esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced SDR-Based Custom OFDM Protocol for Improved Data Rates in HF-NVIS Links
## 2 Materials and Methods
### Receiver Application Flow Chart Development  
The first receiving flowchart illustrates the reception of the signal and the first steps of processing. It starts with a UHD, a USRP source block that receives the signal from a USRP hardware device (Figure 5). A sample rate of 240 kHz and a center frequency of 7 MHz are configured on the USRP. This block provides the output of the received signal, which is split into two parallel paths for further processing.  
Each path contains a signal source block that generates a cosine waveform with a frequency of 50 kHz and a frequency of \(-50\) kHz, respectively. In order to demodulate the respective frequency components, these signals are multiplied by the received signal in  
Figure 4: Set OFDM channels on different subcarriers.  
Figure 5: Receiving IQ streaming and set OFDM channels at baseband.  
Multiply Blocks. The resulting waveforms are then displayed in the QT GUI Frequency Sink block, which displays the full spectrum and specific channels (24 kHz bandwidth) for analysis of the received waveform. After multiplication, each signal is passed through a low-pass filter to isolate the desired frequency component and reduce noise.  
The next flowchart focuses on OFDM demodulation and data recovery (Figure 6). It includes two OFDM receiver blocks that are configured to match the parameters of the transmitter, including the length of the FFT, the length of the cyclic prefix, and the modulation schemes (BPSK for the headers, QPSK for the payloads). These blocks are responsible for the demodulation of the incoming OFDM signals and the extraction of the data packets.  
The output from each OFDM receiver is passed to a tag debug block and then converted from unsigned char to float using UChar To Float blocks for visualization purposes. The demodulated data are then combined using a byte combiner block based on the packet_len tag, where the original data packets are reconstructed. The combined data are then written to a file using a file sink block. QT GUI Time Sink Blocks, which display the data over time for debugging purposes, are also used to visualize the demodulated data streams.
###### Abstract  
The energy dissipation of hydraulic structures is crucial to the overall safety and stability of hydraulic engineering projects. In order to isolate energy dissipators from hydraulic engineering projects and address the issues of vibration damage caused by the discharge structures, a new type of vertical jet energy dissipator was developed by placing crushing needles at the nozzle of the vertical jet pipeline. The crushing needles were mainly used to break the high-energy jet into several smaller jets. As the air is mixed with the water flow, the mechanical energy of the water flow is converted into internal energy and dissipated in the air. The structural parameters of the vertical jet energy dissipators include the size and number of crushing needles. In this paper, the first-order and second-order statistical characteristics and energy dissipation rate of vertical jet energy dissipators under different structural parameters are studied by means of numerical simulation and a physical model test. The results show that, within the scope of this study, the energy dissipation rate of a vertical jet increases with the increase in Reynolds number, the number of crushing needles, and the size of crushing needles; and the energy dissipation rate of the vertical jet increases by 1.04 to 4.89 times compared with that without crushing needles. Under the same Reynolds number, the height of the jet decreases with the increase in the number of crushing needles and the size of the crushing needles. With the vertical development of the flow, the vertical average velocity of the vertical jet energy dissipator is getting smaller and smaller. Adding crushing needles will hinder the upward diffusion of the jet, reduce the height of the jet, and accelerate the attenuation of the jet velocity. As a statistic result regarding the fluid stress, the Reynolds stress along the axis shows a slow upward trend at the potential core, soars at the shear layer, and finally decreases at the end of the jet. The flow has a higher convective transportation intensity in the lateral direction than in other directions. The addition of crushing needles can, to some extent, affect the fluid transport in that area, thereby altering the pressure in the region and dissipating the mechanical energy of the flow-induced vibrations when the jet impacts the crushing needles. The vertical jet-type energy dissipator proposed in this study addresses key engineering challenges, such as terrain constraints and the need for flexible design solutions. Its ability to efficiently dissipate energy while maintaining adaptability makes it a valuable tool for hydraulic engineers designing energy dissipation systems. The conclusions of this study provide a theoretical basis for the application of vertical jet energy dissipators.  
energy dissipators; vertical jet; statistical characteristics; energy dissipation rate analysis of vertical jet energy dissipators
# Mdpj _sciences_
## 3 Review of the DMRN Protocol
### Key Encapsulation Mechanisms (KEM)  
A Key Encapsulation Mechanism (KEM) is a cryptographic protocol designed to securely establish a shared secret key between two communicating parties. This key can then be used for symmetric encryption, where both parties can encrypt and decrypt messages using the same key. A KEM consists of three algorithm parts (KeyGen(), Encaps(), Decaps()), where  
* KeyGen(): Generates a (public) encapsulation key and a (private) decapsulation key (\(p_{k}\), \(s_{k}\)) through a non-deterministic process.
* Encaps(\(p_{k}\)): Utilizes the public encapsulation key (pk) to create a secret key \(k\) and corresponding ciphertext \(c=Enc(p_{k},\ k)\) via a non-deterministic algorithm.
* Decaps(\(s_{k}\), \(c\)): A deterministic key decapsulation algorithm that, given the private decapsulation key (\(s_{k}\)) and a ciphertext \(c\), either returns a secret key \(k\) or fails, being denoted by returning \(\bot\).
###### Abstract  
Changes in cell-matrix and cell-to-cell adhesion patterns are dramatically fostered by the microgravity exposure of living cells. The modification of adhesion properties could promote the emergence of a migrating and invasive phenotype. We previously demonstrated that short exposure to the simulated microgravity of human keratinocytes (HaCaT) promotes an early epithelial-mesenchymal transition (EMT). Herein, we developed this investigation to verify if the cells maintain the acquired invasive phenotype after an extended period of weightlessness exposure. We also evaluated cells' capability in recovering epithelial characteristics when seeded again into a normal gravitational field after short microgravity exposure. We evaluated the ultra-structural junctional features of HaCaT cells by Transmission Electron Microscopy and the distribution pattern of vinculin and E-cadherin by confocal microscopy, observing a rearrangement in cell-cell and cell-matrix interactions. These results are mirrored by data provided by migration and invasion biological assay. Overall, our studies demonstrate that after extended periods of microgravity, HaCaT cells recover an epithelial phenotype by re-establishing E-cadherin-based junctions and cytoskeleton remodeling, both being instrumental in promoting a mesenchymal-epithelial transition (MET). Those findings suggest that cytoskeletal changes noticed during the first weightlessness period have a transitory character, given that they are later reversed and followed by adaptive modifications through which cells miss the acquired mesenchymal phenotype.  
Keywords:simulated microgravity; focal adhesion; vinculin; E-cadherin; cytoskeleton; HaCaT cells; epithelial-mesenchymal transition (EMT) +
Footnote †: journal: Applied Sciences
## 4 Method
### Practical Algorithms: G-MAPPO and G-QMIX
#### 4.3.1 G-Mappo
```
0: Learning rate \(\alpha\), loss weights \(\lambda_{V}\) and \(\lambda_{I}\), number of groups \(K\), number of agents \(N\), max total time-steps \(T^{TOT}\), max episodic time-steps \(T^{EP}\), ppo-update times \(U\). Initialize: Network parameters \(\mathbf{\theta}\), \(\mathbf{\phi}\), and \(\mathbf{\psi}\), total running time-step \(t^{TOT}=0\). 1:while\(t^{TOT}\leq T^{TOT}\)do
2: Set episode step \(t=0\), receive initial state and observation \((s^{0},\mathbf{o}^{0})\) from the environment. 3: Initialize episodic replay buffer \(\mathcal{D}=\{\}\)
4:while\(t\leq T^{EP}\) and **not** terminated**do
5: Choose joint action \(\mathbf{a}^{t}\) using actor network \(\mathbf{\theta}\). 6: Interact with the environment using \(\mathbf{a}^{t}\) and receive reward \(t^{t}\), is_terminated_ and next state and observation \((s^{t+1},\mathbf{o}^{t+1})\). 7: Collect a transition tuple at \(t\) and update the replay buffer \(\mathcal{D}=\mathcal{D}\cup\{(\mathbf{\tau}^{t},s^{t},t^{t})\}\). 8:\(t=t+1\). 9:endwhile
10: Set ppo-update counter \(u=0\). 11:while\(u\leq U\)do
12: Calculate global value \(V_{\mathbf{\psi}}\) and advantage \(A_{\pi_{\mathbf{\theta}}}\) using critic network \(\mathbf{\phi}\). 13: Calculate joint prediction policy \(\theta_{\mathbf{\psi}}^{pred}\). 14: Calculate \(\mathcal{L}(\pi_{\mathbf{\theta}})\) following (8). 15: Calculate \(\mathcal{L}(V_{\mathbf{\phi}})\) following (9). 16: Calculate \(\mathcal{L}(\theta_{\mathbf{\psi}}^{pred})\) following (6). 17: Calculate total loss \(\mathcal{L}_{G-MAPPO}\) following (10) and update \(\mathbf{\theta}\), \(\mathbf{\phi}\), and \(\mathbf{\psi}\). 18:\(u=u+1\). 19:endwhile
20:\(t^{TOT}=t^{TOT}+t\). 21:endwhile
```  
**Algorithm 1** G-MAPPO
## 4 Experimental Results
### Comparative Experiment  
In order to verify the effect and performance of the proposed method, we applied the proposed method to the YOLOv8s model and conducted experiments on the BDD100K dataset. The experimental results are shown in Table 1. This experiment compared the performance of various detection models and evaluated them from multiple aspects, such as accuracy ([EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95), number of parameters, and computational complexity. The results show that ours model achieves the optimal balance between accuracy and efficiency. Compared with other models, ours shows excellent detection performance while maintaining a low parameter count (11.193 M) and computational complexity (30.1 G), especially in complex autonomous driving scenarios, where it exhibits strong generalization capabilities. Compared with the lightweight YOLO series, although ours has slightly higher computational complexity, its accuracy has been significantly improved. Compared with classic Faster-RCNN and SSD, ours not only significantly improves accuracy, but also avoids deployment problems caused by high parameter volume and computational complexity on resource-constrained devices.  
In general, compared with other classic detection algorithms, our method takes into account both detection accuracy and speed and achieves a good balance. At the same time, it also maintains a high level of detection performance under the strict standards of different IoU thresholds, showing good generalization performance and deployment potential, and is more suitable for application in the field of autonomous driving.
# Advances in Simulating Radiative Transfer in Complex Environments
## 1 Introduction
In previous versions of ENVI-met, however, the effects of different materials and surfaces were only calculated explicitly for primary radiation [37]. The distribution of secondary radiative fluxes (reflected shortwave radiation and longwave radiation emitted from objects) was, due to lack of memory and to save computational effort, carried out using a simplification, where instead of the actual reflected shortwave radiation and longwave radiation emitted, averages over all facades and surfaces within the model area were used [37]. This simplification could in some instances lead to a rather low accuracy that has been reported in evaluation studies comparing modeled against measured values of radiation and mean radiant temperature (MRT) [30; 38; 39; 40; 41; 42; 43; 44]. In this paper, a newly developed method is introduced to overcome these shortcomings. It enables the simulation of fluxes of reflected shortwave and emitted longwave radiation within the urban environment with a much higher level of detail, considering the actual objects, i.e., facades, surfaces, and trees \"seen\" by a grid cell. By simulating secondary radiation in greater detail, differences in local microclimates can be identified more clearly and influences of, e.g., highly reflective surfaces or vegetation cover onto bioclimate indices such as physiological equivalent temperature (PET) can be taken into account. In a large ENVI-met proof-of-concept simulation featuring a multitude of different surfaces and plants, the new method is compared against the previous approach. The comparison should evaluate the accuracy of the new radiation scheme and examine the impacts of the more accurate secondary radiation modeling on the simulated local microclimate.
# Tunable 60 GHz Multiwavelength Brillouin Erbium Fiber Laser
## 3 Results and Discussions
Increasing the pump power further, the third sextuple Stokes (18 th Stokes signal) was effective at a pump power value of 450 mW. On other hand, the tuning range also decreased linearly for all pump power ranges. Therefore, the pump power at which the Stokes signal number was saturated was chosen as 450 mW. Figure 7 shows the output spectrum of the generated sextuple Stokes signals at different Bp wavelengths (1560, 1565, 1570, 1580, 1585, and 1590 nm), which lies within the tuning range bandwidth. For all selected Bp wavelengths, the maximum power of Bp (25 mW) and pump power (450 mW) were used. Figure 7a,f at Bp wavelengths of 1560 and 1590 nm, respectively, show three sextuple Stokes signals only as compared to four sextuple Stokes signals generated at the other Bp wavelengths. The reason for this can be explained easily in that these two wavelengths lie at the edge of the free-running modes of the erbium gain, whereas the other Bp wavelengths (1565 to 1585 nm) lie at the central wavelength of these modes. For all generated Stokes signals, a low gain difference between the Bp and the sextuple Stokes signals, high Stokes signal powers, and a high OSNR of more than 55 dB were recorded. The spectrums of these sextuple Stokes signals prove the applicability of such Stokes signals in dense wavelength division multiplexing system applications. In addition, our conducted experimental results are compared with the previous different Brillouin frequency shifts in a multiwavelength fiber laser. Table 1 shows a summary of previous published works and our presented work. Figure 6: Pump power effect on generated Stokes signals and tuning range. Figure 8a,b shows the peak power stability of the generated Stokes signals (Bp, S6, S12, and S18) within a short (one hour: five minutes each) and long (12 h: one hour each) period, respectively. Stability was recorded at the maximum pump powers of the pump and Bp for these two periods. It is clear that S6 and S12 were stable within these periods. The reason for this can be attributed to the gain saturation of both S6 and S12, which makes them stable. On the other hand, a fluctuation of almost 2 dB from 6.5 to 8.5 dBm was observed within the two periods for S18, since this Stokes signal did not reach the saturation level. Figure 7: Different sextuple Stokes signal spectrum at Bp wavelengths of (**a**) 1560 nm, (**b**) 1565 nm, (**c**) 1570 nm, (**d**) 1580 nm, (**e**) 1585 nm, and (**f**) 1590 nm.
## 4 Equalizer Design Techniques  
In the techniques we discuss, the room response equalizer is designed on the basis of measurements of the RIR or RTF in one or more locations within the desired listening area. As we will see in Section 5, the room response is pre-processed in most cases in order to contrast some of the detrimental effects discussed in Section 3. In any case, a prototype room response is usually obtained and used for the equalizer design.  
Most of the equalizer design techniques can be classified into the following five classes:  
* Homomorphic filtering;
* Linear predictive coding (LPC) analysis;
* Least-squares (or other) optimization techniques;
* Frequency domain deconvolution;
* Multiple-input/multiple-output inverse theorem (MINT) solutions.  
The first two techniques are generally used for minimum-phase equalization, the latter three for mixed-phase equalization.
## 3 Results
### Results from GLMM for FMA-UE Score  
Table 2 presents the estimated factor levels for the FMA-UE score derived from the Generalized Mixed Linear Model. The analysis included all 93 patients, with fixed effects comprising the intervention period (Periods), intervention group (Group), and their interaction (Periods \(\times\) Group). The group coefficient (\(\beta\) = 1.363, SE = 0.447, \(p\) = 0.002) indicated that the shaking group had a significantly higher baseline FMA-UE score than the control group. The period coefficient (\(\beta\) = 0.467, SE = 0.298, \(p\) = 0.118) showed the overall change in FMA-UE score over time across both groups, which was not statistically significant. Importantly, the interaction term (\(\beta\) = 1.359, SE = 0.419, \(p\) = 0.001) indicated that the shaking group experienced a significantly greater improvement in FMA-UE scores over time than the control group, demonstrating the effectiveness of the intervention.  
Variance components were significant with group variance (Group Var = 25.368, SE = 4.721, \(p\) < 0.001, 95% CI [16.115, 34.621]), period variance (Periods Var = 25.057, SE = 4.150, \(p\) < 0.001, 95% CI [16.923, 33.191]), and the covariance between group and period (Group \(\times\) Periods Cov = 23.963, SE = 4.300, \(p\) < 0.001, 95% CI [15.534, 32.391]).
## 2 Materials and Methods  
### BOP Measurements and Headform Instrumentation  
The details on the blast exposure experiments were published elsewhere [29; 30; 31] and briefly described here to familiarize the informed reader with the experimental setup. The shock tube at the New Jersey Institute of Technology (NJIT) is a square cross-section (0.71 m \(\times\) 0.71 m) advanced blast simulator with an adjustable breech. The incident BOP is typically monitored along the length of the shock tube using seven high-frequency pressure sensors model 134A24 (PCB Piezotronics, Inc., Depew, NY, USA). In all experiments, the T5 sensor (located in the test section directly above the headform; see Figure 1 in reference [30] for details) was used to measure and quantify the incident overpressure waveforms characteristics. We used circular Mylar membranes with a standard thickness of 0.254 (1\(\times\)) mm and 0.726 (3\(\times\)) mm stacked together to generate shock waves with two discrete peak overpressures of 70 kPa (approximately 10 psi) in the test section.  
The instrumentation of the customized facial and ocular countermeasures safety (FOCUS) headform [32] was also described in detail in the recently published report [30]. Briefly, we used ten high-frequency piezoelectric sensors, model 102B06, manufactured by PCB Piezotronics (Depew, NY, USA), and mounted flush with the surface of the headform via threaded ports. These sensors are divided into two groups, medial, and circumferential: (1) five medial sensors (marked as H1 to H5, Figure 1B) are located along midline anterior-posterior in 45\({}^{\circ}\) intervals; and (2) five circumferential sensors are mounted in the following order: two on the right parietal side (H6 and H7, in 60\({}^{\circ}\) intervals), two in both eye sockets (H8 and H9), and one on the left parietal side of the headform (H10, 90\({}^{\circ}\) interval, Figure 1B). An in-house developed LabView 2016 software was used to capture BOP waveforms. All data were recorded at 1.0 MHz sampling frequency, and the typical acquisition time was 50 ms. The data acquisition system is based on PXI-6133 S Series multifunction DAQ modules and PXIe-1082 PCI Express chassis (National Instruments, Austin, TX, USA). The signals of pressure sensors were fed through 8-channel signal conditioners PCB 483C05 (PCB Piezotronics Inc., Depew, NY, USA) and did not require additional filtration.
## 3 Materials and Methods
### Model Architecture  
The main structure of YOLOv5 can be divided into 3 major parts: the backbone, neck and head. The backbone's main role is in extracting multi-scale visual features from input images by stacking multiple convolution blocks (convolution + batch normalization + sigmoid linear unit (SiLU) activation) and C3 modules [45]. The C3 module can be regarded as a specific implementation of CSPNet with 3 convolution modules. This module ensures that the backbone achieves a rich feature extraction ability along with reducing heavy inference computations that occur by duplicate gradients. A fast implementation of spatial pyramid pooling (SPPF) is attached at the last layer. Compared to the original spatial pyramid pooling (SPP) layer, this layer simplifies the repeated downsampling process by successive max-pooling layers. Its role is in assisting the multi-scale feature expression ability of the backbone and removing the fixed-size constraint of the network. In the neck, the down-sampled feature maps from the backbone are up-sampled to generate multiple new feature maps to detect objects of different scales. In the head, different scales of feature maps are gathered to yield final bounding box candidates and class labels. However, in the case of small object detection, the uppermost module of the head only has role in detecting large bounding boxes. Therefore, we detached the layer in the inference stage for faster  
Figure 1: Overall architecture of the proposed framework.  
calculations and sustained the layer in training stage to allow the model to learn more diverse features.  
Additionally, we attached an auxiliary domain classifier to the YOLOv5 base architecture. Based on this module, the domain adaptation strategy is implemented to the proposed framework. The module is comprised of an average pooling layer followed by four fully connected (FC) layers. The feature map is downsampled and flattened to a 256 dimension vector. The first three 256 dimension FC layers are followed by SiLU activation, and the last FC layer gives a 4-channel output of domain confidence. As the backbone extracts base features from input images, the role of the domain classifier is to infer the domain of the input image. To force the base network to extract domain-invariant features, we calculate domain loss with the output of the domain classifier in the direction of confusing the main classifier. The gradients are then backpropagated, allowing the model to extract visual-shape-oriented features, which are more domain-invariant. In this paper, the domain classifier network classifies 4 categories, which are a combination of two binary classes: (1) spectrum (visible light, infrared) and (2) data source (real and synthesized). Therefore, the domain classifier allows the model to learn its feature extraction capability from multiple datasets while maintaining the detection performance in the domain with less data.