esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms
## 1 Introduction
But regarding fractional-order PID (FOPID) controllers, existing studies have predominantly focused on validating their control effectiveness through dynamic performance indicators such as step response, while theoretical analysis and systematic validation of their stability and robustness against external disturbances remain insufficient. To overcome the bottlenecks of existing support equipment in terms of automation and robustness, this paper proposes a mechanical structure for an anchor drilling rig and a fractional-order control strategy synergistically optimized by intelligent algorithms and robust control. First, the automatic grabbing and loading of anchor bolts is achieved by integrating the anchor bolt magazine and a robotic manipulator; simultaneously, the level of automation is enhanced by adopting extended hollow anchor bolts combined with grouting support. Specifically targeting the robustness challenges in complex coal mine environments, the FOPID controller is optimized using the improved particle swarm optimization (ILPSO) algorithm to enhance dynamic performance. To further analyze and enhance the system's disturbance rejection capability under harsh working conditions, this paper integrates Hoo robust control to formulate a composite control strategy, and employs frequency domain analysis techniques to conduct a detailed analysis and validation of the system's stability and robustness. Simulations and frequency domain analysis demonstrate the advantages of the proposed control strategy in improving the precision and robustness of the support equipment, providing a theoretical and technical basis for enhancing the operational reliability of the support equipment in harsh environments.
## 3 Results
### Characterization of Microcapsules
#### 3.2.3 Water Content  
Drying conditions have an important effect on the moisture content of dried capsules. However, the composition of the formulations can also lead to changes in the water content, which will affect the properties of the dried product [30]. Indeed, the moisture content is an important variable in the shelf life of powders. Table 3 shows the percentage of water content in dried powders. The powders that were dried via SD contained more water compared to the powders dried via FD. Among SD powders, no considerable difference in water content was observed. The water content of SD powders ranged from 4.18% for P7S to 5.02% for L3S.This indicates that the emulsifier tested in this study does not have a big effect on the final water content of powders, except for P7S, which contains a slightly lower amount of water of 4.18%. [PERSON] et al. [31] showed that the water content of SD microparticles containing essential oils from _Varronia Verbenaceae_ and _Achryroline autreoides_ ranged from 3.4 to 5.2% in various storage conditions. In another study [30], SD microcapsules of clove extract with different formulations, two surfactants, and three drying carriers were obtained, and the sample with maltodextrin as the drying carrier showed a moisture content of 4.06%. Among the obtained FD powders, powders prepared using lecithin as emulsifier contained lesser amounts of water. Indeed, L7L and L3L contained 2.03 and 2.29% water, respectively. This is about half of the water content of powders emulsified using pea protein. However, there was no significant difference (\(p>0.05\)) between pea protein-emulsified FD powders at different pHs. The obtained water content was 3.53 and 3.59% for P7L and P3L, respectively, which is in agreement with the EE results. In fact, emulsions stabilized using lecithin and dehydrated via freeze-dryer had lower encapsulation efficiency than those emulsified using pea protein. Small variations were observed between powders produced at different pHs for all powders dried using the same drying method. The powders prepared at pH 3 had slightly more water content compared to the powders prepared at pH 7. In general, spray-drying and freeze-drying processes produce powders with lower water content for a longer storage period. In addition, this property is also important in our case because when microcapsules are added to food matrices, they should not significantly modify the water activity of these foods and, consequently, their physicochemical properties. Another study reported similar results with moisture content ranging from 2.7% to 5.0% in the encapsulation process of orange oil using freeze-drying [32]. This result related to water/encapsulating material interactions will be confirmed using thermogravimetric analysis (next section).
# Improved Arabic-Chinese Machine Translation with Linguistic Input Features
## Challenges and Approach
### Linguistic Issues  
Arabic and Chinese belong to different language families. Arabic is a morphologically rich and complex language with a typical verb-subject-object (VSO) order. Although VSO is the typical word order of Arabic, the corpora used show a mixed distribution of VSO, subject-verb-object (SVO), and verb-object-subject (VOS) clauses (VOS order is admitted in specific contexts, for instance using a pronoun to express the object). By contrast, Chinese lacks morphology and exhibits a systematic word order of SVO. Word order in Chinese is considerably closer to that in English compared with that in Arabic. Figure 1 shows an example of Arabic typical order and Chinese order.  
The different word order of this language pair makes the accuracy of word alignment in MT difficult to achieve. This issue causes data sparseness problems, given that numerous words are misaligned cannot be found in the training corpus, thereby leading to low-quality translation results.  
Furthermore, dealing with morphology-rich languages is difficult in SMT. A word in Arabic may have various morphologies to represent different meanings (affixes, stems, and clitics) with several types of morphological features, such as person, number, case, and tense. Moreover, optional diacritical marks in Arabic words cause further ambiguity. These features of Arabic cause more challenges, as they worsen the data sparsity issue, increase the OOV words, and consequently complicates the alignment of word-level in translation between Arabic and another language.  
In contrast, Chinese is considered a morphology-poor language. A Chinese word can be composed of a single character or multiple characters without word boundaries, and it has a complicated system of quantifier formation and verbal aspects. The authors in [18] discussed in detail the challenges of building a direct MT approach between Arabic and Chinese.  
Consequently, the morphology analysis of word units becomes increasingly necessary to improve the translation results between Arabic and Chinese. In this work, we present solutions for these issues by using preprocessing tokenization schemes and injecting linguistic factors into the source side of the translation corpora.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 5 Parametric Study
### Influence of Rebar Diameter  
Figure 22 illustrates the impact of rebar diameter on the pull-out performance of notched T-perfobond connectors. Increments in rebar diameter from 4 mm to 6 mm, 8 mm, 10 mm, and 12 mm resulted in minimal changes in pull-out resistance of \(-0.12\%\), \(-3.08\%\), \(-1.46\%\), and \(-0.34\%\), respectively. The findings indicate that the rebar diameter exerts a negligible influence on the ultimate pull-out resistance of the specimens. This outcome is likely attributed to the dominant anchorage effect provided by the flange and the notches, which overshadows the impact of rebar diameter variations on the ultimate resistance to pull-out.  
Figure 21: Influence of perfobond length. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.
## 3 Results and Discussion
### Transport Property Evaluation
This microstructure allows decoupling of thermal conductivity from the electronic transport by increased density of grain boundaries, thus increased scattering of phonons (mainly reducing the lattice component of the thermal conductivity). The combined overall positive effects were seen in the details of electronic and thermal transport properties, as presented below. The electrical conductivity (\(\sigma\)) is plotted in Figure 3b, where samples show either very minor change or slight reduction of \(\sigma\) by increasing temperature, which is typical of heavily doped semiconductors, or metallic character. The highest electrical conductivity values were measured for Sb\({}_{2}\)Te\({}_{3}\) and Bi\({}_{1.8}\)Sb\({}_{0.2}\)Te\({}_{3}\) samples. The electrical conductivity of our two n-type and one \(p\)-type Bi\({}_{2-x}\)Sb\({}_{x}\)Te\({}_{3}\) samples are higher than those of other solution derived samples [23; 31; 32] and comparable with that of \(p\)-type alloy ingot (on the level of \(1\times 10^{3}\) S/cm at 300 K) [33]. The lower electrical conductivity of Bi\({}_{2}\)Te\({}_{3}\) in comparison to Sb\({}_{2}\)Te\({}_{3}\) arises from the intrinsically small band gap, \(\mathbf{E}_{\rm g}\), of this compound (\(\sim\)0.13 eV) [34]. Given such a small \(\mathbf{E}_{\rm g}\), the minority carriers are more easily thermally excited, and the resulting detrimental bipolar effect also introduces extra thermal conductivity. Therefore, to increase the electrical conductivity of Bi\({}_{2}\)Te\({}_{3}\), the \(\mathbf{E}_{\rm g}\) has to be widened/broadened. The \(\mathbf{E}_{\rm g}\) of Sb\({}_{2}\)Te\({}_{3}\) is \(\sim\)0.28 eV [35], and Sb doping in Bi\({}_{2}\)Te\({}_{3}\) helps to optimize the carrier concentration and introduces strong point defect scattering of heat-carrying phonons. However, there is an inherent restriction of Sb doping: doping with too high a content of Sb will lead to too great a hole concentration because of the reduced formation energy of antisite defects, resulting in a reduced \(\mathbf{\kappa}\) and an increased \(\mathbf{\kappa}_{\rm e}\)[36]. This is the main reason that Bi\({}_{1.5}\)Sb\({}_{0.5}\)Te\({}_{3}\) shows both higher electrical and thermal conductivity. Similarly, Bi doping in Sb\({}_{2}\)Te\({}_{3}\) reduces the electrical conductivity likely due to decreased energy band gap and increased number of thermally activated minority carriers. As a result, doping with either Bi or Sb in Bi-Sb-Te compounds finely tunes the key electrical parameters such as the valley degeneracy, carrier concentration, effective mass, deformation potential, and band gap [37; 38].
## 3 Results and Discussion
### Fatty Acid Profiles and Health Indexes of Groin, Jowl, and Trimming Shoulder Fats  
In Table 4, the percentage composition of fatty acids in groups of the tested pork fats is presented. Three main groups of fatty acids were identified: monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), and saturated fatty acids (SUFA) in fats extracted from groin, jowl, and trimming shoulder of pigs fed liquid and dry diets. MUFAs were the main group of fatty acids found in groin, jowl, and trimming shoulder fats, their average share ranged from 46.11% \(\pm\) 3.03 for GF_L to 52.15% \(\pm\) 4.14 for TSF_L. The highest MUFA share was found in trimming shoulder fat of pigs fed a liquid diet. In the case of groin fat, no statistically significant differences were found between the average MUFA share in fat of liquid-fed (46.11% \(\pm\) 3.03) and dry-fed (46.50% \(\pm\) 2.84) pigs. The type of feed influenced the MUFA content in jowl fat and trimming shoulder fat. In both cases, the fats of pigs offered liquid feed (JF_L and TSF_L) were characterized by a higher MUFA percentage content. The greatest influence of the type of diet was found in the share of MUFA in trimming shoulder fat. The fat of pigs fed liquid feed (TSF_L) contained 10.72% more MUFA than the fat of pigs offered dry feed (TSF_D). Saturated fatty acids (SFA) were the second group of fatty acids in terms of average percentage content in the fatty acid groups. The highest SFA share was found in the groin fat of liquid-fed pigs (43.11% \(\pm\) 1.32), and the lowest in the jowl fat of pigs fed a dry diet (35.89% \(\pm\) 0.86). Groin and jowl fats extracted from pigs fed dry feed (GF_D, JF_D) contained less SFA than fats from pigs offered liquid feed (GF_L and JF_L). The opposite result was reported in the case of trimming shoulder fat--the fat of pigs offered liquid feed was characterized by a lower SFA share. The consumption of saturated fatty acids, according to the FAO/WHO dietary recommendations [40], should be less than 10% of the daily energy. The high consumption of these fatty acids may lead to the development of diseases such as obesity, circulatory system disorders, and digestive system cancers. Therefore, it is very important to modify the pigs diet to replace saturated fatty acids in the diet with mono- and polyunsaturated fatty acids. It is worth emphasizing that the type of feed system influenced the average percentage content of PUFA in all three types of fat. The average PUFA content in groin, jowl and trimming shoulder fats ranged from 10.78% \(\pm\) 1.02 for GF_L to 13.67% \(\pm\) 1.89 for TSF_D. A higher PUFA content was found in fat extracted from dry-fed pigs compared to the fat of pigs fed a liquid diet. [PERSON] et al. [41] examined the influence of fat in the diet of fatened pigs on the quality of meat, the profile of fatty acids and the sensory quality of pork. The
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 2 Background
### Joint Embedding  
Joint embedding [16], known as multi-modal embedding [17], is a technique that can encode heterogeneous data into a unified vector space. This technique is used for many tasks [18] (e.g., Krugle (Krugle code search, [[http://www.krugle.com/](http://www.krugle.com/)]([http://www.krugle.com/](http://www.krugle.com/)) (accessed on 2 August 2020)), especially in the image processing. For example, in the field of computer vision, [PERSON] and [PERSON] [19] used the joint embedding technique to jointly embed imagesand text into the same vector space for image labeling. In the code search field, we need to treat the code as a structural text and vectorize it.  
Vectorization of a text refers to the process of converting text into numerical tensors. In this paper, we decompose the text into a series of lexical tokens, and then use word embeddings and distributed representations to associate numerical vectors with these tokens. If two words have similar context, then even if the two words themselves are unrelated, they may carry similar semantic features. Since natural language queries and code fragments are heterogeneous, semantically similar codes and natural language queries may not have the same keywords or synonyms, and simply matching based on their keyword tags is prone to term mismatch. To remedy this deficiency, embeddings need to be generated separately for code and natural language descriptions.  
Word embedding is an embedding technique that makes vectors of similar entities close to each other, also known as a distributed semantic model [20,21]. It represents words as fixed-length vectors so that similar words are close to each other in the vector space. Word embeddings are often implemented using machine learning models such as CBOW and Skip Gram [22]. These models build a neural network to capture the relationship between words and their contextual representation.  
In this paper, we want to find the corresponding functional code snippet for the natural language query entered by the user. Considering the heterogeneity of code and natural language, we use a joint embedding technique to map the code and its corresponding language description into a unified vector space to achieve the purpose of code search. Due to many syntactic and structural features unique to the source code, we choose a collection of method names, API calls, and tokens to represent the code, and then combine them into a semantic vector representing the entire code entry.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion  
To illustrate the efficacy of the proposed DVKT framework, four public datasets are applied for evaluation. Furthermore, the experimental setup, comparison with the state of the art, and some ablation studies are given successively.
## 2 Related Works
### The Fuzzy Logic-Based Algorithm Used in Cluster Head Selection  
In [34], the author proposes an energy-aware distributed dynamic clustering protocol (ECPF). In ECPF, the non-probabilistic cluster head selection is implemented by introducing a delay inversely proportional to the residual energy of each node. Based on this, the cluster heads are selected according their remaining energy. The fuzzy logic is used to evaluate the fitness of a node to choose a final cluster head from the neighbor cluster heads. The inputs of the fuzzy logic system are the node degree and the node centrality. In [35], the authors propose a fuzzy logic-based energy-efficient multiple cluster head selection routing protocol for the wireless sensor networks. In this algorithm, the distance from the base station and the residual energy are the inputs of the fuzzy logic system to decidethe possibility of the node to be the cluster head. In [25], considering the expected residual energy, the authors propose the fuzzy logic-based clustering approach with an extension to the energy predication to prolong the lifetime of the wireless sensor network. In this algorithm, the residual energy and the expected residual energy are the inputs of the fuzzy logic system, and the output is the chance of the node to be the cluster head. The new algorithm selects the cluster head considering the expected residual energy of the sensor nodes. More papers on the fuzzy logic-based cluster head selection algorithm can be found in [36; 37; 38; 39].
## 2 Model and Data Description
### Data Description  
In this study, the WRF model is driven by both FNL and ERA5 datasets, and the CMA data are used to drive the Holland and Jelesnianski models. Then, the results are compared with the typhoon wind speed data recorded by the SMAP. Table 1 provides descriptions of the datasets used in this study. It should be noted that the CMA dataset in this study has been linearly interpolated to a 1 h time interval.
## 2 Study Site
### Hydrogeological Conditions  
According to different media, groundwater types include pore water from the porous medium and fracture water from the fractured medium. The pore water is widely distributed in the completely weathered belt and the Quaternary loose strata like \(\mathrm{Q^{al+pl}}\), \(\mathrm{Q^{del}}\), and \(\mathrm{Q^{col+dl}}\), whereas the fracture water is recharged by rainfall and surface runoff and occurs in fractures and fault zones.  
Nine boreholes were arranged to monitor the groundwater level in this study area, as shown in Figure 1a. The groundwater fluctuations of these boreholes during the construction of the underground powerhouse were plotted in Figure 4. It can be seen that the groundwater level of boreholes located in the upper reservoir and diversion tunnels remains almost constant. On the contrary, the groundwater level of boreholes around the underground powerhouse were subjected to an excavation-induced drawdown, with a maximum value of 122 m (YZK4). Therefore, the drainage effect on the water table drawdown of underground caverns has a significant impact on the groundwater environment.  
Figure 3: Stereographic plot of faults and joints around the underground powerhouse. The red line represents the fault, and the black line represents the joint. The blue breakpoint line represents the axis of the underground powerhouse. The number represents the group number of the joints.  
Figure 2: Geological cross section along the diversion tunnel #1 alignment.  
To evaluate the hydraulic conductivity of the rock mass, Lugeon pressure tests were conducted in boreholes, drilled in the upper and lower reservoirs, the waterway system, and the exploratory gallery. The statistical distribution of the measured mean hydraulic conductivities in this project is shown in Figure 5. It can be seen that 67% of the measured values are less than 1 Lu and 33% over. Among the latter, only 2% exceed 10 Lu. Moreover, larger values are widespread at the shallow surface because of the high variability in fractures. It can be clearly seen that the measured values have the tendency to be reduced with increasing depth. In this note, the conversion of the hydraulic conductivity units is 1 Lu for 1 \(\times\) 10\({}^{-7}\) m/s and vice versa.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 4 Results
### Polar-Orbiting Constellation  
Since the navigation service provided by the GNSS has limitations for users in high-latitude regions, a polar-orbiting configuration has been widely used in LEO augmentation navigation systems due to the advantage of polar-orbiting constellations of traveling north-south over both poles of the Earth and covering different sections of the Earth's surface. In this section, we take Iridium as an example and conduct the survey. Iridium consists of 66 satellites in 6 planes inclined at 52 degrees, and the orbital altitude is 778 km [38]. The configuration can be seen in Figure 4b. The result of the satellite PRN codes assignment is shown in Figure 9, and satellites broadcasting the same PRN code are shown to be far enough away from each other. These 10 PRN codes are sufficient for realizing CDMA and ranging of the Iridium constellation.  
In order to study the effect of the satellite orbital altitude on the number of minimum PRN codes, we changed the altitude of the satellite while keeping the other satellite parameters constant. Figure 10 shows the minimum number of PRN codes and the number of conflicts (the number of 1 in the conflict matrix \(R\)) for each altitude. The minimum number of PRN codes was found to be associated with the orbital altitude, and for a given constellation configuration and orbital inclination, the higher the altitude is, the more PRN codes are needed. When our proposed PRN code assignment strategy is applied, even if the LEO orbital altitude grows to 3000 m, 22 PRN codes are sufficient, which reduces the acquisition time by two-thirds and reduces the receiver's memory resources required by two-thirds compared to the method where a unique PRN code is assigned to each satellite.  
As can be seen from Figure 10, the approximate minimum number of PRN codes and the number of conflicts increase in a step-by-step manner. This phenomenon can be explained by Figure 11, which shows the critical state. If the satellite orbital altitude becomes higher, users can receive signals from two satellites at the same time. If the satellite orbital altitude becomes lower, users cannot receive signals from two satellites at the same time. On the one hand, if the other orbital parameters remain unchanged, the higher the satellite orbit is, the greater the satellite signal coverage is, the more satellites that can be seen by users on the ground, and the greater the conflict relationship between satellites is, so more PRN codes are needed. On the other hand, as the satellite altitude  
Figure 9: Graph of Iridium PRN code assignment. Ten colors and numbers represent ten PRN codes, which are enough to realize CDMA for Iridium constellation.  
increases, the number of conflicts is jumpy at the critical point in Figure 11 from 0 to 8, and these conflicts are A-B, B-A, B-C, C-B, C-D, D-C, D-A, and A-D. Because the distribution of satellites is uniform, the number of conflicts is jumpy at the critical point, and the approximate minimum number of PRN codes is jumpy, which is also characteristic of uniform constellations.