esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 UWB Signal Processing Steps
### Target Detection
#### 2.2.1 Path Loss Compensation  
The radar range equation (or simply radar equation) associates the ratio between the received power and the transmitted power with the forth power of the inverse distance to a reflecting point:  
\[\frac{P_{rec}}{P_{T}}=\frac{G_{T}\ A_{e}\ \sigma}{\left(4\pi\right)^{2}}\frac{R ^{4}\ L}{L}\, \tag{11}\]  
where \(P_{rec}\) is the power captured by the receiving antenna, \(P_{T}\) is the transmitted power, \(G_{T}\) is the gain of transmitting antenna, \(A_{e}\) is the effective aperture of the receiving antenna, \(\sigma\) is the radar section, \(R\) is the range (target distance), and \(L\) denotes all losses affecting the radar performance.  
In the development of this range equation, it is assumed that both the radar's transmitter and the target are isotropic radiators. An isotropic radiator does not focus energy; instead it distributes power uniformly over the surface of a sphere centered on the antenna. We note that an isotropic radiator cannot exist in the \"real world\". However, it is a mathematical and conceptual concept that is often used in radar theory.  
Another note is that the development of the above equation makes the tacit assumption that the antenna is pointed exactly at the target, which is not always the case depending on the various positions of the target; otherwise, GT must be modified to account for this.  
All these mathematical assumptions used in the radar theory accept the flexibility in the range power, which can also vary according to the target's physical shape and properties [31]. In this regard, following our experimentation, we have chosen 2 as a power of the range so that the path loss will be compensated with the squared distance of a reflecting point. Therefore, the signal's power at each point was weighted with \(R^{2}\) where \(R\) is the range between the radar and that particular point.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 1 Introduction
The common definitions for channel bandwidth, transmission bandwidth configuration, minimum guard band, and RB alignment in TS 38.104 and TS 38.101-1 can be reused for an NTN satellite system; however, RAN4 analysis and simulation results demonstrated that it is not feasible to implement the Rel.17 NTN for the SCS of UL signals lower than 60 kHz (FR1) since the guard period is insufficient period due to the estimated timing error budget [7]. At first glance, the second approach appears to be more convenient and logical, as 3 GPP has been defining technical specifications for generations of cellular technology for decades, and utilizing satellite mobile service frequencies seems like the most plausible option. However, this approach has several notable flaws, one of which is spectrum assessment. Given that satellite coexistence is a global issue, frequency coordination for satellite networks conducted within ITU-R is a complex and time-consuming procedure. The frequency bands defined by 3 GPP for NTN technologies are already in use by several satellite operators providing mobile communication services, including voice, data, and Internet of Things services. It should be noted that some of these services are critically important for communications. The future NTN satellite 5G-6G networks would require the development of LEO megaconstellations which will have very high activity factors and consequently significantly increase the interference level in these bands for the existing networks. Therefore, it would be challenging for countries wishing to use the n255 and n256 bands to meet the coordination requirements and gain access to this spectrum. In our study, we present an interference analysis using simulated 5G satellite networks with typical characteristics developed by 3 GPP and ITU-R, as well as several satellite networks operating in the n255 and n256 bands, such as Immarsat, Echostar, and Omnispace. The intention of this study is to highlight the compatibility challenges that will be faced by those seeking to utilize the n255 and n256 bands for 5G-6G satellite technologies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NTN Satellite Band** & **Uplink (UJ) Operating Band** & **Downlink (DD) Operating Band** & **Duplex Mode** \\
**Satellite Access Node Receive/UE Transmit** & **Satellite Access Node Transmit/UE Receive** & **Duplex Mode** \\
**F\({}_{\text{UL,low}}\)–F\({}_{\text{UL,high}}\)** & **F\({}_{\text{DL,low}}\)–F\({}_{\text{DL,high}}\)** & **F\({}_{\text{DL,high}}\)** \\ \hline n256 & 1980–2010 MHz & 2170–2200 MHz & FDD \\ n255 & 1626.5–1660.5 MHz & 1525–1559 MHz & FDD \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frequency spectrum in the S-band and L-band (FR1) for NTN usage.
# High-Speed Micro-Grooving of Metal by Angled Irradiation of Single-Mode CW Fiber Laser
## 3 Experimental Results and Discussion
### High-Speed Observation of Processed Area  
High-speed observation of the processed area was conducted in order to analyze molten metal flow and spatter. Figure 5a schematically illustrates the experimental setup to observe the surface of workpiece. The workpiece was moved by the mechanical slider, and the experimental conditions were fixed at the average power of \(P\) = 200 W, incident angle of \(\theta_{i}\) = 45\({}^{\circ}\), and scanning speed of \(V_{s}\) = 2 m/s. Figure 5b,c shows the captured images and its illustrations at the scanning speed of \(V_{s}\) = 1 and 2 m/s. The resolution of these captured images is \(480\times 400\) pixels. The frame rate and the exposure time of camera were 4000 fps and 2 \(\upmu\)s, respectively. The keyhole and the molten metal flow could be recognized as the bright white areas in the images in Figure 5b,c. The white area on the incident side is clearer than that on the reflection side. The volume of molten metal is thought to be larger on the incident side. Note also that the molten metal on the incident side flows toward the center line of the scan path. By contrast, the molten metal flow on the reflection side is almost parallel to the scanning direction. At a point, \(M_{p}\), a certain distance, \(L\), behind the keyhole, the molten metal flow on the incident side reaches the center line of the scan path. The distance, \(L\), increased with increasing scanning velocity of the laser beam, as shown in Figure 5b,c.  
Figure 5: High-speed observation of processed surface.  
Next, to clarify the behavior of the spatter, the processed area on the X-Z plane was observed under the same experimental conditions. Figure 6 shows the experimental setup and results. The frame rate, resolution, and exposure time of the captured image shown in Figure 6 are 23,000 fps, 210 \(\times\) 160 pixels and 1.4 us, respectively. The particles of molten metal clearly spread from the irradiated point and scattered toward the reflection side.
## 3 Results
### Interpretable Machine Learning
#### 3.2.1 Partial Dependent Plot (PDP)  
PDP is one of the interpretable machine learning techniques that enables the interpretation of the relationship between the variable and predicted values of the model or the relationship between variables. Rather than simply showing the importance of a variable, it was possible to understand the degree of contribution to the model according to the change in the variable. In addition, it can be said that it is a technique that makes it easier to interpret the effect of a change in a variable on the model because it can be seen visually through a graph. The formula for PDP follows as Equation (8):  
\[\hat{f}(\mathrm{x}_{i})=E\Big{[}\hat{f}\big{(}x_{i},x_{j}\big{)}\Big{]}=\int \hat{f}\big{(}x_{i},x_{j}\big{)}dP(x_{j}) \tag{8}\]  
Figure 3: The feature importance plot for the selected final model with comprehensively high performance.  
The above formula is a formula for calculating the influence of a variable on the overall model. \(x_{i}\) is the variable that the partial dependency function should visualize, and \(x_{j}\) is the variable used by the model \(\big{(}f\big{)}\). The partial dependence function calculates the difference in the distribution of the model results among various combinations of \(x_{i}\) and \(x_{j}\). Therefore, when \(x_{i}\) is fixed, all values for \(x_{j}\) are reflected in the model and averaged, so that the influence of each variable on the model can be obtained [51].  
The PDP interact plot is a figure that shows partial dependence visually and numerically. By expressing the partial dependence calculation result of two variables on the x-axis and y-axis in a grid format, it is possible to determine how much the change in the two variables affects the prediction result of the model [52]. Figure 4 is the interactive plot of volume and length. In Figure 4, as the length increases, the probability of occurrence of accidents corresponding to each grid square increases, and it can be seen that it remains constant after 200 m. In the case of volume, it can be seen that the probability of an accident increases as the traffic volume continues to increase, and, in particular, it can be seen that the probability of occurrence of an accident is almost 40% in the section with 29,233 veh/day.  
Figure 5 is a graph that calculates the partial dependence of volume and length. As each variable increases, it can be seen that the partial dependence value on the variable of traffic crash increases. Each line corresponds to each sample used for analysis, and it can be seen that as each variable increases, it has a positive effect on the occurrence of accidents.  
Figure 4: PDP interact plot of volume and length.  
However, in the case of traffic volume of about 30,000 veh/day or more, it can be confirmed that the occurrence of accidents decreases after that.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction
### Networking and Network Management
#### 3.3.2 Node Joining and Network Mode Selection  
Setting up a networking trigger condition. The CRI between the first target ship and own ship can be expressed as \(CRI_{TS_{1}}\), and \(CRI_{threshold}\) is a threshold of CRI, when the two indexes meet Formula (4),  
\[CRI_{TS_{1}}\geq CRI_{threshold} \tag{4}\]  
Own ship entered the networking state, so the target ship joined the network as the first node in the network, and own ship began to network with other ships around. We cannot only connect with the target ship whose CRI reaches the above threshold because the collision avoidance action between two ships is likely to have a negative impact on other ships, which is not conducive to the resolution of the situation of own ship. Therefore, we also need to screen the ship objects that are necessary for networking interaction. We set such a condition that for all ships within the communication range of this ship, as long as they meet Formula (5),  
\[\text{CRI}\
eq 0\wedge u(\text{DCPA})\
eq 0\wedge u(\text{TCPA})\
eq 0 \tag{5}\]  
Then such a target ship will join the network as a new node.  
In some narrow waters, such as channels for entry and exit, anchorage (mooring) grounds, fishing areas, traffic flow intensive areas, etc., it can be predicted that this ship will encounter many ships in a period. We suggest that, in such waters, the network mode of ships should choose the cluster mode and its star-ring topology so that we only need to have direct contact with a small number of major ships. In this case, it not only reduces the pressure of the overall network but also relieves the burden of a single node.  
In open waters, we will not encounter a large number of ships that pose a danger to our ship in a short time normally, so we can consider choosing the intercommunication mode and its network topology.
## 5 Simulation Results and Performance Analysis  
To verify the rationality and efficiency of the proposed method, this section compares the network performance of routing in this paper with replication-based routing SprayAnd-Wait [22] and node attribute-based routing RSNA [12] with different buffer sizes, analyzing them specifically.
## 2 Feature Enhancement Point Cloud Data Resampling
### Large-Scale Noise Removal  
For large-scale noise, the first type of noise is generally removed by the enclosing box method and the radius filter method, and the second type of noise is removed by the region growth method and the statistical filter method, which can effectively remove the first and second types of noise, respectively, but different methods are needed to remove different types of noise in this way, and the efficiency is low. Therefore, a median denoising method based on region segmentation is used.  
Figure 1: Denoising algorithm flow.  
The set of point cloud coordinates transformed by the coordinate system is \(Q\). The point cloud region is segmented along the \(u_{1}\) direction, and the set of \(u\) coordinates of data points in each region is \(Q_{j}^{u}[z^{\prime\prime},\sigma](j=1,2\cdots,q)\), \(\sigma\) denotes the \(u_{1}\) direction segmented point cloud region. The \(\sigma\) values are set according to the laser scanning frequency of the acquisition system; as shown in the figure, \(Q_{j}^{u}[z_{k}^{\prime\prime},\sigma](j=1,2\cdots,q)\) is the set of \(u\) coordinates in the segmented area. \(Q_{j}^{u}[z^{\prime\prime},\sigma](j=1,2\cdots,q)\) can be expressed as:  
\[Q_{j}^{u}[z^{\prime\prime},\sigma]=\left\{z_{k}^{\prime\prime}\middle|x_{j}^{ \prime\prime}\leq x_{k}^{\prime\prime}\leq x_{j}^{\prime\prime}+\sigma\right\} \tag{12}\]  
where \(z_{k}^{\prime\prime}(k=1,2\cdots,l)\) denotes the \(u\) coordinates of the data points in the region; \(l\) denotes the number of \(u\) coordinates of the data points in the region; \(x_{j}^{\prime\prime}\) and \(x_{j}^{\prime\prime}+\sigma\) are the \(u_{1}\) coordinates of the \(j_{th}\) segmentation region boundary.  
The elements of \(Q_{j}^{u}[z_{k}^{\prime\prime},\sigma](j=1,2\cdots,q)\) are sorted in the segmentation region, the median \(z_{m}^{\prime\prime}\) of the elements in the segmentation region is obtained, the distance between each element of \(Q_{j}^{u}[z_{k}^{\prime\prime},\sigma](j=1,2\cdots,q)\) and \(z_{m}^{\prime\prime}\) is calculated and recorded as \(d_{k}=\left|z_{k}^{\prime\prime}-z_{m}^{\prime\prime}\right|\), \(d_{k}\) is arranged in the order from smallest to largest, and the threshold \(d_{m}\) is set, remove \(z_{k}^{\prime\prime}\) for \(d_{k}\) values greater than the threshold \(d_{m}\), and mark the remaining set of elements corresponding to the point cloud data sitting as \(Q^{\prime}\).
## 5 Conclusions  
In order to explore the influence of clay content and moisture content on the wave attenuation characteristics of filling fissures in surrounding rock under a high-amplitude seismic wave, the split Hopkinson pressure bar (SHPB) test technique was used to conduct high-amplitude impact tests on artificial filled joints composed of sand-clay mixtures with different clay mass fractions and saturations. The conclusions are as follows:  
1. With the increase of the clay weight fraction, the wave attenuation coefficient decreased first and then increased. When the weight fraction of Kaolin was 50%, the attenuation coefficient was 9.70, which is the minimum among the tested working conditions. As the saturation increased, the wave attenuation coefficient reached a maximum when the saturation was 25%, and then began to decrease as the saturation increased.
2. With the increase of the clay weight fraction, the relative breakage potential \(B\) decreased continuously, and the proportion of dissipation energy to total incident energy also decreased. The variation trend of the transmitted energy ratio was the same as that of the wave attenuation coefficient. When the clay mass fraction was 25%, the proportion of transmitted energy to total incident energy reached the maximum.
3. With the increase of saturation, when the saturation was 25%, the friction between particles decreased due to the lubrication of water, and the corresponding transmitted energy was the lowest. When the saturation reached 100%, the wave attenuation coefficient of sand-clay mixtures was the smallest, and the proportion of transmitted energy reached 42.1%. Compared with quartz sand and sand-clay mixture, the wave attenuation coefficient was similar. It could be considered that the wave transmission characteristics was independent of the filling materials under saturated conditions. However, due to the presence of bound water in clay, the particles crushed due to the presence of tiny bubbles during impact.  
S.L.: Investigation, Methodology, Formal Analysis, Writing--Original Draft; Z.X.: Methodology, Supervision, Validation, Writing--Review & Editing; P.F.: Conceptualization, Funding Acquisition, Resources, Project Administration, Validation, Writing--Review & Editing; K.X.: Data Curation, Investigation. All authors have read and agreed to the published version of the manuscript.  
Figure 10: (**a**) Energy distribution histogram of specimens with different saturations after impact. (**b**) Ratio curve of each part energy to incident energy.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) (grant number 51979280) and the National Natural Science Foundation of China (NSFC) (grant number 52009138).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems
## The Proposed Antenna Design  
### Antenna Structure  
The structure of the antenna array is shown in Figure 1. A \(2\times 2\) array configuration was realized by employing four probe-fed square microstrip patch antennas with a center-to-center distance \(d=0.6\lambda\) (\(\lambda\) calculated at 900 MHz), and a 2 mm-thick aluminum ground plane of size \(440\times 440\) mm(a thick ground plane is employed for increasing the structure robustness). Each microstrip patch antenna was implemented with an air gap of height \(h=20\) mm and an upper 0.5 mm-thick square aluminum patch of edge length \(L=150\) mm. An SICL-based feed network was implemented with an S7136 dielectric substrate (\(\varepsilon_{\mathrm{R}}=3.6\), \(\tan\delta=0.0035\)) fixed on the top side of the ground plane. The feed network substrate did not cover the whole ground plane and was shaped to follow the route of the feed network transmission line. Each antenna element was connected to the feed network through four metallic cylindrical probes of diameter 0.5 mm, positioned at a distance \(p=67.5\) mm from the element center point, to implement a four-probe microstrip patch antenna structure which was employed to radiate CP, as will be better described in the next section. Finally, Teflon screws were used to support the aluminum patches (not shown in Figure 1).
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 4 Target
Increasing the size could lead to a lower production yield due to secondary re-interactions inside the target. The optimization is then a trade-off between mechanical robustness against heating and the effective interaction length crossed by the secondaries. An interesting example is an optimization performed for monitored neutrino beams and, hence, slowly extracted beams, by ENUBET. The team has conducted extensive optimization studies based on the FLUKA [95,96] and G4 beamline [97] simulation codes, using graphite (density 2.2 g/cm\({}^{3}\)), beryllium (density 1.81 g/cm\({}^{3}\)), Inconel (density 8.2 g/cm\({}^{3}\)) and some high-\(Z\) materials. Each target prototype is modeled geometrically as a cylinder with variable radii between 10 and 30 mm and various lengths extending from 5 to 140 cm. The target optimization was based on the production yield of \(K^{+}\) particles into a typical secondary beamline acceptance (\(\sim\)20 mrad) and the intrinsic material characteristics (specific heat and density). The kaon yields are depicted in Figure 4. Eventually, the most promising materials for monitored neutrino beams are graphite and Inconel-718. Graphite is a known and well-tested material employed in several neutrino beams thanks to its heat endurance and production yields [93]; Inconel is quite a novel choice that is under consideration for nuSTORM [98] and ENUBET, but already at use at CERN in other applications (like the new CERN-PS East Area Beam Stoppers). The main cooling technique [94] for targets in Superbeams remains the water-cooling circuit. Heat is transferred to the water through an intermediate step either consisting of solid legs/fins, a gas mixture, or a heat pipe since the high temperature reached by the targets and the low boiling point of water do not allow these elements to be put in direct contact. Water cooling is employed in the NuMI beam serving the NO\(\
u\)A [99] experiment at Fermilab, where a 1.2 m long segmented target made of graphite is cooled by properly spaced fins and water circulating in a base-plate at the bottom of the fins [100].
## 3 Adaptive Compensation through Digital Filters
The capability of the FIR filter to adjust the signal's spectrum is limited by the total number of taps. Usually, the FIR filters in the Tx have many taps and are independently controlled for each tributary. Thus, the Tx FIR allows great capability and flexibility for pre-compensation of the bandwidth narrowing effect. In general, with the bandwidth narrowing effect, one should increase the peaking amplitude, increase the peaking frequency, and reduce the roll-off factor. These parameters can be adjusted independently or simultaneously. We summarize the experimental parameters in Table 2 below for both the Tx FIR filter and the Rx FIR filter.
## 5 Improved SAC (iSAC) Algorithm
### MDP Engineering
#### 5.2.1 State Description  
In the context of the IoV, a computing power allocation process encompasses the processes of task offloading, task queuing, task computing, and result returning. In the preset scenario, the state space consists of six scores, {0, 0, 0, 0, 0, 0}, corresponding to the six servers in the action space. Initially, the system needs to collect server load and task information. Then, a score is assigned. If the remaining storage capacity of the server is greater than the storage requirement of the task, add 1 to the corresponding position in the state space set, otherwise do not add. If the server's remaining computing power is greater than the task's computing power requirement, add 1 to the corresponding position in the state space set, otherwise do not add. Finally, the system will calculate the transmission time and computation time overhead (excluding task queuing time and communication delay) of the computing task to each server and select the server with the minimum time cost to add 1 to its score. Such a state space not only expresses the size of the task, the computing power requirements, the server load, and the task time components but also reduces the dimensionality of the state space, thereby accelerating the convergence of the algorithm. Therefore, state space \(S\) can be represented as in Equation (22):  
\[s(t_{j})=\{score_{0},score_{1},score_{2},score_{3},score_{4},score_{5}\} \tag{22}\]
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 4 Datasets
### ICDAR-17 Pod  
ICDAR-17 Page Object Detection (POD) [5] is a publicly available dataset introduced in the page object detection competition at ICDAR 2017. This dataset is one of the most widely used datasets to evaluate graphical page object detection systems. The dataset comprises a page consisting of various layouts such as single-column, double-columns, and multi-columns. This dataset has an annotation for tables, formulas, figures present in document images. The page objects contain headings, textual, page title, content, captions, etc. This dataset contains 2417 English document images in total, extracted from 1500 scientific papers of CiteSeer. This dataset is split into training and test set, having 1600 and 817 document images, respectively. Contents are as follows: Training set contains 1978 figures, 698 tables, and 3515 formulas, while Test set contains 961 figures, 371 tables, and 1192 formulas.