esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Smart Metering Experiment  
#### 4.1.1 Smart Metering Case Description  
The smart metering case has been overviewed in Figure 18.  
The system has been built around the ADREAM building, and its' main purpose is related to improvement of energy efficiency. The main components in the smart metering system are pointed out and visualized in the Figure 19. The novel prototyped components are autonomic M2M manager, ETSI M2M service capability layer with autonomic service capabilities; CoAP based communication with local low power network.  
Figure 18: Smart metering case.
## 3 Results
### CHSP Up-Regulates the Expression of PTEN and NF-Kappa B Protein in Ovarian Cancer Cells  
As shown in Figure 5, the expression of PTEN was increased by treatment of OVCAR-3 cells with CHSP. Compared to control values, PTEN expression at 10 \(\upmu\)g/mL of CHSP increased by 75.1%. Represented in Figure 5, the expression of NF-kappa B was down-regulated after CHSP treatment. In addition, NF-kappa B expression at 20 \(\upmu\)g/mL of CHSP decreased by 65.8% as compared to the control level.  
Figure 2: CHSP induced apoptosis in ovarian cancer cells (OVCAR-3) and normal ovarian cells (IOSE 364). (**) CHSP induced apoptosis was analyzed by flow cytometry. Cells treated with CHSP for 24 h were collected, stained with PI and Annexin V, and analyzed by flow cytometry. (**) Calculation of the apoptotic rate of the data in A. (**) Induction of caspase by CHSP. OVCAR-3 cells were treated with CHSP for 4 h, and caspase-3/7 enzymatic activity was determined using a Caspase-Glo 3/7 assay kit. *\(p<0.05\) as compared to control. **\(p<0.01\) as compared to control.  
_Appl. Sci._**2020**, _10_, 8615  
Figure 3: CHSP activated the p53-dependent intrinsic pathway. (**a**) CHSP increased the levels of Puma, Bax, Bad, cleaved PARP, and p53 in OVCAR-3 cells. (**b**) p53. (**c**) Puma. (**d**) Bax. (**e**) Bad. (**f**) cleaved PARP. \({}^{*}\)\(p<0.05\) as compared to control. \({}^{**}\)\(p<0.01\) as compared to control.  
Figure 4: CHSP inhibited cell proliferation via the HIF-1\(\alpha\)/VEGF pathway in OVACAR-3 cells. (**a**) Cells were treated with CHSP for 24 h. Culture supernatants were collected for the VEGF assay by ELISA with a Quantikine Human VEGF Immunoassay kit. (**b**) CHSP decreased the levels of HIF-1\(\alpha\) and HIF-1\(\alpha\) bands were quantitated with NIH Image J software for analysis. (**c**) Cells were transfected with VEGF luciferase reporter and HIF-1\(\alpha\) plasmids. After transfection, all the cells were treated with or without CHSP (10 \(\mu\)g/mL) for 16 h and the luciferase activity was determined. * \(p\) < 0.05 as compared to control.  
Figure 5: CHSP up-regulated the expression of PTEN and down-regulated NF-kappa B protein in OVACAR-3 cells. (**a**) CHSP increased the levels of PTEN and decreased the levels of NF-kappa B in OVCAR-3 cells. (**b**) PTEN. (**c**) NF-kappa B. * \(p\) < 0.05 as compared to control. * \(p\) < 0.01 as compared to control.
# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 4 The SIM-D Algorithm  
The SIM-D algorithm is based on information diffusion across a social interaction network, as shown in Algorithm 1. It performs interactions between the persons (also known as agents) according to the interaction graph shown in Figure 3. A brief description of SIM-D algorithm is summarized and given below and summarized in Algorithm 1.  
```
Input :Interaction Network of Agents, disease parameters (transmissibility, state duration) Output :Infections at locations over time-steps, variation of people in disease states initialization();// load interaction graph and disease state for(time-step \(=1\)to N)do for(\(i=1\)to K)do // for each person  prepareInteractions();// prepare list of interactions to perform foreach(\(I_{ij}\in P_{i}\))do // for each interaction of two persons (\(i\) and \(j\)) if(\(i<j\))then  ComputeInteractionOutcome(\(I_{ij}\));// determine probability of transmission  end if if(\(PT_{ij}>0.5\))then \(PM_{ij}\rightarrow\)sendOutcomes();  end if  end for \(P_{i}\leftarrow\)receiveOutcomes();// receive outcomes for\(P_{i}\)  end for UpdateState();// update person status and infections at locations end for
```  
**Algorithm 1**The general SIM-D algorithm, where \(P\) represents a Person, \(I_{ij}\) refers to an interaction between Person \(i\) and \(j\) and \(PT_{ij}\) refers to the outcome of their interaction. \(N\) refers to the total number of simulation time-steps and \(K\) refers to the total number of persons.  
SIM-D runs over sever time-steps also called the simulation days.  
The processing of each iteration involves the following steps:  
1. Each person determines the list of persons that it is going to interact with based on a normative schedule, and individual behaviour and health state.
2. The persons compute interactions with other persons. The computation uses disease transmissibility, person susceptibility and duration of contact. The interaction may or may not result in an infection, depending on a stochastic model. Disease propagation from one person to another person is modelled by Equation (1). \[P_{ij}=1-e^{1-rst}\] (1) where \(r\) is transmissibility, \(s\) is susceptibility, and \(t\) is the duration of contact. If the computed probability is greater than 0.5, the interaction results in the transmission of disease; otherwise, it is not.
3. If a person gets infected, it is notified of the infection.
4. At the end of time-step, the persons with outcomes update their state. The number of infections at locations are also updated.
## 2 WI-SV-Related Work and Overview  
### Related Work  
As earlier stated, perhaps the most popular strategy for the implementation of WI-SV systems was dissimilarity mapping induced by the Dichotomy Transform. Given a pair \((a,b)\) of signature images and their corresponding descriptors, \(f_{a},\,f_{b}\in\mathbb{R}^{d}\), the DT, initially proposed in [47], utilizes a dissimilarity function, \(\Psi_{\mathbf{E}uc}(\cdot,\cdot):\,\mathbb{R}^{d}\times\mathbb{R}^{d}\rightarrow \mathbb{R}^{d}_{+}\), which maps to a dissimilarity space, \(\mathcal{D}\subseteq\mathbb{R}^{d}_{+}\), by means of the following expression: \(\mathcal{D}=|f_{a}-f_{b}|\). By using the DT, a Polychotomizer is transformed into a Dichotomizer through the in troduction of the concept of _\"dissimilarity\"_ between samples of the same and different classes. As a consequence, the similar (\(\omega^{+}\)) or dissimilar (\(\omega^{-}\)) classes are independent of the number of writers involved. This attribute renders the design process intuitive, and it can be applied in a direct transfer learning framework. Feature dissimilarities were used in [41] under a framework which combines graphometric feature sets and an ensemble of classifiers. Following reference [28], the DT was employed under a multiple feature extraction method and a global boosting feature selection. The dissimilarity space was also used in the development of a hybrid WI-WD system in [29]. Partially oriented features were also enabled under the DT framework, with notable success in both blind intra- and cross-lingual experiments [37]. The use of the DT and data-driven features was originally introduced in [58], in which an SVM classifier acted as the training model. In [30], the use of the DT was further augmented by the introduction of a white-box analysis at the instance level using the instance hardness measure. In [59], the DT is utilized with the 256 Local Binary Patterns (LBP) features and a decision tree classifier. All the aforementioned methodologies adhere to the assumption that the feature space--data under examination--follow the vector space axioms, i.e., closed under vector addition and scalar multiplication.  
In recent times, there has been a growing application of geometrically preserving SV systems in a number of research endeavors. All of these methods rely on the use of Symmetric Positive Definite (SPD) matrices as image descriptors, with considerable success in discrimination [60; 61]. It has been demonstrated that SPD matrices are an effective means of authenticating signatures in both WD [52] and WI modes [54; 55; 56], while in [6; 38; 39] metric learning approaches have been enabled in a WI framework with significant success in both intra- and cross-lingual scenarios.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work
### Other Future Trends
#### 4.4.3 Applied to MIMO  
Multiple-input multiple-output (MIMO) technology achieves diversity through the use of multiple-transmit and receive antennas, which improves the SNR at the receiver and minimizes the outage probability [98]. Because the impact of spot quality variations on the receiving end can vary significantly [99; 100; 101], VBDA technology can be applied to MIMO to adjust the beam divergence angle and optical power of multiple transmitting inputs, achieving a good compromise between link availability and communication rate. For example, the dartboard scheme proposed in reference [102] uses two concentric laser beams with different divergence angles for superposition, significantly improving the average data rate of the link. Simulations showed that, at 10 dBm transmit power, the dartboard scheme provides 53% higher data rate than 4-level pulse-amplitude modulation (4-PAM) and 79% higher than OOK. Therefore, VBDA technology can be applied to MIMO in FSO communication in the future to further improve the performance and reliability of the communication link.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Formation Phase
#### 5.2.2 Event-Driven Clustering  
In event-driven clustering, the focus is to generate energy-efficient clusters by preventing unnecessary and pro-active clustering [58]. However, significant overhead can result if clusters are formed in the entire field before the occurrence of an event; this overhead occurs in terms of network energy and processing and does not ensure better network performance in some applications [59].
# Heavy Metals in Post-Exploitation Reservoirs--The Bagry Lake Case Study (Poland)
## 4 Relationships between Metals in Sediment  
Table 3 lists the parameters of the model (1), significance factor (F) and the correlation coefficient R. All models in Table 3 are significant (F \(\leq\) 0.05) with high correlation coefficients R. Figure 17a-f shows the fit of the model (1) to the measured metals concentrations \([Me_{s}]\).  
Table 3 confirms a correlation of iron and manganese co-occurrence. It can be explained by the presence of manganese in iron alloys and co-occurrence of these metals in the environment. In addition, iron and manganese correlate with lead; its presence may be associated with slide bearing alloys used in internal combustion engines and railway electric traction motors [38]. The sunken equipment makes such a correlation reasonable.  
The co-occurrence of copper, lead and zinc (Table 3) is also related to the presence of sunken copper alloys [38].  
Cadmium correlates only with iron and manganese, although it can also be found in sliding bearing alloys (together with tin and lead) or copper alloys [38]. Cadmium does not correlate with either lead or copper. Its association with iron results from the recent use of cadmium for steel surfaces as corrosion protection plating or as decorative plating. Currently, cadmium plating is banned as highly toxic.  
Figure 16: Cd in the supernatant [mg Cd/L].  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multirow{2}{*}{**Constant**} & \multicolumn{4}{c}{**Multiplier for Concentration [g Fe/kg d.m.] Others [mg... /kg d.m.]**} \\ \cline{3-8}  & & **Fe** & **Mn** & **Cu** & **Pb** & **Zn** & **Cd** & **Significance F** & **R** \\  & & **Sediment** & **Sediment** & **Sediment** & **Sediment** & **Sediment** & **Sediment** & & \\ \hline Fe sediment & \(-\)1.185 & & 0.03727 & & 0.2328 & & 2.653 \(\times\) 10\({}^{-8}\) & 0.9652 \\ Mn sediment & 47.02 & 17.85 & & & \(-\)0.9139 & & 4.524 \(\times\) 10\({}^{-7}\) & 0.9457 \\ \hline Cu sediment & 0.7386 & & & 1.718 & 0.1315 & & 3.394 \(\times\) 10\({}^{-4}\) & 0.8411 \\ Pb sediment & 3.087 & 1.551 & \(-\)0.01271 & & & & 3.030 \(\times\) 10\({}^{-5}\) & 0.8934 \\ \hline Zn sediment & \(-\)15.68 & & 0.1369 & 0.7184 & 1.658 & & 2.088 \(\times\) 10\({}^{-6}\) & 0.9415 \\ \hline Cd sediment & 0.4990 & 0.06407 & \(-\)0.003081 & & & & 5.309 \(\times\) 10\({}^{-3}\) & 0.8810 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model parameters for metals concentrations in the sediment (s.—
## 3 Results and Discussion
### Volatile Compounds  
The composition of volatiles compounds is presented in Table 2. The oil from fresh olives presented a profile of volatile compounds characteristic of a fresh oil, with a marked increase in C6 hexanal, (_E_)-2-hexenal compounds in the first months of harvesting; however, oils from frozen olives showed a lower development of volatile compounds during ripening, especially those generated by the enzymes of the lipoxygenase cascade such as aldehydes and C6 alcohols (hexanal, (_E_)-2-hexenal, hexan-1-ol, (_E_)-2-hexen-1-ol). In fact, for all the harvesting months, a significantly lower content of total volatile compounds was observed in oils from frozen olives compared with the oils from fresh olives (_p_ < 0.05). Within each group, no significant differences were observed in the content of total volatile compounds between the different harvesting months (_p_ > 0.05); however, significant differences (_p_ < 0.05) were observed for some individual compounds (Table 2). The reduced development of volatile compounds in oils from frozen olives is due to a decrease in the activity of the enzymes of the lipoxygenase cascade. Thus, in the study by [PERSON] et al. [52], they observed an abrupt drop in the activity of LOX, HPL, ADH, and AAT enzymes in olives damaged by freezing for 3 days at \(-18\)\({}^{\circ}\)C. It was also observed that C6 aldehydes were more affected than C6 alcohols, which would result in oils with less bitterness and intensity of green and grassy aroma, but with a higher intensity of fruitiness and with a milder aroma.  
In the study carried out by [PERSON] et al. [28] using samples with defective frostbitten olives, the characteristic volatile compounds of two types of frost that affect the composition of the VOO were described. One of them, characterized by'soapy' and'strawberry' perceptions, presented a higher content of ethyl 2-methyl butanoate and ethyl propanoate, whereas the other, characterized by the 'wood' and 'humidity' descriptors, presented a high concentration of pentanal and octanal; however, in our study, higher levels of these compounds were not observed in oils from frozen olives. This observation could be explained by the fact that the olives were affected by mild frost (\(-3\)\({}^{\circ}\)C) instead of a severe frost, and therefore, the volatile composition did not change so much as to produce the characteristic volatile compounds of defective frostbitten olives.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 4 Sinkhorn-Knopp with Successive Overrelaxation
### Lyapunov Function  
Convergence of the successive overrelaxed projections is not guaranteed in general. In order to derive a robust algorithm with provable convergence, we introduced the Lyapunov function:  
\[F(\gamma)=\operatorname{KL}(\gamma^{*},\gamma), \tag{23}\]  
where \(\gamma^{*}\) denotes the solution of the regularized OT problem. We used this function to enforce the strict descent criterion \(F(\gamma^{\ell+1})<F(\gamma^{\ell})\) as long as the process has not converged.  
The choice of (23) as a Lyapunov function is of course related to the fact that Bregman projections are used throughout the algorithm. Further, we show (Lemma 1) that its decrease is simple to compute, and this descent criterion still allows enough freedom in the choice of the overrelaxation parameter.  
Crucial properties of this Lyapunov function are gathered in the next lemma.  
**Lemma 1**.: _For any \(M\in\mathbb{R}^{*}_{+}\), the sublevel set \(\{\gamma\mid F(\gamma)\leq M\}\) is compact. Moreover, for any \(\gamma\) in \(\mathbb{R}^{n_{1}n_{2}}_{++}\), the decrease of the Lyapunov function after an overrelaxed projection can be computed as:_  
\[F(\gamma)-F(P_{\mathcal{C}_{k}}^{\omega}(\gamma))=\langle\mu_{k},\varphi_{ \omega}((A_{k}\gamma)\oslash\mu_{k})\rangle, \tag{24}\]  
_where_  
\[\varphi_{\omega}(x)=x(1-x^{-\omega})-\omega\log x \tag{25}\]  
_is a real function, applied coordinate-wise._Proof.: The fact that the Kullback-Leibler divergence is jointly lower semicontinuous implies in particular that \(K\) is closed. Moreover, \(K\subset\mathbb{R}_{+}^{n_{1}\times n_{2}}\) is bounded because \(F\) is the sum of nonnegative, coercive functions of each component of its argument \(\gamma\).  
Formula (24) comes from the expression:  
\[F(\gamma^{1})-F(\gamma^{2})=\sum_{i,j}\Bigl{(}\gamma^{*}_{i,j}\log(\gamma^{2}_ {i,j}/\gamma^{1}_{i,j})+\gamma^{1}_{i,j}-\gamma^{2}_{i,j}\Bigr{)}\]  
and Relations (4) and (20).  
It follows from Lemma 1 that the decrease of \(F\) for an overrelaxed projection is very inexpensive to estimate, since its computational cost is linear with respect to the dimension of data \(\mu_{k}\). In Figure 5, we display the function \(\varphi_{\omega}(x)\). Notice that for the Sinkhorn-Knopp algorithm, which corresponds to \(\omega=1\), the function \(\varphi_{\omega}\) is always nonnegative. For other values \(1\leq\omega<2\), it is nonnegative for \(x\) close to one.
# Extra-Tidal Members and Dynamics of the Open Cluster NGC 6705
## 2 Data and Method  
### Sample Selection  
Compared to Gaia-DR2, Gaia-DR3 provides more precise astrometric data; it offers five-dimensional (5D) astrometric data (position, parallax, and proper motion) for approximately 1.8 billion sources, along with three-band \((G,BP,RP)\) photometric measurements [17; 18]. This enables the extraction of a large number of reliable cluster members (including extra-tidal members) from open clusters. It is crucial to apply appropriate filtering criteria when selecting an initial sample from Gaia-DR3; the initial sample refers to the preliminary dataset from which cluster members will be extracted using the DBSCAN clustering algorithm (see Section 2.2). Based on Gaia-DR3 data, [PERSON] and [PERSON] [7] determined some fundamental parameters for NGC 6705: central positions \((l=27.300^{\circ},b=-2.774^{\circ})\), mean proper motions \((\mu_{a}cos\delta,\mu_{\delta})=(-1.535\pm 0.008,-4.165\pm 0.005)\) masyr\({}^{-1}\), parallax \(\varpi=0.411\pm 0.004\) mas. Based on previous studies, the relevant parameters are shown in Table 1.  
In order to better study this cluster, we attempted to identify reliable cluster members with G = 21 mag within 30 arcmin of the cluster center. According to the estimation by [PERSON] and [PERSON] [7], the tidal radius of the cluster is about 11 arcmin. Our search radius is approximately three times the tidal radius estimated by [PERSON] and [PERSON] [7], allowing us to identify more extra-tidal members of NGC 6705. To reduce field star contamination, we selected sample stars based on the following selection criteria: (1) the stars must have five astrometric parameters (\(l,b,\omega,\mu_{\alpha}cos\delta,\mu_{\delta}\)); (2) the stars must have three-band photometric data (\(G,BP,RP\)); (3) the stars must have proper motions in the ranges of \(-2\)\(<\mu_{\alpha}cos\delta<\)\(-1\) mas/yr and \(-4.6\)\(<\mu_{\delta}<\)\(-3.6\) mas/yr; (4) the stars must have parallaxes in the range of \(-0.2\)\(<\omega<\)1 mas. Finally, we obtained 11,291 sample stars in Gaia-DR3 catalog.
## 5 Simulation Results
### Performance Evaluation of Proposed Method 1 with RFCN Model  
We measured the predicted inference latency of the proposed method 1 in the RFCN model and now compare it to the actual inference latency for the edge and cloud. Figures 12 and 13 show the actual and predicted inference latency with the RFCN model for the cloud and edge, respectively. Overall, as shown in Figures 12 and 13, it can be observed that the predicted inference latency with the RFCN model reflects the actual inference latency for the edge and cloud well. Therefore, it can be confirmed that the proposed inference latency prediction approach is suitable for object detection.  
Figure 12: The actual and predicted inference latency of method 1 with the RFCN model for the cloud: (**a**) gRPC; (**b**) REST.  
When comparing the results for the edge and cloud, the inference latency of the cloud is relatively very low, and so the similarity between the predicted and actual inference latency at the edge is longer than that of the cloud. Although the difference between the predicted and actual inference latency seems small at the edge, the RMSE of the edge is larger than that of the cloud, as shown in Table 6, since the inference latency at the edge is relatively long. The difference between the predicted and actual inference latency for the cloud seems more noticeable than for the edge. However, the RMSE of the cloud is smaller than that of the edge.  
A comparison was also conducted according to the communication protocols gRPC and REST. Two different trends have been observed depending on the communication protocol. First, the RMSE performance of gRPC is lower than that of REST in the cloud. In other words, the predicted inference latency is more accurate when gRPC is used than when REST is used in the cloud. Second, the RMSE performance of REST is lower than that of gRPC at the edge. That is, the predicted inference latency is more accurate when REST is used than when gRPC is used at the edge. Figures 12 and 13 also confirm that using gRPC in the cloud and REST at the edge is better for predicting inference latency.
## 3 Literature Review
### Diverse Applications in Construction  
The versatility of drones in construction is highlighted through their ability to collaborate in lifting heavy loads, assembling structures using prefabrication technology, and even serving as delivery tools in suspension bridges [18; 19]. The direct involvement of drones in the construction process, such as assembling a 6 m tall tower, demonstrates their practical utility.