esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MO Isolator
### Discussion  
In order to optimize the performance of the MO isolator, we will discuss the effect of ferrite structure parameters on the performance of MO isolator. When the length L of the two ferrite sheets is increased, the positive transmission efficiency of the MO isolator does not obviously change. But the reversed isolation peak of the isolator has a sharp rise with the increasing length, as shown in Figure 7a. When the length of the ferrite sheets increases from 14 to 20 mm, the isolation peak rises gradually from 34.58 to 49.49 dB, and the bandwidth is also broadened 27.28%. The improvements of the MO isolator's isolation and bandwidth are owed to its unique working principle because the operational principle of our designed MO isolator is the ferromagnetic resonance absorption principle, where longer ferrite sheets can lead to more energy absorption. Thus, the reverse isolation of the MO isolator increases with the length of the ferrite sheets. The operating bandwidth (isolation \(>\) 15 dB) of the MO isolator is broadened to be 24.85 to 27.15 GHz in Figure 7a, when the length L of the two ferrite sheets increases to be 20 mm.  
Because the forward and reverse attenuation constant of the isolator is related to the size and position of ferrites [24], the optimization design generally minimizes forward attenuation which determined by the position of the ferrites. The H-plane rectangular waveguide resonance isolators were also improved by using ferrites with different materials, structures, and positions [25]. When the distance between the two ferrite sheets and the narrow-edge of waveguide is increasing from 5.59 to 5.84 mm, the loss of the isolator is decreasing, which has been investigated for improving the performance of the isolator in [25]. Thus, we tried to change the position of ferrite sheets in the PCW to optimize the forward transmission efficiency in this work.  
In the frequency range of 24.5 to 27.15 GHz corresponding to the bandwidth of the MO isolator mentioned above, the transmission efficiencies' variation of the isolator are related to the distance \(d\) between the ferrite sheets and the boundary of PCW, as shown in Figure 7b. When the distance \(d\) deviating from 1 mm increases to 1.4 mm or decreases to 0.6 mm, the transmission efficiencies significantly reduced. However, the distance \(d\) hardly influences the isolators' isolation, which is slightly increasing when the distance is changing from 0.6 to 1.4 mm, as shown in Figure 7c. So the optimal distance between the ferrite sheets and the boundary of PCW is 1 mm. Therefore, the transmission performance of the designed MO isolator can be optimized by finding the most appropriate resonant distance.  
Figure 6: The variation of transmission characteristics of MO isolator with frequency.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 1 Introduction  
With urban development, a large number of excavation works have been conducted. Stress relief caused by basement excavation will inevitably affect surrounding subway tunnels. Many research results have been obtained with regard to the effect of tetragonal excavation on the deformation of an adjacent metro tunnel. The main contents of this study are as follows: geometry of the excavation [1; 2]; relative position of tunnel and excavation [3; 4; 5; 6; 7]; excavation conditions and reinforcement method [8; 9; 10; 11]; tunnel lining stiffness [4] and diameter of tunnel lining [1]; different soil constitutive models [12]; soil relative density and retaining wall stiffness [13]; influence zone [14] and calculation methods [15; 16; 17].  
However, existing studies have mainly focused on analyzing the tunnel response caused by the unloading of the tetragonal basement excavation. With the rapid development of urban construction, the development and utilization of underground space is rapidly increasing and more and more foundation basements are constructed as circular foundation basements [18; 19; 20; 21]. Unlike the traditionaltetragonal rectangular basement, a circular basement can make full use of the circular arch effect, such that the force of the structure becomes more reasonable.  
Existing circular basement studies have mainly focused on the soil pressure around the basement and the retaining structure's deformation caused by the excavation [22; 23; 24; 25; 26; 27; 28; 29; 30]. However, few studies have focused on the deformation of surrounding foundations and buildings, which is caused by the unloading of the circular excavation. Considering the many differences between circular and tetragonal basements, it is theoretically and practically significant to evaluate the effect of circular basement excavation on the deformation of the surrounding structure to reduce damages.  
The objective of this study was to investigate the contribution of circular basement construction to the deformation of a nearby existing tunnel, and to reveal the tunnel deformation mechanism. Three-dimensional numerical analyses using advanced constitute model combined with accurate numerical simulation techniques were conducted based on the reported centrifuge model test. The effect of the excavation shape and the tunnel and soil responses were analyzed with the expectation that the results may provide a useful reference for engineering practice.
# Investigation of the Mechanical Properties of Spur Involute Gearing by Infrared Thermography
## 5 Discussion  
The contact of two gears can be simulated in several ways. For the design of the excitation device, we were inspired by publications such as [38; 39]. We consider our test setup to be simple and the infrared camera image processing procedure used to be appropriate. However, it is necessary to discuss some issues that arose during the measurements and the processing of the results:  
1. The design of the test condition. If the opposing tooth performs a translational movement, then due to the displacement of the tooth under test, there is a translation and rotation of this tooth. For large translation and rotations, image stabilization will need to be addressed, e.g., by using reference markers that will be detectable in the infrared range of the camera used.
2. Due to the micromotions of the test wheel and the countertooth, the boundaries of these objects appear in the images that could be misinterpreted as high values of the 1 st invariant. We assume that one of the causes of this phenomenon is the rotation of the surface that represents the edge of the test tooth and countertooth. The algorithm of the lock-in method evaluates this periodic change in the position of the surface (with lock-in frequency) as a value of the 1 st invariant. For this reason, the edge invariant values were not evaluated. We assume that the tooth alignment is improved if the countertooth does not act in translational motion but in rotational motion. The edge problem described above was also the reason why the line on which the 1 st invariant is evaluated is not placed closer to the tooth edge. For the same reason, we  
Figure 6: First stress invariant for the PETG wheel under the force load induced by the modal exciter, and the invariant values at the points on the line segments marked in the contour fiber region (red plot) and in the matrix region (green plot).  
have not yet evaluated the invariant directly in contact between the wheel and the countertooth.
3. For the measurements, we used an infrared camera with a 320 \(\times\) 240 pixels detector. It is reasonable to expect that increasing resolution of the detector (increasing the number of pixels) will improve the resolution when evaluating the invariant. However, due to the micro displacement, this increase may require more precise image stabilization.
4. If we do not evaluate the method and test condition used for the spur gear testing described above, then the roughness of the contact surfaces, their coefficient of friction, the temperature of the teeth during the test, and also the test history for the wheel under test must also be considered to understand the mechanical properties. For longer duration tests or frequently repeated tests, the contact surface of the tested wheel teeth may be worn.
# Strategies for Reducing Automobile Fuel Consumption
## 4 Utilization-Related Factors
### Promotion of Traffic Flow Improvement  
In general, streets with frequent interruptions of vehicular flow, usually caused by intersections or pedestrian facilities, tend to exhibit higher fuel consumption and increased emissions from brake and tire wear [64]. Improving driving speed through smoother traffic flow positively affects the _mileage index_ (e.g., miles per gallon), resulting in reduced fuel consumption. To achieve this, several measures can be implemented. For instance, developing bypass roads and other arterial networks can effectively reduce through-traffic in city centers. Additionally, ensuring smooth and safe transportation services requires intelligent road usage, improved road maintenance practices, and updating of existing road infrastructure.  
Moreover, eliminating _vehicle congestion_ is crucial for economic and environmental reasons. In heavily congested urban areas, fuel consumption can be twice as much as that occurring in free-flow conditions. This fact underscores the importance of conducting studies on traffic-light optimization, considering factors such as road design, intersections, gradients, and topography of commonly used routes within the city. In managing passenger and commercial vehicle operations, substantial fuel economy improvements can be achieved through strategic and operational decisions. These decisions include route selection and vehicle load optimization, all aimed at maximizing fuel efficiency [67; 68].  
The implementation of _Intelligent Transportation Systems_ (ITSs) involves the integration of people, roads, and vehicles using state-of-the-art Information and Communication Technologies (ICTs). These systems have the potential to significantly enhance the utilization of roads, improve transportation efficiency, and tackle various traffic, congestion, and energy-related challenges. As a result, they contribute to creating a more cost-effective society concerning road traffic management. ITSs achieve this by providing real-time traffic information through onboard electronic control units that are compatible with the Vehicle Information and Communication System (VICS). This information includes details such as travel time, congestion conditions, and traffic restrictions. By utilizing these data, drivers can make informed decisions, leading to improved mileage per liter of fuel [69].
## 2 Materials and Methods
### Preparation of Carboxymethylated NFC  
Carboxymethylated nanofibrils of cellulose (cmNFC) were prepared following two main steps. First, cellulose pulp was functionalized by carboxymethylylation with the protocol adapted from [PERSON] et al. [73]. A suspension of 30 g of cellulose pulp was prepared in water at 2 wt % and solvent exchanges were carried out in triplicate by mixing and filtration to replace water by ethanol. The amount of reagent is given by the number of equivalents of OH groups accessible onto cellulose (eq/OH). Two solutions of 1.8 eq/OH of NaOH (4.42 g) in isopropanol (136 mL) and 3 eq/OH of CICH\({}_{2}\)COONa (17.48 g) in isopropanol (291 mL) were separately prepared by magnetic stirring. The cellulose pulp suspension in ethanol was put in the round-bottom flask, supplemented with 550 mL of isopropanol and the solution of NaOH, mechanically stirred for 30 min and heated in the oil bath at 60 \({}^{\circ}\)C. Then, the solution of sodium chloroacetate was added and the chemical reaction occurred for 1.5 h at 60 \({}^{\circ}\)C. After the carboxymethylylation, the pulp was filtrated to extract isopropanol. Then, the pulp was successively washed with distilled water, a solution of acetic acid (0.1 M) and distilled water again. Finally, the modified cellulosic pulp was soaked in a solution of NaHCO\({}_{3}\) at 4 wt % for 1 h before a final washing step with distilled water. Secondly, the carboxymethylated cellulose (CMC) was mechanically fibrillated using a Masuko Grinder at 2 wt % for 1.5 h until reaching a percentage of fines superior to 99%. The amount of anionic charges was determined by conducimetric titration around \(1540\pm 190\) umolg\({}^{-1}\) corresponding to a degree of oxidation around 0.26.
# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 3 Materials and Methods
### Method Applied  
#### 3.2.1 Main Methodological Assumptions and Research Question  
The basic research problem to which this study refers concerns the definition of a Compound Correspondence Index (CCI), at local and regional scales, and the determination of the number and optimal class ranges that explicitly indicate the spatial location of differences in the information capacity of two investigated datasets. The WLC (Weighted Linear Combination) method for comparative, multi-criteria analysis was used on the basis of such criteria as differences in the area covered by buildings, forest and water bodies, and the lengths of roads, railways and rivers. The minimum difference indicates a very similar information volume, while maximum implies large differences between the two sets. It is assumed that the consolidated regional classification of the Compound Correspondence Index (regional CCI, hereinafter referred to as CCIRn), attributed to the 1 km2 hexagonal grid, and their statistical measure of dispersion, enables the comparison of datasets for individual geographically disjoint areas according to uniform criteria. The decision to use hexagons has some advantages, as it is closer in shape to circles than squares, potentially reducing bias due to edge effects [40].  
Therefore, the answering of two research questions is the priority of this study, and the questions are as follows:  
Q1--Is there the difference between the CCI value calculated for grid cells of all research areas together (CCIRn) and the CCI value calculated in grid cells separately for each area (local CCI; hereinafter referred to as CCILn), and if so, how big?  
Q2--How does the value of regional CCI (CCIRn) change if we include another research area in the analysis, i.e., how sensitive is the CCIRn?  
The answers to the questions allow us to verify the hypothesis that CCIRn underestimates the dissimilarity between analyzed datasets, indicating slightly higher compliance than the local CCI.  
This approach is innovative as it allows the comparison of two sets containing qualitative data for geographically disjoint areas, thus enabling the user to choose one of them consciously and responsibly. It also shows the differences between CCI classifications at the local and regional levels.
## 4 Evaluation of One-Hop Neighbor Wireless Link Status Based on Fuzzy Logic Model
### Fuzzification  
Fuzzification is the process of accepting and converting crisp numerical input values into fuzzy linguistic values by employing a predefined membership function to each network metric. For the sake of accurate evaluation of the considered metrics, their values are normalized by considering Min-Max feature scaling normalization, which improves the sensitivity of the metrics to every negligible variation on the neighbor link status. The following equation shows the normalization of the expected distance progress metric:  
\[DP_{c_{i}}^{c_{x},d}=\frac{DP^{c_{x},d}-DP_{min}}{DP_{max}-DP_{min}} \tag{13}\]  
where \(DP_{c_{i}}^{c_{x},d}\) is the normalized distance value of the potential candidate to destination and \(DP_{max}\) and \(DP_{min}\) are the maximum and minimum measured distance span values to destination of all potential candidates \(C_{i}\). Figure 5a illustrates the membership function of candidate distance progress evaluation. The forwarder node employs the developed membership function to map the normalized metric value into the predefined linguistic fuzzy set and measures the degrees of belonging to the linguisticvariables. Thus, the fuzzy set degree values for the distance progress metric input belongs to {VeryClose, Close, Far}.  
The fuzzy membership function for the node transmission power is shown in Figure 5b. The forwarder node uses the membership function to evaluate the neighbor transmission range based on the information included in the exchanged hello packets. Thus, the forwarder node calculates which degree the transmission range of a candidate forwarder belongs to {Long, Short}. The membership function of the ATM metric is depicted in Figure 5c. The forwarder node uses the membership function and the normalized metric to calculate which degree the available throughput value of a candidate node belongs to {Low, Medium, High}, where low represents low achievable throughput and high represents high achievable throughput.  
Figure 5d depicts the fuzzy membership function of the wireless link quality metric. The metric defined in Equation (1) is normalized using min-max normalization as in Equation (15). The current forwarding node uses the fuzzy membership function and the normalized value to calculate which degree the metric value belongs to {Good, Average, Bad}.
# Brief Review of Recent Developments in Fiber Lasers
## 6 Fiber Laser Applications
### Medical Sector  
With the continuous progress of science and technology, fiber lasers are increasingly widely used in laser therapy [106]. Laser energy can be delivered through optical fibers to accurately treat diseased tissues. In laser therapy, fiber lasers have the advantages of high energy density, high orientation, and minimally invasive treatment. The application of fiber lasers in laser therapy includes but is not limited to laser beauty treatment, laser eye therapy, laser tumor therapy, and laser endoscopic therapy.
## 1 Introduction
The time and frequency transfer experiment lasted for 10,000 s. The experimental results showed that the RMS timing fluctuation between master site A and slave site B is 119 ps, with the frequency stability being 3.3 \(\times\) 10\({}^{-11}\) at 1 s and 2.8 \(\times\) 10\({}^{-14}\) at 2000 s. The RMS timing fluctuation between master site A and slave site C is 59.5 ps, with the frequency stability being 3.0 \(\times\) 10\({}^{-11}\) at 1 s and 2.6 \(\times\) 10\({}^{-14}\) at 2000 s. This technique overcomes the shortcomings of multiple round trips and wavelength division multiplexing of optical signals in current techniques and adopts a method of extracting signals of different frequencies with a single photodetector, which greatly reduces the complexity of the system and can provide important support for the application of a multiple-site transfer of time and frequency in synchronization network.
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 2 Standard Planes Recognition with AI
### Scan Plane Detection  
Non-expert physicians may have difficulties determining which organ or part of the body is being scanned with US, as only a limited cross-sectional 2D image is usually available. The lack of information on the specific location of each plane reduces the diagnostic accuracy and makes it quite dependent on the experience and skills of the physician. Automatic plane detection methods can be used in practice in two different ways: in real-time scanning to assist inexperienced sonographers and, as a postprocessing step, retrieving standard scan planes from acquired videos that could be further analyzed afterward. Each use case has its specific challenges, as fast computation is more critical than accuracy in real-time applications, while in the case of offline image analysis it is the opposite. The first developments in automatic scan plane detection date back almost 20 years, with several works based on support vector machines (SVM) [13; 14] and conventional ML [15; 16]. Recently, US view recognition has been further improved with deep-learning techniques. Most of these works are limited to specific solutions for individual organs or organ regions. For instance, fully automatic left ventricle (LV) detection in cardiology is a challenging task. This is due to the noise and artifacts in the moving cardiac ultrasound image and the variety of LV shapes. A faster R-CNN and an active shape model (ASM) were combined to come up with a new way to find and track the LV in imaging sequences [17]. In addition, a fast R-CNN [18] was proposed to identify the ROI, and an ASM [19] obtained the parameters that most precisely characterized the LV's shape. In obstetrics, scans for the detection of possible abnormal fetal development typically involve the acquisition of several standard planes, which are used to obtain biometric measurements and identify possible abnormalities. The earliest works on automated fetal standard plane detection were able to identify only 1-3 standard planes in short videos of 2D fetal US sweeps [17; 18; 19; 20]. Some of these early works tried to figure out where standard planes were by looking for major anatomical structures in 2D US images. [PERSON] et al. [24] proposed detecting these standard planes using local context information and cascading
## 3 Smart Metering in Germany
### Network Architecture and Key Entities  
To implement the stated functions, the SMGW works as a mediator between three different communication networks [6]. Figure 1 depicts a condensed overview of the different networks and authorized roles within the SMGW infrastructure. The home area network (HAN) is the communication network on the consumer side. Within this network are DERs, control boxes, and EMSs, which are collectively called the \"controllable local system\" (CLS) in the SMGW infrastructure. In the HAN, the SMGW provides meter data to the consumer and diagnostic information to the service technician. The local metrological network (LMN) contains all connected metering devices. Finally, the wide area network (WAN) is used for all communication with the \"Smart Meter Gateway administrator\" (GWA), and the \"external market participants\" (EMP).  
The Smart Meter Gateway administrator (GWA) acts as a trustworthy authority for the SMGW. Each SMGW is assigned to one GWA. Only the GWA is authorized to configure the SMGW and perform administrative functions such as software updates. In order to perform these functions, the GWA can securely communicate with the SMGW using the \"management channel\". This channel can be established using the so-called \"wake-up service\". All other communication partners of the SMGW in the WAN are external market participants (EMPs). One must distinguish between active and passive EMPs. While the passive EMP only receives metering data, the active EMP (aEMP) is authorized to communicate with one or more CLS in the HAN, using the transparent data tunnel provided by the SMGW. Therefore, the DSMgr is acting in the role of an aEMP. Consumers are the legal entities or natural persons whose consumption is metered. The SMGW is able to distinguish between multiple consumers, in order to preserve their privacy. Each consumer is the owner of their individual meter data and can retrieve them at a designated interface from the SMGW or metering device. For maintenance and to perform diagnostics, the service technician has local access to the SMGW via the HAN interface.  
The roles within the SMGW architecture are distinct from the defined market roles within the energy economy. While most of the traditional market roles will act as EMPs in the SMGW infrastructure, e.g., energy suppliers, the role of the metering point operator faces new regulations and requirements. The metering point operator has to equip and operate the iMSys according to the regulations and will, in most cases, act as the GWA.