esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flower-Shaped Miniaturized UWB-MIMO Antenna with High Isolation
## 2 The Proposed Antenna System
### Current Distribution  
Figure 8 presents the surface current distribution in the resonance modes, to visually emphasize the decoupling effect of ground plane geometry. In the proposed MIMO system, port 1 is stimulated, while port 2 is terminated with a 50 \(\Omega\) matched load. When just port 1 is stimulated, the current is largely distributed on antenna 1 and its modified T-like branch on the same side, as shown in Figure 8, whereas the current distribution on the surface of antenna 2 is relatively weak. It can be deduced that current-absorbing effect of the improved ground branch successfully improves the port isolation between the two monopole antennas.
# A Force Measurement Method Based on Flexible PDMS Grating
## 2 Property of PDMS and fabrication of PDMS grating
### PDMS Grating Fabrication  
Dow Corning Sylgard 184 silicone elastomer base (Sylgard 184A) and Sylgard 184 silicone elastomer curing agent (Sylgard 184B) are used to fabricate PDMS grating.  
First, the electronic scale (BSA124S, Sartorius) is applied to weighting materials, and three solutions are prepared in mixing ratios of 5:1, 10:1 and 15:1 (Sylgard 184A to Sylgard 184B in weight) respectively.  
Second, to make sure the material is evenly mixed, it is necessary to seal the glass bottle and evenly shake it up by a vortex mixer (Vorte2). Then, the glass bottle is put into a vacuum dryer (DZF-6020) for vacuum treatment for 20 min, through which the bubbles in the material are completely removed.  
Third, a commercial diffraction grating (#48-462, Edmund Optics) with a groove density of 600 line/mm and a size of \(50\times 50\) mm is selected as the master grating. The solution is poured evenly on the master grating and spin coated. The spin coater (KW-A4) rotates at 200r/min for 20s and at 500r/min for 15s. After that, put the samples in the vacuum drying oven (DZF-6020) for heating, of which curing temperatures are set to the 70\({}^{\circ}\)C, 80\({}^{\circ}\)C, and 90\({}^{\circ}\)C respectively for 3h.  
Finally, take the samples out from the vacuum drying oven, and a PDMS film cast with grating structure is obtained. For easily mounting on the tension tester, the sample is cut into a shape of \(50\times 27\) mm (length \(\times\) width) by a fixture and a knife, and the dimension error is \(\sim\)\(\pm\)0.5 mm. The thickness is 0.91\(\pm\)0.05 mm measured by a Vernier caliper (VOGEL 201035). It is worth noting that the thickness can be adjusted by changing the speed and time of spin coating.  
The whole fabrication process of the PDMS grating is shown in Figure 2.  
The PDMS grating is shown in Figure 3a and its micro pattern is observed by SEM (scanning electron microscope), as shown in Figure 3b.  
Figure 2: The fabrication process of flexible PDMS grating.  
In this paper, three different mixing ratios and three different curing temperatures are chosen as the processing parameters, and nine sets of PDMS grating samples are prepared and tested.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 4 Results and Discussion  
This section presents the experimental results obtained from the performance of the closed-loop irrigation system during an evaluation period of four weeks. First, we show the results of each one of the four sampling strategies according to the evaluation parameters. Then, we analyze each transient response to identify the differences for every sampling algorithm. Finally, based on the experimental results, we estimate the power consumption for each strategy. It is important to highlight that, as a result of the use of four different sampling strategies; there were no significant differences in the vegetative evolution of the crops; this means that, in general, the control system worked correctly in the four irrigation areas.
## 2 Methodology
### Design of FCM-Based GA
Step 1: Set the parameter values, namely the number of clusters \(G\), number of generations \(GN\), population size \(PS\), crossover probability \(P_{c}\), mutation probability \(P_{m}\), fuzziness value \(m\), and termination tolerance \(\epsilon\). Step 2: Initialize the population. Chromosomes are generated using the FCM clustering methods as many times as \(PS\). To produce a chromosome, we calculate the membership value \(u_{ic}\) for each job record \(i\) to each cluster \(c\) by randomly generated numbers \(\gamma_{ic},0\leq\gamma_{ic}\leq 1\), as follows  
\[u_{ic}=\gamma_{ic}/\sum_{c\in G}\gamma_{ic}. \tag{8}\]  
Step 3: Apply genetic operations. The value of the fitness function \(F\) (Equation (7)) can be calculated for each individual. We then use genetic operations, namely the selection, crossover, and mutation operators, to improve population diversity. Step 4: Apply optimal preservation. For each generation, the fitness values are re-calculated after the genetic operations have been applied to evaluate each individual. Individuals with higher fitness values are more likely to be chosen for survival. Step 5: Check termination condition. In the present study, the iteration terminates either after a given number of generations \(GN\) or when a given fitness variance value \(\epsilon\) is achieved. If either of these conditions is satisfied, then evolution stops. Otherwise, we return to step 3. For a population of \(PS\), indexed by \(p\), the variance \(\epsilon\) is calculated as  
\[\epsilon=\sum_{p=1}^{PS}\left(F_{p}-\frac{\sum_{p=1}^{PS}F_{p}}{PS}\right)^{2}. \tag{9}\]  
After the evolution process is complete, we can obtain the K-dimensional center for each cluster and the membership values. We then associate each job record to all clusters only if its membership value \(u_{ic}\) exceeds a given threshold value \(\delta\). An auxiliary binary variable \(\epsilon_{ic}\) is used to calculate the expected job cycle time for all clusters. Finally, the expected job cycle time in each cluster \(c\) can be calculated as  
\[e_{c}=\frac{\sum_{i\in\mathcal{R}}u_{ic}-c_{i}\epsilon_{ic}}{\sum_{i\in \mathcal{R}}u_{ic}-c_{i}\epsilon_{ic}}, \tag{10}\]  
\[\text{where, }e_{ic}=\left\{ \begin{array}{ll}1&\text{if }u_{ic}>\delta\\ 0&\text{otherwise}\end{array} \right.. \tag{11}\]  
Figure 5 shows a flowchart of the complete FCM-based GA approach.
# FE Analysis of Motorcycle Helmet Performance under Severe Accidents
## 5 Scenario of Impact Accidents
### Result Comparisons  
All studies examined the head protection performance of motorcycle helmet structures under three cases of accidents with different initial velocities and impact angles. The results in this section were calculated based on Equation (2) using the related results from finite element analysis. Firstly, the lost control speed (47 kph) caused the most severe head injury due to the high peak acceleration, and the probability of head injury for all cases was 100% according to AIS 2+ (Figure 20). The severity of injury was different for all three cases depending on the characteristics and behaviors in each case. An initial velocity of 27 kph at 30 and 45-degree impact angles seemed to be the most effective condition, because the probability of head injury was lower than 60% for all cases, except for 30 degrees in case II. The low impact angles caused an impact area at the top surface of the helmet only and increased the HIC because other surfaces had not absorbed the impact energy. The other impact angles varied based on each impact condition. Impact angles of 60 and 75 degrees also caused high peak acceleration for all cases, because the helmet had a collision with a footpath only, without sliding effects, and was not included for further comparison. However, the defined velocity for those helmet regulations (7.5 m/s or 27 kph) showed safe situations for head injury. In contrast, higher velocities (30 and 47 kph) were associated with more severe injuries, even though those speeds are typically used for driving nationwide.  
The energy absorption of foam components is also a pivotal point to evaluate in terms of the performance of helmet protectability during accidents. The results were compiled with the probability according to AIS 2+ (Figure 21). The highest energy absorption was observed when the impact angles were 30 and 45 degrees at an initial velocity of 27 kph. In all cases present, the foam absorbed over 75% of the energy, effectively reducing the impact of accidents' head injury probability. Thus, the helmet structure should be designed by mainly considering the energy absorption of the foam component, which can enable the most efficient performance for head protectability and reduce the severity of head injury in the event of impact accidents.  
Figure 19: Energy absorption and contact force from simulation at 27 kph for case III.
## 3 System Model
### Problem Formulation  
We optimize the energy consumption of both terminal devices and edge servers to lower the overall energy use of the system, which involves making decisions about offloading, communication resource allocation, and computing resource allocation. Specifically, the optimization problem can be expressed as  
\[\min_{w,p,O,f}E=\sum_{i=1}^{M}\sum_{t=1}^{\mathcal{T}}E_{i,t}^{ sum}, \tag{16}\] \[s.t.\quad\text{C1}:O_{i,j,t}\in\{0,1\},\forall i\in M,j\in N,t \in\mathcal{T},\] \[\quad\text{C2}:0\leq T_{i,t}\leq T_{t}^{\max},\forall i\in M,t \in\mathcal{T}\] \[\quad\text{C3}:p_{\min}\leq p_{i,j,t}\leq p_{\max},\forall i\in M,t\in\mathcal{T},\] \[\quad\text{C4}:w^{\min}\leq w_{i,j,t}\leq w^{\max},\forall i\in M,t\in\mathcal{T},\] \[\quad\text{C5}:f^{\min}\leq f_{i,k,t}\leq f^{\max},\forall i\in M,t\in\mathcal{T},k\in K,\]  
where C1 denotes range of offloading ratio, C2 denotes that each task must be completed within the maximum delay, C3 denotes range of transmission power, C4 denotes range of the subcarriers, and C5 denotes range of the CPU computing frequency of the terminal device.
## 4 Proposed RCSA Method
### Definition of Weight Parameters
In this way, the proposed method can mitigate spectrum fragmentation by exclusively assigning the prioritized FSs to requests with different \(R_{p}\) in each group. In (9), \(W(R_{p})=W_{c}(R_{p})+W_{f}(R_{p})\) becomes 0 when both \(W_{c}(R_{p})\) and \(W_{f}(R_{p})\) are 0. Consequently, lightpaths are expected to be established efficiently by preferentially using both prioritized cores and FSs, as shown in Figure 4, thereby reducing inter-core crosstalk and the blocking probability. Note that in this paper, we define the specific values of this weight parameter \(W_{f}(R_{p})\) according to network models in the simulation experiments, which are discussed in Section 5.
# Quantifying Fermentable Sugars in Beer: Development and Validation of a Reliable HPLC-ELSD Method
## 3 Results and Discussion
### Application of the Method
#### 3.3.2 Fermentation Profile  
The fermentation of word corresponding to Beer A was monitored from initial inoculation with yeast (0.3 days after inoculation) to cold maturation (10.4 days). As shown in Figure 5, sucrose was rapidly hydrolyzed into glucose and fructose by yeast invertase enzymes. This enzymatic activity explains the complete depletion of sucrose within the first 0.5 days. Glucose was metabolized preferentially, serving as the primary carbon source for yeast during early fermentation, producing ethanol and carbon dioxide. Fructose consumption occurred later, consistent with yeast preference hierarchy. The structural complexity of nitrogen sources and oxygen availability influence glucose and fructose fermentation rates [20]. Maltose and maltotriose assimilation began when approximately 50% of glucose had been consumed, as observed at day 0.5, when glucose levels dropped to 7.1 g/L. Maltose and maltotriose uptake and metabolism by yeast during brewing fermentation affect the fermentation rate and beer flavor [21].  
The observed trends are in agreement with studies by [PERSON] et al. [22], which described the sequential fermentation of sugars in brewing. This process can be influenced by various factors, including word composition, yeast strain, and fermentation conditions [23]. Understanding these sugar consumption patterns is crucial for optimizing fermentation processes and selecting appropriate yeast strains.  
The total sugar profile obtained via HPLC-ELSD aligned closely with extract levels determined using the Alcolyzer (Figure S6, Supplementary Material). The discrepancy between the two methods, with extract values consistently higher than HPLC sugars, reflects the inclusion of dextrins and \(\beta\)-glucans in the extract measurement, which are not quantified by HPLC. Total \(\beta\)-glucan content in malt is directly associated with achievable extract in the brewhouse, making it a key determinant of malt quality [24]. \(\beta\)-Glucans increase viscosity and decrease filterability of beer solutions, with molecular weight playing a crucial role [25]. The final attenuation of 68% indicated that 68% of sugars present in the  
Figure 5: Profile of various sugars throughout fermentation for Beer A.  
original word were converted into ethanol, consistent with the expected range (67-73%) for Beer A. Figure 5 and Figure S6 also illustrate a reduction in yeast activity after day 4, likely due to nutrient depletion and ethanol toxicity, as reported in classic fermentation studies [26,27].
## 2 Range Ambiguity Analysis by Alternatively Up and Down Chirp Modulation
As can be seen from (7), the ambiguous signal power is mainly determined by the backscattering intensity of the ambiguous area when the range antenna pattern has already been fixed according to the system design. At certain condition, range antenna pattern and the lower power of the defocusing signal can no longer suppress the ambiguous signal power being lower than the main area signal power, when the backscattering intensity of ambiguous area grows strong enough. This can result in ambiguous range defocusing targets appearing on the product image with a shape of line along range direction, having severe impact on visual effects and subsequent applications.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Harmonic Vibration Testing
#### 4.3.3 Harmonic Vibration Testing: Discussion on the Influence of the Central-Cut Width
The presence of these nonlinearities (nonlinear damping/softening/hardening) should be related to the presence of (1) bolted-joint connections (i) in the boundary conditions, (ii) between the box assembly and the removable component, and (iii) in the removable component connections; (2) large deformation in the removable component or/and box assembly depending on the mode of vibration and the location of the accelerometer and, hence, the possible activation of geometric and inertia nonlinearities.
## 3 Validation of the Calculation Method
### Comparison with the Experimental Data in Undrained Heating Test of [PERSON] et al. (2007)  
Using modified oedometer and triaxial test apparatus, [PERSON] et al. [10] carried out undrained heating tests on soft Bangkok clay to study the thermal pore water pressures under different stress conditions. To obtain soils with different overconsolidation ratios, the consolidation pressure of 200 kPa was unloaded to 100 kPa and 50 kPa. The physical parameters of soft Bangkok clay are shown in Table 2. Meanwhile, although \(\Lambda\) was not reported in this undrained heat test, [PERSON] et al. [9] found that when \(\Lambda\) = 10, their model-predicted results and the experimental results of [PERSON] et al. [10] fit best, so the value of \(\Lambda\) was taken as 10. Furthermore, since the maximum \(OCR\) is 4 and the critical threshold is not reached, the influence of stress history on \(\Lambda\) is ignored.  
The predicted results of Equation (12) are compared with the experimental results of [PERSON] et al. [10], and the comparison results are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\theta\) (\%) & \(S_{r}\) & \(G_{s}\) & \(I_{P}\) & \(\Lambda\) & \(n_{0}\) \\ \hline
90–95 & 98 & 2.68 & 60 & 0.46 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters of soft Bangkok clay obtained from [PERSON] et al. (2007).  
Figure 1: Comparison of predicted thermal pore water pressure against the results of [PERSON] et al. (2017).  
From Figure 2, it can be observed that all experimental data points are around the predicting lines. In addition, through calculation, it was found that the coefficients of determination (\(R^{2}\)) of all predicted results are 0.95 or above, which indicates that the predicted results are highly consistent with the experimental results. The consistency between the predicted results and the experimental results ensures the accuracy of the calculation method established in this paper.
## 1 Introduction
Specific objectives for this study were to:  
* Assess responses to soil water stress of soybean and maize physiology (canopy ET, leaf \(\mathrm{g_{s}}\), leaf \(\mathrm{T_{r}}\) and leaf A), morphology (\(\mathrm{h_{c}}\), LAI), biochemistry (chl), and remote sensing (\(\mathrm{T_{L,Rad}}\) and reflectance) variables. * Investigate PLS-R as a method to model leaf \(\mathrm{g_{s}}\), \(\mathrm{T_{r}}\), and A using hyperspectral data and identify the most relevant wavelengths contributing to the model as well as the model improvement when incorporating \(\mathrm{T_{L,Rad}}\) and \(\mathrm{h_{c}}\) into the models. The results from this study can be useful for applications in growth chambers and outdoors at the farm level using unmanned aerial vehicles (UAVs) carrying hyperspectral, thermal cameras and obtaining \(\mathrm{h_{c}}\) from structure from motion (SfM) photogrammetry or light detection and ranging (LiDAR) sensors.
###### Abstract  
It is well-known that performance of the classical algorithms for active noise control (ANC) systems severely degrades when implemented for controlling the impulsive sources. The objective of this paper is to propose a new recursive least squares (RLS) algorithm (and its variant) for being implemented in the framework of ANC systems. The proposed RLS-based adaptive algorithm employs an objective function designed to achieve robustness against the impulse type sources. The derivation of the algorithm is quite straightforward; however, a few modifications have been incorporated to address the application at hand. In order to improve upon the numerical stability issue of RLS-based adaptation, it is suggested to employ smoothing while updating the inverse correlation matrix. Furthermore, it is proposed to introduce a step size in the update equation of the adaptive algorithm. This results in the fixed step-size modified filtered-x (MFx) robust RLS (FSS-MFxRRLS) algorithm. As expected, a fixed value step size results in a trade-off situation for convergence speed and steady-state misalignment. In order to address this issue of a trade-off situation, the idea of a convex combined step size (CCSS) is introduced into the adaptive procedure to develop the CCSS-MFxRRLS algorithm. When the ANC is started, the CCSS strategy (automatically) selects a large-valued step size to achieve a fast initial convergence. As the ANC system converges at the steady-state, the CCSS is automatically tuned to a small value which improves the steady-state performance of the proposed CCSS-MFxRRLS algorithm. Extensive simulations have been designed to mimic many scenarios for practical applications of ANC for impulsive sources. The simulation results demonstrate that the proposed CCSS-MFxRRLS algorithm is very effective in many practical scenarios involving ANC of impulsive sources.  
robust RLS adaptive filtering; active noise control; impulsive noise; convex-combined step size
## 4 Masked Addressing
### Chip Characterization  
Chip characterization occurs once for each chip, recording \(C\) and \(M\), which are necessary to identify the chip in the future.  
1. A single chip \(A\), to be characterized, is placed in range of the external reader. The external reader transmits a characterize signal.
2. The chip applies a voltage difference \(\Delta V_{R}\) to the ID cells, activates the ID generator, and records the output \(G_{+}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\Delta V_{T}<-V_{R}\) & \(\Delta V_{T}>V_{R}\) & **Classification** & **C** & **M** \\ \hline True & False & “0” & 0 & 1 \\ False & False & Unreliable & X & 0 \\ False & True & “1” & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of ID cells based on reliability tests, where \(V_{R}\) is the magnitude of the threshold voltage difference required for reliable operation, C is ID code, and M is the ID mask.  
3. The chip applies a voltage difference \(-\Delta V_{R}\) to the ID cells, activates the ID generator, and records the output \(G_{-}\).
4. The chip transmits \(G_{+}\) and \(G_{-}\) to the external reader.
5. The external reader computes: \[C_{A}=\overline{G_{+}}\ \ \&\ \ G_{-}\] (12) \[M_{A}=G_{+}\ ||\ G_{-}\] (13)
6. The external reader stores \(C_{A}\) and \(M_{A}\) for chip \(A\).
# A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning
## 3 System Design
### ML Models
#### 3.3.2 RF Structure  
RF is a supervised learning algorithm that constructs multiple decision trees. To get an accurate and stable prediction, the final model's prediction is derived by voting on the class prediction of the individual trees in the forest. Each branch of the tree represents a possible decision, occurrence, or reaction. FR model can be used for both classification and regression problems with a high classification rate.  
We built an RF model with a maximum tree depth of 60 and 8 node splits. For an ensembles construct \(f\) that has a collection of ensemble classifiers \(h_{1}(x)\),..., \(h_{n}(x)\), the ensemble predictor \(f\) for a class \(x\) is calculated as follows:  
\[f(x)=argmax\sum_{i=1}^{n}I(h_{i}(x)) \tag{9}\]  
where \(f(x)\) is the most frequently predicted class determined by voting between the outputs of \(h_{1}(x)\),..., \(h_{n}(x)\) and \(I\) is the indicator function that measures the extent to which the average number of votes for the right class exceeds the average vote for any other class. An immense margin value gives more confidence in the classification results.  
We used an entropy-based splitting criterion that controls how each tree node splits the data. This has a significant effect on how each decision tree in the forest draws its boundaries.
## 4 Discussion
In addition, the integration of AML-derived data with spatial information systems and smart infrastructure frameworks holds significant potential. Recent studies have demonstrated the feasibility of implementing digital twin models for underground utility tunnels using multimodal sensors and BIM-GIS integration [47]. Similarly, methodologies for geospatial data acquisition and real-time service modeling in digital twin environments have shown that such integration can enhance utility monitoring and operational management [48]. Future studies should also consider adopting objective performance evaluation methods for underground sensing technologies, as discussed by [49]. Such standardized frameworks would support the comparison of different detection systems under varied environmental conditions, contributing to more reliable deployment in practice.