esa-sceva/satcom-chunk-collection huggingface dataset:
# Trends in Compliance Monitoring Devices (CMDs) in Ships' Ballast Water Treatment Systems
## 2 A Holistic Approach to Monitoring Threats to the Environment in Ballast Water
### Compliance Monitoring Devices (CMDs) on UV-Treated Water Under IMO Regulations  
The method of adopting rapid ballast water analysis has been considered by scientists in an experiment where four systems were compared for effectiveness in testing for compliance with the IMO standards for ballast water treatment and management. In their paper, [PERSON] et al., following a comparative analysis of four CMDs for testing treated ballast water on ships [18], limited their study to systems that used UV treatment, a system that works to disinfect the ballast water on the ship so that it is safe to discharge at a foreign port of loading [18]. Using the theoretical difference in identifying viable or living organisms that survive the ballast water treatment in their experiment, they showed that if they are stained with the testing material Fluorescein Diacetate (FDA), they will show green fluorescence and though they may be alive, they will not be capable of reproducing after discharge. In the USA, the USCG working together with the Environmental Protection Agency (EPA) refers to microbe indicator tests to determine the ship's ballast water management compliance. The observation noted by the scientists after comparing four CMDs is that compliance monitoring devices (CMDs), are usually less accurate than detailed analyses because they mostly use biomass proxies such as chlorophyll fluorescence intensity or Adenosine TriPhosphate (ATP). After comparing the following CMDs from several brands--Ballastwise, WALZ-PAM, Ballast-Check 2 and B AQUAPLUS ATP--they found that some CMDs are less accurate; however, they are extremely useful for rapid results that technicians who are not biologists may rely on to have a fair assessment and estimation of the safety and compliance measures of the ballast water on board [18] (pp. 2-3).
# Ultra-Thin Metamaterial Beam Splitters
## 5 Non-Polarizing Beam Splitter  
Polarization-independent behavior of the NPBS can be achieved by the same length of wires (i.e., \(w_{x}=w_{y}\)), and the same period (i.e., \(\Lambda_{x}=\Lambda_{y}\)). We can obtain arbitrary splitting ratio by changing \(w_{x}\) and \(\Lambda_{x}\). Figure 4 shows the reflectance, transmittance, and absorbance plot for different polarizations as a function of different geometric parameters at 1.55 \(\upmu\)m. The thickness of Ag and MgF\({}_{2}\) are \(t_{Ag}=45\) nm and  
Figure 3: Effective impedance under (**a**) TE-polarized and (**b**) effective impedance under TM-polarized incident light. (**c**) Absorbance spectra under TE-polarized and TM-polarized light. Vertical dashed lines indicate the at 1.55 \(\upmu\)m operating wavelength of the PBS.  
\(t_{MgF_{2}}=30\) nm, respectively. Figure 4a shows the results by changing \(w_{x}\) at \(\Lambda_{x}=600\) nm. The structure exhibits almost polarization-independent behaviour when \(100\) nm \(<w_{x}<200\) nm. Figure 4b shows the results by changing \(\Lambda_{x}\) at \(w_{x}=100\) nm. In this case, the structure is almost polarization-independent when \(500\) nm \(<\Lambda_{x}<600\) nm. We should notice that the absorbance is always small, less than \(0.05\) under most of the geometries, which indicates a good efficiency. The large range of reflectance and transmittance provides the opportunity to use this NPBS under different application requirements.
# Stability Reinforcement of Slopes Using Vegetation Considering the Existence of Soft Rock
## 1 Introduction
A typical place along the Xining-Chengdu railway, China, has been experiencing slope instability issues and it served as a case in this study. In this paper, four types of vegetation, i.e., grass, shrubs, young forest and mature forest, were considered to reinforce the slope in the case. The stability of the slope under various reinforcement and rainfall conditions was analyzed by using the finite element software Plaxis 2D. The effect of vegetation root was simplified as the addition cohesion of the soils. The numerical results for the various conditions were compared to study the effect of vegetation type on slope stability. It has been found that the vegetation reinforcement indeed increased the safety of slope stability for the dry and rainfall situations, while the increasing proportion of factor of safety (FS) was not large because the vegetation root didn't intersect the relative deeper potential slip surface.
## 3 Databases and Results
### Quantitative Results  
#### 3.3.1 Quantitative Results on Bonn Database  
Algorithm comparisons for two different classification tasks, \"normal versus ictal\" and \"interictal versus ictal,\" on Bonn database, are shown in Tables 1 and 2 respectively. For both classification tasks, RR-DTDWT yielded an sensitivity and specificity of 100%. As a contrast, DTDWT, RR-DWT and DWT achieved a relatively lower performance. To avoid the performance bias caused by feature dimension, we also utilized Fisher score to reduce feature dimensionality (the same dimensionality as RR-DTDWT). As shown in Tables 1 and 2, RR-DTDWT outperformed all other methods on the Bonn database.
## 4 Understanding of TBM Performance
### Selection of Excavation Methods
[32] investigated the thickness of the damage zone through the utilization of seismic refraction techniques during tunnel excavations in sandstone formations employing both TBM and drill and blast methods. Their test findings demonstrated that the damage zone had a thickness of 0.3 m for the TBM method, whereas it ranged from 0.6 to 1.3 m for the drill and blast method. In 2013, [PERSON] et al. [33] and colleagues carried out a comprehensive study involving electron microscope scanning tests, acoustic emission tests, and relaxation depth tests on Jinping marble under diverse excavation conditions. Their observations revealed that under TBM excavation conditions, the predominant damage mechanism was characterized by shear forces, leading to significant deformation in the rocks near the point of peak load. [PERSON] et al. [34] conducted an experimental analysis, which led them to the conclusion that the uniaxial compressive strength of the rock is closely associated with the efficiency of TBM excavation. In general, the drill and blast method, while more complex to coordinate and characterized by an extended construction period, offers a higher degree of adaptability to various geological conditions. Conversely, the TBM method is distinguished by its streamlined procedures, quicker construction pace, and enhanced safety measures. Nevertheless, it exhibits less adaptability to diverse geological conditions and demands the use of costly equipment, as highlighted by [PERSON] et al. [35]. Therefore, in the excavation design phase of a tunnel project, the process of selecting the appropriate construction method necessitates a thorough comparison of technical and economic indicators, all while taking into account the crucial evaluation parameters presented in Table 6. This meticulous approach is implemented to minimize potential project risks. \begin{table}
\begin{tabular}{c c c} \hline
**Comparison** & **Drill and Blast Method** & **Tunnel Boring Machine** \\ \hline Advantages & & & The section is fixed and mainly circular. Generally good stability and low disturbance of the surrounding rock. \\  
TBMs have benefited from more than five decades of extensive research and development, resulting in the establishment of highly efficient construction technology and a wide array of product types. Owing to their remarkable speed and enhanced safety features, TBMs have garnered widespread adoption in tunnel excavation projects for railways, highways, and water conservancy, making them the preferred choice in numerous countries. The shift from traditional drill and blast technology to TBM technology has been primarily motivated by the pursuit of safer and more efficient construction practices. This transition has shaped the trajectory of TBM tunnelling technology, and its future will continue to be guided by these fundamental principles. As a result, future TBM technology is anticipated to surpass the current generation of TBMs in terms of functionality, cost-effectiveness, automation, and flexibility of use, ultimately leading to substantial economic and social benefits.
## 3 Results
### Analyzing the Shear Wave's Characteristics in the Frequency Domain
#### 3.2.2 Fourier Energy Magnitude  
For the applied experiments, coherent compounding decreased the maximal Fourier energy magnitude with a factor of 3.9 for 15% tissue depth and 2.7 for 40% tissue thickness (see Table 3). A similar observation was made for the simulations: compounding reduced the maximal Fourier energy magnitude by factors of 8.0 and 10.5 for 15% and 40% tissue thickness respectively. Furthermore, when comparing the virtual single PWI to the biomechanics simulation, an additional decrease by factors of 6.5 and 9.2 was noticed for the two considered tissue depths. Next to these dissimilarities in maximal Fourier energy magnitude, the relative energy magnitude of secondary to primary mode for the deeper tissue region also differed (see Figure 5). This proportion was 0.5 for the experimental single PWI. For the simulations, this ratio shifted from 1.7 for the virtual biomechanics to 1.3 for single PWI and 0.6 for compounded PWI.
# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 4 The Proposed Method
### R-Kat  
We propose R-KAT, a training strategy for zero-shot cross-lingual media profiling. R-KAT operates primarily during fine-tuning, where it constructs virtual multilingual news source embeddings and regularizes both the multilingual and English embeddings. The training objective is to find optimal parameters that minimize the classification error between virtual multilingual and English news sources. The min-max optimization formulation is as follows:  
\[\underset{\mathcal{L}}{min}\sum_{(A_{i},y_{i})\in\mathcal{D}}\mathcal{L}(P(A_ {i}),P(A_{i}+\delta_{K}),y_{i}) \tag{1}\]  
where \(D\) is the dataset and \(y_{i}\) is the ground-truth label. \(A_{i}\) denotes the input data, and \(\delta_{K}\) represents the difference between the English and other language contextual representations. \(\mathcal{L}\) is the loss function. The perturbation \(\delta_{K}\) is calculated as follows:  
\[\delta_{K}=\delta_{K-1}+\frac{g(P(A_{i}+\delta_{K-1}))}{||g(P(A_{i}+\delta_{ K-1}))||_{2}} \tag{2}\]  
where \(g(P(A_{i}))\) is the gradient of the model with respect to the input \(A_{i}\); when training begins, \(\delta_{K=0}=0\). Each time, we use the inverse of the gradient as a perturbation to align the English representation with the representation of the language with the largest difference. After three iterations, the final perturbation \(\delta_{K=0}\) is generated.  
Our approach simplifies the creation of virtual multilingual embeddings by directly perturbing the embedding vectors, without altering the original text. Through successive perturbations of the Transformer's word embedding matrix, we generate challenging virtual samples, enabling models trained on monolingual data to handle multilingual inputs. For these virtual samples, we compute the corresponding losses using cross-entropy:  
\[loss_{E}=\mathcal{L}(A_{i},y) \tag{3}\]  
\[loss_{M}=\mathcal{L}(A_{i}+\delta_{K},y) \tag{4}\]  
After constructing the virtual multilingual samples, the model performs forward passes on the original English samples and the virtual multilingual samples, respectively, and calculates the classification losses \(loss_{E}\) and \(loss_{M}\) for each pass. In order to align the media profiling classification effects of different languages, we minimize the bidirectional KL divergence between the two output distributions. The regularization formula is as follows:  
\[loss_{KL}=\frac{1}{2}[D_{KL}(P(A_{i})||P(A_{i}+\delta_{K}))+D_{KL}(P(A_{i}+ \delta_{K})||P(A_{i}))] \tag{5}\]  
The final loss for the input sample \(A_{i}\) is formulated as follows:\[loss_{total}=\frac{1}{2}\alpha\cdot(loss_{E}+loss_{M})+\beta\cdot loss_{KL} \tag{6}\]  
where \(\alpha\) and \(\beta\) are coefficient weights that balance the classification loss \(loss_{E}\), \(loss_{M}\), and the KL divergence loss \(loss_{KL}\). The overall training objective is to minimize the total loss \(loss_{total}\). After calculating \(loss_{total}\), the initial word embedding layer is reset, and final backpropagation is performed.
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.3 Resisting Tamper-Proof Device Stolen Attacks  
The \(\mathcal{U}_{k}\) holds \(\{\overline{Y_{k}},Z_{k},TID_{k}\}\) in a tamper-proof device \(TPD_{k}\). It is quite possible that an adversary can steal \(TPD_{k}\), extract all the values stored in it and initiate a valid request message. In our proposed protocol, an adversary cannot compute a valid login request message even after stealing \(TPD_{k}\). If an adversary wants to calculate a valid login request message \(\{M_{1},TID_{k},auth_{k}\}\), he will have to compute a valid \(auth_{k}\). The computation of \(auth_{k}=h(ID_{k}\|ID_{CSP}\|X_{k}\|r_{4})\) is not possible for an adversary as he can neither compute the valid value of \(X_{k}^{\prime}\), nor can he retrieve the value of the random number \(r_{4}\). Further, the adversary does not know the identity \(ID_{k}\) of \(U_{k}\). So, the proposed protocol is secure against tamper-proof device stolen attacks.
# A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
## 2 Related Works  
The consistency problem can be observed in real-world complex problems in fields like weather and climate modeling [22,23], structural health monitoring [24,25], and chemical reaction modeling [26,27], among others. The essential characteristic of these problems is their evolutionary, time- and history-dependent nature. Although the events themselves are continuous, the data points are snapshots of the overall process.  
Several research works address the consistency problem. [PERSON] et al. [28] proposed using Physics-Informed Neural Networks (PINNs) to enhance a model's ability to produce consistent results across different time step sizes, leveraging both numerical and experimental spatio-temporal data. Their approach adjusts the loss terms in the objective function to account for variations in temporal resolution, ensuring stable predictions even as input conditions change. Similarly, [PERSON] et al. [29] demonstrated that using partially averaged density-dependent variables and kinetic energy density (KED) effectively tackles the challenges posed by uneven data distributions and sampling difficulties. This approach retains enough spatial structure to work within the Kinetic Energy Density Functional (KEDF) framework, allowing the model to train with fewer data points while still providing accurate predictions.  
In contrast, these works obtain consistent estimates by injecting physics-based knowledge into the model, such as through PINNs or kinetic energy density. Therefore, these approaches are mostly application-specific. In the current study, we propose an RNN formula that ensures consistency in the estimates directly from the data points it has learned, without requiring any additional physics or engineering-based knowledge. The neural network (NN) architecture learns directly from the data during training, and during estimation, it converges to the learned information. Thus, we argue that our solution is more generic, as it can be applied across a wide range of applications. To support this, we have conducted experiments with both synthetic and real-world datasets.  
Given that history-dependent continuous events are often observed in material science and engineering problems, two notable works [20; 21] have emerged in this field.
## 3 Results and Discussion
Earlier studies have reported that the anti-apoptotic Bcl-2 members, which consist of Bcl-xl, Bcl-2, Bcl-w, and Mcl-1, exert an important role in the resistance of cancer cells to chemotherapy. Therefore, a reduction in Bcl-2 and an increase in Bax stimulate the apoptosis process and eliminate cancer cells [32]. Our western blotting analysis results showed increased Bax expression and decreased Bcl-2 expression in cells co-treated with tanshinone IIA, which was stronger than SMR (Figure 4); however, no difference was observed in poly (ADP-ribose) polymerase (PARP) expression, which is a parameter for stress and DNA damage in cells. In summary, we simultaneously analyzed five compounds (salvianic acid A, rosmarinic acid, salvianic acid B, caffe acid, and tanshinone IIA) from SMR, and determined their cytotoxic effects on HCT-116 human colon cancer cells. Among the five compounds in SMR, only tanshinone IIA significantly decreased cell viability in a concentration-dependent manner. Both SMR and tanshinone IIA increased nuclear condensation, suggesting that SMR and tanshinone IIA successfully induced apoptosis. We also found that tanshinone IIA induced apoptotic cell death and significantly increased cleaved caspases-7, -8, and Bax expression, as well as decreased Bcl-2 expression in the course of apoptosis. Taken together, our data show that tanshinone IIA is an active ingredient of SMR and may be a useful chemotherapeutic strategy for patients with colorectal cancer. Figure 4: Effects of 70% ethanol extract of _Salviae__miliorrhizae_ Radix (SMR) and tanshinone IIA on apoptosis in HCT-116 cells. (**A**) Protein expression of PARP, Bax, Bcl-2, cleaved caspase-7, cleaved caspase-8, cleaved caspase-9, and GAPDH. (**B**) Graph of relative protein expression. Data are the means of experiments performed in triplicate. Data are presented as the mean \(\pm\) SD. and were analyzed using the Student’s \(t\)-test. * \(p<0.05\) versus non-treated cells. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/4/1304/s1](http://www.mdpi.com/2076-3417/10/4/1304/s1)]([http://www.mdpi.com/2076-3417/10/4/1304/s1](http://www.mdpi.com/2076-3417/10/4/1304/s1)). **Author Contributions:** Conceptualization, Y.-K.C. and K.S.K. ; performing experiments and analyzing data, B.K., S.L., and C.-S.S.; C.-S.S.; validation, B.K. and K.S.K. ; writing--original draft preparation, Y.-K.C. and K.S.K. ; writing--review and editing, K.S.K., funding acquisition. All authors have read and agreed to the published version of the manuscript. **Funding:** The present study was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (2019R1F1A1059173). **Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis
## 4 Test Analysis
### D-S Fusion Fault Diagnosis
Comprehensive analysis shows if multiple sensor data sets are fused with D-S diagnosis, the uncertainty of the single sensor method can be reduced and interactions between various sensors can be eliminated, which can effectively improve the reliability of centrifugal pump fault diagnosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Normal State** & **Slight Cavitation** & **Severe Cavitation** & **Loose Foot Bolts** \\ \hline  & 1.0000 & 0.0000 & 0.0000 & 0.0000 \\ Pump & 0.0000 & 0.9333 & 0.0667 & 0.0000 \\ vibration & 0.0000 & 0.0917 & 0.8417 & 0.0667 \\  & 0.0000 & 0.0000 & 0.0417 & 0.9583 \\ \hline  & 0.9583 & 0.0333 & 0.0000 & 0.0083 \\ Torque & 0.0083 & 0.9333 & 0.0417 & 0.0167 \\  & 0.0000 & 0.0667 & 0.8750 & 0.0583 \\  & 0.0000 & 0.0083 & 0.0333 & 0.9583 \\ \hline  & 0.9500 & 0.0417 & 0.0083 & 0.0000 \\ Motor & 0.0083 & 0.9417 & 0.0500 & 0.0000 \\ vibration & 0.0083 & 0.0500 & 0.8833 & 0.0583 \\  & 0.0000 & 0.0000 & 0.0167 & 0.9833 \\ \hline  & 0.9583 & 0.0250 & 0.0167 & 0.0000 \\ Swing & 0.0083 & 0.8333 & 0.1583 & 0.0000 \\  & 0.0000 & 0.0417 & 0.8667 & 0.0917 \\  & 0.0000 & 0.0000 & 0.0083 & 0.9917 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Preliminary diagnosis results of BP neural network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Signal**} & \multicolumn{4}{c}{**Local Credibility**} & \multirow{2}{*}{**Global**} \\ \cline{2-2} \cline{4-5}  & **Normal State** & **Slight Cavitation** & **Severe Cavitation** & **Loose Foot Bolts** & **Credibility** \\ \hline Pump vibration & 1.0000 & 0.9914 & 0.9828 & 0.9914 & 0.9333 \\ Torque & 0.9106 & 0.8960 & 0.9113 & 0.9259 & 0.9313 \\ Motor vibration & 0.8860 & 0.9211 & 0.9217 & 0.8254 & 0.9396 \\ Swing & 0.9350 & 0.9200 & 0.9440 & 0.9154 & 0.9125 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification credibility of BP neural network. Figure 8: Analysis of recognition accuracy under (**a**) normal state by D-S evidence, (**b**) normal state by pump vibration signal, (**c**) slight cavitation by D-S evidence, (**d**) slight cavitation by torque signal, (**e**) severe cavitation by D-S evidence, (**f**) severe cavitation by motor vibration signal, (**g**) loose foot bolts by D-S evidence, and (**h**) loose foot bolts by D-S swing signal.
## 1 Introduction
addressed DR programs as another reserve resource to mitigate uncertainty in wind power output; they developed a robust optimization approach to derive an optimal UC decision by maximizing total social welfare under the joint worst-case wind power output and DR scenario [19]. [PERSON] and [PERSON] presented a unified stochastic and robust UC model by introducing weights for the components of the stochastic and robust parts in the objective function, which was solved by a Benders' decomposition algorithm [20]. This paper explores the optimal DR and UC in a factory's power system, rather than a bulk power system or a DR at home, considering uncertain photovoltaic (PV) power generation. A factory tends to produce more real power from its micro-turbine generators once the time-of-use electricity price set by the utility is high or the PV power is inadequate. The factory has elastic loads, such as production lines, which are associated with interruptible demand and fixed energy consumption in a day. The time periods for operating these elastic loads can be determined by the time-of-use tariff, PV generation, a capacity contract, cost of the fuel of the micro-turbine generator and other operational constraints. The uncertainty of PV power output was addressed by many papers [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. In this paper, the uncertainty of PV power is modeled using stochastic distributions. This problem is expressed by a stochastic mixed integer optimization formulation. The innovations of this paper are summarized as follows:  
1. The problem is solved by a two-level method: the master level determines the optimal states (\(0/1\)) of the generators and the elastic loads using a novel genetic algorithm; and the slave level deals with optimal real power scheduling and power purchase/sale from/to the utility, subject to the system operating constraints, using the interior point algorithm. 2. Two novel encoding schemes associated with new crossover and mutation operations in genetic algorithms are presented. These new operations make this novel GA more efficient to solve the optimal UC and DR in a factory's power system. 3. The uncertainty in the PV power generation is studied by using the point estimate method that integrates the master level with the slave level to gain an optimal stochastic mixed-integer solution. 4. Not only the states of micro-turbine generators in UC but also the states of elastic loads at the production lines in DR are addressed at the same time. The rest of this paper is organized as follows. Section 2 formulates the problem to be solved. Section 3 then presents the methodology based on genetic algorithms. Section 4 summarizes the simulation results of a 30-bus factory power system with PV generations. Section 5 draws conclusions.