esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Reconfigurable Impedance Network Connected by Group  
The group-connected reconfigurable impedance network architecture is proposed to provide improved performance with low complexity [11]. The group-connected impedance network means that the impedance network is connected by the group. In this architecture, the \(N_{I}\) RIS elements are grouped into \(N_{G}\) groups. The elements in a group are connected by the impedance to all other elements in the group to form an \"impedance network\". The impedance networks have impedances connected to each other in the grouped elements, as well as grounded impedances for each element. The reactance matrix is expressed as a block-diagonal matrix that satisfies the symmetric condition; therefore, the RIS elements can be in different states. Then, the scattering matrix of this architecture can be written as a block diagonal matrix, in contrast to a conventional RIS, which can usually be represented as a diagonal matrix. Therefore, the reactance matrix and scattering matrix are given as follows:  
\[\mathbf{X}_{I}=\text{diag}(\mathbf{X}_{I,1},\mathbf{X}_{I,2},\cdots,\mathbf{ X}_{I,N_{G}}),\mathbf{X}_{I,g}=\mathbf{X}_{I,g}^{T}(g=1,2,\cdots,N_{G}), \tag{3}\]  
\[\mathbf{\Phi}=\text{diag}(\mathbf{\Phi}_{1},\mathbf{\Phi}_{2},\cdots,\mathbf{ \Phi}_{N_{G}}),\mathbf{\Phi}_{g}=\mathbf{\Phi}_{g}^{T},\mathbf{\Phi}_{g}^{H} \mathbf{\Phi}_{g}=\mathbf{I}(g=1,2,\cdots,N_{G}). \tag{4}\]  
According to [19], the scattering matrix should satisfy the unitary condition in addition to the symmetric condition.
## 4 Results and Discussion
### Performance under Different Number of Stations  
The performance of the proposed approach was evaluated under the effects of Num\({}_{\text{STA}}\) = 4 and the channel condition SNR = 8 dB for the cases of the Weibull, Pareto, and fBM traffic models. As the results in Figure 4(c) show, when the number of stations ranged from two to four, the system throughput performance was significantly increased in all traffic models, as the traffic rate increased with an increasing number of stations. However, due to the effect of heterogeneous traffic patterns in the different traffic models, the performance of the proposed approach varied even under the same number of stations.  
Figure 4: System throughput versus SNR for different traffic models such as Pareto, Weibull, and fBM when Num\({}_{\text{STAs}}\) = 4.  
The performance of the proposed adaptive aggregation achieved a better performance than the FIFO FA (baseline approach) due to the adaptive aggregation approach it employed. However, the Adaptive FA Conv. approach achieved a better performance in all scenarios because it only focused on optimizing the system throughput at the expense of increasing the delay. On the contrary, the proposed approach achieved the maximum system throughput performance while considering the lowest cost in terms of increasing the delay that was closest to the maximum performance achieved by the Adaptive FA Conv. approach under the effects of a variable number of STAs.  
As the results given in Figure 5 show, the proposed approach always outperformed the FIFO FA (baseline approach), particularly in the Weibull and fBM traffic models, in which the traffic rate variation among streams was less bursty. The proposed approach achieved the maximum performance of 583 Mbps using the Weibull traffic model, whereas a lower performance of 279 Mbps was achieved in the Pareto traffic model with the same number of STAs (four). Likewise, when the number of stations was two, the worst performance of 256 Mbps was achieved by the fBM traffic, but the proposed approach achieved a better performance of 265 Mbps due to its use of an adaptive aggregation approach. These results  
Figure 5: Performance of system throughput versus number of stations when the channel condition was SNR = 8 dB for the Weibull, Pareto, and fBM traffic models.  
showed that the number of stations affected the performance of the system throughput behavior under the conditions of heterogeneous traffic patterns among streams in the downlink MU-MIMO channel and under the given channel condition of SNR = 8 dB. In general, according to the results, the performance of the proposed approach was better at coping with less bursty traffic conditions, such as the Weibull and fBM traffic models. This showed that the performance of different aggregation rules adopted in different aggregation policies [10] were affected by the traffic pattern when attempting to provide the optimal minimum delay in the performance of the proposed adaptive aggregation algorithm.
## Sensor Proxy Mobile IPv6 (SPMIPv6)--A Novel Scheme for Mobility Supported IP-WSNs  
**[PERSON] and [PERSON] *  
Department of Computer Engineering, College of Electronics and Information, Kyung Hee University, Yongin-si, 446-701, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-31-201-3778; Fax: +82-31-204-3778.  
_Received: 18 December 2010; in revised form: 20 January 2011 / Accepted: 23 January 2011 / Published: 1 February 2011_  
**Abstract:** IP based Wireless Sensor Networks (IP-WSNs) are gaining importance for their broad range of applications in health-care, home automation, environmental monitoring, industrial control, vehicle telematics and agricultural monitoring. In all these applications, mobility in the sensor network with special attention to energy efficiency is a major issue to be addressed. Host-based mobility management protocols are not suitable for IP-WSNs because of their energy inefficiency, so network based mobility management protocols can be an alternative for the mobility supported IP-WSNs. In this paper we propose a network based mobility supported IP-WSN protocol called Sensor Proxy Mobile IPv6 (SPMIPv6). We present its architecture, message formats and also evaluate its performance considering signaling cost, mobility cost and energy consumption. Our analysis shows that with respect to the number of IP-WSN nodes, the proposed scheme reduces the signaling cost by 60% and 56%, as well as the mobility cost by 62% and 57%, compared to MIPv6 and PMIPv6, respectively. The simulation results also show that in terms of the number of hops, SPMIPv6 decreases the signaling cost by 56% and 53% as well as mobility cost by 60% and 67% as compared to MIPv6 and PMIPv6 respectively. It also indicates that proposed scheme reduces the level of energy consumption significantly.  
**Keywords:** wireless sensor network; SPMIPv6; IP-WSN; IETF; 6 LoWPAN; IEEE 802.15.4; IP sensor node
## 5 Post Adsorption Results
### SEM Analysis  
The SEM images of AC-MOP after adsorption of MB and IBF were used to assess the stability of the adsorbents (Figure 11a,b). The morphology of AC-MOP changed after the adsorption of MB and IBF, especially after the adsorption of IBF in Figure 11b. The surface of AC-MOP after IBF adsorption is rougher than before the adsorption. AC-MOP appeared similar before and after MB adsorption with grooves and cavities.
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
To address this challenge, a deep transfer-based framework is proposed in which transfer learning is used to effectively train the prediction model using only a small amount of COI stock data. * Second, to enhance the prediction model performance, various types of input features are designed. The objective is to find suitable input features that may affect the quality and performance of a classifier. In our work, for a given COI stock, stocks that have relationships with the COI stock are first selected, and then, model inputs are formed based on features of the COI stock, the index (i.e., the KOSPI 200 or the S&P 500), and selected stocks. Three relationships are proposed: the highest cosine similarity (CS), similar field (SF), and the highest market capitalization (HMC). Specifically, CS selects stocks that have a direction in closing price movement that is most similar to the COI stock; SF finds stocks that have similar industrial products to the COI, even though there may be no direct closing price movement correlation between them; and HMC aims at choosing the largest companies in each stock market. The companies that are selected based on HMC may affect the COI's closing price movements. Moreover, model inputs formed based on features of the COI stock and the index are called CI. * Third, various experiments are conducted to validate our framework's performance. Results show that the proposed approach outperforms four baselines: RF, SVM, K-nearest neighbors (KNN), and a recent model that used LSTM cells [20]. Specifically, our approach achieves a 60.70% average accuracy, compared to a 51.49% average accuracy from RF, a 51.57% average accuracy from SVM, a 51.77% average accuracy from KNN, and a 57.36% average accuracy from the existing model. Moreover, the performance of the SF selection method substantially dominates other considered methods in terms of average prediction accuracy. In addition, the number of similar-field companies in the input features can affect the prediction performance dramatically. The rest of this paper is organized as follows: Section 2 summarizes the existing literature related to stock price prediction. Preliminaries, including base knowledge about long short-term memory and transfer learning, are presented in Section 3. Then, Sections 4 and 5 describe dataset and preprocessing and our proposed framework, respectively. In Section 6, we analyze the evaluation results of the proposed model with different types of input, and we compare the performance of our approach with other benchmark models. Finally, the conclusions from this work are drawn in Section 7.
## 4 Modeling of MD-RC Frame
### Numerical Modeling of MD-RC Frame  
In order to obtain the numerical modeling of MD-RC frame, the initial damage state (DS) of MD-RC frame after mainshock should be first defined, as was mentioned previously. Severe damaged structures might be demolished and lose possibility of repair, according to the definition of structural damage state classification (see in Table 1). In addition, disaster investigation found that most of the earthquake-damaged structures are in a moderately damaged state or below [3]. To this end, two damage states (i.e., minor and moderate damage) of MD-RC frames are considered to investigate the damping effect of the retrofitting schemes on MD-RC frame under different damage levels in the present study. In the first damage state (remark DS1), the maximum IDR of intact structure should be in the range of 1/250 to 1/100. In the second damage state (remark DS2), the maximum IDR of intact structure should be in the range of 1/100 to 1/50. By the trial calculation, the intact structure will enter the above damage state (i.e., DS1 and DS2) when the PGA of the MS1 and MS2 mainshock were both 0.15 g and 0.30 g, respectively. Figure 5 presents the IDR distribution and damage limitation value under MS1 and MS2 mainshock excitation. In Figure 5, it is evident that the maximum IDR values of MD-RC frame fall within the range of minor and moderate damage under MS1 and MS2 mainshock excitation. In addition, all of the maximum IDR occur on the structural bottom floors, so the bottom floors must be retrofitted. Finally, four numerical models of MD-RC frame containing two damage states (i.e., DS1 and DS2) are obtained.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Earthquake** & **Year** & \(\mathbf{M_{w}}\)1 ** & **Station and Comp. 2 ** & \(\mathbf{R_{RUP}}\)3 ** & \(\mathbf{V_{S30}}\)4 ** & **Pulse** \\ \hline MS1 & Loma Prieta & 1989 & 6.9 & SVC270 & 9.3 & 347.9 & yes \\ MS2 & Northridge & 1994 & 6.7 & JGB022 & 5.4 & 525.8 & yes \\ AS1 & Northridge & 1994 & 6.7 & RRS228 & 6.5 & 282.3 & yes \\ AS2 & Imperial Valley & 1979 & 6.5 & E06230 & 1.4 & 203.2 & yes \\ AS3 & Gazli, USSR & 1976 & 6.8 & GAZ090 & 5.5 & 259.6 & no \\ AS4 & Loma Prieta & 1989 & 6.9 & BRN090 & 10.7 & 462.2 & no \\ \hline \end{tabular}
\end{table}
Table 4: Information of selected recorded near-fault ground motions in this study.  
Table 5 lists the natural frequencies of the intact RC frame and four MD-RC frames. It is clear that the natural frequencies of the intact frame are significantly reduced, which indicates that the stiffness and strength of the structural materials have decreased to varying degrees.
# Modeling the Energy Performance of LoRaWAN
## 3 LoRaWAN Overview
### LoRaWAN Physical Layer
#### 3.2.2 Physical Layer Message Format  
LoRaWAN specification defines a physical layer message that comprises a preamble, a physical header (PHDR), a physical header Cyclic Redundancy Check (PHDR_CRC), a physical payload (PHY Payload), and an error detection tail (CRC) [27]. Figure 3 shows the physical layer message structure. PHDR and PHDR_CRC fields have a combined total size of 20 bits. Additionally, 2 bytes of CRC are present only in uplink messages, thus the downlink is optimized for low transmission time.
## 2 Materials and Methods
### Methods
#### 2.2.2 Design of Ice-Templating Moulds  
To control the orientation of the porous microstructure of the scaffolds, three moulds were designed and build in-house to produce scaffolds with isotropic pores and anisotropic pores. One of the moulds was built with aluminium all-around (Figure S1A1,A2; Supplementary Content). It was hypothesised that heat would be isotropically directed into the solution to produce isotropic ice structure and, after freeze drying, an isotropic scaffold. This mould is termed as \"mould-isotropic\". The other two moulds were comprised of aluminium base and PTFE walls. Adding the PTFE walls to the aluminium base, a difference in thermal conductivity was created, and heat was expected to be directed anisotropically into the PGSU solution, forming oriented ice crystals. For this purpose, the moulds were designed in different shapes to create different pore orientations within the scaffold (Figure S1B1,B2,C1,C2; Supplementary Content). The mould designed to produce pore orientation perpendicular to the scaffold will be referred to as \"mould-perpendicular\", and the mould for pore orientation parallel to the scaffold will be referred to as \"mould-parallel\". In both cases, pores were expected to orient perpendicular to the bottom surface of the mould (i.e., the surface in contact with the shelf of the freezing chamber). In the case of mould-perpendicular, the produced scaffold was investigated directly as fabricated, without turning it around (Figure S2A2,B2; Supplementary Content), while, in the case of mould-parallel, the scaffold produced was rotated by 90\({}^{\circ}\) to consider the perpendicular direction as the parallel direction (Figure S2C2,C3; Supplementary Content) in order to mechanically test the scaffolds along the pore orientation direction. For both moulds, the PTFE walls were detachable to allow for easy removal of the scaffolds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Number of** & **Number of Non-** & **Pre-Polymer** & **Glycerol:HDI** & **Nomenclature** \\  & **Porous Layers** & **Porous Layers** & **Concentration** & **Molar Ratio** & **Nomenclature** \\ \hline
1 & 1 & - & 10 & 1:0.6 &
\begin{tabular}{c} PGSU- \\ perpendicular \\ \end{tabular} \\ \hline
2 & 1 & - & 10, 15, 20 & 1:0.6, 1:0.8, 1:1.0 & PGSU-parallel \\ \hline
3 & 2 & - & 10, 15 & 1:0.6 & PGSU-bilayer \\ \hline
4 & 2 & 1 & 5, 10, 15 & 1:0.6 & PGSU-trilayer \\ \hline \end{tabular}
\end{table}
Table 1: Synthesis and fabrication parameters used for the preparation of PGSU scaffolds.
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 7 Results and Discussion
### Cement Sheath  
As shown in Figure 19, the larger the elastic modulus of the cement sheath, the smaller the radial displacement of the sealing section casing under compressive and thermal loads. This results in a smaller change in annular volume, leading to a higher pressure. The Poisson's ratio of the cement sheath had a smaller impact on the annular pressure.
## 4 Layered SDN-Based Optimization Framework
### Centralized Queue Length Minimization  
Before introducing our timescale decomposition, we start from the centralized optimization we wish to emulate, and the logical steps that decompose the problem into layers via the Lagrange decomposition. If the SDN orchestrator, with full control of the total service rate \(Z\), could allocate rates directly to the eNBs, the optimization would be:  
\[\max_{z}\sum_{n\in\mathcal{N}}\mathcal{U}_{n}(z_{n})\text{ s.t.}\sum_{n\in \mathcal{N}}z_{n}\leq Z,\;0\leq z_{n}\leq Q_{n}[t]\;\forall n\in\mathcal{N}, \tag{2}\]  
where we use the QMW policy as objective function with \(\mathcal{U}_{n}(z_{n})=Q_{n}[t]z_{n}\) for the sake of illustrating the decomposition technique. In this formulation, the first constraint represents the overall backhaul capacity, whereas the second constraint defines the feasible region for optimization variable \(z_{n}\) which is limited by serving all packets in the queue per one time slot. We remark that an alternative optimization case study would be to consider the wireless device queues as the bottom layer queues. For such an alternate optimization model with wireless device queues, the utility should include the state \(w\) of the wireless channel which could be incorporated as \(f(Q_{n}[t],w,z_{n}[t])\), whereby \(f\) is a known function of the queue, channel state information \(w\), and service rate \(z_{n}[t]\).  
With the QMW policy, the maximization in (2) leads to the minimization of the long-term average total queue length, which also results in the minimization of the end-to-end delay in the network (a consequence of [PERSON]'s theorem [88] for the simplified scenario of continuous flows and infinite queue backlogs [89]).
# Influencing Factors of Motion Responses for Large-Diameter Tripod Bucket Foundation
## 5 Results and Discussion  
In Section 3, the similarity scale for the models of the prototypes of LDTBF is based on geometric similarity and gravity similarity. The similarity ratio of translational accelerations in surge, sway, and heave motions and the rotational angles in roll, pitch, and yaw motions are all 1/1. Furthermore, LDTBF is usually transported to the installation site with 1# bucket in front, 2# and 3# buckets in rear in practical operation [33]. The safer transportation and construction are guaranteed by the better motion response characteristics of heave and pitch under the coupled marine environmental loads. The maximum amplitudes of heave translation (M\({}_{\text{heave}}\)) and pitch rotation (M\({}_{\text{pitch}}\)) of LDTBF were obtained from the test data and the wave amplitudes (H\({}_{\text{i}}\)) for each period were measured using the wave gauges. The heave and pitch RAOs are defined as M\({}_{\text{heave}}\)/H\({}_{\text{i}}\) and M\({}_{\text{pitch}}\)/H\({}_{\text{i}}\), respectively. For comparison with MOSES results; all the graphs for RAOs are shown at the prototype scale.  
The comparisons of RAOs for heave acceleration and pitch angle between the experimental tests and MOSES numerical simulations for Model 1, Model 2, and Model 3 at a 10.0 m water depth and 4.5 m drawight conditions are shown in Figure 9. The maximum amplitudes occurred in the vicinity of natural periods of heave and pitch motions. The experimental and simulated results in the heaving direction are consistent, while the results in the pitching direction agree very well irrespective of the trend or values, i.e., the air floating hydrodynamics characteristics of LDTBF in the waves can be treated as a three-dimensional scattering problem based on the linear potential theory. Further theoretical analysis for LDTBF can be strengthened in this aspect.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(H_{d}\)**(kg)** & \(M_{s}\)**(kg)** & \(M_{s}\)**+\(M_{w}\)**(kg)** & **External Head (m)** & **Interior Head (m)** & **Pressure Head (m)** \\ \hline
4.0 & 296.09 & 960.51 & 4.0 & 2.80 & 1.23 \\
4.5 & 296.09 & 1080.64 & 4.5 & 3.31 & 1.23 \\
5.0 & 296.09 & 1200.77 & 5.0 & 3.82 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Details of the parameters for the numerical simulations of prototype 2 in MOSES.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(H_{d}\)**(kg)** & \(M_{s}\)**(kg)** & \(M_{s}\)**+\(M_{w}\)**(kg)** & **External Head (m)** & **Interior Head (m)** & **Pressure Head (m)** \\ \hline
4.0 & 300.78 & 960.75 & 4.0 & 2.78 & 1.25 \\
4.5 & 300.78 & 1080.89 & 4.5 & 3.29 & 1.25 \\
5.0 & 300.78 & 1201.02 & 5.0 & 3.80 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Details of the parameters for the numerical simulations of prototype 3 in MOSES.  
Figure 9: Experimental and simulated RAOs for Model 1, Model 2, and Model 3: (**a**) heave RAO and (**b**) pitch RAO.
## 3 Results and Discussion
### Interaction between Extracellular Vesicles and Microcantilevers
This conclusion is further supported by the final absolute deflections of the MCs actuated in dynamic mode (Figure 4C). In this configuration, the system measures both the resonance frequency shift induced upon EV adsorption on MCs and the bending of the MCs due to the surface stress change. Our particular experimental conditions (without any passivation) allows a direct comparison between the surface stress generated on the Au and Si surfaces. In this configuration, the absolute deflection signal considered at equilibrium is \(\Delta\)z = (\(-\)41 \(\pm\) 6) nm. Substitution of this value into Equation (1) indicates that the surface stress exerted by the EVs adsorbed on the top Au face is higher than the one exerted by the EVs adsorbed onto the Si face by \(\Delta\sigma\) = (28 \(\pm\) 4) mJ/m\({}^{2}\). This result can be discussed by taking into account the results reported in the literature about deflection signals upon injection of liposomes onto functionalized SiO\({}_{2}\) and Au surfaces [18]. As already discussed, [PERSON] and co-workers demonstrated that upon incubation with POPC liposomes, a SLB forms on the SiO\({}_{2}\) surface, resulting in a positive deflection due to a compressive stress of \(\Delta\sigma\) = (17.0 \(\pm\) 1.4) mJ/m\({}^{2}\). Instead, they also showed that intact liposomes adsorb onto the Au surface of the MC, exerting a compressive stress \(\Delta\sigma\) = (98 \(\pm\) 7) mJ/m\({}^{2}\). That is, intact vesicles adsorb on the Au surface and generate a surface stress about five times larger than the one generated by SLB formation on the Si surface. This new result suggests that EVs could partially fuse also on the Au surface, generating a lower surface stress than in case of intact vesicles adsorbing on the surface, opening new possibilities for biological applications of EVs in the field of biosensors and organoids on-a-chip.