esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Machine Learning
### Techniques and Algorithms
#### Unsupervised Learning  
Unsupervised learning does not have a training dataset. Some unlabelled data are presented, and the model itself must learn from them, and then predict future results [43]. This type of learning model is the most appropriate when the problem requires a large amount of data that is unlabelled. Unsupervised learning aims to find hidden patterns or specific structures in the data. It is used to extract inferences from datasets consisting of input data without labelled responses.  
This learning model uses dimensionality reduction (e.g., principal component analysis or PCA) and clustering techniques (e.g., \(k\)-means, Fuzzy \(c\)-means, and hierarchical) to develop predictive models. An example of the application of unsupervised ML model is the detection and classification of unwanted mail or spam. These algorithms are explained below:  
* Principal component analysis (PCA) is a procedure of dimension reduction. This statistical method is useful when there are a large number of variables, where each variable has more or less importance. PCA generates a score matrix \(T\) called a score matrix where the correlation between variables is displayed in a maximum of two or three dimensions. This procedure is used to assign a set of interrelated variables to a smaller set of non-linearly correlated variables while representing as much variance as possible in the original data set [44]. Some examples of applications of this method are feature extraction [45], social science, medicine, and genome [46].
* \(k\)-means is a clustering algorithm. This technique consists of selecting the input data into \(k\) clusters for a predefined \(k\) group. Each data point in the input set is unlabelled data. The interpretation for each of the \(k\) groups is that the mean value of the group is representative of all elements in that group. Alternatively, each \(k\) groups could represent a type of input data. The user defines the number \(k\) of clusters. This algorithm uses computational distances to find the distance between two points, for example, the Euclidean distance. Also \(k\)-means can be used in intrusion detection systems (IDS) [28].  
* Fuzzy c-means is a soft clustering algorithm. This method randomly selects the number of clusters; then, each data point is assigned a cluster membership. This process is continuously reviewed to minimise the distance and degree of cluster membership [47].
* Hierarchical clustering is used to cluster data points when the data is unlabelled. This method can be classified into two categories: divisive and agglomerative. In the divisive approach, the data points are considered as one large cluster and then divided into smaller clusters. In the agglomerative approach, each data point is considered as an individual element, and then it is added to a cluster [48].
# CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions
## 3 Fuse Different Features of CDTNet
We used two methods to fuse the features, concatenation (CDT_C) and addition (CDT_A). The details of the fuse process in the lower left part of Figure 4 are shown in Figure 5.  
In Figure 5, each pooling result is transposed with different strides and filter numbers, where TC represents transposed convolution, s is stride, and num is filter number. Several successive max-pooling and transpose operations may lead to the information loss of low-level features, so we set the filter number by dividing by 2 in order, except for the last TC process, where the filter number equals that of the second-to-last TC. Figure 4: CDTNet architecture. After TC, the outputs of pool2 to pool5 have the same size as pool1. The feature maps of all transposed results and pool1, in Figure 5a, are concatenated into a single tensor as the input of the next layer. \[O_{i}=\left[p_{i},p_{i+1}t^{2^{1}},\ p_{i+2}t^{2^{2}}\ldots\ldots,\ p_{i+n}t^{2^{n }}\right] \tag{2}\]  
where \(O_{i}\) concatenates all feature maps, and \(p_{i+n}t^{2^{n}}\) is the result of (\(n\))-th pooling layer transposed with stride \(2^{n}\). The feature maps of all transposed results in Figure 5b are concatenated into a single tensor, and then added with pool1 as input of the next layer. \[C_{i}=\left[p_{i+1}t^{2^{1}},\ p_{i+2}t^{2^{2}}\ldots\ldots,\ p_{i+n}t^{2^{n}}\right] \tag{3}\]  
\[O_{i}=\frac{add(p_{i},C_{i})}{2} \tag{4}\]  
where \(C_{i}\) concatenates all transposed feature maps except \(p_{i}\), and \(O_{i}\) averages \(p_{i}\) and \(C_{i}\). According to the output size, each pooling result performs different times of TC. The TCs of pooling results are shown in Figure 6.  
In Figure 6, pool5 is transposed four times, and the stride is 2, 4, 8, and 16, respectively. The number of channels is 512, 128, 32, and 8, which is based on the filter number of each convolutional layer and the ratio in Figure 3:0  
Figure 3:1 Feature fused methods. (**a**) Concatenate the features of TC results and skip connection. (**b**) Sum the features of TC results, then concatenate with skip connection.
## 1 Introduction
### Key Definitions
#### 1.3.3 e-Government  
e-government is the short form for electronic government and refers to the framework of using information systems and technological communications to provide services to citizens within government. e-government encompasses a diversity of variables, including the use of the Internet and devices such as computers to simplify the work of government in providing various services to citizens [1]. The adoption of an e-government model can provide numerous opportunities for different stakeholders and enhance the convenience and efficiency of services [18]. Citizens can access government services directly without going through different intermediaries within a properly functioning e-government system [12]. It becomes incumbent for government to ensure that such systems are protected, and therefore, to ensure that the entire paradigm of providing services to people is secured. Cybersecurity is an integral aspect of any e-government framework.
## 3 Proposed System  
This section details the proposed multi agent system (MAS) and presents a general diagram of the system's architecture which is shown in Figure 2. Literature shows that multi agent systems have previously been employed in similar tasks like taxi fleet coordination [32] where they offer an ideal solution to abstract away issues of the different existing platforms and communication protocols. The system is divided into the following groups of agents: bike sharing data agents, weather data collector agents, geographical information agents, data persistence agents, demand prediction agents and API agents. Several multi agent platforms such as SPADE [33; 34], JADE [35], PANGEA [36; 37], AIOMAS [38] and osBrain [39] were evaluated and osBrain was finally selected for this system because of its ease of use. Furthermore, it is implemented in Python (like SPADE and AIOMAS) and it is in continuous development nowadays.  
Figure 2: General diagram of the proposed multi agent system for bike sharing systems.  
Each agent group will be described in detail in following sections, including issues encountered, techniques, developed processes and decisions taken.
## 2 Problem Description and System Models
### Constrained Markov Decision Process
#### 2.4.5 Constraints  
The CMDP incorporates safety constraints, including the voltage magnitude limits at all bus nodes. These constraints are critical for ensuring the secure operation of the distribution network. Therefore, an additional set of constraint cost functions is introduced as follows:  
\[R_{c}=\mathbb{E}\left[\sum_{t=0}^{T}\gamma^{t}c^{t}\right]\leqslant d \tag{7}\]  
where \(c=\sum_{i}^{N}[\max(0,V_{i,\tau}-1.05)+\max(0,0.95-V_{i,\tau})]\), \(d\) denotes the predefined constraint threshold.  
The detailed interaction process between the agent and the environment is illustrated in Figure 3. At each time step, the agent first perceives the current state of the distribution network environment, which includes key information such as node voltages, PV states, and capacitor states. Based on the current state, the agent selects an action \(a\) according to its decision-making policy. This action influences the reactive power outputs of the PV inverters, as well as the reactive power compensation provided by the capacitor banks. Subsequently, the environment updates its internal state according to the action, including changes in power flow, PV status, and capacitor status. The updated states are then fed back to the agent, along with corresponding reward and cost signals.
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 2 Basic Theories and Methods
### Construction of Damage Index  
In order to better to understand the damage to the structure, the ratio of its damage frequency band energy to the sum of all frequency band energies is defined as follows:  
\[R_{p}=\frac{E_{j,p}}{\sum\limits_{i=0}^{2j-1}E_{j,i}}\hskip 28.452756 pt(p=1,2, \ldots,m) \tag{9}\]  
The damage state of the structure is characterized by the change in the energy ratio of before to after the damage, and the energy ratio variation of the p band can be expressed as:  
\[ERV_{p}=\abs{R_{bp}-R_{ap}} \tag{10}\]  
In the above expression, \(R_{bp}\), \(R_{ap}\) represent the energy ratio of the p frequency band before and after structural damage, respectively.
## 3 Results and Discussion
### Physisorption Test Parameters  
The BET surface area, pore volume, mean pore diameter, and particle size constitute interconnected characteristics that are essential for optimizing the photocatalytic efficiency of a material [57]. In this case study, these parameters were assessed through physisorption testing, and the findings are detailed in Table 3. The results indicated that the TiO\({}_{2}\)/zeolite material exhibited the highest surface area (169.079 \(\pm\) 0.802 m\({}^{2}\)/g), surpassing those of the synthesized TiO\({}_{2}\) (86.951 \(\pm\) 0.346 m\({}^{2}\)/g), the commercial TiO\({}_{2}\) from Sigma Aldrich (80.571 \(\pm\) 0.278 m\({}^{2}\)/g), and zeolite analcime (1.740 \(\pm\) 0.005 m\({}^{2}\)/g). The zeolite analcime exhibited the lowest surface area value, which was characteristic of this structure type due to its high-temperature synthesis [49]. On the other hand, the value obtained for TiO\({}_{2}\)/zeolite was approximately double that of the synthesized pure TiO\({}_{2}\). The high surface area and porous structure obtained for this material favor the adsorption, desorption, and diffusion of reagents, resulting in high photocatalytic activity [58]. Furthermore, the other measured parameters, such as the pore volume and pore size, in the TiO\({}_{2}\)/zeolite composite also exhibit the highest values compared to those of the other samples, as indicated in Table 3.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Sample** & **Band Gap (eV)** \\ \hline TiO\({}_{2}\)/zeolite & 3.14 \\ TiO\({}_{2}\)–Sigma Aldrich & 3.29 \\ Synthesized TiO\({}_{2}\) & 3.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The calculated band gaps of the samples.  
Figure 6: Indirect band gaps calculated for **(a)** TiO\({}_{2}\) from Sigma Aldrich, **(b)** synthesized TiO\({}_{2}\), and **(c)** TiO\({}_{2}\)/zeolite. The red line represents the Tauc plot image, and the tangent blue line indicates the estimated band gap energy from the linear fit.
# Optimization of the Drying Temperature for High Quality Dried Melissa officinalis
###### Abstract  
The drying temperature is one of the main factors affecting the storage of medicinal and aromatic plants (MAPs). The present study aimed to investigate the impact of different drying temperatures (20, 35, 42, and 49 \({}^{\circ}\)C) on _Mentha officinalis_ quality attributes (moisture content, color, chlorophyll content) and the composition of its essential oil (EO), as well as the environmental impact, to determine the optimum drying temperature for this herb. According to the current findings, higher temperatures resulted in shorter drying times. However, this was accompanied by increased energy consumption and higher carbon footprint per hour of operation. Both room temperature (20 \({}^{\circ}\)C) and high oven temperature (49 \({}^{\circ}\)C) led to a darker colored product (i.e., higher browning index). Drying at 20 \({}^{\circ}\)C resulted in a higher EO yield compared to drying at higher temperatures (42 and 49 \({}^{\circ}\)C). Furthermore, lower temperatures (20 and 35 \({}^{\circ}\)C) and the highest temperature (49 \({}^{\circ}\)C) significantly decreased the levels of the two major EO compounds (geranial and neral), whereas both compounds were found in higher levels when the plants were dried at 42 \({}^{\circ}\)C. On the other hand, plants dried at 42 \({}^{\circ}\)C appeared to have the lowest amount of citronellal, significantly lower than those dried at the other tested temperatures. The results suggest that the optimum temperature for drying _M. officinalis_ is at 42 \({}^{\circ}\)C, as it maintained the quality attributes of the dried product while also resulting in high quality EO.  
drying process; energy consumption; environmental impact; essential oil; lemon balm Article
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 4 Methodology  
We first reviewed existing email security tools. However, we only focused on tools that are integrated with Gmail's web interface. For instance, Tutanota is another email security tool ([PERSON] et al., 2018) that uses a separate website, and it is not integrated with Gmail's web interface. The service provider (Virtru) does not access unencrypted user content or decrypt it, and it has built-in access controls that can help Gmail users control access to their emails after they are sent. While Virtru provides security for email transmission and storage, it does not prevent attackers from compromising email accounts. In this study, we aim to understand how users perceive receiving end-to-end encrypted emails for the first time. In addition, we want to know how they respond to scenarios where sensitive information is sent via an encrypted email (e.g., when a user revokes an access message for a specific action) as well as how they manage access to the confidential information after it has been sent.
# Modelling Regime Changes of Dunes to Upper-Stage Plane Bed in Flumes and in Rivers
## 3 Step Length Models from Literature
### Comparison of Step Length Models  
Both the conceptual model of [PERSON] et al. (2009) [14] and the more physics-based model of [PERSON] and [PERSON] (1992) [42] are applied and tested in the dune model described in 2. For this test calculation scenario, A4 of [PERSON] et al. (2009) [14] was used, which corresponds to a flood wave in a flume setting, see Figure 3. For the model runs, sediment with a \(D_{50}\) of 0.28 mm, a slope \(i\) of 2 \(\times\) 10\({}^{-3}\), and a hydrograph as presented below are used.  
This artificial scenario has not been physically simulated and measured, but corresponds to flume conditions with regard to water depth and dune height. The goal of the present calculations is to investigate if transition to the upper-stage plane bed occurs in roughly the same way as the dune evolution model of [PERSON] et al. (2009) [14] predicts, as well as the general qualitative behaviour of their model. This advanced dune evolution model is provided with a k-\(\epsilon\) turbulence closure model and a separate advection-diffusion model for suspension. Scenario A4 of [PERSON] et al. (2009) [14] started from a flat bed, and  
Figure 3: Hydrograph of scenario A4 after [PERSON] et al. (2009) [14].  
showed dunes growing to a height of about 4 cm. A transition to upper-stage plane bed occurred at about 0.6 h, and a re-emergence of dunes occurred at about 1.7 h. Both step length models in the current model are tested with this scenario. The resulting development of the dune trough and crest positions (in the vertical) and water depth over time are shown in Figure 4.  
Using the [PERSON] et al. (2009) [14] step length model leads to bedform growth, though no upper-stage plane bed occurs. Apparently, this step length model does not lead to strong enough lag in the present study; the dunes do not wash out at all but keep growing at the moment in time where the k-\(\varepsilon\) dune evolution model of [PERSON] et al. (2009) [14] predicted a transition to upper-stage plane bed (0.6 h). Besides growing for too long, the dunes also become too high (10 cm). With the [PERSON] (1992) [42] step length model, no dunes are able to grow at all, which is completely different from the results of the k-\(\varepsilon\) dune evolution model of [PERSON] et al. (2009) [14].
## 4 Results of Numerical Simulation
### Normal Displacement Characteristics  
Figure 9 presents the normal displacement field diagram of group A, under the three conditions of crack initiation, crack propagation, and crack coalescence. The normal stress applied to AI, AII, and AIII was always in 1 MPa, and the shear rate was 0.01 mm/s, 0.02 mm/s, and 0.04 mm/s, respectively. In the crack initiation stage (Figure 9a(1)-a(3)), the crack initiation displacement was 0.04 mm, 0.05 mm, and 0.07 mm, in a(1), a(2), and a(3), respectively. By comparison, it was not difficult to find that the displacement of crack initiation increased with the development of the shear rate. In the crack propagation stage (Figure 9b(1)-b(3)), the displacement value on the specimen's right side was negative, and the left side was positive. Moreover, the absolute displacement value was the largest at the edge-notched cracks on both sides. The reason for this phenomenon was that the shearing action produced a clockwise moment around the center of mass, marking M in Figure 9b(1), which caused opposite displacements on the left and right sides, with the largest displacement at the edge-notched cracks on both sides. In particular, the normal displacement of the right boundary was smaller than that of the left boundary, as seen in Figure 9b(1). This was because the sheer velocity loaded on the end of the left side of the rock bridge, between the holes and the fracture-hole, provided a certain degree of support to reduce the load transfer. Therefore, the right boundary, which was away from the loading end, had a smaller normal displacement than the left boundary. In the crack coalescence stage, the greater the shear rate was, the larger the displacement value of the upper half of the specimen was, reflecting that the specimen's expansion was positively related to the shear rate.  
Three groups of specimens with the same shear rate and normal stresses of 1 MPa, 2 MPa, and 4 MPa, respectively, and marked as case BI, BII, and BIII, were selected. The simulated experimental results of the normal displacement field, shown in Figure 10, were obtained. Three stages of crack initiation, propagation, and coalescence were selected for analysis. In the crack initiation stage (Figure 10a(1)-a(3)), the maximum displacement showed an increasing trend in the increase of normal stress, which was 0.08 mm, 0.10 mm, and 0.14 mm, respectively. It can also be seen that the upper part of the right edge-notched crack, at the stage where it was far away from the loading end of the shear rate (that is, on the right side), is the place with the largest displacement in a(1), a(2), and a(3). In the crack propagation stage (Figure 10b(1)-b(3)), under shear stress, an obvious clockwise moment around the center of the specimen was generated at BI (Figure 10b(1)). The action of the bending moment made the normal phase displacements at both ends of the specimen the opposite. During the crack coalescence stage, in the case of BI (Figure 10c(1)), the upper  
Figure 8: Shear stress–shear displacement curves: (**a**) case A; and (**b**) case B.
# Machine Learning-Based Malicious X.509 Certificates' Detection
## 3 Verification for Extraction (VFE)
### Standard Checking  
The standard checking part carries out criterion checking with the guide of RFC 5280. For example, if the serial number is a positive integer no longer than 20 octets, whether certificates of versions 1 and 2 have extensions or the length of explicit text in certificate policy exceeds 200, etc. Table 2 shows the details of checking items. In a word, what we do in this part is find restrictions of RFC 5280 and check whether certificates are conforming with those restrictions. This is a necessary step for the reason that RFC 5280 is an agreement about X.509 certificates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Value Type** \\ \hline PEM certificate & PEM type certificate & bytes \\ version & version of certificate & integer \\ fingerprint & sha1 and sha256 fingerprints of certificate & string \\ serial number & serial number & integer \\ public key & public key of subject & dictionary \\ validation & validation information of certificate & dictionary \\ issuer & issuer information of certificate & dictionary \\ subject & subject information of certificate & dictionary \\ signature hash algorithm & hash algorithm used in signature & string \\ signature algorithm & asymmetrical algorithm of signing & string \\ signature & the signature of certificate & bytes \\ tbs certificate & the tbs information of certificate & bytes \\ extensions & extensions of certificate & dictionary \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detail of basic fields of X.509 certificates.
# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 2 Literature Review
The technique clusters the data points into a sphere-shaped data structure known as a micro-cluster. The initial radius of the micro-cluster is learned and adjusted according to the average data point density. A cluster graph is constructed in real-time utilizing micro-clusters to generate the arbitrarily shaped cluster. To deal with the developing property, micro-clusters that are irrelevant to recent data-stream content are recognized and eliminated. However, the approach is limited to numerical attributes, rendering it incapable of handling data streams with mixed attribute types. CeAC is another recently proposed completely online clustering technique based on grid density [25]. It is a cost-effective and adaptive clustering technique that can increase computational efficiency while maintaining the clustering result's accuracy. CeAC can generate clusters of any shape and efficiently cope with outliers in parallel. Additionally, it has an adaptive calculating approach that speeds up clustering operations by shedding workload based on the characteristics of the incoming data. The suggested method is accurate and efficient in real-time data stream clustering, as demonstrated by experimental findings. However, the suggested technique does not address the issue of clusters merging incorrectly, which is a regular occurrence when dealing with developing data streams. To summarize, existing literature contains numerous approaches that aim at solving the problem of clustering evolving data streams. Various challenging elements were considered for solving the problem. In the work of CEDAS [5] and BOCEDS [15], the authors have enabled arbitrary shape clustering algorithm to perform the clustering with evolving cluster (the cluster might take any possible shape and its shape might change with respect to time). However, significant issues are observed in these algorithms. There is a lack of quantization stage of the raw data before dealing with it in the creation of the core mini clusters. Other algorithms have adopted the grid concept for this quantization, such as MuDi-Stream [26]; however, they used the static grid in time and spatial attributes.