esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Synthesis of Ti\({}_{2}\)AlN MAX Phase via Step Annealing Method  
Commercial powders of elemental titanium (Ti, \(-\)325 mesh, Sigma Aldrich, St. Louis, MO, USA), aluminum (Al, \(-\)325 mesh, Sigma Aldrich), and aluminum nitride (AlN, Sigma Aldrich) were used as the initial precursors for the preparation of the Ti\({}_{2}\)AlN MAX phase. All powders were used as received without further treatment.  
The precursor ratio of starting powder mixtures of 2 Ti + 0.8 Al + AlN was used. Since Al could evaporate and dissociate during the processing, which inhibits the single-phase synthesis of Ti\({}_{2}\)AlN [47], an excess amount of Al was introduced to compensate for the Al loss. Ti, Al, and AlN powders were weighed on a precision scale (0.0001 g) at a molar ratio of 2:0.8:1. Powder mixtures and zirconia balls were sealed in a dry environment with a dew point of nearly \(-\)55 \({}^{\circ}\)C to avoid the introduction of moisture during the milling process. The powders were thoroughly ball-milled in a stainless-steel vessel by using a Fritsch Pulverisette planetary mill [11]. The powders were finely milled using a unique set of milling conditions in two steps, using a powder-to-ball ratio of 1:4. In step 1, a low rpm of 270 was used, and the powders were milled for 6 h, whereas in step 2, a high rpm of 400 was used for another 6 h of milling. These steps in the milling process enabled the thorough mixing of the precursors. The high-rpm step plays a significant role in the mechanical activation [48] of the mixed powders. Mechanical activation helps the disintegration of constituents to yield smaller sized powders while increasing the active surface area. Consequently, decreasing the time and the temperature of synthesis are desirable for Ti\({}_{2}\)AlN synthesis. The mixed powders were placed in a stainless-steel die and compacted into 13 mm diameter pellets by applying a uniaxial pressure of 4 MPa.  
For this particular step annealing synthesis method, the pellets were placed in an alumina crucible and placed in a double-tube furnace, where a constant flow of argon gas was maintained throughout the thermal annealing process. This method includes two different steps, involving different heating rates and temperatures. First, a heating rate of 5 \({}^{\circ}\)C/min was used to reach 600 \({}^{\circ}\)C with a soaking time of 1 h. Second, a relatively high heating rate of 20 \({}^{\circ}\)C/min was used to reach 1100 \({}^{\circ}\)C from 600 \({}^{\circ}\)C, and the sample was soaked for 3 h at that temperature. After completion of the synthesis process, the pellets were naturally cooled to room temperature and ground with an agate mortar pestle to obtain Ti\({}_{2}\)AlN MAX phase powder.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 1 Introduction
Piecewise continuous-sine functions with cubic polynomial arguments are employed to depict the joint trajectories along the waypoints, where each piece of sine function depicts one joint trajectory between adjacent waypoints. With predefined conditions, each piece of sine function is depicted with one unknown parameter which should be optimized. The MTTP is converted into a parameter-optimization problem. An IGA is developed to optimize the unknown parameters. In the IGA, each chromosome consists of three parts. The first part denotes the waypoint sequence, the second part denotes the sequence of joint configurations corresponding with the first part, and the third part denotes a special value corresponding with the depiction of the joint trajectories. Since the system is redundant, each waypoint corresponds with infinite joint configurations. Moreover, considering the sequence of joint configurations directly leads to combination explosion. An approach based on the concept of the dual-arm angle [32] was employed to formulate joint configurations. At each waypoint, eight joint configurations were derived with an assignment of the arm angle. The rest is organized as follows. Preliminaries and adopted notation for space robotics are presented in Section 2. In the same section, the MTTP, the objective functions, the approach to depicting the joint configurations at each waypoint, and the approach of formulating the joint trajectories are explained. The IGA encoding and updating mechanisms, together with the IGA optimization process, are introduced in Section 3. In Section 4, numerical simulations are carried out to validate IGA, including comparisons with two other approaches. Section 5 concludes the work and gives an outlook for further research.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 5 Deep Learning Techniques in Agriculture
### Framework Used  
Concerning the frameworks used, all the well-known CNN architecture works also used a deep learning framework. The percentage of used frameworks was as follows; Keras (four papers, 32%), TensorFlow (three papers), TensorFlow/Keras (five papers), PyTorch (one paper), TensorFlow/PyTorch (one paper), and Deeplearning4j (one paper). Meanwhile, five papers developed their own framework.
# In-Process Measurement of Barkhausen Noise for Detection of Surface Integrity during Grinding
## 4 Conclusions  
The investigations have shown the suitability of in-process BN measurements for the detection of thermo-mechanical impact on surface integrity during grinding of case hardened steel. Detectability was demonstrated based on surface states from initial residual stress changes to strong tempering zones on workpieces with different initial states. Similar to commonly used post-process BN measurements, maximum (M\({}_{\rm MAX}\)) as well as averaged (M\({}_{\rm MEAN}\)) BN amplitude increase with initial thermo-mechanical influences and decrease with stronger tempering. Differences in the behavior of M\({}_{\rm MAX}\) and M\({}_{\rm MEAN}\) during in-process measurements were explained by mechanical excitation that does not affect the area around coercivity field strength. Both values show a high sensitivity for tempering and changes in the residual stress state, and comparatively low standard deviations. Further investigations are required on the influences from grinding itself on BN signals (especially M\({}_{\rm MEAN}\)), which can overlap the influences from the generated surface area state. Despite this need for further research, the results show the possibility of grinding process monitoring or control using Barkhausen noise, for example in combination with process models. Thus far, limit values for the detection of surface changes cannot be defined universally but have to be determined for individual applications.  
Conceptualization, formal analysis, writing--original draft preparation and visualization, R.J.; investigation, R.J. and J.H.; writing--review and editing, J.H. and J.E.; supervision and project administration, B.K. and J.E. All authors have read and agreed to the published version of the manuscript. This research was funded by the German Research Foundation (DFG), grant number 401804277. This scientific work was supported by the German Research Foundation (DFG) within the research priority program SPP 2086 for projects EP128/3-2 and KA1006/28-2. The authors thank the DFG for this funding and intensive technical support. The authors declare no conflict of interests.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 5 Discussions
In practice, we do not expect the gyroscope sensor in smartphone can always be calibrated. The experiments with noisy headings confirm our fusion positioning approach can be applied to noisy situations. The performance of orientation filtering was also investigated. To check the varied angle at each turn, we computed the differences between the headings around turn locations. The angle differences at the three U-turns of Route 2 are listed in Table 5. In the case of inaccurate initial position, the angle difference at the first U-turn (column '1 st turn raw value') contains obvious error (\(-208.206^{\circ}\)), while the values after orientation filtering are close to the true value (columns 'xx turn filtered value'). With disturbed orientations, the values after orientation filtering are not accurate. This comparison reflects our orientation filtering method may not be valid when the smartphone is swayed. From the test results in Figures 11b-14b, we find the orientation filter can only locally correct locations of each route segment. This orientation error is reduced when the PDR is conducted in a short range and frequently corrected by the BLE-based localization. The PDR enriches the route details during the tracking. Accordingly, 'jumping' cases are reduced as well (see Figure 11c). Regarding the PDR algorithm, we also compared the accuracy of step counting between our method and the Android built-in step counter. Table 6 presents the average of four tests for each route. In this case, the step counts of our PDR algorithm is more accurate than the built-in counter. There are only slight differences between our counts and the actual steps, and these counts are sufficient for us to generate PDR results at a high accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Our Method** & **Android** & **Ground Truth** \\ \hline Route 1 & 104 & 89 & 102 \\ Route 2 & 179 & 174 & 184 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Step counts of our PDR algorithm and Android API. \begin{table}
\begin{tabular}{c} **Rout 2** \\ **Disturbed Orientation** \\ \end{tabular} \\ \hline
**CDF 90\% (m)** & 4.50 & 3.78 & 5.84 & 3.54 & 5.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CDF of the four cases. Figure 17: CDF of the both routes. Orange color indicates the case of inaccurate initial positions, while blue represents the case of disturbed orientations: (**a**) Route 1; and (**b**) Route 2.
## 5 Conclusions  
A radix-8 weighted Booth-encoded multi-modulus multiplier based on an area-saving hard multiple generator (HMG) is proposed in this paper. Compared with the methods previously reported in the literature, the proposed work can achieve better performance with a circuit design that has a lower power, a faster operation, area-saving, and a lower area-delay product (ADP) and power-delay product (PDP). With the multi-modulus HMG, the proposed architecture can save up to \(55.23\%\) (\(n=40\)) of hardware area. With the multi-modulus multiplier, the proposed architecture can save up to \(35.46\%\) (\(n=24\)) of hardware area, up to \(11.15\%\) (\(n=16\)) of delay time, up to \(24.73\%\) (\(n=24\)) of dissipation power, up to \(38.88\%\) (\(n=8\)) of ADP, and up to \(27.85\%\) (\(n=24\)) of PDP compared with previously reported approaches. The Xilinx field programmable gate array Artix-7 XC7A35T-CSG324-1 chipset was used for synthesis and implementation. The proposed approach can be applied in cryptography, error correction codes, digital signal processors, and other fields.  
Conceptualization, C.-T.K. and Y.-C.W.; Methodology, C.-T.K. and Y.-C.W.; Software, Y.-C.W.; Validation, C.-T.K.; Formal analysis, C.-T.K.; Investigation, C.-T.K. and Y.-C.W.; Writing--original draft, C.-T.K.; Writing--review & editing, C.-T.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflict of interest.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.2 The Effect of Enhancing the Task Selection Procedure  
The procedure for selecting tasks runs in the critical path of the kernel scheduler, so the target task for the migration must be identified as efficiently as possible. Therefore, this study selects the target task more quickly to allow more efficient inter-node task migration for multithreaded applications. Only the task in the runqueue for which the thread group leader is currently on nodes rather than destination and source nodes is evaluated. No thread group leader is migrated across nodes.  
Figure 12 shows the different improvement ratios. The Exclbase policy achieves a more significant improvement than the MB policy. This enhancement has a different effect on each because the MB policy evaluates each task in the runqueue to select the most beneficial task for migration. For the MB+ policy, though this enhancement allows faster task selection by only evaluating the subset of tasks in the runqueue, the selected target task may not result in a greater benefit than the most beneficial task. If there are a few tasks in the runqueue, the saved evaluation costs can be negated by selecting a target task that is not the most suitable.  
The Exclbase policy selects the task for which the thread group is least exclusive regarding the thread distribution on nodes. The Exclbase policy does not consider the  
Figure 12: Improvement ratio for enhancing the task selection procedure.  
examined task's thread group leader, so the selected task can be migrated to the node different from where its thread group leader is currently located. Moving tasks away from its thread group leader can involve more remote memory access after migration. In contrast, the Excl policy incorporates the proposed enhancements for task selection procedure into the Exclbase policy. The Excl policy evaluates the task for which the thread group leader is on other nodes and migrates the task to the node the thread group leader is on. Most memory pages are first touched by the thread group leaders, as presented in Section 3.2, so the Excl policy successfully enhances the Exclbase policy and improves the performance.
## 2 Model and Methodology
### The Clearness Index Based Kalman Filter  
In general, the Kalman filter is implemented on a time-sequence of forecasts to reduce the systematical and random forecasting errors. As mentioned earlier, however, Kalman filter is not able to handle substantial day-to-day changes in the forecasting error. For the sake of improving the performance of the Kalman filter for day-ahead solar prediction, a new framework of implementing Kalman filter is proposed in this study based on the clearness index. It is a dimensionless number between 0 and 1, which is usually used to measure the clearness of the atmosphere, and is defined as the surface radiation divided by the horizontal extraterrestrial radiation,  
\[K_{t}=\frac{\sum_{24h}^{i=1}I}{H_{0}}, \tag{6}\]  
where \(I\) and \(H_{0}\) stand for the solar radiation (hourly) and the extraterrestrial horizontal radiation per day, respectively. \(H_{0}\) is the horizontal insolation without any atmosphere effects and usually calculated for specific sites from the following formulation [32],  
\[H_{0}=\frac{24\times 3600G_{sc}}{\pi}(1+0.033\cos\frac{360n}{365})\times(\cos \phi\cos\delta\sin\omega_{s}+\frac{\pi\omega_{s}}{180}\sin\phi\sin\delta), \tag{7}\]  
where \(G_{sc}\) is the solar constant (1367 W/m\({}^{2}\)), \(n\) is the day of the year, \(\phi\) is the latitude of the location in degree, \(\delta\) is the declination angle in degree and \(\omega_{s}\) is the sunset hour angle in degree. The horizontal extraterrestrial radiation \(H_{0}\) is a function of the day of year and latitude. Thus, the \(H_{0}\) at two target sites (i.e., Maizuru and Qinghai) in Japan and China for all years can be easily obtained (Figure 1) and then used to calculate clearness index that is used to define the sky conditions in this study.  
We choose the clearness index defined in Equation (6) to rearrange the time-sequences of forecasts and observations to three new kinds of data series that might have similar forecasting error characteristics, in order to conduct a Kalman filter efficiently. To be specific, three kinds of sky conditions, i.e., clear (\(K_{t}>0.65\)), cloudy (\(0.4\leq K_{t}\leq 0.65\)) and complete overcast (\(K_{t}<0.4\)), are defined firstly based on the values of the clearness index. Then, we implement the Kalman filter on three new data series under different sky conditions. Such a framework for implementing the Kalman  
Figure 1: The extraterrestrial horizontal radiation \(H_{0}\) (W/m\({}^{2}\)) series for two sites.  
filter in solar radiation forecasting, to the best knowledge of the authors, is proposed for the first time. We compare the performance of the clearness index-based Kalman filter against the results of merely implementing the Kalman filter to the time-sequence of forecasts, WRF raw forecasts and the corresponding observed data in Section 4.
## 1 Introduction
Additionally, existing methods often lack dynamism, making them ineffective in predicting and adapting to the temporal variations and impacts of geological disasters. Therefore, this paper proposes an integrated dynamic resilience assessment and optimization framework (SRN-RAOF) for sparse road networks affected by geological disasters in extreme environments. This framework aims to comprehensively understand and en hance the resilience of sparse road networks in regions prone to geological disasters by constructing a dual-layer topological structure model, defining the resilience connotation of sparse road networks, and developing resilience quantification assessment models and optimization strategies. This study specifically focuses on the dynamic changes in road network performance following geological disasters and the corresponding recovery strategies. Specifically, it examines the impact of sparse network structures, disaster duration, and the residual functionality of damaged infrastructure on network resilience in the absence of alternative routes. The study does not address pre-disaster risk prediction or long-term monitoring. The main contributions of this study are as follows:  
* Proposing a two-layer topological modeling approach suitable for road networks in regions prone to frequent geological and meteorological disasters. * Developing a dynamic resilience evaluation method for sparse road networks under high-frequency cumulative disaster events. The remainder of this paper is organized as follows: Section 2 reviews related research on transportation network resilience assessment; Section 3 introduces the mathematical model of SRN-RAOF; Section 4 applies SRN-RAOF to an actual road network in China; and Section 5 presents the conclusions of the paper.
## 4 Results

Table 4: Average packet loss (%) obtained between PCs-UAVs at different gap distances for OLSR, BATMAN-ADV, and Babel, for 2 frequency bands: 2.4 and 5 GHz. Each measurement had a duration of 60 s.  
Figure 3: Average throughput (Mbps) vs. distance (meters) for each protocol and frequency band. between two nodes was 40 m. This is again reflected above 40 m, which is the distance at which the intermediate hop to the relay node existed. If the relay node had been located more than 40 m away, it would have been impossible to use it as an intermediate communication node in the 5 GHz band. We can conclude that Babel and OLSR obtained better results than BATMAN-ADV, given the results obtained. This may have been expected because BATMAN-ADV has a too-high routing update period that was originally not intended for FANETs. Finally, between Babel and OLSR, Babel offered the best overall performance, guaranteeing more stable results in both frequency bands.
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 2 Formulation
### Modal Functions  
The longitudinal modal functions (electric field for TM modes and magnetic field for TE modes) in an equilateral triangular waveguide are calculated as [26; 27; 28]:  
\[\phi_{z}^{\mathrm{TE}}(x,y)=\sum_{q=1}^{3}\left\{\begin{aligned} \cos\bigl{(}k_{yq}(y-h_{t})\bigr{)}\ \cos\bigl{(}k_{xq}x\bigr{)}\,&& \In the case of TM modes, the tangential modal functions are obtained as \(\mathbf{\Phi}^{\text{TM}}(x,y)=\phi_{x}^{\text{TM}}\hat{\mathbf{x}}+\mathbf{\phi}_{y}^{\text{TM }}\hat{\mathbf{y}}=-\mathbf{\
abla}\phi_{z}^{\text{TM}}(x,y)\), leading to  
\[\phi_{x}^{\text{TM}}(x,y)=\sum_{q=1}^{3}k_{xq}\begin{cases}\sin\bigl{(}k_{yq}(y- h_{t})\bigr{)}\,\sin(k_{xq}x)\,&(\text{S})\\ -\sin\bigl{(}k_{yq}(y-h_{t})\bigr{)}\,\cos(k_{xq}x)\,&(\text{A})\end{cases} \tag{17}\]  
\[\phi_{y}^{\text{TM}}(x,y)=-\sum_{q=1}^{3}k_{yq}\begin{cases}\cos\bigl{(}k_{yq}( y-h_{t})\bigr{)}\,\cos(k_{xq}x)\,&(\text{S})\\ \cos\bigl{(}k_{yq}(y-h_{t})\bigr{)}\,\sin(k_{xq}x)\,&(\text{A})\.\end{cases} \tag{18}\]  
Figure 2 plots the normalized magnitude of the tangential modal functions \(\mathbf{\Phi}_{i}(x,y)\) in the equilateral triangular waveguide. The fourteen considered modes are ordered according to their increasing cutoff frequency \(f_{c,mn}=k_{c,mn}c/(2\pi)\), \(k_{c,mn}=4\pi\sqrt{m^{2}+n^{2}+mn}/(3e)\) being the cutoff wavenumber [26]. Additionally, the modes are labeled following the notation \(\text{TM}/\text{TE}_{\text{s}/\text{a}\,mm}\). Note that the total tangential field in the surface of the triangular hole will be a linear combination of the fourteen considered modal functions (see Equations (1) and (2)).
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN  
At present, the research on routing protocols for WBAN is mainly divided into the following methods [14; 15], and this paper refers to the classification method presented by [PERSON] et al. [14], (as shown in Figure 3) posture-based routing, temperature-based routing, cross-layer routing, QoS-based routing, and cluster-based routing.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods
### System Model and Problem Formulation  
#### 3.1.1 IoD Network Architecture  
The Ground Control Station (GCS) edge servers, cloud servers, and drones compromising the Internet of Drones (IoD) network architecture are linked together with wireless communication channels. Drones (D), which are limited by computational and energy budgets, employ sensors and a communication module to sense data and send it to others. Localized control is offered through GCS, which serves as a bridge between drones and the infrastructure backend. Edge servers (ES), which offer computational power with low latency, are located near drone operating areas, while cloud servers (CS) process data and large-scale computations. Drone-to-GCS (D2G) through light protocols, GCS-to-Edge (G2E) through secure wireless links, and Edge-to-Cloud (E2C) via high-bandwidth lines are all components of the communication system. An IoD network architecture is shown in Figure 2.  
Figure 1: Flowchart of proposed framework.