esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design
### Mathematical Model
#### 4.4.4 Core Function Estimators and Local Polynomials  
Among the non-parametric alternative to the regression models, it is assumed that \(Y=m(X)+e\) where \(m\) is a function which is not confined within a parametric family. Subsequently, we need to estimate m from a sample \(\left(X_{i},Y_{i}\right),\ldots,\left(X_{n},Y_{n}\right)\), _where_ is the kernel estimators which are established by the weight of \(\left(X_{i},Y_{i}\right)\), in the estimate m, as considered by (12):  
\[W_{i}\left(t,X_{i}\right)=\frac{\frac{1}{h}K\left(\frac{t-X_{i}}{h}\right)}{ \hat{f}(t)} \tag{12}\]  
where \(K(t)\) is a symmetric density function and \(\hat{f}(t)\) is a kernel density estimator. Furthermore, \(W_{i}(t,X_{i})\), and for each \(i\), a weighting function that gives greater importance to the values \(X_{i}\) of the auxiliary variable that are close to \(t\).  
An alternative expression for \(W_{i}(t,X_{i})\), is \(W_{i}(t,X_{i})=\frac{K\left(\frac{t-X_{i}}{h}\right)}{\sum_{j=1}^{n}K\left( \frac{t-X_{j}}{h}\right)}\), is given after having calculated the weights \(W_{i}\), where the exploration may be resolved by weighted least squares as follows (13):  
\[\min_{a,b}\sum_{i=1}^{n}W_{i}\left(Y_{i}-\left(a+b\left(t-X_{i}\right)\right) \right)^{2} \tag{13}\]  
where the parameters obtained depend on \(t\), because of the weights \(W_{i}\), they also need to depend on \(t\). Therefore, the locally adjusted regression model around t will be: \(I_{t}(X)=a(t)+b(t)(t-X)\)and the estimation of the function at the point of \(X=t\), is \(\hat{m}(t)=l_{i}(t)=a(t)\), where the functions of the nucleus in the parametric estimation of the regression are identical as in the density.  
When generalizing to the local adjustment of polynomial regressions of a higher degree, meaning if it is intended to estimate type models \(\beta_{0}+\beta_{1}X+\beta_{2}X^{2}+\cdots+\beta_{q}X^{q}\), where instead of value \(X_{i}\) it is used the value of \((t-X_{i})\). The local polynomial estimator of grade q assigned weights \(W_{i}\) is obtained through the nucleus function of the polynomial regression that it can be solved hence (14).  
\[\min_{\beta_{0},\cdots,\beta_{n}}\sum_{i=1}^{n}W_{i}\left(Y_{i}-\left(\beta_{0 }+\beta_{i}(t-X_{i})+\cdots+\beta_{q}(t-X_{i})^{q}\right)\right)^{2} \tag{14}\]  
The parameters \(\hat{\beta}_{j}=\hat{\beta}_{j}(t)\) depend on the point \(t\), where the estimate is considered, and the locally adjusted polynomial around \(t\), will be (15):  
\[P_{q,t}(t-X)=\sum_{j=1}^{q}\hat{\beta}_{j}\left(t-X\right)^{j} \tag{15}\]  
Being m(t) the estimated polynomial value at the point where \(X=t\):\(\hat{m}_{q}(t)=P_{q,t}(0)=\hat{\beta}_{0}(t)\) and for the particular case, it is adjusted for zero-grade polynomials, obtaining the Madaraya-Watson estimator, or known as the regression core estimator, using the model (16):  
\[\hat{m}_{k}(t)=\frac{\sum_{i=1}^{n}K\left(\frac{t-X_{i}}{h}\right)Y_{i}}{\sum_ {i=1}^{n}K\left(\frac{t-X_{i}}{h}\right)}=\sum_{i=1}^{n}W(t,X_{i})Y_{i} \tag{16}\]
## 2 Materials and Methods
### Measurements and Data Processing  
Unlike the ELBARA-II radiometer that measures brightness temperature to calculate soil reflectivity and permittivity, the GNSS reflectometry instrument measures direct signals from the GNSS satellites and their reflected signals from the soil surface, thus directly providing surface soil reflectivity. According to the Oceanpal instrument design, the reflected LHCP signals were acquired in channel 1, and direct signals in RHCP were acquired in channel 2, in the first measurement stage (1-LD). In the second stage (2-DR), direct signals were acquired in channel 1, and the reflected RHCP signals were acquired in channel 2, as shown in Table 1. Thus, the calibration box has two functions. Firstly, we could collect signals from 3 different antennas with two input channels; secondly, a calibration process was performed to reduce the impact from the two different channels [24]. Since the LHCP signals were considered to be more sensitive to soil moisture variation, a longer measuring time was set for the first measurement mode.  
LHCP and RHCP reflectivities could then be calculated from equations  
\[K=mPD2\!/\!mPD1 \tag{1}\]  
\[\textit{Reflectivity\_RL}=(mPR1\!/\!mPD1)\!/\!K \tag{2}\]  
\[\textit{Reflectivity\_RR}=(mPR2\!/\!mPD2)\times K \tag{3}\]  
where \(K\) is the ratio of direct waveform peak power from the two different measurement stages, \(mPD1\) and \(mPD2\) are the direct waveform mean peak power measured in the two different measuring modes, and \(mPR1\) and \(mPR2\) represent the LHCP/RHCP-reflected waveform mean peak power measured in the different measuring modes. For GNSS measurements, the amplitude could be associated with soil properties.  
The direct signal waveform peak power ratio between consecutive measurements stages is shown in Figure 4. This represents the estimated calibration constant used for the relative calibration of the two instrument's channels. For each data take, the calibration constant is computed for each PRN (pseudo-random noise) between two consecutive measurement stages [24]. The power ratio among channels was concentrated around 1, with an average value of 1.03 and a mean absolute deviation of 0.091 over the whole observation period (August 2014-May 2016). The fluctu  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement Mode** & **Channels Information** \\ \hline
1-LD (3 min) & Channel 1: Reflected LHCP \\  & Channel 2: Direct RHCP \\
2-DR (0.5 min) & Channel 1: Direct RHCP \\  & Channel 2: Reflected RHCP \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data Acquisition Schedule.  
Figure 3: Soil moisture ThetaProbe sensors and Davis Vantage Pro2 Plus meteorological station.  
be caused by changes in the channel gain due to temperature variations and other environmental factors [24].
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Ketones  
Three ketones, cyclohexanone, acetone, and butanone, were tested, yielding varied results (Figure 4a,b). Cyclohexanone is the most effective, achieving high oil recovery percentages, while the performance of acetone and butanone is mediocre. Focusing on acetone (C3) and butanone (C4), it seems that increasing the number of carbon atoms leads to decreased recovery efficiency, but cyclohexanone, which is a C6, is much more effective. The reason must be the positive effect of the ring structure on oil recovery, as is the case with cyclic hydrocarbons (Figure 2c). Although cyclohexanone does not have as high an efficiency as some other solvents, it could be useful in the industry as it has several  
Figure 3: Percentage of residual oil removed from produced water: (**a**) monohydric alcohols, (**b**) influence of carbon atom count in monohydric alcohols, (**c**) polyhydric alcohols, (**d**) C3 alcohols, (**e**) influence of the number of hydroxyl groups.  
additional advantages. It is readily available, low in cost, can be easily recycled within the industrial process, and has low toxicity.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 3 Architecture for a Cement Factory Based on Blockchain Technology
### Blockchain Service Layer
#### 3.2.2 Consensus Algorithm  
Blockchain networks never require an external third party, and each new transaction validated using a consensus mechanism. The main purpose of the consensus algorithm is to ensure there are no collisions between transactions. The use of the PoAh consensus algorithm helps the blockchain to be lightweight and suitable for resource-constrained devices, and can eliminate centralized dependencies [12; 30]. A wide range of different consensus algorithms has been used in blockchain applications, including PoW, proof of space (PoSpace), a measure of trust (MoT), and proof of stake (PoS).  
Although most classical blockchain networks use PoW as a consensus algorithm, this is not suitable for resource-constrained devices. The processes used in PoW are expensive for IoT and edge computing applications, which adopted with public blockchain [11]; in PoAh, the reverse hash function process used in PoW is eliminated, making the transaction process lightweight [10; 12].  
First, the participant nodes generate a transaction proposal to create a new block and then the nodes sign the new transaction with a private key and directly broadcast it to the blockchain network. After receiving the proposed block in the network, trusted nodes verify the signature with a public source key; the trusted nodes evaluate the MAC address and compare it with the received one. After completing the authentication step, the authenticated block is broadcast to the network with PoAh identification. The user adds a new block to the chain after computing the hash value to link the next block and then retrieves the previous block hash to save it to the current block. Otherwise, the nodes drop the unauthenticated block.  
In our scheme, we used the PoAh consensus mechanism to update the blockchain and to validate each new block, PoAh uses cryptographic authentication to authenticate new blocks that make it faster than other traditional consensus algorithms. The approach means that the blockchain can be efficient integrated with IIoT devices. Algorithm 1 shows the steps of the PoAh consensus algorithm [12].  
Figure 2: Block structure.  
```
Input: All participant nods in the BC network follow ECDSA Output: Add authenticated Block to the Blockchain network
1 start:
2 Nodes integrate transactions to form of blocks
3 Nodes sign blocks with own private key
4 Nodes broadcast signed blocks to the network
5 Trusted nodes verifies signature with source public key
6 Trusted nodes evaluate MAC address
7 if (Block authentication == true)
8 Valid Block \(\mid\) PoAh, broadcast to network
9 User add new blocks into chain
10 else
11 Drop the unauthenticated block
12 end: Go to step 1 for next process
```  
**Algorithm 1**PoAh Execution Process.
## 1 Introduction
The same immobilization process as in the first set of experiments was followed to functionalize the surface. The biotin-coated magnetic beads were added to the medium, containing different concentrations of streptavidin for capturing and separation. After the separation step, the magnetic beads (biotin-coated magnetic beads + streptavidin = streptavidin-loaded magnetic beads) were washed and introduced to the surface forming the sandwich assay on the gold chip as BBSA + streptavidin (target) + immunomagnetic bead (biotin-coated magnetic bead). Then, iron nano particles were added under an external magnetic field for signal amplification (Figure 2). For the second set of experiments, the biotin-coated magnetic beads simultaneously served two main purposes: (1) as immunomagnetic capture agents to capture and separate the target (streptavidin); (2) as base beads immobilized on the surface to attract iron nano particles under an applied magnetic field for signal amplification. Figure 1: Schematic of the first set of experiments: iron nano particle accumulation around immobilized base beads. (**a**) Picture of the microfluidic chamber including gold chip inside and permanent magnets; (**b**) Picture of the measurement setup; (**c**) Illustration of the immobilization of immunomagnetic beads on gold chip and accumulation of iron nano particles. In this method, the biotin-coated magnetic beads serve as target capture and separation agents and as base beads for the magnetic attraction of added uncoated magnetic particles. Applying an external magnetic field and exposing uncoated magnetic particles is the signal amplification step (Video S1). For both steps, images of well-defined and robust patterns of base beads and iron nano particles were recorded to quantify the impact of signal amplification.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 6 Data Analysis
### Sparse LAA with Constraints on the PSLL Minimization and Null  
The fifth example focuses on a 32-element LAA to minimize the PSLL while aiming for nulls at \(\theta=81^{\circ}\) and \(\theta=99^{\circ}\). The main lobe width is configured as \(10^{\circ}\), and the sidelobe regions are designated as \(\theta=\left[0^{\circ},84^{\circ}\right]\cup\left[96^{\circ},180^{\circ}\right]\). To attain the lowest null and enable a fair comparison with other algorithms, the population size of the NCGWO algorithm was set to 30, and the maximum number of iterations was set to 1000.  
Figure 15a illustrates the sparse LAA pattern using the NCGWO algorithm and compares it with the results of the GWO, IGWO, WDO, and ACO algorithms [39; 40]. Figure 15b displays the iterative curves of GWO, IGWO, and NCGWO. The NCGWO algorithm performs better than its IGWO and GWO counterparts due to its rapid convergence and attainment of optimal values. Figure 16 shows the 3D radiation patterns of the 32-element sparse LAA before and after the NCGWO algorithm optimization. NCGWO was successful in greatly reducing the PSLL and lowering the null.  
Table 7 presents the optimization results. In this example, the ACO, CSO, IGWO and GWO algorithm achieved nulls of \(-50\) dB, \(-80\) dB, \(-108.2477\) dB, and \(-106\) dB, respectively. The proposed NCGWO algorithm achieved the deepest null of \(-116\) dB,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Optimized Element Position** & **Peak SLL (dB)** & **Execution Time (s)** \\ \hline GWO & 0.2204, 0.7078, 1.2023, 1.8266, 2.5468 & \(-19.0700\) & 4.9820 \\ \hline IGWO & 0.2234, 0.7196, 1.2224, 1.8604, 2.5966 & \(-19.0712\) & 4.3400 \\ \hline PSO & 0.2515, 0.5550, 1.0650, 1.5000, 2.1100 & \(-17.4100\) & 9.5150 \\ \hline WDO & 0.2233, 0.7179, 1.2221, 1.8591, 2.5936 & \(-19.0500\) & 6.2090 \\ \hline NCGWO & 0.2298, 0.7401, 1.2613, 1.9205, 2.6785 & \(-19.0848\) & 3.3540 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of results of algorithms for 10-element sparse linear array antennas.  
Figure 16: 3D radiation patterns of the SLL minimization and null of 32-element sparse linear array antennas: (**a**) before and (**b**) after.  
Figure 15: (**a**) Radiation patterns of the SLL minimization and null of 32-element sparse linear array antennas; (**b**) iteration curve.  
which surpassed the ACO, CSO, IGWO, and GWO algorithms by 66 dB, 36 dB, 8 dB, and 10 dB, respectively. The NCGWO algorithm exhibits superior performance in generating deep nulls compared with other algorithms.
## 6 Assessment of Integration Within Digital Twin
### Compatibility Between Test Levels and Requirement Types  
The integration of SiL and HiL within a DT framework allows for a structured and scalable validation architecture, where each test level corresponds to specific requirement types. Functional and algorithmic requirements--often related to logic flow, data handling, or embedded algorithms--are most efficiently addressed through system-level (SiL) design during early development phases. In contrast, hardware-dependent or safety-critical requirements demand HiL validation, where real-time electrical signals and physical sensor feedback must be processed under realistic operating constraints.  
Digital twin technology serves as a unifying layer that ensures traceability between requirements, simulation of artefacts, and real-world outcomes. It provides a bidirectional link that helps propagate insights and faults across all development levels. Rather than replacing SiL or HiL, the DT strengthens its impact by enabling continuous validation, synchronizing cross-domain data, and supporting test coverage extension with minimal physical effort.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Network Topology (Structure)
#### 3.6.1 Hierarchical (Cluster-Based)
Each CH transmits its location information to the upper and lower level CH, and each node aggregates and fuses its own data with data received from other sensors and transmits the generated data to the next node in the chain, which narrows the length of packets. Research shows that CCS conserves energy; however, long chains are responsible for large delays [70]. Table 5 establishes a comparison between hierarchical-based routing protocols [71].
## 5 Performance Evaluation
### Parameter Tuning of G-TIAG
#### 5.3.4 Time Interval of Graph Construction  
The TIAGs change significantly within a period after the attack occurs, so comparing TIAGs of different periods can identify attacks. Specifically, if the time interval between constructing two TIAGs is too small, the variability between TIAGs is small, which may directly affect the final performance; if the interval is too large, there is the problem of missing attack features. Therefore, we explore the four different time intervals, and the performance of G-TIAG is shown in Figure 7c. As the increase in time interval, the classification performance shows an overall increasing trend from about 0.78 to 0.92. However, the increase is very slight for time intervals from 200 to 400, and we finally choose 200 as the time interval of graph construction.
## 1 Introduction
The primary contributions of this work are summarized as follows:  
* We introduce the retriever consensus confidence fusion (RCCF) method, which addresses fundamental limitations in hybrid retrieval systems by incorporating consensus measurements, adaptive weight assignment, and confidence estimation to achieve superior retrieval accuracy and reliability. * We develop AM-HDGAT for text, an innovative graph-based classification approach that models inter-document relationships while combining high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms, enhanced with adversarial training for robust security-critical applications. * We propose AttenFlow, a unified context-aware framework that combines advanced retrieval fusion and graph-based classification, setting new standards for intelligent document processing by jointly optimizing efficiency, accuracy, and security to meet real-world organizational needs. * We conduct extensive experiments on benchmark datasets and real-world scenarios, demonstrating notable gains in retrieval accuracy (\(\uparrow\)16.9%), classification performance (\(\uparrow\)2.23% F1-score), and system efficiency (\(\downarrow\)26.1% processing time). Through these contributions, this research advances the state of the art in automated document processing while providing practical solutions that can be readily deployed in real-world organizational contexts, ultimately facilitating the digital transformation of document management practices across diverse domains.
# Kinetic Comparison between Drop Jumps and Horizontal Drop Jumps in Elite Jumps and Sprinters
###### Abstract  
Previous research addressed the spatiotemporal variables of the drop jump (DJ) versus the horizontal drop jump (HDJ). This study compared the kinetic variables of the DJ versus the HDJ in elite jumpers and sprinters. In a single session, sixteen elite jumpers and sprinters performed two DJ attempts with three different fall heights (0.30 m, 0.40 m, and 0.50 m), and after 2 h, performed two HDJ attempts from the same fall heights (0.30 m, 0.40 m, and 0.50 m). Kinetic variables: eccentric ground reaction forces (GRFE) and concentric ground reaction forces; eccentric impulse (PE) and concentric impulse (PC); peak power in the concentric phase; and rate of force decrease (RFDe) were measured using a research-grade force plate. The Wilcoxon test was used to compare the vertical and anteroposterior axes. GRFE was significantly higher (\(p\leq 0.05\)) in the DJ vs the HDJ with large effect sizes. The PE (\(p\leq 0.006\)) and PC (\(p=0.002\)) were significantly lower in the DJ than in the HDJ. The RFDe was also significantly lower in the DJ at 0.30 m vs. the HDJ at 0.30 m (\(p=0.002\)). In summary, elite jumpers and sprinters may benefit from incorporating both the DJ and the HDJ into their training regimens, with the DJ being particularly advantageous for enhancing power metrics and RFDe.  
Keywords:ground reaction force; impulse; power output; concentric phase; eccentric phase; bilateral jumps +
Footnote †: journal: _Sciences_
## 7 Evolutionary Methods
### Multi-Objective Optimization for Trade-Off Analysis  
One of the defining advantages of evolutionary methods lies in their capacity to manage multi-objective optimization challenges effectively. These methods excel in scenarios where conflicting objectives must be addressed simultaneously, offering solutions that balance competing goals. In food systems, such optimization problems often involve trade-offs between minimizing costs, maximizing product quality, and enhancing environmental sustainability. For instance, in supply chain optimization, balancing economic efficiency with environmental impact is a recurring challenge. A notable application of genetic algorithms (GAs) in this context demonstrated their effectiveness: a multi-objective optimization model applied to a food distribution network achieved a 10% reduction in costs while simultaneously lowering carbon emissions by 15% [68; 69].  
Similarly, particle swarm optimization (PSO) has been applied to optimize food packaging design, addressing objectives such as cost-effectiveness, environmental impact, and consumer preferences. By leveraging the ability of PSO to explore complex, multi-dimensional solution spaces, researchers achieved a 12% reduction in packaging costs alongside a 10% improvement in consumer satisfaction ratings. These examples underscore the versatility of evolutionary methods in solving multi-objective problems, particularly in scenarios requiring a nuanced understanding of trade-offs between competing goals [68].  
Recent advancements have further enhanced the scope of these methods. The research highlighted in \"A learning-driven multi-objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations\" (2024) showcases the integration of evolutionary techniques with learning-driven strategies. These hybrid approaches improve the efficiency of convergence and the quality of solutions, particularly in dynamic and distributed systems [70]. Similarly, the integration of Q-learning into evolutionary frameworks, as demonstrated in \"Multi-objective integrated harvest and distribution scheduling for fresh agricultural products with farm-to-door requirements\" (2025) highlights the potential of adaptive learning to tackle real-time optimization challenges. These studies represent significant progress in addressing the inherent complexities of multi-objective problems, particularly in industries with dynamic operational environments [71].
# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 2 Material and Methods  
This study assesses the ability of LDAS-Monde sequential assimilation of satellite derived surface SSM and LAI to represent the impact of the summer 2018 heatwave in Europe on vegetation. The following sections describe LDAS-Monde system as well as 2 other key elements of its setup: atmospheric forcing (LDAS-Monde being an offline system) and satellite derived observations.