esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 3 Preliminary Preparation for Data and Causal Analysis
### Data Preprocessing
#### 3.2.2 Data Balancing  
The experimental data used in this paper are provided by the Civil Aviation Administration of China and are real flight data. Although the probability of occurrence of an anomaly is highest during the approach phase of a flight, it still accounts for a small portion compared to normal data. Details are shown in Figure 2; it can be seen that abnormal data account for 22.21%. Among them, _ILS Glide Slope Deviation_ accounts for the largest proportion, accounting for 42.44% of the entire anomaly, while _Large Approach Roll Angle_ accounts for the smallest proportion, only 0.20% of all anomalies. In conclusion, there is an extreme imbalance in the proportion of various anomalies in the approach phase.  
For multi-class classification models, if there are insufficient training data for a certain category, the model will learn too little \"information\" about that category, ultimately resulting in difficulty in determining which category the data belong to. So, data imbalances will have a significant negative impact on the final classification results and also affect the accuracy of the model. Therefore, this paper uses the Borderline SMOTE algorithm [14] to balance the data, which only uses minority class samples on the boundary to synthesize new samples. The result after data balancing is shown in Figure 3.  
Figure 1: Down-sampling results of pitch angle parameters.  
Figure 2: Distribution of different anomalies in QAR data during approach phase.
# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Opinion Mining
[PERSON] and [PERSON] [89] propose a novel method for sentiment classification of text documents. In particular, they determine the orientation of a term based on the classification of its glosses and its definitions in online dictionaries. In the training phase, a seed term set, representative of the two categories Positive and Negative, is provided as input. By means of a thesaurus (online dictionary) the set is expanded with additional terms, that are lexically related (synonymous) to the seed terms and, therefore, can also be considered as representative of the two categories. This process is applied iteratively, until no new terms are added. For each term in the final set a textual representation is constructed, by collating its glosses, as found in machine-readable dictionaries. In that sense the method is semi-supervised since except for the initial seed terms, all features are selected algorithmically, rather than by human experts. For both the expansion and gloss retrieval operations, the authors employed the Wordnet Ontology, mainly because of its ease of use for automatic processing. Moreover, glosses in WordNet have a regular format that allows the production of clean textual representations without the need for manual text cleaning. After removing stop words, each such representation is mapped to a numerical vector by the standard normalized TFIDF score of its terms. Finally, the set of all vectors is used to train a binary classifier. In the experiments three types of classifiers were used, namely the multinomial naive Bayes, support vector machines with linear kernels, and the PrTFIDF probabilistic version of the Rocchio learner [90]. The algorithm was shown to outperform other state-of-the-art methods in standard benchmarks, while also being computationally much less intensive.
## 4 Experiments
### The Impact of Resolution on Pose Estimation  
#### 4.1.1 Experimental Setup  
The experiment was evaluated under the Lego scene. To simulate situations with inaccurate poses, we introduced Gaussian noise N(0,0.15I) to the ground truth pose data, using it as the initial pose. The evaluation metrics adopted in this paper are rotation error, translation error and the PSNR. The computations were carried out on Ubuntu 20.04 within the Windows Subsystem for Linux (WSL), leveraging an Intel Core i7-13700K CPU and an NVIDIA GeForce RTX 3080 Ti GPU.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 5 Analysis of Results
### In Situ Maximum Printing Speed Test in Terms of Defined Points Spacings
#### 5.4.3 Analysis of Maximum Speed with Different Point Spacing  
A nonlinear regression analysis was conducted to identify the behavior of the speed in the tested intervals. As a consequence, the exponential curve shown in Figure 11 and Equation (1) were found. From this analysis, Figure 11 shows that the greater points spacing the greater the maximum printing speed.  
\[Max\ Speed=0.0804\text{-}e^{0.0301\text{-}sp} \tag{1}\]  
where:  
_Max speed_ = Maximum printing speed (m/s).  
_sp_ = Point spacing (mm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Spacing 12.5 mm** & **Spacing 25 mm** & **Spacing 50 mm** \\ \hline Q\({}_{1}\) (m/s) & 0.1205 & 0.1603 & 0.3650 \\ M\({}_{e}\) (m/s) & 0.1211 & 0.1618 & 0.3676 \\ Q\({}_{3}\) (m/s) & 0.1220 & 0.1627 & 0.3711 \\ L\({}_{\text{lim}}\) & 0.1183 & 0.1567 & 0.3559 \\ U\({}_{\text{lim}}\) & 0.1243 & 0.1661 & 0.3759 \\ Atypical values & 0 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the boxplot applied to different point spacing.  
Figure 10: Boxplots with maximum speeds.  
It is interesting to see how Figure 11 shows that the greater the point spacing, the greater the maximum speed. Equation (1) is especially useful when the user wants to know the approximate maximum printing speed for different point spacings that can be entered manually from the printing code. In addition, in case of needing to control the printing speed more precisely, the user will have sufficiently reliable data to define an acceptable range of speed controls lower than this maximum value, and thus to execute the code with a value of controlled and precise speed. However, it is important to emphasize that, because of the exploratory nature of this study, it is not possible to assure that other printing machines will have the exact same behavior. Despite this fact, the procedure presented here to determine the curve could be replicated to obtain own results for further studies.
# UAV Communication Recovery under Meteorological Conditions
###### Abstract  
Our study proposes a UAV communications recovery strategy under meteorological conditions based on a ray tracing simulation of excessive path loss in four distinct three-dimensional (3D) urban environments. We start by reviewing the air-to-ground propagation loss model under meteorological conditions, as well as the specific attenuation of rain, fog, and snow, and we propose a new expression for line-of-sight (LoS) probability. Using the two frequency bands of 28 GHz and 71 GHz, we investigate the impact of specific attenuation caused by different weather conditions and analyze the relationship between the radius of the UAV coverage area and the elevation angle. Furthermore, we investigate the effects of the rainfall rate, liquid water density, and snowfall rate on the maximum coverage area and optimal height of the UAV. Eventually, we propose a strategy that involves compensating for the maximum path loss and adjusting the UAV's position to recover the coverage of the UAV to ground users. Our results show that rain has the greatest impact on the UAV's coverage area and optimum height among the three types of weather conditions. For various weather conditions, relative to Region 1, the percentage reduction in the maximum coverage radius of Region 2 to Region 4 increases gradually, and the extent of each increase is approximately 10%. Moreover, after adding the compensated path loss, the coverage radius of the UAV in the four regions is restored to a value slightly larger than that before the rain. In addition, rain caused the greatest reduction in UAV coverage for suburban environments and the lowest for high-rise urban environments.  
unmanned aerial vehicle (UAV); air-to-ground (A2G); meteorological conditions; channel models; ray tracing (RT); excessive path loss; LoS probability 2023/7423.009/drones/2070423  
2023/07/0423  
2023/07/0423
## 3 Results
### Single-Scan Point Cloud Processing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filtration Step** & **Min** & **Max** & **Mean** & **Std Dev** \\ \hline Initial PC & 21,350 & 22,266 & 21,880 & 205 \\
5 m Filter & 9170 & 14,395 & 11,133 & 865 \\ Ground Filter & 2327 & 7173 & 4862 & 618 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of points at each stage of point reduction along Path A. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filtration Step** & **Min** & **Max** & **Mean** & **Std Dev** \\ \hline Initial PC & 21,453 & 22,341 & 21,906 & 225 \\
5 m Filter & 8062 & 15,612 & 11,309 & 1804 \\ Ground Filter & 2114 & 8085 & 4772 & 1307 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of points at each stage of point reduction along Path B.  
Figure 7: Two-dimensional histograms for a single scan. (**a**) Projection to the horizontal plane \(H_{XY}\) with manually marked support locations. (**b**) Projection to the front plane \(H_{YZ}\), marked elongated objects. Figure 9 illustrates the results of the Euclidean clustering. The parameters for the clustering function were set as distance tolerance to \(d_{th}=0.5\) m, minimum cluster size to \(c_{min}=20\), and maximum cluster size to \(c_{max}=500\). Figure 10 presents examples of the results of the final classification. The clusters with a width of more than \(i_{w}=0.1\) m and height within \(i_{h}=0.65\) m (bigger frames) and \(i_{h}=0.55\) m (smaller frames) with \(i_{rh}=10\%\) tolerance were classified as idlers and marked in green. The remainder were considered non-idlers and marked in red. Figure 8: The results of the density-based segmentation (the points of interest marked in blue-green). (**a**) Points from the \(XY\) segmentation. (**b**) Points from the \(YZ\) segmentation. (**c**) The set difference of points from the \(XY\) and \(YZ\) segmentations. Figure 9: Clusters representing idler frame candidates. It may be concluded that the green marking was correctly assigned to idler frames. Also, the object to the left marked in red was correctly marked as a non-idler. However, some other idler frames were not classified correctly as such. This happened both for the distant one in the left image and for the close idler in the right image. Closer investigation has shown that the height-based detector prohibits the positive classification of the frames which are not completely visible. This happens often in the case of distant frames as they may be occluded by closer objects, but it also happens when the idler frame is close and it is not visible in full height due to the limited vertical angle of view of the LIDAR.
## 2 Materials and Methodology
### Methodology
#### 2.2.5 Encoding Methods  
Common encoding methods include label encoding, one-hot encoding, and target encoding (also known as mean encoding) [41]. Label encoding maps each category to a unique integer, thereby converting categorical variables to numerical variables. This method is simple and easy to implement, but it assumes an inherent order or ranking between categories, which may not always exist. One-hot encoding creates a binary vector for each category, with a value of 1 for the category and 0 for all others. This approach can handle nominal variables with no inherent order but can lead to the curse of dimensionality and may not work well with high-cardinality variables. Target encoding replaces each category with the mean of the target variable for that category, effectively encoding the relationship between the categorical variable and the target. While it can capture non-linear relationships and reduce dimensionality, it is vulnerable to overfitting and may introduce bias if the target variable is correlated with the categorical variable. Therefore, we will use these three encoding methods in the experimental stage to compare the model effects to obtain the most suitable encoding method.
# Experimental Study on Drying Characteristics of Alfalfa Hay Bales Using Hot Air Convection
## 2 Materials and Methods  
### Experimental Materials  
This study was conducted in the Experimental Building of the Inner Mongolia University of Technology (see Figure 1), Hohhot, Inner Mongolia Autonomous Region, China (111\({}^{\circ}\)40\({}^{\prime}\) E, 40\({}^{\circ}\)50\({}^{\prime}\) N). The alfalfa used in the experiment was harvested in the first flowering stage, leaving a stubble height of ~5 cm after cutting. Then, it is immediately subjected to pretreatment: manual sorting (removal of 5% damaged samples), shade drying (25 \({}^{\circ}\)C, 50% RH, 2 h), and hydraulic baling (10 kN pressure). The initial moisture content of fresh alfalfa was 40.2 \(\pm\) 1.8% (w.b.), which was reduced to 25-30% (w.b.) after pretreatment.
###### Abstract  
Atomic oxygen (AtOx) is a major component of the space environment between 200 and 800 km (LEO--low Earth orbit region) and is the principal source of erosion for exposed aerospace structures. The damage to surface materials is proportional to the AtOx fluence, which depends on altitude, exposure time, orbital inclination, and solar activity, and it is caused by the formation of volatile oxides which do not adhere to the surface; furthermore, the mass loss may also be worsened by UV radiation, which increases the chemical degradation of the exposed material. Carbon/carbon (C/C) is an advanced ceramic composite that is frequently found as a base component of thermal protection systems (TPS), rocket nozzles, or other spacecraft subsystems. In this work, a simulation of the AtOx/UV synergistic effects on C/C plates exposed at different attitude positions were carried out by experimental tests performed at the Aerospace Systems Laboratory (LSA--Sapienza University of Rome) by means of an Atomic Oxygen OS-Prey RF plasma source, which also included a high-power UV-ray generator. The present experimental plan was built on the activity developed during recent years at LSA concerning the study of C/C materials for protecting aerospace structures from thermal shock in re-entry missions. The tests were conceived by considering a fixed time of exposure with a base fluence of \(7.6\times 10^{19}\) n.s./cm\({}^{2}\), as evaluated from the erosion of the reference samples exposed to AtOx flux at a normal incidence; the simulation of the different attitude positions was then analyzed, also considering the simultaneous effect of UV radiation. The results of the aging ground test suggest the following: (i) C/C oxidation in LEO must be taken into full consideration in the TPS design with reference to protective coating solutions, (ii) the LEO environment simulation is closely related to AtOx/UV combined irradiation, as well as to the spacecraft's in-orbit attitude.  
LEO space environment; atomic oxygen; UV; synergistic effects; carbon/carbon composite; space systems testing +
Footnote †: journal: Applied Sciences
## 4 Discussion
purified two thermostable lipases (A and B) from _Bacillus thermooleoorans_ ID-1 [34]. Lipase A and B have optimal temperatures of 60-65 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, respectively, while the pH optima were 9 and 8-9, respectively. The detergent industry is an important application field for lipase. Surfactants and oxidants play a major role in detergents. Generally, the surfactant has negative effects on enzymatic hydrolysis and acts as a competitive inhibitor in the reaction system. The detergent lipases must be highly tolerant to surfactants and detergent additives. In the presence of 1% detergents such as Tween-20 and sodium deoxycholate, lipase P1 showed 14% and 42% relative activity, respectively [33]. LipGt6 retained 30% and 75% relative activity, respectively. In addition, it was reported that 0.1-1% H\({}_{2}\)O\({}_{2}\) completely inhibits the _B. sintthi_ lipase [35]. In the presence of 1% H\({}_{2}\)O\({}_{2}\), LipGt6 retained 69.5% relative activity. Therefore, it has good application prospects in the detergent industry. Researchers have suggested that the change in lipase activity may result from a conformational change in the interaction between lipase and metal ions [36]. The L1 lipase structure shows a novel zinc-coordinated extra domain that is conserved among members of the lipase family L5. The novel zinc site stabilizes the structural domain by coordinating with His-81, Asp-61, and Asp-238. Calcium-dependent stability has been demonstrated by [PERSON] et al. ; lipase L1 begins to unfold at 66 \({}^{\circ}\)C in the presence of calcium ions but at 58 \({}^{\circ}\)C in the absence of calcium. The residues of the Ca\({}^{2+}\)-binding site are located near the catalytic His and Asp residues, stabilizing the active center of these enzymes [37]. Many of the lipases were activated when Ca\({}^{2+}\) and Mg\({}^{2+}\) were added [38; 39]. LipGt6 enzyme activity was increased in the presence of 5 mM Mg\({}^{2+}\) (117.49%) and Ca\({}^{2+}\) (113.50%). However, no similar calcium-binding site has been reported in the lipase II family. Further structural information and experimental data are required to determine whether there are related residues.
## 3 Applications
### Photocatalysis for Environmental Remediation  
The photoactivity of titania has been widely applied for environmental remediation; for instance, through the generation of graphene-bearing membranes for water purification [186; 206]. The mechanisms of titania-photocatalyzed dye degradation proceed through the generation of reactive oxygen species (ROS) that trigger the photooxidation of organic molecules [207]. This phenomenon can be applied to a variety of different pollutants. They include dyes, for which titania co-doping with iron and praseodymium was recently reported to significantly narrow the band gap. Moreover, it promoted the generation of oxygen vacancies, which can trap electrons and thus reduce the recombination of charge carriers [208]. Nitrogen and sulfur co-doping represents a very popular strategy to enhance the photodegradation of cationic dyes [209]. Another type of target pollutants consists of volatile organics, whose photo-degradation is heavily influenced by levels of humidity [210]. Another area that requires urgent action is the environmental pollution by drugs, especially antibiotics, whose mechanisms of photodegradation are still the subject of intense investigations [211]. Clearly, the removal of pesticides is also highly sought after, although a number of variables have to be taken into consideration as they may affect the efficiency of the process [212]. Ammonia is a critical pollutant of agricultural concern, both in gaseous and aqueous matrices, for which heterogeneous nanostructures find application as photocatalysts [213]. In fact, ammonia splitting into nitrogen and hydrogen gases is particularly attractive as it would concomitantly generate clean fuel [214]. The fundamental reaction pathways involved in the process are shown in Figure 9[214].  
Sometimes, different photoactive catalysts are combined together to enhance the performance of the final materials [215]. Alternatively, doping can be an effective strategy; for instance, with bismuth [216], tungsten [217], nickel [218], and so on.
## 2 Methods
### Data Preprocessing and Normalization
#### 2.3.3 Normalization  
Before using the BP neural network for network training and result prediction, it is necessary to normalize the data. Common normalization methods include linear function normalization (min-max scaling) and zero-mean normalization (z-score standardization). The purpose is to \"stretch\" the distribution of data across dimensions and to project them onto a similar scale range. Otherwise, due to the large range discrepancies in different types of collected data, problems such as slow convergence speed and long training time of the BP neural network may arise. This study uses linear normalization processing, as shown in Equation (7).  
\[X_{norm}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{7}\]  
In the formula, \(X_{\text{min}}\) and \(X_{\text{max}}\) are the minimum and maximum values in the same column of data to be processed, and \(X\) is the current data being processed.  
Due to the large range of the data, to improve data accuracy, the results after data preprocessing and normalization are taken to three significant figures. The data after preprocessing and normalization are shown in Table 5.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.3. Scrypt  
The Scrypt [26] is a cryptographic hashing algorithm serving as a validation algorithm for a number of cryptocurrencies. Initially, it was implemented for Tenebrix, further being adopted as the base algorithm for Litecoin and Dogecoin. The Scrypt algorithm is also utilized by a number of other cryptocurrencies, including Monocoin, ProsperCoin, MonaCoin, CashCoin, and many more [3]. Scrypt is a password-based Key Derivation Function (KDF). In the field of cryptography, a KDF is a hash function that generates one or more secret keys, such as a password, master key, or user name and user IDs, from a secret value using a pseudorandom function. KDFs are typically well structured to avoid brute force password guessing attacks. Prior to the advent of Scrypt, KDFs such as Password-Based Key Derivation Function 2 (PBKDF2) demonstrated a certain degree of vulnerability to resist FPGAs and ASIC attacks. While some other password-based KDFs, including PBKDF2, were not memory-intensive, they are computationally intensive. Scrypt was specifically designed to address both aspects, being memory-intensive and computationally demanding [58; 59].  
The Scrypt depends on the following three main parameters: \(N\), \(p\), and \(r\). \(N\) is the parameter that defines the computation and memory cost, impacting the resources needed to perform the execution of the algorithm. The \(p\) is the parallelization degree, while \(r\) shows the size of a memory block, hence directly affecting memory usage. Other parameters include settings for the hash function and the output hash length. The Scrypt operation takes two input parameters: the message to be hashed and a random string called the salt. The addition of this string or salt serves to introduce entropy and thus secure the system against all attacks based on pre-calculated rainbow tables. The association tables offer time-memory cooperation for the recovery of clear encryption keys from hashed keys. Initially, the data are fed into a PBKDF2, which has the objective of improving resistance to brute-force attacks and reducing the number of potential weak points. The output of the PBKDF2 function will be an array of blocks, \([B_{0}...B_{p-1}]\), each of \(128\times r\) bytes, according to the algorithm's parameters. Then, these blocks are processed by the ROMix function.  
The output of the ROMix function becomes an expensive salt, fed into another iteration of PBKDF2 to produce the output key of the desired length [26].