esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption of Sulfamethoxazole  
Figure 5 shows the adsorption isotherms (20 \({}^{\circ}\)C) of SMX with the GSAC-KOH-3, GSAC-FeCl\({}_{3}\)-3 and GSAC-H\({}_{3}\)PO\({}_{4}\)-3 activated carbons and a commercial active carbon. The adsorption capacity seems to be mainly determined by the BET surface area of the carbons rather than the amount and character of the surface groups (Figure 4). Consistently with its much higher surface area, GSHC-KOH-3 yielded the highest adsorption capacity. The only deviation of that behavior occurred with the GSHC-H\({}_{3}\)PO\({}_{4}\)-3 activated carbon, which showed lower adsorption than the expected from its BET surface area. This can be explained in terms of SMX ionization and the pH slurry of this carbon. At solution pH 4.6, SMX (pKa1: 1.7, pKa2: 5.6 [63]) must be majorly made of neutral and anion species; since the pH slurry of GSHC-H\({}_{3}\)PO\({}_{4}\)-3 is 2.2, its surface will be negatively charged, which induces repulsion forces between the molecules of SMX and the carbon surface [64; 65].  
Figure 4: FTIR spectra for GS, GS-220-40 hydrochar and activated carbons. Results of TPD of the activated carbons in the inside Table.  
The experimental data were fitted to the Langmuir equation:  
\[q_{e}=\frac{q_{L}\cdot K_{L}\cdot C_{e}}{1+K_{L}\cdot C_{e}},\]  
where \(q_{e}\) is the equilibrium adsorbate loading onto the adsorbent (mg\(\cdot\)g\({}^{-1}\)), \(C_{e}\) the equilibrium liquid-phase concentration of the adsorbate (mg\(\cdot\)L\({}^{-1}\)), \(q_{L}\) the monolayer saturation capacity (mg\(\cdot\)g\({}^{-1}\)) and \(K_{L}\) the Langmuir constant (L\(\cdot\)mg\({}^{-1}\)). This equation described the equilibrium data well, as can be seen in Figure 5. The values of the fitting parameters and the correlation coefficients are given in Table 4. The calculated monolayer Langmuir capacity of GSHC-KOH-3 reached 650 mg\(\cdot\)g\({}^{-1}\) (notice that in Figure 5 the represented experimental SMX values did not reach the saturation level). The SMX adsorption capacity of this material is higher than the exhibited by other adsorbents, such as activated carbon from pine tree or coconut shell, coal, carbon nanotubes, graphene and organo-montmorillonite at similar operating conditions, which are characterized by maximum adsorption capacities within the range of 122-242 mg\(\cdot\)g\({}^{-1}\)[65; 66; 67].
## 2 Materials and Methods
### Preparation of Yeast Film-Forming Emulsion and Film Casting  
Yeast films (YFs) were prepared from 15% (\(w/w\)) baker yeast suspended into ultrapure water (0.054 uS/cm at 25 \({}^{\circ}\)C; Ultra-pure Water System, SMART N, Heal Force, Shanghai, China) under continuous magnetic stirring (250 rpm, 10 min) following a modified method [16]. The pH of yeast suspension was maintained at physiological levels, approximately pH 6. To facilitate the release of the cytoplasmic protein, rupture of yeast cells was performed through ultrasound treatment and applied in an ice bath for 80 min at a 30% amplitude, a frequency of 20 kHz, and 500 W, using a 13 mm titanium tip probe (VCX 500, Sonics Vibra Cell, Newtown, CT, USA), with a pulsation cycle of 59 s on and 15 s off. The sonication energy released into the system at the end of the treatment was 125 kJ. After sonication, the yeast suspension was thermally treated for protein denaturation, mannans and \(\beta\)-glucans unfolding, and enzymatic inactivation [9] at 92 \({}^{\circ}\)C for 30 min, followed by cooling in the iced bath to stop the denaturation. Glycerol and Tween 80 were used as plasticizers and stabilizer, respectively. To determine the best concentration of plasticizers, initial tests were performed according to other reported protocols [4; 9]. Since high plasticizer concentration produced oiling while lower concentrations determined brittleness and it was not suited for this matrix, the optimum interval was set to be 1.00%-1.50% (\(w/w\)) [17]. Experiments were performed using 1.00, 1.25, and 1.50% \(w/w\) of glycerol and Tween, each added to pretreated cooled yeast suspension, which was further functionalized with 2.00% \(w/w\) from a 1:1 mixture of cinnamon and lavender essential oils (CLEO) (AromaZone, France). The resulting film-forming emulsions were homogenized with an Ultra Turrax T 18 at 12,000 rpm (IKA, Staufen im Breisgau, Germany) to obtain stable film emulsions, which were further subjected to degassing under vacuum to eliminate air bubbles before being casted in silicone trays and dried at 25 \(\pm\) 1 \({}^{\circ}\)C and a 50% RH. The following samples were obtained: YF1 (1.00% \(w/w\) glycerol, 1.00% \(w/w\) Tween 80, and 2% \(w/w\) CLEO), YF2 (1.25% \(w/w\) glycerol, 1.25% \(w/w\) Tween 80, and 2% \(w/w\) CLEO), YF3 (1.50% \(w/w\) glycerol, 1.50% \(w/w\) Tween 80, and 2% \(w/w\) CLEO), and respective control samples, without CLEO-YF1c, YF2c, and YF3c. YFs were conditioned in a desiccator at a 50%RH for a minimum of 72 h before testing. Duplicate samples were analyzed and reported as average values.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Other Signals
#### 3.4.2 Exhaust Gas Temperatures  
In two studies, [PERSON] and [PERSON] [42; 43] presented a novel approach for knock detection using a combination of traditional engine sensors and an exhaust gas temperature sensor as inputs for an artificial neural network. The underlying premise of their model was that pressure and temperature fluctuations in the engine resulted in autoignition and, hence, knock; these fluctuations, in turn, modify the heat transfer out of the engine, resulting in changes in the exhaust gas temperature. Experimental tests on a turbocharged gasoline-powered vehicle generated around 5 million data sets, which were filtered and used for network optimization. The neural network achieved a 92 percent precision in detecting knock occurrences. This approach appears promising, especially since the exhaust gas temperature is a signal that is already measured in all production vehicles.
###### Abstract  
Accurate and low-complexity phase estimation is crucial for optimal data recovery in coherent optical receivers, especially for applications in emerging scenarios such as low-margin optical networks, green networks, probabilistic-shaping modulation, the use of high-order modulation formats in hollow-core fibers, long-haul links operating at low signal-to-noise ratios, and free-space optical links in low-earth orbit satellites, among others. These new developments demand highly efficient and reliable data transmission methods, even under stringent conditions of minimal operational excess margin. This paper introduces an efficient pilot-assisted phase estimation strategy for coherent optical receivers, integrating robust interpolation techniques for quasi-optimal operation. Our approach significantly enhances phase estimation accuracy, addressing the unique challenges posed by these new scenarios. Through comprehensive simulations, we illustrate our method's superiority over conventional methods, showcasing marked improvements in computational complexity and bit error rate. The results highlight the critical role of sophisticated interpolation in bolstering pilot-assisted phase estimation, offering a promising technique for optimizing performance in next-generation coherent optical receivers.  
Keywords:coherent optical receivers; low-margin optical networks; pilot-assisted phase recovery; phase outliers mitigation; robust phase interpolation; high-order modulation _Article_
# Design and Analysis of Independently Adjustable Large In-Pipe Robot for Long-Distance Pipeline
## 2 Overview of Robot
### Detection System and Self-Checking System  
There are types of sensors installed in different parts of the robot, making it not only a mobile platform, but also as a kind of unmanned pipeline detector.  
There are three types of sensors for motion control: pressure sensors, encoders and laser radar. As depicted in Figure 3, pressure sensors were installed between loading wheels and tracked bracket. Independent speed measurement and self-checking system depend on encoders. Abnormal conditions can be detected immediately. The laser radar was installed on an aluminum profile rod in front of the robot. As a crucial sensor of motion control, lidar can monitor the change of pipe diameter in real time. When deformation and siltation cause irregular terrain or meeting reducing coupling, elbows and \(T\)-branches, lidar makes an accurate judgment.  
Getting stuck in pipe would be fatal for any robot. At present, most in-pipe robots are mainly based on artificial judgment and determining status by visual image. We want robot can have certain self-adjusting capabilities in complex environment, so we set up a self-checking system. Its main role is to ensure the safety of the robot's movement and will work in the cable or no cable operation.  
Figure 2: (**a**) CAD model of the crawler. (**b**) CAD model of the pantograph bracket.  
_Appl. Sci._**2020**, _10_, 3637 Where a, b, c, d, e, f, g, h, j, s are the external dimensions of robot,,,, are the posture angle of the pantograph bracket. The dimensions are shown in Figure 5.  
\[\left\{\begin{array}{c}F_{1}sin\alpha+F_{2}sin(\alpha+\theta)+F_{3}sin\beta=G^{ 0}\\ F_{1}cos\alpha+F_{2}cos(\alpha+\theta)=F_{3}cos\beta\\ a\ F_{1}sin\alpha+(a+g)\ F_{2}sin(\alpha+\theta)+(a+g+b)\ F_{3}sin\beta=G^{0}j \end{array}\right. \tag{1}\]  
where \(\theta\) is the angle between \(F_{2}\) and pantograph bracket, \(G0\) is gravity of central body projected on this surface, \(j\) is the distance from \(O_{1}\) to \(G^{0}\).  
Finally, we get the equation of forces acting on the crawler.  
\[\left\{\begin{array}{c}F_{1}^{0}sin\alpha+F_{2}^{0}sin(\alpha\quad\mu)=F_{N} \\ f\ F_{1}^{0}sin\alpha+(f+g)F_{2}^{0}sin(\alpha\quad\mu)=F_{N}\ s\end{array}\right. \tag{2}\]  
where \(F_{N}\) indicates the equivalent force acting on crawler frame, \(s\) indicates its distance from \(O_{3}\).  
The equation become solvable when the external dimensions of the robot are taken. Since the pantograph mechanism was calculated to make the crawler has effective support and to cope with the uneven pipe wall with different length electric putter, the robot is adaptable to the uneven pipe wall, as well as providing average supporting forces during movements.
###### Abstract
For instance, potassic and phyllic alteration are predominantly associated with sulfide mineralization, whereas propylitic and argillic alteration are sulfide-free or contain minor amounts of sulfides [2; 6; 9]. Hydrothermal alteration is characterized by ionic metasomatis that consists of alkali metasomatism, hydrolytic (or acidic) reactions, oxidation/reduction reactions, and hydration-carbonation reactions, among others [9]. Moreover, element gains and losses always occur during metasomatic processes. Investigating the element migration associated with hydrothermal alteration has crucial significance for the establishment of mineralization and alteration zoning and for a deep understanding of the ore-forming process [13; 14; 15]. However, the element migration characteristics in distinctive mineralization and alteration zones of porphyry deposits are relatively unclear and lack a detailed case study. The Beiya gold-polymetallic deposit is typical and representative of Cenozoic porphyry-skarn-type deposits in the Sanjiang metallogenic belt, northwestern Yunnan, China, with gold reserves of more than 200 tonnes and a gold grade of about 2.45 g/t [16]. Three stages of magmatic activities are mainly developed at Beiya: (1) quartz albite porphyry (QBP) and lamorphyre are formed in 65\(\sim\)60 Ma [17; 18]; (2) quartz syenite porphyry (QSP) or monzogranitic porphyry (MGP) are formed at about 37\(\sim\)34 Ma [19; 20; 21; 22]; and (3) there is 3.78\(\sim\)3.66 Ma of biotite syenite porphyry (BSP) [17]. The Re-Os isotope ages of molybden are 36\(\sim\)34 Ma [21; 22], indicating that mineralization is closely related to QSP or MGP in the late Paleogene, and the sources of ore-forming fluids and materials are mainly derived from magma [23; 24; 25; 26]. Five types of mineralization--porphyry, skarn, hydrothermal vein, stratabound, and supergene-enriched--are well developed in the Beiya deposit [16; 27; 28; 29; 30] and are intensely controlled by various structures and lithologies [16; 27; 30]. There are three main types of hydrothermal alteration--porphyry alteration, contact zone alteration, and wall-rock alteration--that occur in the Beiya deposit [16; 19], and [PERSON] et al. [16] proposed the characteristics of a metallogenic element zone. Nevertheless, the element migration of these mineralization-alteration zones has not been discussed. In this paper, we identify the various types of mineralization and alteration and their occurrence features, carry out element migration regularity of different mineralization-alteration zones, and then establish the model of mineralization and alteration zoning, which provides new understandings of metallogenic regularity and a scientific basis for prospecting prediction of the Beiya deposit (Figure 1), as well as the similar porphyry deposits in the region and elsewhere in the world.
# Avoiding Obstacles via Missile Real-Time Inference by Reinforcement Learning
## 2 Missile Dynamics and Environment Modeling
### Environment Modeling
#### 2.3.2 Environment Modeling for Scenario B  
For scenario B, the three-dimensional rough terrain environments are generated as visualized in Figure 4.  
The environment has a planar size of 10,000 m for both the x- and y-axis, and the limitation of height is the highest peak. For each episode, the missile agent must hit the target without colliding with the terrain but while maintaining spatial limits. To formulate the mountainous area, numerical values within the range shown in Table 4 are randomly chosen.  
With the parameters above, the following function generates the height map of the field:  
\[f(x,y)=\sum_{i=0}^{n_{\Lambda}}-\Lambda_{M_{i}}\exp\Biggl{\{}-\frac{(x-x_{M_{ i}})^{2}+(y-y_{M_{i}})^{2}}{\sigma_{M_{i}}}\Biggr{\}} \tag{14}\]  
where \(n_{\Lambda}\) is the total number of peaks and \(\Lambda_{M_{i}}\) is the minimum height of the \(i\)th peak. The height of the \(i\)th peak is higher than the value \(\Lambda_{M_{i}}\) due to the effect of the value of the surrounding peaks. \(x_{M_{i}}\) and \(y_{M_{i}}\) represent the planar position of the \(i\)th peak on the \(x\) and \(y\) axis, respectively. \(\sigma_{M_{i}}\) is the deviation, which shows a more widely spread mountain shape as its magnitude increases.
# Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure
## 2 Methodology and Data
### Attack Strategies
#### 2.2.1 The Initial Load Distribution and Capacity of the Node  
In the cascading model, the initial distribution of load \(L_{i}\) is based on the importance of node \(i\)[46]. Then, the initial load of node \(i\) is usually defined as  
\[L_{i}=\rho k_{i}^{\xi}(\xi>0) \tag{2}\]  
where \(k_{i}\) is the degree of node \(i\). \(\rho\) is a constant. \(\xi\) is a tunable parameter that is used to control the strength of the initial load of node \(i\) in the network. Since the logistics hub city has the characteristics of fast-in and fast-out and large transit volume, the weighted degree centrality is consistent with this characteristic. Therefore, the strength of node \(i\), that is, the weighted degree centrality, is taken as the initial load of node \(i\).  
\[L_{i}=k_{wi} \tag{3}\]  
where \(k_{wi}\) is the weighted degree centrality of the node.  
The capacity \(C_{i}\) of node \(i\) is determined following the ML model [1], assuming that the capacity is proportional to its initial load, namely,  
\[C_{i}=L_{i}(1+\alpha)(0\leq\alpha\leq 1) \tag{4}\]  
where \(\alpha\) is the capacity parameter that represents the capacity or tolerance of the network in cascading failures. The smaller the capacity parameter is, the more sensitive the urban node is to the change in logistics volume, while a larger capacity parameter indicates that the city is basically not affected by the distribution of logistics volume in other cities.
# Learning Robust Shape-Indexed Features for Facial Landmark Detection
## 2 Related Work
In recent years, with more attention to severe occlusion and large pose variations, an increasing number of works [16; 33; 34; 35; 36; 37; 38; 39; 40; 41] have aimed at overcoming such obstacles in facial landmark detection. RCPR [16] detected the occlusion area while estimating the landmarks, and used the occlusion proportion of the area to weight the regressor. PCD-CNN [33] took the detected 3D face pose as the initial condition to detect landmarks under large pose variations. ODN [34] achieved robustness for occlusion by applying adaptive weights to facial regions and restored low-rank features of occluded regions by exploiting the geometric structure of the face. LUVLI [35] used a stacked hourglass network to jointly estimate landmark locations, the uncertainties of these predicted locations, and the visibility of landmarks. CCDN [36] proposed a cross-order cross-semantic deep network to activate multiple related facial parts, which fully explored more discriminative and fined semantic features to solve the problems of partial occlusions and large pose variations. MTAAE [37] proposed a multi-task adversarial autoencoder network based on the idea of multi-task learning, which could learn the more representative facial appearance and improve face alignment performance in the wild. SAAT [38] proposed a sample-adaptive adversarial training approach, in which the attacker generated adversarial perturbations to reflect the weakness of the detector, and the detector must improve its robustness to adversarial perturbations to defend against adversarial attacks. DSCN [39] proposed a dual-attentional spatial-aware capsule network to improve the ability to capture the spatial positional relations between landmarks by using the capsule network that can remember the location information of the entity. MSM [40] used spatial transformer networks, hourglass networks and exemplar-based shape constraint to detect landmark under unconstrained conditions. [PERSON] et al. [41] designed two teacher networks, a Tolerant-Teacher and a Tough-Teacher, to guide the lightweight student network. The Tolerant-Teacher was trained using soft-landmarks created by active shape models, while the Tough-Teacher was trained using the ground truth landmarks. Meanwhile, they designed an assistive loss to determine the landmarks of teacher network prediction as positive or negative auxiliary.
## 4 Case Studies: Using DSM to Address GPM Limitations in DPD
### Modeling a Second Process with DSM (PROVE) and Translating to GPM (OPM)  
In Figure 13, we present a PROVE diagram of a second process description from another case study reported in [24]. This diagram contains 28 representational elements. We convert the process description to an equivalently rigorous OPM model (Figure 14).  
Figure 12: Process description modeled PROVE.  
The manual conversion relies on the same modeling pattern that was used in the first case study, as illustrated in Figure 11. PROVE activities are represented by OPM processes. PROVE artifacts are represented by OPM objects; and PROVE states are represented by OPM objects that are associated with the artifact object by using exhibition relationship (i.e., the OPM artifact object exhibits the OPM state objects); each state object includes two states indicating if this state was achieved or not. Furthermore, the relations to/from an OPM process are limited: relations to a process (i.e., process input/prerequisites) are strictly in the form of an instrument link from an \"achieved\" state (of a state object exhibited by an artifact object); and relations from a process (i.e., process output) are strictly in the form of a result link to an \"achieved\" state (of a state object exhibited by an artifact object).  
The equivalent OPM model contains 74 representational elements, suggesting that the OPM modeling effort for this case is at least 2.6 times more significant, prior to considering  
Figure 14: OPM diagram of the IP Protection Oversight case study.  
Figure 13: PROVE diagram of the Intellectual Property (IP) Protection Oversight case study (adopted from [24]).  
the additional cognitive effort that OPM would have required (had we not provided the singular process modeling method and pattern of Figure 10).
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments  
### HNF4A-MODY (MODY 1)  
MODY 1 is caused by a mutation in the hepatocyte nuclear factor 4A (_HNF4A_) gene, which is expressed primarily in the liver, but also in the pancreas and kidney. The _HNF4A_ gene regulates the expression of genes involved in lipid metabolism and hepatic gluconeogenesis [2, 33, 34]. The mutations in this gene result in decreased insulin production, which is linked to autosomal dominant non-insulin T1 DM [35]. Heterozygous mutations in this gene result in beta cell dysfunction, impaired glucose-stimulated insulin secretion, elevated low-density lipoprotein (LDL), and low levels of high-density lipoprotein (HDL) and triglycerides [35]. Affected individuals may present with fetal macrosonia, transient neonatal hyperinsulinemic hypoglycemia, progressive development of hyperglycemia, and the onset of diabetes mellitus in late adolescence or by the age of 25 years [24]. During the first decade of life, MODY 1 patients present with normal glucose tolerance [34]. At the time of diagnosis and in the early stages of the disease, patients with MODY1 can control their glycemia solely through diet, though both have elevated postprandial glucose levels after eating carbohydrate-rich foods [36]. However, most patients' \(\beta\)-cell function will deteriorate over time, necessitating pharmacological treatment [27]. Individuals with _HNF4A_-MODY are sensitive to sulfonylurea [27] and are best treated with low-dose sulfonylurea rather than insulin (Table 2) [30, 37, 38, 39], due to an increased insulin secretory response in the pancreas to sulfonylurea and increased insulin sensitivity to secreted insulin [30]. However, in advanced disease or during pregnancy, insulin therapy is usually required [34].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene Name** & **Pathophysiology** & **Treatment** \\ \hline _HNF4A_ & \(\beta\)-cell dysfunction & Sulfonylurea, Insulin \\ _GCK_ & Glucose-sensing defects, \(\beta\)-cell dysfunction & Diet, except possibly during \\ _HNF1A_ & \(\beta\)-cell dysfunction & pregnancy \\ _HNF1A_ & \(\beta\)-cell dysfunction & Sulfonylurea \\ _IPF/PDX1_ & \(\beta\)-cell dysfunction & Insulin or Diet \\ _HNF1B_ & \(\beta\)-cell dysfunction & Insulin \\ _NEUROD1_ & \(\beta\)-cell dysfunction & OAD or Insulin \\ \hline _KLF11_ & Decreased glucose sensitivity of \(\beta\)-cells & OAD or Insulin \\ _CEL_ & Pancreatic endocrine and exocrine dysfunction & OAD or Insulin \\ _PAX4_ & \(\beta\)-cell dysfunction & Diet, OAD, or Insulin \\ _INS_ & \(\beta\)-cell dysfunction, insulin gene mutation & OAD OR Insulin \\ _BLK_ & \(\beta\)-cell dysfunction, affected insulin secretion & Diet, OAD, or Insulin \\ _ABCC8_ & Insulin secretion defects, ATP-sensitive potassium & Diet, Insulin, Sulfonylurea \\ _ASCC111_ & ATP-sensitive potassium channel dysfunction & Sulfonylurea \\ _APPL1_ & Insulin secretion defects, development delay & OAD or Insulin \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summaries of MODY subtypes and treatments.