esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiments: Security Analysis and Comparison
### NIST Tests Analysis  
The NIST statistical test suite [47] is a set of statistical tests used to evaluate the degree of randomness in the data. To validate the effectiveness of our text encryption method, we utilized the NIST test suite to assess the randomness of 10 sequences obtained from the cipher texts generated from the proposed scheme. Each sequence consists of 10,000 binary bits obtained by applying modulo 2 on the generated cipher texts.  
The results of the NIST tests are presented in Table 5. According to the information provided in [47], the minimum pass rate for each statistical test listed in Table 5 is approximately 8 when considering a sample of 10 binary sequences. Our findings, as shown in Table 5, indicate that all sequences generated by our proposed method successfully passed all the NIST tests listed in Table 5, with the exception of the serial test and cumulative sum test. These two tests passed in 7 out of 10 sequences, which is close to the acceptable passing threshold of 8. Therefore, it can be concluded that the proposed text encryption scheme has the ability to generate cipher text with a high level of randomness.  
Figure 4: Comparison of the percentage of encryption quality of the proposed scheme with the state-of-the-art schemes [10; 11; 12; 13; 14; 15; 16; 17; 18].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Test Name** & **Proportion** & **Results** \\ \hline Frequency (monobit) & 10/10 & Pass \\ Frequency (block) & 10/10 & Pass \\ Longest run & 9/10 & Pass \\ Rank & 10/10 & Pass \\ Non-overlapping template & 9/10 & Pass \\ Overlapping template & 10/10 & Pass \\ Universal & 10/10 & Pass \\ Linear complexity & 10/10 & Pass \\ Serial & 7/10 & Fail \\ Cumulative sums 1 & 7/10 & Fail \\ Cumulative sums 2 & 8/10 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of NIST tests.
# Manufacture of a Potential Antifungal Ingredient Using Lactic Acid Bacteria from Dry-Cured Sausages
## Materials and Methods
### Volatile Organic Compounds (VOCs) Determination  
Volatile Organic Compounds (VOCs) emitted by the bacteria after fermentation in MRSb and MB10 media were determined by head-space solid phase microextraction (HS-SPME) technique and subsequent analysis in a gas chromatograph coupled to a mass spectrometer (GC-MS) following the methodology of [PERSON] et al. (2018) with minor modifications.  
For this purpose, 10 mL of the BFS, obtained as described in Section 2.6, was placed in a 20 mL glass vial. The VOCs were extracted for 45 min at 50 \({}^{\circ}\)C with a divinylbenzene/carbon wide range/polydimethylsiloxane (DVB/C-WR/PDMS) coated fiber (80 \(\upmu\)m \(\times\) 10 mm) (Agilent Technologies, Santa Clara, CA, USA). Then, the fiber was desorbed for 10 min at 250 \({}^{\circ}\)C in splitless mode on an Agilent 7890A gas chromatograph coupled to an Agilent 7000A triple quadrupole mass spectrometer equipped with an electron impact (EI) source. The column used for chromatographic separation was an HP-5 MS (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m) (Agilent Technologies). The temperature ramp was programmed as follows: 40 \({}^{\circ}\)C held for 2 min and raised to 160 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min; then, it was ramped up to 260 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and held for 4 min. The carrier gas was helium (99.99%) at a flow rate of 2.5 mL/min. Compound detection was performed in Full Scan mode in an \(m/z\) range of 40-450 Da.  
The compounds were identified by comparison of their mass spectra with those recorded in the NIST 09 library. In addition, linear retention indices (LRI) were calculated based on the retention time of a solution of alkanes (C8-C20) tested under the same conditions as the samples and compared with the existing literature.
## 2 Material and Methods
### Roughness Measurement Methodology  
The Alicon Infinite G5 is an optical three-dimensional microscope for complete 3D surface analysis, with micro, macro, and nano resolutions, and it allows for the assessment of thermographic color maps according to the applicable standards of EN ISO 21920 [24]. This optical microscope can analyze larger areas using XY table movement and a problem-free image-joining functionality. It can also assess and analyze the results of 2D and 3D roughness profiles, cylindricity, or profile parameter P. Its vertical resolution is up to 10 nm, while the lateral resolution is 400 nm. For each option, there are statistical functions to statistically define the experiment [25].  
The measurement took place according to the standards EN ISO 4287 [26] and EN ISO 4288 [27]. An Lc filter was used for a random profile of 8 mm and a profile length of 40 mm. For the random profile, the values were chosen according to the roughness parameter Ra. In our case, Ra ranged from 10 to 80 \(\upmu\)m.  
Principle of Roughness Measurement  
The Alicona Infinite G5 microscope works on the principle of structured light vertical scanning and the layering of individual images (focus variation method) using coaxial white light, precisely adjusted through the mirrors into the objective and to the specimen's surface. Light reflection off the specimen is scanned back by the sensor. Each pixel in the image has its own record of the given position in Cartesian data coordinates. In the flat plane, the size of local contrast (blurring) of algorithmic combinations is obtained, as well as information on the adjacent surroundings of the given region and information on the pixel brightness. These calculations were repeated for the same pixel vertically, across all images. After completing the algorithm calculation, a contrast curve was formed (each \"x\" in the plane has many contrast values; the same is true for the \"y\" axis) [28]. Measurement principle using the Alicona Infinite G5 optical microscope is shown in Figure 4.  
Figure 3: Data processing and assessment methodology.
## 3 Results and Discussions
### Transmission Loss in Elastic Metamaterial  
Transmission loss analysis was performed on an elastic metamaterial with different numbers of unit cells to verify the effectiveness of the metamaterial in preventing the transmission of the elastic wave through the material within the bandgap frequencies. Figure 7a shows the transmission loss spectra with one-, three-, and five-unit cells with a filling fraction of 0.125 for each. The peak transmission loss occurred at the bandgap regions, where the metamaterial prohibits wave propagation. It was verified that increasing the  
Figure 6: (**a**) Phononic crystal with four-square inclusions at a filling fraction of 0.2, where the location of the inclusions were varied by changing the distance r. The dispersion curves showing the generated band gaps are plotted in the case of (**b**) r = 0.495, (**c**) r = 0.354, and (**d**) r = 0.212.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\#** & **FF** & \(\mathbf{a_{i1}}\) & \(\mathbf{a_{i2}}\) \\ \hline
1 & 0.105 & 0.05 & 0.1 \\
2 & 0.2175 & 0.1 & 0.15 \\
3 & 0.375 & 0.15 & 0.2 \\
4 & 0.5775 & 0.2 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The dimensions of the phononic crystals with 9 gradient square inclusions.  
number of unit cells results in higher transmission loss and more defined peaks, enabling more effective prohibition of elastic wave propagation through the material.  
The transmission spectra were obtained for normal and oblique incidences of the incoming wave to verify the omnidirectional absorption of the metamaterial, shown in Figure 7b. It was shown that the metamaterial is more effective towards normal incident waves than oblique (45\({}^{\circ}\)) incident waves, where the transmission loss peak reduced from \(-140\) to \(-100\) dB. The corresponding deformation distributions of the metamaterials with normal and oblique incidences are at 1700 Hz, as shown in Figure 7c, where the peak transmission loss occurs. It became evident that the five-array metamaterial is active in terms of prohibiting any wave propagation through the material, resulting in zero transmittance in the specified frequency range. By tuning the geometric and material parameters within the PnC, the elastic metamaterial proposed could be utilized in a variety of passive vibration isolation applications such as in small electronic devices, sensitive lab equipment, or even in civil engineering structures. The simplicity of the design, relying solely on square inclusions, enables the ease of manufacturing of this metamaterial to be implemented in a suitable application.
# Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network
## 2 Method
### Brief Description of Color HMAX Based Feature Descriptor (Opponent Color Channels)  
The biologically-inspired HMAX model [39] was extended to include color cues for recognition from color images and significantly improved performance was reported on different color datasets images [40; 41; 42]. We implemented the extension of HMAX model for pattern recognition to recognize and classify fabric weave patterns and yarn's color [17]. The main difference in the extension was the incorporation of the pattern's color information in the feature descriptor using opponent color channels. The two main parts of this model are the feature descriptor and the classifier. The feature descriptor was based on the fusion of opponent color channel and HMAX model, and the classifier is implemented using deep ELM. The descriptor performs the extraction of the object texture and object color information from the image. While the classifier in the later stage uses this information from the preceding descriptor stage to perform the regression and to perform the classification. To improve the recognition accurateness of the previously developed algorithm [17], we replaced the SVM classifier in the previously proposed algorithm with the ELM-based classifier in this paper.  
Processing starts with an input RGB image (see Figure 1). The processing architecture comprises two stages, namely, the feature descriptor stage and the classification stage. In stage I, individual red, green, and blue color channels are combined in three pairs of opponent color channels, namely, red-green, yellow-blue, and white-black opponent color channels using a Gabor filter. The opponent color channels were integrated with the HMAX model, so that the pattern features and color features be processed simultaneously. The extracted features which correspond to the pattern and color are then fed to the deep neural network-based classification stage. In the classification stage (stage II), we first train the classifier over features of the training image set, and then test the trained classifier with the testing image set.  
The process of filtering the RGB image into individual color channels and then combining individual color channels into opponent color channels and its integration with the HMAX model is explained in detail in [17], as the feature descriptor stage is the same as the implementation in [17]. We further improved the earlier proposed model by replacing the classifier stage with a neural network based classifier. Next, we describe the method used to construct our proposed deep extreme learning network briefly as a weave pattern and yarn color classifier.  
The architecture and hierarchy of the layers of HMAX model and their functionality is discussed in detail elsewhere [39; 43; 44; 45; 46; 47]. The color HMAX implementation for the fabric texture recognition and classification was also achieved in our earlier study [17].
## 3 Results and Discussion
### Discussion
Slice-wise brain hemorrhage detection frameworks generally operate on the entire CT slices or, like our approach, perform some primary ROI extraction to prepare the data for the analysis. The central part of processing is left to the deep learning tool with a limited impact of the operator on the feature extraction. Some targeted localization of selected portions of the image could likely improve the detection and classification scores. Differences between various hemorrhage subtypes are associated with their location and shape. Taking the above into account in more specifically dedicated procedures might benefit classification accuracy improvement. Hemorrhages are frequently caused by mechanical head injuries. Thus, the skull's external area could be examined as well, apart from the deep learning analysis. The lowest metrics obtained in EDH classification raise a question on the imbalance between classes in a single classification task. The number of epidural cases is an order smaller than the other subtypes. That is probably the case in this particular bleeding, and the class weighing employed during training is not sufficient to resolve this problem. However, there is no such correlation throughout all subtypes. The IVH with the top classification accuracy (correctly detecting 29 out of each 30 cases) is not the best-represented hemorrhage; second-best IPH (14 out of 15 correct) is the second-worst-represented subtype of ICH. Thus, the character of a particular hemorrhage appearance in CT has a crucial influence on the detection capabilities. Similarities between different bleeding symptoms and features are also possible (recall that the dataset of negative samples in each subtype classification also contains images with all other hemorrhages). Some complex cases represent a combination of multiple subtypes simultaneously. Therefore, correct classification is undoubtedly a challenging task. To put our study in context, we gathered the F1 score results from corresponding state-of-the-art ICH detection methods [12; 16; 19; 22] along with our double-branch RF model (DB-RF) in Table 4. The F1 score is a reliable measure in highly imbalanced datasets since it ignores the true negatives. The selected methods addressed the detection of at least a half of our hemorrhage subtype set ([PERSON] et al. [16] operate on a joint SDH+EDH class and omit the IVH) and were published from 2018 to 2020. Most importantly, similarly to our approach, they detected hemorrhages in CT slices, not in the entire 3D series. [PERSON] et al. [19] and [PERSON] et al. [22] used the RSNA competition database. We used provided data and assessment results to determine the F1 score wherever it was not given explicitly.
## 3 Decremental Antenna Selection Algorithms
### \(\ell_{0}\)-Norm Minimization (L0 NM) Algorithm
Solve the reformulated problem \(\mathcal{P}_{4}\). If \(\exists\left\|\mathbf{w}_{(r)}\right\|_{0}<\left\|\mathbf{w}_{(r-1)}\right\|_ {0}-\theta/P_{\text{cir}}\), \(S^{(r)}\) is the antenna subset corresponding to the nonzero entries of \(\left\|\mathbf{w}_{(r)}\right\|_{0}\), \(r=r+1\), and repeat this operation. Otherwise, stop and the optimal subset is \(S^{(r-1)}\). 3. **End**  
**Algorithm 1**\(\ell_{0}\)-Norm Minimization (L0 NM) Algorithm  
**Remark 1**. : Considering a high antenna correlation or a large amount of line of sight, the MRDS algorithm is not suitable because the factors have an harmful impact on the channel orthogonality. However, the L0 MN algorithm is not influenced by the antenna correlation and amount of line of sight.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Environment Factors Dimension  
The environmental factors dimension acknowledges that the surrounding environment or field conditions influence the design of an irrigation system. Within our survey, we have identified three factors within this dimension. (i) _Field Conditions_: Several terms are used to describe field conditions. These conditions combined will affect the water flows and water absorption. (ii) _Natural Variations_: Variations in weather patterns, temperature, and precipitation will impact irrigation scheduling and water requirements. (iii) _Crops_: A robust irrigation system must be adept at accommodating the evolving water requirements of different crop types, considering growth stages, crop types, and targeted water distribution. Through the environmental factors dimension, our survey strives to comprehensively analyze the interaction between field conditions, natural variations, and crop specifics in shaping irrigation systems. Through understanding how these factors interact and impact irrigation design, we aim to provide insights into the adaptability, efficiency, and customization of IoT-driven irrigation solutions.
## 3 Our System Model and Primer  
This section describes the background/primer and defines the relevant parameters/variables for our research. We model the positions of the base stations relative to the user and analyze how these positions affect channel performances, including the path loss model and channel capacity. Because the flying base station cannot independently serve as the bridge gateway between wired and wireless communications, it functions as a relay and depends on the stationary terrestrial base station to provide connectivity.
## 2 Detailed Analysis of the Literature
### Prosumer Communities
#### 2.1.5 Prosumer Management
For example, an analysis of the prosumer behavior led to a prototype of an informatics solution that assists in decision-making and selecting optimal use of non-controllable appliances [57]. Likewise, another proposed energy scheduling model for residential consumers aims to optimize the timing of their consumption, generation, and storage [58]. Other similar smart scheduling of electric appliances are also found in the literature [59]. As active participants in energy co-creation, we also discuss prosumers' co-creation activities. These four activities include ideation, development, testing and giving feedback, and providing validation through new functionalities and innovative services [37]. Co-creation is a critical component in ensuring sustainable energy supply to the community grid. Therefore, we propose the following:  
**Proposition 5**. : _Understanding prosumers' energy behavior profile, their organization, and their motivation is an essential building block for effective prosumer management and energy system planning. A goal-oriented PCG builds stronger ties among members, motivates members, reinforces their bargaining power, and sustains the energy sharing process._
## 2 Preliminaries
### Market Rules  
There are many market players in the abovementioned system. Under an incomplete resource aggregation situation, the electricity and heat transaction rules are given as follows:  
(1) VPP can purchase electricity from and sell electricity to SES, the outer power grid, and the PVP alliance. VPP needs to carry out transactions with the transmission rights of the distribution network. The maximum transaction power between VPP and one entity is limited by the rated power of the distribution lines and other factors. Hence, there is a maximum power constraint between VPP and the outer power grid. In some trading hours,  
Figure 1: Overall framework of the day-ahead trading model for VPPs.  
VPP needs to purchase electricity from and sell electricity to SES to avoid large fluctuations in the transaction price or the phenomenon of renewable energy abandonment. Specifically, when the generator output of VPP cannot meet the load demand, it can purchase electricity from the outer power grid and SES and sell electricity to the outer power grid and SES when the generation output of VPP is surplus. Similarly, when the output and the power purchase from the grid cannot meet the power supply demand, it needs to purchase power from SES at this time.  
(2) SES provides services for the VPP and the outer power grid. SES can purchase electricity from the outer power grid and sell electricity to the VPP.  
(3) The power interaction between the VPP and PVP alliance can be realized through the tie line. There are different characteristics among different members of the PVP alliance. The members of the PVP alliance can realize the power interaction through the tie line and P2P mechanism, and the members can cooperate with each other to promote the consumption of renewable energy and reduce the power dependence on VPP.  
(4) The purchase and sale of electricity prices between the outer power grid and other entities are predefined and cannot be regulated by our system. In the meantime, the purchase and sale of electricity price between the SES and VPP are the decision variables, as well as the transaction price between the VPP and PVP alliance. In accordance with the optimal transaction price, the transaction electricity amount between SES and VPP would be regulated to obtain the minimum total energy cost of SES, VPP, and PVP alliance, as well as the transaction electricity and heat amount between VPP and PVP alliance.
## 2 Related Works
Aiming at a cooperative transmission for space-based networks, many scholars have gone deep into the research of the network architecture, protocols, and routing strategy and routing algorithm. Based on the SOS structure and some other protocols, networking technologies and coordination mechanisms were fully researched to improve the cooperative transmission for Earth observation [33]. Additionally, in [34], a kind of multiple access communication systems, with a GEOand a LEO satellite as relays, was designed, based on the resource allocation protocol. [PERSON] [35] improves the dynamic intrusion of complex networks by optimizing the load priority prevention strategy index. [PERSON] [36] proposes a new nondestructive measurement index, which is used as the object function of the optimized and improved particle swarm algorithm to ensure the smooth operation of railway transportation. Four typical capability allocation strategies are proposed by [PERSON] [37] to improve the dynamic resistance of complex networks and effectively prevent cascading failures of complex networks under limited resources. Based on a large amount of relevant research, this paper conducts an in-depth study on the problem of invulnerability optimization of SBIN.
## 2 Data Collection  
Compared with our previous research [7], the scientific geodetic iGMAS receivers (manufactured by one university in China) [7], commercial ComNav M300 Pro (manufactured by ComNav Technology Ltd. in Shanghai, China) receivers, together with two Trimble Zephyr Geodetic 2 antennas are used in the experiments here. All the selected GNSS receivers can simultaneously track GPS L1/L2/L5, Galileo E1/E5a/E5b, BDS-2 B1I/B2I/B3I, and BDS-3 B1I/B3I/B1C/B2a/B2b signals, except that B2b cannot be tracked by M300 Pro. The tracking modes of GPS/Galileo/BDS-3 L1-E1-B1C/L5-E5a-B2a signals, which are our major concerns in the following analysis, are listed in Table 3.  
The observations are also collected with 10 s sampling interval on the roof of the School of Geodesy and Geomatics (SGG) building at Wuhan University but in June 2018, which is different from our previous experiments in [7]. Figure 1 shows the observational environment and the GNSS antennas used in the experiments, while Figure 2 illustrates the sky plot and availability of BDS-3, Galileo, and GPS satellites with 10\({}^{\circ}\) elevation cut-off angle from 3-4 June 2018. 12 GPS Block IIF, 16 Galileo, and 8 BDS-3 operational satellites are tracked during the experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Receiver** & \multicolumn{2}{c}{**GPS**} & \multicolumn{2}{c}{**Galileo**} & \multicolumn{2}{c}{**BDS-3**} \\ \hline iGMAS & L1 C/A & L5I & E1B & E5 aI & B1C Data & B2a Data \\ M300 Pro & L1 C/A & L5I & E1 (B + C) & E5a (I + Q) & B1 CC 1 & B2 aA 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The observables including tracking mode for the iGMAS and M300 Pro receiver.