esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Power Allocation of SCO\(-\)AN
### Objective Function of Power Allocation
#### 3.1.2 Objective Function of Power Allocation for MIMO Communication System
\[\mathbf{A}_{B}\overset{\Delta}{=}\frac{1}{\sqrt{L_{B}}}\big{[}a(\theta_{B,1,m }),a(\theta_{B,2,m}),\dots,a(\theta_{B,L_{B},m})\big{]} \tag{44}\]  
\[\mathbf{A}_{B}\overset{\Delta}{=}\frac{1}{\sqrt{L_{B}}}\big{[}a(\theta_{B,1,m }),a(\theta_{B,2,m}),\dots,a(\theta_{B,L_{B},m})\big{]} \tag{45}\]  
\[a(\theta_{B,l,m})=\frac{\left[1,e^{-j2\pi\frac{d}{\gamma_{c}}\cos(\theta_{B,l,m})},e^{-j\pi\frac{d}{\gamma_{c}}\cos(\theta_{B,l,m})},\dots,e^{-j2\pi\frac{ d}{\gamma_{c}}\cos(\theta_{B,l,m})}\right]^{T}}{\sqrt{N}} \tag{46}\]  
\(L_{B}\) is the number of paths between the transmitter and Alice. For the MMSE estimator,  
\[\overline{\mathbf{h}}_{MMSE}=(\mathbf{I}_{L_{B}}+\mathbf{K}_{B}^{H}R_{dd}^{-1 }\mathbf{K}_{B})^{-1}\mathbf{K}_{B}^{H}R_{dd}^{-1}z, \tag{47}\]  
where \(\overline{\mathbf{h}}_{MMSE}\) denotes the estimate of \(\mathbf{h}\) with MMSE Estimator and \(R_{dd}\overset{\Delta}{=}K_{E}K_{E}^{H}+\sigma_{v}^{2}I_{N}\) The power allocation problem of SCO\(-\)AN for SIMO with active eavesdropper is written as:  
\[\begin{split}\text{min}&\log\left(\frac{\left| \mathbf{K}_{H}+(\mathbf{h}_{eo}+\tilde{\mathbf{h}})\mathbf{Z}_{k}\mathbf{Z}_{ k}^{\dagger}(\mathbf{h}_{eo}+\tilde{\mathbf{h}})^{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\texttexttexttexttexttexttexttexttexttext  
where  
\[ \begin{array}{ll}f(x,y)&=-\log\Bigg{(}\frac{\left|\mathbf{K}_{li +(\mathbf{h}_{\alpha}+\tilde{\mathbf{h}})Z_{\mathbf{z}}Z_{\mathbf{z}}^{\dagger}( \mathbf{h}_{\alpha}+\tilde{\mathbf{h}})^{\dagger}x}\right|}{\left|\mathbf{K}_{li }\right|}\Bigg{)}+\log\Bigg{(}\frac{\left|\mathbf{K}_{G}+\mathbf{G}_{i}Z_{ \mathbf{z}}Z_{\mathbf{z}}^{\dagger}G_{\mathbf{z}}^{\dagger}x\right|}{\left| \mathbf{K}_{G}\right|}\Bigg{)}\\ g_{1}(x,y)&=\log\Bigg{(}\frac{\left|\mathbf{K}_{li+(\mathbf{h}_{\alpha}+\tilde{ \mathbf{h}})Z_{\mathbf{z}}Z_{\mathbf{z}}^{\dagger}(\mathbf{h}_{\alpha}+ \tilde{\mathbf{h}})^{\dagger}x}\right|}{\left|\mathbf{K}_{li}\right|}\Bigg{)} -\log\left(\frac{\left|\mathbf{K}_{G}+\mathbf{G}_{i}Z_{\mathbf{z}}Z_{\mathbf{ z}}^{\dagger}G_{\mathbf{z}}^{\dagger}x\right|}{\left|\mathbf{K}_{G}\right|} \right)-C_{sec,\alpha 0}^{0,m}\\ g_{2}(x,y)&=\log\Bigg{(}\frac{\left|((\mathbf{h}_{\alpha}+\tilde{\mathbf{h}})Z_ {\mathbf{z}}Z_{\mathbf{z}}^{\dagger}(\mathbf{h}_{\alpha}+\tilde{\mathbf{h}})^ {\dagger})y+(\mathbf{h}_{\alpha}+\tilde{\mathbf{h}})Z_{\mathbf{z}}Z_{\mathbf{ z}}^{\dagger}(\mathbf{h}_{\alpha}+\tilde{\mathbf{h}})^{\dagger}x}{\left| \mathbf{K}_{li}\right|}\Bigg{)}-\text{K}\\ g_{3}(x,y)&=x\\ g_{4}(x,y)&=y\\ h_{1}(x,y)&=x+y-P_{0,m}\end{array}  \tag{50}\]  
The power allocation problems of SCO\(-\)AN with perfect channel estimation, imperfect channel and active eavesdropper are similar. Therefore, we use the same algorithm to solve the problem.
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
## 3 Results
### Receiver Side  
* \(r\)1 Message Receiving: The loop antenna receives and transmits the signals to LIA. The receiving terminal continuously reads the voltage value of the frequency on the communicating channel from LIA and judges the bit value. A received signal higher than the reference voltage represents binary 1, and a received signal lower than the reference voltage represents binary 0. When a string of binary 1 is identified, the receiver terminal begins signal reception. The modulated signals received by LIA are shown in Figure 8a. As illustrated in Figure 8b, the LIA filters out the noise and demodulates the received signals to obtain the final demodulated signals. At the receiver terminal, the demodulated signals are sampled and judged according to the reference voltage, and the complete transmitting binary string is ultimately obtained;
* \(r\)2 Message Decoding and Inverse Processing: If the user defines additional data processing work such as encryption and compression, the corresponding inverse processing work of decryption and decompression should also be performed. Since there is no additional user-defined message processing function, the receiver terminal operates as follows: 1. Decode the binary string and obtain the results: \"0 xFF0168656C6C6F20\" in hexadecimal; 2. Obtain the payload data of the message \"0x68656C6C6F\" located by the frame header and frame tail; 3. Convert it into the original message according to ASCII encoding rules: \"hello\";
* \(r\)3 Message Output: Show the received message \"hello\" on the receiver side.  
Figure 8: Measured time domain signals of receiver side. (**a**) Received signals. (**b**) Demodulated signals.  
Figure 7: Measured time domain signals from the transmitter side. (**a**) Source signals. (**b**) Modulated signals.
# An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer
###### Abstract  
The reconstruction of deep in situ environment up to 95 \({}^{\circ}\)C and 70 MPa using water is critical for the fidelity testing of deep Earth rocks. The temperature and pressure of the water have strong coupling in such an environment, which makes the control of temperature and pressure very difficult. The paper firstly presents the design of the system of deep in situ environment reconstruction and core transfer (SERCT); secondly, for the problem of high temperature and pressure control, a pressure-temperature (P-T) interpolation control algorithm based on the iso-density P-T curves of water is proposed. A P-T coupling control path is decomposed into two independent interpolation paths: an iso-thermal pressure control and an iso-mass temperature control, which realizes the decoupling control of temperature and pressure. Then, a fuzzy-PID dual mode method is adopted for the pressure control after decoupling, which reduces the overshoot and the dynamic response time of the system. For temperature control, a segmented and grouped electric heating mode is designed to improve the uniformity of the temperature field. A fuzzy PID temperature control algorithm based on grey prediction is proposed to achieve high precision temperature control with small overshoot. Finally, the effectiveness of the proposed methods is verified by experiments.  
deep in situ environment; deep Earth rock; pressure-temperature decoupling control; fuzzy PID control; grey prediction 20233202023202320232023202320232020232020232020232  
Therefore, it is necessary to analyze the physical and mechanical behaviors of rocks in the deep in situ environment, and develop a rock mechanics theory that considers the influence of the deep in situ environment [4]. To achieve this goal, a platform that can reconstruct and simulate the deep in situ environment of high temperature and high pressure--and complete the transfer, preparation and testing of the core in this environment--is essential. One key difficulty in the development of this platform is the control of temperature and pressure, because under high temperature and pressure, the temperature and the pressure affect each other, and the movement of core may cause great fluctuations in pressure and temperature.  
In this study, the SERCT is designed to reconstruct a deep in situ environment of 2500 m underground. At this depth, the temperature is approximately 95 \({}^{\circ}\)C and the pressure is highly likely to be less than 70 MPa. A P-T decoupling control method is then proposed to achieve the in situ environment reconstruction with high temperature (up to 95 \({}^{\circ}\)C) and high pressure (up to 70 MPa). For the independent controls of pressure and temperature after decoupling, a fuzzy-PID dual mode pressure control and a fuzzy PID temperature control based on grey prediction are presented.
## 4 The Proposed Framework
### Framework Details
#### 4.4.1 Training Procedure  
The training paradigm of DAGA, as delineated in Algorithm 1, employs a mini-batch strategy to expedite computational efficiency, alongside a pre-training and fine-tuning sequence designed to optimize recommendation performance. The core objective encompasses the refinement of model parameters \(\Theta=\{\theta_{m1},\theta_{da},\theta_{m2},\theta_{ag},\theta_{pg},\theta_{ pu},\theta_{dc}\}\) and the accurate prediction of group preference distributions \(Y\). According to Figure 2, \(\theta_{m1},\theta_{da},\theta_{m2},\theta_{ag},\theta_{pg},\theta_{pu}\) denote the model parameters of MLP Encoder1, the multi-head disentangled self-attention encoder, MLP Encoder2, the Factor-Wise Attention Aggregator, the Predictor of Group Embedding and the Shared Encoder (predictor of user embeddings), respectively, while \(\theta_{dc}\) denote the discriminator parameters of Equations (10) and (11).  
Initiated with the stochastic initialization of parameters \(\Theta\), the model undergoes pre-training with \(\mathcal{L}_{user}\), which instills foundational user representation learning and predictive capabilities. Subsequent fine-tuning, leveraging \(\mathcal{L}_{MI}\) and \(\mathcal{L}_{user}\), refines the discriminative prowess of our framework, particularly in discerning complex user-group dynamics. The primary training involves a comprehensive optimization of \(\mathcal{L}_{group}\), \(\mathcal{L}_{MI}\) and \(\mathcal{L}_{user}\), ensuring the model's adeptness in capturing and predicting nuanced group preferences.  
```
0: User, item and group sets \(\mathcal{U}\), \(\mathcal{I}\), \(\mathcal{G}\); historical interaction matrices \(R\), \(Q\)
0: Optimized model parameters \(\Theta\); predicted group preference distribution \(Y\)
1: Initialize: Randomly initialize model parameters \(\Theta\)
2:while in pre-training phase do
3: Generate mini-batches from \(\mathcal{U}\)
4: Update \(\theta_{m1}\), \(\theta_{da}\) by minimizing user-item interaction loss \(\mathcal{L}_{user}\),
5:endwhile
6: Reload \(\Theta\) for fine-tuning
7:while in fine-tuning phase do
8: Generate mini-batches from \(\mathcal{G}\)
9: Identify positive and negative samples for each group
10: Update \(\theta_{m1}\), \(\theta_{da}\), \(\theta_{m2}\), \(\theta_{agg}\), \(\theta_{pu}\), \(\theta_{dc}\) by simultaneously minimizing mutual information loss \(\mathcal{L}_{MI}\) and user-item interaction loss \(\mathcal{L}_{user}\)
11:endwhile
12: Finalize \(\Theta\) for primary training
13:while not converged do
14: Generate mini-batches from both \(\mathcal{U}\) and \(\mathcal{G}\)
15: Identify positive and negative samples for each group
16: Update \(\Theta\) by simultaneously minimizing \(\mathcal{L}_{group}\), \(\mathcal{L}_{MI}\) and \(\mathcal{L}_{user}\)
17:endwhile
18: Predict group preference distribution \(Y\) using optimized \(\Theta\)
```  
**Algorithm 1** Training procedure of DAGA.
## 2 Data Collection and Pre-Processing
### Automatic Identification System Data  
AIS is an automatic identification system that exchanges information at 150 MHz very high frequency (VHF) with nearby ships, stations, and satellites in order to identify and locate ships or other maritime traffic [25]. AIS information includes the date and time and the ship's name, call sign, position (latitude and longitude), speed over ground (SOG, in knots), course over ground (COG), gyro heading, and rate of turn, etc.  
In this study, time and date, position, SOG, and COG details were used as variables to be analyzed for ship trajectory patterning. As of April 2020, when the gantry crane collision occurred, AIS data on ship berthing at the target pier for four months from January  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pier** & **Pier Length** & **Water Depth** & **Simultaneous Berthing Capacity** \\ \hline New Port 2 Pier & 2000 m & 16–17 m & 6 ships/4000 TEU \\ New Port 3 Pier & 1100 m & 18 m & 2 ships/4000 TEU, two ships/2000 TEU \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the target piers.  
Figure 3: Topographical characteristics of Busan New Port: (**a**) British Admiralty Chart; (**b**) ship passing around Todo island; (**c**) location of the target pier.  
2020 were collected. The ship type was a container ship and the target vessels featured a gross tonnage of 100,000 tons or more. The specifics of the collected AIS data are shown in Table 2.  
The peculiarity of the AIS data analyzed is that the data reception interval differed depending on the ship's speed and changing course [26]. For instance, dynamic information on the Class-A AIS used by Safety of Life at Sea ships was received at a cycle of 10 s for ships sailing at less than 14 knots and at 3.3 s when the ship's courses changed. In the case of the Class-B AIS, it was received in 30 s in the same situation. Therefore, in this study, as the time of entry and departure of ships differed, the unity of units was essential for analyzing the time series.
## 2 Materials and Methods  
The structures of the model systems were built with the Molden software package [23]. Neutral clusters made of a dimethylviologen cation and two anions have been considered since the alkyl chains, although important in order to guarantee some solubility in low-polar solvent, does not give any significant contribution to the charge-transfer band between the anions and the bipyridinium core. Energy minimization was run at the B3 LYP-D3(PCM)/6-31+G(d,p), that is including the dispersion correction of [PERSON] [24] and the long-range solvent dielectric response [25]. \(n\)-Hexane (\(\varepsilon=1.8819\)), dichloromethane (\(\varepsilon=8.93\)) and acetone (\(\varepsilon=20.493\)) were selected as solvents. All structures were checked, by frequency calculations, to be true minima with no imaginary frequencies. Xyz coordinates can be found in Supporting Information. Subsequent TD-DFT calculations were run with a variety of functionals and two basis sets of double and triple-\(\zeta\) quality: 6-31+G(d,p) and aug-cc-pVTZ. Functionals used for the TD-DFT, again always including the Polarizable Continuum Model (PCM model) of solvation, were: PBE0 [26], M06-2X [27], CAM-B3 LYP [28], \(\omega\)B97-XD [29] and LC-\(\omega\)HPBE [30; 31]. The number of states used for the calculations was set to 10. All calculations have been run with the software package Gaussian16 [32]. GaussView [33] software was used to simulate the UV-Vis spectrum applying a line broadening of 0.4 eV.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & \(\lambda_{\text{max}}\) **(nm) Lowest Energy Band** & **Ref.** \\ \hline
[OV][PF\({}_{6}\)] in CH\({}_{2}\)Cl\({}_{2}\) & 265 & [20] \\
[OV]Br\({}_{2}\) in CH\({}_{2}\)Cl\({}_{2}\) & 388 & [9] \\
[DTMV]I\({}_{2}\) in CH\({}_{2}\)Cl\({}_{2}\) & 466 & [10] \\
[OV]I\({}_{2}\) in acetone & 482 & [9] \\
[OV]I\({}_{2}\) in CH\({}_{2}\)Cl\({}_{2}\) & 506 & [9] \\ \hline \hline \end{tabular} \(\text{OV}=\text{octylviolgen, 1,1’-dicotyl-4,4’-bipyridinium; DTMV}=\text{decylletramethylviologen, 1,1’-dicely-2,2’,6,6’-tetramethyl-1,1’-bipyridinium.}\)  
\end{table}
Table 1: Experimental maximum absorption wavelength, \(\lambda_{\text{max}}\) in nm, of the lowest-energy band for the compounds in solution. Concentration was in the range 2–5 \(\times\) 10\({}^{-2}\) M.
## 3 Setup for Downscaling Simulations
### Parameter Settings for Soil Profiles  
The study area is set up as a 5 m \(\times\) 5 m vertical profile. We choose loamy soil with moderate soil quality, and its hydraulic parameters are from the USDA (United States Department of Agriculture) for different soil texture categories: saturated water content \(\theta_{\rm s}=0.4\), residual water content \(\theta_{\rm r}=0.07\), a pore size distribution index \(n=1.1\), and the reference value of saturated hydraulic conductivity \(K_{\rm s}\) is 0.28 m d\({}^{-1}\). Here, the proposed inverse method is performed in heterogeneous soil profiles, with the two most variable parameters (\(K_{\rm s}\) and shape parameter characterizing the pore distribution \(\alpha\)) exhibiting spatially variable. The \"true\" two-dimensional \(K_{\rm s}\) fields are generated using KL expansion, with this reference as the mean. \(\alpha\) is considered to be the spatially distributed field associated with \(K_{\rm s}\), that is, \(\alpha(x,z)=\sqrt{K_{\rm s}(x,z)}\). The spatial heterogeneity is quantified by statistical parameters including the spatial variation variance \(\sigma\), the size of characteristic length \(\lambda\) (\(\lambda_{x}\) and \(\lambda_{z}\) in the \(x\) and \(z\) directions, m) and the anisotropic ratios (\(\lambda_{z}/\lambda_{x}\)). These parameters are set as follows: \(\sigma=0.5\), 1.0, 1.2, 2.0; when investigating the effect of different characteristic lengths on the simulation results, we use an isotropic medium, but with varying characteristic lengths, that is, \(\lambda_{x}=\lambda_{z}=4\), 8, 12, 16 m; when investigating the effect of the degree of anisotropy on the simulation results, we use an anisotropic medium by taking the characteristic length in the \(x\)-direction to be 4, but in the \(z\)-direction to be 16, 64, and 180, which gives us four degrees of anisotropy, i.e., \(v=1\), 4, 16, 45.
## 2 Significance of the Study
This is possible due to the small size of THz antennas, which can be densely packed into a small area [23]. Moreover, the capability for omnidirectional broadcasting in wireless setups reduces the time needed for memory synchronization and lowers power consumption compared with wired connections. It also decreases the processing time at intermediate nodes by significantly reducing the number of required hops. A study comparing wired and wireless interconnects in a system with 64 cores found that wireless interconnections improve bandwidth per core and energy efficiency per packet by 15% and 39%, respectively [24]. Additionally, using various multiplexing techniques can further increase the data rate.
## 3 System Model  
The model in this paper is built on a multi-terminal, multi-edge network scenario, in which the set of terminal layer devices are denoted by \(\mathcal{M}=\{1,2,\ldots,M\}\). On each MD (Mobile device), there exists a task queue and a computation queue, where the task queue stores the tasks to be decided for offloading and the computation queue processes the tasks that are executed locally. Additionally, the set of edge layer servers are denoted by \(\mathcal{N}=\{1,2,\ldots,N\}\). Multiple computation queues are included in each edge server for parallel computation of transmission queue offload tasks. Figure 1 shows an illustration of EC system with a mobile device and an edge node. In the following, we first present the task model and the task offloading decision, respectively. Then, we introduce the computation, communication model and prediction model.
_Biomolecules_**2015**, \(5\), 1245-1265; doi:10.3390/biom5031245  
_Biomolecules_  
**ISSN 2218-273X**  
www.mdpi.com/journal/biomolecules/  
_Review_  
**Bacterial Sigma Factors and Anti-Sigma Factors: Structure, Function and Distribution**  
**[PERSON]**  
School of Life Sciences, University of Sussex, Falmer, Brighton BN1 9 QG, UK;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +44-1273-877-764  
Academic Editors: [PERSON] and [PERSON]  
_Received: 20 March 2015 / Accepted: 1 June 2015 / Published: 26 June 2015_  
**Abstract:** Sigma factors are multi-domain subunits of bacterial RNA polymerase (RNAP) that play critical roles in transcription initiation, including the recognition and opening of promoters as well as the initial steps in RNA synthesis. This review focuses on the structure and function of the major sigma-70 class that includes the housekeeping sigma factor (Group 1) that directs the bulk of transcription during active growth, and structurally-related alternative sigma factors (Groups 2-4) that control a wide variety of adaptive responses such as morphological development and the management of stress. A recurring theme in sigma factor control is their sequestration by anti-sigma factors that occlude their RNAP-binding determinants. Sigma factors are then released through a wide variety of mechanisms, often involving branched signal transduction pathways that allow the integration of distinct signals. Three major strategies for sigma release are discussed: regulated proteolysis, partner-switching, and direct sensing by the anti-sigma factor.  
**Keywords:** RNA polymerase; transcription; anti-sigma; sigma; partner-switching; RIP; signal transduction; stress; extracytoplasmic
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 5 Potential lncRNA-Based Biomarkers
### Neat1  
Another lncRNA contributing to the intricate landscape of BC is Nuclear-Enriched Abundant Transcript 1 (NEAT1), which plays a role in the initiation and advancement of various cancer types. Research conducted by [PERSON] et al. showed a notable increase in NEAT1 expression in human BC cells, including MCF-7, MDA-MB-453, MDA-MB-231, and SKBR3 cells, when compared to normal mammary epithelial cells such as MCF-10A. However, the isoforms NEAT1_1 and NEAT1_2 displayed differing expression patterns across various intrinsic subtypes of BC. While NEAT1_2 demonstrated the highest expression levels in HER2-positive and luminal B cancers, NEAT1_1 expression peaked in ER-positive luminal A and luminal B cancers [47].  
[PERSON] et al. observed an elevated expression of NEAT1 in blood samples from BC patients, with the highest levels detected in the TNBC subgroup [48]. A strong correlation was demonstrated between NEAT1 overexpression in BC tissues and LNM, TNM staging, and tumor size. Elevated NEAT1 expression was more commonly detected in M1 and III-IV tumors compared to M0 and I-II tumors [49]. A study by [PERSON] et al. elucidated the role of Forkhead Box N3 (FOXN3) as a transcriptional repressor associated with the Switch-Insensitive 3A (SIN3A) complex in ER-positive BC cells. NEAT1 emerged as a crucial mediator of FOXN3-SIN3A interactions, contributing to the repression of genes involved in the EMT and promoting BC metastasis. The researchers proved that the overexpression of NEAT1 was strongly correlated with LN positivity among ER+ BC patients [50]. An alternative mechanism through which NEAT1 promotes the EMT involves targeting the miR-211/High-Mobility Group AT-Hook 2 (HMGA2) axis. Given the pivotal role of HMGA2 in the EMT, it is hypothesized that NEAT1 contributes to cellular metastasis by modulating the miR-211/HMGA2 axis [49]. The study of [PERSON] et al. reveals NEAT1's oncogenic role in BRCA1-related breast cancer, involving its regulation of miR-129-5p and WNT4. NEAT1 overexpression due to BRCA1 deficiency promotes breast tumorigenicity by epigenetically silencing miR-129-5p, leading to increased WNT4 expression and activation of oncogenic signaling [51]. [PERSON] et al. highlighted the crucial role of NEAT1 in BC progression under hypoxic conditions, mediated by hypoxia-inducible factor 2 (HIF-2) pathways. NEAT1 induction in hypoxia promotes paraspeckle formation which stores RNA in the nucleus, accelerates tumorigenesis, and correlates with worse survival in BC patients [52].