esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Torsional Shear Flow Calculations
\begin{table}
\begin{tabular}{l} \hline Section A: \(a=1/2^{\prime\prime}\), \(d=1^{\prime\prime}\), \(t=1/16^{\prime\prime}\), \(d/a=2\), \(e_{x}/a=3/8\), \(\varpi/a^{2}=27/32\) \\ \(k_{0}/a^{2}=1-3/8=5/8\) \\ \(k_{1}/a^{3}=-(1/4)\times 2+(1/2)(3/8)=-5/16\) \\ \(k_{3}/a^{2}=3/4+3/8+1=17/8\) \\ \(k_{4}/a^{3}=(3/8)\times 4+2(5/8-17/8)-5/16=-29/16\) \\ \(k_{5}/a^{2}=2(1+1)=4\) \\ \(k_{6}/a^{3}=(3)^{2}(1/2-3/16)+3(17/8-4)-29/16=-74/16\) \\ \(Q_{1}=0\) \\ \(Q_{3}/a^{3}=(1/4)\times 2-27/32=-11/32\) \\ \(Q_{4}/a^{3}=(3/16)(1+1)2+(5/8-27/32)\times 2-5/16=0\) (check) \\ \(Q_{5}/a^{3}=-(3/16)\times 3^{2}-(27/32-17/8)\times 3-29/16=11/32\) \\ \(Q_{7}/a^{3}=-(1/2)\times 16-4(27/32-4)-74/16=0\) (check) \\ \hline Section B: \(a=1^{\prime\prime}\), \(d=13/4^{\prime\prime}\), \(t=1/8^{\prime\prime}\), \(d/a=7/4\), \(e_{x}/a=12/31\), \(\varpi/a^{2}=0.7207\) \\ \(k_{0}/a^{2}=7/8-12/31=0.488\) \\ \(k_{1}/a^{3}=(1/4)(7/4-24/31)-0.488=-0.244\) \\ \(k_{3}/a^{2}=(12/31\times 7/4)+12/31+(1/2)(7/4)=1.9375\) \\ \(k_{4}/a^{3}=(12/31)(1+7/8)^{2}+(1+7/8)(0.488-1.9375)-0.244=-1.6047\) \\ \(k_{5}/a^{2}=(7/4)(1+7/8)=3.2813\) \\ \(k_{6}/a^{3}=(11/4)^{2}(7/16-12/62)+(11/4)(1.9375-3.2813)-1.6037=-3.4498\) \\ \(Q_{1}=0\) \\ \(Q_{3}/a^{3}=(1/4)(7/4)-0.7207=-0.2832\) \\ \(Q_{4}/a^{3}=(12/62)(1+7/8)^{2}+(0.488-0.7207)(1+7/8)-0.244=0\) (check) \\ \(Q_{5}/a^{3}=-(12/62)(11/4)^{2}-(0.7207-1.9395)(11/4)-1.6047=0.2832\) \\ \(Q_{7}/a^{3}=-(7/16)(2+7/4)^{2}-(2+7/4)(0.7207-3.2813)-3.4498=0\) (check) \\ \hline Section C: \(a=5/8^{\prime\prime}\), \(d=17/8^{\prime\prime}\), \(t=3/64^{\prime\prime}\), \(d/a=3\), \(e_{x}/a=1/3\), \(\varpi/a^{2}=27/20\) \\ \(k_{0}/a^{2}=3/2-1/3=7/6\) \\ \(k_{1}/a^{3}=(1/4)(3-2/3)-7/6=-7/12\) \\ \(k_{3}/a^{2}=3/2+1+1/3=17/6\) \\ \(The hyperbolic dependence of a given section's torsional shear flow upon length and cross-section geometry appears in the penultimate row for A, B and C in Table 2. There is sufficient variation in geometry between the three channels to reveal both the influence of the cross-section and the length (through \(L\) and \(\mu\)) upon shear flow within Figure 3.  
The greatest beam length of 1 m for section B shows the widest variation in shear flow, with this being near zero at the free end. Reducing this length to \(\approx 1/3\) for A and C shows how their channel section dimensions alter the fall in shear flow from the fixing by \(\approx 27\%\) and \(68\%\) at the free end, respectively. The torsional shear flow at perimeter points \(i=1\), \(2\ldots 7\) for each cross-section at a given length position is found from multiplying the entry in the final row in Table 2 for each channel A, B and C by the appropriate \(Q_{i}\) values (see Equation (5b,c)). Thus, for channel section A at half length, the entry of \(-0.2656\) applies to the section's \(q\) distribution under a unit torque (\(T=1\) Nm) with \(E/G=210/70=3\), as follows:  
\[q_{i} =Et\ (\mathrm{d}^{3}\theta/\mathrm{d}z^{3})Q_{i}=Et\ \times(-0.2656\times 1 0^{-6})T/G\ \times Q_{i}\] \[=3\times(1/16\times 25.4)\ \times(-0.2656\times 10^{-6})\ \times(1\times 10^{3})\ \times Q_{i}\] \[=-(1.265\times 10^{-3})\ Q_{i}\]
# Computational Flow Dynamic Analysis in Left Atrial Appendage Thrombus Formation Risk: A Review
## 2 CFD Simulation for Thrombs Formation Risk  
### Process Description: From Obtaining a CT Scan to CFD Simulation of Left Atrial Appendage  
It is essential for diagnosis, treatment planning, and risk assessment to comprehend the hemodynamics of the left atrial appendage. Obtaining a CT scan and conducting a CFD simulation of the left atrial appendage is necessary to shed light on the methodology and considerations required for an accurate and meaningful analysis. The patient is placed on the examination table and positioned within the CT scanner. Multiple perspectives of the heart and left atrial appendage are captured. The resulting image data is saved for future examination. The CT images acquired are transferred to a computer and processed by specialized software. LA and LAA contours have been defined and segmented to produce a three-dimensional model of the structure. This model is the basis for subsequent CFD simulations. Mesh generation is the process of discretizing a 3D model into a computational mesh. In this phase, the model is subdivided into smaller elements that depict the geometry of the left atrial appendage and its surrounding structures. The resolution of the geometry is crucial for accurate CFD simulations. The CFD simulation's conditions are specified. This includes identifying fluid properties such as viscosity and density, as well as the system's initial and boundary conditions, such as inlet and outlet velocities, pressures, and flow characteristics. After defining the boundary conditions and generating the geometry, specialized software performs the CFD simulation. Mathematical equations are solved numerically, allowing for the prediction of flow behavior. After the CFD simulation has been completed, the results are analyzed and interpreted. Figure 1 presents a scheme of the process.
## 3 Cross-Layer Adaptive Feedback Scheduling
### Cross-Layer Design Methodology  
The design of wireless networks is often based on a layered network protocol stack, and the design and operation of different network layers are separated. As shown in Fig. 2, IEEE 802.11b specifies two layers, i.e., the physical layer and the MAC sub-layer, among the seven-layer OSI reference model. At the physical layer 802.11b specifies four different levels of transmission rates, i.e., 1, 2, 5.5, and 11 Mb/s. At the MAC layer 802.11b exploits CSMA/CA to solve resource contention among multiple nodes. In the context of wireless control, it is intuitive that the control applications are at the application layer.  
When the system operates under dynamic environments, the timing properties of WLAN may vary with different physical layer parameters, which influence in turn the performance of the control  
Figure 2: Cross-layer design framework for wireless control systems.  
systems at the application layer. To maximize QoC, it is necessary to take advantage of dynamic interactions between the physical layer and the application layer. Cross-layer design should be exploited to achieve application adaptation [19, 30].  
In this paper, the sampling periods of control loops are chosen as the parameters of the application layer. The main reason behind this choice is that the sampling periods influence not only the QoC but also the workload on the network, which affects the communication delay and the DMR. In a sense, the DMR can be regarded as an indicator for link quality associated with the physical layer. Since the transmission rate may change at runtime, it naturally becomes another parameter at the physical layer. Consequently, the basic role of feedback scheduling that exploits cross-layer design is to adjust the sampling periods of control systems at the application layer based on information about DMR and transmission rate from the physical layer.  
In wireless networked systems, a straightforward design objective of feedback scheduling is to control the DMR at a desired level. Since WLAN does not support non-destructive communication protocols, it is impossible to analyse the system schedulability for WCSs. Therefore, the network utilization is not a suitable choice for the controlled variable for feedback scheduling. Without loss of generality, the DMR of all control loops is used as the controlled variable of the feedback scheduling system. Actually, because WLAN employs a MAC protocol featuring random medium access, the DMR of control loops also reflects the level of DMR of interfering signals.
# Layout Optimisation of Frame Structures with Multiple Constraints and Geometric Complexity Control
## 1 Introduction
### Precedent Work
#### 1.1.3 Geometric Complexity Control  
The direct results of the layout optimisation of discrete structures may consist of many hair-like features (elements with tiny cross-sectional areas) and present intricate material connectivity, especially when dense ground structures are used. The hair-like features and complicated material connectivity introduce significant challenges to the practical manufacturing of such structures. Typically, the tiny features have negligible mechanical contributions and can be removed if their cross-sectional areas fall below a threshold value. Instead of assigning a static value to the threshold used, [PERSON] et al. [48] employed adaptive thresholds to simultaneously preserve the optimality of solutions and increase the diversity of structural designs. As an alternative, [PERSON] [49] suggested a joint cost function and added it to the material cost when measuring the economy of structural designs. The constant joint cost penalises short members [49; 50], making them less competitive compared to long members; therefore, during the optimisation process, the optimisers favour the removal of short members. Similar to the joint cost, elemental and nodal fabrication costs have been proposed when assessing the economy or performance of structural designs [51; 52]. These cost functions penalise elements with low cross-sectional areas more than those with large cross-sectional areas. As a result, the smaller elements are more prone to removal [51; 52]. Additionally, the control of geometric complexity, particularly in terms of simplifying member connectivity in structural layouts, can be enhanced through various methods. For example, intersecting members can be eliminated [53], the number of nodes can be limited [54], and the angles between elements can be restricted [55]. Furthermore, the significant simplification of structural layouts can be achieved by adjusting nodal position and allowing the coalescence of both members and nodes, typically through geometry optimisation methods [50].
# An Architecture for Reliable Transportation of Delicate Goods
## 5 Workflow
### Merchandise Aggregation and Disaggregation
#### 5.3.2 Aggregation Protocol  
If the terminal is not part of the whitelist of the central, the association state will be false and the mobile app will enable the option to aggregate the terminal to the central, which means associating the merchandise to the container (Figure 11). If this option is selected, the mobile app sends an aggregation request to the cloud. If the state and information is consistent, the cloud service will reply by sending the Authentication token (see Section 5.2.1).  
The Authentication token is used for the mobile app authentication on the central device and activation of the Closed Access BLE Service--Aggregation (Figure 6b). Once authenticated (Figure 11--message 2.1), the central adds the terminal to its whitelist (Figure 11--message 2.2) and the terminal do the same relatively to the central device (Figure 11--message 2.1.1). This will later allow connectivity between devices. With the Closed Access BLE Services enabled (Figure 11--message 2.1.1), the central will configure the type A/B device to work as terminal on the star topology (Figure 11--message 3).  
During the transport period in which the merchandise is in the container, both devices will establish the connection periodically. Besides the time interval (Figure 11--message 4), the central should inform the terminal when the first connection starts (Figure 11--message 5). This time offset is computed considering the time from the present moment to the beginning of the next connection period, the number of terminals hold on the whitelist of the central device, the number of simultaneous connections accepted by the central and the relative position of the terminal on the whitelist--the goal is to prevent that all terminals try to request connection simultaneously promoting access conflicts, which would lead to more connection attempts and higher energy expenditure.  
The terminal gets that data and configures itself to wake up periodically and establish the connection with the central.
## 1 Introduction  
Additive manufacturing (AM) offers new design freedom and thereby high potential for function-oriented part design. Apart from potential geometric complexity achievable only through additive manufacturing, complexity in material composition can be utilized through combination of different materials or material classes. Combining functional materials with standard materials in the same process gives opportunity for the implementation of new features, which would be impossible to realize conventionally. For instance, combination of elastic and stiff materials can be used for the integration of acoustic functions [1], while integration of functional materials like shape memory polymers or conductive polymers can be used for a variety of different applications [2; 3; 4; 5; 6].  
Albeit, in order to design multi material parts for AM efficiently, detailed knowledge of the interface strength on the boundary layer between materials is vital in order to assure a sufficiently strong bonding of the two faces. While some sources rudimentarily show compatibility of different material combinations, the influencing factors on compatibility have not been examined thoroughly. For instance, the website of printer manufacturer Ultimaker B.V. (Geldermansen, Netherlands) shows compatibility in an incomplete table, which does not provide reasons for good or bad compatibility between materials. In addition, the evaluation of materials' compatibilities is limited to combinations of building and support materials [7].  
On simple blunt interfaces, adhesion is the sole phenomenon by which forces can be transmitted, however, a variety of different mechanism based on diffusion, mechanical interlocking, surface energy, chemical bonding, polarity, electrostatics, and interface imperfections have varying influence on adhesion strength [8].  
Thus, in order to optimize part design and process parameters for specific material combinations, the relevant adhesion phenomena need to be identified and their influence on the total adhesive force quantified thoroughly. For this purpose, a systematic approach for optimization of interface strength is necessary to fuel innovation via design freedom of additive manufacturing, in particular regarding functional integration enabled by combining multiple materials. Accordingly, effects of different design measures on the quality of layer bonding are to be determined for selected material combinations. Additionally, apart from influences stemming from the choice of materials, process parameters have a huge impact on the strength of the resulting interface and can be used as levers to improve bonding. Furthermore, AM provides a large design freedom with regard to interlocking features due to its layer by layer material application.
## 2 Precise Orbit Positioning Processing Approach
### Ambiguity Fixing of BeiDouObservations
Therefore, the code residuals of all GPS satellites are ranged according to their elevation angles in ascending order. These code residuals are lumped together to calculate the root mean square (RMS) values with a sampling step size of 0.1 degrees. Such calculation holds for the GLONASS, Galileo and Beidou satellites. Figure 1 shows the RMS of elevation dependent ionosphere-free (IF) code residuals. It can be seen that there exists constant bias in the code residuals of PPP but not POP. That is because when we estimate the satellite clocks in POP, the code bias is not assimilated into the code residuals but into the satellite clocks. What can be concluded here is that the code bias inside the satellite clocks will contribute to the estimation of the float ambiguity and the NL ambiguity fixing since the clocks are involved in the PPP adjustment. Therefore, the estimated UPD should be used together with the corresponding orbit and clock for PPP ambiguity fixing. What can also be found from the POP results is that the code observation residuals are at the same level for each system, therefore, we set equal weights to the code observations of each system. For multi-GNSS ambiguity fixing, the DD ambiguity is fixed within each system. Since BDS does not have a good coverage outside the Asia-Pacific area and only three to six Galileo satellites can be tracked by Antarctica stations. Therefore, in order to overcome the limitation of the number of valid satellites, GLONASS, Galileo and BDS perform together with GPS, respectively.
## 3 Significance of Volatile Organic Compounds in Honey
### Biomedical Activities of Honey and Contribution from Its Volatile Constituents
#### 3.3.1 Terpenes  
Terpenes are a small and heterogeneous class of naturally occurring compounds that serve as important synthetic building blocks for the production of flavors, fragrance, pharmaceuticals and nutraceuticalals. They are oligomers or polymers of isoprene and can be categorized depending on the number of isoprene units into monoterpenes, sesquiterpenes, diterpenes, triterpenes and tetraterpenes. With respect to occurrence in nature, the monoterpenes represent the most important and best investigated class of terpenic compounds.  
Biochemical modifications such as oxidation or rearrangement of monoterpenes produce the related monoterpenoids [65]. Quite a few honey samples have been investigated and the following monoterpenes and monoterpenoids were identified: linalool and its derivative, \(\beta\)-terpineol, dihydrorotronellol, \(\beta\)-citronellol, citronellal, geranyl acetone, limonene, \(\beta\)-pinene, tetrahydrorogeraniol, cavacrol, p-cymene, 1,8-cineol, camphor, isoborneol, p-cymenol _etc._[29,30]. They are known to be active against a wide range of micro-organisms including Gram negative and positive bacteria, viruses as well as fungi [66,67].  
This class of compounds is also known for its biological activities in humans; limonene prevents mammary, liver, lung and other cancers acting in the promotion/progression stage [68]. Besides, their antimicrobial activity, these compounds possess antiseptic and disinfectant qualities and also very great stimulating therapeutic properties; however, they could cause side effects such as skin and mucous membrane irritation and toxicity (www.betterlivingessentials.com/pure-essential- oils/essential- oils).
Sensors_**2011**, _11_, 341-361; doi:10.3390/s110100341  
_Series_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**On-Demand Information Retrieval in Sensor Networks with Localised Query and Energy-Balanced Data Collection**  
**[PERSON] * and [PERSON]**  
National Institute of Information and Communications Technology, Kyoto 619-0288, Japan;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 29 October 2010; in revised form: 8 December 2010 / Accepted: 27 December 2010 / Published: 30 December 2010_  
**Abstract:** On-demand information retrieval enables users to query and collect up-to-date sensing information from sensor nodes. Since high energy efficiency is required in a sensor network, it is desirable to disseminate query messages with small traffic overhead and to collect sensing data with low energy consumption. However, on-demand query messages are generally forwarded to sensor nodes in network-wide broadcasts, which create large traffic overhead. In addition, since on-demand information retrieval may introduce intermittent and spatial data collections, the construction and maintenance of conventional aggregation structures such as clusters and chains will be at high cost. In this paper, we propose an on-demand information retrieval approach that exploits the name resolution of data queries according to the attribute and location of each sensor node. The proposed approach localises each query dissemination and enable localised data collection with maximised aggregation. To illustrate the effectiveness of the proposed approach, an analytical model that describes the criteria of sink proxy selection is provided. The evaluation results reveal that the proposed scheme significantly reduces energy consumption and improves the balance of energy consumption among sensor nodes by alleviating heavy traffic near the sink.  
**Keywords:** on-demand information retrievals; localised data query; data collection; attribute-object name; sink proxy; balancing of energy consumption
## 2 Electronic Address Look-Up Memory Architectures
This configuration ensures that at least one pulldown path from the ML down to the ground exists at any time during a mismatch between the search word and the stored data. On the contrary, a match between the search word bit and the stored data disables both pulldown paths, disconnecting the ML from the ground and feeding its output to the SA. If this matching happens for all the cells of the ML, a NOR logic operation between all CAM outputs, performed at the SA, identifies the exact match of the packet's destination address with the certain RIB prefix entry. Figure 1: (**a**) Electronic AL memory architecture comprising a T-CAM table interconnected to a RAM table and the respective router Routing Information Base (RIB) list; (**b**) Logical circuit of an electronic Matchline architecture; and (**c**) the standard electronic 16T Not OR (NOR) T-CAM cell architecture.
# An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
## 3 Results
### Comparison with Existing Techniques  
In the previous subsections, the performance of our method is compared with that of deep learning methods using vocal tract-based cepstral features and their combinations as references. The results in Figure 10b and Table 6 show that the LPC combination based on the SMOTE + CNN yields the best overall detection accuracy. In this subsection, this optimal combination is compared with existing methodologies and deep learning techniques. Many studies have developed different VPD techniques over the past few decades; in our work, we select four studies with databases and deep learning methods similar to those used in our research, as shown in Table 6.  
Table 6 presents the databases, methodologies, features, deep learning methods, and performances of competing approaches under the binary detection model scenario for voice disorder detection. In [22], the CGAN-IFCM algorithm achieves an accuracy of 95.15%, with 869 normal and 1356 pathological voice data in the SVD. Although our study also uses all the SVD data, we are not sure why the number of points differs from the total amount of data in this study. The authors of [24] prove that the proposed FC-SMOTE method outperforms the other oversampling methods by 100% and 90% in terms of the accuracy of the CNN model with the MEEI and SVD, respectively. Then, for the SVD data, 687 normal and 194 pathological voice data are used, and pathological voice data are augmented to 687 via oversampling. Although the approach in [26] demonstrated good performance, that study used the SPDD. In a comparison with [2,22,24] using the SVD, the proposed combination containing LPCs based on the SMOTE and the CNN improved the resulting accuracy by 3.74-11.29%. Compared to [26], using the SPDD, our proposed method increased the performance by 2.26%. All existing works [2,22,24,26] solved imbalanced class issues with various features, databases, and methodologies. In conclusion, our method (LPCs based on the SMOTE and the CNN) has improved accuracy by up to 11.29% compared to papers using the same parameters (MFCC) and database (SVD) as our experiments.