esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
Moreover, the Autoregressive integrated moving average (ARIMA) model is a popular methodology for load demand evaluation and forecasting [28], and hybrid ARIMA strategies have been studied and implemented to achieve higher efficiency in [28; 29; 30; 31]. In particular, for a Brazilian northeast company [28], the energy demand based on natural gas has been studied and predicted by applying an ARIMA-ANN based hybrid methodology. Furthermore, for the prediction of a single day ahead energy price, ARIMA and Holt-Winters forecasting models have been implemented in [29], achieving results that overcome those of the traditional models. Authors in [30] have proposed adaptive online ensemble learning with a recurrent neural network (RNN)-ARIMA-based hybrid model to reach the target of load demand prediction. Similarly, an ARIMA-GBRT (gradient boosting regression tree)-based hybrid model has also been developed and implemented in [31] to simulate and forecast the electrical energy consumption of residential buildings. The GBRT technique is used in combination with ARIMA in the proposed model to improve the forecast performance with respect to existing prediction models. The comparison of forecasting models is performed based on standard performance indicators and demonstrates the superiority of the proposed ARIMA-GBRT model. In addition, other contributions have proposed methods to identify the energy theft detection in power grids. In this context, the authors in [32; 33] have studied the load profile of various customers to expose the degree of nontechnical losses. The study on load profile and load demand provided satisfactory data to analyze the possibilities of energy theft at the distribution point. In [33; 34], a solution to the fraud activity by implementing the technique of decision tree is discussed. The authors of [35] used a time series neural network to identify the source of power tampering activity. Moreover, a machine learning algorithm allows the comparison of various local households to determine the number of honest customers and the rate of illegal consumers. In [36], the authors applied various classification models on regular energy expenditure data and encoded data. A comparison of accuracies of the adopted models is also presented. In addition, contributions [37; 38; 39; 40; 41] have studied how the power theft phenomenon affects the consumers as well as the power grid. The analysis of the related literature reveals that there is the lack of a unique methodology to predict energy consumption, especially for detecting illegal use of energy, and it does not emerge a suitable and effective strategy to be applied in this context. Therefore, the authors of this paper are motivated to analyze the performance of various demand forecasting techniques and propose an efficient method for energy demand prediction. In addition, in order to protect the domestic household from power theft, a unique methodology is adopted and analyzed.
## 1 Introduction
meshing techniques can help reduce the number of grids to some extent, the time step in the FIT is determined by the smallest grid size in the process of calculating radiation emissions from large and intricate structures. Therefore, there still exists a challenge of long computational time. This paper presents the application of the Huygens equivalence principle for calculating the electromagnetic radiation of complex electronic equipment. Firstly, the main radiation sources are determined by analyzing the propagation path of the conducted interference of the fan. The field-circuit coupled FIT method with the non-uniform grid is utilized to develop a detailed model of the electromagnetic radiation of the fan, including the radiation sources of the winding and the drive circuit PCB. Then, the Huygens equivalence principle is applied to compute the near-field radiation generated by multiple radiation sources of axial fans. This near-field secondary radiation source is used as an equivalent radiation source to realize a fast calculation of electromagnetic radiation in a large space domain, and the equivalent model is validated by simulation and experiment. Finally, the electromagnetic radiation inside the electronic enclosure dissipated by multiple fans is examined as a case study to calculate the electric field distribution within the enclosure, which illustrates the effectiveness of the equivalent model in practical applications.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 5 Adaptive Parameter Tuning
### Training the Predictor
#### 5.1.1 Generating Training Data  
We use cross-validation to train and test our approach. We use 80% of the sparse matrices from the SuiteSparse matrix collection [23] to train our model and then test the trained model on the remaining matrices. For each training matrix, we store the sparse matrix using our hybrid format under a given K. We then profile the resulting SpMV performance to find the best-performing K value.  
As the range of possible values that K can take is too large, it is prohibitively expensive to exhaustively search for the optimal K for every training matrix. Instead, we sample 10% of our training matrices and find that the best K often stays around \(K_{average}\) and \(K_{histogram}\), where \(K_{histogram}\) is the number given by a histogram algorithm similar to HYB. As a result, for each training matrix, we profile the resulting SpMV performance of around \(K_{average}\) or \(K_{histogram}\), [\(K_{average}\)-30, \(K_{average}\)+30] and [\(K_{histogram}\)-30, \(K_{histogram}\)+30], where the lower bound is capped at 0. To generate training data, we measure the resulting SpMV performance for each K value for a given training matrix and record the best-performing value. On average, we profile 30-120 different values of K. To minimize the impact of profiling noise, we profile each K value setting multiple times until the upper and lower confidence bounds are smaller than 5% under a 95% confidence interval setting. As part of the training data collection, we also collect a set of feature values (Section 5.2) for each training matrix.
## 3 Interference simulation of different bandwidth signaling  
Simulation parameters: the seabed was absolutely hard and flat; water depth: 100 m; sound source deployment depth: 95 m (near the seabed); frequency: 1 kHz; horizontal distance of the transmitting and receiving end 200 m; mark bandwidths were 1 Hz, 10 Hz, and 100 Hz.  
Figure 3 shows the signal interference diagrams at different bandwidths. The bandwidths from (a) to (c) were 1 Hz, 10 Hz, and 100 Hz, where the abscissa is the signal sound pressure level and the ordinate is the depth. It can be seen from the comparison of the figures that as the bandwidth increased, the vertical fluctuation range of the sound intensity decreased exponentially until it tended to be stable in the end.  
Figure 1: Signal interferograms at different frequencies: (a) 500 Hz, (b) 2000 Hz, and (c) 5000 Hz.  
Figure 2: Signal interferograms at different horizontal distances: (a) 100 m, (b) 300 m, and (c) 1000 m.  
In summary, from the simulation results shown in Figures 1-3, it can be seen that the signal fluctuation range was proportional to the distance between the sending and receiving ends and was inversely proportional to the signal center frequency, bandwidth, and modem lowering depth, which were related to Formulas (3)-(11) and were consistent with the inferences given.
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 3 Medication Safety WSN Authentication Scheme  
Radiofrequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of things (IoT). Similar to radiofrequency identification, sensor networks have limited transmission capacity. A wireless medical sensor network (WMSN) can be used to construct a pervasive medical system [20]. However, the physical security of sensor nodes cannot be guaranteed because nodes are easily compromised by adversaries. For communication security and personal safety, it is necessary to realize mutual authentication on the Internet of sensors. To achieve local password change and forward security while resisting mobile device loss attacks, we propose a mutual authentication scheme for WMSN.
###### Abstract  
The Deterministic Network (DetNet) is becoming a major feature for 5G and 6G networks to cope with the issue that conventional IT infrastructure cannot efficiently handle latency-sensitive data. The DetNet applies flow virtualization to satisfy time-critical flow requirements, but inevitably, DetNet flows and conventional flows interact/interfere with each other when sharing the same physical resources. This subsequently raises the hybrid DDoS security issue that high malicious traffic not only attacks the DetNet centralized controller itself but also attacks the links that DetNet flows pass through. Previous research focused on either the DDoS type of the centralized controller side or the link side. As DDoS attack techniques are evolving, Hybrid DDoS attacks can attack multiple targets (controllers or links) simultaneously, which are difficultly detected by previous DDoS detection methodologies. This study, therefore, proposes a Flow Differentiation Detector (FDD), a novel approach to detect Hybrid DDoS attacks. The FDD first applies a fuzzy-based mechanism, Target Link Selection, to determine the most valuable links for the DDoS link/server attacker and then statistically evaluates the traffic pattern flowing through these links. Furthermore, the contribution of this study is to deploy the FDD in the SDN controller OpenDayLight to implement a Hybrid DDoS attack detection system. The experimental results show that the FDD has superior detection accuracy (above 90%) than traditional methods under the situation of different ratios of Hybrid DDoS attacks and different types and scales of topology.  
DetNet; SDN; Hybrid DDoS attacks; flow-based detection +
Footnote †: journal:  
Article
## 3 Results  
### Bioactive Compounds  
In the different structures of the xoconostle, there are significant differences (\(p<0.05\)) in all the bioactive compounds (Table 1). In phenols, flavonoids, and tannins, the highest concentration was found in whole fruit extracts (313.26 mg GAE/100 g, 189.53 mg QE/100 g and 71.067 mg CATE/100 g), respectively, and betalains with 17.45 mg/100 g; the highest concentration was found in the seed. In the betalain results of xoconostle Ulapa, variation was found in comparison with xoconostle Rosa since it has been reported values of 31 mg/100 g and 47 mg/100 g in the endocarp and pericarp, respectively, according to [PERSON] et al. [26]. This difference could be mainly due to the species and the ripening of the fruits.  
Biological Activities in In Vitro Tests with Aqueous Extract of Xoconostle (Opuntia oligacantha [PERSON])  
As can be seen in Figure 2a, there were significant differences (\(p<0.05\)) between the different extracts, the whole fruit extract being the one that presented the highest power against urease with 86.1% in the highest concentration of 24 mg/mL.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extract** & \begin{tabular}{c} **Fenolsmg** \\ **GAE/ 100 g** \\ \end{tabular} & \begin{tabular}{c} **Flavonoids** \\ **mgQE/100 g** \\ \end{tabular} & \
\begin{tabular}{c} **Taninsmg** \\ **CAT/100 g** \\ \end{tabular} &
\begin{tabular}{c} **Betalains** \\ **mg/100 g** \\ \end{tabular} \\ \hline Endocarp & 262.00 \(\pm\) 2.42 a  & 151.80 \(\pm\) 3.8 a  & 48.35 \(\pm\) 0.35 b  \\ Percarp & 281.73 \(\pm\) 8.69 b  & 117.80 \(\pm\) 1.60 c  & 27.10 \(\pm\) 8.00 c  & 6.22 \(\pm\) 0.09 c  \\ Whole Fruit & 318.53 \(\pm\) 6.02 d  & 71.07 \(\pm\) 3.20 d  & 9.22 \(\pm\) 0.19 d  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Content of bioactive compounds in extract of xoconostle variety Ulapa (_Opuntia Oligacantha [PERSON])_.  
Figure 2: Urease (%) (**a**), elastase (%) (**b**) and \(\beta\)-Glucuronidase (%) (**c**) inhibition. The lowercase letters a, b, c represent the significant differences (\(p<0.05\)) between the concentrations and the uppercase letters A, B, C mean the significant differences (\(p<0.05\)) between the extracts; the data were analyzed with a Tukey test.  
In Figure 2b, regarding the enzymatic inhibition capacity of the xoconostle extract on elastase, significant differences (\(p<0.05\)) were identified between the extracts, with the effect of the endocarp and pericarp and that of the whole fruit being similar, with the highest potential (79%). Figure 2c shows the inhibitions of the enzyme \(\beta\)-glucuronidase, where it is indicated that there are significant differences (\(p<0.05\)) between the three doses. There are no significant differences between the mesocarp and pericarp (\(p>0.05\)). The whole fruit was the one with the highest inhibition, with 84%.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 4 User's Demands Awareness
### User's Willingness  
In contrast to the transmitter, the energy consumption when receiving the video file mainly comes from its own power use. Therefore, according to the radio energy dissipation model proposed in [30], the energy consumption receiving \(n\) bits for a mobile terminal is given by:  
\[E_{r}(n)=nE_{elc} \tag{8}\]  
where, \(E_{elc}\) denotes the circuit loss constant. Let \(E_{pi}\) denote the total energy that the potential service requester's device can store, \(E_{pi}^{s}\) denotes the current residual energy of the device for potential service requester \(u_{pi}\), and the file size of video \(f_{l}\) is \(n_{l}\) bits. In order to ensure the potential requester's QoE, the device's residual energy ratio \(\varphi_{pi}\) for user \(u_{pi}\) should be greater than the threshold \(\varphi_{th}\) after the video is completely received, which can be expressed as:  
\[\varphi_{pi}=\frac{E_{pi}^{s}-n_{l}E_{elc}}{E_{pi}}>\varphi_{th} \tag{9}\]  
where the value of \(\varphi_{th}\) has been indicated in [31], i.e., \(\varphi_{th}=0.3\). If the ratio of the residual energy of the user's device after receiving the pushed video is lower than the threshold \(\varphi_{th}\), the potential requester will not accept the video push service. Therefore, the expression of the lowest residual energy coefficient \(c\) is expressed as:  
\[c=\left\{\begin{array}{ll}1&\varphi_{pi}>\varphi_{th}\\ 0&\varphi_{pi}\leq\varphi_{th}\end{array}\right. \tag{10}\]  
In summary, when cluster head attempt to improve throughput by pushing video \(f_{l}\) to potential requester \(u_{pi}\), the willingness of user \(u_{pi}\) to accept the pushed video \(f_{l}\) can be expressed as:  
\[P_{pi,l}^{accept}=cP_{pi,l}^{request}=\left\{\begin{array}{ll}\lambda P_{l}^ {pop}+(1-\lambda)P_{pi,l}^{interest}&\varphi_{pi}>\varphi_{th}\\ 0&\varphi_{pi}\leq\varphi_{th}\end{array}\right. \tag{11}\]
# Level of Agreement in Subjective Selection of Gingival Colour
###### Abstract  
_Background and Objectives_: Primary outcome: To assess the level of agreement between the objective and subjective methods for recording gingival colour in each area of the gingiva. Secondary outcome: To compare performance of the subjective visual method of gingival colour selection by a male observer and a female observer. _Materials and Methods_: A chromatic study was conducted on a total of 101 participants, in five gingival zones, from the free gingival margin to the mucogingival line, using a SpectroShade Micro spectrophotometer for the objective method and 21 ad hoc ceramic gingival shade tabs for the subjective method. A man and a woman of the same age, with the same amount of clinical experience in dentistry, selected the tab that most resembled the colour of participants' gingiva. The \"chromatic error\" was then assessed by calculating the colour difference (using the Euclidean and CIEDE2000 formulae) between the CIELAB coordinates of the shade tab selected and the objective coordinates of the gingiva. The unweighted Kappa coefficient was used to calculate the level of agreement between observers. _Results_: For the male observer, the mean chromatic errors varied between \(\Delta\)Eab\({}^{\star}\)10.3 and 13.1 units, while for the female observer, the mean errors varied between \(\Delta\)Eab\({}^{\star}\)11.1 and 12.8: these differences were not statistically significant. Similarly, no statistically significant differences were found between the mean chromatic errors for the five gingival zones in either the male operator (\(p=0.100\)) or the female operator (\(p=0.093\)). The minimum level of agreement (unweighted Kappa) between the observers ranged from 0.1 to 0.4. _Conclusions_: Subjective selection of gingival colour was very inaccurate, by both the male observer and the female observer, for any area of the gingiva, with no differences identified between them. The level of agreement between the observers was low. These findings suggest that gingival colour should not be determined using solely subjective methods, given that the chromatic errors significantly exceeded the clinical acceptability threshold for gingiva (4.1 units for \(\Delta\)Eab\({}^{\star}\) and 2.9 units for \(\Delta\)E00). Both observers showed a tendency to select gingival shade tabs that were redder and bluer than the objective colours.  
Keywords:gingiva colour; agreement between objective and subjective methods; perception of colour gender +
Footnote †: journal: Applied
## 3 Results and Discussion
### Audio Analysis Method
#### 3.4.2 Machine Learning Algorithm
Authors in [53], for example, relied on the spectrogram as input to a CNN with four convolution layers to detect queenless hives. The challenge of training \"hive-independent\" models was emphasized, and an SVM was shown to better generalize to unseen hives. Notwithstanding, state-of-the-art accuracy was achieved with the CNN for the \"hive-dependent\" scenario. CNN architectures were also explored in [47] for bee detection with results outperforming SVM, random forest, and XGBoost (a decision tree-based ensemble learning algorithm [119] that uses a gradient boosting framework) classifiers. In a similar vein, the work in [41] compared the performance of 2D (spectrogram input) versus 1D (raw audio input) CNNs and concluded that the latter resulted in more robust decisions, especially when test data differed from the training data by beehive, location, time, and bee race.
# Advances in Biomaterials for Breast Reconstruction
## 3 Scaffold Guided Breast Tissue Engineering
### Components of Bioink for Breast Tissue
#### 3.2.2 Synthetic Components  
Synthetic biomaterials have the capacity to be modified in ways that provide mechanical strength, biodegradability, and cell adhesion ability. Some synthetic components have the property of nature-derived biomaterials that can be included as bioink and some other components act as mechanical support of the 3D bioprinting structure.  
The most frequently adopted component is poly e-caprolactone (PCL), which provides mechanical strength with sufficient biocompatibility as a characteristic of the additive supportive structure [82]. In a rodent model study, ADSCs seeded to a PCL scaffold increased angiogenesis and minimized the host inflammatory response, resulting in increased tissue growth and biostructure integration [83]. In another study, a 3D-printed PCL device was attached to the surface of a silicone implant and applied to a rodent model to enhance adipogenic differentiation of ADSCs without foreign body reaction [84]. PCL is often researched as a copolymer with other molecular materials. In one study, a copolymer of e-caprolactone and p-dioxanone supported proliferation and differentiation of ADSCs, which is suitable for tissue regeneration [85]. In other study, copolymer of polycaprolactone-co-poly-D,L-lactic acid (PCLLA) was evaluated as a biomaterial for breast tissue reengineering [86]. Although the initial mechanical property of PCLLA has shown potential as an additive during manufacturing, it has limitations in thermal stability and degradation rates [87].  
The exact utility of synthetic biomaterials is uncertain, and research is in progress. However, as shown clinical success in bone tissue engineering, they can be regarded as potentially important 3D bioprinting tools for breast tissue regeneration if harmonized with biologic biomaterials.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 2 Literature Review
### Contributing Factors to Crash Injury Severity  
The severity of crash injuries results from the interaction of environmental characteristics, vehicle characteristics, traffic participants, and road characteristics [38]. Understanding contributing factors that impact injury severity is the key to improving traffic safety.  
Environmental characteristics include weather (clear, rain, ice, etc.) and lighting conditions (daylight, dawn, dark, dark-lighted, etc.). Crashes in clear weather were more likely to cause severe injuries, possibly because of higher driving speeds than in other weather conditions [36; 39]. Dark lighting was statistically significant in multiple studies; however, its effect on injury severity varied [36; 40]. In addition, individual studies have found that factors such as'snow' and 'dawn' also affect the severity of injuries [21].  
As for vehicle characteristics, vehicle type is the focus of existing literature research. Sport utility vehicles and pickup trucks are more likely to be involved in no-injury crashes [26; 41],while motorcycles and public transport are more likely to be involved in severe crashes [29]. The researchers believe it has to do with vehicle usage and the physical design of the vehicle. Specifically, pickup trucks have better shock resistance to protect their occupants. Public transport drivers are prone to fatigue and severe injury due to long-distance, long-term driving tasks.  
Gender, age, and poor driving behavior are the main contributing factors for traffic participants. However, for the same affecting factors, the findings reached by different researchers vary greatly. [PERSON] and [PERSON] [42] found that male drivers were more likely to be seriously injured than females, while [PERSON] et al. [26] came to the opposite conclusion. Some age-related studies argue that older drivers are more vulnerable to severe injury due to their more fragile physiological abilities [43]. Others believe that younger drivers are more likely to be seriously injured due to aggressive driving [39]. In addition, poor driving behaviors such as alcohol involvement, distraction, and drowsy driving were associated with a higher probability of severe injury in most studies [26; 44].  
The roadway characteristic appears in the literature as one of the most common influencing factors of injury severity. Studies have suggested that roadway surface, alignment, and road class are noteworthy. Poor road conditions significantly impact injury severity [21]. Curved roads are often more likely to cause severe and fatal injuries than straight roads [39]. In different grades of roadway, secondary roads and dedicated public transport roadways increase accident injury severity [40].  
In light of the above, this study considers factors including temporal, environmental, road, spatial, vehicle, crash, and human characteristics, which are commonly used in previous studies.