esa-sceva/satcom-chunk-collection huggingface dataset:
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 3 Simulations
### Network Performance
#### 3.1.1 Simulation Setup  
We have deployed sensors and actuators as in Figure 3, and more detailed simulation configurations are as follows;  
* The number of sensors \(n_{s}\) in the sensing area is 30.
* The number of actuators \(n_{a}\) in the actuating area is 10.
* The number of drone \(n_{d}\) is one.
* The distance between the sensing area and actuating area is 160 m.
* Drones going to the actuator pass the sensing area.
* The problematic sensor is the southern-most node in the sensing area.
* All sensors, actuators and drones are equipped with a Wi-Fi interface for communication.
* The maximum throughput of sensors is limited to 100 Kbps  
Figure 3: Node placement of the network performance simulation.  
In our simulations, we select a priority service throughput as a metric to analyze the performance of our algorithms. The priority service throughput is the sum of weighted actuators' throughput. The weight value is the correlation coefficient of actuators regarding the problematic sensor. Therefore, the priority service throughput \(Th_{ps}\) is as follows,  
\[Th_{ps}=\sum_{i=1}^{n_{a}}\omega_{i}\times Th_{i}. \tag{6}\]  
The reason for adopting the new metric is that the main actuator (the highest-correlated actuator) is most important in the service, but other actuators can also monitor the problematic sensor imperfectly. In our simulations, \(\omega_{i}\) is selected in accordance with the reciprocal of the distance between the problematic sensor and the actuators. The set of \(\omega_{i}\) in our simulations is as follows: \(\{0.23,0.30,0.39,0.51,0.67,0.87,1.13,1.47,1.92,2.50\}\).
## 3 Materials and Methods
### Classification Methods
#### 3.2.10 Random Forests  
Random forests (RF) are a classification algorithm that is a combination of decision tree predictors so that each of them depends on the values of a randomly -elected independent vector with the same distribution for all trees in the forest [37]. After training, predictions for unseen samples can be made by taking the majority vote [36].
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.6 SIMVLM: Simple Visual Language Model Pre-Training with Weak Supervision  
[PERSON] et al. [103] present the Simple Visual Language Model (SimVLM), which uses large-scale weak supervision to simplify the vision-language pre-training process by reducing the reliance on expensive annotated and labeled datasets as well as object detection pre-training. Their model is also simpler in that it does not not require the use of multiple auxiliary loss functions (objectives).  
They propose the Prefix Language Modeling (PrefixLM) objective. With PrefixLM, some initial tokens of an input sequence are designated as prefix tokens, which are generally taken from the image represenation. PrefixLM enables bi-directional attention on the prefix tokens whereas the remainder of the tokens in the input sequence, generally taken from textual representation, are processed via the autoregressive factorization (causal attention). The PrefixLM training objective is given by:  
\[\mathcal{L}_{PrefixLM}(\theta)=-\mathbb{E}_{\mathbf{x}\sim D}[\log P_{\theta} (\mathbf{x}_{\geq T_{p}}|\mathbf{x}_{<T_{p}})]=-\mathbb{E}_{\mathbf{x}\sim D }\left[\sum_{t=T_{p}}^{T}\log P_{\theta}(\mathbf{x}_{t}|\mathbf{x}_{[T_{p},t] },\mathbf{x}_{<T_{p}]})\right] \tag{5}\]  
where \(\theta\) are the trainable model parameters, \(D\) is the dataset and \(\mathbf{x}\) is the input sequence. \(\mathbf{x}_{<T_{p}}\) are the prefix tokens and \(\mathbf{x}_{[T_{p},t]}\) are the tokens for the autoregressive factorization. Therefore, PrefixLM takes advantage of bi-directional contextualized representation as well as autoregressive generation. It ends up being modality-agnostic since it can effectively deal with the visual and/or the textual domains.  
The approach allows images to be considered as prefixes for their corresponding textual descriptions. The image section of the input sequence is acquired via a transformer backbone inspired by the Vision Transformer (ViT) [99] and CoAtNet [104]. This approach eliminates the need for an object detection module since they operate on raw image patches. The textual section of the input sequence is acquired via tokenization and embedding. The positional information of the tokens is also incorporated. The bi-directional attention and causal attention are then carried out on the image and textual tokens, respectively.  
For training, they use the image and alt-text pairs taken from ALIGN [54] together with the Colossal Clean Crawled Corpus (C4) [105]. The modality-agnostic nature of their objective allows the model to train on these two datasets made up of image-text pairs and text-only data. Training on the text-only corpora effectively compensates for the inherently noisy nature of the alt-text data. They evaluate their model after fine-tuning on six downstream vision-language tasks, including visual question answering (VQA) and image captioning (using MS COCO and Nocaps). They also show strong generalization and transfer ability in zero-shot settings.
# Spatial-Temporal Self-Focusing of Partially Coherent Pulsed Beams in Dispersive Medium
## 4 Conclusions  
In the present work, a novel class of spatially and temporally PCP source with circular spatial and sinc temporal coherence distribution is introduced. The evolution of pulsed beams generated by this kind of source in dispersive media is investigated. Water and air are chosen as two typical examples of dispersive medium. It is found that the pulsed beams exhibit spatial-temporal self-focusing behavior upon water/air propagation. The relationship between spatial parameters and temporal parameters of pulsed beams is acquired, where self-focusing takes place at spatial and temporal dimension simultaneously. A detailed comparison between the pulsed beams and the normal non-uniformly correlated PCP beams is performed. The results show that the spatial-temporal self-focusing phenomenon of non-uniformly correlated PCP beams with Gaussian weight function is more noticeable, and takes place more early. The results obtained can have potential applications in laser micromachining and laser filamentation [65]. For example, in laser micromachining, a proposed pulsed source might be generated in order to obtain a self-focusing spot upon propagation, where a suitable peak intensity is adopted to ablate the proposed material. In the process of micromachining, the profile of the focal spot can be modulated to improve fabrication resolution. In addition, in the field of laser filamentation, the intensity inside the filament can be enhanced by manipulating the mutual coherent function (MCF) of proposed pulsed source using a Spatial Light Modulater (SLM).  
Z.Z. Data Curation, writing-original draft, writing-review and editing; C.D. formal analysis, methodology; Y.Z. formal analysis; L.P. project administration. All authors read and approved the final manuscript.  
Figure 9: On-axis and off-axis normalized spatial-temporal intensity profile \(I(x,\,t,\,z)\) of PCP beams at \(z\) plane with different values of \(t\) (**a**,**c**) and \(x\) (**b**,**d**).  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant numbers 61575091 and 61675094.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 3 Discussion and Conclusions  
Effective suppression of unwanted sea clutter returns requires an accurate characterization of their spatio-temporal statistics. We have created a WSSUS model for sea clutter that allows for the prediction of the clutter range-Doppler spectrum and provided a mechanism by which internal clutter motion may be accounted for and quickly estimated. Validation against the Ingraa medium grazing angle dataset shows agreement between the WSSUS model and the ensemble average spectrum to within 1 dB at all frequencies.  
Future work could apply this model to developing signal processing techniques such as adaptive transmit waveform design and improved MTI filtering to optimize signal detection in the presence of heterogeneous clutter.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 1 Introduction
* In the mutual authentication between vehicles process, the proposed protocol achieves authentication and session key negotiation without the involvement of TAs and RSUs, enabling vehicles to communicate in scenarios lacking traffic infrastructure. * Using temporary anonymous identities, nodes cannot resolve each other's real identities, thereby protecting privacy. Once the authentication session is initiated, the established temporary session key is used, avoiding the burden of key management and making it difficult for attackers to obtain keys or tamper with messages. * Considering the limited computational resources of vehicles, the authentication protocol is designed to be lightweight and efficient while resisting various complex typical attacks. The remaining paper is organized as follows: Section 2 presents the literature survey. Section 3 explains the background knowledge. Section 4 shows the system model. Section 5 details the proposed protocol. Section 6 presents the security analysis. Section 7 displays the results of the performance evaluation. Section 8 concludes the paper.
# Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
## 2 PHD Filter Based on the PMC Model
### PHD Filter Based on PMC Model  
RFS-based solutions consider that at time \(k\), targets and measurements are two RFS: \(X_{k}=\{\mathbf{x}_{1},\cdots,\mathbf{x}_{n}\}\) and \(Z_{k}=\{\mathbf{z}_{1},\cdots,\mathbf{z}_{m}\}\), where \(n\) and \(m\) are random integers which indicate the number of targets and measurements. The measurements associated to a given state \(\mathbf{x}_{i}\) is noted \(\mathbf{y}_{i}\) and defines the random finite set \(\dot{X}_{k}=\{(\mathbf{x}_{1},\mathbf{y}_{1}),\cdots,(\mathbf{x}_{n},\mathbf{y}_{n})\}\).  
Representing PHD with \(v(\mathbf{x})\), the \(v(\mathbf{x})\) of the random finite set \(X\) is the first order moment of multi-target density and also the spatial density of the expected number of targets. Directly computing \(v_{k}(\mathbf{x})\) in a PMC seems complicated because \(\mathbf{x}_{k}\) is not necessarily Markovian; however, we can propagate a joint intensity \(v_{k}(\mathbf{x},\mathbf{y})\) and then obtain the PHD \(v_{k}(\mathbf{x})\) as \(v_{k}(\mathbf{x})=\int v_{k}(\mathbf{x},\mathbf{y})\mathrm{d}\mathbf{y}\).  
Assuming that there is no spawning (if there is spawning the extension is immediate), according to the multi-target Bayesian principle, the joint PHD can be propagated through the following prediction and update formula:  
\[v_{k|k-1}(\mathbf{x},\mathbf{y})=\int p_{S,k}(\mathbf{x}_{k-1})f_{k|k-1}(\mathbf{x},\mathbf{y}|\bm {x}_{k-1},\mathbf{y}_{k-1})\times v_{k-1}(\mathbf{x}_{k-1},\mathbf{y}_{k-1})d\mathbf{x}_{k-1} d\mathbf{y}_{k-1}+b_{k}(\mathbf{x},\mathbf{y}) \tag{6}\]  
\[v_{k}(\mathbf{x},\mathbf{y})=[1-p_{D,k}(\mathbf{x})]v_{k|k-1}(\mathbf{x},\mathbf{y})+\sum_{\mathbf{z} \in\mathcal{Z}_{k}}\frac{p_{D,k}(\mathbf{x})v_{k|k-1}(\mathbf{x},\mathbf{z})\delta_{\mathbf{z }}(\mathbf{y})}{\mathbf{\kappa}_{\mathbf{z}}(\mathbf{z})+\int p_{D,k}(\mathbf{x})v_{k|k-1}(\mathbf{x}, \mathbf{z})d\mathbf{x}} \tag{7}\]  
where \(p_{S,k}(\mathbf{x})\) is the probability that a target with state \(\mathbf{x}\) at time \(k-1\) still exists at time \(k\); \(p_{D,k}(\mathbf{x})\) is the probability that a target with state \(\mathbf{x}\) is detected at time \(k\); \(b_{k}(\mathbf{x},\mathbf{y})\) is the joint PHD of the birth targets RFS at time \(k\); \(\mathbf{\kappa}_{\mathbf{z}}(\mathbf{z})\) is the PHD of the clutter measurements RFS at time \(k\); and \(\delta_{\mathbf{z}}(\mathbf{y})\) is the Dirac delta function concentrated at \(\mathbf{z}\). Finally remember that \(v_{k}(\mathbf{x})=\int v_{k}(\mathbf{x},\mathbf{y})d\mathbf{y}\), where the integral w.r.t. \(\mathbf{y}\) can reduce to a sum.
## 1 Introduction
Although biological methods such as phytoremediation and phosphorus accumulation are widely used, phosphorus absorbed during plant growth will be reabsorbed by the soil through defoliation, and thus phosphorus removal in the bioretention system mainly depends on the media adsorption and chemical precipitation of the metal salts. The adsorption process takes place quickly. Both electrostatic interaction and ion exchange are reversible processes, which occur on the surface of the media [30]. Compared to the adsorption reaction, the precipitation process is relatively slow and may take several hours [28]. Therefore, fast adsorption is an important technique to remove the phosphorus when the runoff flows through the infiltration medium layer. In the rainstorm runoff, the process of dissolution of phosphorus is random and variable. Proper selection of the media layer is the key to improve phosphorus removal. However, only a few published reports have examined the combined mechanism of removal of pollutants using plants, media, and saturated zones in bioretention systems under different conditions [31; 32; 33]. While higher removal efficiency in bioretention systems provides numerous benefits in urban stormwater management, the impact of bioretention systems on water quality in the groundwater environment cannot be ignored. The aim of this study is to examine the ecological purification mechanism of stormwater runoff in the bioretention system by employing different coupling systems using plants, media, and saturated areas. This was achieved through the following steps: (a) the Chicago model was used to simulate three different intensities of the storm runoff and to evaluate the removal of nitrogen and phosphorus by three systems; (b) the removal effects of NH\({}_{4}\)\({}^{+}\)-N, NO\({}_{3}\)\({}^{-}\)-N, total nitrogen (TN), and total phosphorus (TP) were stimulated by three systems under different wet-dry cycles, rainfall intensities, and concentrations; (c) fuzzy mathematics was used to evaluate the effluent quality of bioretention systems in regular rainfall events; and, (d) static adsorption, scanning electron microscope (SEM), energy-dispersive X-rayspectroscopy (EDS), and X-ray diffraction (XRD) were combined to determine the pollutant removal mechanisms of different combinations of the bioretention system. On this basis, an optimization strategy is proposed for the simultaneous removal of nitrogen and phosphorus in the bioretention system. The results provide a reliable and scientific reference for improving the performance of the bioretention system and removing organic matter, nitrogen, and phosphorus from the initial road runoff.
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### Recombinant Live Vaccines
#### 3.2.6. Vpm1002  
VPM1002 presents another opportunity with recombinant live vaccines. VPM1002 is a recombinant BCG vaccine in which the _urease_\(C\) gene (responsible for the inhibition of phagolysosomal maturation) has been replaced by the _listeriolysin_\(O\)-encoding gene from _Listeria monocytogenes_[28]. It has shown increased immunogenicity, efficacy, and safety in preclinical studies, as well as in phase I and II clinical trials in both infants and adults [28; 29]. A further phase II clinical trial was completed in 2017 investigating the safety and immunogenicity of VPM1002 in comparison with BCG in HIV-exposed and unexposed newborn infants (ClinicalTrials.gov: NCT02391415). In this double-blind randomizedcontrolled trial, 416 participants were enrolled. Although results have not yet been made available, further progress is being made, as evidenced by the currently recruiting phase II/III clinical trial in which two groups of adults successfully cured of category 1 pulmonary TB will receive either VPM1002 or placebo (ClinicalTrials.gov: NCT03152903). The single dose of VPM1002/placebo will be administered, and the efficacy of the vaccine calculated against TB recurrence. In addition, a phase III double-blind randomized clinical trial has begun recruitment in which the efficacy, safety, and immunogenicity of VPM1002, in comparison to BCG, in preventing TB infection in newborn infants will be evaluated (ClinicalTrials.gov: NCT04351685).
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.2.5 Rice Yield Data  
Researchers collected four rice yield per unit area data obtained from the actual measurement of a measured rice field in Tianjin High Quality Agricultural Products Development Demonstration Center from 2017 to 2020, and the rice variety in this field was Xiaozhan rice. In addition, the researchers collected two rice yield data per unit area in Tianjin in 2019 and 2020 through the statistical annals released by the National Bureau of Statistics. In summary, six groups of rice yield per unit area data were used in this study to verify the accuracy of the rice yield estimation model proposed in this paper.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 2 Semianalytical Approach for the Determination of Modulus and Damping
### The Method: Modulus and Damping Nonlinearities Evaluation (MoDaNE)
#### Inversion: Dispersion Relation and Attenuation Coefficient  
The analytical derivation of Equations (7) and (8) can be exploited to extract velocity \(c\) and attenuation coefficient \(\alpha\) from experimental data. To do this, let us consider to excite a 1D sample, at \(x=0\), with a sinusoidal wave of frequency \(\omega\). The output signal at \(x=L\) is recorded and its amplitude \(A\) and phase \(\Phi=\phi+\phi_{0}\) (with \(\phi_{0}=-\omega t_{0}\)) can be derived by fitting experimental data with a cosine function. Furthermore, by using a suitable calibration of the experiment, \(U_{0}\) and \(\phi_{0}=-\omega t_{0}\) can be also derived. Hence, the phase \(\phi=\Phi-\phi_{0}\) can be measured.  
Inverting Equations (7) and (8), the analytical expressions of the physical quantities velocity \(c\) and attenuation coefficient \(\alpha\) can be derived:  
\[c=\omega/k =\frac{\omega}{\frac{1}{L}\big{(}\tau n\pm atan\sqrt{\frac{z}{2\cos^ {2}(\phi)}}\big{)}} \tag{9}\] \[\alpha=\frac{1}{L}atanh\sqrt{\frac{z}{2\sin^{2}(\phi)}} \tag{10}\]  
where the sign is positive if \(\phi+\phi_{0}>0\), \(n\) is the order of the closest mode to \(\omega\) and  
\[z=-(A/U_{0})^{2}-\cos(2\phi)+\sqrt{1+(A/U_{0})^{4}+2\cos(2\phi)(A/U_{0})^{2}} \tag{11}\]
## 3 Simplification of CDM  
The coupling force generated by the projection is the main difference between CDM and the single-track or kinematic model. For simplification, the relationship between different coupling forces in (5)-(7) is analyzed numerically under three typical conditions as shown in Figure 5.  
As shown in Figure 6c, the projection of the lateral tyre force on the longitudinal driving direction varies with \(\delta_{f}\), and the maximum value reaches 354 N. This directly leads to the oscillation of vehicle longitudinal speed (See Figure 5a), which is consistent with the analysis in Section 2.3. Under Condition 2 and 3, though the longitudinal control input, \(F_{T}\), is different, the yaw rate remains unchanged. Because the lateral coupling force varies in a small range (See Figure 6d), which is far less than the longitudinal coupling force. From the above analysis, it is possible to simplify CDM by ignoring the minor coupling part.  
Figure 5: Simulation inputs: (**a**) longitudinal driving force \(F_{T}\); (**b**) front wheel steering angle \(\delta_{f}\).
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 2 Materials and Mix Proportions
### RPC Mixtures and Curing Conditions  
37 RPC samples were prepared for tests of the compressive strength, elastic modulus, shrinkage, and creep. The W/B (water-binder ratio) for all RPC specimens is 0.20. Steel fibre contents of 0%, 1% and 2% by volume of mixture were used. The specific mix proportions are listed in Table 2. The numbers in Table 2 represent mass ratios of the RPC mixtures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constituent** & **Cement (\%)** & **Silica Fume (\%)** \\ \hline CaO & 63.37 & 0.39 \\ SiO\({}_{2}\) & 22.08 & 95.11 \\ Al\({}_{2}\)O\({}_{3}\) & 5.72 & 0.43 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.05 & 0.42 \\ K\({}_{2}\)O & 0.43 & 0.48 \\ MgO & 2.02 & 0.17 \\ Na\({}_{2}\)O & 0.19 & 0.19 \\ Equivalent alkalis (Na\({}_{2}\)O + 0.658K\({}_{2}\)O) & 0.33 & 0.64 \\ SO\({}_{3}\) & 2.10 & 0.28 \\ Loss on ignition & 0.71 & 1.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cement and silica fume.  
Figure 1: Steel fibre used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**W/B** & **Cement** & **SF** & **Quartz Sand** & **Quartz Powder** & **SP** & **ST\({}^{\bf 1}\) (\%)** \\ \hline
0.20 & 1.00 & 0.30 & 0.70 & 0.35 & 0.024 & 0 \\
0.20 & 1.00 & 0.30 & 0.70 & 0.35 & 0.024 & 1 \\
0.20 & 1.00 & 0.30 & 0.70 & 0.35 & 0.024 & 2 \\ \hline \hline \end{tabular} \({}^{1}\) Volume percentage.  
\end{table}
Table 2: RPC sample mixtures for experiments.  
The mixing procedure affects the material properties of RPC [34], and the manufacturing method for RPC is different from that of normal strength concrete. To minimize the impact of the mixing process, all the mixtures were made using the same planetary mixer. Based on previous studies [28; 35], the mixing procedure is as follows:  
1. The cement, silica fume, quartz sand, and quartz powder are mixed in a dry state for approximately 2 min at a low speed of approximately 140 rpm. During this dry mixing process, steel fibre was added to the mixtures.
2. 50% of the water and 50% of the superplasticizer were gradually added to the mixtures, and the mixtures were stirred for 3 min at a high speed of approximately 280 rpm.
3. The remaining 50% of the water and the superplasticizer were added to the mixtures, which were then stirred again for 3 min at a high speed of approximately 280 rpm.
4. After mixing, the RPC was poured into moulds and vibrated until fully consolidated.  
After 24 h, the RPC samples were removed from the moulds, and the samples were cured in a special curing box at a temperature of 90 \({}^{\circ}\)C and relative humidity (RH) of greater than 95% for 48 h. The samples were then moved to a testing room with a temperature of 20 \({}^{\circ}\)C and a relative humidity (RH) of 60% until the experiments were performed.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design
### Network Topology Initialization  
For each satellite node in OCCN, a potential inter-satellite link can be established as long as it satisfies the geometric visibility and inter-satellite optical communication constraints with other satellite nodes. The algorithm for establishing the initial links is shown in Algorithm 1.  
Figure 2: The diagram of MCOTG algorithm.  
```
Input: Satellite connection degree \(d\), satellite node count \(n\), satellite node set \(V\), and matrix \(E^{t}\) Output: The potential inter-satellite links matrix \(E^{t}\)
1 for\(i=1\) to \(n\)do
2 for\(j\in\) Neighbors \((i)\cap V,j>i\)do
3 if\(d_{ij}\leq d_{\max}=\sqrt{R_{i}^{2}-R_{h}^{2}}+\sqrt{R_{j}^{2}-R_{h}^{2}}\mid \mid\delta=\cos^{-1}\left(\frac{R_{i}^{2}+R_{j}^{2}-d^{2}}{2\cdot R_{i}\cdot R _{j}}\right)\leq\delta_{max}\);
4 then
5 Set \(e_{ij}^{t}=1,e_{ij}^{t}\in E^{t}\) //indicate an inter-satellite link between \(v_{i}\) and \(v_{j}\);
6
7 end if
8
9 else
10 Set \(e_{ij}^{t}=0,e_{ij}^{t}\in E^{t}\) //indicate no inter-satellite link between \(v_{i}\) and \(v_{j}\);
11
12 end if
13
14 end for
15 for\(i=1\) to \(n\)do
16 ifdegree (\(i\)) \(>d_{i}\)then
17 Let \(e_{ij}\) be the link with the smallest weight in \(Q\);
18 Set \(e_{ij}^{t}=0,e_{ij}^{t}\in E^{t}\) to indicate no inter-satellite link between \(v_{i}\) and \(v_{j}\);
19 end if
20
21 end for return\(E^{t}\);
```  
**Algorithm 1**Network Topology Initialization Algorithm.