esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 3 IoT Challenges in Energy Systems
### Privacy and Security  
The concepts of privacy and security are tied together; as the level of security becomes lower, the system would be more threatened by unauthorized manipulation and so the privacy may be affected. On the other hand, privacy could be violated in a secure system. As an example, assume that noise pollution is aimed to determine on streets in an IoT framework; hence, sound sensors should be installed on the streets. While the system may be designed in a secure manner and no unauthorized third party may have the access to consumers' information, the very first question arise about privacy is that: \"Is it legal to record people's voice?\" Another example is that in IoT framework for smart buildings, a procedure could be detected for what people watch on television, or when they are awake. Although an appropriate security protocols may be used in such system, the question is that who can access this information, and do people feel pleasant to share this information? This challenge makes IoT processing slow and is extensively reported in the literature [101; 102].  
Generally, there are three main categories for privacy challenges: personal privacy, privacy-preserving data mining, and underlying technologies' privacy. Considering these aspects of privacy, legal regulations, which are globally accepted by governments, are strongly needed. In addition, human rights should be considered in all actions that are done in this regard [103]. Generally speaking, limitations in IoT sensors' capability make challenges in privacy and security issue more complex. This means that they cannot handle complicated security protocols. This challenge is mentioned in [104], and a small cryptographic key size is designed for IoT security.
## 3 Results
### Economic Summary Result  
After the process simulation had been performed, the economic analysis was focused on determining the product price as the first step. The price was determined by trial-and-error to find the minimum price for selling. The minimum selling price was calculated when net present value (NPV) equaled 0. This calculation resulted in USD 0.38 as the minimum selling price. The expected profit margin was 100% and the final product cost with an adjustment was USD 0.76 per capsule. Hence, a bottle with 30 capsules would have a price of USD 22.8.  
The production capacity was targeted at citizens of Jakarta in Indonesia. From the statistical data, more than 70% of the total population of 10.56 million citizens of Jakarta is of productive age [35]. However, only 250,000 individuals in this population were considered, resulting in 2,053,125 longevity spinach extract capsules to supply potential needs [36]. In this simulation, the product sold rate was set at 60%, with an overall production capacity of 10 years in the economic simulation. The economic analysis summary can be seen in Table 4. The capital expenditure consisted of direct and indirect fixed capital and working capital for the first 3 months of operational expenditure. The operating costs consisted of direct production costs, tax and insurance, health, safety, environment (HSE) costs, general affairs, and maintenance costs. The economic analysis also included depreciation costs to obtain gross income before tax. The depreciation was calculated with the single declining method and coefficient from the US Bureau of Economic Analysis [37].
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 4 Experiments
### The Convergence Behavior
#### 4.2.1 Element-Wise Pruning  
We traced both original accuracy and adversarial accuracy of AP and APD with the element-wise pruning scheme in the compression rate of \(\times 2\), \(\times 3\) and \(\times 4\). The results are described at Figure 1. Let us note that the adversarial accuracy is measured against PGD10. APD achieved a significant improvement in the original accuracy in the early stage of optimization with LeNet, VGG16, and ResNet18. With LeNet, the original accuracy of AP fell to lower than 20% on the first epoch whereas the original accuracy of APD was maintained above 90% across the entire optimization process. With VGG16, the original accuracy of both AP and APD was dropped on the first epoch. However, the amount of decrease in the original accuracy on the first epoch of APD was less than that of AP. For instance, in the compression rate of \(\times 4\), the original accuracy on the first epoch of APD was higher than that of AP by about 20%. Moreover, with LeNet and VGG16, APD improved the convergence behavior of both original accuracy and adversarial accuracy compared to AP. For instance, in the compression rate of \(\times 3\) with VGG16, APD only required 40 epochs for the average value of the original accuracy and the adversarial accuracy to reach 61.00% (the maximum average value achieved by AP), whereas AP required 46 epochs to achieve that. With ResNet18, APD reduced the drop of original accuracy on the first epoch by about 10% across the entire compression rates though the improvement in the convergence behavior of both original accuracy and adversarial accuracy is smaller than that of other networks.
# Mechanical and Thermal Properties for Uranium and U-6 Nb Alloy from First-Principles Theory
## 1 Introduction
The reactions producing \(\alpha^{\prime\prime}\) and \(\gamma^{0}\) phases are generally regarded as martensitic (diffusionless) in nature, occurring by shear and/or displacive mechanisms requiring only cooperative, short-range motions of the atoms [4]. The Nb supersaturated metastable \(\alpha^{\prime\prime}\)-phase solid solution exhibits far better corrosion resistance than unalloyed uranium and it is amenable to subsequent age hardening, permitting a wide range of mechanical properties [19; 20; 21; 22; 23] through the selection of aging temperature and time [8; 15]. Additionally, the equation of state, phase stability, and phase transformations under high pressure and temperature, including shock behaviors, of uranium-6 wt.% niobium and uranium-7.7 wt.% niobium alloys have been studied recently [23; 24]. From a modeling standpoint, there are fewer studies on the uranium-niobium system, but there are some recent phenomenological works reported [25; 27; 28; 29]. Uranium-niobium has also been investigated from first-principles electronic structure theory [30; 31; 32; 33]. However, no theoretical or experimental data on elastic properties are found in the literature, even though they are central for understanding the mechanical behavior and the SME of the U-Nb alloys. Constitutive modeling of the uranium-niobium alloys also depend on the elasticity [27] and new data would be helpful for that purpose. In this article, we compute first-principles elastic moduli for uranium metal and uranium-niobium alloy (12.5 at.% Nb). We focus on two phases, namely the orthorhombic ground-state \(\alpha\) phase of uranium and the quenched monoclinic \(\alpha^{\prime\prime}\) phase of the U-6 Nb alloy. The calculated elastic moduli will highlight the strong influence of niobium in the alloy, resulting in softer elastic behavior and a corresponding increase and improvement in ductility. Many of the interesting properties of U-6 Nb, including the shape memory effect, originate from the characteristics of the 5f electrons on the uranium atom. Even though the studies of uranium metal itself, experimental and theoretical, are too plentiful to list here, we note that fundamental aspects of the metal remain unexplained. Specifically, the anomalous negative thermal expansion of one of its lattice parameters is not understood but is likely linked to the mechanical behaviors of uranium and the U-6 Nb alloy. We explain this phenomenon here by computing electronic structure and phonon density of states and couple the results to an ab initio quasi-harmonic free-energy model.
## 4 Network Partition Algorithm for the Joint Placement Problem
### Analysis of the Complexity and Effectiveness of SAPKM  
Compared to the state-of-the-art works, SAPKM has the advantage on computational efficiency due to its optimized initialization. The computational complexity of SAPKM is analyzed in detail as follows.  
Theorem 4.1: _The computational complexity of PKM in Algorithm 1 is \(O(k^{2}\cdot n)\), where \(n\) and \(k\) denote the number of nodes and gateways, respectively._  
Proof: The function of PKM in Algorithm 1 is to iteratively obtain \(k\)-centroids by iteratively updating them from \(1\) sub-domain till \(k\) sub-domains.  
Obviously, the computational complexity of the process of centroid-based network clustering is \(O(k\cdot n)\). Thus, the running time for each iteration of the while-loop in Steps 3-7 is \(O(n+(k+1)n)\). Therefore, the running time of Algorithm 1 is \(O(\frac{(k^{2}+3k-4)}{2}\cdot n)\), which can be computed in \(O(k^{2}\cdot n)\) time.  
Theorem 2: _Each iteration of the while-loop in SAPKM described in Algorithm 2 can be computed in \(O(m^{2}\cdot n+k\cdot n)\) time, where \(n\), \(k\), and \(m\) denote the number of nodes, gateways, and controllers, respectively._  
Proof: SAPKM mainly consists of two parts, i.e., initializing the gateways and controllers by employing PKM and updating the joint placement by implementing SAA to maximize reliability with latency constraints. Specifically, Steps 2 and 3 can be computed in \(O(k^{2}\cdot n)\) and \(O(m^{2}(n-k))\) time, respectively. For Step 4, the running time is \(O(k\cdot n+k\cdot m)\). For Steps 5-20, each iteration of the while-loop runs \((k\cdot n+k\cdot m+m^{2}(n-k))\) times, which can be computed in \(O(m^{2}\cdot n+k\cdot n)\) time.  
From the above, Algorithm 2 can be compute in \(O((k^{2}+(1+t)(m^{2}+k))n)\) time with \(t\) iterations.
## 3 Design Consideration on Radiation-Grafted AEM
### Radiation Technique and Mechanism
#### 3.2.1 Pre-Irradiation  
Pre-irradiation polymer backbone in an inert atmosphere or vacuum produces free radicals, which in turn act as a grafting site for the monomers. A high concentration of free radicals can be obtained with high-energy electron beams at a high dose rate, and grafting is performed instantaneously after the irradiation exposure [74]. In the presence of oxygen, the pre-irradiation of polymer backbone produces peroxides or hydroperoxides, which subsequently decompose into radicals at a higher temperature and initiate the grafting reaction upon interacting with monomers. The lifespan and stability of these radicals depend on the types of polymer backbone, as well as the storage conditions and durations, which permit the grafting process to be performed at a later time and away from the source of radiation [67; 80]. The formation of homopolymer is in very minimal levels for this technique, as the activation of monomer by radiation is absent. Moreover, the degradation and crosslinking of the polymeric substrate are likely to take place during the course reaction, as the protection by monomer is missing as well. Besides, the efficiency of the grafting process is highly dependent on the radical lifetime of the polymer substrate [81]. Generally, the pre-irradiation method is suitable for production on a pilot scale.
## 3 Discussion
Additionally, the group velocity of the optical pump pulse will differ from that of the X-ray probe in a protein crystal: the X-rays will propagate with a refractive index of 1 but a larger refractive index is associated with the optical pulse (\(\approx\) 1.2-1.4 for proteins at crystal growth concentrations [81; 82]), resulting in group velocity mismatch (GVM) between the two. Larger crystals will increase this effect, as they introduce a larger path length for the pulses. Figure 10 plots the expected added delay of the optical pump for a distribution of crystal thicknesses at different possible protein crystal refractive indexes: for a 2-\(\upmu\)m crystal and n = 1.3, for example, the achievable time resolution cannot be higher than 2 fs, as this is the amount of time by which the pump will be slowed down compared to the probe pulse. The added delay is calculated by multiplying the GVM with the crystal thickness. The GVM is given by the pump and probe group velocities (\(v_{\text{pump}}\) and \(v_{\text{probe}}\)) in the crystal and therefore by their refractive indexes:  
\[\text{GVM}=\frac{1}{v_{\text{pump}}}-\frac{1}{v_{\text{probe}}}=\frac{\text{ n}_{\text{pump}}}{c}-\frac{\text{n}_{\text{probe}}}{c}. \tag{3}\]  
Figure 9: Chromatic dispersion effect. Calculated stretching of a femtosecond optical pulse given a range of group dispersion delay (GDD) values. The table shows stretching values for a 5-\(\upmu\)m GDVN jet (GDD = 0.20 fs\({}^{2}\)), a 170-\(\upmu\)m droplet (GDD = 6.50 fs\({}^{2}\)), and a 5-\(\upmu\)m crystal (GDD = 11.29 fs\({}^{2}\)). A final point to consider is the self-focusing that occurs if the pulse power exceeds a critical power, \(P_{\text{cr}}\). The nonlinearity effects that result from self-focusing can lead to a total collapse of the beam. While for CW lasers there is a mathematical relationship to calculate \(P_{\text{cr}}\) from the nonlinear refractive index of a medium, this cannot be used accurately for ultrashort pulses [83] so we cannot predict a specific threshold for self-focusing. This effect is, however, observed regularly with femtosecond pulsed lasers [84; 85; 86; 87] and is expected at the gigawatt peak powers reached by lasers generated through hollow core fiber compression. In a successful experiment, these incredibly high peak powers will have to be contained to avoid self-focusing, beam filamentation, as well as nonlinear absorption effects in the sample.
## 5 Experimental Results
### Buck-Boost Topology  
#### 5.3.1 Step-Down  
Figure 11 shows the power efficiency in the step-down section of the buck-boost topology in the CV and CC modes. The power efficiency is improved in every condition because of the removal of D\({}_{2}\) in the switch-off section. In the CV mode, the efficiency improvement becomes larger with an increase in the output current; \(P_{Diff}\) increases in the high-power region. In the CC mode, the power efficiency improves with a decrease in the input voltage, and \(P_{Diff}\) increases in the low-power region. In the same duty condition, the voltage stress of the diode D\({}_{1}\) increases with the input voltage; \(P_{Diff}\) decreases with an increase in loss.  
Figure 10: Power efficiency comparison of the boost topology: (**a**) CV mode; (**b**) CC mode.
## 4 Methodology
### Preliminary Selection of Features  
The first step of this examination performs the feature preselection by rejecting features that are either strictly infrastructure dependent or redundant. From the set of 14 categorical features, four address features (group II) are withdrawn, as well as DNS_QUERY_ID (from group I). The addresses are firmly connected with the infrastructure in which the network traffic was captured--in this case, the capturing setup of the original dataset. They do not represent any general traffic characteristics, and using them may result in biased classification outcomes. The same reason stands behind the removal of DNS query ID. Then, also ICMP_IPV4_TYPE from group I is withdrawn, as it is already captured in ICMP_TYPE.  
The next step is to compute the correlation on the remaining traffic features to find and reject the redundant ones. In this examination, valuable information on highly correlated features is obtained. An absolute value of the correlation coefficient greater than 0.9 is achieved by five pairs of features. These are as follows:* LONGEST_FLOW_PKT with MAX_IP_PKT_LEN;
* MIN_TTL with MAX_TTL;
* TCP_FLAGS with CLIENT_TCP_FLAGS;
* RETRANSMITTED_OUT_BYTES with RETRANSMITTED_OUT_PKTS;
* OUT_BYTES with NUM_PKTS_1024_TO_1514_BYTES.  
For each feature in the pair, the cumulative value of all absolute correlation coefficient values is calculated. The higher the cumulative correlation value, the less helpful the feature. This way, one receives sufficient information on removing unneeded features--one from each pair. In line with that, the following features are rejected: LONGEST_FLOW_PKT, MAX_TTL, CLIENT_TCP_FLAGS, RETRANSMITTED_OUT_PKTS, and NUM_PKTS_1024_TO_1514_BYTES. To summarize, 32 features, containing 24 numerical and 8 categorical features, are left for further analysis after this stage.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
## 3 Results and Discussion
### Workability  
The slump results are shown in Figure 5. It can be found that the slump of concrete almost linear reduced with the increase of GNP content. At 0.4 wt % GNP, the mixing process of the cement slurry, stone, and graphene-water mixture became difficult, and the slump was only 57 mm. It is universally acknowledged that GNP has larger surface-to-volume ratio and can absorb more water on its surface; nevertheless, the amount of water added to the fresh slurry remains unchanged during the forming process, and therefore, this could reduce its workability. Meanwhile, with the increase of graphene addition, contact degree magnified between graphene particles; some graphene particles are not uniformly dispersed in cement matrix. This phenomenon can be found in subsequent SEM experiments. Consequently, these would have reduced the concrete workability.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 2 Materials and Methods
### Multi-Static Coherence  
For each multi-static interferometric pair, we also estimated the coherence using an estimator with a spatially adaptive window size [31], with estimator window sizes between 6 range looks \(\times\) 4 azimuth looks (corresponding to an area on the ground with a diameter of about 7 m) at high coherence and 21 range looks \(\times\) 14 azimuth looks (about 25 m on the ground) at very low coherence. Using a larger estimator window at low coherence levels reduces the estimation bias [39; 40].
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 4 Creating the Polygonal Graph over the Work Space
### The Weighting Mechanism of the Graph, Calculations of the Weights of Nodes and Edges
#### 4.1.2 The Final Weighting Calculation of the Path  
There are many routes between the START and GOAL positions. Let the index of these paths be \(k=\{1,\ldots,p\}\). Then the final weighting of the selected (\(k\)th) path can be calculated as follows:  
\[sumE_{(k)}=\sum_{i=1}^{n}E_{(i)}+\sum_{j=1}^{m}l_{j} \tag{14}\]  
The final execution path can be chosen based on the minimum cost function (\(C_{f(min)}\)), where:  
\[C_{fmin}=min(sumE_{k}) \tag{15}\]  
Figure 8: Illustration of weight calculations along the path in three different situations, see nodes n(i). The drone is represented as a point and orientation, see the blue full arrow line. The wind direction is represented with dashed arrow line.  
Possible Graph-Search Procedures, Survey and Comparison of the Effectiveness of Different Optimal Path Planning Methods  
In mobile robot platforms, there are several methods and procedures to achieve the desired optimal path between the given points in space. Before determining the most suitable algorithm, or algorithms, for this particular case, the methods should be classified to determine which, or what combinations, might be the best solution. It is essential to differentiate between path search algorithms and trajectory optimization methods. In the case of path search, the routes are found between the START and GOAL points of the space (if such routes can be found). In most cases, this search yields the best possibilities, which means the shortest or the fastest path. In comparison, trajectory optimization methods are usually performed by choosing some optimization goal (parameter) and the optimization algorithm is run in order to approximate this goal function. The graph below displays a possible classification (Figure 9). A detailed description of each method is provided below.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 4 Optimization of the Skeleton Cross-Section
### Optimization of Skeletons of Three Common Shapes  
#### 4.1.1 Rectangular Cross-Section  
A schematic of rectangular cross-section is depicted by Figure 11, in which \(a\) and \(b\) are the lengths of the edges of the cross-section, \(i\) is the measured current flowing through the center of the coil, and \(R_{1}\) is the inner radius of the skeleton. In the case of uniform winding, the magnetic flux through each turn of the winding is  
\[\Phi_{\rm m}=\frac{\mu_{0}i}{2\pi}b\ln\frac{R_{1}+a}{R_{1}}. \tag{12}\]  
Figure 10: Influence of each parameter on the capacitance between a single turn of the winding and the shielding shell \(C_{ts}\). (The reference values are \(r_{c}=0.93\) mm, \(\delta=0.07\) mm, \(e_{r}=5\), \(h=1.1\) mm, \(l_{t}=20\pi\) mm).  
The optimization problem for the dimensions of a rectangular cross-section skeleton can be expressed as follows. Given fixed inner radius \(R_{1}\), measured current \(i\), and perimeter \(l=2(a+b)\), determine the relation between \(a\) and \(b\), such that the magnetic flux \(\Phi_{\text{m}}\) through the rectangular cross-section is maximized.  
In [20], a straightforward derivation is presented as follows. Substituting  
\[b=\frac{l}{2}-a \tag{13}\]  
into Equation (12) yields  
\[\Phi_{\text{m}}=\frac{\mu_{0}i}{2\pi}(\frac{l}{2}-a)\ln\frac{R_{1}+a}{R_{1}}. \tag{14}\]  
The maximum \(\Phi_{\text{m}}\) is achieved when  
\[\frac{\partial\Phi_{\text{m}}}{\partial a}=0, \tag{15}\]  
namely  
\[\frac{\mu_{0}i}{2\pi}\bigg{[}-\ln\frac{R_{1}+a}{R_{1}}+(\frac{l}{2}-a)\frac{1 }{R_{1}+a}\bigg{]}=0. \tag{16}\]  
Recall Equation (13), when \(a\) and \(b\) satisfy  
\[b=(R_{1}+a)\ln\frac{R_{1}+a}{R_{1}}, \tag{17}\]  
The rectangular cross-section is optimized.
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 3 Results  
### Tropospheric Delay Comparison between NWP and GAMIT  
Different regional NWP models may have different accuracy and therefore detailed evaluations are required to assess the accuracy of different models. In this study, the NWP model we used is the main medium-range NWP model from the SMB. To investigate the quality of ZTD parameters derived from the NWP model, we compare the value of the tropospheric delay calculated by NWP and GAMIT. To achieve this objective, one year's NWP data over East China area and GNSS observation on eight meteorological stations in Figure 1 were collected. The tropospheric delays of these eight stations were calculated with GAMIT every 1 h. As the NWP ZTD are sampled every 3 h, we do not interpolate in time but restrict the comparison to the NWP data epochs.  
As typical examples, the ZTD series derived from the medium-range NWP model and GAMIT at stations BXTC, SDJM and SHBS are shown in Figures 3-5. From these figures, we can see the time series of the NWP ZTD are in relatively good agreement with GAMIT ZTD. The correlation coefficient showed in Table 1 are all larger than 0.97, which also suggest that these two kinds of ZTD are highly correlated. Generally, the peaks of these ZTD series appear between the 180 th and 210 th day of year, which are during the rainy season. As a result, rapid and large changes of water vapor would increase the variability of tropospheric delay. Taking the tropospheric delay gained from GAMIT as the precise value, the residuals of the estimation using the NWP method are shown in Figure 6. Table 1 presents the average values and the Root Mean Square error (RMS) of the residuals during the experimental period. From Figure 6, we can see the maximum and minimum values of the residuals are about 200 mm and \(-\)150 mm, and the NWP ZTD residuals are mainly distributed between \(-\)50 mm and 50 mm. The mean residuals are between \(-\)5.50 and 5.60 mm, and the RMS are between 24.02 and 32.62 mm (Table 1). The station of BTLU shares the best precision, while the RMS of station SHBS, SDJM and ZJHZ are relatively large. In this study, we set the mean value of the RMS of these eight stations as the prior variance of tropospheric delay residuals for the stations in this area.