esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## Using Mobile Radio Frequency (RF) Power Charger to Charge the Batteries of Sensors in a WSN
### Problem Formulation  
A brief explanation of the formulation is given as follows:  
* Decision variables: 1. \(l_{xy}\), are binary variables indicating whether a landmark is assigned for the charger at the position (x,y) and 2. \(z^{i}_{xy}\), are binary variable indicating whether sensor \(i\) receives power from landmark (x,y).
* Objective function: is a binary integer linear function that maximizes the total received power of high priority nodes by selecting suitable land marks to be visited by the mobile wireless power charger and is given by \(\sum_{i}\sum_{x}\sum_{y}\beta^{i}P^{i}_{xy}l_{xy}\) where \(\beta^{i}\) is a binary constant whose value is 1 if the sensor node \(i\) is collecting data from critical equipment and hence is high priority and \(P^{i}_{xy}\) is the power received by sensor \(i\) from a landmark positioned at (x,y) coordinates which depends on the pre-known euclidean distance.
* Constraint sets: are all binary integer linear constraints and their interpretations are explained below: 1. A linear constraint set in the binary variables \(l_{xy}\) that ensures that the total number of landmarks assigned to the charger do not exceed a certain pre-selected number. 2. A linear constraint in the variables \(l_{xy}\) and \(z^{i}_{xy}\) enforces that if a landmark at a position (x,y) is chosen, then there has to be at least one sensor that could recharge its batteries from a mobile wireless charger at that position. 3. A linear constraint in \(z^{i}_{xy}\) that ensures that the power supply of the mobile charger is not exceeded, given the replenishment demand \(\delta_{i}\) for node \(i\): \[\sum_{i}z^{i}_{xy}\delta_{i}\leq\tau_{l}\ \ \forall x,y\] (33) where \(\tau_{l}\) is the initial supply of the mobile wireless power transmitter.
4. A linear constraint set in both \(z^{i}_{xy}\) and \(l_{xy}\) to ensure that a sensor can receive power from the position (x,y) only when there is a landmark for the charger in that position.
5. A linear constraint in both \(z^{i}_{xy}\) and \(l_{xy}\) guarantees that each sensor is receiving power from at least one landmark.  
A linear constraint set in \(z^{i}_{xy}\) ensures that sensor \(i\) receives power from a landmark in the position (x,y) only if it is within the transmission range of the mobile charger.
7. A linear constraint set in \(z^{i}_{xy}\) to ensure that the high priority nodes receive power that is at least equal to what the lower priority nodes receive.
## 5 Performance Evaluation
Among the first 87 commodities, the ratio of the average delay of the GAGR algorithm to the average delay of the FW algorithm is 0.8, and the ratio of the average delay of the VTSR algorithm is 0.84. However, both the FW algorithm and VTSR algorithm have 13 commodities that do not arrive at the ground station, resulting in packet loss, while the 100 commodities of the GAGR algorithm do not experience packet loss. Secondly, we calculate the ISL utilization of all commodities under the TCP protocol under the three routing algorithms, as shown in Figure 6. We divide the simulation time into 10 s intervals, which can capture the dynamic changes in the network performance and the influence of our algorithm over time, showing the advantages of the GAGR algorithm in reducing the link utilization. Obviously, in all simulation intervals, the link utilization of the GAGR algorithm is lower than that of the FW algorithm and VTSR algorithm. The average link utilization of the FW algorithm is the highest, followed by that of the VTSR algorithm. Because the paths found by these two algorithms are not satisfactory, the number of forwarding hops is greater and more ISLs need to be passed. Similar to the situation under TCP, the monitoring link utilization under UDP is as shown in Figure 7. The link utilization of the GAGR algorithm is still the lowest among the three, because the approximate optimal solution is found in the optimization and iteration process of the candidate path, which reduces the number of forwarding hops. Figure 4: Average round-trip times under TCP protocol. Figure 5: Average round trip times under UDP protocol. Figure 6: Inter-satellite link utilization under TCP protocol. Furthermore, we sort each commodity based on its receive-send comparison according to the TCP protocol, as shown in Figure 8. Among the receiving performance of 100 commodities, the average receive-send ratio of our proposed GAGR algorithm is 0.49, with that of the FW algorithm being 0.38 and the VTSR algorithm being 0.42. In the UDP protocol, as shown in Figure 9, the average receive-send ratio of our proposed GAGR algorithm is 0.89, with that of the FW algorithm being 0.69 and the VTSR algorithm being 0.72. It can be seen that the GAGR algorithm has better throughput and provides more reliable network services. Figure 8: Received/sent traffic ratio under TCP protocol. Figure 7: Inter-satellite link utilization under UDP protocol. Finally, we compare the three routing strategies in terms of the number of hops for each commodity, as shown in Figure 3:0 FW requires the highest number of forwarding steps to complete a request, while GAGR has an average of 9.35 hops per commodity request and VTSR has an average of 9.44 hops per commodity request. Figure 3:1 Hop count comparison. Figure 3:2 Received/sent traffic ratio under UDP protocol.
## 3 Methodology and Details of Data Analysis
### Case Study II: Analyzing Movements of the Geodetic Pillar
Campaigns ran from 10 March to 6 April 2000 and from 20 February to 22 March 2002 [32]. Figure 12: LS spectrum of inclination data of _Sensor I mounted on the building_ that hosts the pillar. Campaign 2000-Y axis (1 DY-2000) and X axis (1 DX-2000). Campaign 2002-Y axis (1 DY-2002) and X axis (1 DX-2002). Figure 13: LS spectrum of inclination data of _Sensor II mounted on the pillar_. Campaign 2000-axis Y (2 DY-2000) and axis X (2 DX-2000). Campaign 2002-axis Y (2 DY-2002) and axis X (2 DX-2002). Inspecting the deflections of the pillar using the hourly data of continuous GPS reference stations generally supported the results of the previous findings. In the variance _vs._ frequency graphs in Figure 14, we can clearly see the significant peaks derived from the time series of the GPS solutions. Where the signal for the northing component (dN) contains periods with T = 24 h and T = 12 h, the easting component (dE) includes only the period with T = 12 h for both campaigns. In the results, the amplitudes of the significant peaks of the signal contents are around 0.5 mm. \begin{table}
\begin{tabular}{c|c|c|c|c|c} \cline{3-6}  & & \multicolumn{3}{c|}{**Year 2000 Campaign (10 March to 6 April)**} \\ \cline{3-6}  & & \multicolumn{3}{c|}{**Y-axis (east-west)**} & \multicolumn{3}{c}{**X-axis (north-south)**} \\ \cline{3-6}  & **Period** & **Frequency** & **Amplitude** & **Frequency** & **Amplitude** \\  & **(hour)** & **(cyc/hour)** & **(mrad)** & **(cyc/hour)** & **(mrad)** \\ \hline
**SENSOR-I** & **12** & 0.083 & 0.008 & 0.083 & 0.006 \\ \cline{2-6}  & **24** & 0.042 & 0.018 & 0.042 & 0.015 \\ \hline
**SENSOR-II** & **12** & 0.083 & 0.031 & 0.083 & 0.026 \\ \cline{2-6}  & **24** & 0.042 & 0.062 & 0.042 & 0.039 \\ \hline \end{tabular}
\end{table} 
Table 5: Overall LSSA results of inclination data: _geodetic pillar_ (S-II) and _host building_ (S-I). Figure 14: LS spectrum of GPS data (time series of the northing (dN) and easting (dE) components of the _ISTA pillar position_) for both campaigns in 2000 and 2002.
## 3 Results and Discussion
### Chemical Investigation
#### 3.3.2 Characterization of Volatile Organic Compounds (VOCs) by SPME-GC/MS  
The analyses conducted using SPME-GC/MS enabled the identification of numerous volatile components belonging to various chemical classes such as aliphatic alcohols, sesquiterpenes, and monoterpenes (Table 1). These volatiles compounds were found in all analyzed tissues and genotypes, though with varying distributions. The alcohols 1-butanol, 3-methyl- and 1-butanol, 2-methyl- were present in all investigated samples. The highest percentage of 1-butanol, 3-methyl- was found in the stems of Orte 2 F7 P2 (30.2%) and Orte 1 F4 P10 (24.5%), while lower amounts were detected in the same tissue of Grato 1 (13.2%). On the other hand, 1-butanol, 2-methyl- reached its highest percentage in the SFH of Orte 2 F7 P2 (19%) and in the stem of Orte 1 F4 P10 (16.3%), with lower percentage mean values found in both tissues of Grato 1 (5.0% in the stem and 0.3% in the SFH, respectively). In contrast, 1-hexanol was present in the SFHs but not in the stems of the three investigated genotypes, with the highest percentage mean values detected in Grato 1 (33.8%) and Orte 1 F4 P10 (26.3%). Additionally, significant relative amounts of 3-ethyl-4-methylpentan-1-ol were relieved in the stems of [PERSON] 1 F4 P10 (50.1%) and Orte 2 F7 P2 (48.2%). Finally, 1-pentanol, 3,4-dimethyl- was detected in similar percentages only in the stems of Orte 1 F4 P10 (7.0%) and Orte 2 F7 P2 (6.0%). Among sesquiterpenes, \(\beta\)-eudesmene was present in all analyzed samples, but reached significantly higher percentages in the SFHs of all three tested genotypes: 90.8% in [PERSON] 1, 41.1% in Orte 2 F7 P2, and 38.1% in Orte 1 F4 P10 (Table 1). On the other hand, \(\beta\)-caryophyllene was detected in both the stem and SFH of [PERSON] 1 (0.7% and 6.7%, respectively), in the SFH of Orte 2 F7 P2 (5.2%) but was completely absent in both tissues of Orte 1 F4 P10. In contrast, the monoterpene _trans-\(\beta\)_-ocimene was present in both the stems and SFHs of Orte 1 F4 P10 (2.1% and 4.7%, respectively) and Grato 1 (24.1% and 0.9%, respectively), but was missing in either tissue of Orte 2 F7 P2 (Table 1). In general, VOCs have many beneficial effects including antioxidant effects, plant growth stimulation and anti-plant pathogen activity [11]. In particular, sesquiterpene compounds found in these matrices such as, \(\beta\)-eudesmene and \(\beta\)-caryophyllene are well-known for their antioxidant activity [11]. On the other side, branched-chain C5 alcohols such as, 2-methyl-1-butanol and 3-methyl-1-butanol were evaluated for their antifungal activity against _Botrytis cinerea_ of tomato and table grape and between the two compounds, 3-methyl-1-butanol was shown as the more active one [11]. In addition, some volatile compounds impart unique flavor and odor characteristics to food. In this regard, 1-hexanol has a fruity/green/sweet aroma [11] while trans-\(\beta\)-ocimene has been reported as one of the compounds that contributed the most to the aroma of organic passion fruit [11].
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Uncertainties Analysis
#### 3.3.1 Sensor Precision
For the Bossons and Brenva glaciers, where we have a relatively homogeneous number of matching pairs, similar precision is estimated for both zones (15-20 m/year). On other glaciers, we observe differences in precision that do not seem to be completely related to the accumulation or ablation zones. For example, in the lower parts of the Mer de Glace and Mage glaciers that have a gentle slope and where the surface speed is less than 50 m/year, the weighted standard deviation is only 2-4 m/year, thus close to the nominal precision calculated on a stable ice-free ground. Above the altitude of 2400 m, however, the terrain becomes steeper, shows greater spatial variability in ice flow and has fewer surface features to follow due to the presence of snow. Figure 4: Analysis of Sentinel-2 and Landsat 8 precision in ice-free areas as a function of individual repeat cycles for years 2017 and 2018. **(A)** Cumulative distribution of the weighted standard deviation values in ice-free areas as a function of the length of the repeat cycle for Sentinel-2 data. Weighted standard deviation is calculated for each pixel of the grid; **(B)** Same as A for Landsat data; **(C)** Evolution of the precision as a function of the length of the repeat cycle for Sentinel-2 (circle) and Landsat 8 (triangle). Precision is taken as the median of the distribution in **(A,B)**. **(D)** Evolution of the sub-pixel matching precision with the cycle length for Sentinel-2 (circle) and Landsat 8 (triangle) based on **(C)**. Repeat cycle is color-coded from red for long to blue for short repeats for all panels. This results in larger weighted standard deviations, at the order of 15-20 m/year. Another example is the Argentiere glacier, where the weighted standard deviation starts to increases considerably downstream of the ELA (2700 m), where the glacier surface is in the shade of the Aiguille Verte and Droites peaks. In summary, several factors can affect the precision of surface speed estimates: seasonal flow variability, shaded areas, steep slopes, difficult weather conditions (resulting in significant cloud cover) and, most importantly, the absence of surface features due to permanent snow cover. On average for the French Alps, we found a weighted standard deviation of 12.4 m/year in the accumulation zone compared to 12.8 m/year in the ablation zone (Figure 2C), highlighting, for the reasons stated above, the difficulty of estimating the nominal sensor precision for displacement mapping using areas covered by glaciers.
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### Multi-Channel Electronics
#### 2.3.5 Synchronization Scheme  
A clock network makes it possible to combine several systems in synchronous operation. Each sub-system is connected to the PC via its own PCIe interface (Figure 6).  
In this way, all 1024 elements of the matrix array can be addressed individually and in parallel without using a multiplexer. The synchronization of the four sub-systems is achieved by a separate synchronization circuit. Here, one system acts as the master and generates the 20 MHz master clock with which all systems are operated. In addition, the Virtex-6 FPGA of the master system generates a coded 1 MHz synchronization signal, which also serves as a trigger for an ultrasound measurement scan. The master clock and synchronization signal are passed on to the individual sub-systems via a distribution circuit. Compared to Figure 3 in Section 2.3.1, the on-board generated clock is no longer used to drive the PLLs. Instead, the external clock, which is distributed to the individual  
Figure 6: Synchronization scheme with one master and three slave systems.  
systems, is applied via the CLK IN input by selecting it with a switch. The master system itself also receives the two signals again as input to ensure phase equality with the other sub-systems in the sequence control. Other research groups, such as those around the platform ULA-OP, are also pursuing similar approaches [18]. While our method is based on synchronous distribution of clock and trigger signals with equal signal run lengths, ULA-OP's synchronization circuit relies on PLLs with a programmable phase shift and on a trigger delay compensation to realize zero delay of the timing signals between the master and slave systems.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 2 Axial Compression Test
### Material Performance  
#### 2.2.1 Concrete Constituent Materials  
The cement used was P.O 42.5 grade ordinary Portland cement. The coarse aggregate was crushed limestone with a particle size range of 5-20 mm and continuous gradation. Natural river sand with a fineness modulus of 2.87 was selected as the fine aggregate. To improve workability, a polycarboxylate-based high-performance water reducer (transparent light-yellow in appearance) with a water reduction rate of 25% was employed.
# Energy Optimisation of Industrial Limestone Grinding Using ANN
## 7 Optimisation of the Grinding Process Using Artificial Neural Networks
### Design of a Predictive Model for Evaluation Indicators Using ANN
#### 7.1.2 Loss Function and Metrics  
The mean squared error (MSE) was selected as the primary loss function due to its convexity and strong statistical grounding in Gaussian likelihood estimation. It penalizes large deviations more heavily, leading to improved accuracy in most regression tasks:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{7}\]  
To complement MSE, the mean absolute error (MAE) was also computed:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{8}\]  
While MAE treats all errors equally and is robust to outliers, MSE emphasizes larger discrepancies. Together, they provide a comprehensive view of both typical and extreme prediction errors. Additional metrics reported include the root mean squared error (RMSE) and coefficient of determination (\(R^{2}\)), which quantify overall predictive performance.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **Unit** & **Min** & **Max** \\ \hline Mill screw conveyor motor speed & rpm & 29.9 & 51.2 \\ \hline Temperature after the mill & \({}^{\circ}\)C & 67.4 & 80.9 \\ \hline Airflow before the fan & m\({}^{3}\)/h & 160,000 & 190,000 \\ \hline Pressure difference across the mill & mbar & 22.7 & 39.3 \\ \hline \end{tabular}
\end{table}
Table 3: Controlled variables.
## 3 Numerical Results
### Parameter Uncertainty Analysis of Burgers' Equation
#### 3.2.2 Sensitivity Analysis  
To further analyze the parameter uncertainty law of Burgers' equation, the sensitivity of the parameter is studied. Figure 7 reports the standard MC and MFMC estimator, respectively, in \(p=10^{2}\) and \(p=10^{4}\) computational budget, repeated 100 times estimates of the main sensitivity and total sensitivity box plots. It is worth noting that compared with the standard MC estimation, the sensitivity index of the MFMC method estimator decreases and the estimated variance is also reduced. Therefore, in all cases, compared with the standard MC estimation, the multi-fidelity estimation has smaller variance with the budget increases. When the true value of the sensitivity index is closer to zero, the variance of the estimator is similar as the order of sensitivity magnitudes.
## 5 Conclusions  
In this study, an attempt was made to evaluate vegetation health in the CLP in multi-dimensional analysis using GPP and Ec time series data. The short-term abrupt change (disturbance time and recovery rate) and long-term trend change (eight change types) were researched to describe and quantify the vegetation health between 2001 and 2020. BFAST and BFAST01 algorithms were used to obtain the three ecological health assessment indicators. The most important findings and conclusions drawn from this study include the following.  
1. More than 90% of disturbance pixels of GPP and Ec in the short-term change only once and more than 60% of pixels recover after disturbance. However, the recovery rate after disturbance is slow, and the interval with the largest proportion is 0-0.00015. The long-term trend mostly exhibited a monotonic increasing trend.
2. The function of the ecosystem on the CLP has been improved, but the resilience of vegetation is weak. Overall, the health of the ecosystem has been improved, but locally, the ecosystem is not healthy.  
Therefore, we suggest that the ecological restoration project of the CLP should shift its focus from increasing the amount of vegetation to enhancing its resilience.  
Conceptualization, X.L. ([PERSON]) and X.L. ([PERSON]); methodology, B.H. and X.L. ([PERSON]); validation, L.T, Q.Y., L.Z. and Y.M.; writing--original draft preparation, X.L. ([PERSON]); visualization, X.L. ([PERSON]); supervision, X.L. ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Flexible Introduction Team of Ningxia Hui Autonomous Region (Grant No. 2020 RXTDLX03); Remote sensing monitoring and evaluation of ecological status in Ningxia (Grant No. NXCZ202202003).  
The authors would like to thank the anonymous reviewers and the editor for their constructive.  
The authors declare no conflict of interest.
## 4 Evaluation
### Results and Discussion
#### 4.2.1 Performance Evaluation in the Skudai Scenario  
The transmission overhead incurred and average energy consumed by the proposals are recorded in Figures 15 and 16, respectively, while throughput and average delivery delay are recorded in Figures 17 and 18, respectively. PRoPHET without a replication technique incurs a transmission overhead of 866.9. Using fixed replication quotas of 2, 4 and 8 results in 98.4%, 97.9% and 97.3% reduction in transmission overhead, respectively. With ADS, PRoPHET's transmission overhead is reduced by 97.4%. While incorporating only LAMeD into PRoPHET increases transmission overhead by about 12 times, LARep (which includes our proposed replication mechanisms) achieves a reduction of 99.7%. In terms of energy consumption, PRoPHET consumes an average of 176.2 J without a replication technique. Using fixed replication quotas of 2, 4 and 8, reduces this value by 85.5%, 77.1%  
PRoPHET gives a throughput of 54.5% without a replication technique. With fixed replication quotas of 2, 4 and 8, throughput drops by 67.9%, 48.6% and 22.9%, respectively. Also, utilizing ADS reduces throughput by 47%, while LAMeD and LARep increase throughput by 45% and 49.4%, respectively. In terms of delivery delay, PRoPHET without a replication technique records the lowest average at 6.1 h. With fixed replication quotas of 2, 4 and 8, average delivery delay increases by 11.5%, 21.3% and 25%, respectively. Also, utilizing ADS, LAMeD and LARep increases average delivery delay by 20%, 47.5% and 73.8%, respectively.  
In summary, incorporating the replication techniques under comparison into PRoPHET routing protocol reduces transmission overhead and energy consumption, but only LARep increases throughput. LARep also records significantly lower transmission overhead and energy consumption. Average delivery delay on the other hand increases with throughput. We discuss these results and their implications in real-world scenarios next.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
###### Abstract  
This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.  
radar; SLAM; autonomous driving; ego-localization; odometry +
Footnote †: journal: Applied sciences