esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Field Experiment  
The field experiment was carried out at the experimental Organic Farm in Chwalowice (I'hza Municipality, Radom County, Masovia Region, Poland), which belongs to the Agricultural Advisory Center in Brwinow, Radom, Poland (51\({}^{\circ}\)18' N, 21\({}^{\circ}\)30' E). The field trial was set up as a split block design with three replications, on brown soil. Neither pest nor disease and weed control was applied in the trial. Organic fertilization of the plots in the form of compost was applied, in the amount of 60 dt ha\({}^{-1}\).  
Investigations were carried out on 17 spring barley cultivars (_Hordeum vulgare_ L.: Natasia, SU Lolek, Britney, Ella, KWS Dante, KWS Atrika, Olimpic, Rubaszek, Oberk, Barylka, Tocada, Podarek, Soldo, Penguin, Fabienne, Fariba and Argento) and 8 oat cultivars (_Avena sativa_ L.: Harnas, Haker, Arden, Bingo, Nawigator, Krezus, Pascal and Amant). The cultivars were selected from the Polish National List of Agricultural Plant Varieties. The performance of cultivars registered on the National List was previously tested by The Research Centre for Cultivar Testing (COBORU) at average and intensive level of agrotechnics. For our experiment, we selected cultivars recommended for average level of agrotechnics based on their performance in terms of yielding and disease resistance.  
Weather conditions during the development stage of plants are shown in Table 1. In May, the precipitation was found to be higher than the multiannual average. From June to August, a deepening drought occurred, with temperatures higher than the multiannual average.  
The grains of barley and oat cultivars were harvested at the stage of full maturity. The grain samples (500 g each) after collection (2015 harvest) were stored at room conditions (23 \(\pm\) 1 \({}^{\circ}\)C, \(\sim\)60% relative humidity) and milled in the MLU-202 laboratory mill (Buhler GmbH, Uzwil, Switzerland). Until chemical analyses, the milled samples were stored in \(-\)20 \({}^{\circ}\)C.
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Power Reduction Techniques
#### 3.1.3 Data Mitigation
Each sensor node contains four sensors to identify five agricultural field parameters: soil temperature, soil moisture, environmental humidity, soil electrical conductivity, and environmental temperature. The PDA combines the ZigBee coordinator node, GPRS module, and GPS module. The ZigBee WSN gathers farming data and obtains GPS information for each sensor node. It then passes the data to the host PC through GPRS. The experimental results show that the battery power of the sensor nodes can be saved based on the data-gathering scheme. Data compression_ is a useful method for deploying sensor nodes with restricted energy resources. Information encoding can be performed in sensor nodes, whereas information decoding can be performed in sink nodes. Using compression techniques to reduce the size of information that the sink nodes transmit over wireless channels may also reduce the power consumption and extend the battery life of the sensor nodes. One study has applied data compression approaches to reduce the power consumption for auto-irrigation system which based on CC1000 RF module [41]. Based on this method, the battery of the sensor node can be extended to 359 days. The _data-driven_ power reduction technique aims to reduce the amount of sampled data by conserving the sensing accuracy to an appropriate level for a specified application [94]. Thereby, the amount of data to be transmitted to the sink nodes can be minimized. Therefore, this technique can reduce the power consumption of agriculture WSNs and extend their battery life. A data-driven algorithm is considered by [PERSON] and [PERSON] [95] to solve the energy consumption problem of the sensor node in PA with low complexity and maintain the transmission rate. Their experiment reveals that their proposed algorithm has higher energy efficiency than the ESPIN and SPIN algorithms in [96; 97] by 36.84% and 81.53%, respectively. The _data rate_ of RF transceivers can be adjusted in the physical layer of a wireless protocol to reduce the power consumption of the sensor node. [PERSON] et al. [98] investigated the low battery condition of a sensor node to attain prolonged battery life for separate wireless sensor nodes that can preserve sampling suitability and reliability in communicating accurate data. Different sensors, such as those for soil moisture, sap flow, and soil water, were examined to study the effect of sensing debility due to low battery voltage. The comparison of the data sampling rate of these sensors shows that sap flow measurement is the best option for agricultural WSN. This finding is attributed to the low error obtained when the sampling interval increased to 15 min while maintaining measurement accuracy. Table 4 shows the comparison of power reduction technique based on _data mitigation_ schemes for previous research in agricultural applications.
# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 5 Discussion
### Data Analysis for Dataset 3  
Table 6 gives the result of the 7 Machine algorithms and 4 ensemble learning using Dataset 3. The decision Tree attained an accuracy level of 0.9234. KNN had an accuracy of 0.9051. Logistics Regression also has an accuracy of 0.8896. SVM, QDA, and Neural Network also had accuracy results of 0.9243, 0.8528, and 0.9186, respectively. Gaussian NB, however, did not do so well in terms of accuracy, having an accuracy score of 0.8070, making it not so far from the true value but still within range. Table 6 also has other evaluation metrics results, such as Precision, Recall, Specification, F1 Score, and NPV, which we used across the following algorithms, ranging from Decision Trees, KNN, Logistics Regression, Gaussian NB, Neural Network, QDA, and SVM. All attained approximate scores of 0.92, 0.91, 0.89, 0.81, 0.92, 0.85, and 0.92 respectively. The scores obtained for FDR, FNR, and FPR across all 7 algorithms ranged between 0.07 and 0.19, i.e., the range is between 7-19%. This result implies that only one evaluation metric cannot fully be enough to predict the efficiency of an algorithm, except tested with other metrics. Figure 5 gives a clear graphical representation of the Evaluation metrics and the Algorithms using Dataset 3. The Bar graph represents each of the machine learning algorithms and the accuracy results they produced in percentages. The four ensemble learning models used in the course of this project, which include Ada boost, Gradient Boosting, Random Forrest, and Max voting, as mentioned earlier, were extensively trained using our datasets. Outputs were collected from each of the algorithms. Each of the algorithms considering our performance metrics, as stated in chapter three, produced an output result consisting of Accuracy, Precision, Recall, Specificity, MCC, KAPPA, F1 Score, AUC, FDR, FNR, FPR, and NPV. The accuracy levels help us identify which of the ensemble classifiers performs best when it comes to detecting virtual harassment. Table 6 also gives the result of the 4 ensemble learning models, highlighted in green. Random Forest had the highest accuracy level of 0.9258, Max Voting had an accuracy level of 0.9254, Gradient Boosting had 0.8266, and Ada Boost had the least accuracy level of 0.8238. While considering other evaluation metrics, Random Forest turns out to have a KAPPA score of 0.77 (77%), while Gradient Boosting had 0.23 (23%). All four ensemble learning obtained Precision, Recall, Specificity, F1 score, and NPV of 82%, 83%, 93%, and 93%, respectively, starting from Ada Boost, Gradient Boosting, Random Forest, and Max voting, making it very suitable for detecting virtual harassment in Dataset 3. QDA produced an AUC score of 0.89, making it the highest under AUC, showing perfection in the model's prediction.
## 3 Typical Examples of THz Conductivity in Nanomaterials
The physical parameters are shown in the caption of Table 1.2  
Table 1.3 Complex conductivity in single crystalline epitaxial ZnO films. Open circles and triangles are the experimental data extracted from [31]. Solid lines are the conductivity predicted from the SSFTC model. The reason for this can be clearly understood in terms of equivalent electrical circuits. The Drude mechanism, _i.e._, free carrier scattering (and loss), is equivalent to \(LR_{\text{g}}\) series connection, where \(L\) is the inductance and \(R_{\text{g}}\) the resistance in grains. The tunneling mechanism, _i.e._, charge accumulation (and loss), is equivalent to \(CR_{\text{gb}}\) parallel connection, where \(C\) is the capacitance and \(R_{\text{gb}}\) the resistance in grain boundaries. We know that a series connection of these elements produces the Lorentz-type resonance at a certain frequency [2]. Current flow (conductivity) is dominated by \(R_{\text{gb}}\) at lower frequencies and is dominated by \(L\) at a high frequency, _i.e._, zero conductivity at low and high frequencies when \(R_{\text{gb}}\) is large. We recognize, therefore, that a negative imaginary conductivity at a lower frequency is dominated by the grain boundary transport and a positive imaginary conductivity is dominated by the free carrier scattering within the grain. The SSFTC model can judge which factor is dominant in nanostructured materials. Recent reports on the THz conductivity in InP nanowires [33] and silicon nanocrystal superlattices [34] show similar behaviors, as shown in Figure 9, suggesting the importance of the tunneling contribution of carriers in these new materials. We should mention another important modeling technique that is based on computer simulations. It is recognized that Monte Carlo simulations can provide useful insight and information on carrier dynamics [12; 35], which replicate well the non-Drude type behavior. This technique is useful when one discusses the carrier dynamics inside semiconductor nanoparticles. Carrier scattering processes at boundaries have been discussed in great detail [12]. If the carrier mean-free-path (MFP) is much smaller than grain size (or grain size is much larger than MFP), the carriers are regarded as moving in homogeneous media. In this case, the carrier dynamics can be well interpreted by the Drude-Smith model [21; 36]. Finally, it should be mentioned that the interband optical transition (Lorentz oscillator), combined with the Drude contribution of free carriers, also shows a non-Drude behavior, which has been reported in carbon-based nanotubes [37]. Thus, the examination of the SSFTC model for other material systems will shed a great deal of light on the mechanism of optical conductivity in the terahertz range.
## 5 Analysis of Results  
In our research, the ZED-F9P four-system GNSS observations are processed in instantaneous PPP mode, applying the obtained corrections to satellite orbits and clocks through BKG, CNES, IGS, GFZ, and WHU analysis centers. Various single-constellation dual-frequency PPP solutions are adopted, including GPS-PPP, GLONASS-PPP, Galileo-PPP, and BeiDou-PPP solutions; moreover, the acquired datasets from the Trimble R4s  
Figure 4: Visible satellites and PDOP over our examined station.  
receiver are processed in post-processed PPP mode utilizing the traditional dual-frequency GPS-PPP processing model. Thereafter, the resulting coordinates from the ZED-F9P's PPP models are validated with those estimated from the geodetic-grade GPS-PPP solution; our obtained results are validated in static positioning and PWV applications.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 4 Analysis  
As mentioned above, the target detection and surface clutter reduction in the proposed distributed GPR configuration are better than conventional GPR architectures. The previous analysis was performed for a small generic target (both dielectric and metallic), whose behavior is similar to a point source.  
To further analyze this system for the proposed application (landmine and IED detection), a target with characteristics similar to a plastic PMN-type landmine was considered. In particular, the target, which is fully composed by TNT, has a cross-section of 10 cm \(\times\) 4 cm and it has been buried at 25 cm depth under the same rough surface used previously. This is one of the most challenging scenarios for this application mainly due to the major part of the reflected signal from the target being specularly-reflected away from the receiver, the low-contrast between the target and the soil, and the clutter due to the soil surface roughness.  
The SAR images obtained from FDFD simulations for the configurations previously compared (multimonostatic DLGPR, multistatic and multibistatic FLGPR-DLGPR) as well as the multimonostatic FLGPR configuration were compared in this scenario. In the multimonostatic FLGPR configuration (shown in this section for comparison purposes), the TX-RX is moved between down-track positions \(x=-20.8\) m and \(x=-19.2\) m at \(y=2.5\) m height, with main beam pointing at an angle of \(83^{\circ}\). The results for all these configurations are shown in Figure 15. As explained above, the multistatic and multibistatic FLGPR-DLGPR configurations show similar results (Figure 15c,d, respectively). In the multimonostatic configurations (Figure 15a,b), the target cannot be detected at all. Furthermore, in the multimonostatic FLGPR case (Figure 15b), it can also be seen that there is poor resolution in vertical position, which implies that it is not possible to distinguish even whether the target is above or below the surface (in case it was detected) [16]. For this configuration, the bright high-intensity bands shown in the SAR image are generated entirely from the rough surface scattering, and their extended lengths are due to poor synthetic focusing in the down range direction. Their change of direction is due to the fact that the refraction at the soil surface is taken into account in the inversion. However, in the proposed multibistatic FLGPR-DLGPR configuration (Figure 15d), two reflections coming from the target (at \(-15\) dB mainly from its left and right sides) can be clearly distinguished, thus allowing the detection of the target.  
Figure 14: SAR image from FDFD simulations with a dielectric target buried under a rough surface considering TM polarization.
# Exploring Convective Drying Behavior of Hydroxide Sludges Through Micro-Drying Systems
## 5 Future Perspectives  
Moving forward, several research avenues can be explored to expand upon our findings:  
1. Scaling Up the Study: Future work should validate these results at an industrial scale, considering larger sludge volumes and different drying configurations to assess real-world applicability.
2. Advanced Drying Optimization: The integration of machine learning models and intelligent control systems could enhance real-time monitoring and adaptive process control, improving energy efficiency dynamically based on sludge properties.
3. Valorization Pathways: Investigating the potential reuse of dried hydroxide sludge in various industries, such as cement production, agriculture, or energy recovery, could further enhance sustainability in sludge management.
4. Environmental Impact Assessment: A Life Cycle Assessment (LCA) would provide a holistic evaluation of the environmental footprint of different drying strategies, supporting decision-making for more sustainable wastewater treatment processes.  
By addressing these aspects, our study not only advances the scientific knowledge but also lays the groundwork for practical improvements in sludge management, with the ultimate goal of achieving more energy-efficient and sustainable drying operations in DWTPs.
# An Overview of GEO Satellite Communication Simulation Systems
## 2 Coverage Analysis
### Simulation-Based Coverage Modeling  
While fast evaluation algorithms offer computational efficiency, they may compromise accuracy in highly dynamic or complex scenarios. To address this, simulation-based coverage modeling provides an alternative to produce precise and realistic coverage assessments. For example, [PERSON] et al. [17] demonstrated the effectiveness of simulation-based methods by integrating a Systems Tool Kit (STK) with MATLAB to evaluate satellite visibility and target coverage performance. By modeling both individual reconnaissance satellites and Walker constellation networks, the approach enabled comparative analysis of coverage effectiveness in maritime operational areas, supporting mission planning with high-fidelity simulation outputs.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Deployment
#### 5.1.2 Implementation Phase  
After researchers click the Start Button at Web UI, the start signal is sent to the main controller. Based on connection relationships and assigned network parameters recorded in the database, the main controller creates emulation nodes, binds them to SDN switches, and informs the start time of emulation. Then an initialization daemon is activated in each emulation node and reads the database to fulfill automatic configurations, including the implementation of the dynamic link characteristic, routing setting, protocol stacks establishment, and etc. Subsequently, emulation nodes are suspended until the beginning of emulation.  
During the process of emulation, the main controller issues flow entries to SDN switches to realize the dynamic network. Designated source nodes transfer data packets through the emulation network and finally arrive at the destination. If researchers want to change the network scenario, they can go back to the step of creating the network model. If they need to change protocols and services, they can go back to the service modeling step. It is flexible and easy to change the scenario by reconfiguring emulation parameters.
# Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
## 1 Introduction
The advantage of spectral imaging over current imaging techniques is to differentiate and quantify multiple biomarkers and drug penetration into the target tissue at the same scan. At present, spectral CT imaging requires phantoms containing known quantities of known reference materials in order to calibrate the biological data. However, spectral CT decomposed images that show good correlation over the full range of measured concentrations of the materials may contain unrecognized misidentification errors between the different materials. The ground truth for the identity and concentration of all materials in a complex biological specimen is frequently unknown. Therefore, a need has arisen to both measure and monitor changes in material detectability in the post-MD phantom images before they can be used for calibration purposes in specimens whose composition is unknown. The aim of this study is to generate a quantitative metric using MD images of a calibration phantom which can be used to quantify correct material identification at various concentrations. We compare two different imaging protocols using one sparse solution material differentiation method for post material decomposition assessment by measuring the sensitivity (true positive rate) and specificity (true negative rate) of individual high-Z material at each concentration in the material image domain.
## 4 Results and Discussion  
### Criterion Layer Weights  
As shown in Figure 3, Trust and Security emerges as the most critical indicator within the criterion layer, followed by Basic Performance (B1, 0.2710), Features and Technology (B3, 0.1355), and Design and Interface (B2, 0.0442). This reflects the high level of concern users have regarding the protection of personal information and health data when using medical websites. Additionally, Basic Performance and Features and Technology ensure  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criterion** & **Weights** & **Rank** & \
\begin{tabular}{c} **Solution** \\ **Layer** \\ \end{tabular} & **Weights** & **Rank** &
\begin{tabular}{c} **Overall** \\ **Weights** \\ \end{tabular} & **Rank** \\ \hline \multirow{8}{*}{B1} & \multirow{8}{*}{0.2710} & \multirow{8}{*}{2} & C1 & 0.3440 & 1 & 0.0932 & 4 \\ \cline{3-8}  & & & C2 & 0.0760 & 5 & 0.0206 & 12 \\ \cline{3-8}  & & & C3 & 0.1785 & 2 & 0.0484 & 6 \\ \cline{3-8}  & & & C4 & 0.1479 & 4 & 0.0401 & 8 \\ \cline{3-8}  & & & C5 & 0.0482 & 7 & 0.0131 & 17 \\ \cline{3-8}  & & & C6 & 0.0533 & 6 & 0.0144 & 15 \\ \cline{3-8}  & & & C7 & 0.1521 & 3 & 0.0412 & 7 \\ \hline \multirow{8}{*}{B2} & \multirow{8}{*}{0.0442} & \multirow{8}{*}{4} & C8 & 0.1172 & 3 & 0.0052 & 21 \\ \cline{3-8}  & & & C9 & 0.1295 & 2 & 0.0057 & 20 \\ \cline{3-8}  & & & C10 & 0.0410 & 6 & 0.0018 & 24 \\ \cline{3-8}  & & & C11 & 0.0720 & 5 & 0.0032 & 23 \\ \cline{3-8}  & & & C12 & 0.5529 & 1 & 0.0244 & 11 \\ \cline{3-8}  & & & C13 & 0.0873 & 4 & 0.0039 & 22 \\ \hline \multirow{8}{*}{B3} & \multirow{8}{*}{0.1355} & \multirow{8}{*}{3} & C14 & 0.0503 & 6 & 0.0068 & 19 \\ \cline{3-8}  & & & C15 & 0.0970 & 4 & 0.0131 & 16 \\ \cline{3-8}  & & & C16 & 0.4272 & 1 & 0.0579 & 5 \\ \cline{3-8}  & & & C17 & 0.2352 & 2 & 0.0319 & 10 \\ \cline{3-8}  & & & C18 & 0.1282 & 3 & 0.0174 & 14 \\ \cline{3-8}  & & & C19 & 0.0620 & 5 & 0.0084 & 18 \\ \hline \multirow{8}{*}{B4} & \multirow{8}{*}{0.5494} & \multirow{8}{*}{1} & C20 & 0.0358 & 5 & 0.0197 & 13 \\ \cline{3-8}  & & & C21 & 0.3370 & 2 & 0.1852 & 2 \\ \cline{3-8}  & & & C22 & 0.2172 & 3 & 0.1194 & 3 \\ \cline{3-8}  & & & C23 & 0.0653 & 4 & 0.0359 & 9 \\ \cline{3-8}  & & & C24 & 0.3447 & 1 & 0.1894 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Weight summary.  
that the website both meets the basic operational needs of users and offers technological functionality and services. Although Design and Interface carry a lower weight, they still play a significant role in enhancing user experience, boosting user satisfaction, and establishing brand identity. Therefore, in the design and development of medical websites, there should be a greater emphasis on protecting user health information and providing an efficient and convenient service experience, rather than solely focusing on visual appeal.
## 5 Discussion and Future Works
### CAV Integrated with the Cloud
A good example is polymorphic and metamorphic malware, which changes itself each time it replicates. It has been proven that individual vehicles are fragile when they face such powerful attacks [88]. Therefore, taking some defense measures on the cloud will provide extra security to protect CAVs from smart attacks. Basically, intrusion detection can protect CAVs from security issues. Traditional intrusion detection uses some static analysis approaches, such as comparing programs to known malware based on the program code, looking for signatures, or using other heuristics, which gradually expired to defend against the deformation of these attacks because they still expose some security loopholes [88; 131]. Incidentally, the cloud platform presents tremendous advantages for implementing new types of AI-based detection methods [132; 133; 134; 128]. With the management of the cloud, large volumes of data can be captured from the internet and collected from traffic situations by setting up some honeypot and trapping, and sufficiently powerful hardware can be provided to support these sophisticated algorithms. This way, the components relevant to the reliable driving operations of each vehicle can be monitored by the cloud. In practice, the local vehicle can collect anomaly data in the first time. Each vehicle primarily performs core intrusion detection in a distributed manner, which is referred to as local layer intrusion detection. If a local vehicle cannot decide whether the data is malicious, it transmits the data to the cloud for further detection. The remote cloud can provide further verification of the local detection results within the time constraint, which is referred to as outer layer intrusion. Therefore, how to relieve the delay in communication in the multi-layer detection process to meet the requirements imposed by some applications is challenging.