esa-sceva/satcom-chunk-collection huggingface dataset:
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 5 Exosome-Based Therapeutic Applications in Soriasis
### Exosomes as Therapeutic Agents for Soriasis Treatment
Additionally, UCB-MNC-exosomes reduced the proliferation of CD4\({}^{+}\)/CD8\({}^{+}\) T cells and the release of proinflammatory cytokines by PBMCs. UCB-MNC-exosomes also increased _FOXP3_ expression in PBMCs, suggesting that they can restore the immune balance of Th17/Treg. Moreover, UCB-MNC-exosomes significantly reversed acanthosis and decreased the expression of IL-17A and CCL20 in IMQ-induced psoriaticatic mice. These findings indicate that UCB-MNC-exosomes may be harnessed for the treatment of Th17-driven inflammatory skin diseases [91]. Recently, another study reported that patients' serum-derived exosomes containing miR-6785-5p could alleviate psoriasislike skin lesions both in vitro and in vivo. Mechanically, keratinocytes actively took up the serum-exosomes containing miR-6785-5p, which then inhibited the abnormal proliferation and inflammatory state of keratinocytes by interfering with the MNK2/p-elf4E axis, thus alleviating psoriasis-like inflammatory skin lesions [92]. Finally, drugs or carriers need to penetrate the stratum corneum before exerting anti- psoriasis effects. However, none of above studies explored how exosomes penetrate through the stratum corneum to exert anti- psoriasis effects. According to previous knowledge, there are two main ways for exosomes to penetrate the stratum corneum, which are the intercellular interstitial pathway and cutaneous appendages pathway. Due to the small size (30-150 nm) and the lipid bilayer structure (similar to the structure of cell membranes), exosomes can directly penetrate the intercellular interstitial between keratinocytes and reach the epidermis and dermis. Additionally, exosomes may enter the dermis directly via cutaneous appendages, such as hair follicles, sebaceous glands and sweat glands. However, cutaneous appendages occupy a relatively low proportion of the skin surface area; thus, the numbers of exosomes absorbed through this pathway are limited.
# Effect of Variable Conditions of Exposure on the Physical and Mechanical Properties of Blockboards
## 3 Results and Discussion
### Physical Properties
After two months of testing, it was noticed that the MC values of the blockboards with HDF face sheets exceeded the values of those with veneer face sheets for all cases of exposure. This can be attributed to the affinity of fibres for taking up water and degrading after exceeding swelling. Other researchers [27] also observed that the higher density of the surface layers of particleboards appeared to be unsuitable for exposure to high air humidity. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CC-V**} & \multicolumn{3}{c}{**CC-HDF**} \\  & **t, (mm)** & **w, (g)** & **MC, (\%)** & **t, (mm)** & **w, (g)** & **MC, (\%)** \\ \hline \multirow{2}{*}{Before testing} & 18.16 & 948 & 8.05 & 17.72 & 1153 & 6.54 \\  & (0.06) & (3.5) & (0.5) & (0.07) & (6.5) & (0.8) \\  & 18.47 & 986 & 9.23 & 18.25 & 1211 & 8.83 \\  & (0.09) & (6.4) & (0.4) & (0.1) & (8.4) & (0.8) \\  & 18.49 & 994 & 10.65 & 18.37 & 1226 & 10.5 \\  & (0.08) & (8.2) & (0.3) & (0.15) & (10.9) & (1.0) \\  & 18.52 & 998 & 11.01 & 18.40 & 1232 & 11.06 \\  & (0.07) & (8.9) & (0.9) & (0.14) & (10.9) & (1.1) \\  & 18.55 & 984 & 11.37 & 18.48 & 1211 & 11.88 \\  & (0.09) & (9.2) & (0.8) & (0.12) & (8.9) & (0.9) \\ \hline \hline \end{tabular} Values in the parenthesis are standard deviations.  
\end{table} 
Table 5: Variation of the blockboards’ physical parameters during their exposure in the climatic chamber (average values recorded for each month). Figure 4: Variation of MC for all panels in all three situations of exposure.
## 3 Experiment
### Ablation Experiment
#### 3.4.1 Effect of SFA Encoder  
In order to verify the effectiveness of the SFA Encoder, the paper used the SFA Encoder, the encoder (based on recursive gated convolution) of HorNet, and the Swin Transformer [37] of Mask2 Former, respectively, as the backbone of GLFFNet, and other modules of experiments were kept consistent (specifically, to simply compare the advantages of the SFA Encoder, the SPAC Encoder proposed in this paper is not added to any of the three backbones). The experimental results are shown in Table 1. Compared with the other two models, the SFA Encoder shows a significant improvement in terms of these metrics.
## 3 Remote Sensing Image Scene Understanding
### Deep-Learning-Driven Scene Classification
#### 3.2.1 Feature from Pre-Trained CNN for Remote Sensing Image Scene Classification
When the training ratio is 80%, the scene classification performance tested on the UC Merced dataset of DCA fusion can still reach 92.32 \(\pm\) 1.02% with slight improvement (about 4-5% over AlexNet and CaffeNet).
## 3 Numerical Simulation Model
### Model Size and Boundary Conditions  
According to the selected research area, the size of the numerical simulation model is 45 m \(\times\) 80 m \(\times\) 60 m, in which the excavation area of foundation pit is 45 m \(\times\) 20 m \(\times\) 34 m. The diameter of the cast-in-place pile is 1.4 m, the length is 40 m and the embedded depth in soil is 6 m. The size of the crown beam and waist beam in pile top is 1.0 m \(\times\) 1.4 m \(\times\) 45 m, and the size of the ribbed beam of the pile is 0.4 m \(\times\) 40 m \(\times\) 0.4 m. The soil is divided into 6 layers along its depth in the model with thicknesses of 2.0 m, 2.2 m, 7.6 m, 7.0 m, 1.5 m and 39.7 m. The piles are formed in a single pour as a whole on-site, so there is no connection between the distribution of the piles and the strata. The waist beam is of uniform size, and the size of the waist beam does not change with the soil conditions and burial depth. A total of 12 waist beams and anchor cables in each layer are arranged on the cast-in-place pile. The arrangement and prestress of anchor cables are shown in Table 1. The numerical simulation model is shown in Figure 5. The boundary conditions of the model are as follows: fixed constraint at the bottom surface, normal displacement constraint at the side and free boundary at the top surface. Since the top of the foundation pit is adjacent to the road, the road load effect is considered. A vertical uniform load of 30 kPa is applied within the range of 5-25 m on the top of the foundation pit to simulate the road load.
## 6 Conclusions  
Our study starts with an approach analysis for the impact of DDoS attacks on LTE-M networks named the MDP model. A first congestion is expected on an LTE-M network due to the huge number of requests attempting to concurrently link to the network as a result of a DDoS attack, which eventually causes an overload issue. In this paper, a survey was provided for the main literature approaches to address this issue. In our work, we begin to research LTE-M network infrastructure and IoT devices' technological features in order to differentiate among Authentic, Suspicious, or Malicious requests. By modeling the system, we end up with promising results regarding the effect of DDoS attacks on M2M and the bottlenecks that occur due to these attacks on LTE-M networks. We realize that LTE-M networks can be affected by the increase in the number of Read, Modify, or Delete Requests. Under different scenarios, we analyze the data traffic and predict the system state to determine the behavior of the system and its probability of being under attack.  
Our results show that in normal cycle, most of the time the system will stay in the initial phase with a probability of 52%. Meanwhile, the system will reside 63% in the Authentic phase during a Suspicious scenario. Moreover, during the attack scenario, a probability of 53% is calculated in the Suspicious phase.  
In our future work, we INend to study, analyze, and predict potential DDoS attacks or other attacks on H2H traffic and LTE-A networks. Additionally, we will develop our work to find a solution to mitigate the DDoS attack influence by filtering Malicious traffic, which helps in maintaining a good QoS for all traffic.  
Conceptualization, A.H.E.F., M.H.M. and A.M.; methodology, A.H.E.F., M.H.M. and A.M.; software, A.H.E.F. and M.H.M.; validation, A.H.E.F., M.H.M. and A.M.; formal analysis, A.H.E.F., M.H.M. and A.M.; investigation, A.H.E.F, M.H.M., A.N. and A.M.; resources, A.H.E.F. and M.H.M.; data curation, A.H.E.F., M.H.M., A.N. and A.M.; writing--original draft preparation, A.H.E.F. and M.H.M.; writing--review and editing, A.H.E.F., M.H.M., A.N. and A.M.; visualization, A.H.E.F, M.H.M., A.N. and A.M.; supervision, A.M.; project administration, A.M.; funding acquisition, A.H.E.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article in [39].  
The authors declare no conflicts of interest.  
List of Abbreviations  
3 GPP 3 rd Generation Partnership Project  
4G Fourth Generation  
CAIDA Center for Applied Internet Data Analysis  
CC Command Control  
CIC Canadian Institute for Cybersecurity  
DDoS Distributed Denial of Service  
H2H Human-to-Human  
ICS Industrial Control Systems  
IoT Internet of Things  
LPWAN Low Power Wide Area Networks  
LR Logistic Regression  
LTE-A Long-Term Evolution-Advanced  
LTE-M Long-Term Evolution for Machines  
M2M Machine-to-Machine  
MDP Markov Detection and Prediction
# Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning +
## 3 Training Results and Evaluation
### System Evaluation  
Emulating human behavior in a game environment is a complex task that cannot be accurately measured without involving a diverse set of human players. In some cases, however, simulation results can provide valuable guidelines for predicting human behavior, especially when focusing on specific personality traits. This approach guided the evaluation of our models, with the HiDAC crowd simulation serving as the baseline for comparison [26].  
The results of the training process support the initial hypothesis that the agents can emulate human behaviors to some extent. This is evidenced by two key factors: the reward values obtained during training and the visual inspection of the agents' behaviors within the game environment. Each agent exhibited a range of behaviors, shaped by the reward functions, that mimic simplified versions of human actions. While individual agents displayed different gameplay styles and actions, these variations reflect the interplay of multiple personality traits, which manifest at varying levels depending on the mental state of the player at any given moment.  
Although personality is inherently complex, we were able to distill each trait into simplified behavioral patterns, allowing us to create agents that simulate core aspects of a specific trait. This simplification, however, may be considered a limitation of the work, as it reduces the full depth of human personality into more basic representations.  
Figure 8: Extrovert (E) and Introvert (Non-Extrovers—NE), 3 Extroverts and 1 Introvert (EEEI), and 3 Introverts and 1 Extrover (IIIE) agents training results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Personality (+/\(-\))** & **Mean Reward 1** & **Mean Escape Time 2** &
\begin{tabular}{c} **Success Rate** \\ **(Difference 3)** \\ \end{tabular} \\ \hline
3 Extroverts and 1 & & 1150 & 380 & 39\% (+10\%) \\ Introvert & & & \\
3 Introverts + 1 & & 403 & 48\% (**+18**\%) \\ Extrovert & & & \\
3 Neurotic + 1 Non & 1654 & 362 & 36\% (+16\%) \\ Neurotic & & & \\ \hline \hline \end{tabular}  
* The final reward number based on agent actions. 2 The mean play time in seconds. 3 The difference (number in parenthesis) with the corresponding uniform teams.  
\end{table}
Table 5: Non-uniform personality teams and effectiveness.  
For an effective evaluation of the agents' behavior, it is essential to establish clear behavioral ground truths.  
As an initial benchmark, experimental results from Durupinar provide a useful point of comparison. These results, summarized in Table 6, serve as a reference for assessing the agents' performance and offer insights into how well the modeled behaviors align with expected personality traits. This shows that the agents indeed have similarities with the human behaviors that they emulate.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 4 Analysis of the Influence of Vital System Parameters on System Stability
### Recommendations for Parameter Design  
Based on the analysis results in Sections 4.1 and 4.2, some recommendations for the parameter design for the inverter under the unbalanced grid condition are given as follows.  
1. As analyzed in Section 4.1, the stability margin of the inverter is smaller under greater grid impedance unbalance. Therefore, for a inverter that may work under the unbalanced grid condition, the parameter design should be more conservative.
2. For a inverter under the unbalanced grid condition, the current loop bandwidth should be larger than that under the balanced grid condition. However, a larger current bandwidth may introduce oscillation problems at medium and high frequencies. Therefore, the setting of the current loop bandwidth should be considered to satisfy the stability margin requirement within a wide frequency band.
3. According to the analysis results in Section 4.2, it can be concluded that dynamic performance cannot only be pursued when designing the DSOGI-PLL of the grid-connected inverter system. It is necessary to appropriately reduce the damping coefficient and control bandwidth of the PLL to improve the stability of the whole grid-connected system.
# Design of a Solenoid Actuator for a Cylinder Valve in a Fuel Cell Vehicle
## 2 Structure of the Double Plunger Type Solenoid Actuator  
The single plunger type solenoid actuators are commonly used to open and close the flow path at low pressure. According to EU 406/2010 Annex IV, which is the first standard for hydrogen fuel cell cars presented in Europe, the maximum permissible pressure of a cylinder valve should be 875 bar, which is 1.25 times the rated pressure. In the case of ultra-high pressure of 875 bar, however, it can hardly control the flow path because the force needed to pull up the single plunger is higher than the electromagnetic force exerted by the actuator. So the double plunger type solenoid actuator which controls the flow path in two consecutive stages, even with a little bigger force than usual, is suggested shown in Figure 1. The solenoid actuator structure consists of a coil for the formation of electromagnetic fields; a disk and a case that constitute a magnetic circuit; a core that is magnetized by the electromagnetic field generated by the coil to draw the plunger; plungers A and B, which open and close the flow path with mechanical movement; guides for the movements of the plungers; springs that restore the plungers; and O-rings to maintain of air-tightness under ultra-high pressure conditions.  
When a voltage is applied to a solenoid actuator, the core is magnetized and draws plunger B. The hydrogen in the cylinder escapes through a fine hole in plunger A. Due to the escaping hydrogen, a state of pressure equilibrium is achieved, in which the pressure in the solenoid actuator and the  
Figure 1: Construction of solenoid actuator.  
pressure in the flow path connecting to the fuel cell stack become the same. Plunger A is attached to plunger B and moves so that the flow path connecting to the fuel cell stack is completely opened. The attractive force of the solenoid actuator required to open the flow path should be larger than the force imposed on plunger B in the initial closed state, in which voltage is not applied to the solenoid actuator, as shown in Figure 2. The horizontal component (X-direction) of the force applied to plunger B is offset because the shape of the plunger is symmetric across the central axis, and the vertical component (Z-direction) maintains as much of the area of the actuator in contact with plungers A and B as possible.
# Influence of DMSO Non-Toxic Solvent on the Mechanical and Chemical Properties of a PVDF Thin Film
## 2 Materials and Methods
### Chemical Characterization  
According to [PERSON] et al. [34], the phases of PVDF can be completely characterized by performing different chemical analysis together. FTIR, XRD and differential scanning calorimetry (DSC) were performed to distinguish and identify the prevalent electroactive phase, its content and the degree of crystallinity.  
FTIR-attenuated total reflectance (ATR) measurements were conducted using a PerkinElmer FT-IR spectrometer frontier apparatus (Waltham, MA, USA). The analysis was made by considering 64 scans with a resolution of 4 cm\({}^{-1}\) from 4000 to 600 cm\({}^{-1}\). From FTIR-ATR, it is possible to distinguish the non-EA phase (e.g., a phase) from the EA phases (e.g., \(\beta\) and \(\gamma\) phases) present on the material, and from Equation (1) [40], it is possible to calculate the total content of the EA phases (F(EA)).  
\[F(EA)=\frac{A_{840,EA}}{\left(\frac{K_{840}}{K_{766}}\right)\times A_{766,\alpha }+A_{840,EA}} \tag{1}\]  
where A\({}_{766}\) and A\({}_{840}\) are the absorbances at 766 cm\({}^{-1}\) and 840 cm\({}^{-1}\), respectively, of the \(\alpha\) phase and EA phases, and K\({}_{766}\) and K\({}_{840}\) are the corresponding absorption coefficients (6.1 \(\times\) 10\({}^{4}\) and 7.7 \(\times\) 10\({}^{4}\) cm\({}^{2}\)/mol, respectively).  
Figure 2: Mechanical apparatus used to mechanically tension the samples. At left, we present a general overview of the machine used, while at right, we present a detailed view of the mechanical actuators.  
Besides FTIR-ATR, XRD (Bruker D8 Advance DaVinci, Billerica, MA, USA) was also used to identify the phases. This technique, in contrast to FTIR, allows clear distinction of the EA phases, such as the \(\beta\) phases from the \(\gamma\) phases. The method uses a conventional Bragg-Brentano diffractometer, and we adopted the following parameters: wavelength of the incident X-ray beam (\(\lambda\)) of 1.5405 \({}^{\circ}\)A, angle range of 5\({}^{\circ}\)\(\leq\) 2\(\theta\)\(\leq\) 45\({}^{\circ}\), step size of 0.02\({}^{\circ}\), and 1 s per step.  
The combination of both methods (FTIR-ATR and XRD) enables a robust identification of the electroactive phases presented in the material.  
DSC measurement was conducted using a Hitachi DSC7020 calorimeter (Hitachi, Ibaraki, Japan) at 10 \({}^{\circ}\)C/min from 50 \({}^{\circ}\)C to 200 \({}^{\circ}\)C. This complementary technique allows us to obtain the degree of cristalinity, \(\chi\), of the sample through Equation (2) [32], as well as the melting (T\({}_{m}\)) and the onset (T\({}_{onset}\)) temperatures from the resultant curve.  
\[\chi=\frac{\Delta H}{x\Delta H_{a}+y\Delta H_{EA}} \tag{2}\]  
where \(\Delta H\) is the melting enthalpy of the material, \(\Delta H_{a}\) and \(\Delta H_{EA}\) are the melting enthalpies of the \(\alpha\) (93.07 \(\mathrm{J/g}\)) and EA phases (103.4 \(\mathrm{J/g}\)), respectively, and \(x\) and \(y\) are the \(\alpha\) and EA phase proportions, respectively, obtained from FTIR-ATR.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 4 Proposed Ensemble of Deep Learning Models for IoT Intrusion Detection
### Step 4: Classification Using Deep Learning Models  
In the subsequent stages, we employ a diverse ensemble of deep learning architectures to perform the classification task, leveraging the unique strengths of each model:  
Convolutional neural networks (CNN): CNNs are adept at capturing spatial and structural patterns within data, making them particularly suitable for image-based intrusion detection or situations where spatial relationships are crucial. CNNs are particularly effective in capturing spatial patterns within data, making them well-suited for image analysis and feature extraction. In intrusion detection, they can be applied to analyze network traffic data and packet payloads. CNNs excel in recognizing spatial relationships and patterns in such data, which can indicate malicious activity. They can detect irregularities or patterns that might be hard to uncover using traditional methods. By including CNNs in the ensemble, the model becomes proficient at recognizing spatial anomalies in network traffic, contributing to the overall robustness of intrusion detection.  
Long short-term memory (LSTM): LSTMs excel in handling sequential data and are well-suited for capturing temporal dependencies in IoT security datasets. They are effective in recognizing patterns that evolve. LSTMs can learn long-term dependencies in data, ensuring that patterns spanning multiple time steps are not overlooked. Their inclusion in the ensemble deep learning framework provides the ability to capture temporal anomalies and identify sophisticated intrusion patterns that might evade detection through more traditional means.  
Gated recurrent unit (GRU): GRUs are a variation of LSTM and balance computational efficiency and performance in sequence modeling tasks, making them a valuable addition to our ensemble. GRUs, like LSTMs, excel in capturing temporal dependencies. By introducing GRUs into the ensemble, the model benefits from a balance of computational efficiency and the capability to detect sequential anomalies, contributing to the model's ability to detect intrusions resource-efficiently.  
The strength of using this ensemble approach lies in the combined power of these models. CNN, LSTM, and GRUs each focus on different aspects of the data: spatial patterns, temporal dependencies, and efficient sequential analysis, respectively. By integrating them and using a voting mechanism, the ensemble can collectively identify anomalies and intrusions more comprehensively. The diverse perspectives provided by these models help reduce false positives and false negatives, enhancing the overall performance of intrusion detection. The voting policy allows the models to reach a consensus, minimizing the likelihood of misclassification and increasing the system's overall accuracy. This way, the ensemble approach harnesses the strengths of these individual models to create a more robust and effective intrusion detection system.
## 4 Experimentation and Evaluation
### Algorithms under Study  
#### 4.2.1 Decision Tree  
The decision tree model was created by modifying various hyperparameters, which affected the different results and training times. In this research, only the max_depth parameter was altered, influencing the number of depth nodes the model could have. Therefore, to have an adequate number of models of this type for comparison, decision trees with 10, 15, 20, and 25 nodes were chosen for each dataset type. The following figures show the results obtained by the no-balanced (B) and no-balanced (SB) datasets. As (SB) and a balanced dataset as (B).