esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Airport Surface Object Detection Based on Graph Neural Network
## 4 Conclusions  
In this paper, we design a GNN-based feature extraction block, called ViGE, and implement adversarial training for the popular object detector YOLOv5s. Different from CNN which aggregates local messages and transformer which gathers global information through self-attention, ViGE block aggregates information from each node's k-nearest-neighbors in pixel (or representation) space. Compared to the original ViG block, our ViGE block defines edge features that can represent spatial relations more directly. Considering potential perturbation in airport surveillance, it is necessary to enhance the model's robustness, especially towards adversarial attacks. We perform sufficient experiments to prove our methods' effectiveness in detection accuracy and robustness improvement.  
When introducing GNN into image representation learning, how to construct a graph for an image (or a feature map) is the first problem we encounter. Though we have explored several neighbor definitions approaches in this article (i.e., in different spaces with various parameters), there still are other promising approaches we have not discovered. What is more, the noisy node trick [33] may help to improve GNN's robustness. Furthermore, the performance of the GNN on datasets with a broader range of object classes, such as the DOTA dataset [34], remains to be researched. Like transformers, we do anticipate that GNN can also obtain great success in the computer vision domain. However, there is a long way to go.  
Conceptualization, W.T. and H.L.; methodology, W.T.; software, W.T.; validation, W.T. and H.L.; formal analysis, H.L.; investigation, W.T.; resources, W.T.; data curation, W.T.; writing--original draft preparation, W.T.; writing--review and editing, H.L.; visualization, W.T.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangdong Basic and Applied Basic Research Foundation, grant number 2022A1515110837. This research was also funded by the State Key Laboratory of Air Traffic Management System Funding Project, grant number SKLATM202110.  
Not applicable.  
The open remote sensing dataset LEVIR can be accessed from [31].  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Without Attack**} & \multicolumn{2}{c}{**5 Iterations Attack**} & \multicolumn{2}{c}{**10 Iterations Attack**} \\ \cline{2-7}  & **mAP.5** & **mAP.5:95** & **mAP.5** & **mAP.5:95** & **mAP.5** & **mAP.5:95** \\ \hline YOLOv5s-ViGE & **0.958** & **0.737** & 0.486 & 0.171 & 0.320 & 0.156 \\ YOLOv5s-ViGE–A-0.1 & 0.943 & 0.719 & 0.764 & 0.397 & 0.667 & **0.340** \\ YOLOv5s-ViGE–A-0.2 & 0.945 & 0.717 & **0.773** & **0.406** & **0.683** & 0.322 \\ \hline \hline \end{tabular} Note: The best performance is marked in bold text.  
\end{table}
Table 4: Model performance under various settings of adversarial attack.
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 4 T-Move
### Trendiness-Based Cache Replacement Policy in T-Move
The trendiness of data object _x/y/z/c1_ is obtained by \(t_{1}=(4f_{1}+3f_{2}+f_{3})/4\). The trendiness hence takes into account not only the popularity of the data object, but also that of related content. The more closely two data objects are related to one another, the more prefixes match in their names, which increases their impact on the trendiness of each other. The trendiness is normalized with respect to the number of prefixes in the data object for which it is calculated. In the absence of such normalization, data objects with longer prefixes maybe reported to be trendier even if the frequency of access of related objects is less. The trendiness of all data objects with at least one prefix-matching is updated in the TT when a data object is stored or accessed from the cache. Cache replacement is carried out based on the trendiness of the data object. When a router receives a data object, it stores a copy in its cache and forwards the object to the consumer. If there is space in the cache without having to delete anything, the new data object is simply inserted into the cache and an entry into is made in the TT with its name, trendiness, and frequency. If there is no place in the cache, the data object with least trendiness is evicted from the cache to store the new data object. Cache replacement based on trendiness is a simple, yet effective means of measuring the popularity of content. It is dynamic and with low overhead, which makes it an attractive option for reducing traffic and latency.
## 2 Finite Element Formulation of Composite Beam with Viscoelastic Layers
### Elastic Layers  
According to the Euler-Bernoulli theory, the cross-section of a layer is infinitely rigid. It remains plane after deformation and perpendicular to the beam deformation axis.  
If \(u_{k}\) is the horizontal displacement in the center of the \(k\)th elastic layer, \(w\) is the vertical displacement, and \(\theta\) is the angle between a normal to the cross-section before and after  
Figure 1: Multilayered elements: (**a**) elastic core laminated on the top, (**b**) elastic core laminated on the bottom, (**c**) elastic core laminated on both sides, and (**d**) sandwich element.  
Figure 2: \(k\)th layer of element \(e\) (modeled as a beam finite element).  
deformation (\(\varphi=\theta=\partial w/\partial x\)), the displacement field of the \(k\)th elastic layer is expressed as follows:  
\[\mathbf{u}_{k}(x,t)=\mathbf{N}_{k}(x)\mathbf{q}_{k}(t) \tag{1}\]  
where \(\mathbf{u}_{k}(x,t)=\begin{bmatrix}u_{k}(x,t),&w(x,t),&\theta(x,t)\end{bmatrix}^ {T}\) and \(\mathbf{q}_{k}(t)=\begin{bmatrix}u_{1k}(t),&u_{2k}(t),&w_{1}(t),&\varphi_{1}(t), \\ w_{2}(t),&\varphi_{2}(t)\end{bmatrix}^{T}\) is vector of nodal displacements. \(\mathbf{N}_{k}(x)\) denotes matrix of shape functions:  
\[\mathbf{N}_{k}(x)=\begin{bmatrix}N_{1}(x)&N_{4}(x)&0&0&0&0\\ 0&0&N_{2}(x)&N_{3}(x)&N_{5}(x)&N_{6}(x)\\ 0&0&N_{2,x}(x)&N_{3,x}(x)&N_{5,x}(x)&N_{6,x}(x)\end{bmatrix} \tag{2}\]  
where \(\left(\bullet\right)_{x}=\partial(\bullet)/\partial x\). Horizontal displacements are approximated using linear functions, whereas vertical ones are approximated using Hermite polynomials.  
Hence, the generalized strain vector can be written in terms of nodal displacements in the following form:  
\[\mathbf{e}_{k}(x,t)=\mathbf{B}_{k}(x)\mathbf{q}_{k}(t) \tag{3}\]  
where \(\mathbf{e}_{k}(x,t)=\begin{bmatrix}\varepsilon_{k}(x,t),&\kappa(x,t)\end{bmatrix} ^{T}\), \(\varepsilon_{k}\) is the axial strain of the \(k\)th layer (\(\varepsilon(x,t)=u_{,x}(x,t)\)), \(\kappa\) is the curvature \(\begin{pmatrix}\kappa(x,t)=-w_{,xx}(x,t),&\left(\bullet\right)_{,xx}=\partial^ {2}(\bullet)/\partial x^{2}\end{pmatrix}\), and matrix \(\mathbf{B}_{k}\) can be expressed as follows:  
\[\mathbf{B}_{k}(x)=\begin{bmatrix}N_{1,x}(x)&N_{4,x}(x)&0&0&0\\ 0&0&-N_{2,xx}(x)&-N_{3,xx}(x)&-N_{5,xx}(x)&-N_{6,xx}(x)\end{bmatrix}. \tag{4}\]
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 4. **Mathematic Modeling**  
To realize real-time control of the rotor, it is necessary to establish the force and torque model between the stator and the rotor. All the magnetic material can be denoted by the magnetic moment \(M\), whose unit is A\(\cdot\)m\({}^{2}\). For the permanent magnet (PM), its magnetic moment \(M\) is constant. For soft magnetic material, its magnetic moment \(M\) is determined by the intensity of external magnetic field.  
For PM, the torque acting upon it can be expressed as:  
\[T=M\times B \tag{12}\]  
The force acting on PM can be expressed as:  
\[F=(M\cdot\
abla)B \tag{13}\]  
In Equations (12) and (13), \(B\) represents the magnetic flux intensity at the position of PM.  
Since there is no electric current in the area of PM, according to the Maxwell equation, we have:  
\[\
abla\times B=0 \tag{14}\]  
According to Equations (13) and (14), \(F\) can be expressed as:  
\[F=\left[\frac{\partial B}{\partial x}\ \frac{\partial B}{\partial y}\ \frac{\partial B}{\partial z}\right]^{T}M \tag{15}\]  
In order to obtain the force and torque acting on the rotor, the magnetic flux intensity \(B\) produced by the stator and the magnetic moment \(M\) of the rotor needs to be solved first.
## 4 Results and Discussion
### Variation of Membership with Different Number of Interference Data
For Stage II and III, the results demonstrate that the more the data belonging either to \(S_{\text{II}}\) or \(S_{\text{III}}\), the more reliable the stage affiliation judgment could be made. The increasing number of interference data could only weaken the belief degree on the processing stage affiliation judgment, but could not change the stage affiliation being Stage II or III. It could be drawn the conclusion that a reliable judgment on the processing stage affiliation could be made for Stage II or III by the model when conflict and imprecise data exist. The more similar methanogen community structure between Stage I and II than that between Stage I and III, or Stage II and III could also be reflected by the model as \(C(S_{\text{I}})\) values change apparently. Analyzing the results of the three stages, it could come to conclusion that the bigger the number of data from one fuzzy set, in other words, supporting each other, the more credible this group of data, and the more reliable the judgment on the processing stage affiliation. When several interference \(x\) values of \(S_{\text{un}}\) appear, the determination tends to rely more on the \(x\) values belonging to fuzzy sets representing a valid stage among the three stages than that to \(S_{\text{un}}\). In other words, the appearance of interfering \(x\) values could not change the affiliation of the processing stage of the tested paralleling sludge samples, but could increase the uncertainty of the judgment. In hence, the model could reflect the credibility of a group of measured data, and advisably deal with the interference data to determine the most probable processing stage where this data group come from and how likely are these data to belong to this stage. The reliability of the model in addressing data impreciseness and conflict are proved. Figure 8: Performance of the model fusing different numbers of interference data of Stage II: (**a**) Quantity ratios of _Methanoseta_ to _Methanobacterium_ of Stage II with different numbers of interference data (For the x-coordinate, letters a to f represent the six paralleling data, numbers 0 to 5 represent the number of interference data.) (**b**) Integrated memberships of Stage II with different numbers of interference data. (Stage I stands for the state of sludge samples after treated by centrifugal thickening and pre-dewatering, Stage II stands for the state of sludge samples after treated by thermal hydrolysis, Stage III stands for the state of sludge samples after experiencing anaerobic digestion, and stage uncertain stands for the uncertainty of the processing stage affiliation of a sludge sample.)
## 2 Background Theory
### Description of Time Reversal PPM
All binary data, \(a_{j}\in\{0,1\}\), are modulated by the pulse position modulation (PPM) scheme, where \(M\) is the total number of binary bits for each data package to be transmitted.
# Object and Event Detection Pipeline for Rink Hockey Games
## 3 Automatic Pipeline for Object and Event Detection in Rink Hockey Games  
One major contribution of this work is the specification, implementation, and testing of an automatic pipeline capable of detecting and tracking players, the ball, and other in-game objects in real time during fast-paced rink hockey games, as well as some important events, as can be seen in Figure 2.  
That said, the pipeline was designed to meet the following requirements:  
* Object detection capabilities for player tracking, ball tracking, and video analysis.
* Real-time performance for updating object detections regularly during fast-paced games.
* Ability to handle diversity in the appearance of objects due to factors such as lighting conditions, player uniforms, and background noise.
* High accuracy in object detection.
* High speed response in object detection.
* Compatibility with specialized hardware such as GPUs.
* Adaptability to different environments and lighting conditions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Sport** & **Dataset** & **Detection Type** & **Performance** \\ \hline Vats [5] & Ice Hockey & 84 NHL Games & PT (CNN) & 83\% (Acc.) \\ [PERSON] [6] & Ice Hockey & 6 2018 Winter Olympics Games & PT (CNN) & 93.05\% (Acc.) \\ [PERSON] [16] & Ice Hockey & 2 Ice Hockey Games & OD (CNN) & 94.6\% (Acc.) \\  & Soccer & 2 Soccer Games & & \\  & Basketball & APIDIS & & \\  & SPIROUDOME & & & \\ [PERSON] [17] & Ice Hockey & 5 NHL Games & OD (CNN) & 88.7\% (F1) \\ [PERSON] [20] & Soccer & CAVIAR & BSOS, PT & 94.7\% (Acc. 1) \\
**Ours** & Rink Hockey & 2525 Rink Hockey & OD, ED (YOLOv7) & 80\% (Acc.) \\  & Game Frames [22] & & & \\ \hline \hline \end{tabular} Object Detection (OD); Player tracking (PT); Event Detection (ED); Background Subtraction-based Object Segmentation (BSOS). 1 Combined Acc. in detection of two players in video sequence of CAVIAR dataset according to Table 3 in [20].  
\end{table}
Table 1: Comparative analysis between different computer vision systems.  
Figure 2: Pipeline for real-time detection of hockey objects and events.  
* Ability to detect and represent major events based on the stream of visual objects.
###### Abstract
UAV imagery enables data collection for weed mapping using the random forest-OBIA method. Farmers can use weed maps to manage their fields [6]. In this study, aimed to find out whether hyperspectral data can be used to discriminate weeds from crops. Our hypothesis was that hyperspectral data would be able to help in identifying weeds by using the spectral bands to detect the weeds in the field; therefore, the hyperspectral analysis of weed species in sorghum crops was explored. The aim was to discover the significant spectral bands that can be used to discriminate weeds species and how the spectral bands can be used to detect weeds within fields. The data were classified using statistical analysis and image processing. Then, the significant spectral bands were used to detect weeds in the field using multispectral UAV data. If weeds are detectable using image processing, this will help farmers to monitor weeds at earlier stages to avoid weed infestation, meaning farmers will be able to only spray the weed patches in the affected areas, reducing costs and protecting the soil and crops from excessive chemicals.
## 3 Results
### Estimation of \(f_{\mathrm{PSII\_760}}\) and ChlF\({}_{\mathrm{PSII\_760}}\)  
We observed that under low light conditions (\(<\)500 umol m\({}^{-2}\) s\({}^{-1}\)), \(f_{\mathrm{PSII\_760}}\) exhibited a pronounced response to changes in light intensity, increasing from 0.53 to 0.58 as the light intensity rose from 30 to 500 umol m\({}^{-2}\) s\({}^{-1}\). At higher light intensities (\(>\)500 umol m\({}^{-2}\) s\({}^{-1}\)), \(f_{\mathrm{PSII\_760}}\) displayed minimal variation, stabilizing at approximately 0.56 (Figure 4a). While \(f_{\mathrm{PSII\_760}}\) showed moderate variability across light intensities, notable differences were observed among the seven PFTs analyzed in this study (Table 1). Specifically, deciduous needleaf forest (DNF) had the lowest mean \(f_{\mathrm{PSII\_760}}\) value (about 0.27), whereas evergreen broadleaf forest (EBF) had the highest (about 0.71), which was 2.5 times greater than that of DNF. In contrast to the substantial differences observed among the PFTs, variability within individual PFTs was relatively small, except for DNF. Within each PFT, the standard deviations of \(f_{\mathrm{PSII\_760}}\) were substantially lower than their respective means, indicating relatively consistent values within the PFTs (Table 1).  
Figure 4: **(a)** The light response curve of the contribution of PSII to total chlorophyll fluorescence at 760 nm (\(f_{\mathrm{PSII\_760}}\)). **(b)** Comparison of the broadband chlorophyll fluorescence from PSII estimated using\(f_{\mathrm{PSII\_760}}\) (ChlF\({}_{\mathrm{PSII\_761}}\)) with that derived from PAM measurements (ChlF\({}_{\mathrm{PSII\_PAM}}\)). The red circles and error bars in panel **(a)** denote the means and standard deviations for 52 species, respectively, while the gray dots show the results for each species. In panel **(b)**, the black dashed line and red solid line indicate the 1:1 line and the linear regression result, respectively. The color scheme indicates photosynthetically active radiation (PAR, μmol m\({}^{-2}\) s\({}^{-1}\)). The coefficient of determination (\(R^{2}\)), root-mean-square error (RMSE, μmol m\({}^{-2}\) s\({}^{-1}\)), and relative root-mean-square error (rRMSE, %) are reported. The regression slope and intercept are also included.  
In addition, our results reveal a strong correlation between \(\rm{ChlF_{PSII\_IFPSII}}\) and \(\rm{ChlF_{PSII\_PAM}}\). Across all species, \(\rm{ChlF_{PSII\_IFPSII}}\) accounted for approximately 85% of the variance in \(\rm{ChlF_{PSII\_PAM}}\), with an RMSE of 3.44 umol m\({}^{-2}\) s\({}^{-1}\) and an rRMSE of 12.09% (Figure 4b). A slight overestimation in \(\rm{ChlF_{PSII\_IFPSII}}\) (slope = 0.76) was observed, likely attributable to the omission of PSI contributions to the total chlorophyll fluorescence at 686 nm during the calculation of \(f_{\rm{PSII\_760}}\). For the different PFTs, \(\rm{ChlF_{PSII\_IFPSII}}\) also explained the majority of the variation in \(\rm{ChlF_{PSII\_PAM}}\), with \(R^{2}\) values ranging from 0.79 to 0.99. The RMSE varied between 0.58 and 4.66 umol m\({}^{-2}\) s\({}^{-1}\), while the rRMSE remained below 20%, ranging from 2.03% to 18.96% (Table 1).
# Characterization of Six Cold-Pressed Berry Seed Oils and Their Seed Meals
## 3 Results and Discussion
### Antioxidant Activity  
Antioxidants promote human health because they lower the risk of aging-related diseases, e.g., heart diseases and cancer [20; 21]. Conventional oil extraction and processing techniques involve heating. Since a lot of bioactive compounds are thermally instable, they are broken down due to the heating. Because no heating is involved when cold pressing is used, berry seed oils produced in this manner contain higher amounts of natural antioxidants. The antioxidant capacity of oils is connected to the concentration of phenolic compounds [22; 23]. The ferric reducing antioxidant power (FRAP) method was used to determine the antioxidative activity of the cold-pressed seed oils. In the FRAP assay, ferric ions (Fe\({}^{3+}\)) are reduced to ferrous ions (Fe\({}^{2+}\)), which leads to a change in color. The intensity of the color depends on the reducing potential of the compounds present in the oil samples. FRAP values of the cold-pressed oils are shown in Figure 2. In the present study, the highest FRAP activity was detected in one of the sea buckthorn oils. Blackberry and blackcurrant oils exhibited high FRAP activity with FRAP values over 900 \(\upmu\)mol Fe\({}^{2+}\)/100 g oil. These observations are in accordance with results mentioned in literature [24]. Strawberry seed oil had the lowest FRAP value. Like the TP content, the FRAP activity was 10-100 times higher in extracts from the seed meals.  
Another antioxidant parameter is the IC50 value which is used to quantify the free radical scavenging activity. The IC50 value is defined as the extract concentration required for 50% inhibition of DPPH radicals under the given experimental conditions. Although all oil samples exhibited the capacity to react with and quench free DPPH in a dose-dependent way, a clear difference in the DPPH-scavenging capacity can be observed (see Table 2). Blackberry seed oil extracts exhibited the strongest DPPH-scavenging activity, followed by sea buckthorn, raspberry, blueberry, blackcurrant, and strawberry oil extracts.  
Figure 1: Content of total polyphenols in different cold-pressed berry seed oils and their respective seed meals. Results are mean values \(\pm\) standard deviation of triplicates.
# A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks
## 2 System Model  
### System Model and Main Assumptions  
The heterogeneous VLC/RF vehicular communication network provides vehicular mobile terminals with (VMTs) wireless access outdoors, as depicted in Figure 1. The RF APs are assumed to be the base stations of cellular networks, e.g., LTE eNodeB [14]. A VLC AP consists of several uniformly placed LED street lamps configured with communication modules [7]. Each VMT is equipped with at least one single photo-detector (PD) located at the top of the car, thus supporting both RF and VLC transmission in downlink vehicle-to-infrastructure (V2I) connections. As uplink traffic is always considerably less than downlink transmission, only RF transmission is employed in the uplinks [11].  
To avoid inter-cell interference (ICI) for VLC APs, the available transmission spectrum is divided into two bands and consecutive VLC APs use different channel bands [4]. Since the visible light signal overlap only occurs between adjacent VLC APs, this bandplan strategy can eliminate ICI.  
Let us consider a VTM connected to a VLC AP traveling along a given path. The packets are assumed to be transmitted through an LoS link. While driving, the VTM needs to perform HHO from the host VLC AP to the next AP (called the 'Target' AP) [4]. When entering the overlapping area covered by two neighboring VLC APs (denoted by the pink circle labeled with \(h=2\)), the RSSI from the host AP \(P_{H}\) reduces and the RSSI from the target AP \(P_{T}\) increases. A conventional HHO process is triggered when \(P_{T}>P_{H}+\text{HOM}\), where HOM is called the handover margin [1]. The conventional HHO process can be triggered by LoS interruption of the VLC link, while fast recovery triggers switching back operation, which leads to ping-pong handovers consuming unnecessary signaling overheads [7]. Thus, the conventional HHO scheme was not adopted in the considered vehicular VLC/RF networks.  
A soft HHO scheme is assumed to be applied for VLC HHO [4]. The VLC HHO process is triggered when \(P_{T}>P_{R1}\), with \(P_{R1}\) being an appropriate RSSI value for normal HHO executed while driving [4]. We also define an RSSI value \(P_{R0}\) that is able to support VLC access as an early HHO threshold, thus \(P_{R0}<P_{R1}\). The early HHO can be performed if VLC link blockage occurs on the condition of accessible target VLC APs. This threshold-based HHO scheme does not actually cause the ping-pong effect, because the original AP after HHO would not be identified as the target AP.  
The RF link is assumed to be always available, because the coverage of an RF AP is much larger than that of a VLC AP and the RF signals can penetrate opaque obstruction and are less influenced by weather conditions. The mobile switching center is responsible for access selection and the handover process of the VMTs. The packets in the queue follow the first-in-first-out rule. Newly arrived packets are dropped when the buffer of the mobile switching center overflows.