esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 3 Applications of LTP in Anti-Tumor Therapy  
As one of the four states of the basic matter of the universe, plasma is an ionized gas produced by the decomposition of polyatomic gas molecules or the removal of electrons from a monoatomic gas shell. It is composed of ions, electrons, atoms, ultraviolet, visible light, infrared radiation, neutral molecules, and free radicals [9]. The temperature of plasma is determined by thermal motions of electrons and heavy particles such as atoms and ions. In common thermal plasma, when the density of particles is high, due to intensive collisions among electrons and heavy particles, all particles reach thermal equilibrium. The temperature in such plasma is high, over several thousand degrees. If the atmospheric pressure plasma discharge is fast, the electrons and heavy particles are in a thermal non-equilibrium. In this case, the temperature of the particles is much lower than that of the electrons. We call such plasma low-temperature plasma [10].  
Since the first report on the killing effect of LTP on melanoma in 2007 [11], the application of LTP in cancer treatment has experienced fast growth. We have previously demonstrated that in vitro LTP inhibited cell viability of HepG2 in a dose- and time-dependent manner and induced HepG2 cell autophagy [12]. In our previous study, a helium atmospheric pressure plasma jet (APPJ) was used to generate plasma-activated saline (PAS) and plasma-activated medium (PAM). PAS was injected subcutaneously to treat B16-tumor bearing mice in vivo and PAM was used to treat B16 cells in vitro. The results demonstrated that LTP induced melanoma apoptosis in vitro and in vivo [13]. Other studies have reported that local treatment of mice with LTP can shrink tumors and increase survival time [14; 15; 16]. Compared with other anti-cancer methods such as chemotherapy and radiation therapy, LTP is advantageous in its selective anti-cancer ability [17]. That is, when treating tumor cells and normal cells with the same defined dose of LTP, LTP can kill tumor cells without damaging normal ones (Figure 1). Meanwhile, LTP-based cancer therapy is unlikely to result in drug resistance, but able to accurately kill tumors deep in the body [18]. Therefore, LTP has become a new means of treating tumors.  
Figure 1: Selective effects of the optimal dose of LTP. Low doses of LTP can increase the proliferation and differentiation of normal cells, while high doses can cause cell cycle arrest, DNA damage, apoptosis to kill cancer cells.
## 4 Discussion
### ROC Accuracies of These Models  
The prediction performance of the four models was also assessed by the ROC curve method. The \(y\)-axis of an ROC curve indicates the sensitivity values of true positive rates, while the \(x\)-axis suggests the values of \"1-specificity\", which are known as false positive rates. Hence, an ROC curve can be set up as a function of \"1-specificity\" and can further reflect the prediction accuracy of a 0/1 classification system because of its various cut-off threshold evaluations [7; 8; 10].  
The ROC method has been commonly and successfully applied to landslide susceptibility prediction model evaluation [16; 25; 26]. An AUC ranging from 0.5 to 1 is the standard index of model prediction performance. The greater the value of the AUC, the better the model prediction performance. Figure 7 shows that the AUC values of the PSO-MLP, MLP-only, BPNN, and IV models were 0.822, 0.791, 0.800, and 0.786, respectively. This precision index once again reflects that the PSO-MLP model had the best prediction performance among these four models.
# A MAC Protocol to Support Monitoring of Underwater Spaces +
## 7 Conclusions and Future Work  
In this paper, we have proposed the UWSO-TDMA MAC protocol for supporting underwater acoustic sensor networks. The mechanism is based on a TDMA protocol, but without including a master node or a fixed schedule for the nodes. In fact, each node selects the slot in which it will transmit in a distributed way. Time synchronization is kept through a sonobuoy that picks up the time from GPS or the Galileo system. Nodes that are not within the sonobuoy transmission range synchronize their clocks through retransmitting nodes. As the protocol is based on TDMA, transmission delay and latency are bounded, and the network can be used for transmitting messages with real-time constraints.  
The proposal was evaluated using simulations that consider the monitoring of the Bahia Blanca estuary, in order to determine whether its port activities change the beaches' ecosystems. Although the results are still preliminary, they are encouraging. Addressing the data transmission in UWASN opens new opportunities for studying several environments that have been difficult to address for researchers. Particularly, underwater environments can be easily monitored with sensor networks that use the proposed MAC protocol.  
As part of the future work, we intend to evaluate the performance of the proposed protocol in a real-world scenario: throughput, latency and delay against other protocols, like MACA or MACAW. Depending on the obtained results, we will determine specific opportunities and restrictions to use this proposal in physical scenarios.  
This work was partially supported by the European Community through the project Community Networks Testbed for the Future Internet (CONFINE): FP7-288535; by Spanish government under Contract TIN2013-47245-C2-1-R; and by the Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Argentina, PIP 11220110100487.  
[PERSON] and [PERSON] designed the main aspects of the MAC protocol and designed its evaluation. Moreover, [PERSON] conducted the evaluation process. [PERSON] and [PERSON] validated and refined the protocol and the evaluation process. [PERSON] and [PERSON] analyzed the simulation data and delivered the reported results.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## A Distributed Transmission Rate Adjustment Algorithm in Heterogeneous CSMA/CA Networks  
**[PERSON]**\({}^{\dagger}\), **[PERSON]**\({}^{\dagger,\ast}\)**and [PERSON]**\({}^{\dagger}\)  
School of Electrical and Electronic Engineering, Nanyang Technological University,  
639798 Singapore; E-Mails: [EMAIL_ADDRESS] (S.X.); [EMAIL_ADDRESS] (E.G.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +65-6790-4003.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 28 November 2014 / Accepted: 19 March 2015 / Published: 26 March 2015_  
Distributed transmission rate tuning is important for a wide variety of IEEE 802.15.4 network applications such as industrial network control systems. Such systems often require each node to sustain certain throughput demand in order to guarantee the system performance. It is thus essential to determine a proper transmission rate that can meet the application requirement and compensate for network imperfections (e.g., packet loss). Such a tuning in a heterogeneous network is difficult due to the lack of modeling techniques that can deal with the heterogeneity of the network as well as the network traffic changes. In this paper, a distributed transmission rate tuning algorithm in a heterogeneous IEEE 802.15.4 CSMA/CA network is proposed. Each node uses the results of clear channel assessment (CCA) to estimate the busy channel probability. Then a mathematical framework is developed to estimate the on-going heterogeneous traffics using the busy channel probability at runtime. Finally a distributed algorithm is derived to tune the transmission rate of each node to accurately meet the throughput requirement. The algorithm does not require modifications on IEEE 802.15.4 MAC layer and it has been experimentally implemented and extensively tested using TelosB nodes with the TinyOS protocol stack. The results reveal that the algorithm is accurate and can satisfy the throughput demand. Compared with existing techniques, the algorithm is fully distributed and thus does not require any central coordination. With this property, it is able to adapt to traffic changes and re-adjust the transmission rate to the desired level, which cannot be achieved using the traditional modeling techniques.  
keywords: wireless sensor network; CSMA/CA; transmission rate adjustment; distributed processing +
Footnote †: journal: Journal of the Acoustical Society of America
## 2 Principles and Methodologies
### Arch-Dam-Reservoir-Foundation Simulation Method
#### 2.1.2 Fluid-Solid Coupling Model (FSCM)  
The fluid-solid coupling model (FSCM) assumes that the reservoir water is a uniform, inviscid, irrotational, and compressible ideal fluid, and the dynamic water pressure generated by the reservoir water vibration satisfies the following wave equation [39]:  
\[\
abla^{2}p(x,t)-\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}=0 \tag{2}\]  
where \(\
abla\) is the Laplace differential operator, \(c\) is the acoustic wave velocity in the water, \(p\) is the dynamic water pressure, and \(t\) is time.  
The boundary conditions of the coupled dam reservoir-water flow-solid system are categorized into the following four main types:(1) Free surface boundary conditions  
Without considering the free surface micro-amplitude gravity waves, the boundary condition of the reservoir water surface can be expressed as:  
\[p=0 \tag{3}\]  
(2) Fluid-solid coupling boundary conditions  
\[\frac{\partial p}{\partial n}=-\rho_{w}\ddot{u}_{n} \tag{4}\]  
where \(n\) is the outer normal direction of the fluid domain at the dam-reservoir interface, \(\rho_{w}\) is the density of the water, and \(\ddot{u}_{n}\) is the normal absolute acceleration.  
(3) Radiation boundary conditions at the end of the reservoir  
At the tail end of the reservoir, sufficiently far from the dam to satisfy the no-reflection condition:  
\[\frac{\partial p}{\partial n}=-\frac{1}{c}\frac{\partial p}{\partial t} \tag{5}\]  
(4) Absorption boundary conditions at the bottom of the reservoir  
At the bottom boundary of the reservoir, the following boundary conditions are satisfied without considering the influence of the sediment adsorption boundary at the bottom of the reservoir:  
\[\frac{\partial p}{\partial n}=0 \tag{6}\]
## 4 Experiments
### SVHN Dataset  
The SVHN dataset [48] is derived from the Google Street View House Numbers dataset, and each image contained a set of \"0-9\" Arabic numerals. The training and test sets have 73,257 and 26,032 images, respectively, with an image size of 32 \(\times\) 32 pixels (as shown in Figure 10).  
As shown in Table 2, in the SVHN dataset, the model was trained with different numbers of labeled samples and recorded the mean and standard deviation of the five runs of the method. The method improved by 0.79% over the previous method when trained with 40 labeled samples, however, the model does not perform as well in terms of accuracy when trained with 250 and 1000 labeled samples compared to the previous method.  
As shown in Figure 11, it can be seen that the model performs well compared with the previous model, which achieved an accuracy of 96.83% on the test set, given that 40 annotated samples from the SVHN dataset were used to train the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**SVHN**} \\ \hline
**Labeled** & **40** & **250** & **1000** \\ \hline Pseudo-Label [25] & - & 79.79 \(\pm\) 1.09 & 90.06 \(\pm\) 0.61 \\ \hline \(\Pi\)-Model [26] & - & 81.04 \(\pm\) 1.92 & 92.46 \(\pm\) 0.36 \\ Mean-Teacher [27] & - & 96.43 \(\pm\) 0.11 & 96.58 \(\pm\) 0.07 \\ \hline MixMatch [30] & 57.45 \(\pm\) 14.53 & 96.02 \(\pm\) 0.23 & 96.50 \(\pm\) 0.28 \\ UDA [31] & 47.37 \(\pm\) 20.51 & 94.31 \(\pm\) 2.76 & 97.54 \(\pm\) 0.24 \\ Re-MixMatch [32] & 96.66 \(\pm\) 0.20 & 97.08 \(\pm\) 0.48 & 97.35 \(\pm\) 0.08 \\ FixMatch [33] & 96.04 \(\pm\) 2.17 & 97.52 \(\pm\) 0.38 & 97.72 \(\pm\) 0.11 \\ \hline
**Ours** & **96.83 \(\pm\) 0.15** & **97.54 \(\pm\) 0.21** & **97.63 \(\pm\) 0.14** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error rates for the test set on the SVHN dataset.  
Figure 10: (**a**) some actual samples from the SVHN dataset. (**b**) artificial samples of SVHN generated by the image generation model.  
Figure 10b shows some of the generated SVHN samples with labels, and it is easy to see that most of them are the same as the actual training samples of the SVHN dataset. They have different figures as well as critical features.
## 4 Layout and Post-Layout Experimental Results
### Layout of Ring Oscillator  
To evaluate the performance of the proposed low voltage delay element, a 21-stage cold start-up ring oscillator based on the proposed delay cell, which is the minimal system, was implemented in a standard 180 nm CMOS process. The dimension of the transistors in the proposed delay element is listed in Table 2.  
The physical layout of the cold start-up ring oscillator is shown in Figure 5, which occupies an area of 290 \(\upmu\)m \(\times\) 90 \(\upmu\)m, which is much less than the cold start-up scheme based on LC oscillators and off-chip inductors. Every transistor is enclosed by a guard ring considering the reliability.  
The post-layout experimental validation was conducted on a Cadence Spectre simulator. Calibre xRC was used to extract the parasitic capacitors, resistors, and diodes in the physical layout.
# KeyNet: An Asymmetric Key-Style Framework for Watermarking Deep Learning Models
## 4 The _KeyNet_ Framework
### Watermark Carrier Set Signing and Labeling
15:\(signature_{fake}\gets hashAndReshape(fakeStr,s),signature_{fake}\
eq signature\)
16:endfor
17:for each sample \(d\) in \(D_{\mathit{diff}}\)do
18:for each position, label \((p_{k},l_{k})\) in \(PL\)do
19:\(d^{p_{k}}\gets sign(signature,d,p_{k})\)
20:\(Add((d^{p_{k}},0),D_{\mathit{diff}}^{\mathit{signed}})\)
21:endfor
22:endfor return\(D_{wm}^{\mathit{signed}},D_{\mathit{diff}}^{\mathit{signed}},signature\)
```  
**Algorithm 1** Signing a WM carrier Set  
Once we obtain \(signature\), we start the labeling step of the WM carrier set \(D_{wm}\). For each \(c\in D_{\mathit{wm}}\), we replicate \(c\) for \(z\) times where \(z\) is the total number of possible positions \(\{p_{k}\}_{k=1}^{z}\) of the signature. Then, we use the \(sign\) function to place \(signature\) in the position \(p_{k}\) to obtain the \((c^{p_{k}},l_{k})\) pair. We also leave one copy of each sample without signing and assign it the label \(0\). That is, if a carrier set sample \(c\) is not signed with \(signature\), it will be represented by the \((c,0)\) pair. We then do two steps:  
1. We generate a fake information string \(fakeStr\) by making a slight modification to \(infStr\). Then, we generate \(signature_{fake}\) following steps similar to those above followed to generate the real owner's signature \(signature\). After that, we sign the \(D_{wm}\) samples each with a different fake signature and a randomly chosen position \(p_{k}\) and, instead of assigning to the signed sample the corresponding label \(l_{k}\), we assign it the label \(0\). To obtain a new fake signature, we again make a slight random modification in \(infStr\) and generate the fake signature in the same way as above. 2. We take samples from other distributions \(D_{\mathit{diff}}\) and sign them with the real owner's signature as we did with the \(D_{wm}\) samples. We assign them the label \(0\). We use samples from different distributions to avoid triggering the WM just with the owner's real signature. In other words, we make the triggering of the WM from a marked model dependent on the carrier set distribution in addition to the pattern of the owner's signature. The goal of the above two steps is to make the marked model \(h^{*}\) output the information that tells the position of a signature only if we pass to it a sample that _belongs to \(D_{wm}\) and is signed with the real signature_. Otherwise, \(h^{*}\) ignores the presence of any different signature from _signature_ on \(D_{wm}\) samples. This also avoids \(h^{*}\) responding to samples from different distributions than the \(D_{wm}\) distribution. Finally, Algorithm 1 returns the signed WM samples \(D_{wm}^{signed}\), the signed samples from different distributions \(D_{dif}^{signed}\), and the signature \(signature\) that will be used to trigger the marked model \(h^{*}\). Note that this process is performed only once, before the WM is embedded.
## 5 Conclusions  
In this paper, the fatigue life of an underwater vehicle rudder rod mechanism under a polar low-temperature environment is studied, and the following results are obtained through experiments and simulation analysis:  
1. In the polar low-temperature environment, the material properties of the rudder rod mechanism decrease, which leads to the shortening of its fatigue life, especially when the temperature is reduced to \(-40\)\({}^{\circ}\)C, and the fatigue performance of the rudder rod specimen is reduced by 96.7%.
2. Cyclic loading is one of the main factors leading to fatigue cracking of the rudder bar mechanism. In a low-temperature environment, the mechanical properties of the material decrease, making the effect of cyclic loading on the mechanism more significant.
3. Ice-covered conditions have a significant effect on the fatigue life of rudder rod specimens, and the reasons can be summarized as follows: increased structural loads, reduced strength, weakened stability, the formation of local support, corrosion, and hydrogen embrittlement aggravation.  
In summary, we conducted fatigue simulation and experiments on the rudder mechanism of underwater vehicles, achieving the goal of conducting fatigue life analysis on the structure at low temperatures. In the future, we will further select materials and structures suitable for the polar low-temperature environment to improve the fatigue life of the rudder rod mechanism.  
Data curation, H.C.; Supervision, Z.W.; Writing--original draft, L.Y.; Writing--review and editing, W.G.; Modeling, C.C.; Resources and Corresponding, X.L. and S.C. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the \"13 th Five Year Plan\" Dual Program, (grant No. 3901C9), China.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
We acknowledge the support provided to this study by Naval University of Engineering in the form of time and facilities.  
The authors declare no conflicts of interest.
## 1 Introduction
Running offline allows the uncertainty corresponding to process representation to be isolated from climate-related uncertainties, which ultimately can be use to improve ESMs and future climatic projections. This evaluation is key in model development. One important process that remains to be evaluated is the lengthening of the growing season over the Northern Hemisphere. This has been observed by several authors in satellite, modeled and field data [23, 24]. Changes in seasonal variation and the mean values of LAI, mostly due to an increase in temperature at the beginning of the growing season, have important implications on the global carbon cycle. However, considerable uncertainty remains with regard to greening trends and the ability of models to reproduce satellite-derived trends. With new and improved LAI data now available [25, 26, 27, 28], a more precise validation of model output is imperative. The objective of this paper is to compare LAI from satellite-derived measurements with modeled output from a set of 8 LSMs over the Northern Hemisphere. We ask three questions to fulfill this objective:  
* Do uncoupled (LSMs) models correctly reproduce the spatial variability of LAI shown by satellite data over the Northern Hemisphere? * How does the length of the growing season in the different models compare with the satellite data? And where are the main discrepancies (onset or dormancy)? * What are the trends in LAI and the growing season over this period?
## 3 Results
### North Atlantic 2014-03-01  
Figure 2 shows an in-depth look at the retracking of radar echoes in the North Atlantic on 2014-03-01 shortly after 6 am. We see a clear correlation between our retrieved LRM and SAR mode SWH with a linear decrease in SWH in both modes from 70\({}^{\circ}\)N to 76\({}^{\circ}\)N. Our retrievals from the pLRM waveforms show close correlation to those from the SAR mode. The comparison RADS data has the same spatial pattern of SWH with peaks of \(\approx\)5 m SWH at 61\({}^{\circ}\)N and 70\({}^{\circ}\)N. The WaveWatch 3 data (that was supplied alongside the RADS data) has a similar magnitude of SWH to the RADS data with less well correlated spatial pattern of peaks. For SAR and pLRM modes the RADS data are consistent to our SWH. For LRM mode the RADS SWH is approximately 0.34 m higher. While there are discrepancies between our new SAR, LRM, and pLRM, and RADS LRM retracked SWH in Figure 2, this single example is insufficient to draw conclusions from. In Section 3.6, we investigate the bias between our new SAR and LRM processing against examples from [17] and RADS data. There are likely to be further biases between these data and our retracked pLRM examples, though this is beyond the scope of this study as we focus on the construction of a full coherent dataset. The differences to pLRM do not just depend upon the assumptions and representations within the semi-analytical model used here as there is the issue of range walk compensation used within the creation of the pLRM waveforms from the base SAR echoes [22].  
Figure 3: Comparison of the three methods of waveform fitting for 1 Hz downsampling over a section of satellite track in the North Atlantic. Red: fitting of \(\sigma_{\mathrm{s}}\) (SWH) only, blue: \(\sigma_{\mathrm{s}}\) and \(t_{0}\) (waveform leading edge), black: \(\sigma_{\mathrm{s}}\), \(t_{0}\) and \(A\) (waveform power scale). These results are compared to a three parameter fit of each individual 20 Hz waveform (yellow scatter). Retrieved (or default values) of wave power scale, SWH and waveform leading edge are plotted, along with the fit error between the the original and optimal semi-analytical waveform.
# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 4 The Proposed System  
The system has been named the \"Power Consumption Information & Analytics System (PIAS)\". The proposed system can deal with various data types in a heterogeneous environment by hosting the data in two independent databases, namely (i) a structured database for mechanical meters after the digital transformation process and data cleaning process, and (ii) an unstructured (NoSQL) database for smart meters. Meanwhile, the integration is carried out through the system back-end through API and system interfaces that have access to each independent database. The system retrieves the power consumption data from different geographic regions and can be coupled with other information such as climatic conditions. It is structured into four tiers: the data, analytics, application, and presentation tiers. The design is based on a serverless infrastructure design as a block of services suggested in a recent study [45], which also states the scalability and performance discussion in more detail. Figure 1 shows the PIAS tiers diagram.