esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 5 Validation and Discussion
### Results  
#### 5.2.1 Scenario (a): \(\Delta\tau\) Larger than 1 Chip  
In this scenario, we consider a relative delay of two L1 C/A chips, which corresponds to a path difference around 600 m. In this case, the two signals are clearly parted (see Figure 2 (top)) and the estimation of the main signal could almost be decoupled from the estimation of the secondary signal. Figure 3 (left hand side plots) shows the root mean square error (RMSE) for the time-delay estimation. One can see the threshold for the chirp signal at SNR_OUT = 20 dB (i.e., the SNR at the output of the matched filter) and for the GPS L1 C/A signal at SNR_OUT = 21 dB. For a SNR_OUT larger than this threshold the estimator reaches the CRB: (1) this validates the new CRB expression, and (2) provides the receiver conditions to be in a correct operation region. It is worth pointing out the impact that having two sources have on the time-delay estimation w.r.t. the single source case, where the threshold region for a GPS signal is at SNR_OUT = 15 dB (refer to [17,18]). Figure 3 (right hand side plots) show the RMSE for the Doppler frequency estimation. In this case, the performance for both direct and reflected signals is on the CRB in the range of SNR_OUT values considered. Notice that it does not make sense to consider lower SNR values because in such case the time-delay estimate is below the convergence threshold. In any case, this result validates again the CRB expression.
## 3 Materials and Methods
### Data Validation  
The system proposed was tested on indoor and outdoor scenarios and compared against a reference instrument. The first scenario was inside an enclosed room, and the reference instrument was Schneider's thermostat (ST), which measures only temperature and humidity and shows the live measurements through a display.  
Schneider personnel gave access to the site where the thermostat was placed for 1 h (from 12 pm to 1 pm); since photos were not allowed, Figure 7 shows a layout of the area. As illustrated, the proposed OSWS (red rectangle) was placed next to the thermostat (green rectangle) and was configured to have the same measurement interval, in this case, 30 s. It is essential to highlight that there were no windows, and the single door was closed; hence, there was no airflow. During this process, the OSWS was powered with an internal Li-ion battery.  
For outdoor comparison, the OSWS was placed on a house roof exposed to the air for one day. The reference instrument was a Purple Air sensor 2.118 km away, which is considered a small distance, and the weather conditions.  
Figure 8 shows the map and the distance the Google Maps app estimated. The purple point is where the weather station was placed, and the red point represents the Purple Air sensor. The line represents the distance between the OSWS and the Purple Air sensor. The red dots are the specific locations.  
Figure 6: PACF plot of a temperature times series.  
Figure 7: The layout of the measurement site.  
The measuring time was 24 h (from 13 th May at 01:30 to 14 th May at 1:30), with a measurement time interval of 10 min. The data obtained from the OSWS were stored on an SD card for further analysis, and the data from the reference tool were downloaded from Purple Air's website.
# Stochastic Model Predictive Control for Urban Traffic Networks
## 3 Stochastic MPC-Based Model  
### Uncertainty  
Due to the complexity and diversity in the real world, uncertainty widely exists in practical engineering problems, and the urban traffic signal control system is no exception. For instance, the traffic demands at the boundary of a specific urban road network, unexpected traffic fluctuations (caused by accidents or parking garage), and the entering (or exiting) traffic flow of a link between the adjacent intersections. As pointed out in [25; 27], the variance of traffic demand through the network is the most important. Accordingly, in this paper, we mainly focus on traffic signal modeling and optimization of urban traffic networks with uncertain traffic demand and disturbances.  
It is no doubt that the traffic demand (e.g., \(d_{i,r}(k)\) defined in Equation (5)) varies from day to day and from hour to hour. Assume that we can access the historical traffic information, which was collected during the last few months (or years) and recorded in the database of traffic information management system. Then, by utilizing the previous information and statistic techniques (or tools), we can derive the estimation of the variation tendency. In general, normal distribution can effectively describe the stochastic process in real-world system. Without loss of generality, in this paper, we assume that the traffic demand (e.g., \(d_{i,r}(k)\) defined in Equation (5)) follows a normal distribution function, i.e.,\[d_{i,r}(k)\sim N(\mu_{1},\sigma_{1}^{2}),\;\;k=1,2,3,..., \tag{8}\]  
where \(\mu_{1}\) and \(\sigma_{1}^{2}\) denote the mean value and variance of \(d_{i,r}(k)\), respectively.  
Similarly, by using the historical data, we can also approximately derive the probability distribution function of disturbance \(e_{i,r}(k)\). In addition, for the convenience of discussion, we define \(e_{i,r}(k)\), which follows normal distribution. Assume the mean value and variance of \(e_{i,r}\) are \(\mu_{2}\) and \(\sigma_{2}^{2}\), then, the distribution function of \(e_{i,r}(k)\) can be defined as  
\[e_{i,r}(k)\sim N(\mu_{2},\sigma_{2}^{2}),\;\;k=1,2,3,... \tag{9}\]  
It should be noted that we can also define \(e_{i,r}(k)\) (or \(d_{i,r}(k)\)), which obeys Poisson distribution [33; 34]. In summary, in a practical application, we should use the historical data to estimate the distribution function of \(d_{i,r}(k)\) and \(e_{i,r}(k)\) reasonably.
## 3 Experimental Results and Analysis
### Validation of the Effectiveness of the Rail Surface Condition Identification Algorithm
#### 3.3.4 Comparison with Other Algorithms  
To further evaluate the effectiveness of the proposed model, mainstream image classification networks were compared using the same dataset and under identical experimental conditions. The models selected for comparison include AlexNet [21], VGG16 [22], GoogLeNet [19], ResNet18 [23], ResNet34 [23], ResNet50 [23], EfficientNet [14], ShuffleNet [12], and MobileNet [13]. Since GoogLeNet and MobileNet were used as the teacher and student models in the previous experiments, their performance without transfer learning was also included for reference. The evaluation results of all models in the test set are shown in Table 6. From the accuracy results, among the non-lightweight models, GoogLeNet achieved the highest accuracy, followed by the ResNet series. AlexNet had the lowest accuracy, suggesting that deeper networks are better at capturing complex features. Among the lightweight models, the accuracy of all lightweight models was lower than that of non-lightweight models, likely due to shallower network depth and reduced feature extraction capabilities. MobileNet outperformed other lightweight models, confirming its suitability for the student model in the knowledge distillation strategy. The proposed model achieved the highest accuracy of 97.38%, surpassing other models by 1.4% to 17.6%, a model inference time of only 0.0371 s, ranking in the mid-to-upper range among all networks, and a parameter size of 4.21 \(\times\) 10\({}^{6}\), which is significantly smaller than GoogLeNet and ResNet models while maintaining comparable performance. Considering all evaluation metrics, the lightweight rail surface condition identification model based on knowledge distillation and transfer learning achieves the best balance between accuracy, efficiency, and real-time performance. This makes it suitable for deployment on trains, ensuring high identification accuracy while maintaining the real-time processing requirements of onboard systems.  
Furthermore, compared to the model in the literature [11], which shares the same recognition task and exhibits similar rail surface condition characteristics, the proposed model in this paper achieves a significant reduction in the number of parameters, a slight improvement in recognition accuracy, and enhanced suitability for vehicle deployment, making it more practical for real-world applications.
# Exploring Data Model Relations in OpenStreetMap
## 4 Experimental Analysis
### Relation Data Extraction  
To perform the data extraction, we carried out the following set of steps. The OSM XML for Madrid, Rome, Vienna and Zurich was downloaded from GeoFabrik on 30 June 2017. We downloaded the whole country files for Spain, Italy, Austria and Switzerland and then extracted the cities directly from this. Using a Python script, we extracted all of the relations for each of the cities and stored these in a PostgreSQL PostGIS database. Java software was developed to perform the analysis ofthe relations within the PostGIS database. R was used for statistical calculations and visualisations. In summary, the number of relations in each city are as follows: Vienna 9177, Zurich 1855, Rome 13,226 and Madrid 6403. We used the OSM relation representing each city boundary in OSM as a means of extracting the relations in each city in an equivalent manner. In cases where the OSM relation crossed the boundary of the city the entire relation was extracted. All attributes of each relation are stored in the PostGIS database. These include all of the attributes displayed in the XML in Figure 1. We calculate a number of additional attributes including the number of members in each relation, number of tags, etc.
## 3 Results and Discussion
### Drying Shrinkage  
In general, drying shrinkage is considered to be one of the main disadvantages of foamed concrete, because of the absence of coarse aggregates. Figure 5 presents the experimental results of drying shrinkage tests of the foamed concrete, using the Graf-Kaufmann method at an age of 28 days. It is clear that the addition of fly ash reduced drying shrinkage by about 30%. The incorporation of fine perlite increased shrinkage, with this being attributable to the characteristics of perlite; that is, its high water absorption which influences shrinkage. Conversely, the addition of fine Liaver reduced drying shrinkage significantly to about one third (Mix 6) of the drying shrinkage of the control mix. From these results, it can be concluded that drying shrinkage is reliant on mixture composition, as well as on the density of the foamed concrete. Lightweight aggregates can reduce shrinkage, depending on their type and characteristics. Expanded glass (Liaver) exhibited smaller drying shrinkage, since the Liaver grains have a higher restraining capacity for shrinkage as compared to cement particles. A combination of expanded glass (Liaver) and fly ash significantly reduced the drying shrinkage of foamed concrete. Fine Liaver is a non-reactive material, and it therefore does not undergo much volumetric change, meaning that it can restrain the development of cracking and shrinkage. The water absorption of fine perlite is much higher than that of fine Liaver, which can result in significant drying shrinkage.  
Figure 4: Thermal conductivity of foamed concrete mixes.
## 4 Analysis of On-Site Monitoring Results
### Analysis of Monitoring Results and Comparison  
#### 4.2.1 Deformation of Rectangular Pipe Jacking  
The distribution curve of settlement caused by the inclined twin-tunnel construction of the existing tunnel is shown in Figure 9. The initial passage of the new shield tunnel resulted in a maximum settlement of 14.94 mm for Passage A and 9.08 mm for Passage B of the existing tunnel. The maximum settlement occurred directly above the centerline of the new shield tunnel, forming a \"V\"-shaped curve in the settlement distribution. Approximately  
Figure 8: Monitoring point layout (unit: mm). (a) Passage A; (b) Passage B.  
two and a half months after construction of the left tunnel was completed, the right tunnel was constructed in the reverse direction, leading to increased settlement and deformation of the existing tunnel. The maximum settlement of Passage A and Passage B of the existing tunnel caused by the second construction was 17.67 mm and 10.08 mm, respectively. The deformation of the existing tunnel structure also transitioned from the original \"V\"-shape to a \"W\"-shape, displaying an asymmetric distribution.  
Analysis of measured data and settlement curves in conjunction with the real construction conditions indicates a deviation in the center of the settlement trough within the existing tunnel. This deviation is due to the inclined intersecting excavation of the new shield tunnel beneath the existing rectangular pipe-jacking tunnel. The shield cutter head reaches one side of the existing tunnel first, causing the soil in this area to be disturbed  
Figure 9: Settlement distribution of the existing tunnel. (**a**) Passage A; (**b**) Passage B.  
initially by the tunneling process, leading to a relatively higher cumulative settlement in this specific region.
## 4 Materials and Methods
### Analysis of Distinguishability of OTP Extraction Function
#### 4.3.2 Analysis of Distinguishable Security of the OTP Extraction Function  
To represent the distinguishable model, the experiment performed by adversary \(A\) is defined as follows. The model defined in this section is illustrated in Figure 6.  
Figure 5: Conceptual diagram for generalizing the extraction function family.  
Figure 6: The Oracle model selects one instance randomly from the function family or extraction function family \(J\).  
\begin{tabular}{l|l} Experiment 1 & Experiment 0 \\ \(Exp_{j}^{prf-1}(A)\) & \(Exp_{j}^{prf-0}(A)\) \\ \(E_{j}^{i}\gets J\) & \(E_{j}^{i}\gets Func(D,R)\) \\ \(b\gets A^{Ej}\) & \(b\gets A^{S}\) \\ \(Return\ b\) & \(Return\ b\) \\ \end{tabular}  
To distinguish between the OTP extraction function and the random function, the adversary \(A\) algorithm is constructed, as shown in Algorithm 1 for the model defined in the previous section. Adversary \(A\) distinguishes between \(World\) 0 and \(World\) 1 using threshold \(H\) shown in Table 1, and the detailed process consists of five steps.  
```
Input :Queries \(x_{i}\) Output :Decision \(b\)
1:\(b\gets 0\)
2:for\(i\gets 0\) to \(|V|\)do
3:\(cnt_{v_{i}}\gets 0\)
4:endfor
5:for\(i\gets 0\) to \(n\)do
6:\(y_{i}\gets g(x_{i})\)
7:if\(y_{i}\in V\)then
8:\(cnt_{y_{i}}\gets cnt_{y_{i}}+1\)
9:endif
10:endfor
11:for\(i\gets 0\) to \(|V|\)do
12:if\(cnt_{v_{i}}\geq H\)then
13:\(b\gets 1\)
14: Break
15:endif
16:endfor
17:return\(b\)
```  
**Algorithm 1**Algorithm for distinguishing the random function of adversary \(A^{S}\)  
In step 1, \(A\) initializes the final output value of \(b\) to 0 (line 1). In step 2, \(A\) initializes a total of 256 \(cnt_{v_{i}}\) (count values for each element \(v_{i}\) of the reduced set \(V\)) to 0 to check how many times each \(v_{i}\) is output (line 2 to 4). In step 3, \(A\) inputs the query \(x_{i}\) to the oracle and stores the response \(g(x_{i})\) in \(y_{i}\) (line 5 to 10). If \(y_{i}\in V\), then \(A\) updates the \(cnt_{v_{i}}\) initialized in step 2 to \(cnt_{v_{i}}+1\). \(A\) repeats steps 3 and 4 for \(n\) times (for example, \(2^{20}\) times, as shown in Table 1). Based on Table 1 analyzed in Ref [1], adversary algorithm \(A\) succeeds in distinguishing. According to Table 1, static and dynamic extraction functions equivalent to random functions output all elements within the range (\(Z_{10^{6}}\)) in similar frequencies to the expected value, while the improved dynamic extraction function corresponding to the vulnerable function family \(J\) outputs the elements of set \(V\) (\(V\subset Z^{10^{6}}\)) by more than 35 times the expected value. Therefore, if Oracle chooses \(World\) 0, arbitrary \(cnt_{y_{i}}\) values do not exceed \(H\), and if Oracle chooses \(World\) 1, arbitrary \(cnt_{y_{i}}\) values are considered to exceed \(H\). Hence, adversary \(A\) can distinguish \(World\) 0 and \(World\) 1 with high probability by verifying the existence of the \(cnt_{v_{i}}\) output more than threshold \(H\).
## 3 Results and Discussion
### Effect of Solution pH  
The Sb(V) adsorption by Fe-CMB as a function of the solution pH (3-11) is presented in Figure 7. The increase in solution pH reduced the Sb(V) adsorption from 14.2 mg/g to 13.3 mg/g. The solution pH profoundly affects adsorption by influencing the surface charge of the adsorbent and the metal species [9]. Sb(V) mainly exists as Sb(OH)\({}_{6}\)\({}^{-}\) in the solution with pH 2-10 [10], and Sb(V) favors a positively charged surface. The surface of the adsorbent becomes more negatively charged with increasing solution pH, causing repulsion between Sb(OH)\({}_{6}\)\({}^{-}\) and more negatively charged surfaces [9; 10]. Furthermore, competing with OH\({}^{-}\) at higher solution pH also inhibits the Sb(V) adsorption. The Sb(V) adsorption is generally higher under acidic conditions and relatively insensitive to solution pH. Although the solution pH increased from 3 to 11, the Sb(V) adsorption decreased by  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Temp.** (\({}^{\circ}\)C)** & **In K\({}_{\text{e}}\)** & **1/T (1/K)** & \(\Delta H^{0}\) (kJ/mol)** & \(\Delta S^{0}\) (J/K\(\cdot\)mol)** & \(\Delta G^{0}\) (kJ/mol) & \(R^{2}\) \\ \hline
15 & 3.099984 & 0.003470 & & & \(-\)7.35 & \\
25 & 2.71791 & 0.003354 & \(-\)19.86 & \(-\)43.42 & \(-\)6.91 & 0.9514 \\
35 & 2.56438 & 0.003245 & & & \(-\)6.48 & \\ \hline \end{tabular}
\end{table}
Table 4: Enthalpy, entropy, and Gibb’s free energy for the adsorption of Sb(V) by Fe-CMB.  
Figure 5: Van’t Hoff plot for Sb(V) adsorption by Fe-CMB.  
only 6.5%. Fe-CMB maintained its performance at a high pH, indicating that Fe-CMB can be applied to alkaline wastewater.  
Figure 6: Effect of Fe-CMB dose on the adsorbed amount of Sb(V) per unit mass of adsorbent (mg/g) and their corresponding removal percentage (%).  
Figure 7: Adsorbed amounts of Sb(V) and final pH as a function of initial solution pH.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Tracking Optimization Method
#### 5.3.1 Noise-Driven Optimization Methods
A new algorithm combining the real-time of Mean Shift and the robustness of PF is proposed in [94]. The tracking scene in this paper is the swimming pool, for which the color histogram is suitable as the target model because of the stability of the color feature in scale, rotation, and translation. Therefore, the underwater target model is based on RGB histogram according to the kernel function and Mean Shift. Then the tracking process is completed by PF. Owing to the introduction of Mean Shift, the distribution of particles can quickly get closer to the true state of the target. Therefore, the state can be represented by fewer particles, which can further reduce the tracking time to ensure real-time tracking. From the above introduction of various tracking algorithms based on different filtering selections, we know that the EKF, UKF, and PF are commonly used filters in underwater target tracking because the target model of underwater targets is usually nonlinear and the noise is non-Gaussian. By analyzing data collected from the looking forward sonar, the authors in [95] compare the tracking performance of EKF, UKF, and PF for the underwater target with different motion models such as CA, CV, and the variable speed linear model. Except the last model is maneuvering model, the other two target models are non-maneuvering models. In summary, all EKF, UKF, PF based methods can provide accurate tracking results for the linear model. However, for nonlinear stochastic movement and systems with non-Gaussian noise, the effectiveness of methods based on EKF cannot be guaranteed. When systems are nonlinear and with non-Gaussian noise, the method based on PF has superiority over that base on UKF.
## 3 Results
### Mineral Profile
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Water** & **Pertilizer** & \begin{tabular}{c} **Dose** \\ **(g/L)** \\ \end{tabular} & \begin{tabular}{c} **F0** \\ **(-)** \\ \end{tabular} & \begin{tabular}{c} **Fm** \\ **(-)** \\ \end{tabular} & \begin{tabular}{c} **Fv/Fm** \\ **(-)** \\ \end{tabular} & \
\begin{tabular}{c} **Yield** \\ **(-)** \\ \end{tabular} &
\begin{tabular}{c} **ETR** \\ **(-)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Wetered} & Control & - & 206 abc & 1006 d & 0.79 e & 0.20 b & 29.3 c \\  & Cattle & 10 & 208 ab & 1010 d & 0.80 d & 0.21 b & 29.9 c \\  & manure & HI frans & 10 & 192 c & 1016 d & 0.81 bc & 0.23 a & 32.3 b \\  & HI frans & 12.5 & 198 bc & 1021 d & 0.81 cd & 0.23 a & 33.2 a \\ \hline \multirow{4}{*}{Drought} & Control & - & 212 ab & 1081 c & 0.80 cd & 0.10 c & 13.5 f \\  & Cattle & 10 & 214 a & 1129 b & 0.81 bc & 0.10 c & 14.1 ef \\  & manure & 10 & 200 abc & 1135 ab & 0.81 b & 0.10 c & 14.9 de \\  & HI frans & 12.5 & 197 bc & 1146 a & 0.82 a & 0.10 c & 15.2 d \\ \hline \multicolumn{6}{l}{Letters a-f indicate statistically different mean values (\(\alpha\) = 0.05). F0: LSD Fertilizer: 14.85; LSD Water regime: 8.138; LSD Fertilizer \(\times\) Water regime: NS, Fm: LSD Fertilizer: 16.12; LSD Water regime: 460.900; LSD Fertilizer \(\times\) Water regime: NS, Fr\(\vee\)/Fm: LSD Fertilizer: 0.009; LSD Water regime: 0.000; LSD Fertilizer \(\times\) Water regime: NS, Yield: LSD Fertilizer: 0.014; LSD Water regime: 0.000; LSD Fertilizer \(\times\) Water regime: 0.012, ETR: LSD Fertilizer: 0.991; LSD Water regime: 0.908; LSD Fertilizer \(\times\) Water regime: NS.} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Lettuce chlorophyll fluorescence under different fertilizer and water conditions (un-nominated units).
# Advances in Graphene Field Effect Transistors (FETs) for Annine Neurotransmitter Sensing
## 3 Amine Neurotransmitter Detection via GFETs
### Dopamine
Compared to remote top-gate designs, integrated gate designs make it easier to fabricate compact devices [208; 209; 210]. Micron-sized, wafer-level electrolyte-gated GFETs (EG-GFETs) can be incorporated into miniaturised multitransistor array (gMTA) circuits, as reported by [PERSON] et al. [162]. Functionalising these arrays with a short-strand DA-specific DNA aptamer transformed them into aptasensors, which comprised CVD graphene encased in a multi-stack passivation layer [211]. The LOD of gMTAs at 10 nM in undiluted artificial cerebrospinal fluid (aCSF) was one order of magnitude greater than when in PBS [109]. The secondary structure of aCSF causes positively charged molecular regions to move towards the graphene channel when DA is bound [212; 213], adding on the Debye length for each detection event. This suggests that alterations in the electrolytic solution's ionic composition can affect the conformation of aptamers and the functionality of FETs [214; 215]. [PERSON] et al. [163] reported a soft neural probe that enabled the site-selective functionalisation of aptamers with high molecular specificity. The CVD graphene gate channel was functionalised with amine (-NHZ) groups using an electrochemical grafting technique [143; 216], facilitating direct bio-detection under physiological conditions. I\({}_{\text{DS}}\) loss and p-doping effects were recorded due to the disrupted graphene sp\({}^{2}\) bond and introduction of new groups during functionalisation. With increased DA levels (from 1 nM to 100 uM), the electrical response improved because of aptamer structure rearrangement caused by non-specific adsorption on the graphene surface and imprecise binding [217; 218].