esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sample Environment Components
### Sample Rack/Sample Magazines  
The sample rack (Figure 1) holds three magazines for up to 13 standard 1 mm cut-vettes (external dimensions: \(52\,\mathrm{mm}\cdot 12.5\,\mathrm{mm}\cdot 3.5\,\mathrm{mm}\)) each. The cuvettes can be flushed with dried air to prevent condensation when measuring at low temperatures. Each of the magazines has a separate connector (CBI, Staubli, Pfaffikon, Switzerland) for temperature control fluid and is insulated with PEEK, so that three individual temperatures can be used simultaneously, provided enough thermostats are present. Within each magazine, three positions can be equipped with in-cell Pt-100 thermometers (P0K1.161.2K.Y.5000-4.S, Innovative Sensor Technology IST AG, Ebnat-Kappel, Schweiz). The remaining cuvette temperatures can then be interpolated. The Pt-100 thermometers are read out with a temperature monitor (Model 224, Lake Shore Cryotronics, Westerville, OH, USA). The sample holder temperature is controlled with fluid pumped by a thermostat (FP50-HL, [PERSON], Seelbach, Germany).  
Figure 1: Photograph of the sample changer. The sample changer consists of a rack for 3 magazines each holding up to 13 cuvettes. Each magazine’s temperature can be controlled individually. The open sides can be flushed with dried air to prevent condensation, when measuring at low temperatures. This part of the setup can also be used for standard SANS experiments without DLS. The red arrows indicate the cuvette positions, that were used for the temperature measurements in Figure 2.  
Figure 2: Comparison of set-point temperature, bath temperature and the temperature in three different cuvettes inside the sample holder plotted against time.  
The sample aperture is 9 mm \(\cdot\) 20 mm for the incident beam and 10 mm \(\cdot\) 25 mm on the back. The largest scattering angle possible with this sample environment is 42.5\({}^{\circ}\) in the vertical direction. However, it should be noted that the possible scattering angle also depends on the actual configuration of the SANS instruments, e.g., the neutron spot size. However, the sample-to-aperture distance will be a compromise to allow for the incident light to reach the sample cells. The sample environment can be moved along the Y- and Z-axis (in the instrument coordinate system) to change the sample in the neutron beam and laser focus. This is accomplished with two linear translation stages (LS-180, Physik Instrumente, Karlsruhe, Germany).
## 2 Materials and Methods
### WioU Bounding Box Loss Function
#### 2.6.3 WioUv3  
WioUv3 operates based on the outlier degree of anchor frames. The outlier degree coefficient \(\beta\) is shown in Equation (6):  
\[\beta=\frac{L^{*}IoU}{\overline{\text{L}_{IoU}}}\in[0,+\infty) \tag{6}\]  
The smaller the outlier degree, this coefficient \(\beta\) ensures that anchor frames of lower quality exert less influence. In WioUv3, a nonmonotonic focusing coefficient is added to WioUv1. This addition assigns smaller gradient gains to normal quality anchor frames and larger gradients are assigned smaller gradient gains. The use of the outlier degree coefficient \(\beta\) prevents the effects of low quality frames.  
WioUv3 incorporates a nonmonotonic focusing coefficient \(r_{2}\) into WioUv1, as delineated in Equations (7) and (8).  
\[r_{2}=\frac{\beta}{\delta a^{\beta-\delta}} \tag{7}\]  
\[L_{WIoUv3}=r_{2}L_{WIoUv1} \tag{8}\]  
where the anchor frame will receive the maximum gradient gain when \(\beta=\mathbb{C}\). The normalization factor \(\overline{\text{L}_{IoU}}\) in WioUv3 undergoes dynamic transformation and updates, thus, allowing the quality evaluation metrics of the anchor frame to be dynamic. Consequently, WioUv3 can select the most suitable gradient gain allocation strategy for the current sample at varying times.  
This study employs the WioUv3 bounding box loss function to supplant the original CioU bounding box loss function. The WioU loss function mitigates the competitive influence of high-quality anchor frames and curbs the adverse impact of low-quality anchor frames. Combined with the dynamic gradient gain allocation strategy, this approach improves network robustness and augments the model's detection capability.
## 3 Results
### Results Regarding Energy and Distance covered by VIPV
#### 3.1.4 Daily Distance Covered by the VIPV  
Figure 8 presents the simulated average daily distance of VIPV on a passenger car for the three architectures of energy transfer considering battery saturation. When analyzing daily distance, values up to are without shading losses.  
Figure 8: Average daily distance covered by VIPV, passenger car, 0.312 kWp solar roof and 157 Wh/km electrical consumption with battery saturation/charge with the grid every night.  
Figure 7: Yearly distance covered by VIPV in Malaga and Paris, passenger car, 0.312 kWp solar roof, 50 kWh battery, direct charge in HV battery, effect of plugging frequency.  
Figure 8 indicates that for June, the sunniest month, the VIPV at midlife can cover an average distance up to 14 km/day (44 km/day/kWp) in Malaga, shading 0% and directly charging the main battery. The difference between architectures of energy transfer is less than 1 km. It shows, moreover, that in Paris (-average Europe), the VIPV can cover up to 8.7 km with 0% shading and 3.3 km with 49% shading.
# Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms
## 3 A Fractional-Order FOPID Controller Based on Intelligent Optimization Algorithms
### Fractional-Order Controller  
In 1999, Professor [PERSON] introduced the application of fractional-order differential equations in automatic control, marking a significant advancement in control theory. Unlike conventional PID controllers, fractional-order controllers offer enhanced dynamic response characteristics and improved disturbance rejection capabilities, making them more adaptable to complex systems. The structural block diagram of the fractional-order controller is shown in Figure 3.  
However, the performance of fractional-order controllers, including fractional-order PID (FOPID) controllers, heavily depends on the proper selection of their parameters.  
Figure 2: Overall structure of the drilling and anchoring Robot.  
Conventional tuning methods, such as the Ziegler-Nichols and Chien-Hrones-Reswick methods, work well in simple systems, but often yield suboptimal results in complex non-linear systems, particularly when significant model inaccuracies are present [18; 19]. Additionally, the parameter adjustment process is cumbersome, making it challenging to adapt to real-time changes in operating conditions. This limitation becomes even more pronounced for FOPID controllers, due to their higher-dimensional parameter space. To address these issues, many researchers have applied intelligent algorithms to the parameter tuning of PID and FOPID controllers. These algorithms offer significant advantages, including strong optimization capabilities, support for parallel computation, and effectiveness in multi-objective optimization. Therefore, this paper proposes the use of intelligent algorithms to optimize parameter tuning effectively.
# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 4 Experimental Evaluation
### Static Pressure Prediction in Operations with Different Capacity  
To further explore the performance of the LSTM-based predictor on untrained data, we tested the model trained on data from 4 th, 5 th, and 6 th floors. These floors require HVAC systems with the same configuration but different heat loads. The data from May to August on the 4 th floor were used as learning data, and the static pressures on the other two floors were predicted. The input data from the 4 th floor system consisted of 172,205 samples, and the test data from the 5 th and 6 th floors were 172,186 and 171,933, respectively. Note that the test data are all the data from the 5 th and 6 th floors.  
The predicted results for the static pressure on the 5 th floor are 0.298 mmAq for MAE, 1.964% for MAPE, and 0.898 for RMSE. The error characteristics are not as good as those for the 4 th floor, but it is a surprisingly good result considering that the set-point static pressure on the 5 th floor is 18 mmAq, which is twice that on the 4 th floor. The result for the 6 th floor is similar, with an MAE of 0.313 mmAq, MAPE of 2.062%, and RMSE of 0.954. The set-point static pressure on the 6 th floor was even higher at 20 mmAq, but the model trained on the 4 th data worked relatively well for the other floors. Figure 18 graphically compares the prediction and the real values measured by the sensors from May to July and the test period in August. For this experiment, we used the test period data from the 4 th floor in August. When the data are available, the static pressure for other floors with different capacities can be predicted.  
This result implies that, when the data from an AHU are not available, we can use those from other floors and create a trained model as a rough initial predictor. Then, as the data are obtained, we can train for the AHU to refine the model.  
Figure 17: Out-of-season forecast results of the LSTM-based predictor trained using data from May.
## 3 Numerical Results  
Figure 2a is the indoor environment layout, and the actual office was simulated for this paper. In this simulation, the dimension of the office was 5 m long, 10 m wide, and 4.5 m high. A partition, a wooden desk, and an iron cabinet were put inside the office. All the floors, walls, and ceilings were made of 20 cm thick concrete. The transmitter was placed at the center of the office at axis (2.5, 5.0, 2.5) m, and 105 receivers were uniformly distributed in the office, as shown in Figure 2b The height of the receivers was 1.5 m above the ground. The number of fingers for the rake receiver was 4 (\(N_{F}=4\)).  
In a real environment, the dielectric constant and conductivity change with the frequency. Therefore, the frequency dependence for the dielectric constant and conductivity was also considered in the simulation. Since there were many receivers, the average BER and the outage probability were used for evaluating the system.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 5 Bearing Fatigue Life Test
### Comparison Between Fatigue Life Test Results and Model Calculation Results
5, the error decreased from 20.9% to 2.7%, corresponding to a reduction of 18.2 percentage points; and for specimen No. 6, the error was reduced from 25.2% to 8.0%, constituting a decrease of 17.2 percentage points. Comparisons between the lubrication-corrected and De Mul (non-lubricated) models reveal significant convergence trends in the deviations between calculated and measured values across all three specimens. This confirms that the established lubrication-modified bearing fatigue life calculation model effectively enhances prediction accuracy. A further analysis indicates that the lubrication-considered model maintains an average difference of approximately 5.3% from the experimental data, representing a 16.1% improvement in prediction accuracy (mean value) compared with conventional models. These improvements demonstrate the critical role of lubrication factors in bearing life prediction and validate the engineering applicability of the lubrication modification modeling methodology proposed in this study.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Lithology Classification Based on Deep Learning
#### 2.3.1 Pol-SAR Data Pre-Processing  
* Speckle Reduction  
SAR images, as a consequence of their coherent nature, are subject to speckle, which decreases image quality and makes image description and interpretation difficult. Thus, a refined Lee filter [28] whose window size is \(9\times 9\) is applied to the Pol-SAR dataset as a pre-processing process to reduce speckles.  
* Superpixel Segmentation  
A superpixel is a perceptually consistent unit with the pixels in one group being similar in character and texture. Superpixel segmentation can divide an image into a number of small contiguous regions where the pixels in each region are homogeneous. It is thus a type of oversegmentation of an image. The pixels in the same superpixel are considered as the same category in this paper.  
The Pauli RGB image is formed with the intensities of three channels. After oversegmentation, the Pauli RGB image can be segmented as \(\{I_{1},\cdots,I_{N}\}\) using the SLIC superpixel generating algorithm [29]. After segmentation, all the pixels in a superpixel will be labeled in the same class, which can be expressed by the averaged coherency matrix \(\bar{T}_{j}\):  
\[\bar{T}_{j}=\frac{1}{n_{j}}\sum_{i=1}^{n_{j}}T_{i}, \tag{19}\]  
where \(n_{j}\) is the number of pixels in superpixel \(I_{j}\).  
Due to the set average processing, superpixel segmentation mitigates the influence of speckle noise compared with the original coherent matrices. To utilize the spatial features of Pol-SAR data, the polarimetric features used in the novel classification are all extracted from the superpixel segmented image.
# A Nodal Analysis Based Monitoring of an Electric Submersible Pump Operation in Multiphase Flow
## 4 Materials and Methods  
### Model Formulation  
An ESP converts fluid kinetic energy to hydraulic pressure when developing the required differential pressure. Performance is maximal when an ESP manages an incompressible fluid (e.g., water). Liquid with a density similar to water enhances pump performance; thus, fluid density directly affects ESP performance. Any density variation significantly affects the PIP and Pd. In practice, the fluid density significantly changes during oil production when the gas volume fraction (GVF) at the pump intake exceeds the design parameters. Pump operation fails; oil movement from the bottom hole to the surface decreases sharply, then stops [34]. To clarify the effect of free gas on ESP performance, an ESP well was divided into three compartments: from the pump discharge point to the wellhead, across the pump, and from the sand face to the ESP intake (Figure 1). The variation in each compartment was evaluated as the change in fluid density when the GVF increased. [PERSON] et al. [25] showed that a variation in Pd reflected a change in the upper ESP (Equation (1)), for example, at nodal point B of Figure 1. Equation (2) shows that differential pressure fluctuation across the pump can be affected by variations in fluid mixture density under identical pump head conditions; variations in PIP indicate events that occur below the pump (Equation (3)). The combination of these parameters defines the condition of the ESP well.  
\[\mathrm{P_{disch.}=P_{wh}+\Delta P_{garv.}+\Delta P_{fric.}} \tag{1}\]  
\[\mathrm{\Delta P_{pump}=H\times\rho_{mix}} \tag{2}\]  
\[\mathrm{PIP=P_{bh}-\left(\Delta P_{garv.}+\Delta P_{fric.}\right)} \tag{3}\]  
where \(\mathrm{P_{disch.}}\) (psi) and PIP (psi) are defined above, \(\mathrm{P_{wh}}\) is the wellhead pressure (psi), \(\mathrm{P_{bh}}\) is the bottom hole pressure, \(\mathrm{\Delta P_{garv.}}\) is the pressure loss related to gravity (psi), \(\mathrm{\Delta P_{fric.}}\) is the pressure loss related to friction, \(\mathrm{\Delta P_{pump}}\) (psi) is the differential pressure between \(\mathrm{P_{disch.}}\) and PIP (psi), H is the pumping head (ft), and \(\mathrm{\rho_{mix}}\) is the density of the fluid mixture (lb/ft3).
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
## 1 Introduction
In fact, this area of sign language recognition has many points in common with gesture recognition, and it is important to realize that there are several applications of machine learning in this area using neural and diffusion models, some of them convolutional, or even of deep learning techniques [12; 13; 14]. In general, robustness and/or accuracy increase when using multimodal acquisition systems. However, it also increases the information processing required and the complexity of integrating all sensors into the same hybrid acquisition system. It is also expected that in unimodal FMG systems, the number of sensors will strongly influence accuracy as they enable higher spatial resolution and the extraction of a greater number of features [2]. However, there are still several shortcomings [10; 15] that need to be addressed in order to be able to use FMG technology in commercial bionic prostheses. In this paper, we will describe the implementation of a bimodal EMG/FMG system using the physiological signal acquisition platform, BITalino (Plux Wireless Biosignals S.A., Lisbon, Portugal), to make the acquisition of these two different signals from BITalino EMG modules and Flexiforce(tm) sensors from Tekscan(tm). The simultaneous acquisition of EMG and FMG data was then performed, using BITalino and OpenSignals, as well as the optimization of the MATLAB routines for signal processing and onset/offset detection of the acquired signals, implemented in previous works within the scope of this same project [16]. These steps are crucial for the extraction of features and subsequent use of these features to train and compare Machine Learning models in gesture recognition through MATLAB's Classification Learner tool. Exploring the potential of this tool, two studies were developed with on the one hand, the objective of analyzing the impact of each of the characteristics on the success in the recognition of gestures and, on the other hand, the objective of optimizing the classification models, by adjusting some parameters of these same models, for example, the number of layers in a neural network or the number of neighbors in a nearest-neighbors model. Preliminary results point to significant gains in the effectiveness of the classification of gestures, in line with the conclusions of other studies [11; 17].
## 3 Results and Discussion
### Scenario 2
#### 3.2.1 BER Performance  
This part shows the BER performance of both SCU and SEU using scenario 2 for different modulation schemes. The results are given in Figures 14-17.  
In Figure 14, the BER results are derived using BPSK modulation for both SUs, i.e., SCU and SEU, using WHT-NOMA with IRC compared to conventional NOMA with and without IRC. Similar to scenario 1, it can be observed that using WHT-NOMA results in a performance gain of approximately 5-10 dB in SNR for different BER values for both SUs, i.e., SCU and SEU, compared to the conventional NOMA. The receiver performance of WHT-NOMA is better than conventional NOMA for both ideal and nonideal SIC cases. As the SCU is the high-gain user with low power assignment, and further, the SIC is also performed by SCU, it can be seen that the performance of SEU is better compared to SCU. Furthermore, it can also be observed that using IRC results in reduced BER for both SCU and SEU, in all cases showing the suppression of ICI caused by the interfering MU. A similar trend can be observed in Figure 15 where QPSK modulation is used with a WHT matrix of order 4 (\(H_{4}\) from (16)).  
For the higher-order modulation schemes, i.e., QAM and 8-QAM, the BER results for scenario 2 are shown in Figures 16 and 17, respectively. It can be observed that these results also follow the same trend as in Figures 14 and 15, i.e., approximately 5-10 dB performance gain in SNR for different values of BER using WHT-NOMA for both SCU and SEU, compared to conventional NOMA. This shows that there is a significant receiver performance gain in terms of the BER for WHT-NOMA compared with the conventional NOMA, and the use of IRC suppresses the ICI, especially at higher SNR values.
## 3 Network Model
### Flexible Modulation Format
Thus:  
\[\text{OSNR}_{m,D}\propto\frac{\bigg{[}\frac{\alpha|\beta_{2}|}{\ln\big{(} \frac{B}{B_{0}}\big{)}}\bigg{]}^{\frac{1}{3}}}{N_{s}10^{\frac{2\alpha L_{s}}{3 }}\gamma^{\frac{2}{3}}} \tag{4}\]  
\[\text{Q}_{m,D}^{2}\propto\text{OSNR}_{m,D}\propto\frac{\bigg{[}(\alpha|\beta _{2}|)/\ln\big{(}\frac{B}{B_{0}}\big{)}\bigg{]}^{\frac{1}{3}}}{nN_{s}10^{\frac {2\alpha L_{s}}{3}}\gamma^{\frac{2}{3}}} \tag{5}\]  
Let \(\text{Q}_{m,}^{2}=\text{Q}_{\text{F}}^{2}\), where \(\text{Q}_{\text{F}}^{2}\) is the FEC threshold, and suppose that \(\kappa\) is a scaling factor, with:  
\[\text{Q}_{m,D}^{2}=\frac{\kappa(\alpha|\beta_{2}|)^{\frac{1}{3}}}{N_{s}10^{ \frac{2\alpha L_{s}}{3}}\gamma^{\frac{2}{3}}} \tag{6}\]  
\[N_{s}=\frac{\kappa(\alpha|\beta_{2}|)^{\frac{1}{3}}}{\text{Q}_{m,D}^{2}10^{ \frac{2\alpha L_{s}}{3}}\gamma^{\frac{2}{3}}} \tag{7}\]  
Therefore, the achievable transmission distance that is achievable for a DP-\(n\)-QAM signal on the transmission link is given by:  
\[L_{\text{a}}=N_{s}L_{s}=\frac{\kappa(\alpha|\beta_{2}|)^{\frac{1}{3}}L_{s}}{ n10^{\frac{2\alpha L_{s}}{3}}\gamma^{\frac{2}{3}}\text{Q}_{\text{F}}^{2}} \tag{8}\]  
Although the MFs and the corresponding transmission distance of ultra-high-bit-rate signals are still under study, it can be deduced from Equation (1) that the transmission distance of DP-\(n\)-QAM signals is inversely proportional to the modulation format order \(n\). Therefore, assuming that transceivers with the same baud rate are used, the MFs and the transmission distance corresponding to the ultra-high-bit-rate signal can be obtained from the currently known transmission distance of DP-\(n\)-QAM signals. According to existing studies, a 400 Gb/s signal can be obtained by employing 96-Gbaud DP-16 QAM to meet 600 km transmission requirements. A 1000 km transmission of 400 Gb/s signals can be achieved using 96-Gbaud DP-QPSK [51; 52; 53]. In addition, \(\sim\)100 Gbaud optical transceiver technology is expected to mature in the next 2 to 3 years with the development of related standards and technologies [1]. Considering the above, assuming that 112-Gbaud transceivers are used, which transmit/receive a subcarrier that occupies 125 GHz, and then Table 2 can be derived. In the following contents, the spectrum width occupied by an OCh is defined as a spectrum block (SB) and is used as the spectral resource unit in this paper when studying the resource allocation scheme of SCNs. According to Table 2, the flexible MF selection in SCNs can be implemented. As shown in Figure 2, there are multiple MF schemes to choose from when routing path 1\(\rightarrow\)2\(\rightarrow\)3 needs to carry a 2 Tb/s traffic request. Each of the cases in Figure 2 can fulfill the transmission requirements, but the spectrum resources occupied are different. Depending on the distance of the different links of the routing path, the optimal MF combination can be selected.
###### Abstract  
For energy-neutral operation (ENO) of wireless sensor networks (WSNs), we apply a wireless powered communication network (WPCN) to a WSN with a hierarchical structure. In this hierarchical wireless powered sensor network (WPSN), sensor nodes with high harvesting energies and good link budgets have energy remaining after sending their data to the cluster head (CH), whereas the CH suffers from energy scarcity. Thus, we apply the simultaneous wireless information and power transfer (SWIPT) technique to the considered WPSN so that the sensor nodes can transfer their remaining energy to the CH while transmitting data in a cooperative manner. To maximize the achievable rate of sensing data while guaranteeing ENO, we propose a novel ENO framework, which provides a frame structure for SWIPT operation, rate improvement subject to ENO, SWIPT ratio optimization, as well as clustering and CH selection algorithm. The results of extensive simulations demonstrate that the proposed ENO based on SWIPT significantly improves the achievable rate and reduces the energy dissipated in the network while guaranteeing ENO, in comparison with the conventional schemes without SWIPT.  
energy-neutral operation; wireless powered sensor network; simultaneous wireless information and power transfer; energy harvesting; clustering Article