esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Linear Model for the Calculation of Routing Metrics in 802.11s Using ns-3 and RStudio
## 2 Methodology
### ALM Metric Calculation in RStudio  
As stated before, RStudio was used for the statistical analysis of the resulting data base in ns-3.  
From here onward, the simulator variables presented in Table 2 will be called linear model (LM) regressors.  
The database used for this initial analysis was _mesh.csv_. According to the IEEE 802.11s standard, the regressor that determines the ALM value is FAILAV, and the remaining equation terms lack importance since they are constant.  
The LM for the original model IEEE 802.11s is:  
\[\text{lmesh1}=\text{log}\ (\text{METRIC})\sim\text{FAILAVG} \tag{5}\]  
Next, the original model was modified. This is doable with an LM, where all database regressors, the square, and the cube of FAILAVG are included:  
\[\text{lmesh2}=\text{log}\ (\text{METRIC})\sim\text{FAILAVG}+\text{FAILAVG}^{2}+ \text{FAILAVG}^{3}+\text{MXN}+\text{M\_STEP}+\text{M\_PACKET}+\\ \mathit{INTERFACES}+\mathit{L4} \tag{6}\]  
Then, the tests ANOVA and STEPWIZE determined the regressors which were significant for the model. At this point, the regressors can be discarded.  
This final section presents an LM using the regressor COMP as follows:  
\[\text{lmesh3}=\text{METRIC}\sim\text{COMP}+\text{COMP}^{2}+\text{COMP}^{3}+ \text{MXN}+\text{M\_STEP}+\text{M\_PACKET}+\\ \mathit{INTERFACES}+\mathit{L4} \tag{7}\]
## 4 Discussion
### Effects of Ex-Situ Biofloc as Feed Supplement to Improve Shrimp Growth Performance
Most of trace mineral concentrations in shrimp flesh had no significant differences, except for calcium, magnesium and manganese, whose levels were significantly lower in the shrimp-fed with 90% commercial pellet reduction supplemented with ex-situ biofloc(90%C+BF) than those in the control group (\(p<0.05\)) (Figure 3a,b). Considering trace mineral contents in feeds (Table 4), manganese concentration was higher in commercial pellets than that of ex-situ biofoc. Consequently, a lower manganese level in shrimp flesh might be correlated to the reduced level of commercial pellets. Nevertheless, our observation suggests that a lower level of manganese concentration in shrimp-fed with 90%C+BF was sufficient for biological processes, as there were no negative effects on shrimp survival and growth. In contrast, magnesium and calcium concentrations, which were higher in ex-situ biofoc than those of commercial pellets, were found to be significantly lower in shrimp flesh in the 90%C + BF group. Magnesium and calcium are a major part of shrimp exoskeletons and also play an important role for osmoregulatory purposes [46; 57]. Calcium and magnesium are mostly found in the shells of Pacific white shrimp [58]. Thus, we speculated that magnesium and calcium could be mostly deposited in shells, hence lower amounts were observed in the flesh. To elucidate the molecular mechanism, the expression levels of the selected genes related to lipid metabolism and energy metabolic process, namely _fatty acid synthase (Fas)_, _Lipase_, _CArnitine palmitoyltransferase-1 (Cpt-1)_, _cytochrome C oxidase (Cox)_, and _NADH dehydrogenase (NADH)_, were examined and found to be correlated to shrimp growth performance and survival. The lipid metabolism genes (_Fas_, _Lipase_ and _Cpt-1_) were expressed higher in shrimp-fed with 5% and 10% commercial pellet reduction and ex-situ biofoc supplement, which may suggest improvement in fatty acid synthesis. The encoded gene products play a crucial role in the digestion of dietary fat [59]. Previous studies reported that _Bacillus_ spp. could be a probiotic to stimulate digestive enzyme activity in _Fenneropenaeus indicius_[60], and in _L. vannamei_[61; 62; 63]. _Cpt-1_ has a primary function for energy production through the \(\beta\)-oxidation of fatty acids in the mitochondria in Gilthead Sea Bream fish [64; 65]. _Cox_, _ATPase_, and _NADH_ genes are relevant to energy metabolism. _Cox_ plays a crucial role in the metabolic pathway associated with fatty acid \(\beta\)-oxidation of peroxisomal [66]. The higher expression levels of energy-related genes in the biofoc groups indicated that flux energy is increased during the feeding trail. The elevated expression levels of these transcripts suggest that the ex-situ biofoc aids in enhancing digestion of lipid, increasing feed absorption and energy production, resulting in improving feed utilization, growth performance and survival in shrimp.
## 5 Comparative Evaluation of Different FOVs
### 3D Matching Between Virtual Control and Physical Target Objects (Revisited)  
The completion time using the stereo rendering and direct augmentation in the first experiment (Figure 15) took longer than the non-stereo rendering and indirect augmentation with a small FOV. However, there was no significant difference between the two augmentations in the second evaluation (Figure 21). We assumed that the different result was caused by whether the control object was real or virtual. To confirm this assumption, an additional experiment was performed by ten participants (male: six, female: four, mean age: 29.3 years old, SD: 4.9) [44]. The additional experiment was performed with 3D matching in the same environment as the second experiment. However, unlike the second experiment, a virtual object was used as a control object to match the physical object like in the first experiment. A quantitative analysis of completion time was performed. We found that there was a statistically significant difference between the two augmentations, as shown in Figure 22. The stereo rendering and direct augmentation method had approximately double the completion time of the non-stereo rendering and indirect augmentation method (\(p<0.05\)). Note that we obtained similar results for task performance with the virtual control object for 3D matching in Section 4. The conclusion based on the results of the two experiments is as follows: Unlike the virtual object, when the physical control object is used, it is not necessary for the user to continuously look at the 3D matching process as the user can intuitively manipulate the physical control object. On the other hand, the user must look at the virtual control object continuously to perform the 3D matching task.  
Figure 21: Task completion time (s) for 3D matching from a physical object to a virtual object depending on visual contexts and FOV.  
Figure 22: Task completion time (s) for 3D matching.
## 3 Proposed Schemes: Midpoint Relay Selection Using Social Trust and Battery Level
### An Example Scenario of Our Proposed Schemes
Input: probe and data Output: SNR values, data
1: Receive probe messages from \(s\) and \(r\)
2: Send SNR values of probe received from \(s\) and \(r\) to \(s\)
3: Send probe reply to \(s\)
4: Receive data from \(r\) sent by \(s\)
5: Send user data to \(s\) via \(r\)
```  
**Algorithm 6**Tasks performed at Destination  
In our second proposed scheme MRS-SD-BL, the source initially filters the devices that are friends of source and have battery above battery threshold (\(B_{thres}\)). Let us say the selected devices are 6, 10 and 15. If the number of selected devices are more than a probe limit, it filters at most \(l\) (probe limit) devices that are nearest to midpoint of the distance between source and destination. Then, the source filters devices that have social trust above social threshold (\(S_{thres}\)) to probe sequentially. Let us say, only devices 15 and 6 have social trust above social threshold. After probing them and the destination directly, the source compares data rate offered by each of them. If data rate of device 6 is higher than that of device 15 and direct communication, device 6 is selected as a relay. Next, we illustrate the the communication protocol for the proposed relay selection schemes explaining the information exchange between network entities during relay selection.
## 2 General Flow and Basic Scenario
### Basic Scenario of the Proposed Method  
Figure 4 illustrates an overview of the non-cooperative target tracking and measurement scenario in cislunar space based on the CEI and BDS-3 inter-satellite link.  
Figure 3: General flow of the CEI and BDS-3 inter-satellite link-based measurement method for cislunar non-cooperative targets.  
In this model, firstly, based on the pre-observation data and operation law of the non-cooperative targets, the BDS-3 satellites located in each orbit will realize the irradiation of the non-cooperative targets in the cislunar space by applying the inter-satellite link signals between normal missions of inter-satellite link communication. Based on this, the BDS-3 interplanetary link signals reflected by the targets will be transmitted in cislunar space until they reach the ground. Then, the above signals will be reflected by targets and transmitted in cislunar space until they reach the ground. Based on the ground-based CEI receiver array, the above signals are received. Finally, accurate measurement information of the target will be obtained based on the correction of the signal observation error.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 3 Key Design of SRSS/ILMO
### Communication Protocol Design  
The Modbus protocol is a universal UART serial communication protocol for controllers. It is located in the application layer of the OSI model. The Modbus protocol can be used to connect different devices into an industrial network. Modbus has two transmission modes (ASCII and RTU). Referring to the RTU mode of Modbus protocol, the data frame format and detection domain of Modbus protocol is used to form the SRSS/ILMO communication protocol. It makes the system more secure and reliable in control and data transmission.  
As shown in Figure 11, the data frame format of SRSS/ILMO includes device number, function code, address code, number of bytes, total number of packets, number of packets, data, and CRC check. The device number is to distinguish the source of upload data and control command. The fixed device number is set to remote monitoring system, sea surface relay transmission buoy, and seabed observation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Power** & **Work Time** & **Average Power** \\ \hline Seabed observation controller (Work) & 4.44 W & — & — \\ Seabed observation controller (Standby) & 0.96 W & — & — \\ AquaSeNT Moden \({}^{1}\) (Transmit) & 20 W & — & — \\ AquaSeNT Moden \({}^{1}\) (Receive) & 0.7 W & — & — \\ AquaSeNT Moden \({}^{1}\) (Standby) & 0.12 W & — & — \\ Resistivity & 9 W & 8 min/hour & 1.2 W \\ FBG-PPS & 15 W & 4 min/hour & 1 W \\ PA200 & 1.92 W & 1 min/hour & 0.032 W \\ TCM XB & 0.1 W & 1 min/hour & 0.0017 W \\ \hline \hline \end{tabular}  
* Underwater acoustic communication module.  
\end{table}
Table 1: Power consumption of each module of the seabed observation system.  
Figure 10: Hierarchical control of power supply.  
system respectively. The function code is to distinguish whether the transmitted command is a sensor switch or a data command. The address code is to distinguish different sensors. To overcome sudden errors in underwater acoustic communication, data transmission adopts sub-packet transmission. In order to prevent sub-packet loss in data transmission, the total number of packets and number of packets are added in the data frame. CRC-16 check is used to ensure no data corruption during data transfer.  
In Modbus protocol, the message is distinguished by a spatial interval of at least 3.5 characters. Using the interval time, the receiving device can judge whether the transmission ends. The 3.5 characters interval mechanism is shown in Figure 12. SRSS/ILMO uses this time interval method to design an UART serial communication program to improve the reliability of the UART serial interrupt service program.
## 3 Results and Discussion
### The \({}^{13}\)C NMR Spectral Analysis  
The presence of new basic centers was shown by the nuclear magnetic resonance spectrum of the CH-ANP. The spectrum of chitosan showed a characteristic peak for C1 at 105 ppm. The peaks for C2 and C\({}_{3}\)/C\({}_{5}\) appeared at 58 ppm and 75 ppm, respectively. The C\({}_{4}\) and C\({}_{6}\) showed peaks at 84 ppm and 62 ppm, respectively, as indicated in Figure 3. Both peaks at 175 ppm and 19 ppm are due to the presence of carbonyl carbon and the methyl group of chitin due to incomplete deacetylation. The chemically modified chitosan CH-gly indicated two prominent peaks at 128 ppm and 137 ppm, which were due to the double bond of glycidyl methacrylate [22]. Other peaks at 19 ppm and 169 ppm are associated with methyl and carbonyl of glycidyl methacrylate, confirming the first step of the modification of chitosan. However, when this intermediate product was further altered (CH-ANP) in the second step, the two peaks at 128 ppm and 137 ppm due to vinyl carbon disappeared, showing the attachment of 1,4-bis (3-aminopropyl)piperazine molecule to this product. This also supports our results from elemental and FTIR spectroscopy in increasing the Lewis basic centers for cations sorption from aqueous medium.  
Figure 2: Fourier-transform infrared spectroscopy (FTIR) spectra of pristine CH and chemically modified CH-based materials, i.e., CH-gly and CH-ANP.
## 4 Bearing Fault Detection Using YAMNet and Transfer Learning
### Model Validation  
The fine-tuned model was tested under new data to validate its effectiveness, and the capabilities of TL approaches were assessed in limited data scenarios by leveraging on networks pre-trained with sound events. The confusion matrices resulting from the test data are shown in Figure 9, whereas Table 5 shows the overall accuracies achieved at the end of training, validation, and test. There is evidence that the model can properly perform ideal fault diagnosis tasks for dataset A and dataset B with 100% accuracy. Additionally, no overfitting occurred, though extremely small datasets were employed. The class imbalances associated to data availability for the dataset A did not affect model accuracy. In this sense, the TL strategy was effective when the knowledge of spectrograms features is transferred from an SED network to bearing fault detection tasks. Nonetheless, the replacement of a single layer proved to be adequate because the source domain of YAMNet shared numerous features with the domain of vibration signals, as long as these are properly processed. The adoption of Mel spectrum features is part of the pre-processing, and it enhances similarities between transfer domains. Dataset C showed lower accuracies but, when the dropout approach is applied, performances markedly improved.  
The results are consistent with the literature concerning parameter-based TL for CWRU diagnosis. For instance, in [36] it is observed that networks pre-trained on ImageNet are able to perform fault diagnosis tasks with 99.95% accuracy and a training time of 229 s. However, more than one layer required fine-tuning because more training data were available (4000 samples). It can be argued that the dominant features learned on ImageNet were not highly specific of time-frequency images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training Epochs** & **Training Stop Criterion** & **Training Time (s)** \\ \hline A & 5 & Max accuracy & 38 \\ B & 4 & Max accuracy & 29 \\ C & 40 & Max epochs & 330 \\ C with dropout & 40 & Max epochs & 330 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training epochs and training time.  
Figure 8: Fine-tuning: (**a**) dataset C; (**b**) dataset C with dropout layer.
# Magneto-Optical Traps for Cold Atomic Gravimetry: Research Status and Development Trends
## 3 Classification and Application of MOTs
### Other MOT Attempts  
In 1991, [PERSON] et al. found that when four of the six laser beams in a MOT are misaligned, the trapping capacity of the MOT increases, while atomic clusters are formed. They proposed and constructed a MOT with less than six laser beams and designed a MOT scheme with four laser beams, as shown in Figure 6. This MOT successfully trapped \(4\times 10^{7}\) atoms of \({}^{20}Ne\)[51; 52].  
Figure 5: Structure of GAIN gravimeter.  
The advantage of the four-beam MOT is that the laser beam is reflected back along different paths, and the laser beam coming out of the MOT can be used for other purposes. However, due to the difficulty of beam adjustment, the cooling and trapping of atoms is less effective than that of the six-beam MOT; therefore, it is rarely used in cold atomic gravimetry.  
In 1998, [PERSON] and [PERSON] attempted to implement a MOT for Cesium atoms using a five-beam configuration, as shown in Figure 7. In this scheme, two laser beams were projected in opposite directions along the axis of the quadrupole trap, with the polarization states of \(\sigma+\) and \(\sigma-\), respectively. The remaining three laser beams propagate opposite each other at an angle of 120\({}^{\circ}\) in the orthogonal plane, and all of them have a polarization state of \(\sigma+\). Briefly, [PERSON] compared the number and density of atoms trapped in five- and six-beam magneto-optical traps, and showed that the trapped population in the five-beam MOT is 15% smaller than in the six-beam MOT. The atomic densities are of the same order. The lowest temperature achieved with the five-beam MOT is 3.8 \(\upmu\)K, slightly higher than the 2.3 \(\upmu\)K achieved with the six-beam configuration [53].  
In summary, the five-beam MOT provides a convenient alternative to the six-beam configuration, with the advantage of capturing more atoms in less time. In [PERSON]'s study, a slower Zeeman coil was used in conjunction with a five-beam MOT. One beam in the horizontal was directed against the atoms as they entered the trapping region. This increased the effective stopping distance of atoms [54].
# Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method
## 2 Construction of Slope Instability Criteria Using Bayesian Gaussian Mixture Model
### Gaussian Mixture Model (GMM)
It is typically denoted as \(L(\theta;x)\), where \(\theta=\{\theta_{1},\theta_{2},\ldots,\theta_{n}\}\) are the unknown parameters of the model, and the observed data is \(X=\{x_{1},x_{2},\ldots,x_{n}\}\), with the probability density function \(f(x_{i};\theta)\). The expression for the likelihood function is as follows:  
\[L(\theta;x)=f(x_{1};\theta)f(x_{2};\theta)\ldots f(x_{n};\theta)=\prod_{i=1}^ {n}f\left(x_{i};\theta\right) \tag{11}\]  
By replacing the probability density function \(f(x_{i};\theta)\) with the probability density function of the Gaussian Mixture Model, the likelihood function of the Gaussian Mixture Model can be derived as follows:  
\[L(\theta;x)=\prod_{i=1}^{N}\sum_{k=1}^{K}\pi_{k}\mathcal{N}\left(x;\mu_{k}, \sum_{k}\right) \tag{12}\]
# EmuCD: An Emulator for Content Dissemination Protocols in Vehicular Networks
## 5 EmuCD in Action
### Barenetal Performance Results
The spikes in between were due to an increased network activity between containers, with the average CPU usage hovering around 5% during the process, naturally higher than in the previous case when the emulator was not running any protocol. Regarding RAM, BitTorrent can be a lightweight protocol, with an average 35 MB used per container (meaning around 5 MB were used by the protocol). Figure 4: RAM usage of EmuCD without any protocol, 100 nodes. Figure 5: CPU usage of EmuCD running BitTorrent on 100 nodes, 50 MB file. The worst case scenario was an edge case where every node was at full activity. It is very rare to have this behaviour in a VANET, so a stress test was developed, consisting of 95 OBUs and 5 RSUs (a 50 MB file starts only in the RSUs), where each OBU was connected to one of the RSUs and to every other OBU (each OBU has 95 neighbours). This was an extremely dense scenario, built to push the limits of the emulator (in fact it was so demanding that the hardware could only handle 100 nodes). Each RSU was sending the BitTorrent files to the OBUs, which then propagated to other OBUs. Figures 7 and 8 show the CPU and RAM usage during this stress test, respectively. There was an increased CPU usage in the beginning, from 11:40 p.m. until 11:41 p.m., due to the containers being booted. The test started at 11:41 p.m. and an increasing graph was observed, matching the progress of the file distribution: initially only the RSUs had the file's chunks but as they spread, more and more simultaneous transfers between OBUs occurred. At 11:45 p.m. every container was already shutdown and the test was finished. Total RAM usage hovered around 6 GB. Figure 6: RAM usage of EmuCD running BitTorrent on 100 nodes, 50 MB file. Figure 7: CPU usage of BitTorrent during a stress test, 100 nodes, 50 MB file. These results show that, with enough hardware resources, every scenario was possible. Excluding the above mentioned edge case and focusing on the BitTorrent example, in a \"normal\" VANET, at 35 MB per container and with the observed CPU usage, the deciding factor was RAM; therefore, if 35 GB were available to be used by the emulator, 10\(\times\) more nodes could be launched and emulated. Note that, with other more complex protocols, the limiting factor may not be RAM, but CPU instead. The hardware requirements must be adapted according to the protocol and scale of the emulation.
# Relation in Alginate-Based Magnetic Suspensions
## 2 Materials and Methods
### Suspensions and Composites  
To prepare magnetic suspensions first the stock solutions were made by dissolving sodium alginate in distilled water under permanent stirring. Weighted portions of magnetic particles were dispersed in a small amount of water under ultrasound treatment for 5 min, and then mechanically homogenized with viscous alginate solution in a rotational dissolver at 2000 rpm for 30 min. Resulted suspensions were kept overnight for equilibration. As the sterile conditions were not specially maintained, the rheological testing was performed within 36 h after sodium alginate was dissolved to prevent biodegradation of alginate in solutions and in the suspensions.  
Separately, binary polymeric compositions of alginate with different batches of magnetic particles were prepared for thermodynamic measurements. Therefore, magnetic particles were dispersed in a small amount of water under ultra-sound treatment and the resulting magnetic slurry was then mixed with a 7% stock solution of alginate in a rotational dissolver at 2000 rpm for 20 min. Certain proportions among magnetic slurry and alginate solution were kept maintaining the series of suspensions in which the polymer-to-particle weight ratio varied discretely. Resulted suspensions were then cast onto the glass surface and water was evaporated at 50 \({}^{\circ}\)C to the constant weight of the dry polymeric film. Thus, polymeric films of magnetic compositions were prepared with sodium alginate content ranging from 0 to 100% with a 10% step.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Batch** & \(S_{sp}\) **(m2/g)** * & \(D_{w}\) **(nm)** & \(M_{0}\) **(emu/g)** & \(M_{r}\) **(emu/g)** & \(H_{c}\) **(Oe)** \\ \hline Fe\({}_{5}\)O\({}_{4}\) & 6.9 & 305 & 84 & 6.6 & 78 \\ Fe & 9.0 & 86 & 154 & 14.9 & 280 \\ Ni & 12.6 & 85 & 51 & 17.7 & 260 \\ Ni@C & 14.2 & 98 & 47 & 6.9 & 128 \\ \hline \hline \end{tabular}  
* Specific surface area as determined via low-temperature sorption of nitrogen.  
\end{table}
Table 1: Selected properties of magnetic particles used in alginate-based magneto-rheological suspensions.  
Figure 1: TEM microphotographs of magnetic particles: (**a**)—magnetic (Fe\({}_{3}\)O\({}_{4}\)); (**b**)—metallic iron (Fe); (**c**)—metallic nickel (Ni); (**d**)—nickel particles coated with carbon shell (Ni@C); (**e**)—HRTEM image of the carbon layer on the surface of Ni particle.