esa-sceva/satcom-chunk-collection huggingface dataset:
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 3 Results and Discussion  
Food pathogen detection methods have recently shifted from traditional culture-based approaches [45] to faster and more efficient techniques, thereby reducing the time required for pathogen detection. Various rapid detection methods have emerged. They can be categorized into three main groups: immunological reactions, nucleic acid assays, and biosensors [46; 47]. Among the most rapid tests commonly used to detect pathogens are the lateral flow immunoassay, the enzyme-linked immunosorbent assay (ELISA), and the polymerase chain reaction (PCR) [48; 49].
## 5 SINR-Based TPC as a Potential Game
### Interference Cancellation  
If a collision occurs at the simultaneous arrival of two or more packet transmissions at the receiver, the conventional signal extraction process, only the packet with the strongest signal is decipherable, considering all other signals as interference. When the signal of interest is not the strongest, it becomes irretrievable. In contrast, Successive Interference Cancellation (SIC) empowers the retrieval of even weaker signals of interest.  
With SIC, the bits of the strongest signal are prioritized for decoding initially. Subsequently, the original signal corresponding to those bits is reconstructed and subtracted from the combined signal. This iterative method facilitates the recovery of multiple packets, hence earning the moniker successive interference cancellation [39]. The aim here is to decode the signal of interest and cancel out the interference caused by the interfering nodes in order to obtain the SINR of the current node, as can be seen in Figure 1. The A, B, and C circles correspond to nodes, which operate within a transmission range and the boxes the operations that take place during the operation.  
Interference cancellation is employed in multi-hop wireless communication to enhance performance because it directly addresses the adverse effects of interference, which can significantly degrade signal quality, throughput, and overall network efficiency. This technique allows for a more effective use of available spectrum by reducing the interference that degrades the desired signal. Other methods such as beamforming, and frequency reuse are also viable, while interference cancellation offers a targeted solution for improving the SINR.  
In comparison to traditional RF bands, interference cancellation in THz communication presents several unique challenges. THz signals experience greater atmospheric absorption, scattering, and attenuation than RF signals, leading to higher path loss and reduced range. This results in more frequent re-transmissions and increased interference. While traditional RF bands benefit from well-established interference management techniques and longer propagation distances, THz communication, with its shorter wavelengths, offers higher spatial resolution and enables advanced beamforming. However, this requires more precise and dynamic interference cancellation methods to address the higher density and more complex channel conditions inherent in THz communication. Additionally, THz signals are highly sensitive to environmental factors, which complicates the implementation of interference cancellation. These challenges necessitate the implementation of more adaptive algorithms to ensure reliable performance.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Acid Leaching Effects
#### 3.4.2 Effect of Solid-Liquid Ratio  
With an R\({}_{\mathrm{fs}}\) of 1:2, a maximum roasting temperature of 850 \({}^{\circ}\)C, a roasting duration of 2.5 h, a sulfuric acid concentration of 2 mol/L, a leaching temperature of 80 \({}^{\circ}\)C, and a leaching duration of 120 min, this study examined the leaching rate of lithium of fly ash at various solid-liquid ratios in the range of 1:10-1:45. The results are shown in Figure 9. As shown in Figure 9, when the solid-liquid ratio was adjusted from 1 g:10 mL to 1 g:45 mL in increments of 5 mL, the leaching rate was found to be proportional to the solid-liquid ratio up to a point of 1 g:35 mL. Specifically, the leaching rate remained relatively stable between 1 g:15 mL and 1 g:30 mL. The highest leaching rate of 93.08% was achieved at a solid-liquid ratio of 1 g:35 mL. However, beyond this ratio, the leaching rate began to decrease. This decrease was attributed to the product of silicates. At excessively high solid-liquid ratios, the reaction generated a significant amount of silicate, which entrapped the fly ash residue and caused it to adhere to the walls of the reaction vessel. Consequently, some fly ash was unable to react with the acid solution, resulting in a diminished leaching effect.  
Figure 8: Effect of leaching temperature on the leaching rate of lithium.
## 2 AE Technology  
When the material is damaged under external load or internal stress, the strain energy generated by the failure is converted into the elastic wave for release. [PERSON] et al. [36] characterized the damage by releasing elastic energy at each step of the material damage process. AE refers to the collection and analysis of such elastic waves by Acoustic devices. Compared to ordinary concrete, the homogeneity of BFRPC is greatly improved, but there are a large number of micro-pores, and many micro-cracks will be generated during the curing process. During the failure process, micro-cracks initiation and propagation of micro-cracks eventually led to the destruction of BFRPC structure. This failure process contains a large number of AE signals. The information contained in the AE signal of the BFRPC beam during this process and the correlation analysis with the mechanical parameters of the RPC beam can be used to understand damage information of the BFRPC beam.  
The information in AE signals is usually expressed by direct and indirect parameters. Direct parameters are the data recorded directly by AE equipment, while indirect parameters are further calculated by direct parameters. Figure 1 shows the basic parameters of AE signal.  
As can be seen from Figure 1, the direct parameters include threshold, amplitude, counts, duration, rise time, and energy. The setting of the threshold can effectively filter out the influence of ambient noise on the test data, so as to reflect the real failure condition of the material more truly. Amplitude is the maximum voltage corresponding to the signal peak value. Counts are the number of times that the AE signal exceeds the threshold during the duration. Duration refers to the time interval between the AE signal trigger time and the disappearance time. The rise time refers to the time interval between the AE signal trigger time and peak amplitude time. Energy is the measured area below the AE signal envelope.  
Indirect parameters include average frequency (AF) and RA values, etc. In concrete materials, the classification of crack types is proposed, using the combination of the AF values and RA values. This classification technique has been standardized (JCMS 2003) and is shown in Figure 2[37]. The calculation process is shown in Equations (1) and (2).  
\[\text{AF value}= \frac{\text{Counts}}{\text{Duration}},(\text{kHz}) \tag{1}\] \[\text{RA value}= \frac{\text{Rise time}}{\text{Amplitude}},(\text{ms}/\text{V}) \tag{2}\]  
Figure 2: Failures were classified according to average frequency (AF) and RA values.  
Figure 1: Basic parameters of acoustic emission AE.  
When the material deforms locally, tensile waves and shear waves occur at the deformation position. Tensile wave has fast wave velocity, large amplitude, and short rise time, which is characterized as higher AF value and lower RA value in the AE signal. On the contrary, the shear wave velocity is slow. The slower wave velocity leads to longer rise time and duration, which indicates that the AF value is lower, and RA value is higher in the AE signal.
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 4 Results and Discussion
### TensorRT Network Optimization  
To deploy the deep learning model on the edge computing device, especially on Jetson Nano, we utilized the TensorRT framework that enabled us to perform the network optimization. This optimization enables edge devices to perform high-speed inference by utilizing fewer resources efficiently. For this purpose, we applied FP16 optimization on our weapon detection model because Jetson Nano only supports FP16, and this was getting reasonable FPS on Jetson Nano.
## 4 Application
### Clinical Application
#### 4.1.5 Anesthesia Monitoring  
[PERSON] et al. [PERSON] et al. (2018) introduced the concept that a PPG imaging system might be employed to continuously monitor regional anesthesia by monitoring skin microcirculation. Using both an RGB camera and a near-infrared camera, [PERSON] et al. [PERSON] et al. (2019) applied camera-based PPG to monitor patients of cardiovascular disease during surgery in an intraoperative environment. It can help anesthetists to take measurements in response to any cardiovascular events and to administer appropriate medication.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
###### Abstract  
An emerging requirement in multimedia applications over the Internet is the provisioning of synchronized play out of multimedia streams on all the ultimate receivers. Most multimedia applications use real-time transport protocol/real-time control protocol (RTP/RTCP) to transport multimedia data over the internet. Internet Protocol (IP) network incurs a loss and/or delay of media packets resulting in distortions at receivers that can be perceived by the user. These distortions need to be removed before data is presented to the end user. To handle such issues inter-stream and intra-stream synchronization algorithms were developed by the researcher using RTP/RTCP protocols. These schemes could not work under multicasting environment properly. In this paper, we have presented an algorithm based on RTP/RTCP protocol that achieved synchronized playout of multimedia data under multicasting environment. The proposed scheme offers a modification in RTCP and usage of the Network Time Protocol (NTP) as a source of the common clock to achieve synchronized playout of multimedia data on multiple devices at the same time. We have implemented and tested our algorithm in Ericson Research Corporate Unit, Lulea, Sweden. Implementation results show that the proposed algorithm delivered accurately synchronized playout of multimedia data under all the possible scenarios defined in the experimental setup.  
synchronization; multimedia; multicasting; time stamping; playout; streaming 2018 856; doi:10.3390/app8040556
# Influence of Construction Joint and Bridge Geometry on Integral Abument Bridges
## 4 Bridge Response Results
### Girder Moment  
Girder moments with respect to AB joint stiffness, backfill height, and bridge length are presented in Figure 11d,f. Positive and negative maximum moments collected at any point produce the maximum throughout the bridge. During bridge contraction, most of the positive and negative maximum moments occurred at the midspan and the abutment, respectively. During bridge expansion, most of the positive and negative maximum moments occurred at the abutment and the midspan, respectively. Both positive and negative moments of steel (STL) and PSC girders were significantly affected by the following factors, in order of importance: bridge length \(>\) AB joint stiffness \(>\) backfill height.  
The bridge length parameter significantly influenced girder moments. Both positive and negative maximum moments of steel and PSC girder IABs tended to increase as bridge length increased. Owing to time-dependent effects, PSC girders produced relatively smaller negative moments than did steel girders. The maximum positive and negative girder moments of 61.0, 121.9, and 182.9 m steel IABs varied 0.6, 1.3, and 2.5 times and 1.9, 3.6, and 2.9 times compared with those of the 30.5 m bridge, respectively. The maximum positive and negative girder moments of 61.0, 121.9, and 182.9 m PSC IABs varied 1.03, 0.9, and 1.03 times and 0.5, 1.1, and 2.2 times compared with those of the 30.5 m bridge, respectively.  
AB joint stiffness significantly influenced girder moments. Girder moments in steel girder IABs increased as AB joint stiffness increased, while those in PSC girder IABs decreased as AB joint stiffness increased. Positive and negative girder moments of steel girder IABs with PA standard and rigid AB joints varied 1.07 and 1.03 times higher and 1.12 and 1.41 times higher than those of IAB with hinged AB joint. However, positive and negative girder moments of PSC girder IABs with PA standard and rigid AB joints varied 0.69 and 0.57 times and 0.75 and 0.64 times less than those of IAB with hinged AB joint. Thus, hinged and rigid AB joints would reduce girder moments for steel and PSC girder IABs, respectively.  
Backfill height influenced both positive and negative girder moments in steel and PSC girder IABs. Especially, negative girder moments were significantly influenced. All girder moments except the positive moment in steel girder IABs showed tendencies to increase as backfill height increased. Positive and negative PSC girder moments in 4.6 and 6.1 m high backfill increased 1.04 and 1.21 times higher and 3.95 and 5.52 times higher than those in 3.1 m high backfill. Negative steel girder moments in 4.6 and 6.1 m high backfill increased 1.09 and 1.35 times higher than those in 3.1 m backfill, while positive steel girder moments decreased 0.90 and 0.82 times.  
Based on the girder moment investigation, rigid AB joint, lower backfill height, and shorter bridge length reduce the PSC girder moment. Hinged AB joint, lower backfill height, and shorter bridge length reduce the steel girder moment.
# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 2 Methods  
This work proposes a mixture network combining CNN and LSTM, and adds attention mechanism and multi-scale mechanism to the hybrid model. The network can make full use of the ability of convolution neural networks to extract spatial features, long-term memory networks to extract time features, multi-scale convolution to achieve multi-scale feature extraction of time series, realizing the important expressions of different features through attention mechanisms.  
CNN and LSTM are both important methods for time series analysis [33]. The CNN algorithm can effectively extract the spatial features of data in a very abstract way [34]. One-dimensional convolution of the convolution neural network can obtain the spatial structural characteristics of the sequence, i.e., the shape characteristics of the sequence, which can help improve the accuracy of prediction. The LSTM network can extend the temporal characteristics and process data with sequential characteristics. Therefore, combining the advantages of CNN and LSTM can form a new method that gives full play to their respective strengths. In this paper, the multi-scale feature attention mixture network model (MFAMNet) is obtained by combining the two networks to fully utilize temporal and spatial characteristics. The specific framework of the network is shown in Figure 1.  
As shown in Figure 1, the data are first sent into the LSTM module, multi-scale convolution module, LSTM module, and residual attention module, and the operation is cycled three times. Then, the model selects the output of the first LSTM module and the residual attention module in each cycle and feeds them into a one-dimensional convolution. Finally, all the results are added to obtain the predicted results. The purpose of this structure is to enable the network to fully extract the features of multi-scale time series and make the network more capable of feature expression. This multi-level circular structure can extract the multi-level characteristics of the load, i.e., it effectively retains the temporal dependent features of the shallow-level load sequence and also obtains the deeper-level load characteristics. The network reshapes the multi-layer features through the attention mechanism and integrates them with the features at all levels, which greatly improves the diversity and effectiveness of network features, and can effectively express the phenomenon of load climbing and load shock. In particular, the extraction of data features of load peaks can be more sufficient, which is conducive to improving the ability of the network to conduct load forecasting. For household loads, the peak of the load often exists for a short time, so how to obtain the effective characteristics of the load peak will become particularly important, while in the traditional network, it is easy to ignore the features of the load peak because the proportion of the load peak in the whole sequence is too low.
# The Influence of Vertical Ground Motion on the Design of Common R/C Frames
## 3 Evaluation of Frames for Vertical Ground Motion
### Evaluation Through Time History Analysis
It is apparent that the shear force was considerably increased due to vertical ground motion, especially at the beam ends for seismic hazard level zone III. In particular, the increase ranged from 9% to 55% for the ends and from 7% to 36% for the mid-points. The increase was more pronounced for higher seismic intensities. However, for this particular beam, the shear resistance was adequate. Figure 20: _Cont._  
Other non-linear response quantities are not presented due to the following reasons:  
* The horizontal displacements, drifts, bending moments and shear forces of columns because they were not considerably affected by the vertical motion;
* The vertical displacements of beams' mid-points because they were not directly involved in the design of structural members;
* The axial forces of columns and bending moments of beams because their influence was reflected in the plastic hinge formation.
# A Hybrid Intelligent Approach to Predict Discharge Diagnosis in Pediatric Surgical Patients
## 3 Experimental Results
### Main Processing
Although the overlap between classes 1 and 2 in Figure 4b is close to one-third of the decisions, it might be considered that pathologies and complications may appear after the patient is discharged. Similarly, the highest precision presented in all cases corresponds to class 2, and the particular situation for discharging unhealthy patients should require further analysis by practitioners. A higher level of errors is presented between classes 1 (deceased patient) and 2, and close attention must be paid to every particular case. Although approaches in Figure 4a,b,e present a dark diagonal (correct class predictions), the diagonal values for XGBoost are consistently higher than those of other approaches. Finally, Figure 5 presents the receiver operating characteristics (ROC) curves per class computed for the proposed system designed with XGBoost. In order to construct the ROC curves, the output probabilities from each class were computed, and the area under the ROC curve (AUC) was estimated for each curve using the approximation by rectangles. The lowest AUC was achieved by the blue solid ROC curve, which represents the performance of the system for class 3 (deceased patients), and the operational point closest to the upper-left corner corresponds to \(tpr=0.85\) and \(fpr=0.1\). Furthermore, \begin{table}
\begin{tabular}{l c c} \hline \hline
**Technique** & **Accuracy** & **F-Measure** \\ \hline XGBoost & **84.62\%** & **73.99** \\ AdaBoost & 82.83\% & 46.95 \\ Gradient boosting & 78.44\% & 44.96 \\ Random bagging & 84.82\% & 49.77 \\ CART & 77.63\% & 46.79 \\ Voting ensemble & 74.05\% & 46.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of the proposed system with XGBoost, and compared to other decision tree (DT) ensemble algorithms. Figure 4: Confusion matrices for classifier comparison and performance analysis. (**a**) AdaBoost, (**b**) XGBoost, (**c**) Gradient boosting, (**d**) Random bagging, (**e**) CART, and (**f**) Voting ensemble. selecting the correct operational point is relevant to tune the system and reduce errors for specific classes.
## 3 Experimental Results and Analysis
### Results of Full Feature Modeling
Considering both the error and fitting degree comprehensively, the ANN model performs best in the straight channel modeling, followed by the CATBOOST model, with an R\({}^{2}\) value of 0.857127 and an RMSE value of 1426.64, demonstrating a relatively good balance of performance. Figure 4: Modeling results of two flow channel working conditions with different models (The segmentation ratio was 7:3, and R\({}^{2}\), RMSE, and MAE were used as evaluation indicators to evaluate all traditional machine learning models). Figure 4b shows the performance of different models in the modeling of hexagonal channels. By comparing the RMSE, MAE, and R\({}^{2}\) indicators, the following conclusions can be drawn: In the modeling of hexagonal channels, the XGBOOST model has the highest RMSE value, 177.715, indicating a relatively large prediction error. In contrast, the ANN model has the lowest RMSE value, 79.016, demonstrating the best prediction accuracy. In the modeling of hexagonal channels, the ANN model has the highest R\({}^{2}\) value, 0.995199, indicating the best fit to the data. Relatively speaking, the XGBOOST model has the lowest R\({}^{2}\) value, 0.975712, showing a relatively poor fitting effect. Considering both error and fitting degree comprehensively, the ANN model performs best in the modeling of hexagonal channels, followed by the CATBOOST model, which has an R\({}^{2}\) value of 0.991751 and an RMSE of 114.145, demonstrating a relatively good balance of performance.