esa-sceva/satcom-chunk-collection huggingface dataset:
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
## 2 Materials and Methods
### Overview
#### 2.1.2 Determination of Mix Design  
Two approaches were considered for the mix design: (i) CDL cast three different low-strength concretes (named CDL 1-3) mainly differing in the aggregate type used, while all the other parameters were kept virtually constant as much as possible. Obviously, this approach leads to different concrete compressive strengths. All CDL concretes used the same cement type, CEM II 42.5N; (ii) CSTB, on the other side, aimed to have four low-strength concretes with cement type CEM II/B-LL 32.5R (named as CSTB 1a-4a) and four high-strength concretes with CEM I 52.5N cement (named as CSTB 1b-4b). Each concrete of each strength class used different aggregate types. This approach aimed for a largely constant concrete compressive strength within each strength class and required a preliminary experimental campaign in which CSTB optimized the mix designs. The grading curve for all cast concretes respects EOTA Technical Report 48 requirements [9]. Table 1 summarizes the exact mix design for all three concretes cast by the CDL team.  
As can be seen from Table 1, all three CDL concretes have almost the same aggregate-to-cement and water-to-cement ratios. The main difference is the petrography and the granulometric distribution of the coarse aggregate. It has to be noted that it was not possible to obtain a maximum aggregate size of 16 mm (0.63 in.) for concrete CDL 3, and therefore a maximum aggregate size of 22 mm (0.87 in.) was used.  
The mix design parameters for all other concretes cast and tested by CSTB are shown in Tables 2 and 3 for low-strength and high-strength concretes, respectively. For both CSTB concrete classes it can be clearly seen that the mix design parameters significantly differ among concretes of one class. It is well known that petrography, size, and shape of aggregates are important factors influencing concrete mechanical properties [11; 12; 13; 14; 15]. Consequently, it is not surprising that for concretes with different aggregates, different mix design parameters are required in order to achieve the same compressive strength.
## 4 Results and Discussion
### Pineapple Fruit Processing
#### 4.2.3 Pineapple Dehydration Process  
Figure 12 shows the results of carbon, water, and energy footprints by grouped processes of the agricultural and industrial stages for the dehydrated pineapple scenario. As was the case in the two previous industrialization processes, the carbon footprint also showed that a greatest share was generated by the manufacturing and application of fertilizers (43%), followed by irrigation and land preparation, with a share of 18% and 10%, respectively. These three processes come from the agricultural stage. While in the industrial stage, the largest share was produced in the initial stages, which included the reception and preparation of the fruit up to the process of slicing, with a share of 9%. The dehydration process exerted a share of 7%. In the first case, it was caused by electrical consumption from the equipment. In the second one, it was due to the use of energy from fossil fuels for the drying chamber. For the water footprint category, again, its impact was directly related to the supply of water for irrigation during fruit cultivation, with a share of 95%.  
Figure 11: The energy footprint of pineapples in syrup by main unit processes.  
Figure 12: The carbon, water, and energy footprints of dehydrated pineapple by main unit processes.  
For the energy footprint, as expected, most came from the manufacturing and application of fertilizers, with a share of 85%. The remaining proportion corresponded to the industrial stage, as demonstrated in the tracking of energy flow in Figure 13. This shows that energy flows from their primary source of supply up to the processes where they were consumed. Non-renewable energy from fossil fuels supplied a large proportion of the energy required, with a global share of about 92%. As explained above, locally, the energy was supplied to many of the productive sectors and was derived from the oil and natural gas industries, including the electricity generation processes, which had repercussions on the impacts quantified by the energy and carbon footprints.  
In the case of renewable energy, they have little relevance, only a share of 4%; there are few incentives for the use of biomass, with some exceptions, such as the bioenergetic use from bagasse in the production of cane sugar, which was previously mentioned [62; 63; 64]. In the stage that includes the dehydration oven chamber, energy losses of 24% were quantified with respect to the total energy supplied, this should be taken into consideration to improve the efficiency in the design of the dryer or in the implementation of an energy recovery system.
## 4 Results and Discussion
### Flexural Strength  
Figure 4 illustrates the flexural strength of mortars formed using a single binder over a 28-day period. In the initial segment, the cement mortar with the standard composition displayed the highest strength, followed by a notable reduction in strength as the proportion of lime increased. Specifically, mortars containing 15%, 25%, 35%, and 50% lime experienced a decline in flexural strength by 19%, 32%, 56%, and 63%, respectively. Comparable results can be found in existing scientific literature concerning both hydrated lime and cement mortars [37; 38].  
Concerning the latter part of Figure 4, mixtures featuring gramitic aggregates as a partial replacement for sand at ratios of 10%, 20%, and 30%, while maintaining a fixed cement replacement rate of 25%, exhibited an enhancement in flexural strength upon the introduction of fine gramitic aggregates. Subsequently, there was a substantial reduction in flexural strength, reaching 76% when 100% granite aggregate was employed as a partial substitute for sand. Consistent with the findings of [PERSON] et al. [39; 40], it is advisable to avoid employing cement contents at or below 25% in lime-cement mortars, as this may adversely affect strength. This may be attributed to the improved mechanical properties of the mixture containing 25% lime as a partial replacement for cement and 30% fine granite aggregate as a substitute for sand, as evident in the latter segment of Figure 4, a consequence of the use of flexible additives that reduce pore size.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 5 Method
### Evaluation Metrics  
In this study, the performance of classification models for diagnosing AAS is evaluated using a set of metrics. The evaluation metrics employed include accuracy, specificity, sensitivity (recall), Area Under the Curve (AUC), precision, and the F1-score. These metrics provide comprehensive insights into the performance of the classification models, allowing for a thorough assessment of their effectiveness in accurately identifying AAS cases.  
Accuracy measures overall model performance, specificity assesses the model's ability to correctly identify true negatives, and sensitivity evaluates the model's capability to capture true positives. The AUC reflects the model's discriminative ability across various thresholds, while precision quantifies the accuracy of positive predictions. The F1-score provides a balanced measure of precision and recall. These metrics collectively provide insights into the reliability and effectiveness of classification models in diagnosing AAS, aiding clinicians in decision-making.
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 3 Results  
### Regression Models  
In this study, the curing time and shear strength models were trained without overfitting as shown in Figure 3. The loss for the training set and the loss for the validation set decreased with each epoch number, and both loss datasets were converged after the 600 th epoch.  
The calculated MAEs and MAPEs for training and validation datasets for curing time and shear strength models were shown in Table 2. The MAEs for the training and validation datasets for the curing time model were 1.57 s and 1.84 s, respectively. The MAEs for the training and validation datasets for the shear strength were 6.08 MPa and 6.13 MPa, respectively. The MAPEs for the training and validation datasets for the curing time were 24.7 % and 25.4 %, respectively. The MAPEs for the training and validation datasets for the shear strength were 18.8 % and 22.5 %, respectively.  
Figure 2: NCP flip-chip bonding process.  
The curing time model has relatively low accuracy due to limited number of datapoints. Therefore, the model demonstrates a framework, but it is difficult to practically apply to the industry. The shear strength model showed better accuracy. Compared to [PERSON] et al.'s research [23], they suggested R2 of 0.64 for the shear strength prediction model. In this study, the accuracy of the shear strength is 28.9%-35.2% higher than their study. By increasing the number of datapoints, the accuracy of the regression models will be enhanced.
## 3 Results and Discussion
### Measurement  
Figure 15 shows the measurement setup of the proposed MMA. The first three frequency bands were measured with the setup shown in Figure 15a. A vector network analyzer (VNA), coaxial cable, and waveguide to the coaxial adapter (P/N: 75 WCAS, P/N: 51 WCAS_Cu) and 1 \(\times\) 2-unit cell prototype were used in this setup. The upper resonance frequency was measured by a horn antenna with 10 \(\times\) 10-unit cells in the prototype design, as shown in Figure 15b. The agreement of the measurement and simulated values of the \(S_{11}\) (dB) phase in degree and absorption % are shown in Figure 16a,b, respectively. The measured absorption values are indicated in Figure 16b. The measured Q-factor is 28.83, 40.31, 13.91 and 25.76, at 12.4 GHz, 14.11 GHz, 17.56 GHz, and 20.1 GHz, respectively.  
Figure 13: **(a)** MMA base liquid hydrocarbon sensor model; **(b)** absorption plot of different hydrocarbons; **(c)** frequency shift of different hydrocarbons; **(d)** upper-band sensitivity for liquid hydrocarbon sensor model.  
Figure 14: (**a**) MMA base solid material sensor model; (**b**) absorption plot of different Arlon substrates; (**c**) frequency shift of different Arlon substrates; (**d**) upper-band sensitivity for solid material sensor model.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 2 Historical Overview of Post-Earthquake Fire (PEF) Events
### 10.2003 Hokkaido, Japan PEF  
On 26 September 2003, a magnitude 8.0 earthquake struck Hokkaido, Japan, causing significant damage to industrial facilities. A fire broke out at the Hokkaido Refinery in Tomakomai, originating from a tank containing raw oil. The fire spread through the refinery's piping systems, creating a large-scale blaze that took approximately eight hours to control and extinguish. Two days later, another fire ignited from a different tank, but it was swiftly contained. The event highlighted the vulnerability of industrial sites to post-earthquake fires and the challenges associated with fire suppression in such environments [14].  
The historical record of PEF events underscores the devastating impact that fires can have following seismic activity, particularly in densely populated urban areas. These incidents underscore the importance of resilient infrastructure, efficient firefighting systems, and comprehensive disaster response strategies to mitigate fire risks following seismic events. Key factors influencing fire spread include damaged water supply systems, disrupted communication networks, the presence of combustible materials, and adverse weather conditions. The economic losses associated with these events, including the USD 1 billion economic loss from the 1923 Kanto PEF and the USD 350 million loss from the 1906 San Francisco PEF, further illustrate the significant financial burden that PEFs can impose. Insights gained from historical PEFs contribute to advancing emergency preparedness and enhancing urban resilience against future seismic disasters.
## 2 Data Collection
### Training of Railway Bridge Damage Data  
Image data representing various classes of damage observed in different structural components were collected and used to develop a deep-learning-based damage detection and prediction model. These datasets were utilized for supervised learning. Table 4 shows sample data from the ground-truth (GT) annotations used for training. Cracks were annotated using line segments, while other damage types, such as concrete spalling, exposed rebar, and paint peeling, were labeled as polygons to capture their detailed spatial extent. All annotated regions were precisely labeled to reflect the true geometry and boundaries of each damage class. The collected images were manually annotated based on the Korean Railway Bridge Inspection Manual. Damage regions were labeled only when clearly visible defects were identified in the images.  
Table 4 shows full-resolution UAV images labeled for training and evaluation. Crack regions were annotated with lines, and other damage types were labeled using polygons. These examples reflect realistic conditions where multiple damage types and background noise coexist, aiming to improve robustness in practical inspections.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Group** & **Data Labeled by Damage Type** \\ \hline \hline
**GT Sample Data** & \\ \hline \hline \end{tabular}
\end{table}
Table 4: GT data by damage type for training.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Multi-Class Damage Type** & **Number of Images** & **Composition Ratio (\%)** \\ \hline Almost no damage & 5840 & 38.71 \\ Cracks & 5304 & 37.47 \\ Paint peeling & 1415 & 10.00 \\ Concrete Scaling/Spalling & 574 & 4.06 \\ Cracks + Concrete Scaling/Spalling & 771 & 5.45 \\ Cracks + Leak & 75 & 0.53 \\ Cracks + Concrete Scaling/Spalling + Leak & 4 & 0.03 \\ Cracks + Concrete Scaling/Spalling + Rebar exposure & 19 & 0.13 \\ Cracks + Paint peeling & 422 & 2.98 \\ Cracks + Concrete Scaling/Spalling + Paint peeling & 91 & 0.64 \\ \hline
**Total** & **14,155** & **100.00** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of multi-class damage types (Ratio = Damage type/Total images).
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Intrusion Detection System  
An intrusion detection system is implemented by modifying [PERSON]'s [23] source code to detect and remove a sinkhole attack from the network. The modified sections are explained and highlighted in all figures. According to [PERSON] and [PERSON] [24], intrusion detection is the process of capturing and analyzing significant events in a system for the possible presence of an intrusion. Intrusion detection systems aim to prevent unauthorized access to the network and responding to the malicious activity that may be caused by the intruder. Researchers have proposed various approaches to implementing an IDS based on wired networks which are quite different because MANET is a wireless platform.  
The implemented standalone intrusion detection system is customized to function in the two layers of the TCP/IP model, namely, the network and data link layer running the OLSR routing protocol. It uses two mechanisms to achieve its goal and is intended to work for UDP traffic because TCP requires ACKs from the destination. The watchdog analyzes every node that is in transmission proximity. It compares two variables (threshold value and counter) before detecting that the node is showing malicious behavior. If a node withholds a packet, the watchdog algorithm counts the downtime for the delay of the node to forward the message and compares this with the threshold value. The delay must be less than the threshold value for the node to be considered cooperative or else the node will be considered malicious. The watchdog will also check the packet itself for alterations and possible modifications. If any node only accepts the watchdog node and does not forward, then the watchdog declares it as a sinkhole node and isolates that node from the path of packet transmission. A sinkhole node has an infinite ratio of receiving and sending data packets [25].
However, the detection efficiency of this device was only 0.14%. To alleviate this contradiction, a pixelated structure scintillator array was employed in this study. It has been validated that the pixelated structure scintillator array is effective for obtaining improved spatial resolution in X-ray imaging and positron emission tomography (PET) systems [13; 14]. It also works for the neutron detector system. Fast neutron radiography is composed of pixelated scintillators, optical guides, and photosensor arrays. The cut between each scintillator pixel is filled with reflective materials. The position of the incident neutron can be rebuilt by the signal distribution from the photosensor array. The probability of the interaction between fast neutrons and hydrogen nuclei increases with an increase in the thickness of the scintillator. Therefore, it can be speculated the fast neutron detection efficiency may be improved by increasing the thickness of the scintillator. However, if the scintillator has a monolithic structure, without the pixel cut filled with reflective materials, the luminescence will spread out to cause more signals from the photosensor array unit. This will significantly increase the difficulty in position reconstruction and reduce the position resolution of the detector. The pixel scintillator restricts the spread of the luminescence. The optical guide slightly diffuses the luminescence at the end of the pixel scintillator to three to five photosensor units. The position of a scintillation event can be estimated by Anger logic [15]. The pixelated structure can improve the detection efficiency under the premise of ensuring the position resolution. This study was conducted to develop a large-area scintillator detection system with higher detection efficiency and favorable spatial resolution. In this study, the pixelated structure was composed of pixelated scintillator arrays and pixelated photosensitive elements--SiPM arrays. More specifically, the pixelated scintillator arrays comprised hydrogen-rich plastic scintillators, which were designed and optimized by Monte Carlo simulation. They were finally cut, polished, and optically isolated to form arrays with a certain size and shape. A SiPM array with a pixel size of 3.14 mm \(\times\) 3.14 mm was designed to replace conventional photomultiplier tubes (PMTs) to record the output scintillation photons from the scintillator. A total of 256 SiPM units were encapsulated into a 5 cm \(\times\) 5 cm array. Self-designed multiplexing readout electronics and a Data AQuisition (DAQ) system were developed. In this study, Monte Carlo simulation results were obtained. The size of the scintillators was optimized to achieve the optimal balance between detection efficiency, spatial resolution, and processing difficulties. Moreover, a detector prototype was fabricated and tested under a 14 MeV fast neutron source.
## 3 Experimental Results  
### Introduction of the Evaluation Method and the Experiments  
For the experiments, the actual access depth of the dynamic and static contact group is measured by hand using the vernier scale. The access depth accuracy of the dynamic and static contact group is defined as:  
\[T=\left|T_{algorithm}-T_{actual}\right| \tag{6}\]  
where \(T_{algorithm}\) is the distance calculated from the proposed algorithm and \(T_{actual}\) is the actual access depth.  
For the evaluation of the segmentation accuracy, _IoU_ and _Dice_, shown in Figure 10, are used. The calculation formulas are:  
\[IoU=\frac{T\cap P}{T\cup P}=\frac{Tp}{FP+TP+FN} \tag{7}\]  
\[Dice=\frac{2|T\cap P|}{|T|+|P|}=\frac{2 TP}{FP+2*TP+FN} \tag{8}\]  
where _TP_ is True Positive, _TN_ is True Negative, _FP_ is False Positive, and _FN_ is False Negative.  
The platform of the experiment is: Windows 10 Professional 64-bit, Intel i7-8700 @ 3.2 GHz, NVIDIA RTX 3080, Tensorflow-gpu 2.5.0, 32 G RAM, Python 3.6. All the images of the switch machines are obtained from China Shanghai Shentong Metro Co., Ltd., and then the images are rotated and cropped to the fixed size of 1280 \(\times\) 960. There are six types of switch machines which are shown in Figure 11 in the dataset. The number of images in the dataset for training is 2400, in which the training dataset contains 400 images of each kind of switch machine. There are 600 images for testing, in which each kind of switch machine has 100 images. The coco [14] model is used to initialize the network.
# Extraction and Synthesis of Silicon Nanoparticles (SiNPs) from Sugarcane Bagsase Ash: A Mini-Review
## 3 Reduction of Silica into Silicon Nanoparticles
### Magneisothermic Reduction of Silica to Silicon  
The metallothermic reduction of silica via magnesium reduction procedure leads to the formation of silicon using very low temperatures and reaction time. The procedure is not energy-intensive and thus inexpensive. The progress of the reaction depends on the experimental conditions shown in Figure 6, with temperatures below the melting point of magnesium of 650 \({}^{\circ}\)C, moreover, in a solid-state reaction (2) dominates [46; 47].  
\[SiO_{2}+2 Mg\to Si+2 MgO \tag{22}\]  
\[SiO_{2}+4 Mg\to Mg_{2}Si+2 MgO \tag{23}\]  
\[2 Mg+Si\to Mg_{2}Si \tag{24}\]  
\[SiO_{2}+2 MgO\to Mg_{2}SiO_{4} \tag{25}\]  
The magnesium reduction has shown the potential to produce silicon from silica in the temperature range of 500 and 950 \({}^{\circ}\)C, which allows template-assisted design of silicon structures. Furthermore, the method has demonstrated the ability to preserve structural and intricate features in the as-synthesized silicon as small as 15 nm. The production of pure silicon requires one last step, where nitric acid (HNO\({}_{3}\)) and hydrochloric acid (HCI) are added to remove any unreacted silica and excess magnesium present in the sample, essentially, other studies report the use of two acids aqua regia than single acid addition, consequently, leaving silicon replica behind with improved and higher surface area than the starting template [48].