esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamentals of Terahertz Communication
### The Terahertz Spectrum
#### 2.1.3 Higher Data Rates
The changing needs of modern applications can be met using the high bandwidth, shorter wavelength, and ultrafast data rates provided by THz communication systems. Nevertheless, new investigations and developments are required to eliminate the above limitations and harness the capability of THz technologies in next-generation networks [37; 45]. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Studies** & **Technology** & **Data Rates (Gbps)** & **Applications** & **Key Challenges** \\ \hline \multirow{2}{*}{[21]} & Microwave Communication & 10–100 & IoT, mobile networks & Limited spectrum \\ \cline{2-5}  & Optical Communication & 100–400 & Datacenters, fiber- & \multirow{2}{*}{High cost of deployment} \\  & & & optic networks & \\ \hline \multirow{2}{*}{[38]} & THz Communication & 1000–10,000 & 6G networks, inter-satellite links & Atmospheric attenuation \\ \cline{2-5}  & Photonic-Assisted Systems & 1000+ & High-speed backhauling & Limited scalability \\ \hline \multirow{2}{*}{[40]} & Nanofabricated THz Systems & 4000+ & Real-time analytics, AR/VR & Complex integration \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of higher data rates across technologies.
## 2 Materials and Methods
### Mathematical Models
#### 2.2.4 Evaporation Tower Modeling  
The evaporation tower in the desalination process separates the fresh water from the saline water. During the extraction process, the air is allowed to flow at the lower part of the tower, and its temperatureat that point is \(T_{gl,ev}\left(t\right)\) and its humidity is \(W_{g1,ev}\left(t\right)\). Besides, the air has a mass velocity of \(m_{g,ev}\left(t\right)\). When the air exits the tower, its temperature is \(T_{g2,ev}\left(t\right)\), while its moisture is \(W_{g2,ev}\left(t\right)\). Moreover, there is hot water that flows from the collector. The water is adjusted to temperature \(T_{l2,ev}\left(t\right)\). Figure 7 shows how the tower operates as a multivariate system.  
Figure 6: The input/output block diagram of the humidifier.  
Similarly, the evaporation tower works on the following assumptions:  
The system does not transfer heat from one mass to the other;  
The flows of the air and water are in a counter-current and a one-dimensional space;  
The heat of the water remains invariant throughout the process;  
The flow equation shows energy transfer from the air to the water.  
The following system of equations represents the flow of fluids that takes place in the evaporation tower.  
Water stage  
\[\frac{\partial T_{Lev}}{\partial t}=\frac{m_{l}}{M_{l,ev}}\frac{\partial T_{Lev }}{\partial z}-\frac{h_{l}a_{ev}}{M_{l,ev}\mathbb{C}_{l}}(T_{i,ev}-T_{l,ev}) \tag{9}\]  
Air phase  
\[\frac{\partial T_{g,ev}}{\partial t}=-\frac{m_{g,ev}}{M_{g,ev}}\frac{\partial T _{g,ev}}{\partial z}-\frac{h_{g}a_{ev}}{M_{g,ev}\mathbb{C}_{g,ev}}\Big{(}T_{g, ev}-T_{i,ev}\Big{)} \tag{10}\]  
Air-water interphase  
\[h_{l}a_{ev}(T_{i,ev}-T_{l,ev})=h_{g}a_{ev}\Big{(}T_{i,ev}-T_{g,ev}\Big{)}+ \lambda_{\phi}K_{m}a_{ev}\Big{(}W_{i,ev}-W_{g,ev}\Big{)} \tag{11}\]  
and  
\[\frac{\partial W_{g,ev}}{\partial t}=-\frac{m_{g,ev}}{M_{g,ev}}\frac{\partial W _{g,ev}}{\partial z}+\frac{K_{m}a_{ev}}{M_{g,ev}}\Big{(}W_{i,ev}-W_{g,ev}\Big{)} \tag{12}\]
## 4 Results  
### Datasets Used  
In this section, two datasets have been used. On each of those datasets, we used the same data processing architecture as explained in Section 2.  
Figure 5: Convex hull of actives sensors at \(n=0\).  
Figure 6: Evolution of the MZS each n execution.  
**DS1:** The first dataset is an open access dataset made by _[PERSON] et al._, _2017_[24]. This dataset is composed of BLE RSSI values emitted by 13 iBeacons and the actual position of the user. As this dataset has a ground truth, it allows us to compute the accuracy value of our method. **DS1** has a \"grid\" placement. Table 1 is a description of the dataset. The number of instances is the number of RSSI values for all sensors.  
The limited number of instances from this dataset forces us to use a time window size of 15 s. Indeed, to validate our approach we need at least 3 sensors returning an RSSI value between the interval of time. This leaves us with 8 min and 33 s of data to process which is limited but, the ground truth is a precious data, especially from an open access dataset.  
**DS2:** The second dataset is made of data collected during the museum experimentation. This experimentation of indoor positioning held within the museum of natural history of La Rochelle. As museums are usually closed due to the pandemic, the experimentation took place during a visit by students from a local high school.  
Figure 7 describe sensor placement within the museum. We equipped the largest room in the building, and we get positional data when visitors were within this room. The experiment began at ten in the morning and ended at noon. During those two hours, subjects of the experimentation visited the museum freely. We do not have ground truth, as this is not a dataset made and controlled by our team in our office, but \"on the field\", and following visitors could have changed their behavior. Finally, the three trajectories of visitors where build based on 82,630 Bluetooth total footprints collected and process with the methodology explains earlier, except we only consider intervals of time with a mean RSSI value of at least \(-90\) dB as explained in Section 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Sensors** & **Number of Instances** & **Placement** & **Duration** & **Frames** \\ \hline
13 & 1420 & Grid & 8m33 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of **DS1**.  
Figure 7: Placement of sensors within the museum.  
Table 2 show the number of sensors used, the total number of instances, the type of placements and the number of subjects. Table 3 show a description of three subjects from our experimentation. We should note that the number of instances is not an indicator for the time spent in the studied rooms as show tag number 3, where from 24,414 instances, we only got 3 min of exploitable data (3 sensors sending an RSSI value greater than \(-90\) in the same time window).
## 2 Background
### Reference Architecture for Digitalization  
ISO/IEC/IEEE 42010:2022 [39], titled \"_Software, systems and enterprise_--_Architecture description_\", defines architecture as \"the fundamental structure of a system, or more specifically, the structure of system components, their relationships, and the principles and guidelines that govern their design and evolution over time\". Architecture provides a framework for the interactions of system components to reach specific goals.  
Figure 1: Input–Process–Output diagram of a digital maintenance framework as in [13].  
Reference architectures, as common bases for multiple applications, play an important role in generalizing the application of concepts and elements. They provide a foundation that can be adapted and interpreted into concrete architectures for specific system designs. In the context of asset digitization, establishing clear methods for usage and definition in relation to new technologies is crucial. Standardization becomes a key component to ensure the recognizability, interchangeability, and efficient operability of digital assets within digital environments, encompassing various platforms like IoT infrastructures and edge-cloud computational paradigms.  
A comprehensive review of reference architectures, as presented in [10], emphasizes the comparison of layered architectures, particularly RAMI 4.0 and IIRA (Figure 2), against classical pyramid-style architectures like ANSI/ISA-95 or 5C. Layered architectures, gaining dominance in many applications, foster interoperability, communication between layers, and flexibility. This approach aligns with the advancements in digital twin technology.  
RAMI 4.0 and IIRA are now taken as foundational references, given their broad acceptance and influence in both academic and industrial contexts. Both frameworks employ a layered approach to structure systems, promoting modularity, adaptability, and incremental upgrades. Each architecture offers a distinct perspective, but they share fundamental principles and methodologies to address the challenges of industrial digitization.  
RAMI 4.0 uses a dual-axis framework that integrates lifecycle considerations with hierarchical system structures, offering a comprehensive view. In contrast, IIRA adopts a more streamlined approach, emphasizing simplicity. A significant feature of RAMI 4.0 is the introduction of the Asset Administration Shell (AAS), which serves as a cornerstone for driving digital transformation, as detailed in the section on digital twins. Additionally, RAMI 4.0 explicitly highlights the importance of managing the lifecycle of assets and systems, a critical factor in the digitization process.
## 4 Conclusions and Outlook  
A new configuration of discharge chamber and power source was presented utilizing high voltage leakage transformers for discharge current limitation to avoid arcing. The electrical operation of discharges between metal electrodes and a liquid was discussed. It was found that the conductivity of the liquid influenced the discharge current and voltage at breakdown. The kind of liquid influenced the optical appearance of the discharge. Sodium emissions were present in the spectra recorded with saline solution but were almost absent for distilled water and tap water. Except for tap water, the treated liquids became acidic and the conductivity increased. In all liquids, distinct nitrification was observed. The microbiological studies showed that physiological saline solution and tap water became antimicrobial, which is especially noteworthy for the case of tap water. Despite the lack of acidification, an inactivation of the tested microorganisms _E. coli_ and _S. aureus_ was determined.  
In order to obtain more detailed insight in the discharge process itself, additional investigations are needed. To clarify the origin of the antimicrobial properties of tap water, measurements regarding the production of reactive oxygen and nitrogen species (ROS/RNS) and an evaluation of the influence of sodium bicarbonate are necessary.  
With the presented discharge configuration, significant volumes of plasma-treated water can be easily produced in less than 30 min. Because the device is portable, the water can be treated on-site. The properties of the treated water are controllable by setting the treatment time. The antimicrobial effect of the plasma-treated water can be adjusted by varying the exposure time. Thus, the discharge configuration is an appropriate experimental setup for the investigation of practical applications of plasma-treated water. Besides its use as a disinfecting agent, application as a fertilizer with additional disinfecting properties in agriculture is also conceivable. Additionally, the use of the plasma device for cleaning of contaminated water should be considered.  
Conceptualization, M.S.; Formal analysis, M.S., B.A. and I.C.G.; Funding acquisition, K.-D.W. and T.v.W.; Investigation, M.S., V.H., B.A. and T.G.; Methodology, M.S., V.H., B.A. and T.G.; Project administration, M.S., V.H. and T.G.; Resources, M.S., V.H. and T.G.; Supervision, K.-D.W. and T.v.W. Writing--original draft, M.S., V.H., B.A. and I.C.G.; Writing--review & editing, M.S., V.H., T.G., K.-D.W. and T.v.W.  
This work was funded by the German Ministry of Education and Research (BMBF, grant 13N13960) and by the Ministry of Education, Science and Culture of the State of Mecklenburg-Vorpommern (grant: AU 15 001), which is gratefully acknowledged.  
The authors like to thank [PERSON], [PERSON], [PERSON], and [PERSON] for fruitful discussions and laboratory assistance.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Clustering Algorithm for Applying the Path-based Clustering Model
#### 3.3.2 Clustering Process  
The cluster status of this protocol can be divided into three categories: (1) unclustered node, (2) cluster member node, and (3) cluster head node.  
Figure 5 shows this in a state machine diagram. Each state and condition that changes with each other is as follows.  
First, all nodes are in the unclustered node state after initialization. The cluster head periodically broadcasts a head announcement message instead of a discovery message. When the unclustered node receives this information, it first confirms that it has collected all the neighbor's path information. Path similarity is measured using the path information of the announced heads in the state where path information exists. After measuring the similarity of the neighboring clusters through the path data, the unclustered node requests to join the specific cluster with the best path distance. If the confirmation is answered, the unclustered node enters the cluster member state.  
When becoming a cluster member, a node proves its connectivity by responding to the cluster head announcement periodically requested by the discovery answer. However, if an announcement is not received more than three times from the participating cluster head, we return to the unclustered node and perform discovery. In addition, when the path distance value to the cluster head of the neighbor cluster to which the cluster does not belong is better, the cluster head sends a join request message to the cluster head through the cluster assignment message. When a confirmation is received, it becomes a member of the cluster, just like an unclustered node.  
However, if there are no nodes in the vicinity, the node will appoint itself as cluster head and implement cluster head announcement. In this state, if three neighboring nodes have not responded and after three timeouts, they become unclustered nodes again and perform discovery. When the node becomes a cluster head, its ID is used as a cluster ID. The neighboring nodes can confirm that the cluster head is the cluster head by confirming that the cluster ID is the same as the node ID.  
If the node responding to the cluster head announcement message is an unclustered node, it requests the path data and performs the cluster head announcement again.  
In contrast, if the node responding to the cluster head announcement message is another cluster head node and the cluster size of the other party is large, the path distance is checked. If the path distance is within the threshold, it returns to the unclustered node and performs discovery again.  
Each node has a neighbor table as shown in Figure 6, as well as exchanges path information at the first contact. This causes overhead, but since the path information does not change, the exchange can be performed only once. Therefore, in the long term, the ratio of total traffic overhead is small compared to the overhead due to frequent connectivity verification.  
Figure 5: State Diagram of the PCDP Node.
## 2 Materials and Methods
### Random Forest  
The Random Forest method is a non-parametric supervised learning method that belongs to the Bagging type of ensemble learning. By combining multiple weak classifiers, the final result is averaged by voting or taking the mean, which makes the result of the overall model have high accuracy and generalization performance [27]. Generally, there is no overfitting on noisy data. It can also obtain better results, has better classification accuracy, and does not produce overfitting problems. So, the random forest is chosen as the classification model, often applied in traffic accident injury studies to rank the importance of accident severity risk factors. When using a dataset to train a model, the database is first divided into a training dataset (70%) and a test dataset (30%) using a stratified sampling approach, which reduces errors due to sample distribution and ensures that the imbalance rate is consistent between the training and test datasets. Random Forest generates a new training set \((N_{t})\) by repeatedly randomly selecting \(T\) samples from the original set \(N\) by a self-service resampling technique with put-back. Subsequently, \(T\) new training sets and corresponding decision trees are used to form the random forest model. The random forest involves many parameters, the most important of which are as follows:  
1. n_estimators: This is the number of decision trees in the random forest, i.e., the number of base evaluators. Theoretically, more trees create better model, but it is also more likely for models with more trees to encounter problems such as model overfitting and long model computation time. Hence, a reasonable number of decision trees will often achieve good results;
# Quality of Experience in 6G Networks: Outlook and Challenges
## 3 Enabling Technologies of Sixth Generation Networks
### Other Considerations  
Establishing whether 6G will achieve true cognition and/or self-awareness at the network level is still at the early stages. It is more reasonable to expect considerable advances in network operation autonomy to be deployed by 2030. There are also aspects of intelligence and learning that continue to merit scrutiny, such as replicability and explainability. However, given the steadfast development in realizing autonomous systems, project advances in these areas over the next decade can be expected.  
The notion of \"self-aware networking\" has been put forth as part of the more sophisticated expectations from IBN [60]. Realizing self-awareness will depend on two key achievements: a substantial simplification of autonomic operation requirements and a closer understanding of the achievable gains from self-awareness.  
Finally, a crucial aspect that was not discussed in the development above is security. Given the expansive reach of 5G networks and their evolutions, there were numerous significant security enhancements made, which were presented in ref. [62]. Towards 6G, it is expected that blockchain deployments will become more common, especially in services. Meanwhile, continuous advances in quantum computing seem to indicate industrialized instances' viability by 2030, especially in encryption. Notwithstanding this, the increasingly individualized nature of 6G services necessitate the realization of effective security and privacy measures.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 7 Virtual Resource Allocation Algorithms
### Discussion  
The softwarization of network functions with NFV and their live migration thanks to the technological enablers such as SDN and MEC makes it possible to dynamically allocate computation and storage resources. Physical layer abstraction is required for both optical and radio resources of the converged network to achieve joint optimization of physical and virtual network resources at the NFV orchestrator level. Achieving the abstraction of all physical resources is a step towards achieving end-to-end network slicing and translating high-level O&M goals to technical parameters in beyond-5G networks, as seen in the efforts, such as [28,97].  
As seen from Table 6, the addition of the infrastructure provider to the increasing number of the external stakeholders in 5G networks is also taken into account in several works. In addition to the abstraction of all physical and virtual resources of the network, the common abstraction at the network management framework is also required between these stakeholders. A well-known abstraction for the design and implementation of intelligent stakeholders in a distributed fashion is the use of intelligent agents [6]. The collaboration, cooperation, and negotiation of multiple agents to achieve a common goal creates a multi-agent system, which is an ideal candidate to solve complex resource management problems between multiple stakeholders in a distributed fashion. As seen from Table 6, current multiple stakeholder algorithms do not provide solutions that are feasible for a large solution space [91], and complexity grows with the increasing number of users and BSs [47], therefore implementing these solutions require multi-agent architectures that can optimize local network sub-graphs.
# Blind Channel Estimation Method Using CNN-Based Resource Grouping
## 3 Proposed Method
### Data Set Generation
Furthermore, it was confirmed that the amount of data required to ensure the performance of K-means-based channel estimation must also be considered. The input data for the CNN is generated based on simulations. The two-step CNN training process is conducted using the final optimal patterns derived from exhaustive search, characterized by the frequency domain parameter \(f\) and the time domain parameter \(t\). Thus, each step involves distinct input data and correct labels. For instance, during training in the frequency domain, the optimal patterns are classified based on the number of subcarriers \(f\) within the patterns. The grouped patterns are then defined with \(f\) as the correct label. The input data is generated based on the channel conditions associated with the patterns sharing the same correct label. Single or multiple patterns with the defined correct label \(f\) have associated channel conditions, such as delay spread, velocity, and SNR. These channel conditions are used to generate a tapped-delay line (TDL)-D channel. The channel generation process is repeated \(N\) times, and for each iteration, the channel conditions are randomly selected from the patterns with the same \(f\), following a discrete uniform distribution. The transmitted signal \(x_{k,l}\) passes through the generated TDL-D channel, and noise is added to produce \(y_{k,l}\), as expressed in Equation (1). The received \(N\) signals are processed as input data for the CNN by separating the real and imaginary components and adjusting the size to \(1\times 1680\times 2\times N\). The values are normalized between \(-1\) and \(1\). In this study, \(N\) is set to 100,000. The generated dataset of 100,000 samples is divided into training, validation, and test datasets in proportions of 0.8, 0.1, and 0.1, respectively. The training dataset serves as input data for the CNN.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis
### Simulation Setting
#### 5.1.3 Performance Metrics  
The unknown class rejection performance can be characterized by using the overall accuracy of the unknown class detection on a combined dataset of known class and unknown class samples. However, it is unknown in the testing phase how many samples from the unknown classes are in the real world, so it is not appropriate to compare the unknown class rejection performance of the methods using a (or set of) threshold.  
AUROC [26], as a metric to measure the performance of a binary classification model, can be interpreted as the probability that a positive sample is assigned a higher score than a negative sample, which is a threshold-independent metric. Therefore, we use AUROC as the metric for the unknown class rejection performances of all methods.  
An OSR method should not sacrifice the accuracy of closed-set classification while obtaining a better rejection performance of the unknown classes. Therefore, classification accuracy is used to evaluate the multiclassification performance of known classes. All experimental results in the following are averaged over six random experiments.