esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 3 System Model Design and Optimization Framework  
### System Model  
We consider a field of interest (FoI), which is a 2D terrain, with an area of M \(\times\) L m\({}^{2}\). Inside the terrain, we place \(N\), \(N\ \in\ Z^{+}\) in a total wireless sensor network and they form a connected WSN, with one sink node \(H\). The sink node collects all data and is placed at the centroid of the terrain. The nodes are considered homogeneous in terms memory, processing, communicating capabilities, and energy reserves. Their job is to periodically collect data samples and send them to the sink node (i.e., the base station--BS). Initially, the WSN is considered static, which means that no mobility is present, nor do the nodes change their coordinates. Additionally, each node has the same sensing range, \(R_{s}\), and communication range, \(R_{c}\).  
In Figure 1, we present an example of two topologies for a small-scale network case, the size of which is 20 \(\times\) 20 m\({}^{2}\), in which fifteen nodes are distributed. Let us call topology (a) the deterministic deployment of nodes, as produced by the centroidal Voronoi tessellation (CVT) [1], and topology (b) the random deployment, in which the nodes' coordinates are derived by a Gaussian distribution [2]. The position of each node is stored in a discrete set of (x,y) coordinates. Therefore, the WSN is considered as a graph G(V,E), where V expresses the number of nodes, \(N\), and E is the total edges that connect the nodes. Our network system model has the following design features:  
1. All sensors are responsible for monitoring an event inside their sensing area, periodically taking measurements, and sending them to the BS. When they perform that, they are considered active.
2. Topologies in which a node has no neighbor are neither considered connected nor valid. Each sensor \(i\in[1\dots N]\) should communicate with at least one neighbor and send the data to the BS, either directly or via a multihop pattern [5]. In the case of a multihop pattern, relay nodes send all packets to the BS.
3. The coordinates (x,y) of each sensor may change over time due to the neural network solver decision to minimize the total energy consumption.
4. The position of the BS is stationary.
5. We call the initial sensor positions theoretical points, which constitute the CVT topology, in which the optimal sensor distance is \(d_{uv}\leq\sqrt{3}R_{s}\)[1]. We also call each sensor's coordinate query points (i.e., application reserved coordinates) as the application domain dictates and as long as there is a WSN-connected graph at all times. For example, in a smart agriculture application, we may want to mount each sensor at certain terrain locations, i.e., on a tree.
6. The area to be monitored is a rectangular 2D grid terrain of size \(M\times M\), where \(M\in Z^{+}\).  
We define a round as a case where all sensor nodes in the WSN send at least one packet towards the gateway node. Additionally, we define the network lifetime as the total rounds until all nodes reach zero energy reserve (i.e., all the nodes die).
## 6 Performance Evaluations
### Performance with Different Number of Nodes  
We vary the number of nodes in the network from 2 to 20. In this part of the simulation, each node is considered to generate data at a very high rate of 15 Pkts/s and the other simulation parameters remain the same.  
As delineated in Figure 7, the overall average energy consumption of the protocols increases proportionally with the number of network nodes. As shown in Figure 7, for various traffic loads from different numbers of deployed nodes, the proposed ATLAS has the lowest energy consumption in comparison to LDTA-MAC and IEEE 802.15.4 protocols. This is due to the efficient use of energy in handling data traffic under different load scenarios. Conversely, the LDTA-MAC and IEEE 802.15.4 are unable to adapt their operations based on the traffic load, particularly when there are many network nodes generating more traffic. Furthermore, their protocol operation includes long range single-hop transmissions, causing much more energy consumption. However, overall LDTA-MAC consumes less energy than the IEEE 802.15.4 does.  
Figure 6: End-to-end delay over diverse traffic loads.  
Figure 8 illustrates the data fidelity (throughput and delivery ratio) of the protocols for different numbers of deployed nodes. As portrayed in Figure 8(a), in comparison to the other protocols, ATLAS maintains a better throughput for both lower and higher number of sources. When there are 20 nodes in the network, ATLAS ensures a successful delivery of about \(70\%\) of the packets per second. However, the percentage of successful delivery is approximately \(53\%\) and \(24\%\) for LDTA-MAC and IEEE 802.15.4, respectively. Similarly, the delivery ratio shown in Figure 8(b) indicates better results using the proposed MAC protocol. The ATLAS protocol achieves a maximum ratio of \(87.5\%\) for 10 nodes and about \(71\%\) for 20 nodes. In contrast, due to the lack of traffic adaptability in using the CAP, CFP and IP modes of the superframe, the delivery ratio of LDTA-MAC and IEEE 802.15.4 suffer considerably, especially when the network load increases due to heavy traffic from a higher number of nodes.  
Figure 8: Data fidelity over different number of nodes: **(a)** Aggregate throughput; and **(b)** Packet delivery ratio.  
Figure 7: Average energy consumption over different number of nodes.  
The average end-to-end delay is also observed for various numbers of deployed nodes. As depicted in Figure 9, ATLAS has a lower delay in comparison to both LDTA-MAC and IEEE 802.15.4, and the maximum and minimum delay variation is also minimal for ATLAS. However, the average delay increases linearly with the number of nodes. The interpretation of the delay result as shown in the graph is the same as that given for Figure 6 and is not repeated here. Overall, these results support the delay effectiveness of ATLAS and its suitability for MAC operations in diverse WBAN applications.
## 4 Numerical Examples  
In this section, three numerical examples are investigated, including a mathematical example and two structural examples. Through these three examples, the proposed method is compared to the NRBM [30] to evaluate its accuracy and efficiency, where the reliability results from an MCS [26] with \(1\times 10^{8}\) samples are treated as the data.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 2 System Model and Preliminaries
### Orthogonal Allocation  
In this configuration, resources are split between services so that no mutual interference between \(\mathcal{I}_{\text{a}}\) and \(\mathcal{I}_{\text{b}}\) arises. Without loss of generality, we assume orthogonality to be achieved in time and focus on the setup exemplified by Figure 1(a) (the reported analysis and results would hold for an orthogonal allocation in the frequency domain as well). Time is divided into successive frames of equal duration. Within each frame, a fraction \(\alpha\) of the time is granted exclusively to \(\mathsf{s}_{\text{a}}\) for data delivery. During this period, the channel output at the receiver over the \(\ell\)-th channel use takes the following form:  
\[Y_{\ell}=X_{\ell}+W_{\ell}\]  
where \(X_{\ell}\sim\mathcal{CN}(0,\rho_{\text{a}}/\alpha)\) is the complex symbol transmitted by the user, and \(W_{\ell}\sim\mathcal{CN}(0,\sigma^{2})\) is a zero mean, \(\sigma^{2}\) variance, circularly-symmetric complex Gaussian additive noise component. In view of the fraction \(\alpha\) of resources available for transmission, the average signal-to-noise ratio (SNR) at the receiver is thus, as follows:  
\[\gamma_{\text{a}}:=\frac{\rho_{\text{a}}}{\alpha\sigma^{2}}. \tag{1}\]As for users of the best-effort service \(\mathcal{T}_{\mathsf{b}}\), channel access is only permitted during the remaining fraction \((1-\alpha)\) of the time. To instantiate a slotted ALOHA policy, the corresponding portion of the frame is assumed to be divided in slots of equal duration, each allowing the transmission of a single packet of \(\mathcal{T}_{\mathsf{b}}\). Denoting by \(n_{s}\) the number of channel uses over a slot, the input-output relation over the \(\ell\)-th slot conditioned on having \(U=u\) users concurrently transmitting can be expressed as follows:  
\[\mathbf{Y}_{\ell}=\sum_{j=1}^{u}\mathbf{X}_{\ell}^{(\mathsf{b},j)}+\mathbf{W}_ {\ell}. \tag{2}\]  
In (2), \(\mathbf{X}_{\ell}^{(\mathsf{b},j)}\) is the codeword transmitted by the \(j\)-th user of \(\mathcal{T}_{\mathsf{b}}\) active in slot \(\ell\), whose \(n_{s}\) components are modeled as i.i.d. circularly-symmetric normal r.v. with zero mean and variance \(\rho_{\mathsf{b}}\), i.e., \(\mathbf{X}_{\ell}^{(\mathsf{b},j)}\sim\mathcal{CN}(\mathbf{0},\rho_{\mathsf{ b}}\mathbf{I}_{n_{s}})\). In turn, \(\mathbf{W}_{\ell}\sim\mathcal{CN}(\mathbf{0},\sigma^{2}\mathbf{I}_{n_{s}})\) is the additive Gaussian noise, leading to the incoming signal vector at the receiver, \(\mathbf{Y}_{\ell}\in\mathbb{C}^{n_{s}}\). Note that when \(U=0\), no user of \(\mathcal{T}_{\mathsf{b}}\) transmits over the slot, and the summation in (2) brings no contribution so that solely the noise component \(\mathbf{W}_{\ell}\) is observed. As discussed in Section 1, this event, occurring with probability \(e^{-\lambda}\), represents a waste of resources, as bandwidth granted to mMTC is not employed to attempt data delivery.
# Targeted Therapy in Metastatic Bladder Cancer: Present Status and Future Directions
## 6 Targeting UC Vascularization with VEGF Inhibitors  
Solid tumors depend on adequate blood supply in order to grow and metastasize. In the case of hypoxia, cells release vascular endothelial growth factor (VEGF), which activates the VEGFR expressed on vascular endothelial cells, thereby stimulating neovascularization [47]. VEGF or VEGFR have been targeted in several tumor types to inhibit the formation of new blood vessels, thus impairing tumor growth [48]. VEGF/VEGF inhibition is not tumor-specific and there are currently no biomarkers available to select patients with an increased probability of response to therapy. Thus, VEGF/VEGF inhibition can currently be classified as a form of non-personalized, targeted therapy. Several VEGF/VEGF-directed therapies have been evaluated in MIBC. Bevacizumab is an antibody that binds VEGF, preventing the activation of VEGFR. Bevacizumab has been investigated in combination with gem/cis as a first-line treatment option in mUC and showed an ORR of 72% in a phase II trial [49]. Despite these promising initial results, gem/cis + bevacizumab did not increase the overall survival in a follow-up phase III trial [50], demonstrating the importance of confirming phase II ORR results in phase III randomized trials that evaluate overall survival. Ongoing biomarker studies could identify subsets of patients who benefit from anti-angiogenic therapy. Cabozantinib is a small molecule inhibitor of VEGFR that also inhibits other RTKs (c-MET, AXL, and RET) [51]. Cabozantinib treatment for mUC is currently under investigation as monotherapy in a third-line setting (NCT01688999). Another study investigated cabozantinib + nivolumab + ipilimumab and demonstrated a favorable toxicity profile [52; 53]. A phase II trial is currently being performed to evaluate its efficacy (NCT03866382). The ARCADIA (hARnessing CAbozantinib and Durvalumab Immuno-oncology Association) phase II trial investigates the efficacy of a cabozantinib + durvalumab combination therapy in patients with platinum-relapsed mUC (NCT03824691) [54; 55]. The safety and tolerability of cabozantinib + atezolizumab are being investigated in the COSMIC-021 phase Ib trial (NCT03170960) that includes patients with locally advanced or metastatic UC, among other tumor types [56; 57; 58]. Preliminary results based on the castration-resistant prostate cancer cohort point to a tolerable safety profile for the treatment combination [59].  
The inhibition of neovascularization by blocking VEGF or VEGFR has shown favorable toxicity profiles that allow for combination therapy with gem/cis or ICI. Anti-VEGF therapy + gem/cis has not shown a survival benefit despite an impressive ORR of 72% in a phase II trial. Results of clinical trials that combine VEGFR-inhibition with ICI are currently pending and the value of an anti-VEGF/VEGF treatment for mUC is yet unclear. Nonetheless, favorable toxicity profiles support the future evaluation of anti-VEGF/VEGF therapy combined with drugs other than gem/cis or ICI.
## 3 In Situ Sensors Used in the L-PBF Processes  
As discussed in the previous section, many factors contribute to the defect generation in parts manufactured using the L-PBF process. Although, optimizing the process parameter is a promising approach to reduce the possibility of defect generation, it is costly to optimize these parameters experimentally, and computationally expensive to use numerical methods to predict defect formation. Therefore, one trending approach is to integrate in situ sensors to monitor the occurrence of these defects online. The most popular sensing strategies for defect detection in the L-PBF process include capturing photonic, electrical, sonic, and thermal signals. Commercial machines have integrated in situ sensors; however, most of them focus on data collection rather than data analysis [3]. This section gives an overview of the most common signal monitoring sensors used in the L-PBF process. These signals can be categorized into visual signals, temperature signals, spectrum signals, and acoustic signals.
## 3 Methodology
### Lidar Point Cloud Mapping
#### 3.2.2 Loop Closure Factor  
This section constructs a point cloud map using the poses of historical laser keyframes and registers features with the current laser frame. Firstly, as illustrated in Figure 8, it identifies 25 consecutive historical keyframes [27] with a Euclidean distance \(d_{ni}\) greater than \(\varepsilon_{dis}\) from the current laser frame \(\mathbb{F}_{n}\), mapping their feature point clouds to the global coordinate system based on corresponding poses to establish the historical map. Secondly, it maps the feature point cloud of the current laser frame to the global coordinate system using the solved pose, and registers this with the historical map using the ICP method.  
The relative transformation \(\Delta\mathbf{T}_{n}^{\mathrm{ICP}}\) obtained from the above registration serves as the loop closure factor. This factor, along with the polarized Lidar odometry factor, is input into the iSAM2 factor graph optimization to refine the final pose state \(\hat{x}_{n}\) of the current \(n\)th frame. This refined pose state represents the result after correction based on the historical map.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 4 Experiments
### State-of-the-Art Comparison
#### 4.3.2 Results on VOT  
The VOT dataset is mainly derived from the Video Object Tracking Challenge (VOT, Challenge). It not only provides open-source toolkits and many labeled videos, but also provides evaluation criteria and test results for target tracking algorithms. In the VOT dataset, VOT2016 [41] is the most commonly used dataset in order to assess the performance of the algorithm. It has increased from 16 videos to 60 videos, and the difficulty has also increased. VOT2016 mainly uses three indicators to verify the performance of the target tracking algorithm. (1) Accuracy. Computational accuracy is equivalent to counting the number of overlaps between the forecast bounding box and the ground-truth bounding box. (2) Robustness. The steadiness of the tracking target is assessed by utilizing Robustness. (3) Expected Average Overlap, EAO.  
The EAO curve results of the VOT2016 assessment are shown in Figure 7 and Table 3. As is revealed by the results in the figure, SiamFP ranks first among 70 trackers according to the criteria for ranking trackers by EAO score, exceeding the performance of these state-of-the-art trackers. Notably, SiamFP outperforms the second-ranked tracker SiamFC by about 15%.  
Figure 6: Tracking Results of SiamFP with Six Trackers on Video Sequences of OTB100.
## 2 Experimental Procedure
### Experimental Procedures  
The tensile tests of carbon fiber woven composite specimens were carried out with a speed of 1 mm/min by an electronic universal testing machine (CMT5305). Real-time AE signals originating from the damage and evolution of the composites were monitored by using a DS2-8A system under tensile loading conditions, as shown in Figure 2. Two RS-54A broadband sensors with the frequency range 100-900 kHz were mounted on the surface of the specimen with a tape and the distance between them was 55 mm. In order to provide a good acoustic coupling, silicone grease was used as a coupling agent between the sensor and the surface of specimen. The sampling frequency was set at 3.5 MHz. At the start of the tensile test, a pencil lead break test was performed to verify the transducer coupling. Two sensors verify the correctness of AE signals with each other. One sensor with more signals was selected as the source of the analysis data. The morphology of the fractured composite specimen was observed using scanning electron microscopy (SEM, JEOL JSM-7500F).  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline Tensile strength (MPa) & 2550 \\ Tensile modulus (GPa) & 135 \\ Elongation (\%) & 2.1 \\ Density (g/cm\({}^{3}\)) & 1.8 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical and physical properties of the used carbon fiber.  
Figure 1: (**a**) The schematic illustration of weave composite. (**b**) Images of composite specimens.  
Figure 2: Experimental system of tensile tests and acoustic emission (AE) monitoring.
## 3 Results and Discussion
### Electronic Nose Analysis  
The increase in meat production and the need for rapid detection have contributed to the development of simple, fast, accurate, and inexpensive methods to evaluate the classification or quality of meat [48]. Figure 3 shows how the electronic nose simulates the olfaction.  
The multivariate recognition algorithm to process the multi-sensor array signals is based on the linear discriminant analysis method [57]. Because the process is simple and economical, sample preparation is minimal, and reading and interpretation of the measurements are clear, the electronic nose has become a viable alternative to conventional analysis [11; 58] and has been applied to evaluate the shelf-life of livestock products [59], and to analyze meat quality [60].  
In the present study, there were nine eigenvalues of 16 sensors of the electronic nose, including means, integral value, differential value, range, quadratic coefficient, primary coefficient, halfwidth, primary coefficient of logarithmic regression function, and constant term of logarithmic regression function, which were used to analyze these three breeds of chicken [61]. They could be accurately identified with the CDA to analyze the acquired data in order to evaluate the overall flavor characteristics among the three chicken breeds. As shown in Figure 4, the CDA based on the 144 parameters derived the first and second canonical variables (CN1 and CN2, respectively). CAN1 explained 93.4% of the variability and was able to differentiate BJY and LH chickens from AA broilers. Furthermore, CAN2 was able to separate BJY from LH chickens. The results indicate that the distance between the core of BJY chickens and LH chickens was relatively close, but it could also be completely separated. The possible reason is that these two breeds of chicken were fed in the same environment and at the same age.  
The above studies have shown that electronic nose technology can be used to successfully distinguish the volatile compounds among these three breeds of chickens. This method exhibits satisfactory results using appropriate pattern recognition techniques for data analysis, which provides a gratifying analytical method for Beijing-you chicken identification. With the advantages of high sensitivity and excellent selectivity, the electronic nose has broad application prospects for the detection of meat adulteration in the future.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**VOCs**} & \multicolumn{2}{c}{**BJY**} & \multicolumn{2}{c}{**LH**} & \multicolumn{2}{c}{**AA**} \\ \cline{2-7}  & **Quantity** & **Relative Content** & **Quantity** & **Relative Content** & **Quantity** & **Relative Content** \\ \hline Hydrocarbons & 6 & 34.29\% & 9 & 45.68\% & 19 & 89.02\% \\ Alcohols & 6 & 31.36\% & 5 & 32.32\% & 1 & 0.41\% \\ Phenols & 1 & 8.92\% & 1 & 13.36\% & 1 & 0.06\% \\ Aldehydes & 5 & 12.25\% & 3 & 5.53\% & 2 & 5.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the VOCs of different types of chicken breast.  
Figure 3: Simulation of the olfaction by electronic nose technology.
# Evolutionary Design Optimization of an Alkaline Water Electrolysis Cell for Hydrogen Production
## 2 Physics and Economy of the Alkaline Water Electrolysis Cell
### Physics of the Void Fraction  
#### 2.2.1 Mathematical and Numerical Tools  
**Finite Volume Model (CFD)**  
The void fraction can be calculated using numerical simulations. A model has been developed and validated by [PERSON] et al. [1] in a previous study. The following hypothesis have been chosen:  
* The flow is Newtonian, viscous and incompressible;
* the flow is considered isothermal;
* ions distributions are neglected;
* the flow is considered laminar;
* bubble diameter is constant for a given operating condition; and
* The current density is taken as constant.  
For more explanations about the hypotheses, see [PERSON] et al. [1]. The model is a two-fluid Eulerian model Equations (12)-(16).  
\[\frac{\partial\varepsilon_{k}\rho_{k}}{\partial t}\ +\ \vec{\
abla}\Big{(} \varepsilon_{k}\rho_{k}\ \vec{V}_{k}\Big{)}=S_{k} \tag{12}\]  
\(\varepsilon\) is the gas or liquid fraction, the subscript \(k\) can be either \(g\) (O\({}_{2}\), H\({}_{2}\)) or \(liq\), \(\rho\) is the density in kg m\({}^{-3}\), \(V\) the velocity in m s\({}^{-1}\), and \(S_{k}\) is the term source in kg m\({}^{-3}\) s\({}^{-1}\)  
\[\frac{\partial}{\partial t}\Big{(}\varepsilon_{k}\rho_{k}\ \vec{V}_{k}\Big{)}\ +\ \vec{ \
abla}\Big{(}\varepsilon_{k}\rho_{k}\ \vec{V}_{k}\vec{V}_{k}\Big{)}=\ -\ \varepsilon_{k}\vec{\
abla}p\ +\ \vec{ \
abla}\Big{(}\varepsilon_{k}\vec{\tau}\Big{)}\ +\ \varepsilon_{k}\rho\ \vec{g}\ +\ \vec{F}_{k} \tag{13}\]  
\(p\) is the pressure in Pa, \(\vec{\tau}\) is the stress tensor in Pa, \(\vec{g}\) the gravitational acceleration in m s\({}^{-2}\), and \(\vec{F}_{k}\) is the exchange term in N m\({}^{-3}\)  
The stress tensor is written as follows:  
\[\vec{\tau}=\mu_{k}\Big{[}\Big{(}\vec{\
abla}\vec{V}_{k}\ +\ \vec{\
abla}\vec{V}_{k} ^{T}\Big{)}\ -\ \frac{2\vec{\ast}}{3}\vec{\
abla}\vec{V}_{k}\Big{]} \tag{14}\]  
with \(\mu_{k}\) the viscosity of the phase \(k\) in Pa s and \(I\) the unit tensor.  
\[\vec{F}_{k}=\vec{F}_{D}\ +\ \vec{F}_{L}\ +\ \vec{F}_{BD} \tag{15}\]  
\[\vec{F}_{k}=\ \underbrace{-\,\frac{3}{4}\varepsilon_{g}\rho\,\frac{C_{D}}{d_{ b}}|U_{r}|U_{r}}_{Drag\ force}\ -\ \underbrace{\varepsilon_{g}\rho C_{L}|U_{r}|\ rot\Big{(}\vec{V}_{I}\Big{)}}_{Lift\ force}\ -\ \underbrace{ \varepsilon_{g}\rho\,\frac{K_{g}}{d_{b}}|U_{r}|\vec{\
abla}\varepsilon_{g}}_{ Bubble\ dispersion\ force} \tag{16}\]  
Equation (15) is the exchange term. It is the sum of the usual drag force and lift force. The study of [PERSON] et al. [1] showed that it was mandatory to take into account the bubble diffusion force \((\vec{F}_{BD})\) to reproduce the experimental data. The coefficient \(K_{g}\) has been used to fit the experimental data of [PERSON] et al. [23].  
Using the Vaschy-Buckingham theorem, four dimensionless numbers have been determined Equations (17)-(20).  
\[Re_{V_{G}}=\frac{\rho_{l}\,V_{G}\,H_{dec}}{\mu_{L}} \tag{17}\]  
\[Fr_{V_{G}}=\frac{g\,H_{dec}}{{V_{G}}^{2}} \tag{18}\]  
\[r^{*}=\frac{r}{H} \tag{19}\]  
\[h^{*}=\frac{h}{H} \tag{20}\]