esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation Between Co-Design and Psychological Need Satisfaction Based on fNIRs Hyperscanning
## 4 Discussion
### IBS of DLPFC and BROCA Be Related to Semantic Processing and Problem-Solving Ability  
Secondly, the study found that the channels covered by the DLPFC and BROCA brain regions increased in both traditional and co-design, and the increase in IBS was more significant during co-design, indicating better consistency of DLPFC and BROCA for cognitive response under co-design tasks. According to previous research, DLPFC is a subregion of the brain PFC located in the anterior part of the brain, primarily responsible for advanced cognitive functions such as executive control, working memory, and attention regulation [28; 29; 30; 31; 32]. [PERSON] et al. [33] found that compared with the high-creativity subjects, the low-creativity subjects had larger IBS in the right prefrontal lobe DLPFC, indicating that DLPFC is considered to be related to creativity and problem-solving ability, and is an important research object in the fields of neuroscience and cognitive science [34].  
In addition, BROCA is a specific area of the brain located in the posterior part of the inferior frontal gyrus (IFG), responsible for language movement. It is mainly known for its close connection with aphasia, especially motor aphasia. Motor aphasia is a language disorder in which patients are able to understand others' speech but have difficulty expressing their own thoughts, exhibiting characteristics of non-fluent oral communication. Previous studies have found significant activation of BROCA in both public speaking [35] and walking while speaking [36] task detections. In the Stroop task conducted by [PERSON] et al. targeting the elderly, significant activation of DLPFC and BROCA was also observed  
Figure 11: Scatter plot of IBS versus psychological need satisfaction rate in CH2 and CH3 under the draw block condition: (**a**) Traditional CH2 Draw (T2D); (**b**) Traditional CH3 Draw (T3D); (**c**) Co-design CH2 Draw (C2D); (**d**) Co-design CH3 Draw (C3D).  
under word recognition stimulation [37], further indicating that the activation of these two brain regions is related to semantic processing and problem-solving abilities. In this study, design itself is a creative problem-solving task regardless of the type of task. The results showed that both task conditions were effective in activating DLPFC and BROCA, and under co-design tasks, the IBS of related brain channels increased more significantly, further supporting the aforementioned research and expanding the possibility of collaborative task paradigms.
## 2 FES for SCI  
FES is a treatment method that uses electrical stimulation to contract each paralyzed muscle and reconstruct purposeful movements for upper and lower limb motor functions impaired by central nervous system injuries such as stroke or SCI. Although paralysis occurs in SCI due to damage to the upper motor neurons, normal electrical excitement remains in the lower motor neurons and their controlling muscles. Thus, electrical stimulation is used in place of excitatory impulses from the upper motor neurons, which can be applied directly to the lower motor neurons to contract paralytic muscles (Figure 1).  
In 1961, [PERSON] et al. reported the first use of FES, in which ankle dorsiflexion was controlled according to the gait cycle by stimulating the common peroneal nerve with a surface electrode in a patient with varus equinus due to stroke [21]. FES was first applied for SCI in 1963 by [PERSON] et al. [22], who stimulated the extensor digitorum muscle of a patient with cervical SCI to open the hand, in combination with a finger hinge splint to reconstruct ADL [22]. Subsequent studies have reported the application of FES in programmed motion stimuli to the paralyzed limb via multiple stimulus electrodes to reconstruct purposeful motions in the upper limbs for activities such as writing, eating, and drinking; and in the lower limbs for walking and standing [23]. Following these studies, randomized controlled trials were performed to investigate the effectiveness of FES training on the independence or function of SCI patients. [PERSON] et al. performed a randomized controlled trial to examine the efficacy of 40 h of FES therapy with conventional occupational therapy (COT) compared with COT alone [24]. The FES therapy significantly reduced disability and improved voluntary grasping in the subjects with tetraplegia. [PERSON] et al. reported that 16 weeks of thrice-weekly FES-assisted walking program compared to a non-FES exercise program in chronic incomplete traumatic SCI patients [25]. The recent systematic review and meta-analysis have indicated that FES although significantly increased upper extremity  
Figure 1: Schematic diagram of the principle of functional electrical stimulation (FES). Instead of upper motor neuron damaged by spinal cord injury, muscles contract and reconstruct function through lower motor neurons that are not damaged by controlled electrical stimulation.  
independence, there was no obvious difference in overall upper extremity function, lower extremity independence, and life quality of individuals with SCI [26].
## 2 Materials and Methods
### Assessment of Anti-Inflammatory Activity  
The production of NO, IL-6, TNF-\(\alpha\), COX-2, and LOX in lipopolysaccharide-acrylamide (LPS)-stimulated Raw264.7 cells was assessed following a method outlined by [PERSON] et al. (2024) [23]. In brief, Raw264.7 cells were cultured in DMEM-FSP at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\). The culture medium was replenished every 24 h. The cells were seeded (density: 5 \(\times\) 10\({}^{4}\) cells/well) in 24-well plates containing DMEM-FSP plus 2.5 mg/L LPS. Different concentrations of Au-Ag alloy NPs were added to the plates. Two untreated control groups were used. In one group, Raw264.7 cells were cultivated in only DMEM-FSP (negative control). In the other group, the cells were cultivated in DMEM-FSP plus LPS (positive control). After 24 h of incubation, the cells were centrifuged. The supernatant was collected. To evaluate NO production, the supernatant was mixed with an equal volume of Griess reagent and incubated for 10 min. Absorbance was measured at 540 nm by using a microplate reader. NO levels were quantified from a standard curve generated using NaNO\({}_{2}\) solution, as described by [PERSON] and [PERSON] (2017) [24].  
Western blotting was performed to measure the levels of IL-6, TNF-\(\alpha\), COX-2, and LOX in LPS-stimulated Raw264.7 cells [25]. In brief, the cells were lysed using mammalian protein extraction reagent. The cell lysate was incubated on ice for 15 min and then centrifuged at 12,500 rpm for 10 min at 4 \({}^{\circ}\)C. The supernatant was collected. Protein concentration in the supernatant was determined through the Bradford assay. The extracted protein (30 \(\upmu\)g) was separated through sodium dodecyl sulfate polyacrylamide gel (10%) electrophoresis. The resultant protein bands were transferred onto a polyvinylidene fluoride membrane. The membrane was blocked with 5% nonfat dry milk in Tris-buffered saline (TBS)-Tween 20 (0.1%) for 2 h. After washing with TBS-Tween 20, the membrane was incubated with primary antibodies against IL-6, TNF-\(\alpha\), COX-2, LOX, or \(\upbeta\)-actin for 1 h at room temperature. After three 5 min washes with TBS-Tween 20, the membrane was incubated with horseradish peroxidase-conjugated secondary antibodies for 1 h. Then, a chemiluminescence reagent was added to generate a detectable signal. Protein bands were visualized using the LAS3000 Luminescent Image Analyzer (Fujifilm Life Science, Tokyo, Japan). Notably, \(\upbeta\)-actin was used as a control to ensure equal protein loading.
## 3 Materials and Methods
### Sentiment Analysis of Attributes
#### 3.5.1 Classifying Attributes into Kano Categories  
In this study, a new approach to classify hotel attributes into Kano categories is proposed. As the above descriptions in our study, the positive sentiment value \(SC_{i}^{pos}\) of attribute \(A_{i}\) is obtained from customers whose expectations toward hotel attribute \(A_{i}\) has been met or even exceeded. So \(SC_{i}^{pos}\) of attribute \(A_{i}\) indicates the customer satisfaction that attribute \(A_{i}\) can bring when it performs well. Likewise, the negative sentiment value \(SC_{i}^{neg}\) of attribute \(A_{i}\) is obtained from customers who think the attribute realistic performance hasn't met their expectations, which represents customer dissatisfaction that attribute \(A_{i}\) causes when its performance is poor. The overall sentiment value \(SC_{i}\) of attribute \(A_{i}\) is obtained from all customers stayed in the hotels of the same star. Thus, the \(SC_{i}\) is regarded as the expectant customer satisfaction that attribute \(A_{i}\) should generate. In accordance with the obtained \(SC_{i}^{pos}\), \(SC_{i}^{neg}\) and \(SC_{i}\), following the previous index value classifying methods [10; 24], here we define an index \(SI\) to compare the effects of the attributes' good performance and poor performance on customer satisfaction in hotels of the same star rating, and the \(SI\) index of attribute \(A_{i}\) can be calculated as:  
\[SI_{i}=\frac{SC_{i}^{pos}-SC_{i}}{SC_{i}-SC_{i}^{neg}} \tag{14}\]  
Obviously, \(SI_{i}\in[0,+\infty]\). The \(SI\) index indicates the ratio of the customer satisfaction of good performance to the customer dissatisfaction of poor performance comparing with the expectant customer satisfaction of the overall performance concerning attribute \(A_{i}\). To determine the Kano category of each hotel attributes, a cut-off point \(\theta\) is defined subjectively. According to the testing results based on different assignment methods in these review datasets, we define \(\theta=\left({{SI^{MAX}}-{SI^{MIN}}}\right)/6\), where \(SI^{MAX}\) and \(SI^{MIN}\) represent the largest and smallest values of the \(SI\) index among the six hotel attributes. Moreover, the mean of the \(SI\) index among the six hotel attributes is calculated, denoting \(\overline{S}I\). Hence, hotel attributes can be classified into Kano categories as follows:  
If \(0\leq SI_{i}<\overline{S}I-\theta\), attribute \(A_{i}\) is regarded as basic factor, indicating attributes in this category bring more customer dissatisfaction compared to other attributes.  
If \(\overline{S}I-\theta\leq SI_{i}\leq\overline{S}I+\theta\), attribute \(A_{i}\) is regarded as performance factor, indicating attributes in this category bring equal or approximate customer satisfaction and dissatisfaction compared to other attributes.  
If \(SI_{i}>\overline{S}I+\theta\), attribute \(A_{i}\) is regarded as excitement factor, indicating attributes in this category bring more customer satisfaction compared to other attributes.
# On the Modeling of Automotive Security: A Survey of Methods and Perspectives
## 2 Taxonomy of Security Models  
Because of widespread use of the interconnection, security becomes one of the main concerns in the industrial domain, which includes automobiles. Unlike safety, the security of a system is defined in the dimensions of intents and external influence in [34]. Generally, security is the ability to defend the confidentiality and integrity of the system by implementing protections to prevent unauthorized access [35; 36; 37]. In the automotive context, the security means all functions and interfaces of road vehicles and the automotive ecosystem are protected against cybersecurity threats. Since security is related to uncertainties like malicious events and environmental interactions, security models can be used as a tool to rationalize the security of the system and represent it in a structured manner [38].  
Security models are developed to describe the security characteristics of systems formally. They are used to explain the reasons for the security-related behaviors of systems accurately [39]. There are various security models according to different purpose, for instance: the access control model, the integrity model and the threat model. In this paper, we only focus on the threat model to identify the potential threats and risks that could affect the system. It is significant for system security engineering to derive the security requirements and protection mechanisms based on the threat model [40]. Hence, the threat models are commonly used in automotive security. There are several developed threat modeling methods for software systems as illustrated in [41]. We review and categorize these approaches, which stimulate the automotive threat modeling development.  
Threat modeling is a group of planned activities for identifying and assessing application threats and vulnerabilities [28] as shown in Figure 2. We propose a classification scheme to categorize the methods of threat modeling into the qualitative models, which are mainly in descriptive form, and the quantitative models like stochastic models.
# An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems
## 4 Results
### The Selection of Threshold  
As mentioned above, our proposed method will reschedule packets when some conditions are met, one of which is the load factor of the core exceeds a pre-configured threshold. The load factor represents the utilization of the queue of the core; thus, the pre-configured threshold should have the same definition, which is also a representation of the utilization of the queue. There is no doubt that the selection of the threshold has a great impact on the adjustment frequency. Therefore, we first verify the probability of packet drop under different thresholds. The results are shown in Table 1.  
As we can see, when the threshold is 50%, the average probability of packet drop is 55.4%, i.e., there is a higher probability of packet loss when the occupancy of the core's queue exceeds 50%. The larger is the threshold, the higher is that probability that a packet will be dropped. Meanwhile, a smaller threshold leads to adjustment happening frequently, which incurs a large scheduling overhead. Therefore, in this paper, we set the threshold of 25%. This is, only when the utilization of the core' queue exceeds a quarter will our proposed method adjust the scheduling decision.
## 3 Background
### Overview of Target Algorithms  
Block cipher algorithms generally are based on different structures and the number of rounds depending on the key length. Table 3 shows the number of rounds according to the key length of the encryption algorithm. To match the same experimental environment, we set the plaintext and key size of AES, CHAM, and LEA as 128 bits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Platform** & **AES** & **CHAM** & **LEA** \\ \hline [PERSON]. [PERSON] et al. [6] & GTX 680 & 63.9 & - & - \\  & Radeon HD 7970 & 205.8 & - & - \\ [PERSON] et al. [7] & Tesla P100-PCIe & 605.9 & - & - \\ \hline [PERSON] et al. [8] & GTX 480 & 78.6 & - & - \\ \hline [PERSON] et al. [9] & GTX 780 & 80.52 & - & - \\  & GTX TITAN X & 207.25 & - & - \\  & GTX 1080 & 279.86 & - & - \\ \hline [PERSON] et al. [10] & GTX 680 & - & - & 138.8 \\  & K40c & - & - & 260.28 (scaled) \\ \hline [PERSON] et al. [11] & GTX 980 & 149.4 & - & - \\ [PERSON] et al. [12] & K40c & 79 & - & 368 \\  & GTX 980 & 154 & - & 678 \\  & GTX 1080 & 256 & - & 1,478 \\ \hline [PERSON] et al. [13] & i5 Skylake@2.3 GHz (CPU) & - & 1.33 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Throughput (Gbps) table with existing implementation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(X\oplus Y\) & Bit-wise exclusive OR operation \\ \hline \(X\boxplus Y\) & \(X+Y\) mod 232 \\ \hline \(X*Y\) & Multiply X and Y in \(GF(2^{8})\) in AES \\ \hline \(e_{j}\) & \(j\)th columns in AES \(block\) \\ \hline \(x_{i},y_{i},z_{i},w_{i}\) & 32-bit word in \(i-th\) rounds of CHAM/LEA \(block\) \\ \hline \(x\lll i\) & Rotating \(x\) left-side as \(i\) bits \\ \hline \(x\ggg i\) & Rotating \(x\) left-side as \(i\) bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 3 Methods and Materials
### Development Environment  
The development environment where MLS is installed uses the Ubuntu 18.04 operating system or a later version, CMake 3.18.xx, ROS Noetic, C++ 17, and a compiled MLS library. The MLS library used for this implementation is draft C++ version 12 from the IETF MLS Working Group GitHub repository [18]. VSCode is the integrated development environment used due to the simplistic synchronization with the MAUI GitHub repository [19]. Leading up to MLS implementation on physical UxS platforms, we employed virtual instances for application development. These virtual instances are configured to the following parameters: one core CPU and 8 GB of RAM. This configuration sets a minimum baseline for UxS processing requirements for our test, which is below the performance baseline of our use case UxS. We made no modifications to the original MLS source code, functionality, or dependencies required to facilitate our MLS applications.  
After compiling the MLS library, all library files and included directories are manually added to our MLS application folder hierarchy. Our application's CMakeList.txt file is updated to link the necessary MLS libraries to our program. Refer to our GitHub repository [19] for all three phases and a pre-compiled MLS library.
## 3 Methods
### Lucy-Richardson Iterative Algorithm  
According to the Fourier optics theory, the finite aperture is equivalent to a low-pass filter in the spatial light field transformation, which will cause a cutoff effect on the spatial frequency in cases above a certain value [20]. It is represented in the airspace as a high-resolution image convolved with the point spread function, resulting in image blur, as described in Equation (9):  
\[g(x,y)=\iint h(x,y,\epsilon,\eta)f(\epsilon,\eta)ded\eta+n(x,y). \tag{9}\]  
Unlike the super-resolution recovery of ordinary under-sampled images, whose goal is to recover high frequency components from the aliased spectrum, the main purpose of super-resolution in our system is to recover the high-frequency components that are filtered out by the finite aperture, which is called the inverse problem. To solve this problem, many methods have been proposed, such as the Winner inverse filter, projection onto convex sets, etc. Our system adopts the Lucy-Richardson iterative algorithm, and its main principle is the maximum likelihood criterion theory. As shown in Equation (10), it attempts to find a high-resolution image estimate \(f(x,y)\) to maximize the likelihood function \(P(g|f)\), where \(g\) is the low resolution image, resulting in a maximum likelihood estimate \(\hat{f}(x,y)\),  
\[\hat{f}=argmaxP(g|f), \tag{10}\]  
where \(P(g|f)\) is a statistical model that reflects the radiation distribution of the object plane. The iterative equation of this algorithm is as follows:  
\[\hat{f}_{k+1}=\hat{f}_{k}[\frac{g(x,y)}{h(x,y)*\hat{f}_{k}(x,y)}*\hat{h}(x,y)]. \tag{11}\]  
In Equation (11), \({}^{*}\) denotes the convolution operation, \(h(x,y)\) is the PSF obtained from the simulation result of an ideal point source in FEKO, and \(\sum h(x,y)=1,\hat{h}(x,y)\) represents the spatially inverse value of \(h(x,y)\). In addition, the initial iteration condition is generally set to \(\hat{f}_{0}=g(x,y)\). To solve the ill-posed problem, the iteration number should be limited [27; 28] and is set to 300 in this paper based on the restoration effect. Finally, the \(f_{k+1}\) that satisfies the iteration requirement is the estimated high-resolution image.  
In conclusion, the flow chart of our LPCT and L-R iterative method that was presented in Section 3 is shown in Figure 5.  
Figure 4: The diagram of the log-polar transformation: before (**a**) and after(**b**).
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure
### Data Acquisition and Preprocessing
All the resting state scans consisted of 140 functional volumes and were obtained while participants were resting in the scanner with eyes open. The parameters included: TR = 3000 ms, TE = 30 ms, flip angle = 80\({}^{\circ}\), slice thickness = 3.313 mm, and 48 slices. Resting state images and structural scans were elaborated in Nifty format [43]. The images were processed using the CONN functional connectivity toolbox v15.a (nitrc.org, Version 02142, Cambridge, MA, USA). This software exploits SPM routines to perform structural and functional analysis. The preprocessing procedures are summarized in Figure 1. The first step was to realign the MRI structural data to undo the effects of subject movements during the scanning session. The differences between each successive scan and a reference scan, which is the average of all scans in the time series, was minimized [44]. Afterwards, the data were transformed using linear warps into a standard anatomical space, the Montreal Neurological Institute (MNI) space [45]. A slice timing correction step was necessary to compensate for slice acquisition delays, which may add up to significant temporal shifts between the expected and the actually measured BOLD signal [46]. Finally, it was useful to co-register (to realign the 256 \(\times\) 256 MRI images with the 64 \(\times\) 64 fMRI images to work in a unique reference frame) functional and anatomical images [47]. We also included a scrubbing step for both motion artifacts and outliers in fMRI images. The Artifact Detection Tools (ART) software (nitrc.org) was used to identify scans affected by movement-related artifacts. Then, functional images were smoothed using a Gaussian kernel of 8 mm FWHM. The movements and the physiological noise were removed by linear regression. More precisely, the noise source considered was the signals from white matter and cerebrospinal fluid, the six head motion parameters, and the outlier scans detected during the scrubbing process [37]. Then, the preprocessed images were bandpass filtered to 0.001 Hz-0.01 Hz to reduce the noise. \begin{table}
\begin{tabular}{c c c c} \hline  & **HC Subjects** & **AD Patients** & \(p\)**-Value** \\ \hline Number of subjects & 36 (21 F/15 M) & 33 (18 F/15 M) & \(p>0.99\)1  \\ Age, years & 72.51 (\(\pm\)5.40) & 72.72 (\(\pm\)7.52) & \(p>0.05\)2  \\ MMSE & 26.6667 (\(\pm\)1.36) & 22.6667 (\(\pm\)2.45) & \(p=2.828\times 10^{-12}\) 3 \\ \hline \end{tabular}
\end{table} 
Table 1: Demographic and neuropsychological characteristics of the subjects.