-DA model analysis, this study focused on the comparative study of the differences between PRHN and PRGN, PRGN and PRNN, PROY and PQOY, and PRNN and PCON, as shown in Figure 3. The red dots in the graph indicate metabolites with VIP values exceeding 1. Larger VIP values correspond to larger differences between the two factors.  
As shown in Figure 3, there were 17 differential VOCs in 'hongjiang' obtained from half-net room (PRHN) and little-net room (PRGN) cultivation modes, of which four compounds had VIP \(>\) 2, namely, valencene (80), limonene (14), 3,7-dimethyl-2,6-ocatdenial (50), and intermedeol (95). Nine differential VOCs were present in the little-net room (PRGN) and big-net room (PRNN) oranges, of which the 3 compounds with VIP \(>\) 2 were valencene (80), limonene (14), 4a-dimethyl-6-(prop-1-en-2-yl)-1,2,3,4,4a,5,6,7-ocatabrornaphthalene (76). Ten differential VOCs were present in Red River Orange Team 38 (PROY) and Qingping Township (PQOY), of which two compounds with VIP \(>\) 2 were limonene (14) and valencene (80). Twelve differential VOCs were present in the large-net room (PRNN) and open-air cultivation (PCON) products, of which three compounds with VIP \(>\) 2 were 4a-dimethyl-6-(prop-1-en-2-yl)-1,2,3,4,4a,5,6,7-ocatabrornaphthalene (76), limonene (14), and Selina-4,11-diene (78).
# Uniform _vs._ Nonuniform Membership for Mildly Context-Sensitive Languages: A Brief Survey
## 4 Previous Results  
We are now equipped to take a closer look at the complexity of parsing for mildly context-sensitive languages. While the formalisms described above differ in how much they can express, the classes of languages they define all fulfill the following criteria for mild context-sensitivity, suggested by [PERSON].  
1. They extend the context-free languages.
2. They have the constant growth property, _i.e._, the difference in length between a word and the next shorter word is bounded by a constant. (Formally, a language \(\mathcal{L}\) has the constant growth property if there is a constant \(c\) such that for every string \(u\in\mathcal{L}\), if the length \(|u|\) of \(u\) is larger than \(c\), then there is a string \(v\in\mathcal{L}\) such that \(|u|>|v|\) and \(|u|-|v|\leq c\).)
3. They can be parsed in polynomial time.  
Here, it is the polynomial time parsing that interests us in particular. The fact is that while the nonuniform membership problem associated with any of the formalisms is solvable in polynomial time, the _uniform_ membership problem is only known to be polynomial for some of the formalisms.  
For TAGs and similar formalisms, the uniform membership problem is solvable in polynomial time. Concretely, there are algorithms that run in time \(\mathcal{O}(n^{6}\cdot p(|G|))\), where \(n\) is the size of the input string, \(p\) is a polynomial and \(|G|\) is the size of the grammar. For LCFRSs, the situation is different. The best known algorithms for their uniform membership problem have a running time of \(\Theta(|G|\cdot n^{f\cdot(r+1)})\), where \(G\) is the grammar, \(n\) again the length of the string, and \(f\) and \(r\) are the fan-out and rank of the grammar, respectively [9; 15; 16]. For an excellent overview of the available algorithms for parsing mildly context-sensitive languages, see the book by [PERSON] [17].  
In fact, the uniform membership problem for LCFRSs is PSPACE-complete even if the rank is fixed to 1 (1-LCFRS) [18] and NP-hard even if the fan-out is fixed to 2 (LCFRS(2)) [19]. This shows that parsing for such grammars is a far from trivial problem.  
Since the languages generated by DTWTs are the same as those generated by LCFRSs, we know that each such language has a polynomial time nonuniform membership problem. Not much has been written, however, about the uniform membership problem for DTWTs. It is rather easy to show that it is in fact EXPTIME-complete. Membership follows by standard automata-theoretic techniques. For the hardness result, it is already known that the emptiness problem for deterministic tree-walking automata is EXPTIME-hard; see, e.g., [20]. The only remaining step is to argue that a  
Figure 1: An illustration of a LCFRS derivation. The rule \(A\to\varphi(B,C)\) has rank two. Nonterminal \(B\) has fan-out 3, while \(C\) has fan-out 2.  
small technical difference in the definition of the transducer part of DTWTs and tree-walking automata can be overcome by an encoding trick.
## 2 Materials and Methods
### Application of Image Processing for Field Test  
Digital image processing is the implementation of computer algorithms for manipulation and extraction of information from a digital image. A digital image is a numerical representation of a two-dimensional image that can be defined as a function f(x,y) where x and y are spatial coordinates, while f is the value in the coordinates (x,y) [14].  
In this research stage, the image processing method was employed to extract the quantitative data from the image (frames from video). The image processing was performed according to the procedure in [15]. In general, there were two main processes. The first one was the UV count extraction based on optical character recognition (OCR). The second one was the UV pixel percentage calculation based on facular area.  
The image processing required the video as input data. The video was converted to frames. The image on each frame was then processed. The image was an 8-bit image with a size of 768 \(\times\) 576 pixels and comprised 3 components, namely red, green, and blue (RGB). In the mathematical calculation, each component was represented as a matrix I with a size of 768 \(\times\) 576. Since the image was 16-bit data, there were 28 or 256 value levels for each element in the matrix. In this case, 0 was the minimum value and 255 was the maximum value. In addition, there were binary images that were involved in this image processing where there were only two values for each element in the matrix, namely 0 and 1.
## 4 A Coal Rock Strength Prediction Model Based on the AdaBoost Ensemble Method
### Model Structure and Prediction Process
#### 4.1.2 Base Learner  
Given the training dataset (X\({}_{1}\), Y\({}_{1}\)),...,(X\({}_{N}\), Y\({}_{N}\)), where Yi \(\in\) {\(-1\), \(1\)} represents the class labels of the training samples for i = 1,..., N, do the following:  
(1) First, initialize the weight distribution for the training data. Each training sample is assigned the same initial weight: w\({}_{i}\) = 1/N. Therefore, the initial weight distribution of the training sample set D\({}_{1}\)(i) is as follows:  
\[D_{1}(i)=(w_{1},w_{2},\ldots w_{N})=(\frac{1}{N},\ldots,\frac{1}{N}) \tag{10}\]  
(2) Perform iterations for t = 1, 2,..., T.  
a. Select a weak classifier h with the lowest current error rate as the t basic classifier H\({}_{\upsilon}\) and calculate the weak classifier ht:X\(\rightarrow\){\(-1\), 1}. The error of this weak classifier on the distribution D\({}_{t}\) is as follows:  
\[e_{t}=P(H_{t}(x_{i})\
eq y_{i})=\sum_{i=1}^{N}w_{i}I(H_{t}(x_{i})\
eq y_{i}) \tag{11}\]  
b. Calculate the weight of the weak classifier in the final classifier, denoted by alpha (\(\upalpha\)).  
\[\alpha_{t}=\frac{1}{2}\ln\left(\frac{1-e_{t}}{e_{t}}\right) \tag{12}\]  
c. Update the weight distribution D\({}_{t+1}\) of the training samples:  
\[D_{t+1}=\frac{D_{t}(i)\exp(-\alpha_{t}y_{i}H_{t}(x_{i}))}{Z_{t}} \tag{13}\]  
Figure 6: AdaBoost algorithm framework.  
where \(Z_{t}\) is the normalization constant: \(Z_{t}=2\sqrt{e_{t}(1-e_{t})}\)  
(3) Finally, combine the weak classifiers based on their weights, denoted as \(\alpha\).  
\[f(x)=\sum\limits_{t=1}^{T}\alpha_{t}H_{t}(x) \tag{14}\]  
By utilizing the sign function, the weak classifier's sign information is obtained, which contributes to the formation of a strong classifier:  
\[H_{final}=sign(f(x))=sign(\sum\limits_{t=1}^{T}\alpha_{t}H_{t}(x)) \tag{15}\]
# Steering Pull Model and Its Sensitivity Analysis
## 3 Steering System Model
### Steering Column Model  
A steering column is located between the universal joint and the steering wheel. In the case of a column type electric power steering (C-EPS) system, the steering assist components, such as a torque sensor, a worm gear, and an electric motor, are connected to the steering column. The worm gear that consists of a worm shaft and a worm wheel is basically designed to work under the condition in which their pitch circles are mating each other [19]. However, as the vehicle goes on a bumpy road, the worm gear is subjected to vibration which causes the mismatch of the pitch circles so that undesirable rattling of the worm gear occurs. For this reason, an anti-rattle spring (ARS) is usually employed in the C-EPS system [19].  
An ARS applies preload to the worm shaft, and this preload causes misalignment and friction to the worm shaft [20]. The friction appears in two types--Coulomb friction and viscous friction. Coulomb friction is represented by a linear function of the ARS constant while viscous friction is represented by an exponential equation of the steering rate [21]. The entire steering column including steering assist components can be modeled by the following Equation (15).  
\[J_{C}\ddot{\theta}_{C}+B_{C}(\dot{\theta}_{C}-\dot{\theta}_{S})+K_{C}(\theta_{ C}-\theta_{S})=T_{wh}-T_{TF}\cdot sgn(\dot{\theta}_{S})\]  
\[T_{CF}=CK_{ARS},\ \ T_{VF}=a\dot{\theta}_{S}^{b},\ T_{C}=K_{C}(\theta_{S}-\theta_{ C}),\ T_{TF}=T_{CF}+T_{VF} \tag{15}\]  
where \(J_{C}\) = inertia coefficient of steering column, \(\theta_{C}\) = steering column angle, \(B_{C}\) = damping coefficient of steering column, \(K_{C}\) = stiffness of steering column, \(\theta_{S}\) = steering wheel angle, \(T_{wh}\) = steering wheel torque, \(T_{TF}\) = total friction torque of steering column, \(T_{CF}\) = Coulomb friction of steering column, \(C\) = coefficient of Coulomb friction, \(K_{ARS}\) = ARS stiffness, \(T_{VF}\) = viscous friction of steering column, \(a\),\(b\) = coefficient of viscous friction.
## 2 Background  
### Virtual Environments  
Virtual environments offer various forms of interaction and immersion, which allows them to be considered in various fields of application involving simulation and training under different scenarios represented by virtual worlds. In this sense, we can find several classifications of these environments.  
* Augmented Reality (AR) virtual environments provide the user with a semi-immersive, non-intrusive experience and have a wide range of applications such as video games [23], in construction [24], and applications for mobile devices [25], object tracking and recognition, among others. The glasses contain a screen placed in front of the human eye and produce a virtual image, allowing the user to naturally experience a mixture of virtual information from the real world [26].
* Virtual Reality (VR), according to [PERSON] [27], is defined as a digital environment generated by a computer that can experience and interact as if that environment were real. Virtual reality environments contribute to the presence and telepresence [27]; this refers to the feeling of being in an environment in an immersive way. This technology is widely applied to different fields such as entertainment [13], education [14], and healthcare [15]. VR encompasses a variety of users, such as older adults [16] where the use of VR environments is suggested to improve balance and mobility compared to conventional and untreated interventions, and children and youth where the usability of VR is studied in an educational gaming context. Some evaluations show that VR games are useful and enjoyable in students [17].
* Mixed Reality (MR), according to [PERSON] et al. [28], is classified into four strands: that which may or may not include VR, a combination of AR and VR, the ability to combine the two technologies in one system or mobile device, and as an enhancement of AR. That is, it includes physical interaction with virtual elements.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 1 Introduction
### Related Works  
#### 1.2.1 Environmental Conditions  
Several works investigate the connectivity of VANETs operating in dynamic environments such as urban, suburban, and city areas having highways with high mobility, fading channels, and shadowing effects, which typically affect communication links of vehicle nodes [1]. To understand these environmental conditions, ref. [2] authors present an obstacle-based radio propagation model that considers the effect caused by the presence of obstructing vehicles in the Line-of-Sight model. They conducted experiments to evaluate the model under different environmental conditions (city, highway, and rural) by varying vehicle density to study the effect of throughput operation and packet collision considering environmental factors for multi-channel dedicated short-range (DSRC) based vehicle-to-vehicle (V2V) communication. A mesoscopic vehicular mobility model in a multiple-lane highway with a steady-state traffic flow condition is adopted where different inter-vehicle distance distributions are utilized. Authors in [3] propose using the spatial point processes to model random vehicle locations in a two-dimension instead of the one-dimension in small and large cities. Works in [4; 5] investigate vehicle connectivity probability in dynamic environments. Authors in [4] consider the different traffic conditions: high, medium, and low density based on the V2V measured data at 5.9 GHz, then simulate the connectivity of the VANETs. The work in [6] considers both cognitive andcommunication probability to derive cellular vehicular connectivity probability. However, most of these works do not consider the general channel effects that both fading and shadowing have on vehicle communications.
## 2 Concept
### Single- and Multi-Target Scenarios  
Figure 2 shows a schematical drawing of the proposed antenna arrangement for enhancing the cross-range resolution for three antennas and corresponding coding filters in a non-closed setup. While the different beam colors represent the spatial distribution of the antenna arrangement, overlap beams are colored in the corresponding mixed color. The inserted stars represent radar targets in different range resolution cells I to V. This chosen scenario perfectly illustrates all five occurring target interference cases. For the discussion of these cases, we can assume in good approximation that all radar targets provide equal RCSs \(\sigma\) and do not interfere with each other.
# UAV Support for Mission Critical Services
## 4 Potential Roles of UAV in MCS  
In order to define any system architecture, some usage scenarios are typically outlined. These scenarios are used to form the requirements related to the developed system. In the context of MCS, there have been two main scenarios identified in which UAVs may play different roles:  
* zin the case of properly working infrastructure, the UAVs as a part of a MCS can be deployed to extend the coverage and to provide MCS-specific services and platforms for their deployment. In such a case UAVs can be used for rescue action or to perform a dedicated task; however, they may benefit from the connectivity and services offered by the infrastructure.
* If the infrastructure is not functioning correctly (in a case of disasters, power grid failure, etc.), the UAVs can be used for providing ad hoc connectivity or as an ad hoc service platform. In this scenario, it is assumed that UAVs' computing capabilities will be used for MCS services and other purposes (support for Smart City, etc.). Both scenarios will be described in detail in the subsequent sections.  
Figure 3: MEC-enabled UAVs combined with NTN (an example).
## 5 Results and Discussion
### DC Condition (0 Hz)  
Under DC, the current distribution is uniform across each segment's cross section, unaffected by skin or proximity effects [4]. Table 4 shows the extracted total power loss at 0 Hz and total \(R_{\text{DC}}\) (in Ohms) for the 14 models. As each model has an effective length of \(l_{\text{twist}}\), the per-meter DC resistance \(R_{\text{DC-2D}}\) is calculated by dividing the total \(R_{\text{DC}}\) by \(l_{\text{twist}}\). Naturally, the numerically calculated per-meter \(R_{\text{DC-2D-cal}}\) is constant at 6.36 \(\mu\Omega/\text{m}\). In all cases, \(R_{\text{DC-2D}}/R_{\text{DC-2D-cal}}=1.0\).  
Physically, the difference in total power loss (in Watts) between small \(\lambda\) and large \(\lambda\) arises because the helix path is longer for larger \(\lambda\) within each segment. However, when expressed as per-metre (i.e., normalising by \(l_{\text{twist}}\)), the \(R_{\text{DC-2D}}\) is the same for all models, confirming no difference in DC conduction (0 Hz) [1].  
Figure 8: Colour map of the current density \(J\) (A/m\({}^{2}\)) at 50 Hz for the 5-segment model in 2D.
# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 3 Proposed Method
### Task Description  
In this work, we aim to detect 3D bounding boxes of vehicle objects in RGB images using the YOLOv7-3D model. Given an input image \(I\in\mathbb{R}^{H\times W\times 3}\), camera intrinsic parameters \(K\in\mathbb{R}^{3\times 3}\), and camera extrinsic parameters \(P\in\mathbb{R}^{3\times 4}\), the output of the YOLOv7-3D model provides 3D bounding boxes for the traffic objects in the image, which can be represented as \(B=\{B_{1},B_{2},\ldots,B_{n}\}\), where each bounding box \(B_{i}\) consists of seven degrees of freedom:  
\[B_{i}=(x_{i},y_{i},z_{i},l_{i},w_{i},h_{i},\theta_{i}) \tag{1}\]  
Here, \((x,y,z)\) represents the position of the center point of each 3D bounding box in meters. \((l,w,h)\) denote the length, width, and height of the cuboid, respectively, in meters. \(\theta\) represents the global orientation of each traffic object in space, which is the angle between the object's heading direction and the \(x\)-axis of the camera coordinate system, in the range of \([-\pi,\pi]\).  
The camera intrinsic parameters \(K\in\mathbb{R}^{3\times 3}\) include parameters such as focal lengths, optical centers, and pixel sizes, which describe the optical characteristics and imaging process inside the camera. \(f_{u}\) and \(f_{v}\) are the focal lengths, while \(c_{u}\) and \(c_{v}\) represent the pixel coordinates of the optical center.  
\[K=\left[\begin{array}{ccc}f_{u}&0&c_{u}\\ 0&f_{v}&c_{v}\\ 0&0&1\end{array}\right] \tag{2}\]  
The camera extrinsic parameters describe the camera's position and orientation in the world coordinate system. It consists of the translation vector \(T\in\mathbb{R}^{3\times 1}\) and the rotation matrix \(R\in\mathbb{R}^{3\times 3}\), which map points in the camera coordinate system to the world coordinate system.  
For a 3D point \(X=[x,y,z]^{T}\), it can be projected onto the image plane as \(Y=[u,v,1]^{T}\) using the camera projection matrix:
## 3 The Proposed Scheme
### Data Transmission  
In this study, data transmission begins after the tree structure is constructed. Each cell head collects sensing data from nodes in the same cell. After the cell head collects the data, they are transmitted along the paths of the tree structure, and finally transmitted back to the BS making a complete round of data transmission. At the beginning of each round, the selection and tree structure of the cell head is reset. In the proposed GB-PEDAP, by re-selecting the cell head mechanism to make the energy consumption of the cell head more uniform, the excessive energy consumption of some sensors can be avoided.
## 3 Results and Discussion
### Morphology Analysis of LTO  
In order to observe the morphology, the LTO materials were characterized using SEM. Figure 2 shows different magnifications of SEM images for LTO powder with various amounts of NaCl addition via the solid-state reaction. The LTO particles exhibit micro sizes ranging from 1.83 to 2.28 \(\upmu\)m, and all samples display a cubic morphology with a smooth surface. Moreover, previous studies reported that at certain concentrations, salt can prevent the agglomeration of particles during the sintering process [37; 38; 54]. Based on SEM images, the agglomeration of LTO particles is occurred without the NaCl addition (Figure 2a). With the NaCl addition of 2-4 wt %, only a slight agglomeration is formed, indicating an effective inhibition of agglomeration using salt. However, the increasing NaCl addition to 6 wt % lead to more agglomeration of LTO particles as referred by [PERSON] et al. [54]. Based on SEM images, it is predicted that LTO with a slight agglomeration will have better electrochemical performances.