esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
The DC component of the circulating current is essential to keep the SM voltages of MMC around a reference value. 2. Reducing the RMS value of the MMC arm currents. 3. Reducing the voltage ripples in the SM DC-link capacitor. 4. The suppression of circulating current does not disturb the MMC main functionality (in this study, MMC operated as a power quality compensator for electric railway applications). On the other hand, as specific aspects of the deadbeat predictive current control methodology applied for circulating current suppression can be summarized as follow:  
1. Being a deadbeat-based control methodology, unmodeled errors in the SRPC mathematical model often deteriorate the system performance. These unmodeled errors may lead to make the  
Figure 16: Currents when using the predictive current controller under load transients: (**a**) SRPC compensation currents; (**b**) Upper and lower arm currents of section \(x\) converter; (**c**) Upper and lower arm currents of section \(y\) converter; (**d**) MMC circulating currents. calculated voltage references deviate from the expected reference values. However, the one-cycle time delay and arm inductor resistance can be included in a more complex model. 2. The presented control methodology is only valid for fixed switching frequency applications.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 3 Classifications of Cyber-Attacks in SGs
### Man in the Middle (MitM) Attacks  
MitM attack consists of three systems, one attacker and two victim computers. The attack starts when the attacker sending signals to the first victim system claims that it is the second victim system while sending other signals to the second victim system, indicating that it is the first victim system. The first victim sends all packages to the attacker, transmitted to the second victim via himself with the MitM effect. When the fake connection is established, the victim thinks they are using the usual network connection. MitM attacks are most commonly carried out by taking advantage of the Address Resolution Protocol (ARP) and changing the MAC address information expressed as ARP poisoning. In parallel with the proliferation of internet networks, security vulnerabilities have increased [94]. Approximately 30 years have passed since the vulnerability in the ARP protocol was detected. However, damaging systems is still one of the widely used methods. This result shows that the security measures taken were insufficient [95; 96; 97]. Especially considering how easy it is to join and leave the mobile network, which is widespread today; the difficulty of preventing ARP poisoning and MitM attack is clearly understood.
## 3 Numerical Benchmark: System with Three Interacting Diaphrags
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Numerical Value** & **Unit** \\ \hline \(m_{1}=m_{2}=m_{3}\) & \(4.2\times 10^{6}\) & kg \\ \(J_{0,1}\) & \(1.0\times 10^{10}\) & N\(\cdot\)m\({}^{2}\) \\ \(J_{0,2}\) & \(3.2\times 10^{10}\) & N\(\cdot\)m\({}^{2}\) \\ \(J_{0,3}\) & \(7.5\times 10^{10}\) & N\(\cdot\)m\({}^{2}\) \\Figure 4: Variation of the modes and of the natural frequencies of the system from 4 to 6 as a function of \(k_{var}\). Figure 3: Variation of the modes and of the natural frequencies of the system from 1 to 3 as a function of \(k_{var}\). Concerning the mode shapes, when \(k_{var}\) is equal to 0 the diaphragms are uncoupled and show the same three modes. The first mode corresponds to a translational mode along the transversal direction (y-direction) of the system, while the second mode corresponds to a rotational one. While the first mode does not change as a function of \(k_{var}\), in the case of the second mode, the stiffening effect of the springs characterizing the interaction can be clearly observed: indeed, if the presence of the interaction is clearly visible for values of \(k_{var}\) equal to 0.8, in the case of higher values of \(k_{var}\) the three masses tend to rotate as one single mass, showing therefore a monolithic behavior (see Figure 3). If the frequency curves present a crossing, the modes undergo the so-called re-ordering phenomenon, consisting of a change of order of the modes of the system. In the case of this numerical benchmark, a re-ordering can be observed in two cases, as reported in Figure 6.  
Figure 5: Variation of the modes and of the natural frequencies of the system from 7 to 9 as a function of \(k_{var}\). Figure 6: Re-ordering of modes: (**a**) modes 3 and 4; (**b**) modes 6 and 7. A first re-ordering of modes can be observed in correspondence with the third and fourth natural frequencies \(f_{3}\) and \(f_{4}\) of the system for increasing values of \(k_{var}\) (Figure 6a): indeed, in the case of the third one, a translational mode along the longitudinal direction (x-direction) is observed for high values of \(k_{var}\), instead of a mixed torsional-bending one, observed at low values of \(k_{var}\) (the mode shapes can be found in Figures 3 and 4). A similar situation (Figure 6b) can be observed for the sixth and seventh mode (the mode shapes can be found in Figures 4 and 5). Consequently, it can be said that for very high values of \(k_{var}\), i.e., when the three masses behave as one single mass, the first three modes result to be the global modes of the system, corresponding to the translations in the directions x and y and to the rotation. On the other hand, the modes from 4 to 9 can be defined as local modes of the system. The application of the reported dynamic equation on a numerical benchmark highlights the influence of the interaction between adjacent diaphragms on the dynamic behavior of the system.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
###### Abstract  
High precision of measurement of methane potential is important for the economic operation of biogas plants in the future. The biochemical methane potential (BMP) test based on the VDI 4630 protocol is the state-of-the-art method to determine the methane potential in Germany. The coefficient of variation (CV) of methane yield was >10% in several previous inter-laboratory tests. The aim of this work was to investigate the effects of inoculum and the digestion system on the measurement variability. Methane yield and methane percentage of five substrates were investigated in a Hohenheim biogas yield test (D-HBT) by using five inocula, which were used several times in inter- laboratory tests. The same substrates and inocula were also tested in other digestion systems. To control the quality of the inocula, the effect of adding trace elements (TE) and the microbial community was investigated. Adding TE had no influence for the selected, well- supplied inocula and the community composition depended on the source of the inocula. The CV of the specific methane yield was <4.8% by using different inocula in one D-HBT (D-HBT1) and <12.8% by using different digestion systems compared to D-HBT1. Incubation time between 7 and 14 days resulted in a deviation in CV of <4.8%.  
biochemical methane potential test; inoculum; methane; biogas; anaerobic digestion; inter-laboratory test +
Footnote †: journal: Applied Sciences
## 2 Digital Identity and Privacy Implications on Social Media
### Privacy Implications and the Privacy Paradox  
The repetitive posting trend of photographs on SM drew researchers' attention due to the huge amount of personal and/or sensitive information revealed [34]. Users' persistent engagement on SM tends to cultivate \"more lenient privacy attitudes and beliefs\" [40] (p. 4). \"Users' dilemma\", [34] or the privacy paradox, as it is widely known [16; 41; 42], should be considered as users waffle between their privacy concerns and their need to build popular, representative, and conversational profiles [16; 34; 43; 44; 45; 46; 47; 48].  
Photographs unveil details about \"babyfaces, credit cards, phone numbers, social security cards, house keys and other personally identifiable information\" [3] (p. 1) which will not be removed even after deleting the content [3; 49; 50] or the account. Implications related to photographs are referred to as \"visual privacy leaks\" and may harm users' financial or personal lives. Sharing the same content across SM can facilitate users' identification [3; 13].  
[PERSON]'s \"context collapse\" raises privacy implications by discussing the revelation of users' content to unwanted audiences [51]. This revelation hints at social surveillance and surveillance technologies, as far as online social habits of networking are concerned [52]. Bigger audiences than originally intended, or false presuppositions of one's online audience, may lead to self-disclosing to unwanted social groups [15; 16; 53]. \"Today, almost everyone's digital profile is looked at by employers, banks, family, friends and future significant others, educators, and hackers\" [54], imposing implications regarding social surveillance through \"context\" or \"time collapse\". [PERSON]'s notion of \"time collapse\" [55; 56; 57; 58; 59] signifies the consistent omnipeence of digital traces of users' identity through time. As stated in [PERSON]'s remark, \"our pasts are becoming etched like a tatto into our digital skins\" [55] (p. 2). Time collapse is readopted by [PERSON], who underlines that \"Facebook makes things that should just have happened in passing totally permanent and public\" [60] (p. 11).  
Privacy is \"a prerequisite for being included in the participation society\" [17]. It should be warrantied as an individual right and a public good in our digital society to ensure freedom of speech in the electronic sphere [11]. Privacy and freedom are closely associated with the European perception of the \"right to be left alone\", one of the fundamental rights and freedoms protected by the right of data protection in the Charter of Fundamental Rights of the EU [17].  
Focusing on the role of self-determination and self-disclosure as users' unveiling mechanisms on SM can help researchers examine users' representations while managing privacy implications online.
## 3 Experiment Setup and Results
### Transmission Response of the Four Channel Wavelength Demultiplexer  
To determine the insertion loss and crosstalk between the adjacent channels of the 1 \(\times\) 4 channel demultiplexer, we have used a similar setup as in Figure 7b. The optical phase shift in the delay arms of the MZI interferometers was thermally tuned. We applied a set of DC voltages to implement thermal heating in the corresponding delay arms. The thermal heating occurs from the electrical power loss in the resistive load of the heater. Figure 11 shows the optical transmission response of each of the four channels of the demultiplexer. The applied DC voltages in the thermal heaters of the delay arms were \(V_{2}=1.5\) V (0.32 mA), \(V_{3}=4\) V (0.9 mA) and \(V_{5}=2.5\) V (0.55 mA). All the voltages were applied simultaneously to the heaters to minimize the crosstalk between the channels. Each of the heaters has separate signal and ground contact pads, therefore no electrical cross-talk was observed in the simultaneous biasing. From this figure it is seen that the side lobe suppression ratio for _ch-1_, _ch-2_, _ch-3_ and _ch-4_ are 12 dB, 12.8 dB, 12.6 dB and 11.2 dB, respectively from 1530 nm to 1580 nm wavelength range. The free spectral range (FSR) from 1550 nm to 1580 nm wavelength range for _ch-1_, _ch-2_, _ch-3_ and _ch-4_ are 12.85 nm, 12.9 nm, 12.85 dB and 12.9 nm, respectively.  
Figure 12a shows the overlapped transmission responses of all four channels of the demultiplexer. In the transmission response from wavelength range 1550 nm to 1565 nm (Figure 12b), we measured that the transmission peaks of _ch-1_, _ch-2_, _ch-3_ and _ch-4_ are at 1552 nm, 1558.45 nm, 1555.3 nm and 1561.6 nm. Therefore, the wavelength separation between _ch-1_ and _ch-3_ is 3.1 nm, between _ch-2_ and _ch-3_ is 3.15 nm, and between _ch-2_ and _ch-4_ is 3.15 nm. The FSR and wavelength separation between two channels is within the 0.1 nm range of the simulation result presented in Section 2.3. We used the results of Figure 12b to set the four CW laser signals at the wavelength range from 1552 nm to 1561.6 nm to demonstrate wavelength division multiplexing (WDM) operation in Section 3.3.  
Figure 10: Normalized output transmission response at the two output ports of the MMI couplers.
# Adenocarcinoma Recognition in Endoscopy Images Using Optimized Convolutional Neural Networks
## 4 Experiments
### Network Training  
The implementation was based on Keras with a TensorFlow backend. We used stochastic gradient descent with a batch size of 8 for all methods. The learning rate started from 0.001 and decreased by a factor of 0.1 when the tolerance level exceeded 4. We used a weight decay of 0.0001 and a momentum of 0.9 without an accelerated gradient. Overall training was done for different network architectures on a single NVIDIA GTX 1080 Ti GPU. We fine-tuned DenseNet-121, ResNet-152, and VGG16 using RMSProp [31] with a decay of 0.9. Each network was trained for 100 epochs. Figure 8 shows the inference time during training each architecture with same number of batches. As can be seen in the figure, the proposed method processed fewer images, but it had fewer parameters in comparison to other architectures. Due to the increasing number of channels added in the existing CNNs, its complexity overfit the model. However, we used the same number of channels in all layers and increased the convolutional layer, which made the method less complex. Table 5 shows the overview of each network's computation  
In the experimental results with 128 normal images, there were 8 TN cases, which showed food and excrement in colonic mucosa and colorectal crescenti wrinkles similar to polyps. Further, it was also observable that the problem that had the biggest impact on the overall error rate was images with small-sized polyps that were difficult to detect in the normal area. It showed a 4.6% error rate in the experimental results. This was the most error of the total error rate of 5.61%. In all the test data, adenoma images showed the lowest precision by 91.21%, but adenocarcinoma images showed the highest accuracy of 97.05%. The accuracy of the whole image was 94.39%.  
The comparison with the well-known network in the ImageNet Challenge is shown in Table 9. The test result showed 87% on VGG19 [28], 90% on ResNet [29], and 89% on DenseNet [30] when trained with the same dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Actual Class** & **Predicted Class** & **Error Images** \\ \hline (a) & Normal & Adenocarcinoma & \\ (b) & Adenoma & Normal & \\ (c) & Adenoma & Adenocarcinoma & \\ (d) & Adenocarcinoma & Normal & \\ (e) & Adenocarcinoma & Adenoma & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Sensitivity (\%)** & **Precision (\%)** & **Accuracy (\%)** \\ \hline Normal & 93.37 & 95.23 & \\ \hline Adenoma & 95.07 & 91.21 & 94.39 (\%) \\ \hline Adenocarcinoma & 94.28 & 97.05 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CNN Networks** & **Accuracy (\%)** \\ \hline VGG19 & 87 \\ \hline ResNet & 90 \\ \hline DenseNet & 89 \\ \hline
**Proposed Architecture** & **94** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy with other CNN networks.
## 2 Methods
### Wavelet Decomposition  
Wavelet decomposition is a signal processing technique used to decompose complex signals into components of different scales. The fundamental idea of wavelet decomposition is to break down a signal into a series of wavelet functions, each possessing different frequency characteristics at various scales. This enables wavelet decomposition to capture local features of the signal while preserving global information. In this study, wavelet decomposition effectively dissects non-stationary signals and removes noise interference from charge-discharge cycling data. It captures detailed features of the signal, enhancing denoising capabilities for random non-stationary signals [23; 24]. The formula for wavelet decomposition is as follows:  
\[d_{l}{}^{j,n}=\sum_{i}A_{i-2l}d_{i}^{i+1,n} \tag{2}\]  
\[d_{l}{}^{j,n+1}=\sum_{i}B_{i-2l}d_{i}^{j+1,n} \tag{3}\]  
where \(l\) and \(i\) are the decomposition levels, \(n\) and \(j\) are the wavelet packet node numbers, represents the low-frequency coefficients of the \(j\)-th level decomposition, \(d_{l}^{j,n}\) and \(d_{l}^{j,n+1}\) represent the high-frequency coefficients of the j-th level decomposition. \(A_{i-2l}\) and \(B_{i-2l}\) are sets of low-pass and high-pass filters in wavelet packet decomposition. In this study, we choose dbN as the wavelet basis function and set the decomposition level to 4. Wavelet decomposition mainly dissects the State of Health sequence of lithium batteries into high-frequency and low-frequency signals using high-pass and low-pass filters. After decomposition, we obtain one low-frequency signal and four high-frequency signals. The model of the State of Health sequence for lithium batteries is represented as follows:  
\[\text{SOH}(t)=C(t)+uD(t) \tag{4}\]  
where \(\text{SOH}(t)\) represents the overall State of Health sequence of the lithium battery, \(C(t)\) denotes the general degradation trend of the lithium battery's SOH, \(D(t)\) signifies the local fluctuation trend, \(u\) stands for the fluctuation coefficient, and \(t\) represents the time interval.
# Optimal Robust PID Control for First- and Second-Order Plus Dead-Time Processes
###### Abstract  
The present study proposes a new design method for a proportional-integral-derivative (PID) control system for first-order plus dead-time (FOPDT) and over-damped second-order plus dead-time (SOPDT) systems. What is presented is an optimal PID tuning constrained to robust stability. The optimal tuning is defined for each one of the two operation modes the control system may operate in: servo (reference tracking) and regulation (disturbance rejection). The optimization problem is stated for a normalized second-order plant that unifies FOPDT and SOPDT process models. Different robustness levels are considered and for each one of them, the set of optimal controller parameters is obtained. In a second step, suitable formulas are found that provide continuous values for the controller parameters. Finally, the effectiveness of the proposed method is confirmed through numerical examples.  
PID control; FOPDT system; SOPDT system; over-damping; sensitivity function; robust stability Article
## 3 Results and Discussion
### Active Metal Requirement and Reusability  
Since DMF was the better solvent, the catalyst requirement for CNT-NM was calculated. Typical Heck reaction with conventional palladium salts requires 1-2 mol % catalyst [34; 35]. In the present work with MWCNT-Pd hybrids, significantly lower quantities of Pd (order of 0.01 mol % Pd) resulted in higher yield at shorter reaction times. Therefore, this study showed that nan palladium on functionalized CNTs is more effective than conventional palladium salts. The turnover frequency (TOF) of the two catalysts were calculated as follows:  
\[\text{TOF}=\frac{\text{Product (mol)}}{\text{Pd (mol) }\times\text{ Reaction time (h)}} \tag{1}\]  
Under reflux conditions, the TOF of p-MWCNT-Pd and np-MWCNT-Pd in DMF were 9977 and 5863, respectively, and in toluene 1090 and 977, respectively, which shows that the polar p-MWCNT-Pd in polar solvent has higher catalytic activity. Under microwave conditions, the TOF of p-MWCNT-Pd and o-MWCNT-Pd were 9000 and 6681, respectively.  
An important issue has been the reusability of the catalyst. In the case of p-MWCNT-Pd and np-MWCNT-Pd, leaching of Pd from MWCNT support to hot DMF was demonstrated by carrying out a reaction under the aforementioned conditions and filtering the solid catalyst after 30 min while the mixture was still hot (around 150 \({}^{\circ}\)C). The filtrate was allowed to reflux in the absence of solid catalyst and yield from the reaction was determined. The yields were found to remain the same in the presence of the p-MWCNT-Pd as well as np-MWCNT-Pd. This demonstrated that Pd desorbed from the CNT support had remained suspended into the liquid phase in hot condition.  
Irreversible leaching is a major consideration while dealing with solid catalysts due to depletion in the metal content and subsequent loss in activity. In another set of experiments in DMF with p-MWCNT-Pd and np-MWCNT-Pd, the recovered catalysts obtained by filtration after one hour reaction and subsequent cooling to room temperature were reused for a three consecutive reactions. The yield obtained with recovered catalysts in each step was within 2% of the previous step yield. This showed that there was no significant decrease in the catalytic activity of the recovered catalyst. This can be explained by \"boomerang effect\" [45; 46; 47] where catalyst which is dispersed into the bulk solution under the hot condition from the solid support redeposits under cold conditions. In order to study this, the reaction mixture (after 1-h reaction) was filtered in the hot condition to recover the catalyst. Similarly, the reaction mixture (after 1 h) was filtered after cooling to room temperature. The catalysts were analyzed for Pd content. The weight percent of Pd from the hot and cold conditions in the p-MWCNT-Pd were 11.9 and 22.9, respectively, as determined by EDX. This supported the existence of boomerang effect. In the future, poison testing will be conducted for these catalysts to understand the nature of catalysis which assist in designing next generation catalysts.
## 5 Experiments
### Results
#### 5.5.2 Quantitative Evaluation of Models  
To provide a further quantitative evaluation, we have used the measure OAHII (Outliers Association with Hurricane Intensity Index) developed by [PERSON] et al. [6]. OAHII shows how much correlation exists between the outlierness scores generated by an algorithm and the actual impact of the hurricane (which is the ground truth for the given dataset). The ground truth is represented by the actual Katrina path reported in [PERSON] et al. [13], which contains the best track positions and intensities reported once every 6 h. It contains the latitude, longitude, time, wind speed and pressure information around a set of hurricane eye locations during the period from August 23 rd to August 31 st. We have applied linear interpolation to the ground truth data to fill missing hurricane readings and to transform it to an hourly basis like the buoy dataset.  
The anomaly scores of the proposed hybrid deep learning framework (HDLF) and IsolationForest (IF), LDBSCAN, LOF, and ST-BDBCAN algorithms are compared, excluding ST-Outlier detection algorithm as it does not generate a score. The proposed framework is tested against the data prepared with different window sizes to demonstrate that it is the highest performing model at each configuration. We used Euclidean distance for LOF and LDBSCAN algorithms as it gives better results. However, we gave the result for ST-BDBCAN generated by using Mahalanobis distance since it yielded in better results as indicated by [PERSON] et al. [6]. Results of the three OAHII measures, OAHIII, OAHII2, and OAHIII3 are shown in Table 3.  
OAHII measures show the effectiveness of our model over the state-of-the art spatio-temporal anomaly detection algorithms. It is superior to other approaches in terms of both anomaly detection accuracy, which is given in Table 2, and correlation of scores to the ground truth, which is given in Table 3. According to the results given in Table 3, our model performed better than the next best performing algorithm in detecting anomalies scoring more than 10% in all OAHII measures.