esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Piot Online Back-Translation  
Prior work has scaled back-translation to massively many-to-many NMT by performing online back-translation paired with randomly sampled intermediate languages during training [21], called random online back-translation (ROBT). However, with a poor zero-shot initialization (translations are produced on-the-fly by the MNMT model being trained, which initially performs poorly between unseen language pairs), ROBT converges to a sub-optimal state with only a few thousand steps.  
Most sentence pairs for MNMT models are commonly English-centric (i.e., English either as the source or target language) and non-English directions can be pivoted through English. Therefore, we turn to back-translate English sentences (either the source or target side) into a randomly sampled intermediate language, named pivot online back-translation (POBT), which is detailed in Algorithm 1. For each training sample \((\mathbf{x},\mathbf{y},L_{i},L_{j})\), we uniformly sample an intermediate language \(L_{k}\) where \(L_{k}\
eq L_{i}\) and \(L_{k}\
eq L_{j}\), and back-translate \(\mathbf{x}\) (if \(L_{i}=Eng\)) or \(\mathbf{y}\) (if \(L_{j}=Eng\)) into \(L_{k}\) to obtain a new synthetic sample. Both real and synthetic sentence pairs are merged for training. With a better initial performance of English-centric language pairs adequately seen during training, POBT can generate high-quality synthetic sentence pairs and deliver larger benefits for non-English directions than ROBT. Following [PERSON] et al. [21], we used batch-based greedy decoding to reduce the computational cost.  
```
Input : Training data, \(D\);  Initialized MNMT model, \(M\);  Language set, \(L\); Output : Zero-shot improved MNMT model, \(M\) whilenot convergeddo
2\(B\leftarrow\) sample batch from \(D\);
3 for\((\mathbf{x},\mathbf{y},L_{i},L_{j})\gets B\)do
4\(L_{k}\sim\) Uniform(\(L\)) (\(L_{k}\
eq L_{i}\) and \(L_{k}\
eq L_{i}\));
5 if\(L_{i}=Eng\)then
6//back-translate English sentence in source side into Language \(L_{k}\);
7\(\mathbf{x}_{k}\gets M([L_{i},\mathbf{x},L_{k}])\);
8\(B\gets B\cup(\mathbf{x}_{k},\mathbf{y},L_{k},L_{j})\);
9
10 end if
11 if\(L_{j}=Eng\)then
12\(\mathbf{y}\leftarrow\) \(\mathbf{x}_{k}\gets M([L_{j},\mathbf{y},L_{k}])\);
13\(B\gets B\cup(\mathbf{x}_{k},\mathbf{x},L_{k},L_{i})\);
14
15 end if
16
17 end for
18
19 Optimize \(M\) using \(B\)
20 end for return \(M\)
```  
**Algorithm 1**Algorithm for Pivot Online Back-Translation
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 2 State Space Equations and the Henon-RK45 Method  
### State Space Equations of an Aeroelastic System with Freeplay  
The equations of the motion of an aeroelastic system with freeplay can generally be expressed in the form of a set of piecewise linear state space equations (e.g., Equation (5) in [3], Equation (8) in [4], and Equation (1) in [13]):  
\[\dot{\mathbf{x}}=\mathbf{A}_{0}\mathbf{x}+\mathbf{b}f_{\text{FP}}(x_{k}) \tag{1}\]  
where \(\mathbf{x}\) is \(n_{x}\) system states; \(\mathbf{A}_{0}\) is an \(n_{x}\times n_{x}\) constant matrix; \(\mathbf{b}\) is an \(n_{x}\times 1\) constant vector, and \(f_{\text{FP}}\) is the freeplay function, reflecting the nonlinear connection between a displacement and a structurally restoring force. Assume that only one freeplay involved in the present work takes the \(k\)-th state \(x_{k}\) as its input; then, the freeplay nonlinearity can be expressed as:  
\[f_{\text{FP}}(x_{k})=\left\{\begin{array}{ll}K_{\text{lin}}(x_{k}-\delta)&x _{k}>\delta\\ 0&|x_{k}|\leq\delta\\ K_{\text{lin}}(x_{k}+\delta)&x_{k}<-\delta\end{array}\right. \tag{2}\]  
where \(K_{\text{lin}}\) is the underlying linear stiffness of the freeplay and \(2\delta\) is the amount of freeplay. In the present paper, the \(k\)-th degree of freedom (DoF) of the system is called the \"freeplay DoF\" and \(x_{k}\) is called the \"freeplay state\".  
Equation (2) defines two non-smooth boundaries, which are known as the two \"freeplay boundaries\": \(\Sigma_{1}:x_{k}=-\delta\) and \(\Sigma_{2}:x_{k}=\delta\). These two boundaries separate the state space into three subdomains: (1) \(S_{0}\), where \(|x_{k}|\leq\delta\); (2) \(S_{1}\), where \(x_{k}<-\delta\); and (3) \(S_{2}\), where \(x_{k}>\delta\). Within each subdomain, the system operates as a linear system, so the system is actually governed by two linear subsystems: (1) the underlying linear system (ULS), when the state \(\mathbf{x}\) is in \(S_{0}\), and (2) the overlying linear system (OLS), when \(\mathbf{x}\) belongs to \(S_{1}\cup S_{2}\). Figure 1 illustrates freeplay and the three subdomains \(S_{1,2,3}\), separated by two freeplay boundaries \(\Sigma_{1,2}\) in state space.
# The Progress on Lung Computed Tomography Imaging Signs: A Review
## Preliminary  
### Lung CT Public Database  
The lung medical image database is an essential basis for training and evaluating the detection, classification, segmentation, and retrieval models and algorithms of lung lesions. The existing publicly accessible lung imaging databases are categorized as lesion-oriented, lung disease type-oriented, and sign-oriented, with representative datasets provided in Table 1.  
In 2001, the organization of the Lung Image Database Consortium (LIDC) [6] began to build a web-accessible research resource for the development, training, and assessment of CAD approaches for lung lesions. The database was integrated with the Image Database Resource Initiative (IDRI) in 2004, which is a lesion-oriented database. Other lesion-oriented databases include LUNA16 (Lung Nodule Analysis 2016) [7], ANOGE09 (Automatic Nodule Detection) [8], and Lung TIME (Lung Test Images from Motol Environment) [9]. A database for interstitial lung diseases (ILDs) [10] has been developed, which comprises 13 categories such as hypersensitivity pneumonia, pulmonary tuberculosis, pulmonary fibrosis, and interstitial pneumonia. The LISS [11] database is the first public database on CT imaging signs of lung diseases, comprising nine common signs of lung diseases, including lobulation, spiculation, cavity, calcification, pleural indentation, and ground-glass opacity (as illustrated in Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **CTScan** & **Type** & **Number of Class** & **Object** & **Description of the Purpose of Dataset** \\ \hline LIDC-IDRI & 1018 & DICOM & 9 & Lesion & Lesion classification and detection. \\ LUNA16 & 888 & MHD & 9 & Lesion & Lesion detection algorithm development. \\ ANEODE09 & 55 & DICOM & 4 & Lesion & Evaluation of lesion detection algorithms. \\ ILDs & 108 & DICOM & 13 & Lung diseases & Classification of interstitial lung disease. \\ Lung TIME & 157 & DICOM & 4 & Lesion & Lesion classification and detection. \\ LISS & 271 & DICOM & 9 & Sign & CISs classification and detection. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description information.
## 3 Methods
### Network Analysis
#### 3.3.2 Link Analysis  
In this phase, a link-based network analysis methodology was proposed to analyze interactions between a specific node and other nodes. The relationships between the forward and backward transportation and logistics industries (excluding those with zero transaction volumes) were quantified using [PERSON]'s algorithm, the shortest-path algorithm based on links. This approach allows for analyzing not just node connections but also the significance of specific paths within the network, using these numerical values.  
We defined the distance as the inverse of the total inter-industry transaction volume in the network structure and used it as the weight. To make these weights compatible with [PERSON]'s algorithm, which determines the shortest path by minimizing the sum of edge weights, we transformed the transaction volumes into distance-like values by taking their inverse \(\left(d_{ij}=1/w_{ij}\right)\). This transformation allows edges with higher transaction volumes to be interpreted as shorter distances, thereby increasing their priority in shortest-path computation. The shortest path between two industries was calculated using networkx.dijkstra_path, which applies [PERSON]'s algorithm based on edge weights. In other words, distances were defined as the inverse of transaction volumes \(\left(d_{ij}=1/w_{ij}\right)\), and a priority queue was used to iteratively update the shortest known paths. This enabled efficient identification of economically significant routes within the network.  
\[d_{ij}=\frac{1}{w_{ij}},\ for\ w_{ij}>0 \tag{6}\]  
\(w_{ij}=Transaction\ Volume\ of\ nodes\ i\ and\ j\)  
\(d_{ij}:Distance\ between\ node\ i\ and\ j\)  
A priority queue that includes all vertices is created by setting a specific node as the starting vertex and initializing a distance array. Then, the priority of the queue is determined based on the initialized distances, and the shortest-distance vertex (u) is selected from the start vertex in the priority queue. The conditions for all vertices (v) neighboring the shortest-distance vertex (u) can be verified through Formula (7).  
\[[d[v]=min(d[v],d[u]+w(u,v))], \tag{7}\]  
\((w(u,v))=\) The edge weight between vertex (u) and vertex (v)  
A vertex (\(v\)) with an updated distance value is reinserted into the priority queue, ensuring the distance value is kept up to date. Finally, if all vertices have been visited or the priority queue is empty, the process is terminated, and the path, distance, etc., are derived. As mentioned, link-based analysis not only evaluates the connectivity between nodes but also provides the advantage of quantitatively analyzing the relative importance of a specific path within the network.
# Factors Affecting Implementation of Radiological Protection Aspects of Imaging in Radiotherapy
## 1 Introduction
In addition, there will be an increased risk of inducing second cancers in organs and tissues lying within the field being imaged [8; 9; 10; 11; 12]. A study of radiotherapy imaging doses in a US hospital reported average cumulative imaging doses to the brain, lungs and red bone marrow of 380, 188, and 491 mGy, respectively, for which the associated additional average lifetime attributable risks of cancer incidence per 100,000 persons were 78, 271 and 510 for brain cancer, lung cancer and leukaemia, respectively [12]. Therefore, the decrease in exposure that can be achieved through use of smaller high-dose treatment margins and reduction of alignment errors need to be balanced against the detriment from doses to surrounding tissues from more frequent imaging. A study of a group of Finnish hospitals showed that cumulative doses to organs surrounding the PTV can vary by factors of ten or more depending on the imaging technique and frequency, indicating that there is considerable scope for optimisation of radiological protection [11]. Making judgements on potential harm from imaging is difficult without information on the doses received, and this information is limited at the present time. Guidance has been produced about the use of IGRT [13], but this has been largely by high income countries and less information is available on its use in countries at all levels throughout the world and especially on doses delivered by radiologic imaging modalities in radiotherapy. Two projects are described in this paper; the purpose of the first was to gain information about the use of IGRT in countries at different stages of development with varying levels of funding. The type of information sought was how widely IGRT was used, and how much information and knowledge users have about doses from imaging delivered to patients. The second project was to look at methods for recording doses from cone beam CT (CBCT), the imaging method used more widely, and the feasibility of carrying out such measurements in countries with lower levels of funding.
# Aptamer-Based Biosensors for the Analytical Determination of Bisphenol A in Foodstuffs
## 4 Detection Techniques Applied in the Analysis of Real Food Samples
### Optical Atpasensors
The released DNA segment became the essential part of a series of double strand hybridization processes from which the DNAzyme was produced. The obtained DNAzyme cleaved the dual-labeled substrate DNA into two segments and induced the separation of the fluorophore and quencher, generating a fluorescence response whose intensity depended on the BPA level. Berberine has also been used as a fluorescence signal messenger [79]. In a buffered solution, berberine resulted in a weak fluorescence signal that increased with the addition of BPA aptamers and decreased again in the presence of BPA. Of all optical aptasensors investigated for the detection of BPA in food, these appear to be easy to use, and in some cases they show a very low LOD value (Table 2) [45]. Consequently, they seem to be particularly promising for applications in routine analyses.
# New Space Engineering Design: Characterization of Key Drivers
## 3 Results and Discussion
### General Discussion of New Space Design Drivers and Enablers
#### 3.1.1 Category A: Evolution of Traditional Design Drivers
These technologies also enhance safety and reduce mission costs by minimizing the need for human intervention while enabling system maintainability and ensuring interoperability between different system units to support collaborative operations. Maintainability refers to a system's ability to undergo interventions, such as fuel fueling, repair, or upgrading, which has become a typical application scenario in recent years. Interoperability, on the other hand, denotes the capacity of different system units or modules to exchange information effectively and operate together within a shared mission environment, an increasingly critical requirement in distributed and modular architecture. Adaptability is also pursued at the design level to accommodate the wide range of application scenarios that characterize New Space. In this context, system scalability is gaining relevance, as it reflects the ability of a system to satisfy different requirements with minimal redesign efforts [6]. Furthermore, in intensive and exploratory missions, durability emerges as a key driver, often achieved using advanced materials that contribute to extending mission lifetimes, including innovative smart materials, such as shape-memory alloys and under-development self-healing materials [37; 38].
# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 5 Applications of Reinforcement Learning in Additive Manufacturing
### Quality Control
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Applications** & **AM Processes** & **RL Methods** \\ \hline MLMB 1 deposition & WAAM & Model-based RL methods \\ control [173] & & \\ \hline Process optimization for & & \\ quality improvement [174] & LPBF & Model-based RL methods \\ \hline Melting defect & & \\ minimization [175] & LPBF & \begin{tabular}{c} PPO (model-free \\ and policy-based) \\ \end{tabular} \\ \hline Temperature uniformity & LDED & \begin{tabular}{c} PPO (model-free \\ and policy-based) \\ \end{tabular} \\ \hline Product defect & & \begin{tabular}{c} Q-learning (model-free \\ and value-based) \\ \end{tabular} \\ minimization [179] & LDED & \begin{tabular}{c} Q-learning (model-free \\ and value-based) \\ \end{tabular} \\ \hline Defect mitigation & & \\ optimization [181] & FFF & G-learning (model-free) \\ \hline Process parameter & & \
\begin{tabular}{c} Q-learning (model-free \\ and value-based) \\ \end{tabular} \\ optimization [182] & LPBF &
\begin{tabular}{c} Q-learning (model-free \\ and value-based) \\ \end{tabular} \\ \hline Process control & & \\ optimization [183] & WAAM & Value iteration and DDPG 2 \\ \hline \hline \end{tabular}  
* MLMB: multi-layer and multi-bead; 2 DDPG: deep deterministic policy gradient.  
\end{table} 
Table 9: Some studies on reinforcement learning for quality control in AM. printing while reducing the need for extensive training samples. Specifically, continual G-learning integrated both offline and online prior knowledge. Offline prior knowledge was obtained from literature or previously experimental datasets, while online knowledge was acquired during printing. Additionally, both the reward incurred in the AM process and the information cost were considered in the expected return, which was maximized as the objective. The authors designed an experimental platform to evaluate the performance of the proposed RL framework. The results demonstrated that this method significantly improved online defect mitigation in the AM process. [PERSON] et al. proposed a novel approach to optimize process parameters in metal AM processes [182]. The environmental state variables in their method included laser power, scan speed, and hatch spacing. They applied Q-learning to identify optimal parameter combinations and validated their approach through experiments on the LPBF of aluminum alloy. The results highlighted the robustness of the predicted optimal parameters, underscoring the potential of this approach for practical applications. Similarly, [PERSON] et al. utilized a policy-based RL approach, especially deep deterministic policy gradient (DDPG), to develop a control policy for AM processes [183]. This policy was successfully tested on a WAAM simulator, which closely approximated a realistic AM environment.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 5 Results
### Tuning the RX DBB Components
The maximum number of decoding iterations is 10. Figure 12: From left to right: incurred MPDU latency (**a**) and BER (**b**) when setting the largest allowed number of LDPC decoding iterations to 100. MCS colour codes are the same as in previous figures. The white region on the left subplot represents the expected latency region, governed by MCS and \(\frac{E_{b}}{N_{0}}\).
## 4 Experimental Results and Analysis
### System Environment  
We conducted all experiments using Python (v.3.9) on a setup equipped with a standard CPU. The essential libraries installed included opencv-python (v.4.10.0), matplotlib (v.3.8.4), numpy (v.1.23.5), and scipy (v.1.13.1), along with parallel processing libraries such as numba (v.0.60.0) and scikit-learn (v.1.5.1). Additionally, we utilized the SimpleITK (v.2.4.1) library for medical image processing and functools for advanced functional programming.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 3 Numerical Example
### Comparison of the Performance Between the Proposed Method and the Traditional Approach  
To evaluate the performance of the proposed method, we compare the following damage detection results with the traditional approach: damage detection without the effect of noise, damage detection with the effect of noise and damage detection in consideration of different quasi-static loading conditions before and after damage.
###### Abstract  
The shield tunneling method is widely used, especially in urban areas, since it is efficient for minimizing disturbances to surroundings. Although segmental lining is commonly used in this method, in both the research and practice of tunnel lining design, the interaction between the ground and lining in the tangential direction remains unclear; that is, the mobilizing shear stress due to load models and the degree of the bond in the tangential direction. Therefore, to clarify the effects and mechanism of the tangential ground-lining interaction on segmental lining behavior, a parameter study was carried out, taking tangential spring stiffness, load models, soil stiffness, and shallow and deep tunnels as parameters. The interaction conditions were based on the existing literature. It was found that (1) the tangential spring has small effects on lining behavior, (2) the load model significantly affects the sectional forces, (3) the initial tangential earth pressure and slip ground-lining boundary provide more safety from a design viewpoint, and (4) in the case of shallow tunnels in soft ground, tensile stress appears in the lining. Therefore, it is important to take the tangential ground-lining interaction conditions into consideration during tunnel lining analysis.  
shield tunnelling; segmental lining design; ground-lining interaction; beam-spring model; parameter study +
Footnote †: journal: Applied Sciences
## 2 Output Saturation Limits for Classical Stochastic Resonance Models
### Potential Function Model for Bistable Systems
When excited only by random noise \(\Gamma(t)\), the signaling particles reciprocally leap between the two potential wells at the Kramers escape rate \(r_{k}\), which is determined by the noise intensity and the noise probability distribution [14]:  
\[r_{k}=\frac{a}{\sqrt{2\pi}}\exp\left(-\frac{a^{2}}{bD}\right) \tag{6}\]  
When the noise and the periodic signal stimulate the bistable system at the same time, the motion state of the system's potential well and the signaling particle is shown in Figure 2.  
The bistable system is subjected to the joint action of the weak periodic signal and the random noise, and the periodic signal causes the periodic switching of the system's potential wells, which, together with the periodic jumping of the particles caused by the random noise, makes it possible for the particles to reciprocate between the two potential wells in the case of \(A<A_{C}\), and the output of the system (the motion state of the signaling particles) is switched between the two potential wells according to the signal frequency \(\omega_{0}\) (the periodicity of the particle's location switching). When the switching rate \(\omega_{0}\) matches the Kramers escape rate [15], i.e.,  
\[r_{k}=\frac{\omega_{0}}{\pi} \tag{7}\]  
the system's output induced by random noise will have the same frequency as the weak periodic signal. This means that the random noise and the weak periodic signal are output in the form of the same cycle in an orderly manner. This conversion of energy from the noise into the energy of the weak periodic signal augments the weak periodic signal and generates stochastic resonance. Figure 2: Schematic representation of the system state during mixed signal excitation.
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 5 Experimental Results
### Limitations and Future Work  
The limitations of the proposed approach are that the thresholds of W9 and peak are calibrated at 0.12 Nm torque command and for a healthy door. In real-world scenarios, the door friction increases and torque command must be adjusted. The noise in the motor current increases due to brush wear, the thresholds of W9 and peak need recalibration, and the rules in the MATLAB function must be updated.  
These limitations can be addressed in an extension of this study by modeling door friction and motor wear, developing a pre-diagnostic algorithm, and performing FMEA (Failure Modes and Effects Analysis) for train doors. Such an approach would enable the system to send warning messages and facilitate servicing before a potential failure occurs.