esa-sceva/satcom-chunk-collection huggingface dataset:
# An Innovative Adaptive-Dimension Accordion Honeycomb for Circumferential Deformation
## 2 Variable-Dimension Honeycomb Design  
### Unit Cell Geometry  
The unit cell configuration is depicted in Figure 1 and can be easily described within the polar coordinate system, taking point O as the origin and exhibiting symmetry about the angular bisector. The geometry of the unit cell is defined by two internal angles, \(\theta\) and \(\phi\), the wall length \(l\), the radius \(r\), and the thickness \(t\). Additionally, the gauge thickness of the unit cell is denoted as \(H\). Consequently, the lengths of the internal cell walls are determined as follows:  
\[w_{1}=\frac{r\sin\frac{\theta}{2}}{\sin\frac{\phi}{2}} \tag{1}\]  
\[w_{2}=\frac{(r+l)\sin\frac{\theta}{2}}{\sin\frac{\phi}{2}} \tag{2}\]The geometric constraint for the inside cell walls to avoid touching each other is as follows:  
\[l>v=v_{1}+v_{2} \tag{3}\]  
where \(v\) represents the total invagination length, and its formulation is as follows:  
\[v=\frac{2r\text{sin}\frac{\theta}{2}}{\tan\frac{\theta}{2}}+l\left(\frac{\text{ sin}\frac{\theta}{2}}{\tan\frac{\theta}{2}}-\cos\frac{\theta}{2}+1\right) \tag{4}\]  
Therefore, the geometric limit for \(l\) is the following:  
\[l>\frac{2r}{\tan\frac{\theta}{2}/\tan\frac{\theta}{2}-1} \tag{5}\]  
The constraints for the additional parameters are as follows:  
\[\phi>\theta \tag{6}\]  
To simplify the constraints, a new parameter \(p=v/l\) is defined, with an initial range of \(p\) as \((0,1)\). Substituting \(p\) to Equation (4), we obtain the following:  
\[l=\frac{2r\text{sin}\frac{\theta}{2}}{\left(p+\cos\frac{\theta}{2}-1\right) \tan\frac{\phi}{2}-\sin\frac{\theta}{2}} \tag{7}\]  
Hence, the final range of \(p\) is as follows:  
\[1-\cos\frac{\theta}{2}+\frac{\sin\frac{\theta}{2}}{\tan\frac{\phi}{2}}<p<1 \tag{8}\]  
Obviously, for the left side of the inequality, the lower bound of \(p\) increases with \(\theta\) and decreases with \(\phi\). In this paper, the maximum value of \(\theta\) is \(10^{\circ}\) and the minimum value of \(\phi\) is \(45^{\circ}\), and the corresponding maximum lower bound is \(0.2142\). Therefore, the geometric constraint can be satisfied if \(p\) is sampled in (0.2142, 1). In the unit cell geometry, \(l\) can be uniquely described by other parameters and replaced by \(p\) as an independent parameter. Additionally, the six parameters are divided into shape parameters \(\theta\), \(\phi\), and \(p\), and size parameters \(r\), \(H\), and \(t\). Inspired by the properties of triangles, we define that when the shape parameters of two unit cells are the same, the two unit cells are considered similar. Furthermore, if the size parameters of two similar unit cells are also identical, the two unit cells are congruent.  
Figure 1: Geometric parameters of the honeycomb cell.
## 4 Experiments
### Network Structure and Implementation Details  
**Network Structure**. The Canonical Mapping part of CMD-Net is composed of two SA modules plus three FC layers. The first and the second SA modules have 512 and 128 grouping centers, respectively. Both employ Multi-Scale Grouping (MSG) to capture multi-scale patterns and concatenate features at different scales to acquire multi-scale features: The scales of the first module were 0.1, 0.2, and 0.4, and the scales of the second module were 0.2, 0.4, and 0.8. The output of the second module is a 640-dimensional vector representing the input cloud. This feature was fed into the set of three FC layers, FC1, FC2, and FC3, each of which had 512, 256, and 9 neurons, respectively. The FC3 layer generated the rotation matrix that when multiplied with the input cloud transformed it to the canonical space.  
The denoising part of the network was composed of one SA layer (the third one) plus a shared MLP block which was followed by a softmax activation. The design of the third SA module was the same as the second SA module of the Canonical Mapping part of the complete network. The shared MLP block comprised three layers with the neuron numbers 640, 1024, and 2048. We prevented over-fitting of the MLP block by setting the dropout ratio to 0.2.  
**Implementation Details.** We trained a separate CMD-Net for each category of interest and our training was performed on an 8 GB NVIDIA GeForce RTX 2080/PCIe/SSE2 GPU. The training had two stages: the first stage was the auxiliary task training where we trained \"Canonical Mapping\" part of the network for 1000 epochs. The batch size was 16. In the second stage, we trained \"Denoising\" part (downstream task) for 400 epochs freezing the rest of the network. The batch size was nine. We used an initial learning rate of 0.01 and the ADAM optimizer (\(\beta_{1}=0.9\) and \(\beta_{2}=0.9999\)) with a \(10^{-5}\) weight decay.
## 5 Discussion
### Comparative Analysis and Application Considerations of Prediction Models
For building energy consumption data with strong time-correlation, the prediction effect may be inferior to other strategies. For SVR, an SVR model is trained separately for each future time step, and each model is trained and predicts based on the historical data and influencing factors before the corresponding time step. Since SVR can handle non-linear relationships, training a separate model for each time step can better adapt to the characteristics of data at different time steps. However, it also faces the problems of a large number of models and complex management, and there is a lack of information sharing among the models, which may lead to inconsistent prediction results. Under the sequential strategy, MLP constructs an independent MLP network for each time step for training and prediction. Each MLP network learns based on the data before the corresponding time step and uses its non-linear approximation ability to predict the energy consumption of that time step. This method can optimize the model according to the data characteristics of each time step, but the increase in the number of models will bring problems such as long training time and high consumption of computational resources, and it is also difficult to fully utilize the overall information of time-series data. Similarly, LSTM trains an independent LSTM model for each time step. Although LSTM can capture the long-term dependencies in time-series data, due to the independent training of each model, it is impossible to effectively utilize the coherence information between different time steps, and the ability to mine the complex time-series characteristics in building energy consumption data are relatively weak. Moreover, the training of a large number of independent models will consume a large amount of computational resources and time. Under the sequential strategy, Transformer trains a Transformer model for each time step. The powerful self-attention mechanism of Transformer can independently capture the characteristics of the data before the corresponding time step in each model. However, similar to other models, under the sequential strategy, the number of Transformer models is large, the computational cost is extremely high, and there is a lack of collaboration among the models, making it difficult to fully utilize the advantages of Transformer in processing long-sequence data. Different prediction models have their own advantages and disadvantages under the three multi-step prediction strategies of direct, recursive/rolling, and sequential. In practical applications, it is necessary to comprehensively consider factors such as the characteristics of building energy consumption data (such as data stability, non-linear degree, time-correlation, etc. ), computational resources, and prediction accuracy requirements, and make a comprehensive trade-off to select an appropriate prediction model and multi-step prediction strategy to achieve accurate multi-step prediction of building energy consumption.
## 4 Evaluation  
After preparing the data for the dataset, the experiment was performed using a Python application by Jupyter Notebook. In the present study, we assessed the proposed approach. Since the dataset is so big and complex, we attempted to reduce the training time by finding the best type and number of features using the PSO algorithm with CE and KL divergence to obtain a subset of features that have a high rate of detection. To evaluate the performance of our proposed method, a series of experiments were performed on the CCCS-CIC-AndMal-2020 dataset. Meanwhile, we have used a hybrid approach as a classifier to detect attacks with a high degree of accuracy [24]. The performance of the classifiers used in our method was measured by measuring the F1-score, precision, recall, and accuracy. The AUC, the area under the ROC curve, is a popular metric for assessing classifier performance in malware datasets with the following stated levels: 0.5 indicates a poor guess, 0.6 indicates a poor forecast, 0.7 indicates a mediocre forecast, 0.8 indicates a decent prognosis, 0.9 indicates an excellent forecast, and 1.0 indicates an excellent forecast. To summarize the performance of the algorithms that we used in our research paper, a confusion matrix was used in which the number of correct and incorrect predictions by each class is known, as shown below:  
\[\text{F1}-\text{Score}=2*\dfrac{\text{Precision}\,*\,\,\text{Recall}}{\text{ Precision}\,+\,\,\text{Recall}}\]\[\text{Accuracy}=\frac{\text{TP }+\text{ TN}}{\text{TP}+\text{FN}+\text{FP}+ \text{TN}}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]
## 6 Application and Results  
### Framework of the Study  
In the present study available data from the EURO-CORDEX initiative [26; 27] have been considered for daily climate projections of maximum and minimum temperature, \(T_{\text{max}}\), \(T_{\text{min}}\), precipitation, \(p_{r}\), and daily maximum wind speed, \(v_{\text{max}}\). In more detail, they have been examined with data provided by the Danish Meteorological Institute (DMI), the CLM Community (CLMcom), the Royal Netherlands Meteorological Institute (KNMI), the Max Planck Institute (MPI-CSC), the Laboratoire des Sciences du Climat et de l'Environnement--Institute Pierre Simon Laplace (IPSL-INERIS), the and Centre National de Recherches Meteorologiques de Meteo France (CNRM).  
It must be highlighted that each climate projection needs a sort of calibration, which is usually done reproducing past observations in a suitably long period, generally at least 50 years, representing the so-called \"control period\". This calibration process, usually called \"Historical Experiment\", is carried out aiming to fit data predicted by the considered climate model in the control period with measured ones, in order to assess its capability to correctly reproduce future climate. In the EURO-CORDEX framework, the projections were calibrated assuming \"control period\" for the \"historical experiment\" in the period 1951-2005.  
In the calibration, the \"run\" of climate model, being guided by real changes in atmospheric composition in the period 1951-2005, was forced to fit observations. Once calibrated, the climate model was used to predict the \"future\" outcomes in the period 2006-2100 in several emission scenarios [28; 29].  
Starting from the model specifications summarized in Table 1 and considering the medium emission scenario (RCP4.5 experiment) and the highest emission scenario (RCP8.5 experiment), the present study focuses on the elaboration of factors of change maps, derived comparing the future predictions provided by several climate models with the corresponding results of the historical experiment.  
The outcomes of the analysis, in terms of factors of change maps for precipitation, ground snow load, basic wind velocity, and maximum and minimum air-shade temperature, are presented in the following paragraphs for Italy and Germany.
# Matting Algorithm with Improved Portrait Details for Images with Complex Backgrounds
## 3 Method  
To solve the complex background and detail matting problem in image matting, we designed and implemented the MORLIPO network by effectively combining the image background restoration and detail matting modules. Figure 2 shows the network structure of MORLIPO. The network consists of a background restoration module (BRM) and fine-grained matting (FGMatting) for background restoration and detail matting, respectively. To generate the final alpha, the background caused by the background restoration is used as an auxiliary input to facilitate the differentiation of a more apparent foreground, and the detail matting module enhances each other and finally generates a fused alpha, which better extracts the target details while realizing the image matting of complex backgrounds.
# Development of a CALPHAD Thermodynamic Database for Pu-U-Fe-Ga Alloys
## 4 Crystal Structure
### Pu-Fe Assessment  
The assessment of the Pu-Fe phase diagram was performed across the full composition and temperature ranges. The calculated phase diagram for the Pu-Fe system is indicated in Figure 1 and compared to available experimental data from Mardon [45] and Ofte [46]. In Table 5, the current assessment is also compared to that of [PERSON] [3] and considerable improvements are to be noted. For the iron-rich side, the congruent melting temperature of the PuFe\({}_{2}\) compound is improved and the higher solubility limit of Pu in \(\gamma\)-Fe is realized. Note that most of the amendments were made of the Pu-rich side (Figure 2), as that is the domain where iron-impurities are of interest. For the low-temperature allotropes (\(\alpha\), \(\beta\) and \(\gamma\)) of plutonium, iron solubility was not considered as there is limited (or negligible) experimental evidence. An overall improved fit of the liquidus and solidus data on the Pu-rich side was achieved. Specifically, the solubility of iron in both \(\delta\)-Pu and \(\varepsilon\)-Pu were improved (from 0.4 at. % to 0.9 at. %, and 1.9 at. % to 2.7 at. %, respectively) and in better agreement with literature (1.3 at. % Fe and 2.4 at. % Fe, respectively). The invariant reaction temperatures were also ameliorated across the phase diagram, compared to the previous assessment, especially in the Pu-Pu\({}_{6}\)Fe phase domain. The stoichiometric compounds were re-assessed to include the generally used standard reference states (\(\alpha\)-Pu and \(\alpha\)-Fe) compared to [PERSON] et al. [3], who used the liquid phase as their reference state. Table 6 indicates that the formation enthalpy of PuFe\({}_{2}\) calculated from the thermodynamic database in this work is in good agreement with the solution calorimetry measurements of [49] but is much less stable than the experimental value reported in [48] using e.m.f. techniques and the EMTO calculations presented in this work. Initial attempts to use the EMTO calculated values to optimize the Pu-Fe system included a more stable formation enthalpy for PuFe\({}_{2}\) were unsuccessful. Figure 1: Calculated Pu-Fe phase diagram based on the present CALPHAD assessment and compared with experimental data [45; 46]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Reaction Type** & **Reaction** & \multicolumn{3}{c}{**Composition at. \% Fe**} & **Temp \({}^{\text{*}}\)C/K** & **Refs.
# Secure and Efficient Authentication Scheme in IoT Environments
## 3 Proposed ECC-Based Lightweight Authentication Scheme
### Initialization Phase  
When beginning the registration process, a trusted server (\(TS\)) chooses an elliptical curve equation \(E_{p}\): \(y^{2}\equiv x^{3}+m_{1}x+m_{2}\) (mod \(p\)) over \(Z_{p}\), where \(Z_{p}\) (\(p>2^{160}\)) is the finite range of the group. A \(TS\) selects two fields, respectively, \(j_{1}\) and \(j_{2}\), where \(m_{1}\) and \(m_{2}\) must obey condition \(4\ m_{1}^{3}+27\ m_{2}^{2}\ (\text{mod}\ p)\
eq 0\). Let \(G\) be the \(E_{p}\), _the_ base point with a prime order \(n\) (\(n>\ 2^{160}\)), and \(O\) be the point at infinity, such that \(n.G=O\). Then, \(TS\) chooses arbitrary nonce (s) as its secret key.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
###### Abstract  
Ahead geological prospecting, which can estimate adverse geology ahead of the tunnel face, is necessary in the process of tunnel construction. Due to its long detection range and good recognition effect on the interface, the seismic method is widely used in tunnel ahead prospecting. However, the observation space in tunnels is quite narrow compared to ground seismic prospecting, which leads to some problems in the acquisition of wave velocity, including: the velocity of the direct wave is used to replace the wave velocity of the forward rock approximately; the arrival time information of seismic waves is the main factor in time-travel inversion or the tomography method, which is sufficient to provide a simple model rather than deal with complex geological conditions. In view of the above problems, the frequency domain full waveform inversion method in ground prospecting is introduced to tunnel seismic prospecting. In addition, the optimized difference format is given according to the particularity of the tunnel environment. In this method, the kinematics and dynamics of the seismic wavefield are fully used to obtain more accurate wave velocity results. Simultaneously, forward modeling and inversion simulations on tunnel samples with typical adverse geological bodies are given here, which verified the validity and reliability of the proposed method.  
ahead geological prospecting; frequency domain full wave inversion; forward modeling; velocity acquisition +
Footnote †: journal: Applied Sciences  
Article
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 5 Heuristic Algorithm for Energy-Aware Routing
### Adaptive Relay Nodes Activity Control  
The number of active nodes should be adapted to the volume of traffic. The need to wake up additional nodes was discussed in Section 5.1. When the volume of traffic on the network is reduced, as far as possible, some nodes should be placed into low-energy states, i.e., sleeping mode. Too many nodes cannot be deactivated. Therefore, we propose to assign a flag to a candidate planned to be placed into sleeping mode. A marked node has the lowest priority of all active neighboring nodes of \(n\), i.e., \(i\in A_{n}\) in the traffic handling. If there is a possibility of transmitting data through unmarked nodes without exceeding the constraints for maximal load \(\lambda_{N_{s}}\), the marked node should remain unused and can be disabled. Otherwise, marked nodes are used as they have a higher priority than disabled ones. The flag of the selected node is removed, and the node is incorporated into the transmission path as the next hop.  
The marked node is placed into sleeping mode when the flag remains untouched for the selected period. After deactivation, the flag is removed. If this node is re-enabled, it implements the standard priority in traffic handling. Thus, the node can be disabled without changing the state of nodes in an unpredictable manner. Unfortunately, both nodes enabling and disabling take some time and produce an inevitable delay that results in the inefficient node state in the transition period.
# Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications
## 2 Device Structure of Electronic Fibers
Although the integration of electronic fibers has mainly been achieved through weaving, threading, knitting, or by hand, some fiber devices are easily damaged by traditional textile processes. Therefore, automatic processing technologies that can produce well-aligned arrays and robust interconnections between electronic fibers and electrodes have been developed to construct scalable e-textiles [19; 20].
## 3 Recent Results on New Layered Iron and Cobalt Oxylfluoride Compounds  
### Unusual O/F Site Disorder in Layered Cobalt Oxylfluoride  
As reviewed above, a variety of fluorinating agents greatly contributes to oxyfluoride chemistry. Considering the limited variety of transition metals, however, a further search for new oxyfluoride phase is necessary in order to better understand oxyfluoride. Co is among the 3d transition metals studied to a lesser extent. In fact, only one Co-based perovskite compound has been reported: LaSrCoFeO\({}_{5}\)F [40], where O/F sites are randomly distributed as well as Fe/Co sites. High-pressure synthesis is an effective alternative approach to low-temperature fluorination, although expensive apparatus required for the reaction method is necessary. Recently, we have achieved the synthesis of the first example of RP-type layered cobalt oxyfluoride Sr\({}_{2}\)CoO\({}_{3}\)F, under a pressure of 6 GPa at 1,700 \({}^{\circ}\)C [41]. This compound adopts a simple body-centered tetragonal structure with the space group _I4/mmm_ (Figure 2(c)). Magnetic susceptibility measurements revealed an antiferromagnetic phase transition at around 320 K, which is different from the ferromagnetic behavior in the corresponding oxide Sr\({}_{2}\)CoO\({}_{4}\)[42]. Furthermore, neutron powder diffraction study characterized the \(S=2\) high-spin state at Co cations. The structural features in the cobalt oxyfluoride are also worthy of attention. The O/F anions are disordered at the apical sites, and the cobalt cation shifted from the basal plane takes a square pyramidal coordination. We noticed that the coordination environment around Co center is unusual when compared with related oxyfluoride compounds (see Figure 2). As described above, a similar O/F site disorder is observed in Ba\({}_{2}\)ScO\({}_{3}\)F and K\({}_{2}\)NbO\({}_{3}\)F, but each \(B\) cation with \(d^{0}\) electronic configuration takes octahedral coordination without shifting from the basal plane. In comparison, Sr\({}_{2}\)FeO\({}_{3}\)F and Ba\({}_{2}\)InO\({}_{3}\)F have a square-pyramidal coordinated metal center, but O/F site occupation occurs in an ordered manner. Thus, coexistence of anion disorder and square-pyramidal coordination, which has never seen in related oxyfluoride, is realized in the new cobalt oxyfluoride. It was initially assumed that square-pyramidal coordination stabilizes the O/F site ordered state, but this is not the case in Sr\({}_{2}\)CoO\({}_{3}\)F. Square-pyramidal coordination of Co cations is commonly seen in cobalt-based materials with \(d^{6}\) high spin configuration, such as BiCoO\({}_{3}\)[43] and Sr\({}_{2}\)CoO\({}_{3}\)Cl [44]. The observed O/F disorder, however, is non-trivial. The role played in the unusual coordination environment around Co is probably related to the reaction condition employed: high-pressure and high-temperature synthesis. A denser environment at high pressure or entropic effects associated with high temperature are likely to stabilize the anion-disordered phase.