esa-sceva/satcom-chunk-collection huggingface dataset:
# Rio Madeira HVDC System: Commissioning of the Ground Electrode for Bipole 2 at Porto Velho
## 2 The Rio Madeira HVDC Electrode, Bipole 2, at Porto Velho  
Figure 1 presents the top view of the ground electrode, which is a vertical type, with 80 sub-electrodes, consisting of wells distributed along a 5 km asymmetric perimeter. The electrode is divided into 8 sections, each one with 10 wells, which are connected to a central switching house by 8 section feeders.  
A 32 m deep high-resistivity ground layer required the design of the sub-electrodes with an active part between 32 m and 67 m, as illustrated in Figure 2. The first 6 m of each well is of corrugated plastic pipe, and from 6 m to 67 m depth it has a casing of 13\" steel pipes. Each active part of the sub-electrodes is filled with an array of 13 FeSeCr anodes (51 \(\times\) 1524 mm, 20 kg, 0.25 m\({}^{2}\)), inside a special fine carbon coke backfill.  
The following operational conditions apply for this electrode:  
* Bipolar-continuous operation of the electrodes with a maximum unbalance of 40 A;
* Monopolar with ground return of 2625 A \(\times\) 220 h per year (2.5% of the time);
* Short-time overload current of 1.63 \(\times\) 2625 A = 4279 A for 1.5 s;
* Long-time overload current of 1.42 \(\times\) 2625 A = 3727.5 A for 30 min;
* Monopolar with ground return in overload of 3490 A \(\times\) 5 h (300 min/year).  
The following conditions were established for the electrode design:  
* Electrode useful life of 35 years, with resistance limited to 0.35 \(\Omega\);
* Eight independent sections and ability to work without one section;
* All active parts inside the water-saturated ground layer;
* Utilization of 13\" steel pipes for all the well extension, except for the first 5 m on the top;  
Figure 1: The ground electrode of Rio Madeira bipole 2, with its 80 wells along a 5 km asymmetric perimeter. Site 1 is the switching house and the gray lines are the buried section feeders.  
* Iron rods spaced no more than 2 m, to allow for a uniform current distribution at the coke-ground interface (requirement from Cigre [2]);
* Wells at least 30 m distance from the site perimetric fence and low-lying areas, such as ditches or ponds (environmental rule);
* Current density in the coke-soil interface limited to 1 A/m\({}^{2}\) for the nominal monopolar operation.  
Regarding superficial current density, the two main references on HVDC ground electrode design present the following recommendations:  
* Cigre [2] clause 5.1.3.5 states \"A maximum average current density in the range of 0.5 A/m\({}^{2}\) to 1 A/m\({}^{2}\) is recommended for land electrodes to avoid electro-osmosis\";
* IEC [1] clause 5.1.6 states \"For earth electrodes that run in the bipolar mode for long periods or are in soil with high water content, an average current density of 2 A/m\({}^{2}\) or higher may be the permissible value for the rated current of one pole\".  
Figure 2: Well construction, 67 m depth.
## 4 Discussion
The chemical makeup of the portion of WTCO dispersed into the bulk aqueous phase was not analyzed in this work; therefore, we are not able to define whether the portion of oil degraded was the lighter portion of the crude oil, although presumably that is the case. Regarding the biodegradation data presented in Figure 9, the fourth test condition, which did not include the confidential dispersant formulation as did the others, was the condition which indicated the greatest level of dispersed oil biodegradation. Other researchers have also reported hindered dispersed oil biodegradation apparently due to the presence of a dispersant Corexit, as compared to a condition with no dispersant added, under similar 35-day batch test conditions [4]. However, this effect is not consistent with other cases [5]. The dispersants Petroclean and FFT indicated 70 to 80% biodegradation levels at 21 days of culture in shake flasks (Table 2). The biodegradation extent was also positively enhanced by higherlevels of mixing energy (Table 3), which is expected based on the higher mixing energy responsible for small disperse particle sizes. The modeling presented herein is simplistic in that NAPL particle size is an input parameter rather than a calculated result of the model. Even so, the dissolution and biodegradation data presented by model runs conducted at different particle sizes qualitatively reflect the findings determined in the experimental work. In particular, the model results indicate a time frame of 300 to 1200 h (12 to 50 days) to attain biodegradation of 99% of an original xylene NAPL mass, while comparable endpoints from the experimental biodegradation tests were demonstrated by the attainment of 90% or greater biodegradation over 21 or 28 days (Figure 9 and Tables 2 and 3). The additional result of interest arising from the modeling work is the minor contribution that advection may have, as compared to diffusion-only mass transport, on overall time required to achieve 99% biodegradation as a function of the particle size. The results indicated a very minimal effect on the time required to achieve 99% biodegradation for the 150 \(\upmu\)m particle size, with a more pronounced effect of approximately a two-fold difference for the 500 \(\upmu\)m particle size. This difference is expected to be due to the smaller total surface area for mass transfer associated with the larger particle size.
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
## 2 Theory and Software  
### Fuzzy Theory  
Fuzzy theory was formulated by professor [PERSON] and introduced in \"Fuzzy Sets\" [24]. Fuzzy logic [28; 29] aimed the formalization of an approximate reasoning, and it is characterized for employing concepts, such as linguistic variables and fuzzy if-then rules. One of the most important aspects of the concept of linguistic variable is to consider them as a form of compression (called granulation), in which the granules are an overlapping of fuzzy sets [25; 30]. Fuzzy if-then rules translate expert knowledge into a set of linguistic rules. This set characterizes the fuzzy controller behavior. Hence, it can be implied that fuzzy control is based on words rather than complex equations. The idea of fuzzy control is to model control actions based on expert knowledge. That is, the fuzzy controller provides an algorithm that converts qualitative control strategies (based on h of observation of the process) to an automatic control strategy [24; 31; 32].  
Fuzzy logic found strong supporters in Japan. The Sendai Subway 1999 series is still one of the most important applications of fuzzy logic. It successfully increased smoothness during starts and stops (increasing passenger's comfort) and was 10% more energy efficient when compared to human control actions [33; 34]. [PERSON] [35; 36], [PERSON] [36], [PERSON] and [PERSON] [37], and [PERSON] et al. [38] reviewed applications of fuzzy control in bioprocesses, such as Ajnomoto's fuzzy automatic substrate feeder [36; 39], Sankyo's fuzzy pH controller [36; 37], Nippon Roche's fuzzy culture phase identifier, and a fuzzy temperature control for sake brewing [36; 37; 38]. Outside Japan, [PERSON] and [PERSON] [40] applied fuzzy logic in order to determine the optimal moment to start substrate feeding in a _Cephalosporium ceremonium_ cultivation. With three reasoning levels (Attention, Action, and Protection), the fuzzy controller enabled safe automatic feeding operation [40]. Based on [PERSON] and [PERSON] [40], [PERSON] et al. [41] developed a fuzzy controller in order to determine harvesting threshold in a _Bacillus megaterium_ cultivation for Penicillin G oxidase production. Experimental results showed that the CO2 peak was precisely identified by the fuzzy controller [41]. [PERSON] et al. [42] created an automatic solids feeder controlled by fuzzy logic. Based on mass deviation as input, the motor speed was determined by the fuzzy controller. Results showed that the controller outperformed the classic on-off controller in all validation tests with smoother feeding profiles [42].  
Fuzzy-related research studies have been steadily increasing in the last decade [43; 44; 45; 46]. Its application has been effective to overcome the intrinsic non-linearities of biological systems [47]. However, just a fraction of the current papers was dedicated to improve dissolved oxygen control, a closer view shows that these works are mainly focused on improving waste water treatment plants [43; 44; 45; 46].
Porcine Cortical Bone Lamina as a Predictable Technique for Guided Bone Regeneration: Histomorphometric and Radiographic Evaluation  
[PERSON]  
1 Department of Head and Neck and Sensory Organs, Division of Oral Surgery and Implantology, Institute of Clinical Dentistry, Gemelli Foundation for the University Policlinic, Catholic University of the Sacred Heart, 00168 Rome, Italy 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 3  
Autologous bone has osteoinductive, osteosynthetic, and osteoconductive capabilities and has long been defined as the \"gold standard\" in guided bone regeneration (GBR) [4]. Donor sites can either be intra-oral or extra-oral; however, as two surgical accesses are needed, this procedure causes considerable discomfort to the patient and increases morbidity [4]. Heterologous bone grafting requires only one surgical access, significantly reducing the patient's post-operative discomfort and morbidity; on the other hand, the efficacy of this type of material is lower than that of autologous bone due to the lack of osteoinductive and osteogenic properties [2]. Some authors claim that the performance of the two materials overlaps in some clinical situations [5]. In contrast, other authors suggest that using a mix of heterologous and autologous can maintain the latter's properties while decreasing the amount needed [6]. Several techniques can be adopted to regenerate lost bone volumes [7; 8; 9; 10], and different biomaterials are commonly adopted [11; 12]. Over time, several GBR techniques have been proposed in which stability and absence of micromovements are necessary conditions to promote revascularization and graft integration [13; 14]. If these conditions are lacking, the grafted bone may become necrotic or infected, compromising the success of the surgery [13]. For these reasons, when resorbable membranes are adopted, a lower success rate than non-reasorbable membranes is reported in the literature [15]. Recently, new resorbable, porcine heterologous cortical bone lamina was introduced to the market, and some authors have applied them to the regeneration of bone defects to restore the bone volumes needed for placing implants [16; 17].  
After implants insertion, assessment of the marginal bone level (MBL) and clinical data allow the health of the implant to be monitored and the onset of peri-implant disease (mucositis and peri-implantitis) to be adequately intercepted [18]. [PERSON] et al., in a recent systematic review, have shown no statistically significant differences between the native and regenerated bone in terms of implant survival and success [19].  
The aim of this study is to radiographically assess the variation of marginal bone levels around implants inserted in regenerated sites after prosthetic loading and to provide a histomorphometric evaluation of the regenerated tissues. Bone regeneration was performed using a GBR technique with porcine heterologous biomaterials: particulate bone stabilized by a cortical bone lamina and covered by a resorbable collagen membrane.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 4 Experimental Results and Analysis  
### Image Dataset  
Owing to the absence of publicly available image datasets for ice-covered transmission lines, we opted to simulate the conditions of ice-covered transmission lines and build a dataset accordingly. We conducted icing experiments on transmission lines in a low-temperature laboratory, and the materials and specifications of the transmission lines used in the experiments were consistent with those of the actual transmission lines. During the experiment, we controlled the temperature, humidity, and other environmental parameters, and simulated natural icing by adjusting the release of water droplets from spray. Throughout the process, a camera was used to capture images of the ice-covered transmission line. The original image had a resolution of 4608 \(\times\) 3456, and after adjustment, we uniformly scaled it to 512 \(\times\) 512. After strict screening and cleaning of the captured images, a dataset containing 304 images of iced transmission lines was finally obtained.  
These images were then divided into training and test sets at a ratio of 9:1. The experimental setup involved the Pytorch 1.13.1 deep learning framework, an RTX 4090 GPU, and the Ubuntu 20.04.6 operating system. The batch size was set to 16, the learning rate was 0.001, and the EPOCH was 50 rounds.
## 3 Experiments and Results
### Crushing of Hollow Tubes  
The lateral and uniaxial crush tests for hollow composite tubes are shown in Figure 9a,b, respectively. Three identical composite tubes were used in the tests. The ends of all tubes were cut out to ensure that the tubes are free from burrs or uneven edges. The results are reported in Table 6. The tests were all in good correlation.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**HOLLOW**} & \multicolumn{4}{c}{**Lateral**} & \multicolumn{4}{c}{**Uniaxial**} \\ \cline{2-9}  & **1** & **2** & **3** & **Average** & **1** & **2** & **3** & **Average** \\ \hline PCF (kN) & 0.84 & 0.82 & 0.76 & 0.81 \(\pm\) 0.05 & 65.16 & 75.88 & 75.46 & 72.17 \(\pm\) 7.01 \\ MCF (kN) & 0.69 & 0.67 & 0.64 & 0.67 \(\pm\) 0.03 & 39.86 & 40.52 & 38.46 & 39.61 \(\pm\) 1.15 \\ CFE & 0.82 & 0.82 & 0.85 & 0.83 \(\pm\) 0.02 & 0.61 & 0.53 & 0.51 & 0.55 \(\pm\) 0.06 \\ AE (kJ) & 0.017 & 0.017 & 0.016 & 0.017 \(\pm\) 0.001 & 3.19 & 3.22 & 3.05 & 3.12 \(\pm\) 0.10 \\ SEA (J/g) & 0.44 & 0.42 & 0.41 & 0.42 \(\pm\) 0.02 & 80.91 & 81.56 & 77.24 & 79.90 \(\pm\) 2.66 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Properties of the hollow composite tube under lateral and uniaxial crushing.  
Figure 8: Load–displacement curves related to the in-plane and out-of-plane crush of chiral lattices.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**CHIRAL**} & \multicolumn{4}{c}{**In-Plane**} & \multicolumn{4}{c}{**Out-of-Plane**} \\ \cline{2-9}  & **1** & **2** & **3** & **Average** & **1** & **2** & **3** & **Average** \\ \hline PCF (kN) & 11.60 & 11.39 & 11.62 & 11.54 \(\pm\) 0.15 & 29.28 & 28.99 & 29.01 & 29.08 \(\pm\) 0.20 \\ MCF (kN) & 9.38 & 8.44 & 9.47 & 9.10 \(\pm\) 0.66 & 16.72 & 16.52 & 14.20 & 15.81 \(\pm\) 1.61 \\ CFE & 0.
# Nonlinear Stiffness of Semi-Fixed Dowel Joints in Semi-Integral Bridges
###### Abstract  
The semi-fixed dowel joint studied in this paper consists of a steel dowel with rubber sleeves and a rubber pad to connect the superstructure and the substructure and to support the main beam. This type of joint is suitably used in retrofitting a jointed bridge with high-stiffness substructure into a semi-integral bridge. This study aimed to investigate the nonlinear stiffness of the joint, which is the key parameter in the retrofitting design. Taking the real joint in a retrofitted semi-integral bridge as a prototype, tests on full-scale models were carried out, in which the wall thickness of the rubber sleeve and the thickness of the rubber pad were taken as parameters. The test results show that the wall thickness of the rubber sleeve is the main factor affecting the radial stiffness and the flexural stiffness of the semi-fixed dowel joint. With the decrease of the wall thickness of the rubber sleeve, the radial stiffness decreases while the flexural stiffness increases. However, the thickness of the rubber pad has little influence on both the radial and flexural stiffness of the joint, and its main role is only vertical loading-bearing. If the longitudinal deformation of the main beam of a bridge under maximum temperature variation is 10 mm as the allowance value of engineering design, the wall thickness of the rubber sleeve for this joint should not be less than 40 mm. At last, on the basis of experimental research and theoretical analysis, the formulas of radial stiffness and flexural stiffness are fitted; it can be used as reference for practical engineering application, or for formulation of relevant specifications.  
retrofitted bridge; jointless; dowel joint; rubber sleeve; rubber pad; stiffness +
Footnote †: journal: Applied
## 4 Validation Experiment
### Experimental Results and Discussion  
The shape of the samples after upsetting is shown in Figure 7. It can be seen that bulging has obviously formed on the TU sample. Meanwhile, the surface of the TU sample is rough, which indicates poor uniformity of metal flow during upsetting. In contrast, the CEBU sample does not show any bulging. In addition, the surface of the CEBU sample is much smoother than that of the TU sample, indicating that more uniform metal flows have been formed in CEBU. It must be admitted that the shapes of the two deformed samples in CEBU and TU are different from the finite element analysis. This is mainly due to the ideal friction boundary conditions used in the FEA. In the experiments, the friction conditions at the billet end were affected by the deformation process. Nonetheless, the CEBU showed advantages over the TU in terms of both bulging control and deformation uniformity.  
The microstructure in the detection zones of the samples is shown in Figure 8. Using the open-source image processing software ImageJ 1.53v, the average size of the grains in these detection zones can be obtained. For TU, the average grain sizes in the detection zones 1, 2, and 5 were 78.4 \(\upmu\)m, 56.2 \(\upmu\)m, and 51.6 \(\upmu\)m, respectively. In contrast, the average grain sizes of these regions in the CEBU are 62.3 \(\upmu\)m, 53.4 \(\upmu\)m, and 49.7 \(\upmu\)m. The results show that the grain size obtained with CEBU is smaller than that obtained with TU under the same reduction ratio. It is generally accepted that grain size correlates with cumulative plastic strain in metals. Therefore, the grain size results in Figure 8 can indirectly verify the simulation results of the equivalent strain in Figure 4. As discussed in Section 1, due to the friction effect between the billet and the anvil, the metal flow in Zone 1 is restricted.  
Figure 7: Samples used in the upsetting test. (**a**) TU; (**b**) CEBU.  
Accordingly, the grain size in zone 1 is generally larger than that in other zones. In relation to the whole billet, the ability of metal flow in CEBU is better than that in TU. As a result, the grain size obtained in CEBU is smaller than that obtained in TU.  
In order to further validate the differences in grain size and mechanical properties between CEBU and TU, microhardness tests were carried out. The hardness results at different detection zones of the specimen are shown in Figure 9. It can clearly be seen that the hardness in CEBU is generally higher than that in the corresponding regions of TU. In addition, the highest hardness value is obtained at the center of the CEBU sample. According to the Hall-Patch theory [29], the hardness of a metal is directly related to its grain size. Therefore, the hardness results in Figure 9 are consistent with the microstructure observations in Figure 8. In conclusion, the grain size and hardness results indicate that the use of CEBU not only suppresses the formation of bulging but also results in smaller grain sizes.
# Detecting Methotrexate in Pediatric Patients Using Artificial Neural Networks
## 2 Methodology  
This section presents the methodology used for the project development, and the analysis is based on Kendall System Analysis and Design, which proposes seven phases, which, for our analysis, are presented in Figure 1[22]. The Kendall and Kendall methodology for information system development is structured into seven fundamental phases that guide the creation of effective and user-adapted systems. The first phase, the FeasibilityStudy, aims to assess the project's technical, economic, and operational viability. During this phase, the need for an intelligent diagnostic system for acute lymphoblastic leukemia (ALL) in pediatric patients is evaluated, considering available resources and the project's feasibility within the context of the Tuxtla Gutierrez Pediatric Hospital. The second phase, Requirement Analysis, focuses on defining and documenting the system requirements. An exhaustive collection of personal and clinical patient data are examined, alongside the functionalities required for the mobile application, ensuring that these requirements are clear and detailed for subsequent implementation.  
In the third phase, System Design, a detailed design of the system is developed, including architecture, interfaces, and necessary processes. The design of the mobile application for data collection and the neural network for data analysis are developed, specifying the characteristics and integration of each component.  
The fourth phase, Development, involves constructing the system according to the established design. This phase includes programming the mobile application for data capture and storage and implementing the neural network in Matlab 2023a, integrating both components to ensure accurate data analysis.  
During the fifth phase, Testing, the system's functionality is verified to ensure it meets the specified requirements. Extensive testing of the mobile application is performed to ensure its functionality, and the performance of the neural network is evaluated with test data, adjusting the system as needed to enhance accuracy and effectiveness.  
The sixth phase, Implementation, focuses on deploying the system in the real environment. The application is installed on hospital devices, ensuring proper integration and functionality of the intelligent diagnostic system and providing training to end-users to ensure efficient use.  
Finally, the seventh phase, Maintenance and Evaluation, involves continuous monitoring of the system to correct errors, perform updates, and ensure its proper functioning over time. User feedback is collected, and periodic evaluations of the system are conducted to ensure it continues to meet established objectives and adapt to any new requirements.  
Figure 1: Proposed methodology based on [PERSON]’s Life Cycle Management [22].  
The seven phases will be described below.
# Methods to Apply a 3-Parameter Logistic Model to Wind Turbine Data
## 2 Data Filtering  
In order to apply the methods proposed in this work in a proper manner, only valid data need to be considered, which means that raw data have to be filtered. From the point of view of the values, wrong data can be classified into two groups: those values that are clearly outside the proper range and those values that are close to being valid but must be discarded, too. For the first ones, the following procedure was applied:  
* To discard wind speed values that are lower than the cut-in wind speed.
* To discard wind speed values that are 1.5 m/s above the cut-in wind speed, when they provide an output power lower than a 5% of the rated power.
* To discard wind speed values that are 1 m/s above the rated wind speed, when they provide an output power lower than a 75% of the rated power.  
The limits correspond to values clearly out of bounds, i.e., clearly dispersed and out of place in the data cloud. In the second case, and in order to establish a reasonable limit, a filter based in a Normal distribution is applied. In this case, the mean power (\(\mu\)) is obtained for all the wind speed values that correspond to an interval of 0.5 m/s and, centering a Normal distribution in those values, all the values outside the range \(\mu\pm 3\sigma\) are discarded, being \(\sigma\) the standard deviation of the values in that interval. As a final checking, if the percentage of values discarded is over 5%, those limits have to be reconsidered.  
In Figure 1, raw data and filtered data are shown in order to be compared.