esa-sceva/satcom-chunk-collection huggingface dataset:
# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 4 Experimental Results and Analysis
### Experimental Setup
#### 4.2.1 U-Net Model Training Setup  
For the first stage training process, 80 standard patterns collected by the industrial camera are used as the training sample set after Gaussian filtering. The size of the filter matrix used is \(7\times 7\), the mean value is 0, and the variance is 3. In addition, considering that the image captured by the smartphone is affected by the ambient light (light or dark), data augmentation based on gamma correction is conducted on the training samples. The gamma values range from 0.5 to 1.2, and the step size is 0.1. A total of eight values are selected. After augmentation, 80 \(\times\) 8 = 640 training sample images are obtained in all as the train set of U-Net model.  
Figure 8: Smartphone-captured genuine patterns. (**a**) Clear pattern; (**b**) Blurry pattern.  
Figure 6: Digital pattern.  
Figure 7: Standard collected pattern.  
The learning rate of U-Net is set to \(1\times 10^{-4}\), Adam optimizer is applied, the number of training epoch is 1000, and the relative weight of loss items \(\beta\) is set to 25.
## 2 Related Studies  
The proposed systems in this study are inspired by hydroflight equipment, such as jetpacks [18; 19], flyboards [19; 20], jetovator [21], and aquadrone [22]. They have shown the capability of water-powered aerial systems. Unfortunately, these hydroflight equipment are either manual or semi-automated, and their applications are mainly in hydroflight sports, entertainment, and show performances. The way a person leans his/her body to operate hydroflight equipment gives us the idea of the weight distribution actuator in our third design. In 2017, firefighters in Dubai demonstrated the use of a water-powered flyboard to get them to the height they need to tackle a fire [23; 24]. The fire hose pumped water from the source below and gave an infinite water supply.  
[PERSON] and [PERSON] [25] experimented with two actuating mechanisms for unmanned water-powered aerial vehicles, namely nozzle-gimbaled and flow-regulated mechanisms. Similar to our nozzle rotation and flow rate control designs, they all implement the idea of controlling the direction and the amplitude, respectively, of the thrust generated by each nozzle. However, their designs are different from ours, which results in different motion characteristics. For instance, their nozzle-gimbaled mechanism leads to the non-minimum phase phenomena, and their flow-regulated mechanism is not able to control the yaw motion. Additionally, their systems' motion equations are rudimentary such that the motion of the system is neglected in the generation of thrust and the effects of the hose are results from a number of assumptions without the explicit hose's dynamics.  
On the other hand, our systems share the similar objective of using water for propelling and firefighting with the Dragon Firefighter system from Tohoku University, Japan. The system was first proposed in [26], and by considering the system as a flexible continuum robot, multiple problems related to its motion dynamics were subsequently solved [27; 28; 29; 30]. Besides similar purposes, our systems differ from theirs in almost every aspect, such as design, modeling, and control. For instance, the Dragon Firefighter consists of a module of four rotary nozzles arranged differently from ours. Additionally, the water jets from these nozzles not only propel the system, but also suppress the fire simultaneously. For conveying water to the nozzles, the hose in their system is short and kept horizontal, connect directly to the nozzle module with an oscillation suppression mechanism.  
Furthermore, partial and naive ideas of this study have been presented in our previous publications [31; 32]. In both studies, the dynamics of the hose were neglected, which could not help to fully understand the system characteristics. In [31], we presented the idea of separately controlling the flow rate of each thrust nozzle. However, from a practical perspective, it cannot be the case. In [32], we explored the idea of controlling the weight distribution to maneuver the system. The actuating mechanism was different from that in the third system in this paper, and only five degrees of freedom of the system motion were controllable.
## 3 Results  
In Figure 3, the temporal evolution of CO and CO\({}_{2}\) concentrations are shown for the three segments previously described. In Table 1, the corresponding CO and CO\({}_{2}\) amounts are given. During Segment 1, the target CO exit concentration (<100 ppm) was attained for about 30 s. As observed in [48], CO\({}_{2}\) started to be detected in the gas phase only when CO concentration rose. Consequently, CO\({}_{2}\) concentration in the gas flow theoretically entering the fuel cell was negligible. The amount of CO  
Figure 2: Outline of the lab experiment.  
consumed during this segment was about 4.5 \(\times\) 10\({}^{-2}\) mmol and was mostly trapped on the catalyst surface. The corresponding CO/Cu ratio was equal to 0.19.  
In Segment 2, a very narrow peak of CO was observed at the very beginning with a maximum value not exceeding the target exit CO concentration, whereas mainly CO\({}_{2}\) evolved. From a quantitative point of view, about 3.0 \(\times\) 10\({}^{-2}\) mmol of CO\({}_{2}\) were produced, corresponding to 67% of CO consumed in Segment 1, representing a very good result taking into account the low operating temperature. It is worth noting that most of the CO\({}_{2}\) evolution (\(\approx\)55%) occurred in the first few minutes at 55 \({}^{\circ}\)C due to the positive effect of temperature on both CO oxidation (oxygen mobility) and CO\({}_{2}\) desorption rate, in agreement with our previous results [48].  
When segment 1 was repeated (segment 3), a significant reduction of the time when exiting CO concentration was below the target was observed (20 s vs. 30 s). Accordingly, CO adsorbed in Segment 3 was 40% lower than that adsorbed in segment 1 and also slightly lower than the CO\({}_{2}\) evolved during segment 2. This reduction should be related to the initial state of the catalyst surface, still partially covered after the segment 2, and thus not completely available for the fresh catalyst.  
Without exception, the trap provided a clean gas flow with a maximum punctual value of CO outlet concentration of 100 ppm, 100% selectivity (no H\({}_{2}\) can be oxidized under these conditions, as demonstrated in [48]) and no O\({}_{2}\) in the H\({}_{2}\)-rich stream.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Molecule** & **Segment 1** & **Segment 2 (Isothermal)** & **Segment 2 (Total)** & **Segment 3** \\ \hline \multicolumn{5}{c}{**(mmol)**} \\ \hline CO & 4.50 \(\times\) 10\({}^{-2}\) & 0.2 \(\times\) 10\({}^{-2}\) & 0.2 \(\times\) 10\({}^{-2}\) & 2.72 \(\times\) 10\({}^{-2}\) \\ CO\({}_{2}\) & 0.04 \(\times\) 10\({}^{-2}\) & 1.65 \(\times\) 10\({}^{-2}\) & 3.02 \(\times\) 10\({}^{-2}\) & - \\ \hline \end{tabular}
\end{table}
Table 1: CO consumed and CO\({}_{2}\) formed in segments 1, 2 and 3.  
Figure 3: CO (black line) and CO\({}_{2}\) (red line) concentrations and temperature as function of time during segment 1 (**a**), 2 (**b**) and 3 (**c**).
## 3 Research Progress on Adsorption and Separation of Hydrocarbon Molecules
### Aromatic Hydrocarbons
#### 3.2.1 Carbon Materials
The experimental results showed that 0.075 mg GNP/MIL-101 had the highest adsorption efficiency for naphthalene, up to 93% at 298 K.
# Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)
## 3 MinT: Rationale and Architecture  
Aiming at a secure-by-design fair PoW mining network in heterogeneous IoT environments, our MinT scheme leverages DT technology to continuously monitor the usage of containerized miners and discourages misbehaving nodes from unfairly overwhelming the peers by using extra computing power. Figure 1 illustrates the high-level system architecture of MinT, which adopts a hierarchical cloud-fog-edge computing paradigm. Such a hierarchical framework not only provides system scalability for large-scale fair mining tasks based on geographically distributed IoT devices but also supports flexible management and coordinated central and decentralized local decisions given heterogeneous networksand application domains. Moreover, MinT relies on a permissioned network that provides basic security guarantees, such as the public key infrastructure (PKI) and digital signature, data integrity [2], identity authentication [6] and access control [38], etc. In essential, MinT is a partial decentralized PoW mining network. Furthermore, DTs in MinT are mainly used to monitor their associated miners and to support misbehavior detection, and they do not directly participate in the PoW mining task or impose interference on the consensus protocol. Therefore, our Mint is promising in enabling a fair mining network without sacrificing distribution and decentralization. The rationale behind the MinT is described as follows:
## 4 Common/Unifying Theoretical-Conceptual Framework  
A general framework needs to be developed in order to allow researchers and clinicians to organize systematically and understand the enormous diversity of observations related to qEEG characteristics in neuropsychopathology. As a first step, a general concept of _dynamic qEEG oscillatory patterns_ has been proposed [450]. Within this concept, a dynamic qEEG oscillatory pattern is considered as a spatial-temporally organized superimposition of ongoing multiple qEEG oscillations in many frequency bands [26], where different oscillations are mixed in varying proportions based on the vigilance level; perceptual, cognitive, and mental operations; behavior; and the extent of pathologic processes. It seems that different qEEG descriptors can be combined within the dynamic qEEG oscillatory pattern, which is characterized by (a) _frequency content_, including the composition of delta (0.5-3 Hz), theta (3-7 Hz), alpha (7-13 Hz), beta (13-30 Hz), and gamma (>30 Hz) qEEG oscillations, along with their proportions, dominant frequencies, amplitudes and powers, and (b) _spatial heterogeneity_ (expressed in spatially structured extracellular electrical fields), including spatial complexity (amount of brain connectivity), interhemispheric symmetry, and hubs (cortex areas with the highest neuropsychopathology effect or highest functional connectivity). This concept was successfully applied to major depressive disorder [450].  
The concept of dynamic qEEG oscillatory patterns is useful since it enables researchers to combine all known and newfound qEEG characteristics within _one entity_. Studies (see above) have suggested that dynamic qEEG oscillatory patterns reflect the structural and functional integrity of the brain, including an information-processing mode that is dynamically regulated by interactions within a homeostatic system that is mediated by many different neurotransmitters on one side and functional activity and various perceptual and cognitive operations associated with a mental or behavioral condition in health and pathology on the other.  
The categorization of different _types_ of dynamic qEEG oscillatory patterns resulted in the second concept--the _qEEG phenotype_.
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 3 Information Fusion Based on Extended Information Filtering
### Consistence  
Usually, in a distributed structure, the information matrix obtained by information fusion of each node is different. In order to ensure that the UAVs have the same environments awareness, it is necessary to use the consistency algorithm to make the information matrix tending to be consistent. In this paper, the traditional classical consistency algorithm was adopted.  
The algorithm is described as follows [5; 6]:  
\[\xi_{i}[d+1]=W_{ii}\xi_{i}[d]+\sum\limits_{j\in N_{i}}W_{ij}\xi_{j}[d]=\xi_{i} [d]+\sum\limits_{j\in N_{i}}W_{ij}(\xi_{j}[d]-\xi_{i}[d]) \tag{49}\]  
where \(W\) is the consistency weighted matrix. It can use the following expression:  
\[W_{ij}=\left\{\begin{array}{l}\left(\max\{N_{i},N_{j}\}\right)^{-1},j\in N_ {i}\\ 1-\sum\limits_{i,k\in N_{i}}W_{ik},i=j\\ 0,otherwise\end{array}\right.. \tag{50}\]  
Figure 3: The observation configuration when \(N=3\).
# Fast Low-Precision Computer-Generated Holography on GPU
## 2 Methodology  
The Fresnel model for the complex amplitude of a single PSF created by a point emitter located at coordinates \((\delta,\epsilon,\zeta)\) is given by the following expression:  
\[P(x,y)=a\cdot\exp\Big{(}\frac{\pi i}{\lambda\zeta}\big{[}(x-\delta)^{2}+(y- \epsilon)^{2}\big{]}\Big{)} \tag{1}\]  
where \(\lambda\) is the wavelength and \(a\) is the point amplitude. To obtain the complex-valued hologram \(H(x,y)\), we must sum over all \(Q\) points in all pixels of \(H\). This gives us the following:  
\[H(x,y)=\sum_{j=1}^{Q}P_{j}(x,y)=\sum_{j=1}^{Q}a_{j}\cdot\exp\Big{(}\frac{\pi i }{\lambda\zeta_{j}}\big{[}(x-\delta_{j})^{2}+(y-\epsilon_{j})^{2}\big{]}\Big{)}. \tag{2}\]  
In conventional point-based CGH, this expression is evaluated independently for all pixels, typically computed using floating-point arithmetic. We propose to significantly accelerate this by combining two features:  
1. Leveraging the separability of every \(P(x,y)\) along its spatial dimensions \(x\) and \(y\);
2. Using low-precision fixed-point integer approximations for the various mathematical operations of (2). These are described in the remainder of this section.
# Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
## 4 Conclusions  
In this research, we tested a new method called \"Simplified Digital Image Correlation method (SiDIC)\" for detecting non-uniform deformation of the super pressure balloon. In order to confirm the measurement accuracy of the developed SiDIC, first, we assessed the measurement accuracy of DIC using a rubber balloon covered with random spray patterns. Next, we used a rubber balloon covered with both spray patterns and a net to analyze it with DIC and SiDIC. Results of SiDIC and DIC were found to be in good agreement. Next, we applied both DIC and SiDIC to a rubber balloon covered just with a net supposing as an SPB. As a result, DIC recognized the net as a pattern, although it could not measure the whole deformation accurately. On the other hand, SiDIC measured the deformation clearly. Furthermore, SiDIC was able to identify undeformed regions when balloon deformation was restricted by bonding the net to the rubber shell. Therefore, it can be used as a simple deformation measurement method for the balloon. In this research, we used a two-dimensional DIC to find out whether SiDIC could successfully measure the deformation. However, 2D-DIC cannot measure the deformation in the depth direction, which means we have not measured the real deformation of the balloon. In addition, we used visual detection to detect the intersections and the experiment we used to identify the measurement accuracy was rough. From the results, we will continuously upgrade the measurement accuracy and develop an intersection detecting program at the same time; we are attempting to measure the balloon deformation more accurately by evolving SiDIC to \"3D-SiDIC\". We are planning to discuss how to fuse SiDIC and 3D-DIC in the future.  
[PERSON] conceived, designed, and performed the experiments, analyzed the data and wrote the paper. [PERSON] and [PERSON] provided the laboratory support and improved the manuscript.  
This work was supported by JSPS KAKENHI Grant Number 17H01352.  
The authors are grateful to [PERSON] and [PERSON] for suggesting the topic treated in this paper. We also thank them for sharing images and data of the Super pressure balloon with us.  
The authors declare that there is no conflict of interest.
###### Abstract
The MODIS algorithm uses light use efficiency (LUE) model to estimate GPP, which may underestimate ecosystem GPP under varying climatic conditions and vegetation type and overestimate GPP for water-limiting ecosystems due to lack of explicit representation on soil moisture constraint [52]. In addition, MODIS estimates the GPP but leaves an incomplete picture of NEE due to lack of information on ecosystem respiration [47]. On the other hand, SMAP Level 4 carbon products algorithm incorporates soil moisture information, land cover classification and eight-day canopy fraction of photosynthetically active radiation (FPAR) observation from MODIS to provide detailed and improved estimations of terrestrial carbon fluxes [59; 60]. This study used SMAP Level 4 carbon products to understand the spatial and temporal variability of carbon fluxes across Texas. Texas is unique among states because of its large size, geographical location, climate variations, and heterogeneous landscapes, which makes it unique to study ecosystem dynamics. The main goal of this research was to quantify the carbon sequestration potential across Texas and study the combined effects of climate and land-use changes on it. Thus, the specific objectives of this work were to: (1) Evaluate SMAP NEE and GPP products; (2) quantify spatial patterns and temporal distributions of CO\({}_{2}\) flux across Texas; and (3) evaluate the response of CO\({}_{2}\) fluxes to the combined effects of climate and land-use changes in the state of Texas using SMAP Level 4 carbon products.
# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 4 Experiments
### Ablation Studies  
In this experiment, we use the average accuracy as the overall performance measure. However, practical applications often involve diversified metric requirements. In this context, it is difficult for a single solution set to meet the needs of multiple optimization objectives. Borrowing the idea from multi-objective optimization, we introduce the concept of the Pareto-optimal solution: in a multi-objective optimization problem, a solution is Pareto-optimal if it is not dominated by any other solutions in the solution set at the same time.  
Taking the Cifar10 Mnist dataset as an example, we take the accuracy of the two tasks as the optimization objective. While traditional baseline algorithms usually give only one dominated solution, our algorithm produces multiple sets of non-dominated solutions. Figure 5 shows the evolutionary trajectory of these non-dominated solution sets during the training process. The results show that our method gradually approaches the real Pareto frontiers during the training process, which not only reflects the improvement of the adaptive and weight balancing capabilities but also proves that the method can produce richer solution sets, which can be used as a reference for the subsequent extension to more complex multi-task scenarios.  
In addition, Figure 6 shows the trend of the average accuracy of the different methods under complex tasks. As expected, although the HPS method performs well on the MNIST number classification tasks (Task 2 and Task 3), its overall performance is sacrificed. This is due to the fact that traditional weight-assignment strategies cannot simultaneously balance the requirements of low-accuracy tasks and overall performance improvement. In contrast, our approach presents superior performance in all tasks, and the advantage is even more prominent when the task complexity increases. This shows that the framework has good scalability and stability under multi-task conditions.  
Figure 5: Pareto frontiers showing non-dominated solutions for CIFAR10 MNIST tasks. Our method achieves better coverage of high-accuracy regions compared to static weighting approaches.
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 4 Proposed Scheme
The objective of Algorithms 1-3: For outlier detection, the anomaly score threshold limit is calculated. ```
1. Set cluster_head = node with the highest energy
2. Set threshold (value)
3. If route reply of node (i) > threshold (value)
4. Send alert to cluster head and neighboring nodes that the node is an outlier
5. If the node is an outlier, then avoid routing through it
6. Calculate energy discharge
```  
**Algorithm 4**Energy calculation and identification of outliers  
Like outlier detection at the local level, outliers are also identified at the global or network levels, as shown in Figure 2.
## 2 Materials and Methods
### Recurrent Neural Networks (RNN)  
Recurrent Neural Networks (RNNs), however, are especially successful when dealing with sequential data. In opposite to a fully connected layer from an ANN, the recurrent layer in an RNN, consist of a loop (a chainlike structure) which enables information flow \"through time\". A neuron handling data from timestep \(t\)-\(1\), provides an output while also  
Figure 4: Nonlinear model of a neuron (according to: [27]).  
passing information (\"Hidden State\") to the neuron handling timestep \(t\), and so on. The result is better handling of data where the current timestep depends on data from several timesteps before.  
The calculations at each timestep of the input sequence can be represented with the two following equations [28; 29]:  
\[h_{t}=tanh_{h}(W_{h}x_{t}+U_{h}h_{t-1}+b_{h}) \tag{3}\]  
and  
\[y_{t}=tanh_{y}\big{(}W_{y}h_{t}+b_{y}\big{)} \tag{4}\]  
where: \(W_{h}\) is the weight matrix between the input and hidden layers and \(U_{h}\) is the recurrent layer weight matrix at adjacent time steps. The vectors \(b_{h}\) and \(b_{y}\) are the bias terms which give an offset to each node (neuron).  
However, in real world data, a situation where the data multiple time steps before influences the data at the current moment often occurs. No matter the effectivity of RNNs when dealing with timeseries data, in such cases, problems occur in the form of vanishing or exploding gradients. With the increase of the time dependency gap, the performance of traditional RNNs drops significantly [18; 30].