esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Meteorological Data and Solar Hours  
To determine the solar radiation potential, an average value of 4.60 kW/hour/m\({}^{2}\)/day was found at the study site, with a peak during May, June and July and a noticeable decrease in November, December and January. However, 4.60 is a very acceptable value  
\begin{table}
\begin{tabular}{l l l} \hline
**Equipment and Sources of Information** & **Description** & **Specifications** \\ \hline A non-participant observation guide and a survey. & Open-ended questions were used as field tools to develop the energy diagnosis. & Applicable to installed equipment, aeration, pumps and lighting. \\  & & International Electrotechnical \\ Technical data of panels. & With polycrystalline technology. & Commission (IEC) and UL 1703 \\ Inverter technical data. & With grid-connected technology. & IEC 61727 and UL-1741 certifications. \\ Fluke Multimeter. & For voltage and current measurements. & CAT III, 600V, VCA \(\pm\) (1.0\% + 3), \\ Solar radiation meter. & Amprobe Solar-100. & Range: 1999 W/m\({}^{2}\); accuracy \(\pm\) \\ Fluke 434-II Power Quality Analyzer & For measurement of electrical power (W), voltage (V) and current (A) & 5–10 W/m\({}^{2}\); resolution 0.1W/m\({}^{2}\) \\  & & Accuracy: Voltage: 0.5\% of nominal \\  & & voltage, Current: 0.5\%, Power: 1\%, \\  & & Frequency: 0.01 Hz). \\ Software. & & Restcreen® Clean energy management \\  & & expert software version 8 for Windows \\  & & (ON, CA: Government of Canada). \\ Software. & Single-line system diagram design. & Autocad SR, CA: (Autodesk, Inc, San \\ Software. & Descriptive statistics. & JAMOVI software version 2.3 \\  & & (Jamovi.org). \\ Electrical installation calculations. & [34; 39; 42; 71; 74] and Sunny Design Version 5.22.5 (Niestetal, DEU: SMA Solar Technology AG Corp, Rocklin, CA, USA). \\  & & \\ \hline \end{tabular}
\end{table}
Table 2: The calculation and sizing of the On Grid-PV solar photovoltaic energy was carried out with the following instruments.  
for the system calculation. Table 3 shows the data found for the study site obtained with Retscreen expert V.8.  
The basis for the design of an On Grid-PV system is the availability of the solar resource in a solar site and the grid connection; the former is crucial to define the peak power of the system. The data obtained are used for the system design, concluding that the solar radiation potential is feasible, with a historical minimum of 3.55 and a maximum of 5.46 kWh/m\({}^{2}\)/d, very appropriate values for the development of photovoltaic systems. The statistics of the photovoltaic power potential [77] indicate that the average radiation of the country is 5.77 kWh/m\({}^{2}\), which represents a huge potential for electricity generation by solar energy. Internationally, Mexico is considered one of the countries with the highest sunlight capture [40,41].
## 4 Results
### Susceptibility Assessment
#### 4.2.2 Susceptibility Calculation  
The proposed methodology analyses the geological hazard by means of the susceptibility assessment. The term \"susceptibility\" is employed here to define the likelihood of occurrence of hydrates in the sediment column, and subsequently the likelihood of them being affected by dissociation processes resulting from natural or human-induced activities (liquefaction, explosions, collapse, crater-like depressions or submarine landslides). Susceptibility assessment is applied as the first step in a pan-European risk assessment, owing in particular to (i) the regional scope of the assessment (the European continental margins and adjacent areas) and (ii) the current state of European gas hydrate-related information characterized by intensively studied areas with a high density of high-quality data and wide areas of critical knowledge gaps with no data. The baseline scenario (the initial hypothesis) is that gas hydrate occurrence is only possible in seafloor areas where pressure (bathymetry) and seafloor temperature conditions are inside the theoretical GHSZ. In this zone, the occurrence of gas hydrates is directly related to the presence of evidence (direct samples of hydrates) or indicators of it (e.g., pore water and velocity anomalies, BSRs and gas chimneys), as well as the occurrence of hydrocarbon fluid flow structures. Finally, the likelihood of the seafloor being affected by gas hydrate dissociation processes will be great at the base of the GHSZ and in the vicinity of gas hydrate evidence and indicators. In order to prove this initial hypothesis, a susceptibility assessment was carried out through map algebra in a GIS environment from a density map of evidence and indicators and the pan-European map of the GHSZ on the seafloor.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 2 Materials and Methods  
### Finite Element Modelling and Analysis  
To implement pairs of completely integrated spar hulls and mooring lines that are coupled under wave loading, a finite element model (FEM) was used. The spar platform analysis considers the actual physical connection between a vertical rigid floating hull and a mooring line. Such a unique model can capture all different types of loading, nonlinearities, and boundary conditions [21]. Thus, the integrated binding consequence used in this study was examined to demonstrate the drag damping importance in mooring systems under deep water conditions. The FEM code AQUA/ABAQUS was utilized to find the responses of the spar types of the platform.  
The primary purposes of this research are to achieve the right catenary shape of the mooring line under gravity (self-weight), stiffness, and axial tension, all of which are related to its mean curvature. This equilibrium state is obtained conclusively when the spar cylinder is gently released under buoyancy. This dynamic analysis was performed under normal seawater circumstances.  
A decoupled quasi-static method that totally or partially ignores the influences of the interaction between the platforms and mooring lines is the most commonly used strategy for handling the dynamics of a spar platform. In this technique, the mooring lines are used to model as massless linear or nonlinear springs. For the mooring line responses, the platform motions are applied in a comprehensive finite element model of the catenary mooring line as an external loading with regard to the imposed boundary conditions. Hence, the dynamic interactions between mooring lines and platforms are not appropriately modelled in the uncoupled conventional analysis [22]. For platforms in deeper waters, moorings often contribute to considerable damping and inertia due to their longer length, greater weight, and greater dimensions. These damping values must be taken into account in order to provide an appropriate motion analysis of a platform in deep waters.
## 6 Lipidomics
### Mode of Action  
[PERSON] et al. demonstrated the role for lipidomics in mechanism of action studies by mapping the impact of the natural antimycobacterial compound vanillin in _M. smegmatis_. The authors observed that vanillin changes the composition of fatty acids, glycolipids, glycerophospholipids and saccharolipids causing disruption of cell membrane homeostasis [108]. Similarly, lipidomic analysis has been instrumental in investigating the consequences of _M.tb_ drug-resistance conferring mutations to well-characterized anti-TB drugs [109]. [PERSON] et al. demonstrated that rifampicin-resistant _M.tb_ isolates with mutations in _rpoB_ exhibited altered lipid profiles dependent on the specific location of the SNPs [109]. Lipidomic analysis showed that different _rpoB_ SNPs resulted in distinct relative abundances of short-chain and long-chain fatty acid phthiocerol dimyccoerosa (PDIM), which induced alternative macrophage activation pathways and altered macrophage metabolism. Such analyses are useful not only to understand the mode of action of a compound, but to deconvolute the consequences of drug resistance-conferring mutations. Lipidomics is often used as an unbiased approach to complement findings from other methodologies. An example is the evaluation of the mycolic acid transporter, MmpL3, as a druggable target [110]. MmpL3 is an integral inner membrane transporter, with a role in the export of mycolic acids to the periplasmic space in the biosynthesis of the mycobacterial cell wall. Lipidomic analysis of _mmpL3 Mtb_ knockdown mutants using thin layer chromatography of total lipids revealed a fast decline in cell wall-bound mycolic acids and trehalose dimycolates. Combined with confirmation of in vitro and in vivo essentiality, this study confirmed the mycolic acid transporter MmpL3, as a validated druggable target for _M.tb_ drug discovery. Similarly, lipidomics, alongside genomics and transcriptomics, characterized the mode of action of HC2091, a novel compound that likely targets MmpL3. _M.tb_ HC2091-resistant mutants were shown to have SNPs in the _mmpL3_ gene, likely conferring drug resistance. Thin layer chromatography of the total extractable lipids from HC2091-treated _M.tb_ cultures supplemented with radiolabelled-sodium acetate demonstrated a dose-dependent reduction in trehalose dimycolate accumulation with increasing concentration of HC2091. In combination with transcriptional profiling of HC2091-treated bacilli, the authors confirmed that HC2091 targets MmpL3 through a mechanism distinct from other MmpL3 inhibitors [66]. Lipidomics, therefore, often in combination with other omics approaches, is an effective tool, especially in the interrogation of cell wall biosynthetic pathways, a rich source of druggable _M.tb_ targets.
## 4 Computational Complexity  
Each iteration of the multi-sensor IF estimation algorithm involves two major steps: (a) searching the highest energy TF point and (b) IF-estimation at other time-instants. Time-instant with highest instantaneous signal energy is estimated with \(\mathcal{O}(NWM)\), where \(W\) is the width of the window and \(N\) is the number of samples in the signal. The peak frequency bin is searched by using \(L\) Fourier transform operations for \(M\) sensors thus resulting in the computational cost of \(O(MLW\text{log}W)\). The IF at other time-instant is estimated with the computational cost of \(\mathcal{O}(3 PMW)\) because the cost of computing Fouriertransform at a single bin is \(O(W)\) and we compute FT using three different windows at \(P\) number of bins for \(M\) number of sensors. The IF is estimated at \(N\) samples so the computational cost for all time-instants excluding the starting point becomes \(\mathcal{O}(3 PMWN)\). The total computational cost of a single iteration thus becomes \(\mathcal{O}(3 MPWN+MLW\text{log}W+NWM)\). The algorithm is repeated for \(V\) components so the total computational cost is \(\mathcal{O}(3 VMPWN+VMLW\text{log}W+NWMV)\). The computational cost of TF filtering and MUSIC algorithm can be ignored as these operations are \(O(N)\), which is less than the order of the proposed algorithm.
# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 3 Materials and Methods
### Ablation Study  
To verify the significant contributions of the main components in our model, we compared our model with the following variants:  
First, we created a variant, \(\text{LKGA}_{w}/_{o\text{ learner}}\), without collaborative signals from learners, in which we removed the collaborative signals from learners and only used the books that learners directly interacted with as the initial set of entities. Second, we removed the initial connections to create \(\text{LKGAw}/\text{o}\) learner; in this variant, we removed the initial connections in the embedding generation of each layer. The experimental results are shown in Figure 3, from which we can draw the following conclusions.  
1. Compared to our model, removing the collaborative signals from learners led to a decrease in model performance. This fact demonstrates that relying solely on the direct interaction information of learners limited the model's effectiveness, and introducing collaborative signals served as a supplementary means to help the model capture learners' interests and preferences.
2. Removing initial connections resulted in poorer performance, as each layer introduced a certain degree of loss and distortion to the input information, leading to a gradual decrease in the richness and effectiveness of the original information. Retaining the original information through initial connections effectively mitigated information loss.  
Figure 3: The impact of collaborative signals and initial connections.  
3. We can observe that the impact of removing collaborative signals was greater than that of removing initial connections, which also proves that the selection of initial entities had a significant impact on model performance. When the selection of initial entities could represent learners' preferences as much as possible, even without initial connections, good results could be achieved with reasonable parameter settings.  
In summary, collaborative representation learning provided an effective way to model high-order connectivity relationships between learners and books, enabling the model to more accurately depict learner preferences and book characteristics. This refined representation learning is key to improving recommendation performance and helps build more intelligent and personalized recommendation systems.  
Furthermore, considering the potential impact of irrelevant entities on model performance, we introduced an attention mechanism that dynamically adjusted the importance of different neighboring entities, effectively avoiding the interference of irrelevant information.
## 3 Experiments  
### Dataset Collection  
To evaluate the effectiveness of the proposed HST-Net model in the detection of insulator surface defects, we collected a dataset comprising images from four distinct types of defects, namely: (1) flashover damage to insulator shells, (2) insulator shell break, (3) good insulator shell, and (4) insulator string. The images were captured using an industrial-grade array camera, the MV-CE200-10 GM model, within the Songshan Substation, Zhengzhou City, China. Initially, our collection comprised 1600 original images. Then, we enhanced this dataset to a total of 12,500 images by data enhancement. This expanded dataset was then meticulously segregated into training and validation sets, maintaining a ratio of 2:1.  
When capturing images, careful consideration was given to the realistic scenarios of insulator operation on actual industrial lines, striving to include various temporal periods, light angles, shooting distances, and interference damage. This thorough approach ensured the images chosen were representative of the broad range of possible scenarios, thereby boosting the robustness of the dataset. Figure 6 illustrates the variety of insulator surface defects captured in our study.
## 2 Creation of Signals Database Measurement Conditions  
Conventional axle counter systems operate based on the inductive sensors (detectors) mounted on the rails (such devices are located all over the world in many countries). The passage of a wheel changes the signal, which then is subject to analysis and wheel counting. Depending on the design of the sensor the signal may be intensified (Figure 3) or be suppressed while the wheel appears over it.  
In order to detect the direction of train movement, sensors usually comprise two channels which in the end produce signals in a form of voltages u\({}_{1}\) and u\({}_{2}\) (Figure 3). During the wheel passage the signals are changing in time, but in fact they are changing with the movement of the wheel along the sensor. Such signals are then named \"wheel signatures\" as their shape mainly depends on the wheel profile.  
The shape of the signal depends on the following factors:  
* the rail profile and condition (the level of wear & tear);
* the wheel profile and condition;
* the lateral position of a wheel in relation to the rail and wheel detector head (sensor).  
Figure 3: Wheel sensor signals during a wheel passage, an example.  
The input data are gathered in the laboratory conditions on the specially dedicated test stand. During the measurements in the field, even if the known train is moving with the known speed, the lateral position of a wheel is accidental and not repeatable. It is also not feasible in the field to measure all the possible wheel profiles moving on the rails in different conditions.  
The rails, wheels and their position on the rail generates many combinations of measurement configurations. The number of combinations may be decreased. Typically, it is possible in relation to the rail profile and its condition for which the detector is approved to be used (e.g., 60E1 with lateral wear (L) up to 10 mm; vertical wear (V) up to 20 mm and mixed V/L = 10/5 mm). The number of types of wheels may also be reduced, however, it is rather possible in the mass transit applications (metro, trams) due to a limited volume of types of vehicles or into a certain extent in the railways (e.g., TSI compliant rolling stock). Wheel types may also be limited by means of defining the minimum dimensions of a flange, a wheel diameter and a rim width. However these features are not so easy to be monitored, like rail types, which do not change.
# Drum Water Level Control Based on Improved ADRC
## 3 Simulation and Performance Analysis of Drum Water Level ILADRC
### Robustness Analysis  
The control performance of the system after the transfer function changes is discussed. Increase \(\lambda=0.04\) and decrease \(T=20\), so the \(G_{W}\) is significantly increased, and the transfer function \(G_{W}\) is changed to:  
\[G_{W}(s)=\frac{0.04}{s(20s+1)}. \tag{24}\]  
The comparative simulation of LADRC and ILADRC is shown in Figure 6.  
The comparison of the variations of control input between LADRC and ILADRC is shown in Figure 7. As can be seen from Figure 7, In the case of the model parameter \(G_{W}\) increasing, the control input peak of ILADRC is smaller than that of LADRC, and the fluctuation is smaller. According to the output energy of the controller input system through the control channel, ILADRC consumes less energy than that of LADRC.  
Figure 6: Simulation of LADRC and ILADRC with the model parameter \(G_{W}\) increasing.  
Figure 7: Comparison of control input with the model parameter \(G_{W}\) increasing between LADRC and ILADRC.  
With the model parameter \(\lambda\) reduced to 0.02 and \(T\) increased to 40, the \(G_{W}\) is significantly reduced, that is, the \(G_{W}\) is changed to:  
\[G_{W}s=\frac{0.02}{s(40s+1)}. \tag{25}\]  
The comparative simulation of LADRC and ILADRC is shown in Figure 8.  
The comparison of the variations of control input between LADRC and ILADRC is shown in Figure 9. As can be seen from Figure 9, in the case of the model parameter \(G_{W}\) decreasing, although the control input peak of method ILADRC is larger than that of LADRC, the fluctuation is smaller.  
Figure 8: Comparison of LADRC and ILADRC with the model parameter \(G_{W}\) decreasing.  
Figure 9: Comparison of control input with the model parameter \(G_{W}\) decreasing between LADRC and ILADRC.  
The dynamic performance and anti-interference performance of LADRC and ILADRC are compared, and the results are shown in Table 3.  
As can be seen from Figures 6 and 8 and Tables 2 and 3, the Setting time of LADRC and ILADRC is relatively stable. When \(G_{W}\) is increasing, the overshoot of LADRC is 13 times that of ILADRC. When disturbance is added to the main loop, the dynamic landing of LADRC is 1.7 times that of ILADRC. When disturbance is added to the secondary loop, the multiple becomes 3.7. The recovery time of LADRC is obviously greater than that of ILADRC. When \(G_{W}\) is decreasing, the overshoot of LADRC is 3.4 times that of ILADRC. The recovery time of LADRC is 4.8 times that of ILADRC. When disturbance is added to the main loop, the dynamic landing of LADRC is 1.5 times that of ILADRC. When disturbance is added to the secondary loop, this multiple becomes 3.4. The recovery time of LADRC is 50 s, while that of ILADRC is 0 s. The experimental data show that both are more sensitive to \(G_{W}\) decreasing. The ILADRC's indexes change very little and are more stable. ILADRC has better adaptability and robustness to the changes of model parameters.
## 2 UWB Signal Processing Steps
### Data Cleansing and Clutter Rejection
#### 2.1.1 Exponential Averaging  
In [16], a clutter-reduction method was proposed where the foundational theory of this method is based on exponential averaging. Having an initial estimate of the clutter signal \(A_{(k-1)}\), the new estimated clutter signal \(A_{(k)}\) is computed recursively from \(A_{(k-1)}\) and the new incoming radar scan \(X_{k}\), where k is the time index. Thus, \(A_{(k)}\) is derived as:  
\[A_{(k)}\ =\ \alpha A_{(k-1)}\ \ +\ (1-\alpha)\ \ X_{k}\, \tag{2}\]  
where \(\alpha\) is a constant scalar weighting factor which is between 0 and 1, \(A_{(k)}\) is the estimated background at \(k\) time index and \(X_{k}\) is the \(k^{th}\) frame in the dataset. This method is appreciated for its speed for real-time signal processing, but criticized for its very simple clutter estimation leading it to have poor performance in complex scenario cases.
## 5 Frequency Manipulation and Communication Feasibility
### Feasibility of Frequency Manipulation
#### 5.1.1 Procedure of Modified XML-RPC Algorithm  
The algorithm makes procedure calls through the hypertext transfer protocol secure (HTTPS) in a server-client relationship as given in Figure 15. Unlike the traditional client blocks which were typically implemented inside GNU Radio blocks, we modified the concept and implemented an external Python-based client program which is compatible with the latest versions of GNU Radio. The XML-RPC client sends the set messages to the respective XML-RPC server through a reconfiguration command. Acceptance of connection by the server program enables the client's commands to alter SDR parameters inside the GNU Radio. The server facilitates access to an external client-requesting program. This is done through variable call-back functions in the flow graph and procedures that enable tuning of the SDR parameters. The server architecture contains handlers in which the tuning functions and variables are stored. The event manager in the server controls the operation of events or functions requested by the client program. The returned value of the functions is then sent to the client via a secure HTTPS protocol with XML encoding. The encrypted HTTPS uses secure sockets and transport layer security for data integrity and protection.