esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Frozen Storage
To further exemplify the data acquired from the 6-week frozen sampling, a PCA statistical analysis was performed to monitor the odor profile changes throughout the tested period via clustering techniques. Figure 7 displays the PCA of the primary compounds from the SLF eggs and crickets across the 6-week study period. PCA multivariate analysis showed that the first principal component accounted for 44.4% of the variability in the data, while the second principal component accounted for 35.8% of the variability in the data in the eggs. Weeks 0 and 1 are grouped independently in their own quadrants, while weeks 2 and 4 are grouped together in the same quadrant, and weeks 4 and 5 are grouped together in the same quadrant. Samples from the earlier timeline (weeks 0-1) depict compounds such as anisole, dodecane, tridecane, and D-limonene as responsible for the diverging odor profile, while the later timeline samples (weeks 2-5) highlight 3-octanone, 2,3-butanediol, and phenylethyl alcohol as main drivers for the clustering during this period. For the crickets, PCA multivariate analysis showed that the first principal component accounted for 60.6% of variability in the data, while the second principal component accounted for 15.8%. Weeks 0 and 1 are grouped together in the same quadrant, while weeks 2 and 3 are grouped independently in their own quadrants. Weeks 4 and 5 are also grouped together in the same quadrant. For the earlier sampling period, the cricket samples had compounds such as 6-methyl-2-heptanone responsible for the clustering, while aldehydes such as octanal or aromatic hydrocarbons such as p-xylene are responsible for the advanced week odor profile clustering. It can be said that when comparing the SLF eggs and the crickets, dodecane is a compound that affects earlier sampling odor profiles more than the latter time periods of analysis. Figure 6: Bar graph representing the concentration of dodecane and anisole in the frozen storage study (PPM).
# The Effect of Inhaled Corticosteroid Therapy on Periodontal Status in Patients with Asthma
## 2 Materials and Methods  
The study was conducted with the approval of the Bioethics Committee at the Medical University (no. KB-0012/27/11). Patients of periodontal clinics who attended periodontal disease treatment, preventive check-ups, or hygiene visits were included in the study (Group I). Group II and III patients came in for follow-up visits, such as for professional teeth cleaning. Patients were introduced to the study and its purpose. The subjects were informed that participation in the study is entirely voluntary and refusal to participate would not affect their treatment in any way. They were also informed about the right to withdraw from the study at any time without giving any reason and bearing consequences. Having read the information about the research and having had any doubts clarified, the participants agreed in writing to participate in the study. The study was conducted from January to October 2014.  
120 patients were recruited using convenience sampling, ranging from 18 to 71 years (arithmetic mean 41 years). Three 40-person study groups were formed:  
* I group--periodontitis subjects with diagnosed asthma, treated with ICSs (\"PE-RIOASTHMA\" group);
* II group--persons with diagnosed asthma without periodontitis, treated with ICSs (group \"ASTHMA\");
* III group--healthy subjects without asthma and without periodontitis (group \"CONTROL\").
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Frequency-Based Haze and Rain Removal Network (FHRR-Net) Architecture
#### 2.3.1 Dilated Convolution  
Dilated Convolution [29; 30] is a way to increase the receptive field by adding zero padding inside the filter without increasing the parameters. Dilation rate is a hyper parameter for dilated convolution which defines the spacing between kernels. For example, a 3 \(\times\) 3 kernel with dilation rate of 2 has the same view as a 5 \(\times\) 5 kernel, using only nine parameters. A visual example of dilated convolution is shown in Figure 6.  
In Figure 6, 3 \(\times\) 3 dilated convolution with a dilated rate of 1 has the same receptive field as a traditional 3 \(\times\) 3 convolution, but a 3 \(\times\) 3 dilated convolution with a dilated rate of 2 has a receptive field of 5 \(\times\) 5 convolution. Figure 6b shows the same amount of computation as the traditional 3 \(\times\) 3 convolution, but with a 5 \(\times\) 5 receptive field.  
In the haze removal work, contextual information of the input image is helpful for removing haze relevant features [14] and also useful in rain streak removal work [11]. By using a dilated structure, richer contextual information from the input can be obtained.  
Dilated convolutions for all the convolutional layers of proposed encoder-decoder structure and also, due to the nature of the encoder-decoder structure, it has a variety of scale output features. These characteristics make it possible to take a large receptive field and easily obtain contextual information of the input image.
## 5 Simulation and Evaluation Results
### DRTO Algorithm Performance Evaluation
#### 5.1.1 The Convergence Performance  
The DRTO algorithm is evaluated over 30,000 time frames. In Figure 5, we plot the training loss of the DNN, which gradually decreases and stabilizes at around 0.02, whose fluctuation is mainly due to the random sampling of the training data.  
In Figure 6, we plot the normalized offloading cost, which is defined as  
\[\hat{F}(x^{*},\mathbf{\alpha}^{*})=\frac{F(x^{*},\mathbf{\alpha}^{*})}{\min_{\mathbf{x}^{ \prime}\in\{0,1\}^{N}}F(\mathbf{x}^{\prime},\mathbf{\alpha}_{\mathbf{x}^{\prime}})} \tag{17}\]  
where the numerator denotes the optimal offloading cost by the DRTO algorithm, and the denominator denotes the optimal offloading cost by greedily enumerating all the \(2^{N}\) offloading locations. We set the update interval \(\Delta=64\). As we can see, within the first 5000 time frames, the normalized offloading cost significantly fluctuates, indicating that the offloading policy has not fully converged. Finally, most of the normalized offloading cost is converged to 1, only few frames slightly fluctuating above 1 due to the rapid channel  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline
**Parameters** & **Value** \\ \hline Computational intensity \(k\) (cycles/bit) & 10 \\ Computing Power Consumption of SatEC server \(p_{c}\) (W) & 0.5 \\ CPU frequency of SatEC server \(f_{1}\) and TC \(f_{0}\) (GHz) & 0.4, 3 \\ Latency-energy weight parameter \(\lambda\) & 0.5 \\ Training interval \(\delta\) & 10 \\ Random batch size & 128 \\ Replay memory size & 1024 \\ Learning rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: The training loss of DRTO.  
fading when the inter-satellite handover occurs. In spite of this fluctuation, the DRTO algorithm can still achieve near-optimal offloading cost performance.
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 3 Results and Discussion  
### Scanning Patterns  
Figure 1a-d illustrate the scanning patterns formed by different trajectories. While the TL is regular and values of \(H_{max}\), \(D_{min}\), and \(D_{max}\) do not change, the BC, SL, and RL have different \(H_{max}\), \(D_{min}\), and \(D_{max}\) values over the FOV. The pattern produced by SL is denser at the edges and sparser in the middle [30]. Its regularity is better than BC but is not as regular as TL. RL does not scan the entire FOV, producing a pattern that is very dense in the center and moderately dense near the edges. The BC scan trajectory has the lowest regularity and resolution when compared to other scanning trajectories.  
Figure 2a,b depict the relationship between the maximum distance \(H_{max}\) and \(D_{max}\) as a function of \(N_{P}\), respectively. As can be seen, theoretical and numerical values agreed quite well. The inset shows the same plot with both the x and y axes displayed in log10 format. The log-log plot of the data clearly shows that \(H_{max}\) and \(D_{max}\) display a hyperbola, with the values decreasing as \(N_{P}\) increases. Our results support previous estimates that for large values of \(N_{P}\), \(H_{max}\) and \(D_{max}\) of the SL trajectory are proportional to the reciprocal of \(N_{P}\)[18, 25].  
The density of the scanning pattern for \(N_{P}\) = 100 has been proven to be sufficient for producing a good-quality image [8, 24]. Therefore, the pixel size of the phantom image to be utilized for image reconstruction in this work will be determined using the pixel size of this \(N_{P}\) value. Table 2 displays the numerically determined values of \(H_{max}\), \(D_{min}\), and \(D_{max}\) for all four types of scanning trajectories at \(N_{P}\) = 100.
## 4 Results and Discussions
### Testing Period  
Further, the model was validated for the testing period, using statistical metrics. For this, we calculated the mean index value of all the pixels and examined the variation in terms of drought categories. The reason why we are using drought classes is that there are not enough drought periods during the testing period in order to understand the variation in other drought perspectives (like duration and intensity). This is crucial, as the variation provides confidence to further inspect the model at a spatial scale and also along different drought characteristics. The results (Figure 7a,b) show that the prediction capability of the model under both time periods is quite similar, with SPEI 3 being predicted slightly better than SPEI 1, with the values of R\({}^{2}\) being 0.76 and 0.73, respectively. During this period, we evaluated the variation as per the drought class. For example, if the predicted SPEI value was found to be in the same drought class (Table 1), the results were considered to be satisfactory for that particular month. The results show that the observed SPEI 1 value had the most months under near-normal conditions (73%), i.e., values ranging from \(-\)0.99 to 0.99, followed by moderately dry (10%) and moderately wet (6.67%). In the case of predicted values, 43 of the 60 months depicted same drought class between the observed and predicted SPEI 1 values, while the remaining  
Figure 6: Relative importance of the variables for predicting SPEI 1 and SPEI 3 during training period.  
varied. Similarly, for SPEI 3, the near-normal conditions were observed for 78%, followed by severely wet (10%) and moderately dry (8.33%). However, when analyzing the variations between the classes, 45 months depicted the same drought classes between predicted and observed values. The results show that statistical metrics may not always prove to be a useful quantification approach, and therefore it is important to analyze in terms of drought characteristics. For instance, in the present study, we used drought classes, but others can use drought duration and similar classes, provided they have sufficient data to analyze the variation.
## 4 Results
### Correlation Between Operational Parameters and Acoustic Emission Data
#### 4.2.3 Blade Pitch Angle  
The relationships between the blade pitch angle and the signal features are shown in Figure 11. The largest amplitudes are measured at a blade angle of \(-2^{\circ}\) to \(-1^{\circ}\) and average around 105 dB at sensor 3 and around 90 dB at sensor 7. Up to an angle of 28\({}^{\circ}\), the amplitudes decrease to around 101 dB at sensor 3 and 74 dB at sensor 7, beyond which  
Figure 10: Correlation between rotor speed and signal features amplitude in decibel, energy in engineering unit, and spectral centroid in kilohertz highlighted as moving average of the maxima for (**a**) sensor 3 (transition piece) and (**b**) sensor 7 (foundation).  
the acoustic activity remains almost stable. The energy and spectral centroid show a qualitatively similar relationship.  
The blade pitch angle is adjusted depending on the wind speed to ensure optimal machine operation. The analysis indicates that, up to wind speeds of 3.5 \(\,\mathrm{m}\mathrm{/}\mathrm{s}\), the blade position varies between \(-2^{\circ}\) and \(25^{\circ}\). Between 3.5 \(\,\mathrm{m}\mathrm{/}\mathrm{s}\) and 9 \(\,\mathrm{m}\mathrm{/}\mathrm{s}\), the turbine is primarily operated in a neutral position of approximately \(-2^{\circ}\), and only at higher wind speeds does the position change again up to \(25^{\circ}\). This explains why louder background noise occurs at low blade pitch angles, as the rotor speed and wind speed tend to be high at these positions. In addition, this underlines the coupling of aerodynamic noise from the wind turbine blades and their interaction with the air in the tower structure. However, the relationship cannot be considered in isolation here either; it depends on the other operating parameters.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 2 Related Works
CT allows for public monitoring of distributed logs, enabling oversight of CA operations and the management of compelled certificates by empowering legitimate domain owners to reject unauthorized certificates. This log-based approach was later adopted in many research works for building a transparent PKI, such as in enhanced certificate transparency [26], ARPKI [27], and CertLedger [13]. Yet, the log-based PKI approach still relies on current CAs and requires a continuous flow of data for storing records. Also, to revoke a certificate, action must be taken to communicate with the CA once any irregularities are detected in the CT log. Another well-regarded solution that addresses the problems of a centralized PKI is a PKI that combines blockchain with the WoT. This WoT-based PKI ensures the authenticity of a public key without relying on a centralized authority, thus eliminating a single point of failure. DBPKI [28] is a dynamic blockchain-based PKI based on the WoT that distributes trust across various parties, thereby reducing dependence on traditional CAs and Certificate Revocation Lists (CRLs). However, as per the proposal, all the information would be recorded on the blockchain rather than providing digital signatures, indicating that the existing system for certificate verification, such as web browsers, would require substantial modifications in the authentication process. BlockPGP [29] is an alternative blockchain-based PKI that creates PGP certificates and stores the public keys on a PGP key server. It integrates blockchain-related data into the PGP certificates, aligning with the existing OpenPGP standards. Also, existing certificate holders can sign other public key certificates. Similarly, Cecoin [19], SCPKI [30], LRS_PKI [31], and Trustful [32] are PKIs that combine WoT and blockchain technologies. Rather than relying on a centralized CA, these systems employ miners to fulfill the role of certificate authorities.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
###### Abstract  
Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.  
multimemporal DEM; control factors; susceptibility assessment; LRM; historical landslide events +
Footnote †: journal: Applied Sciences
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 3 Research Background
### Location-Allocation
#### 3.1.2 Maximizing Coverage  
Maximizing coverage is used to locate facilities based on the number of facilities, which, in this case, is determined by the decision maker. Additionally, facilities are often required to arrive at all demand points within a specified response time [30]. This model, called MCLP, seeks the location of a fixed number of facilities that are most probably insufficient to cover all demand within the standards, so the demand covered by the service is maximized [22]. The fixed number of facilities is a proxy for a limited budget [22]. Its formulation is as follows (see Equation (4)):Sets:  
\(I\) = set of demand nodes (indexed by \(i\));  
\(J\) = set of eligible facility sites (indexed by \(j\));  
Parameters  
\(p\) = the number of facilities to be deployed;  
\(a_{i}\) = the demand quantities at demand node \(i\);  
\(N_{i}\) = \(\left\{j\right|\left.d_{ji}\leq S\right\}\);  
\(d_{ji}\) = the distance from potential facility location \(j\) to demand node \(i\);  
\(S\) = the distance standard for coverage;  
Decision Variables:  
\(y_{i}\) = binary variable equals 1 if node \(i\) is covered, 0 otherwise;  
\(x_{j}\) = binary variable equals 1 if a facility is located at node \(j\), 0 otherwise.  
\[Max\ \sum_{i\in I}a_{i}y_{i} \tag{4}\]  
subject to  
\[y_{i}\leq\sum_{j\in N_{i}}X_{j}\ \forall i\ e\ I \tag{5}\]  
\[\sum_{i\in I}X_{j}=p \tag{6}\]  
\[x_{j},y_{i}=0,1\ \ \forall j\ e\ J,\forall i\ e\ I \tag{7}\]  
The objective in Equation (4) maximizes the weighted sum of covered demand nodes. Constraints in Equation (5) state that the demand at node \(i\) is covered whenever at least one facility is located within the time or distance standard S. Constraint in Equation (6) gives the total number of facilities that can be sited [22].
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 4 Methodology and Implementation
### Hidden Markov Model (HMM)  
The HMM was trained using the Gaussian HMM from the hmmlearn library in Python. Two scenarios were considered: binary classification as botnet or normal (\(N=2\)), and multiclass classification into the seven botnet types (\(N=8\)). To address data imbalance, a combination of oversampling and undersampling was applied during training. The train-to-test ratio was set at 80:20, and a maximum of 1000 iterations was used for HMM. The model demonstrated an effective training accuracy of 95%.  
Here, we outline a step-by-step recap:  
1. The dataset was divided into training and testing data subsets.
2. The training-to-test data split ratio was set at 80:20.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr. No.** & **Feature Name** & **Description** & **Category** \\ \hline  
ange & Total packets range & Statistical Feature \\
19 & TotBytesACF & Total bytes ACF & Autocorrelation Feature \\
20 & TotBytesPeriodicity & Total bytes periodicity & Autocorrelation Feature \\
21 & TotBytesStrength & Total bytes strength & Autocorrelation Feature \\
22 & TotPktsACF & Total packets ACF & Autocorrelation Feature \\
23 & TotPktsPeriodicity & Total packets periodicity & Autocorrelation Feature \\
24 & TotPktsStrength & Total packets strength & Autocorrelation Feature \\
25 & DurACF & Duration ACF & Autocorrelation Feature \\
26 & DurPeriodicity & Duration periodicity & Autocorrelation Feature \\
27 & DurStrength & Duration strength & Autocorrelation Feature \\
28 & Label & ’Botnet’ or ’Normal’ & Label \\
29 & Class & Botnet type (7 in total) & Label \\
30 & DegreeCentrality & Degree centrality & Topology Feature \\ \hline \hline \end{tabular}
\end{table}
Table 6: Extracted features list after data pre-processing.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attributes** & **Sample 1** & **Sample 2** & **Sample 3** \\ \hline StartTime & 15-08-2011 16:43:20.935 & 15-08-2011 16:43:20.937 & 15-08-2011 16:43:20.949 \\ Duration & 632.765 & 0.162 & 1793:589 \\ Protocol & udp & tcp & icmp \\ SrcAddr & 151.51.231.119 & 147.32.84.229 & 147.32.84.174 \\ DstAddr & 147.32.86.44 & 66.56.30.27 & 147.32.96.69 \\ State & CON & CON & URP \\ TotPkts & 32,055 & 2 & 96 \\ TotBytes & 8,577,197 & 567 & 56,640 \\ SrcBytes & 8,575,244 & 95 & 56,640 \\ Label & flow=Background-UDP- & flow=Background-TCP- & flow=Background \\ Established & Established & & flow=Background \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data after removing unwanted protocols and states.  
Balancing of the dataset was applied.
* The HMM was trained with a maximum of 1000 iterations.
* The HMM was applied to classify data as either botnet or normal.
* The HMM was applied to classify seven malware families plus normal traffic.
## 6 Conclusions  
A novel faulted section location method for a distribution network based on status information of fault-indicating equipment is proposed in this paper. Considering that a lot of feeder terminal units (FTUs) and fault indicators (FIs) are applied in power distribution networks, it provides great convenience for collecting the fault information. However, the method for analyzing electrical quantity is more complex because it is easily affected by fault types and system parameters. Therefore, the method in this paper uses status information of FTUs and FIs, which can reduce the difficulty of applying this method in distribution networks. Furthermore, considering topology structure changes frequently in distribution networks, a method for automatically constructing the line list is proposed, which can dynamically represent the topology structure of power distribution line.  
Figure 13: Simulation results of expanded distribution network.  
In this paper, based on the iterative search method, a topology search algorithm and an information loss detection algorithm are proposed. Considering that information loss may lead to a local optimum, the information loss detection algorithm can eliminate the possibility of a local optimum for the topology search algorithm in advance, which improves the accuracy of the fault location method. Different fault conditions are simulated in MATLAB, and the results prove that the proposed method is applicable for solving complex fault cases in distribution networks.  
Moreover, we also study the factors that affect the rate of the proposed method, and we find an advantage that the running time only depends on the quantity of the FIES and branches between the substation and fault points. When the node scale is expanded, the running time does not necessarily increase. Therefore, the fault location method proposed in this paper can achieve a fast and accurate fault location in power distribution networks.  
This paper was a collaborative effort between the authors. The individual contributions can be stated as follows. Formal analysis, G.L. and J.Z.; Funding acquisition, Q.C.; Investigation, J.Z.; Project administration, Q.C.; Software, G.L.; Supervision, Q.C.; Writing--original draft, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 51877123).  
The authors declare no conflict of interest.