esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Computation of the Scattered Fields from a Spherical, a Circular, and a Square Cylindrical Cavity  
### Spherical Cavity  
Consider a three-dimensional spherical cavity on the surface of an elastic half-space, as demonstrated in Figure 4. The cavity has a depth \(D\), a width \(2R_{0}\), and a cavity radius \(R\). The area \(S\) of the surface trace is defined as \(S=\left\{x,y\right\}\!\!\left|x^{2}+y^{2}\leq R_{0}^{2}\right\}\). In the coordinate system \((x,y,z)\), we have:  
\[R^{2}=x^{2}+y^{2}+(z+Z)^{2},\;\;Z=R-D,\;\;R^{2}=R_{0}^{2}+Z^{2}. \tag{10}\]  
We define the dimensionless quantities \(\overline{r}_{0}=kr_{0}\), \(\overline{R}=kR\), \(\overline{R}_{0}=kR_{0}\), \(\overline{D}=kD\), and \(\overline{z}_{0}=kz_{0}\), which will be used for the expressions of the vertical displacement of scattered wave field.  
The cylindrical coordinates are used for the computation of the scattered field. The total vertical displacement for \(\theta=0\) is computed using:  
Figure 4: A spherical cavity in a half-space.  
Figure 3: Source and observation point.  
\[u_{z}=-\frac{A_{0}}{4E}W(z)\Big{(}\int_{0}^{\overline{R}_{0}}\int_{0}^{2\pi}\Big{[} U(0)\overline{f}_{x}(\tau_{0},\theta_{0},\overline{z}_{0})H_{1}^{(1)}( \overline{r}^{\prime})\cos\theta^{\prime}+U(0)\overline{f}_{y}(\tau_{0},\theta_ {0},\overline{z}_{0})H_{1}^{(1)}(\overline{r}^{\prime})\sin\theta^{\prime}+W(0) \overline{f}_{z}(\overline{\tau}_{0},\theta_{0},\overline{z}_{0})H_{0}^{(1)}( \overline{r}^{\prime})\Big{]}\bar{\rho}_{0}d\theta_{0}\Big{)}, \tag{11}\]  
where  
\[\overline{f}_{x}(\overline{\tau}_{0},\theta_{0},\overline{z}_{0})=\left(\frac {\overline{\tau}_{0}\cos\theta_{0}}{\sqrt{\overline{R}^{2}-\overline{\tau}_{0 }^{2}}}\Big{(}d_{4}e^{-\overline{\tau}_{0}^{2}}+d_{5}e^{-\overline{\sigma}_{ 0}^{2}}\Big{)}+i\Big{(}d_{6}e^{-\overline{\tau}_{0}^{2}}+d_{7}e^{-\overline{ \sigma}_{0}^{2}}\Big{)}\right)\right)\right. \tag{12}\]  
\[\overline{f}_{y}(\overline{\tau}_{0},\theta_{0},\overline{z}_{0})=\left(\frac {\overline{\tau}_{0}\sin\theta_{0}}{\sqrt{\overline{R}^{2}-\overline{\tau}_{0 }^{2}}}d_{10}e^{-\overline{\
u}_{0}^{2}}\right)\right. \tag{13}\]  
\[\overline{f}_{z}(\overline{\tau}_{0},\theta_{0},\overline{z}_{0})=\left(i \frac{\overline{\tau}_{0}\cos\theta_{0}}{\sqrt{\overline{R}^{2}-\overline{ \tau}_{0}^{2}}}\Big{(}d_{6}e^{-\overline{\
u}_{0}^{2}}+d_{7}e^{-\overline{ \sigma}_{0}^{2}}\Big{)}+\Big{(}d_{8}e^{-\overline{\
u}_{0}^{2}}+d_{9}e^{- \overline{\sigma}_{0}^{2}}\Big{)}\right)\right. \tag{14}\]  
with  
\[\overline{r}^{\prime}=\sqrt{\overline{r}_{0}^{2}+\overline{r}^{2}-2\overline{ \tau}_{0}\cos\theta_{0}}, \tag{15}\]  
\[\cos\theta\tau=\frac{\overline{\tau}-\overline{\tau}_{0}\cos\theta_{0}}{\sqrt {\overline{r}_{0}^{2}+\overline{r}^{2}-2\overline{\tau}_{0}\cos\theta_{0}}}, \ \ \sin\theta\tau=\frac{\overline{\tau}_{0}\sin\theta_{0}}{\sqrt{\overline{r}_{0}^{2 }+\overline{r}^{2}-2\overline{\tau}_{0}\cos\theta_{0}}}. \tag{16}\]
## 3 Concept
### Requirements  
Since there are many conceivable applications for control loops in manufacturing companies, the foremost requirement is the suitability for the application. It must allow the control of manufacturing with the aim of improving its key performance indicator  
Figure 4: Description of an agent-environment interaction by a Markov Decision Process [61].  
delivery reliability and considering its influences. The necessity of this requirement results from the motivation to transfer control engineering principles to the interface between supply network and PPC. Strictly speaking, however, this is not about controlling orders at individual machines, but about optimizing the order flow through the entire manufacturing system in terms of delivery reliability (cf. Section 1).  
For this purpose, due to their potential consequences, it is also necessary to consider events from the supply network, so-called external events, in the control loop and that appropriate measures to adjust manufacturing can be taken in response to the former (cf. Sections 1 and 2.1).  
The environment of manufacturing companies is rather complex, whereas the link between supply network and PPC is mostly based on static planning objects. To overcome this, the control loop must be designed in the sense of an adaptive control in such a way that its data-driven model is not only oriented to predefined situations, but also dynamically adapts to unknown situations and resulting events (cf. Sections 2.1 and 2.2.2).  
This adaptivity is determined by the desired learning capability of the controlling element. It enables the controlling element to develop new decisions from data sets and, contrary to mathematical optimization, generalize to unknown data (cf. Section 2.2.3).  
Moreover, a high degree of automation is necessary. First, for the controlling element to learn new situations from data and, thereby, adapting all result variables to changed environmental conditions. Second, to decide for and implement measures in manufacturing without the downsides of manual interventions. The system limits can thus be continuously extended (cf. Section 2.2.3).  
For this purpose, the controller must be able to determine multi-dimensional parameters and their dependencies as manipulated variables. The complexity of the problems in PPC alone determines this complex controller design with multi-dimensional manipulated variables (cf. Section 2.1).  
Since the planning and control tasks in supply network and manufacturing are rather complex and interlinked, a seamless integration of the control loop into the existing manufacturing IT is required. Most preferably, this is achieved by a modular approach (cf. Sections 2.1 and 2.2.2).  
All requirements are summarized in Table 2 and will henceforth be referenced by their corresponding number.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### Congestion Control in IoT Networks
#### 4.2.1 Congestion Detection  
Detecting the onset of congestion is the first step toward activating congestion control mechanisms. IoT networks exhibit unique traffic patterns and requirements compared to traditional networks. Hence, customized congestion detection approaches are necessary. Table 8 shows the key indicators for IoT congestion detection, including the following:  
* Buffer Occupancy: Monitoring buffer utilization at the network nodes can signal congestion. Thresholds can be set to trigger a congestion response when the buffers become full. For example, the CoAR protocol uses current and historical buffer occupancy to detect congestion [1].
* Traffic Load: Observing channel load conditions over time also reveals congestion. The CoAP-R algorithm utilizes present and past traffic loads for congestion detection [2].
* Latency: Increasing delays in packet delivery indicate possible congestion. Some solutions, like adaptive VBS [3], use changes in latency as a congestion marker.
* Packet Loss: When buffers overflow at the congested nodes, packets will be dropped. Monitoring packet loss rates can thus identify congestion.
* Queue Length: Longer queues at the nodes imply traffic accumulation and possible congestion. Techniques like FLCC [21] employ queue length to detect congestion.  
\begin{table}
\begin{tabular}{c c} \hline
**Congestion Detection Technique** & **Description** \\ \hline
**Buffer Occupancy** & Monitoring buffer utilization at nodes \\ \hline
**Traffic Load** & Observing channel load over time \\ \hline
**Latency** & Increasing delays in packet delivery \\ \hline
**Packet Loss** & Dropped packets due to buffer overflow \\ \hline
**Queue Length** & Longer queues indicate traffic accumulation \\ \hline \end{tabular}
\end{table}
Table 8: Key indicators for IoT congestion detection.  
Buffer occupancy is commonly used, as it directly signals the buildup of packets at the nodes. A combination of such indicators provides a comprehensive congestion detection system. Machine-learning methods can also be applied to learn the congestion patterns.
## 3 Results and Discussion
### Mechanical Properties
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**PHBV Sample**} & **Tensile** & **Young’s** & **Flexural** & **Flexural** \\  & **Strength** & **Modulus** & **Strength** & **Modulus** \\ \cline{2-5}  & **MPa** & **GPa** & **MPa** & **GPa** \\ \hline PHBV-15 & 12.3 \(\pm\) 1.0 \({}^{\rm bc}\) & 1.12 \(\pm\) 0.21 \({}^{\rm a}\) & 27.5 \(\pm\) 1.5 \({}^{\rm b}\) & 1.54 \(\pm\) 0.13 \({}^{\rm b}\) \\ PHBV-32 & 13.7 \(\pm\) 1.1 \({}^{\rm b}\) & 0.83 \(\pm\) 0.1 \({}^{\rm b}\) & 27.4 \(\pm\) 1.1 \({}^{\rm b}\) & 1.27 \(\pm\) 0.06 \({}^{\rm c}\) \\ PHBV-39 & 12.7 \(\pm\) 1.9 \({}^{\rm bc}\) & 0.87 \(\pm\) 0.04 \({}^{\rm b}\) & 20.8 \(\pm\) 1.4 \({}^{\rm c}\) & 1.02 \(\pm\) 0.08 \({}^{\rm d}\) \\ PHBV-46 & 12.2 \(\pm\) 1.8 \({}^{\rm bc}\) & 0.57 \(\pm\) 0.07 \({}^{\rm c}\) & 19.8 \(\pm\) 1.6 \({}^{\rm d}\) & 0.82 \(\pm\) 0.06 \({}^{\rm e}\) \\ PHBV-61 & 13.3 \(\pm\) 0.3 \({}^{\rm b}\) & 0.61 \(\pm\) 0.06 \({}^{\rm c}\) & 18.1 \(\pm\) 0.6 \({}^{\rm d}\) & 0.80 \(\pm\) 0.06 \({}^{\rm e}\) \\ PHBV-71 & 10.1 \(\pm\) 1.4 \({}^{\rm c}\) & 0.58 \(\pm\) 0.05 \({}^{\rm c}\) & 20.3 \(\pm\) 1.4 \({}^{\rm c}\) & 0.86 \(\pm\) 0.06 \({}^{\rm e}\) \\ PHBV-78 & 13.2 \(\pm\) 0.7 \({}^{\rm b}\) & 0.67 \(\pm\) 0.03 \({}^{\rm bc}\) & 21.2 \(\pm\) 1.5 \({}^{\rm c}\) & 0.95 \(\pm\) 0.10 \({}^{\rm de}\) \\ PHBV-84 & 11.7 \(\pm\) 0.9 \({}^{\rm bc}\) & 0.79 \(\pm\) 0.16 \({}^{\rm bc}\) & 27.3 \(\pm\) 0.8 \({}^{\rm b}\) & 1.31 \(\pm\) 0.03 \({}^{\rm c}\) \\ Std PHBV-8\%HV & 25.9 \(\pm\) 2.2 \({}^{\rm a}\) & 0.75 \(\pm\) 0.09 \({}^{\rm bc}\) & 59.2 \(\pm\) 0.8 \({}^{\rm a}\) & 4.65 \(\pm\) 0.15 \({}^{\rm a}\) \\ \hline \end{tabular} \({}^{\rm a}\) Values are mean \(\pm\) standard deviation. Where necessary, the standard deviation has been rounded up to the mean’s reported precision. \({}^{\rm b}\) Statistically difference of mean of results is measured via Tukey test (\(p\)-value \(<\) 0.05) and showed by superscript letters (a, b, c, d, e).  
\end{table} 
Table 9: Tensile and flexural properties of purified PHBV from different operational days. flexural strength and PDI (\(R^{2}\) = 0.68), flexural modulus and PDI (\(R^{2}\) = 0.78), tensile strength and PDI (\(R^{2}\) = 0.69), and [PERSON]'s modulus and PDI (\(R^{2}\) = 0.83) were observed.
## 2 Geological Background  
Tarim Basin is surrounded by mountains such as Tianshan, Kunlun, Kuluketage, Keping and Altun Mountain (Figure 1). The Tarim Basin is a large composite basin composed of a Paleozoic cratonic basin and a Mesozoic-Cenozoic foreland basin [38], where thick Paleozoic, Mesozoic, and Cenozoic sedimentary strata are deposited on the Archean and Proterozoic crystalline basement [39; 40; 41]. According to the current structural features and stratigraphic burial depth, Tarim Basin can be divided into seven primary structural units of \"three uplifts and four depressions\". The three uplifts are Tabei Uplift, Central Uplift and Southeast Uplift of the Tarim Basin, and the four depressions are the Kuqa Depression, North Depression, Southwest Depression and Southeast Depression of the Tarim Basin [38].  
The Tarim Basin began to receive sedimentation in the Sinian period, developing a complete set of marine to marine-terrestrial transitional sedimentary systems. Except for the absence of the Cambrian system in the Kuqa Depression, the Tiekelike area of Southwest Tarim, the Southeast Uplift, and the Southeast Depression, the Cambrian system is distributed throughout the entire Tarim Basin. Moreover, the Cambrian system in the Tarim Basin has good exposure in the Keping area. Due to the topography being high in the west and low in the east, the Cambrian deposits show the characteristics of west-east differentiation. Taking the platform margin belt in the east basin as the boundary, the Tarim Basin can be divided into the carbonate basin facies area in the Tadong area and the carbonate platform facies area in the Taxi area [42]. Through the analysis of outcrops, paleontological evidence and drilling data, the Cambrian system in the Taxi platform facies area can be subdivided into three series and six formations. From bottom to top, these are the Yuertusi Formation, Xiaoerbulake Formation and Wusonggeer Formation in the lower Series, the Shayilike Formation and Awatage Formation in the middle Series and the lower Qiulitage Group in the upper series [43; 44]. The Tadong basin facies area can be subdivided into three series and four formations [44]. From bottom to top, these are the Xishanbulake Formation, the Xidashan Formation in the lower series, the Moheershan Formation in the middle series and the Tuershaketage Formation in the upper series (Figure 2).  
Figure 1: Location of study area and structural unit map of Tarim Basin.  
Figure 2: Stratigraphic division and correlation of the Cambrian in the Tarim Basin and the column diagram of the Cambrian strata in the Xiaoerbulake outcrop area.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 3 Results
### Optimization of Model 1: Classification of Positive/Negative Samples  
In the optimization of the first model, each one of the nine aforementioned preprocessing combinations was used as input, each at a time. The models use a kNN algorithm with 3 neighbors, and they were tested at different feature selection thresholds ranging from 0 to 100. The accuracy achieved by each configuration is shown in Figure 3 (left) where the maximum accuracy was obtained with a SNV preprocessing technique, and the feature selection threshold was between 11 and 18.  
Figure 3: Accuracy of a k-NN model 1 in leave-one-cow-out nested cross-validation: with 3 neighbors, according to the preprocessing combinations described in Figure 2 (letters from a to i) and feature threshold (**left**), with different values of k-nearest neighbors, and feature selection thresholds (**right**).  
Once the preprocessing technique had been chosen, the second optimization process was performed. Its objective was to optimize the neighborhood size and the feature selection threshold. Models were tested with k-nearest neighbors between 1 and 10 and different feature selection thresholds from 0 to 100.  
In Figure 3 (right), the accuracy of the model with different neighborhood sizes is shown. It should be highlighted that, with the SNV preprocessing technique, the model achieved accuracies above 0.84 with any of the k-nearest neighbors experimented. However, the best results were achieved when the model used six as number of nearest neighbors. On the other hand, accuracy is deeply affected by the feature selection threshold, since it sets the amount of input values received by the model. A feature selection threshold equal to 12 allowed the model to achieve the best results with 6 nearest neighbors.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 6 Conclusions  
To accurately locate the position of the CSS of a complicated slope in LES, a new method is proposed by combining the WPA and the SRM. The algorithm proposed in this paper includes two approaches. The first approach is an algorithm to bring the slope into the LES. Based on the SRM, the strain field of LES and FOS can be found. The second approach is the wavelet packet filter algorithm for smoothing the CSS. This algorithm applies wavelet packets to SSA. Through the analysis of three different examples and comparison with [PERSON]'s method, the results show that: (1) the proposed method is reasonable and efficient and can be used to locate the CSS; (2) it is reasonable to take the maximum equivalent plastic strain point as the position of CSS. It should be pointed out that this study is limited to two-dimensional SSA with elastic-perfectly plastic material. The validation of the proposed method for the three-dimensional problems and the non-elastic-perfectly plastic material needs further study.  
Conceptualization, Y.C.; Data curation, Z.N.; Formal analysis, C.H. and S.D.; Funding acquisition, Y.C.; Investigation, K.L.; Methodology, Z.N.; Project administration, Z.N.; Supervision, Y.C. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Key Research and Development Program of China, under the Grant Nos. 2018 YFC0809400.  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author.  
The authors declare no conflict of interest.
## 2 Research Context
### Product Architecture
#### 2.3.3 Open Architecture  
Similar to modular architecture, open architecture is based on the principles of modular design [18]. This type of architecture comprises standard and customizable modules, like modular architecture, in addition to personalized modules [4]. Open architecture targets a portion of the same objectives as modular architecture through the use of standard and customizable modules. These common objectives stem from the use of many of the same concepts, such as modular design.  
Open architecture, however, goes beyond modular architecture. This architecture employs the concept of modules for manufacturing, as in modular architecture, but also in other product lifecycle stages. The use of modules throughout the product lifecycle emphasizes product modification and evolution during its useful life. Products can, thus, be repaired, upgraded with new functions, or recycled directly by the customer using the modular concept [18].  
In addition to extending the product's lifecycle, the deployment of modules designed for disassembly [4] enables a focus on the end-of-life stage of products [18]. This approach is indicative of a shift towards a market that values sustainability, evident in the scalability of products and considerations for their dismantling.  
Furthermore, the integration of personalized modules within open architecture fosters a degree of individualization for certain modules, achieved through a collaborative designprocess with the user. Product personalization is, thus, achieved through significant client involvement [4]. Such personalization can occur throughout the product's lifecycle, in line with the objective of employing modules throughout the product's lifespan.  
Customer-driven personalization facilitated by open architecture aligns more closely with the context of MPE [4; 18]. This approach not only increases product durability by enabling repairs but also aligns corporate strategies with sustainable development objectives. The practice of individualizing products during their lifecycle has the potential to foster and maintain enduring customer relationships throughout the duration of the product's life.
## 3 Methods and Results
### Relationship between Vertical Fissure Throws and Fissure Lengths  
To determine the rate of uplift in the study area, alluvial fans with known levels were compared (the youngest being the Shur River alluvial fan at 32\({}^{\circ}\)54\({}^{\prime}\)32.78\({}^{\prime}\) N, 59\({}^{\circ}\)4\({}^{\prime}\)7.79\({}^{\prime}\) E). As a result, the uplift was about 45 m.  
However, interpreting the study area's differences in uplift values as a transgression zone [28] in terms of fissure numbers and lengths should be done with caution. Because the fissuring concentrate in the study area is on the fault's hanging wall, compressional activity can result in greater apparent growth. A high uplift in this area, on the other hand, could indicate an extensional activity. As a result, dilation should be considered when assessing the evolution of the faulting and fissuring intensity.  
The accuracy of the obtained model (Figure 5) enables us to determine the 3D coordinates of each point of the stereo pair with an accuracy of approximately 0.03 m in both horizontal and vertical directions (Table 6). We calculated the difference in elevation between each fissure's two edges (Figure 13).  
By measuring the amount of offset in the fissure, which ranged from 0.3 m to 1.1 m, it was discovered that the longest fissure has the most offset (Table 6). The fissure number, length, and throw of the 20 identified fissures for a part of the study area (Figure 5) were compared (Figure 14). Despite the dispersion, a straight-line fitting of a log-log graph's distribution of points suggests a power-law for the fissure. Although it is difficult to draw definitive conclusions about the variation in fissure activity from these diagrams, it appears that the maximum offset (throw) is perpendicular to the faults. This indicates that the fault is growing upward, forming fissures in the transgression zone.  
It is a common procedure to analyze the swarm of fissures and faults in terms of the size of the frequency distribution. In wide tectonic regions where faults span a wide range of dimensions, a power-law distribution is used to define the size-frequency distribution. The exponent value decreases with increasing strain. Our analysis of fracture patterns in the NW Birjand reveals similar trends in terms of physical properties, as the fracture distribution closely follows a power-law (Figure 12). This behavior suggests that the evolution of the fissure swarm was controlled by fracture nucleation and linkage [35; 36].  
Figure 12: (a) Graph depicting the number and length of fissures (classes of 10 m); (b) the log-log diagram of the number (N) and length of fissures (L), illustrating the explicit linear distribution.
## 4 Discussion  
### [PERSON]'s Model as an Alternative to a Traditional Environment-Emotion Approach  
Three approaches are commonly used to investigate the influential mechanism of environments on human perceptions, emotions and behaviour. The first approach that was widely employed in earlier studies used questionnaires and interviews to ask users to describe what and how certain environmental characteristics make them feel happy or relaxed [72; 73]. This bottom-up way of exploration brings difficulties to practitioners since people's responses can sometimes be vague and inconsistent, requiring a lot of extra effort to translate such information into design languages. A more precise way of indicating human emotions and behaviour in response to their environment is to use psychophysical instruments such as an eye-tracking device to capture eye movement [74], a polygraph to measure physiological parameters (e.g., electroencephalograph, skin conductance and cortisol level) [75] and a portable Global Positioning System (GPS) to track people's activities [76]. However, these have difficulty providing direct and applicable clues on how and how much environments should be improved to benefit people. These shortcomings thus led to a third approach, which was to mathematically establish the relationship between environment and human responses using factor component analysis [77], correlation analysis [78] and regression models [79]. Although they have the advantage of being precise and capable of combining multiple influential indicators into the same model, they cost a great amount of time in construction and their accuracy is susceptible to data errors.  
A practical tool should be efficient to use, easy to comprehend, closely tied in with design perspectives and applicable to other contexts with similar requirements. Each of the three methods has one or two advantages, but none of them has all the necessary conditions simultaneously. The method proposed in this study to link human emotions to design perceptual attributes can not only include more dimensions, but also more directly disclose the relationships between them with less time and effort required. In addition, it provides a technical tool that is easy to understand and use in design practice when a bridge between human emotions and the living environment is needed.