esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
## 3 Feature Extraction and Utilization
### Digital Features
#### 3.1.2 Log Index Features  
In contrast to the log count feature, the log index feature is not based on the frequency of log events. Instead, it is based on the encoding of the log templates index. Each log event template can be encoded into an index number, and the index numbers sorted by execution time are combined to form the log index feature. Each log event template can be encoded into an index number, and the index numbers sorted by execution time are combined to form the log index feature. Furthermore, for the log template sequence shown in Figure 4, its log index vector may be \([1,1,4,2,1,1,2,2,4,2,1]\), as each number represents the index of its corresponding template. Obviously, this method can not only record the number distribution of log events, but can also preserve the positional relationship of log events, which makes the log index feature more widely used. [PERSON] et al. [14] used LSTM to capture the potential log sequence relationship of log index vectors to predict whether the next log event deviated from the normal log event sequence. [PERSON] et al. [42] proposed a CNN-based anomaly detection method by using a parser to find log constants that frequently appeared in log events, named it log-key, and then constructed a one-to-one mapping of the log-key and index. Next, they sorted the index according to the time spent to construct an index vector, encoded the index vector into an index matrix, and finally used three convolutional layers of different sizes of filters for feature extraction. This method can well capture the log features of different ranges in the log index. Similar to [PERSON] et al., [PERSON] et al. [43] also exploited the feature extraction ability of a CNN, used convolution filters of different sizes for feature extraction, and then used LSTM to capture the relationship between sequences for anomaly detection.  
It should be noted that, due to the unevenness of the log partition, the vector lengths generated by the log index feature are often different, so shorter and longer log vectors need to be processed, but deleting the vector length can easily cause data loss, which makes it difficult for the above methods to achieve a better average effect on multiple sets of datasets.
Searching for Appropriate Storage Conditions for Short-Term Wet Preservation of _Porphyridium purpureum_  
[PERSON]  
1 Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium; lise.soetemans@vito.  
as flow cytometry and immunofluorescence microscopy [11; 12]. In addition, literature often highlights the potential use of _P. purpureum_ in food applications where it may serve not only as a colorant but also as a thickening or gelling agent. The viscosity-increasing properties and the (weak) gelling behavior are usually attributed to the presence of sulfated exopolysaccharides (EPS), excreted by _P. purpureum_ and encapsulating its cells [13]. Finally, the lipid fraction including long-chain polyunsaturated fatty acids has been suggested as a potential target for _Porphyridium_ bioreinery and valorization [8]. However, the lipid content of _P. purpureum_ is rather low, often below 15% of its dry matter [12; 14; 15].  
Several studies have provided important information on the effect of cultivation conditions [13; 14; 16; 17] and of down-stream processing operations [13; 17] on _Porphyridium_ phycoerythrin levels, polysaccharide composition, and rheological properties. Yet, very little is known about how this algae should be preserved in between harvest and down-stream processing. Nevertheless, this intermediate storage step is often unavoidable. The course of algae cultivation and the harvesting time is partly unpredictable as it depends on weather conditions. Due to the unpredictability of algae cultivation, it is impossible in practice to carry out harvest and processing immediately when algae are harvest-ready. The algae must, therefore, be temporarily stored.  
Against this background, the current study aims to identify appropriate storage conditions for _P. purpureum_ biomass. Only wet, cooled (T \(>\) 0 \({}^{\circ}\)C) storage will be considered as drying [18; 19] and freezing are rather expensive and because heat-based drying can be expected to reduce B-PE levels [20].  
A first storage study aimed to understand the effect of wet storage on B-PE levels, the formation of odorous short-chain fatty acids, and microbial load. The latter two parameters are important quality parameters when food or feed applications are envisioned. A second preservation test was performed to study organic matter losses, carbohydrate composition, and the algal rheological properties during storage.
## 4 Artificial Intelligence in Ultrasound in Symptomatic Carotid Plaque  
As proposed in the SCOT-HEART and PROMISE II trials [80; 81], the vulnerability of plaque more than its size is responsible for acute vascular events: the rupture of the plaque causes distal embolization or intralesional thrombosis, resulting in the reduction of blood flow [82; 83].  
The vulnerability of plaque depends on morphological and hemodynamic characteristics: hemodynamic factors, such as blood flow volume and velocity, wall shear stress, energy loss, and pressure gradient acting on the plaque surface are the main determinants of the progression of atherosclerotic lesions [84]. Due to the reduction in the vascular lumen and the increased blood flow velocity, high wall shear stress is the primary burden for endothelial cell damage leading to plaque rupture. Ultrasound study is the most widely used technology for the non-invasive examination of carotid disease, allowing the assessment of the morphologic plaque characteristics and the evaluation of the alteration of the hemodynamic factors through the modification of the wave velocity profile on the power Doppler.  
Carotid Doppler ultrasound may stratify the carotid plaque vulnerability based on the echogenicity and surface profile of lesions. However, this technique depends on the operator's ability and experience and cannot identify the internal compounds, reducing  
Figure 2: Artificial intelligence and imaging techniques for symptomatic plaque detection. Atherosclerotic plaque’s evolution is expressed by two major terms “stenosis” and “vulnerability”. These two possible scenarios can be detected thanks to several diagnostic imaging tools. Images can be acquired and elaborated by deep learning patterns, which could make image interpretation faster and more accurate, overcoming some human bias.  
the accuracy in establishing the vulnerability. Ultrasound instability characteristics are hypoechogenicity, an irregular surface, an incomplete fibrous cap, or a plaque blood flow signal suggestive of ulceration [85].  
Conventionally, ML and DL strategies may be used to evaluate atherosclerotic carotid disease. Artificial intelligence consists of extracting, analyzing, and interpreting imaging features by developing complex algorithms producing predictive results [86; 87]. This technology is termed \"radiomics\".  
The application of AI in carotid disease provides automated segmentation methods to ameliorate the work of physicians. Segmentation methods can be based on traditional image processing, requiring more accuracy and expert evaluation. Deep learning-based segmentation methods have been designed to increase diagnostic accuracy in recent decades. However, imaging omics are primarily created from CT and MRI scans, while only a few studies have evaluated the application of AI using vascular ultrasound assessment.
# Improved Honey Badger Algorithm and Its Application to K-Means Clustering
## 3 Improved Honey Badger Algorithm
### Nonlinear Inertia Weights  
In intelligent optimization algorithms, the nonlinear inertial weight refers to a dynamically adjusted weight that varies in a non-linear manner throughout the optimization process. Particle swarm algorithms have demonstrated that inertia weights reflect the ability of the latter follower to escape from the previous position, as well as that larger inertia weights lead to better global search capabilities, and conversely, smaller inertia weights lead to better local exploitation [30]. In the HBA, the update of the individual location largely depends on the optimal location found in the last iteration. This over-reliance on past optimal solutions can lead to an imbalance between the potential for exploration and the ability for exploitation.  
Therefore, nonlinear inertia weights were introduced into the two foraging patterns of honey badgers. The nonlinear inertia weight parameters [31] are shown in Equation (10):  
\[\omega=\frac{1}{1+1.5e^{\frac{10}{t_{max}}-5}}-0.1\times r \tag{10}\]  
where \(r\) is a random number from 0 to 1; \(t\) is the current iteration number; and \(t_{max}\) is the maximum iterations.  
The HBA's mining phase (Equation (4)) and honey phase (Equation (6)) are updated to Equations (11) and (12).  
\[x_{new}=x_{prey}\times\omega+F\times\beta\times I\times x_{prey}+F\times r_{3 }\times\alpha\times d_{l}\times\left|\cos(2\pi r_{4})\times\left[1-\cos(2\pi r _{5})\right]\right| \tag{11}\]  
Figure 4: Comparison of original and improved density factors.  
\[x_{new}=x_{prey}\times\omega+F\times r_{7}\times\alpha\times d_{i} \tag{12}\]  
The inertia weight \(\omega\) decreases as the quantity of iterations increases. The early inertia weight is relatively large, which means that the improved Honey Badger Algorithm can rely more on the standing of the present individual so as to achieve a thorough global search. As the algorithm progresses, the inertia weight gradually goes down, and the impact between successive generations decreases. The use of nonlinear inertia weights facilitates a balance between global exploration and local exploitation, thereby reducing the time required to approach the optimal solution.
# A 2D-View Depth Image- and CNN-Based 3D Model Identification Method
## 1 Introduction  
As three-dimensional (3D) printing [1; 2] and modeling grow in popularity, many 3D models are being distributed and circulated through the Internet. Like other digital multimedia (audio, video, image, etc.), copyright infringement [3; 4] of 3D models is also unavoidable. There are two general methods [5] that can be used to protect copyright interests from infringement: digital watermarking and fingerprinting.  
Digital watermarking is a technique that adds a visible or invisible message to a host's digital content. If the message is discovered in suspected content, the content can be considered as duplicated, and the ownership of the content can be confirmed. However, on applying digital watermarking techniques, damage to the quality of the host content is inevitable because the method of embedding the message is performed by manipulating the data of the host content. This is why most content producers do not like to use digital watermarking to protect their copyrights. Thus, when applying digital watermarking, controlling the trade-off between robustness and imperceptibility is fairly important.  
In contrast to digital watermarking, fingerprinting does not add any message to the host content, which means that no damage occurs. Instead, it extracts unique features from the contents by analyzing the inherent properties of the contents. Ideally, the features are required to be invariant when some modifications are made to the contents. After extracting features from both the host content and the suspect content, the ownership can be proved by comparing the similarity of the two sets of features. Generally, compared with digital watermarking, fingerprinting requires a longer processing time because it has to measure similarity with the information stored in the database. If the database hasthousands of millions of items of data, fingerprinting will be time consuming and also imply high financial costs with respect to purchasing and maintaining servers.  
In this paper, we propose a method using the fingerprinting technique to identify copyrights for 3D models. Inspired by several previous works [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19], our method is mainly based on 2D view depth imaging and the convolutional neural network (CNN). Hence, here we give a brief review about methods for view-based 3D model similarity measurement and CNN.
## 3 Design Solution for FBG Inclinometer for Cantilever-Beam-Type Hydraulic Support
### Working Principle of FBG Inclinometer for Cantilever-Beam-Type Hydraulic Support  
The working principle of the hydraulic support FBG inclinometer is as follows: the top beam of (1) and (2) metal shell are fixed together by (5) fixing bolts; when the angle deflection of (2) metal shell occurs, the pendulum under (1) cantilever beam is deflected due to gravity, which makes the connection between the top beam of (1) cantilever beam and the pendulum undergo a local strain, and be symmetrically laid out on the two sides of the cantilever beam (3). The fiber-optic grating can monitor the angular offset of this connection, and then carry out calibration experiments at different angles to derive the wavelength change values at different angles.  
Figure 3: Model diagram of FBG tilt sensor. 1—Cantilever beam for wire cutting, 2—Metal housing, 3—Fiber-optic grating, 4—Limit screws, 5—Bolts for fixing the cantilever beam, 6—Bonding fixing points.  
Figure 2: Structure diagram of a cantilever beam.  
The cantilever-beam-type hydraulic support FBG inclinometer, engineered in accordance with the aforementioned principle, is capable of enhancing the sensitivity of the fiber-optic grating. Moreover, it is equipped with a zero-calibration device (limit bolt), which significantly improves its measurement accuracy and repeatability. Structurally, its design primarily encompasses two components: the metal casing and the cantilever beam. The design profile of this inclinometer is presented in Figure 4, clearly demonstrating the overall layout and key structural elements.
## 4 Discussion
### Reliability of AOP Measurements  
In this study, the average differences between the first and second measurement of AOP was less than \(1\) mmHg and \(1.3\) mmHg when using the CONT and INCR cuff inflation protocols, respectively (Table 4). The range of differences between the first and second measurement of AOP was less than \(12\) mmHg when using the CONT cuff inflation protocol and less than \(22\) mmHg when using the INCR cuff inflation protocol. This data, combined with high correlations (\(>\)0.99) indicate that sequential measurements of AOP are highly reliable.  
In this study, we measured AOP in each leg using each cuff inflation protocol within a single visit to the laboratory. Thus, we did not assess day-to-day or administrator variability. The recent study by [PERSON] et al. [33] reported that \(47\%\) of the variance in limb occlusion pressure was between participants, \(18\%\) was within participants between days, \(6\%\) was within participants between legs, and \(28\%\) was associated with random error. Due to a high relative error of \(14.4\) mmHg, [PERSON] et al. [33] suggested that the limb occlusion pressure should be measured regularly to ensure effectiveness of BFR during exercise. Thisconcurs with the recommendation of another previous study that AOP should be measured regularly during training (as opposed to a single time point) since AOP may vary within and between days [8].
# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 4 The Three-Stage Stackelberg Game
### Analysis of Stackelberg Equilibrium  
According to [37], we give the definition of Stackelberg equilibrium as follows:  
**Definition 1**. : _Given the strategies of the IPL and NPL, i.e., \(\mathcal{L}\) and \((\mathbf{p},\mathbf{a},\mathbf{x},\mathbf{k})\), the optimal response of the RAL is denoted as_  
\[(\mathbf{y}^{*},\mathbf{w}^{*},\mathbf{e}^{*},\mathbf{s}^{*})=\mathop{\arg\max}_{\mathbf{y},\mathbf{w },\mathbf{e},\mathbf{s}}U^{\mathrm{RA}}(\mathcal{L},(\mathbf{p},\mathbf{a},\mathbf{x},\mathbf{k}),(\bm {y},\mathbf{w},\mathbf{e},\mathbf{s})). \tag{12}\]  
**Definition 2**. : _Given the strategy of the IPL, i.e., \(\mathcal{L}\), the optimal response of the NPL is denoted as_  
Figure 2: Proposed three-stage Stackelberg game model. \[(\mathbf{p}^{*},\mathbf{a}^{*},\mathbf{x}^{*},\mathbf{k}^{*})=\underset{\mathbf{p},\mathbf{a},\mathbf{x},\mathbf{k} }{\arg\max}\;U^{NR}(\mathcal{L},(\mathbf{p},\mathbf{a},\mathbf{x},\mathbf{k}),(\mathbf{y}^{*},\mathbf{w} ^{*},\mathbf{e}^{*},\mathbf{s}^{*})), \tag{13}\]  
_where \((\mathbf{y}^{*},\mathbf{w}^{*},\mathbf{e}^{*},\mathbf{s}^{*})\) is the optimal response of the RAL reacting to the NPL's strategy._  
**Definition 3**. : \(\mathcal{L}^{*}\) _is the [PERSON] equilibrium strategy if and only if_  
\[\mathcal{L}^{*}=\arg\max_{\mathcal{L}}U^{InP}(\mathcal{L},(\mathbf{p}^{*},\mathbf{a}^ {*},\mathbf{x}^{*},\mathbf{k}^{*}),(\mathbf{y}^{*},\mathbf{w}^{*},\mathbf{e}^{*},\mathbf{s}^{*})), \tag{14}\]  
_where \((\mathbf{p}^{*},\mathbf{a}^{*},\mathbf{x}^{*},\mathbf{k}^{*})\) and \((\mathbf{y}^{*},\mathbf{w}^{*},\mathbf{e}^{*},\mathbf{s}^{*})\) are the optimal responses for the NPL and the RAL reacting to the IPL's strategy._  
**Theorem 1**. : _If there exists a set \((\mathcal{L},(\mathbf{p},\mathbf{a},\mathbf{x},\mathbf{k}),(\mathbf{y},\mathbf{w},\mathbf{e},\mathbf{s}))\) such that the constraints of \(P1\), \(P2\) and \(P3\) hold simultaneously, the three-stage stackelberg game must have at least one Stackelberg equilibrium._  
Proof of Theorem 1.: \(P3\) is a mixed-integer nonlinear programming problem.
## 2 Materials and Methods
### Data Processing
#### 2.2.1 Moving Average Method  
The moving average method is frequently used in indoor positioning systems [15; 16; 17] as it reduces the value of outliers and smooths the signal making it easier to process.  
Figure 3 shows the impact of the moving average method on raw data collected by one sensor, smoothing the signal and making it easier to process by removing the drastic variation in our values.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process
### Organic Materials
#### 4.2.1 Wood  
Inscets, like termites, destroy the wood, but they do not destroy the cellulose, which is instead metabolized by microorganisms; different biopathogens mostly work in synergy.  
The most important component of wood is cellulose, and the second is lignin; their structure and behavior towards water are opposite, as you may immediately see from Table 5.  
Biological deterioration of wood may start at humidity levels of 20%. At this humidity level, fungi become active. There are mainly three different types of fungi of interest for CH artifacts, and they are named by the color of the decomposition material they produce; in fact, they are recognizable by the naked eye: white rot, brown rot, and soft rot [103].  
Although fungi and bacteria may also have high toxicity for humans, the worst, in terms of physical destruction of CH artifacts made of organic materials, are insects, because they tremendously speed up the degradation process. Holes and tunnels are the results of insect activity, which become visible only when the damage is at an advanced state.  
Different insects prefer different foods; some are able to eat anything, while others need to live in symbiosis with microorganisms.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 4 Results
### Radiation Properties  
In Figure 9, the proposed irregular octagonal two-port textile MIMO antenna's radiation performance in both the E and H planes is examined in depth. Figure 9 shows 2D radiation patterns calculated at 5.2 GHz according to both the measured and simulated results; these two-dimensional plots reveal that the antenna is radiating omnidirectionally. Notably, both the measured and simulated radiation patterns demonstrate high degrees of agreement in the E and H planes, respectively.  
Figure 8: Current distribution at a frequency of 4.6 GHz.  
Figure 7: S-parameter plot of simulated and measured results for the (**a**) conventional and (**b**) proposed antenna.  
Figure 9b illustrates both the simulated as well as the measured gains and radiation efficiency of the irregular octagonal two-port textile MIMO antenna across frequencies from 3 GHz to 7 GHz. Our wearable textile irregular octagon-shaped two-port MIMO patch antenna exhibits gains of up to 4.1 dB at frequencies as low as 4.6 GHz, with maximum gains reaching 6.5 dB, and the efficiency is 96% when set to 6.75 GHz.
# MM-ConvBERT-LMS: Detecting Malicious Web Pages via Multi-Modal Learning and Pre-Trained Model
## 4 Experiment and Analysis  
### Experimental Datasets and Metrics  
In order to validate the effectiveness of MM-ConvBERT-LMS in the malicious web page detection task, a synthetic malicious web page dataset based on the public dataset [35; 36] is used in this paper, which contains features of both HTML and URL modalities. The length distribution of the two data is shown in Figure 6. In the experiments, 10,000 data are used as the training set, 5000 data are used as the validation set, and 10,000 data are used as the test set, and the distribution of positive and negative samples is balanced. Remarkably, we find that the HTML features extracted directly from the original dataset suffer from severe data duplication problems between the training, validation, and test sets, and one possible reason is that the HTML tag sequences of some of the simpler web pages are likely to be the same. To ensure the fairness of the experiments and to avoid the data pollution problem, we additionally de-duplicated the HTML tag sequence data, even though this duplicated data came from different original web pages. The hardware configuration and software environment of the server used for the experiments are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Device \& Software** & **Information** \\ \hline CPU & Intel(R) Xeon(R) CPU E5-2690 v4 \\ RAM & 12 GB \\ External Storage & 512 GB SSD \\ Operating System & Ubuntu 18.04.3 LTS \\ Python Version & 3.8.8 (AMD64) \\ Machine Learning Library & Pytorch 1.11.0 \\ \hline \end{tabular}
\end{table}
Table 2: Hardware configuration and experimental environment.  
Figure 6: The distribution of the two-modal data.  
For the evaluation metrics, Accuracy, Precision, Recall, and F1 Score were chosen to evaluate the performance of the model, and the calculation procedure is shown in Equations (12)-(15). Where _TP_ and _TN_ represent the number of true positive samples and true negative cases, _FP_ and _FN_ represent the number of false positive samples and false negative samples. Figure 7 provides a graphical representation of these metrics, with _TP_ and _TN_ indicating correctly classified samples, and _FP_ and _FN_ representing misclassified samples.  
\[\text{Accuracy}=\frac{TP+TN}{TP+FN+FP+TN} \tag{12}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{13}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{14}\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{15}\]  
To better train the model, we use the AdamW optimizer and use a warm-up mechanism during the training of the pre-trained models. The number of training epochs was set to 10 for the pre-trained models and made adjustments to a maximum of 20 epochs for the shallow deep learning models.