esa-sceva/satcom-chunk-collection huggingface dataset:
# Safe and Policy Oriented Secure Android-Based Industrial Embedded Control System
## 2 Android-based Industrial Controller for Safety-Critical Applications
### Software Architecture
The resulting Android image was flashed to the internal embedded multimedia controller (eMMC) memory of the JECS-600 ITX. To implement Xenomai, an ADEOS-IPIPE patch compatible with both the Linux kernel and Xenomai itself is required, however, the patch for Linux 4.1.15 is not available in the Xenomai repository. Thus, we have modified the closest available patch source namely, ipipe-core-4.1.18-arm9, ensuring that all patch hunks are successful. After patching the Linux kernel with ADEOS-IPIPE, kernel options related to power management, kernel debugging, memory page migration, and CPU frequency scaling were disabled to ensure that the unwanted voltage and frequency scaling that can affect the real-time performance of Xenomai will not occur [31]. Thereafter, the Xenomai-enabled kernel is flashed to the JECS-600 ITX. The latest official distribution of IgH Etherlab is only compatible with the earlier versions of both Linux kernel and Xenomai (earlier than 4.0 and 3.0, respectively). One advantage of open software is that the source code is available to all developers and everyone can contribute to the improvement of the project. An unofficial patch set for the IgH Etherlab is available, solving the compatibility issues with our development environment, and is currently awaiting approval for mainline merge \begin{table}
\begin{tabular}{c c} \hline
**Software Component** & **Version** \\ \hline Toolchain & gcc-arm-android-gnueabihf-5.4 \\ \hline Boolloader & U-Boot 2015.04 \\ \hline ADEOS-IPIPE & ipipe-core-4.1.18-arm-9 \\ \hline Xenomai & Xenomai 3.0.7 \\ \hline Linux Kernel & Linux 4.1.15 \\ \hline Android & Android 6.0.1 Marshmallow \\ \hline IgH Etherlab & IgH Etherlab 1.5.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Real-time environment for the JECS-600 ITX. available at [34]. IgH Etherlab was compiled, enabling the generic device driver, as the real-time device drivers for i.MX6 platforms are not currently available. The user space libraries of both IgH Etherlab and Xenomai were compiled and copied to the filesystem of the Android environment. As Android does not support typical Linux operating system commands, the IgH Etherlab scripts for loading the EtherCAT master module and device drivers were modified. Instead of using modprobe, we employed the insmod and rrmmod commands to load and unload kernel modules, respectively. Although we have successfully leveraged a real-time environment for the JECS-600 ITX, Android should be able to communicate with Xenomai tasks to be applicable in safety-critical industrial applications. The next section offers a solution, integrating Android applications with Xenomai RT tasks.
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.6 Step 6: Diagnosis Techniques and Priority Index  
The priority index depicted in Table 4 allows the abilities of specific diagnosis techniques to be quantified. This supports identification of the most suitable approach to detect and diagnose specific failure modes of the system under study. As shown, the priority index proposes quantifying the diagnosis by the following two attributes: (1) informativeness of failure mechanism(s) and cause(s) diagnosed, and (2) ability to diagnose the maintainable item(s) supported by individual qualitative descriptions. Respectively, these quantities involve ranking the relevant information about the failure mechanism, severity, and overall level of damage offered by the diagnosis, and the ability to diagnose the maintainable item(s), whether this concerns normal or abnormal behavior, only damaged components, or deterioration levels for all components. The diagnosis level for the specific technique under study is estimated as the product of these two quantities.
In the characterization of VOCs from plant matrices, the sample preparation and detection techniques are critical steps in achieving optimal results [65; 66]. Nowadays, headspace solid-phase microextraction (HS-SPME) sample preparation coupled with gas chromatography/time-of-flight mass spectrometry (GC-TOF-MS) has been widely employed for metabolic profiling of volatiles from highly complex plant matrices. In the past, sample preparation of VOCs analysis relied on different extraction methods, including hydrodistillation (HD), supercritical fluid extraction (SFE), and solvent extractions [66]. These methods are time-consuming; utilize high amounts of solvents, resulting to excessive solvent waste; and causing some temperature-sensitive volatiles to be degraded at high temperatures during extraction [67]. Nowadays, headspace solid-phase microextraction (HS-SPME), a new sample preparation technique, is becoming popular for the screening of volatiles in many plant, food, and beverage matrices [68; 69]. HS-SPME is a fast and powerful method which does not require solvents, is easy to automate, and requires only a small sample size [67; 70]. Moreover, this method offers the ability to isolate or extract trace multifarious volatile or semi-volatile organic compounds from different product substrates [71]. However, expensive operational costs, including the high cost of standards and chemicals, are the major drawback of this method when compared to low-cost HD and SFE methods. In addition, the absorbent fiber's application is limited to only some influential high-boiling compounds, such as alcohols [72; 73]. On the other hand, GC-TOF-MS can screen hundreds of metabolites and offers higher speed, much greater mass accuracy, and a highly sensitive approach compared to the conventional GC-MS [69; 74]. However, it is not capable of quantifying the exact amount of each compound, and is mainly used to explore the overall composition of the target group of compounds [75]. HS-SPME and GC-TOF-MS have been successfully applied as the most modern methods in the metabolomic studies of Zingiberaceae species such as _Curcuma amada_ and _Zingiber barbatum_[76; 77]. The present study applied the HS-SPME extraction method and GC-TOF-MS detection technique to evaluate the VOC profiles of _K. parviflora_ leaves and rhizomes. In addition, the effects of the growing conditions on the volatiles of the tissues were also analyzed to provide a better and more holistic view of the VOC richness of the plant.
# Research on the Influence of Anxiety Psychology on Unsafe Behavior Among Construction Workers
## 2 Literature Review and Theoretical Model
### Mediating Roles of Organizational Climate, Safety Competency, and Job Satisfaction
Social cognitive theory underscores human agency, positing that individual traits critically shape behaviors through dynamic interactions between personal perceptions and environmental contexts. This theoretical lens emphasizes that workers' behavioral responses are adaptive syntheses of internal states and external situational demands. Based on the above analysis, safety competency is hypothesized as follows:  
**H5:**: _Safety competency is negatively correlated with construction workers' unsafe behaviors._
**H6:**: _Safety competency is negatively correlated with construction workers' anxiety psychology._
**H7:**: _Safety competency mediates the relationship between anxiety psychology and unsafe behaviors._  
Job satisfaction is generally defined as an individual's psychological state resulting from their productive activities within an organizational context, a construct widely utilized in psychological research. As a key metric for assessing productivity and operational efficiency in the construction industry, construction workers' job satisfaction profoundly influences organizational behavior optimization [41]. Studies indicate that achieving higher job satisfaction necessitates cultivating work environments characterized by competitive compensation, robust organizational citizenship behaviors, and low turnover rates [42]. [PERSON], [PERSON] et al. [43] demonstrated that job satisfaction significantly corrects safety non-compliance and safety non-participation among construction workers, with work engagement fully mediating these relationships. Enhancing job satisfaction thus serves as a behavioral corrective mechanism for unsafe acts. [PERSON], [PERSON] et al. [44], employing structural equation modeling (SEM), validated the impact of psychological safety climate (PSC) on workers' intent to stay and the mediating role of job satisfaction in Chinese contexts, using Guangzhou construction workers as a case study. [PERSON]'s Two-Factor Theory categorizes determinants of job satisfaction into hygiene factors (e.g., salary, working conditions) and motivational factors (e.g., recognition, career growth). Hygiene factors are essential for maintaining baseline employee morale; their deficiency triggers work withdrawal behaviors, whereas motivational factors must be strategically leveraged to elevate satisfaction levels. Accordingly, the hypotheses for job satisfaction are proposed as follows:  
**H8:**: _Job satisfaction is negatively correlated with construction workers' unsafe behaviors._
**H9:**: _Job satisfaction is negatively correlated with construction workers' anxiety psychology._
**H10:**: _Job satisfaction mediates the relationship between anxiety psychology and unsafe behaviors._
# Production Improvement Rate with Time Series Data on Standard Time at Manufacturing Sites
## 4 Implementation and Results
### Leverage Production Time Improvement Rate  
The production time improvement rates thus generated can be applied to products of the same production plan type to be produced in the future to generate time series production time information for predicting possible future production and capacity overruns, as shown in Table 8.  
Let us consider a future scenario where you have a product labeled AA, which requires 4 min of lead time (L/T) for production in process A. It is noteworthy that the production plan type for this product aligns with the one that generated the production time improvement rate illustrated in this study. By applying the data from the production time improvement rate, we can ascertain that the L/T for process A in the case of product AA stands at 4 min. Furthermore, as production periods accumulate, it becomes evident that the L/T for this process progressively decreases. This transformation of the production time for process A in the context of product AA into time series data effectively encapsulates the productivity improvement rate. These data can serve as a foundational resource within an APS system, enabling the forecasting of potential production and overcapacity. Moreover, they can also serve as valuable reference information for evaluating past and present improvement rates when shaping future productivity enhancement objectives.
## 1 Introduction  
In this study, we present a Contactless Vital Sign Monitoring system (CVSM) for in-cabin heart rate (HR) and respiration rate (RR) measurements using a near-infrared indirect Time-of-Flight (ToF) camera. We address the challenges of varying ambient illumination as well as the interference from excessive motion by utilizing the active illumination and the additional depth information from the ToF camera. The synchronized 850 nm active illumination in the ToF camera allows us to operate independently of the ambient light conditions, while the additional depth information can be used to compensate for motion-induced intensity artifacts. As the users move in the vehicle (passively or actively), their relative position against the ToF camera also changes; thus, the additional depth information measured by the ToF camera is correlated with the motion of the driver. We utilize this correlation to compensate for the motion-induced intensity artifact by differentiating whether the grayscale intensity change is due to motion or the blood volume change from the heartbeat. Furthermore, the depth information is also used to estimate the amount of motion during the measurement period, therefore predicting the quality of the HR measurements. Additionally, the depth change from chest wall motion can be used to directly measure RR, thereby allowing our system to measure both HR and RR with one monolithic sensor. We systematically investigate the performance of our CVSM system by performing HR/RR measurements in various realistic environments with different degrees of motion from the roads and the drivers. We study the benefit of the depth-based motion compensation by comparing the success rate of HR measurements using both grayscale and depth information (\"compensated\" HR measurements) against HR measurements using only the grayscale information of the same video (\"uncompensated\" HR measurements). Our study finds that with the depth-based motion compensation, the HR measurement success rate increase from 13.6% (\"uncompensated\" HR measurements) to 71.9% while the user is driving on the highway. When the user is driving on a local road, the success rate increases to 56% from 12%. As for the RR measurement, our CVSM system measures the RR of the driver on highway scenario with a mean deviation of \(-1.4\) BPM from the reference RR measurements. With the ability to measure two important physiological parameters with one sensor, we believe our ToF based CVSM system can enable a low-cost, compact, and multi-modal driver monitoring system that can be used for applications such as drowsiness or sudden sickness detection in vehicle.
## 8 Key Performance Evaluation  
### Sensitivity to the Initial State  
The main property and advantage of using chaotic systems to generate pseudo-random keys is their high sensitivity to the initial state of the system. This property is exploited to secure the encrypted messages that are communicated between the devices. Even if the attacker possesses the system's equations of the generation process, he/she would not be able to achieve the right Pseudo-Random sequence that the device will use. In Figure 11, we observe the sensitivity of the different keys generated for the same \(stop\_time\) with changes that can reach the order of \(10^{-6}\) and still affect at least 45-50% of the key bits.  
Given the fact that the adversary has no knowledge of the \(stop\_time\) of the system, the key generated for the encryption is fully random at this point of time. Figure 12 demonstrates the sensitivity of the generator towards the number of iterations to produce the key. This sensitivity is significant as we observe that for each different value in the generating seed, the resulting sequence changes drastically.  
Figure 10: Frequency of Occurrence of Different Length-Blocks of Bits in the Generated Keys.  
Figure 11: Sensitivity of Key Bits towards the Initial Conditions Variation. (**a**) Initial value difference in order of \(10^{-2}\); (**b**) Initial value difference in order of \(10^{-6}\).  
Figure 12: Sensitivity of Key Bits towards the Length of the Iteration Cycle.
# A Sustainable Location-Allocation Model for Solar-Powered
## 5 Conclusions  
A location-allocation model was developed in accordance with the flower pollination algorithm (FPA) and sustainability considerations to solve the problem of rice productivity using the innovative technology of solar-powered pest control. The development of UVUS involved open innovation in startups and the adaption of existing technologies, such as batteries, solar panels, and sensors. UVUS was developed with three things in mind: farmers, the energy supply, and midges. The mixed-integer linear programming (MINL) approach was used to determine the decision variable of the number of UVUS in order to minimize the non-covered area. Numerical analysis using a case study at Sanggar Rojdele, Delanggu Village showed that the model can be used to determine the number of UVUS. By installing five UVUSs with an investment cost of IDR 8,500,000, all of Delanggu's rice fields can be covered, and farmers can save IDR 5,346,500 in maintenance costs, which will come to IDR 500,000 every 3 years. The CO\({}_{2}\) per month can be reduced by 13,155 kg. Finally, up to 90% of farmers were satisfied.  
Further research is needed to extend the model by not assuming that the ultraviolet light and ultrasonic sound have the same coverage; instead, the coverage should be calculated separately in detail. Other economic benefits can be calculated for sustainability considerations, such as the positive impacts on air and soil that result from not using pesticides to kill pests.  
Conceptualization, W.S. and M.H.; methodology, M.H.; software, G.T.R.; validation, W.S., M.H. and G.T.R.; formal analysis, M.H. and G.T.R.; investigation, G.T.R.; resources, G.T.R.; data curation, G.T.R.; writing--original draft preparation, G.T.R. and W.S.; writing--review and editing, G.T.R. and W.S.; visualization, G.T.R. and M.H.; supervision, W.S. and M.H.; project administration, G.T.R. and W.S.; funding acquisition, W.S. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by The Ministry of Research, Technology, and Higher Education/National Research and Innovation Agency in \"Kompetifi Nasional--Peneitian Dasar Research Program\" (Contract 221.1/UN27.22/HK.07.00/2021, 10 March 2021).  
Not applicable.
# Freshness Monitoring of Packaged Vegetables
## 3 Factors Indicating the Freshness of Packaged Vegetables
### Chlorophyll Content and Leaf Senescence Effect for Packaged Leafy Greens  
Chlorophyll (Chl) are the green pigments found in plants that allows the absorbance of light energy needed for photosynthesis. These pigments have different absorbance and reflectance corresponding to different light wavelengths. The peak absorbance occurs at the blue light and to a lesser extent in red light, while high reflectance is noticed around green light, that is why we see the leaves green in color. The reason for this strong absorbance is due to the electron transitions between the blue and red energy levels [54].  
Knowing Chl concentration helps to a great extent in assessing the freshness of leafy greens. Spectral reflectance is considered one of the fastest non-destructive methods that can identify Chl concentration in vegetables. It was found that there is a correlation between the maximum slope of reflectance and the Chl content at wavelengths between 690-740 nm, which is known as the \"red edge\" [54]. The higher the concentration of Chl, the broader its absorbability becomes, and the less reflectance occurs shifting the red edge to longer wavelengths. Thus, the red reflectance can be considered as a reliable parameter to indicate the concentration of Chl. It was noticed that the reflectance of fresh lettuce tissue is different from old one at specific wavelengths as shown in Figure 4. Where a distinct reflectance difference is maximized with the use of near infrared light (700-900 nm),similar results were also noticed with spinach [55]. However, the near infra-red (NIR) reflectance is not only sensitive to changes in Chl content, but also to changes in the leaf structure and water content. For this reason, development of decay sensors or indicators based on spectral imaging and Chl fluorescence imaging can help identify the quality of the packaged vegetables whether by quantifying the content of Chl or by measuring the reflectance [56; 57; 58; 59; 60].  
The correlation between the chlorophyll breakdown with time is related to the leaf's senescence. Many other hypotheses evolved trying to explain this phenomenon. For instance, it was recently suggested that this degradation is due to the catabolic process of Chl as a part of senescence [61]. Another study suggested that the decrease of carotenoids which protects Chl from light leads to loss in the green pigments [62]. Other studies suggested that the contact of chlorophyllase, which is present in the outer membrane of the chloroplast, with the Chl inside the chloroplast leads to the destruction of Chl [63]. Regardless of these hypotheses, the common fact is that the Chl concentration degrades with time and hence can be used as indicator to vegetables freshness.
## 5 Illustrative Results
Figure 4 demonstrates the OP against the energy conversion efficiency \(\eta\) (Figure 4a) and the power saturation threshold \(\iota\) (Figure 4b) at the energy harvester of S. It is expected that increasing \(\eta\) and \(\iota\) makes S harvest more energy, therefore decreasing the OP. Figure 4 demonstrates accurately this expectation wherein the reliability of the communication is dramatically ameliorated with increasing \(\eta\) and \(\iota\). Additionally, S incurs outage performance saturation at large \(\iota\) at which the NLEH matches the LEH, as expected. Furthermore, S experiences a complete outage for small \(\iota\), as predicted. Figure 3: OP versus the HWi degree \(\rho\). Figure 2: OP versus \(\bar{P}\). Figure 5 exposes the OP against the time-splitting factor \(\beta\) (Figure 5a) and the target transmission rate \(R_{0}\) (Figure 5b). Figure 5a shows that \(\beta\) can be optimized to attain optimal communications reliability. The optimal value of \(\beta\) is comprehended to poise the duration for energy harvesting (Phase 1) and the duration for transmission (Phase 2). Moreover, Figure 5a shows that S incurs a complete outage for \(\beta\geq 0.827\), which is reasonable as analyzed in Section 3 where the OP is 1 when \(\Lambda_{0}\rho\geq 1\). Given \(R_{0}=0.6\) bps/Hz, \(\rho=0.1\) and \(\Lambda_{0}=2^{R_{0}/(1-\beta)}-1\), it is obvious that \(\Lambda_{0}\rho\geq 1\) is equivalent to \(\beta\geq 0.827\). Additionally, Figure 5b demonstrates the considerable performance degradation with increasing \(R_{0}\), as predicted. Furthermore, S also incurs a complete outage for \(R_{0}\geq 2.076\) bps/Hz, which coincides with the analysis in Section 3 where the OP is 1 when \(\Lambda_{0}\rho\geq 1\). Given \(\beta=0.4\), \(\rho=0.1\) and \(\Lambda_{0}=2^{R_{0}/(1-\beta)}-1\), it is obvious that \(\Lambda_{0}\rho\geq 1\) is equivalent to \(R_{0}\geq 2.076\) bps/Hz. Figure 6 demonstrates the impact of shadowed fading parameters \((\mu,\chi,\kappa)\) on the outage performance. It should be noted that this paper considers integer values of \((\chi,\mu)\) and the scenario of \(\chi\geq\mu\). It is observed from Figure 6 that the reliability of the communication is considerably ameliorated with increasing \((\kappa,\chi,\mu)\), as anticipated. Figure 4: Parameters pertinent to harvested energy. (**a**) OP versus \(\eta\). (**b**) OP versus \(\iota\).
# Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
## 5 Conclusions  
The proposed MIMO-VLC system consists of a display and camera in smart mobile devices as the transmitter and receiver. The basic arrangements comprise modulations (PAM and SPM), the detection of channel conditions, and power allocation. These designs are discussed with the characteristics of optical components in mobile phones. Moreover, the proposed WF-based bit allocation possesses the appropriate design for modulation in the MIMO VLC system. To evaluate this improvement, the optimization in the simulation is proven to have the minimum number of transmission errors in the physical cases. In future research, the proposed bit allocation for the MIMO VLC system can be extended for high-order PAMs and more dynamic channels for mobile platforms with higher resolutions for displays and cameras.  
Investigation, C.-M.L., B.-H.L. and C.-C.C.; Writing--original draft, C.-M.L.; Writing--review & editing, C.-M.L. All authors have read and agreed to the published version of the manuscript.  
Ministry of Science and Technology (Taiwan): MOST 109 2221 E 194 030.  
The authors declare no conflict of interest.