esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 7 Conclusions  
Based on SHM data, this paper analyzes the time-varying characteristics and correlations between structural temperature, equivalent traffic load, and structural vertical frequency. Lasso regression models under different time scales for identifying abnormal patterns are established and verified using a large-span arch bridge. The conclusions obtained were as follows:  
1. For this bridge, the equivalent traffic load can be replaced by the RMS of the structural acceleration;
2. The temperature of the steel bridge structure changes uniformly, without any noticeable temperature lag in different structural components. There is a good linear relationship between the longitudinal displacement of the girder and the structure temperature, which can serve as an important basis for evaluating the performance of expansion joints;
3. It is feasible to eliminate the normal time-varying components of the structural characteristics by establishing a relationship between the action and structural characteristics;
4. The power spectral densities of structural temperature, equivalent traffic load, and structural vertical frequency exhibit two different periodic components, 24 h and 8192 h, corresponding to the daily and annual cycle scales, respectively;
5. Lasso regression models established based on frequency residuals under both daily and annual cycle scales can all identify structural abnormal states, but the predictive performance of the Lasso regression model under the annual cycle scale is better.  
A further future research proposal is to consider the influence of pile-soil interaction on the structural frequency and extract indicators that are more sensitive to the structural state. Furthermore, it is necessary to study whether damage indicators established based on structural frequency are applicable to small- and medium-span bridges.  
Conceptualization, H.Z. and N.H.; methodology, H.Z.; software, H.H.; validation, H.Z., H.H. and Z.F.; formal analysis, H.Z.; investigation, N.H.; resources, H.H.; data curation, Z.F.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z.; visualization, Z.F.; supervision, N.H.; project administration, N.H.; funding acquisition, N.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Zhejiang Engineering Center of Road and Bridge Intelligent Operation and Maintenance Technology, grant number 202302G.  
Figure 10: Structural abnormal pattern recognition results.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## Empirical Modeling of Liquefied Nitrogen Cooling Impact during Machining Inconel 718  
**[PERSON] 1**, [PERSON] 2, [PERSON] 1 and [PERSON] 3,*(c)  
1 Laboratory for Cutting Processes, Faculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, 1000 Ljubljana, Slovenia; [EMAIL_ADDRESS] (M.H.); [EMAIL_ADDRESS] (F.P)  
2 Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia; [EMAIL_ADDRESS]  
3 Laboratory for Machining Processes, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +386-2-220-7601  
**Featured Application: Implementation of a machine learning procedure to model the temperature drop in the cryogenic cooling of Inconel 718. Machine learning is performed by an adaptive neuro-fuzzy inference system (ANFIS). Based on a representative set of cryogenic cooling experiments, the formed response surfaces (of temperature drop in Inconel 718) are represented as a set of fuzzy logic rules. Since we are dealing with a small set of experiments (input-output datapoints), the leave one subject out (LOSO) version of k-fold cross-validation is adopted. LOSO offers to exploit the small datasets and by them assess and tune the overall ANFIS modeling performance. A machine learning-based model is designed to minimize the RMSE prediction error by finding the optimal set of hyper-parameters (external ANFIS input parameters). The optimization is performed with the particle swarm optimization (PSO) algorithm.**  
**Abstract:** This paper explains liquefied nitrogen's cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model's construction and validated via k-fold cross-validation. Optimization of the ANFIS's external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (_test RMSE_) = 0.2620, and _test R_\({}^{2}\) = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen's cooling ability, which has an impact on the surface characteristics of the machined parts.  
**Keywords:** cryogenic machining; cooling impact; Inconel 718; machine learning; adaptive neuro-fuzzy inference system; particle swarm optimization
## 5 Discussion
### Other Findings  
We also obtained other findings from the experiment. For instance, we found it interesting that all the participants did not refer to the minimap all the time during navigation tasks in both modes. During the interview process, we asked them about this, and they explained that the minimap looks like a sandbox from a bird's-eye view, which could give them a brief perspective of the surrounding environment. They could establish spatial cognition relatively comprehensively in a short time. We plan to modify our minimap interface to be opened and hidden according to participant's preferences. Another interesting finding is that when asking about the possible use of this concept in the future, more than one pedestrian mentioned in the interview that the simplified minimap could also be used during running, jogging, bicycling, and other outdoor sports in a specific area of the urban environment. This also inspires us to discuss more specific uses for navigation with this system. We also noticed that some participants mentioned that the minimap could be followed by the motion of the head and eyesight, which inspired us to consider multi-model interaction in the future. In addition, the participants also mentioned that the normal 3D map interface also works in some cases of navigation. For example, the participants said that if the minimap could offer a detailed image of a building from a 3D aerial view, it could help them recognize and identify certain destinations in more complicated environments--for example, an area that has many buildings that have similar appearances and complicated architecture structure. Moreover, one participant suggested that the normal 3D minimap with detailed environmental information could also add the indoor structural information of landmarks and buildings, inspiring us to explore more specific applications of mirrored maps and aerial map modes. Additionally, some participants mentioned that a 3D virtual minimap could also be applied in underground environments where the GPS function might not perform well.
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 4 Results and System Evaluation
### Limitations and Challenges  
The proposed model exhibits several limitations that require future mitigation. One notable constraint pertains to its validation exclusively on buses, which confines its applicability to a single category of public transportation. For watercraft in remote areas or alternative bus models designed for similar locales, the model necessitates adjustments in distinct parameters that describe the operation of these vehicles. Despite the model's universal quality token formula, parameters differ among vehicle models, necessitating human intervention at this juncture for calibration.  
Additionally, another significant limitation relates to the technical peculiarities of components and systems in different modes of transportation. The data transmission from buses is resolved through one method, collecting data from a specific set of components. In the case of waterborne transportation, unique features come into play, including distinct braking systems, the absence of wheels, and other differentiating factors.
## 3 AdViSED Fast Measuring System
### Bounding Box Extraction and Time/Edge Analysis  
After the feature extraction and first decision step discussed above, the survivor blobs are analyzed dynamically (time-based analysis step in Figure 1) considering the characteristic of the bounding box that contains them. The bounding box table is composed by the lists of all bounding boxes and for each of these we have the dimension and origins x and y and a counter called kill time. The kill time is initialized to the same value for each box (i.e., \(kill\_time=frame\_rate\)). The counter is decreased by one for each frame thereafter analyzed. When the counter is zero, the bounding box is deleted. This means that each bounding box has a lifetime of one second by setting the value of \(kill\_time=30\) and considering a \(frame\_rate\) of 30 fps.  
There is also a test in parallel to the count to kill the smoke blobs without marked edges (edge-based analysis step in Figure 1). Therefore, we perform an edge test that deletes the bounding boxes that do not respect the constraint without waiting for the end of the count of the kill time.  
To implement this task, we use the Sobel edge detection method in Equation (5). With \(I\) being the source image, \(G_{x}\) and \(G_{y}\) represent the vertical and horizontal derivative approximation, where \(\star\) denotes the two-dimensional signal processing convolution operation. The resulting gradient approximation can be combined to give the gradient magnitude using Equation (6).  
\[G_{x}=\left(\begin{array}{ccc}1&0&-1\\ 2&0&-2\\ 1&0&-1\end{array}\right)\star I;\ G_{y}=\left(\begin{array}{ccc}-1&-2&-1\\ 0&0&0\\ 1&2&1\end{array}\right)\star I; \tag{5}\]  
\[G=\sqrt{G_{x}^{2}+G_{y}^{2}} \tag{6}\]
## 4 WEAMR Overview
### Inter-Gateway Communication  
The gateways are connected using high-speed Ethernet cables. Gateways are supposed to exchange certain information that would be required for routing and monitoring the state of the links. Our sensor network has been divided into clusters but the overall network can be considered as one autonomous system (AS). We therefore require a protocol configured in gateways that can handle cluster to cluster (Intra-AS) communication. We propose to employ the widely-used Open Shortest Path First (OSPF) protocol for inter-gateway communication. We choose OSPF because of two main reasons: (1) In distance vector routing protocols, each router does not possess information about the complete network topology and consequently there is a slow convergence problem (2) OSPF works over IP and has a richer set of extensions and added features as compared to other link state and distance vector routing protocols. Each gateway has information saved about nodes of the complete network in form of a gateway network table.  
Every time a node is added or removed from a particular cluster, an update is sent to the default gateway. The default gateway accordingly updates the gateway network table. An update message is eventually sent to all the gateways of the network connected via the hotline. This completes one cycle of the update mechanism. This process is already supported in OSPF by the database synchronization mechanism. The synchronization process begins as soon as the gateway attempts to bring up the adjacency. The database is described by each gateway by sending a series of Database Description packets to its neighbors. As gateways are connected in a bus network, via hotline, all the gateways will synchronize their databases. Gateways are also deployed with a monitoring mechanism. Specifically,gateways are aware of the health of neighboring gateways and the corresponding links. This monitoring mechanism helps to ensure the connectivity of the gateways and eventually ensuring the availability of the hotline. This mechanism is supported by the OSPF Hello protocol. The neighbor relationship is maintained with the help of the Hello Protocol. It is also responsible of ensuring the bidirectional communication between neighbors. Hello packets are sent periodically on all router interfaces. In summary, gateways are configured with dual functionality. They are deployed with two different protocols. One protocol (WEAMR) is used for intra-cluster or cluster-node communication, the other protocol (OSPF) is used for inter-gateway communication.
## 4 Experimental Studies  
### Parameters and Simulation Setup  
We operated the simulation experiments using MATLAB (R2018a, MathWorks, Natick, MA, USA), which provides a cost-effective, risk-free environment to explore the traffic management strategy and evaluate the proposed TICA algorithm. The specifications of the system used for the simulation are: Intel Core TM 2 processor, Quad CPUQ6700 @ 2.60 GHz, RAM-8 GB, and OS-Windows 10 Home 64-bit (Intel, Santa Clara, CA, USA). We selected a signal-free intersection of Tuarnje Avenue and Fenglin Road in Wuhan city, Hubei Province, China, as a test scenario, whose geometry is shown in Figure 13. For the sake of simplicity, the intersection has been simplified in the simulated road model, which contains three lanes at each entrance for \"turn left\", \"go straight\", and \"turn right\", respectively.  
As shown in Table 3, the time step was set as 0.01 s, which provides a balance between motion control accuracy and computational time, and the width of the lane was set as 3.5 m, which affects the radius of the turning circle and the size of the intersection. The traffic flow was set from 1080 to 2160 vehicles per hour, and the initial speed of each vehicle was set from 10 to 50 km/h. Besides, the degree of traffic balance was set as [0.25 0.25 0.25 0.25], which represents that the amounts of vehicles coming from different directions are equal; the probability of turning was set as [0.3 0.5 0.2], which represents that 30% of vehicles will turn left, 50% will go straight and 20% will turn right. The length of vehicles was randomly set as 3 or 5 m, representing compact and middle-sized vehicles, respectively.  
Figure 13: The geometry of the unsignalized intersection. (**a**) Overview; (**b**) detailed drawing.  
Before the simulation experiments, we designed 12 groups of test scenarios with different volumes of traffic flows (1080 to 2160 vehicles per hour, veh/h) for evaluating the proposed conflict detection and resolution algorithm (as shown in Table 4). The number of vehicles passing through during a simulation was from 90 to 180 in proportion. Each traffic flow condition contained three randomly generated test scenarios, named A, B, and C. The details of each test scenario are contained in the sign-in sheet, which determines the initial state of vehicles (as shown in Table 5).
## 3 Data and Methodology  
### Training and Test Areas  
The Worldview-3 (WV-3) satellite image used in this study were obtained over the Universiti Putra Malaysia (UPM) campus in Selangor, Malaysia (3\({}^{\circ}\)0\({}^{\prime}\)8.0181\({}^{\prime\prime}\) N, 101\({}^{\circ}\)43\({}^{\prime}\)1.2172\({}^{\prime\prime}\) E). The training and testing sites were chosen from the UPM site (Figure 1).  
The WV-3 image was taken in November 2014 by the Digital Globe (Figure 2). The spatial resolution of the WV-3 image is 0.31 m and 1.24 m for the panchromatic band and multispectral bands, respectively. More specifically, the WV-3 image includes 8 bands as well as the panchromatic band with a radiometric resolution of 11 bits. These bands include (coastal, red, green, blue, yellow, red edge, near-infrared, and near-infrared). Digital Globe has more information on WV3's characteristics (2020). Two images have been extracted from the WV-3 image to implement the training and testing processes with a similar spatial resolution (0.31 m), where the image used for training covers 39.5 hectares, while the image used for testing covers 21 hectares.  
Figure 1: Location of the case study area and the training and test areas selected to develop and assess the proposed classification methods.  
Figure 2: The ground truth data includes the main land cover types in the study area (Bare land,water body, dense vegetation or tress, grass land, roads, and buildings, (**a**) represents the training area and (**b**) represents the testing area.  
The ground truth data were acquired as a land use and land cover (LULC) map in Geographic Information System (GIS) file format. The data were prepared by the Department of Survey and Mapping Malaysia (JUPEM) in 2015. The ground truth data of the training and test areas are presented in Figure 2. There are seven LULC types in the area, including grassland, road, urban/built-up, dense vegetation/trees, bare land, and water. The percentage of ground truth data used in training; grassland (16%), roads (27%), built-up area (26%), dense vegetation/trees (20%), water body (8%), and bare land (2%). On the other hand, the percentage of data used in testing grassland (14.5%), roads (31%), built-up area (24%), dense vegetation/trees (18.5%), water body (9%), and bare land (2.5%).
## 1 Introduction
In the calculations, the frequency of recharge with the grid is a variable parameter. Finally, the energy loss in the electronic components which allow charging the main battery of the vehicle using PV has an influence on VIPV performance. The authors have already partially shown this influence for a city bus [6], demonstrating that in this case directly charging the PV in the main battery is better than charging the low voltage battery first and periodically transfer energy from a low-voltage to high-voltage battery. This paper will study this question more deeply in Section 3.2 for a passenger car. In this paper, we propose to consider all the factors that may influence the performance of VIPV on a passenger car. These factors are location, shading share, charge architecture, power thresholds for using PV, curvature of the solar panel and frequency of recharge with the grid. This study provides the performance of VIPV in terms of the PV energy supplied to the engine, the distance covered by the VIPV and CO\({}_{2}\) emissions estimated using life cycle assessment (LCA). This paper contributes to the state of the art by evaluating the performance of VIPV on a passenger car in European countries. It considers the following factors:  
* The daily use patterns which may vary during the week and the year;
* The monthly variations in solar irradiance and their hourly distribution;
* The PV panel's curvature;
* The solar panel's ageing;
* The shading;
* The system's effectiveness;
* Three architectures to transfer energy from the PV to the battery;
* Battery fullness due to the frequency of recharge with the grid and to rest days. The energy/distance simulation model gives inputs to the life cycle analysis process, which allows the calculation of avoided carbon emissions. This article is structured in five sections. Section 2 describes the model, data and parameters. Section 3 gives the simulation results with three architectures to transfer energy from the VIPV to the main battery (through a 12 V battery, by means of a 48 V additional battery and directly in the high-voltage battery). Section 3.1 gives the results regarding PV energy transferred to the engine and the distance covered by the VIPV. Section 3.2 gives the results of the parametric study of the combination of PV peak power and thresholds for using PV. Section 3.3 gives the LCA results. Section 4 discusses the obtained results. Section 5 contains the conclusions of this study.
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.1 Effect of pH on the Adsorption Process  
The pH of the solution is considered a very important parameter that affects the adsorption process. In order to determine the optimum pH value, the analysis was extended by varying the pH value of the solution in the range 2-12 for the investigated dyes, keeping all other variables constant. The results obtained, presented in Figure 8, indicate a direct relation between the removal efficiency and the pH of the solution.  
The removal efficiency of the investigated dyes was high at pH 2, and decreased with the increase in pH to 12. This can be explained by the fact that the adsorption mechanism of anionic dyes in acid solution is a result of electrostatic attraction forces between the positively charged surface of the adsorbent and the negatively charged dye molecules [57]. On the other hand, as the pH increased, the number of hydroxide anions increased, and these compete with anionic dyes for positively charged sites in the adsorbent, which determines a reduction in the removal efficiency as the pH increases.  
The maximum removal yield was obtained at the optimum solution pH of ~2 as 98.54% for AY 42 and 97.58% in the case of AR 213. It should be noted that the removal efficiencies were higher than 70%, even at the natural pH of the dye solutions--6.8 for AY 42 and 6.9 for AR 213--so we decided to perform further adsorption studies at a natural pH.  
Figure 8: Effect of pH on dye removal efficiency using PM4; initial dye concentration 50 mg/L, 1 g/L PM4, contact time 120 min, 25 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PM1**} & \multicolumn{2}{c}{**PM2**} & \multicolumn{2}{c}{**PM3**} & \multicolumn{2}{c}{**PM4**} \\ \hline  & \(\eta\) & \(q_{\mathrm{i}}\) & \(\eta\) & \(q_{\mathrm{f}}\) & \(\eta\) & \(q_{\mathrm{f}}\) & \(\eta\) & \(q_{\mathrm{i}}\) \\  & (\%) & (mg/g) & (\%) & (mg/g) & (\%) & (mg/g) & (\%) & (mg/g) \\  & 64.91 & 33.81 & 79.04 & 39.38 & 80.59 & 40.29 & 98.54 & 49.27 \\ AR 213 & 69.64 & 34.32 & 79.24 & 39.62 & 83.84 & 41.92 & 97.58 & 48.79 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Removal yields and adsorption capacity of magnetic nanocomposites.