esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
###### Abstract  
Person re-identification (re-ID) technology has attracted extensive interests in critical applications of daily lives, such as autonomous surveillance systems and intelligent control. However, light-weight and efficient person re-ID solutions are rare because the limited computing resources cannot guarantee accuracy and efficiency in detecting person features, which inevitably results in performance bottleneck in real-time applications. Aiming at this research challenge, this study developed a lightweight framework for generation of the person multi-attribute feature. The framework mainly consists of three sub-networks each conforming to a convolutional neural network architecture: (1) the accessory attribute network (a-ANet) grasps the person ornament information for an accessory descriptor; (2) the body attribute network (b-ANet) captures the person region structure for a body descriptor; and (3) the color attribute network (c-ANet) forms the color descriptor to maintain the consistency of the color of the person(s). Inspired by the human visual processing mechanism, these descriptors (each \"descriptor\" corresponds to the attribute of an individual person) are integrated via a tree-based feature-selection method to construct a global \"feature\", i.e., a multi-attribute descriptor of the person serving as the key to identify the person. Distance learning is then exploited to measure the person similarity for the final person re-identification. Experiments have been performed on four public datasets to evaluate the proposed framework: CUHK-01, CUHK-03, Market-1501, and VIPeR. The results indicate that (1) the multi-attribute feature outperforms most of the existing feature-representation methods by 5-10% at rank@1 in terms of the cumulative matching curve criterion; and (2) the time required for recognition is as low as \(O(n)\) for real-time person re-ID applications.  
multi-attribute; person re-identification; global feature generation; autonomous surveillance system; intelligent control Article
## 4 Discussion
### Emergency Response Teams and Emergency Management System in NPP
After the Three Mile Island nuclear accidents sparked the possibility of a nuclear core meltdown, the importance of a knowledge-based decision support system to handle NPP accidents was observed, and prototypes were proposed starting around 30 years ago until recently [82; 83; 84; 85]. However, the implementation of such an emergency management system is much slower. One of the main reasons is that the routine operators (MCR teams) arealready using an NPP operating system: a digitalized program with computerized control and screen-based interfaces. The experts are divided between the need for another separate system created specifically to handle emergencies or to embed the emergency management function into the current NPP operating system. Both approaches have their own merits and disadvantages. Moreover, similar to the emergency response teams/organizations, the emergency management system should differ by country and NPP design. Creating a separate emergency management system makes sense especially during severe accidents (nuclear core meltdown) because the goal shifts from prevention to mitigation of accidents, and the existing day-to-day NPP operating systems cannot support mitigation actions effectively (alarms continue sounding, warnings before mitigation actions, etc.). However, forcing the operators to switch to a different system during a stressful situation might increase the probability of human error, albeit the training provided. Moreover, occurrences of severe accidents are very rare, and no two accidents are exactly alike [82]; thus, an emergency management system should be designed to have the capabilities of a wide range of mitigation actions and in-depth knowledge support. Embedding such a sophisticated system into the existing NPP operating system is also arguable. There is no need for the operators to use an emergency management system during routine operations and having additional labels or buttons (to represent the emergency system's functions) to check before taking an action could drain the operators' cognitive resources. Our research group agrees with the importance of an emergency management system for NPPs, and among other functions, we are especially interested in the functions that support efficient communication and avoid communication errors, which we intend to support in future research on developing the team communication model. Communication is one of the three process models recognized in the design of a decision support system for NPPs; it is as important as decision-making and the management of advice [86].
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### GNSS (Global Navigation System) Signal Relative Measurement Technology  
Mainstream GNSS signals include GPS (the global positioning system) in the United States, the Beidou satellite navigation system in China, Galileo in Europe, and Glonass in Russia. In this paper, GPS is used as an example for measurement analysis. GPS is an efficient and proven relative navigation method among low-orbiting satellites for distributed spacecraft precision-formation missions [29]. The formation spacecraft receives the GPS navigation signal via the GPS receiver, completes the absolute positioning, obtains the absolute speed and position, and realizes the autonomous orbit determination of the formation spacecraft. The spacecraft uses the inter-satellite link to share the GPS signal and to calculate the relative position and altitude information, so as to realize the relative state measurement of the formation flying. The principle is shown in Figure 15.  
GPS relative measurement technology is used to ascertain relative information by obtaining absolute information. Its advantages are its mature technology, it is lightweight, and has low power consumption. Its disadvantages are that its reliability and safety cannot be guaranteed in a highly dynamic space environment. It is only suitable for low-medium-orbit formation tasks and cannot effectively cover high-orbit areas. The GPS measurement algorithm is divided into the carrier phase difference (CDGPS), relative pseudo-range  
Figure 15: Principle diagram of the GPS relative measurement system.  
difference, and relative position difference. The accuracy and scope of action are shown in Table 7.  
CDGPS [30] is currently the most precise method of GPS relative measurement. The resulting accuracy is affected by a variety of errors, some of which can be eliminated or attenuated by differentials, including GPS clock errors, GPS ephemeris errors, ionospheric delays, and receiver clock errors. Some factors that cannot be eliminated differentially include the antenna phase center error, the multipath effect error, and phase observation noise.  
EXPERIMENT EARTH OBSERVATIONS-1 (EO-1) completed an in-orbit experiment of AFF equipment for the first time. AFF used CDGPS for spacecraft distance measurement and configured multiple antennas for relative altitude measurement, as shown in Figure 16. Star Ranger also performed CDGPS for state measurement, using 12 G/15 G inter-satellite links for communication and GPS differential correction information transmission, with an accuracy of 3 mm.
## 2 Background
### An Overview of Internet of Vehicles
#### 2.1.2 **Internet of Vehicles' Characteristics and Challenges**
Thus, nodes must often choose a trustworthy route to ensure that data is delivered to specific destinations to function correctly. The vehicles must be continuously linked to one another.
## 3 Proposed Method  
The performance and complexity of channel estimation algorithms are two key issues in CF M-MIMO systems. The proposed method takes the diagonal of the MMSE matrix, i.e., the element-wise MMSE estimator, and applies QR decomposition to it to find the inverse of the matrix; it then applied it to the estimation process. Since the UE is multi-antenna equipment, a precoding matrix is used at the user side to manage the interference. The estimator is phase-aware because it takes into consideration the received signal's phase information, which is crucial for correctly predicting the channel in a system with many antennas. The estimator estimates the medium coefficients for every element of the channel separately, making it element-wise as well. The received signal's phase information is taken into account during the estimating process in the phase-aware version of the estimator, which might increase the channel estimation's precision. EW-MMSE is a computationally effective method that calculates the channel coefficients of each antenna individually while taking the correlation between several antennas into account. The MMSE estimator may be more successful in capturing this correlation in CF M-MIMO with multi-antenna user equipment since the channel may be highly correlated across antennas. On the other hand, element-wise MMSE may perform better if the channel correlation is minimal by making use of the antennas' independence. The channel estimator for the element-wise estimator \(\hat{h}_{mk}^{exp}\) is defined by  
\[\hat{\mathrm{f}}_{mk}^{exp}=\mathrm{vec}\left(\hat{\mathrm{H}}_{mk}\right)= \mathrm{\bar{H}}_{mk}e^{i\varphi_{mk}}+\mathrm{R}_{mk}\mathrm{\bar{F}}_{k} \mathrm{\Psi}_{mk}^{-1}\mathrm{vec}\Big{(}\mathrm{Y}_{mk}^{p}\Big{)} \tag{17}\]where \(R_{mk}\) and \(\Psi_{mk}^{-1}\) are diagonal matrices defined in Equations (2) and (7), respectively.
## 3 Far-Field Solution  
The far-field can be derived asymptotically by evaluating the integral in Equation (37). For that substitute \(x^{{}^{\prime}}=r\cos\vartheta\), \(\left|y^{{}^{\prime}}\right|=r\sin\vartheta\) and transform the path of integration using the transformation \(\beta^{{}^{\prime}}=-k\cos\tau\), with \(\left(0<\vartheta<\pi,\ -\ <q_{3}<\right).\) Hence for \(kr\rightarrow\), Equation (37) becomes  
\[\widetilde{\varphi}^{separated}(x^{{}^{\prime}},y^{{}^{\prime}},\omega^{{}^{ \prime}})=\frac{\delta^{*}\left(\omega^{{}^{\prime}}-\omega_{1}^{{}^{\prime}} \right)\sin\vartheta_{0}\sin\vartheta\exp\left(ik\left(r+r_{0}\right)\right)} {2\sqrt{rr_{0}}\left(\cos\vartheta+\cos\vartheta_{0}\right)}\]  
\[\times\left[\frac{\exp\left(-i\left(k\cos\vartheta+k_{1}\cos\vartheta_{0} \right)l\right)}{\sqrt{1-\cos\vartheta}\sqrt{k_{1}\cos\vartheta_{0}-k}}+ \frac{1}{i\sqrt{1+\cos\vartheta}\sqrt{k_{1}\cos\vartheta_{0}+k}}\right]\]  
\[+\frac{iu_{0}\sum_{n=-}\sin\vartheta C_{n}\delta^{*}\left(\omega^{{}^{\prime}} -n\omega_{0}^{{}^{\prime}}\right)}{k\cos\vartheta}\left[\frac{\exp\left(ik \cos\vartheta\right)}{\sqrt{1-\cos\vartheta}}+\frac{1}{i\sqrt{1+\cos\vartheta }}\right]\exp\left(ikr-i\frac{\pi}{4}\right)\left(\frac{2\pi}{kr}\right)^{ \frac{1}{2}}. \tag{39}\]  
Inverting the Fourier transform of Equation (39) will recast the diffracted field in time domain as  
\[\widetilde{\varphi}^{separated}(x^{{}^{\prime}},y^{{}^{\prime}},t^{{}^{\prime} })=\frac{\sin\vartheta_{0}\sin\vartheta}{4\pi\sqrt{rr_{0}}\left(\cos\vartheta+ \cos\vartheta_{0}\right)}\]  
\[\times\left[\frac{\exp\left(-ik_{1}\left(\cos\vartheta+\cos\vartheta_{0} \right)l\right)}{\sqrt{1-\cos\vartheta}\sqrt{\cos\vartheta_{0}-1}}+\frac{1}{i \sqrt{1+\cos\vartheta}\sqrt{\cos\vartheta_{0}+1}}\right]\exp\left(ik\left(r+r_ {0}\right)-i\omega_{1}^{{}^{\prime}}t^{{}^{\prime}}\right)\]  
\[+\frac{iu_{0}\sin\vartheta\sum_{n=-}C_{n}}{2\pi\overline{k}\cos\vartheta}\left[ \frac{\exp\left(i\overline{k}l\cos\vartheta\right)}{\sqrt{1-\cos\vartheta}}+ \frac{1}{i\sqrt{1+\cos\vartheta}}\right]\exp\left(i\overline{k}r-i\frac{\pi}{4} -in\omega_{0}^{{}^{\prime}}t^{{}^{\prime}}\right)\left(\frac{2\pi}{kr}\right)^ {\frac{1}{2}}, \tag{40}\]  
where \(\overline{k}=\frac{n\omega_{0}^{{}^{\prime}}}{c}.\) It is important to remark here that we can deduce the results for cylindrical source diffraction by a half-plane situation [22] mathematically from the present analysis. From Equation (38),  
Figure 1: Geometry of the problem.  
expressions for \(\widetilde{T}\left(-\beta^{{}^{\prime}}\right),\widetilde{R}_{1,2}\left(\beta^{{}^{ \prime}}\right)\)and \(\widetilde{C}_{1,2}\) vanish and value of \(\widetilde{E}_{-1}\) can be evaluated under the limit \(l\rightarrow\ \left(\frac{\sqrt{l}}{e^{l}}\right).\) By virtue of these values, Equation (38) vanishes, and we are left with only separated field, containing the contribution of diffracted field for the half-plane in it. Now on extending the finite barrier to a half plane, the diffracted field of [22] can be recovered exactly which serves as a check of correctness of the mathematical results of this manuscript.
## 3 Operational Requirements
### Environmental Conditions
#### 3.3.1 Ground Conditions  
The physical conditions of the field become one of the most obvious and impossible to avoid, it is very different a robot in a controlled environment of a greenhouse or a test laboratory from that of a real field. Terrain with unevenness, slopes, or irregularities can hinder the robot's movement and require advanced navigation and stability systems to maintain harvesting accuracy [49]. In addition, the presence of stones, logs, and other obstacles that the robot must detect and avoid to prevent damage and maintain continuous operation. In fields with high plant density, the robot must navigate through narrow spaces, which requires precise route planning and fine maneuverability. Variability in plant height and thickness can affect the visibility and accessibility of fruits, necessitating adjustments to the robot's perception systems and harvesting mechanisms [50].
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
## 2 Target Peak Enhancement Using Time Reversal Processing
### Time-Reversed Signal Implementation  
The sampled signal \(m[n]\) is given by matched filtering the received signal \(r(t,\tau)\) at time \(t\). The time-reversed retransmitted signal \(s_{tr}^{k}[n]\), \(k=0,\ldots,K-1\), \(n=0,\ldots,N-1\) is given by  
\[\begin{array}{ll}s_{tr}^{k}[n]&=m^{k}[N-1-n]\\ m^{k}[n]&=m[n]\{u[n-kL]-u[n-kL-N+1]\}\end{array}, \tag{6}\]  
where \(N\) is pulse length, \(L\) is sample interval, and \(u[n]\) is the unit step function. Thus, a total of \(K\) time reversed transmission signals are used. Figure 1 describes this implementation in Equation (6).
###### Abstract  
Purpose: A deep learning technique was used to analyze the triceps surae muscle. The devised interpolation method was used to determine muscle's volume and verify the usefulness of the method. Materials and Methods: Thirty-eight TI-weighted cross-sectional magnetic resonance images of the triceps of the lower leg were divided into three classes, i.e., gastrocnemius lateralis (GL), gastrocnemius medialis (GM), and soleus (SOL), and the regions of interest (ROIs) were manually defined. The supervised images were classified as per each patient. A total of 1199 images were prepared. Six different datasets separated patient-wise were prepared for K-fold cross-validation. A network model of the DeepLabv3+ was used for training. The images generated by the created model were divided as per each patient and classified into each muscle types. The model performance and the interpolation method were evaluated by calculating the Dice similarity coefficient (DSC) and error rates of the volume of the predicted and interpolated images, respectively. Results: The mean DSCs for the predicted images were >0.81 for GM and SOL and 0.71 for GL. The mean error rates for volume were approximately 11% for GL, SOL, and total error and 23% for GL. DSCs in the interpolated images were >0.8 for all muscles. The mean error rates of volume were <10% for GL, SOL, and total error and 18% for GM. There was no significant difference between the volumes obtained from the supervised images and interpolated images. Conclusions: Using the semantic segmentation of the deep learning technique, the triceps were detected with high accuracy and the interpolation method used in this study to find the volume was useful.  
deep learning; semantic segmentation; triceps surae muscle +
Footnote †: journal: Medical Physics
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The laboratory dataset underwent coordinate system conversion, low-pass filtering, data extraction, and formatting using MATLAB R2018b (The MathWorks, Natick, MA, USA) [42]. It was filtered using a 6-Hz and 30-Hz fourth-order, zero-phase lag Butterworth low-pass filter for marker trajectories and ground reaction forces [47]. The subsequent procedures were carried out in OpenSim 4.2 (Stanford University, Stanford, CA, USA) following a previously published workflow (Figure 1B) [42]. These steps encompassed anatomical scaling, inverse kinematics, residual reduction, computed muscle control, and forward dynamics.  
Initially, the weight of the marked points in the model was manually fine-tuned. The model was subsequently scaled to align with the anthropometric characteristics of the participants, ensuring that the root mean square error between the marked points and the corresponding virtual marked points in the experiment remained below 0.02 m. The maximum error observed was below 0.04 m. Subsequently, the inverse kinematics algorithm was employed to compute the joint angles that yielded the minimum error between the marked points and the virtual marked points in the experiment. As per the official OpenSim guidelines, prior to implementing Computed Muscle Control (CMC) and Forward Dynamics (FD) analysis, the Residual Reduction Algorithm (RRA) was employed to mitigate the influence of modeling and marker data processing errors. The subtalar and metatarsophalangeal joints were locked during the data processing [40]. In the final stage, muscle activations computed from optimized excitations were used as input for FD, which computed the kinematics of all joints and advanced the motion by one step ahead in time. The FD simulation was executed in multiple steps to address significant perturbations that could cause diverging results.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 5 Implementation Walkthrough
### Agent Hierarchy Implementation
#### 5.3.2 Supervisor Agent Logic  
Supervisor agents implement strategic decision-making through a hybrid architecture that accommodates both rule-based abstractions of human expertise and direct human intervention. The framework supports three operational modes that realize the human-AI collaboration models defined in the architectural specification:  
* _Rule-Based Mode_: Declarative rules serve as abstractions of human-approved decision patterns, operating over consultant-generated tokens to pattern-match against topic distributions and generate directives when activation thresholds are exceeded. These rules encode institutional knowledge and established protocols, enabling consistent application of human strategic judgment.
* _Human-in-the-Loop Mode_: Direct human supervision allows strategic decision-makers to review topic-annotated tokens and issue directives manually, particularly for novel situations or high-stakes decisions that require contextual judgment beyond codified rules.
* _Centaurian Mode_: AI-augmented human decision-making where supervisory agents provide machine-generated recommendations, risk assessments, or scenario analyses to support human strategic reasoning, combining human intuition with computational analysis capabilities.  
This approach ensures modularity--new coordination logic can be added as rules, human oversight can be selectively applied, and AI assistance can be configured per decision context--while maintaining formal semantics via the underlying Petri net substrate.
## 4 Methodologies and Implementation
### Research on the Application of DDPM in Biomedical Image Segmentation
#### 4.1.1 Image Preprocessing Using DDPM  
Biomedical image segmentation is a vital step in medical image analysis, enabling the delineation of anatomical structures and abnormalities. DDPM, with its unique denoising and generative capabilities, holds promise for improving the quality and precision of segmentation in biomedical images.  
One of the primary applications of DDPM in biomedical image processing is denoising. Research has explored the denoising capabilities of DDPM to improve the quality of medical images, particularly in modalities like MRI and CT, where noise is prevalent [13; 43].  
Other research has investigated adaptive denoising strategies using DDPM to address variable noise conditions in different biomedical imaging scenarios. This involves tailoring the denoising process based on the local characteristics of the image [42; 44].  
Utilizing the uncertainty estimates provided by DDPM, researchers integrate uncertainty-aware preprocessing steps. This allows downstream segmentation algorithms to account for the reliability of the image data during the segmentation process [13; 53].  
DDPM's generative nature is harnessed for data augmentation in biomedical image datasets. This approach involves generating synthetic images that closely resemble real data, contributing to more robust segmentation model training [42; 44].
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.7 Ray Bending Based Localization (RBL)  
In all the above mentioned localization algorithms, the path of sound rays is assumed to be straight lines. However, because of the depth dependent sound speed, the path of sound rays bends in water. This causes deterioration in performance of the above algorithms when they are deployed in real UWSNs. In [34], authors present a Ray Bending based Localization (RBL) method that accounts for ray bending in water. With the assumption of straight line trajectory and constant velocity, constant range interval surface is spherical. For the ray tracing model [35], constant range interval surface is not spherical; but it retains axial symmetry about the longitudinal axis containing anchor node, since velocity varies only with depth. Simulation study shows that localization using the ray bending model out-performs that using straight line models.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Positioning Domains
#### 3.1.3 Space Domain  
In the space domain, the ranges are estimated by measuring the angle (or direction) of arrival (AoA) for the signal of interest. Often, this is done by the means of an antenna array or a sectorized antenna. For a given device \(n\), it is possible to describe its measurement at \(m\) as  
\[(x_{n},y_{n})=r_{m}cos(\theta_{m})+r_{m}sin(\theta_{m}), \tag{5}\]  
where \(r_{m}\) is the distance from \(m\) to \(n\) and \(\theta\) the angle of arrival determined at \(m\). Hence, by solving for the unknown coordinates, one can obtain a range estimate.  
In summary, AoA is particularly interesting for IoT, as the major constraint for achieving angle measurements relies only on the antenna design. However, its major drawback is that the error increases with the distance to the transmitter, which means that a small deviation in the angle results in a large error for the devices at the service edge.