esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Methods
#### 2.3.4 Microstructure Experiment  
1.  XRD measurement  
The hydration was stopped by placing about 5 g of sample into 100 mL of anhydrous ethanol and replaced with an equal amount of ethanol after 10 min and 24 h. The samples were removed after 2 d and dried in a vacuum oven at 40 \({}^{\circ}\)C to avoid further hydration. The dried samples were ground into powder and passed through a 300-mesh sieve. The XRD spectra of the samples were measured using a Rigaku Smart Lab SE diffractometer. The XRD scanning range was set from 5\({}^{\circ}\) to 90\({}^{\circ}\), with a scanning step width of 0.02\({}^{\circ}\) and a scanning speed of 2\({}^{\circ}\)/min.
2. Scanning electronic microscopy(SEM) measurement  
The hydrated samples were dried as described above. SEM images of the dried samples were visualized by ultra-deep-field 3D microscopy (TESCAN MIRA LMS, Brno, Czech Republic) with an accelerating voltage of 20 kV.
3.  LF-NMR experiment  
LF-NMR relaxometry is a promising powerful and effective nondestructive method for measuring microstructural features of cement-based materials [49]. NMR spectra of fresh DSPB slurry were measured at different times using a Niumag Low-Frequency NMR Analyzer to determine the transverse relaxation time (T\({}_{2}\)) for the samples. In the LF-NMR experiment, 20 \(\pm\) 0.01 g of the paste was sealed in a sample vial, and the weight of the sealed vial was measured before and after the experiment to confirm that no water had evaporated from the paste sample [50]. The Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence was used for the experiments, with a resonance frequency of 21 MHz and a sampling frequency of 100 kHz, and the temperature of the inner bore of the magnet was set to 25 \({}^{\circ}\)C. For each CPMG sequence, 2000 echoes were acquired, with an echo time of 0.105 ms and a total relaxation delay of 210 ms [51]. The NMR signals were converted to T\({}_{2}\) distributions by inverse Laplace transform (ILT). The distributions of T\({}_{2}\) at 15, 30, 45, and 60 min after mixing were measured by using different DSPB samples according to the set program.  
Figure 3: Dynamic rheology test program.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## Appendix A Zadoff-Chu Base Sequence and Resource Mapping Rules  
### Zadoff-Chu Base Sequence and Variants  
If \(\mathbf{m_{SRS,b}/K_{TC}}<6\), the sequence group is generated from one base sequence with \(\mathbf{v}=0\). Once the value of \(\mathbf{m_{SRS,b}/K_{TC}}\geq 6\), the value of \(\mathbf{v}\) is chosen from zero and one according to Equation (A3). Basically, the values of sequence group \(\mathbf{u}\) are set for enabling group hopping, and sequence number \(\mathbf{v}\) is the set ofsequence hopping functions. \(\mathbf{u}\) can be calculated by Equation (A1a).  
\[\mathbf{u} =(\mathbf{f_{gh}}+\mathbf{n}_{ID}^{SRS})\ mod\ 30\] (A1a) \[\mathbf{f_{gh}} =\sum_{\mathbf{m}=0}^{7}\mathbf{c}\left(8(\mathbf{n}_{\mathbf{s },\mathbf{f}}^{\mu}\mathbf{N_{symb}^{slot}}+\mathbf{l}_{0}+\mathbf{l}^{{}^{ \prime}})+\mathbf{m}\right)\mathbf{2^{m}}\ mod\ 30\] (A1b)  
where \(\mathbf{n}_{ID}^{SRS}\) is the SRS sequence identity received from the RRC message, \(\mathbf{n}_{\mathbf{s},\mathbf{f}}^{\mu}\) is the slot number within a frame for the subcarrier spacing configuration, \(\mathbf{N_{symb}^{slot}}\) is the mean number of OFDM symbols per slot, and \(\mathbf{l}_{0}\) and \(\mathbf{l}^{{}^{\prime}}\) indicate the starting location of the SRS OFDM symbols and the location of the \(\mathbf{l}^{{}^{\prime}}\)th symbol. \(\mathbf{f_{gh}}\) can be calculated from Equation (A1b) when RRC parameter \"groupOrSequenceHopping\" contains information \"groupHopping\"; otherwise, it equals zero. \(\mathbf{c}(\mathbf{n})\) is the PNsequence given by [16] as shown by the equation group (A2):  
\[\mathbf{c}(\mathbf{n}) =(\mathbf{x1}(\mathbf{n}+1600)+\mathbf{x2}(\mathbf{n}+1600))\ mod\ 2\] (A2a) \[\mathbf{x1}(\mathbf{n}+31) =(\mathbf{x1}(\mathbf{n}+3)+\mathbf{x1}(\mathbf{n}))\ mod\ 2,\ (\text{ initial}\ [\mathbf{x1}(0)=1,\mathbf{x1}(\mathbf{n})=0])\] (A2b) \[\mathbf{x1}(\mathbf{n}+31) =(\mathbf{x1}(\mathbf{n}+3)+\mathbf{n}+2)+\mathbf{n}+1)+\mathbf{ n}))\ mod\ 2,\ (\text{(initial}\ [\mathbf{n}_{ID}^{SRS}=\sum_{\mathbf{i}=0}^{30}(\mathbf{x}_{ \mathbf{z}}(\mathbf{i})\mathbf{2}^{\mathbf{i}}]))\] (A2c)  
If the RRC parameter \"groupOrSequenceHopping\" contains information \"sequence-Hopping\" and sequence length \(\mathbf{M}_{sc,b}^{rs}\) is not less than 6 RB in length (6N\({}_{sc}^{RB}\)), \(\mathbf{v}\) can be calculated by Equation (A3):  
\[\mathbf{v}=\mathbf{c}(\mathbf{n}_{\mathbf{s},\mathbf{f}}^{\mu}\mathbf{N_{symb }^{slot}}+\mathbf{l}_{0}+\mathbf{l}^{{}^{\prime}})\] (A3)  
otherwise, \(\mathbf{v}\) equals zero.
# Vibration Response Analysis of Overhead System Regarding Train-Track-Bridge Dynamic Interaction
## 3 Coupling Vibration Analysis of Train-Track-Overhead System
### Influence of Train Speed on the Dynamic Response of Train Bridge System
Figure 16a shows that, when a passenger train is operating, the maximum deflection of the mid-span vertical and horizontal girders rises along with the rise in train running speed, which first increases gradually and then quickens. The maximum deflection is 2.62 mm, which is less than the specification's 10 mm maximum deflection limit. As seen in Figure 16b, the overhead system's lateral displacement is more sensitive to train speed than its horizontal displacement, which is easily altered. As the speed rises, the overall lateral displacement increases, also. According to the study, the speed range is much smaller than the standard requirements of the transverse displacement limit of 2 mm, going up to 0.20 mm. The mid-span of the structure's maximum vertical acceleration is depicted in Figure 17a. Additionally, it shows that the vertical acceleration of the aerial system's midspan is roughly linear but still directly proportional to the increase in velocity. This vertical acceleration's maximum value of 3.88 m/s\({}^{2}\) is higher than the 0.35 m/s\({}^{2}\) specified by the specification [20]. Figure 17b shows the change in the mid-span transverse acceleration of the overhead system, which rises slowly at the beginning with the change in the train speed, rises sharply after the train speed reaches 60 km/h, and reaches the maximum value of 0.41 m/s\({}^{2}\) at 100 km/h. However, the transverse acceleration does not exceed the limit value of 0.2 m/s\({}^{2}\), as required by the specification. Figure 16: Mid-span displacement response under different speed levels (unit: mm): (a) Mid-span maximum vertical deflection. (b) Mid-span lateral displacement. Figure 17: Mid-span acceleration response under different speed levels (unit: m/s\({}^{2}\)): (a) Mid-span vertical acceleration. (b) Mid-span lateral acceleration.
## 3 The Proposed VLSI Architecture for the New Integer DCT Algorithm
### VLSI Implementation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Constrained** \\ **Clock Period/** \\ **Frequency** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Critical Path** \\ **Delay +** \\ **Setup Time** \\ **[ps]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Interconnect** \\ **Area [\(\mu\)m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Combational** \\ **Area [\(\mu\)m\({}^{2}\)]** \\ **[\(\mu\)m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Flop** \\ **Area [\(\mu\)m\({}^{2}\)]** \\ **[\(\mu\)m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Total** \\ **Area [\(\mu\)m\({}^{2}\)]** \\ **[\(\mu\)m\({}^{2}\)]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Equivalent** \\ **Gates Count** \\ **[uW]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Static** \\ **at Constrained** \\ **Clock** \\ **Frequency [mW]** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Dynamic Power** \\ **at Constrained** \\ **Clock** \\ **Frequency [mW]** \\ \end{tabular} } \\ \hline
50 ns/20 MHz & 419 & 1004.41 & 1593.46 & 2095.45 & 4693.32 & 232,833.79 & 132.1 & 0.2 at 20 MHz \\
10 ns/100 MHz & 419 & 1004.41 & 1593.46 & 2095.45 & 4693.32 & 232,833.79 & 132.1 & 1.0 at 100 MHz \\
1 ns/1 GHz & 418 & 998.11 & 1584.27 & 2095.45 & 4677.83 & 232,065.29 & 131.4 & 9.5 at 1 GHz \\
300 ps/3.33 GHz & 300 & 1052.21 & 1786.48 & 2095.45 & 4934.14 & 244,780.93 & 138.8 & 33.6 at 3.33 GHz \\
250 ps/4 GHz & 250 & 1316.08 & 2063.89 & 2095.45 & 5475.42 & 271,633.60 & 161.0 & 48.0 at 4 GHz \\
240 ps/4.17 GHz & 240 & 1380.77 & 2135.07 & 2095.65 & 5611.48 & 278,383.74 & 167.3 & 52.1 at 4.17 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of synthesis results of the IntDCT core. Figure 12: Illustration of the IntDCT systolic array operation for the \(38\ldots 58\) time unit interval.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Transportation Management
#### 4.1.1 Vehicle Routing Solution  
This case study focuses on a large-scale milk production factory serving over 500 customers in Northern Thailand. The company's current delivery system, which operates on a two-day cycle, relies on manually designed routes based on planner experience. This approach has proven costly and inefficient, with transportation costs accounting for 9.5% of sales and a delivery-in-full-on-time (DIFOT) rate of 80%.  
To address these issues, a Vehicle Routing Problem (VRP) optimizer was implemented. The problem was classified as a Capacitated Time-Window Vehicle Routing Problem [35]. A VRP Spreadsheet Solver was employed to develop the solution [36]. After collecting customer location data and requirements, a distance matrix was constructed, enabling the design of more efficient routes (as illustrated in Figure 2).  
The implementation of the optimized routes resulted in significant improvements. Cost savings of 23.7% were achieved through reduced fuel consumption, lower labor costs, and improved fleet utilization. Additionally, the service level saw a marked increase, with the DIFOT rate rising to 95%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Industry/Product**} & \multirow{2}{*}{**Project Focus**} & \multirow{2}{*}{**Cost Impact (\%)**} & \multirow{2}{*}{**Time Impact (\%)**} & \multirow{2}{*}{**Reliability Impact (\%)**} & \multirow{2}{*}{**Cost Saving/yr.**} & \multirow{2}{*}{**Other Benefits**} \\ \hline \multirow{4}{*}{26} & \multirow{4}{*}{L} & \multirow{4}{*}{Frozen Food} & Sales forecasting & & & & \\  & & & (ILPI1) and logistics & & & & \\  & & & communication & & & & \\  & & and order & & & & & \\  & & & processing (ILPI3) & & & & & \\ \hline
27 & L & Food Processing & Transportation (ILPI8) & \(-\)16.25\% & \(-\)22.12\% & & 1,650,000 \\ \hline \multirow{2}{*}{28} & \multirow{2}{*}{L} & \multirow{2}{*}{Frozen Food} & Warehouse management (ILPI6) & \(-\)21.67\% & & & 2,370,551 \\ \hline
29 & L & Dairy & Transportation (ILPI8) & \(-\)23.70\% & & +15\% & 1,178,618 \\ \hline
30 & L & Asphalt & Transportation (ILPI8) & \(-\)26.10\% & & & 3,208,217 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 5 SASE Performance in the Sub-Angstrom Regime
The statistical standard deviations of the achievable SASE intensities are up to 5%. In addition, using 7.3 GeV electron bunches produced with 50 MV/m and 40 MV/m cathode accelerating gradients as presented in Sections 3 and 4, Figure 10 shows the gain curves of the SASE processes at two exemplary photon energies. As discussed, this is in light of a potential CW operation of the facility. Limited by the present (long) undulator period and a lower electron beam energy in this operation mode, photon energies of 5.3 keV and 7.8 keV, out of the angstrom regime, are exemplarily chosen fitting the undulator parameter K via practical gap control. In this work, we only consider existing undulators of the facility aiming to demonstrate the obtained qualities of electron bunches generated at lower cathode accelerating gradients and a reduced final beam energy. As shown in Figure 10a, the obtained electron bunch qualities in both the 40 MV/m and 50 MV/m cases, enable generating SASE intensities above 1.5 mJ at 7.8 keV and above 2.3 mJ at 5.3 keV. The estimated gain lengths are below 3.5 m and 4.5 m at 5.3 keV and 7.8 keV, respectively. As a comparison, using smaller gain lengths, the 50 MV/m case shows higher SASE pulse energies than 40 MV/m at both photon wavelengths. The superiority of better emittance for the 50 MV/m case over 40 MV/m becomes more pronounced towards higher photon energies, in terms of higher SASE intensity and smaller gain length. At a fixed wavelength the advantage of better emittance for the 50 MV/m case can also be seen in the improved transverse coherence. A summary of the results at 7.3 GeV is presented in Table 3. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(\mathbf{E_{e}}\) & \(\mathbf{E_{p}}\) & \(\mathbf{\lambda_{p}}\) & \(\mathbf{P}\) & \(\mathbf{I}\) & \(\mathbf{L_{g}}\) & \(\mathbf{E_{cath}}\) \\ \hline
**[GeV]** & **[keV]** & **[Å]** & **[W]** & **[mJ]** & **[m]** & **[MV/m]** \\ \hline
17.5 & 20 & 0.620 & \(1.953\times 10^{11}\) & 4.23 & 5.47 & 56.3 \\ \hline
17.5 & 30 & 0.413 & \(8.314\times 10^{10}\) & 1.77 & 7.45 & 56.3 \\ \hline
17.5 & 40 & 0.310 & \(2.323\times 10^{10}\) & 0.46 & 11.7 & 56.3 \\ \hline
17.5 & 45 & 0.276 & \(1.054\times 10^{10}\) & 0.16 & 16.4 & 56.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of SASE performance in the sub-angström regime using low-emittance beams produced with 56.3 MV/m cathode accelerating gradient at a beam energy of 17.5 GeV. A undulator period of 4 cm is used. The notations follow those used in Table 1.  
Figure 10: SASE performance of optimized 7.3 GeV electron bunches produced at lower cathode accelerating gradients of 40 MV/m and 50 MV/m for a potential CW operation at exemplary photon energies of 5.3 keV and 7.8 keV. (**a**) gain curves; (**b**) power distributions by the end of the undulator beamline. Dashed lines in (**a**): exponential fits to the gain curves. Solid lines in (**b**): Gaussian fits to the power distributions.
## 4 Results
### Trace Element Characteristics  
Sr is an important trace element in carbonate rocks, and the content of Sr increases gradually from shallow water to deep water. Since the contents of Sr, Na, and Mg in atmospheric precipitation are lower than in seawater and the contents of Mn and Fe in atmospheric precipitation are higher than in seawater, the deposition of dolomites usually leads to a reduction in the content of Sr, Na, and Mg in the quality of atmospheric precipitation. And there is an increase in Mn plus and Fe. Moreover, the form of Mn is relatively stable and accumulates in deep water, so the Mn content gradually increases from shallow water to deep water. The Na content often reflects the salinity of diagenetic fluid. The results of trace elements in carbonate rock samples show that (Table 1) the content of Fe and Mn is inversely proportional to the content of Sr and Na, and the variation in trace elements can be seen from mricitic dolomite-powder crystal dolomite-fine crystal dolomite (Figure 8). The micritic dolomite in the study area has the characteristics of low Fe, low Mn, high Sr, and high Na. The Fe content is between 980 \(\times\) 10\({}^{-6}\) and 1100 \(\times\) 10\({}^{-6}\), with an average of 1660 \(\times\) 10\({}^{-6}\), and the Mn content is between 33 \(\times\) 10\({}^{-6}\) and 89 \(\times\) 10\({}^{-6}\), with an average of 52.67 \(\times\) 10\({}^{-6}\). It has the characteristics of low Fe and low Mn, indicating that its diagenetic environment is a near-surface oxidation environment, reflecting a kind of penetemporaneous dolomitization. The average Sr content is 407.3 \(\times\) 10\({}^{-6}\), indicating that the diagenetic environment is near-surface or shallow burial. The average Na content is 313.3 \(\times\) 10\({}^{-6}\), which is higher than that of most ancient dolomites. The Na content indicates that the dolomite was formed in a marine environment with high salinity, so it has high Sr and Na content. It is also proved that the formation environment is Sabuha pencentemporaneous dolomitization with strong evaporation. One sample has an abnormal Sr content of 861 \(\times\) 10\({}^{-6}\), which may be caused by the reflection of formation water through siliceous clastic sediments. The interaction with carbonate minerals changes the content of Sr pairs. Figure 7: Histogram and correlation chart of porosity and permeability. (**A**) Pore radius distribution. (**B**) Porosity histogram. (**C**) Permeability histogram. (**D**) Correlation diagram of porosity and permeability. The content of Fe and Mn in powder crystal dolomite is obviously higher than that of other lithologies. The content of Fe is between 6888 \(\times\) 10\({}^{-6}\) and 12,761 \(\times\) 10\({}^{-6}\), with an average of 10,116.4 \(\times\) 10\({}^{-6}\), and the average content of Mn is 361.2 \(\times\) 10\({}^{-6}\). It can be seen that the content of Fe and Mn is much higher than that of mud crystal and fine crystal dolomite. The average value of the Na content is 177.32 \(\times\) 10\({}^{-6}\), and the average value of Sr is 91.1 \(\times\) 10\({}^{-6}\). The abnormally low value of the Sr content also indicates that Sr
## 5 Results and Discussion
Finally, the approach is adaptable and can be extended to other tasks beyond detection, such as seg  
Figure 23: Examples of false positives. mentation, with appropriate modifications to the representation of the label and uncertainty region ranges. However, there are some limitations to our study. One limitation is that the label quality assurance model may struggle to detect certain error subtypes, such as the A2 error subtype. This can be addressed by exploring different neural network architectures or training strategies or incorporating additional features to improve the model's ability to distinguish between subtle differences in label quality. Another limitation is that our approach relies on a _small set_ for training the label quality assurance model. The performance of the model may be sensitive to the quality and diversity of this _small set_, which should be carefully curated to ensure its representativeness of the entire dataset. Future research could focus on the investigation of more advanced techniques to improve the detection of challenging error subtypes, such as incorporating additional context information or improving the neural network architecture. Additionally, the approach could be extended and adapted to a wider range of computer vision tasks. By exploring future directions, our approach can pave the way for more efficient, accurate and reliable labeling processes in artificial intelligence projects, ultimately leading to the development of higher-quality and more robust machine learning models. In this paper, we emphasize the importance of data requirements for object detection projects within the field of requirements engineering because it heavily relies on image data with labeled object bounding boxes. We introduce a process for integrating data requirements into the requirements engineering process, with a particular emphasis on label quality assurance, a critical component of the overall data requirements process for creating modern artificial intelligence solutions. Our study presents a unique machine learning-based approach to perform label quality assurance automatically, providing immediate feedback to annotators and project stakeholders about the quality of the labeling work and significantly improving the efficiency of the process. Our approach can be extended to various computer vision tasks and other machine learning subfields by adjusting label representation and setting suitable uncertainty region ranges. To the best of our knowledge, this is the first machine learning-based label quality assurance method for object detection projects, setting our work apart from previous research that relied on manual review, inter-annotator agreement and deep active learning approaches.
## 3 Analysis of Test Results  
### Thixotropy of Soil Surrounding the Pile  
The rapid direct shear tests conducted on the soil surrounding the pile yielded the cohesion (c) and internal friction angle (\(\varphi\)) of the surrounding soil at different resting times, as shown in Table 3.  
As shown in Table 3, the internal friction angle and cohesive strength of the soil surrounding the pile exhibit similar increasing trends as the resting time is increase. Specifically, the growth rate is rapid during the first 30 days, slower during the middle 30 days, and slowest in the final 30 days. Throughout the entire testing period, the cohesive strength increased by 291% and the internal friction angle increased by 73%. The soil surrounding the pile demonstrates a significant thixotropic recovery. Analyzing the reasons, the structural clay of the Zhanjiang Formation exhibits thixotropic properties. During the processes of soil sampling and mixing, the structural integrity of the soil is compromised, resulting in the disruption of aggregate structures, increased particle spacing, reduced interlocking forces between particles, and the breakdown of connections between particles. Cohesive strength arises from factors such as the cementation effect and electrostatic forces between soil particles. After resting, the aggregation of soil particles allows the cohesive strength and internal friction angle of the soil to recover. The disturbance process in the experiment can simulate the disturbance of the soil surrounding the pile during engineering construction, which similarly causes damage to the soil structure. As construction is completed, the surrounding soil is in a resting state, and the strength of the soil gradually recovers with the passage of time.  
The curves of cohesive strength (c) and internal friction angle (\(\varphi\)) of the soil surrounding the pile as a function of resting time (t) were fitted respectively, as shown in Figure 2.  
From Figure 2a, it can be seen that the relationship between the cohesion (\(c\)) of the soil surrounding the pile and the resting time (\(t\)) can be expressed as:  
\[c=4.52 log_{10}(t)+2.05 \tag{1}\]  
From Figure 2b, it can be seen that the relationship between the internal friction angle (\(\varphi\)) of the soil surrounding the pile and the resting time (\(t\)) can be expressed as:  
\[\varphi=1.551 log_{10}(t)+3.69 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Strength**} & \multicolumn{4}{c}{**Different Curing Times of Pile Foundations/d**} & \multirow{2}{*}{**Magnitude of**} \\ \cline{2-2} \cline{4-7}
**Indicators** & **1 d** & **7 d** & **14 d** & **28 d** & **60 d** & **90 d** \\ \hline \(c/\)kPa & 2.85 & 4.84 & 6.51 & 8.86 & 10.65 & 11.14 & 291 \\ \(\varphi/^{\circ}\) & 3.9 & 4.69 & 5.31 & 6.02 & 6.61 & 6.76 & 73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cohesion c and internal friction angle \(\varphi\) of soil around piles after different resting times.