esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Unfettered agricultural activities have severely degraded vast areas of grasslands over the last decade. To rehabilitate and restore the productivity in affected grasslands, rangeland management practices still institute vast nitrogen-based fertilization regimes. However, excessive fertilization can often have damaging environmental effects. Over-fertilization can lead to nitrogen saturation. Although early indicators of nitrogen saturation have been documented, research detailing the near-real-time nitrogen saturation status of grasslands is required to better facilitate management protocols and optimize biomass production within degraded grasslands. Hence, the aim of this study was to discriminate nitrogen-saturated tropical grasses grown under a diverse fertilization treatment trial, using Worldview-3 satellite imagery and decision tree techniques. To accomplish this, nitrogen-saturated plots were first identified through specific physiological-based criteria. Thereafter, Worldview-3 satellite imagery (400-1040 nm) and decision tree techniques were applied to discriminate between nitrogen-saturated and -unsaturated grassland plots. The results showed net nitrate (NO\({}_{3}\)\({}^{-}\)N) concentrations and net pH levels to be significantly different (\(\alpha\) = 0.05) between saturated and non-saturated plots. Moreover, the random forest model (overall accuracy of 91%) demonstrated a greater ability to classify saturated plots as opposed to the classification and regression tree method (overall accuracy of 79%). The most important variables for classifying saturated plots were identified as: the Red-Edge (705-745 nm), Coastal (400-450 nm), Near-Infrared 3 (838-950 nm), Soil-Adjusted Vegetation Index (SAVI) and the Normalized Difference Vegetation Index 3 (NDVI3). These results provide a framework to assist rangeland managers in identifying grasslands within the initial stages of nitrogen saturation. This will enable fertilization treatments to be adjusted in near-real-time according to ecosystem demand and thereby maintain the health and longevity of Southern African grasslands.  
nitrogen; nitrogen saturation; rangeland management; random forest; Worldview-3 Article  
[PERSON], [PERSON], [PERSON], [PERSON], N. The Detection of Nitrogen Saturation for Real-Time Fertilization Management within a Grassland Ecosystem  
1
## 7 3D Printing Technologies Applied to Prostheses
### Fused Deposition Modeling (FDM)  
Fused deposition modeling (FDM) is one of the most widely used 3D printing technologies for prosthetic manufacturing due to its simplicity, low cost, and ease of access (Figure 8). In this process, a filament of thermoplastic material, such as ABS or PLA, is heated and extruded through a nozzle, depositing layer-by-layer to form the prosthesis. FDM has become the preferred technology for many prosthetic manufacturers due to its low cost of materials and the availability of printers in the market, which allows both professionals and amateurs to create custom prostheses.  
In addition, FDM makes it possible to manufacture lightweight but durable prosthetics, which is crucial for users who need functional devices that do not cause discomfort during prolonged use. The technology has proven particularly useful for creating upper limb prostheses, such as hands and fingers, that require a balance between structural rigidity and flexibility in moving parts [9]. However, one of the main limitations of FDM is the relatively low surface quality and limited accuracy compared to other printing technologies, which may require post-processing processes to improve the aesthetics and functionality of the device [10].
# Clipping Noise Compensation with Neural Networks in OFDM Systems
## 4 Conclusions  
In this article, a DL-based scheme to mitigate the clipping effects in OFDM systems is demonstrated. A four-layer NN is used and its output is post processed with a DAR-inspired post processing to protect the unclipped part from undesired distortions. The simulation results show that the MSE and BER performances under different clipping ratios and channel conditions are superior to that of the existing algorithms. To interpret the behavior of the NN, the hidden patterns of the trained weight matrices are revealed, and they facilitate our understanding of the neural network.  
The contributions to this article include: conceptualization, T.-H.S. and Y.-C.X.; methodology, T.-H.S. and Y.-C.X.; software, Y.-C.X.; formal analysis, T.-H.S. and Y.-C.X.; investigation, T.-H.S. and Y.-C.X.; resources, T.-H.S.; data curation, Y.-C.X.; writing--original draft preparation, Y.-C.X.; writing--review and editing, T.-H.S.; visualization, Y.-C.X.; supervision, T.-H.S.; project administration, T.-H.S.; funding acquisition, T.-H.S. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the \"Center for mmWave Smart Radar Systems and Technologies\" under the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), and partially funded by the Ministry of Science and Technology (MOST) of Taiwan under MOST 108-3017-F-009-001 and MOST 108-2218-E-009-018.  
This work was partially supported by the \"Center for mmWave Smart Radar Systems and Technologies\" under the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), and partially supported under MOST 108-3017-F-009-001 and MOST 108-2218-E-009-018, in Taiwan.  
The authors declare no conflict of interest.
# Measurement of Unit Weight of Dry Sand Using Piezoelectric Sensor
## 1 Introduction  
Piezoelectric materials (PZT, i.e., lead zirconate titanate) often find varied applications in civil engineering projects. One of the PZT sensors, called the bender element, has been used to generate shear or compressive waves during laboratory testing [1; 2; 3; 4]. On the other hand, the electro-mechanical impedance (EMI) technique employing PZT sensors has been widely used as a non-destructive testing technique for monitoring concrete structures. When using this technique, the PZT material is usually attached to the surface of concrete and steel bridges and other such concrete structures to monitor their strength or deformation [5; 6; 7]. Recently, the technique was used to measure the curing conditions of concrete [8; 9; 10].  
Soil compaction is the most fundamental procedure in soil engineering. The degree of soil compaction can be determined by measuring the unit weight (density) of compacted soils. Three methods are usually used to determine the in situ density of compacted soils: (a) The sand-cone method; (b) the rubber-balloon method; and (c) the nuclear method. Soil densities around pipes or retaining walls could be reduced owing to soil washing due to leakage or groundwater flow [11]. At times, cavitation may also occur resulting in ground subduction. Recently, many instances of ground failure caused by such changes in soil density have been noticed. The three methods fordetermining the in situ density are not able to measure the continuous variation of density. It has, therefore, become necessary to regularly monitor the unit weight of soils in the field.  
In this study, a PZT sensor was embedded in three different grain sizes (small, medium, and large) of the Nakdong river sand. The sand grains were poured into the small compaction mold and four different levels of surcharge were applied on the surface of each type of sand. Subsequently, the frequency and conductance of the PZT sensor were measured for each type of sand under the different levels of load. The resonant frequency and corresponding conductance of the PZT sensor in a specific frequency range were then correlated with the level of load and corresponding unit weight or density of sand. Such a relationship could be used to predict the unit weight of soil subjected to compaction, loosening, or cavitation in the field.
## 2 Driver/Passenger Acceptance
### Measurement of Driver/Passenger Acceptance  
Measurement of driver/passenger acceptance must be such an uncertain work because it is based on individuals' emotional factors. However, measurement of those uncertain human factors has been researched in the fields of drivers' mental workload. Thus, as well known, the solutions to measure drivers' mental workload are classified into three categories: subjective measures, performance measures, and physiological measures [22].  
One of the representative methodologies of subjective measures is a self-reporting method. Self-reporting methods have always been very attractive to many researchers. No one can provide a more accurate judgment concerning a mental load than the person experiencing it [23]. The self-reporting method is considered one of the best methods since this type of research comes closest to tapping into the essence of mental workload [24].  
Performance measures are classified into three categories: primary-task performance measures, secondary-task performance measures, and reference tasks [23]. Primary task performance is a measure of the overall effectiveness of man-machine interactions [22]. For vehicle driving experiments, the number of errors made in tracking performance, speed of performance, or reaction time measures are frequently used as primary task performance measures. Secondary task measures can be taken when another task is added to the primary task. Reference tasks are standardized tasks that are performed 'before' and 'after' the task under evaluation, and they mainly serve as a validation instrument for trend effects.  
The last type of measures represents those derived from the operator's physiology. The advantage of physiological responses is that they do not require an overt response by the operator, and most cognitive tasks do not require overt behavior. Several disadvantages of physiological measures are mentioned in [25], i.e., the required specialized equipment and technical expertise and the critical signal-to-noise ratios. It is also stated that the operator's physiology, a reflection of bodily functions, is further removed from operator-system performances than primary task performance.  
Considering the measurement of driver acceptance concerning the above-described methodologies, in this study, two methods were employed to measure and investigate driver/passenger acceptance: the subjective measure and the performance measure. Thus, the vehicle experiment program was designed concerning those methods as follows. As mentioned above, the subjective assessment, self-reporting on acceptance, is supposed to be immediately coded between 'Accept' and 'Reject' considering the reliability of true values. Additionally, with respect to the performance measures, dozens of measurable objective parameters were continuously collected for the vehicle experiments. By investigating the relationships between the subjective assessment results and the measured objective parameters, objective methodologies for the evaluation of driver acceptance were herein studied.
## 7 Underwater Localization Techniques
### Sound Localization (SL)  
In AUV-assisted localization systems, there are three types of communications: wake-up, demand, and reaction messages. There are three processes involved in situating [69]. Upon entering the sensing active region, the AUV transmits a wake-up signal. Each sensor node will emit a request signal or packet as soon as it has received the wake-up signal from the other sensor nodes. After that, the AUV replies with a packet comprising its coordinates. For this phase, the AUV must communicate with each detector node at least once. As a result, it is usual to need more energy for localization. Better than the AUV and the AUV is that throughout the localization era, the sensor nodes only receive beacons and do not connect with other nodes due to the high energy consumption of the sensor nodes' communication. An example of this kind of strategy is a \"silent localization technique.\" Beacons were previously swapped between the sensor nodes and the AUV. Last but not least, it is possible to drastically lower the underwater localization power usage by a silent localization.
# Enhancements and Challenges in CoAP--A Survey
## 6 Open Challenges
### Energy Efficiency of CoAP-Enabled Nodes  
As we know, a CoAP node regularly checks the server for resource discovery. To discover a resource, CoAP implements two different approaches i.e., (1) Distributed Resource Discovery (DRD) and (2) Centralized Resource Directory (CRD) [48]. In the case of DRD, a CoAP-enabled device directly communicate with the server for the required resources. However, the CoAP-enabled node required high energy if a resource is not available for a longer time and the node operates on the battery power. Similarly, in the case of CRD to avail resources, CoAP-enabled nodes periodically communicating with a centralized directory. However, in the case of CRD, the directory should be updated regularly, otherwise, a CoAP-enabled node will consume its battery eventually. Such constant failure in getting resources increases the failure probability of the network. Resulting, in the degradation of the services provided by the network.
## 4 Conclusions  
In this paper, the distribution of the initial stress of the configuration-controllable metamaterial under axial displacement was analyzed using numerical and experimental methods. And the influence of initial stress on the bandgap characteristics of the structure was compared with the results without considering initial stress. The numerical results showed that the bandgap characteristics of the CCPM with initial stress are more in accordance with the published experimental transmission spectra. This indicated that initial stresses have a significant influence on bandgap characteristics as the configuration of CCPM changes. The research further revealed the reasons for the errors between numerical and experimental data for paper [11].  
As the initial tensile or compressive stress increases, the first initial bandgap contracts and new bandgaps occur. Existing bandgaps will be closed or new ones will be opened when the initial stress turns to a certain value. This feature can be considered when controlling vibrations of multiple frequencies. Considering the initial compressive stress, the first initial bandgap is reduced to 254 Hz-291 Hz and the second additional bandgap is reduced to 391 Hz-400 Hz when the CCPM is compressed by the displacement of 10 mm. The third additional bandgap occurs as the compression and tension displacements reach 20 mm. The tensile initial stress shifts up the bandgaps, while the compressive initial stress shifts them down. This feature can be used for vibration reduction at high and low frequencies.  
Conceptualization, H.Z. and J.W.; methodology, F.Y., H.Z. and J.W.; software, F.Y. and Q.F.; validation, Q.F.; data curation, F.Y.; writing--original draft preparation, F.Y.; writing--review and editing, H.Z. and J.W.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated and analyzed during this study are included in this article.  
The authors declare no conflict of interest.  
Figure 13: The first initial bandgap width changes with the deformation mode: (**a**) compression; (**b**) tension.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Parameter Sensitivity Experiments  
In this section, parameter experiments were conducted, keeping other parameters constant, for the following four variables: number of base classifiers, \(N\); size of sliding window, _sizeW_; initial value of elements in the minimum marginal threshold matrix, \(\theta_{1}\); and the initial value of elements in the maximum marginal threshold matrix, \(\theta_{2}\). The range of \(N\) was \([5,20]\), the range of _sizeW_ was \([250,1000]\), the range of \(\theta_{1}\) was \([0.4,0.6]\), and the range of \(\theta_{2}\) was \([0.7,0.9]\). Experiments were conducted on two data streams with mixed-type concept drift, as follows: Fixed_Mix_SIG and Var_Mix_SIG. The former has a fixed class proportion, while the latter experiences changes or flips in class proportion as the data stream progresses.  
As shown in Figure 2(a),e, the performance of the algorithm gradually improved with the increase in the number of base classifiers, with the greatest improvement occurring when the number increased from 5 to 10. However, after \(N=10\), further increases in the number of base classifiers did not significantly enhance the algorithm's performance, particularly with the Var_Mix_SIG data stream, where increasing the number of base classifiers hardly improved the performance. This indicates that the ensemble already possesses sufficient diversity, and further increasing the number of base classifiers has little effect on performance improvement. Simultaneously, the default setting for the other ensemble classification algorithms was 10, so the default value for parameter \(N\) was set to 10. As shown in Figure 2(b),f, the best effect was achieved when the window size was \(sizeW=500\). An appropriate window size ensures that each class contains a sufficient number of instances and avoids the influence of outdated concepts. Figure 2(c),g shows the impact of the initial value of the minimum marginal threshold matrix element, \(\theta_{1}\), on the algorithm's classification performance. When \(\theta_{1}\) was 0.5, all of the algorithm's metrics were optimal. When \(\theta_{1}\) was less than 0.5, the identification of difficult instances became stricter, leading to insufficient learning of representative instances and resulting in decreased classification performance. When \(\theta_{1}\) was greater than 0.5, the likelihood of querying labels for nonrepresentative instances increased, which encroached on the learning opportunities for difficult instances, thus affecting the algorithm's performance. Similarly, as shown in Figure 2(d),h, the optimal value for the initial value of the maximum marginal threshold matrix element \(\theta_{2}\) was 0.75.  
In summary, the default values for the parameter combination chosen for the proposed algorithm were \(N=10\), \(sizeW=500\), \(\theta_{1}=0.5\), and \(\theta_{2}=0.75\), and these settings were followed in subsequent experiments.
## 6 Grid and SDG Current Estimations  
The time delay of using PMUs can significantly affect the FIM strategy of separated phase-current control because it needs data from the grid and SDG currents for calculating the current reference. To avoid this delay, this paper also presents the grid and SDG current estimations programmed into the IBDGs.  
The grid and SDG current estimations can be divided into three cases depending on the fault section, as shown in Figure 6. Z1, Z2, and Z3 are impedance per length. Each fault section is distinguished by considering the signal of the directional overcurrent relay (DOCR) placed at the IBDG1 and SDG nodes. Sending the relay signal is carried out by teleprotection. If a fault occurs at point C in the section next to the SDG, IBDG1 and IBDG2 receive the relay signals from both DOCR1 and DOCR2. If the fault is at point B in the section between IBDG1 and SDG, the relay signal is sent by only DOCR1. When the fault occurs upstream of the DOCR, for instance at point A, no relay signal is transmitted by either DOCR1 or DOCR2. Besides, current estimations require the sampled voltage and current data of the IBDGs. These sampled values can be exchanged between IBDGs through smart grid communication.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 4 Scanning Principle
### Non-Resonant Scanner  
Resonant fiber scanners provide a large scan area for a given input power. However, it is difficult to get a resonant fiber scanner for an application requiring low-frequency scanning [81]. From Equation (6), it is clear that very long and slender beams are required to get a small resonant frequency. Moreover, the resonant scanners do not provide the flexibility in offsetting the center of the image field from the optical axis. It is possible to offset the center using additional deflectors, which, on the other hand, make the scanner more complex and bulkier.  
Such problems can be partially solved by exciting the scanning fiber at a frequency away from its natural frequency. In this case, the tip displacement of the fiber, given the excitation power, will be much smaller. There are some non-resonant scanners available using a bimorph piezo bender actuator [86],  
Figure 12: Mode shapes of a cantilever beam describing the beam shape in vibration at resonance.  
and electro-magnetic actuators [87]. As stated earlier, these scanners use a high amount of power to produce the desired tip displacement, which can cause safety issues in such devices.  
[PERSON] et al. developed an MEMS scanner for the design of an endoscopic OCT probe. In this case, a 40-mm-long fiber is actuated using a 3 V drive voltage [88]. [PERSON] et al. designed a non-resonant thin-film piezoelectric actuated scanner for an OCT application [89].  
Non-resonant scanners can be miniaturized by decreasing the length of the cantilevered portion and increasing the tip deflection using the leverage method. [PERSON] and [PERSON] used a double-lever method to amplify the tip displacement of the optical fiber. In this case, a piezo-bender actuator was connected to the fiber at the cross point of the leverage. Such an optical scanner provides a tip deflection comparable to that obtained in resonant scanners, with the possibility of shifting the center of the image [90].