esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microbial Residues and Amino Sugars Analysis  
The concentration of amino sugars (GluN, GaN, and MurN) in the soil was determined according to the method described by [PERSON] and [PERSON] [33]. Briefly, soil samples were hydrolyzed at 105 \({}^{\circ}\)C with 6 M HCl for 8 h. The solutions were filtered and centrifuged after adjusting their pH to a level between 6.6 and 6.8. The supernatant solutions were then freeze-dried, and methanol was used to wash out the AS from the residues. The recovered AS were converted to aldononitrile derivatives and the excess derivatization reagents were removed by a two-step reaction. In the first step, a derivatization reagent containing 40 mg mL\({}^{-1}\) 4-(dimethylamino) pyridine in pyridine methanol (4:1 \(v/v\)) and 32 mg mL\({}^{-1}\) hydroxylamine hydrochloride was added to the dry sample in the airlock, while in the second step, acetic anhydride was added. Dichromethane was used to extract the derivatives, and 1M HCl and distilled water were used to remove excess anhydride. The final AS derivatives were then dissolved in 300 uL ethyl acetate-hexane (\(v\):\(v\) = 1:1) for analysis. Internal standard (myo-inositol) was added before hydrolysis and the derivatives of AS were determined using an Agilent 6890A GC (Agilent Tech. Co., Wilmington, DE,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & **SOC** & **TN** & **NH4\({}^{+}\)N** & **NO3\({}^{-}\)N** & **Olsan—P** & **pH** & **GluN** & **GalN** & **MurN** \\  & **(g kg\({}^{-1}\))** & **(g kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** & **(t2.5)** & **(mg kg\({}^{-1}\))** & **(g kg\({}^{-1}\))** & **(g kg\({}^{-1}\))** \\ \hline Content & 8.63 & 0.95 & 0.78 & 4.74 & 42.70 & 8.22 & 145.09 & 67.42 & 7.31 \\ \hline \hline \end{tabular}  
* SOC: soil organic carbon; TN: total nitrogen; NH4\({}^{+}\)-N: ammonium; NO3\({}^{-}\)-N: nitrate; pH: soil reaction; Olsan—P: available phosphorus; GluN: glucosamine; GalN: galactosamine; and MurN: muramic acid.  
\end{table}
Table 1: Soil properties before the experiment in 2013.  
USA) equipped with a flame ionization detector and an HP-5 (25 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m) fused silica column. Fungal residues in SOC were calculated as the difference between total GluN and bacterial GluN [13; 34], while the bacterial residues in SOC were determined by multiplying the MurN content by 45 [34]. The total microbial residues in SOC were calculated as the sum of bacterial and fungal residues.
Sensors_**2015**, _15_, 28287-28313; doi:10.3390/s151128287  
OPEN Access  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Prototyping a GNSS-Based Passive Radar for UAVs:**  
**An Instrument to Classify the Water Content Feature of Lands**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2,**  
**[PERSON] 1 and [PERSON] 2**  
\({}^{1}\) Istituto Superiore Mario Boella (ISMB), Via P.C. Boggio 61, 10138 Torino, Italy;  
E-Mails: [PERSON]ADDRESS] (G.M.); [PERSON]IL_ADDRESS] (M.P.); [EMAIL_ADDRESS] (E.F.)  
\({}^{2}\) Politecnico di Torino - Corso Duca degli Abruzzi 24, 10129 Torino, Italy;  
E-Mails: [EMAIL_ADDRESS] (S.U.); [EMAIL_ADDRESS] (L.L.P.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-11-2276-447; Fax: +39-11-2276-299.  
Academic Editor: [PERSON]  
_Received: 10 July 2015 / Accepted: 2 November 2015 / Published: 10 November 2015_  
**Abstract:** Global Navigation Satellite Systems (GNSS) broadcast signals for positioning and navigation, which can be also employed for remote sensing applications. Indeed, the satellites of any GNSS can be seen as synchronized sources of electromagnetic radiation, and specific processing of the signals reflected back from the ground can be used to estimate the geophysical properties of the Earth's surface. Several experiments have successfully demonstrated GNSS-reflectometry (GNSS-R), whereas new applications are continuously emerging and are presently under development, either from static or dynamic platforms. GNSS-R can be implemented at a low cost, primarily if small devices are mounted on-board unmanned aerial vehicles (UAVs), which today can be equipped with several types of sensors for environmental monitoring. So far, many instruments for GNSS-R have followed the GNSS bistatic radar architecture and consisted of custom GNSS receivers, often requiring a personal computer and bulky systems to store large amounts of data. This paper presents the development of a GNSS-based sensor for UAVs and small manned aircraft, used to classify lands according to their soil water content. The paper provides details on the design of the major hardware and software components, as well as the description of the results obtained through field tests.  
**Keywords:** UAV; GNSS-reflectometry; GNSS bistatic radar; prototyping
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 6 Conclusions  
In general, geographic routing protocol is more scalable for real-time VANET-based highway scenarios and it requires very low overheads. It is essential to increase the efficiency of the network due to its huge dynamic mobility on the highways. In order to make the vehicles reach the destination in the most effective manner, an effective routing protocol is needed. Due to the unique behavior of the VANET network, next hop selection during communication becomes a challenge. To overcome this flaw and to increase the overall performance, we introduced MAC/NET with Updated Genetic Algorithm--A Cross Layer Approach (MNUG-CLA). The performance of our protocol was analyzed and compared with earlier works. For performance validation, the parameters we concentrated on were energy efficiency, energy consumption, successful packets transmitted and network overhead. From the comparison, it is proven that the proposed system performs better when compared with the earlier approaches. However, we deal with huge data and mobility, leading to increased routing overhead and delay. Therefore, in the future work, we plan to proceed by introducing a novel approach with delay aware routing in geographical routing.  
All authors have contributed to the analysis, programming, writing of original drafts, as well as review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare they have no conflicts of interest.  
Figure 7: Successful Packet Transmission Calculation.
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 3 The Proposed Method
### MultiRes-RCF-Based Subject-Sensitive Hashing for HRRS Images
#### 3.2.2 The Flow of the Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF  
The flow of our subject-sensitive hashing algorithm is shown in Figure 4. Before the feature extraction based on MultiRes-RCF, the HRRS image needs to be preprocessed to make it meet the input conditions of MultiRes-RCF; the output of MultiRes-RCF is then compressed and encoded to obtain a subject-sensitive hash sequence.  
In the process of feature compression, we adopted the principal component analysis (PCA)-based compression method and took the principal components of the feature matrix as the compressed feature, which is similar to the existing subject-sensitive hashing [9; 23]. The encoding process of the features takes the binary high-order part of the principal component value and fills the insufficient parts with zeros. The encoded hash sequence is in binary form and can also be represented as hexadecimal, which is easier to store.  
To ensure the security of the hash sequence itself, the hash sequence can also be encrypted, which is a commonly used security enhancement method for subject-sensitive hashing algorithms. However, in our subject-sensitive hashing algorithm, the process of encrypting the hash sequence is not adopted, because the blockchain mechanism can already guarantee its security and the additional encryption process increases the burden of key management. This is also a feature that distinguishes our algorithm from other subject-sensitive hashings.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Sound  
A sound-actuated robot senses external sound through the internal voice coil to generate vibrations and performs modular communications for monitoring. This actuator consists of coils, permanent magnets, and circuits. Its working principle is that the coil collects external sound waves and vibrates, cuts the permanent magnet to generate current, and the coil carries the current to drive the corresponding sensor.  
[PERSON] et al. presented a 160 mm long bionic robot driven by 3D printed voice coil actuators, relying on electromagnetic coils [162]. Powered by a 7.4 V battery, the robot can move 25 body lengths in 12 min. A series of electromagnetic coils are embedded in the robot (just like annelids) to transmit vibration and realise surface movement. It cannot only be used for sound input and output but also to achieve the purpose of communications within functional modules of the robot. This low-cost robot shows great potential in applications, such as pipeline monitoring and search and rescue scenarios.  
The issues facing sound actuations are that it is susceptible to external noise, and the stability is low. Moreover, its output driving force is still not high enough, which limits its mobility.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 4 Results
### Analysis  
Accuracy and Precision: SAAS demonstrated superior accuracy (0.96) and precision (0.93), highlighting its robustness in delivering pertinent and exact recommendations in comparison to alternative models.  
The sensitivity (0.95) and specificity (0.94) measures of SAAS demonstrate its capacity to accurately detect pertinent recommendations while minimizing incorrect identifications.  
The F1 score of SAAS is 0.94, indicating its ability to achieve a balanced performance by efficiently combining precision and recall.  
Figure 7: The Workflow of the Suggestion System.  
Runtime Performance: SAAS has superior runtime performance, with a mere 32 ms, making it very effective for real-time applications in a shopping assistant system.  
Table 4 presents a qualitative analysis of the suggestion system outcomes, demonstrating that the SAAS model outperforms the other models in terms of relevance, scalability, and overall performance metrics, while also having a moderate level of implementation complexity. The metrics assessed include:  
Relevance: How well the model's suggestions match user preferences and needs.  
Diversity: The range of different suggestions the model can generate.  
Scalability: The model's ability to handle increasing amounts of data and user interactions without performance degradation.  
Implementation Complexity: The difficulty and resource requirements involved in integrating the model into a practical system.  
The scores in Table 4 show that the SAAS model has the highest relevance (0.9), indicating its strong ability to provide pertinent recommendations. It also demonstrates excellent scalability (1), showing it can effectively manage larger datasets and user interactions. Although its diversity (0.8) and implementation complexity (0.6) are on par with other models, the overall performance makes it the most efficient model for delivering personalized shopping recommendations.  
Figure 8 presents a comparative assessment of the models, focusing on their relevance, diversity, scalability, implementation difficulty, and interpretability. It highlights the strengths and shortcomings of each model in real-world scenarios.  
The SAAS model demonstrated superior performance in all essential measures, establishing it as the favored option for building a shopping suggestion system. The algorithm's high level of accuracy, precision, sensitivity, specificity, and balanced F1 score guarantee  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Metric** & **GPT-3** & **BERT** & **T5** & **SAAS** \\ \hline Relevance & 0.8 & 0.8 & 0.8 & 0.9 \\ \hline Diversity & 0.7 & 0.6 & 0.8 & 0.8 \\ \hline Scalability & 0.9 & 0.7 & 0.7 & 1 \\ \hline Implementation Complexity & 0.6 & 0.6 & 0.8 & 0.6 \\ \hline \end{tabular}
\end{table}
Table 4: Qualitative Comparison of Models.  
Figure 8: Qualitative Comparison of Models.  
that it provides relevant and individualized ideas effectively. Although it has a somewhat longer runtime, its overall speed makes it well-suited for real-time applications in e-commerce platforms.
# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
## 3 Results and Discuss
### Odor Identification of Livestock Farms
To summarize the correct rate of KNN's classification of odor in different sampling locations of the chicken house in Table 7. Obviously, the correct rate of KNN's classification of odor in different locations of the chicken copy is above 85%, and the correct rate of SVM classification is above 95%. Similarly, the feature selection has no significant impact on the classification accuracy rate, and SVM is more suitable for chicken house odor recognition. In summary, SVM has the highest classification accuracy rate for VOCs in the pig and chicken houses, followed by KNN, while LDA have the worst classification results. This may be because SVM could call nonlinear kernel functions to solve nonlinear classification problems, while LDA has relatively weak nonlinear classification capabilities. Therefore, SVM was more suitable for the recognition of gas in the chicken houses and piggeries, but the features was the average value or the maximum value, the classification results were not significantly different, and the selection of the feature value has no significant impact on the recognition rate. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **KNN/SVM** & **Chicken-Middle** & **Chicken-Window** \\ \hline \multirow{2}{*}{Average} & KNN & 92.73\% & 78.46\% \\  & SVM & 94.55\% & 92.31\% \\ \multirow{2}{*}{Maximum} & KNN & 90.91\% & 84.62\% \\ \multirow{2}{*}{Maximum} & SVM & 96.36\% & 93.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The accuracy for odor classification in the same sampling location of chicken house at different sampling times. Figure 8: LDA chart of chicken house gas. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **KNN/SVM** & **Moming** & **Afternoon** & **Evening** \\ \hline \multirow{2}{*}{Average} & KNN & 69.51\% & 77.5\% & 82.93\% \\  & SVM & 86.59\% & 92.5\% & 92.68\% \\ \multirow{2}{*}{Maximum} & KNN & 73.17\% & 85\% & 84.15\% \\ \multirow{2}{*}{Maximum} & SVM & 89.02\% & 95\% & 89.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The accuracy for odor classification in different sampling locations of piggery for the same sampling time. Based on the recognition of different locations at a given sampling time and different sampling times at a given location in the livestock house, gas recognition of different locations at different times was carried out. The results are shown in Table 8. It can be seen from the table that the results are consistent with the trend in Tables 4-7--that is, the recognition effect of SVM is better than that of KNN, but the impact of feature on the recognition result is small. Through the comparison of different classification models and features, the electronic nose recognition model for identifying the emission information of livestock and poultry houses at different times and places was obtained. Rapid identification of VOCs in different times and spaces will provide help for the treatment of livestock houses.
## 2 Methods  
### Experimental Set-Up  
The test system for measuring the hand-transmitted vibration response of the hand-arm system is built according to ISO 10819:2013 [9] (as shown in Figures 4 and 5). The test system consists of a single-axis electromagnetic shaker, a vibration controller, and an instrumental handle (as shown in Figure 6), of which the instrumented handle has a diameter of 40 mm and a length of 120 mm (the length of the grip part), the middle section of the handle is cut in half along the axis to form the handle base and the handle cover, and the inside of the handle is hollowed out to install a three-axis acceleration sensor (Dytran 3023A2, sensitivity: 10 mv/g) and two force sensors (ZNHBM161217) which measure the grip force. The handle bracket is connected to the transducer (Kistler 9317 C) for measuring thrust force and is fixed to the electromagnetic shaker by a clamp. The axis of the instrumental handle mounted on the handle bracket is perpendicular to the ground, and the vibration controller drives the electromagnetic shaker to vibrate along \(z\)-axis direction according to the given excitation signal. The \(z\)-axis is parallel to the ground and is perpendicular to the centerline of the handle (\(y\)-axis direction), as shown in Figure 4.  
Figure 4: Schematic diagram of test system.  
Figure 5: The test system.  
The three-axis acceleration sensor in the handle base is installed in the middle of the handle base and the three orthogonal measurement directions of the sensor are aligned with the coordinate system of the hand (as in Figure 7). The sensor is used to measure the z direction acceleration that is the vibration signal of the single-axis electromagnetic shaker. The excitation signals used in the experiment are consistent with those specified in ISO 10819:2013 [9]. Broadband random vibration in the frequency range of 5 to 1600 Hz is shown in Figure 8, with the \(x\)-axis being the frequency (f), the \(y\)-axis being the power spectral density (PSD), the reference signal being the excitation signal set by the controller, and the control signal is the actual feedback excitation signal. The sensor for measuring acceleration at the wrist is fixed using an adapter; one of the directions of the sensor must be parallel to the excitation direction z of the shaker. The shape and dimensions of this adapter are determined by reference to the ISO 10819:2013 [9] and the dimensions of the sensor. The adapter obtained by 3D printing is as shown in Figure 9. The use of this adapter not only ensures that the three orthogonal measurement directions of the sensor coincide with the direction of the vibration transmitted to the hand but also increases the contact area between the sensor and the arm, reducing the influence of the adapter and the sensor on the measurement results [17].  
Figure 8: Excited signal.  
Figure 6: Instrumented handle.  
Figure 7: Coordinate system of the hand.  
Figure 9: Accelerometer adapter.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Comparison Against Related Research  
Our best classifier achieves an AUC of approximately 100% while maintaining an EER of 1.34%. This is done by combining position and accelerometer data using gestures with more than 50 data points and averaging the prediction result of 25 consecutive gestures. While there are other implementations, which reach better results with less grouped gestures, as for example [28] where eleven consecutive inputs lead to an EER of 0.31%, in this section we also want to compare the details of the implementations compared to our system.  
Figure 7: ROC Multiple Gestures for Position, Accelerometer and Timestamp Data with All Gesture Lengths—4 Hidden States and 4 Mixtures.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 1 Introduction
We propose a SLAM positioning method that integrates LiDAR and IMU, effectively addressing the low positioning accuracy of existing SLAM algorithms in the three-dimensional motion of UAVs, reducing cumulative errors, and enhancing map-building results. This paper has five sections. Section 1 introduces the background and significance of the aerial manipulator and summarizes relevant research. Section 2 discusses the related work in the field of aerial manipulators. Section 3 presents the hardware system of the aerial manipulator. The rotary-wing UAV and manipulator each have a control system with coupled interactions to achieve stable flight and precise positioning of the manipulator. Section 4 provides a detailed analysis of the implementation approach for autonomous localization and grasping of the aerial manipulator. Section 5 presents experimental analysis through simulations and physical demonstrations of target grasping tasks. Section 6 presents the conclusions of our work.
# A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs +
## 2 Description of Floorplanning Problem  
Generally, floorplanning involves determining the relative positions of modules. Let \(B=\{b_{i}1\leq i\leq n\}\) be a set of rectangular modules, where each module \(b_{i}\) has a specified width \(w_{i}\) and height \(h_{i}\). \(N=\{n_{i}1\leq i\leq m\}\) represents a netlist that describes the connections between modules. The goal of floorplanning is to assign a set of coordinates to each module \(b_{i}\), while ensuring that no two modules overlap.  
Let \((x_{i},y_{i})\) denote the coordinates of the bottom-left corner of module \(b_{i}\). The floorplan area \(A\) is defined as the minimum rectangular area that encompasses all modules, and it can be calculated as follows:  
\[A=\max(x_{i}+w_{i})\times\max(y_{i}+h_{i}) \tag{1}\]  
We employ the widely used Half-Perimeter Wirelength (HPWL) model [41] as the method to estimate the total wirelength, which is defined as follows:  
\[W=\sum_{i=1}^{m}\left(\max_{b_{i},b_{j}\in n_{i}}\left|x_{i}-x_{j}\right|+\max _{b_{i},b_{j}\in n_{i}}\left|y_{i}-y_{j}\right|\right) \tag{2}\]  
Based on the optimization objective defined by the minimum rectangle area \(A\) and the wirelength \(W\), the formulation is as follows:  
\[F=\alpha A+\beta W \tag{3}\]  
Among these, \(F\) is a feasible floorplan diagram, indicating the weighted sum of the total area \(A\) and the total wirelength \(W\). The coefficients \(\alpha\) and \(\beta\) are weight factors ranging from 0 to 1.