esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hardware Composition of Performance Testing System for Space Harmonic Reducer
### Selection of Hardware Components  
Some of the components selected in this study are shown in Table 1. A Kollmorgen S300 servo driver cooperates with AKM drive motor as input. The EnDat data interface of Heidehan position encoder is a bidirectional digital interface for encoder applications, which can transmit two kinds of position values: incremental and absolute encoders, and such an encoder only needs four signal lines to complete the transmission because of the serial data transmission mode. The mechanical sensors manufactured by German HBM Sensor Company are selected. The HBM torque sensor consists of several parts as follows: torque sensor, flange, and non-rotating torque sensor used to measure reaction force. It has a sliding ring and non-contact signal transmission technology and a variety of coupling components applied to the torque sensor. The rated measurement range of HBM torque sensor is from minimum 0.1 N\(\cdot\)m to maximum 300 kN\(\cdot\)m, and the rated speed can be up to 40,000 rpm. The current/voltage related parameters in power grid are collected by YPD series DC current/voltage transmitters of Suzhou Xunpeng Instrument and three-phase AC current/voltage transmitters, and then the transmitters are connected to the computer through RS485 or RS232 serial bus interface to construct a measurement and control system. Adam3937 which made by Yanhua company is a connection terminal that can be controlled by host PCI bus. It connects to the computer PCI card through a DB37 internal connector and realizes automatic switching-on of the external circuit by software.  
\begin{table}
\begin{tabular}{c c c} \hline
**Name of Selected Component** & **Function** & **Number** \\ \hline Kollmorgen S300 Servo Motor Driver & Input side motor drive & 6 \\ \hline Kollmorgen AKM servo motor & Input side drive motor & 6 \\ \hline Kollmorgen S700 Servo Motor Driver & Output side motor drive & 1 \\ \hline Kollmorgen AKM servo motor & Torque motor, simulating variable load on output side & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Hardware components.  
Figure 3: Overall hardware schematic.
## 3 Results
### Overall Results
#### 3.3.1 Students
Additionally, the sense of belonging and empathy was built from effective communication processes between professors and students, as stated by a student in the focus group: \"I also have to recognize that by having good communication, they (professors) have had the freedom to understand the different complications that may exist in the student body; one never knows what may be happening to another classmate's family\"(focus group). Although there were good communication practices, there were also drawbacks to effective communication. In this case, there were connectivity problems that hindered the teaching-learning process, as one student mentioned when talking about their experience in videoconferences: \"I had trouble understanding and would many of the Zoom meetings we had could cut off at 40 min. I think I told the professor about it and in truth I dunno know if the change was made because the session that was gonna implement them was the one when the earthquake happened\" (focus group). On other occasions, the problem focused on the lack of teaching, specifically on the students' lack of understanding of a specialized language and the professors not giving them enough explanations: \"It seems to me that this form of working is not correct, all that are lectures because sometimes we don't understand some terms. I'm from the technology department, and I don't know management terms\". Moreover, there was an absence of feedback on the work presented by the students: \"Going back to (the issue of) feedback, if I have a question about a class or how to deliver a paper, they will give it. The problem is when I have done that, they don't respond to any of the work I handed in. Like, I literally don't know about how it came out.\" (Focus group). Finally, in the socio-political agreement, the meaningful relationships of the actors fostered the construction of communities with motivation for learning. One of the students stated that their educational experience changed thanks to the relationship and didactic structure that the professor implemented with their students since it gave them certainty and confidence: \"In my opinion, the one was most successful in this semester was the UEA of human-computer interaction with Professor [PERSON]. It started from before the term, always gave us the guideline and always gave us a bit of certainty within all the uncertainty\" (focus group). Additionally, there was confidence in the students to approach their professors to solve academic and personal problems, as can be seen in this extract from the focus group: \"I can't complain much about the communication. I mean, there was communication with my professors because I had a problem with re-enrollment in which all of my subjects disappeared. I contacted them, they told me it was fine, and we resolved it\" (focus group).
# Experimental Study of Nanosecond Laser-Generated Plasma Channels
## 3 Experimental Setup
In fact, the latter could be measured only using an optical probe with a shorter pulse duration, along with an interferometric method to estimate the channel inner density profile that is out of the scope of the present work. Figure 5: Experimental setup. A laser pulse of 1–5-joule and 10-ns time duration with a 1030-nm wavelength, is sent towards an optical system based on a plano-convex and a cylindrical lens to generate an elongated focus in a gas jet target. A portion of the main laser pulse is used as optical probe. L1 is an f = 200-mm plano-convex lens, L2 is an f = 100-mm cylindrical lens. L3 is an f = 150-mm plano-convex lens used for shadowgraphy on CAM2. CAM1 is used as top-view image of the optical plasma emission. BD is a beam-dump. It is worth mentioning that the optical probe beam propagated through the plasma channel on the same plane of the main laser axis, i.e., through the elongated focus (45\({}^{\circ}\) incidence angle). Consequently, shadowgraphy images could provide only an averaged estimation of the channel inner diameter, which was transverse to such a plane (x-y plane). Thus, any channel asymmetry occurring along the longitudinal direction (z-axis) could not be measured. In fact, as mentioned above, estimation of the significant-intensity-thresholds was the main aim of this experimental work and no additional shadowgraphy line was setup.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 3 Wavelet and Wavelet Packet Transformation and Reconstruction  
### Wavelet Transformation and Reconstruction  
A wavelet is a special kind of function with oscillation and can quickly decay to zero, and tends to be irregular and asymmetric [53]. In general, the mother wavelet \(\psi(t)\) is restricted, and function \(\psi(t)\) should be governed as follows  
\[\int_{-\infty}^{+\infty}\frac{\left|\hat{\psi}(\omega)\right|^{2}}{\left| \omega\right|}d\omega<\infty \tag{2}\]  
where \(\hat{\psi}(\omega)\) is Fourier transform of \(\psi(t)\).  
By the dilation and translation of \(\psi(t)\), the wavelet basis \(\psi_{a,b}(t)\) can be defined as follows  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi\bigg{(}\frac{t-b}{a}\bigg{)} \tag{3}\]  
where the parameter \(a>0\) indicates the scale factor and the parameter \(b{\in}R\) represents the displacement factor.  
For the signal function \(f(t)\in L^{2}(R)\), after wavelet decomposition, the coefficients are  
\[W_{f}(a,b)=\big{<}f(t),\psi_{a,b}\big{>}=\frac{1}{\sqrt{a}}\int f(t)\psi^{*} \bigg{(}\frac{t-b}{a}\bigg{)}dt \tag{4}\]  
where \(\psi^{*}(t)\) is the complex conjugate function of \(\psi(t)\).  
The inverse wavelet transform formula is  
\[f(t)=\frac{1}{C_{\psi}}\int_{0}^{+\infty}\frac{da}{a^{2}}\int_{-\infty}^{+ \infty}W_{f}(a,b)\psi\bigg{(}\frac{t-b}{a}\bigg{)}db \tag{5}\]  
Defining \(a=2^{-j}\), \(b=k2^{-j}\), and \(j,k\in Z\), the corresponding binary discrete wavelet function sequence \(\psi_{j,k}\) can be formulated as  
\[\psi_{j,k}=2^{j/2}\psi\Big{(}2^{j}t-k\Big{)} \tag{6}\]  
and when \(f(t)\in L^{2}(R)\), the discrete wavelet transform is  
\[C_{j,k}=f,\psi_{j,k}=\int_{-\infty}^{+\infty}f(t)\psi_{j,k}^{*}(t)dt \tag{7}\]
## 5 Numerical Simulations
Figure 3: The control signals with curve indices from 1 to 9 represent LESO, NLESO types I, II, III, IV(a), IV(b), V, and VI, and NHOESO, respectively of Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\beta_{1}\) & 46.4494 \\ \hline \(\beta_{2}\) & 731.7998 \\ \hline \(\beta_{3}\) & 762.1222 \\ \hline \(\beta_{4}\) & 2879.2 \\ \hline \(\delta_{1}\) & 0.0150 \\ \hline \(\delta_{2}\) & 0.3316 \\ \hline \(\alpha_{1}\) & 0.0047 \\ \hline \(\alpha_{2}\) & 0.0497 \\ \hline \(\alpha\) & 0.5331 \\ \hline \(\beta\) & 0.3342 \\ \hline \(k_{\alpha}\) & 0.6166 \\ \hline \(k_{\beta}\) & 0.5432 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample values of the ADRC units. Figure 5: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **ESO** & **ITAE** & **ISU** \\ \hline \(1\) & LESO & 1.714331 & 7.168854 \\ \hline \(2\) & NLESO type I & 1.560685 & 6.682492 \\ \hline \(3\) & NLESO type II & 1.613618 & 6.809034 \\ \hline \(4\) & NLESO type III & 1.604350 & 6.779854 \\ \hline \(5\) & NLESO type IV(a) & 1.630781 & 6.835949 \\ \hline \(6\) & NLESO type IV(b) & 1.619501 & 6.696178 \\ \hline \(7\) & NLESO type V & 1.657292 & 6.853151 \\ \hline \(8\) & NLESO type VI & 1.610722 & 6.768733 \\ \hline \(9\) & NHOESO & 0.937766 & 5.373665 \\ \hline \end{tabular}
\end{table} 
Table 3: The performance indices of the numerical simulations. Figure 6b shows an estimated state \(\xi_{2}\) that is smoother than the one obtained by the NLSSO type I. In the estimated state of the earlier case, the high-frequency harmonic disappeared before the fifth second; however, in the case of NLSSO type I, it is still there beyond the fifth second. Moreover, an accurate estimation of the generalized disturbance was noticed in Figure 6c compared to that in Figure 5c. Figure 6: _Cont._  
In the NHOESO, the smoothness of the control signal \(u\) and the minimum overshoot in the output response were due to using the proposed nonlinear error function \(\mathcal{G}\) with the following features: it is a smooth function; it has high gain near the origin and a small gain with large error values. Another reason for these enhancements was the extra augmented \(\xi_{\rho+2}\) state in the proposed NHOESO, which allowed for the precise observation of the generalized disturbance \(\xi_{\rho+1}\) with higher-order derivatives.
## 4 MLP Neural Network  
In the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study, such as fluid mechanic engineering [26, 27, 28, 29, 30, 31, 32, 33, 34], chemical engineering [35, 36, 37, 38, 39, 40, 41], electrical and computer engineering [42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72], petrochemical engineering [73, 74, 75, 76, 77], petroleum engineering [78, 79, 80, 81, 82], mathematics and physics [93, 94, 95, 96, 97, 98, 99, 100, 101, 102], and environmental engineering [103, 104, 105, 106, 107]. The ANN has been demonstrated to be the most potent technique for classification and prediction among the aforementioned computational methods.  
Perceptron is a kind of neural network in terms of computational unit. In fact, perceptron is a single-layer neural network; a multilayer perceptron is called a neural network. Taking of real values of input vectors and calculating a linear composition of these inputs are perceptron's duties. If the result value is less than the threshold value, the perceptron output will be \(-1\) and, otherwise, will be \(1\). The following equation determines the perceptron output [108, 109]:  
\[y=f(\sum_{i=1}^{u}w_{i}x_{i}+w_{i}) \tag{4}\]  
The page is divided into two parts if the perceptron has two inputs \(x_{1}\) and \(x_{2}\) and the equation of the dividing line is defined as follows:  
\[w_{1}x_{1}+w_{2}x_{2}+w_{0}=0\]In the n-dimensional space of instances, perceptron is thought-outed as a hyperplane. Perceptron only learns instances that are linearly separable so that it can completely divide the instances into two parts by a hyperplane and apply the values of \(-1\) to one side and \(1\) to the other side. Obtaining the values of perceptron weights is the goal of training it so that perceptron produces the real value of training instances. Perceptron is taught according to the following algorithm:  
1. Weights get random values
2. For each training instance, perceptron is applied. If the samples are misjudged, the perceptron weight values are corrected.
3. Are all trainings evaluated correctly?
4. Yes, the end of the algorithm.
5. No, back to step 2.  
When the network is single-neuron, it loses the ability to implement nonlinear functions. Multilayer perceptron networks (MLP) are very useful and can be offered as a solution because they perform nonlinear mapping with high accuracy, which is considered as the main solution in many engineering problems. MLP is a type of feed-forward network; the output is calculated directly from the input without any feedback. In the MLP network, the neuron model consists of a nonlinear activation function. Nonlinearity, continuity, and derivability at all points are some of the features that an activation function should have. If the activation function is not nonlinear, the network performance will be reduced to the level of monolayer perceptron.
## 2 Review of Related Works  
Understanding a language is fundamental before reasoning in such a language can occur. NLU is, therefore, aptly described in [20] as a machine's ability to grasp and make sense of a prompt in a natural language, either text or speech, and derive meaning from such input. NLR can be described as the ability of a machine to produce new statements, declarations, or actions based on existing knowledge without relying solely on its memorized facts, accumulated knowledge base, or explicit contextual input [21].  
NLU in low-resource languages is a daunting challenge owing to the lack of sufficient data for training LLMs. There are several approaches to mitigate this challenge. The transfer of the capabilities of pre-trained language models from high-resource languages to low-resource languages, leveraging vocabulary matching, was proposed in [22]. This technique has been shown to be effective in improving the performance of BERT models, even with minimal training data in the target language. Another proposed approach involves retrieval-augmented language models. This model leverages an external voluminous collection of documents to aid the improved performance of knowledge-intensive tasks [23; 24]. It has been demonstrated that the approach is effective in few-shot learning settings, with language models picking up knowledge-intensive tasks with very few training examples. The use of natural language descriptions of slots has also been proposed as a method to improve NLU models in LRLs [25].  
The need for robust multilingual NLU benchmarks to evaluate the performance of NLU models on LRLs was addressed in [26] through the creation of SIB-200 for such an evaluation exercise. The benchmark, like many other multilingual benchmarks, is designed for the evaluation of the performance of NLU models in many languages, including LRLs, to facilitate a standardized comparison of the performance of different models and pinpoint areas for improvement. [PERSON]. [27], however, submitted that a fundamental limitation of multilingual LLMs is their poor ability to generalize when it comes to NLU tasks in LRLs, when included in multilingual benchmarks, are highly limited compared to other languages. [PERSON]. [27] proposed the LM contamination index, which seeks to assess the impact of data contamination on the performance of LLMs as an alternative evaluation metric.  
Overall, natural language understanding in low-resource languages is a challenging task that requires the development of new approaches and models that can leverage limited resources and data [23].
###### Abstract  
Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial details of objects. The Flexible Spatiotemporal DAta Fusion method (FSDAF) was proposed, which can monitor the abrupt change events, but its predicted images lacked intra-class variability and spatial details. To overcome the above limitations, this study proposed a comprehensive and automated fusion method, the Enhanced FSDAF (EFSDAF) method and tested it for Landsat-MODIS image fusion. Compared with FSDAF, the EFSDAF has the following strengths: (1) it considers the mixed pixels phenomenon of a Landsat image, and the predicted images by EFSDAF have more intra-class variability and spatial details; (2) it adjusts the differences between Landsat images and MODIS images; and (3) it improves the fusion accuracy in the abrupt change area by introducing a new residual index (\(R\)J). Vegetation phenology and flood events were selected to evaluate the performance of EFSDAF. Its performance was compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Spatial and Temporal Reflectance Unmixing Model (STRUM), and FSDAF. Results show that EFSDAF can monitor the changes of vegetation (gradual change) and flood (abrupt change), and the fusion images by EFSDAF are the best from both visual and quantitative evaluations. More importantly, EFSDAF can accurately generate the spatial details of the object and has strong robustness. Due to the above advantages of EFSDAF, it has great potential to monitor long-term dynamic changes of land surface.  
image fusion; high spatiotemporal resolution; EFSDAF; FSDAF; Landsat; MODIS
# Parameterized Optimization in Uncertain Graphs--A Survey and Some Results
## 2 Distribution Models for Uncertain Graphs
### Independent Cascade Model  
The sampling procedure to define the Independent Cascade (IC) model is very naturally described by a diffusion process which proceeds in rounds. The process is as defined in Reference [14]. The diffusion process starts after round 0. In round 0, \(A_{0}\) is a non-empty set of vertices and these are called _active_ vertices. The process maintains a set of edges \(A_{t}\) after round \(t\geq 0\). The vertices in \(A_{t}\) are said to be active. The sampling process is as follows. If a vertex \(v\) is in \(A_{t-1}\), then it remains active in round \(t\) also, that is \(v\in A_{t}\). The diffusion process in round \(t+1\) is as follows: If \(v\) is in \(A_{t}\setminus A_{t-1}\), that is \(v\) first becomes active after round \(t-1\), then consider each edge \(e=uv\) such that \(u\) is not in \(A_{t}\). Each such edge \(e\) is sampled with probability \(p(e)\) independent of the other outcomes. If \(e=uv\) is selected (that is, it survives), then \(u\) is added to \(A_{t+1}\). We reiterate that the edges incident on \(v\) will not be sampled in subsequent rounds. The sampling procedure terminates in not more than \(|E|\) rounds. The outcome of this sampling procedure is the edge subgraph formed by a set of those edges which were selected when the first end point of the edge becomes active. [PERSON] et al. [14] showed that for any edge subgraph \(H\) of \(G=(V,E)\), the probability that \(H\) is the outcome is given by \(P(H)=\prod_{e\in E(H)}p(e)\prod_{e\in E\setminus E(H)}(1-p(e))\).  
**Observation 1**.: _For an uncertain graph \(\mathcal{G}=(V,E,p)\) the RF Model and the IC Model are identical._  
The next distribution model is a generalization of the RF model and was introduced by [PERSON] and [PERSON] [43].
## 5 Discussion
### Selection of Experimental Parameters for Improved LOS  
This paper presents simulation experiments aimed at simulating the path-following effect of the improved LOS method during robot flight, especially for frequently changing paths. The results of the experiments indicate that the 3D path-following method using the improved LOS method is able to adaptively adjust the size of the acceptance circle radius based on the path angle and the current flight speed of the robot. This allows it to switch reference paths while reducing following error and satisfying the robot's performance constraints. It is also able to prioritize horizontal and vertical paths based on their real-time distance from the reference paths, guiding the robot toward rapid convergence with the reference paths.  
In the simulation experiments on adaptive heading control, the robot's forward-looking distance is set to 0.5 m at a flight speed of 0.2 m/s. This is because the distance between the phase wire and the ground wire in an environment with power line divisions is usually between 1 and 2 m. When maintaining a safe distance, the robot's flight envelope becomes narrow; thus, a small forward-looking distance is chosen. Additionally, the robot should follow the planned path at a slower flight speed for robot safety. In the case of the adaptive acceptance circle strategy, the speed and angle of the path segment are weighted at 2 and 1, respectively, with a flight speed of 0.5 m/s. Therefore, when planning of the adaptive acceptance circle, it is expected that the priority of the current flight speed will exceed the angle of the path segment. In order to achieve a higher accuracy of the path-following control, the adaptive heading control strategy adopts a smaller scale coefficient, \(m\), based on the robot's horizontal error requirements in flight. This increases the priority of horizontal control in the heading control process.  
Given the results of simulation experiments, both the improved LOS method and the traditional LOS method are able to effectively follow the preset paths of the robot, and compared with the traditional LOS method, the improved LOS method has faster convergence speed; smaller following errors; and better performance in terms of stability, robustness, and response speed in path following, which is important for improving the flight accuracy and safety of the FPLIR in spaces segmented by power lines.