esa-sceva/satcom-chunk-collection huggingface dataset:
# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 4 Experiments
### Quantitative Evaluation  
We conduct evaluations for the proposed tracker given the testing splits of the LaSOT and GOT-10k benchmarks, and Tables 1 and 2 show the comparisons for the respective benchmarks. We name our algorithm, the target-adaptive feature bottleneck tracker, as **TAFT** in the subsequent tables and figures. From the performance metrics in Table 1, we observe that the proposed algorithm outperforms other ResNet-based trackers, such as GlobalTrack [29], ATOM [68], SPLT [69], and SiamRPN++ [10], and shows competitive performance compared to DiMP-50 [70] and Ocean [44]. Because the majority of these ResNet-based trackers are based on the ResNet-50 backbone, which is a much heavier network compared to the ResNet-18 backbone used in our tracker, our tracker runs at a much faster speed of 74 fps, thanks to the lighter backbone network and our proposed feature bottleneck module. Additional figures with comparisons for the success, precision, and normalized precision plots with varying overlap and location error threshold values are also shown in Figure 4.  
The results for the test split of GOT-10k are shown in Table 2 where all the quantitative metrics are obtained by uploading the outputs for the test sequences to the authors' online evaluation server ([[http://got-10k.aitestunion.com](http://got-10k.aitestunion.com)]([http://got-10k.aitestunion.com](http://got-10k.aitestunion.com)) (accessed on 8 September 2023)). Our proposed TAFT also shows competitive performance compared to other trackers on the task of relatively short-term sequences, even though our baseline tracker GlobalTrack [29] is a long-term oriented global search-based tracker. Although every other tracker requires meticulous tuning for the hyperparameters regarding temporal smoothness, motion priors, and cosine window weighting, our proposed TAFT does not employ any of these motion priors and thus performs generally well on a wide variety of tracking scenarios. Additionally, because removing the motion prior also removes the dependency between consecutive frames, our tracker can be executed in a batch-wise fashion where multiple frames can be processed at once, enabling further acceleration of the tracking speed.
## 4 Discussion
### Limitation  
The current subanalysis has some limitations. Firstly, we assessed adherence to comprehensive telerehabilitation and its impact on prognosis only in the HCTR group patients. More information would be provided by comparing the adherence between the HCTR and UC groups. However, we did not collect such data, especially since only 12% of UC group patients participated in cardiac rehabilitation programs.  
Secondly, we did not gather data on maintaining patients' adherence to medical recommendations, as well as data regarding physical capacity and repeating any training programs in the long-term follow-up after the 9-week intervention period. Thirdly, in this subanalysis, women constituted only 11% of HCTR group patients; therefore, extrapolating those results to the female population may not be reliable. Additionally, we did not collect data on the socioeconomic status of patients, which would provide more information on the factors favoring full adherence. Finally, the low numbers of partially or non-adherent patients are very satisfactory, but on the other hand, this may have had an impact on the statistical analyses, and this way could have affected the results.
## 5 Conclusions  
In this paper, an effort towards consistent adaptive routing in backbone networks under extreme congested conditions was proposed, with the main goal to avoid unnecessary path rerouting and reduce consequent network fluctuation and channel deterioration, by predicting the onset of an obvious and steady relative channel deterioration compared to the backup path. First we proposed a model to describe the differential channel congestion between the selected path and the backup one. Then, a metric for consistent routing, _PSI_, was designed, which is a boolean indicator for rerouting between the current path and the backup one. When the PSI is on, \(I_{t}=1\), it denotes that the backup path has and will have steady superior performance compared to the current path; thus it works as a sign to reroute traffic to the backup path. When the PSI is off, it denotes a steady inferior performance compared to the backup path.  
Evaluation experiments have been carried out on real networks virtualized with the Docker network, using real-life Abilene network traffic datasets. Results were presented and analyzed in detail. It is evident from the results that the PSI is able to predict the trend of channel congestion consistently; once PSI changes, it will take effect for more than 1000 s on average and more than 3000 s at most, while improving the congestion situation by more than 300% on average. The parameter selection was also discussed: different parameter values were used and their effects were compared. It turns out that the design of the PSI is flexible with configurable parameters. For future work, the effect of the PSI will be further evaluated by implementing it in real-life backbone networks with various, and its effects in terms of metrics like the call block probabilities will be studied to compare with existing approaches.  
Conceptualization, D.P. and Q.Z.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z.; formal analysis, Q.Z.; investigation, Q.Z. and D.P.; resources, Q.Z. and D.P.; data curation, Q.Z. and D.P.; writing--original draft preparation, Q.Z.; writing--review and editing, D.P.; visualization, Q.Z.; supervision, D.P.; project administration, D.P.; funding acquisition, D.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC) projects EP/N033957/1, and EP/P004024/1; by the European Cooperation in Science and Technology (COST) Action CA 15127: RECODIS--Resilient communication and services; by the EU H2020 GNFUV Project RAWFIE-OC2-EXPSCI (Grant No. 645220), under the EC FIRE+ initiative; and by the Huawei Innovation Research Program (Grant No. 300952).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(L_{i}(t)\) & Latency of the Path \(i\) at time \(t\) \\ \(L_{j}(t)\) & Latency of the Path \(j\) at time \(t\) \\  & Latency Differential Sensitivity \\ \(\theta\) & represent a certain amount of congestion \\  & by a certain amount of latency. \\ \end{tabular}
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 4 Human Tracking Module
### System Calibration  
The OptiTrack system needs two different calibration tools to ensure accurate motion tracking and system alignment: the CW-500 tool and the CS-400 tool. Figure 3 shows the calibration tool; each one is used for specific calibration purposes: the calibration with the CW-500 is performed for environment identification, while the calibration with the CS-400 is mandatory for reference system definition.  
The CW-500 tool is a calibration tool that is manually moved throughout the working volume for wanding acquisition, as shown in Figure 3 left and center. This process allows the Motive 3.1.0 software to dynamically gather the poses of the three retro-reflectors attached to the tool from all the cameras. The captured data help the system to identify the working environment by defining the tracking boundaries and optimizing the intrinsic configurations of the cameras to ensure accurate coverage of the entire space. Reference [20] details the calibration procedure that has been followed.  
The CS-400 tool is a Custom Calibration Square used to define the reference frame of the OptiTrack system, \(T_{opt}\), against which each rigid body pose is accurately expressed. For the proposed application, the tool was positioned on the edge of the robot workbench, as shown in the Figure 3, on the right. This choice was made to calibrate the base frame of the robot, \(T_{base}\), intuitively and accurately with respect to \(T_{opt}\), which provides sensory vision data. In this way, the kinematic chain of the robot, contained in the URDF file that simulates the environment in ROS2, is accurately connected to the reference frame of the vision system, avoiding positioning errors of the rigid bodies. To do that, four markers were positioned on the robot base; then, a rigid body was created including these markers in order to obtain the transformation matrix \(T_{base}^{opt}\) of the centroid of the rigid body itself. Finally, the original OptiTrack drivers for ROS2 were edited in order to send to the Ubuntu PC the tracked rigid bodies expressed in the \(T_{base}\) reference system, i.e., \(T_{RB_{i}}^{base}\).
# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
#### 3.1.1 Analysis Based on Usage and Purpose  
Figure 3 presents the distribution of publications based on local explanation methods' usage and purpose for industrial AI applications, as derived from Table 2. The survey shows that the SHAP (Shapley additive explanations), LIME (local interpretable model-agnostic explanations), and various Grad-CAM (gradient-weighted class activation mapping) methods are the most commonly used local explanation techniques, accounting for 39.47%, 22.81%, and 18.42% of the studies, respectively. Other local explanation methods have been used less frequently. Regarding usage and purpose, most of the SHAP and LIME applications in the surveyed literature are agnostic and post hoc. At the same time, the specific and ante hoc of local explanations are primarily used in various Grad-CAM methods for industrial AI applications.
# Remote Sensing in Urban Forestry:
###### Abstract  
Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.  
remote sensing; urban forest; Ecosystem services; LiDAR; multi-source data [table]capposition=top
## 2 Results
### McJAZ Proteins Interact with McCOI1a  
Previous studies report that JAZ proteins play a vital role in JA response through interacting with the JA receptor, CORONATINE INSENSITIVE 1 (COI1) in a JA-Ile- or coronatine (COR, a functional mimic of bioactive JA-Ile)-dependent manner rather than a JA- or MeJA-dependent manner [19; 23; 71; 72]. Some JAZ proteins in non-model plants may also interact with AtCOI1 [71]. To determine the role of McJAZ proteins in the JA response in _M. canadensis_, we first examined the interaction between AtCOI1 and McJAZ proteins by yeast two-hybrid (Y2H) assays. As shown in Figure 8, all of the yeast transformations  
Figure 7: **Subcellular localization analysis of McJAZ proteins in _N. benthamiana_ leaf cells. The GFP-tagged McJAZ fusion proteins and empty green fluorescence protein (GFP) alone were transiently expressed in the epidermal cells of _N. benthamiana_ leaf under the control of the CaMV 35S promoter. GFP fluorescence was detected at two days after infiltration in _N. benthamiana_ leaves by confocal laser microscopy. GFP alone is labeled as GFP control. The red arrow indicates the nucleus within each image. Bar = 20 \(\upmu\)m.**  
grew normally on the control medium (DDO, Figure 8A), whereas none of the McJAZ proteins interacted with AtCOI1 regardless of whether the selection medium (QDO) was supplemented with the JA-Ile mimic, COR (Figure 8B,C). Subsequently, given that protein-protein interaction might occur in a context-dependent manner in different plant species, we next cloned the homologous protein of AtCOI1, designated this as McCOI1a given that other unidentified copies of McCOI1 may exist in _M. canadensis_, and then examined the interaction between McCOI1a and McJAZ proteins. As expected, interaction was also not detected for McCOI1a and all seven McJAZ proteins under selection medium (QDO) in the absence of COR (Figure 8B). In contrast, except for McJAZ6 and McJAZ7, most of McJAZ proteins including McJAZ1, McJAZ2, McJAZ3, McJAZ4 and McJAZ5 interacted with McCOI1a in selection medium (QDO) supplemented with COR respectively (Figure 8C), suggesting that a conserved interaction between COI1 and JAZ is present in different plant species.
## 2 Survey of Commercial Small Platforms
### Active Microwave
#### 3.3.2 Real Aperture Radar Scatterometers  
Current and planned scatterometers missions have been identified and are summarized in Table 11. Earth Observation missions based on scatterometers typically operate at C-, and Ku-bands, and present spatial resolutions from 10 to 50 km. Current and future contributing missions to the Copernicus system with radar scatterometer are: ASCAT and SCA, ASCAT/Metop-A/B/C (2007 to 2021), with global coverage every 1.5 days and 12.5 km spatial resolution for basic sampling, SCA/Metop-SG-B1/B2/B3 (from 2022 to 2030) with near global coverage every 1.5 days, from 15 to 20 km of spatial resolution with sampling at 6.25 km intervals.  
The main variables derived from radar scatterometer data are wind speed and vector over sea surface [66], but scatterometers are also capable to obtain surface soil moisture indices [67], leaf area index [68], snow water equivalent, snow cover [69], and sea ice extent measurements [70] Table 11 shows the characteristics of the radar scatterometer. The power consumption of these sensors is in the range of 210-540 W, and mass is in the range from 260 to 600 kg. According to the requirements of power consumption, size and mass, this payload can be carried over mini- or large-satellites.
## 5 Proposed Interface Design Method
### Prototyping and Evaluation  
Designers may apply any of the various software development methods to prototype the interface, including the agile, lean, and waterfall, to mention a few. However, a method particularly well-aligned with the SA-oriented design is the Lean startup method by [PERSON] [38]. It is an incremental iterative design and development process that focuses on quickly formulating and validating ideas with end users to gain early insights into critical design features and trade-offs that allow for refining the design concept and reducing uncertainties [39; 40]. Using a build-measure-learn feedback loop, the method enables rapid interface prototyping, from basic versions, such as paper-based mockups and storyboards, to high-fidelity prototypes.  
The system interface prototype, materializing the developed design concepts, can be assessed for SA using one or a set of SA measurement methods, as mentioned in Section 2.1. Among them, the situation awareness global assessment technique (SAGAT) is recommended. In this study, SAGAT was selected over other techniques because it objectively measures SA, which is crucial for evaluating the effectiveness of the AR and MR interfaces designed through the proposed method. SAGAT measures SA by querying users during a simulated operational environment and comparing their perception of the situation with what is actually happening to determine the accuracy of their SA [15]. This approach contrasts with subjective tools such as self-rating techniques (e.g., SART), which can be influenced by user biases, or with performance-based or behavioral measures, which may not accurately reflect the user's understanding of the situation [15]. Moreover, SAGAT's validity and reliability have been empirically proved [41], and it is a well-known SA measurement tool [42; 43]. The reader can refer to [15] to explore SAGAT and other SA measurement methods.  
The feedback from the evaluation processes must be analyzed and integrated into the design matrix for further prototyping-evaluation iterations until an acceptable SA score and desired design are achieved. Notably, there is no predefined minimum score for effective SA enablement. The target SA score depends on the margin for error that one is willing to accept [44].
## 4 FTGC Design-Based LOS Angular Rate and the Target Motion Estimation Information  
In this section, the LOS angular rate and the target acceleration are firstly estimated with the application of the Extended State Observer (ESO) based on the measured body-LOS angle information. In what follows, an FTGC law is proposed in which the DSC technique is used to avoid the problem of explosion of the differential. The control structure diagram is described in Figure 5.
## 3 Research Problems  
Existing literature has some studies on resisting potential threats in a wireless network. However, specific open-ended issues are explored, which must be solved to mitigate the rising security concerns. The identified research problems are as follows:  
* Gap between IoT and ad hoc network security: The current state of security solutions in ad hoc networks is not applicable when integrated with cloud or IoT systems. Due to the progress of technologies, the necessary revision has not been carried out considering the practical deployment environment. For example, when a mobile ad hoc network (MANET) node is deployed in IoT, the security protocol for it [13] differs from the security protocol of IoT [50]. Moreover, the ad hoc network concept is not considered much while all nodes are deployed in the IoT scenario; they are considered in terms of individual nodes and not in the form of a network. Currently, there are more IoT security schemes than conventional ad hoc security.
* Sophisticated Game Modelling: Studies prove that the game concept is one of the most rapidly evolving security solutions in the network [51; 52]. However, most game concepts deployed to date associated the model with developing interactive game stages, including multiple steps. Although it was quite possible to use this multi-stage gaming model to address various traits of security, in the case of a complex environment, sophisticated measures had to be taken and there was less assurance of model sustainability. Hence, there is a need for a simplified and straightforward game model to perform better decision-making. Furthermore, it is notable that most of the existing game models have extensive payoff matrix computation, which also demands resources. Therefore, when applied to a sizeable practical network, such a sophisticated game model will call for a delay and higher response time. Thus, a lightweight game model is needed to secure a challenging communication environment.
* Computational Burden not emphasised: There are two forms of threats in the network; one form is when the attacker's identity is well-known based on their attack patterns, while the second form is unknown. Hence, security modelling confirming the presence of misbehaved nodes calls for an extensive set of observations to be carried out. Such threat monitoring calls for deploying a method with a lesser dependence on resources and the inclusion of smart operational processes geared towards identifying the attacker's intention in the least amount of time possible.  
Therefore, existing studies do not emphasise balancing the computational demand with security on dynamic networks.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 4 Data Acquisition Tool--IT System Design
### Maintainability Analysis Module
#### 4.2.3 Reliability Assessment  
The reliability of the maintenance process is modeled as a function of task completion times and their variability.  
* Maintainability Function \(M(t)\): \[M(t)=P(D_{i}\ \leq\ t)=F_{D_{i}}\ (t)\] (9) where \(t\) is the time threshold for task completion.
* Variance of Task Duration: Variance helps in identifying bottlenecks. \[Var(D_{i})=E[D_{i}^{2}]-(E[D_{i}])^{2}\] (10)
## 3 Results
### Connectivity Substructures  
After verification of the reliability of the algorithm with the results shown above, the next step was estimating the substructures from the eigendecomposition of Graph kernels for sample epochs of each category of the given dataset. The graph kernels encode the non-linear spatial connectivity substructures and temporal metastable states. For example, the plots in Figure 3 below show the existence of connectivity substructures (clusters) for sample epileptiform data. Figure 3a shows the correlation matrix heatmap for the preictal-state sample. Figure 3b shows the inherent clustering substructures for the preictal-state sample. Figure 3c shows the correlation matrix heatmap for the ictal-state sample. Figure 3d shows the inherent clustering substructures for the ictal-state sample. Figure 4 shows the distinct patterns of substructures for the cognitive load dataset. Figure 4a shows the heatmap of the connectivity matrix for the idle-state sample. Figure 4b shows the inherent clustering substructures for the idle-state sample. Figure 4c shows the heatmap of the connectivity- matrix for the d1B-state sample. Figure 4d shows the inherent clustering substructures for the d1B-state sample.  
Figure 2: Plots show the optimal clusters of eigenvectors for a synthetic dataset: (**a**) synthetic dataset with 5 clusters, (**b**) optimal clustering structure for top-2 eigenvectors, (**c**) silhouette scores, (**d**) SSE distance scores.  
Figure 4: Plots show the existence of connectivity substructures (clusters) for sample cognitive load EEG-ERPs. (**a**) Heatmap of the correlation matrix (>0.5) for idle-state sample; (**b**) inherent clustering structure for the idle-state sample; (**c**) heatmap of the correlation matrix (>0.5) for d1B-state sample; (**d**) inherent clustering structure for the d1B-state sample.  
Figure 3: Plots show the existence of connectivity substructures (clusters) for sample MIT CHB Epileptiform EEG data. (**a**) Heatmap of connectivity matrix (>0.5) for preictal-state sample; (**b**) inherent clustering structure for the preictal-state sample; (**c**) heatmap of connectivity matrix (>0.5) for ictal-state sample; (**d**) inherent clustering structure for the ictal-state sample.  
The distinct connectivity substructures occurring in samples of different epileptiform categories are shown in Figure 3, and those for the cognitive state categories are shown in Figure 4. Furthermore, the pair-wise channel-connectivity states are sparse, which was also observable in Figures 3 and 4.