esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates
## 2 Specimens
### Homogeneous Particleboards of Sugarcane Bagasse  
The high-density homogeneous particleboards of SCB and polyurethane resin based on castor oil followed the methodology defined in [24], assuming a nominal density of 800 kg/m\({}^{3}\) and dimensions of 0.55 \(\times\) 0.55 \(\times\) 0.016 m. Initially, the SCB was oven dried at a temperature of 60 \({}^{\circ}\)C for 24 h to reduce the moisture content to around 12%. After drying, it was sieved in a sieve shaker. The bagasse particles retained in the sieve mesh with openings greater than 2 mm were milled in a knife mill to produce particles of size up to 8 mm. The particles with a size of less than 2 mm were removed from the manufacturing process because they are not suitable for the step of dispersing the resin. Subsequently, the particles of SCB were introduced in a planetary mixer and the adhesive polyurethane resin based on castor oil was added, in a proportion of 15% by weight of the dry mass of bagasse, for better uniformity in the distribution of the adhesive in particles. After this mixing period, the particles were introduced into a mattress forming mold, with dimensions of 0.55 \(\times\) 0.55 m and pressed with a pressure of 5 MPa at a temperature of 100 \({}^{\circ}\)C, for a period of 10 min. At the end of the pressing process, the complete curing process of the resin required a period of 72 h [25].  
Low velocity impact tests at four different energy levels, namely 5, 10, 20 and 30 J (see Figure 3) were carried out using an instrumented drop-weight impact testing machine (CEAST/Instron 9340). A steel mass was attached to the steel impactor with a hemispherical tip of 20 mm diameter for a total weight of 3 kg. The different energy levels were obtained by changing the height of release of the impactor. The specimens were clamped circumferentially along a diameter of 40 mm in a pneumatic-actuated clamping fixture. During the test, the time histories of impact force and the initial impact velocity were recorded. Post-impact, the permanent dent depth of each coupon was measured using a non-contact profilometer (Taylor-Hobson Talyscan 150) with a scanning speed of 10,500 \(\upmu\)m/s and a nominal vertical resolution of 1 \(\upmu\)m. The dent depth as a function of impact energy is shown in Table 3[25].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plate No.** & **Max Load (N)** & **Displacement at Maximum Load (mm)** & **Final Displacement (mm)** \\ \hline
1 & 2339 & 3.88 & 5.25 \\
2 & 2141 & 3.73 & 5.14 \\
3 & 1843 & 3.18 & 5.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indentation test on jute/hemp fiber hybrid laminates.  
Figure 2: Photographs of the jute/hemp fiber hybrid specimens from the back side.
# Improved PEG-Based Construction of Analog Fountain Codes
## 2 Analog Fountain Codes and the PEG Algorithm  
In this section, encoding and decoding processes of AFCs are briefly introduced. Then, PEG applied to AFC construction is reviewed.
### Sensor Applications in Industry  
It can be seen from Table 1 that a diverse range of applications have already had initial investigation into the use of LCI sensors for monitoring processes. Recent work demonstrating the use of LCI in the domain of a liquid environment has shown that the system is able to provide stable, accurate data during profilometry measurements across various step-heights [12]. An extension of this work demonstrates an embedded LCI sensor for absolute distance measurement of samples operating within a jet of water has also shown significant data stability [13], demonstrating the potential usefulness of the LCI sensor for post-processing verification of parts, utilising a liquid jet for flushing of debris and coolant during measurement. This growing interest in the use of LCI sensors in industrial applications indicated a requirement of a text focused on optimising systems for such environments, along with expected outcomes and potential issues commonly found.  
Although there are several implementations of OCT configurations, due to the sensitivity advantages of Fourier-domain approaches over time-domain ones [14] and the prohibitive cost of operating with laser-swept light sources, this paper focuses on the use of spectrometer or spectral-domain based implementations.
## 5 Results  
### Single-Cycle Tensioning of Strands  
In single-cycle tensioning, the goal is to apply forces to individual strands of the stay cable in a sequential manner, ensuring that the final tension across all strands is uniform and meets the calculated requirements. The process starts with an initial tension force and proceeds the tension across all 12 strands is equalized. In Table 3, the forces required to individually apply to the strands of the stay cable for conducting the cable tensioning procedure in a single cycle are presented. The intensities of forces to be applied are determined according to the algorithm shown in Figure 6. This implies that the calculation according to the algorithm for single-cycle tensioning starts from the condition where the cable is tensioned with the force predicted by the calculation, and when the tension forces in all 12 strands are equalized, and the deformation of the cable axis corresponds to the calculated one, according to the numerical model of the bridge. By conducting the analysis in the opposite direction from the direction of the single-cycle tensioning process, the required tensioning forces in each strand are determined for phases where the number of tensioned and anchored strands is always one less. Ultimately, the force required to be applied to the first strand for the implementation of the given procedure is obtained. Table 3 details the forces applied to each strand (denoted as \(\mathrm{Z_{i}}\)) and the resulting tension forces in the cable (\(\mathrm{Z_{ki}}\)) after each strand is stressed. For example, when the force of 131.26 kN is applied to the first strand, the cable tension force is 131.26 kN. The tension force in the cable after the last strand is stressed reaches 1200 kN. The table also shows the shortening of the cable axis (\(\mathrm{\xi_{ki}}\)) with each additional tensioned strand, indicating how the cable deforms under increasing tension. \begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Symbol** & **Value** \\ \hline External diameter of pipe & \(\mathrm{d_{P}}\) & 110 mm (\(\mathrm{D_{P}}=+1.0\) mm) \\ \hline Pipe wall thickness & s & 6.6 mm (\(\mathrm{Ds}=+0.9\) mm) \\ \hline Mass of the HDFE pipe & \(\mathrm{m_{P}}\) & 2.166 kg/m\({}^{1}\) \\ \hline \end{tabular}
\end{table} 
Table 2: High-density polyethylene (HDPE) pipe properties. Figure 9: Mathematical model of stay cable for numerical example. Strand-by-strand tensioning in a single cycle is effectively illustrated through Figures 10 and 11. The force in the cable after tensioning each successive strand equals the force applied to the last strand, multiplied by the total number of tensioned strands (Figure 10). Figure 11 graphically represents the relationship between the number of tensioned strands and the cable's deformation.
### Contributions  
We implemented PRESENT, the first lightweight block cipher, and GIFT, one of the most popular lightweight block ciphers. The bit-slicing technique was applied to improve the throughput of PRESENT and GIFT and optimize the algorithm to minimize overhead. The contributions of this paper are summarized below.  
* Fast GPU implementation of PRESENT and GIFT Both PRESENT and GIFT block ciphers utilize S-box, which can be pre-computed and stored on various GPU memories to achieve high encryption performance. In this paper, we move away from this traditional technique and focus on the advanced bit-slicing technique to further improve the throughput performance. Existing bit-slice implementation on GPU suffers from excessive use of registers. To avoid this issue, the proposed implementation generates the round keys on the fly instead of pre-computing them.
* First GPU-based exhaustive key search and bulk data encryption for both PRESENT and GIFT block ciphers. We performed practical analysis by testing practical use cases (i.e., exhaustive key search and bulk data encryption). When implementing an exhaustive key search, the counter value is directly generated in the bit-sliced form on the GPU kernel. In this way, we do need to transfer the counter values to the GPU. This also reduces the memory copy delay and improves performance significantly.
* Speed record result on modern GPU architecture (RTX) The proposed implementation was evaluated on the RTX 3060 with NVIDIA Ampere architecture. We are able to achieve 553.932 Gbps PRESENT-80, 529.952 Gbps PRESENT-128, 583.859 Gbps GIFT-64, and 214.284 Gbps GIFT-128 throughput for encryption, respectively. For exhaustive search, 24.264 Gbps PRESENT-80, 24.522 Gbps PRESENT-128, 85.283 Gbps GIFT-64, and 10.723 Gbps GIFT-128 throughput are achieved, respectively. Note that our implementation of PRESENT is approximately 4\(\times\) higher performance than the latest work that implements PRESENT [10].
## 3 System Model and Problem Formulation
### Cellular and D2D Communication Mode
So, from the above expressions, we can infer that the intensity of interference (i.e., outage probabilities of a SCeNB link) depends on small cells and DUEs density, distances between the receiver and transmitter of those links, SINR requirements, and transmit powers. By increasing DUE receiver distances from CUEs or SUEs will increase the success probability of D2D links. Similarly, the outage probability of SCeNB links will decrease by increasing distances between the D2D transmitter and CUEs or SUEs [1].
## 2 Adjacent Pixel Difference (APD)
The procedure of extracting the confidential data from the stego-image and restoring the original cover image from the stego-image is shown in Figure 3. By following this procedure, the confidential data can be extracted from the stego-image without loss and the original cover image can be restored without distortion. Therefore, the APD technique is an excellent reversible data hiding technique that can embed large amounts of confidential data. An example of confidential data embedding in APD is shown in Figure 4, and an example of the confidential data extraction and the original cover image restoration procedure is shown in Figure 5. By constructing the histogram of the adjacent pixel difference sequence (D) of the cover image in Figure 4, it can be seen that \(\text{PP}_{1}\) = 0, \(\text{PP}_{2}\) = 1, h(\(\text{PP}_{1}\)) = 8, and h(\(\text{PP}_{2}\)) = 7. Therefore, the number of data bits that can be embedded in the cover image is h(\(\text{PP}_{1}\)) + h(\(\text{PP}_{2}\)) = 15 bits. As shown in Figure 4, the stego-image generated by embedding confidential data in the cover image was slightly different from the original cover image, and the image quality of the stego-image was slightly degraded. As shown in Figure 5, the confidential data was extracted from the stego-image without loss, and the original cover image was recovered without distortion. Therefore, the APD technique could be used in digital image watermarking, military, and medical applications. Step 1. Generate the stego-image sequence (S) by scanning the pixel values of the stego-image in inverse s-order. Step 2. Restore the embedded adjacent pixel difference sequence (DE) and the cover image sequence (C) by repeatedly executing Step 2.1 and Step 2.2 while increasing i. Step 2.1. Restore the embedded adjacent pixel difference sequence (DE) using Equation (6). \[\text{DE}_{\text{i}}= \begin{cases}\text{C}_{\text{i}}&\text{if (i=0)}\\ \text{C}_{\text{i-1}}-\text{S}_{\text{i}}&\text{otherwise}\end{cases}  \tag{6}\]  
Step 2.2. Restore the cover image sequence (C) using Equation (7). \[\text{C}_{\text{i}}= \begin{cases}\text{S}_{\text{i}}&\text{if (i=0)}\\ \text{S}_{\text{i}}+\text{sd}_{\text{j}}&\text{if (1\leq i\leq n-1\text{ and }C_{\text{i-1}}-\text{S}_{\text{i}}\in[\text{PP}_{ \text{j}}+\text{sd}_{\text{j}},\text{CZB}])}\\ \text{S}_{\text{i}}&\text{otherwise}\end{cases}  \tag{7}\]  
where \(\text{j}\in\{1,2\}\)  
Step 3. Extract the confidential data embedded in the stego-image by applying Equation (8) to each element DE of the embedded adjacent pixel difference sequence (DE). \[\text{Extracted bit}= \begin{cases}0&\text{if (DE}_{\text{i}}=\text{PP}_{ \text{j}})\\ 1&\text{if (DE}_{\text{i}}=\text{PP}_{\text{j}}+\text{sd}_{\text{j}})\end{cases}  \tag{8}\]  
Step 4. The original cover image is generated in inverse s-order using C.  
Figure 4: An example of data embedding using the APD technique. Figure 5: An example of data extraction and original cover image restoration using the APD technique. Figure 3: Data extraction and original cover image restoration procedure of APD.
## 2 Parabolic Antenna Analysis Method
### Induced Amplitude Calculation
Here, the values of the parameters are specified as follows: D is set to 30\(\lambda\), the FD ratio (\(F/D\)) is set to 0.35, \(\left(g_{\phi},\ \ g_{\phi}\right)\) are set to (0.5\(\lambda\), 0.5\(\lambda\)), \(\left(f_{x},\ f_{y},\ f_{z}\right)\) are set to (1\(\lambda\), 1\(\lambda\), 0), (\(\theta_{s}\), \(\phi_{s}\)) are set to (160\({}^{\circ}\), 35\({}^{\circ}\)), and \(q\) is set to 1. In Figure 3, the square marker represents the feeder position, and the direction of the arrow indicates the beam axis of the feeder. Figure 4 shows the distributions of normalized induced signal amplitudes on the parabolic reflector surface, considering the effects of induced signals due to wave propagation from the feeder and those based on the radiation pattern of the feeder. Here, the antenna configuration is identical to that shown in Figure 3a. Figure 4a displays the amplitude of the induced signals resulting from the electric field effects caused by wave propagation between each point charge source from the feeder. It is evident that the distribution adheres to an inverse distance relationship, as described by Equation (11). Owing to the feeder's offset \(\left(f_{x},\ f_{y},\ f_{z}\right)\) from the focal position, as presented in Figure 3a, the distribution around the reflector surface is observed to be asymmetrically distributed. Figure 3: Parabolic antenna geometry for induced amplitude: (**a**) general geometry; (**b**) moved and rotated geometry. Figure 4b illustrates the amplitude of the induced signals for each point charge source due to the feeder's radiation pattern. The influence of the radiation pattern simulated by the cosine-q model (\(q=1\)) and the impact of beam steering (\(\theta_{s}\), \(\phi_{\mathrm{s}}\)) from the feeder lead to an asymmetric distribution.
## 3 Results  
### Structural Characterization of LSC  
The XRD pattern of LSC is presented in Figure 4. It is observed that there are no impurity peaks from the XRD pattern, and the XRD diagram of LSC shows the characteristic of sharp peaks, indicating that the crystallization of LSC was excellent. The XRD pattern of LSC displays characteristic peaks at 2\(\theta\) = (23.4\({}^{\circ}\), 33.2\({}^{\circ}\), 40.8\({}^{\circ}\), 47.6\({}^{\circ}\), 53.5\({}^{\circ}\), 59.1\({}^{\circ}\), 69.6\({}^{\circ}\), 79.2\({}^{\circ}\), 83.7\({}^{\circ}\), and 88.2\({}^{\circ}\),) which correspond to the planes of (012), (110), (202), (024), (122), (300), (220), (134), (042), and (404) simultaneously, consistent with the standard reference data (JCPDF:89-5719).  
Figure 3: Schematic diagram of thermal stimulation current method.  
Figure 2: Illustration of the pulsed electroacoustic (PEA) measurement.
## 4 Case Study
### Data Preparation
#### 4.2.3 Travel Time Data  
Travel time data consist of two components: baseline travel time under normal conditions and travel time under disaster impact.  
To estimate the baseline travel time under normal conditions, this study utilizes Gaode Map's daytime route planning data. Gaode Map's route planning function integrates historical traffic data, real-time traffic conditions, and predictive models, incorporating floating vehicle trajectories, large-scale user trajectory data (e.g., GPS and mobile signaling), traffic monitoring equipment (e.g., cameras, geomagnetic sensors, and radar detectors), and road network topology. This integration provides optimal route recommendations and estimated travel times. Consequently, in the absence of significant traffic disruptions, Gaode Map's planned travel times can approximate actual conditions. To mitigate short-term traffic fluctuations, this study collects data during non-peak weekday hours using the Gaode Map API to obtain recommended routes and corresponding travel times between designated origin-destination pairs. Data are collected continuously for at least seven days, and the average value is computed to reduce single-query bias.  
For post-disaster travel time estimation, this study employs the Simulation of Urban MObility (SUMO) traffic simulation software (version 1.19.0) to reconstruct travel times across affected road network segments. Based on the constructed road network topology, disaster-induced conditions such as road closures, reduced traffic capacity, and node disruptions are simulated. The maximum traffic capacity of affected road segments is adjusted accordingly. SUMO's built-in tripinfo.xml and edge-based statistics modules are used to record vehicle travel times at different time points. The final simulation results are employed to compute the average travel delay caused by the disaster.
## 2 Materials and Methods
### System Architecture
#### 2.2.3 Non-Relational Database  
The non-relational database module (Figure 4) is represented by a MongoDB database, a NoSQL document-oriented model. This database can store data in the JSON format; each dataset has a unique structure, organized in collections. The module is composed of collection and pipeline algorithms, which are used for data aggregation based on different rules. The aggregation pipeline is a MongoDB framework that is designed for data transformation based on custom filters, grouping rules, and sorting. The data are retrieved using the Fitbit Web API, using functions described in the official SDK documentation. The data retrieved are in the standard JSON format and can be used in front-end routines.  
The purpose of this module is to store the FIDS data, aggregate them according to specific rules, and make these data available for further processing and access. The structure of the database collection is represented in Figure 5. Each collection and pipeline have the following roles:  
Figure 4: MongoDB database structure.  
* FIDSData--stores raw data received from the smartwatch.
* ActivityPrediction--stores activity-type predictions (5 s granularity).
* EnergyExpenditure--stores energy expenditure for each activity (minute granularity).
* HourExpenditure--stores energy expenditure for each activity (hour granularity)--this collection is created by the MinuteToHour pipeline and has as the main source the EnergyExpenditure collection.
* SedentaryPrediction--stores how many minutes each activity is performed (hour granularity)--this collection is created by the SedentaryPrediction pipeline and has as its main source the EnergyExpenditure collection.
* UserInformation--stores information provided by the user through self-report forms (weight and mood).  
Each collection is later accessed by using the GET endpoint /getData from NodeJS modules and used for both processing and displaying purposes.
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
###### Abstract
To overcome the limitations generated by neural networks and the high-dimensional feature vectors used in statistical classifiers, this study proposes a novel technique for the unbalance fault diagnosis based on texture features and a classification approach. We have named our proposal VTA for Variance-correlation Texture Analysis. Although texture features have been proposed for the classification of different motor faults [24; 25], texture has never been explored for the detection of unbalance faults. This approach consists of analyzing vibration signals by using a subset of texture features proposed by [PERSON] [26]. Subsequently, a support vector machine (SVM) [27] is trained and validated to infer the unbalance and healthy classes in the IM. Finally, a test set is used to measure the performance of the classifier. Experiments from this study may lead to an improvement in the accuracy and training time in comparison to other models established in the state-of-the-art. The rest of the manuscript is structured as follows: Section 2 describes the methodology used to build our VTA approach with an SVM classifier using texture features. In Section 3 the experiments and results are discussed. Finally, in Section 4 the conclusions are presented.
## 5 Methods
### Experimental Setup
In this specific scenario, the delays are set as \(d1=48-28=20\) and the _batch_size_ should include the half-hour delayed data. Thus, the delay parameters are designed as \(d1=20\) and \(d2=d1+T-1=20+48-1=67\). In the case of a FNN, the input is transformed from \([x(t-\tau+1),x(t-\tau+2),\dots,x(t)]\) to \([x^{1}(t-\tau+1),x^{2}(t-\tau+1),\dots,x^{k}(t-\tau+1),x^{1}(t-\tau+2),\dots,x^ {k}(t-\tau+2),\dots,x^{1}(t),\dots,\)\(x^{k}(t)]\). Here, \(x(t)\) is assumed to consist of \(k\) variables, denoted as \([x^{1}(t),x^{2}(t),\dots,x^{k}(t)]\). Thus, the FNN maps from \(k\tau\) inputs to one output. In order to account for dummies that have the same value for a given day, we retain only one value per day. Each \(x(t)\) in the samples should include the demand at time \(t\), the forecasted temperature at time \(t+d2\), and _DayOfWeek_ dummies for the next day (tomorrow, if \(t\) is today). The prediction half-hours in this case range from \(t+d1\) to \(t+d2\); thus, if we are interested in considering the days after holidays, we should incorporate dummies for the two days following tomorrow. It is important to note that due to holidays falling on weekdays there are irregularities in the _DayOfWeek_ dummies for adjacent days in the dataset. For example, it may follow a pattern such as Monday, Tuesday, Thursday, Friday, Tuesday, etc. As a result, the _DayOfWeek_ dummies are random in nature, not periodic. When considering the historical data to predict \(target(t)\), particularly when the look-back \(\tau\) is large, the LSTM model proves beneficial by avoiding the issue of vanishing or exploding gradients. However, it is important to note that including too much fluctuated information for a large \(\tau\) may adversely impact the accuracy of the predictions, as depicted in Figure 13. In Figure 13, impact of off-peaks and peaks are illustrated. During the prediction using pre-trained model, i.e., 1 the upper off-peak, i.e., 2 is affected. Such effect can be seen through out the test dataset. In the case of holiday data, it may be necessary to have a large \(\tau\) spanning one or multiple years. Conversely, for non-holiday data the required lookback period is typically on the order of a few days or weeks, with the exception of the days surrounding Songkran and New Year, which may require a lookback period on the order of years. To address  
Figure 13: Visualization of pretrained model, lookback period, and prediction. this, an approach of treating the interim days together with the holidays was attempted; however, this resulted in poor performance [22].