esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Volatile Organic Compounds Released in Infested Plants with Bacterien cockerelli  
When the three varieties of tomato were infested with nymphs of _B. cockerelli_, changes in VOC profiles emitted among varieties were evident. In Floradade, the infested leaves released a blend of volatiles which included the following: 2, 6, 7, 8, 10, 11, 14, 18, 20, and 22 (Table 1, Figure 3). For the wild type, the following volatiles were detected in the infested leaves: 1, 3, 6, 8, 9, 10, 11, 12, 14, 18, and 20 (Table 1, Figure 3). Micro-Tom showed three volatiles emitted in response to nymph feeding: 1, 5 and 18 (Table 1, Figure 3).  
In Floradade-_B. cockerelli_ interaction, compounds (+)-4-carene, \(\alpha\)-thujene and (+)-sabinene (peak area \(>10^{8}\)) were the most abundant VOC emitted in response to nymph feeding (Table 1). Meanwhile, in wild-_B. cockerelli_ interaction, compounds (+)-4-carene and \(\beta\)-phellandrene (peak area \(>10^{8}\)) were the most abundant emissions (Table 1, Figure 3). Lastly, the compounds \(\alpha\)-pinene and \(\beta\)-pinene (peak area \(>10^{7}\)) were the most abundant in Micro-Tom infested after 2 h of herbivory on tomato leaves (Table 1, Figure 3).  
Those plants that were infested with psyllids showed a differential profile for the eight VOCs identified, in which the compounds (+)-4-carene, \(\beta\)-phellandrene and (+)-sabinene (peak area \(>10^{8}\)) were the most abundant (Figure 3). Nevertheless, only the compound \(\alpha\)-pinene was statistically different between wild and Micro-Tom plants (\(P=0.000154\)).  
The three varieties of tomato plants emitted qualitative and quantitative changes in VOC emissions. For example, Floradade plants presented eight compounds and the majority of these compounds were reduced in infested plants (Table 1, Figure 4B); further, seven VOCs were exclusive to healthy plants (Table 1). In wild plants, five compounds were exclusive to healthy plants and two compounds were found as exclusive to infested plants (Table 1). Micro-Tom plants presented very poor emissions in all assays performed here (Table 1).  
Figure 3: Plant volatiles from infested plants of three varieties of tomato. Compounds were presented in profile and were obtained from the three varieties for two bioassays in infested tomato plants: Floradade (white bars), wild (black bars), and Micro-Tom (gray bars). Compounds: \(\alpha\)-pinene (**1**), \(\beta\)-pinene (**5**), (+)-4-carene (**6**), (+)-2-carene (**8**), \(\beta\)-phellandrene (**9**), (+)-sabinene (**11**), caryophyllene (**18**), and phenol, 2,6-bis(1,1-dimethylethyl)-4-(1-ethylpropyl)- (**22**). Values are means \(\pm\) SE, \(n=6\). Level of significance: ***, \(P<0.001\). (Univariate ANOVA, non-parametric _Kruskal-Wallis_ test at \(P=<0.05\)).
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
###### Abstract  
Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and illumination changes. In this paper, we utilize the hierarchical features of convolutional neural networks (CNNs) and learn a spatial-temporal context correlation filter on convolutional layers. Then, the translation is estimated by fusing the response score of the filters on the three convolutional layers. In terms of scale estimation, we learn a discriminative correlation filter to estimate scale from the best confidence results. Furthermore, we proposed a re-detection activation discrimination method to improve the robustness of visual tracking in the case of tracking failure and an adaptive model update method to reduce tracking drift caused by noisy updates. We evaluate the proposed tracker with DCFs and deep features on OTB benchmark datasets. The tracking results demonstrated that the proposed algorithm is superior to several state-of-the-art DCF methods in terms of accuracy and robustness.  
Keywords:visual tracking; discriminative correlation filters; hierarchical convolutional features; re-detection; model update +
Footnote †: journal:
## 3 Theoretical Analysis
### Case Study: Transmitter Localization by LDWuR for a Mobile Network
#### 3.6.1 Static Antennas  
The receiver adopts the number of LDWuRs in different directions. The receiver polls each LDWuR for each direction in turn to explore the transmitter's direction. The average total power consumption is the same as WuRs Equation (15) with a larger cover range. The average total power consumption, \(P_{t}^{sta}\), is given by:  
\[P_{t}^{sta}=\frac{\sum\limits_{i=1}^{N_{dir}}P_{t}\,T_{scan}}{N_{dir}\,T_{scan }}\,=\,P_{t} \tag{17}\]  
where \(T_{scan}\) represents the scan time for each direction and \(N_{dir}\) is the number of different directions, which means the number of LDWuRs. For example, the receiver adopts four LDWuRs in different directions, such as east, south, west and north. Equation (17) shows that LDWuRs consume the same power consumption to explore the transmitter's direction. Figure 2 shows that one of four LDWuRs scans the north direction with \(90\,^{\circ}\).  
The total latency is \(N_{dir}\) times the total latency of WuRs Equation (16), as the receiver spends more time on the scan of each direction. The total latency is given by:  
\[T_{t}^{sta}=N_{dir}\,T_{t} \tag{18}\]  
Figure 2: One of four long-range directional wake-up radios’ (LDWuRs) scans the north direction.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### With Nanotechnologies  
The use of nanotechnologies has attracted many researchers' attention for various industrial uses [74], including wastewater recovery with different residues, such as oil. Studies have been carried out using zeolites and nanoparticles that function mainly as catalysts and adsorbents, which have been used in refineries and petrochemical plants. Studies show that nanostructured zeolite can extract up to 40% more gasoline compared to other catalysts.  
Some studies have been successful in designing highly selective nanocomposite membranes using a zeolite membrane. This membrane is effective in separating O\({}_{2}\)/N\({}_{2}\) molecules in air. To date, no studies have yet been conducted on the direct application of zeolites to the enhanced oil recovery (EOR) process. However, the potential of the ion-exchange nature of zeolite given its porous structure may need to be investigated. This zeolite property could be useful for adsorbing cations, especially under high salinity conditions [16].  
Nanoscale adsorbents generally have advantages over other conventional bulk elements thanks to their large specific surface area and high surface reactivities [20]. For example, modified nanoclays have been used as absorbents for non-ionic, anionic, and cationic dyes. From the differences in absorption between different chemical and morphological dye and clay structures, the adsorption forces that perform important functions have been identified.  
Nanoclay can easily have an absorption capacity of more than 600 mg of substrate per gram of sorbent and may also have 90% absorption at an initial dye concentration of 6 g/L, or of 60% based on the adsorbent's weight, which indicates an extremely high dye affinity. Studies show that, with certain modifications, nanoclay, e.g., montmorillonite, could easily become an excellent adsorbent for anionic, cationic, and non-ionic dyes [75].
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 2 Related Work
### Object Detection Methods  
#### 2.2.1 Single-Stage Object Detection Method  
Single-stage object detection methods refer to those that require only one feature extraction process to achieve object detection. Compared to multi-stage detection methods, single-stage methods are faster in terms of speed, although they might have slightly lower accuracy. Some typical single-stage object detection methods include the YOLO series, SSD, and RefineDet. The YOLO model was initially introduced by [PERSON] et al. [51] in 2015. Its core idea is to divide the image into S*S grids, and each grid is responsible for predicting and generating multiple bounding boxes if the center point of the object is within that grid. However, during training, each bounding box is allowed to predict only one object, limiting its ability to detect multiple objects and thus affecting detection accuracy. Nonetheless, it significantly improves detection speed. Shortly after the publication of the YOLO model, the SSD model was also introduced [52]. SSD uses VGG16 [53] as its backbone network and does not generate candidate boxes. Instead, it allows feature extraction at multiple scales. The SSD algorithm trains the model using a weighted combination of data augmentation, localization loss, and confidence loss, resulting in fast speeds. However, due to its challenging training process, its accuracy is relatively lower. In 2018, [PERSON]. [54] proposed the RefineDet detection algorithm. This algorithm comprises the ARM module, TCB module, and ODM module. The ARM module focuses on refining anchor boxes, reducing the proportion of negative samples, and adjusting the size and position of anchor boxes to provide better results for regression and classification. The TCB module transforms the output of the ARM into the input of ODM, achieving the fusion of high-level semantic information and low-level feature information. RefineDet uses two-stage regression to improve object detection accuracy and achieve end-to-end multi-task training.
## 3 Results and Discussions
### Validation of the VSFlab and Comparison with LISST-VSF
All these validations fully proved the reliability of our VSFlab in measuring the VSFs in the backward direction of 1\({}^{\circ}\) to 178.5\({}^{\circ}\).
## 6 Experiments
### Parameters to Perform the Loop Closure Processes  
When a new image arrives, the first step is to know if it belongs to an existing cluster. As detailed in Section 4, to perform the Node Level Loop Closure, the _Mahalanobis distance_ is used, (Equation (3)). After that, it is possible to detect if the image descriptor \(\vec{d}_{q}\) may belong to a specific node \(N_{i}\), storing all the candidate solutions in the set \(N^{*}\).  
Once the candidate nodes are selected, their images are compared with \(\vec{d}_{q}\) using _Euclidean distance_, Equation (5), where \(d^{N_{i}}\) are all the candidate descriptors from the Node Level Loop Closure. The Image Level Loop Closure detects the most similar image among them, named \(I_{i}\). After detecting \(I_{i}\), the image has to fulfill the Prominence and Centroid Conditions (Sections 4.3 and 4.4). Furthermore, the process evaluates if two nodes are similar enough to be merged (Section 4.6).  
As described in Section 4, these equations depend on different parameters that have an influence on the results. Equation (4) depends on \(x\). It establishes how restrictive the process is to close the loop in the node level. The lower \(x\) is, the easier to close the loop. \(x\) must depend on the number of clusters (C) and it is tuned as \(x=3.05-0.15*C\), with limits depending on \(\Omega\), Figure 5 shows graphically these values. Another parameter is \(y\), which is used in Equation (11) and it influences the node merging; the lower \(y\), the easier to merge similar nodes. Finally, the parameter \(\gamma\) appears in Equation (8). Once \(I_{i}\) is retrieved, this equation evaluates the prominence of the result to ensure that the retrieved image has a peak on the similarity curve higher enough compared to its the neighbours. The higher \(\gamma\) is, the more prominent the peak must be. During the experiments \(\gamma\) is constant, \(\gamma=5\). These parameters are summarized in Table S4.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
## 3 Results and Discussion
### Characterization of Spheronized JMGW  
The morphology, PS, SSA, and TD of spheronized JMGW, denoted as SGW\(X\) (\(X\) = spheronization time (min)), were characterized. The results are presented in Figures 4 and S2, and Table 2. The SEM images of JMGW and SGW\(X\)s (Figures 4 and S2) clearly demonstrate that the spheronization process induced a morphological change in JMGW. Based on these changes, it was predicted that the spheronization process would influence the PS, SSA, and TD of JMGW. However, as illustrated in Figure 4 and Table 2, PS and SSA exhibited only slight variations during the spheronization process, while TD gradually increased. The observed change in tap density (TD) can be associated with the reduction in span after the spheronization process. While the mean particle size of JMGW showed negligible variation, the reduced span indicates a change in the distribution of coarse and fine particles. This change in particle size distribution is identified as a key factor contributing to the increase in TD. This suggests that although the applied spheronization process had minimal effect on the PS and SSA of JMGW, it significantly improved TD, which is a favorable outcome. Furthermore, it was confirmed that the spheronization process did not affect the crystallinity of GW (Figure 3 and Table 1).  
which is coal-tar (CT) with the quinoline insoluble (QI) removed (Table S5), was utilized for the carbon coating and granulation of SGW9 (Figure 5). In other words, to improve the PS, SSA, and TD of SGW9 to a level comparable to commercial products, a method involving mixing with RCT followed by carbonization was employed. Figure 5a shows the manufacturing flow for RCT, while Figure 5b illustrates the carbon coating and granulation processes through RCT coating and carbonization. The RCT-derived carbon-coated SGW9 was labeled as SGW9@RCT.  
From the TEM images of SGW9 and SGW9@RCT (Figure 6), we directly observed a carbon-coated layer on SGW9. This confirms that the applied carbon coating process was successfully implemented. Subsequently, we measured the PS, SSA, and TD of SGW9@RCT (Table 3). Compared to SGW9, both PS and TD increased, while SSA significantly decreased through the applied carbon coating process. All of these trends represent positive changes from the perspective of anode material performance. Moreover, the properties of SGW9@RCT were found to be at a level comparable to commercial anode materials (CAM). Consequently, we successfully converted GW into SGW9@RCT, an anode material that is competitive with commercial products.  
Figure 5: (**a**) Manufacturing flow for RCT and (**b**) carbon coating processes.  
Figure 6: TEM images of SGW9 and SGW9@RCT.
## 5 Conclusions and Future Work  
The main contributions of this article are as follows: First, a novel ranging scheme based on CSI and RSSI using EKF is proposed. Second, an indoor ranging algorithm based on RSSI and CSI in the environment of high-load AP is proposed, which solves the ranging accuracy problem under high load AP. This paper demonstrates the feasibility of this method in a high-load AP environment and proves the accuracy of the algorithm through related experiments. We also compared it with several other related algorithms. Third, we use the DELL Inspiron n4010 laptop and commercial 802.11n wireless network card driver to build the experimental environment. Compared with existing other ranging methods, the proposed method does not require additional equipment and can be applied to a wider range of indoor situations. The experimental results show that the proposed ranging method effectively improves the accuracy of the indoor environment. Of course, the current system is still insufficient and we need to continue improving it in our future work. We could consider the participation of multiple APs in the experiment. Additionally, as the indoor environments are complex and vary with time due to people, we will analyze impacts brought about by these factors.  
[PERSON] proposed the idea and implementation methodology, performed all experiments and wrote the paper, verified the experiment process and results; [PERSON] reviewed and edited paper, and supervised the work. All authors have read and agreed to the published version of the manuscript. This study is supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011). This study is supported by Smart City R&D project of the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18 NSPS-B149843-01). The authors declare no conflict of interest.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 5 Detailed System Analysis
### Protocol Description
#### 5.3.2 Advert Collection Sub-Protocol  
The Advert Collection sub-protocol that is depicted in Figure 11 is used for the acquisition of adverts and may run multiple times as needed.  
1. The user computes a pair of asymmetric keys \(U^{t}_{EK}\) and \(U^{t}_{DK}\). The user stores the decryption key \(U^{t}_{DK}\) and then composes an Advert Request Message (_ARM_) which contains the encryption key \(U^{t}_{EK}\) and his advertising interest (referenced by known interest identifies that are common throughout the system).
2. The _ARM_ is encrypted with a public key \(D_{PuK}\) and sent to one of the Ad-Dealers, either directly or via an Agent.
3. The responding Ad-Dealer decrypts the _ARM_ with the corresponding key \(D_{PrK}\) which is kept private among all the Ad-Dealers who use the system and obtains \(U^{t}_{EK}\) as well as the user's advertising interest.
4. The Ad-Dealer determines which specific adverts would best suit the needs of the user and files a request to the appropriate Advertisers.
5. Upon receiving the Ad-Dealer's request, the Advertisers respond with the adverts in plain form.
6. The Ad-Dealer aggregates all of the received adverts into an Advert Delivery Message _ADM_.
7. The _ADM_ is first encrypted with the \(U^{t}_{EK}\) and then returned to the user either directly or via the same Agent who delivered the original _ARM_.
8. The user receives the decrypts the _ADM_ with his \(U^{t}_{DK}\) in order to obtain the adverts which are stored for future use by the Publishers.  
Figure 11: Advert Collection sub-protocol.
Sensors_**2014**, _14_, 5552-5572; doi:10.3390/s140305552  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Estimates of Minor Ocean Tide Loading Displacement and Its Impact on Continuous GPS Coordinate Time Series  
**[PERSON] 1, [PERSON] 2,*, [PERSON] 3, [PERSON] 1 and [PERSON] 2**  
1 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; E-Mails: [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (L.D.)  
2 GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; E-Mail: [EMAIL_ADDRESS]  
3 Institute of Geodesy and Geophysics, Chinese Academy of Sciences, 340 XuDong Road, Wuhan 430077, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-27-6877-8985; Fax: +86-27-6877-8890.  
_Received: 7 January 2014; in revised version: 13 February 2014 / Accepted: 18 February 2014 / Published: 20 March 2014_  
**Abstract:** The site displacement due to ocean tidal loading is regarded as one of the largest uncertainties in precise geodetic positioning measurements, among which the effect of minor ocean tides (MOT), except for the 11 main tidal constituents, are sometimes neglected in routine precise global positioning system (GPS) data processing. We find that MOT can cause large vertical loading displacements with peak-to-peak variations reaching more than 8 mm at coastal/island stations. The impact of MOT on the 24-hour GPS solution is slightly larger than the magnitude of MOT loading itself, with peak-to-peak displacement variation at about 10 mm for the horizontal and 30 mm for the vertical components. We also find that the vertical velocity of all the selected stations in the Southwest Pacific was reduced by more than 10% after considering the MOT effect, while stations with weighted root mean square reduced data account for 62%, 59%, and 36% for the up, east, and north components respectively, in particular for most coastal/island stations. Furthermore, MOT correction could significantly reduce the annual signal of the global stacked east component, the near fortnightly and the long-term periodic signals in the up component. The power of some anomalous harmonics of 1.04 cycle per year is also decreased to some extent. These results further proved the benefits of MOT correction in precise GPS data processing.  
keywords: ocean tidal loading; minor ocean tides; GAMIT/GLOBK; GPS data reprocessing; weighted root mean square analysis; spectrum analysis; anomalous harmonics +
Footnote †: journal: Journal of Ocean Energy and Oceanography
## 6 Closed-Loop Simulation  
The simulations in Section 5 are only open-loop simulations for the ideal dynamic model. However, there are complex environmental disturbances and thrust execution errors in real flight. Therefore, it is necessary to introduce closed-loop control to achieve a high-precision landing. In this section, a closed-loop guidance algorithm based on MPC is proposed. The proposed algorithm's accuracy and robustness are verified by adding aerodynamic drag, thrust execution error, and initial state errors to the model.  
In the implementation of the MPC-based closed-loop guidance algorithm, the navigation system first obtains the state variables, including the flight velocity, position, and the remaining mass. The obtained data is then transmitted to the DNN optimal predictor, which estimates the optimal landing time. Then, the state variables and the optimal landing time are transmitted to LCvx to obtain the optimal control profile. Finally, the average value of the control variables in the first guidance period is transmitted to the dynamic model, and the state variables are updated. This process will repeat after one guidance period until the rocket safely reaches the landing site. The algorithm flow chart is shown in Figure 18.
## 2 Test Setup  
### Shaking Table  
The tests were conducted at the State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, using a shaking table with a vertical load capacity of 30 t within the multi-point shaking table test system, featuring a table size of 6 m \(\times\) 4 m. The shaking table has three degrees of freedom (two translational and one rotational) with a displacement of \(+/-500\) mm, a maximum acceleration of 1.5 g, and an operating frequency range of 0.1 to 50 Hz. The shaking table system can simulate the occurrence of real ground motions, adjusting the table input according to the acceleration signal of the ground target seismic wave in an iterative process until the target ground acceleration response is achieved.