esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 4 Proposed Scheme
### Registration  
Smart meters within the jurisdiction of the data concentrator \(DC_{j}\) receive public parameters. The smart meter \(SM_{i}\) selects a random number \(k\in Z_{p}^{*}\) as its private key, calculates the public key \(K=T_{k}(x)\) and publishes it. \(SM_{i}\) then computes the \(XID_{i}=ID_{i}T_{k}(S)\ mod\ p\) and sends \(\{XID_{i},t_{0}\}\) to \(DC_{j}\), where \(t_{0}\) is the timestamp and \(ID_{i}\) is the unique identification of the smart meter, which is bound to the user's identity, corresponding to their home address, bank account and IP of the smart meter, relating to absolute privacy.  
Upon receiving \(\{XID_{i},t_{0}\}\), \(DC_{j}\) first verifies the timeliness of the message, and if \(|t_{current}-t_{0}|\leq\Delta T\), it then proceeds to the next step of calculation: \(ID_{i}=XID_{i}/T_{s}(T_{k}(x))\). After obtaining the \(ID_{i}\), \(DC_{j}\) selects the random number \(e_{i},r_{i}\in Z_{p}^{*}\) and computes  
\[PID_{i}=H(ID_{i}||e_{i}) \tag{7}\]  
\[E_{i}=H(ID_{i}||s||r_{i}||PID_{i}) \tag{8}\]  
where \(PID_{i}\) is the pseudo identity of the smart meter \(SM_{i}\), and \(DC_{j}\) sends \(\{PID_{i},E_{i}\}\) to \(SM_{i}\) and saves \(\{ID_{i},r_{i}\}\).
## 2 Materials and Methods
### Environmental Capacity Assessment System (ECAS)  
#### 2.2.1 Environmental Capacity Theory  
Environmental capacity (EC) refers to a characteristic of the environment, which is the ability of the environment to accommodate a certain pollutant without causing an unbearable impact on the environment [36]. This feature contains three environmental implications: (1) pollutants existing in the environment will not affect the environment as long as they do not exceed the specified limit value; (2) no environment has an unlimited capacity to contain pollutants without affecting the physical and ecological functions of a specific ecosystem; and (3) EC can be quantified. Applying this concept to the ocean for further analysis, marine EC depends not only on the size of the space, location, tidal pattern, temperature and other hydrological conditions of the sea, as well as on physical, chemical, and biological migration and transformation conditions, but also depends on the environment quality standards that the sea should meet in order to maintain a certain sea environment function. In other words, the capacity of the marine environment is closely related to the natural background value of a particular sea area, the characteristics of various environmental elements, social functions, physical and chemical properties of pollutants, and the self-purification capacity of the marine environment, which can better reflect the impact of reclamation activities on the environment than water quality indicators. Therefore, we chose the EC of several typical pollutants in the coastal zone as the evaluation index, and conducted an in-depth consideration of the feasibility of the superimposed implementation of the reclamation scheme.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 2 SEG Process Description and Model Development
### Hydrodynamics
#### 2.3.1 Dense Phase  
In the model, the dense phase is considered to be a perfused pack with porosity \(\varepsilon_{d}\) and gas velocity \(u_{d}\). The calculation of \(u_{d}\) and \(\varepsilon_{d}\) is performed following the methods presented by [PERSON] [26]. The minimum fluidization gas velocity, \(u_{m\!f}\), is determined using the Ergun equation (Equation (1)), which is formulated based on the definitions of the Archimedes and Reynolds numbers, as well as the  
Figure 3: Simplified reactor scheme of the modeled process.  
Figure 2: Modeling concept of SEG in a dual fluidized bed system.  
Sauter diameter (Equations (2)-(4)). In the present work, a sphericity of \(\psi\) = 0.75 [27] and a porosity at minimum fluidization \(\varepsilon_{m\!f}\) = 0.45 [27] are assumed.  
\[Ar=150\frac{1-\varepsilon_{m\!f}}{\psi^{2}\!\cdot\!\varepsilon_{m\!f}^{3}}Re_{ m\!f}+\frac{1.75}{\psi\!\cdot\!\varepsilon_{m\!f}^{3}}Re_{mf}^{2} \tag{1}\]  
\[Ar=\frac{g\!\cdot\!d_{sv}^{3}}{\
u_{g}^{2}}\frac{\rho_{p}-\rho_{g}}{\rho_{g}} \tag{2}\]  
\[Re_{mf}=\frac{d_{sv}\!\cdot\!u_{mf}}{\
u_{g}} \tag{3}\]  
\[d_{sv}=\sqrt{\varphi\!\cdot\!d_{p}} \tag{4}\]  
Analysis of the fluidized bed inventory showed a mean particle size, \(\overline{d_{p}}\), of 350 \(\upmu\)m [11]. According to [PERSON] [26], the real velocity in the dense phase is higher than the calculated minimum fluidization gas velocity, \(u_{mf}\), and it can be estimated with the following empirical equation:  
\[u_{d}|_{h=0}=\ u_{mf}+\ \frac{1}{4}\big{(}u_{empty}\big{)}|_{h=0}-\ u_{mf} \big{)} \tag{5}\]  
For the remaining reactor heights \(u_{d}(h)\) is determined by the continuity equation.  
The porosity \(\varepsilon_{d}\) is calculated, as proposed by [PERSON] and [PERSON] [28], as  
\[\varepsilon_{d}(h)=\varepsilon_{m\!f}\bigg{(}\frac{u_{d}(h)}{u_{mf}}\bigg{)} ^{\frac{1}{R_{2}}}. \tag{6}\]  
For the parameter \(n_{Rz}\), [PERSON] and [PERSON] [28] provided the empirical equation:  
\[n_{Rz}=\left\{\begin{array}{rl}4.65&\text{if}\ \ Re_{s}\leq 0.2\\ 4.4\!\cdot\!Re_{s}^{-0.03}&\text{if}\ 0.2\leq Re_{s}\leq 1\\ 4.4\!\cdot\!Re_{s}^{-0.1}&\text{if}\ 1\leq Re_{s}\leq 500\\ 2.4&\text{if}\ Re_{s}>500\end{array}\right. \tag{7}\]  
where \(Re_{s}\) is the Reynolds number from the rate of descent of a single particle. In this work, the parameter \(n_{Rz}\) is used to adjust the calculated bed height to the experimental values. With \(n_{Rz}\) = 5.5, the model could be fitted to the real bed height determined experimentally.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
###### Abstract  
Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency.  
wireless sensor network; mobile agent; ant colony optimization; particle swarm optimization; moving trajectory +
Footnote †: journal: Sensors  
_Article_  
A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 3 Proposed IOTA-Based FTN-DFT-s-OFDM Waveform Design
### Transmission Waveform Design
#### 3.1.2 FTN Signaling  
Given the discrete transmit symbol (1), FTN signaling aims to transmit each symbol with a shorter time interval to reserve the time resources. We reuse the above notations, and denote \(x(n)\) as the input to the FTN module. Denote \(T_{0}\) as the normal symbol transmission interval (i.e., the interval under Nyquist sampling), the exact symbol interval after FTN signaling is given by \(T=\alpha T_{0}\), where \(\alpha\) is the acceleration factor ranging within \([0,1]\). The acceleration factor \(\alpha\) represents the compression degree of FTN, where the FTN waveform under small \(\alpha\) has large spectral efficiency. Denote \(g(t-nT)\) as the shaping filter and overall received signal is derived by  
\[y(t)=\sqrt{E_{s}}\sum_{n}x(n)g(t-nT)+n(t), \tag{5}\]  
where \(n(t)\) denotes the additive white Gaussian noise (AWGN), \(g(t-nT)\) denotes the shaping filter and \(E_{s}\) denotes the average power of the transmitting signal.  
FTN can be defined as pulses that are shorter in time and thus no longer orthogonal. We can easily observe that smaller \(\alpha\) results in a higher data rate. However, due to the non-orthogonality of pulses in FTN signaling, there are overlaps among carriers, and ISI appears. The performance of communication systems thus degrade because of the presence of ISI. In the next, we introduce IOTA filter, which helps to resolve the ISI brought by FTN signaling.
# The Impact of Drilling Parameters on Drilling Temperature in High-Strength Steel Thin-Walled Parts
## 2 Experimental Condition
### Temperature Measurement System for Drilling of Thin-Walled Workpieces  
The experiments were carried out on a VMC850E vertical machining centre manufactured by Shenyang Machine Tool Co., Ltd. In order to facilitate the observation of temperature changes on the bottom surface of the drilled holes, a special fixture was used to fix the thin-walled workpiece surface vertically on the table, and an angular head was mounted on the main spindle of the vertical machining centre to convert the vertical machining into horizontal machining. A Fotric 388s infrared thermal imager was employed to monitor, in real time, the evolution of the temperature field on the bottom surface of the workpiece during drilling. The camera was calibrated using the contact-thermometer  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Elemental** & \(C\) & \(Co\) & \(Cr\) & \(Mo\) & \(Si\) & \(Mn\) & \(P\) & \(S\) \\ \hline mass fraction/\% & 0.16 & 13.83 & 1.95 & 1.04 & 0.01 & 0.02 & 0.006 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of AF1410 steel [32].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Property Index** & **Numerical Value** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & 7860 \\ Modulus of elasticity (GPa) & 203 \\ Yield strength \(\sigma_{0.2}\) (MPa) & 1580 \\ Thermal conductivity (W/mK) & 27.8 \\ Specific heat capacity (J\(\cdot\)kg\({}^{-1}\)K\({}^{-1}\)) & 490 \\ Poisson’s ratio \(\mu\) & 0.29 \\ Tensile strength \(\sigma\) (MPa) & 1700 \\ Melting temperature (K) & 1623\({}^{-1}\)695 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some physical and mechanical properties of AF1410 steel [33].  
method, yielding an adjusted emissivity of 0.3. The measurement accuracy of the Fotric 388s is \(\pm 1\)\({}^{\circ}\)C or \(\pm 2\)% of the reading, whichever is larger, and its instantaneous field of view (IFOV) is 0.68 mrad. The infrared resolution is at least 640 \(\times\) 480 pixels. The drilling force measurement system consisted of a Kistler9257B flatbed force measuring instrument, a Kistler5070A charge amplifier, a Kistler5697A data collector (Kistler, Winterthur, Switzerland), and a computer. The AF1410 steel thin-wall drilling temperature test system is shown in Figure 1.
# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Observation
#### 4.3.3 Mobility  
Mobility, which is also called monitoring continuity [36], is one of the main characteristics that enables identification of the state of sensors and actuators and their capability of movement [26; 36; 39; 43; 44; 80], with significant implications on device operation, connectivity, and location management [30; 48; 80]. Devices are classified into two categories: static/immobile/fixed and mobile [26; 77; 82; 113].  
* Fixed/static/immobile: Objects that remain static to a specific location or cannot move. Their observations are restricted to a specific location, in a static or very constrained (in terms of mobility) environment that is not designed to move (relative to their point of installation) without being uninstalled.
* Mobile: The objects move [44], and their location may be calculated in absolute coordinates or relative to reference nodes in the network [81], requiring wireless communications to transmit data and allow configuration and control [113]. Their movement and mobility capability are controlled independently (or autonomously) or dependently through device users [43].  
A self-moving device moves autonomously and relative to its setup/installation point, without being uninstalled (e.g., smart car), whereas a non-self-moving device does not move autonomously but can still move relative to its original location without being uninstalled [26; 77].  
Mobility of the things in the system is dependent on the collaboration of the items physically coupled with the humans in the system [28], as in crowdsensing applications, in which geographically dispersed users actively (participatory) or passively (opportunistic) collect data with their smartphones [51; 75]. Classifications between mobility-agnostic and mobility-aware [82] highlight an approach that ignores knowledge about mobility and the ones that consider and exploit it for optimization [12].  
Challenges related to mobility include frequent disconnections and handoffs, which affect perfect connectivity [126]. Mobility techniques in the cloud, fog, and edge architectures [60] support mobility, and other protocols apply routing and resource discovery mechanisms [39].
# RJA-Star Algorithm for UAV Path Planning Based on Improved RSDOS Model
## 2 Materials and Methods
### Brief Introduction to A-Star Algorithm  
The A-star algorithm is a method of finding the shortest path in static scenes. The calculation principle of the A-star algorithm is both simple and fast, but this will slow down with the increase of map size. The increase in computing space will also be exponential, with the larger the map, the greater the computing time and storage footprint. The A-star algorithm will take significant time and space for path calculation. In addition, the computation time will determine the routing efficiency of A star algorithm.  
The A-star algorithm is a fast and efficient way to find the right heuristic. Among them, \(H(n)\) is the function expression of the shortest path cost of UAV starting from the nth node \((x_{u},y_{u},z_{n})\) to the target \((x_{t},y_{t},z_{t})\). \(G(n)\) represents the shortest path between the starting point \((x_{s},y_{s},z_{s})\) and the nth node. Wherein, \(F_{cot}(n)\), \(H(n)\), \(G(n)\) can be expressed by Equation (1).  
\[\begin{split} H(n)&=\sqrt{\left(x_{t}-x_{n}\right)^{2 }+\left(y_{t}-y_{n}\right)^{2}+\left(z_{t}-z_{n}\right)^{2}}\\ G(n)&=\sqrt{\left(x_{n}-x_{s}\right)^{2}+\left(y_{n}- y_{s}\right)^{2}+\left(z_{n}-z_{s}\right)^{2}}\\ &\qquad\qquad F_{cot}(n)=G(n)+H(n)\end{split} \tag{1}\]  
Figure 1: Definition and representation of five topological relationships.  
Figure 2: Abstract topology representation of UAV area.
# A Bayesian Control Chart for Monitoring Process Variance
## 4 The Construction of the EWMA Variance Chart with the Bayesian Approach
### Performance Comparison of the EWMA Variance Control Chart
Hence, we conclude that the out-of-control detection performance of the proposed Bayesian EWMA variance chart is always better than those of the SS EWMA-V chart, no matter whether the shift from \(p_{0}/E_{0}(p)\) to \(p_{1}/E_{1}(p)\) is small, medium, or large.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Energy Efficiency  
The laser-powered drone technology comes at the expense of several factors. For instance, a significant amount of optical energy is wasted in free space. Due to this feature, laser-powered drones require high power laser sources. It is still unclear that performance gain can justify the additional energy costs. Several industries are investigating methods to enhance the efficiency of LPT by significantly reducing energy losses. Hence, future studies must address this critical challenge and target further improving the PV receiver's efficiency in harvesting energy from the laser transmitter.
# Evaluation of Horizontal Shear Performance of Composite Girders Using Separable Shear Connectors
## 2 Design Standards for Horizontal Shear Strength of Composite Concrete Members  
Figure 2 depicts the mechanism of horizontal shear force generation at the interface between girder and deck in composite members under vertical loading. The horizontal shear force generated here is resisted by the shear resistance of shear connectors installed between the girder and deck, and the friction resistance according to the roughness level of each interface.  
Consequently, the design of these composite members focuses on ensuring adequate resistance to the relative displacements due to bending. Design standards across various countries provide equations for the horizontal shear strength that depend on the roughness of the interface between concrete members cast at various intervals (Table 1). Shear connectors, typically specified as U-shaped stirrup rebars, are embedded within concrete girders to facilitate this interaction.  
Figure 2: Bending–horizontal shear force mechanism of a prestressed concrete (PSC) composite girder.
## 3 System Model
### Transmission Model  
After specifying an application delay of \(T_{QoS}\), when \(k\) paths are generated between Source and Ink, and the end-to-end delay control is close to \(T_{QoS}\), the energy consumption of each path is the smallest. \(D_{st}\) represents the shortest path, then \(T_{QoS}\) needs to satisfy \(T_{QoS}\geq\frac{D_{st}}{R_{\max}}\times T_{hop}\), otherwise there will be no path to satisfy q's needs. On the other hand, Q can't be too big. For a given network topology, multimedia applications, set the w range:  
\[\frac{C.D_{st}}{R_{\max}}\times T_{hop}\geq T_{QoS}\geq\frac{D_{st}}{R_{\max}} \times T_{hop} \tag{9}\]  
where \(C\) is constant, generally set to 2, \(R_{\max}\) is the maximum transmission distance. Assuming that \(R^{*}\) is the average minimum distance per hop, there are:  
\[R^{*}\geq\frac{D_{st}}{H_{QoS}} \tag{10}\]  
When constructing \(k\) paths, the optimal transmission distance \(R_{k}\) satisfies the following formula:  
\[R^{*}\leq R_{k}\leq R_{ma} \tag{11}\]  
Suppose that for multiple paths of \(P_{1},P_{2},\cdots,P\), the corresponding lengths are \(L_{1},L_{2},\cdots,L_{k}\). The average transmission distance per hop of the \(k\)-th path is:  
\[R_{k}=\frac{L_{k}}{H_{QoS}} \tag{12}\]  
\(R^{*}\times H_{QoS}\leq L_{k}\leq R_{\max}\leq H_{QoS}\) can be obtained, where \(L_{\max}=R_{\max}\times H_{QoS}\).  
The energy consumed by the \(k\)-th path is \(E_{k}=\sum\limits_{i=1}^{H_{QoS}}\left(Dn_{i-1}n_{i}\right)^{2}\), which can be obtained by applying Cauchy inequality.  
\[E_{k}=\sum\limits_{i=1}^{H_{QoS}}\left(Dn_{i-1}n_{i}\right)^{2}\geq\frac{ \left(\sum\limits_{i=1}^{H_{QoS}}\left(Dn_{i-1}n_{i}\right)\right)^{2}}{H_{QoS }}\approx\frac{L_{k}^{2}}{H_{QoS}} \tag{13}\]  
The condition of the above formula is \(D_{n_{0}n_{1}}=D_{n_{1}n_{2}}=\ldots=D_{n_{H_{QoS}}\sim 1}n_{H_{QoS}}\approx \frac{L_{k}}{H_{QoS}}R_{k}\). That is to say, when the distance per hop is equal (\(R_{k}\)), the energy consumption is the smallest.
## 5 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Study** & **Method** & **Dataset** & **Subjects** & **Results, \%** \\ \hline \multirow{4}{*}{[PERSON] et al. [59]} & Typographic error-based & 78 records, 51 with & & Accuracy 94.87, *TP rate for \\  & feature extraction, & EDSS 0–3.5, 18 with & & low class 90.40, *TP rate for \\  & oversampling based feature & EDSS 4.0–5.0, 10 with & & medium-class 94.20, *TP rate \\  & selection and classification & EDSS 5.5–10.0 & & for high class 100.00 \\ \hline \multirow{2}{*}{[PERSON] et al. [58]} & CNN with 14 layers & eHealth Lab and clinic & 38 MS, 26 healthy & & Accuracy 98.77, Sensitivity \\  & & & & 98.77, Specificity 98.76 \\ \hline \multirow{4}{*}{[PERSON] et al. [60]} & CNN pretrained on & & & \\  & Alzheimer’s & & & \\  & neuroimaging initiative & Clinic & 76 MS, 71 healthy & Accuracy 87.04, AUC 96.08 \\  & dataset & & & \\ \hline [PERSON] et al. [61] & Graph neural network & Clinic & 90 MS & Specificity 82, F1-Score 80 \\ \hline [PERSON] [62] & Graph CNN & Clinic & 90 MS & Specificity 92, F1-Score 92 \\ \hline \multirow{4}{*}{Our model} & & & & Axial: Accuracy 98.37, \\  & & & & Sensitivity 96.46, \\  & & & & Specificity 99.60 \\  & & & & Sagittal: Accuracy 97.75, \\  & & & & Sensitivity 95.01. \\  & & & & Specificity 99.80 \\  & & & & Hybrid: Accuracy 98.22, \\  & & & & Sensitivity 96.39, \\  & & & & Specificity 99.50 \\ \hline \hline \multicolumn{4}{l}{*TP: true positive rate.} \\ \end{tabular}
\end{table} 
Table 6: Comparison with other state-of-the-art MS brain MRI classification methods. * Patients under 18 years of age were excluded from the study.
## 3 Results and Discussions
### Results and Discussions of Trajectory Prediction for Deceleration Phase  
Based on the experimental design, the proposed approach is applied to predict the trajectory for a flight decelerating from 216 to 186 km/h. The prediction errors of the flight time and trajectory are reported in Figure 14, from which we can see that GP-based approaches obtain higher prediction accuracy than that of the baseline. Since regulating aircraft speed has more variable parameters, the prediction error of flight time is obviously larger than that of regulating flight altitude, as shown in Figure 15. After predicting the flight time with respect to the flight speed, the positional offsets of the flight trajectories are estimated based on a DGP model. Finally, the flight trajectory is obtained by accumulating the positions of last instant and its positional offset. In Figure 16, the positional offsets at a different instant are shown to illustrate the transition patterns of flight trajectory for  
Figure 12: Experimental results for a descent flight.  
Figure 13: The predicted flight time of its CI for a descent flight from 8100 to 7500 m.  
performing the flight deceleration. In general, the positional offsets are distributed in a divergent interval, which indicates that the aircraft maneuverability degenerates the prediction accuracy of the flight trajectory. Fortunately, the intrinsic advantages of the GP promote the prediction performance of the proposed approach.
# Effects of Growth Parameters on the Analysis of _Aspergillus flavus_ Volatile Metabolites
## Materials and Methods
### Selection of SPME Fibers  
Carboxen/polydimethylsiloxane (CAR/PDMS), divinylbenzene/polydimethylsiloxane (DVB/PDMS) and DVB/CAR/PDMS SPME fibers were purchased from Sigma-Aldrich (St. Louis, MO, USA). The aflatoxinic isolate K73 was used for the SPME fiber comparison study. The growth medium used was MEA, and the spores' concentration was maintained at 1 \(\times\) 10\({}^{6}\) spores/mL. Fungal cultures were incubated in the absence of light at 30 \({}^{\circ}\)C for 7 days. Fungal cultures were prepared in 6 replicates for each type of SPME fiber for a total of 18 experiments. After 7 days of culture incubation, SPME fibers were exposed to the headspace of the Erlenmeyer flasks containing the fungus at 30 \({}^{\circ}\)C for 5 h. After the sampling period, the fiber was pulled into the needle sheath; the SPME device was removed from the flask and then inserted into the hot injection port of the GC-MS for thermal desorption.