esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Current Status of Foreign Research  
#### 2.5.1 Bi-Encoder-Based Word Sense Disambiguation (WSD)  
An article proposes a dual-encoder (Bi-encoder)-based model for word sense disambiguation (WSD) [12]. The model employs two independent BERT encoders: one encodes the target word and its context (context encoder), and the other encodes the word sense definition in WordNet (gloss encoder). The two encoders generate representations in a jointly optimized embedding space, and disambiguate by calculating the similarity (dot product) between the target word context embedding and the gloss embedding of each lexical meaning, selecting the closest lexical meaning. The model optimizes cross-entropy loss through end-to-end training and effectively utilizes the semantic information of the glosses to significantly improve the disambiguation of low-frequency lexical senses (LFSs) and zero-sample lexical senses, achieving SOTA on several English WSD datasets, and in particular, reducing the error rate of low-frequency lexical senses by 31.1%.
## 2 Datasets and Pre-Processing Steps
### Surface Environmental Parameters Related to CO\({}_{2}\) Uptake and Release  
Surface parameters that we explored included land surface skin temperature (temp), scPDSI, BA, and GPP. We obtained the temperature data from NASA [64], which uses a standard physical retrieval that is combination of data from the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) [65]. We used the level 3 monthly product with spatial resolution of 1 \(\times\) 1 degrees from January 2009 to September 2016 for AMSU limitation later. The drought severity index, scPDSI, used for quantifying long-term drought conditions, are calculated with time series of temperature, together with fixed parameters related to the soil/surface characteristics at each location, which aims to make results from different climate regimes more comparable [54]. This index was available as monthly 0.5 degree from January 2009 to December 2016. Monthly BA, from Global Fire Emissions Database, Version 4.0 (GFED4), had a spatial resolution of 0.25 degree from January 2009 to December 2016. The product combined Moderate Resolution Imaging Spectroradiometer (MODIS) burned area maps with active fire data from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and the Along-Track Scanning Radiometer (ATSR) family of sensors [55]. GPP, a parameter related to carbon uptake by vegetation photosynthesis, was obtained from MODIS. Monthly GPP was produced with the data of time resolution of 8 days [56; 57]. GPP with spatial resolution of 1 km data from 2009 to 2015 was collected from [66], and data of 2016 are calculated with data accumulation directly. In addition, The Land Cover Type Climate Modeling Grid (CMG) product (MCD12C1) from MODIS [67] in 2009 was used for land cover classification over detected results.  
Quality control was performed on all datasets according to their respective user guides and resampled to 1 \(\times\) 1\({}^{\circ}\) degree spatial resolution to match GM-XCO\({}_{2}\) using grid averaging. A mean seasonal cycle was calculated for each time series similar to GM-XCO\({}_{2}\) in order to identify anomalies in time and space.
# Protection of Personal Data in the Context of E-Commerce
## 4 Online Store in Croatia
### The Importance of GDPR for the Growth of Online Consumption  
A significant concern when buying from online stores is the possible exposure of private data. Although customers frequently voice privacy concerns when shopping online, their behavior often contradicts these worries, which is called the privacy paradox.  
Figure 2: Many conveniences of online shopping for customers.  
Marketing firms have developed a high level of proficiency in collecting and analyzing consumer data and closely monitoring user activity to obtain valuable insights into purchasing patterns. While the ethical use of this data can be helpful, there are concerns about potential misuse and privacy violations due to the combination of aggressive advertising and the sale of visitor data.  
Authorities have implemented stricter legislative frameworks, like the General Data Protection Regulation (GDPR) in the European Union, in response to privacy concerns. The GDPR has strengthened security measures to ensure that merchants do not retain payment data if users withdraw consent. This regulation is being implemented as part of a larger initiative to minimize potential negative impacts on consumers and rebuild confidence in online transactions. Nevertheless, tension persists between consumers and e-commerce platforms, as personal data collection is necessary to facilitate transactions.  
Consumers need to be aware of the potential risks of online shopping, including identity theft. Caution should be exercised when sharing personal information. Creating a secure environment that fosters trust is crucial for organizations, as they must acknowledge and cater to the preferences and needs of online consumers. Implementing stricter regulations is anticipated to be vital in restoring consumer confidence in online shopping security. This, in turn, is expected to foster the expansion of e-commerce and favorably impact consumer behavior. This paper aims to investigate enhanced privacy laws' influence on online commerce's growth and their implications for consumer behavior.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 2 Communication Network Architecture for Smart Metering  
In the Smart Metering environment, a communication network architecture could be represented by a hierarchical architecture classified by data rate and coverage. This hierarchical architecture for Smart Metering comprises (see Figure 1):  
* Neighbourhood area network (NAN) or field area network (FAN). These networks provide communication from end devices (e.g., Smart Meters) to data concentrators located at medium voltage/low voltage (MV/LV) transformer substations.
* Wide area network (WAN). This network provides communication from the utility head end out to data concentrators located at MV/LV transformer substations.  
Optical communication, cellular and WiMAX are commonly used as a communication medium between LV transformer substations and utility head end due to their high capacity and low latency.  
Power line communication [11; 12] and radio mesh [13] are widely used for communication from end devices (e.g., Smart Meters) to data concentrators located at MV/LV transformer substations due to low cost and simplicity.  
Figure 2 shows a typical communication network architecture, which is considered for pilots, future deployments, or national roll-out in the Czech Republic. There is no doubt that the Smart Metering access networks (NAN, FAN) will be deployed mainly by PLC and for remote rural Smart Meters via wireless communications. A BB-PLC solution could be deployed in both MV and LV segments [11; 12]. A comprehensive review of PLC standards can be found in previous studies [11; 13].  
Figure 1: Smart Metering communication network architecture with several technologies.
## 2 Background
### Driving Technologies of Industry 4.0
#### 2.2.5 Cybersecurity  
Cybersecurity refers to the protection and security of data and information collected through digital technologies, ensuring that they are not tampered with or leaked. It is considered one of the main challenges for the implementation of Industry 4.0 [64; 66]. It involves protecting data from systems that can be intercepted or hacked [97]. The more connected and integrated a system is, the greater the vulnerability, requiring greater data security [64].
## 4 OWS Q-Learning Algorithm
### Q-Learning Algorithm  
The Q-learning algorithm was first proposed by [PERSON] et al. in 1989 as a method for solving reinforcement learning tasks [54], using a state-action value function \(Q(s,a)\). The general idea of the algorithm is that the agent keeps trying in an unknown environment. In the process of trying, the agent constantly adjusts the strategy based on the feedback obtained, and finally generates a better strategy. The update process of the Q-learning algorithm's value function \(Q(s,a)\) is as follows:  
\[Q(s,a)\gets Q(s,a)+\alpha\left[R+\gamma\underset{a^{\prime}}{max}Q(s^{ \prime},a^{\prime})-Q(s,a)\right] \tag{14}\]  
where \(Q(s,a)\) indicates that the agent's state at \(t\) moment is \(s_{t}\), the sum of the optimal reward discount obtained after executing the action \(a_{t}\). \(\alpha\) is the learning rate, which represents the impact of the current training sample on the current learning. \(\gamma\) is the discount factor [55], which reflects the proportion of the value of future rewards at the current moment.  
The updates of the value function \(Q(s,a)\) in the Q-learning algorithm are as follows: under the state \(S\), the agent uses the \(\varepsilon-greedy\) strategy to select the action \(a\), executes the action \(a\), obtains the immediate reward \(r\), and the agent transfers to the next state \(S^{\prime}\), at which time the agent selects the \(a\) that makes the largest \(Q(s^{\prime},a)\) as the next action \(a^{\prime}\) to update the value function (12). \(a^{\prime}\) only participates in the update of the value function and does not really execute. The action performed in the next state \(S^{\prime}\) is re-selected using the \(\varepsilon-greedy\) strategy after the value function is updated. The agent from the beginning to the endpoint is called an episode. The pseudocode for the Q-learning Algorithm 1 is as follows:  
```
Input: state set \(S\), action set \(A\), reward function \(R\) Output: optimal state-action value function \(Q_{s}(s,a)\), from the optimal state-action value function to obtain the optimal strategy \(\pi_{*}(S)\)  Process:
1. Initialize the Q table and have a Q value of 0 for all state-action pairs
2. \(\pi\): derived from Q
3. If strategy \(\pi\) is not optimal
4. For all states \(s\in S,a\in A\)
5. Update the state-action value function \(Q(s,a)\): \[Q(s,a)\gets Q(s,a)+\alpha\left[R+\gamma\underset{a^{\prime}}{max}Q(s^{ \prime},a^{\prime})-Q(s,a)\right]\]
6. If the strategy is optimal at this point, end the calculation, otherwise skip back to step four
```  
**Algorithm 1** Q-learning algorithm
## 5 Evaluation
### Discussion of Additional Cost  
LI-RAID [5] and e-SAP [12] are the most similar to our proposed Differentiated Protection architecture. Therefore, the overhead of the proposed method is similar to these two methods. LI-RAID composes multiple pages with different error rates into one stripe. In this process, program interference may occur between pages. LI-RAID leaves one wordline blank to mitigate the impact of program interference. These blank pages incur a small additional storage overhead (0.8% of total capacity). In the case of e-SAP, partial stripe parity overhead is added, but the parity write overhead is greatly reduced compared to the traditional RAID policy since stripes are dynamically constructed in a log-write fashion. Since these two methods are similar to the general RAID structure, other than that, the operation dependent on the method is not added in FTL and GC operations. Therefore, the proposed policy should incur overhead similar to these methods. The Differentiated Protection policy does not apply the same strength of data protection to all flash memory pages. Protection strength can be flexibly applied according to the user's request. Figure 8 shows that the difference in error rates with or without parity overhead is not distinct. WARM [6] is similar to the hot/cold-aware data placement policy. WARM dealing with retention errors minimizes additional overhead by eliminating redundant refresh operations for write-hot data. As with WARM, our hot/cold-aware data placement policy also migrates data only when data movement is required such as garbage collection or scrubbing operation. Thus, we believe that our proposed policy causes an overhead similar to WARM.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 3 UWB Notch Antenna Design Approach
### Parametric Analysis of UWB Notch Antenna
#### 3.2.1 Effect of Upper Resonator-Width 't'  
To analyze the effects of width 't' on the S\({}_{11}\) characteristics, the variation in width 't' was simulated and analyzed. The S\({}_{11}\) characteristics are represented in Figure 7. When the width is 0.5 mm, the result is optimum.
###### Abstract  
Emerging technologies such as Software-Defined Networks (SDN) and Network Function Virtualization (NFV) promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA) technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.  
network function virtualization; software defined networking; service-oriented architecture; network service; service chaining; virtual network function +
Footnote †: journal:
## 5 Future Perspectives
### Innnersive Training and Learning  
Immersive learning refers to the process by which individuals engage in an immersive learning experience within a physical or virtual interactive learning environment. The creation of such an environment plays a crucial role in facilitating the immersive learning experience and typically involves simulation, cognition, and association [76]. Additionally, the design of immersive learning emphasizes the principles of realism, achievement, and presence [77]. As the metaverse emerges as a new stage in the development of immersive technology, it integrates various technologies such as 5G, VR/AR/MR/XR, big data, blockchain, artificial intelligence, and digital twin. This integration empowers the high-quality development of immersive learning and, to some extent, resolves challenges related to immersion, presence, interactivity, and responsiveness. Furthermore, the metaverse provides a robust network for 5G and AR, supporting a virtual-real overlay experience and offering a more effective avenue for immersive learning to unfold.  
Drawing upon metaverse theory, we have devised a model diagram for immersive training learning (refer to Figure 7), which can be employed in future scenarios such as staff training (e.g., TBM driver operation training and on-site accident hazard demonstration) and the promotion of safety knowledge. The combination of 5G and AR overcomes the technical barriers associated with AR. The 5G + AR foundation layer (red layer) encompasses low latency, precise positioning, and high quality, providing fundamental technical support for immersive learning while enhancing the overall immersive learning experience. The 5G + AR transformational layer (yellow layer) incorporates mobile ubiquity, sensory internet, multimodal interaction, and personalized learning analytics with 5G technology, bringing about further advancements in the development of immersive learning. Guided by metaverse theory, immersive learning driven by 5G and AR is characterized by realism, presence, interactivity, and adaptability, forming the characteristic layer (blue layer) of immersive learning. At the elemental level (pink layer), immersive learning involves learners, 5G and AR technologies, mobile learning media, immersive learning environments, and enriched learning resources.  
With the ongoing development and maturation of the metaverse and its applications, immersive learning can be redefined, and metaverse-based immersive learning is poised to become a new industry in the \"Internet+\" era. As two pivotal technologies within the metaverse, 5G and AR synergistically enhance connectivity and expand the possibilities for user engagement, thereby creating numerous opportunities for immersive learning.
# Potential of Room Acoustic Solver with Plane-Wave Enriched Finite Element Method
## 2 Brief Preliminaries for Room Acoustic Simulations Using PUFEM
### Plane-Wave Enriched Finite Elements in 2D Analysis  
In FEM, sound pressure at an arbitrary point \((x,y)\) within an element \(\Omega_{\text{e}}\) is approximated by product of shape functions \(N_{i}(\xi,\eta)\) and nodal values of sound pressure \(p_{i}\)  
\[p(x,y)=\sum_{i=1}^{n}N_{i}(\xi,\eta)p_{i}, \tag{4}\]  
where \(\xi\) and \(\eta\) are the local coordinate system. In plane-wave enriched finite elements, the plane wave, which is the general solution of Helmholtz equation, is incorporated into shape functions via the partition of unity property [33; 34]. Nodal values of sound pressure \(p_{i}\) are approximated with the superposition of plane waves propagating in various directions as  
\[p_{i}=\sum_{l=1}^{q}A_{i}^{l}e^{jk(x\cos\theta_{l}+y\sin\theta_{l})}, \tag{5}\]  
where \(q\) stands for the number of plane waves, \(\theta_{l}\) denotes the angles of plane waves in polar coordinate systems, and \(A_{i}^{l}\) expresses the amplitude of plane wave propagating in a direction \(\theta_{l}\). As presented in this report, \(\theta_{l}\) are set as evenly distributed angles \(\theta_{l}=2\pi l/q\). Substituting Equation (5) into Equation (4) engenders a plane wave enriched approximation of sound pressure at an arbitrary point within an element as  
\[p(x,y)=\sum_{i=1}^{n}\sum_{l=1}^{q}N_{i}(\xi,\eta)e^{jk(x\cos\theta_{l}+y\sin \theta_{l})}A_{i}^{l}. \tag{6}\]  
Figure 1: Interior sound field \(\Omega\) where \(\Gamma_{0}\), \(\Gamma_{\text{V}}\) and \(\Gamma_{Z}\) respectively represent a rigid boundary, a vibration boundary, and an impedance boundary. In addition, \(\mathbf{n}\) is the outward normal.  
As presented in Equation (6), the plane wave enrichment is applicable to any finite element with a different shape function. By defining a new shape function \(P\) as the product of the shape function and the plane wave with unit amplitude, Equation (6) can be expressed as shown below:  
\[p(x,y)=\sum_{i=1}^{n}\sum_{l=1}^{q}P_{(i-1)q+l}A_{i}^{l} \tag{7}\]  
The equation above is used for PUFEM discretization of the weak form of Equation (3).
## 2 Materials and Methods
### 3D Virtual Reconstruction of the Power Corridors
#### 2.5.2 3D Reconstruction of the Power Corridors  
We complete all power corridors' description data following the above steps. For each power corridor, these description data should comprise all towers' geo-located 3D positions, heading angles, and 3D virtual models, identified with their filename and hanging points. Based on these data or parameters, we can easily load and render the power towers in the Cesium virtual globe. The power lines between two adjacent towers (i.e., over a span) have the shape of a catenary curve and thus can be modeled by fitting catenary curves to the measured hanging points [50]. As presented in Algorithm 1, Cesium-based rendering and visualization methods were used for the 3D reconstruction of power corridors. Figure 7d shows the partially reconstructed results of one power corridor.  
```
1:\(n\_pc\): the number of power corridors, \(n\_tower\): the number of towers in a power corridor, \(n\_TowerType\): The number of tower types, model data: \(M=[n\_type,url]\): description data of a power corridor: \(T=[n\_ModelType,(lat, lon, alt),(heading, pitch, roll)]\); begin
2:for\(i\) in \(n\_pc\)
3:for\(j\) in \(n\_tower\)
4:for\(k\) in \(n\_towertype\)
5:if\(T[j].nModelType=M[k]n\_type\)
6: Add a 3D model (\(url[k]\), \(T[j].(lon,lat, alt)\), \(T[j].(heading,pitch=0, roll=0)\));
7:endif
8:endfor
9:// Draw powerlines
10:Get hanging points of the adjacent towers (\(T[j].(lon,lat, alt)\), \(T[j+1].(lon,lat, alt)\));
11:Calculate the catenary curve;
12:Render powerlines between the two adjacent towers;
13:endfor
14:endfor end
```  
**Algorithm 1**3D reconstruction (rendering) of power corridors in Cesium virtual globe
# Performance of Differential-Phase OFDM Systems over Selective Multipath Fading Channels
## 6 Numerical Results and Conclusion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Guard Period (ns)** & **Frame Duration \(T_{f}\)**\((\mu\)s) & \((T_{g}/T_{f})\) \% & **Average SIR per Frame (dB)** \\ \hline
200 & 3.4 & 5.88\% & 19.00 \\
400 & 3.6 & 11.11\% & 26.29 \\
600 & 3.8 & 15.79\% & 36.75 \\
800 & 4.0 & 20\% & No Interference \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average signal-to-interference ratio Vs. the guard period. in Figure 5a,b. The probability of error is also calculated for different values of \(M\)-ary sizes for 64 subcarriers and 400 ns guard period duration as in Figure 6.  
In this paper, the performance of differential-phase OFDM has been investigated in the presence of AWGN and the multipath fading channel with ISI and ICI. A closed form formula for the average signal to noise/interference ratio has been derived. The system probability of error performance is also studied for the differential-phase OFDM under different conditions and parameters. The IEEE 802.11a standard parameters are used in the simulation along with a decaying exponential power delay  
Figure 5: OFDM–QDPSK system with (**a**) \(N=64\) and different guard period durations and severity parameter (\(m\)) values (**b**) \(T_{g}=400\) ns and \(BW=20\) MHz with different numbers of subcarriers and severity parameter (\(m\)) values. Figure 6: OFDM–MDPSK system with \(N=64\), \(T_{g}=400\) ns and different M-ary sizes. profile channel. The results indicate that at a low severity Nakagami parameter, the guard period plays an imperative role in improving the system performance when compared to higher severity parameters. The system performance can be significantly improved by increasing the number of subchannels from 8 to 64. The results show that a limited improvement can be added to the system when the number of subchannels is increased to 256 or 1024. This also justifies the use of 64 subchannels in the IEEE 802.11a standard since the system complexity will significantly drop when the number of subchannels is decreased from 1024 to 64 with limited degradation in the system performance occurring. This work could be expanded in the future with other types of noises to complete the study of differential phase OFDM. The system could also be studied by implementing diversity reception such as equal gain combing (EGC) and maximal ratio combining (MRC) methods. Another featured addition to this work could be studying the differential phase OFDM-CDMA system with direct sequence (DS) and frequency hopping (FH) [22; 23; 24].