esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Dynamics of Global Plate Motion Based on the Granger Causality Test
## 3 Data Analysis and Results
### Results of the Causality Test between Plate Boundaries  
This paper has proven that energy releases at plate boundaries have an order of integration of zero. The next step was to investigate the possibility of cointegration among the energy releases at plate boundaries. For this, the Engle-Granger test was used. The results for cointegration are reported in Table 3. The Engle-Granger test indicated that cointegration is present between the energy releases at most plate boundaries at the 1% significant value because the null hypothesis of no cointegration was rejected. This outcome indicated a long-term relationship. However, the Engle-Granger test indicated that the statistics between AN-AU and AU-EU, as well as AN-AU and AU-PA, were lower than the critical value at the 5% level. Therefore, there was no cointegration among the energy releases at these plate boundaries. The existence of a cointegrating relationship among energy releases at plate boundaries suggests the presence of Granger causality, at least in one direction, but it does not suggest the direction of causality between plate boundaries. In other words, the existence of causality in either direction cannot be ruled out. The direction of Granger causality would be determined by a traditional \(F\) test through constructing a VAR model and its associated probability value. Based on the Akaike information criterion (AIC), the optimal lag length was chosen [57]. As shown in Table 3, the \(F\)-statistics and the associated probabilities permit us to reject the null hypothesis that there is non-causality between the two plate boundaries. Therefore, there is unidirectional Granger causality between two plate boundaries, such as from PA-PS to NZ-PA, from PA-PS to AN-PA, from AU-PA to NZ-PA, from NA-SA to NZ-PA, from NZ-SA to AN-SA, from SA-AF to AN-SA, from AN-AF to AF-SO, from  
Figure 3: Graph of energy release versus time for the plate boundary NA-PA: The dotted line represents the energy release of the earthquakes, and the solid line represents the energy release after transformation into natural logs. This is one example from all 28 plate boundaries. AF-EU to AN-AF, from PS-EU to PA-PS, from PS-EU to AF-EU, from PS-EU to IN-EU, and from AU-EU to AN-AU. This list means that the energy releases at the former plate boundaries are the Granger causes of the energy releases at the latter. In addition to this result, there is bidirectional Granger causality between some pairs of plate boundaries, such as between AN-PA and AU-PA, between AN-SA and SA-NA, between NZ-SA and SA-AF, between SO-AR and AR-EU, between AN-PA and AN-NZ, between AN-PA and AN-SA, and between AN-AU and AU-PA. In addition, the results were highly significant. For demonstration and visualization, a map of Granger causality between energy releases at plate boundaries is reported (Figure 4).
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
###### Abstract  
Cyber ranges have become essential platforms for realistic cybersecurity training, research, and development. Existing taxonomies often describe the functional aspects of cyber ranges--scenario design, team configurations, and evaluation metrics-- focusing less on the underlying technologies that enable modern training. In this paper, we highlight the current trends and persistent gaps in the existing literature and propose a taxonomy that decouples functional capabilities from the enabling infrastructure, adding a dedicated _Technology_ dimension. We derived and refined this taxonomy through an iterative literature mapping process, culminating in a proposed taxonomy that highlights key emerging trends such as cyber-physical integration, federation across multiple sites, and AI-driven orchestration. Key findings include the identification of clear convergences and divergences in existing taxonomies and concrete recommendations for future research directions, such as integrating socio-technical considerations and conducting systematic empirical validation. Our framework aims to guide researchers, developers, and practitioners in designing, implementing, and comparing cyber range solutions. An online Taxonomy Mapping Toolkit is developed to allow the cyber ranges research community to take advantage of the proposed taxonomy and build upon it as new advancements emerge.  
cyber range; cyber range taxonomy; systematic literature review; cyber range classification; cybersecurity training platform +
Footnote †: journal: _Future Internet_  
to cybersecurity training, the increasing complexity of these digital ecosystems demands continuous research and methodological development in their application [4; 5; 6; 7].  
Over time, these environments have been referred to by different names, for example, testbeds or digital training platforms, highlighting the breadth of solutions that fall under the \"cyber range\" umbrella. In 2013, [PERSON] and [PERSON] [8] surveyed early platforms and identified various labels such as attack lab and computer network operation (CNO) testbeds. Although these early platforms primarily focused on simulating isolated attack scenarios, they laid crucial groundwork for what has become an increasingly complex and comprehensive area. As the literature has expanded, the need for the categorization and classification of these environments has become apparent.
## 3 Results and Discussion  
### Uniaxial Compression Behavior Modeling for the Target Shipping Container  
Figure 3 shows the stress-train (SS) curves obtained through the compression test for the target shipping container. Among grapes of the same color, a grape of a thick solid line represents the average for three repetitions. The maximum stress on the SS curve is CS, and the corresponding deformation is the failure strain (FS). In the case of a container, most of the compressive load is supported by the four vertical edges and side panels; therefore, the CS of the container is expressed as the load per unit peripheral length, considering the structural characteristics of the container [16].  
Significant differences were observed in the CS and FS of the target shipping containers with respect to the relative humidity (Figure 3 and Table 4). In the case of the'vertical long' Containers A and B, the CS in the standard condition decreased by approximately 15% and 24% as the relative humidity increased to 70% and 90%, respectively. In the case of the 'horizontal long' Container C, it decreased by approximately 24% and 42%, respectively (Table 4). As shown in Table 1, although the outer liners of the target shipping containers that come in direct contact with the humid outside air were the same (paperboard), the  
Figure 3: Stress–strain curves for the target shipping containers.  
Figure 2: Uniaxial compression creep test apparatus for the shipping containers.  
primary cause of this difference is believed to be the difference in the geometric shape of the containers. In addition, in the case of Container C (Figure 3c), the FS decreased with the increase in the relative humidity; therefore, we deduced that the 'horizontal long' container is more sensitive to changes in the relative humidity.  
Modeling each SS curve, as shown in Figure 3, was required to determine the CAE-predicted compression behavior in the multi-stacked packages, as applied to the target shipping containers. A procedure such as 3D response surface analysis is required to derive a model (3D, stress-strain-humidity) representing the more general SS behavior of a container from the experimental data (2D, stress-strain) in Figure 3. Figure 4 shows the results of the 3D response surface analysis of the SS behavior with respect to the relative humidity of each target shipping container. The modeling results are presented in Table 5. The shape of the response surface of stress-strain-relative humidity and the constant value of the model reflect the geometric shape of the container.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
## 2 Materials and Methods
### Link between Meteorological and Hydrological Droughts  
#### 3.2.1 Establishing Regression Function  
The coefficient of determination (R2) and regression coefficient (a) extracted from linear regression equations (Table 1) showed that a significant relationship existed between SDI and SPI, and it increased up to the 9-month time scale and then decrease in the case of the 12-month time scale. The coefficient of determination (R2) of the 6-months and the 9-month time scale was relatively higher as compared to the other two time scales. Moreover, the correlation of SPI and SDI was higher at upstream sub-basin 1 of the Soan River Basin as compared to downstream. For instance, sub-basin 1 and sub-basin 2 had 0.66 and 0.63 values of R2 based on a 9-month time scale respectively. The regression lines with the related data points are shown in Figure 6 which may be utilized for the prediction of hydrological droughts using the meteorological droughts information.  
Figure 4: Temporal variation of hydrological drought periods in sub-basins 1 and 2 at different time scales.  
Figure 5: Difference in Hydrological drought (SDI–12) severity at both sub-basins representing the spatial variations across the Soan river basin.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(K\) & \multicolumn{2}{c}{**Sub-Basin 1**} & **Sub-Basin 2** \\ \hline \multirow{2}{*}{3-Months} & a & 0.35 & 0.37 \\ \cline{2-4}  & R\({}^{2}\) & 0.125 & 0.14 \\ \hline \multirow{2}{*}{6-months} & a & 0.67 & 0.64 \\ \cline{2-4}  & R\({}^{2}\) & 0.45 & 0.4 \\ \hline \multirow{2}{*}{9-Months} & a & 0.66 & 0.63 \\ \cline{2-4}  & R\({}^{2}\) & 0.44 & 0.4 \\ \hline \multirow{2}{*}{12-Months} & a & 0.54 & 0.45 \\ \cline{2-4}  & R\({}^{2}\) & 0.29 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of Regression modeling developed between SDI on SPI.  
Figure 6: _Cont._
## 3 Results and Discussion
### Analysis of Total Polyphenol Content  
_S. purpurea_ L. remains a source of many valuable compounds, among them polyphenols and triterpenenes (Table 3). Thus the aim of this study was to determine the level of total polyphenol content in modified _Sarracenia_ plants. The increase in the content of total polyphenols was observed for all induced hairy roots as compared to untransformed roots (Figure 3). The highest increase was noticed for two lines, 7#1 and 7#2, for which 9-fold and 7-fold higher polyphenol content was measured, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Variant** & **A (mg/L)** & **B (mg/mL)** \\ \hline Total polyphenol content & 121.86 & 126.0606 \(\pm\) 5.3569 \\ \hline Total triterpene content & 4.46 & 4.5737 \(\pm\) 0.1552 \\ \hline \end{tabular}
\end{table}
Table 2: Expected (A) and real (B) values of the content of polyphenols and triterpenes from extracts prepared in optimal extraction conditions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Effect** & **Compound** & **Reference** \\ \hline Analgesic therapy & Sarapin & [5,27] \\ \hline \multirow{6}{*}{Anticancer properties} & Betulinic acid & \multirow{6}{*}{[28]} \\  & Betulinaldehyde & \\ \cline{3-3}  & Ursolic acid & \\ \cline{3-3}  & Plumagin & \\ \cline{3-3}  & Ramentaceon & \\ \cline{3-3}  & Quercetin & \\ \hline \multirow{6}{*}{Antidiabetic properties (T2D)} & Quercetin & \multirow{6}{*}{[5,29]} \\  & Morroniside & \\ \cline{3-3}  & Quercetin-3-O-glucose & \\ \cline{3-3}  & Rutin & \\ \hline \multirow{6}{*}{Antiglycation activity} & Catechin & \multirow{6}{*}{[5,29,30]} \\  & Myricetin & \\ \cline{3-3}  & Quercetin-3-O-galactoside & \\ \cline{3-3}  & Rutin & \\ \hline \multirow{2}{*}{Antimycobacterial properties (Mycobacterium tuberculosis)} & Betulinic acid & \multirow{2}{*}{[31]} \\  & Ursolic acid & \\ \hline \multirow{6}{*}{Antipomavirus properties (monkeyprox-MPXV, poxvirus} & \multirow{6}{*}{Quercetin} & \multirow{6}{*}{[32,33]} \\  & bovis-VACV, variola & \\ \cline{3-3}  & virus-VARV) & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & Dyseppesia and constipation & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & (lavative properties) & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & Gynecological disorders & Betulinic acid & [5] \\ \hline \multirow{2}{*}{Neurodegenerative diseases (mainly Parkinson’s disease, Alzheimer’s disease)} & \multirow{2}{*}{Luteolin} & \multirow{2}{*}{[1]} \\  & Quercetin-3-glucoside & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & Quercetin-3-O-galactoside & \\ \cline{1-1} \cline{2-2} \cline{4-4}  & Rutin & \\ \hline \end{tabular}
\end{table}
Table 3: Potential bioactive properties of compounds found in plants of the genus _Sarracenia_.  
The leaves of plants did not exhibit this tendency, and the total polyphenol content was not changed when compared to that in the wild-type plant leaves.
## 1 Introduction  
In the last few decades, screw fixation of acute scaphoid fractures has become a favorable alternative after the development of headless cannulated screws [1; 2; 3]. Some surgeons have used this technique for displaced scaphoid fractures and nonunions [4]. In general, screw fixation of scaphoid fractures may be performed through a dorsal or polar approach [1; 4; 5]. Nowadays, both approaches are used for waist scaphoid fractures on the basis of the surgeon's preference. Theoretically, the dorsal percutaneous approach to screw fixation of the scaphoid waist fractures allows for a more central placement [6], but some studies did not support that this might translate into a stronger fixation. [PERSON] and [PERSON] [7] conducted a study using three-dimensional (3D) computer modelling with computed tomography (CT) scans of the scaphoid bone to compare two approaches and showed that a volar approach could allow the surgeon to use longer screws. Another finite element analysis demonstratedthat centrally placed polar compression screw fixation might be biomechanically advantageous over dorsal screw fixation [8]. Therefore, some surgeons preferred solar screw fixation [9] and conducted biomechanical studies to analyze the stability of different scaphoid screw placement [10; 11].  
According to the literature, two main techniques can be used for solar scaphoid fixation [10]. One is the eccentric screw placement [1; 11]. The other is the central screw placement technique [10; 12]. The optimal screw placement for solar fixation of scaphoid waist fractures, herein, continues to be a subject of debate. Recently, some surgeons performed computerized 3D analyses and reported that most waist fractures were horizontal oblique and not transverse in orientation [13]. To date, only a few studies have directly compared different screw placements for horizontal oblique scaphoid fractures in similar groups of patients managed with the same advanced anesthetic and rehabilitation program. There is a need to know whether different screw placements for the most common scaphoid waist fractures affect fixation strength.  
We designed this cadaveric study to biomechanically compare fixation strength between central and eccentric screw placements for the solar fixation of horizontal oblique scaphoid fractures. We hypothesized that central screw placement would achieve superior fixation strength for scaphoid fractures. This study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional review board of our hospital on 30 March 2018 (EMRP-107-011).
## 3 Design and Implementation of the Proposed Scheme  
A cryptosystem is a system designed for secure communication that employs a set of algorithms and protocols to encode and decode messages. This study presents a technique that combines multiple encryption methods to enhance the security of digital images. The proposed approach first scrambles the pixels of an image and then applies confusion and diffusion techniques. The following steps outline the process used in this study.
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 4 Conclusions  
An STLF method using a semi-parametric model and RNN with selected bases is presented. This tool has been adopted by TPC Operation Department for daily operation purposes since 2019. Due to the weather characteristics in Taiwan, test results indicate that STLF is especially challenging at season transitions: from spring to summer and from summer to autumn. The main advantage of an RNN-based STLF proposed here is that with the calibrated forecasted temperatures and features extracted from the load series, through ensembles of B-spline and multi-resolution bases after statistical variable selection approach, it can avoid the overfitting problems in the deep learning stage and adapt to these weather changing patterns earlier than other methods. Noticeable improvements of the MAPEs in 2019 for the RNN model with calibrating temperatures as compared with other methods are observed. However, the intra-day load forecasts are sometimes far off due to unexpected meteorologic factors. The real-time adaptive load forecasts for the next one hour with every 5-min interval and helps the system operator to adjust the ancillary service requirement to meet the electricity demand changes. In [15; 16], both have used LSTM as the deep learning methodology; we have also tried the LSTM model, where results for the STLF show that the improvements on the accuracies of the forecasts are  
Figure 10: The Model Performance of Monthly MAPEs on the Days (D + 2) to (D + 7), for the RNN Model.  
limited and the model is more complicated and takes much more time to compute, which is not that feasible for daily use in practice. In [16], a similar-days selection procedure is adopted, which is worthy of more studies to see the advantages and shortcomings of this approach for our dataset with fast changing weather patterns. Besides the techniques presented, an optimal model averaging various load forecasting models is a topic for further investigation, as is how to extend the training samples to special day load forecasts.  
[PERSON] has assisted in data analysis, programming, validation of result, and writing original draft. [PERSON], [PERSON], [PERSON] have contributed to programming and data analysis. [PERSON] has contributed to data curation and supervise the research study. [PERSON] and [PERSON] supervise the research study, and assistance in project administration, formal analysis and have reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
The Taiwan Power Company under contract No. TPC-546-4841-0405 and TPC-546-4840-0706.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Design Challenges for mHealth Applications
Hence, on one hand, interference caused by WBANs is relatively low. On the other hand, interference tolerance and coexistence with other wireless systems can be a challenging issue, especially for WBANs operating at the industrial, scientific, and medical (ISM) frequency band and deployed in environments with many interfering sources. * _Topology_: Typically, the most common WBAN configuration is the star topology, where all the sensor devices are connected to the WBAN coordinator via a direct link. However, it is also possible to use part of the deployed sensors as relays [25], to improve connectivity in case of poor quality direct links, thus enhancing reliability [26, 27] and energy efficiency [28]. In the M2M access communication network, a point-to-point medium/long-range connection between the WBAN gateway and the core network is usually assumed (e.g., through a WLAN access point or an LTE base station), without excluding the possibility of more complex topologies. For instance, it is possible to exploit the presence of multiple WBANs [29] or ambient sensor network deployments [30] in order to establish multihop links, enabling the design of efficient routing and cooperative schemes. * _Security_: A key challenge of mHealth applications is to guarantee of the confidentiality of medical information, which faces additional threats due to the M2M architecture, mainly due to: (i) the wireless transmission of medical-sensitive data; and (ii) the data storage in multiple locations, both locally and in remote servers and devices. Hence, a need for a holistic approach is generated in order to ensure secure M2M communication [31]. To this direction, new schemes have to be designed, taking into account the specific characteristics of different technologies, in order to create interoperable and technology-independent protocols and security mechanisms. * _Technology integration_: The wireless technologies employed in the different stages of the M2M system architecture have diverse characteristics and challenges that are often studied separately. Nevertheless, the integration of these technologies into a unified mHealth application is an open issue that must be considered carefully. Therefore, in order to guarantee end-to-end QoS, scalability and ubiquitous connectivity, it is important to adapt the access communication technologies, such as LTE, WiMAX and IEEE 802.11 WLAN, to the characteristics of WBANs, taking into account the requirements of pervasive mHealth applications.
# Semantic 3D Mapping from Deep Image Segmentation
## 2 Related Work
### Object Detection and Segmentation  
Several authors base their work on single-stage detectors like YOLO v2 [16], YOLO v3 [17], or ResNet [18] and add depth information to the network. For example, [PERSON] et al. in [19] based their work on SegNet [20]. They duplicate the encoder part, extracting features both from the RGB and depth images, to finally fuse both as the network decodes them. [PERSON] in [21] compared how fussing depth and RGB features at different layers resulted in better performances for a YOLO v2 CNN. However, their output is still a bounding box, not including any 3D information in the end. [PERSON] et al. in [22] modified a YOLO v3 CNN to include depth information and produce a 3D centroid of human detections. Others, like [23] proposed an architecture that deals with both 2D and 3D object detection, supported by a ResNet-18 CNN.  
Alternatively, [PERSON] et al. in [24] introduced depth-aware convolution and depth-aware average pooling operations for a new kind of CNN. These operations avoid the introduction of additional parameters, and keep the computation complexity to the conventional CNN.  
[PERSON] et al. in [25] addressed the problem using a 3D CNN. Initially limited in scalability, other recent works are improving the initial memory and computational costs derived from a pure 3D approach. [PERSON] et al. [26] created PointNet, a 3D k-nearest neighbor (kNN) graph neural network based on point cloud data which achieved state-of-the-art performance on RGB-D segmentation. Real-time instance segmentation has been finally achieved by Yolact [1], a method based on the use of RetinaNet using ResNet-101 and Feature pyramid networks as default feature backbone. In addition, MSeg [2] offers robust real time segmentation, plus a single, composite dataset combining the largest semantic segmentation datasets.  
These deep learning approaches have been interfaced with ROS [27] to provide semantic segmentation in robotic applications, e.g., wrappers for Deepnet [28] or custom CNNs based on PyTorch [28] or Tensorflow [29]. Their main drawback is that the output is always given as images, limiting their use in 3D applications.