esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluated Performance
### Acoustically Transported Power  
The time-averaged rate of acoustically-transported mechanical energy is denoted as acoustic power \(\dot{W}\) [W], while the time-averaged rate of acoustically-transported thermal energy is denoted as acoustical thermal power \(\dot{Q}\) [W] [12]. Acoustic power \(\dot{W}\) and acoustical thermal power \(\dot{Q}\) are expressed by oscillatory pressure \(P\) and the cross-sectional mean of oscillatory velocity, \(U\), and they are given in Equations (1) and (2) [2,13] as follows:  
\[\dot{W}=\frac{A}{2}\mathrm{Re}[P\dot{\Omega}], \tag{1}\]  
\[\begin{array}{ll}\dot{Q}&=\frac{4}{2}\mathrm{Re}\left[P\dot{\Omega}\left( \frac{\delta_{v}-\chi_{\alpha}}{(1+\sigma)(1-\delta_{v})}\right)\right]\\ &-\frac{A\rho_{m}\epsilon_{p}||\mathcal{U}^{2}}{2\omega(1-\sigma^{2})|1-\chi_ {\
u}|^{2}}\mathrm{Im}\left[\chi_{\alpha}+\sigma\hat{\chi}_{\
u}\right]\frac{dT _{\pi}}{dx}.\end{array} \tag{2}\]  
In the two equations, the tilde refers to complex conjugation, \(x\) denotes the axial coordinate along the looped tube, \(A\) denotes cross-sectional area of the tube, \(T_{m}\) denotes time-averaged gas temperature, and \(\rho_{m}\), \(\gamma\), \(\epsilon_{p}\), and \(\sigma\) denote mean density, specific heat ratio, specific heat at constant pressure, and Prandtl number of the working gas, respectively. Additionally, \(\chi_{\alpha}\) and \(\chi_{\
u}\) represent the thermoacoustic functions that depend on the ratio of the radius \(r\) of the flow channel(s) and the penetration depth \(\delta\)[3], where \(\delta\) is given by \(\delta=\sqrt{2\kappa/(c_{p}\rho_{m}\omega)}\), in which \(\omega\) denotes angular frequency of the acousticwave, and \(\kappa\) denotes thermal conductivity of the working gas. In the study, it is assumed that the heat capacity of the tube wall significantly exceeds that of the working gas. This assumption allows setting the value of the tube-wall temperature constant.
# Research on a Piezoelectric Pump with Flexible Valves
## 4 Experimental Results and Discussion  
### Fabrication  
In order to verify the availability of the pump and the correctness of the theoretical analysis, a prototype of the PPFV has been fabricated. To facilitate the observation of the state of the valve, the pump body and the valve covers were fabricated with transparent photosensitive resin and 3D-printed. The flexible valves were made of brass and their shape was produced by laser-cutting technology. Piezo-element (KS-412T19A, Cosson, Dongguan, China) was used as the PZT vibrator, which was mainly composed of a PZT-8 ceramic wafer and a brass substrate. Photos of the prototype are shown in Figure 4. The dimensions of the prototype are shown in Table 1, and the size parameters of the PZT vibrator are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Size (mm)** \\ \hline \(Y_{0}\) & 0.8 \\ \(L\) & 5 \\ \(h\) & 1.9 \\ \(b\) & 0.05 \\ Flow channel width & 3.8 \\ Pump chamber diameter & 37 \\ Pump chamber height & 1 \\ \hline \end{tabular}
\end{table}
Table 1: The size of the PPFV.  
After preparing the parts needed for the manufacturing of the prototype, it was assembled and combined. Firstly, the PZT vibrator was fixed in the center of the pump body with epoxy adhesion. Secondly, the flexible valves were made and installed. The uniform hinges on the leaflets were made with a sharp blade, the fixed ends of the leaflets were glued into the fixing grooves of the pump body with vulcanized silicone rubber, and the initial clearance of the free ends was adjusted with tweezers under the super depth microscope system (VHX-600, Keyence, Osaka, Japan). Finally, the valve covers were glued to the corresponding position of the pump body with vulcanized silicone rubber to seal the flow channel of the pump body.
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
## 2 Material and Methods  
The main features of the TETRA standard are [24]: 25 kHz channel bandwidth; 200 channels in a 5 MHz frequency band; duplex working mode (between 380 and 385 MHz for mobile station (MS) and between 390 and 395 MHz for base station (BS)); access to communication channel resources based on frequency access techniques (Frequency Division Multiplexing Access--FDMA) but also in time (Time Division Multiplexing Access--TDMA). From the perspective of real-time acquisition, the following characteristics must be taken into account: frame length: 56.67 ms; slot duration: 14.167 ms; duration of a multiframe (18 frames): 1.02 s. According to the specifications of the TETRA standard, a monitoring station devoted to BS measurement should simultaneously acquire 200 channels, each with a bandwidth of 25 kHz, in the frequency range from 390 to 395 MHz, with an acquisition time of less than 14 ms.
## 7 Challenges and Limitations  
The challenges encountered by CRNs with PU and SU delays may be multi-faceted and depend on the characteristics of the communication protocol. This challenge in CRNs is mostly encountered due to dependency over arbitrary thresholds towards allocation of servers, which may in turn lead to suboptimal performance under dynamic network conditions [32]. Furthermore, CRNs can face challenges due to limited capabilities with limited sensitivity for accurately identifying the available spectrum, contributing to efficient resource utilization and causing congestion problems in practical applications, especially in different environments with different models and data causing delay and packet loss. Besides the optimization of resource allocation between active PU-SU links, the selection of optimal requirements for admission-control policies poses computational and heuristic challenges, and for understanding network dynamics [32].  
For CRNs, the mentioned constraints provide valuable insights for improving system performance and QoS [32, 33]. By addressing the reliance on arbitrary thresholds defining hysteresis paths, researchers can optimize responsive control mechanisms for server reservation, enabling more efficient spectrum management by increasing sensing capacity through accurate identification of available spectra to optimize resource usage and maximize spectrum utilization. In addition, the development of congestion control techniques helps to reduce collisions and ensure services with optimal QoS by prioritizing network resources and prioritizing critical services. The integration of deep learning techniques can further enhance CRNs through dynamic scheduling and resource allocation based on real-time rate of congestion in the CRN, ultimately providing improved network efficiency and experience [33, 34, 35, 36].
## 3 Vcc-Ssf
### Architecture of VCC-SSF  
VCC-SSF for the VCC environment is composed of three layers: Core Technology, Security, and Application Services. Figure 1 shows the architecture of VCC-SSF.  
The _Core Technology Layer_ is the layer handling the V2X of vehicle computing and Cloud Computing technologies, _i.e._, the core technology of VCC-SSF. The V2X comprises the V2V, V2I, IVN, and V2N technologies, and it facilitates communication between vehicles and between the vehicle and infrastructure. In addition, its technologies are related to Cloud Computing, such as storage virtualization, server virtualization, and cloud storage management.  
The _Security Layer_ is the layer providing functions such as authentication, encryption, access control, and privacy protection. Furthermore, it authenticates stationary and driving vehicles and encrypts personal identification and sensitive information (e.g., location, payment, traffic, and accident information). In addition, it provides access control to the internal and external systems of the vehicle for permitted objects, and protects personal privacy such as video and voice data in the vehicle black box, payment information, personal identification information, location information, _etc._  
The _Application Service Layer_ provides services to the driver or user utilizing V2X communication to access the collected data in a VCC environment. Two services are provided: Payment Service and Accident Management Service. The Payment Service allows the user to automatically pay for desired goods and consumables inside the vehicle in advance. The Accident Management Service prevents accidents that may occur on the road, provides response when an accident occurs, and provides management for vehicles involved in the accident.
# Study on the Propulsion Characteristics of a Flapping Flat-Plate Pumping Device
## 3 Numerical Simulation Method
### Grid Independence Verification  
The mesh refinement level directly affects the accuracy and computational speed of numerical results. Therefore, conducting grid independence verification is a crucial step to ensure the accuracy and reliability of the computational outcomes. The test condition for verification involves a flapping flat-plate hydrofoil with chord length \(c=1.0\) m, frequency \(f=1\) Hz, having amplitude \(A=0.45\) m, pitching amplitude \(\theta_{max}=30^{\circ}\), and \(\Phi=-\pi/2\).  
To verify grid independence, numerical simulations were performed with three different mesh sizes (10,000, 160,000, and 330,000 cells), with results shown in Figure 7. Comparative analysis revealed that the instantaneous thrust coefficient results are essentially consistent across the three mesh sizes. Considering the balance between computational efficiency and accuracy, a mesh of 160,000 cells was selected for further calculations. This mesh refinement study serves as a grid convergence analysis, confirming that the 160,000-cell mesh provides accurate and stable results for the hydrofoil thrust coefficient with deviations under 5% compared to finer grids.
## 4 Types of Robots
### Unmanned Ground Vehicles (UGV)  
UGVs, also known as rovers, are the simplest in the design of all the robots discussed in this review. These robots work well on flat surfaces but perform poorly on cluttered surfaces [85]. UGVs can be wheel-driven, using pneumatic wheels to move [86], or crawler-mounted or tracked systems [87]. Crawlers, which are also used in military tanks and heavy equipment, have better traction on slick or wet terrain. If the surface conditions are suitable, wheeled UGVs are the most efficient in terms of power consumption, cost, control, robustness, and speed [85]. Owing to lower power consumption and longer runtime,UGVs can be used as human assistants for long inspection rounds [88]. Some examples of commercially available UGVs used for research are the Clearpath Jackal [89] and Clearpath Husky [90].  
Due to their low center of gravity, UGVs are the most stable and can carry large payloads. Some common payloads attached to UGVs in the built environment for inspection and monitoring purposes are (a) regular fixed cameras and stereo cameras for visual data collection [90,91]; (b) LiDAR and laser scanners for 3D data capture [89,91]; (c) mechanical arms for obstacle removal [90]; (d) ground-penetrating RADAR (GPR), ultrasonic sensors, and infrared sensors for behind-wall and under-ground sensing [92,93,94,95]; (e) a graphics processing unit (GPU) for processing [89]; and (f) IMU, GPS, and UWB sensors for localizing and navigation [89,96].  
UGVs have been used in a variety of situations other than buildings due to their ease of design and operation. They have been used for the inspection of bridge decks [89,97], storage tanks [98], HVAC ducts [5], and even sewer and water pipelines [99]. Owing to their adaptability, they can perform floor cleaning, wall construction, and wall painting in addition to inspection [90]. They make an excellent test bed for algorithms and sensor design due to their ease of operation.  
The low height of UGVs is also a disadvantage because it limits their reach in large halls or spaces with high ceilings. As a result, UGVs are also used in close collaboration with other robots, such as UAVs. Such multi-robot collaborations are explained in more detail in Section 4.9.
# Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
## 3 Models Results and Measurements
### Thermal Model Validation  
In order to validate the proposed Simulink thermal model, the calculated temperature of the PV cell was compared with the NOCT given by the PV module manufacturer. The environment parameters defining the NOCT were: wind speed 1 m/s; air temperature 20 \({}^{\circ}\)C; solar radiation 800 W/m\({}^{2}\); tilt angle of PV module 45\({}^{\circ}\). In the case study, NOCT = 46 \({}^{\circ}\)C (the temperature-power coefficients \(-0.52\%/^{\circ}\)C). The corresponding PV cell temperature obtained by the Simulink thermal model (without storage batteries) was \(\approx\)45 \({}^{\circ}\)C, with an acceptable matching.  
Thermal models were also validated by experimental measurements on real PV module prototypes, with and without the battery storage system, using a thermographic camera, an anemometer, and a few thermometers. At first, the thermographic camera was set up with precision in consideration of the reflected temperature compensation (RTC), distance of object, relative humidity, and air temperature. After these settings were established, several thermal pictures were taken, and the temperatures were compared. Some example results are given in Figures 6 and 7. It is interesting to notice that, as expected, front surface temperature increased in the area corresponding to the back side battery areas (Figure 6). Measurements were taken in different ambient conditions and thermal pictures of both front and back sides of PV module were taken, for both the cases with and without storage batteries. The hotspots introduced in the PV module by the batteries obviously cause a slight decrease in the voltage of the corresponding PV cells (approx. \(-0.5\%/^{\circ}\)C), and a general decrease in the PV module power and efficiency [16].  
Temperatures estimated by the thermographic camera (\(T_{f}\), \(T_{b}\), \(T_{batt\text{-}b}\), \(T_{batt\text{-}g}\)) were properly averaged, and compared to the corresponding temperatures obtained by the thermal model. In general, the matching was satisfactory in the areas without the batteries, the difference in temperature being in the order of 1 \({}^{\circ}\)C. In the areas occupied by the batteries the difference was in the order of 3 \({}^{\circ}\)C. The difference was greater in the model with the batteries, but it was still somewhat acceptable, considering that all the edge effects were neglected in the 1D thermal model.  
Figure 6: Thermal (**left**) and visual (**right**) images of the PV modules with and without the battery storage system for ambient conditions \(T_{a}\cong 8\) \({}^{\circ}\)C and \(G_{n}\cong 600\) W/m\({}^{2}\).  
Figure 7: Thermal images of front and back sides of the PV modules, with and without the battery storage system for ambient conditions \(T_{a}\cong 15\)\({}^{\circ}\)C and \(G_{n}\cong 900\) W/m\({}^{2}\).
## 4 Treating Bone Diseases: Evidence from Bone Imaging and Analysis
### Cell and Gene Therapy  
Many pathological conditions of bone are genetic in origin, with intrinsic molecular-level defects or cellular-level damage such as defective collagen in OI pathology or ill-regulated osteoblasts in Paget's disease. Therefore, cell and gene therapies are better suited to correct these skeletal disorders. Cell and gene therapy relies on the change or introduction of genetic material that resides in the cell nucleus: cell therapy is via blood transfusion of ex-vivo modified cells, whereas gene therapy typically operates through targeted delivery for in vivo modification of the genetic material of the patient. To treat OI pathology, bone marrow stem cells were transplanted into OIM mice or children [177; 178; 179]. Both \(\alpha\)1 and \(\alpha\)2 type I collagens were correctly expressed following cell engraftment. There was a partial recovery in bone quality, where increased bone mineralization density and improved homogeneity and, thus, better organization in mineral distribution were observed [178]. Notably, nanoindentation and the macroscale four-point bending test revealed mitigation of the severe brittleness of OI bone, with some mechanical properties being similar to those of healthy WT bone [177; 178]. For in vivo gene therapy, an ovariectomized osteopororotic mouse showed improved micro-architecture and reduced bone loss following the transfer of the human interleukin-1 receptor antagonist gene [180].  
Hypophosphatasia (HPP), a metabolic disorder of bone, is caused by mutations in the _ALPL_ gene and is linked to a deficiency in the NCP tissue-nonspecific alkaline phosphatase, or TNAP. As a result of TNAP-D\({}_{10}\) administration, treated mice with infantile HPP showed improved survival and extended lifespan [181]. There were also few signs of rickets-like bone deformities in the mice treated with this virus-mediated gene therapy, and the resulting bone shared many microstructural similarities to normal WT bone [181]. Similar attempts were also made for other pathologies, including osteogenesis imperfects or osteoarthritis [182; 183; 184]. Since application-based studies in this area remain scarce, collagen-mineral ultrastructural examination of the treated bone is largely absent in the literature at this stage. One other limiting factor is that the efficacy of many of these treatments has not reached observable macroscopic phenotypes that resemble healthy WTs, rendering high-resolution imaging non-essential. Studies tend to focus exclusively on biological and biochemical analyses and visualization using histomorphometry. Despite this, nanoscale imaging and analysis can still offer appreciable information, including the identification of favorable (and possibly localized) changes in the mineral or collagenous ultrastructure of the treated bone and the determination of whether the newly produced collagen can self-assemble into the correct organization.
## 3 Methods
### Weakly Supervised Fine-Tuning  
In order to adapt SAM to specific colonoscopy images, it is necessary to select appropriate prompt information and network components for fine-tuning. Based on the results in Figure 1, the bounding box is the optimal choice as the prompt information for generating segmentation masks. It is crucial to highlight that the bounding box is derived from the ground-truth label, with each polyp region aligning with a specific bounding box. The architectural framework of SAM is constituted by three principal elements: an image encoder for feature extraction, a prompt encoder to integrate user inputs, and a mask decoder that generates the segmentation outputs. Due to the significant computational load primarily on the image encoder, which is built on visual transformers, we maintain the image encoder's frozen state and solely fine-tune the prompt encoder and mask decoder components.  
Moreover, the choice of loss function is crucial during model training due to the disparities between pseudo-labels and ground-truth labels. We incorporate the weighted Intersection over Union (IoU) loss from the original SAM loss function, alongside popular segmentation loss functions such as the binary cross-entropy loss and dice coefficient loss, which have demonstrated robustness across various segmentation tasks [44, 45]. Figure 2 supplies an overview of the WSPolpy-SAM framework, where the SAM model's parameters remain frozen during the generation of pseudo-label \(\tilde{Y}_{i}\). Subsequently, we guide SAM's fine-tuning process using \(\tilde{Y}_{i}\) as the supervision mask, focusing solely on fine-tuning the prompt encoder and mask decoder. The total loss function is presented in the formula below:  
\[\mathcal{L}_{Total}=\mathcal{L}_{\mathit{IoU}}(Y_{i}^{\prime},\tilde{Y}_{i})+ \mathcal{L}_{\mathit{BCE}}(Y_{i}^{\prime},\tilde{Y}_{i})+\mathcal{L}_{ \mathit{Dice}}(Y_{i}^{\prime},\tilde{Y}_{i}), \tag{7}\]  
where \(Y_{i}^{\prime}\) represents the predicted mask, i.e., \(Y_{i}^{\prime}=SAM(X_{i},Y_{i})\). Fine-tuning the SAM model helps it better understand and segment the colonoscopy polyp regions, adapting to the image features generated by the pseudo-labels.
###### Abstract  
In order to improve the photocatalytic effect of Nb\({}_{2}\)O\({}_{5}\), manganese from 1% to 10% was added by the polymeric precursor's method. The samples obtained were subjected to different calcination temperatures from 400 to 700 \({}^{\circ}\)C. Characterization of the synthesized materials was performed by XRD, IR, Raman and Diffuse Reflectance Spectroscopy (DRS) spectroscopy, FE-SEM, Brunauer-Emmett-Teller (BET) method, Photoluminescence (PL) and Dynamic Light Scattering (DLS). Photocatalytic performance for degradation of Rhodamine B was also evaluated. The Rietveld refined X-Ray Diffraction (XRD) pattern of Nb\({}_{2}\)O\({}_{5}\):Mn 1.0 wt.% (700 \({}^{\circ}\)C) sample was similar to that of the un-doped oxide, therefore indicating that Nb was replaced by Mn and, consequently, there are not significant variations in the oxide structure. The Nb\({}_{2}\)O\({}_{5}\):Mn 10.0 wt.% (700 \({}^{\circ}\)C) sample has the lowest band gap energy. The specific surface area (SBET) PL value increased as manganese concentration increased. The Nb\({}_{2}\)O\({}_{5}\):Mn 5.0 wt.% (700 \({}^{\circ}\)C) sample has an Eg of 3.15 eV and morphological and surface characteristics that made it an appropriate photocatalyst in the Rhodamine B degradation. The novelty of this work relies on the use of a small quantity of Mn ions as dopants leading to Nb\({}_{2}\)O\({}_{5}\):Mn nanostructured particles without using any surfactant or other additives.  
Mn-doped Nb\({}_{2}\)O\({}_{5}\); polymeric precursor; characterization; photocatalysis +
Footnote †: journal: Applied Physics Letters
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Technical Benefits to IoT Devices  
CR-LPWAN presents a number of technical benefits to IoT at both the device and network level, which we discuss as follows: