esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Production and Characterization of SLN  
Blank SLN (tRES-free) and tRES-loaded SLN formulations were prepared using the ultrasound (US) technique, modified after [PERSON] et al. [47]. The developed formulation consisted of 5.0% of stearic acid as solid lipid (Galena, Campinas, SP, Brazil), with or without of 1.2% soy phosphatidylcholine (Epikuron(r) 200, Cargill, Hamburg, Germany); and the water phase consisted of 3.5% of poloxamer 407 (Pluronic(r) F127, Sigma-Aldrich, St. Louis, MO, USA); 0.18% of methyl paraben (Nipagin(r) M, PharmaSpecial, Itapevi, SP, Brazil), and 0.02% of propyl paraben (Nipasol(r) M, PharmaSpecial, Itapevi, SP, Brazil) (Table 1).  
The water phase was firstly heated up to 70 \({}^{\circ}\)C, and then added to a previously prepared mixture of solid lipid (stearic acid) and soy phosphatidylcholine (Epikuron(r) 200) for F2 and F2.RES heated at  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Ingredients/\%** & & \\ \cline{2-7}  & **SA** & **SPC** & **P407** & **MP** & **PP** & **RES** & **MQ Water \$** \\ \hline F1 & 5.0 & - & 3.5 & 0.18 & 0.02 & - & Sq \({}^{*}\) \\ F2 & 5.0 & 1.2 & 3.5 & 0.18 & 0.02 & - & Sq \({}^{*}\) \\ F1.RES & 5.0 & - & 3.5 & 0.18 & 0.02 & 0.1 & Sq \({}^{*}\) \\ F2.RES & 5.0 & 1.2 & 3.5 & 0.18 & 0.02 & 0.1 & Sq \({}^{*}\) \\ \hline \hline \end{tabular}  
* SA: Stearic acid; SPC: Soy phosphatidylcholine; P407: Polaxamer 407; MP: Methylparaben; PP: Propylparaben; RES: _Trans_-resveratrol; MQ \$: Mili-Q \$: sufficient quantity.  
\end{table}
Table 1: Composition of solid lipid nanoparticles (SLN) formulations.  
the same temperature, or to the melted solid lipid in case of F1 and F1.RES, to produce a pre-emulsion. The obtained pre-emulsion was stirred with a 752A-2 magnetic stirrer (Fisatom, Sao Paulo, SP, Brazil) for 1 min, and heat was kept constant. A sonication probe (Branson Sonifier 250, Branson Ultrasonics Corporation, Frederick, MD, USA) was placed in the pre-emulsion for 20 min, applying a power output of amplitude of 47%. Ice was used to lower the temperature during sonication, because preliminary studies showed that in these conditions, the produced nanoparticles would have a lower mean hydrodynamic diameter and polydispersity index. For tRES-loaded SLN (F1.RES and F2.RES), the drug was added to melted stearic acid prior to sonication of the pre-emulsion. tRES (Resveratrol extract 100%, Galena, Campinas, SP, Brazil) was used in a concentration of 0.1% (wt/wt). All formulations were prepared following the same procedure [48]. After sonication, the formulations were centrifuged for 10 min at 5000 rpm to eventually eliminate the titanium fragments that could be released from the probe tip during sonication [49]. The mean hydrodynamic diameter and polydispersity index were determined by dynamic light scattering (Zetasizer Nano ZS, Malvern Instruments, Malvern UK). Samples were previously diluted with ultra-purified water to suitable concentration and analyzed in triplicate.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Communication Quality Performance Evaluation
#### 4.3.1 Analysis of the Average Network Throughput of Different Solutions  
The average network throughput under the comparison of various schemes is shown in Figure 10. The I-VHO scheme has the lowest average network throughput of 142.3349 Mbps due to the ping-pong effect caused by the frequent handover. It has an average network throughput of 159.3888 Mbps. The CBDHVHO scheme takes into account the continuous and stable communication of user-receiving devices, and the handover technique achieves a seamless connection, so the average network throughput is the highest at 195.2826 Mbps.  
Figure 10: Comparison of the average network throughput of the proposed I-VHO, D-VHO, and CBDHVHO schemes.  
Figure 9: Comparison of the average number of handovers for the proposed I-VHO, D-VHO, CBDVHO, and CBDHHO schemes.
## 3 Selection of Spatial Predictive Methods  
### Spatial Predictive Methods  
For spatial predictive modeling, there are many methods available [3]. Previously, over 20 spatial predictive methods were grouped into (1) non-geostatistical methods (e.g., inverse distance weighting (IDW)), (2) geostatistical methods (e.g., ordinary kriging (OK)), and (3) combined or hybrid methods [10]. Collectively, these methods are largely non-machine learning methods and a small portion of these methods, like regression tree (RT) and linear regression models (LM), use secondary information.  
When sufficient secondary information is available, a number of other methods could be used. These methods include traditional statistical methods, machine learning methods, the hybrid methods of traditional statistical methods and geostatistical methods, and the hybrid methods of machine learning and geostatistical methods (Table 1). These methods were applied or compared in various spatial predictive modeling studies [47; 48; 49; 50; 51; 12; 41; 42; 41; 43; 44; 45; 12; 46; 47; 48; 49; 52; 41; 42; 43; 45; 13; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55]. Of these methods, random forest (RF), hybrid method of RF and OK (RFOK), and hybrid method of RF and IDW (RFIDW) were among the most accurate methods in these applications. Generalized boosted regression modeling (GBM), hybrid method of GBM and OK (GBMOK), and hybrid method of GBM and IDW (GBMIDW) showed great potential based on our unpublished study. In the current study, these methods are presented in three main groups: (1) non-machine learning methods; (2) machine learning methods; and (3) the hybrid methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Non-Machine Learning Method and Hybrid Methods** & **Machine Learning Method and Hybrid Methods** \\ \hline Non-machine learning method & Hybrid methods & Machine learning method & Hybrid methods \\ Generalized additive models & & Cubist & Cubist and OK (cubistOK) \\ Generalized least squares trend estimation (GLS) & GLS and OK & Generalized boosted regression modeling (GBM) & GBM and IDS (GBMIDS) \\ Generalized linear models (GLM) & GLM and IDW (GLMIDW) & GBM and OK (GBMMC) \\  & GLM and OK (GLMOK) & General regression neural network (GRNN) & GRNN and IDS (GRNNIDS) \\ GLM with lasso or elastic net regularization & & GRNN and OK (GRNNOK) \\ Linear models and OK & Multivariate adaptive regression splines & \\ RT and IDS (RTIDS) & Naive Bayes & \\ RT and OK (RTOK) & Random forest (RF) & RF and IDS (RKIDS) \\  & & RF and OK (RKOK) \\ Support vector machine (SVM) & SVM and OK (SVMOK) \\ SVM and OK (SVMIDS) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial predictive methods using predictive variables [41, 48, 49, 50, 51, 52, 53, 54, 55, 6, 12, 55, 41].
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Direction of Blockchain-Enabled Federated Learning
#### 4.2.2 Defense Mechanisms  
Defense mechanisms refer to the system of updating and verifying the model and attacking the confrontation. The solution of the defense mechanism plays an important role in improving the security of federated learning, and with the support of blockchain, federated learning realizes the improvement of defense capabilities.  
The consensus mechanism of the blockchain can verify the model update to cope with the poison attack of federated learning. There are many application cases in blockchain-based federated learning. For example, [PERSON] et al. [73] proposed a completely decentralized peer-to-peer (P2P) multi-party ML method, developed Multi-Krum defense, and used Byzantine fault-tolerant aggregation scheme to verify local model updates and enhance FL defense capabilities. [PERSON] et al. [103] proposed the VFChain system to prevent poison attacks by developing an effective committee selection scheme and blockchain authentication data structure.  
Taking advantage of the immutable nature of blockchain records can improve the defense capability of federated learning. One of the current challenges faced by federated learning is the problem of countering attacks, such as the tampering of local data and model parameters, which will lead to the decline of model performance. However, current federated learning solutions, such as finding abnormal data or clients through anomaly detection, experience difficulties in effectively solving this problem. In this way, as long as one party's data or parameters are tampered with, its information will be judged to be invalid [104]. Alternatively, IPFS can be introduced to store the training model and retrieve participants by unique hash values. In this way, storage costs are reduced and the model is protected from member inference attacks [55]. [PERSON] et al. [87] presented a blockchain-based federated learning security and privacy protection framework (BFLSP) to achieve the security of models and data during training.  
Blockchain can help federated learning to identify and defend against potential attacks, punishing malicious actors [75]. Additionally, due to the large number and dispersion of customer terminals, it is difficult for malicious clients to backtrack, and the traceability of blockchain can be used to trace and punish the participants who launch malicious attacks [91], improving the accuracy of model training in federated learning. Under a consensus mechanism, the blockchain-based federated system BELIEFS [88] achieves a tolerance for malicious attacks.
## 3 Barriers to the Use of Lidar for Control
### Risk Assessment and Reliability Issues  
On the basis of the preceding section, there are general concerns that come up when implementing LAC on a wind turbine. The question \"what happens if there is no or a faulty signal from the lidar?\" is ubiquitous among all workshop participants. Understandably, no one would risk harming the turbine or at worst putting people in danger, if the safe operation of the turbine depends on the reliability of the lidar itself. Because of failures in hardware components and/or in software applications, lidars have not always been able to provide service reliably in the past. It is claimed that the technology readiness level (TRL) of currently-available lidar systems for LAC is still too low. As a result, there are not only the aforementioned safety concerns but also the high costs of troubleshooting and repair. Overall, considerations must be made on how long the lidar's lifetime should be and how it should be designed to require the least maintenance effort.
# How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region
## 3 Results
### The Annual and Monthly Dynamic Trends of Environmental Factors  
In the period 2005-2019, the AUC of the LR model exceeds 90% in all years, and RH\({}_{\text{min}}\) is the most important environmental factor affecting the forest fire probability in the model in all years (Figure 4a). GPP and T\({}_{\text{max}}\) are the second and the third most important environmental factors, respectively. In most years, the relative importance of RH\({}_{\text{min}}\) exceeds 50% (Figure 3), and the relative importance of RH\({}_{\text{min}}\) varies significantly between years, with 2010 having the largest relative importance, with 72% of the total, and 2018 having the lowest at 33.46% of the total. The Spearman correlation coefficient for RH\({}_{\text{min}}\) and GPP indicates that no significant relationship exists between the two (\(p\) = 0.416), RH\({}_{\text{min}}\) correlates negatively with T\({}_{\text{max}}\) (\(r\) = \(-\)0.67, \(p\) = 0.007), and the corresponding values of RH\({}_{\text{min}}\) threshold and forest fire for 50% of the train were not significant, and their inter-annual variation is not significant (Figure 4b,c).  
Between January and June, over 98% of forest fires occurred in the first half of the year (Figure 2c), whereas the results for the other months are not statistically significant because of the small amount of data. Therefore, we use herein the LR model from January to June to study the monthly dynamics of forest fires. RH\({}_{\text{min}}\) is the most important environmental factor from January to May (Figure 5a), during which time the AUC of the related model exceeds 90% (except for April, which is 85%). GPP is the second-most important among the relevant models from April to May (Figure 5a), and it becomes the most important environmental factor in June, with an AUC of 93.5% for this model. No significant correlation exists between these two factors (\(p\) = 0.208), and the variation of RH\({}_{\text{min}}\) and the threshold of 50% forest fire probability is more significant, reaching its maximum threshold in June. The GPP threshold at 50% forest fire probability is approximately 2.7 \(\times\) 10\({}^{3}\) (kg K) (Figure 5b) and does not change significantly from January to June. The partial derivative of RH\({}_{\text{min}}\) and GPP at 50% forest fire probability also does not change significantly (Figure 5b,c, respectively).  
Figure 3: Ranking of relative importance of environmental variables.  
Figure 4: (**a**) Ranking of relative importance of environmental variables for each year. (**b**) Interannual thresholds for the most important environmental variables for each year. (**c**) Interannual partial derivatives of the most important environmental variables for each year.  
Figure 5: **(a)** Ranking of relative importance of environmental variables by month in the first half of the year. **(b)** Thresholds of \(\mathrm{RH}_{\mathrm{min}}\) and GPP for each month in the first half of the year. **(c)** Partial derivatives of \(\mathrm{RH}_{\mathrm{min}}\) and GPP in each month of the first half of the year.
## 5 Verification of Simulation Results
### Verification of the Strain Gauges Test  
In order to verify the accuracy of the finite element model, strain gauges were pasted at the outlet of the clamp. When \(Y_{b}\) changed, \(\sigma_{a}\) was measured under different wind loads and compared with the simulated stress. Because the strain gauge needed to be pasted on a smooth surface, the surface of the single strand of aluminum wire to be pasted with the strain gauge was lightly polished with a file. The thickness of the polished part accounted for about one fifth of the diameter of the single strand. The model of static strain instrument used was YE2538A (Yutian Technology Company, Wuxi, China). The strain gauge used was a conventional small resistance strain gauge with the size of 0.5 mm \(\times\) 0.5 mm and a sensitivity of 2.00 kV. Figure 12 shows the installation position of strain gauges.  
As shown in Figure 13, with the increase of the wind load, the \(\sigma_{a}\) obtained using the simulation and \(\sigma_{a}\) obtained using the test both increased, and the maximum error between \(\sigma_{a}\) obtained using the simulation and \(\sigma_{a}\) obtained using the test was about 20 MPa. On one hand, the error was the error of experimental measurement, on the other hand, it was the error of the finite element numerical calculation itself. In conclusion, although there was an error between \(\sigma_{a}\) obtained using the simulation and \(\sigma_{a}\) obtained using the test, it also proved that the accuracy of the finite element model results were within a reasonable range.  
Figure 11: (**a**) Wear defects on the surface of the third layer of conductor, and (**b**) wear defects of the third layer of conductor at the outlet of the tension clamp after cutting.  
Figure 12: Installation positions of the strain gauges.
# The Role of Data Analytics in the Assessment of Pathological Speech--A Critical Appraisal
## 4 Discussion
### Analysis of Results
Clarifying the main aim of problems treated, whether that be detection, classification, prediction, regression, longitudinal monitoring, etc. The objectives of the study must be clearly and accordingly fixed to one or several of the mentioned problems. * **Clinical acceptance**. There must be a clear applicability orientation to clinical semantics, and efficient computer-assisted monitoring, as well as to treating confounding factors. * **Ethical implications, and patient and data protection**. Evidence should be shown on compliance with ethical standards. The origin, protection, and protocol implementation requirements have to be explicitly declared, even when using second-source databases. * **Information integrity**. Possible information content alteration due to too strong feature selection algorithms must be well explained. Feature selection algorithms used in data pruning must be explicitly commented on ([PERSON]'s linear discriminant analysis, relief feature selection, embedded random forests, least absolute shrinkage, and selection operator, etc. [88; 89; 90]). * **Linguistic issues**. The relevance of different linguistic origins of the databases should be taken into account. Although the effect of this difference could be considered negligible as far as open vowel phonation is concerned, it should not be the case when other speech tests are considered. * **Relevant metadata**. The demographic description of both pathological and control participants, is another question to be taken into account. Inclusion and exclusion criteria have to be declared. Medication and other perturbation factors must be commented on. * **Model selection and description**. Decisions on this important issue have to be documented, specifying the type as supervised vs. unsupervised approaches. The classification models have to be described or properly referenced, as well as the problems found in hyper-parameter settings, training, testing, validation, and evaluation. * **Objective performance improvements**. The studies should show real significant performance improvements or knowledge advancement in the field. Orienting the research to simply outperform others' scores showing slight subjective or not statistically supported gains would not be considered sufficient for publication by many journals unless providing clear quality, innovation, and work interest. * **Preservation of semantic load and future hypothesis casting**. The risks of using too exhaustive and tight DD and EE approaches not caring about preserving feature semantics may be hampering scientific reasoning, explainability, and the production of new hypotheses and testing paradigms. The belief that \"data speak from and by themselves\" is not only to be declared but also explained and proven.
# Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention
## 3 Review of Similar Work
For energy efficient routing in SDN, [37] proposed a deep Q-network (DQN)-based Energy Efficient routing (DQN-EER) algorithm to find energy-aware data paths between OpenFlow switches. The RL agent is implemented with deep convolutional neural network and modelled with MDP to interact with the environment. Three controllers with 16 hosts and 20 switches were used to set up the network topology. After 2300 training episodes, theDQN-EER was compared with CPLEX and CERA algorithms. Results show that DQN-EER outperforms CERA and CPLEX for energy-efficient routing when the state becomes larger. In optimizing network routing, [38] proposed a DRL agent targeted at optimizing network delay in SDN environment. The proposed set up involves a network topology of 14 nodes with 10 traffic intensity that ranges incrementally from 12.5% to 125% relative to the aggregated network capacity. With 1000 distinct traffic configurations, the DRL agent was trained with 100,000 step traffic matrixes. Results show that, as the training time increases, the DRL agent's performance also increases and improves the overall network delay. [PERSON] et al. [39] proposed a MADDPG integrated algorithm in multi-agents with cooperation to solve the internet of vehicles (IoV) dynamic controller assignment problem while minimizing network delays and packet loss. The architecture is deployed in the data and the control plane of the SD Networks. The data plane is split into two sub-layers: mobile and stationary layer. The mobile layer has on-board units integrated in moving vehicles for communication with roadside units (RSUs) and base stations (BSs). The control plane has a hierarchical distribution with multiple edge controllers at the lower tier with the top tier core controllers in remote data centers. [PERSON] et al. [40], in minimizing packet loss rate and increasing packet throughput in a network, proposed a MADDPG based traffic control and multi-channel reassignment (TCCA-MADDPG) algorithm in SDN-IoT. The heterogeneous IoT structure consisting of LTE network, WIFI network, and 3 GPP network forms the edge layer in the architecture as layer three. The fourth layer, which consists of IoT devices, is the fog layer. The second layer is the core backbone layer that bridges the third layer to the first layer, the data center layer. [PERSON] et al. [41], in optimizing routing in SDN, proposed a DDPG Routing Optimization Mechanism (DROM) framework using DRL agent integrated within the control layer. The DROM agent uses a Traffic Matrix to receive the current network load state [10]\(s\) from the environment and changes the weight links of the network through an action \(a\). The DROM agent receives a reward \(r\), and the state changes from \(s\) to \(s^{\prime}\).
## 5 Principles Related to Traveling Wave Detection Methods
### A Dual-End Positioning Algorithm Independent of Wave Velocity  
The basic principle of the dual-end traveling wave fault location method is to utilize the propagation characteristics of the fault-generated traveling wave signal on the line and to locate the fault with the help of measurement information from both ends. The formula for calculating the fault wave velocity is given by \(V=1/\sqrt{LC}\), where \(C\) and \(L\) represent the capacitance and inductance per unit length of the line, respectively. Due to variations in wave velocity among different lines and differences caused by changes in environmental and line parameters, the numerical values of wave velocities at different times and positions on the same line will also differ.  
By employing the Karhunen-Loeve transform, when a fault occurs in a mixed-line power system, the fault traveling wave signal can be decoupled into line-mode and zero-mode components, with the wave velocity values of the zero-mode component set to \(v_{0}\) and the line-mode component set to \(v_{1}\). Through the propagation process of the fault traveling wave's zero mode and line-mode components on the mixed line, the following formula for calculating the positioning distance can be obtained:  
\[\left\{\begin{array}{l}l_{\text{MN}}=v_{1}(t_{\text{M1}}-t_{0}+t_{\text{N1}}-t_ {0})\\ l_{\text{MN}}=v_{0}(t_{\text{M0}}-t_{0}+t_{\text{N0}}-t_{0})\\ v_{0}(t_{\text{M0}}-t_{0})=v_{1}(t_{\text{M1}}-t_{0})\end{array}\right. \tag{10}\]  
The calculation formula obtained after eliminating equations \(v_{1}\), \(t_{0}\), and \(v_{0}\) is (11):  
\[d_{\text{M}}=\frac{t_{\text{Mo}}-t_{\text{M1}}}{t_{\text{Mo}}-t_{\text{M1}}+t_ {\text{N0}}-t_{\text{N1}}}l_{\text{MN}} \tag{11}\]  
In the equation, \(t_{\text{Mo}}\) and \(t_{\text{N0}}\) represent the time for zero-mode traveling waves to reach the ends M and N, respectively; \(t_{\text{M1}}\) and \(t_{\text{N1}}\) represent the time for line-mode traveling waves to reach the ends M and N, respectively; \(v_{1}\) and \(v_{0}\) represent the velocity values of the line-mode and zero-mode traveling waves, respectively; \(l_{\text{MN}}\) represents the length of line MN; and \(t_{0}\) represents intermediate parameters.  
The model defined by Equations (10)-(12) is newly introduced in this work. The positioning results of this algorithm are independent of the wave velocity and changes in line parameters. Moreover, it does not require measuring and recording the occurrence time of fault reflection waves, thereby reducing the difficulty of wavefront exploration and enhancing the accuracy of fault positioning.