esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Nitrogen and Phosphorus Removal
## 3 Results
### Nutrient Concentrations  
#### 3.3.1 Nitrogen  
Total N concentrations were highest at the System Inflow, and generally decreased as the runoff was treated in the system (Figure 4). Ammonium-N concentrations increased within the treatment tanks (i.e., between System Inflow and Tank Outflow), decreased from the WB (i.e., between Tank Outflow and WB Outflow) and were generally similar or increased within the EB (i.e., between Tank Outflow and EB Outflow) (Figure 4). All means, sample sizes, and standard deviations are shown in Supplementary Materials Table S2.  
System Inflow median N concentrations were 49.84 (range: 18.8-158.36) mg L\({}^{-1}\) for TN, 6.6 (range: 3.7-25.0) mg L\({}^{-1}\) for NH\({}_{4}\)\({}^{+}\)-N, and 0.065 (range: 0.025-1.11) mg L\({}^{-1}\) for NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N. Median N  
Figure 3: Distribution of storm event depths during the 2019 sampling season.  
concentrations measured at Tank Outflow were 45.74 (range: 27.12-71.54) mg L\({}^{-1}\) for TN, 17.2 (range: 4.74-40.7) mg L\({}^{-1}\) for NH\({}_{4}\)\({}^{+}\)-N, and 0.025 (0.025-7.7) mg L\({}^{-1}\) for NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N. Compared to the System Inflow, NH\({}_{4}\)\({}^{+}\)-N median concentration in Tank Outflow increased, and that difference was found to be statistically significant (\(p<0.001\)).  
The WB was most effective at reducing N concentrations. Median concentrations measured at the WB Outflow were 16.61 (range: 12.15-52.34) mg L\({}^{-1}\) for TN, 5.14 (2.75-39.55) mg L\({}^{-1}\) for NH\({}_{4}\)\({}^{+}\)-N, and 0.025 (0.025-0.13) mg L\({}^{-1}\) for NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N. The median effluent concentrations of TN and NH\({}_{4}\)\({}^{+}\)-N at the EB Outflow were both reduced relative to Tank Outflow (i.e., inflow to the WB) and those differences were found to be statistically significant (\(p<0.001\), \(p=0.002\), respectively).  
The EB Outflow median TN concentrations were higher than the WB Outflow. At the EB Outflow, the median TN concentration was 39.74 (range: 17.7-92.34) mg L\({}^{-1}\), the NH\({}_{4}\)\({}^{+}\)-N median concentration was 24.5 (range: 1.88-75.5) mg L\({}^{-1}\), and the NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N median concentration was 0.025 (range: 0.025-0.089) mg L\({}^{-1}\). Differences in concentrations compared to Tank Outflow were not found to be significant.
# High-Precision Combined Tidal Forecasting Model
## 1 Introduction
The ARIMA-SVR prediction model is a data-driven model. It has unique advantages in solving numerical predictions, rebuilding highly non-linear functions, time series analysis, and so on. It does not need to consider the physical mechanism of the tidal formation process, but establishes a mathematical analysis of time analysis. By learning the given samples, we can find the statistical or causal relationship among the variables of water level, which has broad prospects in tidal prediction. By using the complete data to extract information, including the astronomical tide level and the non-astronomical tide level, the proposed model greatly improves the accuracy of tidal prediction. The model was validated against the measured tide data at the port of Bay Waveland Yacht Club in Mississippi, USA. The verification proves that the combined model effectively ameliorates the low accuracy of a single model and provides effective tidal prediction.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 1 Introduction
### Contributions  
The key contributions of this work are outlined below:  
* The EE problem in the SISO-OFDMA communication system for the DL direction is formulated by us. This formulation takes into account THE subchannel and power allocation within the OFDMA network, as well as the phase shift of the RIS. The intricate relationship among the optimization variables renders the problem non-convex, posing a significant challenge to finding an optimal solution directly.
* Within the framework of AO, a highly efficient sub-optimal solution strategy is introduced by us. This approach capitalizes on the lower bound of the EE problem by introducing a novel parameter into the EE formula. The integration of this new parameter allows for the dissection of the intricate objective function of EE into individual components for each user. Subsequently, a resourceful greedy method called the maximizing-EE-lower-bound-based downlink subcarrier assignment (MDSA) is proposed by us for subcarrier allocation, ensuring the effective utilization of available subchannels. Simultaneously, the approach employs the sequential quadratic programming method (SQP) for power allocation, a versatile optimization technique. Furthermore, the RIS phase shift is optimized using the particle swarm optimization (PSO) algorithm, recognized for its adaptability in finding optimal solutions within complex optimization spaces.
* The simulation results demonstrate that the proposed low-complexity sub-optimal method, employing MDSA and PSO, enhances the performance of RIS-enabled SISO-OFDMA communication systems compared to benchmark methods.  
Notation: In this paper, vectors and matrices are denoted by bold-face lower-case and upper-case letters, respectively. Sets are designated by upper-case calligraphic letters. Additionally, the functions \(\mathcal{R}(\cdot)\), \(\mathcal{I}(\cdot)\), \(|\cdot|\), \((\cdot)^{*}\), and \(\arg(\cdot)\) indicate distinct properties of a complex number, namely, its real part, imaginary part, modulus, complex conjugate, and angle, in that order. \(\mathbf{I}_{M}\) denotes the identity matrix of size \(M\times M\), and \(\mathbf{e}_{m}\) denotes the \(m_{\text{th}}\) column of \(\mathbf{I}_{M}\). \(\text{diag}(\mathbf{x})\) denotes a square diagonal matrix with the elements of \(\mathbf{x}\) on the main diagonal. \(\|.\|\) denotes the \(l_{2}\) norm. The distribution of a circularly symmetric complex Gaussian (CSCG) random variable with mean \(\mu\) and variance \(\sigma^{2}\) is denoted by \(\mathcal{CN}(\mu,\sigma^{2})\). Some other notations used in the paper are defined in Table 2.
## 3 Spatial Characterization Methods
### Minimum Volume Enclosing Ellipsoid Algorithm  
Compared with the 3D convex hull, the ellipsoid is more prominent in spatial characteristics. It has clear three-axis distribution characteristics, which can be used to calculate geometric characteristics such as crack direction and crack surface. The equation of the ellipsoidal surface is given as:  
\[ax^{2}+by^{2}+cz^{2}+2 dxy+2 exz+2 fyz+2 gx+2 lty+2 iz=1 \tag{1}\]  
Nine parameters from \(a\) to \(i\) can be solved with more than 9 points [29]. However, an ellipsoidal surface may not be formed when solving the surface equation, due to the irregular distribution of scattered points. In the field of convex optimization, the MVEE can be used to solve this obstacle [40].  
Consider a set of \(m\) points in \(n\) dimensional crack space \(R^{n}\): \(S=\{s_{1},s_{2},\cdots s_{m}\}\in R^{n}\). To guarantee that the ellipsoid has positive volume in any condition, it is assumed that the convex hull of the set S spans \(R^{n}\). Therefore, the ellipsoid \(\varepsilon\) in center form is given by:  
\[\varepsilon=\left\{s\in R^{n}\middle|(s-c)^{T}E_{m}(s-c)\leq 1\right\} \tag{2}\]  
where \(c\) is the center of the ellipsoid, \(E_{m}\in S^{n}_{++}\), and \(E_{m}\) is a positive definite matrix. Thus, the volume of ellipsoid \(\varepsilon\) is given by:  
\[V(\varepsilon)=\frac{v_{0}}{\sqrt{\det(E_{m})}} \tag{3}\]  
where \(v_{0}\) is the volume of the unit hypersphere. Therefore, this problem is equivalent to finding a vector \(c\) and a positive definite matrix \(E_{m}\), which minimizes \(\det(E_{m})\).  
Optimizing the problem into a convex optimization problem, the problem can be solved easier in the following dual problem:  
\[\left\{\begin{aligned} &\max\log\det V(\mathbf{u})\\ &\text{subject to }\mathbf{1}^{T}\mathbf{u}=1\end{aligned}\right. \tag{4}\]  
where \(\mathbf{V}(u)=\mathbf{Q}\text{dig}(\mathbf{u})\mathbf{Q}^{T}\), \(\mathbf{Q}=\left[\begin{matrix}\mathbf{P}&\mathbf{1}^{T}\end{matrix}\right]^{T} \in\bm  
An ascent method is adopted to find the maximum. The ascent direction at a feasible point \(\overline{\mathbf{u}}\) is \(\Delta u=\mathbf{e}_{j}-\overline{\mathbf{u}}\), where \(j=\underset{i}{\text{argmax}}g_{i}(\overline{\mathbf{u}})\) in the \(j\)-th unit vector \(\mathbf{e}_{j}\), and \(g_{i}(\mathbf{u})=\mathbf{q}_{i}{}^{T}V(u)^{-1}\mathbf{q}_{i}\). The problem can be approached [41] with  
\[\max_{\alpha\in[0,1]}\log\det V\big{(}\overline{\mathbf{u}}+\alpha\big{(}\mathbf{e}_{j }-\overline{\mathbf{u}}\big{)}\big{)} \tag{5}\]  
where the \(\alpha\) is  
\[\alpha=\frac{g_{i}(\overline{\mathbf{u}})-(n+1)}{(n+1)(g_{i}(\overline{\mathbf{u}})- 1)} \tag{6}\]
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 2 Related Work  
In this part, we firstly review the traditional trusted routing schemes for improving the routing security and reliability. Then, we introduce the relevant research approaches of routing schemes using the blockchain technology. Finally, we investigate the state-of-the-art of the application of the reinforcement learning in routing networks.
## 5 Experimental Results and Discussion
### Test 2: Analysis of the PLR in the UL Due to DL  
We have deployed 8 EDs where ED1-7 transmit asynchronous RPs every T = 70 s (with SF7) whereas ED8 is triggered to transmit also the UP at a random interval of 120-130 s with SF9. According to the notation used in (5) the considered scenario corresponds to N = 8, \(\mathbb{E}(\tau)=72\) ms, \(\mathbb{E}(D)=247\) ms, yielding a predicted PLR of 3.6%.  
In the considered setting we force ED1-7 to use a random channel from sub-band g1 whereas ED8 is allowed to transmit using Ch0 from sub-band g. Table 5 reports a PLR of 3.66% which is indeed a very high value and is certainly unacceptable in our case. It is worth noting that this value almost perfectly matches the PLR predicted by (5).  
A possible way for overcoming this problem is to duplicate each GW. More specifically, only one of the 2 GWs is configured to transmit the DCPs, so that the other is always free to receive UPS.  
As a matter of fact, there are several papers that propose full-duplex or multi-cast GWs to overcome this problem. However, as discussed in Section 2, such feature is not present in off-the-shelf GW solutions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **SF** & **PLR (\%)** \\ \hline ED1 & 9 & 5.18 \\ ED2 & 10 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of PLR between SF9 and SF10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **SF** & **PLR (\%)** \\ \hline ED1 & 7 & 94.3 \\ ED2 & 7 & 35 \\ Both & 7 & 29.66 \\ \hline
**Node** & **SF** & **PLR (\%)** \\ \hline ED1 & 7 & 4.7 \\ ED2 & 8 & 3.23 \\ Both & 7 and 8 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of PLR between SF7 and SF8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **SF** & **PLR (\%)** \\ \hline ED1 & 8 & 17.96 \\ ED2 & 8 & 84.88 \\ Both & 8 & 3.46 \\ \hline
**Node** & **SF** & **PLR (\%)** \\ \hline ED1 & 8 & 32 \\ ED2 & 9 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of PLR between SF8 and SF9.
# Adding Flavor to Beverages with Non-Conventional Yeasts
## 3 Results
### Fermentation Performance  
We compared the fermenting capacity of the strains with very strong volatile compound formation to the lager yeast strain _S. pastorianus WS34/70_ and other _S. cerevisiae_ strains. All strains were adjusted to the same optical density prior to the start of fermentation to allow for comparison of the speed of fermentation between strains. Fermentation rates were followed by measuring the CO\({}_{2}\) release daily (Figure 3). All strains were able to ferment the liquid within one week except for the _W. anomalous_ strains CBS 248; CBS 249 and CBS 261. However, in contrast to the other strains, the _W. anomalous_ strains showed an almost linear fermentation curve with equal amounts of CO\({}_{2}\) released per day. During fermentation the _W. anomalous_ strains produced oily top layers and biofilms of cells. This oily phase may have reduced the CO\({}_{2}\) release as we observed the lowest pH in the _W. anomalous_ fermentations measured among all strains: for strain CBS 261 the pH reached 4.79 and for strain CBS 262 the pH was 4.59, while the liquids fermented by other yeast strains showed a pH of \(>\)5. This suggests that the dissolved CO\({}_{2}\) contributed to the acidification of the liquid in the _W. anomalous_ fermentations. The most rapid fermentations as represented by fast CO\({}_{2}\) loss were observed with the _S. cerevisiae_ and lager beer production strains. Yet, in addition to the _Wickerhamonyces_ strains, several other strains showed prolonged CO\({}_{2}\) loss, including _P. kluyveri_ CBS 188 (Figure 3).  
For most of the strains we could observe a correlation between fermentation speed, residual sugar concentration and ethanol production. The _S. cerevisiae_ and lager yeast strains left 2-2.5% of sugar in the medium, while the residual sugar in _Candida_ and _Pichia_ strains was between 3-3.5%. _Wickerhamonyces anomalous_ strains left 4.5-5% of sugars unfermmented (Figure 4). Concomitantly, _S. cerevisiae_ and lager yeast strains produced the highest amount of alcohol (up to 8%) and the _W. anomalous_ strains only 4%.  
Figure 3: Fermentation profiles of the selected strains representing the detected flavor diversity. The CO\({}_{2}\) release was measured daily for each individual strain (see Table 1 for the nomenclature of strains). Fermentations were followed for 13 days.
## 2 Theoretical Analysis of the near Field of an Ultra-Large-Scale Metasurface
### Calculation of Noisy Near-Field Metasurface
#### 2.2.1 Observation Surface Distance Is 0.9 m  
First, when the observation surface is positioned at a distance of 0.9 m from the metasurface, the noise amplitude is set as _amp_ = 35. The observation surface in Figure 4a,b is 1.407 m \(\times\) 1.407 m, while that in Figure 4c-g is 1.005 m \(\times\) 1.005 m. It can be observed from Figure 4 that for the small metasurface depicted in Figure 4a,b, both the 3D and 2D normalized amplitude diagrams indicate that crosstalk occurs and is predominantly overshadowed by noise interference. The phase diagram also reveals the excitation of vortex waves with \(l=-5\) mode; however, due to excessive noise presence, it becomes challenging to discern the vortex phase except within a limited area at approximately  
Figure 3: Near-field simulation of metasurfaces with different sizes at an observation distance of 1.2 m. (**a**) \(R=20\times p\), \(R=0.2\) m, sampling interval = 0.007 m, and metasurface size of 6.66 \(\times\) 6.66302; (**b**) \(R=40\times p\), \(R=0.4\) m, sampling interval = 0.005 m, and super surface size of 13.33 \(\times\) 13.3302; (**c**) \(R=80\times p\), \(R=0.8\) m, sampling interval = 0.005 m, and metasurface size of 26.66 \(\times\) 26.66302; (**d**) \(R=120\times p\), \(R=1.2\) m, sampling interval = 0.005 m, and metasurface size of 40 \(\times\) 40302.  
one-fourth of its center (3.14 \(\times\)\(R^{2}\)/4). On the other hand, for the large metasurfaces shown in Figure 4c-e, where \(R\) equals 40 \(\times\)\(p\), \(R\) equals 60 \(\times\)\(p\), and \(R\) equals 80 \(\times\)\(p\), respectively, the 3D and 2D amplitude diagrams demonstrate an absence of crosstalk as well as the effective isolation of electric field amplitudes from disruptive noise interference. Similarly, the phase diagram confirms the excitation of vortex waves with \(l=-5\) mode, and notably, the extent of the vortex phase on the sampling surface gradually expands with increasing array size. Furthermore, in the ultra-large-scale metasurface illustrated in Figure 4f,g, multiple concentric circles are excited by vortex wave amplitudes, resulting in efficient noise isolation. The phase diagram indicates the successful acquisition of undisturbed vortex phases on the observation surface without any interference caused by noise. In addition, the OAM spectrum exhibits high mode purity throughout all scenarios in Figure 4, and this remains true even after superimposing noise. It can be inferred that a larger metasurface possesses superior anti-interference capabilities under identical levels of noise interference.  
Figure 4: _Cont._
## 4 Discussion
### Influence of Thermal Conductivity  
According to Equation (12), it can be seen that the thermal conductivity affects the thermal resistance value in the model. The smaller the thermal conductivity, the greater the thermal resistance. The thermal conductivity is set to be 0.68 W/(m/K), 1.68 W/(m/K), 2.68 W/(m/K), 3.68 W/(m/K), and 4.68 W/(m/K) in five different numerical tests, while keeping the other parameters constant. Figure 10a shows that the average temperature changes of the five experiments before the peak almost coincide. Although there is a slight fluctuation after the peak, the difference is not obvious. As shown in Figure 10b, the peak strength is also relatively stable. It can be concluded that the thermal conductivity in a certain range has little effect on the average temperature and peak strength of the model.  
Figure 9: Axial stress, number of micro-cracks, and average temperature change with the axial strain during the numerical experiment under uniaxial compression.  
This result proves that the heat conduction effect does not produce heat but rather determines the amount of heat transferring between particles, so the average temperature change of the model is almost independent of the thermal conductivity.
## 2 Methods  
### List of Instrumentation and Software  
The tools and software, including (1) DJI Phantom 4 Pro drone (resolution: 4096 \(\times\) 2160), controller, Secure Digital (SD) card, and charged battery pack; (2) MAPIR Survey3N NIR camera and SD card; (3) MAPIR Camera Control (MCC) software(v2019.10.16); (4) iPad (DJI GS Pro app included); (5) WebOpenDroneMap (WebODM); (6) ArcMap; (7) PyCharm; and (8) Canopeo, were used to evaluate the plant density and health within the GSIs. The DJI Phantom 4 Pro drone (resolution: 4096 \(\times\) 2160) has (effectively) a 20-megapixel camera with a focal length of 8.6 mm, a lens FOV of 84\({}^{\circ}\), a CMOS sensor size of 1 inch, and it captures images at ISO-100 (Dymatize, Tustin, CA, USA) with a shutter speed of 1/320 s [36]. The Survey3N NIR 12 MP camera (MAPIR, San Diego, CA, USA) has a focal length of 8 mm, a 41o FOV, and imagery captured at ISO-400 with a shutter speed of 1/500 s [37]. Survey3N takes pictures every 0.5 s during the flight and saves the photos in RAW image format [37]. Using the MAPIR Camera Control software, the data from the image RAW files and the Global navigation satellite system (GNSS) data stored with the image files are combined. DJI GS Pro app in iPad was used for designing an appropriate flight plan of each studied site. WebODM is an open-source drone mapping software that was implemented to generate orthorectified maps, point clouds, and digital surface models (DSMs) from aerial imagery using image processing libraries including OpenSfM and Primitive Machine Vision System (PMVS) [38,39]. ArcMap was used to view, edit, and analyze geospatial data and maps while PyCharm is an integrated development environment (IDE) which was used in computer programming, specifically for the Python programming language and codes. Python (version 3.9.13) along with libraries (e.g., numpy, cv2, pyplot) were used for developing the framework, and alternative code (e.g., Visual Studio Code) can be used based on individual skills, availability, and familiarity.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## 3 Results
### Our Encryption and Decryption Scheme for Delivering in SC-CDM
#### 3.4.2 Secure Proof of Ownership over Secure Transaction  
Proof of ownership is similar to the integrity auditing protocol [37], but with the role of prover and verifier changed. In our construction, only clients with intact data can pass the proof-of-ownership protocol. Moreover, since the proof of ownership protocol is performed over encrypted data [38], the protocol does not expose any information, since CDM delivered is persisted in the IPFS as blockchain.  
The SC-Cloud handles CDM data in IPFS in order to support only secure temporary storage of the transferred CDM data, thereby making file information of the data confidential. Currently, IPFS does not provide separate access control for external access, so it needs to be resolved.  
In order to handle the problem of keeping data of a large volume of CDM in the distributed ledger of the SC-CDM, IPFS is applied to perform effective and secure management of CDM data. The following is a transaction for delivering the CDM. The transaction for CDM is signed with SHA-256. Table 3 shows the contents of the transaction for CDM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Item** & **Contents** \\ \hline
1 & Sender & fK5q+LBx9 DXQm9 vxAJPaw1 zBWA9 UT60V5 Ma12 uxXGDQ= \\ \hline
2 & Recipient & HhhAXOj+iMqRRNJJgDaRD/sk78x \(\times\) 1248F7 WNr9 eEjxHL= \\ \hline
3 & Key & KMFFK44 HR3 IFSHYMYUITY2 VYZRCZZPR44H86K7D3 BNZCGJ7D6C3 OXA=== \\ \hline
4 & IV & F9A90A83 DA5E986 ACEE3 EE4F7 AEA8343 \\ \hline
5 & Hash1 & QmPs2 BsjB9 WuV6 pHxdVYJ8 fsnbLu8 BTY93b9 anwwW6 SM \\ \hline
6 & Hash2 & QmffHCn3 qnnWobcmmWrVZTioWW6 UdWxMrUqkBbD1 BiTihiv \\ \hline \hline \end{tabular}  
IV = Initialization Vector.  
\end{table}
Table 3: The contents of the transaction for CDM.  
Figure 7: Testing of encryption and decryption of a file in SC-CDM.
## 4 Integrity Monitoring  
This section presents the approach for the IM for the ambiguity-fixed RTK positioning under quiet atmosphere conditions. The mean values and standard deviations of the overbounding EMCs are first searched for phase and code residuals on the between-receiver level for both frequencies. Using the determined overbounding EMCs, the threat model protects against the onset of satellite faults, which is assumed to have the probability \(10^{-5}\) per satellite per approach [(42; 43)]. Assuming that both receivers are tracking dual-frequency phase and code signals from the same \(m\) satellites, in addition to the all-in-view solutions, in the DD mode, \(m-1\) subset solutions can be computed excluding all observations from one satellite in each subset solution.  
Figure 5: L1 and L2 residuals introducing the ground truth of the baselines and the resolved ambiguities for (**a**) the open-sky scenario and (**b**) the suburban scenario.
## 2 Materials and Methods
### UHPLC-MS/MS Calibration for AFB1 Analysis  
Two different types of calibration samples were prepared for the UHPLC-MS/MS: (A) A multi-point matrix-matched calibration in the supernatant of the fava bean suspension without AFB1 and LAB and (B) a multi-point calibration in water. All calibration samples were prepared as real samples. Both calibration curves contained seven different concentration levels including a zero level without AFB1. The linear quantitative area with 2 uL injection was 0.2 ng/mL-50 ng/mL. The matrix-matched curve was used for the quantification of the samples. The LOD of the method was 0.1 ug/kg and the LOQ was 0.2 ug/kg.