esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Introduction:  
Element data obtained by plane-wave acquisitions were beam-formed to reconstruct radio frequency (RF) data for displacement estimation. In this study, three beam-forming strategies were used: delay-and-sum (DaS), Lu's-fk, and Stolt's-fk. In DaS beam-forming, dynamic focusing in receive was applied with and without apodization (Hamm function) in which the F-number had to be set (Equation (1)). Furthermore, angular weighting was applied in Lu's-fk and Stolt's-fk beam-forming in which the migrated k-space spectrum was multiplied with a template designed such that wave directions closer aligned to the beam-steering direction were multiplied with higher weights compared to waves deviating from the steering angle. In angular weighting, the angular range had to be set: wave directions within this range were weighted by Hann function and outside were weighted by zero. Summarized, the optimal F-number in DaS with and without apodization and the range in angular weighting were investigated.  
**Method and Materials:** Plane-wave element data were obtained by three transducers (L7-4, 12L4 VF, L12-5) in a multi-purpose phantom (model 539; ATS Laboratories, Norfolk, VA, USA) containing circular lesions and needles to evaluate contrast and resolution, respectively. Element data were beam-formed by above strategies in which the F-number (DaS) or angular range (angular weighting) were varied between 0 and 2.0, and \(\pm\)10 and \(\pm\)30. The mean contrast-to-noise ratio (CNR) of the lesions and mean axial and lateral resolution (full width at half maximum) of the needles were calculated to a depth of 50 mm (Figure A1) using software provided by the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS) of the IEEE International Ultrasonics Symposium 2016 [21].  
**Results and Discussion:** In DaS, an F-number of 0.875 seemed to be most optimal for all transducers: contrast decreased while lateral resolution remained constant at values below 0.875 and contrast remained constant above 0.875 while lateral resolution increased. The axial resolution seemed to be independent of the F-number. These results were found in DaS with and without apodization. As can be seen in Table A1, apodization increased contrast, which came at the cost of increased lateral resolution.  
Similar results were found for angular weighting (Table A2), a range of \(\pm\)20\({}^{\circ}\) seemed to be optimal for all transducers and both Lu's-fk and Stolt's-fk, since the lateral resolution and contrast did not improve anymore at larger and smaller angle ranges, respectively. The axial resolution was unaffected by the angular range. Furthermore, angular weighting improved contrast at the cost of lateral resolution, as can be seen in comparing Tables A2 and A3.
## 4 State of Art
### Magnetic Force Sensors  
Optical fiber magnetic field sensors exploit the magnetostrictive effect that produces a mechanical strain in a ferromagnetic material when it is subjected to an external magnetic field. For that an optical fiber is bonded to a sample, or by thin-film deposition with a magnetostrictive material. The most commonly used magnetostrictive sensing elements were optical fibers coated with nickel [43], metallic glasses such as Metglass 2605S2 or Vitrovac 40-60 [44; 45; 46; 47] and ceramic thin-films (Fe\({}_{2}\)O\({}_{4}\), NiFe\({}_{2}\)O\({}_{4}\) or Ni\({}_{x}\)Co\({}_{1-x}\)Fe\({}_{2}\)O\({}_{3}\)) [48; 49].  
Since 2000, an intensive investigation on an alloy material with high magnetostriction coefficient (Terfenol-D) has been reported on literature. The most studied optical configuration is a rod or ribbon shape of Terfenol-D attached to a fiber Bragg grating (FBG) [50]. The advantages of this type of sensors are the small size and possibility to have various sensing elements in the same optical fiber, thus allowing optical multiplexing. Alloys using Terfenol-D have also been tested in microstructuredfibers [51]. Recently another kind of sensors to detect magnetic fields has been proposed, where the sensor is based on magnetic fluid.
## 5 Connectivity Manager
### Managing Security Threats  
Autonomous and remote-controlled operations involve many risks that need to be handled carefully. Autonomy is mainly achieved by means of software and thus, assuring the cybersecurity of the software and the whole platform is essential. On the other hand, the other key area is the cybersecurity of the communication system. The ability to control the ship remotely also leads to new attacking possibilities.  
All the systems in the ship, especially safety/critical ones need to be designed so that a single failure such as breaking a connectivity router will not have a significant impact on the ability of the ship to operate in all relevant modes and environments. Moreover, the ship should be able to locate itself using other means than relying only on satellite geolocation, since it can be jammed easily. Redundant mechanisms and, e.g., shielding the GNSS receiver from horizontal interference helps in  
Figure 5: Vertical handover for autonomous ship connectivity.  
increasing the resistance to malicious users. Shielding means adding a physical shield to the antenna against potential interferers to reduce the effect of jamming signals by tens of decibels.  
The main cybersecurity dimensions for autonomous operations include confidentiality, integrity and availability. Confidentiality means that only those with rights to access can access systems and resources. Integrity means that the data are precise and error free during the lifetime of data. Availability complements the previous dimensions, ensuring that the systems and data related to autonomous operation are available when needed. These dimensions need to be taken account in connectivity system design.  
According to our previous analysis, the most important threats for autonomous ship communications are [5] (1) losing the data, (2) data are changed and (3) hijacking the data. The first threat relates to availability, the second to integrity and the last to confidentiality. There has to be redundancy in the network, good coding and cyber security protocols and mechanisms to avoid any of these happening. Risk identification and management need to be performed carefully. In addition, the people onboard as well as in the remote operation centers need to have cybersecurity training to ensure that people are not neglectful and do not expose the systems to unauthorized use.  
Authentication mechanisms play a key role both in the remote operation center and in the ship gateway to ensure that only the authenticated persons can operate the ships. One of the interviewed companies stated this as follows: the cybersecurity mechanisms need to ensure that \"The ship should not obey if the commands come from the wrong direction\".  
From the data security and integrity point of view, adopting, e.g., distributed ledger solutions such as blockchain [51], could be used in the maritime field. This could enable accurate container tracking, automatic shipping transactions and protection against unauthorized data tampering.
# On the Performance Analysis for CSIDH-Based Cryptosystems
## 3 CSIDH-Based Schemes  
In this section, we introduce the CSIDH key exchange and two main CSIDH-based algorithms--CSURF [14] and collision-free CSIDH proposed by [PERSON] and [PERSON] [13]--to compare the performance and security. As CSIDH made a noticeable improvement by exploiting supersingular elliptic curves to instantiate the CRS scheme, various methods began to propose in order to optimize the performance and improve the security. The former is the CSURF, which proposes a way to exploit efficient horizontal 2-isogenies for a speed-up, and the later is the method by [PERSON] and [PERSON], where they analyzed the existence of a collision in the private keyspace and provided a method to eliminate such collisions. Before going into the details of the algorithms, we present three primes, \(p_{1}\), \(p_{2}\), and \(p_{3}\), which will be used throughout the paper.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Montgomery Curves** & **Tweaked Montgomery Curves** \\ \hline DBLADD & \(8\mathbf{M}+4\mathbf{S}\) & \(11\mathbf{M}+5\mathbf{S}\) \\ DBL & \(4\mathbf{M}+2\mathbf{S}\) & \(5\mathbf{M}+3\mathbf{S}\) \\ \hline \(\ell\)-isogeny eval. & \((4d)\mathbf{M}+2\mathbf{S}\) & \((6d)\mathbf{M}+2\mathbf{S}\) \\ \(\ell\)-isogeny coeff. & \((6d-2)\mathbf{M}+3\mathbf{S}\) & \((6d-2)\mathbf{M}+3\mathbf{S}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational costs of lower-level functions on Montgomery and tweaked Montgomery curves.  
First, we use the primes \(p_{1}\) and \(p_{2}\) presented as below, in order to match the size of the base field for a fair comparison.  
\[p_{1} =4\cdot\begin{array}{c}(3\cdot\ldots\cdot 373)\\ 73\text{ first odd primes}\end{array}\cdot 587-1\approx 2^{510.668},\] \[p_{2} =4\cdot 2^{2}\cdot 3^{2}\cdot 11\cdot\begin{array}{c}(3\cdot \ldots\cdot 373)\\ 73\text{ first odd primes}\end{array}-1\approx 2^{510.100}.\]  
The first prime \(p_{1}\equiv 3\mod 8\), presented in [6], will be used to compare the original CSIDH and [PERSON]'s CSIDH. The second prime \(p_{2}\equiv 7\mod 8\), presented in [11], will be used to compare the original CSIDH and CSURF.  
On the other hand, in [14], [PERSON] and [PERSON] used the prime \(p_{3}\) defined as below for CSURF. In this paper, we use \(p_{3}\) to explain the CSURF algorithm, but \(p_{3}\) will not be used for the implementation as the size of the prime is larger than \(p_{1}\).  
\[p_{3}=4\cdot 2\cdot 3\cdot\begin{array}{c}(3\cdot\ldots\cdot 389)\\ 74\text{ consecutive primes},\end{array}-1\approx 2^{512.880}.\]
## 3 Results and Discussion
### Visual Evaluation  
The detection results from the DOTA test set and Pleiades large-scale image set were interpreted visually to assess the performance of algorithms. According to the detection results of the DOTA test set, Yolo-v3 is more successful than the other networks. Although the selected samples provided in Figure 12 include different sized aircrafts, and the image patches have illuminance differences, background complexities, and different band information, the Yolo-v3 provided a lesser amount of missing objects, while SSD provided the worst results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**DOTA Training Set**} & \multicolumn{3}{c}{**DOTA Test Set**} & \multicolumn{3}{c}{**Large Scale Image Set**} \\ \hline
**Method/Metric** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** \\ \hline Yolo-v3 & 0.99 & 0.95 & 0.97 & 0.96 & 0.89 & 0.92 & 0.97 & 0.87 & 0.91 \\ SSD (1 st) & 0.99 & 0.44 & 0.61 & 0.96 & 0.43 & 0.59 & 0.65 & 0.36 & 0.46 \\ SSD (2 nd) & 0.89 & 0.73 & 0.80 & 0.86 & 0.68 & 0.76 & 0.87 & 0.65 & 0.74 \\ Faster R-CNN (1 st) & 0.99 & 0.51 & 0.67 & 0.99 & 0.47 & 0.63 & 0.74 & 0.41 & 0.52 \\ Faster R-CNN (2 nd) & 0.97 & 0.92 & 0.95 & 0.98 & 0.89 & 0.93 & 0.98 & 0.92 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision, recall, and F1 score of all datasets.  
Figure 11: Graphic representation of the precision, recall, and F1 scores.  
The aircraft detection from the large-scale Pleiades image set, which covers a 53-km\({}^{2}\) area in total, lasted around 37 s for SSD, 97 s for Yolo v3, and 102 s for the Faster R-CNN with the proposed detection flow approach. The results from Sabiha Gokcen Airport and Antalya Airport are provided in Figures A4 and A5, respectively. In Figure A4, non-detected objects are observable in the center and northern part of the image for SSD. YOLO-v3 missed only two airplanes for that image scene, however, it faced multiple detections at the bottom left part of the image scene where several airplanes are grouped. Faster R-CNN provided a balanced performance with a high detection rate and good localization of the objects. For Figure A5, similar results were achieved, and some false detections were also observed in the SSD case.
## 2 Results and Discussion
#### 2.1.1 Characteristics of Selected _Brassicaceae RSH_ Genes
Further in silico comparative analysis of the intron-exon organization of _RSH_ genes in selected _Brassicaceae_ species (Figure 2 and Supplementary Figure S1) showed that the number of exons and introns, and the location of introns in different types of _RSH_ genes, was preserved in the plants analysed. Plant _RSH1_ genes are characterized by very complex structures, with over 20 introns and exons in each analysed gene, except for _BnRSH1\(e\) and _RsRSH1\(c\) (Table 1). The high number of introns and exons is a common feature of _RSH1_ genes from both mono- and di-cotyledonous plants [25] (data from the NCBI Gene Database). The average number of introns per gene in plants is about 4 [46; 47], which raises a question about the possible role of such great complexity in the _RSH1_ gene. It is widely accepted that introns fulfil different roles, i.e., introns may contain regulatory elements, they may serve as alternative promoters, or they may be a template for synthesis of non-coding regulatory RNAs [46]. Moreover, introns are crucial for alternative splicing and, in plants, intron retention is a widely observed phenomenon [48]. The presence of introns enhances the expression of genes in varied organisms [49]; however, interestingly, in plants in contrast to animals, higher expression is observed for genes containing more and longer introns [50]. The highly complex structure of _RSH1_ genes in plants may suggest their high expression and important roles in many metabolic pathways. Other _RSH_ genes  
Figure 1: Chromosomal localization of _RSH_ genes in _A. thaliana_, _B. napus_, _B. oleracea_, _B. rapa_ and _C. sativa_. in plants are much more compact than _RSH1_, containing approximately 5 introns in _RSH2_/3 genes, and 2-3 introns in _CRSH_ genes (Table 1, Figure 2 and Supplementary Figure S1).
## 2 System Description
### Synchronization Signal  
Upon the initial power-up of the UE, it retrieves two synchronization parameters, CIDS \(w\) and CIDG \(g\), to find and identify the network. The NR system specifies 1008 PCIs, each uniquely derived by concatenating one NR-PSS and one NR-SSS from the available candidates. The NR-PSS is characterized by the CIDS value, represented as \(w\in\{0,1,2\}\), while the NR-SSS is distinguished by the CIDG value, denoted as \(g\in\{0,1,2,\cdots,335\}\). The assignment of PCIs to UEs is carried out using the formula \(N_{PCI}=g+336w\).  
The frequency domain NR-PSS can be generated using a maximum length sequence (m-sequence) of length 127 points. This m-sequence occupies 127 subcarriers of the synchronization signal block (SSB) bandwidth. By introducing a cyclic shift to a base m-sequence denoted as \(c(m^{\prime})\), three unique NR-PSS symbols are obtained. The generation of these symbols can be described as  
\[p_{w}(m^{\prime})=1-2c([m^{\prime}+43w]\bmod N),\ 0\leq m^{\prime}<M_{s}, \tag{4}\]  
where \(w\in\{0,1,2\}\) is an identifier of the NR-PSS among the potential candidates, \(M_{s}=127\) is the length of the NR-PSS symbol. We denote \(P_{w}(m)\) as the NR-PSS broadcasted at subcarrier \(m\), where it spans across 240 subcarriers in the frequency domain. With this notation in mind, the NR-PSS sequence equipped with CIDS \(w\) can be expressed as  
\[P_{w}(m)=\begin{cases}p_{w}(m-56),&56\leq m\leq 182\\ 0,&\text{otherwise},\end{cases} \tag{5}\]  
which says that out of the total 240 available subcarriers, the NR-PSS binary modulates only 127 subcarriers, with the remaining subcarriers being zero-inserted. Importantly, three NR-PSSs, each corresponding to a different CIDS value, are cyclically shifted versions of one another. This property ensures that the cross-correlation between any two NR-PSSs approaches zero when \(M_{s}\gg 1\).  
The NR-SSS is derived by combining two m-sequences to address the issue of poor cross-correlation. The resulting sequence produces a gold sequence with a length of 127 points and thus the NR-SSS is formulated as  
\[\begin{split} s_{g}(m^{\prime})&=[1-2c_{0}([m^{\prime }+15[g/112]+5w]\bmod M_{s})]\\ &\quad\times[1-2c_{1}([m^{\prime}+(g\bmod 112)]\bmod N_{s})],\ 0 \leq m^{\prime}<M_{s},\end{split} \tag{6}\]  
where \(g\in\{0,1,2,\cdots,335\}\) represents an identifier of the NR-SSS among the potential candidates, the operator \(\lfloor x\rfloor\) returns the largest integer that does not exceed \(x\), and the operator \((x\bmod y)\) returns the remainder after dividing \(x\) by \(y\). The generation of two sequences \(c_{0}(m^{\prime})\) and \(c_{1}(m^{\prime})\) is thoroughly formulated in [1]. The sequence \(s_{g}(m^{\prime})\) is then allocated onto \(M_{s}\) NR-SSS subcarriers, where \(m\in\mathcal{S}=\{56\leq m\leq 182\}\), resulting in \(S_{g}(m)\) as follows  
\[S_{g}(m)=\begin{cases}s_{g}(m-56),&56\leq m\leq 182\\ 0,&48\leq m\leq 55,183\leq m\leq 191,\end{cases} \tag{7}\]where the NR-SSS occupies 127 subcarriers within the frequency domain and the other subcarriers out of the 240 available subcarriers are allocated exclusively for the PBCH signal.
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 2 Mobile Device 3D Measurement Technology
### LiDAR-Based Body Measurement Technology  
Recently, research on body measurement utilizing LiDAR sensors has been increasing. For example, ref. [22] proposed a method to automatically recognize and measure human body parts using LiDAR data. In this study, 3D point clouds from LiDAR were used to accurately identify various body parts and measure body circumferences based on them. However, this method primarily assumes measurements in a fixed environment, limiting its applicability in various everyday situations. Another study [23] proposed a method to predict the locations of body parts based on LiDAR data using a deep learning model. This study effectively utilized depth information from LiDAR to create a 3D body model, showing higher accuracy than conventional 2D image-based methods, but research on real-time processing and user-friendly interface implementation is still lacking.
## 3 Design Overview
### ID-Based Multipath Transmission Protocol
Producers send NDC data packets to consumers using the data chunk ID as the source ID and the consumer device ID as the destination ID. According to the source ID field of the NDC data packet, the edge node of the producer changes the destination IP of the NDC data packet to the multipath service node IP at the index position of the local multipath forwarding information table through the ID protocol and forwards it. The multipath service node can identify NDC data packets with multipath preference and query the consumer's IP to NMRS through the destination ID field of NDC data packets. Based on the query results, the multipath service node changes the destination IP of the NDC data packet to the consumer's IP and forwards it. In the above approach, the NDC message sent by the producer reaches the consumer through multiple paths. In the process of multipath transmission, the ID field of the NDC data packet remains unchanged, and the destination IP of the data packet is modified by the edge node and the multipath service node to realize the rerouting. During rerouting, sub-traffic between producers and consumers is transmitted in two stages. Firstly, the producer-side edge node orients each sub-flow to the multipath service node on its sub-path. Subsequently, each multipath service node forwards the received sub-traffic to the final consumer end. In physical networks, traffic is first routed from the producer to the multipath service node according to the shortest path-based protocol (e.g., OSPF), and then from the multipath service node to the consumer.
## 3 Proposed Method
### Bfwls  
Since the useful details of the image can be enhanced by fusion of RGB and NIR images [34], the BFWLS is proposed to fuse the rich detail information of the input NIR image into the initial colorized RGB image to obtain optimized output color image.  
The fusion process is shown in Figure 6. In BFWLS, BF can preserve the image content structure well and WLS is used to preserve shading distribution of reference information. In order to ensure the color fidelity of output image of the ColorNet, the initial RGB image is decomposed into luminance and chrominance and a new luminance layer is restructured by fusing the luminance of the RGB image with the NIR image. In our method, the luminance layer of RGB image is derived from the YUV color space, because it is suitable for situations where the luminance decreases significantly from RGB to NIR images [7].  
The BFWLS fusion consists of three steps. In the first step, the NIR image is decomposed into base and detail images by BF and WLS filters, respectively. The base image refers to the filtered smooth image. The detail images are obtained by subtracting the base image from NIR image. The solution process of detail images is shown in Equations (6) and (7):  
\[NIR_{BF}^{d}=NIR-NIR_{BF}^{b} \tag{6}\]  
\[NIR_{WLS}^{d}=NIR-NIR_{WLS}^{b} \tag{7}\]  
where the superscript \(d\) indicates the detail image and the superscript \(b\) indicates the base image. \(NIR_{BF}^{b}\) represents the NIR base image after BF filtering, \(NIR_{WLS}^{b}\) represents the NIR base image after WLS filtering, \(NIR_{BF}^{d}\) represents the NIR detail image after BF filtering, and \(NIR_{WLS}^{d}\) represents the NIR detail image after WLS filtering. Finally, the average values of the detail-WLS and detail-BF that are extracted from the NIR image are retained. The final detail layer is obtained by Equation (8):  
\[NIR^{d}=\frac{1}{2}\Big{(}NIR_{BF}^{d}+NIR_{WLS}^{d}\Big{)} \tag{8}\]  
In the second step, color space is transformed from RGB to YUV and then WLS filtering is performed on the \(Y\) channel. The fused detail layer \(NIR_{d}\) obtained by the first step and the base-WLS of the \(Y\) channel are combined to obtain a new luminance layer. The mathematical expression is shown in Equation (9):  
\[Y^{\prime}=NIR^{d}+Y_{WLS}^{b} \tag{9}\]  
Figure 6: The flow chart of BFWLS fusion.  
where \(Y_{WLS}^{b}\) represents the \(Y\) channel base image after WLS filtering and \(Y^{\prime}\) represents the final luminance layer.  
In the third step, the new luminance layer obtained by the second step and chrominance channel UV are combined and transform to RGB color space to obtain the final fused RGB image.