esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
## 1 Introduction
In addition, the effect of thermal stress on other layers in the device architecture has also been reviewed, particularly noting the degradation of charge transport layers and metal contacts, and presents solutions to slow down the degradation. Finally, the available strategies to improve the thermal stability of perovskite solar cells are depicted.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Polysaccharides
#### 2.1.3 Starch  
The use of natural and modified starches as biodegradable, renewable, biocompatible, and non-toxic polysaccharides has been widely used in several medicinal applications, including tissue engineering and drug delivery [57; 58; 59; 60; 61; 62]. Natural sources including wheat, rice, corn, and potatoes can be utilized to separate starch, which is then employed primarily as a carrier and in some applications like bone replacement and healing [29]. Besides these advantages, native starch also presents some disadvantages such as low water solubility, the formation of gel or pastes with non-uniform texture and viscosity, deterioration in different conditions of high temperature and pHs, and freeze-thaw variations [63; 64]. Therefore, in order to improve native starch's physicochemical or biological properties and make it suitable for producing DDSs, several modifications (chemical, physical, and enzymatic) are required [65].  
Using broken rice, [PERSON] and colleagues created a novel nanoparticulate system for acetylated starch nanocrystals. Acetylated starch nanocrystals with different degrees of substitution were prepared using acetic anhydride as an acetylating agent through a reaction with starch nanocrystals. These findings suggest that the acetylated starch nanocrystals made from broken rice are a potentially useful tool for the regulated administration of doxorubicin in cancer treatment [66]. Other examples of starch derivatives that demonstrated benefits for drug delivery applications are presented in Table 1.  
Starch-modified alginate nanoparticles for drug delivery were developed by [PERSON] and coworkers using an environmentally friendly method. For the determination of their potential in controlled drug delivery applications, theophylline and bovine serum albumin were used as model drugs. The obtained nanoparticles had good encapsulation efficiency, and the in vitro drug release studies showed pH dependency characteristics. Also, it was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Starch Type** & **Drug** & **References** \\ \hline Oxidized starch & Tobramycin; colistin & [67] \\ Acetylated starch & Ibuprofen & [68] \\ Hydroxyethyl starch & Doxorubicin & [69] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Starch types used for intravenous drug delivery.  
shown that the created nanoparticles were biocompatible with L929 fibroblast cell lines. Thus, the developed materials were demonstrated to be a promising tool for drug delivery application [70].
# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 4 Experiments and Results Analysis  
### Description of Wind Speed Data  
The research data comes from a field measured wind speed observation tower erected on south bank of the Qiongzhou Strait Bridge site. In order to verify the effectiveness and stability of the hybrid prediction model, typhoons data from typhoons Wutip and Ramason are selected as sample data. Ramason appeared in the Pacific Ocean, west of Guam, on the  
Figure 1: Framework of the hybrid forecasting model.  
afternoon of 12 July 2014. Soon, it is upgraded to a strong typhoon and made landfall along the coast of Longtang Town, Xuwen County, Guangdong Province, China, on the 18 th. The maximum wind speed was 60 m/s and the minimum pressure of the center was 950 hPa. Data collection lasted for 72 h, from 00:00 on 17 July 2014 to 24:00 on 19 July 2014. Tropical Storm Wutip formed at 14:00 on 27 September 2013 in the central South China Sea. On the 29 th, it was upgraded to a strong typhoon in the waters of Sansha City, Hainan Province, and made landfall in Quang Binh Province, Vietnam on the 30 th. The maximum wind speed was 35 m/s and the minimum pressure of the center was 970 hPa. Data collection lasted for 48 h from 00:00 on 29 September 2013 to 24:00 on 30 September 2013.  
The time intervals of each step of the original wind speed data is 10 min. Dataset 1 comes from Typhoon Wutip. Sample data of Typhoon Ramason is divided into two datasets, i.e., dataset 2 and dataset 3, according to the variation of the mean wind speed. The wind speed fluctuation range of dataset 2 changes dramatically, while the wind speed of dataset 3 varies smoothly. The statistics of the three datasets are shown in Table 1, and the time-history of the datasets is depicted in Figure 2.
## 4 Numerical Results and Discussion  
The double cantilever composite beam test described in Section 3 is modeled using the two-dimensional OSB PD composite model in Section 2. The DCB specimen shown in Figure 4 is made of CFRP composite with material properties shown in Table 2. The horizon of the present two-dimensional OSB PD model is \(\delta=3\) dx, dx is the grid spacing. The boundary conditions for PD DCB model are shown in Figure 5. Displacement boundary conditions are applied at the loading end. The loading increment for each iteration step is noted as dL. No-fail zones are set for preventing possible premature failure due to boundary effects [12]. The modeling flowchart is shown in Figure 6. For each loading step, firstly, a displacement boundary condition dL is applied; secondly, ADR is used to get the static results; thirdly, the revised energy-based failure criteria derived in Section 2.2 are used to check bond failure; and finally, a new loading step is applied. The two-dimensional PD modeling deformed displacement field in thickness direction is shown in Figure 3b.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **DELL** \\ **(mm)** \\ \end{tabular} } & \multicolumn{2}{c}{**DCB-1**} & \multicolumn{2}{c}{**DCB-2**} & \multicolumn{2}{c}{**DCB-3**} & \multicolumn{2}{c}{**DCB-4**} & \multicolumn{2}{c}{**DCB-5**} \\  & **Disp.** & **Load** & **Disp.** & **Load** & **Disp.** & **Load** & **Disp.** & **Load** & **Disp.** & **Load** \\  & **(mm)** & **(N)** & **(mm)** & **(N)** & **(mm)** & **(N)** & **(mm)** & **(N)** & **(mm)** & **(N)** \\ \hline
50 & 4.77 & 88.04 & 5.07 & 97.22 & 5.52 & 86.16 & 5.27 & 94.29 & 4.79 & 91.07 \\
51 & 5.17 & 87.59 & 5.23 & 86.22 & 5.59 & 79.09 & 5.27 & 94.29 & 5.24 & 91.09 \\
52 & 5.18 & 85.84 & 5.26 & 82.09 & 5.60 & 77.87 & 5.75 & 89.35 & 5.34 & 90.19 \\
53 & 5.20 & 85.23 & 5.29 & 81.60 & 5.62 & 77.53 & 5.75 & 89.35 & 5.62 & 92.44 \\
54 & 5.26 & 85.60 & 5.30 & 80.78 & 5.65 & 72.54 & 5.75 & 89.35 & 5.62 & 92.44 \\
55 & 5.54 & 84.56 & 5.34 & 80.10 & 5.67 & 69.59 & 5.96 & 81.77 & 6.02 & 93.91 \\
60 & 6.15 & 62.19 & 5.52 & 77.25 & 5.89 & 55.74 & 6.47 & 74.20 & 6.42 & 66.33 \\
65 & 6.79 & 68.00 & 6.15 & 66.52 & 6.03 & 47.80 & 7.13 & 62.66 & 8.12 & 73.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental delamination growth process observed in DCB test (DELL: delamination length).  
Figure 4: CFRP double cantilever beam (DCB) test specimen.  
Figure 5: Boundary conditions for PD DCB model.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
###### Abstract  
Software Defined Networks (SDN) and Mobile Edge Computing (MEC), capable of dynamically managing and satisfying the end-users computing demands, have emerged as key enabling technologies of 5G networks. In this paper, the joint problem of MEC server selection by the end-users and their optimal data offloading, as well as the optimal price setting by the MEC servers is studied in a multiple MEC servers and multiple end-users environment. The flexibility and programmability offered by the SDN technology enables the realistic implementation of the proposed framework. Initially, an SDN controller executes a reinforcement learning framework based on the theory of stochastic learning automata towards enabling the end-users to select a MEC server to offload their data. The discount offered by the MEC server, its congestion and its penetration in terms of serving end-users' computing tasks, and its announced pricing for its computing services are considered in the overall MEC selection process. To determine the end-users' data offloading portion to the selected MEC server, a non-cooperative game among the end-users of each server is formulated and the existence and uniqueness of the corresponding Nash Equilibrium is shown. An optimization problem of maximizing the MEC servers' profit is formulated and solved to determine the MEC servers' optimal pricing with respect to their offered computing services and the received offloaded data. To realize the proposed framework, an iterative and low-complexity algorithm is introduced and designed. The performance of the proposed approach was evaluated through modeling and simulation under several scenarios, with both homogeneous and heterogeneous end-users.  
software defined networks; mobile edge computing; reinforcement learning; stochastic learning automata; game theory; data offloading; pricing; optimization +
Footnote †: journal:  
_Future Internet_
## 2 Port Cranes Overview
### Evolution Timeline of Port Cranes
#### 2.2.5 Specialized Cranes for Larger Vessels (1970s-1990s)  
* Panamax/Post-Panamax STSCs Development (1970s-1980s)--As container ships continued to grow, the cranes had to adapt. In the 1970s, Panamax cranes were developed to handle ships passing through the original Panama Canal. These cranes had a reach of 13 container rows and lifting height of approximately 38 m. In the 1980s, post-Panamax cranes were introduced for even wider vessels, extending their reach to 16 rows and increasing lifting capacity to 40-50 tons. Seismic design innovations were also incorporated to ensure the stability of large cranes in regions prone to high winds and earthquakes [25].  
RTGCs Introduction (1987)--In 1987, Valmet (later known as Kalmar) delivered the first batch of RTGCs to the Tanzanian port of Dar es Salaam. RTGCs have several significant advantages over their predecessors such as mobile gantry cranes. They have eight wheels, allowing them to stack containers in a 1 over 3 configurations, which increases the storage density in the container yards. With a lifting capacity of up to 41 tons and 400 horsepower, compared to the 22-30-ton capacity and 130-horsepower of mobile gantry cranes, RTGCs were better equipped to meet the high-productivity demands of modern ports [26].
## 3 Proposed Method
### Dynamic Channel-Selection-LSTM  
For the channel feature maps obtained after ACFF, to establish the connection between different timestep feature vectors, we use two proposed DCS-LSTM modules to extract the temporal context information of the sensor signals, the structure of which is shown in Figure 2g. In addition, [PERSON] et al. [46] pointed out that models containing at least two recurrent layers work better in processing sequence data. Here, we similarly use the ACS operation to obtain the contributions of different channels, adaptively learn the weight coefficients of each channel, and strengthen the ability to characterize features for the classification of confusable behaviors. The structure of the basic LSTM network cell is shown in Figure 4.  
The forgetting gate decides what information to let continue through that neuron. The input gate decides how much information to update to the state matrix. The output gate combines the neuron's state vectors, the input vectors, and the output vectors of the previous neuron to arrive at the output value for the current moment. Its vector update operation is represented as  
\[i_{t}=\sigma(W_{ai}a_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_{i}) \tag{19}\]  
\[f_{t}=\sigma\left(W_{af}a_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_{f}\right) \tag{20}\]  
\[o_{t}=\sigma(W_{ao}a_{t}+W_{ho}h_{t-1}+W_{co}c_{t}+b_{o}) \tag{21}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\sigma(W_{ac}a_{t}+W_{hc}h_{t-1}+b_{c}) \tag{22}\]  
\[h_{t}=o_{t}\sigma(c_{t}) \tag{23}\]  
where \(i_{t}\), \(f_{t}\), and \(o_{t}\) are the output vectors of the input, forgetting, and output gates of the LSTM cell at time \(t\), respectively; \(c_{t}\) is the state vector of the LSTM cell at time \(t\); \(\sigma\) is a sigmoid nonlinear excitation function that introduces a nonlinear factor; \(a_{t}\) is the input vector of the LSTM cell at time \(t\); \(W\) stands for the weight matrix for the connection between different gates; and \(b\) is the bias vector.  
LSTM can record the feature representation of longer sequence data. Therefore, we proposed the DCS-LSTM network to implement the time-series modeling work on the data to facilitate the extraction of temporal contextual information of the sensor signals and weight the channel features by an ACS module to improve the model's ability to discriminate individual channel features and classify confusable behaviors. The hidden cells of the LSTM are set to 128.  
Figure 4: Structure of the LSTM cell unit. It updates the state of the unit through input gates, output gates, and forgetting gates. The upper horizontal line ensures that vectors pass through the neurons with only a few linear operations, enabling long memory retention.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 8 Evaluation
### LNN-RLSTM Labels and Real Labels  
The quartile method is suitable for excluding only the most prominent feature anomalies and this task is very meaningful for LNN-RLSTM, which has good noise immunity by training to describe the real data feature distribution. During the training process, although the proportion of anomalous data is small, each anomaly may significantly affect the parameters of the model, thus, improving the sensitivity of the model to anomalies. As shown in Figure 14, the time to train the model with the traditional machine learning algorithm with the addition of novel anomalies at a later stage is illustrated. Although the quartile method has better robustness in anomaly detection, the quartile method is not sensitive to novel anomalies added at a later stage because it is an unsupervised learning algorithm and does not learn from the later dataset. Although the training time of Random Forest and LightGBM is shorter than LNN-RLSTM in the model pre-training stage, the model needs to be trained once again as a whole every time a new anomaly type is added manually, thus, not improving in time and even increasing the time complexity. The LNN, on the other hand, can recognize the newly added data anomalies in one cycle length unit and, thus, the LNN training is much faster. In the early stage of model pre-training, the time consumption is relatively high because LNN- RLSTM needs to train a dual network. However, when the model is trained, for variable real-time monitoring environments, further updates to the model can be achieved by simply varying the relevant parameters of the LNN, because the operator's marking law is also checked in one cycle.  
Figure 13: Performance based on four metrics under different models with three KPIs.  
The time complexity of LNN and RLSTM is low, as shown in Table 3. The time complexity of LNN is mainly related to the number of layers in its stack. Since the concept of sliding window is introduced in this paper to help LNNs perform local detection, the number of layers of LNN stacking does not need to be excessive, about 4-7 layers are sufficient. The semi-supervised learning approach of this algorithm is further optimized to improve the accuracy of the algorithm by introducing artificial labeling. The fuzzy weight of the sequence memory in the previous cycle is realized by adding the concept of correlation gate to RLSTM. The parameters in RLSTM can be simplified to two matrices, U and V, which are mapped to input and output, the dimension of U is hidden number * Input and the dimension of V is hidden number * hidden number, so the time complexity is lower than other deep learning algorithms.
## 5 Discussion
Finally, tangential distortion (keystoning) caused by projectors being non-perpendicularly aligned to the object surface, as well as projection distortion on non-planar surfaces, needs to be overcome. To solve the aforementioned problems, a projector-camera system is suggested. Here, cameras are used as a proxy for the viewer to calculate the geometric correspondence between points in the projected images and the captured images of the object by the cameras, allowing the projected images to be aligned with the objects [43]. Geometric correspondence is generated by the calibration process of this system, while the calibration is self-forwarded [44]. Calibration methods such as those based on a checkerboard [45] and structured light have demonstrated the potential and speed of self-calibrated projector-camera systems to provide geometric information and deal with registration issues, allowing them to accurately match the projected image with the object. Using such a system would avoid unnecessary manual calibration and achieve faster dynamic projection of design changes. By pre-wrapping the images in terms of the object information derived from camera scanning, automatic calibration of a projector-camera system could also handle keystoning, blending issues as a result of using multiple projectors, and projection on 3D surfaces of complex shapes [44; 46; 47; 48]. Research into automatic calibration for projection mapping has been focused and evolved. However, the tools for automatic calibration are still being developed [49], especially in terms of integrating different functions such as colorimetric correction, edging blending, and accusation of geometric information. At the same time, design software tools specific to the building industry also need to be developed. In addition to the stated future studies, additional technical components need to be added in the proposed method for more interactive SAR building designs that would allow users to interact with augmented information, such as controlling the projected contents by gestures or other interactive mediums. For this functionality, a camera-projector system that enables tracking of a physical model and gesture recognition as an interactive medium could also be applied [50; 51; 52]. Also, considering that the range and amount of information in the BIM model increases as design progress, the proposed method needs to be extended to incorporate diverse building information from BIM models. For example, projecting information provided by various project participants, such as the HVAC information (heating, ventilation, and air conditioning) and structural details inside the building may need to be included in the proposed SAR-based design review framework. This will allow the simulation of building components and functions by using physical mass models, expanding the applicability of the proposed method not only for design phases, but also construction phases to check constructability.
###### Abstract  
Background:This study evaluated the postoperative effects of hyaluronic acid (HA) on pain, swelling, and trismus following mandibular third molar surgery. **Material and Methods:** Thirty healthy patients with bilateral impacted mandibular third molars underwent two surgeries at 21-day intervals. In a split-mouth design, one extraction socket was treated with 0.2 mL of high-molecular-weight hyaluronic acid gel (Monovise(tm) [molecular weight \(\approx\) 1.5-2.2 million Da)], while the contralateral socket received no additional treatment. Perioperative medications, including NSAIDs, were standardized for all patients. Data collection included postoperative pain, swelling (using [PERSON]'s method), analgesic consumption, and trismus (mouth opening) on designated days. Data were analyzed using the Mann-Whitney U and Wilcoxon signed-rank tests with Bonferroni correction (adjusted significance level: \(p\) > 0.0083). **Results:** The mean VAS pain scores on day 1 were 63.5 \(\pm\) 22.3 in the HA group and 61.9 \(\pm\) 12.5 in the control group, decreasing to 3.9 \(\pm\) 7.6 and 3.3 \(\pm\) 7.2, respectively, by day 7 (\(p\) > 0.0083). The maximum interincisal distance on day 7 was 45.9 \(\pm\) 7.4 mm in the HA group and 43.5 \(\pm\) 7.3 mm in the control group, showing a slight improvement (\(p\) = 0.002). Swelling, measured using the tragus-pogonion distance, was 164.6 \(\pm\) 20.7 mm in the HA group and 166.3 \(\pm\) 18.9 mm in the control group on day 7 (\(p\) > 0.0083). Analgesic consumption remained comparable across all postoperative days (\(p\) > 0.0083). No statistically significant differences were observed between the HA and the control groups at any evaluated time point. **Conclusions:** Hyaluronic acid application after mandibular third molar surgery demonstrated a slight improvement in trismus on day 7, but no significant long-term advantages in pain or swelling. While early postoperative improvements in trismus were observed, these findings require further validation. Additional studies are needed to explore HA's potential clinical applications in oral surgery.  
m _Applied Sciences_  
_Article_
## 4 Technology Evaluation Criteria
### Data Rate and Network Delay  
Given definition:  
The data rate describes the throughput a given wireless technology can offer. Latency or network delay describes the time a bit of information needs to get from one communication endpoint to the other communication endpoint.  
Suggested scale:  
The higher the communication requirements of an application, the higher the requirements for large throughput and low latency. We therefore propose a scale from low to high, where throughputs in the range of bit/s to kbit/s and latencies in the range of up to one second are acceptable (category 'low'-e.g., applications for sensor data acquisition) up to throughs in the order of Mbit/s and latencies in the range of milliseconds are required (category 'high' e.g., teleoperation).  
low -bit/s-kbit/s, single seconds  
medium kbit/s-Mbit/s, tenth of a second  
high -10 Mbit/s or possibly higher, milliseconds