esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this work, the comparison between two VCO architectures designed in a commercial 65 nm technology was made. Targeting high-frequency space applications, as the SpaceFibre protocol, a CML approach was adopted for the design of the RO-VCO. CML architecture was preferred, targeting high frequency, thanks to its lower voltage swing than a CMOS. The RO-VCO was an appetible VCO configuration in terms of technology scaling, high-integration density, and area occupancy, which was about 35% of the total silicon area required for the LC-VCO. Although the RO-VCO resulted as strongly dependent on the device parameters, in space applications, the best components should be selected. To overcome the effects of the device parameters deviation on the oscillation frequency, an LC-tank VCO architecture was designed. This architecture, despite its large area, mainly occupied by the inductor, presented promising performances in terms of the frequency range, covering the 5.35 GHz to 6.55 GHz range, in the typical case, with a control voltage swing of V\({}_{\text{DD}}\). SEE simulation results highlighted the output nodes as the most sensitive nodes for both VCOs, for the effects due to the varactors. Although the LC-tank VCO used one order greater varactor values than RO, and the ionizing particle hits generated higher amplitude variation on its output signals, the frequency variations of this VCO were lower than that showed by the RO architecture, thanks to the different relationship between frequency and capacitance. In the literature, VCOs based on Colpitts architecture for space applications are not available because of their large silicon area. The LC system, whose layout is shown in Figure 7, would be integrated into a 1 mm\({}^{2}\) chip containing a SERDES (Serializer-Deserializer) to test system-level performance. The whole chip would be electrically tested in standard condition, then it would be exposed to X-rays to achieve the 300 krad TID and to heavy ions for SEE characterization.  
**Author Contributions:** D.M.: investigation, methodology, conceptualization, software, writing-review and editing; G.C.: investigation, methodology, conceptualization, software, writing-review and editing; S.S.: investigation, methodology, conceptualization, software, writing-review and editing; S.S.: investigation, methodology, conceptualization, writing-review and editing; All authors contributed to the present paper with the same effort in finding available literature resources, conducting simulations, as well as writing the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by PHOS4 BRAIN and ISHTAR Projects by INFN and the University of Pisa, as well as by the Dipartimento di Eccellenza Crosslab project by MIUR.  
**Acknowledgments:** The authors would like to thank [PERSON], [PERSON], and all the other guys from IMEC (Leuven) for useful discussions and support on the preliminary VCO design. Discussions with [PERSON] and [PERSON] are also acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on the Influence of Moisture Content and Void Ratio on the Disintegration of Red Clay
## 4 The Influence of Moisture Content and Void Ratio on Disintegration
### Disintegration Rate and Combined Water Saturation  
As shown in Table 5 and Figure 6, when the moisture content is constant, the disintegration rate increases as combined water saturation increases. When e = 1.0, the disintegration rate of red clay decreases as combined water saturation increases, and the soil does not disintegrate when the combined water saturation is above 72.0%. When e = 1.1, all soils disintegrate, and the disintegration rate of red clay decreases as combined water saturation increases. When e = 1.15, all soils disintegrate, and the disintegration rate decreases first and then increases with the increase of combined water saturation. This abnormal phenomenon may be caused by the large pore volume in soils with high moisture content and high void ratio; the cohesion between adjacent soil particles is very weak, which easily disrupts stable state of soil. Through fitting with origin 8.0, the disintegration rate of red clay is exponentially related to the combined water saturation. The adjusted R\({}^{2}\) is 0.8989, indicating a good fit. The fitting equation is:  
\[v=7.96\times exp^{(-S_{r}^{\prime}/2.92)}+0.02 \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Initial** & & & **Integral Area** & **Half-Point of** & **Integral Area of** & **Integral Area** & **Combined** \\
**Moisture** & **Void** & **Half-Point of** & **of Combined** & **Saturation** & **Combined Water** & **of Free Water** & **Water** \\
**Content** & **Ratio** & **Natural State** & **Water in** & **State** & **in Saturation** & **in Saturation** & **Saturation** \\
**(\%)** & & & **Natural State** & **State** & **State** & **State** & **State** \\ \hline
24 & 1.00 & 1.36557 & 611.79 & 1.9698 & 998.29 & 112,368.50 & 61.28\% \\
24 & 1.10 & 1.34987 & 574.89 & 1.98423 & 992.36 & 158,682.55 & 57.93\% \\
24 & 1.15 & 1.33775 & 550.78 & 1.97718 & 968.15 & 157,313.24 & 56.89\% \\
26 & 1.00 & 1.41757 & 698.12 & 1.90246 & 968.71 & 70,606.10 & 72.07\% \\
26 & 1.10 & 1.41675 & 669.31 & 1.91512 & 959.48 & 141,921.18 & 69.76\% \\
26 & 1.15 & 1.40762 & 646.92 & 1.94936 & 950.03 & 154,143.16 & 68.09\% \\
28 & 1.00 & 1.47127 & 782.73 & 1.81833 & 966.80 & 88,476.18 & 80.96\% \\
28 & 1.10 & 1.45999 & 757.39 & 1.83971 & 939.67 & 119,127.19 & 80.60\% \\
28 & 1.15 & 1.45734 & 735.71 & 1.86865 & 945.35 & 160,775.53 & 77.82\% \\
30 & 1.00 & 1.55293 & 908.91 & 1.77622 & 984.96 & 71,251.05 & 92.28\% \\
30 & 1.10 & 1.55323 & 858.61 & 1.8195 & 974.33 & 108,895.42 & 88.12\% \\
30 & 1.15 & 1.53142 & 831.13 & 1.7999 & 959.75 & 180,072.03 & 86.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Right half point\({}^{\prime}\) and combined water saturation statistics table.
## 3 Research about Impact of AI on Education
### Current Applications of AI in Education
However, [PERSON] et al. [35] argued that the tools could lead students to use paper mill websites or platforms, encouraging dishonest behavior and jeopardizing academic integrity. For international students whose native language is not English studying in English-speaking countries, AI-powered translator and writing tools, voice recognition and dictation tools, and language learning tools can assist their learning. For example, as well as Google Translate, Grammarly, a writing support application, facilitates academic writing for international students, faculties, and researchers [36; 37]. Similar tools, such as ProWritingAid, Quillbot, and Ginger, also positively impact English for foreign language students in English academic writing [38; 39; 40]. In addition to academic writing skills, listening and oral skills are equally critical for international students. [PERSON] [41] highlighted that international students often need to make more of an effort than native-English-speaker peers to achieve higher academic achievement due to their lack of discussion skills, lower listening skills for long lectures, and unfamiliarity with the instructors' accents, humor, and examples. Many AI English voice chat applications have been developed recently with diverse goals. For instance, instructors can use ELIZA and ALICE chatbots to allow students to check grammar errors and incorrectly used words. Andy and Mondly interpret sentences' meaning and provide appropriate answers and can be recommended for individuals who want to practice more complex sentences [40]. Based on empirical research, AI voice chatbots have been shown to positively impact students' English communication ability by enriching language input and expanding interaction. Furthermore, chatbots increase students' English learning motivation, self-confidence, and interest in learning [42; 43]. Several general AI languages learning apps and platforms, such as Duolingo and Rosetta Stone, offer personalized interactive lessons, exercises and quizzes, and feedback to help learners improve their vocabulary, grammar, and oral skills [44]. However, despite their potential, AI English learning applications are limited in their ability to interact with humans, replicate cultural and contextual differences in language, understand or produce creative or original language, and recognize errors [45].
## 3 Results
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**G [W/m\({}^{2}\)]** & \(\sigma_{G}\) [W/m\({}^{2}\)]** & **T1 [\({}^{\circ}\)C]** & **T2 [\({}^{\circ}\)C]** & **T3 [\({}^{\circ}\)C]** & **T4 [\({}^{\circ}\)C]** & **T5 [\({}^{\circ}\)C]** & \(\sigma_{T}\) [\({}^{\circ}\)C]** \\ \hline
914.26 & 1.11 & 48.73 & 46.16 & 46.05 & 52.02 & 53.08 & 2.84 \\
914.08 & & 49.41 & 47.48 & 46.52 & 52.40 & 53.40 & 2.76 \\
914.27 & & 49.49 & 47.64 & 46.81 & 52.52 & 53.35 & 2.73 \\
914.68 & & 48.27 & 46.23 & 46.23 & 52.18 & 53.01 & 2.55 \\
915.55 & & 47.14 & 45.87 & 45.66 & 51.75 & 52.64 & 2.47 \\
914.88 & & 46.96 & 45.68 & 45.50 & 51.65 & 52.57 & 2.44 \\
914.26 & & 47.57 & 46.13 & 45.58 & 51.53 & 52.70 & 2.43 \\
913.44 & & 47.56 & 46.41 & 45.64 & 50.82 & 52.43 & 2.41 \\
912.82 & & 47.40 & 46.08 & 45.41 & 50.47 & 52.30 & 2.46 \\
912.20 & & 47.60 & 46.28 & 45.83 & 50.45 & 52.39 & 2.40 \\
912.07 & & 47.47 & 46.49 & 45.98 & 50.91 & 52.40 & 2.42 \\ \hline \end{tabular}
\end{table} 
Table 7: Temperature and solar irradiance measurements. Figure 12: Model results with new optimized parameters: (**a**) I–V curves and (**b**) MAPE. 0.8932 for current, indicating a much better model fit. These improvements reinforce the importance of tailored parameter calibration under varying field conditions to enhance model reliability. These results show the importance of the development and use of a data acquisition system that can perform a voltage sweep on the PV module to obtain the I-V curves in real conditions of operation. These valuable data can be used both to compare different operational conditions and to obtain real-time data to optimize PV panel models. The comparison in Table 10 highlights the performance and trade-offs of the proposed Arduino-based I-V tracer relative to three widely used commercial systems. While the commercial instruments deliver higher accuracy, faster acquisition times, and comprehensive automation, their high cost and proprietary architecture limit accessibility, particularly in educational, low-resource, or off-grid contexts. In contrast, the proposed system offers a low-cost, open-source alternative with sufficient resolution to capture the critical features of a PV panel's performance curve, including the maximum power point. Its portability, simplicity, and transparency make it an ideal platform for teaching photovoltaic concepts, prototyping control algorithms, or enabling low-cost diagnostics in decentralized energy applications. This underscores the educational and practical value of developing open-hardware tools for renewable energy research and training.
# Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces
## 2 Proposed Method
### Pre-Training Binary Segmentation Model for Crack Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block Name** & **Layer** & **Kernel Size** & **Stride** & **Channels** \\ \hline \multirow{3}{*}{Con-1} & Convolution & \(3\times 3\) & 1 & 3\(\rightarrow\)64 \\  & Convolution & \(3\times 3\) & 1 & 64\(\rightarrow\)64 \\  & Maxpool & \(2\times 2\) & 2 & - \\ \hline \multirow{3}{*}{Conv-2} & Convolution & \(3\times 3\) & 1 & 64\(\rightarrow\)128 \\  & Convolution & \(3\times 3\) & 1 & 128\(\rightarrow\)128 \\  & Maxpool & \(2\times 2\) & 2 & - \\ \hline \multirow{3}{*}{Conv-3} & Convolution & \(3\times 3\) & 1 & 128\(\rightarrow\)256 \\  & Convolution & \(3\times 3\) & 1 & 256\(\rightarrow\)256 \\  & Convolution & \(3\times 3\) & 1 & 256\(\rightarrow\)256 \\  & Maxpool & \(2\times 2\) & 2 & - \\ \hline \multirow{3}{*}{Conv-4} & Convolution & \(3\times 3\) & 1 & 256\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Maxpool & \(2\times 2\) & 2 & - \\ \hline \multirow{3}{*}{Conv-5} & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Maxpool & \(2\times 2\) & 2 & - \\ \hline \multirow{3}{*}{Center-Block} & Up-sampling & \(2\times 2\) & Scale factor: 2 & - \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)256 \\ \hline \multirow{3}{*}{Deconv-5} & Up-sampling & \(2\times 2\) & Scale factor: 2 & - \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)256 \\ \hline \multirow{3}{*}{Deconv-4} & Up-sampling & \(2\times 2\) & Scale factor: 2 & - \\  & Convolution & \(3\times 3\) & 1 & 7698\(\rightarrow\)512 \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)256 \\ \hline \multirow{3}{*}{Deconv-3} & Up-sampling & \(2\times 2\) & Scale factor: 2 & - \\  & Convolution & \(3\times 3\) & 1 & 512\(\rightarrow\)256 \\  & Convolution & \(3\times 3\) & 1 & 256\(\rightarrow\)64 \\ \hline \multirow{3}{*}{Deconv-2} & Up-sampling & \(2\times 2\) & Scale factor: 2 & - \\  & Convolution & \(3\times 3\) & 1 & 192\(\rightarrow\)128 \\  & Convolution & \(3\times 3\) & 1 & 128\(\rightarrow\)32 \\ \hline \multirow{3}{*}{Deconv-1} & Convolution & \(3\times 3\) & 1 & 96\(\rightarrow\)32 \\ \hline \multirow{3}{*}{Conv-F} & Convolution & \(3\times 3\) & 1 & 32\(\rightarrow\)1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Complete composition of the employed U-Net-based model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Ratio** & **Number of Samples** & **Percentage** \\ \hline Training & 6 & 12,044 & 60.22\% \\ Validation & 1 & 2123 & 10.615\% \\ Test & 3 & 5833 & 29.165\% \\ \hline Total & 10 & 20,000 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data distribution in the dataset.
# Freshness Monitoring of Packaged Vegetables
## 4 Intelligent Packaging (IP)
### Sensors
#### 4.1.1 Optical Sensors
The choice of which imaging technique to be used depends on the color of the product. Both parameters (LEDI4 and LEDICF) showed high accuracy of 96.7-96.9% in evaluating the quality of bagged leafy greens [58]. Hence, there is a possibility of using commercial imaging sensors to identify and monitor the freshness of such products. For pathogen detection, the use of hyperspectral imaging (HSI) together with the integration of chemometric and an artificial neural network was developed in [79]. This technology depends on the formation of metabolic by-products because of pathogens activity that induces biochemical changes and indicates contamination of food. Taking hyperspectral images of bagged leafy greens, spinach for instance, over the range 400-1000 nm will give a spectrum with varied reflectance depending on the intensity of _E. coli_ present. The outcome of these images is a 3D cube that gives information about total spatial distance, wavelength, and intensity of the image from where the quantitative and qualitative data can be extracted using principal component analysis (PCA) [79]. For prediction and computing of _E. coli_ number in packaged spinach, artificial neural network (ANN) can be applied on the hyperspectral data. In [79], the ANN used consisted of seven artificial neurons to obtain better performance in counts prediction. Regression analysis used showed a good fit between the predicted from ANN and true values obtained. For more convenient interpretation, a prediction map is constructed to give a visual indication based on the fingerprint left by the _E. coli_ count on the leaves of each pixel in the spatial plane of the hyperspectral image based on the chemometric techniques. This map shows visual color changes as the count increase, which allows for an easy, rapid, and accurate detection. The blue color is considered the control and as the concentration of _E. coli_ on the leaves increase, color shift towards green, yellow, orange, and then red [79]. This allows for easier detection of the contaminated samples with visual colors as shown in Figure 7. This simple method shows the high possibility of using such a technique for the detection of pathogens. Testing of this technology is done only on certain strain of _E. coli_ (_K12_); hence, more tests are needed to validate and ensure its compatibility to detect other strains of _E. coli_ or even other pathogens. Many other techniques were used for detection of contamination of vegetables with the use of conventional NIR spectroscopy [80; 81; 82; 83]. However, the information provided from these techniques lacks the spatial information as only one spectrum is provided for each sample. Thus, information about chemical composition will not be extracted compared to hyperspectral imaging where the information is represented in 3-D.  
Figure 6: Decay detection of lettuce with LEDI4 and LEDICF versus visual rating, the black line represents the theoretical perfect match. (reproduced with permission from Postharvest Biology and Technology journal [58]).
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 3 Construction of the Experimental Platform  
To verify the feasibility of the model identification method and the effectiveness of compensation, this section introduces a 16-bit, four-channel TIADC high-speed data acquisition system hardware platform. This hardware platform is mainly composed of an FPGA (XC7K410T) processing motherboard, a collection board containing two AD9653 chips, a clock board, a power divider and a computer. The clock board is configured by the host computer to generate the sampling clock and phase required by each sub-channel of the TIADC acquisition system, and a sinusoidal signal is generated as the analog input signal of the acquisition system. The analog signals are connected to the power divider and divided into four signals, which are sent to four ADC sub-channels. The data collected by the ADC acquisition board are sent to the DDR3 cache after FPGA preprocessing, and the cached data are transmitted to the computer through the USB3.0 data post-processing interface. Figure 7 shows the overall structure of the TIADC data acquisition system.
## 3 Channel Model  
Channel model is discussed in this section. Each source node modulates its data using Binary Phase Shift Keying (BPSK) modulation scheme. In water (as a communication medium), the transmitted signal suffers fading and noise. Fading is due to multiple paths traversed by the transmitted signal and noise is due to shipping, turbulence, wind, thermal activities, _etc_. In under water environment, typical considerations involve absence of the dominant component. Thus, we assume Rayleigh fading and subject to noise we assume the AWGN model. Equations (1)-(3) [19] describe relationship between the signals that are transmitted and received by source, relay and destination nodes. \(X_{s}\) is the original signal, \(Y_{sd}\) and \(Y_{sr}\) are signals received at destination and relay nodes via source node. \(Y_{rd}\) is signal received at destination node via relay node. \(n_{sd}\) and \(n_{sr}\) are channel noises existing over source to destination and source to relay links. \(n_{rd}\) represents channel noise existing over relay to destination link. \(g_{sd}\) and \(g_{sr}\) are channel gains existing over source to destination and source to relay links. \(g_{rd}\) represents channel gain existing over relay to destination link. Signals received at relay and destination nodes via source node are represented as:  
\[Y_{sr}=X_{s}g_{sr}+n_{sr} \tag{1}\]  
\[Y_{sd}=X_{s}g_{sd}+n_{sd} \tag{2}\]  
Signal received at destination node via relay node is represented as:  
\[Y_{rd}=Y_{sr}g_{rd}+n_{rd} \tag{3}\]  
Two independently faded copies (\(Y_{sd}\) and \(Y_{rd}\)) of source's data are combined at destination node by using Maximal Ratio Combining (MRC) as a diversity combining technique [19].
## 4 Performance Evaluation
### Metrics  
The FPBR and DDOR performance was evaluated using the following metrics:  
* Packet delivery ratio (PDR). This metric is measured as the ratio between the number of data packets received by the destination and the number of data packets transmitted by the source.
* Delay per hop. This is the average time a data packet requires for a single hop transmission.
* Average end-to-end delay (EED). The EED is the average time for a data packet to reach its destination.
* Network overhead. This metric is measured in terms of the number of routing control packets transmitted per second normalized by the vehicle density. This metric includes the number of beacons plus the discovery control packets.
* MAC Overhead. This metric is measured in terms of the number of control packets that the MAC layer transmits per second normalized by the vehicle density.
* Number of hops (NH). The NH is the average number of hops required for a data packet to reach its destination.
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 4 Simulation and Results
### Simulation Setup  
To measure the performance of our approach, a simulated UAV-WSN testbed for data collection was configured. The simulated environment is distributed in an area of 1000 m in length and 1000 m in width. We assume that (1) Each node has its own unique identification code and GPS coordinate; (2) The whole wireless network operates on a single fixed radio channel, (3) The UAV works at the same speed during the entire process of data collection; (4) The value sensing and event radius \(Rs\) and \(Ru\) are fixed. In total, 1000 sensors were randomly placed in the field and the whole network uses a data packet size of 100k bits. The emerging events occur randomly in the simulation area; the priority and the number of events is also randomly assigned. The flying altitude of UAV is 30 m, and its velocity is 5 m/s. The sensor node, the cluster head and the UAV communicate using the ZigBee protocol. Other parameters of the network are shown in Table 1. The simulation runs 100 times, and the evaluation indexes take the averages.  
The virtual testbed is initialized as follows: First, the sensor nodes are randomly deployed on the simulation site. Then, using the k-means clustering algorithm, cluster heads are created. When the emerging events occur, the Algorithms 1 and 2 are applied. The parameters of the network are shown in Table 1.