esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Device Fabrication and Materials  
Figure 1a presents the energy-level diagram and the structure of the blue TADF OLEDs used as the reference (device 1) in this study. The device was fabricated as follows: on an indium tin oxide (ITO) substrate, N,N\({}^{\prime}\)-bis(naphthalen-1-yl)-N,N\({}^{\prime}\)-bis(phenyl)-benzidine (NPB), 4\(A^{\prime}A^{\prime\prime}\)-tris(carbazol-9-yl)triphenylamine (TCTA), and 1,3-bis(carbazol-9-yl)benzene (mCP) were used as the HTL, and 10 wt% of 10,10\({}^{\prime}\)-(4,4\({}^{\prime}\)-sulfonylbis(4,1-phenylene))bis(9,9-dimethyl-9,10-dihydroracidine) (DMAC-DPS)-doped bis [2-(diphenyl-phosphino)phenyl]ether oxide (DPPEPO) was used as the EML. The chemical structures of the materials used for the EML are shown in Figure 1b. An additional DPPEPO layer without a dopant was used as the ExBL, and 1,3,5-Tris(1-phenyl-11 Hbenzimidazol-2-yl)benzene (TPBi) was used as the electron transport layer (ETL). Finally, lithium fluoride (LiF) and aluminum (Al), which were used as the electron injection layer (EIL) and cathode, respectively, were deposited.  
Before device fabrication, the ITO-precoated substrate was cleaned in acetone and methanol and rinsed in deionized water with sonication for 10 min. The emitting area, 6.25 mm \(\times\) 6.25 mm, was fixed by a photoresist layer. The organic materials and metal structures were deposited at rates of 0.5-1 and 1-3 A/s, respectively, under high-vacuum conditions.  
To determine the relationship between the device stability and C-V characteristics, the structure of the OLED was varied in terms of the HTL and ETL, as summarized in Table 1. As  
Figure 1: (**a**) Energy-diagram and structure of blue TADF OLEDs and (**b**) chemical structures of organic materials used in the EML.  
devices 1, 2, and 3 demonstrate different HTL/EML interfaces, the degradation characteristics of each HTL layer could be analyzed. For analysis from the ETL perspective, devices 1, 4, and 5 were fabricated, and the thicknesses of the ExBL were set to 10, 0, and 40 nm, respectively.
# Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device
## 2 Materials and Methods  
### Extraction Device Modeling  
The initial design of the extraction device is shown in Figure 1. The left side of the device consists of a gripping part for holding the pipe, and the right side is composed of an active part to which the user applies force. The action part is depicted in detail in an enlarged view. In this study, it was assumed that the tongs gripped the pipe with sufficient force and that the subject of the study was limited to the action part to which the user applied a force.  
The operating mechanism of the extraction device presented in this study is shown in Figure 2. When the user exerts pedal effort on this device, the force converted into vertical force by the link structure is transmitted to the gripper holding the agricultural pipe. It is a device that uses the principle that the pipe embedded in the farmland is lifted and removed by the force. This allows users to quickly and easily remove agricultural pipes by simply pedaling with their feet.  
Figure 1: Initial form of the device.  
Figure 2: Operating mechanism of the device.  
In order to facilitate the analysis, Figure 1 was schematized as a two-dimensional plane, as shown in Figure 3. Because point \(A\) is fixed and does not move when the extraction device is used, point \(A\) is regarded as the reference point (origin of the coordinate system).  
Link3 consists of a sliding joint capable of only up-and-down translational movement, and point \(A\) is a fixed-rotation joint. Points \(B\) and \(C\) are rotational joints that are not fixed and are configured to move in connection with each link. The coordinates of each point are shown in Figure 3, where each link is assumed to be a rigid body. The mass and moment of inertia of each link are not considered because their influence is negligible compared with the force factor considered in the analysis. The variables to be considered in the design are the length of Link1 (\(L_{1}\)), length of the left side of Link2 from point \(B\) (\(L_{2}\)), length of Link2 from the right side of point \(B\) (\(L_{3}\)), the horizontal distance between point \(C\) and point \(A\) (\(k\)), angle (\(\alpha\)) made by the extension line of Link3 with Link2, and angle (\(\beta\)) made by Link1 with the ground. Among the six variables, \(\beta\) can be expressed as a function of the remaining variables. Therefore, this final link system can be modeled with the five variables. \(\beta\) can be represented by Equation (1).  
\[\beta=\cos^{-1}(\frac{L_{2}\cos\alpha-k}{L_{1}}) \tag{1}\]
## 4 Results  
### Calibration and Validation of Two Hydrological Models  
Based on daily meteorological and hydrological observations within the study area, the periods from 2014 to 2017 were designated as the calibration period, and from 2018 to 2020 as the validation period. During these periods, the XAJ and GR4J hydrological models were calibrated and validated. Figure 4 displays the observed and simulated hydrological process graphs for both XAJ and GR4J hydrological models during the calibration and validation periods. The graphs indicate differences in the models' abilities to capture flood peaks, which can be attributed to their distinct structural characteristics. However, both models effectively captured flood peaks during the calibration period.  
Moreover, during the calibration and validation periods, the _NSE_ values for the XAJ model are 0.86 and 0.88, respectively, and the _RE_ values are 8.58% and 7.26%, respectively. For the GR4J model, the _NSE_ values are 0.92 and 0.91, and the _RE_ values are \(-\)0.15% and 8.26%, respectively. Compared to earlier studies [57; 58], both the XAJ and GR4J models demonstrate good applicability, as indicated by _NSE_ and _RE_ values, which suggests that the models are well calibrated and reliable for further flood forecasting research. The calibrated parameters for both hydrological models are detailed in Table 2.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 6 Conclusions  
In this paper, we proposed an HSI inpainting algorithm based on online robust spectral dictionary learning. A spectral dictionary adapted to image inpainting was obtained using a new algorithm proposed in this paper for constructing a dictionary directly from hyperspectral data. The spectral dictionary was subsequently used in the sparse reconstruction of the image, thereby restoring missing or corrupted pixels. Compared to the most advanced HSI inpainting algorithms, the algorithm herein possessed a better inpainting ability for additive Gaussian noise, Laplacian noise, and Poisson noise. Compared to other image inpainting algorithms based on dictionary learning, this algorithm was faster. However, this algorithm requires complex calculations, and thus future research should focus on its rapid implementation. In addition, the research object of this paper is limited to visible near-infrared (VNIR) and short-wave infrared (SWIR) HSIs. However, in the preprocessing of medium-wave infrared (MWIR) HSIs, dead pixels inpainting is a critical problem. The imaging principle of MWIR HSIs is very different from VNIR/SWIR HSIs. In our future research, MWIR HSI inpainting will also be an important direction.  
[PERSON] conceived and designed the method; [PERSON] guided the students to complete the research; [PERSON] performed the simulation and experiment tests; [PERSON] helped in the simulation and experiment tests; and [PERSON] wrote the paper.  
This research was supported by the National Natural Science Foundation of China under Grant No. 61801513.  
The authors declare no conflict of interest.
## 2 Statistical Models
### Latent Semantic Analysis  
Latent Semantic Analysis (LSA) [11] is a statistical method used for high dimensional data. It performs singular value decomposition (Appendix A) of the original matrix, a word-document co-occurrence matrix in the case of documents.his results in a more dense, lower-dimensional representation, and is called latent semantic space.  
Let us denote a collection of \(N\) documents \(D=d_{1},d_{2},\ldots,d_{N}\) with words \(w\) from dictionary \(V\) of size \(M\), where \(V=w_{1},w_{2},\ldots,w_{M}\). By ignoring the order of words, we can represent those data in a word-document co-occurrence matrix \(\Sigma\) of size \(N\times M\) with values \(Z=z(d_{i},w_{j})_{ij}\). Function \(z\) denotes the frequency of word \(w\) in document \(d\) or its TFIDF weight.  
To compute the similarity between two words, the dot product of the \(i\)-th column and the \(j\)-th row of matrix \(\Sigma\) is calculated because it contains words and their weights in latent semantic space. To calculate the similarity between two documents, the dot product of the \(i\)-th column and the \(j\)-th row of matrix \(\Sigma V\) is computed.  
The original co-occurrence matrix is usually sparse but vectors computed using singular value decomposition are dense. It is possible to create valuable representations of two documents even when they do not have common words, because synonyms and ambiguous words are transformed similarly. Due to dimension reduction of the representation vectors, similar words are merged and infrequent words are filtered out. This feature works only in case when the averaged meaning of words is close to the original. In other cases, it may introduce noise to the model.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 2 VMD Parameter Optimization and Wavelet Noise Reduction
### Improved Wavelet Thresholding Denoising
The value of \(a\) can be adjusted based on the requirements of different vibration signals, facilitating \(a\)'s seamless transition between the new and traditional hard threshold functions. Figure 1 illustrates the function graphs of the new wavelet threshold at varying values of \(a\).
## 1 Introduction
The following enzymes and  
compounds are involved in the MEP pathway: DXS, 1-deoxy-p-xylulose-5-phosphate synthase; DXR, 1-deoxy-p-xylulose-5-phosphate reductase; MCT, 2-C-methyl-p-erythnitol-4-phosphate cytidyltransferase; CMK, cytidyl (4-diphospho)-2-C-methyl-p-erythnitol kinase; MDS, 2-C-methyl-p-erythnitol-2,4-cyclodiphohate synthase; HDS, 1-hydroxyl-2-methyl-2-(\(E\))-butenyl-diphosphate synthase; HDR, 4-hydroxy-3-methylbut-2-enyldiphosphate reductase; IDI, isopentenyl pyro-phosphate isomerase. The following enzymes are involved in the sesquiterpenes biosynthetic pathway: FDS, farnesyl diphosphate; FPP, farnesyl pyrophosphate; FAS, farnesene synthase; HUS, humulene synthase; NES, nerolidol synthase; SQS, squalene synthase. The following enzymes are involved in the pathways toward monoterpene and diterpene formation: GDS, Geranyl diphosphate; GPP, Geranyl pyrophosphate; LIS, linalool synthase; LS, limonene synthase; MYS, \(\beta\)-Myrcene synthase; OCS, \(\beta\)-Ocimene synthase; NUDX1, Nudix hydrolase 1. To characterize terpene biosynthesis, here we report the use of an integrated approach combining metabolites, target gene transcripts and gene function analyses to reveal a gene-to-terpene scenarios network in tea plants. Terpene synthases whose expression positively correlated with farnesene were selected and both in vitro and in planta function analysis confirmed that TPS2 involved in the biosynthesis of \(\alpha\)-farnesene, \(\beta\)-ocimene, as well as \(\beta\)-farnesene, a very important and conserved alarm pheromone in response to aphids, in tea plants, suggested the gene-to-terpenes network was a vigorous and reliable method for the discovery of novel genes in terpene pathways.
# O-Band and C/L-Band III-V Quantum Dot Lasers Monolithically Grown on Ge and Si Substrate
## 1 Introduction
In addition, most of experimental results reported so far are referring to the 1.3 \(\upmu\)m wavelength at the O-band telecom window [41; 42; 44; 45; 49; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. Most of the works intentionally utilized offcut silicon substrates to suppress antiphase disorder arising from the interface between III-V (polar) and silicon (non-polar). Otherwise, patterned (001) silicon substrate or InP was utilized in the heterostructures. For the C-band or L-band telecom window, there are very few works that have been reported [50; 64]. Since many of the Si photonic passive and active devices are based on C-band applications, III-V light sources on Si in the long-wavelength range are becoming strongly demanded. Especially, for future Si photonic integration, Si-based high-gain III-V semiconductor optical amplifiers (SOAs) for long-haul transmission at the C/L-band are also essential components as on-chip replacements for erbium-doped fiber amplifier (EDFA). Here, we reviewed some of recent works of III-V/Si hybrid materials and devices at both O-band and C/L-band telecom windows, via a novel (111)-faceted silicon hollow structure which is capable of suppressing both APBs and thermal cracks simultaneously.
###### Abstract  
In Underwater Wireless Sensor Networks (UWSNs), data should be transmitted to data centers reliably and efficiently. However, due to the harsh channel conditions, reliable data transmission is a challenge for large-scale UWSNs. Thus, opportunistic routing (OR) protocols with high reliability, strong robustness, low end-to-end delay, and high energy efficiency are widely applied. However, OR in UWSNs is vulnerable to routing attacks. For example, sinkhole attack nodes can attract traffic from surrounding nodes by forging information such as the distance to the sink node. In order to reduce the negative impact of malicious nodes on data transmission, we propose an intrusion detection scheme (IDS) based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm for OR (DOIDS) in this paper. DOIDS is based on small-sample IDS and is suitable for UWSNs with sparse node deployment. In DOIDS, the local monitoring mechanism is adopted. Every node in the network running DOIDS can select the trusted next hop. Firstly, according to the behavior characteristics of common routing attack nodes and unreliable underwater acoustic channel characteristics, DOIDS selected the energy consumption, forwarding, and link quality information of candidate nodes as the detection feature values. Then, the collected feature information is used to detect potential abnormal nodes through the DBSCAN clustering algorithm. Finally, a decision function is defined according to the time decay function to reduce the false detection rate of DOIDS. It makes a final judgment on whether the potential abnormal node is malicious. The simulation results show that the algorithm can effectively improve the detection accuracy rate (3% to 15% for different scenarios) and reduce the false positive rate, respectively.  
underwater wireless sensor networks; opportunistic routing security; intrusion detection scheme +
Footnote †: journal: Sensors  
0000-0001-6500-0001-6500][PERSON]  
11 January 2023
## 4 Discussion
For O\({}_{2}\) and CO\({}_{2}\) at high O\({}_{2}\) values, lower CO\({}_{2}\) values were measured. The dependence between O\({}_{2}\) and CO\({}_{2}\) is confirmed here, as the roots of the plants contain CO\({}_{2}\) and they receive it through the leaves. The next finding was that CO\({}_{2}\) contains O\({}_{2}\) and that the amount of CO\({}_{2}\) was increasing by decreasing depth. Indirect concentration was also related to the root system of plants containing CO\({}_{2}\) and O\({}_{2}\), according to Figure 3, Table 4. The larger root system was located at the depth of two meters and drops with depth. Higher CO\({}_{2}\) levels indicate that if there is pollution at the measuring site, it is at a depth of up to 2 m.  
Wilcox test results, according to Figures 4 and 5 and Tables 5 and 6, showed statistically significant variability in measured CO\({}_{2}\) values due to influence of both, date and measurement depth. Measurements taken at a depth of 2 m have higher CO\({}_{2}\) values than those taken at a depth of 4 m. The connection is seen in the depth of the root system of the plants that covered the surface, and there was no growth of trees with deep roots in the measured places, i.e., CO\({}_{2}\) with depth dropped and also due to late measurement date. When the soil temperature drops, the steams density rises and the dispersion rate is lower, this is also related to the fact that there was no organic pollution. 3. Analysis of PID variability by date, based on the results in Figure 6, showed statistically significant variability of measured PID values due to date. However, this analysis may be insignificant if the pollution of volatile substances has not occurred at all in these wells. However, the weather can also have an impact on this change. In wetter and colder weather, the effective porosity is lower, which has a negative impact on migration and the volume of soil air for measurement. 4. The relationship between TP (total hydrocarbons) and CH\({}_{4}\) is correct by statistical confirmation of dependency. CH\({}_{4}\) is contained in TP, i.e., when measuring hydrocarbons, total TP methane values according to Figure 2, Table 3 are also included. With increasing CH\({}_{4}\) values, TP values also increase. While detecting TP (without methane), it is necessary to subtract CH\({}_{4}\) from the measured values of TP. Confirmation of these connections is beneficial not only during evaluation of pollution from the measured values, but also for a better understanding of the pollution migration parameters, the time evolution of pollution, the valorization of endangered objects by the spread of contamination, and the assessment of the need for and method of remediation. Crucial is also knowledge of the relationship between CO\({}_{2}\), CH\({}_{4}\) and O\({}_{2}\) in natural bioremediation that occurs everywhere in the soil. As a theme for further research, it may be interesting to monitor the specific time evolution of organic pollution in soil non-homogeneous environments.
## 2 Results and Discussion
### Volatile Composition in Wine Made of Dried and Fresh Goji Berry
#### Benzenes and Volatile Phenols  
Although the levels of benzenes were not as high as those of alcohols and esters in the goji berry fermented wines, a significant difference on their concentration was also observed (Table 4). The fresh goji berry fermented wine contained higher concentrations of methyl 2-(pentyloxy) benzoate, ethyl dihydrocinnamate, coumaran, 4-ethylphenol and 4-vinylguaiacol. On average, the concentrations of 4-ethylphenol and 4-vinylguaiacol were 1474 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{/}\mathrm{L}\) and 346 \(\mathrm{\SIUnitSymbolMicro g}\mathrm{/}\mathrm{L}\) in the dried berry fermented wine, which corresponds to only about 16% and 54% of that in the fresh berry fermented wine, respectively. Considering the differences of their amount between these wines, we speculated that the drying process decreased the level of their precursors in the goji berry fruits [31; 32; 33; 34]. Besides, the drying process also reduced the population of microorganisms, such as _Dekken bruxellensis_, on the surface of goji berry fruits. These microorganisms have also been reported to convert \(p\)-coumaric and ferulic acid into 4-ethylphenol and 4-vinylguaiacol during the fermentation process [34; 35]. The dried goji berry fermented wine possessed higher concentrations of isodurene, 1,1,4,6-tetramethylindane, 2,5-diisopropyl-\(p\)-xylene, naphthalene, 2,5,8-trimethyl-1,2-dihydronaphthalene, phenethyl acetate, \(\beta\)-methylnaphthalene, \(\alpha\)-methylnaphthalene, and 4-tolylcarbonl although these volatiles were all detected at low levels. Both the fresh and the dried goji berry fermented wines contained similar concentrations of ethyl cinnamate, 2,4-di-tert-butylphenol, and ethyl salicate.
# Modeling the Energy Performance of LoRaWAN
## 3 LoRaWAN Overview
### LoRaWAN Physical Layer
#### 3.2.3 Receive Window Parameters  
The DR, the frequency channel and the time between the end of an uplink transmission and the start time for the first and second receive windows (_RECEIVE_DELAY1_ and _RECEIVE_DELAY2_, respectively, see Figure 2) are defined in the LoRaWAN regional parameters document [39]. Table 4 shows the main basic physical layer parameters, and their default values.  
The DR to be used in the first receive window (RX1) can be set as the uplink DR minus the DR offset RX1 parameter, _RX1 DROffset_, if the latter is equal to or less than the DR of the uplink; otherwise, the DR for the first receive window will be DR0. _RX1 DROffset_ can take values in the range of 0 to 5. Since _RX1 DROffset_ has a default value of zero, the DR for the first receive window is by default the same one used in the last uplink transmission.  
The frequency channel used in the first receive window is the same as the one used for the preceding uplink transmission, while the second receive window uses a fixed frequency and data rate configuration (by default, frequency 869.525 MHz, and DR0). These parameters can be customized in the hardware platform used in the study. However, other platforms (e.g., RN2483, [36; 37]) do not support such configurability.  
The duration of a receive window has to be at least the time required to effectively detect a downlink preamble [27]. If a preamble is detected during a receive window, the end-device radio remains active until the reception of a complete message. If this is done successfully in the first window, the end-device does not use the second receive window. If a transmission is not detected within a receive window, the radio transceiver and, possibly, the microcomputer chip that support the device will be in an active state, with a significant current consumption, for a certain period of time during  
Figure 3: LoRaWAN physical layer message format. (*) Cyclic Redundancy Check (CRC) field is only present in uplink transmissions. The preamble length is configurable; for the device used in this study, preamble length is \(n=8\) symbols.  
Figure 2: Basic transmission scheduling in LoRaWAN Class A functionality.  
the window. This time is an important factor for the end-device lifetime and it is strongly related to the specific transceiver hardware used (see Section 4).  
In the hardware platform we have used in this paper, the time spent in preamble detection in a first receive window is determined as the symbol timeout parameter (_SymbTimeout_), which is set to 8 symbols for DR0 (SF12) and DR1 (SF11), and 12 symbols for the rest of DRs. On the other hand, for the second receive window, a feature called Channel Activity Detection (CAD) is used. This mechanism reduces channel listening time. If no incoming signal is detected, the end-device decides earlier to stop preamble detection, thus saving radio on time and current consumption.
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 6 Cooperative Content Caching Framework
### Popularity Prediction  
After getting user preferences, we estimate the overall popularity of movies. Those movies which are rated by most of the users and whose mean rating is higher are considered the top-most popular movies. MBS predicts the popularity, downloads that content, and delivers popular content to the corresponding RSUs. When a user's request arrives at the RSU, then content will be provided directly to the user without using the back-haul bandwidth. Otherwise, the content will be fetched from the core network through the back-haul link with extra overhead and delay.  
Location-based files served by RSU are represented as, \(N_{R}=LF^{R}\), and popular files served by MBS are expressed as, \(M=PF^{B}\), hence the total number of files stored in RSU can be determined by Equation (12).  
\[F_{R}=M+\sum_{R=1}^{n}\ N_{R}{}^{f} \tag{12}\]  
The popularity is calculated by two variables, \(F_{j}\) showing the number of files at MBS, and \(F_{i}\) representing the number of files at RSU keeping the \(K\) as a constant of popularity. Equation (13) finally expresses the content popularity.  
\[P_{j,k}\ =\ P_{r,\ j}\cdot(1/\ K)/\sum_{n=1}^{F_{j}}\ 1/n \tag{13}\]