esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Temperature Stability
#### 3.1.2 Temperature Stability Test of Bare and Coated Walls  
Figure 6a details the temperature measurements for the block wall, both bare and after applying a layer of paint. Initially, the room temperature was recorded at 16.2 \({}^{\circ}\)C and 20.4 \({}^{\circ}\)C. The graph shows that one minute of heat exposure resulted in a temperature of 18.2 \({}^{\circ}\)C behind the bare wall, and 18.1 \({}^{\circ}\)C after ten minutes. The temperature at the measurement point behind the wall was consistently higher than the room temperature. With a layer of paint, the temperature behind the wall was 17.6 \({}^{\circ}\)C after both one minute and ten minutes of heat exposure. The temperature difference between the measurement point and the room temperature increased by \(-\)4.7 \({}^{\circ}\)C after painting, demonstrating the paint's effective insulation.  
Figure 6b presents the temperature measurements for the brick wall, bare and painted. The room temperatures were 16.2 \({}^{\circ}\)C and 20.4 \({}^{\circ}\)C, respectively. Initially, the temperature behind the bare wall was 17.8 \({}^{\circ}\)C after one minute of exposure and 18.2 \({}^{\circ}\)C after ten minutes, exceeding the room temperature. After applying the paint, the temperature behind the wall remained at 18.3 \({}^{\circ}\)C after both one minute and ten minutes of exposure. The temperature difference between the measurement point and the room temperature increased by \(-\)4.1 \({}^{\circ}\)C after painting, indicating a favorable insulation effect.  
As depicted in Figure 6c, the temperature measurements for the bare earth wall and the painted brick wall showed initial room temperatures of 16.2 \({}^{\circ}\)C and 20.4 \({}^{\circ}\)C. One minute of exposure resulted in a temperature of 17.7 \({}^{\circ}\)C behind the bare wall, and 17.4 \({}^{\circ}\)C after ten minutes, slightly above the room temperature. Following painting, the temperature behind the wall was 19.6 \({}^{\circ}\)C after one minute and 19.7 \({}^{\circ}\)C after ten minutes of exposure, with a temperature increase of \(-\)1.9 \({}^{\circ}\)C compared to the room temperature, suggesting effective  
Figure 5: (**a**) Schematic diagram of the temperature stability test. (**b**) Schematic diagram of the temperature stability test in front of and behind three bare walls at 0 10\({}^{\circ}\).  
Figure 6: (**a**) Temperature change of the block wall before and after coating under the heat source. (**b**) Temperature change of the brick wall before and after coating under the heat source. (**c**) Temperature change of the earth wall before and after coating under the heat source (0 is 10\({}^{\circ}\)).  
insulation. These findings preliminarily indicate that the insulation effectiveness is related to the wall material.
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 3 Proposed Adaptive Reconfigurable Cache Scheme Based on Decision Tree
### Adaptive Decision-Making Algorithm Based on Decision Tree  
The determination of the optimal cache associativity based on the statistical information of the application run time is a typical classification task. The decision tree algorithm can be used to solve classification problems. The decision tree model consists of a root, internal nodes, branches, and leaf nodes. Its exact structure is determined during the training process. The classification rules of the decision tree are easy to understand, and the hardware implementation of the model is simple because it can be implemented in an if-then-else way. This paper uses the J48 algorithm in WEKA [20], which is an open-source machine learning framework, to train a decision tree model of the optimal associativity. The J48 algorithm is a widely used decision tree algorithm. The J48 algorithm can automatically prune the decision tree model to avoid overfitting. It can train a decision tree model with more accuracy and less manual intervention. The pseudocode of the J48 algorithm is shown in Algorithm 1[21]. A set of training data \(D\) and an attribute list \(A\) are the input of the J48 algorithm. Starting from the root, the J48 algorithm calculates the information gain ratio of each attribute in list \(A\) and selects the attribute \(X\) with the largest information gain ratio as the classification criterion of the current node. Then, it creates branches and splits samples into subsets \(D_{1}\), \(D_{2}\),..., \(D_{\text{k}}\). \(D_{1}\), \(D_{2}\),..., \(D_{\text{k}}\) are created as child nodes of the current decision node and split recursively on these child nodes until the samples in each partition have the same label. ```
J48 (Training data \(D\), Attribute \(A\)): if all samples in \(D\) have the same label: return a leaf node with that label let\(X\)\(\in\)\(A\) be the attribute with the largest information gain ratio let\(R\) be a tree root labeled with attribute \(X\) let\(D_{1}\), \(D_{2}\),..., \(D_{\text{k}}\) be the partition produced by splitting \(D\) on attribute \(X\) for each \(D_{1}\)\(\in\)\(D_{1}\), \(D_{2}\),..., \(D_{\text{k}}\): let\(R_{\text{i}}\) = J48(\(D_{i}\), \(A-[X]\)) add \(R_{\text{i}}\) as a new branch of \(R\) return\(R\)
```  
**Algorithm 1** Pseudocode of the J48 algorithm [21]. The trained decision tree model is shown in Figure 4. Each node of the tree is assigned an attribute selected by the J48 algorithm. The root is the cache miss rate in the current reconfiguration period, and the attribute value is divided into four intervals by the J48 algorithm in the training process, which are _miss rate_ < 0.27, 0.27 \(\leq\)_miss rate_ < 0.48, 0.48 \(\leq\)_miss rate_ \(\leq\) 2.52, and _miss rate_ > 2.52. Starting from the root, the decision is made by taking a branch depending on the attribute miss rate and reaching the next node. The branch is continuously taken depending on the attribute corresponding to the current node, and this process is repeated until a leaf node is reached, which is the decision result.
# Two-Phase Deep Learning-Based EDoS Detection System
## 5 Experimental Setup
### Attack Scenarios  
As discussed in Section 2, there are two common types of EDoS attacks, bandwidth depletion, and resource depletion. To prove the efficiency of our proposed mechanism, we simulate an ICMP flooding attack, a TCP SYN flooding attack, and an HTTP flooding attack. The ICMP flooding attack is a representative of bandwidth depletion attacks, and the others are resource depletion attacks.  
* ICMP flooding attack, also known as the Ping flood attack is a common EDoS attack in which an attacker takes down a victim server by overwhelming it with ICMP echo requests, i.e., ping request. The network bandwidth of the victim server will be overloaded by the attacker's ping request and thus, the victim server has to request more resource usage to reply to other normal requests.
* TCP SYN flooding attack, also known as \"half-open attack\", exploits part of the normal TCP three-way handshake to consume resources on the victim server and render it unresponsive. An attack continuously sends an initial connection request to the victim server making all ports unavailable to respond to upcoming legitimate traffic.
* TCP-HTTP flooding attack is one of the EDoS attacks where a web server is exploited by an attacker through seemingly-legitimate HTTP GET or POST requests. The attacker forces the targeted webserver to allocate the utmost resources for each request. Since the attacker does not use the spoofed IP request as a TCP SYN flooding attack, this kind of attack is not established in the network layer. Instead, it is established in the application layer.  
EDoS attacks are similar to low-rate DDoS attacks [19]. A packet per second (attack rate) of 10,000 is considered the standard rate to differentiate between a low-rate DDoS and high-rate EDoS [15]. Following the instructions in [14; 15], we use the Bonesi tool [27]  
Figure 6: The proposed LSTM model.  
to simulate an ICMP flooding attack and TCP SYN flooding attack. In reality, a real EDoS attack scenario not only contains attack traffic but also contains normal traffic. To create a realistic EDoS attack, we mix EDoS attack traffic and normal traffic. We simulated different levels of EDoS attacks whose request rate ranged from 1000 to 7000 and mixed with a fixed rate of 400 normal requests per second following the EDoS evaluation scheme proposed by [PERSON] et al. [28], as shown in Table 6.
## 5 Experiments
### Home Automation Experiments  
The first set of tests was carried out in a static indoor environment, as shown in Figure 12, which represents the interior of a home, where there were typical barriers, such as walls and furniture. The distances between the deployed nodes were receiving (4-6 m). Figure 12 shows on a floor plan the four different positions where the receiving node (in red) was deployed around the environment and only one position for the sender (in green), which was utilized to send data packets to each node.  
Figure 11: Screenshots of the nRF mesh app.  
Figure 12: Floor plan of the home automation scenario.  
Figures 13-16 show the communications latency of messages transmitted using the developed protocol at 9 s intervals from four distinct places in a house: the kitchen, the entrance, the living room, and the bedroom. On the one hand, Figures 13 and 14 show slightly more stable behavior than for the other two locations. It is worth noting that in all the performed experiments, there is a peak around packet 60, indicated by vertical red lines in Figure 13. After checking the connection logs and ruling out possible connectivity failures, it can be confirmed that this peak occurs because after a certain number of minutes, the protocol verifies whether the communication is still active, which causes a delay in the remaining packets. Notwithstanding this anomaly, there are hardly any spikes in latency (i.e., latency variability is low). The typical delay is approximately 8 to 8.5 s. This suggests that both locations benefit from better signal propagation conditions, possibly due to fewer obstacles and shorter distances to the transmission point. On the other hand, the results shown in Figures 15 and 16 show less homogeneity in latency, with more spikes. Latency readings typically range from 8 to 9 s, indicating that higher variability could result from increased attenuation caused by a greater number of obstacles or longer distances to the transmission point.  
Figure 17 depicts the standard deviation of latency for the four different locations of the receiving node, showing how latency differs in terms of consistency between environments. When comparing the results shown in Figure 17 with the ones shown in Figures 13-16, a huge spike can be observed on packet 60 that indicates the exception shown on the previous latency graphs. If such a spike is ignored, it can be concluded that the latency stability is slightly better for locations 1 (living room) and 3 (entrance), whereas oscillations are greater for locations 2 (kitchen) and 4 (bedroom). These locations show higher variability, likely caused by increased signal attenuation, the presence of more obstacles, or less favorable propagation conditions in those areas.  
Figure 16: Latency for node at position 4.  
Figure 17: Latency variability (standard deviation) for the four analyzed home scenarios.  
Figure 15: Latency for node at position 2.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 5 Hansel Mobile Applications
### SNApp  
The SNAP service makes possible the sensor-to-platform communication via a dedicated Application Programming Interface (API), which defines a series of predefined requests to interact with the service core facility. Routines for the sensor to execute these requests are integrated in a software package called SNAP Application (SNApp), installed in all the SNAP sensors. The software package is responsible for the following actions:  
1. Asynchronous configuration of the sensor parameters;
2. Retrieve jobs/requests;
3. Record live RF signals;
4. 1-bit compression to reduce the size of the recorded signal file;
5. Transmission of the recorded signal file to the cloud platform for its processing.  
SNApp has been designed as an easily-installable software package conceived for sensors to interact with the SNAP API in a secure and autonomous manner. The software is also responsible for the manipulation of the sensor hardware, namely a RF front-end based in a Software Defined Radio (SDR) device, whose main purpose is to gather live RF signals, and a UNIX-based control board responsible for data forwarding over a wireless connection. A prototype of hardware sensor was developed in course of the HANSEL project and the details can be found in [55]. The routines implemented within SNApp are designed to support sensors implementing any SDR-based device, as long as its hardware configuration is properly set (i.e., sampling frequency, bit encoding, etc.). The SNApp routines are entirely implemented in Python and require the sensor control board to install Python v3.0 or higher. In our case, a Raspberry Pi Zero W [56] was used to run the SNApp routines controlling a RTL-SDR RF front end [57] and to provide Wi-Fi(tm)connectivity for delivering the gathered RF samples to the cloud platform.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 4 Results
### Qualitative Comparison  
In this experiment, 3D-Unet was chosen as the baseline model. Typical semantic segmentation results of the LFNet and the 3D-Unet model for the SemanticKITTI dataset are shown in Figure 2. The 3D-Unet model's predictions are consistent with the true value category for most objects. For example, the 3D-Unet model is able to identify the \"car\" and separate it from the \"ground\". However, the model's predictions are relatively coarse as it only detects the general outline of the object and is unable to distinguish well between the edge boundaries of the object. For example, some of the \"sidewalk\" in the 3D-Unet model is incorrectly identified as \"terrain\", and some of the 'buildings' are incorrectly identified as 'fences', etc. Compared with the 3D-Unet model, the LFNet focuses on instances where the boundary lines and contours are not clear enough to learn the details of the object. The segmentation results show that the LFNet is able to accurately identify boundary information, such as sidewalks, street building outlines, etc.  
Typical semantic segmentation results of the LFNet and the 3D-Unet model for the NuScenes dataset are shown in Figure 3. The 3D-Unet model is able to distinguish moving objects such as cars and trucks to a certain extent, but its predictions are not accurate, and there are many misclassifications. For example, the 3D-Unet model does not accurately identify the outlines of vehicles, and the model confuses \"trucks\" with \"trailers\". In addition, the model is not accurate in identifying pedestrians and cannot identify all pedestrians in outdoor scenes. The majority of LFNet's predictions are consistent with the category of ground truth, and it can accurately identify pedestrians, vehicle contours, etc.  
From the segmentation results, it is clear that the LFNet model is able to focus on small objects as well as boundary information through the GFLM and the MFFM. Thus, it learns more detailed features in large scenes and eventually obtains better semantic segmentation results.  
Figure 2: LFNet visualization results for the SemanticKITTI dataset.
# Antenna on Chip (AoC) Design Using Metasurface and SIW Technologies for THz Wireless Applications
## 4 Conclusions  
The innovative design of an antenna is described for on-chip applications for terahertz applications. It is shown that by combining metasurface and SIW technologies in the antenna design, its impedance bandwidth, radiation gain, and efficiency performance are substantially enhanced. This is achieved without increasing the aperture area of the antenna. The antenna is constructed on a GaAs substrate and is composed of a 2 \(\times\) 4 array of rectangular patches with a row of circular slots etched on it. It is excited through a microstrip coplanar waveguide feedline. Compared to AoC reported in the literature, the proposed antenna has a larger frequency bandwidth and operates at a much higher frequency with comparable gain and radiation efficiency.  
Conceptualization, A.A.A., M.A., B.S.V., H.B., F.F., and E.L.; methodology, M.A., B.S.V., and E.L.; software, A.A.A., M.A., and B.S.V.; validation, A.A.A., M.A., B.S.V., H.B., F.F., and E.L.; formal analysis, M.A., B.S.V., and F.F.; investigation, M.A., B.S.V., F.F., and E.L.; resources, A.A.A., M.A., B.S.V., H.B., F.F., and E.L.; data curation, A.A.A., M.A., B.S.V., H.B., F.F., and E.L.; writing--original draft preparation, A.A.A. and M.A.; writing--review and editing, A.A.A., M.A., B.S.V., H.B., F.F., and E.L.; visualization, M.A., B.S.V., F.F., and E.L.; supervision, M.A., F.F., and E.L.; project administration, M.A., F.F., and E.L.; funding acquisition, M.A., F.F., and E.L. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by RTI2018-095499-B-C31, Funded by Ministerio de Ciencia, Innovacion y Universidades, Gobierno de Espana (MCIU/ AEI/FEDER, UE).  
Not applicable.  
Data available statement: Data are contained within the article.
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 2 Module-Level FRM
### Variable \(F\) is Associated with Fault States of FRM
#### 2.2.2 Indicator \(T\) for Fault Detection  
Indicators for fault detection, as the objective detection results of automatically testing and diagnosing equipment when fault appears, can be used for probabilistic inference of fault causes and modes. For a two-state system, the indicators for fault detection tend to be fixed values, while in a multi-state system, indicators for fault detection theoretically are values continuously changing in a certain interval, that is, test indicators change with operating environment and age of electronic equipment.  
In order to comprehensively show fault states of electronic equipment, multiple examples of test equipment are generally used for testing multiple indicators. In addition, due to application of complex and multiple test parameters with different units, expected outputs of each parameter are different when a circuit is in different states. For the convenience of modeling, all indicators are unified into a framework. After calculating membership degrees of faults of each parameter, experts calculate the synthesized membership degree of all test parameters according to importance weights of parameters.
## 1 Introduction
WSN (wireless sensor network) technology is applied in this system to transmit the ECG data wirelessly from the patient's body to an Android smart phone device. In order to achieve maximum mobility and flexibility in ubiquitous healthcare, the design and the size of the sensor node used are taken into consideration during the hardware implementation. As for medicine care assistance, barcode technology is applied to assist out-patients in medication administration, by capturing and decoding the barcode on medicines using the Android smart phone's embedded camera. This reduces the occurrence of medical errors caused by consumption of the wrong medicine which is often life threatening. The idea of this proposal can drive healthcare provision out of hospitals and into the home environment by utilizing a mobile phone, with benefits in medicine, health and social care. It also reduces the hassles, queues and crowds in hospital as well as providing more healthcare services and focus to patients who are seriously and urgently in need of such services. The remainder of this paper is organized as follows. The background and related works which inspired the idea of developing a comprehensive ubiquitous healthcare solution are described in Section 2. Section 3 presents the system design of the ubiquitous healthcare system and the implementation of the methods used in obtaining, handling and processing the sensory data from the biomedical device on the human's body, followed by descriptions of experimental results in Section 4. Lastly, Section 5 contains some conclusions.
# Fully Open-Source Meeting Minutes Generation Tool
## 3 Proposal of Meeting Minutes Generation System
### System Overview  
The proposed system aims to provide users with flexibility in its use of open-source models and accessibility. For this purpose, the proposed system is implemented on top of several functions and deployed as a web application, as shown in the Figure 1. This system architecture consists of the _client side_ and the _server side_. The _client side_ comprises the input function and the output function. The input function accepts a pair consisting of a recorded online presentation and the document used during the presentation. The output function visualizes the results of the transcribed audio, generates summaries, extracts keywords, and matches slides through the _user interface_ (_UI_). The _server side_ is responsible for processing the video to generate meeting minutes. Since each slide in the video may contain different information, the system first segments the video by slides. This process uses the _split video function_, which includes the _scene-change detection_ algorithm and the segmentation mechanism [29]. The _scene-change detection_ algorithm compares visual information between adjacent frames and splits the video at the frame where the visual information changes.  
Then, the audio from each segmented video is transcribed into a text using the _audio-to-text_ function [30; 31]. This process converts the speaker's voice into a text format. From the text, the _information extraction function_ generates the summary and extracts the keywords to help users quickly grasp the key points of the speaker's message [19; 32].  
At this stage, the system has generated the transcription, summary, and keywords for each video segment. However, these data still need to be linked to the corresponding slide. To achieve this, the _information correlation function_ is applied. This function uses _OCR_, the _python-pptx_ library, and _regular expression (regex)_ to match the extracted information with the corresponding slide [33]. Finally, the system organizes the correct information through the slide output information function, which is displayed to the user through the _client-side_ interface.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 4 Multi-Sensor Data Fusion Framework  
### Definition of the Framework  
The overall framework of this paper is illustrated in Figure 2. In this framework, the IMU serves as a source for predicting filtered states, while the 3D lidar provides precise observations through point cloud matching. Additionally, an RAC (Realtime Array Calibration) relocalization algorithm is designed to ensure the algorithm's stability in challenging environments. Compensation mechanisms are established among multiple sensors such as point cloud distortion correction and global relocalization, which are detailed in Section 4.3.3.  
Figure 2: Framework of the proposed localization algorithm.