esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis and Parameter Optimization of the Optimal Fixed-Point Quantum Search
## 4 Optimization of Parameters of the OFPQS Algorithm  
In practical applications of the OFPQS algorithm, if set \(\delta=0\), then, from Equation (12) or Equation (13) it can be found that the quadratic speedup over classical algorithms will be lost; while, if set \(\delta>0\), the output of the OFPQS algorithm is not necessarily a target item. Inspired by [4], which achieves about \(12\%\) reduction of the expected queries through stopping the Grover algorithm short of the optimal number of iterations and restarting again in case of failure, to find a target item as soon as possible, a natural strategy is to set the lower bound of success probability \(1-\delta^{2}<100\%\) and repeat the OFPQS algorithm until it succeeds. Under this strategy, in order to find the optimal parameter \(\delta\), we shall estimate the expected number of Oracle queries of the OFPQS algorithm before a target item is found.  
First, a single execution of the OFPQS algorithm requires at least \(2l_{opt}\) queries, since \(l_{opt}\) iterations are required in the sequence \(S_{L}\) of Equation (4). Second, judging whether the algorithm is successful from the measurement result also takes one query. Then, define \(L_{opt}=2l_{opt}+1\), we can get the expected number of Oracle queries as  
\[L_{E} = \sum_{j=1}^{\infty}p_{j}L_{opt}, \tag{21}\]  
where  
\[p_{j}=\left[1-P_{L_{opt}}\left(\lambda\right)\right]^{j-1} \tag{22}\]  
Note that, with the above optimal parameters, we can obtain the corresponding upper bound of the expected number of queries, i.e.,  
\[L_{E,opt}^{Up}\left(\lambda_{0}\right)\equiv L_{E}^{Up}\left(\lambda_{0},l_{opt },\delta_{opt}\right)\,. \tag{29}\]
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 2 Related Works
### Zero-Shot Point Cloud Semantic Segmentation  
Although there have been significant advances in zero-shot learning for images [30; 31], research on zero-shot segmentation for 3D point clouds is still underexplored. The pi  
Figure 1: We propose a visual-semantic fusion branch that effectively aligns and fuses the features of the two modalities to obtain auxiliary predictions that are not biased toward seen classes. Additionally, we introduce an adaptive bias calibration module, which dynamically combines the predictions from the main and auxiliary branches.  
oneering work in this area was carried out by [PERSON] et al. [32], who adapted the two-dimensional (2D) Zero-Shot Learning (ZSL) framework to 3D point cloud data by aligning the PointNet features and semantic embeddings in the same space. Subsequently, they further expanded 3D ZSL through several studies, including introducing transductive learning strategies [33] and building geometric primitive transfer models [34]. However, these methods assume the availability of unlabeled samples from unseen categories, which greatly limits their applicability in real-world situations.  
To eliminate reliance on samples from unseen categories during training, [PERSON] et al. [18] proposed a generative framework that utilizes a generator to synthesize pseudo-features based on the semantic information of unseen classes, achieving breakthrough progress in 3D ZSL and GZSL. Building on this, subsequent work by [PERSON] [19] improved feature diversity by enhancing the multi-dimensional alignment of the visual-semantic space. Since these methods rely on data from seen classes to learn models for recognizing both seen and unseen data, they tend to exhibit bias toward seen classes.  
By contrast, we propose a dual-branch network architecture that includes a visual-semantic fusion branch. This branch takes advantage of the higher semantic relevance of synthetic features for unseen classes compared to seen classes. By doing so, it allows us to obtain auxiliary predictions that are unbiased toward seen classes through visual-semantic alignment. This adaptive approach helps guide the calibration for semantic segmentation.
## Materials and Methods
### Chromatographic Analyses
#### 2.3.2 Gas Chromatography-Mass Spectrometry (GC-MS) Analysis  
Compounds' identification was performed on gas chromatograph coupled with triple quadrupole mass spectrometer (7890A/7000B, Agilent Technologies, Santa Clara, CA) working in a single quadrupole mode. The mass spectrometer was operated in electron ionization (EI) mode at 70 eV, in a scan range of _m/z_ 33-333. Samples were injected in a splitless mode onto SPB-5 (30 m \(\times\) 0.20 mm \(\times\) 0.50 \(\upmu\)m, Supelco Bellefonte, PA, USA) column. The oven temperature program during analysis was as follows: 40 \({}^{\circ}\)C (1 min), then 6 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C and 25 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C (3 min). Temperatures of the injector and mass spectrometer ion source were 250 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively. Helium was used as a carrier at a flow of 0.8 mL/min. Data acquisition and analyses were performed using the Agilent Technologies MassHunter Workstation (B 07.00, Santa Clara, CA, USA) software. Tentative identification was accomplished using the National Institute of Standards and Technology (NIST, Gaithersburgh, MD, USA) library (version 2.0) of mass spectra.  
3.3 Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry (GC\(\times\)GC-ToFMS) Analysis  
Comprehensive two-dimensional gas chromatography analysis was performed on a GC\(\times\)GC-ToF-MS system (Pegasus 4D, LECO, St. Joseph, MI, USA). The GC was equipped with a DB-5 primary column (25 m \(\times\) 0.2 mm \(\times\) 0.33 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA) and Supelcowax 10 (1.2 m \(\times\) 0.1 mm \(\times\) 0.1 \(\upmu\)m, Supelco, Bellefonte, PA, USA) as a secondary column. The injector temperature was set at 250 \({}^{\circ}\)C and injection was performed in splitless mode. The gas flow was set at 0.8 mL/min. The entry oven temperature was programed as follows: 40 \({}^{\circ}\)C (1 min)60 \({}^{\circ}\)C/1 min to 200 \({}^{\circ}\)C (0 min) 25 \({}^{\circ}\)C/1 min to 235 \({}^{\circ}\)C (5 min). Secondary oven: 65 \({}^{\circ}\)C (1 min) 6 \({}^{\circ}\)C/1 min to 225 \({}^{\circ}\)C (0 min) 25 \({}^{\circ}\)C/1 min to 260 \({}^{\circ}\)C (5 min). Transfer line temperature was 260 \({}^{\circ}\)C. The modulation time was 4 s. The time-of-flight mass spectrometer was operated at a mass range of _m_/z 33-383 and detector voltage \(-\)1700 \(V\) at 150 spectra/s. The data were collected and processed using LECO ChromaTOF v.4.40 Total analysis time was 34.07 min. For SAFE and SDE samples, 3 min of solvent delay was applied.
## 3 Problem Formulation
### UCAV Path Planning Model  
According to [44], a mathematical modeling of this problem is carried out as follows:  
1. A segment ST that directly connects the starting and terminal point is drawn,
2. The segment is divided into \((\mathrm{D}+1)\) equal parts by \(\mathrm{D}\) perpendicular lines,
3. All \(\mathrm{D}\) lines are taken as new axes, and a series of points are assigned and connected on them to form a complete path.  
The whole path is divided into \((\mathrm{D}+1)\) steps, represented by a vector \(S_{i}=Y_{i}=(x_{i1},x_{i2},\ldots,x_{iD})\), where \(x_{ij}\) represents the \(j\)th dimension of the \(i\)th solution vector and indicates the discrete position of each step in the vertical axis. In reality, the components of the \(S_{i}\) vector represent the ordinates of the \(\mathrm{D}\) path points relative to the \(ST\) segment, and their abscissae are \(X_{l}=(x_{1},x_{2},\ldots,x_{D})=((\left\|ST\right\|/D),(\left\|ST\right\|/D) \times 2,\ldots,\left\|ST\right\|)\), \(l=1,\ldots D\), where \(\left\|ST\right\|\) represents the length of the segment ST. For example, if \((0,0)\) is the starting point and \((\left\|ST\right\|,0)\) is the endpoint, then the distance between them is \(\left\|ST\right\|\). The vector \(S_{i}\) can be used to create a path with \(\mathrm{D}\) nodes, where the coordinates of each node are \(\mathrm{P}_{1}((\left\|ST\right\|/D),x_{i1})\), \(\mathrm{P}_{2}((\left\|ST\right\|/D)\times 2,x_{i2})\), and so on. Linking all the nodes results in the complete path. Restrictions can be imposed on each solution path to keep it within the battle area, according to the size of the battlefield (refer to Figure 2). It is important to note that the coordinates \((x^{*},y^{*})\) of the generated nodes \(\mathrm{P}_{1},\ldots\mathrm{P}_{D}\) in the plane \(XX^{*}Y^{*}\) are related to their coordinates \((x,y)\) in the plane \(OXY\) through a simple transformation. This transformation can be expressed as \(\begin{bmatrix}x^{*}\\ y^{*}\end{bmatrix}=\begin{bmatrix}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{bmatrix}\times\begin{bmatrix}x\\ y\end{bmatrix}\).  
Figure 2: Thorough graphical depiction of the path planning modeling procedure (e.g., here \(D=7\) and \(n_{Hir}=2\)).
## 2 Materials and Methods  
### Research Sample Collection and Preparation  
The developed spectral band selection algorithm was extensively tested on three hyperspectral datasets. The datasets were made to develop aflatoxin b1 quantification and classification in almonds for industrial in-line application. All datasets contain both aflatoxin B1 contamination and contamination-free almond's hyperspectral response. The contamination-free samples were considered zero aflatoxin B1 concentration. Almonds were artificially contaminated in four different aflatoxin B1 contamination levels in each dataset. Since the aflatoxin B1 contamination rate is low, the presence of highly contaminated almonds significantly impacts the test results [32]. Therefore, in this research, almonds were contaminated in 250, 500, 750, and 1000 ppb. In dataset 1, almonds of similar thickness and shape were used to obtain a consistent spectral response. The almond's thick ness was measured using a vernier caliper. Then all contaminated and contamination-free almonds were used to acquire hyperspectral images using an SWIR camera. The camera has an operating wavelength range between 900 and 1700 nm, divided into 224 spectral bands. The length of the acquired image was set to 200 pixels, while the width was set to 640 pixels. Consequently, the dimension of the acquired hyperspectral image cube was \(640\times 200\times 224\). To reduce the data volume, a mean value was calculated for each spectral image and converted each hyperspectral image data cube into 224 spectral data points. The artificial aflatoxin B1-contaminated concentration was used as the ground truth. In this way, dataset 1 was created using 471 almond samples. Similarly, dataset 2 was made of 465 contaminated and non-contaminated almonds. In dataset 2, almonds were chosen by visual inspection with similar shape, thickness, and texture to create data diversity.  
However, almonds naturally vary in shape, thickness, and texture. Therefore, dataset 3 was created, which consisted of almonds with different thicknesses, shapes, and sizes. These three datasets were utilized to assess the classification performance of the developed spectral band selection algorithm for detecting aflatoxin B1 contamination. The feature spectra were selected for both raw and standard normal variate (SNV) processed data. The mean spectral response is called raw data, which could be affected by the light scattering phenomenon. In the SNV, each spectrum is normalized by subtracting its mean and dividing by its standard deviation. This process effectively reduces the spectral scattering effect caused by variations in sample size or surface properties, while preserving the original spectral features and response. By minimizing unwanted variations, SNV enhances the signal quality without altering the essential information in the data. In comparison to the developed spectral band selection algorithm, all three datasets were tested on existing classical feature selection algorithms. The developed hybrid spectral band selection algorithm based on MLP network weights and spectral refinement (MLPW-SR) is described in the subsequent subsection.
## 2 Feasibility on ODEON-Based Auditory Scene Simulation in Closed Spaces  
ODEON [18,19] is an architectural acoustic simulation software based on geometric acoustics developed by the Technical University of Denmark. The software uses the hybrid method [20] combined with the virtual sound source method [21,22] and the sound ray tracking method [23,24,25] as well as the second source method [26], which can calculate the internal sound field of various types of closed spaces and simulate the noise control measures to be taken and their effects. Many scholars [27,28,29,30] have studied and confirmed the credibility of the software in simulating indoor sound field. In this paper, we will use hearing experiments to study the feasibility of ODEON 8.0 in simulating simplified closed-field auditory scenes.  
First, the binaural signals in two real rooms are collected by a spherical sound source and a dummy head (see Table 1 for details). Then, ODEON 8.0 is used to establish the indoor sound field models of the two rooms in order to simulate the corresponding binaural room impulse responses. After that, simulated binaural signals can be achieved by means of virtual auditory technology, referring the process of simulating the transmission of sound waves to both ears meanwhile obtaining the information of time and space. Finally, after the hearing experiment is designed and completed, the degree of consistency in the auditory perception is compared between the actual binaural signals recorded in the real rooms and the simulated binaural signal by use of ODEON 8.0. The feasibility of the software in spatial sound field auditory perception research is tested.
## 1 Introduction  
Effective screening for early Parkinson's disease (PD) is essential, as early diagnosis and treatment can postpone the progression of symptoms and complications caused by the disease [1]. Early detection can also economically reduce the burden of elderly patients [2]. Despite the importance of early diagnosis, PD is still under-recognized in its early stages [3], perhaps partially because existing diagnostic criteria are mainly based on subjective symptoms [4]. Additionally, in the early stages of PD, nonmotor symptoms (NMS), such as olfactory problems, depression, and rapid eye movement sleep disorder, are more prominent than motor symptoms, further complicating the early diagnosis of this disease [3; 5].  
Recent studies have demonstrated that iron accumulation in the deep grey matter (e.g., the substantia nigra (SN), globus pallidus (GP), dentate nucleus (DN), red nucleus (RN), caudate nucleus (CN), and putamen (PUT)) may serve as a potential biomarker of PD [6; 7; 8]. Quantitative susceptibly mapping (QSM) on magnetic resonance imaging (MRI) may be useful in detecting this iron accumulation [9]. QSM uses the phase difference induced by differences in tissue susceptibility to estimate magnetic susceptibility. Because iron is the primary source of these susceptibility changes, QSM has been used in patients PD patients to measure iron distribution [8; 10]. In this research, patients with PD demonstrated significantly increased QSM values in the basal ganglia areas than healthy volunteers [10]. Research has also shown that the amount and extent of iron deposition in the brain vary depending on the severity of disease [6]. Thus, QSM may prove valuable in the diagnosis of PD.  
Recently, researchers have begun to assess the use of machine learning techniques for the diagnosis of PD. A support vector machine (SVM), one of the most potent classification algorithms based on machine learning techniques, has been used in such studies [11; 12], demonstrating accuracy rates of 86% [12] and 97% [11] for the detection of moderately advanced PD in patients with atypical forms of parkinsonism using diffuse tensor imaging and susceptibility-weighted imaging each. However, these studies focused on patients with advanced PD; no previous studies have assessed the use of an SVM model to detect early PD.  
Therefore, the purpose of this study was to assess the potential of machine learning-based screening for early PD. We constructed an SVM model using QSM values to distinguish between patients with early PD and healthy volunteers and compared them with those from logistic regression (LR) models. As non-motor symptoms and motor symptoms are related to the pathologic severity of PD [13], we constructed separate SVM and LR models to distinguish between patients with low Non-Motor Symptoms Scale (NMSS) scores and those with high NMSS scores.
## 4 Experiments
### Experimental Settings
#### 4.1.2 Baselines  
Five baseline algorithms are the most related or latest algorithms in the literature and they are described below.  
* **YuAlg:** A relation-flow algorithm presented [17] in 2023. This algorithm is the most advanced algorithm in the relation-flow approach for solving AWPs so far. It synergizes the \(S^{2}\) method for extracting explicit relations and a neural miner for extracting implicit relations. It develops a symbolic solver to reason the relation set.
* **ZhangAlg:** A Sequence-to-Sequence (Seq2 Seq) algorithm presented in [19] in 2020. It constructs a graph encoder with a tree decoder to generate an arithmetic expression for a problem text. It achieved the highest solving accuracy without the use of a pre-trained model.
* **JieAlg:** A Seq2 Seq algorithm presented in [28] in 2022. It uses BERT as the pre-trained encoder and proposes a deductive reasoner to construct target expression iteratively. It achieves SOTA performance on Math23k and MAWPS.
* **YasuAlg:** An LLM-based algorithm presented in [32] in 2023. It proposes an analogy-based prompting method, prompting the LLM to first recall relevant exemplars before solving the initial problem. This method significantly improves the performance of LLMs on mathematical reasoning tasks.
* **LiuAlg:** An LLM-based algorithm presented in [14] in 2023. It introduces an XOT method, which integrates a problem-solving framework by prompting LLM with diverse reasoning thoughts. This method achieves SOTA performance on the GSM8K dataset under the same LLM settings.  
The implementation details of the algorithm described in this paper are as follows. **YuAlg,** a relation-flow algorithm, employs the same model pool and classifiers as the original work to solve the dataset. **ZhangAlg** and **JieAlg**, both Seq2 Seq-based algorithms, utilize the code and training set provided by the original work to train solvers, which are then used to solve the datasets in our experiments (excluding the AWPs involved in training). Due to the lack of expression annotation in the original GSM8K, our experiments utilized the expressions converted by LILA [39] to enable training on GSM8K. **YaAlg** and **LiuAlg,** as LLM-based algorithms, both use gpt-3.5-turbo as the base generation model and apply different prompting methods to solve the datasets. In order to conduct a fair comparison with baselines, the algorithm proposed in this paper (hereinafter referred to as **PROP**) also uses gpt-3.5-turbo as the base generation model and employs the same classifier as **YuAlg** for classifying scene types. In addition, our experiments also tested the solving accuracy of **PROP** using different LLMs.
###### Abstract  
In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.  
fuzzy comprehensive evaluation; fuzzy Markov; availability analysis; reliability index; uncertainty; wellhead connector
# A Minimal GBT Model for Distortional-Twist Elastic Analysis of Box-Girder Bridges
## 5 Numerical Results  
A case study is considered, consisting of a reinforced concrete, rectangular box-girder bridge, taken from the literature [3]. The box-girder has the following geometric characteristics: length \(\ell=30\,\mathrm{m}\), width \(b=6\,\mathrm{m}\), height \(h=1.5\,\mathrm{m}\), flange thickness \(t_{1}=0.25\,\mathrm{m}\), web thickness \(t_{2}=0.35\,\mathrm{m}\); elastic moduli \(E=35654\times 10^{3}\,\mathrm{kN}/\mathrm{m}^{2}\),and \(G=E/2\). The GBT constants consequently assume the values in Table 1. Moreover, the joint distortional rotation is \(\alpha=-0.83\) and the warping ratio \(\beta=0.70\).  
The girder is assumed to be simply supported at the end, where it is free to warp. It is loaded at the two webs by equal and opposite forces \(P=\pm 750\,\mathrm{kN}\), uniformly distributed on a length \(\Delta=\frac{\ell}{4}\), as \(p_{0}:=\pm\frac{P}{\Delta}=100\,\mathrm{kN}/\mathrm{m}\), and centered at the abscissa: \(\xi=\frac{\ell}{4}\) (Figure 12). The relevant modal loads \(c_{t}(z),c_{d}(z)\) are evaluated by Equation (9), as the virtual work spent at the abscissa \(z\) by the external forces in the displacements caused by \(\theta=1\) and \(\varphi=1\), respectively. Since, at the application points of the loads (i.e., at the corners \(C,D\) of the box, see Figure 1), it is \(V=\pm\frac{P}{2}\) in both modes, it follows that  
\[c_{a}(x)=\begin{cases}p_{0}b&z\in\left(\xi-\frac{\Delta}{2},\xi+\frac{\Delta}{ 2}\right)\\ 0&z\
otin\left(\xi-\frac{\Delta}{2},\xi+\frac{\Delta}{2}\right)\end{cases} \qquad\alpha=t,d \tag{36}\]  
The elastic problem was tackled in different ways: (a) by solving the GBT Equation (22) (with no terms neglected) via Fourier series; (b) by exactly integrating the differential Equations (32) and (33) after having neglected the coupling terms; and (c) via a FEM analysis. The latter was carried out by using a commercial software by implementing a model made of 40 \(\times\) 60 = 2400 shell elements (along the directrix and the axis, respectively) for an overall number of 2440 degrees of freedom. The results were then compared.