esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Objective OPP Model
### The Second Objective Function Considering ANOBC
#### 3.2.2 Number of Observable Buses under D-PMU \(N\)-1 Contingencies  
When one D-PMU fails, some buses in the network cannot be observed. The number of unobservable buses differs when the locations of D-PMUs are different. Ensuring the network is completely observable in the event of any D-PMU outages can double the number of D-PMUs that need to be provided. This is extremely uneconomical. Therefore, the second objective function is to maximize the average number of observable buses under D-PMU \(N\)-1 contingencies. This is taken as the value of the ANOBC.  
Define \(\mathbf{X}_{\text{S}}\) as the placement scheme, of which the total row number is \(N_{\text{D-PMU}}\); Define \(\mathbf{X}_{\text{cd}}\) as the \(k\)-th outage scheme, which is generated to simulate the \(N\)-1 contingencies. \(N_{\text{D-PMU}}\) outage schemes are generated to simulate the \(N\)-1 contingencies. Using an IEEE 13-bus system as an example, if installing three D-PMUs in bus 2, bus 4 and bus 9, the placement scheme is \(\mathbf{X}_{\text{S}}=\left[\begin{array}{cccccccccccccccc}0&1&0&1&0&0&0&0&1&0&0& 0&0\end{array}\right]^{\text{T}}\), and the three outage schemes are defined as:  
\[\begin{array}{cccccccccccccccc}\mathbf{X}_{\text{01}}=\left[\begin{array}{cccccccccccc}0&0&0&1&0&0&0&0&1&0&0&0 &0\end{array}\right]^{\text{T}}\\ \mathbf{X}_{\text{02}}=\left[\begin{array}{cccccccccccccccc}0&1&0&0&0&0&0&0&1&0&0&0 &0&0\end{array}\right]^{\text{T}}\\ \mathbf{X}_{\text{03}}=\left[\begin{array}{cccccccccccccccc}0&1&0&1&0&0&0&0&0&0&0&0 &0&0\end{array}\right]^{\text{T}}\end{array}. \tag{7}\]The second objective function is calculated as:  
\[\max F_{2}(\mathbf{X})=ANOBCS=\sum\limits_{k=1}^{N_{\text{D-PMU}}}n_{\text{ok}}/N_{ \text{D-PMU}}\,, \tag{8}\]  
where \(n_{\text{ok}}\) is the number of observable buses of outage scheme \(\mathbf{X}_{\text{ok}}\), which can be calculated as:  
\[n_{\text{ok}}=\sum\limits_{j=1}^{N}g_{\text{oj}}, \tag{9}\]  
where \(g_{\text{oj}}\) is a 0-1 auxiliary variable, which represents the observability of bus \(i\), and is defined as:  
\[g_{\text{oj}}=\begin{cases}1&m_{\text{oj}}\geq 1\\ 0&m_{\text{oj}}=0\end{cases}, \tag{10}\]  
where \(m_{\text{oj}}\) is the number of observations representing the NMR of bus \(j\). The measurement redundancy vector \(\mathbf{M}_{\text{ok}}=\left[m_{\text{o1}},m_{\text{o2}},\ \cdots\ m_{\text{oN}}\right]^{\text{T}}\) is calculated by:  
\[\mathbf{M}_{\text{ok}}=\mathbf{A}\mathbf{X}_{\text{ok}}, \tag{11}\]  
where \(\mathbf{A}\) is the connectivity matrix, in which the elements are defined as follows:  
\[A(i,j)=\begin{cases}1&\text{ifbus $i$ is connected to bus $j$}\\ 1&\text{if $\ \ $i=j$}\\ 0&\text{other}\end{cases}. \tag{12}\]
## 3 Results  
THg, expressed on a dry weight (dw) basis, in sponges collected in the area of Montecristo and Giglio islands, are reported in Figure 2A,B, respectively.  
Total mercury showed high variability in sponges coming from the marine area of Montecristo island, ranging from 0.033 to 0.64 mg\(\cdot\)kg\({}^{-1}\), with the minimum content in _Cliona viridis_ (0.033 \(\pm\) 0.001 mg\(\cdot\)kg\({}^{-1}\)) and the maximum (20-fold higher) in _Sarcotragus spinolus_ (0.64 \(\pm\) 0.01 mg\(\cdot\)kg\({}^{-1}\)). Values ranging from about 0.11 to 0.29 mg\(\cdot\)kg\({}^{-1}\) were recorded in _Agelas oroides_, _Axinella damicornis_, _Halicona_ (_Halicolona_)  
Figure 2: Mercury levels (\(\pm\) standard deviation (SD)) in the sponges collected in Montecristo (**A**) and Giglio (**B**) islands. In each graph, different letters (a, b, c) indicate statistically significant differences among sponges (Kruskal–Wallis test, \(p<0.05\)). Ad = _Axinella damicornis_; Ao = _Agelas oroides_; Cv = _Cliona viridis_; Hf = _Halicolona_ (_Halicolona_) _fulv_; Hf = _Halicolona_ (_Soestella_) _mucosa_; Pe = _Penares eustrum_; Ss = _Sarcotragus spinosulus_; Cc = _Crambe crambe_; Cr = _Chondrosia reinformis_; Hb = _Hymedesmia_ (_Hymedesmia_) _baculifera_; Hc = _Heminycale columella_; Pf = _Petrosia_ (_Petrosia_) _ficiformis_.  
fulva_ and _H. (Soestella) mucosa_, whereas slightly higher values were found for the three samples of _Penares eustarum_ (1), (2) and (3) (0.35 \(\pm\) 0.03 mg\(\cdot\)kg\({}^{-1}\)), that showed similar concentration among them. Statistical analysis of the data showed a significant difference (Kruskal-Wallis test, \(p\) \(<\) 0.05) between _C. viridis_ vs. _P. eustarum_ (3) and _S. spinosulus_, _H. (S.) mucosa_ vs. _P. eustarum_ (3) and _S. spinosulus_ (Figure 2A).  
A high variability of THg content was also found in sponges collected in the marine area of Giglio Island: total Hg ranged from 0.017 to 0.73 mg\(\cdot\)kg\({}^{-1}\) dw. Even in this case, the minimum content was measured in _C. viridis_ (0.0167 \(\pm\) 0.0003 mg\(\cdot\)kg\({}^{-1}\)), which showed values from 10 to 70 times lower than other species. On the other hand, the three samples of _Chondrosia reinformis_ (1), (2) and (3) showed the highest concentrations (ranging from 0.39 to 0.73 mg\(\cdot\)kg\({}^{-1}\)). In the other sponges, the THg content ranged from \(\approx\)0.1 (_Crambe crambe, Heninycale columella_) to about 0.22 mg\(\cdot\)kg\({}^{-1}\) in _H. (H.) fulva, Hymedesmia baculifera_ and _Petrosia (P.) ficiformis_. The Kruskal-Wallis test highlighted significant differences (_p_ \(<\) 0.05) between _C. reinformis_ (1) vs. _C. crambe, C. viridis_ vs. _C. reinformis_ (1) and _C. reinformis_ (3) (Figure 2B).  
Since samples of _C. viridis_ and _H. (H.) fulva_ were found in both islands, differences in the THg content between the two islands have been investigated, to compare the possible influence of different sites. _C. viridis_ showed a higher THg in Montecristo island (Kruskal-Wallis test, \(p\) \(<\) 0.05), while the concentration of mercury in _H. (H.) fulva_ was higher in Giglio (Kruskal-Wallis test, \(p\) \(<\) 0.05).
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 2 Pharmacological Mechanisms of Drug-Food Interactions
### Pharmacokine Drug-Food Interactions (PK-DFIs)
#### 2.1.2 Distribution  
Drug distribution describes the reversible transfer of a drug from the blood to organs through vascular permeability into interstitial-intracellular space, and from there inside the cells [78]. Drug's physicochemical properties, plasma and/or tissue protein binding, tissue composition and the presence of several barriers (i.e., blood-brain barrier) are factors that play roles in distribution [79]. Drug's distribution is often related with the volume of distribution (Vd). It is defined as the apparent volume in which the existing amount of drug in the body must be dissolved to give its measured plasma concentration. Low Vd values are for drugs that remain mainly in plasma while high values are for lipophilic molecules that are distributed in tissues with high body-fat (i.e., CNS drugs). Regarding drugs in plasma, they are either bound (fb) or unbound (fu) to plasma proteins (i.e., human serum albumin (HAS), a1-acid-glycoprotein) depending on their affinity with them. This is important since only unbound drugs are distributed in the tissues and give the pharmacological action, as well as are eliminated. Protein binding is a site of potential drug interactions and alteration of protein binding can be significant in cases of fu < 0.05, low Vd (high plasma concentration) and narrow therapeutic index (NTI) [80]. For example, if a drug with fu = 0.05 changes it to fu = 0.1 and no added elimination occurs, the drug will double its action which can potentially lead in ADRs. Phenolic derivatives (i.e., flavonoids, phenolic acids, anthocyanins, gallic acid derivatives, stilbene derivatives etc.) can form reversible complexes with plasma proteins such as albumin [81; 82]. Flavonoids such as baicalin, rutin and quercetin that are found in several DS have been suggested to alter the albumin binding of drugs such as theophylline, nifedipine, promethazine, and ticagrelor [82]. As far as displacement of warfarin, experimental results vary from no risk up to clinically significant DFIs. For DFIs in protein binding with warfarin, an allosteric alteration of different binding regions is proposed, but the interaction is a minor one due also to the fact that S-warfarin (the active enantiomer) has lower protein binding [83].
## 1 Introduction
Ultimately, these metrics are jointly considered for target recognition. Once the data of a new class is obtained, it suffices to learn its corresponding  
Figure 1: Incremental learning for the SAR ATR model. The model can be updated at a minimal cost when it encounters the targets from new classes. class-specific dictionary independently. Experiments conducted on the MSTAR dataset have verified the efficacy of the proposed method. To summarize, the main contributions of this paper are as follows:  
(1) A novel incremental SAR ATR method utilizing class-specific dictionaries is developed. This approach enables incremental learning without the necessity of retaining historical data, optimizing storage and computational resources. (2) Intra-class divergence is integrated into the dictionary learning framework as a constraint, significantly enhancing the discriminative power of class-specific dictionaries. This innovation improves the ability to distinguish between different target classes. (3) Comparative analysis demonstrates that the proposed method outperforms existing incremental learning techniques in SAR ATR tasks, demonstrating superior recognition accuracy and robustness. The following sections of this paper are systematically arranged. Section II reviews the related studies, including incremental learning and sparse representation classifiers. In Section III, the detailed implementation of our proposed incremental SAR ATR method using class-specific dictionaries is elaborated. Section IV conducts multiple experiments on the MSTAR dataset, presenting and analyzing the corresponding experimental results. Finally, Section V summarizes the research conclusions and prospects for future work.
# An Empirical Study on Ensemble of Segmentation Approaches
## 2 Materials and Methods
### Loss Functions
#### 2.2.2 Cross-Entropy  
The cross-entropy (CE) loss function provides us a measure of the difference between two probability distributions. The goal is to minimize this difference and, in doing so, it has no bias between small or large regions.  
This could be an issue when dealing with imbalanced datasets. Hence, the weighted cross-entropy loss was introduced and it resulted in well-balanced classifiers for imbalanced scenarios [32].  
The formula for weighted binary cross-entropy is presented in (1). In this equation, \(T\) refers to the ground truth label image, while \(T_{ik}\) is the true value for the pixel \(i\) and it can be equal to either 0 or 1. It is equal to 0 if the pixel \(i\) belongs to the class \(k\), 0 otherwise.  
\(P\) is the prediction for the output image and \(P_{ik}\) is the probability of the \(i\)-th pixel to belong to the \(k\)-th class obtained by using the sigmoid activation function. For \(P\), we used the softmax activation function which returns probabilities.  
\(w_{ik}\) is the weight given to the \(i\)-th pixel of the image for the class \(k\). These weights were calculated by using an average pooling over the mask with a kernel \(31\times 31\) and a stride of 1 in order to consider also nonmaximal activations.  
\[L_{WBCE}=-\sum_{k=1}^{K}\sum_{i=1}^{N}w_{ik}\times T_{ik}\times\log(P_{ik}) \tag{1}\]  
where \(K\) is the number of classes and \(N\) the number of pixels.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications  
The realm of geo-hydrological applications is very wide. In this review, we focus on those applications that deal with the so-called geomorphic processes. Substantially, they are natural processes originated at or near the Earth's surface which include but are not limited to: expansive soils, soil erosion, slope failures, ground subsidence, river channel changes, glaciers, and coastal erosion [98]. They can have human-induced or natural causes, and at the same time, they can determine casualties and huge economic losses: when geomorphic processes threaten human populations they are properly called geomorphic hazards. The relation is unfortunately bi-directional: as the population grows, more people are likely to be exposed to the effects of hazardous geomorphic processes. At the same time, the population growth often accentuates the instability of geomorphic processes at the origin of the hazards.  
Great attention is paid among the general public to such natural hazards that represent abrupt changes, such as earthquakes and volcanic eruptions. Nonetheless, larger economic losses and casualties are caused by relatively slow progressive geomorphic hazards like landslides, soil erosions, subsidence, and leveee collapses, whose effects may be mitigated by proper monitoring.  
In light of that, in the following sections we will present and discuss the applications of DOFS to the three most common geomorphic processes: soil erosion in levees and embankments, slope failures and landslides, and ground subsidence.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 4 Results
### Energy Consumption  
The motors used by the drone are DJI 920 kV motors with a size of 22 mm \(\times\) 12 mm and 1045 propeller. Table 4 presents the details of energy consumed by the drone in the different throttle levels. The battery utilized is a 3-cell Li-po battery with an output of a maximum of 12.6 V and has a capacity of 6000 mAh with a discharge rate of 60 C.  
To determine the flight time, \(T\), of the drone, the total current, \(I_{t}\), needs to be calculated. In this regard, first, the total current consumed by the motors, \(I_{tm}\), should be obtained [41].  
\[I_{tm}=N\times I, \tag{11}\]  
Figure 21: Packets data transmitted and received by the drone (**a**) and its delays (**b**).  
where \(N\) is the number of motors, and \(I\) is the current for a single motor.  
After the total current of the motors is obtained, the total current consumed by the drone can be obtained by adding it up with the total current consumed by every component on the drone, \(I_{s}\) as in Table 5:  
\[I_{t}=~{}I_{tm}+\sum I_{s}. \tag{12}\]  
Thus, the flight time is the summation of battery capacity, \(C\), divide by the total current consumed:  
\[T=~{}\sum\frac{C}{I_{t}}\times 1000. \tag{13}\]  
As can be seen in Figure 22, the drone's power and current consumption are inversely related to efficiency. In other words, by further pushing the throttle, the rate of power consumption enhancement is more than the thrust enhancement and a larger part of the energy will be lost at higher throttle levels. Hence, as the throttle is pushed further, the drone's flight time will decrease, as shown in Figure 23. Moreover, the results show that the energy consumption by control and telecommunications components is relatively high, compared to the energy consumed by the motors. Therefore, to enhance the drone's flight time, not only should the drone weight, battery capacity, and throttle level be optimized, but also more energy-efficient components and algorithms should be developed for communications and flight control.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Throttle** & **Current (A)** & **Power (W)** & **Thrust (g)** & **Efficiency (g/W)** & **Temperature (\({}^{\circ}\)C)** \\ \hline
50\% & 2.6 & 28.9 & 290 & 10.0 & \\
65\% & 5.1 & 56.6 & 460 & 8.1 & \\
75\% & 7.4 & 82.1 & 590 & 7.2 & 55 \({}^{\circ}\)C \\
85\% & 10.1 & 112.1 & 730 & 6.5 & \\
100\% & 13.4 & 148.7 & 860 & 5.8 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specification for the energy consumption of the drone.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Current (D)** & **Power (W)** & **Percentage** \\ \hline Raspberry Pi & 360 mA & 1.8 W & 49.29\% \\ Pi Camera & 90 mA & 0.45 W & 12.32\% \\ LTE Modem & 290 mA & 1.45 W & 38.34\% \\ GPS Module & 0.06 mA & 0.003 W & 0.01\% \\ Pixhawk FC & 0.25 mA & 0.0125 W & 0.03\% \\
**Total** & **730.31 mA** & **3.7155 W** & **100\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Current consumption on every component.  
Figure 22: Power and current consumption versus efficiency.
## 5 Conclusions  
Landslides remain the most significant challenge in disaster prevention and mitigation, particularly along highway buffer zones, where multiple fatal accidents last year have drawn increased attention. In this study, NB, SVM, SVM-RF, and XGBoost models, along with two different non-landslide sampling strategies (GLHM and Random), were employed to assess landslide susceptibility in a 2 km buffer zone of the G3033 Duku Expressway. Sixteen contributing factors were preliminary selected. To avoid multicollinearity, we excluded plain curvature, roughness, rainfall, and snowmelt due to their high correlation (\(\left|\,\mathrm{r}\right|>0.5\)) with other indicators, a pattern consistent with known topography--precipitation relationships in alpine regions.  
The key findings are summarized below:  
* XGBoost consistently achieved the best performance under both sampling strategies. Under GLHM sampling, XGBoost obtained the highest AUROC (94.61%, IQR: 3.58) and accuracy (84.30%, IQR: 2.81), outperforming the other models. Pairwise comparisons further confirmed the statistical superiority of XGBoost, with significant differences observed especially when compared to NB and SVM models (adjusted \(p\)-values \(<0.05\) or \(<0.001\) in multiple comparisons). These results highlight the strong generalization capability of XGBoost in complex landslide susceptibility assessments, benefiting from its ability to model nonlinear interactions and handle high-dimensional feature spaces efficiently.
* The GLHM sampling method demonstrated a clear advantage over random sampling across all models. Both AUROC and accuracy were consistently improved under GLHM. For AUROC, the improvements for NB, SVM, SVM-RF, and XGBoost under GLHM reached +8.44 (\(p<0.001\)), +7.11 (\(p<0.001\)), +3.45 (\(p=0.023\)), and +3.04 (\(p=0.029\)), respectively. A similar trend was observed for accuracy, with increases of +11.30% (\(p<0.001\)) for NB, +8.33% (\(p<0.001\)) for SVM, +7.40% (\(p=0.002\)) for SVM-RF, and +8.31% (\(p<0.001\)) for XGBoost. These results indicate that GLHM effectively enhances model performance, likely by improving the representativeness of the training data and better capturing the underlying distribution of landslide and non-landslide units.
* Interpretability analysis using SHAP values further demonstrated that the choice of sampling method not only affects model performance but also the attribution of contributing factors. Under GLHM, top-ranked features were consistent across models, with STI (e.g., 0.2936 in SVM, 0.2947 in SVM-RF), NDVI (e.g., 0.3202 in SVM-RF, 0.2358 in SVM), and slope (e.g., 0.1374 in NB) appearing most frequently among the top three features. In contrast, under random sampling, feature rankings varied more widely, and models exhibited greater reliance on features such as elevation (e.g., 0.1260 in SVM) and lithology, which may reflect artifacts introduced by sampling from spatially mixed hazard contexts.
# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 5 Example--Simulation Models  
In order to analyse the presented problem, we have used the FlexSim software, which allows computer-modelling and simulation of discrete production processes with the use of human re-sources as well as robots.  
In the stage of conceptual design of the manufacturing system, some scenarios were considered, and computer models of lines operated by human operators or robots have been developed (Supplementary Materials), taking into account the planned breaks at work, failure rates, handling and quality parameters.  
In the first stage a simplified reference model, that only included machinery workstations (with necessary connections, input and output elements) was created (Figure 4). It represented the production flow in ideal conditions. To achieve greater production volume, two machining cells with a total of 8 milling machines were introduced.  
In the next phase a transport system with conveyors was included into model. Different scenarios were considered, including manual handling and some levels of automation and use of different number of industrial robots. The main two variants were, a human operated line (Figure 5) with 6 operators and robotic line with 3 robots and 1 human operator on the inspection station (Figure 6).  
Figure 4: Reference model of the manufacturing line (Supplementary Materials).  
Figure 5: Model of human operated line.  
Processing parameters were assumed based on MTM and time study of similar processes, including:  
* Milling process mean time = 450 s;
* Milling machine setup per part = 30 s;
* Drilling = 30 s;
* Automatic deburring = 30 s;
* Hand deburring = 40-50 s;
* Washing = 180 s (capacity 10 parts);
* Flow test = 60 s;
* Leak test = 60 s;
* Inspection = 50 s;
* Load/unload time of human operator = 20-30 s;
* Load/unload time of robot = 5 s;
* Conveyor speed = 1 m/s;
* Quality level: 99% for human operated line, 99.9 % for robot-operated line.
# Value of Information Based Data Retrieval in UWSNs
## 3 Value of Information in Underwater Sensor Networks
### Problem Definition
#### 3.4.2 Role of \(\tau_{f}\) in Infotentials  
The time-stamp \(\tau_{f}\) determines the Vol extracted from an information segment based on its infotential. Therefore, Vol of a data report varies according to the time instant at which the information is gathered by an end-processing agent. In the given UWSN scenario, we have two different final time-stamp \(\tau_{f}\) definitions. The definitions are based on who the end-processing agent is; the one who is responsible for triggering an actuation response after processing the data. If the AUV is equipped with the ability to process the data such that it can initiate an actuation response then \(\tau_{f}\) is the time at which the AUV retrieves the data from the respective sensor node. However,if the end-processing agent is above the sea surface, as in [50], then \(\tau_{f}\) is determined when the information is received by the end-processing agent (for which the AUV will have to resurface [50]). The time stamp \(\tau_{f}\) definition can crucially impact the design of AUV path planning algorithms for Vol maximization. In this paper we assume the AUV to be the end-processing agent which implies that \(\tau_{f}\) will be determined by instant at which the information segment was offloaded from the sensor node on to the AUV.
# Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs
## 2 Materials and Methods
### A Two-Stage Voltage Regulation Scheme for Distribution Networks with PVs and EVs  
The conventional voltage regulation resources in distribution networks include the OLTC transformers and the shunted CBs, which are featured by slow response and limited daily action times. By contrast, PVs and EVs are connected to the grid via power electronic converters, making them capable of real-time controlling their reactive powers. To fully exploit the voltage control capability of EVs and PVs, a two-stage coordinated EV and PV control scheme for the grid voltage regulation is proposed in this section, with its general framework illustrated in Figure 9. The coordinated EV and PV control scheme is divided into two stages-the day-ahead control stage and the intraday control stage, which will be discussed in the following subsections.