esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 1 Introduction
High concentrations of laser-active RE ions (like Erbium, Ytterbium and Neodymium) can thus be incorporated into the glass matrix without clustering, which is responsible to degrade the performance of the glass enhancing quenching effects [11; 12; 13; 14]. Fiber laser length can be then substantially reduced, minimizing nonlinearities that grow with fiber length. Moreover, phosphate glasses are substantially immune to photodarkening. It was demonstrated that the maximum concentration of Yb\({}^{3+}\) ions allowed in a phosphate fiber before the onset of photodarkening at 660 nm was at least 56 times higher than a standard commercial silica fiber and 6 times higher than a highly Al-doped silica fiber [15]. In terms of thermo-mechanical properties, phosphate glasses have typically a low glass transition temperatures (400-700 \({}^{\circ}\)C), if compared to silica (1000-1200 \({}^{\circ}\)C), and also lower softening temperatures (500-800 \({}^{\circ}\)C instead of 1500-1600 \({}^{\circ}\)C) [10; 16; 17; 18]. Although these thermal features present some advantages in terms of glass processing for fabricating the fiber preform, the drawback is that phosphate fiber can suffer from thermal degradation under high-power operation. Special care and subsequent cooling apparatus may therefore be required during high power laser operation. In addition, the characteristic temperatures difference between phosphates and silica, as well as the sharp difference in physical properties between standard commercial silica based fibers and phosphate ones, has slowed down the exploitation of these fibers due to the more challenging integration in all-fiber devices. Nonetheless, splicing of phosphate fibers to silica based fibers and fiber components (for example couplers and Fiber Bragg Gratings) is nowadays commonly employed, with relatively low loss (0.2 dB per joint) and high strength [19; 20; 21]. In the last decades, remarkable results in the field of phosphate based fiber lasers and amplifiers have been obtained in terms of high output power, new operating wavelengths, single-frequency operation, pulse duration and compact configurations. The aim of this paper is to review major results obtained in the field of phosphate fiber laser technology to highlight its complementarity to silica fiber technology and discuss its future prospect. The paper is organized as follows. In Section 2 a brief review of spectroscopic properties of main RE ions used for the development of phosphate fiber lasers and amplifiers is presented. Section 3 reports the current state of the art of CW phosphate fiber lasers and amplifiers. In Section 4 main results of CW single-frequency fiber lasers and amplifiers are reviewed. Pulsed single-frequency phosphate fiber lasers and amplifiers are addressed in Section 5.
# Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals
###### Abstract  
The lifetime of induction motors can be significantly extended by installing diagnostic systems for monitoring their operating conditions. In particular, detecting broken bar failures in motors is important for avoiding the risk of short circuits or other accidents with serious consequences. In the literature, many approaches have been proposed for motor fault detection; however, additional generalized methods based on local and statistical analysis could provide a low-complexity and feasible solution in this field of research. The proposed work presents a methodology for detecting one or two broken rotor bars using the sums and differences histograms (SDH) and machine learning classifiers in this context. From the SDH computed in one phase of the motor's current, nine texture features are calculated for different displacements. Then, all features are used to train two classifiers and to find the best displacements for faults and health identification in the induction motors. A final experimental evaluation considering the best displacements shows an accuracy of 98.16% for the homogeneity feature and a few signal samples used in a decision tree classifier. Additionally, a polynomial regression curve validates the use of 50 samples to obtain an accuracy of 88.15%, whereas the highest performance is achieved for 250 samples.  
broken rotor bars; SDH; current signals; induction motors; texture features; regression analysis +
Footnote †: journal: _sciences_
## 2 Multi-Constellation PPP Model
### Multi-Constellation PPP
\right) \tag{4}\]  
where \(V_{\rho IF}\) and \(V_{\phi IF}\) are the GPS observation residuals. \(V_{\rho IF_{1}}\) and \(V_{\phi IF_{1}}\) are residuals of other navigation satellites but not GPS satellites. \(L_{\rho IF}\) and \(L_{\rho IF_{1}}\) are ionosphere-free code observations of GPS and another GNSS, respectively. \(V_{\phi IF}\) and \(V_{\phi IF_{1}}\) are the ionosphere-free carrier phases observable. For \(B_{1}\), \(\lambda_{other}\) is the corresponding coefficient matrix of other navigation satellites. \(dt_{gps}\) is the clock offset of GPS, and \(dt_{other}\) is the system time difference parameter. In the tests performed in this study, the ambiguities were treated as constants (assuming no cycle-slips), and other parameters were all epoch dependent, as shown in Equation (4). Combining multiple GNSSs is associated some issues, such as the integration of the time reference system, the coordinate reference systems, and the inter-frequency biases. These issues also represent challenges affecting multiple-constellation PPP. Because the individual GNSSs have different frequencies and signal structures, the code bias values are different in a multiple-GNSS receiver. These biases are the inter-system biases for the code observation. The phase delays are also different, and their differences represent inter-system biases for phase observations. GLONASS satellites emit their signals on individual frequencies, which will also lead to frequency-dependent biases in the receivers. For the GLONASS satellites with different frequency factors, the receiver code biases differ from the carrier phase bias. These differences are usually called inter-frequency biases. Both the inter-system and inter-frequency biases must be considered in a combined analysis of multi-GNSS data. The corresponding parameters should be estimated for all multi-GNSS receivers: one bias for the code measurements of each system and each frequency for GLONASS. However, the inter-system/inter-frequency biases and obtained satellite clocks are fully correlated. Consequently, when satellite clocks are used, the corresponding biases must also be estimated or corrected for these GNSS receivers. It should be noted that such a receiver internal bias is relevant only when processing the code data. Indeed, when analysing the phase measurements, the corresponding phase ambiguity parameters will absorb the phase delays. These will only be relevant if ambiguities are resolved to their integer values (i.e., mixed ambiguity resolution between different GNSSs, GLONASS ambiguity resolution or un-differenced ambiguity resolution).
## 2 Proposed Method
### Class Activation Intervals  
In this section, we describe the procedure for generating class activation interval (CAIs) using a (3+2)D fully-convolutional neural network. A CAI for a particular category indicates the discriminating intervals used by CNN to identify that category (see some examples in Figures 1-3 and 5). The procedure for locating these time intervals is illustrated in Figure 5. As shown in Figure 2, two convolutional layers with filters 2D of size \(K\times K=3\times 3\) were used to obtain the last two layers to which the two loss of Equations (3) and (4) are associated. Suppose we have a problem with \(C\) classes and therefore in correspondence of each pixel \((i^{\prime},j^{\prime})\) in input, in output we have a vector of elements \([y_{1},y_{2},\ldots,y_{C}]\). As described in the following Equation (6), each of these elements \(y_{c}\) is obtained as the output of a convolution, and therefore depends only on the elements contained in a neighborhood \((i^{\prime},j^{\prime},0)\ldots(i^{\prime}+K,j^{\prime}+K,T)\) of the previous layer. More formally  
\[y_{i^{\prime},j^{\prime},c}=\sum_{t=1}^{T}\sum_{j=1}^{K}\sum_{i=1}^{K}w_{i,j, t}^{c}\cdot\hat{g}_{i+i^{\prime},j+i^{\prime},t}=\sum_{t=1}^{T}n_{i^{\prime},j^ {\prime},t}^{c} \tag{6}\]  
Analyzing the Equation (6) it is clear that, once the label \(c\) of the winner class has been obtained, it is possible to go back to understand on which time interval this output value depends. So, to get the Class Activation Intervals in a point \((i^{\prime},j^{\prime})\), that is \(CAI_{i^{\prime},j^{\prime}}=[n_{i^{\prime},j^{\prime},1}^{c},\ldots,n_{i^{ \prime},j^{\prime},t}^{c}]\) we have to do as follows  
\[n_{i^{\prime},j^{\prime},t}^{c}=\sum_{j=1}^{K}\sum_{i=1}^{K}w_{i,j,t}^{c}\cdot \hat{g}_{i+i^{\prime},j+i^{\prime},t} \tag{7}\]  
Positive \(n_{i^{\prime},j^{\prime},t}^{c}\) values indicate that the date \(t\) of the time series contains information useful for determining the class, while negative values indicate that the date \(t\) does not contribute to the determination of the class.  
Note that the loss of Equation (5) can be computed in parallel, but the NDVI layer must be placed before the output segmentation layer if we want each output class label to depend on NDVI values. If we put the two layers in parallel, we cannot say anything about the dependence between class label and NDVI activation with respect to the input time series and therefore we cannot calculate the CAI.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### Multi-Terminal Transmission Line Fault Location Method
#### 4.2.1 Branch Line Failure  
When a fault occurs in a branch line, as shown in Figure 5 (((((())))), the fault occurs on M1-T1, which is thought to start from M1. The following relationship holds:  
\[d_{M_{1}FN_{i}}-L_{M_{1}T_{1}}<0d_{N_{1}FM_{1}}-L_{N_{T_{1}}}>0 \tag{29}\]  
where, \(N_{i}\), I \(\in\) (1,2,3..., k), represents the endpoints of each branch line, and \(T_{i}\), I \(\in\) (1,2,3..., k) denotes each T node.  
Therefore, the fault branch judgment matrix is as follows:  
\[\begin{bmatrix}&M_{1}&N_{1}&N_{2}&N_{3}&\cdots&N_{k}&M_{2}\\ M_{1}&\times&<0&<0&<0&\cdots&<0&<0\\ N_{1}&>0&\times&<0&<0&\cdots&<0&<0\\ N_{2}&>0&0&\times&<0&\cdots&<0&<0\\ N_{3}&>0&0&0&\times&\cdots&<0&<0\\ \vdots&\vdots&\vdots&\vdots&\vdots&&\vdots&\vdots\\ N_{k}&>0&0&0&0&\cdots&\times&0\\ M_{2}&>0&0 fault branch judgment matrix are all equal to 0. The row elements are all less than 0, the fault is determined to occur in the branch node where the branch endpoint is located.
## 2 Materials and Methods
### System and Data Acquisition  
The tabletop hyperspectral fluorescence line-scan imaging system used in this study is shown in Figure 1. The hyperspectral fluorescence imaging system comprises a hyperspectral imaging device, transfer table, and ultraviolet-A (UV-A) light source. To identify food components such as chlorophyll a, UV-A (365 nm) excitation light, whose fluorescence emission range is in the blue-to-near-infrared region (up to 730 nm), was used [32]. The imaging device comprises a 14-bit electron-multiplying charge-coupled-device (EMCCD) camera (MegaLuca R EMCCD camera, Andor Technology PLC, Belfast, Northern Ireland), spectrograph (Hyperspec VS imaging spectrograph, Headwall Photonics, Fitchburg, MA, USA) with a 60-um slit and a C-mount lens (Schneider-Xenoplan 1.4/23 C-mount lens, Schneider Optics, Hauppauge, NY, USA). The transfer table includes a step motor (Velmex, Bloomfield, NY, USA) and linear motion guide. The UV-A light source is composed of two pairs of four 10-W light-emitting diodes (LEDs; LedEngin, San Jose, CA, USA) that emit excitation light at 365-nm [26].  
Hyperspectral line-scan images of the juice residues on SSS were obtained by moving the samples by 740 steps at 0.5 nm per step for an exposure time of 0.2 s in a darkened room. Each line-scan contained 502 \(\times\) 501 spatial pixels obtained by pairwise binning (averaging) of the 1004 \(\times\) 1002 available pixels in the spatial dimension, and 70 wavebands spanning 450-730 nm in the spectral dimension that were obtained by binning at approximately 4 nm intervals. Since the wavelength region after 730 nm is a region that is repeated as the effect of second order, this region was removed by a filter (Kodak Wratten Gelatin Filter, No. 8, Kodak, Rochester, NY, USA). Through this acquisition process, hyperspectral image cubes composed of 502 \(\times\) 501 \(\times\) 70 (spatial \(\times\) spatial \(\times\) spectral) were collected using the in-house developed software [10; 26].  
Figure 1: (**a**) Illustration and (**b**) photo of the hyperspectral imaging system.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 2 Preliminaries
### Learning with Errors  
The LWE problem, a classic lattice problem defined by [PERSON], underpins the security of all the constructions presented in this paper. The LWE problems are divided into two primary categories: worst-case problems and average-case problems. In lattice cryptography schemes, the most frequently employed problems are LWE problem and the Small Integer Solution (SIS) assumption. The LWE problem is predominantly used for constructing public key encryption, attribute-based encryption schemes, and identity-based encryption. Conversely, the SIS assumption is mainly utilized in the creation of one-way hash functions, collision-resistant hash functions, digital signatures, and authentication schemes.  
Definition 11 (LWE Distribution): For a uniformly random and fixed secret vector \(\mathbf{s}\in\mathbb{Z}_{q}^{n}\), a vector \(\mathbf{a}\leftarrow\mathbb{Z}_{q}^{n}\) is selected uniformly at random, and a random number \(e\) is drawn from a distribution \(\chi\), where \(\chi\) is a discrete Gaussian error distribution over \(\mathbb{Z}_{q}\). The LWE distribution is defined as the output of uniform samples of the form \(\big{(}\mathbf{a},\mathbf{b}=\langle\mathbf{a},\mathbf{s}\rangle+\mathbf{e} \text{ mod }q\big{)}\in\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\), denoted as \(\mathbf{A}_{\mathbf{s},\chi}\).  
Definition 12 (Search LWE): Given \(m\) independent and uniformly random samples \(\big{(}\mathbf{a}_{i},\mathbf{b}_{i}\big{)}\in\mathbb{Z}_{q}^{n}\times\mathbb{Z }_{q}\) from the LWE distribution \(\mathbf{A}_{\mathbf{s},\chi}\), the objective is to recover the unknown secret vector \(\mathbf{s}\).  
Definition 13 (Decision LWE): Let \(\lambda\) be the security parameter, \(q=q(\lambda)\geq 2\) be a prime, and \(\chi=\chi(\lambda)\) be a discrete Gaussian error distribution over \(\mathbb{Z}_{q}\). An instance of the \(LWE_{\lambda,q,\chi}\) problem is to distinguish between the following two challenge oracles \(\mathcal{O}\): \(\mathcal{O}_{\mathcal{G}}\): Outputs uniformly random samples \(\big{(}\mathbf{a}_{i},\mathbf{b}_{i}\big{)}\) from \(\mathbb{Z}_{q}^{n}\times\mathbb{Z}_{q}\), where \(\mathbf{a}_{i}\stackrel{{\mathcal{S}}}{{\leftarrow}}\mathbb{Z}_{q}^ {n}\) and \(\mathbf{b}_{i}\stackrel{{\mathcal{S}}}{{\leftarrow}}\mathbb{Z}_{q} \), \"\(\stackrel{{\mathcal{S}}}{{\leftarrow}}\)\" denotes uniform random sampling. \(\mathcal{O}_{\mathcal{G}}\): Selects a uniformly random fixed secret vector \(\mathbf{s}\leftarrow\mathbb{Z}_{q}^{n}\), then selects \(\mathbf{e}_{i}\leftarrow\chi\) and samples \(\mathbf{a}_{i}\leftarrow\mathbb{Z}_{q}^{n}\) uniformly, with \(\mathbf{b}_{i}=\langle\mathbf{a}_{i},\mathbf{s}\rangle+\mathbf{e}_{i}\). Outputs \(\big{(}\mathbf{a}_{i},\mathbf{b}_{i}\big{)}\in\mathbb{Z}_{q}^{n}\times \mathbb{Z}_{q}\).  
The LWE problem is that it is difficult to distinguish between the two oracle outputs mentioned above.
## 2 Study Area and Dataset
### Sentinel-1 Data  
Sentinel-1 is a two satellites constellation that carries C-band SAR sensors (\(\sim\)5.6 cm wavelength) under the Copernicus Programme coordinated and managed by the European Space Agency (ESA). Sentinel-1 data are provided to users free of charge. The revisit cycle of Sentinel-1 single satellite A or B is 12 days in all weather conditions, day and night [57]. In this study, a total of 284 scenes of descending Sentinel-1 TOPS data along two tracks covering two frames from 2017 to 2021, were collected in the northern QTP. The Sentinel-1 datasets were acquired in interferometric wide swath (IW) mode. The acquired Sentinel-1 data are shown in Table 3.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Artificial Intelligence Method
#### 5.3.5 Just-In-Time Learning (JITL) Method  
The JITL method comprises a stacked autoencoder (SAE) and CCA neural network. In deep canonical correlation analysis (DCCA), it is divided into a joint learning process of dividing historical data, extracting input and output, constructing SAE personal learning and optimizing SAE parameters to improve parameter relevance. A CNN is introduced into the two processes to construct a residual monitoring mode. The monitoring statistical limit can be obtained by selecting and estimating historical data from online data. The model can be used to monitor the complex non-linear mode, but it may cause misjudgment due to the impact of small interferences on data monitoring statistics [97; 98]. [PERSON] et al. [99] proposed an improved JITL-CCA method. They constructed a CCA model through K-means to classify the minimum input-output-related dataset obtained from the training data. After removing the omitted data, the CCA model was reconstructed. The parameter matrix was calculated, and the statistical limit was monitored. This algorithm is poor in dealing with new operation modes and faults due to its high dependence on learning datasets.  
To realize the division of phase and transition, [PERSON] et al. [100] combined statistics pattern analysis (SPA) and slow feature analysis (SFA) models to form statistics slow feature analysis (SSFA). The most relevant samples were selected from local data by a JITL algorithm to construct a similarity matrix and monitor the current test samples. [PERSON] et al. constructed a global preserving SSFA model based on real-time learning by introducing global analysis and local statistical matrixes to shorten the monitoring time of abnormal states and improve monitoring accuracy.
## 1 Introduction
However, to the best of our knowledge, UAV-enabled ISWPT systems have not yet been studied, despite the significant utility of UAV-enabled radar sensing in various tasks, such as target detection and tracking, surveillance, environmental monitoring, and autonomous UAV operation. In this study, we investigate UAV-enabled ISWPT systems, where a UAV is equipped with multiple antennas to sense multiple targets while transferring power to multiple devices on the ground at the same time. We propose a method for jointly designing the transmit waveform and UAV altitude in order to enhance both radar sensing performance and wireless power transfer efficiency. The main contributions of our study are summarized as follows:  
* We devise a method for jointly designing the transmit waveform and UAV altitude, taking into account the fundamental trade-off between the desired beam pattern for accurate radar sensing and the desired harvested power of the devices for efficient WPT operation. * As the corresponding design problem results in challenging non-convex optimization, we first explore an optimal solution and then develop a method to find the optimal waveform and altitude by leveraging the Particle Swarm Optimization (PSO) algorithm. Based on the analysis, we further characterize the maximum desired harvested power of the devices for UAV-enabled ISWPT systems. * In order to lessen power consumption in UAVs by reducing computational complexity, we also develop an efficient, low-complexity method for jointly designing the transmit waveform and UAV altitude. A novel transmit waveform is devised based on insights from the joint optimization problem, and its design parameters, along with the UAV altitude, are jointly optimized by investigating the optimal conditions and developing an efficient algorithm. Our numerical results demonstrate that the proposed joint design method considerably enhances both radar sensing and WPT performance while benefiting from substantially decreased computational complexity. The rest of this paper is organized as follows. In Section 2, we present the system model and introduce our design problem. In Section 3, we describe the steps taken to obtain the optimal design and investigate the maximum desired harvested power of the devices. We propose an efficient method for jointly designing the waveform and UAV altitude in Section 4. We present the evaluation of the proposed method in Section 5 and conclude our paper in Section 6.
## 4 Results and Discussion
### RQ9 and RQ10: What Is the Research Focus and Limitations According to the PS?
#### 4.9.6 Deep Feed Forward Networks
They implemented a DFFN with an attention mechanism to classify the source code as vulnerable or not to XSS attacks. The proposed approach outperforms existing static analysis tools in every classification performance metric. However, it also has different limitations that undermine its applicability to real-world scenarios. Indeed, the model is evaluated using a synthetic dataset. In addition, the code2 vec method does not scale to large source code files, and both representation techniques do not take into account invocations between different source code files.
# Addressable Refraction and Curved Soliton Waveguides Using Electric Interfaces
## 4 Discussion  
An electric interface interposed along the propagation of a soliton waveguide may indeed act as an electric reflector. The advantage of an electrical interface with respect to a physical interface is its modulability, i.e., the possibility of varying its contrast in a continuous and gradual manner. On the other hand, the achievable contrasts are not very high as the variation of the refractive index is induced by the electro-optical effect. Even if we talk about nonlinearities of the second order, the values of contrast of refractive index are still limited. In the present work, we analyzed contrasts up to \((2.0\div 2.5)\times 10^{-4}\). This means angles of incidence on the interface were extremely large, and therefore had small complementary angles. With electric reflectors, curves with radiuses of hundreds of microns to millimeters can be obtained, thus offering a rather large panorama of possibilities. Moreover, it is worth noting that propagation losses even for very marked curves remain extremely steep, lower than the guides made with more traditional techniques such as ion exchange or epitaxial growth. The limit of 0.07 dB/cm for straight propagation is not achieved by any other technology. This value increases again by 0.2 dB for tight bends with a radius of 200 \(\upmu\)m, and then falls back to the reference value of the straight guides for radius curves of 250-300 \(\upmu\)m.  
The solitonic technology is rapidly opening doors to a new circuit geometry for integrated photonics, using very low loss waveguides, addressable and plastic, in the sense that they are modifiable according to the external stimuli that are sent to them [19; 22; 23].  
[PERSON] performed the simulations, interpreted the data and wrote the paper; [PERSON] wrote the numerical code; [PERSON] wrote the numerical code and participated to the data interpretation.  
This research was funded by Sapienza Universita di Roma, grant \"Progetti di Ricerca--Piccoli\" number RP116155012228B3-2016.  
The authors declare no conflict of interest.