esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Recent Works and Methods
On the other hand, every algorithm had its own relevant set of features leading to better results. For that, a Performance-based Method of Ranking (PMR) FS algorithm was also proposed. As with previous works, FE, DP and FS were not considered nor mentioned. The CTU-13 dataset for botnet anomaly detection was created and introduced by [PERSON] et al. [26]. The authors compared three NIDS methods in their work: Cooperative Adaptive Mechanism for Network Protection (CAMNEP), BCplus and BotHunter. Besides, a methodology for comparison of the previous methods and NIDS solutions was proposed, which are, in general, fed with NetFlow-based data sources. Such a methodology just compares predictions obtained by the methods under evaluation for each observation in terms of standard and well-known metrics such as F1, precision, accuracy, etc., and another one proposed by the authors: the error metric. However, a common way to do crucial tasks in network attack detection such as FE, DP, or FS is also not proposed. Through the work in [27], the authors consider all the steps we claim as mandatory for network attack detection and remark the importance of feature extraction and feature selection steps for DoS, DDoS and network attacks classification in general. Firstly, they chose and extracted a vast number of network traffic features from previous works in the field. Secondly, they proposed a new feature selection method that mixes the Random Forest and Pearson's correlation coefficient, called Random Forest and Pearson wrap (RFPW) algorithm. Their approach was tested through the MAWI dataset with a Gradient Boosted Decision Tree (GBDT) algorithm and some other classical ML algorithms, showing improvements introduced not only in the performance of the algorithms but also in the time required for the training phases. In summary, almost all studied proposals avoid, elude or do not mention critical steps to evaluate ML-based algorithms for network attack detection. Therefore, the authors of this paper claim that it is hard and, sometimes, impossible to reproduce the obtained performance. Additionally, carrying out a fair comparison between different NIDS solutions only having information about the performance results is questionable. It is also worth noting that one of the most used datasets is the obsolete KDDCup'99 and its derived and improved version NSL-KDD which could be a handicap when evaluating NIDSs. In conclusion, some standard procedures or methodologies comprising all necessary steps, from the considered raw data to the outcome, should be proposed allowing honest and equitable comparisons.
## 4 Adaptive Neuromorphic Joint FRESH Equalizer and Detector
### Sparse De-Noising Deep Auto-Encoders as Cyclostationary FRESH Equalizers
From the discussion above, it is evident that some similarities and dissimilarities exist between the DDA-FRESH and RLS-FRESH structures. These are tabulated below (Table 1). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **RLS-FRESH** & **DDA-FRESH** \\ \hline Adaptive & Yes & Yes \\ De-noising Ability & Yes & Yes \\ Tracking Ability & Yes & Yes \\ Non-linear & No & Yes \\ Inter-connectivity & Limited & Massive \\ Architecture & Single Layer Parallel Filter-bank & Multiple Cascaded Layer \\ Error Propagation & Feed-back & Back-propagation \\ Convergence Speed & Faster & Slower \\ Blind Adaptive Capability & Little or No training data required & Certain amount of training data required \\ Prediction Performance & Good, under Gaussian noise & Better than RLS-FRESH, under non-Gaussian noise \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative Features between RLS-FRESH and DDA-FRESH. The general block diagram depicting the overall interference cancellation and estimation procedure is presented in Figure 3. In this iterative scheme, the PU-OFDM signal can be treated as cyclostationary noise, which is based by a FRESH filter based on knowledge of its cyclic period. An initial estimate of the SU-FC-FB can be obtained by subtracting the estimated noisy PU-OFDM from the received mixture. As evident, the 1 st adaptive FRESH filter operating based on PU cyclic parameters can be either RLS based FRESH or DDA based FRESH filter. The initial estimate is passed through a serial-to-parallel (S/P) converter which divides the incoming signal into parallel sub-channels. Each sub-channel corresponds to each sub-band of the transmitted FC-FB. There is M such sub-bands. Each sub-channel is operated by a block diagonal matrix with R AFB transform matrices on the main diagonal. Each AFB transform matrix corresponds to each sub-block of that sub-band. Therefore, the AFB sub-matrices act as match filters (MFs) to recover the CP-OFDM based sub-blocks. Then, FRESH filter, adapted to each sub-block CP-OFDM's cyclic frequency, can jointly act as a channel equalizer and noise canceller. The FRESH based equalized sub-block symbols are then demodulated to obtain an estimate of the transmitted bit-stream.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 2 Autonomous DDboats
### Modeling of the DDboats
#### 2.3.2 Signal-Based Representation of the Effective System Inputs  
Alternatively, the property of differential flatness [21] of the model (10) can be exploited to approximate the input signals  
\[u_{v}^{(i)}(t) =\frac{\text{d}\sqrt{\left(\dot{x}_{1,\text{m}}^{(i)}(t)\right)^ {2}+\left(\dot{x}_{2,\text{m}}^{(i)}(t)\right)^{2}}}{\text{d}t}\] \[\approx\frac{1}{T_{\text{s}}^{2}}\cdot\sqrt{\left(x_{1,\text{m}}^ {(i)}(t_{k+1})-x_{1,\text{m}}^{(i)}(t_{k})\right)^{2}+\left(x_{2,\text{m}}^{( i)}(t_{k+1})-x_{2,\text{m}}^{(i)}(t_{k})\right)^{2}} \tag{14}\]  
and  
\[u_{\phi}^{(i)}(t) =\frac{\text{d}\phi_{\text{m}}^{(i)}(t)}{\text{d}t}\] \[\approx\frac{1}{T_{\text{s}}}\cdot\left(\phi_{\text{m}}^{(i)}(t_ {k+1})-\phi_{\text{m}}^{(i)}(t_{k})\right) \tag{15}\]  
after setting the disturbance inputs \(z_{1}^{(i)}(t)\) and \(z_{2}^{(i)}(t)\) to zero with \(T_{\text{s}}=t_{k+1}-t_{k}\).  
To avoid the amplification of measurement noise in the following reconstruction of input disturbances, the signals \(u_{v}^{(i)}(t)\) and \(u_{\phi}^{(i)}(t)\) are filtered offline (i.e., after the completion of the complete trajectory tracking experiment) by a 12-th order infinite impulse response low pass filter with a cut-off frequency of 0.2 Hz.
# A Fast Sparse Coding Method for Image Classification
## 4 Discussion  
Image classification is an important research field in computer vision. The ScSPM method, which represents an image through SIFT extraction, sparse coding, and spatial pooling, has shown its advantage in this field. However, the dictionary learning involved in sparse coding demands high computational complexity and is very unstable. To solve these problems, in this paper, we propose an image classification framework, whose dictionary learning and feature quantization are developed as two independent parts of sparse coding. Firstly, SIFT descriptors are extracted from each of subregions. Secondly, \(k\)-medoids cluster is applied to learn a dictionary from SIFT descriptors. Then, ScSPM method is employed to generate image feature. Finally, classifier is applied to classify images according to image feature.  
We evaluated our approach on two diverse datasets: Caltech-101 and UIUC-Sports. The results show that our method can increase accuracy of spatial pyramid matching, which suggests that our method can improve performance of sparse coding features.  
Although the proposed method has improved the classification performance, similar categories may be misclassified mutually. To improve this situation, in our future work, we will try to use more efficient low-level features, and apply multiple stage classifiers for boosting the classify performance.  
Conceptualization, M.Z. and D.W.; methodology, M.Z., D.W. and H.Z.; software, T.L.; validation, M.Z., T.L. and C.L.; formal analysis, H.Z.; investigation, C.L.; resources, M.Z.; data curation, T.L.; writing--original draft preparation, M.Z.; writing--review and editing, D.W. and H.Z.; visualization, T.L. and C.L.; supervision, D.W.; project administration, M.Z.; and funding acquisition, M.Z.  
This research was funded by the National Natural Science Foundation of China under Grant No.61741311, National Natural Science Foundation of China under Grant No.61702249, and Natural Science Foundation of Shandong Province under Grant No.ZR2017 PF010.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithms** & **Avg. Accuracy** \\ \hline LScSPM [16] & 85.3\% \\ MR-BoF [36] & 85.1\% \\ HILLC+SPM [37] & 85.0\% \\ SNDL [38] & 85.2\% \\
**FScSPM (Our Approach)** & **86.7\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy comparison between our approach and other SPM based features on UIUC-Sports dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithms** & **Avg. Accuracy** \\ \hline Local Soft Assignment [39] & 82.3\% \\ LLKC [33] & 86.4\% \\ \(N^{3}\)SC encoder [29] & 85.5\% \\ CLGC(RGB-RGB) [31] & 86.4\% \\ MUSIC [40] & 81.8\% \\ OB2014 [41] & 82.3\% \\ Hybrid-CNN [25] & **94.2\%** \\ TPN-FS [34] & **95.2\%** \\ DeepSCNet [35] & **87.1\%** \\
**FScSPM (Our Approach)** & **86.7\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy comparison between our approach and state-of-the-art on UIUC-Sports dataset.
## 2 Related Works
### Existing Data Compression Approaches  
Various data reduction approaches for energy consumption of IoT has been deployed in the state of art. In [PERSON] et al. [15], researchers have introduced a data reduction approach according to the dual prediction. The merits of the prediction model are that the edge forecasts the sensed value with no requirement of radio communication is processed till reaching the predefined threshold value. In [PERSON] [16], developers have presented a data aggregation model for input data stream from IoT relied monitoring networks. The accomplished results have implied that it is applicable to gain a compression to the greater iterations on temperature data. In [PERSON] et al. [17], researchers have projected data compression mechanism which is advantageous of temporal correlation in gathered data. It depends upon elegant and effective processing in 1D Discrete Wavelet Transform (DWT) by lifting models as well as Differential Pulse Code Modulation (DPCM).  
Compressive Sensing (CS) as well as transform domain compression mechanism are prominently applied for images and multivariate time series compression in IoT domains. In [PERSON] et al. [18], developers have employed multi-signal compression mechanism on the basis of fuzzy transform. It is utilized on multi-signal surrounding data gathered using Wireless Sensor Network (WSN) and limited the data to certain extent. In [PERSON] et al. [19], developers have presented 2-D lifting wavelet transformation for the purpose of compressing multi-signal data gathered from various sensor nodes. Hence, the newly developed model applies Haar wavelet and accomplished a better compression ratio as well as recovery accuracy. Transform domain compression is classified by the potential of recovering data. Besides, CS definition is generated as effective model energy-proficiency in IoT modalities. In addition, some context-adaptive binary arithmetic coding (CABAC) technique and their extended versions are available in [20; 21].
# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System
### Feature Engineering
#### 3.3.2 Selection and Extraction  
FE is based on selection and extraction mechanisms. Firstly, selection is the most intuitive class of FE techniques. Selection mechanisms analyze the set of available indicators and provide a set of weights or ranking associated with the level of relevance of each of them in the further classifications processes. Human experts classically performed this process, based on their knowledge on failure cases. In this way, the objective is to automatically establish which indicator features can contribute the most to further steps.  
On the other hand, extraction mechanisms are intended to generate new \"synthetic\" features, which are richer in information than the original ones. These are constructed based on the statistical analysis and subsequent combinations of the original features, creating a new more effective set of indicators, which provides better information to the posterior inference process and the distinction between different cases [21].  
Selection and extraction can both be applied separately or sequentially. In the latest, selection is used to obtain a subset of all the generated indicators. Based on this, a new set of synthetic metrics are generated and fed to the inference stage.  
The added value of applying FE covers not only the computational costs of posterior stages. Firstly, the fact of having selected specific metrics can allow avoiding the gathering of certain variables in the acquisition stages as well as their computation in the generation phase, also reducing its computational and memory costs. This is represented by the adjustment flows going from the FE stages to the data acquisition and indicators generation blocks. Secondly, as it is going to be further demonstrated in Section 4, the reduction in the number of indicators will typically allow important improvements in the posterior inference phase, as it avoids the inclusion of features that do not provide relevant information for the process, but that can introduce noise in their results [21].
## 3 Results and Discussion
### Photoelectrochemical Characterization
The main evidence from the comparison of these data can be summarized as follows: (i) akin to NiO electrodes of type A, the attachment of Pt onto the sensitized NiO surface provides catalytic sites for proton reduction to dihydrogen, leading to enhanced photocurrent densities relative to those observed with the blank sample without metal on top; (ii) the measured photocurrent densities are overall lower than those observed with NiO electrodes of type A, which can be attributed to the reduced dye loading and corresponding light-harvesting ability as well as to the different morphology of the NiO film (mainly size and connectivity of the NiO nanoparticles), which might influence the efficiency of charge recombination phenomena [31]; (iii) within this limitation, potentiostatic methods (Methods 1 and 2) perform slightly better than the CV-based ones (Methods 3-5); (iv) the potentiostatic Method 1, which combines both potential bias and light, provides more active photocathodes (maximum photocurrent densities of \(-\)22 \(\mu\)A\(\cdot\)cm\({}^{-2}\) for the NiO-B \(\mid\)**P1**\(\mid\)**Pt-**1** sample at 20 s deposition time) than the analogue Method 2 without applied illumination (maximum photocurrent densities of \(-\)13 \(\mu\)A\(\cdot\)cm\({}^{-2}\) for the NiO-B \(\mid\)**P1**\(\mid\)**Pt-**2** sample at 20 s deposition time; see Figure 5A); (v) as to the potentiodynamic procedures (Methods 3-5), improved performances are attained with the NiO-B \(\mid\)**P1**\(\mid\)**Pt-**3** electrode (maximum photocurrent densities of \(-18\)\(\mu\)A\(\cdot\)cm\({}^{-2}\)) with respect to both NiO-B \(|\)**P1**\(|\)Pt-4 and NiO-B \(|\)**P1**\(|\)Pt-5 (maximum photocurrent densities of \(-14\) and \(-15\)\(\mu\)A\(\cdot\)cm\({}^{-2}\), respectively); (vi) similar to what was observed for NiO electrodes of type A, the importance of merging both potential and light is clearly evident for both potentiostatic and potentiodynamic methods, suggesting that the investigated deposition protocols could be well suited for future studies aimed at photoelectrochemical hydrogen generation using different sensitized NiO substrates.
## 2 Materials and Methods
### Characterization  
Briefly, the phase composition of all MBG samples was determined using an X-ray diffractometer (D2 phaser, Bruker, Karlsruhe, Germany) with Cu K\(\alpha\) (\(\lambda\) = 0.154 nm) radiation at 30 kV and the current at 10 mA. The XRD samples were prepared by loading a small amount of MBG powders onto a 25 mm zero-background holder. The scanning angle (20) was set in the range of 20-80\({}^{\circ}\) with the scanning rate at 4\({}^{\circ}\)/min. FE-SEM (JSM-6500F, JEOL, Tokyo, Japan) mainly uses a focused electron beam to identify the sample surface morphology and microstructure. Energy-dispersive spectroscopy (EDS) analysis was used to describe the material compositions by using INCA software 7582-M (Oxford Instrument, Abingdon, UK). The MBG powders were fixed onto an SEM stub using double-sided carbon-conductive tape. The samples were then sputtered using platinum with a deposition rate of 30 s. The adsorption/desorption Isotherm (Novatouch LX2, Quantachrome, Boynton Beach, FL, USA) measurement method was proposed by Brunauer-Emmett-Teller (BET), which is determined via the physical adsorption of nitrogen gas on the pores/surface of the particles and Barrett-Joyner-Halenda (BJH) also measured pore size and pore volume. Nitrogen gas was used because it is abundant, inert, and high in purity. The microstructure of the un-doped, Ag-doped, Ce-doped, and Ag-Ce co-doped MBG were analyzed via TEM (Tecnai G2 F20, FEI, Hillsboro, OR, USA). For the preparation of the TEM sample, MBG powders were dispersed into an acetone solution using ultrasonic vibration for 10 min. Then, the dispersion was dropped on the surface of 300-mesh copper-coated TEM grids. Lastly, the principal hydroxyl apatite of the functional groups and structural bands in the dried MBG powders were assessed using Fourier-transform infrared spectroscopy (FTIR) (Thermo Nicolet AVATAR 370, Thermofisher, Waltham, MA, USA) in the wave number range from 400 to 4000 cm\({}^{-1}\).
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 4 The Proposed Protocol
### Registration Phase  
As shown in Figure 2, taking the registration of the server \(SP\) as an example, the processes are as follows:  
R1: Firstly, SP chooses a random \(r_{sp}\in Z_{q}^{*}\) and its identifier \(ID_{sp}\in Z_{q}^{*}\) and computes \(R_{sp}=r_{sp}\cdot P\). Then, SP transmits a registration request, \(\{ID_{sp},R_{sp}\}\), to TA securely.  
R2: In response, first, TA chooses \(r_{ta}^{sp}\in Z_{q}^{*}\) randomly to calculate the public key of \(SP\). \(PK_{sp}=R_{sp}+r_{ta}^{sp}\cdot P\). Next, TA sends \(\{PK_{sp},r_{ta}^{sp},ID_{s},PK_{s}\}\) to SP via a secure channel.  
R3: In response, \(SP\) takes \(r_{ta}^{sp}\) as part of its private key and obtains its private key, \(k_{sp}=((r_{sp}+r_{ta}^{sp})\bmod q)\). Then, SP checks whether \(PK_{sp}?=k_{sp}\cdot P\); if it holds, then SP computes \(WS_{s}=k_{sp}\cdot PK_{s}\) and stores \((ID_{sp},k_{sp},ID_{s},WS_{s})\).  
Similarly, \(S\) stores \((ID_{s},k_{s},ID_{sp},WS_{sp})\) after registration. When a new end device \(S^{{}^{\prime}}\) joins and registers the system, TA sends \(\{ID_{s^{\prime}},PK_{s^{\prime}}\}\) to SP securely.
## 3 Experimental Setup
### Ablation Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **JARVIS-DFT** & **Zenodo** & **ICME** & **Materials Project** \\ \hline ResNet [48] & 0.056 \(\pm\) 0.01 & 0.061 \(\pm\) 0.01 & 0.071 \(\pm\) 0.01 & 0.065 \(\pm\) 0.01 \\ EfficientNet [49] & 0.062 \(\pm\) 0.01 & 0.059 \(\pm\) 0.01 & 0.069 \(\pm\) 0.01 & 0.068 \(\pm\) 0.01 \\ U-Net [50] & 0.051 \(\pm\) 0.01 & 0.058 \(\pm\) 0.01 & 0.062 \(\pm\) 0.01 & 0.059 \(\pm\) 0.01 \\ Swin-Transformer [51] & 0.048 \(\pm\) 0.01 & 0.053 \(\pm\) 0.01 & 0.058 \(\pm\) 0.01 & 0.061 \(\pm\) 0.01 \\ MobileNet [52] & 0.066 \(\pm\) 0.01 & 0.071 \(\pm\) 0.01 & 0.078 \(\pm\) 0.01 & 0.073 \(\pm\) 0.01 \\ DenseNet [53] & 0.055 \(\pm\) 0.01 & 0.060 \(\pm\) 0.01 & 0.060 \(\pm\) 0.01 & 0.064 \(\pm\) 0.01 \\ Ours & **0.044 \(\pm\) 0.01** & **0.049 \(\pm\) 0.01** & **0.054 \(\pm\) 0.01** & **0.057 \(\pm\) 0.01** \\ \hline \hline \end{tabular}  
**Bold** indicates the best performance.  
\end{table} 
Table 5: Error analysis of different models on JARVIS-DFT, Zenodo, ICME, and Materials Project datasets.
# Phytocannabinoids Biosynthesis in Angiosperms, Fungi, and Liverworts and Their Versatile Role
## 3 Biosynthesis of Phytocannabinoids
### Production of Phytocannabinoids through Synthetic Approaches  
Phytocannabinoids are terpenophenic compounds that are produced by polyketide and the MEP pathway. In the heterologous biosynthesis approach, two important pathways that are optimizing and engineering provide abundant precursors for cannabinoid production. Additionally, these pathways are linked via aromatic PT, ubiquitous in plants, animals, bacteria, and fungi [52; 53]. Thus, this helps in the production of aromatic metabolites such as coumarin, flavonoid, and phenylpropanol at different reaction spectra [54]. As discussed, biosynthesis of phytocannabinoid is different in plants, fungi, and liverworks; thus, the recent techniques which emerge out to be beneficial is by using the aromatic PT-based approach to generate novel phytocannabinoids in the heterologous hosts formed on combinational utilization of module over several species [52; 53; 54].  
Similar to _Humulus lupulus_, inside trichomes of _C. sativa_, chalcone isomerases such as proteins (CHILs) are expressed. Additionally, CHILs are polyketide binding proteins, and their co-expression in heterologous cannabinoid production mechanism plays a pivotal role in augmenting biosynthesis. Apart from combinatorial biochemistry techniques, enzyme-based approaches also play a remarkable role in phytocannabinoid production. Furthermore, some non-natural precursors such as pentanoic acid, hexanoic acid, heptanoic acid are incorporated inside the generated cannabinoid. Derivatization of phytocannabinoid diversifies their functionality. Glycoslation of phytocannabinoid reduces cell damage and autotoxicity and increases cell stability and life; in a therapeutic context, glycosylated phytocannabinoid enhances absorption, distribution, metabolism and, excretion (ADME) features. Halogenation of cannabinoids (like DCA) by co-expression of halogenase AscD derived from _Fusarium_ exhibits several antifungal, antibacterial, antitumor, antiparasitic properties. Thus, the synthetic biology technique serves as a new insight into the production and designing of phytocannabinoids [55; 56; 57; 58].