esa-sceva/satcom-chunk-collection huggingface dataset:
# Packet-Forwarding Algorithm in DTN Based on the Pheromone of Destination Node
## 2 Related Works
In this manner, the similarity of the community that contains the node with ID = 29 for five consecutive days can be computed, which is a discrete value that is distributed from 0.4 to 0.6. Accordingly, 70% of the community members are relatively stable. This finding is important for our research. We can utilize the relatively stable structure of the network to improve the efficiency of packet forwarding. By studying the recently published typical DTN routing algorithms [2; 5; 7; 10; 11], we can know that the betweenness centrality of a node is most important in the process of selecting a relay node; it represents the capability of the current node to forward a packet to the destination. [PERSON] et al. [16] proposed a technique to compute the betweenness centrality of a node by collecting all node information in DTN. However, given that the node location is easy to change in DTN, one node cannot easily obtain global information. This algorithm may consequently cause a considerable deviation in the betweenness centrality of a node. If every packet forwarded by the nodes has high betweenness centrality, then excessive consumption of the resources of these nodes may occur. To overcome the problem of requiring global information in computing betweenness centrality, we utilize the pheromone held by a candidate relay node to represent the betweenness centrality of a current node. Considering that the pheromone can be periodically flood from the destination node, the pheromone can be easily generated without global information. The excessive consumption of the resources of a relay node can, thus, be avoided because different destination nodes spread various pheromones.
## 1 Introduction  
As the country actively promotes the energy revolution and implements the \"dual-carbon\" goal, a new type of power system has emerged to take on the heavy responsibility of carbon peaking, carbon neutrality, and realizing the new development concept. Accurate power load forecasting can significantly improve the power utilization efficiency and reduce carbon emissions [1]. However, large quantities of electricity are difficult to store for long periods of time and to supply at any time, which makes it difficult to maintain a balance between supply and demand in the operation of the power system [2]. How to maintain the flexibility, stability, economy and security of the power grid in the future has become an urgent issue.  
Precise power load forecasting is vital for the effective operation and planning of power systems. It not only guarantees the stability of the power system but also empowers power grid companies to strategically organize power generation schedules, ensuringample power supply to cater to diverse user demands. This approach helps minimize power disruptions due to inadequate supply and enhances the overall reliability of power provision [3]. Through accurate load forecasting, power grid companies can rationally plan and dispatch power generation resources to avoid resource wastage and cost increase. Accurate forecasts can provide valuable information to electric utilities, power plants, and grid operators to help them formulate reasonable generation plans, optimize power dispatch, and improve the reliability and economics of the power supply [4].
## 3 Results and Discussion
### LC-MS/MS Optimisation
Choosing the appropriate stationary phase was a crucial step in developing the reversed-phase separation method. For this purpose, tests were performed on a series of columns with diverse parameters (length, diameter, grain size, and polarity) specifically designed for ultra-efficient liquid chromatography. The best results were obtained with a core-shell Kinetex C18 column (100 mm \(\times\) 2.1 mm, 2.6 \(\upmu\)m). A fixed, non-porous silicone core is the packing of this column, on which a porous silica layer is deposited, and a monolayer of dimethyl-n-octadecyl silane is immobilized on its surface. Such a phase is \"endcapped\" by means of the appropriate reagent to deactivate the silica as much as possible. This configuration ensured high efficiency and a sharp peak resolution. The composition of the mobile phase also had a significant impact on chromatographic performance. Water served as the main component, while methanol and acetonitrile were tested as organic modifiers. Both solvents provided good retention, but methanol was chosen due to its lower cost. Adding acidic modifiers--acetic or formic acid--enhanced analyte retention, with formic acid proving more effective and thus being selected for final use. Lowering the mobile phase pH was found to improve peak shape, increase selectivity, and enhance sensitivity. Gradient elution was necessary to effectively separate the broad range of pharmaceuticals. Adjustments to the gradient profile--such as modifying the initial proportion of the organic solvent--helped improve resolution. In early elution stages, weakly retained compounds (e.g., atenol and metoprolol) migrated rapidly due to their low logP values. Shortening the gradient duration for the organic phase allowed better peak shapes for hydrophobic compounds and reduced the analysis time without compromising efficiency. The developed chromatographic method was characterized by satisfactory reproducibility of retention times regardless of the number of determined pharmaceuticals or their physicochemical properties. Moreover, a short time of analysis, and thus low cost and solvent consumption, are fundamental advantages of the developed methodology.
## 4 Data Analysis
### Statistical Analysis
#### 4.1.2 B200a-J Evaluation  
Figures 22-24 present the RMSD values for all the PZTs of beam \"B200a-J\". For the PZTs located in the interfacial zone, \"J3\" exhibits an incremental trend, either from the first loading level until the failure, while \"J6\" shows a significant increment in the RMSD values, reaching the ultimate ones at the 5 th loading levels (Figure 22b). Both PZTs are located in the right span, where the fatal shear failure crack occurred. All the other \"Js\" demonstrate insignificant variations, following the structural integrity condition of their efficient monitoring area, in terms of their location, except for \"J2\" at Dam, and Dam3 loading states, where the flexural crack at the middle of the beam occurred. In these states, the RMSD values of the PZT exhibit significant augmentation, while after that, they return to lower insignificant values.  
Moreover, the \"SA\" RMSD values (Figure 23b) follow the formation of the developed cracking pattern (Figure 10). Thus, SA3 and SA4 demonstrate significantly higher values of RMSD, compared to all the relative embedded PZTs. The latter comes in agreement with the beam's structural integrity with respect to the PZTs' locations. Further, even though the externally epoxy-bonded PZTs are not as sensitive as the embedded ones, \"X2\" shows higher RMSD values than the relative of \"X3\", as depicted in Figure 24b.  
Figure 22: (**a**) Cracking at failure and “J” PZTs’ notation and position for “B200a-J”, and (**b**) RMSD index values for “J” of “B200a-J”.  
From all the above observations and the cracking pattern formation presented for each set of PZTs, it could be extracted that all the different PZT patches' configurations achieved successfully to identify the critical effect that was assigned to investigate. Hence, the formation and the development of the damage, which finally led to a fatal shear critical crack in the right span of the beam for both the jacket and retrofitted beam, has been successfully detected.  
Figure 23: (**a**) Cracking at failure and “SA” PZTs’ notation and position for “B200a-J”, and (**b**) RMSD index values for “SA” of “B200a-J”.  
Figure 24: (**a**) Cracking at failure and “X” PZTs’ notation and position for “B200a-J”, and (**b**) RMSD index values for “X” of “B200a-J”.
## 4 Discussion
### Rq.1.--What is the trend and actual situation of LSS applied to manufacturing chemicals?
The low implementation also contributes to scarce industry collaboration as well as limited expertise on LSS in the chemical industry, resulting in a shortage of professionals with expertise in this specific field. Another important factor is the lack of well-defined guidelines or framework describing a clear and suitable adoption of LSS within this specific sector. According to [PERSON] et al. [72], practitioners need clear guidance on which strategy should be applied first, i.e., L or SS, and which tools should be used, especially during the initial stages of LSS implementation. In this regard, several authors have emphasized the need to standardize LSS curricula to enhance learning in companies [73; 74]. There are also other contributing factors, such as a lack of understanding of the benefits, lack of leadership, employee resistance, or issues related to time consumption, which arise from the previous ones and could be potentially resolved by effectively tackling the main barriers. R.Q. 4.--What is the future research direction for the chemical manufacturing companies after the LSSM implantation? The barriers and factors mentioned above can serve as a basis for future research on this topic. For instance, there is a real need to define a systematic framework that guides companies to a suitable adoption and get the most out of these methodologies within the chemical manufacturing sector. Standardizing the LSS curriculum would ensure that the knowledge and skills acquired by learners in different organizations are consistent and comparable. The latter will also encourage collaboration and facilitate the transferability of LSS skills across different sectors and organizations. Additionally, due to the lack of empirical studies published on this topic, research on LSS practices in this sector represents an excellent opportunity for future research. Last but not least, the existence of counter-intuitive or simply the non-existence of scenarios that need to be considered require the development of simulation models that consider multiple variables, their interactions, and variability [75]. The creation of simulation and optimization-based systems based on the application of LSS techniques in chemical manufacturing processes represents a novel and important challenge, as well as an advance in the way of minimizing the waste generated and the search for reliability.
## 4 Simulation Results
### Performance Evaluation of MMMR
Furthermore, we can notice that it is possible to have good metric combinations that improve packet losses and delay, as is the case for \"distance + density\" \((w_{1},w_{2},w_{3},w_{4})=(0.5,0,0.5,0)\) in the medium density scenario. In Tables 5 and 6, we summarize some combination of the weights in the low and medium density scenarios, respectively. Both tables show the percentage of losses and the average delay obtained with each combination of weights. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance** & **Trajectory** & **Density** & **ABE** & **\% Losses** & **Average Delay** \\ \(\mathbf{w_{1}}\) & \(\mathbf{w_{2}}\) & \(\mathbf{w_{3}}\) & \(\mathbf{w_{4}}\) & **[s]** \\ \hline
0.25 & 0.25 & 0.25 & 0.25 & 40.58 & 1.14 \\
1 & 0 & 0 & 0 & 44.36 & 1.22 \\
0 & 1 & 0 & 0 & 42.13 & 1.20 \\
0 & 0 & 1 & 0 & 45.47 & 1.27 \\
0 & 0 & 0 & 1 & 53.81 & 1.40 \\
0.5 & 0 & 0 & 0.5 & 41.75 & 1.16 \\
0 & 0.5 & 0 & 0.5 & 41.56 & 1.20 \\
**0.5** & **0** & **0.5** & **0** & **40.29** & **1.12** \\
0 & 0.5 & 0.5 & 0 & 45.75 & 1.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A summary of the results of the evaluation of some combination of the weights for each metric (in a medium density scenario). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance** & **Trajectory** & **Density** & **ABE** & **\% Losses** & **Average Delay** \\ \(\mathbf{w_{1}}\) & \(\mathbf{w_{2}}\) & \(\mathbf{w_{3}}\) & \(\mathbf{w_{4}}\) & **[s]** \\ \hline
**0.25** & **0.25** & **0.25** & **0.25** & **65.66** & **1.45** \\
1 & 0 & 0 & 0 & 79.17 & 2.26 \\
0 & 1 & 0 & 0 & 70.02 & 1.56 \\
0 & 0 & 1 & 0 & 72.79 & 1.91 \\
0 & 0 & 0 We can see in Table 5 that the best results, in terms of packet losses and average delay, are obtained with the MMMR combination of weights \((w_{1},w_{2},w_{3},w_{4})=(0.25,0.25,0.25,0.25)\). In the case of the medium density scenario (see Table 6), the best option is the combination \"distance + density\" \((w_{1},w_{2},w_{3},w_{4})=(0.5,0,0.5,0)\). The second best option is our proposal \((w_{1},w_{2},w_{3},w_{4})=(0.25,0.25,0.25,0.25)\), which, differently from the \"distance + density\" option, includes a bandwidth guarantee by using the ABE algorithm. The possible drawback of MMMR is the simple way of assigning weights to each metric, _i.e._, equitably.
## 4 Results
### PSIIRC Linear Optics Simulations with Different MC  
The example of dimer absorption modeling clearly shows how important it is to understand from what value of MC the obtained spectra can be considered sufficiently averaged. Naturally, the extreme high MC provides proper averaging; however, it is always better to know the moment when we can stop MCM without loss of accuracy to save computational time.  
Before starting the fitting of the measured experimental data, preliminary simulations of PSIIRC optical properties were made in order to estimate MC. We used the parameters of Table 1 from [40] to create the 8 \(\times\) 8 exciton Hamiltonian, Q\({}_{\text{y}}\) electron transition energies \(\Omega_{n}\) for each PSIIRC cofactor and \(J_{nm}\) interaction energies in the dipole-dipole approximation. The absorption profile of monomeric pigments was calculated in the same way as for the Chl dimer. We used the following set of MC to evaluate Equations (7)-(10): 100, 200, 500, 1000, 2000, 5000 and 10,000. Fluctuations of the diagonal elements of the Hamiltonian were estimated for \(FWHM_{\Omega}=180\) cm\({}^{-1}\). For each MC, linear spectra were simulated seven times and the differences between absorption spectra were calculated according to formula: \(\theta_{qr}^{\text{MC}}=\frac{1}{N}\sum_{n=1}^{N}\left[\sigma_{abs}^{\text{MC} }\left(\omega_{n}\right)_{q}-\sigma_{abs}^{\text{MC}}\left(\omega_{n}\right)_{ r}\right]^{2}\), where \(N=2048\), \(q,r=[1,2,\ldots,7]\) are the indexes enumerating PSIIRC spectra for each MC. The results of the simulation are listed in Table 1. Graphic visualization of \(\theta_{qr}^{\text{MC}}\) as well as histograms of fluctuation parameter distribution \(\delta_{1}^{j}\) for the first pigment in PSIIRC are represented in Figure 2. The differences between spectra calculated at MC = 10,000 and the other six values of MC are shown in Figure 3.  
Figure 3: The same data as in Figure 2. Demonstration of the differences between spectra calculated at MC = 10,000 and the other six values of MC. X-axis of each matrix corresponds to seven spectra with MC = 10,000, Y-axis enumerates spectra for MC = 100 (**A**), 200 (**B**), 500 (**C**), 1000 (**D**), 2000 (**E**), and 5000 (**F**). Color bars reflect the variations of \(\chi^{2}\).  
Figure 2: Distribution of the fluctuation parameter \(\delta_{1}^{j}\) for the first transition energy \(\Omega_{1}\) in the Hamiltonian (5) for the following MC values: 100 (**A**), 200 (**C**), 500 (**E**), 1000 (**G**), 2000 (**I**), 5000 (**K**), 10,000 (**M**). For each MC, absorption spectrum of PSIIRC was simulated seven times, and then the spectra were compared with each other by calculating \(\chi^{2}\) for each pair of 21 combinations of the spectra. The resulting matrices of \(\chi^{2}\) are shown for MC = 100 (**B**), 200 (**D**), 500 (**F**), 1000 (**H**), 2000 (**J**), 5000 (**L**), 10,000 (**N**). Color bars reflect the variations of \(\chi^{2}\).
## 2 Related Works
### Challenges and Solutions for Packet Disorder in Multipath Transmission
[14] explored multipath transmission in ICN using an independent name resolution system, treating it as a service within ICN and assigning the corresponding service MPSID. ICN routers bound with multipath service EIDs act as relay nodes to build multipath topologies. Building on this work, the authors further proposed a relay node selection method based on path correlation in [15], which reduces congestion and packet loss by avoiding path overlap, thereby lowering the likelihood of out-of-order arrivals. They also introduced a data scheduling algorithm based on path state value calculations, which reduces disorder by prioritizing paths with smaller RTTs and lower packet loss rates. In summary, discussions on reordering buffer management in TCP/IP networks are relatively straightforward, while in the ICN domain, most research has focused on optimizing the transmission process. This study addresses the gap by designing a reordering module at the receiver-side gateway for ICN multipath transmission scenarios and conducting an investigation into reordering buffer management strategies.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 4 Evaluation
### Separation Model Configuration  
The audio file format is mono and 16 kHz PCM (pulse code modulation). To obtain spectrogram features from audio signals, we applied STFT to each analysis frame of 64 milliseconds (1024 samples), while making a 25% overlap with a shift size of 16 milliseconds. After STFT, only the first half of the STFT frequency bins are used because the second half is the complex conjugate of the first one. The number of frequency bins of the STFT spectrogram in Equation (2) is the half of the frame size, \(F=1024/2=512\). The extracted spectrogram features are component-wisely rescaled so that all the elements belong to \([0,\ 1]\).  
The first baseline model is implemented by a stacked RNN similar to that in References [4; 5; 6]. The number of layers and the number of hidden nodes in each layer are set to 3 and 1024, respectively. ReLU activation functions are used for all layers including the output, because spectral magnitudes are nonnegative. Hence, no further post-processing is required. The second baseline is an U-Netimplemented by the description given in References [7; 29]. Its detailed architecture is given in Table 1. The encoder consists of six convolution layers with a kernel size of 5 and a stride of 2. Each layer uses batch normalization [33] and leakyReLU [34] with 0.2 sloop. The encoder consists of six transposed convolution layers with a kernel size of 5 and a stride of 2. As shown in Table 1, the number of channels of the encoder in each layer doubles in the next layer, except layer 0. At the decoder, the number of channels decreases by half in the next layer to reconstruct the spectrogram as the original size.  
Each layer uses a batch normalization and a plain ReLU activation function [34]. The first three layers of the decoder use dropout [35] with the drop probability of 0.5. All models are trained by Adam optimizer [36] with its initial learning rate of \(10^{-4}\) and a batch size of 128. The number of training steps of each model is 10,000. Baseline models use the mean squared error (MSE) loss given in Equation (4), and the proposed method uses the weighted loss function in Equation (16) with the weights computed by Equation (15). All the experiments are performed on an Intel i5 4-core desktop computer with 64 gigabytes main memory, equipped with NVIDIA GTX 1080 GPU with 8 gigabytes memory. The batch size (128) was determined considering that the GPU memory should hold all the weights of a single batch to compute the gradients of the weights at the same time.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations
### Doubly Fed Induction Generator Model  
The mathematical modeling of the Doubly Fed Induction Generator is defined by the expressions provided in [26; 27]. The \(dq\) components of both the stator and rotor voltages are defined as:  
\[\left\{\begin{array}{l}v_{ds}=R_{s}i_{ds}+\frac{d\psi_{ds}}{dt}- \omega_{s}\psi_{qs}\\ v_{qs}=R_{s}i_{qs}+\frac{d\psi_{qs}}{dt}+\omega_{s}\psi_{ds}\\ \end{array}\right. \tag{13}\] \[\left\{\begin{array}{l}v_{dr}=R_{r}i_{dr}+\frac{d\psi_{dr}}{dt}- \omega_{r}\psi_{qr}\\ v_{qr}=R_{r}i_{qr}+\frac{d\psi_{qr}}{dt}+\omega_{r}\psi_{dr}\\ \end{array}\right. \tag{14}\]  
with \(R_{s}\) and \(R_{r}\) are the stator and the rotor resistances, \(\omega_{s}\) and \(\omega_{r}\) are the stator and the rotor angular velocity, \(i_{ds}\) and \(i_{qs}\) are the \(d\)-\(q\) stator currents, \(i_{dr}\) and \(i_{qr}\) are the \(d\)-\(q\) rotor currents.  
The \(dq\) components of the flux linkage are defined by:  
\[\left\{\begin{array}{l}\psi_{ds}=L_{ss}i_{ds}+L_{m}i_{dr}\\ \psi_{qs}=L_{ss}i_{qs}+L_{m}i_{qr}\\ \end{array}\right. \tag{15}\] \[\left\{\begin{array}{l}\psi_{dr}=L_{rr}i_{dr}+L_{m}i_{ds}\\ \psi_{qr}=L_{rr}i_{qr}+L_{m}i_{qs}\\ \end{array}\right. \tag{16}\]  
with \(L_{ss}\) and \(L_{rr}\) are stator and rotor inductances and \(L_{m}\) the magnetizing inductance.  
The electromagnetic torque obtained from the DFIG is defined by:  
\[T_{e}=\frac{3}{2}p\left(\psi_{ds}i_{qs}-\psi_{qs}i_{ds}\right) \tag{17}\]  
with \(p\) is the pair pole number.  
The mechanical interaction between the turbine and the generator can be defined as:  
\[\frac{J}{p}\frac{d\omega_{r}}{dt}=T_{e}-T_{t} \tag{18}\]  
with \(J\) is the inertia of the system.  
Figure 3: Characteristic curves of the studied Wells turbine [20]. (**a**) Power coefficient vs. flow coefficient. (**b**) Torque coefficient vs. flow coefficient.
## 2 Materials and Methods
### Measurement of Flavor Compounds  
Flavor compounds in all samples were measured using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). Briefly, each sample (5 mL) was enclosed in a 20 mL SPME glass vial with 1.2 g of NaCl and 3 \(\upmu\)L of the internal standard 2-octanol (328.8 \(\upmu\)g/mL in absolute chromatographic grade ethanol). A DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA) was used to extract flavor compounds at 45 \({}^{\circ}\)C for 45 min. The compounds were desorbed and detected via GC-MS (7000D; Agilent Technologies, Santa Clara, CA, USA) equipped with an HP-5 MS column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m; Agilent Technologies, USA). The initial temperature of the oven was held at 40 \({}^{\circ}\)C for 2 min, and then increased to 140 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, heated at 10 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, and held for 5 min. The ion energy for the electron impact (EI) was kept at 70 eV and used a full scan ranging from 40 to 500 amu. The constituents were identified by matching the mass spectrum with the NIST14 and Kovats retention indices (RI). Semi-quantitative compound analysis was performed by comparing the area with the internal standard [12]. The odor activity value (OAV) was calculated according to OAV = C/T (C was the concentration of flavor compounds; T was the odor threshold of the compound). All sample analyses were carried out in triplicate.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### Variable Definition  
#### 3.2.1 Coordination Systems  
In this section, we describe the coordination systems used in the proposed model. We define the global coordination system as \((x,y,z)\). The camera coordination system is also defined as \((X,Y,Z)\). The \(Y\)-axis is aligned with the center line of the image. Note that the origin is set to the receiver camera at \((x_{c}(t),y_{c}(t),z_{c}(t))\). The elevation angle of the camera is defined as \(\theta\).