esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross Talk between Synthetic Food Colors (Azo Dyes), Oral Flora, and Cardiovascular Disorders
They are regarded as relatively persistent pollutants because of their ability to not degrade under aerobic conditions. There is more reduction in anaerobic conditions by oral and bacterial microbiome, having toxic, mutagenic, and carcinogenic effects on humans and animals. Azo dyes have also been identified in many tattoo colorants, including the derivatives of 3,3'-dichlorobenzeneidine, phthalocyanine derivatives, and azo-derivatives of 2-amino-4-nitrotoluene. Azo dyes used in food are reduced to aromatic amines by the azoreductase enzyme. The azoreductase enzyme is present in several bacteria as well as hepatic cells. In the liver, it is present in a less active state compared to the equivalent enzyme present in several bacteria. The activity of the bacterial azoreductase enzyme present in skin flora, gut flora, and oral flora has been studied thoroughly, such as _Neisseria flavescens_, _Staphylococcus hominis_, _Staphylococcus lentiis_ and _Lactobacillus_ (oral bacteria) show azoreductase activity. In this review, the presence of azo dyes (synthetic food colors) in food products and their association with periodontal and cardiovascular disease were addressed, such as their contribution to plaque buildup in the teeth and their reduction into aromatic amines through the azoreductase enzyme, released by the oral microbiome, causing different periodontal and gums disease, which contribute cardiovascular disorders. Figure 1: Chemical structures of (**a**) Tartrazine and (**b**) Allura red.
## 3 Data Analysis Algorithm
### Regression Analysis  
Regression analysis was used to analyze the power-generation characteristics of the floating PV system, according to the change in module temperature. For describing the dependent variable \(Y\), the multiple regression model was defined as in Equation (6) with the \(k\) number of \(X_{k}\)[19; 20; 21].  
\[Y_{i}=\beta_{0}+\beta_{1}X_{1i}+\beta_{2}X_{2i}+\cdots+\beta_{k}X_{ki}+ \epsilon_{i}, \tag{6}\]  
where, \(Y\) = dependent variable, PV generated power. \(X_{k}\)= independent variables, such as module temp, slope irradiation, water temp, and so on. \(\beta_{0},\beta_{1}\),..., \(\beta_{k}\) are the regression coefficients to be estimated, \(\epsilon_{i}\). is the residual error, and \(i\) is the data number.  
The progress of estimating the regression coefficients in the multiple regression models is as follows. Assuming that the estimates of the regression coefficients, \(\beta_{0},\beta_{1},\ \ldots,\ \beta_{k}\), are \(\hat{\beta}_{0},\hat{\beta}_{1},\ \cdots,\ \hat{\beta}_{k}\), the least-squares method is applied to obtain \(\hat{\beta}_{0},\hat{\beta}_{1},\ \cdots,\ \hat{\beta}_{k}\), that minimizes the squared sum of the residuals.
## 2 Results and Discussion  
### FTIR Results  
Visual analysis was used to count particles and fibres. In total, 39.6% of the MPLs were fibres while 60.4% were fragments. The samples whose filters presented particles/fibres bigger than 20 um were analysed with FTIR. The results showed that the composition of most of the fibres bigger than 20 um was cellulose (64%), 15% of the analysed fibres were polyester, 12% were of PE, 5% were PET, and 4% PP. In Figure S1, an example of a cellulose fibre is shown. Some fibres were not identified by FTIR due to their thickness. The observations show that there were, on average, 29 fibres/sample and 360 black particles/sample. Carnivorous and omnivorous fishes showed a higher presence of particles than detritivorous fishes, corresponding to the fact that fish that have a predatory diet (i.e., elevated trophic level) may accumulate and present higher levels of contamination than those that eat detritus [22] The results can be seen in Table S1 of the Supplementary Materials section.
## 4 Experimental Results
### Implementation Details  
In this study, we applied UAV-NeRF to generate a DSM from drone-captured images. During training, we updated the parameters via the adaptive moment estimation (Adam) optimizer with an initial learning rate of 0.0005 and a weight decay factor of 0.0001 following the StepLR learning rate adjustment strategy. We discretized each ray into 64 uniformly distributed 3D points for volume rendering. The total number of training iterations was 300k. All experiments were conducted via the Sat-NeRF framework and executed on an NVIDIA GeForce RTX 3090, manufactured by NVIDIA Corporation, located in Santa Clara, United States, with a batch size of 1024 rays. with a batch size of 1024 rays. Based on the computer specifications and the number of iterations mentioned in this section, the training of each model in the experiment took approximately 15 h.
## 5 Results and Discussion
### Effect of Sample Size
Figure 2: Dependence of the stability measure \(STma\) upon \(n\) calculated using the BOOTres method for confidence ellipses along with the MDI distance measure and the MDS techniques mMDS (\(\bullet\)) and PCoA (o). most likely dendrogram, which tends to 1 (Figure 4--right). In most cases, the model that describes this increase is the logarithmic one \(Pr=A\ ln(n)+B\). However, this tendency may be very slow, especially when the ED distance measure is used. To sum up, of the four methods examined, BOOTres, PCE, JACKmds, and MAPSov, the first two do not calculate confidence ellipses, the JACKmds gives low precision confidence regions, and only the MAPSov method produces reliable MDS confidence ellipses, that is, confidence ellipses that tend to zero when the sample size increases; they do not exhibit extreme values, whereas their variability at a fixed \(n\) value is rather small (Figure 4--left). For these reasons, only the MAPSov method was selected for constructing confidence ellipses in MDS plots. At this point, we should also stress that, depending on the dataset, the performance of the MAPSov method may be affected by the MDS technique used. In fact, a satisfactory MDS technique should generate points within a confidence ellipse that are not spread away or form two or more accumulation regions but gather in a compact fashion within the elliptic region. Examining the various confidence ellipses involving MDS map points and constructed using the four MDS techniques adopted in the present study, we found that the most satisfactory of these techniques are the nMDS and PCoA.
## 3 Methods
### Word-Embedding Schemes  
After the text preprocessing stages, we used word-embedding to generate a vector representation of each word. We explored six different word-embedding schemes with the aim of evaluating which was the best scheme to represent the episodes in the EHRs.  
First, we trained a Word2 Vec model using a continuous bag of words (CBOW) to obtain a vector representation for each word in the dictionary constructed by all the words contained in our EHR database. We trained the model from scratch using a neural network with 300 neurons in the hidden layer. Once the data were preprocessed, we created our own word-embeddings representation using the training data with 300 dimensions and a context windows of 5 tokens. To differentiate if there is a change in the sense of the paragraph, we added the special token \(<SEP>\) after each period or before a new line according to [26]. At the end of the training stage, we obtained \(147,272\) vectors with 300 dimensions.  
Second, we used a pretrained Word2 Vec model in the Spanish Billion Word Corpus (SBWC) [27]. The pretrained word embedding models employed in this study were chosen due to their pretraining being carried out in Spanish. In particular, this model contains 1,000,653 vectors with \(m=300\), where \(m\) is the vector size. Taking into account that the progress notes analyzed from EHRs contain a huge amount of words with typos and misspellings, there is a high probability that those words will not be found in the dictionary of the SBWC. We implemented a method to be able to assign a vector value to words with typos or misspellings based on searching the closest word in the dictionary. This adjustment was implemented by computing the Levenshtein distance between the misspelling or typo words and the vocabulary of the SBWC. The minimum Levenshtein distance was found using a searching technique based on a tree data structure, which is faster than a traditional search. The Word2 Vec model was implemented with and without the misspelling and typos adjustment.  
To compare different word-embedding approaches, we implemented three additional embeddings schemes. First, the GloVe Vectors (GloVe) embeddings were used with a model pretrained from the SBWC. Second, the GloVe was used with and without the misspelling and typos adjustment (same algorithm as in Word2 Vec). And third, we implemented FastText using a model pretrained in Common Crawl and Wikipedia. The FastText model was trained using CBOW, with dimension 300, a character n-grams of length 5, a window of size 5, and 10 negatives. We did not use the misspelling adjustment in FastText because it works with subword information.  
The six embedding schemes used in this work are listed below:  
* Word2 Vec from scratch trained in our dataset.  
* Pretrained Word2 Vec.
* Pretrained Word2 Vec with typos and spelling correction.
* Pretrained GloVe.
* Pretrained GloVe with typos and spelling correction.
* FastText.
# Identification of Emission Source Using a Micro Sampler Carried by a Drone
## 3 Result  
### Identifying VOC Emission Sources around a Dormitory Community by Drone Sampling  
After the residents of the dormitory at Site A had contacted the KEPB regarding specific chemical emission events, the auditors from the KEPB rushed to the dormitory community and took air samples on the reporters' balcony. The air samples were brought to the laboratory, where their constituents and their concentrations were determined. Table 1 presents the results of the analysis. The main emitted VOCs were aromatic hydrocarbons, including toluene, ethylbenzene, and p-xylene (5.5-14.0 ppb). KEPB selected three suspected factories that used organic solvents as raw materials, and their space distance (\(d_{\mathrm{s}}\)) were less than 1 km from the dormitory. Plant C (\(d_{\mathrm{s}}\) = 388 m) is a chemical manufacturing factory, whose main products are epoxy resin and polymers. Plant S (\(d_{\mathrm{s}}\) = 598 m) is a steel plant, whose main products are steel drums and galvanized drums. Plant P (\(d_{\mathrm{s}}\) = 677 m) is a panel factory, which fabricates high-end panels using the low-temperature poly-silicon (LTPS) technology.  
An assistant operated the drone, which carried an NTS, to collect air samples upwind and downwind of each of the above plants. Wind speeds at each sampling were determined to be lower than 5 m/s. Those wind speeds are based on the data of the Taiwan Central Weather Bureau. Table 1 summarizes the concentrations of the VOCs from the three potential stationary sources as determined using the NTS sampling device. Downwind of Plant C, the concentration of ethylene oxide, which was emitted in the manufacture of epoxy resin, was as low as 2.2 ppb. More important, ethylene oxide was not the compound that had been detected at the dormitory. Downwind of Plant P, propylene glycol methyl ether (PGMEA), which is a solvent that is used in fabricating panels, was identified at a low concentration of 2.0 ppb. PGMEA also did not match the emission that was detected at the dormitory. Finally, downwind of Plant S, toluene, ethylbenzene, and p-xylene (TEX, 9.0-15.0 ppb) were identified in the air samples, and these VOCs were characterized as similar to the aromatic hydrocarbons that were detected at the dormitory.
## 5 Conclusions  
This study proposed a method for performing time synchronization on a CAN-Ethernet heterogeneous network connected by a gateway. In particular, the study proposed a time-synchronization method that considered the processing delay in the gateway required to convert the messages. Moreover, a synchronization method for CAN networks was proposed in which the slaves shared the calculated delay to simplify the procedure by exploiting CAN's bus topology. In addition, the CAN message format was proposed to convey PTP messages over the CAN network. Finally, a test bed was constructed using automotive microcontroller modules, and used to evaluate the time- synchronization performance of the proposed method for heterogeneous CAN-Ethernet networks. The following conclusions may be drawn from the obtained results.  
First, the synchronization method of IEEE 1588 was extended to develop a synchronization method for heterogeneous networks connected by a gateway. For this purpose, equations were derived to calculate the propagation delay and offset by considering the asymmetric processing time of the gateway. Furthermore, to implement the required synchronization technique, this study proposed a method that accommodates the maximum data size constraint of the CAN and the difference in bus transmission methods.  
Second, the evaluation of synchronization performance demonstrated that the proposed method has an error of approximately 7 us. While this does not satisfy the 1 us error suggested in IEEE 1588, it indicates that the method can provide satisfactory synchronization precision for applications, such as autonomous driving, despite the use of software timestamps. Therefore, because the output of the sensors connected to the synchronized CAN nodes is measured within approximately 7 us, it is expected to enhance context recognition performance via sensor fusion.  
In terms of future research directions, follow-up studies may replace the software timestamps with hardware timestamps and experimentally verify the extent to which the system performance can be improved. Research is also required concerning methods to introduce the P2P method of IEEE 1588 and the selection of the appropriate CAN nodes to perform synchronization with the Ethernet master node. In addition, more research effort on synchronization should be directed to more complicated network structures such as Ethernet connected with tandem CAN networks and other types of heterogeneous networks, namely, CAN-MOST and CAN-FlexRay.  
Conceptualization, H.J.K. and U.L.; Project administration, S.L.; Software, H.J.K. and U.L.; Supervision, M.K.; Writing--original draft, H.J.K.; Writing--review & editing, M.K. and S.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the System Industrial Strategic Technology Development Program (10079961, \"Development of a deterministic DCU platform with less than 1 us synchronization for autonomous driving system control) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea). The authors declare no conflict of interest.
# Detection of Intelligent Intruders in Wireless Sensor Networks
## 2 System Modeling and Definitions
### Problem Formulation  
The intelligent intruder detection problem in a WSN system can be formulated as a \"game\" according to \"game theory\" [24], which can be roughly abstracted as \"a set of tools and a language for describing and predicting strategic behavior\" [25], helping us abstract and understand how decision makers, _i.e._, the intelligent intruders, interact with the target WSN. In this paper, the interactions between an intruder and a WSN are terminated when either the intruder or the WSN wins. To be specific, the WSN is considered the \"winner\" if (1) the intruder is detected by a connected sensor before reaching its destination or (2) the intruder is still inside the WSN when the allotted time \(T_{max}\) expires. Otherwise, the intruder is considered the \"winner\". In other words, the intruder, modeled as an intelligent decision maker, is able to detect nearby sensors and take actions to circumvent the sensing coverage area of the WSN while heading for its destination.  
Without loss of generosity and for the similarity of analysis and boundary effects' avoidance, we formulate the given WSN field of interest \(A\) as a slightly bigger area with horizontal side length \(L+\delta_{L}\) (\(\delta_{L}\ll L\)), while the vertical side length remains the same as \(L\), as shown in Figure 1. The intruder will start from a random point on the imaginary left periphery and move toward another random point on the imaginary right periphery, which is the intruder's destination.
## 4 New Skyhook and Displacement-Velocity Control Method
### Design and Analysis of the New Skyhook Control Algorithm
Unlike conventional SH control, the new SH control algorithm performed highly across the entire domain of vibration frequencies, including the resonance frequencies of the bogie and carbody [Figure 4b]. The trends in the acceleration of the carbody were similar to  
Figure 2: (**a**) The velocity region division and (**b**) the contribution coefficient for the new SH control under the vibration velocity of the carbody. (**a**): the regions (1) 0 to 0.0075 m/s, (2) 0.0075 to 0.025 m/s, (3) 0.025 to 0.1 m/s, and (4) \(\geq\)0.1 m/s, assumed to correspond the carbody vibration at the high frequency domain, medium frequency domain, around the second-order resonance frequency domain, and around the first-order resonance frequency domain, respectively. A, C, D, E, G, and H are the intersections of region thresholds and line graph of vibration velocity of carbody; B and F are the peak at the first and second-order resonance frequencies, respectively. those in the velocity of the carbody under the three control methods [Figure 4c]. Compared with conventional SH control, the new SH control algorithm effectively attenuated vibrations in the lateral suspension system, reducing the vibration velocity and acceleration of the carbody by 51.86% and 51.21%, respectively, at a frequency of 14.6 Hz. Figure 4: _Cont._  
Figure 3: Comparisons of the vibration transmissibility under different controls.
### Motivations and Contributions  
This paper presents an efficient and effective cooperative method for smart ERP IoT-Fog-Cloud service selection and composition. It extends the CCS-2S component of the global framework architecture introduced in [25] to enhance the precision and optimize the execution time of IoT with Fog and Cloud services composition in order to offer useful and optimal business processes depending on the user's QoS constraints and contextual preferences. In particular, we introduce a new QoS energy violation with a leverage energy-efficient cooperative strategy. Since there exist different types of cooperative strategies for service composition, we propose a QoS-Energy aware multi-agent strategy, which leverages relevant services in the selection phase and optimizes energy consumption. The proposed study is very important because it is carried out in one of the new research fields, especially in light of the improvement of distributed business processes in Algerian companies, since semantic-based context-aware strategies can help to filter business services that violate both energy and customer constraints. An evaluation was undertaken of the proposed method using several kinds of agents through the IoT business process of the IRIS company in Setif city in Algeria. The study achieved promising ERP performance in terms of QoS and saving energy. The contributions to this dynamic and adaptive smart ERP are outlined as follows:  
* We extend the previous model of CxQSCloudSERP ontology [25] with a variety of industrial IoT concepts such as robotics, drones, I-IoT, etc. for describing rich information of ERP services to ensure shifting and business agility. It enables the classification of services by category, role, QoS, and energy consumption.
* We propose a Semantic Context-aware Agent-based Concrete Composite Service with three stages (SCwA-CCS-3S) for smart ERP to accelerate the service composition task and offer customers a flexible personalized smart ERP.
* We propose an improved SCwA-CCS-3S, which provides a three-stage composition and optimization process. First, we introduced a novel dominance relationship with a new QoS energy violation degree through NSGA-II (Non-Dominated Sorting Genetic Algorithm II) [17]. Intelligent cooperative agents aim to quickly discover available composite services that meet customer's constraints. We then selected a composite service (concrete Business Process) among the set of all generated composite services, according to customer's QoS preferences. Finally, we adapted a composite service for managing unpredicted events, such as changes of customers' QoS needs, critical sensor events, and low latency that have stopped working.
* The proposed approach targeted the problem of agility and dynamicity of ERP in IRIS Setif Company of Algeria. It is based on different cooperative multi-agent strategies to compose business services quickly and inexpensively.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 4 ETSI PoC
### Security Assets  
Security assets are the core components that enable protection measures to be enforced within our ZSM architecture. These assets include instantiable entities, such as Virtual Security Functions (VSFs), as well as configurable physical and virtual components, like switches or SDN controllers. Both types are responsible for implementing advanced security functions. Assets are managed using a modular approach, allowing seamless integration into the framework. This modularity not only improves system scalability but also enhances security flexibility by enabling the on-demand addition of new security functions as they become available. This approach ensures that the system can evolve rapidly and adapt to new security threats without disrupting existing operations.  
The asset integration process starts by expanding the policy model to define the capabilities and configuration parameters of the new security components. This step ensures that the framework understands the asset's functionality. Next, a plugin is developed to translate high-level policies into technology-specific configurations, such as YANG. Finally, a driver is created to directly operate and configure the asset, for example, using NETCONF, thus completing the life-cycle management process.  
In this work, several security assets have been integrated into the autonomous management of the ZSM framework. These include as follows:  
* STA: Analyzes 5G core control traffic and detects anomalous encrypted traffic.
* I2 NSF: Establishes a secure communication channel.
* PoT: Verifies that traffic follows the secure communication channel.
* Fifth generation core agent: Acts as a middleware controller to manage network functions (NFs) and retrieve contextual UE data from the 5 GC.  
For each of these assets, the aforementioned integration steps have been applied. This section introduces these security modules and explains how they have been integrated into the proposed architecture as part of this work.