esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biofertilizers Improved Date Palm Growth Parameters under Salt Stress  
Biofertilizers significantly affected the height (\(p<0.001\), F = 35.08) of vitroplants under all salinity levels. The two tripartite combinations PGPR+AMF1+Comp and PGPR+AMF2+Comp in particular efficiently increased plant height compared to the controls, with respective enhancements of 47% and 48% under non-stressed conditions (0 mM), 44% and 43% under 120 mM NaCl and 42% and 41% under 240 mM NaCl (Table 2). Figure 1: Effects of salt stress and biofertilizers on arbuscular mycorrhizal fungal (AMF) root colonization frequency and intensity of date palm vitroplants. Comp: compost; AMF\({}_{1}\): native AMF consortium; AMF\({}_{2}\): AMF pure strain; PGPR plant growth promoting rhizobacteria. Data presented are means \(\pm\) standard deviations, n = 3. Bars followed by the same letters within the same salt stress level are not significantly different according to Student–Newman–Keuls test (\(p<0.05\)). The accumulation of biomass (shoot dry weight and root dry weight) are critical growth parameters since the impact of salt stress on plant growth and development is a consequence of the effects of this constraint on physiological, biochemical and nutritional aspects. In this study, shoot dry weight (SDW) was significantly affected by biofertilizers under all salinity levels (\(p<0.001\), F = 41.58). Under non-stressed conditions, all biofertilizers alone or in combination significantly increased SDW compared to control plants (Figure 2), except the bipartite combination PGPR+Comp. At 120 mM NaCl level, all treatments except the Comp, AMF\({}_{1}\), PGPR and PGPR+Comp treatments increased SDW compared to control plants. The bipartite combination AMF\({}_{1}\)+Comp resulted in the highest enhancement of 189% compared to the control. Under 240 mM NaCl level, the PGPR, AMF\({}_{1}\)+Comp and PGPR+AMF1+Compretanents were the most effective in increasing the SDW by 128%, 122% and 113% respectively compared to the 240 mM NaCl-stressed control plants.
## 6 Discussion and Future Research Directions
### Challenges in ML Application for 6G Network Optimization  
While ML promises to assist the incoming 6G network integrate seamlessly with other enabling 6G technologies, its application is lached with critical implementation challenges. One such issue is the convergence time for most ML applications. Due to large datasets being fed into ML systems during model training, convergence time becomes critical as most DRL often take longer time to converge [39; 58]. Researchers suggest that adopting an optimization function can enhance convergence time during model training [39]. Since the 6G network will support critical time-dependent and dynamic applications, a long ML time convergence can impact the performance of the networks [58].  
Furthermore, the 6G network will be heterogeneous in design as it will encompass different networks, protocols, users, and network demands. Consequently, the network performance will be characterized by different QoS and QoE requirements [58]. However, the 6G is expected to usher in a reliable and personalizing QoE and QoS [15]. To achieve this, ML is designed to be flexible and implement an action-based protocol for different applications. This approach will ensure that the incoming 6G network actualizes its personalized QoE requirement and provides critical network resources.  
Another critical challenge in the upcoming 6G network is the concerns of data privacy and security of the network. It also impacts the quality of reception of signals and exposes the network to threats such as jamming [15]. FL has been implemented to address the security challenges in wireless networks; however, the communication overhead cost for FL is somewhat expensive [225; 226]. Choosing and applying the appropriate algorithms and frameworks that will enhance the communication overhead is one way to overcome the challenge of the cost of implementing ML in wireless networks.
## 4 Proposed Routing Protocol
### Data Reception and Forwarding  
As shown in Figure 6, in underwater sensor networks, a node receiving a data packet first checks whether it is the destination. If so, it accepts the packet, updates its neighbor table, and may act as a source or a relay. If not, it listens, updates the neighbor information, and discards the packet. For forwarding, it selects the best relay based on forwarding factors, transmits with initial power, and listens for ACK or forwarding behavior. If there is no response, the power gradually increases and retries. If unsuccessful, the relay is removed and a new one is selected. The non-selected neighbors monitor the process, update their tables, and may join the next relay selection, thereby improving the reliability and efficiency.  
The dynamic power adjustment mechanism adapts to varying link conditions to ensure successful data transmission. The listening mechanism enables real-time monitoring of the transmission status. This design simplifies the data forwarding process, reduces energy consumption, and provides strong support for the efficient operation of underwater sensor networks.
## 2 Materials and Methods
### Data Sets of Gross Primary Productivity
#### 2.2.2 NIRv-GPP  
The monthly global NIRv-GPP data set was derived based on the Advanced Very High Resolution Radiometer (AVHRR) reflectance from LTDR (Land Long Term Data Record v4) product [24]. NIRv is the product of total scene near-infrared reflectance (NIR) and NDVI, commonly used as a proxy to represent vegetation productivity [23]. Based on the established linear relationship between NIRv and EC flux-derived GPP from 104 flux towers, the global monthly 0.05\({}^{\circ}\) of GPP data set was estimated. This approach yielded a mean \(R^{2}\), around 0.70, for the validation sites. The NIRv-GPP data set uses no climate data as input.
# Analysis of Satellite Compass Error's Spectrum
## 2 Materials and Methods
### Background
These are extremely useful for measuring the heave, which is thought to be important on small hydrographic units. In more extensive systems, there is also an option to include information from magnetic sensors or (and) a barometric measuring element. Possible block-diagram of a standard satellite compass was depicted in Figure 3.  
Studies published in [18] proved that a satellite compass behaves similarly to a standard gyro or fiber-optic gyro (FOG) on a ship in motion. In the Figure 4 a small, systematic shift of measurements from individual devices can be noticed, however, this is due to inaccuracies in the installation of the satellite compass and FOG for the time of experiments. In general, all compasses seem to show almost  
Figure 3: Possible block-diagram of a standard satellite compass. (Source: [PERSON]). the same values, and visible oscillations are probably due to imperfections in the control system and inertia of the ship. The existence of a very low frequency (Schuler tuning) characteristic of a classic gyro compass (NAVIGAT X) is noticeable. The most changes in the heading presented in the image occur due the behavior of the ship, so they are very similar to each other. The spectral analysis of these measurements proves that low-frequency oscillations dominate, and one can also distinguish oscillations common to all three compasses, i.e., yaw resulting from the characteristics of the ships movement (0.01 Hz). On the other hand, various frequency bands are not clearly repeated in registrations made with individual compasses. For example, significant differences occur at around 0.008 Hz for the satellite compass (as shown in Figure 5). Figure 4: Example of registered headings from a classic gyrocompass (NAVIGAT X) fiber-optic gyro (NAVIGAT FOG 3000) and satellite compass FURUNO SC50 [18]. Figure 5: Single-side amplitude spectrum of oscillations presented in Figure 4[18].
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Minimum Perturbation Random Initialization  
Random initialization is effective and popular in multi-objective optimization algorithms, whereas the random initialization will introduce an intolerable perturbation in the MODMSA. To generate an initial population with less perturbations and good diversity, a minimum perturbation random initialization is proposed in this section. In the minimum perturbation random initialization, we first allocate the scheduled missions in the initial observation plan \(\Pi_{ini}\) the same VTW as in the initial observation plan and randomly allocate a VTW to the unscheduled missions and the dynamic missions. Then, the VTW chromosomes can be determined according to the VTW allocation result \(W_{M}\). Secondly, all missions are allocated to the unscheduled mission set of corresponding orbits based on the VTW chromosomes. Thirdly, the scheduled missions in the initial observation plan \(\Pi_{ini}\) are first allocated the same OTW as that in \(\Pi_{ini}\). After all the scheduled missions are allocated, other missions in the unscheduled mission set are attempted to be inserted directly into the current observation sequence on the orbit in descending order of the profits. Therefore, the minimum perturbation random initialization effectively avoids perturbation and maintains population diversity.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### Technical and Non-Technical Skills in Surgery (GQ2)  
Mastery of both technical and non-technical skills is required to perform a safe procedure. The former are defined as psychomotor actions or related mental faculties acquired through practice and learning, pertaining to a particular craft or profession. The latter are defined as the social (teamwork, leadership, communication), cognitive (situation awareness, decision-making), and personal resource skills (stress, fatigue, and stress management) that complement technical skills, contributing to safe and effective performance [45]. As reported by [46], the technical skills are no longer enough for the delivery of a modern and safe surgical practice; indeed, more frequently the risk to the patient is from failure of non-technical skills [45; 46].  
Regarding question GQ2 (\"Which skills are addressed by simulator? (technical skills, non-technical skills)\"), in the largest part of the analyzed studies [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 33; 34; 37; 38; 40; 41; 44], the simulators address both the technical and non-technical skills. Only in five studies [35; 36; 39; 42; 43] do the simulators address only the technical skills. Indeed, many studies show that simulation-based education is a powerful tool to teach both technical and non-technical skills to individual surgeons and surgical teams without risking patient safety.
## 3 Our Proposed Work
### Feature Selection Module  
Once the data are binary encoded, not only redundant, but also irrelevant samples need to be removed from the lagged input data samples. In removing redundant features, the execution time during the training process is minimized. On the other hand, removal of irrelevant features leads to improvement in forecast accuracy, because the outliers are removed.  
In order to remove the irrelevant and redundant features from the binary encoded input data matrix \(P_{b}\), an entropy-based mutual information technique is used in [9,12], which defines the mutual information between input \(Q\) and target \(T\) by the following formula,  
\[MI(Q,T)=\sum_{i}\sum_{j}p(Q_{i},T_{j})log_{2  
In order to determine the \(MI\) value between \(Q\) and \(T\), the joint and independent probabilities need to be determined. For this purpose, an auxiliary variable \(A_{v}\) is introduced.  
\[A_{v}=4T+2M+Q\ \ \ \forall T,M,Q\in\{0,1\} \tag{5}\]  
It is clear from Equation (5) that \(A_{v}\) ranges between zero and seven. \(A_{0v}\), \(A_{1v}\), \(A_{2v}\), \(A_{3v}\),..., \(A_{7v}\) counts the number of sample data points (out of total \(l\) data points) for which \(A_{v}=0\), \(A_{v}=1\), \(A_{v}=2\), \(A_{v}=3\),..., \(A_{v}=7\), respectively. In this way, we can now easily determine the joint and independent probabilities as follows.  
\[\begin{split} p(Q=0,T=0,M=0)&=\frac{A_{0v}}{l}\\ p(Q=0,T=0,M=1)&=\frac{A_{2v}}{l}\\ p(Q=0,T=1,M=0)&=\frac{A_{4v}}{l}\\ p(Q=0,T=1,M=1)&=\frac{A_{6v}}{l}\\ p(Q=1,T=0,M=0)&=\frac{A_{1v}}{l}\\ p(Q=1,T=0,M=1)&=\frac{A_{3v}}{l}\\ p(Q=1,T=1,M=0)&=\frac{A_{5v}}{l}\\ p(Q=1,T=1,M=1)&=\frac{A_{7v}}{l}\\ \end{split} \tag{6}\]  
\[\begin{split} p(Q=0)&=\frac{A_{0v}+A_{2v}+A_{4v}+A_{6v }}{l}\\ p(Q=1)&=\frac{A_{1v}+A_{3v}+A_{5v}+A_{7v}}{l}\\ p(T=0)&=\frac{A_{0v}+A_{1v}+A_{2v}+A_{3v}}{l}\\ p(T=1)&=\frac{A_{4v}+A_{4v}+A_{5v}+A_{7v}}{l}\\ p(M=0)&=\frac{A_{0v}+A_{1v}+A_{4v}+A_{5v}}{l}\\ p(M=1)&=\frac{A_{2v}+A_{3v}+A_{6v}+A_{7v}}{l}\\ \end{split} \tag{7}\]  
Based on Equation (4), mutual information between \(Q\) and \(T\) is calculated, and thus, redundancy and irrelevancy are removed from the input samples. This mutual information-based technique is computed with a reasonable execution time and acceptable accuracy.
## 4 Experiments  
For the evaluation of proposed method, comprehensive experimentations have been conducted on a well-known multiview IXMAS [47] dataset. The leave-one-sequence-out (LOSO) scheme has been used for view-invariance evaluation. In this scheme, the classifier is trained on all sequences except one, which is used for testing. This process is repeated for all possible combinations and results are averaged. This is a common strategy used by different researchers such as [19; 29] for evaluation of their methods. This is helpful to compare our results with these state-of-the-art methods.
# Exploring ISAC: Information-Theoretic Insights
## 3 Mono-Static ISAC with Sensing Distortion
### The Capacity-Distortion-Cost Tradeoff
The best constant estimator for this channel is \(\delta=0\), and the communication mode without sensing achieves the rate-distortion pair \((\mathsf{C}_{\text{NoDist}}(\mathsf{B}),\mathsf{D}_{\text{trivial}}(\mathsf{B })=1)\). The sensing mode without communication achieves the rate-distortion pair \((0,\mathsf{D}_{\min}(\mathsf{B}))\). In Figure 3, the rate-distortion tradeoff achieved by these two TS baseline schemes is compared with a numerical approximation of the capacity-distortion-cost tradeoff \(\mathsf{C}(\mathsf{D},\mathsf{B})\) of this channel. As previously explained, \(\mathsf{C}(\mathsf{D},\mathsf{B})\) also passes through the two end points \((R_{\min}(\mathsf{B}),\mathsf{D}_{\min}(\mathsf{B}))\) and \((\mathsf{C}_{\text{NoEst}}(\mathsf{B}),\mathsf{D}_{\max}(\mathsf{B}))\) of the improved TS scheme. To obtain a numerical approximation of the points on \(\mathsf{C}(\mathsf{D},\mathsf{B})\) in between these two operating points an alternating optimization method similar to the Blahut-Arimoto algorithm is used in [56].
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation
### Goodput Measurements
#### 5.1.2 Impact of Correlation on Average Goodput  
The incoming signal can be correlated with the transmitting and receiving antennas. The higher correlation of antennas leads to degradation of the overall system performance. As 5G uses MIMO technology, it is important to measure the correlation. In this paper, we have considered the standard high, low and medium correlation profiles, as shown in Table 2, where \(\alpha\) means the base station and \(\beta\)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Channel Model**} & \multirow{3}{*}{**Doppler Frequency (Hz)**} & \multicolumn{5}{c}{**Correlation Profiles**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Low**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**High**} \\ \cline{3-6}  & & \(\alpha\) & \(\beta\) & \(\alpha\) & \(\beta\) & \(\alpha\) & \(\beta\) \\ \hline EPA & 5 & 0 & 0 & 0.3 & 0.9 & 0.9 & 0.9 \\ EVA & 5, 50 & 0 & 0 & 0.3 & 0.9 & 0.9 & 0.9 \\ ETU & 70, 300 & 0 & 0 & 0.3 & 0.9 & 0.9 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Channel model parameters [83].  
means the UE side correlation, as per [83]. The results are shown for the LTE, URLLC and eMTC use cases in Figure 4 with EPA, EVA and ETU channel models and low, medium and high correlation.  
The overall trends show that the proposed dynamic approach achieves similar performance to that of the baseline for the low correlation profile as plotted in Figure 4a-c for LTE, Figure 4d-f for URLLC and Figure 4g-i for eMTC. When there are less than six users in a cell with respect to the LTE use case, dynamic improves the goodput by approximately 8-10 Mbps (\(\approx\)11.1-18.1%), as shown in Figure 4a-c. The biggest goodput enhancement is approximately 14 Mbps (\(\approx\)60%), achieved by dynamic-FBMC-high over greedy-FBMC-high (the black solid and dotted curves in Figure 4a). The dynamic-FBMC outperforms the greedy-FBMC by up to 4 Mbps (\(\approx\)7.27%) in the case of medium correlation and up to 5 Mbps (\(\approx\)9.09%) for high correlation profiles, as shown in Figure 4d-f for URLLC and Figure 4g-i for eMTC, respectively. Overall, dynamic-FBMC outperforms dynamic-OFDM by up to 8 Mbps (\(\approx\)16%), as shown in Figure 4d-f for URLLC and Figure 4g-i for eMTC, respectively.
## 5 ASTI Protocol  
The ASTI protocol [7] aims at predicting the injection volume based on past consumption records. It applies the technique smoothing factor, which is similar to reinforcement learning. One of the strategies ASTI uses is classifying transactions into various categories. For simplicity, let us assume it is transactions of \(\theta\) type being processed while its corresponding batch size is \(q_{\theta}^{*}\) and its periodicity is \(T_{\theta}^{*}\) under the PITEC setting. Each class of transactions of type \(\theta\) has its optimum performance that requires periodic injection of \(q_{\theta}^{*}\) unverified transactions in every cycle.  
Suppose the last cycle has a time lag of \(T_{last}\) while \(q_{last}\) transactions were injected in the last cycle. If the performance of the network remains the same as last cycle, one would need to adjust the injection volume that  
\[q_{(next,\theta)}=q_{(last,\theta)}\frac{T_{\theta}^{*}}{T_{(last,\theta)}}. \tag{16}\]  
However, this is naive because one cannot simply predict the performance of the network based on only previous cycle that Equation (16) proposes. To consider effects from more earlier cycles for the performance prediction, one can do  
\[\hat{q}_{(next,\theta)}=\gamma q_{(last,\theta)}\frac{T_{\theta}^{*}}{T_{( last,\theta)}}+(1-\gamma)\hat{q}_{(last,\theta)} \tag{17}\]where \(0<\gamma<1\) with initial expectation value  
\[\hat{q}_{(last,\theta)}=q_{\theta}^{*},\quad T_{(last,\theta)}=T_{\theta}^{*} \tag{18}\]  
One can visualized the process as follows (Figure 3):  
ASTI protocol can be further extended to multiple categories of transactions, based on the nature of the transactions. Some transactions might have more importance than others. This could be extended further as future work for another optimization scheme based on the importance of the types. Furthermore, initially when the unverified transactions are entered into the system, the representation of the system can be treated as a temporal graph. One can further improve the temporal graph by the Pregel-based approach in [8].
## 6 Performance Evaluations
### Performance with Varying Traffic Loads
As shown in Figure 6, the end-to-end delay of the IEEE 802.15.4 is significantly higher than that in ATLAS and LDTA-MAC, since the protocol is unable to make a timely delivery of the data packets due to the high contention and collision losses at both low and high traffic. In contrast, both ATLAS and LDTA-MAC experience a lower delay under low traffic until 9 Pkts/s. However, as the traffic load increases beyond this, the delay performance of LDTA-MAC suffers due to the lack of traffic load awareness and adaptation. Conversely, the ATLAS protocol can adaptively make efficient use of the different modes of MAC operation and is thereby able to transmit the data with minimum delay, even at high traffic loads. In the graph of Figure 6, the vertical line shows the maximum and minimum delay variation of the different simulations runs, and it is evident that the ATLAS protocol has the least variation.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 1 Introduction
### Comparison of Related Surveys
**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**LLM**} & \multicolumn{2}{c}{**Countermeasures**} & \multicolumn{3}{c}{**Traditional AI**} \\ \cline{2-9}  & & **Taxonomy** & **Applications** & **Challenges** & **UAV Safety** & **UAV Security** & **Taxonomy** & **Applications** & **Limitations** \\ \hline
[43] & 2022 & & & ✓ & & ✓ & ✓ & ✓ \\ \hline
[39] & 2022 & & & ✓ & ✓ & & ✓ & \\ \hline
[40] & 2022 & & & ✓ & ✓ & & ✓ & \\ \hline
[35] & 2022 & & & ✓ & ✓ & & & \\ \hline
[44] & 2023 & ✓  
on a structured taxonomy from [18, 19]. Unlike prior surveys, we compare traditional AI (ML and DL) with LLMs for UAV safety and security. We analyze traditional AI applications and their limitations, like poor generalizability and lack of interpretability, and we show how LLMs overcome these through multimodal data fusion and chain-of-thought reasoning. We also identify the challenges posed by the current deployment of LLMs and highlight future research directions. To ensure scientific rigor, we followed a systematic literature selection process [52], conducting a comprehensive database search with relevant keywords, screening articles by titles and abstracts, and reviewing full texts for quality and relevance. Articles were categorized into directly related, indirectly related, and background knowledge groups, and evaluated for transparency, rigor, credibility, and relevance. This methodological rigor, paired with our novel framework, ensures a robust and targeted review, offering actionable insights into LLM applications for UAV safety and security, distinguishing our work from existing surveys. The contributions of this paper are as follows:  
* We present a comprehensive review of traditional AI methods in the context of UAV safety and security, synthesizing their pivotal role in mitigating operational threats;
* We critically evaluate the limitations of conventional AI models, focus on their shortcomings in dynamic, real-time UAV environments, and propose essential areas for advancement to achieve robust and reliable safety and security frameworks;
* We provide an in-depth analysis of the emerging integration of LLMs into UAV safety and security domains, demonstrating their transformative potential through multimodal data fusion, real-time decision-making, and adaptive threat detection, offering insights that may be useful for future research;
* We outline innovative future research directions, emphasizing novel solutions such as multimodal embodied intelligence systems and satellite networks, to address LLM deployment challenges, paving the way for secure and efficient operations.