esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
###### Abstract  
The 3 rd Generation Partnership Project (3 GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation loss, but suffers from high Doppler shift. The 3 GPP proposes Doppler shift pre-compensation for each beam region of the satellite. However, user equipment farther from the beam center has significant residual Doppler shifts even after pre-compensation, which degrades link performance. This study proposes residual Doppler shift compensation by adding demodulation reference signal symbols and reducing satellite beam coverage. The block error rate (_BLER_) data are obtained using link-level simulation with the proposed technique. Since the communication time provided by a single LEO satellite moving fast is short, many LEO satellites are necessary for seamless 24-hour communication. Therefore, with the _BLER_ data, we analyze the link budget for actual three-dimensional orbits with a maximum of 162 LEO satellites. We finally investigate the effect of the proposed technique on performance metrics such as the per-day total service time and maximum persistent service time, considering the number of satellites and the satellite spacing. The results show that a more prolonged and continuous communication service is possible with significantly fewer satellites using the proposed technique.  
wireless communications; Internet of Things; non-terrestrial networks; doppler shift; NB-IoT; LEO satellite +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 6 Simulation Results with Discussion
For example, one can notice from Figure 33 that at low-speed mobile, the blocking probability at traffic intensity of 450 Erlang is less than 0.10 for data service and around 0.001 for voice service; on the other hand, considering high-speed mobiles and at the same traffic intensity of 450 Erlang, clear degradation of the performance occurs where the blocking in Figure 34 is more than 0.10 for data service and around 0.01 for voice service. This status can be explained as follows. In real life, the signal strength fluctuates while being on the move. In all circumstances, thanks to the CACA, it works well in balancing the load all over the network and keeping the blocking and dropping probability at lower levels. In the final set of results [37; 38], the proposed algorithm is evaluated against similar findings from [30]. According to [30], the outcomes are presented for the average number of dropped packets (Figure 37) and the average utility performance (Figure 38) relative to the data arrival rate (Mbit/s). It is evident from Figure 37 that up to an arrival rate of 2.7 Mbit/s, there is nearly a total correspondence between our algorithm and the one proposed in [30]. However, when the rate exceeds 2.7 Mbit/s, our algorithm demonstrates superiority over the suggested method by achieving a better performance in terms of the number of packets dropped. Conversely, from Figure 38, it is clear that our algorithm exceeds the performance of the proposed algorithm across all traffic levels. This is predictable since the authors in [30] indicated that an increase in locally processed computational tasks results in an increase in CPU energy consumption, which leads to a decline in performance. Figure 8:8 Average blocking probability. Figure 8:9 Grade of service (GoS). Figure 7:0 Average handover dropping probability. Figure 7:1 Average blocking probability with a 60% increase in the proportion of data services. Figure 7:2 Average handover dropping probability with a 60% increase in the proportion of data services. Figure 7:3 Grade of service (GoS) with a 60% increase in the proportion of data services. Figure 7:4 Average blocking probability with 40% high-speed mobile users. Figure 7:5 Average handover dropping probability Average blocking probability with 40% high-speed mobile users. Figure 7:6 Grade of service (GoS) Average blocking probability with 40% high-speed mobile users.
# Orbital Angular Momentum Generation and Detection by Geometric-Phase Based Metasurfaces
## 3 Metasurfaces for OAM Generation
### Artifical PEC-PMC Metasurface
A metallic via with the radius of \(r=0.25\) mm and height of \(d_{1}=2\) mm connects the patch to the ground plane. Reproduced with permission from [84], Copyright AIP Publishing LLC, 2016. Figure 2: The amplitude and phase distributions of reflected electric fields from the PEC-PMC metasurface at a transverse plane \(z=20\) mm: (**a**) Amplitude; (**b**) Phase. Reproduced with permission from [84], Copyright AIP Publishing LLC, 2016.  
distribution is not uniform any more and ripples are observed in the phase distribution. As discussed before, the ripples are caused by evanescent field components scattered by the dipoles. The distortion only exists in the near field and will become less serious when the observation plane moves further away from the metasurface.
###### Abstract  
As the depth of petroleum drilling increases, the strata environment becomes more complex. The efficiency and lifespan of Polycrystalline Diamond Compact (PDC) drill bits fail to meet current drilling demands. However, the structure and arrangement of PDC cutters are valuable determinants of drilling efficiency, although related research still has gaps and deficiencies. This study focuses on PDC cutters in axe, triangular prism, and circular forms. It establishes an inhomogeneous granite model based on the actual measurements of granite and verifies the accuracy of this model through uniaxial compression simulation. Finite element models of three types of cutters in various combination schemes are constructed to examine rock-breaking effects, with the best scheme optimized using Box-Behnken response surface methodology. The rock-breaking process of the optimal PDC drill bit layout has been compared to that of a single cutter bit. Field drilling has demonstrated the effectiveness of a mixed cutter arrangement. The results show that the inhomogeneous granite model can be trusted. The optimal arrangement involves axe cutters in the front row and an alternate arrangement of triangular prism cutters and axe cutters in the back row. The optimal lateral and longitudinal distances for the triangular cutters from the front row of axe cutters are 10 mm and 7 mm, respectively, while those for the back row of axe cutters from the front row are 10.06 mm and 7 mm, respectively. The ROP standard deviation in the drilling process of mixed cutter bits decreases by 53.06% and 43.08% compared to axe and triangular prism cutter bits, respectively. The drilling efficiency increases by 16.8% and 16.6%, respectively, demonstrating higher efficiency and stability. Field drilling results indicate that a mixed cutter bit increases efficiency by 23.5% compared to a bit with only triangular prism cutters. This study posits that research on the combination schemes and parameters of PDC cutters can significantly enhance drilling efficiency, thereby reducing the drilling cycle and costs.  
PDC drill bit; heterogeneous granite; mixed cutter arrangement; Box-Behnken; finite element analysis Article
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### Existing Land Use Data Set  
As described in Section 1.2, state-wide land use information is mapped by QLUMP. Land use is mapped to a national standard, according to the ALUM Classification--which has a three-tiered hierarchical structure broadly structured by the potential degree of modification from essentially native land cover [13]. The (six) primary and (32) secondary classes relate to land use, and (159) tertiary classes include commodity and land management practice information (e.g., \"Tree fruits\" as demonstrated in Figure 2. While tertiary-level information is particularly valuable for many applications, including biosecurity response, it has historically been expensive and impractical to collect, and as a result not consistently recorded.  
The QLUMP methodology has been an accurate, reliable, and cost-effective option since the late 90s--making use of available technology, data, and imagery. Mapping is undertaken primarily at the desktop, combining imagery interpretation and ancillary data to derive land use products. These products are field validated, peer reviewed, and accuracy assessed prior to publishing.  
Because of the large area of Queensland (1.85 million squared kilometers), it has not been feasible to update land use information across the entire state at once, therefore updates occur regionally, using natural resource management (NRM) region boundaries. As a result, the currency of data varies from region to region. The most recent data is 2017 (Fitzroy and Burnett Mary NRM regions) and the most dated is 2012 (South East Queensland NRM region). The Wet Tropics NRM region was last updated to 2015. Regional updates occur on an ad hoc basis, dependent on state government priorities--for example the most recent updates were in the Great Barrier Reef catchments to support the Paddock to Reef Monitoring, Modelling and Reporting Program, and the Reef 2050 Water Quality Improvement Plan. A user survey conducted by QLUMP in 2020 indicates that there is a growing need for more current, and higher resolution land use information. The current QLUMP methodology, while proven, requires an intensive amount of manual image interpretation and spatial data analysis. There is a need for a more automated methodology that enables faster publishing of land use information, and CNNs are a possible solution.
## 4 Description of the Experiment
### Datasets  
#### 4.3.1 Basic Introduction to Datasets  
The experiments used five datasets: ATEC, BQ, LCQMC, PAWS-X, and Chinese STS-B, which are briefly described here.  
ATEC-Question Similarity Calculation is a competition question hosted by Ant Financial Services that uses an algorithm to determine whether two given sentences have the same semantic meaning, which can be applied in a customer service system, and all its data comes from actual application scenarios of Ant Financial Brain. The dataset has 62,477 training samples, 20,000 samples in the development set, and 20,000 samples in the public test set.  
The Bank Question (BQ) dataset [16] is a large-scale domain-specific Chinese dataset for Sentence Semantic Equivalence Recognition (SSEI). The BQ dataset contains 120,000 question pairs from the online banking customization service logs. It is divided into three parts: 100,000 pairs for training, 10,000 pairs for validation, and 10,000 pairs for testing.  
LCQMC [17] is a large-scale Chinese question-matching corpus covering a large number of question-pair data. The corpus contains 260,068 question pairs with manual annotations and is divided into a training set, a development set, and a test set. The training set contains a total of about 238,766 question pairs, the development set contains a total of about 8802 questions, and the test set contains a total of about 12,500 questions.  
PAWS-X [18] is a new dataset consisting of 23,659 human-translated PAWS evaluation pairs in six different types of languages: French, Spanish, German, Chinese, Japanese, and Korean. PAWS-X demonstrates the effectiveness of deep, multilingual pre-training while leaving considerable scope to drive multilingual research that better captures structural and contextual information. The number of training set samples in this dataset is 49,129, with a positive and negative sample count of 27,445 and 21,684, respectively.  
STS-B [19] (Semantic Textual Similarity Benchmark) is a dataset for measuring textual similarity. sts-b contains a set of sentence pairs about textual similarity, each with a score that measures how similar the two sentences are semantically. The score is a real number from 0 to 5, where 0 means that the two sentences are not related at all and 5 means that the two sentences are identical or very similar. The Chinese STS-B dataset is mainly translated from the original English STS-B dataset and is used for fine-grained Chinese text-matching tasks or Chinese natural language inference tasks. The Chinese STS-B contains a total of 5231 training samples, 1458 validation samples, and 1361 test samples.
## 3 Experimental Results and Analysis
### Performance Metrics  
This section is devoted to the illustration of the performance metrics employed in our experiments to measure the system performance objectively. Furthermore, we give a subjective evaluation of our approach in Figure 6 using several examples of forged images. The performance metrics are elaborated below.
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## 5 Conclusions  
In this paper, we focused on the optimal cooperative routing path from a source node to a destination node through multi-hop wireless transmission. A TSC transmission model was proposed and the cooperative link cost was formulated. Based on the proposed two-stage link cost, a distributed TSCR scheme was proposed to achieve a high energy-efficient packet transmission and prolong the network lifetime. Simulation results have verified the effectiveness of the proposed TSCR scheme on the energy savings and energy balance. Compared with the existing schemes, the proposed TSCR can simultaneously prolong the network lifetime, shorten the end-to-end transmission delay and improve the energy efficiency. In addition, the proposed TSCR scheme can be adjusted to accommodate the different performance goals through the parameter \(x\).  
It has been assumed in this paper that there is only one active communication session in the whole network. The future work will extend the proposed TSCR scheme to multiple-source multi-destination scenario as studied in [27; 28]. To this end, the interference between different data flows needs to be carefully addressed.  
**Author Contributions**: [PERSON] and [PERSON] developed the algorithm and analysed the results, [PERSON] and [PERSON] performed the simulation and wrote the paper, [PERSON] and [PERSON] reviewed the paper. All authors read and approved the final version of the paper.  
Figure 6: Energy efficiency versus different number of nodes.  
**Funding:** This paper is sponsored by National Key Technology R&D Program of China under grant No. 2018 ZX03001024-006.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Mathematical Models
### Quadratic Programming Representation of Single Index Model  
The return on an investment portfolio (\(R_{P}\)) is defined as the weighted sum of the returns of its constituent securities:  
\[R_{P}=\sum_{i=1}^{N}\varepsilon_{i}\Re_{i},\]  
where \(N\) is the number of assets and \(\varepsilon_{i}\) denotes the respective weighting of each asset within the portfolio. By substituting the single-index model (2) into this portfolio return equation, we obtain  
\[R_{P}=\sum_{i=1}^{N}\varepsilon_{i}(A_{i}+\beta_{i}I+C_{i}).\]  
Following [PERSON] [21], this can be further simplified to  
\[R_{P}=\sum_{i=1}^{N+1}\varepsilon_{i}(A_{i}+C_{i}).\]  
Consequently, the expected return (\(E[R_{P}]\)) and portfolio risk (\(Var[R_{P}]\)) are determined as follows:  
\[E[R_{P}]=\sum_{i=1}^{N+1}\varepsilon_{i}A_{i},\]  
and  
\[Var[R_{P}]=\sum_{i=1}^{N+1}\varepsilon_{i}^{2}\mathcal{Q}_{i}.\]  
Thus, the single index model naturally lends itself to a quadratic programming formulation, presented as  
\[\begin{array}{l}\max\bar{\lambda}\sum_{i=1}^{N+1}\varepsilon_{i}A_{i}-\sum_ {i=1}^{N+1}\varepsilon_{i}^{2}\mathcal{Q}_{i}\\ s.t\ \ \sum_{i=1}^{N}\varepsilon_{i}=1,\ \ \varepsilon_{i}\geq 0,\\ \sum_{i=1}^{N}\varepsilon_{i}\beta_{i}=\varepsilon_{n+1},\end{array} \tag{4}\]  
where \(\bar{\lambda}\) signifies the investor's level of risk aversion, and \(\varepsilon_{n+1}\) represents the weighted average responsiveness of the portfolio return (\(R_{P}\)) to the level of the index (\(I\)).
# Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement
## 2 Experimental Setup  
### Measurement Devices  
The experimental investigation was performed with four different measurement devices. Table 1 summarizes the relevant specifications of measurement devices used in this study to detect particle mass and number concentrations in aerosols. They differ in the measurement method, measured particle size range, and cost.  
The Scanning Mobility Particle Sizer (SMPS, TSI Model No. 3934) from TSI GmbH is a commonly used laboratory-scale device for particles in the nanometer size range. It classifies the particles based on their electric potential and then determines the number of particles in individual fractions by spectrometry [38]. For the investigations with the low-cost sensors, two different models were selected: the SDS011 from NovaFitness and the HPMA compact from Honeywell. Their photometric measuring method operates via transmission effects, which results in a wider detectable particle-size range than with the SMPS. There are already a number of studies with the mentioned low-cost sensors [39; 40; 41; 42; 43; 44], where the comparison to different laboratory measuring devices (Optical Particle Counter, SMPS, etc.) showed that the PM2.5 values are comparable [39; 41; 43]. As soon as the range allowed by the manufacturer for the relative humidity is exceeded, the measured values are detected incorrectly [41; 44]. The low-cost sensors output their results in PM2.5 and PM10, which correspond to the particle mass concentration of all particles up to 2.5 and 10 \(\upmu\)m, respectively. In addition, the Air Quality Guard (AQ Guard) from Palas GmbH is used, which promises to detect a wide range of particle sizes, including their mass and number concentration, using the photometric measuring method [14]. Considering this, it is referred to as a mid-cost device.
## 2 Limit State Design for a Pultruded GFRP Bridge Deck
### Serviceability Limit State (SLS)  
In SLS, the combinations of unfactored loads acting under normal operating conditions and wind loads (25 m/s) are utilized. In this load combination, the self-weight of GFRP deck and asphalt pavement is not considered; only the impact factor due to the dynamic effect is considered. Table 1 compares the prescribed design wheel loads and tire contact area in the design standards [13; 14].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Design Standard** & **Designation** & **Truck Weight (kN)** & **Rear Axle Load (kN)** & **Impact Factor (\%)** & **Design** & **Tire Contact** & **Area** \\ \hline AASHTO LRFD [13] & HL-93 & 320 & 142 & 33 & 94.4 & 254 \(\times\) 508 \\ \hline Korean Hwy. LSD [14] & KL-510 & 510 & 192 & 25 & 120.0 & 231 \(\times\) 577 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design wheel loads in the design standards [13; 14].  
Figure 1: Configuration of section and adhesive layer in TG200 bridge deck. (**a**) Section dimensions; (**b**) stepped lap joint; (**c**) dimensions of adhesive layer.  
The design truck load (KL-510) of the Korean Highway Bridge Design Code for Limit State Design (LSD) with a 25% impact factor is utilized in the analysis for the GFRP decks. The design single-wheel load of KL-510 is more conservative than that (HL-93) of AASHTO LRFD, as shown in Table 1. Figure 2 shows the design wheel loads of KL-510, where half of the rear axle load is the maximum single-wheel load (96 kN = 192/2 kN). This is increased by a 25% impact factor to 120 kN, as shown in Table 1.  
As shown in Figure 3, and according to the Korean Highway Bridge Design Code [14], two loading configurations are considered: (1) the force effect due to a single rear wheel (LC1 hereafter) and (2) the force effect due to two adjacent middle wheels (LC2 hereafter). The LC1 wheel load (120 kN) is distributed over the tire contact area (231 mm long and 577 mm wide) at the top surface of the deck. The two adjacent loads (84.4 kN each) in LC2 are distributed over the tire contact area (each 217 mm long and 540 mm wide) 1.2 m apart, as shown in Figures 2 and 3.  
The deflection criteria for GFRP bridge decks in different design standards and related documents are shown in Table 2. As mentioned above, the GFRP deck deflections are calculated via FE analysis and should satisfy these deflection criteria for SLS.  
Figure 3: Load configurations per Korean Highway Bridge Design Code [14]. (**a**) Load configuration 1 (LC1); (**b**) load configuration 2 (LC2).  
Figure 2: KL-510 design truck load per Korean Highway Bridge Design Code [14].
###### Abstract  
Background: The quadriceps tendon (QT) autograft has recently drawn attention for anterior cruciate ligament reconstruction (ACLR). Finding the best autograft option for adolescents after an ACL injury is essential to ensure them a high-quality active lifestyle. There are no studies comparing the all-soft-tissue QT autograft with the hamstring tendon (HT) autograft in such population. Methods: In this study, 68 patients younger than 18 years of age were assigned to the HT (38 patients) or the QT (30 patients) ACLR group. The groups were similar, allowing their comparison. The instrumented knee laxity was evaluated with a Genourob thermometer. In total, 48 patients (27 HT and 21 QT) were tested 3 months post-op, and 45 patients (26 HT and 19 QT) were tested 6 months post-op. Results: We found that 3 months post-op, the side-to-side anterior tibial translation measurements (GNRB1) were worse in the HT than in the QT group (1.4 (0.2-5.2; 1.715) vs. 0.6 (0.1-2.1; 0.905) mm, \(p\) = 0.02). There was no difference in side-to-side anterior tibial translation at 6 months post-op (GNRB2) between the HT and the QT group (1 (0.2-5.3; 1.519) vs. 1.1 (0.3-3.4; 1.279) mm, \(p\) = 0.927). At 3 months post-op, the side-to-side anterior tibial translation (GNRB1) was worse in males than in females. irrespective of the graft choice (1.45 (0.1-5.2; 1.696) vs. 0.4 (0.1-3.4; 0.89) mm, \(p\) = 0.016). The displacement curve slopes 6 months post-op were better for females (3 (0-13.1; 3.335) vs. 5.3 (0-26.1; 7.848), \(p\) = 0.014). At 3 months post-op, the GNRB measurements showed that the side-to-side anterior tibial translation (GNRB1) was better for females when the HT autograft had been used (0.45 (0.2-3.4; 0.942) vs. 2.4 (0.3-5.2; 2.333) mm, \(p\) = 0.003). In general, both autografts provided excellent objective outcomes at early follow-up. Conclusions: The all-soft-tissue QT autograft should be considered as a reliable alternative for ACLR in adolescents. This autograft has at least the same properties as the HT autograft in the early stages after ACL reconstruction in adolescent athletes and, in some respects, seems superior to the HT autograft. Research should continue to find the best possible graft choice for the most active and willing-to-return-to-sport population.  
anterior cruciate ligament reconstruction; adolescent athletes; all-soft-tissue quadriceps tendon autograft; pediatric anterior cruciate ligament reconstruction