esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
## 1 Introduction
Based on the expressions, we consider the impact of \(m\) and \(D\) to the per-node throughput capacity and have some interesting findings. Our main contributions can be summarized as follows:  
1. We propose a new heterogeneous mobile network model, which consists of \(n\) mobile normal nodes and \(m\) stable helping nodes. The helping nodes are more powerful and they only work in routing and relaying data. Compared with the traditional hybrid network model, setting up such a backbone with helping nodes saves a lot of time and money. 2. According to the time scales and space dimensions, we consider four types of network model: two-dimensional i.i.d. fast mobility model, two-dimensional i.i.d. slow mobility model, three-dimensional i.i.d. fast mobility model and three-dimensional i.i.d. slow mobility model. We derive the capacities of the four types of mobility by both intuitive analysis and strict mathematical proof. 3. We analyze the asymptotic expressions on the network capacity. We find that the number of helping nodes can affect the throughput capacity significantly. The network capacity can achieve constant order if the number of helping nodes is large enough, and in this case the time delay can be ensured. We also conclude that the delay constraint \(D\) affects the throughput capacity. If \(D\) is not limited, the throughput can also stay constant order, while the time delay can not be ensured.
# Contextual Explanations for Decision Support in Predictive Maintenance
## 2 Related Work
It indicates that there is space for further research on XAI and time series in the context of PdM. Another substantial work [8] categorises the domain of this study as \"explainable predictive maintenance\". It presents reasons why explanations are required for PdM, discusses predictive maintenance tasks and XAI techniques and presents four industry-based use cases. Examples of predictive maintenance systems that are based on outlier detection methods and that offer SHAP-based explanations are presented in [11; 30; 31; 32]. These works deal with different applications of predictive maintenance, use SHAP as a ready-to-use method and do not analyse how comprehensible its explanations are to the user. Another approach, based on a semi-supervised model to identify the degradation of machine elements is presented in [33]. Counterfactual explanations were used in this study, which can be considered an easier to understand solution for the user. Finally, there are approaches to the predictive maintenance task [34; 35] where the aim of the explanations is to support a user in defining what maintenance actions should be performed and how the maintenance process may be optimised. The above review of related work shows numerous XAI methods, along with a growing array of applications employing these methods to explain the decisions of predictive maintenance models. Different papers in the field under review focus on different aspects--some on the model generated, others on explanations for time series, and finally there are those that take a user as a central reference point for the generated explanations. However, to the best of the author's knowledge, no research has been conducted that combined the above issues to improve the quality of explanations and support user decision-making, given the specifics of PdM, such as outlier analysis and temporal data. None of the aforementioned studies considered the explanation context that can be determined when data with temporal characteristics are analysed. Furthermore, there is a lack of research addressing the simplification of explanations for time series analysis using an extended representation composed of multiple derived variables.
## 2 Results
### Integration of Omics (Microbiomics and Metabolomics) Data and Patient Metadata
Pairwise similarity values represent both positive and negative connections between all parameters in the different model blocks. Similarity connections are considered as edges that connect nodes in the network graph. Solid gray lines represent positive associations, while dashed red lines represent negative associations. The thickness of the line indicates the strength of the association. Two modules—shown as separate circles—were identified, and they contained features that were strongly associated with each other. BMI, body mass index; FFM, fat-free mass; NLR, neutrophil-to-lymphocyte ratio; SM, skeletal muscle; SMI, skeletal muscle index; TAT, total adipose tissue; VATSAT, visceral-to-subcutaneous adipose tissue ratio. Finally, given that therapeutic response was inversely associated with the presence of sarcopenia, we specifically examined the differences in gut microbiota and metabolome between sarcopenia and non-sarcopenia patients (Figure 5). The gut microbiota of sarcomigenic patients were enriched in _Streptococcaceae_, _Enterobacteriaceae_, _Veillonella_, _Ruminococcus_, _Streptococcus_, _Butyricimonas_, and _Lactobacillus_, while they were depleted in _Ruminococcaceae_, _Oscillospira_, and _Akkermanisia_ (\(p\leq 0.05\), Wilcoxon test). In addition, sarcopenia and non-sarcopenia patients differed in many metabolites, including 2-heptanone, 6-methyl-, and nonanal, which were overabundant in the former patient type, and 1-pentadecene, 2-butanone, dichloroacetic acid 4-pentadecenyl ester, 1-hexanol 2-ethyl-, 2-hexanone, 2-pentanone, acetone, 1-butanol 3-methyl-, 2-pentadecanone, and butanal 3-methyl-, which were overabundant in the latter patient type. Figure 4: Elevated fecal levels of butyric acid at baseline are associated with a decreased survival rate in advanced melanoma patients. Kaplan–Meier curves for overall survival in the entire cohort. Patients were stratified based on basal fecal butyrate levels above or below the whole cohort median. Measurements were available through day 405 (vertical dashed red line), after which point a linear model trained on known data was used to predict future survival trends (through day 840). Cox proportional hazards model for days 0 to 405, \(p=0.05\); days 0 to 840, \(p=0.00018\).
## 3 Results and Discussion
### Peroxide Value of Groin, Jowl, and Trimming Shoulder Fats  
The peroxide values of the analyzed pork fat samples are presented in Table 3. Regardless of the fat type, pork fats of pigs fed a liquid diet were characterized by a higher peroxide value than fats of pigs fed dry feed. The highest peroxide values were determined for the groin and jowl fats of pigs fed liquid feed: 1.42 meq O\({}_{2}\)/kg of fat and 1.41 meq O\({}_{2}\)/kg of fat, respectively. The lowest peroxide value was determined in the case of jowl fat of dry-fed pigs (0.71 meq O\({}_{2}\)/kg of fat). The largest difference between fat peroxide values depending on the type of feed was observed in the case of jowl fat (1.41 meq O\({}_{2}\)/kg of fat for JF_L versus 0.71 meq O\({}_{2}\)/kg of fat for JF_D). In the case of groin or trimming shoulder fat, the differences in the peroxide value were much smaller than those obtained for the jowl fat. In the case of groin fat of dry-fed pigs, a 17.6% lower peroxide value than that for the fat of pigs offered liquid feed was observed, while in the case of trimming shoulder fat, a 13.01% reduction in peroxide value was determined. Based on the obtained results, it can be stated that the type of diet influenced the peroxide value of tested fats. The use of dry feed in pig nutrition reduced the peroxide value of pork fats extracted from groin, jowl, and trimming shoulder.  
[PERSON] and [PERSON] [39] conducted research on the influence of the location of fat in a pork carcass on its physicochemical properties. The authors determined the peroxide, acid, and iodine values of jowl, groin, beacon, and lard fats. For groin fat, the peroxide value reached 1.09 meq O\({}_{2}\)/kg of fat. This result was similar to the value obtained for the groin fat of dry-fed pigs in this study (1.17 meq O\({}_{2}\)/kg of fat). The jowl fat of pigs fed a liquid diet was characterized by an average peroxide value of 1.41 meq O\({}_{2}\)/kg of fat. [PERSON] and [PERSON] [39] reported a similar peroxide value for jowl fat (1.37 meq O\({}_{2}\)/kg of fat).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **GF\_L** & **GF\_D** & **JF\_L** & **JF\_D** & **TSF\_L** & **TSF\_D** \\ \hline Peroxide value (meq O\({}_{2}\)/kg of fat) & 1.42 \(\pm\) 0.39 \({}^{\mathrm{d}}\) & 1.17 \(\pm\) 0.24 \({}^{\mathrm{b,c}}\) & 1.41 \(\pm\) 0.24 \({}^{\mathrm{d}}\) & 0.71 \(\pm\) 0.24 \({}^{\mathrm{a}}\) & 1.23 \(\pm\) 0.24 \({}^{\mathrm{c}}\) & 1.07 \(\pm\) 0.24 \({}^{\mathrm{b}}\) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline  & GF\_L—groin fat (liquid feed); GF\_D—groin fat (dry feed); IF\_L—jowl fat (liquid feed); IF\_D—jowl fat (dry feed); TSF\_L—trimming shoulder fat (liquid feed); TSF\_D—trimming shoulder fat (dry feed). Data denoted by the same lowercase letters are not statistically different (\(\alpha\) = 0.05) in terms of peroxide value.  
\end{table}
Table 3: Peroxide value of pork fats.
## 1 Introduction
In that case, the numerical simulation must predict the structural behavior under load with very high accuracy. The present study constitutes part of the work towards that end. Its main contribution is the characterization of flexural properties of 3D printed CFC with a sufficient sample size to reduce epistemic uncertainty. It is demonstrated that a larger sample size leads to more accurate quantification of the mechanical properties and their variabilities. This fills a current knowledge gap, as no study with a significantly large sample size has been published to date. Tensile tests were intentionally not performed, as they often yield invalid measurement results. For instance, there is a risk of premature failure near the clamps caused by the superposition of tensile stress and surface pressure if clamping jaws are tightened too much. If the clamps are not tightened strongly enough, the sample can slip. The specimen must be discarded in both cases [48; 49]. In this work, the flexural properties and their variabilities were experimentally quantified using a three-point bending test and a total of 30 test specimens. 15 specimens with 0\({}^{\circ}\) and 15 specimens with 90\({}^{\circ}\) fiber direction were tested to determine the longitudinal and the transverse flexural properties. Among fiber reinforcement materials, carbon fiber offers the highest strength and stiffness. Therefore, lightweight industries are particularly interested in carbon fiber composites. In addition, the availability of 3D printing systems plays an important role in industrial adoption. Markforged is a market leader of CFF 3D printers. Its systems are widely used in the industry (in the lightweight sector, especially). Also, filaments tailored for the printer are advantageously available directly from the manufacturer. In the present study, a CFF desktop 3D printer (Mark Two, Markforged) and carbon fiber reinforced nylon filament were used to manufacture the test specimens. The next chapters are as follows: Section 2 describes microstructure analysis methods for the composite material before and after printing, and the mechanical testing method used to determine the flexural properties of the material. Section 3 presents the results of the material analysis and mechanical experiments. Section 4 provides a discussion of the previous results. Especially, the large observed variability of the mechanical properties is discussed. A conclusion and an outlook on future work constitute the final section of this paper.
Bark from the okra plant can be utilized as a source of natural fibers. Similar to other conventional plant fibers, various methods can be used to obtain the fibers from okra plant bark, including water-retting, dew-retting, chemical-retting and mechanical extraction by a number of degumming techniques based on the biological activity of microorganisms, the effects of chemical agents or mechanical means [3; 24]. Unlike cotton, which has single-cell elementary fibers, okra past fibers are technical fibers that are multicellular and lignocellulosic, which is similar to jute and hemp. Okra past fibers are partially separated bundles of cellulosic fibers attached by hemicellulose, lignin, and pectin [19; 25]. The literature on okra past fibers is limited to a few studies. The influence of some chemical treatments on the physical characteristics of okra past fibers was investigated by [PERSON] et al. [26]. The mechanical, chemical, morphological, and thermal performance of okra bast fibers and the effects of some chemical treatments, including scouring, bleaching, and acetylation, on these were studied by [PERSON] et al. (2018); [PERSON] et al. (2018). [PERSON] et al. (2018) investigated the influence of some chemical and enzymatic treatments on the physical, mechanical, and chemical characteristics of okra bast fibers. They also dyed the okra fibers with an industrial biowaste colorant (green mate tea waste) and studied the ability of okra fibers to remove heavy metal ions from aqueous solutions. [PERSON] and [PERSON] (2018) have reported that the chemical composition of okra bast fibers is cellulose (60-74%), hemicellulose (15-20%), lignin (5-10%), and pectin and waxes. Even though the literature contains some studies on okra bast fibers, there are none available on dew-retting okra bast fibers, as all of the mentioned studies have investigated water-retted okra bast fibers. Furthermore, there are no systematic studies of the thermal properties of okra bast fibers. The aim of this work is to investigate and compare the properties of okra fibers obtained from various parts of the plant via dew-retting and water-retting. Special emphasis has been given to the thermal performance of okra bast fibers as they are processed under various conditions, and as part of composite systems operating at elevated temperatures.
## 2 Observation Methods
### Vertical Total Electron Content VTEC  
One of the key parameters used to characterize ionospheric conditions is the vertical total electron content (VTEC). The TEC represents the total number of free electrons integrated along the ray path between a satellite of the Global Navigation Satellite System (GNSS) located at an altitude of 19,100 km to 23,300 km (thus, a radial distance from the centre of the Earth of approximately 4 Earth radii, Re) and the receiver on the ground. The TEC is measured in TEC units (TECu), where 1 TECu = 10\({}^{16}\) electrons/m\({}^{2}\). The VTEC is the vertical projection of TEC at the Ionospheric Pierce Point (IPP), which intersects above the electron density peak at a 450 km height.  
The VTEC is directly related to the GNSS signal propagation delay caused by the ionosphere. These ionospheric conditions are continuously monitored by the STCE in Brussels based on the GNSS observations of the EUREF Permanent Network (EPN) [21] and processed using the ROB-IONO software [22].  
Real-time GNSS data from \(\sim\)150 stations (GPS + Galileo + GLONASS) are used to estimate the VTEC at the IPP every 30 s, covering a 5 min time span. This approach provides extensive spatial and temporal coverage for monitoring the ionosphere state. For each 5 min interval, median VTECs at the IPP for each satellite-receiver pair are estimated along with their standard deviation and are then interpolated to produce VTEC and VTEC variability maps above Europe. The methodology for producing these maps and their validation against widely used post-processed Global Ionospheric Maps such as IGS and ESA, with mean differences of 1.3 \(\pm\) 0.9 and 0.4 \(\pm\) 1.6 TECu, respectively, are detailed in [22].  
The VTEC time series provided in this paper are extracted from these maps at three locations:  
1. In the northern part of Europe (61\({}^{\circ}\) N, 5\({}^{\circ}\) E);
2. Above Brussels, in mid-latitude Europe (50.5\({}^{\circ}\) N, 4.5\({}^{\circ}\) E);
3. In North Africa (36\({}^{\circ}\) N, 5\({}^{\circ}\) E).  
The expected VTEC at each location corresponds to the median of the 15 previous ones for the same local time.
# Impact and Stability of mandibular Setback after Intraoral Vertical Ramus Osteotomy
## 4 Discussion
### TMJ Dysfunction  
In the existing literature, there is controversy on the effect of orthognathic surgery on the TMJ. Several studies [20, 21, 22] reported that performing orthognathic surgery, especially IVRO, improves TMJ disorders and pain due to the fact that the condyle tends to move in an anterior and inferior direction, reducing pressure on the articular disc. Additionally, the absence of rigid fixation in IVRO allows the condyle to return to its physiologic equilibrium position. Another explanation could be related to the hypothesis that there is a difference in the morphological TMJ in skeletal class III that affects TMJ function, supporting the fact that after orthognathic surgery, the condyle is restored to its natural position and that may contribute to the improvement of TMJ disorders [23]. In this study, TMJ disorders and dysfunctions were examined before and after the surgery. Twenty-three percent of the patients reported at least one symptom regarding TMJ at T0, as opposed to only 15.3% at T2; thus, improvement was observed, but it was not statistically significant (\(p=0.089\)). A lack of a statistical difference could be caused by the small number of patients examined.
# CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning
## 5 Discussion and Conclusions  
This study proposed CIMI, a deep-learning-based medical image AI processing system that classifies and extracts text from medical images. CIMI not only classifies PFT results, but also extracts medical images to text in a standardized medical data form for big data or AI researchers. We also provided a CIMI validation tool for related researchers to assess the accuracy of their algorithm. The accuracy of Tesseract OCR, which is mainly used in CIMI, is not always constant and is greatly affected by the application of various pre-processes and whitelists [32]. The PFT used in our study was particularly affected by the number of characters, size, thickness, color, and margins between the letters. The higher the number of characters, the larger the size, the thicker the characters, and the greater the color difference between the characters and the background, the higher the accuracy. The margins between the letters showed greatest impact in accuracy when characters and margins were best distinguished. In other words, if the margins between characters were extremely high or small, the boundaries between characters became blurred, which resulted in lowered accuracy. Because of these characteristics, the same Tesseract OCR base algorithm showed different accuracy according to PFT type or algorithm. (Table 2)  
Type 1 and type 2 format was found to be similar (Figure 7a). This resulted in little difference between the CIMI algorithm (86%) and vanilla algorithm (72%) accuracy. This was because the letter of these PFTs are thicker, larger in size, and have proper margins between the letters than other PFTs. These types generally showed lower accuracy than other types; for example, CIMI with a standard of 80%. This was because within these PFTs, there were more \"%Chg\" items that consist mostly of one word compared to other PFTs. For types 3, 4, and 7, where format is similar to each other (Figure 7b), the accuracy difference between CIMI and vanilla, grayscale algorithms is large, because the letters in these PFTs are thin and small. Difference is most severe in type 3, moderately severe in type 7, less in type 4, because type 3 has the narrowest margins between letters, whereas type 7 has more \"%Chg\" items than other PFTs. Type 4 had a similar font size and thickness compared to type 3 and type 7, but the margins between the characters were wide, and there was no \"%Chg\" items, therefore showing a moderately severe difference. For type 5 and type 6, which were similar (Figure 7c), despite the smallest letter size compared to other PFTs, the accuracy between CIMI and other algorithms were relatively small, because the letter
###### Abstract  
A new method for the synchronous vibration parameter identification of constant-speed rotating blades based on blade tip clearance (BTC) measurement and blade tip timing (BTT) is introduced. A BTC sensor is used to measure the BTC when the blade tip passes through each sensor. The BTT method is used to determine whether the blade tip arrives in advance or lags. The geometric model between the BTC and the blade tip vibration displacement (BTVD) is established, and the BTVD of the blade tip passing through each sensor is obtained. Then, the nonlinear least squares method is used to determine the synchronous vibration parameters of the constant-speed rotating blade. The results show that with an increase in amplitude, the higher the accuracy of the vibration parameter identification proposed in this paper; with a decrease in the random error of the BTC measurement, the higher the accuracy of the vibration parameter identification proposed in this paper; with a decrease in the random error in the measurement of the blade disk dimensions, the higher the accuracy of the vibration parameter identification proposed in this paper. In addition, the smaller the ratio of the blade length to the blade disk radius, the higher the accuracy of the vibration parameter identification method introduced in this paper. Because the structure of the gas turbine compressor and turbine blade disk has a small blade disk ratio, the method proposed in this paper is suitable for the simultaneous vibration parameter identification of gas turbine compressor blades and turbine blades.  
constant rotating speed; synchronous vibration; parameter recognition; blade tip clearance measurement