esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Kinematics
#### 3.2.2 Joint Angles during Landing Phase
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Joint**} & \multirow{2}{*}{**Plane**} & \multicolumn{2}{c}{**Pre**} & \multicolumn{2}{c}{**Post**} \\  & & **Max** & **Min** & **ROM** & **Max** & **Min** & **ROM** \\ \hline Ankle (\({}^{\circ}\)) & Sagittal & 25.23 (6.33) \({}^{\star}\) & \(-\)16.58 (4.53) & 41.80 (4.31) & 25.83 (8.73) & \(-\)15.10 (6.50) & 40.93 (4.16) \\  & Frontal & **7.35 (4.33) \({}^{\star}\)** & \(-\)6.86 (3.29) \({}^{\star}\)** & **14.20 (4.10) \({}^{\star}\)** & **10.29 (4.67) \({}^{\star}\)** & \(-\)7.45 (4.17) & **17.75 (4.09) \({}^{\star}\)** \\  & Horizontal & 9.14 (6.31) & **-3.08 (6.11) \({}^{\star}\)** & **12.22 (3.87) \({}^{\star}\)** & 7.90 (6.88) & **-6.55 (7.89) \({}^{\star}\)** & **14.45 (5.31) \({}^{\star}\)** \\ Knee (\({}^{\circ}\)) & Sagittal & -1.84 (2.55) & \(-\)7.695 (27.27) & 75.11 (7.84) & -3.80 (4.96) & \(-\)7.74 (35.40) & 73.66 (33.08) \\  & Frontal & 6.63 (4.85) & \(-\)**1.87 (3.89) \({}^{\star}\)** & **8.49 (2.41) \({}^{\star}\)** & 9.32 (7.44) & **-5.75 (7.70) \({}^{\star}\)** & **15.07 (5.27) \({}^{\star}\)** \\  & Horizontal & 8.66 (7.42) & \(-\)6.31 (3.85) & 14.98 (4.10) & 8.35 (21.74) & \(-\)7.31 (20.43) & 15.66 (30.01) \\ Hip (\({}^{\circ}\)) & Sagittal & 41.08 (27.17) & \(-\)4.69 (6.25) & 45.77 (30.61) & 43.12 (34.20) & \(-\)2.88 (5.92) & 46.00 (33.65) \\  & Frontal & 3.22 (1.97) & **-8.85 (5.45) \({}^{\star}\)** & 12.07 (3.68) & 4.62 (4.46) & **-6.65 (5.91) \({}^{\star}\)** & 11.27 (1.83) \\  & Horizontal & 5.68 (6.02) & \(-\)5.72 (4.43) & 11.41 (5.13) & 7.86 (15.01) & \(-\)3.63 (14.30) & 11.49 (4.41) \\ \hline \end{tabular}
\end{table} 
Table 3: Peak joint angles and ROM of pre-fatigue and post-fatigue during the landing phase.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz
### Sub-Arrays
#### 3.2.2 Non-Symmetric Array
The size and requirements of the smartphone to design it do not allow for the use of 3D models or non-symmetric subarrays in contrast to the base station, which can be used in one or many designs. Base station technique requirements \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multicolumn{2}{c}{**No. of**} & \multirow{2}{*}{\begin{tabular}{c} **Elements/** \\ **Ports** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Type of 5G** \\ **Antenna** \\ \end{tabular} } & \multirow{2}{*}{**Size (mm)**} & \multirow{2}{*}{**B.W (GHz)**} & \multirow{2}{*}{
\begin{tabular}{c} **Return Loss** \\ **(S11/dB)** \\ \end{tabular} } & \multirow{2}{*}{**Isolation**} & \multirow{2}{*}{**Gain**} & \multirow{2}{*}{**Eff. (\%)**} \\ \hline
[82] & 1/2 & 3D-model & 82 \(\times\) 82 \(\times\) 11.8 & 3.14–3.81 & - & \(>\)43 & \(>\)8.1 & 83 \\
[86] & 1/1 & 3D-model & 75 \(\times\) 80 \(\times\) 19 & 3.15–3.67 & 33 & - & 8.2 & - \\
[95] & 1/1 & 2D-model & 43.36 \(\times\) 35 \(\times\) 1.575 & 3.392–3.598 & 28.774 & - & 6.58 & 83 \\
[97] & 1/1 & 2D-model & 55 \(\times\) 40 \(\times\) 1.6 & 3–4.136 & 41.31 & - & 4.45 & \\
[97] & 1/1 & 2D-model & 44 \(\times\) 30 \(\times\) 1.6 & 3.14–4.167 & 43.95 & - & 4.36 & \(>\)88 \\  & & 46 \(\times\) 26 \(\times\) 1.6 & 3.154–5.31 & 20.44 & & 4.42 & \\
[102] & 4/4 &
\begin{tabular}{c} Symmetric \\ sub-array \\ \end{tabular} & 129.5 \(\times\) 129.5 \(\times\) 28.2 & 1.55–6 & - & 16 & - & 84 \\
[106] & 4/4 &
\begin{tabular}{c} Symmetric \\ sub-array \\ \end{tabular} & 120 \(\times\) 65 \(\times\) 1.6 & 3.3–5 & - & 18.8 & 4.71 & - \\
[107] & 3/6 &
\begin{tabular}{c} Non- \\ symmetric \\ sub-array \\ \end{tabular} & 160 \(\times\) 16 \(\times\) 35 & 1.63 to 3.73 & - & 25 & 12.9 & - \\
[114] & 8/16 &
\begin{tabular}{c} Symmetric \\ sub-array \\ \end{tabular} & 95 \(\times\) 455 \(\times\) 11 & 3.3–5.9 & 15 & 28–30 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between the reference works. permit the use of these types of 5G antennas in 2D and 3D models, and symmetric or non-symmetric sub-arrays for small and large sizes.
## 6 Result and Conclusions  
The comparison results of \(\mathrm{DAF}_{\mathrm{Eisenmann}}\), \(\mathrm{DAF}_{\mathrm{Area}}\), and \(\mathrm{DAF}_{\mathrm{Field}}\) is as follows. As the variables for the \(\mathrm{DAF}_{\mathrm{Eisenmann}}\) and \(\mathrm{DAF}_{\mathrm{Area}}\) calculation for both ballasted and concrete track structures are the same (using Equations (8) and (9), respectively), their results are obviously the same when compared with one another for each train speed conditions. Between the two dynamic factor calculation types, however, slope of the \(\mathrm{DAF}_{\mathrm{Area}}\) is visibly higher than that of the \(\mathrm{DAF}_{\mathrm{Eisenmann}}\) (this result is again expected as it has been established since the research results outlined in Figure 1). However, the difference between the \(\mathrm{DAF}_{\mathrm{Field}}\) between the concrete and ballasted tracks were relatively more noticeable. Throughout the speed variables, the comparison of \(\mathrm{DAF}_{\mathrm{Field}}\) of ballasted tracks to the \(\mathrm{DAF}_{\mathrm{Eisenmann}}\) and \(\mathrm{DAF}_{\mathrm{Area}}\) showed a range from 17% up to 32%, and 18% to 34% respectively. In the case of the \(\mathrm{DAF}_{\mathrm{Field}}\) of concrete track, again the difference was higher, showing a range of from 14% to 42% for the \(\mathrm{DAF}_{\mathrm{Eisenmann}}\) and 23% to 30% for the \(\mathrm{DAF}_{\mathrm{Area}}\). Between the track types of the \(\mathrm{DAF}_{\mathrm{Field}}\), a difference from 4% to 8% was observed. Results of this study proposed and demonstrated that dynamic factor calculations based on the standard methods deviate at a relatively large degree from the track results, and there is a difference in the results between the track types. In theory, the dynamic factor should be a reliable indicator of a track structure's dynamic response performance. However, the lack of consistency and the deviation from the standard dynamic factor calculation between the track types shows that there is bound to be a margin of error in the dynamic factor evaluation of tracks if the current standard codes are continued to be used without distinction between the track classes.  
Conceptualization, J.L., and K.O.; methodology, K.O.; validation, Y.P. and J.C.; formal analysis, J.L.; investigation, J.L. and K.O.; data curation, J.L.; writing--original draft preparation, J.L. and K.O.; writing--review and editing, J.L. and K.O.; supervision, J.C. and Y.P.; project administration, J.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C152026-02). Also, this work was supported by 2020 BUCHEON UNIVERSITY Research Grant.  
The authors declare no conflict of interest.  
Figure 12: Comprehensive dynamic factor comparison.
## 2 System Model and Proposed Scheme  
### System Model  
Figure 1 shows the system model considered in this paper. Assuming a multicarrier transmission system, the secondary system with STPA-BAA is laid over the primary one on the superpose spectrum. It is noted that the primary system does not have the interference suppression function. Basically, in the cognitive radio network, the secondary system must not interfere with the primary system on the same frequency resources. Therefore, the secondary system should suppress interference from the primary transmitter as well as reduce interference to the primary receiver. \(\text{SIR}_{n}\) and \(\text{SIR}_{\text{total}}\) are defined as the SIR at the \(n\)-th (\(1\leq n\leq N\)) subcarrier and the total SIR among all subcarriers, respectively. Their relationship can be formulated as  
\[\text{SIR}_{n}=\frac{\text{S}_{n}}{\text{I}_{n}}, \tag{1}\] \[\text{SIR}_{\text{total}}=\frac{\sum\limits_{n=1}^{N}\text{S}_{n }}{\sum\limits_{n=1}^{N}\text{I}_{n}}, \tag{2}\]  
where \(\text{S}_{n}\) and \(\text{I}_{n}\) stand for the desired and interference signal power at the \(n\)-th subcarrier, respectively. Let \(x_{l}(n,i)\) denote the \(i\)-th symbol on the \(n\)-th subcarrier in the signal received by the \(l\)-th (\(1\leq l\leq L\)) antenna element. The \(L\)-by-1 received signal vector of the secondary system, \(\textbf{X}(n,i)\), is defined as  
\[\textbf{X}(n,i)=[\begin{array}{cccc}x_{1}(n,i)&x_{2}(n,i)&\cdots&x_{L}(n,i),\end{array}]^{T}, \tag{3}\]  
where \((.)^{T}\) denotes transposition. Received signal includes the desired signal for the secondary system and the inter-system interference signal from the primary one. The inter-system interference can be suppressed by BAA, which multiplies the received signal per subcarrier by a weight vector.
## 2 Materials and Methods  
Search strategy. This literature review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (see Figure 2, PRISMA flow chart diagram). A preliminary review registration in PROSPERO was considered unnecessary and impractical given the limited number of controlled prospective trials. We conducted a comprehensive electronic search across four major databases, Medline/PubMed, EMBASE, Medline/Metacrawler and the Cochrane Library, to identify relevant publications for this systematic review. The search strategy utilized Medical Subject Heading (MeSH) terms and keywords, focusing on the intersection of pancreas divisum and endoscopic ultrasonography (EUS) with the following \"SEARCH QUERY\": ('pancreas divisum'/exp OR 'pancreas divisum') AND ('endoscopic ultrasonographyraphy'/exp OR 'endoscopic ultrasonographyraphy'). EUS started becoming more widespread in the mid-1990s with the development of linear echoendoscopes, which allowed more detailed evaluation of pancreatic ductal system conditions such as pancreas divisum. For this reason, our search encompassed all types of articles published in English from January 1993 to December 2024.  
Figure 1: Drawings illustrate three types of pancreas divisum. (**A**) Type 1, or classical divisum, with total fusion failure. (**B**) Type 2 with dominant dorsal drainage in the absence of [PERSON]’s duct. (**C**) Type 3 or incomplete divisum with a small communicating branch (blue arrow).  
To ensure the quality and relevance of the included studies, we established specific exclusion and inclusion criteria as follows.  
Figure 2: PRISMA flow chart diagram.  
Exclusion criteria were applied to eliminate editorials, commentaries, letters, conference abstracts, case reports, histopathology and cytology studies, as well as animal studies, duplicate publications or multiple reports of the same study. All non-peer-reviewed sources and animal articles were also excluded.  
To include studies investigating the diagnostic and clinical implications of pancreas divisum and its management, a set of inclusion criteria was developed, with a particular focus on the diagnostic value of EUS in pancreas divisum even if it was not the study's primary objective. We included original prospective and retrospective studies, anatomical and radiological research findings, systematic reviews, meta-analyses, and observational studies.  
Furthermore, to ensure the relevance and applicability of the findings to contemporary clinical practice, only studies published in peer-reviewed journals were considered for inclusion.  
The articles were grouped into four major subheadings:  
A. Diagnostic accuracy of pancreas divisum,  
B. Prospective studies for intermethod comparisons,  
C. Reviews, expert reviews, systematic reviews and meta-analyses,  
D. Retrospective studies for intermethod comparisons.
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 2 Systems and Methods
### Doppler Estimation and Compensation Implementation
#### 2.3.2 Conventional Doppler Compensation Method  
After obtaining the Doppler factor, the conventional Doppler compensation approach is to resample the received data frame packets with linear interpolation for accomplishing Doppler compensation. The implementation process of Doppler compensation is as follows.  
First, \(r(n)\) is processed by upsampling at \(M\) times the original sampling rate; then, the received sequence can be rewritten as  
\[r(m)=\sum_{n=-\infty}^{L_{n}}r(n)h(m-nM), \tag{20}\]  
where \(h(m)=sinc(m/M)\), and \(L_{n}\) represents the length of \(r(n)\). Second, the sampling rate of \(r(m)\) is downsampled to \(\frac{1}{N}\) times of the sampling rate of \(r(n)\) after the upsampling process is completed; accordingly, the received sequence can be formulated as  
\[\hat{r}(m)=\sum_{n=-\infty}^{L_{n}}r(n)h(mN-nM). \tag{21}\]  
where \(h(m)=\text{sinc}[m/\text{max}(M,N)]\). Accordingly, the Doppler compensation can be accomplished through the above variable sampling process. The implementation of this compensation process on the hardware platform requires two sampling processes on the received signal, which imposes a huge burden on the calculation capacity, memory space and processing delay of the central processing unit (CPU).
# Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations
## 4 Results and Discussion
### Randomness Extraction  
With the aforementioned conditions, the effective entropy of our implementation converges to a sequence length of \(4\,\mathrm{bit}\), after which no significant increase in the effective bits extracted is achieved. This can be seen in Figure 8. The distribution is thus divided into 16 bins and the measurements are assigned to a 4-bit Gray sequence. The electronic noise yields a contribution of roughly \(3.50\,\mathrm{bit}\), which corresponds to an extraction ratio of \(0.50\,\mathrm{bit}\) from each sample. As expected, due to the QCNR value of \(5\,\mathrm{dB}\) obtained, only a relatively small portion of bits is available to generate true RNs, which constitutes the main technical challenge of this implementation.  
Figure 7: Distribution of the homodyne measurements and electronic noise for \(10^{7}\) samples taken at \(12\,\mathrm{dB}\). Here, each bin corresponds to a unique ADC value.  
Two distinct randomness extractors were applied to the unprocessed bits: the SHA-512 cryptographic hash function and the Toeplitz-hashing extractor. The desired extraction ratio is achieved by hashing data subsets of appropriate length and merging the results. For the Toeplitz implementation, pseudorandom numbers are used as a seed of the hashing matrix, which can potentially influence the final RN security [44]. Ideally, sacrificing part of the QRNG output should be explored or, since this method constructs a strong extractor, a second source of randomness can be used to obtain a reusable seed [45]. The raw bits were thus subdivided into sequences of \(2^{12}\) bits (\(n=4096\)) and hashed with different seeds. The output sequence length, \(m\), of the Toeplitz hashing algorithm is chosen, such that the ratio is lower than the maximum allowed:  
\[m=\lfloor 2^{12}p\frac{H_{\text{quant}}}{H_{\text{total}}}\rfloor, \tag{19}\]  
where \(p\) was arbitrarily chosen as 80% to account for any potential entropy overestimation. Thus approximately 0.10 random bits are obtained from each raw bit. By contrast, this consideration was disregarded for the SHA-512 extractor, and thus an extraction ratio of approximately 0.13 random bits for each raw bit is roughly preserved. In this case, the output length \(m\) is set as 512 bit and the input sequence length is chosen such that:  
\[n=\lceil 512\frac{H_{\text{total}}}{H_{\text{quant}}}\rceil. \tag{20}\]  
The post-processing execution time severely limits the final bit rate since the algorithm implementation is incapable of keeping pace with the physical data generation rate. As such, a rate of 278 kbit/s and 1.68 Mbit/s were reached with the Toeplitz and SHA-512 extractor, respectively. Nevertheless, this setup could theoretically achieve approximately 19.69 Mbit/s.
# A Multi-Period Optimal Reactive Power Dispatch Approach Considering Multiple Operative Goals
## 3 Tests and Results
### Test Systems
#### 3.2.2 IEEE 118-Bus Test Case  
Several simulations were performed on the IEEE 118-bus test system, which has 186 branches and 77 control variables; these consist of 54 generation units, 9 transformers, 12 capacitors, and 2 reactors. The single-line diagram of the system is depicted in Figure 3, where each color represents a different voltage control area with a single pilot node. The total active and reactive base loads are 4242 MW and 1438 MVAr for period 11 on the 100 MVA base. The initial output of active and reactive power from the generators comes from the solution of the UC problem reported in [41]. Additionally, the initial settings of the tap transformers are in the nominal ratio and the states of the shunt elements are all connected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pilot Node** & **Lower Voltage Limit (p.u)** & **Shunt** & **MVAr** \\ \hline V\({}_{\text{np1}}\) & 0.9097 & Q\({}_{\text{C18}}\) & 10 \\ V\({}_{\text{np4}}\) & 0.9177 & Q\({}_{\text{C25}}\) & 25 \\ V\({}_{\text{np10}}\) & 0.9244 & Q\({}_{\text{C33}}\) & 53 \\ V\({}_{\text{np12}}\) & 0.9252 & & \\ V\({}_{\text{np13}}\) & 0.9039 & & \\ V\({}_{\text{np22}}\) & 0.8927 & & \\ V\({}_{\text{np29}}\) & 0.9274 & & \\ V\({}_{\text{np31}}\) & 0.8500 & & \\ V\({}_{\text{np36}}\) & 0.8640 & & \\ V\({}_{\text{np41}}\) & 0.9119 & & \\ V\({}_{\text{np48}}\) & 0.9038 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lower voltage limits of pilot nodes and MVAr parameters of shunt elements (IEEE 57-bus test system).  
Figure 3: Single-line diagram of the IEEE118-bus test system with pilot nodes and voltage control areas.  
The minimum and maximum limits of control variables for the IEEE 118-bus test system are as follows. Each transformer ratio varies from 0.9 to 1.1 p.u in equal steps of 0.01. The shunt elements are between 0 and its MVAr parameter, which have no steps. The voltage set points of generators vary in the range of [0.9, 1.1] p.u (except for lower voltage limits of pilot nodes, which are reported in Table 2). Table 2 shows the shunt MVAr parameter and lower voltage limit for pilot nodes taken from [44]. In this case, \(\overline{M}\) is set to 100 for all operation days.
# Review on Soft Mobility Infrastructure Design Codes
## 2 Codes for Soft Mobility Infrastructure
### Supporting Facilities  
Supporting facilities concern both pedestrians and bicycles and mainly include: (i) parking facilities and (ii) end-of-trip (EOT) facilities.  
Bicycle parking facilities are regulated by size, facility type, and location in various regions. In TW, sizes are categorized into basic, parallel, and angled types, with specified dimensions (Figure 12). CN specifies a single bicycle size of 0.6 to 0.8 m wide and 2.0 m long, allowing diagonal arrangement in limited space without specific angle regulations. Residential areas, public buildings, and transportation hubs with high bicycle parking demand should allocate facilities according to construction guidelines and signage. Sections of roads designated for motor vehicle parking can be repurposed for bicycle parking when demand is high, ensuring accessibility near rail stations, bus stops, schools, and hospitals within 50 m. Importantly, bicycle parking facilities must not obstruct emergency passages or encroach on infrastructure like manholes or street signs.  
Figure 11: Detailed page of Competeurs velo à Paris (Bike counters in Paris) (Source: [[https://compteurs.parisenselle.fr/details/Boulevard-de-Sebastopol](https://compteurs.parisenselle.fr/details/Boulevard-de-Sebastopol)]([https://compteurs.parisenselle.fr/details/Boulevard-de-Sebastopol](https://compteurs.parisenselle.fr/details/Boulevard-de-Sebastopol)) (accessed on 3 June 2025)).  
Figure 10: Front page of Competeurs velo à Paris (Bike counters in Paris) (Source: [[https://compteurs.parisenselle.fr/](https://compteurs.parisenselle.fr/)]([https://compteurs.parisenselle.fr/](https://compteurs.parisenselle.fr/)) (accessed on 3 June 2025)).  
EOT facilities cater to SMI users, such as pedestrians, cyclists, e-cyclists, or runners, providing rest, cleaning, refueling, and repair services. Although EOT facilities exist globally, they are not widespread. Singapore exemplifies this with various amenities like changing rooms, showers, and bike repair tools. Additionally, to accommodate e-bicycles and e-scooters, some companies have developed charging stations as part of EOT infrastructure.
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 2 In Vivo Confocal Laser Scanning Microscopy
### Other Applications of In Vivo CLSM  
In vivo CLSM can be employed for identification of specific clues and monitoring the effects of treatment in inflammatory/autoimmune/infectious skin diseases, such as psoriasis, skin aging and hydration, together with results of rejuvenation and scar treatments, skin pigmentation and related disorders, such as melasma and vitiligo [66; 67; 68; 69; 70; 71; 72; 73; 74].  
Psoriasis is characterized by scales and vessels, both visible with RCM, with scales corresponding to hyperkeratosis, associated with an increased epidermal thickness (>60-80 um), measured through the number of stacks from the top of the stratum corneum to the first cellulated epidermal layer [75].  
The exploration with RCM at epidermal level enables the visualization of a regular/irregular honeycomb pattern or eventual pigmentation corresponding to mottled pigmentation while at DEJ/superficial dermis blood vessels and collagen can be observed [61]. Interestingly, variation in pigmentation and collagen remodeling, have been described after laser and rejuvenation treatments [76; 77; 78; 79; 80; 68; 69; 70; 68; 68; 69; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80].  
RCM has been used to study acne skin and different features corresponding to comedy, papules and pustules have been reported. Additionally, RCM has been applied to treatment monitoring, showing the reduction of both inflammatory and hyperkeratotic features with RCM [72; 73; 81].