esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
With X-ray, SAM, and TTA, three NDTs were evaluated on the three most common interconnect technologies in the power electronics and opto-electronics industries, namely, reflow soldering, sintering, and adhesive interconnects, to prove their suitability for thermal performance and interconnect quality testing. For soldered interconnects, an X-ray inspection was sufficient to detect voids and nonwetted areas. This allowed a qualitative and fast testing with commercial X-ray equipment even at production speed for in-line measurement while manufacturing. SAM instead had a smaller resolution unable to detect small voids. TTA was able to detect nonwetted areas and revealed that void rates under a certain limit had only a minor influence on the thermal performance. However, when it came to sintered and adhesive interconnects, X-ray was unable to provide adequate data. The micro and nanopores of sintered interconnects were too small for X-ray resolution, and delaminations in the sinter interface could not be detected. In addition, the density of adhesives was too low compared to the surrounding materials of the DBC and heat sink to be visible in X-ray. With SAM, most degradation of sintered interconnects and adhesives were detectable; however, data quality strongly depended on the thermomechanical set-up. Components and wire bonds impeded the evaluation, and material thickness limited the transducer frequency and, therefore, the resolution. Moreover, the samples had to be placed in water for inspection. With TTA, all degradations of adhesive were detectable, with small differences able to be inspected even for sintered interconnects. Through the use of automatized TTA equipment, the required effort when using this method is drastically reduced and brought to a level comparable with other NDTs.  
Conceptualization, M.S. and G.E.; methodology, M.S. and S.K.B.; software, M.S.; validation, M.S., S.K.B., and E.L.; formal analysis, M.S.; investigation, M.S., S.K.B., E.L., and G.E.; resources, G.E.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S., S.K.B., E.L., and G.E.; visualization, M.S.; supervision, G.E.; project administration, G.E.; funding acquisition, G.E. All authors read and agreed to the published version of the manuscript.  
The research was funded by the Bavarian Ministry of Science and Art in the project IQLED (13 FH044 PX8).  
The authors acknowledge [PERSON] from Robert Bosch GmbH Ansbach for the provided materials and [PERSON] from Hochschule Landshut for the technical support in X-ray analysis.  
The authors declare no conflict of interest.
## 4 Numerical Simulations  
In this section, numerical simulations are carried out to verify the effectiveness of the criterion obtained in the previous section for the appearance of LS between systems (2) and (3). When \(a=35\), \(b=3\), \(c=12\), \(d=7\) and \(0\leq r\leq 0.085\), system (1) is chaotic; when \(a=35\), \(b=3\), \(c=12\), \(d=7\) and \(0.085<r\leq 0.798\), system (1) is hyperchaotic; and when \(a=35\), \(b=3\), \(c=12\), \(d=7\) and \(0.798<r\leq 0.90\), system (1) is periodic. In the following, we consider the hyperchaotic case. By calculation, when \(a=35\), \(b=3\), \(c=12\), \(d=7\) and \(0.085<r\leq 0.798\), the hyperchaotic Chen system (1) only has one equilibrium \((0,0,0,0)\).  
In the following simulations, the parameters in systems (2), (3) and (5) are taken as \(a=35\), \(b=3\), \(c=12\), \(d=7\) and \(r=0.5\). From condition (21), the functional relationship \(y_{i}=x_{i}(t-\tau)\), \(i=1,2,3,4\) between systems (2) and (3) can be achieved if one chooses \(k_{1}=0\), \(k_{2}=19\), \(k_{3}=-2\) and \(k_{4}=0.6\). The numerical results are presented in Figures 1-4 for different values of the time delay \(\tau\), where the initial conditions are taken as \(x_{1}(-\tau,0]=0.1\); \(x_{2}(-\tau,0]=0.2\); \(x_{3}(-\tau,0]=0.3\); \(x_{4}(-\tau,0]=0.4\); \(y_{1}(-\tau,0]=0.2\); \(y_{2}(-\tau,0]=0.2\); \(y_{3}(-\tau,0]=0.3\); \(y_{4}(-\tau,0]=0.4\); \(z_{1}(-\tau,0]=0.3\); \(z_{2}(-\tau,0]=0.4\); \(z_{3}(-\tau,0]=0.3\); and \(z_{4}(-\tau,0]=0.4\). It is depicted that the synchronization errors \(y_{i}-z_{i}\), \(i=1,2,3,4\) between systems (3) and (5) approach zero and the functional relation \(y_{i}=x_{i}(t-\tau)\), \(i=1,2,3,4\) between systems (2) and (3) can be achieved for different values of the time delay: \(\tau=0,0.2,1\) and \(2\), respectively. The time delay in the signal transmission results in the transition from CS to LS between two hyperchaotic Chen systems under condition (21). In fact, the synchronization condition obtained in this paper for exact LS between systems (2) and (3) is not involved with the value of \(\tau\). We show in Figure 5 that even if the value of \(\tau\) is extremely large, exact LS between systems (2) and (3) still can be achieved, where the initial conditions remain as those in Figures 1-4.  
Figure 3: (**a**) Synchronizationerrors between systems (3) and (5); (**b**) The time series of \(x_{i}\), \(y_{i}\), \(i=1,2,3,4\), in systems (2) and (3) with \(k_{1}=0\), \(k_{2}=19\), \(k_{3}=-2\), \(k_{4}=0.6\), \(\tau=1\).  
Figure 2: (**a**) Synchronization errors between systems (3) and (5); (**b**) the time series of \(x_{i}\), \(y_{i}\), \(i=1,2,3,4\), in systems (2) and (3) with \(k_{1}=0\), \(k_{2}=19\), \(k_{3}=-2\), \(k_{4}=0.6\) and \(\tau=0.2\).
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 3 The Proposed SET Algorithm
### Energy Consumption of eNodeB Using Congested and Non-congested Network Loads  
Recently, global warming and CO\({}_{2}\) emission have been extensively studied from the perspectives of environmental and economic impacts [26]. In this regard, energy consumption at eNodeB has become a key issue in cellular networks. Studies have determined that eNodeB operations are accountable for a major part of cellular energy consumption [27] wherein up to 80% of energy is consumed. Therefore, minimizing the power consumption of eNodeB can considerably reduce overall network energy consumption. The scheme proposed in this section relies on the path loss and energy consumption of eNodeB. Accordingly, we adopt and modify the energy consumption model in references [28; 29], as shown in Figure 3.  
From Figure 3, the total energy consumption can be formulated as:  
\[E_{Total}=i(E(R_{TX})+E(S_{TX})) \tag{8}\]  
where i denotes the number of users, \(E_{Total}\) denotes the total energy consumption, \(R_{TX}\) and \(S_{TX}\) denote the transmission and receiving power levels measured in ijoules/bits, respectively.
_applied sciences_  
_Article_  
Exact Mathematical Solution for Maximum Transient Offtracking Calculation of a Single-Unit Vehicle Negotiating Circular Curves  
[PERSON]*\({}^{\copyright}\), [PERSON]+\({}^{\copyright}\), [PERSON], [PERSON], [PERSON], [PERSON]+  
and [PERSON]  
Faculty of Civil Engineering, University of Belgrade, 11120 Belgrade, Serbia; [PERSON]_ADDRESS] (M.L.); [PERSON]_ADDRESS] (D.G.); [PERSON]L_ADDRESS] (S.F.); [PERSON]DDRESS] (F.T.); [PERSON]RESS] (S.V.); [PERSON]SS] (N.M.)  
* Correspondence: [PERSON]L_ADDRESS]  
Footnote †: journal: Applied Sciences  
**Featured Application: The best use of this paper is for the revision of the worldwide used ICAO calculation procedure for fillet design elements at airports. Also, the developed calculation method for maximum transient offtracking calculation can be used for more accurate positioning of single-unit autonomous road vehicles negotiating circular curves.**  
The low-speed turning maneuverability of vehicles is closely related to a well-known offtracking phenomenon which occurs when the rear wheels of a turning vehicle deviate towards the inside of a horizontal curve. Although numerous mathematical models and computer programs for vehicle swept path analysis have been developed in the past, only a few of them can calculate the maximum transient offtracking of a turning vehicle, yet with limited accuracy. The authors were motivated by this fact to find a new mathematical solution for maximum transient offtracking calculation of a single-unit vehicle when negotiating circular curves. In the first stage, a transcendental equation defining vehicle maximum transient offtracking position is derived and numerically solved by Python 3.10.12. In the second stage, the polynomial regression model predicting accurate numerical solutions of the transcendental equation with the desired level of accuracy was developed and tested. The new calculation method is simple enough to simply take the vehicle datum length, circular curve radii, and turn angle, while instantly producing the maximum transient offtracking value, without the need to draw any of the vehicle movement trajectories.  
road design; low-speed offtracking; maximum transient offtracking; vehicle movement; ICAO; transcendental equation; numerical solution; Python; polynomial regression model
## 2 Materials and Methods
### Extreme Vertices Design  
An experimental framework used to study items with several ingredients or components is called a mixture design. Extreme vertices designs are employed when the mixing design only covers a tiny area or sub-portion of the simplex. These designs are required when the selected design space does not follow the rules of a simplex design, usually because the components are subject to both upper- and lower-bound limitations. The constraints are written as \(\text{Ii}\leq\text{x}\text{i}\leq\text{Si}\) (\(\text{i}=1,2;\,\ldots\,\text{-q}\)), where \(\text{Ii}\) denotes the \(\text{i}\)th component's lower bound and \(\text{Si}\) denotes its upper bound [23; 24].  
The general form of a constrained mixing problem (extreme vertices design) is:  
\[X_{1}+X_{2}+X_{2}\ldots X_{q}=1 \tag{1}\]  
\[\text{Ii}\;\leq\;\text{Xi}\;\leq\;\text{Si for}\;\text{i}\;=\;1,\,2,\cdots,\; \text{q}\;\text{ with}:\;\text{Ii}\;\geq\;0\;\text{et}\;\text{Si}\leq 1\]An extreme vertices design was used in this work to optimize a sub-portion or a smaller space within the simplex. The sub-portion consists of a mixture of added starches with 30% in total formula (X\({}_{1}\): corn starch; X\({}_{2}\): potato starch; X\({}_{3}\): tapioca starch):  
\[X_{1}+X_{2}+X_{3}=0.3 \tag{2}\]  
The results enabled the calculation of regression coefficients, with each response being modeled as a function of the coded factors as follows:  
\[\mathrm{Y}=\sum_{\mathrm{i=1}}^{\mathrm{P}}\alpha_{\mathrm{i}}X_{\mathrm{i}}+ \sum_{\mathrm{i<j}}^{\mathrm{P}}\alpha_{\mathrm{ij}}X_{\mathrm{i}}X_{\mathrm{ j}}+\epsilon \tag{3}\]  
where Y represents the predicted response value; X\({}_{\mathrm{i}}\) are independent factors or variables; \(\alpha\)i represents the linear effect of each component, and \(\alpha\)ij represents the interaction effects. \(\epsilon\) represents the error.  
The choice of responses was based on the basic criteria of the quality of gluten-free cookies: Y\({}_{1}\): specific volume (cm\({}^{3}\)/g), Y\({}_{2}\): spread ratio, Y\({}_{3}\): expansion factor, and Y\({}_{4}\): overall acceptability. The maximum and minimum levels of the components were determined based on preliminary tests and literature data [21; 22]. Any variation from these concentrations results in gluten-free cookies with low technological properties.  
Based on factors such as the acceptability of the cookies and their overall quality, the amounts of corn starch ranged from 12 to 20 g, potato starch from 6 to 12 g and tapioca starch from 0 to 6 g. A total of eleven trials were carried out for the selected mixture design (Table 1):
## 3 Results
### Illumination  
In Reggio Calabria, public lighting is not fully standardized. While there is a growing trend toward adopting LED lamps, many of the city's streets are still illuminated using older technologies, such as high-pressure sodium lamps. These lamps have been standard in many European cities for several years. Given the environmental and economic benefits of LED lamps--including greater energy efficiency, longer lifespan, and improved control over light direction and dispersion--it is particularly beneficial to introduce LED lighting in the city.  
To optimize public lighting and manage light pollution effectively, an accurate mapping of the current lighting situation is essential. Advanced technological tools, such as digital simulations, can help identify the best interventions for regulating lighting. By using satellite data on light pollution and the actual specifications of the streetlamps in the city, we conducted an initial analysis of light pollution levels within Reggio Calabria.  
Additionally, data obtained from on-site measurements were also analyzed and integrated into the system. Figure 5 presents the results from this proposed methodology, highlighting the initial findings related to the city's light pollution and the potential impact of different lighting technologies.  
The representation shows the intensity of light pollution, which increases with distance from the city center but also reveals significant issues in the suburbs. The initial results obtained from the proposed system can be continuously updated by incorporating a mathematical model. Once defined, this model can be integrated into the platform, which is already structured to accommodate such functional models. The model will adjust the results in real-time based on input variables from brightness sensors deployed across the territory and the acquired satellite images.  
This system not only enables the real-time visualization of changes but also allows for the simulation of modifications, analyzing their potential impacts. The goal of this model is to facilitate evaluations based on different territorial scenarios and, potentially, to monitor the evolution of light pollution over time. This ongoing development will further enhance the system's ability to manage and mitigate light pollution effectively.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
## 3 Materials and Methods
### Lighting Control Based on Rules  
This section describes the open-loop lighting control system. This control system is energy efficient and simple to design. An important component of this system is the rule base consisting of if-then rules. The output of the rules is the dimming level of a LED luminaire \(n\) at time \(t\), denoted as \(o_{n}(t)\), and the input consists of the occupancy state \(p_{n}(t)\) detected by the sensing module connected to LED \(n\), schedule \(s(t)\), location \(r(n)\), distance between LED \(n\) and windows \(l(n)\), and daylight illuminance value \(d(t)\). The control rule can be expressed by:  
\[o_{n}(t)=f(p_{n}(t),s(t),r(n),l(n),d(t)). \tag{2}\]  
In Equation (2), the schedule is preferred by the user, which defines activities in different times. The location represents the type of space, such as a meeting room, a manager room, and an office room. If the luminaire located in a zone where daylight harvesting is implemented, the distance between \(n\) and windows decides the types of subzones for daylight harvesting and the value of the distance is replaced with the subzone type. All three values are set by the user through a supplied software. \(p_{n}(t)\) and \(d(t)\) are determined by the occupancy sensors, the sensing module colocated with luminaire \(n\), and the light sensor nearest to windows. An example of the function is introduced in Section 3.3.1.  
Since the illumination at the associated zone of LED \(n\) is influenced by the light outputs of LEDs in its neighborhood, the controller of LED \(n\) seeks aid from neighboring LEDs by transmitting a request frame. The request frame contains two types of data: address of transmitter \(A_{n}\) and forwarding count \(T_{c}\). Consider the controller of wireless sensing module \(m\) shown in Figure 5. After reception of the request frame from LED \(n\), the controller compares address \(A_{n}\) with the stored record at its one-hop neighbor table to determine the occupancy state of \(m\). If there is a match, which indicates \(m\) is a one-hop neighbor of \(n\), \(p_{m}(t)\) is set to one, and the dimming level \(o_{m}(t)\) is calculated using Equation (2); namely, \(p_{m}(t)=1\) indicates that the associated area of \(m\) is occupied, while \(p_{m}(t)=0\) indicates that the associated area of \(m\) is unoccupied. Further, the controller of \(m\) determines whether to forward a request frame to its one-hop neighbors continuously. The timer value of \(T_{c}-1>0\) indicates that one-hop neighbors of \(m\) are needed to provide aid to \(n\). In this case, module \(m\) transmits a new request frame with address \(A_{m}\) and forwarding count \(T_{c}-1\). Once _Timer_ of \(m\) is \(T_{0}\), \(p_{m}(T_{0})=0\), and \(O_{m}(T_{0})\) reset to the unoccupied-state values. The controller of \(n\) keeps on broadcasting request frames to alert other nodes if its occupancy state is occupied.  
Figure 5: Flowchart of the neighbor-aided illumination control of node \(m\).
## 4 Simulation Experiments and Results
### Optimum Back-Rake Angle
[28], consequently increasing maximum stress within the core. The x-axis represents the standard deviation of the cutting force at a back-rack angle of 15-25\({}^{\circ}\), while the y-axis represents the stress at the outermost edge of the core at different dip angles. The z-axis corresponds to various cutting angles' MSE values. These variables are used to create a scatter plot, and a surface is fitted to the data in the XYZ-axis. From Figure 12, it is evident that the back-rake angle of the drill has an impact on the maximum stress and MSE, aligning with the findings of the theoretical model. As the back-rake angle increases, the standard deviation of the mean cutting force exhibits a decreasing and then increasing trend. Based on the distribution and aggregation of points, when the standard deviation of the cutting force is at large, the maximum stress in the core also becomes larger, inferring that there is a relationship between the maximum stress in the core and the standard deviation of the cutting force. Furthermore, the back-rake angles ranged from 15\({}^{\circ}\) to 25\({}^{\circ}\), with the optimum back-rake angle of 21\({}^{\circ}\) producing a maximum stress and mechanical specific energy that were 0.69 and 0.85 times higher than the highest point, respectively. Based on the simulation results, the optimal back-rack angle is 21\({}^{\circ}\), considering the maximum stress, MSE, and average cutting force. Figure 11: Maximum stress and MSE values with back-rake angle.
# Discoloration Mechanisms of Natural Rubber and Its Control
## 6 Methods to Control the Discoloration in Natural Rubber
### Chemical Treatments  
#### 6.4.1 Removal of Non-Rubber Components  
One way to reduce the yellow color of NR is to remove non-rubber components, for example, lipids and proteins from NR. The discoloration of NR latex can be prevented by inhibiting either enzymatic activity or eliminating the substrates concerned by chemical treatment or the combination of both [45]. The removal of non-rubber components, in particular proteins and lipids in NR by chemical methods, can help reduce the yellowness index of NR to a level comparable to that of synthetic polyisoprene.  
Purifications by deproteinization and transesterification [52] are methods for removing proteins and lipids from NR latex/solution, respectively, that could reduce coloring substances present in NR [53]. Vulcanized NR prepared from highly purified rubber by saponification was light-yellow in color [54; 55]. The employment of some chemicals in latex, i.e., a proteolytic enzyme, sodium hydroxide, to reduce rubber discoloration. It was found that the color index of NaOH-treated rubber or saponified rubber (SP) was lightest, whereas the color of urea-treated rubber or deproteinized rubber (DP) was close to the centrifugation rubber. DP and SP may effectively produce light-colored NR [55]. Since non-rubber components were removed into the serum during centrifugation, saponification effectively removes proteins and gives the lightest color film. Thus, these may be effective methods to produce light-colored NR.
# Biosynthesis of Camphane Volatile terpenes in _Annomum_ _villosum_ Lour: Involved Genes and Enzymes
## 2 Results
### Unigenes Involved in Backbone Biosynthesis of Camphane Volatile Terpenes
Figure 6: Putative amphane volatile terpene biosynthesis pathway in _Anomoun villosum_ Lour. DXS: 1-deoxy-D-xylulose-5-phosphate synthase, EC: 2.2.1.7; DXR: 1-deoxy-D-xylulose-5-phosphate reductio-somerase, EC:1.1.1.267; MCT: 2-C-methyl-D-eryththird 4-phosphate cytidylyltransferase, EC:2.7.7.60; CMK: 4-(cytidine 5”diphospho)-2-C-methyl-D-eryththril kinase, EC:2.7.1.148; MCS: 2-C-methyl-D-eryththril 2,4-cyclodextrin diphosphate synthase, EC:4.6.1.12; HDS: 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase, EC:1.17.7.1; IDS: 4-hydroxy-3-methylbut-2-en-1-yl diphosphate reductase, EC:1.17.1.2; AACT: acetyl-CoA acetyltransferase, EC:2.3.1.9; HMGS: 3-hydroxy-3-methylglutural coenzyme A synthase, EC:2.3.3.10; HMGR: 3-hydroxy-3-methylglutural coenzyme A reductase, EC:1.1.1.34; MK: mexulonate kinase, EC:2.7.1.36; PMK: phosphomevalonate kinase, EC:2.7.4.2; MVD: diphosphomevalonate decarboxylase, EC:4.1.1.33; IPI: isopentenyl-diphosphate Delta-isomerase, EC:5.3.3.2; GPPS: geranyl-diphosphate synthase, EC:2.5.1.1; BPPS: bornyl diphosphate synthase, EC: 5.5.1.8; BPPH: bornyl pyrophosphate hydrolase, EC: 3.1.7.3; BDH: bornel dehydrogenase, EC: 1.1.1.198; BAHD: BAHD acyltransferase, EC: 2.3.1. Figure 7: Expression profile of DEGs in terpenoid and polyketide biosynthesis pathways and putative transcription factors. (**A**) Expressions of DEGs associated with biosynthesis pathways of terpenoids and polyketides. (**B**) Expressions of DEGs involved in transcription factors.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.3 Quadratic Discriminant Analysis (QDA)  
Quadratic discriminant analysis (QDA) shares conceptual similarities with LDA but employs a quadratic decision boundary for class separation [18]. QDA assumes Gaussian-distributed data for every class, in contrast to LDA. Class-specific mean vectors, the average feature values within each class, and class-specific priors, the relative frequency of data points per class, are incorporated into the model.
###### Abstract  
Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the poor signal-to-noise ratio of radial accelerometers hardly obtains the correct horizontal attitude, especially the roll angle. In this paper, a novel near-vertical attitude measurement method was proposed to address this issue. The redundant micro-electromechanical system (MEMS) accelerometers were employed to replace the original accelerometers from MEMS inertial measurement unit (IMU)/magnetometers for calculating horizontal attitude under near-vertical conditions. In addition, a simplified four-position calibration method for the redundant accelerometers was proposed to compensate for the installation and non-orthogonal error. We found that the redundant accelerometers enhanced the signal-to-noise ratio to upgrade the azimuth accuracy at the near-vertical well section. Compared with the traditional method, the experiment results show that the average azimuth errors and roll errors are reduced from 34.45\({}^{\circ}\) and 27.09\({}^{\circ}\) to 5.7\({}^{\circ}\) and 0.61\({}^{\circ}\), respectively. The designed configuration scheme is conducive to the miniaturized design and low-cost requirements of wellbore measuring tools. The proposed attitude measurement method can effectively improve the attitude accuracy of near-vertical wells.  
wellbore attitude; near-vertical attitude; MEMS inertial measurement unit; magnetometer; redundant accelerometer +
Footnote †: journal: Applied  
0000-0001-4414-46138