esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Toward a Theoretical Framework
### Security, Privacy, and Familiarity Affecting Trust in the 6 GCloHE System
#### 8.2.1 Security  
Researchers reveal significant insecurity in the IoHE devices. The IoHE wireless devices pose many security challenges such as intrusion, denial of service, forgery, or heterogeneous network threats. Hackers can use ransom ware to target vulnerabilities in the Windows operating system to prevent healthcare professionals from accessing affected devices.  
Security is defined as the protection of software and hardware from misuse, malfunction, unauthorized access, damage, disruption, and misdirection. Security is important to the IoHE applications because of sensitive health data privacy; hence, the following security solutions are imperative to protect data from attacks: (i) access control is a key step in protecting IoHE applications and health data, therefore, well-designed access control and strong access management must be implemented to ensure healthcare data security and privacy. Through access control systems, an organization can restrict and monitor the use of critical data and protect privacy and security. Furthermore, healthcare stakeholders should have training and awareness of information security to provide security of IoHE applications and health data; (ii) the IoHE devices collect data from various environments, hence, physical security is paramount for the IoHE devices. Physical security of IoT health devices and health data involves protection against environmental threats, accidents, physical sabotage, and theft; (iii) IoHE devices should have replacement devices for protecting physical attacks so that the IoHE devices continue collecting and transferring data without interruption; (iv) network security, such as 6G Tactile Internet, is an important issue for IoHE devices and applications. All IoT devices connect to networks and communicate with each other over a network. Firewalls, filtering structures, internet protocol (IP) security, and secure sockets layer/transport layer security should be used to ensure network security.  
Ethical hacking can identify security vulnerabilities and risks for an organization. Moreover, most of the devices are designed without security, making them easy goals for safety breaches. By using the IoHE hacking tools, one can secure the system and the infrastructure within the organization. This process helps to create an ideal strategy that effectively and efficiently incorporates tools to identify and resolve security vulnerabilities.  
Ways to secure the IoHE devices include: (i) developing an exhaustive map of healthcare assets; (ii) following the best security practices; (iii) effective authentication implementation; (iv) insulating devices that do not have a built-in control; and (v) usage of appropriate tools.
## 3 Experiments and Analyses
### Experiment Setup
#### 3.1.4 Visual Information Fidelity  
VIF is the novel full reference image quality metric. VIF quantifies the mutual information between the reference and test images based on natural scene statistics (NSS) theory and human visual system (HVS) model. It can be expressed as the ratio between the distorted test image information and the reference image information, the calculation equation of VIF is shown in Equation (17).  
\[VIF=\frac{\sum_{i\in subbands}I(\overrightarrow{C^{Nj}_{i}};\overrightarrow {F^{Nj}_{i}})}{\sum_{i\in subbands}I(\overrightarrow{C^{Nj}_{i}}; \overrightarrow{F^{Nj}_{i}})}\enspace, \tag{17}\]  
where \(I(\overrightarrow{C^{Nj}_{i}};\overrightarrow{F^{Nj}_{i}})\) and \(I(\overrightarrow{C^{Nj}_{i}};\overrightarrow{F^{Nj}_{i}})\) represent the mutual information, which are extracted from a particular subband in the reference and the test images respectively. \(\overrightarrow{C^{N}}\) denotes N elements from a random field, \(\overrightarrow{E^{N}}\) and \(\overrightarrow{F^{N}}\) are visual signals at the output of HVS model from the reference and the test images respectively.  
An average VIF value of each input image and integrated image is used to evaluate the fused image. The evaluation function of VIF for image fusion is shown in Equation (18) [37].  
\[VIF(A,B,F)=\frac{VIF(A,F)+VIF(B,F)}{2}\ \, \tag{18}\]  
where \(VIF(A,F)\) is the VIF value between input image A and fused image F; \(VIF(B,F)\) is the VIF value between input image B and fused image F.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 5 Conclusions  
Active flow control and sliding mode control have been integrated together to tackle the problem of modeling and control morphing aircraft at a high AoA. The dynamic model has been established with a consideration of roll damping, the morphing motion, and aerodynamic fluctuation induced by SJA. The synthetic jet technique with an optimized cavity shape has not only been used to improve the aerodynamic performance of aircraft at a high AoA, but also to execute flight control along with the morphing span. The simulation results show that the proposed control strategy for roll control could track the command signal quickly and effectively. Furthermore, the roll motion associated with a given morphing strategy could be maintained via SJA, and the following conclusions can be obtained:  
1. The roll dynamic with a low frequency of morphing aircraft can be controlled by the morphing motion, which can track the ideal response within a few seconds, and
2. By introducing active flow control based on SJA, roll motion with a higher frequency at a high AOA can be controlled within a fraction of a second, and an additional redundant controlled input provided by motion morphing will be conducive to improving the control performance of the system.  
Conceptualization, W.L. and W.W.; methodology, W.L.; software, W.W.; validation, S.Z. and W.L.; investigation, S.Z.; data curation, S.Z.; writing--original draft preparation, W.W.; writing--review and editing, X.H., C.L. and W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (2662018 QD029), the Natural Science Foundation of Hubei Province of China (2020 CFB309), and the Key Research and Development Program of Hubei Province of China (2020 BBB062).  
Please contact the authors for the raw/processed data required to reproduce these findings.  
The authors would like to thank [PERSON] and [PERSON] of Huazhong Agricultural University for their contributions to this work.  
The authors declare no conflict of interest.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 4 Test Procedure
### Experiment 2--Spatial Displacement Repeatability  
The purpose of the second experiment was to validate the efficacy of the FAGPS technique over a short range real-world environment with both multipath and non-multipath errors present. Hence, the aim of this experiment is to verify that performing a calibration at one geolocated landmark should improve GPS performance when the receiver is moved and tested at another point (for a limited period of time).  
Two survey points were identified (Table 2 and Figure 4) and located. These survey points were approximately 95 m apart and are located in a residential area.  
Figure 4: Locations of survey data points.  
The first survey point (PCM113580231) was selected as the geolocated reference point. The GPS was turned on at survey point PCM113580231 and was left static for 5 min to ensure that it had a reliable fix. Based on the known position of PCM113580231 the measured position offsets in X and Y were computed to correct the data (latitude offset = 0.0000546667, longitude offset = 0.0000326667).  
The GPS receiver was then repositioned at PCM113580230 for an hour in order to assess the performance of the correction over time at a different survey point.
# From 5G to 6G--Challenges, Technologies, and Applications
## 2 Fifth-Generation Network's Shortcomings  
This section looks at how well 5G is expected to perform as it is being rolled out in more and more global markets recently. It is appropriate first to examine the key technologies of 5G that are quickly becoming outdated. Network densification is a key player in 5G through the very wide deployment of small cells. However, the benefits of this deployment, i.e., enhanced coverage and higher data transfer rates, represent diminishing returns as more and more small cells are deployed due to the significant increase in infrastructure cost. Another technology is carrier aggregation, which allows users to be served by more than a single-component carrier to offer a higher bandwidth [7]. However, this has implications for hardware on the end users' side to support different frequency bands. It is worth looking at the cloud radio access network (C-RAN) as being a primary component of 5G to mitigate the hardware limitations of end devices. However, as networks grow exponentially in size, it becomes evident that the cloud alone is not enough, and fog and edge node computations are needed. Moreover, security in the main 5G technologies is not advanced enough to be deployed on very large scales, such as in software-defined networks (SDNs), where it lacks the mechanisms to verify trust between the management apps and the controller. Another example is network function virtualization (NFV), where attackers can target software-level components, such as the virtual infrastructure manager, and generate fake logs that hinder the operation of NFV [8]. Furthermore, 5G offers ultra-reliable and low-latency communication (URLLC) as one of its key drivers. However, it is limited to the edge of the network without real integration across the entire network (including the core) [9]. Moreover, the concept of heterogeneous networks (HetNets) is at the core of 5G technologies, but currently, such network integration is limited to terrestrial networks. This has to be further expanded to be three-dimensional by including aerial and space mesh networks in the main network. It is also important to note that 5G is not immune to denial of service (DoS) attacks or threats that compromise its availability [10]. It is crucial that this be improved in future networks to adjust for the size of ever-growing networks of billions of nodes. Next, we present the global communication network requirements and demands that are expected to surpass 5G's capabilities.
## 3 Results and Discussions
### Metal Removal Efficiency and Resin Selectivity
Although lignin contains some polar functional groups, such as phenolic hydroxyls and carboxylic acids, their lower density and accessibility compared to the sulfonic groups of Nafion further reduce its efficiency in the exchange of metal cations in solution. These characteristics highlight the challenges associated with using lignin in applications requiring strong water affinity, making its chemical engineering essential to transform it into a competitive material.
# Malware Variants Detection Model Based on MFF-HDBA
## 5 Experimental Results and Discussion
### Effects of Different Malware Image Sizes on Model Performance  
The input size of the model based on CNN must be a fixed size. Therefore, we reshaped the malware image to a fixed square size using bilinear interpolation algorithm. In addition, the scale and performance of the model are closely related to the malware image sizes which are input to the proposed model. The smaller the input size of model, the more difficult it is to retain all the texture information of the image, making it easy to misclassify similar malware family samples. Conversely, the larger the input size costs more computation time, and even causes model overfitting. In order to obtain an optimal input size, the malware images were normalized to 32 \(\times\) 32, 64 \(\times\) 64, 128 \(\times\) 128, 256 \(\times\) 256, and 512 \(\times\) 512, using bilinear interpolation; the results are shown in Table 5.  
Figure 6: Classification confusion matrix.  
The results show that the classification accuracy increased initially from 84.28% to 99.36%, and then decreased to 99.04% when increasing the size of malicious code images from 64 \(\times\) 64 to 512 \(\times\) 512. This phenomenon indicated that the model was overfitted, and the input size of 256 \(\times\) 256 was critical. In addition, the model parameters increased with increasing malware image size. Moreover, the larger number of parameters indicated that there was a larger consumption of computer resources, which also led to an increase in the training time of the model. Considering the classification performance and parameters, we selected a malware image size of 256 \(\times\) 256 as the input for the model.
## 3 Results
### Optimization of the Suction Temperature
The effects of \(AT_{sahx}\) on the determination of suction temperatures can also be observed in Figure 7. The minimum \(ST_{com2}\) (14.01 \({}^{\circ}\)C) and \(ST_{com3}\) (23.66 \({}^{\circ}\)C) can be simultaneously achieved only when the \(AT_{sahx}\) is below a minimum value (\(AT_{sahx,min}\)). With an \(AT_{sahx}\) smaller than \(AT_{sahx,min}\) (solid line in Figure 7, \(AT_{sahx}\) = 10 \({}^{\circ}\)C), the SAHEXs undertake a greater cooling load of the compressed air, thus leading to a lower cooling load for the CWAHEXs; thus, the heat obtained from the SAHEXs is more than is needed for the generation heat of the OAR to produce enough chilled water for the CWAHEXs. Once \(AT_{sahx}\) is higher than \(AT_{sahx,min}\) (dotted line in Figure 7, \(AT_{sahx}\) = 15 \({}^{\circ}\)C), the cooling capacity of the SAHEXs drops, while the cooling load for the CWAHEXs increases until the heat obtained from the SAHEXs is insufficient to drive the OAR to cool the compressed air to the minimum suction temperature. \(ST_{com2}\) or \(ST_{com3}\) has to be enhanced alone or simultaneously with the increase in \(AT_{sahx}\). Allowable \(AT_{sahx,max}\) corresponds to the working condition that \(ST_{com2}\) and \(ST_{com3}\) are both 40 \({}^{\circ}\)C.  
Figures 6 and 7 also show that the allowable range of \(AT_{sahx}\) is closely related to \(T_{eva}\) and \(FR_{s\cdot a}\). \(AT_{sahx,min}\) and \(AT_{sahx,max}\) both increase with the increase in \(T_{eva}\) and \(FR_{s\cdot a}\). This result is supported by the fact that _COP_ of OAR increases with a higher \(T_{eva}\) and \(FR_{s\cdot a}\), under which condition less generation heat is required by OAR for the same cooling load of the evaporator. This means that the heat allocation ratio between the SAHEXs and CWAHEXs can be decreased as a result of higher \(AT_{sahx,min}\) and \(AT_{sahx,max}\). Based on the optimization of the suction temperatures of the compressors, the thermodynamic characteristics of the combined systems are analyzed below.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 2 Related Work
### Probabilistic Models in MOT  
[PERSON] and [PERSON] [10] introduce a latent data association framework that models state nodes across continuous time slices as interconnected through probabilistic dependencies, reframing data association and model selection as a unified inference problem. To decrease computational complexity, [PERSON] [11] employs Markov random fields with a Local Binary Pattern algorithm, replacing cluster graphs with a more tractable model with the assumption of prior knowledge of target count, detection probability, and clutter density. [PERSON], [PERSON], and [PERSON] [12] tackle the challenges of image noise, cluttered backgrounds, and camera motion by integrating a pairwise Markov random field into a joint 2.5D layered model, which enables simultaneous image segmentation and tracking.  
However, these methods often overlook the distinct stochastic motion patterns of pedestrians and the inherent instability of tracking cues, treating them the same as other dynamic objects in the scene. To address this, we emphasize the dual-phase pattern of targets by formulating tracking within an HMM framework. And we introduce the weighted entropy mechanism to quantify the uncertainty of feature characterization capacity during multi-cue fusion, enabling the adaptive recalibration of weight to mitigate bias in noisy or cluttered conditions.
# Increasing the Bioactive Compound Content of Olive Oil by Acidification of Olive Paste
###### Abstract  
This study investigated the impact of acidification on olive paste to enhance the extraction of olive oil enriched in bioactive phenolic compounds, aiming to develop a novel functional food. Recognizing that acidic pH promotes the activity of _g-glucosidase_ responsible for oleuropein and ligtsroside hydrolysis, food-grade organic acids--citric, ascorbic, and acetic acid--were added prior to malaxation to improve the bioactive compound content in the resulting oils. A randomized experimental design was employed, using three different doses of each acid (1, 2, and 4%) with three replicates per dose, alongside control trials without acid addition. Acidification did not affect olive oil extraction efficiency or alter quality parameters for extra virgin olive oils. Treatment with 1% ascorbic acid significantly increased phenolic compound content by 37% compared to controls. Secoiridoids comprised 79.4% of the total phenolic content, with oleacein as the predominant compound (237.58 \(\pm\) 9.18 mg/kg), representing over 50% of the total. This increased oleacein concentration led to up to a 67% enhancement in antioxidant capacity (428.63 \(\pm\) 31.57 mg Trolox/kg) compared to controls. The antioxidant capacities of 3,4-DHPEA, 3,4-DHPEA-EDA, and 3,4-DHPEA-EA were determined to be 12.05, 5.80, and 4.91 umol TE/mg, respectively. Furthermore, the 1% ascorbic acid treatment enhanced volatile compounds associated with the lipoxygenase (LOX) pathway while reducing ethanol levels.  
o 2025141336
# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 2 Channel Measurement
### Signal Model
We only consider the correlation characteristic on the base station; thus, we have the correlation matrix on user ends as \(R_{Rx}\,=\,I_{N\times N}\,\). Here \(I_{N\times N}\) represents the unit diagonal array. Thus, the \(K\times M\)-dimensional channel matrix can be expressed as  
\[H=W\cdot R_{Tx}^{1/2} \tag{5}\]  
Assuming that the system model of Massive MIMO is frequency-flat and slow fading, the channel matrix \(H\) is usually modelled as independent and identically distributed with mean value of zero and variance of one. According to the Marchenko-Pastur Law, each row of the \(N_{rx}\times N_{tx}\) dimensional channel matrix \(H\) is zero mean, the variance is \(1/N_{rx}\), and the eigenvalue distribution of the matrix \(H^{H}H\) and the matrix \(HH^{H}\) is a deterministic distribution of convergence. At that time, the channel gradually \"hardened\", and the channel information was mainly concentrated on the main diagonal. That is to say, the value of the diagonal elements tends to one gradually, while the value of the non-diagonal elements tends to zero gradually. The phenomenon of channel hardening comes from the fact that the variance of the mutual information grows very slowly relative to its mean, or even shrinks as the number of antennas grows, which has been proven true [19; 20] and illustrated in Figure 4.  
Based on the above analysis, the correlation matrix \(R_{Tx}\) can be expressed in the form of local stationary autocorrelation. As an example, we assumed that the base station had  
Figure 4: Power distribution of channel correlation matrix. 32 antenna array elements. If the channel is stationary, the correlation matrix can be shown in Figure 5a. If the channel is non-stationary, the correlation matrix can be divided into several stationary regions. The Massive MIMO non-stationary channel can be composed by several stationary sub-channels. As shown in Figure 5b.
## Nomenclature  
\begin{tabular}{l l l} Parameter & Definition & Units \\ \(\hat{i},\hat{j},\hat{k}\) & Unit vectors, for defining the direction of shear forces & \([\mathrm{Adim}]\) \\ \(a_{c}\) & Cutting depth & \([\mathrm{mm}]\) \\ \(a_{c\sigma}\) & Optimum cutting depth & \([\mathrm{mm}]\) \\ \(\Delta a_{c}\) & Cutting depth adjustment parameter & \([\mathrm{mm}]\) \\ \(D_{c}\) & Major diameter of the work material & \([\mathrm{mm}]\) \\ \(d_{m}\) & Machined (minor) diameter of the workpiece & \([\mathrm{mm}]\) \\ \(E_{prom}\) & Average steady-state energy consumed by the process & \([\mathrm{J}]\) \\ \(F_{c}\) & Tangential shear force & \([\mathrm{N}]\) \\ \(F_{a}\) & Axial cutting force & \([\mathrm{N}]\) \\ \(F_{p}\) & Radial cutting force & \([\mathrm{N}]\) \\ \(F_{res}\) & Resulting machining force & \([\mathrm{N}]\) \\ \(\overset{\rightarrow}{F}_{res}\) & Vector resulting machining force & \([\mathrm{N}]\) \\ \(F_{v}\) & Feed rate of the cutting tool & \([\mathrm{mm}/\mathrm{min}]\) \\ \(K_{1}\) & Constant of electrical power parameters & \([\mathrm{N}/\mathrm{s}]\) \\ \(K_{2}\) & Surface roughness parameter constant & \([\mathrm{m}^{3}/\mathrm{s}^{2}]\) \\ \(K_{F}\) & Constant of proportionality to the load force & \([\mathrm{Adim}]\) \\ \(K_{M}\) & Constant of proportionality to the load moment & \([\mathrm{mm}]\) \\ \(k_{s}\) & Cutting pressure, interaction between tool and material & \([\mathrm{MPa}]\) \\ \end{tabular}