esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Orthogonal frequency division multiplexing (OFDM) technology is presented for use in free-space optical (FSO) communications accompanied by the spatial diversity reception. Using quadrature phase shift keying (QPSK) modulation formats, the OFDM signals show robustness to support high spectral efficiency and compatibility with the spatial diversity reception to improve receiver sensitivity. Compared with the single-carrier QPSK signal, the OFDM-QPSK signal with 64 sub-carriers can reduce the BER from 2.87 \(\times\) 10\({}^{-3}\) to 2.98 \(\times\) 10\({}^{-4}\) at the SNR of 6 dB. Using a two-aperture spatial diversity reception with OFDM, the BER can be reduced from 2.45 \(\times\) 10\({}^{-3}\) of a single receiver to 6.10 \(\times\) 10\({}^{-4}\) under moderate turbulence conditions. Under strong turbulence, the BER of the single receiver is 2.14 \(\times\) 10\({}^{-2}\). It can be improved to 1.16 \(\times\) 10\({}^{-3}\) by using four-aperture receivers, and even 6.87 \(\times\) 10\({}^{-4}\) by using six-aperture receivers. The optimized aperture number should be selected according to channel conditions.  
free-space optical communications; orthogonal frequency division multiplexing; spatial diversity reception +
Footnote †: journal: Applied Sciences
## 3 Analysis and Numerical Results
### Numerical Results
Because an environment where a sink node is deployed on the center,  
Figure 8: Average power depending on the transmission rates. (**a**) Wide range; (**b**) High rate range. Figure 9: Average power levels according to the numbers of nodes. as shown in Figure 3b, is being analyzed, the entire nodes can be divided into multiple groups and a sink node can be deployed on the center of each group. The results are shown in Figure 10 in which, it can be seen that in the case of the three groups, the average power consumption decreases to 1/10 at the maximum compared to the case of a single group.
###### Abstract  
The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA's FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September-October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This campaign combined two unique airborne instrument packages to obtain simultaneous observations of solar-induced fluorescence (SIF), LiDAR-based canopy structural information, visible through shortwave infrared (VSWIR) reflectance spectra, and surface temperature, to advance vegetation studies of carbon cycle dynamics and ecosystem health. We obtained statistically significant results for fluorescence, canopy temperature, and tower fluxes from data collected at four times of day over two consecutive autumn days across an age class chronosequence. Both the red fluorescence (F685) and far-red fluorescence (F740) radiances had highest values at mid-day, but their fluorescence yields exhibited different diurnal responses across LP age classes. The diurnal trends for F685 varied with forest canopy temperature difference (canopy _minus_ air), having a stronger daily amplitude change for young vs. old canopies. The photochemical Reflectance Index (PRI) was positively correlated with this temperature variable over the diurnal cycle. Tower measurements from mature loblolly stand showed the red/far-red fluorescence ratio was linearly related to canopy light use efficiency (LUE) over the diurnal cycle, but performed even better for the combined morning/afternoon (without midday) observations. This study demonstrates the importance of diurnal observations for interpretation of fluorescence dynamics, the need for red fluorescence to understand canopy physiological processes, and the benefits of combining fluorescence, reflectance, and structure information to clarify canopy function versus structure characteristics for a coniferous forest.  
Keywords:chlorophyll fluorescence; diurnal responses; red SF; far-red SF; SIF ratio; Fratio; _HyPlant_; G-LiHT; GEP; LUE
# Ultrafast Optical Signal Processing with Bragg Structures
## 5 Ultrafast Pulse Modulations Based on Cholesteric Liquid Crystal (CLCs) Bragg Gratings
As a result, an incident 100 femtoseconds laser pulse can be stretched to nearly 2 picoseconds by the first CLC cell, and then recompressed to the original pulse length by the second CLC cell, as seen in Figure 16. Such pulse stretching and recompressing operations are commonly employed in chirped pulse amplification (CPA) systems [71] that require an intermediate pulse stretching process to prevent amplification saturation. In this case, the entire stretch-recompress operation can be done all-optically with CLC cells measuring <1 mm in interaction length, contrasting greatly with the bulky optics employed in conventional CPA systems.
## 2 Islanding Detection Methods
### Active Methods
#### 2.2.7. Negative Sequence Current Injection Method (NSC)  
This method involves injecting a negative sequence current into the system to track the PCC's negative sequence voltage and identify an islanding condition. Due to only offering low impedance while the main grid is connected, the injected negative sequence current that flows into the load will not alter the voltage at PCC. However, if the main grid connection is severed, the injected negative current will flow to the load and cause an unbalanced voltage at the PCC. If this voltage exceeds the threshold, the islanding will be detected in accordance with the established criteria [23]. NDZ is not presented using this method and the load change doesn't affect the effectiveness of NSC.
## 4 Results and Discussion
### Algorithm Performance  
Figure 9a-e depicts the convergence curve for IBFOA and BFOA, which is represented by the fitness value (MAPE) over the iteration. Table 10 represents the convergence time obtained for each day type, which shows that the IBFOA has a shorter convergence time than the BFOA. The IBFOA improves the convergence speed and accuracy of BFOA. SCA has a high speed due to its simple structure. At the same time, the reproduction and elimination dispersal processes give BFOA an advantage in accelerating the exploitation phase and preventing the algorithm from falling into local optima.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Day Type** & **Algorithm** &
\begin{tabular}{c} **Convergence** \\ **Time (Minutes)** \\ \end{tabular} \\ \hline \multirow{3}{*}{**Monday**} & BFOA & 44.6826 \\  & IBFOA & 38.1281 \\  & BFOA & 31.2697 \\  & IBFOA & 26.9290 \\  & BFOA & 25.8514 \\  & IBFOA & 16.5285 \\  & BFOA & 35.5887 \\  & IBFOA & 21.7109 \\  & BFOA & 33.8185 \\  & IBFOA & 31.6932 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of the best convergence time for BFOA and IBFOA.  
Figure 8: Illustrations of plots for MAPE and MAE.  
Therefore, the IBFOA has better performance in terms of convergence speed, accuracy, and local optima avoidance by synergizing the strengths of both algorithms. From the analysis, considering five-day types in a week, the IBFOA converges at 398 th, 31 st, 92 nd, 231 th, and 532 th iterations, respectively. In contrast, the BFOA converges at the 477 th, 111 th, 143 rd, 301 st, and 682 nd iterations, respectively.
###### Abstract  
The proportion of instances belonging to each class in a data-set plays an important role in machine learning. However, the real world data often suffer from class imbalance. Dealing with multi-class tasks with different misclassification costs of classes is harder than dealing with two-class ones. Undersampling and oversampling are two of the most popular data preprocessing techniques dealing with imbalanced data-sets. Ensemble classifiers have been shown to be more effective than data sampling techniques to enhance the classification performance of imbalanced data. Moreover, the combination of ensemble learning with sampling methods to tackle the class imbalance problem has led to several proposals in the literature, with positive results. The ensemble margin is a fundamental concept in ensemble learning. Several studies have shown that the generalization performance of an ensemble classifier is related to the distribution of its margins on the training examples. In this paper, we propose a novel ensemble margin based algorithm, which handles imbalanced classification by employing more low margin examples which are more informative than high margin samples. This algorithm combines ensemble learning with undersampling, but instead of balancing classes randomly such as _UnderBagging_, our method pays attention to constructing higher quality balanced sets for each base classifier. In order to demonstrate the effectiveness of the proposed method in handling class imbalanced data, _UnderBagging_ and _SMOTEBagging_ are used in a comparative analysis. In addition, we also compare the performances of different ensemble margin definitions, including both supervised and unsupervised margins, in class imbalance learning.  
Keywords:classification; ensemble margin; imbalance learning; ensemble learning; multi-class +
Footnote †: journal: Applied Soft. 2018, 815; doi:10.3390/app8050815
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 3 Experimental Section  
### Survey Area  
A channel wave transmission survey was developed at the Guoerzhuang Coal Mine, Handan, Shanxi province, China. The survey area is shown in the following Figure 2. It corresponds to the trapezoidal region covered by the ray. The No. 2 coal seam is mined in the survey area, and the variation range of coal thickness in the survey area is 0.4-2.4 m. The roof of the coal seam is mainly sandstone and the floor is siltstone, and a total of 5 normal faults have been exposed. The coal thickness and fault distribution exposed by the roadways are shown in Figure 2.
## 5 Technologies for Automated Observation-Based Identity Confirmation
### Radio Frequency Identification  
In addition to a visual observation, a short-range RF link was also adopted to demonstrate a different class of observation node. Although a number of technologies were initially considered (including Bluetooth [12], and the IEEE 802.15.4 Zigbee standard [13]), the best guarantees as to the physical location of the device in question were achieved when using ultra short-range RF communications such as those used for Radio-Frequency Identification (RFID).  
RFID technologies fall into two broad classes: low-frequency devices (operating at 125 kHz in Europe), and high-frequency devices operating at 13.56 MHz [14].  
Low frequency systems typically have an operating range of several inches, but have very low data transfer capabilities and are only used with simple passive _tags_ containing a static serial number determined at the time of manufacture. Such devices are typically used for asset tracking: High frequency systems may store up to 4 Kb of data [15], and may be writable as well as readable. This class of tag is often produced in a _credit-card sized_ form-factor, and is often used to provide security access tokens, as well as cashless ticketing in public transportation systems.  
Near-Field Communication (NFC) also operates on the 13.56 MHz frequency and supports active communication between two devices, over a range of a few centimetres.  
Despite (or perhaps because of) its ubiquity within the security (RFID) and banking sector (NFC for contactless payments), the state of easily accessible and open-source software to support operations more complex than reading or writing to simple tags is somewhat limited.  
Most devices capable of reading NFC data (including most mobile phones) are able to read static tags which have been formatted using the NFC Data Exchange Format (NDEF) [16] standard; but there is somewhat limited support for the active data exchange provided by the Simple NDEF Exchange Protocol (SNEP) [17; 18].  
A simple device was constructed to utilize Simple NDEF Exchange Protocol (SNEP), utilizing open-source example C code and based on the PN532 NFC [19] chipset connected using the Inter-Integrated Circuit Protocol (I2C). This was interfaced to a Raspberry Pi as a USB serial device, using an Arduino development board, based on the Atmel ATMega32U [20] microcontroller. This is shown in Figure 7.
## 4 Testbed Environments--Physical and Virtualized
### Physical Environment--Host  
The Host test environment consisted of 17 (identical hardware specifications) physical computers connected to LAN via Ethernet with a speed of 1 Gbps. Each of these machines was equipped with an Intel (R) Core (TM) i7-7700 CPU @ 3.60 GHz (4 physical cores, 8 logical threads) and 16 GB of RAM. The operating system installed on these computers was Linux Ubuntu 20.04.3 LTS. For testing purposes, the GCC 9.3.0 compiler version and the OpenMPI 3.1.1 library were installed on all computers in the Host environment.
## 2 Suggested Topology and Its Operation
### Operating States
#### 2.2.3 Zero-State  
This operating state is very critical due to the presence of an inductor at the intermediate state. Its presence ensures the elimination of the possible shoot-through caused by the turning on of the complementary controlled switching units. The sudden interruption of the inductor current takes place once the output is forced to change from non-inverting or inverting to zero; this change of the output at non-zero input voltage leads to the induction of the voltage or current surges that may damage switching devices. The output voltage is zero if all the controlled switching units are maintained in their off state. This is only possible if the intermediate inductor is completely de-energized. This is ensured by adding extra turn-off delays in the controlled switching units that are not directly connected to the input source. For example, during the non-inverting state of positive input voltage as illustrated in Figure 5b, the controlled switching units \(Q_{1}\) and \(Q_{5}\) are conducting to connect load with the source. In this state, although the controlled switch \(S_{3}\) is on, this branch is in the non-conducting state as its series-connected diode is reverse biased. The issue of inductor current interruption is resolved by adding the turn-off delays (\(\theta_{o}\)) in gating signals connected to control switching units \(Q_{3}\) and \(Q_{5}\). With this arrangement of the switching signals, the controlled switch \(Q_{1}\) turns off before the turning off of the controlled switches \(Q_{3}\) and \(Q_{5}\). This arrangement avoids the current interruption problem of the inductor safely. Then all switching devices maintain their off states to have zero voltage at the output according to the requirement. The closed current loop with corresponding gating  
Figure 6: Voltage inverting operation: (**a**) switching control scheme; (**b**) current closed-loop during positive input; (**c**) current closed-loop during negative input.  
signals during the operation in which the output changed from non-inverting to zero-state is illustrated in Figure 7a,b respectively. A similar approach is considered a non-inverting state for negative input and inverting state both for positive and negative input voltage. The output during this state is realized as  
\[v_{o}(\omega t)\ =\ 0 \tag{7}\]
## 4 Experiments
### The Impact of Resolution on Pose Estimation
#### 4.1.3 Results of Different Resolution Hash Encoding  
Figure 5a,b illustrate the trend of pose errors during the training process under different resolutions. As can be seen from the figure, at resolutions 16, 32 and 64, the errors are small and close to the real pose. When the resolution increases, the error becomes larger. At resolutions 96 and 128, it can be considered that a few cameras have pose deviations. At  
resolutions of 256 and 1024, the error in pose is too large, suggesting that the majority of cameras have pose deviations and cannot complete the pose adjustment task. Figure 5c shows the trend of PSNR during the training process. Among them, the accuracy is the highest at a resolution of 96 and the lowest at resolutions 256 and 1024.  
Through experiments, we have verified that the network based on hash encoding has the capability for pose estimation and rapid scene reconstruction. Additionally, it was validated that low-resolution hash encoding networks can rapidly and effectively perform pose estimation, whereas high-resolution hash encoding cannot function properly in this task. Moreover, we found that the highest rendering accuracy of the image is not the one with the smallest pose error. As shown in Figure 5, the rendering accuracy is the highest at a resolution of 96, but its pose estimation is not the most accurate. Accurate pose estimation is the foundation for high-precision rendering, so we cannot solely rely on rendering accuracy to judge the quality of the final result.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method
### Method
#### 3.5.3 Attention Layer  
The attention mechanism [12] allows the model to capture the most important parts of the PM\({}_{2.5}\) concentration when different features of past states are considered. In order to take advantage of the information of the past states, an attention-based layer is added to the LSTM layer in the proposed AC-LSTM model. It ranks the importance degrees of different feature states in the past as follows, where \(H=[h_{1},h_{2},\cdots,h_{t}]\) is the feature state matrix in the attention layer:  
\[u_{t}=\tanh(W_{h}h_{t}+b_{h}), \tag{10}\]  
\[\alpha_{t}=\frac{\exp(u^{\top}v)}{\sum\limits_{t}\exp(u_{t}^{\top}v)}, \tag{11}\]  
\[s=\sum\limits_{t}\alpha_{t}h_{t}, \tag{12}\]  
where \(u_{t}\) and \(v\) denote the projection vectors, \(\alpha_{t}\) is the normalized attention weight of \(h_{t}\), and \(s\) is the weighted output vector of the attention layer.  
According to the importance of each vector in the feature state matrix \(H\), Equations (10) and (11) can calculate the normalized weight of each vector. Equation (12) gives the weighted vector \(s\). This achieves the importance of measuring feature states at different times Eventually, the weighted vector \(s\) passes through a layer of a fully connected network to obtain the PM\({}_{2.5}\) concentration of the prediction task.
# Averting BER Floor with Iterative Source and Channel Decoding for Layered Steered Space-Time Codes
## 5 Future Work  
In near future, we aim to incorporate the neural network and deep learning applications to multimedia streaming. More specifically, we aim to simulate the proposed system using convolutional neural networks (CNN) in order to further enhance the iterative decoding performance. The learning process for channel estimation and its behaviour can  
Figure 5: PSNR versus \(E_{b}/N_{0}\) performance curves of the SBC schemes presented in Table 2.  
be devised by allowing the LLR values to the CNN along with the iterative decoding process. Moreover, we expect to extend the existing HEVC setup to low bit rate 3D and multi-view video. Furthermore, we plan to simulate our proposed three-stage system using the H.266 codec (developed in mid-2020) for supporting ultra-high-definition video streaming. Interestingly, the H.266 codec further compresses the data stream with the employment of sophisticated encoders and decoders.
## 3 Results and Discussion
### Headspace Volatile Analysis Using SPME-GC-MS of Mallab Samples (Roasted/Unroasted)
#### 3.4.1 Aldehydes/Ketones  
Aldehydes represented the major class of volatiles in specimens at ca. 37.34, 32.52, 44.60 and 19.48% in WG, WE, RE and RRE, respectively. Benzaldehyde (peak 36) was the most abundant aldehyde, being more enriched in RE samples (ca.18.71%), followed by (_E_)-cinnamaldehyde (peak 47), present in a relatively high amount at ca. 12.3, 11.2 and 7.2% in WE, RE and WG, respectively. The content of both aldehydes (peaks 36 and 47) was found at different levels among seed accessions, but most notably was found to decline upon roasting to reach 0.8 and 1.9%, respectively. Benzaldehyde is known to account for the characteristic smell of bitter almonds produced as a result of amygdaline glycoside hydrolysis [32] and it likely to lead to a similar scenario in mahlab seeds. (_E_)-cinnamaldehyde exhibits a pungent cinnamon-like aroma and is likely to contribute to _P. mahaleb_ seeds. The well-reported antimicrobial activity of (_E_)-cinnamaldehyde against a wide range of microorganisms [33] might contribute to the _P. mahaleb_ seeds' use as an antidiarrheal agent in folk medicine. Unlike aldehydes, ketones were detected at much lower levels in all specimens, ca. 3.7, 2.5, 5.3 and 3.7% of the total volatiles in WG, WE, RE and RRE samples, respectively. Moreover, 3-Octen-2-one (peak 102) was the most abundant ketone, detected at the highest levels in WG and RE seeds (2.3 and 2.8%, respectively).
## 5 Conclusions  
In order to realize bit synchronization for BeiDou weak signals with large frequency deviation, a bit synchronization algorithm based on differential correlation and maximum likelihood is proposed. Firstly, the differential correlation is used to remove the effect of frequency deviation, and the differential delay time is set to be a multiple of bit cycle to remove the influence of NH code. Secondly, the maximum likelihood function detection is used to improve the detection probability of weak signals.  
Monte Carlo simulations are carried out to analyze the detection performance of the proposed algorithm compared with a traditional algorithm under the CN0s of 20-40 dB-Hz and different frequency deviations. The results show that the proposed algorithm outperforms the traditional method when the frequency deviation is 50 Hz. This algorithm can remove the effect of BeiDou NH code effectively and weaken the influence of frequency deviation. This algorithm is suitable for BeiDou weak signal bit synchronization with large frequency deviation. In addition, this method is applicable to BeiDou signals and it is also applicable to GPS, Galileo and other satellites navigation systems.  
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61374115, 61328301), the Funding for Outstanding Doctoral Dissertation in NUAA (Grant No. BCJ14-06), the Funding of Jiangsu Innovation Program for Graduate Education (Grant No. KYLX_0283) \"the Fundamental Research Funds for the Central Universities\". The author would like to thank the anonymous reviewers for helpful comments and valuable remarks. [PERSON] conceived the idea and wrote the paper; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] coordinated the research.  
The authors declare no conflict of interest.