esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 1 Introduction
### UAV Characteristics
Apart from using a small number of UAVs, a swarm of UAVs can also cooperatively work to carry out complex tasks in significantly large areas [19] and especially in monitoring and surveillance applications, whereas the flying ad hoc networks (FANETs) [20], in which multiple UAVs communicate in an ad-hoc manner, can effectively expand the connectivity and communication range in scenarios with terrestrial network constraints, i.e., remote nodes, highly mobile nodes, and highly dispersed nodes. Nevertheless, challenges regarding the mobility, resource management, and control of the UAVs are imposed, especially due to large UAV swarms and the variability of their types, while the successful and long-term operation of UAV-based networks necessitates effective interference mitigation along with coordination and interoperability between heterogeneous wireless systems. Moreover, the limited endurance of the UAVs with respect not only to the networking and on-board processing tasks but also to the power demand of their engines and the flight control is currently one of the major practical factors restricting the full-scale deployment of UAVs in NTNs. In thisrespect, drone lifetime prolongation is a major concern, strongly related to flight characteristics and mission parameters. Nevertheless, compared to terrestrial wireless networks, UAV networks have many distinctive features, such as highly dynamic network topologies, orbits or flight paths, and weakly connected communication nodes. Since the power supply is limited, energy-efficient design of airborne systems with respect to path planning and battery scheduling is also required to extend flight duration. Moreover, the mobility and the respective Doppler shift is increased and the Quality of Service (QoS) in data transmissions may be asymmetric. Overall, the communication requirements need to be adapted to the rate and quality of the data transmission in order to achieve the desired performance metrics. Additionally, existing conventional communications techniques encompass inherent limitations, particularly in the case of complex communication scenarios, where unexpected and nonlinear phenomena prevail. As dynamic and mission-critical UAV-based communications lead to particular complexity, uncertainty, and high degree of variability, AI/ML is the key technology that enables fundamentally different decision-making capabilities to obtain a proper UAV placement and trajectory.
# Antioxidant, Antimicrobial, Antidiabetic and Cytotoxic Activity of _Crocus sativus_ L. Petals
###### Abstract  
The purpose of this research is to examine in vitro antioxidant, antimicrobial, antidiabetic and cytotoxic efficacy of different extracts of _Crocus sativus_ L. petals. Antioxidant activity of extracts was assessed by DPPH and ABTS (2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) assay. Among all extracts, ethanol (SPE) had the maximum radical scavenging activity with IC\({}_{50}\) values 86.63 \(\pm\) 7.53 ug/mL. The antimicrobial activity was determined by the evaluation of the minimum inhibitory concentration using the agar well plate procedure. The most effective extract was SPE with a minimum inhibitory concentration varying between 500 ug/mL, 250 ug/mL, 125 ug/mL, 62.5 ug/mL, 31.25 ug/mL, 15.63 ug/mL. Cytotoxic activity was tested against MDA-MB-231 cell lines using the MTT method whereas, antidiabetic activity was evaluated using an alpha-glucosidase inhibition assay. All extracts were found to have significant antidiabetic activity.  
Keywords:anti-diabetic; _Crocus sativus_ L. petals; ABTS assay; cytotoxic; polyphenols; FTIR +
Footnote †: journal: Applied
## 3 Experiments and Discussion
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **3 DCNN** & **GCN** & **MiniGCN** & **HybirdSN** & **DBDA** & **MDGCN** & **FCGN** \\ \hline
1 & 80.00 & 94.66 & 93.73 & 83.44 & 99.51 & 88.88 & **100.00** \\
2 & 90.16 & 70.37 & 72.54 & 80.38 & 93.55 & 96.16 & **96.51** \\
3 & 80.05 & 66.89 & 76.83 & 82.21 & 94.11 & 94.17 & **98.46** \\
4 & 86.54 & 86.88 & 98.16 & 99.**19** & 96.42 & 92.59 & 97.36 \\
5 & 84.43 & 89.27 & 93.40 & 96.47 & 97.64 & **96.79** & 97.13 \\
6 & 79.12 & 93.38 & 92.64 & 98.81 & 96.23 & 99.50 & **100.00** \\
7 & 67.22 & 92.91 & 88.48 & 86.89 & 96.66 & **96.82** & 92.89 \\
8 & 90.87 & 96.14 & 97.59 & 98.04 & 91.35 & 95.72 & **98.68** \\
9 & 70.00 & **100.00** & 99.73 & 73.11 & 89.37 & 99.98 & **100.00** \\
10 & 79.11 & 85.37 & 75.98 & 90.41 & 70.30 & 85.70 & **97.30** \\
11 & 90.81 & 68.45 & 79.42 & 74.23 & 90.32 & 96.06 & 98.63 \\
12 & 72.50 & 78.90 & 79.51 & 91.00 & 97.38 & **98.88** & 95.17 \\
13 & 70.88 & **99.84** & 98.93 & 71.88 & 97.99 & 97.21 & 99.59 \\
14 & 85.83 & 85.12 & 87.88 & 98.33 & 98.34 & **99.93** & 99.33 \\
15 & 92.30 & 82.67 & 89.82 & 94.48 & **96.25** & 96.17 & 95.71 \\
16 & 69.22 & 97.41 & **100.00** & 70.22 & 86.66 & 94.82 & 97.75 \\ \hline OA(\%) & 78.47 & 86.67 & 88.67 & 87.99 & 94.55 & 97.62 & **98.78** \\ AA(\%) & 80.57 & 86.77 & 89.04 & 86.82 & 93.26 & 95.58 & **97.80** \\ Kappa & 80.70 & 84.38 & 88.39 & 87.44 & 94.01 & 95.49 & **97.99** \\ Train time(s) & 250.11 & 59.02 & 342.55 & 220.99 & 298.32 & 1204.15 & 204.50 \\ Test time(s) & 15.04 & 5.60 & 15.38 & 14.51 & 21.77 & 20.33 & 5.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of the IP dataset. Figure 8: The classification maps for Salinas. (**a**) False-color image; (**b**) ground truth; (**c**) 3 DCNN; (**d**) GCN; (**e**) MiniGCN; (**f**) HybridSN; (**g**) DBDA; (**h**) MDGCN; (**i**) FCGN; (**j**) Figure legend. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **3 DCNN** & **GCN** & **MiniGCN** & **HybirdSN** & **DBDA** & **MDGCN** & **FCGN** \\ \hline
1 & 80.85 & 77.26 & 86.22 & 94.10 & 96.36 & **99.00** & 98.41 \\
2 & 80.49 & 76.97 & 92.21 & 94.36 & 99.11 & 98.21 & **99.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of the PU dataset. Table 6 shows the classification results of the different models on the Salinas dataset. We can see that the FCGN was superior to other methods in terms of the OA, AA, and Kappa coefficient, proving the effectiveness of the FCGN algorithm again. By observing the classification results of Grapes untrained and Vineyard untrained ground features in the Salinas dataset, the classification accuracy is relatively low; this is largely because the two ground features are mixed together. We can see from Figure 8 that the FCGN method had fewer misclassified pixels than other methods and was more accurate for classifying large-scale regions.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 5 Experiments
### Baseline Algorithms  
Many state-of-the-art open-set recognition methods are compared below, including _OpenMax_[21], _G-OpenMax_[38], _CROSR_[23], _ARPL_[26], and _SARPL_[26]. Furthermore, we compared our proposed algorithm with some ablation versions in order to show the efficiency of the introduced sub-modules. Those methods are briefly introduced below:  
1. _OpenMax_[21]: A typical discriminative model-based method with OpenMax layer;
2. _G-OpenMax_[38]: A generative model-based method extended from OpenMax, which employs the conditional generation countermeasure network (GAN) to generate unknown class samples;
3. _CROSR_[23]: An improved version of OpenMax, which is obtained through reconstructing samples and encoding more original samples to improve the performance in open set scenarios;
4. _ARPL_[26]: The reciprocal points are introduced to separate the feature spaces of the known and unknown classes, and the classification is determined by a threshold method.
5. _SARPL_[26]: Based on the classical ARPL method, we introduced the proposed \(\mathcal{L}_{ac}\) loss function to constrain the convergence direction of the reciprocal points. This SARPL can be considered as an ablation version of our proposed algorithm \(\text{OSSG}_{TW}\) without the ViT, Wasserstein distance, and central loss techniques.
6. _Ablation versions_: \(\text{OSSG}_{TW}\)(w/o CL) and \(\text{OSSG}_{TW}\)(w/o W/CL) are the versions without central loss, and without both Wasserstein distance and central loss, respectively.
## 3 The Proposed HSTMAC Framework and System Model
### Foundation of the HSTMAC Protocol
#### 3.1.1 System Capacity of S-ALOHA Protocol  
Let \(d\) denote the packet size and \(L\) denote the total available resource blocks. If the number of active devices is \(X\), the average resource utilization \(R\) is calculated as:  
\[R=\delta\cdot Xe^{-\frac{X}{L/\delta}} \tag{1}\]  
where the available data channels is \(L/\delta\). Then the well-known capacity of S-ALOHA system \(C_{S\text{-}ALOHA}\) is obtained when \(X=L/\delta\):  
\[C_{S-ALOHA}=Le^{-1} \tag{2}\] \[X^{*}_{S-ALOHA}=L/\delta\]  
where \(X^{*}_{S-ALOHA}\) is the maximum supportable devices of the S-ALOHA system.
# Pharmaceutical Industry in Vietnam: Sluggish Sector in a Growing Market
## 3 Evolution of Pharmaceutical Industry  
### Overview of the Industry  
#### 3.1.1 State of Development of the Domestic Production  
According to some reports, the pharmaceutical industry in Vietnam has reached an intermediate level of international integration resulting in the development of a domestic pharmaceutical industry that is specialized in manufacturing generics and exporting non sophisticated pharmaceutical products to other countries [3]. However, it should be noted that so far the domestic industry mainly produces drugs out of imported raw materials while the value-added of exports is very small in comparison to the inputs imported for production.  
The value of drugs produced in Vietnam accounted for 0.72% of GDP in 2014 and only 2.18% of national industrial revenues [20]. It is forecasted that the pharmaceutical industry will expand at 15.5% in the next 5 years, contributing to 2.2% GDP in 2017 [2].  
In 2015, the domestic industry supplied up to 50% of the domestic demand while imports covered the remaining half, not yet counting values of input materials and active ingredients [20]. In this regards, approximately 60% of pharmaceutical end products, 90% of active pharmaceutical ingredients, and most raw materials for the production of pharmaceuticals are currently imported. On the other side, foreign enterprises are responsible for an estimated 20% of the domestic pharmaceutical production [38].  
In 2015, the distribution system consisted of 1200 companies (both domestic and foreign manufacturers); 1180 public health hospitals; 170 private hospitals (mainly located in the big cities) and 54,250 retail drug stores [17]. Foreign Direct Investment logistic companies and foreign pharmaceutical companies are not permitted to distribute pharmaceutical products directly in Vietnam.
## 4 Analysis
### Neural Networking for the Centralized Approach  
Backpropagation Learning Algorithm  
This subsection reviews the neural network algorithm [30] for analyzing the performance of the centralized method. Assume that the network under consideration has a general architecture with three layers of neurons. In our case, input and output layer neurons are linear, whereas neurons in the hidden  
Figure 3: Virtual sensor scheduling flowchart for the DASA algorithm.  
layer are log-sigmoidal. Let the vector pairs in \(\mathcal{T}\) be sample representation of the unknown function \(f:\mathcal{R}^{n}\rightarrow\mathcal{R}^{p}\):  
\[T=\left\{(X_{k},D_{k})\right\}_{k=1}^{Q}, \tag{6}\]  
where \(n\) is the neuron index range in the input layer, \(p\) is the neuron index range in the output layer, \(X_{k}\in\mathcal{R}^{n}\), \(D_{k}\in\mathcal{R}^{p}\), \(Q\) is the number of training vector pairs, and \(k\) is the iteration index. Note that \(D_{k}\) is the desired vector response for the network input \(X_{k}\). Thus, the mean square error of the entire training set is:  
\[\mathcal{E}=\frac{1}{Q}\sum_{k=1}^{Q}\mathcal{E}_{k} \tag{7}\]  
where \(\mathcal{E}_{k}=\frac{1}{2}E_{k}^{T}E_{k}\), and \(E_{k}\) is the instantaneous error of the training pair \((X_{k},D_{k})\). Based on the above description, the update of neuron activations can be formulated as follows.  
For the hidden layer:  
\[z_{h}^{k}=\sum_{i=0}^{n}w_{ih}^{k}x_{i}^{k},\ h=1,\ldots,q \tag{8}\]  
\[\mathcal{S}(z_{h}^{k})=\frac{1}{1+e^{-z_{h}^{k}}},\ h=1,\ldots,q \tag{9}\]  
\[w_{ih}^{k+1}=w_{ih}^{k}+\eta(-\frac{\partial\mathcal{E}_{k}}{\partial w_{ih} ^{k}}). \tag{10}\]  
For the output layer:  
\[y_{j}^{k}=\sum_{h=0}^{q}\frac{w_{hj}^{k}}{1+e^{-z_{h}^{k}}},\ j=1,\ldots,p \tag{11}\]  
Figure 4: The round ID updating process of the DASA algorithm; the \((\cdot)\) represents the round ID.  
\[\mathcal{S}(y_{j}^{k})=y_{j}^{k},\ j=1,\ldots,p \tag{12}\] \[w_{hj}^{k+1}=w_{hj}^{k}+\eta(-\frac{\partial\mathcal{E}_{k}}{ \partial w_{hj}^{k}}). \tag{13}\]  
Note that \(q\) is the neuron index range in the hidden layer, \(x_{i}^{k}\) and \(y_{j}^{k}\) are the _i_th and _j_th component of the input vector \(X_{k}\) and the output vector \(Y_{k}\), respectively, \(w_{ih}^{k}\) and \(w_{hj}^{k}\) are the biases of the hidden and output neurons, respectively, \(\mathcal{S}(\cdot)\) is the signal function, and \(\eta\) is the learning rate in the back-propagation algorithm.
## 3 Methodology for Change Detection
### Forest Decrease Detection  
The forest decrease change detection techniques applied in this study rely on the computation of height differences. In the selected test region, trees are harvested applying a selective logging system, the windthrow in 2007 is therefore the main reason for height decrease in large regions. The DSMs taken before and after 2007 can be used as pre- and post-event data, respectively. Tree change monitoring from pixel based DSM data is a difficult task, as the tree crown representation in the DSMs may differ in different acquisitions due to local variations in reflection properties. Moreover, DSMs generated using stereoscopic or similar imaging data may contain imprecise height values due to matching errors. Finally, the resolution differences among the DSMs as well as tree growths might influence local change detection results. Resampling all DSMs to the same resolution and median filtering by a small kernel does not significantly change the original quality of the DSMs. Thus, in order to allow a reliable comparison, a median filter with window size of \(5\times 5\) pixels was applied to all DSMs before the post-change DSMs were subtracted from the pre-change DSMs. The height change \(\Delta H(i,j)\) of pixel \(H(i,j)\) at coordinates \(i,j\) is defined as,  
\[\Delta H(i,j)=H^{\prime}_{pre}(i,j)-H^{\prime}_{after}(i,j) \tag{1}\]  
where \(H^{\prime}_{pre}(i,j)\) and \(H^{\prime}_{after}(i,j)\) represent the height values in the pre- and post-event DSMs after applying a median filtering, respectively. The windowsize of \(5\times 5\) pixels is set due to the resolution differences among the DSMs. Based on the generated height difference map, a change mask _ChangeMask_ can be derived by setting a threshold \(T\) as:  
\[ChangeMask(i,j)=\begin{cases}1&\Delta H(i,j)\geq T\\ 0&\Delta H(i,j)<T\end{cases} \tag{2}\]  
Figure 3: Distribution of 184 selected inventory plots overlaid on the forest mask which was manually digitized from WorldView-2 data, where black pixels represent the center of each plot and the forest mask is shown in light grey color.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 5 Conclusions and Suggestions  
In this paper, the long-term performance of SMA-13 containing different fibers was compared. The following conclusions can be drawn after conducting this research:  
1. The high-temperature performance of the BFSMA-13 is better than that of the LFSMA-13 at different aging degrees. The high-temperature performance of BFSMA-13 increases with the increase of the aging degree, while the aging process decreases the high-temperature property of LFSMA-13.
2. The cracking resistance of BFSMA-13 is better than that of LFSMA-13 at different aging stages. The test results proved that BFSMA-13 is more capable of deformation and less prone to cracking at low and medium temperatures.
3. The Marshall stability, immersed Marshall stability, and the residual Marshall stability of BFSMA-13 are greater than that of LFSMA-13, which shows that basalt fiber can better improve the strength and the water stability of SMA-13 mixture than lignin fiber.
4. Different properties of BF and LF lead to their different roles in the mixture. BF can improve the performance of asphalt mixtures by acting in concert with the asphalt mixture. LF can absorb excess free asphalt in SMA-13, and it is not as good as BF in improving the performance of the mixture.
5. At the characteristic peak, the increasing percentage of the peak areas in the spectrum of SBSBF is lower than those in the SBSLF as the mixture ages, indicating that the aging process has a greater impact on the chemical composition of SBSLF than the SBSBF.  
Some suggestions are made for further works: (1) the aging method used in this paper only involves thermal aging, and other aging conditions should be adopted, such as ultraviolet light aging; (2) the fatigue properties of asphalt mixtures were not tested in this paper and should be conducted in the future; (3) properties of fiber asphalt mastic at different aging conditions should be tested in further studies; and (4) the reinforcement mechanism of fiber on asphalt mixtures needs to be further clarified.  
Conceptualization, B.W. and P.X.; methodology, K.L.; software, K.L.; validation, J.X. and C.C.; formal analysis, X.W.; investigation, C.C.; resources, J.X.; data curation, K.L.; writing--original draft preparation, X.W.; writing--review and editing, B.W.; visualization, C.C.; supervision, P.X.; project administration, P.X.; funding acquisition, P.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant-Number 52008365).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors want to appreciate the Yangzhou University Test Center for buying and offering some of the test instruments and materials, and also want to thank Applied Sciences Editorial Office for their patient editing and support during the writing of this manuscript.  
The authors declare no conflict of interest.
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 3 Paint Industry Wastewater Treatment Methods
### Physicochemical treatment  
Physicochemical treatment methods are the most common techniques for paint industrial wastewater treatment. Table 1 contains recently used methods, such as sedimentation, filtration, adsorption, ion exchange, coagulation-flocculation, and oxidation primarily focusing on dividing the colloidal and suspended particles.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 4 MADM Handover Algorithm
### Quality of Service Attributes and User Preferences  
Handover decision-making chooses the best network depending on a set of attributes that include the network conditions, user preferences, devices, applications and traffic. In this paper, we focus on several QoS attributes (such as the received signal strength indicator-RSSI, delay, utilization and packet loss) and user preferences (cost per byte) that are suitable for the WBAN traffic categories in Section 3.2. These parameters are calculated according to the expression detailed hereafter.  
In radio propagation environments, the RSSI can be calculated as follows [28]:  
\[RSSI(\mathrm{dBm})=P_{t}-10\eta\log\frac{d}{d_{0}}-X \tag{1}\]  
\[f_{x}(x)=\delta_{x}^{-1}\times e^{\frac{\eta+b_{x}}{\delta_{x}}}\times e^{-e (\frac{\tau+b_{x}}{\delta_{x}})} \tag{2}\]  
where \(P_{t}\) denotes the transmission power of the sender (dBm). Here, \(\eta\) is the path loss exponent (e.g., \(\eta=2\) in a free-space environment); and \(d\) and \(d_{0}\) are the distance from the sender to the receiver and the reference distance, respectively. Additionally, \(X\) is a Gaussian random variable with zero mean and deviation \(\delta_{x}\) in Equation (2), shown in Figure 2.  
The utilization is given by Equation (3)  
\[U_{utilization}\ (\%)=\frac{data\ (\mathrm{bit})}{Bandwidth\ (\mathrm{Hz})\times Interval (\mathrm{s})}\times 100 \tag{3}\]  
Figure 2: Distribution of the probability density function (Pdf) of \(X\).  
The delays in the WiFi, UMTS and LTE are shown in Equations (4)-(6), respectively, as follows [29]:  
\[D_{WiFi}=T_{diffs}+T_{sifs}+T_{boff}+T_{data}+T_{ack} \tag{4}\]  
The data transmission time \(T_{data}\), back-off slots time \(T_{boff}\), and acknowledgement time \(T_{ack}\) are given by the following equations [27]:  
\[T_{data}=\frac{L_{phy}+L_{maxcht}+payload+L_{maxft}}{R_{data}}+ \frac{d}{s}\] \[T_{boff}=bo_{slots}\times T_{boslots}\] \[T_{ack}=\frac{L_{phy}+L_{maxcht}+L_{maxft}}{R_{data}}\]  
\[D_{UMTS}=5\;\text{ms}+X\times 10\;\text{ms}+\frac{l}{\alpha}\times 10\;\text{ms} \tag{5}\] \[D_{LTE}=T_{up}+T_{Buff}+T_{re}+T_{U\_sch_r}+T_{U\_sch_g}+T_{UE}+T_{ eNodeB}+T_{core} \tag{6}\]  
The remaining parameters used in this paper are listed in Table 1.
## 6 Application of the Optimization Techniques: Routing Protocols
### Attribute-based or Data-centric Routing Protocols
#### 6.1.5 AcquiRE (Active Query Forwarding in Sensor Networks)  
This algorithm [13] also considers the wireless sensor network as a distributed database. In this scheme, a node injects an active query packet into the network. Neighboring nodes that detects that the packet contains obsolete information, emits an update message to the node. Then, the node randomly selects a neighbor to propagate the query which needs to resolve it. As the active query progress through network, it is progressively resolved into smaller and smaller components until it is completely solved. Then, the query is returned back to the querying node as a completed response.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Aluminum Oxide Nanoparticles  
Aluminum is a very light whitish-gray metal that is a good conductor of electric current. Aluminum oxide is a bio-inert heat-resistant crystalline substance suitable for biological environments in the tissue engineering field. It serves as a catalyst in the chemical and petrochemical industries [160].  
Aluminum oxide nanoparticles (Al\({}_{2}\)O\({}_{3}\)-NPs) integrated with ceramic coatings show good osseointegration, low cytotoxicity, good biodegradability, and are more resilient to long-term implantation failure. In view of their good mechanical behavior, aluminum implants are characterized by long-time survival predictions with an enhanced proliferation of osteoclastic cells [161].  
[PERSON] et al. compared the 3D porous \(\beta\)-Ca\({}_{2}\)SiO\({}_{4}\) scaffold and its modification by the aluminum ions solid solution. Significantly higher proliferation and differentiation of rat BM-MSCs on the aluminum-modified scaffolds were observed; also, their degradation rate was slower. Enrichment with Al\({}_{2}\)O\({}_{3}\) exhibited enhanced bone formation, which has been proven by increased expression of osteogenic markers such as OC, OPN, and Col1 [162].  
Reinforcement of polyhydroxy butyrate-chitosan scaffolds with Al\({}_{2}\)O\({}_{3}\) nanowires enhanced mechanical properties with a tensile strength of 10 times higher compared to the same basic scaffold without aluminum nanowires [163].