esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Classical Standard Static Measurements Approach  
Considering the IEC 60193 [21] standard recommendations, the performance test shall be performed at constant speed or at constant testing head. To facilitate the results post-processing, it is recommended to perform the tests at constant speed and water temperature in order to maintain the Reynolds number approximately constant. Then, to draw the tri-dimensional hydraulic efficiency surface, a sufficient number of measurement points covering the entire operating range must be considered.  
Now let us focus on a double-regulated turbine, such as a micro-turbine with two independent counter-rotating variable speed runners. In terms of hill-chart construction approach, the case is similar to a Kaplan turbine with variable angles of guide vanes and runner blades. Once the set of measurement points selected, the classical standard measurement approach consists of regulating one operating point, waiting several tens of seconds to ensure a stabilized flow condition and then perform the measurement, recording usually the average and the standard deviation values of each parameter from signals of several seconds of acquisition.  
The three main sequences of measurement/processing operations to draw the final efficiency hill-chart of the machine are illustrated in Figure 1. The first step consists on several sets of measurements at constant testing head H\({}_{\text{i}}\), using combinations of turbine runners' speeds (N\({}_{\text{A}}\) and N\({}_{\text{B}}\)) while keeping constant the speed ratio between the two runners, \(\alpha_{\text{i}}\). For each selected testing head, the result is a tri-dimensional efficiency hill-chart, figured in the right side of the first step. Then, in the second step, for each testing head, the variations of efficiency \(\eta\) with the discharge Q and with the head H are drawn for all sets of constant runners' speed ratio, and their envelope is calculated. In the third phase, the previously obtained envelopes are used to construct the final full 3D hill-chart of the machine by surface interpolation.  
Figure 1: Standard methodology to reconstruct the full hydraulic efficiency hill-chart of a double-regulated machine using the classical static measurements method: (**a**) First step: measurements of several efficiency hill-charts at constant testing head H\({}_{\text{i}}\) and constant speed ratio between the two runners, \(\alpha_{\text{i}}\); (**b**) Second step: calculation of discharge-efficiency and head-efficiency envelopes for all testing heads; (**c**) Third step: construction of the final full 3D hill-chart of the machine by surface interpolation using the previously obtained envelopes.
## 2 Background
### Lidar System Optimization for Control Applications
#### 2.4.2 Optimizing Lidars by Assessing the Accuracy of Simulated Lidar Measurements
With induction zone effects included, the optimal scan radii and preview distances for measuring rotor effective wind speed and shear were found to be 70% \(R\) (44 m) and 60% to 70% \(D\) (76-88 m),respectively. These values are only slightly smaller than the optimal parameters without induction zone effects.
# An Experimental Study on the Large-Volume Liquid Hydrogen Release in an Open Space
## 3 Results and Discussion
### Effect of Release Conditions
#### 3.5.1 Release Hole Diameter  
The release hole diameters are 100 mm and 50 mm in Test 7 and Test 8, and it takes 30 s and 60 s for Test 7 and Test 8 to release both 2.5 m\({}^{3}\) of liquid hydrogen. The average jet velocity of liquid hydrogen can be calculated by Equation (1):  
\[v=\frac{4q}{\pi d^{2}t} \tag{1}\]  
where \(v\) is the jet velocity of liquid hydrogen, \(q\) is the total volume of the released liquid hydrogen, \(d\) is the hole diameter, and \(t\) is the release duration. Therefore, the liquid hydrogen jet velocity in Test 8 is greater than that in Test 7; the diffusion of liquid hydrogen and cryogenic hydrogen clouds is expected to show different behaviors in these two tests. For Test 7, the liquid hydrogen jet velocity is smaller, but the released volume of liquid hydrogen per unit time is larger, so the liquid hydrogen released in Test 7 tends to accumulate on the ground more often compared to Test 8. The sensors that monitored significant hydrogen concentration fluctuations in both tests are N28 and N29 at a height of 2 m and N13 at a height of 8 m, as shown in Figure 9,b.  
The hydrogen concentration fluctuations at sensors N28 and N29 in Test 7 and Test 8 are compared in Figures 10 and 11. Unsurprisingly, the near-source sensors N28 and N29 always monitor the hydrogen concentration fluctuations earlier in Test 8 due to the higher jet velocity. However, when it comes to the far-field sensor N13, the hydrogen concentration fluctuations are monitored in Test 7 about 13 s earlier than in Test 8, as presented in Figure 12.  
Figure 8: The visible cloud behaviors after terminating the release in the atmosphere in Test 8.  
In Test 7 and Test 8, the liquid hydrogen pools are almost confined within the cobble-stone cover due to the high flow resistance, so the fluctuation in hydrogen concentration at the far-field sensor should be more dependent on spontaneous hydrogen diffusion. As previously mentioned, the liquid hydrogen jet in Test 7 tends to accumulate more on the ground, the near-source concentration of the cryogenic hydrogen cloud is higher, and the temperature difference between the cryogenic hydrogen cloud and the atmosphere is larger, as shown in Figure 13, and therefore the spontaneous hydrogen diffusion will be enhanced, which leads to an earlier monitoring of hydrogen concentration fluctuations in the far-field sensors. In addition, for sensor 13 in Test 7, the temperature here starts to decrease significantly at 5 s, and accordingly, the hydrogen concentration starts to increase dramatically at this point. Similarly in Test 8, at about 18 s, the temperature at sensor 13 begins to decrease and the concentration of hydrogen begins to increase. The results indicate a correlation between hydrogen concentration and temperature fluctuations.  
Figure 10: Comparison of hydrogen concentration fluctuations at sensor N28.  
Figure 9: The sensors acquired hydrogen concentration fluctuations at heights 2 m and 8 m.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 4 Results  
In this part, the experimental setup is introduced firstly. A variety of evaluation indicators are then used to comprehensively evaluate the experimental results. In order to prove the effectiveness of the proposed method, experiments were conducted on UC Merced and SIRI-WHU datasets. Furthermore, the accuracy of the proposed method is compared with traditional methods and deep learning methods. In order to verify the method proposed in this paper, seven models are created for relevant verification. For the sake of simplicity, each model is named according to its characteristic, as shown in Table 1. The following is a detailed explanation.  
(a) WMS-RFPNet represents the convolutional neural network with RFPNet structure and all optimization methods but without the multi-view scaling strategy.  
(b) MFPNet represents the convolutional neural network that adopts the multi-view scaling strategy, fusion strategy of pooling layer feature maps, and all optimization methods, but does not use the residual block.  
(c) MRNet represents the convolutional neural network that adopts the multi-view scaling strategy, the residual block, and all optimization methods but does not adopt the fusion strategy of pooling layer feature maps.  
(d) WD-RFPNet represents the convolutional neural network with the multi-view scaling strategy, RFPNet structure, parameter norm penalty, and the moving average model, but without the Dropout strategy.  
(e) WPNP-RFPNet represents the convolutional neural network with the multi-view scaling strategy, RFPNet structure, Dropout, and the moving average model, but without the parameter norm penalty strategy.  
(f) WWAM-RFPNet represents the convolutional neural network with the multi-view scaling strategy, RFPNet structure, Dropout, and parameter norm penalty, but without the moving average model.  
(g) RFPNet represents the proposed method with the multi-view scaling strategy, RFPNet structure, and all optimization methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Multi-View** & **The** & **Fusion** & & **Parameter** & **Moving** \\  & **Scaling** & **Residual** & **Strategy** & & **Norm** & **Average** \\  & **Strategy** & **Block** & & & **Penalty** & **Model** \\ \hline WMS-RFPNet & & & & & & \\ MFPNet & \(\surd\) & & & & & \\ MRNet & \(\surd\) & & & & & \\ WD-RFPNet & \(\surd\) & & & & & \\ WPNP-RFPNet & \(\surd\) & & & & & \\ WWAM-RFPNet & \(\surd\) & & & & & \\ RFPNet & \(\surd\) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The models created for verification.
## 3 Instrumental Analytical Techniques
### Gas Chromatography (GC)
#### 3.1.2 Gas Chromatography Coupled to an Electroantennography Detector (GC-EAD)  
In this technique, the compounds partitioned in chromatographic columns are analyzed on an EAD, which consists of two electrodes (one for recording, and the other one for reference) connected to a recently excised insect olfactory organ (antenna), head, or even the whole body from alive or sacrificed individuals [13]. When active VOCs touch the biological receptor, an electric potential difference is produced as the compound passes through with a signal commensurate to the concentration and response factor that is produced in the insect, and for compounds with no stimulating effect, the electric potential remains without remarkable alterations. The electric potential difference produces a current that is measured by an electrode, and then an electroantennogram is generated.  
When a GC-EAD is utilized for VOC profiling as a function of antennal response, the real-time recognition of chromatographic peaks having such an effect can be achieved [102]. Nevertheless, the chemical identity of profiled compounds cannot be determined when using this method separately, and for this reason, it is common that the EAD is used in complement with FID in split configuration. Additionally, EAD does not provide accurate information about the type of effect that a compound produces on a live insect because it only shows whether an olfactive response is generated. The utilization of GC-EAD has been intensively used since the 1980s, and some examples include the investigation of pollinator-attractive compounds [21; 41], parasitoids [37], pests [67], and the identification of pheromones in complex extracts [29].
# An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network
## 3 Algorithm Principle
### FOA Optimized BP Algorithm  
In order to make up for the shortcomings of the BP algorithm, this paper uses the FOA algorithm to optimize the parameters of the BP algorithm and improve the generalization ability and output accuracy of the BP algorithm. The algorithm flow is shown in Figure 6.  
According to Equation (6), \(p_{x},\ p_{y},\ p_{z},\ \alpha,\ \beta,\ \gamma\) are input, and \(\theta_{1},\ \theta_{2},\ \theta_{3}\) are output. A BP neural network structure is constructed with six inputs and three outputs. The input and output samples are normalized. The initial weight and initial threshold of the neural network are randomly generated. In the FOA optimized BP neural network algorithm, the initial weights and initial thresholds are used as the initial position of the fruit fly. The seven steps of the fruit fly optimization algorithm are followed, the value of the corresponding fitness calculated, and then the optimal weight and threshold are found through iteration.  
Figure 6: The flow chart of the FOA optimized BP algorithm.  
Among them, the fitness function is represented by the variance function of the joint angle output by the BP and the expected joint angle, expressed as:  
\[J(k)\ =\ \frac{1}{N}\sum_{i=1}^{N}\left(\theta_{i}(k)\ -\ \theta_{i}\right)^{2} \tag{13}\]  
where \(N\) represents the number of training samples, \(\theta_{i}(k)\) represents the output of the \(i\)-th sample at the \(k\)-th iteration, and \(\theta_{i}\) represents the expected output value of the \(i\)-th sample.  
The optimal weights and thresholds found by the fruit fly optimization algorithm are used as the initial weights and initial thresholds of the BP algorithm to train the neural network. Finally, the trained neural network is used to solve the inverse kinematics. In the FOA optimized BP algorithm, the fruit fly population size is set to 30 and set up to 50 iterations.  
The FOA optimized BP algorithm uses the FOA algorithm to find the global optimum, and the BP algorithm to find the local optimum. The algorithm's convergence speed, generalization ability, and convergence accuracy are improved.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
#### 4.3.3 Comparison and Analysis on Real Data Streams  
This section presents the experimental results on the real data streams. As shown in Tables 2-6, the AdaAL-MID ranked first in recall and G-mean on the diabetes data stream, first in accuracy on the hypothyroid and connect-4 data streams, and first in F1-score on the shuttle data stream. It is worth noting that in multiclass classification evaluation metrics, G-mean, as the geometric mean of class recall rates, may be 0, for example, LB, SRP, ROALE-DI, and MicFool-E for the shuttle data stream. This is because in real multiclass data streams, instances of the smallest class may be extremely limited, resulting in a class recall rate of 0. Although the AdaAL-MID's performance was relatively inferior to supervised learning algorithms on some data streams, the applicability of each supervised algorithm varied. It can be observed that no single algorithm can maintain a first rank on all data streams. The AdaAL-MID's average rankings for the five indicators of real data streams were 2.50, 3.00, 2.83, 2.67, and 3.33. With the exception of being lower than the SRP's 2.00 on accuracy and lower than the ROSE's 2.50 and SRP's 3.00 on kappa, it ranked first for the other three indicators. In addition, the AdaAL-MID's performance on each real data stream was superior to the current state-of-the-art active learning algorithms.  
Figure 6 displays the real-time recall change curves of all algorithms on the five real data streams. On the Covtype, hypothyroid, connect-4, and diabetes data streams, the AdaAL-MID outperformed the other active learning algorithms significantly, except for the initial stage of the Shuttle data stream, where it performed relatively poorly compared to CALMID. Except for the hypothyroid data stream, the AdaAL-MID exhibited a competitive performance against the supervised algorithms and outperformed the current state-of-the-art ensemble algorithm ROSE on four data streams. Additionally, while some algorithms may perform well on certain data streams, their performances may deteriorate on others. For example, although SRP's recall rate on the Covtype data stream was significantly better than the other algorithms, its performance on the Shuttle and hypothyroid data streams  
Figure 5: The recall curves of the comparison algorithms for the variable class proportion data streams.  
was basically at the bottom. It can be seen that the AdaAL-MID exhibits strong adaptability to different types of real data streams.
## 4 Factors with Unknown Functions  
In contrast to GreA/B and DksA (TraR), other SCBFs, such as Gfh1 (Figure 1B) from _Deinococcus-Thermus_ phylum, and Rnk (Figure 1B) from Gram-negative proteobacteria, do not appear to have any specific physiological role of their own. The best-studied example of this type of factor is Gfh1, which inhibits all the catalytic activities of RNAP--NTP addition, intrinsic hydrolysis and pyrophospholysis [33,55]. In a manner similar to the Gre factors, a coiled-coil domain of Gfh1 displaces the TL catalytic domain away from the active center [27] (Figure 1C), causing inhibition of transcription [37]. However, unlike the Gre factors, Gfh1 cannot hydrolyse RNA, despite the presence of conserved acidic residues at the tip of its coiled-coil domain. Interestingly, Gfh1 is active only at acidic pHs, at which it adopts GreA-like conformation. A pH increase causes inversion of the Gfh1 C-terminal domain relative to its N-terminal domain, abolishing the ability of Gfh1 to bind RNAP [33]. This pH-dependent activation mechanism suggests that Gfh1 modulates transcription under conditions where intracellular pH is lowered. Recently, a functionally similar SCBF from _Mycobacterium tuberculosis_, Rv3788, has been characterized [34]. Rv3788 competes with GreA for binding to RNAP, prevents nucleotide addition, and its two conserved acidic residues are critical for this inhibition [34]. The inhibition is also stronger at lower pH, suggesting a structural similarity with Gfh1.  
The _E.coli_ factor Rnk is currently assigned as regulator of nucleoside diphosphate kinase [18]. Although Rnk is structurally similar to the other SCBFs, its N-terminal coiled-coil domain is much shorter, and would not reach the RNAP active center if bound in a manner similar to Gre or Gfh1 (Figure 1B). Additionally, its C-terminal domain is turned 104\({}^{\circ}\) compared to that of GreA. Nevertheless, it has been shown that Rnk can bind RNAP and competes with Gre factors and DksA for this binding [18]. _E. coli_ cells deleted for Rnk have no phenotype [18] in normal growth conditions.  
Gfh1, Rv3788 and Rnk are all able to displace bound Gre proteins and DksA from RNAP _in vitro_, due to their higher affinity for RNAP, but the significance of this _in vivo_ remains to be shown. It is possible that these factors may act as anti-Gre and/or anti-DksA factors. It is also possible that the observed inhibitory activity of some of these factors may make a significant contribution to cell maintenance in some as yet unknown conditions.
## 3 Methodologies
### Distance and Perplexity Minimization  
Knowledge distillation can be seen as a problem of transferring information from a teacher model to a student model, aiming to align their levels of entropy. From the perspective of natural language processing, this alignment can also be understood in terms of perplexity. Perplexity is used to measure how well a probabilistic model predicts a given sample. In our case, _sBert_ demonstrates superior representational power compared to _FastText_, which enhances its robustness in prediction accuracy.  
Figure 2: The _Distance Minimization_ model consists of a word-embedding layer for updating _FastText_ word embeddings, a transformer encoder structure which projects the outputs of the encoder, and combined loss for comparing the distance with _sBert_ sentence vectors.  
To facilitate the information transfer, we propose utilizing a sequence-to-sequence transformer model (Figure 3). In this method, the word embedding layers are shared between the encoder and the decoder parts of the model. The input sentences are initially processed by the encoder, during which a set of representations are generated for each word based on their positions in the sentences. These representations are then provided to the cross-attention module within the decoder. For each position in the target sequence, the decoder leverages the cross-attention module to interpret the relationship between the encoder's representations (key-value pairs) and its own queries to predict the subsequent word in the output sequence, utilizing cross-entropy for training, hence minimizing the perplexity values. After the decoder generates a set of representations for each word in the output sequence, these representations are then averaged to obtain sentence vectors. Finally, all generated sentence vectors are compared with their _sBert_ counterparts.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 2 Classification and Progress of Power Industry Optical Wireless Communications
### Optical Wireless Power Inspection
#### 2.5.2 Optical Wireless Electric Tunnel Inspection  
In the course of tunnel inspection, power tunnel detection is a dangerous and complicated task. Since the length of the tunnel is long and relatively closed, general network signals cannot be transmitted, patrol officers may be out of touch, not to mention the real-time detection of data sent back. The inspectors hope to keep communication with the ground centralized control center. In this way, in the event of a line failure or accident, it will keep in touch with the ground centralized control center, and work together to troubleshoot and carry out on-site rescue. At the same time, since the tunnel patrol is a dangerous work, it has a strong demand for personnel positioning and navigation. However, because the cable tunnel is usually located at a depth of more than 10 m underground, and the influence of the structural characteristics and electromagnetic characteristics of the tunnel, all kinds of RF signals in the tunnel are seriously attenuated, which makes it difficult to satisfy the above requirements.  
IWOC-PLC system could solve the above problems of substation and cable tunnel patrol. This is because when LED lights are used to communicate in substations or cable tunnels, they neither generate radio waves nor interfere with equipment in substations or cable tunnels. Using the LED as the communication node in the workstation, inspectors can always maintain a network connection with the background operating system, obtain information services and conduct confidential communication during the inspection. At the same time, it can precisely locate inspectors and the equipment they operate.  
In [81], [PERSON] et al. proposed an intelligent detection network structure for substations or cable tunnels. Where the working data of the business system is transmitted through the network to the lighting LEDs in different positions in the substation or cable tunnel; the developed light source transceiver modulates the data into light signals and loads them on the LEDs for transmission. On-site inspection personnel receive VLC signals through user transceivers to realize data communication services and high-precision positioning services. At the same time, the user transceiver uses visible light signals to uplink location information and communication data to realize real-time monitoring and interaction between on-site operators and background managers.  
Another possible solution is a LiFi+PLC-IoT solution. Since power tunnels lack wires the most, allowing the wires to transmit data and tunnel ceiling lights to act as transmitters form a complete solution. Wherever they go, the inspectors can send back data in real-time and talk to the people behind them in real time, at speeds currently as high as 20 Mbps.