esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Method  
Two Heraeus 312 fused silica samples were pre-polished by the supplier to avoid the uncertain effects of different polishing qualities. The size of the samples is 50 mm \(\times\) 50 mm \(\times\) 10 mm, and all samples had bright surfaces with no obvious brittle scratches. Samples 1# and 2# were first etched with a 5% HF acid solution supplemented by megasonic vibration. The etching process parameters have been optimized, and each round of operation includes 30 min of HF acid etching and 30 min of megasonic cleaning by deionized water. Then, the samples were dried with absolute alcohol. The ion beam sputtering parameters are shown in Table 1.  
Both surfaces of Sample 1# were sputtered with the same removal depth before LIDT tests. The removal depths of the three IBS treatments were 900 nm, 1500 nm, and 2000 nm, respectively. The LIDT test is performed using an R-on-1 method, which irradiates the test point with a gradually increasing laser flux until the imaging Charge Coupled Device (CCD) can observe the damage. For the LIDT test, 10 test points are selected randomly in the corresponding area, and the average of the damage thresholds is taken as the damage threshold corresponding to the removal depth. A 3\(\omega\) Nd:YAG laser was employed in the LIDT test; the pulse width is 7 ns, and the repetition rate is 1 Hz. The laser spot is close to a flat Gaussian type, and its diameter is about 1.2 mm. The laser beam is focused on the back surface of the sample. When both surfaces of Sample 2# are removed by IBS at a certain depth each time, the LIDT tests are carried out first, and then the sample is soaked in deionized water for 30 min, and then the LIDT tests are carried out again after anhydrous alcohol dehydration.  
Infrared spectroscopy is the main way to study the OH- group structure characteristics of fused silica. The absorption peak in the infrared spectrum corresponds to the vibrational form of the OH- group, and the characteristic absorption peak near 3660 cm\({}^{-1}\) indicates the presence of OH- groups [20]. A Bruker Vertex70 (Bruker Corporation, llerica, MA, USA) high-resolution Fourier transform infrared spectroscopy (FTIR) was used to measure the hydroxyl content after different surface treatment methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Beam Voltage** & **Beam Current** & **Incidence Angle** & **Working Pressure** & **Cathode Airflow** & **Neutralizes Airflow** \\ \hline
900 eV & 5 mA & 0\({}^{\circ}\) & 5 \(\times\) 10\({}^{-3}\) Pa & 6 sccm & 6 sccm \\ \hline \end{tabular}
\end{table}
Table 1: Process parameters of IBS.  
Figure 1: Chemical structure defects in fused silica due to the introduction of OH- and F- ions.  
The photothermal weak absorption technology is used to detect and evaluate the surface absorption characteristics of fused silica. The photothermal weak absorption detection system is provided by Hefei Zhichang Optoelectronic Technology Co., Ltd. (Hefei, China), and the product type is PTS-2000-RT-C.
# Pricing Strategies of Logistics Distribution Services for Perishable Commodities
## 4 Comparison Analysis of Four Pricing Models  
### Comparison of Optimal Delivery Pricing  
Because the distribution pricing model with time constraints adds time constraints to the original model, this paper only compares the maximum distribution pricing between the fixed pricing model and the dynamic pricing model.  
**Proposition 1**.: _Under the same distribution time, if \(0<t<\overline{t}\), then the distribution pricing under the dynamic pricing model is higher than that under the fixed pricing model. If \(\overline{t}<t<T\), the distribution price under the dynamic pricing model is lower than the fixed price model. Here, the parameter \(\overline{t}\) satisfies \(V_{\overline{t}}+h\overline{t}\big{(}T-\overline{t}\big{)}=h+\frac{(V_{0}-V_{ \overline{t}})}{\lambda T}\), and \(T>\overline{t}>\frac{T}{2}\)._  
**Proof**. We compare formulas (3) and (9), and consider the description of the first increase and decrease of \(P_{D}(t)^{*}\) in property 5. Firstly, compare \(P_{D}(0)^{*}\) with \({P_{F}}^{*}\). \(P_{D}(0)^{*}=\frac{kI+C+V_{0}}{2}\), while \(t=0\), we get \({P_{F}}^{*}=\frac{kI+h+C}{2\lambda T}+\frac{V_{0}\big{(}1-e^{-\lambda T}\big{)}} {2\lambda T}\). If \(h>V_{0}+\frac{V_{\overline{t}}-V_{0}}{\lambda T}\), \({P_{F}}^{*}>P_{D}(0)^{*}\), otherwise, \({P_{F}}^{*}<P_{D}(0)^{*}\). Let \(y(t)=\frac{kI+C+V_{0}}{2}-\frac{kI+h+C}{2\lambda T}+\frac{V_{0}\big{(}1-e^{- \lambda T}\big{)}}{2\lambda T}\), which can be transferred to \(y(t)=\frac{V_{0}-h}{2}-\frac{V_{0}-V_{\overline{t}}}{2\lambda T}=\frac{ \lambda TV_{0}-hkT-V_{0}+V_{\overline{t}}}{2\lambda T}=\frac{(\lambda T-1)V_{0 }^{2}-hkT+V_{\overline{t}}}{2\lambda T}>\frac{V_{\overline{t}}+V_{\overline{t }}-hkT}{2\lambda T}>0\), in other words \({P_{F}}^{*}<P_{D}(0)^{*}\). Because \(P_{D}(t)^{*}\) monotonically increases if \(0<t<\frac{T}{2}\) and \({P_{F}}^{*}\) remains unchanged, \({P_{F}}^{*}<P_{D}(t)^{*}\) holds if \(0<t<\frac{T}{2}\). If \(t=T\), \({P_{F}}^{*}=\frac{kI+h+C}{2}+\frac{V_{0}\big{(}1-e^{-\lambda T}\big{)}}{2 \lambda T}\), \(P_{D}(T)^{*}=\frac{kI+C+V_{\overline{t}}}{2}\), let \(u(t)=\frac{kI+h+C}{2}+\frac{V_{0}\big{(}1-e^{-\lambda T}\big{)}}{2\lambda T}- \frac{kI+C+V_{\overline{t}}}{2\lambda T}\), then \(\mathfrak{u}(t)=\frac{h-V_{\overline{t}}}{2}+\frac{V_{0}\big{(}1-e^{-\lambda T }\big{)}}{2\lambda T}=\frac{hkT-\lambda TV_{\overline{t}}+V_{0}-V_{\overline{t }}}{2\lambda T}=\frac{V_{0}-(\lambda T+1)V_{\overline{t}}+hkT}{2\lambda T}>0\), if \(t=\frac{T}{2}\), \({P_{F}}^{*}<P_{D}\big{(}\frac{T}{2}\big{)}^{*}\). If \(t=T\), \({P_{F}}^{*}>P_{D}(T)^{*}\), so there is \(\frac{T}{2}<\overline{t}<T\). If \(0<t<\overline{t}\), the distribution price under the dynamic pricing model is higher than that under the fixed pricing model. On the contrary, If \(\overline{t}<t<T\), the distribution price under the dynamic pricing model is lower than the fixed price model. \(\square\)
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 4 Results
### Results  
A comparison of the resulting metrics on the KITTI dataset is shown in Table 2. We show the results on the cross-area test set, which consists of areas that are not part of the training set [13], and compare the previous state of the art with our best-performing model. Our model has the best latitude recall rate within 1 m and the best azimuth recall rate within 3 and 5 degrees. OrienterNet is the most capable model, obtaining better lateral and longitudinal results overall, but is unable to incorporate a rotation prior, resulting in a lower angular recall rate [12]. Overall, we find the results of our methods to be competitive with the current state of the art, showing the LM optimization algorithm to be a promising direction for small-scale localization problems.
# An Oligoimide Particle as a Pickering Emulsion Stabilizer
## 4 Conclusions  
The PMDA-ODA oligoimide particles were synthesized by a one-step procedure using water as a solvent. The PMDA-ODA particles were found to be relatively hydrophilic, and it is considered that the carboxyl and amino end groups contribute importantly to the hydrophilicity of the particles because PMDA-ODA is oligomeric. The PMDA-ODA particles showed partial wetting properties and were stably dispersed in both water and oil phases. In contrast, a stable dispersion could not be prepared from a control sample of PMDA-ODA particles synthesized using an organic solvent (NMP). SEM and DLS showed that the size of the PMDA-ODA particles synthesized in water ranged from 100 to 300 nm after ultrasonication. Both oil-in-water and water-in-oil Pickering emulsions were prepared using the oligoimide particles, and the size of the emulsion droplet was controlled by oligoimide particle concentration. The oil-in-water emulsions were successfully applied to prepare microcapsules of the emulsion droplets. The new oligoimide stabilizer has the advantages of easy synthesis, no need for surface modification, no need for pH adjustment of continuous phase, and the capability of stabilizing both oil-in-water and water-in-oil emulsions.  
The following are available online at [[http://www.mdpi.com/2073-4360/10/10/1071/s1](http://www.mdpi.com/2073-4360/10/10/1071/s1)]([http://www.mdpi.com/2073-4360/10/10/1071/s1](http://www.mdpi.com/2073-4360/10/10/1071/s1)). Figure S1: FT-IR spectrum of PMDA-ODA polyimide synthesized in NMP. Figure S2: FE-SEM image of PMDA-ODA particles synthesized in NMP: (a) particles prepared by sieving with a 200 \(\upmu\)m sieve and ultrasonication of its dispersion at a frequency of 30 kHz for 30 min; (b) particles prepared by ball milling using a planetary ball mill with a speed of 800 rpm for 22 h. Figure S3: Oil droplet size distributions of PMDA-ODA particle-stabilized \(n\)-hexadecane-in-water emulsions prepared at various particle concentrations (0.1-1.0 wt %). Figure S4: A photograph and optical micrographs of water-in-phenyl acetate Pickering emulsions prepared with different PMDA-ODA particle concentrations: 0.5, 1.0, and 1.5 wt %.  
Conceptualization, Y-J.C., K.C., and C.-M.C.; Experiments and Analyses, Y-J.C., D.-M.K., I.-H.S., J.-Y.C., S.-W.J., B.-J.K., J.-W.J., and C.-E.J.; Writing-Original Draft Preparation, Y-J.C. and C.-M.C.; Writing-Review & Editing, Y-J.C. and C.-M.C.  
This research was supported by a grant (18 SCIP-B103706-04) from the Construction Technology Research Program funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.  
The authors declare no conflict of interest.  
Figure 11: TGA thermograms of PMDA-ODA, the shell wall, poly(MMA-\(\infty\)-DVB), and the microcapsule. The shell wall sample was obtained by pressing the microcapsules between two slide glasses and drying.
# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 12 Practical Contributions to the Literature  
Renewable-energy-source-based distributed generation integrates 6G wireless networks so as to process collected sensory data swiftly [38], reducing power consumption by optimized wireless energy transfer, smart grid and batteries, and energy management and cyber-physical systems. Low-latency ultra-fast 6G wireless communications assist big-data-driven sensor connectivity in reducing carbon emissions. Industry 4.0 automation, connected IoT devices, and cloud computing and augmented reality technologies can focus on renewable and sustainable energy sources. Smart UAV technologies can optimize collaborative green IoT and sustainable Industry 4.0 environments by collected data sharing through fog nodes [28], saving energy and ensuring battery longevity by distributed device operation mode control. UAV-technology-based green IoT facilitate energy efficiency, enhance real-time sensor node transmission power, and reduce carbon footprint. UAV technologies can reduce IoT device power consumption by close reliable data transfer in smart environments, while extending the coverage area by green wireless communication technologies and clustering techniques. Dynamic wireless charging infrastructures, vehicle-to-everything (V2X) communications, battery swapping stations, smart green Internet of Vehicles, and fast charging stations [77] can enable electric grid renewable energy resource integration while preventing grid overload, decrease greenhouse gas (GHG) emissions and oil consumption, and improve air quality and sustainable vehicular connectivity and networking. Energy-aware scheduling algorithm-based 6G green communication systems [78] can be deployed for optimal resource sharing and scheduling decision management in terms of energy consumption efficiency. Non-orthogonal multiple access (NOMA), 6G IIoT communications, and statistical signal transmission [79] can be harnessed for ubiquitous connectivity and detection performance due to monitoring service sensing accuracy.
# Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot
## 2 Methods
### Sensing System Design
#### 2.3.4 Foot Contact Forces (FCFs) Measurements  
A special shoe was developed to measure FCF in the sagittal plane. Note that the FCF is an interaction force between the foot and shoe. It is different from GRF. GRFs only appear when the foot touches the ground, but FCFs exist in both the stance and swing phases. There are three components of FCF in the sagittal plane, which include \(F_{Fx}\) in the front-rear direction, \(F_{Fz}\) in the up-down direction, and torque \(T_{Fy}\) on the dorsiflexion axis. The force condition of the sole is complex. There are many contact states of the sole during exercise, such as heel strike, full foot landing, forefoot landing, and toe landing, while ground unevenness can also cause unpredictable deformation of the sole. Moreover, rotational freedom of the human toe joint is required for walking stability and energy saving [33], so the sole should be flexible. Thus, a double-layer elastic steel sole structure was designed, as shown in Figure 6a.  
The proposed design utilizes a combination of a 3D force sensor and two 1D force sensors. The 3D force sensor is arranged slightly behind the toe joint, and the sensor can obtain the up-down direction force (\(F_{z2}\)) and the front-rear direction force (\(F_{x2}\)). A 1D force sensor with a high overload margin is set under the heel to measure the GRF (\(F_{z1}\)) on the heel. A 1D force sensor is placed under the toe to measure the supporting force (\(F_{z3}\)). The sensor layout and force analysis model are shown in Figure 6b.  
For computational purposes, the force signals obtained from the force sensors are synthesized to the ankle joint. The resultant forces can be calculated based on the location of the force sensors:  
\[\left\{\begin{array}{l}F_{Fx}=F_{x2}\\ F_{Fz}=F_{z1}+F_{z2}+F_{z3}\\ \tau_{Fy}=F_{x2}l_{4}-F_{z1}l_{1}+F_{z2}l_{2}+F_{z3}l_{3}.\end{array}\right. \tag{11}\]  
Figure 6: Foot force measurement device: (**a**) sole structure; (**b**) force sensor signals of the sole.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 4 Simulations and Results
### Experiment 4: Spoofing Localization Effects Considering Varying Numbers of Observation Points  
Experiment 4 tests the effectiveness of the proposed method in spoofing interference localization under different numbers of observation points. The spoofing interference source settings, carrier phase measurement error settings, antenna positioning error settings, and the positioning accuracy statistical methods for deception interference sources in the experiment are consistent with Section 4.2. The experimental path used in the experiment is recommended Path 5, with the number of observation points set to 11, 21, 41, 61, and 81, respectively, and each observation point number setting is uniformly sampled of Path 5. The experimental results are shown in Figure 13, where the horizontal and vertical coordinate settings are consistent with those of Figure 10.  
Simulation results show that the spoofing interference localization effect of the proposed method improves as the number of observation points increases. However, as the number of observation points increases, the improvement of the spoofing interference localization effect decreases, and the increase in the number of observation points leads to a significant increase in computational complexity. Considering the cost of the implementa  
Figure 12: Test results from Experiment 3. (**a**) Comparison of localization effects under different antenna positioning error settings of the proposed method; (**b**) Comparison of localization effects under different antenna positioning error settings of the Two-step Method 1.  
tion of the method, the recommended number of observation points for the monitoring region set in the experimental scenario in this paper is 21 or 41.
## 3 Results
### Comparison between BDS Single-System and Joint POD
#### 3.3.1. Overlapping Arcs Precision
The results show that the along, cross-track, and radial-track directions were improved by 49.3%, 37.2%, and 48.5%, respectively. This result indicates that the joint orbit determination has the greatest impact on the radial direction, followed by the along-track direction, and has the least impact on the cross-track direction, excluding GEO satellites.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
###### Abstract
Furthermore, the selection of these nations takes the highest and lowest population densities into consideration. The report will allow:  
* Consumers to comprehend and compare the quality of the communication service they are receiving as well as the government's/regulator's relentless efforts in ensuring a high level of service quality and guaranteeing investments to improve said quality for its citizens;
* Countries to continue as leading nations in communication service and meet the targets set out by the broadband commission;
* Mobile broadband companies to understand their status within and outside the country, which aids their R & D efforts to stay ahead of competitors such that the country meets its \"5G+ Strategy\";
* Regulators to find out new and suitable metrics and synchronize them. The rest of this paper is organized as follows: In Section 2, the methodology of the study is discussed. The case studies from each nation are presented in Section 3. Section 4 \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Indicator** & **2018 Data** & **Penetration** & **Source** \\ \hline World Population & 7.6 billion & - & UN \\ Mobile Broadband Subscription & 5.3 billion & 69\% & ITU \\ Unique Mobile Subscribers & 5.1 billion & 67\% & GSMAi \\ Unique Mobile Internet Subscribers & 3.5 billion & 47\% & GSMAi \\ Internet Users & 3.9 billion & 51 \% & ITU \\ Active Social media Users & 3.5 billion & 45\% & Datareportal (Hootsuite) \\ Fixed Broadband Subscription & 1.1 billion & 14\% & ITU \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage of world population with communication as of 2018.  
is dedicated to examining the progress made towards 5G. Finally, the summary and conclusion of this paper are presented in the last section.
###### Abstract  
Ultrasonic Guided-wave (UGW) testing of pipelines allows long-range assessment of pipe integrity from a single point of inspection. This technology uses a number of arrays of transducers separated by a distance from each other to generate a single axisymmetric (torsional) wave mode. The location of anomalies in the pipe is determined by inspectors using the received signal. Guided-waves are multimodal and dispersive. In practical tests, nonaxisymmetric waves are also received due to the nonideal testing conditions, such as presence of variable transfer function of transducers. These waves are considered as the main source of noise in the guided-wave inspection of pipelines. In this paper, we propose a method to exploit the differences in the power spectrum of the torsional wave and flexural waves, in order to detect the torsional wave, leading to the defect location. The method is based on a sliding moving window, where in each iteration the signals are normalised and their power spectra are calculated. Each power spectrum is compared with the previously known spectrum of excitation sequence. Five binary conditions are defined; all of these need to be met in order for a window to be marked as defect signal. This method is validated using a synthesised test case generated by a Finite Element Model (FEM) as well as real test data gathered from laboratory trials. In laboratory trials, three different pipes with defects sizes of 4%, 3% and 2% cross-sectional area (CSA) material loss were evaluated. In order to find the optimum frequency, the varying excitation frequency of 30 to 50 kHz (in steps of 2 kHz) were used. The results demonstrate the capability of this algorithm in detecting torsional waves with low signal-to-noise ratio (SNR) without requiring any change in the excitation sequence. This can help inspectors by validating the frequency response of the received sequence and give more confidence in the detection of defects in guided-wave testing of pipelines.  
Keywords:signal processing; defect detection; torsional wave; power spectrum; sliding window; pipeline inspection; ultrasonic guided-waves (UGWs) +
Footnote †: journal: Applied
## 3 Results and Discussion
### Phosphorus Removal in Batch Adsorption Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Pseudo-First-Order**} & \multicolumn{3}{c}{**Pseudo-Second-Order**} \\ \hline \(\mathbf{q_{e,exp}}\) & \(\mathbf{q_{e,cal}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{q_{e,exp}}\) & \(\mathbf{q_{e,cal}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{k^{2}}\) \\ (mg/g) & (mg/g) & (1/min) & \(\mathbf{R^{2}}\) & (mg/g) & (mg/g) & (g/mg min) & \(\mathbf{R^{2}}\) \\ \hline
1.25 & 0.92 & 0.00601 & 0.7785 & 1.25 & 1.10 & 0.02325 & 0.9924 \\
1.32 & 1.37 & & & 1.32 & 1.37 & & \\
1.36 & 1.60 & & & 1.36 & 1.49 & & \\
1.49 & 1.71 & & & 1.49 & 1.56 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Kinetic parameters of phosphorus adsorption onto HAOPs. the removal process of phosphorus compounds is controlled mainly by chemisorption or chemical bonding. The rapid phosphorus adsorption was also attributed to the boundary layer diffusion or macro-pore diffusion, while the slow adsorption was attributed to the intraparticle diffusion or micro-sore diffusion [41]. The high applicability of the pseudo-second-order model for the presented kinetic data is also in agreement with other studies [36; 41].
# Re-Understanding the Sedimentary Environment of the Wufeng-Longmaxi Shales in the Sichuan Basin
## 2 Evidence That Marine Source Rocks Developed in a Shallow-Water Restricted Environment
### Fossil Evidence  
The development of graptolite, radiolaria, and siliceous rocks does not necessarily indicate a deep-water environment.  
[PERSON] (1993) [5] argues that the primary condition for the existence of graptolites is the degree of water calmness, rather than water depth. Enclosed basins are particularly conducive to the growth of graptolites. [PERSON] et al. (1993) [38] examined the paleosalinity of the upper Yangtze region during the Wufeng period and concluded that the peak of seawater desalination in Upper Yangtze during this period was fully synchronized with the development of graptolites, suggesting a strong association with freshened environments.  
It is evident that radiolarian fossils are unreliable indicators of deep-sea facies, as the silica in siliceous rocks may originate from the deposition of biogenic siliceous skeletons [39]. Calcareous and siliceous shales are commonly found in various environments, including semi-deep to deep-sea settings and enclosed bays [40]. Modern radiolarians primarily inhabit depths ranging from 0 to 75 m and can serve as proxy indicators for primary productivity in the ocean's surface layer [41; 42]. In the South China Sea, high concentrations of living radiolarians correspond well with peak levels of chlorophyll and primary productivity, occurring at water depths between 0 and 25 m [43].  
Thus far, we cannot verify whether the extinct graptolites originated from deep-water environments; however, the radiolarians that coexisted with them do have living counterparts in modern oceans and inhabit shallow-water environments. While it cannot be denied that graptolites and radiolarians may exist in deep-water shelves (60-200 m) or even on the abyssal seabed, statistically, the probability of their development in shallow-water environments is significantly higher than in deep-water environments.