esa-sceva/satcom-chunk-collection huggingface dataset:
# Steering the Clinical Translation of Delivery Systems for Drugs and Health Products
## 3 Poster Presentations
### Nanoemulsions as Delivery Systems for Poly-Chemotherapy Aiming to Melanoma Treatment  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 3, [PERSON] 3, [PERSON] 4, [PERSON] 3 and [PERSON] 1,*  
1 Universita degli Studi di Torino, Dipartimento di Scienza e Tecnologia del Farmaco, via [PERSON] 9, 10124 Torino, Italy  
2 Universita degli Studi di Torino, Dipartimento di Scienze Veterinarie, via [PERSON] 10, 10095 Grugliasco (TO), Italy  
3 Universita del Piemonte Orientale, Dipartimento di Scienze della Salute, via Solaroli 17, 28100 Novara, Italy  
4 Ospedale San Giovanni Bosco, Piazza del Donatore di Sangue 3, 10154 Torino, Italy  
* Correspondence: [EMAIL_ADDRESS]  
IV stage melanoma is the most advanced and critical stage, and leads to metastases or relapses, in the case of previous surgical removal. Despite the available current treatments being increased inrecent years, current pharmacological therapies are only palliative care, which do not affect the final outcome, but whose primary end point is prolonging patient's life. Therefore, the improvement of current chemotherapy is worthy of investigation.  
In this experimental work a nanotechnology-based poly-chemotherapy aiming to treat IV stage melanoma is proposed and tested at pre-clinical level. Temozolomide, rapamycin and bevacizumab were co-loaded in injectable nanoemulsions for total parenteral nutrition (Intralipid(r)), owing to suitable devices, and preliminarily tested in vitro on human and mouse cell models and in vivo in B16-F10 melanoma mouse model.  
The combination of drugs was efficiently loaded into the liquid lipid matrix of Intralipid(r), including bevacizumab monoclonal antibody, leading to a fast internalization in tumor cells, as assessed through flow cytometry. An increased cytotoxicity towards melanoma cells, as well as an improved inhibition of tumor relapse, migration and angiogenesis was demonstrated in cell models for the Intralipid(r) loaded drug combination. In preliminary in vivo studies, the proposed approach was able to reduce tumor growth significantly compared to controls. A relevant efficacy towards tumor angiogenesis and mitotic index was assessed, and immune response was probably involved.  
In conclusion, in preliminary pre-clinical studies Intralipid(r) proved to be a safe and versatile poly-chemotherapy delivery system for advanced melanoma treatment, by acting on multiple mechanisms, and allowing the perspective of a personalized nanomedicine.  
The authors thank Italian Ministry of Education and University for funding (FFABR 2017, Ricerca Locale 2017-2018).
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 2 Theoretical Model of Specular Returns from Inland Waters
### The Theoretical Complex Radar Altimeter Echo, Z(n,r)
In fact, there is a significant echo power leaking into a range bin from outside the \(Sr\) annulus. This fact turns out to be surprisingly beneficial, because it enables sub-range bin resolution in the water level assessment, and this is made possible by a simple algorithm. For completeness, Equation (1) should also include the antenna beam response function. In all practical cases that will be considered in the paper, the water body is at or very near nadir, with a maximum off-nadir angle <0.1\({}^{\circ}\). Thus, the beam term has been left out, given that the Envisat beamwidth is 1.29\({}^{\circ}\). It may be of interest to some readers to compare this model with the well-known Brownian [10] radar altimeter model. The [PERSON] formula has been used for three decades of sea-level measurements. At the core the two models start with the same basic physics, i.e., summing flat elemental scatterers. Neither has wind surface roughening. The [PERSON] model incorporates wind roughening only as a constant sigma-0 for all scatterers. One significant and meaningful difference is that here water is flat, \(\xi=constant\), whereas over ocean \(\xi\) has the distribution of wave height (the [PERSON] model, however, can be \"dialed\" down to a flat ocean). Another difference is that the ocean is boundless (at least for [PERSON]'s theory) whereas here we look at bounded water surfaces such as lakes and rivers. A very important difference is that in our model we retain the phase information, which can be useful if multiple echoes are combined coherently. [PERSON] did not envision coherence between successive echoes. In fact, the PRF of early radar altimeters was specifically chosen to minimize inter-echocoherence [11]. However, on inland waters there is inter-echo coherence even with low PRF (such as Envisat) and the coherence can be exploited. This is a favorable condition for the altimeter Poseidon-4, which will be onboard the Sentinel-6 mission, with its medium PRF of 9200 Hz. The integration in Equation (1) is implemented with the Earth map \(M\) discretized into square \(dS\) cells. The computation time goes as \(1/dS^{2}\). We verified the numerical accuracy of Equation (1) on simple geometric shapes to reproduce the classic and well know RCS patterns, e.g., the thin rectangular plate [12]. With Envisat parameters we found that 1 m \(\times\) 1 m cells provide sufficient accuracy. There is no discernable change with smaller cells; 2 m \(\times\) 2 m cells begin showing discernable noise in the second and third sidelobes. The model is computationally intensive but we experienced 10\(\times\) or more acceleration with GPUs.
###### Abstract  
Localization methods of unknown emitters are used for the monitoring of illegal radio waves. The localization methods using ground-based sensors suffer from a degradation of localization accuracy in environments where the distance between the emitter and the sensor is non-line-of-sight (NLoS). Therefore, research is being conducted to improve localization accuracy by utilizing Unmanned Aerial Vehicles (UAVs) as sensors to ensure a line-of-sight (LoS) condition. However, UAVs can fly freely over the sky, making it difficult to optimize flight paths based on particle swarm optimization (PSO) for efficient and accurate localization. This paper examines the optimization of UAV flight paths to achieve highly efficient and accurate outdoor localization of unknown emitters via two approaches, a circular orbit and free-path trajectory, respectively. Our numerical results reveal the improved localization estimation error performance of our proposed approach. Particularly, when evaluating at the 90 th percentile of the error's cumulative distribution function (CDF), the proposed approach can reach an error of 28.59 m with a circular orbit and 12.91 m with a free-path orbit, as compared to the conventional fixed sensor case whose localization estimation error is 55.02 m.  
RF fingerprint; localization; UAV; route optimization; PSO Article
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Online Learning Direct-to-Satellite Packet Scheduling
#### 2.4.5 Algorithmic Formulation  
Since we are developing a learning approach to the LEO transmission scheduling, we will rely on the activation function for classification/learning, softmax. For the set of values \(x_{j}\), \(\{j,1,N\}\), it is for each value \(x_{i}\) from as:  
\[softmax(x_{i})=\frac{exp(x_{i})}{\sum_{j}exp(x_{j})}. \tag{2}\]  
Since softmax adds up to 1 across all inputs, it effectively creates a probability distribution function that disproportionally favors larger values of \(x_{i}\).  
Let \(r_{data}\) be the data point generation rate (in data points per hour), and _bundlesize_ be the number of data points that comprise a full bundle. Then, the rate of full packet bundling \(r_{pkt}\) is: \(r_{pkt}=\frac{r_{data}}{bundlesize}\). Let \(\mathrm{softmax}(\mathbf{z})\) be the vectorized softmax function where \(\mathrm{softmax}(\mathbf{z})_{i}\) is the softmax of the i-th element of \(\mathbf{z}\), and let \(v(\mathbf{s})\) the vectorized value function. We express the policy \(\pi\) as:  
\[\pi(a_{i}|\mathbf{s}_{i})=\mathrm{softmax}(\lambda^{\mathrm{t}\odot t_{\min} }\odot v(\mathbf{s}))_{i} \tag{3}\]  
where the \(\odot\) and \(\odot\) symbols operating on vectors \(\mathbf{t}\) and \(\mathbf{s}\) are the element-wise subtraction and multiplication, respectively. Equation (3) expresses the probability of selecting a satellite pass \(a_{i}\) from interval \(\big{[}t_{min},t_{max}\big{]}\) as the softmax of the estimated transmission success probability for the pass, multiplied by a discount factor for future passes. Pass quality and promptness will be prioritized, while still giving a chance for exploration of passes currently predicted to be worse. This preference allows Monte Carlo learning to improve the value function estimates with time.
# Arithmetic Study about Efficiency in Network Topologies for Data Centers
## 5 Developing Some Typical Use Cases  
After having presented some instances of network topologies fit for data centers along with the definition of coefficient \(\eta\), some typical use cases are going to be developed, taking into account that the number of nodes that will be up and runningis considered to be small to medium. In order to cope with this, a limit of 16 nodes per topology is going to be imposed, although smaller numbers are going to be exposed as well. Basically, a systematic approach by means of powers of two is going to be described in the following subsections. Additionally, another one is going to be devoted to fit those designs not matching any power of two, and eventually, a further one is dedicated to some other commonly used network topologies in larger data centers.  
Regarding the calculations for all topologies, the average number of hops has been undertaken by first selecting a given end host and, in turn, adding up the number of hops to reach all its peers within the topology and dividing into the count of such peers. Otherwise, the average number of links has been carried out by adding up the links of each device, no matter if they are end hosts or switches, and dividing into the count of all those devices.  
It is to be reminded that data center network topologies need to avoid single points of failure in critical points; thus, redundancy is a must. From that point of view, single hub and spoke topologies have not been taken into account, whilst redundant hub and spoke topologies have been included into this study. Further, it happens that the definition of this coefficient gives advantage to topologies with fewer average links, which may benefit non-redundant topologies against redundant ones. Hence, in order to avoid this situation, this coefficient was restricted only to redundant topologies. On the other hand, topologies with more than two redundant links are penalized against topologies with just two of such links, although those cases are not considered herein, as the most common layout is to have just two redundant devices.
# Smart Inverters for Microgrid Applications: A Review
## 2 Smart Inverters
### Adaptability  
It is crucial for a smart component, in this case, a smart inverter, to be able to adapt or adjust itself to the changes and occurrences of the system in which it operates. This means the ability to estimate the parameters, specifically the impedance of the grid, and self-synchronize in terms of frequency. This is adaptability and is another crucial characteristic of a smart inverter. One of themost important operational problems of microgrids is unintended islanding. This is mainly due to grid failure, and the inverters are required to be included with islanding detection algorithms in order to be able to self-adapt if required. Furthermore, being fault tolerant is another operational ability that falls in this category [34]. The importance of fault-tolerant operation becomes relevant when it is not possible to partially or fully shut down a system due to its critical application even in faulty conditions. For example, hospitals require constant and stable electrical power, and if a fault occurs the grid is required to continue the supply until the problem is solved, otherwise it can cause devastating consequences [74].
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results
### Simulation 3: Effect of Signal Constellation  
Figure 12 shows the comparison of BER in MSC-AmBC and TD-AmBC with respect to the modulation order \(M\) and the reflection coefficient \(\alpha\). Based on \(K_{1}=5\)-dimensional signal space, the MSC-AmBC can support \(M=2^{K_{1}}=32\)-ary modulation using only \(\bar{M}=2\) load impedances which are very simple to implement at the IoT devices with small form-factor, such as biosensors and wearables. As a result, the high-order modulation with \(M\geq 32\) can be successfully supported in the MSC-AmBC while only 16-ary PSK modulation available at the conventional TD-AmBC due to the hardware limitation of RF tags. Interestingly, formation of the multi-dimensional constellation is helpful not only for practical implementations but also for multiplexing data. For example, 32-ary signal constellation of the MSC-AmBC has the minimum Euclidean distance of \(d_{\min}=2\sqrt{\alpha}\) which is equal to that of BPSK modulation of the TD-AmBC. As demonstrated in Figure 12, 32-ary modulation of the MSC-AmBC performs similar to BPSK modulation of the TD-AmBC while multiplexing \(V=\log_{2}M=5\) bits per OFDM symbol effectively. Consequently, high-rate and long-range AmBC can be achieved with sparse codes by overcoming both the hardware and channel limitations inherent in AmBC.  
Figure 11: Effect of the duty cycle \(D\) and the reflection coefficient \(\alpha\) for detection and energy harvesting performances (\(D=0.1\) represents the TD-AmBC system). (**a**) BER performance in MSC-AmBC (\(K=10\), \(\zeta=0.16\), \(M=2\)). (**b**) Harvesting probability in MSC-AmBC (\(K=10\), \(\zeta=0.16\), \(M=2\)).  
Figure 12: Effect of the modulation order \(M\) and the reflection coefficient \(\alpha\) for detection and energy harvesting performances (MSC-AmBC: modulation with iterative projections and expansions using \(\bar{M}=2\) impedances, TD-AmBC: \(M\)-PSK modulation using \(\bar{M}=16\) impedances). (**a**) BER performance in MSC-AmBC (\(K=10\), \(\zeta=0.16\), \(D=0.5\)). (**b**) BER performance in TD-AmBC (\(K=10\), \(\zeta=0.16\), \(D=0.1\)).
# Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication
## 2 Methodology
### Testbed Setup
#### 2.3.2 Heterogeneous Connection  
To ensure communication protocol compatibility, we use Ethernet connections to emulate the VLC and WLAN links. Linux wondershaper [25] is adopted to manually set the uplink and downlink speed of network interface cards (NICs) based on the throughput measurement of VLC and WLAN links. The heterogeneous testbed includes a host (two NICs), a switch, and a server (one NIC). Two Ethernet connections are set up between the host and the switch. One Ethernet connection is set up between the server and the switch. Using the socket programming written in C code ([[https://github.com/mathewsalas/Hetnet-Socket-Programming.git](https://github.com/mathewsalas/Hetnet-Socket-Programming.git)]([https://github.com/mathewsalas/Hetnet-Socket-Programming.git](https://github.com/mathewsalas/Hetnet-Socket-Programming.git)), accessed on 29 November 2022), the host binds two sockets to two Ethernet NICs, respectively, and the server binds one socket to its Ethernet NIC. The key exchange process is implemented by letting the server listen to its socket for a key request and sending the encryption key once an authorized request is received. The host sends a key request over the NIC emulating Retro-VLC uplink, receives an encryption key on the same NIC emulating the VLC downlink, and then applies the key to data encryption over the other NIC emulating the WLAN link. To evaluate the time cost of the key exchange process, we use the packet analyzer Wireshark on the host side to measure the time between the key request and the reception of the encryption key. According to the measurements based on the Retro-VLC testbed (Section 2.3.1), the data rates are set to 1 kbps and 10 Mbps for uplink and downlink data transmission, respectively.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 4 Load Modeling Influence on Grid Dynamics
### Static and Dynamic Load Modeling--Sensitivity Analysis
#### 4.2.3 Induction Motor Load Percentage--Sensitivity Analysis  
In this subsection, it is intended to evaluate the influence of the IM load percentage on the BESS power converter sizing. Note that in the base case scenario (Section 4.2.1), 26.5% of IM load was considered. The results are presented in Table 3 (for the cases where L3 and/or L5 were not modeled as IM loads, their modeling is based on a constant impedance). A general trend to reduce the BESS power converter capacity with the reduction in the IM load ratio is verified. In fact, higher shares of IM loads will require larger reactive current consumption during the post-fault period, which in turn will induce a larger reactive current injection from the BESS power converter. However, the IM load location also presents some influence on the BESS power converter sizing. For instance, for the case where the IM load is only modeled by L1, the required BESS capacity is smaller than the base case, which presents a lower IM load ratio.  
In addition, the short-circuit critical clearing time (CCT) for different shares of IM load integration was analyzed, considering a 12 MVA BESS power converter sizing (suitable for system operation while considering static load modeling--Section 4.1). As can be observed in Table 4, there is a clear trend for the CCT reduction as a function of the IM load ratio reduction. Note that for the case where L2 is modeled as an IM load, a 12 MVA BESS power converter is not enough to guarantee the IM load recovery after a fault occurrence, independently of its duration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IM Load (\%)** & **IM Load** & **CCT (ms)** \\ \hline
11\% & L5 & 86 \\
15.5\% & L3 & 80 \\
21\% & L4 + L5 & 63 \\
26.5\% & L3 + L5 & 62 \\
27\% & L1 & 61 \\
36.5\% & L3 + L4 + L5 & 46 \\
36.5\% & L2 & – \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network fault critical clearance time vs. IM load percentage.  
Figure 12: BESS power converter rating vs. induction motor (IM) load inertia sensitivity analysis with respect to different motor load types.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IM Load (\%)** & **IM Load** & **S\({}_{\text{BESS}}\) (MVA)** \\ \hline
11\% & L5 & 18 \\
15.5\% & L3 & 28 \\
21\% & L4 + L5 & 28 \\
26.5\% & L3 + L5 & 38 \\
27\% & L1 & 25 \\
36.5\% & L3 + L4 + L5 & 46 \\
36.5\% & L2 & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grid-forming BESS power converter sizing vs. IM load percentage.
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 2 Material and Methods
### Data
#### 2.2.4 Analysis of Anomalies  
For the analysis of anomalies, the monthly values of the gauges and the monthly products of IMERG were used. The investigation of the volume of precipitation for each month concerning its average (2000-2018), was carried out by calculating the normalized anomalies of precipitation with standard deviation (Equation (1)) [44].  
\[X_{\text{Anomaly}}=\frac{\left(X_{\text{i}}-\overline{X}_{\text{2000-2018}} \right)}{\sigma_{\text{2000-2018}}} \tag{1}\]  
where, Xi is the month of the year analyzed, \(X\) is the monthly average of the 2000-2018 series, and \(\sigma\) is the monthly standard deviation of the time series. The mean (\(\overline{X}\)) and standard deviation (\(\sigma\)) were calculated using Equations (2) and (3), respectively.  
\[\sigma\ =\sqrt{\frac{\sum_{i=1}^{n}\left(X-\overline{X}\right)^{2}}{n}} \tag{2}\]  
\[\overline{X}=\frac{\sum_{i=1}^{n}\left(X_{\text{i}}\right)}{n} \tag{3}\]  
Significant anomalies (95% confidence interval) were \(X_{\text{Anomaly}}\) values ranging between \(\pm\)1.96 as adopted by [45; 46; 47]. Thus, a \(X_{\text{Anomaly}}>1.96\,|\) gives us a 95% confidence to assume the observations as an anomaly, i.e., different than the historical mean.  
A schematic diagram is presented in Figure 3, with the data and performance for the overall evaluation process of this study.
## 4 Simulation Results
### Performance Evaluation
#### 4.3.1 A Comparison of the Experiments Performed Under Active Power Change
However, the VSG system successfully restrains the system's output voltage from decreasing greatly, thereby stabilizing the voltage. The TD3-based system outperforms the other two VSG algorithms and brings the properties of damping and inertia into the control of the MMC, allowing the output voltage and frequency to be self-adaptive in response to changes in load. This significantly increases the power system's capacity to operate stably. In conclusion, when compared to the vector control, the TD3 control does not result in a significant reduction in voltage amplitude and demonstrates better performance and robustness against system uncertainty.