esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Multi-Cell Cooperative Control Algorithm Based on Fuzzy Brain Emotional Learning
## 5 Simulation Results
### Control Multiple Cells
Figures 14 and 15 show the trajectories of the FBELNN-based cell cooperative controller manipulating cells and forming vertical or triangular formations under different solution environment coefficients and new ideal desired trajectories. In the new multi-cell experiment, different experimental conditions are set, and the cell cooperative controller can still move the captured cells to the desired formation in time, and can manipulate the three cells to converge from their respective initial positions to the desired spacing position, effectively. The multi-cell cooperative control task in different environments has been completed. In the experiments in this section, the ideal trajectory defined in this paper can greatly guarantee the safety of the cells during the movement and the effectiveness of the control method. From the simulation results, it can be seen that the multi-cell cooperative controller based on FBELNN has achieved excellent results in controlling cells in different paths. The controller can set different paths and cell formations according to actual needs to realize automatic multi-cell cooperative control tasks. In addition, the adaptability of the FBELNN controller is also very strong, and it can complete the coordinated control of cells in a variety of different environments, which shows that the proposed multi-cell cooperative controller based on FBELNN has good robustness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Main Controller** & **Cell 1** & **Cell 2** & **Cell 3** \\ \hline \multirow{4}{*}{RMSE of each cell in the \(Y\)-axis direction} & PI & \(5.0930\times 10^{-6}\) & \(4.3229\times 10^{-6}\) & \(1.9634\times 10^{-6}\) \\  & PID & \(4.8497\times 10^{-6}\) & \(4.1085\times 10^{-6}\) & \(1.8935\times 10^{-6}\) \\  & BP & \(3.3223\times 10^{-6}\) & \(3.6277\times 10^{-6}\) & \(1.4233\times 10^{-6}\) \\  & RBFNN & \(3.2235\times 10^{-6}\) & \(3.0946\times 10^{-6}\) & \(1.3616\times 10^{-6}\) \\  & FCMNN & \(2.9536\times 10^{-6}\) & \(2.5967\times 10^{-6}\) & \(1.2155\times 10^{-6}\) \\  & This work & \(2.1391\times 10^{-6}\) & \(0.5858\times 10^{-6}\) & \(0.5920\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Captured Cell** & **Initial Position (\(\upmu\)m)** \\ \hline Yeast cell 1 & (21.39, 42.96) \\ \hline Yeast cell 2 & (0.13, 76.59) \\ \hline Yeast cell 3 & (\(-1.32\), \(-11.53\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Initial position of the captured cells.
## 3 Dark Net and IoT Integration: Use Cases, Legal Considerations, and Ethical Frameworks  
Recent research has highlighted the importance of integrating technologies like the onion router (TOR) into IoT systems, especially for applications requiring high levels of privacy. In IoT, this is particularly relevant in fields such as healthcare and industrial automation, where sensitive data is transmitted.  
[PERSON] et al. [4] emphasize the vulnerability of IoT devices to cyberattacks due to the lack of encryption in many communication protocols. They propose that combining IoT with TOR can enhance the security of personal and industrial data by anonymizing communications, thereby preventing surveillance and data breaches in critical environments like healthcare and smart cities.  
In the context of IoT, one significant application is in the field of wearable health devices. As noted by [PERSON] et al. [5], the anonymity provided by networks like TOR can be crucial for protecting sensitive medical data transmitted by IoT devices. This is particularly relevant for wearable health applications, where the privacy of personal health data is of utmost importance.  
[PERSON] et al. [6] explore how blockchain technology can be combined with TOR to improve security in IoT networks. They suggest that blockchain-based access control can ensure both data integrity and privacy, which is crucial for industries managing large amounts of sensitive data, such as energy and manufacturing.  
In the industrial IoT (IIoT) domain, secure communication protocols such as MQTT, when used in conjunction with TOR, have been recognized as effective in protecting critical infrastructure. Although TOR can introduce latency due to its layered encryption, [PERSON] et al. [7] argue that the privacy benefits outweigh the performance drawbacks, especially in sensitive sectors like manufacturing and energy.  
From a legal perspective, the use of TOR in IoT must comply with regulations such as GDPR, which govern the handling of personal data. [PERSON] et al. [8] highlight the challenges of ensuring compliance with these frameworks when using privacy-enhancing technologies, particularly concerning the processing of metadata and device interactions.  
Ethically, while TOR offers strong privacy protections, it also presents risks of misuse. [PERSON] et al. [9] discuss how the anonymity provided by TOR can be exploited for malicious purposes, but they emphasize that with proper legal frameworks and oversight, the advantages for protecting privacy-sensitive IoT applications outweigh these risks.
# An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning
## 1 Introduction  
In recent years, Japan's agricultural industry has faced a number of challenges, including a decline in production due to a decrease in farmland area, a shortage of labor due to a decrease in the number of producers, and an aging population [1]. In 2015, 64.9% of agricultural workers were aged 65 years old or above; however, by 2020, this percentage had increased to 69.8%. In addition, the arable land area in Japan has been decreasing, as shown in Figure 1. The total area of arable land as of July 2020 was approximately 4.37 million hectares. Therefore, in recent years, smart agriculture using robots and IoT has been studied. Smart agriculture aims to solve the shortage of human labor by automating tasks that are normally performed by humans. It also aims to improve the efficiency of work by using computers to manage and analyze data on crops and the environment, thereby increasing production per unit area and improving quality.  
The purpose of this research was to automatically analyze the growth status of tomatoes using camera images. To achieve that, farmers measured the thickness of the stems near the flower clusters, and the length of flower clusters and growing points. In fact, a caliper is often used to analyze the growth of tomatoes in a plant factory, but this method may damage the stems and also put strain on the person conducting the measurement [2]. We believe that this required time can be reduced by using cameras installed on farms and image analysis techniques. In this study, we aimed to create a system to perform such analysis. This system should be inexpensive and easy to use so that farmers who are not familiar with computers can easily adopt it. Therefore, our goal is to obtain sufficient measurement data with a small number of cameras by using cameras with a pan-tilt functionthat can capture multiple plants. Additionally, another objective of this work is to reduce the cost of the hardware used in this system as much as possible, and to create a system that is easy to use and does not require complicated operations by the user.
## Materials and Methods
### Quantitative Real Time PCR (qRT-PCR)  
Total RNA was extracted from C2C12 myotubes using homemade Trizol reagent according to the manufacturer's instructions. The RNA (1 ug) was reverse-transcribed into cDNA with a Compact cDNA Synthesis Kit (Smart Gene, Daejeon, Republic of Korea) in a GeneAmp PCR System 9700 (PerkinElmer, Inc., Waltham, MA, USA). qRT-PCR was conducted using the SYBR Green Q-PCR Master Mix (Smart Gene) in a QuantStudio(tm) 1 Real-Time PCR system (Thermo Fisher Scientific) under the standard thermal cycle conditions: polymerase activation for 2 min at 95 \({}^{\circ}\)C followed by 40 cycles of denaturation for 5 s at 95 \({}^{\circ}\)C, then annealing/extension for 30 s at 60 \({}^{\circ}\)C. The list of primers is provided in Table 1. All mRNA levels were normalized using the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as an internal control. Relative expression values were calculated using 2\({}^{-\Delta\Delta\text{CI}}\) method.  
\begin{table}
\begin{tabular}{c c c} \hline
**Primer** & **F/R** & **Sequences (5\({}^{\prime}\) to 3\({}^{\prime}\) Direction)** \\ \hline FoxO3a & F & GAG TGA CTC CAG CAG CCT TG \\  & R & ATT CCA AGC TCC CAT TGA AC \\ \hline MuRF1 & F & TGG AAA CGC TAT GGA SAA CC \\  & R & AAC GAC CTC CAG ACA TGG AC \\ \hline MAFbx & F & CTC TGC TGT GAG TGC CAC AT \\  & R & CAA TGA GCC TGG GTA CCA CT \\ \hline IL-6 & F & CCC ACC AAG AAC GAT GAT CA \\  & R & CTC CGA CTTG G AAG TGG TA \\ \hline IL-1\(\beta\) & F & GTC ACA AGA AAC CAT GGC ACA T \\  & R & GCC CAT CAG AGG CAA GGA \\ \hline iNOS & F & AAT CIT GGA GGG AGT TGT GG \\  & R & CAG GAA GTA GGT GAG GGC TTG \\ \hline COX-2 & F & GCC TAC TAC AAG TGT TTC TTT TTG CA \\  & R & CAT TTT GTT TGA TTG TTC ACA CCA T \\ \hline GAPDH & F & GGT GCT CTC TCA CT CT CAA CA \\  & R & GTT GCT GTA GCC AAA TTC GTT GT \\ \hline \end{tabular}
\end{table}
Table 1: Primers for real-time PCR amplification of gene expression.
## Materials and Methods
### GC-MS Conditions  
Chromotographic separations of VOCs and SVOCs from fruits and vegetables were performed using an Agilent Technologies 6890N (Palo Alto, CA, USA) gas chromatography system equipped with a BP-20 fused silica capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness) supplied by SGE (Darmstadt, Germany) with helium (Helium N60, Air Liquid, Portugal) as carrier gas at a flow rate of 1 mL \(\times\) min\({}^{-1}\) (column-head pressure: 13 psi). The injector temperature was fixed at 250 \({}^{\circ}\)Cpsi and a splitless injector equipped with an insert of 0.75 mm i.d. was used. The temperature program was set up as follows: initial temperature 40 \({}^{\circ}\)Cpsi for 1 min, 1.2 \({}^{\circ}\)Cpsi \(\times\) min\({}^{-1}\) ramp until 80 \({}^{\circ}\)Cpsi, held for 2 min, 3 \({}^{\circ}\)Cpsi \(\times\) min\({}^{-1}\) ramp until 150 \({}^{\circ}\)Cpsi, held for 2 min, 40 \({}^{\circ}\)Cpsi \(\times\) min\({}^{-1}\) ramp until 220 \({}^{\circ}\)Cpsi and then held isothermally at 220 \({}^{\circ}\)Cpsi for 10 min. MS detection was performed in full scan in an Agilent 5975 quadrupole inert mass selective detector, the ion energy used for the electron impact (EI) was 70 eV and the source temperature was 230 \({}^{\circ}\)Cpsi. The electron multiplier was set to the autotune procedure. The mass acquisition range, made in full scan mode, was 30-300 \(m/z\).  
Volatile metabolite identification was achieved by comparing the retention times (RT) of the chromatographic peaks with those, when available, of authentic standards (ST) run under the same conditions. For metabolites whose reference substances were not available, the identification was performed by matching their retention indices (RI) determined relative to the retention time of a series of n-alkanes (C8-C20) with linear interpolation, with those of literature data, and by comparing their mass spectra using the National Institute of Standards and Technology (NIST) MS 05 spectral database (Gaithersburg, MD, USA).
## 4 Results and Discussion
### Different Algorithms Comparison  
To validate the proposed RT-DETR-Tomato-BS model, we conducted a comparative analysis with other improved models and the baseline model to assess its performance. The evaluation metrics used for comparison were precision, recall, F1 score, and mAP50, as shown in Table 1. The mAP50 values for the RT-DETR-Tomato-S model (88.0%), RT-DETR-Tomato-B model (87.4%), and RT-DETR-Tomato-BS model (88.7%) demonstrated that each of these models achieved the highest recall, precision, and F1 scores in comparison to the baseline RT-DETR, indicating the superiority of the proposed method. Additionally, the P-R curves in Figure 11 show that the AUC for the models follows the order RT-DETR-Tomato-BS \(>\) RT-DETR-Tomato-S \(>\) RT-DETR-Tomato-B \(>\) RT-DETR. At the same time, the precision curves in Figure 12 also show the results as RT-DETR-Tomato-BS \(>\) RT-DETR-Tomato-S \(>\) RT-DETR-Tomato-B \(>\) RT-DETR.  
Figure 11: P–R curves of different methods for ablation study.  
Figure 10: Comparison of tomato image detection between RT-DETR and RT-DETR-Tomato-BS models based on the natural scene dataset.
# A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
## 1 Introduction  
The location-routing problem (LRP) is one of the most important combinatorial optimization problems in supply chain management and logistics system planning [1]. Its processing directly affects logistics distribution costs and efficiency, customer satisfaction, and environmental issues. Low-carbon LRP (LCLRP) [2] is the problem of driving route planning and depot location while considering carbon emission factors. In recent years, vehicle routing problems and depot location issues have become increasingly important for the economy, society, and the environment. Therefore, we construct a mathematical model of regional LCLRP, taking into consideration time windows, simultaneous pickup and delivery with heterogeneous fleets (RLCLRPTWSPDHF). Its purpose is to analyze the factors affecting logistics costs, vehicle carbon emissions, and customer satisfaction. Simultaneously, a novel hyper-heuristic (HH) algorithm was developed by applying the second exponential smoothing method (SESM) combined with an adaptive mechanism as hyper-level selection strategies, and a self-adaptive acceptance criterion was designed to accelerate convergence and improve accuracy. We named this novel algorithm SESMAM-HH.  
The main contributions and innovations of this paper are as follows:  
1. For the first time, the effects of multiple models, simultaneous delivery, time windows, and speed regionalization on carbon emissions were considered, and the RLCLRIPTWSPDHF mathematical model was constructed.  
The second exponential smoothing method, combined with the adaptive mechanism, was used as the high-level selection strategy of the hyper-heuristic algorithm, and an adaptive receiving mechanism is proposed.
3. The impacts of vehicle composition, depot distribution and cost, and customer parameters on carbon emissions, logistics costs, and customer satisfaction are evaluated, and management advice and guidance on how to plan and manage distribution decisions are provided.  
The construction of this paper is as follows: the second section briefly reviews related work; the third section establishes the RLCLRPTWSPDHF integrated three-dimensional exponential model; the fourth section describes the proposed algorithm; the fifth section designs the simulation experiment to verify the correctness of the RLCLRPTWSPDHF and the efficiency of the proposed algorithm, and analyzes the impact of various parameters on logistics costs, carbon emissions, and customer satisfaction; the last section contains the conclusion and outlook.
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 3 Results and Discussion
### Blackcurrant Physicochemical Changes During Storage  
The blackcurrant soluble solid content was not statistically different on day 0 and day 3 (Table 1). The \({}^{\circ}\)Brix was from 14.1 to 15.1 on average. On day 7, only UVA had significantly different \({}^{\circ}\)Brix (15.9 on average) compared with the control (\({}^{\circ}\)Brix = 14.3 on average). Similarly, the significance was observed on day 11 as well. Meanwhile, the UVA-Chito treatment had a significant difference of \({}^{\circ}\)Brix (16.2 on average) compared with the control. On day 15, significance was observed in the UVA-Chito treatment compared with the control treatment, whereas no significant differences were observed in the UVA and chitosan treatments compared with the control treatment. With the storage, blackcurrant soluble solid content was significantly changed since \(p\)-values were 1.78\(\times\)10\({}^{-4}\). The pH, on the other hand, was not significantly changed during storage, ranging from 2.90 to 3.09 on average. The acidity, as citric acid, ranged from 1.43 to 2.17% on average. In this research, postharvest treatments did not influence the acidity of blackcurrant berries in the observation time range.  
Figure 2: Blackcurrant berry firmness changes during storage. Each treatment contains three replicates. Bars indicate values are the mean \(\pm\) standard deviations of replicates. Different letters correspond to mean values significantly different (\(p\) < 0.05) by analysis of variance (ANOVA) and Tukey post hoc test across storage.
# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 2 Related Work  
In many countries across the world, authorities in charge of maintaining roads surface primarily rely on statistical analyses of the acquired data, visual field inspections, or vehicles outfitted with specialized measurement equipment to keep an eye on roads condition [5; 6]. These monitoring techniques are labor-intensive, ineffectual, and time-consuming. Furthermore, they usually lack adequate data coverage to provide a complete overview of a road surface status, and they are susceptible to human errors in many situations. Smartphone-based sensing has been a significant addition to environmental monitoring since the emergence of microelectromechanical systems (MEMS) and other types of sensors that are now equipping recent smartphones [7]. In this direction, several research initiatives were conducted to gather data from smartphone sensors installed on moving vehicles and to analyze roadway surface issues [8; 9; 10]. Next, we give a brief overview of some recent work dealing with the use of smartphone sensors for road quality estimation. Based on crowdsourcing, the authors of [11] presented a new technique to identify road surface irregularities, such as bumps, cracks, and holes using smartphone sensors. Other similar works based on crowdsourcing were presented in [12; 13; 14; 15]. The idea behind crowdsourcing is to collect data from a number of drivers simultaneously and transmit them to a centralized server for further processing and deducing useful insights about the monitored roads. In [12], the authors presented a mobile application called \"Asfault\",which was used for monitoring the state of asphalt pavements. In [13], a system called \"smart-patrolling\" was proposed to analyze the quality of road pavements. The authors of [14] presented a mobile application called \"SmartRoadSense\" that relies on GPS and accelerometer data of a driver's smartphone for detecting road irregularities and problems. In more recent research, the authors of [16] reported on the use of the so-called \"RCT\" mobile application, which allows for identifying the position, speed, and linear acceleration of each vehicle. In [17], an Android application was used to collect accelerometer data at a low rate from a driver's mobile phone for estimating road quality in some streets in Ireland. In the same direction, the authors of [18] presented the mobile application \"RoadSense\", which uses a frequency of 50 Hz for collecting large volumes of data about road surfaces. However, in this case, the smartphone's battery was depleted due to the high sampling frequency. Works presented in [19] and [20] used the integrated smartphone accelerometer sensor for analyzing signals of vertical vibration of the supervised vehicles. In a different application, the authors of [21] concentrate on evaluating abnormalities in pedestrian and bicycle road segments. The proposed technique consists of estimating pavement states using data extracted from the MEMS modules of a bicycle-mounted mobile phone. A summary of the previous related works is provided in Table 1.
###### Abstract
In the case of the tested high-power pulsed laser, the direct modulation rate is the highest one using such lasing structures. For both lasers, the shapes of the switching current include some disturbances (oscillations) caused by the electrical parameters of their signal interfaces. Therefore, a dedicated technology of these interfaces or switching electronics integration is critical for high-speed QC laser applications. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Wavelength**} & \multirow{2}{*}{**Laser**} & \multicolumn{2}{c}{**Data Rate/ Modulation**} & \multirow{2}{*}{**Distance**} & \multirow{2}{*}{**Temp.**} & \multirow{2}{*}{**Year**} \\  & & & **Modulation Rate** & & **Type \({}^{*}\)** & \\ \hline
3.0 \(\upmu\)m & ICL & 70 Mb/s & NRZ-OOK & 1 m & 77 K & 2009 [9] \\
4.7 \(\upmu\)m & QCL & 40 MHz & Analog & 2.5 m & 293 K & 2015 [10] \\
4.65 \(\upmu\)m & QCL & 3 Gb/s & NRZ-OOK, PAM-4, PAM-8 & 5 cm & 293 K & 2017 [11] \\
4.65 \(\upmu\)m & QCL & 4 Gb/s & PAM-4, DMT & 5 cm & 293 K & 2017 [11] \\
4.8 \(\upmu\)m & QCL & 10 MHz & Analog & a few m & 293 K & 2020 [12] \\
3.0 \(\upmu\)m & ICL & 2 GHz & Analog & x cm & 77 K & 2009 [13] \\
3.0 \(\upmu\)m & ICL & 70 Mb/s & OKK & x cm & 293 K & 2010 [14] \\
3.6 \(\upmu\)m & DFG & 200 Mb/s & OKK & 10 m & 293 K & 2017 [15] \\ \hline \end{tabular}  
* NRZ-OOK, on-off keying with non-return-to-zero coding format; PAM-x, x-level pulse amplitude modulation; DMT, discrete multitone modulation; DFG, difference frequency generation laser.  
\end{table} 
Table 1: Free-space optics (FSO) systems operated in medium-wavelength infrared (MWIR) range. Summarizing, the paper describes a practical study of a state-of-the-art MWIR quantum cascade laser technology considering the application of both cw and pulsed lasers in free space data transmission.