esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In general, users accept the data rates offered to them by the radio chip rather than specifying the data rate required for the particular link. While links close to a root node may have a requirement for considerable throughput, those closer to the edge of the network rarely need the rate provided by the radio, e.g., 50 kbps for the Fleck-3B or 250 kbps for IEEE 802.15.4 compliant radios. With suitable coding we could, for example, halve the data bit rate and achieve 3 dBm of extra gain (see Section 3.). The resulting increase in SNR could move us from the transition zone to the region of good reception, significantly reducing the requirement for packet retransmissions. We can therefore achieve better link performance with less energy consumption by trading off what we do not actually need: high data bit rates. This is the fundamental premise of our new rate adaptive data-link algorithm, RA-MAC: we dynamically trade data bit rate to improve the link quality, rather than use retransmissions. The RA-MAC approach therefore _reduces the number of transmissions_, reduces channel utilization and _lowers the energy_ required to send messages within the network. The contributions of this paper include:  
* RA-MAC, which is, to the best of our knowledge, the _first rate-adaptive Media Access Protocol_ for WSNs with the purpose of _minimizing network energy consumption_. In contrast to conventional Wireless Local Area Networks (WLANs), where optimizing link throughput is the main focus, node energy consumption is typically a more important performance metric in WSNs. Further, rate-adaptive protocols in WLANs are typically deeply coupled with 802.11 Request To Send (RTS) and Clear to Send (CTS) message exchanges, but the packets are typically too small in WSNs to justify the RTS/CTS exchanges (e.g., the default MAC payload size is 28 bytes in TinyOS 2.x). Moreover, WLAN MAC typically do not take _the cost of acknowledgement packets_ into account because the sizes of the data packets are significantly larger than the acknowledgement packets. * An analytical model of the energy consumption under different channel conditions, which we use to optimize the overall network energy consumption. * Evaluation of RA-MAC using both simulation and experiment demonstrates the performance of the protocol. We have demonstrated the feasibility of our RA-MAC protocol using TinyOS [8] running on the off-the-shelf TinyNode platform [9]. The rest of the paper is organized as follows. We present related work in Section 2. We discuss our hypothesis regarding adaptive data rates in Section 3. and set up an analytical model to optimize node energy consumption under different channel conditions in Section 4. We present the details of the proposed RA-MAC protocol in Section 5. Section 6. presents the experimental results that compare the performance of RA-MAC to that of SCP-MAC [10], followed by the simulation comparison between a conventional WLAN rate adaptive algorithm and the proposed RA-MAC in Section 7. Finally, we present our conclusions in Section 8.
###### Abstract  
Radiated noise is a major topic of interest regarding the brake squeal phenomenon as it is directly linked to the noise generated which can be potentially detrimental to user comfort and perception. However, very few studies offer in-depth and comprehensive insight into the analysis and understanding of acoustic noise during squeal events. This study is intended to provide an original contribution to this issue by investigating acoustic phenomena occurring during the squeal phenomenon via a digital antenna composed of 117 microphones. Experiments are performed on the Friction-Induced Vibration and noise test bench at Ecole Centrale de Lyon (FIVE@ECL). The first main aim is to investigate the characteristics of the acoustic radiated field during brake squeal and more particularly to describe the evolution of the radiated field patterns per revolution of the disc system. The second major aim is to illustrate the possibility of reconstructing the radiated acoustic field everywhere in the space surrounding the brake system, leading to the construction of a robust representation of 3D acoustic patterns, providing acoustic squeal events in the physical space around the brake system. Results show that the vibratory signature remains identical during squeal event braking test. The acoustic signature of squeal noise consists mainly of a fundamental frequency and its harmonic components, with secondary lower contributions from other fundamental frequencies. The associated radiated acoustic field during squeal events are characterized by different directivities and intensities of the acoustic radiated field for each squeal frequency, with potential changes in these directivities and intensities over short times corresponding to the rotation period of the disc.  
friction-induced vibration; noise; acoustic; experiments; brake squeal; acoustic field reconstruction +
Footnote †: journal: Applied sciences
###### Abstract  
It has been found in engineering practice that the degree of rockburst risk increases when roadway excavation occurs near the stratigraphical boundary of different lithologies. This study uses the 1276 m deep-buried roadway of a lead-zinc mine in Yunnan, China, as its engineering background. Based on a numerical analysis of this case, it investigates the mechanical behavior of surrounding rocks in different lithological formations and explores the causes of excavation-induced rockburst. Additionally, by changing the excavation strategy in a numerical simulation, the influence of the direction of roadway excavation on the degree of rockburst risk in the construction of different lithological formations is assessed. The results are summarized as follows: (1) When the tunnel passes from the C1b stratum (limestone) to the D3 zg stratum (dolomite), an abnormal stress zone forms in the roof rock strata of the D3 zg stratum (the lower plate of the stratum boundary). The rockburst risk level was evaluated by introducing the numerical rockburst index in this abnormal stress zone, which aligns closely with on-site rockburst investigation results. The rockburst risk is the greatest in the abnormal stress zone, which provides an external energy storage environment for the development of rockburst disasters. (2) Near the stratum boundary, the rockburst risk level when excavating from the D3 zg stratum to the C1b stratum is greater than that when excavating from the C1b stratum to the D3 zg stratum. The direction of tunnel excavation significantly affects the rockburst risk level during construction that crosses different lithological strata. These findings can provide a theoretical basis for the construction design of similar underground projects.  
rockburst; stratigraphical boundary; excavation direction; numerical simulation; brittle shear rate; mechanical behavior 2025
## 6 Performance Evaluation
### Simulation Results
#### 6.2.1 Stability Period  
Figure 3 depicts the stability period for SEP-E, DEEC-E, EBCS, TEEN and HFECS against different network sizes. It can be observed from Figure 3 that the stability periods of HFECS and EBCS outperform all other clustering schemes as both of them are entropy-based clustering schemes. Furthermore, the stability period of all clustering schemes is decreased with the increase in network size due to the reason that the distance between sensor nodes and the energy consumption is increased as we increase our network size. For most of the clustering schemes, energy is consumed for performing data forwarding and lossy aggregation tasks but in the case of HFECS, we perform lossless aggregation at CHs by concatenating multiple data packets into a single large data packet of specified length to reduce the energy consumption, thus resulting in an enhanced stability period in comparison to other clustering schemes for larger network sizes. As DEEC-E works on the basis of absolute threshold value calculated in each round, the residual energy of all sensor nodes is then compared with this threshold value. As this threshold value does not take into account the network size, the stability period of this scheme is significantly reduced when the network size increases up to \(350\times 350\) m\({}^{2}\). The degraded performance of SEP-E in comparison to EBCS and HFECS can be explained as the probability of each type of sensor node do not consider the remaining energy while selecting the new CH. Also, when the network size increases, the low density regions in the network have a greater number of CHs, which results in the inefficient utilization of energy by the CMs and correspondingly the stability period of SEP-E is reduced. The performance of EBCS in terms of the stability period is better than SEP-E, DEEC-E, and TEEN for all the network sizes.  
The performance of EBCS for network sizes up to \(200\times 200\) m\({}^{2}\) is slightly better than for HFECS. The reason for this is that when sensor nodes supporting the asynchronous working-sleeping cycle are deployed in a smaller network area, we have a high density of sensor nodes in every cluster, i.e., greater number of cluster members to be facilitated by their assigned CH, so it is highly likely that a potential CH or a CM will lose all of its residual energy in a slightly less time than EBCS due to the extra computation of link connectivity calculations performed in every cluster. This extra computation is due to the increased number of sensor nodes present in every cluster. As we increase the network size, the uniformly random deployment of sensor nodes create clusters with a lower number of sensor nodes as compared to the scenario of smaller network sizes, so the probability of a sensor node in HFECS for losing all of its energy decreases in comparison to the probability of a sensor node operating in EBCS. Table 9 shows the percentage improvement in terms of the stability period.  
Figure 3: Stability period in terms of rounds against variable network size.
## 4 Discussion
In terms of long-term forecasting, although ICS-SNN performs well, the accuracy of predictions may decrease due to the complex factors affecting the financial market, especially unforeseeable macro events over a long time span. Although the ICS-SNN model proposed in this study performed well in experimental environments, it still faces several challenges when applied in practical financial markets. Firstly, transaction costs directly affect the profitability of the strategy, especially in high-frequency trading scenarios where even small costs can accumulate into significant burdens. Secondly, liquidity constraints in the market may result in some predictions being unable to be executed, especially on low-liquidity assets. Finally, the issue of market delays cannot be ignored, as any delay may result in missing the best trading opportunity. Therefore, future research needs to focus more on how to overcome these practical obstacles to enhance the practical application value of the model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Name** & **Time Step** & **Neuron Number** & **Batch Size** & **Scaler Scope** & **Decay Rate** & **Threshold** \\ \hline ICS-SNN & 11 & 952 & 1000 & 4.4328 & 0.5078 & 0.8413 \\ \hline CS-SNN & 15 & 1809 & 1845 & 4.4615 & 0.6208 & 0.9459 \\ \hline SNN & 25 & 1050 & 1500 & 5.0 & 0.55 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The final hyperparameter values of ICS-SNN, CS-SNN, and SNN.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### Hyperspectral Imaging Technology
#### 2.2.3 Application Status and Prospects  
Hyperspectral imaging--a quantitative remote sensing approach--has been widely applied in environmental monitoring, vegetation analysis, geologic mapping, atmospheric characterisation, biological detection, camouflage detection and disaster assessment [135; 136; 137; 138; 139; 140; 141]. However, the application requirements for hyperspectral sensors underwent significant variations with the development of advanced sensors. First, a wide spectrum covering range is required to enhance the monitoring and detection capabilities of this system in various applications. This can be achieved by combining multiple sensors with different wavelength detection ranges [142] or by using an integrated system with a wide spectral range [80]. Second, the system sensitivity and preprocessing accuracies are equally important along with the spatial and spectral resolutions. For example, the AVIRIS next generation system [143] has been applied to detect methane, owing to its high signal-to-noise ratio and high data preprocessing accuracy. Finally, this technology facilitates the advantages of characterisation and quantification of targets; for example, hyperspectral imagers have been used for road network extraction.
## 4 Tool Application to Evaluate Pollutant Emissions
### Carbon Balance Method
#### 4.2.1 Demonstration of the Carbon Balance Method
\tag{19}\]  
_Carbon out_  
The carbon-outlet quantity is calculated through the following formula:  
\[Carbon_{\text{out}}\ =\ C_{\text{atomic mass}}\left[\frac{CO_{2}\ \text{ emissions}}{CO_{2\ molar\ mass}}\ +\ \frac{CO_{\text{ emissions}}}{CO_{\text{ molar mass}}}\ +\ \frac{HC_{\text{ emissions}}}{HC_{\text{ molar mass}}}\right] \tag{20}\]  
in which:  
\(C_{\text{atomic mass}}\ =\ 12.011\) g is the relative carbon mass;  
\(CO_{2\ \text{emissions}},CO_{\text{emissions}}\) and \(HC_{\text{emissions}}\) [g/h] are the substances flowrate emissions;  
\(CO_{2\ molar\ mass}\), \(CO_{\text{molar mass}}\) and \(HC_{\text{molar mass}}\)[g/mol] are the molar masses of pollutant. Emissions can be calculated through the following general equation:  
\[Pollutant_{\text{emissions}}=\frac{Pollutant_{\text{molar mass}}}{Pollutant_{ \text{molar volume/ peak}}}Pollutant_{\text{concentration}}\dot{m}_{\text{exh}} \tag{21}\]  
where \(Pollutant_{\text{molar volume}}\) [m\({}^{3}\)/mol] is the molar mass and the molar volume of pollutant CO\({}_{2}\), CO, or HC, respectively. It is worth noting that the molar mass/molar volume ratio of a pollutant is equal to its density, as shown below:  
\[\rho_{pollutant}\ =\ \frac{Pollutant_{\text{molar mass}}}{Pollutant_{\text{ molar volume}}}. \tag{22}\]  
As a result, by substituting the density in the carbon out formulation, the following equation is obtained:  
\[Carbon_{\text{out}}\ =\ C_{\text{atomic mass}}\frac{\dot{m}_{\text{exh}}}{ \rho_{\text{exh}}}\left[\frac{CO_{2\ concentration}}{CO_{\text{molar volume}}}\ +\ \frac{CO_{\text{ concentration}}}{CO_{\text{molar volume}}}\ +\ \frac{HC_{\text{ concentration}}}{HC_{\text{molar volume}}}\right] \tag{23}\]  
_Exhaust gas density calculation_  
The exhaust gas density is equal to the ratio between mass flowrate \(\dot{m}_{\text{exh}}\) [kg/h ] and volumetric flowrate \(\dot{V}_{\text{exh}}\) [m\({}^{3}\)/h]:  
\[\rho_{\text{exh}}\ =\ \frac{\dot{m}_{\text{exh}}}{V_{\text{exh}}}. \tag{24}\]The volumetric flowrate \(\dot{V}_{exh}\) can be calculated by summing up the volumetric flowrate of all the exhaust gas components; however, since they are dependent mainly on CO, CO\({}_{2}\), and HC content, the formula can be simplified as follows:  
\[ \begin{array}{c}\dot{V}_{exh}\;=\;\dot{V}_{CO}\;+\;\dot{V}_{NO}\;+\;\dot{V}_{NO _{2}}\;+\;\dot{V}_{HC}\;+\;\dot{V}_{H_{2}O}\;+\;\dot{V}_{CO_{2}}\;+\;\dot{V}_{ O_{2}}\;+\;\dot{V}_{N_{2}}\;+\;\dot{V}_{SO_{2}}\to\\ \dot{V}_{exh}\;\approx\;\dot{V}_{CO}\;+\;\dot{V}_{HC}\;+\;\dot{V}_{H_{2}O}\;+\; \dot{V}_{CO_{2}}.\end{array}  \tag{25}\]  
The last expression of \(\dot{V}_{exh}\) implies that the exhaust gas volumetric flowrate is calculated by taking into account also the water content (i.e., it is a wet quantity).
## 7 Conclusions  
In this research, we propose a formalized process of deploying the YOLOv3 object detection model on FPGA, which can detect dishes with acceleration and high accuracy for empty-dish recycling robots. In order to implement the YOLOv3 model on FPGA, we first build up a unique dataset containing 16 types of dishes for model training and testing. Then, we use our unique dataset to train the YOLOv3 model on software platforms, improve the accuracy of the model, and reduce the size of the model through pruning. After the software process, we deploy the pruned model to FPGA through Vitis AI as the hardware process. Compared to the original model, the pruned model with the highest comprehensive performance has the following features: the model size is reduced from 62 MB to 12 MB, which is only 19% of origin; the number of parameters is reduced from 61,657,117 to 9,900,539, which is only 16% of origin; the running time is reduced from 14.411 s to 6.828 s, which is only less than half of origin, while the detection accuracy is decreased from 97% to 94.1%, which is only less than 3%. For future research, we will improve detection accuracy by adjusting the pruning method to alleviate the accuracy decrease, and improve the dish dataset to increase the detection accuracy. We will also add evaluation criteria such as results compared with the ground truth on public datasets, and quantitative details on the FPGA resource utilization. In addition, we will formalize a process flow for implementing CNN on FPGA, and use 3D coordinates to achieve more accurate detection for empty-dish recycling robots.  
Data curation, X.Y.; Investigation, Z.W.; Methodology, H.L.; Project administration, L.M.; Software, Z.W.; Supervision, L.M.; Writing--original draft, Z.W.; Writing--review and editing, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by JST, the establishment of university fellowships towards the creation of science technology innovation, Grant No. JPMJFS2146.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Advantages of Using Cerberis3 in a Hub-and-Spoke Network Topology
#### 1.3.1 Monitoring and Visualization  
To ensure the reliable and secure operation of the point-to-point QKD network, it is essential to have a comprehensive monitoring and visualization system. The Cerberis3 QKD system from ID Quantique offers advanced monitoring capabilities that enable network administrators to monitor and visualize key performance metrics.
# Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
## 4 Results and Discussions
### Performance Optimization of Model-Parameters
As expected, the \(\epsilon\) required for the calculation of the three different statistical measures \(\lambda_{5}\) (which implies using IF, SE and SK for each signal) is the lowest. Figure 9 provides the confusion matrix, which details the testing performance for \(d_{\text{LSTM}}\) = 500 and by considering the combination of IF, SE and SK as input (\(\lambda_{5}\)). Overall, the accuracy as calculated on the testing dataset (1650 samples of equally balanced classes) accounts for 92%. As it can be observed, the better intra-class accuracy is achieved by classifying shear events (93.1%), followed by tensile event (91.9%), where the lowest is mixed-mode (90.0%). The model finds most difficult to identify mixed-modes and mainly confuses them with actual tensile solicitations (or vice versa). On the other hand, the shear events appear to be relatively easier to be classified when compared against the other classes, with a limited number of false positives mainly concentrated on mixed-modes. Figure 8: (**a–d**) Effects of model-parameters in tuning of Bi-LSTM network with a dataset Q = 15.000 AE events and testing of 1650 events. (**a**) Performance of training, validation and testing with different input configurations (\(\lambda_{1}\)-\(\lambda_{5}\)) using \(d_{\text{LSTM}}\) = 200 (**a**), \(d_{\text{LSTM}}\) = 500 (**b**), \(d_{\text{LSTM}}\) = 800 and (**c**), respectively. (**d**) Trend of epoch number \(\epsilon\) = 300 as a function of the different input configurations (\(\lambda_{1}\)-\(\lambda_{5}\)) and for \(d_{\text{LSTM}}\) = 200, 500 and 800, respectively. Figure 10 shows the learning error by the value of the loss function for both training (Figure 10a) and validation (Figure 10b) tasks. Based on these analyses, we set the number of the LSTM cells in our Bi-LSTM model to \(d_{\text{LSTM}}=500\), since it provides the lowest validation loss (\(J_{\text{VAL}}(\Theta)\)) and the better testing accuracy (according to Figure 8c), and choose the input configuration \(\lambda_{5}\) where we take into account IE, SE and SK as the representative properties of the signal.
## 3 Results and Discussion
### Comparison to Expanded Polyethylene (EPE)  
The direct comparison of the results of this study to previous published results is relatively difficult. Studies on the topic of transmissibility and damping ratio are limited. As stated in the introduction, the effect of temperature on resonant frequencies of plastic foams were investigated only by [PERSON] et al. [55]. However, strict values are mostly not available from that study; they can be only estimated based on two figures: resonant frequencies of expanded polyethylene as a function of temperature, and resonant frequencies of expanded polystyrene as a function of temperature. Furthermore, the transmissibility values and damping ratios were not published directly. The results for EPE (expanded polyethylene, \(\sim\)35.24 kg/m\({}^{3}\)) showed an effect of temperature similar to what was found in our study. When the temperature decreased, the resonant frequency mostly increased. The only exception was the observed resonant frequency at 0.5 psi (3.447 kPa), where it was approximately 70 Hz at 3 \({}^{\circ}\)C and 68 Hz at \(-17\)\({}^{\circ}\)C. In contrast, in our study, the resonant frequencies of PE25, PE30, and XPE at 3.488 kPa (\(\sim\)0.506 psi) were 44.4331 Hz, 84.3035 Hz, and 133.3350 Hz at \(-20\)\({}^{\circ}\)C; and 40.1499 Hz, 62.4857 Hz, and 114.5280 Hz at 0 \({}^{\circ}\)C. All resonant frequencies decreased when the temperature was increased. In [55], the vibration tests were performed at normal room temperature after preconditioning.  
Furthermore, as [55] showed, the resonant frequencies at 3 \({}^{\circ}\)C and 23 \({}^{\circ}\)C at 0.75 psi (5.17 kPa) and 1.0 psi (6.89 kPa) were quite equal. The differences were also minimal at 23\({}^{\circ}\) C and 43\({}^{\circ}\) C with 1.5 psi (10.34 kPa). In our study, the resonant frequency of PE25 decreased at 6.976 kPa (\(\sim\)1.012 psi) from 40.1499 Hz to 34.0133 Hz when the temperature was increased from 0 \({}^{\circ}\)C to +20 \({}^{\circ}\)C. These values were 62.4857 Hz and 48.1642 Hz for PE30, and 114.5280 Hz and 94.8137 Hz for XPE. The resonant frequency of XPE and PE30 at \(-20\)\({}^{\circ}\)C was about two times as much as that at 40 \({}^{\circ}\)C. In [55], the resonant frequency decreased from 68 Hz to 52 Hz at 0.5 psi (\(\sim\)3.447 kPa) when the temperature increased from \(-17\)\({}^{\circ}\)C to 43 \({}^{\circ}\)C. In our study, these values were 44.4331 Hz and 32.5566 Hz for PE25 at 3.488 kPa when the temperature increased from \(-20\)\({}^{\circ}\)C to 40 \({}^{\circ}\)C. Generally, PE25 was the least influenced by temperature changes in our study. PE25 at 1.1922 Hz (PE25, 6.976 kPa static load, +20 \({}^{\circ}\)C to +40 \({}^{\circ}\)C) also showed the smallest changes. Generally, the changes in PE25 were similar to the results for EPE35 in [55].