esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Validation and Results Analysis
### Cylindrical Particle Case  
The modeling capability of the IIM T-Matrix model is investigated for cylindrical particles. In this test, the light wavelength is taken as 0.5 \(\upmu\)m, the refractive index of the particle is set as \(m=1.60-0.0008\)i, and the diameter (\(D\)) and length (\(L\)) of the cylinder are set as 2 \(\upmu\)m. The scattering phase matrices are calculated by the EBCM and IIM T-Matrix methods, and the results are shown in Figure 6.  
Figure 6: The scattering phase matrix obtained by the IIM and EBCM T-Matrix methods for cylindrical particles. The simulation errors of F\({}_{11}\) are denoted by relative errors. For F\({}_{12}\), F\({}_{34}\), and F\({}_{44}\), they are firstly normalized by F\({}_{11}\), and their simulation errors are evaluated by the absolute errors.  
It can be seen that the results calculated by the IIM T-Matrix model show a good agreement with those of EBCM, indicating that the IIM T-Matrix method can simulate the light scattering process of a cylinder with a high accuracy. For F\({}_{11}\), its relative errors are less than 10% in most scattering directions, and for F\({}_{12}\)/F\({}_{11}\), F\({}_{34}\)/F\({}_{11}\), and F\({}_{44}\)/F\({}_{11}\), their absolute errors are generally smaller than 0.1. Similar to the results of spheroidal particles, the spatial distribution of simulation errors is similar to the variation pattern of the phase matrix elements; namely, in the scattering directions where the curves of the phase matrix elements change dramatically, their simulation errors will increase as well.  
The integral scattering parameters of different models are also calculated, as shown in Table 3. It can be found that the results of the IIM T-Matrix method show a good agreement with those of EBCM, which validates the modeling accuracy of the IIM T-Matrix method.
# Characteristic of _Pseudomonas syringae_ pv. _atrofaciens_ Isolated from Weeds of Wheat Field
## 3 Results  
In the wheat fields examined in the Kyiv region, weeds with the symptoms characteristic of phytopathogenic bacteria were found. Particularly, _S. arreensis_ plants had light brown angular spots and _P. argemone_ plants had a roundish shape with whithis spots on the leaves (Figure 1). From 53 symptomatic plants, 25 bacterial isolates were obtained. Among them, seven isolates (L8, L9, L11, L11a, L13, L15, L16) were found to be aerobic non-spore-forming Gram-negative motile rods. These strains formed gray, translucent, smooth, rounded colonies that were 1.0-2.5 mm in diameter and had slightly wavy edges with a compacted and convex center. These isolates were oxidase-negative and induced a hypersensitivity reaction (HR) on tobacco leaves. According to the results of LOPAT tests (levan production, oxidase, protopentinase activity on potato, arginine dihydrolase, HR on tobacco leaves) (L+, O\(-\), P\(-\), A\(-\), T+), these seven strains were assigned to LOPAT group 1a (_P. syringae_) (Table 1). The basic properties of _P. syringae_ strains L8, L9, L11, L11a, L13, L15, L16 were similar to the properties of neopathotype strain _P. syringae_ pv. _atrofaciens_ UCM B-1011 (PDDCC 4394) (Table 1). All the new isolated strains utilized citrate, mannitol, xylose, arabinose, galactose, saccharose, and did not use lactose, trehalose, malonate, maltose, and cellobiose. These strains showed reactions as \(\beta\)-glucosidase, phosphatase, aesculin hydrolysis, and did not demonstrate urease, ornithine decarboxylase, lysine decarboxylase, n-acetyl-\(\beta\)-d  
Figure 1: Symptoms on of _Sonchus arreensis_ L. (**A**) and _Papaver argemone_ L. (**B**) found in wheat fields. glucosaminidaseidase, \(\alpha\)-galactosidaseosidase, \(\gamma\)-glutamyl transferase activities. Some newly isolated strains showed weak utilization of acetamide (L11, L11a, L13, L15, L16) and inositol fermentation (L8, L9, L11, L11a, L16). Strains of bacteria isolated from weeds were different in aggressiveness against wheat and host plants (Table 2). The highly aggressive against wheat strains of _P. syringae_ (L15 and L16) were isolated from the _S. arreensis_. The score of aggressiveness of these strains was 3 (5-point scale) for wheat. At the same time, the strain L16 was medium aggressive against the host plant--_S. arreensis_ (score of aggressiveness was 3 on a 5-point scale). Strains L8, L9, L11, L11a, and L13 were medium or weak aggressive against weeds and wheat (score of aggressiveness 1-2).
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 5 Performance Evaluation
### Evaluation of Object Detection and Encoding  
The majority of the encoding time for sensitive objects is determined by the object detection time, which relies on the inference performance of the YOLOv8 model. Table 6 illustrates the comparative performance of CPU and GPU for object detection, with the GPU demonstrating a considerable reduction in processing latency. In our experiments, we observed that object detection using a GPU was approximately 12.57 times faster than using a CPU for a set of 10 images. When the number of images was increased to 100, the GPU-based object detection was still significantly faster, at about 11.24 times the speed of the CPU. We also observed that the standard deviation of the object detection time for images was smaller when using a GPU compared to a CPU. This indicates that the object detection time remains consistent, leading to more stable and efficient processing. Our evaluation  
Figure 14: (**a**) Image with objects detected by Yolo8, (**b**) encoded the image contained hidden human objects.  
demonstrates that object detection utilizing GPU-accelerated Jetson equipment achieves an average processing time of 65 ms per image, indicating the feasibility of real-time operation.  
The comparative object detection performance of CPU and GPU is evaluated using a statistical hypothesis test, \(p\)-value. The obtained \(p\)-value of 0.029, below the conventional significance level of 0.05, confirms a statistically significant improvement in object detection performance when using the GPU.  
Figure 16 shows the performance of encoding images using CPU and GPU. As seen in Figure 16, the encoding time of the video using the GPU is, on average, 13.59 times faster  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No of Images**} & \multicolumn{2}{c}{**CPU**} & \multicolumn{2}{c}{**GPU**} \\ \cline{2-5}  & **Mean** & **Std Dev** & **Mean** & **Std Dev** \\ \hline
10 & 8.67 & 1.09 & 0.69 & 0.04 \\ \hline
30 & 34.99 & 0.10 & 1.97 & 0.05 \\ \hline
60 & 50.07 & 0.12 & 3.91 & 0.06 \\ \hline
100 & 72.49 & 5.24 & 6.45 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Elapsed time of detecting object.  
Figure 15: Elapsed time and std. deviation per logger threads.  
compared to the CPU. Additionally, the standard deviation is 0.05 ms, confirming excellent processing performance.
## 3 Calibration Artefacts
### 3D Aztec Artefact  
Geometry calibration standards are essential for the verification or calibration of geometric errors of micro-CMMs. A number of standards have been developed worldwide. Most of them look like a miniature version of popular artefacts applied in large CMMs, for instance, mini ball bar [43], mini ball array [44] or mini ball plate. Photos of some representative calibration artefacts studied at PTB are illustrated in Figure 8.  
However, there is a practical limit in applying the artefacts mentioned above. The surface area of reference planes/spheres measurable by microprobes is limited by either the structure of the artefacts or the styli length, consequently impacting the calibration accuracy. In addition, the precise assembling of tiny reference spheres on substrates is also not a trivial task.  
To mitigate the problems mentioned above, recently a type of 3D Aztec artefact manufactured from crystal silicon using the wet-etching technique has been developed. One basic unit of the Aztec artefact is shown in Figure 9a. It has a pyramidal shape and consists of a few plateaus. On each plateau, micro-pyramidal marks are fabricated, as detailed in Figure 9b. To calibrate the marks, usually four sidewall planes of marks are measured and their intersection point is calculated as the reference coordinate. In the given design example, the overall size of one pyramid artefact is 6.5 mm \(\times\) 6.5 mm \(\times\) 1.4 mm. A photo of a fabricated Aztec artefact in 4-inch wafer size is shown in Figure 9c. It can be conveniently saved into a suitable size to fit the measurement volume of different micro-CMMs.  
The Aztec artefact has several advantages. For instance, the artefact can be mass-produced using the wet-etching technique cost-effectively; due to the crystal nature of the silicon material, the marks have high sidewall surface quality and well-defined geometry (angle between sidewalls and upper surface: 54.7\({}^{\circ}\)); the layout of the artefact has a wide open space from the top, offering better accessibility for the probing styli.  
Figure 8: Photos of several geometry calibration standards for micro-CMMs, shown as: (**a**) a Zerodur gauge block bridge; (**b**) a ball plate with nine hemispheres made of silicon nitride manufactured by Carl Zeiss IMT; and (**c**) a micro-tetrahedron artefact consisting of four spheres with (\(\O\) 0.5 mm).  
Figure 7: (**a**) Recorded probing curve for estimating the adhesive forces between probing sphere and sample; and (**b**) different probe-sample interaction phases.  
Figure 10 shows the repeatability of a calibration of the 3D Aztec artefact using the NMM equipped with a boss-membrane probe. Two repeated measurements are run, with each offering a set of reference coordinates. Their coordinate difference is shown as vectors for the \(x\)- and \(y\)-coordinates in Figure 10a and that for the \(z\)-coordinate in Figure 10b. The scale of the vectors is shown in the bottom left region of the plot. It can be seen that for most measurements, the deviation is less than 10 nm, indicating excellent measurement repeatability.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 1 Introduction  
In recent years, the fifth generation (5G) mobile communication system has evolved as a valuable tool to meet the rapid development of MIMO communication. The 5G wireless communication is used in a wide range of emerging applications to meet highly stringent latency, dependability, mobility, and security requirements. However, with the improvement of application demands, more stringent latency and reliability are required in fields such as unmanned driving and remote surgery [1; 2; 3], so it is necessary to develop towards the sixth generation (6G) mobile communication. In almost all application scenarios, low cost, low power consumption, and high stability are common requirements, which undoubtedly become the challenge of mobile communication.  
MIMO technology is one of the key technologies used for mobile communication, which can greatly improve the capacity and spectrum utilization of communication systems without increasing the bandwidth. MIMO technology is widely used in wireless communication, such as Internet of Things (IoT) and wireless sensor networks (WSNs). Antenna transmission, both in IoT and WSNs, is an important part of a wireless communication system, and the antenna structure of the MIMO system directly affects the transmission performance. In order to optimize the channel transmission performance, this paper derives the relationship between the channel matrix phase and channel capacity, and designs the optimal transceiver antenna configuration.
# Privacy-Preserving Data Mining on Blockchain-Based WSNs
## 4 Methodology
### Distributed Data Mining
#### 4.3.4 Message Propagation Time  
The system we are presenting makes use of the onion routing technique described in Section 3.1 to convey a DM model through the nodes of a WSN. Therefore, messages are routed through several nodes of a wireless multi-hop network and relayed multiple times, introducing substantial latency before receiving the result. To assess the response time of the proposed architecture operating over a large WSN, we look at the publicly released NS3-based simulator described in [52]. NS3 [53] is a discrete-event network simulator for internet systems, and it is the most-used network simulator by the IoT research community [54]. The simulator described in [52] is designed to simulate the technique described in Section 3.1 over a WSN that varies in node number, topology, routing, etc. Furthermore, it allows for the manipulation of message structures by defining nodes in the message path (corresponding to layers of the layered object), detailing the payload content, and other settings such as maintaining message uniformity.  
We set up a simulation that constructs a WSN of 200 nodes. Nodes are deployed at random locations on a disc-shaped plane with a radius of 300 m. We selected the deployment scheme affected by randomness to model the usually non-uniform node density of WSNs. Nodes have a wireless communication range of approximately 30 m, operating based on the IEEE 802.11n standard at 2.4 GHz and with a data rate of 13 Mbps. The maximum transmission unit and maximum segment size are set to the ns3 default value, namely, 2296 bytes and 536 bytes, respectively.  
Messages are transmitted over the TCP protocol, multi-hop routing is conducted using the Optimized Link State Routing Protocol (OLSR) [55], and cryptography is completed with the Libsodium library [56].  
The simulator is set up to issue 20 messages for each value of \(M\), with \(M\) being the size in bytes of the model carried in the message payload. The layered object included in the message is constructed to lead the message through 20 randomly selected nodes. Our previous work [35] stated that for privacy requirements, half the nodes in the message path are only performing routing operations, without access to the payload content (decoy nodes). Therefore, this setup allows for estimations of the propagation time of messages constructed to perform model processing at 10 nodes. Message size is maintained as uniform after the onion routing operation, with padding when necessary. Figure 5 shows the simulated WSN and an onion message routed through 20 sensor nodes.
## 2 Study Area and Data Sources
### Data
#### 2.2.4 MODIS Data  
In this study, the MOD09A1.061 product is used to calculate vegetation indices. This data product has a spatial resolution of 1 km and a daily temporal resolution. Reflectance band data from the corresponding grid points are extracted, and data from the blue, red, near-infrared, and shortwave infrared bands are selected. The Enhanced Vegetation Index (_EVI_) and the Land Surface Water Index (_LSWI_) are then calculated. The calculation formulas for these indices are as follows [37]:  
\[EVI=2.5\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+1+6\rho_{red}+7.5\rho_{blue}}, \tag{1}\]  
\[LSWI=\frac{\rho_{NIR}-\rho_{SWIR}}{\rho_{NIR}+\rho_{SWIR}}, \tag{2}\]  
where \(\rho_{NIR}\) represents the surface reflectance in the near-infrared band, \(\rho_{red}\) represents the surface reflectance in the red band \(\rho_{blue}\) represents the surface reflectance in the blue band, and \(\rho_{SWIR}\) represents the surface reflectance in the shortwave infrared band.  
This study utilizes the MOD11A1.061 product to obtain land surface temperature (_LST_), which has a spatial resolution of 500 m and a daily temporal resolution.
# Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET
## 6 Results and Discussion
### Secure Routing Mechanism  
Although secure routing mechanism was not the specific focus of this research work, it is worthful evaluating the routing-based network performance once our proposed scheme was utilized. During vehicular network communication, data packets move through intermediate nodes from source to destination. Due to highly dynamic mobility and frequent change in topologies, the chances of link breakage are substantially high. Thus, it is not advisable to maintain per-destination routing information. In this research, we investigated the geographical routing protocol greedy perimeter stateless routing (GPSR), which only keeps local information of the neighbor. However, GPSR is proven to be exposed to various insider attacks [45; 46]. Later, secure-geographical routing protocol greedy perimeter stateless routing (S-GPSR) was proposed to address the security concerns of the baseline protocol. Thus, we adopted the S-GPSR routing protocol to execute our proposed scheme.  
In this protocol, during packet forwarding to the destination, each node must scan its neighborhood routing table to obtain the next hop information, thus it selects the node with high trust value, also known as the highly trusted route, rather than the default minimum distance. S-GPSR uses the trust update interval (TUI) model to decide the duration of time waited before calculating the trust values to each node. Each node in S-GPSR performs two functions, forwarding the packets and checking the integrity of the packets. The trust values of the participating nodes increase if the nodes successfully forward the packets in an appropriate manner and succeed in the integrity check. In case the integrity check fails and packets are not broadcasted in an appropriate way, the trust values decrease and the nodes are considered as malicious nodes. Figure 21 demonstrates the influence of overall network performance in terms of throughput once denial of service attack is induced in networks. When the proposed scheme was executed by utilizing the S-GPSR routing protocol, it showed a greater throughput of up to 50% as compared to other baseline security protocols. This was due to our proposed scheme utilizing its self-generated blockchain-based security mechanism, where evaluation of integrity and trust are embedded within each participating device. Thus, other protocols required some additional processing delay to implement the security mechanism. This delay affected the packet deadline, which led to the data packets missing the end-to-end deadline. Furthermore, an in-depth study will be carried out in future work.
## 1 Introduction
In the case of smartphones, GNSS observations suffer not only from high measurement noise, atmospheric refractions or multipath, but also from anomalies such as duty cycling and gradual accumulation of phase errors. Such a problem occurred with GNSS chipsets built in mobile phones, such as the Xiaomi Mi 8 [7; 8; 9]. The duty cycling is a process where the GNSS chipset of the smartphone works in a discontinuous way, which causes the hardware clock to be active only for a fraction of each second to support low power consumption and thus prevent battery discharge [10; 11; 12; 13]. This action limited the use of smartphone observations for precise positioning techniques such as RTK and PPP. Fortunately, Google Nexus 9 was the first mobile device which enabled continuous recording of GNSS observations. First research results of the static data analysis showed that the horizontal and vertical RMS position errors were less than 0.8 and 1.4 m [4]. Other researchers [14; 15] performed the studies using the same device. Position accuracy ranged from below 1 m to a few decimeters depending on experimental setups for positioning based on carrier-phase observations. Unfortunately, during signal analysis, various drifts for the code and phase observations appeared. In later Android versions, it was possible to turn off the duty cycling mode, making the observables applicable for phase-based positioning methods. Based on this, first single-baseline RTK positioning experiments considering two different kinds of multi-frequency and multi-constellation master stations took place [16]. This research work demonstrated that it is not possible to fix phase ambiguities and therefore to reach cm-accuracy in real-time, mostly due to the low quality of GNSS raw measurements collected by a mobile phone. For float-only solutions, researchers proved to obtain better precision with respect to the Global Positioning System (GPS)-only results. What they additionally realized,when the GLONASS constellation is also added, the results are becoming worse. Additional works on recent GNSS smartphone positioning using single-frequency precise positioning in RTK or static PPP modes were presented in References [17,18]. Their investigation showed that the performance of current mobile phones is quite superior with respect to previous models of smart devices, but it is still not available to reach high coordinate accuracies. The authors of this paper focused on assessment of carrier phase observations in precise smartphone positioning, and above all, in ambiguity fixing performances. Since November 2019, we have been doing research on various GNSS chipsets built into the mobile devices. Since then, we paid special attention to the latest dual-frequency GNSS Kirin 980 chipset, and in December 2019, we obtained a Huawei P30 Pro smartphone based on this chipset. In December 2019 and January 2020, we conducted several static measurement sessions in various configurations. Based on collected RINEX data, we analyzed the signals, the quality of carrier phase observations and finally single-base positioning results according to the nearest GNSS reference station.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Code Design for Gaussian Interference
#### 3.4.1 Ad-Hoc LDPC Code Design
Thus, for each \(\alpha^{(\ell)}\), we aim at determining the maximum amount of interference \(\sigma_{i,\text{th}}^{2}(\alpha^{(\ell)})\), such that error-free decoding is possible. Following the results in [40], this requires that the log-likelihood a posteriori probability (APP) mutual information \(\mathsf{l}_{\text{APP}}(j)\to 1\ \forall j\in\{0,\ldots,n_{b}-1\}\) (assuming that the block length and the number of decoding iterations go to infinity). For a general code ensemble, the computation of the iterative decoding threshold has to take into account the respective cases of the interferer being at the beginning or end of the codeword. Let us denote by \(\sigma_{i,\text{th}}^{2}(\alpha^{(\ell)},\text{b})\) and by \(\sigma_{i,\text{th}}^{2}(\alpha^{(\ell)},\text{e})\) the iterative decoding threshold for a chosen \(\alpha^{(\ell)}\), when the interferer affects the beginning or end of the codeword, respectively. We evaluate the following gain function \(g\),  
\[g=\prod_{\ell}\frac{\sigma_{i,\text{th}}^{2}(\alpha^{(\ell)},\text{b})}{\sigma _{i,\text{o}}^{2}(\alpha^{(\ell)})}\cdot\frac{\sigma_{i,\text{th}}^{2}(\alpha^ {(\ell)},\text{e})}{\sigma_{i,\text{o}}^{2}(\alpha^{(\ell)})}, \tag{7}\]  
where \(\sigma_{i,\text{o}}^{2}\) is obtained from the outage capacity expression in (3). Note that due to the symmetry constraint in (6), \(\sigma_{i,\text{th}}^{2}(\alpha^{(\ell)},\text{b})=\sigma_{i,\text{th}}^{2}( \alpha^{(\ell)},\text{e})\) and (7) can be simplified. **Example 2**. : _Fix \(n_{b}=11\), \(m_{b}=6\), \(p_{b}=1\), \(R_{c}=1/2\), where the first column is punctured. For the multi-target optimization, we impose the symmetry constraint in (6). For complexity reasons, we optimize the threshold for only two different values of \(\alpha\), namely \(\alpha^{(0)}=6/10\) and \(\alpha^{(1)}=9/10\). The differential evolution algorithm in [39] was executed with the following parameters: crossover probability of 0.6, population size of 200, and number of generations of 4000. The multi-target optimization yields:_  
\[\textbf{B}_{\textbf{A}}=\left[ \begin{array}{cccccccccccc}1&0&0&2&2&0&0&0&0&1&1 \\ 2&0&0&0&0&0&1&0&1&1&0\\ 2&0&1&0&1&0&0&0&0&0&1\\ 2&1&0&0&0&0&0&1&0&1&0\\ 2&0&1&1&0&1&0&0&0&0&0\\ 1&1&1&0&0&0&0&2&2&0&0\end{array} \right].\]