esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study  
In the process of analyzing the voltage profiles in a distribution system, it is important to use an accurate continuous power flow calculation methodology. Recently, novel methodologies have been proposed for solving both Power-Flow and Continuation Power-Flow in distribution systems. Reference [24] proposed several corrector techniques based on efficient Newton-like methods to improve the computational performance of conventional continuation power flow analysis. Reference [25] studied some relevant  
Figure 7: Normalized PV generation and load consumption profiles.  
aspects related with the power flow solution of ill-conditioned cases using the current injection formulation. Reference [26] solve the OPF problem using meta-heuristic MPA algorithm. In this study, we used the OpenDSS as the power flow calculation tool which is an electric power distribution system simulator developed by the Electric Power Research Institute [27]. OpenDSS has great advantage that determines the voltage and operating point of each inverter based on its volt-var curve. Moreover, OpenDSS offers a Python COM interface that enables users to script customized simulations adopting Python's reinforcement learning package PyTorch, which provides NN and training for agents [28]. To demonstrate the effectiveness of the system, a simulation was conducted on the modified IEEE 13 bus system from [8] and the modified IEEE 34 bus system from [29], as shown in Figure 8. In this study, we did not consider the size and the connected position of PV systems. Once the PV system installed by its owner is in the distribution system, the size and the connected position of the PV system cannot change at all. We focused on the operation strategy of the distribution system operator in the situation after the topology of the distribution network has already been determined. Therefore, the data about the topology of the test system is extracted from other references which are adopting PV inverters in distribution systems. In this section, the detailed experimental environment and a comparison with other control methods are presented.
## 2 Materials and Methods
### Vis-NIR Reflectance Spectroscopy and Acquisition System  
Spectroscopy studies the interaction between matter and electromagnetic radiation. Reflectance is the potency of a material to reflect electromagnetic radiation (EMR). The union of these features gives rise to four fundamental vibrational modes: symmetric stretch (\(\
u\)1), symmetric bend (\(\
u\)2), asymmetric stretch (\(\
u\)3), and asymmetric bend (\(\
u\)4). Bending vibrations include molecular scissoring, rocking, wagging, and twisting. The Vis-NIR range was chosen to realize the acquisition system of the _Exoland Simulator_ as it provides the identification and information of many minerals and molecules [51], such as the following:  
* Molecular water (adsorbed, interlayer, water ice) and hydrated salts (e.g., sulfates) due to overtone and combinations of H\({}_{2}\)O/O-H groups in the 1.4-1.6 \(\upmu\)m range [28];
* Ortho- and chain silicates through Fe\({}^{2+}\) crystal field transitions that produce large absorption bands around 1.0 and 2.0 \(\upmu\)m (pyroxene or olivine) [52];
* Sheet silicates (phylosilicates) due to the hydroxyl vibrational mode OH at 1.4 \(\upmu\)m combined with the Al-OH or Fe/Mg-OH modes at \(\simeq\)2.2 and \(\simeq\)2.3 \(\upmu\)m, respectively [33];
* Carbonates through overtone and the combination of the CO\({}_{3}\) groups 3\(\
u\)3 at 2.3 \(\upmu\)m [28];
* NH\({}_{4}^{+}\) compounds due to the presence of overtone and the combination at \(\simeq\)1.08, 1.56, 2.01, and 2.2 \(\upmu\)m [53].  
As the acquisition system must work in an overall range of wavelengths equal to 0.38-2.2 \(\upmu\)m, two different spectrometers were selected during the design phase: an SR2 model (0.38-1.08 \(\upmu\)m), shown in Figure 1a, and an NIRQuest+ model (0.9-2.2 \(\upmu\)m), shown in Figure 1b (both Ocean Insight Inc., Orlando, FL, USA). The collimator is a Gershun Tube, as shown in Figure 2, and it is connected to the two spectrometers by an optical fiber.  
The collimator is able to collect light from an area of about 10 cm of diameter and allows changing the angle of view between 1 and 28 degrees. The optical system must be moved with respect to a worktable, where the soil samples will be placed, while the two spectrometers will be placed in a steady position on the table, where the test rig is positioned.  
Figure 1: Spectrometers: (**a**) SR-2 VIS400-25; (**b**) NIRQuest+.
## 4 Performance Evaluation
### Simulation Results and Discussion
#### 4.2.4 Performance Comparison and Analysis of Fair PA with Fixed PA  
The outage probability with fair PA and fixed PA are plotted against the target rate in a two-user scenario with \(\eta=4\) (Figure 7a,b). For this simulation, the achievable sum rate derived in Equation (15) for User 1 (far from BS) is considered the same as the target rate for User 1 (far). The PA coefficients \(\alpha_{1}\) and \(\alpha_{2}\) are calculated using derived Equations (19) and (20) for fair PA. The fixed PA coefficients for Users 1 and 2 are used as \(\alpha_{1}=0.80\) and \(\alpha_{2}=0.20\), respectively (for the transmitted power of 40 dBm).  
By looking at Figure 7a, one can observe that Users 1 and 2 in fixed PA coefficients are in outage condition when the target rate is greater than 2 bps/Hz. This means that Users 1 and 2 in fixed PA will not experience an outage if the target rate is less than 2 bps/Hz. To infer, users in fixed PA are poorly performing and saturated to 1, as fixed PA neither considers the user's target rate requirements nor utilises instantaneous CSI conditions. Hence, it is not an optimal strategy to allocate power to users in the network. On the other hand, for a fair PA case, coefficients \(\alpha_{1}\) and \(\alpha_{2}\) are derived by considering target rate requirements dependent on the achievable sum rate of User 1 (far), i.e., CSI. As the target rate increases, User 1 (far) gradually goes into an outage condition, thus increasing the outage probability. However, User 2 (near) shows a sudden change in the outage probability trend when the target rate is between 5 bps/Hz and 8.5 bps/Hz. After 8.5 bps/Hz, User 2 also goes into an outage condition.  
Figure 7b shows the improved outage probability for a fair PA case for the required target rate. The improved outage probability trend is achieved by forcefully setting User 1 (far) and User 2 (near) PA coefficients as \(\alpha_{1}=0\) and \(\alpha_{2}=1\). This setting is done when it does not affect the outage condition of User 1. When the target rate is less than 8.5 bps/Hz, derived PA coefficient \(\alpha_{1}\) is assigned to User 1 (far) without bothering about User 2 (near), thus focussing more on User 1's performance. However, when the target rate is above  
Figure 6: Outage probability of SISO-NOMA scheme.  
8.5 bps/Hz, the PA coefficient \(\alpha_{2}=1\) is assigned to User 2 (near), thus allocating the whole power to it and focussing on User 2's performance. Hence, User 2's outage probability is improved from 8.5 bps/Hz. User 1 has left no power (\(\alpha_{1}=0\)), which does not affect its outage condition.  
Figure 8 shows that the achievable sum rate for having a fair PA is higher than for having a fixed PA when plotted against transmitted power. Because of varying CSI conditions, the fair PA coefficients \(\alpha_{1}\) and \(\alpha_{2}\) are calculated to meet the desired target rate requirements, thus achieving an improved achievable sum rate in a fair PA case.
## 2 Remote Sensing Surface Albedo Products  
The main features of the three SA products investigated in this work (C3S PROBA-V SA v1.0, C3S SPOT/VGT SA v1.0 and MCD43A3 C6) are summarized in Table 1. The information of the spectral characteristics of the four optical bands of the VEGETATION sensors onboard the PROBA and SPOT satellites, and its equivalent Moderate resolution Imaging Spectroradiometer (MODIS) bands, is provided in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Product**} & **Satellite** & \multicolumn{2}{c}{**BDF Model:**} & \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**GSD**} \\  & **/Sensor** & & **Volumetric** & & \\  & & **/Geometrical Kernels** & & & \\ \hline
**C3S** & & & & & \\
**PROBA-V** & & & & & \\
**SA v1.0** & & & & & \\ \hline
**C3S** & & & & & \\
**SPOT/VGT** & & & & & \\
**SA v1.0** & & & & & \\ \hline
**NASA** & & & & & \\
**MCD43A3 C6** & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the global remote sensing SA products under study. GSD stands for Ground Sampling Distance.  
The quality flag information of each product was used to filter low-quality pixels (Table 3). For the C3S PROBA-V SA v1.0 products, the land pixels showing an input status \"out of range\" or \"invalid\" or \"saturated\" in the B2 and B0 channels were discarded. The SPOT/VGT SA v1.0 pixels where the algorithm failed were not considered in the validation exercise. Additionally, two ancillary variables were also taken into account: The uncertainty (ERR) and the mean age (AGE, in number of days) of the observations that are used to produce the SA. The SPOT/VGT SA v1.0 pixels with associated uncertainty greater than 0.2 and an AGE greater than 20 were discarded. For MODIS C6, the quality flags are given in the MCD43A2 product, and those pixels with fewer than seven valid observations were discarded.
## 3 Results and Discussion
### Synthesis of MIP on Terbium Nitrate
#### 3.2.1 Yield of the Products
at 1597.10 cm\({}^{-1}\) for Tb-MIP and 1601.07 cm\({}^{-1}\) for NIP; -CH\({}_{2}\)-CO- at 1407.65 cm\({}^{-1}\) for Tb-MIP and 1406.63 cm\({}^{-1}\) for NIP. All these band characterize the presence of MAA in the composition of the MIP and NIP. The presence of the second monomer 2-VP could be proved by the very intensive and narrow band at 1027.51 cm\({}^{-1}\) for MIP and 1026.89 cm\({}^{-1}\) for NIP, which is characteristic of a pyridine ring. Also, the band of 1,3-substitution in the benzene ring could be observed in the IR spectrum, which proves the presence of the cross-linker DVB in the imprinting structures. Based on the analysis of the IR spectra, as well as based on the literature data, it is possible to characterize how the reaction took place (Figure 7). In the synthesis of the MIPs and NIPs, it is proposed that the method of non-covalent imprinting was used. In the non-covalent imprinting approach, a highly cross-linked polymer containing template molecules is generated during the polymerization of functional monomers with a large amount of cross-linking reagent in the presence of template molecules. At the first stage, to form a pre-polymerization complex, terbium nitrate is added to the mixture along with monomers to create molecular imprints. With non-covalent imprinting, the template binds with the monomers due to the hydrogen bond, the O\(\cdots\)H bond is formed with MAA, and the N\(\cdots\)H bond is formed with 2-VP. Then, an initiator AlBN is added to the mixture, which undergoes thermal decomposition with the formation of radical polymerization centers. Along with the initiator, a binding agent divinylbenzene is added, which just makes it possible to make cross-connections between the fingerprint and free functional groups of DVB. As a result, the molecular imprints bind to each other and to the DVB, forming a network in which the molecular imprints remain permanent. In addition, DVB causes a narrowing of the pores in the structure, which increases the efficiency of extracting target molecules from the solution. After polymerization, the material is subjected to the extraction of a template that leaves empty spaces inside the polymer structure with the shape of a molecular imprint of terbium nitrate. The resulting polymer is separated from the remaining solution, and then, it is cyclically treated with some solutions to remove terbium and other undesirable impurities. Figure 6: IR spectroscopy of the terbium(III) nitrate’s MIP (red line) and NIP (black line).
## 5 Analysis of the Transmission Delay
### One-Hop Delay/Distance for Intra-/Inter-Lane Scenario
#### 5.2.4 One-Hop Transmission Distance for the Inter-Lane Scenario  
Let \(D^{(1,2)}\) denote the distance between the selected relay vehicles in the inter-lane case. From Equation (21), we can formulate the relation between the one-hop timer value and the transmission distance, which can be represented as:  
\[D^{(1,2)}=\alpha_{D}T^{(1,2)}+\beta_{D}, \tag{27}\]  
where \(\alpha_{D}=R\alpha_{2}\) and \(\beta_{D}=R\beta_{2}\). \(\alpha_{2}\) and \(\alpha_{D}\) are positive for \(\epsilon>\frac{v_{\Delta}L_{\max}}{v_{\Delta}L_{\max}+R}\). Hence, if \(\epsilon=1/2\), we can see that \(\alpha_{2}>0\), \(\alpha_{D}>0\) when \(R>v_{\Delta}L_{\max}\).
## 2 Literature Review
[30] used ETA to identify the event chain from 39 ship grounding accident reports, thereby combining all the event chains to develop the CN, which was quite time-consuming. However, AR could discover potential, meaningful associations in a dataset without prior knowledge [32]. It has been widely used in the domains of metro engineering [33], aviation [34], and road traffic [35]. To the authors' knowledge, only a few studies have utilized AR to establish CN in the field of maritime accidents. The purpose of the present study is to reveal the complex evolution law of seafarers' unsafe acts involved in maritime accidents, based on which tailored safety management recommendations are made to reduce the risks of maritime transport activities. Specifically, the present study introduces association rule (AR) technique into complex network (CN) to avoid the defect that the edge weights are over-reliant on experts' subjective experience in the traditional complex network development process, and to develop a directed weighted interaction network of seafarers' unsafe acts and their influencing factors. Furthermore, through global network topology analysis and local network community structure extraction, the risk evolution characteristics of seafarers' unsafe acts in maritime accidents are analyzed from a multidimensional perspective, and the risk propagation paths of seafarers' unsafe acts and their influencing factors are quantitatively revealed to provide strong support for interrupting the risk propagation of seafarers' unsafe acts.
## 3 Results and Discussion
### Oxidation-Reduction Potential, pH, and TBARS Index  
The physicochemical parameters of model meat products (pH, ORP, and TBARS) were affected by the addition of apple vinegar. Table 4 presents the physicochemical properties of model sausages. The sausages were analyzed for their pH levels, which varied between 5.46 and 5.85, and an ORP index ranging from 358.50 to 396.15 mV. The TBARS values were between 0.54 and 0.91 mg MDA/kg. Table 3 shows the pH changes in sausages that were treated with apple vinegar during refrigerated storage at 4 C.  
The samples containing vinegar experienced a decrease in pH values immediately after production (0 days). However, these pH values steadily increased as the samples were stored for a longer period. This was associated with the presence of organic acids in naturally fermented apple vinegar absorbed by sausage in the process of curing. At the highest concentration of vinegar (V5), the pH level was significantly reduced. [PERSON] and [PERSON] [40] found that marinating beef _Longissimus dorsi_ muscle with weak organic acids and salts impacted its quality. According to their findings, the type and concentration of the organic acid used had a significant influence on the pH values and weight changes. Acid treatment caused lower pH values, denaturing proteins below the isoelectric point and altering their water-binding ability. Previous studies have shown that marinating meat with organic acids significantly lowers its pH value [41; 42]. These results have important implications for the food industry, as controlling the pH level is crucial to ensuring the quality and safety of meat products. In a study conducted by [PERSON] et al. [41], it was observed that the addition of vinegar to biflong caused a decrease in the pH value from 5.64 to 4.91. Similarly, [PERSON] et al. [42] found that the pH values of beef, which varied between 5.51 and 5.60, decreased and varied between 5.11 and 5.37 after undergoing the marination
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 4 Experimental Results and Analysis
### Evaluation Metrics  
For the quantitative evaluation of the imperceptibility of DARI-Mark, we used the classic objective standard PSNR and SSIM to measure the degree of image distortion. PSNR is defined as:  
\[V_{\text{PSNR}}(\mathbf{I},\mathbf{I}_{\text{W}})=10\text{log}_{10}\frac{W\times H \times P_{\text{MAX}}^{2}}{\sum\limits_{i=0}^{W-1}\sum\limits_{j=0}^{H-1}\left[ \mathbf{I}(i,j)-\mathbf{I}_{\text{W}}(i,j)\right]^{2}} \tag{4}\]  
where \(W\) is the width of the image; \(H\) is the height of the image; \(P_{\text{MAX}}\) is the maximum value of image pixels; and \(i,j\) are the \(x\), \(y\) coordinates of each pixel, respectively. The higher the value of \(V_{\text{PSNR}}\), the better the imperceptibility of DARI-Mark.  
For the quantitative evaluation of robustness, we used the bit error rate (BER) and normalized cross-correlation (NCC) as the evaluation index. The BER was used to evaluate the error rate of the original watermark and the extracted watermark, defined as:  
\[V_{\text{BER}}(w,w^{\prime})=\frac{1}{L_{\text{W}}}\sum\limits_{i=1}^{L}\left| w_{i}-w^{\prime}{}_{i}\right| \tag{5}\]  
where \(w\) is the original watermark; \(w^{\prime}\) is the extracted watermark; \(w_{i}\) and \(w^{\prime}{}_{i}\), respectively, represent the information of the original watermark and the extracted watermark at position \(i\); and \(L_{\text{W}}\) is the length of the watermark information. The NCC was used to evaluate the similarity between the original watermark and the extracted watermark, defined as:  
\[V_{\text{NCC}}(w,w^{\prime})=\frac{\sum\limits_{i=1}^{L}\left|w_{i}\times w^{ \prime}{}_{i}\right|}{\sqrt{\sum\limits_{i=1}^{L}w_{i}^{2}}\times\sqrt{\sum \limits_{i=1}^{L}w^{\prime}{}_{i}^{2}}} \tag{6}\]  
\(V_{\text{BER}}\in[0,1]\), \(V_{\text{NCC}}\in[0,1]\). The lower the BER, the larger the NCC value, the better the robustness of DARI-Mark.
# Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks
## 4 Performance Evaluation
### Time Complexity Analysis  
In this section, to verify the capability of the node to perform the proposed scheme, we present the time complexity analysis results. Table 2 summarizes the time complexity of the proposed scheme and other protocols. The proposed scheme is formed by two nested loops: (1) the inner loop that has a running time proportional to the number of nodes (\(N\)) to calculate the required number of paths and (2) the outer loop that has a running time depending upon the number of paths (\(P\)). Therefore, the complexities of the inner and of the outer loops are equal to \(O(N)\) and \(O(P)\), respectively. In addition, the running time of the exceptional handling process is \(O(1)\) because the process would be terminated within a fixed time. As a result, the time complexity of the proposed scheme could be expressed as \(O(N\times P)+O(1)\). Therefore, the overall time complexity of the proposed scheme is \(O(N\times P)\). As the number of nodes \(n\) is much more than the number of paths \(P\), the time complexity of the proposed scheme could be considered as \(O(N)\). Compared to the other protocols, REER has a running time proportional to the number of nodes (\(N\)) to calculate the link cost. Thus, the time complexity of REER is \(O(N)\). RGMR is formed by two nested loops: (1) the inner loop that has a running time proportional to the number of nodes (\(N\)) to calculate required number of paths and (2) the outer loop that has a running time according to number of paths (\(P\)) to calculate enter/exit point of each path. Thus, the time complexity of RGMR is \(O(N\times P)\). Finally, the RRDLA also has two nested loops: (1) the inner loop that has a running time proportional to the number of nodes (\(N\)) and (2) the outer loop that has a running time to meet the condition (\(K\)). Thus, the time complexity of RRDLA is \(O(N\times K)\).
## 3 Materials and Methods
### Methods
#### 3.2.3 Application of Medical-Knowledge Graph  
A knowledge graph is a structured management tool for knowledge, with medical-knowledge graphs extensively used to integrate and organize a vast array of medical and health-related information. Leveraging the foundations of artificial intelligence and data science, these graphs aim to provide comprehensive medical knowledge and insights and have broad applications.  
1. Knowledge generation  
When encountering complex cases with scarce relevant Q&A examples, difficulties arise in matching appropriate medical advice due to a lack of precision. In such instances, the characteristics of medical-knowledge graphs can be utilized to provide universal and structured medical information as a supplement, achieving the goal of generating universally applicable medical advice. The process of knowledge generation is shown in Figure 9.  
Initially, the patient's disease assessment results obtained from the M.I.N.I auxiliary assessment tool are used as input. Then, multiple combinational searches are conducted in the mental and psychological health domain-knowledge graph. The search results include information on disease symptoms, treatment methods (including treatment modalities, medications, recommended diets), prevention measures, etc. Finally, after the classification and organization of knowledge, the information is compiled into a knowledge list.
# Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning
## 2 Notation and Overview of Techniques
### Logistic Regression  
In logistic regression, the response \(Y_{i}\) is a Bernoulli \((\pi_{i})\) variable with a probability mass function (pmf) given by  
\[P(Y_{i}=y_{i})=(1-\pi_{i})^{1-y_{i}}\pi_{i}^{y_{i}}, \tag{1}\]  
where \(Y_{i}=1\) if the \(i\)-th claim is observed with probability \(\pi_{i}\) and \(Y_{i}=0\) with probability \(1-\pi_{i}\) otherwise, for \(i=1,\ldots,n\).  
Furthermore, we assume that the \(i\)-th claim will be observed or not using covariate information \(x_{i}=(x_{i,0},\ldots,x_{i,d})\in\)X\(\subseteq\{1\}\times\mathbb{R}^{d}\), with \(x_{i,0}=1\) corresponding to the intercept component.  
As is well known, \(Y_{i}\) has an exponential dispersion family (EDF) representation with canonical link function given by  
\[g(\pi_{i})=\log\left(\frac{\pi_{i}}{1-\pi_{i}}\right)=x_{i}^{T}\beta, \tag{2}\]  
where \(\beta=(\beta_{0},\ldots,\beta_{d})^{T}\in\mathbb{R}^{d+1}\) and where the design matrix \(X=(x_{1},\ldots,x_{n})^{T}\in\mathbb{R}^{n\times(d+1)}\) is of full rank \(d+1\). The use of the canonical link function implies that we receive unbiased estimates on a portfolio level, as can be seen in [46].  
Additionally, from Equation (2), we obtain the sigmoid, or the inverse of the logit, function \(\sigma:X\rightarrow(0,1)\) of the prediction  
\[x\mapsto\sigma(x;\beta)=\pi_{i}=\frac{1}{1+\exp(-x_{i}^{T}\beta)}. \tag{3}\]  
The sigmoid function has the advantage that the predicted values are probabilities which yield more easily interpretable results. Furthermore, the individual coefficients in the vector \(\beta\) can be treated as feature importance weights which result in explainable models.  
Finally, it is worth noting that in addition to the logistic sigmoid function, which is given by Equation (3), there are several other sigmoid functions, such as the hyperbolic tangent and the arctangent which are also monotonic and have a bell-shaped first derivative. Sigmoid functions were inspired by the activation potential in biological neural networks and have been widely used when designing ML algorithms, particularly as activation functions in artificial neural networks.