esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Bacterial Cell Death Inducing Agents
#### 2.1.4 Biosurfactants  
The extracellular, amphiphilic surface-active compounds produced by microbes are referred to as biosurfactants. An interesting utility of these biosurfactants as anti-cancer agents is noteworthy. Biosurfactants interfere with the cancer progression processes by inhibiting crucial signaling pathways such as PI3K/Akt, JAK/STAT pathway and MAPK signaling. In addition, biosurfactants can stimulate Natural killer (NK) cells, inhibit angiogenesis, and induce apoptosis via death receptors in cancer cells [42; 43]. Surfactin, a cyclic lipoprotein biosurfactant has demonstrated anti-cancer activity by cell cycle arrest, apoptosis, and inhibiting metastatic escape. The ability of these surfactin molecules to be incorporated in nanoformulations such as polymeric nanoparticles, micelles, and liposomes is a major advantage in targeting the cancer cells for improved delivery [44]. Surfactin from Bacillus safensis F4 exhibited significant anti-biofilm and anti-tumoral efficacy against T47D breast cancer cells and B16F10 mouse melanoma cells [45]. Biosurfactant isolated from the bacteria Acinetobacter inducing M6 demonstrated anti-proliferative activity in lung cancer cell line A549 and the non-toxic nature of biosurfactants was demonstrated on the non-tumor MC-3 T3-E1 cell line. The G1 phase was arrested in A549 cells indicating inhibition of DNA synthesis as the mechanism of cell death. The protein moiety of the biosurfactant was responsible for the lysis of the cell membrane, owing to its detergent activity [46]. Rakicidins from marine bacterium _Micromonospora_, possesses hypoxia selective toxicity against several cell lines such as colon cancer (HCT-8), lung cancer cells (A549), gastric cancer (SGC7901), uterine cervix cancer cells (HeLa), and hepatocellular carcinoma cells (HepG2). Rakicidins induced apoptosis by activation of caspase-mediated pathways and by blocking signaling pathways such as MAPK and JNK/p38 [42].
## 6 Conclusions  
This paper investigated both active and semi-active tuned mass dampers with relative displacement and velocity feedback. Following the approach in [42], both mass dampers were calibrated for the specific parameter \(K\), ensuring that their effectiveness in reducing harmonic vibrations is comparable to that of the [PERSON]'s TMD with \(K\) times larger mass. The key conclusions drawn from this work are as follows:  
1. The optimal ATMD with acceleration feedback proposed by [PERSON] et al. [17] can alternatively be realized based on a relative displacement and velocity feedback, without relying on acceleration feedback.
2. Both the sub-optimal STMD and the optimized STMD with the clipped viscous damping-stiffness control force have a clearly defined efficiency limit.
3. The application of the numerically optimized correction factors in the semi-active control force (46)-(48) allowed us to achieve an enhanced performance of the optimized STMD compared to the sub-optimal STMD.
4. The highest effectiveness of the optimized STMD in reducing harmonic vibrations corresponds to the TMD with roughly three times greater mass.  
Although the results presented in this work are promising, they should be treated as primarily theoretical due to the assumptions made regarding the use of an ideal semi-active damper in the STMD and an ideal active actuator in the ATMD. In practice, the  
Figure 11: Further characterization of the control force of the optimized STMD in comparison to that of the sub-optimal STMD: (**a**) Force–displacement loops at the frequency corresponding to the left peak of the frequency response; (**b**) force–displacement loops at the frequency corresponding to the right peak; (**c**) comparison of the optimal controlled stiffness of the ATMD with the equivalent stiffnesses resulting from control forces of both STMDs. All results for \(\mu=1\%\) and \(K=3\).  
effectiveness of both systems will be limited, in part, due to the force tracking errors, which are discrepancies between the ideal control force and its actual value. These aspects require further investigation in future research.  
This research was funded by the AGH University of Krakow within the scope of the Research Program No. 16.16.130.942.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The author declares no conflict of interest.
# Intelligent Total Transportation Management System for Future Smart Cities
## 3 Results--Examples of Sub-Model Developments
### Intelligent Total Transportation Managing System in Smart Cities
#### 3.3.4 Priority Transport Management  
The total transportation managing system uses passive methods to monitor the non-cooperative vehicles, semi-active methods for the cooperating vehicles, an active approach to support the contract-based transportation, and a proactive approach for managing priority vehicles.  
The priority vehicles, such as police, fire machines, and ambulances, might be supported by free lanes/freeways by the total transportation managing system.
# A Real-Time Numerical Decoupling Method for Multi-DoF Magnetic Levitation Rotary Table
## 2 Magnetic Levitation Rotary Table and Modeling Method  
### Working Principle of Maglev Actuator  
The structure of the maglev actuator for the magnetic levitation rotary table is given in Figure 1. The rectangular permanent magnets and every two coils are symmetrical around the vertical axis. Additionally, the magnet array is formed in Halbach mode along the rotor circumference, and its geometric centers are located on a circle with radius \(R\), as depicted in Figure 1a. Since there exist no contacts between stator and rotor, the motion system is able to undertake rotations and translations in all axes. Only the long side of each racetrack coil is located underneath the magnet array in the working stroke, so the moving range is decided by the size of the employed coils. Different from existing maglev rotary tables with the current in the coil radially flowing, this design, employing long racetrack coils, possesses the following advantages:  
1. This prototype is more suitable to realize translation in the horizontal plane because the area of one phase coil under the magnet array is constant when the rotor translates in the stroke.
2. The design expense of the rotor is significantly reduced because it is not required to decrease the gap between the neighboring permanent magnets like the existing actuator in the maglev rotary table.
3. The racetrack coil is easier to fabricate, install, and alter to other design parameters.  
Figure 1: Structure of a magnetically levitated (maglev) actuator for six degrees of freedom (DoF) magnetic levitation rotary table: (**a**) Top view of coil set in stator and magnet array in rotor with corresponding coordinate system, (**b**) side view of coil set and magnet array, (**c**) partial view of one coil and two permanent magnets with a coil coordinate system and magnet coordinate system.  
With the relative angles between coils and magnets varying when the system works, thrust and torsion ripples are more obvious compared with a traditional planar magnetically levitated actuator. Therefore, the accuracy of the force and torque model is more vital for stable motion control. Furthermore, as per the advantages of the design mentioned above, the structure of the coil set is not periodic along the argument, and the gap of the neighboring magnets in the array cannot be ignored. Therefore, traditional harmonic analysis cannot model an accurate magnetic-field distribution. In this work, the force and torque resulting from each coil are solved due to the magnetic charge method and Lorenz force integral independently.  
To analyze the magnetic force and torque of each coil, several different coordinate systems \(\{r\}\),
## 3 Results and Discussion
### Chemical Properties of Beetroot Samples
#### 3.3.2 Antioxidant Activity and Polyphenol Content  
In yellow beetroot, the content of vitamins, particularly vitamin C, polyphenols and the beetroot pigments themselves (betalains), flavonoids, and polyphenolic acids are correlated with antioxidant properties. The correlations shown between the pigments and the fermentation time and the salt content in the brine, as well as the condition of the sample (non-drying and freeze-drying), demonstrated an increase in both the red and yellow pigments for the beetroot immediately after fermentation. Similar relationships can be seen for the polyphenolic compounds [64; 65].  
The ability to reduce free radicals was reduced in the analyzed samples (Figure 3c,d), but an increase in the antioxidant capacity was observed after the log phase in bacterial growth (after day 8 of the process). This may be due to the reduction in the sugars contained in the fermented beetroot tissue and the penetration of salts into the tissue, which may result in greater availability of vitamins and compounds during extraction performed before spectrophotometric analysis. At the same time, a significant reduction (by about 50%) in the antioxidant capacity was found in the samples after freeze-drying. The drying process lasted 24 h, during which degradation of the active ingredients could occur, which was also confirmed in the case of the yellow and red pigment contents of the samples analyzed (see Figure 2).  
Figure 3: \(Cont.\)  
For the polyphenol content of yellow beetroot, in the samples analyzed after the fermentation process as well as after freeze-drying, an increase in the polyphenol content was observed, as was also found in previous studies on red beetroot juice and pepper samples. For raw yellow beetroots, values similar to those reported in the literature by [PERSON] et al. [4] were obtained. However, an inverse relationship was shown by [PERSON] et al. [64] in their study on red beetroot, where both the phenolic acid content and the total phenolic content decreased, respectively, from 32 to 17 \(\mu\)g/g d.m. and from 33 to 18 \(\mu\)g/g d.m. They have correlated this decrease in tissues with the activity of phenolic acids decaboxylases and reductases. In our research in the yellow variety, these enzymes are maybe in a lower level of activity.  
A different approach using the Folin-Ciocalteu (F-C) reagent was tried out, but it was discovered that it does not just react with phenolic compounds alone. It reacts with other substances that can be oxidized, such as ascorbic acid, aromatic amines, reducing sugars, and aromatic amino acids. Moreover, the presence of proteins in the sample can interfere with the assessment [66]. That is the reason for the possibly incorrect interpretation of the antioxidant properties measured by means of the polyphenol content. Based on our experience, such a high increase could be related to measuring other substances that could react with this F-C reagent.
## 1 Introduction
### State of the Art  
Studies on the use of hydrogen for the aircraft propulsion started around 1950, inside programs aimed at the introduction of new fuels to extend the range and ceiling of military spy planes, trying to avoid combustion shortcomings of traditional JP fuels at high altitudes. One of these projects is the NACA \"Project Bee\" ([13]), which developed a notable series of reports around 1956 ([14; 15; 16; 17]). Later, in the 1970s, in response to oil crises, NASA commissioned studies for the replacement of conventional aviation fuel: the candidates were hydrogen and methane. The main U.S. aircraft manufacturers proposed a wide range of aircraft designs, ranging from short-haul to intercontinental jets ([18; 19; 20]), along with airport integration feasibility studies ([21]) and plans for development programs. In 1988, a USSR project developed the TU-155 testbed, which used hydrogen and other gasses for the turboief feeding.  
In more recent years, the European Union gave a new motivation to the research about hydrogen as an energy vector, setting up dedicated research programs, such as CRYOPLANE ([22]) and CHATT ([23]). The aim of these studies is the decarbonisation of the transport industry, the development of a more sustainable energetic market, and an improvement in the EU energetic independence ([1, 2]).  
Concerning the aircraft design perspective, reference works are [18; 19; 20; 24; 25] and [26]. In such studies, the influence of the hydrogen storage state, engine technology, and tank-aircraft configuration integration are studied. Specific information about engines can be found in [15; 17; 27; 28], with different engine architecture options aiming to enhance performance or to minimise the changes from current technology. Tank design is studied in [14; 16; 25; 29; 30; 31; 32], where the problems of the insulation, structural design, and thermodynamic state simulation are addressed. The level of detail of the aforementioned studies is summarised in Table 2.
# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Application of Deep Learning in PV Power Forecasting  
Table 3 reports a summary of different deep learning-based techniques published during the period 2011-2019 for forecasting the power produced by PV plants.  
In [66] five LSTM-based neural networks have been designed to forecast the hourly PV output power. The proposed model, that does not use any meteorological data and is based on historical powers, offered a reduction in the forecasting error compared with other methods.  
A six-layer feedforward deep neural network for one day-ahead PV power forecasting of a grid connected photovoltaic system installed in Seoul, Korea has been presented in [67]. The method, that does not require the use of any on-site sensors, has shown better performance than other models using local measurements. Nevertheless, the achieved errors during summer and cloudy weather were not satisfactory.  
In [68], the authors proposed a comparative study between different deep neural networks-based one-day ahead forecasters. The study includes conventional neural networks (CNNs), LSTM, and a hybrid model that combine CNNs and LSTMs. It has been shown that the accuracy of the three models mainly depends on the size of the available database. Generally, the experimental results show that the deep learning network has a good effect on the prediction of photovoltaic power generation and the stability and robustness of the model are high.  
A recurrent LSTM-based method has been designed for the hourly short-term forecasting of the power produced by a PV plant installed in Gumi, South of Korea [69]. The model accepts as an input the solar irradiance, the ambient temperature, and the cloudiness index. The results showed the best performance compared with other approaches based on DNN, ANN, auto regressive integrated moving average (ARIMA), and seasonal-ARIMA. LSTMs perform particularly well, especially in the case of instable power output.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref and Authors** & **Year** & **Method** & **Time Horizon** & **Parameters Used** & **Point for Regional** & **Region and PV** & **Accuracy** \\ \hline
[66] & 2017 & Deep LSTM & \multirow{2}{*}{1 h ahead} & Historical powers & 1 point & \multirow{2}{*}{Assume, Egypt} & RMSE = 82.15 \\ [PERSON] et al. & & & & & & \\ \hline
[67] & 2018 & DNN & 24 h ahead & Weather forecast & 1 point & \multirow{2}{*}{Mean, Egypt} & MAE = 29\% \\ [PERSON] et al. & & & & & & & \\ \hline
[68] & 2019 & CNN, LSTM and CNN+LSTM & \multirow{2}{*}{1 day ahead} & On-site & \multirow{2}{*}{1 point} & \multirow{2}{*}{[PERSON], China, 23.4 Kyp} & RMSE = 0.343\%, \\ [PERSON] et al. & & & & & & & MAE = 0.126\%, \\ \hline
[69] & 2019 & RNN-LSTM DNN & 1 h ahead & On-site measurement and cloudiness data & 1 point & \multirow{2}{*}{[PERSON], China, 23.4 Kyp} & MAE = 0.22\% \\ [PERSON] et al. & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: DL-based methods for the forecast of PV power-period 2017-2019.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
## 2 Computational Methods
### Numerical Method  
This work presents an arbitrary-Lagrangian-Eulerian(ALE) method [40; 41; 42] to simulate fish moving in the fluid. The governing equations of ALE formulation for the two-dimensional viscous incompressible flow are expressed as follows:  
\[\
abla\cdot\mathbf{u}=\mathbf{0}, \tag{3}\]  
\[\frac{\partial\mathbf{u}}{\partial t}+\mathbf{c}\cdot\
abla\mathbf{u}=-\frac{\
abla p}{ \rho}+\
u\
abla^{2}\mathbf{u}+\mathbf{b}. \tag{4}\]  
In these equations, \(\rho\) and \(\
u\) denote the density and the kinematic viscosity of the fluid respectively; \(p\) and \(\mathbf{u}\) represent the pressure and velocity of the flow respectively; \(\mathbf{b}\) is the body force per unit mass,  
Figure 1: Illustration of the analytical description of the fish-like robot body. **(a)** Half outline of the body; **(b)** mesh distribution around the body.  
\(\mathbf{c}\) is the convective velocity which represents the relative velocity between the moving mesh and the fluid. The boundary conditions are set as follows: uniform velocity at the inlet, constant static pressure and zero-gradient velocity at the outlet, slip wall at the lateral boundaries, and no-slip wall on the surface of the cylinder. The interaction of the fish and the fluid is realized by setting a no-slip boundary condition on the robot surface and introducing a relation:  
\[\mathbf{u}_{b}=\mathbf{u}_{f}, \tag{5}\]  
where \(\mathbf{u}_{b}\) is the fish body velocity, \(\mathbf{u}_{f}\) is the fluid velocity at the surface of fish body.  
The governing equations were discretized by a finite volume approach with the second-order backward scheme in time and second-order liner scheme in space. The pressure-implicit split-operator (PISO) algorithm [43] was used to solve Equations (3) and (4) for velocity and pressure. The whole computational domain was decomposed by triangle mesh, as illustrated in Figure 1b. As the fields around the undulating fish may had a large gradient, mesh near the robot was refined with high resolution. The surface of the fish body was composed of 110-160 cells. All the simulations were based on the mesh scale from \(5\times 10^{4}-9\times 10^{4}\) cells which has been tested to be large enough for our two-dimensional simulations. We used a parallel dynamic mesh method based on the radial basis function to move mesh on OpenFOAM.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Application to Floor Classification and Landmark Detection
#### 3.3.2 Landmark Detection  
Landmark detection intends to recognize whether a user is located at preliminarily defined points or not. Landmarks in an indoor environment can be toilets, elevators, and rooms, which create a particular pattern of Wi-Fi RSSI measurements such that their distinct features can be distinctly extracted. In this paper, the landmark detection is used for the trajectory learning, which will be introduced in Section 4.2.  
The landmark detection implementation is as follows. First, similar to the floor classification, the label of the training data should be defined as the landmark index for the semisupervised discriminant analysis algorithm in the training phase, which is described in Section 3.2.4. In the test phase, when a test RSSI measurements set has come, the distance on the signal space is calculated between each landmark's feature set and the current RSSI feature vector. Let \(D_{c}\) be the distance given by \[D_{\epsilon}=\|\tilde{\mu}_{c}-z_{*}\|,\;\;c\in\{\text{landmark}_{1},\text{landmark }_{2},\cdots\}, \tag{36}\]  
where \(\tilde{\mu}_{c}=T^{\text{semi}^{\text{T}}}\mu_{c}\) is the center of the training data points belong to the same landmark and \(z_{*}=T^{\text{semi}^{\text{T}}}y_{*}\) is the test RSSI feature vector.  
In sum, the semisupervised feature extraction is proposed to deal with the nonlinearity and sparsity of the raw Wi-Fi RSSI measurements. Two independent feature extraction models according to the different label type are applied to the floor classification and the landmark detection. In particular, the landmark detection can trim a trajectory sample by detecting two landmarks as the start and end points of a path, which is used for the trajectory learning in the next section.
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 3 Methodology
### Sentence Representation
#### 3.1.1 Generation of Relation Indicator  
The relation indicators represent the key words and cue phrases that reference to different relation types. They are prior knowledge for the GCN model to capture the linguistic clues of certain relation in texts, which can be obtained from lexical resource. In this paper, we collect the relation indicators from a large-scale lexical resource called FrameNet. The FrameNet is a publicly available lexical resource, which categorizes words and sentences into high level semantic frames to express different concepts. Each semantic frame describes a type of relation, event, or object in the form of a conceptual structure, which consists of definition, frame elements, FE core sets, examples, lexical units (LUs), and frame-frame relations. The corresponding semantic frame of the relation _founder_ is illustrated as Figure 1. There are over 1200 semantic frames and 13,000 LUs in the FrameNet, most of them describe different semantic relations.  
In addition to FrameNet, there are some other popular lexical resources, such as PropBank [33] and VerbNet [34], which can also be utilized to extract the linguistic knowledge of entity relations. However, unlike FrameNet which is semantically motivated and contains lexical units with various part of speeches, PropBank and VerbNet are verb-oriented and focus more on syntactic level. Hence, many important linguistic clues of entity relations cannot be extracted, and many verbs with no relational meaning may be extracted unexpectedly, producing more noises to the relation extraction system.  
For each relation type in our relation extraction, we first obtain the corresponding semantic frames by traversing the FrameNet. All the LUs involved in these semantic frames are relation indicators, which are actually the keywords that often used to express such relation. We eventually identify 62 semantic frames and 1136 LUs from the FrameNet. Each LU is a discrete word or phrase. In order to leverage the relation indicators to provide knowledge attention, we project each word and phrase of the corresponding LU into low-dimensional vector \(\mathbf{u}_{i}\in\mathbb{R}^{d_{w}}\) by looking up the pre-trained word embedding matrix, where \(d_{w}\) is the size of the LU embeddings. If a LU consists of multiple words, such as the LU \"_set up_\" in Figure 1, we calculate the mean of the embeddings of these words to form the corresponding relation indicator. We aggregate all the relation indicators to form a indicator set \(\mathbf{U}=\{\mathbf{u}_{1},\mathbf{u}_{2},...,\mathbf{u}_{n}\}\), where \(n\) is the number of LUs. This relation indicator set can be used as the prior knowledge for relation extraction.