esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Notation and Background
### Pushforward Measures  
Let \(\pi\colon G\to M\) be the projection to the homogeneous space \(M=G/H\). Then, \(\pi\) is a measurable map, and if \(\mu\) is a measure on \(G\), the pushforward of \(\mu\) by \(\pi\), defined by \(\pi_{*}\mu(B)=\mu\left(\pi^{-1}(B)\right)\), for all measurable subsets \(B\subseteq M\), is a measure on \(M\). A numerical example is provided in Figure 1, showing anisotropic distributions on the homogeneous space \(\mathbb{S}^{2}\) obtained from pushing forward Brownian motions of a non-invariant metric on the top space \(\operatorname{SO}(3)\).  
The Riemannian volume measure \(\operatorname{Vol}_{G}\) on \(G\) decomposes into a product measure consisting of the volume measure on fibers in \(G\), e.g., \(\pi^{-1}(z)\), and the volume measure on their horizontal complement, i.e., \(d\operatorname{Vol}_{G}=d\operatorname{Vol}_{\pi^{-1}(z)}d\operatorname{Vol} _{|H}(z)\), where \(d\operatorname{Vol}_{|H}\) is the horizontal restriction of the volume measure in \(G\). The measure of a process \(g_{t}\) on \(G\) pushes forward to \(M\), and we denote the corresponding density with respect to the volume measure on \(M\) for \(p_{t}^{M}\). Then, \(p_{t}^{M}(x)=\int_{\pi^{-1}(x)}p_{t}^{G}(g_{0},y)d\operatorname{Vol}_{\pi^{-1} (z)}(y)\).  
**Lemma 1**.: _Let \(g_{t}\) be a Markov process on \(G\), started at \(g_{0}\in G\), with density \(p_{t}^{G}(g_{0},\cdot)\), and let \(X_{t}=\pi(g_{t})\). The conditional expectation on \(M\) satisfies_  
\[\mathbb{E}[f(X)|X_{T}=v]=\mathbb{E}\Bigg{[}f(X)\frac{p_{T-t}^{M}(X_{t},v)}{p_ {T}^{M}(x_{0},v)}\Bigg{]},\]  
_for all bounded, continuous, and non-negative \(\mathcal{F}_{t}\)-measurable \(f\) on \(M\). Furthermore,_  
\[\mathbb{E}[\tilde{f}(g)|g_{T}\in N]=\mathbb{E}[f(X)|X_{T}=v],\]  
_where \(\tilde{f}=f\circ\pi\)._Proof.: Let \(f\) be a bounded, continuous, and non-negative measurable function on \(M\), and let \(\tilde{f}=f\circ\pi\). Then, it follows directly from (7) and (11) that  
\[\mathbb{E}[\tilde{f}(g)|g_{T}\in N]= \mathbb{E}\bigg{[}f(\pi(g_{t}))\frac{p_{T-t}^{G}(g_{t},N)}{p_{T}^ {G}(g_{0},N)}\bigg{]}=\mathbb{E}\bigg{[}f(\pi(g_{t}))\frac{p_{T-t}^{G}(g_{t}, \pi^{-1}(v))}{p_{T}^{G}(g_{0},\pi^{-1}(v))}\bigg{]}\] \[= \mathbb{E}\bigg{[}f(\pi(g_{t}))\frac{\pi_{*}p_{T-t}^{G}(g_{t},v)} {\pi_{*}p_{T}^{G}(g_{0},v)}\bigg{]}=\mathbb{E}\bigg{[}f(X_{t})\frac{p_{T-t}^{M }(X_{t},v)}{p_{T}^{M}(x_{0},v)}\bigg{]}.\]
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 5 Conclusions  
In this paper, we present a Doppler estimation and timing synchronization method for underwater acoustic communication that is affected by a Doppler shift and Doppler spread, as well as an MCSS communication method that provides beneficial modulation in these fluctuating channels. The proposed method's performance was validated through simulation by comparing it to an existing spread spectrum method in a channel with multipath and Doppler shifts. The BER results for each SNR of the simulation in a channel with signal interference and a Doppler shift were compared, and the nonlinear change in Doppler was well-captured and unaffected by the cumulative effects of synchronization errors. In addition, the performance was verified through a sea trial, and an artificial Doppler shift was added to the transmission signal because the nonlinear part of the  
Figure 16: (a) Constellation diagram of DSSS. (b) Artificial Doppler factor and results of measured Doppler factor from the received data.  
Doppler shift could not be visually seen in the simulation. As a result, a channel with a Doppler shift, Doppler spread, and multipath was created, and when the performance of these channels was compared, it was discovered that the conventional spread spectrum methods achieved better BER at the same symbol rate.  
The reception process requires more computation than the baseband; a tradeoff for this might be higher accuracy. Developing hardware that can cover many computations and finding a way to achieve better accuracy with less computation is an important task for future research. Experiments have shown that the Doppler estimation method or transmission method described in this paper is feasible and that higher speeds of autonomous underwater vehicles will be detectable in the future. We also investigated and tested ways to improve communication within the underwater vehicle itself.  
Data curation, H.R. and C.Y.; Methodology, K.K.; Software, H.R.; Supervision, K.K.; Writing--original draft, H.R.; Writing--review and editing, K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Agency for Defense Development, South Korea, grant number UD200010 DD.  
Not applicable.  
The authors declare no conflict of interest.
## 7 Conclusions  
While general-domain NER algorithms have advanced significantly, their adaptation to specialized industrial applications such as shearer maintenance remains limited, with conventional fine-tuning approaches dominating existing solutions. This study addresses this gap by proposing a multi-level clue-guided prompt learning (MCP) framework that harnesses the few-shot learning capabilities of LLMs to achieve resource-efficient named entity recognition in shearer maintenance scenarios. Our experimental evaluation demonstrates the superiority of MCP over mainstream baselines, yielding F1-score improvements of 4.85% (vs. BiLSTM-CRF) and 1.19% (vs. BERT-BiLSTM-CRF), while eliminating dependency on large annotated datasets and exhibiting enhanced generalization potential.  
Figure 6: Results of ablation experiments.  
Our study has several limitations that warrant further investigation. First, the current MCP framework's performance is contingent upon manually engineered prompt templates, which introduces subjectivity and may compromise robustness across diverse maintenance contexts. Second, while achieving state-of-the-art results on shearer maintenance datasets, the method's generalizability to other industrial domains (e.g., hydraulic systems or mining conveyors) remains unverified.  
Future research should focus on three strategic directions to address these challenges. Automated prompt optimization strategies, potentially leveraging reinforcement learning or gradient-based template search, could minimize human intervention in template design. Cross-domain validation studies are needed to systematically quantify the framework's adaptability to broader industrial NLP tasks, including fault diagnosis in interconnected mechanical systems. Finally, hybrid architectures integrating LLM-based few-shot learning with model compression techniques (e.g., knowledge distillation) or traditional supervised models may offer viable pathways to balance accuracy, computational efficiency, and extreme low-resource adaptability.  
Methodology, X.C. and L.S.; Software, X.W. and X.Y.; Validation, X.Z.; Data curation, L.S. and Y.D.; Writing--original draft, X.C. and L.S.; Writing--review and editing, X.C. and L.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China (52274158 and 51834006).  
Not applicable.  
The data and materials used in this research are available upon reasonable request to the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 3 Inter-Satellite Links Pointing and Tracking in-Orbit Test Method
### Virtual Target Satellite Based on Ground Stations
\tag{15}\]  
The solution of Equation (14) can be derived as:  
\[k=\frac{-a_{2}+\sqrt{a_{2}^{2}-4a_{1}a_{3}}}{2a_{1}} \tag{16}\]  
According to the geometry, the space vector pointing from the geocentric to the virtual target satellite at time \(t_{1}\) in the J2000 coordinate system is obtained as follows:  
\[\mathbf{r}_{A}(t_{1})=\mathbf{r}_{dA}(t_{1})+k\cdot\mathbf{r}_{dAs}(t_{1}) \tag{17}\]  
For the ground station B, the same algorithm is used:  
\[\mathbf{r}_{B}(t_{1})=\mathbf{r}_{dB}(t_{1})+k\cdot\mathbf{r}_{dBs}(t_{1}) \tag{18}\]  
where \(\mathbf{r}_{dB}(t_{1})\) represents the position vector of station B in the J2000 geocentric frame at time \(t_{1}\); and \(\mathbf{r}_{dBs}(t_{1})\) represents the relative position vector from the ground station B to the local satellite.
## 5 Conclusions  
This paper focused on the algorithm for the inverse Laplace transform in 2.5D airborne TEM for marine investigation. Considering the masking effect of seawater, a trapezoidal wave was selected as the source waveform because its frequency spectrum mainly aggregates in the low-frequency range. For the inverse Laplace transform, the Talbot algorithm was found to have the capacity to match this waveform better than the common G-S algorithm owing to a higher accuracy and robustness. Based on the Talbot algorithm with the trapezoidal waveform, two typical applications--a rugged seafloor and a subsea tunnel--were modeled by using the 2.5D FEM. The voltage response for the number of terms and the height of the loop source were investigated. It was demonstrated that the Talbot algorithm with a trapezoidal waveform provides good performance for the complex marine geology model, where the suitable detecting height of the loop source is selected based on the subsea terrain and target. The airborne TEM method with the Talbot algorithm can effectively and rapidly detect an anomaly in shallow water at low cost, and therefore is expected to be a promising technology in marine investigations.  
In future work, the actual geologic model, such as seafloor topography or subsea tunnels, will be investigated by the existing airborne TEM system. By comparing the measured data with the simulation results, the parameters of the simulation model will be optimized in future research. Furthermore, the forward algorithm will also be optimized. The results and data could provide a basis for the development of a compact airborne TEM system for marine investigations.  
[PERSON] proposed the idea and drafted the manuscript, [PERSON] and [PERSON] performed the simulations and composed the figures, [PERSON] and [PERSON] conducted the review and editing, [PERSON] and H.C. conducted funding acquisition conducted funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 61671333), Natural Science Foundation of Hubei Province (No. 2014 CFA093), the Fundamental Research Funds for the Central Universities (No. 2042019K50264 and No. 2042019 GF0013) and the Fundamental Research Funds for the Wuhan Maritime Communication Research Institute (No. 2017)-13).  
The authors declare no conflict of interest.
## 2 Methodology
### Integrated Analysis Model  
#### 2.2.1 Domain and Grid  
The simulation domain was defined in the geometric specifications of the target engine, P300-RX. The fluid domain was divided into five regions, as illustrated in Figure 4: the inlet region, compressor, combustor, turbine, and exhaust nozzle, and the outlet region. The start motor was excluded from the simulation domain; tip clearances of 0.3 mm and 0.2 mm were applied to the compressor and turbine, respectively. Furthermore, the analysis was performed on the sector geometry rather than the full annular geometry to reduce the analysis time, as demonstrated in Table 2.  
The grid was generated via the compressor or turbine as an aligned grid in the hex/prism format using TurboGrid software (2021 R1) from ANSYS (Canonsburg, PA, USA). The remaining portion of the domain was generated as a non-aligned grid utilizing a combination of the hex/prism and tetrahedral formats, leveraging the meshing program of ANSYS. The grid configuration resulted in approximately 9 million nodes and 14 million elements across the entire domain. The grid geometry and the number of nodes and elements for each domain are presented in Figure 5 and Table 2, respectively.  
To validate the grid, the Y+ values were examined on the analysis results at engine speeds of 50,000 RPM and 104,000 RPM, respectively. As illustrated in Figure 6, the Y+ values were found to be no higher than 10 in most regions, even at higher RPM, except in the limited region near the impeller tip. Furthermore, as shown in Figure 7 below, a grid independence check was conducted by varying the grids from 10 million to 20 million. The results demonstrated that, compared to the present grid system, the thrust difference was 8.7% for the 10 million grid system. This difference was reduced to less than 1.3% for the 20 million grid system, which is 1.5 times the present grid system, confirming that there is no problem with the present grid system.  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Sector** & **\# of Elements** \\ \hline Entry & \(360/7^{\circ}\) & 821,614 \\ Impeller & \(360/7^{\circ}\) & 3,007,260 \\ Diffuser & \(360/15^{\circ}\) & 961,150 \\ Combustor & \(360/12^{\circ}\) & 3,847,684 \\ Turbine stator & \(360/21^{\circ}\) & 1,007,880 \\ Turbine rotor & \(360/29^{\circ}\) & 1,082,720 \\ Nozzle & \(360/6^{\circ}\) & 3,804,753 \\ Total & - & 14,533,061 \\ \hline \end{tabular}
\end{table}
Table 2: Grid Information.  
Figure 5: Grid System of Integrated Analysis Model: (**a**) Compressor; (**b**) Combustor; (**c**) Turbine; (**d**) Exhaust Nozzle.
Future Internet_**2010**, \(2\), 417-430; doi:10.3390/fi2030417  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Anticipation of Traffic Demands to Guarantee QoS in IP/Optical Networks**  
**[PERSON]**  
Centre Tecnologic de Telecomunicacions de Catalunya, Av. [PERSON] 7, 08860, Castelldefels, Spain; E-Mail: [EMAIL_ADDRESS]; Tel.: +34 93 645 29 00; Fax: +34 93 645 29 01  
_Received: 21 August 2010; in revised form: 2 September 2010 / Accepted: 2 September 2010 / Published: 21 September 2010_  
**Abstract:** Traffic in the Internet backbone is expected to grow above a few Tbit/s in 2020. To cope with this, operators are moving to IP/optical network architectures, where IP is the convergence layer for all services. On the other hand, the quality of service (QoS) requirements of future applications encompasses the individualization of services and the assurance of stricter quality parameters such as latency, jitter or capacity. In other words, future optical networks will not only transport more IP data, but they will also have to offer differentiated QoS requirements to services. Finally, some emerging applications, e.g., grid computing, need greater flexibility in the usage of network resources, which involves establishing and releasing connections as if they were virtualized resources controlled by other elements or layers. In this context, traffic-driven lightpath provisioning and service-plane approaches arise as very interesting candidate solutions to solve the main challenges described above. This work reviews the concepts of service-oriented and self-managed networks and relates them to propose an integrated approach to assure QoS by offering flow-aware networking in the sense that traffic demands will be anticipated in a suitable way, lightpaths will be established taking into account QoS information (_i.e._, impairments) and complex services will be decomposed into optical connections so that the above techniques can be employed to assure QoS for any service.  
**Keywords:** IP convergence; QoS; optical networking; service plane
# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 4 Trends of HSI/MSI in Monitoring Plant Growth Status
### Monitoring Nitrogen Status  
The nitrogen (N) status of plants is known by evaluating the N content within plant tissues. Chemical methods are commonly used to examine total nitrogen content (TNC), which are destructive to the plant body [117]. HSI/MSI offers spatial information and, later, can be used to visualize the distribution of N content in the plant body, instead of using soil and plant analyzer development (SPAD). As a common approach used in spectroscopy, SPAD evaluates TNC based on the point-scanned method. As mentioned above, point-scanned techniques do not cover the whole body and do not offer pixel information [118]. Table 6 shows recent publications using HSI/MSI to investigate the N status of plants.
# Measurement of Dielectric Properties of Thin Materials for Radomes Using Waveguide Cavities
## 3 Results
### Measurement Factors
The goal is to ease the process of finding appropriate materials when designing radomes and antennas, as well as identify cost-effective fabrics for these applications.
## 6 Simulation Results
### RVQ-Based CSI Feedback  
The sum-rate curves of IA with perfect CSI and RVQ-based CSI feedback with different quantization bits in three \(2\times 2\) transceiver pairs are compared in Figure 4, and the corresponding approximation errors are calculated in Figure 5. The theoretical sum-rate curves are based on (17), and the other curves, including the perfect IA and RVQ-based feedback, are all from simulation. In each snapshot, the channel matrices were quantized using RVQ, the precoders and decoders were computed from the quantized CSI, and the decoders were perfectly known to the receivers. From Figures 4 and 5, we can see that the approximation accuracy of (17) is acceptable when quantizing the \(2\times 2\) complex matrices with more than 10 bits, and the normalized sum-rate error between the theoretical and simulation results is generally less than 5%.  
When the SNR is higher than 20 dB in Figure 4, the sum-rate with quantized CSI gradually saturates, and the performance difference between perfect CSI and quantized CSI is significant. To guarantee the DoF performance, the quantization bits should increase with the SNR. However, since the codebook size increases exponentially with the number of quantization bits, the storage space for the codebook and the complexity for codeword searching become obstacles in practical applications. For example, the quantization of a \(2\times 2\) complex matrix with 20 bits results in a codebook with more than 60 KB memory space, and \(2^{20}\) chordal distances need to be calculated and compared for the optimal codeword in each quantization. Since the \((2\times 2,1)3\) case is the simplest scenario for IA, the codebook oversizing and complexity issues are worse for larger channel matrices. Therefore, a more practical approach to CSI quantization should be adopted in general IA cases.  
In Figure 6, the theoretical results of three sum-rate analysis methods with RVQ-based CSI feedback are compared in case \((2\times 2,1)3\), including our proposed method and two other methods separately from [2,16]. The latter two methods derived the rate loss upper bound in LFB-based IA and further discussed the DoF performance. It can be seen from Figure 6 that the proposed sum-rate approximation method achieves much higher accuracy than the rate loss-based analysis methods. Moreover, our closed-form sum-rate formula is much more concise than the accurate but tedious expressions in [15,24], which are in the form of a sum of complicated series.  
Figure 4: Performance comparison of theoretical and simulation results with RVQ-based CSI feedback with different quantization bits in \((2\times 2,1)3\).  
Figure 5: Normalized error of the theoretical IA sum-rate with RVQ-based CSI feedback in \((2\times 2,1)3\).
## 3 Proposed Method
### Fingertip Detection  
After extracting the hand contour, the K-cosine corner detection [33] algorithm computes the fingertip points based on the coordinates of the detected hand contours. This is a well-known algorithm used to detect the shapes of certain objects, and it is also used in fingertip detection. It attempts to determine the angle between the vectors of a finger, as shown in Figure 4.  
\[\cos\alpha_{i}=\cos(\overset{\rightarrow}{a_{ik}},\overset{\rightarrow}{b_ {ik}})=\frac{\overset{\rightarrow}{a_{ik}}\cdot\overset{\rightarrow}{b_{ik} }}{\left|\overset{\rightarrow}{a_{ik}}\right|\left|\overset{\rightarrow}{b_ {ik}}\right|} \tag{1}\]  
Equation (1) is used to determine the fingertip locations, where \(i\) is a boundary point and \(k\) is a constant value that obtained from the experiment. In this calculation, 10 is the best choice for value \(k\) and is suitable for almost all situations. The \(k\)-vectors at the point \(P_{i}(x_{i},y_{i})\) are \(\overset{\rightarrow}{a_{ik}}=(x_{i}-x_{i+k},y_{i}-y_{i+k})\) and \(\overset{\rightarrow}{b_{ik}}=(x_{i}-x_{i-k},y_{i}-y_{i-k})\). \(\cos\alpha_{i}\) denotes the cosine of angle between \(\overset{\rightarrow}{a_{ik}}\) and \(\overset{\rightarrow}{b_{ik}}\) for a given pixel \(P_{i}\).  
The angle \(\alpha_{i}\) obtained by the K-cosine algorithm, is used to discriminate between the fingertips and the valleys as a threshold. The fingertips are considered as a positive curvature, while the valleys are a negative curvature. If the point value of \(\cos\alpha_{i}\) is the greater or equal to threshold, it is considered to be the fingertip candidate. This process results in sets of pixels belonging to the hands and fingertip locations, as shown in Figure 5b.  
Figure 4: Fingertip detection using the K-cosine algorithm.  
Figure 3: Skeletal joint information based on the Kinect skeletal tracker.
###### Abstract  
The Wyoming Department of Transportation (WYDOT) initiated a project to optimize the heights of barriers that are not satisfying the barrier design criteria, while prioritizing them based on an ability to achieve higher monetary benefits. The equivalent property damage only (EPDO) was used in this study to account for both aspects of crash frequency and severity. Data of this type are known to have overdispersion, that is having a variance greater than the mean. Thus, a negative binomial model was implemented to address the over-dispersion issue of the dataset. Another challenge of the dataset used in this study was the heterogeneity of the dataset. The data heterogeneity resulted from various factors such as data being aggregated across two highway systems, and the presence of two barrier types in the whole state. Thus, it is not practical to assign a subjective hierarchy such as a highway system or barrier types to address the issue of severe heterogeneity in the dataset. Under these conditions, a finite mixture model (FMM) was implemented to find a best distribution parameter to characterize the observations. With this technique, after the optimum number of mixtures was identified, those clusters were assigned to various observations. However, previous studies mostly employed just the finite mixture model (FMM), with various distributions, to account for unobserved heterogeneity. The problem with the FMM approach is that it results in a loss of information: for instance, it would come up with N number of equations, where each result would use only part of the whole dataset. On the other hand, some studies used a subjective hierarchy to account for the heterogeneity in the dataset, such as the effect of seasonality or highway system; however, those subjective hierarchies might not account for the optimum heterogeneity in the dataset. Thus, we implement a new methodology, the Bayesian Hierarchical Finite Mixture (BHFMM) to employ the FMM without losing information, while also accounting for the heterogeneity in the dataset, by considering objective and unbiased hierarchies. As the Bayesian technique has the shortcoming of labeling the observations due to label switching; the FMM parameters were estimated by maximum likelihood technique. Results of the identified model were converted to an equation for implementation of machine learning techniques. The heights were optimized to an optimal value and the EPDO was predicted based on the changes. The results of the cost-benefit analysis indicated that after spending about 4 million dollars, the WYDOT would not only recover the expenses, but could also expect to save more than $4 million additional dollars through traffic barrier crash reduction.  
bayesian hierarchical finite mixture model; machine learning; cost-benefit analysis +
Footnote †: journal: