esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Performance Analysis
### Resonator Design and Simulation
In summary, choosing appropriate values for Nt and W can enhance the performance of the resonator, but an increase in both parameters leads to a larger device size. Therefore, the selection of Nt and W should be based on the desired performance of the SAW device, the required dimensions, and the complexity of the manufacturing process. The final structural design parameters of this paper are shown in Table 2.
## 2 Materials and Methods
### Spectral Data Preprocessing  
Spectra, extracted from hyperspectral images, contained abnormalities such as noises, uncertainties, variabilities, interactions, and unrecognized features, which disrupted the modeling processes. To minimalize these effects, calibrated spectra obtained from the plant leaves were transformed using baseline correction, in which the lowest value was subtracted from all the remaining values in the spectrum. The values below 400 nm and above 2400 nm remained noisy and were abandoned. In addition, values in the 1000-1200 nm range have also been removed, as this range is in the marginal registration zones of both sensors. Then, the second derivative Savitzky-Golay (SG) filter (second-order polynomial fitting and nine-data-points window size) was applied [42; 45]. In the process of SG smoothing, the linear least squares method is used to fit a small set of consecutive data points to a polynomial. The calculated central point of the fitted polynomial curve is taken as the new smoothed data point. After this calculation, some data points at both ends of the spectrum are lost in order to allow the filter to be fully contained within the available data [46].  
To reduce the high dimensionality, enhance learning efficiency and improve the calculation speed, only some of the wavelengths were selected from the full spectra. These wavelengths bring out the most important information which could efficiently predict nitrogen deficiency in plants growing under different water conditions. In the current study, the most informative wavelengths were determined using a Correlation-based Feature Selection (CFS) algorithm with a greedy stepwise selection method. The data processingmethods were implemented using the Unscramble X10.3 software (CAMO Software, Inc., Oslo, Norway, 133).
## 2 Materials and Methods
### Mobility Model
#### 2.1.2 Gaussian-Markov Mobility Model  
When the movement of a node is time-dependent, it means that the movement of the node is governed by the laws of physical kinematics, and the movement of the current moment is closely related to the movement of the previous moment. For example, the current speed of a node depends on the speed of the previous moment, and in most cases, the node moves along a given path. Gaussian-Markov (GM) [21] model was originally proposed to implement the simulation of personal communication systems. GM models can be used in networks to capture the correlation between the time and speed of nodes.  
In this model, each node is initially assigned a velocity, including the value of velocity \(v\) and direction \(\theta\). For every fixed time interval \(\Delta t\), the node updates the speed and direction once, and this update is based on the previous update. The calculation formula is as follows:  
\[v_{n}=av_{n-1}+(1-a)\overline{v}+\sqrt{1-a^{2}}w_{v-1} \tag{3}\]  
\[\theta_{n}=a\theta_{n-1}+(1-a)\overline{\theta}+\sqrt{1-a^{2}}w_{\theta-1} \tag{4}\]  
where \(\overline{v}\) and \(\overline{\theta}\) are the mean values of the velocity and direction when n approaches infinity, respectively; \(w_{v-1}\) and \(w_{\theta-1}\) are two unrelated Gaussian random variables with a mean \(\alpha\) value of 0 and a variance of 1; and \(\alpha\) is an adjustable parameter within the range of [0, 1] to control the randomness of the nodes. By adjusting the value of \(\alpha\), the strength of the node motion correlation can be adjusted. At the n-th time interval, the current position of the node can be obtained using Formulas (1) and (2). The motion trajectory of the node is shown in Figure 2.  
Figure 1: A node movement trajectory in RWP mobility model.  
The model effectively solves the phenomenon of the sudden turning of nodes in a random mobility model, which is more in line with the movement of entities. However, in GM, the speed of a node at any time interval is a dependent Gaussian variable of the speed of the previous time interval, which makes it impossible to maintain the motion trajectory in a straight line under certain parameters, and the entire simulation process does not pause, which is inconsistent with the movement of UAVs in reality [3].
# Photonics-Based Microwave Image-Reject Mixer
## 3 Photonic IRM Based on Phase-Cancellation Techniques
### Photonic IRMs Based on 90\({}^{\circ}\) Electrical Hybrid  
Methods in the first category apply an electrical 90\({}^{\circ}\) hybrid to generate a pair of quadrature LOs [18] or a pair of quadrature RF signals (containing the images) [19; 20]. Then, RF and LO signals are mixed in the optical domain. A pair of quadrature IF outputs will be generated, which are combined by another electrical 90\({}^{\circ}\) hybrid, to produce a downconverted IF signal, with the downconverted image being rejected. In [PERSON] et al.'s paper [18], the in-phase RF signals were modulated on two lasers by direct modulation, and then injected into two separate electro-optic modulators driven by the pair of quadrature LOs from an electrical 90\({}^{\circ}\) hybrid. A separate study [19] describes two quadrature RF signals being generated by an electrical 90\({}^{\circ}\) hybrid, and two out-of-phase LOs being produced by an out-of-phase divider, that formed two RF and LO pairs, which were mixed at two directly modulated lasers. Optical signals output from the two lasers were detected by two photodetectors (PDs), generating a pair of quadrature IFs. The two IFs are then combined by another electrical quadrature hybrid to achieve image-reject downconversion. For both approaches, two mixed RF and LO pairs were separated, and the difference between two separated branches affected the system performance and stability. By using integrated modulators to perform modulation by the LO and RF signals simultaneously, this problem can be avoided. For example, a single integrated dual-parallel Mach-Zehnder modulator (DPMZM) has been previously used [20], which greatly improved the stability of the system. The key problem associated with approaches in this category [18; 19; 20] is that they rely on an electrical 90\({}^{\circ}\) hybrid to generate quadrature RF signals or LOs. Since electrical 90\({}^{\circ}\) hybrids cannot maintain a precise 90\({}^{\circ}\) phase difference across a wide frequency range  
Figure 5: Schematic diagram of a photonic IRM, based on phase cancellation. IF: intermediate frequency.  
at high frequencies, the bandwidth of IRMs is usually very limited, or, the image suppression ratio is small (Figure 6).
## 5 Discussion
### Method Comparison  
By integrating the dual-channel method and XFAST method, we have developed a new method that has the advantages of both, although the integration is not a simple combination.  
Like the dual-channel method, the byproduct of correlation, partial peaks, are considered in the acquisition verification instead of being neglected. The difference being, in DC-XFAST, that partial peaks are stacked so that the detection performance is not affected by their magnitude. Moreover, the entire local code segment is folded to have half of the incoming signal block length, and folding local replicas are zero-padded to full length. The use of the folding technique reduces the acquisition time.  
Both the DC-XFAST method and XFAST method adopt folding techniques to achieve rapid acquisition, but the DC-XFAST method applies max-value detection to achieve a high detection probability. Moreover, DC-XFAST performs the subsequent multi-peak verification, which is the core step of DC-XFAST to address SNR deterioration resulting from folding.
## 5 Analysis  
Due to different measurement principles, the advantages, shortcomings, and application scenarios of various measurement techniques are also different, as shown in Table 3.  
Multimodal data fusion, as a method for the joint detection of multiple signals, can leverage strengths and avoid weaknesses, helping to improve detection accuracy. It is very common in bearing detection technology [95; 96; 97; 98; 99; 100; 101]. In comprehensive applications, vibration, analysis, and thermal imaging are the most commonly used combinations for detecting internal and surface faults. Electromagnetic and acoustic detection have high sensitivity and are suitable for early fault identification, while visual detection is suitable for \begin{table}
\begin{tabular}{c c c} \hline
**Techniques** & **Accuracy** & **Literature** \\ \hline Vibration Analysis & 98.91\% & \begin{tabular}{c} Rolling bearing fault diagnosis method using \\ time-frequency information integration and \\ multi-scale TransFusion network \\ \end{tabular} \\ \hline Acoustic Emission & 93\% & \begin{tabular}{c} Acoustic emission monitoring of naturally \\ developed damage in large-scale low-speed \\ roller bearings \\ \end{tabular} \\ \hline Current Analysis & 97\% & Motor Bearing Fault Diagnosis Bas \\ \hline Electromagnetic Detection & Less than \(\pm\)8\% & \begin{tabular}{c} Bottom Crack Detection with Real-Time Signal \\ Amplitude Correction Using EMAT-PEC \\ Composite Sensor \\ \end{tabular} \\ \hline Infrared Thermography & 94.30\% & \begin{tabular}{c} Unsupervised Feature-Preserving CycleGAN for \\ Fault Diagnosis of Rolling Bearings Using \\ Unbalanced Infrared Thermal Imaging Sample \\ \end{tabular} \\ \hline Vision Detection & 99.5\% & \
\begin{tabular}{c} Detection and Classification of Bearing Surface \\ Defects Based on Machine Vision \\ \end{tabular} \\ \hline Intelligent Detection &
\begin{tabular}{c} Outperformed the baseline by \\ 6.03\% and 8.76\% in terms of \\ mean average precision (mAP) \\ and mean average recall (mAR) \\ \end{tabular} & A hierarchical attention detector for bearing \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of certain performance data against the latest detection methods. surface defects and is easy to integrate with automation systems. The choice of appropriate detection method depends on specific detection requirements and fault types.
# New Space Engineering Design: Characterization of Key Drivers
## 2 Methodology
### Categorization of Design Drivers and Enablers  
The outcome of this screening process is a qualitative group of design drivers and enablers of different natures, each assigned to a specific category for a clearer understanding of its function and significance within the New Space scenario. The categories are as follows:  
* Category A: Evolution of Traditional Design Drivers: This includes elements that were historically present in space missions that have been redefined or adapted in response to the New Space paradigm.
* Category B: Emerging Manufacturing and Integration Practices: This encompasses new industrial needs and technologies that were adopted by New Space companies to better respond to market needs.
* Category C: Sustainability and Technology Independence: This reflects the increased maturity and strategic awareness of the sector, targeting long-term viability and autonomy.  
This categorization of design drivers and enablers can serve as a baseline for implementing a design methodology aimed at enhancing the competitiveness of new space products. This can be achieved by integrating stakeholder needs derived from the current trends, which are translated into actionable innovation and design drivers. Tailored solutions and enabling technologies aligned with these drivers can then be adopted to address evolving market demands.
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## 3 Results  
### The Architecture of the Simulation Platform Used for the Evaluation of the LB Algorithms  
The architecture of the simulation platform used to assess the performance of the proposed LB algorithms is depicted in Figure 6.  
Figure 6: The block diagram of the simulation platform used for the evaluation of the LB algorithms.  
The Traffic Generator module generates the data flows for each user according to the specified traffic mixture and statistical characterization of each traffic type [43]. The generated data flows are routed through the tunnels based on the decisions taken by the LB module. The Wireless Channel Simulator module simulates the wireless links instantiated in the cellular networks according to the considered channel models. To each simulated wireless link a Channel Estimator module is associated, these modules perform a short-term prediction of the channel parameters based on the latest simulated/computed values. In this way, it is possible to make a short-term prediction of the tunnels' instantaneous capacity. The LB module receives the statistical data characterizing the generated data flows, the Queue State Information (QSI) associated with each tunnel, and the Channel State Information (CSI) generated by the Wireless Channel Simulator module. Using all this information, for each input flow, the LB module tries to choose the tunnel which ensures the smallest delay accumulated through the system. At the output of the tunnels, the accumulated delay and other performance indicators are evaluated for each packet, and statistical data are collected for each flow. These results together with the data describing the status of the system can be stored in a database for further evaluation. The following subsections describe each constituent block and the considered algorithms.
# Microbial Biosynthesis of Lactones: Gaps and Opportunities towards Sustainable Production
## 4 Microbial Production  
### Biotransformation  
Biotransformation processes for the production of lactones, mainly \(\gamma\)-decalactone, have a long history. Early in 1963, the production of lactones resulting from the catabolism of ricinoleic acid was reported for several species of the genus _Candida_, with the authors suggesting that \(\gamma\)-decalactone was derived from the intermediate 4-hydroxydecanoic acid [50]. Basically, the biosynthetic process is dependent on the shortening of a hydroxy fatty acid in the \(\beta\)-oxidation pathway and spontaneous lactonization. To produce different lactones, different fatty acids must be used, in particular with the hydroxy group in different positions. However, lactone production by biotransformation was reported from other sources, such as cheese [51]. Biotransformation of hydroxy fatty acids for the production of lactones was, since the 1990s, the chosen alternative to achieve a high-yield process. In contrast with de novo biosynthesis, microorganisms that are able to convert fatty acids into lactones are extensive. In particular, the production of \(\gamma\)-decalactone from castor/ricinoleic acid was the subject of intense investigation mainly in the workhorseY. lipolytica, which led to several patent applications [41]. Other patents and processes covering high-yield biotransformation of lactones were related to \(\gamma\)-dodecalactone and \(\gamma\)-octadecone, for instance [6; 47]. Some patents report titers above 10 g/L (e.g., 12.5 g/L) by using _Y. lipolytica_ in fermenters up to 300 L [11]. The first biotransformation processes were established in the early 1980s and with the boost of novel processes, the price of lactones declined from approximately US$ 20,000/kg to US$ 1200/kg in the late 1990s [51]. In the beginning of the last decade at least one company (Safisis--Lessafre, France) had a commercial biotransformation process of ricinoleic acid to \(\gamma\)-decalactone employing _Y. lipolytica_[52].
# MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss
## 3 Proposed Methods
### Loss Function  
FaceNet is a facial recognition model proposed by the Google team [44] which mainly uses triplet loss for model training. A triplet is composed of three samples (\(x^{a}\), \(x^{p}\), \(x^{n}\)), where \(x^{a}\) and \(x^{p}\) are two face images with the same identity (positive pair), where \(x^{a}\) and \(x^{n}\) are two  
According to the characteristics of the masked-face dataset, ideally, triplets such as (Anchor, Positive1, Negative) and (Anchor, Positive3, Negative) in Figure 2 will be selected for training, because they contain more mask changes; this helps the model learn the relationships between mask changes and facial feature changes. Since the sample feature distances were \(L1<L2<L3\) in the feature space, the model chose triplets such as (Anchor, Positive2, Negative) for training after adopting the semi-hard strategy, which contains fewer mask changes. Therefore, it is difficult for the model to extract the facial features occluded by a mask.  
In order to solve this problem, we used the large margin cosine loss function to train the model. The large margin cosine loss function replaces the selection of triplet training with a non-grouping learning method, and its expression is as follows:  
\[L=-\frac{1}{N}\sum_{i=1}^{N}\log\frac{e^{s\cdot(\cos(\theta_{yi}i)-m)}}{e^{s \cdot(\cos(\theta_{yi}i)-m)}+\sum_{j\
eq yi}^{n}e^{s\cdot\cos(\theta_{j}i)}} \tag{4}\]  
subject to  
\[W =\frac{W*}{\left\|W*\right\|}, \tag{5}\] \[x =\frac{x*}{\left\|x*\right\|},\] \[\cos(\theta_{j},i) =W_{j}{}^{T}x_{i},\]  
where \(N\) is the number of training samples, \(x_{i}\in R^{d}\) represents the feature vector of the \(i\)-th sample, and its identity label is \(y\). \(W_{j}\in R^{d}\) is the weight of the class \(j\). \(\theta\) is the angle between \(W_{j}\) and \(x_{i}\), \(S\) is the scaling factor, and m is the margin of the angle, used to limit the distance between classes. The large margin cosine loss function effectively solves the problem of insufficient mask changes in the model training process; all masked-face images are used for model training. Figure 3 shows its representation in the feature space; \(\alpha\) and \(\beta\), respectively, represent the angles between sample \(x\) and W1 and W2. For each sample \(x\) belonging to class X, \(\cos\beta-\cos\alpha\geq m\) should be satisfied, and the margin m also increases the inter-class difference while further compressing the intra-class distance. Experiments have also proved that this method greatly improves the recognition accuracy.  
Figure 2: A representation of triplets in feature space. Anchor, Positive1, Positive2, and Positive3 belong to class X; Negative belongs to class Y; L represents the feature distance between different samples.
# Expanded Scene Image Preprocessing Method for the Shack-Hartmann Wavefront Sensor
## 2 Methods
### Generalized Anscombe Transform  
In the extended target Shack-Hartmann wavefront sensor for measurement, the transmitted signal is affected and interfered with by a variety of factors, including environmental stray light, device characteristics, detector characteristics, and other factors, which introduces a variety of noise. According to statistical law, its noise distribution includes two types: signal-independent Gaussian noise and signal-dependent Poisson noise [29; 30; 31]. The noise model equation is represented by Equation (9):  
\[s(x)=a\cdot p(s_{0}(x))+n(x), \tag{9}\]  
where \(s(x)\) is a noisy image, \(s_{0}(x)\) is a clear image, \(x\) denotes any pixel point, \(a\) is the gain, \(n\) is Gaussian noise with mean \(m\) and standard deviation \(\sigma\), and \(p\) is the Poisson noise.  
The mutual correlation method is commonly employed to mitigate the impact of noise signals in the sub-image array on the relative error of offsets. However, this method overlooks the characteristics of the image noise. Therefore, this paper employs a method to enhance the signal-to-noise ratio. Conventional image noise processing algorithms typically assume the noise to be Gaussian white noise, while in practical detection, the detector generates Gaussian-Poisson noise. To tackle this issue, the detection noise is initially transformed into Gaussian noise. Subsequently, an algorithm is applied to effectively eliminate the Gaussian noise, thereby reducing computational complexity and saving processing time. This conversion process is known as the variance stabilizing transformation (VST), where the generalized Anscombe transform is frequently employed as a nonlinear transformation in image processing.  
According to Equation (9), each pixel value \(x_{1}\) observed can be expressed as:  
\[x_{1}=a\cdot p(y_{1})+n, \tag{10}\]  
Figure 1: Flowchart of the method.  
where \(n\big{(}m,\sigma_{1}^{2}\big{)}\), the Poisson distribution \(p\) parameter is \(\lambda_{0}\), \(a\) is the gain, and \(y_{1}\) is the pixel point of the clear image corresponding to the acquisition of pixel \(x_{1}\). At this point, the variance is \(Var(x_{1})=\sigma^{2}+a^{2}\lambda_{0}\), and the process of the generalized Anscombe transform is shown in Equation (11) below:  
\[f(x_{1})=\left\{\begin{array}{rl}\frac{2}{a}\sqrt{ax_{1}+\frac{3}{8}a^{2}+{ \sigma_{1}}^{2}-am},x_{1}>-\frac{3}{8}a-\frac{{\sigma_{1}}^{2}}{a}+m\\ 0,x_{1}\leq-\frac{3}{8}a-\frac{{\sigma_{1}}^{2}}{a}+m\end{array}\right.. \tag{11}\]  
The transformed variance \(Var(f(x_{1}))=a/4\), and there is only Gaussian noise in the subaperture image.
## 2 Materials and Methods
### Field Trials
#### 2.2.3 Study Site and Experimental Plot Layout  
The spray trials were conducted in an apple orchard (cv. Cosmic Crisp) planted on M9-NIC29 rootstock in year 2013 and was trained in a tall spindle architecture. The research orchard was located in Roza Farm (46.29\({}^{\circ}\) N, 119.73\({}^{\circ}\) W) of Washington State University. The planting density of the experimental plot was 4284 tree ha\({}^{-1}\) with an inter-row spacing of 3 m, plant to plant distance of 0.9 m, and mean tree height of 3 m.  
A set of 11 apple trees planted between two wooden posts, positioned 10 m apart (hereafter termed as blocks), were designated for the system installation. Out of the 35 blocks in the experimental orchard, 6 were randomly selected for the spray trials and, in each of the blocks, a 10 m long pneumatic spray delivery based SSCDS was installed with modified micro-emitters or hollow cone nozzles in a 3-tier arrangement. Three blocks were treated with a modified irrigation micro-emitter (T1), while the other three blocks were treated with a hollow cone nozzle SSCDS (T2) (Figure 3). To ensure that the two treatments did not interact, the treatment specific spray trials were conducted on two different dates (i.e., T1: 22 July 2019 and T2: 24 July 2019). On a given day, three replicate trials were conducted within 45 min of time window.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Emitter** & **Model** & **Manufacture** & **Spray Pattern** & **Flow Rate (L min\({}^{-1}\))** & **Cost (USD \$/Unit)** \\ \hline Modified micro-emitter & Modified & Jain Irrigation Inc. & Hollow cone & 0.9 & 1.5 \\ Hollow cone nozzle & TXVS12 & Teejet Technologies & Hollow cone & 0.8 & 20.0 \\ \hline \end{tabular}
\end{table}
Table 1: Specification of emitters tested in this study.