esa-sceva/satcom-chunk-collection huggingface dataset:
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 1 Introduction
Specifically, the obtainable orbit elements deviate from the prior conclusion under a more complex dynamic. For example, perturbations affect the orbits and contribute to deviations of real orbits from ideal two-body dynamics, which may improve the observability of a system. Moreover, with a calibrated thrust maneuver, observability can be guaranteed for possible situations [33]. Observability analysis is necessary to determine whether a particular measurement system is sufficient to solve the state estimation problem [34; 35; 36], and a number of researchers have carried out this analysis in the context of the OD problem [37; 38; 39]. One unwieldy approach to establish observability is to use classical nonlinear techniques that rely on Lie-derivative criterion methods [24; 25]. In general, it is difficult to provide further results on the system observability based on the Lie derivative criterion because the analytical expressions of higher-order Lie derivatives become progressively more complicated. Hence, discussing observability for nonlinear systems using Lie algebra and differential geometry methods becomes quite difficult. Another type of approach for establishing observability is the observability matrix (OM) [1; 11]. The OM method can be used to determine whether a system can be observed using sequential measurements, which are easy to obtain through numerical propagation. In this paper, a novel concept for autonomous OD is proposed based on a two-spacecraft system with angle-only measurements. Differently from the traditional autonomous OD using intersatellite angle measurements, the inertial attitude of the observer is also added to be estimated. In this manuscript, we present for the first time the observability analysis and the orbit estimation results for a two-spacecraft system with different additional conditions. Five conditions are considered and for each condition, the observability of the OD system is investigated using the OM. The remainder of this paper is organized as follows. The state model and the measurement model used is firstly presented in Section 2, followed by brief reviews of the OM and the fifth-order CKF. Five conditions, as well as the corresponding OMs, are discussed in Section 3. The observability analysis and filter estimation results are shown in Section 4. Finally, conclusions are given in Section 5.
## 5 Conclusions  
In this paper, we have proposed a method for reconstructing the scene image with extended depth of field (EDF) from a hologram that is captured with an OSH system. Our proposed method is based on a scheme that is referred to as \"non-overlapping block-based entropy minimization\" method, which is applied to determine the depth (focus) map of the scene image in an autonomous, block-by-block manner. From the depth value, each block is reconstructed as a focused image tile. Subsequently, the complete scene image is reconstructed with all the constituting objects appearing as sharp focused images. Further enhancement on the visual quality of the reconstructed image is attained by smoothing the depth map, so that the blocking effect at the object boundaries is reduced. Experimental results reveal that our proposed method is capable of reconstructing EDF images from OSH holograms representing both continuous tone and binary object scenes with favorable quality.  
Further research on the proposed method could be conducted through applying different kinds of focus measurements in the auto-focus detection process. An extensive review on the topic has been reported in [23].  
Conceptualization, [PERSON], [PERSON] and [PERSON]; Methodology, [PERSON]; Software, [PERSON]. [PERSON]; Validation, [PERSON], [PERSON] and [PERSON]; Formal Analysis, [PERSON]; Investigation, [PERSON]; Resources, [PERSON], [PERSON] and [PERSON]; Data Curation, [PERSON], [PERSON] and [PERSON]; Writing-Original Draft Preparation, [PERSON]; Writing-Review & Editing, [PERSON], [PERSON] and [PERSON]; Visualization, [PERSON], [PERSON] and [PERSON]; Supervision, [PERSON]; Project Administration, [PERSON].  
Figure 9: (**a**) EDF image of hologram “A” reconstructed with the method in [18]; (**b**) EDF image of hologram “B” reconstructed with the method in [18].  
**Acknowledgments:** The authors would like to express their thanks to [PERSON] for setting up the experiment. This work is partially sponsored by Ministry of Science and Technology of Taiwan under contract number MOST103-2221-E-035-037-MY3.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
Currently, automated prostate volume (PV) estimation primarily relies on segmentation methods based on transrectal ultrasound (TRUS). Our search strategy revealed no available literature on prostate segmentation using transabdominal ultrasound (TAUS). A single research group focused on automating the Ellipsoid formula on TAUS images to obtain the PV. In this section, we explore the significant advancements in deep learning (DL) methods for prostate segmentation using ultrasound (US) imaging. Generally, the deep learning (DL) architectures reviewed utilize a traditional U-Net as the backbone. Various authors have enhanced the traditional U-Net by implementing residual connections or substituting the encoding path with alternative models. However, the majority of architectural designs incorporate additional modules aimed at improving segmentation accuracy. Table 1 provides a summary of studies focused on prostate segmentation using TRUS, highlighting the modifications and improvements in different DL architectures. In this table, studies that do not focus on DL methods for prostate segmentation are excluded. The latter subsections explain various techniques and innovations that enhance segmentation accuracy and robustness. We begin with pre-processing methodologies aimed at improving image quality by mitigating artifacts inherent to US imaging. This is followed by a discussion on the employment of multi-directional image data, which leverage different imaging perspectives to improve segmentation outcomes. We then cover the implementation of additional shape information, which helps in accurately segmenting ambiguous prostate regions by incorporating anatomical landmarks and shape models. Attention mechanisms, on both image and feature levels, are examined for their ability to focus on relevant regions and improve segmentation precision. Further, we discuss feature map refinement techniques that enhance the quality of feature extraction in DL architectures. The consistency and robustness of DL models across diverse datasets are also considered, highlighting strategies to ensure reliable performance. Finally, a quantitative evaluation of various DL architectures is provided, comparing their performance metrics to establish state-of-the-art prostate segmentation in US images. Figure 2: Flowchart visualizing the results of the PRISMA-based article selection process.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 2 Theoretical Framework
### Propagation of Wi-Fi Signals  
With wave-particle duality, electromagnetic waves are the method of Wi-Fi propagation, which exhibit both particle properties and wave properties during propagation [36; 37]. In a wide space, electromagnetic waves are only transmitted directly without obstacles. While in an indoor environment, the propagation characteristics will change due to the existence of various objects, causing reflection and diffraction, etc. In addition, electromagnetic waves have a certain penetration ability and can  
Figure 1: Procedure of Wi-Fi fine timing measurement (FTM).  
spread across walls. As a result, the actual propagation process of electromagnetic waves is varied and complex.  
The propagation of electromagnetic waves follows the'skin effect' [38]. That is, when there is an alternating current or alternating electromagnetic field in a conductor, the current distributes unevenly and concentrates in the'skin' part of the conductor, that is, the current is concentrated on the thin layer of the outer surface of the conductor. As a result, the resistance of the conductor is increased, and its power loss is also increased. When an electromagnetic wave carrying a signal passes through some media, it is restricted by the skin effect, such that a part of the electromagnetic wave stays on the surface of the media and cannot completely pass through the media. The better the conductivity of the medium, the more obvious the effect of blocking in the electromagnetic wave is, and the more obvious the attenuation of the signal intensity is. Table 1 presents the signal attenuation of electromagnetic waves with a frequency of 2.4 GHz when crossing various common obstacles in indoor buildings. It can be obtained through observation that the better the conductivity of an obstacle is, the more obvious signal attenuation will be.  
Electromagnetic waves will wear out when propagating in air. The loss in free space is mainly caused by the air's own propagation and the scattering and refraction of fine dust particles. In the case of no obstacles in the air, the propagation of electromagnetic waves can be expressed as per Equation (4) [39], where d is the distance from the MT to the FT; \(d_{0}\) is the reference distance; \(L(d)\) is the received power of the MT when the distance from the FT is d; \(L(d_{0})\) is the received power when the distance from the FT to the MT is 1 m; \(\eta\) is the attenuation coefficient; and k is the shadowing factor, obeying a Gaussian distribution with a mean of 0. Since the model is in the case of LOS, k is 0.  
\[L(d)=L(d_{0})+10\cdot\eta\cdot lg(\frac{d}{d_{0}})+k \tag{4}\]
## 3 System Model and Problem Formulation
### Federated Learning Task Modeling  
We consider a cross-device federated learning scenario consisting of a central server and a set of edge clients \(\mathcal{C}=\{c_{1},c_{2},\dots,c_{N}\}\), where each client \(c_{i}\) owns a local dataset \(\mathcal{D}_{i}\) that is typically highly non-IID. The goal of the system is to collaboratively train a robust and high-performing global model without sharing raw data.  
During each communication round \(t\), the server selects a subset of clients \(\mathcal{S}_{t}\subseteq\mathcal{C}\) to participate in training. Each selected client performs the following steps:  
1. Downloads the current global model \(w_{t}\);
2. Trains locally using its own dataset and computes a model update \(\Delta w_{i}^{t}\);
3. Uploads \(\Delta w_{i}^{t}\) to the server for aggregation.  
The server aggregates the received updates via weighted averaging to produce a new global model \(w_{t+1}\), which is then broadcast to all clients, completing one round of training.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline \(\mathcal{C}\) & Set of all clients \\ \(c_{i}\) & The \(i\)-th client \\ \(\mathcal{D}_{i}\) & Local dataset of client \(c_{i}\) \\ \(w_{t}\) & Global model parameters in round \(t\) \\ \(\Delta w_{i}^{t}\) & Model update from client \(c_{i}\) in round \(t\) \\ \(\mathcal{S}_{t}\) & Selected subset of clients in round \(t\) \\ \(\tau_{i}^{t}\) & Trust score of client \(c_{i}\) in round \(t\) \\ \(\tilde{\tau}_{i}^{t}\) & Instantaneous trust score of client \(c_{i}\) \\ \(\phi_{t}^{t}\) & Participation frequency of client \(c_{i}\) \\ \(\psi_{t}^{t}\) & Gradient consistency of client \(c_{i}\) \\ \(\omega_{i}^{t}\) & Contribution effectiveness of client \(c_{i}\) \\ \(r_{i}^{t}\) & Incentive reward for client \(c_{i}\) in round \(t\) \\ \(\tilde{R}_{t}\) & Total incentive budget in round \(t\) \\ \(v_{i}^{t}\) & Raw contribution score of client \(c_{i}\) \\ \(\tilde{\tau}_{i}^{t}\) & Adjusted contribution score based on validation \\ \(g_{i}^{t}\) & Validation error reduction from client update \\ \(\alpha_{i}^{t}\) & Aggregation weight for client \(c_{i}\) \\ \(\zeta_{i}^{t}\) & Deviation indicator of client update \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and definitions.  
Figure 4: System model.
# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 4 Trustworthiness Model Specification
### Data Trustworthiness Layer  
This layer aims to ascertain the correctness of the source's collected data. We propose the measurement of this layer's trustworthiness with the metric faulty sensing ratio (\(FSR\)), defined in Equation (2) as the proportion of false sensed values (\(FSV\)) by all nodes and total sensed values (\(TSV\)) in a defined period. The lower the \(FSR\), the better the data trustworthiness.  
\[FSR=\frac{FSV}{TSV}. \tag{2}\]  
Corrective methods (e.g., [51; 52]) which try to detect abnormal data (\(FSV\)) stored in the source node due to a sensor malfunctioning, a misreading of the sensed data, or erratic writing in the node's memory, can be applied. Additional examples of corrective methods are hashes, checksums, and parity bits, among others (see Figure 6).
## 4 Hardware Implementation Design
### Implementation Results  
In Table 2, the processing performance characteristics are presented regarding the processing delay of one block of data and the corresponding throughput considering a relative low frequency clock, adequate for embedded systems.  
_Calculation of the throughput rate:_  
A data block of 500, 16 bit samples will be processed at worst case every:  
4 cc (data-path latency) + 500 cc (input samples) + 4175 c (processing delay) = 4679 cc.  
\[\text{Throughput rate (bps)}=(16\times 500\text{ bits})/(4679\times\text{Tclk}), \tag{1}\]  
For 62.5 MHz CLK, we get worst case processing latency estimation at 106.8 Mbps, which is equal to 13.375 \"500 \(\times\) 16 bits samples\" data-blocks per second. Based on such throughput capacity the following comparison with software-based respective implementation can be made.  
Considering 10-minute EEG signals acquired from the MIT data base [22] the corresponding file is 307.200 bytes and the required compression delay exhibited by the software-based algorithm was measured to be approximately 20 s considering an i7 dual core pc. Based on the previous calculation, the respective delay of the hardware accelerator is anticipated to be around 20 ms, thus offering a drastic decrease regarding delay as well as resource consumption. Furthermore, such evaluation indicates that the presented hardware accelerator comprises a highly efficient solution when considering multiple channels acquired concurrently or/and low frequency processing units being available by the sensors.  
Additionally, the implementation presented is resource conservative as indicated in Table 3, depicting the degree of FPGA resources required by the specific implementation. Such measurements  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Processing Delay (In Clock Cycles)** & **Throughput Rate (Worst Case)** \\ \hline
4175 (Worst Case) & 106.8 Mbps (62.5 MHz Clk) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compression module processing delay and throughput rate (Assuming \(\times\) MHz operating frequency).  
Figure 5: Compression module Input/Output block timing. Start of block.  
Figure 6: Compression module Input/Output block timing. End of block.  
effectively indicate that more than one hardware accelerator could coexist in the same FPGA board offering increased flexibility.
## 2 Materials and Methods
### Volatile Compound Profile  
Volatile compounds were extracted by headspace solid-phase micro-extraction (HS-SPME). Analysis was carried out according to [PERSON] et al. [25] with some modifications. Briefly, 10 mL of olive oil was added to a 15 mL glass vial with 10 \(\upmu\)L of benzyl acetate as internal standard (1000 mg L\({}^{-1}\)) using 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco Company, Bellefonte, PA, USA). Vials were maintained in a temperature-controlled water bath at 40 \({}^{\circ}\)C for 60 min (10 for equilibration and 50 min for extraction) with continuous agitation (500 rpm) in a magnetic stirrer (IKA C-MAG HS 4, IKA-Werke GmbH & Co. KG, Staufen, Germany). After that, fiber was inserted into the injector at 250 \({}^{\circ}\)C for 3 min to directly desorb volatile compounds into the GC column.  
A gas chromatograph Shimadzu GC-17A coupled to a mass spectrometer (Shimadzu QP-5050A (Shimadzu Corporation, Kyoto, Japan) was used for the isolation and identification of the volatile compounds. To separate volatile compounds, an Rci-1301 Sil MS (Restek Corporation, Bellefonte, PA, USA) column was used (length: 30 m, internal diameter: 0.25 mm and film thickness: 1.0 \(\upmu\)m), using helium as a carrier gas at 0.6 mL s\({}^{-1}\) in a splitless mode. Injector and detector temperatures were 230 and 300 \({}^{\circ}\)C, respectively. The oven program was as follows: initial temperature 40 \({}^{\circ}\)C for 3 min; rate of 5 \({}^{\circ}\)C min\({}^{-1}\) up to 100 \({}^{\circ}\)C; and rate of 3 \({}^{\circ}\)C min\({}^{-1}\) until 300 \({}^{\circ}\)C and held for 3 min.  
To characterize volatile compounds, chromatogram was analyzed using GCMS Postrun Analysis (Shimadzu Corporation, Kyoto, Japan) software and individual compounds were identified by three methods: (i) mass spectrum (original chemical compound and collection of the Wiley 229 and NIST 14 spectrum libraries); (ii) retention index of standards (RI); and (iii) retention indexes calculated using the C7 to C16 \(n\)-alkane mix (Sigma-Aldrich,Steinheim, Germany). Experimental retention index was compared with literature index obtained from National Institute of Standards and Technology [26].
## 5 Monitoring Program and Results
### Monitoring of Soil Displacement  
A Trimble DiNi03 electronic static level is used for the automated monitoring of the surface settlement at the center axis of the two tunnels and within a certain width on both sides. Starting from the starting shaft, nine monitoring sections are laid along the jacking direction, with a spacing of about 10 m, and the measurement points are numbered as Di, where i is the ith monitoring section, and Di (i = 1 to 9) is arranged directly above the central axis of the channel and the central axis of the two tunnels. In the distance from the viaduct abutment near the pipe jacking side of each of the three soil-deep lateral displacement monitoring holes, the hole depth longer than the abutment buried in the ground is 3 m, that is, the soil tilt hole depth is about 45 m, as shown in Figure 13.
## 2 Related Work
### DCDM Architecture Functionality  
To carry out the integration of software units employing _Agnostic Message_ in order to improve the low coupling, the functionality of the architecture presented in this article is explained in this sub-section.  
Figure 7 shows a sequence diagram where the three components that conform to the DCDM architecture communicate. The Figure shows how a client initiates the integration using the architecture proposed in this article. The process starts when the client sends a message addressed to the _Agnostic Message component_, carried out through the _sendMessage_ operation. As an input parameter, a _MessageFacade_ type object named _Payload_ is sent, which represents the _Composite_ of the _Envelope_ component where the message is hosted. The _Composite Payload_ message is divided into two essential parts; the first one is the _Properties Entity_ and _Transaction Entity_ section, as shown in Figure 2. The first section of the message contains the entity's name to be searched in the metadata of the repository or database, and this will be the starting point to create the instance or the DML (Data Manipulation Language) sequence. This will allow executing the order according to the _Action Type_ property, which indicates the action to be performed in the database repository. In the second part, the transactions are represented by a MAP data structure. These structures allow for managing the fields and their respective values, which makes it possible to store them in _key/value_ pairs, where the key is the entity's field and the value is the field's value. Once the client has sent the message, the _Strategy_ pattern is accessed from the _Payload_ compound with the _StrategyCDDM_ implementation where the algorithms that will provide a solution to the message request are stored. This access is provided through an interface called _Context_, the strategy to be taken according to the _Action Type_ property, where a new instance of the algorithm to be implemented is created. _StrategyDML_ and _StrategyProcess_ will take care of the message and provide a solution to the integration with the _managementStrategy_ operation, which has as input parameter of the composite _Message_. Once the entity is generated and the message persists, a response is given to the client, indicating if the execution was correct or if there is a problem.  
Figure 6: Representation of _StrategyCDDM_ strategy implementation.
# An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications
## 5 Scientific Applications and Evaluation
### Power Flow Problem in Power Grid
#### 5.1.1 Settings of the Numerical Experiments  
The test case is the four buses and two generators problem in ([66], p. 377), coded in a MATLAB package called MATPOWER [67]. Based on the framework built in [68], we incorporate HHL circuits and quantum simulators into the solving process in MATPOWER. The linear systems of our interest are all \(5\times 5\) systems but not Hermitian. So, the actual input system is first expanded to \(8\times 8\) so the size of the RHS vector is the power of 2, and then it is enlarged to \(16\times 16\) following Equation (1). So, we eventually use 4 qubits to encode the vector \(\vec{b}\). This process is illustrated in Figure 2(a).  
The default value of \(n_{c}\) set by [35] using Equation (3) is 6. To demonstrate how the accuracy of eigenvalues affects an iterative algorithm, we select \(n_{c}\) from 4 to 7. With 4 clock qubits in QPE and an ancillary qubit required by the HHL algorithm, the number of qubits in each HHL circuit ranges from 9 to 12. The N-R method converges when  
\[\left\|\begin{bmatrix}\Delta\vec{P}\\ \Delta\vec{Q}\end{bmatrix}\right\|_{\infty}\ <\ 10^{-8}.\]  
However, because the linear system formed in an N-R iteration depends on the solution from the previous N-R iteration, the linear systems at Iteration \(j\) with different \(n_{c}\) will differ. Our comparison focuses on the convergence speed and the final solution at the convergence instead of errors at each iteration across different \(n_{c}\).