esa-sceva/satcom-chunk-collection huggingface dataset:
# AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement
## 4 Experiment and Analysis
### AE-DTI Effectiveness Experiment
#### 4.4.2 Comparison of AE-DTI with Other Methods  
This section examines the classification performance and effectiveness of four models. These models include the following: [PERSON] et al. [21] utilized time-based features extracted from 15-s Tor traffic to train a random forest model; [PERSON] et al. [29] employed sliding time windows to segment network traffic and calculated the relative entropy of traffic within the time window to identify Tor traffic; [PERSON] et al. [30] proposed a deep-learning-based scheme for detecting dark web traffic (Tor traffic); and this paper presents a darknet traffic identification method based on autoencoder (AE-DTI). All of these models were trained on the ISCXTor dataset.  
The experimental results are presented in Table 5. With the exception of [PERSON] et al.'s method, the accuracy of the other methods exceeds 90%, and the accuracy of AE-DTI surpasses 98%. Particularly, when the dataset is reduced to 16 dimensions by AE-FS, AE-DTI requires minimal features for training. However, AE-DTI performs better than other methods in every evaluation index. Consequently, AE-DTI not only enhances the efficiency of traffic identification but also significantly improves classification accuracy. This is of substantial importance in enhancing the efficiency and accuracy of traffic identification, and it can serve as a valuable reference for research and practical applications in related fields.  
Figure 12: Comparison of the training time and accuracy of the model between the reduced-dimensional dataset and the original dataset.
# A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem
## 1 Introduction
#### The Need for Modifying the Firefly Algorithm  
Classical FA could fall either in the stagnation (solutions do not enhance anymore) or premature convergence (trap into the local minimum) in the searching space. This is due to the excessive exploration and exploitation elements in the classical FA ([PERSON] et al., 2013; [PERSON] et al., 2011).  
Different strategies can be employed to avoid stagnation and premature convergence. For example, [PERSON] et al. (2012) suggested the balancing process explicitly for exploitation and exploration through sorting and splitting the initial population of FA in two equal groups (first half assigned to first group and second half is considered into the second group). While FA is applied for the first group, they proved neighbourhood searching of the second group can efficiently enhance the overall performance of standard FA, mainly stagnation, premature convergence and computational time.  
For solving the various classes of problems, there is a need to modify the classical FA ([PERSON], 2010b). Furthermore, the FA relies on the attractiveness formulation and variation of light intensity. Each of these aspects allows a substantial opportunity for algorithm enhancement. Several attempts have been made to modify the firefly algorithm [5; 6]. The main classifications can be found in Table 1.  
There are six main directions in literature for modifying FA (Table 1). According to Table 1, these directions have gone into the development of parallel FA, elitist FA, Levy flights randomized FA, binary represented, Gaussian randomized FA and chaos randomized FA. In standard FA, the brightest firefly (the current global best solution) moves randomly which may decrease its brightness depending on the direction. This issue leads to decrease the algorithm performance. However, if the brightest firefly is allowed to move only if in a direction its brightness improves, the performance of algorithm will not decrease. Levy flights randomized, Gaussian randomized and Chaos randomized were considered for this purpose. Note that the strengths and weaknesses of different ways for modifying of FA are highly depended on the problem at hand. For example, the modify FA based on binary represented is more proper for problems with binary variables.
# A Framework for Symmetric-Quality S3D Video Streaming Services
## 2 Related Work
### Conditional Replenishment Algorithm
As a result, Video Enhancement Information (VEI) is generated, which contains compressed data such as the mode, size of each block, and IVV. Figure 6 visually demonstrates the application of the conditional replenishment algorithm, showcasing the process where low-resolution blocks from the right-view video are replaced with high-quality substitute blocks extracted from the left-view video. The blocks designated for conditional replacement are marked in gray, with each block assigned a specific mode. Figure 7 briefly illustrates an example of the bits allocated according to the modes of these blocks, as the stream is constructed by sequentially traversing the blocks in the order shown on the right. For instance, if the mode of a block is 01, it is further divided into smaller blocks. If the mode is 10, the upscale mode is applied, and for 11, the replenishment mode is applied along with the assignment of IVV information. In blocks that are no longer divided, only upscale and replenishment modes are present, and VEI is structured in a manner that distinguishes them using 00 and 01, respectively. Figure 5: Conditional replenishment algorithm. Figure 8 illustrates the SHVC codec integrated with the aforementioned technology. The conditional replenishment algorithm improves video quality by 2 to 5 dB in terms of _PSNR_ and enhances RD performance through inter-frame encoding of disparity information [21]. This helps resolve the issue of asymmetric image quality between the left and right views, providing a more comfortable 3D viewing experience. Figure 8: SHVC codec incorporated with VEI. Figure 6: Conditional replenishment algorithm application results. Figure 7: VEI stream construction method.
## 5 Discussion
However, due to the limitations of HR satellite data, such as those for HJ-1, in the neural network training process, the input parameters included only the reflectance of the red and NIR bands, which are fairly sensitive to vegetation. If shortwave infrared bands are measured by satellites, these data can be introduced as a modification to counteract satellite data noise. Due to the HR data having a high spatial resolution, minor deviation in geometric positioning can generate substantial errors. Therefore, precise geometric corrections are required during initial processing. Although the retrieval model presented here has achieved good results in croplands, applications in other land types need to be further validated. Moreover, the quality of the training samples has a serious impact on the estimation results, MODIS over-estimating LAI in sparse vegetation areas and underestimating LAI in dense vegetation areas [59] and the effect of using MODIS LAI as a training sample on the results needs further analysis. The NARX neural network needs to be trained for the time series of each pixel, it will take a long time to estimate in a large area. On average, it took about 8 minutes of training time for 300 \(\times\) 300-pixel image size. The time was measured on a computer with Intel Core i7 3.4 GHz processor and 4 GB of random access memory (RAM). We will try to improve the algorithm and shorten the training time.
# Arabidopsis Disulfide Reductase, Trx-h2, Functions as an RNA Chaperone under Cold Stress
## 3 Results
### Trx-h2 Exports Nuclear mRNA to the Cytoplasm under Cold Stress  
Because Trx-h2 harbors a putative NLS at its C-terminal end (Figure 1) and functions as an RNA chaperone (Figures 4 and 5), we investigated its role in the export of mRNAs from the nucleus to the cytoplasm at different temperatures. Leaves of 14-day-old WT (Col-0), _trx-h2_, and Trx-h2 Oe Arabidopsis plants were incubated with a 45-mer oligo(dT) probe labeled with fluorescein at its end, and traced the localization of poly(A) mRNAs by in situ hybridization, as described previously [26].  
Under normal conditions (22 \({}^{\circ}\)C), fluorescence signals were uniformly distributed throughout the nucleus and cytoplasm in WT (Col-0), _trx-h2_, and Trx-h2 Oe cells, indicating that mRNAs were effectively transported from the nucleus to the cytoplasm (Figure 6, upper panel). When incubated at 0 \({}^{\circ}\)C for 2 days, although WT (Col-0) and Trx-h2 Oe leaves showed no difference in the distribution of fluorescence signals between the cytoplasm and nucleus, parts of strong fluorescence signals were remained at the nucleus in _trx-h2_ leaves, suggesting that the export system of mRNAs transport was critically disrupted in the _trx-h2_ mutant plant, resulting in the accumulation of mRNAs in the nucleus (Figure 6, lower panel). Together, our data suggest that Trx-h2 has dual functions acting as a disulfide reductase and as an RNA chaperone, whose function should be controlled upon the environmental temperature changes.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.2 Meteorological Data  
Meteorological data were derived from Global Land Data Assimilation System (GLDAS) products [36]. GLDAS products were generated using four land surface models and data assimilation techniques with both satellite data and ground based observational data [37; 38]. Daily near surface air temperature, near surface specific humidity, and net shortwave radiation were obtained in our study by averaging the GLDAS three-hour products. As the spatial resolution of this product was 0.25\({}^{\circ}\), bilinear interpolation was adopted to generate the 1 km meteorological datasets.
# Oriented Coloring on Recursively Defined Digraphs
## 5 Longest Oriented Path for Oriented Graphs  
**Name**: Oriented Path (OP)
**Instance**: An oriented graph \(G=(V,A)\) and a positive integer \(k\leq|V|\).
**Question**: Is there an oriented path of length at least \(k\) in \(G\)?  
We can bound the path length through the oriented chromatic number, when considering oriented co-graphs. Please note that in the subsequent results the oriented path \(\overrightarrow{P_{k+1}}\) does not necessarily refer to an induced path (though, its definition implies no chord on the path).  
**Proposition 3** ([24]).: _A directed graph \(G\) has a homomorphism to the transitive tournament \(\overrightarrow{T_{k}}\) if and only if there is no homomorphism of the oriented path \(\overrightarrow{P_{k+1}}\) to \(G\)._  
By Corollary 3 this leads to the next result.  
**Corollary 4**.: _The oriented chromatic number of an oriented co-graph \(G\) is the minimum number \(k\) such that there is no homomorphism of the oriented path \(\overrightarrow{P_{k+1}}\) to \(G\)._  
In order to compute the length of a longest oriented path \(\ell(G)\) for an oriented graph \(G\), we give the next result.  
**Lemma 6**.: _Let \(G_{1},\ldots,G_{k}\) be \(k\) vertex-disjoint oriented graphs._  
1. \(\ell(G_{1}\oplus\ldots\oplus G_{k})=\max(\ell(G_{1}),\ldots,\ell(G_{k}))\)__
2. \(\ell(G_{1}\oslash\ldots\oslash G_{k})=\ell(G_{1})+\ldots+\ell(G_{k})+k-1\)__  
**Theorem 4**.: _Let \(G\) be an oriented co-graph. Then, the length of a longest oriented path \(\ell(G)\) can be computed in linear time._  
**Proposition 4**.: _Let \(G\) be an oriented co-graph. Then, it holds that \(\ell(G)=\chi_{o}(G)-1\)._  
Proof.: The statement can be shown recursively on the structure of an oriented co-graph \(G\).  
* If \(G=\bullet\), then it obviously holds \(\ell(G)=0=\chi_{o}(G)-1\).
* If \(G=G_{1}\oplus\ldots\oplus G_{k}\) \[\ell(G) = \ell(G_{1}\oplus\ldots\oplus G_{k})\] \[= \max(\ell(G_{1}),\ldots,\ell(G_{k}))\] by Lemma 6 \[= \max(\chi_{o}(G_{1})-1,\ldots,\chi_{o}(G_{k})-1)\] by induction hypothesis \[= \max(\chi_{o}(G_{1}),\ldots,\chi_{o}(G_{k}))-1\] \[= \chi_{o}(G_{1}\oplus\ldots\oplus G_{k})-1\] by Lemma 1 \[= \chi_{o}(G)-1\]
* If \(G=G_{1}\osldots\osl G_{k}\) \[\ell(G) = \ell(G_{1}\osldots\osl G_{k})\] \[= \ell(G_{1})+\ldots+\ell(G_{k})+k-1\] by Lemma 6 \[= \chi_{o}(G_{1})-1+\ldots+\chi_{o}(G_{k})-1+k-1\] by induction hypothesis \[= \chi_{o}(G_{1})+\ldots+\chi_{o}(G_{k})-1\] \[= \chi_{o}(G_{1}\osldots\osl G_{k})-1\] by Lemma 1 \[= \chi_{o}(G)-1\]  
This shows the statements of the lemma.  
The previous lemma implies that for every oriented co-graph the upper bound of Corollary 4 is strict.  
In order to state the next result, let \(\omega(G)\) be the number of vertices in a largest clique in graph \(G\).  
Corollary 5: _Let \(G\) be an oriented co-graph, then \(\chi_{o}(G)=\ell(G)+1=\chi(und(G))=\omega(und(G))\) and all values can be computed in linear time._  
Proof: The first equality holds by Proposition 4 and remaining equality follows by Lemma 1.
## 2 Background and Related Works
### Constrained Mining  
Allowing users to specify their interest via the use of constraints [35; 39; 40] helps guide the data mining process so that only those sets of frequently co-occurring items satisfying the user-specified constraints can be found, which in turn avoids unnecessary computation for mining those uninteresting frequent patterns. These user-specified constraints can be imposed on items, events or objects in various domains, including shopper market baskets, meteorological records, and event planning calendars. In general, these constraints can be categorized into several overlapping classes according to the properties that they possess. In this article, we examine one class of constraints called anti-monotone (AM)constraints, which possess the properties of anti-monotonicity. This class of constraints can be further subdivided into the following two sub-classes:  
1. Frequency constraints include the following: \(C_{1}\equiv\textit{sup}(X)\geq\textit{minsup}\) expresses the user interest in finding frequent patterns from precise data, _i.e._, every pattern \(X\) with actual support (or frequency) meeting or exceeding the user-specified minimum support threshold _minsup_; and \(C_{2}\equiv\textit{expSup}(X)\geq\textit{minsup}\) expresses the user interest in finding frequent patterns from uncertain data, _i.e._, every pattern \(X\) with expected support meeting or exceeding the user-specified minimum support threshold _minsup_.
2. Non-frequency AM constraints, with examples include the following: * \(C_{3}\equiv\) min(_X.RewardPoints_) \(\geq\) 2000 expresses the user interest in finding every pattern \(X\) such that the minimum reward points earned by travellers among all airports visited are at least 2000; * \(C_{4}\equiv\) max(_X.Rainfall_) \(\leq\) 10 mm says that the maximum rainfall among all meteorological records in \(X\) is at most 10 mm (_i.e._, \"relatively dry\"); * \(C_{5}\equiv\)_X.Location_ = Europe expresses the user interest in finding every pattern \(X\) such that all places in \(X\) are located in Europe; * \(C_{6}\equiv\)_X.Weight_\(\leq\) 23 kg says that the weight of each object in \(X\) is at most 23 kg (e.g., no heavy checked baggage for a trip); and * \(C_{7}\equiv\) sum(_X.Expenses_) \(<\) 300 CHF says that the total expenses on all items in \(X\) is less than 300 CHF.  
Note that a constraint \(C\) is anti-monotone [40] if and only if all subsets of a pattern satisfying \(C\) also satisfy \(C\). Equivalently, a constraint \(C\) is anti-monotone (AM) if and only if all supersets of a pattern violating \(C\) also violate \(C\). A pattern \(X\) is valid in a database if such a pattern also satisfies the user-specified constraints. Given (i) a database; (ii) user-specified _minsup_; and (iii) user-specified AM constraints, the research problem of constrained pattern mining from uncertain data is to discover from the database a complete set of patterns satisfying the user-specified AM constraints (_i.e._, valid patterns).
# Atom Probe Tomography for Catalysis Applications: A Review
## 2 Atom Probe Tomography  
### Atom Probe Tomography: Basic Principles  
Since its infancy in 1967 [58], atom probe tomography has become a well-established method for studying the 3D structure and composition of materials with high mass resolution, sub-nanometer spatial resolution and ppm elemental sensitivity [59; 60; 61; 62]. APT is based on the phenomenon of field desorption, which consists of applying an electric field sufficiently high to desorb atoms from a surface [59; 60; 61; 62]. By combining a static electric field with a pulsed electric field (or other pulsed energy source) and controlling the amplitude and frequency of the pulses, it is possible to field-evaporate a specimen, atom by atom, atomic layer by atomic layer, in a controlled fashion. A position-sensitivedetector registers the position of impact of the ionized species (in \(x\) and \(y\) directions), and time-of-flight mass spectrometry (ToF-MS) is used to determine the chemical nature of the individual atoms. From the order of arrival of the desorbed species, \(z\)-axis information is recovered, and further data treatments allow the reconstruction of the three-dimensional structure of the initial specimen [63; 64; 65; 66; 67]. A schematic representation of the technique is depicted in Figure 1. APT is a destructive technique, performed under ultra-high vacuum (UHV) conditions, and the electric field required to desorb species varies with the chemical element and its close neighbors (typically in the 25-50 V\(\cdot\)nm\({}^{-1}\) range). Later advances greatly expanded the capabilities of the technique: the use of a local electrode to enhance the field effect (leading to the LEAP systems--Local Electrode Atom Probe), the use of a reflectron to increase the mass resolution, and the use of a pulsed laser to initiate evaporation permitting the study of non-conductive materials by APT [68; 69; 70; 71; 72; 73]. In addition, advances in electronic components and computerization now allow collection of millions of atoms per hour as compared to a few hundred per day in the early years of the atom probe. Detailed experimental protocols to perform APT experiments and 3D reconstruction are widely available [59; 60; 61; 62; 63; 64; 65; 66; 67].
## 2 Materials and Methods  
### Sequential Methods for FSM State Encoding  
The core principle of the sequential state encoding methods involves selecting FSM states in a specific order and assigning a unique code to each state. We put forward the following hypotheses to guide our research.  
**Hypothesis 1**.: _The outcome of FSM state encoding is influenced by the sequence in which codes are allocated to the states._  
**Hypothesis 2**.: _States that are most interconnected with already encoded states should be encoded first._  
Let \(S=\{s_{0},\ldots,s_{M-1}\}\) represent the set of FSM states, and \(K=\{k_{0},\ldots,k_{Q-1}\}\) represent the set of all available codes, where \(M\) is the number of FSM states and \(Q\) is the number of available codes, with \(s_{0}\) being the FSM's start state. Let \(Q=2^{R}\), where \(R=\lceil log_{2}M\rceil\), and let \(\lceil A\rceil\) denote the smallest integer greater than or equal to \(A\).  
Sequential FSM state encoding can be represented by the following algorithm.  
Algorithm 1 begins by assigning the zero code \(k_{0}\) to the initial state \(s_{0}\) and updates the sets of states (\(S\)) and codes (\(K\)) by removing \(s_{0}\) and \(k_{0}\), respectively. The main encoding process is carried out in a while loop, which continues until all states have been encoded. Within the loop, the next state \(s_{i}\) is selected from \(S\) according to a specified state selection mode. Then, using Algorithm 2, the appropriate code \(k_{j}\) is assigned to \(s_{i}\). After assigning the code, both \(s_{i}\) and \(k_{j}\) are removed from their respective sets.  
```
1. Assign \(s_{0}\) <= \(k_{0}\) (zero code \(k_{0}\) to the start state \(s_{0}\));
2. Update \(S:=S\setminus\{s_{0}\mid\), \(K:=K\setminus\{k_{0}\}\).
3. While (\(S\
eq\varnothing\)) do
4. Select the state \(s_{i}\) from \(S\) according to the specified state selection mode.
5. Select the code \(k_{j}\) from \(K\) to assign to \(s_{i}\) using Algorithm 2.
6. Assign \(s_{i}\) <= \(k_{j}\) (code \(k_{j}\) to state \(s_{i}\)). Update \(S:=S\setminus\{s_{i}\mid\), \(K:=K\setminus\{k_{j}\}\).
7. end while.
```  
**Algorithm 1** State encoding procedure.  
The key steps in Algorithm 1 are selecting the state \(s_{i}\) for encoding (step 3) and finding the code \(k_{j}\) that is optimal for assignment to the state \(s_{i}\) (step 4). When choosing \(k_{j}\), it is crucial to calculate the increase in FSM area resulting from this assignment.
## 2 Materials and Methods
### Multispectral Imagery Data Collection  
The MikroKopter JR11X UAV and multiple camera array 6 (MCA 6) sensor (Tetracam Inc., California (USA)) were used to collect the aerial images at week 3 (17 December 2014). The MCA 6 camera has six sensors that can be fitted in the UAV. The six bands were chosen based on the results in Section 2.3. The multispectral data were collected for different spatial resolutions, which were 5.43 mm (flight height: 10 m), 10.83 mm (flight height: 20 m), and 20.31 mm (flight height: 37.5 m). The RGB imagery was collected at a height of 1.6 m (0.87 mm spatial resolution). The field of view (FOV) for MCA 6 was 38.3\({}^{\circ}\)\(\times\) 31.0\({}^{\circ}\). The image size was 1280 \(\times\) 1024. The advantages of MCA 6 are that it can provide 8 or 10 bit data and its weight of 790 g enables it to be attached to UAVs.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 3 DC Motor Speed Consensus Control
### Fixed Reference  
Similarly to [28], based on Figure 1a, we use  
\[\tilde{u}_{1} = k_{p}\left(\xi_{2}-\xi_{1}\right)+u_{1} \tag{13a}\] \[\tilde{u}_{2} = k_{p}\left(\xi_{3}-\xi_{2}\right)\] (13b) \[\tilde{u}_{3} = k_{p}\left(\xi_{1}-\xi_{3}\right) \tag{13c}\]  
so the resulting state space representation is  
\[\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \xi_{3}\end{array}\right]=k_{p}\left[\begin{array}{ccc}-1&1&0\\ 0&-1&1\\ 1&0&-1\end{array}\right]\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \xi_{3}\end{array}\right]+\left[\begin{array}{c}u_{1}\\ 0\\ 0\end{array}\right] \tag{14}\]  
The resulting matrix \(\mathcal{L}_{r}\) has an eigenvector \(\omega^{T}=\left[1\,11\right]\). Pre multiplying (14) by \(\omega^{T}\),  
\[\dot{\xi}_{1}+\dot{\xi}_{2}+\dot{\xi}_{3}\;=\;u_{1} \tag{15}\]  
The center of mass of the states is  
\[\xi_{cm}=\frac{\xi_{1}+\xi_{2}+\xi_{3}}{3} \tag{16}\]  
Then, we take the leader control law (5) with \(n=3\)  
\[u_{1}=3k_{cm}\,\left(\xi_{cm}^{d}-\xi_{cm}\right) \tag{17}\]  
where \(\xi_{cm}^{d}\) is the desired center of mass value and \(k_{cm}\) is a constant. By substituting (17) in (15), we obtain  
\[\dot{\xi}_{cm}=-k_{cm}\,\xi_{cm}+k_{cm}\,\xi_{cm}^{d} \tag{18}\]  
If \(\xi_{cm}^{d}\) is a constant and \(k_{cm}=1\), then \(\left(\xi_{cm}-\xi_{cm}^{d}\right)\to 0\) as \(t\to\infty\). By substituting (17) in (14), we obtain  
\[\left[\begin{array}{c}\dot{\xi}_{1}\\ \dot{\xi}_{2}\\ \dot{\xi}_{3}\end{array}\right]=\left[\begin{array}{ccc}-2&0&-1\\ 0&-1&1\\ 1&0&-1\end{array}\right]\left[\begin{array}{c}\xi_{1}\\ \xi_{2}\\ \xi_{3}\end{array}\right]+\left[\begin{array}{c}3\\ 0\\ 0\end{array}\right]\,\xi_{cm}^{d} \tag{19}\]  
From (19), it is possible to see that \(\xi_{1}\), \(\xi_{2}\) and \(\xi_{3}\) converges to \(\xi_{cm}^{d}\).  
**Remark 1**.: _It is worth to recall that since the leader control is based on the center of mass of the agent's states, the general case for DC motor synchronization can be achieved by using control law (5)._