esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Estimation of Gross Primary Productivity (GPP) and Evaporranspiration (ET)  
The estimation methods of GPP and ET have been studied deeply. For instance, the VPM model performed better than other light use efficiency models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM)) for GPP in on a large scale [44; 45]. The revised PM model could obtain the accurate daily ET data with MODIS time-series products [46; 47; 48; 49]. Given the applicability and accuracy of the model via satellite data in large-scale dryland, the modified Vegetation Photosynthesis Model (VPM) and revised Penman-Monteith (PM) model were applied to estimate GPP and ET in this study, respectively. The details can be referred to in the flow chart in Figure 2.  
\[GPP=\varepsilon_{g}\times APAR\times Rsd\times 0.48 \tag{1}\]  
\[\varepsilon_{g}=\varepsilon_{max}\times min(F_{t},F_{w}) \tag{2}\]  
\[F_{t}=\frac{(T-T_{min})(T-T_{max})}{\left[(T-T_{min})(T-T_{max})\right]-\left( T-T_{opt}\right)^{2}} \tag{3}\]  
\[F_{w}=WI\times VPDs \tag{4}\]  
where \(R_{sd}\) is the download shortwave radiation ([46], Figure 2). \(\varepsilon_{max}\) is the potential LUE (g cm\({}^{-2}\) MJ\({}^{-1}\)) and \(\varepsilon_{max}\) for barren/sparse vegetation is 0.389 g cm\({}^{-2}\) MJ\({}^{-1}\)[50; 51]. \(F_{t}\) represents the effects of temperature on \(\varepsilon_{max}\). \(T_{min}\), \(T_{max}\) and \(T_{opt}\) are the minimum, maximum and optimum temperature for photosynthetic activities, respectively. \(F_{w}\) is reflected by \(VPD_{5}\) and land water index (\(WI\)) that was calculated by satellite-derived land surface water index (LSWI) with consideration of the phenology effects on \(\varepsilon_{max}\), [11; 45].  
To estimate total ET, the canopy ET (\(\lambda E_{vgg}\); Equation (6)) and soil evaporation (\(\lambda E_{soil}\); Equation (7)) were calculated [46].  
\[\lambda E=\lambda E_{vgg}+\lambda E_{soil} \tag{5}\]  
Figure 1: The study area map.  
\[\lambda E_{soil}=\frac{\Delta R_{n,soil}+\rho c_{p}(e_{s}-e_{a})/r_{a}}{\Delta+ \gamma\times(r_{tot}/r_{a})}\times\left(\frac{RH}{100}\right)^{(e_{s}-e_{a})/100} \tag{7}\]  
where \(\Delta\) (Pa K\({}^{-1}\)) is the slope of the curve relating saturated water vapor pressure (\(e_{s}\), Pa) to temperature (K); \(\gamma\) (Pa K\({}^{-1}\)) is psychrometric constant; \(R_{n}\) (W m\({}^{-2}\)) is available energy; \(\rho\) (kg m\({}^{-3}\)) is air density, \(c_{p}\) (J kg\({}^{-1}\) K\({}^{-1}\)) is the specific heat capacity of air; \(e_{a}\) (Pa) is the actual water vapor pressure; \(r_{a}\) (s m\({}^{-1}\)) is the aerodynamic resistance; and the surface resistance (\(r_{s}\), s m\({}^{-1}\)) is the effective resistance to transpiration from the plant canopy.
# Real-Time DTM Generation with Sequential Estimation and OptD Method
## 2 Materials and Methods  
The new concept of DTM generation from real-time data acquired is based on the OptD method and sequential estimation principles. Among existing techniques for DTM generation, methods such as inverse distance weighting, kriging, nearest neighbor, and spline methods are typically used [14; 15]. All these methods give one 3D model after processing. For DTMs generated using the existing methods, the surface is described by one value root mean square error (RMSE). Other errors related to DTM generation are also provided to demonstrate the impact of processing stages on model error or model accuracy depending on the accuracy of the data and measurement technology used [16; 17]. For DTMs generated using sequential estimation, the value of \(m_{0}\) for each sequence is calculated. The use of sequential estimation can give a completely different perspective on the accuracy of the generated DTM and is an innovation and novelty in the approach to DTM quality assessment. This means that a significant property of the proposed methodology, namely, _the variability of accuracy characteristics_ of the DTM, can be demonstrated, i.e., determining different accuracies in different fragments of the model.  
Moreover, DTM can be stored in the form of _a characteristics file_, which gives the possibility of calculating the height of a point at any area of object.
# Solving Multi-Document Summarization as an Orienteering Problem
## 2 Related Work
### Statistical Approaches  
This approach has been followed since the first development of the field [12; 13]. An example of a statistical feature that has been used frequently in the summarization studies is _word frequency_. [PERSON] [14] proposed a frequency-based method for sentence ranking, whereby the summary is generated by extracting the top ranked ones while checking and eliminating complete duplicates. [PERSON] et al. [15] proposed a summarization system which exploits the highest ranked terms, called _topic signature terms_. Each term is given a weight based on its relative frequency in a relevant cluster of documents. In more detail, the system scores sentences based on these terms. Then, to deal with the redundancies, it chooses a sentence to be part of the summary if the number of overlapped topic signature terms between this sentence and the already selected ones is below a predefined threshold. Another statistical feature used in summarization is _term frequency times inverse document frequency_ (TF-IDF), which is an information-retrieval word importance measure that is also used to calculate sentence scores. [PERSON] and [PERSON] [16] used this feature along with other features to give a score to each sentence. Nevertheless, to improve the performance, the position feature is only used with document clusters in which the key sentences occur at the beginning of each document. Finally, the redundancy problem is handled by computing the similarity between sentences.  
[PERSON] et al. [17] examined the effects of four different term weighting approaches, including term frequency and nonnegative matrix factorization, as well as different sentence segmentation and tokenization methods on the performance of multi-lingual single document and multi document summarizers. In the sentence extraction phase, they used OCCAMS [18] algorithm. [PERSON] and [PERSON] [19] proposed an MDS approach in which enhanced text representations are produced by a neural network and used to extract summary sentences based on the cosine similarity measure. Each sentence is compared to the most frequent terms and to the title of the document to which it belongs. A _text mining_ algorithm was also used. [PERSON] et al. [20] scored sentences based on a sequential pattern (n-grams and gappy n-grams), which is extracted using a text mining algorithm [20].  
[PERSON] et al. [21] examined the _hierarchical Latent Dirichlet Allocation_ (hLDA) model for text summarization. A hierarchical topic tree is built for each set of documents. Each node represents a latent topic and each sentence is assigned to a path which starts at the root and ends at a leaf. Summary sentences are extracted in a way that maximizes the coverage of the important sub topics and ensures that the redundancy rate among these sentences is less than 0.5.
## 3 Results
### Analysis of Response Surface Test Results
#### 3.4.2 Interaction Effect Analysis of Regression Model  
According to the variance analysis results of the optimized regression model, the interaction item (\(A\)\(\times\)\(B\)) between scraper shaft speed and scraper space had a significant  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of** & & & & & \\
**Variance** & **Mean Squares** & **Degrees of Freedom** & **Sum of Square** & **Ratio of F** & **\(p\)-Value** & **Degree of Influence** \\ \hline Model & 596.19 & 14 & 42.59 & 30.01 & \textless{}0.0001 & **\\ A & 466.84 & 1 & 466.84 & 329.03 & \textless{}0.0001 & **\\ B & 7.54 & 1 & 7.54 & 5.31 & 0.0359 & * \\ C & 4.92 & 1 & 4.92 & 3.47 & 0.0822 & \\ D & 28.58 & 1 & 28.58 & 20.14 & 0.0004 & **\\ AB & 7.77 & 1 & 7.77 & 5.48 & 0.0335 & * \\ AC & 1.41 & 1 & 1.41 & 0.99 & 0.3346 & \\ AD & 1.33 & 1 & 1.33 & 0.94 & 0.3486 & \\ BC & 0.50 & 1 & 0.50 & 0.35 & 0.5614 & \\ BD & 0.22 & 1 & 0.22 & 0.16 & 0.6972 & \\ CD & 6.04 & 1 & 6.04 & 4.26 & 0.0569 & \\ A2 & 6.607 \(\times\) 10\({}^{-3}\) & 1 & 6.607 \(\times\) 10\({}^{-3}\) & 4.657 \(\times\) 10\({}^{-3}\) & 0.9465 & \\ B2 & 31.08 & 1 & 31.08 & 21.90 & 0.0003 & ** \\ C2 & 0.92 & 1 & 0.92 & 0.65 & 0.4330 & \\ D2 & 46.50 & 1 & 46.50 & 32.77 & \textless{}0.0001 & ** \\ Residual & 21.28 & 15 & 1.42 & & & \\ Lack of Fit & 8.70 & 10 & 0.87 & 0.35 & 0.9281 & \\ Pure Error & 12.58 & 5 & 2.52 & & & \\ Sum & 617.47 & 29 & & 21.90 & 0.0003 & \\  & & & & & 21.90 & \\  & & & & & \\ \end{tabular} \({}^{*}\) Shows the term is significant (\(p\)\(<\) 0.05); ** shows the term is very significant (\(p\)\(<\) 0.01).  
\end{table}
Table 7: Variance analysis of the quadratic regression model for the Central Composite Design response surface test.  
influence on the comprehensive qualified rate of fertilization (\(p<0.05\)). When the scraper shaft speed was 40 r/min, Design-Expert software was used to draw the response surface and contour line of interaction effects between scraper shaft speed and scraper space (\(A\times B\)). The interaction effect between the two factors can be vividly reflected, as shown in Figure 7. It can be seen from the response surface and contour line of the interaction item \(A\times B\) that the comprehensive qualified rate of fertilization showed a downward trend as the two factors increased. The response surface curve of the scraper shaft speed (\(A\)) was steeper than that of the scraper space (\(B\)), indicating that scraper shaft speed had a significant impact on the comprehensive qualified rate of fertilization.
## 4 Discussion
However, the preliminary results are primarily intended as a proof of concept for the sensor-fusion apparatus. The focus of this work and our main objective is to demonstrate the feasibility of comprehensive handwriting data collection using multiple sensors, different handwriting forms (signatures, short phrases, words, and individual letters), and different input modalities (finger, stylus), rather than generalizing the findings across a larger population. Additionally, the constraints of the S Pen Remote SDK prevented detailed capture of pressure and tilt data, and manufacturer restrictions required using a smartphone as an intermediary between the tablet and smartwatch, affecting performance. The piezoelectric sensors used were chosen for their availability and cost rather than their sensitivity, and the use of multiple devices increased battery consumption, limiting continuous use. Future research will address these limitations by expanding the sample size, exploring advanced sensor integration, applying sophisticated machine-learning techniques, and considering longitudinal studies to assess the stability of handwriting biometrics over time. We have deliberately chosen commercially available devices to make the apparatus more accessible and feasible for a wide range of applications. This choice balances cost and complexity, as off-the-shelf components are often more affordable and easier to integrate than custom hardware, simplifying the experimental setup and enhancing the reproducibility of the findings. Unlike comparable studies that often focus on a single sensor type and specialized hardware (e.g., custom-designed pens), our solution involves an implementation of the sensor-fusion concept, which enables an in-depth analysis of handwriting dynamics. We remain open to the possibility of testing devices and sensors from other manufacturers. The proposed apparatus supports such customization, as one of its features is high flexibility with regard to the introduction of new sensors.
## 5 Economic Impact of the Integration of SCP Practices
### Job Creation  
The SCP implementation by MSMEs seems to have a positive impact on local job creation. The MSMEs that integrated SCP practices into their farms saw an increase in the number of workers they could employ. During the semi-structured interviews with the five showcased MSMEs, it was found out that the MSMEs now could offer higher wages to their workers. The analysis showed that MSMEs employed an average of four paid workers before 2018, when the GOALAN project was initiated. Halfway through the project, i.e., by the end of 2019, it was found out that the project beneficiaries could employ an average of six persons. According to the GOALAN Project Midterm Review Report [38], the wage rate was almost doubled, from a range of Ksh 150-200 per day before 2018 to Ksh 300 in 2019. In addition, a number of the MSME beneficiaries who used to work as casual labourers  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **General Agricultural** & **Concrete SCP Practices** & **Percentage of Project** & **Percentage of Non-beneficiaries** \\  & **Practices** & **Concrete SCP Practices** & **Practices** & **Adopting SCP Practices** \\ \hline \multirow{2}{*}{1} & Irrigation and water & Implementing water & \multirow{2}{*}{51\% (N = 40)} & \multirow{2}{*}{32\% (N = 32)} \\  & management & harvesting storage & & \\ \hline \multirow{2}{*}{2} & Soil conservation & Implementing crop rotation, & \multirow{2}{*}{92\% (N = 73)} & \multirow{2}{*}{14\% (N = 11)} \\  & Soil conservation & cover cropping and & & \\  & & mulching & & \\ \hline \multirow{2}{*}{3} & Pest management and use & Applying integrated pest & \multirow{2}{*}{62\% (N = 49)} & \multirow{2}{*}{9\% (N = 7)} \\  & of agricultural input & management (IPM) & & \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Value addition} & Processing of “ugly” & \multirow{2}{*}{49\% (N = 39)} & \multirow{2}{*}{1.2\% (N = 1)} \\  & & vegetables into paste or chips & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{Post-harvesting techniques} & Using clean and adequate & \multirow{4}{*}{85\% (N = 76)} & \multirow{2}{*}{8\% (N = 6)} \\  & & containers for products, & & \\ \cline{1-1}  & & proper handling during & & \\ \cline{1-1}  & & transport, packaging of & & \\ \cline{1-1}  & & products & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentage of project beneficiaries adopting SCP practices in relation to non-beneficiaries.  
outside their farms reported that they were now employers. This is an indication that the adoption of SCP has a positive impact in creating jobs and increasing labour wages.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
## 2 Conventional Equalization Algorithm
### DDLMS Algorithm  
The DDLMS algorithm is a further improvement on the LMS algorithm. The algorithm does not require a training sequence, which maintains the advantages of the LMS algorithm without introducing additional computational complexity. The cost function of the DDLMS algorithm can be expressed as:  
\[J(k)=\frac{1}{2}E\Big{[}\big{(}\hat{y}(k)-y(k)\big{)}^{2}\Big{]}, \tag{10}\]  
where \(\hat{y}(k)\) is the decision output. The DDLMS error signal can be expressed as:  
\[e(k)=\hat{y}(k)-y(k), \tag{11}\]  
The formula for updating the filter coefficients can be expressed as:  
\[\mathbf{w}(k+1)=\mathbf{w}(k)+\mu e^{*}(k)\mathbf{x}(k), \tag{12}\]  
The DDLMS algorithm needs to judge the output signal of the equalizer one by one and subtract the decision output \(\hat{y}(k)\) from the equalizer output \(y(k)\) to obtain the error function [11]. The residual error after convergence is small, but the algorithm cannot converge when the decision error of the signal is large. It is usually used in conjunction with other equalization algorithms. In addition, the output of the adjudicator makes a decision based on the known modulation method, and the symbol decision is more complicated in higher-order modulations. In the case of mode switching in the VM system, decision switching is required according to the current modulations. The DDLMS algorithm is not suitable for VM systems. In the VM system, the DDLMS algorithm needs to make a decision switch based on the current modulation, and the modulation mode cannot be determined at this time. Therefore, the DDLMS algorithm is not suitable for VM systems
## 6 Discussion of Results  
Our proposed EDB-CPS model consists of five phases that constitute an effective and secure cyber-physical system. Although the IA-CPS presented in Reference [26] introduced a clustering strategy in a WSN to minimize energy consumption, it failed to account for circumstances of a limited number of sensors in a cluster, which likely occurs in practical use. The sensor recruitment and node selection modules provide a solution for clusters with a minimum number of sensors, where the actuator is responsible for sending a recruitment request to the other clusters to determine the expected number of sensors. The mobile robot sensors monitor the moving patients (there is a certain region where any mobile robot sensor that enters may become a head static head sensor or a mobile robot sensor), and the actuator (head sensor) is identified in this model. The latency mobile model is deployed to handle the moment of patients [34]. Therefore, it improves the system's reliability but sometimes reduces efficiency because of the actuator, as it is located farther, which causes a back-transmission penalty. To overcome this limitation, we introduced an average midpoint in the optimal actuator algorithm (Algorithm 1). In the future, we can specify a certain range in which sensors can directly send data to the selected BS without redundant calculations to improve work performance. Furthermore, the sensor advertisement is quite efficient; it showed a better performance compared to other state-of-the-art methods (A-CPS, BSMIC, and StreamLAB) in terms of throughput and hop-to-hop delay. The results in Table 3 confirm the effectiveness of our proposed model (EDB-CPS). The model proposed in Reference [27] only supports static sensors, whereas our proposed model supports static and mobile robot sensors. To handle mobile sensors, a lattice mobility model can be deployed. In Table 3, the proposed EDB-CPS produces a better throughput of 445.2 kb/s without malicious nodes. When the 5% malicious nodes are generated, then the proposed EDB-CPS also produces a better throughput as compared to contending methods. The proposed EDB-CPS gets a better hop-to-hop delay (it means lower hop-to-hop delay). The lower hop-to-hop delay causes increasing the throughput. The reason for getting the better performance of the EDB-CPS is to use NSR, NA, and OD DMP processes.  
Figure 5: (**a**) Number of hops versus the required amount of time for the proposed EDB-CPS and the contending models, IA-CPS, BS-MIC, and StreamLAB, with a maximum of 27 hops. (**b**) Number of hops versus the required amount of time for the proposed EDB-CPS and the contending models, IA-CPS, BS-MIC, and StreamLAB, with a maximum of 54 hops.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 6 A Case Study: Remote Patient Monitoring of a Heart Patient in the IoMT Networks
### Proposed Methodology
#### 6.3.2 Second Combination: MSA + ML  
The second combination of ML and SDN benefits the IoMT infrastructure by providing functional decentralization in the IoMT networks [105]. Important features like monitoring and controlling the data traffic should be distributed intelligently into the network so that a single node failure (such as failure of a controller node) will not be a cause of the entire IoMT network becoming non-functional. Moreover, this combination provides time to recover from the failures. ML can predict which particular edge servers are most vulnerable to failure through real-time data analytics [106; 107]. MSA is then applied on those edge servers to recover from failure and satisfy the QoS requirement (low latency, high throughput) to nodes connected to those edge servers [108].  
MSA also guarantees the proper authorization and authentication on the multiple points of microservices applications on different edge servers to protect from malicious attacks [86]. Overall, this combination helps to provide distributed functionality and manage the resources on demand in the IoMT networks [109] through access control. In Figure 6, different groups of patients at different places should be provided with these functionalities, which are represented by M, C, D, and R, during the central controller failure by using this combination.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 2 Design of TEG Simulation System
### Overall Design of TEG System  
The direct disposal of low-grade flue gas waste heat is one of the main reasons for high energy consumption. Flue gas heat loss is one of the largest heat loss in power station boilers, accounting for 80% or more of the total heat loss of boilers. The most important factor affecting the heat loss of flue gas is the boiler exhaust temperature, which is generally maintained at 110 \({}^{\circ}\)C\(\sim\)150 \({}^{\circ}\)C [27]. Therefore, the waste heat of boiler flue gas mostly belongs to low-temperature waste heat, and the use of TEG technology will have better commercial competitiveness.  
The TEG system designed in the paper is composed of TEG module, hot end module for heat exchange of boiler flue gas waste heat, cold end module, detection circuit, MPPT controller, PWM controller, DC-DC converter, battery, insulated water tank, etc. It is shown in Figure 3.  
Figure 3: Schematic diagram of TEG system.  
The TEG module is composed of multiple semiconductor thermoelectric plates in series or parallel, which can be encapsulated electronically in industrial applications. The function of the TEG system is to convert waste heat of boiler flue gas into electric energy. The expelled flue gas from the boiler is directed into a purpose-built pipe located at the hot end module. The pipe incorporates a spoiler mechanism to ensure that the waste heat temperature of the flue gas is evenly distributed around the pipe. Multiple thermoelectric plates can be arranged around the pipe and connected in series or in parallel to increase the output power. At the same time, tap water is heated by the TEG module, and then enters the special water tank to meet the demand for production of domestic hot water.  
The working process of the system is as follows: The TEG module converts the waste heat energy of boiler flue gas into electric energy, and the output voltage and current are measured and converted into digital signals. Then, it is sent to MPPT and PWM controller to generate PWM pulse signal to control DC-DC converter. The voltage is adjusted and the maximum power output is achieved. The output energy can be used for normal operation of DC or AC load, and the function of the battery is to store energy.