esa-sceva/satcom-chunk-collection huggingface dataset:
# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 3 Feature-selective and Spatial Receptive Fields (FSRF) Blocks
### Multi-Branch Convolution  
The goal of using multi-branch convolution is to provide various filters for multiple branches, ultimately aggregating more informative and multifarious features. For any given feature map  
Figure 1: The structure of the “Feature-selective and Spatial Receptive Fields” (FSRF) block.  
\(\mathbb{R}^{H^{r}\times W^{r}\times C}\), firstly we conduct two transformations \(\overline{\mathrm{F}}_{\mathrm{tr}}\): \(X\rightarrow\overline{\mathrm{U}}\in\mathbb{R}^{H\times W\times C}\) and \(\overline{\mathrm{F}}_{\mathrm{tr}}\): \(X\rightarrow\overline{\mathrm{U}}\in\mathbb{R}^{H\times W\times C}\). Note that \(\overline{\mathrm{F}}_{\mathrm{tr}}\) and \(\overline{\mathrm{F}}_{\mathrm{tr}}\) are composed of efficient convolutions, with batch normalization and ReLU functioning in sequence. A \(3\times 3\) convolution kernel is used in the transformation \(\overline{\mathrm{F}}_{\mathrm{tr}}\), and a \(5\times 5\) convolution kernel is used in the transformation \(\overline{\mathrm{F}}_{\mathrm{tr}}\). \(H\), \(W\), and \(C\) denote the height, width, and number of channels of the feature map, respectively. Let \(W_{c}=[w_{1},w_{2},\ldots,w_{c}]\) and \(O_{c}=[o_{1},o_{2},\ldots,o_{c}]\) denote the learned set of \(3\times 3\) and \(5\times 5\) convolution kernels, where \(W_{c}\) and \(O_{c}\) refers to the parameters of the corresponding \(c\)-th convolution kernel. We can then write the outputs of \(\overline{\mathrm{F}}_{\mathrm{tr}}\) and \(\overline{\mathrm{F}}_{\mathrm{tr}}\) as \(\overline{\mathrm{U}}_{c}=[\overline{u}_{1},\overline{u}_{2},\ldots,\overline {u}_{c}]\) and \(\overline{\mathrm{U}}_{c}=[\overline{u}_{1},\overline{u}_{2},\ldots,\overline {u}_{c}]\), where  
\[\overline{u}_{c}=w_{c}*X=\sum_{s=1}^{C^{\prime}}w_{c}^{s}*x^{s},\ \overline{u}_{c}=o_{c}*X=\sum_{s=1}^{C^{\prime}}o_{c}^{s}*x^{s} \tag{1}\]  
Here \(*\) denotes convolution, \(w_{c}=\left[w_{1}^{s},w_{2}^{s},\ldots,w_{c}^{s}\right]\)\(o_{c}=\left[o_{1}^{s},o_{2}^{s},\ldots,o_{c}^{s}\right]\) and \(X=\left[x^{1},x^{2},\ldots,X^{C}\right]\), \(w_{c}^{s}\) and \(o_{c}^{s}\) is a 2D spatial kernel representing a single channel of \(w_{c}\) and \(o_{c}\), respectively.
## 4 Discussion
It was suggested that the decrease in phosphatase activities during composting could be attributed to the formation of complexes between enzymes and humic substances, thus disrupting enzyme-substrate interactions [64]. It can be equally explained by the global decrease in biological activity and abiotic parameters during the cooling phase of composting, which was confirmed by statistical analysis (Table 4). Alkaline phosphatase is the main phosphatase active during the composting process. Since plant roots are devoid of alkaline phosphatase, it can be inferred that the alkaline phosphatase should be attributed to bacteria and fungi [47]. However, it was suggested that, unlike microorganisms, plants produce only acid phosphomonoesterases [62]. Consequently, it is suggested that the phosphatase in the compost samples originated from microorganisms and not from green waste. NGS analysis demonstrated the microbial diversity of species solubilizing the phosphate. In this context, several types of bacteria transform the non-bioavailable phosphate present in compost into forms usable by plants. Among the bacteria that are able to solubilize phosphate by producing alkaline and acid phosphatases are species belonging to _Rhizobium_, _Enterobacter_, _Serratia_, _Citrobacter_, _Proteus_, _Klebsiella_, _Pseudomonas (P.cepacia)_, _Flavobacterium_, _Achromobacter_, _Agrobacteriumrium_, _Erwinia (E. herbicola)_, _Escherichia_, _Mycobacterium_, and _Bacillus_[61]. The high concentration of phosphatase activity recorded with mixture B could be attributed to the bacterial diversity identified by NGS, which is in agreement with the results of the microflora microbial concentration obtained--higher in compost B compared to the other two compost mixes. Indeed, there are also bacteria belonging to the class of Gammaproteobacteria, such as _Burkholderia cepacia_ and _Morganella morganii_, which contain _bceD_ and _napA_ genes, respectively, that encode phosphatases [65; 66]. For fungal species, several authors showed that many species of _Aspergillus_ (like _A. terreus_), _Saccharomyces cerevisiae_, _Neurospora crassa_, _Rhizopus delemar_, _Cryptococcus neoformans_, _Fesencaea pedrosoi_, _Humicola lutea_, _Sporothrix shenckii_, and _Fusarium moniliforme_ possess the ability to produce huge amounts of acid phosphatase [62; 64; 67; 68; 69]. Other species have the ability to produce both types of phosphatases at the same time, such as _Aspergillus_ (_A. niger_, _A. awamori_, and _A. fumigatus_), _Emmericella_ (_E.nidulans_ and _E.rugulos_), _Penicillium_ (_P.simplicissimum_ and _P.rubrum_), _Acrophialophora_, _Alternaria_, _Microrouxii_, _Coriolus versicolor_, and _Schizophyllum_[70; 71]. In addition, alkaline phosphatase activity was reported for _Rhizopus stolonifer_ and _A. rugulosus_[72].
## 5 Results and Discussion
### Hyperparameter Experiments
#### 5.4.3 Effects of Value and Channel Embedding Dimensions  
Figure 14 shows the impacts of value and channel embedding dimensions, which provide a larger feature space for subsequent feature extraction, allowing for deeper nonlinear transformations to capture more intricate spatiotemporal relationships. The value and channel embedding dimensions cannot be too large, which can lead to overfitting. Therefore, the best prediction result is achieved when the value and channel embedding dimensions are set to 32 and 64, respectively.
## 4 Numerical Results
### ABER Results
Under the different \(P_{j}\) conditions, the system's ABER increases with the increase in \(\sigma_{\text{o}}\). This indicates that we can obtain a smaller ABER by reducing the value of \(\sigma_{\text{o}}\). In addition, when \(P_{j}\) is large, the system's ABER does not decrease with the increase in \(P_{j}\) but approaches different asymptotic bounds because of different values of \(\sigma_{\text{o}}\). This indicates that the system's ABER cannot always be decreased by increasing \(P_{j}\), which verifies the accuracy of Remark 2. Figure 12: System ABER versus \(P_{j}\) for different \(\theta_{\text{FoV},m,j}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. Figure 13: System ABER versus FoV for different \(P_{j}\) when \(M=2\) and \(Z_{m,j}=0.5\) km. From Figures 9-14, we can conclude that all theoretical values match the simulation values well. Thus, the obtained ABER expression could be directly employed to evaluate the system's error performance without the need for time-consuming simulations.
## 4 Results and Discussion
### Bigram Features  
Using bigrams, the best performance obtained using the logistic regression model was 85.12% accuracy; almost all the models were among the worst performing. Naive Bayes also showed a considerable increase from its unigram accuracy of 80.21%, which goes on to show that in some instances, the pair of words captures the offensive type of language better than single words. The SVM dropped to 84.82% and the Random Forest decreased to 75.77% when bigrams were used. Results show that while bigrams do carry more contextual information as compared to unigrams, they do not add much to the model in terms of offensive language detection in Urdu.
## 3 Results
### Testing Results  
The training process was repeated three times in total. To compare the generalization effect, the selection of weight files preferred the validation loss over the validation accuracy. Therefore, for different batch sizes, the set of model weights chosen to be retained is the one with the lowest validation loss of the model. Finally, 54 results were obtained. The models were applied to the test set, and their predictions were used as the basis for evaluating their performance. The precision, recall, and f1-score of all models for each type of distress in the test set are organized in Tables 3-5. In addition, the last column of each table shows the macro-average precision, macro-average recall, and macro-average f1-score to evaluate the overall performance of the models. For ease of analysis, the naming rule for the models is to add an underscore to the name of the network model followed by the value of the corresponding batch size, e.g., VGG13_16 and VGG13_64 stand for VGG13 trained at a batch size of 16 and a batch size of 64. For precision, the best models overall are VGG13_16 and VGG13_64 (both 98.426%). The model with the lowest overall performance was MobileNetV3_16 (96.938%). Regarding the interlayer debonding category of distress, the model with the highest precision was VGG13_32 (99.469%). In contrast, the model with the lowest precision was DenseNet169_32 (98.649%). Regarding the interlayer water seepage category of distress, the best model was SqueezeNet1\(1\)16 (99.138%). In contrast, the model with the lowest precision was VGG19_32 (97.430%). ResNet34_16 performed the best for the interlayer loosening distress category with a precision of 98.755%. In contrast, InceptionV3_32 had the lowest precision of 96.713%. For the structural loosening distress category, both VGG13_16 and VGG13_64 performed the best, with a precision of 98.537%. In contrast, MobileNetV3_16 had the lowest precision of only 91.964%. Figure 6: (**a**) Training accuracy curves and validation accuracy curves of AlexNet, InceptionV3, VGG13, and VGG16 for four distress categories at batch sizes 16, 32, and 64, respectively. (**b**) Training accuracy curves and validation accuracy curves of ResNet18, ResNet50, DenseNet169, and MobileNetV2 for four distress categories at batch sizes 16, 32, and 64, respectively. (**c**) Training accuracy curves and validation accuracy curves of EfficientNetB3 and SqueezeNet1_1 for four distress categories at batch sizes 16, 32, and 64, respectively. Figure 7: Training loss and validation loss curves for CNNs of 10 network models with batch sizes 16 and 32, respectively. The solid line represents the training loss, while the dashed line represents the validation loss. The curves with different colors represent different batch sizes, which are illustrated in the legend with “val loss” and “train loss”, followed by an underscore and the specific values of the batch sizes.
## 4 Numerical Results and Discussions
The PDF of the GG channel with the combined effect of AT and PE is calculated in [3] as  
\[f_{h}(h)=\frac{\alpha\beta\xi^{2}}{A_{0}\Gamma(\alpha)\Gamma(\beta)}G_{1,3}^{3,0}\left(\frac{\alpha\beta h}{A_{0}}\left|\xi^{2}-1,\alpha-1,\beta-1\right. \right), \tag{49}\]  
where \(G_{p,d}^{m,n}(.|)\) is the Meijer-G function (Section 2.24 in [32]). The parameters \(\alpha\) and \(\beta\) characterize the irradiance fluctuations [2]. In this work, we consider moderate and strong AT regimes with \(\alpha\) and \(\beta\) equal to \([4,1.9]\) and \([4.2,1.4]\), respectively. Since BER signifies the system's performance, we present the numerical results of bit error of the system with varying AT and PE conditions for different degrees of the jammer's activity. Figure 9 presents the simulation results obtained after performing Monte-Carlo simulations modeling the GG fading channel following (49) with altering AT conditions and an active jammer. It demonstrates the improvement in bit error as \(N\) increases for both strong and moderate AT regimes. For the moderate AT condition, ABER improves remarkably from \(1\times 10^{-2}\) to \(3.76\times 10^{-3}\) and \(1.76\times 10^{-3}\) for \(N=2,3\), and \(4\), respectively, for \(\gamma=20\) dB. It can be seen from the figure that the advantages of transmit aperture selection are noticed for GG channel fading as well. As the SJNR increases, the effect becomes more striking. Moreover, as the AT conditions worsen, the system's performance takes a toll.
## 4 SEA Modeling
### Test-Simulation Comparison  
Figure 19 shows the mean RMS acceleration of each subsystem in the third octave bands. The simulated results (blue lines) are compared with experimental data obtained by laser vibrometry measurements (black lines with stars). The time acceleration response is measured at several points of each subsystem (46 and 20 measurements on a line along the x-axis for the subsystems SEA 2D 1 and SEA 2D 2, and 175 measurements covering all the surface for the bottom subsystem SEA 2D 3).  
The Power Spectral Density is then computed for each point. Then, for each subsystem, a spatial average of the PSD of all points belonging to the subsystem is performed. As explained in Section 2.3, with scanning laser vibrometry, the measurements at different points are not synchronous but sequential, allowing only the computation of the PSD \(S_{i}\) and not the cross power spectral densities \(S_{ij}\) between two points \(i,j\).  
Thus, the experimental results shown in Figure 19 correspond to the average of the PSD (equal to \((S_{i}+S_{j})/2\)), which would be overestimated compared to the real average value, which accounts for cross spectral densities \(((S_{i}+S_{j})/4)+S_{ij}/2)\). Finally, the averaged PSD value of each subsystem is converted to RMS acceleration in each third octave band. The results shown in Figure 19 show good test-simulation correlation. In subsystem SEA 2D 1, an averaged difference of \(-1.9\) dB between the mean simulation results and measurements is observed.  
In subsystem SEA 2D 2, this difference reaches \(-3.8\) dB, revealing a slight underestimation of levels by SEA simulation or a slight overestimation of the experimental averaged PSD because of the lack of cross-spectral PSD data. In subsystem SEA 2D 3, simulation underestimates levels below 2.8 kHz; however, above 2.8 kHz, the averaged difference between simulation and experiments is only \(-1.1\) dB.  
Figure 19: Test (black lines with stars) -simulation (blue lines) comparison for one-third octave band mean acceleration in the three SEA subsystems. The simulation spread displayed is obtained varying the damping loss factor in accordance with measurements shown in Figure 17.
## 3 Results and Discussion
### Optical Properties of N-CQDs
As the excitation wavelength escalates from 380 to 440 nm, the fluorescence peak's shape becomes progressively symmetrical, whereas its intensity starts to decline. The emission wavelength redshift from 500 to 600 nm. Quantum effects elucidate this phenomena; under conditions of quantum confinement, reduced diameters of nitrogen-doped carbon quantum dots (N-CQDs) correlate with broader band gaps, requiring shorter excitation wavelengths and yielding shorter emission wavelengths [40]. Figure 4c demonstrates that when the fluorescence peak attains its maximum, the emission energy of the major N-CQDs (4-6 nm) across all sizes is anticipated to be at its peak, with the emission wavelength primarily focused in the blue-green light spectrum. This elucidates the presence of a matching peak in fluorescence intensity at 510 nm. Figure 4d distinctly illustrates the exceptionally bright green luminescence of the 0.25 mg/mL N-CQDs solution when subjected to ultraviolet (365-395 nm) light irradiation, whereas under visible light, the N-CQDs aqueous solution appears light yellow. The quantum yield (QY) of the N-CQDs was determined to be 3.65%. Furthermore, as depicted in Figure 4d, the chromaticity coordinate of this fluorescent solution is (0.2576, 0.3855). According to the CIE 1931 standard [41] chromaticity values, its chromaticity coordinate predominantly falls within the green light emission region [20].
# Tutorial Review on Space Manipulators for Space Debris Mitigation
## 12 The Nature of Dexterity and Tactility
The linear Kalman filter comprises two parts:  
(i) the prediction part estimates a posteriori state \(\hat{x}(k)\) and its covariance \(P(k)\) from the process model:  
\[\hat{x}(k)=A(k)x(k-1)+B(k)u(k-1) \tag{148}\]  
\[P(k)=A(k)P(k-1)A^{T}(k)+Q(k) \tag{149}\]  
(ii) the correction part updates the previous estimate \(\hat{x}(k-1)\) based on measurements \(z(k)\):  
\[\hat{x}(k)=\hat{x}(k-1)+K(k)[z(k)-H(k)\hat{x}(k-1)] \tag{150}\]  
\[P(k)=[I-K(k)H(k)]P(k-1)\]  
\[K(k)=P(k-1)H^{T}(k)\Big{[}H(k)P(k-1)H^{T}(k)+R(k)\Big{]}^{-1}=\text{Kalman gain} \tag{151}\]The primary problem in multisensory fusion is in uncertainty, which may be implemented through Bayesian probability, Dempster-Shafer evidence or fuzzy sets [298]. These types of capabilities will require high computational capabilities for rapid sensor fusion _in real time_, imposing severe computational burdens--a high-speed hardware sensor fusion system comprising massively parallel multiprocessors has achieved rapid 1 msec visual/force feedback rates [299]. The question is whether this degree of sophistication in tactility involving artificial skins and sensor fusion is necessary for active debris removal. The short answer is almost certainly \"no\" for grappling and securing the target debris but if thermal blanket handling or salvaging of potentially useful or valuable parts (essentially a form of space mining) becomes necessary then the answer will be \"yes\" if that eventuality arises. Nevertheless, dextrous grasping involves the combination of visual imaging and tactile sensing--grasp planning requires visual contour extraction to determine finger placements [300]. It is clear that a form of multisensory fusion might be valuable for active debris removal suggesting that some of these more sophisticated methods will add value to these new tasks.
## 3 Results and Discussions
### Question Analysis
#### 3.3.1 Approaches and Application Domains (RQ1 e RQ2)
Finally, 6 of the selected studies fell into the category of visual analysis or applications of visual techniques. Figure 4 presents the number of studies published per year, quantifying by category, where it is possible to notice that there are studies of category CD1 from 2005 to 2019 which shows a tendency of studies that apply approaches that seek to identify suspicions transactions. It should be added that the approaches were classified into categories with greater prominence within the study but some works fell into more than one category, such as [S8] in which despite being in the CD3 category for presenting an intelligent method for money laundering risk estimation, also makes it possible to track and recognize suspicious transactions. [S69] is another example that, while adopting visual analysis and techniques, focuses on analyzing and assessing risks and is categorized on CD3 (other examples involve work that applies pattern or group detection but focuses on suspicious transaction detection).
## 4 Analysis and Evaluation
### Fault Tolerance  
A blockchain is essentially a distributed and decentralized network that shares a common state. However, even though various consensus protocols have been designed to maintain the agreement on all nodes, it is still possible that this agreement does not occur due to some fault. The endeavor to achieve and maintain this agreement under possible faults is called fault tolerance.  
We note that our proposed consensus protocol is based on secret sharing, which refers to splitting one secret \(S\) to \(m\) shares \((S_{1},S_{2},...,S_{m})\) and distributing shares to \(m\) participants, and one important feature of secret sharing is that the secret can be reconstructed with \(t\) (\(t\) stands for a threshold and \(t<m\)) or more shares presented. Such a \((t,n)\)-threshold secret sharing system can be used to implement the fault tolerance in the following way:  
* Information of any \(t\) or more \(S_{i}\) shares makes \(S\) easily computable. This means that the secret \(S\) can be easily reconstructed from any combination of \(t\) shares.  
Figure 1: A \((2,3)\)-threshold secret sharing. Any \(2\) shares or all \(3\) shares can reconstruct the original secret.  
* Information of any \(t-1\) or fewer \(S_{i}\) shares will leave the secret \(S\) completely undetermined. This means that the secret \(S\) cannot be reconstructed with fewer than \(t\) shares.
* One extreme case of such a secret sharing system is that \(t=m\) so that all shares are required to build the original secret.  
With the \((t,n)\)-threshold secret sharing system, the fault tolerance depends on a majority of the tellers, so the tolerance rate is less than 51% of tellers. For instance, if the transaction was split into 12 shares, then setting \(t=6\) cannot make sure the reconstruction of the secret since these 6 tellers could be all controlled by the attacker, even though the possibility of device failure still exists. If the value of \(t\) is set to be larger than 6, then the secret can be recovered, and the consensus can still be reached.
# Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
## 3 Results and Discussion
### Effect of Contact Time and Temperature  
The effect of adsorption time on the adsorption capacity of Alg-Mel was also evaluated with contact time in the range of 0.5-12 h under pH 5.0 at room temperature. It can be seen from Figure 5a that the adsorption capacity of the Alg-Mel for Pb\({}^{2+}\) increases with the increasing of contact time and then remains stable. More than 95% of Pb\({}^{2+}\) was adsorbed onto the Alg-Mel sorbent within 5 h. On another hand, when \(\sim\)20 mg of Alg-Mel was used for treating 40 mL of 0.48 mM Pb\({}^{2+}\) solution, it took more than 12 h for the adsorption to reach equilibrium (Figure 5b).  
Figure 4: Effect of pH on Pb\({}^{2+}\) adsorption of the Alg-Mel. (\(\sim\)100 mg of Alg-Mel was equilibrated with 40 mL of 0.48 mM Pb\({}^{2+}\) at 25 \({}^{\circ}\)C for 6 h).  
Furthermore, the influence of ambient temperature on adsorption abilities of the Alg-Mel was also studied. It can be seen from Figure 6 that the adsorption ability of the Alg-Mel for Pb\({}^{2+}\) increases slowly with the ambient temperature rose from 20 \({}^{\circ}\)C to 45 \({}^{\circ}\)C, which indicates that the adsorption process is an endothermic process.