esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Machine Job-Shop Scheduling Problem to Minimize the Makespan with Uncertain Job Durations
## 3 Problem Settings and Notations
: _A minimal (with respect to the inclusion) set of pairs of job permutations \(S(T)\subseteq S\) is called a \(J\)-solution for the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) with set \(\mathcal{J}\) of the given jobs if, for each scenario \(p\in T\), the set \(S(T)\) contains at least one pair \((\pi^{\prime},\pi^{\prime\prime})\in S\) of the job permutations, which is optimal for the individual deterministic problem \(J2|p,n_{i}\leq 2|C_{max}\) associated with scenario \(p\)._  
From Definition 1, it follows that, for any proper subset \(S^{\prime}\) of the set \(S(T)\)\(S^{\prime}\subset S(T)\), there exists at least one scenario \(p^{\prime}\in T\) such that set \(S^{\prime}\) does not contain an optimal pair of job permutations for the individual deterministic problem \(J2|p^{\prime},n_{i}\leq 2|C_{max}\) associated with scenario \(p^{\prime}\), i.e., set \(S(T)\) is a minimal (with respect to the inclusion) set possessing the property indicated in Definition 1. The uncertain job-shop problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) is a generalization of the uncertain flow-shop problem \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\), where all jobs from the set \(\mathcal{J}\) have the same machine route. Two flow-shop problems are associated with the individual job-shop problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\). In one of these flow-shop problems, an optimal schedule for processing jobs \(\mathcal{J}_{1,2}\) has to be determined, i.e., \(\mathcal{J}_{2,1}=\mathcal{J}_{1}=\mathcal{J}_{2}=\emptyset\). In another flow-shop problem, an optimal schedule for processing jobs \(\mathcal{J}_{2,1}\) has to be determined, i.e., \(\mathcal{J}_{1,2}=\mathcal{J}_{1}=\mathcal{J}_{2}=\emptyset\). Thus, a solution of the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij}|,n_{i}\leq 2|C_{max}\) may be based on solutions of the two associated problems \(F2|l_{ij}\leq p_{ij}\leq u_{ij}|C_{max}\) with job set \(\mathcal{J}_{1,2}\) and with job set \(\mathcal{J}_{2,1}\). The permutation \(\pi_{1,2}\) of all jobs from set \(\mathcal{J}_{1,2}\) (the permutation \(\pi_{2,1}\) of all jobs from set \(\mathcal{J}_{2,1}\), respectively) is called a _Johnson's permutation_, if the inequality in Equation (1) holds for the permutation \(\pi_{1,2}\) (the inequality in Equation (2) holds for the permutation \(\pi_{2,1}\), respectively). As it is proven in Reference [28], a Johnson's permutation is optimal for the deterministic problem \(F2||C_{max}\).
## 5 Conclusions  
The following conclusions were made:  
1. The disadvantages of the existing methods of repairing main oil and gas pipelines involving cutting out defective sections are justified.
2. The design of devices for fixing and centering the ends of the pipeline has been developed and patented, which makes it possible to avoid a sharp displacement of the ends of the pipeline and ensure their centering before welding a new section without the use of pipelayers.
3. A mathematical model has been developed for calculating the reaction forces arising in the hydraulic cylinders of the firing and centering devices, the forces required to center the ends of the pipeline as well as the resulting stresses in the pipeline wall. The initial data for calculations based on the mathematical model are data on the position of the pipeline in the repair trench. The mathematical model will ensure the safe automatic operation of devices without the presence of working personnel in the trench.
4. The choice of the method of laser scanning of the pipeline to determine the position of the pipeline in the repair trench is justified.
5. A method for processing a point cloud of a pipeline obtained as a result of laser scanning has been developed. The method allows us to obtain a polynomial that describes the spatial position of the central axis of the pipeline, based on its point cloud.
6. As a result of a laboratory study, it was found that the developed method for processing a point cloud of a scanned pipeline makes it possible to obtain a polynomial, on the basis of which it is possible to estimate with sufficient accuracy (up to 5%) the parameters calculated by the mathematical model, in comparison with the method of photographing cross markers glued on the side generatrix of the pipeline.
7. As a result of modeling the process of cutting the main pipeline and centering its ends by the method of finite element computer simulation, a sufficient convergence (up to 5%) of the simulation results with the results of calculations using the proposed mathematical model was obtained.
8. According to the results of calculating the financial costs for the production of repair work according to the currently used method, it was revealed that the main costs (transportation permit for one pipelayer, depreciation and fuel costs) fall on the use of pipelayers. The devices proposed in the work can significantly reduce these cost items and completely eliminate the largest one, which is the cost of a transportation permit for oversized equipment.
9. The developed method of repairing main oil and gas pipelines and the mathematical model will improve the level of industrial safety of the repair process, as well as its technological and economic efficiency.
# Dictionary Encoding Based on Tagged Sentential Decision Diagrams
## 4 Encoding Dictionaries into TSDDs  
A dictionary includes a large number of words, and we intend to transform it into a decision diagram that stores the whole dictionary. In this way, we can complete some common operations in the dictionary just by performing some binary operations on decision diagrams. When we want to verify whether a word exists in the dictionary, we just need to compute the result \(Apply(F,G,\wedge)\), where \(F\) is the decision diagram representing the dictionary, and \(G\) is the decision diagram representing the word. If the result is \(false\), then the word does not exist in the dictionary; otherwise, it exists in the dictionary. When we want to remove some words from the dictionary, we just need to construct the decision diagram \(G\) representing the set of words. And then, we compute the result \(Apply(F,Negate(G),\wedge)\), where \(F\) represents the dictionary. Changing the traversal process on dictionaries to several binary operations on decision diagrams can remove the traversal process. This is why we encode dictionaries into decision diagrams.  
In this section, we first introduce the process of encoding a dictionary into a decision diagram in four different ways. Ref. [1] pointed out the use of tries when encoding dictionaries into decision diagrams but did not provide a detailed description of the process. Hence, we were inspired by them and designed our method of encoding dictionaries with the help of tries, which greatly accelerated the compilation process. In the following, we intend to explain in detail how to use tries to complete the encoding of a dictionary and present the algorithm for decoding TSDDs.
## 7 Network Performance Analysis: NB-IoT vs. LoRaWAN
### Throughput
#### 7.2.1 Battery Life
In the LoRaWAN test, the IoT module (Fipy is used as the LoRa gateway and LoRa Node) and the test parameters include a coding rate of 4/5, 125 kHz bandwidth, class A, and a spread factor (SF) of 7. The SF influences the power consumption level since it determines the time it takes radio links to transfer data. The LoRa network gateways are diverse, as a receiving gateway can become a serving gateway. This reduces end devices' power consumption since devices closer to the edge can become gateways. The asynchronous nature of the LoRaWAN allows UE to sleep as often as desirable based on the class of configuration. To achieve an instant downlink communication, the IoT device must be configured as a class C, and scheduled transmission nodes in classes B and A is recommended for efficient power consumption. In both networks, allowing more frequent data transmission would reduce the battery lifespan. The total energy required to perform a single test procedure in LoRaWAN was measured as 1 mAh. Similarly, the uplink transmission for LoRa consumed approximately 100 uAh, which is far lower than 1.8 mAh consumed in NB-IoT network. This shows that LoRa is more power efficient than NB-IoT and should be considered as a primary deployment option for extremely low data applications. To achieve a reduction in energy consumption in LoRaWAN such as water quality monitoring, the transmission should be scheduled based on use case.
## 3 Materials and Methods
### Feature Extraction  
The extraction of radiomic features from the CTV was performed using the python library pyradiomics [10], a highly tested and maintained open-source platform and also available as an extension from 3 DSlicer. Most PyRadiomics features are in compliance with the Image Biomarker Standardisation Initiative (IBSI), an independent international collaboration that aims at standardizing the extraction of image biomarkers for high-throughput quantitative image analysis (radiomics) [32]. A total of 107 features were extracted volume-wise with the default settings: only the original images (no filter applied), minimum Region Of Interest (ROI) dimensions of 2, a pad distance of 5, a bin width of 25 and a cubic voxel size of 1 mm, since the voxel size will vary as it depends on the acquisition settings of each LINAC, as shown in Table 1. Table 3 shows the radiomic feature classes extracted from the dataset.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Feature Class** & **\# Features** \\ \hline Shape-based & 14 \\ First order & 18 \\ GLCM & 24 \\ GLDM & 14 \\ GLRLM & 16 \\ GLSZM & 16 \\ NGTDM & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted radiomics feature classes.  
Figure 2: Initial CT scan with manually drawn prostate (yellow) and registered CBCT. (**a**) Initial CT scan. (**b**) First EBRT session CBCT.  
The extracted features were then saved to a tab-separated values file for later processing in a python environment.
## 1 Introduction
It is also known to support osteoblast attachment and proliferation, whichmakes it suitable for GBR and GTR applications. As PLA degrades, it releases lactic acid, a metabolite that is naturally processed by the body, reducing the risk of adverse tissue reactions. Nevertheless, it may cause a local pH decrease, leading to inflammation. Thus, it is important to acknowledge while developing a novel biomaterial for this purpose that its degradation rate is affected by various factors such as molecular weight, crystallinity, and environmental conditions. An appropriate choice of PLA type may help to have an impact on its performance over time. PLA's chemical structure allows for modifications and the addition of bioactive materials. These may include ceramics, nanoparticles, or other polymers to improve their mechanical properties, bioactivity, and ability to support bone regeneration. Although PLA has many advantages, it still has some challenges that must be overcome, including its hydrophobic nature and slow degradation under physiological conditions. Advanced fabrication methods like electrospinning enable PLA processing, followed by the integration of functional nanomaterials with its matrix, which have shown promise in addressing these limitations. Recent research highlights the potential of PLA-based nanofibrous scaffolds to deliver the mechanical strength, porosity, and bioactivity needed for effective bone regeneration. Particularly promising ones are those enhanced with bioactive additives [29; 30; 31; 32]. Polydopamine (PDA), a polymer derived from natural sources, is biocompatible, hydrophilic, and uncoadhesive. It also exhibits antioxidant, metal-ion chelating, and antibacterial properties due to its catecholamine and hydroxyl functional groups. PDA enables bioconjugation with a wide range of biomedical materials and facilitates cellular interactions. It is synthesized through the auto-oxidation and polymerization of dopamine. Growth factors are soluble proteins secreted by cells that regulate proliferation, migration, and differentiation by binding to specific transmembrane receptors, thus promoting wound healing. Despite their considerable therapeutic potential, growth factors are limited by their short half-lives, low stability, and rapid enzymatic degradation at body temperature. Encapsulation within a polymer matrix can address these challenges by providing protection and enabling controlled release. The nanofibrous composite presented in this study represents an innovative biomaterial for GBR and GTR applications. It offers a combination of advantageous biological and mechanical properties, making it a promising candidate for use in tissue regeneration thanks to the use of potentially bioactive components, which may affect osseointegration as well as new bone formation [33; 34; 35; 36].
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis  
### Data Sources and Structure  
The experimental study focused on data collected within specific geographic coordinates in Jinniu District, Chengdu City, spanning latitude 30.60-30.70 and longitude 104.00-104.06. The temporal scope included 4 days, from 7 to 10 August 2014, with 7-8 August classified as weekdays and 9-10 August as weekends. The data comprised 15,190,410 entries gathered from approximately 7000 taxis. The data's attribute structure is detailed in Table 2.  
From Table 2, we can gather information about the longitude and latitude positions and the passenger status of a taxi collection terminal at a specific moment. However, it is important to note that the longitude and latitude coordinates in the trajectory data are in an encrypted format known as the GCJ-02 coordinate system. To perform map matching and subsequent analysis, we need to convert these coordinates to the WGS-84 coordinate system. Additionally, we can determine the sampling interval of the terminal data by analyzing the timestamp field in the taxi trajectory data. It appears that data with a 10 s interval makes up approximately 78% of the total data, constituting the majority. As a result, it is essential to filter out data with sampling intervals that are either too short or too long in accordance with the experimental requirements.
# Fully Attentional Network for Low-Resource Academic Machine Translation and Post Editing
## 2 Related Works
### Turkish English Machine Translation  
Machine translation from Turkish to English can be examined in 2 parts so far. Part 1 is Statistical Machine Translation systems and part 2 is Neural Machine Translation systems tested as a low source language pair. Part 1: In 2007 [15] and 2009 [16], carried out the first major studies for Turkish-English machine translation with SMT application. In these studies, they tried to solve sentence alignment and rare word problems by applying morphological word segmentation. Based on the Moses Statistical Machine Translation Toolkit [17], hierarchical expression-based models were presented in The International Conference on Spoken Language Translation (IWSLT) 2010 evaluation campaign. The SMT method is generated with an approach using a representation equivalent to the word form without splitting the Turkish words into morphemes. Generated the SMT method by using a representation equivalent to the word [18] form without splitting the Turkish words into morphemes and achieved 23.78 BLEU scores. The study prepared for the IWSLT 2013 conference was able to achieve 17.14 BLEU scores at the morpheme level and with a hierarchical phrase-based SMT system [19]. In 2019, a tree-based English Turkish statistical translation system was introduced [20]. The system prepared has achieved 21.4 BLEU scores. Part 2: In 2017, ref. [21] studied the IWSLT data set using the language model created with RNN in the deep fusion method with monolingual data. They received a score of 21.34 BLEU in the analysis using Neural Machine Translation. In the study [22] conducted to improve the translation quality due to the low resource available for Turkish-English machine translation, monolingual data were used with the training set, as in study [21], and reached 21.8 BLEU score with the IWSLT data set. Modern NMT systems were used in studies conducted in 2017, and solutions to the low-resource problem were pursued through methods such as transfer learning, byte pair encoding, and back-translation. In these studies, refs. [23; 24; 25; 26] BLEU scores were obtained as 25.42, 18.9, 22.56, and 13.2 respectively. In the study in 2020 [27], a transformer-based Turkish-English NMT system was designed and a score of 28.48 BLEU was achieved. Created 5000-sentence dataset for Turkish Statistical machine translation [28] and a parallel corpus of 1M sentences were presented in the study prepared in 2021 [29].
## 2 Experimental  
The ChG SIF used in this work had a core made of Ge\({}_{12.5}\)Sb\({}_{15}\)Se\({}_{72.5}\) glass and a cladding made of Ge\({}_{15}\)Sb\({}_{10}\)Se\({}_{75}\) glass; both had sufficient thermal compatibility so that the components could be drawn down in the same fiber. A vacuumed melt quenching technique was used to synthesize the glass samples with high-purity elemental raw materials of Ge, Sb, Se (99.999%), similar to that previously reported [32]. SC generation was measured further based on the Ge-Sb-Se fiber. The experimental set-up is depicted in Figure 1. A tunable MIR OPA system (\(\sim\)150 fs full-width at half maximum (FWHM), 1 kHz repetition rate, and 2\(\sim\)10 \(\upmu\)m tunable wavelength) was used as an exciting source. A pair of polarizers was used to control the output power of the pump, and the beam from the OPA was coupled to the fiber via a calcium fluoride lens with a focal length of a 75 mm. The output SC from the fiber was collected into the input slit of a monochromator, and the spectra were measured by an infrared spectrometer (Infrared systems, FPAS, Winter Park, FL, USA) with a liquid nitrogen-cooled HgCdTe (MCT) detector (1-16 \(\upmu\)m wavelength range). Furthermore, the refractive indices were measured using an infrared spectroscopy ellipsometer (J.A. Woollam IR-Vase II, Lincoln, NE, USA). The Raman spectra of the samples were obtained in the range of 25-400 cm\({}^{-1}\) using a Raman spectroscopic (Renishaw InVia, Renishaw, Gloucestershire, UK). All these measurements were carried out at room temperature.
## 4 Qualitative Findings and Discussion
### Providing Necessary Assistance
#### 4.2.2 Prompts  
Some participants indicated that posture reminders are necessary for individuals who experience back pain due to prolonged sitting at work. For instance, participants suggested incorporating proactive features such as vibrations or audible alerts to monitor sitting posture.  
\"It would be much better to have such a reminder because poor sitting posture can aggravate lumbar muscle fatigue. Anyway, it's good to have such a reminder in the end.\" (Participant 11)  
Some participants expressed expectations of smart technology, hoping that LSAD could provide precise health feedback through sensors, data analysis, and other means: \"If it could monitor my sitting posture in real-time and even offer some simple exercise suggestions, that would be perfect. I believe such features can help me in my recovery process\" (Participant 1). Additionally, participants also reported that scheduled activity reminders would be more beneficial for their recovery. Participant 6 mentioned, \"Vibrations or audible alerts can, to some extent, compel us to engage in simple activities, promoting blood circulation and muscle movement in the lower back\".  
In this study, participants underscored uncertainty regarding the effective wearing of LSAD as a motivating factor necessitating assistance. Participants expressed a desire for guidance based on scientific principles to help them ascertain the effectiveness of wearing LSAD. Indeed, in a similar study, an investigation was conducted on the impact of the tightness of abdominal belts on the maximum acceptable weightlifting, and it was determined that the effectiveness of abdominal belts depends on their tightness as well as on task variables [48]. This highlights the critical role of tightness in the effective wearing of LSAD. In addition, providing appropriate cues based on the needs of different scenariosmay be more helpful to the rehabilitation outcomes of patients with lower-back pain. Accordingly, when designing LSAD for individuals with lower-back pain, the incorporation of smart technology or wearable materials should be considered. This, combined with factors such as the patient's activity level and the severity of lower-back pain, can offer personalized exercise services for patients [49; 50].
# Rock Slope Instability Mechanism Induced by Repeated Mining in Mountain Mining Areas
## 5 Slope Instability Slip Mechanism  
As shown in Figure 16, combined with the results of model test and numerical simulation analysis, the instability evolution of the slope under underground repeated mining disturbance can be divided into four stages as follows: roof caving and overlaying rock subsidence, joint rock toppling, fracture penetration, and slope toe shearing and slope slipping. The failure mechanism of slope instability can be summarized as strata collapse-joint rock toppling-shear slip. The main reasons for the instability and slip of the slope will be divided into geological factors and non-geological factors. In the collapse area, the geological factors were characterized as the typical upper hard and lower soft, upper steep and lower gentle slope. During the long period of weathering and karstification, many deep and large joints are developed at the top of the slope, and the stability of the mountain will be impacted. Non-geological factors include external disturbances such as underground excavation, rainfall and earthquakes.  
Figure 16: Slope instability process. (**a**) Roof caving and overlaying rock subsidence; (**b**) joint rock toppling; (**c**) crack penetration; (**d**) slope toe shearing and slope slipping.  
Figure 15: The displacement of measuring points at the surface of the slope. (**a**) Horizontal displacement; (**b**) vertical displacement.  
When the underground resource is mined repeatedly, the joint rock can be broken during the mining process of the upper coal seam. The deep and large fissures of the slope continuously developed, and the mining-induced cracks will connect with natural fissures. In addition, water can penetrate into the slope along the cracks, and the conditions of the slope are further disturbed and deteriorated. Finally, the potential slip surface will be formed. When the rock mass at the foot of the slope is sheared and squeezed out, the rock of the slope loses support, forming a potential sliding zone and eventually leading to the slip of the slope.
## 4 Dynamic Feature Based XGBoost Model
### Semi-Supervised Soft Sensor SSCI-XGBoost  
Considering the mismatch of the quantitative relationship between process variables and quality variables caused by the inconsistent sampling rate in industrial processes, if only labeled samples are used for modeling, the dynamic information contained in many process variables will be ignored.  
In order to fully capture the potential dynamic information in the industrial process and use unlabeled samples to improve the performance of the model, this paper proposes a semi-supervised soft sensor model SSCI-XGBoost based on dynamic feature extraction. The full data are used to train the SCINet feature extractor. Then, the labeled sample and its dynamic features are used to train the XGBoost regressor. The modeling flow chart is shown in Figure 4. The modeling process is divided into two stages: offline training and online testing. In the offline stage, historical data are organized to determine the optimal parameters of the soft sensor model, and then, the model is trained. In the testing phase, the online data and the trained model are loaded to realize the online measurement.  
Figure 4: SSCI-XGBoost modeling flow chart.