esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Volatiles' Profile by ITEX/GC-MS  
The extraction followed by separation of volatile compounds was performed using the \"in-tube extraction\" ITEX/GC-MS technique applied on a GC-MS Shimadzu QP-2010 (Shimadzu Scientific Instruments, Kyoto, Japan) equipped with AOC-5000 Combinatorial autosampler (CTC Analytics, Zwingen, Switzerland) and ZB-5 ms capillary column, 30 m \(\times\) 0.25 mm id \(\times\) 0.25 \(\upmu\)m (film thickness) (Phenomenex, Torrance USA). A volume of 0.1 mL oil was placed in the headspace vial, sealed, and incubated at 60 \({}^{\circ}\)C for 20 min with continuous stirring. The volatile compounds accumulated in the headspace of the ampoule were adsorbed, using a syringe, into a Tenax fiber (ITEX-2 TRAPTXTA, (G23)-Siliconert 2000, Tenax 80/100 mesh, Switzerland) and then thermally desorbed in the GC injector. After each analysis, the fibre was cleaned by passing a warm stream of Nitrogen. The temperature program for the chromatographic column for the separation of volatile compounds was as follows: maintenance 2 min at 50 \({}^{\circ}\)C, followed by an increase at 160 \({}^{\circ}\)C by 4 \({}^{\circ}\)C/min and then an increase at 255 \({}^{\circ}\)C by 15 \({}^{\circ}\)C/min and maintenance at this temperature for 10 min. The carrier gas was helium, at a constant flow rate of 1 mL/min. The injector temperature, ion source and interface have been set to 250 \({}^{\circ}\)C. The MS detector used the electronic impact ionization (EI) mode in a scanning range of 40-350 \(m/z\). The split ratio was 1:20. Separated volatiles were identified by comparing the mass spectra with those from NIST27 and NIST147 mass spectrum libraries and verified by comparison with retention indices extracted from www.pheobase.com, accession 12 December 2021) and www.flavornet.org, accessed 10 December 2021) (for single-phase columns stationary like ZB-5 ms). The results are expressed as a percentage of the total peak area.
# Optimization and Experimental Study of Penetration Depth of Pocket Penetrometer
## 4 Conclusions and Prospects  
### Conclusions  
1. The improved specimen preparation technique performed better in terms of specimen uniformity and preparation efficiency. The density, water content, and shear strength errors of the samples were controlled, providing more reliable baseline data for penetration testing.
2. Penetration curves were quantified using critical depth theory and curve fitting. The ExpAssocDelay function was used to optimize the analysis of penetration curves, reduce the influence of human factors, and improve the objectivity and accuracy of data processing to a certain extent.
3. The results of the penetration tests on different remolded soils showed that the relationship equations between the liquidity index, shear strength, and penetration resistance  
Figure 16: Fitting curves of different water content and penetration resistance.  
did not change significantly in different soils, although there were differences in soil properties.
* The results of the penetration tests on the remolded soil and the in situ soil showed that the empirical equations between penetration resistance and shear strength showed a correlation under certain conditions. The empirical equation is informative for evaluating the shear strength of soils despite the differences in the soils tested.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 6 Conclusions  
With the development and deployment of 5G networks, network planning puts forward higher requirements on the accuracy, complexity, and versatility of path loss prediction. Machine learning methods, especially supervised learning, can model hidden non-linear relationships and thus can be used for path loss prediction. Based on historical data, machine-learning-based models can build relationship between path loss and input features. It has been shown that machine-learning-based models, including ANN, SVR, and RF, are in good agreement with measured data. In order to satisfy the demand for training data, two data expansion schemes have been proposed to make full use of existing data and classical models. Through the measured data, the feasibility of the proposed schemes has also been verified. Finally, we have summarized the problems still faced by the machine-learning-based path loss prediction.  
Conceptualization, Y.Z. and J.W. ([PERSON]); methodology, [PERSON] and [PERSON]; software, [PERSON] ([PERSON]), and [PERSON]; validation, [PERSON], [PERSON] ([PERSON]), [PERSON], and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] ([PERSON]), and [PERSON]; writing--original draft preparation, [PERSON], [PERSON] ([PERSON]), and [PERSON]; writing--review and editing, [PERSON], [PERSON] ([PERSON]), [PERSON], and [PERSON]; supervision, [PERSON].  
This work was supported in part by National Natural Science Foundation of China (Nos. 61201192, 61871035) and National High Technology Research and Development Program of China (No. 2015 AA01A706).  
We would like to dedicate this paper to [PERSON], who unfortunately passed away just before the paper was submitted for publication. [PERSON] played an essential role in the research described here and he is greatly missed.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Cost Comparison of Different Intelligence Allocations  
Because the application scenarios of low- and medium-level intelligent driving are limited, which means that the economy of large-scale infrastructure deployment is low, the technical route dispute of intelligent allocation mainly exists at the high level. Table 7 shows the cost composition of four high-level technical routes. The cost comparison of different intelligent allocations is shown by the purple arrow in Figure 4. Although collaborative intelligence can reduce the on-board hardware of high-level intelligent driving, to ensure basic safety, the vehicle still needs to retain the necessary sensors and computational capacity. Therefore, in the case of vision-only routes, single-vehicle intelligence uses low-cost hardware, and the cost reduction brought by collaborative intelligence is very limited. In the case of multi-source fusion routes, collaborative intelligence can reduce the cost by about 30%. In the case where the cost decreases even more, by transferring expensive sensors to the roadside, the system changes from the original single-vehicle intelligence with multi-source fusion to the collaborative intelligence with vision-only, and the cost can be reduced by 41.7%.
## 3 Results and Discussion
### Sequencing, Assembly, and Genomic Characteristics  
The genome of _[PERSON]_ sp. JNUCC 0626 was successfully sequenced using the PacBio Sequel II platform, resulting in an assembled genome of 10.78 Mb. This included a single linear chromosome with a length of 10,602,950 bp (Contig 01) and a plasmid measuring 177,940 bp (Contig 02), with no assembly gaps (Figure 2). The PacBio sequencing provided further insight, revealing that 165,349 subreads were obtained, with a mean subread length of 9791 bp and an N50 value of 12,162 bp. These metrics demonstrate the high quality and length of the sequencing reads. The overall genome coverage was calculated to be 150.17\(\times\), and the DNA G + C content was measured at 69.02% for the chromosome and 70.12% for the plasmid. The GenBank accession numbers for the genome are to be specified. The genomic analysis of _Lentzea_ sp. JNUCC 0626 revealed that the chromosome contains 9761 proteine-coding sequences (CDSs), along with 15 rRNAs and 87 tRNAs. In contrast, the plasmid consists of 197 CDSs and 2 tRNAs. These findings highlight the genetic composition of both the chromosomal and plasmid components, providing valuable insights for further functional and comparative genomic studies. The gene ontology (GO) annotation for _Lentzea_ sp. JNUCC 0626 was performed to categorize its protein-coding sequences (CDSs). GO terms link gene products to three main categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CP). In the Biological Process category, 2449 CDSs were associated with metabolic processes, and 2356 were linked to cellular processes. For Molecular Function, 2772 CDSs exhibited catalytic activity, and 2211 CDSs were involved in binding activities. In the Cellular Component category, 1813 CDSs were related to cellular anatomical entities. These annotations help in understanding the functional roles of genes in the context of the organism's biology (Table S4).  
Figure 2: Circular genome map (**a**) and plasmid map (**b**) of _Lentzea_ sp. JNUCC 0626. Marked characteristics are shown from outside to the center; coding DNA sequences (CDS) on forward strand, CDS on reverse strand, tRNA, rRNA, GC content, and GC skew.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
### Objectives  
Going a step further, the objective of this research was to offer a complete and easy-to-use commercial solution for the profitability assessment of precision agriculture applications in extended crops on an annual basis.  
Accordingly, a new module was developed within the ifarma farm management platform using PreFer functionalities, especially its mapping environment and algorithms, thus facilitating interoperability, swiftness, and ease. In this respect, the next Section will present the materials and methods employed; Section 3 will demonstrate the results along with discussion; and Section 4 will provide the main conclusions of this work.
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 3 System Design and Architecture
### Controller Modules
#### 3.2.2 Payload Validation Module  
This module validates the certificate and verifies the signature of the payload before the payload is further processed and flows are installed into the individual nodes (i.e., switches or routers). The certificate is validated via chain of trust. A root certificate of the CA is present in the trusted store. The certificate is validated against the trusted CA. Upon the successful validation of the certificate, the signature of the payload is validated for checking the integrity of the message. The IP addresses contained in the payload are forwarded to the connected nodes upon successful signature verification.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 2 Related Work  
Point cloud downsampling is a crucial step in point cloud processing, as it effectively reduces the size of point cloud data, decreases computational load, and accelerates subsequent operations. Point cloud downsampling methods can be categorized into deep learning-based, grid-based, clustering-based, and voxel-based downsampling.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Introduction to DL-Based Methods for Wake Detection
#### 3.3.3 Anchors and Key-Points  
Detection models can also be divided into anchor-based and anchor-free categories [57; 80]. Anchors are predefined BBs of various scales and aspect ratios that serve as references for predicting the presence and location of objects within an image [81]. These anchors are densely tiled across the image, ensuring comprehensive coverage and the ability to detect objects at different scales and orientations. The primary advantage of using anchors is their capacity to standardize the prediction process, allowing models to learn to predict offsets from these anchor boxes to the actual object boundaries, thereby facilitating the detection of a wide range of object sizes and shapes with a unified framework. It is important to note that the use of anchors requires deep contextual knowledge of the objects of interest by the developer.  
In response to the complexities and limitations associated with anchor-based methods, anchor-free models have emerged as a simpler yet effective alternative. These models eliminate the need for predefined anchors, instead relying on other mechanisms to identify and localize objects.  
Another possible solution is the use of key-points [57]. Models like CornerNet [82] and CenterNet [83] represent a shift towards using key-points such as object corners or centers to define the spatial extent of objects. In particular, CornerNet detects objects by predicting paired key-points for the corners of BBs, while CenterNet identifies the central point of objects and estimates their size directly. These types of solutions can also effectively make use of heat-maps, which are, in general, a natural output of CNNs.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 3 Methods  
We introduce the E-GTN framework, a specialized pipeline designed for the autonomous excavation domain, focusing primarily on the extraction of terrain information pertinent to reinforcement learning tasks in unmanned excavation operations. The overall structure of E-GTN is shown in Figure 1.  
In the terrain information processing stage of our E-GTN framework, we commence with the capture of raw point cloud data using laser radar. These data are then processed using voxel grid downsampling and normal estimation to reduce complexity and enhance feature representation. The resulting downsampled point cloud is essential for creating a detailed and computationally manageable model of the terrain, which is critical for accurate environmental reconstruction and subsequent analysis.  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  & **Input** & **Features** & **Our Contributions** \\ \hline \multirow{3}{*}{[6,7]} & \multirow{3}{*}{LiDAR + Cameras + GPS} & Integration of early environmental perception with 3D information for excavation projects & Advanced multisensor fusion and terrain processing network based on deep learning \\ \hline \multirow{3}{*}{[8]} & \multirow{3}{*}{Movable 3D laser scanners} & Mobile scanning for civil engineering & Efficient data processing through pseudo-imaging \\ \hline \multirow{3}{*}{[9]} & \multirow{3}{*}{Cameras} & Rich feature extraction but low efficiency & Custom GridNet for salient terrain feature extraction \\ \hline \multirow{3}{*}{[3,10]} & \multirow{3}{*}{LiDAR} & Large-scale 3D point cloud reconstruction and traditional control algorithm for excavation & Combination of reduced dimensionality and DRL algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of excavator environmental perception methods.  
Figure 1: Overall architecture of E-GTN.  
The terrain feature extraction module is the centerpiece of the E-GTN framework, where the downsampled point cloud is meticulously transformed into a structured grid map. This grid map undergoes normalization and is treated as a pseudo-image, allowing us to leverage convolutional neural networks (CNNs) for advanced feature extraction. Our tailored model GridNet adeptly processes the grid data to distill critical terrain features that are instrumental for the reinforcement learning tasks in autonomous excavation.  
Finally, the decision-making module utilizes the features extracted by GridNet to inform the reinforcement learning algorithm. While this section is less emphasized, it is still pivotal as it enables the autonomous system to make strategic decisions based on the terrain information provided. The integration of these modules within the E-GTN framework ensures a seamless flow from data acquisition to decision execution, facilitating efficient and intelligent autonomous excavator operations.
###### Abstract  
Human-machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human-machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human-machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies--specifically, smart gloves--is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user's glass with the haptic ones provided by the piezoelectric sensors.  
haptic interfaces; human-machine interfaces; virtual reality; tactile feedback; sensors; actuators; virtual interaction model +
Footnote †: journal:  
0022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 7 Performances
### FOV, Beamwidth, and Sidelobe Suppression
The OPA with an 11 \(\times\) 11 beam array and a flat-top envelope was proposed at a wavelength of 905 nm [113]. The OPA consists of non-uniformly distributed antennas with specially designed Dammann gratings. A total scanning FOV of 68.8\({}^{\circ}\)\(\times\) 77\({}^{\circ}\) was achieved by the OPA with a beamwidth of 6.5\({}^{\circ}\) perpendicular to the WGAs [113]. Moreover, a kind of complex circular OPAs can be used to realize wide FOVs and even 360\({}^{\circ}\) scanning [138; 139; 140; 141]. Complex control circuits [139; 140] and downward radiation [138; 141] of circular OPAs are the issues to be solved. An FOV of 180\({}^{\circ}\) has been aimed by an EF OPA [94] and a WGA-based OPA [114]. Other relatively wide FOVs of WGA-based OPAs presented in recent years are 97\({}^{\circ}\)\(\times\) 17.1\({}^{\circ}\)[117], 80\({}^{\circ}\)\(\times\) 17\({}^{\circ}\)[12], 70\({}^{\circ}\)\(\times\) 14\({}^{\circ}\)[104], 70\({}^{\circ}\)\(\times\) 6\({}^{\circ}\)[103], 56\({}^{\circ}\)\(\times\) 15\({}^{\circ}\)[11], 51\({}^
## 3 Results and Discussion
### Electrical Conductivity Measurements on Activated CF Yarns  
The electrical conductivity of CF yarns prior and after the laser beam irradiation is illustrated in Figure 10. The electrical conductivity of both specimens (TCF, PCF) was calculated using Equation (6) as described on Section 2.7 at materials and methods. bTCF and bPCF exhibited a conductivity of approximately 220 S-cm\({}^{-1}\) and 180 S cm\({}^{-1}\) respectively, which increased after irradiation. The highest electrical conductivity value was observed for TCF\({}_{4.75\,\mathrm{W}}\) = 260 S-cm\({}^{-1}\), displaying an increase of approximately 18.4%. Overall, irradiated specimens displayed an increase in conductivity with increasing laser fluence when compared to control non-exposed samples (bTCF and bPCF) (Table 2). This enhancement in electrical conductivity can be attributed to the microstructure of polyacrylamide based CFs (PAN CFs), which possess a turbostratic nature consists of graphitic, arbitrarily folded sheets, and amorphous carbon [47; 48]. Upon CO\({}_{2}\) laser irradiation, due to heating nature of the irradiation, the amorphous carbon can be transformed to graphitic, enhancing the CFs' electrical conductivity [49; 50; 51; 52; 53].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Tensile Strength (GPa)**} & \multicolumn{2}{c}{**Electrical Conductivity**} \\  & & \(\sigma\) **(S/cm)** \\ \hline bTCF & 3.21 \(\pm\) 0.22 & 220 \(\pm\) 11 \\ \hline TCF\({}_{3.75\,\mathrm{W}}\) & 1.59 \(\pm\) 0.21 & 222 \(\pm\) 15 \\ \hline TCF\({}_{4.25\,\mathrm{W}}\) & 1.34 \(\pm\) 0.19 & 257 \(\pm\) 13 \\ \hline TCF\({}_{4.75\,\mathrm{W}}\) & 1.25 \(\pm\) 0.23 & 261 \(\pm\) 17 \\ \hline bPCF & 3.19 \(\pm\) 0.26 & 180 \(\pm\) 12 \\ \hline PCF\({}_{4.25\,\mathrm{W}}\) & 2.14 \(\pm\) 0.28 & 199 \(\pm\) 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tensile strength and electrical conductivity (\(\sigma\)) of all irradiated samples.  
Figure 9: Single carbon fiber tensile strength for all irradiated samples.
## 4 Simulation Results
### Simulation Environments
Scrambling is applied to avoid periodic interference and randomize it, and the combination of repetition and scrambling is similar to pseudo-noise code spreading in code division multiple access systems. The channels are obtained by a Bellhop simulation using ray-tracing. The target spot is at a latitude of 36.7 and longitude of 129.7 in the East Sea of Korea, as shown in Figure 11. The average depth is 200 m on the spot. The transmitter and the receiver are located 2 m above the floor. Ten channel impulse responses are obtained for distances from 100 m to 1 km by 100 m. Table 2 gives an example of the relative power values of the delay taps for 100 m. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time (s)** & 0 & 0.0000676 & 0.225 & 0.227 & 0.229 & 0.231 \\
**Relative Power (W/W)** & 1 & 0.871 & 0.0522 & \(3.17\times 10^{-4}\) & \(3.20\times 10^{-4}\) & \(1.95\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Channel impulse response for a distance of 100 m.  
In this section, the proposed Scheme 2 is compared with the packet train method because they have the same objective to enhance channel utilization. In the packet train method, each node relays data from the previous node to the next node, and the node transmits its own data simultaneously. In this scenario of data flooding to the sink, the amount of data to be transmitted is accumulated because relaying is processed toward the sink. Therefore, the number of frames for transmission proportionally increases with the node index in Figure 12. In the figure, the \(i\)th node uses \(i\) frames for transmission. Note that each node uses the same number of frames for transmission in Schemes 1-3, but the amount of data is different depending on the node. Therefore, if the last node (the 10 th node) uses 100% of the frame capacity, the utilization of the 1 st node can be said to be 10%. This is unavoidable because the amount of data is not the same. For the packet train method in Figure 12, there are many unused frames, neither for transmission nor for reception, but each transmission fully uses 100% of the frame capacity. For two schemes for comparison, the average utilizations are obtained. For the packet train method, 55 (=1 + 2 +...+ 10) data frames are transmitted for 65 (= 2 + 3 +...+ 11) time frames. The average is 55/65 = 0.846. For Scheme 2, 10 data frames are transmitted for two time frames, but the average utilization of a data frame is 55% (= (10% + 20% +...+ 100%)/10). Therefore, the average is 10/2 \(\times\) 0.55 = 2.75. This means that the proposed Scheme 2 has 3.25 times more efficiency than the packet train method. For a fair comparison, a repetition of two times is applied to the packet train method because a nominal repetition of six times is used for Scheme 2. The difference in data rate is within 8.4%. Figure 11: Target spot for underwater channels.