esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of ElectroSpark Process Parameters on the WE43 Magnesium Alloy Deposition Quality
## 3 Results and Discussion
### Effect of the Electric Parameters on Mechanical Properties and Thickness of Deposits  
The mechanical properties of the deposits as a function of process parameters were investigated by indentations of micro-hardness (HV\({}_{0.3/15}\)) on polished cross-sections of the deposits. Average micro-hardness value of deposits are shown in Table 3, as well as the average micro-hardness value of measurements performed in the substrate close to the interface. The results show that the sample processed at the lowest E\({}_{\text{s}}\) (s1) exhibits the lowest hardness value, while no significant difference exists between the average hardness values of deposits processed at E\({}_{\text{s}}\) higher of 0.75 J. In addition, even if the deposits microstructure was finer grained respect to the substrate (see Figure 5), it can be noticed that all deposits present an average micro-hardness value lower than that of the substrate (\(75\pm 4\) HV), regardless of the processing parameters. The lower hardness of the deposits respect to the base material can be attributed to the presence of widespread defectiveness within them. Indeed the deposits performed using low E\({}_{\text{s}}\) (0.15 and 0.75 J) exhibited higher percentage area fraction of defects than those performed with E\({}_{\text{s}}\) of 0.9, 1.125 and 1.69 J. These last ones displays both constant and slightly higher micro-hardness value (Table 3) due to the lower percentage area fraction value. In the substrate micro-hardness values at 100 \(\upmu\)m far from the interface (Table 3) does not vary respect  
Figure 11: Analysis of the defects in the deposits cross-section. (**a**) percentage number of defects and (**b**) percentage area of defects vs the three classes of shape factor (\(f_{circle}\)): (0–0.4) laminar defects, (0.4–0.7) spherical defects and (0.7–1) random shaped defects.  
to the base material, confirming (as shown in Figure 7) that the low thermal contribution of the process does not change significantly the microstructure and mechanical properties of the substrate.  
The results presented in Figure 12 demonstrate that the WE43 deposits thickness is related to the pulse-spark energy. Indeed, the thickness increases with deposition pulse energy, ranging from 82.6 \(\upmu\)m for the deposit produced at the lowest energy to 407.6 \(\upmu\)m for the deposit produced at the highest E\({}_{\text{s}}\). This is due to the increased amount of material being transferred at higher spark energy (E\({}_{\text{s}}\)) [41], that rises the deposit growth rate (GR). Therefore, for a given number of deposited layers, the increasing of E\({}_{\text{s}}\), lead also to a reduced deposition time.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization
### Wireless Sensor Deployment
#### 2.1.2 Objective Function of Sensor Deployment Considering Communication Cost  
When deploying sensors, we must consider not only the number of sensors, but also the energy consumption during wireless sensor network transmission, because energy consumption is related to the distance between sensors. However, we cannot determine which sensors will communicate to accurately reduce the communication distance between them. Therefore, the communication distance between all the sensors is selected to be reduced so that the communication distance between subsequent sensors is reduced. Assume that there are \(N\) optional points. Node \(i\) and node \(j\) are selected to deploy sensors. The communication cost between the two nodes is defined as follows.  
\[d_{i,j}=\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i}\right)^{2}}. \tag{2}\]  
In Equation (2), \((x_{i},y_{i})\) is the node \(i\) coordinate, and \((x_{j},y_{j})\) is the node \(j\) coordinate.  
\[C(A)=\sum_{k=1}^{n=|A|}\sqrt{\left(x_{j}-x_{i}\right)^{2}+\left(y_{j}-y_{i} \right)^{2}}. \tag{3}\]  
In Equation (3), \((x_{i},y_{i})\) and \((x_{j},y_{j})\) are the coordinates of the nodes in the set \(A\).  
In this paper, the problem of improving the efficiency of the sensor submodularity and reducing the communication cost \((C)\) is transformed into a combination optimization problem. The objective function is as follows.  
\[\max_{A\subseteq V}F(A),s.t.C(A)\leq B. \tag{4}\]  
For the communication cost budget \(B>0\), Equation (4) aims to find the solution set with the maximum mutual information within a low communication cost.
###### Abstract  
In an aging global society, a few complex problems have been occurring due to falls among the increasing elderly population. Therefore, falls are detected using a pendant-type sensor that can be worn comfortably for fall detection. The sensed data are processed by the embedded environment and classified by a long-term memory (LSTM). A fall detection system that combines a simple threshold method (STM) and LSTM, the STM-LSTM-based fall detection system, is introduced. In terms of training data accuracy, the proposed STM-LSTM-based fall detection system is compared with the previously reported STM-hidden Markov model (HMM)-based fall detection system. The training accuracy of the STM-LSTM fall detection system is 100%, while the highest training accuracy by the STM-HMM-based one is 99.5%, which is 0.5% less than the best of the STM-LSTM-based system. In addition, in the optimized LSTM fall detection system, this may be overfitted because all data are trained without separating any validation data. In order to resolve the possible overfitting issue, training and validation data are evaluated separately in 4.1, and then in terms of validation data accuracy of the STM-LSTM-based fall detection system, optimal values of the parameters in LSTM and normalization method are found as follows: best accuracy of 98.21% at no-normalization, no-sampling, 128 hidden layer nodes, and regularization rate of 0.015. It is also observed that as the number of hidden layer nodes or sampling interval increases, the regularization rate at the highest value of accuracy increases. This means that overfitting can be suppressed by increasing the regularization, and thus an appropriate number of hidden layer nodes and a regularization rate must be selected to improve the fall detection efficiency.  
all detection; the elderly; long short-term memory (LSTM); overfitting; regularization Article
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **0 s, Sv/h** & **0 s, Sv/h** & **1 Month, Sv/h** & **1 Month, Sv/h** & **1 Year, Sv/h** & **1 Year, Sv/h** \\ \hline Silver & \(3.52\times 10^{-8}\) & \(3.58\times 10^{-8}\) & \(3.02\times 10^{-8}\) & \(3.08\times 10^{-8}\) & \(1.30\times 10^{-8}\) & \(1.32\times 10^{-8}\) \\ Gold & \(4.36\times 10^{-8}\) & \(7.13\times 10^{-9}\) & \(2.27\times 10^{-11}\) & \(3.70\times 10^{-12}\) & \(1.17\times 10^{-15}\) & \(8.01\times 10^{-16}\) \\ Manganese/Nickel & \(2.25\times 10^{-8}\) & \(2.39\times 10^{-8}\) & \(4.13\times 10^{-11}\) & \(3.89\times 10^{-11}\) & \(1.83\times 10^{-11}\) & \(1.72\times 10^{-11}\) \\ Niobium & \(6.23\times 10^{-1}\) & \(3.57\times 10^{-1}\) & \(1.27\times 10^{-12}\) & \(1.20\times 10^{-12}\) & \(9.44\times 10^{-13}\) & \(8.81\times 10^{-13}\) \\ Nickel & \(7.14\times 10^{-11}\) & \(7.05\times 10^{-11}\) & \(5.12\times 10^{-11}\) & \(5.10\times 10^{-11}\) & \(1.21\times 10^{-11}\) & \(1.26\times 10^{-11}\) \\ Rhodium & \(4.34\times 10^{-9}\) & \(3.97\times 10^{-9}\) & \(2.87\times 10^{-11}\) & \(2.76\times 10^{-11}\) & \(1.80\times 10^{-11}\) & \(1.61\times 10^{-11}\) \\ Titanium & \(1.09\times 10^{-11}\) & \(1.10\times 10^{-11}\) & \(3.21\times 10^{-12}\) & \(3.25\times 10^{-12}\) & \(2.03\times 10^{-13}\) & \(2.09\times 10^{-13}\) \\ Yttrium & \(1.09\times 10^{-1}\) & \(1.12\times 10^{-1}\) & \(8.95\times 10^{-11}\) & \(8.76\times 10^{-11}\) & \(1.02\times 10^{-11}\) & \(9.85\times 10^{-12}\) \\ \hline Data Library & EAF-2010 & TENDL-2021 & EAF-2010 & TENDL-2021 & EAF-2010 & TENDL-2021 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dose rates at 30 cm distance from 1 g of material. Comparison between the EAF-2010 and TENDL-2021 nuclear data libraries. **Author Contributions:** Conceptualization, A.T.; Formal analysis, A.T. and S.B. ; Investigation, S.B. ; Writing--original draft, A.T.; Writing--review & editing, G.S. ; Supervision, G.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No. 101052200--EUROfusion). The views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** JET Contributors: See the author list of \"Overview of JET results for optimising ITER operation\" by [PERSON] et al. published in the Nuclear Fusion Special Issue: Overview and Summary Papers from the 28 th Fusion Energy Conference (Nice, France, 10-15 May 2021). **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Determination of AA Content  
For the analysis of AAs, 1 g of fresh samples was transferred to a 50 mL centrifuge tube containing 15 mL of a 70% ethanol extraction solution. The mixture underwent ultransonication for 5 min at 210 W, followed by centrifugation at 10,000\(\times\)\(g\) rpm for 10 min. Subsequently, 1 mL of the supernatant was filtered through a 0.22 \(\upmu\)m microporous membrane. AAs were separated through pre-column derivatization and analyzed using HPLC on an Agilent 1290 system, fitted with an Agilent Zorbax SB-C18 Rapid Resolution HT column (3.0 mm \(\times\) 50 mm, 1.8 \(\upmu\)m). This analysis was conducted in accordance with the method outlined by [PERSON] et al. [25]. The mobile phase comprised a 25 mM acetate buffer (pH 5.8) containing 0.02% sodium azide (Phase A) and 100% acetonitrile (Phase B). The flow rate was maintained at 0.6 mL/min. The gradient elution program was meticulously structured as follows: an initial 6% B was held for 3 min; B was then increased from 6% to 14% over 7 min and held at 14% for 3.5 min; subsequently, B was raised from 14% to 19.5% over 2 min, then to 20% over 2.5 min, and held for 1 min. The gradient continued with an increase from 20% to 26% B over 1 min, from 26% to 30% B over 3 min, and from 30% to 50% B over 1 min. Afterward, B was raised from 50% to 63% over 1 min and held for 1.5 min; it was then increased to 100% B in 1 min and maintained for an additional 1 min. Following this, the column underwent washing and reconditioning processes. The detailed gradient profile was as follows: 0-3 min, 5% B; 3-6 min, 20% B; 6-8 min, 30% B; 8-10 min, 40% B; 10-15 min, 50% B; 15-17 min, 5% B. The injection volume was set at 2 \(\upmu\)L. Detection was performed using a photodiode array at 280 nm, and the column temperature was controlled at 16 \({}^{\circ}\)C. AAs were identified by comparing their retention times with those of reference standards. Standard curves were prepared, and quantitative analysis of the samples was executed following the methodology proposed by [PERSON] et al. [25]. All samples were analyzed in triplicate.
## 4 Discussion  
### Equilibrium Structure  
Based on a previous systematic study of the first-order satellite reflections, [PERSON] et al. argue that the main contribution to the first-order satellite reflections is a result of a displacement of the tin atoms in the _ab_ plane [35]. The main new aspect in the measurements is our observation of the weak second-order satellite reflections around some reflections, which were previously not observed due to signal-to-noise limitations [35, 36]. If we accept the mechanism for the superlattice proposed by [PERSON] et al. [17] where the incommensurate modulation is seen as a renormalization of a TA and a TO mode with a wavevector of \(q=(0.1~{}0~{}-0.1)\), the presence of the second-order reflections indicates that there is an additional weak coupling to phonon modes with a resulting wavevector of \(2q\). Such harmonic contributions have been observed in some other systems with incommensurate modulations such as thiourea [9, 37]. These \(2q\) modulations can arise from cubic anharmonic terms in the Hamiltonian [1, 38]. The different temperature dependences of the second-order satellites would then indicate that these anharmonic terms change strongly with temperature over the stability range  
Figure 3: (**a**) Intensity of the superlattice reflection (\(-0.9~{}0.0~{}-2.1\)) as a function of pump-probe delay for X-ray penetration depths of \(\blacktriangledown\), \(\blacktriangledown\), \(\blacktriangledown\), \(\blacktriangledown\) = 71, 130, 262, 455 nm at a fluence of 3.5 mJ/cm\({}^{2}\). Traces have been vertically offset for clarity. Red lines show the result of fitting the model described in the text to the data. (**b**) Total intensity reduction as a function of probing depth with a linear fit. (**c**) Probing depth as a function of relaxation time with a linear fit whose slope is 3.7 m/s.  
of the incommensurate phase. The disappearance of the higher-order modulations at around 207.5 K leave no signatures in measurements of the heat capacity [39] or spontaneous deformation [40] at within the temperature of the incommensurate phase, while stark changes occur at the phase boundary both at 193 K and 220 K.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 3 Results  
### NDVI Temporal Annual Variation Characteristics  
To study the annual changes in the NDVI during 2000-2019 in Inner Mongolia, a time series analysis of the vegetation cover was conducted for each year (Figure 3). The results showed that the annual NDVI values in Inner Mongolia fluctuated upward from 0.42 to 0.51 between 2000 and 2019, at a growth rate of 0.0028/year (\(p<0.05\)) and a multi-year average of 0.46. The NDVI fluctuated less throughout Inner Mongolia considering interannual variation, with the maximum NDVI of 0.51 recorded in 2018, and the minimum of 0.42 recorded in 2001.  
Considering each vegetation type as a unit, forest had the highest mean NDVI value of 0.83, which fluctuated moderately, with a maximum NDVI value of 0.85 occurring in 2019 and a minimum of 0.81 in 2000. The difference between the annual maximum (0.75) and minimum (0.66) values of the NDVI for the meadow steppe was 0.09. The fluctuating trends were similar to those of typical steppe, with the maximum and minimum values occurring in 2018 and 2007, respectively. Meanwhile, the typical steppe NDVI values showed an overall increasing trend over the 20 year period, with NDVI values decreasing from 2000 to 2009, reaching a minimum value of 0.42 in 2009, and then increasing for three consecutive years. Relatively large fluctuations were observed from 2010 to 2013, reaching a maximum value of 0.57 in 2018. The NDVI value for the desert steppe was relatively low at 0.19, with a peak of 0.25 in 2012. It is noteworthy that the desert had the lowest NDVI value of 0.1 and the value fluctuated the least.  
Figure 3: Interannual variation of NDVI by vegetation types in Inner Mongolia from 2000 to 2019.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 2 Fundamentals of Optical Wireless Communications
### The Hidden Node Problem in VLC Networks  
The hidden node problem is a well known problem in radio wireless networks using channel access mechanisms based on contention techniques as CSMA/CA [4]. CSMA/CA based methods are used in most wireless networks such as Wireless LAN (WLAN) [27] or sensor networks [28]. The hidden node problem is caused by signal coverage constraints, resulting in network nodes not being able to detect transmissions performed by other nodes. Thus, a node performing a carrier detection to check the status of the communication channel may detect the channel idle and continue to transmit, leading to possible collisions. Most research has been undertaken to evaluate the effects of this problem in radio frequency based networks, such as [5; 6; 29; 30; 31]. In VLC networks, the hidden node problem is also produced in a similar way to radio wireless networks, but, in this case, the problem is further aggravated by the directive characteristics of optical transmitters and receivers.  
Figure 4 shows a sample scenario of a VLC network which presents the hidden node problem. In this example, two user terminals try to make an uplink transmission to a data/illumination infrastructure device, located at the ceiling of the enclosure. All nodes in the network employ a medium access control procedure based on CSMA/CA method.  
In Figure 4, if terminal 2 performs a carrier detect procedure while terminal 1 transmits to the infrastructure, due to the directivity of terminal 1 optical transmitter (\(\theta_{1/2}\)) and terminal 2 optical receiver (FOV), transmitted signal of terminal 1 is not detected by terminal 2. Consequently, terminal 2 will consider the medium is idle and will transmit at the same time terminal 1 is transmitting, thus producing a collision at the infrastructure. IEEE 802.1 medium access control procedure based on the CSMA/CA method; therefore, uplink transmissions made according to this procedure are susceptible to be affected by the hidden node problem. This situation was revealed in some works published during the standard development [32] and in some documents [33] with proposals to take into consideration by the group responsible of developing the standard. However, in the current version of the standard [3], there are not specific mechanisms designed to solve this problem. To the best of our knowledge, there are not any published works that evaluate the impact of hidden nodes in performance of uplink transmissions made using CSMA/CA in the IEEE 802.15.7 standard.
## 2 Description of the Proposed Method
### Frequency-Power Droop Coefficient Determination Method
#### 2.2.1 Stochastic Optimization Based on the Monte Carlo Sampling Method  
To determine the droop coefficient in the proposed method, the Monte Carlo sampling method was utilized [38]. In the Monte Carlo sampling method, a number of load profiles are generated and are utilized for solving the optimization problem. All cases are generated based on probabilistic function, such as normal distribution, to represent possible realizations in the presence of forecast errors. For each load profile, the optimization problem of the previous section is solved, and operating points of HVDCs are calculated. The sampling number is represented by \(i\), and the output of LCC and VSC HVDCs connected to the \(n\)-th bus at the \(i\)-th sample is represented as \(P_{VSC}(n\),\(i\)) and \(P_{LCC}(n\),\(i\)), respectively.  
Furthermore, frequency deviation for each load profile is also calculated in this stage. To derive frequency deviation, the relationship between load profile and grid frequency should be established. Therefore, on the assumption that only HVDCs participate in frequency regulation, we use the swing equations of the power network, including frequency-supporting HVDCs and a load with damping effects, as follows [21]:  
\[\sum\limits_{j=n}^{N}\{P_{GEN}(n,i)+P_{VSC}(n,i)+P_{LCC}(n,i)-P_{LOAD}(n,i)\}= M_{\alpha q}\frac{d\Delta\omega(i)}{dt}+D\Delta\omega(i) \tag{12}\]  
where \(M_{\alpha q}\) is the moment of inertia and \(D\) is the load damping coefficient. The deviation of grid frequency is represented by \(\Delta\omega\). As the steady-state value (i.e., \(\Delta\omega=0\)) of the left side of Equation (12) is equal to zero, and the active power of the HVDCs can be represented by the corresponding droop coefficients, the frequency deviation can be described as  
\[\Delta\omega(i)=\frac{-\sum\limits_{n=1}^{N}\Delta P_{LOAD}(n,i)}{\sum \limits_{n=1}^{N}\frac{1}{R_{VSC}(n)}+\sum\limits_{n=1}^{N}\frac{1}{R_{LCC}(n )}+D}, \tag{13}\]  
where \(\Delta P_{LOAD}(n\),\(i\)) is the change in the load profile of the \(n\)-th bus at the \(i\)-th sample, which is defined by the Monte Carlo sampling method. \(R_{VSC}(n)\) and \(R_{LCC}(n)\) represent the droop coefficient of the VSC and LCC HVDC connected to the \(n\)-th bus, respectively. The frequency deviation cannot be derived directly from Equation (13), because the droop coefficients of the HVDCs, which are not determined yet, are utilized to derive the frequency deviation. Therefore, we assume that the sum of coefficients is constant, as follows:  
\[\sum\limits_{n=1}^{N}\frac{1}{R_{VSC}(n)}+\sum\limits_{n=1}^{N}\frac{1}{R_{ LCC}(n)}=\frac{1}{R_{eq}} \tag{14}\]  
where \(R_{eq}\) is an equivalent droop coefficient of the total system. Note that the active power of HVDCs during grid frequency only depends on the ratio of the droop coefficient, so power sharing between multiple HVDCs can be regulated properly while satisfying the constraint (14).