esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-to-Insulator Transition in Ultrathin Manganite Heterostructures
## 4 Mechanism of the MIT
### Overview of the Mechanism of MIT in LSMO  
Many models have been proposed to explain the origin of thickness-driven MIT in LSMO, Though the debates still remain, the proposed models and related experiment evidences give us quite different aspects to look into the underlying physics, and imply the cooperative effects on the dead layer. The extrinsic defects (oxygen vacancies, dislocations, etc.) induced by growth generally destroy the long range magnetic order, and thus increases the dead layer thickness. The strain, which is heavily investigated in the past, is another important effect that will degrade the properties especially for ultrathin films. However, in the situation of small strain, which can only result in minor orbital reconstruction, the octahedral rotation pattern near the interface modified by interface octahedral  
Figure 14: Annular dark field image of the cross-section of (La\({}_{0}\)\({}_{2}\)Sr\({}_{0}\)\({}_{3}\)MnO\({}_{3}\))\({}_{5}\) superlattices grown at different laser spot size (**a**) 10.2, (**b**) 7.5, (**c**) 5 and (**d**) 1.6 mm\({}^{2}\). The laser energy during growth was fixed at 35 mJ. (**e–g**) are the elemental mapping of (**e**) Ti, (**f**) Mn, and (**g**) La for LSMO grown at a laser spot size of 1.6 mm\({}^{2}\). (**h**) Simultaneously recorded annular dark field image. Figures are adapted from Ref. [50].  
coupling emerges to be a key ingredient that determines both the magnetic and transport properties. In terms of a widely observed dead layer in many metallic oxides, as discussed in next section, the dimensionality effect is also believed to play a vital role, though the pure dimensionality effect is hard to obtain in real films, due to the unavoidable interface effect, stain, and broken symmetry. More experimental and theoretical efforts are still required to eventually figure out the origin of the dead layer. For examples, some dynamic strategies to control the MIT, such as charge modulation by the external electric field [75; 76], may help us to separately understand the individual effects on MIT, and pave a new path to understand the physics of MIT in LSMO. Developing a method to fabricate a freestanding ultrathin LSMO is also highly desired in the future, in order to really gain an insight into the dimensionality effect [77].
## 3 The Proposed Methodology
### T Component: Extracting LDA Topics  
At first, fastTNT employs Latent Dirichlet Allocation (LDA), an unsupervised generative probabilistic topic modeling algorithm, to extract hidden semantic themes, known as topics, from a preprocessed collection of documents (refer to Figure 3). This algorithm requires two main parameters from the users: the number of topics (\(k\)) and words per topic (\(m\)). After LDA training, it produces two byproducts: the topic-word matrix (\(TW\)) and the document-topic matrix (\(DT\)). The document-topic matrix distribution indicates the extent to which each document is associated with each topic. In contrast, the topic-word matrix distribution probabilities indicate the extent to which each word is associated with each topic. fastTNT uses only the topic-word matrix for further analysis.  
Initially, faSTNT utilizes the topic-word matrix (\(TW\)) to generate a word list (\(W\)). The \(W\) will consist of \(n\) distinct words present across all topics. In other words, faSTNT eliminates duplicate words across different topics in \(TW\), retaining unique ones.  
Afterward, faSTNT leverages the training document-word dataset (\(D_{train}\)), containing \(d\) documents and \(z\) words, where \(z\) represents the total number of distinct words in the preprocessed dataset, along with the word list (\(W\)), comprising \(n\) distinct words, to generate  
Figure 1: The general framework of the faSTNT approach, illustrating the working mechanisms of the T, F, and G components (Part 1).  
portance values for each word in \(W\). The importance of each feature (word) was estimated by averaging the results from \(j\)-executed iterations.  
The MCCV process ensures robust evaluation and results in a mean feature weights list that indicates the importance of each feature (word) in \(W\), denoted as \(FI=\{\textit{F}\textit{I}w_{1},\textit{F}\textit{I}w_{2},\ldots,\textit{F}\textit{I }w_{n}\}\), where \(n\) is the total number of features in \(W\), and \(\textit{F}\textit{I}w_{i}\) represents the mean importance of feature \(wi\) in \(W\).  
According to the feature importance values obtained (\(FI\)), words in \(W\) are ranked accordingly. The newly ranked word list is denoted as \(W_{R}\).  
To this end, a user-specified \(v\) percent of words with the lowest feature importance values are discarded from the ranked word list (\(W_{R}\)). This refined list, now referred to as the final word list (\(W_{F}\)), consists of \(f\) words (where \(f<n\)). The feature importance values  
Figure 4: The working mechanism of the F component.  
Figure 3: The working mechanism of the T component.  
for the words in \(W_{F}\) are retained for subsequent analysis, forming a new feature weight list referred to as \(Fl_{F}\).  
The \(W_{F}\) will be used in the next G component, while the \(Fl_{F}\) will be utilized in the S component for topic scoring purposes.
# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 2 MTD Techniques
### Design and Classification
#### 2.2.3 When to Move  
MTD techniques need to determine when to update the current state of the MTD system to maximize the invalidation of the relevant resource information obtained by an attacker. The conditions for triggering updates can be divided into fixed-time triggering [6] and ad hoc event triggering.  
* Fixed-time triggering: MTD techniques periodically shift the attack surface at fixed intervals. Setting the triggering interval requires a technique-specific analysis, but for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multicolumn{4}{c}{**Typical Techniques**} & \multicolumn{1}{c}{**Features**} \\ \cline{2-7}  & **Network L.** & **Platform L.** & **Ex. Env. L.** & **Software L.** & **Data L.** & \\ \hline \multirow{4}{*}{Shuffling} & IP hopping & VM migration & OS rotation & Software rearrangement & Keys rotation & These techniques own a low burden in terms of development costs and resource consumption, ease of operation and high compatibility. Security is highly dependent on the quality of existing techniques.  
each technique, researchers need to find the right triggering point. If the interval is too long, attackers have enough time to penetrate the system and launch an attack. If it is too short, the MTD mechanism is triggered frequently, leading to wasted resources and degraded performance. Additionally, frequent triggering of MTD can significantly degrade the QoS and users' experience.  
As a result, self-adaptive MTD techniques based on ad hoc event triggering are becoming increasingly favored. These techniques can effectively avoid the problems associated with selecting fixed intervals. By adapting to changes in the system or network environment, self-adaptive MTD techniques can ensure optimal defense mechanisms that minimize the likelihood of successful attacks while maintaining high QoS and user usage satisfaction.  
* Ad hoc event triggering: MTD shifts the attack surface when the system detects an attacker's access or a precursor to an attack. Self-adaptive MTD adopts this approach, and its main challenge is accurately predicting attacks that can trigger MTD effectively.  
Machine learning can be a helpful tool in addressing this challenge by assisting in the achievement of the self-adaptive triggering of MTD. By analyzing patterns in system behavior, machine learning algorithms can identify potential threats and predict future attacks more accurately than traditional rule-based systems. In Section 3, we further discuss how machine learning can be leveraged to improve the effectiveness of self-adaptive MTD techniques.
## 2 Design Aspects
### Meteorological Data  
This section will highlight the analysis of potential solar data in Baghdad, Iraq. The main reason for this is to discover the potential power of renewable energy available at the location of operation. The meteorological conditions of Iraq correspond to a warm and dry climate during the summer season. Iraq has abundant solar energy capability with a significant amount of sunlight throughout the year as it is located in the Global Sunbelt. Solar energy can be widely deployed throughout two-thirds of Iraq. In the western and southern areas, daily average radiation ranges between 2800 and 3000 h, with relatively high average daily solar radiation of 6.5-7 kWh/m\({}^{2}\). The direct and global solar irradiation is given in Figure 2, [26]. Thence, the study location has great potential for solar energy, allowing sufficient use of solar thermal power as a main prime mover for the absorption cooling system.  
The solar radiation data and environmental conditions used correspond to the TMY2 (typical meteorological year) format for Baghdad, the capital of Iraq (latitude is 33.3 N, longitude is 44.6 E, and Altitude 3.8 m). These data are provided by TRNSYS and have been obtained with Version 5 of the Meteorom program.  
Solar insolation varies according to the time of year. The daily highest solar irradiation of the globe is almost 8 kWh/m\({}^{2}\) and the daily highest temperature reaches over 45 \({}^{\circ}\)C (sometimes in summer  
Figure 2: Iraq solar annual direct normal and global horizontal Irradiation map © 2019 The World Bank, Source: Global Solar Atlas 2.0, Solar resource data: Solargis [26].  
season, the temperatures exceed 50 \({}^{\circ}\)C). A cooling effect is needed for seven months (April-October). During these months, the sunshine lasts for almost 10 hours per day, with an average total daylight of 13 hours per day [36].
## 2 Materials
### Data Preprocessing  
The objective of atmospheric correction is to determine the true surface reflectance values by removing atmospheric effects from satellite images, while the derivation of the TCT coefficients is generally based on surface reflectance. Although orthorectification and image fusion does not affect the effect of pseudo-TCT, orthorectification can reduce the distortion of remote sensing images and improve the image fusion effect, and image fusion can improve the resolution of images and facilitate the subsequent observation of the details of the enhanced images. Therefore, atmospheric correction, orthorectification and image fusion were performed on image A. It should be noted that when the resolution of multispectral images is 4 times that of panchromatic images, and their resolutions were integers, the effects of image fusion will be better. During orthorectification, the resolutions of multispectral images and panchromatic images would be resampled to 4 m and 1 m, respectively. Image B did not have a panchromatic image, so no image preprocessing was performed.
## 3 Results & Observations
### Capacity of Spreading Factors Clusters  
The first thing a LoRaWAN deployer has in mind is that different spreading factors are directly related only to the range of the communication. Instead, spreading factor allocation is argued widely as an essential factor for the reduction of interference between node transmissions, due to the demodulation process. In general cases when the number of EDs is increased above 100 the PDR is decreased. Apart from certain observations, we have to mention that when we compared the total simulations where PDR exceed or is equal to 0.95, we notice that for experiments from 300 to 1000 EDs, 16 simulations out of 77 were on that level, while for simulations from 80 to 100 EDs, 30 out of 53 simulations were on that level. Specifically, we have noticed for example that for a deployment with 10 min transmission intervals for 1000 Eds, the PDR due to collisions drops from 0.90 for DR5 to 0.10 for DR0. This happens because when the number of EDs increases the possibility for interference increases as per the ToA which is SF-related. The same is also performed in high SFs for channel availability. So, we getting more arguments for assumption that as SF increases, fewer EDs on the network can be set to this. Thus, schematically, we can tentatively claim that each transmission interval has a corresponding maximum supported number of EDs, different for each DR cluster. In simulations with a setup of 1 h transmission interval, the capacity of EDs when the goal is to achieve more than 0.90 PDR is greatly reduced based on the DR allocation (Figure 4). DR4 and DR5 clusters showed that they can handle successful transmissions for 5000 or more EDs respectively, DR3 around 3000 EDs, DR2 1500 EDs, DR1 nearly 500 EDs and DR0 around 200 EDs. On the other hand, we have noticed that when the distance is increased the ToA is increased which leads to slight PDR reduction caused by interference.  
Figure 4: How DR allocation affects network capacity.  
On the other hand, if we increase the frequency of transmissions, we can see that DR cluster capacity is degraded significantly. We are seeing this impact on simulations with a 10 min transmission interval and 50 byte payloads (Figure 5). The corresponding DR cluster for this case drops to around 800 for DR5, 600 for DR4, 400 for DR3, 200 for DR2, 100 for DR1 and below 20 for DR0.
## 2 Methods
### Application Example  
To verify the robustness and applicability of the algorithm in practical applications, we digitized the Gazelle Fault in the South Aswan region of Egypt to obtain its Bouguer gravity profile [9]. We then used the C-AWPSO algorithm for inversion. The estimated parameters are \(M=19.18\) mGal, \(z=153.6\) m, \(\theta=88.5^{\circ}\), \(w=560.53\) m, and \(x_{0}=4.99\) m (Table 6), with an RMS error of 0.68 mGal. From Table 6, we can conclude that the present method gives the minimum RMS error when compared with the previous methods in the literature. Compared with previous studies, the accuracy of the method proposed in this paper has been significantly improved, with the RMS errors increasing by 85.12%, 64.4%, and 87.38%, respectively. Figure 4 presents a comparison between the predicted gravity anomalies of the Gazelle Fault calculated by previous researchers [6; 9; 17] and those calculated using the C-AWPSO algorithm. The results obtained by the C-AWPSO method are closer to the observed gravity anomalies in distribution. In Figure 4, the predicted anomaly curve from the C-AWPSO algorithm aligns well with the curve obtained using [PERSON]'s (2021) method on the left side [9]. Although the predicted anomaly curve from the C-AWPSO algorithm is very close to the observed anomaly curve, there are still noticeable differences in trends. These phenomena could be due to the presence of other nearby structures generating gravity effects apart from the fault, or there might be noise in the vicinity that is difficult for the algorithm to identify.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Used** & \multirow{2}{*}{**5\% Noise**} & \multirow{2}{*}{**e (\%)**} & \multirow{2}{*}{**10\% Noise**} & \multirow{2}{*}{**e (\%)**} & \multirow{2}{*}{**15\% Noise**} & \multirow{2}{*}{**e (\%)**} \\  & **Ranges** & & & & & & \\ \hline \(M\) (mGal) & 0–100 & 14.99 & 0.07 & 14.96 & 0.27 & 15.05 & 0.33 \\ \(z\) (m) & 0–10 & 4.88 & 2.4 & 5.18 & 3.6 & 4.84 & 3.2 \\ \(\theta\) (\({}^{\circ}\)) & 0–90 & 29.99 & 0.03 & 30.58 & 1.93 & 29.30 & 2.33 \\ \(w\) (m) & 10–20 & 14.81
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 2 Literature Review
### Related Work
#### 2.1.2 Research on Taxi Time Prediction
[14] proposed a deep learning model including a temporal flow sub-model (airport capacity, number of taxiing flight, different time periods), a spatial sub-model (taxizing distances), and an environmental sub-model (weather, air traffic control, runway configurations, aircraft categories) to incorporate the influence of spatiotemporal factors, external environmental conditions, and spatial characteristics by three sub-models. [PERSON] et al. [15] proposed a taxi-out time prediction model for departure flights based on local weighted support vector regression, and the prediction results show that the accuracy within \(\pm\)3 min can reach 83.3%, which is better than the traditional support vector regression model. [PERSON] et al. [16] performed the prediction in two stages, first predicting the taxi path of the flight and then predicting the taxi time according to the taxi path and taxi distance. The prediction results show that the accuracy within \(\pm\)5 min can reach 95%. [PERSON] et al. [17] used a BP neural network model to predict the taxi-out time, and the prediction results of the taxi-out time of departure flight fitted well with the true values. [PERSON] et al. [18] innovatively proposed a deep metric learning model to predict taxi time by finding the most similar scenes in the history. They filter out the most similar scenarios, and then calculate the average taxi time as the prediction of the taxi time for the current scenario. [PERSON] et al. [19] used a neural network (NN), support vector machines (SVMs), and a regression tree (RT) to predict the taxi-out time at Mohammed V Casablanca Airport and the experimental results show that the prediction performance of the NN is better. This study also found that machine learning models with lower training performance can still show better prediction performance. From the above research, we can see that the existing works studied the taxi-out time as a whole without considering its components and lacked the optimization of the prediction algorithms in terms of methodology. Based on the above works, in this paper, we propose an ATT prediction model based on the nutcracker optimization algorithm (NOA) and XGBoost. We first calculate the UTT, and then propose three features: the corridor departure flow, the corridor arrival flow, and the departure flow proportion of ODP, and use the NOA algorithm to improve the XGBoost algorithm for ATT prediction.
## 5 Implementation of the Operational System  
The crop yield forecasting system is fully automatic which implies that data collection, processing, crop yield forecasting, and the generation of graphical and tabular outputs do not require a human operator. Figure 16 presents the workflow of the crop yield forecasting system along with software that was used at each processing step. The system is implemented as an R package, so any external functions (CDO, Python, SNAP) are called from the R environment. Python is used to retrieve satellite and ERA-5 data, CDO to process the ERA-5 data, and SNAP to process Sentinel-3 images. Processing of MODIS data, aggregation to administrative units, preparation of predictors, training and application of crop yield models, as well as generation of final outputs are implemented in R. Figure 17 shows an example of graphical system output, which complements the tabular data (CSV) and geospatial vector data (SHP).  
To guarantee the portability of the system, everything was installed on a virtual machine (VirtualBox v6.1.20 with Xubuntu v20.04.2). This allows migration to cloud computing environments such as Data and Information Access Services (DIAS) or Amazon Web Services (AWS), where the Copernicus data sets are directly accessible.  
Figure 16: Workflow of the crop yield forecasting system, data streams, and software used (‘cdo’— Climate Data Operators v1.9.9, R v4.1.1, Python v3.8.10).
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
## 3 Results and Discussion
### Calculate Bottomhole Temperature  
By calculating the effect of different nanoparticles on the bottomhole temperature, the effect of nanoparticles on the thermal management performance of drilling fluids is explored so as to effectively reduce the bottomhole temperature during high-temperature drilling operations and ensure the stable operation of drilling equipment. The effects of different nanoparticles on the bottomhole temperature are shown in Figure 8. CNTs significantly reduced the bottomhole temperature, and the bottomhole temperature gradually decreased with the increase of CNTs addition. When the additional amount of CNTs was 3%, the bottomhole temperature decreased to 99.74 \({}^{\circ}\)C. Compared with the base drilling fluids without nanoparticle additions, the temperature was reduced by 4.53 \({}^{\circ}\)C, indicating that CNTs showed excellent temperature reduction in improving the thermal management  
Figure 7: HTHP filtration loss after hot rolling of nanoparticle-enhanced OBDFs.  
performance of drilling fluids. Similarly, SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) nanoparticles also helped to reduce the bottomhole temperature, which decreased by 1.47 \({}^{\circ}\)C and 0.88 \({}^{\circ}\)C, respectively, when the addition was 3%. Although the cooling effect of different nanoparticles varied, the results showed that all three nanoparticles, CNTs, SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\), were able to reduce the bottomhole temperature to some extent, which suggests that nanoparticle-enhanced OBDFs have the potential to improve thermal management in high-temperature drilling environments.
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 4 Back-End
### Object Goal Space Generation
Similar to the definition of the pixel neighborhoods, the rectangles of the four neighborhoods of the BRs are the theoretical affordance space \(\mathcal{N}_{a}^{\prime}(i,\ j)\). The rectangles belonging to the eight neighborhoods but not the four neighborhoods are the theoretical nonaffordance space \(\mathcal{N}_{na}^{\prime}(i,j)\):  
\[\left\{ \begin{array}{c}\mathcal{N}_{a}^{\prime}(i,j)=\mathcal{N}_{4}(i,j) \\ \mathcal{N}_{na}^{\prime}(i,j)=\mathcal{N}_{8}(i,j)-\mathcal{N}_{4}(i,j) \end{array} \right.. \tag{21}\]  
Assuming the width of a BR is \(l_{w}\), and the height is \(l_{h}\). The affordance space contains four rectangles. One edge length of each rectangle is \(l_{e}\), and the other is \(l_{w}\) or \(l_{h}\). The nonaffordance space is four squares with the edge length \(l_{e}\):  
\[l_{e}=s_{r}l_{r}. \tag{22}\]  
where \(s_{r}\) is the safety factor, and \(l_{r}\) is the radius of the robot circumscribed circle. There are two shortcomings in the theoretical model: (1) It does not consider the uncertainty of the BR of the object. Therefore, it may contain free grids. (2) The real affordance and nonaffordance spaces are a subset of the theoretical ones. For example, the couch has only one rectangle in Figure 10(a). We use the grid map to partially solve this problem. For the former, we also consider the free grids in the BR as affordance space; for the latter, we use the occupied and unknown girds to remove some rectangles. Therefore, the real affordance \(\mathcal{N}_{a}(i,j)\) and nonaffordance spaces and \(\mathcal{N}_{na}(i,j)\) are:  
\[\left\{ \begin{array}{c}\mathcal{N}_{a}(i,j)=\{\mathcal{N}_{a}^{\prime}(i,j) \cup\mathcal{B}(i,j)\}\cap\mathcal{F}\\ \mathcal{N}_{na}(i,j)=\mathcal{N}_{na}^{\prime}(i,j)\cap\mathcal{F}\end{array}  \right., \tag{23}\]  
where \(\mathcal{B}(i,j)\) is the grid set of the BR, and \(\mathcal{F}\) is the free grid set of the map. Based on the above method, we build the object semantic grid map, as shown in Figure 13.  
Figure 12: Object goal space: (**a**) 4-connected pixels, (**b**) 8-connected pixels, (**c**) theoretical model of the object goal space. NAS is short for nonaffordance space. The square’s edge length of NAS is \(l_{e}\). The width and height of the affordable space are \(l_{w}\) and \(l_{h}\).