esa-sceva/satcom-chunk-collection huggingface dataset:
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 3 Background: Challenges in Detecting Three Behaviors
### Violence  
Concerning violence, the current study is focused on the violent behavior in uncrowded scenes involving two persons only such as fighting, kicking, and punching of two people. In this way, violence detection involves challenges due to two main factors: diversity of the fight patterns and variety of appearance. The typical patterns in fights include punching, kicking, jabbing, hitting with an object, and clinching. Hockey fights can be a useful source of information to learn each of these diverse patterns as most of them are explicitly found therein. In this way, the system must be provided with exclusive examples containing reliable cues of each pattern. However, as the sports footage is usually consistent in appearance, the other challenge here is to ensure that the system has obtained adequate knowledge of patterns that enables it to generalize and translate the learned features to other scenarios. This must be addressed by ensuring that fight scenes with a varied context are provided.
# Molecularly Imprinted Filtering Adsorbents for Odor Sensing
## 3 Results and Discussion  
### Influence of DOP Concentration on the Adsorption Properties of PVC-DOP Composite Adsorbents  
The specific adsorption properties of PVC-DOP composite adsorbents with different mixing ratios were evaluated using the GC-MS/SPME method. In this experiment, 10 odorant gases (benzene, 2-hexanone, heptanal, anisole, 2-nonanone, propanoic acid, salicaldehyde, methyl salicate, \(o\)-cresol, hexanoic acid) were used as odor samples. These gases are typical bio-volatile organic compounds (BVOCs) from various chemical groups which are important analytes for food production and living environmental monitoring. Figure 5 illustrates the dependence of the amount of adsorbed gases on the DOP mixing ratio, and the amounts of adsorbed gas normalized to those adsorbed by a pure PDMS adsorbent are shown in Figure 6. The normalized values indicate whether the composite adsorbs greater amounts of gas than PDMS does. Figure 6 shows the individual values for 2-hexanone, propanoic acid, benzene, and \(o\)-cresol. The adsorbed gas amounts increased with the increasing DOP ratio for benzene, 2-hexanone, and propanoic acid; the change in the adsorbed amounts of propanoic acid is especially large. In contrast, the adsorbed amount of \(o\)-cresol decreased with the increasing DOP content. These specific absorption properties can be attributed to the change in solubility parameters on mixing DOP and PVC; hydrophobic adsorbents have a higher concentrating ability toward hydrophobic odorants. This result shows that the specific adsorption of gases by PVC-DOP composite adsorbents can be controlled by adjusting the mixing ratio. This composite is especially promising as an adsorbent for fatty acids.  
Figure 4: Adsorption experiment system. CMS: carbon molecular sieve column for obtaining clean air. MFC: mass flow controller. Sample adsorbents were placed in the sample chamber.  
Figure 5: Adsorbed odorant amount (TIC: total ion current obtained from gas chromatography–mass spectrometry/solid-phase microextraction) of polyvinyl chloride–dicotyl phthalate (PVC-DOP) adsorbents as a function of DOP content.
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 4 Case Study of Seasonal Variation in Light-Use Efficiency and SIF across the Amazon Basin
### Discussion
#### 4.3.3 PRI and Eddy-Flux Measurements of Light Use Efficiency  
The general standard for measurement of GPP is from the acquisition of eddy covariance data of CO\({}_{2}\) exchange. A basic assumption in the calculation of GPP is that the relation between temperature and ecosystem respiration derived from nighttime measurements applies to daytime conditions. This assumption has been questioned based on an analysis of the differential fractionation of stable carbon and oxygen isotopes collected seasonally above a temperate deciduous forest in Massachusetts [69].  
Even if accurate, GPP estimates derived from eddy-flux from towers are difficult to extrapolate across landscapes with a variety of topography and cover types. Multi-directional sensors borne on satellites can, we believe, aid considerably in the pursuit of better estimates of GPP and related ecosystem exchanges (water vapor, methane, etc.). The possibility of supplementing ground-based measurements with those obtained with multi-directional sensors borne on satellites is, we believe, an approach worth pursuing. Our results suggest that PRI is able to capture seasonal changes in \(\varepsilon\) across large areas of tropical forests. However, we observed that the correlations of PRI and \(\varepsilon\) varied greatly between flux sites: \(\text{PRI}_{\text{diff}}\) was more sensitive to variations in \(\varepsilon\) for southern sites than northern sites. This is in concert with reduced rainfall measured from GRACE for the southern flux sites. We surmise that \(\text{PRI}_{\text{diff}}\) becomes more sensitive to variations in \(\varepsilon\) under stressed conditions. We observed the strongest correlation (\(r^{2}=0.70,p<0.01\)) for the dry period between June and September (Figure 12). Our finding is corroborated by [PERSON] et al. [9], where PRI was significantly correlated with \(\varepsilon\) during most of the dry season for an evergreen coniferous forest in China.  
Our study also substantiates earlier work by [16; 17] in temperate coniferous and deciduous forests that reported strong non-linear relationship between satellite-derived PRI and \(\varepsilon\) obtained from eddy covariance measurements (although a different approach was used to account for shadow effects).
## 3 Experimental Verification
### Noise Generation
It is determined by averaging the DNN response (behind the softmax layer) over all epochs and all trained models. This reflects the tendency of the networks to alter the designated class of a given image during the learning process. Examples of four misclassified images from the MNIST database are presented in Figure 4. For all 10 classes, the decision outputs \(V_{i},i\in\{0,\ldots,9\}\) of a sample DNN architecture (ResNeXt29 in this case) are shown below the images. The output corresponding with the target class--the class to which the image actually belongs, according to the reference data from the MNIST database--is highlighted in red. These are obvious examples for which human evaluation would produce a similar outcome to that of a DNN. Such examples confirm that misclassification chance is primarily dependent on the specific image, not on the class to which the image belongs. We define \(\overline{V}\) as the classification output, averaged over all 200 epochs and all eight utilized DNN architectures. Figure 5 shows boxplots of \(\overline{V}\) for the 30 most difficult images to classify. That is, the images with the lowest median \(\overline{V}\). The figure additionally presents the six most difficult images below each plot. The CIFAR10 and CIFAR100 datasets include images of objects positioned or viewed in a manner that is very rare in the learning database (e.g., the ostrich or the horse's head images in CIFAR10) or images that are extremely difficult to classify for a human (e.g., the lion or the beetle images in CIFAR100). Moreover,  
Figure 4: Examples of misclassified MNIST images. The \(V_{0}\),..., \(V_{9}\) outputs from a sample DNN model (ResNeXt29 architecture) for all 10 classes are presented on plots below each image. For each image, the target class is highlighted in red. some of the most difficult images are degraded or have very low resolutions (e.g., the majority of the examples from GTSRB). The application of IDN noise to the datasets required the retrieval of the most difficult-to-classify images, such as those presented in Figure 5. For each image, \(\overline{V_{i}}\) was determined, where \(i\) denotes the classification number produced by the DNN output. Following this, the sum of probabilities was normalized over all classes, such that \(\sum\overline{V_{i}}=1\). With the probabilities normalized, the new corrupted class could be determined randomly. Moving through the images from most difficult, noise was applied to each image label until the proportion of affected labels reached the designated noise level. Figure 5: Boxplots of \(\overline{V}\), the classification output averaged over all 200 epochs and all eight utilized DNN architectures, for the 30 most difficult-to-classify images within each data set. The six most difficult images are presented below each plot.
# Computational Evaluation of Shock Wave Interaction with a Cylindrical Water Column
## 2 Computational Model
### CFD Analysis  
#### 2.2.1 VOF Method  
The present CFD analysis employed the VOF method for the transient tracking of the liquid-gas interface during the interaction process, which allowed approximating the boundary between the two different phases on the fixed numerical mesh. According to this classical methodology, volume fractions were introduced to represent the volume occupied by each phase in each computational cell. The VOF technique was shown to be more efficient and flexible than other methods when treating the complex interface between two immiscible fluids [18] and is often employed to study the interaction of water droplets with shock waves, e.g., [4; 12].  
Practically, the compressible Navier-Stokes equations were written for volume averaged fields that were shared by the two different phases. These equations were solved for an effective fluid, whose averaged properties were evaluated according to the volume fractions. For example, the averaged density was determined by:  
\[\rho=\alpha\rho_{l}+(1-\alpha)\rho_{g}, \tag{6}\]  
where \(\rho_{g}\) represents the variable gas density and \(\alpha\) stands for the volume fraction of liquid water (secondary phase). The field variable \(\alpha\) was evaluated throughout the computational domain by solving the associated continuity equation, while the volume fraction of the ideal gas air (primary phase) was trivially computed. In any computational cell, depending on the local volume fractions, the variables and properties were representative of either one of the two phases or a mixture of them.  
Among the advantages of the VOF method, the effect of surface tension forces along the air-water interface can be simply simulated. The present analysis was carried out by employing the continuum surface force (CSF) model proposed in [21], with the momentum equation being supplied with an additional source term due to surface tension, as done in similar studies, e.g., [15].
# Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump
## 3 Results and Discussion
### Complexity
#### 3.1.1 Neural Networks  
For neural networks, the most crucial aspect is the size of the network. The more neurons the network has, the more complex it becomes. The number of neurons is strictly related to inference time and the use of computational resources at the place where the neural network is located [66]. Based on the parameter sets presented in Table 3, grid search was performed and all available network architectures were implemented and trained. A comparison of the architectures in confrontation with MAPE metrics is presented in Figures 11-13. MAPE metrics were chosen because, at first glance among other used metrics, they give the most insight into results, without knowing the predicted values. A detailed discussion of the metrics used is presented in Section 2.3.1.  
Several conclusions could be drawn based on the plots presented in Figures 11-13. For all types of network, the most abundant set of the input variables generally results in significantly better performance. Also, for all types of models and for all sets of input variables, the best model is characterized by 10 neurons in the hidden layers. Furthermore, for architecture of this property, in almost all cases, the value of MAPE of the best models is significantly lower than for networks with five neurons in the hidden layers. The exceptions are RNN with the [output pressure, outlet flow] input variables set, and LSTM with the [output pressure, outlet flow, bleed pressure] input variables set. The length of time window which impacts complexity in context of the number of input neurons is very varied for the best models. In Figure 11, the top three models do not differ much in terms of performance, but are characterized by three different numbers of input neurons. For almost the best model of each type and each set of input variables, the best one is the most complex. That is, it has 10 neurons in each hidden layer and five hidden layers. The only exceptions are the dense model with the [output pressure, outlet flow, bleed pressure] variable set and  
Figure 11: Quality metrics comparison for top 10 dense models for each selected set of model input variables.  
Figure 12: Quality metrics comparison for top 10 RNN models for each selected set of model input variables with respect to network architecture.  
the LSTM model with the [output pressure, outlet flow] variable set, which both have four hidden layers.  
The degree of complexity of the implementation is comparable for all presented models. The implementation with Python using TensorFlow enables us to change the network type and its hyperparameters in a very convenient, fast and flexible way.  
Taking the above analysis into consideration, the best neural network model is characterized by the properties presented in Table 5. This model has slightly worse performance than the best model among all available, but it has significantly simpler architecture.
## 4 Conclusions  
In this work, the effect of calcium hydroxide on the geopolymer derived from kaolin, fly ash, and red mud was investigated using compressive strength tests, X-ray diffraction, infrared spectroscopy, BET method, and scanning electron microscopy. For all ranges of NaOH activator concentrations investigated from 4 M to 10 M, the compressive strength of the geopolymer first increased, then decreased with the increasing calcium hydroxide content. The geopolymer materials produced at the 8 M NaOH activator had higher compressive strength than those of materials prepared at 4 M, 6 M, and 10 M NaOH. The critical value of calcium hydroxide content, which can give the highest compressive strength of the geopolymer prepared, is about 13% wt. of solid raw materials. There is a coexistence of geopolymerization gel and C-S-H/C-A-S-H gel in the materials prepared. Both porosity and the formation of N-A-S-H/C-S-H/C-A-S-H during the polymerization process are important for the mechanical properties of materials. The highest compressive strength, 19.7 MPa, was obtained for the material prepared with the presence of 45 g Ca(OH)\({}_{2}\), 8 M NaOH, and kaolin: fly ash: red mud ratio of 150 g: 75 g: 75 g. The findings of this study can help develop a better understanding of the effect of calcium hydroxide on the properties of geopolymeric products for further research and practical applications.  
Figure 7: Pore volume, average pore diameter, and compressive strength of materials prepared by using 8 M NaOH and different Ca(OH)\({}_{2}\) contents.  
**Author Contributions:** Conceptualization, T.D.C. and T.P.; methodology, T.H.N.; formal analysis, T.D.C. and T.P.; investigation, T.D.C., M.T.V. and T.H.N.; data curation, T.P.; writing--original draft preparation, T.D.C. and T.P.; writing--review and editing, T.D.C.; supervision, T.D.C. and T.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Vietnam Ministry of Education and Training, grant number B2019-MDA-03.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on reasonable request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 4 Experimental Verification
### Parameter Settings
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(N\) & Total of location number for possibly-deployed sensors & 86 \\ \(\alpha\) & Path weight & 0.1 \\ \(\beta\) & Heuristic information weight & 0.1 \\ \(\xi\) & Local pheromone evaporation coefficient & 0.1 \\ \(\rho\) & Global information system evaporation coefficient & 0.1 \\ \(w\) & Weight & 0.5 \\ \(Q\) & Pheromone quality coefficient & 1 \\ \(\tau_{0}\) & Pheromone under initial conditions & 0.0003 \\ \(\varepsilon\) & Constant & 1 \\ \(p_{s}\) & Probability that habitat \(i\) has species \(s\) & 0.1 \\ \(p_{max}\) & Maximum of \(p_{s}\) & 1 \\ \(I\) & Maximum immigration rate & 1 \\ \(E\) & Maximum emigration rate & 1 \\ \(E_{0}\) & Sensor node’s initial energy & 0.5 \\ \(E_{elc}\) & Energy consumption per bit for transmitting data & 50 nl/bit \\ \(E_{mp}\) & Energy consumption of multipath model transmitter & 0.013 p/bit/m\({}^{4}\) \\ \(E_{fs}\) & Energy consumption of free space model transmitter & 10 pj/bit/m\({}^{2}\) \\ \(round_{max}\) & Maximum round number & 2500 \\ \(n_{max}\) & Maximum iteration number & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings.
## 6 Key Issues and Challenges
### Security  
Security is an important aspect of wireless monitoring schemes [134]. The data transmitted from the sensor node to the central station node may be affected [127]. The functionality of the system could be compromised due to the existence of weak security protocols. Security against any cyber-attack and physical tampering should be employed in all the layers including authentication for legitimate users and end-to-end encryption to provide privacy and confidentiality. Moreover, only authorized persons should be allowed to perform a certain task while complying with security protocols [135]. The data transmission modules are prone to external tampering; thus, they pose huge security issues.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technical Specifications** & **Rating** & **Technology** & **Remark** \\ \hline Storage/memory & \begin{tabular}{c} In kB \\ Internal storage + \\ micro SD slot \\ In \(MB\) \\ \end{tabular} & \begin{tabular}{c} Microcontroller/Arduino \\ BeagleBone/Raspberry Pi \\ PLC \\ \end{tabular} & \begin{tabular}{c} Consideration has to be taken whether data \\ needs to be stored locally or sent to the cloud. \\ \end{tabular} \\ \hline Supply Voltage & 5 V & \begin{tabular}{c} Microcontroller \\ BeagleBone/ \\ Raspberry Pi/ \\ Arduino \\ PLC \\ \end{tabular} & \begin{tabular}{c} For the development of an efficient solar PV \\ monitoring system, the technology chosen \\ must match the available power supply \\ \end{tabular} \\ \hline Range & \(<\)300 m & \begin{tabular}{c} ZigBee/ \\ Wi-Fi/ \\ Bluetooth \\ GSM/ \\ LoRa \\ \end{tabular} & \begin{tabular}{c} The range of data transmission depends on \\ the distance to the remote control center. \\ \end{tabular} \\ \hline Data transmission rate & \begin{tabular}{c} In kbps \\ In Mbps \\ Bluetooth \\ In bps \\ \end{tabular} & \
\begin{tabular}{c} ZigBee/GSM \\ Wi-Fi/ \\ Bluetooth \\ LoRa \\ \end{tabular} &
\begin{tabular}{c} The rate of data transmission must be \\ considered according to the requirements of \\ the system. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Guidelines for the selection of appropriate technology for solar PV monitoring systems.  
When an unknown device is included in a ZigBee network, unprotected data can be sent from the device, hence affecting the whole network. The data in a Wi-Fi network can easily be captured within some ranges. The attackers in a Bluetooth system can interfere and steal encryption keys, thus accessing the data between the devices. Although LoRa provides end-to-end security through several steps such as application and network key, the network key can be extracted if an attacker gains physical access to the device [136].
# Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication
## 3 ICI Unaware Cost Function Based Soft Feedback Iterative Receiver
### OMP Channel Estimation  
The OMP algorithm is a kind of greedy algorithm, which first searches the dictionary for elements that match the received signal, orthogonalizes the selected element, removes the effect of the element from the signal and the dictionary, and obtains the signal residual. Then in the remaining dictionary, it continues to search for the element that has the best match with the signal residual, and the above process is repeated until the residual satisfies the set threshold [21]. The following linear model is usually used to analyze the sparse channel estimation problem:  
\[\mathbf{y=Ax+\eta}, \tag{7}\]  
During the channel estimation, \(\mathbf{y}\) shows the received pilot information, \(\mathbf{\eta}\) is the noise vector, \(\mathbf{x}\in R^{M}\) shows the channel information to be estimated and \(\mathbf{A}\) is the constructed dictionary vector as given by:  
\[\mathbf{A}=[a_{1},a_{2},\cdots a_{N_{\mathrm{r}}}], \tag{8}\]  
where \(a_{i}\), \(i=1,2,\cdots,N_{\mathrm{r}}\) shows the elements in the dictionary and represents the amplitudes corresponding to different paths in the channel. Let \(\mathbf{r}_{p}\) be the residual after \(p\) iterations with initial value \(\mathbf{r}_{0}=\mathbf{y}\), search for the elements in the dictionary that have the largest inner product of residuals and get the index of the matching element in the dictionary:  
\[s_{p}=\arg\max_{j=1,\ldots,N_{\mathrm{r}},j\
otin\mathrm{I}_{p-1}}\frac{\left| \mathbf{a}_{j}^{H}\mathbf{r}_{p-1}\right|^{2}}{\left\|\mathbf{a}_{j}\right\|_ {2}^{2}}, \tag{9}\]  
where \(I_{p-1}=\left\{s_{1},s_{2},\cdots,s_{p-1}\right\}\) is the index of the previous \(p-1\) iterations. Schmidt orthogonalization of the selected elements is given by:  
\[\mathbf{u}_{s_{p}}=\mathbf{a}_{s_{p}}-\sum_{i=1}^{p-1}\frac{\left\langle \mathbf{a}_{s_{p}},\mathbf{u}_{i}\right\rangle}{\left\langle\mathbf{u}_{i}, \mathbf{u}_{i}\right\rangle}\mathbf{u}_{i}, \tag{10}\]  
where \(\mathbf{u}_{i}\) is the value of the orthogonalized element chosen for the first time and the estimated values of elements in signal \(\mathbf{x}\) is given by:  
\[\hat{x}_{p}=\frac{\left\langle\mathbf{u}_{s_{j}},\mathbf{r}_{p-1}\right\rangle }{\left\|\mathbf{u}_{s_{p}}\right\|_{2}^{2}}, \tag{11}\]  
and the residual signal is calculated as:  
\[\mathbf{r}_{p}=\mathbf{r}_{p-1}-\hat{x}_{p}\mathbf{a}_{s_{p}}, \tag{12}\]  
Stop the iterations when \(\left\|r_{p}\right\|_{2}^{2}<\epsilon\)(where \(\epsilon\) is the residual threshold). Finally the delay estimation value can be obtained from Equation (7), with \(\hat{x}_{p}\) being the corresponding path gain, the resulting channel frequency estimate is given by:  
\[\mathbf{H}=\sum_{p=1}^{N_{\mathrm{r}}}\hat{x}_{p}\mathbf{\Lambda}_{p}, \tag{13}\]  
where \(\mathbf{\Lambda}_{p}\) is a diagonal matrix with the diagonal elements satisfying the following equation;  
\[\left[\mathbf{\Lambda}_{p}\right]_{m,m}=e^{-2\pi\tau_{p}m/T}. \tag{14}\]