esa-sceva/satcom-chunk-collection huggingface dataset:
# Hot Topic Community Discovery on Cross Social Networks
## 3 Hot Topic Community Discovery Model
### LB-LDA Model
#### 3.2.3 LB-LDA Model Inference  
In text mining, documents and words are visible while the distributions are invisible. Therefore, the parameter distributions need to be estimated, including \(\theta\) and \(\varphi\). Similar to LDA, the Gibbs  
Figure 4: Generation process of LB-LDA.  
Sampling algorithm is used to estimate these parameter distributions. For one bitterm, the two words share the same latent topic label. If other bitterm latent topic labels are known, Equation (2) can be used to estimate this bitterm existence probability in different the topic labels. The meaning of each element in Equation (2) is showed in Table 1.  
\[p(z_{i}=k|\overrightarrow{z_{i}},\overrightarrow{B})\propto\frac{N_{d,k,i}+ \alpha_{k}}{N_{d,i}+\overrightarrow{\alpha}\cdot\overrightarrow{1}}\frac{N_ {k,w_{i,1},i}+\beta_{i}}{N_{k,i}+\overrightarrow{\beta}\cdot\overrightarrow{ \cdot\cdot\cdot\cdot}}\frac{N_{k,w_{i,2},i}+\beta_{i}}{N_{k,i}+\overrightarrow{ \beta}\cdot\overrightarrow{1}} \tag{2}\]  
The Gibbs sampling procedure is used to update each bitterm's latent topic label. Firstly, topic labels are assigned to each bitterm in the corpus randomly. In every iteration, elements in Table 1 are updated. Then, Equation (2) is used to update each bitterm's topic label. When the specified number of iterations reaches, it stops. The Gibbs sampling procedure is shown in Algorithm 1.  
```
Input  Enlarged corpus C', topic labels set \(T\), hyper-parameters \(\overrightarrow{\alpha},\overrightarrow{\beta}\), iteration times iter Output  Document-topic distribution \(\theta\), topic-word distribution \(\varphi\)
1 Initialize each bitterm's the topic label randomly
2 For iter_times = 1 to iter
3 For each document \(d^{\prime}\) in corpus C'do
4 For each bitterm \(b\) in document \(d^{\prime}\) do
5 Calculate the probability of each topic label by Equation (1)
6 Sample \(b^{\prime}\)s topic label based on the result of step 5;
7 Calculate \(\theta\) and \(\varphi\) based on Equations (2) and (3)
```  
**Algorithm 1 Gibbs Sampling Process.**  
The equations to estimate the parameters \(\theta,\varphi\) are shown as Equations (3) and (4). \(\theta\) is a \(M\times K\) matrix and represents topic distribution over each document. \(\varphi\) is a \(K\times V\) matrix and represents the word distribution over each topic label.  
\[\theta_{k,d}=\frac{N_{k,d}+\alpha_{k}}{N_{d}+\sum_{t=1}^{K}\alpha_{t}} \tag{3}\]  
\[\varphi_{i,k}=\frac{N_{i,k}+\beta_{i}}{N_{k}+\sum_{t=1}^{V}\beta_{t}} \tag{4}\]
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 5 Challenges in Smart Grids
### Challenges in State Estimation  
As one of the most critical components in energy management systems, state estimation has been challenged through more dispersed generation, demand-responsive loads, data-rate devices, and advanced cyber attacks [116]. On the one hand, extending the conventional SE approaches to distribution system state estimation (DSSE) is challenging because the radial or weakly meshed topology will cause observability problems, high R/X ratios for cable, few real-time measurements lead to low estimation accuracy, and unbalanced loads result in high computational complexity [73; 117]. On the other hand, cyber attacks like replay attacks, stealthy FDI attacks, and other advanced attacks continuously challenge state estimation.  
Future research in the field of distribution system state estimation (DSSE) should focus on the integration of Demand Response (DR) programs and the price sensitivity of active distribution networks, addressing the challenges of uncertainty and variability [116]. Exploring data-driven methods for system monitoring and learning before, during, and after high-impact, low-frequency (HILF) events is essential. These efforts aim to improve the distribution system observability and develop effective system restoration strategies that leverage the real-time knowledge of system states, thereby ensuring resilience and efficiency in future distribution systems with high penetration of renewable resources and DR capabilities [118].
# O-Band and C/L-Band III-V Quantum Dot Lasers Monolithically Grown on Ge and Si Substrate
## 2 O-Band and C/L-Band III-V Quantum Dot Devices via III-V/IV Hybrid Growth  
As previously stated, APDs are planar defects and debilitate device performance; they are known to be electrically active and can be detrimental for laser diode efficiency [65]. For conventional III-V devices, any threading dislocation or APDs propagating through the active layers will act as a non-radiative recombination center, leading to an increased threshold current density for III-V lasers on Ge or Si substrates [36]. Therefore, most QD lasers were grown on offcut Si substrate to avoid the formation of APDs. An alternative method is to grow GaAs on Si with an intermediate Ge epitaxial layer, which offers a near-GaAs on Si lattice constant but has a few defects. This is because the lattice mismatch between Ge and GaAs is very small, only a 0.08% mismatch. Since the realization of a quantum dot laser diode on miscut Ge substrate was reported by [PERSON] et al. [36], extensive investigation of the monolithic growth of III-V materials on a group IV platform has been carried out. Their novel growth method shows great promise of the ideal virtual substrate of Ge substrates and Ge/Si substrates for subsequent epitaxially grown III-V materials, thus promoting the development of III-V quantum dot lasers. In 2012, with the initial GaAs nucleation layer at the proper growth temperature, the InAs/GaAs quantum dots grown on a Ge substrate were realized [37; 66]. This was an essential step towards the monolithic integration of III-V QD lasers on Si substrates by using Ge-on-Si substrate. In addition, the room temperature threshold current density of 64.3 A/cm\({}^{2}\) has been reported. Then, the dislocation filter layer was introduced to improve the 1.3 \(\upmu\)m emission [40; 41; 67]. InAlAs/GaAs and InGaAs/GaAs strained-layer superlattice (SLSs) structures were used to reduce the density of TDs generated at the interface between III-V buffer layer and the IV substrate, and it was found that InAlAs is more effective than InGaAs/GaAs SLSs in suppressing the propagation of TDs. Further reduction of density of TDs was then achieved by additional elevated temperature steps (annealing) during the growth of InAs/GaAs QD structures on Si substrates [47]. The annealing process also results in significant improvements in optical properties and laser device performance. These attempts have been carried out upon Si or Ge substrates with 4 or 6 degrees offcut to the [110] direction. However, the GaAs buffer layers on these structures are at least 1 \(\upmu\)m thick, which renders the height difficulty for subsequent fabrication and integration. Therefore, a novel method in which only a thinner buffer layer is required would be more promising for future on-chip silicon photonic integration.
## 1 Introduction  
Sustainable agroecosystems tend to balance land exploitation for human needs such as food, fiber, and wood with the long-term conservation of natural resources. Sustainable intensification (SI) is defined as a process or system where the agricultural yield is increased, or additional nonagricultural land is converted without adverse environmental impact [1]. Some benefits of this system, including productivity, decrease in erosion, conservation of soil moisture, greater soil biological activity, and reduction in production costs, have been previously described [2]. Global carbon balance is maintained when plant growth creates the ideal conditions for the decomposition of organic matter, while live roots contribute to respiration, according to Reference [3]. However, soil scientists are alarmed by the current status of global warming and positive carbon balance in Earth's atmosphere. This may be resolved by increasing soil-organic-carbon (SOC) sequestration and identifying areas and soils with the greatest potential to undergo this process by the spatial quantification of SOC, not only in the topsoil layer but also in deeper ones [4; 5].  
The province of Manabi has the largest concentration of agricultural land in Ecuador, 766.744 ha, including natural and cultivated pastures (INEC, 2017). The largest territory has slopes greater than 12% [6]. The main water reservoir in the province, which has a capacity of 450 Mm\({}^{3}\), is regulated by the La Esperanza dam. Its content is intended for human consumption and irrigation [7]. The current landscape in the study area, the Carrizal-Chone valley, is dominated by a conventional system of cultivated pastures, forestry, and crops such as banana, cacao, coffee, and citrus. The physical and chemical properties of the soil have been improved through the management of grazing [7]. However, the impact of these systems on soil-carbon sequestration have not been evaluated.  
Proper soil-management practices benefit the sequestration of organic carbon, which has an essential role in soil properties [8]. However, the intensification of conventional agriculture means that soils are losing the capacity to sequester carbon, causing soil to become impoverished and lose its diversity over time [9]. Agroforestry systems aid the recuperation of degraded soils. Some soil factors, such as soil structure and aggregate stability, may improve porosity, decrease erosion, and increase soil productivity [10]. It is important to describe these and other factors that improve the capacity of soil to store carbon. Organic-carbon concentration and stock may vary by type of land use, as demonstrated by Reference [8], who found higher carbon accumulation in perennial and woody plantations compared to those of a short cycle and rotation. SOC storage and dynamics have long been known to depend on the region, soil-forming factors, climate, parent material, organisms, relief, time, and soil management [11, 12]. Therefore, the aim of this study was to compare the influence of different land-management types on SOC.
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 2 Noise K-Means Clustering Algorithm
### The Realization of the Noise-Based K-Means Clustering Algorithm  
The detailed steps of the noise-based K-means clustering algorithm are described as follows.  
**Step 1. Set the clustering number K**  
According to [43; 44; 45; 46], it is generally believed that the clustering number of a K-means algorithm is between 2 and \(\sqrt{N}\), where \(N\) represents the number of GPS data points. In this paper, more clusters are required to describe the distribution of urban hotspots. The GPS data points are intensive data, so the clustering number was set as \([\frac{\sqrt{N}}{2},\sqrt{N}]\).  
**Step 2. Optimize the clustering number K using binary inversion**  
The clustering number \(K\) is converted from decimal to binary (the binary digits is rounded by \(\sqrt{N}\)). Then, one digit of the binary number is randomly flipped to generate a new binary number. Finally, the binary number is converted to a decimal and a new \(K\)_is obtained_. The binary inversion of the solution is shown in Figure 3.  
**Step 3. Optimize the clustering center**  
Since the location of the center point of the clustering is not fixed under the same number of clustering K values, in order to obtain a better initial distribution of the center point, the sum of squares for frror (SSE) is used to evaluate and find the optimal center of the clustering.  
\[\text{SSE}=\sum_{i=1}^{K}\sum_{j=1}^{N}\bigl{(}X_{ij}-\overline{X_{i}}\bigr{)} ^{2} \tag{1}\]  
where \(K\) represents the number of clusters, \(N\) represents the number of taxi GPS data points in clustering, and \(\bigl{(}X_{ij}-\overline{X_{i}}\bigr{)}^{2}\) represents the error sum of squares for each GPS data point.  
**Step 4. Output optimized clustering center and capture urban hotspots**  
According to the found optimal center of the clustering, it is set as the clustering center of the noise-based K-means clustering algorithm (Algorithm 1), which is used to solve the urban road planning problem in order to obtain the clustering results for the given taxi GPS data and thus capture an excellent center cluster, i.e., urban hotspots.  
It can see that the complexity of the noise-based K-means clustering algorithm is related to the number of data points in each dataset. It is not directly related to the number of other noises and iteration times of the algorithm. As such, its time complexity is \(\text{O}\bigl{(}\text{n}^{2}\bigr{)}\)  
Figure 3: The binary inversion of noise.
## 2 Cost Model of Predictive Maintenance
### Maintenance Costs
#### 2.2.1 Cost of Predictive Maintenance  
Opting for PdM for the system is not systematic. In fact, in some cases, choosing to perform CM can be preferable to PdM. The expected cost of PdM during the time horizon \(D_{T}\), denoted by \(C_{p}\), is expressed by the following equation:  
\[C_{p}=c_{p}\cdot\sum_{i=1}^{N_{in}}N_{i} \tag{1}\]  
where \(c_{p}\) is the constant cost of a single PdM action and \(\{N_{i}\}_{1\leq i\leq N_{in}}\) is the set of binary decision variables where \(N_{i}=1\) if PdM is required between inspection \(i\) and \(i\)+1 and 0 elsewhere.
## 3 Experimental Results, Analysis and Discussion  
The testing procedure described in Section 2.2 was applied to structure-A and -B for detecting failing joints and identifying those that are failing, where applicable. Three test runs for each configuration of structure-A (NJ-A, FJ1-A) and structure-B (NJ-B, FJ1-B, FJ2-B) were conducted, with the exciter driven by a triangular signal at 80 Hz and an amplitude of 600 mVpp (structure-A and -B) and 800 mVpp (structure-B). The case of detecting failing joints in a structure is treated in Section 3.1. It is shown that, in general, shifted frequency for specific modes observed in the power spectrum of structure response may be indicative of failing joints. The case of identifying the particular joint that is failing is treated in Section 3.2. It is shown that the task of failing joint identification is more complicated than the detection task of Section 3.1. For the identification task, both damping factor and shifted frequency characteristics should be simultaneously evaluated by using a specific feature defined in Section 3.2.
# Angiography Simulation and Planning Using a Multi-Fluid Approach
## 3 Diffusion Simulation of Contrast Media
### Boundary Conditions  
The simulation of a SPH fluid always shows artifacts at the boundary. Therefore, many boundary handling techniques [26; 27] for SPH-based fluid simulations have been proposed. The blood vessel is an elastic object and its deformation is influenced by the heart beat and movements of nearby tissues. It is extremely challenging to achieve a real-time performance for training while addressing the fluid-elastic deformation coupling problem with complex conditions. Therefore, in our 3D angiography simulation, we treat the blood vessel wall as a static solid and do not consider the influence of heart beating at present. In the future, we plan to incorporate real-time soft tissue modelling algorithm to address the fluid-elastic deformation coupling [28]. By treating the blood vessel wall as solid allows the fluid velocity at all fluid-solid boundaries is equal to that of the solid boundary [29]. Therefore, the fluid flow satisfies the non-slip boundary condition at a static solid boundary. Moreover, the density of a SPH fluid will exist obviously deficiency at the boundary which makes the fluid flow unstable.  
To address these problems at the boundary, we introduce a simple approach that requires very little extra computations for handling boundary conditions. In addition, this method can be applied to arbitrary blood vessel models for SPH fluid models that use multi-layer boundary particles to cover the blood vessel and use the SPH formulation directly for density corrections and non-slip conditions.
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 3 LWM2M Proxy
### Proxy Deployment  
As we highlighted in the introduction, we propose to deploy the LWM2M Proxy at the network edge in between Clients and Servers, so that it can take advantage of the proximity with the end devices. However, we highlight that the Proxy should not be deployed even closer to the end-devices and within the access network itself, e.g., within a node of the IoT network. As a matter of fact, in case of dynamic IoT deployment, e.g., scenarios involving frequent topology changes due to channel variation or node mobility, a Proxy deployed in an IoT node might fall in a sub-optimal placement with respect to the Client-Server path. If instead, an edge-placement is considered, topology changes can be easily managed by the Proxy, which need only to reconfigure few parameters of the extended functions that are influenced by the latency and the topology of the network, e.g., the request shaper and the cache.  
As an example, let us consider the case of node mobility. When using an access network technology such as LoRaWAN or 6 LoWPAN, node mobility is transparent to the Proxy. Indeed, when a node moves within the 6 LoWPAN domain, the mobility is supported by the routing protocol used within the 6 LoWPAN and it is transparent to the Proxy that is deployed at the edge on the 6 LoWPAN Border Router or on a node in the backend near to the 6 LoWPAN Border Router. In LoRaWAN, nodes are not associated with a specific gateway and data transmitted by a node can be received by multiple gateways. Each gateway will forward the packet to the network server, so complexity is transferred to the network server: if a node moves there is no handover needed from gateway to gateway. So, also in this case, node mobility is managed by the access network technology, and it is transparent to the Proxy that runs on the LoRaWAN Application Server.  
If the access-network technology is a cellular network, e.g., Narrowband IoT, node mobility among the base stations of the network is possible and is managed by the handover function of the technology itself. In this case, the Proxy can still be deployed close to the serving base station, e.g., exploiting a Multi-Access Edge Computing (MEC) architecture.
## 2 Materials and Methods
### Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Spatial Resolution (Deg)** & **Source** \\ \hline Solar-Induced chlorophyll & 0.05 \(\times\) 0.05 & GOSIF V2 \\ Fluorescence & 0.25 \(\times\) 0.25 & NOAA OISST V2.1 \\ Sea Surface Temperature & 0.5 \(\times\) 0.625 & MERRA-2 Model \\ Air Temperature at 2 m & 0.5 \(\times\) 0.625 & MERRA-2 Model \\ Specific humidity at 850 hPa & 0.5 \(\times\) 0.625 & MERRA-2 Model \\ Rainfall & 0.25 \(\times\) 0.25 & IMD \\ Wind components at 850 hPa & 0.25 \(\times\) 0.25 & ERA5 \\ Soil Moisture & 0.1 \(\times\) 0.1 & FLDAS Model \\ NINO3 SST and DMI & - & NOAA PSL GCOS \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of data used from 2001–2018 at monthly temporal resolution. yearly basis [58]. We used high-resolution daily rainfall data from the India Meteorological Department (IMD) [59]. The recent IMD rainfall data is gridded at 0.25\({}^{\circ}\) spatial resolution, available for 120 years and arranged in 135 \(\times\) 129 grid points which include rainfall records from 6955 rain gauge stations over India. To analyse the circulation features and moisture transport associated with the summer monsoon, the zonal (u) and meridional (v) wind components and specific humidity at 850 hPa level are used [60; 61]. The wind components were collected from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset. This ECMWF data product is based on the integrated forecasting system Cy41r2 and includes hourly output, available at a horizontal resolution of 31 km [62].
## 3 Results
### Linear and Conformational B-Cell Epitopes Prediction  
According to ABCpred, the linear B-cell epitope densities for all the proteins ranged from 0.49 to 0.55. CAM07737 predicted by the BepiPred server showed high linear B-cell epitope densities. In the case of the BCpred server, CAM07737 and CBA07476 showed high epitope densities. Additionally, the ElliPro server also predicted CBA07476 with high epitope densities relative to other proteins (Table 3).  
The DiscoTope server analysis resulted in a list of proteins (e.g., CBA09441, CBA07476, CAM07737, and CBA03648) with conformational epitope density scores of 0.49, 0.49, 0.43, and 0.50, respectively, which was different from the Ellipro server scores of 0.48, 0.47, 0.50, and 0.48, respectively. It was demonstrated based on the DiscoTope server that CBA03648 should have a high conformational epitope density. With the Ellipro server, CAM07737 also exhibited high densities of conformational B-cell epitopes (e.g., relative to other proteins), as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Accession No.**} & \multicolumn{2}{c}{**DiscoTope**} & \multicolumn{2}{c}{**Ellipro**} & \multirow{2}{*}{**Length**} \\ \cline{2-2} \cline{6-7}  & **EN** & & & & & & \\ \hline CBA09441 & 351 & 0.49 & 343 & 0.48 & 713 \\ CBA07476 & 354 & 0.49 & 342 & 0.47 & 721 \\ CAM07737 & 313 & 0.43 & 357 & 0.50 & 714 \\ CBA03648 & 360 & 0.50 & 349 & 0.48 & 723 \\ \hline \hline \end{tabular}  
EN = Epitope residue number, ED = Epitope density.  
\end{table}
Table 4: Conformational B-cell epitope densities of proteins predicted with DiscoTope, and ElliPro.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Accession No.**} & \multicolumn{2}{c}{**DiscoTope**} & \multicolumn{2}{c}{**Ellipro**} & \multirow{2}{*}{**Length**} \\ \cline{2-2} \cline{6-7}  & **EN** & & & & & & \\ \hline CBA09441 & 2.176 & 1.007 & 1.025 & 0.7022 & 0.909208 \\ CBA07476 & 2.167 & 1.006 & 1.025 & 0.7186 & 0.90866 \\ CAM07737 & 2.18 & 1.007 & 1.025 & 0.7092 & 0.910421 \\ CBA03648 & 2.177 & 1.007 & 1.029 & 0.6939 & 0.9154 \\ \hline \hline \end{tabular}  
\end{table}
Table 4: Confidential B-cell epitope densities of proteins predicted with DiscoTope, and ElliPro.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Accession No.**} & \multicolumn{2}{c}{**Ellp**} & \multirow{2}{*}{**CBA09441**} & \multicolumn{2}{c}{**SOLpro**} & \multirow{2}{*}{**CBA07737**} \\ \cline{2-2} \cline{6-7}  & & **EN** & & & & & & \\ \hline CBA09441 & 713 & 352 & 0.49 & 149 & 0.20 & 140 & 0.19 & 216 & 0.30 \\ CBA07476 & 721 & 368 & 0.51 & 143 & 0.19 & 160 & 0.22 & 226 & 0.31 \\ CAM07737 & 714 & 368 & 0.52 & 157 & 0.21 & 160 & 0.22 & 206 & 0.28 \\ CBA03648 & 723 & 400 & 0.55 & 148 & 0.20 & 140 & 0.19 & 219 & 0.30 \\ \hline \hline \end{tabular}  
EN = Epitope starting residue number, ED = Epitope density.  
\end{table}
Table 3: Linear B-cell epitope densities of proteins.