esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Recently, many authors have reported using NIR-HSI to assess quality attributes in food and other products [21; 22; 23; 24]. In addition, it has been extensively used for the physical and biochemical constituent characterization of potatoes and sweet potatoes [25; 26; 27; 28; 29; 30]. HSI is currently used to determine both the appearance and internal chemical characteristics of food quality, such as rice fungal growth [31], the hardness of maize kernels [32], and the sugar content in potatoes [33]. This technology is a powerful method for the nondestructive evaluation of various quality traits of processed and raw food crops. Specific physical properties, such as the color of potato tubers while processed [25] and the textural attributes of potato tubers, have been studied [34]. In addition, biochemical constituents in sweet potatoes, such as starch, cellulose, and the distribution of soluble solid content, have been estimated using HSI [35]. Adulterants in tapioca starch have also been detected using NIR-HSI, with excellent prediction performance (R = 0.99) and a Root Mean Square Error in Calibration (RMSEC) of 2.47% [36]. Hyperspectral imaging technology has an advantage over conventional spectroscopy due to its ability to integrate imaging and conventional spectroscopy for the rapid and simultaneous assessment of physical and chemical components of various food products. This review highlights the principles of near-infrared hyperspectral imaging techniques combined with chemometrics tools. It then provides cases of its application in determining important biophysical quality traits and texture quality in root and tuber crops, especially yam and cassava. It also presents the drawbacks and the way forward for this technology. This work will contribute significantly to understanding quality trait assessment using the NIR-HSI method.
## 4 Results and Discussion
### Reliability Model  
Using the maximum likelihood estimation method, an exponential distribution is modeled considering both ceramic plate failure data and their preventive substitutions (right-censored data). In Figure 9, a graphical summary of the model is presented, as well as its main parameters and statistics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hours** & **Data** & **Failures** \\
**Operated** & **Operation** & **Failures** \\ \hline
0–1000 & 29,119 & 120 \\ \hline
1000–2000 & 29,068 & 226 \\ \hline
2000–3000 & 27,761 & 504 \\ \hline
3000–4000 & 25,219 & 644 \\ \hline
4000–5000 & 18,058 & 457 \\ \hline
5000–6000 & 4549 & 97 \\ \hline
6000–7000 & 288 & 6 \\ \hline Total & 134,062 & 2054 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of data by period of hours operated.  
Figure 8: Distribution of the time intervals of complete ceramic plate set renewals.  
Failure data records considered 2795 ceramic plate failures and 7731 right-censored replacements. In the left graph, the survival function represents the probability of survival over the usage of ceramic plates. In the middle graph, an exponential Q-Q plot is presented as a qualitative assessment of how well data can be represented by the exponential distribution. On the right, a box presents a summary of the main statistics of the model. Based on failure and replacement records, ceramic plates have a survival probability of 90% at 1388 operating hours, 80% at 2940 operating hours, 70% at 4700 operating hours and 60% at 12,074 operating hours. As Figure 8 shows, complete ceramic plate renewal occurs on average at 4822 operating hours, and a set never operated longer than 7285 h.
## 4 Blackout Analysis
### Preconditions
#### 4.1.5 Natural Phenomena (PREC_5)  
Natural disasters consist of a broad category that includes a variety of tragic occurrences, such as earthquakes, lightning strokes, hurricanes, tornadoes, avalanches, forest fires, volcanic eruptions, landslides, floods, etc., that can cause short circuits and can play a significant role in blackouts. An earthquake, for instance, may cause trips to transformers in the region it hits due to the Buchholz relay operation. In a forest through which transmission lines cross, during a fire, the transmission line circuit breakers may be tripped due to acting between phases or between phases and earth due to the reduction in the dielectric strength of the air caused by the smoke.
# Digital Twin Conceptual Model within the Context of Internet of Things
## 3 Physical Space Layer  
In the context of IoT and Industry 4.0, any object, product, or system consists of two spaces: The physical space and the digital space. This section describes the basic building blocks of the physical space. The physical space layer (PSL) consists of the physical objects, sensors, signal conditioning circuits, edge computing devices, power driving circuits, and actuators. The sensors collect the  
Figure 1: An end-to-end digital twin proposed conceptual model within the context of Internet of Things (IoT).  
physical object's operational and surrounding environment parameters. The sensors convert the object's non-electrical phenomena into electrical signals after which the signal conditioning circuit shifts, scales, and/or amplifies these signals to be compatible with the edge computing device's digital, analog, and serial communication ports.  
Once the parameters are collected, some local decisions are made (with the aid of the edge computing devices) to command some actuators through the driving circuits to operate based on a process algorithm. Furthermore, selected object operational and surrounding parameters will be transmitted through the communication layer for the purposes of data storage and processing in the above layers. The edge computing device, single system on a chip (SoC), could be a low-cost, low-power, compact, stamp-sized microcontroller [13] or a credit card-sized microcomputer [14].  
These edge computing devices are equipped with high speed CPU's, a large program and data memory, gigabyte Secure Digital (SD) storage, and large number of digital and analog ports. Furthermore, they also have wired communication ports, such as RS485, I2C, SPI, USB, and CAN. Moreover, they contain proprietary ports such as cameras, GPS, keypads, and LCD. Additionally, wireless access points, Ethernet ports, GPRS, and Bluetooth ports are also available on these devices. In addition to the sensors, signal conditioning circuits, and edge computing devices, there are driving circuits such as power amplifiers, relays, silicon-controlled rectifiers (SCRs), and triodes for alternating current (TRIACs) [15]. These driving circuits provide the interface between the edge computing device's output ports and the physical object to be actuated according to the process algorithm. Some of the edge computing devices and microcontroller/microcomputing units have add-on hardware on the top (HAT) [16]. These add-on boards have several sensors that can be interfaced with the edge computing device. Each of these HATs have open source software libraries that can be included and invoked in the user program to configure and collect data from these sensors.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis  
In this section, we analyse the performance results obtained in the different scenarios. First, the parameters used are defined. Secondly, tests with the proposed implementation are performed to evaluate the performance. We measure the overall traffic rate, the Packet Delivery Ratio (PDR), the average delay and the BERs using USRPs. Finally, a comparison between the simulation results obtained in [13] and the experimental ones is made.
## 3 Experimental Results
### Strain Development of Stirrup  
Strain development of stirrups for all the specimens is similar. Take specimen M4-D12-7.5d as an example (Figure 15). The stirrups can be divided into two types in this study, the confined stirrup and the general stirrup (Figure 5b). The confined stirrup locates at the lap length zone between the heads and across the diagonal cracks. For the confined stirrups, the strains develop slow before the peak load of the specimens. At peak load, the diagonal cracks are developed at the concrete surface and the strains of confined stirrups develop fast, which can restrain the expansion of cracks. At the post-peak load stage, the strains of the confined stirrup develop rapidly. In terms of general stirrups, although the strains of general stirrups develop rapidly in the post-peak load stage, the strain values of the general stirrups are still smaller than 1000 \(\mu\varepsilon\), far below the yielding strain, during the whole loading process. Since the adjacent headed bars are not coaxial and it can result in the rotation of CHBC, the general stirrups are adopted to simulate the constraint of the other reinforcements in structure members on CHBC. The strain values of the general stirrup being small indicates that while CHBC is applied in the concrete structure, adequate constraint can be guaranteed to limit the adverse effects caused by the rotation of CHBC.  
The confined stirrup can strengthen the core concrete, which can improve the bearing capacity of CHBC. In addition, the confined stirrups are across the diagonal crack region. Then, the confined stirrup may be under shear deformation due to dowel action, which can contribute to the bearing capacity of CHBC directly by the projection of shear force in the axial direction of headed bar. The removal of concrete after the test has been conducted shows that the confined stirrup skeleton is integrated and little shear deformation is observed (Figure 16). Therefore, the confined stirrup mainly sustains tension force and functions to provide confinement to the core concrete instead of bearing the shear force.  
Figure 15: Strain development of stirrup (**a**) M4-D12-7.5d-1; (**b**) M4-D12-7.5d-2; (**c**) M4-D12-7.5d-3.  
To achieve a good confinement to the core concrete, the spacing between the confined stirrups should not be too large within the lap length. It is recommended in this paper that the spacing of the confined stirrups should meet the requirement that at least two confined stirrups should be placed between the heads in order to provide a good confinement. Additionally, the diameter of the confined stirrup rebar is recommended to be greater than 6 mm.
## 4 Results and Discussion
### Analyzing Possibility of W-Oil as Rejuvenator  
The pavement performance of asphalt becomes poor due to the aging effect, and the aim of adding rejuvenator is to restore the pavement performance of aged asphalt. First of all, the optimum dosage of W-oil shall be determined. In this paper, the optimum dosage is determined by the rheological index, because the rheological index can be directly correlated to the field pavement performance. According to the test results of performance indices obtained from the rheological test, with the increasing dosage of W-oil, the workability, fatigue property and low-temperature performance of rejuvenated asphalt are improved. However, the rutting resistance of rejuvenated asphalt is declining. The principles of determining the optimal dosage of W-oil are: under the premise of ensuring the high-temperature performance of rejuvenated asphalt, improve the workability, fatigue property, and low-temperature performance of aged asphalt to the maximum. Taking the \(J_{nr}\) value of rejuvenated asphalt no lower than that of virgin asphalt as the criteria, the calculated dosage, making \(J_{nr}\) in the case of 3.2 kPa and 0.1 kPa restore to the level of virgin asphalt, is respectively 13.0% and 13.7%, and the average value of 13.4% is taken as the optimum dosage of W-oil.  
The feasibility of the rejuvenator is usually evaluated by comparing the properties of virgin asphalt and rejuvenated asphalt. For carrying out a comparative analysis on the performance of 13.4% W-oil rejuvenated asphalt and virgin asphalt, the safety, aging, and pavement performance indices of virgin and rejuvenated asphalt shall be processed for normalization, as shown in Figure 21. The optimum dosage of W-oil is determined as 13.4%, which is based on the same high-temperature stability as virgin asphalt. The safety of rejuvenated asphalt with 13.4% W-oil is poorer than that of virgin asphalt, while the safety can still meet the specification requirements. The aging property is superior to that of virgin asphalt. The ductility is better than that of virgin asphalt. The workability is slightly poorer than virgin asphalt, and will be further improved. The fatigue life of rejuvenated asphalt is longer than that of virgin asphalt by approximately 33%. The value m of rejuvenated asphalt is close to that of virgin asphalt, but the value S is lower by 47% than virgin asphalt; therefore, the low-temperature property of rejuvenated asphalt is better than that of virgin asphalt. In summary, W-oil can significantly improve the properties of aged asphalt, and it is of great potential to serve as an asphalt rejuvenator.  
Figure 20: S and m Values of Virgin, Aged, and Rejuvenated Asphalt.
# Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples
## 3 Results and Discussion
### Influence of Laser Power and Scanning Speed  
Figure 9 shows the relationship between the laser power, scanning speed and surface roughness (\(S_{\text{a}}\)) values. It is obvious that, after laser polishing, the surface roughness of the materials will be reduced. When the laser scanning speed remains constant, the surface roughness decreases with an increase in the laser power. Meanwhile, when the laser power remains constant, a lower roughness value is obtained with a smaller scanning speed. When the laser powers are 300 W and 400 W, the influence of the scanning speed on the surface roughness is more obvious.  
Figure 8: Data plot of line profiles extracted from three-dimensional contours: (**a**) Ti6 Al4V, (**b**) AlSi10 Mg, (**c**) 316L and (**d**) IN718.  
Figure 9: Relationship between laser power, scanning speed and roughness.  
When the scanning speed is 1 m/s, the surface roughness is not significantly affected by the smaller laser-polishing power, especially at 100 W. This is mainly due to the fact that the small energy input only partially melts the material surface, resulting in a smaller molten pool, which is not conducive to the wettability of the materials in the adjacent areas. After solidification, there are obvious scanning marks on the surface, and the roughness exhibits very little change. Owing to the decrease of the laser moving speed, a scanning speed of 0.5 m/s produces a molten pool that is able to wet the adjacent area so that a larger molten pool can be obtained, producing a smoother surface after solidification. Similarly, when the laser scanning speed is constant, an increase in the laser power causes the molten pool to increase in size. Therefore, a smoother surface with a smaller roughness value can be obtained. For all four materials, the polishing effect is optimum when the laser speed is 0.5 m/s and the laser power is 400 W.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 2 Methodologies
### Validation Approach  
In order to validate the performance of the prediction of the models, a simulated historical forecast (SMH) approach is conducted. SMH is based on the rolling origin cross-validation approach, or otherwise known as forward chaining, where the data are split into training and testing sets and each day is tested based on the prior training data. However, this method would be computationallyintensive due to conducting tests each day and would result in more forecasts that are likely to have correlated estimates of errors. While with SMH, fewer forecasts can be defined at cut-off dates and the total error can be evaluated based on all the forecast horizons. Figure 3 illustrates the process of the rolling origin cross-validation. This approach would allow the models to train on a dataset and test on another unseen dataset of the time series. Moreover, the SMH approach could be used as an indicator of the models' generalizability in forecasting the maximum load.  
In addition, to assess the performance of each model in terms of accuracy, various statistical metrics were adopted, such as root mean square error (RMSE), the mean absolute percentage error (MAPE), coefficient of determination (R\({}^{2}\)), mean absolute error (MAE), and coefficient of variation of root mean square error (CVRMSE) that can be expressed as in the following equations:  
\[RMSE=\frac{\sqrt{\sum_{i=1}^{n}{({{{\hat{y}}_{i}}-{{y_{i}}}})}^{2}}}{n} \tag{11}\]  
\[CVRMSE=\frac{\sqrt{\sum_{i=1}^{n}{({{{\hat{y}}_{i}}-{{y_{i}}}})}^{2}}}{{{{\hat {y}}}}} \tag{12}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}{\left|{{{y_{i}}}-{{{\hat{y}}_{i}}}}\right|} \tag{13}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}{\left|{{{{\hat{y}}_{i}}-{{y_{i}}}}}\right|} \times 100\% \tag{14}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{({{y_{i}}}-{{{\hat{y}}_{i}}})}^{2}}{{\sum_{i=1}^ {n}{({{y_{i}}}-{{{\hat{y}}}})}^{2}}} \tag{15}\]  
where \(y\), \({{{\hat{y}}}}\) and \({{{\hat{y}}}}\) represent the measured, predicted, and averaged values respectively.
## 2 Methodology
Figure 2 illustrates the principle of combinatorial search for load combinations with \(x_{i}(t)\) in Equation (2) for load disaggregation, which aims to optimize load combinations by \(x_{i}(t)\). In Figure 2, \(P_{i}(t)\) represents the assumed power demand of the \(i\)-th electrical appliance. \(x_{i}(t)\) associated with \(P_{i}(t)\) is represented as a binary vector, evolved through metaheuristics, and used to minimize Equation (2) (the minimal error, \(err(t)\), to be obtained for load disaggregation) between the summation of superimposed absorptions \(\Sigma x_{i}(t)P_{i}(t)\) with an unmonitored base load \(P_{\text{hase}}(t)\) and the total load \(P(t)\) (\(P(t)\) to be approximated from a pack of \(P_{i}(t)\) with \(P_{\text{hase}}(t)\)). A metaheuristic algorithm, GA, is suitable for load disaggregation formulated in Equation (2) (the objective metric is the metric we are trying to optimize and the fitness metric is the algorithm's guide to doing so), where concerned electrical appliances are recognized by parallel computing accelerated GA for the declared objective metric. Concerned electrical appliances are constant or time-varying resistive, inductive, or capacitive loads. The NIALM investigated in this paper has the main objective of recognizing concerned electrical appliances for (residential) DSM according to a composition of appliance-level real power consumption, which is disaggregated from total real power consumption acquired apart for load disaggregation. That is, with the minimum in Equation (2), acquired total real power consumption is the total of real power consumption by all individual electrical appliances concerned and operated where a base load  
Figure 2: An illustration of the principle of combinatorial search for load combinations to \(x_{i}(t)\) for load disaggregation in this paper, whereby load combinations by \(x_{i}(t)\) are optimized. \(x_{i}(t)\) is represented as a binary vector, evolved through metaheuristics, and used to minimize Equation (2) between the summation of superimposed absorptions \(\Sigma x_{i}(t)P_{i}(t)\) with an unmonitored base load \(P_{\text{hase}}(t)\) and the total load \(P(t)\). should be considered. To Equation (2), a parallel computing accelerated GA as a load recognizer for load recognition of the NIALM in this paper is used to recognize the correct \(x_{i}(t)\) to gain the minimum between the acquired total real power consumption and the sum of superimposed real power consumption by concerned electrical appliances. In this paper, real power consumption, \(P\), is extracted, as the electrical feature for load recognition in Figure 1, from acquired total real power consumption, which is disaggregated into appliance-level real power consumption through the parallel computing accelerated GA-based NIALM of Equation (2). Concerned electrical appliances to be recognized can be constant or time-varying resistive, inductive, and capacitive loads [4; 25].
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 2 Satellite Networks Penetration Testing Method  
Due to the lack of existing methods for satellite network penetration testing, we adapted the cyber-physical system security methodology from [19] to develop a three-dimensional testing method.  
As illustrated in Figure 1, this method comprises three functional dimensions: (1) Satellite network segmentation (\(x\)-axis) divides the network into user, ground, and space segments, enabling focused evaluation of mission-critical components rather than comprehensive system coverage. (2) Automation degree (\(y\)-axis) provides manual, semi-automatic, and fully automatic operational modes for adaptable test execution. (3) Penetration process (\(z\)-axis) enforces sequential progression through planning, vulnerability discovery, analysis, exploitation, and remediation phases. This triaxial structure systematically integrates network partitioning, automated adaptability, and procedural discipline, offering security practitioners a comprehensive testing methodology for heterogeneous satellite architectures. The visual method in Figure 1 clarifies operational workflows while maintaining mission-specific prioritization.