esa-sceva/satcom-chunk-collection huggingface dataset:
applied sciences  
Anhancing Crop Yield Predictions with PENemble 4: IoT and ML-Driven for Precision Agriculture  
[PERSON] 1\({}^{\copyright}\), [PERSON] 1\({}^{,\star}\)\({}^{\copyright}\) and [PERSON] 2  
1 Department of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, 58 Moo 9, Km.42, Paholyothin Highway, Klong Luang 12120, Thailand; [EMAIL_ADDRESS]  
2 Department of Agricultural Engineering, Faculty of Engineering, Rajamangala University of Technology Thanyaburi, 39 Moo 1, Rangist-Nakhomayok Road, Thanyaburi 12110, Thailand; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
This research introduces the PENemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PENemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PENemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PENemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices.  
ML-based precision agriculture; IoT-based precision agriculture; sustainable agriculture; PENemble 4; maize yield prediction; spatial prediction; remote sensing
# A Robust Security Architecture for SDN-Based 5G Networks
## 1 Introduction
Introducing an overarching security architecture requires a comprehensive and protracted approach to SDN. Since it was introduced, designers are looking at possible ways to secure SDN that do not compromise scalability. One architecture called SN-SECA (SDN+NFV) Security Architecture [17]. Figure 1: 5G networks. In this paper, we target at handling the security problems in SDN-based 5G networks. As we mentioned, the SDN 5G network has two planes: the control plane and the data plane. We add a security module between the two planes. This security will synchronize secrets between the physical layer devices and the SDN controller. This secret used in our scheme is generated by the hash function. During each authentication, the security module will first issue the 5G device for its secret. Since the security module knows the hash function in the device in advance, it can determine whether the reply is correct; the incorrect secrets indicate the request is from an attacker. After a successful authentication, the security module will update the secret seed in the physical layer device to prevent the attacker from decoding the hash function by eavesdropping for a long time. Note that our scheme is low cost and is fully compatible with the SDN architecture, making it competitive when deploying in today's SDN-based applications. Finally, we simulate our scheme under different kinds of attacks. The simulation results show that our work has a disconnect rate of 0.01, which is much lower than the state of art. Our contributions are three fold:  
1. In this paper, we propose a security architecture for SDN-based 5G networks. To the best of our knowledge, there is no other work in this field. 2. We detail the security problems of SDN-based 5G network in the communication plane. We propose a low-cost security authentication scheme for these problems. 3. We detail how our scheme raises the security of a current SDN based 5G network. In addition, we compare our work with the state of the art. The simulation results show that our work is robust while it has a low disconnect rate of 0.01%, whose rate is about 80% in the state of the art when the number of attacks is 50,000. The rest of the paper is organized as follows. Section 2 introduces the background of SDN-based 5G network and its security drawbacks. Then, in Section 3, we give our security architecture in details. After that, the simulation results are provided to demonstrate the superiority of the proposed schemes in Section 4. Finally, Section 5 briefly concludes this manuscript.
## 2 Materials and Methods
### SiTPS Expression in Planta and In Vitro  
Tomato seedlings were harvested at 11 dpi (days post inoculation) and roots from seven plants were pooled together in each replication. Before RNA extraction, the samples were freeze-dried overnight. Fungal biomass from mature _S. indica_ cultures, grown for two weeks on CM agar plates, was also harvested. Total RNA from root and fungal samples was extracted using the Spectrum(tm) Plant Total RNA Kit (Sigma-Aldrich, St. Louis, MO, USA) and its integrity was validated with an agarose gel 1% (_w/v_). For the plant root samples 1.5 \(\upmu\)g of total RNA was used to synthesize cDNA, while for the fungal samples, cDNA was synthesized using 250 ng total RNA. cDNA synthesis for root and fungal samples was performed with the Revert First Strand cDNA Synthesis kit and an oligo-dT primer (Thermo Fisher Scientific, Waltham, MA, USA) with a final volume of 20 \(\upmu\)L.  
For the RT-qPCR, 2 \(\upmu\)L of 1/10 diluted cDNA were used as a template in a 10 \(\upmu\)L reaction, using 5 \(\upmu\)L of the Brilliant III Ultra-Fast SYBR(r) Green (Agilent Technologies, Santa Clara, CA, USA) and SiTPS-specific primers (Supplementary Table S1). Each RT-qPCR reaction was performed with three technical replications for the four biological replications. RT-qPCR was performed using the AriaMx Real-Time PCR System-G8830A (Agilent Technologies, USA), with a program of 95 \({}^{\circ}\)C for 5 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 30 s, 60 \({}^{\circ}\)C for 1 min and 72 \({}^{\circ}\)C for 1 min. A final dissociation step was performed to assess the quality of amplified products and the specificity of the primers. Expression of _SiTPS_ was normalized using _S. indica_ glyceraldehyde-3-phosphate dehydrogenase gene (_GAPDH_, GenBank: FJ810523.1) expression levels and calculated with the 2\({}^{\text{-}\Delta\text{C}}\) method [40; 41].
## 3 Proposed Metrics of Protocol Compliance
### Sugeno Integral-Based Compliance Measure
\[c_{S}(t_{j})=\max_{\alpha\in[0,1]}\bigl{(}\min(f(\alpha),g_{1}(t_{j\mu})) \bigr{)} \tag{15}\]  
where \(t_{j\mu}=\{a_{kj}:k=1,\ldots,r_{j}\) and \(\mu(a_{kj})>\alpha\}\), i.e., a set of activities performed in the trace \(t_{j}\) that are compliant at least with the level \(\alpha\). Once the compliance levels for the traces are calculated, we can aggregate those values using the Sugeno integral again. Here, \(X=t_{1},\ldots,t_{m}\). Since each trace (patient) is equally important, we use a probability measure and the densities are  
\[g_{2}(t_{j})=\frac{1}{m} \tag{16}\]  
We define the compliance for a protocol \(P\) as the Sugeno integral of function \(f(x)=x\), with the measure \(g_{2}\). \[C(P)=\max_{\alpha\in[0,1]}(\min(f(\alpha),g_{2}(T_{\alpha}))) \tag{17}\]where \(T_{\alpha}=\{t_{j}:j=1,\ldots,m\) and \(c_{S}(t_{j}))>\alpha\}\), i.e., a set of traces \(t_{j}\) that are compliant with at least the \(\alpha\) level. The pseudo-code for calculating the Sugeno integral-based compliance measure is listed in Algorithm 4. ```
establish the importance of individual activities, if needed for each patient in the protocol calculate densities as (13) or (14) find \(\lambda\) using (11) calculate compliance per patient using (15) calculate densities as (16) calculate overall compliance using (17)
```  
**Algorithm 4** Sugeno integral-based compliance measure  
The advantage of this method is that it combines activity compliance, patient compliance and protocol compliance as a single number. This number could be used as a KPI, and the calculations behind it could help identify the biggest discrepancies at the patient and activity levels.
## 2 The Selection Problem of the UAV-LEO Backhaul Links  
### Architecture of LEO-UAV-Sensor-Based IoRT Network System  
An IoRT network system with wireless UAV-LEO backhaul links is considered, as illustrated in Figure 1, where the 5G core networks are far away from the IoRT sensors. The sensors on the ground transmit the data to UAVs. UAVs are referred to as aerial base stations and work as hubs between the access network and the backhaul network. The part of the UAV-sensor is the access network. Then, the data are transmitted to the core network through the backhaul links. The backhaul links in the IoRT network architecture are from the UAV to the ground gateway. Note that if the ground gateway is remote from the sensors, the data can be transmitted among the inter-satellite links and then downloaded to the gateway. LEO satellites serve as relays of backhaul networks. Thus, the backhaul links are divided into three parts: LEO-UAV, LEO-LEO, and gateway-LEO. Finally, the LEO-UAV part of the backhaul links is the focus of our research. Specially, the LEO satellites have higher store-and-forward capability to process tasks than the UAVs. Therefore, one LEO satellite can be associated with more than one UAV at the same time.
# Sirtuin 1 and Hormonal Regulations in Aging
## 4 Natural Activators of SIRT1 and Their Use in the Prevention of Aging
### Paeonol as a SIRT1-Activating Anti-Aging Compound  
Paeonol, a phenolic compound extracted from the bark of _Paeonia suffruticosa_ (Moutan Cortex) Andr. is widely known for its anti-inflammatory, antioxidant, and neuroprotective properties [90; 91]. Paeonol has been found to bind directly to SIRT1, increasing its activity and stimulating the SIRT1/p53/telomeric repeat-binding factor 2 (TRF2) pathway in vascular smooth muscle cells isolated from C57 BL/6] mouse arteries, which protects against vascular senescence and endothelial dysfunction, which are major contributors to cardiovascular aging [92]. Animal experiments demonstrated that paeonol (20 mg/kg/oral gavage) reduces endothelial senescence in male C57 BL/6] mice (8-weeks-old) by enhancing SIRT1 expression, leading to improved vascular function and reduced oxidative stress [93]. Moreover, a study in an [PERSON]'s disease model of APP/PSI double transgenic mice reported that paeonol alleviated cognitive impairment by activating SIRT1-mediated neuroprotective pathways [94]. Studies have also shown SIRT1-mediated neuroprotective effect of paeonol as it reduces neuroinflammation and improves cognitive function [91; 95].  
Despite promising preclinical data, clinical trials investigating the efficacy of paeonol in humans are limited. However, a pilot study suggested that paeonol-containing formulations improved arterial stiffness and reduced inflammation in elderly patients with chronic kidney disease (stages I-IV), warranting further exploration [96]. These findings highlight the potential of paeonol as a therapeutic compound for addressing age-related vascular and neurodegenerative disorders.
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 3 2D-BIBD System Description
### Construction of 2D-BIBD Transmitter Architecture  
The transmitter module employs a combination of FBG filters and optical splitters with \(k_{pd}\) optical switches to perform the required spectral-spatial encoding operation. For a single user, a LED is used to generate the required spectrum for encoding and transmission of binary data over the optical medium. The end-face of the LED is connected  
Figure 1: Transmitter structure of the 2D balanced incomplete block design (BIBD) OCDMA system.  
to a MZM, which uses on-off keying (OOK) to modulate the user's binary data sequence with the LED spectrum (Figure 1). The output of the MZM is fed to a special arrangement of FBG filters that encodes the modulated spectrum in the spectral domain. Figure 1 shows a combination of two filters, FBG#1 and FBG#2, an optical splitter, and \(M_{2}\) star couplers for user 1, to allow the spectrum in accordance with the \(X_{1}\) coding scheme of the proposed code.  
In the process of spectral encoding, optical pulses are initially delivered to FBG#1, which is used to reflect the spectral components according to the position of 1s in the \(X_{1}\) coding scheme. The rest of the spectrum is filtered out by FBG#1. The round trip delays of the matched spectral components are different as the matched spectral components are reflected by different gratings. To compensate for the delay, FBG#2 is used.  
This process introduces the \(X_{h}\) spectral encoding to the 1D-BIBD code. FBG#1 and FBG#2 have the same grating characteristics. However, the FBG#1 grating structure is made in the reverse order to that of the FBG#2 to avoid time spreading.  
The FBG#2 reflected pulses are sent to an optical splitter that is used to perform a \(Y_{g}^{T}\) spatial encoding operation and produces the intended \(C_{g,h}\) 2D-BIBD code sets. In spatial encoding, the optical splitter equally divides each matched spectral component power into \(w2\) portions due to the \(w2\) code weight of the spatial code pattern. Finally, according to the spatial code pattern \(Y_{g}\), these \(w2\) split portions of the encoded spectrum are further sent to \(M_{2}\) star couplers, which correspond to the 1s of the spatial code pattern, through \(k_{pd}\) optical switches.
# Improvement of the Cross-Scale Multi-Feature Stereo Matching Algorithm
## 2 Algorithm Description  
The proposed algorithm employs a binocular system constructed with two monocular cameras as the experimental setup. Taking rectified left-right image pairs as the input, it sequentially performs cost computation, cost aggregation, disparity calculation, and disparity post-processing [11], ultimately outputting the final disparity map. The detailed workflow is illustrated in Figure 1. The workflow starts by computing matching costs across candidate disparities using distinct cost functions between pixels in the target image and their correspondences in the reference image. Subsequently, the cost-aggregation phase fuses SAD (sum of absolute differences) with an enhanced census transform metric. A multi-scale representation of the input stereo pair is then constructed, where cost aggregation is performed through a cross-scale guided filtering framework. Finally, the winner-takes-all (WTA) strategy is applied for disparity selection, followed by post-processing operations to refine the disparity map.
# Beyond Kanban: POLCA-Constrained Scheduling for Job Shops
## 4 Industrial Case: Aerospace Manufacturing Firm
### Computational Experiments
#### 4.3.3 Stakeholder Feedback  
Managers and production planners recognize that the optimization model effectively reduces worst-case waiting times and alleviates persistent bottlenecks; flows that pre  
\begin{table}
\begin{tabular}{l r r r r r} \hline \hline
**KPI** & **Run 1** & **Run 2** & **Run 3** & **Run 4** & **Run 5** \\ \hline
**Commercial Scheduler** & & & & & \\ Max Lead Time [days] & 614.45 & 678.17 & 496.34 & 726.35 & 337.02 \\ Sum of Lead Times [days] & 83,174.74 & 97,395.33 & 61,859.58 & 71,348.79 & 43,385.88 \\ Number of Tardy Orders & 941.00 & 928.00 & 857.00 & 935.00 & 863.00 \\ Sum of Tardiness [days] & 88,851.72 & 112,643.83 & 93,476.54 & 104,375.12 & 116,645.50 \\ Makespan [days] & 615.70 & 725.95 & 584.93 & 821.93 & 518.27 \\ Sum of Completion Times [days] & 91,803.64 & 168,673.14 & 192,756.19 & 215,136.93 & 287,286.95 \\ \hline
**CP Model** & & & & & \\ Max Lead Time [days] & 381.02 & 336.02 & 386.02 & 378.69 & 293.02 \\ Sum of Lead Times [days] & 55,786.78 & 50,133.72 & 42,947.60 & 44,207.31 & 34,769.23 \\ Number of Tardy Orders & 819.00 & 777.00 & 771.00 & 800.00 & 834.00 \\ Sum of Tardiness [days] & 68,136.81 & 75,059.27 & 84,392.82 & 88,183.42 & 111,596.68 \\ Makespan [days] & 382.27 & 382.27 & 474.27 & 543.68 & 474.27 \\ Sum of Completion Times [days] & 65,311.06 & 125,158.34 & 179,170.48 & 194,153.20 & 282,755.81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: KPI Comparison for Commercial Scheduler and CP Model.  
viously accumulated excessive work-in-process now run more evenly and predictably. They also recognize the value of having a clearer and more actionable view of capacity constraints resulting from the direct integration of POLCA rules within the scheduling process. In addition, stakeholders acknowledge that the model's ability to proactively prevent deadlocks contributes to greater operational reliability on a daily basis. Overall, the optimization model is regarded as a practical and targeted improvement to the planning process, enabling steadier flow control and enhanced predictability of performance while remaining compatible with existing environment.
### Contributions  
In this study, we consider random access wireless mesh networks with multi-packet reception capabilities. For such networks, we address the issue of jointly allocating flow on multiple paths while also performing SIC. More precisely, following our work in [22], we explore a distributed flow allocation scheme aimed at maximizing the average aggregate flow throughput, while providing bounded delay, when SIC is employed for handling interference. The proposed framework can be applied in arbitrary topologies, however, three simple topologies are simulated in Ns2 for the evaluation process. Using simple topologies allows for gaining insights, regarding the relation of SIC with the flow allocated on each path. Moreover, it allows us to explore the potential gains of SIC over IAN and especially the effect of link asymmetry on this gain.  
The evaluation process of the aforementioned scheme consists of four parts. In the first one, we investigate the accuracy of the model employed by the _Throughput Optimal Flow Rate Allocation (TOFRA)_ scheme, for capturing the average aggregate flow throughput (AAT) observed in the simulated scenarios. In the second part, the gain in terms of throughput that can be achieved at the network level, by combining TOFRA flow allocation scheme with SIC is explored. In this part, the proposed scheme is also compared with other simple flow allocation schemes in terms of throughput. The effect of SIC on end-to-end flow delay is also explored. Finally, we evaluate the accuracy of the simulated annealing method for identifying the flow rates that achieve the globally maximum average aggregate throughput.  
Our results show that the proposed flow allocation scheme achieves up to 15.2% higher AAT when combined with SIC, instead of treating interference as noise, for an SINR threshold (\(\gamma\)) value equal to 0.5. For larger \(\gamma\) values, this improvement either becomes negligible or lower AAT is achieved. The reason is that SIC improves the success probability on certain links, and consequently these links forward more packets on the next hops. This in turn results in increased interference in neighboring links. For large \(\gamma\) values, the effect of interference becomes more significant and is not compensated by the gain in terms of throughput. Finally, both the analytical and simulation results verify that the improvement in terms of throughput by employing SIC instead of treating interference as noise, increases with the asymmetry among interfering links.  
The rest of the paper is organized as follows: Section 2 presents the system model considered, while Section 3 overviews the analysis employed. The simulation setup is presented in Section 4 while the evaluation process is presented in Section 5. The study is concluded in Section 6.
## 2 Study Area, Data and Methods
### Data  
#### 2.2.1 Eddy Covariance Flux Data  
Flux data from three rice-wheat-rotation cropland EC stations within the study area--the Shouxian site in Anhui and the Dongtai and Dafeng sites in Jiangsu--were selected for model training and prediction (Figure 1). At the Shouxian site, the EC sensors were mounted 2.5 m above the ground and consisted of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA) along with a CO\({}_{2}\)/H\({}_{2}\)O open-path infrared gas analyzer (EC 150, Campbell Scientific Inc., Logan, UT, USA). At the Dongtai and Dafeng sites, virtual temperature and wind velocity components were monitored using a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA). To measure H\({}_{2}\)O and CO\({}_{2}\) density, a fast response open path gas analyzer (LI-7500, LI-COR, Inc., Lincoln, NE, USA) was used. The installation height of the sensors for the Dongtai site was 10 m, whereas for the Dafeng site it was 6.3 m above the ground. As mentioned in the previous studies ([26; 27; 29]), the three EC sites are relatively flat, with more than 90% of the flux primarily contributed by the cropland. EddyPro 5.2.1 (LI-COR, Inc., Lincoln, NE, USA, 2015) software was applied to calculate hourly CO\({}_{2}\) fluxes and to correct for CO\({}_{2}\) canopy storage to obtain NEE values. Data pre-processing in the EddyPro software mainly included averaging and statistical tests [30], time lag compensation, double coordinate rotation, spectral correction [31], and Webb-Pearman-Leuning density correction [32]. The poor-quality fluxes (Eddypro quality check flag value = 2) were further discarded. The REddyProc R package ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebPackage](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebPackage)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebPackage](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebPackage)), accessed on 10 July 2021) inputted pre-processed half-hourly EC data and supported further processing [33]. Firstly, a quality-check and filtering were performed based on the relationship between observed flux and friction velocity to discard biased data [34]. Then, the flux data were gap-filled using the marginal distribution sampling approach [35]. NEE was separated into GPP and ecosystem respiration based on the nighttime partitioning algorithms [35]. The gap-filled hourly GPP data were summed to compute cumulative GPP for daily, 8 day, seasonal and annual time resolution for further analysis [36]. Data from these three sites were processed using the same methods. Details of the agricultural practices and processing methods at these three sites can be obtained from the references in Table 1[26; 27; 29].