esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures
## 2 Background
### Virtual Machine Allocation on NUMA Nodes  
The number of cores per chip is constantly increasing, meaning that the memory bandwidth is becoming a performance bottleneck in centralized memory architectures such as uniform memory access (UMA). The processing speeds of CPUs are becoming faster than those of memory, and memory access, especially when it is remote, is typically inefficient. NUMA architecture helps to solve this problem, as it allows each processor to access the memory used by other processors [12]. On-chip interconnections allow processors to access each other's memory using their memory controller, which reduces memory contention and increases the memory bandwidth per core [12]. The efficient allocation of NUMA domains and shared memory can impact the performance of virtual machines, and these should therefore be considered when designing an efficient virtual machine allocation scheme.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods
### Multi-Label Classification Model for Apple Disease and Pest Areas
#### 2.3.4 Classifier  
Apple diseases and pests often exhibit different geometric shapes and visual characteristics, and there can be multiple types of pests and diseases present within a single region. Therefore, it is difficult for a single classifier to capture the complex characteristics of various types of diseases and pests at the same time, resulting in poor classification performance. For the features output by the feature extraction network, AMMFNet uses multiple binary classifiers to realize multi-label classification tasks. As shown in Figure 5, six binary classifiers (background and five types of diseases and insect pests) were adopted. Each binary classifier was specifically aimed at the classification of a type of disease or pests, thus realizing the multi-label classification task. This enables the model to carry out flexible learning according to the characteristics of different diseases and insect pests and finally improves the recognition accuracy and robustness of multi-label diseases and pests. Furthermore, the usage of multiple binary classifiers can also enhance the prediction interpretability of the model, as the output of each binary classifier can directly reflect the confidence level or probability of the model to the corresponding pest labels, which is convenient to understand and explain the prediction process of the model.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 3 Experiment and Discussion
### Evaluation Metrics  
Samples working in different environments have different life cycle lengths. In this paper, therefore, the remaining useful life is set as the percentage of useful times. A linear function represents the degradation trajectory of system, and the remaining useful life \(y_{t}\) is expressed by Equation (18).  
\[y(t)=1-\frac{t}{q}. \tag{18}\]  
\(\hat{y}\) and \(y\) represent the predicted result and the real result. If \(\hat{y}<y\), the prediction result is considered as an early prediction. If \(\hat{y}>y\), the prediction result is considered as a late prediction. Considering that late predictions have more serious consequences, two evaluation metrics, the root mean squared error (RMSE) and scores are used to evaluate the performance of the proposed method. Root mean squared error gives an equal penalty to late and early predictions, as in Equation (19):  
\[RMSE=\sqrt{\frac{1}{q}\sum\limits_{t=1}^{q}y^{\prime}(t)^{2}}, \tag{19}\]  
which is \(y^{\prime}(t)=\hat{y}(t)-y(t)\). The score metric was proposed in the PHM 2008 Data Challenge [38] and defined as an asymmetric function that penalizes late predictions more than the early predictions. The expression of score function is a weighted sum of remaining useful life errors, as in Equation (20):  
\[\begin{array}{l}\mathit{Scores}=\sum\limits_{t=1}^{q}\mathit{score}(t),\\ \mathit{score}(t)=\left\{\begin{array}{ll}e^{-\frac{y^{\prime}(t)}{13}}-1, &\quad ify^{\prime}(t)<0\\ e^{\frac{y^{\prime}(t)}{10}}-1,&\quad ify^{\prime}(t)\geq 0.\end{array}\right.\end{array} \tag{20}\]
# IG-Based Method for Voiceprint Universal Adversarial Perturbation Generation
## 6 Experimental Analysis
### Untargeted Attack
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Attack Methods**} & \multicolumn{6}{c}{**Parameter Type**} \\ \hline \multirow{2}{*}{**-**} & \multirow{2}{*}{\(\mathbf{\alpha}\)} & \multirow{2}{*}{\(\mathbf{\epsilon}\)} & \multirow{2}{*}{**Learning Rate**} & \multirow{2}{*}{**Epoch**} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**Confidence \(\mathbf{\kappa}\)**} & \multicolumn{2}{c}{**Expected Spoofing**} & \multirow{2}{*}{**Penalty**} \\  & & & & & & & **Rate \(\delta\)** & **Factor c** \\ \hline IG-UAP & - & - & - & 100 & 32 & 40 & 0.1 & 0.2 \\ \hline FGSM-UAP & - & 0.1 & - & 100 & 32 & - & - & - \\ \hline PGD-UAP & 0.1 & 0.0005 & 0.00001 & 100 & 32 & - & - & - \\ \hline C\&W-UAP & 0.1 & - & 0.0001 & 100 & 32 & - & - & - \\ \hline MSCW-UAP & 1.5 & - & 0.001 & 100 & 32 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters of experiments. was employed for an experimental analysis of untargeted attacks on the 1 DCNN Gamma model, whereby a single sample from each category in the UrbanSound8k dataset was taken. Table 3 shows the experimental hyperparameters, with the horizontal coordinates ranging from 0 to 9 in representation of the categories in the UrbanSound8k dataset. Figure 5 illustrates the experimental outcomes. The results of the experiment demonstrate that even when a single sample is taken from each category in the UrbanSound8k dataset, the IG-UAP method remains capable of computing effective acoustic generality against perturbations. For the small-sample size dataset, the average ASR of the acoustic generality anti-perturbation generated by the IG-UAP method is 68.8%, the average SNR is 19.125 dB, and the \(l_{dB}\) is \(-\)15.026 dB.
## 4 Discussion
### Influence of Rock Structure on Elastic Properties  
Previously, we have seen that the microstructure of the rock affects the propagation of acoustic waves in samples. Porosity is the main parameter responsible for the variation of acoustic waves velocities. Indeed, velocities of \(P\) and \(S\) waves decrease when porosity increases [10]. Consequently, \(Vp\) and \(Vs\) from chalk are lower than limestone and dolomite ones, due to their lower porosities. Nevertheless, for a same porosity, very different \(Vp\) and \(Vs\) can occurred, which can be due to differences in pore structure [49]. Two equations relate \(Vp\) to porosity through pore-fluid compressibility: WTA (Wyllie's Time Average) [50] and RHG (Raymer-Hunt-Gardner) [51]. Our samples are situated below both trends (Figure 12 left), similarly to the main values plotted in literature [6,26]. The latter stated that the total travel time of a wave in a medium has no physical reason to be the sum of the travel times in the individual components. The low values of \(Vp\) are therefore mainly due to a high microporosity [27], which are observed in our data set.  
Moreover, \(Vp\) and \(Vs\) are related in a same equation with a positive correlation [52,53], where if one increases, the other follows (Figure 12 right). These equations fit the chalk and limestone samples, contrary to dolomite ones. [PERSON]'s equation stated \(Vp=1.9 Vs\) for limestones, while our data set gives \(Vp=1.33 Vs\) (with \(R^{2}=0.74\)) for Normandie and \(Vp=1.82 Vs\) (with \(R^{2}=0.86\)) for Euville samples. The last one is therefore correlated with [PERSON]'s equation, while Normandie gives a remote result, probably due to its high proportion of small pores. However, Lexos samples present a significant number of variations due to the complexe structure of dolomite, and those two equations do not seem appropriate for our data.  
Consequently, propagation of acoustic waves in rock seems to be strongly influenced by microstructure.  
Figure 12: **Left**: P-wave evolution with porosity. **Right**: P-wave evolution with S-wave evolution.
## 5 Kramers-Kronig Detection Scheme
### Related Impact on the KK Scheme
#### 5.2.1 Opto-Electronic Frontend  
The KK receivers have demonstrated high potential for full-optical field reconstruction in one polarization by employing a single PD such as the heterodyne detection. However, unlike heterodyne detection, the required CSPR is much more relaxed. Furthermore, signal distortions (i.e., chromatic dispersion) that might have occurred prior to the square law detection can be compensated after optical field reconstruction [121]. Nonetheless, the KK reconstruction algorithm is susceptible to the signal distortion that occurs between square law detection and KK processing [19]. Therefore, for an effective optical field reconstruction, the squared magnitude of the optical field has to be fed into the KK algorithm without further distortions that might emanate from the digitization processing and optical-to-electrical (O/E) conversion. In a practical receiver, this is not usually the case, and distortion is normally induced by the O/E conversion frontend transfer function. In general, the receiver has bandwidth limitations and the residual phase and magnitude responses of its associated components such as transimpedance amplifier, PD, and ADC. It should be noted that if the electrical distortion is not compensated, it can bring about residual SSBN and can equally affect the optical field reconstruction [19; 121].  
Moreover, the impact of receiver bandwidth limitations on the KK algorithm has been studied, and it has been numerically demonstrated that even with moderate receiver bandwidth limitations, a significant OSNR penalty can be induced. Similarly, it has been experimentally demonstrated that adaptive equalizer employment prior to the KK processing can alleviate the O/E frontend bandwidth limitation impact on the KK field reconstruction and subsequently enhance the system performance [19; 121]. Furthermore, the impact of the magnitude and phase distortions has been considered, and it has been revealed that the magnitude response presents a much more stringent impact compared with the phase response. However, in the course of KK detection, a slight phase response can induce an \(\sim\)0.5 dB penalty [19]. An abrupt change in the amplitude or phase can also result in a more severe penalty. In general, for an effective optical field reconstruction, the phase and magnitude distortions that might have occurred within the signal bandwidth have to be compensated prior to the KK algorithm's application [19].
###### Abstract  
The study aimed to investigate the effectiveness of post-isometric relaxation (PIR) compared to self-stretching (SS) in working-age individuals with chronic non-specific neck pain. A parallel-group study was conducted with 50 patients who were randomized to either the self-stretching (SS, n = 25) or post-isometric relaxation (PIR, n = 25) group and received interventions three times a week for four weeks. In addition to these interventions, all patients were prescribed transcutaneous electrical nerve stimulation (TENS). Outcome measures were neck pain, the neck disability index (NDI), hand grip strength (HGS), and cervical spine range of motion (ROM). Data were collected at baseline and after four weeks of outpatient rehabilitation. Repeated measures ANOVA was used to determine within-group differences, and an independent \(t\)-test compared between-group differences. There were no significant differences between the groups at baseline in neck pain intensity. Following both interventions, pain intensity and functional disability decreased, cervical spine ROM increased, and HGS improved (\(p\) < 0.05). Between-group analysis showed that participants in the PIR group achieved lower pain intensity (\(p\) = 0.032, [PERSON]'s d = 0.81), greater neck extension ROM (\(p\) = 0.001, [PERSON]'s d = 0.96), and lower neck disability index (\(p\) = 0.004, [PERSON]'s d = 0.85) compared to the SS group. In conclusion, both PIR and SS interventions effectively reduced neck pain, increased range of motion, and decreased the neck disability index in working-age individuals. Neither technique showed superiority in increasing neck ROM and HGS. However, PIR was superior to SS in reducing neck pain and NDI, indicating potential benefits from integrating these interventions during treatment sessions.  
non-specific neck pain; autogenic inhibition; stretching exercises; TENS +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Numerical Approach  
In order to support the findings obtained from the experiments and diagnostics as well as to improve the understanding of the occurring physical phenomena, numerical modeling of two experimental cases was carried out. The first simulation is concerned with the undisturbed ion beam and subsequent self-neutralization through pseudo SEE at the chamber walls (ions crossing the _vacuum chamber_ boundary condition emit a specific averaged number of electrons (i.e., yield) based on their impact kinetic energy). The second simulation features nearly the same setup, with the only difference being the four measurement probes located inside the ion beam, which produce a disturbance. All simulations are carried out using the open-source code PICLas [21], which employs the higher-order discontinuous Galerkin (HODG) method as the electromagnetic field solver and the DSMC method for modeling particle-particle collisions. A detailed overview of the implemented models and the numerical framework is given in [22; 23]. We take advantage of modeling background species in PICLas and perform a preliminary DSMC simulation of the neutral gas only. The resulting cell-wise distribution of velocity, temperature and number density is then interpolated onto the mesh used for the PIC simulations.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 1 Introduction
#### Contributions  
The main contributions of this paper are summarized as follows:  
* We propose a comprehensive and adaptive security framework called Enhanced SCAFFOLD tailored for IoT environments. The framework establishes secure communication channels between IoT nodes using software-defined networking (SDN) and machine learning techniques.
* Key components of Enhanced SCAFFOLD include channels encrypted using session keys, continuous traffic monitoring by the SDN controller, ensemble machine learning for attack detection, precision mitigation via SDN reconfiguration, and periodic reauthentication for freshness.
* A mathematical model is presented to formally define the protocol. Detailed pseudocode specifies the algorithms for key generation, encryption, attack detection, and mitigation.
* Extensive simulations are conducted to evaluate the performance of the Enhanced SCAFFOLD framework. The ability to reliably detect and rapidly mitigate various attacks with minimal latency and energy consumption overhead is demonstrated across diverse IoT network scenarios and traffic patterns.  
The rest of the paper is organized as follows: Section 2 discusses the background. Related work is discussed in Section 3. Section 4 discusses the IoT security and existing defense approaches. Section 5 provides an overview of the Enhanced SCAFFOLD protocol, detailing its components, modeling, and algorithms. Section 6 presents the simulation setup and performance analysis. Finally, Section 7 concludes the paper and outlines future research directions.
## Abstract  
High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results. However, they ignore the reality that traffic flows can propagate backwards on road networks when modeling spatial relationships, as well as associations between distant nodes. In addition, more effective model components for modeling temporal relationships remain to be developed. To address the above challenges, we propose a local-global features fusion temporal convolutional network (LGTCN) for spatio-temporal traffic flow prediction, which incorporates a bidirectional graph convolutional network, probabilistic sparse self-attention, and a multichannel temporal convolutional network. To extract the bidirectional propagation relationship of traffic flow on the road network, we improve the traditional graph convolutional network so that information can be propagated in multiple directions. In addition, in spatial global dimensions, we propose probabilistic sparse self-attention to effectively perceive global data correlations and reduce the computational complexity caused by the finite perspective graph. Furthermore, we develop a multichannel temporal convolutional network. It not only retains the temporal learning capability of temporal convolutional networks, but also corresponds each channel to a node, and it realizes the interaction of node features through output interoperation. Extensive experiments on four open access benchmark traffic flow datasets demonstrate the effectiveness of our model.  
1 School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China;  
[EMAIL_ADDRESS] (W.Y.); [EMAIL_ADDRESS] (H.K.); [EMAIL_ADDRESS] (K.D.); [EMAIL_ADDRESS] (D.L.)  
2 China Mobile Internet Co., Ltd., Guangzhou 510510, China  
* Correspondence: [EMAIL_ADDRESS]  
t traffic flow prediction; spatial-temporal feature; local-global feature fusion; probabilistic sparse self-attention; temporal convolutional network
# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 8 Future Scope of WBANs  
WBANs are growing at an insurmountable rate in the market for medical fields and industry fields for entertainment. In the near future, WBAN systems will change the way people think about  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accelerometer from Gait Signal** \\ \hline
**References** & [24] & [25] \\ \hline \multirow{3}{*}{**Accuracy**} & EER of 7\% with signal corr., & EER of 9.4\% with phone accel. \\  & 10\%, 18\%, and 19\% & EER of 9.8\% with phone gyro. \\  & with other methods. & EER of 8.0\% with combined \\  & & accel. and gyro. \\ \hline \multirow{2}{*}{**Sample Size**} & 36 & 51 \\ \hline \multirow{3}{*}{**Requirements**} & Three-dimensional accel., & Google Nexus 5 or \\  & worn on belt of user & Samsung Galaxy S5 \\ \hline \multirow{2}{*}{**Advantages**} & Low-cost, easy to implement & Readily available, easy to implement \\ \hline \multirow{3}{*}{**Disadvantages**} & Not viable for disabled individuals, & Not viable for disabled individuals, \\  & results affected by shoes, leg & results affected by shoes, leg \\ \cline{1-1}  & injuries, walking surface & injuries, walking surface; \\ \cline{1-1}  & & some people do not carry \\ \cline{1-1}  & & their phone in their pockets \\ \hline \hline \end{tabular}
\end{table}
Table 7: Body Portable Device Evaluation by technique.  
managing and monitoring their health. This will also reduce healthcare costs because it will provide more preventive healthcare. WBANs require small, low-cost, low-energy devices such as sensor nodes. New generations of smartphones can store the medical data that is sent from the sensors, and in the future, with the growth of the smartphones, we will see improvements in this technology. The S-MAC and T-MAC protocols still need more research and improvements regarding the energy efficiency for the WBAN sensors. The user configuration systems and interfaces for the personal monitoring systems require more enhancements. This will be helpful for the users to interact efficiently with the device and improve their quality of life.  
WBAN authentication and identification fields require further research and studies in terms of efficiency, accuracy, and reliability for the users' personal sensitive information. This will help to implement it in the market to increase the usability and availability.  
We can now formulate several potential authentication schemes based on biometric techniques we discussed. Compared to the previous category that used the devices as a base form, on this analysis form we discussed the techniques that were used in papers [14; 16; 24; 26; 27; 28; 29; 30; 32; 33; 34; 35; 36; 32; 35]. Each technique has been used in a different way on the same device. For improved understanding and simplification, we re-categorized the use of each technique based on the way it is used and we added the possibility of combining two techniques or more together. We created new methods for improved comprehension of the WBAN authentication systems and the possibility of combining two or more techniques together.
# A LoRa-Based Linear Sensor Network for Location Data in Underground Mining
## 6 Conclusions  
In this paper we have introduced a linear network based system we have developed for the transmission of location data in underground mines. The purpose of the system is to transmit low  
\begin{table}
\begin{tabular}{l|l} \hline
**Tag Distribution** & **Success Probability** \\ \hline
16 0 0 0 0 0 0 0 & 0.974 \\
8 8 0 0 0 0 0 0 & 0.966 \\
4 4 4 4 0 0 0 0 & 0.948 \\
2 2 2 2 2 2 2 & 0.924 \\
0 0 0 0 4 4 4 & 0.923 \\
0 0 0 0 0 0 8 & 0.920 \\
0 0 0 0 0 0 0 16 & 0.924 \\ \hline \end{tabular}
\end{table}
Table 1: Probability of successful delivery with skewed tag distributions.  
Figure 5: Throughput, \(n=10,\mu=100\).  
bit-rate data, primarily location data generated by tags carried by personnel and equipment, to a headend. The system is used during emergencies or when the main network has failed.  
We have used the LoRa physical layer as the transmission technology and developed a protocol for message transmission. The protocol incorporates three key design decisions: flooding for message forwarding, sequence numbers and time-to-live fields for managing broadcast storms, and a simplified CSMA scheme for managing contention.  
We have implemented and trialed a small version of the system which has given promising results. However, to explore the scalability of the system without having to deploy a large amount of hardware we have developed analytical and simulation models of the system. These have given us an indication of the number of tags and relays that can be expected to be able to be supported. We have found that large numbers of lightly loaded relays performs better than smaller numbers of heavily loaded relays. This may have some consequences for how the system is installed with larger numbers of low powered relays and tags preferred to smaller numbers of higher powered relays and tags.  
Future work includes incorporation of location devices into the system, optimization of CSMA mechanism and larger trials involving the testing underground of larger numbers of nodes and tags.  
Location data underground can be based on received signal strength and accelerometer data. Incorporating this into the system is an important step. However, there are issues to be explored relating to determining the location of the relays. As relays are put in place accelerometer data carried by the installers can give an indication as to the location of the relay. How accurate might that data be? There is the optimization of the delay in the CSMA scheme to be addressed. What is the optimal delay that maximizes throughput? We also plan to conduct larger underground trials.  
Safety underground is a matter of profound concern to the mining industry. LoRa used in an emergency location system can help add to that safety.  
All authors contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.