esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussions
### Effects of SPME Fiber Type on TWA Sampling with Glass Liner  
A glass liner facilitating TWA SPME was used. Two adsorptive SPME coatings were tested, i.e., 85-\(\upmu\)m Car/PDMS and 50/30-\(\upmu\)m (divinylbenzene, DVB) DVB/Car/PDMS, and both types of coatings effectively extracted acetic acid (Figure 3) for up to 12 h. Mass extracted by the fibers showed a linear response with sampling time (\(R^{2}\) > 0.99). However, the results show that the average masses extracted by both SPME fibers were higher than the theoretical value (Equation (1)) by \(11.1\%\) and \(3.7\%\) on average for Car/PDMS and DVB/Car/PDMS fibers, respectively. This (relatively small and reproducible) discrepancy from theory (Equation (1)) could be considered excellent, considering that no effects of metallic SPME fiber assembly were taken into account.  
The 85-\(\upmu\)m Car/PDMS fiber provided a slightly higher response than the DVB/Car/PDMS fiber for acetic acid, which is consistent with the studies of [PERSON] et al. [22] and [PERSON] et al. [33]. The total mass extracted by the SPME fibers was reproducible. The RSDs of MS responses with Car/PDMS (ranging from 2.3% to 12.2%) were lower in comparison with the DVB/Car/PDMS fiber (ranging from 3.2% to 14.7%). A linear regression model with a log-transformed response showed that masses extracted were not significantly different between the two SPME fibers (\(p\)-value = 0.44), as well as between both fibers and theoretical values (\(p\)-value = 0.43). The differences in mass extracted with 50/30-\(\upmu\)m DVB/Car/PDMS at every sampling time were 9% less than the mass extracted with 85-\(\upmu\)  
Figure 3: TWA SPME where fiber is retracted into a glass liner. Comparison of the extraction efficiency of acetic acid by 85-\(\upmu\)m Car/PDMS and 50/30-\(\upmu\)m DVB/Car/PDMS SPME fibers. The theoretical mass on the SPME fiber (shown as a solid line) was calculated using Equation (1) ([PERSON]’s law of diffusion). The experimental masses are shown as dotted and dash lines for Car/PDMS and DVB/Car/PDMS fibers, respectively. Extraction conditions: 85-\(\upmu\)m Car/PDMS fiber exposed inside a glass liner, standard gas (acetic acid, \(C_{\text{gs}}\) = 617 \(\upmu\)g\(\cdot\)m\({}^{-3}\)). Retraction depth was 1.75 cm. SIM mode at \(m/z\) 60.0 was used for acetic acid detection and quantification. Experiments were completed in triplicate.  
Car/PDMS, respectively. Log-transformation of mass extracted on SPME fiber was performed because there was non-constant variance in the residuals.
# U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
## 3 Methodology
### Problem Formulation and Assumptions
Since the approach deals with unsupervised generating speaker embedding vectors from speech data (without domain adaptation), the output embedding vectors are named as unsupervised vectors (u-vectors). Formally u-vector is defined as follows,  
**Definition 1**. : _Unsupervised vectors (u-vectors) refer to a set of DNN generated speech embeddings, which are clusterable based on speakers, trained from unlabeled speech segments._  
In the formal definition, by DNN, any specific implementation of substantially deep neural networks are indicated, such as convolutional, feedforward, recurrent, etc.
## 2 Materials and Methods
### Experimental Data Processing
Since the complex refractive index of the SPPs is defined as the ratio of the SPP wave number \(k_{\rm s}\) to the wave number of the radiation from the source in vacuum \(k_{0}=2\pi/\lambda_{0}\),  
\[\widetilde{n}_{\rm s}=\frac{k_{\rm s}}{k_{0}}=n_{\rm s}+i\!\cdot\!\kappa_{\rm s} \tag{1}\]  
the real part \(n_{\rm s}\) was found from the spectra shown in Figure 3 using the following formula:  
\[n_{\rm s}=\left(\frac{\lambda_{\rm spp1}/\lambda_{\rm spp2}}{\lambda_{01}/ \lambda_{02}}\right)\!\cdot\!{\rm Re}(\widetilde{n}_{\rm a}) \tag{2}\]  
where the normalization to \(\lambda_{01}/\lambda_{02}\) takes into account the shift in the NovoFEL generation spectrum that could occur during the time between recordings of the interferograms with and without SPP excitation (see Section 2.1). The factor \({\rm Re}(\widetilde{n}_{\rm a})\) in Expression (2) makes it possible to take into account the fact that the laser radiation propagates not in vacuum but in air with the complex refractive index \(\widetilde{n}_{\rm a}=1.0002726+i\!\cdot\!0.0000039\)[51; 52]. For each sample, the average value of \(n_{\rm s}\) was found from four sets of interferograms. To find the \(\kappa_{\rm s}\) value, we measured the attenuation of the SPP intensity at the displacement of the movable mirror \(M_{2}\) (see Figure 1) in the plasmon interferometer. In this case, the fixed mirror \(M_{1}\) was covered by an absorbing plate. A typical dependence, as measured on a sample with a ZnS layer 470 nm thick, is shown in Figure 3. The figure also shows the result of the approximation of the measurement results with a function of the form \(g(x)=B\!\cdot\!\exp(2x/L_{\rm s})+g_{0}\), where \(L_{\rm s}\) is the SPP propagation length. The exponent takes into account the \(2x\) increase in the SPP path at the displacement of the mirror by \(x\). The noise level \(g_{0}\) was measured in the experiment and was set as an approximation parameter. For each sample, the average SPP propagation length \(L_{\rm av}\) over four measurements was found and the \(\kappa_{\rm s}\) value was calculated using the formula [11]  
\[\kappa_{\rm s}=\frac{\lambda_{0}}{4\pi\!\cdot\!L_{\rm av}} \tag{3}\]
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 2 Fundamental Principles  
The principle of adaptive traffic light control using deep reinforcement learning (DRL) and fuzzy logic involves combining DRL, which utilizes neural networks and reinforcement learning, with fuzzy logic to manage traffic lights based on current traffic conditions. The DRL model learns the most effective control strategies by interacting with the traffic environment and receives rewards in the form of reduced waiting times to determine the traffic light phases. Meanwhile, fuzzy logic determines the duration of the green light based on the number of cars at the intersection. The fundamental principles of the methods used for adaptive traffic light control in this study divide the content into subsections as follows.
# Fiber Monitoring System Applied to Railway Bridge Structures in a Near-Fault Region
## 3 Research Design and Data Collection
### Installation of Sensing Fiber  
The fiber cables used in this research are single-mode optical fiber with four cables running parallel to each other on the box girder bridge. The working temperature ranges from \(-20\)\({}^{\circ}\)C to 80 \({}^{\circ}\)C. The optimal strain-sensing range of the fiber cable used in this research is set at 5000 \(\mu\epsilon\). To account for the expansion of the bridge structure, fiber cables that have steel wires embedded are chosen for testing in this research, as shown in Figure 6a. Steel wires in fiber cable greatly improve its tensile strength, and the maximum tensile strength for this fiber is 15 kg-f.  
The cross-section of the High-Speed Rail bridge box girder is in the shape of a trapezoid. It would make sense to assume that four sensing fibers on the four corners of the trapezoid are a suitable setup that could collect the necessary data for horizontal and vertical strain measurement of the structure. The sensing fibers are laid out to be parallel to each other. With the same axial position of the strain measurement point for each sensing fiber, it forms a cross-section for strain and deformation analysis.  
The installation and verification of the sensing fibers are closely considered to ensure the accuracy of data collection. To accurately represent the strain of the box girder, the contact between the sensing fiber and the surface of the box girder needs to be non-slip. First, due to the height of the bridge, it is necessary to work aloft to clean and smooth out the surface of the box girder. The next step would be to apply industrial-level adhesive to the surface and place the sensing fiber. Another industrial-level silicone-based protection is applied on top of the fiber to further protect it from possible environmental damage, as shown in Figure 6. Lastly, BOTDR is used to make sure the installed fiber is of high quality and there is no breakage of the fiber during the installation process.
###### Abstract  
The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from features other than mobile ad hoc networks (MANET), such as aerial mobility in 3D space and frequently changing topology. This paper analyzes the performance of four topology-based routing protocols, dynamic source routing (DSR), ad hoc on-demand distance vector (AODV), geographic routing protocol (GRP), and optimized link state routing (OLSR), by using practical simulation software OPNET 14.5. Performance evaluation carries out various metrics such as throughput, delay, and data drop rate. Moreover, the performance of the OLSR routing protocol is enhanced and named \"E-OLSR\" by tuning parameters and reducing holding time. The optimized E-OLSR settings provide better performance than the conventional request for comments (RFC 3626) in the experiment, making it suitable for use in UAV ad hoc network (UANET) environments. Simulation results indicate the proposed E-OLSR outperforms the existing OLSR and achieves supremacy over other protocols mentioned in this paper.  
enhanced optimized link state routing (E-OLSR); OPNET; routing protocols; unmanned aerial vehicles (UAVs); UAV Ad hoc network (UANET) 2022
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 3 Experimental Results and Discussion
### Prediction Comparison of Different Geomagnetic Conditions  
In order to further study the prediction ability of the MA-LSTM model under different geomagnetic activities, we divided the test data into a magnetic quiet period and a magnetic storm period for comparative analysis (in this paper, samples with Kp \(<\) 3 are taken as magnetic quiet days, and samples with Kp \(>\) 3 and \(-100<\) Dst \(\leq-50\) are taken as magnetic storm periods). Histograms of absolute error distribution during magnetic quiet periods and magnetic storm periods are shown in Figure 10, from which we can see that during the magnetic quiet period, the absolute error distribution of the prediction is more concentrated, basically between [\(-\)2,2], while during the magnetic storm, the absolute error distribution of the model is wider, reaching [\(-\)3,3], that is, the prediction effect of the MA-LSTM model during the magnetic quiet period is better than that during the magnetic storm. However, even during the magnetic storm period, the R-square of our model can still reach 0.989 in the selected area.  
Figure 11 shows the comparison between the predicted value and the true value of a magnetic quiet day and a magnetic storm day, from which we can see that on the magnetostatic day, the predicted value matches the real value perfectly, while on a magnetic storm day, the error between the predicted value and the true value is relatively large, and the fitting is not very good.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 5 Discussion and Open Research Challenges
### Backbone Network Design  
To provide broadband access, all VAPs should be connected to the rest of the world. It would be a challenging problem to connect all the VAPs to internet considering the dense deployment for illumination purposes. Creating a new wired network of VAP with Ethernet or fibre is not a cost-effective solution. Wireless connectivity or power line communication could be a potential solution. However, it has limitations such as interference of wireless connections, the low data rate of PLC, the additional cost of integrating with VLC. A potential solution could be to free up the ISM band (2.4 and 5 GHz) providing approximately 200 MHz of a band that could be used to provide backbone connectivity to VAP.
## 4 Experimental Results and Discussions
### Experimental Results  
#### 4.3.1 Results on NWPU VHR-10  
In this experiment, we compared the proposed SSD7-FFAM with other state-of-the-art detectors on the NWPU VHR-10 dataset. The results are shown in Table 1. The proposed SSD7-FFAM achieved an mAP of 83.7%, which is a 13% improvement over the original SSD7 [33]. SSD7-FFAM has higher accuracy than the state-of-the-art two-stage methods RICNN [42], R-P-Faster R-CNN [43], and Faster R-CNN [6].  
The SSD7-FFAM (0.033 s) also has the shortest average running time than other single-stage methods such as NEOON [44] and AF-SSD [14]. Although the mAP of AF-SSD is higher than that of SSD7-FFAM, the parameters of the SSD7-FFAM model only account for 29% of AF-SSD (1.66 vs. 5.7 MB). The results show that the proposed SSD7-FFAM is a lightweight real-time object detection network more suitable for embedded devices. Figure 7 shows some prediction results of the proposed SSD7-FFAM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mAP (\%)** & **Average Running Time (s)** \\ \hline COPD [37] & 54.6 & 1.070 \\ RICNN [42] & 72.6 & 8.770 \\ R-P-Faster R-CNN [43] & 76.5 & 0.150 \\ Faster R-CNN [6] & 80.9 & 0.430 \\ SSD7 & 70.7 & 0.178 \\ NEOON [44] & 77.5 & 0.059 \\ AF-SSD [14] & 88.7 & 0.035 \\ SSD7-FFAM & 83.7 & 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison result of the proposed SSD7-FFAM and state-of-the-art methods on the NWPU VHR-10 dataset.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 3 Existing Problems
### Better Understanding of the Coherent and Incoherent Scattering of GNSS Signal  
In most cases, the sea surface is in a non-stationary state. Affected by wind stress and swells propagating in the adjacent sea area, the sea surface roughness scale corresponding to the generated sea surface waves is much larger than the wavelength scale of the GNSS signal. Therefore, the GNSS signal mainly produces diffuse scattering on the sea surface. In the ocean applications of spaceborne GNSS-R, the discussion of the coherence of GNSS signals scattered from the sea surface is less discussed.  
The GNSS signal scattering on the land surface is more complicated. In addition to being affected by the surface soil moisture, the scattering signal will also be affected by the surface roughness, vegetation, terrain, soil texture, and inland water bodies. The incident GNSS signal usually undergoes coherent and noncoherent scattering on the actual land surface. There are several works [61; 62] concentrated on this topic. However, the analysis of spaceborne data (CYGNSS and TDS-1) mostly thinks that the received energy is mainly coherent scattering, and the simulation analysis of the theoretical model also points out that the coherent scattering energy is larger than the incoherent scattering energy [63]. In GNSS-R, coherent and incoherent observations have different surface scattering mechanisms. This difference is mainly manifested in the power extension and magnitude difference of the observed DDM. A coherent DDM is closer to the WAF (Woodward Ambiguity Function) of the GNSS navigation signal itself, while a noncoherent DDM has very obvious power spread characteristics in terms of delay and Doppler [64].  
The articles [65; 66] propose a classification method to determine the coherence of CYGNSS 454 terrestrial observation DDM. Since the GNSS signals received by the satellite receiver from the non-calm sea surface are all non-coherent signals, the power distribution of the coherent DDM received from the flat surface is close to the WAF. According to the power spread and magnitude characteristics of typical coherent and noncoherent DDM, six classification estimators are established to identify the coherence of CYGNSS terrestrial DDM. The classification results show that when the estimator based on the DDM absolute scattering power feature is used, it is more difficult to distinguish the coherence of the DDM and the estimator that characterizes the power spread feature can better separate the coherent and incoherent DDM [65; 66]. In the future, how to combine the scattering physical mechanism (especially microwave scattering mechanism) and signal explanation to fully utilize the coherent and noncoherent signals together should be paid much more attention.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates  
The second adaptive method is the standard in ODE solvers. This method uses local error estimates and does not yield global error bounds or near optimal grids, but works very well in practice and is computationally cheap. It is convergent under a smoothness assumption.  
The results from this section for one-step methods and the deadbeat controller (Equation (10)) are in principal classic [23]. Here, we present a new convergence proof, following the same principle techniques as in [23], but separating requirements on the error estimate, timesteps, and \(\Delta t_{0}\), for generic one-step methods. This prepares convergence proofs for general controllers and estimates, and we use it to show convergence in the QoI for the goal oriented adaptive method in Section 4. We first introduce the relevant notation.  
**Definition 2**.: _The flow [24] of an IVP (Equation (1)) is the map_  
\[\mathcal{M}^{t,\Delta t}:\mathbf{u}(t)\to\mathbf{u}(t+\Delta t),\]  
_where \(t\in[t_{0},t_{e}]\) and \(t+\Delta t\leq t_{e}\) for \(\Delta t>0\)._  
The flow is the solution operator for \(\mathbf{u}(t)\). To numerically solve an IVP means to approximate the flow by a _numerical flow map_\(\mathcal{N}^{t,\Delta t}:\mathbb{R}^{d}\to\mathbb{R}^{d}\) defined by some numerical scheme. The solution after a timestep can be written in the form  
\[\mathbf{u}_{n+1}=\mathcal{N}^{t_{n},\Delta t_{n}}\mathbf{u}_{n}.\]We generally assume Problem (1) to have the unique solution \(\mathbf{u}\) guaranteeing existence of the flow map \(\mathcal{M}^{t,\Delta t}\). We define the _global error_ by  
\[\mathbf{e}_{n+1} :=\mathbf{u}_{n+1}-\mathbf{u}(t_{n+1}) \tag{7}\] \[=\underbrace{(\mathcal{N}^{t_{n},\Delta t_{n}}-\mathcal{M}^{t_{n} \Delta t_{n}})\mathbf{u}_{n}}_{\text{global error increment}}+\underbrace{\mathcal{M}^{t_{n},\Delta t_{n}}\mathbf{u}_{n}-\mathcal{M}^{t_{n},\Delta t_{n}}\mathbf{u}(t_{n})}_{ \text{global error propagation}}.\]  
The dynamics of global error propagation is usually not known. The global error increments, however, have a known structure. Given a sufficiently smooth right-hand side \(\mathbf{f}\) for Equation (1), the _local error_ of a scheme \(\mathcal{N}^{t,\Delta t}\) of order \(p\) is  
\[\left(\mathcal{N}^{t_{n},\Delta t_{n}}-\mathcal{M}^{t_{n},\Delta t_{n}}\right) \mathbf{u}_{n}=\Delta t_{n}^{p+1}\mathbf{\phi}(t_{n},\mathbf{u}_{n})+\mathcal{O}(\Delta t _{n}^{p+2})\]  
with the _principal error function_\(\mathbf{\phi}\)[11].
## 3 Methodology  
The developed investigation approach followed in the present study was a four-step procedure: (i) data selection, generation of the spring inventory map and selection of nonspring areas, (ii) application of the Frequency Ration (FR) method and the linear support vector machine (LSVM) as a feature selection method so as to quantify the contribution of each explanatory factor and determine the optimal set of factors that have high predictive power, construction of the training and validation dataset, (iii) application of the Kernel logistic regression (KLR), Random Forest (RF), and Alternating Decision Tree (ADTree) models, and (iv) validation and comparison of the developed models. Figure 2 highlights the flowchart of the followed methodology, and each method used in our study will be briefly described in the following paragraphs.
###### Abstract  
Conventional visual SLAM systems often struggle with degraded pose estimation accuracy in dynamic environments due to the interference of moving objects and unstable feature tracking. To address this critical challenge, we present a groundbreaking enhancement to visual SLAM by introducing an innovative architecture that integrates advanced feature extraction and dynamic object filtering mechanisms. At the core of our approach lies a novel Multi-Scale Feature Consolidation (MSFConv) module, which we have developed to significantly boost the feature extraction capabilities of the YOLOv8 network. This module enables superior multi-scale feature representation, leading to significant improvements in object detection accuracy and robustness. Furthermore, we have developed a Dynamic Object Filtering Framework (DOFF) that seamlessly integrates with the ORB-SLAM3 architecture. By leveraging the Lucas-Kanade (LK) optical flow method, DOFF effectively distinguishes and removes dynamic feature points while preserving the integrity of static features. This ensures high-precision pose estimation in highly dynamic environments. Comprehensive experiments on the TUM RGB-D dataset validate the exceptional performance of our proposed method, demonstrating 93.34% and 94.43% improvements in pose estimation accuracy over the baseline ORB-SLAM3 in challenging dynamic sequences. These substantial improvements are achieved through the synergistic combination of enhanced feature extraction and precise dynamic object filtering. Our work represents a significant leap forward in visual SLAM technology, offering a robust solution to the long-standing problem of dynamic environment handling. The proposed innovations not only advance the state-of-the-art in SLAM research but also pave the way for more reliable real-world applications in robotics and autonomous systems.  
ORB-SLAM3; dynamic feature removal; YOLOv8
## 2 Materials and Methods
### Single Fiber Pull-Out Test  
Fibers were embedded in the cementitious matrix using a special equipment designed and constructed at IPF [56; 57; 58]. For the realization of the pull-out specimens, the fine grained matrix was mixed with tap water with a speed mixer. The same matrix has been employed in previous study with a water/binder ratio of 0.7 [55]. However, in this work, a water/binder ratio of 1.14 was used. This was necessary in order to obtain a fluid paste that enables the realization of matrix droplets with an adequate shape. The mixture was then filled in a sample holder with cylindrical shape and an inner diameter of about 2.6 mm. One end of the fibers was embedded by a computer-controlled process for 1000 \(\upmu\)m length under controlled atmosphere and temperature (23 \({}^{\circ}\)C, 50% relative humidity (RH)).  
About 19-20 specimens were prepared for each coating and cured for 28 days at constant temperature of 23 \({}^{\circ}\)C and 90% RH. The SFPO tests were conducted using a specialized pull-out apparatus, for which a detailed description can be found in Reference [56, 58]. The single fiber model composite was placed in the pull-out device and the upper fiber end was fixed at a mandrel with a cyanoacrylate adhesive in such a way that the free length was minimized. Subsequently, the pull-out test was performed under quasi-static loading conditions using a loading rate of 0.01 \(\upmu\)m/s.