esa-sceva/satcom-chunk-collection huggingface dataset:
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 4 The Durability Prediction Calculation Methods for Square Rebar Components
### The Calculation Method of the Critical Corrosion Depth of Square Rebar  
Concrete strength, the ratio of cover depth, and rebar diameter are seen as factors affecting the critical corrosion depth at the cover cracking time [2; 4; 18]. Therefore, these factors and the form of empirical equations were also used in this study. Based on the experimental data in Table 2, an empirical equation was obtained to calculate the critical corrosion depth of the square rebar by multiple linear regression. The calculation method is shown in Equation (14).  
\[\delta_{cr}=0.0407c/d^{\prime}+0.0012f_{cu,e}-0.0237 \tag{14}\]  
Figure 7: Schematic diagram of electrochemical accelerated corrosion test using the method of the parallel circuit.  
where \(d^{\prime}\) is the width of the square rebar section (mm).  
The decisive coefficient in the least square method is a statistical index reflecting the reliability of the change of dependent variables for the multiple linear regression results. [PERSON] et al. [34] presented the decisive coefficients for the Equations (11) and (13), and both values were 0.645. However, the value of the decisive coefficient in the proposed equation was 0.738, which indicated that the collected two factors were highly related to the critical corrosion depth of the square rebar. Thereby, it can be concluded that the empirical equation is reliable and can meet the accuracy requirements of engineering applications. From Table 3, the average error between test values and calculated values from Equation (14) can be calculated, and the value is 8.42% (calculated by the absolute values of the error data), which is more accurate than the results from equations for round rebars, and the calculation accuracy of Equation (14) is acceptable for engineering application.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 5 Results and Discussion
### Data Reduction  
We designed a flow clustering technique described in Section 3.3 to reduce the data to be evaluated and keep the relevance of the analysis. Its final step consists of classifying flows between the functional zones. We use a derivative of the curvature equation for this classification. Figure 5 shows the curve of the magnitude of the flows. We smooth the Original Data line, producing the Smoothed line. Later, we use Equation (1) to produce the Curvature line in Figure 5. Its derivative was used to evaluate the maximum, i.e., the Selected Point for the classification (vertical dashed line). Flows with a magnitude above it are considered trending, while the remaining are secondary.  
Figure 4: Distribution of trips and passengers in weekdays and weekends per hour; 48 classes were created for every hour of the day in weekdays and weekends [7].  
Figure 6 shows the classified clustered flows for datasets with and without geo-coordinates. Figure 6a [7] uses the first variation of the flow clustering technique, leading to clusters with a deformed-looking shape. This shape is due to their formation made out of coordinates that were collected from the performed trips. These clusters were obtained from the Yellow Taxis dataset 2016 when geo-coordinates for trip pickup and drop-off points were still available. The clusters shown in Figure 6b,c were tailored--in the anonymization process before data releasing--according to the shapes of some districts and neighborhoods of the city, thus leading to more regular shapes.  
Figure 6b,c show a difference in the locations of the departures and arrivals clusters. Yellow Taxis have a historical trend to attend users in Manhattan, while Green Taxis were created to serve the more peripheral zones, being prohibited from starting trips in Manhattan. The focus on covering Manhattan is not strongly present in this sample of flows shown in Figure 6a. Green Taxis are also prohibited from taking passengers to airports, while Yellow Taxis are not, which results in a flow from the bottom-right corner of Figure 6a (where JFK Airport is located) to a cluster near the Brooklyn neighborhood. This is another trend of the Yellow Taxis.  
The geo-coordinates allow us to obtain more relevant flows. In contrast, the flows that were obtained with the anonymized dataset resulted in many flows starting and finishing in the same regions. The flows that were obtained in this data reduction phase were used to evaluate the impacts of the hybrid routing, which are described in Section 5.6.  
Figure 5: Selection of classification threshold using the derivative of the curvature equation.
## 3 Results and Discussion
### Maximum Swelling Capability (\(\%\)) and Porosity (\(\%\)) of R4  
The maximum swelling capability percentage of R4 by volume and weight was determined after 15 min of sonication and 9 min of degassing of a fine dispersion of R4 (0.65 mL, 188.7 mg) in an excess of water using Equations (3) and (4). The data obtained by such experiments were useful for finding the porosity ratio percentage using Equations (1) and (2). The determinations were made in triplicate and the results are reported in Table 1, expressed as the mean of three independent experiments \(\pm\) standard deviation (SD). Figure S1 in the Supplementary Materials (SM) associated with this paper shows the aspect of swollen R4 obtained from hydrating the dry resin with an excess of 10 mL of water and recovered by centrifugation at 4000 rpm for 30 min while Figure 5 shows a representative optical microphotograph of the swollen resin.  
As reported in Table 1, R4 was capable of absorbing so much water that it increased its volume by 731% and its weight by 244%, respectively. The porosity was 88% and 96%, thus confirming both a remarkable porosity and absorbing ability. The optical microphotograph of the swollen R4 (Figure 5) substantiated the high absorbing capability of R4, showing pseudo-spherical beads unequivocally imbibed with water, with sizes up to 1300 \(\upmu\)M. This meant that swollen R4 showed beads up to 10.4-fold greater than the smallest dried particles (1040%), thus cross-validating the data reported in Table 1. These characteristics,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Vi (mLJ/Wi (mg)** & **Vf (mL)/Wf (mg)** & **P (\%)** & **S (\%)** \\ \hline R4 & 0.65/188.7 & 5.4/4800 & 88/96 & 731/2444 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results from the swelling experiments.  
Figure 4: Optical microphotographs of dried R4 (letter a indicates the scale bar).  
in a possible use of R4 as a material to reduce bacterial contamination in water, should favor electrostatic interactions with the bacteria contained in it. In addition, the same characteristics of high porosity and hydrophilicity indicate the great compatibility of R4 with water and good flow properties, which, in a potential use as a material to build columns for water disinfections, could allow an easy and rapid flux of contaminated waters.
## 7 Simulations and Results
### Performance Metrics  
To evaluate the simulation, six different performance metrics were used in our results:  
* Total number of interests (NI): The total NI packets transmitted in the network from the consumer side.
* Number of high-priority interests (HPI): The total number of high-priority interests successfully communicated in the network.
* Total number of Retransmissions (RTx): RTx shows the total number of retransmitted interest packets.
* Average energy consumption (AEC): The average power consumption of all the sensor nodes in the network.
* Content retrieval time (CRT): The time it takes for the consumer to send an interest and receive data.
* Network lifetime (NLT): The time when the first node dies in the network.
## 3 Proposed Methodology
### Proposed Architecture Using Concatenating Features
#### 3.3.2 Support Vector Machine  
Support Vector Machine (SVM) was selected for its robustness in high-dimensional feature spaces, which is particularly relevant for our concatenated feature vectors. SVMs have shown superior performance in text classification tasks where feature spaces are sparse [58]. Their ability to handle non-linear decision boundaries through kernel functions makes them especially suitable for capturing complex patterns in offensive language [59].  
We have used SVM due to having a small-size dataset and it assigns data points to the appropriate side of the margin based on the input feature vector f. We have used Equation (25) to determine the belongings of particular data points, where \(y_{i}\) represents the true label of the ith training samples, which can take values of 0, 1, 2. The variable w denotes the weight vector, which is perpendicular to decision boundary. \(f_{i}\) represents the feature vector of ith training samples, and \(b_{y_{i}}\) represents the bias term associated with the true label \(y_{i}\).  
\[y_{i}\Bigl{(}\mathbf{w}^{T}\mathbf{x}_{i}-b_{y_{i}}\Bigr{)}\geq 1\text{, for every1}\leq i\leq n \tag{25}\]
## 1 Introduction
It is remarked that for the case of sliding along very flexible beam, the IGA method takes more advantage on accuracy and efficiency when using the \(C^{k}\)-continuous NURBS basic function (k \(\geq\) 1). However, under the concept of IGA, the dynamic analysis of the beam structure with a sliding joint, based on the GEBT and Lagrangian multiplier method, has rarely been reported. The objective of this paper is to propose a geometrically exact beam element based on the local frame approach under the concept of IGA. The proposed element can be applied to the cases of finite deformation and rotation in dynamic analyses of flexible multibody systems, especially for the deployable spatial truss structures with a sliding joint. The analytical expressions of internal and inertial forces and their Jacobian matrices for present beam element are strictly derived based on the framework of Lie group. To obtain a symmetric tangent stiffness, the increments are described based on the previously converged configuration, as suggested by [PERSON] and [PERSON] [10]. To promote the integration of CAD and CAE, the displacement and rotation fields are interpolated separately using the NURBS basis functions. Due to the application of the Crisfield and Jelenic interpolation algorithm [12], the strain measures automatically satisfy the property of objectivity. Besides, the dynamic equations expressed in a set of Differential Algebraic Equations (DAEs) are solvedby the generalized-\(\alpha\) method [30; 31]. The sliding joint is modeled using the Lagrangian multiplier method. The constraints equations relative to the sliding joint and its Jacobian matrix are also given. The paper is organized as follows: The basic theory of the geometrically exact beam theory including beam kinematics, static equations, dynamic equations, and their linearization are introduced in Section 2. Finite element strategies are employed in Section 3. In Section 4, the modeling of sliding joint is conducted. In Section 5, two dynamic numerical examples and a deployment dynamic analysis of spatial truss structure are implemented to verify the availability and the applicability of the formulation proposed in this paper. Conclusions are presented in Section 6.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 4 Performance Evaluation and Discussion: Example of GEO SAR Design
### Impact of Bistatic Scattering RFI
#### 4.2.3 Doppler Filtering Analysis  
As we discussed in Section 3.2, we can conduct the Doppler filtering for the LEO SAR signal. Shown in Figure 9 (assuming the power of the LEO SAR signal is the same with that of the GEO SAR signal), a large Doppler difference bias will be helpful for reducing the LEO SAR RFI energy after imaging. When the Doppler rate difference is large, shown in Figure 9a, the LEO SAR signal after filtering is noise-like with only about \(-\)40 dB level. Otherwise, the peak of the LEO SAR signal after filtering will be comparable with the amplitude of the GEO SAR signal shown in Figure 9b. The relationship between the various LOS angles, the PRF of the GEO SAR, the working frequency and the ratio are given in Figure 10. From the figure, we can find that a larger LOS angle, a higher PRF of the GEO SAR and the lower working frequency are the effective methods to improve the performance of Doppler filtering in the presence of the LEO SAR RFI. The improvement derives from that they are helpful to filter the RFI energy outside the GEO SAR Doppler bandwidth by increasing the difference of the Doppler spectra between the LEO SAR and GEO SAR, as well as decreasing the alias. However, as the PRF and the working frequency also depend on the imaging swath and applications, they should be traded-off during the GEO SAR system design. In the meantime, LOS angle is determined by the geometry of the LEO SAR system. Because the LEO SAR generally works in broadside imaging, LOS angle is often large enough for Doppler filtering.  
Figure 10: (**a**) ratio and the LOS angle \(\hat{\theta}\) (L band); (**b**) average ratio (averaged by \(\hat{\theta}\) within 0°-90°) and the PRF (L band); (**c**) ratio and the working frequency (\(\hat{\theta}\) = 90°).  
Figure 9: GEO SAR and LEO SAR RFI signals after Doppler filtering: (**a**) about 300 Hz/s\({}^{2}\) Doppler rate difference; (**b**) about 0.024 Hz/s\({}^{2}\) Doppler rate difference.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 3 Deep Learning-Based Decoder
### DNN-Based Denoiser for Soft Decision Decoding  
[PERSON] and [PERSON] [14] presented a new decoding architecture in Figure 6, which uses the DNN as the denoiser. This approach was inspired by the noise removal of image processing [19]. In this architecture, the denoised signal sequence \(\mathbf{dy}\) is obtained by the DNN and the received word \(\mathbf{r}\) can be determined based on \(\mathbf{dy}\). Finally, the HDD produces the codeword based on \(\mathbf{r}\). This architecture combines the DNN-based denoiser and the  
Figure 4: Decoding Architecture based on DNN-based Binary Classification.  
Figure 5: DNN Architecture in [13].  
traditional HDD, resulting in better decoding performance as compared to the HDD only. The DNN architecture [14] in Figure 7 consists of three fully connected layers. The total numbers of nodes in these three layers are 256, 128 and 64, respectively. The activation function used in each layer is the ReLU function.
# Non-Invasive Hydration Monitoring with a Graphene Dual Sweat Sensor
## 2 Materials and Methods
### Sensor's Performance and Reliability  
To assess the run-to-run repeatability of the sensor's detection repeatability tests, repeat measurements were conducted on the same sensors for sodium and potassium, with concentrations from 0.1 mM to 100 mM, increasing in tenfold steps. The reproducibility tests were performed via identical measurements on the five different sodium and potassium sensors, with concentrations from 0.1 mM to 100 mM, increasing in tenfold steps.  
Other ions present in human sweat may interfere with the detection of target ions, making it necessary to evaluate the sensor's interference resistance. For this, the sensor was immersed in the test ion solution (10 mM NaCl or 10 mM KCl), and interfering ions were sequentially added. The voltage output detected by the sensor was monitored. The interference ions tested for both the Na\({}^{+}\) and K\({}^{+}\) sensors were 10 mM NaCl, 10 mM MgCl\({}_{2}\), 10 mM CaCl\({}_{2}\), 10 mM KCl, 10 mM NH\({}_{4}\)Cl, and a mixed solution containing 10 mM of each chemical (NaCl, MgCl\({}_{2}\), CaCl\({}_{2}\), KCl and NH\({}_{4}\)Cl).  
To assess the practical performance of the sensor, a test was conducted using human sweat. A trained athlete participated in an experimental trial that involved three, 30 min intervals of endurance running to induce sweating. Sweat was then collected from the forearm, upper arm, and chest after a 15 min seated rest using sweat patches (3M\({}^{\text{TM}}\) Tegaderm\({}^{\text{TM}}\) +Pad Film Dressing with Non-Adherent Pad, Saint Paul, MN, USA) (Figure 2). A total of nine samples were collected from the different body parts at the three intervals and were immediately placed in an ice box before being stored at -20 \({}^{\circ}\)C.  
Figure 1: (**a**) Graphene dual-sensor design. The graphene-based sensor consists of two working graphene electrodes, a graphene counter, and one reference electrode; (**b**) cyclic voltammetry of bare sensors under 2 mM K\({}_{3}\)Fe(CN)\({}_{6}\), 0.1 M HCl, and 0.1 M KCl over five cycles across five different dual sensors.  
The collected sweat samples were then analyzed using one graphene-based dual-sensing sensor. The results were compared with those from a commercial analyzer and with analytical results from inductively coupled plasma atomic emission spectroscopy (ICP-OES). The commercial analyzer used was from HORIBA (Kyoto, Japan), specifically the LAQUAtwin-Na-11 and LAQUAtwin-K-11 models. The sweat samples were tested without any dilution or cleaning procedures. For ICP-OES analysis, the samples were aliquoted and diluted 100 times with ultrapure water. Each sample was analyzed in triplicate, and a 1 ppm sodium or potassium standard was concurrently analyzed for validation.
## 2 Platform Configuration and Mooring Layout  
In this study, the simulated floating platforms contain a spar buoy at two different water depths, i.e., the OC3-Hywind spar at a depth of 320 m and the UMaine spar at 200 m; and two TLPs, i.e., the MIT/NREL TLP and the UMaine TLP. The aforementioned floating platforms were designed specifically to support the NREL 5-MW wind turbine [26], including the rotor, nacelle, and tower. Meanwhile, properties of mooring systems including taut-leg and catenary lines for the simulated floating platforms [1] are also presented. The particulars of four floating platforms with respect to the mooring properties are given in Table 1.
## 3 Localization Algorithms in WSNs  
Based on the measurement of inter-sensor distance, localization algorithms in WSNs can be broadly classified into two categories: centralized and distributed [50]. In centralized localization technique, all the inter-sensor measured distances are sent to the central location where the positions of each and every sensor node are calculated. On the other hand, in distributed localization technique, the individual sensor nodes calculate their own position by utilizing the distance measurement from other anchor nodes. Major approaches for designing centralized algorithms are Multi Dimensional Scaling (MDS) [59], linear programming [60] and stochastic optimization algorithms [61; 62]. Some well known distributed localization algorithms are DV-Hop [52], DV-Distance [14] and a number of other algorithms based on the above two algorithms [17; 18; 63].  
Centralized and distributed localization algorithms are further subdivided into range based and range free algorithm. Moreover, fusing the information from different positioning systems with different physical principles can improve the accuracy and robustness of the overall system. This leads to the development of another category known as hybrid data fusion [8].  
Range based localization technique utilizes the measurement techniques such as AOA, TOA, TDOA and RSSI as is discussed in the previous section to estimate the distance between sensor nodes and then calculates the position. Range based technique usually achieves high ranging accuracy but requires extra hardware and consumes more energy. In the following sub-section, we focus on range free localization and hybrid data fusion techniques.
## 2 The Robotic Machining System and Combined Calibration Method
Since the camera view cannot cover the entire workpiece, an XY Dual-Axis servo module is used to move the 3D camera in sections, completing a panoramic merge after capture. Next, the collected point cloud data features are matched with the training template's point cloud features, calculating the difference between the actual workpiece pose and the training template workpiece pose. Finally, this difference is compensated into the initially established workpiece coordinate system to achieve workpiece coarse positioning. Figure 1: The robotic composite machining system. The contact-based calibration method represents the final step in calibrating the workpiece. Therefore, the precision of the calibration achieved with the contact-based method directly impacts the accuracy of workpiece positioning. Figure 3 illustrates the principle of using a contact probe to calibrate a hole feature. The probe touches the sidewall sequentially, recording points of contact, with a minimum of three points measured on the feature to calculate the coordinates of the center. The workflow for the robot to calibrate the workpiece using a probe is as follows:  
1. The machining robot retrieves the Renishaw probe tool from the tool magazine and activates the probe tool. 2. Based on the preliminary position coordinates of the workpiece feature obtained from the 3D visual system coarse calibration, the robot acquires the initial position coordinates for the feature to be calibrated. 3. The robot uses the contact probe to sequentially measure the corresponding workpiece features. 4. An accurate workpiece coordinate system {W} is established, completing the precise workpiece positioning. Since robot contact-based calibration is the final step and directly affects the accuracy of workpiece positioning, improving the precision of robot contact-based positioning is of vital significance for enhancing the overall precision of robot machining. To accomplish contact-based calibration, the precise calibration of the probe's TCP is the initial and crucial step. Figure 2: The 3D visual system coarse positioning approach.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 3 Methodology
### Proposed Protocol Design
#### 3.2.3 Client Authentication  
Upon receiving the client's message, the server computes the local key \(k_{local}\) using its private key and the client's public key:  
\[\text{Shared Secret}=ECDH(k_{pr,S},k_{pub,C_{i}})\]  
\[k_{local}=\text{SHA-3}(\text{Shared Secret})\]  
Using \(k_{local}\), the server verifies the HMAC and decrypts \(m^{r}_{C_{i}}\) using AES to extract the client's certificate and nonce. The client's certificate is then validated using the CA's public key (\(k_{pub,CA}\)). The server responds by generating a symmetric key (\(k_{sym}\)), assigning it a lifetime \(T\), and encrypting these values, along with the nonce and server's ID (\(ID_{S}\)) using the derived \(k_{local}\):  
\[y_{C_{i}}=e_{k_{local}}(k_{sym},r_{i},T,\text{ID}_{S})\]  
where \(e_{k_{local}}\) denotes the encryption operation with key \(k_{local}\). In our protocol, we have used AES-256 in CBC mode for this encryption. An HMAC (\(h_{y}\)) is also computed of \(y_{C_{i}}\). The server sends \(y_{C_{i}}||h_{y}\) to the client.