esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## Materials and Methods
### Multivariate Data Analysis
#### 2.5.4 Visualization Map of Classification  
Simultaneously obtaining spectral and spatial information, hyperspectral imaging technology can combine with images' processing method to locate individual wheat seed and visualize their corresponding varieties accurately. The specific steps were as follows:  
1. Isolating samples from background and extracting the average spectrum of each ROI and the spatial locations of each pixel in this ROI.
2. Developing the optimal discrimination model.
3. Predicting the given wheat samples' variety according to the optimal model.
4. Assigning the variety label to all pixels of spatial position of corresponding sample and forming visualization classification by pseudo-color map.  
Varieties visualization of wheat seeds is beneficial for convenient and clear discrimination, which can contribute to detecting the quality of grains on a large scale in modern seed industry.
## 1 Introduction
designed a PD-fuzzy-bang-bang composite algorithm for tracked harvesters, and, for a guided trajectory that travels at 0.7 m/s, the maximum lateral tracking error reached 12 cm after tuning the parameters through trial and error [20]. To further improve the adaptability of tracking parameters, self-tuning methods based on the radial basis function (RBF) neural network [21] and particle swarm optimization (PSO) [22] have been proposed, which, however, cannot satisfy the requirement of real-time parameter adjustment because of the complicated environment disturbances and noise in actual working scenarios. Because the PP and SM algorithms only require the tuning of a single parameter to achieve good tracking performance, self-tuning methods for geometric-based tracking have been intensively investigated. [PERSON] et al. designed an ANS based on an improved PP algorithm, which employed PID control to optimize the forward distance, reducing the average lateral tracking error by 42.83% compared to the PP algorithm without optimization [23]. In the application of SM for different automatic turning routes, the multiple-population genetic algorithm was employed to enhance the SM, showing an improvement of 41.72% in terms of the lateral error for U routes [24]. The above-mentioned self-tuning geometric-based tracking methods are either not suitable for the RTK-based navigation decision framework or have only been tested using numerical simulation. How to efficiently integrate an easy to-implement navigation decision and path-tracking algorithm for ANSs is still a matter of debate. Fuzzy logic control is an effective way to adjust the look-ahead distance for PP and SM [25], which can cater to the variations in agricultural machinery ANSs regarding the travel speed and soil conditions. In our previous work, a fuzzy SM (FSM) and PSO-enhanced FSM were proposed and verified based on numerical simulation and mobile robot automatic navigation, and primary results on a self-driving wheeled combine harvester were also reported [26]. However, the navigation decision framework and the applicability of the FSM in different self-driving platforms have not been tested and discussed. In this contribution, the dynamic path search algorithm developed in [27] was successfully combined with the FSM, for which an easily implemented ANS was designed for the self-driving tractor to perform autonomous tillage with whole-field coverage. The Dongfanghong 1104 tractor (Yto Group Co., Luoyang, China) was selected as the experiment platform, and both straight path-tracking and whole-field path-tracking results are reported.
# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 2 Literature Review  
Several studies in the literature have demonstrated the effectiveness of genetic algorithms and other metaheuristic methods in the optimization of audio systems. [PERSON] and [PERSON] (2012) developed an automatic parameter adjustment method for audio equalizers using an interactive genetic algorithm (IGA). In their study, user satisfaction was evaluated by measuring the difference between the user-preferred frequency response and the optimized frequency response [9].  
[PERSON] and [PERSON] (2018) proposed a 16 th-order IIR filter-based graphic equalizer design optimized using a genetic algorithm. In their study, they succeeded in improving filter performance by reducing the interference between adjacent frequency bands and maximizing gain. The average root mean square error (RMSE) value was reported as 0.05, and the maximum gain achieved was 12 dB [10].  
[PERSON] and [PERSON] (2020) demonstrated superior performance in IIR filter-based audio equalization using the Gravitational Search Algorithm (GSA), achieving lower error rates compared to traditional methods. The RMSE value was reported as 0.06, and the maximum gain was found to be 11 dB [11].  
In these studies, the RMSE was used to measure the difference between the optimized frequency response and the target frequency response. Gain refers to the capacity of the filters to enhance the signal at specific frequencies.  
This study differs from other studies in the literature in several key aspects. Previous studies have typically focused on users' satisfaction or on achieving specific frequency responses. In this study, optimizations aimed at enhancing the performance of in-vehicle audio systems are conducted according to ideal hearing curves specified by standards. This ensures that the speaker output conforms more closely to the principle of equal loudness.  
Additionally, while most studies in the literature primarily use the RMSE or mean square error (MSE) as performance metrics, this study also incorporates the correlation metric. Correlation is included to better analyze the similarity between the optimized frequency response and the ideal curve. This allows for a more comprehensive evaluation of how closely the speaker output matches the ideal curve.
## 2 Description of the LMS-Based Algorithms
### Filtered-U Recursive LMS Algorithm Applied to a Feed-Forward Configuration  
In some cases, the reference microphone could be exposed to the acoustic feedback generated by a control source. In such cases, it is necessary to take into account the feedback path of wave propagation from the control source to the reference microphone, because the formation of poles could make the system unstable. A simple extension of the FXLMS algorithm (FBFXLMS) was proposed [4], in which a sufficiently long FIR filter, usually estimated in a preliminary stage together with the secondary path, approximates the impulse response from the control source to the reference microphone. The contribution of the filtered control signal is afterward subtracted from what the microphone measures, reconstructing in this way the reference signal. [PERSON] et al. proposed the use of an IIR filter [23] so that the poles of the adaptive filter remove the poles created by the acoustic feedback. This technique makes it possible to use a lower-order filter with respect to a FIR filter without a preliminary model of the feedback path that can be dynamically tracked during cancellation operations. However, these advantages come at the price of having filters that are not unconditionally stable and possible convergence to a local minimum of the cost function because the mean square error is generally non-quadratic.  
Hereinafter, the application of an IIR filter with the recursive LMS (RLMS) algorithm is reported as developed by [PERSON] [24]. Considering the scheme illustrated in Figure 4, after the preliminary stage of offline estimation of the secondary paths, the algorithm works as described in the following. The filtered reference signals are still computed as defined in Equation (1), while the filtered output signals are computed as:  
\[y^{\prime}_{ji}(n-1)=\sum_{m=0}^{M-1}s^{\prime}_{j,m}(n)y_{j}(n-m-1). \tag{10}\]  
The updating law defined in Equation (3) is substituted by two updating equations, Equation (11) for the feed-forward coefficient vector \(a_{j}(n)=[a_{j,0}(n)\ a_{j,1}(n)\ \ldots\ a_{j,K-1}(n)]\) and Equation (12) for the feedback coefficient vector \(b_{j}(n)=[b_{j,1}(n)\ b_{j,2}(n)\ \ldots\ b_{j,K}(n)]\). Both vectors are assumed to have the same length \(K\), whilst the two step-size parameters \(\mu_{a}\) and \(\mu_{b}\) are kept distinct for the two equations:  
\[a_{j,k}(n+1)=a_{j,k}(n)-\mu_{a}\sum_{i=1}^{2}e_{i}(n)x^{\prime}_{ji}(n-k)\ \ \ k=0,1,\ldots,K-1. \tag{11}\]  
\[b_{j,k}(n+1)=b_{j,k}(n)-\mu_{b}\sum_{i=1}^{2}e_{i}(n)y^{\prime}_{ji}(n-k)\ \ \ k=1,2,\ldots,K. \tag{12}\]  
Figure 4: Schematic representation of FURLMS algorithm for a feed-forward configuration.  
Finally, the two control signals are computed as in Equation (13):  
\[y_{j}(n)=\sum_{k=0}^{K-1}a_{j,k}(n)x(n-k)+\sum_{k=1}^{K}b_{j,k}(n)y_{j}(n-k). \tag{13}\]  
This procedure is repeated at any generic instant \(n\).
## 3 Detailed Design of Viscous Plane Damper
### Theory of Viscous Plane Dampers Using Orifices
#### 3.2.1 Generation of Damping Force  
The damping force is created as the piston moves, forcing fluid through the orifices. The force \(\mathrm{F_{d}}\) is proportional to the resistance encountered by the fluid. For laminar flow, this force can be approximated as:  
\[\mathrm{F_{d}}=\mathrm{C}\times\mathrm{\dot{x}}\]  
where C is a damping coefficient that depends on the orifice size and fluid properties, and \(\mathrm{\dot{x}}\) is the velocity of the piston.  
In reality, for higher flow rates, the flow may become turbulent, and the damping force becomes nonlinear (proportional to \(\mathrm{\dot{x}}^{2}\)).
# Application Study of the High-Strain Direct Dynamic Testing Method
## 2 Theory of the High-Strain Direct Dynamic Testing Method  
### Measuring Instruments  
For a foundation pile with length L and diameter D, as shown in Figure 1, the pile top center is taken as the origin of coordinates, and the \(x\)-axis is established along the pile shaft direction, with time t as the horizontal axis. Strain sensors are installed at positions with depth x, with N + 1 strain sensors in total. The strain sensor at depth should be as close to the pile bottom as possible to reflect the actual end-bearing force more accurately. The arrangement of strain sensors along the pile shaft should be based on the distribution of soil layers on-site. For this study, the sensor placement was conducted following the Code for Testing of Building Foundation (DBJ/T 15-60-2019) [39].  
A complete measurement line is formed by the pile top accelerometer SA. Many strain sensors are used, one at the pile top and the rest at nodes distributed at various depths along the pile shaft. Each measurement line should be parallel to the central axis of the pile and evenly distributed across the pile's cross-section.  
The piling equipment and impact on the pile top with a hammer after setting the sampling interval \(dt\) for the testing instruments are activated, with a total of M samples collected. The mass of the hammer, the impact method, the sampling interval dt, and the number of samples M should all agree with relevant standards and specifications.
## 3 Analytical Methods
### Whole-Rock Sr and Nd Isotopes Analyses  
The Rb-Sr and Sm-Nd isotopic analysis followed procedures similar to those described by reference [45]. Whole-rock powders for Sr and Nd isotopic analyses were dissolved in Savillex Teflon screw-top capsule after being spiked with the mixed \({}^{87}\)Rb-\({}^{84}\)Sr and \({}^{149}\)Sm-\({}^{150}\)Nd tracers prior to HF + HNO\({}_{3}\) + HClO\({}_{4}\) dissolution. Rb, Sr, Sm, and Nd were separated by use of the classical two-step ion exchange chromatographic method and the isotopic compositions were obtained using a Thermo Fisher Scientific Triton Plus multi-collector thermal ionization mass spectrometer at IGGCAS (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China). The whole procedure blank was lower than 250 pg for Rb-Sr and 100 pg for Sm-Nd. The isotopic ratios were corrected for mass fractionation by normalizing to \({}^{88}\)Sr/\({}^{86}\)Sr = 8.375209 and \({}^{146}\)Nd/\({}^{144}\)Nd = 0.7219, respectively. The international standard samples, NBS-987 and JNdi-1, were employed to evaluate instrument stability during the period of data collection. The measured values for the NBS-987 Sr standard and JNdi-1 Nd standard were \({}^{87}\)Sr/\({}^{86}\)Sr = 0.710264 \(\pm\) 0.000012 (n = 4, 2 SD) and \({}^{143}\)Nd/\({}^{144}\)Nd = 0.512114 \(\pm\) 0.000009 (n = 3, 2 SD), respectively. USGS reference material BCR-2 was measured to monitor the accuracy of the analytical procedures, with the following results: \({}^{87}\)Sr/\({}^{86}\)Sr = 0.705022 \(\pm\) 0.000011 and \({}^{143}\)Nd/\({}^{144}\)Nd = 0.512632 \(\pm\) 0.000010. The \({}^{87}\)Sr/\({}^{86}\)Sr and \({}^{143}\)Nd/\({}^{144}\)Nd data of BCR-2 show good agreement with previously published data by TIMS technique [45].
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 3 Discussion  
In this paper, an implantable dual-band antenna for biomedical telemetry was designed to operate at MedRadio and ISM bands. Miniaturization and dual-band function were attained by adding a shorting pin and cutting U-shaped slots on the patch surface. The performance of the proposed antenna was evaluated inside a pancreas equivalent model, and the resonance and radiation performance were found to be within acceptable limits for an implantable device.  
In addition, for a more accurate estimation of the implantable antenna behavior, the antenna was simulated and measured in the pancreas' surface of a male and a female human phantom. As seen from the analysis of the simulation results, resonance, radiation, and patient safety performance were considered satisfactory for the desired frequencies in both human phantoms. The reflection coefficient in both cases satisfied the \(-10\) dB limit. Nonetheless, at 402.5 MHz, there was a shift in the S11 resonance as compared with the cubic model. Finally, gain and radiated power were low due the antenna's small size and the absorption of radiation by the tissues surrounding the pancreas, with slightly better values in the female human phantom.  
Figure 26: Attenuation of electric field along the x-cut of the female human phantom at **(a)** 402.5 MHz and (b) 2.45 GHz.  
The possibilities that implantable devices can offer to medicine and human body networks in general offer a variety of research perspectives. Future extensions of this work for further investigation of the wireless channel within the human body could be the following:  
* Application of the proposed antenna to the other anatomical models provided by Sim4 Life to study its performance and compare results.
* Extension of the wireless antenna connection study, within an anatomical model with an external receiver, inside a medical room.
* Further downsizing of the proposed antenna designed in this work, with structure modifications and algorithms.
* The construction of the proposed antenna could be carried out to study in vitro its behavior in a real environment within a liquid or gel that simulates the dielectric characteristics of human tissue. Such methods have been used in Reference [3] for exporting experimental data.  
Writing--original draft preparation, M.M.; writing--review and editing, S.K. ([PERSON]); supervision, S.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This article is an extended version of our paper [5] presented in MOCAST Conference.  
The authors declare no conflict of interest.
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Supported Network Topologies  
Mobile ad-hoc networks (MANET) [84; 85] and hybrid wireless sensor networks (WSNs) [86; 87] are the main tactical topologies.The authors of [88] evaluate the performance of network coding in the context of multi-hop military wireless networks. The researchers prove its efficiency in multicast and broadcast communications. They also test the optimal capacity of the system and its ability to recover from lost packets. Network coding operates well even with highly lossy and unreliable links.  
Interest has grown in opportunistic sensing systems [89], particularly on those that take advantage of smartphone-embedded sensors [90]. A network of opportunistic sensing systems can automatically discover and select sensor platforms based on the operational scenario, detecting the appropriate set of features and optimal means for data collection, obtaining missing information by querying resources available, and using appropriate methods to fuse data. Thus, the system results in an adaptive network that automatically finds scenario-dependent objective-driven opportunities with optimized performance. For example, Mission-Driven Tasking of Information Producers (MTIP) [91], is a prototype system for sharing of airborne sensors that focuses on the effective allocation of a large number of potentially competing individual tasks to individual sensors. Nevertheless, specific protocols are needed for advancing autonomous sensing that not only ensures effective utilization of sensing assets but also provides robust optimal performance [92].  
Moreover, the development of a decentralized infrastructure is needed to avoid a single point of failure. Bandwidth is perhaps one of the most precious resources in a tactical environment. It is expected that in dynamic battlefield environments large-scale data analysis will be conducted in near real-time. This fact implies constraints on data analysis coupled with connectivity challenges. Decentralizing computational resources by creating multiple and local cloudlets is insufficient if the overall approach still consists in sending raw data from transducers to a local cloud for processing.
## Materials and Methods
### Attention-Based Feature-Fusion VGG19
#### 2.1.2 Feature-Fusion Modification  
Feature-fusion [34] is a technique used in CNNs to combine features learned from different layers or branches of the network. The goal is to improve the network's performance by taking advantage of complementary information learned at different stages of the network.  
Technically, feature-fusion is implemented by concatenating or adding the feature maps from different layers or branches of the network. The resulting feature maps are then passed through a nonlinear activation function and used as the input to the next network layer. The choice of fusion operation (concatenation or addition) and the specific layers to be fused can be designed based on the problem being solved.  
Figure 1 illustrates the feature-fusion blocks connected to the final three max-pooling layers of the baseline VGG19. A feature-fusion block tracks the output of the max-pooling layer, performs a batch normalisation and a random 50% dropout, and connects the product directly to the concatenation layer.  
The concatenation layer consists of two components: (i) the global average pooling layer and (ii) the densely connected layer, which flattens the extracted features.  
In this way, the low-level features are retained and not refiltered due to the hierarchical nature of the VGG19 network.
## 4 Trajectory-Planning Method  
The flight status and the TDOS of the failure point can provide the direction of mission degradation when the failure occurs, and the ATU-IGM can determine the target orbit-injection capability of the launch vehicle after each degradation step. Therefore, the combination of the ATU-IGM and the TDOS can be used to determine the launch vehicle's orbit-injection ability under different failures. Then, an effective and sufficient decision-making sample can be obtained by traversing the failure states. On this basis, the RBFNN can train the decision-making samples to obtain a decision-making model under power failures for online trajectory planning of launch vehicles. When the launch vehicle carries out mission online planning under failure conditions, the optimal orbital elements can be obtained from the flight status, the failure information, and the TDOS. Finally, the ATU-IGM is used to complete the online trajectory planning of the mission. The method proposed in this paper can enter the enabled state at the time of fault because it can judge the carrying capacity in real time through the fault information and decide whether to re-plan the mission and trajectory independently.  
For the generation of failure samples, the ATU-IGM only needs to obtain the remaining flight time required to enter the target orbit and obtain the maximum flight time of available fuel during transfer by calculating fuel consumption to determine whether the launch vehicle has enough fuel for the target orbit. The ATU-IGM needs to calculate the complete flight trajectory for online trajectory planning and give real-time guidance. Due to the limitation of article length, the RBFNN section is not described in this paper. In summary, the launch vehicle trajectory-planning process under power failures is shown in Figure 3.