esa-sceva/satcom-chunk-collection huggingface dataset:
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 7 Challenges and Future Directions  
Despite many benefits of exosome-based therapeutic applications in psoriasis, very few of them are being developed into nanomedicine. Several challenges limit the clinical translation of exosomes in the treatment of psoriasis. Firstly, the existing isolation and separation methods of exosomes exhibit low efficiency, low yields and high costs of scale production, which restricts the large-scale production and application of exosomes. Secondly, the drug loading efficiency of exosomes is less than 20% in most studies of psoriasis treatment. Novel drug loading methods need to be developed for increasing the drug loading efficiency of exosomes to reduce the manufacturing costs. Thirdly, the stability of exosomes in vitro and in vivo needs to be improved. [PERSON] et al. found that the particle size of exosomes significantly increased after storing at \(-80\)\({}^{\circ}\)C for 6 months, suggesting the occurrence of fusion between exosomes [100]. Moreover, exosomes may be degraded by enzymes in the inflammatory microenvironment of psoriatic lesions.  
The future research directions of exosomes for the treatment of psoriasis should focus on the following aspects. Firstly, further studies using engineered exosomes to target certain subsets of immune cells, especially the IL-23/Th17 axis, as well as their signaling pathways to treat psoriasis, are warranted. Secondly, future studies may focus on exosomes targeting memory T cells to prevent the frequent relapse of psoriasis. Finally, efforts may be made to engineer exosomes that can serve as delivery systems for efficient delivery of specific proteins, nucleic acids and/or small molecular drugs to treat psoriasis.
## Materials and Methods
### Antimicrobial Activity against _S. mutans_  
Spread plate and microdilution methods were used for determining the antimicrobial activity of the EOs MD-1 and MF-1. For determining the antimicrobial activity using a 96-well plate (microdilution method), 90 \(\mu\)L of BHIS broth was added to each well. Essential oils were dissolved and diluted in 5% DMSO. MD-1, MF-1, clove oil and chlorheximide digluconate (CHX) were added to final concentrations of 0, 2, 4, 8, 10, 12, 14 and 16 \(\mu\)g/mL. CHX, a widely used antimicrobial agent in mouth wash and other oral hygiene products, was used as a positive control. Aliquots of 10 \(\mu\)L from overnight grown _S. mutans_ culture were added to each well. Finally, the 96 well plate was incubated at 37 \({}^{\circ}\)C for 24 h. For determining the inhibition of colony forming units in the presence of essential oils (MD-1, MF-1 and clove oil) and CHX, aliquots of 500 \(\mu\)L from actively growing culture of _S. mutans_ were added to tubes containing 5 mL BHIS broth. Essential oils and CHX were added to final concentrations of 0, 5, 10, 15 and 20 \(\mu\)g/mL to these tubes. The cultures were incubated on a shaker maintained at a speed of 150 rpm and a temperature of 37 \({}^{\circ}\)C for 12 h. After incubation, the CFU counts were determined by spreading 0.1 mL from appropriate dilutions of untreated and treated cells on BHI agar plates. The plates were finally incubated for 3 d at 37 \({}^{\circ}\)C and CFUs were counted and recorded.
# Shear Strength and Durability of Expansive Soil Treated with Recycled Gypsum and Rice Husk Ash
## 2 Materials and Methods
### Criteria for Assessment  
The shear strength of the treated specimen was examined based on resulting trends in peak shear strengths and angles of internal friction versus the variation in additive content. The stress-strain behavior of each specimen was compared with a modified nonlinear elastic constitutive model that illustrates the correlation between stress-strain parameters using an approximate hyperbolic function.  
The durability of the specimen was assessed using the collapse index and strength loss. ASTM D5333-03 classified collapse indices that qualitatively described the degree  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Collapse Index** & **Degree of Collapsibility** \\ \hline
0\% & None \\
0.1–2\% & Slight \\
2.1–6.0\% & Moderate \\
6.1–10\% & Moderately severe \\ Greater than 10\% & Severe \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification of Collapse Index, I\({}_{c}\)[49].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Collapse Index** & **Degree of Collapsibility** \\ \hline
0\% & No Collapsibility \\
1–2\% & Low Collapsibility \\
5–10\% & Moderate Collapsibility \\
10–20\% & High Collapsibility \\ Greater than 20\% & Very High Collapsibility \\ \hline \hline \end{tabular}
\end{table}
Table 5: Index of defining the intensity of collapsibility [49].  
of specimen collapse after multiple wet-dry cycles. [PERSON] [45] also considered strength losses reaching 50% reasonable when subjected to durability tests. A response surface model was generated to predict the unconfined compression strength of the treated soil.  
Treatment of specimens with expansion indices below 20 after their respective durability tests was deemed effective per the National Structural Code of the Philippines (NSCP) 2015 Section 303.5 [37]. Four criteria are used to consider a sample as expansive. The first three are optional and can be satisfied if the last requirement is achieved.  
* Plasticity index of 15 or less and liquid limit higher than 50.
* More than 10% of soil particles pass the No. 200 sieve.
* More than 10% less than 5 \(\upmu\)m in size.
* Expansion index greater than 20.
# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 4 Evacuation Simulation Model
### Emergency Event  
The simulation assumes that the building is affected by a spatially-extended and temporally-varying event such as a fire emergency, and adopts the emergency simulation developed by [PERSON] and [PERSON] [2]. This emergency simulation assumes a virtual sensor network covering the whole area of the indoor environment (Figure 3). Each sensor captures the environmental factors within a circular detecting area (radius r = 6 m) if no walls shield the reception of this sensor. Otherwise, the detection area of the sensor is the intersection between the circular area and the boundary of the region where the virtual sensor is located. The sensor network is generated by adding an edge between two sensor nodes if their detection areas are intersecting. Two sensor nodes are neighboring each other if they are connected with an edge. Each sensor node has two possible states: _normal_ and _active_. A sensor stays in the _normal_ state when its detecting area is safe, and will shift to _active_ when its detecting area is blocked by the emergency event. Before the event, all sensors should be in the _normal_ state. The event is marked by setting one sensor to be _active_. As the event expands, the next _active_ sensors should be one or more neighbored sensors of the already _active_ one. Every two steps, this simulation randomly picks up one of the already _active_ sensors, and set all its neighboring sensors as _active_. Thus, an extended emergency process has been simulated through simulating the state transfer of the virtual sensors. The main parameter that differentiates a simulated emergency event is the location where the first virtual sensor that detects the emergency events.  
Figure 1: The 3D model of the five level departmental building.  
Figure 2: The route graph of the departmental building.  
The emergency event is modeled independently from the route graph, so that the virtual sensors can be distributed equidistantly and can cover also spaces that are not directly covered by the route graph. Such simulations represent spatially-extended events, starting from a single location and continuing to expand. Events of other characteristics, such as earthquakes that impact at multiple locations at the same time or bomb explosions that do not expand after going off, will not be covered by this particular simulation, but could be tested in the same way in differently designed simulations.
## 6 Simulation and Results Discussion
### Results and Discussion
#### 6.2.3 Lifetime with Routing Control Overhead  
The average path lifetime is the minimum time in which the maximum number of mobile nodes is desired to shut down. The routing control overhead is the number of control packets required to send each data packet in a network. We have used the channel-access AODV protocol. Figure 8 shows the lifetime and routing control overhead with the secured DSB and other existing algorithms. Our proposed model, QoSDSBS, with the desired channel-access AODV protocol is depicted in Table 5. Table 5 deliberates that the proposed QoSDSBS algorithm attains minimal routing overhead compared to the other existing algorithms. However, the proposed algorithm stagnates in a lifetime of 4 ms. Although, in a later case, it improves nearly 20% in a lifetime of 12 ms, due to the efficient broker-selection process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Throughput (Mbps)**} & \multicolumn{4}{c}{**Delivery Ratio (\%)**} \\ \cline{2-5}  & **EE-OHRA** & **EA-DRP** & **NETAR** & **QoSDSBS** \\ \hline
5 & 42.75 & 47.13 & 53.45 & 69.51 \\
25 & 52.87 & 59.94 & 65.84 & 78.63 \\
45 & 65.78 & 71.48 & 79.51 & 85.91 \\
65 & 74.84 & 79.76 & 84.17 & 89.42 \\
85 & 83.75 & 88.45 & 93.75 & 96.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of throughput vs. delivery ratio.  
Figure 7: Throughput vs. delivery ratio.
# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 4 Method
### ML Algorithms  
For our study we used various classification and regression techniques for predicting the final student performance. We made sure to choose one representative example out of the six main categories of learners, i.e., Bayes classifiers, rule-based, tree-based, function-based, lazy and meta classifiers [71].  
1. Bayes classifiers: Based on the Bayes theorem, the Bayes classifiers constitute a simple approach that often achieves impressive results. Naive Bayes is a well-known probabilistic induction method [72].
2. Rule-based classifiers: In general, rule-based classifiers classify records by using a collection of \"if... then... \" rules. PART uses separate-and-conquer. In each iteration the algorithm builds a partial C4.5 decision tree and makes the \"best\" leaf into a rule [73]. M5 Rules is a rule learning algorithm for regression problems. It builds a model tree using M5 and makes the \"best\" leaf into a rule [74].
3. Tree-based classifiers: Tree-based classifiers is another approach to the problem of learning. An example is Random Forest classifier that generates a large number of random trees (i.e., forest) and uses the majority voting to classify a new instance [75].
4. Function-cased classifiers: Function-based classifiers build a discriminant function that separates selected instances as widely as possible. SMO and SMOreg implement the support vector machine for classification [76; 77; 78] and regression [79; 80] respectively.
5. Lazy classifiers: Lazy learners do not train a specific model. At the prediction time, they evaluate an unknown instance based on the most related instances stored as training data. IBk is the k-nearest neighbor's classifier that is able to analyze the closest k number of training instances (nearest neighbors) and returns the most common class or the mean of k nearest neighbors for the classification and regression task respectively [81].
6. Meta classifiers: Meta classifiers either enhance a single classifier or combine several classifiers. Bagging predictors is a method for generating multiple versions of a predictor and using them in order to get an aggregated predictor [82].  
On the other hand, Auto-WEKA was restricted to use tree-based classifiers - Decision Stump, J48, Logistic model tree (LMT), REPTree, RT and M5P - and rule-based classifiers - JRip, One Rule (OneR), PART and M5 Rules.
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Simulation Results and Analysis of UAV Path Planning in Complex Terrain  
#### 5.3.1 Parameter Settings  
The simulation is conducted in MATLAB R2022a in Microsoft Windows 10 with a computer hardware platform configuration of Intel(r) Core(tm) i7-9750H @ 2.60 GH and RAM 16 G. The workspace of UAV is \([0,100]\) km long, \([0,100]\) km wide, and \([0,10]\) km high. The UAV starting point coordinate is (0, 0, 0.5), and the target point coordinate is (200, 200, 1). The simulation scene of the UAV path planning workspace is depicted in Figure 9. The plane coordinates and radius of the three threat area centers are shown in Table 11. Moreover, the maximum turning angles \(\phi=60^{\lx@math@degree}\), \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are 0.4, 0.4, and 0.2, respectively.  
In this experiment, we use the basic SSA and AO to compare with the proposed IHSSAO. For all these algorithms, the population number and the maximum number of iterations are set to 30 and 50, respectively.
## 4 Performance Evaluation
### Performance Evaluation
#### 4.4.1 Packet Delivery Ratio of Routing Protocols  
In our evaluation, we examine the performance of three routing protocol schemes in a network environment comprising 30 randomly distributed Relay Nodes (RNs). We investigate how the Packet Delivery Ratio (PDR) of these schemes is affected when varying the number of traffic sources and the relay node density. Figure 5a illustrates the results, which clearly indicate that our proposed scheme outperforms the OSPF routing protocol by a significant margin. Specifically, our scheme achieves a PDR improvement of approximately 10% to 20% for conditions with both low and high traffic rates. Unlike the OSPF, which prioritizes shorter paths without considering real-time network conditions, the DOS-RL and SDN-Q are capable of applying intelligent decision making which achieves a relatively better performance. The better performance exhibited by the proposed DOS-RL compared to that of the SDN-Q is due to its adaptability features to the dynamic changing network environment assisted by shaping rewards, which assists it in making decisions somewhat carefully to avoid poor energy consumption by dynamically adjusting objectives based on the measured networking conditions to avoid congestion and minimize packet loss.  
Next, we explore the impact of varying the number of deployed nodes (RNs) in the network while keeping other parameters, such as the number of traffic sources, constant at five. Interestingly, we observe that the difference in the PDR remains relatively small across the different numbers of the RNs. In Figure 5b, we notice a gradual decline in the PDR as the number of nodes increases. This decline can be attributed to factors such as increased network complexity, scalability, congestion, and increased overhead resulting from a larger number of nodes sharing the network resources. Nevertheless, our proposed scheme continues to outperform the other routing schemes by maintaining a comparatively higher PDR, even when the number of RNs is doubled.
## 3 Results
### Field Trials: Experiment 2  
Independent Samples _T_-test analysis showed no significant (\(F\) = 0.225 and \(p\) = 0.648) difference in the percentage of germination in all five furrows of negative control plants (mean 91.20\(\%\)\(\pm\) 2.8) and five furrows of _B. bassiana_-treated plants (mean 87.20\(\%\)\(\pm\) 3.5). Independent sample _T_-test analysis showed no significant difference in the number of leaves and plant height. The average number of leaves in negative controls and _B. bassiana_-treated plants at the third week after planting the seeds were 5.20 \(\pm\) 0.055 and 5.23 \(\pm\) 0.054, respectively. The average plant height for the negative control and _B. bassiana_-treated plants was 12.04 \(\pm\) 0.16 cm and 12.36 \(\pm\) 0.15 cm, respectively. In this trial, _S. frugiperda_ larvae were not detected, neither in negative control plants nor in _B. bassiana_ treated plants at the time of data collection. Independent Samples _T_-test analysis showed no significant difference between negative controls and _B. bassiana_-treated plants in corn cob length with \(F\) = 0.006 and \(p\) = 0.937, whereas no significant differences in corn cob weight were observed (\(F\) = 0.048 and \(p\) = 0.831) (Table 2). Although numbers were not recorded at harvesting time,  
Figure 6: Representative virgin female moth having laid fertile eggs showing apparent parthenogenesis.  
it was observed the presence of more than two corn cobs per plant in the _B. bassiana_-treated plants, compared with the negative control plants (Figure S6).
## 2 Methods
### Phase 3
#### 2.3.2 Procedure improve_tour  
We also use our algorithm _construct_tour_ to improve a feasible solution \(T=(t_{1},t_{2},\cdots,t_{n},t_{1})\), \(n=|V|\). Iteratively, point \(t_{i+1}\), \(1\leq i<n\), is removed from the tour \(T\) and is reinserted by a call of procedure _construct_tour\((V,T\setminus\{t_{i+1}\})\)_. If a removed point gets reinserted in the same position, then \(i:=i+1\) and the procedure continues until \(i\leq n\) (see Table 6).  
Figure 11 illustrates the iterative improvement in the cost of the solutions obtained at Phase 3 for a sample problem instance _usa115475_. The initial solution \(T_{0}\) of Phase 2 is iteratively improved as shown in the diagram.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{2}{l}{**PROCEDURE**_improve_tour_(\(V\),\(T\))} \\ \hline \(1\) & \(i:=1\) \\ \(2\) & **WHILE**\(i<n\)**DO** \\ \(3\) & \(t_{j}:=t_{i+1}\) \\ \(4\) & remove \(t_{i+1}\) from the tour \(T\) & //now \(T\) is infeasible \\ \(5\) & _construct_tour_(\(V\), \(T\setminus\{t_{i+1}\}\)) & //\(T\) is feasible again \\ \(6\) & **IF**\(t_{i+1}=t_{j}\)**THEN** \\ \(7\) & \(i:=i+1\) \\ \(8\) & **RETURN**\(T\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Procedure _improve_tour_.  
Figure 11: The improvement rate at Phase 3 for instance _usa115475_.  
Figure 10: (a) a fragment of a solution before applying the algorithm _2-Opt_; (b) the corresponding fragment after applying algorithm _2-Opt_.  
**Lemma 4**: _The time complexity of the Procedure improve_tour is \(O(n^{2})\)._  
**Proof of Lemma 4.** In lines 2-7, there is a _while_ statement with \(n-1\) repetitions. The call of Procedure _construct_tour_ in line 5 yields the cost \(O(n)\) since with \(m=n-1\), \(h=1\); see the proof of Lemma 3 (\(m\) is the number of points in the current partial tour). The lemma follows.
## 4 Results and Discussion
### Results Using BoW and without the SMOTE Technique  
Initially, experiments are performed on the original dataset with class imbalance using BoW features. The results of all models in terms of accuracy, precision, recall, and F1 score are shown in Table 10. SVM and SGD outperform other models with significant accuracy of 0.94 each followed by LR with 0.93 accuracy. Results indicate that linear models perform better on the dataset when BoW features are used. The primary reason is the large feature set used for training as using the BoW technique feature space is large and the linear model performs well when a large feature set is available for training. While KNN, GNB, and AdaBoost show poor performance as they require a small feature set for a good fit, and they need categorical data for the significant results. Tree-based models RF, ETC, and DT show average accuracy scores.
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 1 Introduction
However, the complexity of the convolution in the time domain is still high. In [44], a delayless FDAF algorithm originally used in echo cancellation [45] was extended to ANC, but the computational burden at certain sampling periods is rather high. Two computationally efficient FDAF algorithms for ANC were proposed in [46; 47] using the delayless approach in [48; 49], but the delay in the filter adaptation is not removed. Although many efforts have been devoted to the frequency-domain FxLMS algorithms, these algorithms are not well compared and analyzed. To fill this gap, this paper presents a comprehensive review of the frequency-domain FxLMS algorithms. Only mono-channel algorithms are considered in this paper, since they can be straightforwardly extended to the multi-channel case. The conventional frequency-domain FxLMS algorithms are reviewed, and then the effects of the two kinds of delays on the overall performance are analyzed. Specifically, it was found that calculating the adaptive filter output and the weight vector update in different ways leads to different convergence performances, which is quite different from system identification problems. The update-first approach is then proposed to improve the stability. Several delayless frequency-domain algorithms for ANC are surveyed, but these algorithms did not remove the delay related to the secondary path. To address this problem, we present a new delayless frequency-domain FxLMS algorithm to completely remove the aforementioned two types of delays and overcome the shortcomings of the state-of-the-art frequency-domain FxLMS algorithms. The complexity of the frequency-domain approaches in terms of both peak and average multiplications per sample are evaluated. Simulations are carried out to evaluate the convergence performance and stability of the frequency-domain FxLMS algorithms.
# The Ocean-Going Autonomous Ship--Challenges and Threats
## 4 The Case of the First Unmanned Ship Crossing the English Channel
### Lessons Learned from the Participation in the Unmanned Vessel Operation  
Based on the considerations above, the following observations have been listed:  
1. The unmanned and autonomous vessels must be tested in real traffic conditions to obtain non-simulation data for further considerations, although a proper level of information and multiple back-up control methods must be ensured and provided to maintain at least the existing safety level of maritime operations.
2. Taking into account all the restrictions related to the situational awareness sensors nowadays, radar provides the most valuable information to the operator on-shore. Improvements to vessels' radar data transfer on-shore are needed.
3. Self-diagnosis systems, including position determining devices, communication and power supply play crucial role in the vessel remote control process and should be taken into account in the autonomous units decision making algorithms. The redundancy of the positioning and movements parameters systems should be ensured, preferably using the cross-check methods.
4. In the situation of a lack of regulations for unmanned vessels operator's qualifications, it is the operating companies' role to ensure that the proper people control this equipment. The extensive works towards the clarification of theoretical and practical training requirements for MASS operators will certainly be conducted in the near future, although it requires open discussion between education institutions, policy makers and industry representatives, where each voice, based on real experience, will be valuable.