esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 2 Numerical Simulation Method
### Numerical Simulation Method for Laser Atmospheric Transmission
\[u(r,z_{i})=F^{-1}\left\{\exp\left[-\frac{i\Delta z}{2k}\left(K_{x}^{2}+K_{y}^{2} \right)\right]\times F\left[e^{is(r,z_{i})}u(r,z_{i-1})\right]\right\} \tag{6}\]  
Repeat the above process for m times (where m is the number of phase screens) until the beam reaches the transmission distance L.  
Additionally, in the process of laser transmission through the marine atmosphere, the laser beam is affected by solid particles, aerosols, and other substances, leading to scattering, refraction, and absorption phenomena, which result in the attenuation of the laser signal. In this paper, we assume uniform horizontal extinction, and the path loss can be expressed by the following empirical formula [30]:  
\[h_{l}=\exp\left[-\int_{0}^{L}\sigma(\lambda)dz\right] \tag{7}\]  
where \(\sigma(\lambda)\) is the extinction coefficient, which is a parameter related to the wavelength \(\lambda\) and the horizontal visibility \(V_{h}\). The extinction coefficient is given by [30]:  
\[\sigma(\lambda)=\frac{3.912}{V_{h}}\left(\frac{\lambda}{550}\right)^{-q} \tag{8}\]  
where \(q\) is the correction factor for the wavelength, and its value is determined as follows:  
\[q= \begin{cases}0.585V_{h}^{1/3}&V_{h}<6\text{km}\\ 1.3&6\text{km}\leq V_{h}<50\text{km}\\ 1.6&V_{h}>50\text{km}\end{cases}  \tag{9}\]  
Thus, the intensity distribution information on the target surface can be expressed by the following:  
\[I(r,z=L)=\left|u(r,z=\text{L})\right|^{2}\exp\left[-\int_{0}^{L}\sigma(\lambda )\text{d}z\right] \tag{10}\]
# A Combined Method for Localizing Two Overlapping Acoustic Sources Based on Deep Learning
## 3 Sound Source Separation
### Data Preparation and Feature Extraction  
To illustrate the data preparation and feature extraction process for the source separation problem, we present a specific example. The separation task was considered in a closed acoustic environment, a room with dimensions of \(15\times 9\times 4\) m\({}^{3}\). An orthogonal microphone array was positioned at point (7.5, 4.5, 1.5) m, with an array size of \(d=0.2\) m. The acoustic sources were placed 2 m from the array center and were positioned at the same height as the array (1.5 m).  
To train the source separation model, signals from each microphone were used to simulate a wide range of possible source locations within the room. A total of 6000 directions were randomly and uniformly sampled from the range \([-180^{\circ},180^{\circ})\). For data synthesis, 300 audio files--each 500 ms long and sampled at 16 kHz--were randomly selected from the training portion (4620 files) of the TIMIT database [19]. Each audio file was assigned to 20 different randomly generated directions. These audio signals were treated as sound sources and were convolved with the corresponding room impulse responses (RIRs) to produce 6000 direction-specific audio files for each of the four microphones. Next, 3000 random pairs of audio files were selected, each representing two independent sources. These paired files were mixed to simulate overlapping source signals, resulting in 3000 mixed audio files (raw data) per microphone.  
The RIRs between each source and microphone were generated using the RIR Generator software [[https://github.com/ehabets/RIR-Generator](https://github.com/ehabets/RIR-Generator)]([https://github.com/ehabets/RIR-Generator](https://github.com/ehabets/RIR-Generator)) (accessed on 5 September 2024) [21], which is based on the image source method [22]. Assuming an RIR length of 4096 samples and a reverberation time RT60 = 0.36 s (the time it takes for the sound energy to decay by 60 dB), the software automatically determines the maximum order of reflections. Small rooms are less reverberant while bigger rooms are more reverberant. To achieve this, the RT60 follows linearly the volume as stated by [PERSON]'s formula [23].  
Feature extraction was performed by applying STFT, using a Hanning window of 256 points, corresponding to a 16 ms window length and yielding 129 frequency bins. To augment the dataset, a 25% overlap between consecutive temporal segments was introduced.  
The resulting spectrograms of the microphone signals were segmented into fixed-size blocks of 100 \(\times\) 129 (time frames \(\times\) frequency bins), ensuring consistent input dimensions for the neural network. Additionally, the spectrograms of the two individual sources were extracted to compute the ideal binary mask (IBM), which was used to calculate the loss function during model training. The final training dataset consisted of 23,040 samples, each with dimensions 100 \(\times\) 129.
## 3 Materials and Methods
### Static Adsorption and Desorption Experiments  
The following equations were used to quantify the capacity of adsorption and adsorption/desorption ratio.  
Capacity of adsorption:  
\[\mathrm{q_{e}}=\frac{(C_{0}-C_{e})\times V}{m} \tag{5}\]adsorption ratio:  
\[\mathrm{E}=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{e}})}{\mathrm{C}_{0}}\times 100\% \tag{6}\]  
where \(\mathrm{q}_{\mathrm{e}}\) (mg/g) represents the adsorption capacity; \(\mathrm{E}\) represents the adsorption ratio (%); \(\mathrm{C}_{0}\) and \(\mathrm{C}_{\mathrm{e}}\) are the concentrations of glucoerucin in solution at the initial and equilibrium time, respectively (mg/mL), \(\mathrm{V}\) and \(\mathrm{m}\) represents the volume of the sample solution (mL) and the mass of the matrix (g).  
Desorption ratio:  
\[\mathrm{D}=\frac{\mathrm{C}_{\mathrm{d}}\mathrm{V}_{\mathrm{d}}}{(\mathrm{C}_ {0}-\mathrm{C}_{\mathrm{e}})\times\mathrm{V}}\times 100\% \tag{7}\]  
where \(\mathrm{D}\) represents the desorption ratio (%), \(\mathrm{C}_{\mathrm{d}}\) represents the concentration of glucoerucin in the eluent (mg/mL), \(\mathrm{V}_{\mathrm{d}}\) represents the volume of the eluent (mL), and \(\mathrm{C}_{0}\), \(\mathrm{C}_{\mathrm{e}}\) and \(\mathrm{V}\) are the same as defined above.  
The static adsorption tests of crude extracts were carried out as follows: 0.5 g test adsorbent was put into a flask with a lid, 40 mL sample solution of glucoerucin extracts was added. The flask was then shaken at 150 rpm for 12 h at a constant temperature of 30 \({}^{\circ}\)C. The solutions before and after adsorption were analyzed by HPLC.  
After the adsorption process reached equilibrium, the adsorbents were washed by deionized water. Then desorbed using a desorption solution which was ammonia water solution or KCl solution and shaking for 12 h at 30 \({}^{\circ}\)C.  
The preliminary choice of adsorbent used to separate glucoerucin was evaluated by their capacities of adsorption and their ratios of adsorption and desorption. The adsorption and desorption properties were also compared, including the sample pH value and the concentration of ammonia water or KCl used for desorption. The adsorption isotherms of glucoerucin were studied. Their Langmuir and Freundlich equations were evaluated. In addition, adsorption kinetic curve was also evaluated by the pseudo-first-order kinetic model and pseudo-second-order kinetic model.
## 3 Study on Real-Time Testing of an Isolated Microgrid
### Case Base Implementation under Actual Laboratory Conditions  
The variability in daily PV generation profoundly impacts isolated system operations and the design of BESS or management systems. Analyzing the daily solar energy production curve with high and low fluctuations is crucial for optimizing PV equipment to suit specific project conditions. For emulation purposes, a power curve (\(P_{PV}\)) selected for this study (see Figure 6, highlighted in red) exhibits significant variability influenced by weather, location, and system characteristics. Figure 7 depicts the daily power curve based on actual measurements from the Micro-Grid Laboratory in Cuenca, Ecuador (geographical coordinates: \(-2.891918819933002\), \(-79.03857439068271\)). Choosing a highly variable generation profile is essential as it represents worst-case conditions for rigorously testing the V2G strategy and validating corrective actions. Managing grid stability and ESS efficiently under such variability poses substantial challenges, making it imperative to assess system response comprehensively under realistic conditions.  
Figure 8 shows the results of applying one hour of high variability PV production in the test system. As expected, integrating a generation source with high variability (Figure 8a) forces the operational synchronous generation to compensate for such power imbalances (Figure 8b). Due to this case's severe power control requirements, the grid frequency varies substantially (Figure 8c). Additionally, the voltage of each phase at the PCC also experiences significant fluctuations, adversely affecting the power quality of the isolated system. This experiment demonstrates the need to mitigate the operational issues of high-variability PV power integration in weak and isolated power systems.  
Figure 7: Typical power fluctuations in the daily PV production with a photographic record of the sun every hour during the day. Reproduced with permission from [33].
# Semantic Multigranularity Feature Learning for
## 4 Experiments
### Baselines  
Baselines in our experiments could be divided into three categories: the methods that extract global features, the methods that extract local features, and the FGVC methods that localize critical regions and fuse global and local features. Some dedicated efforts on RS image scene classification are also taken into account for comparison. These methods are briefly introduced in this subsection.  
* **Global methods**. The deep CNNs designed for generic image classification such as VGG [3] and ResNet [4] are used as global methods in our experiments. These networks have achieved SOTA performance on large-scale datasets, such as ImageNet [7].
* **Local methods**. Different from the designed networks for generic object recognition, local CNN-based methods such as bilinear-CNN [16] capture orderless statistical information without spatial information.
* **FGVC methods**. In the experiments, we select several FGVC methods related to the design idea of the proposed SMGFL-Net. In NTS-Net [9], critical regions with different sizes and aspect ratio are automatically selected through the region proposal network.  
It could fuse local and global features for recognition. ResNet50 is the backbone network of NTS-Net. DCL [17] encourages the backbone network to extract local features by destroying the spatial distribution of the training images. Another highly relevant study, progressive-MG [22], can learn multigranularity features through jigsaw puzzle generator with different patch size in a progressive way of training.
* **RS image scene classification methods.** The related methods in this category include the following: CNN-CapsNet [35] can achieve better affine transformation invariance; InceptionV3-CapsNet [35] achieves the best performance on NWPU-RESISC45 when the training ratios are 0.1 and 0.2; further, multilayer stacked covariance pooling (MSCP) [34] is applied to the VGG16 backbone and consistently outperforms single-layer models on three challenging datasets. MGML-FENet [36] has achieved SOTA performance on both NWPU-RESISC45 and AID.
# Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling
## 3 Results and Discussion  
### Application  
In this section, the performance adaption method with cluster sampling is applied to a real E-class gas turbine power plant. The gas-path schematic of the gas turbine and the measurable gas-path parameters are shown in Figure 2 and Table 1, respectively. The main parameters in ISO conditions are summarized in Table 2. It was a 127.6 MW gas turbine, with an system efficiency of 33.6%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Output power (MW) & 127.6 \\ System efficiency (\%) & 33.6 \\ Compressor pressure ratio & 12.75 \\ Turbine inlet temperature (K) & 1397.15 \\ Turbine outlet temperature (K) & 821.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of the gas turbine power plant in ISO conditions.  
The data came from the steady-state operating conditions of the E-class gas turbine from June to November, totally 146 data points. Some measurements of the collected field data are shown in Figure 4. The ranges of the measurements are fuel flow rate \(m_{f}\in[14.531\,\mathrm{lb/s},\,16.79\,\mathrm{lb/s}]\), compressor inlet temperature \(T_{1}\in[294.085\,\mathrm{K},315.054\,\mathrm{K}]\), power output \(P_{w}\in[100.717\,\mathrm{MW},120.850\,\mathrm{MW}]\), and turbine outlet temperature \(T_{4}\in[832.134\,\mathrm{K},849.513\,\mathrm{K}]\).
## 3 Experimental Data and Results
### Usefulness of Camera-Based Optical Communications in the Detection Process
#### 3.1.3 Projective Exposure of Regressive Transformations by Parameterizable Methods and Histograms
Thus, these cases lead to signal irradiation that can reach 0.076 uW/cm\({}^{2}\); the reference is taken into account based on optical irradiation measured at distances of 1 m. At a later stage, communication is improved by introducing an external filter to reduce the field of view. For distances up to 15 m according to the literature, FOV is compatible with mobile applications because the angle between the traffic light and the receiver is less than 3.5\({}^{\circ}\). Binarization as a standard process depends on the threshold and the performance formed between the direct connection with the power of ambient light. Therefore, to be able to expose data as real as possible and as close as possible to something true, the Otsu method was used for threshold comparisons and passing the level of ambient light. If we assume that the total number of recognized pixels is found in the main mapping, then RX should scan the entire frame in a set order, from right to left and from top to bottom, to reconsolidate the reconstruction process. In the initial cells, we had a percentage of about 60% of the total number of pixels on the black gradient; the remaining pixels were converted later. The last step was to turn the entire image into black pixels and the final illustration of the messages through the quantization process (see Figure 11). As a result of the measurements performed, the variety of transmission and the proposed distance do not show disturbances, and in the conditions in which a fixed position is maintained for the mobile device, the movement on the communication line cannot be disturbed. A captured background image that does not contain relevant objects is contrasted by focusing directly on the communication path. Both Rx and Tx were gradually exposed to various types of lighting conditions, gradually using more panoramic lighting with fluorescent lamps and more precisely ambient font noise. The cameras of the phones were used to maintain the Tx image; the cameras used have an optical stabilizer (OIS) and automatic adjustment with a 64-megapixel image sensor (see Figure 12). The filtering stage  
Figure 10: Detection process in an indoor scenario with disturbing lighting sources over a distance of 15 m.  
retains only those quadrilaterals that contain the necessary characteristics; calibrating the position determines the respective court extremely precisely. Bit rates of approximately 11 kbps were obtained with an exposure of 2 at an interval of 0.7 ms, which can be adjusted by the method used or the encoding type. The experimental evaluation can be defined as follows: the performance was shown by the success rate of image transfer and processing (BER); in this case, an error rate of approximately \(10^{-3}\) was obtained by correlating the data between transmission and reception.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.2 O-Du  
The O-DU is designed as a white box that performs the O-DU functions, such as upper L1 and lower L2 functions. The hardware includes a motherboard that contains a processing unit, memory, the internal I/O interfaces, and external connection ports. There are two split options for the O-DU, which are O-DU\({}_{(6)}\) and O-DU\({}_{(7-2)}\). WG4 considers the O-DU\({}_{(7-2)}\) functional split option due to the two competing interests. The first is to keep an O-RU as simple as possible, because size, weight, and power draw are the primary deciding considerations, and the more complex an O-RU, the larger, heavier, and more power-hungry the O-RU tends to be. The second is to have the interface at a higher level, which tends to reduce the interface throughput relative to a lower-level interface. However, the O-RU tends to be the more complex with higher levels of interface.  
The fronthaul and backhaul interface are used to carry the traffic between O-RU\({}_{(7-2)}\), \(FHM_{(7-2)}\), \(FHQ_{(7-2)\to 8}\) and O-DU\({}_{(7-2)}\), as well as O-CU and O-DU\({}_{(7-2)}\). The O-DU\({}_{(7-2)}\) design may also provide an interface for hardware accelerator option design. The other hardware functional components include synchronization and timing, the storage for software, hardware and system debugging interfaces, and board management controller, just to name a few; the O-DU\({}_{(7-2)}\) designer will make decision based on the specific needs of the implementation. Note that the O-DU\({}_{(7-2)}\) hardware reference design is also feasible for O-CU and integrated O-CU/O-DU\({}_{(7-2)}\).
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Five-Element Microphone Array Localization Algorithm in the X0Z Plane  
For the X0Z plane, M1, M3, M5, M6, and M0 form the second single-plane five-element microphone array, setting the rectangular coordinate parameters of the sound source as \(S_{1}(x_{1},z_{1},r_{1})\), and the spherical coordinates as \(S_{1}(r_{1},\theta_{1},\varphi_{1})\). According to the principle of sound source geometric localization, using the distance formula between S and M0, M1, M3, M5, and M6, \(S_{1}(x_{1},z_{1},r_{1})\) is derived:  
\[\left\{\begin{array}{l}x_{1}\approx\frac{\left(\tau_{30}-\tau_{10}\right) \left(4a^{2}-c^{2}n_{1}\right)}{4 am_{1}}\\ z_{1}\approx\frac{\left(\tau_{40}-\tau_{50}\right)\left(4a^{2}-c^{2}n_{1} \right)}{4 am_{1}}\\ r_{1}=\frac{4a^{2}-c^{2}n_{1}}{2m_{1}c}\end{array}\right. \tag{4}\]  
where \(m_{1}=\tau_{10}+\tau_{30}+\tau_{50}+\tau_{60}\), \(n_{1}={\tau_{10}}^{2}+{\tau_{30}}^{2}+{\tau_{50}}^{2}+{\tau_{60}}^{2}\), and the values of \(x_{1}\), \(z_{1}\), and \(r_{1}\) are related only to the time-delay values \(\tau_{10}\), \(\tau_{30}\), \(\tau_{50}\), and \(\tau_{60}\), the sound velocity c, and the array element spacing a.  
Combining Equations (2) and (4), we obtain \(S_{1}(r_{1},\theta_{1},\varphi_{1})\):  
\[\left\{\begin{array}{l}r_{1}=\frac{4a^{2}-c^{2}n_{1}}{2m_{1}c}\\ \theta_{1}=\textit{arcsin}\frac{c\left(\tau_{40}-\tau_{50}\right)}{2a}\\ \varphi_{1}=\textit{arccos}\frac{c\left(\tau_{30}-\tau_{10}\right)}{\sqrt{4a^ {2}-\left[c\left(\tau_{60}-\tau_{50}\right)\right]^{2}}}\end{array}\right. \tag{5}\]  
According to Equation (5), when the array element spacing a and sound velocity c are fixed, the value of the distance \(r_{1}\) from the sound source S to the coordinate origin, the elevation angle \(\theta_{1}\), and the horizontal angle \(\varphi_{1}\) are related only to \(\tau_{10}\), \(\tau_{30}\), \(\tau_{50}\), and \(\tau_{60}\).
## 3 Electronic Devices under Radiation Environment
Before predicting the reliability of this material in the proposed applications, the degradation mechanism of this material under the deep space environment was studied. They employed finite element analysis (FEA) to study the degradation physics, and their results were verified with their experiments. Their studies showed an elasticity degradation during the thermal shock, which induces lattice shift and dislocation slip in the microstructure. The dislocation piling up causes the effective elastic modulus of sintered joints to decline due to broken atomic bonds. With the understanding of the degradation physics, degradation model can be developed to extrapolate the lifetime of such material from accelerated test condition to predict its reliability and suitability in deep space usage. The increasing important of electronics operating under radiation environment can be seen from the above description, and from the above few works, one can see that there are still a lot more work needed in this area.
# Analysis and Design of Single-Phase Unidirectional Transducers with High Directivity
###### Abstract  
Electrode-width-controlled (EWC) single-phase unidirectional transducers (SPUDT) contribute to reduction of insertion loss of surface acoustic wave (SAW) devices due to their strong unidirectional properties. In this work, we propose a method to optimize the unidirectionality of EWC-SPUDT based on our research results that the unidirectionality of the EWC-SPUDT cell is strongly related to its reflectivity and its unidirectional angle. Furthermore, in order to ensure strong unidirectionality to achieve low insertion loss, a simulator based on the finite element method (FEM) is used to study the relationship between geometrical configuration of the EWC-SPUDT cell and its reflection coefficient, as well as its transduction coefficient. Simulation results indicate that the reflection coefficient of the optimized EWC-SPUDT cell composed of 128\({}^{\circ}\) YX lithium niobite (LiNbO\({}_{3}\)) substrate and Al electrodes with thickness of 0.3\(\upmu\)m reaches the optimal value of 5.17% when the unidirectional angle is designed to be \(-90^{\circ}\). A SAW delay line is developed with the optimized EWC-SPUDT cell without weighing, and the simulation results are verified by experiments. The experimental results show that the directivity exceeds 30 dB at the center frequency and the insertion loss is just 6.7 dB.  
single-phase unidirectional transducers (SPUDT); directivity; finite element method (FEM); Coupling-of-Modes (COM) theory +
Footnote †: journal: Applied Sciences
## 3 IEEE 802.15.6: The Key-Enabling Standard for WBANs
### Coexistence and Interoperability
#### 3.5.2 Channel Hopping  
A coordinator may change its operating channel periodically by choosing a particular channel hopping sequence, which is not being used by any neighbor coordinators. Moreover, the coordinator should hop to another channel after dwelling on the current channel for a fixed number of beacon periods and not in the middle of a super frame.  
A prospective WBAN coordinator should select a channel to operate prior to starting a new network, which could be done by performing a channel scan (_i.e._, active, passive or energy detection based) over a given list of channels in a specific frequency band. At the end of the scan, the coordinator may select an unoccupied channel for network operation. If no free channels have been detected, the coordinator selects an occupied channel and uses the information gathered during the scan to operate in Time Sharing, Offset Synchronization or Interference Mitigation mode. Further details on channel hopping can be found in section 6.13.2 of the standard.