esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MSP Algorithms and Master Data in Production Planning System
### Production Plan System  
Prior to the concept of SCM, companies connected various processes such as production, logistics, and accounting. Production, logistics, finance, accounting, and so on share information and resources through ERP systems [40]. However, as the supply chain complexity increases, a new system for managing the planning process becomes necessary. As a system to manage production planning, it was introduced with the characteristics of an advanced planning and scheduling (APS) hierarchy. The APS system is used to develop a production plan based on these two characteristics and existing data. The supply chain can function as a solution to multiple planning problems by optimizing goals and constraints through a model definition of the plan [41]. The master data (which contain basic information related to products, inventory, production facilities, supply chains, etc.) play a highly important role in production planning and management. This directly affects the accuracy and efficiency of production planning. Maintaining accurate and updated data and formulating production plans based on these improves the accuracy and flexibility of production schedules and enables effective inventory management and efficient supply chain operations.  
In this study, a production plan was established using the APS system by reflecting the master data predicted by machine-learning results. A comparison of the established plan with the existing plan provided indicators that can be used as a reference for comparing results that affect the availability of facilities, such as the input quantity.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Research Challenges and Future Work
#### 6.2.1 Noise and Interference  
Many systems and their designs do not manage noise and interference or over-simplify the modeling. For example, in [48; 121], the modeling of the channel multipath only assumes light power due to the first reflection. However, can we simply ignore the light power arriving at a transmitter after several reflections, for example, due to the walls and environment? In addition, in many experiments and simulations, results consider only direct light and ignore the impact of multipath signal strength [54; 56; 60; 73]. Sometimes, it is even assumed that the environment is noiseless or that the experiment is conducted in the dark. Some also consider that the interference due to other light sources can be avoided due to the use of different frequencies [14]. However, it should be noted that ambient light cannot be ignored for the accuracy of positioning.  
In general, the noise in VLC measurement [48; 51; 121] can be expressed as  
\[\sigma_{noise}^{2}=\sigma_{thermal}^{2}+\sigma_{slot}^{2} \tag{8}\]where \(\sigma^{2}_{thermal}\) denotes the thermal noise in the optical channel while \(\sigma^{2}_{shot}\) denotes the shot noise, which depends on the light-induced current and can be modeled as Gaussian noise.  
There are some solutions to measure the noise level in the visible light channel. In [41; 82], the first time slot in the TDMA is reserved for measuring the background power while all the transmitters are turned off. As shown in [96], coding can be used to minimize the noise variance experienced by receivers in a system where background power is estimated. On the other hand, the scheme in [92] uses the last few bits to measure the ambient noise, which is similar to the solution in [41; 82].  
In [122], the authors investigate the impact of a multipath in VLC. The results show that the impact is more severe when the receiver device is located at the corner or the edge of a room environment compared to that when the receiver device is located in the center of the room [77; 93]. It is also shown that the multipath can cause a more significant impact to indoor positioning than that due to shot noise or thermal noise, so the multipath cannot be ignored. Until now, there have not been many research studies focused on how to mitigate this kind of influence.
## 3 Materials and Methods
### Data Analyses
#### 3.3.4 Adaptive Capacity Index  
In our approach, poverty and literacy rates were used to assess and compute the adaptive capacity index. These proxies were selected because of the scarcity of data for other proxies such as transportation, safety nets, and natural resources. Furthermore, these two proxies accurately reflect adaptive capacity and have an impact on all other proxies. These proxies have an inverse connection, although they are not completely independent. Poverty reduction can boost literacy rates and resistance to climatic shocks by increasing access to climate knowledge and investing in more resilient farming systems [59]. It can be observed through the Green Morocco Plan (GMP) that the technical and material support offered to help small and medium scale farmers is able to somehow reduce the poverty rate in the agricultural sector in Morocco. Therefore, it turns out that reducing the poverty rate can lead to improved access to irrigation through easy access to knowledge and rationalization of new irrigation practices, which can also help in reducing the effects of problems associated with declining rainfall, as well as rainwater harvesting. Equation (8) was used to compute the adaptive capacity index:  
\[AdC_{\_synsn}=\frac{10^{2}-P_{\_ptsn}}{10^{2}}+\frac{L_{\_}{ytnsn}}{10^{2}} \tag{8}\]  
where \(AdC_{\_synsn}\) is the crop yield adaptive capacity index at the national and sub-national scale, \(P_{\_ptsn}\) and \(L_{\_}{ytnsn}\) are the poverty and literacy rates (%) at both the national and sub-national scale, respectively, and \(y\) represents the year notation.
## 4 Discussion
### Limitations  
This study is subject to some limitations. It is based on a cross-sectional study method; thus, any causality can be referred to as relations with significant results. However, due to the scant bibliography that exists so far on the subject, the results found may be a good starting point for the future development of prospective longitudinal studies to clarify the repercussions of populations' generalized use of meal-tracking platforms. Previous studies have shown that during weight loss interventions, to guaranty ad-herence, to track the food intake diary, at least in two occasions must be achieved [76]. In our study, the number of times or the frequency at which the participants tracked their food intake was not registered. The fact that the use of a meal tracker platform is not related to a better BMI might be related to an inconstant adherence to the tracking habit in our study. Participants were questioned only about their usage of the meal-tracking platform over the last month to facilitate a concrete response and not provoke an inferred response because the event would not be concretely recalled [77]; this might also be considered a short period where the monitoring of changes in BMI can occur. Millenials are the predominant generation represented in the study. Previously, it has been stated that older adults are less prone to adopt the use of digital health technologies [78], but this fact might compromise the representativeness of our sample to other populations with a more balanced representation of the different generations. The sampled participants have a high and homogeneous internet literacy. Expanding the survey link through universities might have conditioned the access to the survey link to highly educated individuals. Thus, the results might be generalizable only to populations with similar characteristics.
## 2 Methods
### Experimental Data Analysis
#### 2.4.1 Data Acquisition and Preprocessing  
The experiment involved measuring three types of data: subjective evaluation data, physiological signal data, and performance data. The NASA-TLX scale was used to assess the workload of subjects under various experimental tasks.  
The average value of bilateral mastoid electrode points was used as a pre-reference value. The EEG signals are high-pass filtered at 0.1 Hz and low-pass filtered at 40 Hz. A eyes-close pre-recording stage from 5 s before the start of the experiment was used as the baseline. The Independent Component Analysis (ICA) algorithm was used to differentiate the EEG signal into independent components and remove impurity components such as blinks, eye movements, head movements, electrocardiography (ECG) data, and work frequency interference from the EEG signal.  
The Theta band (4-8 Hz) was chosen for its established role in working memory and attention [38]. The Alpha band (8-13 Hz), associated with relaxation, was analyzed to observe decreases in power during cognitive engagement [38]. The Low Beta band (13-17 Hz) was selected for its relevance to alertness, vigilance, and cognitive processing [38]. Fourteen electrode sites in the parietal and occipital electrodes (Figure 3) have been demonstrated to correlate with vigilance. Using fast Fourier transform (FFT), the power of Theta, Alpha, Low Beta, and the Theta/Alpha ratio were calculated for these 14 electrode points, yielding 56 EEG indicators.  
For the ECG data, a bandpass filter (0.03-20 Hz) was applied to remove baseline wander and high-frequency noise, preserving essential low-frequency components and heart rate variability data. After filtering, R-peak detection and power calculations were performed to extract 8 features for analysis. Combined with 4 eye movement metrics, a total of 68 physiological metrics were obtained, as summarized in Table 1.  
The performance data included emergency deceleration event response time, self-vehicle speed when DRT appeared, DRT response time, and following distance, recorded at 20 Hz. The DRT was used to measure the subject's vigilance state during the experiment. When DRT appeared, a red clock pattern was displayed in the upper left quadrant of the driver's field of view for two seconds. The interval between appearances was randomly distributed, ranging from 46 to 50 s. The driver needed to press a button on the steering wheel's right side to react.  
Figure 3: Position of 14 electrode channels. The yellow dots represent 9-channel EEG signals of the parietal site, and the green dots represent 5-channel EEG signals of the occipital site.
## 3 Results and Analysis
### Permeability of Lime-Stabilized Slurry  
Figure 5 below shows the slurry log \(k\)-log \(p\) curves for each group with different lime dosages. It can be observed that although lime stabilization effectively improved the permeability coefficient of the slurry with high water content, this permeability coefficient did not increase indefinitely with the increase in lime dosage. Instead, the permeability of the stabilized slurry was found to decrease when the lime dosage exceeded a certain threshold value. Taking the Hangzhou slurry and the Wenzhou slurry as examples, while the permeability coefficient of the Hangzhou slurry reached its peak with 2% lime dosage, it decreased significantly after the lime dosage was further increased; in contrast, the permeability coefficient of the Wenzhou slurry consistently increased along with the increase in lime dosage within the range of 1% to 3%.  
The granular gradation was furthermore observed to affect the permeability coefficient of the lime-stabilized slurry. The permeability coefficient of the stabilized slurry was more substantially enhanced with the increase in clay content. Under a vertical effective stress of 100 kPa, the permeability coefficients of the Hangzhou and Wenzhou slurries rose by 178.0% and 55.1%, respectively, with a 1% lime dosage. It can thus be seen that clay proved more sensitive to lime than silt and that the slurry with high clay content achieved a greater improvement in permeability after lime stabilization than that with silt.
## Appendix B Secondary Control  
Secondary control allows the regulation of voltage and frequency, which are not adjusted by the primary control loop. Implementing a local secondary controller at each distributed generator (DG) results in improved output quality and offers a significant advantage compared to centralized controller approaches [10].  
Each DG measures its frequency at each sampling time, averaging the received information from other units and then broadcasting its average version (\(\omega_{DG}\)) to the other DGs. Then, the consensus data is compared with the nominal frequency of the MG (\(\omega_{ref}\)) and sent to the secondary controller of \(DG_{i}\) to restore the frequency using:  
\[\delta\omega_{DG_{k}}=k_{p_{\omega}}(\omega_{ref}-\omega_{DG_{k}})+k_{i_{\omega }}\int(\omega_{ref}-\omega_{DG_{k}})dt\] (A6)  
\[\bar{\omega}_{DG_{k}}=\frac{\sum_{i=1}^{N}\omega_{DG_{i}}}{N}\] (A7)  
where \(k_{p_{\omega}}\) and \(k_{i_{\omega}}\) are the controller parameters of PI and \(\delta\omega_{DG_{k}}\) is the compensator signal for primary control (refer to Secondary Control block shown in Figure 6). \(\omega_{DG_{k}}\) and \(\omega_{ref}\) are the averages of frequency for all DGs and reference frequency of the MG, respectively.  
After calculating the average voltage received from the communication network (\(v_{MG}\)), the local controller determines the error between this value and the voltage reference \(v_{ref}\) as shown in Equation (A8). Finally, \(\delta v_{DC_{k}}\) is sent to the primary control to compensate for the voltage deviation. The strategy is shown in the Power System layer of Figure 6.  
\[\delta v_{DG_{k}}=k_{p_{v}}(v_{abc}-v_{DC_{k}})+k_{i_{v}}\int(v_{abc}-v_{DC_{ k}})dt\] (A8)\[v_{DG_{k}}=\frac{\sum_{i=1}^{N}v_{DG_{i}}}{N} \tag{12}\]  
where \(v_{DG_{k}}\) refers to average voltages broadcasted from each DG at the sampling time. Small signal representations of the frequency and voltage for secondary control are detailed in [63].
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Nondispersive Infrared Absorption Spectroscopy (NDIR)  
The non-dispersive infrared spectroscopy (NDIR) method is a mid-IR spectroscopic gas monitoring technology, which analyzes gases according to the mid-infrared characteristic absorption wavelength caused by the vibration of the gas molecules. In the actual operation and production of power equipment, the NDIR sensor has been used to locate the sulfur hexafluoride leakage area through multi-point arrangement, data acquisition, data analysis and processing, and communication technology and to monitor the operation status of equipment at the remote end [21].
## 3 Fluorocarbon OligoNucleotide Amphiphiles (FONs) and Fluorinated Oligonucleotide Analogues
### Fluorocarbon OligoNucleotide Amphiphiles (FONs)  
Recently our group has reported on the synthesis and biological properties of FONs [63]. In previous studies, our group already reported on the synthesis and cellular uptake studies of lipid-oligonucleotide conjugates (LONs) [64; 65]. The main purpose of attaching a fluorocarbon chain to a polyanionic oligonucleotide (ONs) was to study how cellular uptake or internalization of ONs are influenced by the nature of the hydrophobic moieties. To address this issue, [PERSON] _et al_. have synthesized two FONs featuring C\({}_{8}\)F\({}_{17}\) and C\({}_{6}\)F\({}_{13}\) chains using 1,3-dipolar Huisgen's reaction [66] via a \"_post synthetic_\" modification approach. Three different human cell lines were incubated with either FONs or LONs or unconjugated ONs for comparative study of cellular internalization (Figure 5). Moreover, internalization kinetics of FONs, LONs and unconjugated ONs with Huh7 cells were also evaluated. Keeping in mind the hydrophobic and lipophilic properties of fluorocarbon chains, it can be hypothesized that FONs self-assemble outside the cell to form nano aggregates. It is likely that these nanoobjects would be uptaken by the cell via an endocytosis pathway. It is worth mentioning here that to the best of our knowledge, there is no earlier report on oligonucleotide amphiphiles featuring perfluorinated chains.
## 4 Numerical Results
In the low-SNR region, the multiuser interference dominates the residual LI, so the effect of the residual LI is negligible. Figure 3: Achievable rate versus SNR\({}_{s-r}\) with SNR\({}_{r-d}=30\) dB. Figure 2: SMSE versus SNR\({}_{s-r}\) with SNR\({}_{r-d}=30\) dB.
###### Abstract  
Delivery service sharing (**DSS**) has made an important contribution in the optimization of daily order delivery applications. Existing DSS algorithms introduce two major limitations. First, due to computational reasons, most DSS algorithms focus on the fixed pickup/drop-off time scenario, which is inconvenient for real-world scenarios where customers can choose the pickup/drop-off time flexibly. On the other hand, to address the intractable DSS with the flexible time windows (**DSS-Fle**), local search-based heuristics are widely employed; however, they have no theoretical results on the advantage of order sharing. Against this background, this paper designs a novel algorithm for DSS-Fle, which is efficient on both time complexity and system throughput. Inspired by the efficiency of shareability network on the delivery service routing (**DSR**) variant where orders cannot be shared and have the fixed time window, we first consider the variant of DSR with flexible time windows (**DSR-Fle**). For DSR-Fle, the order's flexible time windows are split into multiple virtual fixed time windows, one of which is chosen by the shareability network as the order's service time. On the other hand, inspired by efficiency of local search heuristics, we further consider the variant of DSS with fixed time window (**DSS-Fix**). For DSS-Fix, the beneficial sharing orders are searched and inserted to the shareability network. Finally, combining the spitting mechanism proposed in DSR-Fle and the inserting mechanism proposed in DSS-Fix together, an efficient algorithm is proposed for DSS-Fle. Simulation results show that the proposed DSS-Fle variant algorithm can scale to city-scale scenarios with thousands of regions, orders and couriers, and has the significant advantage on improving system throughput.  
delivery service sharing; shareability network; approximation algorithm; time windows +
Footnote †: journal: Applied Physics Letters
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 1 Introduction
A refined composite multiscale dispersion entropy and deep belief network-extreme learning machine based sub-health recognition offline algorithm is proposed and optimized by an improved firework algorithm in [20]. For bearing fault detection, a new framework based on wavelet transform and discrete Fourier transform with deep learning is proposed in [21]. The chapter \"Vibration-Based Condition Monitoring Using Machine Learning\" in book [22] describes the fault-detection and -diagnosis framework, and provides many machine learning algorithms, such as unsupervised/semisupervised learning, reinforcement, and transfer learning. However, machine learning requires huge amounts of valid data. The visualized real-time vibration analyzer designed in this paper stores the data sampled from measurements to a local database and uploads them to a server through a 5G network. The proposed analyzer provides an effective data source for machine learning. Meanwhile, it also has the traditional spectrum analysis function. The main contributions of this paper are given as follows. 1. A digital integrator in frequency domain is proposed by combining fast Fourier transform with digital filtering. The analyzer uses a vibration acceleration sensor to sample acceleration signal. The corresponding velocity and displacement signals are, respectively, obtained from the acceleration sensor signal by means of two digital integrations. Frequency-domain integration has higher accuracy, faster computation speed, and better stability than the time-domain integration;
2. By using the spectral properties of the signals, a fast method for calculating the envelope spectrum and instantaneous frequency of the narrowband modulated signal is derived. In order to detect an unidentifiable periodic signal in the power spectrum, cepstrum is introduced. As a result, the analyzer can perform power spectrum,envelope spectrum, and cepstrum analysis simultaneously. Further, a fault prognosis algorithm is given by exploiting these spectral analyses;
3. A Raspberry Pi-based analyzer with integrated vibration signal acquisition, analysis, display, data storage, and uploading is proposed. The application running on the Raspberry Pi uses TCP protocol to transmit to a remote server the raw vibration data that the fault prognosis algorithm has assessed to be faulty. Except for the general-purpose hardware, the system is implemented and upgraded by software. The rest of this paper is organized as follows. Section 2 describes key algorithms based on spectral analysis, focusing on digital integrator, envelope spectrum, and cepstrum. Section 3 introduces the overall system design of the test platform and software flow. Section 4 presents the practical measurements and the analysis of the results, and finally, Section 5 concludes this paper.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Discussion  
### Shape of Farm Field  
The evaluation of our system prototype was conducted within the confines of a rectangular field. However, real-world fields exhibit a predominantly irregular shape, necessitating a nuanced approach to the placement of PIR sensors. While the established strategy of aligning sensors along straight boundaries remains consistent and effective, addressing corners emerges as a significant challenge. The diversity in corner shapes demands varied coping mechanisms. Two critical considerations come to the forefront: ensuring an extended detectable distance from the boundary and enhancing seamless coverage. Achieving a longer detectable distance is facilitated by the horizontal projection of PIR sensors outward from the corners. Simultaneously, the goal of enlarging seamless coverage is pursued through the strategic combination of vertically placed sensors or the exclusive use of horizontally placed ones, contingent on the specific field scenarios.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 2 Background and Related Work
### Architectures for Securing ITS Networks
#### 2.4.2 Mfspv  
[PERSON] et al. [14] proposed a multi-factor secured and lightweight privacy-preserving authentication scheme for a vehicular ad hoc network (MFSPV), which employs a combination of physically unclonable functions (PUF) [36] and one-time dynamic pseudo-identities (\(PID_{v}\)) as authentication factors. The intent is to mitigate the heavy dependency that other protocols [13; 31] have on the system's key and long-term sensitive data stored in an ideal TPD, which may not be realistic. It aims to decentralise the wide domain of the CA into regional domains by assigning an autonomous regional domain key (\(R_{k}\)) for each region.  
The instant a message is ready to be transmitted, it is generated a \(PID_{v}\), as shown in (3). The message hash signature (\(\phi_{vi}\)) is then calculated, as in Equation (4).  
\[PID_{v}=h(API_{new}||V_{\text{sk}}||ID_{v}||k_{mbr})\oplus h(API_{new}||t) \tag{3}\]  
\[\phi_{vi}=h(PID_{v}\ ||\ R_{k}\ ||\ m\ ||\ t) \tag{4}\]  
The protocol's proposed message format attaches the chosen pseudo-identity, hash signature and current timestamp (\(t\)) to the message \(m\) (Figure 5).  
Regarding message verification, when a surrounding ITS station receives the message, it validates the timestamp. If it is valid, it checks the signature, as shown in Equation (5).  
\[\phi_{vi}^{\prime}=h(PID_{v}\ ||\ R_{k}\ ||\ m\ ||\ t) \tag{5}\]  
According to the authors, the computation cost for one message verification is 0.006 ms, which offers from 64.0% to 99.9% lighter computation than the protocols that they compare MFSPV to. The authors state that their proposed protocol achieves a communication overhead reduction of 6.4% to 89.2% when compared to other schemes. They conclude that the MFSPV offers superior performance and features over the existing and related schemes.