esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Hybrid Deep-Learning-Model-Based Diamond-Tool Wear Prediction  
This paper developed a demonstrative ultra-precision diamond cutting machine tool, in which the fast tool servo (FTS) machining system adopts the extra fast servo tool installed on the \(Z\)-axis of the machine tool to realize the fast \(Z\)-direction feed and completes the surface topography processing. The fast tool servo device adopted in this paper is designed with a voice coil motor as the driver, grating ruler as the position feedback sensor, flexible hinge as the transmission and stop mechanism of large stroke, and high-precision electromagnetic drive fast tool servo device. Figure 1 is a schematic diagram of its overall mechanism.  
In the digital twin system, state recognition is the core part of the system, which directly determines the accuracy of the system. To recognize the wear state of diamond tools, feature extraction of machining signals is needed. However, due to the slight change of signals caused by tiny DTW, it is very difficult to manually complete the feature extraction process. The adaptive feature extraction of the deep learning method can also reasonably solve the difficulty of feature extraction caused by slight signal changes. This paper proposed an HDL (i.e., hybrid deep learning) model to predict the wear state of diamond tools. Furthermore, the TCM system is established to realize the online monitoring of diamond tools. The flow diagram of HDL-based diamond-tool prediction is shown in Figure 2.  
In the process of tool monitoring, the cutting force contains a lot of information and characteristics about tool wear, so it is applied in the monitoring of the tool state. However, the force sensor (including the piezoelectric approach and strain approach) is not easy to install because of the high cost of measurement, which is the reason for other ways to predict the tool state in this paper. A self-sensing cutting force estimation method is established based on machine learning. According to the machining signal and control signal of the FTS device in the ultra-precision machining process, the cutting force signal is estimated by using a multi-layer fully connected neural network trained by the special machining process signal. This method does not need to rely on a force sensor and can achieve an accurate estimation of cutting force.  
Figure 1: Schematic diagram of the overall mechanism of FTS [25].  
Combined with the cutting force estimation method, machining signals of the FTS device are used to estimate and reconstruct cutting force. Since the convolutional neural network is sensitive to the spatial characteristics of the signal, a one-dimensional signal of estimated cutting force is converted into a two-dimensional signal by the time series imaging method. The transformed two-dimensional image data are input into the HDL model for training, and the trained state recognition effect is verified. To train the HDL model for diamond-tool wear state prediction, a large number of high-quality data samples containing different wear states of diamond tools need to be obtained through a large number of experiments.
## 3 Case Study: Design and Implementation of the Photovoltaic Control System
### Optimization of the Photovoltaic Control System
Let us now evaluate the proposed optimization technique by the analysis of the utilization of the FPGA resources. Finally, the system is physically implemented and validated within the real FPGA device. To perform this, the Nexys4 DDR prototyping board was used. The presented concurrent control system was implemented with XC7A100T FPGA. This board contains switches and a 7-segment display, that are used to stimulate remaining input and output values, respectively. Due to the limited number of switches, there is a possibility of controlling the first five panels (it is just assumed that the remaining three are working properly). Additionally, the seven-segment display is used to indicate the number of panels successfully powered on, with correct power and optimal temperature. The count of panels with proper power or temperature is visible on the display after pressing the corresponding momentary button. Figure 14 shows the experimental setup of the discussed system. Note that the presented figure corresponds to the results of simulation shown in Figure 13. At the illustrated state (marking) of the system, six panels are enabled (which is signalized by the seven-segment display). Indeed, two panels are turned off since switches that correspond to signals \(e1\) and \(e3\) are in a low state. Figure 12: Logical synthesis and implementation of the system within Vivado 2023.1. Figure 13: Validation of the designed photovoltaic system.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 3 Advanced LSFP
### Lsfp
#### 3.1.2 Uplink LSFP  
Similarly, the uplink transmission process consists of the following steps.  
In Step 1, network hub controller of a group receives the large-scale fading factors sent by all base stations in the group and forms a large-scale fading factor decoding matrix.  
In Step 2, network hub controller sends the large-scale fading factor decoding matrix to each base station.  
In Step 3, when a base station receives the information sent by the user, the signal is decoded according to the large-scale fading factor decoding matrix.
## 2 Materials and Methods
### SPME/GC-MS Characterization of Extracts Volatile Fraction  
Chemical characterizations of extracts volatile fraction were performed by solid-phase microextraction/gas chromatography coupled to mass spectrometry (SPME/GC-MS). SPMEs were obtained by a Supelco-57299-U SPME DVB/CAR/PDMS (Divinylbenzene/Carboxen/Polydimethylsiloxane) fiber (Sigma Aldrich-Saint Louis, MO, USA). All extracts were processed as follows: 0.50 g of dry extract were put into a 20 mL capacity glass vial and sealed with a rubber septum and an aluminum. The vial was placed on a heated plate (50 \({}^{\circ}\)C) and the SPME needle was inserted into the vial. The grey fiber was exposed to the headspace for 20 min. After exposure, the fiber was retracted into a needle and loaded into the injection port of the gas chromatographer for fiber desorption at 250 \({}^{\circ}\)C for 15 min.  
A Clarus 580 GC apparatus (PerkinElmer-Waltham, MA, USA) coupled to a Clarus SQ 8 S GC/MS (PerkinElmer-Waltham, MA, USA) was used for GC-MS analysis. Separations were achieved on a fused silica Zebron-ZB-SemiVolatile column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m--Phenomenex, Torrance, CA, USA). Analyses were carried following a different temperature gradient depending on samples.  
The temperature gradient for hemp extracts was as follows: starting temperature 50 \({}^{\circ}\)C (hold 1 min), up to 145 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min (hold 5 min), up to 175 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and up to 250 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min (hold 5 min). The carrier gas was Helium (flow rate 1 mL/min). The split of the injector was set to 1:50, while the injector and the transfer line temperature were set at 250 \({}^{\circ}\)C.  
The temperature gradient for thyme and coriander extracts was as follows: starting temperature 45 \({}^{\circ}\)C (hold 10 min), up to 180 \({}^{\circ}\)C at 2.5 \({}^{\circ}\)C/min (hold 5 min). The carrier gas was Helium (flow 1 mL/min), while the injector and the transfer line temperature were set at 250 \({}^{\circ}\)C.  
The semi-quantitative characterization was carried out through Turbomass 6.1.0.1963 software (PerkinElmer-Waltham, MA, USA). The unknown compounds were identified by matching the obtained spectra with the NIST Mass Spectral Library 2.0 (NIST-Gaithersburg, MD, USA) and confirmed by comparison of the retention index (RI) with those retrieved from [[http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/)]([http://webbook.nist.gov/chemistry/](http://webbook.nist.gov/chemistry/)). A mix of \(n\)-alkanes, ranging from octane (C8) to triacontane (C30) was obtained from Supelco (Bellefonte, CA, USA) and injected using the analytical conditions above reported to determine the retention index (RI) as proposed by [PERSON] et al. [20].  
Semi-quantitative analysis was made by peak area normalization without response factors. Relative abundances (%) were the mean of two replicates.
## 5 Experimental Validation (Simulated)  
To validate and demonstrate the effectiveness of the proposed ISAF-IoT framework, a simulated environment was designed and implemented in Python 3.13.3. The simulation setup comprised 1000 heterogeneous devices of various power capabilities representing IoT devices. These IoT devices were configured with distinct computational capabilities to represent the various IoT devices typically found in IoT environments. Device specifications included processing power ranging from 40 to 120 MHz, memory capacities between 32 and 128 MB, and battery sizes varying from 500 to 2000 mAh. Security checks were scheduled at random intervals, occurring between 3 to 12 times per hour. These were configured to generate a security data overhead from 5 to 20 KB for each check. The simulations were conducted using a custom-developed Python environment. They used NumPy and Pandas libraries for data simulation and analysis, while Matplotlib 3.10.0 was employed for the graphical visualization of performance metrics.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Examples of Performance-Improved BOCDA Systems
#### 4.4.3 BOCDA System with High-Speed Random Accessibility
[68], a lock-in detection circuit was newly established, in which the reference frequency can be changed at a high speed, for example, in the order of kHz. The measurement performances, such as a 200-m measurement range, six-cm spatial resolution, 1000 points/s random access speed, and 1.7-MHz BFS measurement accuracy, have been totally demonstrated. Recently, we have been studying the limitations of the total performance under random accessibility. One of the dominant restriction factors is the integration time to realize the position-selective function of the correlation-domain technologies, basically which must be at least one period of the LD frequency modulation in the basic BOCDA system. In the case of the double modulation scheme, the integration time should correspond to the lower SOCF frequency. When the temporal gating scheme is introduced to elongate the measurement range, the restricting time becomes one period of the intensity modulation waveform for the gating. This restricting time is required to measure one point in the BGS shape measurement. Namely, to make the BGS spectral shape through measuring, for example, 100 data points, the total measurement time to have one BGS spectral shape  
Figure 30: Experimental results of 5000 point/s high-speed random accessibility measurement by the system shown in Figure 29; five points are selected arbitrarily along the fiber [67]. Simultaneous dynamic strain measurements can equivalently be realized. (**a,b**) are the BGS and BFS change as a function of time, respectively. takes 100 times longer than the minimum restricting time mentioned above. This means that the random access speed depends closely on the spectral range and the spectral interval in the BGS shape measurement, which correspond to the strain dynamic range and the accuracy. The position-changing time, or the SOCF frequency-changing time, is related to the total random access performances, but it is not always dominant. This was shown in the measurement of Figure 30.  
By introducing some additional idea to manage the measurement procedure, we have recently demonstrated the total performance of a seven-cm spatial resolution, 1000-m measurement range, 2500 points/s random access measurement speed, and about 80-\(\upmu\varepsilon\) strain accuracy. Here, we introduced both the temporal gating scheme and the double modulation scheme to elongate the measurement range. This study has not been published yet, but will be presented in the near future.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Security Requirements
#### 3.2.2 Confidentiality  
Confidentiality or privacy of the wireless communication channel protects from the unauthorized disclosure of information [24]. Another significant barrier to IoD implementation is making data available, and controlling access to that data (data confidentiality). For instance, when a group of drones collects road traffic data from several places, there is a continuing problem in sharing this data safely and effectively [20].
## 3 Results
### Results of Phosphorus Adsorption Experiments  
The conducted experiments on the sorption capacity of bottom sediments in relation to phosphorus showed that the tested sediments adsorbed phosphorus quite well, but the course of adsorption was unusual. First, the obtained adsorption characteristics indicate that it is not monolayered but multi-layered. The double Langmuir and Langmuir models did not show a significant fit to experimental data. In contrast, the BET, general Freundlich-Langmuir, and Freundlich models were suitable for describing the phosphorus adsorption processes. Those models can predict a convex adsorption isotherm, but only at much higher adsorbate concentrations--at lower concentrations, the isotherm was concave. This was indicated by 1/n coefficients higher than 1 both in the Freundlich model (1/n ranges between1.263-1.802 for sediment taken at St. 1 and between 1.164-1.615 for St. 2.) as well as for the general Freundlich-Langmuir model (1/n ranges between 1.21-2.547 at St. 1 and between 1.28-2.852 at St. 2) (Tables 3 and 4). An exemplary course of adsorption isotherms is presented in Figure 9 (in this case, analysis was performed for S = S\({}^{\prime}\) + S\({}_{0}\)). The points represent mean values of adsorbed P for different final P concentration Ce. Phosphorus adsorption isotherms (for surficial deposits layers) were fit to the general Freundlich-Langmuir model, the deeper sediment layers to the BET model or Freundlich model, as it was indicated by determination coefficient (R\({}^{2}\)) values (Tables 3 and 4). However, the differences in R\({}^{2}\) values between tested models were rather small (in the range between 0.9607 to 0.9987) for all analyzed sediment layers. The assessed maximum adsorption capacities (S\({}_{\text{BET}}\) and S\({}_{\text{FL}}\)) were different, depending on the model, and varied between two orders of magnitude. It is worth noting that the much higher values of this parameter were assessed for wear-fitting the models to experimental data (lower R\({}^{2}\) values). The maximum capacity of a single monolayer in the BET model was the highest for the surficial sediment (0-5 cm) at both research stations (15,649.34 mg P kg \({}^{-1}\) DW at St. 1, and 13,121.06 mg P kg \({}^{-1}\) DW at St. 2). The values of this parameter declined with sediment depth (to 354.64 mg P kg \({}^{-1}\) DW at St. 1 for sediment layer 10-15 cm, and to 498.84 mg P kg\({}^{-1}\) DW, for deposit layer 15-20 cm at St. 2) (Tables 3 and 4). On the contrary, k\({}_{\text{BET}}\) values rose with sediment depth, amounting to a maximum for deeper sediment layers (7.20 dm\({}^{3}\) mg\({}^{-1}\) for layer 10-15 cm at St. 1 and 2.266 dm\({}^{3}\) mg\({}^{-1}\) for layer 15-20 cm at St. 2). The values of this parameter declined with sediment depth (to 354.64 mg P kg \({}^{-1}\) DW at St. 1 for sediment layer 10-15 cm, and to 498.84 mg P kg\({}^{-1}\) DW, for deposit layer 15-20 cm at St.  
Figure 9: Examples of modeled P adsorption isotherms fitting to experimental P adsorption data.
## 3 Experimental Methods
### Sensing Material Fabrication  
Two different sensing materials were fabricated. The first sensor is a heterostructure combining graphene and MoS2 flakes grown by chemical vapor deposition (CVD). The second sensor is a heterostructure combining graphene and MoS2 film grown by RF magnetron sputtering. Figure 4 shows the SAW sensor using graphene/MoS2 flakes. A mechanical exfoliation was used to transfer both graphene and MoS2 flakes to the SAW sensor's cavity region. Graphene, grown on Cu foil, was attached to the thermal release tape (TRT), and the Cu was then removed with a Cu etchant. Afterward, the graphene was transferred to the SAW sensor's surface. Heat was applied to leave only the graphene on the surface, as TRT loses its adhesive properties at 110 \({}^{\circ}\)C. Next, MoS2 flakes were transferred onto the graphene. For this, MoS2, grown by CVD on a SiO2 substrate, was spin-coated with PMMA and then placed in DI water to separate the PMMA/MoS2 flakes from the SiO2 substrate. The separated PMMA/MoS2 flakes were then placed on top of the previously transferred graphene and heat-treated at 90 \({}^{\circ}\)C for 1 h to transfer the MoS2 flakes onto the graphene. PMMA was then removed using acetone. Similar to the graphene/MoS2 flake transfer method, a graphene/MoS2 film was also fabricated to compare the sensor properties with those of a sensor utilizing a larger volume of sensing material. The graphene was transferred using TRT, followed by MoS2 deposition via RF sputtering to a thickness of 30 nm, with a shadow mask used to confine the film to the designated regions. The RF power was set to 120 W in an argon environment with a flow rate of 30 sccm. MoS2 was deposited at a rate of 1 nm/min for 30 min. To improve crystallinity, an annealing treatment was performed in a rapid thermal annealing (RTA) chamber under an Ar atmosphere at 300 \({}^{\circ}\)C for 30 min.  
Figure 4: The fabrication procedure of the two-port SAW delay line, graphene/MoS2 flake transfer, and microheater.
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 2 Materials and Methods
### Normalizing Sequence Length  
Another common way to deal with global time warping is to normalize the sequence lengths before estimating the alignment. This technique has been explored in many forms in previous works [43; 44; 45]. Let \(x_{0}\), \(x_{1}\),..., \(x_{N-1}\) and \(y_{0}\), \(y_{1}\),..., \(y_{M-1}\) be the two sequences that we would like to align, where \(N\leq M\). Before computing the pairwise cost matrix \(C\), we can downsample the sequence \(y_{0}\), \(y_{1}\),..., \(y_{M-1}\) to match the length of the other sequence, yielding a modified sequence \(\hat{y}_{0}\), \(\hat{y}_{1}\),..., \(\hat{y}_{N-1}\). One simple way to perform this downsampling is to simply calculate a weighted combination of the two nearest neighbors. In this approach, we first create \(N\) linearly spaced indices between 0 and \(M-1\), and then use these indices to compute values as a weighted combination. For example, a desired sample at index 4.4 would be calculated as \(0.6\cdot y_{4}+0.4\cdot y_{5}\). Once both sequences have been normalized in length, we can use standard DTW to estimate the alignment between \(x_{0}\), \(x_{1}\),..., \(x_{N-1}\) and \(\hat{y}_{0}\), \(\hat{y}_{1}\),..., \(\hat{y}_{N-1}\), and then account for the global downsampling factor to infer the alignment between \(x_{0}\), \(x_{1}\),..., \(x_{N-1}\) and \(y_{0}\), \(y_{1}\),..., \(y_{M-1}\).  
There are multiple ways one might normalize sequence length. In our experiments, we consider two different dimensions of behavior. One dimension is to either (a) downsample the longer sequence to match the length of the shorter sequence, or (b) upsample the shorter sequence to match the length of the longer sequence. The second dimension is to perform upsampling/downsampling by either (a) using linear interpolation between the two nearest neighbors (as described in the previous paragraph) or (b) simply usingthe nearest neighbor (e.g., a desired sample at index 4.4 would be represented as \(y_{4}\)). In Sections 3 and 4, we characterize the performance and runtime of the four possible combinations: downsampling with linear interpolation, downsampling with nearest neighbor, upsampling with linear interpolation, and upsampling with nearest neighbor.
## 5 Conclusions  
The proposed work, with the help of the available data set, managed to generate sufficient amounts of data with the help of GAN. Perfect samples were considered here for the generation of data. Hence, the data generated assured the quality that was used for training the network. For training the GAN, 440 images and augmented images were used. A total of 1,001,000 images were generated and used for training the SegNet architecture. The segmentation showed improved accuracy in the case of benign samples, as it had a perfect shape compared to malignant samples. Through the evaluation of the parameters, it is clear that the GAN with image augmentation showed better performance.  
The evaluation is clearly presented in the tabulation with comparison of parameters. The proposed model outperformed the SegNet model by only 9% and the GAN with SegNet model by 2%. Different encoder-decoder combinations were also considered here, and the comparison of parameters is tabulated in Table 3. SegNet with GAN performed well in comparison with VAE, Autoencoder, and U Net.  
The proposed model outperformed the VAE scheme by 7% and the U Net with GAN by 2%. The receiver operating curve was also plotted here to validate the result. SegNet and SegNet with GAN were plotted for comparing the AUC. The AUC of the proposed scheme outperformed the former.  
The time needed for the training process was very high with the i5 processor with 3.2 Ghz clock speed and 8 GB RAM, which was used in the initial stages of the study. The processing time was reduced when the processor configuration switched to a higher level. The processing time can be reduced to 120 min if it is assisted by a GPU (NVIDIA RTX 4090) integrated within the system.  
[PERSON] designed the research study, provided research ideas, read the literature, and collected medical records. [PERSON] was a major contributor in the research work and in writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be acquired from a publicly available dataset [[https://zenodo.org/record/3723295/files/](https://zenodo.org/record/3723295/files/)]([https://zenodo.org/record/3723295/files/](https://zenodo.org/record/3723295/files/)) subset0.zip (accessed on 28 February 2023). The data sets and materials used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest. None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.