esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
## 4 Results of Overview
### AI Methodologies and Tools Utilized for Anxiety and Depression Management
#### 4.3.2 Chatbots  
Internet-based cognitive behavioral therapy has been available since the 1990s, although it is known for having a poor rate of adherence [65]. CBT chatbots, designed to emulate regular chat interactions, can enhance emotional connection and deliver supplementary benefits. One such platform is \"Woebot\", which offers direct assistance to patients and members through chat, along with extensive tools and information to optimize their treatment, improve the general health of the population, and increase the efficiency of health care practitioners. Extensive study supports it, and it has been utilized by 1.5 million individuals. This platform is specifically designed to target those who may be on the periphery of conventional health care, aiding beyond the confines of regular clinical environments [9].  
Youper is a virtual therapist that uses artificial intelligence to provide cognitive behavioral therapy using a smartphone app. Youper enables users to engage in cognitive behavioral therapy sessions directly on their mobile devices, allowing them to specifically address and manage symptoms associated with different mental health disorders, such as anxiety or depression. The application utilizes artificial intelligence to tailor the therapy process, offering personalized exercises, strategies, and concepts that are specifically designed to meet the particular requirements and objectives of the user [66].  
Tess, however, is a chatbot focused on mental health, namely providing therapy and support to individuals who are exhibiting signs of despair and anxiety. \"Tess\" is an artificial intelligence program that mimics human behavior and offers techniques for dealing with emotions shown by the user [66]. Research data indicate that Tess has demonstrated a 28% reduction in depression and an 18% reduction in anxiety [67]. The most effective utilization of such a chatbot remains uncertain. Virtual therapists who aid those with anxiety and depression are currently accessible in the market. Extensive research has been conductedto assess their efficacy [64]. In addition to providing tailored therapeutic benefits, they also enhance accessibility by circumventing some stigmas associated with mental illness.
_Applied_  
_sciences_  
Brevinin-2 Drug Family--New Applied Peptide Candidates Against Methicillin-Resistant _Staphylococcus aureus_ and Their Effects on Lys-7 Expression of Innate Immune Pathway  
**[PERSON]**\({}^{\copyright}\), **[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**\({}^{\ast}\)  
School of Life Science and Technology, Xidian University, Xi'an 710126, China; [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (Q.Z.); [EMAIL_ADDRESS] (D.C.)  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +86-029-8189-1060  
Received: 6 November 2018; Accepted: 11 December 2018; Published: 14 December 2018  
Featured Application: In this study, antimicrobial peptides belonging to the Brevinin-2 family were tested against multidrug resistant bacteria and found to be effective. These substances are expected to be successfully utilized in inflammatory therapy and antibiotic replacement. Furthermore, screening methods for anti-inflammatory drugs carried out using the differential expression of immune regulatory pathways can be widely applied to drug testing.  
**Abstract:** The issue of _Staphylococcus aureus_ (MRSA) developing a resistance to drugs such as methicillin has long been the focus for new drug development. In recent years, antimicrobial peptides, such as small molecular peptides with broad-spectrum antibacterial activity and special antibacterial mechanism, have shown a strong medicinal potential. In particular, the Brevinin-2 family has been shown to have a significant inhibitory effect against gram-positive bacteria (G\({}^{+}\)). In this study, we researched the influence of MRSA on the behavior and survival rate of nematodes. We established an assay of _Caenorhabditis elegans_-MRSA antimicrobial peptides to screen for new potent anti-infective peptides against MRSA. From the Brevinin-2 family, 13 peptides that had shown strong effects on G\({}^{+}\) were screened for their ability to prolong the lifespan of infected worms. Real-time Polymerase Chain Reaction (PCR) tests were used to evaluate the effect on the innate immune pathway dauer formation defective (DAF)-2/DAF-16 of _C. elegans_. The assay successfully screened and filtered out four of the 13 peptides that significantly improved the survival rate of MRSA-infected worms. The result of real-time PCR indicated that the mRNA and protein expression levels of _lys-7_ were consistently upregulated by being treated with four of the Brevinin-2 family. The Brevinin-2 family peptides, including Brevinin-2, Brevinin-2-OA3, Brevinin-2 ISb, and Brevinin-2 TSa, also played an active role in the DAF-2/DAF-16 pathway in _C. elegans_. We successfully demonstrated the utility of anti-infective peptides that prolong the survival rate of the MRSA-infected host and discovered the relationship between antibacterial peptides and the innate immune system of _C. elegans_. We demonstrated the antimicrobial effects of Brevinin-2 family peptides, indicating their potential for use as new drug candidates against MRSA infections.  
**Keywords:** Brevinin-2 family; _C. elegans_; MRSA; DAF-2/DAF-16 pathway; _lys-7_
## Proposed Method
### AI Verification Environment Model  
In the AI learning environment, data should be verifiable through tracking of raw data and processing data. Figure 3 shows the configuration to verify the integrity of AI learning data. The verification node of the system connected with the AI server over wired and wireless networks conducts verification of the data through connection with the blockchain server. The verification node receives data from the data provision node and stores it in blockchain, and upon the AI server's request for data, the data stored in blockchain are provided to the AI server. This process forms a verification environment for AI learning. The data provider layer in the verification environment model transmits raw data and their hashcode to the data layer. The data layer for data management and collection  
Figure 2: Blockchain-based AI learning data open network.  
encrypts the data and hashcode received from the provider layer and stores them in blockchain. The AI machine learning layer receives the data and encrypted hashcode from the data layer, teaches the AI, and derives results through learning patterns for model management.  
The verification node encrypts and stores data received from the data provision node in blockchain with the encryption key and decryption key, or encryption and decryption keys created in the verification node can be received by the data provision node to enable the data provider to provide data after encryption using the encryption key received at the time of data provision. This allows data encryption using the same encryption key for a number of data users in an open network, which has the effectiveness of managing only a single decryption key.  
Table 2 shows components in the AI Verification Environment Model and consists of storage, providers, and validation.  
Figure 4 shows the overall structure of the AI environment for verification of the proposed model.  
Upon request for the verification of data and its hashcode being received from the AI server, the encrypted hashcode of the corresponding data is transmitted to the provision module from the block chain ledger in the data storage module. In the provision module, the encrypted hash value is decrypted and transmitted to the verification module. In the verification module, the decrypted hashcode and the hashcode of the data received from AI are compared to verify whether it is forged.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scope** & **Description** \\ \hline Storage & Save data hashcode with data storage. Store hashcodes together in one blockchain or save each one separately. \\ \hline Provider & When requesting data from AI server, it is transmitted to the data server and stored in blockchain. Provided after decrypting the encrypted data in data encryption mode. \\ \hline Verification & Integrity verification using the hashcode of data stored in blockchain. Compare the hashcode of data received from the AI server to the hashcode of data stored in blockchain. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construct of verification.  
Figure 3: Proposed block configuration.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 1 Introduction
Through the simulation results, it can be confirmed that when the PSO is performed in a limited region, high positioning accuracy and fast processing time are achieved. This paper is structured as follows. Section 2 analyzes related work for performing indoor localization. Section 3 describes the system model. Section 4 describes the proposed positioning scheme in detail. Sections 4 and 5 describe the parameter values used in the simulation and the simulation results. Finally, Section 6 presents the conclusion of this paper.
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 5 Performance Evaluations
### Remote Device Service Deployment  
In this section, we evaluate the dynamical deployment the _Device Service_ to EdgeX nodes in a Kubernetes cluster and the time required to remotely deploy a service in EoK. The service deployment time was measured from the point at which the deployment command was executed in the Kubernetes controller until the service ran in the EdgeX node. Note that the service image was not downloaded to the EdgeX node in advance in this evaluation, and the service status was checked every second to verify the time of deployment completion.  
Figure 5 shows the total deployment time and time of the first to the fourth replicas deployed to the EdgeX node, expressed by a cumulative distribution function, where deployment time includes downloading from the repository and running the pod replica in each node as per the master node's _kubectl_ command. As shown in Figure 5, we can observe that 75% of the first replica deployment time was approximately under 20 s to start, regardless of how many replicas had to be deployed. Moreover, the time after the first replica deployment until the remaining replicas were deployed gradually decreased, as shown in Figure 5b,c, because the first replica had to download the image for operation while the subsequent replicas did not. Thus, the total deployment time increased with the number of replicas, but not linearly. Although the total deployment time increases with the number of replicas, this result proves that service deployment through EoK can be completed within several tens of seconds via the deployment command at the master node located remotely. Therefore, we can conclude that EoK improves the manageability by allowing geographically remote deployment of services at edges.  
Figure 5: Total time for remote deployment and setup time for (**a**) 1 replica, (**b**) 2 replicas, and (**c**) 4 replicas.
# A Simplified Query-Only Attention for Encoder-Based Transformer Models
## 5 Discussion  
This study proposed a simplified query-only attention mechanism for encoder-based transformer models. The primary goal was to improve the model's efficiency and interpretability by reducing the complexity of the attention mechanism, without compromising performance. Our experimental results indicate that the query-only attention mechanism can achieve a performance that is comparable to the traditional \(Q\)\(K\)\(V\) attention mechanism, which uses separate query, key, and value vectors.
## 2 Materials and Methods
### Modeling and Control
#### 2.2.3 Manipulator Workspace  
The planar workspace is defined as the total 2D area that the manipulator can reach given a set of angle limits. The angle limits of all links are defined by the physical limitations of the design [22]. \(\theta_{1}\) is limited from \(-120^{\circ}\) to \(-10^{\circ}\) as derived from the physical interference limits of link 1. \(\theta_{2}\) is limited from \(0^{\circ}\) to \(140^{\circ}\) as derived from the physical interference limits of link 2. \(\theta_{2}\), absolute is limited from \(-90^{\circ}\) to \(-13^{\circ}\) as derived from the physical limits of the servo motor actuating link 2. By adding an additional DoF from the drone rotor rotation, the 3D workspace is also computed. The resulting planar and 3D workspaces of all possible end-effector positions within the workspace limits are shown in Figure 10.  
The payload position is also limited to the positive \(x\) values and the negative \(y\) values to simplify the analysis and prevent interference with the drone rotors. Any points with negative \(x\) values can be reached by the drone turning \(180^{\circ}\). Furthermore, the manipulator is limited to orientations in which the joint connecting links 1 and 2 points downward.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Value** \\ \hline \(l_{1},\ l_{2}\) (mm) & 150, 140 \\ \hline \(m_{1},m_{2},\ m_{p},\ m_{b}\) (g) & 53, 35, 200, 545 \\ \hline \(\alpha_{1},\ \alpha_{2}\) & 53.3\%, 58.6\% \\ \hline \(\rho_{1,2}\), \(\rho_{b}\) (g/cm\({}^{3}\)) & 1.25, 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the aerial manipulator.  
Figure 10: (**a**) Aerial manipulator planar workspace; (**b**) 3D workspace.
# Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems
## 3 Design and Analysis of Fault-Tolerant Consensus Controllers
\begin{split}& J_{i}=\int_{0}^{\infty}[\Big{(}u_{Gi}^{f}(t) \Big{)}^{T}H_{G}u_{Gi}^{f}(t)+e_{Gi}^{T}Q_{G}e_{Gi}]dt,\\ & W_{i}=\int_{0}^{\infty}[\Big{(}u_{Ai}^{f}(t)\Big{)}^{T}H_{A}u _{Ai}^{f}(t)+e_{Ai}^{T}Q_{A}e_{Ai}]dt,\end{split}  \tag{29}\]  
where when \(i=1,2\ldots,m\) is used, it represents UGV, and when \(i=m+1,\ m+2,\ m+3,\ \ldots,\ n\) is used, it represents UAV. \(H_{G},Q_{G},H_{A},Q_{A}\) are both positive definite symmetric matrices. Due to the different dimensions of the state vectors of UAVs and UGVs, weight coefficient matrices are designed as follows:  
\[ \begin{split}& Q_{G}=60*I_{4},H_{G}=5*I_{2}\\ & Q_{A}=60*I_{12},H_{A}=5*I_{3}\end{split} \]  
According to Assumption 1, it can be inferred that if all \(J_{i}\) and \(W_{i}\) reach minimum values, the entire leader-following multi-agent system's state can achieve consensus. Therefore, from Theorem 1, it can be concluded that the controller \(u_{i}^{*}\) can achieve consensus on the multi-agent system's state. Obtain the optimal fault-tolerant consensus controllers for each UAV and UGV, respectively:  
For UGV:  
\[\left\{ \begin{array}{l}u_{Gi}^{*}(t)=-\frac{|\mathcal{N}_{i}|+a_{0i}}{2}(I _{2}-\mathcal{P}_{Gi})^{-1}H_{G}^{-1}B_{G}^{T}\eta_{i}^{*}(t)\\ \eta_{i}^{*}(t)=-Q_{G}e_{G}-A_{G}^{T}\eta_{i}(t)\end{array} ,i=1,2,\ldots,m. \tag{30}\]UGV control law:  
\[u_{Gi}^{*}(t)=-\frac{|\mathcal{N}_{i}|+a_{0i}}{2}(I_{2}-\mathcal{P}_{Gi})^{-1}H_{ G}^{-1}B_{G}^{T}(x_{Gi}(t)-x_{A0}(t)) \tag{31}\]  
For UAV:  
\[\left\{ \begin{array}{ll}u_{Ai}^{*}(t)=-\frac{|\mathcal{N}_{i}|+a_{0i}}{2}(I_{3 }-\mathcal{P}_{Ai})^{-1}H_{A}^{-1}B_{A}^{T}\eta_{i}^{*}(t)&i=m+1,\ldots n\\ \eta_{i}^{*}(t)=-Q_{A}e_{A}-A_{A}^{T}\eta_{i}(t)\end{array} \right. \tag{32}\]  
UAV control law:  
\[u_{Ai}^{*}(t)=-\frac{|\mathcal{N}_{i}|+a_{0i}}{2}(I_{3}-\mathcal{P}_{Ai})^{-1} H_{A}^{-1}B_{A}^{T}(x_{Ai}(t)-x_{A0}(t)) \tag{33}\]  
For any two agents \(i\) and \(j\) in the leader-following multi-agent system, there are two cases for an agent \(j\), (1) \(j\in\mathcal{N}_{i}\), in which case, according to the above analysis, \(e_{i}^{*}(t)\) asymptotically converges to zero, thus indicating that agents \(i\)'s states and agents \(j\)'s states tend to be consistent; (2) \(j\
otin\mathcal{N}_{i}\), according to Assumption 1, it is known that there is a path between any two agents, and by the transitivity of states, the states of agents \(i\) and \(j\) tend to be consistent. It can be inferred that the consistency error of the system (11) under actuator failure can asymptotically converge to zero at this time.
## 3 Pathogenesis of AD
### The Amyloid Hypothesis
#### 3.1.1 Nonamyloidogenic Pathway  
APP is a glycoprotein that is produced in the brain and central nervous system as an integral membrane glycoprotein [63]. APP consists of 770 amino acids, where A\(\beta\) includes 28 residues just outside the membrane in addition to 14 residues from the APP transmembrane domain [64]. It can be processed sequentially by two proteolytic pathways, namely the non-amyloidogenic and the amyloidogenic route [63]. The non-amyloidogenic route leads \(\alpha\) secretase-mediated cleavage of full-length APP. The cleavage removes the sAPP ectodomain from the cell membrane and leaves an 83-amino-acid-long C-terminal APP fragment (\(\alpha\)-CTF) inside the plasma membrane. The \(\alpha\)-CTF will then be cleaved further by \(\gamma\)-secretase, releasing a tiny p3 fragment into the extracellular area while the rest of APP intracellular domain remains in the cytoplasm [65]. The major \(\alpha\)-secretases are ADAM10 and ADAM17, which are the metalloproteinases in neurons. A small p3 hydrophobic fragment is generated by \(\alpha\)-secretase, which plays a role in normal synaptic signaling and is further sliced at residue 711 by \(\gamma\)-secretase; however, its exact functions still need to be clarified. Endocytosis of cell-surface APP leads to endosome growth of A\(\beta\) that contributes to extracellular discharge and accumulation of A\(\beta\)[66].
## 3 Results
### Surface Electromyography (sEMG) Results of Lower-Limb Muscles
#### 3.2.3. Coactivation Ratio Results During Pre-Landing and Landing Phases  
The Shapiro-Wilk test indicated that the coactivation ratio data during landing followed a normal distribution.  
A paired t-test showed that the coactivation ratio (CAR) of the biceps femoris to rectus femoris (BF:RF) significantly decreased post-fatigue in the LR1 phase (FDR-adjusted \(p\) = 0.0033, t = 4.238) and LR2 phase (FDR-adjusted \(p\) = 0.0057, t = 4.981). Similarly, the CAR of the tibialis anterior to gastrocnemius (TA:GAS) significantly decreased post-fatigue in the LR2 phase (FDR-adjusted \(p\) = 0.0057, t = 4.842). No significant changes were observed in other phases (\(p\) > 0.05). The detailed results are shown in Table 7 and Figure 6.
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 4 Results
### Data Used in the Evaluation
The thermal stimulation has been applied for 10 s, homogeneously distributed over the entire external surface with an energy of \(F=1.5\,\mathrm{K/s}\). This value was previously estimated experimentally through laboratory tests. The spatial and temporal evolution of temperature has been calculated from (24) by programming an algorithm for numerical resolution by finite differences and considering representative parameters of NDT thermographic inspections and the inspected material (Figure 4c). The theoretical thermal evolution was corrupted with noise to obtain data closer to real conditions. It was demonstrated in previous studies that the noise affecting IR sensors can be modeled by Gaussian white noise [41; 42], so this was the type of noise used to approximate the theoretical data to actual experimental situations (Figure 4d). The next stage in the analysis of the proposed methods has been carried out by using data collected from experimental tests performed in the laboratory. OSHT inspections have been conducted using two 1000 W halogen lamps at 80% of their capacity. The lamps heated the material under inspection for 10 s and both the heating and the cooling phases were recorded, making a total inspection time of 20 s. Infrared images have been acquired with a FLIR SC5500 model and a front face test setup (Figure 5a). A slight deviation was applied in the perpendicular orientation of the camera with respect to the inspected sample to avoid reflections of the sensor itself on the surface of the material. The sample used in the experimental analysis was made in a sandwich structure of composite material with 1 mm thick skins and 5 mm thick foam core. The defects intentionally introduced in this sample were small sheets of PTFE of sizes \(10\times 10\) mm\({}^{2}\), \(5\times 5\) mm\({}^{2}\), \(5\times 3\) mm\({}^{2}\) and \(3\times 3\) mm\({}^{2}\). The defects were located at two different depths, under 1 mm skin and under 1 mm skin and additional 0.1 mm adhesive layer (Figure 5b). For repeatability analysis, two defects of each size were set at each depth. The emissivity of this sample was estimated experimentally as 0.91.
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 5 Results and Discussion
### Simulation Results
#### 5.1.1 Message-Delayed Assaults  
Figure 6 depicts E2 ED in the presence of MITM, which caused message delays of 2 ms. It is clear that adding harmful nodes that delay valid communications within the network causes the E2 ED to rise. Nevertheless, an MITM assailant with message-delay capabilities prevent the legal nodes from receiving the messages on time. Ideally, the legal nodes should receive such legal communications with the least amount of delay. Additionally, Figure 6 shows that E2 ED grows as assailants are dispersed around the network.  
Next, Figure 7 shows the number of delayed messages produced by the harmful nodes. It is evident that when harmful nodes proliferate in the network, the number of delayed messages rises. The delayed messages increase to 55.02% when seven assailants are introduced into the network.  
Figure 6: End-to-End delay caused by message-delayed assaults.  
Figure 8 shows the Packet Loss Ratio (PLR) in the presence of harmful nodes. It demonstrates that PLR rises as the number of harmful nodes in the network rises. For example, when the network was flooded with seven harmful nodes, about 55.11% more packets were lost in the presence of attackers. Based on our experiment, this is because the destination that discarded the packets due to the excessive delay was in the presence of harmful nodes.