esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Adsorption Isotherms Models
### Langmuir Adsorption Isotherm Model  
A Langmuir adsorption isotherm model is utilized to explain the equilibrium between an adsorbate and an adsorbent system when the surface of the adsorbent is homogeneous. It also indicates that the adsorption is monolayer as the adsorbent has equal affinity for all adsorbate molecules [134]. Adsorption phenomena depend on the number of sites uncovered on the adsorbent, and desorption depends on the number of sites of the adsorbent covered.  
The Langmuir isotherm model is expressed as:  
\[C_{\rm e}/q_{\rm e}=1/q_{\rm Q}K_{\rm L}+C_{\rm e}\ q_{0} \tag{3}\]  
The quantity of solute adsorbed onto the adsorbent at adsorption equilibrium is represented by q\({}_{\rm e}\) (mg/g, mmol/g); C\({}_{\rm e}\) (mg/L, mmol/L) is the solute concentration in solution at adsorption equilibrium; q\({}_{\rm o}\) (mg/g, mmol/g) is the highest adsorption capability; and K\({}_{\rm L}\) (L/mg, L/mmol) is the Langmuir isotherm constant. By plotting C\({}_{\rm e}\)/q\({}_{\rm e}\) versus C\({}_{\rm e}\), we obtain a linear relationship. The values of both q\({}_{\rm o}\) and K\({}_{\rm L}\) can be obtained from the slope (1/q\({}_{\rm o}\)) and the intercept (1/q\({}_{\rm o}\) K\({}_{\rm L}\)), respectively.  
The advantage of this model is that the surface of the adsorbent is uniform, and there is no interaction among adsorbates. It follows [PERSON]'s law at low concentration levels. Moreover, the adsorption process is unimolecular, and when the whole surface is covered by solute, further adsorption does not occur, and this indicates a maximum of adsorption saturation of adsorption. The disadvantage is that, according to [PERSON], a saturated value of adsorption is independent of temperature, but experimentally it was found that adsorption decreases with a rise in temperature. This model does not give an idea about the roughness of the adsorbent surface or heterogeneous adsorption [135].
## 3 Results and Discussion
### Responses of ELUE and GPP to Bio-Physical and Environmental Factors
#### 3.2.3 Responses of ELUE and GPP to Water Availability
The effect of precipitation on the temporal variations of ELUE was analyzed, and only the data during the active growing season was considered to exclude the effects of growth phenology changes on ELUE. Figure 8 shows that ELUE increased sharply, which may be caused by the decline in PAR when there is rainfall during cloudy conditions, since then ELUE gradually decreased. These results are the first evidence showing that the ELUE variation trend is approximately proportional to the square root of inverse time after a precipitation event; a low and relatively steady ELUE was found during a long period without precipitation or irrigation, reflecting the fact that a soil water deficit will suppress ELUE due to declining GPP (Figure 9). The effect of precipitation on ELUE is mainly in the way of replenishing soil moisture content. The relationship between SWC and ELUE was simple and coherent, exhibiting a trend in which ELUE increased with increasing SWC during the active growing season. Previous studies have concluded water stress will reduce the carbon exchange between the ecosystem and atmosphere physiologically as a result of the combination of stomatal, mesophyll conductance and biochemical limitations [8; 78; 79]. Such results can provide a theoretical basis for the development of carbon flux estimation and crop yield prediction models in water-limited environments. Figure 8: The seasonal variation of ecosystem light use efficiency (ELUE) at a representative site of soybean (**a1,a2**) (US-CRT), (**b1,b2**) winter wheat (CH-Oe2) and (**c1,c2**) summer maize (US-Ne1). Series 2 is an enlarged part of the gray shade of Series 1. Bar plots show precipitation. The equations inserted in Series 2 show the ELUE variation with the number of days after a certain precipitation event. Figure 7: Relationships between the total precipitation and (**a**) ecosystem light use efficiency (ELUE), (**b**) gross primarily productivity (GPP) and (**c**) photosynthetic active radiation (PAR) during the crop growing season at the four agroecosystems. Ns represents that the trend is not significant.
# Time Series Feature Selection Method Based on Mutual Information
## 4 Result Analysis
### Effect Analysis of Working Condition Recognition Based on the Selected Features  
Figure 7 shows the effect of working condition recognition based on the selected system time series features. In the CMAPSS dataset, there are a total of six different operating conditions for the engines, and they continuously alternate between the six different operating conditions throughout their entire lifetime. Accurately identifying the system working conditions based on the selected sensor signals is an important foundation for subsequent research such as system operability analysis, etc.  
Figure 8a shows the effect of recognition based solely on sensor signal s9, Figure 8b shows the effect of working condition recognition based on s9 and s11, and Figure 8c shows working condition recognition based on s9, s11, and s12. From Figure 8, it can be seen that Figure 8a,b can achieve working condition recognition to a certain extent. However, there are some data points that overlap, which means that the system's working condition identification cannot be effectively and completely achieved. By selecting the first three  
Figure 7: F-test and mutual information comparison of the nine sensor signals (figure drawn by matplotlib).  
features, the recognition of complex working conditions of the system can be effectively realized, which shows the effectiveness of selecting time series features of the system based on mutual information.
###### Abstract
In such a system, a conventional controller, such as a PID FLC, optimization-based controller may not suffice. Toa certain extent as reported, the optimization-based controller was successful, but one of the drawbacks was a long converging time. Therefore, the design of a robust intelligent controller was required for the minimization of frequency error. In this way, this paper presents an interval type-2 fuzzy logic-based, dual-mode gain scheduling (DMGS) of the proportional and integral controller in which the gains of PI controller were scheduled through the dynamic selector switch instead of the optimization algorithm. The proposed technique was a combination of IT2 FL, PI controller and dynamic selector switch; hence, the technique is said to be a hybrid controller-based technique. This paper is structured as described: Section 2 provides the system description and modeling of the proposed automatic generation control (AGC) model considering RES power fluctuation. Section 3 presents the application of IT2 FLC in a proposed interconnected system. Section 4 describes the results and discussion. Conclusions are drawn in Section 5.
## 3 Results and Discussion
### Prediction Model Based on NIRS Data  
The prediction performances of the PCR and PLSR methods based on the NIRS data are shown in Table 6. The R\({}_{\text{cv}}^{2}\) values of the electronic nose ranged from 0.9797 to 0.9989, and the RMSEC values ranged from 0.2003 to 0.8585. Because the R\({}^{2}\) value was larger than 0.8, the application of NIRS for predicting the concentration of SDPP mixed with WPC is feasible. In addition, the optimal algorithm for NIRS was the PLSR algorithm with the Savitzky-Golay first derivative and mean-center preprocessing method, which again confirmed the accuracy of the CR analysis.
# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 5 Conclusions  
In 3D film image, it is not easy to inspect whether the 3D film image is good or bad because in 3D film images, the pattern contour is not clear, and the contrast of pixels in the image is low. In addition, there are few methods used to classify good or bad 3D film images since they are a recently developed product. In this paper, we proposed a quality inspection method that classifies good and bad 3D film images based on a threshold value by calculating the width for a specific height of the image histogram that shows the characteristics of the image. Through the experiment, it was shown that the classification accuracy of the proposed algorithm was the highest, at 99.34%, at 3/10 of the height of the image histogram. The proposed quality inspection method took a little bit longer than other algorithms, but the algorithm was simple, and the test accuracy was much higher than other algorithms. Additionally, it was shown that there was no need to detect patterns in the image, and quality inspection was possible even if the contrast of the image was low. In future work, we plan to study methods for automatic quality inspection of 3D film images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Accuracy (\%)** & **Computation Time (sec)** \\ \hline Proposed algorithm & 99.34 & 3240.225 \\ Image subtraction & 76.75 & 12.126 \\ Canny edge detection & 22.62 & 12.158 \\ Canny edge detection with HSV [17] & 25.05 & 54.869 \\ Otsu thresholding & 39.28 & 8.135 \\ Morphological geodesic active contour [20] & 72.07 & 2074.59 \\ SVM with Canny edge detection [23] & 76.58 & 21.314 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification accuracy and computation time of the proposed and comparison algorithms.  
**Author Contributions:** Conceptualization, J.L. and J.K.; methodology, J.L. and J.K.; software, J.L. and J.K.; validation, J.L. and J.K.; formal analysis, J.L. and J.K.; investigation, J.L. and J.K.; resources, J.L. and J.K.; data curation, J.L. and J.K.; writing--original draft preparation, J.L.; writing--review and editing, J.K.; visualization, J.K.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Research Foundation of Korea, grant number CD202112500001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 3 Efficient Mobility Support for Sink Group Utilizing Region Shift  
This section presents our approach for Region-Shift-based Mobile Geocasting Protocol (RSMGP). We define and assume related side technical issues as a network model. Then, the concepts and detailed mechanisms of RSMGP are proposed.
## 5 Conclusions  
This paper proposes a novel refined answer selection method with attentive bidirectional long short-term memory network and a self-attention mechanism for an intelligent medical service robot. Firstly, the required knowledge-based text was constructed as background information and converts the question and answer from words to vectors, separately. Secondly, self-attention mechanism was adopted to extract the global features from the vectors. Thirdly, an attentive Bi-LSTM network was designed to better understand semantic information of the dataset and calculate the similarity between the question and answer considering background knowledge information. To evaluate the performance of the proposed approach, this study constructed a knowledge-based QA dataset including multiple medical QA pairs and conducted a series of experiments to verify the effectiveness of the proposed method. Through the experiments, the proposed method improved 6% in accuracy and decreased the BLEU indicator to 3.10. MAP could reach 68.6%. It effectively verifies its feasibility for answer selection in intelligent Q&A systems, which is crucial for a medical service robot to realize an intelligent question answering service.  
In future work, we will perform transfer learning to see whether the proposed method can be applied to other lower-level applications in the natural language processing domain, such as machine translation and sentiment analysis. We will try to construct a knowledge graph (KG) as our background information to mine hidden relations beyond the question and answer. We will also further explore the impact of other existing pre-training models on answer selection, such as GPT, BERT, and so on. We will further expand the medical service data through python crawlers and build a special medical dataset to study the proposed answer selection task deeply for a medical service robot.  
Conceptualization, D.W. and Y.L.; methodology, D.W.; software, Y.L.; validation, H.M. and F.X.; formal analysis, F.X.; investigation, Y.L.; resources, H.M.; data curation, D.W.; writing\(-\)original draft preparation, D.W. and Y.L.; writing\(-\)review and editing, H.M. and F.X.; visualization, H.M.; supervision, F.X.; project administration, D.W; funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (No. 61902051), by the International Science and Technology Cooperation Program of Liaoning Province (No.2022 JH2/10700012), by the Applied Basic Research Program of Liaoning Province(No.2022 JH2/101300269), by the Dalian Science and Technology Innovation Fund (No. 2021J)I12 GX013, No.2021J12 GX014), and by the Research Foundation of Liaoning Province (No. LJKQZ20222447).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 3 Topological Structure Mesh Routing
### Handoff  
To maintain connection with the network, the leaf device periodically checks the link quality with the parent router through the received beacon frame. If the RSSI of the current  
Figure 10: Data flow of the mesh network configured using the TSMR.  
parent router is lower than the defined threshold (equivalent to \(-70\) dB), the leaf device searches for another router with a higher RSSI. When a router with a higher RSSI exists, the device can participate in the new network by sending an association-request frame to the new router. Figure 11 shows an example of the proposed handover procedure.  
In Figure 11, a mobile node [1.1.1] moves into the communication area of the router [1.0.0] and starts the handover process by sending a handover-request frame to the router [1.1.0]. Figure 12 shows the format of the proposed handover-request frame.  
The router [1.1.0] that receives the handover-request frame allocates a new address [1.0.1] to the mobile node and broadcasts the handover-response frame to all neighboring nodes. Figure 13 shows the proposed handover-response frame structure.  
Figure 11: Example of the proposed handover procedure.  
Figure 12: The format of the proposed handover-request frame.  
Figure 13: The proposed handover-response frame structure.  
The mobile node that receives the handover-response frame starts the data transmission process. The base station that receives the handover-response frame relays the received frame to the neighboring router [0.1.0].  
In the TSMR protocol, because coordinators rebroadcast the received handover-response frame to share information regarding the movement of a mobile device, some intermediate routers can recognize the new address of the moved device. Consequently, the source device can transmit a data frame to the new destination through an intermediate router that obtains the new address of the moved destination device, although it does not recognize the handover of the destination device. Also, if there is no response from the leaf device, the router buffers the packet. Upon receiving the handover response frame, the buffered packets are forwarded to the router that sent the handover response frame, and the router transmits the packets that are forwarded to the newly subscribed leaf device. This process cannot completely prevent packet loss, but it can minimize loss.  
When the router moves, it broadcasts a handover-notification frame to its leaf device. The leaf devices that receive it transmits dummy data to the router, and the router selects the leaf device with the highest RSSI to the new router. The selected device changes its leaf ID to zero, and the legacy router performs a handover process.
# Biotechnological Fungal Platforms for the Production of Biosynthetic Cannabinoids
## 4 Development of Biotechnological Fungal Platforms for the Biosynthesis of Cannabinoids  
Fungi produce a broad diversity of primary and secondary metabolites with different bioactivities. Different biotechnological processes have been implemented with the yeasts _Saccharomyces cerevisiae_[41; 42; 43], _Yarrowia lipolitica_[44; 45], and _Komagataella phaffi_ (formerly _Pichia pastoris_[46; 47]) [48; 49]. The production of high-value metabolites through different biotechnological processes is also achieved by means of filamentous fungi, such as _Aspergillus_ and _Penicillium_ species, _Acremonium chrysogenum_, _Blakeslea trispora_, and _Trichoderma reseei_[50; 51; 52]. Among these filamentous fungi, _P. chrysogenum_ (_P. rubens_) stands out for its adaptability to the massive industrial overproduction of penicillin [53; 54; 55], which suggests the use of this microorganism as a cell factory for products other than \(\beta\)-lactam antibiotics [56; 57; 58]. The complete genome sequences of yeasts and filamentous fungi obtained in recent years have provided the basic information for the development of new approaches aimed at improving production strains, increasing metabolite yields, and designing new fungal production platforms for natural products with potential commercial value [51; 59; 60].  
Although bacteria such as _Escherichia coli_, _Bacillus subtilis_, _Streptomyces livedans_, and _Corynebacterium glutamicum_ are easier to manipulate genetically, these microorganisms are not always suitable for the heterologous expression of proteins of eukaryotic origin due to their lack of post-translational modifications and their limited ability to secrete heterologous proteins. On the contrary, some yeasts and filamentous fungi have the ability to biosynthesize biologically active proteins with a degree of glycosylation similar to the original proteins [52] and possess a great secreting capacity for different enzymes and metabolites. It should be noted that 82% of the commercial enzymes used in the food industry are manufactured using fungal hosts [61], which confirms the role of these microorganisms as cell factories for heterologous protein production [62].  
The application of new genetic engineering techniques at the molecular level has allowed a considerable increase in the production of recombinant proteins and secondary metabolites in yeast and filamentous fungi [49; 63; 64; 65; 66; 67]. This has contributed to the design of fungal hosts for the heterologous expression of biosynthetic gene clusters that could work as efficient platforms to produce high-added-value secondary metabolites, such as cannabinoids.
## 1 Introduction
These technologies, known as Multi-RAT Dual Connectivity (MR-DC) [7], allow, for example, a user equipment to connect to an LTE and NR access node simultaneously. The question also raises of determining in which situations it is convenient to switch from one technology to the other in order to make communications more efficient, i.e., to determine how and when to use a certain communication mode and interface depending on the usage scenario and the potential requirements of the applications being used. The necessary architectures are further complex when interconnection mechanisms must be defined where multiple operators are presented and a User Equipment (UE) must be managed so that the use of V2X is authorized independently of the operator to which it is subscribed. This paper provides a review of basic and advanced use cases defined for vehicular communications and their corresponding potential requirements. Thus, it describes the need for high performance networks, communication modes and efficient internetworking mechanisms to meet the potential requirements of different services. The simulations carried out of LTE transmission modes allow obtaining Packet Delivery Ratio (PDR) data based on the distance between transmitter and receiver, which indicates the limitations of this technology and suggests the need for new technologies with better Key Performance Indicators (KPIs). The present paper is organized as follows. Section 2 reviews the standards for vehicular communications, communication modes and use cases defined by 3 GPP. Section 3 details the resource management mechanisms defined for LTE mode 4. Section 4 shows simulation results of the PDR in LTE Mode 4 and LTE Mode 3 to support that a centralized approach offers better performance, at the cost of cellular network coverage, also asserting that LTE V2X can only address rudimentary use cases. Finally, Section 5 discusses how being aware of the MR-DC nature of 5G non-standalone deployments may be beneficial for the sustainable growth of capacity provisioning planned by a Mobile Network Operator (MNO) and the ongoing adoption of advanced V2X services.