esa-sceva/satcom-chunk-collection huggingface dataset:
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation
### Zero-Energy Interface  
In terms of energy efficiency, the ultimate 6G zero-energy vision can largely be achieved through a combination of efficient hardware design for on-device intelligence, ultra-low-power receiver architecture, enhanced energy harvesting techniques, and zero-energy communication schemes in the massive but scalable machine type communication (MTC) [116]. In this context, significant power reduction can be explored by considering energy consumption of the intelligent device ecosystem as a whole, instead of individual elements with individual antennas and transceivers. Secondly, with the booming energy harvested-based devices coming out, MTC network architecture needs to be advanced to accommodate the development with substantially different requirements. For example, it can complement ambient backscatter communications (AmBC), a promising technology that separates carriers needed to transfer power to the devices and allows backscatter communications. In AmBC, backscatter devices can communicate between one another by modulating and reflecting received RF signals from ambient sources such as waves from nearby RF towers [117]. Most recently, [118; 119] developed the complementary security and quality requirement aspects for such schemes for greener communication. The approach consumes significantly less power than the conventional transmitter by replacing the voltage-controlled oscillator and power amplifiers. However, this approach is still challenged with direct path interference, backscatter propagation, co-existence with legacy receivers, and the ambient signal of unknown fast-changing phase and amplitude.  
To complement the evolution of hardware for zero-energy, on-device intelligence and zero communication schemes are expected to be fully applied in 6G. The first refers to the application of ML and AI to create context- or application-aware energy management for efficient battery usage, smart compressed sensing, event-driven sensor interfaces, or adaptive communication protocols to optimize data transfer [120]. There is still a lot of ongoing concept development for zero communication schemes, but it can mainly be seen as quality requirement optimization via architecture or device design while considering the PHY to APP layers end-to-end (E2E) aspect, especially with the introduction of THz band, technologies such as CF-mMIMO and IRS, heterogeneous MTC scenarios, and divergent application requirements [121].
## 3 Results
### Dynamic Grasping
#### 3.3.2 Results of Dynamic Grasping  
The dynamic grasping process executed by our robotic system integrates a visual module and a KF + Low-Pass filter combination to ensure accuracy. The visual module first captures images of the oysters on the conveyor belt, after which the state of each oyster is determined by the object detection algorithm. The KF + Low-Pass filter method processes this information to refine the prediction of the optimal state of the oyster, including location and velocity. Using these refined data, the robot calculates the predicted grasping position t seconds ahead and executes the grasping action, placing the oyster into the designated box.  
As the conveyor belt has an infinitely variable speed, the belt speed is unknown. For comparison, three different belt speeds were tested. The belt speed increased from Level 1 to Level 3, and each group underwent 25 grasping tests. The results, shown in Table 3, reveal a progressive decline in success rates as the belt speed increased.  
According to the data in Table 3, the dynamic grabbing experiments were conducted in three sets of tests at different band speeds, each containing 25 grabbing operations. As the belt speed increases from Level 1 to Level 3, the success rate of grabbing shows a gradual decrease. Specifically, Group 1 consisted of 25 tests, of which 21 were successful grabs, with a success rate of 84%. Group 2 had only 19 successful grabs, although the sample size was the same, and the success rate dropped to 72%. Group 3 performed the worst with only 12 successful grabs, dropping the success rate to 53%. Instances of failed grasping were analyzed and several factors were identified that led to a reduced success rate, especially  
Figure 10: Filtering results of KF combined with the Low-Pass filter and Moving Average under three conveyor speed conditions: (**a**) high speed (12 s), (**b**) medium speed (15 s), and (**c**) low speed (18 s).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Belt Speed** & **Number of Samples** & **Number of Grabs** & **Average Success Rate** \\ \hline \multirow{3}{*}{1} & & 21 & \\  & & 20 & 84\% \\  & & 22 & \\  & & 19 & \\
2 & 25 & 18 & 72\% \\  & & 17 & \\  & & 12 & \\
3 & 25 & 13 & 53\% \\  & & 15 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of dynamic grasping.  
when the conveyor speed increased. In addition to the challenges encountered with static grasping, it was found that the grasp success rate decreased significantly at higher belt speeds.
# Implementing and Testing a U-Space System: Lessons Learnt
## 4 GCS Integration
### Inta  
INTA's aircrew was formed by a Pilot-in-Command (PiC), an external pilot, two payload operators (one of them also in charge of the U-space communications using the Web app), one visual observer, a platform engineer and the subject matter expert of the exercise (the final user). At a given moment, the PiC was managing up to three drones at the same time flying BVLOS, including communications with air traffic control, since the drones took off from the aerodrome Rozas Airborne Research Center (CIAR). However, they concluded that an improved integration would allow the PiC to also assume the U-space communication tasks. Examples of that improvement would be to automatically load the plan into the drone when the PiC accepts the route suggested by the Plan Processing service, or the automated loading in the drone of the commands when an instruction is _wilceed_ by the pilot. At the moment of the tests, such steps were only partially automated, requiring some coordination between the PiC and the person in charge of the U-space communications.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design  
In this study, we introduce a methodology for predicting stock movement utilizing SA on Twitter and StockTwits data. Exploiting APIs, we first extract tweets from Twitter and stock tweets from StockTwits. We then use methods such as TextBlob and VADER to apply SA to text. VADER is an SM data lexicon and a rule-based SA tool. It not only returns positive, neutral, or negative numbers, but also evaluates how positive or negative a sentiment is [34]. TextBlob, on the other hand, is a SA library that returns a sentiment rating for each tweet [35].  
Following that, financial information from Yahoo Finance was pulled. The stock price movements, the stock change variable, are then combined with the sentiment scores, and the SMP model is built using seven classifiers. To evaluate the suggested model, we divided the data into training and test sets. We appoint 80% for training the model and 20% for testing in accordance with the Pareto Principle [36]. The proposed approach for anticipating stock movement is displayed in Figure 1.
## 4 Mission S-Net
### S Band Isl System
#### 4.3.3 Antenna System  
Two circular polarized directional planar antennas are used for down- and up-link. Five additional planar antennas with a beam pattern of approximately \(\pm 30^{\circ}\) (pattern shown in Figure 12; nominal gain 6 dbi), one mounted on each non-nadir surface, are responsible for the ISL. Limited onboard resources prohibit activating all ISL antennas simultaneously. Instead, an active antenna switch unit is connected to all ISL antennas. The line-of-sight antenna pair is selected by a scan algorithm, where the caller and responder antennas are switched consecutively until a match is detected. Alternatively, if position and attitude information of both caller and responder are available, the line-of-sight antenna pair can be analytically estimated by orbit propagation, thus shortening the build-up delay. Once a session is established on the physical layer (OSI layer 1), attitude control must be used to keep the line-of-sight stable within the antenna field-of-view. Figure 13 illustrates the antenna concept for the ISL and also the UHF TM/TC channel. The corresponding link budgets for 10, 65, 148, and 400 km ISL distances are summarized in Table 10.  
Figure 12: Horizontal (phi = 0) and vertical (phi = 90) antenna pattern of the ISL and downlink antenna.  
Figure 13: Antenna characteristics for UHF and Sband communications.
## 3 Results and Discussion
### Electrochemical Behavior of Hydroxylamine on the PPy NTs/GSPE  
Since the selected analyte oxidation depends on the pH, the influence of the pH on the hydroxylamine oxidation was determined for the optimization of the operational conditions to reach the highest response. The electrochemical behavior of hydroxylamine on the PPy NTs/GSPE was examined in 0.1 M PBS at variable pHs of 2.0-9.0 using cyclic voltammetry (CV) (Figure 4). Reportedly, the hydroxylamine electro-oxidation on the PPy NTs/GSPE surface was higher in neutral conditions than in an alkaline medium or acidic medium (Figure 4A). Consequently, a pH of 7.0 was chosen as optimal for the hydroxylamine electro-oxidation on the PPy NTs/GSPE surface. In addition, it was discovered that the values of the peak potential shift to lower values with increasing pH, as shown in Figure 4B. Thus, the peak potential is linearly dependent on the pH, according to the following equation: Ep (mV) = \(-\)59.786 pH + 1208.6 (R\({}^{2}\) = 0.9997). The value of the slope (\(-\)59.786 mV/pH) was very close to the theoretical value (\(-\)59 mV/pH), indicating that the same electron and proton numbers participate in the electrochemical oxidation process.  
The electrochemical behavior of 100.0 uM hydroxylamine in the buffer solution (pH 7.0) was illustrated by using differential pulse voltammetry (DPV) techniques on the surfaces of bare GSPE (Figure 5, curve b) and the PPy NTs/GSPE (Figure 5, curve a). The anodic peak potential was approximately 1000 mV for the hydroxylamine oxidation on the bare GSPE, while it was approximately 790 mV on the PPy NTs/GSPE. On other hand, the GSPE exhibited a minimal current (4.0 uA), and the PPy NTs/GSPE exhibited an elevation in the oxidation current (14.5 uA). Accordingly, the greatest electrocatalytic impact on hydroxylamine oxidation was on the surface of the PPy NTs/GSPE as opposed to the bare GSPE, which was caused by increasing the simplification of the electron transfer process.  
Figure 3: XRD pattern of the PPy NTs.  
Figure 2: The FT-IR spectrum obtained of the PPy NTs.  
Figure 4: CV response of 100.0 \(\upmu\)M hydroxylamine on the PPy NTs/GSPE in 0.1 M PBS at different pH values (2.0–9.0). (**A**) Plot of the oxidation peak current of hydroxylamine as a function of the pH solution. (**B**) Plot of the oxidation peak potential of hydroxylamine as a function of the pH solution.  
Figure 5: DPV recorded for (a) the PPy NTs/GSPE and (b) the un-modified GSPE in 0.1 M PBS at pH = 7 with 100.0 \(\upmu\)M hydroxylamine at a 50-mV/s scan rate.
## 2 Materials and Methods
### Validation of the DEGs by qRT-PCR  
To validate the RNA-Seq results, 11 DEGs were subjected to qRT-PCR analysis performed on the LightCycler480 real-time PCR system (Roche, Basel, Switzerland) in a 20 \(\upmu\)L final volume containing 10 \(\upmu\)L of 2 \(\times\) SYBR Premix Ex TaqMan (Takara, Shanghai, China), 1 \(\upmu\)L of 10 \(\times\) diluted cDNA, and 0.8 \(\upmu\)L specific primer pairs, and 7.4 \(\upmu\)L of ddH2O. The changes in mRNA expression were calculated using the comparative 2\({}^{-\Delta\Delta\mathrm{CI}}\) method. Specific primers were designed using DNAMAN V6.0; the primer pair sequences are listed in Table S1. All treatments were analyzed in three biological replicates.
## 2 Methodology
### Model Accuracy Performance Evaluation
#### 2.3.1 Mean Square Error (MSE)  
The mean square error (MSE) evaluates the average squared difference between observed and predicted values. When a model exhibits no error, the MSE equals zero. As the model error increases, its value increases. Recognizing the mean square error as the mean square deviation (MSD) denotes the portion of variation that the regression model does not explain.  
\[\text{MSE}=\frac{1}{\text{N}}\sum_{i=1}^{\text{N}}\left(y_{i}^{\text{predicted }}-y_{i}^{\text{Actual}}\right)^{2} \tag{15}\]
# Digital 3D Hologram Generation Using Spatial and Elevation Information
## 2 Materials and Methods
### Obtaining Spatial Information from Preexisting GIS Datasets  
To create an accurate virtual terrain map, detailed geographic data regarding the specific area of interest need to be gathered [13]. As illustrated in step 2 of Figure 1, obtaining the most suitable dataset for creating a virtual model of a specific area streamlines the workflow. We can acquire height maps, aerial photographs, and various GIS data layers from numerous free resources [14]. For this experiment, the SwissTopo dataset was selected due to its up-to-date and high-quality information about the chosen area [27].  
The SwissTopo dataset is divided into several distinct sections. For the experiment, it was essential to create a virtual terrain map of the area, which required the use of a height map, aerial photos, and topographic maps to develop the model. We obtained a topographical map from the Swiss Map Raster 25 data collection, and aerial photos from the SWISSIMAGE 10 CM dataset, and derived a height map using the swissALTI3D height model. SwissALTI3D is a highly accurate digital elevation model that depicts the surface of Switzerland, excluding vegetation and human-made structures. The orthophotos from SWISSIMAGE 10 CM comprise current digital color aerial photographs covering the entire landscape of Switzerland [27].  
As shown in step 3 of Figure 1, we utilized the special GIS software QGIS 3.36.3 to refine the acquired spatial data for further processing. The processed data from QGIS 3.36.3 were subsequently used to develop a virtual terrain map model with the Blender 4.1 3D modeling software.
# Adaptive Learning in AI Agents for the Metaverse:
## 4 ALMA: A Framework for Adaptive AI in the Metaverse
### Theoretical Justification  
#### 4.2.1 Theoretical Alignments  
ALMAA is based on a combination of theoretical principles that define its philosophy of what artificial intelligence in the Metaverse should be like. Such alignments provide a strong academic base and improve the practical efficiency of AI agents in complex virtual environments.  
(1) Cognitive Psychology and Machine Learning  
ALMAA includes concepts from cognitive psychology that resemble human learning, consistent with machine learning algorithms that adjust based on experience. The interdisciplinary approach helps AI agents achieve human-like adaptability, making their interactions more user-friendly and intuitive. The psychological factor concerns pattern detection and problem-solving, which is reflected in machine learning through predictive and reactive algorithms.  
Figure 4: Interaction of components within the ALMAA framework.  
Computational Neuroscience  
Computationally, this model is grounded in computational neuroscience, mostly concerning neural networks' organizational and functional aspects. This theoretical framework makes it possible to design neural architectures that imitate some aspects of human brain performance, allowing AI agents to perform complex information processing and decision-making. This alignment enables AI agents to handle multiple concurrent interactions and to deal with various sensory inputs that are necessary for functioning within the elaborately detailed settings of the Metaverse.  
ALMAA is underlined by systems theory, which studies the interactions and dynamics of complex systems. This theory lends credence to the said model's accent on the dynamic interplay of the AI agents with the Metaverse environment, suggesting that feedback loops and adaptation should not be overlooked. Systems theory assists in comprehending the feedback systems, which are essential in the adaptive learning processes in ALMAA.  
Central to the theoretical alignment of ALMAA is reinforcement learning as the basis of behavior adaptation according to the outcome of actions. This method is critical for training AI agents to choose the best actions for the given objectives, which is essential for keeping the Metaverse engagement and effectiveness high. Principles of reinforcement learning dictate the development of the model's decision-making engine, allowing AI agents to learn from their actions and refine their strategies over time.  
ALMAA follows the emerging ethical AI development research [35]. In this way, AI agents will adhere to the guidelines regarding user privacy, data security, and ethical interaction standards. By incorporating these factors, ALMAA is by wider societal values and regulatory regimes that make it flexible and long-lasting in worldwide virtual environments.  
Theoretical alignments are vital to the construction and operation of ALMAA, ensuring the model works well in Metaverse and within strict academic and ethical standards.
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
## 3 Multi-Radio Dynamic and Adaptive Routing Algorithm (mDARAL)  
This work focuses on expanding the features of _DARP_, adapting the _DARAL_ algorithm to make it usable within a multi-radio and multi-channel environment. Based on the analysis of _DARP_, it has been observed that the clustering and the use of virtual sub-networks is suited very well to a multi-radio environment. Moreover, the development of \(DARP\) is focused on Smart Cities, where the density and number of nodes tend to increase, thus the multi-radio scheme seems an interesting option for future developments.  
First of all, it has been intended to keep the functionality of \(DARAL\) (dynamical clustering based on the link quality between nodes), but including the required mechanisms to make possible handling a second radio. Moreover, other interesting features from \(DARAL\) are maintained, like the sub-network structure, the parallel execution on each sub-network and the Dynamical Role Selection Process (\(DRSP\)) algorithm used in the original \(DARP\) protocol.  
Based on \(DARAL\), we have carried out a deep analysis of the multi-radio scenarios, concluding that some of the basic features such as the state machine or the link quality indicator (LQI) based route selection algorithm offer a promising base for the development of a native multi-radio network protocol.  
The virtual sub-network concept developed in \(DARAL\) is exploited to support a pre-defined control channel. As long as the devices use a multi-radio approach (considering multi-radio as the use of two radios), one of the radios is assigned to this control channel and the other one to the communication within the sub-network, as it is shown in Figure 1. This behaviour involves only virtual coordinators (VCs) being suitable to use both radios regularly. For end nodes (ENs), the second radio is usually switched off and it would be switched on only for some specific operations like channel migration or channel analysis. This modification to be able to use different radios leads to changes in several parts of \(DARAL\) and \(DRSP\), which are explained in detail as follows.
## 5 Case Study
### Problem Definition  
The scarcity of water is one of the most important challenges facing the world. Therefore, water consumption and irrigation management are significant. The irrigation in conventional agriculture consumes a huge amount of water. Therefore, it has become necessary to propose smart irrigation techniques aimed at reducing water consumption without affecting the quality of agricultural crops. Using recent technologies such as WSNs can achieve this aim.
## 5 What Is C-RAN?
### How Can C-RAN Improve the Sustainability of Cellular Networks?  
The initial concept of C-RAN separated the very power-consuming amplifier from the baseband functions to be installed in the antenna mast. This was done to minimize the loss  
Figure 15: The six functional placement scenarios proposed by Next Generation Mobile Network [114]. The figure shows the deployment agility of C-RAN, where the deployment can adapt to the current scenario with more or less functions located on the cell site, for low-latency and low-fronthaul requirements, but at the cost of less shared processing.  
provided by the cable connecting the RU to the baseband, greatly reducing the size of the amplifier and also to be able to benefit from air cooling in the RU. This was the first step towards centralized processing and led to a huge reduction in energy consumption.  
C-RAN will have many smaller contributions to the energy consumption budget. These will be related to the centralization and virtualization of the network. Hence, C-RAN will benefit from both. Centralizing the baseband processing will enable improved cell cooperation, leading to improved spectral efficiency, and thus, there might be small energy savings to catch due to reduced re-transmissions and lowered interference. Moving all processing to one location will bypass the need for air conditioning locally on the cell sites, as it will only be necessary in the data centers. However, this is only for countries with very warm climates. Virtualizing the baseband processing will enable easier installation of new power-saving features as well as the opportunity to run network functions on any hardware, maximizing the utilization of hardware resources, and thus saving energy from potentially less hardware when the load is consolidated. In C-RAN, the baseband processing is centralized and virtualized, which will give the additional benefit of utilizing users' movement patterns; hence, baseband processing from sites, which users move into at different times of the day, can share the processing hardware because they utilize the resources at different times of the day. Furthermore, the agile capacity assignment of C-RAN will make sure no capacity is wasted because it is assigned to the area where it is needed. However, it must be noted that, according to the study from NGMN [118], baseband processing is only responsible for 6% of the RAN energy consumption.