esa-sceva/satcom-chunk-collection huggingface dataset:
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 3 Conclusions  
This review has predominantly focused on the utilization of titanium dioxide (TiO\({}_{2}\)) in the dual capacity of hydrogen (H\({}_{2}\)) production and H\({}_{2}\) storage. Concerning the aspect of H\({}_{2}\) production, extensive efforts have been dedicated to enhancing the photocatalytic efficiency of TiO\({}_{2}\) under both visible and solar light, all while abstaining from the utilization of precious metals. In the realm of solar-driven H\({}_{2}\) production, TiO\({}_{2}\) has frequently undergone modifications involving non-precious metals and metal oxides. Glycerol has consistently featured as the sacrificial agent in the majority of the documented research endeavors. In the context of visible-light-driven H\({}_{2}\) production, the strategies have commonly entailed the amalgamation of TiO\({}_{2}\) with MoS\({}_{2}\) complemented by the incorporation of a photosensitizer, and the fusion of TiO\({}_{2}\) with CdS. Turning our attention to the subject of H\({}_{2}\) storage, this review has unequivocally demonstrated the catalytic prowess of TiO\({}_{2}\) in augmenting the H\({}_{2}\) storage performance of magnesium hydride (MgH\({}_{2}\)). A myriad of strategies has been employed, encompassing the synthesis of TiO\({}_{2}\) nanosheets, flower-like TiO\({}_{2}\) structures, TiO\({}_{2}\) nanorods, TiO\({}_{2}\) nanoparticles, TiO\({}_{2}\)/Ti\({}_{3}\)C\({}_{2}\)Tx heterostructures, graphene-supported TiO\({}_{2}\) nanoparticles, carbon-enveloped nanocrystalline TiO\({}_{2}\), Co/TiO\({}_{2}\) nanocomposites, and Ni@TiO\({}_{2}\) core-shell structures. The integration of MgH\({}_{2}\) with TiO\({}_{2}\)-based catalysts has yielded notable enhancements in H\({}_{2}\) absorption and desorption kinetics, leading to lower operating temperatures for H\({}_{2}\) desorption and absorption processes, especially when compared to pure MgH\({}_{2}\)
# A Local Feature Engineering Strategy to Improve Network Anomaly Detection +
## 4 Formal Notation and Problem Formulation
### Problem Formulation  
On the basis of the previous formal notation, by denoting as \(\lambda\) the classification process performed by the proposed LFE strategy and as \(Evaluate(\hat{e},\lambda)\) an evaluation function that returns \(1\) in case of correct classification or \(0\) otherwise, we can formalize our goal in terms of maximization of the sum \(\Omega\) of its results, as shown in Equation (1):  
\[\max_{0\leq\Omega\subseteq|\hat{E}|}\Omega=\sum_{z=1}^{|\hat{E}|}\text{Evaluate }(\hat{e}_{z},\lambda) \tag{1}\]  
In agreement with most of the literature in this area, we consider the intrusion detection in terms of a binary classification task, where the two mutually exclusive classes of destination for each analyzed  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Notation** & **Description** & **Note** \\ \hline \(E=\{e_{1},e_{2},\ldots,e_{X}\}\) & _Set of classified events_ & \\ \(E^{+}=\{e^{+}_{1},e^{+}_{2},\ldots,e^{+}_{Y}\}\) & _Subset of normal events_ & \(E^{+}\subseteq E\) \\ \(E^{-}=\{e_{1},e^{-}_{2},\ldots,e^{-}_{W}\}\) & _Subset of intrusion events_ & \(E^{-}\subseteq E\) \\ \(\hat{E}=\{\hat{e}_{1},e_{2},\ldots,\hat{e}_{Z}\}\) & _Set of unclassified events_ & \\ \hline \(F=\{f_{1},f_{2},\ldots,f_{N}\}\) & _Set of event features_ & \\ \(C=\{normal,intrusion\}\) & _Set of event classifications_ & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Formal notation.  
\begin{table}
\begin{tabular}{l c|c c} \hline \hline  & \multicolumn{3}{c}{**Predicted**} \\ \hline \hline  & \multicolumn{2}{c}{_Normal_} & _Intrusion_ \\ \hline
**Actual** & _Normal_ & **TP** & **FN** \\ \cline{2-4}  & _Intrusion_ & **FP** & **TN** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
event are _normal_ or _intrusion_, according to the set of event classifications previously formalized in Table 4.
## Appendix A Gradient: Divergence, and Curl in Cylindrical Coordinates  
### The Nabla Operator \(\
abla\)  
The _gradient_, the _divergence_, and the _curl_ are first-order kinematic operators acting on fields. The conventional way to express them is via a vector called _nabla_, whose components are partial derivatives with respect to certain coordinates system. For instance, in Cartesian coordinates, the _nabla_ operator \(\
abla\) is defined as:  
\[\
abla=\hat{t}\frac{\partial}{\partial x}+\hat{j}\frac{\partial}{\partial y}+ \hat{k}\frac{\partial}{\partial z} \tag{1}\]  
Given that the _nabla_ operator is a vector, we can conduct some vector operations, such as dot product and cross product, which leads to the concept of divergence and curl in Cartesian coordinates, as shown in Equations (1) and (2):  
\[\
abla\cdot U=\frac{\partial U_{x}}{\partial x}+\frac{\partial U_{y}}{ \partial y}+\frac{\partial U_{z}}{\partial z} \tag{2}\]  
\[\
abla\times U=\hat{t}(\frac{\partial U_{z}}{\partial y}-\frac{\partial U_{y} }{\partial z})+\hat{j}(\frac{\partial U_{x}}{\partial z}-\frac{\partial U_{ z}}{\partial x})+\hat{k}(\frac{\partial U_{y}}{\partial x}-\frac{\partial U_{x}}{ \partial y}) \tag{3}\]
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 2 Background Knowledge
### Edwards-Curve Digital Signature Algorithm  
The EdDSA is based on the Edwards curves developed by [PERSON] et al. The security of Ed25519 and Curve25519 are based on ECDLP. Ed25519 has three stages. In the key-generation stage, a public key is generated from a secret key and the based point of Curve25519. In the signature-generation stage, a pair of signatures is produced from the private key and the hash of the input message. The signature is then attached with the message and sent to the receiver. Finally, the received signature and the public key by the receiver are used to verify the message in the verification stage. Algorithm 2 reviews EdDSA in detail.  
```
1:Key-generation
2:Prime \(p\), base point \(G\), private key \(k\)
3:Public key \(Q\)
4:Compute \((hk,lk)=Hash(k)\) where \(hk\) is 32 bytes MSB and \(lk\) is 32 bytes LSB.
5:Assign \(a=hk\) as little-endian notation
6:Compute \(Q=a*G\) over the prime field \(F_{p}\)
7:Signature-generation
8:Prime \(p\) and \(q\), base point \(G\), private key \(k\), public key \(Q\), and message \(m\)
9:Signature \((R,s)\) for the message \(m\)
10:Compute \((hk,lk)=Hash(k)\) where \(hk\) is 32 bytes MSB and \(lk\) is 32 bytes LSB.
11:Compute \(r=Hash(lk,m)\) mod \(q\)
12:Compute \(R=r*G\) over the prime field \(F_{p}\)
13:Compute \(h=Hash(R,Q,m)\) mod \(q\)
14:Compute \(s=(r+h*a)\) mod \(q\)
15:Verification
16:Prime \(p\) and \(q\), base point \(G\), signature \((R,s)\) of the input message \(m\), public key \(Q\)
17:The input message \(m\) is valid or invalid
18:Compute \(h=Hash(R,Q,m)\) mod \(q\)
19:Compute \(hQ=R+h*Q\) over the prime field \(F_{p}\)
20:Compute \(sG=s*G\) over the prime field \(F_{p}\)
21:if\(hQ=sG\)then
22:State message \(m\) is valid
23:else
24:State message \(m\) is invalid
25:endif
```  
**Algorithm 2** Edwards-curve digital signature algorithm (EdDSA).
## 3 Related Work
### Data Security
For these reasons, there are trends away from 'what you know' (e.g., password-based approaches) or 'what you have' (token-based approaches) authentication toward 'what you are' authentication [134]. In attribute-based approaches, an entity is authenticated based on certain properties it has in the current situation. Especially in dynamic and rapidly changing environments, such a context-based description is advantageous [135]. Due to the high flexibility of attribute-based approaches, they are particularly suitable for IoT applications [136] or cloud-based applications [137]. In addition to users, however, it must also be ensured that data are authentic, in terms of, they come from the specified sources and have not been falsified [138]. For digital media, such as image, sound, or video data, digital watermarking can be used for this purpose. That is, an identification tag is inseparably burned into the carrier medium. If it can be ensured that no unauthorized third parties have access to the identification tag, authenticity can be verified by the presence of the digital watermark [139]. Similar techniques can also be applied to data in relational databases. Here, individual marker bits are inserted into the payload data [140]. While the origin of the data can be proven in this way, watermarking approaches inevitably contaminate the actual data with the inserted identification tags. Digital signatures represent a noise-free approach to ensure the authenticity of data [141]. Here, methods of asymmetric cryptography are used. While asymmetric cryptography uses the public key of a recipient for encryption--thereby ensuring that the message can only be decrypted with the corresponding private key, i.e., only by the recipient--the sender uses his or her own private key for signing. Since the sender's public key is freely available, anyone can decrypt the message. However, this verifies that the sender has used the corresponding private key, which proves the origin of the data beyond doubt [142]. In the IoT, attribute-based signatures are suitable. Here, the attributes of a data source are stored in the signature (e.g., technical specifications of a sensor) and the receiver can verify whether these attributes are sufficient for the data to be authentic (e.g., does the sender have the capabilities to capture the data in the requiredquality) [143]. Yet, attribute-based signatures are computationally expensive, which is a particular problem in the context of lightweight IoT devices. Thus, research approaches aim to outsource part of the heavy workload to the cloud [144]. Other approaches deal with the problem that the attributes in the signature might contain sensitive information. To this end, a trusted intermediary is installed that preverifies the signatures and removes the sensitive attributes from it [145]. For areas like social media, where such an authentication of sources is not possible, the authenticity of data can be verified based on their content [146]. Then, fake news can either be blocked [147] or overridden by authentic data [148].
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### HetNet Congestion Indicator
#### 3.2.1 Cell Selection Scheme  
When a UE has more than one BS to join, it selects the one with least HCI. The key idea of HCI is to estimate the congestion level of a base station based on the competitive online algorithm proposed in [27]. Specifically, the HCI cell selection scheme consists of two steps:  
Figure 4: Macro BS adopts threshold-based RB allocation before applying the max-min scheduling.  
1. A UE continuously measures the SINR among neighboring cells. If the SINR of current serving BS becomes weaker and some neighboring BSs become stronger, then the UE enters the cell selection procedure (step 2).
2. The UE selects a list of candidate BSs which have higher SINRs. For each BS, the UE estimates and reports its Channel Quality Indicator (CQI) to the BS. By taking account of the QoS requirement of the UE and the status of RB usage, the BS replies to the UE with its HCI which is calculated as follows. Assume the maximum number of RBs per time slot (system capacity) is \(C\), number of RBs which have been used (allocated) is \(R_{i}\), the number of RBs required by the UE (based on its CQI and QoS requirement) is \(b\), the HCI is given by: \[HCI(b)=cost(R_{i}+b,\ C)-cost(R_{i},C)\] (12) where the \(cost()\) function is defined as follows. \[cost(R_{i},C)=\mu^{(\frac{R_{i}}{C}-1)}\] (13) where \(\mu\) is a constant parameter which is usually set to \(C\) (_in our case, it is set to 50_).  
After obtaining the HCI of each candidate BS, it selects the BS with the least HCI to join. The HCI function is meant to estimate the congestion level or traffic load of a BS.
# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## 2 Background
### Markov Decision Process (MDP)  
Reinforcement learning centers around the sequential decision-making process in the interaction between an agent (as the subject) and the environment (as the object). In a specific environment, there exists an agent capable of perceiving the current environmental information and generating actions based on this information. These actions lead to changes in the observed environmental information for the agent and, simultaneously, a reward signal is provided to the agent when the environmental state undergoes a change. This process continues until the agent ceases its actions or the environment reaches a terminal state. The objective of reinforcement learning is to train the agent's action policy, enabling it to attain the maximum cumulative reward during interaction with the environment [13]. MDP is a classical model for intelligent decision-making. Its core theory posits that if the future state of a state is independent of its past state, i.e., it depends solely on the present state, then this state possesses the Markov property. We define the information involved in this process as follows:  
* \(S\): a finite set of states, where \(s_{i}\) represents the state at step \(i\).
* \(A\): a finite set of actions, where \(a_{i}\) represents the action at step \(i\).
* \(P_{s,a}\): the probability of state transition, which denotes the probability of transitioning to other states given the action \(a\) in the current state \(s\).
* \(R\): the immediate or expected reward obtained from the state transition.
* \(\gamma\): the discount factor that adjusts the influence of future rewards on the current state.  
Thus, MDP can be represented as a quintuple:  
\[M=(S,A,P_{s,a},R,\gamma) \tag{1}\]  
Due to the existence of the reward value \(R\), the action \(a\) chosen by the agent is no longer random, but is aimed at obtaining the maximum cumulative reward. The state transition in this system is determined by both the current state \(s_{t}\) and the action taken \(a_{t}\), the next state \(s_{t+1}\) is determined by the transition probability model \(P(s_{t+1}|s_{t},a_{t})\), and the reward value \(r_{t}\) obtained at the current time is also determined by the probability distribution \(P(r_{t}|s_{t},a_{t})\).
## 6 Performance Evaluation
### Simulation Environment
It is pertinent to mention here that we considered various studies for acquiring the parameters like \(E_{A}\), \(\varepsilon_{fs}\), \(\varepsilon_{mp}\) and energy consumed by CHs during data fusion [19; 39]. The performance of our proposed scheme is evaluated by comparing it with various routing protocols like SEP-E [34], DEEC-E [37], EBCS [39] and TEEN [38] based on the performance metrics like stability period, half-life time, average residual energy, network lifetime and PDR. * Stability period--can be defined as the time difference between two specific points. The first point is when the simulation starts and the second point is when first sensor node dies. * Half-life time--can be defined as the time duration from start of the network simulation until the time when 50% of the sensor nodes have no residual energy left to continue their data delivery tasks. * Energy Consumption--can be defined as the total energy consumed in the network during data transmission, reception, sensing, status transitions (\(CM\to CH\)) and aggregation functions. * Network Lifetime--this parameter is used to demonstrate the complete lifetime of sensor nodes per communication round for varying network size in terms of stacked group bar chart. * Packet Delivery Ratio (PDR)--defined as the ratio of total number of successfully received data packets at BS corresponding to total number of data packets generated in the network by all sensor nodes.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 2 Proposed REC Model
### Constrains: Cash Flow Evaluation  
The investment is equal to:  
\[Inv=\sum_{j\in G}P_{j}\cdot Capex_{j}+ESS_{cap}\cdot Capex_{ESS} \tag{24}\]  
For each year \(y\) the net cash flow is evaluated as:  
\[CF(y)=Sav(y)+Rev(y)+Fin_{inc}(y)-Cost(y) \tag{25}\]  
The net present value of the investment is then calculated as:  
\[NPV=-Inv+\sum_{y=1}^{EL}\frac{CF(y)}{(1+DR)^{y}} \tag{26}\]The savings for each year \(y\) are the sum of the instantaneous savings due to self-consumption (SC) of each member \(i\) and the saving due to shared energy (SE) in the considered year.  
\[Sav(y)=\sum_{t\in y}\biggl{(}\sum_{i\in M}\left(E_{i}^{selfcons}(t)\cdot(PE^{ \epsilon-market}(t,y)+SCsi)\right)+E_{cc}^{shared}(t)\cdot(PE^{\epsilon-market }(t,y)+SEsi)\biggr{)} \tag{27}\]  
The revenues at year \(y\) come from the energy sold to the market in each time step or from the incentive.  
\[Rev(y)=\sum_{t\in y}\biggl{(}E_{cc}^{\epsilon-market}(t)\cdot PE^{\epsilon- market}(t,y)+E_{cc}^{shared}(t)\cdot Inc\biggr{)} \tag{28}\]  
The market price profiles used in Equations (27) and (28) are evaluated for the entire lifetime depending on the expected growth or decrease.  
\[PE^{\epsilon-market}(t,y)=\hat{PE}^{\epsilon-market}(t)\cdot(1+(y-1)\cdot price _{trend}) \tag{29}\]  
\[PE^{\epsilon-market}(t,y)=\hat{PE}^{\epsilon-market}(t)\cdot(1+(y-1)\cdot price _{trend}) \tag{30}\]  
In some particular cases of the Italian context there is the possibility to deduct a part of the investments in photovoltaic power plants from the taxes of the years following the investment. This financial incentive can be treated as a yearly revenue from year 1 to \(year_{ded}\) and can be evaluated as:  
\[Fin_{inc}(y)=\begin{cases}\frac{\frac{\gamma_{old}\cdot Inv}{year_{ded}}}{ \text{if }1\leq y\leq year_{ded}}\\ 0\qquad\qquad\text{if }y>year_{ded}\end{cases} \tag{31}\]  
The total cost at year \(y\) is:  
\[Cost(y)=\sum_{j\in G}\bigl{(}P_{j}\cdot Opex_{j}\bigr{)}+ESS_{cap}\cdot Opex_{ ESS}+Rep_{ESS}(y)+EC_{cost} \tag{32}\]  
where the replacement cost of the battery is:  
\[Cost_{ESSrep}(y)=\frac{\sum_{t}E_{cc}^{\epsilon-ESS}(t,y)\cdot\eta_{CH}+\frac{ E_{cc}^{\epsilon-ESS}(t,y)}{\eta_{DIS}}}{2\cdot Cycles_{max}\cdot(1-SOC_{min})} \cdot Rep_{ESS} \tag{33}\]
# Enhancing Conversational Agent Development Through a Semi-Automatization Development Proposal
## 5 Application Example
### Context  
The Universidad Autonoma de Sinaloa (UAS) offers advanced academic training in the field of Information Sciences through its Postgraduate Program in Information Sciences (PIS). This program encompasses Master's and Doctorate degrees, with two distinct areas of specialization, Computing and Systems and Geomatics and Geodesy. The program holds official recognition within the National Postgraduate System of the National Secretary of Science, Humanities, Technology and Innovation (SECIHTI) in Mexico, highlighting its academic rigor and alignment with national standards for graduate education.  
Although the Postgraduate Program in Information Sciences (PIS) conducts annual admissions with promotional activities, interest in the program extends beyond these periods, necessitating continuous access to information. The current dissemination of information through Facebook and the program website often proves insufficient to address specific user queries and navigation challenges. To address this, a 24/7 accessible conversational agent is proposed to enhance the user experience and support prospective students throughout the year.  
In response to the identified challenges, this case study focuses on the design, development, implementation, and evaluation of a conversational agent tailored to the needs of the PIS. This research encompasses three key objectives: (1) developing and deploying a WhatsApp-based chatbot leveraging the Xatkit framework and the proposed MDD methodology to ensure efficient creation and adaptability; (2) conducting a comprehensive user satisfaction survey to evaluate the chatbot's effectiveness, focusing on user preferencesand experiences within the WhatsApp instant messaging context, specifically assessing its ability to provide accurate and relevant information; and (3) evaluating user satisfaction with the chatbot's GPT-3-generated suggestions in scenarios where predefined knowledge is insufficient, analyzing its capacity to provide contextually appropriate responses and maintain conversational flow.  
The findings are expected to provide information on the feasibility of deploying conversational agents on widely used platforms such as WhatsApp, the effectiveness of combining structured MDD methodologies with AI-driven functionalities to improve chatbot performance, and the overall impact on user satisfaction and institutional outreach. This research underscores the transformative potential of conversational agents in improving information accessibility, particularly where traditional communication channels fall short.
## 4 Results and Discussion  
### Analysis of the Response Characteristics of the Filling Valve  
Figures 6 and 7 show the results of comparison of the valve response time characteristics of the solenoid valves of M company and F company in designing the filling valves.  
The average response rates of the two models of M company ranged from 12 ms to 16 ms, while the average response rate of the solenoid valves of F company was 10 ms. In addition, the differences of the response rates of the solenoid valves were 3 ms for M company and 1 ms for F company, indicating that the valves had a good response rate and speed difference.  
As for the response rate of the filling valve, the maximum rate differences of the solenoid valves of M company ranged from 14 ms to 16 ms, which is significant. However, the result was 10-11 ms for F company, indicating that the products of this company performed better in terms of the differences.  
Figure 5: Schematic diagram for measuring the response time.  
Figure 6: M Company’s valve response characteristics.