esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Procedure
### Construction and Test Setup  
The BMRC1, NEWRC columns, and NEWRCF frame specimens were constructed and tested at the National Center for Research on Earthquake Engineering (NCREE, Taipei) laboratory. A schematic diagram of the test setup is shown in Figure 3. First, the stub footing of all the columns and frames was post-tensioned using four tie-down rods with a clamping force of 320 kN and anchored into the laboratory's strong floor. Then, constant axial compression was applied to the test column through a loading system using a steel beam, an oil pump, a pin connection, and high-tension rods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Concrete**} & \multirow{2}{*}{\begin{tabular}{c} **Longitudinal Reinforcement**, \\ **D25 (\#8)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Transverse Reinforcement**, **D13 (\#4)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Axial** \\ **Compression** \\ **Ratio** \\ \end{tabular} } \\ \hline  & \(f_{c}\)(MPa) & \(f_{yt}\)(MPa) & \(\rho_{t}\)(\%) & \(f_{yt}\)(MPa) & \(\rho_{t}\)(\%) & s (mm) & \(P/Ag_{c}^{\prime}\)(\%) \\ \hline BMRC1 & 37 & 534 & 2.26 & 341 & 1.08 & 100 & 0.066 \\ NEWRC1 & 80.9 & 736 & 2.44 & 813 & 1.03 & 100 & 0.066 \\ NEWRC2 & 80.9 & 736 & 2.44 & 813 & 0.516 & 200 & 0.066 \\ NEWRC3 & 80.9 & 736 & 2.44 & 813 & 0.344 & 300 & 0.066 \\ NEWRC4 & 80.9 & 736 & 2.44 & 813 & 1.03 & 100 & 0.132 \\ NEWRC5 & 80.9 & 736 & 2.44 & 341 & 1.03 & 100 & 0.066 \\ NEWRCF & 80 & 723 & 2.3 & 833 & 1.29 & 100 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Column and frame design parameters.  
Figure 3: Test setup: (**a**) NEWRC columns; (**b**) NEWRCF. Unit: mm.
###### Abstract  
Ammonia emissions are an important issue in livestock production. Many mitigation measures have been proposed in order to reduce the environmental impact of livestock farms, and reliable field measurements are required to evaluate the amount of released or reduced ammonia while applying these measures. Following the guideline of the Verification of Environmental Technologies for Agricultural Production test protocol, five commercially available gas analysers, i.e., INNOVA 1314, Picarro G2103, Rosemount CT5100, Gasmet CX4000, and Akertis LGD F200-A, were validated as alternative methods to the wet-chemistry method (reference method) for measuring ammonia in livestock houses. High correlations (\(r>0.99\)) were found between the analysers and the reference method. The measurement errors of the tested analysers were below 2 ppm\({}_{v}\) or 10%. Equivalence to the wet-chemistry method was demonstrated for the INNOVA and Rosemount analysers without a recalibration and for the Picarro and Akertis analysers with a recalibration. The Gasmet analyser was seemingly subjected to an interference from carbon-dioxide and, after compensating for the cross-sensitivity, the equivalence to the wet-chemistry method could also be demonstrated. Calibration curves that were based on a certified gas cylinder were inconsistent with that based on wet-chemistry measurements, which suggested that field calibration might be necessary for optimal measurement accuracy.  
ammonia; evaluation; analyser; poultry; pig; emission; VERA Article
## 2 Methodologies
### Random-Delay-Corrected DRL Framework
\[\begin{split}\mathbb{T}&=\{\frac{a_{0},s_{0},r_{0}, a_{1},a_{2},s_{1},r_{1},a_{3},s_{2},r_{2},s_{3},r_{3},a_{4},a_{5},a_{6},s_{4} },\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\  
As we can see from (14), the trajectory \(\mathbb{T}\) of the PH-MDP is different from the standard MDP. Between two adjacent actions, the agent may receive 0, 1, multiple, or outdated observation(s) from the environment. For example, there is only one observation \(s_{0}\) received by the agent between \(a_{0}\) and \(a_{1}\); there is no observation between \(a_{1}\) and \(a_{2}\); there are two observations \(s_{2}\) and \(s_{3}\) between \(a_{3}\) and \(a_{4}\); there is one outdated observation \(s_{7}\) between \(a_{9}\) and \(a_{10}\) because newer observation \(s_{8}\) is received by the agent between \(a_{8}\) and \(a_{9}\). Despite this, the PH-MDP can be transformed into the standard MDP. The state of the environment is also changed by the executed action, but what is different is that the changed state will be received with a random observation delay. Therefore, the DRL algorithms with this framework and DCTS are off-policy algorithms that can train neural networks by sampling the trajectory \(\mathbb{T}\). Therefore, we can re-sample \(\mathbb{T}\) and generate two buffer \(\mathbb{D}_{fm}\) and \(\mathbb{D}_{dtl}\) to train the forward model and the actor/critic of DRL algorithms. The two buffers can be defined as shown in (15).  
\[\mathbb{D}_{fm} =\{s_{i},a_{i},s_{i+1}\} \tag{15}\] \[\mathbb{D}_{dtl} =\{s_{i},a_{i},r_{i+1},s_{i+1}\}\]  
Example DRL algorithm: Delayed-DQN. This algorithm integrates the deep Q network (DQN) and the random-delay-corrected DRL framework to help improve the performance in a delayed environment. Delayed-DQN consists of three neural networks, including a forward model, evaluation network, and target network parameterized by \(\theta\), \(\omega\), and \(\omega^{\prime}\), respectively. Delayed-DQN firstly predicts the state \(\hat{s}_{t}\) following Equation (6) and then outputs an action \(a_{t}=\arg\max_{a}Q_{\omega^{\prime}}(\hat{s}_{t})\) with a probability \(1-\epsilon\). Enabled by the DCTS, the DQN networks can be trained with the loss function as follows:  
\[\mathcal{L}(\omega)=(r+\gamma\max_{a^{\prime}}Q_{\omega^{\prime}}(\hat{s}^{ \prime},a^{\prime})-Q_{\omega}(\hat{s},a))^{2} \tag{16}\]  
The complete random-delay-corrected DRL framework with DCTS is illustrated in Algorithm 1 and an example delayed-DQN is proposed.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### Determine the Input Variables  
Before runoff forecasting, selecting the appropriate number of input data can effectively improve the accuracy of model construction. When the input data is insufficient in number, the model training is inadequate to capture the changes and characteristics of the data, resulting in a large difference between the model fitting series and the actual series, and the final forecasting results are not good enough. When the input data is too much, the model training is easy to overfit, and the model fitting series is too close to the input time series, which cannot accurately predict and will also affect the accuracy of the forecasting. This article tests the effect of lag time under different scenarios by referencing previous studies, and the simulation results are compared. Finally, 11 kinds of preferred sets are selected for forecasting.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 3 Preliminaries and Model Definitions
### System Model
In harsh environment, UEs are involved in the data transmission that is routed over the SN and D2D network structure. That is, the participating UE not only delivers the messages generated by itself, but also forwards the routed information originated from other UEs. As for certain UEs, its interaction with neighboring devices provides high connectivity to all the remaining UEs, even though some devices are out of the SN cellular coverage. It is worth noting that the UEs should be authenticated before accessing the D2D network for security assurance. Besides, the UE (smartphone) is equipped with various behavioral sensors such as the accelerometer and gyroscope. Therefore, vital biometric data can be collected by the smartphone sensors, revealing the personal behavioral features. In our design, the user's unique behavioral patterns are applied for continuous authentication. That is, after the initial authentication operation, the specific verification is conducted periodically for the purpose of guaranteeing secure data transmission throughout the entire process. To be concluded, the UEs perform as both the D2D terminal device and biometric data collector as well.
## 2 PFAC Infrastructure & Instrumentation  
The Sentinel-3 satellite flies at an altitude of 814.5 km following a near-polar sun-synchronous orbit that ensures 104 km ground track separation at the Equator. It needs 385 orbits to perform a complete Earth cycle. The STM element has a 27-day revisit time upon the same ground location. This temporal repeatability of Sentinel-3 lies between that of Envisat's and SARAL/AltiKa's (35-days) and CryoSat-2 (369-days) and Jason (10-days) satellite altimeters.  
The Sentinel-3A & Sentinel-3B ground tracks over the island of Crete, Greece are illustrated in Figure 1. The locations of the independent Cal/Val sites that compose the Permanent Facility for Altimetry Calibration are also presented in the same Figure. Two of these sites are employed for the calibration of Sentinel-3A STM element: the CDN1 transponder and the Gavdos sea-surface Cal/Val facilities.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 1 Introduction
In 2022, [PERSON] verified the effectiveness of BiLSTM in literature [17]. Especially under the low signal-to-noise ratios, it had a better recognition effect than LSTM in open RML datasets. In 2020, [PERSON] proposed a spatiotemporal multi-channel learning framework (MCLDNN) for automatic modulation recognition [18]. This network could extract features more effectively from the perspective of time and space. Experiments on reference datasets showed that the proposed framework had high convergence speed and higher recognition accuracy. Based on the existing modulation signal recognition network models, this paper proposes an improved spatiotemporal multi-channel network (IQGMCL) to solve the problem of low signal recognition accuracy. Firstly, the relevant characteristics of the IQ signal are added to the original MCLDNN network data input. After introducing the related features of IQ signals, drawing on the idea of a small convolution kernel in the VGG network, a small convolution kernel (1,3) is used to extract time series features further. These increase the number of model training features and improve the training accuracy and verification accuracy of the model. Secondly, LSTM is replaced with Bi-LSTM. On the basis of one-way flow from the past to the future of the LSTM network, data flow is increased from the future to the past. Bi-LSTM can better explore the temporal characteristics of data and improve the robustness of the model. Thirdly, a full connection layer is removed on the basis of the MCLDNN network. Due to the increase in training characteristics, it can not only prevent overfitting but also reduce model training parameters and also accelerate the convergence speed of the model. Finally, multiplicative Gaussian noise is introduced before classification to randomly erode features. It effectively alleviates the problem of training overfitting. It also improves the robustness of the model and the generalization ability of the network.
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 3 The LHP Based Functional Devices
### Photodetectors
#### 3.2.2 Photodetectors by Combining LHP with 1D Materials  
In addition to 2D materials, LHP has also been combined with 1D materials such as nanotubes and nanowires. These 1D structures with large surface-to-volume ratio enable prolonged charge carrier lifetime, increased interfacial area, and suppressed light reflection, which is preferential for photodetection [9; 109]. As one example, TiO\({}_{2}\) nanotubes (NTs) or nanorods (NRs) have been utilized in photodetection recently [109], but the application has been limited to UV region due to its large bandgap. LHPs are thus used to combine with TiO\({}_{2}\) to broaden the effective absorption band covering the UV and visible range [81; 90]. In a MAPbBr\({}_{3}\) QDs/TiO\({}_{2}\) NTs device [81], it is found that both the responsivity and detectivityin the visible range were increased considerably as compared to the NT-only device. In addition, a hybrid device based on TiO\({}_{2}\) NRs and MAPbI\({}_{3}\) has been found to exhibit self-powered characteristics at UV illumination, demonstrating a current of 15 \(\upmu\)A at 0 V [90]. This self-powered characteristic has been attributed to the blocking of electrons from LHP to anode by the HTM layer. [PERSON] et al. [9] reported a silicon/LHP core-shell photodetector by depositing a layer of Cs-doped FAPbI\({}_{3}\) onto a silicon nanowire arrays, and the device was also found to be self-powered. When the bias was set to be \(-\)0.9 V, the performance could be further improved to show a high responsivity of 844.33 mA/W and detectivity of 3.2 \(\times\) 10\({}^{11}\) Jones. The device was swift in response, demonstrating a rise and decay time of 4 \(\upmu\)s and 8 \(\upmu\)s respectively, very close to the commercial silicon photodiodes [110]. [PERSON] et al. have proposed a photodetector combining LHP and Ga-doped ZnO NRs [18], which demonstrates a high-performance comparable to commercial IR detectors [111], and a wideband detection range from 250 to 1357 nm. This IR detection ability was enabled by the intraband transition in LHP, and a Fano-like transition was observed in the IR range of the absorption spectra.
## 2 Materials and Methods
### Pre-Processing of Change Detection Results
#### 2.3.1 Pixel-Level Probability Normalization  
The final layer of the change detection neural network performs a Softmax classification of each pixel of the feature map and generates a probability distribution map. A cross-entropy loss function is then used to iterate over the model parameters. The Softmax function converts the output values of multiple classifications into a probability distribution in the range of [0, 1]. The classification probability of each pixel is calculated by the following equation:  
\[\mathrm{P}=\mathrm{Softmax}(z_{i})=\frac{e^{z_{i}}}{\sum_{j=1}^{C}e^{z_{j}}} \tag{1}\]  
where \(C\) is the total number of categories (or nodes) for classification, \(i\) denotes the change category, \(z_{i}\) is the output value of the \(i\)th node, and \(\mathrm{P}\) represents the predicted change probability of the pixel. The information to be extracted in single-element change detection tasks is usually divided into two main categories: change and background.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Network Structure-Based Routing
#### 4.1.2 Hierarchical Routing
The probability that the line of sight (LoS) between UAVs and sensor nodes is given depends on the environment, device location, and elevation angle. UAVs use nodes' geo-location from their GPS to find the optimal route. The authors of Reference [45] compared their proposed algorithm with a greedy algorithm. The greedy algorithm could find a local solution, but it was not possible for the algorithm to observe the situation globally. As the goal was to find an energy-saving and time-bound method, it seems that the proposed scheme outperformed the greedy approach. Figure 5: UAV and WSN communication. Projection-based comprehensive data gathering (PCDG): [PERSON] et al. proposed the use of UAVs to collect data from extremely dense WSNs using projection-based comprehensive data gathering (PCDG) [46]. Comprehensive data gathering (CDG) is used to aggregate data from cluster members to their CHs. This can successfully reduce the number of transmissions that lead to the reduction of energy consumption. The UAV is responsible for data transfer to a remote sink node, avoiding the need for long-range transmissions or multi-hop communication among sensors. After an optimized forwarding tree per cluster is constructed, the sensed data are gathered from selected CHs based on projection-based CDG with minimized UAV flight distance. In Reference [46], the joint problem of optimized node clustering, forwarding tree construction, CH selection per cluster, and UAV trajectory planning for energy-efficient data collection was mathematically modeled and analyzed. According to the performance results, the PCGD outperformed other approaches for small-, medium-, and large-size network scenarios.
## 3 Results and Discussion
### Polar Extract Characterization  
The methanol:water (50:50) extraction yield obtained from the lipophilic-free residue of elderberry stalks was 34.4% dw. This value is considerably higher than that previously reported for grape stalks residue (although using methanol:water (75:25) as extraction media) after dichloromethane extraction (6.4%) [35].  
The TPC of elderberry stalks polar fraction was 0.83 g \(\pm\) 0.21 GAE/100 g fw, or, in a dry weight basis, 1.7 \(\pm\) 0.42 g GAE 100 g\({}^{-1}\) dw. This content is in the range of that already obtained for elderberry stalks (0.71 GAE 100 g\({}^{-1}\) fw) [34] and is also within the values known for elderberries (0.36-1.95 g GAE 100 g\({}^{-1}\) fw) [60; 61; 62]. Furthermore, the TPC obtained is very similar to those verified previously in similar matrices such as grape stalks, which presented 1.6 g GAE 100 g\({}^{-1}\) dw for Chardonnay and 2.0 g GAE 100 g\({}^{-1}\) dw for Muller-Thurgau varieties [55].  
The polar extract was also evaluated for its radical scavenging activity by both ABTS and DPPH assays. The activities, expressed as Trolox equivalents accounted, respectively for 1.04 \(\pm\) 0.14 mmol TE g\({}^{-1}\) extract and 0.37 \(\pm\) 0.08 mmol TE g\({}^{-1}\) extract, which are slightly lower values than those obtained previously for elderberries, 1.74-2.20 mmol TE g\({}^{-1}\) extract and 0.62-0.89 mmol TE g\({}^{-1}\) extract [63], against, respectively, ABTS and DPPH radicals. However, this is an expected difference since most of the antioxidant compounds are supposed to be stored in berries. Our elderberry stalks showed slightly higher antioxidant activity (17.31 mmol TE 100 g\({}^{-1}\) fw) when compared with the value obtained by [PERSON] et al. [34] for elderberry stalks methanol extract (10.7 mmol TE 100 g\({}^{-1}\) fw).  
Elderberry stalks, as elderberries or grape stalks, have a high amount of individual phenolic compounds, most predominantly anthocyanins that are commonly used as colorants in food and are well known for their antioxidant properties [34; 31]. Moreover, elderberry stalks show a total phenolic content and antioxidant activities similar to elderberries and to grape stalks, which demonstrates the potential of this by-product to be valorized.
## 6 Discussion and Future Directions
### Performance Evaluation Metrics
#### 6.4.3 Packet Delivery Rate (PDR)  
PDR [170] is the ratio of the total number of data packets successfully received at the destination pt to the total number of data packets coming from the source node.
## 3 Simulation Results and Discussions  
### Check on Thermodynamic Database  
The default PHREEQ database (Phreeqc.dat) uses the extended Debye-Huckel equation, or the Truesdell and Jones equation [22], for activity coefficient computation. It is well known that the usable range of the equation is limited, up to 2 of ionic strength in chloride solution [22]. However, the cementitious material environment considered in this study consists of a high concentration of Na\({}_{2}\)SO\({}_{4}\) solution and thus, the use of the extended Debye-Huckel equation for speciation calculation would be questionable. On the other hand, the calculation using [PERSON]'s model would provide a good approximation for activity coefficients in high concentration [30]. In order to check the database or the activity coefficient equation, a calculation for Na\({}_{2}\)SO\({}_{4}\) solubility was performed using the PHREEQ default database [23] (Phreeqc.dat) and the PITZER database [30] (Pitzer.dat), which uses the PITZER equation described in the Appendix A, Equations (A2-A5), with interaction parameters for various ions. As shown in Figure 2, Pitzer.dat allowed us to predict the correct solubility of Na\({}_{2}\)SO\({}_{4}\) as a function of concentration, but Phreeqc.dat could reasonably predict the experimental results at lower concentration only. Before applying the Pitzer model for the cement system at high ionic strength, it needed to be checked with the conventional database in terms of hydration. Figure 3 shows the influence of the two databases on the hydration of PC in water as a function of curing age. Both ionic profiles and mineralogical distribution were independent of the activity coefficient equation. This was due to low ionic strength of pore solution, which was lower than 0.52, and the Pitzer model showed the same results as the Debye-Huckel equation or the Truesdell and [PERSON] equation.  
The hydration model has already been validated in an authors' previous study by comparing simulation results with experimental data on hydrate assemblage and composition of pore solution for hydrating PC, cement blended with slag, and blended cement containing limestone [21]. The validity of the reactive transport model was also established for short- and long-term ionic ingress in both cement paste and sulphate-resistant PC concrete [25; 26]. Therefore, the models can be used to evaluate the long-term performance of cementitious materials in various environments.  
Figure 2: Influence of activity coefficient on Na\({}_{2}\)SO\({}_{4}\) solubility [30].