esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Flight Test Results and Discussion
### Latency Time Analysis
#### 4.2.2 Flying Condition Analysis  
In this analysis, the flight test data were grouped based on the flying condition of the Remote ID module. Module 1 (flying in a UAV) from scenario 1 is categorized as a flying condition combined with both modules in scenario 2. The ground category was only  
Figure 12: Boxplots of the latency time analysis based on the livelihood condition (rural and suburban area): (**a**) broadcast time [s], (**b**) process time [s], (**c**) query time [s], (**d**) latency time [s], and (**e**) interval time [s].  
module 2 (stay on the ground) from scenario 1. The resulting boxplots for the broadcast time, process time, query time, latency time, and interval time are shown in Figure 13.  
The broadcast time and query time shown in Figure 13a,c had similar trends, which, in flying conditions, had a higher average value (broadcast time: 0.33613 s, query time: 1.066873 s) and variation than in the ground condition (broadcast time: 0.299612 s, query time: 0.784528 s). This indicates that the 4G signal quality on the ground was better than in the air. This is confirmed by the result from other research that measured 4G signal performance using UAVs at several heights above the ground [46].  
Meanwhile, the process time shown in Figure 13b indicates that the flying condition had slightly larger average and variation values. The flying condition should not affect the process time since the system uses the same application and computer hardware. Because the process time was large when a detection event happens, this indicates that the flight test in the flying condition had more frequent or longer warning events.  
In total, the latency time in flying conditions had a larger average value (1.566143 s) and variation than on the ground (1.220758 s). This is in line with the finding from the trend of broadcast and query time earlier showing that the 4G signal quality on the ground  
Figure 13: Boxplots of the latency time analysis based on the flying condition (fly in UAV or stay on the ground): (**a**) broadcast time [s], (**b**) process time [s], (**c**) query time [s], (**d**) latency time [s], and (**e**) interval time [s].  
was better than in the air. According to Figure 13d, the longest latency time was around 24 s. Although it is considered an outlier in the boxplot, the value was much larger than the average and its standard deviation. The larger contribution to this large value came from the query time, which was affected by the internet signal quality.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 3 Methodology  
This study evaluates the energy consumption in existing government school buildings. It considered three schools in Buraidah City in the Qassim region: Anas bin Malik Primary School (school 1), Buraidah Secondary School (school 2), and Prince Abdulelah Secondary School (school 3). The electrical energy consumption in these schools was estimated based on actual electricity consumption readings, and then the integration of PV systems with buildings was studied. An environmental study was also conducted to determine whether PV systems were applied to the KSA's government schools. Figure 1 shows the flow chart diagram of the research methodology.
## Authenticity Preservation with Histogram-Based Reversible Data Hiding and Quadtree Concepts  
**[PERSON] 1,* and [PERSON] 2**  
* Department of Electrical Engineering, National University of Kaohsiung, 700 University Road, Kaohsiung 811, Taiwan
* Department of Electronics Engineering, National Chiao-Tung University, 1001 Ta-Hsueh Road, Hsinchu 300, Taiwan; E-Mail: [PERSON]DDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-7-591-9373; Fax: +886-7-591-9374.  
_Received: 27 August 2011; in revised form: 4 October 2011 / Accepted: 6 October 2011 / Published: 13 October 2011_  
**Abstract:** With the widespread use of identification systems, establishing authenticity with sensors has become an important research issue. Among the schemes for making authenticity verification based on information security possible, reversible data hiding has attracted much attention during the past few years. With its characteristics of reversibility, the scheme is required to fulfill the goals from two aspects. On the one hand, at the encoder, the secret information needs to be embedded into the original image by some algorithms, such that the output image will resemble the input one as much as possible. On the other hand, at the decoder, both the secret information and the original image must be correctly extracted and recovered, and they should be identical to their embedding counterparts. Under the requirement of reversibility, for evaluating the performance of the data hiding algorithm, the output image quality, named imperceptibility, and the number of bits for embedding, called capacity, are the two key factors to access the effectiveness of the algorithm. Besides, the size of side information for making decoding possible should also be evaluated. Here we consider using the characteristics of original images for developing our method with better performance. In this paper, we propose an algorithm that has the ability to provide more capacity than conventional algorithms, with similar output image quality after embedding, and comparable side information produced. Simulation results demonstrate the applicability and better performance of our algorithm.  
keywords:reversible data hiding; histogram; quadtree; imperceptibility; capacity; side information +
Footnote †: journal: Journal of Computational Physics
## 3 Results and Discussions
### Thermal Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & \\ \hline \hline
**Sample** & \(T_{m}\) PCL [\({}^{\text{th}}\) ] & \(\Delta H_{m, PCL}\) [\({}^{\text{th}}\) ] & \(\chi\)_PCL [\%]_ & \(T_{m1}\) P46 [\({}^{\text{th}}\) ] & \(T_{m2}\) P46 [\({}^{\text{th}}\) ] & \(\Delta H_{m,\text{pts}}\) [\({}^{\text{th}}\) ] & \(\chi\)_P46 [\%]_ \\ \hline PA6 & - & - & - & - & 219.8 & 226.3 & 79.8 & 34.7 \\ PA6\_5 PCL & 60.3 & 6.2 & 69.2 & 217.6 & 224.2 & 67.3 & 30.8 \\ PA6\_10 PCL & 62.4 & 10.9 & 78.1 & - & 225.2 & 78.0 & 37.7 \\ PA6\_20 PCL & 63.7 & 20.7 & 74.1 & 220.0 & 225.9 & 55.6 & 30.2 \\ PA6\_30 PCL & 64.6 & 26.3 & 62.7 & 220.3 & 226.8 & 54.3 & 33.8 \\ PCL & 66.8 & 74.9 & 53.7 & - & - & - & - \\ \hline \hline \multicolumn{8}{c}{**Coiling Scan**} \\ \hline
**Sample** & \(T_{m}\) P46 [\({}^{\text{th}}\) ] & \(\Delta H_{m, PCL}\) [\({}^{\text{th}}\) ] & \(\chi\)_P46 [\%]_ & \(T_{m1}\) P46 [\({}^{\text{th}}\) ] & \(\chi\)_P46 [\%]_ & \(\chi\)_PCL [\%]_ & \(T_{g}\)_PCL [\%]_ & \(\chi\)_P46 [\%]_ \\ \hline PA6 & 190.7 & 89.7 & 39.0 & - & - & - & - \\ PA6\_5 PCL & 190.5 & 80.2 & 36.7 & 30.3 & 2.4 & 34.4 & - \\ PA6\_10 PCL & 190.3 & 75.0 & 36.2 & 30.7 & 5.7 & 40.9 & - \\ PA6\_20 PCL & 190.5 & 67.9 & 36.9 & 29.7 & 12.3 & 44.1 & - \\ PA6\_30 PCL & 190.6 & 61.9 & 38.4 & 28.5 & 17.7 & 42.3 & - \\ PCL & - & - & - & 23.3 & 62.0 & 44.4 & -61.0 \\ \hline \multicolumn{8}{c}{**Second Heating Scan**} \\ \hline
**Sample** & \(T_{m}\) _PCL [\({}^{\text{th}}\) ] & \(\Delta H_{m, PCL}\) [\({}^{\text{th}}\) ] & \(\chi\)_PCL [\%]_ & \(T_{m1}\) P46 [\({}^{\text{th}}\) ] & \(T_{m1}\) P46 [\({}^{\text{th}}\) ] & \(\chi\)_P46 [\%]_ & \(\chi\)_P46 [\%]_ \\ \hline PA6 & - & - & 42.4 & - & 224.3 & 90.1 & 39.2 \\ PA6\_5 PCL & 53.5 & 3.0 & 40.7 & - & 224.2 & 79.9 & 36.6 \\ PA6\_10 PCL & 54.1 & 5.7 & 41.6 & 218.6 & 224.9 & 75.8 & 36.6 \\ PA6\_20 PCL & 54.3 & 11.6 & 39.3 & 217.9 & 224.3 & 71.2 & 38.7 \\ PA6\_30 PCL & 54.4 & 16.4 & 52.5 & 217.8 & 224.4 & 62.6 & 38.9 \\ PCL & 54.5 & 73.3 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the DSC tests on the prepared PA6/PCL blends. Figure 7: DSC thermograms of the produced PA6/PCL blends. (**a**) First heating scan, (**b**) cooling scan, (**c**) second heating scan. Figure 8a,b shows the TGA thermograms and the derivative thermogravimetric (DTG) curves of the constituents and of the prepared blends, whilst the most important results are summarized in Table 2. It can be observed that no residual mass is present at the end of the analysis, since the analyzed blend is fully organic and its decomposition products are volatiles. Given the degradation profile of the produced blends, it is thus possible to infer that the thermal degradation resistance is decreased by increasing the PCL amount in the blends. Both the \(T_{1\%}\) and \(T_{5\%}\) tend to decrease with the PCL concentration, going from 326.5 and 398.3 \({}^{\circ}\)C for neat PA6 down to 249.3 and 389.2 \({}^{\circ}\)C for neat PCL, respectively. However, the produced blends maintain acceptable thermal stability even at elevated PCL amounts.
## Flexible Unicast-Based Group Communication for CoAP-Enabled Devices \({}^{\dagger}\)  
**[PERSON] 1,2,*, [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\),**  
**[PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Information Technology (INTEC), Ghent University - iMinds, Gaston  
Crommenlaan 8 Bus 201, Ghent 9050, Belgium; E-Mails: [PERSON]] (J.H.);  
[EMAIL_ADDRESS] (F.V.A.); [EMAIL_ADDRESS] (J.R.);  
[EMAIL_ADDRESS] (I.M.); [PERSON]] (P.D.)  
\({}^{2}\) Said Khoury IT Center of Excellence (SKITCE), Al-Quds University, Abu Deis,  
Jerusalem 51000, Palestine  
\({}^{\dagger}\) This paper is a revised and expanded version of a paper entitled \"Group Communication in Constrained Environments Using CoAP-based Entities\" presented at the 2013 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Cambridge, MA, USA, 21-23 May 2013 [1].  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +32-933-14-900; Fax: +32-933-14-899.  
_Received: 15 April 2014; in revised form: 28 May 2014 / Accepted: 29 May 2014 / Published: 4 June 2014_  
**Abstract:** Smart embedded objects will become an important part of what is called the Internet of Things. Applications often require concurrent interactions with several of these objects and their resources. Existing solutions have several limitations in terms of reliability, flexibility and manageability of such groups of objects. To overcome these limitations we propose an intermediately level of intelligence to easily manipulate a group of resources across multiple smart objects, building upon the Constrained Application Protocol (CoAP). We describe the design of our solution to create and manipulate a group of CoAP resources using a single client request. Furthermore we introduce the concept of profiles for the created groups. The use of profiles allows the client to specify in more detail how the group should behave. We have implemented our solution and demonstrate that it covers the complete group life-cycle, _i.e._, creation, validation, flexible usage and deletion. Finally, we quantitatively analyze the performance of our solution and compare it against multicast-based CoAP group communication. The results show that our solution improves reliability and flexibility with a trade-off in increased communication overhead.  
keywords:Internet of Things; CoAP; sensors; wireless sensor networks; group communication; entities +
Footnote †: journal: Journal of Communications and Information Theory
# Traffic Sign Detection Based on the Improved YOLOv5
## 2 Improved YOLOv5 Algorithm
### ACONC Activation Function  
In the convolutional neural network structure, the output result of each layer is composed of the result obtained by the input of the upper layer through the linear function, but due to the limited expression space of the linear function, it cannot play a role for nonlinear objects, thus affecting the capability of the neural network. The activation function itself is nonlinear, so the introduction of the activation function into the neural network can make the neural network arbitrarily approach a nonlinear function, enhancing the expressive ability of the model. Thus, choosing a suitable activation function is very important for the model. The ACONC activation function is utilized as the activation function in the model [19]. Compared with the traditional sigmoid activation function, the ACONC activation function decides whether to activate a neuron by introducing a switching factor to learn the parameter switching between nonlinear (activation) and linear  
Figure 3: Network framework.  
Figure 2: Flowchart of YOLOv5 algorithm.  
(nonactivation). This activation behavior effectively improves the generalization ability and transmission performance of the model. Its formula is shown in Equation (3).  
\[\text{ACONC}=(p_{1}-p_{2})x\cdot\sigma[\beta(p_{1}-p_{2})]+p_{2}x \tag{3}\]  
where \(s_{1}\) and \(s_{2}\) denote the parameters that are learned with initial values (\(s_{1}=1\) and \(s_{2}=0\)); \(\sigma\) is the sigmoid activation function; and \(\beta\) is the switching factor.  
The switching factor directly determines the degree of the activation function's nonlinearity, and the magnitude of its value depends mainly on the quantity of channels of the model structure (\(C\)) and the feature map's size (\(H\times W\)); its formula is shown in Equation (4).  
\[\beta=\sigma\sum_{c=1}^{C}\sum_{h=1}^{H}\sum_{w=1}^{W}x_{c,h,w} \tag{4}\]
###### Abstract  
In this study, the solution model based on the stochastic harmony search algorithm was proposed to obtain real ground motion (GM) records for nonlinear dynamic analysis of structures. Obtaining the GM record problem was formulated as a constrained engineering optimization problem. The solution model ensures spectral compatibility between the mean horizontal spectrum of selected ground motion (GM) records and the target horizontal spectrum, as well as the mean vertical spectrum of the selected GMs with the target vertical spectrum. This model also allows the management of record-to-record variability in both horizontal and vertical components of the selected GMs. Moreover, the model effectively addresses the period-dependent record-to-record variability in all orientations of seismic excitations simultaneously using a single-scale value, preserving the relative amplitude and phasing of actual GM components. The efficiency of the model has been demonstrated through numerical examples with various uniform hazard spectra, specifically those based on Eurocode-8 and the Turkish Building Earthquake Code, as well as scenario-based target spectra. The results demonstrate that through using the proposed model it is possible to obtain GM records with the desired spectral compatibility and spectral dispersion for both horizontal and vertical GM components. Thus, the model can be used as an efficient way to obtain appropriate GM records for nonlinear dynamic analyses of both two- and three-dimensional structural models for performance-based designs and/or evaluation frameworks, considering seismic excitations in both horizontal and vertical directions.  
ground motion selection; harmony search algorithm; horizontal components; nonlinear dynamic analysis; vertical components Article
## 2 Related Work
This method involves weighting the activation maps from a given layer and finding the average contribution of each pixel to the model's decision. Grad-CAM, by [53], generalizes CAMs by redefining the weights in terms of the gradient produced by the model. However, this technique uses a global average for weight calculations, assuming each activation to be equally important. Grad-Cam++ [54] then redefines Grad-Cam to be in terms of a weighted average, instead of global. Next, LayerCAM enables the use of CAMs for any convolutional layer [55]. By utilizing the backward class-specific gradients, it is possible to generate a separate weight for each spatial location. Another strategy for improving EEG classification and interpretability is to incorporate information from different domains into the DL structure. Multi-Domain Fusion Deep Graph CNN (MdGCNN), by [56], fuses time-frequency and spatial information through the use of graph convolutions, which learn the discriminant features across domains, followed by a sort pooling layer to act as a fusion stage that bridges the extracted information to regular convolutions, which then produce the output. Still, this approach does not include information from external sources to the EEG, limiting its interpretability, unlike the authors of [57], who perform emotion recognition through RNNs using visual information from videos in addition to the EEG, fusing into a hierarchical attention mechanism to organize features based on their perceived significance. This method allows the analysis of how physiological and neurological responses relate to each other during classification. Lastly, Ref. [58] uses a deep and wide CNN to pull out features and perform an initial classification. Kernel matching via Gaussian embedding is then used to combine data from questionnaires and make the model output more accurate. Ultimately, it is evident that traditional feature extraction algorithms, like CSP, provide the best interpretability but are also the least powerful for EEG-based classification, primarily due to their need for manual tuning. Complex DL solutions such as autoencoders, RNNs, and transformers offer the ability to extract much more information when compared to other solutions, but their results are not easily interpretable or require large datasets [59]. This, in turn, leaves CNN-based EEGNet variants as the best compromise between the two. They work similarly to traditional CSP algorithms, requiring no manual tuning for the filters; they can be expanded upon to exploit information present in much deeper structures, and they possess extremely useful interpretation tools in the form of CAMs.
## 3 Results
### Results Comparison  
The results obtained from the DMOSM-GP strategy are compared with some state-of-the-art results in terms of classification accuracy. However, since the proposed methodology uses generative models for discriminative purposes, it has to be stated that the comparison should be made on a few additional items than the classification accuracy. As can be seen in Tables 5 and 6, the average classification results obtained by the DMOSM-GP strategy are competitive among the state-of-art works. It has to be noticed that this work does not implement a preprocessing or feature extraction stage but uses the acquired data to train the MOSM-GPs and perform the discriminative task.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Reference** & **Approach** & **Accuracy** \\ \hline [PERSON] et al [32] & ICA-ELM & 94.29 \\ [PERSON] et al. [33] & CSP-SVM & 78.78 \\ [PERSON] et al. [34] & PDC-MEMD & 70.22 \\ Elastuy and Eldawlatly [35] & DBN & 73.44 \\ [PERSON] et al. [36] & CSP-SVM & 81.41 \\ This work & DMOSM-GP & 82.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean emotion classification results [%] for MI dataset, only mean value for comparison consistency against reported works.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject ID** & **Accuracy** & **Subject ID** & **Accuracy** \\ \hline
17 & \(0.65\pm 4.32\) & 2 & \(0.70\pm 6.19\) \\
18 & \(0.78\pm 8.12\) & 16 & \(0.50\pm 5.28\) \\
22 & \(0.60\pm 3.49\) & 19 & \(0.65\pm 5.66\) \\
29 & \(0.65\pm 4.28\) & 31 & \(0.50\pm 6.77\) \\
30 & \(0.70\pm 5.28\) & **Average** & \(\mathbf{0.64\pm 0.09}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Discriminative test for subjects from the DEAP database using the DMOSM-GP scheme, the test are developed within the valence emotional dimension.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject ID** & **Accuracy** & **Subject ID** & **Accuracy** \\ \hline
1 & \(0.82\pm 3.45\) & 6 & \(0.85\pm 3.18\) \\
2 & \(0.87\pm 4.16\) & 7 & \(0.80\pm 3.73\) \\
3 & \(0.85\pm 2.56\) & 8 & \(0.82\pm 4.21\) \\
4 & \(0.77\pm 4.56\) & 9 & \(0.79\pm 2.66\) \\
5 & \(0.82\pm 4.03\) & **Average** & \(\mathbf{0.82\pm 0.03}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Discriminative test for subjects from the MI database using the DMOSM-GP scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Approach** & **Accuracy** \\ \hline [PERSON] et al. [25] & PSD-SVM & 57.50 \\ [PERSON] et al. [3] & PSD-SVM & 62.00 \\ [PERSON] et al. [30] & PSD-HJORT-SVM & 60.00 \\ [PERSON] et al. [17] & MSP-SVM & 55.76 \\ [PERSON] and [PERSON] [31] & Wavelet-SVM & 65.00 \\ [PERSON] et al. [28] & RFCV-KNN & 65.73 \\ [PERSON] et.al. [11] & PSD-LORSAL & 63.29 \\ [PERSON] et.al. [11] & DE-LORSAL & 77.17 \\ This work & DMOSM-GP & 64.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean emotion classification results [%] for selected DEAP subjects, only mean value for comparison consistency against reported works.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 2 Study Sites and Methods
### Applications
#### 2.4.3 Outburst Flood  
Three outburst floods were simulated along the Marsyangdi using a lake volume of 281,051 m\({}^{3}\) and drainage following a triangle hydrograph with times to peak discharge (Qp) of 5, 10, and 20 min. The lake volume corresponded to a 15-m-high simulated dam on the Marsyangdi River (Section 2.4.1), which produced a lake of comparable length (1.1 km) to that reported by [5] following the 2015 Gorkha earthquake. Floods had peak discharges of 937, 468, and 234 m\({}^{3}\) s\({}^{-1}\) for simulations 1-3 respectively. A fourth simulation was carried out with a Qp of 1874 m\({}^{3}\) s\({}^{-1}\), which represented a doubling of lake volume to 562,102 m\({}^{2}\) draining with a time to Qp of 10 min. Upstream landslide-dammed lakes were identified by [5] that could have caused a cascading hazard. Therefore, we took Simulation 4 to be a worst-case scenario. Simulations were carried out in HEC-RAS 5.0.5 (2D) using the UAV-derived 2 m DEM. Manning's n values were allocated to land cover classes following [41] and inspection of the UAV-derived orthophoto (channel = 0.04, sparse tree coverage = 0.06, woodland = 0.1, urban area = 0.1, bare ground and agricultural land = 0.03). The flood models did not consider sediment entrainment or bank erosion.