esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## Appendix A  
In the real-world model, besides the attitude, velocity, position, and lever arm errors listed in (13), the measurement error models of the gyros and accelerometers are extended as follows:  
\[\delta\vec{\omega}_{ib}^{b}=\delta\mathbf{K}_{g}\mathbf{\omega}_{ib}^{b}+\mathbf{\varepsilon }^{b}=\left[\begin{array}{ccc}\delta K_{gxx}&\delta K_{gxy}&\delta K_{gxz}\\ \delta K_{gyx}&\delta K_{gyy}&\delta K_{gyz}\\ \delta K_{gxx}&\delta K_{gzy}&\delta K_{gzz}\end{array}\right]\left[\begin{array} []{c}\omega_{ib}^{bx}\\ \omega_{ib}^{bx}\\ \omega_{ib}^{bz}\end{array}\right]+\mathbf{\varepsilon}^{b}+\mathbf{\varepsilon}^{b} _{w}, \tag{13}\]  
\[\delta\vec{\mathbf{f}}^{b}=\delta\mathbf{K}_{a}\vec{\mathbf{f}}^{b}+\
abla^{b}=\left[ \begin{array}{ccc}\delta K_{gxx}&\delta K_{gxy}&\delta K_{gxz}\\ \delta K_{gyx}&\delta K_{gxy}&\delta K_{gxz}\\ \delta K_{gxx}&\delta K_{gxy}&\delta K_{gxz}\end{array}\right]\left[ \begin{array}{c}f_{x}\\ f_{y}\\ f_{z}\end{array}\right]+\
abla^{b}+\
abla^{b}_{w}, \tag{14}\]  
where \(\delta\mathbf{K}_{g}\) and \(\delta\mathbf{K}_{a}\) are error transfer matrices of the gyros and accelerometers, in which the diagonal elements denote the scale factor errors, while the off-diagonal elements express the quadrature errors, respectively; \(\mathbf{\varepsilon}^{b}\) and \(\
abla^{b}\) are the gyro and accelerometer biases; \(\mathbf{\varepsilon}^{b}_{w}\) and \(\
abla^{b}_{w}\) denote the random walk noises of gyros and accelerometers.  
In summary, the 39-dimensional state error vector in the real-world model is formed as follows:  
\[\begin{split}\mathbf{x}_{r}(t)=\left[\begin{array}{cc}(\mathbf{\phi}^ {n})^{\rm T}&(\delta\mathbf{v}^{n})^{\rm T}&(\delta\mathbf{p}_{1})^{\rm T}&(\mathbf{ \varepsilon}^{b})^{\rm T}&(\
abla^{b})^{\rm T}&(\mathbf{L}^{b}_{OI})^{\rm T}&(\bm {  
Accordingly, the position measurement equation of the full-order design filter is:  
\[\mathbf{z}^{*}=\mathbf{H}_{p}^{*}\mathbf{x}^{*}(\mathbf{t})+\mathbf{w}_{gp}+\mathbf{w}_{\mu_{c}}+\mathbf{ w}_{dt} \tag{10}\]  
in which:  
\[\mathbf{H}_{p}^{*}=\left[\begin{array}{cc}\mathbf{H}_{p}&\mathbf{0}_{18\times 3}\end{array} \right], \tag{11}\]  
where \(\mathbf{w}_{gp}\) represents the positioning noise of the GPS; \(\mathbf{w}_{\mu_{c}}\) and \(\mathbf{w}_{dt}\) are the position noises caused by encoder measuring error and time-asynchrony error, with the covariance as:  
\[\mathbf{R}_{\mu_{c}}=\text{diag}(\left[\mathbf{M}_{P}\mathbf{C}_{b_{c}}^{n}\left(\mathbf{L}_{ \text{OG}}^{b_{c}}\times\right)\mathbf{\sigma}_{\mu_{c}}\right])^{2} \tag{12}\]  
\[\mathbf{R}_{dt}=\text{diag}(\mathbf{M}_{P}\mathbf{v}^{n}\sigma_{dt})^{2} \tag{13}\]  
in which \(\sigma_{\mu_{c}}\) and \(\sigma_{dt}\) are the mean square deviation of the encoder measurement noise and time-asynchrony noise.
## 3 Results
### Optimization of Procedure  
Having applied the Design-Expert software, we built the 3D response surface and 2D contour plots about this model to elucidate the relationship between the extraction time, the ethanol concentration, the extraction temperature, and the yield of TPC. As shown in Figures 2A and 3A, the interaction influencing the TPC between the extraction solvent concentration and the extraction temperature were obtained. When the extraction time was set at zero, the TPC was improved with the ethanol concentration increased from 50 to 70%, on the other hand, with the extraction temperature increasing from 80 to almost 90 \({}^{\circ}\)C, the extraction of TPC reached the maximum value, and this value then decreased, followed by the continuous growth of the extraction temperature. Based on this observation, both the extraction temperature and the ethanol concentration imparted a positive effect on the extraction of TPC when the ranges of these two variables were 80-89.36 \({}^{\circ}\)C and 50-70%, respectively.  
In addition, Figures 2B and 3B reveal the effect of extraction time and temperature on the TPC. When the concentration of extraction solvent was zero, both the mutual interaction and quadratic effects of extraction time and temperature on the TPC extraction were exhibited. Maintaining the extraction temperature at 80 \({}^{\circ}\)C and increasing extraction time from 9 to 11 min raised the TPC value dramatically and then kept it steadily saturating. Besides, as the extraction time was kept at 9 min, the TPC value increased sharply, followed by the temperature increase from 80 to 90 \({}^{\circ}\)C similarly to finding in Figures 2B and 3B.  
Moreover, a general outline of those 3D response surface plots states is that when the extraction temperature was at a zero level, the yield of TPC was affected by the reciprocal interaction between the extraction time and the ethanol concentration. Both the extraction time and the ethanol concentration had a positive effect on the yield of TPC when another parameter was fixed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **DF** & **Mean Square** & **F-Value** & _p-Value Prob > F_ \\ \hline Model & 51.66 & 9 & 5.74 & 49.97 & \textless{}00001 \\ Residual & 8 & 7 & 0.11 & & \\ Lack of fit & 0.076 & 3 & 25 & 0.14 & 0.9318 \\ Pure error & 0.73 & 4 & 0.18 & & \\ Cor total & 52.46 & 16 & & & \\  & R\({}^{2}\) = 0.9847 & R\({}^{2}\)\({}_{\text{adj}}\) = 0.9650 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA for the fitted quadratic polynomial model of the black goji berry extraction.  
Figure 2: _Cont._Figure 3: _Cont._  
Figure 2: Response surface plots showing the effect of extraction temperature, ethanol concentration and extraction time on the total phenolics content (TPC) from black goji berry. (**A**) Interactive effects of extraction solvent concentration and extraction temperature on TPC; (**B**) interactive effects of extraction time and temperature on the TPC; (**C**) interactive effects of extraction time and extraction solvent concentration on the TPC.
# Fatigue Performance Test and Numerical Analysis of Composite Girders with CSW-CFST Truss Chords
## 6 Conclusions  
1. The fatigue performance test conducted on a composite girder with CSW-CFST truss chords demonstrated that the hot-spot stress in a composite girder with CSW-CFST and CSW-ST truss chords can be obtained by the two-point extrapolation method. The extrapolation interval and measurement point layout can be determined from the CIDECT specifications. The maximum hot-spot stress position was at point S, which was the next-nearest point to the mid-span section.
2. The maximum hot-spot stress at the left- and right-hand-sides of the intersecting weld did not occur at point S of the same section, and the hot-spot stress in the inclined web segment was markedly greater than that in the straight web segment.
3. When the chord is filled with concrete, the hot-spot stress distribution in the intersecting weld zone between the corrugated steel web and the chord is more uniform. The maximum hot-spot stress of a composite girder with CSW-CFST truss chords can be decreased by 18.5% to 60.1% of that of composite girders with CSW-ST truss chords.
4. Under the same fatigue load, concrete in the chord increased the fatigue life by 61.5% compared to composite girders with CSW-ST truss chords. The fatigue crack growth rates of the composite girder with CSW-CFST truss chords along both depth and length directions were slower than those of the composite girder with CSW-ST truss chords during crack propagation and failure stages.
5. Based on the existing fatigue design S-N curve, it was found that when there is no fatigue design S-N curve with higher accuracy, the fatigue design S-N curve given in API can be used to evaluate the fatigue life of composite girders with CSW-CFST truss chords, and the deviation is less than 17.4%.  
Figure 23: Comparison of welding details: (**a**) welding between the corrugated steel web and the chord; and (**b**) welding between the brace and the chord.  
Figure 24: Comparison of measured and calculated fatigue lives.  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON], and [PERSON] contributed reagents/materials/analysis tools; and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (No. 2017 YFE0130300), National Natural Science Foundation of China (No. 52078137), and Natural Science Foundation of Fujian Province (No. 2019J06009).  
**Data Availability Statement:** The data generated or used during the study are included within the submitted article.  
**Acknowledgments:** The tests were conducted at the Fujian Provincial Key Laboratory on Multi-Disaster Prevention and Mitigation in Civil Engineering at Fuzhou University. The support provided by the laboratory staff is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
###### Abstract
The main contributions of the paper are:  
* The detailed design of the MEDIWARN network architecture, a clear description of the role of each component involved in the MEDIWARN communication system and the description of a novel lightweight routing protocol, called the MultiPath Routing Protocol for MEDIWARN (MP-RPM), which offers path redundancy and mobility support without significantly increasing the network workload and latency, are presented. * The implementation of the MEDIWARN communication system on COTS devices is described, which shows the feasibility of the proposed network architecture and offers an experimental performance evaluation in a use case scenario. * The simulative assessment of the MEDIWARN communication system performance in the same scenario used for evaluating the implemented use case is presented to assess to what extent the simulative results and the experimental ones correspond. * A comparative assessment of the timeliness and reliability results obtained by the MEDIWARN communication system using three different routing protocols, i.e., the MP-RPM presented here, the AODV [11] (an on-demand protocol) and the DSDV [12] (a proactive protocol), is presented to highlight the impact of the routing protocol on the MEDIWARN communication system performance. The paper is organized as follows. Section 2 deals with related works. Section 3 presents the MEDIWARN communication system. Section 4 discusses the relevant research challenges and the solutions, while Section 5 focuses on the MP-RPM routing protocol. Section 6 addresses an implementation of the MEDIWARN system on COTS devices and discusses the results obtained through an experimental evaluation. Section 7 presents a simulative evaluation of the MEDIWARN communication system. Finally, Section 8 gives conclusions and hints for future works.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Kinematic Constraints  
Kinematic constraints refer to the restrictions that the human body should obey when moving, such as body structural connectivity constraints, distance constraints between joints, the range constraints of joint angles, and the constraints that each part of the body cannot penetrate each other. These constraints can be used as hard constraints to divide the state space into legal and illegal parts to reduce the search range [21] and can also be used as soft constraints, namely penalty factors [22].
## 2 Experimental Development
### Electrochemical Tests of Resistance to Linear Polarization with Rotating Cylinder  
Tests were carried out at a laboratory scale to assess the efficiency of inhibitors on the corrosion rate, using linear polarization resistance (LPR) in dynamic conditions in an electrochemical cell.  
In the linear polarization tests, the Metrohm AUTOLAB potentiostat/galvanostat was used, along with its corresponding software, NOVA 2.1.  
The electrochemical cell used has three electrodes: the reference electrode, which was AgCl/Ag in a KCl 3M solution brought to closer contact with the working electrode using a Luggin capillary, thus lowering the ohm resistance between the reference electrode and the working electrode; the working electrode, a 1045 carbon steel cylinder with a 2.47 cm\({}^{2}\) exposed area; finally, a graphite counter-electrode was used. All electrodes were submerged in the NACE solution.  
To carry out the tests in dynamic conditions, a rotating disk electrode was used, consisting of a rotating rod. The rotation working speed was, in accordance with the oil company's protocol, a series of 6000-4000-2000-4000-6000 rpm for each studied inhibitor. The working electrode rotated in the core of the NACE solution at a working temperature of 65 \({}^{\circ}\)C with CO\({}_{2}\) bubbling.  
To assess the efficiency of the inhibitors (ASTM G185-06), the instantaneous corrosion rate was measured using the LPR method at the aforementioned speeds, with constant CO\({}_{2}\) bubbling at 65 \({}^{\circ}\)C. After one-hour gas saturation, the open-loop potential was measured for one hour, with and without a corrosion inhibitor, followed by a polarization resistance test between \(-20\) mV and \(+20\) mV of the open-loop potential.  
To calculate the efficiency, Equation (3) is used, where CR is the corrosion rate:  
\[Efficiency=\frac{CR\ without\ inhibitor-CR\ with\ inhibitor}{CR\ without\ inhibitor}*100 \tag{3}\]
## 4 Theoretical Framework  
### Time Series  
Time series are sequences of one-variable samples typically taken at successive and evenly spaced instances of time, which, in this document, will be used to represent the losses in the channel, given the temporal dependence that the data bursts present in a data transmission. These series are statistical tools used to explain (and in some cases predict) the value that the analyzed variable takes at a given moment of time [14].
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Concepts  
#### 7.2.1 Quantum LiDAR  
Quantum-enhanced LiDAR techniques have the potential to surpass classical noise limits and offer improvements in both range resolution and sensitivity. Although still in the research phase, the use of quantum phenomena such as entanglement can improve detection accuracy and signal robustness, even in low-reflectivity or high-noise environments. In addition, quantum illumination protocols that include entangled photon pairs were shown to provide a quantum advantage in detection probability compared to classical strategies, especially in scenarios dominated by background noise and loss [175].  
Recent theoretical advances suggest that quantum LiDAR systems employing squeezed states and frequency-entangled photon pairs could achieve enhanced precision in target velocity estimation by reaching the Heisenberg limit [176]. Additionally, the resilience of certain quantum states against photon losses offers a substantial advantage over classical limited coherent state protocols. The utilization of multimode squeezed vacuum states in quantum Doppler LiDAR has been shown to enable better velocity estimation [176].  
However, the practical realization of a Quantum LiDAR is complex. It necessitates the development of highly efficient entanglement sources and low-loss quantum memories to preserve the idler photon over extended durations. Moreover, the optimized receiver designs can extract the full quantum advantage [175]. As quantum technologies evolve, future spaceborne LiDAR systems may leverage these advancements to achieve superior performance while operating at lower power budgets.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### False Positives vs. Dropped Messages  
As previously described, the simulation incorporated a random number generator within the attacker node code to introduce false positives. This generator produced random numbers for both the root and chain elements, which were then added to the message payload. This approach stresses the Bloom filter's ability to differentiate between legitimate messages and unexpected data, beyond simply dropping packets with no payload. Table 2 presents the results.  
Despite the use of a random seed to generate these attached numbers, the number of false positives across all simulation runs roughly doubled with two attacker nodes compared to one, with only one exception. However, the overall ratio between dropped messages and false positives remained below 5%. This difference, seen at the 4 s frequency, can be attributed to the combination of the random number generator and the random seed used for multiple simulation iterations.
## 3 Physicochemical Properties of Honey
### Organic Acids, Acidity and pH  
Even if organic acids are present in honey's composition in small quantities (less than 0.6%), their importance is still critical for some of its physicochemical parameters such as flavour, colour and preservation [53; 109]. Depending on the amount of acids contained within it, honey has the ability to prevent microorganisms developing [110; 111; 112], and thus the presence of acids as an indicator for honey's authenticity and quality [113; 114; 115]. Literature shows that plants can be considered the foremost source of organic acids present in honey [116; 117] other than the enzymes secreted by the bees during the honey process making [61]. In this sense, organic acids are often used to determine the botanical origins of honey [32; 109]. Many authors have identified the profile of organic acids in various honey samples through analytical methods such as high-performance liquid chromatography, enzymatic determination, or capillary zone electrophoresis with direct ultraviolet detection [88; 112; 118; 119; 120], leading to up to 32 compounds. The most common organic acids present in honey's composition are acetic, butyric, citric, formic, fumaric, glyoxylic, propionic, lactic, malicic, malic, gluconic, pyroglutamic, oxalic and succinic acids among others [53; 60; 61]. Gluconic acid is considered the main organic acid present in honey's composition, its proportion representing 70-90% of the total [42; 110; 112]. Apart from gluconic acid, citric or malic acids are also part of honey's composition [32; 120] their concentration being an indicator of discrimination among Blossom and Honeydew honey [43; 118; 121], the latter suggesting a higher concentration of these organic acids [50; 102; 122]. The presence of organic acids in correlation with other compounds such as esters, lactones and inorganic ions, determines the free acidity of honey [54]. This parameter, when determined, can evaluate the freshness of honey and the degree of possible fermentation [43; 123]. The recommended value for free acidity, as imposed by the European regulations, is 50 mEq/kg [86]. If values are higher, it is an indication of incipient sugar fermentation, leading to the presence of acetic acid, formed by alcohol hydrolysis [61; 103; 123]. The pH is another parameter for the determination of acidity, and its value can vary according to the honey extraction and storage conditions [59; 86]. Honey pH should be situated between 3.2 and 4.5 in order to avoid microorganism contamination [124]. Various honey samples from different geographical regions have been analysed in order to determine their acidity. For Acacia honey, the pH reported had similar values in Romania [123], Italy [105] and Serbia [74], respectively 3.94, 3.92 and 3.99. Moreover, the pH for thyme honey in Romania had much the same values as in Tunisian samples [86].
## 1 Introduction
The evaluation phase encompasses training the models on datasets such as KDEF, Filtered FER2013, and CK+, followed by an assessment using various evaluation metrics. These metrics include the accuracy, AUC-ROC [26], AUC-PRC, and Weighted F1 score [27]. A significant contribution of this study is on optimizing the performance of a pre-trained simple architecture such as a VGG on well-known FER image datasets. Instead of opting for complex deep neural network models, this study demonstrates that careful fine-tuning can lead to better classification accuracy with simpler architectures. This study investigated the efficacy of histogram equalization and data augmentation in improving the FER accuracy, alongside optimizing the performances of pre-trained architectures like VGG on three benchmark FER datasets. Additionally, this study showcases the effectiveness of  
Figure 1: The overall workflow of the proposed framework. different regularization techniques, callbacks, and learning schedulers in enhancing the model performance for FER by conducting extensive experiments. The subsequent sections of this paper are structured as follows. Section 2 presents a review of the related literature in the field. Section 3 introduces histogram equalization and cosine annealing to aid in understanding the proposed models. Section 4 elaborates on the transfer learning-based model, datasets used, and the experiment pipeline. Section 5 presents the experimental results, while Section 6 provides a thorough discussion of these results. Finally, Section 7 concludes this paper by discussing its significance and outlining potential future work.
## 1 Introduction
However, a reliable database which has enough data is one of the key requirements for using such a BP model. It is hard to obtain such a consistent database with the traditional experimental methods since their testing results are dependent on the testing methods, materials, specification and so on. One advantage of the DEM-based modeling approaches is lab-independent. First of all, with the DEM model, users can control one or a few key factors without effects from the other factors. For instance, it is possible to build a DEM model of mixture where all the particles have identical shapes if one wants to explore effects of gradations without influences of particle shapes. As a result, it is possible to obtain consistent results which stick to certain conditions. Secondly, limited by materials and testing conditions, it is very difficult to obtain a large amount of consistent experimental data. With the DEM model, however, it becomes possible to obtain a large amount of data as long as one has the DEM modeling techniques and computer facilities. Against the background discussed above, the main objectives of this research are to explore the prediction of granular materials' porosities by combining the DEM simulation and the BPNN methods. The granular materials in this research refer to mineral aggregates whose sizes range from 2.36 mm to 19 mm. They are key constituents of asphalt mixtures which take over 90% of the overall mass and their porosities may significantly impact mixture volumetric and mechanical properties [27; 28]. Compared to aggregates, asphalt acts more like lubricant during the compaction stage and its impact to mixture porosity can be ignored in this article [12; 29]. According to the Chinese specifications, mixtures whose maximum particle sizes are from 13.2 mm to 19.0 mm are popularly used as high-quality pavement construction. As demonstrated in the previous research [13; 30], both gradations and particle shapes could have certain impacts on mixture volumetric properties. When both gradations and particle shapes are considered simultaneously, it is not easy to make a consistent conclusion. Therefore, in this article the aggregate gradations were selected as the major control parameters, while the particle shapes were considered as minor parameters by introducing a reduction factor.