esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Routing protocols are crucial to self-organize wireless sensor networks (WSNs), which have been widely studied in recent years. For some specific applications, both energy aware and reliable data transmission need to be considered together. Historical link status should be captured and taken into account in making data forwarding decisions to achieve the data reliability and energy efficiency tradeoff. In this paper, a dynamic window concept (\(m,k\)) is presented to record the link historical information and a link quality estimation based routing protocol (LQER) are proposed, which integrates the approach of minimum hop field and (\(m,k\)). The performance of LQER is evaluated by extensive simulation experiments to be more energy-aware, with lower loss rate and better scalability than MHFR [1] and MCR [2]. Thus the WSNs with LQER get longer lifetime of networks and better link quality.  
Wireless Sensor Networks, Energy Efficiency, Dynamic Window (\(m,k\)), Link Quality Estimation, Scalability
# Communication Transport Protocol Strategies for Rail Applications
## 2 The Railway Context
### The ACS Approach and Its Architecture
The ACS GW can not control the bearer itself but it can select the most suitable bearer according to the measured QoS parameters (e.g., latency, packet loss and bit rate), as provided by the Mobile Network Operator (MNO) based on the requirement of the specific rail application, such as those in Table 1[12]. To this purpose, ACS-GW implements an additional communication layer based on IP protocol and independent of each bearer, as shown in Figure 3.  
Figure 1: Separation of the communication technology (or bearer) with the rail application. Figure 2: ACS Conceptual Diagram. The On-Board ACS GW (OG) is the communication node between the local device (host) where the On-Board Application (OA) runs and the communication bearers are, provided for example by 3 GPP terrestrial networks such as 4G LTE or 5G and non-3 GPP networks (such as satellite [14]). The OG is directly connected to the ACS GW on the network side (namely Network ACS GW or NG) through an ACS tunnel over one or more radio communication bearers available in a specific area and time. Above the ACS tunnel between the OG and the NG, the OA is committed to establish an IP link directly to the application on network side, namely Network Application (NA). In this way, the ACS GW can guarantee the required QoS/QoE targets thanks to multi-link connections in different wireless networks and an intelligent and efficient use of the available communication bearers. From a network topology point of view, two or more ACS GWs can also be connected using a peer-to-peer network scheme enabling ad-hoc or mesh connectivity.
# Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction
## 6 Discussion  
The proposed AMSeWL-R system offers significant advancements in enhancing the intelligence and operational capabilities of AMRs. Future work could explore the development of multi-agent systems where multiple AMRs collaborate to complete tasks more efficiently. Such an approach would enhance productivity and open up new possibilities for complex operations in various industries. By leveraging the coordinated efforts of multiple robots, tasks could be completed faster and with greater accuracy, thereby optimizing the overall performance.  
Additionally, AMRs can be combined with the Segment Anything Model (SAM) [43], a large zero-shot segmentation model, to create automatic inspection robots capable of detecting road damage such as cracks. This application has significant market potential and practical value in infrastructure maintenance. Integrating SLAM-based localization and navigation would further enhance task completion, making this a highly promising research direction. Furthermore, the potential for multi-agent collaboration introduces an additional layer of efficiency and innovation, expanding the capabilities and applications of AMRs across various sectors.  
Practical applications of the AMSeWL-R system are diverse and include roles in libraries, museums, construction sites, and infrastructure maintenance. These applications require careful consideration of the dimensions, geometries, and weights of the objects that the equipment can handle as well as the robot's ability to navigate barriers and understand its maximum loading height and minimum corridor width. By addressing these considerations, the AMSeWL-R system can significantly enhance the practical applications of AMRs, making them more versatile and efficient in various real-world scenarios.  
Figure 9: (**a**) Wheeled lifting robot in working condition, demonstrating its capability to handle various objects. (**b**) Environment from the camera’s perspective, showcasing the robot’s view of and interaction with the surroundings. (**c**,**d**) Real-world tests to detect a cola and box using YOLOv8-R.
## 4 Results from Case Studies
### Dynamic Grid Simulations
#### 4.1.2 Dynamic Simulation Results  
For a better illustration of the dynamic grid model, some exemplary contingencies are presented for simulated frequency and voltage magnitude signals from four different PMUs. Figure 10 shows the corresponding simulations results over a period of 10 s for a partial PV plant outage of 50% installed capacity for two different operational points. In both cases, a frequency drop of about 20 mHz can be noticed resulting from a 0.5 MW PV capacity loss (about 1 MW nominal capacity). The voltage magnitudes increase from steady conditions of up to 1.5 kV in adjacent substations. A comparison between both operational points reveals minor differences between frequency signals but larger deviations between voltage magnitude signals due to the difference in the supplied reactive power. A short circuit at 90% length of line L19 is shown in Figure 11 for the same PMU sensor signals and operational points. Compared to the previous contingency, the frequency is oscillating within the first seconds of the disturbance between +20/-40 mHz and returns slowly to the pre-disturbance frequency level of 50 Hz. Characteristic voltage drops of about 110 kV can be observed for both operational points shortly after the disturbance event.  
Figure 10: Exemplary simulations of four PMUs for a partial PV outage at station 2D3 (50% loss of installed capacity) including frequencies (**top and bottom left**) and voltage magnitudes (**top and bottom right**) for two operational points.
## Appendix A
### Pseudocode of Random Forest Algorithm  
```
1:Input:\(D_{\text{train}}\): Training dataset; \(D_{\text{test}}\): Test dataset; \(n\_trees\): Nr of trees in the forest; \(max\_depth\): Max depth of each tree; \(min\_smp\_split\) and \(min\_smp\_leaf\): Min and max nr of samples for leaf node
2:Output:\(D_{\text{predicted}}\): Predicted labels for the test dataset
3:Program Body
4:Initialize an empty list \(forest\): List of decision trees
5:for tree = 1 to \(n\_trees\)do
6: let \(bootstrap\_sample\) be a random subset of \(D_{\text{train}}\) (sampling with replacement)
7: let \(tree\) be a DT initialized with \(max\_depth\), \(min\_smp\_split\), and \(min\_smp\_leaf\)
8: train \(tree\) on \(bootstrap\_sample\); append \(tree\) to \(forest\)
9:endfor
10:Prediction for\(D_{\text{test}}\)
11:for each \(x_{\text{test}}\) in \(D_{\text{test}}\)do
12: let \(votes\) be an empty dictionary
13:for each \(tree\) in \(forest\)do
14: let \(prediction\) be the result of \(tree\) on \(x_{\text{test}}\); increment \(votes[prediction]\) by \(1\)
15:endfor
16: let \(predicted\_label\) be the label with the highest count in \(votes\); assign \(predicted\_label\) to \(x_{\text{test}}\) in \(D_{\text{predicted}}\)
17:endfor
18:Return\(D_{\text{predicted}}\)
```  
**Algorithm A3** Random Forest Algorithm [50]
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 4 System Architecture
### Utilizing ULP Timer
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Voltage** & **Current** & **Power** \\ \hline \multicolumn{4}{c}{Using micro USB connector} \\ \hline Wireless Stick ON & 5.01 V & 51 mA & 255.51 mW \\ Wireless Stick low power & 5.01 V & 13 mA & 65.13 mW \\ \hline \multicolumn{4}{c}{Using 5 V pin} \\ \hline Wireless Stick ON & 7.01 V & 44.15 mA & 309.49 mW \\ Wireless Stick low power & 7.09 V & 11.43 mA & 81.04 mW \\ Power OFF by TPL5111 & 7.11 V & 37 μA & 0.26 mW \\ \hline \multicolumn{4}{c}{Using 3.3 V pin} \\ \hline Wireless Stick ON & 4.91 V & 32.95 mA & 161.78 mW \\ Wireless Stick low power & 5.00 V & 2.27 mA & 11.35 mW \\ Power OFF by TPL5111 & 5.00 V & 26 μA & 0.13 mW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Power consumption based on Vdd input pin [50]. collection is infrequent and the data packet transmitted via LoRa is small, we expect most applications to have a very low duty cycle of under ten minutes per day. \[T=\frac{(C)\mathrm{mA\,h}}{24\,\mathrm{h}\cdot(I_{ON}\frac{x}{1440}+I_{ULP}\frac {1440-x}{1440})\mathrm{mA}} \tag{3}\]  
If we take for example a 2000 mA h battery, and we replace the measured values in Equation (3), the resulting is shown in Equation (4). We can then plot this result as shown in Figure 8, where we draw both the ratio for battery life extension using TPL5111 and the built-in ULP state of the microcontroller. \[T_{TPL5111}=\frac{2000\,\mathrm{mA\,h}}{24\,\mathrm{h}\cdot(I_{ON}\frac{x}{14 40}+0.026\frac{1440-x}{1440})\mathrm{mA}} \tag{4}\]  
From Figure 8, we notice that sensors that operate for 10 min per day can last one year on a charge of 2000 mA h battery when using an external ULP timer. While this is the best-case scenario for this microcontroller, if we account for radio communication and other energy-intensive tasks, the energy consumption will increase. In Figure 9 we also include the variable of average current during the active period from the minimal value od 33 mA to 100 mA graphed on the y-axes, while the z-axes indicate the number of days the battery charge is expected to last. The top chart in orange indicates the battery lifespan when the TPL5111 timer is used, while the bottom chart in blue indicates the usage of the onboard sleep state. The maximum shown in the plot is 200 days, and the area in gray indicates a lifespan of at least 200 days. Thus, we see that the ratio drastically increases when the active state is short and energy-efficient. For sensors that require a longer active state, the energy efficiency should further be improved by using microcontrollers capable of using the multiple ESP32 power modes [36]. Figure 8: 2000 mAh battery lifetime when using TPL5111 and ULP in sleep mode, based on node active period.
## 4 Developed Constitutive Model  
### Constitutive Model Expression  
In a previous study, it was noted from the experimental results that the relationship between stress and strain could be considered an elastic process when the surcharge ranges from zero to the vertical swelling pressure measured under constant volume conditions [10]. The measured vertical swelling strain and lateral pressure under certain surcharges with saturated conditions were found to be quite close during the loading and unloading process [32]. Additionally, the earth pressure of unsaturated expansive soil would mobilize due to matric suction changes after rainfall. This single swelling process differs from the drying and wetting cyclical process, which can be regarded and assumed to be an elastic behavior. Therefore, to analyze the earth pressure of unsaturated expansive soil acting on a vertical retaining wall and investigate the mobilization of earth pressure distribution from the initial state to the saturation state, the interaction between the unsaturated expansive soil and the vertical retaining wall was simplified and regarded as a two-dimensional elastic problem.  
Modifications were made to [PERSON]'s isotropic elastic model to characterize the anisotropic swelling behavior of expansive soil. The elastic modulus with respect to matric suction (\(H\)) was modified to consider two elastic moduli with respect to matric suction (\(H_{v}\) and \(H_{h}\)) in the vertical and horizontal directions, respectively. For simplicity, the elastic modulus with respect to net normal stress \(E\) was still considered isotropic. Therefore, the three-dimensional nonlinear elastic model can be further simplified as follows:  
\[\left\{\begin{array}{l}\mathrm{d}\varepsilon_{h}=\frac{\mathrm{d}( \varepsilon_{h}-u_{a})}{E}-\frac{\mu}{\mathrm{d}}\mathrm{d}(\varepsilon_{h}+ \sigma_{v}-2u_{a})+\frac{\mathrm{d}s}{H_{v}}\\ \mathrm{d}\varepsilon_{v}=\frac{\mathrm{d}(\varepsilon_{v}-u_{h})}{E}-\frac{ \mu}{\mathrm{d}}(\varepsilon_{h}+\sigma_{h}-2u_{a})+\frac{\mathrm{d}s}{H_{v}} \end{array}\right., \tag{1}\]  
where \(\varepsilon_{v}\) and \(\varepsilon_{h}\) are the vertical and horizontal strain, respectively, in %; (\(\sigma_{v}-u_{a}\)) and (\(\sigma_{h}-u_{a}\)) are the vertical and horizontal net normal stress, respectively, in kPa; \(\mu\) is the Poisson ratio; \(s\) is the matric suction, \(s=u_{a}-u_{w}\), kPa; \(E\) is the elastic modulus with respect to net normal stress, in kPa; and \(H_{v}\) and \(H_{h}\) are the vertical and horizontal elastic moduli with respect to matric suction, respectively, in kPa.
###### Abstract  
Quantum repeater is a key component of quantum network, and atomic memory is one of the important candidates for constructing quantum repeater. However, the atomic transition wavelength is not suitable for long-distance transmission in optical fiber. To bridge atomic memory and fiber communication, we demonstrate a frequency conversion interface from rubidium D1 line (795 nm) to the optical communication L-band (1621 nm) based on difference frequency generation. To reduce broadband noise of spontaneous Raman scattering caused by strong pumping light, we use a combination of two cascaded etalons and a Fabry-Perot cavity with low finesse to narrow the noise bandwidth to 11.7 MHz. The filtering system is built by common optical elements and is easy to use; it can be widely applied in frequency conversion process. We show that the signal-noise ratio of the converted field is good enough to reduce the input photon number below 1 under the condition of low external device conversion efficiency (0.51%) and large duration of input pulse (250 ns). The demonstrated frequency conversion interface has important potential application in quantum networks.  
quantum network; frequency conversion interface; difference frequency generation; optical communication band +
Footnote †: journal: Applied sciences
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Convolutional Neural Network  
#### 4.3.1 CNN on SAR Temporal Series  
As discussed in Section 3.7, the CNN was applied on the SAR temporal series of \(\sigma^{0}_{\text{P,VV}},\sigma^{0}_{\text{P,VH}},\Delta\sigma^{0}_{\text{PG,VV}}, \Delta\sigma^{0}_{\text{PG,VH}}\) using the SAR branch of the developed CNN1D model. The data was divided into 50% for training, 40% for validation and 10% for testing. Table 7 summarizes the accuracy obtained when applying the CNN method on the SAR temporal series with the corresponding standard deviation of 10 different iterations. The CNN approach reports very high accuracy where the overall  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Class** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{4}{*}{**15 variable SAR PC-RF + 7 variable**} & **Irrigated** & 0.95 & 0.82 & 0.88 \\  & **Non-Irrigated** & 0.91 & 0.98 & 0.94 \\  & **OA** & & 92.3\% & \\ \cline{1-1}  & **Kappa** & & 0.82 & \\ \cline{1-1}  & **F-score** & & 0.91 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The values of the overall accuracy, Kappa, F-score, precision, and recall obtained from the RF classifier using 15 important SAR PC variables and the seven important NDVI PC variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Class** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{4}{*}{**NDVI-RF 17 Variables**} & **Irrigated** & 0.94 & 0.78 & 0.85 \\  & **Non-Irrigated** & 0.89 & 0.97 & 0.93 \\  & **OA** & & 90.5\% & \\  & **Kappa** & & 0.78 & \\  & **F-score** & & 0.91 & \\ \hline \multirow{4}{*}{**NDVI-RF 7 Important Variables**} & **Irrigated** & 0.92 & 0.76 & 0.84 \\  & **Non-Irrigated** & 0.88 & 0.96 & 0.92 \\ \cline{1-1}  & **OA** & & 89.5\% & \\ \cline{1-1}  & **Kappa** & & 0.76 & \\ \cline{1-1}  & **F-score** & & 0.88 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The values of the overall accuracy, Kappa, F-score, precision, and recall obtained from the RF classifier using the 17 normalized differential vegetation index (NDVI)-PC and the seven important NDVI PC values.  
accuracy reaches 94.1%. The kappa value of the classification reaches 0.87 and the F-score of is 0.94. The F-score of the irrigated class reaches 0.91 which is less than that of the non-irrigated class (0.96).
###### Abstract  
GNSS single-frequency occultation processing technology has the advantage of simple instrumentation, but it is not clear about the accuracy of the Beidou-based single-frequency occultation processing. This paper verifies the single-frequency occultation processing algorithm of the BeiDou navigation system (BDS) and analyzes its accuracy based on occultation observation data from the FY3E satellite. The research aimed to verify the single-frequency ionospheric relative total electron content (reITEC), analyze the accuracy of the reconstructed second frequency B\({}_{3}^{*}\)s excess phase Doppler, and analyze the accuracy of the refractive index products. Results: (1) As for reITEC and excess phase Doppler, the correlation coefficient between single-frequency occultation processing and dual-frequency occultation processing is greater than 0.95. (2) The relative average deviations of the excess phase Doppler of B\({}_{3}^{*}\) are mostly less than 0.2%, and the relative standard deviations are mostly around 0.5%. (3) The bias index and root mean square index of single/dual-frequency inversion have good consistency compared with ERA5 data. All the results show that the single- and dual-frequency inversion refractive index products have comparable accuracies, and the accuracy of the standard deviation of single-frequency inversion refractive index products over 25 km being slightly lower than that of dual-frequency inversion refractive index products.  
BDS occultation; single-frequency processing; excess phase; refractive index 2023, 14, 742. [[https://doi.org/10.3390/attnos14040742](https://doi.org/10.3390/attnos14040742)]([https://doi.org/10.3390/attnos14040742](https://doi.org/10.3390/attnos14040742))
# Thermal Energy Storage for Building Load Management: Application to Electrically Heated Floor
## 4 Discussion  
The thermal comfort study showed that a complete night-control strategy was not possible in half of the zones (depending on the amount of external walls and their orientation). Thus, it was proven that an acceptable thermal comfort was achieved with only 16% of the energy consumption during the day (only between 9:30 a.m. and 4:00 p.m.). However, results show an overheating of some zones at some times during the day. In fact, the stored energy amount is not always required, involving overheating, especially in the zones facing the south. Then, using a different control strategy, which takes into consideration the amount of energy required for the next day in conjunction with the weather prediction, is required to improve the thermal comfort. Moreover, it is important to notice that, if the used building is representative of the dwellings in Quebec, results are situational to the example. Studies on different types of buildings are required draw conclusions about the feasibility of a night-control strategy for an EHF in cold climate.  
Figure 9: Minimal peak electricity price in function the off-peak electricity price to have a payback period of 15 years.  
Especially for the EHF, the payback period is an important indicator. Even if an EHF may have some financial issue (considering the initial cost and the increase of the consumption) it shows that the system may be financially profitable for both customer and supplier thanks to the shift in consumption. The economic viability depends on the off-peak electricity price/cost and the difference between it and the peak electricity price/cost. It is important to notice that this definition gives a conservative result of the real payback amount, since the simulation was only done during the month of January, one of the months with the highest heating loads. Then, for milder months, because the required energy is lower, a higher percentage of this amount may be stored in comparison to January. If a higher precision is required, instead of analyzing one month to determine the amount of energy consumed respectively during peak and off-peak periods, the analysis can be carried out for the entire year. Moreover, in this study, the cost of a system without storage is not considered, giving a conservative result of the real payback period. However, the maintenance cost of the system is ignored.
## 4 Results and Comparison
### Comparison with Previous Studies
#### 4.2.2 Performance Comparison

Table 9: Comparison of relative errors of the proposed approach with the previous solutions. We can also summarize that the previous studies generally adopted the same technical path. Firstly, they extracted a set of prognostic features for health indicator (HI) construction using different statistical analysis, signal processing, and machine learning methods [35, 36, 37, 38, 39, 40, 41, 42]. With advances in deep learning technology, the latest three solutions employed RNN-based models to estimate HI [40, 41, 42]. Secondly, previous studies attempted to model bearing degradation profiles depicted by HI. Subsequently, bearing RUL is estimated by predicting when the HI crossed a predefined threshold. A variety of time-series modeling techniques were employed to build the RUL prediction model, including conventional linear and nonlinear regression/extrapolation, GPR, EKF (with exponential model), and PF (with Paris-Erdogan model and exponential model). Unlike the existing solutions, we proposed an IBL approach where RUL for a given test bearing is estimated based on a run-to-failure example showing similar degradation behavior. Instead of modeling the bearing degradation process, we performed similarity comparisons directly using entire bearing degradation profiles. To deal with the challenges of limited run-to-failure examples in the learning datasets, a data augmentation technique is applied to generate multiple synthetic prognostic feature sequences by modifying the learning instance. To extract prognostic feature sequences, the PCA technique is applied to the spectrograms of vibration signals. To evaluate the similarity between a pair of the learning and test instances, the data projection base (i.e., set of PCs) is derived from the spectrogram of the learning instance. Therefore, the proposed approach can be considered an alternative approach for the challenging bearing datasets. It is also our intent to produce a comparative review of the previous studies with the proposed approach that can serve as a reference for researchers interested in the RUL estimation problem particularly using the PRONOSTIA datasets.