esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Automatic POD Diagnosis Method
### Time-Frequency Analysis of Disturbance Signals
It is worth mentioning that the 256 and 512 are the length of the frequency axis and the time axis, respectively. For the 50 Hz fundamental frequency, there will be 512 points in 10 periods. Meanwhile, the length of the frequency axis of the time-frequency matrix is half of the time axis since the results of the FFT is symmetrical. Thus, the size of the amplitude time-frequency matrix is 256 \(\times\) 512. The equations of discrete DRST are listed as follows. Consider the input signal \(x(t)\) sampled at a fixed sampling frequency \(f_{s}\) corresponding to the time interval of \(T_{s}=1/f_{s}\). The discrete Fourier transform (DFT) of sampled signal is  
\[X(k)=\frac{1}{N}\sum_{l=0}^{N-1}x(l)e^{-\frac{2\pi mk}{N}} \tag{2}\]  
where \(x(l)\) represents the discrete-time sampling sequence of \(x(t)\) and \(k=0,1,\ldots,N-1\). By using the Equation (2) to the sampled signal, we get an \(N\)-point vector \(\mathbf{X}_{N}\) of frequency samples as  
\[\mathbf{X}_{N}=\left[ \begin{array}{cccc}X_{0}&X_{1}\ldots&X_{N-1}\end{array} \right] \tag{3}\]  
Define a 2-D matrix \(P_{N\times M}\) as  
\[P_{N\times M}=\left[ \begin{array}{cccc}X_{1}&X_{2}&\cdots&X_{M}\\ X_{2}&X_{3}&\cdots&X_{M+1}\\ \vdots&\vdots&\ddots&\vdots\\ X_{N-1}&X_{0}&\cdots&X_{M-2}\\ X_{0}&X_{1}&\cdots&X_{M-1}\end{array} \right] \tag{4}\]  
where \(M=2N\) is owing to the symmetry property of DRST. Another 2-D matrix \(G_{N\times M}\) is defined as  
\[G_{N\times M}=\left[ \begin{array}{cccc}G(0,1)&G(0,2)&\cdots&G(0,M)\\ G(1,1)&G(1,2)&\cdots&G(1,M)\\ \vdots&\vdots&\ddots&\vdots\\ G(N-1,1)&G(N-1,2)&\cdots&G(N-1,M)\end{array} \right] \tag{5}\]  
Figure 2: Comparison result on transients disturbance with different parameter \(\beta_{1,2}\). (**a**) Input signal, (**b**) \(\beta_{1}=150,\beta_{2}=40\), (**c**) \(\beta_{1}=121,\beta_{2}=10\). where \(G(n,m)=e^{-\left(2\pi^{2}n^{2}/m\lambda_{1,2}\right)}\) is the FFT for the redefined Gaussian window, \(n=0,1,\ldots,N-1\) and \(m=0,1,\ldots,M\). Thus, by utilizing the convolution theorem and frequency shifting property of Fourier transform, the \(N\)-point discrete DRST can be described in matrix form as  
\[\text{DRST}_{N\times M}=\text{IDFT}\{P_{N\times M}\circ G_{N\times M}\} \tag{6}\]  
where \(\circ\) represents the Hadamard product of the matrix and IDFT representation of the inverse DFT(IDFT). In applications, the FFT and inverse FFT(IFFT), instead of DFT and IDFT, respectively, are used to improve the computational efficiency. The \(\text{DRST}(n,m)\) is, in general, a complex-valued matrix. Therefore, it can be expressed as  
\[\text{DRST}(n,m)=|\,\text{DRST}(n,m)|e^{|\phi(n,m)} \tag{7}\]  
where \(|\,\text{DRST}(n,m)|\) is the magnitude of \(\text{DRST}(n,m)\) and \(\phi(n,m)=\angle\,\text{DRST}(n,m)\) is the phase of \(\text{DRST}(n,m)\). Clearly, the amplitude associated with DRST matrix can be written as  
\[A(n,m)=2|\,\text{DRST}(n,m)| \tag{8}\]  
Next, the amplitude time-frequency matrix of DRST is fed to RDCA.
## 4 Discussion
### Possible Source of Heavy Metals  
As shown in Table 2, the Pb and Cr content in MSW was significant positively correlated at 0.01 level, which suggested that they potentially came from the same source. The As content was significant positively correlated with mass fraction of soil and the Cd content was significant positively correlated with mass fraction of food waste, indicating that the As and Cd in MSW were possibly derived from soil and food waste, respectively. The Pb content in MSW was significant positively correlated the mass fractions of glass, textile, food waste, and white plastic, which suggested that these mentioned components were Pb's potential sources. Furthermore, the significant positive correlation between and among food waste, glass, white plastic, plastic and paper implied that the generation of glass, white plastic, plastic and paper were to a certain extent related to food waste, because the glass, plastic, white plastic, and paper are usually used as packaging materials for food. In addition to these positive correlations, the As content with mass fractions of food waste, the Cd content with mass fractions of paper, the Hg and Cr contents with mass fractions of soil were negatively correlated. These negative correlations indicated that the concentrations of negatively correlated heavy metals decreased as the mass fraction of these components increased in MSW.  
The factor analysis results (refer to Table 3) showed that two principal components could explain the sources of As, Cd, Hg, Cr and Pb in the Eastern Guangdong MSW. The first factor, F1, accounted for 72.1% of the total variance. Hg, Cr and Pb had high F1 loadings. Pearson correlation analysis indicated that Cr and Pb have the same source and Pb might derived from glass, textile, food waste, and white plastic. In addition, as packaging materials, the glass, plastic, paper, and white plastic have strong correlations with food waste. Thus, F1 could be explained as food waste and packaging materials, including plastic, paper, and glass, which was the main influencing factor affect heavy metal contents in the Eastern Guangdong MSW. The second factor, F2, accounted for 24.0% of the total variance. As and Cd had high F2 loadings. As previously described, As and Cd in MSW were possible respectively derived from soil and food waste. Therefore, F2 could be explained by soil components being mixed into food waste. As the third major component in the MSW, mass fraction of soil component in the MSW has reached up to 16.98%, being another major factor affecting heavy metal contents in the Eastern Guangdong MSW.
## 3 Results and Discussion
### Statistical Analysis  
The one-way ANOVA test was performed for the statistical analysis of the results, with the aim of evaluating the effect of pH control on bacterial growth and lactic acid production, as this factor is the only input varying parameter, while the others, such as substrate, temperature, and percentage of glucose have been fixed, as detailed in Section 2.1. To this end, the influence of the pH control was evaluated for each sampling time, i.e., at 0, 2, 4, 6, 8, 14, 16, 18, 20, 22, 24 h, for a total of 11 analyses, the results of which are shown in Table 1. It is worth noting that for the lactic acid production only the values starting from 14 h of fermentation are reported since there is no production before (Figure 4). Moreover, only the contributions considered statistically significant are shown, i.e., with the \(F\)-value greater than the corresponding tabulated one, called the critical \(F\)-value (in this case equal to 6.61), with the \(p\)-value less than 0.05, and a \(\Pi\)% greater than 40%. The complete set of results can be found as Supplementary Materials (Tables S1 and S2).  
The analysis of the results shows how the pH control significantly affects the bacterial growth and production of lactic acid. For the bacterial growth, the main effect occurs at the beginning of the exponential phase, between 8 and 14 h. In fact, once the exponential growth is established, it indistinctly continues until the stationary phase is reached, beyond 16 h, at which the fermentation process is again influenced by the presence or absence of the pH control. About the lactic acid production, the process is significantly influenced during each step, underlining how the use of pH control favourably promotes the production of lactic acid, as shown in Figure 4d.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Output** & **Time, h** & **Source** & \(F\)**-Value** & \(p\)**-Value** & \(\Pi\)**\%** \\ \hline \multirow{8}{*}{Bacterial growth rate} & 2 & pH control & 14.434 & 0.019 & 78.29 \\  & & Error & & 21.71 \\  & 8 & pH control & 131.630 & 0.000 & 97.20 \\  & & Error & & & 2.80 \\  & & pH control & 33.741 & 0.004 & 89.60 \\  & 14 & Error & & & 10.40 \\  & 18 & pH control & 18.758 & 0.012 & 82.54 \\  & 18 & Error & & & 17.46 \\  & 22 & pH control & 42.854 & 0.003 & 91.40 \\  & & Error & & & 8.60 \\ \hline \multirow{8}{*}{Lactic acid concentration} & 14 & pH control & 31.199 & 0.005 & 88.56 \\  & 16 & pH control & 94.330 & 0.001 & 95.93 \\  & & Error & & & 4.07 \\  & 18 & pH control & 33.142 & 0.005 & 89.23 \\  & & Error & & & 10.77 \\  & & pH control & 178.937 & 0.000 & 97.81 \\  & & Error & & & 2.19 \\  & 22 & pH control & 144.268 & 0.000 & 97.31 \\  & & Error & & & 2.69 \\  & 24 & pH control & 671.710 & 0.000 & 99.41 \\  & & Error & & & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: One-way ANOVA results.  
Finally, Figure 5 reports the ANOM results, which show how, on average, both bacterial growth and lactic acid production are positively influenced by the adoption of pH control.
# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 4 Experiment
### The Experiment's Models' Training and Evaluation
#### 4.2.2 Evaluation of Machine Learning with a Distance-Based Flipping Attack with a Threshold of 0.5  
With a distance threshold of 0.5, the distance-based flipping attack had a lower success rate than the previous attack. This is evident from the lower recall values. When comparing this attack to the other attacks, as shown in Table 4, some models perform worse, however, some models performs better than under the label flipping data poisoning attack, like Random Forest, Figure 9b. Some models were also highly affected, such as the SVC and the logistic regression models which could either no longer identify True Labels or barely identify them, as shown in Figure 9a-c.  
It can be concluded that the distance-based flipping attack with a threshold of 0.5 has a moderate success rate compared to the other attacks, but it may not be as effective as the other attacks for some classifiers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LR** & **Random Forest** & **SVC** & **XGB** \\ \hline Acc & 0.5491039744499645 & 0.8286905606813343 & 0.5692423704755145 & 0.839513839602555 \\ F1 & 0.7066293399520911 & 0.8264893521430497 & 0.7254996183961331 & 0.839613440907882 \\ Prec & 0.5611477814448111 & 0.9759151193633953 & 0.5692423704755145 & 0.9737789203084833 \\ Recall & 0.9539468557624874 & 0.7167458895036235 & 1 & 0.7379412452271488 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model evaluation metrics with distance-based label flipping attack.  
Figure 8: (**a**) Logistic regression output with random label flipping attack; (**b**) random forest output with random label flipping attack; (**c**) support vector machine output with random label flipping attack; and (**d**) XGB output with random label flipping attack.
# From Waste to Strength: Applying Wastepaper, Fungi and Bacteria for Soil Stabilization
## 1 Introduction
The strength characteristics of MICP-treated soils can also be improved by incorporating reinforcing additives. According to a study [24], the addition of jute fibers enhanced the engineering properties of MICP-treated sand, increasing its durability by more than 50%. [PERSON] showed that adding 0.1% wool fiber to MICP-treated sand increased the compressive strength threefold and significantly improved its flexural strength [25]. [PERSON] reported that MICP treatment combined with bamboo fibers boosted the ductility as well as the flexural strength of sand specimens by several times [26]. To reduce the cost of soil strengthening technology by MICP, it is advisable to incorporate organic waste at various stages of the process [27,28]. In our previous study [29], we demonstrated that replacing expensive components of Luria-Bertani (LB) medium with spent brewer's yeast extract did not reduce the ability of ureolytic bacteria to bio-mineralize. Additionally, we demonstrated that, among the three types of cellulose-containing waste (wastepaper, flax shives and sawdust), the incorporation of wastewater into MICP-treated soil samples resulted in a 2.1-fold increase in compressive strength and nearly a 1.5-fold increase in the precipitated calcite percentage compared to samples without additives. This finding aligns with another study [27], which reported that adding wastewater as a reinforcing material to MICP-treated soil significantly improved its mechanical strength. This study aimed to determine the optimal wastewater content to be introduced with microbial biomass to enhance soil strength through calcium carbonate deposition by MICP. We studied the yeast-like fungus _Scytalidium candium_ 3C [30], used for the first time for this purpose; the previously selected ureolytic mineralizing bacterial strain _Bacillus licheniformis_ DSMZ 8782 [31]; and their combination. We evaluated the impact of these microorganismson the microbiota of soil samples and highlighted the potential of the mycelial fungus _Sc. candidium_ to enhance soil strength.
## 3 Frequency Response Analysis Considering the PFR Dead Zone  
In order to establish a frequency response model of the power grid considering the PFR dead zone, the expression of WTG in the frequency regulation process is derived first. Furthermore, the modeling of the PFR dead zone is quantified. On this basis, the frequency characteristic expressions of the whole process after power disturbance are derived, which lays the foundation for the formulation of a frequency regulation parameter in the next step.
## 2 Feasibility on ODEON-Based Auditory Scene Simulation in Closed Spaces
### Experimental Results  
The evaluation results are analyzed and processed as shown in Figure 4. The red bar denotes the number of listeners who sense that the \"real\" and \"simulated\" binaural signals are coincident; the black bar denotes the number of listeners who sense that the \"real\" and \"simulated\" binaural signals are different. The longer the bar, the more numerous the selected times.  
Apparently, the \"real\" and \"simulated\" binaural signals are largely aurally consistent, with no obvious difference in terms of auditory perception. At significant level of \(\alpha=0.05\), a \(t\)-test is employed to explore if there is significant difference between the selection of \"same\" and \"different\". The result (\(p=0.0126<\alpha\)) indicates that the selection of \"same\" and \"different\" are significantly different to each other. Therefore, with ODEON 8.0, a virtual auditory environment with high fidelity can be created. In conducting closed-space auditory simulations and related researches, using ODEON 8.0 to simulate indoor sound fields is feasible.  
Figure 4: The distinguishability between real and simulated binaural signals. (**a**) Room A (**b**) Room B.  
Figure 3: Sound field model by use of ODEON 8.0 in Room A.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 6 Performance Comparison and Analysis
### Simulation Results in the Whale Pods Routing Scenario
Although our proposed protocol outperforms the aforementioned performance metrics, we observe some limitations and constraints in certain specific scenarios as discussed below:  
In sparse networks, there are probably fewer PFNs present at each node in the network due to which overall improvement seems to be negligible; however, the computational cost still becomes greater. The abovementioned constraint in DOW-PR will become even more pressing when transmission range is also low. WDFAD-DBR focuses on the packet advancement at the first and second hop. It works well compared to DOW-PR in delay intolerant ad hoc underwater networks, where prolonged network lifetime is not required. DOW-PR has other limitations in the scenario where the source node is in a void and its predecessors also don't have PFNs other than the source node. Figure 14: **(a)** PDR vs. number of nodes; **(b)** energy tax vs. number of nodes; **(c)** end-to-end delay vs. number of nodes; **(d)** APD vs. number of nodes. Comparison of dolphin pods with whale pods/routing. In this case, the node will drop the packet or have progressive backward transmissions. In sparse networks with low average data packet generation, a low number of duplicate packets occurs due to which energy losses reduce in both DOW-PR and WDFAD-DBR, but DOW-PR will carry out more complex computational algorithms. In sparse networks, the probability of the void hole occurrence is high due to which DOW-PR will produce a huge number of void announcement messages, which will be disseminated in the entire network. It has been observed that, contrary to WDFAD-DBR, DOW-PR undergoes additional energy loss in transmitting void announcement control packets and thereby collision probability is also increased. In the future, the authors plan to undertake research that will overcome the above-mentioned constraints.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 10: Military Intelligence Operations  
These operations serve as crucial foundations for the triumph of military endeavors. Their role encompasses gathering and scrutinizing data pertaining to the forces, strategies,and operations of other nations. This aids in uncovering potential intentions of adversaries and criminal organizations, allowing for the exploitation of their weaknesses and furnishing commanders with vital insights for orientation and decision-making.  
Within intelligence operations, one technology of paramount significance is trusted computing. Given the extensive global data exchange, ensuring information security is of the utmost importance. Trusted computing offers a key advantage in safeguarding information from exposure to adversaries. SDN and blockchain are related to trusted computing and can mutually benefit from ensuring security both in the software that defines the networks and in the transactions carried out on the blockchain. Network virtualization enables the enhancement of network security through increased robustness achieved via automated processes. Semantic communication and AINAI play crucial roles in identifying data within this specific type of operation. It is also worth mentioning UAVs, which are one of the advanced low-cost solutions for intelligence missions in short-range operations.
## 5 Conclusions
#### Practical Applications  
1. As described in this study, after applying an NHE protocol to athletes from various sport disciplines, the focus was placed on improving absolute strength and vertical jump. The findings indicate that not all sports lead to gains in these physical capacities.
2. In this context, physicians, physiotherapists, and other professionals knowledgeable about the protocol can tailor the strength training program to suit the athlete's current phase within the macrocycle.
3. Based on the results, vertical jump performance improved in most of the studied disciplines except cycling and teakwondo; therefore, its application may be more appropriate for sports where Nordic stimulation has demonstrated positive effects.
4. With respect to absolute strength, the implemented protocol showed positive effects, particularly in basketball and soccer.
5. The scope of the 7-week Nordic training protocol appears to have limited effects; thus, the observed improvements may be temporary or influenced by load management within each macrocycle.  
Conceptualization: [PERSON]; methodology: [PERSON] and [PERSON]; validation: [PERSON] and [PERSON]; formal analysis: [PERSON], [PERSON] and [PERSON]; investigation: [PERSON] and [PERSON]; resources [PERSON]; data curation: [PERSON] and [PERSON]; writing--original draft preparation: [PERSON] and [PERSON]; funding acquisition: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any external funding.
# Norovirus Detection in Ready-To-Eat Salads by Propidium Monoazide Real Time RT-PCR Assay
## Materials and Methods
### Detection of NoV GI and GII with PMAxx Real Time RT-PCR
#### 2.3.2 Virus Concentration and Nucleic Acid Extraction  
Viruses concentration and viral RNA extraction were performed following the method specified by ISO/TS 15216-2 (2017) for the qualitative detection of NoV genogroups I (GI) and II (GII) from RTE salad samples. Briefly, 25 g of each sample were cut into little pieces and homogenized with TGBE buffer pH 9.5 (100 mM Tris-HCl, 50 mM glycine, 1% beef extract) and 10 uL of process control virus material (Mengo virus, kindly provided by IZS Foggia, Italy). The eluate was concentrated with 5X PEG/NaCl solution (50% (_w/v_) PEG 8000, 1.5 M NaCl). The pellet obtained was resuspended in 500 uL of PBS buffer pH 9.5 (0.15 M sodium phosphate) and divided into two aliquots (250 uL). With the aim of discriminating between native and inactivated viruses, one aliquot (250 uL) was treated with PMAxx-Triton x-100 as previously described [14; 17; 19; 22]. In brief, 250 uL of each aliquot obtained as described above were incubated with 50 uM PMAxx (Biotinum) and 5% Triton X-100 in the dark at RT for 10 min [17]. Samples were then exposed to light for 15 min using a photoactivation system (Led-ActiveBlue, GenIUL). Then, the viral nucleic acids were extracted and purified using a commercial kit (NucliSENS miniMAG kit, bioRxiv.Firenze, Italy) following the manufacturer's standard protocols. All RNA obtained was subjected to real time RT-PCR in compliance with ISO/TS 15216-2 (2017), and each experiment was carried out in duplicate and repeated twice.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 6 Conclusions  
A novel VSS INC MPPT method with adaptive scaling factor for rapid irradiance changes has been proposed in this paper. Using the irradiance level value determined by the model-based state estimation method, an appropriate scaling factor can be determined to enhance the performance of the conventional VSS INC technique. By adjusting the scaling factor according to the irradiance level, the tracking speed, tracking accuracy, and tracking energy loss can all be improved comparing with the VSS INC MPPT method using fixed scaling factor values. Specifically, the tracking energy loss can be reduced by 43.9% and 29.9% under the tested conditions according to the experimental results. Hence, the proposed method can yield more energy and is suitable for fast-changing environments. Another contribution of this study is that fast and accurate tracking can be achieved without the need for extra expensive irradiance and temperature sensors. The proposed method is simple and can easily be integrated into conventional VSS INC algorithms, which makes the developed MPPT solution more suitable for solar generation system applications. Future works include the development of a GMPP tracking algorithm suitable for PSC, and develop an optimal design algorithm to obtain the adequate scaling factor automatically.  
[PERSON], [PERSON], and [PERSON] made considerable contributions in this research, [PERSON] and [PERSON] proposed ideas and research concepts at the beginning of the study, [PERSON] and [PERSON] completed the construction of the simulation platform and the construction of the experimental platform during the research process. All authors are responsible for the integrity of the work as a whole. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.