esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition to conventional RO techniques, recent developments in signal processing algorithms and sensor calibration methods have further enhanced the accuracy and reliability of atmospheric profiles obtained from satellite observations [7]. These advancements have led to the emergence of new satellite missions, such as the European Space Agency's Aeolus mission, dedicated to measuring global wind profiles using a novel Doppler wind lidar [8]. The synergistic combination of RO data with other remote sensing techniques, such as lidar and passive microwave radiometry, holds immense potential for advancing our understanding of key atmospheric processes, such as convection, turbulence, and moisture transport [9]. By incorporating insights from these studies, this research aims to contribute to the ongoing efforts to improve the accuracy and reliability of atmospheric observations for enhanced weather forecasting and climate modeling.
# Numerical 3D Modeling: Microwave Plasma Torch at Intermediate Pressure
###### Abstract  
This study represents a self-consistent three-dimensional (3D) fluid plasma model coupled with Maxwell equations at an intermediate pressure between 1000 and 5000 Pa. The model was established using the finite element method to analyze the effects of time-space characteristics, which is the variation of plasma parameters with time and the 3D spatial distribution of plasma parameters in the plasma torch at various times. The numerical modeling was demonstrated in three different stages, where the growth of electron density is associated with time. From the distribution characteristics of molecular ions, it can be concluded that they are distributed mainly at the port of the quartz tube of the torch, which is larger than the center of the tube. The density ratio of molecular ion to electron is decreased because of the reduction of pressure and distance, which has been calculated from the port to the center of the quartz tube. The analysis of microwave plasma parameters indicated that intermediate pressure is useful for modeling and plasma source designing, especially for carbon dioxide conversion.  
electron density; microwave plasma; molecular ion; modeling; space-time characteristics Article
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 4 Reliability Analysis of CFRP-Packaged FBG Sensors Using the FTA Technique
### Results and Discussion  
By analyzing the minimum cut sets of each fault tree, we can ascertain the weak links of the system. In this paper, the minimum cut sets of each fault tree are obtained using the descending method, as shown in Table 5.  
Without considering the correlation between basic events, the failure probability of the three top events can be calculated:  
\[\begin{split}& P(T)=P(K_{1}\cup K_{2}\cup\cdots\cup K_{r})\\ &=\sum\limits_{i=1}^{r}P(K_{i})-\sum\limits_{i<j=2}^{r}P\big{(}K_ {i}K_{j}\big{)}+\sum\limits_{i<j<k=3}^{r}P\big{(}K_{i}K_{j}K_{j}\big{)}+\cdots+( -1)^{r-1}P(K_{1}K_{2}\cdots K_{r})\\ &\approx\sum\limits_{i=1}^{r}P(K_{i})\end{split} \tag{3}\]  
where \(K_{i},K_{j},K_{j}\) are the \(i\)-th, \(j\)-th, and \(k\)-th minimum cut sets, respectively, and \(r\) is the number of the minimum cut set. Thus, the failure probabilities of \"FBG-FM7\", \"BL-FM15\", and \"CC-FM17\" are 0.06242, 0.03554, and 0.01398, respectively.  
The property weightiness of basic events in each FTA can be calculated:  
\[\Delta g_{i}(t)=\frac{\partial F_{\text{s}}(t)}{\partial F_{i}(t)} \tag{4}\]  
where \(F_{\text{s}}(t),F_{i}(t)\) are the failure probability of the top event and the basic event, respectively. The property weightiness of basic events in FTAs of \"FBG-FM7\", \"BL-FM15\", and \"CC-FM17\" are shown in Figure 13. From Figure 13, we can infer that the property weightiness of all basic events are approximately equivalent. The reason is related to the production and arrangement process of the CFRP-packaged FBG sensors, which are mainly manually operated without unified standards. In addition, the key faults of \"FBG-FM7\", \"BL-FM15\", and \"CC-FM17\" are mainly caused by the manufacturing process (see X1-X3, X6, X9, X10, etc.) and stress conditions (X4-X6, X7, X8, etc.); thus, further study on the standard manufacturing process, the periodic quality testing of the PL, BL, and CC, and the routine calibration of FBG sensors is important.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Failure** & \multirow{2}{*}{**No.**} & **Failure** & \multirow{2}{*}{**No.**} & **Failure** & \multirow{2}{*}{**No.**} & **Failure** \\  & **Probability** & & & & & **Probability** & **No.** & **Probability** \\ \hline X1 & \(4\times 10^{-4}\) & X6 & \(10^{-2}\) & X11 & \(10^{-2}\) & X16 & \(2\times 10^{-3}\) \\ X2 & \(4\times 10^{-4}\) & X7 & \(10^{-2}\) & X12 & \(2\times 10^{-3}\) & X17 & \(4\times 10^{-4}\) \\ X3 & \(4\times 10^{-4}\) & X8 & \(4\times 10^{-4}\) & X13 & \(2\times
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Transformer-Based Approaches
#### 3.3.2 Sparse Attention-Based Transformer Models  
Sparse attention-based transformer models have proven effective for video captioning by focusing on informative regions, reducing redundancy and computational complexity. These models enhance efficiency and caption quality via sparse attention mechanisms. Notable examples include the Sparse Boundary-Aware Transformer (SBAT) by [PERSON] et al. [34], which uses boundary-aware pooling and local correlation to capture features and outperform state-of-the-art methods, and the Universal Attention Transformer (UAT) by [PERSON] et al. [35], which employs a full transformer structure with ViT, FEGs, and UEA for competitive performance. As illustrated in Figure 8, the encoder-decoder structure with sparse boundary-aware attention is used for video captioning.  
However, these models have limitations. Performance hinges on the accuracy of sparse attention, and misidentification or omission of key details can degrade captions. Designing these mechanisms requires careful hyperparameter tuning, which is time-consuming and dataset-specific. Moreover, fast-moving objects or complex scenes pose challenges for selecting sparse, informative regions. Efficient models like SBAT and SnapCap aim to reduce complexity and improve speed, with SnapCap generating captions directly from the compressed video to avoid costly processing.
## 2 Literature Review
### DoS Countermeasures
Finally, [PERSON] and [PERSON] [15] propose a combination of a security-enhanced SIP proxy server and an enhanced application layer stateless firewall to help mitigate DoS attacks targeting VoIP systems. Their proposed solution aims to maintain a trusted list of user IP addresses through continuous communication between the SIP server and the firewall. Using this method, the firewall rules can be adjusted automatically based on the information received from the SIP server. Similarly, the 'Call Me Maybe' framework suggests the use of a UDP-restricted stateless firewall that is automatically activated based on the information received from a Control Centre monitoring latency in a network. Additionally, as the design proposed in this paper only incorporates the stateless firewall temporarily, a stateful firewall could also be in place on the network for regular use, reducing the risks associated with using a stateless firewall full-time--namely, lack of traffic and packet inspection. Due to the success of the above methods, a holistic and dynamic approach to detection was chosen for the method in this paper. By taking the average response times of each device and comparing them to an established 'normal' threshold, a DoS attack can be detected and reactively countered with minimal impact on the VoIP traffic, and therefore minimal disruption of communications.
###### Abstract
Various functions can be employed for the geometric transformation [21]. Physical transformation models are usually preferred, while the empirical models provide a good--and less complex--alternative. Therefore, the empirical rational function model (RFM) is commonly used for the geometric correction of commercial VHR satellites. Various automatic geometric correction procedures utilize the RFM. [PERSON] and [PERSON] [22] developed an algorithm for automatic bias-compensation of rational polynomial coefficients (RPCs) that are based on topographic maps. Their matching accuracy was approximately one pixel. [PERSON] et al. [23] automatically orthorectified Pleiades imagery with a SIFT-based RPC refinement. Images with low incidence angles provided higher accuracy (better than 1 m) and a greater number of GCPs. [PERSON] et al. [24] used the SIFT algorithm to automatically georeference the remotely-sensed images of spatial resolutions from 30 m to 2 m. Although many other studies addressed the problem of automatic geometric corrections, it is still challenging to deliver the georeferenced VHR panchromatic images with a high spatial accuracy (pixel and less), regardless of the images acquisition date and incidence angle. The challenge is even bigger when addressing images with (predominantly) urban land cover. Commonly, the final goal of any automatic processing chains is the generation of geometrically corrected images through orthorectification. While the precise automatic orthorectification of medium and high resolution optical satellite images is already viable, a development of a robust procedure to obtain an accurate geometric correction of VHR panchromatic images is still challenging. This paper presents a completely automatic workflow for the generation of orthoimages from very-high resolution optical panchromatic satellite images. The workflow is composed of an automatic GCPs extraction procedure that works in various steps and it uses image matching techniques and precise feature extraction algorithms, geometric modelling with a RFM, and orthorectification. The research is focused on densely populated urban areas, composed of their typical elements, such as high buildings, their shadows, and wide streets, which present a great challenge in terms of feature extraction and matching. The aim of this study is to implement a procedure for a relatively fast extraction of GCPs with sub-pixel accuracy on different VHR panchromatic images of different dates and off-nadir angles that represent the urban areas to perform GCP refinement and precise orthorectification. Section 2 describes the algorithm and procedures of the automatic geometric corrections. Section 3 deals with the tested data and the evaluation methodology. Section 4 presents the results of the geometric corrections on a set of VHR panchromatic Pleiades images. Section 5 discusses the outcomes, and Section 6 draws the conclusion.
# Molecular and Functional Evolution of the Spermat Sesquiterpene Synthases
## 2 Results
### The Phylogeny of Spermat STSs in the TPS-f Subfamily  
The TPS-f clade is closest to the common ancestor of typical plant TPSs, but the function and evolutionary process of the members are not the same. In order to better understand the STS evolutionary events in the TPS-f subfamily, we performed the phylogenetic analysis separately (Figure 4). Within the TPS-f subfamily, _LnTPS3_[19] and _TwGES1_[20] are real diterpene synthases and the main product is geranyllinalool. They also have the ability to produce acyclic sesquiterpene (_E_)-nerolidol with the substrate FPP. _VvCSENerG1_ and _VvPNNerG1_ from grape also show the same product spectrum [21]. _AdAFS1_ have typical diterpene synthase conserved domain but can produce acyclic (_E_,_E_)  
Figure 2: Phylogeny of functional plant STSs. Green clades represent non-seed plant STSs, blue clades represent gymnosperm STSs, red clades represent monocotyledon STSs, yellow clades represent magnolias STSs, and black clades represent dicotyledon STSs.  
farnesene only [22]. The same product is also formed by _LoTPS2_ lacking two conserved domains at the N-terminus [23].  
Figure 4: The phylogeny of spermatophyte STSs in the TPS-f subfamily. The colored boxes represent different conserved motifs in spermatophyte STSs predicted by MEME online tool ([[https://meme-suite.org/meme/](https://meme-suite.org/meme/)]([https://meme-suite.org/meme/](https://meme-suite.org/meme/))) in 11 March 2021.  
Figure 3: Phylogeny of functional spermatophyte STSs. Outer ring is the phylogenetic tree of all 394 spermatophyte STSs, inner ring is the global phylogenetic tree of representative plant STSs. Red branches represent TPS-a subfamily STSs, light blue branches represent TPS-b subfamily STSs, green branches represent TPS-d subfamily STSs, blue branches represent TPS-f subfamily STSs, pink branches represent TPS-g subfamily STSs.  
In the other subclade of TPS-f, there are three actual STSs. There is a common sequence at the head of _ShSBS_ and _ShZIS_, which is unique to TPS in tomato (Figure 4). Subcellular localization of _ShSBS_ shows that it contains the plastid-targeting peptide and locates in the chloroplast. The first 36 residues of the common sequence may be responsible for chloroplast localization and play an important role in the evolution of these special TPSs. _ShSBS_ and _ShZIS_ may originate from the original TPS-e/f dipterpene synthase, occasionally obtaining the ability to synthesize monoterpenes and sesquiterpenes through exon exchange [24]. _ZmTPS1_ has almost completely lost the N-terminus protonation domain and is highly similar to maize KS, which is different from other TPS-f proteins that retain the basic structure of the original dipterpene synthase, more similar to typical plant STSs [25]. _ZmTPS1_ may have directly evolved from maize KS, but it has completely lost the ability to produce diterpenes. The phylogeny of STSs in the TPS-f subfamily indicates that TPS obtained the ability of producing sesquiterpene from the ancestral diterpene synthase by different evolutionary routes.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.8 Connection and Reconfiguration Methods  
In [104; 105; 106], the existing techniques for solving the connectivity issues in UIoT are discussed, and some methods are indicated herewith. In [105], [PERSON], a self-reconfiguration algorithm is formulated for underwater robots based on energy heuristics. In [106], a full-duplex, parameter configurable, multiple-user modem is developed and tested to improve the throughput level in the UIoT environment.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 5 Results and Discussion
### Fault Detection, Diagnosis and MPC Reconfiguration Tests
#### 5.3.1 Scenario 2 Results  
Fault in the tank 1 level sensor: alarm percentage before a fault detection: 2.2%. Detection instant: 2.285 days. MPC controller is reconfigured using Equation (20) to estimate tank 1 level assuming tank 1 outlet flow rate measure is correct. Figure 13 shows the \(Q\) threshold and the \(Q\) statistic calculated online for fault detection. Detection occurs when \(Q\) exceeds the threshold 20 consecutive times. The following table provides the comparative data of system performance, including normal operating situation, fault without reconfiguration, and fault with controller reconfiguration (FTMPC). Note that the sewer configuration causes the overflowed volume in tank 1, \(V_{\text{ov},1}\) returns to the network, and for this reason, it is not added to \(V_{\text{ov}}\) in all tables [40]. For simplicity, the main indices to be considered are \(V_{\text{ov}}\), \(G_{\text{u}}\) and \(S\). As can be seen in Table 5, a normal MPC controller offers the best performance because the total overflow is the lowest as well as the smoothness in the control actions and provides the highest degree of utilization of the WWTP. MPC with the fault considered reduces the system performance, worsening all indices, but by comparing with no control case, its performance is much better since \(G_{\text{u}}\) is 57.92% vs. 53.96%, \(V_{\text{ov}}\) is 3.8003 \(\times\) 10\({}^{4}\) vs. 6.8473 \(\times\) 10\({}^{4}\) (m\({}^{3}\)). Figure 13: \(Q\) statistic and threshold calculated in level fault situation (scenario 2). Finally, by comparing an MPC with a fault with FTCMPC, this one improves the system performance since the total overflow volume is reduced from 3.8003 \(\times\) 10\({}^{4}\) to 3.4520 \(\times\) 10\({}^{4}\) (m\({}^{3}\)) and the degree of WWTP utilization increases from 57.92% to 58.55%, although \(S\) is worse because the system needs greater control efforts. Therefore, this reconfiguration strategy improves system performance when this fault occurs. * Fault in the tank 3 gate: alarm percentage before a fault detection: 2.01%. Detection instant: 2.31 days. MPC controller is reconfigured by Equation (21) by adding a new constraint to the MPC problem, assuming the tank 3 outlet flow rate measure is correct. Figure 14 shows the \(Q\) threshold calculated online and the value of the \(Q\) statistic for fault detection. Detection happens when \(Q\) exceeds the threshold 20 consecutive times. Table 6 provides the comparative data of system performance in each case for scenario 2. Figure 14: \(Q\) statistic and threshold calculated in gate fault situation (scenario 2).
# Optimization of Phase-Only Computer-Generated Holograms Based on the Gradient Descent Method
## 5 Experiments  
The proposed technique was experimentally verified utilizing the experimental system shown in Figure 10. A PLUTO-2 phase-only SLM (HOLOEYE Photonics AG) was utilized. The resolution was 1920 \(\times\) 1080, the pixel pitch was 8 \(\upmu\)m, and the active area size was 15.36 \(\times\) 8.64 mm. A He-Ne laser with a wavelength of 633 nm was used as the light source. The focal length of the collimator lens was 150 mm, and that of the Fourier transform lens was 100 mm. The reconstructed images were captured using the digital camera (Canon EOS kiss X9 camera, Canon Inc.) The resolution was 6000 \(\times\) 4000 and the sensor size was 22.3 \(\times\) 14.9 mm\({}^{2}\).  
Figure 11a-c show the phase distributions used for the experiments, which correspond to the reconstructed images shown in Figures 6a-c and 11d-f are the phase distributions correspond to Figure 6d-f. The experimentally obtained reconstructed images by proposed algorithm are shown in Figure 12a-c, and the reconstructed images by GS algorithm are shown in Figure 12d-f.  
From Figure 12, the test images were reconstructed. Figure 12a-c show the reconstructed images obtained using the phase-only holograms calculated by the proposed method, while Figure 12d-f illustrate those obtained using the holograms calculated by the GS algorithm. From these results, the intensity variations in the white regions were smaller for the proposed algorithm than the GS algorithm. However, the difference was not so obvious because of some noises caused by the optical system and speckles in the reconstructed images. The reconstructed images contained the speckles because the phase distributions of the reconstructed images were not controlled. Although the images \"hikari\" and \"circle\" were reconstructed clearly, the image \"mandrill\" was not as clear as them. Because the \"hikari\" and \"circle\" were binary images, the image contrast was not affected so much by the speckles. On the contrary, because the \"mandrill\" was a gray-scale image, the image contrast was heavily affected.  
Figure 10: Experimental system.  
Figure 11: Optimized phase distributions by proposed algorithm and GS algorithm used for experiments: (**a–c**) were produced by proposed algorithm, (**d–f**) were produced by GS algorithm. (**a,d**) are “mandrill”, (**b,e**) are “hikari”, (**d,f**) are “circle”.  
Figure 12: Experimentally obtained reconstructed images: (**a–c**) were reconstructed images obtained by the proposed algorithm, (**d–f**) were those obtained by the GS algorithm. (**a,d**) are “mandrill”, (**b,e**) are “hikari”, and (**d,f**) are “circle”.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 4 Performance Analysis  
This section provides the results of the performance assessment for the beamformers described in Section 3 in the context of a handheld device with either two- or four-antenna elements. Without loss of generality, GNSS signals are considered, where the despreading process leads to a correlation peak with time support of \(\pm T_{\text{c}}\) around \(\tau_{0}\), with \(T_{\text{c}}\) termed _clip period_. In both urban and suburban scenarios considered herein, the received signal consists of the superposition of a LOSS and an undesired replica (i.e., \(M=1\)), with carrier-to-noise ratios (\(C/N_{0}\)) of 44 dB-Hz and 41 dB-Hz, respectively. URA4 and ULA2 antenna distributions are considered, where the antennas are separated half a carrier wavelength apart from one another. The array is assumed to be perfectly calibrated for simplicity, and additive white Gaussian noise (AWGN)-independent antenna channels are adopted. The beamformers are implemented within a closed loop where the determination of \(\tau_{0}\) and \(\varphi_{0}\), denoted as \(\hat{\tau}_{0}\) and \(\hat{\varphi}_{0}\), is carried out through a delay-locked loop (DLL) and a phase-locked loop (PLL), respectively, thus providing estimates in a timely manner and, consequently, allowing for some statistical averaging over time. For the correlation terms \(\mathbf{R_{X}}\) and \(\mathbf{r_{Xs}}(\tau_{0},\varphi_{0})\), we resort to the sample correlations \(\mathbf{\hat{R}_{X}}\doteq\frac{1}{N}\mathbf{X}\mathbf{X}^{H}\) and \(\mathbf{\hat{r}_{Xs}}(\hat{\tau}_{0},\hat{\varphi}_{0})\doteq\frac{1}{N} \mathbf{Xs}^{*}(\hat{\tau}_{0},\hat{\varphi}_{0})\), respectively, with \(N=49\) samples. Two different metrics are employed for the performance analysis. On the one hand, there is the cancellation of the replica, which measures the ratio between the amount of power of the replica canceled out by the beamformers and the LOSS power. On the other hand, there is the well-known _multipath error envelope_ (MEE), which measures the errors induced by the computation of \(\hat{\tau}_{0}\) compared to the true value \(\tau_{0}\). In GNSS, the MEE is directly linked to the error in the estimated receiver position.
# Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy
## 4 Numerical Results
### Obtained Results for the IEEE 30-Node System
#### 4.2.3 Results Obtained for Case 4  
For finding the best solution of using wind turbine in the IEEE 30-node system, Case 4 has been implemented by using only JSA and applying the best location bus 5 and the best size 10 MW. JSA with the best performance has been run 50 times for reaching other parameters of the transmission network. As a result, fifty optimal solutions collected for determining profit values and summary of profits consist of the best, mean, and worst profits with $5035.9, $5026.0, and $4997.3, respectively. The results of Case 4 are compared to those from Case 3, as shown in Figure 18. In general, all profits of Case 4 are slightly greater than those of Case 3. Namely, Case 4 reaches greater maximum, mean, and minimum profits than Case 3 by $0.1, $2.3, and $7.0, respectively.  
It is recalled that Case 3 optimizes location of wind turbine, rated power of the wind turbine, and all parameters of transmission power network. However, Case 4 uses the predetermined location and predetermined rated power of Case 3 and only optimizes all parameters of transmission power network. So, the small profit deviation between Case 3 and Case 4 indicates that JSA is very effective for a complicated OPF problem with renewable energy.