esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Performance Measures  
The evaluation of the clustering result quality is referred to as the evaluation of its validity, for which external and internal indicators are utilised. The external validation index is the most common validation method used in the clustering method. It is based on prior knowledge of the data and measures the similarity of clustering results to external ground truth information. Hence, any valid similarity metric suitable for partition comparison can be used as an external indicator [42].  
In contrast, the internal validation index relies only on the information in the data without any additional information. Most internal validation indicators are based on two criteria, i.e., coherence and separation [43]. Compression is defined as a measure of how close objects are in mass. It is often measured by variance; a lower variance indicates better compressibility. Segregation is a measure of how separated a cluster is from other clusters. The distance between the cluster centroids is usually measured.  
In order to assess the current model, the Rand index and purity score were utilised as external validation indicators, and the Davies-Bouldin (DB) index was used as the internal validation index.  
The classification performance refers to how well a classification model can correctly predict the class labels of a given data set. The following metrics were applied as performance measures in order to appraise the proposed model:  
* Accuracy, which measures the proportion of correctly classified instances out of the total number of instances (Equation (11)): \[\text{Accuracy }=\frac{TP+TN}{TP+TN+FP+FN}\] (11)
* Precision, which measures the proportion of true positives out of all the instances classified as positive (Equation (12)): \[\text{Precision }=\frac{TP}{TP+TN+FP+FN}\] (12)
* Recall or sensitivity, which is defined as the proportion of true positives out of all the positive instances (Equation (13)): \[\text{Recall }=\frac{TP}{TP+TN}\] (13)
* F1 score, which is a metric which combines precision and recall into a single value and provides a balanced measure of a model's accuracy (Equation (14)): \[\text{F1}=\frac{2\times\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}}\] (14)
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 4 Experiments for LoRa PHY
### Outdoor-Only Environment  
The purpose of the first experiment is to evaluate the communication range of the testbed system in an outdoor-only environment and to verify whether the LoRa performance stated in the official specification can be reproduced in a real-world scenario. Figure 7a captures the scene of the first experiment on the campus, representing the LOS environment. The gateway (the blue dot) is on the roof of Building #7 at a height of about 15 m, and the node (the top right \(\blacktriangle\) mark) is on the ground and 10 m from the gateway. As shown in Figure 7b, the node is on the outside, and a laptop computer powers the node and feeds it data to send. It moves around 200 m at intervals of 4 m to the lower left on the map. At each location, the node sends 100 packets to the gateway with no retransmission, and the gateway records the received signal power (i.e., RSSI).  
Figure 7: Experimental setup for the outdoor-only environment: a gateway and a moving node. (**a**) The gateway (blue dot) is on the roof of Building #7 at a height of about 15 m, and the node (\(\blacktriangle\) mark) is on the ground 10 m from the gateway. Thus, the straight-line distance between them is 18.028 m; (**b**) The LoRa node is placed on the ground for experiments and is controlled with a laptop computer that powers the node and feeds it data to send. It transmits 100 packets to the gateway once it stops at a location.  
Figure 8 shows the experimental results for the received power of the data signals measured and estimated with increasing distance from the gateway. The blue x marks represent the measurement data in the experiment from which we find the \(\gamma\) value and complete Equation (3) for the outdoor-only environment. The path loss exponent is computed to be 2.977. It is noted that the value is in the range of 2-4 in a normal LOS environment, but it increases to 4\(\sim\)6 in a relatively lossy environment, such as buildings and other indoor environments.  
The red line in the figure illustrates the computation of Equation 3. Using this model, we can estimate the maximum transmission range in which the gateway recognizes signals from the node. The technical document from Semtech indicates that the communication module has a receiving sensitivity of \(-137\) dBm in a LoRa network with a 125 kHz bandwidth [24]. When applying the sensitivity value to Figure 8, we can conclude that the maximum communication range becomes around 5.9 km in the outdoor-only environment. This result is almost the same as the value in the LoRa specification. However, using a lower SF value to increase the data rate can significantly limit the coverage. For instance, the communication distance decreases to 2.38 km because of the low sensitivity of \(-124\) dBm when the SF value is set to 7 at the node. Table 4 compares the maximum communication ranges in the outdoor environment reported in other experiments with our results.
## 3 Results
### Monitoring of the Curing Process Using the B-H Looper  
The B-H looper was used to evaluate the magnetic signal of adhesive samples containing 5% MENs to monitor their curing process. The initial magnetic signal (at curing hour zero) of the three samples was measured. Subsequent measurements were recorded, and the percentage increase with respect to the initial signal was obtained. The average percentage increase of the three samples was calculated and is graphed in red in Figure 5. It can be observed that there was an increasing trend in the magnetic signal during the first curing hours, which started converging after approximately 8 h of curing time.  
Figure 3: (**a**) Magnetic response of free MENs vs. polymer-coated MENs; (**b**) influence of MEN doping on the magnetic signal on a polymer adhesive.  
To confirm the magnetic signal results obtained with the B-H looper, tensile testing was performed on the undoped adhesive to study whether a similar type of convergence was observed on the adhesive's mechanical properties. The peak stress with respect to curing time is plotted in Figure 5 in blue. A similar trend was appreciated, in which there was a rapid increase in tensile strength during the first curing hours and then it started converging at around 8 hours of curing time until it reached \(\sim\)41 MPa. This trend was also confirmed by the progression of the adhesive's glass transition temperature (plotted in black) as it cured, which converged at \(\sim\)137 \({}^{\circ}\)C.
## 1 Introduction
Therefore, this study aims to investigate the ability of TGANs to generate data for geopolymers and the impacts of synthetic data on various algorithms with different mathematical principles and assumptions, including tree-based models, SVM, and neural networks. To achieve this objective, this study uses datasets collected from the open literature to ensure diversity in sources and the chemical composite of precursors. The dataset collected for this study consists of 930 instances. Specifically, 10 variables are considered as inputs, including the content of fly ash (FA), slag (S), sodium silicate (SS), sodium hydroxide (SH), superplasticizer (SP), fine aggregate (Fag), coarse aggregate (Cag), added water (W), curing temperature (CT), and specimen age (Age). The output of the models is the compressive strength (CS). Subsequently, a TGAN was employed to generate data pertaining to fly ash- and slag-based geopolymer concrete mixtures and their corresponding strengths based on the collected dataset. The generated data was then utilized to train different models, including LightGBM, SVM, and cascade forward neural networks (CFNNs). Next, models using synthetic data will be compared with models using original data with evaluation metrics such as R-squared (R\({}^{2}\)), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Residual (RSR), and Weighted Mean Absolute Percentage Error (WMAPE). Additionally, SHAP analysis was performed to facilitate the interpretation of model performance and data influence.
# Study on Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
###### Abstract  
The geological structure of the upper reaches of the Yellow River is complex, especially in the Sigouxia-Laganxia section. It has always been a high-incidence area of landslide disasters, which poses a threat to the safe operation of the upper reaches of the Yellow River. In this study, based on the high-precision remote sensing image data, the spatial distribution of each landslide was obtained by superimposing the remote sensing image and the 1:50,000 digital elevation model (DEM). Some typical landslides were selected for detailed field investigation and field verification. The results show that the remote sensing image characteristics of landslides in the upper reaches of the Yellow River are obvious. Through remote sensing interpretation and field investigation, a total of 508 landslides of various types were found, including 24 giant landslides. The spatial spreading patterns of landslides mainly include six types: dumb-bell shape, rectangle, saddle type, long arc shape, triangle, and side-by-side shape. The length and width of the landslide deposit are mainly concentrated at 550-1500 m and 600-1500 m, and the average elevation of the sliding body is mainly concentrated between 2000 and 2800 m. The average slope of the landslide is mainly distributed between 15-20\({}^{\circ}\). Giant landslides are mainly distributed in the Jianzha basin area, surrounded by the Jishishshan fault and the Lajshan fault in the West Qinling Mountains. The spatial distribution characteristics of giant landslides have obvious regional differences due to different factors such as lithologic differences and riverside erosion. The research results are of great significance for the early identification, prevention, and mitigation of landslide disasters in the upper reaches of the Yellow River.  
lands lide; the upper reaches of the Yellow River; remote sensing interpretation; spatial and temporal distribution; disaster prevention and mitigation 2020 IEEE/RSJ Web Conference ()
## 7 IoT-Defined Application Protocols
### Advance Messaging Queuing Protocol (AMQP)
Transaction monitors and databases support XA-Transactions for its guaranteed features of update on all the connected data upon any update. MQTT does not provide support for its memoryless broadcasting features and lack of queuing. 5. Kerberos authentication, which is based on active dictionary (AD) key underpinning, has the facility to avoid the usernames and passwords traveling via network. The mechanism uses tickets to get entry to the remote server or database, which is based on an encrypted hash containing time stamps. A domain controller (DC) then receives the access request and grants a ticket-granting ticket (TGT), which can be used for the application server as login and session key. The facility of Kerberos is limited to AMQP; thus, private keys can be attacked in MQTT. 6. There is no flow-control mechanism and selective acknowledgment to pause and resume applications in MQTT. 7. Multiplexing is relatively easy in AMQP because MQTT supports the at most one, exactly one, and at least one message delivery schemes. These schemes may be valid for a single receiver, but the result may be different when much information is incorporated for different network nodes, resulting in network overhead and inefficient use of bandwidth. 8. Multiplexing in MQTT can cause flooding, and error control is also affected. 9. AMQP has flexible topology, and the information can traverse the internet or any broker AMQP. 10. AMQP supports ASP.NET, Java, and PHP, while MQTT is dedicated for small data packets and frequent server replies and data updates. If we compare AMQP with other protocols such as RESTful, MQTT, XMPP, and CoAP, AMQP proves to be more advanced and prone to develop more for its modular inter-platform secure connectivity. Despite this, AMQP lacks support for constrained environment and real-time applications, which is indeed important from an IoT perspective. It also does not support an automatic route-discovery mechanism and IoT node-discovery mechanism.
# Selection Methodology of an Electric Actuator for
###### Abstract  
Landing gear system of an aircraft enables it to take off and land with safety and comfort. Because of the horizontal and vertical velocity of aircraft, upon landing, the complete aircraft undergoes different forcing functions in the form of the impact force that is absorbed by landing gears, shock absorbers, and actuators. In this research, a selection methodology has been proposed for an electrical actuator to be installed in the retraction mechanism of nose landing gear of an aircraft having 1600 kg gross takeoff weight. Nose landing gear and its associated components, like strut and shock absorbers, were modeled in CAD software. Analytical expressions were then developed in order to calculate the actuator stroke, translational velocity, force, and power for complete cycle of retraction, and some were subsequently compared with the computational results that were obtained using MSC ADAMS(r). Air in the oleo-pneumatic shock absorber of nose landing gear was modeled as a nonlinear spring with equivalent spring constant, whereas hydraulic oil was modeled as a nonlinear damper with equivalent damping constant. The nose landing gear system was modeled as a mass-spring-damper system for which a solution for sinusoidal forcing functions is proposed. Finally, an electrical actuator has been selected, which can retract and extend nose landing gear, meeting all of the constraints of aircraft, like fuselage space, aircraft ground clearance, locking loads, power consumption, retraction and extension time, and dynamic response of aircraft. It was found that the selection of an electrical actuator is based upon the quantification of forces transmitted to electrical actuator during one point load at gross takeoff weight. The ability of retraction and extension time, as dictated by Federal Aviation Regulation, has also been given due consideration in the proposed methodology as significant criteria. The proposed system is now in the process of ground testing, followed by flight testing in the near future.  
nose landing gear; retraction mechanism; electro-mechanical actuators; impact force; locking loads +
Footnote †: journal: Applied Mechanics and Physics Letters
## 2 Data and Methodology
### Bibliometric Methods and Visualization Tools Used in the Analysis  
In this research, the utilization of bibliometric mapping methods and tools facilitated the visualization of scientific data through diverse procedures. Bibliometric mapping, as a research methodology, involves the application of quantitative techniques to visually depict the scientific literature derived from bibliographic data.  
VOSviewer, a software tool developed by [PERSON] and [PERSON] in 2010, specifically designed for the analysis and visualization of the scientific literature, was used in this present work. VOSviewer is a software widely used in the bibliometric field and in different disciplinary areas [24; 25]. VOSviewer offers the capability to depict co-authorship networks among authors, institutions and geographic regions, co-citation networks of articles and journals, as well as co-occurrence networks of keywords and terms. This tool leverages clustering techniques for the visual detection of structural patterns within the research domain, and harnesses text-mining functionalities for recognizing trends and patterns in the addressed topics [26]. In this paper, the version 1.6.19 of VOSviewer was used for further research.  
For a comprehensive understanding of the techniques and concepts underpinning bibliometric mapping, [PERSON] et al. [27] have provided an informative overview. Moreover, VOSviewer has gained widespread utilization in the realm of bibliometric analysis within the marine science field, as evidenced by various studies [20; 28; 29; 30]. For a detailed examination of these applications, please refer to [PERSON] et al. [27].  
Furthermore, two parameters, Impact Factor (IF) and H-index, were utilized in this paper to measure the impact of the journal or author. The IF and the H-index are two prominent metrics utilized to assess the significance and widespread influence of a publication [20]. They serve as valuable benchmarks when making informed decisions regarding the choice of journals and publications within a particular research domain. The ArcGIS, a comprehensive Geographic Information System (GIS) software that enables users to create, manage, analyze and visualize spatial data was utilized to facilitate the understanding of complex geospatial relationships of the publications, to aid in decision-making processes and to support the creation of detailed maps and visualizations for various applications.  
Lastly, this study made use of the data analysis functionalities of WoS, in conjunction with VOSviewer, to conduct citation analysis and track the number of publications per year, with the overview methods shown in Figure 2.  
Figure 1: Research framework.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 5 Transition Modes Process Monitoring Method
### Artificial Intelligence Method
#### 5.3.2 Autoencoder Model-Based Method  
As a classical method of the deep neural network, the depth learning monitoring method based on autoencoder projects the data samples into the output space through encoding and decoding steps. [PERSON] et al. [84, 85] proposed a representation learning adaptive monitoring method based on a stacked sparse autoencoder (SSAE). The SSAE model is constructed by training the uppermost autoencoder to the latent layer as the input of the next encoder for multiple superpositions and is combined with the autoencoder network to monitor the transition mode. [PERSON] [86] proposed a dynamic sparse stack autoencoder model (DSSAE) which used an unsupervised data dynamic expansion matrix to extract the dynamic characteristics of DSSAE and to improve the monitoring and classification ability of the system by training the model with a back-propagation (BP) algorithm and gradient descent method.  
To monitor the non-linear mode, [PERSON] et al. [87] constructed a variational autoencoder (VAE) model, which is composed of encoders and decoders with multilayer neural networks. The encoder reduces the dimension by mapping the dataset to the latent space, and the ReLU function is used as the activation function. The decoder maps the latent vector to the output space. [PERSON] et al. [88] proposed an adaptive monitoring method based on local adaptive standardization and variational autoencoder bidirectional long short-term memory (LAS-VB). They preprocessed local moving window data for standardization by the LAS method, constructed a VAE model composed of multiple bidirectional long-term and short-term memory layers, and trained the model to calculate the standardized data to obtain the monitoring threshold and to assess the system state. The model has a strong ability to deal with the new mode, but it cannot handle slightly variable drift faults or distinguish the difference between normal deviations and faults. [PERSON] et al. [89] used a variational automatic encoder and random forest information fusion method for bearing fault diagnosis and residual life prediction.
## 5 Data Acquisition and Processing
### Data Processing  
#### 5.2.1 HVSR Technique  
The HVSR technique was applied to the ambient vibration measurements using Geopsy software as a processing tool [45]. The recordings of the 164 measurement points have been processed as follows: The time series were divided into 30-s windows that were 5% cosine-tapered. The windows were selected automatically using the anti-triggering algorithm, which allows for the avoidance of transients and the selection of only windows with stationary ambient vibrations. The anti-trigger parameters were used as recommended by the SESAME project [54]. The fast Fourier transform (FFT) was computed for each window. After that, the Konno-Ohmachi algorithm was applied in order to smooth the amplitude spectra [56], with a smoothing coefficient of 40. The HVSR curve was calculated for each selected window, and an averaged HVSR curve was retrieved. Some examples of the obtained HVSR curves at each zone are shown in Figure 7.
# An Efficient Network Resource Management in SDN for Cloud Services
## 4 Proposed Network Resource Management Method in SDN  
The proposed method manages resources efficiently at the network level when providing services in a cloud environment. Figure 3 shows the proposed system structure for efficient network resource management. As the figure indicates, the SDN controller is periodically notified regarding the amount of data and the number of packets transmitted. The information collected by the SDN controller is sent to the priority calculator, which uses it to calculate the weight of the flows. The ranking table manager sorts and stores the flows by priority based on the calculated weight. It then determines the network path of the flow using the set threshold. The ranking table manager continues to update and store the priority of the services. The flow rule generator creates a flow rule by setting the differential network paths for the services after checking the ranking table. The generated flow rule is sent back to the controller, which sends it to the switch through the Openflow protocol. Finally, each switch forwards packets based on the updated flow rule.  
It is difficult to predict exactly how much data a specific service will send at a specific point in the future. Therefore, in this study, we propose a scheduling method that predicts the amount of future traffic required by the service by analyzing the amount of traffic that has been generated by the service flow. The proposed method includes a procedure for calculating the priority and a procedure for allocating the network path based on the priority. The priority is contingent on how much data have been sent at a certain time in the past. The priority increases for flows that have recently sent large amounts of data, whereas the priority decreases for flows that have not. If the priority of flows does not change over time, current high demanding flows may not use more efficient network paths because past high demanding flows are occupying it. For solving this, we use the exponential weighted moving average \(SP_{k}^{t}\) for the amount of data transmitted in the past for each flow \(k\) at time \(t\), as expressed in Equation (1).  
\[SP_{k}^{t}=(1-\alpha)\cdot SP_{k}^{t-1}+\alpha\cdot B_{k}^{t} \tag{1}\]  
where, \(B_{k}^{t}\) refers to the amount of data transmitted for flow \(k\) between time \(t\)-1 and \(t\), and \(\alpha\) refers to the weight.  
Figure 4 shows the procedure for allocating the network path based on the priority through the ranking table in the application. The controller periodically calculates \(SP_{k}^{t}\) and forwards it to the ranking table. The ranking table then manages the collected flows by sorting them based on \(SP_{k}^{t}\). It then applies a threshold for differential network path allocation. This threshold can be set differently depending on the characteristics of the service and is used as the criterion for allocating efficient network paths in the SDN.  
Figure 3: Proposed system architecture for network resource management.