esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Incremental Conductance with Integral Compensator (IC-INC)  
### Converter Topology  
PV panels usually output a relatively low voltage; thus, step-up (boost) converter topologies are frequently used to connect the PV panels to systems that require higher voltage levels, while performing tracking of the MPP [43]. The switching boost converter is connected to a capacitor \(C_{in}\) at the PV panel output (Figure 8) to reduce the voltage variation around the MPP. The converter controls the extracted current/power from the PV panel by varying the duty cycle, or by using a hysteretic controller if current control is used. In this work, for ANN and IC-INC, we chose to use hysteretic controllers, while for P&O, INC, and FL, we used a PWM modulator and duty-cycle variation.  
The output of the boost converter is connected to a power source (\(V_{mg}\)) and a resistor \(R_{mg}\) that emulates a DC microgrid at 48V, which is typically used in telecom equipment as required by the IEEE DC microgrid standards [44].  
The high-frequency \(f_{sw}\) switching boost converter stores energy in the inductor \(L\) before releasing it in the output capacitor \(C_{out}\) at higher voltage. The high-frequency voltage ripple in the PV (\(V_{PV}\)) should be as minimal as possible, as the MPP is dependent on voltage and current, and oscillations in voltage or current around the MPP can cause a loss of power. Hence, the input capacitor should have enough energy to keep the PV voltage nearly constant during the high-frequency switching period and during abrupt irradiance changes.  
Figure 8: Schematic of implemented topology.  
Figure 7: Relationship of MPP current with irradiance and temperature.  
An inductor \(L\) was selected to allow for a variation of 10% in the boost input current. Although the current \(i_{L}\) has a higher variation than \(i_{PV}\), both have the same average value (\(I_{L}=I_{PV}\)). This variation in currents should be supported by \(C_{in}\).  
The \(C_{in}\) capacitor is appropriately sized for very low ripple voltage (0.05% of \(V_{PV}\)); thus, the \(I_{PV}\) current will also present very little rippling, preventing limit cycles around the MPPT. The \(C_{in}\) capacitor keeps the PV voltage and current nearly constant during the switching period.  
The output capacitor \(C_{out}\) is appropriately sized to support discontinuities in the current while presenting low ripple voltage (0.5%). All the system parameters are presented in Table 2.
## 4 Results and Discussions
### Signal to Noise Ratio  
The complete UM-VLC model and the effect on it of the scattering channel component together with the presented hemispherical distribution is also evaluated by implementing the SNR metric, which is calculated based on Equation (42) and presented as empirical CDF curves. As we can see in the expression (42), the received power is used to compute the  
Figure 5: Empirical CDF and distribution of the received power in the UM-VLC scenario with (**a**) 40, (**b**) 60, (**c**) 100, (**d**) 150, (**e**) 200 dust particles in the hemispheric area, and (**f**) UM-VLC reference scenario, Reprinted from ref. [12].  
SNR. Therefore, the dependence of the SNR on the received power and its trend is reflected in the CDFs curves obtained. In this context, Figure 6 shows the empirical CDFs of the SNR for different \(N\) values between 40 and 200 dust particles.  
Based on these curves, several important findings are highlighted. We observe that the best SNR performance of the UM-VLC system is obtained when \(N=40\) and the worst performance is obtained when \(N=200\). This reaffirms the idea based on the results of the previous subsections, which implies that as the value of \(N\) increases, the performance of the UM-VLC system decreases. However, for values of \(N=100\) and \(N=150\) and for an SNR between 27 and 29 dB, the CDF curves have similar behavior. This behavior allows us to infer that, between these SNR values, the effect of increasing dust particles is not decisive. We must also remember that not only scattering channel component is evaluated, but we also evaluate the UM-VLC channel with all the environmental factors that affect it.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 5 Treatments
### Drugs Targeting Biofilm
#### 5.1.2 Intervening in Trehalose Catalytic Shift  
Trehalose is a glucose disaccharide in Mtb. It is a pivotal constituent of the cell wall components trehalose monomycolate (TMM) and trehalose dimycolate (TDM) [99]. As mentioned above, the former is needed to transport mycolic acid, a component of biofilm EPS in mycobacteria [26], while the latter can release free mycolic acid through enzymatic hydrolysis [100]. These facts indicate that trehalose plays a crucial role in biofilm formation. When facing hypoxia, there is a catalytic shift in trehalose metabolism, which deprives trehalose of TMM and TDM biosynthesis and utilizes it for central carbon metabolism (CCM) intermediate production instead. CCM promotes ATP and antioxidant production and, finally, contributes to drug tolerance. This process is closely associated with TreS, a trehalose synthase that catalyzes the conversion between trehalose and maltose [93]. [PERSON] and colleagues synthesized a series of trehalose analogs (including 2-, 5-, 6-TreAz) and proved their antibiofilm activity towards _M. smegmatis_ after being transported into cells by the trehalose-specific LpqY-SugABC transporter [94]. [PERSON] et al. ascribed this ability of trehalose analogs to their competitive inhibition of TreS, which has an identical architecture to trehalose [93]. Recently, they synthesized a panel of azidodeoxy and aminodeoxy-D-trehalose (TreAz and TreNH2) analogs and determined that 6-TreAz and 2-TreNH2 selectively inhibit biofilm formation in an LpqY-SugABC-dependent manner by interfering with the trehalose catalytic shift in Mtb [95]. All of these findings suggest that trehalose analogs can be regarded as a novel tool for inhibiting mycobacterial biofilm.
# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 3 Materials and Methods
### Signals Generators
#### 3.7.2 Machine Learning Signals  
Financial Signals are based on predicted close values, which are formed to belong to one of the following categories: BUY and SELL. For the extraction of the predicted values, a deep learning method that is based on a modified version of WaveNet model [50] was found to be effective in the multivariate forecasting of financial time series [5].  
The prediction of time series is implemented under the condition of other highly correlated time series based on Wavenet model standards. The WaveNet model [50] is implemented with the application of a dilated convolution neural network to distinguish the predicted prices from the actual prices, instead of utilizing the recurrent connections in which the preceding values are taken into account. In the dilated convolution process, a filter is applied in a more extended area than its length and the input values are skipped by a stable step.  
The architecture of the method combines one convolutional layer and two dense layers with Relu activation function. The hyper-parameters of the model were set as follows: the number of kernels equal to 64, the kernel size equal to 2, the time-step equal to 1 and the processing window equal to 64. The input vector is based on 60 financial indices found to be effective in stock price forecasting [51] and is estimated by the combination of open, close, low, high, volume and adjusted close values of market stocks. Input data are initially preprocessed by applying the min-max normalization method on them.  
Feature selection is based on method developed by [PERSON] et al. [52]. More specifically, the Random Forest feature selection algorithm is applied on the input data. A financial index considered more important when it increases the difference between the out of bag score in a feature vector with added random noise and the out of bag score in the initial feature vector. Finally, 44 financial indices out of 60 are selected. The post-processing of predicted values is defined as their transformation to financial signals, so each prediction should be mapped to one of the two aforementioned categories. For this purpose, each prediction is transformed to its percentage variation in relation with the preceding prediction value to be estimated as the return values of each asset. In the case of the return value is higher than double the summation of average and standard deviation of the return distribution, the extracted signal is equal to BUY. When the return value is lower than double the standard deviation minus the average of return distribution, the signal is assigned to be equal to SELL.
## 4 Conclusions  
To our knowledge, no methods have been published for the analysis of volatile sulphur compounds in wine distillates using SBSE-GC-MS. The method was successfully validated and shown to be suitable for the determination of eight volatile sulphur compounds: DES, DMDS, DPS, DEDS, DBS, DMTs, DPDS, and TC. The proposed method had a good analytical performance when compared with other analytical methodologies optimized and validated for similar analytes in similar samples. After optimization and validation, it was successfully applied for the analysis of 34 wine distillate samples. The most significant volatile sulphur compounds found in the samples were DBS, DMDS, and DPS. In relation to the possible influence of aging in wood on the volatile sulphur compound content, the preliminary results seem to indicate that these exhibit a decreasing tendency during the aging of distillates in wood.  
Conceptualization, R.C. and M.d.V.G.-M.; methodology, M.S.; software, R.C.; validation, R.C. and M.d.V.G.-M.; formal analysis, R.C. and M.d.V.G.-M.; investigation, M.S.; resources, R.C.; data curation, R.C., M.d.V.G.-M. and M.S.; writing--original draft preparation, M.d.V.G.-M. and R.C.; writing--review and editing, M.d.V.G.-M. and R.C.; visualization, M.d.V.G.-M. and R.C.; supervision, M.d.V.G.-M. and R.C.; project administration, R.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Plan Propio de Apoyo y Estimulo a la Investigacion y la Transferencia of University of Cadiz (Project PR2022-021).  
Not applicable.  
The raw data are available from the authors upon request.  
The authors would like to thank Grupo Caballero and Bodegas Gonzalez Bysass for providing the distillate samples.  
The authors declare no conflicts of interest.  
Figure 3: Cluster Analysis according to content in volatile sulphur compounds. Codes in Table A1.
# Numerical Simulation of Subdam Settlement in Ash Disposal Based on CGSW Optimization
## 1 Introduction
Firstly, an accurate CGSW distribution model is established based on the drilling database, and the effect of different CGSW treatments (the replacement method and gravel piles with different parameters) on the uneven settlement of the subdam is studied from the perspective of static analysis. The previous studies on ash disposals are mainly divided into the categories of geotechnical parameters, the stability of ash dumps under seepage conditions, static stability, and dynamic stability. In this paper, an unprecedented challenge has been encountered: the localized concentration of CGSW in the ash dump area, which should not be present. Consequently, the treatment method has become a dilemma for the management. Therefore, this paper designs a series of orthogonal test scenarios to evaluate the impact of each scenario on the settlement of the proposed subdams and finally selects an optimal treatment option to provide recommendations based on the orthogonal test results. The previous studies basically focused on the bearing capacity [32; 33; 34], damage mode [35], deformation [36; 37], and consolidation [38; 39] of single piles or pile groups, and the depth of pile burial was less than 10 m. The study of the uneven settlement of the superstructure by deep pile groups has rarely been mentioned. In this paper, the pile groups are buried at a depth of about 80 m. Using a complex three-dimensional model established by numerical simulation, the effects of different pile group displacements on the settlement of the superstructures under complex original terrain conditions are calculated.
## 4 Case Analysis
### Data Acquisition  
Through the acquisition of borehole optical images, the borehole wall expansion images of the whole hole section of the three boreholes in the survey area were obtained. Some of the borehole wall expansion images are shown in Figure 12. Figure 12a shows the expanded image of the borehole rock wall at 4.5-6.5 m corresponding to borehole ZK I. Figure 12a shows the expanded image of the borehole rock wall at 4.5-6.5 m corresponding to borehole ZKII. Figure 12a shows the expanded image of the borehole rock wall at 4.5-6.5 m corresponding to borehole ZK III. From Figure 12, we can intuitively grasp the distribution and morphological characteristics of the rock mass structure of the borehole. The adverse geological bodies in Figure 12a are mainly structural planes and small holes. The adverse geological bodies in Figure 12b are mainly karst caves and structural planes. The adverse geological bodies in Figure 12c are mainly karst caves.  
Figure 11: borehole multi-source data acquisition equipment. (**a**) Borehole optical imaging equipment. (**b**) Borehole directional acoustic scanning equipment. (**c**) Borehole radar equipment.  
Figure 12: borehole wall in the borehole image. (**a**) Borehole ZK I. (**b**) Borehole ZK II. (**c**) Borehole ZK III.  
Through the acquisition of directional acoustic wave scanning data of boreholes, the full-pulse acoustic wave scanning data of the borehole wall of the three boreholes in the survey area were obtained. Some of the borehole acoustic wave scanning data are shown in Figure 13. Figure 13a shows the borehole directional acoustic scanning data at 4.5-6.5 m corresponding to borehole ZK I. Figure 13b shows the borehole directional acoustic scanning data at 4.5-6.5 m corresponding to borehole ZK II. Figure 13c shows the borehole directional acoustic scanning data at 4.5-6.5 m corresponding to borehole ZK III.  
Through the acquisition of the borehole radar technology, the borehole wall radar detection data of the whole borehole section of the three boreholes in the survey area were obtained. Some of the borehole radar data are shown in Figure 14. Figure 14a shows the borehole radar detection data at 4.5-6.5 m corresponding to borehole ZKI. Figure 14b shows the borehole radar detection data at 4.5-6.5 m corresponding to borehole ZKII. Figure 14c shows the borehole radar detection data at 4.5-6.5 m corresponding to borehole ZK III. The image in Figure 14 shows the horizontal direction of drilling depth, with the left side representing 4.5 m and the right side representing 6.5 m. The image in Figure 14 is the reflection time of the radar wave in the longitudinal direction.  
Figure 14: borehole radar data. (**a**) Borehole ZK I. (**b**) Borehole ZK II. (**c**) Borehole ZK III.  
Figure 13: Directional acoustic scanning of borehole. (**a**) Borehole ZK I. (**b**) Borehole ZK II. (**c**) Borehole ZK III.
# Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies
## 4 Experiments
### Experimental Setup  
We set an object detection benchmark for traffic sign detection processed in the following way. Out of TT100K's 45 instance categories, we randomly selected nine classes as the novel ones, while keeping the other 36 as the base. During the initialization term of meta-learning, only the base class objects are considered. In the adaptation term, there are K-shot annotated bounding boxes for objects in each novel class and 3K annotated bounding boxes for objects in each base class for training, where K is set at 1, 2, 3, 5 and 10. We adopted the mean Average Precision (mAP) as the evaluation metric and a qualified prediction that ought to be more than 0.5 IoU with the ground truth. To design a few-shot learning setup, we considered three different novel/base class split settings, i.e., (\"w1\", \"w27\", \"pd\", \"ip\", \"i6\"/rest); (\"ip\", \"p8\", \"w27\", \"w1\", \"i6\"/rest) and (\"w1\", \"p7\", \"w27\", \"i6\", \"pd\"/rest).  
Similarly, on the GTSDB dataset, we had 300 images from the validation set for evaluation, and the remaining images for training. Out of its 43 instance categories, we randomly selected 10 classes overlapped with TT100K as the novel ones while keeping the remaining 33 as the base. We also considered the proposed model learning on the 43 base classes from GTSDB and exploiting it to detect the two novel objects in TT100K. This setting features a cross-dataset problem that we called GTSDB to TT100K.  
We took a similar approach to the MIST dataset as the previous two datasets. We selected 55 classes as the novel classes, while keeping the rest as base classes.  
As for the computer platform settings, a standard PC was used for all the experiments, whose hardware and software configuration are listed as follows:  
* NVIDIA GeForce RTX 2080 Ti GPU
* Dynamic Memory: 128G DDR3 RAM
* Python + Pytorch(GPU)
# Approximate Networking for Universal Internet Access
###### Abstract  
Despite the best efforts of networking researchers and practitioners, an ideal Internet experience is inaccessible to an overwhelming majority of people the world over, mainly due to the lack of cost-efficient ways of provisioning high-performance, global Internet. In this paper, we argue that instead of an exclusive focus on a utopian goal of universally accessible \"ideal networking\" (in which we have a high throughput and quality of service as well as low latency and congestion), we should consider providing \"approximate networking\" through the adoption of context-appropriate trade-offs. In this regard, we propose to leverage the advances in the emerging trend of \"approximate computing\" that rely on relaxing the bounds of precise/exact computing to provide new opportunities for improving the area, power, and performance efficiency of systems by orders of magnitude by embracing output errors in resilient applications. Furthermore, we propose to extend the dimensions of approximate computing towards various knobs available at network layers. Approximate networking can be used to provision \"Global Access to the Internet for All\" (GAIA) in a pragmatically tiered fashion, in which different users around the world are provided a different context-appropriate (but still contextually functional) Internet experience.  
universal Internet access; approximate networking; Global Access to the Internet for All (GAIA) +
Footnote †: journal: Computer Science and Communications
## 2 Materials and Methods
### Modifications Performed to Original ALO RPA
#### 2.2.2 Electronic Speed Controller  
The used ESC for this motor is the HBC series V7 25063-3 by MGM-ComPro(r). It is a controller for low-power programmable brushless electric motors.  
This motor speed controller provides high-quality and smooth motor control for the RPA and gives the possibility for safe disconnection of the battery when running the motor (emergency STOP).  
ESC uses the best available power MOSFETs (metal oxide semiconductor field effect transistor) or IGBT (Insulated gate bipolar transistor) modules for higher voltage and synchronous pulse-width modulation (PWM) and very powerful 32-bit ARM(r) microcontrollers to achieve very high efficiency. Software and firmware updates can be carried out by connecting to the internet at any time. Thanks to this, ESC is easily accessible and customer-specific modifications and adjustments can be performed. Its basic features are:  
* Working range: from low voltage up to 63 V, up to 250 A continuous power.
* Small dimension and weight.
* Very clear and easy parameter settings by PC.
* Data transfer to PC (or other equipment) and displaying in real-time (monitoring).
* Synchronous rectification, very high efficiency 98%-99%.
* Motor PWM in range 8-32 kHz.
* Suitable for motors without pole limiting.
* Sensor motors as well as sensorless motors are supported.
* Support regenerative braking and battery charging under braking.
* Support many working modes (PWM, constant rpm, constant torque/car mode, boat mode, plain mode).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Voltage (V)** & **14 \(\times\) 3.0 V** & **42 V** \\ \hline Maximum current (I) & 308 A \\ Motor Efficiency (\(\eta\)) & 87\% \\ Maximum power: & P = V \(\times\) 1\(\times\) \(\eta\) & 11.25 kW \\ \hline \hline \end{tabular}
\end{table}
Table 7: Electric engine characteristics.  
* Electronic reversing.
* Support measuring (monitoring) of each battery cell.
* Support any type of battery: NiCd, NiMH, Lipo, Li-Ion, LiFePo4, Pb, etc.
* Monitors controller temperature, motor temperature, and battery temperature.  
With respect to the possibility of parameters setting, it is necessary to have a PC running the Microsoft operating system and a USB cable. The PC must have the \"Controller 2\" software installed, see Figure 6.  
By default, settings of parameters in \"Controller 2\" software represents only average values. These values are usable as \"starting values\" for starting the system testing. In this case, it was necessary to adapt many parameters for the best behavior of the ESC with the available hardware. The description and the possible values for all the parameters are available in the operating manual of the controller [74].
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
## 2 Magnetic Behavior  
As a first step towards the investigation of the energy conversion abilities of Metglas, this section aims at assessing the magnetic responses of the considered material, along with the development of a theoretical framework to predict the energy conversion performance. Starting from the experimental characterization of Metglas 2605 SA1, a model is thus proposed to predict the ultimate energy conversion capability.