esa-sceva/satcom-chunk-collection huggingface dataset:
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## Abbreviations  
### Acronyms  
**3 GPP** Third Generation Partnership Project.  
**BS** Base Station.  
**C-V2X** Cellular Vehicle-to-Everything.  
**CPOFDM** Cyclic Prefix Orthogonal Frequency-Division Multiplexing.  
**DENM** Decentralized Environmental Messages.  
**DL** Downlink.  
**DMRS** Demodulation Reference Signal.  
**LTE** Long-Term Evolution.  
**NR** New Radio.  
**OFDM** Orthogonal Frequency-Division Multiplexing.  
**PRB** Physical Resource Block.  
**PSSCH** Physical Sidelink Broadcast Channel.  
**PSCCH** Physical Sidelink Control Channel.  
**PSSCH** Physical Sidelink Shared Channel.  
**QAM** Quadrature Amplitude Modulation.  
**QPSK** Quadrature Phase Shift Keying  
**RB** Resource Block.  
**RR** Radio Resources.  
**RSRP** Reference Signal Received Power.  
**SCFDMA** Single Carrier Frequency Division Multiple Access.  
**SCI** Sidelink Control Information.  
**SPS** Semi Persistent Scheduling.  
**TB** Transport Block.  
**UE** User Equipment.  
**UL** Uplink.  
**V2V** Vehicle-to-Vehicle.  
**V2X** Vehicle-to-Everything.
## 2 Materials and Methods
### Materials
#### 2.1.1 Study Area and Landslide Inventory Information  
Shicheng County is located in the southeastern part of Jiangxi Province and has a longitude of 116\({}^{\circ}\)05'46\" E, 116\({}^{\circ}\)38'03\" E and a latitude of 25\({}^{\circ}\)57'47\" N, 26\({}^{\circ}\)36'13\" N (Figure 1). Its total area is about 1581.5 km\({}^{2}\) with a length of 71.8 km and a width of 53.7 km. The total population is about \(3.33\times 10^{5}\). Shicheng County belongs to the subtropical monsoon humid climate zone and has abundant sunshine, four distinctive seasons, and rich rainfall. Its average annual precipitation was about 1748.6 mm between 1970 and 2015, and the total precipitation of the main flood season (April-June) accounts for 50.1% of the total annual precipitation. Both precipitation and temperature are non-uniformly distributed in Shicheng County due to the complex terrain characteristics and the relationship between land and sea locations.  
Shicheng County belongs to a mountainous area with a developed river system and a dense river network (0.6 km of each km\({}^{2}\)). The Qin River flows through the whole area from northeast to southwest and finally flows into the Ganjiang River. The groundwater type of Shicheng County is mainly shallow groundwater of shallow depth, good recharge conditions, rapid regeneration speed, and easy extraction. Furthermore, Shicheng County is in a mountain basin surrounded by the Wuyi Mountains. These mountains are mainly composed of pre-Devonian metamorphic rocks, Devonian quartz sandstone, sandy conglomerate, and sandy shale. In general, Shicheng County is in a typical southeast hilly region, with many mountains in the northeast area, rolling hills in the southwest area, and flat terrain in the central area.  
Based on investigations by the Land and Resources Department of Jiangxi Province, the landslide inventory map shown in Figure 1 suggests that 369 landslides occurred in the study area from 1970 to 2012. The sliding masses of these landslides are mainly composed of quaternary silty clay intercalated with crushed stones that have a thickness ranging from 2 to 8 m. These landslides can be mainly classified as shallow soil landslides with a movement type of clay/silt slide [42]. The main features of these shallow soil landslides are their small scale, high frequency, group occurrence, and wide distribution. The total cover area of the recorded landslides in Shicheng County is about \(2.44\times 10^{6}\) m\({}^{2}\), and the area of these landslides ranges from about \(1.0\times 10^{3}\) to \(1.6\times 10^{4}\) m\({}^{2}\). Furthermore, the main direct trigger factors of these landslides are seasonal heavy rainfall and frequent unreasonable human engineering activities, such as slope toe cutting and road excavation.
## 4 Datasets and Experimental Results
### Comparison to Baselines
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **S-SVM** & **SF-SVM** & **S-CNN** & **SF-CNN** & **SF-ICNN** \\ \hline
1 & 79.25\% & 84.03\% & 90.37\% & **91.04\%** & 90.96\% \\
2 & 94.71\% & 97.83\% & 97.71\% & 98.87\% & **99.44\%** \\
3 & 65.94\% & 76.91\% & 67.58\% & 72.10\% & **81.14\%** \\
4 & 70.79\% & **97.82\%** & 84.97\% & 93.83\% & 94.75\% \\
5 & 99.32\% & 99.77\% & 96.77\% & 96.32\% & 100\% \\
6 & 44.90\% & 88.17\% & 80.91\% & 96.22\% & **99.05\%** \\
7 & 71.22\% & 70.15\% & 62.33\% & 64.00\% & **82.30\%** \\
8 & 77.15\% & 85.32\% & 87.35\% & 95.69\% & 96.62\% \\
9 & **99.89\%** & 92.21\% & 75.26\% & 79.53\% & 72.81\% \\ \(OA\) & 81.35\% & 91.53\% & 89.69\% & 93.80\% & **95.50\%** \\ _[PERSON]_ & 0.745 & 0.887 & 0.861 & 0.917 & **0.943** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the classification for the PU HSI dataset (the best results of each row are highlighted). Figure 6: GT and other techniques’ classification results for the PU HSI dataset. (**a**) GT. (**b**) S-SVM. (**c**) SF-SVM. (**d**) S-CNN. (**e**) SF-CNN. (**f**) Suggested SF-ICNN.
# Bitumen and Bitumen Modification: A Review on Latest Advances
## 3 Bitumen Chemical Modifiers
### PPA-Based Techniques  
Among the different acids that can be used to improve bitumen properties through chemical modification, polyphosphoric acid (PPA) has been studied in numerous papers and patent publications [172; 173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185]. From a chemical point of view PPA is an oligomer of phosphoric acid (H\({}_{3}\)PO\({}_{4}\)), with no free water, which may have more than 10 repeating units [122].  
The bitumen modification of PPA appears to be a complex physico chemical process, and the resulting properties may be strongly dependent on the bitumen nature. The difficult investigation of the reactions between the bitumen molecules and PPA is due to the large number of molecules with different chemical structures and their possible interactions. In this respect, an NMR investigation has shown that PPA tends to revert to orthophosphoric acid after mixing with bitumen [123]. [PERSON] et al. claim that PPA neutralizes polar interactions between the asphaltene molecules, by protonation of basic sites or through esterification [186]. Co-polymerization, alkyl aromatization of saturates, and so on are other classes of reactions that have been proposed. A more detailed description can be found in [186; 4; 122; 187]. From a thermal and rheological point of view bitumen/PPA blends [188; 185; 186; 187] have shown an increased high-temperature performance with no significant changes in the low-temperature ones. It has been also found that PPA can improve the storage stability of PmBs. [PERSON] et al. [112] used three ethylene-propylene copolymers of different molecular weights (MWs) and crystallinity to modify four different bitumens. In all cases, at a minimum amount of 3 %wt. of PPA added polymer segregation showed a remarkable reduction.  
However, it should be noted that this improvement could be dependent on many factors, like polymer type and the quantity of PPA, as shown recently by [PERSON] and [PERSON] [189].
# Low Profile Monopole Meander Line Antenna for WLAN Applications
## 4 Parametric Study
### Lower Meander Line Length  
Figure 6 illustrates the effect of a lower meander line length whereas the frequency resonance has been slightly shifted. It is demonstrated unequivocally that when the meander line is at its optimal length, L1 olv = 15.95 mm, the reflection coefficient is \(-22.16\) dB at the 2.43 GHz centre frequency. While significantly reducing the meander line's length to L1 dlv = 7.98 mm, this results in a reflection coefficient of \(-23.44\) dB at the 2.45 GHz centre frequency. When the ML length is maximised to L1 ilv = 20 mm at 2.41 GHz, the reflection coefficient decreases to \(-19.85\) dB. Finally, without the ML, the reflection coefficient is \(-32.37\) dB at 2.44 GHz centre frequency with L1 no line = 0 mm.  
As shown in the Figure below, the change in the lower ML length has no effect on the bandwidth but has an overall impact on the frequency range. The geometry of the lower ML length has been shown in Figure as well.
# A Method for Evaluating the Spatial Layout of Fire Stations in Chemical Industrial Parks
## 2 Methods
### Rationality Analysis  
The coverage rate and overlap-coverage rate of fire stations are used to evaluate the rationality of their layout. According to the survey results on the current layout of urban fire stations nationwide in China, this article determines the standard for the coverage rate, denoted as \(\alpha_{s}\), to be 80%. Specifically, if the PTBRs are covered by fire stations at a rate of 80% within the chemical industrial park, it is considered sufficient to meet the coverage needs. Otherwise, it is considered insufficient to meet the coverage needs. A higher coverage rate value indicates a more efficient fire rescue operation and a better fire safety performance. For the fire station overlap-coverage rate, a value of \(\gamma_{s}=30\%\) is adopted. If the overlap-coverage rate is less than 30%, it meets the requirements. A smaller overlap-coverage rate value indicates a more efficient economic layout of the fire stations. The criteria for these two parameters may vary in regions with different levels of local economic development. Regions with a more advanced economy may require a 100% coverage rate in order to achieve a higher level of fire rescue capability. However, they may be more lenient in accepting a high percentage of overlap-coverage rate.
## 2 Engineering Background  
The 91-105 working face of the No. 3 coal seam is currently being mined in Wangzhuang Coal Mine, operated by the Lu'an Group. The coal seam has an average thickness of 6.15 m, a burial depth ranging from 442 to 535 m, and a recoverable strike length of 2496 m. The layout of the working face is shown in Figure 1. The maximum gas pressure reaches 0.48 MPa, with an original gas content of 7-9 m\({}^{3}\)/t, and the absolute gas emission rate of the working face is 31.49 m\({}^{3}\)/min, classifying it as a high-gas coal mine. Based on on-site borehole coring, borehole imaging, and rock mechanics testing [34; 35], the distribution and mechanical properties of the coal and rock strata in the roof of the 91-105 working face have been determined, as summarized in Table 1.  
In order to improve resource recovery, the 91-105 working face utilizes coal pillar-free mining technology with gob-side entry retaining and employs a \"pier column + flexible formwork\" filling structure to support the tail roadway, which serves as the return airway. This configuration forms a Y-type ventilation system consisting of two intake roadways and one return airway. In this structure, the pier column unit consists of steel pipes with a diameter of 1.2 m, filled with C40 concrete. The flexible formwork unit is constructed using a frame composed of through-bolts and steel ladder beams, wrapped externally with mesh fabric and internally filled with C40 concrete. The concrete-filled steel piers are spaced at 2.4 m intervals and connected at the top using flexible formwork concrete. The layout of the \"pier column + flexible formwork\" structure is illustrated in Figure 1, and the corresponding structural parameters are provided in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Rock Stratum Name** & **Thickness (m)** & **Tension Strength (MPa)** & **Compressive Strength (MPa)** & **Elastic Modulus (GPa)** & **Poisson Ratio** \\ \hline
1 & Mudstone 1 & 4.62 & 2.3 & 24.6 & 4.2 & 0.28 \\
2 & Kern stone 1 & 13.06 & 7.9 & 51.4 & 4.8 & 0.23 \\
3 & Limestone & 3.47 & 5.0 & 47.3 & 8.1 & 0.28 \\
4 & Mudstone 2 & 6.50 & 1.2 & 28.1 & 4.1 & 0.24 \\
5 & Kern stone 2 & 10.05 & 2.9 & 42.3 & 4.3 & 0.32 \\
6 & Sandy mudstone & 3.67 & 3.3 & 37.5 & 4.0 & 0.22 \\
7 & Mudstone 3 & 5.10 & 2.1 & 30.8 & 5.4 & 0.26 \\
8 & Kern stone 3 & 6.90 & 3.7 & 50.3 & 4.8 & 0.23 \\
9 & Mudstone 4 & 4.00 & 2.6 & 29.5 & 3.9 & 0.32 \\
10 & Coal 3 & 6.15 & 1.6 & 17.0 & 2.0 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock mechanics parameters.  
Figure 1: Layout of the 91–105 working face with gob-side entry retaining with “pier column + flexible formwork”.
# Statistical Analysis and Modeling for Optical Networks
###### Abstract  
Optical networks serve as the backbone of modern communication, requiring statistical analysis and modeling to optimize performance, reliability, and scalability. This review paper explores statistical methodologies for analyzing network characteristics, dimensioning, parameter estimation, and cost prediction of optical networks, and provides a generalized framework based on the idea of convex areas, and link length and shortest path length distributions. Accurate dimensioning and cost estimation are crucial for optical network planning, especially during early-stage design, network upgrades, and optimization. However, detailed information is often unavailable or too complex to compute. Basic parameters like coverage area and node count, along with statistical insights such as distribution patterns and moments, aid in determining the appropriate modulation schemes, compensation techniques, repeater placement, and in estimating the fiber length. Statistical models also help predict link lengths and shortest path lengths, ensuring efficiency in design. Probability distributions, stochastic processes, and machine learning improve network optimization and fault prediction. Metrics like bit error rate, quality of service, and spectral efficiency can be statistically assessed to enhance data transmission. This paper provides a review on statistical analysis and modeling of optical networks, which supports intelligent optical network management, dimensioning of optical networks, performance prediction, and estimation of important optical network parameters with partial information.  
optical networks; statistical analysis; machine learning; network optimization; QoS; predictive modeling; shortest path lengths 2025
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 4 Data Collection
### Defect Simulation  
In case of alterations in the production process, the system needs to be quickly retrainable to minimize production downtime. In order to make this possible, production-like conditions have been set for training. This means, that the neural network is given only few data samples. Most neural networks are trained on a balanced dataset to avoid bias in the trained system. However, this requires massive amounts of defective rolling elements. Since the production of rolling elements is already a highly optimized process, defective parts are a rarity. To avoid the need for real defective parts, we propose the use of simulated defects. These defects are digitally added to the scans of defect-free rolling elements. Furthermore, limiting the training dataset to be sourced from defect-free parts also greatly reduces the cost of manual data labeling. Most traditional segmentation approaches require manually created labels for their training. Our approach, however, only requires ensuring that the data samples include no defects. Scans of defective parts captured during the retraining's data collection process need to be taken out of the training set. These can still be used for evaluation purposes.  
Defects on surface scans are simulated by masking the windows with ellipses. These are applied to the image at a randomized position, orientation and with a randomized brightness. Low amplitude noise is then added to texture the ellipse. The ellipses have a minimum size that escapes the tolerance limit set by quality control. Figure 3 showcases a few examples of such defect simulations.  
Simultaneously, a segmentation mask is created for every sample. The mask features the same ellipse with white color on a black background. These segmentation masks are used as targets during the training of the neural network. Additionally, the original defect-free windows are also added to the training set. The segmentation masks corresponding to these windows are all black. For every defect-free window in the original dataset, one simulated defect with exactly one ellipse is created. This doubles the size of the training dataset to 8.000 samples.
## 3 Evaluation
### Elemental Image Compared to Viewpoint Image
However, the evaluation of disparity using an edge-preserving approach is conducted between individual EIs. This metric is capable of detecting both the grid of EIs and the edges of the features within them. By utilising individual EIs, more reliable results can be obtained. The edge alignment bar graph in Figure 18(top) illustrates the MAE values for 12 distinct raw real Holoscopic images, which range between approximately 0.352 and 0.781. The changes seen can be attributed to disparities in the scene, texture, colour, and complexity throughout the images. The results generally show lower values (better) in comparison to the edge-alignment metric results derived using VPI disparity where the range of values for different images is approximately 0.498 to 0.797. Overall, EIs demonstrate better results in comparison to VPIs, with an average MAE of approximately 0.523, whereas VPIs have an average MAE of approximately 0.681, which can be viewed in the averaged bar \"All\". Figure 17: This is a close-up view of a raw Holoscopic image, along with the disparity maps derived from EIs and VPIs. The disparity map created from EIs has greater clarity compared to the one derived from VPIs. Figure 18: The bar graphs display the edge-alignment matrices (**top**) and consistency check matrices (**bottom**) calculated from 12 raw Holoscopic images captured by the Brunel Holoscopic camera. The averaged result is labelled as “All”. EIs generally outperform VPIs, as seen by their lower average MAE and consistency check metric. The bar graph depicted in Figure 18(bottom) illustrates the range of values for the consistency check metric derived from the disparity of EIs and VPIs. The values vary between approximately 0.334 and 0.756. The results exhibit lower values when compared to the consistency check metric results generated using VPI disparity, where the values range between approximately 0.548 and 0.881 for different images. Overall, EIs yield better results compared to VPIs, exhibiting average values of roughly 0.520, while VPIs demonstrate an average value of around 0.731. A selection of four raw Holoscopic images is shown in Figure 19. Simple scenes were captured to compare the disparity of EIs vs. VPIs directly, rather than assessing the algorithm in a complicated scene configuration.
## 3 Drilling
### Azimuthal Hold Technology  
In the Vaca Muerta formation, directional control during drilling was compromised due to limitations in the existing azimuthal hold technology. To address this, a firmware upgrade was introduced to refine the estimation of the near-bit rotational azimuth, thereby enhancing the precision of the steering head.  
Field tests across the various stratigraphic layers of Vaca Muerta were executed in order to assess the impact of the updated azimuthal hold technology on the drilling performance. The firmware advancements, coupled with modifications in the drilling system architecture, led to a marked decrease in wellbore tortuosity and a surge in the rate of penetration (ROP) by over 32% for the lateral sections in the Loma Campana field.  
These improvements translated into significant operational efficiencies, including a reduction of more than 40 h in the drilling cycle time for the production zones. The diminished tortuosity also expedited the tripping and casing installation processes by 6-10 h. Enhanced trajectory control not only minimized wellbore tortuosity, but also reduced uncertainties in wellbore placement, thereby optimizing reservoir navigation. This had the added advantage of facilitating subsequent completion operations by lowering wellbore friction, which impacts wireline and coiled tubing activities.  
Situated in the southeastern quadrant of Argentina's Neuquen Basin, the Loma Campana field has been the site of over 150 horizontal wells since 2015. The implementation of the new azimuthal hold firmware, along with the alterations to the bit's cutting structure, resulted in a 32% reduction in absolute tortuosity when benchmarked against wells that were drilled in the same stratigraphic layer (as indicated in Figure 3). This enhanced trajectory control also led to a friction factor reduction of up to 45%, significantly boost ing the operational performance and reducing the time required for tripping and casing installations [49].
## 2 Materials and Methods
### Risk Stratification  
Risk stratification was quantified using the following BIRADS scores for each nodule: the BIRADS US, defined according to ACR guidelines; BIRADS EFSUMB, according to EFSUMB guidelines; and the two new proposed risk-stratification reporting systems BIRADS TM and BIRADS worst case.  
The BIRADS US score, based solely on ultrasound evaluation results, was calculated separately per nodule on a five-point grading scale, increasing with the likelihood of malignancy. Furthermore, a BIRADS score of 4 can be divided into 4A, 4B or 4C if 1, 2 or more than 3 malignancy US markers were observed. For each nodule, we assigned the appropriate BIRADS score as follows: 1 indicates a normal result; 2, a benign lesion; 3, a solid hypoechoic lesion without any US malignancy marker; 4, a lesion with one or more US malignancy markers; 5, a malignant lesion; 0, an indeterminate result.  
For qualitative elastography, we defined the following risk categories--low risk: Ueno 1 or 2; intermediate risk: Ueno 3; and high risk: Ueno 4 and 5--and for semiquantitative elastography, low risk: FLR <2.8; intermediate risk: FLR between 2.8 and 4.5; and high risk: FLR >=4.5.  
We defined and integrated the BIRADS EFSUMB score (US-ELASTO BIRADS) [19], where stiffness was considered an extra risk factor according to the current recommendations of the EFSUMB guidelines [36]. The conventional downgrade of low stiffness was performed for BIRADS 4A and below, with cases 4B, 4C and 5 not downgraded.  
Our proposed algorithm \"BIRADS TM\" reassessed classical ultrasound BIRADS scores, both upgrading and downgrading based on elastographic results. The scores were upgraded irrespective of the initial result if high stiffness was found, but only scores equal to or lower than 4B were downgraded in the case of low stiffness.  
Finally, a \"worst-case scenario\" (BIRADS worst case) was also analyzed, considering the highest risk category defined by any of the previous evaluations, allowing only a risk upgrade strategy and no downgrade regardless of any additional information.
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Infrastructure and Operation
In [48], a study compares the operational reliability, operating costs, maintenance costs, environmental impact, ease of handling, and system limitations of vibration-based automatic mooring systems, magnetic mooring systems, and conventional mooring systems using ropes and windlasses. As these studies show, many factors are being considered for tugboats, including scheduling, safety, and eco-friendliness. If autonomous tugboats take these factors into account, they will be economical, efficient, and reliable and play an important role in logistics and international trade. They will also help the shipping industry to be sustainable and develop in the future. Figure 23: Tugging processes with time uncertainty [45].