esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CRKG in Massive MIMO and mmWave Systems
### MmWave Communications  
MmWave communications are usually combined with a large number of antennas. Specially, the BS and the user sides are both equipped with massive antennas. Thus, the spatial sparse property of mmWave massive MIMO channel still holds in [46]. Different from the conventional sub 6 GHz wireless channels, the mmWave massive MIMO channels have some different propagation characteristics. The main novel channel properties include the non-stationary property over antenna array axis [47]. Some measurement results has shown that the multipath components parameters may vary over the large physical array, e.g., received power, delay spread, and azimuth angular spread [47]. The new channel characteristics provide more channel temporal-spatial parameters to generate the secret keys.  
The mmWave communications also bring new challenges due to the path and penetration loss [48]. Along with the frequency growth, the mmWave channel suffers severe path loss. The path loss in free space is proportional to the square of the frequency. Because the frequency of mmWave signals is much higher than conventional microwave communications, it leads to higher path loss. Penetration loss is another propagation loss. While sub 6 GHz signals penetrate through some objects, mmWave signals are easily blocked by most solid materials. Even for tinted glass, the penetration loss is high for 28 GHz signals. These two propagation losses lead to lower numbers of propagation paths, and thereby independent stochastic sources. Specifically, when the propagation experiences a significant line of sight, the channel randomness is limited.  
The directional beamforming provides a potential solution to the path loss challenge. The narrow beams concentrate the transmitted power to the directions of the user, compensating the path loss and improving the received SNR. For low number of channel paths, the work in [49] chooses randomly generated perturbations to increase the secret key rate. After the initialization of the communication link, the BS and user know the transmitted and received angles. Then, a transmitter defined randomness (a perturbation angle) is integrated into key generation. Moreover, the non-stationary channel property also allows generating secret keys from the spatial randomness.
## 5 In-Line Application of LIBS in Industrial Production
### Waste Polymers
The elemental monitoring allows to control the polymer composition and to automatically discard undesirable fractions of the recycled material.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 2 Proposed Method
### Selection of Embeddable Pixels
Suppose that the directly decrypted value of marked pixel is \(X\). Then the 1 th\(\sim\)\(m\)th LSBs of \(X\) remain unchanged, and each bit of the (\(m\)+1)th \(\sim\) 8 th MSBs of \(X\) may be 0 or 1, so there are \(2^{8-m}\) different possible values for different combinations of the (\(m\)+1)th \(\sim\) 8 th MSBs. Suppose that these different values are \(X_{l}\) (\(l=1\),..., \(2^{8-m}\)), then one of them must be equal to the original pixel value. Let \(X_{k}\) (\(1\leq k\leq 2^{8-m}\)) be equal to the original pixel value (i.e., \(X_{k}=I_{W}^{s}(i,j)\)). Then, according to Equation (3), \(X_{k}\) satisfies Equation (4):  
\[\left|X_{k}-I_{W}^{s}(i,j)\right|\leq 2^{m-1}-1 \tag{4}\]  
Note that the black pixels surrounding \(I_{W}^{s}(i,j)\) are not modified; thus, the original value of these pixels can be restored losslessly after directly decryption. Therefore, the same predicted value \(I_{W}^{s}(i,j)\) can be obtained according to Equation (2). Next, we will prove that only one value of \(X_{l}\) (i.e., \(X_{k}\)) satisfies Equation (4). Here we assume that both two possible values \(X_{k_{1}}\), \(X_{k_{2}}\)(\(1\leq k_{1}\), \(k_{2}\leq 2^{8-m}\), \(k_{1}\)+\(k_{2}\)) satisfy Equation (4); then:  
\[\left|X_{k_{1}}-I_{W}^{s}(i,j)\right|\leq 2^{m-1}-1 \tag{5}\]  
\[\left|X_{k_{2}}-I_{W}^{s}(i,j)\right|\leq 2^For example, if \(m=6\), the current pixel \(I(i,j)\) belongs to \(I_{W}^{s}\), \(I(i-1,j)\)\(\in\)\(I_{B}\), \(I(i+1,j)\)\(\in\)\(I_{B}\) and \(I(i-1,j)=104\), \(I(i+1,j)=112\), then:  
\[I^{s}_{W}(i,j)=\frac{104+112}{2}=108\]  
Suppose that the directly decrypted value of the marked pixel is \(X\), and the 1 th-6 th LSBs of \(X\) are '101001'. Obviously, the 1 th-6 th LSBs of original pixel \(I(i,j)\) are also '101001', and the two MSBs of \(I(i,j)\) may be '00', '01', '10', or '11'. Then we can obtain the four possible values: 41('00101001'), 105('01101001'), 169('10101001'), 233('11101001'). It is obvious that only 105 satisfies Equation (4) among these four values, i.e. :  
\[|105-108|\leq 2^{5}-1\]  
Therefore, we can recover the original value 105 of the pixel \(I(i,j)\). Since the pixels in \(I_{W}^{s*}\) are not suitable for data embedding, these pixels should be marked. To address this problem, a location map \(LM\) is utilized which is a 0-1 matrix with same size as the original image. In the \(LM\), the pixels in \(I_{W}^{s*}\) are marked with 1, and the other pixels are marked with 0.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 4 Numerical Simulation, Experimental Results and Discussions
### Experimental Platform Description  
#### 4.1.1 Hardware and Avionics Devices  
We use a DJI-F450 quadcopter as the experimental platform (Figure 5) which is operated by an onboard flight computer unit (FCU) Pixhawk [48; 49]. The quadcopter attitude and acceleration are provided by an inertial navigation system (INS). We use a commercially available laser ranging sensor LidarLite V3 to measure the altitude and a commercial global positioning system (GPS) receiver to determine the position of the vehicle. Additionally, a power supplying system (including battery, power adapter module), a set of remote control transmitter/receiver for the manual pilot, and a set of radio telemetry transmitter/receiver for ground station monitoring are used. The quadcopter dynamical parameters are listed in Table 1.
## 2 Materials and Methods  
### Strategy of the Analysis  
In this study, a two-step procedure is used where the ML algorithms are applied to actual and simulated BE data. Figure 1 shows an outline of the strategy that was used in order to address to the questions posed in this study, namely to identify how the PK parameters relate to each other and find the best possible metric for absorption rate.  
Initially, from the actual concentration (C) and time (t) data of the donepezil BE study, pharmacokinetic parameters were estimated using the classic non-compartmental (NCA) approach (Figure 1). Then, principal component analysis (PCA) was applied to the previously calculated PK data. Afterwards, correlation analysis was used to explore the bivariate relationships in the original scale of the PK variables. In order to uncover the contribution of PK characteristics into each possible absorption rate metric (Cmax, Cmax/AUC, and Cmax/Tmax), the second ML technique, random forest (RF), was applied.  
Aiming to explore more conditions with faster or slower absorption rates and identify the performance of PK metrics, further simulations were performed (Figure 1). To fulfill this task, the first step was to develop a PK model to describe the kinetics of donepezil. Non-linear mixed effect modeling (NLME) was employed on the actual C-t data, and a population PK model for donepezil was developed. In a subsequent step, using the population estimates (and all variabilities calculated from the NLME analysis), three scenarios were simulated: (a) slower absorption by adjusting the absorption rate constant (Ka) to be half of the original (0.5x-), (b) equal to the original Ka (i.e., 1x-), and (c) faster absorption where Ka was set to be twice the original (2x-). Thus, three BE datasets of donepezil were simulated, and the two ML techniques (PCA and RF) were re-applied. The latter allows detecting the relationships among the PK variables and the contribution of each one to another, under different absorption rates. In other words, it was an effort to isolate the impact of absorption rate and elaborate on it.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 4 Drug Release Mechanisms from Biodegradable Polymeric Formulations
In general, biodegradable polymers can be classified as bulk erosion polymers and surface erosion polymers [303]. They have distinct erosion processes in the release environment that affect the drug release profile [302]. In some circumstances, the erosion mechanism for a particular biodegradable polymer may change from one to the other [304]. Typically, most poly(esters), for example, PLGA (poly(lactide-co-glycolide)) and PLA (poly(lactic acid)), undergo degradation by a bulk erosion process, which means the simultaneous degradation of entire matrices [305]. For some poly(ortho esters) [306] and poly(anhydrides) [14], they undergo surface erosion by a mild process from the surface into the core [21], which lead to a relatively slow degradation and water diffusion process [307].
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 6 Conclusions and Future Recommendations  
The allocation algorithm based on the many-to-one Gale-Shapley algorithm proposed in this paper can effectively solve the resource allocation problem of D2D users using multiplexed cellular user channels in heterogeneous cellular network environments. In order to improve the utilization of the wireless spectrum, the algorithm allows multiple D2D users to share the channel resources of one cellular user and ensures the communication service quality of the cellular users and D2D users by setting the SINR threshold. A D2D user and channel preference list are established based onthe implemented system capacity to maximize the system total capacity objective function. The KM algorithm is used to allocate channels for D2D clusters to maximize the total capacity of D2D users [21]. The MATLAB simulation is used to compare and analyze the total system capacity of the proposed algorithm, the resource allocation algorithm based on the delay acceptance algorithm, the random resource allocation algorithm and the optimal exhaustive search algorithm, and the maximum allowable access D2D users. The simulation results show that the proposed algorithm has fast convergence and lower complexity than the state-of-the-art schemes [16; 20] and random resource allocation. The total capacity is close to the optimal algorithm. The next research area will be to extend this work to consider error margin in D2D underlaying cellular networks with a focus on power allocation and other important aspects [26].  
Conceptualization, O.A.S. and I.K.; Data curation, S.L.M. and S.A.; Formal analysis, O.A.S.; Funding acquisition I.K. and K.R.; Investigation, I.K. and O.A.S.; Methodology, S.L.M. and K.R.; Project administration, K.R. and I.K.; Software, O.A.S and I.K.; Writing--original draft, I.K., S.A. and S.L.M.; Writing--review & editing, I.K., O.A.S. and S.L.M.  
This research work is funded by the Manchester Metropolitan University, UK.  
The authors declared that they have no conflict of interest.
# Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
## 5 Experimental Results
### Benchmark Function Validation
#### 5.1.2 Comparison with the Excellent Algorithms  
In order to test the effectiveness of the proposed CBBOA, several high-quality algorithms are selected for comparison with CBBOA in this section. The tested algorithms include BMWOA [35], ACWOA [36], COSCA [37], CESCA [38], CGPSO [39], ALCPSO [40], GL25 [41], DECLS [42], DE [43], GWO [44], and BOA. Table 2 records the experimental results of CBBOA and the above algorithms on the CEC14 function.  
In the table, Avg represents the average result, and Std represents the standard deviation. The best performing results on each function in the table have been bolded. We can see nine of the results of CBBOA ranking first. In the F1-F16, CBBOA ranks in the top three out of 15 functions. On F17-F30, which have more complicated function structures, CBBOA ranks in the top three among the nine functions. In contrast, the original BOA only performed well in F23, F24, and F25, and ranked at the end among other functions. This shows that the improvement for BOA is effective. CBBOA can achieve good results on different kinds of functions. These functions are more comprehensive and more versatile.  
In order to analyze the experimental results more comprehensively, we used the Wilcoxon signed-rank test [45] to analyze the experimental results. In the Wilcoxon signed-rank test, when the \(p\)-value is less than 0.05, it indicates that this algorithm has a significant improvement compared with another algorithm in statistics.  
Table 3 shows the \(p\)-value of CBBOA compared with other algorithms. Values greater than or equal to 0.05 are indicated in bold. From the table, we can see that CBBOA has improved significantly over CESCA in all functions. Compared with BMWOA, it can be found that only one function has a test value not less than 0.05. Compared with the original BOA, CBBOA has significant improvements in functions other than F24-F27. This once again proves that the performance of CBBOA is superior and more comprehensive.  
The convergence curves of this experiment can be seen in Figure 3. From the figure, we can see that in F1, F2, F6, and F17, the convergence curve of CBBOA has obvious advantages. In F9, algorithms such as ALCPSO and GL25 have stabilized in the early stage of iteration, while CBBOA can continue to decline. This shows that CBBOA doesn't easily fall into the local optimum. This is where CBBOA has improved compared with BOA.
## 3 Physiological Processes Mediating Cadmium Accumulation in Plants
### Intracellular Cd Sequestration  
Cd sequestration contributes significantly to Cd translocation prevention, thereby reducing Cd accumulation in cereal grains. The main Cd sequestration sites in plants are cell walls and vacuoles. The cell wall is the first line of defense against toxic metals from the external environment. When captured by root cells, metal ions are largely bound by some cell wall components, such as cellulose, hemicellulose, lignin, and pectin [8]. As a result, the highest Cd concentration in root tissues occurs in the apoplast, particularly in the walls of rhizodermal and cortical cells [64]. Such binding of Cd to cell walls can efficiently prevent 36-70% of Cd from being transported across the plasma membrane intotoplasts [75], thereby reducing the Cd translocation between cells and tissues.  
Within the root cells, Cd is mainly located in vacuoles, which are generally considered to be the main storage sites for metals [76]. [PERSON] et al. [77] found that 51% of Cd in barley roots was present in the soluble fraction of the vacuole. After entering the aerial plant parts, Cd is also mainly sequestered in the vacuoles of parenchyma cells in the leaf mesophyll, stem path, and cortex [78; 79]. Vacuolar sequestration of Cd has been demonstrated to reduce Cd concentration in the cytoplasm of roots and leaves as well as alleviate Cd toxicity in plants [80]. Several transporter families, such as HMAs, Ca\({}^{2+}\) exchangers (CAXs), NRAMPs, metal tolerance proteins (MTPs), and adenosine triphosphate (ATP)-binding cassette subfamily C proteins (ABCCs), have been identified as being responsible for the vacuolar sequestration of Cd [80; 81; 82]. It is generally assumed that Cd\({}^{2+}\) in the cytosol first forms low-molecular-weight (LMW) complexes by binding with metal ligands, such as glutathione (GSH), nicotianamine (NA), and organic and amino acids; subsequently, the complexes are transported into the vacuoles, where more Cd\({}^{2+}\) and thiol-containing chelators such as phytochelatins (PCs) are incorporated to produce high-molecular-weight (HMW) complexes like Cd-PC [83]. In addition, the formation of the Cd-malate complex in vacuoles has also been found to reduce the subsequent Cd efflux from the vacuoles to the cytoplasm [84].
## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.5 Evaluation Metrics in Molecular Structure Generation  
_MRR (Mean Reciprocal Rank):_ Mean reciprocal rank (MRR) is an evaluation metric employed in the context of generating or retrieving molecular structures from textual descriptions. It measures the average reciprocal rank of the correct answer among a set of possible answers. This metric is commonly used in the Text2 Mol [60] technique, which focuses on generating or retrieving molecular structures based on textual descriptions. By utilizing MRR, the Text2 Mol [60] technique can assess how well the generated structures align with the given textual descriptions, ultimately ranking the correct molecular structure higher among the generated options.  
_QED (Quantitative Estimate of Drug-Likeness):_ Quantitative estimate of drug-likeness (QED) is an evaluation metric extensively used in molecular graph generation and design, particularly in the domain of molecular design and drug discovery. QED provides a quantitative measure of the drug-likeness of a generated molecule, indicating its potential to be developed into a safe and effective drug. The CGVAE [79] technique, employed in molecular design and drug discovery tasks, incorporates QED as an evaluation metric. The technique leverages QED to assess the drug-likeness of generated molecules, considering various molecular properties such as stability, solubility, and bioactivity. Higher QED values indicate a higher likelihood of a molecule being considered drug-like, facilitating the prioritization of molecules with greater potential.  
_FCD (Frechet ChemNet Distance):_ Frechet ChemNet distance (FCD) is an evaluation metric commonly utilized in text-based de novo molecule generation and molecule captioning tasks. FCD quantifies the similarity between the distributions of molecular structures generated by a model and a reference distribution, typically a dataset of known molecules. The MoITS (Molecular T5) [58] technique, known for text-based de novo molecule generation and molecule captioning, incorporates FCD as an evaluation metric. By leveraging FCD, the MoIT5 [58] technique can assess the similarity of generated molecules with the reference distribution in terms of chemical features and properties. A lower FCD value indicates a higher similarity between the generated molecules and the reference distribution, indicating better quality and alignment with the desired chemical characteristics.
###### Abstract  
The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.  
Keywords:landslide; weights of evidence; logistic regression; random forest; hybrid model +
Footnote †: journal: Applied Sciences
# Temperature Effects in Packaged RF MEMS Switches with Optimized Gold Electroplating Process
## 3 Fabrication
### Characterization of the Optimized Electroplated Gold  
The characterization of the optimized electroplated gold mainly includes a surface roughness scan and a profile scan.  
Firstly, AFM is used to test the surface roughness of the electroplated gold structure, and the test results are shown in Figure 5. It can be seen that the surface grains of electroplated gold are fine and uniform. The measured Roughness Average (Ra) is 46 nm, RMS Roughness (Rq) is 57 nm, Maximum Profile Valley Depth (Rv) is 138 nm, and Maximum Height of the Profile (Rt) is 286 nm.  
A profile scan is conducted on the released electroplated gold beam, and the temperature stability of the beam profile is tested at different temperatures. The test results are shown in Figure 6, in which the inset displays the released gold beam and profile scanning path. Firstly, from the scanned profile, it can be seen that the gold beam does not show significant warping after release, indicating that the internal stress of the electroplated gold layer is relatively low. Secondly, at the range of 25 to 75 \({}^{\circ}\)C, the profiles of the cantilever show very small deviates, which means the electroplated gold layer has excellent thermal stability.  
The process flow of the switch as well as its hermetic package method can refer to our previous work [20]. The SEM of the fabricated RF MEMS switch using the optimized gold electroplating process above is shown in Figure 7a, and the SEM image of the hermetic packaged switch with an HRS cap is shown in Figure 7b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Current density & 0.4–0.6 A/dm\({}^{2}\) \\ Temperature & \(50\pm 5\,^{\circ}\)C \\ Frequency & 1 kHz \\ Duty ratio & 50\% \\ Flow rate & 10–30 L/min \\ \hline \hline \end{tabular}
\end{table}
Table 2: The optimized parameters of gold electroplating.  
Figure 5: The surface roughness of the electroplated gold tested by AFM.
# Layer-Wise Compressive Training for Convolutional Neural Networks
## 2 Background
### Pruning  
One of the most simple, yet effective techniques for reducing the number of weights is called _pruning_. It is based on the idea of removing some neural connections by setting their weights to zero. This concept is inspired by several motivations, either philosophical or empirical ones. First of all, it emulates the mechanisms that control and modify neural connections in the human brain, especially during the growth of a human being. Indeed, both artificial and biological neural networks are error-resilient: they can work if damaged, or when connections are cut off. As demonstrated in previous work, it is possible to simplify the neural network model still meeting the same original accuracy [14; 23]. The second motivation is related to the redundancy of neural networks, where neurons are fully connected to all inputs, even if some connections can be useless. It may happen that a specific input does not contribute, or does not contribute in a sensible way, to a particular neuron functionality. Even worse, a connection can even alter the neuron's functionality, for example because its weight is far from its optimal value. In this latter case, the model may stack in a local minimum where all other neurons have learned to compensate its error. In this sense, pruning can be used to jump throughout the weights space and improve the ability of the SGD method to escape from a local optimum. It also acts as an extreme form of regularization: it reduces the number of parameters in order to prevent overfitting. In this work, pruning is used as a compression strategy to reduce the model complexity.  
From a practical viewpoint, each weight is a coefficient multiplying an input in a matrix-vector manner. A small weight makes a specific input quite irrelevant in the total summation; this behavior can be approximated by dropping that input. In other words, small weights can be approximated by zero-values. Generally speaking, the training algorithm sets a weight _high_ (in the absolute sense) if that connection must have a strong excitatory or inhibitory effect, and hence a significant influence. Such selection can be obtained by defining a threshold \(\tau\) as in the relationship in Equation (10):  
\[\omega_{j}=\begin{cases}\omega_{j},&\text{if }|\omega_{j}|\geq\tau,\\ 0,&\text{otherwise.}\end{cases} \tag{10}\]