esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mixed Clustering Approach for Real-Time Anomaly Detection
## 7 Conclusions, Limitations and Lines for Future Works  
### Conclusions  
In this article, a clustering-based anomaly detection algorithm is proposed for real-time data with numeric and categorical attributes. The algorithm follows two-stage approach. Firstly, it follows \(k\)-means algorithmic approach and then an agglomerative hierarchical approach. The algorithm also takes into account the time-stamp associated with each data instance (the time of generation or occurrence of data). At the end of phase one, a set of \(k\)-clusters along with their lifetimes is found, where each cluster will have a lifetime. In the phase two, the similar clusters are merged depending on the non-empty intersections of their lifetimes. The algorithm stops when no further merger is possible. The proposed algorithm has the following features.  
* It supplies a set of clusters, where each cluster will have an associated fuzzy time interval describing its period or lifetime.  
Figure 8: Data sizes vs. time of execution.  
* One of the most challenging issues in \(k\)-means algorithm is the selection of \(k\). The proposed algorithm has overcome this problem by producing the same number of stable clusters, as the number of clusters reduces due to merging of clusters.
* Obviously, the number of output clusters is less than or equal to that at the beginning.
* The data instance or group of data instances, which does not belong to any of the cluster or belongs to sparse clusters or falling in the lifetime with very low membership values will be treated as anomalies.
* An experimental study with a synthetic dataset and a real world dataset is conducted and comparative analysis is carried out against a couple of clustering-based anomaly detection algorithms namely \(k\)-means [5], PCM (Partitioning clustering with merging) [14], ACA (agglomerative clustering algorithm) [39], IF (IsolationForest) [39], and OnCAD [31]. It has been found that our algorithm outperforms others in terms of accuracy, specificity, sensitivity, number of anomalies found, number of clusters generated, execution time and the stability of the output clusters.
* The algorithm is found to be run in cubic time.
## 4 Results  
Using the linear regression model obtained from Task 7, we could derive the following equation:  
\[\hat{T}_{air}=2.8447+0.09588\cdot T_{dA}+0.38685\cdot T_{nA}+0.14467\cdot T_{dT }+0.34914\cdot T_{nT} \tag{6}\]  
where \(\hat{T}_{air}\) represents the estimated temperature of a given pixel, using inputs from the MODIS satellite. Specifically:  
* \(T_{dA}\) = LST day temperature from MODIS--Aqua,
* \(T_{nA}\) = LST night temperature from MODIS--Aqua,
* \(T_{dT}\) = LST day temperature from MODIS--Terra,
* \(T_{nT}\) = LST night temperature from MODIS--Terra.  
From the analysis of the coefficients of the equation, we can observe that nighttime temperatures (\(T_{nA}\) and \(T_{nT}\)) have a stronger influence on the estimation of air temperature compared to daytime temperatures. With a Root Mean Squared Error (RSME) of 1.11 \({}^{\circ}\)C, an R-squared of 0.981, and a near-zero \(p\)-value, the model performance has overall a good fit [28]. Using this calibration, we were able to estimate air temperatures from MODIS data at the coordinates of ARPA monitoring stations. These estimations, as shown in Figure 13, were then compared with the actual air temperature measurements recorded by ARPA.  
The continuous line represents the air temperature (Tair) measured by the ARPA Monitoring Station located in Fortunago (ID 8007), while the dotted line shows the estimated air temperature derived from MODIS after applying the linear regression. The gray line represents the difference (\(\Delta\)T) between MODIS-derived estimated \(\hat{T}_{air}\), and ARPA \(T_{air}\).  
Appendix A (Figures A13-A18) provides a comprehensive comparison of MODIS-derived and ARPA-measured Tair.  
Figure 14 shows an extract of the results obtained, with reference to the data of the 8-day period from 20 July 2022 to 28 July 2022. By applying the coefficients derived from the linear regression model to each pixel of the four MODIS-LST images related to the same time, we generated raster maps that estimate air temperature across the entire study area with a resolution of 1 km\({}^{2}\). In this way, we can obtain a detailed spatial representation of the air temperature, which would not be possible with in situ measurements alone. By increasing the spatial granularity of temperature data, this technique allows for better estimation  
of bioclimatic indicators, enabling informed decisions related to resource management, land use planning, and assessment of climate impact on agriculture.
# Mediterranean Aquaponics: Fasting and Refeeding in a Polyculture Aquaponic System
## 4 Discussion
### Water Quality
[38] reported nitrate ion levels of 116.71 \(\pm\) 17.57 mg/L and ammonia levels of 0.2 \(\pm\) 0.02 mg/L, in a glasswort culture experiment conducted with marine aquaculture wastewater. In the present study, the nitrate ion levels at the control point, where water exits the hydroponic subsystems (NO3\({}^{-}\)out), indicated the efficient operation of the coupled aquarium system and the oxidizing capacity of the filter [5]. When sea bass and rock samphire were grown in a brackish aquaponic system at three different salinities (8 g/kg, 14 g/kg, 20 g/kg), lower levels of nitrate ions were measured. Specifically, levels were found to be between 93.94 \(\pm\) 7.81 mg/L and 119.81 \(\pm\) 7.36 mg/L [35]. Moreover, ref. [5] observed significantly lower nitrate concentrations, ranging from 76.4 to 77.2 mg/L, in a recirculating brackish aquaponic system that co-cultivated bass and prawns at salinities of 8 g/kg and 14 g/kg, respectively. In the present study, the pH values remained consistent across the various experimental treatments. For optimal nutrient uptake, plants require a pH level between 5.5 and 6.5 [39]. Bacteria are most likely to flourish in a pH range of 7.0 to 8.0, whereas the optimal pH for aquaculture is between 6.5 and 8.5 [39]. It can thus be posited that the optimal pH range for aquaculture is 6.5 to 7.0. When pH values exceed 7.0, the solubility of phosphorus and other nutrients may be diminished [40].
# Random Access for Underwater Acoustic Cellular Systems
## 4 RAP Detection
However, when there is a Doppler frequency, the performance of  
RAP 1 decreases significantly owing to its Doppler sensitivity. As shown in Figure 13a, the detection probability is 1 at the false alarm probability of \(10^{-9}\) when \(f_{D}\) = 0 Hz, and 0.2 when \(f_{D}\) = 20 Hz. However, RAP 2 is robust against Doppler shift, as shown in Figure 13b. For the case of a single UE, the ROC curve at \(f_{D}\) = 20 Hz is almost the same as that at \(f_{D}\) = 0. However, the performance of RAP 2 decreases significantly when an interfering UE exists. As shown in Figure 13b, the detection probability is close to 1 at the false alarm probability of \(10^{-9}\) for the case of a single UE and 0.3 for the case of two UEs. Figure 14 shows the ROC curves of RAP 2 for different values of \(\Delta\beta\) and \(f_{D}\) when two UEs exist. Here, the value of \(\Delta\beta\) is changed from 50 Hz to 150 Hz, when \(f_{D}\) = 0 Hz, \(\beta_{c}\) = 0.5 kHz, and \(\beta_{d}\) = \(\beta_{c}\) + \(\Delta\beta\). As shown in Figure 14a, the detection probability increases with the increase of \(\Delta\beta\). Notably, the maximum value of CAF of the LFM waveform decreases with the increase of \(\Delta\beta\), as shown in Figure 9. Figure 14b shows the ROC curves of RAP 2 for different values of Doppler shift when \(\Delta\beta\) = 110 Hz. As expected, the detection probability decreases with the increase of Doppler shift. The distance can be estimated from the estimated value of time delay as follows:  
\[\hat{d}=v_{c}\hat{t} \tag{33}\]  
Figure 14: ROC curves of RAP 2 when two UEs exist. (**a**) Different \(\Delta\beta\); (**b**) Different \(f_{D}\). Figure 13: ROC curves (analytical vs. simulation). (**a**) RAP 1; (**b**) RAP 2. The time delay, \(\hat{t}\), can be estimated using (25). Here, it is assumed that the speed of sound is constant although the speed is known to depend on the water properties of temperature, salinity, and pressure. Figure 15 compares the performances of time delay estimation in a Doppler environment when RAP 1 and RAP 2 are used. As shown in Figure 15a, the timing error of RAP 1 with three different values of root indices is up to 60 ms when there is a Doppler shift. On the other hand, the timing error of RAP 2 with three different values of \(\beta_{c}\) is up to 15 ms, which is much smaller than that of RAP 1, as shown in Figure 15b. In terms of distance errors, RAP 1 and RAP 2 can result in errors up to 90 m (60 \(\times\) 1.5) and 22.5 m (15 \(\times\) 1.5), respectively. Based on the analytical and simulation results in this section, RAP 2 is selected for our experiment, because RAP 2 is robust to Doppler shift. Although the detection probability of RAP 2 decreases in a multi-UE environment, it can be increased to a certain extent by selecting an appropriate value of \(\Delta\beta\). The timing error can also be reduced to a certain extent with an appropriate value of \(\Delta\beta\).
## 3 CS Application for Gravitational Microlensing
### CS Application  
Transient events such as gravitational microlensing events can be detected using differenced imaging. In differenced imaging, we take a difference of a good seeing reference image with an observed image. We show a CS architecture for microlensing events, to obtain the differenced images, which contain the source star magnification flux as shown in Figure 1.  
In architecture (Figure 1), differencing is applied to the CS measurements, resulting in a reconstructed differenced image. A photometric curve can then be generated using the flux of the source star pixels of the differenced image over time.  
The architecture is implemented in the following manner:  
1. Obtain CS based measurements, \(y_{o}\), for a spatial image. CS can be applied by projecting a matrix, \(A\), onto the region of interest, \(x_{o}\). This can be done on a column-by-column basis for a \(n\times n\) spatial region, \(x_{o}\). Thus, for 2D images, \(y_{0}\) and \(A\) are of size \(m\times n\), where \(m<<n\).
2. Given \(A\) and a clean reference image, \(x_{r}\), construct measurements matrix \(y_{r}\), where \(y_{r}=Ax_{r}\).
3. Apply a 2D differencing algorithm on \(y_{o}\) and \(y_{r}\) to obtain a differenced image, \(y_{\text{diff}}\), and the corresponding convolution kernel, M, which is used to match the observed and reference CS measurement vectors, \(y_{o}\) and \(y_{r}\)[11]. In our modelling, we use \(y_{\text{diff}}=y_{o}-y_{r}\), by using the assumption that the PSF of the reference and observed image is the same. This assumption is valid for space flight instruments when both observed and reference images are obtained on-board the instrument.
4. Reconstruct the differenced image, \(x^{\prime}_{\text{diff}}\) using CS reconstruction algorithms, given \(A\) and \(y_{\text{diff}}\).  
In our modelling, we assume the same PSF for the reference and observed image; hence, in an ideal scenario, we would obtain a differenced image with only the microlensing event present. A sample reference image, the observed image, a differenced image, is shown in Figures 2-4, respectively.  
Figure 1: CS Architecture. The blue block represents CS data acquisition which can be performed on-board a spaceflight instrument, while the orange blocks represent computations which can be performed on the ground. Image differencing can also be performed on-board to further reduce data volume.  
Figure 2: Sample reference image.
## 8 Speaker Direction and Signal Obstruction
### Discussion  
For each sample the MPV was calculated and the difference between MPV and true position of the check point was calculated. The results for each checkpoint were divided into two groups: blocked (B, D, F, H), where the user stands in the way of one of the microphones and direct (A, C, E, G), where the user is not in the path to any microphone. The results can be found in Table 5.  
In terms of accuracy, upward speaker orientation gives consistent results in the 10-20 cm range regardless of where the user stands, which suggests that in this orientation, the user's location has little or no effect on accuracy. Regarding the other four configurations, forward-blocked is consistently very inaccurate across all four check points, a direct result of a disadvantageous combination of user location and speaker orientation. Ultrasound is very directional and will have the least energy directly behind the speaker. In forward orientation, the user stands directly behind the speaker, making the signal even weaker in this direction. In back-blocked configurations, a microphone that is directly in front of the speaker is the one being blocked, which makes the two factors cancel each other out. As a result, back-blocked and the other two horizontal configurations where the user does not block any microphones show very similar accuracy for check points 1-3, ranging between 20 and 40 cm. Results for check point 4 show the worse accuracy ranging from 40 cm to 1.5 m. However, these results are less reliable since around half of the signals were not detected at all, which resulted in a smaller sample size.  
Analysis of the signal detection failure rate reveals similar trends with discrepancy between the first three check points and the last check point being more pronounced. Upward orientation has 0% failure rate at check points 1-3. At check point 4 there was 2.5% failure rate in direct configuration and 10% failure rate in blocked configuration. Because the phone is far from the centre of the room at check point 4, the signal reception is very weak at the opposite corner and almost as weak at the two other corners.
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 4 Results
### Baseline and Metric  
In the experiment, the baseline models were divided into two major categories, spatiotemporal graph models and temporal models, based on whether they handled spatial features. RNN, AE [33], Seq2 Seq [34], WaveNet [9], and Transformer [10] are classical temporal prediction methods that have proven effective in various domains, such as NLP and traffic prediction. STGCN [18], AGCRN [35], and the proposed model GAGW in this paper belong to spatiotemporal graph models. On the other hand, based on different time layer processing strategies, models can be further categorized into RNN-based prediction models led by RNN and DCRNN [21], TCN models such as GWNET [36] and STMGAT [37], and self-attention-mechanism-based models such as STTN [38].  
The evaluation metrics used in the experiment include commonly employed measures in traffic flow prediction, MAE, MAPE, and RMSE, as depicted in Equations (7)-(9), respectively. MAE, known as the mean absolute error, is a widely used performance metric in regression tasks, directly quantifying the average difference between predicted values and actual values. MAPE, built upon MAE, measures the average percentage error of the experimental results. RMSE, or the root mean square error, in comparison to MAE, is more sensitive to large errors, thus challenging the stability and robustness of the predictive model.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|\hat{y_{i}}-y_{i}\right| \tag{7}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{\hat{y_{i}}-y_{i}}{y_{i}}\right|*100\% \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\hat{y_{i}}-y_{i}\right)^{2}} \tag{9}\]
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
## 4 Results and Discussion
### NF Performance in Single Salt Solutions
This shows that Membrane A produces more consistent and stable rejection results compared to NF270, which might be also associated with its denser polymeric structure and, therefore, the stronger contribution of steric (size-exclusion) compared to charge-related effects. Sulfate rejection was determined in two different single salt solutions: 4000 mg/L magnesium sulfate and 100 mg/L copper (II) sulfate. It might be expected that in these solutions, due to complex co-adsorption of both the cations and the anions on the membrane surface, the IEP remains the same, but the overall surface charge becomes lower. As seen in Figure 3d, the rejection of sulfate in both solutions was relatively constant above the membrane IEP. This shows that the rejection of salt was governed mainly by the electrostatic repulsion of sulfate (SO\({}_{4}\)\({}^{2-}\)) and the negatively charged membrane. Below the IEP, a sharp drop in sulfate rejection was observed, most remarkably at pH 2, which is close to the pKa value of sulfate (pKa = 1.92). This could be attributed to the increase in bisulfate (HSO\({}_{4}\)\({}^{-}\)) concentration in the feed solution due to chemical speciation of sulfate at lower pH values, which could trigger the Donnan effect [32]; at pH 2, sulfate ions are present as SO\({}_{4}\)\({}^{2-}\) and HSO\({}_{4}\)\({}^{-}\) with a ratio of ca. 10% and 50%, respectively [33]. The co-ion Mg\({}^{2+}\) was rejected at a greater extent than H\({}^{+}\) due to its higher valence charge. Therefore, SO\({}_{4}\)\({}^{2-}\) anion remained with Mg\({}^{2+}\) cation to maintain the overall electroneutrality on the feed side. At the same time, the monovalent HSO\({}_{4}\)\({}^{-}\) ions coupled with H\({}^{+}\) ions present in the solution, passing through the membrane to the permeate side, thus resulting in a lower permeate pH. The latter is also favored by the significantly higher concentration of very mobile protons (a \"conductivity jump\") at a feed pH lower than 3, which can lead to protons permeation (together with Cl\({}^{-}\)) across the membrane. The overall membrane surface charge is also more shielded under such conditions. As a result of the permeation of HSO\({}_{4}\)\({}^{-}\), the overall sulfate rejection significantly decreased at pH values below the membrane IEP. This phenomenon was also observed by [PERSON] et al. [34] and [PERSON] et al. [14], where sulfate rejection in a mixed system decreased drastically at pH 2 and 3. The fall in sulfate rejection at low pH (36.3% at pH = 2) was more drastic for the CuSO\({}_{4}\) experiment compared to the MgSO\({}_{4}\) experiment. The explanation for this phenomenon is that the higher permeation of SO\({}_{4}\)\({}^{2-}\) ions in the CuSO\({}_{4}\) solution can be attributed to the reduced effect of membrane charge shielding due to the lower salt concentration (100 mg/L of CuSO\({}_{4}\), in comparison to 4000 mg/L of MgSO\({}_{4}\)).
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation
### Solution of the Charger Deployment
#### 5.3.5 Varying the Number of \(L_{c}\)  
Since the charger discretization method is not used to deploy chargers based on the PSO solution, we only analyze the chargers deployed based on the GH solution. The results in Figure 8 show that as \(L_{c}\) increases, the number of chargers deployed by the GH solution increases. When \(L_{c}\) = 1.5 (m), the number of deployed chargers has an inflection point, indicating that when \(L_{c}\) < 1.5 (m), \(L_{c}\) does not affect the number of deployed chargers. When \(L_{c}\) > 1.5 (m), the number of deployed chargers increases with \(L_{c}\). As \(L_{c}\) increases, according to Equation (2), the distance between candidate chargers increases, and the distance between the candidate chargers and the sensor increases, resulting in an increase in the number of deployed chargers. In Figure 11a, the GHDSAE-based GH solution saves 5.82-24.84% (with an average of 18.97%) more chargers than the GHDS-based GH solution. In Figure 11b, the GHDSAE-based GH solution saves \(-\)2.09-12.88% (with an average of 4.29%) less time than the GHDS-based GH solution. As a result, the deployed chargers of the GHDSAE-based solution are fewer than the GHDS-based solution with the cost of  
Figure 10: Deployed chargers with the increase of transmitting Power. (**a**) Deployed chargers of GH; (**b**) Solving time of GH; (**c**) Deployed chargers of PSO; (**d**) Solving time of PSO.  
acceptable solving time growth, and the improvement of discrete accuracy for charging area can reduce the number of chargers deployed, but there is also a performance limit.  
In conclusion, the proposed solutions for charger deployment are scalable as the number of PoIs, the accuracy of sensor discretization, the probability of sensor perception, the transmitting power of chargers, and the accuracy of charge discretization grow. The aggregation effect of sensors can effectively reduce the deployment of chargers at the cost of slightly incremental computing time. In addition, the PSO solution always outperforms the GH solution by deploying fewer chargers with the cost of acceptable calculation time expenses.
## 2 Measurement Principles and Methods
### Flow and Implementation of the Method  
According to the principle of the monocular visual subsidence detection method proposed in Section 2.2, the specific flow of the method is shown in Figure 4. The method consists of three parts: wheel-terrain boundary (WTB) fitting, wheel center position (WCP) acquisition, and wheel sinkage value calculation. Among them, the acquisition of the wheel center position requires Hough circle detection and identification of the wheel center and radius to the image; wheel-terrain boundary requires Canny edge detection and wheel-terrain boundary fitting to the image. Finally, the wheel sinkage value is calculated according to the wheel sinkage model with WTB-WCP. In addition, a specific implementation of the monocular vision sinkage detection method is shown in Algorithm 1.  
Figure 4: Flow chart of the monocular vision sinkage detection method.  
Figure 3: (**a**) The process of identifying the wheel center; (**b**) the process of identifying the wheel pixel radius.  
```
Data: capture image _frame_, original image coordinates _psrc_[4], transform image coordinates _pdst_[4], wheel-terrain junction _ROI_ Result: wheel sinkage \(z\)
1. _transM_\(\leftarrow\) getPerspectiveTransform(_psrc_[4], _pdst_[4]);
2. _frame_\(\leftarrow\) warpPerspective(_transM_);
3. _graylImage_\(\leftarrow\)_cvColor(frame)_;
4. _graylImage_\(\leftarrow\) GaussianBlur(Size(3,3));
5. _circle_\(\leftarrow\) HoughCircles (_graylImage_, _CV_HOUGH_GRADIENT_);
6. _dst_\(\leftarrow\) Canny(_frame_);
7. _points_\(\leftarrow\)dst(_ROI_);
8. \(n\leftarrow\)_points_.size(0);
9. _for\(i\gets 0\)_to_\(n\)do
10. _sumx_\(\leftarrow\)_sumx_\(+\)_points_\({}_{i}\);x_;
11. _sumy_\(\leftarrow\)_sumy_\(+\)_points_\({}_{i}\);y_;
12. _sumx_\(\leftarrow\)_sumx_\(+\)_points_\({}_{i}\);x_\(\times\)_points_\({}_{i}\);y_;
13. _sumx_\(\leftarrow\)_sumy_\(+\)_points_\({}_{i}\);x_\(\times\)_points_\({}_{i}\);y_;
14. _end_
15. \(b\leftarrow\)_(_n_\(\times\)_sumy_\(-\)_sumx_\(\times\)_sumy_)/(_n_\(\times\)_sumx_\(2-\)_sumx_\({}^{2}\));
16. _a_\(\leftarrow\)_(_sumy_\(-\)_b_\(\times\)_sumx_)/n;
17. _d_\(\leftarrow\)_(_a_\(\times\)_circle_center_\(x-\)_circle_\(.\)_centery_\(+\)_b_)/(_a_\({}^{2}+b^{2}\))\({}^{0.5}\)
18. \(z\leftarrow\)_(circle_\(.\)_r_\(-\)_d_) \(\times\)_80/circle_\(.\)r;
```  
**Algorithm 1**Monocular visual sinkage detection method