esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The phenomenon of CPP-ACP dissolution, could also be correlated to the different percentage of roughness increase of G1 (26%) and G2 (76%) at T3: assuming that, thanks to the different remineralizing action of Elm-EP and GC-TM, a \"mixed layer\" nestling CPP-ACP clusters could form onto G2 specimen, in case of an acid attack, not only the CPP-ACP clusters on the surface would be removed, but also the ones included in the \"mixed layer\", increasing the surface roughness. Even though the tandem use of Elm-EP and GC-TM gave indeed promising results, the present study evidenced a poor stabilization of the CPP-ACP clusters onto the specimen surface in the considered conditions. To justify this trend, chemical and mechanical interaction between the active components of Elm-EP and GC-TM should be deeper studied. However, it is possible to speculate about some explanations: for instance, it has been previously reported that for obtaining optimal remineralization and protection results, CPP should bind to salivary pellicle and plaque bacteria [27]. It is then possible, that the formation of a thin film of Elm-EP on the specimen surfaces would have limited the bonding process, decreasing the cluster stability to the acid attack. Moreover, the inclusion of the CPP-ACP nanocomplexes in the formation of the assumed \"mixed layer\" could be insufficient for their stability in comparison to the presence of the biologically complex matrix typical of an in vivo environment. The complete dissolution of CPP-ACP nanocomplexes and the consequent limitation of the GC-TM protection efficiency, seem to be in disagreement with some results reported in literature, where the use of CPP-ACP-containing systems showed a clear effect against enamel demineralization in vitro [28; 29]. However, it is important to keep in mind that data discrepancies among studies can be caused by the use of different experimental protocols, for instance, the use of bovine teeth model instead of human specimens. Even though bovine and human enamels possess many similarities, such as chemical compositions and physicochemical properties, the different arrangements of prisms and interprisms might result in some different outcomes [30]. Moreover, bovine enamel is more porous than human enamel, probably because it consists of larger crystals [31], and these morphological aspects may be correlated to the demineralization of bovine enamel which progresses about three times faster than that of human enamel. Therefore, the performance of the materials in human enamel may be different from the results of our study.
## 5 Results and Discussion  
This study performed simulations for a typical household, located in Rafsanjan, Iran. The hourly insolation and ambient temperature data profiles obtained for a year (8760 h) are shown in Figure 4. The speed of wind and user's load data profiles during a year are represented in Figure 5 [41]. Since the RESs were locally placed near electrical consumption, therefore no electrical losses were caused due to the electricity distribution. MATLAB R2016a software was used with a processor of 2.9 GHz Intel Core i7 with 8 GB of installed memory to implement the proposed algorithms to obtain the simulation results.  
Figure 3: EESA composed of TLBO and SSA.  
Depending on the system's reliability, which was considered via various \(LPS^{max}\) values, components like PV modules, WTs, and battery units were sized to supply the user's load. This paper considers three different scenarios with different configurations of RESs by incorporating reliability at various \(LPS^{max}\) values of the user's choice. These scenarios are given below:  
1. PV-WT-battery: (\(S=[s_{1}\;\;s_{2}\;\;s_{3}]\)),
2. PV-battery: (\(S=[s_{1}\;\;0\;\;s_{3}]\)), and
3. WT-battery: (\(S=[0\;\;s_{2}\;\;s_{3}]\)).  
The three scenarios were optimally sized in terms of their configurations based on the TAC, reliability, and other constraints.
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 2 Materials and Methods
### Initial Exchange  
The primary purpose of the initial exchange is to establish a common understanding of the firmware update status between the base station and receiving device. The base station will initiate the firmware update by sending a short command and the device will respond with the device ID, battery capacity and health status, and current firmware version. The  
Figure 2: The functional diagram of the hardware required for the firmware update process.  
device will also transmit the status of any new firmware that has been received and how much of the data has been transferred into the device. This information is relevant when a partial firmware update has occurred, to avoid re-transferring the same firmware image. The status of the device firmware update could be stored in the base station, but that would prevent the firmware from being loaded by multiple base stations, which is a capability that we want to maintain due to cattle mobility.  
If it is determined that a new firmware update is required, the base station will send a new firmware version number, the starting memory address to load the application code, and the cyclic redundancy check (CRC) of the entire firmware image. The starting address is used by the boot loader to instruct it on where to load the code into memory, and the CRC is used to verify that a valid firmware image has been received. If it is determined that an existing firmware update needs to continue, the base station will inform the device and continue from the point of interruption. These communications are signed using Advanced Encryption Standard-Cipher-based Message Authentication Code (AES-CMAC). The details of the packet structures used in the message exchange between the base station and receiving device are given in Figure 3.
# Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space
## 2 Related Work
### Representation Learning of Entity Descriptions  
Most knowledge representation learning models only use triple structured information, and do not make effective use of other information of entities, such as entity descriptions, image descriptions. We treat the knowledge as auxiliary information for knowledge graph completion, which can effectively discover new triples in knowledge graph.  
The knowledge representation ability of the entity is improved by combining with entity descriptions. The DKRL model [22] uses the description of entities and achieves better results than TransE [4] in link prediction. On its basis, [PERSON] et al. [35] propose the Jointly model, which used NBOW, LSTM, and Att-LSTM three text encoding ways to encode entity descriptions, and LSTM encoding method achieves good results in link prediction. Moreover, [PERSON] et al. [36] propose the model SSP that a semantic space projection by jointly learning from triple structured information and entity descriptions. Analogous to the TransH, the EHP proposed by [PERSON] et al. [24] projects the entity structured representation onto semantic hyperplane of the entity descriptions.  
Specifically, most models that encoding the description of entities choose the way of neural network. Recently, [PERSON] et al. [37] also propose three encoding methods, CNN, Bi-LSTM, and Transformer, for entity descriptions, which are combined with the TransE method to improve the experimental results. [PERSON] et al. [38] use Bi-LSTM to encode entity descriptions, and guide the representation of entity descriptions and entity structured representation interactively. [PERSON] et al. [39] think that the latent semantic information of entity descriptions obtained by CNN and LSTM is limited and propose a new method that incorporates the entity description and network structure. [PERSON] et al. [23] encode entity descriptions through Transformer and attention, and utilize capsule network to capture global semantic features between entities and relations, and achieve good results in link prediction. Considering that previous models rarely pay attention to the global semantics of text information, [PERSON] et al. [25] use text graph neural network to preserve it. However, these models are all performed in Euclidean space. Entity descriptions are rarely introduced into hyperbolic space.  
To sum up, we improve the representation ability of the entity by integrating entity descriptions into the representation of knowledge graph, and represent the embeddings of the entity in hyperbolic space. Based on the research of [PERSON] et al. [22] and [PERSON] et al. [18], a new method of knowledge graph completion is proposed that combines the description of entities with the Poincare ball model of hyperbolic space.
## 5 The HE-BIA Maturity Model v2.0
### The Technology Part
#### 5.1.3 Technical Foundations  
The Technical foundations category groups the underlying technical backend factors that make the deployment of frontend data products possible. These factors are directly relevant to the production of data products. Three maturity dimensions are grouped here. Architecture aims to capture the practices of how to store and organize the central data repository (with less potential problems, less maintenance, and more flexibility to accommodate emerging needs) to best support BIA frontend activities (such as interactive dashboards and ad hoc analytical needs) across the whole organization.  
Technical integration captures the best practices of integrating source data into a central repository. Traditionally, ETL (Extract, Transform, Load) processes are used to integrate data  
Figure 3: The HE-BIA maturity model v.2.0: maturity dimensions and levels.  
into the data warehouse [43]. Recently, development on sourcing unstructured and real-time data has led to the development of data lakes, where ELT (Extract, Load, Transform) is often used to integrate the source data [44].  
The last dimension in this category is IT infrastructure. This dimension encapsulates the required university's information technology infrastructure capabilities to support the development of BIA products. While IT infrastructure is typically not considered a motivation factor for BIA development, nonetheless, we have decided to include it in the maturity model as a hygiene factor. The rationale is that if this element is absent or insufficient, it hampers the development of BIA solutions. For example, lack of possibility or flexibility in accessing the needed infrastructure for testing and deployment of new BIA products have a tendency to either slow down the projects or entail workarounds (e.g., BIA team seeking public cloud infrastructure services instead).
## 3 Network Model
### Sensitivity Area  
As stated before, a sensor covers a circumference of radius \(r_{s}\) and area \(\pi r_{s}^{2}\). Therefore, at time \(t\), the WSN sensitivity area is calculated as the union of the areas of the active sensors at time \(t\) with a path to the sink node, \(s_{s}(t)\). The intersection calculation is a known complex problem, where computational effort increases exponentially with the number of circles [50]. As a usual approach to approximate this calculation, the set of binary demand points \(\tilde{D}_{p}(t)\) is uniformly distributed on the surface [51; 52; 53]. Then, the number of demand points \(\tilde{d}_{p}(t)\) with an active sensor at a distance lower than \(r_{s}\) are counted. According to this approximation, the sensitivity area \(A(t)\) provided by a WSN at time \(t>0\) is given by  
\[A(t)=\frac{1}{\tilde{d}_{p}(t)}\sum_{p\in\tilde{D}_{p}(t)}a_{p}(t), \tag{5}\]  
where \(a_{p}(t)\) is the indicator function defined as  
\[a_{p}(t)=\left\{\begin{array}{ll}1&\text{if}\quad\exists i\in S_{s}(t):||p-i ||_{d}<r_{s}\\ 0&\text{otherwise}\end{array}\right., \tag{6}\]  
i.e., \(a_{p}(t)\) equals 1 if there is an active sensor \(i\) at a distance lower than \(r_{s}\) from the demand point \(p\).  
Within this approach, we consider that demand points follow a grid distribution with a distance between neighbouring points of \(d_{pn}\). Hence, the number of demand points at time \(t>0\) is  
\[\tilde{d}_{p}(t)=\frac{d_{x}\;d_{y}}{d_{pn}}. \tag{7}\]
# New Chaotic Dynamical System with a Conic-Shaped Equilibrium Located on the Plane Structure
## 2 Mathematical Methods, Discovered Model and Numerical Analysis
### New Mathematical Model of Chaotic Dynamical System  
By considering motivation and the numerical approach described above, the following set of three dimensionless differential equations has been discovered and positively tested for chaotic solution:  
\[\begin{array}{lcl}\frac{dx}{dt}&=&z\\ \frac{dy}{dt}&=&-z(a\cdot y+b\cdot x\cdot z)\\ \frac{dz}{dt}&=&x+y^{2}-r\end{array} \tag{1}\]  
where \(a\) = 12 \(b\) = 0.4 are internal system parameters and \(r\) = 1 represents the offset of hyperbolic equilibrium structure. The last equation directly defines the equilibrium object located on plane \(z\) = 0. Both parameters \(a\) and \(b\) can be used to observe different route to chaos scenarios and evolution of a typical strange attractor, while parameter \(r\) can be a fixed value. Dynamical system (1) is dissipative and volume element \(V_{0}\) is contracted by the flow into \(V_{0}\cdot\exp(-a\cdot z)\). That is, the rate of contraction is a function of state variable \(z\). Flow linearization given by the Jacobi matrix defined around equilibrium structure equals  
\[\mathbf{J}(\mathbf{x})=\left(\begin{array}{ccc}0&0&1\\ -b\cdot z^{2}&-a\cdot z&-a\cdot y-2b\cdot x\cdot z\\ 1&2\cdot y&0\end{array}\right) \tag{2}\]  
Due to this expression of Jacobi matrix, a corresponding characteristic equation is also the function of point on the hyperbolic-shaped equilibrium structure, namely  
\[\lambda^{3}+a\cdot z\cdot\lambda^{2}+\left(2a\cdot y^{2}+4b\cdot x\cdot y \cdot z-1\right)\lambda+2b\cdot y\cdot z^{2}-a\cdot z=0 \tag{3}\]  
Considering equilibrium plane and using curve parametrization, we can get the following simplified characteristic polynomial along with associated eigenvalues:  
\[\lambda\left(\lambda^{2}+2a\cdot y^{2}-1\right)=0\quad\Rightarrow\quad \lambda_{1}=0\quad\lambda_{2,3}=\pm\sqrt{1-2a\cdot y^{2}} \tag{4}\]  
Thus, one eigenvalue is always zero and the remaining two eigenvalues form a complex conjugated pure imaginary pair if \(y>1/\sqrt{(}2a)\) and two real eigenvalues with opposite signs otherwise. Dynamical flow near the equilibrium structure forms the most unexpected type of local geometry: center. Choice of the initial conditions close to equilibrium leads to the periodic oscillations. There is a coexistence of three (at least, as other attracting sets are not impossible but remain a mystery) types of solution: limit cycle, chaos and unbounded. Dynamical motion is invariant under the coordinate change \(y\leftrightarrow-y\) and \(z\leftrightarrow-z\).
###### Abstract
Through these efforts, the study aims to provide a robust and interpretable tool for predicting and managing the performance of Li-ion batteries in drones, contributing to safer and more efficient drone operations. The study will provide significant scientific and practical contributions, particularly in drone technology and battery management. Scientifically, it will advance predictive modeling by leveraging a CNN-RNN-based ensemble learning model to capture spatial and temporal features of Li-ion battery performance data. Integrating XAI techniques will offer deeper insights into the model's decision-making process, enhancing transparency and trust in AI-based predictions [12]. Extensive testing and validation will establish a benchmark for future research and applications in battery performance prediction, contributing to the broader fields of data science and artificial intelligence. The study will enhance drone safety by accurately predicting battery performance, preventing unexpected failures, and optimizing drone operations for improved flight efficiency and battery management. Real-time monitoring capabilities will enable timely interventions, maintain operational continuity, and reduce costs through better maintenance scheduling and battery replacement planning. Additionally, improved battery management will extend battery life, reducing environmental impact by minimizing electronic waste. The study's findings and methodologies will also promote technological advancement in drones and battery systems, fostering innovation and the development of more efficient and reliable drone technologies. Ultimately, the study aims to provide a robust and interpretable tool for predicting and managing the performance of Li-ion batteries in drones, contributing to safer, more efficient, and cost-effective drone operations. In this study, the decisions made by the algorithm were interpreted with PDP (Partial Dependence Plots), one of the XAI techniques. PDP is a technique used to visualize how models' predictions are related to certain features. It is used primarily to increase the explainability of complex models [13]. This technique visualizes the effect of certain features of the artificial intelligence model on the model's predictions. It helps to understand how the model responds to certain features. PDP examines a particular feature's effect on the model's predictions.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 2 Literature Review and Related Work  
Traffic between the source and destination of the entire network is not necessarily the same because many devices such as switches and routers may change the pattern and characteristics of the traffic. Using SDN controllers in the 5G network communication systems simplifies the data traffic challenges such as minimizing delays and latency. This simplification allows us to manage the data traffic through the appropriate measurements and optimizations. With an efficient optimization algorithm, a selected data traffic path between the controllers and switches minimizes delays [4]. Although SDN is employed to control the data path in 5G systems, emerging technologies such as 5G based on SDN enhance the controllability and the reliability of the data path when the delay exceeds the limit.  
According to [5], a Named Data Networking (NDN) innovation can be applied to efficiently handle content-based services in future 5G networks. All services depend on traffic management, which simplifies the services according to the users' preferences. Although NDN improves 5G networks, changing legacy networks will be a significant step for more flexibly and feasibly disseminating content. Regarding the implementation of NDN, the existing network infrastructure of the legacy systems needs to be modified according to the 5G requirements, such as energy efficiency. Although authors focused on efficient caching through stateful SDN in NDN [5], SDN will improve the management of the network traffic of vehicular communication and 5G-based transportation systems. These improvements minimize energy consumption.
## 3 Background
### CSI-Based Attack
#### 3.2.3 Requirements for CSI-Based Attack  
The implementation of CSI-based attack only needs a receiver for the attacker, and the receiver can be a network NIC or a laptop. All the devices are commercial off-the-self (COTS) devices; thus, it can be easily achieved and deployed by an aggrieved attacker. Because the attacker does not need to be near the user or obtain any displayed information on the screen, so the attacker can mingle in the crowd and look unsuspicious, as shown in Figure 1.  
After obtaining the CSI measurements, the input sensitive information can be recovered successfully by the attacker using the above techniques, as shown in Figure 2. However, a successful CSI-based attack needs the following several requirements, which can be equivalent to the following equation:  
CSI-based attack \(\Leftrightarrow\) (wireless transmitter, signal receiver, ICMP ring packets at a high rate from transmitter, communication channel between transmitter and receiver, Quality\({}_{CSI}\))  
The detailed information of the above equation can be interpreted as follows: there must be a wireless transmitter and a signal receiver. The wireless transmitter is used to transmit the wireless signal, and the signal receiver is used to receive the time series CSI measurements. In order to recover the input sensitive information, especially for those similar finger motions, the ICMP ring packets from the transmitter must be at a very high rate. In addition, the communication channel between the transmitter and the receiver must remain stable. If the communication channel between the transmitter and the receiver is disturbed and unstable, the receiver will not receive the complete ICMP ring packet, which will be unable to recover finger motions successfully. Quality\({}_{CSI}\) describes the quality of received time series CSI measurements. If the wireless signal is interfered with in the multi-path propagation process, it will be changed at the receiver end.
## 3 Research Framework of Internet of Emergency Services
### Computation Paradigms
#### 3.6.3 Challenges and Limitations of Using IoES for Coordination and Communication  
While IoES has the potential to greatly improve coordination and communication in emergency management and response, there are also several challenges and limitations that need to be addressed. One major challenge is interoperability, as different emergency services and systems may use different technologies and standards, making it difficult to exchange data and communicate effectively. This can result in delays and errors in the transmission and interpretation of information, which can be critical in emergency situations. Another challenge is privacy and security, as sensitive data is often shared and stored through IoES platforms. It is important to ensure that appropriate measures are in place to protect the confidentiality, integrity, and availability of this data, and to comply with relevant laws and regulations. There are also challenges related to the scalability and reliability of IoES infrastructure, as emergency situations can involve large volumes of data and users that can strain the system. It is important to design and deploy IoES solutions that can handle high volumes of traffic, while maintaining performance and availability. Finally, there are challenges related to human factors, such as training and awareness of IoES technologies among emergency responders and the public. It is important to ensure that users are familiar with the capabilities and limitations of IoES technologies, and that they have the necessary skills and knowledge to use them effectively in emergency situations.  
Addressing these challenges and limitations will require collaboration and coordination among stakeholders, including emergency services, technology providers, regulators, and the public. It will also require ongoing research and development to improve the design and implementation of IoES solutions, and to ensure that they meet the evolving needs of emergency management and response.
# Study of NVIS Channel for USN Protocol Definition in Antarctica
## 3 NVIS Channel Analysis  
As stated before, we performed two simultaneous tests between Argentina Cove and the Antarctic Base Juan Carlos I and between Rockie Glacier and the base. These two locations allow us to distinguish the groundwave from the skywave, since the two locations have a similar behavior in terms of ionospheric reflection.