esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 4 Comparison of Analytical and Numerical Results  
Analytical approaches are divided into three categories depending on the degree of shear connection and propped/unpropped construction in non-linear bending resistance  
Figure 17: Deformed shape and von Mises stress filed for model 400_SP10_CW10_SC12S_02: (**a**) left end of the beam; (**b**) right end of the beam.  
Figure 18: Influence of steel beam height on models with partial shear connections and different spot weld densities: (**a**) steel beam heights of 400 mm and 500 mm; (**b**) steel beam height of 600 mm; (**c**) steel beam heights of 500 mm and 600 mm.  
cases. It is necessary to emphasize the fact that in the analytical approaches, it is not possible to take into account the thickness of the CW, and it is not possible to consider the type of connection between steel elements, nor the number of spot welds. For that reason, in the comparison of analytical and numerical results, the decisive parameters for model selection are the steel beam height and the degree of shear connection. Results are divided into subchapters 4.1-4.3, in accordance with the steel beam height.  
For models with a shear connection secured with a tie constraint (which constitutes a full shear connection), bending resistance is calculated in accordance with Equation (2) to ascertain the plastic resistance. For partial shear connections (when shear connectors are positioned in pairs or in a staggered arrangement), bending resistances are calculated in accordance with Equations (3), (16) and (17), to find the linear elastic-plastic or non-linear elastic resistance, respectively.
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 7 Limitations  
* Although the datasets used in this study are comprehensive, they may not fully capture the diversity of real-world IoT devices and attack types. Future research should aim to validate the model on a broader range of datasets to improve its generalizability. While the model demonstrates low computational overhead, further evaluation is required to assess its performance and efficiency in real-time applications, particularly on resource-constrained devices.
* The static training process employed by this model limits its ability to adapt to new attack types and evolving threat landscapes. Future research should explore the development of models with adaptive learning capabilities to dynamically update security measures in response to emerging threats.
* In conclusion, this research confirms the applicability and effectiveness of deep learning techniques in improving IoT security. The proposed model, with its low computational requirements and high performance, presents a valuable tool for real-world IoT operations, enabling the detection and mitigation of large-scale attacks. Future research could focus on testing the model with larger and more diverse datasets and incorporating adaptive learning mechanisms to further enhance its robustness.
# An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
## 2 Materials and Methods
### Smartphone RTK Model
Firstly, it is converted to the ENU (East, North, Up) coordinate system in order to calculate horizontal velocity. Then, the horizontal velocity is projected to the DR heading angle direction and decomposed into the N and E directions. In DR, only the horizontal position is focused, so the geodetic height is not updated, and the RTK geodetic height is directly assigned to DR. The velocity update formula is as follows:  
\[\left\{ \begin{array}{l}v_{n}^{DR}=v_{p}^{RTK}\cos(d\psi)\cos(\psi_{r}^{DR}) \\ v_{e}^{DR}=v_{p}^{RTK}\cos(d\psi)\sin(\psi_{r}^{DR})\\ v_{u}^{DR}=0\end{array} \right. \tag{14}\]  
In the above formula, \(v_{p}^{RTK}\) represents the horizontal velocity of the smartphone RTK solution; \(\psi_{r}^{DR}\) represents the DR heading angle corrected using the OSM road direction; and \(d\psi\) represents the angle between \(\psi_{r}^{DR}\) and \(\psi^{RTK}\). The last step is the position update:  
\[\left\{ \begin{array}{l}B_{k}^{DR}=B_{k-1}^{DR}+\frac{v_{u}^{DR}}{R_{n}+H_{k- 1}^{DR}}dt\\ L_{k}^{DR}=L_{k-1}^{DR}+\frac{v_{u}^{DR}}{(R_{n}+H_{k-1}^{DR})\cos B_{k-1}^{DR} }dt\\ H_{k}=H_{k}^{RK}\end{array} \right. \tag{15}\]  
In the above formula, \(R_{m}\) represents the main curvature radius of the meridian circle; and \(R_{n}\) represents the main radius of curvature of the unitary circle.
## 2 Ciguatoxins Structures and Reference Standards
### Availability of Ciguatoxin Reference Standards
Indeed, the isolation and structural characterisation of pure toxins requires large amounts of highly toxic fish tissues collected in ciguatera endemic areas. In the 1990s, 125 kg of the viscera, including 43 kg of livers from four tons of moray eels (_Gymnothorax javanicus_), were collected in French Polynesia (Pacific Ocean) and extracted by [PERSON] et al. to obtain 350 ug of pure P-CTX-1 [127]. [PERSON] et al. extracted 48.3 kg of viscera from moray eels (_Lycodontis javanicus_) from the Republic of Kiribati (Pacific Ocean) to obtain 409 ug of P-CTX-1 [122]. [PERSON] et al. isolated 100 ug of pure C-CTX-1 from 164 kg of highly toxic specimens of barracuda and horse-eye jack [102], and [PERSON] and [PERSON] isolated 150 ug of pure C-CTX-1 from 51 kg of liver, viscera, and flesh of horse-eye jack (_Caranx latus_) from Saint-Barthelemy (French West Indies) [38]. Currently, only the chemical structure of C-CTX-1 has been determined using nuclear magnetic resonance (NMR) data by [PERSON] et al. [25]. The structures of Pacific analogues have also been inferred using NMR and fast-atom bombardment mass spectrometry (FAB-MS) for analogues in low amounts [98; 119; 122; 128]. The only CTX standards currently available commercially are: synthetic CTX-3C and P-CTX-1 from FUJIFILM Wako Chemicals Corporation; pure P-CTX-1, CTX-3C, CTX-3B, P-CTX-4A standards from the Institute Louis Malarde Phyconesia; and pure P-CTX-2 available from one of the authors (RJL). Recent production of NMR-calibrated standards of P-CTX-1, P-CTX-2 (or 52-epi-54-deoxyCTX1B), CTX-3C, 51-OH-CTX-3C, and P-CTX-4A from Japan [129] provide important, though incomplete, coverage of the ciguatoroxins present in ciguateric fishes in the Pacific. Additionally, C-CTX-1 reference materials prepared from several fish from the northeast Atlantic Ocean [130] were recently characterised using capillary liquid chromatography-high-resolution mass spectrometry (LC-HRMS) [131]. Stable isotope standards of C-CTX-1/-2 using \({}^{18}\)O labeling of the hydroxy group of the hemiketal at C56 and \({}^{18}\)O-labeled C-CTX-3/-4 obtained through the reduction of C-CTX-1/-2 were produced by [PERSON] et al., in addition to two _Gambierdiscus_ spp. metabolites: \({}^{18}\)O-labeled gambierone and 44-methylgambierone [132]. These new reference standards provide useful analytical tools for the identification and quantification of C-CTXs using isotope dilution mass spectrometry.
# Single-Shot Compressed Imaging via Random Phase Modulation
## 3 Principles of Phase Modulation  
In optical sensing and imaging system, light modulation has the potential to exceed the diffract limit and achieve higher contrast in imaging; among various light modulation techniques, phase modulation has gained more attention than its amplitude modulation due to its notable sensitivity and close relation to spectrum, coherence and polarization of light, and so on. Moreover, phase modulation can be easily implemented using various optical devices, spatial light modulator (SLM), liquid crystal display (LCD) and so on, which makes its use more efficient and convenient.  
The phase component carries much more information than the amplitude component, such as the shape, depth, polarization, and so on. Therefore, phase plays a role in many applications, such as system encryption [9] or electro-optic conversion of microwave signals [10]. For example, as illustrated in Figure 1, exchange the Fourier phase of the two images and keep the amplitude unchanged, then the major contexts of both image exchange, which means that the Fourier phase contains the most amount of information about the image.  
Phase modulation (PM) is the modulation technique that utilizes various optical devices to change the optical path difference (OPD) of an incident light to produce different phase delays [11]. Phase modulation can be achieved either by changing the refractive index or the thickness of an optical device, as indicated in the following equation  
\[u_{\text{out}}=u_{\text{in}}\exp(-j\varphi) \tag{6}\]where \(u_{\rm in}\) is the incident light, and \(u_{\rm out}\) is the exit light, \(\varphi\) is the modulated phase of the optical device expressed by  
\[\varphi=2\pi n_{0}d/\lambda \tag{7}\]  
where \(d\) is the thickness of the devices or equivalently the thickness caused by the change of the refractive index, \(n_{0}\) denotes the refractive index of the passing-through material and \(\lambda\) is the wavelength of the incident light. The phase modulation can vary the direction or the shape of the wavefront of incident light.
# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
## 4 Experiments and Analyses
### Performance Comparison Experiment and Result Analysis
In comparison to CS and its variants, BASCS consistently shows a superior fitness range, particularly in the 50-dimensional scenario, where its confidence interval (\(1.98\times 10^{-12}\) to 6.60 \(\times\)\(10^{-12}\)) underscores its dominant performance. Moreover, BASCS maintains consistency across varying experimental conditions, with minimal fluctuations in confidence intervals, thereby boosting its predictability in practical applications. These significant distinctions strongly support BASCS's efficacy in high-dimensional and complex problem-solving, further validating its superiority over the other five algorithms. To further scrutinize the experimental outcomes, the Wilcoxon rank-sum test evaluated whether there are significant differences among BASCS, CS, and its four variants. The Wilcoxon rank-sum test is preferred over alternatives like the t-test because it does not assume normality or homogeneity of variances, making it more suitable for small sample sizes and non-parametric data. Since fitness scores may not consistently meet the assumptions required by parametric tests, the Wilcoxon rank-sum test offers a more robust and reliable comparison of the algorithms' performances. Table 7 details the test findings, calculated from the mean ranks of the best fitness scores from each algorithm's 30 trials on the 10 benchmark functions. The \"Wilcox\" column delineates the comparative results between BASCS and the other algorithms, with '+' for dominance, '-' for subordination, and \"=\" for equivalence. The \"Rank\" column ranks the optimization performances of the six algorithms on the benchmarks, arranged from the smallest to the largest. As seen in Table 7, the rank order of the optimization results for BASCS on the 10 benchmark functions is mostly 1 and 2. Additionally, BASCS demonstrates superior optimization results compared to the other five algorithms on functions \(\mathsf{f_{3}(x)}\), \(\mathsf{f_{4}(x)}\), \(\mathsf{f_{5}(x)}\), \(\mathsf{f_{7}(x)}\), and \(\mathsf{f_{10}(x)}\). For the 50-dimensional functions \(\mathsf{f_{2}(x)}\) and \(\mathsf{f_{9}(x)}\), BASCS's optimization results are second only to ASCSA.
## 3 Results
### DEAP Model
#### 3.1.3 Feature Selection  
A principal component analysis was performed on all the input features from each signal independently as well as on a combined model. Using the PCA decreased the time to fit the model. For example, the time to process using the Gradient Boosting model decreased by 26 s (the largest differential).  
When applying the PCA to each of the signals independently, 21, 24, and 14 features were selected for the PPG, GSR, and RESP signals, respectively. Applying the PCA independently to each of the signals and then summing the total resulted in 59 selected features. However, when the PCA was applied to all three signals combined, 54 features were identified (Table 2). This could indicate that the combined PCA removed some of the features that were already identified within another principal component.
## 3 Proposed System
### Post-Processing  
The output of the sound event detector is a frame-wise probability of each class. Generally, the classifier output is post-processed with a certain threshold to determine whether the class is activated or not. The magnitude of the threshold value influences the classification performance. For example, a high threshold value can reduce false-positive errors but sometimes underestimates the length of the event, and vice versa. In this paper, the classifier output is first processed with a frame-wise threshold and then with an event-wise threshold to eliminate false-positive errors. The threshold value of the event-wise post-processing is set to be larger than that of the frame-wise post-processing.  
When we refer to the classifier output as a matrix \(\mathbf{P}\) of \(N_{out}\times C\), the frame-wise binary matrix \(\mathbf{B}_{frame}\) is calculated as  
\[\left[\mathbf{B}_{frame}\right]_{n,c}=\left\{\begin{array}{ll}1&\text{if } \left[\mathbf{P}\right]_{n,c}\geq\tau_{frame},\\ 0&\text{if }\left[\mathbf{P}\right]_{n,c}<\tau_{frame},\end{array}\right. \tag{24}\]  
where \(C\) denotes the number of classes and \(\tau_{frame}\) is the threshold for the frame-wise post-processing. Let us define \(\chi(i,c)\) as the \(i\)th event set of class \(c\) as  
\[\chi(i,c)=\Big{\{}(n,c)|n_{onset}(i,c)\leq n\leq n_{offset}(i,c)\Big{\}}, \tag{25}\]  
Figure 4: Block diagram of the network structure for the CRNN classifier.  
Figure 5: Block diagram of the mean-teacher model.  
where \(n_{onset}(i,c)\) and \(n_{offset}(i,c)\) are the frame numbers of the \(i\)th onset and offset of class \(c\), respectively. The result of the event-wise thresholding, \(\mathbf{B}_{event}\), is obtained as  
\[\left[\mathbf{B}_{event}\right]_{n,c}=\left\{\begin{array}{ll}1&\text{if} \left(\max_{(n^{\prime},c)\in\chi^{\prime}}[\mathbf{P}]_{n^{\prime},c}\right) \geq\tau_{event}\\ 0&\text{otherwise}\end{array}\right., \tag{26}\]  
where \((n^{\prime},c)\in\chi^{\prime}\) means that \((n^{\prime},c)\) belongs to \(\chi^{\prime}\), and \(\chi^{\prime}\) denotes a certain event set that contains \((n,c)\) as an element. Figure 6 presents a flow chart and an illustrative example of the proposed event-wise post-processing.
## 2 Materials and Methods  
### Analytical Method  
Though the Dulong-Petit rule predicts a constant value for the volume specific heat capacity (\(\rho\)-\(c\)) for all solids, where \(\rho\) is the solid density and \(c\) the specific heat capacity of the solid material, real metals and alloys show individual values that grow with rising temperature. At ambient temperature, 20 \({}^{\circ}\)C, (\(\rho\)-\(c\)) = 2.44 MJ/(m\({}^{3}\)K) for silver and rises (nearly) linearly to 2.73 MJ/(m\({}^{3}\)K) at 500 \({}^{\circ}\)C, as shown in Figure 1. While the density of Inconel is lower than that of silver, its volume specific heat capacity is significantly higher. It grows (nearly) linearly from 3.76 at 20 \({}^{\circ}\)C to 4.77 MJ/(m\({}^{3}\)K) at 600 \({}^{\circ}\)C. The heat transfer is hardly influenced by the probe material, and by experience, we expect it to be in the order of 200 to 300 W/(m\({}^{2}\)K).  
Probes of different materials (everything else equal) lead to different \(Bi\) numbers according to their different thermal conductivities, as known from the definition of the Biot number [21]. Pure silver shows rather high conductivity. At ambient temperature, that is, \(\lambda_{20\text{'C}}\approx\) 430 W/(mK). The strong condition for a lumped system is \(Bi\leq\) 0.1, which is, in the case of silver, \(h\leq\) 3558 W/(m\({}^{2}\)K). Therefore, a probe of silver and 12 mm diameter will always behave as a lumped system.  
The heat conductivity of alloys such as Inconel 600 is more than ten times lower. A typical conductivity at ambient is \(\lambda\approx\) 15 W/(mK). \(Bi\leq\) 0.1 leads to \(h\leq\) 124 W/(m\({}^{2}\)K). As this boundary is not sharp, we may exceed the limit to \(Bi\leq\) 0.25, and we may expect nearly lumped conditions for approximately \(h\leq\) 300 W/(m\({}^{2}\)K).  
Hence, within the scope of the present research, we would apply similar heat transfer coefficient inputs as the starting point in the estimation of the heat transfer coefficient using the lumped heat conduction model.  
Generally speaking, the heat transfer coefficient could be related to the unsteady temperature distributions using the equation for the determination of the average heat transfer coefficient during the immersion quenching process defined in Bourouga [22] that reads  
\[h_{avg}=\frac{1}{\left(T_{init}-T_{\mathrm{b}}\right)}\int_{T_{init}}^{T_{ \mathrm{b}}}h(T_{\mathrm{s}})dT_{\mathrm{s}} \tag{1}\]  
where \(T_{\mathrm{init}}\) is the initial temperature of the specimen, \(T_{\mathrm{b}}\) is the bulk fluid temperature, \(T_{\mathrm{s}}\) is the specimen's surface temperature, and \(h(T_{\mathrm{s}})\) is the heat transfer coefficient's dependence function with respect to the specimen's surface temperature. This function may be determined by inverse analysis from the recorded cooling characteristics using the suitable temperature measuring probe [11; 13].
## 6 Results
### T-function Estimation
#### 6.4.2 T-Junction Branch Radius Estimation  
Figure 28 shows the estimation result of the T-junction branch radius. The horizontal red line shows the designed radius of the branch of 246.10 mm. Two experiments are overestimating and two others underestimating the radius. The mean estimated radius is 239.32 mm, while the standard deviation is 17.25 mm. The bias of the estimation is less than 7 mm, which is less than 3% of the radius. The standard deviation is about 7% of the radius. The bias and the standard deviation are acceptable for the PIG navigation. Since T-junction length is equal to branch diameter, this result also reflects the T-junction length estimation.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 5 The Proposed Prediction Models
### Neighborhood Construction and QoS Prediction  
#### 5.4.1 Neighbor Selection with Network Location  
It is an important step to select similar neighbors for neighborhood-based CF. The number of neighbors and selection method directly affect the prediction accuracy. There are two methods that are widely used for neighbor selection, topK method and threshold method. The topK method selects \(K\) users that are most similar to the target user, as his or her neighbors. The threshold method predefines a threshold, and a user whose similarity is higher than the threshold will be selected to be a neighbor of the target user. However, the traditional neighbor selection methods usually only utilize the service invocation records, but fail to utilize the context information.  
To solve this issue, this paper integrates the network location with the topK method. In the proposed method, let \(A(u)\) be the subset of users who are in the same autonomous system as the target user \(u\). Let \(U(u)\) be the subset of all users. The similar neighbor set \(N(u)\) of the target user \(u\) is generated following the steps below.  
Step 1: this step computes the similarity between the target user \(u\) and any other user \(v\in A(u)\), and then find the \(K\) most similar users in \(A(u)\). The current neighbors in step 1 form the neighbor set \(NA(u)\).  
Step 2: Besides the neighbors in \(NA(u)\) that are selected in step 1, we also select the rest neighbors from \(U(u)\). We compute the similarity between the target user \(u\) and any other user \(v(v\in U(u)\wedge v\
otin A(u))\).
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 2 Related Work
### Application of the SMGW for the Implementation of Value-Added Services  
One major purpose of _Smart Metering Systems_ is the enablement of Demand Side Management. In addition, the German SMGW approach with a dedicated communication gateway that ensures a trustworthy communication between external parties in the WAN (EMP) and internal devices in the HAN (CLS), sets the foundation for further use cases that are not necessarily energy-related, e.g., in the area of building automation. However, scientific work addressing value-added services in the context of the German SMGW architecture is rare, and related work in the context of Demand Side Management is mostly not considering a specific smart metering infrastructure. Possibilities for the integration of building automation applications (\"smart home\") into the SMGW architecture based on the open-source project openHAB are discussed in [22]. The paper proposes to define devices that are controllable via openHAB as CLS in the SMGW's HAN. In [23], different use cases built upon the SMGW architecture are discussed for regulated services, such as energy usage visualization, and for non-regulated services, such as virtual power plants. In the research project \"CLS App\" [24], use cases for the application of CLS control boxes for energy management purposes, such as battery storage control, electric vehicle charging, or feed-in-management, are discussed with an emphasis on implementation aspects. In [25], a Java-based open-source SMGW experimentation framework (jOSEF) is presented as tool for validating smart metering communication use cases involving SMGWs.