esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Process Re-Engineering Models in Cement Production to Improve Energy Efficiency
## 3 Results
### Selection of Energy Intensive Parameters  
Figure 2 shows a Pareto chart analysing the energy loss details in Plant A. The graph identifies and prioritises the factors that contribute to energy loss. The graphic indicates that the total energy loss in Plant A is 1889.07 MJ per year. The graph also shows that the most significant sources of energy loss are the flue gas temperature leaving the kiln (\(FgTK\)), clinker discharge temperature (\(CDT\)), flue gas temperature at the recovery point (\(FgTR\)), and overload flue gas temperature (\(OFgT\)). These four parameters alone account for 96% of total energy losses, as seen in Equation (11).  
\[L_{t}=FgTK+CDT+FgTR+LR+TcKs+EaR \tag{11}\]  
Figure 2 also highlights essential opportunities where solutions to improving energy efficiency can be most successfully applied by identifying these key factors. For example, high temperatures in the flue gases leaving the kiln indicate large heat losses, meaning that enhancing heat recovery systems or optimising kiln operation might significantly minimise these losses. Similarly, high clinker discharge temperatures suggest inefficient heat transfer processes in the kiln and cooling systems. Enhancing the efficiency of clinker cooling can result in significant energy savings. High temperatures at the flue gas recovery point indicate possible inefficiencies in heat recovery units, implying that improvements could include greater insulation, enhanced heat exchanger design, or sophisticated control systems to maximise heat recovery. The factors should be considered for model design with a direct correlation to overall energy losses for Plant A.  
Figure 3 shows a Pareto chart for energy loss details in Plant B. The figure provides a clear visual representation of the numerous aspects contributing to energy losses, allowing for prioritising energy improvement measures. Figure 3 indicates that the total energy loss for Plant B is 1839.87 MJ annually. The graph shows the most significant sources of energy loss, with the tertiary air duct damper temperature (\(TaDT\)), cooler air exhaust temperature (\(CaET\)), flue gas temperature at the recovery point (\(FgTR\)), and overload flue  
Figure 2: Pareto chart to analyse the energy loss parameters in Plant A.  
gas temperature (\(OF_{g}T\)) being the main contributors. These components account for 95% of total energy losses, as seen in Equation (12).  
\[L_{t}=TaDT+CaET+F_{g}TR+OFgT+DPC+LFa+others \tag{12}\]  
The substantial proportion of energy loss (95%) attributed to these specific factors suggests that these are important factors for designing the model that can optimise energy consumption for Plant B.
## 2 Strengthening Techniques
### Reduction of the Seismic Demand and Energy Dissipation  
The techniques used to reduce the seismic demand and dissipate energy include base isolation and passive energy dissipation devices (PED), also known as dampers. These methods are rarely used in existing URM buildings, although researchers are currently assessing and developing more suitable devices for URM buildings. Among these solutions, base isolation has been the most frequently applied technique to this type of structures [43]. Base isolation aims to decouple the building from the ground to decrease the seismic excitation transmitted to the structure by placing a flexible layer between the foundation and the structure [44; 45].  
There are five types of PEDs, namely friction, yielding, phase transformation, viscous fluid, and viscoelastic devices. Friction devices dissipate energy when two or more surfaces slide one on top of the other [46]. In yielding devices, the energy is dissipated through the plastic deformation of a metal element and should be changed after a strong earthquake if it is yielded [47]. Special friction and yielding connectors have been developed to fix floors to walls [48], to link planks to each other for timber floors [49], or as a part of the anchoring systems to tie perpendicular walls [50]. Phase transformation devices normally use wires made of shape memory alloys, which also yield but with the capability of recovering the original shape [51]. One end of the device is attached to a rigid roof, while the other end is anchored to the walls. The devices can also be introduced into post-tensioned vertical ties in slender structures such as towers [52]. Viscous fluid (VF) devices dissipate energy when a solid body (usually a piston with holes) moves in a viscous fluid [46]. These devices have been implemented as a connection point between the wall and the steel rod in tie systems [53]. Viscoelastic (VE) devices draw on the shear deformation of viscoelastic layers bonded to steel plates that move one with respect to the other [46]. [PERSON] et al. [43] furnish detailed information regarding energy dissipation devices for historical constructions.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Application Support Layer (IoT Middleware)
#### 2.6.1 Open Source Middleware Platforms
It supports device management, device interaction, remote device configuration and distributed remote device firmware updates, cloud service creation, data collection and analysis, user behavior analysis, target event notification, and data storage based on big data. In the literature [76], Kaa has been deployed in clusters as an application server for distributed data collection. * PlatformIO [77]: This is an open source IoT development ecosystem. It operates at the application layer and provides a cross-platform build system that integrates a continuous development environment and library management environment. Its main role lies in being an integrated development environment and framework that supports multiple hardware platforms. There is no support for device networking, discovery, and semantic processing. Companies such as ARM, Freescale, and TI provide support and the Apache 2.0 license is used. * Mainflux [78]: This is a hardware-independent IoT cloud platform based on the Go language and micro-service architecture, supporting multiple protocols, device management, OTA-FW-based firmware update, application management, data storage, and fine-grained access control based on customizable API keys and scoped JWT. TLS and DTLS security mechanisms are also supported. The Apache-2.0 License is used. * Patchwork Toolkit [79]: Patchwork provides lightweight tools for the interconnection of a variety of heterogeneous devices which are within the LAN. It only retains the necessary features and therefore is easy to deploy with good scalability. It also provides service registration and discovery functions.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description  
Without loss of generality, let us assume that the optimal path has been planned (in this case, the optimal path is optimized based on the A* algorithm with several waypoints [22], which is depicted in Figure 1); therefore the energy-efficient motion control problem of the DDWMR becomes the problem of designing an energy-efficient motion controller so that the robot can go to the goal through all waypoints efficiently.  
In Figure 1, the differential drive wheeled mobile robot tracks the optimal path connecting the waypoints from the starting point (\(x_{0}\), \(y_{0}\)) to the goal \(G(x_{g}\), \(y_{g})\) through several waypoints \(W_{k}(x_{\text{zw}}\), \(y_{\text{wk}})\), \(k=2\), \(3\),..., \(n-1\). (\(n\) is the total number of waypoints). The waypoints are determined by the planning layer of the robot (using the supervisory controller).  
Navigating the DDWMR to the goal can be done with help of many different motion control algorithms. A general motion control system of the differential drive wheeled mobile robot is depicted in Figure 2. This scheme can be applied to most motion control algorithms to navigate the DDWMR to a given destination.  
At first, an FLC is designed to navigate the DDWMR to the goal through all the waypoints sequentially. Then, a GA-based method is used to optimize the input and output membership functions for the FLC. To execute the optimization process of the FLC using the GA, as an optimizer of created Fitness Function (FF) based on simulated FLC performance, the \"pymoo\" library is employed.  
Figure 1: The differential drive wheeled mobile robot tracks the optimal path with several waypoints.  
Figure 2: A general mobile robot motion control system.  
A kinematic model and an energy model of the DDWMR are needed to design the optimal FLC. Thus, the following subsections will present them.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 4 Proposed Model
### TGAN-AD Architecture  
As shown in Figure 3, TGAN-AD uses the Transformer as the generator and discriminator of the GAN framework to capture the temporal correlation and other hidden behaviors in the multivariate time series. To improve the efficiency of our framework, the multivariate time series data are divided into sub-sequences using sliding windows during the pre-processing, as shown in Figure 4, and the window size \(t_{w}\) is set empirically.  
The real time series \(X\) and the random hidden variables \(Z\) are both input into the generator (\(G\)) to generate a fake time series \(G(X,Z)\): \(X,Z\to G(X,Z)\). Then the fake time series and the real time series are input into the discriminator (\(D\)), where the discriminator learns the parameters to distinguish real and fake time series data. The parameters of \(G\) and \(D\) are both updated according to the output of \(D\), and \(G\) generates fake samples approaching the existing normal samples \(X\). Moreover, \(D\) can improve its discrimination  
Figure 2: Generative adversarial networks.  
ability and distinguish fake time series \(G(X,Z)\) and normal time series \(X\). By iterative training, \(D\)'s discrimination ability can accurately distinguish normal sequence \(X\) and fake sequence \(G(X,Z)\). Then \(G\) can capture the hidden time correlation in the normal time series \(X\) and generate a fake time series \(G(X,Z)\) that can deceive \(D\).  
After the offline training, all the parameters of \(G\) and \(D\) are fixed. The test time series \(X^{\text{test}}\) is encoded as the hidden space. The optimal \(Z^{\text{test}}\) is trained by gradient descent and is input into \(G\) to reconstruct the test sample: \(X^{\text{test}},Z^{\text{test}}\to G(X^{\text{test}},Z^{\text{test}})\). The reconstruction loss \(G_{loss}\) is calculated by the deviation of \(X^{\text{test}}\) and \(G(X^{\text{test}},Z^{\text{test}})\). Meanwhile, the test time series \(X^{\text{test}}\) is input into the trained \(D\) to calculate the discrimination loss \(D_{loss}\). Finally, the anomaly score (\(AD-Score\)) of the test data is calculated with two kinds of losses: \((G_{loss},D_{loss})\to AD-Score\). Then the anomaly state of the test data is determined according to the anomaly score.  
Figure 4: Multivariate time series sliding window.  
Figure 3: The overall framework of TGAN-AD. The left part is the offline training process of TGAN-AD, and the right part is the process of online anomaly detection. Both the generator and discriminator are constructed based on Transformer.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 2 Proposed Tunnel-Free DMM Support Protocol
### Protocol Operation  
The steps of the protocol operation of proposed approach, as shown in Figure 2 are as follows:  
1. Connection to BMA1.
2. (RS) Router solicitation notification to BMA1.
3. (RA) Router advertisement notification.
4. Address configuration  
Figure 1: Architecture of the proposed approach. (**a**) MN communicating with CN1 via BMA1. (**b**) [PERSON] on the move, handover initiated to BMA2. (**c**) Handover complete. Session 1 (S1) continued via BMA2, meanwhile establishing new session with CN2. (**d**) [PERSON] on the move, handover initiated towards BMA3. (**e**) Handover complete. Both sessions now continued via BMA3.  
* BU notification to BMA1.
* BA notification to [PERSON].
* MN connected with CN for communication via BMA1.
* Handover requested by [PERSON] from BMA1.
* Handover initiated after scanning and finding BMA2.
* [PERSON]'s connection established with BMA2.
* BMA2 informs the CN about the MN's updated path.
* BMA2 informed of handover completion.
* NBU from BMA1 to BMA2.
* MN's connection with CN via BMA2
* NBAck from BMA2 to BMA1.
* BMA1 informed of handover completion.
## 1 Introduction  
### Lithium Ion Battery Packs in Electric Vehicles and Their Safety Issues  
Over the years, lithium ion batteries (LIBs), introduced by Sony [1] in 1991, have established themselves as the dominant choice for powering a wide array of consumer electronics due to their high energy and power density compared to other types of batteries, as well as falling costs. Lithium ion batteries have also been widely adopted in the automotive industry, significantly propelling vehicle electrification. This is demonstrated by the increasing global production of various EVs, exceeding 10 million units in 2022, with forecasts for 2023 projecting sales of about 14 million units [2, 3], including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCVs).  
Electric vehicles use lithium ion batteries in the form of battery packs, which consist of connected cells, as shown in Figure 1[4]. These cells are often first assembled into modules and then combined into battery packs. A battery pack also includes a battery management system (BMS), battery enclosures, thermal management system (TMS), connectors and cables, a voltage regulator, and safety features [5].  
In automotive engineering, the design of battery packs involves a large number of cells, their complex interconnections, and monitoring systems. This approach not only offers benefits but also entails risks, which include the following [6; 7; 8; 9; 10; 11]:  
* Voltage and temperature sensitivity--LIBs are sensitive to high temperatures and voltage irregularities, which can lead to malfunctions or degradation of battery performance;
* Electrical hazards--short circuits and overcharging, which can cause overheating, fire, or explosion may occur;
* Physical damage risks--physical impacts during vehicle collisions lead to safety hazards, like short circuits or leakage of hazardous materials;
* Environmental hazards during transport--the batteries pose risks during transportation due to their reactive nature;
* Fire and explosion hazards--due to the chemical composition and energy density of Liion batteries, there is a risk of fire and explosion, which can be caused by different types of abuse (e.g., thermal abuse, electrical abuse, and mechanical abuse) and internal short circuit;
* Thermal runaway risk--the dense packing of a large number of lithium ion batteries (LIBs) in a battery pack increases the risk of thermal runaway (TR) due to the self-heating of the LIBs;
* Risk of cascading failures--in battery packs, failure in one cell can lead to a cascading effect, causing widespread damage to the entire battery system.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 6 Performance Evaluation
### Extended Sensing Evaluation  
In this section, the performances of E-OBU are reported. In particular, in Figure 14a the trend of collisions encountered in the monitored area is shown. In case of vehicles equipped with the E-OBU, there are more collected information about surrounding area. In case of suddenly direction/speed change it is possible to avoid pileup if warning messages between nearest vehicles are exchanged; this trend is depicted in Figure 14a as well.  
In Figure 14b the average vehicles' speed trend is depicted. The higher is the density of the cars in the area the higher is the probability to have collisions. This affects the average speed that decreases because of the road congestion.  
Regarding the Traveling time experienced by vehicles (due to the higher number of collisions and because of rerouting strategy), vehicles that are not equipped with E-OBU measure a higher traveling time as shown in Figure 15. In particular, with a lower number of vehicles in the considered area, in case of E-OBU vehicles measure a higher traveling time because their average speed decrease due to alerting strategies. Once the number of vehicles increases over a certain value, then the higher number of collisions influences the whole area and vehicles experience a higher traveling time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Param. Name** & **Param. Value** & **Description** \\ \hline Simulation time & From 0 a.m. to 8 p.m. & Time of the day take into consideration for sims \\ RSUs coverage & 100–300 m & Coverage radius area for RSU devices \\ Vehicle warning propagation & 80–300 m & coverage distance for flooding warning \\ RSU dissemination & 1–16 Hops & RSU multi-hop warning dissemination \\ E-OBU Sensing Distance & 3–15 m & Sensors coverage distance \\ Tx Power for Vanet Device & 20 dBm & \\ Wave lenght & 0.051 & 5.0 GHz of frequency (IEEE802.11p) \\ Fading Factor & 2 & fading factor in the vehicular environment \\ Rx Antenna Gain & 2 & Rx antenna gain \\ Tx Antenna Gain & 2 & Tx antenna gain \\ Antenna Height of vehicle & 1.50 m & Vehicular antenna height from the ground \\ Antenna Height of RSU & 3.0 m & Rsu antenna height from the ground \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(Cont.\)  
Figure 14: (**a**) Collision number in urban area; (**b**) Average speed in urban area.
# Machine Learning in Detecting COVID-19 Misinformation on Twitter
## 2 Related Work
### Misinformation Corpus  
Several COVID-19 misinformation corpora were created by various researchers. A few are multilingual, while others are monolingual. This is discussed in provided next.  
[PERSON] and [PERSON] [87] presented a multilingual data corpus for fake-news detection named FakeCovid. It is a cross-domain dataset consisting of 7623 COVID-19 related fact-checked news from 92 various websites for fact checking. The news articles were collected between 4 January and 5 May 2020. The collected news articles were manually annotated into 11 different classes on the basis of their content. The presented corpus was collected from 105 countries, consisting of 40 different languages.  
COVID-Lies dataset is an annotated corpus for identifying misconceptions on Twitter, and was presented by [PERSON] et al. [86]. They developed this corpus to accelerate research in the automatic detection of COVID-19 misinformation, specifically on Twitter. They mainly concentrated on 62 different misconceptions, which are most common about the new pandemic, and it contains 6591 tweets related to those misconceptions. Researchers working at the UCI School of Medicine analyzed and annotated these tweets. The annotators first checked whether each tweet conveyed any misconception. If so, researchers also identified whether it propagated this misconception, was not a misconception, or it did not carry any misconception and informative content. These categories are labeled by the categories of agree (pos), disagree (neg), and nostance (na), respectively. It is a well-annotated corpus.  
[PERSON] et al. [88] proposed a multimodel misinformation corpus for facilitating research works related to the detection of conspiracies and fake news regarding COVID-19 on social media. They investigated the credibility of news from around 2000 news publishers from January to May 2020 and collected tweets for discussing the spread of this news through Twitter. The annotated collected tweets as reliable or unreliable with distant supervision, and made this misinformation corpus available for noncommercial purposes.  
COVID19-Misinformation-Dataset is a large Arabic annotated dataset for misinformation detection constructed by [PERSON] et al. [84]. They constructed this corpus as a part of their actual work of detecting real-time Arabic misinformation on Twitter by implementing machine-learning models. This repository supplies tweet IDs that were spread between March 2020 and April 2020 with their corresponding annotation. They covered all misinformation in Arabic spread through Arab Twitter in the initial stages of COVID-19, which was significant, misguiding, and had imprecise content. The annotated tweets were classified into 1311 misinformation and 7475 not misinformation instances. The collected tweets were labeled as 1 or 0 on the basis of their credibility, whether misinformation or not. Annotation was performed by two native Arabic speakers, and they reviewed the reported misinformation, which had been collected from the websites of WHO and the Saudi Arabian Health Ministry.
# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 3 Robust Spatial-temporal Context Hierarchical Response Fusion
### The Overall Flowchart of The Proposed Algorithm
#### 3.1.3 Multi-Response Maps Fusion  
The context-aware filter mainly integrates the context background information around the target into the filter to learn together and obtains a correlation filter with high discriminative performance through training. The advantage is that it can effectively utilize the context information of the surrounding area of the target and can make the target a better robustness in complex scenes such as occlusion, background clutter, and fast motion.  
In order to improve tracking robustness and take full advantage of hierarchical features and each layer of filters, we use context-aware correlation filters to output response values on the third, fourth and fifth convolutional layers, respectively, recorded as \(R_{context3}\), \(R_{context4}\), \(R_{context5}\), and then calculate the weight of each layer's response map normalized in \(t\) frame:  
\[context3\_\overline{w}_{t}=\frac{\max(R_{context3})}{\max(R_{context3}+R_ {context4}+R_{context5})}, \tag{4a}\] \[context4\_\overline{w}_{t}=\frac{\max(R_{context4})}{\max(R_{context3}+R_ {context4}+R_{context5})},\] (4b) \[context5\_\overline{w}_{t}=\frac{\max(R_{context5})}{\max(R_{context3}+R_ {context4}+R_{context5})}. \tag{4c}\]  
The filter response map accounts for a larger proportion and assigns a higher weight. Update the original response weight with the weight of the \(t\) frame:  
\[context3\_w_{t+1}=(1-\tau)\times context3\_w_{t}+\tau\times context3\_ \overline{w}_{t}, \tag{5a}\] \[context4\_w_{t+1}=(1-\tau)\times context4\_w_{t}+\tau\times context4\_ \overline{w}_{t},\] (5b) \[context5\_w_{t+1}=(1-\tau)\times context5\_w_{t}+\tau\times context5\_ \overline{w}_{t}. \tag{5c}\]  
Here, \(\tau\) is the weight update parameter; and \(context3\_w_{t}\), \(context4\_w_{t}\) and \(context5\_w_{t}\) denote the original response weight in \(t\) frame. At \(t\) frame, the final response is obtained by fusing each response map (\(R_{context3}\), \(R_{context4}\), \(R_{context5}\)):  
\[\begin{array}{c}R_{t}=\\ context3\_w_{t}\times context3\_R_{t}\\ +context4\_w_{t}\times\ context4\_R_{t}\\ +context5\_w_{t}\times context5\_R_{t}\end{array} \tag{6}\]