esa-sceva/satcom-chunk-collection huggingface dataset:
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods
### Intrusion Detection System
#### 4.4.2 Detection of Spoofing Attack  
In this section, we outline the detection of spoofing attacks. Spoofing attacks involve the injection of a significant number of data frames with specific IDs. As a result, the proper functioning of key ECUs within the vehicle can be compromised. Spoofing attacks aim to deceive the system and disrupt the normal operation of the vehicle by introducing malicious data frames with altered content. We examine two types of spoofing attacks that specifically target critical components of the vehicle: gear and rpm. Both attacks are executed using a similar methodology, with the distinction lying in the specific components they target.  
Due to the unique characteristics of spoofing attacks, we establish specific thresholds and benchmarks for these two attack types. Our method is similar to that of study [13] that combines signature-based and threshold-based methods. The signature-based method involves creating signatures for known attacks, and since our dataset already contains these two attack types, we can easily define signatures for them.  
However, the benchmark for spoofing attacks differs. We consider the data from the vehicle when subjected to these two types of spoofing attacks as a benchmark and utilize these attack data to compute similarity. Unlike the detection of DoS and fuzzy attacks, where the similarity measure is compared against a threshold, in the case of spoofing attacks, if the computed similarity measure falls within the threshold range, it indicates the occurrence of a specific spoofing attack. We can differentiate between the types of spoofing attacks based on thresholds.  
We illustrate the detection for two attack types in Figure 6. Specifically, Figure 5 illustrates a spoofing attack on gear, while Figure 5 represents a spoofing attack on rpm. Similar to above, in both figures, the blue color is indicative of normal data, while the red color signifies abnormal data. However, for detecting spoofing attacks, the attack data fall  
Figure 5: DoS and fuzzy detection.  
within the threshold range, whereas the normal data lie outside the threshold range. From the figure, it is evident that our method effectively distinguishes between normal data and attack data.
## 3 Modeling of the System
### Other Thermodynamic Components  
The compression and expansion process are calculated from the inlet. The calculation procedure is shown as below:  
\[(h_{c\_out}-h_{c\_in})=(h_{c\_out\_id}-h_{c\_in})/\eta_{c} \tag{22}\]  
\[(h_{t\_out}-h_{t\_in})=(h_{t\_out\_id}-h_{t\_in})\times\eta_{t} \tag{23}\]  
In the above two equations, \(h\) is the enthalpy and \(\eta\) is the isentropic efficiency. For the subscripts, c stands for compressor and t stands for turbine. Moreover, 'in' refers to inlet conditions; 'out' refers to outlet conditions, and 'id' represents ideal state with no entropy increase.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Inner diameter of absorber/m & 0.066 \\ Outer diameter of absorber/m & 0.070 \\ Inner diameter of glass envelope/m & 0.109 \\ Outer diameter of glass envelope/m & 0.115 \\ Opening width of collector/m & 5.0 \\ Focal length/m & 0.98 \\ Concentration ratio & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Purchasing cost functions for main equipment units.  
In this work, printed circuit heat exchanger (PCHE) is used for the heat exchanger and the minimum end difference during heat exchange is set to 5 \({}^{\circ}\)C in the calculation. Then, a few assumptions are made for simplification. The mass flow through each channel and the geometry of each channel are the same. Based on the assumptions, heat exchanger is divided into several axial units, shown in Figure 5 (the number of units is i in the figure). The j-th node is taken out to establish the heat transfer equation.  
Then, the calculation formula of overall heat transfer coefficient for unit j can be obtained according to [37]:  
\[U_{\text{j}}=(\frac{1}{U_{\text{j},h}}+\frac{k}{t}+\frac{1}{U_{\text{j},c}})^ {-1} \tag{24}\]  
where the subscripts h and c here represent hot side and cold side respectively. They are similar during the calculation. Besides, \(k\) refers to the thermal conductivity of material and \(t\) stands for the plate thickness. Where \(U_{\text{j},h}\) can be calculated as [37]:  
\[U_{\text{j},h}=(\frac{\frac{\frac{f_{h}}{8}}{8}\cdot(\text{Re}_{h}-1000)\cdot \text{Pr}_{\text{h}}}{1+12.7(\text{Pr}_{h}{}^{2/3}-1)\cdot\sqrt{\frac{f_{h}}{8 }\cdot(1+\frac{PD}{L})^{2/3}}})\cdot\frac{L}{PD} \tag{25}\]  
where:  
\[f_{h}=(\frac{1}{1.8\text{lg}(\text{Re}_{\text{h}})-1.5})^{2} \tag{26}\]  
\[PD=\frac{4\pi D^{2}/8}{\pi D/2+D} \tag{27}\]  
In the heat exchangers, the equations above are also adopted to calculate \(U_{\text{j},\text{c}}\).
## 5 Conclusions  
This paper reveals the gas-sensing mechanism of pristine and Ag\({}_{2}\)O-doped InN to SF\({}_{6}\)-N\({}_{2}\) decomposition components. Studies show that pristine InN exhibits poor sensing ability for SF\({}_{6}\)-N\({}_{2}\) decomposition components. The Ag\({}_{2}\)O dopant is the active site on the substrate material surface, and the four gas molecules tend to be adsorbed on Ag\({}_{2}\)O. The order of adsorption capacity is: NO\({}_{2}\) > SOF\({}_{2}\) > SOF\({}_{4}\) > SO\({}_{2}\)F\({}_{2}\). The doping of Ag\({}_{2}\)O dramatically increases the absolute value of the adsorption energy of NO\({}_{2}\) to 2.021 eV, resulting in that NO\({}_{2}\) cannot be desorbed from Ag\({}_{2}\)O-InN. In addition, moderate adsorption energy, large charge transfer, and obvious conductivity change make Ag\({}_{2}\)O-InN a potential material for the fabrication of SOF\({}_{4}\), SOF\({}_{2}\), and SO\({}_{2}\)F\({}_{2}\) sensors, and the extremely short desorption time reveals the reusability of Ag\({}_{2}\)O-InN as a gas sensor.  
Conceptualization and methodology, H.D.; data curation, writing--original draft, writing--review and editing, W.L.; Supervision, software, funding acquisition and resources, M.J.; formal analysis, visualization, and investigation, Z.L., H.L. and W.S. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline
**Adsorption System** & **K\({}_{\mathbf{B}}\) (K)** & **\(\mathbf{\tau}\) (s)** \\ \hline \multirow{4}{*}{NO\({}_{2}\)/Ag\({}_{2}\)O-InN} & 298 & \(1.474\times 10^{23}\) \\  & 348 & \(1.817\times 10^{18}\) \\  & 418 & \(2.288\times 10^{13}\) \\  & 298 & \(1.342\times 10^{-8}\) \\ SOF\({}_{4}\)/Ag\({}_{2}\)O-InN & 348 & \(4.768\times 10^{-9}\) \\  & 418 & \(1.698\times 10^{-9}\) \\  & 298 & \(55.94\) \\ SOF\({}_{2}\)/Ag\({}_{2}\)O-InN & 348 & \(8.244\times 10^{-1}\) \\  & 418 & \(1.225\times 10^{-2}\) \\  & 298 & \(8.411\times 10^{-9}\) \\ SO\({}_{2}\)F\({}_{2}\)/Ag\({}_{2}\)O-InN & 348 & \(3.196\times 10^{-9}\) \\  & 418 & \(1.217\times 10^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 4: Recovery time of NO\({}_{2}\), SOF\({}_{4}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\) on Ag\({}_{2}\)O-InN monolayer.  
**Funding:** This work was supported by the National Natural Science Foundation of China, \"Research Fund for International Young Scientist (RFIS-1)\", Project: 52150410419, China University of Mining and Technology, \"Science and Technology Fund for the Young Scientist\", Project: 2021 QN1069, 2022 Open funding of State Key Laboratory of Electrical Insulation and Power Equipment (EIPE22211), and 2021 Jiangsu \"Shuang-Chuang Doctor (Mass Innovation and Entrepreneurship) Talent Program\", Fund: JSSCBS20211187.  
**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.
## 5 Experiments and Results
### Comparison Results
#### 5.3.4 Computational Cost of G-QMIX and G-MAPPO  
Due to the complexity of multi-threaded environments, it is difficult to theoretically analyze the computing complexity of time. In order to quantitatively analyze the computational cost of GAPO methods against QMIX and MAPPO, we summarize the actual computing time of the algorithms in MMM2 in Table 8. Results prove that the computational time costs of G-QMIX and G-MAPPO are within the linear magnitude compared to QMIX and MAPPO. All of the experiments are conducted on the same workstation with AMD Ryzen 7950x CPU, Gigabyte Nvidia RTX 4090 GPU, and Kingston 128 G RAM with twenty sampling threads and five learning threads. The equipment was bought in a second-hand market in Beijing, China in the year 2023.
## 1 Introduction  
### Context and Motivation  
In the modern technology-centric era, digital transformation has made accessibility and cybersecurity critical components for the creation of any inclusive platforms which are essential in day-to-day operations [1]. With both the government and business sectors having to find ways to put most of their services online, the call for accessibility and cybersecurity has grown exponentially. The accessibility and cybersecurity of these platforms not only improve user experience but also protect sensitive information while promoting inclusion and trust. Accessible and cyber-safe web interfaces are also crucial for children, students, senior citizens and people who have disabilities, due to the specific needs and vulnerabilities of these groups. Optimizing accessibility and security not only enhancesthe overall user experience but also serves as a safegu against different dangers and risks associated with present-day digital interactions. Nowadays, a wide range of digital technologies is employed by businesses and institutions to facilitate communication, conduct transactions, and store data. Under these conditions, the risks of cyberattacks have also increased [2]. Furthermore, in today's digital age, the terms UX (user experience) and UI (user interface) frequently appear. UI refers to visual aspects, while UX focuses on functionality and user interaction [3]. Both concepts play crucial roles in the development of digital products. Successful digital products require both UX and UI design. UI design emphasizes the visual and interactive elements, making the product visually appealing and engaging. On the other hand, UX design ensures that the product is functional, meets user needs, and provides a seamless experience. Essentially, the UI designer is responsible for creating beautiful and functional interfaces. In the development of web applications, the integration of cybersecurity, UX/UI and accessibility in the best way possible is essential to assure all the users that they can access sensitive information on the Internet in a comfortable and confident way.
# A Survey of GIS and IoT Integration: Applications and Architecture
## 3 GIS and IoT Integration Use Cases
### Urban Infrastructure  
Growth estimations of city populations from 3.6 to 6.3 billion by 2050 [34] can be a testimony to a great tendency toward urbanization [35; 36]. As expected, modern societies are seeking indices and solutions to evaluate urban smartness [37; 38] and to enhance citizens' life quality [35; 39; 40]. The endeavor to improve life quality encompasses all aspects of a city such as healthcare [41], transportation [42; 43]. IoT is considered as a catalyst in the process of transformation of cities into smart ones [44]. Reference [45] highlights the revolutionary solutions of IoT for infrastructure needs such as Industry 4.0, power and water grids, and smart cities. According to Reference [46], three main areas must be taken into consideration to be improved in order to develop IoT technologies in an efficient way. Those three main areas are sensor-oriented technologies, network services, and energy-related technologies.  
Utilizing GIS holds some benefits in the phases of developing a new infrastructure namely planning, designing, construction, and operation [19; 47; 48]. The capability of handling both spatial and non-spatial data makes GIS a great tool for performing efficient management. In addition, marking the ability to provide digital representations of the Earth's surface, Reference [49] suggests developing digital urban infrastructure. With the great functionalities provided by IoT and GIS mentioned above, the integration of these two technologies would result in more effective systems. Reference [44] investigates other technologies like GIS to be integrated with IoT to be used in different aspects of urban infrastructure.
###### Abstract  
The purpose of the paper is to introduce a new bi-directional approach to assess the gap between the customer needs and technology developers' perception on the value propositions of innovations which includes storages. The paper used two methods; the first comprehensive sense and respond analysis investigated technology developers' perceptions using the value propositions defined under the EU-funded H2020 RESOLVD project. The second method focused on customers and collected a survey which covered challenges, value propositions and preparedness to adopt new technology. The H2020 RESOLVD project has developed an intelligent low-voltage grid management system with storage. The results from the sense and respond analysis showed that most of the value propositions aligned with the responses from the broader survey which are needed within five years (e.g., improved power quality of grid, fault detection, reduced technical loss). However, the cybersecurity perception differed between developers and distribution system operators (DSOs). The customer survey highlighted that certain value propositions of technological solutions are needed more urgently than others, and therefore, technology developers should prioritize these in further developments. Regarding the use of flexibility to manage the LV grid, unclear regulations were expressed as a key barrier, thereby affecting business feasibility around battery storage.  
distribution system operator; low voltage grid; power electronic device; sense and respond; smart grid; storage Article
###### Abstract
However, the gradient in CO\({}_{2}\) concentration between the leaf interior and the atmosphere was not considered, resulting in a bias in the GPP estimation. In the improved EC-LUE model, the effect of atmospheric CO\({}_{2}\) on GPP was estimated considering the CO\({}_{2}\) concentration inside the leaf and the CO\({}_{2}\) compensation point without dark respiration [8]. In this study, the ratio of inter-leaf to atmospheric CO\({}_{2}\) concentration was estimated from VPD and temperature. However, the EC-LUE model is much more complex and parametric, and errors in the parametric data result in large uncertainty in the GPP estimation. The CASA model, as a simple structure and widely used LUE model, has not been considered in existing studies to introduce atmospheric CO\({}_{2}\) concentration to improve the GPP estimation accuracy. Therefore, it is necessary to design a straightforward and effective improved CASA model to enhance the GPP estimation accuracy while considering the effect of CO\({}_{2}\) fertilization. The Qinghai-Tibet Plateau serves as an essential ecological protection barrier for China and is among the most fragile and sensitive regions within terrestrial ecosystems. Studies have shown that the Qinghai-Tibet Plateau is warming earlier than the rest of China, and its annual mean temperature is rising faster than the trend in China [9]. With global warming, the impact of escalating atmospheric CO\({}_{2}\) concentration and temperature on the GPP of the Qinghai-Tibet Plateau has become a vital issue. It is important to understand the GPP changes to the Qinghai-Tibet Plateau to study the carbon cycle within terrestrial ecosystems. Therefore, a long time series of spatio-temporally continuous GPP data with high accuracy can provide data support for the scientific study of the Qinghai-Tibet Plateau ecological security. Therefore, the main purpose of this study is to propose an improved GPP estimation method and generate a long time series, high-accuracy GPP dataset for the Qinghai-Tibet Plateau, enabling analysis of spatio-temporal GPP patterns and trends within the context of climate change. Firstly, the atmospheric CO\({}_{2}\) fertilization effect was introduced into the CASA model which converted the canopy CO\({}_{2}\) into leaf internal CO\({}_{2}\) to develop a more effective GPP estimation model. Secondly, the improved GPP estimation model was evaluated using validation data collected from the literature and global FLUXNET stations in the Chinese region. Finally, a long time series, spatio-temporally continuous GPP dataset of the Qinghai-Tibet Plateau was generated and used to analyze its spatio-temporal patterns. This dataset will facilitate research on climate change and carbon cycling within terrestrial ecosystems on the Qinghai-Tibet Plateau for sustainable ecosystem management.
# Design of a Low-Resolution Gamma-ray Spectrometer for Monitoring Radioactive Levels of Wastewater
###### Abstract  
Wastewater containing radioactive materials is stored in decay tanks, which are typically installed in basements to restrict public access. Specific activity is measured by a gamma spectroscopy system or a gamma counter for several hours per sample before discharge. To simplify the sample collection and measurement procedures before draining, the specific activity conversion coefficient for converting a measured value from a 1.5\" NaI (Tl) detector installed on a tank into specific activity was calculated. The experimental calibration was performed in a test water tank with a volume of 14 L filled with diluted reference source 18F, which is most commonly used in hospitals. The specific activity of the test water was measured with a gamma spectroscopy system using a high-purity germanium detector calibrated with certified reference materials. The change in the conversion coefficient according to the change in the volume of the water tank was corrected based on a Monte Carlo calculation. Finally, it was determined whether the minimum detectable activity (_MDA_) of this system was lower than that of the draining standard. The counts for the specific activity conversion factor and _MDA_ were evaluated as 5.5389 \(\times\) 10\({}^{10}\) and 2.09 \(\times\) 10\({}^{5}\) for 18F and for the one-hour count, respectively. The _MDA_ was lower than the standard value. Thus, this system can be used for discharge determination.  
NaI(Tl); MCNP; MC simulation; wastewater; draining; MDA +
Footnote †: journal: Medical Imaging  
1 Radiation Safety Section, Korea Institute of Radiation and Medical Sciences, Seoul 01812, Korea;  
1 kimkrizans.re.kr  
2 Poton Therapy Center, Research Institute and Hospital, National Cancer Center, Goyang 10408, Korea;  
1 [EMAIL_ADDRESS]
# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
###### Abstract  
The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results' analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.  
ICN; cache scheduling; wireless network; energy efficiency; IoT; big data; algorithm +
Footnote †: journal: Medical Computing
## 4 Experimental Analysis
### Parameter Analysis
By observing the trend and fluctuation of the curve, the sensitivity of hyperparameters to fine-grained image-recognition performance can be analyzed. The superparameter sensitivity analysis results are shown in Figure 1.  
It shows in Figure 1,b that when the hyperparameters \(\gamma\) and \(\varepsilon\), and the feature selection rate changes, the Kappa coefficient of fine-grained image recognition did not fluctuate significantly. The experimental results suggest that the AM-softmax loss function in this method was insensitive to two superparameters.
## 5 Discussion
### Visualization
#### 5.2.3 Visualization of Confusion Matrix  
To provide a more intuitive representation of the impact of IMAs, IUAs, and fusion strategies on the performance of multi-modal fusion models, we visualize the confusion matrices of these models. The results are shown in Figure 8.  
Comparing the left side of Figure 8a,c,e,g with the right side Figure 8b,d,f,h, it can be observed that regardless of the fusion strategy employed, models trained with IUAs outperform those trained with IMAs. Models trained with IMAs tend to have higher recognition accuracy for the negative class but lower accuracy for the positive and neutral classes. On the other hand, models trained with IUAs exhibit better recognition capabilities for both the positive and neutral classes, especially those based on the Conformer fusion method, which show improvements in recognition accuracy for all three classes.  
Figure 8: Confusion matrix for MEC models. (**a**,**c**,**e**,**g**) are from models trained with IMAs, while (**b**,**d**,**f**,**h**) are from models trained with IUAs. (**a**,**b**) belong to the concatenate-based fusion models, (**c**,**d**) belong to the multi-head attention-based fusion models, (**e**,**f**) belong to the transformer-based fusion models, and (**g**,**h**) belong to the Conformer-based fusion models.  
This research, however, has several limitations. The first limitation is the lack of additional datasets for extensive validation. The second limitation is that CH-SIMS is a dataset with imbalanced samples, specifically a small number of positive, especially neutral emotion samples. The adaptability of the model to imbalanced data can also be improved.