esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 VLC-IoT: Existing and Proposed Approaches
### Handover Mechanisms in VLC-IoT
#### 6.3.2 Proposed Approaches  
A review of the relevant literature shows that a few handover techniques have been proposed for VLC-IoT to improve specific key performance metrics of the VLC-IoT system by taking into consideration the unique characteristics of VLC systems. We provide a brief account of such a handover mechanism below.  
1. Received signal intensity-based vertical handover (RSI-VH)  
This handover mechanism is designed based on the received signal intensity (RSI) values of the APs at the VLC-IoT devices. This handover process is usually triggered when the RSI value of any neighboring APs of a VLC-IoT device is greater than the RSI value of the current AP serving the VLC-IoT device [57]. This handover technique was majorly proposed to improve throughput and QoS satisfaction levels of VLC-IoT systems [58].  
2. Non-line-of-sight-based vertical handover  
This handover process is proposed to reduce unnecessary/avoid frequent handover in VLC-IoT systems to improve system performance. In the proposed handover mechanism, a VLC-IoT device waits for a set time (dwell period) when it detects an LOS blockage instead of immediately implementing a handover [56]. When the dwell period expires, if a neighboring AP with better channel resources is available, the VLC-IoT devices handovers to it. Otherwise, the VLC-IoT device will remain with it current serving AP until the blocked link is restored [57].
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 2 Influencing Factors of City Gas Daily Load
### Holidays  
Holidays have a great impact on the daily gas load, and the daily gas load curve shows obvious differences from working days. By sorting out the daily gas load data of major holidays in a city for three consecutive years, it was found that the daily load changes during holidays tended to be consistent. The daily gas load curves during the National Day and 5 days before and after are shown in Figure 3, and the daily gas load curves during the Spring Festival and 5 days before and after are shown in Figure 4.  
The analysis shows that the daily gas load during the National Day and Spring Festival was less than that on weekdays, and the daily gas load before and after the festival fluctuated obviously. Due to the obvious difference between the daily gas load on holidays and the daily gas load on working days, the daily gas load data on holidays should be processed separately when studying the daily gas load prediction.
## 3 Provisioning Resource Pool View
### Quantitative Evaluation
In addition, the initialization time of the method using IaC was 455.384 s on average, and that of the method using BVMO was 92.584 s. This was confirmed by an increase in the load on the system as the software was added. However, it was confirmed through several experiments that BVMO, which only performs volume connections, produces consistent results. As for the total execution time according to the increase in software, it was confirmed that IaC had an average of 458 s and BVMO had an average of 103.2 s.  
Figure 17 shows the results of each experiment performed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \multirow{3}{*}{\begin{tabular}{c} **Iteration** \\ **(Count)** \\ \end{tabular} } & \multicolumn{3}{c}{**VM Creation Phase**} & \multirow{3}{*}{\begin{tabular}{c} **Initialization** \\ **(Seconds)** \\ \end{tabular} } \\ \cline{3-5}  & & \begin{tabular}{c} **Instance** \\ **Spawn** \\ **(Seconds)** \\ \end{tabular} & & \
\begin{tabular}{c} **Instance** \\ **Build** \\ **(Seconds)** \\ \end{tabular} &
\begin{tabular}{c} **Initialization** \\ **(Seconds)** \\ \end{tabular} \\ \hline \multirow{3}{*}{IaC} & 1 st & 2.64 & 2.89 & 461.26 & 469 \\ \cline{2-5}  & 2 nd & 2.7 & 3 & 440.83 & 449 \\ \cline{2-5}  & 3 rd & 2.55 & 3.16 & 455.66 & 451 \\ \cline{2-5}  & 4 th & 2.1 & 2.96 & 460.43 & 455 \\ \cline{2-5}  & 5 th & 2.62 & 3.2 & 458.74 & 466 \\ \hline \multirow{3}{*}{BVMO} & 1 st & 3.13 & 5.39 & 93.95 & 105 \\ \cline{2-5}  & 2 nd & 3.09 & 5.31 & 89.91 & 101 \\ \cline{1-1} \cline{2-5}  & 3 rd & 3.17 & 5.42 & 93.24 & 105 \\ \cline{1-1} \cline{2-5}  & 4 th & 3.06 & 5.57 & 92.83 & 104 \\ \cline{1-1} \cline{2-5}  & 5 th & 3.16 & 5.28 & 91.34 & 101 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Test 3—VM with Number of Two Software. As a result, it was confirmed that BVMO consistently has a time-reduction effect compared with the method using IaC. In contrast to the IaC method, which differs in time depending on the software provision, BVMO showed a consistent required time in each experiment. This is assumed to occur because BVMO utilizes provisioned resources, does not require additional resource-preparation time and operates consistently to create virtual machines (volume mapping after instance preparation).
## 5 Conclusions  
The effects of the CAP treatment evaluated by MTT and SRB assays suggest that this approach can reduce the metabolic activity and the protein content with relatively short periods of time exposure in most cell lines tested. Maximal effects are seen at the longest time exposures tested, 60, 90, and 120 s, with exposures recorded before 60 s not revealing any damage to the cancer cells.  
Furthermore, exposure during 60 s highlights the more pronounced selective effects, considering the periods and electrical output settings evaluated. Concluding, these results are promising for future studies as plasma might offer a new anti-cancer treatment. Currently, several studies are being performed to evaluate the type of cell death and mechanism, considering different plasma conditions. Thus, CAP should be the target of a more refined investigation to confirm its potential as an anticancer therapy.  
Plasma is envisioned as a local treatment, whether through irradiation of body surfaces, exposed or available by endoscopy, or exposure of cavities to activated media. Moreover, current literature points to a penetration of several millimeters into tissue [82]. In the near future, we aim to address these topics.  
Conceptualization, R.S.-T., M.L., A.M.-A., F.C., and M.F.B.; methodology, R.S.-T., M.L., and F.C.; validation, M.L.; formal analysis, R.S.-T. and F.C.; investigation, C.A.-F., R.S.-T., N.A., G.B., J.D.-F., I.M., R.N., B.S., and R.T.; resources, A.M.A. and M.F.B.; data curation, C.A.-F., R.S.-T., and M.L.; writing--original draft preparation, C.A.-F. and R.S.-T.; writing--review and editing, M.L., F.C., and M.F.B.; visualization, C.A.-F. and R.S.-T.; supervision, M.L., F.C., and M.F.B.; project administration, M.F.B.; funding acquisition, M.F.B. All authors have read and agreed to the published version of the manuscript.  
CIBB was funded by the Foundation for Science and Technology (FCT, Portugal) through the Strategic Project UIDB/04539/2020 and UIDP/04539/2020. PhD Scholarship grant from FCT to [PERSON] [SFRH/BD/136973/2018] and [PERSON] [SFRH/BD/116794/2016].  
The authors declare no conflict of interest.  
Appendix AFigure 12: SRB assay results 24 h after plasma therapy application in different human cell lines: hormonal receptor positive breast cancer (MCF7), triple-negative breast cancer (HCC1806), prostate (PC3 and LnCap), esophageal adenocarcinoma (OE19), urinary bladder grade 3 carcinoma (HT1376), osteosarcoma (MNNG-HOS), extrahepatic bile duct carcinoma (TFK-1), endometrial cancer (ECC-1), lung cancer (H1299), melanoma (A375), colon carcinoma (WilDr), and fibroblast (HFF1) at distinct times: 15, 30, 60, 90, and 120 s. Results are expressed as the percentage of protein content normalized to control. Statistical significance in relation to control and between cell populations is represented as * for \(p<0.05\), ** for \(p<0.01\), and *** for \(p<0.001\) (\(n=9\)).
# Bearing Fault Diagnostics Based on the Square of the Amplitude Gains Method
## 4 Experimental Results
Bearing variants are marked on the horizontal axis (a-undamaged bearing; b-with a damaged outer race; with the cut ball at the depth of: c-1 mm; d-2 mm; e-3 mm; f-with a damaged inner race), and on the vertical axis the number occurrences of a given color. It can be seen in both types of figures that the undamaged variant significantly dominates over the others in the case of the threshold not exceeding \(\mu\pm\sigma\) (_green_), while the values of the thresholds indicating damage have the lowest. It can also be noticed that in the case of bearing balls with cuts 1, 2 and 3 mm deep, the threshold \(\mu\pm\sigma\) decreases (_green_) while the threshold \(\mu\pm 2\sigma\) increases (_black_). High values of _black_ and _red_ colors are visible for the variant of the bearing's outer race damage, as a result of which, during the rotation of the shaft, all of the balls in the bearing hit the discontinuity of the race, generating vibrations. The highest values of _black_ color can be seen for the bearing with the cut inner race. In this case, balls more frequently hit the cut point on the inner race than in the outer race. Summarizing the results presented in the collective characteristics (Figure 10), the number of occurrences of the _black_, _red_, _blue_, and _green_ color fields is collected in Table 5. As can be seen from the damage maps, where the damage to the ball was 1 mm deep (Figure 9c) and 2 mm deep (Figure 9d), it is difficult to see an increase in the number of parameters. Therefore, a good complement to the damage maps of the technical condition is their quantitative analysis, from which it is already clear that there was an increase in the number of _red_ and _black_ fields between the indicated damage. The presented results reflect the technical condition of the bearing. In its current form, the method cannot determine the type of bearing damage based on the damage map, e.g., damage to the bearing ball to a depth of 1 or 3 mm. However, it is very effective for detecting when a bearing goes from a usefulness condition to failure (damage). Figure 4:0 Collective characteristics of the damage maps of the SKF 1207K bearing from the side of: (**a**) the drive (**b**) the brake for the shaft speed of 900 rpm. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Selection** & & \multicolumn{3}{c}{**Bearing No 1**} & \multicolumn{3}{c}{**Bearing No 2**} \\ \hline  & _black_ & _red_ & _blue_ & _green_ & _black_ & _red_ & _blue_ & _green_ \\ \hline Undamaged bearing & 0 & 11 & 77 & 272 & 1 & 13 & 117 & 229 \\ Damaged outer race & 101 & 53 & 96 & 110 & 149 & 31 & 57 & 123 \\ Cut ball at a depth of 1 mm & 6 & 40 & 102 & 212 & 63 & 42 & 113 & 142 \\ Cut ball at a depth of 2 mm & 6 & 43 & 109 & 202 & 91 & 56 & 81 & 130 \\ Cut ball at a depth of 3 mm & 35 & 34 & 107 & 184 & 157 & 39 & 66 & 96 \\ Damaged inner race & 259 & 18 & 34 & 49 & 256 & 9 & 41 & 54 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of color fields in subsequent damage maps presented in Figures 8 and 9.
## 1 Introduction
### Focus and Contributions  
In this paper, to deal with the energy supply and information backhaul problems for wireless sensor networks in remote places, we propose an airborne massive MIMO system for WET by combining the AP with massive MIMO technology. The system consists of a low-altitude AP equipped with a two-dimensional (2D) rectangular antenna array and a number of sensors on the ground, as shown in Figure 1. The transmission phase is divided into a WET phase during which the AP charges sensors through downlink WET and an uplink wireless information transmission (WIT) phase during which the AP collects data sent by sensors.  
In the considered hybrid air-terrestrial network, the biggest challenge is the design of practical energy and information beamforming schemes. Different from the BS of a terrestrial network, AP is usually hardware- and computational complexity-limited due to the consideration of weight,  
Figure 1: System model.  
fabricating cost and energy supply [8]. Traditional linear beamforming schemes, such as match filter (MF) beamforming [10] and zero-forcing beamforming [12], require a large number of radio frequency (RF) chains at AP and have high computational complexity. This may make them infeasible when applied on AP. Another challenge lies in the acquisition of instantaneous channel state information (CSI) at the AP. Due to the mobility of AP, the sensor must transmit the uplink pilot sequence frequently, which causes high training overhead and energy consumption. This paper studies the three-dimensional (3D) energy and information beamforming scheme for an airborne massive MIMO system considering the above challenges. The main contributions are as follows.  
* By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR scheme. Although the scheme does not rely on instantaneous CSI, its implementation still requires large numbers of RF chains at AP. When the number of RF chains is limited, a heuristic strongest-path energy and information beamforming scheme is proposed. The scheme requires only virtual angle of departure (AoD) information of sensors and can be implemented in the analog-domain with low hardware complexity. The analysis of the relation between two schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP's antennas tends to infinity.
* Based on the asymptotic approximation of average received SINR at AP, the system parameters, including transmit power, number of active antennas of AP and duration of the WET phase, are jointly optimized to maximize the system EE under the proposed strongest-path beamforming scheme.
* Numerical simulations are presented to evaluate the spectral and energy efficiency performances of the proposed beamforming schemes under different system parameters. The results demonstrate the superiority of the proposed schemes in the airborne massive MIMO systems.
## 3 Background  
### PID Controller  
The PID control is the most common control method of industrious automation. As a linear controller, it constitutes the deviation \(e(t)\) derived from the set point \(r(t)\) and the actual output value \(y(t)\). Through the calculation of proportion, integration and differentiation operation in the continuoustime domain, the output value \(u(t)\) is computed by the following formula: where \(K_{p}\) is the proportion coefficient, \(K_{i}\) is the integration coefficient, \(K_{d}\) is the differentiation coefficient.  
\[e(t)\ =\ r(t)\ -\ y(t) \tag{1}\]  
\[u(t)\ =\ K_{p}e(t)\ +\ K_{i}\int_{0}^{t}e(t)\mathrm{d}t+K_{d}\frac{\mathrm{d}e( t)}{\mathrm{d}t} \tag{2}\]  
In order to induce the calculated quantity, the sampling instant \(kT\) replaces the continuous time \(t\). The integral item and differentiation item are discretized. Through the transformation, the incremental PID controller is presented as:  
\[\Delta u(k)=K_{p}[e(k)-e(k-1)]+K_{i}e(k)+K_{d}[e(k)-2e(k-1)+e(k-2)] \tag{3}\] \[=\ \big{(}K_{p}+K_{i}+K_{d}\big{)}e(k)-\big{(}K_{p}+2K_{d}\big{)} )e(k-1)+K_{d}(k-2)\]  
\[u(k)\ =\ u(k\ -\ 1)\ +\ \Delta u(k) \tag{4}\]  
where \(\Delta u(k)\) is the control increment, \(u(k)\) is the output value of \(kT\) time.
# Experimental Verification of Yam (_Dioscorea rotundata_) Drying with Solar Adsorption Drying
## 2 Materials and Methods
### Description of Dryer Systems  
Three experimental drying methods were employed in this study: traditional open sun drying (OSD), solar drying (SD), and the solar adsorption dryer (SAD) as described in Figure 1. OSD and SD were applied during the daytime, while the SAD is also applied during part of the night. The experiments on the three systems were performed in parallel in an open space near the Agricultural Engineering Department, KNUST, Kumasi, Ghana. For OSD, samples were placed in three trays next to each other in the open sun with air passing through freely. Drying for this system depends on the solar radiation, ambient temperature, humidity,, and air circulation along and around the trays. These conditions were subject to natural and uncontrolled variations. The SD system is made up of a solar collector system and a dryer chamber (i.e., equivalent to path 1\(\rightarrow\)2\(\rightarrow\)3 in Figure 1). The collector system consisted of glass cover, air gap, absorber plate and insulation material at the backside. The collector can achieve air temperatures up to 60 \({}^{\circ}\)C. [PERSON] et al. provide details of the collector design and the performance. [20].  
The SAD was organized as presented in Figure 1. During the day-time, the SAD was operated as a SD, while, during night drying, the air passed through a bed of silica gel, which was used as adsorbent.  
Figure 1: Solar-adsorbent dryer (SAD). 1\(\rightarrow\)2\(\rightarrow\)3: air flow during drying, 4\(\rightarrow\)5\(\rightarrow\)3: air flow during night drying, 6\(\rightarrow\)7\(\rightarrow\)8: air flow for regeneration during day-time.  
The applied solar collector was designed to reach air temperatures up to 60 \({}^{\circ}\)C under Ghanaian radiation conditions. For adsorbent regeneration, another solar collector is used. This collector was larger and could reach air temperatures between 80 to 85 \({}^{\circ}\)C. Details about the design and performance are given by [PERSON] et al. (2010). In the drying cabinet, the air flowed freely along the product particles at the wired trays by natural (OSD) or forced convection (SD and SAD).
# Generalized Optimality Criteria Method for
## 4 Conclusions  
In this article, a new topology optimization algorithm, the generalized optimality criteria method (GOCM), was proposed. It is based on the conventional optimality criteria method (OCM), wherein both Lagrange multiplier and design variables are updated. The main difference is that the proposed method can have multiple inequality constraints, while the OCM can only support a single constraint (e.g., volume fraction). The Lagrange multipliers were updated on the basis of the constraint violation and constraint change. Then, the design variables were updated toward the direction to satisfy the optimality criteria. Therefore, constraints may not have been satisfied during the optimization iteration, but they were satisfied upon convergence. Numerical examples showed that the proposed method was faster than the OCM and the method of moving asymptotes (MMA). In the case of a MATLAB-based 2D model with 5000 design variables, GOCM was 10 times faster than OCM. In the case of the gripper-arm model (68,000 elements), GOCM was more than 1000 times faster than MMA and 330 times faster than OCM. When there were multiple constraints, the optimization time became comparable with that of finite element simulation times. In the case of MMA, the optimization algorithm time was about 35% of the total time, while the optimization time of GOCM was negligible. Therefore, the proposed method was versatile to handle multiple constraints, while computationally efficient.  
For future research, it would be beneficial to handle active and inactive constraints separately. The current implementation updated the Lagrange multipliers for both active and inactive constraints, but it would be necessary to remove the effect of inactive constraints completely from optimality criteria. In GOCM, it is possible that constraints are consistently violated during iterations and only the final converged design satisfies the constraints. In order to make un-converged designs useful, it would be beneficial that designs are updated in the feasible region.  
Conceptualization, N.H.K.; methodology, N.H.K. and T.D.; software, D.W.; validation, N.H.K.; formal analysis, T.D.; investigation, J.D.; resources, D.W.; data curation, T.D.; writing--original draft preparation, N.H.K.; writing--review and editing, N.H.K.; visualization, J.D.; supervision, N.H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Autodesk Nasiran input files are available.  
The authors declare no conflict of interest.  
Figure 7: Accumulated optimization algorithm time for the method of moving asymptotes (MMA).
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 3 Research and Current Status of Energy Management Strategies for HEVs
### Optimization-Based Energy Management Strategies  
An optimization-based EMS is one that uses optimization algorithms to search for and apply optimal or sub-optimal control sequences after establishing the system control objective function and constraints. This type of strategy usually employs known or predicted driving condition information to calculate the actuator settings that minimize the set cost function as the global or local optimum solution [82; 83; 84; 85; 86; 87]. The core problem is generally expressed as finding the objective function's minimal value in the feasible domain. Presently, optimization-based energy management strategies consist mainly of global optimization and instantaneous optimization [88; 89].  
Control strategies based on global optimization generally require an optimized action search under conditions where the global disturbances are known, such as in dynamic programming (DP), genetic algorithms, and game theory methods [90; 91; 92]. However, due to its computationally intensive and complex preview, this class of algorithms is not usually supported for real-time applications. As such, the results of global optimization strategies might be used as benchmarks for evaluating the merits of other energy management algorithms [93; 94; 95]. Meanwhile, the strategy based on instantaneous optimization defines a transient cost function at each moment and solves for the optimal amount of control for the vehicle, which is able to achieve the target of a global cost function approximation through iterative and roll optimization [96]. The main methods based on real-time optimization include equivalent consumption minimization strategies (ECMS), robust control, model predictive control (MPC), and Pontryagin's minimum principle (PMP). The expansion of these techniques is already a hot topic of current research in the field [97; 98; 99; 100].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Initial SOC** & **Final SOC** &
\begin{tabular}{c} **Used Fuel Energy** \\ **(10\({}^{8}\) J)** \\ \end{tabular} & **Savings (\%)** \\ \hline Rule-based & 0.8 & 0.2893 & 2.2730 & - \\ DP & 0.8 & 0.3339 & 2.0146 & 11.37\% \\ DOIP & 0.8 & 0.3093 & 2.0644 & 9.18\% \\ Rule-based & 0.2 & 0.2893 & 3.0951 & - \\ DP & 0.2 & 0.3891 & 2.6383 & 14.76\% \\ DOIP & 0.2 & 0.3348 & 2.7300 & 11.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicle performance with different control strategies over WLTP (Data from the source [75]).