esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 4 Experiment and Result Analysis
### Exponential Distribution
(**b**) Performance comparison between the EKF, IMM-EKF, MPDA, and the proposed algorithm under different NLOS error probabilities \(P_{NLOS}\), where \(M=\ 6,\ N\big{(}0,1^{2}\big{)}\), and \(E\)(8). (**c**) Performance comparison between the EKF, IMM-EKF, MPDA, and the proposed algorithm under different parameters (\(\lambda\)) the exponential distribution, where \(M=6,\ P_{NLOS}=\ 0.5\), and \(N\big{(}0,1^{2}\big{)}\). (**d**) The CDF of the localization error.
## 2 Physical and Mathematical Model
### Verification of Grid-Independence  
The model grid is efficiently divided using ICEM. The model divides the flow field into seven regions based on the characteristics of the core jet, diffusion, inlet, and outlet regions of the leakage. The mesh of the leakage holes, inlet, and outlet is locally encrypted to improve calculation accuracy. Figure 2 displays the meshing and local encryption used in this investigation.  
To assess grid independence, three different grid schemes with 125,610, 309,350, and 409,350 grid counts are used in case 1 for comparative analysis. Figure 3 illustrates the methane distribution of the three grid schemes at test points 1 (7.5, 1.9) and 2 (10, 1.8).  
The results indicate that when the grid reaches 3 \(\times\) 10\({}^{5}\), the concentration generally agrees with the calculated solution for a grid of 4 \(\times\) 10\({}^{5}\) magnitude, with a relative error of less than 2.3%. Considering the precision requirements of engineering and computational efficiency, a grid size of 3 \(\times\) 10\({}^{5}\) is chosen for division.  
The selected grids' overall numerical domain grid size is 0.1 mm, and the maximum size is 50 mm. Five hundred meshes were divided in the area above the 4 mm leakage hole for local encryption, with a grid size growth rate of 1.05.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Pipeline** & **Leakage Hole** & **Leak Hole** & \multirow{2}{*}{**HBR (\%)**} \\  & **Pressure (MPa)** & **Diameter (mm)** & **Location** & \\ \hline Case1 & 0.4 & 5 & X = 0 & 0 \\ Case2 & 0.4 & 5 & X = 0 & 5 \\ Case3 & 0.4 & 5 & X = 0 & 10 \\ Case4 & 0.4 & 5 & X = 0 & 15 \\ Case5 & 0.4 & 5 & X = 0 & 20 \\ Case6 & 0.4 & 4 & X = 0 & 15 \\ Case7 & 0.4 & 6 & X = 0 & 15 \\ Case8 & 0.8 & 5 & X = 0 & 15 \\ Case9 & 1.6 & 5 & X = 0 & 15 \\ Case10 & 0.4 & 5 & X = \(-\)50 & 15 \\ Case11 & 0.4 & 5 & X = \(-\)97.5 & 15 \\ Case12 & 0.4 & 5 & X = \(-\)99.5 & 15 \\ Case13 & 0.4 & 5 & X = 50 & 15 \\ Case14 & 0.4 & 5 & X = 97.5 & 15 \\ Case15 & 0.4 & 5 & X = 99.5 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Working conditions of numerical simulation.  
Figure 2: The meshing and local encryption.
# A New Monocular Pose Estimation Method for the Coplanar P4P Problem
## 1 Introduction  
In optical measurement and machine vision, there is a kind of classical pose estimation problem named PNP (Perspective of N Points) problem, proposed by [PERSON] and [PERSON] [1]. The PNP problem can be described as how to estimate the pose parameters between object and camera if there are N points on an object and their corresponding projections in the image captured by the camera.  
Because at least three points are needed to determine the full pose parameters of an object, if N \(\leq\) 2, the PNP problem will have no determinate solution, while, if N \(\geq\) 6 and the points are non-coplanar, the PNP problem can be easily solved by a direct linear transformation (DLT) algorithm. As a result, the P3P, P4P and P5P problems receive the most attention in the literature.  
For the P4P problem, if the four points are non-coplanar, the P4P problem can be translated into a problem of solving a biquadratic equation with five groups of solutions at most [2; 3; 4]. Otherwise, a unique solution will exist if the four points are coplanar. Linear algorithms can solve not only the rotation matrix and the translation vector, but also any two intrinsic parameters of the camera, e.g., the equivalent focal lengths can be solved [5; 6].  
In addition, the non-coplanar P4P problem may have a unique solution if the points meet some spatial distribution restrictions [7]. If a PNP problem has a unique solution, linear algorithms can obtain the solution [8; 9; 10]. The PNP problems with multi-groups of solutions are usually solved by iterative algorithms with initial values [11; 12], such as the Bundle Adjustment (BA) method [13; 14]. In recent years, methods for PNP problems have been used in indoor positioning, navigation and monitoring [15; 16; 17; 18].  
In this paper, the coplanar P4P problem is studied when the four points are just the four vertices of a parallelogram. For traditional P4P algorithms, the four points' coordinates must be known, or the four points must form a rectangle [19; 20]. For the pose and shape estimation of parallelogram objects with unknown shape parameters, such as someparallelogram structures on buildings as shown in Figure 1 and the parallelogram formed by a walking man's body standing at two positions (discussed at the end of this paper), the solution conditions for traditional P4P methods do not exist.  
According to the projection relationships and the relative coordinate constraints of the parallelogram's vertices, a new monocular pose estimation method is presented in this paper to solve the coplanar P4P problem of a parallelogram object without shape parameters. By the new method, a parallelogram object's pose parameters (translation vectors and rotation angles relative to the camera) and shape parameters (the four vertices' coordinates) can be obtained from one image of the object when one of the parallelogram's side lengths is known. If there is no known scale information, a scale factor between the estimation results and the real values will exist.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
## 4 Numerical Results and Discussions
### Sum Data Rate of the System  
Figure 12 displays the sum data rate of the four algorithms versus the number of users in the network. In the four algorithms, the sum data rate of the system increases with a changing speed that gradually slows down as the number of users increases. When the user number increases and the total energy of the BS remains unchanged, the power of CUs is reduced. With the increasing number of D2D users, more CUs need to consume power on extra data transmission, which leads to further energy consumption. As the user number increases, the BS provides more SCs to the network. For example, when the user number is 80, there are 4 SCs in the network with 20 users in each SC. From Figure 12, the increment of sum data rate FSPA is smaller than the other three algorithms and the sum rate performance of FRPA becomes closer to TSA as the number of users of the system increases.
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
## 2 Production of 3 HP from Glycerol  
The use of glycerol, which is a renewable resource, as starting material for the biotechnological production of 3 HP, is a common strategy. It can be obtained as a by-product of biodiesel production. Biodiesel is produced from the transesterification of oils with a short-chain alcohol (mainly methanol) in the presence of a catalyst [5]. During biodiesel production, crude glycerol is generated as by-product in a ratio of about 100 kg glycerol to 1 ton of biodiesel produced [6]. As the worldwide production of biodiesel is tending to increase, the amounts of crude glycerol that are available will follow the same trend in the future. It is estimated that the production of glycerol will reach 4.2 \(\times\) 10\({}^{9}\) L in 2020, and that the world glycerol market will have a value of about 2.52 billion US dollars (USD) [7,8]. Moreover, the oversupply of glycerol leads to a decrease in its price [9]. During 2013, the prices of refined glycerol varied from 900 USD/ton to 965 USD/ton, with the prices of the unrefined crude glycerol being as low as 240 USD/ton [7]. The high quantities of crude glycerol that are produced require its treatment or further use, which, together with the low price, creates an ideal situation for the incorporation of crude glycerol into the biotechnological production of a variety of fuels and chemicals [8,9]. Moreover, upgrading of biodiesel-derived glycerol to high added-value products will boost the economy of the biodiesel production sector [10]. Conversion of glycerol to 3 HP is one of the available options for the utilization of glycerol, and much research effort is being made towards this conversion. Several bacterial strains, such as _Klebsiella_ sp., are capable of natural uptake of glycerol through oxidative or reductive pathways. In the oxidative pathway, this leads to the formation of pyruvate through the Embden-Meyerhof-Parnas pathway, whereas the reductive pathway results in the formation of 1,3-propanediol [11]. Genetic manipulation of the natural glycerol consuming strains can result in the conversion of glycerol to 3 HP, through 3-hydroxypropionaldehyde as an intermediate. To achieve this conversion, the genes of either the _dha_ operon or the _pdu_ operon are commonly used, as summarized in the following sections.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 3 Engineering Application
### Engineering Data Processing
Since tunnel pipelines may introduce multiple erroneous boundary features during segmentation, as shown in Figure 17, we manually determined the positions of tunnel joints in the original structure and adjusted the segmentation to reduce errors and ensure more accurate results. By applying this method, the study effectively minimizes errors caused by tunnel structural irregularities or pipeline interference, thereby improving the accuracy of manual segmentation. A random sampling of 10 tunnel rings was conducted for verification, and the average values of tunnel segmentation statistical indicators are presented in Table 1.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
###### Abstract  
LoRaWAN has become a popular technology for the Internet of Things (IoT) device connectivity. One of the expected properties of LoRaWAN is high network scalability. However, LoRaWAN network performance may be compromised when even a relatively small number of devices use link-layer reliability. After failed frame delivery, such devices typically tend to reduce their physical layer bit rate by increasing their spreading factor (SF). This reaction increases channel utilization, which may further degrade network performance, even into congestion collapse. When this problem arises, all the devices performing reliable frame transmission end up using SF12 (i.e., the highest SF in LoRaWAN). In this paper, we identify and characterize the described network condition, which we call the SF12 Well, in a range of scenarios and by means of extensive simulations. The results show that by using alternative SF-management techniques it is possible to avoid the problem, while achieving a packet delivery ratio increase of up to a factor of 4.7.  
LoRaWAN; LPWAN; SF12 well; congestion; well fall time; AFLoRa +
Footnote †: journal: _Sensors_  
0021216478. [[https://doi.org/10.3390/s21196478](https://doi.org/10.3390/s21196478)]([https://doi.org/10.3390/s21196478](https://doi.org/10.3390/s21196478))
## 1 Introduction
Thus, the received second harmonic amplitude is greatly increased due to the constructive interference of these two components and can be measured in the pulse-echo mode. This study was conducted in fluids where a focused beam can easily be formed. Phased array transducers can produce focused beams in flat solid samples, but it is not easy to create a tightly focused beam, especially with a small number of elements. Therefore, we need to devise another way to create nonlinear beam fields that can produce a phase difference of about \(\pi/2\) in the presence of the stress-free boundary. A dual element transducer with a phase shift of the input signal in one element relative to the other was found to improve the SHG in the pulse-echo test simulation of thick samples [28]. A multiple element transducer can provide more options in the control of phase shifts and improve SHG in the pulse-echo setup of thin solid samples. This paper investigates the efficient SHG from the stress-free boundary of a nonlinear solid by control of the transmission phase shift of array elements. When a sound beam radiates from annular array elements with different phase shifts, the fundamental and second harmonic waves, which are reflected from the rigid or stress-free boundary, are calculated in the pulse-echo setup and their characteristic behavior are examined based on the magnitude of received amplitudes. The phase difference between the reflected and newly generated second harmonic components is completely analyzed to explain the enhanced SHG in solid samples. We also study the effects of various phase shifts applied to array transducers. Simulation results are also given for the case of the plane wave and a single element transducer to check the diffraction effects and the through-transmission results. Optimal phase shifts for a given sample thickness and frequency are illustrated in terms of SHG and nonlinear parameter determination.
## 3 Results and Discussion
### Single-Layer Devices  
In Figure 3, we present the output and transfer characteristics of Dev-A under both dark and illuminated conditions. Notably, the PbPc single-layer OPTs display typical p-channel operation characteristics under negative gate voltage and drain voltage. Specifically, in the absence of light, the drain current (\(I_{\text{d}}\)) at a drain voltage (\(V_{\text{d}}\)) of \(-50\) V and gate voltage (\(V_{\text{g}}\)) of \(-50\) V was measured to be merely \(-14.41\) nA. Under illumination with an intensity of \(3.44\) mW/cm\({}^{2}\), the drain current increases to \(-25.62\) nA. The photocurrent (\(I_{\text{ph}}\)) of the PbPc single-layer OPT is notably small, registering at \(11.21\) nA. Consequently, it exhibits a low \(R\) of \(4.34\) mA/W, a maximum photo-to-dark current ratio (\(P_{\text{max}}\)) of \(9.3\), and an _EQE_ of \(0.65\%\). Due to its remarkably low carrier mobility of \(8.5\times 10^{-4}\) cm\({}^{2}\)/V\(\cdot\)s (as outlined in Table 1), Dev-A demonstrates suboptimal performance.
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 3 Methodology
### Slot Filling and Intent Detection  
Figure 2 illustrates the flow of a user's query. In the query \"  
To ensure the conversational system is able to deliver the correct intents and related entities, the system analyzes a soldier's utterances first to identify the entities mentioned and match them with intents stored in the mission list database, and then orders the appropriate response sentences. Because traditional retrieval systems rely on retrieving full-text search results for user queries, retrieval models are based on the similarity between the query and the text (e.g., vector space models). Therefore, the user may miss the correct answer because the intent of sentences with high similarity may not match the intent of the user's question. In other words, we prioritize intent and entity accuracy before evaluating similarity.
## 2 System Schematics and Frequency Regulation Structure  
### System Schematics  
The increasing requirements for reducing carbon footprint and energy conservation have challenged traditional power systems. As an enabling technology to address these concerns, power electronics are ubiquitous in modern power systems. In particular, the function of frequency regulation previously provided by synchronous generators will be gradually taken over by grid-tied power converters, which are controlled as virtual synchronous machines (VSMs) to mimic the frequency regulation characteristics of synchronous generators [11; 12; 13]. Figure 1 illustrate the schematic diagram of a typical grid-tied EV charger operating as a VSM. As observed, the VSM serves as an interface between renewable generators and electric loads.
## 3 Results
### Echo Sounder Mapping of Shallow Zooplankton Layers  
The Wave Glider and the Sailbuoy successfully collected good quality echo-sounder data along the Norwegian coast from 66.5 N to 70.9 N, from March to September 2018. The data allowed for mapping of the spatiotemporal and vertical distribution and density of zooplankton down to approximately 100 m depth. Using the WBT Mini echo sounder on the Sailbuoy, dense spring zooplankton concentrations were detected throughout the deployment period (Figure 6).  
In early spring (April), when copepods ascend from overwintering depths, weak layers of zooplankton were registered from approximately 20-30 m depth. Later in the season (June), a persistent high-density layer of zooplankton was observed in the upper 10-40 m. In late summer (July-August), the zooplankton layers were distributed across a wider depth range, exhibiting diel vertical migration patterns [49]. Stronger individual scatterers were also observed within, below and above the zooplankton layer, likely fish and fish larvae grazing on the zooplankton layer. Net sampling in April and June in adjacent areas revealed that zooplankton composition comprised mainly the copep _Calanus finmarchicus_, a dominant zooplankton species in the area [39].  
The Sailbuoy concurrently measured environmental variables, including temperature, salinity, and oxygen saturation. Surface water temperature and salinity increased from April to July/August while the highest oxygen saturation was found in the period of maximum zooplankton surface layer density (Table 2). The fluorescence measurements recorded by the Wave Glider at 0.5 m revealed a diurnal cycle of the fluorescence throughout the study period, with minimum values recorded at noon, and maximum values at night (Figure 5). This was observed in other studies using gliders (e.g., [62; 63; 64]) and reflects physiological quenching, an adaptation of photosynthetic organisms to avoid photo damage when exposed to excess light that leads to a massive decrease in in situ fluorescence [65].  
Figure 5: Subset of environmental variables (fluorescence, temperature, salinity, and oxygen) recorded by the Wave Glider during a 1-month period (June–July 2018) across a transect in the LoVe region.
## 3 Results  
### Spatial and Temporal Sampling  
Pseudo-SWOT data were generated for the full WMOP time period (1 January 2009 to 11 September 2015). This was done for the Science orbit and corresponds to a total of 123 cycles. In a complete cycle, 292 passes are available over the globe, 12 of them crossing our study region (Figure 3). Data were ingested and processed by the SWOT simulator at an across and along track resolution of 2 km. As mentioned in Section 1, one of the specificities of future SWOT data will be their irregular temporal sampling. To better illustrate this, the passes of cycle 2 are plotted in Figure 3. During each cycle, there are periods of time with a higher temporal sampling. This is due to a longer revisit time so that SWOT also fulfills its hydrological objectives as described in Section 1. For instance, the temporal sampling during cycle 2 is as follows: from day 21.3 to 23.9 during which five passesare made; and from day 31.3 to 33.8 during which six passes are made. Then, from day 23.9 to 31.3 and from day 33.8 to 41.2, there are no measurements. Consequently, during each day within a cycle in this study region, there can be two, one or no passes at all. Even with this irregular sampling and without any processing of the data, the final SSH\({}_{obs}\) map (subplot of day 41.2) allows us to observe some features such as, for example, the signal of the Algerian Current following the north African coastline and several cyclonic and anticyclonic mesoscale eddies.  
Figure 3. SSH\({}_{obs}\) (m) obtained for cycle 2. Time increasing from left to right, top to bottom. Days from the beginning of the simulation are shown at the top left corner and the corresponding pass number at the bottom right corner. Outline of the active pass is shown in black. The red boxes show box 1 (pass 15) and box 2 (pass 168).
## 2 Materials and Methods
### Measurement of ROS  
DCFH-DA, a fluorescent probe, was used to determine the intracellular ROS generation. In brief, RAW 264.7 cells were seeded in 96-well plates at a concentration of 2 \(\times\) 10\({}^{5}\) cells/mL. Cells were then treated with SHE at a concentration of 25, 50, or 100 \(\upmu\)g/mL for 1 h before stimulation with LPS (1 \(\upmu\)g/mL) for 24 h. The medium was shaken off, and cells were washed once with PBS and discarded. Afterward, the cells were treated with DCFH-DA diluted to a concentration of 20 \(\upmu\)M in warm DMEM (no phenol red, no FBS) for 30 min. Next, the cells were washed twice with warm PBS. The fluorescence levels were assessed with a microplate fluorometer (Molecular Device, Danville, PA, USA), and an analysis was performed using Softmax Pro v5.4.1 (Molecular Device, Danville, PA, USA). The localization of DCFH-DA was determined using a fluorescence microscope (Leica-Microsystems, Wetzlar, Germany). Image analysis was performed using MetaMorph software (Molecular Devices, Danville, PA, USA).
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
### Problem Statement  
Supervised models are proficient at identifying known attacks; however, they depend significantly on the attack patterns learned during their training phase. Consequently, they find it challenging to detect unknown or emerging attacks when such threats manifest in network traffic. Special attention is devoted to novel attacks commonly known as zero-day attacks and false negatives, which can lead to undetected security breaches, heightened vulnerability, and damage to reputation [3; 4]. To overcome this limitation, we have implemented an unsupervised learning approach that can detect unknown, emerging, or zero-day attack vectors. This approach, which is trained on imbalanced and unlabeled data, is capable of identifying abnormal behavior in network traffic without the need for pre-labeled attack patterns. Unsupervised learning models provide significant benefits for IDSs, especially in situations where labeled data are limited or unavailable. Unlike supervised learning, which relies on labeled datasets for training, unsupervised learning can function effectively using unlabeled data. This is particularly advantageous in the context of IDS, where acquiring a complete and precisely labeled dataset encompassing all possible attack types can be difficult and expensive.  
Leveraging these techniques strengthens cybersecurity, enabling real-time risk detection and mitigation. The quality of training data profoundly affects unsupervised anomaly detection's efficacy, emphasizing the need for diverse datasets to address traffic feature variations [5; 6; 7; 8]. Neglecting complex data patterns can impair detection. Addressing these challenges ensures system robustness, aiding proactive cybersecurity measures against emerging threats in the digital realm.