esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multi-Attribute Recognition Using Exclusive and Correlated Relationships
### Single Task Learning for Exclusive Attributes  
As the first step to consider attribute relationship in multi-attribute recognition, we took an approach of grouping attributes based on their mutually exclusive relationship. Two attributes are said to be mutually exclusive when they cannot be satisfied at the same time. For example, a facial image, cannot satisfy both male and female attributes at the same time, and these two attributes are grouped together. In this manner, all the attributes were grouped into several groups, we treated each group as a single recognition task. Accordingly, the activation function of output nodes and corresponding target needed to be redefined. In this section, we describe the learning for a single task, and then extend it to multiple tasks in the next section.  
When a group of \(M\) attributes \(A_{1},A_{2},\ldots,A_{M}\) are composed by a mutually exclusive relationship, task **T** can be defined to assign each input to one of the attributes, which corresponds to one of \(M\) output nodes of the learning network. The network structure for the single task **T** using output representation of an exclusive relationship is shown in Figure 1. Given an input data \(\mathbf{x_{n}}\) the target output for \(m\)th output node \(y_{m}^{n}(m=1,\ \ldots,M)\) needs to satisfy the conditions: \(\sum_{m=1}^{M}y_{m}^{n}=1\) and \(y_{m}^{n}\in\{0,1\}\). In order to design a network satisfying the conditions, the output value of \(m\)th output node \(f_{m}(\mathbf{x_{n}},\mathbf{\theta})\) is defined by using softmax activation function, which can be written as  
\[f_{m}(\mathbf{x_{n}})=\frac{e^{\text{i}m}}{\sum_{i=1}^{M}e^{\text{i}u_{i}}}\, \tag{1}\]  
where \(u_{m}\) denotes the weighted sum of input to the \(m\)th node. For training the network, we can use the conventional cross-entropy error function for the multi-class classification problem, which is written as  
\[E_{ce}(\mathbf{\theta})=\ -\sum_{n=1}^{N}\sum_{m=1}^{M}y_{m}^{n}\ln f_{m}(\mathbf{x_{n}}, \mathbf{\theta})\, \tag{2}\]  
where \(N\) is the number of training data, and \(\mathbf{\theta}\) is the vector of all weight parameters in the network. Although this is the conventional setting for multi-class classification, it should be noted that the group of mutually exclusive attributes can only satisfy the underlying assumption, and thus our proposed grouping process is important in recognizing various attributes in a single network.
## 2 Related Works
### Beamforming
LOS is disturbed by an obstacle such as a building which decreases the efficiency of the beamforming. An effective multiuser transmission scheme in three-dimensional massive MIMO cellular systems with a full duplex base station is proposed [41]. In 3D massive MIMO cellular systems, a full duplex base station equips two separate large-scale uniform planar antenna arrays. This process introduces the 3D Multiuser Beam Space Transmission (MUBT) scheme that requires the spatial angular information of the users and the SI channel. Due to the reduced dimension property of the effective beams pace channel, the overhead for channel estimation is reduced. In this paper scheduling algorithm is proposed to enable the 3D MUBT scheme in the FD systems. In this work, two separate large-scale uniform planar antenna array is proposed, which is two-dimensional only so that the coverage area is limited.
###### Abstract  
The paper deals with the navigation data exchange between two satellites moving in a swarm. It is focused on the reduction of the inter-satellite demanded communication channel capacity taking into account the dynamics of the satellites relative motion and possible erasures in the channel navigation data. The feedback control law is designed ensuring the regulation of the relative satellites motion. The adaptive binary coding/decoding procedure for the satellites navigation data transmission over the limited capacity communication channel is proposed and studied for the cases of ideal and erasure channels. Results of the numerical study of the closed-loop system performance and accuracy of the data transmission algorithm on the communication channel bitrate and erasure probability are obtained by extensive simulations. It is shown that both data transmission error and regulation time depend approximately inversely proportionally on the communication rate. In addition the erasure of data in the channel with probability up to 0.3 does not influence the regulation time for sufficiently high data transmission rate.  
Keywords:data transmission; erasure communication channel; adaptive coding; satellite formation +
Footnote †: journal: Accepted: 23 November 2020; Published: 1 December 2020
## 2 Materials and Methods  
### Experimental Setup  
Figure 2 illustrates the diagram of the LIBS system. This system comprises a Q-switched Nd:YAG laser (Litron Laser Nano 120-20) with an output wavelength of 1064 nm, an energy of 100 mJ per pulse, a pulse width of 7 ns, and a repetition rate of 20 Hz. The laser beam is focused through an aperture in the collecting spherical mirror onto the sample surface using a focusing optical system with a focal length of 400 mm. The plasma emission spectrum is collected by a spherical mirror (F = 150 mm) positioned 300 mm from the focal point and directed onto a fiber input with a numerical aperture (NA) of 0.22. An optical fiber (in the original configuration) transmits the LIBS signal to an Echelle spectrograph (Aryelle 200; aperture of f/10; spectral resolving power of 9000; 40 um input slit; spectral resolution of 22-83 pm) paired with an intensified charge-coupled device (ICCD) camera (DH334T-18F-03-27A, Andor Technology, London, UK).  
However, as shown in the figure, we have modified the LIBS system by replacing the optical fiber with two CaF\({}_{2}\) collection lenses. This modification improved coupling to the spectrometer and increased the overall signal severalfold.  
The delay between the optical pulse and the ICCD gate is adjusted using a pulse generator. Sophy Software version 7.8.3 (LTB Lasertechnik Berlin, GmbH, Berlin, Germany) manages the ICCD gate width, delay time, and pulse averaging. The detection platform's sensitivity is calibrated with the 63358 Oriel lamp. An XYZ translational stage adjusts the position of the platform holding the samples. LIBS spectra were measured in the 200-850 nm spectral range.  
The volume of the LIBS system chamber was approximately 100 L. An argon flow rate of 13 L per minute was used to continuously purge the chamber under atmospheric pressure to measure As and Mn concentrations in soil samples.  
All measurements were performed in a laser laboratory with controlled environment conditions with the temperature set at 21 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C and humidity at 55% \(\pm\) 5%.
## 4 Methodology
### Selection  
Initially, we extracted 35 features using the software Time Series Feature Extraction Library (TSFE) [28], a comprehensive toolbox that computes temporal, statistical, and spectral features from a given time series. Over these features, we performed a Principal Component Analysis [29] selecting several components able to explain 99% of the variance; we found out that only two components were sufficient to satisfy our criterion, see Figure 5. From the construction of the principal components, we used the weights (loadings) of the linear combination of the features as an indicator of feature importance [30]. At this point, we iteratively excluded one by one the weakest features and recomputed the PCA-based variance explanation, and metric Bayesian Information Criterion (BIC) [31]--refer to Section 5.2; we stopped when the value of BIC increased while PCA did not change. This attribute selection process demonstrated adequate for a machine-learning process that is unsupervised.  
We ended up with five features, including temporal features, absolute energy, and autocorrelation; statistical features maximum and standard deviation; and spectral features power bandwidth, and spectral distance--for details, refer to library TSFE [28].  
Figure 4: Example of a time series with two detected snippets along with the main matches of each one. By exact match, we mean the snippet has maximum MPdist similarity to a subsequence in the original time series; a partial match means the similarity is high but not maximal.  
Figure 5: Visualization of the two components that explain 99% of variance as identified by method Principal Component Analysis.
# Ascertaining the Ideality of Photometric Stereo Datasets under Unknown Lighting
## 4 Dealing with Nonideal Data
```
0: PS data matrix \(M\) of size \(p\times q\) (\(q\) pictures, each with \(p\) pixels)
0: Set \(\mathcal{S}\) containing the indices of the images to be kept in the dataset
1:\(\mathcal{S}=\{1,\ldots,q\}\)
2:\(k=0\), \(\rho_{0}=0\)
3:repeat
4:\(k=k+1\)
5:if fast version then
6: Let \(\widetilde{M}\) contain the columns of \(M\) indexed in \(\mathcal{S}\)
7: Compute the compact SVD \(\widetilde{M}=U\Sigma V^{T}\), with \(V=[\mathbf{v}_{1},\ldots,\mathbf{v}_{q}]\)
8:\(\widetilde{L}=[\mathbf{v}_{1},\mathbf{v}_{2},\mathbf{v}_{3}]^{T}\)
9:endif
10:for\(i=1,\ldots,q\)do
11:if fast version then
12: Let \(L\) be \(\widetilde{L}\) with column \(i\) removed
13:else
14: Let \(\widetilde{M}\) be \(M\) with column \(i\) removed
15: Compute the compact SVD \(\widetilde{M}=U\Sigma V^{T}\), with \(V=[\mathbf{v}_{1},\ldots,\mathbf{v}_{q}]\)
16:\(L=[\mathbf{v}_{1},\mathbf{v}_{2},\mathbf{v}_{3}]^{T}\)
17:endif
18: Run mngn2 [31] on Problem (10) and obtain \(\eta_{i}=\gamma_{6}/\gamma_{5}\) at convergence
19:endfor
20:\(\eta_{\ell}=\max\{\eta_{1},\ldots,\eta_{q}\}\)
21: Store in \(t\) the \(\ell\)th element of \(\mathcal{S}\)
22: Remove \(t\) from \(\mathcal{S}\)
23:\(q=q-1\)
24:\(\rho_{k}=\eta_{\ell}\)
25:until\(\rho_{k}<\rho_{k-1}\) or \(q=6\)
26:\(\mathcal{S}=\mathcal{S}\cup\{t\}\)
```  
**Algorithm 2** Removal of \"unideal\" images from a PS dataset: nonlinear approach. After initializing the set \(\mathcal{S}\) to the column indices of the data matrix \(M\), the main loop starts. There, the algorithm removes one column from \(M\), computes its SVD decomposition, and constructs a tentative matrix \(L\) (see Lines 14-16). Then, (Line 18), the nonlinear least-squares method mngn2 from [31] is applied to Problem (10). The function returns the value of the ratio \(\eta\) in (12) for the Jacobian evaluated at the converged iteration. When all the columns have been analyzed, the index of the maximum value of \(\eta\) identifies the best column configuration, so it is removed from the set \(\mathcal{S}\) (Line 22), and the iteration is restarted. The computation ends when the sequence of ratios stops increasing, or when just six images are left in the dataset. Algorithm 2 also contains a \"fast\" version of the method. To reduce the complexity, the SVD factorization is not computed at each step of the for loop, but just before the loop starts (Lines 6-8). Then, the matrix \(L\) is constructed by selecting the relevant columns from the unupdated factor \(V\) (Line 12). This version of the method produces slightly different results. Its performance will be discussed in the numerical experiments. We remark that the computational cost of Algorithms 1 and 2 does not usually have an impact on real-time processing. Indeed, in most applications, a dataset is analyzed only once, right after acquisition, and a reduced dataset is then stored for further processing.
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
## 2 A Review of Traditional Sensor Placement Techniques for Modal Testing  
Several methodologies have been developed to assist engineers in determining appropriate sensor placement for modal testing and structural health monitoring based on a finite element model. Some of the current existing methodologies include the Effective Independence Method (EIM), the Mass-Weighted Effective Independence Method (MEIM), and the Residual Kinetic Energy approach (RKE) [16; 19]. These existing methods are based on the modal analysis characteristics of the undamped structural dynamic system, which are the solutions of the following real symmetric eigenvalue problem:  
\[\mathbf{K\Phi}-\mathbf{M\Phi}\lambda=\mathbf{0} \tag{1}\]  
where \(\mathbf{K}\) is the stiffness matrix of the structural system, \(\mathbf{M}\) is the mass matrix, \(\mathbf{\Phi}\) is the matrix containing the eigenvectors (modes) of the system, and \(\lambda\) is the eigenvalue of the system; each \(n\)-th eigenvalue \(\lambda_{n}\) corresponds to the \(n\)-th natural frequency \(\omega_{n}\) through \(\lambda_{n}=\omega_{n}^{2}\). Bold notation indicates a matrix or vector quantity.  
When modes are normalized to unit modal mass, the orthogonality parameter \(\mathbf{OR}\) is defined as follows:  
\[\mathbf{OR}=\mathbf{\Phi}^{T}\mathbf{M\Phi}=\mathbf{I} \tag{2}\]  
The kinetic and strain energy distributions for each \(n\)-th mode (\(\mathbf{KE}_{n}\) and \(\mathbf{SE}_{n}\), respectively) are the term-by-term products (operator \(\otimes\) represents element-wise matrix multiplication):  
\[\mathbf{KE}_{n}=\{\mathbf{M\Phi}_{n}\}\otimes\mathbf{\Phi}_{n} \tag{3}\]  
\[\mathbf{SE}_{n}=\{\mathbf{K\Phi}_{n}\}\otimes\mathbf{\Phi}_{n}/\lambda_{n} \tag{4}\]  
The sum of the kinetic and strain energy distributions for each \(n\)-th mode is always equal to 1 when the modes are mass normalized, such that  
\[\mathbf{KE}_{\mathrm{TOT}\ n}=\sum_{i=1}^{\mathsf{DOF}}\mathbf{KE}_{i,n}=\mathbf{1} \tag{5}\]  
\[\mathbf{SE}_{\mathrm{TOT}\ n}=\sum_{i=1}^{\mathsf{DOF}}\mathbf{SE}_{i,n}=\mathbf{1} \tag{6}\]  
DOF stands for \"degrees of freedom\".
## 4 Conclusions
Sequence representing the PAS domain from the photoactivated yellow protein (PYP) from _Halorhodospira halophila_ was used as template for the sequence alignment analysis. Amino acid residues in solid box are representing the PAS core motif responsible for the generation and propagation of the signal to the adjoining effector domain, Figure S4: Multiple sequence alignment of the BLUF coupled vitamin B12 binding domain. Sequence representing the B12 binding domain from the CarH and AerR proteins was used as template for the sequence alignment analysis. The conserved amino acids (Trp 131, Val138, Glu141 and His142) essential for forming the binding pocket for the substrate, i.e., AdoB12, is not found in the aligned portion of the BLUF coupled vitamin B12 binding domain, Figure S5: Multiple sequence alignment of the BLUF coupled DNA pol III \(\gamma\) III domain. The sequence of the well characterized truncated (1-373 amino acids) DNA polymerase III subunit gamma/tau (WP_113440333.1) from _E. coli_ was used as template for the sequence alignment analysis. Amino acid residues in solid box represent the important residues crucial for the enzyme activity, Figure S6: Multiple sequence alignment of the BLUF coupled p450 and the well characterized CYP protein from _Bacillus subtilis_ (used as template). Amino acid residues in solid box representing the conserved (Arg) and altered (Pro to Ser) amino acid residues essential for the substrate binding, Figure S7: Protein-Protein interaction network depicting interacting partners of the selected effector domain (EAL, CHD, PAS, B12, DNA POL III \(\gamma\) III and p450) of the BLUF modular proteins. Protein highlighted in yellow is the query protein. Protein-protein interaction analysis was performed by using String version 11 ([[https://string-db.org/](https://string-db.org/)]([https://string-db.org/](https://string-db.org/))). Details of the query proteins, domains, along with the annotations are given in Table S1, Table S1: Output showing the details of query proteins, domains, interacting proteins and annotated functions. **Author Contributions:** [PERSON] had analysed all the sequences in details and wrote the manuscript. [PERSON] had done preliminary analysis of the BLUF coupled proteins. [PERSON] had conceptualized protein-protein interaction of the BLUF coupled domains and wrote manuscript. [PERSON] had done detailed structure-function analysis of the BLUF and its modular domains and wrote relevant part of the paper. [PERSON] had conceptualized, outlined and wrote the manuscript. **Funding:** M.S.K. research associateship was supported from SR/NM/NB-1087/2017(G)-JNU. S.K. was supported by the SR/NM/NB-1087/2017(G)-JNU. S.K.S. was supported by the research endowment fund from MUJ. **Conflicts of Interest:** The authors have no conflict of interest.
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 2 Datasets
### ECS Description  
The ECS is an important aircraft system that supplies conditioned air for cabin press-surisation and equipment cooling [15]. For the Boeing 737-800, the essential subsystem within the ECS that conditions the engine's bleed air is called the Passenger Air Conditioner (PACK) [18], a schematic of which is shown in Figure 1. Hot bleed air is drawn from the engine compressor at a flow rate commanded by the PACK valve (PV). A proportion of it goes through the temperature control valve (TCV), which mixes with the rest of the bleed air that has been cooled. Ram air serves as the heat sink for the cooling process at the two heat exchangers: the primary heat exchanger (PHX) and the secondary heat exchanger (SHX) [18]. The compressor in the air cycle machine (ACM) raises the pressure and temperature of the air after it passes through the PHX to improve the effectiveness of SHX [15]. A high-pressure water separator (HPWS) regulates the humidity before the air reaches the PACK outlet [18].  
Data have been collected for the B737-800 PACK from a validated simulation platform, SESAC (Simscape ECS Simulation under All Conditions) [15]. The simulation of PACK was conducted at four different operating conditions, including three cruising conditions and one ground-running condition, all based on real flight data. The temperature profile was taken for each case and was determined to be adequate for fault classification. Each temperature profile is first normalised by the corresponding target temperature, and then the deviation from its respective healthy baseline case is taken to produce the input data as a residual. A plot of representative cases for the ECS is shown in Figure 2, with parameters aligned with the locations shown in Figure 1. Five single-fault mode cases and one healthy  
Figure 1: Schematic of B737-800 PACK [18].  
case are shown in Figure 2. It is clear that each different fault mode exhibits a distinct fault pattern. More details regarding the ECS dataset can be found in [18; 19].
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 1 Introduction
### The Structure of This Paper  
Applications of PhC cavities include PhC lasers, which are promising sources for optical interconnects [26], since they feature small footprint, low threshold current and reduced energy cost. As a result of the enhanced light-matter interaction, devices based on optical nonlinearities are also more energy-efficient [27; 28]. Sub-femtojoule PhC switches have been demonstrated [29], as have PhC memories with ultralow power consumption in the nanowatt range [30] and large-scale integration [31]. Other applications include sensors with improved detection sensitivity [32], on-chip photodetectors with high quantum efficiency [33] and quantum cavity electrodynamics [34; 35]. The high Q-factor of PhC cavities also enables adiabatic frequency conversion [36; 37]. This phenomenon suggests a way to store and release photons on demand, with arbitrary timing. For thorough reviews of the many possible applications of PhC cavities, we refer to [22; 28]. Specifically, excellent reviews of PhC lasers can be found in [38; 39].  
This paper is structured as follows. In Section 2, we give an overview of PhC cavities. In Section 3, we discuss threshold current and energy cost of microcavity lasers, such as PhC lasers. In Section 4, we cover the modal properties of passive, line-defect PhC cavities. In particular, we model a PhC cavity as an effective Fabry-Perot (FP) resonator, whose travel=ling modes are the _Bloch_ modes of the waveguide on which the cavity is based. By this approach, we derive compact and transparent expressions for the resonance condition and field distribution. Furthermore, we discuss the impact of slow-light on threshold gain and out-coupling efficiency of line-defect PhC lasers. In Section 5, we review a new kind of PhC laser, known as the Fano laser [40], with a special focus on the tuning characteristics. In Section 6, we finally discuss cavities for extreme dielectric confinement, which promise even superior light-matter interaction than conventional PhC cavities.
### Related Works, Research Gaps, and Questions  
#### 1.4.1 Related Works  
Recent research on RL in portfolio optimization has employed a variety of data sources. The majority of studies use OHLC (Open, High, Low, Close) price data, augmented by technical indicators such as moving averages and volume indicators [21]. Certain studies incorporate macroeconomic factors and volatility measures to capture broader market sentiment [13]. Moreover, feature extractors like multi-layer perceptrons (MLPs), Convolutional Neural Networks (CNNs) [22], and LSTM networks have been used to detect patterns in these time-series data, enabling RL agents to make informed decisions about market conditions [5]. The choice of feature extractor plays a critical role in shaping the agent's capacity to detect trends and forecast market behavior [13]. Studies have explored a wide range of investment instruments, including equity indices, cryptocurrencies, and bonds, with many focusing on highly liquid assets to guarantee reliable historical data for training RL agents [13; 23].  
RL agents such as Deep Q-Networks (DQNs) [24], Deep Deterministic Policy Gradient (DDPG) [25], Proximal Policy Optimization (PPO) [26], and Soft Actor-Critic (SAC) [27] have been applied to portfolio optimization [5; 13; 23; 28].  
These agents vary in their approach to handling decision-making under uncertainty, with some, such as DDPG, being particularly useful for continuous action spaces like portfolio weight adjustments. Rebalancing strategies have also been studied, ranging from continuous rebalancing to periodic intervals [29]. This plays a crucial role in determining the costs associated with transaction fees and slippage, factors that are critical in real-world implementations.  
The reward function typically used reflects cumulative returns or risk-adjusted measures like the Sharpe ratio, but other metrics, such as maximum drawdown, have also been considered [30].
# Four-Channel Polarimetric-Spectral Intensity Modulation Imager
## 5 Discussion  
Due to the complexity of FCPS system calibration, it is necessary to carry out four separate calibration groups. In order to improve the calibration efficiency, the calibration method of the system will be improved. At the same time, in the aspect of data demodulation, a wavelet change and optimization algorithm will be considered to realize spectral data demodulation.