esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Ensemble of Resnet for Dolphin Whistle Detection
## 2 Materials and Methods
### Baseline Detection  
PamGuard [14] is a widely used software designed to automatically recognize marine mammal vocalizations. It provides an interesting baseline method since it is widely used. The operational parameters of PamGuard were used as follows:  
* The \"Sound Acquisition\" module from the \"Sound Processing\" section was included to manage the data acquisition device and convey its data to other modules;
* The \"FFT (spectrogram) Engine\" module from the \"Sound Processing\" section was incorporated to calculate spectrograms;
* The \"Whistle and Moan Detector\" module from the \"Detectors\" section was added for detecting dolphin whistles;
* The \"Binary Storage\" module from the \"Utilities\" section was incorporated to preserve information from various modules;
* A new spectrogram display was created by adding the \"User Display\" module from the \"Displays\" section.  
Input spectrograms were devised utilizing the FFT analysis mentioned above with identical parameters: FFT window length was assigned 2048 points, and the hop size was set to the length multiplied by 0.8 using the Blackman window in the \"FFT (spectrogram) Engine\" module under the software settings. The frequency range was determined between 3 and 20 kHz, and the \"FFT (spectrogram) Engine Noise free FFT data\" was chosen as the source of FFT data in the \"Whistle and Moan Detector\" module settings. During the creation of a new spectrogram display, the number of panels was assigned as 2 to visualize both channels. A detection by PamGuard was classified as a true positive if the signal window identified by the software overlapped with at least 5% of the ground truth signal interval. While this criterion may appear lenient, it allowed for the inclusion of many PamGuard detections that might have otherwise been disregarded.
# Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River
## 3 Results
### Nutrients' Statistics per Site  
Nitrogen-nitrate in almost all samples was very high, with values ranging from close to 0 up to 20 mg/L (Figure 3). More than 90% of the total samples fell in the category of \"bad quality\" for NO3 - N, and the sites having more than 50% of their samples with very high concentrations (more than 7 mg/L) are K 12 (approx. in the middle of the catchment) and K Ekv (close to the river mouth). K MD, which is in the upstream area, had the lowest NO3 - N values, but those values were still high enough to be classified as \"bad quality.\" Total nitrogen (TN) values were very similar to NO3 - N values in most cases, which means that most of the nitrogen-related pollution is inorganic and in the form of nitrates, while a significant difference is observed at the K 5 site since TN values are generally much higher than NO3 - N concentrations (Figure 3). This is due to the fact that NH4+-N values are also very high at the specific site, which could be related to domestic or industrial sewage in close vicinity to the sampling area.  
Similarly, for phosphorus, most of the samples indicated high concentrations and therefore poor and bad quality. Only the river mouth area (K Ekv) presented a slightly better picture, with more than 50% of the samples to fluctuate between the moderate and  
Figure 3: Distribution of NO3 – N and TN values at the sampling sites from upstream (K MD) to the river mouth (K Ekv). The dotted lines indicate good (green) and bad (red) thresholds for NO3 – N according to the WFD provisions.  
poor classifications (Figure 4). This is likely due to the effect of the sea, which generally has significantly lower P concentrations, and therefore, the mixing processes that occur at K Ekv improve slightly the quality of the site. The sites that present the highest PO\({}_{4}\)-P and TP concentrations are K 17 (close to the industrial area of Metamorphosis) and K 5 (in another industrial area close to Athens city center) that also had very high NH\({}_{4}\)+-N levels. Nevertheless, even in the upstream part of the catchment (K MD), more than 75% of the samples were classified as of bad quality.  
According to the HWQI, the sampling sites K Ekv, K 12 and K 17 could be characterized as of bad or poor physicochemical status in the majority of the measurements. Approximately 50% of the samples in K MD (upstream) and 25% of the samples in K Ekv and K 12 indicated moderate physicochemical quality (Figure 5). The worst results in terms of WFD quality class were recorded in K 5, with more than 50% of the samples classified as of bad quality and all the rest as of poor quality. K 17 follows, with almost 80% of the samples classified as of poor physicochemical quality.  
Figure 4: Distribution of PO\({}_{4}\)-P and TP values in the sites from upstream (K MD) to the river mouth (K Ekv). The dotted lines indicate good (green) and bad (red) thresholds for PO\({}_{4}\)-P according to the WFD provisions.
## 4 Discussion
The scatter plots between the VPD and SWC at the grassland sites (Figure 10) also proved the close relationships, with the correlation coefficients being \(-0.74\) and \(-0.58\) at the CH-Oe1 site and the CH-Cha site, respectively. Such analyses again implied the reasonability by using the VPD as a proxy for drought conditions. Figure 9: The variability in VPD and soil water content (SWC) at the two grassland flux sites ((**a**) CH-Oe1; (**b**) CH-Cha) over each weekly period. Owing to the data availability, only the grassland sites were analysed here. Resistance and resilience metrics are often used to assess the sensitivity of ecosystems to climate extremes [55; 56]. Whether forest and grassland follow similar or unique patterns regarding GPP, Re, and NEE resistance and resilience to drought remains unclear. Ecological theories and observations suggest that there is a trade-off between the resistance and resilience of ecosystem structures and functions [57]. This is because of the different life and evolutionary histories of resident plant species in grasslands and forests that result in different functional responses to extreme drought. Generally, forests have a higher resistance to drought than grasslands, because of their deeper root systems and greater water storage capacity [58]. In addition, forests have a greater ability to regulate their water use through stomatal control, which enables them to maintain their water balance even during periods of drought [3]. The higher resistance of forests to drought means that they are better able to maintain their structure and function and continue to sequester carbon, even during periods of drought [59]. This study also confirmed this point, as shown in Figures 7 and 8. [PERSON] et al. (2018) also indicated that grasslands were vulnerable to hydroclimatic differences [60]. Nevertheless, many studies suggest that grasslands have a higher resilience to drought than forests, because of their ability to respond quickly to changes in environmental conditions [61; 62]. Grasslands can quickly re-establish themselves after a drought, due to their ability to rapidly allocate resources to new growth. In addition, grasslands have a more flexible root system that enables them to adapt to changing soil moisture conditions. The higher resilience of grasslands to drought means that they are able to recover more quickly from the impacts of drought and continue to provide ecosystem services. This study highlighted the stronger Rs of grassland ecosystems to droughts compared with forest ecosystems when derived from GPP (Figures 7 and 8). However, both forest and grassland sites exhibited weaker NEE-derived Rt but stronger NEE-derived Rs. Although this study revealed many interesting findings, more flux sites of diverse ecosystems are necessary to consolidate these results in future work.
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 2 Multiple Linear Regression Inversion Principle of Initial Ground Stress Field
### Multiple Linear Regression of Ground Stress Field
#### 2.2.2 Multiple Linear Regression Equation  
According to the principle of the multiple linear regression method [41], taking the calculated value of initial ground stress corresponding to the on-site point under different boundary conditions as the independent variable and the calculated value of ground stress regression as the dependent variable, the regression calculation equation is obtained as follows.  
\[\hat{\sigma}_{jk}=C_{0}+\sum_{i=1}^{n}C_{i}\sigma_{jk}^{i} \tag{2}\]  
In the formula:  
\(\hat{\sigma}_{jk}\)--initial ground stress calculation value of stress component \(j\) at measured point \(k\);  
\(C_{0}\), \(C_{i}\)--undetermined coefficient in multiple linear regression equation;  
\(n\)--number of boundary conditions, five working conditions are discussed in this study;  
\(\sigma_{jk}^{i}\)--in-situ ground stress component caused by the gravity stress mode and each sub tectonic stress mode, respectively.  
\[\sigma_{jk}=\hat{\sigma}_{jk}+\varepsilon \tag{3}\]  
In the formula:  
\(\sigma_{jk}\)--initial ground stress measured value of stress component \(j\) at measured point \(k\);  
\(\varepsilon\)--the error between the in-situ value of initial ground stress and the calculated regression value, which obeys normal distribution and is independent of each other.  
To maximize the fitting degree of the multiple linear regression curve and obtain the regression equation that can best reflect the actual ground stress field, it is necessary to minimize the sum of total deviation squares between the measured data and the regression calculation data while minimizing the sum of the regression squares. Assuming the number of on-site measured points is \(n\), and the stress state of a point in the area is represented by 6 stress components. The following calculation equation for the sum of total deviation squares \(S_{T}\), the sum of regression squares \(S_{R}\), and the sum of residual squares \(S_{e}\) can be obtained. See Equations (4)-(6):  
\[S_{T}=S_{R}+S_{e}=\sum_{k=1}^{n}\sum_{j=1}^{6}\left(s_{jk}-\overline{s_{jk}} \right)^{2} \tag{4}\]  
\[S_{R}=\sum_{k=1}^{n}\sum_{j=1}^{6}\left(\hat{s}_{jk}-\overline{s_{jk}}\right) ^{2} \tag{5}\]  
\[S_{e}=\sum_{k=1}^{n}\sum_{j=1}^{6}\left(\sigma_{jk}-\hat{\sigma}_{jk}\right)^{ 2}=\sum_{k=1}^{n}\sum_{j=1}^{6}\left(\sigma_{jk}-\left(C_{0}+\sum_{i=1}^{n}C _{i}\sigma_{jk}^{i}\right)\right)^{2} \tag{6}\]  
Following calculating the partial derivative \(C_{i}\), its partial derivative is made equal to zero, and the normal Equation (7) for calculating the regression coefficient \(C_{i}\) are obtained.
## 3 Results and Discussion
### Pca  
The score plot and correlation loading expressed the relationship between some crucial meat characteristics/compounds and the sensory scores of SV-cooked samples, as shown in Figure 2a,b, respectively. Approximately 71% of the total variation was counted to explain this relationship. From Figure 2b, all variables located in the outer circle region exhibited a significant correlation with the seven experimental groups with a variance greater than 70%. It was found that the 60-45 and 60-60 samples, which were located at the lower part of the right side, were positively correlated with IMP, acetoin as well as flavor- and overall liking scores. For the control, which were located at the upper part of the right side, a positive correlation was found with HxR, FFA, -SH and carbonyl content as well as 2-pentylfuran, whereas the 50-30 and 50-45 samples, located at the lower part of the left side, were significantly correlated with ADP, Lys and hexanal. Conversely, the 50-60 and 60-30 samples appeared to show little correlation with other quality parameters.  
Based on the PCA results, it helps to confirm that the flavor-liking score was positively correlated with the overall-liking score of SV-cooked fish. The highest flavor-liking score of the 60-45 and 60-60 samples might be influenced by higher IMP and acetoin. The results also suggested that IMP and acetoin are crucial flavor enhancers for cooked tilapia. The positive correlation between FFA, -SH and carbonyl content along with the control sample indicated that protein and lipid oxidation mainly occurred when the fish was processed by the traditional method, compared with SV cooking at 50-60 \({}^{\circ}\)C for up to 1 h. These oxidation products were also significantly correlated with the generation of 2-pentylfuran. Even furan does not contribute a lot to the overall aroma in boiled fish, as stated by [PERSON] et al. [34]. However, 2-pentylfuran is known to be mainly responsible for the undesirable reversion flavor of other foods, i.e., Dezhou braised chicken [38], soybean oil [39], apple juice [40], etc. The higher rate of oxidation of traditional cooking, therefore, may influence the lower flavor-likeness of the control sample. Overall, this study confirmed that the SV-cooking technique can improve the meat quality of cooked fish, in terms of flavor/taste characteristics, particularly when processed at 60 \({}^{\circ}\)C for 45 and 60 min.  
Figure 2: _Cont._
## 2 Methodology
### Filtering Algorithms and Phase Sequence Retrieval  
The phase map derived using the approach presented in the previous section contains a certain amount of noise, which needs to be filtered to achieve accurate results through further quantitative analysis. The WFF (windowed Fourier filtering) algorithm [23] is adopted here as it does not take much computational calculation and achieves a relatively more accurate phase map. The theory and principle of WFF can be found in [23; 24; 25; 26]. The filtered phase map can be expressed as \(\dot{\phi}\), and its complex domain equivalent can be expressed as \(\dot{\mathcal{R}}\).  
The vital significance of the inspection of WTB using dynamic interferometric methods is to view changes of the phase states between current and initial times, including stress concentration, displacement, and strain change while load is exerted on the sample surface. The defects can be further analysed through dynamic changes i phase status in a more intuitive way. In previous studies, most of the methods have concentrated on deriving the discrete phase maps at a certain time instant with less analysis of deriving phase changing sequences over a period of time. Thus, it is crucial to form a dynamic phase change sequence over time. The phase change at a certain time, \(t_{x}\), in comparison to that at the initial time, \(t_{0}\), can be expressed as \(\dot{\Delta\phi}_{t_{x}}\). The sequence from the initial time of loading \(t_{0}\) to time \(t_{x}\) is thus:  
\[\Delta\dot{\phi}_{t_{0}}^{t_{x}}=\left\{\dot{\Delta\phi}_{t_{1}},\dot{\Delta \phi}_{t_{2}},\ldots,\dot{\Delta\phi}_{t_{x}}\right\} \tag{10}\]
## 4 Experiments and Results
### Results and Discussion
#### 4.2.3 Experiments on the Reliability of Our QAAPA Method  
We evaluated our proposed method in the test set and compared the performance on the test set with those on the train and valid set. The results of the test are shown in Table 2. As we can see from it, the compression effectiveness observed in the test set  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Average Coding Lengths (bits)**} \\ \cline{2-3}  & **Training Dataset** & **Validation Dataset** \\ \hline Original & 1312 & 1312 \\ Q & 504 & 504 \\ QA & 263 & 274 \\ QLRA & 122 & 120 \\ QCNNPA & 216 & 240 \\ QAAPA & **110** & **108** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study: average coding length of different data compression methods.  
Figure 7: The separate information entropy of data fields of the training dataset obtained using linear regression predictor (LR), CNN predictor (CNN), and our proposed attention-averaging-based predictor (Attention Averaging).  
remained consistent with the training and validation sets, demonstrating stability and absence of overfitting. The graphical representation of generated code length versus time (Figure 8) illustrates the stability of compression effectiveness over time. The average coding length achieved by the proposed QAAPA method closely aligns with the sequential information entropy estimated earlier, indicating compression results nearing the theoretical minimum. These findings confirm the robustness and reliability of the QAAPA method for compressing real-time ship status data without significant overfitting issues.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 2 Formation Transformation in UAVs-LS
### Collision-Free Trajectory Planning  
In this paper, we assume that there is no obstacle in the air and the UAVs only need to avoid inter-UAV collisions. Artificial potential field (APF) has been applied to obstacle avoidance and path planning for decades. In APF, a potential field is constructed where each UAV is attracted by the target and repelled by the obstacles. The attractive, repulsive and the resultant potential field \(U_{att},U_{rep},U\) of the \(i\)-th UAV can be written as:\[U_{att,i}(t) =\frac{1}{2}\eta(\chi_{i}(t)-\mathbf{\tilde{t}_{i}})^{2} \tag{7}\] \[U_{rep,i}(t) =\begin{cases}\frac{1}{2}k_{p}(\frac{1}{\rho_{i}(t)}-\frac{1}{\rho _{o}})^{2},&\rho_{i}\leq\rho_{o},\\ 0,&\rho_{i}>\rho_{o}\end{cases}\] (8) \[U_{i}(t) =U_{att,i}(t)+U_{rep,i}(t) \tag{9}\]  
where \(\eta\) and \(k_{p}\) are attractive and repulsive force gain coefficients, respectively; \(\chi_{i}\) is the actual position of the \(i\)-th UAV; \(\rho_{o}\) is the minimum distance under which the repulsive potential field does not make effect; \(\rho_{i}\) is the actual distance to an obstacle of the \(i\)-th UAV. The corresponding attractive, repulsive, and the resultant forces \(F_{att,i}\), \(F_{rep,i}\), \(F_{i}\) are:  
\[F_{att,i}(t) =-\
abla U_{att,i}(t)=-\eta(\chi_{i}(t)-\mathbf{\tilde{t}_{i}}), \tag{10}\] \[F_{rep,i}(t) =-\
abla U_{rep,i}(t),\] \[=\begin{cases}\frac{1}{2}k_{p}(\frac{1}{\rho_{i}(t)}-\frac{1}{ \rho_{o}})\frac{1}{\rho^{2}}\frac{\partial\rho_{i}(t)}{\partial\chi},&\rho\leq \rho_{o}\\ 0,&\rho>\rho_{o}\end{cases}\] (11) \[F_{i}(t) =F_{att,i}(t)+\sum_{k=1}^{n_{i}}F_{rep,i}(t), \tag{12}\]  
where \(n_{i}\) is the number of obstacles detected within a radius of \(\rho_{o}\) of the \(i\)-th UAV at time \(t\).
## 4 Methodology
### Interconnection between Gas and Power Systems  
As discussed in the previous sections, gas and electric power systems are physically interconnected at different facilities. In this paper, we consider the most important connections between both systems as follows:  
1. _Power supply to electric drivers installed in gas compressor stations:_ The electric power consumed by the compressor station can be described by the following expression (derived from the first and second law of thermodynamics for an isentropic compression process) describing the required driver power \(P_{D,j}^{CS}\) for compressing the gas flow \(Q\) from inlet pressure \(p_{1}\) to outlet pressure \(p_{2}\)[56; 57]: \[P_{D,i}^{CS}=f\frac{\kappa}{\kappa-1}\frac{Z_{1}T_{1}R\rho_{n}Q}{\eta_{ad}\eta_ {m}}\left[\frac{p_{2}}{p_{1}}\frac{\kappa-1}{\kappa}-1\right],\;i=1\dots N_{CS}\] (27) where \(f\) is a factor describing the fraction of total driver power provided by electric drivers, \(\eta_{ad}\) the average adiabatic efficiency of the compressors, \(\eta_{m}\) the average mechanical efficiency of the installed drivers, \(p_{2}\) the outlet pressure, \(p_{1}\), \(Z_{1}\), \(T_{1}\) the inlet pressure, compressibility factor, temperature, respectively, \(R\) the gas constant, \(\kappa\) the isentropic exponent. The power supply of the gas network is added to the active power demand in the electric model.
2. _Electric power supply to LNG terminals and UGS facilities:_ We capture this interaction by assuming a generic linear function in terms of the regasification or withdrawal rate \(L_{rw}\), respectively: \[P_{D,i}^{rw}=k_{i,0}+k_{i,1}\cdot L_{rw,i}\] (28)
3. _Fuel gas offtake from gas pipelines for power generation in GFPPs:_ The required fuel gas \(L_{GFPP,i}\) for active power generation \(P_{G,i}\) at plant \(i\) can be expressed in terms of the thermal efficiency \(\eta_{T}\) of the GFPP and the gross calorific value \(GCV\) of the fuel gas, as follows: \[L_{GFPP,i}=\frac{P_{G,i}}{\eta_{T}\cdot GCV},\quad i=1\dots N_{GFPP}\] (29)
## 2 Materials and Methods
### Layer 3  
**Step 1**. Determination of the hourly reactive powers using the active powers measured in the pilot EDSs and estimated for the EDSs non-integrated in the \"candidate\" classes and the regression models established in Step 2 from Layer 1.  
\[Q_{t,m}^{(e)}=f(P_{t,m}^{(e)},\beta_{m}),\ m\in\{k_{nc}\},\ t=1,\ldots,T \tag{19}\]  
\[Q_{t,p}^{(e)}=f(P_{t,p}^{(r)},\beta_{p}),\ p\in\left\{N_{pilot}^{EDS}\right\}, \ t=1,\ldots,T \tag{20}\]  
where Q(e),tm is the estimated value of the reactive power in the EDS m belonging to the set of the EDSs non-included in the \"candidate\" classes at the hour t, t = 1, \(\ldots\), T, and Q(e),tp is the estimated value of the reactive power in the EDS m belonging to the set of the pilot EDSs with SCADA measurements integrated at the hour t, t = 1, \(\ldots\), T.  
**Step 2**. Power flow calculation using the Newton-Raphson method to estimate the state of the EDN. The state variables recorded refer to the voltages on the MV side of the HV/MV EDS, the active and reactive power flows, the power/energy losses, and the active/reactive powers injected in the slack bus (the MV bus of the HV/MV EDS).  
**Step 3**. Verifying the technical constraints regarding the voltages and loading of the lines. If there are violations of some limits, then the DNO can apply technical measures to bring the state values to the admissible limits provided in the performance standards or imposed by the manufacturers.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 5 Framework Design for 5G Mm-Wave Backhaul  
Aiming to achieve the desired goals in Section 3 with the support of emerging techniques in Section 4, we propose a framework for mm-wave backhaul, an overview of which is shown in Figure 3. According to the backhaul data demands, a routing scheme is needed for every flow to guarantee a robust performance in terms of latency and throughput. Then, the STDMA scheduling in the MAC layer is followed to assign specific time slots to different hops of every flow, where multiple transmissions can be allocated in the overlapping time periods to fully exploit spatial reuse gain. Finally, physical layer technologies, especially emerging high rate and reliability-aided techniques, such as hybrid beamforming and full duplex transmission, are utilized to satisfy the transmission requirements in the 5G mm-wave backhaul system.