esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results  
### Learning Achievement  
To explore the differences in learning performance between the experimental group and the control group, a Mann-Whitney U test was conducted. The results are shown in Table 2. The findings indicate that there was no significant difference in learning performance between the two groups before the experiment (\(p\) = 0.063 \(>\) 0.05). However, after the experiment, the learning performance scores of the experimental group and the control group were 71.71 and 63.21, respectively, with a significant difference between the two groups (\(p\) = 0.005). This suggests that the digital game-based AI chatbot was more effective in improving learners' academic performance compared to the traditional AI chatbot.  
\begin{table}
\begin{tabular}{c c c} \hline
**Code** & **Description** & **Behavior Explanation** \\ \hline K & Skip video & Click the Skip Video button to skip the instructional video \\ \hline V & Skip the questions & Click the Leave a Question button to skip asking AI chatbot a question \\ \hline Q & Starting a mission & Click on the Explore Map mission button to start the game mission \\ \hline H & Tap on the help guide & Ask AI chatbot for help \\ \hline AF & Free conversation with AI chatbot & Open the dialog window to talk freely with AI chatbot \\ \hline AS & Selective dialogue with AI chatbot & Open the dialog window to select a conversation with the AI chatbot \\ \hline S & Start answering questions & Start answering questions \\ \hline W & Wrong answer & Submit a wrong answer \\ \hline C & Correct answer & Submit a correct answer \\ \hline L & Read the assignment thread & Read the contents of the tip box in the game \\ \hline I & Watch instructional videos & Watch instructional video resources from Game Quest \\ \hline \end{tabular}
\end{table}
Table 1: Behavior coding scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Group** & **N** & **Mean** & **Mean Rank** & **U** & **Z** & \(p\) \\ \hline learning achievement & CG & 39 & 55.51 & 43.59 & 562.00 & \(-\)1.856 & 0.063 \\ (pre-test) & EG & 38 & 50.79 & 34.29 & & & \\ \hline learning achievement & CG & 39 & 63.21 & 31.97 & 467.00 & \(-\)2.809 & 0.005 ** \\ (post-test) & EG & 38 & 71.71 & 46.21 & & & \\ \hline \end{tabular} \({}^{\textbf{st}}\)\(p<0.01\)  
\end{table}
Table 2: The Mann–Whitney U test results for the two groups’ learning achievements.
## 3 The Case Study
### Design Requirements and Architecture  
The crucial requirement for the MUZA was the transportability of the building; therefore, during the design of the building, the reference was made to the dimensions of 9 m shipping container, as they are transportable on all major roads using a semi-truck. This ensures the mobility of MUZA as well as its use in different locations and in different climatic conditions. Another important aspect was the speed of construction and possibility of using already trained workers. From these, it was evident that at least three different construction systems with dry construction process are feasible, namely cross-laminated timber (CLT) panels, timber-frame, and LSF systems. Due to the lack of competency of construction workers in Croatia to construct CLT, as well as high material prices and the need to import the panels, CLT structure was rejected. On the other hand, the ability to find large quantities of material easily on the market, as well as the availability of competent metal workers in Croatia (a large number of manufacturers on the market are able to meet the requests with no special machinery) and higher rigidity of metal structures, steered the design of MUZA towards the LSF system.  
The second crucial requirement for the design of the MUZA was to pay particular attention to minimizing heating and cooling energy needs, and to ensure the highest possible indoor environment quality.  
The MUZA (Figures 3 and 4) has floor plan dimensions of 9.00 \(\times\) 3.00 m and height 3.25 m (on flatbed truck under 4.20 m) which are within the dimensions allowed for road freight.  
Figure 3: Exterior views of the MUZA pavilion: (**a**) main facade, (**b**) back facade, (**c**) side facades.  
Table 1 presents the basic geometric characteristics of the MUZA used in energy balance modelling.
## 3 Results
### Heavy Metal Removal
#### 3.2.2 Manganese Removal Rate  
Figure 5 shows the manganese removal rate for kaolin and kaolin/nanomaterial samples over 90 days. The manganese removal rates of kaolin on the 30 th and 60 th days were 23.63% and 43.63%, respectively. The manganese removal rates of kaolin/nanomaterial samples varied between 31.81 and 58.18% and 47.27 and 75.45% on the 30 th and 60 th days, respectively. The lowest manganese concentration in the outlet of the reactors was obtained on the 60 th day. When the ratio of all nanomaterials included in the kaolin was increased from 1% to 5%, the manganese removal rates also increased.  
Figure 4: Iron removal rate of kaolin and kaolin/nanomaterial samples.  
The manganese removal rate increased with the addition of iron oxide, aluminum oxide, and Oltu clay to kaolin, respectively. The relatively large size and small surface area of iron oxide resulted in lower manganese removal, while Oltu clay with small size and high surface area allowed higher manganese removal. In addition, even if the amount of nanomaterials with higher surface area in kaolin was small, it showed better manganese removal performance compared to nanomaterials with higher amount and low surface area. For example, the addition of 1% aluminum oxide (85-115 g/m\({}^{2}\)) into kaolin provided better manganese removal than with the 5% iron oxide additive (6-8 g/m\({}^{2}\)).  
With the addition of 5% iron oxide, 5% aluminum oxide, and 5% Oltu clay to kaolin, the manganese values at the exit of the reactor were 0.5 mg/L (54.54% removal), 0.38 mg/L (65.45% removal), and 0.27 mg/L (75.45% removal), respectively. On the 90 th day, there was a slight decrease in the manganese removal rate due to desorption. The samples with the lowest and highest manganese removal rates on the 90 th day were found to be kaolin (40% removal) and kaolin + 5% Oltu clay (70% removal), respectively. In this study, manganese is the heavy metal with the highest removal rate after iron. Similar to the removal rate of iron, iron hydroxide and manganese dioxide flocks formed in the upper part of the kaolin samples, and the cavities contributed to the adsorption of manganese, thereby increasing the removal rate [42].
## 6 Technology Specifications  
Two types of RFID systems were used in this case study: an active tag for the receiving warehouse and a passive tag for bags and pallets identification in the central and shipping warehouses. The use of the passive tags for the last two blocks was encouraged by the low investment required (less than \(\copy\)0.90 each) for the purchasing of the tags. These tags are often lost or damaged during de-palletizing and the de-packaging processes. RFID tags include an RFID Chip that stores small amounts of data; the RFID antenna that sends and receives radio waves, allowing the tag to communicate with an RFID reader and the substrate to protect the chip and antenna from damage.  
Passive tags do not have a built-in power source and rely on the energy transmitted by the RFID reader to function. The RFID reader sends out a radio wave which excites the tag and powers it. The tag uses this energy to transmit its signal to the reader.  
This technology is called inductive coupling. The distance between the tag and the reader determines how much power is transferred to the tag. If the tag is too far away, it will not receive enough power to operate. Passive RFID tags are more cost effective than active tags and require no maintenance.  
Therefore, passive tags were used for the last two blocks in our study. These tags are often lost or damaged during the de-palletizing and the de-packaging processes and the choice was encouraged by the low investment required (less than \(\copy\)0.90 each) to purchase the tags. The two warehouses were also provided with a wireless network to allow for the communication of the controller with the handy RFID readers and with the information-system repository.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 3 Multicarrier Data Frames of the DSRC and LTE-V2X Standards
### Mathematical Model of the Received Signal in Multicarrier Systems  
The channel simulator proposed in this paper is based on a signal model that is suitable for multicarrier communications that transmit the information employing data frames. This signal model is valid for the data frames specified by the IEEE 802.11p and 3 GPP LTE-V2X standards. We consider \(N_{p}\) subcarriers for data transmission. Therefore, we model the transmitted symbols in the complex baseband equivalent as column vectors of \(N_{p}\) elements where each element corresponds to the data in the \(n\)-th subcarrier. Then, considering a frame composed of \(K\) multicarrier (OFDM) symbols, the transmitted signal is described by an \(N_{p}\times K\) matrix \(\mathbf{X}\in\mathbb{C}^{N_{p}\times K}\).  
Considering the transmission of the data frame, the received signal can be expressed as:  
\[\mathbf{Y}=\mathbf{H}\circ\mathbf{X}+\mathbf{N} \tag{1}\]  
where \(\mathbf{Y}\), \(\mathbf{H}\), and \(\mathbf{N}\in\mathbb{C}^{N_{p}\times K}\) represent respectively the received symbols matrix, the channel transfer function matrix, and the additive white Gaussian noise (AWGN) matrix. Furthermore, the operator \(\circ\) represents the Hadamard product.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### On-Chain Data Storage Methods
#### 2.1.3 Blockchain Based Techniques  
Blockchain-based techniques have been developed to ensure permanent, secure, and efficient data storage on-chain. These techniques introduce innovative solutions to enhance the functionality of blockchain networks:  
* _Blockweave_: A blockchain structure developed by the Arweave platform, designed for long-term data storage. It enables low-cost permanent data storage, but data cannot be modified, and storage costs remain relatively high [64; 65; 66]. In addition to permanent  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Data Storage** & **Storage** & **Description** & **Advantages** & **Disadvantages** & **Examples** \\ \hline Logs and Transaction Data & Blockchain Logs [71; 72; 73] & Maintaining transaction history and event records & Transparency and transaction traceability & Scalability issues in big data & Ethereum event logs \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._data storage, the use of transaction-related metadata in blockchain improves data management and provides supplementary on-chain information.
* _Transaction Metadata_: Used to store additional information related to transactions. In Bitcoin, the OP_RETURN field is a widely adopted method for storing small-sized data. While it enhances verifiability, it is not suitable for large-scale data storage [67; 68; 69; 70].  
In addition to how blockchain-based techniques store data, the traceability and transparency of on-chain transactions are equally significant. In this context, logs and transaction data play a crucial role in ensuring auditability and system integrity.
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 2 Methodology
### Outer Loop: Stanley Controller  
Figure 4 shows the configuration of the Stanley controller which applied in the path tracking control system. The Stanley controller produces the wheel angle, \(\delta_{\rm s}\) based on the desired trajectory.  
Based on Figure 4, the control law of the Stanley can be derived as follows [28]:  
\[\delta_{\rm s}(t)=\varnothing+tan^{-1}\Bigg{(}\frac{k_{\rm s}e(t)}{v(t)}\Bigg{)} \tag{6}\]  
where \(k_{\rm s}\) is the gain parameter.  
The control law in (6) is saturated by \(\delta_{max}\) as demonstrated in (7). Considering the limitation of the system in delivering fast response, \(\delta_{max}\) is set to be \(\pm 17^{\circ}\) in both directions. This is the region in which the wheel is tested and managed to achieve satisfactory performance in the matter of accuracy and delay.  
\[\delta_{\rm s}(t)=\left\{\begin{array}{cl}\delta_{max}&if\ \varnothing+tan^{-1} \Bigg{(}\frac{k_{\rm s}e(t)}{v(t)}\Bigg{)}\geq\delta_{max}\\ \varnothing+tan^{-1}\Bigg{(}\frac{k_{\rm s}e(t)}{v(t)}\Bigg{)}&if\ \left| \varnothing+tan^{-1}\Bigg{(}\frac{k_{\rm s}e(t)}{v(t)}\Bigg{)}\right|<\delta_{ max}\\ -\delta_{max}&if\ \varnothing+tan^{-1}\Bigg{(}\frac{k_{\rm s}e(t)}{v(t)}\Bigg{)} \leq\delta_{max}\end{array}\right. \tag{7}\]  
At this juncture, the value of \(k_{\rm s}\) was determined by simulating the outer loop of the proposed control structure using the pre-defined path in Figure 5. The slalom path is derived from the average driving pattern during the data collection experiment. The details of the experiment will be elaborated later in Section 2.4. Based on the saturation limit in (7), the appropriate value of \(k_{\rm s}\) chosen here is 4.3.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Data Preparation  
#### 5.2.1 Cleaning  
The original dataset contained 2,075,259 collected measurements. First, the missing values were cleaned during importation by replacing them with the mean values and removing outliers. Second, the original dataset had more than 2 million records in which energy consumption was collected every minute. Thus, a new dataset was created from the original dataset by resampling hourly, which we used to train the models, and it contained 34,589 rows. This decreased the model's training time without affecting the results that we would have had if we had used the original dataset.
## 4 Discussion  
### Uncertainties Between the Two GPP Products  
This study utilized two commonly used GPP products, GPPGOSIF and GPPFluxSat, to analyze the response of terrestrial ecosystem photosynthesis to extreme CHD events. Overall, the two datasets demonstrated consistent responses but also exhibited some differences. For example, compared to the GPPGOSIF, the GPPFluxSat product showed stronger negative GPP anomalies (Figure 4) and greater differences in GPP anomalies among the five vegetation types (Figure 6) in 2022. These differences highlight the inherent uncertainties of the data products, which may be related to differences in data algorithmic processing.  
Figure 9: The percentage (%) of grid points with significant changes exceeding one standard deviation for the CHD years (**a**) 2003, (**b**) 2013, and (**c**) 2022, respectively. Here, significant positive anomalies were counted for 2 m air temperature (T2m), vapor pressure deficit (VPD), and downward solar radiation (DSR), while significant negative anomalies were counted for soil moisture (SM), GPP, and EVI.  
Despite the uncertainties, the overall consistency in responses between the two GPP products is evident, especially in 2022. Figure 10 presents the scatterplot of GPP anomalies from GPPGOSIF and GPPFluxSat. It clearly shows that the GPP anomalies of each vegetation type for each year are significantly correlated between the two GPP products, particularly for shrubs, with correlation coefficients exceeding 0.60. This indicates that the response behaviors of GPP anomalies reflected by the two GPP products are highly consistent, providing a reliable depiction of how terrestrial ecosystem photosynthesis responds to extreme compound drought and heatwave events.
## 3 Materials and Methods
### Assessment of the Transformation from Industry 3.0 to 4.0 According to RAMI
#### 3.5.1. RAMI 4.0: Hierarchical Layers in Implementation  
The hierarchical layers of RAMI 4.0 (Assets, Integration, Communication, Information, Functional and Business) are shown in this project, highlighting the transformation of a conventional production plant into a smart and connected environment:  
Asset layer: Here, the physical and virtual elements are identified. On the one hand, the plant includes the S7-300 PLCs that control the storage, sorting and distribution stations. The quality control station is controlled by an MPU card, which operates in parallel, while virtual assets such as computer vision algorithms and cloud services are managed by the MES. The integration of legacy technologies with new solutions, such as the installation of ESP32 for wireless communication with the PLCs, enables the convergence between the physical and the digital in the manufacturing environment.  
Integration layer: The interaction between the different components is realized through recent technologies. For example, ESP32s are used as wireless intermediaries for connecting legacy devices (PLCs) to the MES, while Node-RED orchestrates the communication and data flows between the MES and cloud services. This layer ensures that physical and virtual assets interact in a coordinated manner, thus enabling synchronization between the MES and the cloud.  
Communication layer: The architecture implements communication protocols such as AS-i to coordinate stations within the plant and MQTT for connection with the cloud; this protocol also allows synchronization and coordination between the MFC and FCS nodes, distributing tasks in the plant according to operational conditions. In this way, critical processes are transmitted securely and in real time, reflecting the information flow necessary for an Industry 4.0 environment.  
Information layer: Information is managed and stored both locally and in the cloud. For example, the MySQL database in the cloud stores the orders, while the MES takes care of downloading these data to a local database as a backup. In addition, the system collects data on the CPU load of the FCS nodes, which is used to optimize the distribution of tasks, thus avoiding saturation of the embedded cards. This layer not only ensures the availability of decision-critical data in real time, but also ensures redundancy and operational continuity in case of connection failures.  
Functional layer: The functional layer is embodied in the management performed by the MES and the MFC by coordinating the stations within the production operations  
Figure 6. Transition from an unstructured environment to RAMI 4.0 architecture.  
and distributing the tasks to the FCS nodes, thus automating the replenishment of the warehouse according to the orders. In addition, the FCS nodes run computer vision algorithms for quality control and facial recognition, showing how the functional layer integrates advanced and distributed tasks, optimizing the production flow.
# Navigation of Autonomous Tug via Evolutionary Algorithms with Radar Plot Fitness Evaluation
###### Abstract  
This paper presents an innovative route planning method for autonomous port tugs, using an evolutionary algorithm with radar fitness assessment. The proposed solution takes into account the specifics of tug operation in a complex port environment, characterized by the presence of numerous static obstacles (port infrastructure, islands) and dynamic obstacles (other vessels). The presented radar fitness assessment method allows for taking into account many optimization criteria, such as route length, number of turn points or safety margin with respect to obstacles. The algorithm was tested in four different navigation scenarios of varying complexity. The results of the research showed that, compared to the classic genetic algorithm, the radar method generates routes with fewer turn points (reduction by 1-2 points) and significantly reduces the total course change (up to 53.6%). Additionally, the routes generated by the radar algorithm consistently maintained a greater safety margin with respect to obstacles. The algorithm is prepared to take advantage of data interchange according to International Maritime Organization's e-navigation proposal via its VHF Data Exchange System (VDES) messages, which could improve the safety of proposed routes and their efficiency. This gives a basis to use the proposed solution as an autonomous vessels and/or formation control algorithm in future automated transport systems.  
autonomous tugboat; ship route; evolutionary algorithm; fitness estimation; e-navigation; VHF Data Exchange System +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://doi.org/10.3390/app15042139](https://doi.org/10.3390/app15042139)]([https://doi.org/10.3390/app15042139](https://doi.org/10.3390/app15042139))
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 3 Materials and Method
### Distance of the Moon from Earth  
The distance of the Moon from the Earth is obtained using a formula that calculates the varying distance along the axes of the Moon's orbit around the Earth [25]. The formula is as follows (3).  
\[\text{Distance}=\sqrt{[(x2-x1)^{2}+(y2-y1)^{2}+(z2-z1)^{2}]} \tag{3}\]  
In this formula, (x1, y1, and z1) represent the coordinates of the center of the Earth, and (x2, y2, and z2) represent the coordinates of the center of the Moon, respectively. The coordinates are typically presented in a three-dimensional Cartesian coordinate system. It is important to note that the distance between the Earth and the Moon is not constant due to their elliptical orbits and various factors such as gravitational interactions with other celestial bodies. The average distance is approximately 384,400 km or 238,900 miles. The formula used to obtain this distance takes into account the fact that the Moon's orbit around the Earth is elliptical and is related to the varying distance of the Moon along the axes of its orbit around the Earth [26].  
The average distance of the Moon from the Earth, based on the dates and locations of earthquakes in the Duzce Province between 1990 and 2022, can be found on the website timeanddate.com [27].
## 3 Data Analysis Algorithm
### Correlation Analysis  
Correlation analysis is a method of numerically expressing the correlations of the mutual effects in the data [17]. [PERSON]'s correlation, which is the correlation statistic most widely used to measure the degree of the relationship between linearly related variables, was used to analyze the relationship between the temperatures of the PV modules and the environmental factors, including water temperature and wind speed. [PERSON]'s correlation coefficient, \(r\), is described in Equation (2).  
\[r=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i= 1}^{n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}}, \tag{2}\]  
where, \(\overline{x}=\frac{1}{n}\sum_{i=1}^{n}x_{n}\), \(\overline{y}=\frac{1}{n}\sum_{i=1}^{n}y_{n}\), \(n\) is the number of samples, and \(-1\leq r\leq 1\)[18].  
The variables \(x_{i}\) and \(y_{i}\), which affect the module temperature of the floating PV, are defined as in Equations (3)-(5).  
\[x_{i}=Tc_{spv}-Tc_{fpv}, \tag{3}\]  
\[y_{i1}=T_{a}-T_{w}, \tag{4}\]  
\[y_{i2}=v_{spv}-v_{fpv}, \tag{5}\]  
where \(x_{i}\) is the module temperature difference between the temperatures of the land-based PV module, \(Tc_{spv}\), and floating PV module, \(Tc_{fpv}\). \(y_{i1}\) is the temperature difference between the PV system on land, \(T_{a}\), and the water surface, \(T_{w}\), and \(y_{i2}\) is the wind speed difference between land, \(v_{spv}\), and water surface, \(v_{fpv}\). The reason for setting the temperature differences, \(x_{i}\), \(y_{i1}\), and \(y_{i2}\), as above is to focus on the precise influence of each variable.