esa-sceva/satcom-chunk-collection huggingface dataset:
# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 2 The Proposed System Overview
### Data Preparation  
#### 2.1.1 Input Image Normalization  
To eliminate the differences in the intensity levels of the mammograms used in the databases, the histogram transfer method was applied from the INbreast dataset to all other images. This normalization preprocessing step in CAD systems is crucial as it can account for large intensity variations that are typically attributed to the use of different scanners with varying parameters in the image-capturing process. These intensity variations can also severely affect the performance of processing and analysis steps, such as image registration, segmentation, and tissue volume estimation. To ensure objective image comparison between different mammograms, a normalization algorithm is performed in advance to modify the distribution of intensity values of each scan and match the selected baseline image. This preprocessing step was adopted from [24].  
In order to help radiologists detect abnormalities, the adaptive histogram enhancement (AHE) [25; 26] method is typically applied as a preprocessing step in CAD systems. The AHE method is a contrast-boosting technique that enhances local contrast and image details. Medical images can benefit greatly from this preprocessing step, but it can also generate a lot of noise as a side effect. To increase the image contrast and eliminate the noise enhancement, a variation known as the contrast-limited adaptive histogram equalization (CLAHE) technique [27; 28; 29] is used, as proposed in the literature.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 2 RPL: Routing Protocol for Low-Power and Lossy Networks
### DODAG Construction  
Initially, the gateway/root multicasts the DIO message. Upon receipt of the DIO message, direct neighbors of the gateway decide to join the DODAG advertised in the DIO message based on their Objective Function (OF). If a node joins the DODAG, using the RPL trickle timer [1], the node also multicasts the DIO message. This process continues and eventually DODAG information reaches all the nodes in a network. If any node in the network is not interested in joining the DODAG advertised in the DIO message, the node ignores the message. Multiple DODAGs can exist in the same network, and they are differentiated by their instance ID.
## 2 Materials and Methods
### Stability Study
#### 2.4.5 Andersen Cascade Impactor Measurement  
The aerosolization properties of both freshly prepared and stored spray-dried samples were evaluated using an Andersen Cascade Impactor (ACI) from Copley Scientific Ltd., Nottingham, UK. An inhalation flow rate of 60 L/min was generated by a vacuum pump (High-capacity Pump Model HCP5, coupled with a Critical Flow Controller Model TPK, Copley Scientific Ltd.) and verified with a mass flow meter (Flow Meter Model DFM 2000, Copley Scientific Ltd.). Samples were loaded into size 3 Ezeeflo(tm) hydroxypropyl methylcellulose capsules (ACG-Associated Capsules Pvt. Ltd., Mumbai, India) and placed in a Breezhaler(r) single-dose device. The device was activated, and inhalation was performed over 4 s. To replicate lung adhesive properties, the stage plates were pre-coated with a Span 85 and cyclohexane mixture (1:99 \(v\)/\(\%\)), which was allowed to dry. After inhalation, all ACI parts were rinsed with 50% methanol (\(v\)/\(v\)\(\%\)) to retrieve deposited KTP. The KTP concentration at each stage was measured via UV/VIS spectrophotometry (ATI-UNICAM UV/VIS Spectrophotometer, Cambridge, UK) at 258 nm. Inhalyti(tm) software (Copley Scientific Ltd., Nottingham, UK), ([[https://www.copleyscientific.com/inhaler-testing/apsd-data-analysis-software/inhalytix/](https://www.copleyscientific.com/inhaler-testing/apsd-data-analysis-software/inhalytix/)]([https://www.copleyscientific.com/inhaler-testing/apsd-data-analysis-software/inhalytix/](https://www.copleyscientific.com/inhaler-testing/apsd-data-analysis-software/inhalytix/)), accessed on 10 October 2024), was used to analyze the in vitro aerodynamic properties of the samples. Aerodynamic profiles were characterized by several metrics: MMAD (Mass Median Aerodynamic Diameter) representing true particle size during inhalation; FPF (Fine Particle Fraction), the percentage of drug particles under 5 \(\upmu\)m; and EF (Emitted Fraction), denoting the percentage of drug mass reaching the impactor compared to the initially loaded KTP amount.
# Comparison of Damage Indexes for Assessing Seismic Fragility of Bearings in an Offshore Bridge
## 4 Results and Discussion
### Comparison of Various Damage Indexes for Bearings
Additionally, the probability for the bearing obtained using the relative displacement is larger, and its increment during the service period is smaller. On the contrary, the probabilities for the bearings obtained using the relative displacement ductility ratio are relatively small, but their increments during the service period are significant. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **LS1** & **LS2** & **LS3** & **LS4** \\ \hline Security & & Case 1 & \\ Economy & Case 3 & & Case 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Application of damage indexes (based on the order of exceedance probability). Figure 16: Time-varying fragility curves of bearing 5#: (**a**) minor damage; (**b**) medium damage; (**c**) serious damage; and (**d**) complete destruction. This phenomenon can be attributed to the fact that piers, especially high piers, have more prominent bending deformation under earthquakes. In a similar scenario, the concept of performance-based ductility design is incorporated by considering the relative displacement ductility ratio, which accounts for the shear deformation of the piers. As a result, the relative displacement ductility ratio-based index for each damage state tends to be higher, resulting in a lower probability of exceeding. By exclusively focusing on relative displacement without considering shear deformation, the resulting indexes for damage states tend to be small. Consequently, this leads to a higher failure probability. In conclusion, Table 10, based on the order of exceedance probability, is more suitable for the division of the damaged state of bearings subject to earthquakes.
## 3 Classification of the Reviewed Literature
### Application Domain and Their Incorporated Contexts  
In this section, the various application domains and the different contexts used for making recommendations are addressed.  
Deciding on the domain of application is the initial stage of any context-aware recommendation systems. This is important because the remaining stages heavily depend on it and different domain of application may contain different contextual information. The authors of [42,44] asserted that the main focus of current RS researchers is the development of RS applications due to the rapid increase in big data. Although several types of academic research exist on RS, there is a lack of comprehensive literature review and classification of those researches. A total of 210 papers are reviewed in [42], from 46 journal articles published within 2001 and 2010. The authors classified and examined the 210 RS papers into their various domains of applications using different data mining techniques. The review gives an insight on the information of RS research trends and provides future directions to both practitioners and researchers. RS is also examined with respect to real-world application domains  
Figure 2: Distribution based on publication year.  
in [13]. In contrast, this paper identifies the various application domains in the current RS researches and also identifies their incorporated contextual information.  
Based on closeness and relatedness, and based on the classification scheme presented in [22; 42], the findings of our review demonstrated that the current application domains in CARS could be classified into travel and tourism, places, e-documents, multimedia, e-commerce, and others (see Figure 3). The fields of multimedia (22%) and e-commerce (19%) had taken the majority of the publications. This may result in the availability of the datasets _Movielens_ ([[http://www.grouplens.org/](http://www.grouplens.org/)]([http://www.grouplens.org/](http://www.grouplens.org/))) and _LastFm_ ([[http://www.last.fm/](http://www.last.fm/)]([http://www.last.fm/](http://www.last.fm/))) that are used for movies and music respectively to provide rich information needed for performance evaluation and which are freely accessible. Therefore, it is easier to test multimedia applications with real datasets than any other field.  
In these sub-sections, each classification is studied and a microscopic view is presented in order to analyze the merit and demerit of the previous research works and the evolving processes between them. In this way, the challenges and future research directions are highlighted to provide better advice to other researchers (especially the novice and the new researchers).
###### Abstract  
The new fifth-generation (5G) cellular networks dramatically improve the speed of message transmissions. Most existing authentication schemes that secure 5G communication rely heavily on the vehicle's tamper-proof device (TPD) and roadside units (RSUs) to store the system's master key. However, it only takes a single compromised TPD to render the whole system insecure. We propose a password-guessing attack-aware authentication scheme based on the Chinese Remainder Theorem (CRT) to secure inter-vehicle communication on 5G-enabled vehicular networks to address this issue. The trusted authorities (TAs) in the proposed scheme generate and broadcast new group keys to the vehicles assisted by CRT. Moreover, since the system's master key does not need to be preloaded, the proposed scheme only requires realistic TPDs. The proposed scheme overcomes password-guessing attacks and guarantees top-level security for entire 5G-enabled vehicular networks. The security analysis indicates that the proposed scheme is secure against adaptive chosen-message attacks under the random oracle model and meets the security requirements of a 5G-enabled vehicular network. Since cryptographic operations based on elliptic curve cryptography are employed, the performance evaluation shows that the proposed scheme outperforms the eight existing schemes in terms of computation and communication costs.  
Keywords:fifth-generation (5G) cellular networks; 5G-enabled vehicular networks; Chinese remainder theorem (CRT); password-guessing attacks; tamper-proof device (TPD) +
Footnote †: journal: Applied  
_Article_  
**Password-Guessing Attack-Aware Authentication Scheme Based on Chinese Remainder Theorem for 5G-Enabled Vehicular Networks. _Appl. Sci._**2022**, 12, 1383. [[https://doi.org/10.3390/app12031383](https://doi.org/10.3390/app12031383)]([https://doi.org/10.3390/app12031383](https://doi.org/10.3390/app12031383))
## 5 Conclusions  
Existing arterial green wave control methods primarily focus on motor vehicles, ignoring the important traffic mode of electric bicycles. To address this problem, this paper presents an arterial multi-path green wave control model that considers both motor vehicles and electric bicycles. The conclusions of this study can be summarized as follows.  
(1) This paper introduces a model that can simultaneously optimize the paths of multiple traffic modes and coordinated variables, namely offset, phase sequence, and common cycle time.  
(2) Traditional arterial green wave control models mainly focus on motor vehicles, ignoring electric bicycles, which results in electric bicycles only occasionally benefiting from the green wave bands. In contrast, the presented model can simultaneously consider the green wave control demands of both motor vehicles and electric bicycles, ensuring that electric bicycles can regularly benefit from the green wave bands.  
(3) The proposed model can solve the multi-path green wave control problem of multiple traffic modes at an arterial level, but it cannot solve the network multi-path green wave control problem of multiple traffic modes. In the future, research can be extended from the arterial studied in this paper to a closed traffic network [22] to investigate the green wave control method for multiple traffic modes at a road network level.  
(4) The model proposed in this paper assumes that each path for every traffic mode is equally important and fails to fully consider the heterogeneity of different paths. Therefore, how to propose appropriate weight coefficients to assess the importance of different traffic modes on the same path and the importance of different paths for the same traffic mode is another worthwhile research direction.  
Conceptualization, B.J.; methodology, B.J. and F.Y.; software, B.J.; validation, B.J. and F.Y.; formal analysis, B.J.; investigation, B.J.; resources, B.J.; data curation, B.J.; writing--original draft preparation, B.J. and F.Y.; writing--review and editing, B.J.; visualization, F.Y. and B.J.; supervision, B.J.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52102395) and by the Philosophy and Social Science Research of the Higher Education Institutions of Jiangsu Province: Research on the method of data-driven bandwidth-based network traffic signal coordination control for passenger cars and buses considering the comprehensive cost (No. 2021 SJA1595).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 2 Optimized Link State Routing Protocol
### Hello Messages  
Ad hoc networks are characterized by a changing topology over time. Thus, in order to detect these changes and maintain a list of neighbors for each node, OLSR uses HELLO messages. During neighborhood detection, each node regularly transmits HELLO messages to advertise its presence and its links with its neighbors, leading to the update of topology information [8]. We can classify the links between two OLSR nodes into three categories:  
* Asymmetric: If the node receives messages from another that does not contain confirmation that it heard them then the link is asymmetrical.
* Symmetric: If two connected nodes hear each other, then the link is symmetric.
* Lost: If the link was considered symmetric or asymmetric and no information was received after a while, then the link is considered lost.  
Figure 3 shows a very simplified version of a neighbor discovery session using HELLO messages. These messages consist of important information fields, the meanings of which are as follows:  
Hello messages consist of important fields of information, the meanings of which are as follows:  
* Reserved: Should be configured with the value \"0.\"
* HTime: Its value represents the HELLO transmission interval used by the node on this specific interface, indicating the time remaining until the next HELLO transmission. This information can be employed for advanced link-sensing purposes.
* Willingness: Indicates a node's ability to relay messages to other nodes. A node with a Willingness value of \"WILL_NEVER\" should never be selected as an MPR by other nodes. In the other case, a node with a Willingness value of \"WILL_ALWAYS\" should always be chosen as an MPR. In most cases, a node should announce a default Willingness value of \"WILL_DEFAULT.\"
* Link Code: Provides information about the link between the sender's interface and the subsequent list of neighboring interfaces. It also indicates the neighbor's status.
* Link Message Size: Determines the link message length, measured in bytes from the \"Link Code\" field to the next \"Link Code.\"
* Neighbor Interface Address: Identifies the neighbor node's interface address.  
Figure 3: HELLO messages Neighbors Discovery [8].  
The sequence of events unfolds as follows: Firstly, Node A initiates communication by sending an empty HELLO message. Node B receives this message and, since it cannot find its own address in the HELLO message, considers Node A an asymmetric neighbor. In response, Node B sends a HELLO message, designating Node A as an asymmetric neighbor. Upon receiving this message, Node A finds its own address in it and upgrades Node B has a symmetric neighbor. To complete the cycle, Node A includes Node B in the HELLO message it sends, and upon reception, Node B registers Node A as a symmetric neighbor.  
HELLO messages have additional applications, being generated and transmitted to all one-hop neighbors for link-sensing, neighbor-sensing, two-hop neighbor-sensing, and MPR selector sensing.
## 2 Related Work
### Standardization Activities
\"The goals are to accelerate the proliferation of SDN services and applications and to offer a more efficient way of providing them through a service-architecture ecosystem of one-stop shopping for service-specific challenges\" [20]. In [11] the authors report on their contributions to IEEE NGSON architecture to include SDN and NFV technologies and to provide a more powerful service composition and orchestration functions through generalized service chains including both application and network services while dynamically establishing data delivery paths across services. The Open Networking Foundation (ONF) is a user-driven organization dedicated to the promotion and adoption of SDN through the development of the OpenFlow specifications as an open standard for the communication between the controller and the data forwarding network elements. ONF is also devoted to the promotion of open source developments as a way of consolidating impacts of SDN in the Industry. Moreover, ONF is very active in the definition of an SDN architecture framework with focus on the controller capabilities and on the interfaces with the other elements in the architecture both at north-bound and south-bound with applications and network elements, respectively [21; 22]. Within the services area, an initiative started about providing an end-to-end orchestration, abstraction and resource optimization across data center SDN controllers and Wide Area Network (WAN) SDN controllers so that user-applications can be created and managed seamlessly [23]. In the NFV arena, the ONF has proposed a flexible NFV networking solution [24] for an NFV deployment of an OpenFlow-enabled SDN approach to deal with the dynamic provisioning of networking services. However, both initiatives do not cover the dynamic composition and orchestration aspects as this work does. Other related standardization efforts are in progress in the Broadband Forum (BBF) [25], which is working on how cloud-based technologies including NFV can be used in the implementation of the Multi Service Broadband Network. The TMForum (TMF) rolled out the Zero-touch Orchestration, Operations and Management (ZOOM) program to develop best practices and standards to deliver true business agility and new digital services and revenue opportunities in the area of virtualization, NFV, and SDN [26].
## 4 Experiments
### Results and Discussion  
The results of four evaluation measures on the multi-modality FLDL dataset are shown in Figure 6. The four evaluation measures for FLDL, listed in Table 4 are the distance measurements of the possibility distribution, and the smaller their value, the better the performance. \begin{table}
\begin{tabular}{c l l} \hline \hline  & **Name** & **Formula** \\ \hline
1 & Hamming distance & \(Dis(D,\bar{D})=\frac{1}{L}\sum\limits_{i=1}^{L}|d_{j}-\tilde{d}_{j}|\) \\
2 & Euclid distance & \(Dis(D,\bar{D})=\left(\frac{1}{L}\sum\limits_{i=1}^{L}\left(d_{j}-\tilde{d}_{j} \right)^{2}\right)^{\frac{1}{2}}\) \\
3 & Lambert distance & \(Dis(D,\bar{D})=\frac{1}{L}\sum\limits_{i=1}^{L}\frac{|d_{j}-\tilde{d}_{j}|}{d_ {i}+\tilde{d}_{j}}\) \\
4 & Fuzzy absolute distance & \(Dis(D,\bar{D})=\frac{\frac{1}{L}|d_{j}-\tilde{d}_{j}|}{\sum\limits_{i=1}^{L} \left(d_{j}+\tilde{d}_{j}\right)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation measures of LDL. The results indicate that FFRS-w exhibited better performance than all other algorithms. FFRS-u and FFRS-l exhibited the worst performances of all four evaluation measures. The results were consistent with the previous analysis. FFRS-u and FFRS-l may lead to overestimation and underestimation. FFRS-m and AA-kNN were similar; they avoided the estimation problems by averaging the outputs. However, the average method can easily ignore the important information included in the data. Therefore, FFRS-w exhibited the best performance. The mean and standard deviation of results are shown from Tables 5-8. The best results are highlighted in boldface. In order to evaluate the performance of each algorithm on all datasets, the average ranks of each algorithm are listed in the bottom row. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Name** & **Formula** \\ \hline
1 & Cheb & \(Dis(D,\bar{D})=\max\limits_{1\leq j\leq|}|d_{l}-\bar{d}_{l}|\) \\
2 & Clark & \(Dis(D,\bar{D})=\sqrt{\sum\limits_{l=1}^{L}\frac{(d_{l}-\bar{d}_{l})^{2}}{(d_{l }+\bar{d}_{l})^{2}}}\) \\
3 & Cosine & \(Sim(D,\bar{D})=\frac{\sum\limits_{l=1}^{L}d_{l}}{\sqrt{\sum\limits_{l=1}^{L}d_{ l}}^{2}\sqrt{\sum\limits_{l=1}^{L}\bar{d}_{l}}}\) \\
4 & Intersection & \(Sim(D,\bar{D})=\sum\limits_{l=1}^{L}\min(d_{l},\bar{d}_{l})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation measures of FLDL. Figure 6: Results of four evaluation measures on CHEAD dataset. The results demonstrate that the proposed FFRS-w algorithm exhibited the best performance. AA-kNN exhibited better performance than all other algorithms, while FFRS
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Loss Function  
The log-cosh function (see Equation (8)) was used as an error function in solving this type of regression problem. Its advantage over the typically used L2 loss function is its robustness against noise and outliers in the training data. With L2, it would be easier to become stuck in a local minimum. In comparison with another popular error function L1, which is robust to noise in the data, the log-cosh error function is smooth, which is better for the smoothness of the training process [28]. The log-cosh error function has proven to be a robust error function for the task of solar irradiance prediction.  
\[f(t,\ a)=\frac{1}{a}\log(\cosh(at))=\frac{1}{a}\log\left(\frac{e^{at}+e^{-at} }{2}\right) \tag{8}\]