esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Object-of-Interest Annotation
### Recognition Approach Selection
This keyframe level comparison is possible because every object instance includes the information in which keyframe it was detected, as shown in the annotation schema of Figure 2. When region information is given, all object instances are individually compared against the left-out object by computation of the region overlap using a polygon intersection. Figure 4 shows this evaluation for a task where three object instances (\"persons\") have been selected (right image) with exact object boundaries. The suggested object instances from a recognition approach (left image) are given as bounding boxes. In this figure, the person shown in the middle of the keyframe was used as left-out object. Thus, the evaluation tries to find out if this object instance was returned and at which position in the ordered result list. During evaluation, every object instance that was generated from this keyframe is compared against the left-out instance using polygon intersection. Those instances that are above a certain intersection (default value is 60%) are true positives (bold bounding boxes) and all other are false positives (thin bounding boxes). The position of the best true positive is then returned, which is 17 in the shown example. Note that the proposed evaluation process does not guarantee that the best recognition approaches are selected. However, the experiments of Section 5 show that it works well if the initial object examples present the object in the videos well. **Fusion:** Finally, the one-left-out results of each approach as well as the recognition approaches that achieved the best evaluation results are combined using following fusion strategy. \[rank_{i}=best_{i}*\#lists*\frac{\#entries_{i}}{\#possibleEntries}-best_{i} \tag{2}\]  
In this fusion, we compute the rank of each object instance \(i\) in the result _lists_ based on its _best_ position in all result lists and the number of _entries_ of this instance in all lists in relation to the _possible_ amount of entries. Depending on the recognition approach, multiple entries of the same instance can either be given  
Figure 4: Evaluation for the first iteration of the one-left-out approach. in one result list or not. After this, we order all instances according to this ranking value. The intuition behind this fusion is to order the instances similar to a zip fastener starting from their best entries. The order of entries with the same best value (originating from different result lists) is determined from the total number of entries. In an alternative fusion approach we additionally used the number of top \(x\) ranked entries (x \(\in\) 50, 100, 500).
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 2 Single Diode Model for Describing Degradation Phenomena
### Effect of PV Degradation on the _I-V_ Characteristic and the SDM Parameters
Since all degradation phenomena have an impact on the delivered power, the normalized sensitivity (\(\mathbf{S_{n}}\)) of the PV output power with respect to the SDM parameters variation is calculated as follows:  
\[\mathbf{S_{n}}=[s_{n,I_{ph}},s_{n,I_{sat}},s_{n,Jt},s_{n,R_{s}},s_{n,R_{sh}}]=\] \[= \left[\frac{\Delta P_{mpp}}{\Delta I_{ph}}\frac{I_{ph,H}}{P_{mpp, H}},\frac{\Delta P_{mpp}}{\Delta I_{sat}}\frac{I_{sat,H}}{P_{mpp,H}},\frac{ \Delta P_{mpp}}{\Delta\eta}\frac{\eta_{H}}{P_{mpp,H}},\frac{\Delta P_{mpp}}{ \Delta R_{s}}\frac{R_{s,H}}{P_{mpp,H}},\frac{\Delta P_{mpp}}{\Delta R_{sh}} \frac{R_{sh,H}}{P_{mpp,H}}\right] \tag{3}\]  
where the \(s_{n,k}\) is the normalized sensitivity calculated by introducing the variation \(\Delta k\) to the k-parameter. The subscript \(I_{H}\) refers to the values in healthy conditions. Due to the non-linearities of PV power with respect to the SDM parameters, the sensitivity is not constant and it should be calculated locally and for the different environmental operating conditions. The normalized sensitivity values of the delivered PV power with respect to the five parameters' variation shown in Figure 2 is reported in (4). \[\mathbf{S_{n}}=[1.04,\,-0.0606,\,1.12,\,-0.158,\,0.031] \tag{4}\]  
The results show that the PV output sensitivity with respect to the \(I_{ph}\) and \(\eta\) is close to one. As a consequence, few percentage variation of \(I_{ph}\) and \(\eta\) is reflected in a significant variation of PV power. \(R_{sh}\) and \(I_{sat}\) have lower impact at less than one order of magnitude. The sensitivity to \(R_{s}\) is somewhere in between. This means that a small variation of these parameters can be tolerated since the corresponding degradation process is not yet detrimental.
# Performance Analysis of Thread Block Schedulers in GPGPU and Its Implications
## 5 Conclusions  
In this article, we presented a scheduler model for the thread block scheduling in GPGPU and developed an open-source GPGPU simulator based on the proposed model. We observed that the current Round-Robin hardware scheduler used in GPGPU is not efficient when the workload of GPGPU increases. In particular, the current hardware scheduler is simple and fast, and it shows reasonable performances when the load of GPGPU is not heavy [26]. However, when a large number of GPGPU  
Figure 8: Performance comparison under workloads of uniform resource demand.  
Figure 9: Performance comparison under workloads of extremely biased resource demand.  
tasks are requested simultaneously and the throughput and the execution time for multiple tasks are important, an efficient thread block scheduling is required to maximize the utilization of SM [27]. The proposed thread block scheduling model manages the available resources for each thread block by the unit of micro thread block (mTB), and evaluates the execution overhead based on the usage of the computing and memory resources. Unlike gpgpusim [28; 29], which accurately emulates hardware in component basis, the proposed model simulates the characteristics of GPGPU with respect to the thread block scheduling. Experimental results using our simulator showed that BFA and DFA perform over 70% better than Round-Robin by making use of the resource usage in SM efficiently as the load of GPGPU increases.  
In reality, the commercial GPGPU device does not open their interface for thread block scheduling, and thus it is not possible to implement our model in real GPGPU devices. Thus, developing simulators is an alternative way for evaluating the thread block schedulers. As our future study, we plan to implement an efficient thread block scheduler based on machine learning for considering the workload characteristics. For example, the scheduling performance can be improved even more by considering the heaviness of workloads and the homogeneity/heterogeneity of resource demands.  
[PERSON] implemented the architecture and algorithm and performed the experiments. [PERSON] designed the work and provided expertise. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the ICT R&D program of MSIP/IITP (2018-0-00549, Extremely scalable order preserving OS for manycore and non-volatile memory).  
The authors declare no conflict of interest.
# MobiRes-Net: A Hybrid Deep Learning Model for Detecting and Classifying Olive Leaf Diseases
## 4 Proposed Hybrid DL Model
### Deep MobiRes-Net Model Training  
In earlier studies, researchers have separately used the Resnet-50 and MobileNet models for olive leaf diseases. In this study, the proposed hybrid DL model extends the design of the Resnet-50 model with the MobileNet network to design the MobiRes-Net model. Figure 7 shows the architecture of the proposed deep MobiRes-Net model, which is divided into two parts: Resnet-50 and MobileNet. Firstly, the previously trained weights of each model, as shown in Figure 7, were loaded. Then, the classification half of each model was replaced by an average pooling layer and a fully connected layer with 1024 neurons. The weights of the fully connected layers of each branch were then merged via concatenation and three more fully connected layers of sizes 1024, 512, and 256 neurons. Olive leaf diseases were finally classified making use of the Softmax classifier in the output layer. The function appearing in the output layer is a type of activation function that is used in multi-class classification problems.  
Figure 6: Slices of typical images with four types of olive leaf disease findings: (**a**) Aculus Olearius; (**b**) olive scab; (**c**) peacock spot; (**d**) healthy.
## 2 Materials and Methods
### ERA5 Reanalysis Data  
ERA5 reanalysis data are the latest generation of reanalysis data created by the Copernicus climate change service C3S, funded by the European Union and operated by the European Centre for Medium-Range Weather Forecasts (ECWMF). ECWMF is an organization that carried out data reanalysis research very early. Its reanalysis data have gone  
Figure 2: Calculation area and water depth (validation sites S1 and L1 marked within the black box).  
through four generations: FGGE, ERA-15, ERA-40, and ERA-Interim. ECMWF launched its 5 th generation reanalysis product (ERA5) in November 2016.  
ERA 5 has been greatly upgraded on the basis of its predecessor, ERA-Interim. Firstly, it has improved from the original ERA-Interim with spatial-temporal resolution of 80 km and 6 h to a horizontal resolution of 31 km and 3 h, respectively, and from 60 layers from the ground to 0.1 hPa in the vertical direction to 137 model layers from ground to 0.01 hPa (about 80 km from the ground). Secondly, ERA5 uses the data assimilation ensemble (EDA) system developed by ECMWF for the first time to adopt more historical observation data, especially satellite data, into the advanced data assimilation and model system to estimate more accurate atmospheric conditions and make the data more accurate at different times and locations.  
The ERA5 wind field stands as a valuable repository of information for storm surge simulations, proffering a global perspective on wind speed and direction. The ERA5 wind field is conventionally harnessed as an influential force field to propel computational models, serving as a crucial driver for estimating wind stress at the ocean surface. This estimated wind stress subsequently fuels the computation of surface ocean currents and the ensuing genesis of storm surges. As a consequence, these surges are predictive of the height and spatial extent of storm surges incited by typhoons.
## Appendix C  
To ensure that the results and findings of this work are applicable to other FE model simulations, mesh sensitivity analysis was performed for the case of a full-scale 3D model. According to the Abaqus Analysis User's Manual [27], the stress-strain relationship that is used to define material behavior, can no longer represent material behavior after the onset of material damage (stated as damage initiation point in Figure 10). If the finite element model were to continue following the behavior defined in the stress-strain relationship, a strong mesh dependency would occur based on strain localization. In order to overcome this issue, Abaqus uses a different approach (Damage Evolution Law) to model material softening behavior past the damage initiation point. Specifically, [PERSON] et al. fracture energy proposal [31] is adopted, which decouples mesh dependency from material behavior once damage is initiated. In their proposal, fracture energy is defined according to Equation (A1).  
\[G_{f}=\int_{\pi_{0}^{pl}}^{\pi_{f}^{pl}}L\sigma_{y}d\overline{\epsilon}^{pl}= \int_{0}^{\overline{u}_{f}^{pl}}\sigma_{y}d\overline{u}^{pl}\] (A1)  
where \(\overline{u}^{pl}\) is the equivalent plastic displacement as work per unit area of crack that has formed. Prior to damage initiation point, \(\dot{\overline{u}}^{pl}\) is considered as zero, and after that, it is calculated based on Equation (A2).  
\[\dot{\overline{u}}^{pl}=L\dot{\overline{\epsilon}}^{pl}\] (A2)  
where \(L\) is defined as the characteristic element length, and for 3D elements used in this work, it is calculated as the ratio of element volume to area of the largest face of the element (\(L=Vol./L\). _Area_). To illustrate the mesh independency from the JC model and damage parameters (Tables 3 and 4), in Figure A7, a comparison can be seen between average experimental results for the 0.001 s\({}^{-1}\) strain rate from Table 2 and FE simulation for average mesh sizes of \(1\times 1\times 1\) (\(L=1\)) and \(2\times 2\times 2\) (\(L=2\)) mm that were used in the gauge length region of the full-scale FE model.  
It can be seen from Figure 17 that the FE analysis results closely match regardless of the mesh size that was utilized. The results of this analysis were expected and can be used to further validate the element damage evolution law followed by Abaqus. In Figure 18, strain map results similar to Figure 20, can be seen for DIC, and average mesh sizes of \(1\times 1\times 1\) and \(2\times 2\times 2\) mm are aligned around the necking region for smooth specimens at the last captured frame before rupture. It can be seen that regardless of the mesh size, similar results are obtained, but due to the decreased number of elements along the gauge length, small strain discrepancies are observed.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 4 Simulation Experiment  
Ideally, SatScope should be capable of meeting research needs under a diverse range of conditions. In this paper, we investigate SatScope's simulation capabilities under specific network conditions and compare it with several recently studied simulators. Additionally, we analyze SatScope's lightweight design, which enables researchers to deploy it across various hardware environments.  
In this simulation experiment, all environments were configured on a personal computer equipped with an i7-13700 @ 2.10 GHz processor and 32 GB of DDR4 RAM, running the 64-bit Windows 11 operating system. The entire simulation was developed using Python 3.9. SatScope primarily relies on two libraries: VTK and Skyfield.  
VTK is an open-source software system widely employed for 3D computer graphics and data visualization, capable of rendering complex datasets and supporting interactive applications. Skyfield, on the other hand, is an astronomy computation library that accurately calculates celestial positions and movements, making it suitable for high-precision astronomical research. These tools were essential to our experiments, ensuring both the accuracy of data processing and the effectiveness of visualizations.
# Allogeneicic Demineralized Dentin Matrix Graft for Guided Bone Regeneration in Dental Implants
###### Abstract  
Autogenous and allogeneic demineralized dentin matrices (Auto-DDM and Allo-DDM, respectively) are currently used for guided bone regeneration (GBR). Buccal marginal bone (BMB) resorption is critical for successful implant integration. This study analyzed BMB resorption around dental implants for GBR between the control group (Auto-DDM graft) and experimental group (Allo-DDM graft). From 2014 to 2019, we enrolled 96 patients (59 males, 37 females, average 57.13 years) who received GBR (52 and 44 using Allo-DDM and Auto-DDM, respectively,) without a barrier membrane and a simultaneous single dental implantation (54 in the maxilla and 42 in the mandible). BMB height was measured immediately after GBR, at prosthetic loading, and 12 months after loading. BMB resorption was classified as initial resorption (between GBR and prosthetic loading) and functional resorption (during 12 months after prosthetic loading). The differences in the BMB levels of Auto-DDM and Allo-DDM were analyzed between the initial and functional resorption stages by independent sample \(t\)-test. Auto-DDM and Allo-DDM showed similar BMB changes in initial resorption (\(0.73\pm 0.97\) and \(0.72\pm 0.77\) mm, respectively) and functional resorption (\(0.69\pm 0.81\) and \(0.48\pm 0.58\) mm, respectively) without a significant difference between the maxilla and mandible. For GBR, Allo-DDM is comparable to Auto-DDM in terms of BMB resorption.  
allogeneic demineralized dentin matrix; buccal marginal bone; demineralized dentin matrix; dental implant; guided bone regeneration +
Footnote †: journal: Applied Sciences
## 2 Background Research
### Adversarial Attacks
Figure 1: Clean (**a**) and adversarial (**b,c**) images of a Labrador-breed dog, showing the classification results and epsilon values for adversarial perturbation (Image Credit: TensorFlow Authors 2019 [32]). * Basic Iterative Method (BIM) [21]: BIM extends the idea of FGSM and implements the attack multiple times with smaller step sizes. * Unrestricted Adversarial Examples [19; 20]: the authors who proposed this idea took a different approach to generating adversarial examples; in this approach, a GAN learns to create adversarial examples from scratch by searching the latent space of the model that is being attacked. Because our architecture is based on their proposed solution, this alternative is discussed in detail in the following sub-section. Machine learning plays many essential and security-sensitive roles, from image classification to spam filtering and malware detection. For this reason, adversarial attacks pose a huge risk to many application areas. The most common security issue here is adversarial attacks in the form of images. Many of the introductory works in this area were carried out using image data from [PERSON]. [PERSON] et al. (2014) [2] and [PERSON] et al. (2015) [1]. These authors focused on image-based attacks that engineer adversarial perturbation to add to the sample image. Despite the initial research focus on images, there has been research on adversarial examples that has successfully reduced the performance of machine learning models that work with other forms of data. Studies such as [4; 5; 37] have proven that adding perturbation to audio input can affect speech-to-text transcription neural networks. Similar attacks have been able to affect other Machine Learning applications as well, such as Natural Language Processing (NLP), areas including sentiment classification, fake news detection, neural machine translation, spam filtering, malware detection, bot detection, network intrusion detection, and even self-driving cars, as shown in [3; 13; 16; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]. In this paper, we use PGD [9] and Unrestricted Adversarial Examples [19; 20] for adversarial training and use PGD [9], FGSM [1], SPSA [34; 35], Unrestricted Adversarial Examples [19; 20], MIM [36], and BIM [21] for testing purposes. FGSM and PGD are both classified as gradient-based adversarial example generation algorithms. These attacks use the gradients produced during backpropagation to generate an optimal perturbation added to the original input. Despite most attacks being gradient-based, attacks such as SPSA use \"gradient-free optimization\" to generate adversarial examples. In the specific case of SPSA, [PERSON] et al. (2018) [35] states that a technique proposed in [34] can be used to approximate the gradients through finite difference estimates in random directions, providing an efficient way of producing adversarial examples. Gradient-based and gradient-free attacks were used to challenge the universality of both previously proposed adversarial training methods and our proposed method.
# A Deep Attention Model for Action Recognition from Skeleton Data
## 4 Experimental Results
### Results on UOW Large-Scale Combined (LSC) Dataset  
The UOW LSC dataset [44] is a large dataset composed of nine publicly available datasets including MSR Action3D Ext [47], UTKinect [48], MSR DailyActivity [49], MSR ActionPair [50], CAD120 [51], CAD60 [52], G3D [53], RGBD-HuDa [54], and UTD-MHAD [55]. There are 94 actions, from 107 subjects and 4953 samples in total. However, some samples in some action classes do no contain skeleton modality and thus are excluded in the experiment, resulting in 88 action classes and 3897 samples. Due to the nature of the LSC dataset combined from several existing datasets, the intra-class variation is much larger than a single dataset captured in a rather fixed environment. Moreover, the number of samples in different action classes are highly imbalanced, ranging from over 100 samples to only four samples in a class. The protocols developed in [44] were used for evaluation, including random cross-subject and random cross-sample. The average recall and precision of all classes as suggested in [44] is used for comparison. Table 2 shows the result of the proposed model compared with the existing methods. It can be seen that better performance can be achieved than the LSTM models [29].  
Figure 5: Compasion of the attention on the joints of feet and hands over time for different action classes. (**a**) Progress of the attention on the joints of feet over time for “hand waving” and “kicking something”. (**b**) Progress of the attention on the joints of hands over time for “playing with phone/tablet” and “kicking something”.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 2 The Reliability Evaluation of PR Data by Comparisons with Ground-Based Instruments
### Comparisons of Rainfall Rate with Rain Gauges
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Period** & **Sites** & **Products (Version) of PR** & **Temporal Scale** & **Conclusion** & **Study Region** \\ \hline [PERSON] et al. (2012) & 1998-2014 & 22 rain gauges & 3843 & monthly, seasonal and annual & underestimation & Singapore \\ [PERSON] et al. (2019) & Jul. 2009 to & 14 rain gauges & 3842 (version7) & daily & overestimation & south-soutwest region of \\ Jun. 2015 & Jun. 2015 & 3842 (version 6 and & daily, monthly and annual & 3042 V7 outperforms 3842 V6 & Northern Morocco \\ [PERSON] et al. (2015) & 1998-2012 & 125 rain gauges & 3842 (version 6 and & monthly, monthly and annual & 3042 V7 outperforms 3842 V6 & United Arab Emirates \\ [PERSON] et al. (2016) & 2000-2010 & 53 rain gauges & 3842 (version 7) & monthly, monthly and underestimation in rainy regions & Iran \\ [PERSON] et al. (2016) & 1998-2013 & 157 synoptic stations & 3842 and 3843 (version7) & annual & underestimation for noisy rain \\ [PERSON] et al. (2014) & Apr. 2014 to & 53 rain gauge stations & 3842 (version7) & daily & underestimation for noisy rain \\ Jun. 2016 & May to Oct. & 194 rain gauges & 3842 (version7) & daily and monthly & overestimation \\ [PERSON] et al. (2014) & 2004 & 194 rain gauges & 3842 (version7) & daily, monthly and annual & underestimation in wet season and \\ [PERSON] et al. (2014) & 2000-2013 & 5-208 rain gauges & 3842 (version7) & annual & overestimation in dry season & across the globe \\ [PERSON] et al. (2018) & 2004-2013 & 36 tipping bucket ratingsupages & 2.425 (version7) & instantaneous & underestimations during the monsoon season & subcontinent \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._