esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### System Results
Figure 10: The cumulative energy demand of the biomass for heating, cooling, and total. Figure 12: System energy performance during the heating and cooling periods. Figure 13: System exergy efficiency during the heating and cooling periods. and it is a critical parameter that determines the mean efficiency of the heating mode. Also, it is important to state that it influences the mean heating performance in a great way. Figure 12 shows that the system operates during the heating mode with an efficiency of around 1.55 and 0.90 depending on the ambient temperature. So, the mean value of around 1.31 is found after the integration during the winter period and a critical parameter in the final result is the percentage of the heating mode operation with the absorption heat pump. A simplistic mathematical analysis would state that [63%\(\cdot\)1.55 + 37%\(\cdot\)0.9 \(\approx\) 1.31]. This calculation validates the analysis that has been presented regarding the variation of the modes during the heating period. At this point, it is useful to separate the heating input in the system in order to determine the contribution of the ambient to heat production. The yearly heating load is calculated at 9136 kWh and the useful heat from biomass that inserts into the system (in the heat pump or in the building directly) is found to be 6290 kWh. Consequently, the heat input from the ambient to the evaporator of the heat pump is around 2846 kWh. Therefore, it can be said that the ambient gives the 31.1% of the heating demand, which is a significant amount that indicates the existence of fuel savings. Another interesting conclusion is that the heating mode is always more efficient than the cooling mode, as Figure 7 indicates. Thus, it is critical to state that the present system is an ideal choice for locations with high both heating and cooling loads. Moreover, this system has to be avoided in very cold climates because in these locations the heating heat pump does not operate easily, and also there are no cooling loads. Additionally, in extremely hot climates, this system is not an optimum choice because there is a lack of heating demand, and also the cooling demand increases due to the high ambient temperatures, the fact that reduces the performance. To conclude, climate conditions such as Athens (Greece), which is a typically Mediterranean city, are ideal for applying the present configuration.
## 2 Related Work
However, the maintenance cost of secret shares over multiple user devices also increases because the maintenance operation requires communication among the user devices; according to [12], the maintenance cost of sharing a secret takes about \(t^{2}\) to \((n-1)^{2}\) under the assumption of \((t,n)\)-threshold scheme, where at least \(t\) out of \(n\) user devices are required for user authentication. In this paper, we considered a more realistic and practical deployment achieving a balance between the security and the convenience. In our system, we particularly design a secret sharing scheme using PBKDF (Password-based key derivation function). Specifically, our approach encrypts a user's sign key and physically separates the secret parameters used to generate the encryption key into the primary and the secondary user devices. With this simplified system model, the proposed system can reduce the management cost while achieving a reasonable level of security. In addition, unlikeprevious studies, we implemented a fully working system and evaluated its performance in real network settings. Certificate-based authentication has been still widely used in a variety of applications. [PERSON] et al. [14] presented a certificate-based user authentication method of leveraging smart cards to mitigate the threats on user authentication in Internet banking applications. Certificate-based authentication is also commonly used by open source IoT frameworks (e.g., AllJoyn [15], OCF (Open Connectivity Foundation) specification [16], IoTivity [17], etc.) to establish secure sessions between devices while minimizing user intervention. To the best of our knowledge, no previous study has presented an offline guessing attack scheme against certificate-based authentication with the goal of obtaining a user's private key from the password-protected private key file. As such, we analyzed this type of threat in the context of user authentication to industrial controllers, and developed a countermeasure to mitigate the threat.
# Detecting Phishing Domains Using Machine Learning
## 4 Methodology
### Dataset Used: UCI Phishing Websites  
Standard datasets already exist for the development of phishing website detection algorithms. Other studies classified websites to establish a list of legitimate and phishing sites for further consideration. This work, on the other hand, utilizes the freely accessible phishing dataset from UCI machine learning repository that can be found in [68], and was prepared by [69]. This dataset was created to build machine learning-based phishing website detection algorithms. It is comprised of extensive properties that span four distinct categories [70]. They designed and extracted characteristics from the following categories: Address Bar, HTML and JavaScript, Abnormal, and Domain. This study was performed using a phishing domain dataset with 31 attributes that can either take a binary or ternary  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dataset** & **Algorithm** & **Accuracy** \\ \hline [PERSON] et al. [37] & URLs & IBK, SVM, NB & 89.75\% \\ \hline [PERSON] et al. [17] & website & ANN, KNN, RF, SVM, C4.5, RF & 97.36\% \\ \hline [PERSON] et al. [50] & Websites & SVM, RF, DT, AB & 93\% \\ \hline [PERSON] et al. [51] & URLs & DT, RF, GBM & 98.40\% \\ \hline [PERSON] and [PERSON] [52] & websites & ELM, SVM, LR, C4.5, LC-ELM, KNN, XGB & 99.2\% \\ \hline [PERSON] et al. [41] & Websites & RF & 97.63\% \\ \hline [PERSON] et al. [42] & UCI & Ensemble bagging, boosting, stacking & 95.4\% \\ \hline [PERSON] et al. [56] & Websites & SVM, DT, RF, KNN, KS, NB & 97.98\% \\ \hline [PERSON] et al. [53] & URLs & eDRI & 93.5\% \\ \hline [PERSON] et al. [40] & URLs & LR, DT, RF & 96.58\% \\ \hline [PERSON] and [PERSON] [54] & Websites & RF & 99.57\% \\ \hline [PERSON] et al. [57] & URLs & RF, SVM & 95.11\% \\ \hline [PERSON] et al. [58] & Websites & RC, kNN, IBK, LR, PART & 97.3\% \\ \hline [PERSON] et al. [59] & URLs &
\begin{tabular}{c} RF, C4.5, PART, \\ SVM, NB \\ \end{tabular} & 96.17\% \\ \hline [PERSON] et al. [60] & Websites & SVM, RF & 94\% \\ \hline [PERSON] and [PERSON] [61] & Websites & Genetic algorithm (GA) + DNN & 89.50\% \\ \hline [PERSON] et al. [62] & Websites & CNN & 95.02\% \\ \hline [PERSON] [63] & Websites & Convolutional auto encoder + DNN & 89.00\% \\ \hline [PERSON] and [PERSON] [64] & Websites & PSL 1 + PART & 99.30\% \\ \hline [PERSON] et al. [65] & Websites & RNN + CNN & 95.79\% \\ \hline [PERSON] et al. [55] & UCI & DNN +[PERSON] & 96.00\% \\ \hline [PERSON] et al. [43] & URLs & GBDT, XGBoost and LightGBM & 98.60\% \\ \hline [PERSON] et al. [66] & Websites & Auto encoder + NIOSELM & 94.60\% \\ \hline [PERSON] and [PERSON] [67] & Websites & Grey wolf optimizer + SVM & 90.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of the latest research focusing on machine learning phishing detection techniques.  
value. This dataset has 11,055 records, and each record includes 31 characteristics. The characteristics of the collection are identified by names, such as URL Length, Submitting to Email, Shortening Service, Abnormal URL, Having an At Symbol, and Redirect.
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
## 4 Conclusions  
This work sought to quantify the yam drying performance in a solar adsorption dryer (SAD) compared to a solar dryer (SD) and open sun drying (OSD) and to validate the effect on vitamin C and composition retention in these three systems. The dryer performance, vitamin C retention, and product color of different cultivars were investigated.  
The SAD drying works the best in the initial period of drying. In this period, the night contributes to drying and reduces the total drying cycle in terms of days due to the application of night drying. At the end of drying, the benefit of night drying is minor due to the low driving force for water transfer. At this stage, dehumidification of the air hardly helps the drying at night. In this period, an elevated temperature is more effective. The drying time in the SAD is affected by seasonal variations and weather conditions.  
For three cultivars, 'Lilii' dried faster than 'Pona' and 'Dente' with 'Dente' drying slowest, which results in differences in granule shape and sizes of these cultivars. The changes in color are for two cultivars of yam comparable for SAD and SD drying and for the third cultivar the SAD dried product was closer to the original whitish color than with SD drying. Long exposure of yam to drying conditions had more influence on the deterioration of vitamin C, but did not affect the composition. The deterioration of vitamin C is the strongest for OSD, and is close for SD and SAD drying with a preference for SD.  
Conceptualization, E.A. and A.v.B.; methodology, E.A. and A.v.B.; software, G.K.and [PERSON]; validation, G.K. and H.K.; formal analysis, E.A; investigation, G.K. and H.K.; resources, E.A; data E.A. and A.v.B.; writing--original draft preparation, E.A; writing--review and editing, A.v.B.; visualization, E.A.; supervision, A.v.B.; project administration, E.A.; funding acquisition, E.A. and A.v.B. The APC was funded by Wageningen University Research and experimental support from Kwame Nkrumah University of Science and Technology, Ghana.  
We acknowledge the Wageningen University and Research, The Netherlands and The University of Science and Technology, Kumasi, Ghana for their financial support of this work. The authors declare no conflict of interest.
## 3 Results  
### Retrievals from \(R_{\text{rs}}(\lambda)\)  
#### 3.1.1 \(a_{\text{Gau}}(\lambda)\) Validation  
The MuPI model was first tested with datasets that contained different levels of chlorophyll \(a\) concentration from the IOCCG synthesized data and different cruises in the global ocean. The main purpose of this test was to evaluate, and validate, the implementation of the MuPI approach for a wide range of environments. A mean UAPD of 36% was obtained between \(a_{\text{Gau}}(\lambda)\) from \(R_{\text{rs}}(\lambda)\) inversion and \(a_{\text{Gau}}(\lambda)\) from water samples throughout the data range for different datasets (see Figure 3). The differences in statistical results, noted in Table 5 for \(a_{\text{Gau}}(\lambda)\) retrieval from different datasets, are strongly influenced by their different dynamic ranges and characteristics as implied in Section 2.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pigments** & \(a_{\text{Gau}}(\lambda)\) & **Parameters (\(a_{\text{0}}\), \(a_{\text{1}}\), \(\dots\), \(a_{\text{1}}\))** & **R\({}^{2}\)** \\ \hline Chl-a & 675 & 1.804, 0.975 & 0.89 \\ Chl-b & 434, 453, 470 & \(-\)0.066, 2.470, \(-\)3.073, 1.379 & 0.72 \\ Chl-c & 470, 492, 523, 675 & 1.334, 2.022, \(-\)3.125, 0.745, & 0.83 \\ PPC & 453, 470 & 0.734, 1.311, \(-\)0.416 & 0.76 \\ PSC & 470, 492, 523 & 1.67, 3.034, \(-\)2.670, 0.725 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for estimation of pigment concentrations: the pigment-specific \(a_{\text{Gau}}(\lambda)\), coefficients and R\({}^{2}\). Chl-a: chlorophyll \(a\), Chl-b: chlorophyll \(b\), Chl-c: chlorophyll \(c\), PPC: photo-protective carotenoids, PSC: photosynthetic carotenoids.  
Figure 3: \(a_{\text{Gau}}(\lambda)\) heights retrieved from \(R_{\text{rs}}(\lambda)\) at MEdium Resolution Imaging Spectrometer (MERIS) bands versus measured Gaussian peak (decomposed from \(a_{\text{ph}}(\lambda)\)) for the data from different datasets (shown in different colors).
# Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection
## 2 Extraction of Multi-Scale Recurrence Quantification Measurements (MRQMs)
### Recurrence Quantification Analysis (RQA)
#### 2.2.1 Recurrence Plots (RPs)
Obviously, the loop points on the long diagonal lines of the pathological glottal signal are discontinuous, which represents the degree of signal amplitude fluctuation. Vocal fold lesions cause the speaker to work harder to produce vowels. The vibration period of the vocal folds determines the pitch frequency of the vocal folds. Therefore, the diagonals on the RP cannot be kept parallel, and there are many scattered and isolated points. A specific analysis can refer to Table 2.
# Research on the Tooth Surface Integrity of Non-Circular Gear WEDM Based on HPSO Optimization SVR
## 4 Results and Discussion
### Analysis of Surface Roughness  
In this study, ANOVA was performed to determine the influence of process parameters on the surface integrity indicators. The confidence level of 95% was used throughout the analyses of the experiment. The corresponding \(p\)-value evaluated the statistical significance of the process parameters. When the \(p\)-value was less than 0.05, the parameters were said to have statistically significant effects on the surface integrity indicators [34].  
In the ANOVA analysis shown in Table 8, the \(p\)-values of peak current, pulse-on time, pulse-off time, and tracking are all less than 0.05. Therefore, at a 95% confidence level, the above parameters are all significant for surface roughness. Among them, the pulse-on time had the most significant effect on surface roughness, with a contribution rate of 56.98%, followed by the peak current, with a contribution rate of 18.64%. Figure 6 shows the effect of selected process parameters on the surface roughness. It can be seen from Figure 6 that the surface roughness increased with the increase of peak current and pulse-on time. The main reason for this was that the discharge energy had the greatest influence on the surface roughness, and the increase of the discharge energy of a single pulse makes the discharge pit larger and the surface roughness worse. The peak current and pulse-on time are proportional to the discharge energy and then affect the surface roughness [35]; with an increase of pulse-off time and tracking, the surface roughness gradually decreases.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Trial** & **A** & **B** & **C** & **D** & **SR (\(\upmu\)m)** & **RS (MPa)** & **MH (HV)** \\ \hline
1 & 1 & 1 & 1 & 1 & 1 & 3.7545 & 95.19544 & 356.4 \\
2 & 1 & 2 & 2 & 2 & 3.9214 & 101.1058 & 364.6 \\
3 & 1 & 3 & 3 & 3 & 3.7459 & 107.491 & 375.0 \\
4 & 1 & 4 & 4 & 4 & 3.6147 & 114.351 & 382.7 \\
5 & 2 & 1 & 2 & 3 & 2.7551 & 99.78 & 368.0 \\
6 & 2 & 2 & 1 & 4 & 3.0905 & 106.234 & 377.78 \\
7 & 2 & 3 & 4 & 1 & 4.2163 & 114.491 & 391.95 \\
8 & 2 & 4 & 3 & 2 & 5.558 & 121.351 & 397.5 \\
9 & 3 & 1 & 3 & 4 & 3.6059 & 105.0054 & 381.5 \\
10 & 3 & 2 & 4 & 3 & 4.5576 & 110.4558 & 395.7 \\
11 & 3 & 3 & 1 & 2 & 5.51 & 117.381 & 415.1 \\
12 & 3 & 4 & 2 & 1 & 6.46 & 147.781 & 418.7 \\
13 & 4 & 1 & 4 & 2 & 3.6672 & 127.4054 & 390.3 \\
14 & 4 & 2 & 3 & 1 & 4.1078 & 156.8558 & 396.8 \\
15 & 4 & 3 & 2 & 4 & 5.0015 & 139.7 & 422.6 \\
16 & 4 & 4 & 1 & 3 & 6.35 & 170.101 & 432.2 \\ \hline \end{tabular}
\end{table}
Table 7: Orthogonal experimental results.
## 2 Port-Hamiltonian Systems
### Formalism and Property
#### 2.1.1. Components  
Electronic circuit components are sorted as (or can be a combination of):  
internal components that store energy \(\mathtt{E}\geq 0\) (capacitors or inductors), internal components that dissipate power \(\mathtt{D}\geq 0\) (resistors, diodes, transistors, etc.), external ports that convey power \(\mathtt{S}\ (\in\mathbb{R})\) from sources (voltage or current generators) or any external system (active, dissipative, or mixed).  
The behavior of each component is described by a relation between two _power variables_: the current \(i\) and the voltage \(v\), defined in _receiver convention_ (the received power is \(\mathtt{P}=v\cdot i\)).  
The energy \(\mathtt{E}_{s}\) stored in storage component \(s\in[1\cdots n_{S}]\) is expressed as a _storage function_\(h_{s}\) of an appropriate _state_\(x_{s}\): \(\mathtt{E}_{s}(t)=h_{s}\big{(}x_{s}(t)\big{)}\geq 0\). Typically, for a linear capacitor with capacitance \(C\), the state can be the charge \(x=q\) and the positive definite function is \(h(q)=q^{2}/(2C)\). Storage power variables \((v_{s},i_{s})\) are related to the variation of the state \(\frac{\mathtt{d}x_{s}}{\mathtt{d}t}\) and the gradient of the storage function \(h^{\prime}_{s}(x_{s})\), the product of which is precisely the _received power_: \(v_{s}\cdot i_{s}=\frac{\mathtt{d}\mathtt{E}_{s}}{\mathtt{d}t}=h^{\prime}_{s} \cdot\frac{\mathtt{d}x_{s}}{\mathtt{d}t}\). For the capacitance, these _constitutive laws_ are \(i=\frac{\mathtt{d}q}{\mathtt{d}t}=\frac{\mathtt{d}x}{\mathtt{d}t}\) and \(v=q/C=h^{\prime}\). Note that these definitions apply equally for non-quadratic storage functions \(h(x)\geq 0\) for which \(h^{\prime\prime}(x)\) is not constant.  
The power \(\mathtt{D}_{d}\) instantaneously dissipated by the dissipative component \(d\in[1\cdots n_{D}]\) is expressed with respect to an appropriate _dissipation variable_\(w_{d}\): \(\mathtt{D}_{d}(t)\equiv\mathtt{D}_{d}(w_{d}(t))\geq 0\). Typically, for a linear resistance \(R\), \(w\) can be a current \(w=i\) and \(\mathtt{D}(i)=R\cdot i^{2}\). As for storage components, a mapping of the dissipative power variables \((v_{d},i_{d})\) is provided, based on the factorization \(\mathtt{D}_{d}(w_{d})=w_{d}\cdot z_{d}(w_{d})\), introducing a _dissipation function_\(z_{d}\). For the resistance, \(i=w\) and \(v=R\cdot i=z(w)\).  
The power instantaneously provided to the system through external port \(p\in[1\cdots n_{P}]\) is \(\mathtt{S}_{p}(t)\), and we arrange the source variables \((v_{p},i_{p})\) in two vectors: one is considered as an _input_\(u_{p}\), and the other as the associated _output_\(y_{p}\), so that the power received from sources on port \(p\) is \(\mathtt{S}_{p}=y_{p}\cdot u_{p}=-v_{p}\cdot i_{p}\) (receiver convention, with \(v_{p}\cdot i_{p}\) the power received by the sources).
## 3 Machine Learning Modeling
### Fundamental Theory of PCA, LSTM, and GA
#### 3.1.2 LSTM Networks  
Long short-term memory (LSTM) is a promoted recurrent neural network (RNN)-based network structure, and its particularity lies in the fact that the nerve cells in the RNN are replaced with memory blocks [50; 51]. Each memory block includes one or more memory cells and three non-linear summation units, and the whole neuron is controlled by the gate. Therefore, the LSTM not only is pretty proficient in handling issues highly related to time series, but also resolves gradient dispersion of the RNN occurring during training [52].  
Figure 5 shows the internal structure of a nerve cell of the LSTM at three consecutive time points. It can be seen that the input part (marked in red) includes the current input \(x_{t}\), the memory from the last LSTM unit \(C_{t-1}\) and the output of the last LSTM unit \(h_{t-1}\), and the output part (marked in blue) includes the current output \(h_{t}\), the next hidden state \(h_{t}\), and the next cell state \(C_{t}\). The keys of the LSTM are the cell state and the gates (including the forget gate \(f_{t}\), the input gate \(i_{t}\), and the output gate \(O_{t}\)). The cell state resembles a conveyor belt to some extent. The structures of the gates can remove or add information to the cell state, and thus describe the quantity of information contained in the current network. First, the data pass through the forget gate for information filtering, during which \(x_{t}\) (input at time \(t\)) and \(h_{t-1}\) (output of the hidden layer at the previous moment)
### Organisation of the Paper  
Our paper is organised as follows. Section 2 recalls briefly what are the target and untarget scenarios in \(\mathcal{R}\), what their versions in \(\mathcal{H}\), fixes some notations, and lists a series of indicators (\(L_{p}\) norms and FID) used to assess the human perception of distinct images. Section 3 formalises the noise blowing-up strategy, provides the scheme of the attack \(atk_{\mathcal{H},\mathcal{C}}\) that lifts to \(\mathcal{H}\) any attack \(atk_{\mathcal{R},\mathcal{C}}\) against a CNN \(\mathcal{C}\) that works in the \(\mathcal{R}\) domain, and that takes advantage of lifting the adversarial noise only. It recalls some complementary indicators used to assess the impact of the obtained tentative adversarial images (Loss function \(\mathcal{L}_{\mathcal{C}}\), \"safety buffer\" \(\Delta_{\mathcal{C}}\)), and again fixes some notations. The experimental study is performed in the subsequent Sections. Section 4 describes the ingredients of the experiments: the resizing functions, the 10 CNNs, the 100 clean high-resolution images, the target categories considered in the target scenario, and the 7 attacks. Section 5 provides the results of the experiments performed under these conditions: success rate, visual quality, and imperceptibility of the difference between adversarial and clean images, timing, and overhead of the noise blowing-up strategy. The cases, where the standard implementation of the strategy failed to succeed, are revisited in Section 6 thanks to the \"safety buffer\" \(\Delta_{\mathcal{C}}\). Finally, Section 7 provides a comparison of the noise blowing-up method with the generic lifting method [29; 30] on three challenging high-resolution images, one CNN, and one attack for the target scenario. Section 8 summarizes our findings, and indicates directions for future research. An Appendix completes the paper with additional data and evidence.  
All algorithms and experiments were implemented using Python 3.9 [32] with NumPy 1.23.5 [33], TensorFlow 2.14.0 [34], Keras 3 [35], and Scikit 0.22 [36] libraries. Computations were performed on nodes with Nvidia Tesla V100 GPGPUs of the IRIS HPC Cluster at the University of Luxembourg.