esa-sceva/satcom-chunk-collection huggingface dataset:
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 1 Introduction
Through our study, we are able to effectively calculate UTT and ATT for flights and establish a corresponding database, which provides guiding recommendations for taxi time statistics and on-time performance calculation methods in China. Furthermore, the prediction of additional taxi time allows for a more accurate estimation of aircraft pushback times based on CTOT, contributing to the reduction of surface congestion and the improvement of operational efficiency at airports. The main contributions of this paper can be summarized as follows:  
1. The arrival and departure flows of different ODPs are analyzed, and a multiple linear regression model of taxi-out time is constructed to calculate the UTT. 2. Three new features are proposed to improve the ATT feature set and dataset: corridor departure flow, corridor arrival flow, and the departure flow proportion of ODP. 3. An ATT prediction model based on NOA-XGBoost is proposed. 4. The effectiveness of the proposed features and prediction model is verified through comparative experiments and ablation experiments on the operation data of Shanghai Pudong International Airport. Figure 1: Composition of taxi-out time.
## 8 Discussion and Open Issues
### Open Issues in IoT for Smart Cities and Its Solutions in 6G  
The 6G system can be equipped with the necessary support to significantly improve the performance of smart cities [53]. It will provide the required infrastructure and data transmission rates to support massive deployment of smart sensors and fully functioning devices (FDs). The 6G also will enable real-time processing of data, as well as data analytics in IoT. The following are the potential open issues that will drive the attention of future research in smart cities and IoT with respect to 6G: 6G requires the support of a large number of IoT nodes and enough bandwidth to support these devices. The capacity of smart cities is directly associated with large-scale device and data-rate support in 6G.  
1. As IoT requires low-power usage-based networking, future 6G will need this support for enabling low-power wide-area networking (LPWAN).
2. Real-time IoT-based control and information exchange systems for critical operation in industries require low-latency-based communication. Thus, to enable low latency and improve reliability, 6G will need ultra-reliable low-latency communication (URLLC).
3. Multi-access edge computing (MEC) is the support required for enabling end nodes in remote IoTs to perform various computations, the results and status of which require multiple access support; 6G will need this MEC to achieve appreciating edge computing.
4. The 6G system requires modular connectivity and network slices to support exclusive IoT tunnels and dissociated processes.
5. IoT devices are exposed to intruders in a network because of its constrained messaging protocols; 6G will need to provide a mechanism to support the encapsulation of such barely protected data and deny unauthorized usage.
6. The 6G system will require an advanced support and management mechanism to efficiently operate IoT networks in smart environments.
7. Technologies such as BLE PANs, Zigbee, LoRaWAN, etc. require integration for the smooth operation of various transactions and network/process integrations; 6G must ensure this interoperability of the new advancements with such existing technologies.
8. Smart cities and environments require dynamic configuration for their ubiquitous operation. To enable such dynamic and retainment, 6G must possess the required tools to enable the automatic configuration of a network and its on-demand reorganization.
9. Generally, IoTs require various levels of QoS. To improve the operation of IoTs in smart environments, 6G needs the support of advanced QoS mechanisms.
## 2 Basic Notions and Preliminary Results
### Quantizer Description
#### 2.2.3 Uniform Quantizer  
The uniform quantizer was proposed in [38], which had relatively simple form and was described as  
\[q_{u}(u)=\left\lfloor\frac{u}{\varpi}+\frac{1}{2}\right\rfloor\varpi \tag{4}\]  
where \(\lfloor a\rfloor\) denotes the greatest integer, it is less than or equal to \(a\), \(\varpi\) is the parameter \((\varpi>0)\) of the uniform quantizer, which can be found in [38]. For this kind of quantizer, the following inequality is satisfied.  
\[|\Delta q_{u}(u)|\leq\frac{1}{2}\varpi. \tag{5}\]  
where the quantization error of \(q_{u}\) is \(\Delta q_{u}\), the logarithmic quantizer and the hysteresis quantizer satisfy property with \(0<\delta<1\) based on [39]. For the uniform quantizer, the sector bound property is also true with \(\delta=0\) and \(d=\frac{\varpi}{2}\).
## 2 Raw Materials and Testing Methods
### Testing Methods
#### 2.3.5 Random Forest Analysis Method  
The random forest model is an integrated model that generates multiple sets of decision trees jointly for prediction by using a subset of training samples [24]. Each decision tree is generated independently without any pruning, and each node is segmented using a user-defined number of features, chosen randomly. The random forest technique may also provide variable importance assessments to evaluate the intensity of each independent variable's influence (degree of relevance) on the anticipated dependent variable.  
Random forest analysis (RFA) grows and combines plentiful decision trees to construct a powerful \"forest\". The most intuitive benefit of RFA is its capability to correct for ordinary regression's habit of overfitting. When we input a new unlabeled datum, it will be evaluated against all the decision trees created in the set and each tree votes for a class member. The category with the most votes will be the final selected category, and the random forest concept diagram is shown in Figure 2.
# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 3 Materials and Methods
### Proposed Modified LSB Extracting Process for Medical Images  
The embedded information extraction process involves retrieving the hidden message from the stego object. To extract an embedded message from a stego image, we use a method known as extraction. This concept originates from cryptographic techniques, though there are certain differences when it comes to breaking the steganographic framework. In the context of creating and transmitting the stego object over an assumed ideal communication channel, we rely on a decoder circuit equipped with two inputs, a secret key for extraction and the cover image, to unveil the hidden message within the stego object. This decoder circuit outputs a single piece of data, the concealed text.  
This extraction process involves several steps performed in reverse order, starting with manipulating the LSB of the stego image, as shown in Figure 5. Following this, the encrypted text is retrieved in byte form, culminating with the final stage of AES128 decryption. The images that contain the embedded ciphertext are converted to a one-dimensional array of pixel values, from which bits are extracted according to a specifiedpattern determined by the embedding process. These bits are then assembled to form the ciphertext array. Once the complete ciphertext is extracted and converted from its bit representation, AES128 decryption is applied to decipher the original text, which contains sensitive information, such as patient prescriptions. This marks the completion of the extraction process, successfully retrieving the encrypted data without visibly altering the image, thereby maintaining confidentiality.  
Figure 5: Visual quality comparison for the proposed approach with different patient prescription text samples with the original MRI samples.
###### Abstract  
The uneven soil fertility made it difficult to implement the recommended nitrogen (N) management practices in the North China Plain (NCP). In order to clarify the effect of N managements in high-fertility soil with a perennial wheat yield of 10,500 kg ha\({}^{-1}\) on photosynthetic characteristics, grain yield, N agronomic efficiency (NAE), and water use efficiency (WUE), a trial was conducted from 2022 to 2024. Main plots were N rates of 0 (N1), 150 (N2), 210 (N3), and 270 (N4) kg N ha\({}^{-1}\); The sub-plots adopted fertigation (F) and traditional fertilization method (T). The results showed that, compared with T, F increased the intercept rate of photosynthetic effective radiation of canopy, net photosynthetic rate, stomatal conductance, and transpiration rate of flag leaves, as well as the activity of phosphate sucrose synthase and sucrose content. It enhanced dry matter transport and contribution to grain. Under N2, the time required to reach the maximum grain filling rate, duration of grain filling and active grain-filling period of F were improved. Grain yield of N2 was increased by 27.81% and 6.75% compared to N1 and N3, respectively. NAE was improved by 48.63% and 51.47%, and WUE was improved by 20.71% and 9.85%. Therefore, the best effect was achieved by using fertigation and the N rate of 210 kg ha\({}^{-1}\) in high-fertility soil.  
high soil fertility field; grain yield; photosynthetic characteristics; accumulation and immobilization of dry matter; grain filling traits; water and N use efficiency +
Footnote †: journal:  
0000-0001-9390-3333][PERSON]  
1 National Key Laboratory of Wheat Improvement, Agricultural College, Shandong Agricultural University, Tai'an 271018, China; [EMAIL_ADDRESS] (Z.M.); [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (Z.Y.)
# Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces
## 3 Results
### Effects of N Deposition on Soil Bacterial Richness and Diversity  
With the increase of N deposition in time and dosages, ecological parameters, such as richness and diversity of soil bacteria, decreased (Figures 1 and 2). At 6 months, soil bacterial richness and diversity were lower than those at 3 months, but the difference among the four dosages was not significant. After 12 months of N treatment, richness and diversity showed an ordered array N3 \(<\) N2 \(<\) N1 \(<\) N0, and there was a significant difference between N3 and N2 treatments (\(p<0.05\)), and a highly significant difference between N0 and N1 treatments (\(p
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 2 Existing Methods
### Scene-Based Method  
1. Filter-based methods  
Filter-based methods are widely used [17; 18; 19], due to their simplicity and ease of use. The filter-based methods require that the FPN has a certain periodicity in order to have a relatively good effect. In fact, this is difficult to satisfy, and most CIS FPNs are non-periodic. It is difficult to separate the FPN from the image if the filtering method is not used on the basis of periodicity.  
2. Statistical methods  
The main idea of statistical methods is to modify the distribution of FPN to a reference distribution. For this method to be effective, many statistical similarity assumptions [20; 21] must be satisfied. However, at most times, this assumption of similarity is difficult to satisfy, so it is generally difficult to achieve an ideal noise reduction effect.  
3. Optimization method  
Based on the optimization methods, it has been the most studied methods in recent years. These types of methods can achieve a good denoising effect [22; 23; 24; 25; 26; 27; 28; 29; 30]. Essentially, these methods are to solve an underdetermined equation. Most of these methods, which are based on the variational  
Figure 6: Schematic diagram of sectional correction method (x coordinate: incident light energy; y coordinate: gray value).  
method, add reasonable prior constraints to complete the transformation of undetermined equations to well-defined equations. The low-rank sparse variational method (LRSUTV), which we will propose next, is also a category of this type of method. At present, some scholars also use the low-rank sparse method to remove stripes. However, most of them remove noise from the image itself, but little attention is paid to the structure of the stripes themselves. A small number of scholars have considered the structural characteristics of the stripes [31; 32; 33; 34; 35], but they lack the consideration of the statistical characteristics of the stripes. The stripe removal method we use considers not only the continuity of the image itself, but also the structural and statistical characteristics of the stripes. At the same time, adaptive adjustment measurement of some parameters is added, which simplifies the adjustment process of parameters to a certain extent. Overall, our approach is a more comprehensive approach to noise reduction.
### Selection Method of Analyzed Articles Based on PRISMA Methodologies
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Al Algorithm**}} & \multicolumn{1}{c}{**Security**} & \multicolumn{1}{c}{**Predictive**} & \multicolumn{1}{c}{**Process Control**} & \multicolumn{1}{c}{**Characteristics**} \\ \cline{2-7}  & **Worker Security** & **Cybersecurity** & **Maintenance** & & & \\ \hline \multirow{3}{*}{Decision Trees} & Used for workers’ diagnosis; & & & Used to identify failure & & Used to classify or predict \\  & disease classification, and treatment decisions & & & conditions based on decision & outcomes: interpretable and & Easy to interpret and visualize; tend \\  & Used for workers’ disease & & & & easy to & to overfit and are unstable \\ \hline \multirow{3}{*}{Random Forests} & Used for workers’ disease & & & & \\  & classification and complex data & & & & \\  & non-linear databases & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{SVM} & Used for medical & & & & \\  & image classification and & & & & \\  & diagnosis & & & & \\  & & & & & \\ \hline \multirow{3}{*}{KNN} & Used for workers’ disease & & & & \\   classification and & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{CNN} & Used for analyzing medical & & & & Particularly effective for processing \\  & images, such as MRIs and & & & & & grid-structured feature they require \\  & X-rays & & & & & large datasets to be trained \\  & & & & & & effectively and are susceptible to overfitting \\ \hline \multirow{3}{*}{ANN} & Used for predicting clinical & & & Used to model non-linear & & & They are versatile and can model \\  & outcomes and diagnoses based & & relationships in data and are & & complex, nonlinear relationships; & they require significant data \\  & on complex data & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{Recurrent Neural Networks (RNN)} & Used for analyzing time & & & Used for time-series & & \\  & & analysis, can capture & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Recurrent Neural Networks (RNN) \\ and LSTM \\ \end{tabular} } & Used for analyzing time & & & Used for time-series & & Time-prediction algorithms, & & with long sequences and are more \\  & sequences, such as patient data & & long-term dependencies in & & predicting sequential data & & difficult to train than other \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: ML and DP algorithm used in smart manufacturing for security, predictive maintenance, and process control.
# Monitoring of Evolving Laser Induced Periodic Surface Structures
## 4 Results
### Role of Optical versus Geometric Properties  
So far, we used the optical properties of cold titanium surface for all simulations. However, the experimental results show that very similar fringe patterns are observed for both the titanium and Si\({}_{3}\)N\({}_{4}\) samples. This is particularly interesting since not only the structures in both cases are significantly different but also the optical properties, that is, one question that needs to be addressed is the role of the carrier density. It is indeed conceivable that the increase of carrier density during the interaction with the laser pulse is essential for the generation of the fringe pattern as this modifies the optical properties significantly. Si\({}_{3}\)N\({}_{4}\) has a wide bandgap (\(\sim\)4.5 eV), that is, at least the absorption of three photons is required to generate an electron hole pair. Nevertheless with the applied fluences and intensities a significant increase of carrier density during the laser solid interaction must be expected. We have compared the structure-induced changes for a titanium and a Si\({}_{3}\)N\({}_{4}\) sample with the same structural parameters (200 nm structure period, 50 nm structure height). Fringe periods are 1.56 um and 1.28 um for titanium and Si\({}_{3}\)N\({}_{4}\).  
The far-field strength is significantly higher in case of the metal surface. While the circular fringe pattern originating from diffraction from the circular structured shape is clearly visible in both cases, the vertical fringe pattern is only present in case of a metal, that is, for high carrier density. Thus, to become sensitive to LIPSS formation, a high carrier density is needed in the instant at which the backscattered signal is generated. that is, backscattered light from a cold insulator (low carrier density) will not carry sufficient information about the presence of the structure. Analyzing the backscattered light from the pulse generating the structure is a very simple approach to gain information about the structure presence.  
The results of these simulations are supported by experimental observations: We did not succeed to experimentally reproduce the fringe pattern in the backreflected light when illuminating LIPSS at much lower intensities.
## Theoretical Modeling
### Mixed Time-Marching Integration Algorithm  
The mixed time-marching algorithm is applied in the implicit predictor-corrector time integration scheme for the first time, in which the end-point algorithm is used in the predictor step for efficiency and mid-point algorithm with numerical damping in corrector step for energy conserving and computation accuracy and stability.
## 5 Results and Discussion
### Evaluation Metrics  
The primary purpose of phase I analysis is to accurately determine and remove OOC profiles to establish a stable IC state model. In related studies, type I and type II errors are usually employed to assess the performance of phase I analysis [33; 55]. In this study, we evaluate the proposed method using the standard evaluation technique. Some performance metrics commonly used in machine learning were also adopted. The anomaly-detection performance is usually measured in terms of precision, recall, and \(F_{1}\) score [21; 27; 28], each of which is defined as follows:  
\[precision=\frac{TP}{TP+FP} \tag{9}\]  
\[recall=\frac{TP}{TP+FN} \tag{10}\]  
\[F_{1}=\frac{2\times precision\times recall}{precision+recall} \tag{11}\]  
In the field of profile monitoring, outlying profiles are considered as positive class (belonging to the OOC process) and normal profiles (belonging to the IC process) as negative class. True positives (TP) refer to the number of outlier profiles that are successfully detected, false positives (FP) refer to the number of normal profiles that are incorrectly detected as outlying profiles, true negatives (TN) refer to the number of normal profiles that are correctly detected, and false negatives (FN) refer to the number of outlying profiles that are incorrectly detected as normal profiles. The \(F_{1}\) score is the harmonic mean of the precision and recall of a model. A high \(F_{1}\) score indicates that low FP and FN are achieved.  
Another metric of anomaly-detection performance is \(F_{\beta}\)[56; 57], which is defined as follows:  
\[F_{\beta}=\left(1+\beta^{2}\right)\times\frac{precision\times recall}{\left( \beta^{2}\times precision\right)+recall} \tag{12}\]  
The \(F_{1}\) score is a special case of the general function \(F_{\beta}\) when \(\beta~{}=1\). If \(\beta>1\), recall is considered more important than precision and the priority is to minimize false negatives. In this study, the most commonly used \(F_{2}~{}(\beta~{}=2)\) is adopted as the performance metric. The reason for selecting \(F_{2}\) is explained in the following text. It is commonly agreed [33; 55] that in phase I monitoring, the type II error (false negative) is more important than the type I error. Typically, the higher the type II error, the more outlying profiles are treated as  
Figure 6: Nonlinear profiles examined in this study. (**a**) Nonlinear profiles without noise; (**b**) Nonlinear profiles with noise added.  
the IC profiles. Consequently, this will create an inadequate decision function of phase II and deteriorate the performance.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 7 Conclusions  
In this research, the reasons for the recent increase in the number of cybersecurity incidents involving IoT devices and systems have been detailed. The weaknesses in their security measures have been discussed, highlighting the need to improve them, given the sensitivity of the information that they handle, added to the important role they have in certain contexts, such as critical environments. It is essential that cyber criminals should not find it easy to compromise them.  
Regarding IoT forensics, it has been explained why the conventional forensic approach cannot satisfy the requirements of the IoT environment, so there needs to be an improvement in the field in order to provide techniques to perform complete and efficient investigations. In addition, the characteristics of IoT devices have been described in order to comprehend what features make this context unique and how they affect the examinations. On reviewing the related work, it has been recognized that an effective method to address this issue is by analyzing IoT devices and systems in order to understand how they operate and what information can be retrieved from them.  
By following this approach, a forensic analysis of the non-volatile memory of the Windows 10 IoT Core operating system has been performed, offering guidelines on how to conduct it, detailing aspects such as the analysis, acquisition, and evaluation of the pieces of evidence detected. This provides investigators with a study that they can use as an aid when investigating the same operating system, especially when, at the time of this proposal, there are no specific IoT methodologies to follow.  
Furthermore, the sources of relevant information that can be retrieved from the storage have been listed, creating a useful handbook that describes what artifacts have been identified, their purpose and location. In addition, it has been seen that the desktop Windows 10 version and the IoT-based one share relevant characteristics and data, consequently meaning that studying other similar systems prior to performing an investigation can very beneficial in order to determine how to approach it, particularly when they are based on the same concept.  
In addition, it has been proven that the forensic examination of IoT systems ultimately leads to the development of specific tools that can facilitate the investigation process, making it more efficient than when only general ones are used. A module for KAPE, a forensic program, has been developed  
Figure 16: Execution of the target developed.  
to collect all the relevant sources of information stored in the non-volatile memory of Windows 10 IoT Core. This allows investigators to automatize the evidence retrieval task in future investigations in which this operating system is present. Furthermore, it also shows that the increase in functionality in general forensic programs is a useful approach to follow when developing IoT tools, instead of focusing on creating independent ones.