esa-sceva/satcom-chunk-collection huggingface dataset:
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 3 Qualitative Benchmark for Analysis
### Criteria for Requirement Mapping for Adaptability  
By satisfying parameter AL1, the MM mechanism (understudy) fulfills requirements 1 st, 8 th, and 12 th of Table 1. Similarly, when the AL2 parameter is considered, it assists in achieving the 1 st and 9 th requirements of Table 1 [32]. Further, when the condition of the AL3 parameter is fulfilled, then it will satisfy requirements 1, 3, and 11 of Table 1. Furthermore, parameter AL4 will assist in fulfilling requirements 1 and 8 of Table 1. Lastly, the requirements mapping criteria for the MM mechanism are considered to fulfill the adaptability parameter AL5, congruently assisting in achieving the requirements 1 and 2 mentioned in Table 1.
## 5 Conclusions  
Pipe roof pre-support (PRPS) and curtain grouting pre-reinforcement (CGPR) were successfully used in the closely spaced large span triple tunnels of the Badaling Great Wall Underground Station. The following conclusions were obtained:  
1. Based on preliminary field monitoring, it indicated that the rock mass ahead of the tunnel face was severely broken due to the severe multiple interactions among triple tunnels, and the current supports could not meet the safety requirements. Therefore, PRPS and CGPR should be applied.
2. A series of numerical simulations were conducted to optimize design parameters. The optimal pipe diameter, arrangement angle of PRPS were determined to be 108 mm and 150\({}^{\circ}\). The grouting thickness of CGPR was optimized as 3 m.
3. According to numerical results, the PRPS showed a better effect in controlling MCS, while the CGPR showed better performance on reducing PPR. It could be attributed to their different working mechanism, the PRPS predominantly bore external load and prevented tunnel collapse, while the CGPR primarily improved the mechanical properties and enhanced the self-stability of the rock mass.
4. PRPS and CGPR had complementary advantages in time, space, and mechanical properties. PRPS could timely form a strong arch structure far ahead of the tunnel face, CGPR could  
Figure 17: Crown settlements of triple tunnels with PRPS and CGPR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & **Max Surrounding Rock** & **Mean Surrounding Rock** & **Max Crown** & **Mean Crown** \\  & **Pressure (kPa)** & **Pressure (kPa)** & **Settlement (mm)** & **Settlement (mm)** \\ \hline Case 1 & 547 & 202.5 & 76.6 & 44.5 \\ Case 2 & 403 & 134.8 & 28.7 & 18.4 \\ Reduction rate & 26.3\% & 33.4\% & 62.5\% & 58.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Monitoring data comparison of two cases.  
effectively restrict the development of the plastic zone. They mutually and actively formed a strong permanent ring in front of the tunnel face.
* The combined implementation of PRPS and CGPR were adopted in subsequent tunnel excavation, the mean surrounding rock pressure decreased by 33.4%, the MCS decreased by 58.7%, respectively, and no significant tunnel damages were observed. The triple tunnel excavation was effectively safeguarded.  
Project administration: D.Z.; Conceptualization, R.L. and P.W.; Data curation, R.L. and Q.F.; Formal analysis, R.L. and D.Z.; Investigation, R.L. and A.L.; Methodology, R.L. and D.Z.; Validation, L.C. and P.W.; Writing--Original draft preparation, R.L.; Writing--Review & editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support by the Fundamental Research Funds for the Central Universities (Grant No. 2020 YJS103), the National Natural Science Foundation of China (Grant No. 51738002) and the National Key Research and Development Program of China (Grant No. 2017 YFC0805401).  
The authors declare no conflict of interest.
## 5 Discussion
### Risks and Concerns Associated with the Use of IoES in Emergency Management and Response  
The deployment of IoES in emergency management and response presents several risks and concerns, including:  
* Security and Privacy Risks: IoES relies on the exchange of sensitive and critical data among various stakeholders, such as emergency responders, healthcare providers, and the public [26]. As a result, there is a significant risk of cyber-attacks [97; 149], data breaches [150], and unauthorized access to critical information [151]. The use of cloud computing and other third-party services can also raise concerns about the security and privacy of data [77; 119; 152; 153].
* Interoperability and Compatibility: IoES requires seamless integration and interoperability among various systems, platforms, and devices [154; 116]. The lack of standardization [155] and compatibility among different technologies can cause interoperability issues [110], which can delay or complicate the emergency response.
* Reliability and Availability: The reliability and availability of IoES infrastructure, such as communication networks, cloud computing, and IoT devices, can significantly impact the emergency response [106; 131; 156]. Any disruption in the availability or functionality of these systems can have severe consequences on the emergency response [97].
* Ethical Concerns: The use of emerging technologies, such as AI and wearables in IoES, can raise ethical concerns about the collection, processing, and use of personal data. The potential misuse or abuse of such technologies can result in unintended consequences and erode public trust in emergency management and response.
* Legal and Regulatory Compliance: The deployment of IoES in emergency management and response must comply with various legal and regulatory requirements, such as data protection, privacy, and security regulations. Non-compliance can result in legal and financial consequences, as well as damage to the reputation of emergency response agencies and organizations.
* Social and Behavioral Issues: The adoption and use of IoES in an emergency response must consider social and behavioral factors that can impact its effectiveness. For instance, the public's trust in emergency response agencies and their willingness to use IoES technologies can affect the adoption and effectiveness of IoES.  
The deployment of IoES in emergency management and response presents several risks and concerns that must be addressed to ensure its effectiveness and reliability. Emergency response agencies and organizations must carefully consider these risks and concerns and implement appropriate measures to mitigate them.
## 3 Results and Discussion
#### 3.2.2 Relationships between Daily Air Temperature and CO\({}_{2}\) Flux Anomalies
The statistically significant (\(p<0.05\)) correlation for 95% quantile threshold was found in the Australian and Congo savannas, the evergreen forests in Australia (the maximum correlation--0.81) and Malaysia, and the deciduous needle-leaf forest in Brazil. The statistically significant correlation for the 90% quantile threshold was found only in two stations (Au-Rob and MY-PSO) in the evergreen forests. At two stations, the MKL in monsoon forests in Thailand and the GH-Ank in evergreen forests in Ghana, a negative correlation between precipitation and CO\({}_{2}\) fluxes was found, but it was not statistically significant. The extreme temperatures had a positive and negative correlation with CO\({}_{2}\) flux anomalies. A positive correlation was found in the savanna in Sudan and the evergreen forests in Australia (R = 0.39 for 95% quantile) and Ghana. In these ecosystems, the high temperatures corresponded to increased CO\({}_{2}\) releases, and a decrease in temperature led to higher CO\({}_{2}\) uptake. Most ecosystems were characterized by a negative correlation between temperature and CO\({}_{2}\) flux anomalies, i.e., positive CO\({}_{2}\) flux anomalies occurred during periods of low temperature and negative anomalies during rising temperatures. The maximum correlation (R = \(-\)0.51, \(p\) < 0.05) was found in the Brazilian needle-leaf forest.
# Robust Full-Screen Wavelength Calibration Algorithm
## 3 The Proposed Calibration Method  
As there are high requirements regarding spectrometer accuracy, we have to solve the calibration problem of the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer. The main difficulties that were encountered are as follows. First the spectral wavelength at the central pixels must be accurately determined using the wavelengths obtained from the limited standard light source, and good calibration accuracy must simultaneously be achieved at other wavelengths. Secondly, the wavelength we must be accurately calculated at the non-central pixels, while simultaneously making the algorithm robust. Finally, a continuous algorithm that can simultaneously calibrate all pixels should be generated.  
After analyzing the working principles of a spectrometer and the causes of the above-mentioned difficulties, we propose the following calibration algorithm. First, the wavelengths of the center pixels are calibrated. According to Equation (4), we conducted a rough calibration, obtained the data pairs needed for the subsequent accurate calibration, and then corrected them using our algorithm's sinusoidal function. Then, when the feature peak of the wavelength reached the non-central pixel, the feedback value of the grating ruler (feedback value) of the central pixel was recorded. To convert the feedback value recorded at the central pixel when the wavelength reaches the non-central pixel into the feedback value of the wavelength at the central pixel, the functional relationship between the pixel's difference and the feedback value is established. Finally, the spectral wavelength calibration algorithm of full-screen pixels was presented by integrating the previous two steps.  
It should be noted that the 1200-line grating of this spectrometer has a spectral range from 200 nm to 1000 nm, and the 2400-line grating has a spectral range from 200 nm to 650 nm. Without loss of generality, we use the 2400-line grating as an example in our discussion. The calibration method can be directly applied to the 1200-line grating.
## 2 Materials and Methods
### Sample Preparation and Testing  
All samples were processed within 24 h of collection. Firstly, the visible and separable components were roughly sorted in each MSW sample manually and the remaining sample was sieved through to a mesh with the pore size of 10 mm. The screen overflow was sorted to corresponding components and the underflow was sorted according to its components. Subsequently, the sorted samples were placed in separated containers and then moved to the electric thermostatic-drying oven for drying at \((105\pm 5)\,^{\circ}\)C until a constant weight was reached. Each component was weighed on a balance (0.001 kg accuracy), so their mass fractions can be calculated. After weighing, a crasher was used to grind the samples to the diameter of less than 0.5 mm. After adequate mixing, the quartering method was used on a continued basis to contract and split the sample until the mass was reduced to approximately 100 g and then stored in dried jars at room temperature. When the heavy metal test was conducted, 2 g of sample was extracted and the grinder was used to pulverize the particle to a diameter of less than 0.5 mm. For each specimen, 1 g was taken for testing.  
The typical toxic heavy metals (As, Cd, Hg, Cr, and Pb) were selected to carry out the test. For As, Cd, Cr, and Pb to be tested, the dry samples were dissolved at \((95\pm 5)\,^{\circ}\)C by 10 mL of 1:1 HNO\({}_{3}\), 5 mL of 65% HNO\({}_{3}\), 3 mL of 30% H\({}_{2}\)O\({}_{2}\) and 10 mL of HCl. Then, the inductively coupled plasma optical emission spectrometry method (ICP-OES) were used to conduct the test (Instrument Model PerkinElmer Optima 8000 DV). For Hg to be tested, the samples were digested using HNO\({}_{3}\), HCl, KMnO\({}_{4}\) and K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) and cold atomic absorption spectrophotometry was applied for testing (Instrument Model Leemanlabs Hydra II AF Gold). To ensure quality, heavy metals were tested to such national standards as GB 5085.3-2007 Appendix A and CJ/T 98-1999 published by State Environmental Protection Administration and Ministry of construction of the People's Republic of China, respectively [20; 21]. The container used in the experiment was first washed by 10% hot nitric acid and then rinsed by de-ionized water to achieve a blank background. The instrument was pre-heated 1 h prior to use in order to avoid wavelength drift. Moreover, the samples were tested in different batches, and two blank samples and reference samples were added for every ten groups of sample test to examine the degree of wavelength drift. To ensure precision and accuracy, each sample was assigned with four parallel samples, and the mean values of results were indicated by the final concentration of heavy metals.
## 3 Mathematical Model Combining MoM with FEM
 \tag{5}\]  
where \(\Delta_{i}\) is the definitional domain of the buried conductors so \(J_{\text{leak}}(\overset{\rightarrow}{r})\) can be expressed as follows:  
\[J_{\text{leak}}(\overset{\rightarrow}{r}^{\prime})=\sum_{i=1}^{M+N}c_{i}v_{i} \tag{6}\]  
\(c_{1},c_{2},\cdots c_{M}\) are the current density of the electrode conductor segments. \(c_{M+1},c_{M+2}\cdots\)\(c_{M+N}\) are the current density of the pipe conductor segments. Based on the MoM and point match method the following algebraic equation set can be formed. \[\left[ \begin{array}{ccccc}G(r_{1,1})&\cdots&G(r_{1,M})&G(r_{1,M+1})&\cdots&G (r_{1,M+N})\\ \vdots&\ddots&\vdots&\vdots&\ddots&\vdots\\ G(r_{M,1})&\cdots&G(r_{M,M})&G(r_{M,M+1})&\cdots&G(r_{M,M+N})\\ G(r_{M+1,1})&\cdots&G(r_{M+1,M})&G(r_{M+1,M+1})&\cdots&G(r_{M+1,M+N})\\ \vdots&\ddots&\vdots&\vdots&\ddots&\vdots\\ G(r_{M+N,1})&\cdots&G(r_{M+N,M})&G(r_{M+N,M+1})&\cdots&G(r_{M+N,M+N})\\ \end{array} \right]\left[ \begin{array}{c}c_{1}\\ \vdots\\ c_{M}\\ c_{M+1}\\ \vdots\\ c_{M+N}\end{array} \right]=\left[ \begin{array}{c}\varphi_{(\text{e},0)}\\ \vdots\\ \varphi_{(\text{s},1)}\\ \vdots\\ \varphi_{(\text{s},N)}\end{array} \right] \tag{7}\]  
The electrode is considered as an equipotential \(\varphi_{(\text{e},0)}\). The pipe potential on the soil side is \(\varphi_{(\text{s},1)},\varphi_{(\text{s},2)},\cdots\varphi_{(\text{s},N)}\). Using the finite element Galerkin method for the pipe conductor area, another equation set can be formed as follows:  
\[\left[\begin{array}{ccccc}K_{1,1}&\cdots&K_{1,N}&K_{1,N+1}&\cdots&K_{1,N+P} \\ \vdots&\ddots&\vdots&\vdots  
The scale electrode was a 30 m diameter steel circle buried at a depth of 1.5 m. The pipeline was buried 28 m away from the electrode at a depth of 1 m. The grounding current was supplied by a current generator with 300 A maximum output current. The pipe material was X80 steel. The soil was considered as a homogeneous medium and the soil resistivity is \(r\), pH = 8. The leakage current density and PSP along the pipeline were measured with the test coupons. The test points layout is shown in Figure 5.  
For each test point, the test circuit is shown in Figure 6. based on the external coupon method [21]. Figure 4: Layout of the scale experiment system. Figure 5: Test points layout along the pipeline. Figure 6: Test circuit based on external coupon method. Figure 7 shows the polarization relationship for the above-mentioned soil and pipe material. The current density is plotted with logarithmic coordinates for a better view. \"log J\" means the common logarithm of the leakage current density.
## 3 Formulation of the Satellite Module Layout Problems
### Design Variables  
In the multi-container configuration, the components must be assigned to one of the four surfaces and be laid out on the corresponding surface. Therefore, the design variables are the following, where purely continuous variables are denoted \(\mathbf{p}^{c}\) and discrete variables are denoted by \(\mathbf{p}^{d}\).  
* For each component, the number of its assigned surface \(\mathbf{p}^{d}_{s}=\{p^{d}_{s,i}\}\), \(i\in\{1,...,N\}\), where \(p^{d}_{s,i}\) are defined as unordered discrete variables;
* The location of the center of inertia of each component on the assigned surface considered as continuous variables \(\mathbf{p}^{c}_{i}=\{p^{c}_{x,i},p^{c}_{y,i}\}\), \(i\in\{1,...,N\}\);
* The orientations of cuboid components on the assigned surface are considered as discrete variables with values of \(0^{\circ}\) or \(90^{\circ}\), \(\mathbf{p}^{d}_{\alpha}=\{p^{d}_{x,i}\}\), \(i\in\{1,...,N_{cub}\}\). Thus, the number of design variables \(n_{var}\) is defined as: \[n_{var}=N+2N_{cyl}+3N_{cub}=3N_{cyl}+4N_{cub}\] (1)
## 6 TTL Estimation  
The five types of data mentioned in Section 5 are used for TTL estimation as shown in Figure 7. The simulated SCA1B data are used to rotate the simulated ACC1B measurements from SF to IF. Then, the simulated GN1B was used to calculate the differential non-gravitational accelerations on the LOS. After integrating twice, the generated differential non-gravitational range was subtracted from the simulated LR1B range data, as shown in Equation (32). The processed range data were then band-pass-filtered together with the simulated LSM1B angles and the LSQ estimation was performed to obtain the values of the coupling factors and the TTL error. The cutoff frequencies of the band-pass filter were set to 50 and 100 mHz in the estimation process.  
The impact of different conditions, including the CM-VP offset, the pointing angle noise, and the laser frequency noise for TTL estimation, was investigated. We divide the results into two types: results under the S-type longitudinal offset (i.e., \(\Delta x=0.5\,\mathrm{mm}\), like the small CM-VP offset of GRACE-FO's LRI) and the results under the L-type longitudinal offset (i.e., \(\Delta x=1.5\,\mathrm{m}\), like the large CM-APC offset of KBR). The difference between the estimated and the true value should be lower than the GRACE-FO's TTL error for successful TTL estimation.
# Fit for What Purpose? NER Certification of Automatic Captions in English and Spanish
## 3 Materials and Methodology: Assessment of AI-Media's Automatic Captions in English and Spanish  
### Software to Be Tested: Lexi  
AI-Media was funded by deaf activist [PERSON] and ally [PERSON] in Australia in 2003 to provide captions for people with hearing loss. In the past two decades, it has become one of the leading global access providers, with offices in the US, UK, Canada and Australia and delivering over 7 million minutes of captioning, transcription and translation for live and recorded media content, online events and web streams every month. Until 2023, most live captions by AI-Media were produced through respeaking, that is, with human captioners trained and assessed in-house with the NER model. Over the past two years, there has been a steady transition towards automatic captions, which are now estimated to take up 80% of the live captions produced by the company. These captions require no human intervention and rely entirely on a software called Lexi (v.3.0), which uses cutting-edge, AI-based large language models created by the company Speechmatics (Cambridge, UK). AI-Media advertises Lexi, which is currently available in 47 languages, as \"the world's most advanced automatic captioning solution\" and adds that these automatic captions are fast closing the gap on human captions, consistently delivering over 98% accuracy and, in some cases, even 99.5% accuracy [23]. The LiRICS test conducted in this article may help to ascertain whether this claim can be backed with empirical data regarding the two languages Lexi is most used for: English and Spanish.
# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS
### Data from a Third Party  
Detecting a parking slot is carried out by obtaining DFTP. This may be a government unit that controls public parking [30], a communications company that has its own detection system [24], or another available SPS [112]. For example, in Reference [210], the database used there contained 3791 images collected from parking places at the Federal University of Parana, in Curitiba, Brazil, to predict available parking.  
Another case, for example, is where two sets of parking occupancy data were used to feed an algorithm based on a model of estimated time variance. One set of data came from the public parking data near the Carnegie Mellon University campus and the other from the SPS deployed in San Francisco, United States of America (USA) (SFPark) [295]. For the estimates model, use is made of the payment schemes obtained from the previous datasets. To achieve an accurate estimate and avoid the infrastructure overload that the installation of a sensor network entails, these transaction-based estimates consider the implicit uncertainty regarding vehicles that pay less than they should and are exempt from overpaying [288].  
Although this technique is not widely applied, it has certain advantages since there is no need to implement a detection system. Therefore, it can be less expensive. The data already exist; thus, it is only necessary to obtain some kind of license or agreement with the holder of the information to be able to access it. In this way, information on the availability of parking slots can be obtained either to predict occupancy.
## 4 Discussion  
TiO\({}_{2}\) thin films with different thicknesses were grown by ALD at a temperature of 200 \({}^{\circ}\)C for silicon surface passivation, anti-reflection coatings and passivating contact. From the characterization of 66-nm-thick TiO\({}_{2}\) thin films by TEM, SIMS, Raman spectroscopy, and ellipsometry, we observed the phase formation of anatase TiO\({}_{2}\) distributing non-homogeneously along the growth direction, deduced its crystal size, completed a depth profile analysis of atomic hydrogen and investigated the optical properties. Amorphous TiO\({}_{2}\) thin films grown by ALD at a temperature of 200 \({}^{\circ}\)C have a good performance in silicon surface passivation. The surface recombination velocity of 8-nm-thick TiO\({}_{2}\) films can reach 14.93 cm/s at the injection level of 1 \(\times\) 10\({}^{15}\) cm\({}^{-3}\). However, the Si surface passivation of TiO\({}_{2}\) film strongly depends on its thickness (8 nm, 15 nm, 35 nm and 66 nm), which is associated with stress effects and atomic hydrogen relating to the phase transformation and thermal stability of TiO\({}_{2}\) films. For further applications on the passivating contact of c-Si solar cells, we also studied the performance of anti-reflection and Si surface passivation for a stack composed of an 8-nm-thick TiO\({}_{2}\) film deposited by ALD and a capping layer of 72-nm-thick SiN\({}_{x}\) deposited by PECVD. Low reflectivity and some degree of surface passivation were observed when using TiO\({}_{2}\)/SiN\({}_{x}\) stacks on Si wafers.  
Figure 9: The average effective lifetime at the injection level 1 \(\times\) 10\({}^{15}\) cm\({}^{-3}\) for bare Si wafers, TiO\({}_{2}\)/SiN\({}_{x}\) stacks, 80-nm-thick SiN\({}_{x}\) films by PECVD, and 66-nm-thick TiO\({}_{2}\) films by ALD.  
**Acknowledgments:** The authors acknowledge the Ministry of Science and Technology R.O.C. (MOST 104-2221-E-218-002, 104-2221-E-259-001 and 104-2221-E-009-167) for financially supporting this study. Thanks to the R&D department of E-Ton Solar Tech. Co. LTD for the measurements of the WCT-120 and Hitachi U-4100.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.