esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Using GNSS Software  
GAMIT (GNSS Analysis at MIT) and GLOBK (Global Kalman Filter) are widely used GNSS processing tools developed at MIT and maintained in collaboration with institutions such as SIO and CfA. Along with GIPSY-OASIS, they support precise positioning, Earth rotation monitoring, and crustal deformation analysis, and are used by organizations like IGS, SOPAC, and UNAVCO [46; 47]. Freely available for academic use, GAMIT/GLOBK has been broadly adopted in geodetic research. Essential supporting datasets--such as precise orbits, station coordinates, Earth orientation parameters, and antenna models--are provided by centers like SOPAC to ensure high-accuracy positioning and seamless integration into global reference frames.  
GAMIT processes GNSS data using both carrier-phase (L1/L2) and pseudo range code observations. Its baseline analysis primarily uses the ionosphere-free linear combination of dual-frequency carrier-phase data, which effectively eliminates first-order ionospheric errors. Code observations are utilized for initial satellite clock synchronization and phase ambiguity resolution. Observed baseline vectors are estimated using a weighted least squares approach, where the variance in each observation is derived based on its stochastic characteristics [44; 47].  
GLOBK supports both daily and long-term network adjustments. For daily solutions, the glred module processes individual GAMIT session outputs. For multi-day integration, the globk module combines daily solution files (H-files) using Kalman filtering to estimate consistent station coordinates, velocities, and frame parameters over time. As part of quality control, GLOBK computes chi-square (\(\chi^{2}\)) statistics for each baseline; those exceeding predefined thresholds (typically \(\chi^{2}>10\)) are excluded from the final solution to preserve network statistical integrity [44; 47].  
The GAMIT/GLOBK processing workflow follows a structured and modular sequence that begins with the formatting of raw GNSS observations and ends with a finalized, statistically validated geodetic network solution. GAMIT modules--such as makej, arc, cfmrg, and solf--perform critical tasks including data preprocessing, modeling of satellite geometry and atmospheric delays, resolution of carrier phase ambiguities, and baseline estimation. Subsequently, GLOBK modules--such as hotglb and glred--integrate these solutions over time, apply robust weighting strategies, and stabilize network parameters through Kalman filtering and reference frame alignment. At each stage, the workflow incorporates automated quality control mechanisms, such as cycle-slip detection, residual analysis, and model misfit diagnostics [13; 44; 46; 47]. Figure 3 illustrates this sequential data flow, emphasizing the systematic design of the software and its emphasis on rigorous statistical filtering to ensure that only high-quality data contribute to the final geodetic solution.
## 3 Models
### Model Architecture
#### 3.1.3 Decision Module  
With the above steps, each decision module outputs a probability distribution and an answer. When selecting the final answer from these intermediate prediction results, we apply two manners for performance improvement and inference acceleration, respectively.  
Performance first. For each layer, the probability of the predicted answer where \(a_{n}\) is denoted as where \(P_{n}(a_{n}|S)\). The answer with the highest probability is selected as the final answer.  
\[a=\operatorname*{argmax}_{\{a_{n}|n:1\leq n\leq N\}}P_{n}(a_{n}|S) \tag{6}\]  
Speed first. If a decision module is confident enough about its predicted answer, it is not necessary to execute the following inference steps. This is also beneficial in terms of reducing the inference time. Inspired by [41], we use a normalized entropy as the uncertainty to measure whether to terminate the current inference. The uncertainty of the \(n\)th decision module is defined as,  
\[U_{n}=\frac{\sum_{c\in\mathcal{C}}P_{n}(c|S)*\log P_{n}(c|S)}{-\log|C|} \tag{7}\]  
where \(|C|\) is the number of candidate answers. During inference, if \(U_{n}\leq v\), then the inference is stopped and \(a_{n}\) is selected as the final answer; otherwise the model will move on to the next layer. \(v\in(0,1)\) is a threshold to control the inference speed. If alluncertainties of the decision modules are greater than the threshold, we adopt the first method to determine the final answer.
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
###### Abstract
Subsequently, these poses are mapped to a 3D coordinate-encoded representation through Sequential Least Squares Programming (SLSQP). By employing this approach, the impact of biomechanical variability and annotation standards across different datasets are mitigated. In addition, the HPBM can facilitate the development of a weakly supervised training methodology that does not require any labeled data but rather relies on prior knowledge, such as skeletal length and articulation range of motion. By fixing the static parameters of the HPBM-encoded pose and generating bone curvatures within the biomechanically feasible range, the 3D normalized hand pose dataset can be expanded without the need for manual labeling. This new dataset offers a greater variety of poses, effectively tackling the challenge of 3D keypoint annotation and thereby enhancing the estimation accuracy of the model. For instance, through weakly supervised training, the error is minimized from 4.85 to 4.57 when the model is fed with ground truth 2D keypoints on the RHD datasets. The main contribution of this work lies in three folds:  
1. In consideration of the anatomical characteristics of the hand, a novel HPBM is introduced, specifically crafted to intuitively depict the biomechanical features of hand poses. This model addresses the limitations associated with traditional 3D coordinate-encoded pose representations. 2. A universal and effective dataset normalization strategy was explored, utilizing the HPBM to unify biomechanical features and standardize root joint positions within and across datasets. In addition, a weakly supervised training methodology was also introduced, utilizing the HPBM to autonomously generate robust 2D-3D pose pairs without the need for manual annotations. 3. Compared to training on non-normalized data, models trained on the proposed normalized dataset demonstrated significant improvements. On the RHD and STBdatasets, notable reductions of 45.1% and 43.4% in error were observed when the model utilized the ground truth 2D pose. Weakly supervised training further achieved an additional 5.7% reduction. The proposed framework proves effective, delivering advanced estimation performance.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 2 Overview of Nanomaterials in Crani Tissue Engineering
### Carbon-Based Nanomaterials  
Carbon-based nanomaterials have unique physical, chemical, and biological properties, such as remarkable mechanical strength, electrical conductivity, and thermal stability [19]. These properties allow carbon-based nanomaterials to exhibit features similar to those of a biological ECM, in addition to promoting adhesion, proliferation, and osteogenic differentiation of bone marrow-derived stem cells (BMSCs) [20]. Graphene and carbon nanotubes are commonly used examples of carbon-based nanomaterials.
## 4 Performance Comparison between Protocols
### Optical Perturbation Statistics
The propagation time difference (skew) in the two cores of a 53 km-long multicore fiber was found to fluctuate in the order of picoseconds in a laboratory experiment at a stabilized temperature [44]. In terms of phase, this corresponds to huge phase excursions of around \(400\pi\) and therefore to a value of \(\gamma_{\omega}=\gamma_{\varphi}\sim 3\times 10^{4}\) km\({}^{-1}\). Consequently, \(\omega\) and \(\varphi\) are fully random in the wrapped range \([0,2\pi)\) for typical propagation distances in QKD. Since this effect is dominant, we even could neglect the dispersion of \(\theta\) in Equation (8), which reduces to (9) with a completely random phase; consequently, Equation (10) predicts that X-base measurements provide no information at all: \(\langle p_{err}^{X}\rangle=1/2\). The original BBM92 protocol does not work in this passive encoding due to the phase noise introduced by the fiber. In polarization encoding, the evolution of the perturbations is different. The fiber can be modeled as a succession of sections with randomly oriented residual anisotropies [43]. The amount of random anisotropy can be estimated from the polarization mode dispersion of the optical fiber as \(0.04\) ps\(/\sqrt{\text{km}}\) in a good modern fiber. This leads to a somewhat better dispersion of \(\omega\) (\(\gamma_{\omega}\sim 2.5\times 10^{3}\) rad\({}^{2}\)/km); however, it is still too high to be useful. Moreover, unlike the previous case, the final transformation is now completely arbitrary for typical propagation distances. Finally, a similar situation is to be expected with respect to collinear encoding, since crosstalk between degenerate spatial modes is important in modern few-mode fibers.
# A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition
## 3 Background and Problem Formulation  
### Running Example  
To illustrate the web service composition problem, we take travel planning as an example. As shown in Figure 1, a user (usually advanced) submits a query to obtain travel recommendations. Services that meet users' partial requirements can be found in the real world. In this case, it is not necessary to define the service composition process in advance. So, services published by different developers or companies can also be automatically composited.  
Users provide their GPS location, destination, departure time, and return time. The set of these initial inputs is defined as a start node.  
The departure city is obtained by invoking the location-city-transfer service through GPS location.  
The air service is called to obtain transportation arrangements by departure time, departure city, return time, and destination.  
The hotel service is called to obtain hotel arrangements by departure time, destination, and return time.  
The scenic service is used to obtain scenic recommendations through transportation arrangements and hotel arrangements.  
The combination of the above information results in transportation arrangements, hotel arrangements, and scenic recommendation. The set of those outputs is defined as an end node.  
In this example, GPS location, destination, departure time, and return time represent the user's input, while transportation arrangements, hotel arrangements, and scenic recommendation represent the required output. Location-city-transfer service, air service, hotel service, and scenic service represent some services in the candidate service set. The algorithm operates by identifying services in the CS that can accept input and produce the desired output. It then constructs the topology of these services to complete the process of service composition.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 4 Performance Evaluation
### Evaluation of the Metering Delay (Comparison between Traditional CCN Schemes)  
In smart metering infrastructure, end-to-end delay have to be optimized to metering depending on metering application requirement. Figure 11 shows the end-to-end delay of the various delay stringent SG applications such as time synchronization, price alert and load control traffic. In this result, the proposed TOP-CCN with FC shows better performance compared to other schemes, because data caching can considerably reduce average hop distance that a data must travel. We can observe that even though the network density increases, TOP-CCN with FC is the only technique that can guarantee less than 2 s end-to-end delay, which is the requirement time synchronization and time alert applications.  
Figure 11: End-to-end delay of the delay limited applications.  
Figure 10: Average MAC retransmission per packet.  
Figure 12 shows the content downloading time of remote firmware update file in the SG network. The size of firmware update content is 50 Kb and this file is fragmented by 10-byte unit chunks in the simulated CCN schemes. Thus all devices need to receive 5000 packets from the SG server. In the RPL scheme, all device in the network can receive the firmware content from the SG server by multicast and broadcast transmission. If some of the devices do not receive the transmitted firmware packet then retransmission procedure occurs in the application layer. This retransmission procedure increases the average hop distance, network congestion and interference of the network. However, the proposed TOP-CCN based architecture can eliminate application layer retransmission by utilizing the cache within SG devices. Thus, the content downloading time of the TOP-CCN is dramatically reduced compared to address based RPL protocol.
## 2 Preliminaries Work and CDR Introduction
### Cross-Domain Robot  
The CDR designed by us can work on the ground, water surface, and in the air, but the aerial control of the CDR is not the key point in this paper. We mainly study the robot control on the ground and the water surface.  
To reduce the mass of the robot, an integrated wheel-paddle design is adopted. The CDR uses the same drive motor on the ground and the water surface. In the air, the CDR adopts tri-copter mechanical structure. The structure of the CDR is as shown in Figure 1.  
In Figure 1b, the main actuators of the robot are introduced. The CDR uses three T-Motor U7 motors in the air. These motors are matched with three T-MOTOR FLAME 60 AHV electronic speed controller. Each motor adopts 16-inch blade. The three aerial drive units can provide a maximum lift of 12 kg for the robot. Two DJI RobotMaster M2006 motors are used on the ground. These motors are equipped with RobotMaster C610 electric speed controller. The tail servo of CDR adopts KST waterproof servo, which has a maximum output torque of 19 kg\(\cdot\)cm. The robot uses the same driving unit when moving on the water surface and on the ground, so we have designed a waterproof shell for M2006 motor. The main sensors and controllers of the CDR are shown in Figure 2.  
In Figure 2, the main sensors and controllers are introduced. To realize the ability of the robot to avoid obstacles autonomously, we use three HY-SR05 ultrasonic sensors and RPLIDAR-S1 laser to get the local obstacles information around the robot. BT-PPK4 is used for the GPS module to provide global localization messages for the robot. Three TFminiPlus  
Figure 1: The structure and actuators of the CDR. (**a**) The structure of the CDR. (**b**) The actuators of the CDR. The CDR uses three motors in the air and two motors on the ground and the water surface.  
Figure 2: The main sensors and controllers of CDR. From bottom to top, the first layer is the power supply layer for generating 5 v, 7.4 v and 24 v voltages. The second layer is the main control layer, from which the driving signal of the motor is output. The third layer is the sub-controller layer, which is used to collect messages of the sensors. The fourth and fifth layers are the sensor layers, where the sensors used by the robot are placed.  
laser ranging sensors is used to get the negative obstacle, to prevent the robot from falling into the trap. IST8 of CUAV is used as the compass. STM32F4 is the main control chip on the sub controller to collect the messages of sensors and send them to the main controller. The main controller is Pixhack-V3x of CUAV, which integrates inertial measuring unit and barometer to obtain the attitude and height of the robot. On-board computer uses Raspberry pi 4B+ to plan the desired trajectory of the robot to avoid obstacles. The on-board computer is hidden in the cabin of the robot.  
The main parameters of the CDR are shown in Table 1.  
Autonomous trajectory tracking of the CDR on the water surface and the ground are shown in Figure 3.
## 3 System Configuration
### Mid-Infrared Tunable Lasers  
MIR tunable laser technology has undergone tremendous development in the past decade, which involves QCL, ICL, difference frequency generator (DFG) [37], optical parametric oscillator (OPO) [38], fluoride fiber lasers [39], hollow-core fiber gas laser [40], VCSEL [41], and II-VI chalcogenides-based MIR lasers [42]. As there have been many excellent reviews of MIR light sources [43; 44; 25], here we present only a brief overview of the newly developed, highly reliable, and most widely used in spectroscopy, focusing on QCL, ICL, VCSEL, and optical frequency comb (OFC).
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG
### Range Signal Tracking  
During the range finding signal tracking phase, first the receiver signal channel obtains the carrier frequency estimates from signal acquisition capture phase, and then fine estimates of the carrier frequency and phase is obtained by tracking loop. Finally, the replica carrier is used for phase measurement or transmission to master node.  
The carrier tracking discriminator defines the type of phase-locked loop and frequency-locked loop. The phase-locked loop adopts narrow noise bandwidth and can track the signal closely. The phase precision of the output carrier of phase-locked loop is most accurate, but it is more sensitive to dynamic stress than the frequency-locked loop. The frequency-locked loop adopts the wide noise bandwidth, so it has good dynamic performance and can be more robust when tracking during tolerance of high dynamic stress. However, the signal tracking is slightly less dense due to the higher of loop noise, so the precision of output carrier phase of frequency-locked loop is poor. The main factors influencing the accuracy of ranging in PRG method is carrier loop tracking accuracy to carrier phase. We adopt the phase-locked loop in the carrier tracking loop at the low dynamic scenario. A typical carrier tracking loop is shown in Figure 7.  
Figure 6: Tong search detector.  
We can see from Figure 7 that the tracking performance is relate to the phase detector, loop filter and coherent integration time.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 6 Conclusions  
Generally, for SAR image segmentation, there are two main troubles. First, there are no freely available and labeled datasets, and images are not convenient for use due to noise. Secondly, a great success was lacking, although a certain degree of success was achieved with deep learning methods. Most of the researchers pay no attention how to obtain a new dataset and use the datasets that are detailed in Section 2. Additionally, unsupervised image segmentation methods are preferred due to being simple and practical.  
By this study, a pipeline was suggested with a dataset which had been created by us. We experimented with this dataset using state-of-the-art segmentation models for testing. We obtained a 92.23% overall PA, with HRNET as the best model according to performance. When results achieved were examined, we realized that models trained on our newly created dataset had not achieved success only in the segmentation of urban areas. When we examined the proportions of labeled classes in our dataset, we found that the percentage of urban areas in the dataset is very few compared to that of the others. The effectiveness of our proposed denoising pipeline for creating a dataset, one which takes care of noises caused by clouds and weather conditions, is apparently seen with examination of the results. As a result, the compared deep learning models achieved better accuracy results on a noise-free dataset setup. Additionally, we evaluated the segmentation performance of models with [PERSON]'s test. This test proved that HRNet is the best model for our dataset.  
Conceptualization, E.B. and M.S.G.; Methodology, H.E.; Software, H.E., E.B. and M.S.G.; Validation, T.A., K.A. and A.A.; Formal Analysis, T.A. and K.A.; Data Curation, H.E.; Writing--Original Draft Preparation, H.E.; Writing--Review and Editing, T.A., K.A. and A.A.; Visualization, H.E.; Supervision, E.B. and M.S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 The Synchronization Condition for Exact LS between Systems (2) and (3)
Moreover, the following successive approximations  
\[\omega_{0}(t)=0,\quad\omega_{n+1}(t)=\Phi(t)+\int_{0}^{t}g(t-\tau)h(\tau, \omega_{n}(\tau))d\tau,\quad n=0,1,2,\cdots \tag{18}\]  
will uniformly converge to the unique continuous solution to Equation (17). It is clear that the functions and parameters in Equation (16) satisfy the assumptions for Equation (17). Then, Equation (16) has the unique continuous solution \(e_{i}=0\), \(i=1,2,3,4\). **Remark 1**. : _The steps required to derive the synchronization condition for CS between systems (3) and (5) are listed below:_  
* _System (_7_) is obtained with variable substitution;_
* _Through the Laplace transform and the convolution theorem, system (_7_) is converted into a set of Volterra integral equations (system(_15_)), which can be further simplified to Equation (_16_) based on the convergence requirement;_
* _The successive approximation method_ _[_30_]_ _is used to solve Equation (_16_). Then, the synchronization condition for CS between systems (_3_) and (_5_) is determined._  
**Remark 2**. : _The condition that all roots of \(D(s)\) defined in Equation (13) have negative real parts guarantees the occurrence of CS between systems (3) and (5). \(D(s)\) is the characteristic polynomial of matrix (11). From the auxiliary system approach, under such conditions the functional relationship \(y_{i}=x_{i}(t-\tau)\), \(i=1,2,3,4\) will be achieved between systems (2) and (3)._  
**Remark 3**. : _According to the Routh-Hurwitz criteria, the necessary and sufficient condition that all roots of \(D(s)\) have negative real parts can be written as_  
\[\beta_{1}>0,\quad\beta_{3}>0,\quad\beta_{4}>0,\quad\text{and}\quad\beta_{1} \beta_{2}\beta_{3}>\beta_{3}^{2}+\beta_{1}^{2}\beta_{4}. \tag{19}\]  
_In particular, if \(u_{0}=v_{0}=w_{0}=p_{0}=0\), Equation (13) becomes_  
\[D(s)=[s^{2}+(k_{1}+k_{2}-c+a)s+(k_{1}+a)(k_{2}-c)-ad](s+k_{3}+b)(s+k_{4}-r). \tag{20}\]  
_Condition (19) can be written as:_  
\[k_{1}+k_{2}>a-c,\quad(k_{1}+a)(k_{2}-c)-ad>0,\quad k_{3}<-b,\quad\text{and} \quad k_{4}>r. \tag{21}\]  
**Remark 4**. : _The time delay \(\tau\) has nothing to do with conditions (19) or (21), which implies that the synchronization condition obtained in this paper is effective for any value of \(\tau\). In the next section, we numerically verify the effectiveness of condition (21)._