esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 2 Materials and Methods
### Ensemble-Based Feature Selection
This process is repeated up until the  
Figure 4: Illustration of candidate feature subsets. termination criteria is met, then the convergence is reached, producing a final subset of the fittest feature set of bands over all produced generations. Let X be our feature spectral input (i.e., training set) defined as follows:  
\[X=\left\{x_{i}\in X|i=1,\ldots,N\right\} \tag{1}\]  
where \(x_{i}\) is a single feature vector sample from the \(i^{th}\) position with size \(N\), given that \(N\) is the total number of spectra feature bends. Let P be its corresponding binary set with size \(2^{N}*N\), where \(2^{N}\) is the combination of all possible features subset to represent our GA chromosome pool. The underlying idea behind the proposed method is to select the best subset of feature bands maximizing or at least roughly maintaining the accuracy at the same level compared to when exploiting the whole spectra. Thus, the constraint of our GA objective function would be a trade-off between the classification accuracy and the size of feature subset X. [PERSON] et al. [4] exploited GA to investigate the optimal spectral bands to be selected for FD. However, GA can be pushed further by customizing its cost function to reduce considerably the resulting subset of features by formulating it, adding a constraint trade-off between the accuracy performance and the size subset of features. The objective function to assess each potential solution chromosome is defined as follows:  
\[\textit{Fitness function}=max\ \sum_{j=1}^{M}\sum_{k=0}^{T-1}f\ \left(P_{k}^{j} \right)\ \text{such that}\ P_{k}^{j}\ \in\ P_{k}\ \text{given that}\ j\ \in\ \left\{1,\ \ldots,\ \text{M}\right\} \tag{2}\]  
where \(M\) is the population size and k is the generation tournament \(k\ \in\ \left\{0,\ \ldots,\ \text{T}-1\right\}\). T is the maximum number of generations used as a stop criterion for our GA.  
We define \(f(P_{k}^{j})\) as follows [PERSON] et al. [34]:  
\[f\left(P_{k}^{j}\right)=\left(\alpha*\left(1-Acc_{k}^{j}\right)+\left(1-\alpha \right)*\left(1-\left(x_{k}^{j}.nbrFeatures\right)/N\right)\right)\ \text{given that}\ 0\ \leq\ \alpha\ \leq 1 \tag{3}\]  
where \(Acc\) is the classifier performance accuracy on the \(X_{k}^{j}\) which is the data feature subset selected by the chromosome individual computed as follows:  
\[X_{k}^{j}=X\ *\ P_{k}^{j} \tag{4}\]  
with \(X_{k}^{j}.nbrFeatures\) as being its size (i.e., the sum of genes selected within the \(P_{k}^{j}\) chromosome set to 1); the constant is used as weight to leverage between the accuracy performance and the subset of size of features. In our case, it has been fixed to 0.1. It works as a penalization factor to the chromosomes with high number of genes (i.e., when two chromosomes have a similar accuracy, it favors the ones with less features). The pseudo code algorithm below (Table 2) illustrates the related GA framework pseudo code. Figure 5: Illustration of Mutation and Crossover in Genetic Algorithms (GA).
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Contingency Response
#### 4.4.1 Robustness  
The system resiliency performance between \(t_{0}\) (incident start) and \(t_{1}\) (degradation start) can be considered as the robustness capability of the smart grid. In [89], the author introduced the normalised degradation index (DI) to evaluate the system degradation extent according to the time \(t\) during the incident  
\[DI=\frac{\int_{t_{1}}^{t_{2}}(M_{0}-M(t))dt}{M_{0}(t_{2}-t_{1})} \tag{11}\]  
[PERSON] mentioned in [79] that North American Electric Reliability Corporation (NERC) regulates that the Electric Power Grid (EPG) should be able to operate normally in the \"N-1\" case; however, this regulation does not take cyber aspects' impact into account. As a result, one of the security topics that has been discussed frequently is establishing a cyber-physical vulnerability model in the smart grid.  
In another cyber-physical vulnerability-related report [27], [PERSON], focused on studying cyber-physical attacks like the Aurora-like attack against the smart grid. Commonly, the smart grid is able to handle \"N-1\" cases without any interruption; however, an Aurora-like cyber attack will lead to an \"N-X\" contingency, which means the grid could potentially lose multiple critical power assets (generators) simultaneously.  
Figure 3: System’s resilient performance during an incident.  
To improve the robustness of the smart grid, the common strategies include (1) hardening the power transmitting infrastructures; (2) managing the trees and flora near the transmitting lines; and (3) introducing substitute/redundant system design [90].
_applied_  
_sciences_  
_Article_  
Development of a Temperature Management System for Battery Packs Using Phase Change Materials and Additive Manufacturing Options  
**[PERSON] 1,2,*, [PERSON] 2, [PERSON] 2, [PERSON] 2 and [PERSON] 1**  
1 Material-Technology Innsbruck (MTI), University of Innsbruck, 6020 Innsbruck, Austria  
2 Carinthia Institute for Smart Materials (CSMAT), Carinthia University of Applied Sciences,  
9524 Villach, Austria  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +43-5-90500-2114  
**Abstract:** The broad acceptance of electromobility depends heavily on the performance and service life of the battery technology used. Lithium-ion battery technology is currently the most promising energy storage technology for mobile applications, but the performance and capacity of the cells are extremely temperature sensitive. Especially in these new complex and powerful applications, there is a significant need for thermal management. Depending on the ambient conditions, part of the effective battery energy must, therefore, be used to temper the system. So far, mainly active components have been used for this task. This not only reduces the overall efficiency but also increases the weight, volume, and maintenance requirements of the system. In the present work, an alternative battery temperature management system was developed. A concept based on latent heat storage materials was created, which uses the advantages of thermal energy storage and the design freedom of additive manufacturing technology, and its suitability for use in battery applications was checked. On the one hand, the use of systems for storing thermal energy was researched and, on the other hand, an innovative cooling concept was created considering new production possibilities.  
**Keywords:** lithium-ion battery; smart materials; phase change material; latent thermal energy storage; battery temperature management system; direct metal laser sintering
## 4 Use Cases and Application Scenarios of Internet of Emergency Services  
### Outline  
The Internet of Emergency Services (IoES) has the potential to revolutionize emergency response and management. Some use cases and application scenarios of IoES are (Table 13):  
* Disaster Response: IoES can be used to enhance disaster response efforts by providing real-time data on the location, severity, and nature of the disaster. This data can be collected from IoT devices, such as sensors, cameras, and drones, and analyzed in the cloud using artificial intelligence and machine learning algorithms. This can enable emergency responders to make informed decisions and allocate resources more effectively. * Medical Emergencies: IoES can be used to improve the response time for medical emergencies by providing accurate and real-time information to emergency medical services (EMS) [3; 5; 6]. Wearable devices and sensors can monitor a patient's vital signs and provide this information to the EMS, who can then prepare for the patient's arrival and provide a more effective treatment. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Context of Use** & **Description of Use** \\ \hline [PERSON] et al., (2022) [61] & Smart fire brigade service as a PES & BEST framework for PES using blockchain \\  & & and IoT \\ \hline [PERSON] et al., (2022) [77] & Various healthcare applications for & Review of SC-based networking, \\  & COV/ID-19 & including blockchain and IoT \\ \hline [PERSON] et al., (2022) [62] & Immediate fire brigade service & FLAME framework for enterprises using \\  & and insurance claim & blockchain and smart contracts \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Use of blockchain for emergency management and response. * Public Safety: IoES can be used to improve public safety by providing real-time information on criminal activity, natural disasters, and other threats. IoT devices, such as security cameras and sensors, can be used to detect suspicious behavior and alert law enforcement agencies. This can enable them to respond more quickly and effectively to potential threats. * Smart Transportation: IoES can be used to improve the safety and efficiency of transportation systems. IoT devices, such as sensors and cameras, can monitor traffic conditions and provide real-time information to drivers, transit agencies, and emergency responders. This can enable them to respond more quickly to accidents and other emergencies and help reduce congestion on the roads. * Industrial Accidents: IoES can be used to improve the response to industrial accidents by providing real-time data on the location, severity, and nature of the incident. IoT devices, such as sensors and drones, can be used to detect hazardous conditions and alert emergency responders. This can enable them to respond more quickly and effectively to the incident and help prevent further damage.
# Secure D2D Communication for 5G IoT Network Based on Lightweight Cryptography
## 5 Security Analysis
### Security against Typical Threats  
* Impersonation attack. In D2D the token generation step, each UE is issued a D2D token, which is signed by the gNB. When gNBs generate the token, they authenticate the validity of the UE by comparing the identity of the UE in UDM. After this authentication process, gNBs complete the generation of D2D tokens by using their private key. Because of this procedure, the attacker cannot impersonate other UE.
* Eavesdropping. In a secure data transmission step, every instance of data transmission is protected by the lightweight AEAD cipher. In the AEAD encryption process, UE uses its D2D token and context sequence as associated data. Using this associated data, MAC is generated, and thereby UE can check the integrity of the message and the validity of the sender UE. For these reasons, the attacker cannot eavesdrop and cannot fabricate a message.
* Privacy sniffing. The proposed D2D communication system uses the D2D token, which is generated based on SUCI of UE and digital signature of gNB using ECDSA. The D2D token can provide anonymity as a cryptographic identity. For this reason, the attacker cannot recognize the original identity of the UE.
* Free riding attack and location spoofing. When the D2D token is generated by gNB, the validity of the UE is authenticated. This means that each instance of validating a UE is managed by gNB. The D2D token is authenticated in the link setup step in the proposed D2D communication system, and the data transmission is protected by AEAD encryption using a D2D token. Therefore, if a free-riding attack or location spoofing occurs in D2D communication, gNB can handle these situations by eliminating malicious UE.
## 4 Conclusions  
In this paper, the water stability, mechanical properties, thermal constants, heating and healing properties of four asphalt mixtures BA, SF, SS and SF + SS were compared. The following conclusions can be drawn.  
1. The addition of steel slag and/or steel fibers improves the water stability of asphalt mixture. Marshall Stability and residual Marshall Stability ratio of asphalt mixture are increased by adding steel slag and/or steel fibers.
2. The addition of steel slag and/or steel fiber can improve the particle loss resistance and the fracture energy of asphalt mixture. Steel fiber increases the toughness of asphalt mixture, while steel slag reduce the toughness of the mixture.
3. Steel fibers increase the thermal conductivity and thermal diffusion of asphalt mixture, and steel slag shows a reverse effect. The volumetric heat capacity of steel slag mixture is higher than plain and steel fiber asphalt mixtures.
4. The composite of steel fibers and steel slag can enhance the induction heating speed of asphalt mixture. The heating speed ranking of different asphalt mixtures is SF + SS \(>\) SF \(>\) SS. The cooling rate of the SF and SF + SS mixture show different ranking SF \(>\) SF + SS, which can be explained according to the thermal constants of the mixtures.
5. The composite of steel fibers and steel slag can increase the heating homogeneity and thus enhance the induction healing ratio of asphalt mixture. Due to different heating units in different asphalt mixtures, the healing ratio ranking of the three mixtures is SF + SS \(>\) SF \(>\) SS at the same surface temperature.  
This research was supported by the Natural Science Foundation of China (No. 51508433 and No. 51778515), the Open Fund of the Key Laboratory of Highway Engineering in Special Regions of Ministry of Education of China, Chang'an University (No. 310821161105), the Open Fund of the Key Laboratory of Road Structure and Material of Ministry of Transport (Changsha University of Science & Technology, No. kfj150302), and the Scientific Research Foundation for Returned Overseas Chinese Scholars, State Education Ministry.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 3 System Model
### Content Popularity and User Preference
Subsequently, the resulting products are fed into a linear layer with an activation function. For a given content embedding \(x_{c_{i}}\) and a user embedding \(y_{u_{j}}\), the output is computed as follows:  
\[\hat{m}_{i,j}=\sigma(\mathbf{w}^{T}(x_{c_{i}}\odot y_{u_{j}})) \tag{2}\]in this equation, \(\sigma\) denotes the sigmoid activation function, \(\mathbf{w}\) represents a trainable matrix, and \(\odot\) signifies the element-wise product. To train the matrix factorization model, we minimize the binary cross-entropy (BCE) loss between the ground truth values \(m_{i,j}\) and the predicted values \(\hat{m}_{i,j}\). It is worth mentioning that we label \(m_{i,j}\) as 1 if the \(j^{\text{th}}\) user has provided a rating for the \(i^{\text{th}}\) content item, and 0 otherwise. The key training parameters for the Neural Collaborative Filtering (NCF) model are summarized in Table 2.  
In order to fit the number of content items and users in our network, all users and content items in the dataset are divided into groups. If the content embeddings are close to each other, we cluster them into groups, and users are grouped in the same way. We utilize a Gaussian mixture model (GMM) to cluster the embedding vectors, and then compute a representative embedding for each group by taking the element-wise mean. Since the inner product \({x_{c_{i}}}^{T}y_{u_{j}}\) captures the correlation between content \(c_{i}\) and a user \(u_{j}\), we apply the softmax function on the inner products to obtain the probability \(P(u_{j}|c_{i})\) for given content \(c_{i}\). This probability represents the preference of user \(u_{j}\) for content \(c_{i}\). Inspired by the works [43; 44], we calculate the joint probability \(P(c_{i},u_{j})\) of content \(c_{i}\) being requested by user \(u_{j}\) as follows:  
\[P(c_{i},u_{j}) =P(c_{i})P(u_{j}|c_{i})\] \[=P(c_{i})\frac{\exp({x_{c_{i}}}^{T}y_{u_{j}})}{\sum_{j=1}^{U}\exp( {x_{c_{i}}}^{T}y_{u_{j}})} \tag{3}\]  
where \(P(c_{i})\) represents the content popularity, while \(P(u_{j}|c_{i})\) reflects the preference of user \(u_{j}\) for content \(c_{i}\). Combining these probabilities establishes a link between the user preference and content popularity. It is important to note that our approach differs from the methods proposed in [43; 44], as we obtain user and content embeddings from a real-world dataset. Furthermore, we consider the inner products of the learned user embeddings and content embeddings to measure their associations, enabling us to capture the relationships between users and content meaningfully.
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 7 PFAS Diagnosis
### Exploratory Approaches in Food Allergy Testing
#### 7.5.1. Mast Cell Activation Test (MAT)  
Similar to the Basophil Activation Test (BAT), MAT measures in vitro activation of mast cells, either derived from mast cell lines or generated from peripheral blood hematopoietic stem cells, using flow cytometry to determine allergic responses. A key distinction is that MAT uses patient serum or plasma to sensitize the mast cells, enabling retrospective testing of stored samples, unlike BAT, which requires fresh blood. However, challenges include difficulty in isolating mast cells, high variability of primary cells, and lower sensitivity than BAT, making MAT particularly useful only as a secondary diagnostic option for basophil non-responders [197; 219; 220; 217].
# Noise Reduction Using Active Vibration Control Methods in CAD/CAM Dental Milling Machines
## 1 Introduction  
Workplace noise exposure directly influences the health of workers. Dental professionals are exposed to high levels of noise while working in dental clinics or laboratories. Both the intensity and long duration of noise from dental work environments are known to contribute to hearing loss [1].  
Recent studies have assessed noise exposure and hearing problems in dental practices on students from a dental college located in the United Arab Emirates (UAE); about 80% of students experienced some noise, 54% reported that they have hearing problems, and 10% reported hearing loss. The study recommended the use of sound absorbing materials in the construction of dental offices and laboratories in order to reduce the noise levels [2]. Dental professionals who regularly used high-speed hand pieces had worse hearing than those who did not use these hand pieces [3].  
In addition to the existing noises, studies by [PERSON], [PERSON] et al. and [PERSON] indicate that the introduction of new technologies such as three-dimensional (3D) CAD/CAM milling machines have contributed to the existing noise source of dental clinics and laboratories, causing greater than expected hearing loss in dental professionals than before [4; 5].  
In general, there are two ways to reduce environmental noise: passive and active. The passive method uses techniques such as enclosures, barriers, and silencers [6]. While these passive noise control techniques can help facilitate noise control of mid and high frequencies, they are inefficient and costly at lower frequencies. The active method uses an active noise control (ANC) system to cancel the unwanted noise based on the principle of superposition [7]. Specifically, identical amplitude and antiphase anti-noise is created and actively integrated with the first noise, thereby triggering the elimination of both noises [8]. Several studies were published on the application of ANC methods (such as active noise canceling for headphones using a hybrid structure, active noise control in headsets by using a low-cost microcontroller, a low-power broad-bandwidth noise cancellation for in-ear headphones, adaptive feedback active noise control headset and sound quality of the machine) [9; 10; 11; 12; 13; 14; 15]. Although various problems are raised and relevant studies are constantly increasing to address noise problems, studies on noise in the dental treatment room including the medical industry are inadequate. In addition, efficient production methods considering costs and methods are needed to apply this noise elimination method into machines aiming for commercialization [12; 14]. We recently investigated a more effective approach to noise reduction in the CAD/CAM milling machine using tactile transducers to produce tactile sound. This study was designed to determine the main noise source generated from CAD/CAM milling machines installed in dental clinics or the laboratory, and to create a pleasant clinic environment by applying a noise reduction method.
# The New Era of Business Digitization through the Implementation of 5G Technology in Romania
## 3 Materials and Methods  
To validate the research results, we carried out empirical research based on an opinion poll (questionnaire). The design of the questionnaire and the formulation of the questions are in accordance with the main objective of this research, namely to observe the degree of use of 5G technology in Romanian economic entities and to observe the impact of this technology on the national business environment (business environment represents the totality of internal and external factors of the economic entity, such as management, employees, customers, suppliers, etc.). The questionnaire consisted of 12 questions with closed answers and 2 questions with precoded answers, structured on a 5-stage Likert scale. 5G technology will be promoted on the market by telecommunications providers, but what we intend to observe through the present research is what 5G technology represents for economic organizations, how 5G technology will affect economic activity, and what are the major benefits that 5G networks have offers to companies? To answer these questions, we have established the following research hypotheses: H1--the use of 5G technology is absolutely necessary for the Romanian business environment; H2--the implementation of 5G technologies within the economic entities is influenced by the activity field, the size and the form of ownership; H3--5G technology innovations bring economic and social benefits to economic entities.  
The questionnaire was sent to 440 economic entities from different fields of activity, by email, to facilitate their filling in and retransmission. A random sampling method was used, the target group of the study was represented by Romanian economic entities from related and diverse fields such as the extractive industry, processing industry, energy industry, transport, construction, hotels and restaurants, education, health and other administrative and social services. From the 440 questionnaires sent, we received a response from 362 respondents. Following the verification of the questionnaires received, 36 of them could not be validated, because the answers were not relevant, so we recorded 326 valid questionnaires and a response rate of 74%. After collecting the completed questionnaires and removing the invalid questionnaires, the collected data was centralized and recorded with the help of Microsoft Excel, creating the database needed to interpret the results. Interpretation and statistical analysis of data collected from questionnaires, as well as graphical representations, were made with the help of the IBM SPSS Statistics 20 specialized application. The SPSS application is one of the most used in the statistical analysis of data in various fields starting with marketing, experimental research, education, and ending with health, insurance industry, etc. [87; 88].