esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Model Architecture for Proposed Approach
Unlike ReLU, which truncates negative values, Mish retains negative information through a smooth, non-monotonic curve, improving the model's capacity to learn subtle patterns and enhancing its generalization capabilities [39; 40]. Supporting its adoption in this work, prior studies show Mish's superior performance compared to ReLU and Swish in CNN-based tasks, particularly within the field of medical imaging, including applications for pediatric pneumonia recognition [41]. Moreover, we conducted experiments comparing our model using the Mish activation function against the same model using ReLU. This comparative analysis allowed us to em pirically evaluate the impact of Mish on performance metrics and validate its effectiveness in our specific application. Overall, the proposed approach is demonstrated in Algorithm 2. ```
1. Step 1: Define Mish Activation Function
2. Define the Mish activation function:
3. \(\textit{Mish}(x)=x^{*}\textit{tanh}(\textit{softplus}(x))\), where:
4. \(\textit{softplus}(x)=log(1+\textit{exp}(x))\),
5. \(\textit{tanh}(x)=(\textit{exp}(x)-\textit{exp}(\textit{-x}))/(\textit{exp}(x)+ \textit{exp}(\textit{-x}))\). 6. Step 2: Load Pretrained Model
7. Load DenseNet201 pretrained on ImageNet without the top layers. 8. Freeze the pretrained layers for feature extraction. 9. Step 3: Add Multi-Scale Convolutions
10. Extract features from DenseNet201 (\(\textit{base\_model.output}\)). 11. Apply multi-scale convolutional layers:
12. \(\textit{conv\_small}\) with filter size 2 \(\times\) 2, activation: _mish_. 13. \(\textit{conv\_medium}\) with filter size 4 \(\times\) 4, activation: _mish_. 14. \(\textit{conv\_large}\) with filter size 8 \(\times\) 8, activation: _mish_. 15. Concatenate multi-scale features. 16. Step 4: Global Pooling and Classification
17. Apply GlobalMaxPooling2D on concatenated features. 18. Add a Dense layer with sigmoid activation for binary classification. 19. Step 5: Compile and Train the Model
20. Compile the model using:
21. Optimizer: [PERSON]. 22. Loss function: _binary_crossentropy_. 23. Metrics: _Accuracy and AUC_. 24. Train the model with the training generator. ```  
**Algorithm 2**Multi-Scale Convolution and Mish activation function with Pretrained DenseNet201
## 3 Method Design
### Dependency Information Fusion Attention Graph Convolutional Network
#### 3.2.2 BERT Encoding Layer  
Machine learning methods cannot directly process text data and require suitable methods to convert text data into digital data, thus introducing the concept of word vectors. Word vectors are representations of words as low-dimensional dense vectors for subsequent tasks, such as the traditional word vectors word2 vec and glove, which are based on the assumption of the distribution of word meanings to obtain its unique mapping vector. However, this approach ignores the problem of multiple meanings of words and does not take into account the semantics of the context, and is therefore a static word vector. In 2018, Google proposed the pre-trained language model BERT [20], which uses a self-supervised method to learn a feature representation for each word on the basis of a massive corpus. The structure of previous pre-trained models would be limited by the one-way language model (left-to-right or right-to-left), thus also limiting the representational power  
Figure 7: Model diagram.  
of the model to acquire only unidirectional contextual information. BERT uses a masked language model (MLM) for pre-training and a deep bi-directional Transformer component to construct the entire model, thus generating a deep bi-directional linguistic representation that incorporates left and right contextual information. In this paper, we first obtain the word vector and sentence vector representations of each data through the BERT model.  
Assuming \(X=[x_{1},x_{2},\ldots,x_{n}]\) is an input text sentence in the dataset, where \(x_{j}\) (\(1\leq j\leq n\)) is the \(j\)th word in the sentence. In this paper, we use BERT to encode \(X\) as follows:  
\[H=[h_{1},h_{2},\ldots,h_{n}]=BERT([x_{1},x_{2},\ldots,x_{n}]) \tag{1}\]  
where \(h_{j}\) is the hidden vector of the last layer of BERT output.
## 3 BER Performance Estimation
### Instantaneous BER Performances for Various Modulation Schemes  
Firstly, it is necessary for the instantaneous BER of any individual signal to be expressed in terms of instantaneous electrical signal-to-noise ratio (SNR), \(\gamma_{n}=\eta^{2}l_{n}^{2}/N_{0}\), and expected SNR, \(\mu_{n}=\eta^{2}E^{2}[l_{n}]/N_{0}\), with \(E[l_{n}]=A_{0,n}g_{n}(c_{n}+\Omega_{n})/\left(1+\xi_{n}^{-2}\right)\) being the expected value of the \(n\)-th received signal, concluding with the following expressions for OOK-RZ/NRZ, \(L\)-PPM, and \(L\)-PAM, respectively [43; 44]:  
\[P_{b,n,OOK-RZ}(I_{n}) =Q\biggl{(}\frac{1}{\sqrt{2}}\frac{\eta_{n}I_{n}}{\sqrt{N_{0}}} \biggr{)}=Q\Biggl{(}\frac{1}{\sqrt{2}}\frac{\left(1+\xi_{n}^{-2}\right)\sqrt{ \mu_{n}}I_{n}}{A_{0,n}g_{n}(c_{n}+\Omega_{n})}\Biggr{)} \tag{8a}\] \[P_{b,n,OOK-NRZ}(I_{n}) =Q\biggl{(}\frac{1}{2}\frac{\eta_{n}I_{n}}{\sqrt{N_{0}}}\biggr{)}=Q \Biggl{(}\frac{1}{2}\frac{\left(1+\xi_{n}^{-2}\right)\sqrt{\mu_{n}}I_{n}}{A_{ 0,n}g_{n}(c_{n}+\Omega_{n})}\Biggr{)}\] (8b) \[P_{b,n,L-PPM}(I_{n}) =\frac{L}{2}Q\Biggl{(}\frac{\sqrt{L\log_{2}L}}{2}\frac{\eta_{n}I_ {n}}{\sqrt{N_{0}}}\Biggr{)}=\frac{L}{2}Q\Biggl{(}\frac{\sqrt{L\log_{2}L}}{2} \frac{\left(1+\xi_{n}^{-2}\right)\sqrt{\mu_{n}}I_{n}}{A_{0,n}g_{n}(c_{n}+ \Omega_{n})}\Biggr{)}\] (8c) \[P_{b,n,L-PAM}(I_{n}) =\frac{2(L-1)}{L\log_{2}L}Q\Biggl{(}\frac{\sqrt{\log_{2}L}}{\sqrt {2}(L-1)}\frac{\eta_{n}I_{n}}{\sqrt{N_{0}}}\Biggr{)}=\frac{2(L-1)}{L\log_{2}L }Q\Biggl{(}\frac{\sqrt{\log_{2}L}}{\sqrt{2}(L-1)}\frac{\left(1+\xi_{n}^{-2} \right)\sqrt{\mu_{n}}I_{n}}{A_{0,n}g_{n}(c_{n}+\Omega_{n})}\Biggr{)} \tag{8d}\]  
The previous modulation schemes' expression can be formulated through the following equation:  
\[P_{b,n,MS}(I_{n}) =p_{MS}Q\biggl{(}q_{MS}\frac{\eta_{n}I_{n}}{\sqrt{N_{0}}}\biggr{)} =p_{MS}Q\Biggl{(}q_{MS}\frac{\left(1+\xi_{n}^{-2}\right)\sqrt{\mu_{n}}I_{n}}{A _{0,n}g_{n}(c_{n}+\Omega_{n})}\Biggr{)} \tag{9}\]  
\[\text{where, }p_{MS} =\left[\begin{array}{c}p_{OOK}\\ p_{L-PPM}\\ p_{L-PAM}\end{array}\right] =\left[\begin{array}{c}1\\ L/2\\ 2(L-1)/(L\log_{2}L)\end{array}\right]\text{ and }q_{MS}=\left[\begin{array}{c}q_{OOK- RZ}\\ q_{OOK-NRZ}\\ q_{L-PPM}\\ q_{L-PAM}\end{array}\right] =\] \[\left[\begin{array}{c}\sqrt{2}/2\\ 1/2\\ \sqrt{\log_{2}L}/2\\ \sqrt{\log_{2}L}/\left[\sqrt{2}(L-1)\right]\end{array}\right].\]  
Next, taking OC diversity into consideration, the instantaneous BER performance is rewritten as:  
\[P_{b,N,MS}(I_{n}) =p_{MS}Q\Biggl{(}q_{MS}\sqrt{\frac{\frac{N}{n}\left(\eta_{n}I_{n} \right)^{2}}{NN_{0}}}\Biggr{)}=p_{MS}Q\Biggl{(}q_{MS}\sqrt{\sum_{n}^{N}\frac{ \left(1+\xi_{n}^{-2}\right)^{2}\mu_{n}I_{n}^{2}}{A_{0,n}g_{n}^{2}(c_{n}+\Omega_ {n})^{2}}}/N\Biggr{)} \tag{10}\]
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 16 Enhancement Prospects
#### 2.4.3 Enhanced Group Message Mechanism  
In Release 16, there should be more enhancements to support downlink command between user groups and group RNTIs. This is because MBMS which was proposed in Release 14 is only efficient for large size downlink command message transmission and requires many UEs to be deployed. For example, the application layer common message can be very small but sent to many UEs under a small group of UEs hence making MBMS not efficient for such applications.
# Enhanced Beacons Dynamic Transmission over TSCH
## 5 Experimental Results and Discussion
### A Ring Network Scenario  
To have a closer look at the network topologies implemented in the real scenarios, simulations of scenarios where the nodes belonging to the network are distributed in the form of rings are also presented. The join-seekers are deployed forming rings around the coordinator node, which acts as a sink as shown in Figure 11. In addition, the link quality is also considered to provide the simulations with a metric that allows the representation of deficiencies in the communication channel. The link quality parameter indicates the probability of successful transmissions.  
Figure 11: Simulation scenario with ring topology. Node 1 is the network coordinator and nodes 2–11 are join-seekers. The green circle depicts the transmission range of node 1, while the grey circle depicts the interference range.  
Figure 10: Average energy consumption of the linear network scenario until all nodes get associated.  
The scenario displays two rings of nodes, accumulating ten join-seekers and one coordinator node. As in the previous case, a comparison is made between the same three different configurations, \(\mathit{Teb}=4\) s, and a total of 4 available channels are set. Figure 12 shows the average network association time.  
Also in this scenario, the network performance under the implementation of EBDT-TSCH improves the behavior obtained by default minimal 6 TiSCH during the network formation process despite the link quality between nodes. For example, in the scenario that has 0.75 probability of successful transmission, the configurations of EBDT-TSCH with \(\beta=0.8\) and \(\beta=1.8\) manage to reduce the average network formation time obtained by 6 TiSCH by 17.20% and 28.77%, respectively.  
Figure 13 shows the average energy consumption during network formation corresponding to the scenario whose link quality value is 0.75. The consumption generated by the network is shown, composed of the contribution of each ring of join-seekers and the coordinator. The contribution of each ring is estimated as the sum of the average energy consumption of each join-seeker node of the ring. The simulation results show that the EBDT-TSCH configurations decrease energy consumption obtained by the default minimal 6 TiSCH configuration by 21.27% for \(\beta=0.8\) and 33.65% for \(\beta=1.8\), respectively.  
Figure 12: Network formation time in a ring topology for different link quality values.  
Figure 13: Average energy consumption of the network in ring topology.
## 5 A Case Study  
In this section, we present a relatively simple but illustrative case study aimed at addressing a straightforward problem concerning V2G systems. The problem revolves around minimizing the cost of charging electric vehicles. We will assume that each vehicle takes one hour to charge and discharge completely. We also assume that every hour of the  
Figure 3: Agile optimization framework for dynamic and real-time optimization problems.  
Figure 2: Learnheuristic framework to address V2G optimization problems.  
day, a decision regarding whether a vehicle should be charged or discharged is made. Only full charge and discharge are allowed each hour, and three distinct pricing schedules are considered: expensive hours, medium hours, and cheap hours. These prices are scheduled throughout the day, selecting a price among the three options every four hours. This schedule is known at the start of the day, and once a vehicle arrives at the charging station, its departure time is known. Once a vehicle's departure time arrives, the vehicle needs to be fully charged. We will compare two scenarios. In the first scenario, a decision-making system is implemented, aiming to minimize the vehicles' charging cost. Conversely, in the second scenario, a decision-making method is not implemented, and vehicles are charged in the last hour before their departure, regardless of the price.
## 2 Characterization of FBG
### Sensing Characteristics of FBGs During Stress Changes  
When a section of the grating is subjected to axially applied tension, the grating area of the grating spacing increases, the strain is uniform, the temperature is constant, the fiber diameter is \(D\), its core radius is \(a\), the grating length is \(L\), and the grating spacing is \(\Lambda\), when the grating area is subjected to axial force, as shown in Figure 1.  
Figure 1: Structure of grating axial strain.  
The following is known from mechanical analysis:  
\[\varepsilon_{x}=\varepsilon_{y}=-\mu\varepsilon_{z} \tag{3}\]  
The axial tension causes the grating area to change, and by differentiating Equation (1), Equation (2) can be obtained. Then, (4) is associated with (1):  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=\frac{\Delta\Lambda}{\Lambda}+\frac{ \Delta n_{eff}}{n_{eff}} \tag{4}\]  
Due to the principle of elasticity:  
\[\Delta\Bigg{(}\frac{1}{n_{eff}^{2}}\Bigg{)}=(p_{11}+p_{12})\varepsilon_{x}+p _{12}\varepsilon_{z} \tag{5}\]  
And:  
\[\frac{\Delta n_{eff}}{n_{eff}}=-\frac{n_{eff}^{2}}{2}[(p_{11}+p_{12}) \varepsilon_{x}+p_{12}\varepsilon_{z}] \tag{6}\]  
\(\varepsilon_{x}=-\mu\varepsilon_{z}\) substitute into (6):  
\[\frac{\Delta n_{eff}}{n_{eff}}=-\frac{n_{eff}^{2}}{2}[p_{12}-\mu(p_{11}+p_{12 })]\varepsilon_{z} \tag{7}\]  
The following is also observed:  
\[\frac{\Delta\Lambda}{\Lambda}=\varepsilon_{z} \tag{8}\]  
Substituting Equations (7) and (8) into Equation (4), we have:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=\Bigg{\{}1-\frac{n_{eff}^{2}}{2}[p_{12 }-\mu(p_{11}+p_{12})]\Bigg{\}}\varepsilon_{z} \tag{9}\]  
where \(p_{11}\) and \(p_{12}\) represent the elasticity coefficient; \(\mu\) is Poisson's ratio; \(\varepsilon_{z}\) is the axial strain.  
The following is also observed: \(p_{e}=\frac{n_{eff}^{2}}{2}[p_{12}-\mu(p_{11}+p_{12})]\)  
Simplifying gives:  
\[\frac{\Delta\lambda_{B}}{\lambda_{B}}=(1-p_{e})\varepsilon_{z} \tag{10}\]  
For a particular grating, the material properties are determined after the production of the grating; so, \(p_{e}\) is constant. Therefore, it can be concluded that \(\Delta\lambda_{B}\) and \(\varepsilon_{z}\) are linearly related [19].
## 3 Results and Discussion
### Stress-Strain Dependency and Comparison with the Experimental Measurements  
DEM simulations of the uniaxial compression test were performed to obtain numerical results as close as possible to the experimentally measured response of the NiAl sample. The macroscopic response of the sample subjected to uniaxial compression load is represented by the stress-stain dependency. The total force of particles on the compressing plate is divided by the area of the sample to compute the stress values. The average strain is obtained by dividing the prescribed displacement of either the upper or lower compressing plates by the initial height of the sample. Figure 3 shows the quantitative comparison of the numerical results with the experimental measurements.  
Figure 2(a) presents the stress dependency on strain obtained from the experimental uniaxial compression test (the curve \"EXP\") and that obtained from DEM computations (the curve \"DEM\"). Three snapshots of particles colored according to their percentage of broken bonds at different stages of loading are included. Figure 2(b) presents the count of broken bonds, which describes the damaging process and supplements the stress-strain curve. During the performed simulations, the count of broken bonds \(N_{bb}\) increases from 0 to 19,504. In the generated discrete element packing, the total count of bonds is equal to 441,954.  
Initially, when the strain varies from 0.000 to 0.010, the count of broken bonds is negligibly small (Figure 2(b)). Therefore, the elastic behavior of the material is dominant, and the numerical stress-strain curve is linear (Figure 2(a)). When the strain rises from 0.010 to 0.0261, the count of broken bonds exponentially increases (Figure 2(b)) and some random microcracks initiate (the second snapshot in Figure 2(a)), which leads to slow weakening of the sample. The macroscopic stress-strain curve starts deviating from the line, but only small differences can be observed (Figure 2(a)). In this strain interval, the experimental stress-strain curve shows some features of ductile behavior. However, the low value of the index B15 [40], equal to 0.51%, indicates more brittle behavior of the considered NiAl material. Finally, when the strain values exceed 0.0261, the count of broken bonds grows very quickly (Figure 2(b)) and macroscopic tensile cracks form in the sample (the third snapshot in Figure 2(a)). The macroscopic stress-strain curve falls down, indicating the beginning of the sample failure (Figure 2(a)). DEM simulations accurately predict the  
Figure 3: Comparison of the numerical results with the experimental measurements: (**a**) the macroscopic stress–strain curves; (**b**) the percentage of broken bonds.  
highest stress in the stress-strain curve. The difference between the numerically and experimentally obtained values does not exceed 0.213 MPa, which is less than 0.1% of the highest experimentally measured stress value. However, the strains of the stress peaks obtained numerically and experimentally differ by a significant value, equal to 0.0011, which is 4.2% of the experimentally measured strain.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 2 On-Board Ship Detection Based on Deep Learning
### Region Proposal Network with Square Anchor
\begin{split}& t_{x}=(x-x_{a})/w_{a},t_{y}=(y-y_{a})/h_{a}\\ & t_{w}=log(w-w_{a}),t_{h}=log(h-h_{a})\\ & t_{x}^{*}=(x^{*}-x_{a})/w_{a},t_{y}^{*}=(y^{*}-y_{a})/h_{a}\\ & t_{w}^{*}=log(w^{*}-w_{a}),t_{h}^{*}=log(h^{*}-h_{a})\end{split}  \tag{2}\]  
Figure 3: Some positive examples of different types of anchors. B and S represent original anchor and square anchor, respectively, followed by the number of anchors. where \(x\), \(y\), \(w\), and \(h\) denote the bounding box's center coordinates, width and height, and \((x,y,w,h)\), \((x_{a},y_{a},w_{a},h_{a})\), and \((x^{*},y^{*},w^{*},h^{*})\) are the parameters of the predicted box, anchor, and ground-truth respectively.
## 4 Problem Formulation
### Probability of UAV Becoming Cluster Head  
UAVs broadcast hello packet to neighbor UAVs. When a UAV receives a packet, it will calculate the sum of the value of \(\Delta P_{ij}\ (t)/dt\) of its neighbor, which represents the probability of the neighbor UAV becoming CH. The process of calculation is as follows:  
\[\textbf{CHP}_{i}(t)=\sum_{j\in N}\frac{d}{dt}\Delta P_{ij}(t) \tag{10}\]  
\(\textbf{CHP}_{i}\) represents the probability of the current UAV \(i\) becoming CH. The higher the value, the higher the chance of UAVi becoming CH. N is the set of neighbor UAVs of \(i\).  
(1) CLUSTER FORMATION: The process of CH selection contains the following step: when a UAV receives two packets, it starts calculating the PLS and stable moment between itself and neighbor UAVs. As each UAV calculates the probability of itself becoming CH (Equation (10)), they broadcast the packet, and then the received UAV compares their CHP value with the received packet; if the CHPi is greater than the CHPj, then \(i\) doesn't broadcast the packet. If the CHPi is less than CHPj, then \(i\) broadcasts the packet without the CHPi.  
(2) CLUSTER MEMBER: There is a static cluster member for each UAV. As the number of UAVs increases in a cluster, they become a burden for the CH. In AoI, the lifetime of a network is very important. If the UAV receives more than one CH request, then the current UAV will calculate the PLS through Equation (9) and join the CH whose value is higher.  
(3) MERGING CLUSTER: There is a situation when two clusters intersect at the point where the merging concept takes place that causes the size of the cluster to exceed the predefined threshold value; the merging procedure will not take place, as managing a larger cluster is also a major issue for the CHs. In this case, both CHs will continue to perform their roles and manage their clusters.
## Results
### Expression of Camptothecin Biosynthetic Genes in OpWRKY3 Transgenic Hairy Roots  
To identify the camptothecin biosynthetic genes regulated by _OpWRKY3_ in _O. pumila_, transcript levels of eight genes (_OpTSB_, _OpTDC_, _OpG10H_, _Op10 HGO_, _OpSLS_, _OpCPR_, _OpSTR_ and _OpSGD_) were analyzed by qRT-PCR (Figure S5). Compared with the empty vector (control), the _OpTDC_ and _OpCPR_ genes were up-regulated in _OpWRKY3_ overexpressing hairy roots, which were approximately 5-fold and 2.5-fold more respectively as compared with the control (Figure 6). However, _OpG10H_, _Op10 HGO_, _OpSLS_, _OpSTR_ and _OpSGD_ genes were down-regulated in the five independent _OpWRKY3_ overexpression hairy roots. In contrast, _OpTDC_ and _OpCPR_ genes were significantly decreased in the five independent _OpWRKY3-SRDX_ lines, while _OpG10H_, _Op10 HGO_, _OpSLS_, _OpSTR_ and _OpSGD_ genes were significantly increased compared with control. In addition, the _OpTSB_ gene expression level of transgenic hairy roots was no significant change. All these results suggested that _OpWRKY3_ may be involved in the regulation of camptothecin biosynthesis.  
Figure 5: The total production of CPT and its precursors in _OpWRKY3_ transgenic hairy roots. (**A**) The total production of tryptamine in OpWRKY3 transgenic hairy roots and medium by high performance liquid chromatography (HPLC); (**B**) The total production of loganin in _OpWRKY3_ transgenic hairy roots and medium by HPLC; (**C**) The total production of secologanin in _OpWRKY3_ transgenic hairy roots by HPLC; (**D**) The total production of CPT in _OpWRKY3_ transgenic hairy roots and medium by HPLC. Values are means \(\pm\) standard deviation of triplicate analyses. *, significant at \(P\) < 0.05, **, highly significant at \(P\) < 0.01.  
Figure 6: Relative expression levels of selected genes in _OpWRKY3_ transgenic hairy roots. Expression levels of these genes were normalized to the empty vector control line. The _actin_ gene was used as an internal control. Values are means \(\pm\) standard deviation of triplicate analyses. _OpTSB_, _OpTDC_, _OpG10H_, _Op10 HGO_, _OpSLS_, _OpCPR_, _OpSTR_, and _OpSGD_ genes are involved in camptothecin biosynthesis.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 5 Research Challenges of IRS-Assisted UAV Communications  
As introduced in the previous section, there have been many studies on IRS-assisted UAV communications systems, and the results confirmed the potential of the technique in future wireless communications. However, there are practical issues and research challenges that need to be addressed to realize and advance IRS-assisted UAV communications systems. This section presents some of them.