esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecular and Functional Evolution of the Spermat Sesquiterpene Synthases
## 4 Materials and Methods
### Heterologous Expression of CaTPSs in Yeast  
An engineered _S. cerevisiae_ strain LWG003 [34] (overexpression of _tHMGR_, _ERG8_, _ERG10_, _ERG12_, _ERG13_, _ERG19_, _ERG20_, _IDI_1, and upc2-1 genes; deletion of _GAL80_ gene; and replacement of native promoter of the _ERG9_ with the glucose-sensing promoter _HXT1_) was used for in vivo characterization of _CaTPS_ genes. The codon optimized genes were synthesized (Genewiz, Nanjing, Suzhou, China) and subcloned into a yeast expression vector pESC-URA. The constructed plasmids were heterologously expressed in LWG003 for the production of sesquiterpenenes. An empty vector pESC-URA was also heterologously expressed in Sc027 as control. The plasmids and strains used in this study are listed in Table S4.  
For shake-flask fermentation, colonies of recombinant yeast strains were picked into 10 mL of synthetic medium with 20 g/L glucose, 6.7 g/L yeast nitrogen base, 5 g/L (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), and 2 g/L amino acid mix lacking uracil and grown at 220 rpm and 30 \({}^{\circ}\)C in a shaking incubator for 16-18 h. Subsequently, the culture was inoculated to an initial OD600 of 0.05 from the precultures and then grown for 96 h using the same conditions. Five milliliters of dodecane were added to the 50 mL culture after 10 h for diphasic fermentation. After dehydration with anhydrous magnesium sulfate, the dodecane phase was used for GC-MS (DB-5 MS, 30 m \(\times\) 0.25 mm \(\times\) 0.25 um) analysis directly. The injector temperature was 250 \({}^{\circ}\)C. One microliter of sample was injected in split mode (1:10), and the GC oven temperature program was applied with 1 mL/min nitrogen as the carrier gas: 70 \({}^{\circ}\)C for 2 min, 10 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C, and hold for 5 min. The MS scan range (\(m/z\)) was from 35 to 350. The fermentation products were identified based on comparison of their MS spectra and retention times with the NIST17 library.  
Supplementary Materials: Supplementary Materials are available online at [[https://www.mdpi.com/article/10.3390/ijms22126348/s1](https://www.mdpi.com/article/10.3390/ijms22126348/s1)]([https://www.mdpi.com/article/10.3390/ijms22126348/s1](https://www.mdpi.com/article/10.3390/ijms22126348/s1)).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed most of the experiments; [PERSON], [PERSON] and [PERSON] provided assistance; and [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially supported by the National Key R&D Program of China (2017 YFD0201400; 31 July 2017).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Oil Seal Control with Deep Learning
#### 2.2.5 Experiments  
In our experiments, we aimed to trade-off accuracy and model complexity in order to obtain a solution that does not require large computational resources and could be easily embedded within an on-board system. We used a CNN with a single convolutional layer consisting of 32 filters with dimension 3 \(\times\) 3 and rectifier activation function. We then applied a max pooling layer with size 2 \(\times\) 2. After flattening the output, we used a dense layer with 64 neurons, a rectifier activation function, a dropout layer with a probability of 0.5, and finally an output layer with 3 neurons to perform multi-class classification.  
The Adam optimiser was used to train the network, using categorical cross-entropy as the loss function, as is customary in multi-class problems. During training, we used mini-batches of size 32 and performed early stopping on the validation loss with a patience equal to 15 epochs. The training phase took place in the cloud through the use of the GPUs offered by the Google Colab service ([[https://colab.research.google.com/](https://colab.research.google.com/)]([https://colab.research.google.com/](https://colab.research.google.com/)) accessed on 5 October 2022). Note that the hyper-parameters defined so far are the result of a trial and error phase, where their combination provided the best result on the validation set. An explanatory loss graph is reported in Figure 20. An early stop occurred at the 30 th epoch over 500.  
Figure 18: Operational flow diagram.  
For the evaluation of our approach, we performed a repeated \(k\)-fold cross-validation. In a \(k\)-fold cross-validation, the original data set is partitioned into \(k\) sets: in turn, each set acts as the test set, while the remaining \(k-1\) sets are used for training and validation. Results can be aggregated either via the macro-average or micro-average. To make the  
Figure 19: Example images of the data collected. The first three pictures represent the correct position of the oil seal, the second three the oil seal positioned upside-down, and the last three is the absent oil seal.  
Figure 20: Graph of the loss trend with respect to epochs: the train loss is reported in red, while the validation is in blue. A smooth factor of 0.6 has been applied.  
procedure robust, the \(k\)-fold cross-validation can be repeated \(n\) times, and average performance measures are usually reported. In our case, the number of folds \(k\) was set equal to 5.  
The average micro-accuracy on the five folds is equal to 98.8%, ranging from 97.0% up to 99.1%. Given the small amount of images used in the training set, the performance of the CNN is indeed very encouraging, as the model is capable of recognising the different orientations of the target object even if the details to consider are very small.
## 4 Results and Discussion  
### Data Description  
In this study, solar PV power data from two PV systems are adopted to verify the performance of the eight deep learning-driven forecasting methods.  
* **Data Set 1:** The first historical solar-PV power dataset used are collected from a parking lot canopy array monitored by the National Institute of Standard and Technology (NIST) [52]. The PV system contains eight canopies tilted 5 degrees down from horizontal, four canopies tilt to the west, and the other four canopies tilt to the east. The modules are installed with their longer dimension running east-west. Each shed contains 129 modules laid out in a \(3\left(\mathrm{E}-\mathrm{W}\right)\times 43\left(\mathrm{N}-\mathrm{S}\right)\) grid. This power system has a rated DC power output of 243 kW. The first dataset is collected from January 2015 to December 2017 with a one-minute temporal resolution. The distribution of the Parking Lot Canopy Array dataset collected from January 2015 to December 2015 are shown in Figure 5a.
* **Data Set 2:** The second solar-PV power dataset is collected from a grid-connected plant in Algeria with a peak power of 9 MWp from January 2018 to December 2018 with 15 min temporal resolution. This PV plant consists of nine identical mini-PV plants of one mega each. Indeed, a set of 93 PV array provides one MWp of DC power, two central inverters with 500 kVA each, allow to connect the 93 PV array to one transformer of 1250 kVA. The hourly distribution of the first dataset are shown in Figure 5b.  
Figure 6 depicts the boxplots of DC power output (Data Set 1 and Data Set 2) in Figure 5 to show the distribution of DC power in the daytime. The maximum power is generated around mid-day.  
Figure 5: **(a)** distribution of solar PV power output from Parking Lot Canopy Array dataset. **(b)** Hourly distribution of solar PV power output from January to December 2018.  
Figure 6: Boxplots of PV power output during daytime hours: **(a)** Data Set 1 and **(b)** Data Set 2.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results
### Result Representation of the Three Evaluated Classifiers Using the Confusion Matrix  
To examine the study more extensively, a comparative analysis of the two test scenarios for each classifier was conducted using the confusion matrix in this section. In the confusion matrix (Figures 8-10), the output class in the Y-axis refers to the prediction of the classifier, and the target class in the X-axis refers to the true reference class. The overall accuracy of the classifier is given in the bottom right corner of each confusion matrix. The last column in each category of  
Figure 7: Performance comparison of the three evaluated classifiers based on the macro-averaging F1-score in two different scenarios: (i) dotted lines denote that the training and test data came from the same environment; (ii) solid lines denote that the training and test data came from different environments.  
the confusion matrix indicates the precision (positive predictive value) and its counterpart the false discovery rate (FDR) of the classifier. Likewise, the last row in each category gives the recall (sensitivity or true positive rate) and its complement the false-negative rate (FNR). The correct predictions for each category are expressed in the diagonal of the confusion matrix. The values in the off-diagonal correspond to the Type-I and Type-II errors. For a scenario where training and test datasets were collected in different environments, the confusion matrices presented in this section were based on the mode of each classifier (i.e., we chose the most frequently predicted class in each trial as our estimator output). For a scenario where the training and test environments were the same, the confusion matrix was based on Trial No. 5 out of the 10 trials described in Section 7.1. The reason was that the random splitting of the test dataset for the true class in this scenario was different for each trial. Moreover, all trials in this scenario gave comparable results as reported in Figure 7.
# Research Trends in Collaborative Drones
## 3 Countering Drone Threats
### Approach and Methodology  
The sub-problems can be approached either passively or actively. Table 1 lists the countering drone threat tasks together with various ideas that exist and sometimes have been investigated in the research community. Conventional radar technology is not practical for spotting and following drones, especially for micro-drones flying at slow speed and low altitudes and for areas such as urban environments. Luckily, spotting and following can be achieved passively using audio, video, and radio frequency observations [26; 27; 28]. The use of artificial intelligence and Machine Learning (ML) for solving problems related to countering drone threats has grown considerably. Agents learn and exploit what they have learned to spot and follow drones. [PERSON] et al. have created a Data Base (DB) of drone Radio Frequency (RF) signals. Together with the use of neural networks, the database can be used to detect drone presence, their type, and kind of flight [29]. [PERSON] et al. use micro-Doppler signatures for drone rotary-wing determination, i.e., helicopter, bi-rotor, quadcopter, hexacopter, or octocopter [30]. Research has also been completed to determine the load carried by a drone. Using micro-Doppler signatures, [PERSON] developed an approach to determine whether a drone carries a load or not [31]. [PERSON] and [PERSON] further investigated this question and developed a method to determine the weight of the payload of a drone [32]. [PERSON] et al. [33] use deep learning to build classifiers to discriminate drones from birds. The approaches considered have limited success due to the weaknesses of the classifiers to adversarial learning attacks, as demonstrated by [PERSON] et al. [34] and [PERSON] et al. [35].  
On the intention determination front, drone maneuvers recognition has also been investigated, such as the work of [PERSON] and [PERSON] using Reinforcement Learning (RL) [36]. Using telemetry data, a Supervised Learning (SL) model to predict the formation of a drone swarm aims has been proposed by [PERSON] and [PERSON] [37].  
Ideas for riposte in response to detecting malicious drone behavior have been quite limited. Attempts to jam the signals of autonomous drones may not be applicable. Shooting flying drones may have their risk.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Passive** & **Active** \\ \hline _Spotting and following_ & Audio, video and RF & Autonomy rev. Turing test \\  & observations and ML & RF signal DB \\  & Micro-Doppler signatures & \\ \hline _Intention determination_ & Maneuvers recognition w. RL & \\  & Formation prediction w. SL & \\ \hline _Riposte_ & & RF jamming \\  & & Counterattack drones \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tackling the countering drone threats. The first column lists the tasks. The second column provides passive solutions based solely on observations. The third column indicates active solutions, acting on threatening drone behavior.
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 3 Results and Discussion  
### Micro XRF Analyses  
Table 1 summarizes the mass percentage composition of various elements detected in the sand samples from Foce Verde, Capo Portiere, and Sabaudia. This information was retrieved to evaluate if resulting differences in classification results could be eventually attributable to different concentrations of elements in sand samples.  
In more detail, the Foce Verde sample shows a high concentration of calcium (25.51%) and a high concentration of chlorine (60.34%), indicating substantial amounts of limestone and/or shell material and sea salt. Iron (6.39%) is present in moderate amounts, suggesting the presence of iron-bearing minerals. The amounts of silicon (5.98%) and titanium (0.49%) reflect minimal quartz and titanium-bearing minerals. Additionally, aluminum (0.39%) and potassium (0.37%) suggest some feldspar minerals. Strontium (0.15%), chromium (0.05%), nickel (0.04%), and zinc (0.03%) are present in trace amounts.  
Similarly, the Capo Portiere sand has a very high concentration of chlorine (82.52%), reflecting significant sea salt accumulation.  
Discrete levels of iron (6.44%) suggest a higher presence of ferrous minerals. The lowest calcium level (7.87%) among the samples suggests minimal calcareous material presence. The presence of titanium (0.50%) and chromium (0.17%) in trace amounts may indicate mineral diversity or industrial contamination. The slight potassium content (0.41%) reflects the presence of feldspar minerals. Aluminum (0.12%), manganese (0.15%), and strontium (0.09%) are also present in trace amounts, along with minimal amounts of nickel (0.02%) and zinc (0.03%). Phosphorus (0.02%) is detected in trace amounts.  
Finally, the Sabaudia sample exhibits a high chlorine concentration (74.39%), indicating substantial sea salt. The lowest iron levels (0.88%) among the three sand samples suggest minimal ferrous materials. The higher silicon concentration (9.46%) indicates a significant presence of quartz or silicate minerals. The presence of potassium (2.05%) and trace manganese (0.11%) aligns with minor feldspar or clay minerals. Aluminum (0.34%), strontium (0.32%), and titanium (0.06%) are also detected in smaller amounts, with trace amounts of chromium (0.04%), nickel (0.02%), and zinc (0.02%).
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## Materials and Methods
### Yield Estimation and Prediction  
Given the total cotton GPP (gC\(\cdot\)m\({}^{-2}\)-year\({}^{-1}\)) in the growing season simulated by BEPS, the cotton lint yield (Y, in units of kg\(\cdot\)ha\({}^{-1}\)) was estimated as follows:  
\[\text{Y}=\text{GPP}\times\text{CUE}\times b\times\text{HI}\times l\times 10, \tag{3}\]  
where CUE is the carbon use efficiency or the NPP-to-GPP ratio, ranging from 0 to 1 for different species [67] and often set to 0.5 as an average of global ecosystems [68]; \(b\) is the biomass to carbon ratio; since [PERSON] [69] noted that the carbon content of biomass is almost always found to be between 45 and 50% (by oven-dry mass), \(b\) is close to 2:1; HI is the harvest index as the ratio of harvested product (grain for food crops, lint and seed for cotton) to total dry biomass (sometimes refer to the total dry aboveground biomass); this is largely determined by the percentage of bolls in the total dry biomass of cotton; \(l\) is the lint percentage of cotton (lint weight to seed cotton weight), and 10 is the ratio from g\(\cdot\)m\({}^{-2}\) to kg\(\cdot\)ha\({}^{-1}\).  
The cotton HI varies with species and is affected by human management. The HI (seed cotton yield to total aboveground biomass) shown in [PERSON] et al. [70] was 0.34, and the lint percentage was 41%. [PERSON] et al. [71] reported an HI of 0.3; however, the cotton HI was less than 0.1 according to the report by [PERSON] et al. [72], with a lint percentage of 41.4% on average under the rotation conditions. [PERSON] [73] reported HI values (seed cotton to aboveground biomass) from 0.28 to 0.33. According to [PERSON] et al. [74], the seed-to-fiber ratio was \(1.41\pm 0.11\), corresponding to a lint percentage of 41% (1/2.41). [PERSON], [PERSON], and [PERSON] [72] reported similar lint percentages (41%). If the HI includes both seed cotton and boll trash, then \(l\) is the gin turnout ratio, which is less than the lint percentage.  
Given the small variations in CUE, \(b\), HI, and \(l\) for cotton, it is desirable to directly link GPP to yield by one coefficient C for practical application as follows:  
\[Y=GPP\cdot C \tag{4}\]  
Equation (4) assumes a linear relationship between GPP and yield; the correlation coefficient is examined below. Such a linear relationship is backed up by the strong correlation (\(R^{2}=0.93\)) between biomass and seed cotton yield as measured by [PERSON] [73]; in contrast, the LAI can only explain 51% of variance in seed cotton yield as shown in the same study. Similarly, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [42] reported that the aboveground biomass explains more variation in crop yield than the peak LAI.  
Once the coefficient C is derived, the yield for each pixel can be estimated with cotton GPP at the end of the growing season, and the cotton production (P) for a given region can be estimated by  
\[P=\sum_{i=1}^{n}Y_{i}\cdot A \tag{5}\]  
where \(Y_{i}\) is the cotton yield estimation for each pixel, \(A\) is the pixel area, and \(n\) is the number of pixels.
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Network Infrastructure  
A resilient and high-capacity network infrastructure is essential for accommodating the multitude of IoT devices in smart buildings and handling the significant data they produce. Numerous current network systems may lack the capacity to accommodate a substantial number of devices, resulting in bottlenecks that impair performance. Real-time applications, such as security monitoring and energy management, need low-latency communication since any delay or network disruption might compromise essential services. Moreover, the substantial amount of data generated by IoT devices requires sophisticated data management systems to guarantee efficient flow and processing. Insufficient network infrastructure may impede the performance of IoT applications, hence impacting operational efficiency and user pleasure.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model
### Sensor Architecture
#### 2.4.2 Packing  
The positions of the lenses were chosen from known results of a proven algorithm, known as \"circle packing in a circle\" [26], using a non-congruent approach to maximize the area occupied by the lenses. After defining a number of lenses N and finding their packing positions, another lens in the center was added with the maximum possible radius.  
A diagram of the positions of the various lenses for a number of lenses from 3 to 8 is portrayed in Figure 8. The optimal scheme will be analyzed and discussed later in Section 3.2. A multi-lens-sensor pair configuration is discussed instead of a single lens and one sensor since it has the added advantage of providing spatial information on the transmitter and receiver misalignment, which will be explored later in Section 4.
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 5 Applications for Two Party Cryptography
### Bit Commitment  
In this subsection, we redesign bit commitment protocol using joint measurement and prove the security of this protocol. Quantum bit commitment protocol is one of the earliest proposed quantum two-party encryption protocols. The original version of quantum bit commitment is a variant of quantum coin tossing proposed by [PERSON] and [PERSON] [1]. In fact, quantum bit commitment is easy to adapt from 1-2 ROT protocol.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 2 Methodology Model
### Digital Filter  
The data of wind speed measured in real conditions contains a lot of noise that can affect the learning of the model, so it is necessary to process the noise to enhance the data quality. In this study, the digital filter Savitzky-Golay (SG) filter was applied to enhance the accuracy of the data signal by subtracting noise.  
SG filter is a type of Finite Impulse Response (FIR) digital filter; it achieves noise reduction by convolution operations on the data frames. The relative extremes and width distribution of the original sequence should be preserved when using it. SG filter has two critical degrees of freedom [33], polynomial order and data frame length. With the flexible adjustment of these two degrees of freedom, the SG filter can perform its excellent performance in many signal-processing scenarios. Considering these advantages, the SG filter was adopted in this study.  
SG filter operates by local least squares approximation fitting to filter the noise signal [34]. The local polynomial is expressed as follows, with a data frame containing a total of \(2M\) +1 sample points centered at \(n=0\):  
\[p(n)=\sum_{k=0}^{N}a_{k}n^{k} \tag{1}\]  
where \(N(N\leq 2M+1)\) is the power of the polynomial.  
In order to find the desired polynomial result, the error between the estimated and original values is evaluated to minimize it, and the error is calculated by the following equation:  
\[\varepsilon_{N}=\sum_{n=-M}^{M}(p(n)-x[n])^{2}=\sum_{n=-M}^{M}\left(\sum_{k=0} ^{N}a_{k}n^{k}-x[n]\right)^{2} \tag{2}\]  
Figure 1: Data flow diagram of wind power short-term forecasting task framework.  
The output of the filter can be represented by a discrete convolution of the following form:  
\[y[n]=\sum_{m=-M}^{M}h[m]x[n-m]=\sum_{m=-n-M}^{n+M}h[n-m]x[m] \tag{3}\]