esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spectral Line Selection  
Coal ash is the residue left after the high temperature burning of coal, which is mainly composed of minerals, usually in the form of metal oxides and a small amount of sulfo-compound, such as Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), Na\({}_{2}\)O, K\({}_{2}\)O, CaO, MgO, Fe\({}_{2}\)O\({}_{3}\), etc. The volatile content of coal is mainly composed of carbon, water, hydrocarbons, and Si, Al, Ca, Fe, Mg and other easily ionized metals. The calorific value is closely related to the content of elements that releases heat during coal combustion, especially C, H, O, and inorganic substances and major elements in ash also have a certain impact on the prediction of calorific value. So, the spectral line intensity of C, H, O, Si, Al, Fe, Na, Mg, K, Ca, Ti and Li elements is selected for the quantitative analysis of the ash content, volatile matter and calorific value of coal samples. Table 1 lists the main characteristic spectral lines of the elements in coal selected in this study, using data from the NIST atomic spectrum database [26].  
\begin{table}
\begin{tabular}{l c} \hline
**Element** & **Wavelength/nm** \\ \hline C & 247.8561 \\ H & 434.0462; 656.2819; 656.2852 \\ O & 407.5862; 441.4905; 777.1944; 777.5388 \\ Si & 288.1577 \\ Al & 308.2153; 309.2710; 394.4006; 396.1520 \\ Mg & 279.5528; 280.2704; 285.2127 \\ Ca & 315.8869; 317.9332; 393.3661; 396.8467; 422.6727 \\ Fe & 259.9396; 404.5813; 438.35449 \\ K & 766.4899; 769.8965 \\ Ti & 334.9405; 336.1227; 337.2798; 338.3769 \\ Na & 588.9950; 589.5924 \\ Li & 460.2898; 670.7775 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristic spectral lines selected for coal industry index analysis.  
Figure 1: The laser-induced breakdown spectrum of coal sample.
## 2 Background
### WBASN Global Connectivity  
WBASNs are capable of interaction with the internet and other existing wireless technologies, including ZigBee, Bluetooth, WLANs and cellular networks, etc. There are various ways to connect WBASNs to the internet; usually, it connects with the help of ambient sensors. Figure 6 shows a generic scenario of WBASN global connectivity.  
The IPv6 over low-power wireless personal area networks (6 LoWPAN) standard helps to connect the devices to the internet. The concept of 6 LoWPAN follows the idea  
Figure 6: WBASN’s global connectivity.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Diseases** & **Physiological Parameters** & **Biomedical Sensor Type** \\ \hline Cancer & Body fat sensor, weight loss & Implantable/Wearable \\  & indication sensor & Implantable/Wearable \\ Hypertension & BP & Implantable/Wearable \\ Heart Disease & ECG, BP, heart rate & Implantable/Wearable \\ Asthma & Respiration and oxygen saturation & Implantable/Wearable \\ Diabetes & Visual impairment & Wearable \\ Rheumatoid Arthritis & Joint stiffness & Wearable \\ Renal Failure & Urine output & Implantable \\ Vascular Diseases & blood pressure and & Implantable/Wearable \\  & peripheral perfusion & \\ Infectious Diseases & Temperature & Wearable \\ Stroke & Activity recognition, impaired & Implantable/Wearable \\  & speech, memory etc. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chronic monitoring diseases with usable sensor types.  
that internet protocol can be applied even to small, low-power devices that have limited processing capabilities to make them a part of the IoTs. 6 LoWPAN is a working group of the Internet Engineering Task Force (IETF) and defines encapsulation and header compression mechanisms that allow IPv6 packets to interact with IEEE 802.15.4 standard. The focus of IP networking for low-power radio communication is those applications that require wireless internet connectivity at lower data rates. The Thread consortium is a consortium that makes the protocol that is run in the 6 LoWPAN standard.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results
### Comparison with Other Methods
#### 5.1.1 Comparisons on FLIR  
We selected several recently proposed multispectral feature fusion-based object detection methods and compared them with our YOLO-MEDet algorithm, using [EMAIL_ADDRESS] (mean Average Precision at an Intersection over Union (IoU) threshold of 0.5) and [EMAIL_ADDRESS]:0.95 (mean Average Precision averaged over IoU thresholds from 0.5 to 0.95) as the evaluation metrics. Table 5 presents the evaluation results of these algorithms on the FLIR aligned test set, comparing HalfwayFusion [10], CFR_3 [10], GAFF [13], CAPTM [37], YOLO-MS [20], MRD-YOLO [38], CFT [39], CMAFF [19], CSAA [40], and ICAFusion [25].  
As shown in Table 5, YOLO-MEDet outperformed the state-of-the-art methods on this dataset, achieving an 80.1% [EMAIL_ADDRESS] score and a 38.5% [EMAIL_ADDRESS]:0.95 score, significantly surpassing the unimodal YOLOv5 model. Compared to ICAFusion, YOLO-MEDet improved the [EMAIL_ADDRESS] score by 1.6%, demonstrating its superior capability in multispectral object detection tasks.  
Notably, although ICAFusion employs an intermediate feature fusion strategy, it did not achieve the same level of feature integration as YOLO-MEDet, which leverages a multidimensional fusion approach for more precise feature representation. This limitation arises because ICAFusion primarily focuses on global information fusion and interaction, whereas YOLO-MEDet integrates both local and global feature representations more effectively. Consequently, YOLO-MEDet surpassed ICAFusion and several other state-of-the-art methods in terms of its feature fusion capability and detection accuracy. Furthermore, while methods such as GAFF and CSAA have achieved strong results in this task, YOLO-MEDet further enhanced the detection accuracy through a more efficient feature fusion strategy, particularly in multispectral data processing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Environment** & **Hyperparameter** & **Setting** \\ \hline OS & Windows 11 & Input Resolution & 640 \(\times\) 640 \\ CPU & Intel Core i7-14700 KF & Initial Learning Rate & 0.01 \\ GPU & RTX 4080 SUPER & Learning Rate Decay & Cosine Annealing \\ CUDA & 12.8 & Batch Size & 8 \\ Pytorch & 2.5.1 & Momentum & 0.937 \\ Python & 3.9 & Epochs & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental environment configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv5 & RGB & 58.6 & 26.2 \\ YOLOv5 & IR & 73.1 & 36.2 \\ HalfwayFusion [10] & RGB + IR & 71.2 & - \\ CFR\_3 [10] & RGB + IR & 72.4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different methods on the FLIR aligned dataset.
## 4 Experiments
### Evaluation Metrics  
The state-of-the-art mean absolute percentage error, root mean squared error, mean absolute error, and coefficient of determination (\(R^{2}\) score) were used to assess the accuracy of the proposed model. If \(r\), \(p\) are the actual and predicted values, \(N\) is the number of samples, and \(r_{i}\) and \(p_{i}\) are values of the \(i\)th samples in \(r\) and \(p\), respectively, then, their formulations have the following forms:  
The average absolute percent error between the actual and predicted values determines the MAPE for each period [31]:  
\[MAPE(r,p)=\frac{100\%}{N}\sum i=1^{N}\left|\frac{r_{i}-p_{i}}{r_{i}}\right| \tag{12}\]  
The mean absolute error is simply an arithmetic average of the absolute errors:  
\[MAE(r,p)=\frac{1}{N}\sum_{i=1}^{N}\left|p_{i}-r_{i}\right| \tag{13}\]  
The root mean squared error is the standard deviation of the residuals, i.e., prediction errors; in mathematical form:  
\[RMSE(r,p)=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(r_{i}-p_{i})^{2}} \tag{14}\]  
Equation (15) presents the \(R^{2}\) score that computes the coefficient of determination. The explained variance ratio indicates the model's goodness of fit and thus, indicates how well the model would predict the unseen, i.e., missing, data samples:  
\[R^{2}(r,p)=1-\frac{\sum_{i=1}^{N}(r_{i}-p_{i})^{2}}{\sum_{i=1}^{N}(r_{i}-p_{i}) ^{2}} \tag{15}\]  
where \(\bar{p}\) represents the mean of the true values and is equal to \(\frac{1}{N}\sum_{i=1}^{N}r_{i}\).
## 4 Discussion  
Historical buildings in many countries and regions are often threatened by seasonal typhoons or strong winds [2], which can cause irreparable damage [30]. Researchers recognize the importance of protecting these structures from wind disasters [31], employing qualitative and quantitative methods to assess and predict risks in windy environments [32; 33]. However, less attention has been paid to how retrofitting and adaptive reuse affect the original wind resistance of historical buildings. In fact, such interventions may increase vulnerabilities to wind, a critical concern that warrants attention.  
To advance this field, our study focused on the Lujiang Academy, a representative brick-and-wood ancestral hall in Guangzhou. CFD simulations under typhoon conditions were employed to compare the impacts of various retrofit strategies on wind resistance. CFD simulations are widely acknowledged for their effectiveness in such studies. Specifically, this study analyzed the impact of two retrofit approaches on wind pressure distribution within the ancestral hall's internal courtyard: installing glass curtain walls and enclosing the courtyard with a glass canopy. Additionally, this study examined the wind pressure distribution on the original, unaltered roof. These retrofit methods are common in Guangzhou's ancestral hall renovations, aiming to enclose courtyards and install indoor air conditioning systems for comfort, aligning with historical preservation goals [34; 35].  
Our findings revealed significant insights: installing glass curtain walls enhances the roof's wind resistance, reducing internal pressure in the main hall and mitigating risks of eaves damage from negative pressure. Conversely, enclosing the canopy alters the building's aerodynamic profile, potentially increasing roof damage risks and amplifying negative pressure effects on eaves and corridor roofs. This underscores the need for caution in retrofit strategies for historical buildings vulnerable to wind disasters, particularly in typhoon-prone areas or valleys.  
Our study primarily focused on mean wind speeds to determine the average wind load distribution on the roof and facades of the ancestral hall. However, limitations in Reynolds-averaged methods precluded capturing information on surface fluctuating wind speeds and loads. Future studies will explore Large Eddy Simulation (LES) to better simulate fluctuating flow fields and assess peak wind loads on structural surfaces [36]. Furthermore, for a comprehensive analysis of roof structural deformation under wind loads, mechanical monitoring and finite element simulation analysis will provide detailed mechanical behavior data, aiding in the preservation of historical building integrity [37]. The insights gained will be valuable for similar contexts [38]. Various modeling methods can be employed in the investigation of historical buildings instead of relying solely on SketchUp. For example, BIM (Building Information Modeling)-based models of historical buildings can help analyze changes in the overall structural forces under external wind forces after alterations to the building's shape and contour [39].
# Multi-Objective Design Optimization of the Reinforced Composite Roof in a Solar Vehicle
## Materials and Methods
### Optimization Parameters  
In general terms, the design optimization of the roof in the case of this solar vehicle was based on a combination of mechanical and functional features, as reported in Table 3. The first ones, i.e., the mechanical features, are related to the structural stiffness respect to static and dynamic loads and are required to assure the best conditions in terms of integrity, safety and modal response. They can be measured by flexural stiffness, torsional stiffness and (first) resonance frequency in the way that the higher their values, the better for the design solution is. The second ones, i.e., the functional features, are also desired to assure the best performances in terms of energy efficiency, including the reduction of inertial masses and possibility of maximizing the area exposed to heat exchange.  
At the same time, since these features are clearly interconnected, it was preferred to reduce the size of the system by fixing one parameter, the weight (= 0.250 \(\pm\) 0.010 kg) as common design target, and performing the optimization based on the other factors. Since each factor has to be maximized, it was possible to express the performance index, the index for comparison (IC) as:  
\[\text{IC}=\frac{1}{\sum_{i=1}^{n}p_{i}}\sum_{i=1}^{n}p_{i}\bigg{(}\frac{y_{i}-y _{i\ max}}{y_{i\ min}-y_{i\ max}}\bigg{)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Unit** & **T1000** & **T800** & **PVC** \\ \hline Type & - & Unidirectional & Twill & Foam \\ Density & Kg/m\({}^{3}\) & 1490 & 1420 & 100 \\ Young’s Modulus & MPa & 121,000, 8600, 8600 & 61,340, 61,340, 6900 & 125 \\ Poisson’s Ratio & - & 0.27, 0.4, 0.27 & 0.04, 0.3, 0.3 & 0.40 \\ Tensile Stress Limit & Mpa & 2231, 29, 29 & 805, 805, 50 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main material characteristics and properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Output** & **Unit** & **Target** & **Weighting** \\ \hline \multirow{3}{*}{Mechanical} & Flexural stiffness & N/mm & Highest & 0.15 \\  & Torsional stiffness & N-mm/rad & Highest & 0.15 \\  & Resonance & Hz & Highest & 0.30 \\  & frequency & & & \\ \hline \multirow{3}{*}{Functional} & Heat transfer & mm\({}^{2}\) & Highest & 0.40 \\  & surface & Weight & Kg & Lowest & (fixed) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization objectives and related weighting coefficients.  
where \(p_{i}\) represents the weighting coefficients (with \(i=1\dots\) 4 and \(\Sigma\,p_{i}=1\)) and \(y_{i}\) is the \(i\)-th optimization parameter which can vary between \(y_{imin}\) and \(y_{max}\), representing the minimum and maximum value assumed by each parameter \(y_{i}\).
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry
#### 4.1.7 Instrumented Sites for TOA Radiometric Calibration  
The RadCalNet network [107] ensures free and open access to SI-traceable TOA spectrally resolved reflectances with associated uncertainties over five globally spread sites. These sites provide nadir-view TOA reflectances every 30 min from 9:00 AM to 3:00 PM local time at 10 nm intervals in the spectral range from 400 nm to 2500 nm [108]. The TOA reflectances are computed from ground-based spectral measurements, using a common methodology for propagating the surface reflectance to TOA level [107]. Each member site takes responsibility for the quality assurance of the surface/atmosphere measurements provided and is subject to peer review and rigorous comparison to ensure site-to-site consistency and SI traceability.  
The RadCalNet initiative is a successful example of international collaboration between Space Agencies coordinated in the frame of the CEOS-WGCV, fulfilling a clear need from the scientific community. RadCalNet measurements are now being extensively used for systematic assessment of TOA radiometry for a large number of institutional and commercial space missions, with a main focus on high-spatial resolution sensors [109; 110; 111; 112]. Evolutions of the RadCalNet service were suggested by the EO community [109; 110], including, in particular, the provision of TOA measurements at enhanced spectral sampling intervals and the inclusion of a full BRDF characterisation to improve the radiometric assessment of off-nadir observations. These proposed improvements are now being considered within RadCalNet evolution activities, together with the inclusion of new sites to increase global coverage.
# Reliability by Using Weibull Distribution Based on Vibration Fatigue Damage
## 6 Conclusions  
1. A probabilistic alternative of the Weibull distribution to vibration fatigue analysis is developed, which allows it to define reliability and probability of failure.
2. Contrary to other models that use the median rank method as the cumulated failure percentile, the proposed methodology considers the accumulated fatigue damage for a platform in which the component's probabilistic life, the probability of failure, and the reliability index are estimated.
3. A methodology is developed based on the model presented to permit a probabilistic approach to vibration fatigue damage accumulation which allows the probabilistic failure and reliability estimation for mechanical components and structures subjected to variable amplitude loading, specifically random vibration.
4. The model and methodology included in this work are applied to mechanical components used in the telecommunication industry to assist in real and practical structural fatigue analysis.
5. An application case is selected to illustrate the proposed Weibull model and its parameter estimation methodology based on the cumulated vibration damage included in this paper.  
Conceptualization, J.M.B.-C., M.R.P.-M. and R.C.T.-V.; methodology, J.M.B.-C. and M.R.P.-M.; data analysis, J.M.B.-C. and R.C.T.-V.; writing--original draft preparation, J.M.B.-C. and M.R.P.-M; writing--review and editing, J.M.B.-C., M.R.P.-M. and R.C.T.-V.; supervision, M.R.P.-M.; funding acquisition, J.M.B.-C. and R.C.T.-V. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.  
The authors declare no conflict of interest.
## 4 Discussion
#### 4.2.1 Advantages  
The GB method improves the lateral spatial resolution, signal-to-clutter ratio, and localization error compared to the PS and conventional reconstruction techniques (see Table 1). It provides an optimal phase aberration correction everywhere in image as the true location and geometry of the aberrator is used to correct the phase aberration (see panel d in Figures 5, 7 and 8). In a previous publication [34], our group demonstrated that the GB method has the potential to enable real-time transcranial imaging (>10 frames/s) without injection of a contrast agent, and its performance is not affected by the heterogeneity (speckle) of the brain tissue; it only requires estimating the position and geometry of the bone layer, as shown in our previous publications [31; 32; 33; 44]. The GB method is not limited to a maximum bone thickness as long as the attenuation caused by a thick bone layer allows detection of echo signals with sufficient signal-to-noise ratio.
# Cow Dung Ash in Mortar: An Experimental Study
## 3 Results and Discussion
### Effects of CDA on Microstructure of Mortar
#### 3.3.2 Mineralogical Composition  
The result of the Fourier-transform infrared spectroscopy (FTIR) analysis conducted on the mortar samples containing CDA content of 0%, 10%, and 30% after 7 and 28 days of curing is shown in Figures 10 and 11, respectively. The FTIR spectra for the 7-day samples, as shown in Figure 10, reveal major peaks at 3860-3640, 2930-2115, 1750-1345, and 1100-450 cm\({}^{-1}\). On the other hand, the spectra for the 28-day samples (Figure 11) exhibit major peaks at 3760-3640, 2930-2280, 2100-1300, 1016-620, and 490-420 cm\({}^{-1}\). The wavenumbers, curves, peaks, and valleys observed in the FTIR spectra of the selected mortar samples, i.e., CDA0, CDA10, and CDA30 were almost identical for both curing periods. This suggested that the CDA content had no significant effect on the hydration product during the development of the hydration process, despite minor differences in the spectra.  
Figure 8: TGA curve of CDA20 after 28 days of curing.  
Figure 9: TGA curve of CDA30 after 28 days of curing.  
wavenumbers observed between the 7 th and the 28 th day samples. Figure 10 illustrates that unreacted particles were detected at the major peak wavenumbers of 744 cm\({}^{-1}\) for CDA0 and CDA10 samples. However, for CDA30 samples, the wavenumbers increased and extended to the range of 813-681 cm\({}^{-1}\). The abundance of unreacted particles can impede the activation reaction, resulting in a delayed early-age strength development. Additionally, the production of a C-S-H gel was lower in the CDA10 and CDA30 mortars, while CDA0 exhibited superior C-S-H gel production compared to CDA10 and CDA30. The wavenumber band between 1644 and 1000 cm\({}^{-1}\) indicates the presence of small amounts of carbon and the O-H-O extending and stretching vibration between 3460 and 1727 cm\({}^{-1}\) suggests the presence of free water.  
Based on the FTIR analysis depicted in Figures 10 and 11, the curve pattern observed for 28-day samples were comparable to that of the 7-day samples. Notably, unreacted particles were identified at identical wavenumbers within the range of 780-450 cm\({}^{-1}\), with a major peak observed at 700 cm\({}^{-1}\) for CDA10. The formation of a C-S-H gel was evident from the peak observed at 1016-1115 cm\({}^{-1}\). The stretching vibration mode of Si-O-T (T: tetrahedral Si or Al) was assigned to the band observed between 950 and 1200 cm\({}^{-1}\). This specific frequency range of 1018-1045 cm\({}^{-1}\) was noted to be characteristic of the presence of silicon tetrahedra (SiO\({}_{4}\)) in the chain structure of the C-S-H gel [37]. Furthermore, both figures provided further insight into the formation of the C-S-H gel, with the frequency range of 1110-970 cm\({}^{-1}\) indicating its formation. Unreacted particles were observed in the 600-400 cm\({}^{-1}\) range, while O-H-O was detected in the 3440-2328 cm\({}^{-1}\) range.  
Figure 10: FTIR spectra of paste from a control and CDA-containing mortar cured for seven days.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 4 Experimental Results and Performance Evaluations  
In this section, the compression ratio and energy consumption of IoT in BESDC for IEI applications are evaluated with four different datasets. Different scales of bounded errors are applied in these datasets. These environmental datasets are temperature, PM 2.5 and CO of air quality monitoring from [43] in Taiwan and seismic wave [44] from the Taiwan Chi-Chi earthquake which happened on 21 September 1999. Table 1 shows the features of correlation in each dataset.  
This experiment with performance evaluation is divided into four parts. The first part introduces the processing in two-tier BEA for this experiment. Then, the second part introduces the calculation of compression ratio and energy consumption in performance evaluations from experiments. The third part evaluates the performance improvement from both temporal and data correlation compression of EB-RLE and EB-HC. The fourth part evaluates the performance improvement from spatial correlation compression of SN in BESDC scheme. The performance improvement is focused on the sensor data compression and energy saving ratios as compared with the original sensor data without compression.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 2 Materials and Methods
### Queueing Problems  
It is important to understand where problems exist within queueing systems. We accessed a database of support tickets relating to the queueing system to understand this. Many of these tickets had been classified by the DevOps team, and we used these to get a sense of the team's concerns.  
Message re-processing can have an impact on the queueing system. Messages can be re-processed if the content within the message is bad or if the message has formatting errors. We attempt to identify whether messages are re-processed in the system by looking at the unique ids of each message.  
We note from Figure 3 the complex traversal of a message through the supply chain network. We attempt to establish if messages suffer from EDI malformation and understand whether particular EDI transformations are more susceptible to errors than others. We try to address this via specific attributes of an EDI message.