esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Biophysical Performance of Vegetation Indices in Typical Coastal Wetland  
Monitoring vegetation change over time at larger scales using vegetation indices has greatly enhanced our understanding of the changing environment [58; 59]. Different vegetation indices vary in their biophysical expression and they can offer distinct information for the leaf phenological cycle, which in turn, affect plant photosynthesis and regulate ecosystem carbon balance [10; 22; 36]. Some researchers have reported that time series of NDVI, EVI (greenness-related vegetation indices), and LSWI (water-related vegetation index) can be used to delineate the green-up and the senescence phase at the canopy level [36; 60]. However, which is the best vegetation index for the model to more accurately predict the plant production in coastal wetlands remains unknown. Therefore, it is important to evaluate the biophysical and phenological expression of different vegetation indices in coastal wetland ecosystems to accurately assess vegetation phenology and predict plant production [61; 62].  
In this study, we examined the relationship between NDVI, EVI, LSWI, and estimated \(\text{GPP}_{\text{EC}}\), Ta, and evaluated the biophysical expression of vegetation indices in a typical coastal wetland. Both EVI and NDVI have significant correlations with \(\text{GPP}_{\text{EC}}\), but EVI has a stronger correlation with \(\text{GPP}_{\text{EC}}\), and hence has more biological and phenological significance in GPP predictions than the NDVI in this coastal wetland (Figures 5 and 6). EVI can minimize the influences of soil condition and atmosphere in reflectance data by incorporating soil adjustment and atmospheric resistance factors [58]. Time series of LSWI also provide valuable information (e.g., water status) for simulating plant growth and wetland carbon exchange, even though it has a lower relationship between Ta and \(\text{GPP}_{\text{EC}}\). It has a great potential in obtaining the water content information of leaf and canopy, and can more accurately identify the vegetation phenology by combining both NIR and SWIR bands [63; 22; 64; 22]. The results in this study are consistent with earlier studies [10; 15; 22; 23; 33; 40; 65; 66], in which all indirectly prove the hypothesis of chlorophyll-\(\text{FPAR}_{\text{chl}}\)-EVI and leaf 1 water-LSWI, as applied in the VPM model. However, it is necessary to further develop the LSWI-based phenology algorithm in the near future [40].
## 4 The WBANs Communication Stack
### Mobility and Radio Channel Modeling for WBANs
#### 4.1.2 State-of-the-Art on WBAN Channel Models
Periodic characteristics of wireless body area networks wireless channels measured using custom hardware in the 900-MHz and 2.4-GHz bands are presented in [41]. Received signal strength indication (RSSI) values of both bands were simultaneously sampled at a rate of 1.3 kS/s. Results are exploited from the measurements campaign of WBANs, which shows that characteristics of WBANs channels (such as periodicity) can be used for reducing the power consumption of wireless communication. A new channel model is introduced to add periodicity on-top- of the existing IEEE 802.15.6 WBAN path loss equations (expressed as Equation (2) in previous sections). Parameters such as activity factor and location factor are introduced to estimate the modified model parameters. Finally, a strategy for exploiting the periodic characteristics of the WBAN channel is presented as an example, along with the power savings from using this strategy. In later work on channel modeling, the authors in [37] suggested that the virtual Multiple Input Multiple Output (MIMO) can benefit in WBANs from the capacity gain and enhancement of the overall performance of on- and off-body links. They propose a \(2\times 2\) MIMO-based model that includes the coupling between wearable antennas and the body, the body dynamics, and a realistic environment. The coupling between wearable antennas and the body was obtained through simulations. Then, user dynamics have been used to include the influence of the body posture on the orientation of the antenna. Finally, a geometrically based statistical channel model adapted to WBANs has been used in a propagation environment (clusters of scatters) to perform bounces of the transmitted signal over randomly distributed scatters to obtain multi-path components and channel impulse responses. For the case of on-body communications, the proposed approach considered that the received signals are resulted from the contribution of one on-body component and several multi-path components present in the environment and obtained from the channel model. A WBAN with patch antennas operating at 2.45 GHz and nine possible locations on a female body have been analyzed. An indoor scenario is considered, and the user is walking. Based on proper metrics, optimum 2 \(\times\) 2 antenna placements are suggested. Concerning on-body communications, the MIMO capacity was estimated for selected configurations and shows that the best performance is obtained when the sink nodes (RXs) are on front and back of the body, and the data sensors (TXs) are on the head, with a
## 3 Results
### Sedimentation
#### 3.3.2 Sedimentation Experiments with Limestone  
The sedimentation experiments conducted with limestone also yielded significant findings on turbidity reduction. The experiments demonstrated varying effectiveness of limestone sedimentation depending on the amount and form of limestone used (Figure 12). In the smallest volume container (0.05 L), using lots of limestone dust significantly reduced turbidity to 29.5 NTU (Table S8). In the larger volume container (6 L), using a cup of limestone dust (300 g) reduced turbidity to 207 NTU, whereas a cup of uncrushed stones (300 g) resulted in high turbidity levels of 10,000 NTU (Table S9). This highlights the importance of the physical form of the limestone in turbidity reduction. This statistically significant result (\(p<0.05\)) demonstrates that adding crushed limestone significantly reduces turbidity.  
Figure 11: Summary of sedimentation experiments with chalks.  
The bar graph shows various water parameters after filtration with limestone. According to the graph, sedimentation notably contributes to reducing most contaminants in the water.
## 3 Software Defined Radio Testbed for Experiments  
The testbed used for experiments is based on two SDR (NI-USRP2922), one is used for Primary Base Station (PBS) generation and the other one is used for a Mobile Cognitive Base Station (MCBS). A spectrum analyzer RTL2832U is used for spectrum sensing, software systems OpenBTS and GNURadio [31; 32; 33; 34] are installed in the computer that operates the MCBS and a code variation is made to enable a cognitive protocol, so that it can perform permanent spectrum detection and channel assignment to communicate to the SUs. The PBS and MCBS allow the connection with primary and secondary users and the available services are phone calls and short messages services (SMS). Power measurements are taken from the frequency channel when the PUs in motion are connected to the PBS. The testbed is shown in Figure 1. The system considers two PUs and two SUs in communication between them with GMSK or OFDM. During the experiments, the location of the PU can be fixed or dynamic as shown in Figure 2. as well as the location of the SU. User movement is random and changes constantly. The objective is to observe the behavior of Renyi-entropy based on the DFT of the power measurements of any PU present and decide whether a PU signal is present.  
The GSM frequency is ARFCN 166 which allows communication in DL and UL. In this case, we use the UL frequency to identify the power of the PU at 831.8 MHz. The RTL2832U is used to constantly measure the frequency spectrum and transmit it to make decision regarding the spectrum.  
The experiments are carried out in an indoor environment with elements such as walls, desks, chairs, among others. The distance between the components of the cognitive base station and the PU is 10 m. To carry out the experiments with a fixed PUE, the direct connection of the antenna is established. In the case of a dynamic PUE, the antenna is adapted into a drone that allows its mobility. Only the antenna is moved and not the entire equipment. Figure 2 shows the fixed and dynamic hardware used.  
Figure 1: Software Defined Radio Test-bed.  
Figure 2: (a) Static location Scenario; (b) Dynamic location Scenario.
## 2 Literature Review
### Energy Efficiency in 5G Networks  
Recently, energy efficiency has emerged as a key performance indicator and primary concern in the design of 5G networks; there has been a paradigm shift from throughput-optimized to energy-efficiency-optimized communications [20]. Certainly, 5G systems will serve an unprecedented number of devices, providing ubiquitous connectivity, as well as innovative and rate-demanding services, as shown by the work conducted in [21; 22]. In mobile communication, the process of implementing hardware efficiency plays a significant role in increasing the energy efficiency at the device and infrastructure levels. According to [23], to support mobile data traffic, several specific metrics are expected, including latency/QoS and energy efficiency. In [24], with a range of maximum transmitting powers, the results illustrated that the maximum energy efficiency roughly equates to maximizing the spectral efficiency for low transmitting power in 5G networks. But this did not succeed in providing the latency/QoS and data rate requirements for the 5G network. In [25], the maximization of energy efficiency could be carried out considering all the practical factors, which are normally enforced in 5G communication systems. Besides this, QoS metrics such as delay, throughput, and energy efficiency have started being enforced, including rate guarantees, minimum delay, and maximal delay bound [26].  
5G D2D communication is considered to be a suitable technology for proximity-based data-sharing services, making it an appropriate candidate for SBD2D communications [26; 27]. To address the increasing demand for data rates, 5G enables network compaction through the organization of cellular networks into small cells. Such a kind of compaction results in having a higher spectral efficiency, which can also lead to a reduction in the power consumption of beekeepers' mobile devices due to their communication with a nearby pico-cell. Consequently, this solution significantly improves the network coverage in SBD2D communication. The simultaneous operation of these macro-, pico-, and femto-cells is named HetNets (Heterogeneous networks) and they play an important role during network performance analyses. Interference remains a critical challenge in 5G heterogeneous networks due to its uncoordinated nature. The authors are thus motivated to analyze the performance metrics of SBD2D when deploying the 5G D2D configuration for SBD2D communication.
## 3 Architecture and Problem Formulation
### System Model
#### 3.2.2 Individual Battery Lifetime  
The optimization of expected battery lifetimes of MTC devices is an important consideration to enhance device performance and ensure a longer period of operation in the absence of recharging opportunity. Accurate modeling of the battery consumption pattern of MTCDs becomes critical considering the variance of energy consumption levels during the duty cycle of a typical MTC node as shown in Figure 2. The energy consumption per device is considered as a semi-regenerative process with regeneration occurring at the end of each interval of successful data transmission. The remaining battery energy of any \(k_{th}\) MTCD at time instant \(t^{o}\) is denoted as \(E_{k}(t^{o})\), and the transmission interval period and the average packet size being denoted as \(T_{k}\) and \(D_{k}\) respectively. Power consumption for sleeping and transmission modes is denoted as \(p_{s}\) and \((p_{k}+p_{e})\) respectively, where \(p_{e}\) is the consumed power by the electronic components during data transmission and \(p_{k}\) is the required transmit power necessary for reliable data transmission. The expected battery lifetime of MTC devices for each transmission interval \(T_{k}\) for any node \(k\) is defined as the direct product of ratio between remaining energy to average energy consumption and the data transmission interval period described as:  
\[L_{k}(t^{o})=\frac{E_{k}(t^{o})T_{k}}{E_{s}+p_{s}\Big{[}T_{k}-\frac{D_{k}}{R_{k }}-n_{a}^{k}T_{a}^{k}\Big{]}+n_{a}^{k}T_{a}^{k}p_{a}+\frac{D_{k}}{R_{k}}[p_{e} +\eta p_{k}\,\Big{]}}, \tag{4}\]  
where \(R_{k}\) is the averaged data rate for any \(k_{th}\) MTCD, \(\eta\) is the inverse of the efficiency of power amplifier, and \(E_{s}\) is the averaged static energy consumption for synchronization, admission control, etc. in each data transmission interval. \(p_{a}\) is the consumed power during data collection in the active mode with a duration of \(T_{a}^{k}\) and \(n_{a}^{k}\) being the total number of active modes during each data transmission interval \(T_{k}\). The consumed energy in transmitting and non-transmitting modes is denoted as \(\dot{E_{s}}^{k}\) and \(\dot{E_{d}}^{k}\), respectively. The simplified form of individual battery lifetime in (4) is as follows:  
\[L_{k}(t^{o})=\frac{E_{k}(t^{o})T_{k}}{\dot{E_{s}}^{k}+\dot{E_{d}}^{k}}, \tag{5}\]  
where:  
\[\dot{E}_{s}^{k}=\begin{array}{c}E_{s}+\begin{array}{c}p_{s}\Big{[}T_{k}- \frac{D_{k}}{R_{k}}-\begin{array}{c}n_{a}^{k}T_{a}^{k}\end{array}\Big{]}+ \begin{array}{c}n_{a}^{k}T_{a}^{k}p_{a}\\ \dot{E_{d}}^{k}=\begin{array}{c}\frac{D_{k}}{R_{k}}[p_{e}+\begin{array}{c} \eta p_{k}].\end{array}\end{array}\end{array}\end{array} \tag{6}\]
## 7 Mode-Locking Techniques
### Hard/Soft Aperture Mode-Locking
#### 7.1.2 Soft Aperture  
As the crystal is translated away from the focus towards \(M2\), \(\gamma\) becomes positive, thereby providing a mode size for ML operation, which is larger than the CW mode. In this situation, the stability limit for ML operation is pulled down to lower values of \(\delta\) by the added Kerr lens. Therefore, a physical aperture cannot be used at \(EM1\). However, the larger mode size for ML at \(EM1\) corresponds to a smaller mode size at the crystal compared to the CW mode. This enables the pump beam inside the crystal to be used as a \"soft\" aperture, by setting a small pump mode to overlap with the smaller ML mode at the focus. Consequently, the pump mode acts as a \"soft aperture\", in which the CW mode suffers from poor pump-mode overlap compared to the ML mode. As can be seen from Figure 5a, the soft aperture technique for mode-locking near \(\delta_{1}\) is less efficient, because the translation of the crystal away from the focus increases the mode size in the crystal, lowering the value of \(|\gamma|\) and also increasing the laser threshold. The efficiency of soft aperture compared to hard aperture mode-locking is better when mode-locking near the second stability zone, \(\delta\gtrsim\delta_{2}\), as shown in Figure 5b, where \(\gamma\) is plotted as a function of the crystal position on the end mirror of the long arm (\(EM2\)). It is therefore beneficial to mode-lock with hard aperture near \(\delta_{1}\) and with soft aperture near \(\delta_{2}\).
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
### Prior Works for Optical Modulation Technologies
Despite its excellent nonlinearity resistance and transmission performance, the Apol-CRZ-FSK modulation format faces some potential limitations and challenges in practical applications. First, the generation and processing of Apol-CRZ-FSK signals require more complex equipment and technical support, which may increase the cost and complexity of the system. Second, in real-world network environments, nonlinear effects may become more severe due to factors such as fiber aging and temperature variations, thereby impacting the quality of signal transmission. Additionally, although the demodulation process of Apol-CRZ-FSK signals does not require complex coherent demodulation, direct detection methods may not achieve optimal performance in certain scenarios. Therefore, optimizing the generation and demodulation techniques of the Apol-CRZ-FSK modulation format to further enhance its performance in practical applications remains a critical direction for future research.
# Compressive Sensing-Based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems
###### Abstract  
Aiming at a massive multi-input multi-output (MIMO) system with unknown channel path number, a sparse adaptive compressed sensing channel estimation algorithm is proposed, which is the block sparsity adaptive matching pursuit (BSAMP) algorithm. Based on the joint sparsity of subchannels in massive MIMO systems, the initial set of support elements can be quickly and selectively selected by setting the threshold and finding the maximum backward difference position. At the same time, the energy dispersal caused by the non-orthogonality of the observation matrix is considered, and the estimation performance of the algorithm is improved. The regularization of the elements secondary screening is deployed, in order to improve the stability of the algorithm. Simulation results show that the proposed algorithm can quickly and accurately recover massive MIMO channel state information with unknown channel sparsity and high computational efficiency compared with other algorithms.  
5G; massive MIMO; compressive sensing; sparsity adaptive; a channel estimation +
Footnote †: journal: Applied  
_Article_
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Functional Characteristics of the Tumor Vascular System
#### 3.4.1 Angiogenesis
PIGF and VEGF-B bind to VEGFR-1, while VEGF-C and VEGF-D bind to VEGFR-2 and VEGFR-3, promoting angiogenesis and lymphangiogenesis [87]. Angiogenic factors are secreted by various host cells, including platelets, muscle cells, and tumor-associated stromal cells. VEGFRs are extensively expressed in endothelial cells [98]. Angiopoietins, also a family of angiogenesis stimulators, are proteins that act as ligands for the Tie receptor family in endothelial cells, playing a critical role in vascular remodeling and stabilization. The Tie-2 receptor facilitates budding and branching, whereas both Tie-1 and Tie-2 maintain vascular integrity. Angiopoietin-1 (Ang-1) activates Tie-2, recruits pericytes to stabilize blood vessels, reduces vascular permeability, and supports endothelial cell survival. In contrast, Ang-2 functions as an antagonist [77; 94]. Among chemokines, CXC chemokines with the Glu-Leu-Arg amino acid motif activate endothelium, while others inhibit angiogenesis. Other growth factors, such as transforming growth factor-alpha (TGF-alpha) and platelet-derived growth factor-BB (PDGF-BB), and protein factors, such as pleiotrophin, angiogenin, platelet-activating factor (PAF), and the HIV tat gene product, promote endothelial cell proliferation and migration [94]. Transforming growth factor-\(\beta\) (TGF-\(\beta\)), whose receptors are expressed by endothelial cells and pericytes, at low concentrations, upregulates angiogenic factors and proteases, whereas, at high concentrations, it inhibits endothelial cell growth, promotes basement membrane remodeling, and stimulates smooth muscle cell differentiation [77]. Other promoters of angiogenesis include fibroblast growth factors (FGFs), platelet-derived growth factors (PDGFs), interferons (IFNs), prostaglandandins E1 and E2, nicotinamide, and heparin [94; 78]. Negative Regulators of Angiogenesis  
The genomic instability of cancer cells leads to the overexpression of multiple angiogenic factors. The inhibition of angiogenesis can prevent tumor growth and cause tumor regression. Inhibitors target specific angiogenic factors and are classified as direct or indirect. Direct inhibitors, such as angiostatin, act directly on endothelial cells to suppress angiogenesis, whereas indirect inhibitors affect other cell types [87; 94]. Angiostatin, for example, an anti-angiogenic protein derived from plasminogen, reduces the growth of distant metastases by enhancing apoptosis in metastatic tumors. Specifically, it attacks the energy system of metastatic tissue by inhibiting ATP synthase F1F0. Another example is interleukins (ILs), a group of cytokines released by leukocytes, in which the presence or absence of the Glu-Leu-Arg amino acid motif at the NH\({}_{2}\) end of their structure (as in IL-8 and IL-4 respectively) enhances or inhibits angiogenesis [77].
## 2 Design Methodology
### Input/Output Stiffness Analysis Using the Matrix Method  
In the general matrix method, each elastic joint has 6 DOFs in its local coordinate, and is therefore modeled using a full \([6\times 6]\) matrix [32]. In this study, to simplify the model of elastic joints, they are assumed to have 3 DOFs, including axial and bending deformation, and rotation around their axis. This assumption is feasible because the designed mechanism has a sizeable out-of-plane thickness that can be treated as a plane strain model. The other three out-of-plane deformations are restricted, and not considered in the calculation model. Consequently, modeling the elastic joint with 3 DOFs can ensure deformation accuracy and allow full description of the design characteristics. A compliance matrix \([C_{i}]\) of an elastic joint is defined as a ratio of the displacement vector \(\left\{u_{i}\right\}=\left[u_{x}\ u_{y}\ \theta_{z}\right]^{T}\) when it endures an applied force \(\left\{F_{i}\right\}=\left[F_{x}\ F_{y}\ M_{z}\ \right]^{T}\)[33]. Follow [PERSON]'s law, this \(\left\{u_{i}\right\}\) can be calculated as [34]:  
\[\left\{u_{i}\right\}=\left[C_{i}\right]\{F_{i}\} \tag{5}\]  
Figure 3: Displacement amplification analysis model. \[\text{or} \begin{Bmatrix}u_{x}\\ u_{y}\\ \theta_{z}\end{Bmatrix} = \begin{bmatrix}\mathbf{C}_{11}&0&0\\ 0&\mathbf{C}_{22}&\mathbf{C}_{23}\\ 0&\mathbf{C}_{32}&\mathbf{C}_{33}\end{bmatrix}\begin{Bmatrix}F_{x}\\ F_{y}\\ M_{z}\end{Bmatrix} .\]  
The general compliance matrix for different flexure hinge cross-sections has been proposed and developed by [PERSON] and [PERSON] [33; 35]. The final form of the matrix for each hinge should be performed by a coordinate transformation from its local coordinate system \((O_{i})\) to a global coordinate system \((O_{j})\)[32]. The transformation manipulation of a compliance matrix from \(O_{i}\) coordinate to \(O_{j}\) coordinate can be expressed as follows:  
\[\mathbf{C}_{i}^{j}=\mathbf{T}_{i}^{j}\mathbf{C}_{i}\Big{(}\mathbf{T}_{i}^{j} \Big{)}^{T} \tag{6}\]  
The transformation matrix \(\mathbf{T}_{i}^{j}\) is illustrated in Equation (7):  
\[\mathbf{T}_{i}^{j}=\mathbf{R}_{i}^{j}\mathbf{P}_{i}^{j} \tag{7}\]  
where, \(\mathbf{P}_{i}^{j}\) and \(\mathbf{R}_{i}^{j}\) are the translational matrix and the rotational matrix, respectively, for the coordinate transformation \(O_{i}\) to \(O_{j}\). The matrices \(\mathbf{P}_{i}^{j}\) and \(\mathbf{R}_{i}^{j}\) can be described in Equations (8) and (9). \[\mathbf{P}_{i}^{j}= \begin{bmatrix}1&0&P_{y}\\ 0&1&-P_{x}\\ 0&0&1\end{bmatrix}  \tag{8}\]  
\[\mathbf{R}_{i}^{j}= \begin{bmatrix}cos\theta&-sin\theta&0\\ sin\theta&cos\theta&0\\ 0&0&1\end{bmatrix}  \tag{9}\]  
where, \(\theta\) is the rotational angle around the \(z\) axis. In Figure 4a, the flexure hinges \(H_{i}(i=1,\,2\,\ldots 7)\), as well as the rigid links \(G_{j}(j=1,\,2\,\ldots 4)\), are either serially or parallel connected to form the overall structure of the mechanism. The calculation for the equivalent stiffness of the hinges or the links in this section uses the PRBM [36; 37].