esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Resolution Analysis with Visualization to Determine Network Attack Patterns
## 6 Results and Discussion
Although it is helpful for high-dimensional applications like genomics data, especially where the number of variables exceeds the size of the observations [69], it is not suitable for analyzing highly imbalanced data. Since the intrusion detection datasets (i.e., Kyoto2006+ and CIC-IDS2017) are highly imbalanced (having a lower number of attack instances), we also found a major limitation of using RF to analyze such datasets because it produced high-performance scores (close to 0.99 or 1.0) even if different wavelet features were utilized. Thus, we excluded RF from our classification analysis. However, since it has been broadly used in network traffic analysis by researchers [64; 70; 71], an extensive analysis study needs to be performed to evaluate its effectiveness in analyzing highly imbalanced intrusion detection datasets. From the evaluation studies, we determined that having a window size larger than or equal to 150 would be good for analyzing network traffic data. However, this claim needs further analysis because if the data has high similarities between normal and attack events (for example, the Kyoto2016 dataset), the distinction between them cannot easily be determined even if a higher window size is applied. As discussed above, we found that a high number of significant features were determined with increased window sizes. However, it produced fewer numbers of instances. Utilizing a relatively large window size with a small step size is highly recommended because it produces sufficient amounts of data that eventually helps analyze network traffic patterns. Additionally, an extensive evaluation must be performed to find an optimal window size. Because of the highly complex nature of network traffic data (often highly imbalanced), these findings lead us to initiate our future research in understanding the characteristics of network traffic data to determine the optimal alpha value automatically.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 2 System Model
### Path Loss and User Association  
We consider both small- and large-scale propagation effects in the channel model. In particular, given a transmitter at \(x\in\mathbb{R}^{2}\), the receiving power at \(y\in\mathbb{R}^{2}\) is given by \(P_{t,x}Ah_{x,y}L^{-1}\left(x,y\right)\), where \(P_{t,x}\) is the transmit power, \(A\) is a propagation constant, \(h_{x,y}\) denotes the fading channel power due to multi-path propagation from \(x\) to \(y\). Moreover, \(L\left(x,y\right)=\left\|x-y\right\|^{\alpha}\) models the channel variations caused by path loss \(\left\|x-y\right\|\), where \(\alpha\) is the path loss exponent and \(\left\|x-y\right\|\) denotes the Euclidean distance between \(x\) and \(y\). We consider Rayleigh multipath fading and log-normal shadowing, i.e., \(h_{x,y}\sim\exp(1)\) is exponentially distributed with unit mean power. For deriving the analytical results, it is assumed that it is rare for the path loss exponent to vary across different tiers.  
Each node associates to the BS that provides the maximum average bias-received-power (BRP). For example, the node located at \(y\) is associated to the BS at \(x\) in tier \(y\) if and only if \(P_{t,k}L^{-1}\left(x,y\right)B_{k}\geq P_{t,j}L^{-1}_{min,j}\left(y\right)B_{j}\) for \(j=1,...,K\). When \(B_{k}\) is constant for any \(K\)-tier, the biased cell association policy will reduce to maximum average received power policy. The maximum average BRP association is stationary [26], i.e., the association pattern is invariant under translation with any displacement. According to the Palm theory [27], the analytical results of a typical cell \(C_{0}\) in tier \(k\) can be extended to other cells \(C_{i}\left(i=1,2,...\right)\) in the same tier. Therefore, we only need to focus on the cell \(C_{0}\) for the analysis in the remainder of this paper.  
On the condition that the path loss exponent is the same in all tiers, the probability of a node associated with \(k\)-th tier \(A_{k}\) under maximum average BRP policy can be obtained as [28]  
\[A_{k} = \frac{\lambda_{k}(P_{t,k}B_{k})^{2/\alpha}}{\sum_{j=1}^{K}\lambda_ {j}\big{(}P_{t,j}B_{j}\big{)}^{2/\alpha}}. \tag{1}\]  
We can see that the association probability depends on the cell association biases, the densities of BSs, and the transmit powers in each tier.
# A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
## 4 Base Networks
### U-Net Architecture  
U-Net [34] is a deep learning image segmentation model proposed by [PERSON] et al. in 2015. It has a symmetric encoder and decoder structure that resembles the letter U, hence its name. The current diffusion model still uses U-Net, which has been applied for a long time. The diffusion model based on U-Net performs well in practice and has also achieved some success in SR reconstruction [22; 23; 24; 25; 26].  
As shown in Figure 2, the original U-Net structure used in SR3 has three important types of modules: DownSample, UpSample, and ResnetBlockwitAttention (RBA).  
Figure 1: SR3 operational flow.  
Figure 2: U-Net operational architecture.  
The DownSample and UpSample modules do not change the number of extracted feature channels, and they are responsible for adjusting the image size to obtain information at multiple scales. The DownSample is implemented by using a convolution of size \(3\times 3\) with a step size of 2 and a circle around the zero-filled image. The UpSample is implemented by using an interpolation method. It is worth noting that during the up-sampling process, the input feature map will be spliced with the same size feature map obtained with the previous down-sampling, which is the so-called skip connection. The skip connections ensure that the features in the decoding process retain most of the original information while including high-frequency details and low-frequency contours.  
RBA is composed of several residual blocks and an attention module that serves as a transition layer, which does not alter the size of the image but serves to enrich the feature representation; however, such a structure also induces temporal problems in the overhead.  
Furthermore, we present a comprehensive exposition of the architecture of the U-Net depicted in Figure 2: the input is a concatenated \(LR\) and \(HR_{t}\) image with six channels. After repeated up-sampling and down-sampling at different scales, a final RGB image with three channels was generated. Here, \(RBA^{mult\_size}\) represents the RBA with a given feature growth sequence. In Figure 2, the channels of each RBA module increase exponentially from top to bottom based on the \(mult\_size\) sequence and a basic channel number. For instance, if \(mult\_size\) = {1, 2, 4, 8, 8}, and the basic channel number is 64, there are five groups of symmetric RBA modules that scale down the image to 1/32 of its original size. The first group outputs 64-channel features, the second group outputs 128-channel features, the third group outputs 256-channel features, and so on.
# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 2 Legal Document Ranking Using Diversification
### Relevance Judgments
For the data given in Table 4, based on the relative proportions, with an acceptance threshold of 20%, we can infer that Document Number 08_711 is relevant for Topics 1 and 2, while Document Number 07_924 is relevant for Topics 1 and 5. In other words from the corresponding topic model distribution, we acquire binary assessments for each query and document in the result set. Thus, using LDA, we create our ground-truth data consisting of binary aspect assessments for each query. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Document** & **Topic 1** & **Topic 2** & **Topic 3** & **Topic 4** & **Topic 5** \\ \hline
08\_711 & 0.731 & 0.267 & 0.00172 & 8.79 \(\times\) 10\({}^{-5}\) & 4 \(\times\) 10\({}^{-5}\) \\
09\_1395 & 0.467 & \(2.25\times 10^{-5}\) & 0.459 & 0.0746 & 1.04 \(\times\) 10\({}^{-5}\) \\
09\_1383 & 0.99 & \(2.85\times 10^{-5}\) & 0.00944 & 0.000298 & 1.31 \(\times\) 10\({}^{-5}\) \\
06\_1169 & 0.994 & \(5.33\times 10^{-5}\) & 0.00559 & \(5.41\times 10^{-5}\) & 2.46 \(\times\) 10\({}^{-5}\) \\
07\_924 & 0.237 & \(4.83\times 10^{-5}\) & \(4.64\times 10^{-5}\) & \(4.9\times 10^{-5}\) & 0.763 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Topic composition for five random documents on the result set for Query 1: Abandoned and Lost Property. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Topic** & **Top Words** \\ \hline
1 & court appliciant property respondent claim order costs company trustee trust \\
2 & evidence agreement contract business drtime hamiltonrespondent applicant sales \\
3 & tribunal rights land title native evidence area claim interests appellant \\
4 & evidence agreement meeting conduct time tiliformsecond brand septemberaustralia \\
5 & evidence professor university dr property gray patent unwacaimnrc \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top key words for topics for documents on the result set for Query 1: Abandoned and Lost Property. We have made available our complete dataset, ground-truth data, queries and relevance assessments in standard TREC format (qrel files), so as to enhance collaboration and contribution with respect to diversification issues in legal IR ([[https://github.com/mkoniari/LegalDivEval](https://github.com/mkoniari/LegalDivEval)]([https://github.com/mkoniari/LegalDivEval](https://github.com/mkoniari/LegalDivEval))).
## 4 Discussion
### Spatial Resolution and Sensors Characteristics  
We generally found that the strength of the NDVI\({}_{is}\)-NDVI\({}_{MD}\) relationship tended to decrease with decreasing spatial resolution (from \(0.5\times 0.5\) to \(3.5\times 3.5\) km, Figure 3), this is due to a closer spatial correlation between the NDVI\({}_{is}\) and NDVI\({}_{MD}\) 0.5 km product as a consequence of the limited spatial representation for the in situ NDVI. In situ radiation sensors are generally mounted a few meters above the canopy, limiting radiation measurements to a small area that is not representative of the biome or landscape. This is particularly true in heterogeneous sites [35], while the instant field of view (IFOV) of the radiometric measurements tends to be too small to effectively represent a landscape or plant functional type [36; 40]. The potential use of in situ radiation measurement data in the upscaling of site responses may be improved by increasing the spatial representativeness of the canopy dynamics. For example, the radiometer should be placed at the convenient distance above the canopy to sense a larger area that is comparable at the satellite pixels. To reduce the mismatch between in situ and satellite measurements, it could be helpful to have more than one radiation sensor for at least downward radiation measurements in the flux footprint area (i.e., a mobile track or transect). This allows to better identify the in situ spatial variability of canopy reflectance. A precise estimation of the flux footprint could also help to improve the upscaling of in situ data. Flux footprint is assumed to vary between 10 to 100 times of the measurements height; however, this assumption tends to overestimate the flux source area [49]. The recent launch of the space borne high-resolution ESA Sentinel-2 mission (10 m \(\times\) 10 m) opens a unique opportunity in phenological application and upscaling flux and in situ data.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 3 A Survey of the Literature
### Review of Symmetric-Key Based Security for Mobile Systems
To the best of our knowledge, until now there is no authentication scheme for wireless communications based on symmetric key cryptography has been proposed to achieve all the security requirement compared with the proposed PKI-based security schemes, but still are cost-effective solutions. However, from the above analysis we consider the proposed schemes in [48; 49] are relatively more secure and efficient protocols, indeed they are more suitable and flexible with mobile systems. Finally, this review only covered some of many research works that are related to mobile communications security and focus on PKI cryptosystems, in particular the authentication and key agreement approaches, while making reference to the works of many other researchers [50; 51; 52; 53; 54; 55; 56; 57; 58; 59] whose research works proposed ways using this approach to improve many problems facing mobile communications security such as replay, IMSI catcher, and false base station (fake MME) attacks. Their research helped provide authentication, key agreement, user anonymity, and privacy to secure network entities from such attacks. Moreover, there are still many other works proposing different methods to protect mobile devices. For instance, [PERSON] et al. [60] proposed a concealment scheme to improve the security of non-protection in mobile operating systems such as iPhone operating system (iOS) devices due to the lack of confidentiality and privacy of these mobile devices [61; 62].
## 2 Background Knowledge
### Low-Rank Representation  
Due to the problem of the signal-to-noise ratio, the current data acquisition methods will inevitably cause varying degrees of noise pollution in the datasets. To reconstruct a low-rank matrix from polluted data, LRR was developed. Given a dataset \(\mathbf{X}\) that has been polluted by errors \(\mathbf{P}\), \(\mathbf{X}\) can be thought of as a mixture of two parts (\(\mathbf{X}=\mathbf{U}+\mathbf{P}\)), where the raw data \(\mathbf{U}\) should be of a low rank. In order to achieve the above objectives, the subsequent regularization rank minimization equation could be formulated as  
\[\min_{\mathbf{U},\mathbf{P}}~{}rank(\mathbf{U})+\lambda\|\mathbf{P}\|_{l}~{}~{}s.t.~{}~{}\mathbf{ X}=\mathbf{U}+\mathbf{P}, \tag{1}\]  
where \(\lambda\) is a trade-off parameter and \(\|\cdot\|_{l}\) also indicates a specific regularization technique for the error term.  
After introducing an extra dictionary matrix, the more general rank minimization problem can be applied to more real mixed data as follows:  
\[\min_{\mathbf{C},\mathbf{P}}~{}rank(\mathbf{C})+\lambda\|\mathbf{P}\|_{l}~{}~{}s.t.~{}~{}\mathbf{ X}=\mathbf{U}\mathbf{C}+\mathbf{P}, \tag{2}\]  
where \(\mathbf{U}\) is a dictionary. Under the circumstances, the minimum solution of \(\mathbf{C}\) which can be abbreviated as \(\mathbf{C}^{*}\) is the \"lowest rank representation\" of \(\mathbf{X}\). In the process of practical application, it is difficult to build an appropriate dictionary. Therefore, the self-representation method of using itself [21] as a dictionary came into being. At this time, \(\mathbf{C}^{*}\) represents the sample's subspace knowledge and can be used for clustering.
# CBMDB: A Database for Accessing, Analyzing, and Mining CBM Information
## 3 Results and Discussion
### Comparison to Other Databases  
To further demonstrate the contribution of our database in efficiently accessing CBM information, in exploring potential CBMs, and in speculating on their functions, the CBMDB was compared with two other commonly used CBM-related databases: CAZy ([[http://www.cazy.org/](http://www.cazy.org/)]([http://www.cazy.org/](http://www.cazy.org/))) and dbCAN ([[https://bcb.unl.edu/dbCAN2/](https://bcb.unl.edu/dbCAN2/)]([https://bcb.unl.edu/dbCAN2/](https://bcb.unl.edu/dbCAN2/))).  
CAZy has been used for nearly three decades and has become the main access point for researchers to collect and classify information on CAZymes, some of which contain CBM domains [25]. The dbCAN, another popular database, integrates with the HMMER, DIAMOND, and Hotpep search tools and provides automated CAZyme annotations for newly sequenced genomes [26]. Both databases mainly focus on providing information on CAZymes and are not specifically directed at CBMs. Furthermore, only limited information on CBMs, including classification, strain origin, and GenBank accession number are available. Compared to CAZy and dbCAN, the CBMDB integrates more direct data related to CBMs, including the amino acid sequence, the structure, source enzymes, the position of CBMs in the enzyme, etc. In addition, unlike the CAZy and dbCAN, the CBMDB combines more powerful analysis tools that enable multidimensional information access, such as the sequence alignment tools for classification and conserved site confirmation, the PDB-based search tool for structural information, the visual analysis tool for phylogenetic trees, and the HMMs-based tools for CBMs annotation. Consequently, the CBMDB will be a unique and powerful resource for CBM-related research.  
Figure 4: Analysis results of an unknown protein with potential CBM functions. (**a**) Results of sequence similarity alignment; (**b**) results of multiple sequence alignment; (**c**) results of structural similarity search; (**d**) visualized results of phylogenetic tree files.
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
## 5 Conclusions  
In this paper, a multi-axis laser interferometer is proposed, whose measurement is not affected by the installation errors. The interferometer measurement model shows that there is no need for the accurate installation of lasers, and the displacement computation shows the irrelevance between high measurement accuracy and installation errors, which is proven by the proposed solution component uniqueness theory for nonlinear equation systems. The simulation shows that even with installation errors close to 1 \(\upmu\)m and 1 \(\upmu\)rad, the interferometer is still able to measure the multi-DOF displacement accurately. For the case of where there is reading noise with RMS=0.1 nm, the STD of the displacement error is 1.884 nm and 5.871 \(\times\) 10\({}^{-7}\) mrad.  
This paper provides a novel way for the design and development of multi-axis interferometers, that is, with today's more and more developed computer processing ability, using a high-performance numerical computation to compensate for the lack of low mechanical manufacturing and assembly level, so that the measurement accuracy approaches the theoretical limit. The modeling method proposed is not limited to the topology of the interferometer in the paper, but can also be applied to other types of multi-axis interference systems. It is only necessary to take the installation parameters such as the laser angle as unknowns into the optical path model, and then the nonlinear computation method can be used to solve the model to obtain accurate displacement measurement results.  
In the future, experiments will be designed to verify the measurement performance of the proposed interferometer. The optimized iteration algorithms with a lower complexity and higher speed will also be studied.  
Figure 14: (**a**) Simulation result of \(x\) with reading noise (RMS = 0.1 nm). (**b**) Simulation result of \(\theta\) with reading noise (RMS = 0.1 nm).  
**Author Contributions:** Writing--original draft, T.W.; Writing--review and editing, J.H., Y.Z. and G.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51175296 and 51677104, and by the Autonomous Scientific Research Project of the State Key Laboratory of Tribology, Tsinghua University, grant number SKLT2018C07.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Problem Description and Model Setting
### Problem Formulation  
The mathematical model of the F-MSRCPSP is as follows.  
\[Min\ Makespan=\max_{j\in A}(f_{j}) \tag{4}\]  
\[Min\ Cost=\sum_{r=1}^{n_{r}}\sum_{t=1}^{T}c_{r}y_{rt} \tag{5}\]  
Subject to:  
\[\sum_{k}x_{rkt}\leq 1;\forall j\in A,r\in R,t\in T \tag{6}\]  
\[AK_{j}^{k}RK_{r}^{k}x_{rkt}z_{jrt}>0;\forall j\in A,r\in R,k\in S,t\in[s_{j},f _{j}] \tag{7}\]  
\[0<f_{i}<s_{j};\forall j\in A,i\in P_{j} \tag{8}\]  
\[z_{jrt}=1;\forall j\in A,\exists r\in R,t\in T \tag{9}\]  
\[\sum_{r=1}^{n_{r}}\sum_{k=1}^{n_{k}}x_{rkt}z_{jrt}\leq 1;\forall t\in T \tag{10}\]  
The first objective function in Equation (4) minimizes the project make-span. The second objective function in Equation (5) minimizes the total cost, which consists of the cost of using resources in each time period. Constraint (6) guarantees that only one skill is required to complete an activity, and constraint (7) ensures that only the resources that master the required skill of the activity can be allocated to the activity in each discrete period. The precedence constraints among activities of the project are ensured by constraint (8). Each activity should be processed once, and each resource can only use one skill to deal with one activity in one time period. The constraints are shown by constraints (9) and (10), respectively.  
To further illustrate the F-MSRCPSP, an illustrative project with five activities, three resources, and three skills is described as follows. Table 2 represents the skill distribution and corresponding mastery level of each resource, as well as its unit salary. Table 3 shows the lower and upper usage bounds, the skill requirements, and their predecessors. The comparison Gantt chart of a feasible solution to the MSRCPSP and F-MSRCPSP is depicted in Figure 1. In the MSRCPSP, a single resource is allocated to an activity and remains constant during the processing of the activity, such as allocate \(R_{2}\) to \(A_{1}\), \(R_{3}\) to \(A_{3}\), \(R_{1}\) to \(A_{5}\), and so on. In the F-MSRCPSP, a resource combination could be allocated to an activity without violating constraints and may change with the implementation of activities and the use of resources, such as for the resource combination, \(A_{1}\) changes from \(R_{2}\) to \([R_{2},R_{3}]\) during the 12 th time period, \(A_{3}\) changes from \(R_{1}\) to \(R_{3}\) during the 7 th time period, and \(A_{5}\) also changes the resource combination in the 8 th and 11 th time period, respectively. Compared with the MSRCPSP, the F-MSRCPSP can obtain a better scheduling plan with a shorter make-span and lower total cost.
## 3 Results and Discussion
### Gas Sensing Investigations
Therefore, the sensor is fast enough to detect any leakage of H\({}_{2}\) gas in real applications. In addition, as expected, the response time decreases with increasing H\({}_{2}\) gas concentration because more H\({}_{2}\) molecules were easily adsorbed onto the sensor surface. The selectivity of the optimized sensor was also checked by exposing it to 10 ppm H\({}_{2}\), toluene, benzene, ammonia, and acetone at 300 \({}^{\circ}\)C. As shown in Figure 10, the sensor  
Figure 8: Response of optimized SnO\({}_{2}\) sensor versus annealing temperature to 10 ppm H\({}_{2}\) gas at 300 \({}^{\circ}\)C. Inset depicts dynamic resistance curves of SnO\({}_{2}\) sensors applied on tubular substrates under optimized conditions and annealed at various temperatures (400–700 \({}^{\circ}\)C) to 10 ppm H\({}_{2}\) gas. Figure 9: Response time of optimized SnO\({}_{2}\) sensor to 10, 50, 100, 500, and 1000 ppm H\({}_{2}\) gas at 300 \({}^{\circ}\)C.  
shows a higher response to H\({}_{2}\) gas relative to other gases. This is mainly due to the small size of the H\({}_{2}\) molecules in comparison with other gases, which leads to the easy and fast diffusion of this gas into deep parts of the sensor, resulting in more adsorption and more sensing reactions. Furthermore, the sensing temperature can affect the selectivity of the sensors, and it seems that 300 \({}^{\circ}\)C is sufficient for the H\({}_{2}\) gas to be sufficiently adsorbed on the sensor surface, and as a result of the subsequent reactions, a higher sensing signal relative to other gases will be generated.