esa-sceva/satcom-chunk-collection huggingface dataset:
# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
## 2 Materials and Methods
### Creating a Dataset
The details of the algorithm of GRU units are shown below:  
\[r_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{3}\]  
\[z_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}) \tag{4}\]  
\[\widetilde{h}_{t}=\tanh(W_{xc}x_{t}+W_{hc}(r_{t}\odot h_{t-1})+b_{c}) \tag{5}\]  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\widetilde{h}_{t} \tag{6}\]  
\(\sigma\) represents the sigmoid function, \(\odot\) represents the element-by-element multiplication, \(W\) and \(b\) represent the weight matrix and bias vector, respectively, \(x_{t}\) represents the input at the current moment, \(h_{t-1}\) represents the hidden state at the previous moment, \(\widetilde{h}_{t}\) represents the candidate hidden state, \(h_{t}\) represents the updated hidden state, \(r_{t}\) represents the reset gate, which controls which information in the hidden state at the previous moment needs to be reset, \(z_{t}\) represents the update gate, which controls the extent to which the previous state information is incorporated into the current state. (3) Fully connected layers: Multiple fully connected layers (also called Dense layers) are used for feature fusion and output, and finally a linear activation function is used as the activation function of the output layer to output the predicted wind and PV power load values. The model takes the time series of wind and PV load forecasts, along with the corresponding weather condition data at moment t, as inputs. These inputs are then processed by the model, which subsequently generates wind and PV generation forecasts for pre-t + 1 moments through multi-layer processing.
## 4 Neurodegenerative Diseases
### Alzheimer's Disease  
AD is the most common type of dementia, more frequent in old age, and was discovered in 1907. Almost 50 million people are affected by dementia, and this figure is predicted to quadruple by 2050 [7; 35]. AD is an irreversible and developing disorder characterized by memory loss, personality modification, behavioral matters, and an impairment in thinking capacity [7; 36]. AD brains display the extracellular gathering of insoluble A\(\beta\) peptides and intraneuronal neurofibrillary tangles produced by pathological alterations of the tau protein [4; 7; 8; 35; 36].  
Gut microbiota, due to its ability to produce pro-inflammatory cytokines (IFN-\(\gamma\), IL-6, TNF-\(\alpha\), IL-18, IL-1\(\beta\)) and metabolites that pass through the BBB, provokes neuroinflammation-mation in the AD brain. High levels of IL-6 lead to the hyperphosphorylation of tau and neuronal degeneration. [37]. Furthermore, gut microbiota produces insoluble amyloids that could increase the development of plaque and contribute to the pathogenesis of AD [38; 39].  
AD patients display in their feces great amounts of _Escherichia_ spp. and _Shigella_ spp., which are pro-inflammatory bacteria, and low concentrations of Eubacterium rectale, known as anti-inflammatory bacteria [4; 9; 10; 16]. The bacteria found in AD patients are _Actinobacteria_, _Bacteroides_, _Lachnospiraceae_, and _Ruminococcus_, with lower levels of _Firmicutes_ and _Bifidobacterium_ and higher levels of _Bacteroidetes_[10].  
The dysbiosis of gut microbiota may be one of the major causes of AD pathogenesis [4; 5; 16]. The ENS can be triggered to create and aggregate AS proteins by various bacteria such as _Escherichia coli_, _Klebsiella pneumoniae_, _Streptococcus_, _Staphylococcus aureus_, _Salmonella_, and _Mycobacterium_[40]. Metabolites produced from pro-inflammatory bacterial species increase brain neuroinflammation and worsen AD [10]. Furthermore, a leaky gut allows bacterial amyloids to pass into the blood circulation and intensify the brain's neuroinflammation [10].  
On the other hand, the age-related decline in gut microbiota diversity is correlated with AD, with a decrease in _Bifidobacterium_ spp. and an increase in _Proteobacteria_ influencing lipid metabolism and memory function [5; 10]. All these results discussed above indicate a relationship between GBA, microbial metabolism, and the progress of AD.
## 2 Numerical Analysis Model  
### Geometry of Analysis Model  
To efficiently simulate the charging process of a tube skid, we simplified the analysis model. Although a typical trailer houses around 40 hydrogen tanks, as depicted in Figure 1, analyzing each tank simultaneously would be impractical. Therefore, we focused our numerical analysis on a single tank, based on the assumption that the slow flow velocity of hydrogen--resulting from the tanks' parallel configuration and their substantial combined storage capacity--ensures uniform charging across all tanks, rendering the analysis of a single tank representative of the entire system. Furthermore, we neglected the influence of the connecting conduit, considering it inconsequential to the overall charging dynamics.  
The tank was modeled in 2D axisymmetric geometry for efficient analysis. Figure 2 illustrates the geometry and dimensions of an individual tank, where the inlet's complex structure was simplified for numerical analysis. The materials constituting the inlet, shell, and liner were aluminum, carbon fiber-reinforced polymer (CFRP), and polyamide (PA), respectively. The tank measured approximately 2.6 m in length and 0.55 m in diameter, with a total volume of 400 L. With the cylinder positioned vertically within a tube trailer, hydrogen was introduced via an inlet at the bottom of the cylinder.  
Figure 2: Geometry of the tube skid.  
Given the tank's vertical orientation, buoyancy aligned with the tank's axis. Furthermore, the tank's geometry and the primary forces acting on the hydrogen exhibited symmetry relative to the central axis, enabling a 2D axisymmetric analysis to accurately account for buoyancy effects. Figure 3 shows the numerical analysis domain and the generated mesh representing the central cross section of the 3D tank geometry for 2D axisymmetric analysis. Comprising approximately 40,000 quadrilateral cells--determined optimal through grid testing--the grid's average quality was assessed at 0.71.
## 3 Proposed Mechanism
### Channel Allocation and Deallocation (CAD)
The channel allocation mechanism first checks the number of TBs that use the overloaded channel. If a single TB is using the overloaded channel, then CAD calls the path update component (see next sub-section). If there are multiple TBs, then CAD first tries to allocate a _used_ channel; otherwise CAD allocates an _unused_ channel if there are any. The channel allocation mechanism finds the TB that has the maximum loss rate. CAD tries to find a used channel with a remaining load that can accommodate the average load of the TB with the highest loss rate. However, the remaining load of a used channel is an estimated value, and an imprecise estimation can cause that channel (_i.e._, the channel which will be assigned) to be overloaded again. This might enforce another channel assignment. To avoid this, we have used a safeguard, \(\beta\), which ensures that a certain percentage of the remaining load of a used channel is not considered when it is allocated. In the simulation, we have set the value of \(\beta\) to be 0.1. In contrast, the channel deallocation mechanism removes channel when the load decreases. The NLD indicates when a channel is overloaded and CAD runs the _ChannelAllocation_ function, whereas every time NLD updates the load of the TBs, CAD runs the greedy _ChannelDeallocaton_ function. The ChannelDeallocation function checks whether a single channel (in addition to its current load) can accommodate the current load of another channel or not. If it finds such a channel, then, that channel is allocated the load of both the channels and the other channel is marked as unused. The capacity of a channel might decrease for to many reasons, for example, very bad link quality, external interference, or even jamming by malicious nodes. LPMC changes the channel if the overall capacity of the channel is decreased to a certain fraction of the \(max\_load\) of the channel. In this case, CAD assigns an unused channel for the TB(s). This feature of LPMC has an inherent benefit over static channel allocation schemes (_i.e._, TMCP) where it is not possible to dynamically measure the channel capacity and switch to an unused channel.
## 3 Simulation and Data Center Experimental Results
### Simulation Results  
Figure 4 shows the BER performance comparison for LDPC-coded 8-PAM signals with PS and uniform distributions for different LDPC code rates. As the PS distribution has a smaller entropy, we needed to make sure that each modulation scheme had the same information rate to guarantee that the comparison was fair; in other words, they had the same FEC overhead. The code rate of the uniform distribution was 0.6, so the information rate was 1.8 bits/symbol, with a 66.7% FEC overhead. From Figure 4, we can see that all PS distributions outperformed the uniform distribution, and the best improvement offered a 0.8 dB signal-to-noise ratio (SNR) gain over the uniform distribution at a BER of \(10^{-5}\), which appeared when code rate (\(r\)) equal to 0.8.  
Figure 5 shows the BER performance of the LDPC-coded PS and uniform distribution for different values of the ROF. We can see for both distribution schemes that a smaller ROF achieved a better  
Figure 4: Bit error ratio (BER) performance for different LDPC code rates (\(r\)). SNR: Signal-to-noise ratio.  
Figure 3: Structure of the PS-NPS-8-PAM-based DWDM system. DEMUX: demultiplexer.  
BER performance, which was caused by the reason shown in Figure 2: a smaller ROF value can lead to the frequency response of a rectangular shape, improving the spectral efficiency. For every ROF value considered, we found that the PS distribution-based scheme always outperformed uniformly distribution-based one.  
Figure 5a shows a very clear error flow when the ROF was set to 0.5. Namely, every LDPC code had an effective SNR region, in which it sufficiently improved the BER performance after a corresponding SNR threshold. According to Figure 2, a larger ROF brought a wider bandwidth and more inter-channel crosstalk, which prevented us reaching the SNR threshold of the employed LDPC code.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 4 Performance of fs-LIMS Instruments in the Element and Isotope Analysis
### Time of Flight, Mass Range, and Mass Resolution
In TOFMS systems, the mass resolution depends on the mass, but also other less controllable experimental effects, such as surface and space charging that can affect the mass resolution by introducing an ion packet temporal jitter due to varying fields at the surface and coulombic repulsion if the ion concentration becomes larger than critical density (\(\sim\)10\({}^{7}\)/cm\({}^{3}\)), respectively. These effects are less severe in LIMS with a laser desorption ion source [135].
# Determination of Optimum Frequency for Electromagnetic-Assisted Nanofluid Core Flooding
## 2 Materials and Methods  
### Sample Preparation  
Berea sandstone samples with a porosity of 0.21 and permeability 230 mD (7.5 cm long and 3.8 cm in diameter) were prepared for saturation and EM properties were measurement. Crude oil of density 0.802 g/cm\({}^{3}\) and viscosity 7.5 cp obtained from Angsi field was used in this study, while magnetite nanoparticles were purchased from Sigma-Aldrich for nanofluid preparation. An 11,000 ppm brine solution was prepared through the dissolution of 0.11 g of NaCl in 100 mL of distilled water. Fe\({}_{3}\)O\({}_{4}\) nanofluid (0.05 wt%) was subsequently prepared by dispersing 0.05 g of Fe\({}_{3}\)O\({}_{4}\) in 100 mL of the earlier prepared brine solution and sonicated for 30 min to achieve homogenous dispersion. The sandstone samples were saturated with the respective formation fluid (prime, crude oil and nanofluid) in a pressurized (1.85 MPa) stainless-steel autoclave placed in an oven at 120 \({}^{\circ}\)C for 48 h for effective saturation (Figure 1). The shape and size distribution of the dry and saturated sandstone grain were examined with the aid of a field emission scanning electron microscope (FESEM) operated at 5 kV. The analysis was conducted using a FESEM (SUPRA 55 VP Carl Zeiss AG, Germany) equipped with an energy dispersive spectrum (EDS) analyzer, which gives the elemental distribution to confirm the inclusion of the saturating fluid (prime, crude oil and nanofluid) in the rock sample.
## 4 Results and Discussions
### LoRaWAN(r) Performance under Single-Channel and Multi-Channel GW  
As discussed earlier, the performance of single-channel and multi-channel GWs differs in some aspects, such as packet transmission reliability and the maximum supported ENs. In this section, these differences in performance are further investigated to understand PDR variation by comparing both GWs, under different SFs (7 and 12). In addition, the comparison also considers the time interval of the LoRa transmitter (i.e., the duty cycle period) and the number of ENs operating simultaneously (to test how these GWs cope with various node sizes).  
From the results shown in Figures 13 and 14, it can be observed that the SF, duration of sleep time, and the number of ENs have a significant impact on the PDR. Furthermore, the multi-channel GW performed better than the single-channel GW due to its capability to support many ENs (up to 800). On the other hand, by increasing the SF, number of operating ENs, and decreasing the sleep time, the packet loss increased.  
Figure 14: Performance comparison between the LoRaWAN® multi-channel and single-channel GWs.  
Figure 13: The percentage of packet loss during data transmission between the LoRaWAN® GW and EN.
## 3 Results
### Data Repository  
We created an open access FTP repository containing all the publicly available data of the TCGA program of the GDC in their original and new standardized extended version (genomic data in BED format and metadata in key-value format). The repository is available at [42]. The data are firstly divided in two branches, original GDC data and extended BED ones (_original_ and _bed_ folders, respectively). The structure of the FTP space is then organized within the two branches using the following structure: _program_ (e.g., TCGA), _tumor_ (e.g., TCGA-BRCA, TCGA-KIRP, TCGA-OV, etc.), and finally _data type_ (e.g., gene_expression_quantification, methylation_beta_value, clinical_and_biospecimen_supplements, etc.). For each data type, the genomic and metadata are separately provided for each aliquot. Currently, a total volume of 2.7 TB of data (1.4 TB of original GDC data and 1.3 TB of converted data) of 33 different tumors is maintained. Table 3 shows the number of aliquots, patients and samples available for each tumor.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Tumor** & **Aliquots** & **Samples** & **Patients** \\ \hline Acute Myeloid Leukemia & 1605 & 1605 & 1211 \\ Adrenocortical Carcinoma & 771 & 771 & 595 \\ Bladder Urothelial Carcinoma & 3786 & 3762 & 2873 \\ Brain Lower Grade Glioma & 4674 & 4674 & 3590 \\ Breast Invasive Carcinoma & 10,305 & 10,280 & 7520 \\ Cervical Squamous Cell Carcinoma and Endocerical Adenocarcinoma & 2706 & 2706 & 2118 \\ Cholangiocarcinoma & 401 & 401 & 267 \\ Colon Adenocarcinoma & 4358 & 4244 & 3121 \\ Esophageal Carcinoma & 1705 & 1701 & 1271 \\ Glioblastoma Multiforme & 3347 & 3282 & 2190 \\ Head and Neck Squamous Cell Carcinoma & 4955 & 4951 & 3636 \\ Kidney Chromophobe & 667 & 667 & 462 \\ Kidney Renal Clear Cell Carcinoma & 5322 & 5155 & 3499 \\ Kidney Renal Papillary Cell Carcinoma & 2812 & 2784 & 2023 \\ Liver Hepatocellular Val Carcinoma & 3604 & 3602 & 2610 \\ Lung Adenocarcinoma & 5245 & 5146 & 3722 \\ Lung Squamous Cell Carcinoma & 4780 & 4736 & 3460 \\ Lymphoid Neoplasm Diffuse Large B-cell Lymphoma & 423 & 423 & 327 \\ Mesothelioma & 775 & 775 & 603 \\ Ovarian Serous Cystadenocarcinoma & 4825 & 4777 & 3586 \\ Pancreatic Adenocarcinoma & 1659 & 1659 & 1267 \\ Pheochromocytoma and Paragangioma & 1652 & 1652 & 1253 \\ Prostate Adenocarcinoma & 4778 & 4778 & 3473 \\ Rectum Adenocarcinoma & 1462 & 1453 & 1124 \\ Sarcoma & 2341 & 2335 & 1797 \\ Skin Cutaneous Melanoma & 4197 & 4197 & 3242 \\ Stomach Adenocarcinoma & 4108 & 4080 & 3018 \\ Testicular Germ Cell Tumors & 1377 & 1377 & 1045 \\ Thymoma & 1120 & 1120 & 862 \\ Thyroid Carcinoma & 4827 & 4827 & 3523 \\ Uterine Carcinosarcoma & 504 & 504 & 398 \\ Uterine Corpus Endomet Carcinoma & 5088 & 5058 & 3860 \\ Uveal Melanoma & 720 & 720 & 560 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of processed tumors with the related number of involved aliquots, samples and patients.  
Structure and content of the created FTP repository are described in detail in the Supplementary File 4 and Supplementary File 5, respectively.
## 2 Materials and Methods
### Calculation of the Developed Emission Factors
#### 2.3.3 Marginal Emission Factor  
The direct (short-term) MEF presented in this paper is calculated according to [30] using the linear regression method from the amount of net power production provided by dispatchable (\(d\)) and fossil-fueled power plants \(E_{d,l}^{NPP}\) and the resulting emissions \(Em_{d}\) between two consecutive hours \(t-1\) and \(t\) (Equation (5)). The gradient of the regression line from \(\Delta Em_{d,l}\) over \(\Delta E_{d,l}^{NPP}\) represents the yearly average MEF. The regression line is derived from 8760 hourly values for each milestone-year. The authors of [49] demonstrate that a more detailed temporal granularity for Germany is associated with an increasing deterioration of the coefficient of determination.  
\[\Delta E_{d,t}^{NPP}=E_{d,t}^{NPP}-E_{d,l-1}^{NPP} \tag{5}\]The MEF is intended to reflect the change in emissions through an active change in demand within Germany. Renewables such as wind power and photovoltaics will not take on this active role due to the functioning of the German electricity market and the feed-in priority of RES [35; 36; 39]. Thus, the MEF does not take into account renewable energy production, storage, and trading volumes. Storage technologies can only provide a limited amount of power under certain conditions. Although electricity trading volumes can be considered a hypothetical marginal \"power plant\" [39], they are not included in this analysis. Theoretically dispatchable power plants include thermal power plants burning fossil fuels and nuclear power plants. Similar to the AEF, the MEF is referred to as MEF\({}_{\text{fc}}\) for fuel combustion or MEF\({}_{\text{Ica}}\) for life cycle assessment.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
###### Abstract  
Unmanned ocean vehicles can be guided and controlled autonomously or remotely, and even remote operation can be automated significantly. Classical methods use trajectory tracking errors in negative feedback. Recently published methods are proposed instead. Deterministic (non-stochastic) artificial intelligence (DAI) combines optimal learning with an asserted self awareness statement in the form of the governing mathematical model (based on physics in this instantiation) to allow control that can be alternatively adaptive (i.e., capable of reacting to changing system dynamics) or learning (i.e., able to provide information about what aspects of the system dynamics have changed). In this manuscript, deterministic artificial intelligence is applied to the heading control of a simulated remotely operated underwater vehicle (ROV). Research is presented illustrating autonomous control of a Seabotix vLBV 300 remotely operated vehicle within milli-degrees on the very first step of a shaped square wave command, and error decreased an additional sixty-two percent by the third step of the square wave command.  
marine engineering; artificial intelligence; dynamics; guidance and control; ocean vehicles +
Footnote †: journal: Applied Sciences  
1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA; [EMAIL_ADDRESS]  
2
# Collision Study on New Aluminum Alloy W-Beam Guardrail
## 5 Finite Element Simulation Results
### Guardrail Energy Absorption  
During the simulation of vehicle-guardrail collisions, energy absorption is one of the crucial metrics to evaluate guardrail performance. The energy absorption equation in finite element analysis is expressed by Equation (10).  
\[\dot{E}=VS_{ij}\dot{\varepsilon}_{ij}-(p+q)\dot{V} \tag{10}\]  
where \(\dot{E}\) represents the energy of the configuration at a certain time, \(\dot{V}\) represents the volume of the configuration at a certain time, \(S_{ij}\) represents the deviatoric stress tensor, \(\dot{\varepsilon}_{ij}\) represents the strain rate tensor, \(p\) represents the pressure, and \(q\) represents the volumetric viscous resistance.  
The energy absorption curves of the guardrails, as shown in Figure 12, exhibit a tendency to stabilize after reaching a certain threshold. The traditional steel guardrail absorbs a maximum of 77.0 kJ of energy at 0.48 s, while the new steel guardrail achieves the highest energy absorption of 85.9 kJ at 0.46 s. In contrast, the new aluminum alloy guardrail demonstrates optimal energy absorption performance, reaching a maximum of 97.7 kJ at 0.39 s.
# Effect of Physical Exercise on the Release of Micorparticles with Angiogenic Potential
## 8 MPs and Physical Exercise
### The Effect of Physical Exercise on MPs with Angiogenic Potential in Athletes  
Endurance athletes have the characteristics to present greater adaptation of the cardiorespiratory and neuromuscular systems resulting in the increase of the delivery and the consumption of oxygen [118]. Two of the sports that highly rely on these adaptations are marathon and half-marathon. [PERSON] et al. compared the amounts of E-MPs and Plt-MPs in a group of half-marathon runners with a control group of gender-matched healthy controls showing that CD51\({}^{+}\) E-MPs and CD42\({}^{+}\)/CD31\({}^{+}\) Plt-MPs did not significantly differ between the two groups [52]. This data suggests that athletes exposed to chronic endurance training can benefit from exercise effects but that such type of training does not lead to further decrease in MPs compared with a healthy non-athlete population. On the other hand, E-MPs and Plt-MPs significantly increase following a marathon run and return to baseline values within 2 days; conversely, leukocyte and monocyte-derived MPs decrease after such run [114].  
Like long distance running, road cycling and triathlon are two sports heavily relying on cardiorespiratory endurance [119]. In triathletes/cyclists, high-volume (2 h at 55% of peak power output) and high intensity (four sets of minutes at 90-95% of peak power output interspersed by 3 min of active recovery or four sets of 30 s at maximal effort separated by 7:30 of active recovery) trials on cycle ergometer, significantly decrease CD31\({}^{+}\)/CD42b\({}^{-}\) apoptotic E-MPs 60 and 180 min after the termination of the trials [117]. It seems that such decrease was due to an increased uptake of MPs by cells. Indeed, triathletes/cyclists' serum collected 180 min after high intensities exercises promote the uptake of CD31\({}^{+}\)/CD42b\({}^{-}\) apoptotic E-MPs in endothelial cells in vitro [117]. Another recent study involving male athletes showed that an incremental cycling protocol until exhaustion determined a significant increase of CD105\({}^{+}\), CD146\({}^{+}\) E-MPs, and CD62P\({}^{+}\) Plt-MPs at the peak of the exercise session [108].