esa-sceva/satcom-chunk-collection huggingface dataset:
# Lateral Loading of a Rock-Socketed Pile Using the Strain Wedge Model Based on Hoek-Brown Criterion
## 2 Parameters Study  
### The Origin of the Hoek-Brown Criterion  
The Hoek-Brown failure criterion [14; 15; 16; 17] for intact rock was first proposed by [PERSON] and [PERSON] based on a large number of rock tests and statistics:  
\[\sigma_{1}=\sigma_{3}+\sigma_{\text{ci}}\sqrt{m_{\text{i}}\frac{\sigma_{3}}{ \sigma_{\text{ci}}}+1} \tag{1}\]  
The influence of confining pressure on the rock failure was considered, shown in Figure 2. For the surface rock mass, because the confining pressure is small, the failure is mainly controlled by the discontinuous surface rather than along the intact rock.  
\[\Delta\sigma_{h}=\sigma_{1}-\sigma_{3}=\sigma_{\text{ci}}\sqrt{m_{\text{i}} \frac{\sigma_{3}}{\sigma_{\text{ci}}}+1} \tag{2}\]  
[PERSON] et al. [18] simplified and rationalized the formula based on the M-C failure criterion using [PERSON]'s variables:  
\[p=\frac{\sigma_{1}+\sigma_{3}}{2},q=\frac{\sigma_{1}-\sigma_{3}}{2} \tag{3}\]  
It should be noted that \(p\) and \(q\) is used to replace [PERSON]'s variables \(p^{*}\) and \(q^{*}\) adopted in ref. [18] because of the deference of Equation (1) in this paper and Equation (2) -in reference [18]. Moreover, the dimensionless and normalized [PERSON]'s variables are represented by \(p^{*}\) and \(q^{*}\) in this paper.  
The principal stress can be obtained:  
\[\sigma_{3}=p-q,\sigma_{1}-\sigma_{3}=2q \tag{4}\]  
Figure 2: Failure envelope of intact rock.  
Substituting the principal stress into Equation (2) can obtain:  
\[\frac{2q}{\sigma_{\rm ci}}=\sqrt{m_{\rm i}\frac{p-q}{\sigma_{\rm ci}}+1} \tag{5}\]  
Simplified to obtain:  
\[\frac{q}{\beta}=\sqrt{2\times\left(\frac{p}{\beta}+\zeta\right)+1}-1 \tag{6}\]  
where \(\beta=\frac{m_{\rm i}\sigma_{\rm ci}}{8}\), \(\zeta=\frac{8}{(m_{\rm i})^{2}}\).  
Dimensionless and normalized form with a pressure modulus \(\beta\), becomes:  
\[q*=\sqrt{2\times(p*+\zeta)+1}-1 \tag{7}\]  
where \(p^{*}\) and \(q^{*}\) are dimensionless and normalized Lambe's variables \(p^{*}=\frac{p}{\beta}\), \(q^{*}=\frac{q}{\beta}\).
## 2 Measurement Principles and Methods
### Sub-Pixel Edge Detection and Fitting
#### 2.1.2 Bilinear Interpolation (BLI) Method  
Bilinear interpolation, an advancement of linear interpolation in two dimensions, facilitates the estimation of pixel values at undefined points within an image. This method approximates the value of a new pixel point by utilizing the values of its four neighboring pixels. It is particularly effective for tasks like image scaling, rotation, and sub-pixel edge detection [38].  
Consider a pixel point with a sub-pixel location designated as \((x,y)\). To employ bilinear interpolation, it is necessary to identify the four nearest integer pixel locations surrounding this point. Typically, these locations are represented as \((x_{1},y_{1})\), \((x_{1},y_{2})\), \((x_{2},y_{1})\), \((x_{2},y_{2})\), where \(x_{1},x_{2}\) are the nearest integers to x, obtained by rounding down and up, respectively, and similarly for \(y_{1},y_{2}\) with respect to y.  
Initially, linear interpolation is conducted in the x-direction, which entails calculating the values at points \((x,y_{1})\) and \((x,y_{2})\).  
\[\begin{array}{l}f(x,y_{1})=f(x_{1},y_{1})+(x-x_{1})\cdot(f(x_{2},y_{1})-f(x_ {1},y_{1}))\\ f(x,y_{2})=f(x_{1},y_{2})+(x-x_{1})\cdot(f(x_{2},y_{2})-f(x_{1},y_{2}))\end{array} \tag{9}\]  
A linear interpolation is then conducted in the direction where the values of \((x,y_{1})\) and \((x,y_{2})\) have already been calculated.  
\[f(x,y)=f(x,y_{1})+(y-y_{1})\cdot(f(x,y_{2})-f(x,y_{1})) \tag{10}\]  
The interpolation results from the two aforementioned directions are combined to derive the final interpolation expression, incorporating the respective weights of these two directions:  
\[\begin{array}{l}f(x,y)=(1-t)(1-u)f(x_{1},y_{1})+t(1-u)f(x_{2},y_{1})\\ +(1-t)uf(x_{1},y_{2})+tuf(x_{2},y_{2})\end{array} \tag{11}\]  
where \(t,u\) represent the differences in distances along the x-axis and y-axis, respectively, defined as \(t=x-x_{1}\) and \(u=y-y_{1}\), where \(x_{1},y_{1}\) correspond to the coordinates of the lower left pixel point.
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Types of Cyber-Physical Attacks
#### 3.2.4 False Data Injection Attacks  
In systems where few or no authentication mechanisms are present, false data injection attacks can be used to inject malicious code and commands into control systems networks [3]. Lack of authentication mechanisms means that there is no way the targeted equipment can verify the authenticity of any of the commands it receives. Therefore, it is a vulnerability that cyber attackers with malicious intent can exploit relatively easily. Such attacks can range from commanding industrial control systems to performing actions that are outside of safe operating margins, to completely reconfiguring the control systems' equipment to perform totally differently from how it had originally been designed to function (the Stuxnet worm is an example of such an attack).  
If attackers can gain undetected access to an industrial control systems network with weak security, entire production lines can be compromised by commanding manufacturing equipment to assemble manufactured products incorrectly, which will compromise the proper functionality of the product.
## 2 Defining Obesity  
Conceptually, the World Health Organization defines obesity as \"abnormal or excessive fat accumulation that presents a risk to health\" [10]. Quantification of excess body fat makes use of the Body Mass Index (BMI) in adults. The BMI is defined by the following formula: \(\mathrm{BMI}=\frac{\mathrm{BW}}{\mathrm{H}^{2}}\), where BW represents body weight (kilograms) and H represents height (meters) [1].  
Quantifying obesity in children can prove to be problematic, due to several reasons. Childhood and adolescence, the latter being defined by WHO as \"the phase of life between childhood and adulthood, from ages 10 to 19\" [11], are marked by a series of significant physiological somatic changes in relatively short periods of time; while in adults, establishing normal values using statistical analysis of anthropometric parameters can yield satisfactory guidelines, pediatric populations tend to be characterized by a greater inhomogeneity in relation to several confounding factors such as age, sex, pubertal stage, and even ethnicity [12; 13]. Alternative techniques that can be implemented to quantify weight excess in children are outlined in Section 5, each with its own advantages and caveats.  
The currently accepted method to determine a child's weight status makes use of weight charts endorsed by the CDC and WHO that take into account the influence of age and gender. For children under the age of 2, the use of BMI is not recommended. In this age bracket, assessing body weight is accomplished using gender-specific weight-for-height charts. A value greater than two standard deviations above the median for this parameter defines overweight, while a value higher than three standard deviations above the median defines obesity. Gender-specific weight-for-height charts can also be used up to the age of 5. From ages 2 and up, gender-specific BMI for age charts are advocated for determining weight status. The CDC recommends the 85 th and 95 th percentiles as cutoff points for overweight and obesity, respectively. For children above the age of 5, the World Health Organization defines overweight by a BMI-for-age greater than one standard deviation above the WHO Growth Reference median, and obesity by a BMI-for-age greater than two standard deviations above the WHO Growth Reference median [14; 15].  
Several authors have proposed a further stratification of obesity with cutoff points at the 95 th percentile of BMI-for age (grade 1 obesity), 120% of the 95 th percentile (grade 2 obesity), and 140% of the 95 th percentile (grade 3 obesity), in order to better define the degree of obesity-associated risk in pediatric populations [16; 17].
## 3 Uncertainty of Scheduling in the ADN  
### Uncertainty of User-Side Response  
According to the principles of consumer psychology, the incentive policy given by the grid has a threshold and saturation value for each user's stimulus [34]. For distribution network users, the uncertainty can be reflected in the users' demand response participation rate, that is, the response range under certain incentive conditions.  
The response model in this paper uses random numbers in a certain interval [\(\lambda_{i2}\), \(\lambda_{i1}\)] to characterize the uncertain behavior of user participation responses [35; 36], as shown in Figure 2, and \(\lambda\) represents the load shedding rate. The price \(pd\) is the start incentive, representing the minimum incentive price for users to start to respond; \(pc\) is the critical incentive making the response greater than zero considering the response uncertainty; \(pm\) is the saturation incentive making users' responses reach the upper limit. The corresponding load shedding rate can be calculated as Equation (1).  
\[[\lambda_{i2},\lambda_{i1}]=\left\{\begin{array}{ll}\left[0,\lambda_{m}\frac {p-p_{d}}{p_{m}-p_{d}}\right]&0\leq p<p_{c}\\ \left[\lambda_{m}\frac{p-p_{c}}{p_{m}-p_{c}},\lambda_{m}\frac{p-p_{d}}{p_{m}- p_{d}}\right]&p_{c}\leq p<p_{m}\end{array}\right. \tag{1}\]
## 3 Results and Discussion
### Damage Identification Using AE
On the other hand, the average rise time values were significantly lower in the \([0^{\circ}/60^{\circ}]_{\text{2s}}\) laminates. At 300 MPa stress, no rise time value is plotted, as no AE activity was recorded until this stress level. Figure 19: Evolution of average rise time values for the angle-ply laminates. Figure 18: Typical schematic of an AE waveform. Figure 20: Evolution of average rise time versus the delamination length in the \([0^{\circ}/30^{\circ}]_{\text{2s}}\) laminates. When the matrix cracks started appearing, the rise time increased above 80 us, but it remained more stable during the test, following the continuous development of matrix cracks, which are characteristic mode I fracture mechanisms, where two opposing stress components are applied on the crack sides, both vertical to the crack plane. A greater increase of the rise time was only measured towards the end of the test, coinciding with the appearance of delaminations, as recorded with the microscope, and coming to a good agreement with the corresponding increase in the [0\({}^{\circ}\)/30\({}^{\circ}\)]\({}_{\text{2s}}\) laminates. The above results lead to the significant conclusion that high rise time values, higher than 100 us, are linked to shear related phenomena, like intralaminar shear debondings and interlaminar delaminations, with increasing values when damage accumulates. On the other hand, smaller rise time values are expected when matrix cracks occur, with more stable values during the damage formation. Supporting conclusions regarding the evolution of the rise time, when tensile or shear related fracture phenomena occur, are found in the literature for other materials, like, for instance, [25] for glass/epoxy composites and [33] for the more traditional concrete. This means that the rise time can be used for the identification of damage modes in CFREs and for the estimation of the occurring stresses at the same time, as increasing shear stresses result in increasing rise time values. Nevertheless, it should be mentioned that the aforementioned results have been obtained for a specific type of CFRE laminate. Different constituents of the composite material, as well as the size and the geometry of the tested specimens, may have an effect, therefore, ongoing research regarding the different parameters that affect the evolution of the AE parameters and the applicability of these findings on larger scale components is performed. Although similar parameters (like rise time) are expected to be sensitive to damage, the numerical exact values are expected to differ when measured in another scale.
## 1 Introduction
The findings of the studies showed that asphalt mixtures composed of steel slag aggregate as a coarse portion enhanced the stiffness modulus, rutting resistance, and cracking resistance, as well as it displayed slightly lower tensile strength ratio as compared to the control mix. Furthermore, limited studies have investigated WMA containing steel slag aggregates. [PERSON] et al. [16] observed that the WMA produced by Sasobit additive and composed of steel slag aggregates as a coarse portion has improved Marshal stability, resilient modulus, permanent deformation, and moisture sensitivity. [PERSON] et al. [17] investigated the performance of WMA incorporating steel slag aggregates. The incorporation of additives Sasobit and Rediset at a dosage of 2% by weight of the bitumen was used to produce WMA. The findings of the study showed that the WMA composed coarse steel aggregate exhibited better performance than HMA and steel slag aggregate replacements in terms of permanent deformation, fatigue life, and moisture susceptibility. Similarly, [PERSON] et al. [18] concluded that utilizing coarse basic oxygen furnace steel slag aggregate in warm mix asphalt manifested better performance than asphalt mixtures incorporating conventional aggregates. [PERSON] and [PERSON] [19] assessed WMA modified with 1.5% of Sasobit and coarse steel slag aggregates. The laboratory experiments of the study showed that WMA incorporating coarse steel slag aggregate was significantly improved in stiffness modulus, permanent deformation, and tensile strength, in comparison to HMA. Permanent deformation and moisture damage are among the main drawbacks of warm mix asphalt, due to the fact that reducing the production temperature may cause inchoate drying of the aggregates, insufficient film thickness, and reduced hardening of the binder [20]. Few studies have evaluated warm mix asphalt at various production temperatures. Furthermore, all the previous studies have assessed the performance of warm mix asphalt incorporating steel slag aggregates using the additive Sasobit. Therefore, the objectives of this study are to investigate the characteristics of warm mix asphalt containing coarse steel slag aggregates, modified with LEADCAP. The warm mix asphalt was investigated at different mixing temperatures of, 150, 140, and 130 \({}^{\circ}\)C to introduce a better understanding and evaluation of the behavior of warm mix asphalt at different production temperatures. A response surface methodology by means of DESIGN EXPERIM 11 [21] was conducted to develop prediction models for the performance of the warm mix asphalt as a contribution in terms of identifying the performances of the asphalt mixes at various production temperatures. The laboratory performance tests were stiffness modulus at the temperatures of 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, dynamic stability, and moisture sensitivity. This study focused on the tests for permanent deformation and moisture sensitivity because warm mix asphalt is highly susceptible to permanent deformation and moisture damage.
# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
## 2 Axial Tensile Performance of TR-UHPC  
### Materials  
#### 2.1.1 UHPC Matrix  
The UHPC used in the test was provided by Hunan Zhonglu Huacheng Bridge Technology Corporation. The main components of the UHPC included silicate cement, quartz sand, quartz powder, silica fume, fly ash, high-efficiency water reducer, and mixed steel fibers. Table 1 shows the UHPC mix proportion. The volume fraction of steel fibers was 2%, comprising end-hooked steel fibers with a diameter of 0.2 mm and a length of 13 mm. These steel fibers had a tensile strength of 2000 MPa and a modulus of elasticity of 200 GPa.  
The mechanical properties of the UHPC were tested according to the standard test methods given by 'Standard Test Methods for Physical and Mechanical Properties of Concrete' (GB/T 50081-2019) [26] and 'Reactive Powder Concrete' (GB/T 31387-2015) [27]. The test for cubic compressive strength involved the use of cubic specimens measuring \(100\times 100\times 100\) mm for the modulus of elasticity test, prismatic specimens measuring \(100\times 100\times 300\) mm were used, and for the prismatic compressive strength test, prismatic specimens measuring \(100\times 100\times 300\) mm were used, with three specimens in each experimental set. The axial tensile strength of the UHPC matrix was obtained by the axial tension test conducted on dog-bone shaped specimens, which is introduced in Section 2.2. Table 2 shows the mechanical properties of the UHPC matrix.
## 3 Findings for Each of the Three Layers
### Market Layer
#### 3.2.2 Flexibility of the Market  
In terms of the dynamics in the electricity supply system, and due to system stability reasons, demand and supply need to match at any instant in time; otherwise, the system is in imbalance. In liberalized electricity systems, market mechanisms implemented to maintain the balance are increasing. In short-term markets and balancing especially, flexibilities aggregated from end-users (customers or prosumers) provide an increasing potential for smart grids. Such flexibility can be used for several use cases, such as balancing, optimizing trading costs and minimizing costs from the imbalance settlement (e.g., caused by forecast errors in renewable electricity generation) or for the customer to increase their own consumption, whereby these use cases can be associated with different roles/actors.  
When using the flexibility for one use case, however, several other actors may be influenced by this activation, either positively or negatively. Future markets in smart grid environments indicate an increased coordination need between several actors in regard to the integration of flexibility, and different flexibility use cases are depicted based on different roles, as shown in Figure 3.
# Low-Cost Active Anomaly Detection with Switching Latency
## 2 System Model and Problem Formulation
### Notations  
Since only one process can be probed at a time, let \(\mathbf{1}_{m}(n)\) be the indicator function of whether process \(m\) is probed at time \(n\). \(\mathbf{1}_{m}(n)=1\) if process \(m\) is probed at time \(n\), and \(\mathbf{1}_{m}(n)=0\) otherwise.  
We use the log-likelihood ratio (LLR), a statistic used in classical sequential hypothesis testing [1], as the decision statistic between two hypotheses. The LLR of process \(m\) at time \(n\) is denoted as  
\[l_{m}(n)\triangleq\log\frac{g(y_{m}(n))}{f(y_{m}(n))}, \tag{5}\]  
which reflects the goodness-of-fit of which distribution a certain process belongs to. The sum log-likelihood ratio (sum-LLRs) of process \(m\) at time \(n\) is given by  
\[S_{m}(n)\triangleq\sum_{t=1}^{n}l_{m}(t)\mathbf{1}_{m}(t), \tag{6}\]  
which combines the observations of the process \(m\) at multiple times, and can be regarded as the score of whether the process is in an abnormal state.  
Let \(D(g||f)\) and \(D(f||g)\) denote the KL divergence between two distributions \(g\) and \(f\) which are given by  
\[\begin{split} D(g||f)&\triangleq\int_{-\infty}^{ \infty}\log\frac{g(x)}{f(x)}g(x)dx,\\ D(f||g)&\triangleq\int_{-\infty}^{\infty}\log\frac {f(x)}{g(x)}f(x)dx.\end{split} \tag{7}\]  
If process \(m\) is in an abnormal state, the sum-LLRs \(S_{m}(n)\) of process \(m\) is a random walk with positive expected increment \(E_{m}(l_{m}(n))=D(g||f)>0\), while \(S_{j}(n)\) of process \(j\) which is in a normal state is a random walk with negative expected increment \(E_{m}(l_{j}(n))=-D(f||g)<0\). Thus, when the sum-LLRs of process \(m\) is sufficiently large, we can declare with a sufficient accuracy that process \(m\) is in an abnormal process. Similarly, when the sum-LLRs of process \(j\) is sufficiently small, we can declare its state as normal.
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm
### Algorithm Optimization and Acceleration  
#### 4.4.1 Parallel Computing Strategy  
The algorithm encompasses a significant number of loops, including those for fitness calculation, population generation, and Pareto front calculation. These loops exhibit no sequential dependencies and are not interdependent. Consequently, a parallel computing approach is proposed to enhance the efficiency of the original algorithm. During the task division phase, the population initialization task is segmented into multiple subtasks. Utilizing a multi-threaded computing framework, distinct iterative steps are allocated to different threads, thereby leveraging the multi-threading capabilities of the computer to execute multiple iterative steps concurrently. Additionally, a dynamic scheduling algorithm is implemented; when a thread becomes idle and awaits further instructions, the program automatically reallocates tasks to that thread.
# Analysis of Electromagnetic Field Characteristics of Wave Glider
## 3 Analysis of Electric Field Noise Characteristics  
### Installation Design of Electric Field Sensors  
To avoid direct contact between the electric field sensors and the stainless-steel traction machine, a wooden board is added between the electric field sensor and the stainless steel to provide isolation and support. The electric field sensor uses a typical Ag/AgCl sensor, with the underwater traction body keep direction as the horizontal direction. The electric field sensor is divided into horizontal and vertical directions, and two sensors are installed in each direction. The electric field collection and storage device is installed at the head of the traction machine to reduce the impact of the collection device on the movement of the underwater traction body. The specific installation structure is shown in Figure 3.  
Figure 3: Installation diagram of electric field sensor.  
The experiment was conducted at a dock in Qingdao. Due to the depth of seawater at the experimental site being over 2 m, and considering that the height of the underwater towing body is about 30 cm, in order to ensure that the underwater towing machine and electric field sensor do not touch the bottom, the connecting cable between the surface mother ship of the wave glider and the underwater towing machine was shrunk to a length of about 1.2 m, which means that the working depth of the electric field sensor is about 1.5 m.