esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
From the results, it can be found that the prediction results are not good in most cases. In fact, because deep learning modeling requires a large amount of data, in the case of insufficient data, the results of each trained model are different. In this study, modeling training was repeated 20 times for each case, and the changes in the accuracy of these models were observed. As can be seen, under the same data usage, not only are the results very different, but also the accuracy cannot be effectively improved. This is not to say that deep learning is useless, but it should be used in a more suitable situation. In this study, to collect and forecast data for a large number of parking lots simultaneously, the data collection frequency is one hour, and the rule-based modeling method is used for prediction and estimation to improve computing efficiency. In practice, we can also combine deep learning techniques with rule-based modeling methods and use this to achieve higher prediction accuracy. Since the optimized part is not the focus of this research, it will not be discussed in depth for now. Evaluation of various dirty data is another issue that must be addressed. For example, a certain field may have fixed opening hours, or many vehicles may leave at the same time before closing, and the number of parking spaces used may fluctuate significantly at those times, or there may be machine equipment maintenance, which may cause abnormal transmission of various data. Furthermore, even the connection of some fields is unstable, which will cause the connection to be interrupted every few hours and the data to be abnormal, or, due to some particular factors, the field will be closed continuously for several weeks. When these conditions occur, the data in this period will significantly impact the modeling. These scenarios will cause unpredictable non-linear changes in the available parking spaces in the parking lot and cause a substantial increase in the difficulty of estimation. Since the modeled data cannot be too messy, how to exclude the dirty data obtained from these conditions is another problem that can be investigated in-depth. Due to the complexity of this part of the work, it is not within the scope of this study. For the above-mentioned particular scenarios with poor fitting results, other rules can be given to improve them. The critical point is the use of feature rules. Since the discussion of this part is beyond the scope of the core discussion of this study, it will not be discussed in depth here. As enough data are often not accumulated in the early stage of field operations, the use of  
Overall, using the approach proposed in this study can achieve satisfactory prediction results using less computing power and without relying on big data, compared to solutions that rely solely on deep learning.
## 3 Results
### Temporal Variation of Vegetation Recovery in the Damage Area from 2009 to 2013
#### 3.4.2 The Transition Matrix for Vegetation Recovery Types  
From 2009 to 2013, the vegetation that had largely recovered indicated the highest rate of increase, at 13.73% per year. The vegetation not recovering indicated the highest rate of decrease at 14.02% per year. During the five-year period, the area of vegetation that had largely recovered reached 115,888 ha, of which 52,956 ha (45.69%) was transformed by vegetation that recovered slightly. The vegetation not recovering lost 76,394 ha during the five-year period, of which 28,806 ha (37.71%) became vegetation that had largely recovered, and 24,713 ha (32.35%) was transformed into vegetation that had recovered fully. The area of vegetation recovering slightly decreased by 81,856 ha, of which 52,956 ha (64.69%) became vegetation that had largely recovered. From 2009 to 2013, the most prominent transition of the vegetation in the damaged area was from vegetation not recovering and vegetation recovering slightly to vegetation that had largely recovered (Tables 7 and 8).  
However, 81,338 ha of vegetation is currently undergoing degradation, which accounts for 17.10% of the damaged area. These degradation areas are primarily distributed on ridges or along rivers (Figure 12). The primary vegetation types in these areas are coniferous forest (31.39%) and scrub (34.17%). Of the 81,338 ha, approximately half of the vegetation (34,125 ha or 41.95%) had been transformed from fully recovered vegetation to vegetation that had largely recovered.
### Rationale and Research Goals  
The literature review shows that the identification of DLD in multilingual children is challenging but also possible from a (cross-)linguistic perspective. This implies that not only are computerised language assessments available in various languages, but they are also language-specific, i.e., they take into consideration specific and disparate markers of DLD for each of the languages assessed. Furthermore, research has shown that computer-based methods are efficient in assessing the same, even remote-based, methods. No previous research studies known to the researchers have yet applied these resources to identify DLD among Spanish-speaking preschool children in Italy. The present screening tasks were developed to automatise the bilingual screening of preschool children from migrant backgrounds living in Italy.  
This research aimed to preliminarily validate the remote screening procedure and tests its applicability for the detection of DLD among bilingual preschool children from migrant backgrounds. As mentioned above, Spanish is among the most common languages of the population with a migration background in the Lombardy region. More specifically, the data obtained in the sample of Spanish-Italian speaking children will be analysed in terms of discriminant and convergent validity as well as diagnostic accuracy:  
1. Construct and convergent validity will be assessed through the correlations between standardised and screening tests and questionnaires assessing the same or similar abilities.
2. The diagnostic accuracy of the experimental battery will be exploratorily investigated through the analysis of the different receiver operating characteristic (ROC) curves. With ROC curves, we will distinguish the clinical condition vs. typical language acquisition, allowing for the definition of ideal cut-off points for the single tasks. To this end, the Youden index (\(J\)) will be considered. The parameters describing the ROC curves of the total screening battery as well as those of the single tasks will be taken as indexes of discriminant validity.
## 2 System Model
### System Setup  
With the purpose of studying the maximum practical transfer data rate of a system consisting of one base station (BS) and multiple nodes under the rules of a learning automaton-based reception algorithm, we devised a number of simulation scenarios. The simulator was developed on an agent-based paradigm, and for this purpose the ANYLOGIC commercial simulation platform [18] was utilized. Based on the principles of IEEE P1906.1/Draft 1.0 Recommended Practice for Nanoscale and Molecular Communication Framework [19], the appropriate simulation elements were considered (transmitter, receiver, message, message carrier and the medium). Moreover, the requirements for any molecular communication framework were also met, discerning five components: message  
Figure 1: Ligand–receptor connection model for biological nanonetworks.  
carrier, motion component, field component, perturbation and specificity (for a complete analysis, the reader is referred to [20]).  
The starting point of the channel modeling is [PERSON]'s laws. We focus on the point-to-point link between the BS and one node, Figure 2, even though all communication nodes are able to retransmit without any operational outage in a broadcast scheme. Information molecules propagate from the transmitter to the receiver according to the concentration gradient. The BS initiates the communication by releasing a pulse and the nodes respond back as soon as they detect the pulse.  
Figure 2 shows the simulation system used in this work. At the center of the simulation area resides the BS, and someone can discern its sensing radius, being the circle that envelops the red figure. Accordingly, all the other nodes have the same sensing radius, inside which the signaling molecules can be trapped from the receptors. Different colors stand as the different types of information molecules released from the communication parties. In a multinode environment, the BS is able to release different types of molecules for different receiver nodes. In this work, we consider a unicast type of communication in order to remove the possibility of saturation for the binding sites.  
Upon reception, BS sends the next message immediately in order to minimize delay and maximize transfer data rate, in a continuous process. Every receiver has its own type of information molecules in order to mitigate the inter-link interference (ILI), which emerges when the receiver receives other transmitters' molecules [21].
## 2 Signal Processing and Height Inversion
### Signal Processing
#### 2.1.3 Retracking and Incoherent Average  
The complex waveforms after coherent integration need to be incoherently averaged to degrade the influence of speckle noise. With the vertical drift of the aircraft during this period, the waveform series need to be compensated with respect to the first one (retracking) [25]. Figure 2A shows a power waveform obtained after incoherent averaging.
## 3 Methodology
### Rotational Speed Control  
While the weight is rotating, the force deviates more toward the weight as the rotational speed of the weight changes. Therefore, the rotation of the weight accelerates before reaching its maximum velocity in the target direction and then decelerating. The graph of rotational speed starting at the angle at which the acceleration starts (_Ps_) and ending at the angle at the end of the deceleration phase (_Pe_) can be plotted, as shown in Figure 2. However, in contrast to its behavior in computer simulations, the motor control behaves distinctly in physical world. The unfilled blue circles in Figure 3 (actual motor movement) shows a rotational speed curve that does not reach its target value due to the load (i.e., weight of the device and other assembly components) applied and the limitations of instantaneous acceleration. The rotational speed and acceleration/deceleration directions of the real motor must be identical with the programmed values to maximize the force concentration in the target direction. In the light of these considerations, we developed a control model that optimizes the speed changes of the motor.
## 3 Results and Discussion
### Future Research Directions  
To build on the findings of this study and further advance the understanding of CO\({}_{2}\) diffusion in brine systems, several important research directions are recommended. First, a deeper investigation into long-term CO\({}_{2}\)-brine-rock interactions is necessary. These include geochemical and mineralogical changes, wettability alterations, and pore-scale structural evolution, all of which directly influence multiphase flow dynamics and storage integrity. Future studies should also focus on improving diffusion coefficient measurement techniques under reservoir conditions, especially by incorporating density-driven convection effects and exploring the role of advanced materials such as green nanofluids on interfacial behavior. Machine learning models capable of predicting CO\({}_{2}\)-brine interfacial tension would significantly enhance the reliability of diffusion estimations in heterogeneous systems.  
Moreover, addressing subsurface heterogeneity and associated uncertainties remains a key priority. This can be achieved through stochastic modeling that incorporates spatial variability in porosity and permeability, as well as improved formation characterization  
Figure 12: Dependency plots show the impact of (**a**) pressure (MPa), (**b**) temperature (K), and (**c**) salinity (mol/L) on the diffusion coefficient, as predicted by the RF model.  
techniques that reduce prediction errors in CO2 injection scenarios. Operational variables such as CO2-brine co-injection strategies, salinity variations, and salt precipitation effects also require further study, particularly in how they influence injectivity and storage efficiency. In parallel, the development of advanced monitoring tools for early detection of CO2 leakage and assessment of its impact on groundwater quality is critical for long-term risk management.  
Finally, special attention should be paid to hydrate formation and stability in depleted gas reservoirs under CO2 injection, especially near the wellbore region where pressure and thermal gradients are prominent. Addressing these multifaceted challenges will not only close existing knowledge gaps but also enhance the accuracy, scalability, and field relevance of predictive models. These efforts will collectively support the safe and efficient deployment of carbon capture and storage technologies, contributing to global climate mitigation strategies.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 4 Simulation Results and Discussion
### Pair of Turbines with Two Interconnected Turbines
#### 4.3.1 Impact of Indirect Lightning  
The peak value of the lightning current is assumed to be 200 kA with a duration of 10/350 \(\upmu\)s, the worst situation that a pair of turbines can experience. This subsection analyzes its impact on one of the towers when the strike hits the soil near the wind turbine. The outcomes show that the capability of integrated PMGSs to reduce the rate of overvoltages, even during the occurrence of harsh strikes with a peak value of 200 kA, is greater than the capabilities of separate PMGSs.  
As Figure 16a-c shows, even though connecting PMGSs in a pair of turbines can reduce the peak overvoltages from 60 kV-50 kV at the control system, from 75 kV-60 kV on the HV side and from 20 kV-14.5 kV on the LV side of the first wind turbine, they still remain much higher than the upper limits the electrical devices can withstand. However, in contrast to the first turbine, the second tower does not suffer from hazardous overvoltages and can bear such transitions without disruption (see Figure 16d-f).  
Figure 16: _Cont._  
Figure 15: EMTP-RV circuit of the pair of turbines. (**a**) Interconnected PMGS; (**b**) separated PMGS.
## 2 Materials and Methods
### Plant Materials
It continued until the 78 th day, with measurements of taproot length (TRL, in cm) and lateral root length (LRL, in cm) taken every 5 days for each progeny. All 156 clonal lines demonstrated normal root development and data collection under control conditions, while under salt stress, 117 clonal lines managed to root successfully and yield root growth data. Leaf samples from the F1 generation were rapidly frozen in liquid nitrogen before being preserved at \(-80\)\({}^{\circ}\)C in an ultra-low temperature freezer for further examination. Ge-nomic DNA was extracted using a kit provided by Beijing Tiangen Biotechnology Co., Ltd., Beijing, China, and its purity and concentration were evaluated through agarose gel electrophoresis and quantified with a Nanodrop 2000 spectrophotometer from Thermo Fisher Scientific, Wilmington, DE, USA. Only DNA samples that met the required standards for quality and concentration were forwarded to Megio Biotechnology Co., Ltd., located in Shanghai, for high-throughput sequencing, which was performed on the Illumina HiSeq2500 platform utilizing the restriction enzyme cleavage site-associated DNA (RAD) sequencing approach. Sequencing data were compared with the reference genome [35] using Burrows-Wheeler Aligner (BWA) software v0.7.8. The alignment results were sorted using SAMtools v0.1.18, and of the results produced, 8305 SNPs passed the quality control for association mapping. These SNPs were used to construct a full-sibling population-based genetic linkage map for _Populus euphratica_, which included 19 linkage groups with a total length of 4574.89 cM and an average distance between markers of 0.55 cM. Based on the principles of Mendelian segregation, the markers were divided into two groups: testcross markers, comprising the types Im \(\times\) ll and nn \(\times\) np, and intercross hybrid markers, denoted by hk \(\times\) hk, with 6886 for the former and 1419 for the latter. We performed functional annotation and prediction for all significant QTLs using the BLAST tool on the \"nucleotide collection (nr/nt)\" database at the National Center for Bio-technology Information (NCBI) website ([[http://blast.ncbi.nlm.nih.gov/](http://blast.ncbi.nlm.nih.gov/)]([http://blast.ncbi.nlm.nih.gov/](http://blast.ncbi.nlm.nih.gov/))) (accessed on 5 January 2024). The comparisons were conducted under the default settings to balance efficiency and accuracy. To ensure the statistical significance of the alignment results, we only retained matches with an E-value less than 0.001.
# Modified Crack Detection of Sewer Conduit with Low-Resolution Images
## 1 Introduction  
The recent urban sinkholes in Korea are often caused by the loss of surrounding soil due to the aging of a sewer built intensively in the 1970s and 1980s. In the case of Korea, 980 local governments nationwide have conducted a detailed investigation of 12,000 km of the entire sewer, which is over 20 years old, to prevent sinkholes.  
Since the current structure maintenance technique evaluates the condition with the naked eye of the inspector based on manpower, a lot of time and effort is consumed, resulting in the poor objectivity and reliability of the survey. One of the ways to compensate for this problem is to use digital image processing technology.  
Recently, studies on the introduction of an image processing technique capable of analyzing the CCTV images of a sewer or the development of the devices are being conducted. [PERSON] et al. [1] measured the deformation of the sewer through image analysis using a probe equipped with an annular laser pointer. [PERSON] et al. [2] extracted information on cracks in a sewer using basic image processing techniques, and [PERSON] et al. [3] applied artificial neural network method complementarily. Later, [PERSON] and [PERSON] [4] studied an advanced image processing method of applying machine learning, and [PERSON] et al. [5] also proposed a method for evaluating the quality of images taken from the sewer. [PERSON] et al. [6] studied the automated detection of cracks in buried concrete pipe images, and [PERSON] et al. [7] tested the proposed algorithm on a set of CCTV videos obtained from the cities of Regina and Calgary in Canada. Recently, [PERSON] et al. [8] analyzed papers on CCTV sewer survey images. However, there is a limit to the application of the developed image processing method, because the quality of the image taken while navigating in such a dark environment with a lot of obstacles as the sewer is very low.  
As for the sewer exploration device, a high-resolution digital camera of more than 2 million pixels has been developed. However, devices of less than 300,000 pixels aremostly being used in Korea because of the small size of the Korean survey industry and the low awareness of the severity of the sewer problem in Korea.  
The sewer conduit images covered in this study are of 76,800 pixels (240 \(\times\) 320), and it is difficult to detect cracks at such low resolution. [PERSON] et al. [9, 10] studied the crack detection method of these low-resolution sewer images twice in 2017 [9] and 2019 [10]. Their 2019 [8] study showed relatively high accuracy research results, but it is a bit complicated to apply. An investigation improving crack detection to compensate for these problems was conducted in this study.
## 4 Results and Discussion
### Evaluation Metrics
#### 4.4.1 Accuracy  
Accuracy is the capability of a measure to match the original value of the quantity being estimated.
# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 1 Introduction
Presentation attacks involve the action of deceiving the biometric system with a spoof biometric characteristic or behavior. In general, presentation attacks are classified into two main categories: impersonation and obfuscation attacks. In impersonation attacks, the attacker attempts to deceive the system sensor to impersonate a legitimate user. While with obfuscation attack, the attacker attempts to fool the system sensor so the system does not recognize him/her. ISO/IEC 30107 terms, definitions, and standards [10] are used in this research to describe presentation attack and PAD systems, and to evaluate PAD system performance. ISO/IEC 30107 defines presentation attack detection as discovering presentation attacks using an automated system. The subject of a presentation attack can be either impostor or identity concealer. The impostor attempts to impersonate a legitimate user of the system through being matched to his/her biometric records, whereas the identity concealer attempts to avoid matching his/her capture to his biometric records. The biometric feature used in the presentation attack is called the presentation attack instrument (PAI) and the object used as the fake biometric feature is called artefact. PAD systems have gone through many stages of development since 2004. First PAD systems used basic methods that were based on extracting Liveness features to decide whether it is a bona-fide presentation or an attack presentation. Ultimately, PAD technology was applied with more effective methods with remote photoplethysmography (rPPG), domain adaption, and deep learning [4]. The contribution of this research includes the following:  
* We evaluate the fairness of current CNN-based PAD methods. * We study the effect of the size of the training and test data on bias in the performance of PAD systems. * We explore the capability of the novel and most recent debiasing variational autoencoder [9] to mitigate bias in PAD. The rest of the paper is organized as follows: Section 2: Related work, Section 3: Methodology, Section 4: Results, Section 5: Discussion and Section 6: Conclusions.