esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A high-rise steel frame structure with viscous dampers was designed in this paper and corresponding scaled models were made for verification by shaking table tests to solve the problem of simulating traveling wave effects and slope amplification effects when inputting seismic waves in shaking table tests of large-span structures in mountain areas. The following conclusions could be drawn for reference in subsequent shaking table tests and related research by analyzing the test data:  
1. It was feasible to add viscous dampers to steel frame structures to amplify seismic waves in a delayed manner. The specific dimensions of steel frame members and the number of parameters of viscous dampers could be determined through finite element analysis and trial calculation based on the expected goals;
2. In the shaking table test, the model made from the original steel frame structure could also delay and amplify the seismic wave after adding a scaled viscous damper and could achieve the effect set in the test plan;
3. The acceleration response of the model under the frequent level eight earthquakes had no significant change before and after the seismic wave delay and amplification. The maximum difference in the acceleration amplification coefficient was 6.85%, indicating that the seismic wave delay and amplification had no impact on the acceleration of the structure;
4. The displacement response of the model in the X-direction under the frequent level eight earthquakes had significantly changed before and after the amplification of the seismic wave delay. The floor lateral displacement has increased by 77.10% at most, and the increase or decrease in the floor lateral displacement caused by the spectral differences of the three seismic waves were different. However, the displacement response in the Y direction had not changed and the floor lateral displacement had increased by 9.57%. From this, it could be judged that the delay effect of seismic waves significantly impacted the displacement response of the structure, while the amplification effect had almost no impact;
5. Limited by experimental conditions with only one model, this shaking table test only compared the dynamic response of the model in the elastic state before and after the seismic wave delay and amplification and did not involve the comparison of the dynamic response of the model in the elastic-plastic state.  
**Author Contributions:** Conceptualization, L.S. and Y.B.; methodology, Z.L.; software, L.S.; validation, Y.B. and Z.L.; formal analysis, L.S.; investigation, L.S.; resources, Y.B.; data curation, Z.L.; writing--original draft preparation, L.S.; writing--review and editing, L.S.; visualization, Z.L.; supervision, Y.B.; project administration, Y.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**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.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 7 Ongoing and Future Developments
### General Considerations and Future Technologies Developments
With the same transmission range there exists also the possibility to adopt the LWMesh protocol, which seems to ensure better performance in terms of data rate, but, with the lack of being proprietary and, thus, available only employing specific hardware devices. Considering instead target applications where a short transmission range is required, another interesting and upcoming mesh technology is represented by BLE, which increases the data rate over that typical of LWMesh and provides good performance in terms of network topology, power consumption and network coverage (even if it requires a large number of connected nodes with respect to other technologies) [88; 89]. Finally, the IEEE 802.11 mesh protocol guarantees an acceptable transmission range, with the highest data rate among all considered communication protocols, but with a power consumption higher than other transmission protocols--it is well-known that an IEEE 802.11-based chip require more energy rather than transmission chips based on alternative protocols [90]. Another remark is that complex systems often require the employment of different and heterogeneous technologies as the ones considered in this survey. Since each solution is characterized by different objectives and data handling policies, in order to integrate data flows across different technologies, it is expedient to define an abstraction middleware placed on top of all the considered stacks. In this way, there is no need to directly modify a predefined protocol stack, but it is sufficient to prepare a \"connector\" which allows to extract the data arriving through a specific mesh protocol and translate them for the destination mesh protocol. As a final remark, future research works should involve the performance analysis of several mesh networks, each of them composed by a different number of devices and denoted by different network depths. The performance is expected to degrade for increasing values of the network depth. Figure 17: Comparative performance analysis, with 9 relevant metrics, of 4 wireless mesh technologies.
## 1 Introduction
Besides, if the robot calculated that a checkpoint was reached, the blue lamp mounted on its board stated to flash. By design, numbers from 0 to 16 are encoded in the ARTags. To accurately determine the position of the robot, the vision system needed to recognize three markers. If two markers had been detected, two robot positions were determined and fortunately, one of which could easily be rejected. If one marker was detected, the robot could be located with lower accuracy, one was positioned on the circle indicated by the distance between the robot and the marker. To calculate the distance to the markers, a specially designed head with two cameras was used. The appearance of the head and the ARTag are shown in Figure 1.  
The software that interpreted the robot's space was initially tested on a laptop with an Intel Core i7 processor and 32 GB RAM, which at 1024 \(\times\) 760 resolution allowed to run the ARTag recognition algorithm 15 frames per second. The vision system communicated with the robot's main processor via a serial bus and transmitted the numbers of founded markers and the distances to them. Information from the wheel odometry was also transmitted to the main processor. Ultimately, the important step of the whole task was to combine the subsystems described above into one decision-making control system, i.e., calculating signals fusion. Tests for the accuracy and speed of the vision system were described in [21]. A general structure of the robot system is shown in the Figure 2.  
All algorithms pertaining to the wheels odometry were implemented in C language and are flashed in the STM processor. The vision system was written in C++ language. A photograph of the robot is shown in Figure 3.  
Figure 1: A mobile a vehicle with vision system on the test track. In the background, there is shown ARTag with coded number 10. Figure 2: Block diagram of the construction of a mobile vehicle.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 4 Track Condition Monitoring Results Using Lateral Vibration Acceleration
### Diagnosis for Section CD  
The diagnosis for Section CD was conducted in a manner similar to the vertical vibration acceleration-based diagnosis. Figure 7 shows the track condition diagram for this section. The diagram can be described similarly as presented in Section 3.2, except that the red line in the lower section shows the registered track curvature as a reference.  
As shown in Figure 7, significant vibrations occurred in the sections from 1.1 to 1.2 km and from 1.4 to 1.5 km. We investigated whether alignment irregularities occurred in these sections. Tables 3 and 4 show the locations where the measurements exceeded the target value of track irregularity correction for riding comfort and the corresponding level of irregularity, as indicated by the track geometry measurement results for the sections from 1.05 to 1.15 km and from 1.4 to 1.5 km. As shown in Tables 3 and 4, track irregularities occurred continuously in both sections, indicating track degradation. Hence, the sections with degraded tracks were identifiable. Next, we discuss the changes in the vibration acceleration over time in these sections.  
Figure 5: Changes in maximum vertical acceleration in a section between 1.1 and 1.2 km.  
Figure 6: Changes in maximum vertical acceleration in a section between 1.4 and 1.5 km.
## 4 Experiments
### Evaluation Metrics  
Let us define the following basic terms:  
* True positives (_TP_)--number of correctly predicted malicious log entries;
* False positives (_FP_)--number of normal log entries classified as anomalies (false alarms);
* True negatives (_TN_)--number of correctly predicted normal log entries;
* False negatives (_FN_)--number of malicious log entries classified as normal log entries.  
The performance of our models was analyzed using several metrics:  
* _Precision_, which indicates what percentage of all positive predictions were actually malicious log entries:  
\[Precision=\frac{TP}{TP+FP}\]
* _Recall_, which is a fraction of true positives among all malicious log entries:  
\[Recall=\frac{TP}{TP+FN}\]
* \(F1\)-score, which considers both precision and recall and is their harmonic mean:  
\[F1=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}\]  
We also analyzed recall vs. precision (PR) curves, which are effective diagnostic tools for binary classification models built on imbalanced data. Apart from this, we calculated the area under the PR curve (PR AUC), which is the average of precision scores calculated for each recall threshold. Additionally, we present the receiver operator characteristic (ROC) curves, which display the _FP_ rate vs. _TP_ rate, to observe the performance of a given model on the positive class. Finally, the area under the ROC Curve (ROC AUC) delivered single evaluation scores for the models across all threshold values.
## 3 System Design and Implementation
### Hardware Design
#### 3.2.2 Power Supply Module  
The USART to RS485 module operates on a 5 V power supply, incorporating a USB-powered 5 V source. For the STM32 microcontroller requiring 3.3 V operation, a dedicated 5 V to 3.3 V voltage regulator circuit is implemented to ensure stable 3.3 V output (see power supply module schematic in Figure 7).
## 1 Introduction
The network based on spectral-spatial feature extraction currently being studied is not suitable for HSI classification because it dilutes spectral information in HSI classification. Therefore, a network based on spectral feature extraction is more suitable for HSI classification than a network based on spectral-spatial feature extraction.We train and compare the hyperspectral image data with dis  
Figure 1: Differences of 1D-Convolution and 1D-Atrous Convolution. (**a**) 1D-Convolution. (**b**) 1D-Atrous Convolution. The gray regions are not included in the calculation when calculating one kernel in the convolution. In general, the 1D-Convolution calculates only limited neighbors, but the 1D-Atrous Convolution calculates far distances as well. data of the hyperspectral image with randomized spatial information in our proposed network and 3D-CNNs [15; 25; 26; 27];
2. Existing 1D-CNN extracts features from adjacent spectral information. This refers to the limitation of 1D-CNN and is a weak point in the spectral information feature extraction network. So, we propose a spectral information feature extraction network using the Atrous Convolution;
3. When using the Atrous Convolution Layer, spectral information features of various distances can be extracted through parallel processing. So we propose a parallel layer model of the 1D-Atrous Convolution Neural Network. This paper is organized as follows. Atrous convolution is described in Section 2. In Section 3, we describe our proposed network, which is named ODPA-CNN. The experiment with HSI data is described in Section 4 and compared with other techniques. We conclude the paper in Section 5.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Cloud Radio Access Network
#### 2.3.1 BBU Pool  
The BBU pool is a centralized, shared, and virtualized site functioning as a data processing center. Individual units, i.e., virtual BBUs, can be stacked together without direct linkage or interconnected to allocate resources based on the dynamic user demand on the connected RRHs. Each BBU pool can support multiple RRHs. In the BBU pool, multiple BBUs are interconnected via the X2 interface, which is highly cost-effective and yields improved performance. The S1 interface connects a BBU to the mobile core network, also called backhaul link. BBUs are composed of high-performance programmable processors and apply real-time virtualization technology. From the implementation perspective, BBUs are installed on virtual machines (VMs) using a hypervisor over physical computing cores present in the cloud data center [28]. Further details on the virtualization of BBUs are provided in [4; 5; 28; 29; 30; 31].  
Figure 2: (**a**) Architecture of RAN with distributed RRH, (**b**) Architecture of cloud RAN.
## 3 Running Logic and Data Organization of the M\({}^{2}\)ES Apps System
### Characteristics of M\({}^{2}\)ES Apps System
#### 3.1.3 Data Organizational Logic When Running an M\({}^{2}\)ES App  
As mentioned above, each M\({}^{2}\)ES App performs only one independent function. What it needs to do is to process the input data, calculate and optimize, and format the output in accordance with certain organizational logics. Figure 4 shows how a user (entity) uses an M\({}^{2}\)ES App.  
Figure 4: Input & Output of an M\({}^{2}\)ES App.  
Figure 3: Running logic of the M\({}^{2}\)ES Apps.  
When the user opts an M\({}^{2}\)ES App from the App Store, this selected will collect data as needed. The input data of the App is primarily from four aspects. The first is the attributes and the status data of resource entity from the Basic DB. The second is the private data taken out from the independent DB of this App according to the entity's URI. The third is the linked data from other Apps' independent DBs, which is pulled according to the entity's URI via API. The last is the raw running data from sensors, which represents the working conditions of equipment and devices.  
The output of an M\({}^{2}\)ES App usually includes two aspects, i.e., the running results and newly generated data. Running results are used to guide production and decision making, and all procedures and final data will be saved reliably. Some basic attribute data will be modified in Basic DB, and procedure and result data related to the function of the App will be stored in its independent DB.
## 5 Experiments
### Evaluation Metrics  
The experiment used three metrics that are widely used to assess the accuracy of traffic prediction, which are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The details of the equations are as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\Bigl{|}y_{true(i)}-y_{pred(i)}\Bigr{|} \tag{12}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{true(i)}-y_{pred(i)}\Bigr{)}^{2}} \tag{13}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\Bigl{|}\frac{y_{true(i)}-y_{pred(i)}}{y_{true(i) }}\Bigr{|}\times 100\% \tag{14}\]  
Among them, MAE is the average of the absolute errors between the predicted values and the ground truth, reflecting the prediction accuracy. RMSE measures the concentration of prediction results around the line of best fit. MAPE reflects the relative magnitude of the deviations between the predicted values and the ground truth. \(n\) is the number of samples. \(y_{true(i)}\) and \(y_{pred(i)}\) denote the ground truth and the predicted values of the \(i^{th}\) sample. Smaller values indicate better prediction performance for the above three metrics.
# ADAfinder Tool Applied to EGMS Data for the Structural Health Monitoring of Urban Settlements
## 1 Introduction
The tool was born with the aim to extract ADA in any area (vegetated, urbanized, etc.) characterized by geohazards, as also shown in [PERSON] et al. [36]. Working in this direction, in this paper, a methodology based on application of the ADAfinder tool to datasets of PSs--downloaded from EGMS--for the monitoring of the deformations of urban settlements, is proposed. The aim of the methodology is the identification of the buildings affected by displacements above a given threshold, among all the buildings included in the investigated area. The proposed methodology provides real support for the management of the urban areas. In fact, the buildings included in an ADA are those that should be studied more in depth, for example, through surveys or installation of on-site monitoring systems. The novelty of the work is related to the inclusion of the ADAfinder tool in a procedure that for the first time is specific for the buildings located in mainly urbanized areas. Moreover, it is worth highlighting that, even if a lot of applications using product derived from Sentinel-1 images exist, e.g., [33], the applications described in this paper are some of the first using precisely EGMS data, that have a free and open data access policy, so they are available to every user that wants to use it. For this reason, according to the authors, putting effort into creating tools for their exploitation is a challenge to undertake. The proposed methodology is applied to two different case study areas in the city of Barcelona (Spain): the _Example_, in the heart of the city, and the _Zona Franca_, an industrial area near the harbor. The paper is articulated as follows. First, the used data (the EGMS data) and tool (the ADAfinder tool) are described in Section 2. The applications of the full procedure to the two case studies are reported in Section 3, highlighting the differences between their results. The potential and the limitations of the proposed methodology are discussed in Section 4. Finally, the Conclusions of the work are argued in Section 5.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 5 Analyzing the Challenges and Advantages of Protocols in Underwater Communication
### MAC Layer Protocols
#### 5.1.2 Contension Based MAC Protocol Design  
S-Aloha [20] was developed in 2006. In underwater acoustic communication, the propagation delay is high, so there is no coordination between nodes. Based on an analysis, the system performance is not much different from the Aloha and S-Aloha systems. T-Lohi [21] was developed in 2007; this mechanism exploits space-time uncertainty problems and high latency problems to detect collisions. The power consumption is low for wake-up, and this protocol gives good throughput. CUMAC [22] was developed in 2012; it is considered a cost-effective technique, thanks to its use of only one transmitter to transmit the data. The hidden terminal problem is still not solved in this approach. MACA-MN [23] was developed in 2008. This approach solves the hidden terminal problem. It can increase the packet delivery rate compared to MACA. R-MAC [24] was developed in 2007; in it, data and control packets are scheduled on both sides such as sender and receiver. UMIMO-MAC [25] was developed in 2011. UMIMO-MAC is designed to: (1) adaptively leverage the tradeoff between multiplexing and diversity gain, (2) select suitable transmit power to reduce energy consumption, and (3) efficiently exploit the UW channel and minimize the propagation delay underwater. Channel Stealing MAC (CS-MAC) was developed in 2011 [26] to solve the hidden and exposed terminal problems. This protocol is satisfactory regarding channel utilization and delay in transmission. MACA adaptation for underwater network (MACA-U) [27] was developed in 2008. In this approach the MACA protocol was tested for underwater communication, focusing on areas such as transaction rules, forwarding packets, and back-off methods. The performance evaluation was good in multi-hop underwater communication. Contention-based Parallel rEservation MAC (COPE-MAC) [28] was developed in 2010 for underwater acoustic communication. In this approach, two techniques, parallel reservation and cyber carrier sensing, are introduced. The advantage of this protocol is its ability to increase the throughput; furthermore, it is very comfortable in large networks. Multiple-rendezvous Multichannel MAC (MM-MAC) [29] was developed in 2010. In this mechanism, the cyclic quorum approach was used to reduce the probability of collisions in underwater communication; it increased the performance in multi-hop underwater wireless sensor networks. The Receiver Initiated Packet Train (RIPT) protocol was developed in 2008 [30]. This protocol was developed to address propagation delays in underwater channels. Its advantage over other protocols is the throughput level, because of the minimum collision rate.
## 3 Results and Discussion
### Evaluation of Direct Indexes
#### 3.3.2 Analysis of Angularity Indexes  
The measured angularity indexes, surface parameter \(SP\), perimeter ratio of equivalent ellipse \(A_{I}\), convex ratio \(C_{R}\), shape factor \(SF\) and fractal dimension \(D\) of the selected 9 kinds of FAs based on the low-resolution images are shown in Table 7. It shows that the natural sand (FA1) always has the smallest value in terms of the five angularity indexes. The one-way ANOVA analysis was further conducted to analyze the effect of FA type on the angularity indexes and results were shown in Table 8. The results imply that the \(F\) value for surface parameter \(SP\) is greater, but the \(F\) value for the other four indexes are smaller than \(F_{0.05}=5.59\). This suggests that, among the angularity indexes, only the surface parameter \(SP\) can effectively distinguish the geometric characteristic difference of different FAs under the significance level of 95%.  
\begin{table}
\begin{tabular}{c