esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
By constructing a discrete-time queueing model, we provide a performance analysis and energy consumption evaluation method to effectively balance energy consumption and latency. The novelty of this paper is summarized as follows:  
1. A packet grading mechanism with preemptive priority is proposed to satisfy the diversified latency tolerance of packets. 2. A hybrid packet forwarding mechanism is proposed to effectively balance latency and energy consumption. 3. A new performance quantitative analysis method using discrete-time queueing theory is provided.
## 4 Conclusions  
The performance of the ISD for drying paddy rice in Burkina Faso was analysed and compared to conventional sun drying. Several improvement strategies for the ISD were pointed out in this study. Temperature in the ISD was higher than ambient as well as conventional sun drying. Under specific weather conditions, the drying performance of the ISD was not substantially faster than for sun drying. A considerable temperature change of paddy rice during mixing was observed. In the ISD, temperature promptly raised from the collector to the middle of the ISD. Nevertheless, the shadow casted in the ISD by the high and round black plastic film caused a slower drying time and required more intense mixing. Under some circumstances on sandy soils and in dusty environments, the ISD could reduce impurities in paddy rice. Solar drying proofed to significantly reduce the aflatoxin level in fresh paddy rice (\(p\leq 0.05\)). The aflatoxin level generally fulfilled the EU regulation.  
Reducing the ratio of the black plastic film of the ISD is necessary to reduce the shady area. Investigation on the proper capacity of paddy rice filled into the ISD with regard to weather conditions should be further conducted. Performance evaluation of the ISD in drying other bulk materials such as maize and sesame grains should be conducted. Further adaptation of the ISD design should be investigated, in order to ensure a successful dissemination in sub-Saharan Africa.  
Conceptualization, S.R. and J.M.; methodology, S.R.; software, S.R.; validation, S.R., S.S. and J.M.; formal analysis, S.R. and M.K.S.; investigation, S.R. and M.K.S.; resources, S.R., S.S., M.K.S., and J.M.; data curation, S.R.; writing--original draft preparation, S.R.; writing--review and editing, S.R., S.S., M.K.S., and J.M; visualization, S.R.; supervision, J.M.; project administration, S.S.; funding [PERSON], S.R., S.S., and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by _Bundesministerium fur wirtschaftliche Zusammenarbeit und Entwicklung_ (BMZ), grant number 13.5254.0-004.0, under the project \"Strengthening Post-Harvest Processes in Africa using Improved Solar Drying\", in the framework of _Deutsche Gesellschaft fur Internationale Zusammenarbeit_ (GIZ) Powering Agriculture.  
The authors thank [PERSON], [PERSON] and [PERSON] for their field assistance. [PERSON] is acknowledged for the data analysis. Great gratitude is also expressed to [PERSON], [PERSON], [PERSON] and [PERSON] from GIZ Green Innovation Centres in Burkina Faso, and Dr. [PERSON] from _Universite Polytechnique de Bobo-Dioulasso_, for their support.  
The authors declare no conflict of interest[PERSON]  
Figure 10: Aflatoxins (AF) of rice dried in the inflatable solar dryer (ISD) and under the sun in batch 1 (20 November 2017) compared to fresh material. Different small letters (a, b and c) within groups (AFB1, AFB2, AFG1 and AFG2) indicate a significant difference at \(p\leq 0.05\).
# Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework
## 1 Introduction
The U-Net model modified and expanded the network on the basis of FCN, so that it can use very few training images to obtain very accurate segmentation results. In addition, an upsampling stage is added, which adds lots of feature channels, allowing more texture information of the original image to spread in high-resolution layers. U-net does not have a fully convolutional layer and uses valid for convolution throughout, which ensures that the results of the segmentation are based on no missing context features. In this paper, a two-step deep-learning based velocity anomalies detection workflow is established. The workflow starts from the pre-migration shot gathers directly, justifiying the presence of anomalies firstly and then predicting accurate location information of the velocity anomalies prior to VMB process by employing a modified U-Net neural network. A set of two-dimensional synthetic model tests are presented to evaluate the effectiveness of the proposed workflow.
# A FBG Intensity Modulation System Combined with an Optical Whispering Gallery Mode Edge Filter
###### Abstract  
In this study, we demonstrated an edge filter-based fiber Bragg grating (FBG) intensity modulation system to realize strain measurement. In order to establish a precise and highly sensitive intensity modulation system, we utilized a bent single-mode fiber to induce whispering gallery mode (WGM) interference as an edge filter and combined that with a FBG sensor. The interference spectra of the attenuation band for the WGM edge filter were tuned by adjusting the bending radii. In addition, we compared and analyzed the signals from the proposed vibration interrogating system and a strain gauge. The measured voltage signals from the proposed interrogation system were in close agreement with measured strains of the strain gauge. The experimental results showed that when the resonant wavelength of the WGM edge filter was 1535.10 nm, the filtration was better and the noise was lower within 100 Hz. Moreover, as the frequency of piezoelectric transducer (PZT) was at 400 and 1000 Hz, the better signal-to-noise ratios (SNRs) of 28.54 and 25.97 were measured at wavelength 1542.05 nm of the edge filter.  
fiber Bragg grating; whispering gallery mode edge filter; intensity modulation
## 4 Results and Discussion
### Performance Evaluation  
The performance of the proposed predictive models was evaluated using scatter plots and statistical indicators such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), and variance accounted for (VAF). These error metrics may address different requirements depending on the dataset, allowing for a comprehensive evaluation of errors from various perspectives. The mathematical formulae of the above-mentioned error metrics are given by Equations (16)-(19).  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\big{(}y_{m}-y_{p}\big{)}^{2}}{\sum\limits _{i=1}^{n}\big{(}y_{m}-y_{ave}\big{)}^{2}} \tag{16}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\big{(}y_{m}-y_{p}\big{)}^{2}}{n}} \tag{17}\]  
\[MAP  
inputs or new datasets may be required to improve the prediction accuracy of the MSP model, as it shows a clear trend of undulating results at higher BAV values.  
The performance of the established predictive models was further evaluated using previously published data. Table 5 lists several dimension stones with associated non-destructive properties gathered through a literature survey. The predictive models were then assessed by implementing them with these data.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Soft Computing Method** & \(\mathbf{R^{2}}\) & **RMSE** & **MAPE** & **VAF** \\ \hline MARS & 0.876 & 2.432 & 14.138 & 87.72 \\ \hline MSP & 0.832 & 2.726 & 10.772 & 83.24 \\ \hline ANN & 0.911 & 1.979 & 10.463 & 91.17 \\ \hline \end{tabular}
\end{table}
Table 4: Statistical indicators for performance evaluation.  
Figure 7: Scatter plots of the predictive models (**a**) MARS, (**b**) MSP, and (**c**) ANN.  
The results indicate that the predictive models can generally be considered acceptable tools for estimating the BAV of various dimension stones. However, it is important to consider the intervals of the non-destructive properties (Table 3) when applying these predictive models. Exceeding the defined intervals of the non-destructive input parameters may lead to undulating results.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 5 Evaluations
### Simulation Results
This shows that the BR in middle is an efficient strategy to reach multiple consumers at the same time. Figure 9b visualizes the percentage of run time for which the radio was kept active. The first box shows the transmitter being active, and the second box shows the receiver active time. The transmitter is kept below 1% for models except the Top1 and Dist4, and receivers are kept active for less than 2%. Keeping the radios idle for longer helps conserve energy in the wireless network. In order to understand the energy utilization of nodes over time, the simulation is run for one hour with the energyst metric report once every 5 min. The graph in Figure 10a showcases the average energy utilization of a node in different models. The initial spike is attributed to the network formation. There is a clear ranking in the energy consumption with Top1 being the highest with more packet transmission due to their longer distance to the BR. In the Dist4 model, energy utilization for a node is 22.7% higher than a node in the Mid1 or Mid4 models. Figure 10b displays the total charge consumed by a node in one  
Figure 8: Metrics for the communication models. (**a**) Latency for occupancy data packets. (**b**) Arrival of occupancy data from all nodes. Figure 9: Metrics for the communication models. (**a**) Time to reach local consumers. (**b**) Percentage of time when radio was active. hour. This also confirms the earlier findings and denotes that the Mid1 and Mid4 models outperform others in terms of efficient data collation and dissemination.
# Temperature Effects on the Natural Frequencies of Composite Girders
###### Abstract  
Composite bridges are typically exposed to temperature variations due to heat radiation, conduction, and convection. Temperature affects the modal parameters of bridges, hindering the application of damage detection methods based on the dynamic properties of bridges. In this study, the effects of temperature on the natural frequencies of composite bridges were investigated experimentally and numerically to derive a basis for separating temperature effects from the natural frequencies. A temperature-controllable girder specimen was developed for modal testing. Additionally, finite element (FE) analysis was conducted to analyze the effects of temperature. The FE analysis results were validated by comparing them to the static response results of the test specimen. The results of the experiments and FE simulations verified that temperature variation can affect the material properties, particularly the modulus of elasticity, of a composite girder, consequently influencing its natural frequency. Based on the tests and simulations, a linear relationship between the temperature and the natural frequency was proposed to remove the temperature effects from the natural frequency.  
temperature effect; natural frequency; composite girder; temperature gradient; heat transfer +
Footnote †: journal: Applied sciences
# A Field Investigation on Vortex-Induced Vibrations of Stay Cables in a Cable-Stayed Bridge
## 1 Introduction
[11] performed experimental investigations on VIVs of a very long (152.4 m) flexible riser with a very large aspect ratio (more than 3000) in the ocean. VIV mainly behaved in a traveling wave at high mode numbers with strongly-high harmonic components and the stable figures of eight trajectories. [PERSON] et al. [12] studied VIVs of a long and flexible cable, which has an aspect ratio of 200 under a sheared flow. For the multimode VIV, the flow structures are dominated by the traveling waves between the cylinder ends and predominated by the standing waves close to the ends. Nearly 30% of the cylinder length was covered by the synchronization region, which was usually observed within the zone of high-velocity. The results indicated that the frequency ratio of the IL and CF vibrations was approximately equal to two. However, [PERSON] [13] and [PERSON] and [PERSON] [14] performed experimental investigations on the dynamic response of a vertical long flexible cylinder with an aspect ratio of 94, vibrating at low-mode frequencies. It was determined that the dominant frequencies between the in-line and cross-flow vibrations are the same because the IL response frequency would not lock onto the second mode even with added mass. While it is difficult to perform wind tunnel tests of VIVs with full-scale cables, the field observations are also hitherto limited. With these considerations, we performed a field study to investigate the VIVs of long-span cables in a real bridge. In this study, the VIVs of stay cables for a cable-stayed bridge were examined based on a structural health-monitoring system installed on-site. In Section 2, the full-scale health-monitoring system to measure the wind and the cable vibration is introduced. In Section 3, the characteristics of the cable vibrations are presented and analyzed. In Section 4, the correlation between the cable response and the wind characteristics are subsequently analyzed, followed by some concluding remarks.
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 3 Results and Discussion
### Afterpulsing  
Traps in the active area of the SPAD \"trap\" charges during an avalanche event and release it later. The time delay of release ranges from a few tens to hundreds of nanoseconds. This adds co-related noise to the measurement and is known as afterpulsing. Since the active area of the CASPAD is very small, low afterpulsing is expected. Figure 5c shows the interavalanche time (\(\Delta\)t) histogram [15,16] of the device with an exponential fit (\(\Delta\)t = 50 \(\mu\)s). With negligible afterpulsing, the interavalanche curve should follow an exponential distribution. The deviation from this exponential distribution is a result of correlated noise. The afterpulsing probability is calculated by dividing the number of counts above the exponential fit by the total number of counts. The afterpulsing probability is calculated to be 5.7% at an excess bias voltage of 0.86 V. Although the afterpulsing probability is not very high, it could be attenuated by using on-chip quenching with low parasitic capacitance to reduce the number of carriers per avalanche event.
## 2 Related Works
### Our Merged Action Dataset: ALMOND
With this operation the training set still results unbalanced, so we balanced each action by randomly duplicating the inner samples to reach the same number of samples of the most populated action in the group. With this method we obtained a balanced and heterogeneous training set, and an unbalanced and heterogeneous test set. The test set unbalancing is not an issue since the performance will not be computed globally but over each class. The dataset was the result of conducted experiments that will be detailed in Section 5. The final dataset, called ALMOND (Assisted Living MONitoring Dataset) is made available for public download [54], and will be used for all the following experiments for action recognition. Classes population details are shown in Table 5, with number of samples of test and training set respectively of 790 and 6775 for a total of 7565. The acquisition resolutions of ALMOND vary from \(320\times 240\) pixels of images coming from the UWA3 DII dataset, to \(1920\times 1080\) pixels from NTU. N-UCLA and MSR3D have the camera positioned at eye-level, while UWA3D II and NTU have acquisitions at both ceiling-level and eye-level, and the IXMAS is only at ceiling-level. All datasets depict the subject from different sides. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Action** & **Train** & **Test** \\ \hline Seated & 110 & 40 \\ Standing & 110 & 50 \\ Walking & 110 & 40 \\ Lying & 110 & 10 \\ On the floor (seated/lying) & 110 & 30 \\ \hline Touching head & 360 & 20 \\ Touching back & 360 & 20 \\ Touching torso & 360 & 20 \\ Touching neck & 360 & 10 \\ Vomiting & 360 & 10 \\ Coughing/sneezing & 360 & 30 \\ Waving hands & 360 & 10 \\ Exercising & 360 & 10 \\ Falling & 360 & 20 \\ Reject & 360 & 180 \\ \hline Drinking & 375 & 20 \\ Eating & 375 & 20 \\ Reading & 375 & 20 \\ Using phone & 375 & 20 \\ Dressing/undressing & 375 & 40 \\ Using laptop & 375 & 20 \\ Reject & 375 & 150 \\ \hline TOTAL & 6775 & 790 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ALMOND dataset training and test set division, with classes cardinality. This variety in the acquisition setup creates the conditions for high generalization capabilities in recognizing the performed action in different environments.
## 3 Methodology  
To address the aforementioned challenges, this study proposes ICP-BGCN (Integrated Content-Propagation Bidirectional Graph Convolutional Network), a novel framework for false information detection that integrates original text semantics, propagation context, and structural diffusion patterns. As illustrated in Figure 1, the framework comprises three interconnected modules:  
Module 1. Propagation Structure Modeling: A hierarchical propagation graph is constructed using comment-retweet relationships. Semantic embeddings generated by Module 2 are mapped to graph nodes to initialize their representations. The DropEdge technique [37] is then applied to both top-down and bottom-up propagation graphs to mitigate over-smoothing and enhance model robustness. Finally, a bidirectional graph convolutional network (GCN) captures nuanced node interaction patterns within the propagation structure.  
Module 2. Textual Feature Extraction: A pre-trained language representation model (BERT) encodes the source post and its associated comments/retweets to extract deep semantic features.  
Module 3. False information Detection: Propagation structural features are fused with textual semantics through attention mechanisms. The enriched representations are fed into a classifier to predict whether the source post is false information.
## 3 Results and Discussion
### Ultrasound Micromolding of PLA:PEG:NaCl Mixtures. Salt Leaching and Scaffold Characterization
Recently, it has been discussed that the geometry of the surface can influence the measurements of the contact angles [52]. Incorporation of PEG clearly diminishes the contact angle being observed a regular evolution with the composition. The value of 43\({}^{\circ}\) measured for a 50 wt-% content of PEG is close to the asymptotic angle of 41\({}^{\circ}\) evaluated for PEG. Results clearly indicated that the specimen was hydrophilic at the selected PEG content, allowing the water penetration. It is also important to note that the incorporation of NaCl caused an important decrease of the contact angle and specifically a decrease from 69\({}^{\circ}\) to 57\({}^{\circ}\) was determined when the specimen was loaded with a 35 wt-% of NaCl. In summary, selected PLA:PEG-10:NaCl-40 specimens were clearly hydrophilic with a contact angle of 46\({}^{\circ}\) that approaches also to the minimum value expected for a PEG sample. Figure 11: TGA profiles of PLA:NaCl-35 (**a**) and PLA:PEG-10:NaCl-40 (**b**) specimens as molded (black lines), and after being washed (blue lines). (**c,d**) SEM micrographs of cross sections of both samples after being washed. Figure 13 compares the porosity, total mass extraction and NaCl extraction yields of representative samples with and without PEG. Samples loaded with the antibacterial drug, PLA:PEG-8:NaCl-36:TCS-6 is also considered. The incorporation of PEG allowed to increase the porosity from 20% to 40-42%, a feature that demonstrates the significant role displayed by the soluble PEG to increase pore connection and enhance NaCl extraction. The extraction yield for PLA:NaCl-40 samples was lower than 40% while values around 90-98% were found for PLA:PEG-10:NaCl-40 and PLA:PEG-8:NaCl-36:TCS-6. In the case of the PLA:PEG-10:NaCl-40 sample, the difference between total mass extraction and NaCl extraction was higher than expected considering the amount of PEG. This point suggests that some PLA was lost during the leaching process probably as a consequence of dragging. It should also to be pointed that 2-3.5% of NaCl could still be present in the PLA:PEG-8:NaCl-36:TCS-6 sample according to the extraction yield. Figure 12: Contact angle for representative micromolded specimens. On the bars are shown the images of the water droplets on the respective material whose composition is shown in the table below. Figure 13: Extraction yields and porosity for PLA:NaCl-40, PLA:PEG-10:NaCl-40, and PLA:PEG-8: NaCl-36:TCS-6.
## 4 Platform and Sensors Implementation and Evaluation
### Sensor Platform  
All reviewed hardware platforms are listed in Table 1. Regarding the two Arduinos, the MEGA 2560 model is a better quality model, bigger, with better technical characteristics and a higher cost (MEGA: 41 ; UNO: 20 ). A key difference is that Arduino UNO has only one serial port, while the Arduino MEGA has four, making it more flexible and useful. Additionally, the Arduino Mega has four times more SRAM which is very important to allow implementing some algorithms which are important for the proposed system and are described in the following sections (e.g., FFT analysis). On the other hand, BeagleBone Black and Raspberry PI are less constrained devices but with higher energy consumption. The most viable option both from an economic point of view and for flexibility reasons is the use of the open code platform Arduino. This platform offers a good balance between capabilities and energy consumption at a reasonable price. The MEGA 2560 model has been chosen as it provides more than one serial port which is necessary for some of the sensors selected in the following sections.  
Figure 2: Hardware Architecture.  
We note that although a prototyping platform like Arduino has been chosen for the prototype, the final release of the product will more than likely use a custom-made sensor board tailored to the project requirements which means that energy consumption and costs can be considerably reduced.
## 3 Methods
### Similarity Analysis of Meteorological Parameters  
For similarity analysis, we compared the meteorological parameters for every station pair of each group against the acceptable instrumental error ranges that were found in the standard operating procedure (SOP; [46; 47; 48; 49]) using the following expression:  
\[\mathrm{PS}= \frac{\mathrm{N2}}{\mathrm{N1}}\times 100 \tag{3}\]  
where N1 and N2 are the total and final data counts, respectively. Notably, N2 requires to meet the following criteria:  
* If D1-D2 is \(\pm 0.5\,\mathrm{\SIUnitSymbolDegree}\) for AT, \(\pm 2.5\,\mathrm{cm}\) for SD, and \(\pm 5\%\) for RH as per SOP's recommendation.
* If the percentage deviation for D1 and D2 is \(\leq\)20% for hourly SR, 10% for daily SR, 1% for BP, and 2% for PR as per SOP's recommendation. Here, we used the larger one between D1 and D2 in calculating the deviation. Notably, the larger one between D1 and D2 is used to compute the percentage deviation, and
* If D1-D2 is \(\pm 0.5\,\mathrm{m/s}\) (i.e., 1.8 km/h) for wind speed and \(\pm 5\,\mathrm{\SIUnitSymbolDegree}\) for wind direction as per SOP's recommendation.  
Upon determining the similarities, we summarized them to identify the required stations in the scope of each individual group. An example of computing PS is shown in Figure 2.  
Figure 2: Example calculation of percentage of similarity (PS) for the parameter of air temperature (AT) for station-pair C3 vs. JP104 in Group 3 along with their time-series (**a**), and differences (**b**).