esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Determination of Crop Seeding and Harvest Dates  
Although detailed field information was not available to allow a more robust and definitive interpretation of the time-series InSAR signatures, yet the examples of these four crop fields showed great promise for using InSAR data to determine the dates of crop seeding and harvest. Two scenarios have been given in previous section to detect the occurrence of change in a field using InSAR coherence, i.e., transition of coherence level from high to low or vice versa. The second scenario is more proper for this study because a relative high coherence is anticipated to last during a period of time after crop seeding but before significant plant development and after harvest when crop fields become more coherent. The following algorithm was thus developed:  
(1) Find all candidate InSAR pairs from the beginning to the end of the season, each with an increase of coherence larger than a given threshold value \(\delta^{\circ}\) = 0.05.  
(2) Determine the time period for seeding activity, with the first candidate date set starting from May 1 to mid-July.  
(3) Determine the time period for the harvest activity, with the first candidate date starting from August 1 to end of October.  
The threshold of 0.05 was determined based on inspecting the time-series InSAR coherence of example fields. The searching windows for seeding and harvest activities were determined based on local knowledge of general crop phenology in the study region. The period of spring snowmelt (in April) could cause false alarm of seeding activity, thus was excluded from the searching window as seeding can only take place after snowmelt. Due to the more complex situation associated with harvest operation, an additional compound condition was added using VH:  
(3a) If VH decreases by more than 2 dB during the same period, or less than 2 dB and is lower than \(-\)19 dB at the end of the period, then accept the candidate; otherwise switch to the next candidate and evaluate this additional condition again.  
Results using the above algorithm at the field level are reported in Table 2. The accuracy for correct detection of seeding date was 85% (n = 67) and 56% for harvest date (n = 77). For some fields, the observed seeding or harvest dates differed from the detected dates by one satellite acquisition cycle (12 days, the \"\(\pm\)1 Cycle\" column on Table 2). This could possibly be related to the fact that field operations such as seeding could sometimes last for several days or longer for a single field. In the case of harvesting, it can even span more than 10 days to complete a field due to various constraints. When these fields are counted, the detection rate increases to 91% for seeding and 88% for harvest. In total, this study failed to detect six fields for seeding and nine fields for harvest, either because no valid change was detected or the detected date was different from the observed date by more than one satellite acquisition cycle.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 2 Common Methods for Detecting Underwater Cracks in the Surface of Concrete Dams
### Intelligent Monitoring Techniques
#### 2.2.3 Temperature Tracing Method  
The temperature tracing method is based on the theory of heat conduction and identifies the crack according to the difference in the cooling rate of the heat source between the crack and the surrounding medium. The schematic diagram of the temperature tracing method is shown in Figure 1. [PERSON] et al. [47] measured the underwater concrete cracks according to the change of heat source cooling rate caused by the change of water content in the porous casing before and after the crack, but this method was less sensitive and could not measure the crack width. [PERSON] et al. [48] proposed a temperature tracing method and monitoring system for the detection of underwater concrete cracks in dams based on the theory of heat conduction. [PERSON] et al. [49] amplified the difference in the cooling rate of the heat source before and after crack emergence by using a hollow sleeve, and measured the crack width using an improved monitoring system. However, these methods require a large number of detection pipes to be pre-embedded in the underwater concrete structure, which will have some impact on the performance of the dam during its normal use.
# Development of a Wireless System to Control a Trombe Wall for Poultry Brooding
## 1 Introduction
### LoRa  
LoRa is a modulation technique in a wireless transmitting technology where an emitter sends small data packets at a low power (between 0.25 and 50 Kbps, in Europe) to a long-range receiver, as seen in Table 1[12, 13]. In one line of sight, there was once a record of 766 km [14]. This technology was developed by Cycleo, lately acquired by Semtech [15].  
Essentially, LoRa is a way of operating radio waves to code information in a symbol format and an astute method of having good receiver sensitivity with a low bit error rate, in a long range [15, 16].  
In Europe, the modulation and the frequency band transmission belongs to the ISM (Industrial Scientific Medical, used to scientific means, industrial and medic ones that are not telecommunications) and is an open band, meaning that no license is required for its use. However, that creates disadvantages because there are more interference and low data rates [17]. Center propagation frequency is located between 863 and 870 MHz, and there are bandwidths of 125, 250 and 500 KHz to the information. Divided in 10 channels, eight of them diversify their data rates from 250 bps to 5.5 Kbps, one with 11 Kbps and another FSK channel working at 50 Kbps [12].  
The modulation technique is called Chirp Spread Spectrum (CSS) and consists of the deliberate lengthening in frequency domain, obtaining more robust signals in the presence of noise, interference or jamming, thereby providing a basic reliability, whereas there is a higher safety when it comes to third parties interpretation of the signal. In exchange, there is an increase in bandwidth and a higher circuit complexity. These deliberate lengthening in the frequency spectrum have the designation of down-chirps (when frequency decreases over time) and up-chirps (when frequency increases over time), as seen in Figure 1[18].  
The Spreading Factor (SF) is the number of bits that can be encoded in a symbol and it can vary between 7 and 12. The higher this value, the greater the transmission range is, and consequently, the smaller the duration of the emitter's battery, which is suitable to the range/battery. In brief, the bigger this parameter, the lower the data, rate and hence, the greater the time on air, making the range larger. This can be observed in Table 2 which represents an 11 byte packet being submitted to different SF values [19].  
\begin{table}
\begin{tabular}{l c} \hline
**Environment** & **Reach (km)** \\ \hline Urban Areas & 2–5 \\ Rural Areas & 5–15 \\ Line of Sight & \(>\)15 \\ \hline \end{tabular}
\end{table}
Table 1: LoRa extension.  
Figure 1: CSS illustration (Up-Chirp).
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Optimization and Simulation  
Optimization must be integrated with simulation tools in designing dynamic Low-Emission Zones (LEZs) to ensure real-world feasibility and adaptability. Optimization models, such as Mixed Integer Linear Programming (MILP) or genetic algorithms, define optimal LEZ boundaries based on predefined objectives like emission reduction and traffic efficiency. However, these models rely on assumptions that may not fully capture complex urban dynamics.  
Simulation tools, like SUMO, provide high-fidelity traffic and emission modeling, allowing researchers to test, validate, and refine optimized LEZ designs under real-time conditions. They account for nonlinear traffic behaviors, congestion spillovers, and dynamic mobility patterns, ensuring optimized policies remain practical, scalable, and effective.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 2 Tracking Error Estimation
### Coherent Prefilter Design  
The coherent pre-filter is based on EKF because of the non-linearity of the observation model. In this case, the correlator outputs are used directly to estimate the signal amplitude, initial carrier phase error, initial carrier frequency error, and initial carrier frequency rate error (\"initial\" in this context refers to the beginning of coherent integration interval). The state vector and observation vector of coherent pre-filter are shown as shown in Equations (26) and (27), respectively.  
\[X_{\rm coh}=\left[A\;\delta\tau\;\delta\rho\;\delta\;\dot{\delta}f\right]^{ \rm T} \tag{26}\]  
\[Z_{\rm coh}=\left[I_{\rm E}\;Q_{\rm E}\;I_{\rm P}\;Q_{\rm P}\;I_{\rm L}\;Q_{\rm L }\right]^{\rm T} \tag{27}\]  
The state transition matrix of coherent pre-filter is written as follows:  
\[A_{\rm coh}=\left[\begin{array}{cccc}0&0&0&0&0\\ 0&1&0&\beta\cdot T_{\rm coh}&0\\ 0&0&1&T_{\rm coh}&\frac{T_{\rm coh}{2}^{2}}{2}\\ 0&0&0&1&T_{\rm coh}\\ 0&0&0&0&1\end{array}\right] \tag{28}\]  
The process noise vector of coherent pre-filter is:  
\[w_{2}=\left[w_{\rm A}\;w_{\rm code}\;w_{\rm clock}\;w_{\rm drift}\;w_{\rm accel }\right]^{\rm T} \tag{29}\]  
where \(w_{\rm A}\) is the process noise for the signal amplitude.  
The corresponding covariance matrix of the system model for coherent pre-filter is given by:  
\[Q_{\rm coh}=E[w_{2}w_{2}{}^{\rm T}] \tag{30}\]  
The measurement vector is a nonlinear function of state variables for coherent pre-filter. Thus, a Jacobian matrix is constructed to accomplish the correction in EKF. Detailed discussion of Jacobian matrix construction for coherent pre-filter is provided in a great deal of literature, and will not be shown here [13; 17; 19; 20]. The observation noise variance of coherent pre-filter can be calculated as Equation (5), as long as the \(C/N_{0}\) estimation is available.
# Centralized Energy Prediction in Wireless Sensor Networks
## 1 Introduction
Our hypothesis is that by centralizing the prediction we can improve the prediction accuracy and network performance if compared with the case that every node runs the prediction. This is important for two main reasons: (i) improving network performance means improving the delivery rate and decreasing delay of energy information required for control decisions without impacting on other packets traffic, and (ii) in case of missing packets, the controller will be able to predict the missing information. The second point is not possible to solve in the cases without prediction or executing the prediction on every node unless the node retransmits the packet, which means more packets traffic. Previous works tested Markov chain [18; 19] for energy consumption prediction in SDWSNs but, to the best of our knowledge, only our previous work [20] implemented the Markov chain running in the controller to predict the network energy consumption of each node in the network. Here, we extend our previous research by implementing and comparing two strategies: the prediction algorithm runs on all the sensor nodes and the prediction algorithm runs only on the controller node. The metrics we used to compare the performance were prediction accuracy, energy consumption, packets delay, delivery rate, and control packets overhead. Despite being widely used to support prediction in networks, machine learning strategies do not apply to our problem as one could not run the ML prediction algorithm on resource-constrained devices given a large amount of data necessary [21]. Our key findings are that by moving the prediction processing overhead to the SDN controller, we obtain a better prediction accuracy, improved network performance, and reduced energy consumption. However, this approach increases the traffic of packets related to energy consumption information. The remainder of this paper is organized as follows. Section 2 reviews related works about energy consumption prediction using Markov chain in WSN. Section 3 explains the Markov chain based energy consumption prediction. Section 4 details our energy prediction proposal. Section 5 shows and discusses the experiments and results, and Section 6 concludes the paper.
## 3 Material and Test Program
### Tension Load and Creep Testing  
Tension load and creep tests were conducted followed by [PERSON] et al. and ASTM D3689M-07(2013)e1 [14; 27] to evaluate the elastic and plastic characteristics of micropiles. During the tension load test, loads were incremented as 0, 0.15, 0.30, 0.60, 0.75, 0.90, 1.00, 1.15, 1.30, 1.45, and 1.60 times of the target load (\(TL\) = 1000 kN) for loading, and 1.60, 1.30, 1.00, 0.75, 0.50, 0.25, and 0 times of \(TL\) for unloading. Applied loads remained for 2.5 minutes. A tensile tester (2.75 MN max. capacity), a hydraulic pump (70 MPa), a pressure transducer (100 MPa), a linear variable differential transformer (LVDT, 200 mm), a data logger (_DC-105R_ and _DC-7104R_), and reference beams were used. All devices were equipped by \(TML\), Japan. The loads were applied by the tensile tester and the hydraulic pump attached to the reference beam. The LVDT was fixed to a reference beam placed at a distance at least five times the diameter of the tested micropile away. After completing the test, an unrecoverable displacement (i.e., plastic displacement, \(\delta_{\text{p}}\)) was calculated by subtracting the elastic displacement (\(\delta_{\text{e}}\)) from total displacement (\(\delta_{\text{t}}\)).  
A creep test was performed at 1.30 \(TL\). The displacements at 0, 1, 3, 4, and 10 minutes were recorded. The creep is defined as the difference between the total displacement measured at 10 min and the displacement measured at 1 min. The creep should not exceed 1 mm [14].
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 2 SSA Forecasting
### Recurrent Forecasting  
Recurrent forecasting in SSA is also known as _R-forecasting_, and the findings in [25] indicate that _R-forecasting_ is best when dealing with large samples. If \(\mathbf{u}_{j}^{\
abla}=(u_{1j},\dots,u_{(m-1)j})^{\prime}\) is the vector of the first \(m-1\) elements of the \(j\)th eigenvector \(\mathbf{u}_{j}\), and \(u_{mj}\) is the last element of \(\mathbf{u}_{j}\). The coefficients of linear recurrent equation can be estimated as  
\[\mathbf{a}=(a_{(m-1)},\dots,a_{1})^{\prime}=\frac{1}{1-\sum_{j=1}^{k}u_{mj}^{2}} \sum_{j=1}^{k}u_{mj}\mathbf{u}_{j}^{\
abla}. \tag{4}\]  
With the parameters in Equation (4), a linear recurrent equation of the form  
\[\widetilde{x}_{t}=\sum_{i=1}^{m-1}a_{(m-i)}\widetilde{x}_{t-m+i} \tag{5}\]  
is used to obtain a one-step-ahead recursive forecast [29]. This linear recurrent formula in Equation (5) is used to forecast the signal at time \(t+1\) given the signal at time \(t,t-1,\dots,t-m+2\)[28] (Section 2.1, Equations (1)-(6)), and the one-step-ahead recursive forecast of \(x_{N+j}\) is  
\[\hat{x}_{N+j}=\left\{\begin{array}{ll}\sum_{i=1}^{j-1}a_{i}\hat{x}_{N+j-i}+ \sum_{i=1}^{m-j}a_{m-i}\widetilde{x}_{N+j-m+i}&\text{for $j\leq m-1$;}\\ \sum_{i=1}^{m-1}a_{i}\hat{x}_{N+j-i}&\text{for $j>m-1$.}\end{array}\right. \tag{6}\]  
We apply the recursive forecasting method in Equation (6) to obtain a one-step-ahead forecast.
## 3 Results and Discussion
### Multivariate Analysis  
#### 3.3.1 Principal Component Analysis  
A PCA was conducted using the AC and TPC results obtained for extracts of six spices using three new DESs and two conventional solvents. The results of the score plot and loading plot of the PCA were presented as a bi-plot in Figure 4.  
The first principal component (PC1) was 95.86% and had the highest eigenvalue of 3.83, while the second principal component (PC2) was 2.20% with a significantly lower eigenvalue of 0.088. As can be seen, PC1 was inversely correlated with all variables: DPPH (\(-\)0.9673), ABTS (\(-\)0.9893), FRAP (\(-\)0.9839), and F-C (\(-\)0.9757), whereas PC2 was positively contributed by DPPH (0.2521).  
Moreover, the bi-plot showed that the clove extracts, as well as cinnamon and ginger extracts obtained using traditional solvents (ethanol and a mixture of ethanol and water (70:30 \(v/v\))) with the highest AC and TPC, were located to the left in the score bi-plot and had negative values for PC1. However, other tested extracts with lower DPPH, ABTS, FRAP, and TPC values were clustered together on the right-hand side of the bi-plot. Interestingly, clove extracts were located some distance away from all of the other sample types. This suggests that their antioxidant composition differed significantly from the studied extracts. Additionally, spice extracts with low and moderate AC and ethanolic extract of clove (CloEt) with the highest ABTS, FRAP, and TPC determined by F-C method were situated under the A1 axis. In contrast, extracts of clove prepared with an ethanol:water (70:30 \(v/v\)) mixture (CloEtW) and the proposed DESs (CloDES1, CloDES2, and CloDES3), as well as ginger, nutmeg, and cinnamon obtained by using traditional solvents with the highest DPPH results, were located upper the A1 axis (Figure 4).  
It is noteworthy that the investigated spice extracts fell into three distinct groups, respectively. Three clove extracts (CloEt, CloEtW, and CloDES3) and an ethanolic extract of cinnamon (CinEt) with the highest antioxidant properties created an evidently separate cluster. Moreover, five extracts of clove (CloDES1, CloDES2), nutmeg (NutEt), and ginger (GinEt and GinEtW) with similar moderate AC and TPC evidently separated from the third cluster, including all the other extracts with the lowest antioxidant properties.  
Figure 4: Principal component analysis bi-plot of antioxidant capacity determination and total phenolic content in the studied extracts of different spices analyzed using DPPH, ABTS, FRAP, and F-C methods.
###### Abstract  
Environmental remediation requires ion-selective polymers that operate under a wide range of solution conditions. In one example, removal of trivalent and divalent metal ions from waste streams resulting from mining operations before they enter the environment requires treatment at acidic pH. The monoethyl ester phosphate ligands developed in this report operate from acidic solutions. They have been prepared on polystyrene-bound ethylene glycol, glycerol, and pentaerythritol, and it is found that intra-ligand hydrogen bonding affects their metal ion affinities. The affinity for a set of trivalent (Fe(III), Al(III), La(III), and Lu(III)) and divalent (Pb(II), Cd(II), Cu(II), and Zn(II)) ions is greater than that of corresponding neutral diethyl esters and phosphonic acid. In an earlier study, hydrogen bonding was found important in determining the metal ion affinities of immobilized phosphorylated polyol diethyl ester coordinating ligands; their Fourier transform infrared (FTIR) band shifts indicated that the basicity of the phosphoryl oxygen increased by hydrogen bonding to auxiliary -OH groups on the neighboring polyol. The same mechanism is operative with the monoprotic resins along with hydrogen bonding to the P-OH acid site. This is reflected in the FTIR spectra: the neutral phosphate diethyl ester resins have the P=O band at 1265 cm\({}^{-1}\) while the monoethyl ester resins have the band shifted to 1230 cm\({}^{-1}\); hydrogen bonding is further indicated by the broadness of this region down to 900 cm\({}^{-1}\). The monoprotic pentaerythritol has the highest metal ion affinities of the polymers studied.  
polymer; ligand; metal ions; separations +
Footnote †: journal: materials
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.18 Utchem  
UTCHEM is a 3D model for multiphase flow and reactive transport of multiple compounds that can simulate a variety of processes in differently saturated media [741]. Accordingly, this multidisciplinary approach can be applied to many applications, especially surfactant enhanced aquifer remediation (SEAR). In addition, physical, chemical, and biological processes associated with the fate and transport of NAPLs were included in the model. These processes include the dissolution and/or mobilisation of NAPLs by undiluted remediation fluids, chemical and microbiological transformations, and changes in fluid properties during site remediation. UTCHEM enables the simulation of remediation fluids with variable density, temperature, and viscosity. The GMS software can provide the interface to UTCHEM. Some recent applications relate to oil/water partitioning (Huynh et al. [742]) and transport of DNAPL in deep aquifers (Wenyi et al. [743]).
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 3 Verification Methods for Quantum Money in Quantum Wallets
### Zero-Knowledge Proofs for Quantum States  
Quantum zero-knowledge proofs (QZKP) provide a sophisticated approach to verification. With QZKPs, the quantum wallet can demonstrate possession of a specific amount of quantum money to a verifier without revealing the actual quantum states. In a zero-knowledge proof, the wallet engages in a protocol with the verifier, allowing them to verify the currency amount while the individual states remain secret and intact. Quantum zero-knowledge protocols have roots in classical cryptography and, when adapted for quantum systems, offer a robust verification method that maintains state confidentiality and coherence.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 5 Experimental Results and Discussion
### Experimental Results
#### 5.1.1 Effects of the Payload Length  
Figure 9 presents the PDR as a function of the payload length with a constant SF12, \(CR=4/5\) and \(BW=125\) KHz in the 868 MHz frequency band. We can notice that the PDR curves are descending with increasing payload length, which demonstrates an impact of the packet length on the frame reception. To observe the shape of the curves better, Figure 9 presents the same data in the log scale to show the limit behaviour of the \(PDR\). In general, on wireless channels, \(PDR\) is a function of the bit error rate and the packet length. The \(PDR\) drops when the packet length increases. This assertion on the impact of packet length on the wireless channels corroborates with our LoRa experimental analysis depicted in Figure 9. We also notice that the shape of the PDR curves is different for each device depending on the position, as described in Table 4, which strongly impacts the channel quality. We can also observe that the device in POS B has a better PDR than the device in POS 2, despite the fact that the distance from both POS B and POS 2 is almost the same. The effect comes from the fact that POS 2 is placed in a much thicker vegetation area (in the green set area as described in Figure 7), which denotes more path loss and more possibility of the collision of our packets.  
We present in Figure 10, the \(PDR\) with different payload length in POS 2 operating in the 433 MHz and 868 MHz band for different \(SF\) and a constant \(CR=4/5\). We draw up the following remarks: the \(PDR\) decreases with increasing payload length. A larger number of symbols increases the probability to encounter channel attenuation variations, thus rejecting the reception of some symbols. In the case of higher \(CR\) (figure not presented), the trade-off is favourable because of the added redundancy; thereby, the problem is denoted by a slight reduction in the \(SNR\). From the figure presented, increasing the payload increases the number of symbols, which also increases the likelihood to encounter channel variation attenuation. In this case, symbols are rejected, bits are lost, the CRC fails and the whole packet is discarded, which leads to a severe reduction in the \(PDR\), and hence the performance.  
Figure 9: (a) PDR vs. payload length for SF12, CR = 4/5, BW = 125 KHz @ 868 MHz (b) log(PDR) vs. payload length for SF12.