esa-sceva/satcom-chunk-collection huggingface dataset:
# Pressure Drop and Particle Settlement of Gas-Solid Two-Phase Flow in a Pipe
## 4 Results and Discussion
### Relationship of Settlement Index, Pressure Drop and Related Parameters  
It is necessary to build a relationship between the settlement index, pressure drop per unit length, and related parameters in order to effectively characterize the gas-solid two-phase flow in a pipe. As shown in Figures 7-9, the settlement index \(Se\) is inversely proportional to the inlet velocity \(V\), particle volume concentrations \(\alpha_{s}\), and particle mass flow \(m_{s}\), while it is also directly proportional to the particle diameter \(d_{p}\). As shown in Figures 10-12, the pressure drop per unit length \(p/L\) is proportional to the inlet velocity \(V\), particle volume concentrations \(\alpha_{s}\) and particle mass flow \(m_{s}\), while it is inversely proportional to the particle diameter \(d_{p}\). As such, we can combine \(V\), \(\alpha_{s}\), \(m_{s}\), and \(d_{p}\) into a synthetic parameter:  
\[\eta=\frac{d_{p}}{V\alpha_{s}m_{s}},\ \ \ \ \ \zeta=\frac{V\alpha_{s}m_{s}}{d_{p}}. \tag{9}\]  
Based on the above numerical data and expression (9), we can establish the following settlement index \(Se\) and pressure drop per unit length \(p/L\) formula:  
\[Se=0.69854+2.68062\log\eta-0.15859(\log\eta)^{2}, \tag{10}\]  
\[\frac{p}{L}=119.33207\exp(\frac{\zeta}{1.35994})-117.66952. \tag{11}\]  
The settlement index and pressure drop per unit length as a function of related synthetic parameter are shown in Figures 13 and 14, where each solid dot represents different numerical data under different \(\eta\) and \(\zeta\), which are composed of different \(V\), \(\alpha_{s}\), \(m_{s}\), and \(d_{p}\).
## 6 Discussion  
The convergence speed in this paper is expressed by the time number of the minimum RMS. The convergence accuracy in this paper is expressed by the minimum RMS. The stability in this paper is expressed by the divergent proportion of the RMS. The divergent proportion is defined as the difference between the final RMS and the minimum RMS, divided by the number of last iteration and the number of iterations at the minimum RMS.  
According to the figures and tables in Sections 5.1-5.3, method 1 converges slightly slower than method 2; however, the RMS minimum of method 1 is the smallest, so the convergence precision is the best, and the divergence ratio is smaller than method 2, which is relatively more stable. The convergence speed of method 2 is the fastest, but the minimum value is slightly larger than method 1, and the divergence ratio is larger than that of method 1, which is relatively more unstable. The method 3 converges at the same speed as method 2, but its minimum value is larg, and the  
Figure 15: System TF estimation (color from light to dark, represents the progress of the iterative process).  
divergence ratio is the largest, making it the most unstable. Method 4 has the slowest convergence, the largest minimum, the worst convergence precision, and a large divergence ratio--which is also very unstable. Method 5 has a slow convergence, a large minimum and a poor convergence precision.  
Method 1 and method 2 have great advantages over manual strategy in convergence effect, convergence speed and iteration stability. The convergence effect of method 1 is the best, although the convergence speed of strategy 1 is not the fastest, the iteration stability is good. Compared with method 1, method 2 has the best convergence speed, but the convergence precision and iteration stability are slightly worse.  
According to the figures in Section 5.4, the iteration coefficient can control the error ratio in the range close to the unit circle. In contrast, if there is no iteration coefficient involved in iteration control, the error ratio will be larger and larger as the iteration progresses, which will cause the iteration to diverge.
## 2 Results and Discussion
### Global Overview of the Rsh\({}_{\text{US6-1}}\) Regulon
DksA, a (p)ppGpp cofactor, can bind the secondary channel and sensitize RNAP to (p)ppGpp at many promoters [3]. The previous study showed that DksA could also function without ppGpp [50]. In _P. aeruginosa_, DksA represses the expression of _rllI_ during the exponential growth [51]. The gene encoding an ATP-dependent Lon protease (WP_007012358) was up-regulated 2.01-fold due to the Rsh\({}_{\text{US6-1}}\) absence. The gene is involved in the toxin-antitoxin (TA) module, a (p)ppGpp-dependent mechanism related to antibiotic tolerance and resistance [3]. Lon protease is a powerful negative regulator of two HSL-mediated QS systems in _P. aeruginosa_[52]. In the TA model, (p)ppGpp activates the Lon protease [3]. Interestingly, our data indicated an opposite type of the TA module regulation of ppGpp in strain US6-1. Moreover, the expression levels of two genes involved in the putative functions of RNA degradation (WP_007014797 and WP_007015015) were increased 2.62 and 2.19-fold respectively in \(\Delta\)_rsh_. The enzyme responsible for RNA degradation has been reported to degrade mRNA of AHL synthase genes and thus decrease the AHL production rapidly [53]. Overall, the above transcriptome data showed that ppGpp could regulate several factors including the ECF sigma factor, DksA, ATP-dependent Lon protease and RNA degradation enzymes. These factors have been proved to affect the expression of _luxI/R_ homologs in other bacteria. However, the relationship between these factors and _luxI/R_ homologs in sphingomonads remains unknown and needs further studies. We proposed these potential molecular mechanisms of ppGpp-dependent expression of _novl_ and _novR_, which might contribute to giving insight into the complicated cross-talk between stringent response and QS in sphingomonads.
## 3 Pharmacological Properties of GP
### Anti-Cancer Effect
#### 3.1.2 Induction of Cell Apoptosis  
Inducing apoptosis was one of the effective ways to treat tumor, which could improve the recovery of tumor patients. [PERSON] et al. [30] evaluated the cytotoxicity of GPS in human colorectal cancer SW-480 cells, and the results showed that GPS (70-130 mg/mL) could decrease the percentage of viable cells and increase the plasma membrane permeability of SW-480 cells in a dose- and time-dependent manner. More importantly, reactive oxygen species (ROS) play an important role in GPS-induced cell toxicity and apoptosis. After the GPS treatment, the level of intracellular ROS level was increased, and DNA fragmentation and apoptotic morphology were observed. [PERSON] et al. [41] investigated the effects of GPS on renal cell carcinoma (RCC) and its molecular mechanism for the first time. Studies concluded that the cell viability of 786-O and Caki-1 treated with gradient concentration of GPS (0, 75, 150, 300, 600 mg/mL) for 24 h showed a significant decrease. In addition, GPS induced the apoptosis of RCC cells through regulating the phosphoinositide 3-kinases/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) signaling pathway.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 2 UWB Forklift Localization Method
### Forklift Motion Model
The UKF filter needs, for several initial parameters, to run, for instance, the parameters \(\alpha\), \(K\), and \(\beta\)[59]. \(\alpha\) and \(K\) size the spread of the sigma-points, whereas the parameter \(\beta\) must be chosen according to the data distribution. Typical recommendation is \(\alpha=10^{-3}\), \(K=0\), and \(\beta=2\)[60]. The initial covariance matrix \(\mathbf{P_{0}}\in\mathbb{R}^{8\times 8}\) is set to the following:  
\[\mathbf{P_{0}}= \begin{bmatrix}\sigma_{UIWB}^{2}&0&0&0&0&0&0\\ 0&\sigma_{UIWB}^{2}&0&0&0&0&0\\ 0&0&\dfrac{r^{2}}{12\Delta t^{2}}&0&0&0&0\\ 0&0&0&\dfrac{r^{2}}{12\Delta t^{2}}&0&0&0&0\\ 0&0&0&0&\sigma_{a}^{2}&0&0\\ 0&0&0&0&0&\sigma_{\omega}^{2}&0\\ 0&0&0&0&0&0&\sigma_{\theta}^{2}\end{bmatrix}  \tag{4}\]  
where \(\sigma_{\theta}\) is the standard deviation of the forklift initial orientation error.
## 3 Research Methodology
### Methods Proposed for Enhancing IoT Data Transmission Security  
Stream encryption is a symmetric-key encryption algorithm, where both encryption and decryption parties use the same key. The sender utilizes this key as a seed for a Pseudo Random Number Generator (PRNG) [45] to generate a key stream. This key stream is then sequentially used to encrypt a plaintext data stream, resulting in a ciphertext data stream. The receiver can decrypt the data if they possess the same key. Building upon traditional encryption methods, our research introduces a method for secure IoT communication, referred to as D-OTP, based on the previous work [4].  
D-OTP combines two aspects: \"Operation Password\" and \"Encryption Key\", briefly explained as follows: (1) The \"Operation Password\" refers to a method for users to utilize to gain system access. One-Time Password (hereafter referred to as OTP password) is a single-use password used by the user during login. OTPs are widely used security authentication tools, commonly seen in financial, telecom, and online gaming transactions, and the applications applied in [46]. (2) The \"Encryption Key\" includes critical data used for computational operations with plaintext data to obtain ciphertext data within the system. One-Time Pad (hereafter referred to as OTP-key) is a key that can only be used once during encryption operations, and OTP-key is a classical encryption algorithm in cryptography and is proven by [PERSON] [16] to possess theoretical un-breakability. For zero-trust network device access, this paper's innovation lies in further integrating OTP password and OTP-key with the \"process of hardware accelerated stream data encryption\", namely the One-Time Password Pad Process (OTP\({}^{3}\)), which aims to fulfill the future stringent confidentiality requirements for zero-trust secure data transmission [47]. The detailed specification of OTP\({}^{3}\) will be further explained in the following section.
## 3 Experimental Results
### Quantitative Evaluation
#### 3.4.3 PA Results on 20 Test Samples  
The PA comparison of TongueNet and REF on 20 test images is illustrated in Figure 13. The proposed TongueNet obtained over 97% for all test samples, with the highest being 99.33% (the 9 th sample). This means TongueNet can correctly predict on average over 97% of the pixels from the images in the test set, indicating that TongueNet holds a high prediction accuracy. Similar to the IOU results (above), TongueNet also outperformed REF on all test samples, where the mean PA (MPA) of TongueNet is 98.44%, while the MPA of REF is 96.8%. Thus, it proves that TongueNet has a stronger ability at pixel prediction.  
Figure 12: IOU comparison of TongueNet (green) and REF (red).
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 3 Designed Channel Code-Book MappingNet Based on Vector Quantization
### Channel Code-Book MappingNet  
Faced with the complex satellite-ground environments, a single channel parameter expression cannot accurately describe the current actual situation. Therefore, to improve the performance and reliability of communication systems, we propose the Channel Code-Book MappingNet. The network consists of two main modules: Channel Code-Book and Denoising Parameter. Channel Code-Book is used to store different channel parameters, and Denoising Parameter is used to store the denoising model parameters under different channel conditions. The channel state mapping network realizes the accurate description and optimization of the channel environment by extracting and mapping the channel state information to the corresponding denoising parameters.
###### Abstract
However, in both of these methods, the FORM sensitivity factors \(\alpha_{\rm R}\) and \(\alpha_{\rm E}\), which are direct parameters used in calculation, are set equal to \(0.8\) and \(-0.7\) for dominant variables, respectively, and are equal to \(0.32\) and \(-0.28\) for nondominant variables. A resistance variable is considered dominant if its sensitivity factor calculated as \(\alpha_{Xi}=COV_{Xi}/\sqrt{\sum_{j}COV_{Xi}^{2}}\), (where COV is coefficient of variation and summation is undertaken over all basic resistance variables) exceeds that of the other resistance variables. The same procedure is performed for determining the dominant load variable. This approach, suggested by [4], has been applied, for example, in [5] for the assessment of the existing sample bridge by recalculating the partial factors. Further, a recalibration of the partial factors, with the corresponding use of sensitivity factors, is often of interest, for example, for the reassessment of the traffic load model [6] or for the assessment of railway bridge reliability [7]. The goal of this paper is to provide a guideline for the correct calculation of the sensitivity factors, and to show that correct values differ significantly from the proposed values from the Eurocode. Without having the accurate sensitivity factors, a proper partial factor calibration is not possible. Taking into account the dependence of the sensitivity factor on the ratio between traffic load and dead load, the correct partial factors for new or existing bridges can be calibrated in defining the load models, satisfying the target safety index (and consequently the probability of failure). Figure 1: Design point and reliability index \(\beta\) according to FORM for normally distributed variables (EN1990).
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 4 Multi Topology Routing Algorithm
### Initial Link Weight  
According to the above classification of spatial tasks, QoS optimization objectives usually include the following: dedicated bandwidth, end-to-end delay, delay jitter and packet loss rate. According to the above optimization objectives, E is used to represent the initial link weight, s and d are used to represent the source and destination nodes of a path respectively, \((i,j)\) represents a link between node and destination node, from node \(i\) to node \(j\), _band \((i,j)\)_ represents the residual bandwidth of the link \((i\), \(j)\), _delay \((i,j)\)_ represents the end-to-end delay of link \((i,j)\), _jitter \((i,j)\)_ represents the delay jitter of link \((i,j)\), _loss \((i,j)\)_ represents the packet loss rate of the link \((i,j)\). \(B\), \(D\), \(J\) and \(L\) respectively represent the requirements of spatial tasks on residual bandwidth, end-to-end delay, delay jitter and packet loss rate. The end-to-end delay consists of four parts: propagation delay on the link, processing delay on the node, transmission delay on the node port and queuing delay on the node. The remaining bandwidth, end-to-end delay, delay jitter and packet loss rate constraint formulas are as follows:  
\[\text{Remaining bandwidth}:\ \min_{(i,j)\in path(s,d)}(band(i,j))\geq B \tag{1}\]  
\[\text{End-to-end delay}:\ delay(path(s,d))\leq D \tag{2}\]  
\[\text{Delay jitter}:\ jitter(path(s,d))\leq J \tag{3}\]  
\[\text{Packet loss rate}:\ 1-\prod_{(i,j)\in path(s,d)}(1-loss(i,j))\leq L \tag{4}\]The initial link weight is calculated according to the business requirement, and the initial link weight calculation model is:  
\[\min(delay(s,d)/D,B/B(s,d), jitter(s,d)/J,loss(s,d)/L) \tag{5}\]  
The constraints of various performance parameters are  
\[s.t.\left\{\begin{array}{l}delay(path(s,d))\leq D\\ \min_{(i,j)\in path(s,d)}(band(i,j))\geq B\\ jitter(path(s,d))\leq J\\ 1-\prod_{(i,j)\in path(s,d)}(1-loss(i,j))\leq L\end{array}\right. \tag{6}\]  
\(\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4}\) represent the calculation coefficients of residual bandwidth, end-to-end delay, delay jitter and packet loss rate respectively, and the initial link weight formula is as follows.  
\[E=\alpha_{1}\frac{delay(s,d)}{D}+a_{2}\frac{B}{B(s,d)}+a_{3}\frac{ jitter(s,d)}{J}+a_{4}\frac{loss(s,d)}{L} \tag{7}\]  
Here, \(\alpha_{1}+\alpha_{2}+\alpha_{3}+\alpha_{4}=1\).  
The SIN is large in scale and complex in structure. Different tasks have different attributes, different requirements for link attributes and different parameter settings. Therefore, the initial link weights for different tasks are different.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 3 Method
### Grayscale Spatial-Temporal Features  
Grayscale spatial-temporal feature includes two features: target-STF and region-STF. These two spatial-temporal features of all targets in each frame are saved with two grayscale images, respectively, and each grayscale value in the map corresponds to a spatial-temporal feature. As shown in Figure 2, the grayscale maps on the left are target-STF maps, and the grayscale maps on the right are region-STF maps. In the target-STF map, each target-STF corresponds to a target. In the region-STF map, each region-STF corresponds to one or more targets.  
The target-STF applies different grayscale values to mark targets. We obtain the bounding box of the target and find the grayscale values of the previous frame within the bounding box. Then, we can quickly find the targets that need to be matched based on these grayscale values. As shown in Figure 2, take the pink target as an example. At frame \(t=0\), assign an ID to the target and obtain the target-STF. At frame \(t-1\) and \(t\), after matching the target-STF, obtain the target ID that needs to be matched by grayscale value, then perform target ID matching.  
For re-identified targets, they were not detected and did not have the target-STF in the previous frame. Therefore, we proposed region-STF. The region-STF uses different grayscale values to mark regions. For a re-identified target, we find the region that the target previously belonged to by region-STF and perform target ID matching in this region. As shown in Figure 2, take the green target as an example. At frame \(t=0\), assign an ID to the target and obtain the region-STF. Store the successfully matched target ID in the region where the target is located. A target ID that has not been successfully matched for a long time will be removed from this region. At frame \(t-1\), the green target detection failed. At frame \(t\), the green target is re-detected, and we use region-STF to find the area in which the green target belongs and match the green target with the target ID contained in this area. The region-STF effectively handles the problems caused by occlusion and improves the robustness of tracking.  
Figure 2: Target spatial-temporal features and region spatial-temporal features. The red bounding box is target-STF, the purple bounding box is region-STF.
### Morphology of Allophane and Imogolite  
Allophanic soils are composed principally of two minerals, allophane and imogolite, mixed with vitreous material. Several investigators have studied the morphology of these two minerals using TEM and SEM microscopy. Some works study only allophane [16; 17; 18] or imogolite [19; 20], but as these minerals usually occur together, most studies describe the combination of the two minerals that naturally occurs in soils [21; 22; 23; 24]. According to these studies, the allophane morphology is described as having two principal forms. The first form is a hollow sphere of 35 to 50 A in diameter, while the second is a discontinuous annular ring with an inside diameter of 35 A. The imogolite is conformed as a shape of hollow microfibers, with diameters of 10 to 30 A and of variable length [25]. In nature, allophane and imogolite usually occur together in clusters. Thus, the allophane forms point clusters, while the imogolite fibers are intertwined with each other [21; 22; 23; 24].  
The allophane and imogolite morphologies confer that these types of soils can retain a large amount of water. The retention of water occurs both inside the particles and their pores' macro-structures. This fact plays an essential role in the behavior of these types of soils.
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 3 Materials
### Integrated GNSS/IMU Gyrocompass Structure and Features
It should be pointed out that using the phase single difference requires no additional (special) processing of satellite signals with frequency division (GLONASS). One should underline that with GNSS data available, the applied more accurate FOGs can be calibrated by rotation with the accuracy sufficient to guarantee the acceptable accuracy of INS attitude with completely lost satellite data for a rather long time (over 1 h). However, during satellite signal outages additional linear velocity and altitude (depth) measurements should be applied.
## 4 Proposed Extension to CARLA
### Designing CARLA+
#### 4.2.1 CARLA Architecture  
CARLA utilizes the client-server model for its operation where the server runs the actual simulation and the client is responsible to communicate and controlling that simulation. The CARLA client communicates with the CARLA server using the provided API. Therefore, to use the CARLA client, we first establish a connection with the CARLA server running on a specific IP and port. Multiple clients can also be used to communicate simultaneously with a server. The main functionality of the client is to ask for information from the server, load different maps, record simulations, and initialize the traffic manager. The classical architecture of CARLA is presented in Figure 2.  
In CARLA, the World represents an object, encapsulating the actual simulation. It is an abstract layer that is used to spawn different actors (vehicles, pedestrians, etc.), change weather conditions, obtain the current state of the world, etc. Each simulation will contain only one world object, which will be destroyed and replaced when loading a new map.  
CARLA also provides a Traffic Manager module which controls vehicles in autopilot mode. This is the default implementation for traffic simulation in CARLA. Its behaviors can be modified by the user/client, e.g., forcing a lane change, over-speeding, ignoring traffic lights, stopping signs, etc. It can be used to orchestrate carefully designed driving scenarios to train autonomous agents.  
Actors are those elements in CARLA that can perform some action to affect the environment or other actors. These include different sensors, traffic signs, traffic lights, vehicles, pedestrians, etc. These actors can be spawned, handled according to the requirements, and destroyed, by the World. Sensors are special actors that are used to retrieve data from their surroundings.
## 3 Results and Discussion
### Verification of Detection Efficiency  
The table below presents a comparison of the values of the parameters for the assessment of hyperbola detection efficiency in reference to GPR images recorded from channels that are characterized by extreme values of the indicators (Laplacian variance, PIQE, and entropy) in the obtained quality assessment. Table 5 presents the differences in the values of the M and L parameters that may be obtained by the user for images of the lowest and highest quality from the selected three measurement routes.  
Figure 12: Resultant images for the detection, based on the example of images of the (**a**) highest and (**b**) lowest quality of the radargram.  
Areas characterized by lower entropy values may indicate a lower quality of data, including the blurring of specific areas in the image.
# An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
## 4 Simulation Results and Discussion
### Analysis of Simulation
Throughput has nothing to do with the number of iterations and convergence, the main reason being that users cannot perceive the transmission signals of other users; at the same time it will not be able to eliminate mutual interference due to the user frequency: random selection after the encountered jamming in channel transmission strategy cannot be efficient due to multichannel random disturbance. Therefore, it has the worst anti-interference effect and cannot guarantee the normal communication of users.