esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 3 Results and Discussion  
### Logging Parameter Analysis  
In building the prediction model, TOC content cannot be accurately predicted by a single logging parameter or all logging parameters; hence, sensitive logging parameters must be selected as input variables. After data normalization, the sensitive characteristics of total organic carbon content were analyzed based on [PERSON]'s correlation coefficient and heat maps. The results are shown in Figures 2 and 3. The results show that w(TOC) is highly correlated with natural gamma (GR) and acoustic (AC), followed by compensated density (DEN), compensated neutron (CNL), shallow lateral resistivity (LLS) and deep lateral resistivity (LLD), low correlation with microsphere focusing resistivity (MSFL), and natural potential (SP). Therefore, GR, AC, DEN, CNL, LLS, and LLD logging parameters were selected as input variables for the TOC content prediction model. In addition, there is a multiple covariance relationship between logging parameters, such as compensated density (DEN) and acoustic (AC), compensated neutron (CNL) and compensated density (DEN), and shallow lateral resistivity (LLS) and deep lateral resistivity (LLD), which have high similarity and need further processing.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### UAV Networks and Communications
#### 2.3.3 Navigation Systems  
There are two general ways a UAV can move around. In automatic UAVs, a controller is used by the pilot and typically operated through the pilot's vision or a camera on the UAV. In autonomous UAVs, there are navigation systems. They have this for the apparent reason of no one piloting them. These navigation systems need to be extensively precise [26]. These UAVs are often fitted with a GPS or GNSS device to get the UAV's location. This would then translate to the navigation and autopilot software to find the UAV's bearing and distance left.  
Because location, bearing, and distance are all variable during a flight mission, the GPS/GNSS systems send data frequently so the UAV autopilot can control the direction of flight [24]. [PERSON] et al. proposed a method of navigation that is extremely simple for the delivery of UAVs [24]. After the user inputs the target location, the UAV is to accept this data. Once accepted, the UAV uses the navigation systems to reach this location. After landing at the target, the UAV returns to its home (the take-off point), where it is to wait for another set of target data. Then, the UAV would repeat the process.  
A significant navigation system goal is to reduce the probability of a crash and when pathfinding puts into account: stealth, feasibility, performance, and implementation. With stealth, many UAVs are used in dangerous environments; therefore, it is paramount to avoid detection. Feasibility refers to the physical capabilities regarding endurance and path length. Performance looks at minimum altitude and flight angles (turning, climbing, and diving). Implementation is likely the most important when stealth is not as important. Implementation refers to the actual efficiency of the route calculated [29].
## 3 Results
### HS-GC-IMS Analysis of Smoke-Flavored Sea Bass in Different Production Steps
#### 3.2.2 Changes in the Volatile Compounds of Different Smoke-Flavored Sea Bass
The content of 3-methylbutanol monomer did not vary significantly (\(p<0.05\)), while hexanol was highest in fresh samples and 1-propanol increased in dried samples. After treated with smoke flavors, the content of 2-propanol increased, but it significantly decreased in the heated samples (\(p<0.05\)). Among the other volatile compounds, ethyl acetate and acetic acid did not change significantly (\(p<0.05\)). The relative contents of 3-pentanone, pentane-2,3-dione, 3-hydroxy-2-butanone and 2-methylbutyric acid were significantly increased in the dried samples. The relative contents of 2,3-butanedione and 2-acetylfuran showed relatively high contents in the smoked samples. After high-temperature heating, the contents of 3-pentanone, 2,3-butanedione, and 2-acetylfuran decreased. Additionally, ANOVA showed that 14 volatiles had notable differences between samples (\(p<0.05\)). 1-Hexanol had the highest ionic signal intensity in fresh fish samples; heptanal, 2,4-heptadienal, hexanal, 3-pentanone, pentane-2,3-dione and 2-methylbutyric acid had the highest ion signal intensity in the dried samples; 3-methylbutanol, 2-propanol, furfural, 2,3-butanedione and 2-acetylfuran had the highest ion signal intensity in the smoked samples, and benzaldehyde and 2-methylpropanal had the highest ion signal intensities in the heated samples. By GC-IMS analysis, these 14 compounds may be considered potential key compounds.
## 4 Results
### Penetration Outcome
#### 4.2.3 Rogue BTS  
The packets captured by Wireshark during the implementation of the Rogue BTS confirmed the encrypted network connection. Cross-checked with the public transport operator team, it was asserted that a Virtual Private Network (VPN) connection is built between the OEM's backend servers and the vehicle. Hence, the data in transit is encrypted from end to end and can therefore be considered secure. However, it is still possible to break the encryption keys in offline mode using existing tools such as Hashcat. Such a practice is usually too time-consuming to be cost-effective [51]. Additionally, if the encryption keys are changed regularly (respecting perfect forward secrecy), breaking one of them will not allow the decryption of all communications but only those of the specific session of the key. Thus, Rogue BTS attacks can be considered ineffective against this vehicle.
## 5 Simulation Result and Discussion
### Simulation Setup
#### 5.1.3 Fusion and Validation Stage Two  
At this stage, the experiment aims to enhance the model's performance by fusing the classification results of every single model. The combination of models was determined based on the single classification result. The model with the highest accuracy rate was combined with other models to achieve the best performance. The model was then validated using a different validation dataset from the training dataset.
## 2 Dynamics of MEMS Gyroscopes
The lumped disturbance is further described such that \(\mathbf{F}=-(\Delta\mathbf{D}+2\Delta\Omega)\dot{\mathbf{q}}-\Delta\mathbf{K }\mathbf{q}+\mathbf{d}\) where \(\Delta\mathbf{D}\), \(\Delta\mathbf{\Omega}\), and \(\Delta\mathbf{K}\) denote parameter uncertainties of \(\mathbf{D}\), \(\mathbf{\Omega}\), and \(\mathbf{K}\), respectively, and we allow 10% parameter variations for the spring and damping coefficients with respect to their nominal values such that \(\Delta\mathbf{D}=0.1\mathbf{D}\), \(\Delta\mathbf{\Omega}=0.1\mathbf{\Omega}\), and \(\Delta\mathbf{K}=0.1\mathbf{K}\). \(\mathbf{d}\) is external disturbance vector caused by temperature change, humidity change, shock, and so on. State space form of gyroscope dynamics is:  
\[\left\{ \begin{array}{l}\dot{\mathbf{q}}_{\mathbf{1}}=\mathbf{q}_{\mathbf{2} }\\ \dot{\mathbf{q}}_{\mathbf{2}}=-(\mathbf{D}+2\mathbf{\Omega})\dot{\mathbf{q}}_{ \mathbf{1}}-\mathbf{K}\mathbf{q}_{\mathbf{1}}+\mathbf{F}+\mathbf{U}\end{array} \right. \tag{5}\]  
where system states \(\mathbf{q_{1}}\) and \(\mathbf{q}_{\mathbf{2}}\) are the displacement and velocity vectors of the detect mass and they are denoted as \(\mathbf{q_{1}}=\mathbf{q}=\left[ \begin{array}{c}q_{x}\\ q_{y}\end{array} \right]\)and \(\mathbf{q_{2}}=\dot{\mathbf{q}}=\left[ \begin{array}{c}\dot{q}_{x}\\ \ddot{q}_{y}\end{array} \right]\).\(\mathbf{F}=\left[ \begin{array}{c}F_{1}\\ F_{2}\end{array} \right]\) represents the lumped disturbance vector. Assumption 1 regarding \(\mathbf{F}\) is made for further discussion. Assumption 1: _The lumped disturbance is bounded [12] such that \(|F_{i}|\leq F_{di},i=1,2\). \(F_{1}\), \(F_{2}\) are lumped disturbances in the X and Y axis, respectively, and \(F_{d1}\) and \(F_{d2}\) are disturbance bounds in each direction._  
The control objective for MEMS gyroscopes is: 1) to propose an adaptive controller so that the proof mass can be driven into oscillation motion at given amplitudes and frequencies in the driving and sensing axis, or in other words, to reach the target that the gyroscope state \(\mathbf{q}\) can track a reference trajectory \(\mathbf{q_{d}}\) and the tracking error satisfies specific prescribed performance requirement and 2) controller parameters can be tuned by adaptive laws.
## 2 Robotic System  
The design of a robotic system depends on the type of structure to be examined, inspection method, payload, other control mechanisms required to use the inspection device (e.g., fixed or scanning type), and environmental loads. Figure 1 shows a 3-d model of such a system for the inspection of risers or pipelines. It includes a rotary scanning mechanism as the inspection device requires full 360-degree access to the structure.  
To provide stability during scanning, a gripping mechanism was developed using a combination of linear actuators that grasp the structure during scanning. Other sensing devices included in the design are the vision camera and robot positioning sensor. To deploy and monitor the robotic system, the topside unit includes the deployment mechanism; sensors to monitor system parameters such as temperature, humidity, and gas detection; and a power supply. Figure 2 shows the system configuration involving various components of the system. The system consists of multiple actuators and sensors which are located at different places. The connectivity among these and the real-time monitoring and control of each are especially important.  
Various design configurations for controlling the system are possible. There could be a powerful centralized controller that can access all the devices, implement the control algorithms, and drive the actuation systems as required. However, the distances involved in the positioning of subunits can introduce latencies in the implementation of real-time control of the actuators and also need a large number of wiring connections between all devices and the centralized controller. With advancements in the communication technologies and the design of integrated devices, there has been a shift from centralized to distributed control, and this concept has been used to address the above issues. The distributed control system configuration used was a combination of field  
Figure 1: 3-d concept model of the robotic inspection system.  
Figure 2: System configuration components.  
performance and safety requirements and associated costs to achieve the required functionality of the system in real time.
## 4 Main Result
### Erasure Channel Description  
By an analogy with [47; 93; 94], let us assume that output measurement is encoded by an encoder and transmitted to a decoder through packet erasure channel with erasure probability \(p\). In addition, suppose that there exists a feedback from decoder to the encoder for acknowledgment whether the packet was erased or not. Therefore the encoder knows what information has been delivered to the decoder (i.e., the aforementioned equi-memory condition is fulfilled). Let the acknowledgment signal at time \(k\) which is sent by the decoder and received by the encoder be represented by \(\zeta[k]\in\{0,1\}\) as follows:  
\[\zeta[k]=\begin{cases}0,&\text{no erasure at instant $k$},\\ 1,&\text{otherwise}.\end{cases} \tag{18}\]  
Random variables \(\zeta[k]\), \(k=0,1,\dots\) are assumed to be independent and identically distributed with common distribution: \(P_{r}(\zeta[k]=0)=1-p\) and \(P_{r}(\zeta[k]=1)=p\).
###### Abstract  
In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show that the accuracies of integrating the STPs of LEOs at 30 s intervals are better than 0.01 m when the forces (\(<\)10\({}^{-5}\) ms\({}^{-2}\)) acting on the LEOs are ignored. Therefore, only using the Earth's gravity model is good enough for the proposed RD_STP method. All unmodeled dynamic models (e.g., luni-solar gravitation, tide forces) are treated as the error sources of the STP pseudo-observation. In addition, there are no pseudo-stochastic orbit parameters to be estimated in the RD_STP method. Finally, we use the RD_STP method to process 15 days of GPS data from the GOCE mission. The results show that the accuracy of the RD_STP solution is more accurate and smoother than the kinematic solution in nearly polar and equatorial regions, and consistent with the RD solution. The 3D RMS of the differences between the RD_STP and RD solutions is 1.93 cm for 1 s sampling. This indicates that the proposed method has a performance comparable to the RD method, and could be an alternative for the POD of LEOs.  
POD; GPS; LEO; GOCE; reduced-dynamic method; kinematic method +
Footnote †: journal: remote sensing
## 3 Simulation Results  
Aiming to validate the presented analytical model by the two above-described scenarios, the model is implemented by means of ns-3 network simulator (release 3.32 [31]), running in the Linux environment and enabling the testing of various scenarios and environments with appropriate LTE RF modules and extension options towards 5G [32].  
The following set up data were used for simulation, with the values presented in Table 2:  
* count of macro cell BSs: \(N_{\text{macro}}\),
* maximal output transmit power of the macro-cell BS: \(P_{\text{cell\_macro}}\),
* maximal output transmit power of the small-cell BS: \(P_{\text{cell\_small}}\),
* count of small-cell BSs: \(N_{\text{small}}\),
* population density per m\({}^{2}\): \(D\),
* maximal distance between BSs in the macro cell: \(r_{\text{macro}}\),
* maximal distance between BSs in the small cell: \(r_{\text{small}}\),
* count of resource blocks in accordance to the standard for the 5 MHz channel: \(n_{RB}\)[1],
* center of the frequency operating band: \(f\),
* LTE channel bandwidth: \(B\).  
We applied the Rayleigh statistical propagation model for the downlink channel [33], as it enables a drastically shorter program execution time than the computationally much more demanding three-dimensional 3 GPP model [23].  
The simulations were performed in five rounds, where the accuracy of the obtained results--_SINR_ in particular, was improved by averaging, and the results were statistically processed in accordance with standard procedures for large data volumes [34].  
In Table 3, the simulation results are presented and conform to the setup data from Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(N_{\text{macro}}\) & 5 & \(r_{\text{macro}}\) & 500 m \\ \(P_{\text{cell\_macro}}\) & 40 W & \(r_{\text{small}}\) & 50 m \\ \(P_{\text{cell\_small}}\) & 250 mW & \(n_{\text{RB}}\) & 25 \\ \(N_{\text{small}}\) & 250 & \(F\) & 2.1 GHz \\ \(D\) & \(3.8\times 10^{-4}\) & \(B\) & 5 MHz \\ \hline \end{tabular}
\end{table}
Table 2: Network parameters used in ns-3 simulation.  
Figure 3: Total deployment costs and efficiency in the two-tier example: (**a**) deployment cost vs. transmit powers ratio: (**b**) deployment efficiency (_DE_) vs. _SE_.  
Finally, in order to estimate \(DE\) for the two considered scenarios, we used (15) taking simulation results to calculate \(C_{TOT}\), and (13) for \(SE_{TOT}\).  
Accordingly, the diagrams in Figure 4a-c represent \(SE\), \(EE\), and \(DE\), respectively, obtained by the simulation of the two scenarios, using the parameters' values listed in Table 2.
# Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
## 2 System Model of V2X Communications
Since higher transmit power is required for the vehicle with a weaker link, we let \(a_{1}>a_{2}\) in our model. Figure 1: System model of Non-orthogonal multiple access (NOMA)-based vehicle-to-everything (V2X) systems. Suppose that each vehicle chooses one antenna having the highest channel gain among multiple antennas to communicate with RSU. In the first phase, the signal to interference plus noise ratio (SINR) experienced by the RSU when detecting \(x_{1}\) sent from D\({}_{1}\) and \(x_{2}\) sent from D\({}_{2}\) are obtained by  
\[\gamma_{D_{1}\to R}=\frac{{{{|{h_{n^{*},1}}|}^{2}}\rho}}{{{{|{h_{k^{*},2}}|}^{2 }}\rho+1}} \tag{1}\]  
and  
\[\gamma_{D_{2}\to R}=\frac{{{{|{h_{k^{*},2}}|}^{2}}\rho}}{{{{|{f_{1}}|}^{2}} \sigma\rho+1}}, \tag{2}\]  
respectively, where \(\rho=\frac{P}{{{N_{0}}}}\) is the common signal-to-noise ratio (SNR) without fading and \(f_{1}\) denotes the IS channel coefficient at the RSU. Here, \(\alpha=0\) and \(\alpha=1\) indicate that pSIC and ipSIC is used at the receiver, respectively, and \(n^{*}=\operatorname*{argmax}_{1\leq n\leq N}{{{|{h_{n,1}}|}^{2}}}\), \(k^{*}=\operatorname*{argmax}_{1\leq n\leq K}{{{|{h_{k,2}}|}^{2}}}\). In the second phase, two vehicles further process the received NOMA signals sent from the RSU. Note that the signal \(x_{1}\) has a higher priority than \(x_{2}\) under the condition of \(a_{1}>a_{2}\). The SINR at D\({}_{2}\) when detecting \(x_{1}\) is written as  
\[\gamma_{D_{2},x_{1}}=\frac{{{{|{g_{2,k^{*}}}|}^{2}}\rho a_{1}}}{{{{|{g_{2,k^{* }}}|}^{2}}\rho a_{2}+1}}, \tag{3}\]  
where \(k^{*}=\operatorname*{argmax}_{1\leq k\leq K}{{{|{g_{2,k}}|}^{2}}}\). According to NOMA, the SIC algorithm is adopted at D\({}_{1}\) as well. Then, the SINR at D\({}_{1}\) for detecting \(x_{1}\) is given by  
\[\gamma_{D_{1},x_{1}}=\frac{{{{|{g_{1,n^{*}}}|}^{2}}\rho a_{1}}}{{{{|{g_{1,n^{* }}}|}^{2}}\rho a_{2}+1}} \tag{4}\]  
and the SINR at D\({}_{1}\) for detecting \(x_{2}\) is written as  
\[\gamma_{D_{1},x_{2}}=\frac{{{{|{g_{1,n^{*}}}|}^{2}}\rho a_{2}}}{{{{|{f_{2}}|}^ {2}}\sigma\rho+1}}, \tag{5}\]  
where \(n^{*}=\operatorname*{argmax}_{1\leq n\leq N}{{{|{g_{1,n}}|}^{2}}}\) and \(f_{2}\) denotes the IS channel coefficients at D\({}_{1}\). It is expected from the above analysis that the received SINR depends on instantaneous channel gains and power allocation ratio \(a_{1}:a_{2}\). The communication process of V2X is affected by the number of antennas implemented at each vehicle, although the ability of receivers is related to which SIC is used between pSIC or ipSIC. It is predicted that different performances are obtained by considering different SIC, i.e., pSIC or ipSIC, different qualities for channels, and different numbers of antennas.
## 1 Introduction  
Age-related macular degeneration (AMD) is a chronic neurodegenerative disease leading to loss of central vision due to the progressive degeneration of photoreceptor cells located in the central point of the retina--macula [1]. In developed countries, AMD is a major cause of central vision loss among the elderly population [2]. There are two main types of age-related macular degeneration--dry and wet (nAMD). AMD typically begins as a dry form, and then, in 10% of the affected population, transforms into a wet form (nAMD) [1], which is characterized by the presence of choroidal neovascularization (CNV)--abnormal and excessive growth of choroid vessels in the submacular area [2, 3]. Despite the high interest of researchers, the pathophysiology of AMD has not been fully understood; however, it is assumed that inflammation and abnormal immune regulation are focal factors leading to the development of AMD [1].  
Inflammation is the body's defense response precisely coordinated by complex immune processes. Inflammatory response can be initiated by non-infectious agents (e.g., cell damage, toxins and drugs) or infectious agents (e.g., bacteria and viruses) [4]. Acute inflammation usually leads to rapid neutralization of the causative agent; however, in the case of abnormalities in control mechanisms, it can become a chronic form and promote the development of atherosclerosis and neurodegenerative diseases [4; 5], such as Parkinson disease, Alzheimer disease and AMD [2]. Previous studies have shown that macrophages and leukocytes--key cells for the inflammatory response--occur in 90-95% of removed CNV tissue [3]. It has been proven that monocyte chemoattractant protein 1 (MCP-1, also known as CCL2) and interleukin 17A (IL-17A) can participate in the development of nAMD by both regulating the inflammatory response and stimulating angiogenesis [6; 7]. Vascular endothelial growth factor (VEGF) and placental growth factor (PIGF) are pivotal factors that stimulate angiogenesis and lead to the development of CNV; in addition, their ability to promote inflammation has also been proven [8; 9].  
Aflibercept--one of the newest approved monoclonal antibodies for the treatment of nAMD--is capable of binding the VEGF-A, VEGF-B and PIGF isoforms [10]. In vitro studies with vascular endothelial cells have shown that affibercept, after binding to VEGF-A, can initiate an inflammatory response by the stimulation of toll-like receptor 4 (TLR 4), with subsequent activation of the nuclear factor kappa B (NF-kB) pathway [6]; meanwhile, in vivo studies have shown a decrease in the concentration of MCP-1 and other proinflammatory and proangiogenic factors in aqueous humor after intravitreal affibercept treatment [11; 12]. However, systemic levels of MCP-1 and IL-17A after intravitreal injection of affibercept have not yet been studied. Therefore, the purpose of our study was to assess the effect of repeated intravitreal allibercept injections on the systemic inflammatory response mediated by MCP-1 and IL-17A, as well as the concentration of VEGF and PIGF in the blood serum of patients with nAMD.
# An Edge-Guided Deep Learning Solar Panel Hotspot Thermal Image Segmentation Algorithm
## 3 Proposed Algorithm  
Mask R-CNN itself has excellent segmentation performance, but it is mainly used to deal with visible light images. However, it is not ideal enough for thermal infrared image processing. In order to improve the accuracy of the network for hot spot segmentation of thermal infrared images, it mainly contains the following three aspects: Firstly, in thermal infrared images, there is the problem of low resolution and unclear target edges, which can  
Figure 1: Mask R-CNN network structure.  
not obtain satisfactory segmentation results. Secondly, during the feature extraction process, the network does not pay special attention to the effective information in the thermal infrared image. Finally, the anchor box is the benchmark used by the algorithm to predict the boundary box. There is a significant difference between the shape of the original algorithm's training and testing targets and the numerical values of the hot spot data, making it difficult to obtain satisfactory accuracy by directly using the initial anchor box values.  
Therefore, based on the Mask R-CNN network, the following improvements are made to address the above three problems in this paper: Firstly, the target edge features are extracted using an edge-aware module, and then the edge-guided feature pyramid structure proposed is used to fuse the extracted edge features with the main features to enhance the boundary representation of the features. Secondly, this paper introduces an infrared spatial attention module into the feature extraction network to optimize the extracted infrared image features and make the segmentation attention focus on the informative pixels in the infrared image. Finally, in the region of interest network, the size of the anchor box is adaptively adjusted by analyzing the aspect ratio of the self-made hot spot datasets via the K-Means algorithm to reduce the probability of missing detection of small targets.