esa-sceva/satcom-chunk-collection huggingface dataset:
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Attack Attempts
#### 4.7.3 Example  
Let us consider \(\mathcal{SF}\) for \(r=15\). The separating family is composed of six sets  
\[\{F_{1},F_{2},F_{3},F_{4},F_{5},F_{6}\},\]  
and every element of \(\{1,2,\ldots,15\}\) belongs to exactly two sets (see Table 6). Hence, \(\ell=6\), and each node can decode two chunks \(t_{i}\) in Formula (11). Assume that each \(t_{i}\) is 20 bits long, meaning that each receiver has access to 40 bits of the tag \(T_{1}\).  
Now, assume that node 2 has been compromised. Consequently, the adversary knows the keys to generate the chunks \(t_{2}\) and \(t_{3}\). For node 3, the situation does not change because \(3\
ot\in F_{2}\cup F_{3}\). Therefore, the adversary must guess 40 bits of \(T_{1}\) verifiable by node 3 in tag \(T_{1}\) to cheat node 3 for a single message. In turn, considering node 4, chunk \(t_{2}\) will be manipulated by the adversary. However, the adversary does not have the key necessary to manipulate chunk \(t_{4}\); hence, to cheat node 4 for a single message, the adversary must guess the tag \(t_{4}\) calculated for the message, with a probability of success \(2^{-20}\). Thus, the probability that node 4 will accept three manipulated messages in a row is \(2^{-60}\).
## 3 System Model
### PDR Review
#### 3.5.2 Step Length  
To calculate the step length, we first determine the vertical displacement by double-integrating the vertical acceleration. The step length is then calculated using the inverted-pendulum model.  
\[S_{VD}=\int_{t1}^{t2}\int_{t1}^{t2}a_{z}(t)dtdt \tag{11}\]  
where \(S_{VD}\) represents the vertical displacement of the pedestrian's body during a step, \(a_{z}(t)\) denotes vertical acceleration as a function of time measured by the accelerometer  
Figure 6: Comparative analysis: PSO versus WKNN: (**a**) Traditional WKNN positioning with k points dependent on fingerprint database, highlighting areas of less accuracy (red circle); (**b**) PSO-generated positioning with k points covering all areas, showing improved accuracy with final particle positions marked (red circle).  
along the vertical axis (z-axis), and \(t1\) and \(t2\) represent start and end times of the double-integration interval.  
\[D_{SL}=2\sqrt{L^{2}-\left(L-S_{VD}\right)^{2}} \tag{12}\]  
where \(D_{SL}\) denotes the step length representing the horizontal distance covered by a pedestrian in one step, \(L\) denotes the height from the ground to the smartphone. Figure 7 below is a step length diagram that visually illustrates the calculation method for step length.
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 3 Fundamentals of Hydrogels
### Preparation of Hydrogels
#### 3.3.1 Physically Crosslinked Hydrogels  
Physical crosslinking methods encompass hydrogen bonding, amphiphilic grafting, block polymer formation, and crystallization. Hydrogen bonding takes place when there are N-H, O-H, or F-H functional groups present. Hydrogen bonding with fluorine (F) in hydrogels is not common. Fluorine is highly electronegative, which makes it difficult for hydrogen bonding to occur with other atoms [51]. An example of physical crosslinking through hydrogen bonding is evident in the bond between oxygen in PEG and carboxylic acid groups in PAA [33]. In amphiphilic grafting and block polymer formation, crosslinking occurs by the self-assembly of hydrophilic or hydrophobic solvents based on their amphiphilic affinity. The crystallization crosslinking process involves the adjustment of crystallization temperature by freeze-thawing or the heating of polymers. PVAs form hydrogels by freeze-thawing [33]. Another example is the application of heat to a PEO copolymer to form a hydrogel. Physical gels have attracted attention because of their straightforward production process and the absence of crosslinkers, which reduce the risk of toxicity [52]. Nonetheless, these gels have a drawback in that their stability can be compromised by changes in factors like stress, pH, temperature, ionic strength, and the presence of solute particles) [50].
## 4 Material and Methods
### Figures of Merit  
The performance parameters used as figures of merit in our simulations were the BER, Signal to Noise Ratio (SNR), MER, and EVM.  
* The BER is defined as the ratio of the number of erroneous bits to the number of transmitted bits within a period. The lower the number of erroneous bits, the better the system performance. In this work, the BER was computed after the constellation demapping and LDPC decoder.
* The SNR is described as the fraction of the received signal strength over the noise strength within the frequency bandwidth range of operation. It measures the quality of the signal relative to the background noise.
* The MER is viewed as an indicator of the modulation quality of the received signal. Defined in [43], this metric measures the QAM symbols' accuracy. A high MER value means good signal quality. The MER is approximately equal to the SNR when only white Gaussian noise is present in the signal. The EVM is a parameter also presented in [43]; the lower its value, the better the system's performance.
## 3 Results and Discussion
### Drought Trend
#### 3.2.2 Seasonal Drought Analyses
The eastern half of the study area along with the north of Paveh, the south of Gilan-e-Gharb, and the south of Somar underwent severe drought and the SPI values for these regions ran between \(-1.5\) and \(-1.9\). The most widespread drought occurred in autumn with the same severity throughout the points of the network belonging to the year of 2014. In autumn of 2014, no rainfall occurred, whence the three month SPI value up to December reached \(-1.6\), and a severe drought struck the whole study area (Figure 7c). To investigate the prevalence of drought in winter, spring, and autumn during the period of study, the frequency percentage of different categorizations of SPI were analyzed. The study of very prevalent droughts frequencies in the winter showed that this type of drought in the study area occurred in the years 2009, 2012, and 2013. In 2012 and 2013, the major zones of the study area were subject to mild or medium droughts. This was the case, while in 2009 the most frequencies were allocated to medium and very severe droughts striking 53% and 27% of the study area, respectively. Therefore, the total severity of the province's drought was evaluated as extreme. In spring, the frequency of very severe wetness was far less than that of very severe droughts, to the extent that in 2002, enjoying the wettest spring, the SPI of 13% of the region was above \(+2\). In the dry spring of 2008, however, 47% of the study area lay in the very severe drought category. Nevertheless, the wet periods have experienced more severity relative to the dry ones. In autumn, no very severe drought or wetness was noticed in any of the stations during 1998 to 2014. In most years, the maximum frequency corresponded to the normal category. All the same, no station experienced the normal conditions for the duration of five years. Of these five years, three were inclusive of widespread drought, and two were with prevalent wet years. The frequencies of severe and medium droughts were more than those of respective wet years. To put it another way, the intensity of drought in different parts of the province was more than that of wetness, though its  
Figure 7: Drought zoning: (**a**) winter 2009; (**b**) spring 2008; (**c**) autumn 2014.  
frequency may be lesser. The reason is that the wet years are frequently of mild severity in most periods and region.
###### Abstract  
The integrity and functionality of the control and instrumentation (C&I) cable systems are essential when it comes to ensuring the reliability and safety of system operations, especially in vehicles or power plants. Whenever a fault occurs in a multi-core cable, it not only affects signals of the individual faulty line but inflicts the rest through crosstalk and noise interference. Thus, it is imperative that cable diagnostic technologies are eligible of detecting the fault and further differentiating the faulty line to prevent the original fault from jeopardizing the entire system operation. We propose here a diagnostic method which detects the presence and the location of a fault, and further differentiates the faulty line within the multi-core C&I cables using a machine learning algorithm based on the time-frequency domain reflectometry results. Neural networks and the hierarchy clustering algorithm are used for fault detection and the identification of the faulty line. The proposed clustering algorithm is verified via experiments with four possible fault scenarios using automotive wires and C&I cables for nuclear power plants. Hence, the proposed algorithm allows a fault in multi-core cables to be accurately detected and estimated when given the location and the reflection coefficient of a fault.  
artificial neural networks; cable insulation; fault diagnosis; hierarchy clustering; reflectometry; signal crosstalk; time-frequency analysis Article
## 1 Introduction
In recent years, methods using model-free adaptive control have been widely adopted, such as chemical processes [18; 19] and injection molding control [20]; however, there has been almost no application in fault-tolerant control. According to the above, it is clear that most research on fault-tolerant control systems now relies on the system model and does not consider the cooperation of fault-tolerant control of the multi-actuators under different failure conditions of the drive system. Thus, it is important to establish a method that does not rely on the vehicle system model and simply relies on the system I/O data for active fault-tolerant control for failure of 4 WID electric vehicle drive systems. In this paper, based on the multi-input multi-output model-free adaptive control (MIMO-MFAC) method proposed by [PERSON] [15], an active fault-tolerant control method for 4 WID electric vehicles is proposed. The proposed method offers two advantages, as follows. (1) The MIMO-MFA active fault-tolerant control method can realize cooperative fault-tolerant control of the drive and steering systems under different failure conditions of 4 WID electric vehicles, avoiding the limit condition and using only a single actuator, such as the drive system or steering system, for fault-tolerant control, making it extremely easy to exceed the workload. This results in failure to complete fault-tolerant control successfully; (2) The control system design does not rely on an accurate system model; it only uses the I/O data to solve the complex, nonlinear and uncertain effects of the vehicle system. Furthermore, this method does not rely on accurate fault diagnosis information. The remainder of this paper is organized as follows. The MIMO-MFA active fault-tolerant control architecture and method are presented in Section 2. The error convergence and input-output boundedness of the control system are proven in Section 3. Simulations based on an SUV vehicle model by means of CarSim-Simulink and experiments based on the driving simulator are presented in Section 4, followed by the conclusion in Section 5.
## 2 Materials and Methods
### Field Campaigns in Fanger Bay  
The observational data corresponded to two field campaigns (each of around two months) from 25 July to 5 September 2017 (FANGAR-I) and from 5 October to 16 November 2017 (FANGAR-II), hence summer and autumn, respectively. The data set consisted of two Acoustic Doppler Current Profilers (ADCPs, from NORTEK, model AQUADOPP 2 MHz) (mooring points M and B in Figure 1), with the velocity and the direction of the water currents obtained every 10 min in 25 cm layers distributed from the bottom to the surface. Moreover, the systems were equipped with pressure systems and a temperature sensor (Vaisala PTB110 and HMP40). A set of CTD (conductivity, temperature and depth, model SeaBird 19 plus) campaigns were conducted. During FANGAR-I, 16 CTD campaigns were carried from 11 July to 5 September (two campaigns per week). During FANGAR-II, only two surveys were undertaken, one at the beginning of the campaign (18 October) and the other at the end (16 November). For each of the CTD campaigns, twenty points were chosen, including both the inner and the outside sections of the bay, where temperature and salinity were measured (see black dots in Figure 1).  
During FANGAR-I, data from Servei Meteorologic de Catalunya (MeteoCat, [[http://www.meteo.cat/](http://www.meteo.cat/)]([http://www.meteo.cat/](http://www.meteo.cat/)) (accessed on 30 January 2020)) of the Illa de Buda, located about 12 km from Fangar Bay, were used. This station records wind data every 30 min. During FANGAR-II, a meteorological station (with temperature and humidity sensor, air pressure and acoustic wind sensor) was mounted on a mussel raft near the mouth (Met in Figure 1) to measure wind, atmospheric pressure, air temperature and humidity every 10 min. The measurement periods and instruments are summarized in Table 1.  
Figure 1: Location of the study area. The red circles show the two main points of freshwater discharges (Bassa de les Olles and Illa de Mar), the red diamonds show the points of measurement during the field campaigns (M: mouth; B: bay) and the green triangle shows the location of the meteorological station (Met) during FANGAR-II. The black point shows the conductivity, temperature and depth (CTD) profiles, the blue lines show the bathymetry and the red line (A–A’) shows the longitudinal section presented in Figure 9.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
###### Abstract  
Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping.  
blood cell detection; blood cell counting; deep learning; convolutional neural network Article
## 2 Materials and Methods
### Catalytic Reduction of NO with \(H_{2}\)  
All catalysts prepared as described above were tested in a fixed bed reactor, using a gas hourly space velocity (GHSV) = 2250 h\({}^{-1}\) defined as feed flow/catalytic bed volume. The catalytic tests were performed maintaining a constant copper content. The typical reacting mixture gases consisted of [NO]\({}_{0}\) = 2000 ppm, [H\({}_{2}\)]\({}_{0}\) = 3% (v/v), balanced at 1 atm with He. The catalysts were pre-reduced at 400 \({}^{\circ}\)C in a 3% H\({}_{2}\) flow for 1 h. Before the catalytic tests, the solids were cooled to room temperature. Then, the catalytic activity was progressively measured at 300, 400, and 500 \({}^{\circ}\)C. The catalytic measurements were replicated three times for each catalyst with no significant variations on the final outcome (standard deviations below 5%). Every catalyst was kept under reaction conditions for at least six accumulated hours, not detecting any sign of deactivation in each fix reaction temperature interval of 2 h. The reactor effluent concentrations (ppm) of [NO\({}_{x}\)] = [NO] + [NO\({}_{2}\)] + [N\({}_{2}\)O], and [NH\({}_{3}\)] were monitored every 2 min with an on line FTIR Mattson Genesis II (resolution of 4 cm\({}^{-1}\)) instrument equipped with a 15 cm path length gas IR cell with CaF\({}_{2}\) windows at RT. The N\({}_{2}\) production was calculated by atomic nitrogen balance. The NO conversion (CNO, %) and the selectivity to N\({}_{2}\) (S, %) was calculated as follows:  
\[\mathrm{C_{NO}}\;(\%)=([\mathrm{NO}]_{0}-[\mathrm{NO}])/[\mathrm{NO}]_{0}*100\] (I)  
\[\mathrm{S}\;(\%)=([\mathrm{NO}]_{0}-[\mathrm{NO}]-[\mathrm{NO}_{2}]-2[ \mathrm{N}_{2}\mathrm{O}])/[\mathrm{NO}]_{0}*100\] (II)  
Figure 2: Schematic sequence of the different reaction steps in the solvothermal approach to obtain CuNPs using DEG as solvent.  
where [NO]\({}_{0}\) is the initial concentration of NO (2000 ppm), whereas [NO], [NO\({}_{2}\)] and [N\({}_{2}\)O] are the concentrations of the corresponding components in the reactor effluent
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 3 Performance Evaluation
This scenario allows for us to evaluate the behavior of RAAs in a standard deployment with one Access Point through which all the network traffic should pass in order to be relayed to a wired network. In such a scenario, we have only one receiver on which the SINR values for each link with the other mobile nodes of the network will vary according to their mobility patterns. (iii) Ad hoc mode network, which is the most complex deployment in terms of interference. In this scenario, receivers are spread randomly in the network and the interference levels will be very unstable due to mobility scenarios. All of the nodes have to adapt their rates depending on the channel conditions and network dynamics (node density and mobility). In the second and the third scenarios, all nodes are placed in the communication range of each other to avoid the routing protocol impact and the cases where the receivers become transmitters. This allows for us to concentrate our study on interference impact and mobility impact on RAAs. Each simulation result presented below is the mean value of 50 simulations, the bars in the graphs represent the standard deviation. The values of the network parameters configured in the simulation scripts are summarized in Table 3. A simulation duration of 30 s is sufficient to ensure randomness in the movement of the nodes inside the boundaries of the square, and it is enough for the selected algorithms to converge. Log-distance and Nakagami-m loss models were used to make the simulations more realistic in terms of link quality and link stability. We used UDP over TCP for traffic generation in order to avoid the overhead of TCP and its rate adaptation which will interfere with the rate adaption of IEEE 802.11. Finally, as for packet size and mobility speed, we decided not to study their impacts in this paper. The chosen values are representative of average to big sized frames and relatively fast moving nodes. \begin{table}
\begin{tabular}{l l l} \hline \hline
**MCS Index** & **Modulation Type** & **Coding Rate** \\ \hline
0 & BPSK & 1/2 \\
1 & QPSK & 1/2 \\
2 & QPSK & 3/4 \\
3 & 16-QAM & 1/2 \\
4 & 16-QAM & 3/4 \\
5 & 64-QAM & 2/3 \\
6 & 64-QAM & 3/4 \\
7 & 64-QAM & 5/6 \\
8 & 256-QAM & 3/4 \\
9 & 256-QAM & 5/6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Modulation and coding schemes.
## 5 HAWT Blade Design
### Smart Blade Design  
The current research trend in blade design is the so called \"Smart Blades\", which alter their shape depending on the wind conditions. Within this category of blade design are numerous approaches which are either aerodynamic control surfaces or smart actuator materials. An extensive review of this subject is given by [PERSON] [31]. The driver behind this research is to limit ultimate (extreme) loads and fatigue loads or to increase dynamic energy capture. Research is mainly initiated based on similar concepts from helicopter control and is being investigated by various wind energy research institutes. The work package \"Smart rotor blades and rotor control\" in the Upwind EU framework programme, the project \"Smart dynamic control of large offshore wind turbines\" and the Danish project \"ADAPWING\" all deal with the subject of Smart rotor control. In the framework of the International Energy Agency, two expert meetings were held on \"The application of smart structures for large wind turbine rotors\", by Delft University and Sandia National Labs, respectively. The proceedings show a variety of topics, methods and solutions, which reflects the on-going research [32,33].  
The use of aerodynamic control surfaces includes aileron style flaps, camber control, active twist and boundary layer control. Figures 6 and 7 show a comparison graph of aerodynamic performance (lift control capability) of a variety of aerodynamic control surface based conceptsSmart actuator materials include conventional actuators, smart material actuators, piezoelectric and shape memory alloys. Traditional actuators probably do not meet minimum requirements for such concepts. Furthermore, proposed concepts of aerodynamic control surfaces (distributed along the blade span) require fast actuation without complex mechanical systems and large energy to weight ratios.  
Figure 6: Schematics of smart structure concepts.  
Figure 7: Comparison of aerodynamic device concepts in terms of lift control capability [31].  
Promising solution for this purpose is the use of smart material actuator systems. By definition, smart materials are materials which possess the capability to sense and actuate in a controlled way in response to variable ambient stimuli. Generally known types of smart materials are ferroelectric materials (piezoelectric, electrostrictive, magnetostrictive), variable rheology materials (electrorheological, magnetorheological) and shape memory alloys. Piezoelectric materials and shape memory alloys are generally the most famous smart materials used in actuators in various applications. The development of their technology has reached a quite high level and commercial solutions are available and widely used [31].
## 3 Sensor System
The coefficients of the dynamic terms are referred to as sensitivities in amplitude mode (\(S_{\rm AM}\)) and phase mode (\(S_{\rm PM}\)), respectively. Expressions are given, and more details are discussed, in Section 4. Considering additional random fractional amplitude fluctuations \(\alpha(t)\) and random phase fluctuations \(\varphi(t)\)[40] (p. 3), the total differential voltage is given by  
\[v_{\rm d}(t)=-\overbrace{\frac{\tilde{V}_{\rm ex}\cdot|\tilde{ G}(f_{\rm ex},B_{\rm bias})|}{2\pi f_{\rm ex}C_{\rm f}\cdot QR_{\rm m}}}^{ \tilde{V}_{\rm d}}\cdot\left[1+\overbrace{S_{\rm AM}B_{\rm x}(t)}^{\rm amplitude \ modulation}+\overbrace{\alpha(t)}^{\rm amplitude\ noise}\right] \tag{17}\] \[\qquad\qquad\left.\cdot\cos\left(2\pi f_{\rm ex}t+\tilde{\gamma}(f _{\rm ex},B_{\rm bias})+\underbrace{S_{\rm PM}B_{\rm x}(t)}_{\rm phase\ modulation}+\underbrace{\varphi(t)}_{\rm phase\ noise}\right). \tag{18}\]  
As depicted in Figure 3, the modulated signal \(v_{\rm d}(t)\) is demodulated employing a quadrature detector, yielding the amplitude-demodulated voltage  
\[V_{\text{n}}(f)=T(f)I_{\text{n}}(f)=T(f)V_{\text{ex}}(f)\cdot j2\pi fC_{\text{ n}} \tag{11}\]0  
and the phase-demodulated signal  
\[V_{\text{n}}(f)=T(f)I_{\text{n}}(f)=T(f)V_{\text{ex}}(f)\cdot j2\pi fC_{\text{ n}} \tag{11}\]1if assuming that the two low-pass filters (LPF) ideally suppress the frequency components around \(2f_{\text{ex}}\).