esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Increasing system bandwidth or using a continuous-time linear equalizer (CTLE) to compensate for channel losses are other potential solutions [11; 12; 13]. Receivers with equalization can overcome problems caused by channel loss, such as intersymbol interference. Effective high-performance receivers must meet numerous requirements, including those for power consumption, area, bit error rate (BER), and equalization. In receiver analog front-ends (AFEs), equalizers such as CTLE are commonly used (Figure 1). CTLE has lower power consumption than other equalizers in high-speed applications. Moreover, as the data rate increases, the proposed CTLE can achieve different  
Figure 1: Block Diagram of a Receiver with an AFE. equalization for different cable lengths by tuning the tail current with a fixed capacitor and resistor. The architecture presented in [1] is generally used in high-speed AFEs. Typically, transimpedance amplifiers (TIAs) are used to limit receiver noise. Common-gate stages are effective amplifiers for high frequencies but have poor noise characteristics [14; 15]. The TIAs based on the shunt-feedback architecture have a low-noise topology that is advantageous for high-data-rate applications. However, a trade-off exists between the performance of the circuit and the suitability of the DC operating point in conventional linear transconductance (Gm)-TIA systems. To achieve a suitable DC operating point, the size of the transistors must be increased, resulting in a large parasitic capacitance at the output nodes and reducing the bandwidth of the circuit. To overcome this problem, source follower (SF) stages were used to design the proposed AFE. This paper is organized as follows: Section 2 details the design considerations, Section 3 presents the simulation results, and Section 4 concludes the research.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Tensile Strength  
As shown in Figure 8, on days 3, 28, and 91 with the W/B at 0.5, the tensile strengths with different proportions of waste PE were 6.7 MPa to 5.1 MPa, 10.2 MPa to 8 MPa, and 14 MPa to 11.8 MPa. On the 3 days without adding GGBFS, the added volume of waste PE increased from 0% to 4%, and the tensile strength decreased by 2.1% to 15.7%. This result indicated that the tensile strength had a slightly decreasing trend as the waste PE increased. After 91 days, only 1% of the waste PE was added, and the tensile strength was 13.7 MPa. When 1% of the waste PE was added together with 10% and 20% GGBFS, the tensile strengths were 14.6 MPa and 15.7 MPa, respectively.  
From days 28 to 91, after adding 1% waste PE, the tensile strength increased by 38.4%. When 1% waste PE was added together with 10% and 20% GGBFS, the tensile strength was enhanced by 46.0% and 53.9%, respectively. This result indicated that the added GGBFS continued the pozzolanic reaction in the late period, which further enhanced the tensile strength of the cement mortar, and the compressive and flexural strength also showed the same trend.
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 6 Conclusions  
In this work, we proposed a simple but effective method, KGEL, to introduce knowledge graph structure information into entity linking. In addition to considering the relevance of entities at the semantic level, the relations between entities were also considered from the perspective of structure. We first obtained the triples and then trained them using the knowledge graph embedding method to obtain the entity embeddings and relation embeddings that contained the graph structure. Finally, the entity embeddings and relation embeddings obtained above were used in the calculation of the global score. Extensive experiments on multiple datasets prove the effectiveness of our method; that is, the knowledge graph structure is useful for entity linking tasks. In addition, KGEmbs can be used as a module to enhance the effects of other baseline models.  
In future work, we will solve the sparsity problem of the knowledge graph. Not every entity has a corresponding triple, nor is there a relation between every pair of entities. In addition, we will try to use better methods to utilize the knowledge graph structure, such as other knowledge graph embedding methods. As introduced in Section 2.3, some recent knowledge graph embedding methods such as HAKE [44], PairRE [45], DualE [46], and EIGAT [47] can better encode entities and relations in knowledge graphs, and theoretically they should further improve the performance of entity linking.  
Conceptualization, Q.L. ([PERSON]) and F.L.; methodology, Q.L. ([PERSON]) and S.L.; validation, Q.L. ([PERSON]), X.L. and K.L.; formal analysis, Q.L. ([PERSON]) and S.L.; investigation, Q.L. ([PERSON]); resources, Q.L. ([PERSON]); data curation, P.D.; writing--original draft preparation, Q.L. ([PERSON]); writing--review and editing, F.L. and S.L.; visualization, X.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Train Time/Epoch** & **Inference Time** \\ \hline Ment-Norm & 23 s & 9 s \\ KGEL & 25 s & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: The execution times of the models.  
Figure 6: The knowledge graph structure contained in the example.  
**Funding:** This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number Y835120378).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** [[https://github.com/lephong/mulrel-nel](https://github.com/lephong/mulrel-nel)]([https://github.com/lephong/mulrel-nel](https://github.com/lephong/mulrel-nel)) (accessed on 1 March 2022).  
**Conflicts of Interest:** The authors declare that they do not have any conflict of interest. This research does not involve any human or animal participation. All authors have checked and agreed with the submission.
## 3 Results
### Effects of Illumination on the Estimation of LAI
#### 3.1.3 Estimation of LAI by Regression Analysis of CIs  
We selected five CIs with the highest correlations with LAI and intercorrelations below 0.8 to construct a multiple linear regression model to further examine the impact of illumination on LAI estimation. The performance of the models is shown in Table 3. In the tillering stage, the R\({}^{2}\) value of the model based on the original CIs was 0.47, while the model based on Retinex-corrected CIs achieved an R\({}^{2}\) of 0.57. Retinex correction effectively improved the accuracy of the LAI estimation model. Similar observations were made at other stages. These results indicated that variation in illumination significantly affected the  
Figure 4: Absolute value of Pearson correlation between CIs (original CIs and Retinex-corrected CIs) and LAI. OC represents the original CIs; RC represents the Retinex-corrected CIs. T, E, B, H, and G represent tillering stage, elongation stage, booting stage, heading stage, and grouting stage, respectively.  
performance of the rice LAI estimation model. Moreover, the performance of the estimation model was subpar when the images captured by unmanned aerial vehicles were not subject to illumination correction.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Fruit Counting  
The difficulty of identifying and counting the fruits on trees is crucial in crop yield estimation in agriculture. Manual counting is time-consuming and labor-intensive. The automated crop counting approach can help project yields and organize harvesting plans to increase productivity and profit margin. In [54], a simulated model of a deep convolutional neural network for yield estimation is developed and tested to know the exact number of fruits. It used a modified version of Inception-ResNet architecture, and experimental results showed an average test accuracy of 91%. A comparison of different research studies in terms of the study area, experiment conducted, the method used, and results achieved are shown in Table 1.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening  
Rubber toughening of epoxy resins is one of the earliest toughening methods, and the research on this technique is relatively mature [21]. Rubber toughening is characterized by its simple preparation process and low cost, making rubber the most extensively studied and widely used material for toughening epoxy resins. Researchers have continually optimized the toughening effect by selecting, synthesizing, and characterizing various types of rubber elastomers. As a result, rubber-toughened epoxy resins have found widespread applications in fields such as aerospace, automotive manufacturing, and composite materials.
## 3 Data Acquisition
### Close-Range Photogrammetry  
The geometry of the site is rather peculiar, with a huge reservoir in front of the hillside (Figure 1), with many crossing hydroelectric power lines from one side to the other, which hamper the exploitation of close-range imagery with RPAS-UAVs (remotely piloted aircraft system-unmanned aerial vehicles). The approximate dimension of the interest hillside displayed in Figure 1 (yellow box) is 700 m wide and 200 m high. In addition, pictures were taken not from ideally planned positions but realistically ideal positions on-site, after defining some minimum constraints such as ideal ground sampling distance (GSD) e.g., following roads parallel to the hillside, and from surrounding hills.  
For the CRP campaign, two 21 Mpix full-frame cameras were used mounted on stable tripods: Canon EOS D5 Mark II with a normal Canon EF 50 mm f/1.4 USM lens, and a Canon 1 Ds Mark III with a telephoto Canon EF 200 mm f/2,8 L II USMlens (Figure 4). The first camera was used to get the whole area, summing 280 normal and convergent imagery; the second camera was used to measure only three areas with the fixed on-the-rock 15 check points, summing 404 imagery. The data acquisition was planned to achieve a GSD of 3.5 cm. The mean camera-object distance was 512 m. A total of 6 h were devoted to taking images on site.  
The three-step workflow followed to achieve accuracy better than 3 cm is presented next. First step, each camera was calibrated on the job selecting a subsampled ideal network with convergent and rotated imagery, summing up to 57 images for the 50 mm lens and 47 images for the 200 mm lens. For the normal lens, 8 interior orientation parameters (f, cx, cy, K1, K2, K3, P1, P2) were computed to achieve 0.34 pixel image reprojection error; for the telephoto lens, 4 additional parameters (f, cx, cy, K1) were computed to achieve a 0.50 pixel image reprojection error. Second step, a free bundle block adjustment with 230 imagery (142 normal + 88 tele) was undertaken. This step included the manual measurement of the white spherical targets (Figures 2b and 3) on the imagery, including 4 (visible) control points on pillars of the reference frame and 15 check points (Figure 5). A mean reprojection error of 0.50 pixels was achieved in the photogrammetric adjustment. Third step, absolute orientation by means of a 3D similarity transformation yielding an RMSE (root mean square error) of 1.55 cm.
###### Abstract  
An all-diode laser-based master oscillator power amplifier (MOPA) configuration for the generation of ns-pulses with high peak power, stable wavelength and small spectral line width is presented. The MOPA emits alternating at two wavelengths in the spectral range between 964 nm and 968 nm, suitable for the detection of water vapor by absorption spectroscopy. The monolithic master oscillator (MO) consists of two slightly detuned distributed feedback laser branches, whose emission is combined in a Y-coupler. The two emission wavelengths can be adjusted by varying the current or temperature to an absorption line and to a non-absorbing region. The power amplifier (PA) consists of a ridge-waveguide (RW) section and a tapered section, monolithically integrated within one chip. The RW section of the PA acts as an optical gate and converts the continuous wave input beam emitted by the MO into a sequence of short optical pulses, which are subsequently amplified by the tapered section to boost the output power. For a pulse width of 8 ns, a peak power of 16.3 W and a side mode suppression ratio of more than 37 dB are achieved at a repetition rate of 25 kHz. The measured spectral width of 10 pm, i.e., 0.1 cm\({}^{-1}\), is limited by the resolution of the optical spectrum analyzer. The generated pulses emitting alternating at two wavelengths can be utilized in a differential absorption light detection and ranging system.  
Y-branch DFB laser; MOPA; ns pulse; DIAL; water vapor +
Footnote †: journal: Applied Sciences
# Waste Cork in Metakaolin-Geopolymer Matrix: Physico-Mechanical Characterization
###### Abstract  
Cork powdery waste (CW) from agglomerated cork caps manufacturing is commonly transported to waste-to-energy plants, although it could be locally exploited for lightweight building materials. The transformation of CW into a geopolymer formulation to obtain a novel composite formulation suitable for insulating panels is presented in this contribution. The geopolymer mix was based on metakaolin added to NaOH and Na silicate solutions, to which 2.4, 4.8 and 9.1 wt% (calculated upon dry metakaolin) of CW in the form of as-received powdery waste were added. No pre-treatments were performed on CW and no thermal curing was conducted for the alkali-activated product that was consolidated at room temperature to improve product sustainability. The insulating panel presented an apparent density of about 1.521 to 0.990 \(\pm\) 0.001 g/cm\({}^{3}\), combined with a total porosity in the range of 35.61 to 56.22 \(\pm\) 0.003 % for 2.4 to 9.1 wt% of CW, respectively, and this was dependent upon ageing time. The values of its mechanical properties (compressive strength ranged from 2.5 to 1.5 MPa at 28 and 90 days of curing time, complying with UNI EN 998-2) and thermal insulating properties (thermal conductivity around 0.1146 W/mK) indicated that the highest percentage of CW in the formulations, i.e., 9.1 wt%, was suitable to obtain self-standing insulating panels.  
cork waste; metakaolin; alkali activation; room temperature consolidation; thermal insulation; zero waste process
## 1 Introduction
### Contribution and Paper Organization  
This paper proposes the use of differential evolution (DE) to optimize FOCNNs utilizing Caputo fractional-order derivatives, with the aim of enhancing CNN performance for groove classification. Specifically, Caputo derivatives are employed to update weights and biases, while DE is applied to identify the optimal initial biases and weight settings. The key contributions of this work are as follows:  
1. Development of a DE-based optimization approach for FOCNNs: We introduce a novel approach to identify the optimal selection from a broad range of initial biases and weights, effectively enhancing the model's convergence and accuracy.
2. Comparative experiments between DE-FOCNN and the original FOCNN: We perform extensive comparisons across various fractional-order values, highlighting the efficacy of DE in optimizing FOCNNs.
3. Implementation of experiments on the Janata dataset: We demonstrate that DE-FOCNN outperforms other popular optimization algorithms in terms of accuracy and robustness in groove classification  
The remainder of this paper is organized as follows. Section 2 provides an overview of related work in groove classification and FOCNN optimization. Section 3 presents the proposed DE-based optimization approach for FOCNNs. Section 4 details the experimental setup, including data collection and preprocessing. Section 5 discusses the results, and Section 6 concludes the paper with potential future research directions.  
This research contributes to the optimization of fractional-order convolutional neural networks by introducing a novel DE-based approach for the automated analysis and classification of musical groove. The proposed method offers potential applications in music recommendation, education, and therapeutic settings.
## 6 Numerical Simulation of the Temperature Field of a Block-Stone Subgrade
### Model Description
#### 6.1.2 Mathematical Model  
To simplify the calculations, the temperature field model of the block-stone subgrade assumes that the gas inside the blocks is incompressible, in accordance with the Boussinesq assumption. In addition to the temperature field model, a natural convection heat transfer model within the blocks is also considered. The controlling equations construct the equations in two parts. Firstly, for the soil layer zone, the controlling equations are the same as in the previous section of this paper for ordinary subgrade. Secondly, for the block-stone layer zone, which is a porous medium containing a large number of pores, the controlling equations are as follows (28).  
1. Continuity equation In natural convection in porous media, air is considered as an incompressible fluid, and the equation simplifies to: \[\frac{\partial\mathrm{v_{x}}}{\partial\mathrm{x}}+\frac{\partial\mathrm{v_{y }}}{\partial\mathrm{y}}=0\] (13) where \(\mathrm{v_{x}}\) and \(\mathrm{v_{y}}\) are the seepage velocity of air in the x and y directions in the block-stone subgrade, respectively (m/s).
2. Momentum equation The Brinkman equation is utilized to simulate within the block-stone layer. \[\frac{\partial\mathrm{p}}{\partial\mathrm{x}}=-\frac{\mu}{\mathrm{k}}\mathrm{ v_{x}}-\rho\mathrm{B}\Big{(}\mathrm{v_{x}^{2}}+\mathrm{v_{y}^{2}}\Big{)}^{1/2} \mathrm{v_{x}}\] (14) \[\frac{\partial\mathrm{p}}{\partial\mathrm{x}}=-\frac{\mu}{\mathrm{k}}\mathrm{ v_{y}}-\rho\mathrm{B}\Big{(}\mathrm{v_{x}^{2}}+\mathrm{v_{y}^{2}}\Big{)}^{1/2} \mathrm{v_{y}}-\rho_{\mathrm{a}}\mathrm{g}\] (15)  
Figure 11: Geometric model and calculation unit division for block-stone subgrade.  
where \(\rho\) is the air density; \(\rho_{\rm a}\) is the function of temperature with respect to air density  
\[\rho_{\rm a} =0.641[1-\beta(\rm T-T_{0})] \tag{16}\]  
where B is the inertia drag coefficient; k is the medium permeability (m\({}^{2}\)); \(\mu\) is the aerodynamic coefficient of adhesion, 1.75 \(\times\) 10\({}^{-5}\) kg/(m\(\cdot\)s); \(\beta\) is the coefficient of thermal expansion of air, 3.50 \(\times\) 10\({}^{-3}\); and T is the air temperature (\({}^{\circ}\)C).  
Since the porous medium model includes both lumped solid and fluid components, the energy equation is as follows:  
\[C\frac{\partial\rm T}{\partial t}=\lambda\Bigg{(}\frac{\partial^{2}\rm T}{ \partial x^{2}}+\frac{\partial^{2}\rm T}{\partial y^{2}}\Bigg{)}+\rm C_{L} \Bigg{(}V_{\rm x}\frac{\partial^{2}\rm T}{\partial x^{2}}+V_{\rm y}\frac{ \partial^{2}\rm T}{\partial y^{2}}\Bigg{)} \tag{17}\]  
where: C is the equivalent specific heat capacity of the medium layer, 1004 J/(m\({}^{3}\cdot\)K); \(\lambda\) is the air thermal conductivity, 0.02 W/(m\(\cdot\)K); C\({}_{\rm L}\) is the specific heat capacity of air.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Whisper
#### 3.4.4 Measurement Techniques: Waveforms  
The wideband sounder of WHISPER project used maximal length pseudo noise sequences (M-sequences) that were bi-phase shift keyed (BPSK) modulated. M sequences have good properties of auto-correlation and cross-correlation ([55]). Due to the requirements of bandwidth and delay time ranges they use M-sequences of length 511 and 1023 chips. They chose BPSK modulation since it is very simple to implement at baseband, it has constant amplitude and hence maximises the transmitted signal power and provides maximum distance between symbols, which is a very interesting characteristic in hostile HF channels. Table 2 summarizes the relevant characteristics of a number of waveform signals used as sounding signals with different bandwidth (chip-rate is \(\sim\) 80% of the sounding bandwidth), including a narrowband variant compatible with those used in DAMSON project.
# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
## 2 Methods of Quantitative and Value Strategic Planning of Budget
### Forecasting Methods  
Forecasting methods are useful for budgeting, as they enable forecasting future values. Traditional econometric methods, such as exponential smoothing, ARIMA, and regression models, have long dominated time series analysis, providing tools to interpret trends and seasonality. Additionaly, advances in technology and machine learning have opened new opportunities, introducing more sophisticated models capable of processing complex patterns of data. In recent years, models based on Transformer architectures have received particular attention, such as NBEATS [16], N-HiTS [17], TFT [18] and PatchTST [19], which are characterized by their ability to efficiently process and forecast time series data. These models are distinguished by their high performance on benchmark sets.  
However, there is still a gap in business where forecasting tools are not used due to a lack of employee skills [20]. Therefore, in the first stage of our research, closely related to business needs, we start with the following classical methods: exponential smoothing, linear regression, ARIMA, and XGBoost. Below is a brief description of the exponential smoothing, linear regression, XGBoost and ARIMA methods with examples of use.
## 5 Sensitivity Analysis  
Sensitivity analysis was conducted to compare the new rankings with the initial rankings to assess how sensitive the final results are to changes in the weights. If significant shifts occur in the rankings, the model is sensitive to those changes, suggesting that the results are influenced by the specific weight settings. In the sensitivity analysis, we focused on three basic scenarios as follows:  
High Weight on Severity: We'll further increase the weight on the severity and equally decrease the occurrence and detection weights.  
Balanced Weights: We'll assume all three factors (severity, occurrence, detection) have equal weights.  
Low Weight on Severity: We will lower the weight of severity and increase the weights for occurrence and detection.  
The corresponding weights are given in Table 19.  
As demonstrated in Figure 14, EdX and Khan Academy consistently demonstrate low sensitivity to changes in severity, occurrence, and detection, making them the most  
Figure 12: Grey scale calculation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**MLP** & **Original\_RPN** & \begin{tabular}{c} **Original\_** \\ **Ranking** \\ \end{tabular} & \begin{tabular}{c} **RPN\_Severity\_** \\ **Increased** \\ \end{tabular} & \begin{tabular}{c} **RPN\_Occurrence\_** \\ **Increased** \\ \end{tabular} & \
\begin{tabular}{c} **RPN\_Detection\_** \\ **Increased** \\ \end{tabular} &
\begin{tabular}{c} **RPN\_Severity\_** \\ **Low** \\ \end{tabular} \\ \hline EdX & 19 & 1 & 19.5 & 18.8 & 19.2 & 18.5 \\ Khan Academy & 19.5 & 2 & 20 & 19.3 & 19.8 & 19.1 \\ Duolingo & 22.91 & 3 & 23.5 & 22.5 & 23 & 22.3 \\ Sololearn & 25.1 & 4 & 25.8 & 24.7 & 25.5 & 24.5 \\ Pluralsight & 25.26 & 5 & 25.9 & 24.85 & 25.7 & 24.7 \\ \hline \hline \end{tabular}
\end{table}
Table 19: RPN risk factor weights for sensitivity analysis.  
Figure 13: Mobile Learning Platform Final Ranking.  
reliable platforms based on the analysis. Pluralsight and SoloLearn are more sensitive to increases in severity, indicating they are more prone to significant issues when critical failures occur. Changes in the detection weight seem to have the least impact across all platforms, suggesting that detection is not as influential in determining the overall risk as severity or occurrence. This sensitivity analysis helps to confirm the robustness of the rankings and shows that certain platforms (like EdX and Khan Academy) maintain stability, while others (like SoloLearn and Pluralsight) are more sensitive to specific risk factors.