esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Building integrated photovoltaic (BIPV) is one of the most efficient ways to utilize renewable energy in buildings. However, the stochastic characteristic of PV power generation and load challenges the optimal dispatch of the BIPV. This paper proposes an optimal scheduling strategy of BIPV microgrid considering virtual energy storage (VES), which intends to further improve the operating economy of a BIPV microgrid. Firstly, existing research findings and gaps in this research area were analyzed. Then, the mathematical model of each unit in the system was created, meanwhile, the air conditioner-based VES (ACVES) model and electric water heater-based VES (EWHVES) model are built by exploiting the heat/cold storage capability of building and the electric water heater, respectively. Next, the optimal scheduling model considering the VES is designed. Finally, the optimal scheduling model is solved by CPLEX to obtain the optimal scheduling strategy that reduces the operating cost of the system. Simulation results demonstrate that the proposed strategy can reduce the operating cost of the system. Furthermore, the VES shows the potential to replace the battery energy storage system.  
BIPV; microgrid; VES; air conditioner; electric water heater; optimal scheduling +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
In this study, we identified 7 full-length _RcTPT_ genes and 49 putative functional RcTPSs in the _R. ditnensis_ genome. There are 20 genes, expressed in the opening petals of butterfly rose, were involved in middle and downstream pathway of volatile terpene biosynthesis, including 9 _RcTPS_, 5 short-chain _RcTPTs_, and 6 _RcNUDX1_. These terpene-related genes exhibited different expression patterns during five different flower developmental stages. The emissions of geraniol were higher from old MU petals than from fresh MU petals, which might be related to the _RcGPPS_ expression. Combining volatile emissions, bioinformatic analysis and differential expression analysis, it is indicated that _RcTPS18_, a member of the TPS-b subfamily, may encode (_E,E_)-\(\alpha\)-farnesene synthase. The highly expressed _RcTPS32_, a predicted sesquiterpene synthase, exhibited increased expression in senescent petals, deserves further study on its products and functions. The present study provided valuable insights into the terpenoid biosynthesis mechanism in rose flowers.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/genes13071177/s1](https://www.mdpi.com/article/10.3390/genes13071177/s1)]([https://www.mdpi.com/article/10.3390/genes13071177/s1](https://www.mdpi.com/article/10.3390/genes13071177/s1)), Table S1: Characterized _trans_-phenyltransferases in _Arabidopsis thaliana_, _Solanum lycopersicum_ and other plants; Table S2: Primer sequences of genes used for qRT-PCR analysis; Table S3: Members of TPT-p and TPS-p genes in _Rosa chitensis_; Table S4: The corresponding IDs of _RcNUDX1_ genes in _Rosa chitensis_; Table S5: Characterized terpene synthase in other plants; Table S6: Numbers of TPT subfamilies in the genomes of nine plant. Figure S1: Alignments of seven _trans_-phenyltransferases amino acid sequences in _Rosa chitensis_; Figure S2: Phylogenetic relationships (a), conserved motifs (b), and gene structure analysis (c) of the _Rosa chitensis_ TPS gene family; Figure S3: Collinearity analysis of TPT genes among _Rosa chitensis_ and other representative plant species; Figure S4: qRT-PCR validation of eight _RcTPSs_ expressed in five samples of butterfly rose.  
Y.K.: Conceptualization, Methodology, Data Curation, Writing--Review and Editing. H.W.: Investigation, Writing--Review and Editing. L.L.: Investigation, Resources. X.D.: Investigation, Resources. J.B.: Conceptualization, Supervision, Project administration, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (31401901), China National Key R&D Program (2019 YFD1000400), and the Natural Science Foundation of Beijing, China (6222007).  
Not applicable.  
We thank [PERSON], Kunming Yang Chinese Rose Gardening Co., Ltd., for his assistance in the experiments.  
The authors declare no conflict of interest.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 4 System Model
### DRAMBR Framework  
The DRAMBR framework is shown in Figure 8. We introduce a new entity, the RS, which provides the RV. The RS is linked to the MA in the SCMS. During the reputation retrieval process, the RS will pre-sign the RV using the _Pre-Signature_ scheme proposed in [2]. The RV is then sent to the requested vehicle to complete the signature and attach it to the message with the PC.  
All MRs are submitted to the RS, which checks and evaluates all the received MRs. In this context, we set a threshold for accepting the MR as \(\tau_{Rep}\), and we denote the vehicle reporting the misbehavior as \((Rep_{\text{Veh}})\) and the target vehicle as \((Tar_{\text{Veh}})\). Our reputation system is designed to consider the MRs if they meet \(\tau_{Rep}\) and ignore any MR from \(Rep_{\text{Veh}}\) with a \(RV<\tau_{Rep}\).  
Figure 8: Proposed misbehavior reporting system (DRAMBR).  
Figure 7: \(MR\) attack.  
The system's efficiency is highlighted by its ability to handle contradictions between the MRs accurately. If most MRs contradict a specific MR, indicating the same misbehavior, the RS does not immediately classify this as malicious reporting. Instead, it evaluates the context to determine whether the discrepancy is due to an error or potential attack activity to make a fair and accurate decision while maintaining system reliability.  
To set the stage for the DRAMBR valuation process, we first outline the main entities involved, as shown in Table 2.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 4 Trials in the Baltic Sea Corridor (Tallinn-Helsinki)
### Description of Planned Coastal 5G Connectivity Trials  
A trial plan for testing the maritime solution involves several key steps to ensure the success of the implementation. Firstly, the installation and integration of a private network core and radio base stations on Eckero Line's Finbo ferry will be performed. Secondly, a controlled environment test will be carried out to verify that the private network core and radio base stations are working correctly. Next, a series of tests will be conducted on the ferry to evaluate the network's performance and identify any potential improvements or issues.  
The tests will be started by testing one frequency and connectivity solution first. This functional testing will be repeated for each solution, and when all solutions have been proven to work as planned, the next phase of testing will be started. During the next phase, the different solutions will be tested together and will focus on the balancing between the solutions. This dual testing will be repeated for the different solution scenarios. When all the scenarios have been proven to work, then the final phase of tests can be started. The third phase of testing will cover all the connectivity solutions, and the aim is to find the best overall solution to fulfill the demand of continuous and high-capacity connectivity solutions for maritime environments.  
Simulations of the multi-hop connectivity will be performed by setting up a number of vessels as moving base stations or multi-hop nodes in the maritime corridor. During normal operation, the network will be monitored to ensure stable and reliable coverage. The network's ability to maintain 5G coverage and local services in the event of a lost shore link will also be tested, simulating scenarios such as search and rescue operations, disaster relief or armed conflicts.  
Key performance indicators such as latency, throughput and reliability will be measured and analyzed. Based on the results, any necessary changes will be made, and additional testing will be conducted to verify the effectiveness of these changes. Data will be collected throughout the testing process to inform future developments and scaling decisions.  
Finally, the results of the trial will be compared with existing solutions to evaluate the benefits and limitations of the multi-hop approach. The findings will be shared with relevant stakeholders to guide future developments.
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 3 Case Analysis
### The Prediction of the SARIMA Model  
#### 3.1.1 The Stationarization of Series  
The time series of traffic flow from January 2014 to December 2018 and its breakdown are shown in Figure 3, and it can be seen that there is a noticeable trend and seasonality. Since the traffic flow is monthly collected, the number of periods is m = 12. At the same time, difference processing needs to be performed to obtain a steady time series for model construction, during which the original series, one-order non-seasonal difference series, one-order seasonal difference series, one-order non-seasonal difference, and one-order seasonal difference series were tested for unit root (ADF). The test results are shown in Table 2.  
It can be seen that when one-order non-seasonal difference and one-order seasonal difference (\(d\) = 1, \(D\) = 1) and one-order seasonal difference (\(d\) = 0, \(D\) = 1) were respectively performed on the series, the results of the ADF test are relatively similar; thus, the SARIMA (\(p\), 1, \(q\)) (\(P\), 1, \(Q\))\({}_{12}\) and SARIMA (\(p\), 0, \(q\)) (\(P\), 1, \(Q\))\({}_{12}\) models can be constructed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Differential Order**} & \multirow{2}{*}{\(\mathbf{t}\)} & \multirow{2}{*}{\(\mathbf{p}\)} & \multicolumn{4}{c}{**Critical Value**} \\ \cline{3-6}  & & & **1\%** & **5\%** & **10\%** \\ \hline \(d\) = 0, \(D\) = 0 & 0.862 & 0.993 & \(-\)3.616 & \(-\)2.941 & \(-\)2.609 \\ \(d\) = 1, \(D\) = 0 & \(-\)3.826 & 0.003 & \(-\)3.621 & \(-\)2.944 & \(-\)2.610 \\ \(d\) = 0, \(D\) = 1 & \(-\)4.802 & \(\leq\)0.001 & \(-\)3.629 & \(-\)2.945 & \(-\)2.611 \\ \(d\) = 1, \(D\) = 1 & \(-\)4.874 & \(\leq\)0.001 & \(-\)3.633 & \(-\)2.949 & \(-\)2.613 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ADF inspection result.
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
###### Abstract  
In the intermittent machining of hardened steel for the die and mold industry, determining how to reduce the wear of PCBN (Polycrystalline Cubic Boron Nitride) tools and improve their machining efficiency and quality is an important subject. This study investigated the intermittent machining of hardened steel (Cr12 MoV, 59 HRC (Rockwell hardness)) using uncoated PCBN tools to determine the cutting performance (cutting force, chip morphology, surface quality, tool life, cutting temperature) and the wear/damage characteristics of the tools. The results showed that the cutting performance of a PCBN tool was better than that of a cemented carbide tool. The wear mechanism on the PCBN tool flank was diffusion wear, adhesive wear, and oxidation wear. The main failure modes of the PCBN tool in the machining process of hardened steel at low speed were tool micro-chipping, the conchoidal damage of the rake face, and the larger damaged area of the flank face. The main failure modes of the PCBN tool in the machining process of hardened steel at high speed were flank wear and high-rate fatigue damage.  
cutting performance; wear characteristics; damage characteristics; PCBN tool; low-rate damage Article
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### 5G and IoT Sensor Technologies for UAVs  
The 5G technology is expected to enhance mobile broadband, enable applications that require ultrareliable very low latency and very high availability networks, improve traffic safety and control, support industrial applications, remote manufacturing, training, surgery, logistics, tracking, and fleet management. It will be utilized for smart agriculture, precision farming, smart buildings, smart meters, support of 4K/8K UHD broadcasting, virtual and augmented reality without range limitations, including homes, enterprises, and large venues offering massive and critical Machine-To-Machine-Type Communications (MTMC) [29]. This type of device communications can be integrated with typical Human-Type Communications (HTC) through suitable gateways in the context of a 5G architecture, as presented in Reference [30] and illustrated in Figure 1.  
Other 5G use cases that are linked to drones involve automation and robotics. Drones with the support of 5G networks would be able to offer a wide variety of tasks and applications providing new benefits to a wide range of industries. Among the top use cases of 5G-enabled drones, we consider applications related to construction, agriculture, insurance claims, police, fire, coast guard, border control, journalism, news, utilities, filmography, and logistics. All these applications will be feasible since autonomous and beyond line-of-sight control will be supported. In order to enable these use cases, specific minimum requirements for aerial vehicles are essential in terms of equipment and sensors.  
The main types of sensor technologies that are supported and are a part of drones today can be separated into three main categories: (a) flight control, (b) data acquisition, and (c) communication sensors.
## 3 Theoretical Formulation and Numerical Modeling
### Effect of The Shapes of Strips
Figure 15 compares the pressure coefficient between the smooth cylinder and cylinder with strips of different shapes attached at \(\alpha=90^{\circ}\). Only the pressure of the upper-half cylinder is shown, because of the symmetrical distribution. Due to the installation of the strips, the pressure coefficient fluctuates significantly at \(\theta=90^{\circ}\). The difference is that a valley is created at the arc strips, while the peaks are created at the rectangular strips, the trapezoidal strips and the triangular strips. When T = 1, the pressure coefficient curve of the rectangular strips, the trapezoidal strips and the triangular strips have a valley at \(\theta=125^{\circ}\) and gradually moves to \(\theta=150^{\circ}\), as time goes on. When T = 3, the valleys disappeared, indicating that the effect of the strips gradually diminishes with time. Figure 14: The vorticity contours of the wake flow for different shapes of strips for Re = 55,000. Figure 16 compares the velocity distribution at different moments on the axis between the smooth cylinder and cylinder with strips of different shapes attached at \(\alpha=90^{\circ}\). The effect of the arc strips on the velocity distribution on the axis is weaker compared to that of the other three shapes. However, the maximum value of the negative velocity and the length of the recirculating zone are still higher than that of the cylinder without strips. The other three shapes of strips have the same effect on the velocity distribution on the axis. In the early stages, the maximum value of the negative velocity and the length of the recirculating zone are smaller than that of the smooth cylinder. As time goes on, the distance between the location of the maximum value of the negative velocity and the surface of the cylinder increases, and the maximum value of the negative velocity and the length of the recirculating zone are large than that of the smooth cylinder. In general, the arc-shaped strips mainly affect the maximum value of the negative velocity, and the other three shapes mainly affect the length of the wake. Figure 15: The comparison of the pressure coefficient between the smooth cylinder and cylinder with strips of different shapes for Re = 55,000.
## 4 WiFi 6/6E: IEEE 802.11 ax
### Orthogonal Frequency-Division Multiple Access (OFDMA)  
The channel access of WiFi 5 and older networks is governed by the carrier-sense multiple access/collision avoidance (CSMA/CA) mechanism, which works well for lightly crowded networks. However, when the network load increases, the CSMA/CA networks become inefficient quickly. Introducing OFDMA into WiFi networks improves the efficiency of the underlying networks in many fronts. Firstly, as shown in Figure 3, OFDMA is able to allocate a single subcarrier frequency among multiple users. OFDMA also enables the transmitters to avoid frequency selective fading. However, the most important advantage in WiFi networks is attributed to the small payload generated by existing and upcoming indoor applications. As a result of these small payloads, the entire bandwidth of a single subcarrier is not required for data transmission. As OFDM allows for sharing the same subcarrier among multiple users, OFDMA improves the resource utilization, specifically for applications with smaller payloads. For instance, legacy WiFi networks would allocate all their subcarriers for every single transmission, as shown in the Figure 3a, which results in a waste of bandwidth resources for applications with small payloads. With OFDMA, as shown in Figure 3b, a transmitter can allocate portions of bandwidth depending on the requirement. Further, it allows for parallel transmissions in the time domain; hence, it achieves lower latency values compared to OFDM systems using effective resource allocation algorithms [37].  
Figure 3: (**a**) OFDM systems allocate all the subcarriers to a single user; (**b**) OFDMA systems can allocate sets of subcarriers to different users.
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 3 Results and Discussion
### Characterization of RSPRC  
The microstructure of RSPRC was observed by a Nova scanning electron microscope (SEM) using Nano SEM 450. Table 4 presents the physical properties of RSPRC used in experiments.  
An SEM image of RSPRC is shown in Figure 5. The SEM image shows that RSPRC is composed of particles with different properties, of which spherical particles account for more than 60% (Figure 5a). After analysis, we established that these are the glass bodies in the fly ash, which have stored a high chemical internal energy after high-temperature calcination and are the source of the fly ash activity. Figure 5b shows that the glass body is a hollow sphere. When the sphere breaks, the Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) inside are released, the broken bond increases, the specific surface area increases and the reaction contact area increases, which increases the number of activated molecules and effectively improves the early chemical activity of the RSPRC. Figure 5c presents the fracture surface of the glass body. RSPRC contains a lot of minerals. Steel slag and activated clay are its main sources. They are 2 CaO-SiO\({}_{2}\), 3 CaO-SiO\({}_{2}\), 2 FeO-SiO\({}_{2}\), 2 CaO-Fe\({}_{2}\)O\({}_{3}\) and free calcium oxide (f-CaO), etc., many of which possess a phosphorus removal ability (Figure 5d) [22].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Physical Parameter** & **Grain Size (mm)** & **Density (g/cm\({}^{3}\))** & **Porosity (\%)** & **Specific Surface Area (m\({}^{2}\)/g)** \\ \hline
**RSPRC** & 5–20 & 1.16 & 30.98 & 2.62–8.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical properties of RSPRC used in experiments.  
Figure 4: Effect of proportion of activated clay on Phosphorus removal efficiency.
## 3 Results
### DAP Measurement  
DAP measured with MC of 32 energies is shown in Figure 5. Obvious large error bars can be seen in the low energy region, ranging from 70.6 MeV to 86.8 MeV compared to that in the high energy region. This deviation illustrates the discrepancy between different measurement times. DAP at energy 83.8 MeV measured with MC illustrates the largest error bar, 1.89 Gy\({}^{\star}\)mm\({}^{2}\)/MU. This shows the instability of the scanning field at low energy 83.8 MeV.  
Figure 4: (**a**) Reconstructed proton dose map of incoming energy 83.8 MeV at depth 2 cm and (**b**) lateral dose profile.  
Comparison of DAP measured with MC and BPC is shown in Figure 6. The error bar demonstrates the standard deviation from the repeating measurements. Mean DAP is the average of DAP measured with MC and BPC (Table 1). The percentage difference illustrates the discrepancy of DAP measured with BPC to that with MC, ranging from \(-0.19\%\) to \(1.26\%\). The single-layer method can be used as reference dosimetry. The percentage difference between DAP measured with the BPC and MC ranges from \(-2.5\%\) to \(0.39\%\). The largest deviation occurred at energy \(83.8\) MeV due to the instability of scanning field at low energy during the commissioning stage of SAPT.  
Figure 5: DAP of 32 different energies measured by the single-layer method with the Markus chamber.  
Figure 6: Comparison of DAP determination with Markus chamber and Bragg peak chamber.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 3 Performance of Single Link
### Propagation Analysis
by the fact that if the link distance is set be be longer than 28 m, the RSSI almost reaches to \(-90\) dBm; according to the analysis in Figure 10, the RSSI may be inaccurate to evaluate a link with a distance longer than 28 m.  
When the transmit power level is set to 31, from Figure 12a, we obtain \(P_{0}=-50.839\) and \(n=2.277\) with the RSSI samples with a link distance no larger than 28 m and \(P_{0}=-56.359\) and \(n=1.687\) with all of the RSSI samples. For the transmit power level of 11, in Figure 12b, we get \(P_{0}=-66.966\) and \(n=1.654\) with the RSSI samples with a link distance no larger than 28 m and \(P_{0}=-70.558\) and \(n=1.256\) with all RSSI samples. From Figure 12, it can been seen that under relatively low transmit power, the RSSI decays slowly as the link distance increases. This numerical result indicates that for in-forest wireless sensor networks, the higher transmit power cannot always overwhelm the lower one for any link distance in terms of link quality; so, we might adopt lower transmit power levels to prolong the network lifetime, while meeting the link quality requirement. Although such fitting results alone cannot achieve a universally sound path loss model, they provide approximated parameters for the simulation-based study of the target wireless sensor network deployed in forests.
## 4 Results and Discussion
The proposed model successfully predicted 970 fake images and 962 real images from a set of 1000 each. Only 30 fake images were misclassified as real, and 38 real images were misclassified as fake. These numbers demonstrate that the model performs well across both classes and does not exhibit a bias toward one class over the other. Thus, the outcomes validate the model's accuracy and balance in recognizing between real and AI-generated images. The confusion matrix in Figure 12 further illustrates the model's balanced performance. PV-ISM correctly classified 970 out of 1000 fake images and 962 out of 1000 real images, resulting in 30 false negatives and 38 false positives, respectively. This nearly symmetric outcome suggests that the model does not favor one class over the other. The low number of classification errors across both classes demonstrates the robustness of the model's decision boundary and its applicability to real-world deepfake detection scenarios. Based  
Figure 11: Graph of loss vs. epoch for best-performing PV-ISM. Figure 12: Confusion matrix of best-performing PV-ISM. on performance trends and model behavior, the patch encoding and transformer-based attention layers appear to be the most impactful. The patch-wise tokenization enables fine-grained feature localization, while the attention mechanism enhances global feature correlation, both essential for distinguishing subtle generative artifacts.