esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 4 Model Validation Results and Analysis
### Analysis of Results  
Refining the Knothe-n model has yielded notable advancements in prediction accuracy, reaffirming the efficacy of the enhanced time function in capturing the evolving relationship between surface subsidence and time. The refined Knothe-n model demonstrates superior predictive capabilities, particularly when factoring in the hysteresis effect. By incorporating additional parameters and implementing segmentation techniques on top of the traditional Knothe time function, we have achieved more precise predictions.  
Compared to the original Knothe model [12], our segmented Knothe-n model offers enhanced granularity by delineating the subsidence process into three distinct stages. This segmentation allows for tailored expressions for each stage, enabling better alignment with the nuanced patterns observed in subsidence data and thereby improving predictive accuracy. Furthermore, the introduction of additional parameters enables comparability with the segmentation function refined by [PERSON] et al. [14]. This multi-parameter approach facilitates parameter adjustments to accommodate varying geological conditions, enhancing the model's versatility and applicability across different scenarios.  
Despite these advancements, it is crucial to acknowledge the limitations of the segmented Knothe-n model. Effective determination of added parameters may necessitate extensive field data, the quality of which directly impacts the accuracy of predictions. Additionally, in cases where the mechanism of ground subsidence is exceptionally complex, a single model may struggle to fully encapsulate all pertinent physical processes.  
In conclusion, the refined segmented Knothe-n model represents a significant step forward in subsidence prediction, offering improved accuracy while maintaining practical simplicity. However, ongoing refinement and validation efforts will be necessary to address the inherent complexities and limitations inherent in modeling ground subsidence phenomena.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 4 Spatiotemporal Traffic Prediction Models  
Spatiotemporal traffic prediction techniques are statistical methods that model given traffic data to study the patterns of traffic flow and construct knowledge to predict the future traffic status [5]. Figure 2 shows the schematic diagram of the most common spatiotemporal models that are used in predicting different real-world applications, such as environment and healthcare. Among these spatiotemporal models are Bayesian inference, ST-Kriging, and artificial neural networks (ANNs). ANNs are considered one of the most widely used models in the traffic domain. The following section provides a comprehensive review of three state-of-the-art spatiotemporal methods and investigates the research gap in these existing models.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 3 Machine Learning for Time Series Prediction
### K-Step-Ahead Prediction
A standard method to solve these problems is to build a specific model from the past values of the time sequence and then apply it gradually to predict their following values. This method is called multi-step prediction. Since it uses the expected information from the previous one, it can be experimentally demonstrated that multi-step prediction is prone to rounding errors, i.e., errors made in the previous one are carried over to the following prediction [8; 9; 10; 21; 31].
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 4 Experimental Results
### Parameters Affecting Liquefaction Behavior of Partially Saturated Sands
#### 4.3.2 Degree of Saturation  
Figure 9 depicts the variation in the \(CSR\) with \(N_{liq}\) at different degrees of saturation in the range of 79-100% under a constant relative density, effective confining stress, and back pressure. The cyclic-stress ratios at the same number of cycles were found to increase as the nearly/fully saturated sand specimens were desaturated using the IPS method. Irrespective of the level of saturation, the liquefaction resistance of the partially saturated specimens was larger than that of the nearly/fully saturated specimens. Further improvement in the liquefaction resistance was obtained via a further reduction in the degree of saturation, which was in agreement with previous experimental observations [41; 61; 62; 63].  
Figure 7: Effect of confining stress on liquefaction resistance of nearly/fully saturated sand specimens.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 3 Materials and Methods
### Definitions
A Mamdani fuzzy logic controller (FLC) is designed to dynamically optimize system performance by adjusting service parameters based on real-time conditions. The FLC takes three input variables: queue length, arrival rate, and server utilization, which are fuzzified into linguistic terms. Using a Mamdani inference system, a set of fuzzy rules is applied to determine the appropriate service batch size and vacation duration based on real-time queueing conditions. The fuzzy rule base is constructed using If-Then logic, where MIN-MAX operations evaluate rule strength, and the Centroid Method is used for defuzzification. This inference mechanism ensures adaptive decision-making, optimizing service efficiency while balancing system load and minimizing idle time.
## 5 Problems and Development Trends  
### Problems and Challenges  
#### 5.1.1 High Technical Costs  
Currently, the introduction of advanced technologies such as genomics, marker-assisted selection (MAS), and intelligent monitoring in the selection of large yellow croaker fry has significantly improved breeding efficiency and accuracy. However, these technologies also come with high technical thresholds and financial costs [47,130]. These technologies depend on high-precision instruments and professional talent support, requiring significant initial investment and high maintenance costs, which particularly pose a considerable economic burden for small and medium-sized farms. Moreover, most farming units are still in the early stages of mastering and applying these technologies, and the adoption rate remains low. Therefore, a key challenge in promoting industrial applications is how to reduce technical costs while ensuring the effectiveness of breeding, and to improve the accessibility and practicality of these technologies.
## 3 Results
### Position Accuracy in X-Direction  
The judgments of position accuracy in X-direction (horizontal position) were assessed against the anticipated (reference) positions. The ANOVA result revealed that the difference the environment caused to the position estimation was significant (F [1, 15] = 19.27, \(p<0.01\)), and the accuracy in the SWDs was 0.916 (SD = 0.07), which is greater than that of the HMDs with the corresponding accuracy of 0.863 (SD = 0.06). Moreover, the main effect of both exocentric distance (F [1, 15] = 5.15, \(p\) = 0.04) and size (F [1, 15] = 4.66, \(p\) = 0.04) were significant, but not the effect of target distances (F [2, 30] = 2.58, \(p\) = 0.09). Thus, it can be observed that the horizontal position accuracy decreased as exocentric distance increased (Figure 5d) and it has improved as size decreased (Figure 5e).  
The results also revealed that the interaction between environment and exocentric distance (F [1, 15] = 6.11, \(p<0.03\)) was significant; while the interaction results between environment and target distance (F [1, 15] = 1.99, \(p\) = 0.17) and between environment and size (F [1, 15] = 0.04, \(p<0.85\)) were not significant. A significant effect of exocentric distance was observed in the SWDs (Figure 5a-c), where the horizontal position accuracy was worsened as exocentric distance increase; whereas no significant effect of exocentric distance was observed in the case of the HMDs (Figure 5a-c). All the three-way interactions and the four-way interaction did not show significant difference on the exocentric distance judgment accuracy.  
Figure 4: (**a**) Exocentric distance estimation accuracy with respect to target distance, where the significant effect of target distance observed in the SWDs. This phenomenon could be explained by accommodation–convergence conflict at the nearest target distance [11,32]; (**b**) the main effect of exocentric distance on distance judgment accuracy. It shows that the overall distance judgment accuracy increases as exocentric distance increases.  
Figure 5: \(Cont.\)
# Multifunctional Hyperelastic Structured Surface for Tunable and Switchable Transparency
## 2 Materials and Methods
### Experimental Section
#### 2.2.3 Measurements and Characterization  
In order to experimentally study the electromagnetic behavior of this HSS, we used a custom-made setup shown in Figure 6. To apply the deformation, we designed a stretching apparatus. We attached Polymethylmeth-acrylate (PMMA) plates, which have good affinity with PDMS films, to the motion controller (Thorlabs XR25C/M) and the bracket (Thorlabs VB01B/M) to make movable and fixed grippers, respectively. We fixed 40 mm of the HSS on the PMMA plate using cloth tapes (tesa 4651) to assure a complete fixation and application of a uniform pressure to the boundaries of the HSS. Using the installed micrometer on the motion controller, the distance change between the grippers could be precisely controlled with an accuracy of 0.01 mm. Moreover, we numerically checked the generated reaction forces on the grippers to be less than the maximum limit supported by the motion controller. We applied grease, a semisolid lubricant, underneath the moving gripper to create a more uniform motion. To easily transfer the HSS to the setup without applying an excess deformation during the mounting process, we did not remove the supporting plastic sheet underneath the PDMS film until the very end of the mounting process.  
Figure 6: Measurement set-up: (**a**) the whole set-up, (**b**) zoomed view of the mechanical set up, (**c**) standard gain horn antenna, (**d**) monopole antenna.  
To send and receive the electromagnetic waves, we used a standard gain horn antenna (Narda corporation, model 640, 8.2 GHz-12.4 GHz) and a monopole antenna (19 mm wire fixed on a probe), respectively. The horn antenna was placed in front of the surface in a distance of 650 mm from the front side of the HSS. The monopole antenna was placed on the other side of the HSS in line with the horn antenna with a distance of 60 mm from the backside of the HSS. The whole mechanical setup, as well as the monopole antenna was placed in a compact anechoic enclosure with pyramidal absorbers to minimize the reflections from surroundings and to avoid interference from the environment. Taking advantage of the embedded micrometer on the motion controller, the displacement was precisely applied to the movable gripper and the scattering parameter S\({}_{21}\) was measured at each step using a vector network analyzer (VNA Rohde & Schwarz ZNB 20. 100 kHz-20 GHz).
## 3 Classical Routing Protocols
### Leach
#### 3.1.1 Advantages of LEACH  
1. The concept of clustering in LEACH effectively increases the network lifetime;
2. Traffic reduction due to aggregation of data at the cluster head level;
3. One-hop data routing from node to CH reduces energy consumption;
4. Global knowledge of the network is not required as LEACH is a distributed protocol, and BS does not control nodes in cluster formation.
5. Intra-cluster collisions are avoided due to the allocation of TDMA slots for data transfer.
## 5 Communication Overhead Analysis
### Proposed TCA Method  
The message structure of the proposed authentication TCA method is shown in Figure 10a, which is analyzed as follows. In the TCA method for single-zone communication, the transmitted message structure is represented by Equation (21).  
\[m=\{m_{i},L_{ZID},pid_{i},T_{s},pk_{i},\sigma_{i}\} \tag{21}\]
## 2 Materials and Methods
### Meteorological data  
To investigate the feasibility of sensor web enablement (SWE) for operational management of meteorological sensors within the Royal Dutch Meteorological Institute (KNMI), a KNMI SWE server was established. Through this SWE server data for nine meteorological parameters of 16 stations in the Netherlands was made available starting from August 2007 onwards. Real time data from the KNMI Meteorological Data distribution System (KMDS) are exported through a ftp server and a feeder to an independent PostGIS database. Because the KMDS database is used in an operational environment all SWE related operations were carried on the PostGIS database which is hosted at the KNMI SWE server. This database stores both the observation values and corresponding meta data. Using a Sensor Observation Service (SOS) [28], available data and metadata can be accessed, queried and downloaded by using the operations DescribeSensor, GetCapabilities and GetObservation, respectively. The SOS is one of a family of standards and specifications that make up the OGC Sensor Web Enablement activity. Queries to and replies from the SOS are XML based and require a XML viewer for further processing. The implementation of the KNMI SWE server was based on the open source software provided by the 52 degNorth Initiative ([http://52 north.org](http://52 north.org)).  
The required meteorological parameters for the vegetation productivity model (equation 3) were acquired from the KNMI SWE server using the SOS GetObservation request. No direct measurements for PAR which designates the spectral range of solar light between 400 to 700 nm were available from the SWE server. Instead PAR was derived from the measured parameter global solar radiation which is the total incoming radiation between 300 and 3,000 nm. In the literature [29] several constants dependent on latitude and cloud conditions are reported to transform global solar radiation to PAR. The reported values vary around 0.45 which was used for the transformation of global radiation to PAR in this study. The 10 minute instantaneous measurements over the day were transformed to an average daily PAR value and used as input for the vegetation productivity model.  
For the calculation of the two scalar values of the vegetation productivity model, S\({}_{\rm{Tmin}}\) and S\({}_{\rm{VPD}}\), additional time-series for air temperature (at 1.5 m in \({}^{\circ}\)C) and relative humidity (at 1.5 m in %) were requested from the KNMI SWE server. The minimum temperature over a day was derived from the 10 minute air temperature time-series and used as input for calculation of the S\({}_{\rm{Tmin}}\) scalar. Both the air temperature and relative humidity were adopted to calculate the S\({}_{\rm{VPD}}\) according to method described by [PERSON] [30]. Continuous 250 m ASCII rasters for the three meteorological parameters PAR, S\({}_{\rm{Tmin}}\) and S\({}_{\rm{VPD}}\) were prepared by using Thiessen polygons as spatial interpolation method.
# Random Forest Prediction of IPO Underpricing
## 2 Materials and Methods  
Random forests [10] are ensemble learning methods that combine trees of predictors. This approach, which is usually treated as a supervised method, creates sets of decision trees that vote for the most popular class for vectors according to a number of features.  
The trees in a forest are created according to a procedure that is similar to classification and regression trees. The previous element is combined with randomized node optimization and bagging. According to [PERSON], the author who introduced the technique, the process follows three key ideas:  
* Each tree is grown based on a unique training set that is generated by a random sampling with replacement from the original dataset. The size of this new sample mirrors the size of the initial one.
* For every node, a subset of the original input variables is selected at random, and the best-performing split on these is used to split the node. The size of the mentioned subset is kept constant during the forest growing.
* Trees are grown to the largest extent possible while complying with a minimum number of training patterns at a terminal node constraint, leaf size, and there is no pruning.  
The resulting set can then be used to make predictions using a voting mechanism. The process simply entails obtaining individual class predictions for unseen samples from all the components of the forest, and assigning the data pattern to the majority vote.  
This technique is applicable to both classification and regression tasks. IPO underpricing prediction falls in the latter category. In this case, tree predictors take on numerical values instead of class labels, and the predictor is formed by taking the average prediction from the tree set.  
Random forests show a major strength vs. other tree-based techniques, such as bagging, that is likely to be useful in the IPO domain. Specifically, the ability to combine strong and weak variables to generate models where the former do not dominate tree generation is a key property. The reason is that it affects the correlation among tree output and, therefore, the generalization error of the model. [PERSON] et al. [11] show that random forests are the dominant single method for deriving trading decisions on the Standard and Poor's (S&P) 500 stock universe vis-a-vis deep neural networks and gradient-boosted trees. Other general properties that are relevant in the IPO underpricing prediction domain are its relative robustness with regard to outliers in training data and the fact that it does not overfit.  
The use of random forests to tackle financial problems is not new. Recent papers illustrate potential application areas, such as trading [12], bankruptcy prediction [13; 14], or credit rating prediction [15], but, as we mentioned in the introduction, despite the desirable traits, its suitability for IPO underpricing prediction is still to be studied.
## 3 Methods for the Characterisation and Evaluation of the Skid Resistance of Asphalt Pavements
### Effect of Macrotexture on Skid Resistance of Asphalt Pavements
#### 4.1.2 Wet Pavement  
In 2013, [PERSON] et al. proposed the concept of three skid-resistant regions for a pavement. This concept explains the interaction between the macroscopic texture of the pavement and the tyre-contact region on a wet pavement [106]. In Zone 1, the water flows through the macroscopic texture of the road surface, increasing the contact area between the tyre and the road surface and reducing the effect of the water's lubricating action on the contact position. In Zone 2, the pavement microtexture plays a crucial role in reducing the thickness of the surface water film. Based on [PERSON]'s energy principle, the dynamic water pressure generated by a tyre on a wet pavement surface increases quadratically with vehicle speed [107]. As vehicle speed increases, the corresponding rise in dynamic pressure promotes a progressive thinning of the water film layer [108]. In Zone 3, direct contact between the tyre and the pavement surface is established. The main mechanisms contributing to frictional interaction are adhesion and hysteresis. During this phase, pavement skid resistance is significantly affected by environmental factors, the viscoelastic properties of the tyre rubber, and the macrotexture of the pavement surface. Under rainfall conditions, a lubrication effect develops, which results in a substantial decrease in adhesive forces and a corresponding modification of the hysteresis component [109]. The hysteresis force depends mainly on the structural characteristics of the pavement, but for the same maximum nominal aggregate size, an appropriate increase in the macroscopic texture depth of the pavement will improve the wet pavement skid resistance.
## 1 Introduction  
In the past decades, unconventional resources, especially shale oil and gas, have played an increasing role in the world's fossil fuel demand market. In China, annual shale gas production exceeded 200 \(\times\) 10\({}^{8}\) m\({}^{3}\) in 2020, and most of the shale gas was produced from the Silurian-Ordovician Wufeng-Longmaxi (WF-LMX) formations in the Sichuan Basin and adjacent areas, southwest China [1]. The WF-LMX formations are a set of high-quality shale gas-producing layers with huge hydrocarbon generation and storage capacity [2, 3], and these successions were characterized by high TOC content, high thermal maturity, and varying mineral compositions [4, 5], leading to complicated diagentic pathways and pore structure systems.  
The shale reservoir pore systems have been fairly characterized by many studies [6, 7, 8], and four major types of pores were identified, including organic matter pores, interparticlepores, intraparticle pores and microfractures [8; 9]. In shale reservoirs, diagenesis including mechanical and chemical modifications after deposition greatly affects porosity development either by destroying primary porosity or generating secondary porosity [10; 11; 12; 13]. During the burial process, with the change in temperature, pressure, and other conditions, the reservoir was modified by compaction, cementation, types of clay mineral, clay mineral transformation, thermal maturation of organic matter, and dissolution, and the porosity of shale and coal beds could be affected [7; 14; 15; 16; 17; 18; 19]. Due to the different primary TOC and mineral composition, diagenesis has different effects on the pore structure, and it is necessary to build the relationship between shale lithofacies and diagenetic pathways that control pore development.  
The aims of this study are twofold. First, the pore structure and controlling factors were characterized by applying a suite of analytical techniques, including low-pressure gas adsorption and high-resolution scanning electron microscope (SEM). Second, with the application of SEM, energy dispersive spectroscopy (EDS), and cathodoluminescence (CL) technique, the major diagenetic reactions were identified, and the effect of diagenetic alterations on porosity development was discussed. Moreover, this study suggests the application of the combustion method to completely remove organic matter, and then calculate the pore volume (PV) and specific surface area (SSA) that the organic matter can contribute by comparing pore structure parameters between pre-combustion and after-combustion samples. This study is of great significance to understand porosity evolution mechanisms in different shale lithofacies.