esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Dynamic Viewpoint Selection  
Dynamic viewpoint selection, denoted also as active sensing, active perception, dynamic sensing, or next-best-viewpoint planning, is a well-investigated research topic in robotics research [33; 34; 35]. Active-vision systems assume data measurements to be expensive or slow, and therefore aim to predict the next viewpoint so as to learn as much as possible [36] by controlling the camera's pose towards an object [33]. To date, most robotics research has focused on incorporating dynamic viewpoint selection for object grasping in structured scenes and/or for objects that can be modeled [37; 38]. Selecting the next-best viewpoint is difficult in agricultural applications due to the complex and uncertain environment, which limits the ability to model the scene [38]. The agriculture scene is highly  
Figure 1: Six different sides of the same pepper; some parts can be green while other parts are fully colored.  
variable and unstructured [1; 38] due to variations within the crop (variability in colors, shape, and texture inherent to the biological nature of the product) and the environment (caused by changing illumination, layout of plants, obstructions). To apply the concept of dynamic viewpoint selection in agricultural scenes, methods should be able to deal with these variations to predict a next-best viewpoint.  
The concept of dynamic viewpoint selection was introduced for sweet pepper detection and has proven to improve detection by 19% with 5-10% decreased costs [38]. The decision whether or not to take an additional viewpoint considered the economic costs for moving the camera to the next viewpoint and the economic benefit of additional peppers revealed from an additional viewpoint.
# ErrorExplainer: Automated Extraction of Error Contexts from Smart Contracts
## 6 Error Description Generation
### Matching Process  
The goal is to map the runtime error information captured in \(\mathbb{U}\) to a unique static error description produced by the analysis in Section 5. ErrorExplainer matches the error information in three filters: (1) function selector, (2) call-graph reachability, and (3) encoded error output. The following steps expand on this pipeline.  
1. **Choose the Richest Error Context:** Because information flows forward along \(G_{\mathbb{C}}\), the deployed contract (i.e., the last vertex in topological order) contains the most complete context. We denote this context by \[\mathbb{R}_{0}\ =\ \mathbb{E}_{c},\] where \(c\) is the final component.
2. **Build Candidate Sets (Selector Filter):** Two candidate pools are constructed from \(\mathbb{R}_{0}\) using the function selector observed at runtime (\(\mathbb{U}.f_{s}\)).  
* _Function-level set_ contains only the normalized errors that belong to the function whose selector exactly matches the call site: \[\mathbb{R}_{1}\ =\ \big{\{}\ (e,f)\ \big{|}\ e\in\mathbb{R}_{0}.\mathbb{N}_{c}(f),\ f= \mathbb{U}.f_{s}\big{\}}.\]
* _Trace-level set_ is a superset that includes errors located in any function transitively reachable from the entry point. This set is used if the function-level match fails: \[\mathbb{R}_{1}^{\prime}\ =\ \big{\{}\ (e,f)\ \big{|}\ e\in\mathbb{R}_{0}. \mathbb{N}_{c}(f),\ \mathbb{U}.f_{s}\in\mathbb{R}_{0}.R_{c}(f),\ f=\mathbb{U}.f_{s}\big{\}}.\]
3. **Compare Encoded Error Outputs (Output Filter):** Each candidate carries a normalized error output \(e.\tilde{e}\). The binary error payload emitted on-chain is compared as follows: \[\mathbb{R}_{2}\ =\ \Big{\{}\ (e,f)\in\mathbb{R}_{1}\ \bigg{|}\ \begin{cases} \mathbb{U}.\tilde{e}=e.\tilde{e},&\text{if }e.\tau=\texttt{string}\\ \mathbb{U}.\tilde{e}|_{32}=e.\tilde{e}|_{32},&\text{if }e.\tau=\texttt{custom} \end{cases}\bigg{\}}.\] For custom errors, only the first four bytes (the error selector) are stable across deployments and therefore used in the comparison.
4. **Fallback to Trace-Level Candidates:** If \(\mathbb{R}_{2}=\varnothing\), the procedure is repeated with \(\mathbb{R}_{1}^{\prime}\) in place of \(\mathbb{R}_{1}\). This handles the case where a proxy causes the initial selector match to fail.
5. **Emit the Final Match:** The final set \(\hat{e}\) is chosen as \[\hat{e}\ =\ \begin{cases}\big{\sqcap}_{e\in\mathbb{R}_{2}}e,&\text{if }|\mathbb{R}_{2}|\geq 1,\\ e\in\mathbb{R}_{2},&\text{otherwise}.\end{cases}\] A singleton \(\mathbb{R}_{2}\) presents an unambiguous error description. Multiple entries imply that the developer reuses identical messages or empty strings. In such cases, ErrorExplainer concatenates each field of the entries (denoted by the meet operator \(\sqcap\)).
## 2 Materials and Methods  
### Study Area  
Central Asia, located in the heart of the Eurasian continent (Figure 1a), is defined by its continental arid and semiarid climate. The region extends from east to west, encompassing Xinjiang, in China, as well as Turkmenistan, Uzbekistan, Kazakhstan, Kyragyzstan, and Tajikistan, covering an area of roughly 5.61 \(\times\) 10\({}^{4}\) km\({}^{2}\)[48]. Situated far from the ocean, this region receives minimal rainfall (less than 300 mm annually) and experiences significant temperature variations, with daily fluctuations ranging from 20 to 30 \({}^{\circ}\)C. High evaporation rates also contribute to its characteristic arid environment [20]. Most of Central Asia consists of arid and semiarid zones (Figure 1b) with varied and complex landscapes, including plains, basins, and mountains (Figure 1c). Vegetation in this area is primarily composed of shrubs, grasslands, and croplands, with grasslands covering the largest area, predominantly located in the more sensitive arid and semiarid zones (Figure 1d). Central Asia features a diverse and complex landscape with significant topographic variation. The southeastern region is characterized mainly by hills and plains, while the northwestern region predominantly consists of lowlands [49]. Soil types in Central Asia are diverse, but soils are low in organic matter and have a fragile soil structure.  
Figure 1: Overview of the study area: (**a**) geographic location of Central Asia; (**b**) climate region distribution in Central Asia; (**c**) elevation in Central Asia; and (**d**) land use in Central Asia.
###### Abstract  
Background: In orthodontic treatment, mini-screws can provide maximum anchorage conditions. If the miniscrew stability is compromised, this could jeopardize the outcomes of the orthodontic treatment or biomechanics. Photobiomodulation therapy (PBMt) is beneficial for biological tissues since it promotes wound healing with its anti-inflammatory and osteo-stimulatory properties. Objectives: The purpose of this study was to evaluate the effects of 810-nm Diode PBMt on the stability of orthodontic mini-screws for three months. Trial Design: Parallel, allocation ratio 1:1. Methods: Using a split-mouth technique, a total of 40 mini-screws were randomly allocated (1:1) to either a placebo laser application (P group) or a laser PBMt (L group). An 810-nm diode laser irradiation of 1 W output power, operating in continuous wave mode for 50 s, was applied in a sweeping movement at a 22 mm distance from the mini-screws. The allocated mini-screws first received PBMt at baseline, then every other day for five days, and then at each orthodontic visit (every three weeks) for a period of three months. At each visit, the stability of the mini-screws was measured by the Ossetll Implant Stability Quotient (Ossetl ISQ) at three points: lower, upper, and distal to the mini-screw. Results: The lower, upper, and distal ISQ values of irradiated and non-irradiated mini-screws significantly decreased at each point over time (\(p<\) 0.001). No significant difference was found in the global ISQ values between the laser and the placebo group (\(p>\) 0.05). Conclusion: PBM did not significantly increase the stability of orthodontic mini-screws over a period of three months. Trial Registration: Protocol was approved by the Research Ethics Committee of the Faculty of Dental Medicine of Saint Joseph University, Beirut, Lebanon University (#USJ/2019/161).  
low level laser therapy; photobiomodulation therapy; photobiomodulation; orthodontic mini-screw 2023
## 5 Conclusions  
A new method based on a polyhedral prism is proposed to simultaneously measure the six DOF geometric motion errors of a rotary axis by using autocollimators and laser displacement sensors. An analysis model of 46 errors was established, which included the motion errors of the target rotary axis and the manufacturing errors and installation errors of each component. The results simulated by the professional matrix analysis software show that the six DOF motion errors of the target rotary axis after being compensated by our model are essentially equal to the preset errors. This provides a theoretical basis for the high precision of this method. The six DOF geometric motion errors of a rotary axis were measured ten times by the measurement device built on the optical platform. After compensation, the motion errors of the target rotary axis, \(\varepsilon_{Z}\), \(\varepsilon_{Y}\), \(\varepsilon_{X}\), \(\delta_{X}\), \(\delta_{Y}\), and \(\delta_{Z}\), were, 27.38'', 16.69'', 15.93'', 12.74 um, 11.12 um, and 2.57 um, respectively. The maximum reduction is 79.1%. The repeatability values were 15.13'', 1.28'', 0.87'', 0.74 um, 0.89 um, and 0.34 um, respectively. Repeatability was significantly improved, with a maximum of up to 54.69%. The results verified the effectiveness of our method and error model. It can be used as a new reliable measurement method of six DOF geometric motion errors of a rotary axis. Subsequently, by analyzing the influence of the higher order errors on the geometric motion errors of the six DOF rotary axis, measurement accuracy and the repeatability can be further improved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Motion Errors** & **Before Compensation** & **After Compensation** \\ \hline \(\varepsilon_{x}\) & 26.15′′ & 15.93′′ \\ \(\varepsilon_{y}\) & 33.36′′ & 16.69′′ \\ \(\varepsilon_{z}\) & 27.28′′ & 27.38′′ \\ \(\delta_{x}\) & 37.65 μm & 12.74 μm \\ \(\delta_{y}\) & 53.09 μm & 11.12 μm \\ \(\delta_{z}\) & 2.66 μm & 2.57 μm \\ \hline \hline \end{tabular}
\end{table}
Table 3: The measurement results before and after the compensation of the six DOF motion errors of the rotary axis.  
Figure 5: Measurement results of compensated six DOF geometric motion errors of a rotary axis.  
**Author Contributions:** All works in relation to this paper were accomplished by all the authors' efforts. Conceptualization, [PERSON], [PERSON] and D.M. The experiment was carried out by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) (51905030, 51527806) and the Fundamental Research Funds for the Central Universities (2019 JBM064).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Related Work
#### 1.3.2 ML-Based Methods for SQA  
[PERSON] et al. [13] used a three-stage algorithm to filter low-quality ECG signals using the PhysioNet/CinC Challenge 2011 dataset (Table 1). Initially, basic features like the variance, covariance, maxima, dynamic range, and averages were computed and compared to a threshold to assign a score. Additional features like the time-lagged covariance metrics, mean, kurtosis, and QRS complex count were processed in the second stage and used as the input to an SVM. The final assessments of the signal quality were combined, resulting in 99.9% for the training set and 83.6% for the test set.  
In [14], the authors developed a single-channel ECG quality metric using six SQIs (pSQI, kSQI, basSQI, pSQI, rSQI, and pcaSQI) and a SVM classifier. To address a data imbalance, noisy data were generated by adding noise from the Noise Stress Test Database to clean records from the PhysioNet/CinC Challenge 2011 and MIT-BIH Arrhythmia Database (Table 1). The system was trained to classify ECG segments as clean or noisy, ensuring that only high-quality data were considered suitable for clinical use. The best results on the extended database were an Ac of 97.9%, Se of 97.6%, and Sp of 98.1% on the training set and an Ac of 97.1%, Se of 97.7%, and Sp of 96.5% on the test set.  
[PERSON] et al. [15] trained an SVM to classify the ECG signal quality into five classes using 13 SQIs. This approach aimed to provide a more detailed noise level classification for various clinical applications, surpassing the traditional binary classification of clean or noisy ECG signals. The results showed a classification Ac of 80.26% and an overlap accuracy (OAc) of 98.60% on the test set using 10 selected metrics. For unseen MIT-BIH Arrhythmia Database (Table 1) validation data, the Ac was 57.26% and theOAc was 94.23%. Fivefold cross-validation yielded an Ac of 88.07% \(\pm\) 0.32% and anOAc of 99.34% \(\pm\) 0.07%.
# Wrinkling Patterns and Stress Analysis of Tensile Membrane with Rigid Elements
## 1 Introduction  
Membrane structures are widely used in the field of aerospace engineering due to their lightweight characteristics [1; 2; 3]. However, the bending stiffness of the membrane is low, and wrinkles appear under compressive load, which has a significant negative impact on the mechanical properties and stability of the membrane structure [4; 5]. Therefore, the wrinkling problem of the membrane structure has become a major research focus.  
Previous studies mostly focused on uniform membrane structures [6; 7; 8], but current membrane structures are usually heterogeneous, such as membrane antenna structures with rigid components [9; 10] or flexible electronic devices with rigid circuits in aerospace engineering [11; 12]. [PERSON] et al. studied the wrinkle characteristics of thin films with local microstructures, such as square rigid elements and circular holes [13; 14], but the calculation method of the non-uniform stress field they proposed is based on infinite thin plates. [PERSON] et al. studied the stretch wrinkle characteristics of graphene membranes with frozen zones by the finite-element method [15], but they did not study the influence mechanism of the frozen zone on the membrane wrinkling. [PERSON] et al. studied the influence of the actuator on the membrane surfaces on wrinkling and found that the actuator changes the wrinkling morphology of the membrane; however, they did not study the relationship between the position of the actuator and the wrinkling [10].  
In this article, we propose a method for calculating the non-uniform stress field based on the Eshelby elastic inclusion theory and analyze the stress distribution of a membrane with four square rigid elements. In this method, the perturbation stress of the square rigid element on the membrane is approximately calculated by the Eshelby cylindrical core, and the concentrated stress at the corner of the square element is fitted by the finite-elementmethod. The compressive-stress region where the second principal stress on the membrane is less than zero at different positions of the periodic rigid element is obtained.
## 4 System Design
### Data Preprocessing  
Once the data we will use in our study has been obtained, we will apply a series of NLP techniques [42] to clean and standardize the text. Since we chose to search for social posts in the country's native language, assuming a greater reach, we must deal with the steps required in order to work with the selected lexicon, which in our case is XANEW. By using an English annotated dictionary, we had two options: either find lexicons for each of the languages we had or translate all posts into English. The main problem of the first option is that, although there exists such annotated dictionaries, they may not be as extensive as XANEW. Therefore, we chose to apply machine translation to all social posts by using Google Translate API (Google Cloud Translate [[https://cloud.google.com/translate/](https://cloud.google.com/translate/)]([https://cloud.google.com/translate/](https://cloud.google.com/translate/)), accessed on 24 February 2021). The results obtained were manually reviewed in random chunks by native speakers, also assuming the performance offered by this service (Table 2), since a full manual inspection was unrealistic due to the volume of data.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Language** & **Bard** & **Tarzan** & **Fail** & **BLEU1** & **BLEU2** & **Tarzan2** & **BLEU3** \\ \hline French & 45 & 60 & 40 & 91 & 89 & 95 & 88 \\ German & 60 & 82.5 & 18 & 77 & 72 & 99 & 81 \\ Italian & 50 & 60 & 40 & 87 & 100 & 99 & 90 \\ Polish & 56.25 & 72.5 & 28 & 66 & 79 & 93 & 84 \\ Spanish & 56.2 & 75 & 25 & 67 & 78 & 98 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Google Translate Scores for selected Languages [43].  
In further steps, we will apply a series of corrections:  
1. Undo contractions.
2. Lowercase text.
3. Correct identified misspellings.
4. Replacement of consecutive non-ASCII characters and hyperlinks.
5. Tokenization of posts.  
As we can see in Figure 3, these techniques help us improve the quality of our dataset and remove unnecessary information, such as smilies or hyperlinks.  
An additional and important step will apply lemmatization to all tweets, so that we are left with terms that are mappable to our lexicon, aiming to capture a greater range of expressions.
## 4 Experimental Verification and Discussion
### Software Design  
The flow chart of the software control procedure of the proposed WPT constant voltage output and maximum efficiency tracking coordinated control algorithm is shown in Figure 8. The main control chips used in the WPT primary and secondary sides are TMS320F28335. First, the initialization of the controller needs to be completed, including the configuration of the system clock, analog-to-digital converter (ADC) interrupt, PWM, and other peripherals. In the DSP controller on the WPT secondary side, the ADC interrupt service function mainly completes the sampling of the load voltage \(U_{\text{out}}\), the load current \(I_{\text{out}}\), and the rectifier output voltage \(U_{\text{f}}\). While completing the voltage closed-loop control, the inverter works at the optimal phase shift angle.  
Figure 8: Flow chart of the software control procedure of the proposed WPT constant voltage output and maximum efficiency tracking coordinated control.  
In the main program, the optimal phase shift angle \(\varphi_{\text{opt}}\) calculated by the ADC interrupt is sent to the Bluetooth module at the WPT secondary side via the serial port. The Bluetooth module at the WPT secondary side sends \(\varphi_{\text{opt}}\) to the Bluetooth module at the WPT primary side. In the DSP controller on the WPT primary side, the program is adopted to process the received optimal phase shift angle \(\varphi_{\text{opt}}\), and output two PWM signals with complementary phase shift to control the inverter.
## 4 Results and Discussion
### Experimental Results
CO\({}_{2}\) emissions increased when mixing nano-particles with biofuel and pure fuel (D80B20N50, D80B20N100, and D80B20N150), respectively reaching 66%, 67%, and 69% compared to D100 fuel. Moreover, adding nanomaterials with diesel and biodiesel fuel has a clear effect on slightly lifting the CO\({}_{2}\) emissions ratio. Carbon relies on reducing the viscosity of the fuel and increasing oxygen in the mixture, which helps improve combustion and increase the injection angle, thus increasing atomization and improving combustion. The higher the load, the higher the CO\({}_{2}\) emissions due to the higher cylinder pressure and temperature, which helps improve CO\({}_{2}\) oxidation, and the opposite happens when the load level decreases. With the increase in the CO\({}_{2}\) emissions rate, these results are consistent with the results obtained by [PERSON] et al. [25]. Figure 7: Variation in the CO emissions with the load for all fuel blend samples: D100, D80B20, D80B20N50, D80B20N100, and D80B20N150. Figure 9 shows the variation of unburned hydrocarbon (HC) emissions with the engine load for all mixtures tested: D100, D80B20, D80B20N50, D80B20N100, and D100B20N150. The HC emission changes are similar to the CO emission changes. Thus, HC emissions are the highest for D100 and less for the D80B20, D80B20N50, D80B20N100, and D80B20N150 fuel blends. The reasons for such compartment of HC emissions is described in terms of CO emissions. The low carbon-to-hydrogen ratio and oxygen component in biodiesel with nanomaterial fuels have caused improved combustion, reducing HC emissions [5]. This result was an increase in flame speed and post-flame oxidation due to the Cetane number. It can also be attributed to the catalytic activity of the metal-based nanomaterial during the combustion process. Through the incorporation of Al\({}_{2}\)O\({}_{3}\) nanoparticles, in biodiesel blends, the calorific value improves, the viscosity decreases, and the fuel is completely burned, which reduces HC emissions. Figure 8: Variation of the CO\({}_{2}\) emissions with the load for all fuel blend samples: D100, D80B20, D80B20N50, D80B20N100, and D80B20N150. Figure 9: Variation of the HC emissions with the load for all fuel blend samples: D100, D80B20, D80B20N50, D80B20N100, and D80B20N150. Figure 10 shows the nitrogen oxide NOx emissions resulting from the oxidation of nitrogen at a high temperature. The variation of NOx emissions with the load for all mixer types tested, D100, D80B20, D80B20N50, D80B20N100, and D80B20N150, is studied. The NOx emission increased with an increasing engine load, where high combustion temperatures decrease the power of the triple bond of nitrogen molecules, dissociate into its atomic state, and participate in a series of reactions with oxygen and generate thermal nitrogen oxides. The result is consistent with the conclusions [9; 10].