esa-sceva/satcom-chunk-collection huggingface dataset:
# Handover Management for Drones in Future Mobile Networks--A Survey
## 6 Related Works
### Machine Learning-Based Technique  
Due to the high mobility of drones, it is difficult to accomplish global information exchange due to unnecessary overheads since the network becomes significantly convoluted. With the advancements in machine learning disciplines, the number of applications throughout various fields has also increased. Several previous studies were conducted concerning the applications of ML in drone networks.  
In 2020 [32], the authors focused on real terrestrial network data for Stockholm. System models were developed using Key Performance Indicators (KPIs), communication delay, and interference to simulate the Handover and Radio Resource Management (H-RRM) optimization problem. This issue was then converted to a machine learning problem, presenting a reinforcement learning solution that detects the temporal and spatial level connections to produce seamless handover decision The system model was performed by the air-to-ground channel where the LoS pathway is predominant. The buffer line was utilized to identify the upcoming data rate, the specified band, and the interference from the BSs. The optimization problem was then developed, and the algorithms were used to execute the decision-making task. The overall handover process was also updated.  
In 2021 [123], the authors have addressed the optimal location of multiple DBSs in a MIMO wireless network setting. They developed a low machine learning-basedalgorithm for optimizing DBS location by minimizing the total wireless RSS occurring by active terminals. When compared to the Euclidean cost benchmark, the proposed algorithm decreases the propagation loss in the system and achieves a lower bit error rate. Nonetheless, energy-related issues have not been fully covered.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
###### Abstract  
This paper introduces a method for the range localization of a moving ship based on the vertical hydrophone line array. The main implementation steps of the proposed algorithm are as follows. First, the stable low-frequency line spectrum component of the broadband radiated noise from the moving ship was extracted through the Detection of Envelope Modulation on Noise (DEMON) spectrum analysis method. Second, the pressure difference between the two different ranges was derived, and the corresponding interference fringes were observed in the plane of time and time interval. Then, the radial velocity of the moving ship could be obtained based on the period of the pattern oscillations of the interference fringes. Further, we estimated the time and range information of the Closest Point of Approach (CPA) and computed the ship range versus time. Finally, each element of the vertical hydrophone line array was processed by the method proposed above, and data fusion technology was adopted to reduce the impact of ineffective elements and improve the range estimation accuracy. The results of the simulation and experiment of a 16-element vertical array performed in the South China Sea verified the effectiveness of the algorithm.  
Keywords:range localization; vertical hydrophone line array; DEMON spectrum; data fusion; moving ship +
Footnote †: journal: Applied Sciences
## 2 Communication Architecture
#### 2.2.1 Heterogeneous Communication Network Fusion  
The traditional load management system's single communication network technology is gradually difficult to carry differentiated and diversified data center power services, while the data center management system uses remote communication and local communication technologies at the network layer and access layer, respectively, to jointly carry power services and provide diversified communication services. In response to the problem of heterogeneity in local and remote communication technologies, the data center management system adopts heterogeneous communication network fusion to achieve organic integration and efficient interconnection between local and remote heterogeneous communication technologies [11], fully utilizing different network characteristics to ensure the service quality of power service communication.
## 3 POD Strategy  
Table 1 shows the overview of the observation and dynamic models used in the POD of the TH-2. The GPS, BDS2, and GC combined POD are implemented by the reduced-dynamic orbit determination approach [30] and the experiments are carried out in the National University of Defense Technology orbit determination toolkit (NUDTTK) software [21,31]. The position and velocity \((\mathbf{x},\mathbf{v})\) of the LEOs are integrated by an 11 th-order Adams-Cowell method [32,33] with a 10 s step-size:  
\[\left(\begin{array}{c}\mathbf{x}\\ \mathbf{v}\end{array}\right)=\int_{t_{0}}^{t}\left(\begin{array}{c}\mathbf{v }\\ \mathbf{a}(\tau,\mathbf{x},\mathbf{v})\end{array}\right)\mathrm{d}\tau \tag{1}\]  
Figure 6: Multipath combinations of GPS and BDS2 pseudorange observations from TH-2A as a function of the elevation angle.  
Figure 7: Noise levels of GPS and BDS2 carrier phase observations from TH-2A as a function of the elevation angle.  
where \(t_{0}\) is the initial epoch and **a** denotes the accelerations of the gravitational and nongravitational perturbations acting on the satellites. During the test periods, the TH-2A performed three maneuvers per day in the along-track direction, whose durations vary from 1 to 5 s. In the POD processing, maneuver accelerations \(\textbf{a}_{\text{man}}\) are considered as constants over the predefined thrust durations and estimated together with the initial state vector, receiver clock offsets, ambiguities, as well as dynamical relations by the batch least-square estimator. The velocity change for each maneuver can be obtained by \(\delta v=\|\textbf{a}_{\text{man}}\Delta t\|\), where \(\Delta t\) is the thrust duration. Since irregular thruster operation times may cause the intervals of the Adam-Cowell integrator to not match with the maneuver durations, we adopt an eighth order single-step Runge-Kutta method [34] with a 0.001 s step-size to increase the integration accuracy in the vicinities of the maneuvers. Specific mathematic formulas can be found in the strategy of the NUDTTK [21]. Since the impact of the receiver phase center variation (PCV) is non-negligible in the POD processing [6], we generate the PCV maps by the residual method [35] based on in-flight data and apply them in the GPS, BDS2, as well as GC combined POD. With respect to the GC combined processing, due to the fact that the current precision of the orbit and clock products for BDS2 is weaker than GPS [36; 37], the weights of the GPS and BDS2 observations are set to 1 and 0.5, respectively. Meanwhile, the independent epoch-wise receiver clock offsets are introduced for each system because the time tag corrections for the GPS measurements are different than those for the BDS2. Detailed explanations are illustrated in Section 4.3.
## 2 Concept
### Single- and Multi-Target Scenarios
#### 2.2.5 Scenario V  
Scenario V contains two targets within the same range resolution cell. Consequently, the separation of target #7 and target #8 by using the aforementioned time gating operation is not possible. Therefore, the coding filter functions superimpose, leading to the following RX signal:  
\[s_{\mathrm{r,V}}(t)=s_{\mathrm{t}}(t)*h_{\mathrm{sys}}(t)*h_{\mathrm{fs}}(t- \tau_{\mathrm{V}})*(h_{1}(t)+h_{3}(t)) \tag{11}\]  
The spectral analysis of \(s_{\mathrm{r,V}}(t)\) will reveal both coding filter transfer functions of filter #1 and #3. However, due to the known antenna arrangement, no intersection area is present so the superposition of two coding functions of non-neighboring areas leads to the interpretation of two separate signals in the corresponding regions.
## 4 Results and Analysis
### Analysis of the Accuracy of the Different Models  
After the first part of the analysis, NTUSU was used as the sentiment dictionary to analyze the accuracy rates of the five text-based sentiment analysis models. A comparison of the accuracy rates of the different models is presented in Table 4. The results of this study showed that the accuracy rates of Models D and E were higher than those of the original models. The best model is Model E, a hybrid model combining the SnowNLP and Jieba assertions, with a 95.35% accuracy rate. This shows that the hybrid model proposed in this study is effective for analyzing learners' emotions. Further analysis of the model improvement showed that the hybrid model (Model E) improved 55.04% over the original SnowNLP model, 7.75% over the Jieba model (Model B), and 6.2% over the retrained SnowNLP model (Model D) using the NTUSU emotion dictionary.  
The accuracy rates of the different models are plotted as shown in Figure 4. Both models proposed in this study have better accuracy than the original model, and the hybrid model is the best model, with 95.35% accuracy.
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 4 Benefit Factors
### BF-4: Time and Inventory Cost Savings in Cargo Delivery by eVTOLs
#### 4.5.2 Cost Savings in Bridge Inspections  
Implementation of drones in bridge inspections can result in significant cost savings. The infrastructure such as bridges and highways, public buildings, and locomotives are subjected to systematic and regular inspection. The most common type of checking process is based on visual inspection carried out by certified inspectors. It is time-consuming and costly, whereas drones are emerging as a potential alternative by ensuring that the activities for inspecting infrastructure will continue with less expensive operation [67].  
Skyward researched to see the return on investment in Ohio from drone bridge inspections [68]. Their analysis calculated that $3143 is needed to inspect a bridge using only snooper considering core hours and $4152 is needed considering nights or weekends. Core hours are the intervals of the day when all employees are required to work, and night or weekend means by the intervals when workers work at night or on the weekend, outside the core hours. On the other hand, $522 (based on core hours) or $735 (based on nights or weekends) is needed to inspect a bridge using drones (see Appendix A). Employee-related costs such as health plan expenses, workman's compensation charges, and pension plan expenses are referred to as fringe benefits, which are considered as 45% for both of the cases. ODOT estimated that 80% inspections are done during core hours and 20% during nights or weekends [68]. We used their data for calculating the cost of 400 bridge inspections. The yearly cost for 400 bridge inspections using only snooper (\(C_{1}\)) is  
\[C_{1}=400\times 80\%\times 3143+400\times 20\%\times 4152. \tag{33}\]  
Though sUAS will not replace all the inspection cases, ODOT expects to handle nearly half of the 400 snooper truck inspections. Thus, out of 400, 200 bridge inspections can be done using drones, while the other 200 inspections will be carried out using snoopers. The yearly cost for 200 bridge inspections using drones (\(C_{2}\)) is  
\[C_{2}=200\times 80\%\times 522+200\times 20\%\times 735. \tag{34}\]  
Using Equation (35), we computed the yearly cost for 200 bridge inspections done by snooper trucks (\(C_{3}\)):  
\[C_{3}=200\times 80\%\times 3143+200\times 20\%\times 4152. \tag{35}\]  
Using Equation (36), we determined the total yearly cost savings in bridge inspections (\(CS^{\beta}\)).  
\[CS^{\beta}=CR_{1}-(CR_{2}+CR_{3}) \tag{36}\]
## 3 Results and Discussion  
### General Characteristics of De Novo Bacidia rubella Genome  
We report the de novo assembled genome of the lichen-forming fungus _Bacidia rubella_ obtained from an axenic fungal culture. Our hybrid approach to sequencing, using Illumina short-read with Oxford Nanopore long-read, resulted in a high-quality genome assembly of _B. rubella_. The final assembly had a size of 33.52 Mb in 51 scaffolds, an N50 of 1.77 Mb (Table 1), and was 98% BUSCO complete (fungi_odb9). Using multiple ab-initio gene-calling methods, 8773 genes were identified (Table 1). The similarity of standard genome metrics between _B. rubella_ and _B. gigantensis_[63] suggests a high-quality _B. rubella_ genome (Table 1). The genome of _B. rubella_ contains 31 BGCs (Table 2), including six non-reducing and four reducing TI-PKS sequences. This TI-PKS biosynthetic arsenal is similar to that of _B. gigantensis_, containing 11 identified TI-PKS genes (seven NR-PKSs and four R-PKSs, respectively; Table 2). Despite the overall similarity of TI-PKS gene numbers in _B. rubella_ and _B. gigantensis_, our BLAST-based comparison of these biosynthetic clusters showed only two significant hits between the two species: one for R-PKS (bacgigpred_003963 and bacrubpred_000636), and one for NR-PKS (bacgigpred_008278 and bacrubpred_00020), with 62.8 and 63% similarity in amino-acid sequences, respectively. This result is not surprising, given that both fungal species differ in their secondary metabolite profiles. The only secondary compound of _B. rubella_ is atranorin, which is the most widespread secondary metabolite found in the genus _Bacidia_ s. lat. ([PERSON] 1996) [64]. In contrast, _B. gigantensis_ is currently the only known _Bacidia_ species to produce homosekikaic acid [65].
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.10 GST-Rheb Pulldown Identified Rheb as an mTOR1 Activator  
The evidence for an association of Rheb with mTOR was obtained by using GST-Rheb proteins as baits to pull down endogenous mTOR. As a result, the amino-terminal portion of mTOR kinase domain (aa 2148-2300) was mapped as a Rheb-binding region [44]. The mechanism for how Rheb activates mTOR1 remained elusive until it was observed by confocal imaging that Rheb localized with mTOR on lysosomes upon growth factor stimulation for mTOR1 activation [45]. Notably, a weak endosomal membrane association of Rheb through its C-terminal farnesylation may also facilitate its association with mTOR for mTOR1 activation using a CSU-X1 spin disc confocal scan head [46] bearing a faster scanning speed and a higher use efficiency of the laser transmittance to significantly increase image resolution compared with canonical confocal microscopes.
## 2 Modeling of Subsystem
### Probability Density Function Fitting
#### 2.3.1 Exponential Distribution  
The accumulative distribution function of the exponential distribution is shown in Equation (7) and has one parameter, \(\lambda\).  
\[F(t_{i})=1-e^{-\lambda t_{i}} \tag{7}\]  
Taking the logarithm on both sides of Equation (7), the linear equation can be created. If the sample data meet exponential distribution, \(t_{i}\) and \(\ln[1-F(t_{i})]\) are linear and the observed value of parameters \(\lambda\) can be calculated by Equation (8).  
\[\hat{\lambda}=-a \tag{8}\]
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 3 System Model and Problem Formulation
### Energy Detection  
After the combiner, energy detection is then used to recover the signal transmitted from the backscatter tag. According to the proposed RIS AmBC system model, both \(s(n)\) and \(u(n)\) follow the circularly symmetric complex Gaussian (CSCG) distribution, and the received signal \(r(n)\) and the combined signal \(y(n)\) also follow CSCG distribution, which can be express as:  
\[y(n)\sim\begin{cases}\mathcal{CN}(0,W_{0}),d=0\\ \mathcal{CN}(0,W_{1}),d=1\end{cases} \tag{9}\]  
where  
\[W_{0}=\boldsymbol{f}^{H}\mathbf{H}_{1}\mathbf{H}_{1}^{H}\boldsymbol{f}+ \sigma^{2}\boldsymbol{I} \tag{10}\]  
\[W_{1}=\boldsymbol{f}^{H}(\mathbf{H}_{1}+\mathbf{H}_{2})(\mathbf{H}_{1}+ \mathbf{H}_{2})^{H}\boldsymbol{f}+\sigma^{2}\boldsymbol{I} \tag{11}\]  
According to [17], the energy detector is the optimal detector to estimate the information of \(d\):  
\[Z=\frac{\rho}{L}\sum_{n=1}^{L}|y(n)|^{2}\overset{d=1}{\underset{d=0}{\geq}} \frac{\rho W_{0}W_{1}}{W_{1}-W_{0}}\ln\frac{W_{1}}{W_{0}} \tag{12}\]  
where \(\rho=\text{sgn}(W_{1}-W_{0})\). As shown in Equation (12), when \(d=0\), the mean of \(Z\) is \(\mu_{0}=W_{0}\), and the variance of \(Z\) is \(\sigma_{0}^{2}=W_{0}^{2}/N\). When \(d=1\), the mean of \(Z\) is \(\mu_{1}=W_{1}\), and the variance of \(Z\) is \(\sigma_{1}^{2}=W_{1}^{2}/N\). In the case of very large \(N\), applying the central limit law, the BER for backscatter tag signal detection can be obtained when \(\rho>0\) as follows:  
\[P_{e}=\begin{cases}\frac{1}{2}Q\Big{(}\frac{T_{h}-\mu_{0}}{\sigma_{0}}\Big{)}+ \frac{1}{2}Q\Big{(}\frac{\mu_{1}-T_{h}}{\sigma_{1}}\Big{)},\text{ when }\rho>0\\ \frac{1}{2}Q\Big{(}\frac{T_{h}-\mu_{1}}{\sigma_{1}}\Big{)}+\frac{1}{2}Q\Big{(} \frac{\mu_{0}-T_{h}}{\sigma_{0}}\Big{)},\text{ when }\rho<0\end{cases} \tag{13}\]  
where \(T_{h}=\frac{\mu_{0}\mu_{1}}{\mu_{1}-\mu_{0}}\ln\frac{\mu_{1}}{\mu_{0}}\).
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 5 Experiment
### Routing Using Multi-Commodity Flow Problem
#### 5.2.1 Results of Experiment 1  
**Number of sources: 3, number of destinations: 1**  
The destination for all three commodities is node 9, with node 1 as the source for commodity 1, node 2 as the source for commodity 2, and node 8 as the source for commodity 3. Before comparing with OSPF, the results of experiments with GLPK using the original objective function in Table 1 are shown in Figure 6. The results of verifying packet arrival rate with ns-3 are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Packet Size & 1500 byte \\ \hline Packet Transmission Time & 10 s \\ \hline OSPF Reference Bandwidth & 10 Mbps \\ \hline Link Bandwidth (Experiment 1) & Select a bandwidth with a uniform random number that results in a cost of 10 to 20. \\ \hline Link Bandwidth (Experiment 2) & 1 Mbps \\ \hline  & 100 kbps, 200 kbps, 300 kbps, 400 kbps, 500 kbps, \\ Bitrate (Experiment 1) & 600 kbps, 700 kbps, 800 kbps, 900 kbps, 1 Mbps, \\ Bitrate (Experiment 1) & 1.1 Mbps, 1.2 Mbps, 1.3 Mbps, 1.4 Mbps, \\  & 1.5 Mbps, 1.6 Mbps, 1.7 Mbps \\ \hline Bitrate (Experiment 2) & 800 kbps, 900 kbps, 1 Mbps, 1.1 Mbps, 1.2 Mbps, \\  & 1.3 Mbps, 1.4 Mbps, 1.5 Mbps, 1.6 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of experiment.  
Figure 5: Cost239 topology.  
The maximum flow in the graph was 82 (Figure 6), but flow bias occurred in each commodity. The packet arrival rate for commodity 1, which had the larger flow, was higher. However, that of commodity 3, which had a smaller flow, was a single path through node 11. Therefore, the arrival rate of commodity 3 was considerably reduced (Figure 7).  
Next, Figure 8 shows the output of GLPK when using the method in Section 4.1. Figure 9 compares the results of the proposed method and OSPF in ns-3. All the objective functions of the subsequent experiments used the method of Section 4.1.  
The maximum flow of the graph was approximately 82 (Figure 8). As the transmit bit rate increased, the packet arrival rate of the proposed method was higher than that of OSPF (Figure 9). In addition, the flow bias of each commodity was improved such that there was no longer a significant difference in the packet arrival rate of each commodity. The packet arrival rate of commodity 3 was higher than when the original objective function was used. The packet arrival rate of OSPF dropped sharply from 100%, while the proposed method showed a slower decline in arrival rate.  
Table 4 compares the average end-to-end delay between OSPF and the proposed method at 200 kbps where the OSPF packet arrival rate for all commodities was 100%.  
Figure 8: Result of GLPK execution.  
Figure 6: Result of GLPK execution.  
Figure 7: Packet arrival rate at original objective function.  
The average end-to-end delay was shorter for OSPF than for the proposed method for all commodities (Table 4).
## 3 Results
### Afterberg Limits of DKM
#### 3.2.3 Effect of Pore Fluid Ionic Concentration  
Figure 10 shows the test results for the Atterberg limits of the DKM samples with different values of NaCl concentration. For the DKM samples, \(w_{\text{L}}\), \(w_{\text{p}}\), and \(I_{\text{P}}\) scarcely change with increasing NaCl concentration; however, for pure kaolin, \(w_{\text{L}}\), and \(w_{\text{p}}\) increase initially with increasing NaCl concentration and then decrease when the latter reaches 0.1 mol/L, while \(I_{\text{P}}\) tends to decrease with increasing NaCl concentration. Similar conclusions for kaolin were reached by [PERSON] and [PERSON] [PERSON] and [PERSON] (2018), who reported that NaCl concentration significantly affects the interparticle association of the kaolinite fabric, thereby affecting the Atterberg limits. For kaolin with a high ionic concentration (\(>\)0.1 mol/L), face-to-face contacts dominate and the double layer is thin, leading to low \(w_{\text{L}}\) and \(w_{\text{p}}\); this effect of NaCl concentration on the kaolinite fabric weakens as more diatoms are added to the kaolin [PERSON] and [PERSON] (2018). Although the Atterberg limits of DKM are insensitive to the ionic concentration, they increase apparently with increasing diatom content; this indicates that diatom content has far more influence on the Atterberg limits than does ionic concentration, which is consistent with the findings of [PERSON] et al. (2018).  
Figure 9: Values of \(w_{\text{p}}\), \(w_{\text{L}}\), and \(I_{\text{P}}\) for DKM samples with different diatom contents.
# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 4 Result Analysis
### Validation of the Green Carbon Footprint Algorithm
#### 4.3.2 Empirical Validation and Adjustments  
To validate these factors, a series of controlled experiments and simulations are conducted. These tests involve running typical machine learning models under varied conditions to capture real-time data on power consumption, memory usage, and CPU temperatures. The resulting emissions are then calculated using the initial set of conversion factors.  
* Iterative Adjustments: The initial results are compared against independently verified benchmarks of carbon emissions for similar operations [31]. Discrepancies lead to iterative adjustments in the conversion factors. This iterative process continues until the calculated carbon emissions align closely with the benchmark data, ensuring accuracy and reliability.  
Algorithm Sensitivity Analysis: Sensitivity analysis is also performed to understand how changes in each factor affect the overall carbon footprint. This analysis helps prioritize adjustments in the conversion factors to minimize errors in the footprint estimation.