esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Simulation and Experimental Methods  
Here, we present a detailed description of the finite-element thermal simulation process for chip packaging defects, the YOLO11-based target detection architecture, and the infrared thermal image acquisition platform. The overall technical workflow is illustrated in Figure 1. First, temperature distribution images of packaged chips under various operating conditions are generated through finite-element simulations and infrared thermal imaging experiments, resulting in two types of datasets: simulated images and experimental images. These datasets are then merged and expanded through data augmentation techniques, including random rotation, occlusion, noise injection, and flipping, to enhance model robustness and generalization. The augmented dataset is subsequently fed into the YOLO11 detection model, which comprises three primary components: a backbone network for feature extraction, a neck network for multi-scale feature fusion, and a detection head for defect localization and classification. The model outputs include detected defect regions, defect types, and corresponding confidence scores. By combining physics-based simulation with data-driven learning, this method leverages both physical interpretability and high-precision detection capability, providing a comprehensive solution for automated chip packaging defect identification.  
Figure 1: Schematic of the proposed multimodal defect detection workflow, integrating finite-element thermal simulation, infrared thermal imaging, data augmentation, and YOLO11-based deep learning for automatic defect identification in chip packaging.
## 3 Results and Discussion
### Error Indicators of Static Measurement  
The static measurements (Table 3) involved a smaller set of samples than the dynamic measurements (Table 2), but the entire recording of the three days together was approximately one hour and twenty minutes.  
Figure 6: The density of deviations in the graphs of the four RTK receivers (**a–d**), together with the calculated error indicators \(\mu_{err}\), \(\sigma_{err}\) and \(RMS_{err}\) from the measurement period “FIX”. The density of the values was calculated identically for all the four receivers from the range (0–5 m) in the number of 2000 bins (resolution 2.5 mm).  
The stable unchanged position was mainly reflected in the ability to solve the ambiguity of the carrier phase (\(SSR\)). Receiver B and Receiver C were successful throughout all the measurements, and Receiver A was not able to resolve the ambiguity in one short period of three seconds. Receiver D was able to solve the phase ambiguity of the carrier wave in a static measurement almost three times more often than in a dynamic measurement. Nevertheless, compared to the results of a previous study [17], this receiver achieved an even worse result, which indicates that the established measurement conditions were not ideal for this low-cost RTK receiver.  
The \(SSR\) result was reflected in the positioning indicators (\(\mu_{err}\), \(\sigma_{err}\) and \(RMS_{err}\)) from the whole measurement period, while these errors were still smaller compared to the dynamic measurements. The error indicators determined based on the selection of samples from the \"FIX\" period showed smaller errors than in the dynamic measurements. A comparison of the resulting \(RMS_{err}\) values with the catalogue values specified by the manufacturers revealed that Receiver A (\(RMS_{err}\) = 0.01 m + 1 ppm) and Receiver D (\(RMS_{err}\) = 0.025 m + 1 ppm) met the stated values. Receiver B and Receiver C did not narrow the stated values (\(RMS_{err}\) = 0.01 m + 1 ppm).
# Soft Contrastive Cross-Modal Retrieval
## 2 Related Work
### Cross-Modal Retrieval  
Cross-modal retrieval [22; 23; 24] is an important issue in the field of information retrieval and machine learning, as shown in Figure 1. It aims at retrieval or correlation matching between different types of data (such as images, text, audio, etc.), thereby achieving the effective interaction and correlation of multimodal data [25]. It has a wide range of needs in practical applications, such as multimedia retrieval [26], content recommendation systems, and intelligent search engines. How to diminish the heterogeneous gap and bridge the semantic gap between different modalities is a significant problem; hence, considerable researchers have proposed lots of methods from their unique perspectives [27]. Furthermore, we divide the existing methods into two categories: Traditional Cross-Modal Methods and Deep Learning-Based Methods.  
**Traditional Cross-Modal Methods.** Canonical correlation analysis (CCA) [13] stands as a classic multivariate method used to examine correlations between two sets of variables, or modalities. MCCA [14], building on CCA, seeks to uncover linear relationships and common structures across multiple datasets. Furthermore, Multiple View Discriminant Analysis (MvDA) [28] is designed to maximize the variability between different representations of the same object while minimizing variations within the same class. [PERSON] et al. [28] applied MvDA to ensure consistent views across multiple linear transformations. Similarly, [PERSON] et al. [29] introduced an approach for joint representation learning to identify semantic connections and relevant features in cross-media data.  
**Deep Learning-Based Methods.** Over the past two decades, deep learning has made significant advances in Natural Language Processing, with an increasing number of methods leveraging deep learning to address the challenge of semantic representation and bridge the heterogeneous gap [30]. For example, [PERSON] et al. [31] introduced a deep multigraph-based hierarchical enhanced semantic representation model (MG-HSF) to align semantic distributions at a fine-grained level. [PERSON] et al. [32] proposed an adversarial cross-modal retrieval method (ACMR) that uses adversarial learning to discover a shared subspace. [PERSON] et al. [33] developed a method for deep semantic matching to enhance the representational capabilities of CNNs. [PERSON] et al. [34] suggested utilizing multiple deep networks to explore correlations in cross-media data through hierarchical learning. Conversely, [PERSON] et al. [35] presented deep canonical correlation analysis, which combines CCA with deep neural networks. [PERSON] et al. [36] proposed a deep canonically correlated autoencoder approach for learning representations using DNNs.
# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 4 Analysis of Experimental Data  
### Analysis of Modeling Results  
Modeling was performed according to the steps of the process, as shown in Figure 3. First, the images were aligned by analyzing the position of the camera for each image, and after entering the measured GCPs for geometric correction, the Align Photos step was performed to extract tie points from the overlapping area in the image. In the Build Dense  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & & \\ \hline Scenario 1 & & & & & & & \\ No GCP & – & – & – & – & – & – \\ \hline Scenario 2 & & & & & & & \\
1 GCP & 1 & 2 & 3 & 4 & – & – \\ \hline Scenario 3 & & & & & & & \\
2 GCPs & 1, 2 & 1, 3 & 1, 4 & 2, 3 & 2, 4 & 3, 4 \\ \hline Scenario 4 & & & & & & & \\
3 GCPs & 1, 2, 3 & 1, 2, 4 & 1, 3, 4 & 2, 3, 4 & – & – \\ \hline Scenario 5 & & & & & & & \\
4 GCPs & 1, 2, 3, 4 & – & – & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: GCP locations for Scenarios 1-5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Scenario**} & **Geolocation RMSE X [m]** & **Geolocation RMSE Y [m]** \\ \hline S1 & 0.161024 & 0.120645 \\ \hline S2-1 & 0.072354 & 0.093217 \\ \hline S2-2 & 0.07112 & 0.091593 \\ \hline S2-3 & 0.073097 & 0.084644 \\ \hline S2-4 & 0.089338 & 0.100365 \\ \hline S3-1 & 0.059623 & 0.071285 \\ \hline S3-2 & 0.065001 & 0.091031 \\ \hline S3-3 & 0.066761 & 0.081339 \\ \hline S3-4 & 0.03989 & 0.050022 \\ \hline S3-5 & 0.051569 & 0.0809 \\ \hline S3-6 & 0.041449 & 0.054192 \\ \hline S4-1 & 0.024585 & 0.031112 \\ \hline S4-2 & 0.017019 & 0.033018 \\ \hline S4-3 & 0.021665 & 0.030613 \\ \hline S4-4 & 0.010256 & 0.030092 \\ \hline S5 & 0.008412 & 0.011855 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE values calculated for each scenario in the study site.  
Point Cloud step, depth information was calculated from individual images based on the point cloud model built during the Align Photos step to create a denser point cloud model. In the Build Mesh step, a polygon mesh model was created using the mesh-building process based on the dense point cloud model. Finally, in the Build Texture step, which was a step that involved creating texture based on the original image and mapping the texture to the polygon mesh model, a 2D orthophoto was produced.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.1 Overall Model Performance
Additionally, it is notable that the most frequently compared models in the surveyed papers were XGBoost, RF, and LSTM, with 47, 43, and 39 instances, respectively.
# New Advances and Challenges of Fall Detection Systems: A Survey
## 4 Challenges and Potentials
### How to Protect Privacy  
From the perspective of users, privacy is another challenge for fall detection, especially when using vision-based algorithms, since no one wants to put a camera in his/her room. This is another reason many algorithms cannot be used in reality. Kinect provides an alternative way. It only collects depth image and skeleton data, in which blurred information has been used to describe human action.  
Furthermore, the methods to detect fall based on Wi-Fi technology are promising and open a door in the future. It is ubiquitous and device free in contrast to methods based on wearable sensors. However, currently, it is hard to detect falls from several persons simultaneously, due to signal interference.
## 3 SRB Activities in Acidic Environments
### Response of SRB to Acidic Environments
#### 3.1.1 The Maintenance of pH in Cytoplasm
It was reported this reaction of F\({}_{1}\)F\({}_{0}\)-ATPase might be a result of reorientation of the \(\varepsilon\) subunit which is known as ATPase endogenous inhibitor, toward F\({}_{0}\) and away from the \(\beta\) subunit of F\({}_{1}\). The rearrangement consequently may enable the F\({}_{1}\) part to hydrolyse ATP and export protons. The atp operon contains 8 genes encoding subunits of F\({}_{1}\)F\({}_{0}\)-ATPase. Previous research in _E. coli_ showed that in low pH conditions, _atpA_, \(B\), \(C\), \(D\), \(E\), \(F\), \(G\) and \(I\) expression was decreased [42]. This gene expression correlated negatively in F\({}_{1}\)F\({}_{0}\)-ATPase under acidic conditions. However, _D. vulgaris_ was shown to have _atpA_, \(B\) and \(H\) expression upregulated [35] which have positive effect on F\({}_{1}\)F\({}_{0}\)-ATPase function. This reaction supported bacterial survival and growth at low environmental pH. Besides controlling the influx and outflux of proton flow, bacteria promote a strategy to combat acid stress by increase the consumption of proton. It has been reported that some bacteria develop enzymes to produce alkaline products in order to neutralize excessive cytoplasmic protons, e.g., the ammonium-forming enzymes in urease systems such as _Helicobacter pylori_, _Bacillus cereus_[43; 44]. Amino acid synthesized by bacteria also was found to help maintaining cytoplasm pH homeostasis by increasing intracellular pH during metabolism [45]. _E. coli_[46]_and Listeria monocytogenes_[47] activates different acid resistance systems under acid conditions, where cells attempt to alkaline cytoplasmic pH, and require the presence of amino acids during acid challenge. These systems are known as the glutamate decarboxylase system (gadABC operon) [48], the arginine decarboxylase system (adiaA) and lysine decarboxylase (CadA) [49]. _D. vulgaris_ was reported to have gene expression of synthesis of amino acid under acid stress [35], however it is still unclear if this amino acid metabolism involved in neutralising cytoplasmic pH.
## Abbreviations  
The main abbreviations used throughout the paper are shown as below:  
\begin{tabular}{l l l l}
3 GPP & the 3 rd Generation Partnership Project & LEO & Low Earth Orbit \\
5G & 5 th generation mobile communication & LoS & Line-of-Sight \\
6G & 6 th generation mobile communication & MANET & Mobile Ad-Hoc networks \\ A2G & Air-to-Ground & MEC & Mobile Edge Computing \\ AAP & Aerial Access Point & MEO & Medium Earth Orbit \\ ACO & Ant Colony Optimization & ML & Machine Learning \\ AI & Artificial Intelligence & mMTC & massive Machine-Type Communication \\ BS & Base Station & NFV & Network Function Virtualization \\ CIDS & Collaborative Intrusion Detection System & NTN & Non-Terrestrial Network \\ DDoS & Distributed Denial of Service & PSO & Particle Swarm Optimization \\ DIDS & Distributed Intrusion Detection System & PUF & Physically Unclonable Function \\ DRL & Deep Reinforcement Learning & QoS & Quality of Service \\ DTN & Delay Tolerant Networking & RF & Radio Frequency \\ DWT & Discrete Wavelet Transform & RFID & Radio Frequency Identification \\ E2E & End-to-End & RIS & Reconfigurable Intelligent Surface \\ EA & Eavesdropping Attack & SA & Simulated Annealing \\ EIGRP & Enhanced Interior Gateway Routing Protocol & SAGIN & Space-Air-Ground Integrated Network \\ ELM & Extreme Learning Machine & SATCOM & Satellite Communication \\ \end{tabular}
# Track-Bridge Interaction of CWR on Chinese Large-Span Bridge of High-Speed Railway
## 3 Track-Bridge Interaction Law of Different Bridge Types
### Continuous Beam Bridge
#### 3.2.2 Track-Bridge Interaction Law  
In the case of full bridge loading, the stress and deformation of the track structure on the long-span continuous beam bridge are obviously greater than that of the multi-span simply supported beam bridge. The bending force of the steel rails on the continuous beam main bridge appears as tension on both side spans and appears on the middle span as the pressure, and its maximum value appears at the side span of the main bridge and the middle of the main span. The maximum relative displacement of the rail slab appears on the top of the fixed support abutment. For continuous beam bridges, the right side of the continuous beam main bridge two-span loading and full-bridge loading are the most unfavorable conditions. When calculating the bending force and displacement of the CRTS-III slab ballastless track seamless track on a long-span continuous beam bridge, the most unfavorable train load action length should be selected according to different check components. Under the vertical temperature gradient, the rail stress peaks all appear near the middle of the full bridge span. The increase of the vertical temperature gradient of the beam will cause the deformation of the rail and the force of the bridge pier to increase [36].  
The use of low-resistance fasteners on the bridge can significantly reduce the rail deflection, displacement, and the longitudinal force of the fixed support pier top [37], but it will weaken the nonlinear constraint between the bridge and the track and make the collision effect stronger [38]. Controlling the longitudinal stiffness of the top of the pier and abutment within the range of 1.0 to 1.5 times the design value is beneficial to the force and deformation of the track and bridge structure under the action of the train load [37].
# Practical Use of Starlink Downlink Tones for Positioning
## 5 Conclusions  
We have investigated the feasibility of tracking Starlink downlink tones for opportunistic positioning in a practical situation, when signals are received without a parabolic reflector.  
Based on the processing of real-life signals, the tones are detected and then tracked by means of an FLL-assisted-PLL. The results have shown that up to six satellites are tracked simultaneously with a C/No between 24 and 36 dB-Hz. In addition, without antenna reflector, tone tracking is often interrupted due to the weak strength of the tones combined with the oscillation in the tones' magnitude. This suggests that in this situation, we cannot afford to track only a single tone for a satellite. Since such interruptions do not usually occur at the same time for tones of the same satellite, tones' measurements have been aggregated in order to obtain the most continuous frequency shift measurement for each tracked satellite.  
In order to determine the potential of Starlink downlink tones in positioning, the aggregated frequency shifts are then used to solve for the position of a static user. Multi-epoch positioning has been considered, in which frequency errors induced by satellite-imprecise orbits are captured in satellite-dependent frequency error states. The results showed that the measurement rate should not exceed 1 Hz to avoid any singularity in the observation model. In addition, the duration of the multi-epoch interval must satisfy a trade-off between good satellite geometry and meeting the assumption of constant satellite-dependent frequency error states.  
The resulting positioning error, using 1 Hz rate measurements stacked over a 30 s duration, is 375 m. Note that this result is obtained with inaccurate satellite orbits (TLE file), inaccurate timing, and a relatively low signal strength. Optimizing the antenna and RF front end will improve both signal quality and satellite availability for better positioning with Ku-band signals.  
Formal analysis, N.J.; Investigation, N.J. and R.A.; Methodology, N.J.; Data acquisition hardware, R.A. and N.J.; Software, N.J.; Validation, N.J. and R.A.; Writing--review and editing, N.J. and R.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Deriving Performance Level from Measurements  
A single package loss event usually does not lead to a failure in the safety function if the next package is transmitted successfully; e.g., when transmitting the position of a truck to another truck for collision avoidance, it is okay if the first package transmission fails as long as the second one is transmitted correctly \(100\,\mathrm{ms}\) later. It becomes unsafe only when a specific number of consecutive transmissions fail in succession. In the following, the variable \(k\) represents the number of consecutive package loss events that are required in a system to lead to a failure in the safety function.  
A typical network trace might have a loss burst distribution such as that presented in Table 4. If \(k\) is the critical number of consecutive loss events that leads to an outage, for the example of [23] from Table 4 for \(k\geq 4\), we would receive a \(PFH_{\mathrm{D}}=0.00113\). Since \(0.00113>10^{-4}\), we cannot even assign PL \(a\).  
Furthermore, since this is a small sample, let us use the 95% confidence interval given by \(\mathrm{CI}=\hat{p}\pm 1.96\sqrt{\frac{b(1-\hat{p})}{n}}\). For the trace of [23], we receive the interval \([0.000492,0.00177]\). This means with a likelihood of 95% we can guarantee that the probability of a dangerous outage is at most \(0.00177\). This would already be too high to assign a performance level.  
In our own trace, no losses with \(k\geq 4\) appeared, since we only measured for an hour and such events are very rare in our system. This demonstrates that just measuring is unreliable, since in safety systems, dangerous outages may only occur a few times per year. This is why, in the following section, we describe how we can use models to derive a good estimate for the frequency of dangerous outages that might not occur during measurement.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## 9 Estimated versus Actual CELP Codec Performance
\begin{table}
\begin{tabular}{c c} \hline \hline
**Codec** & \(R\) **Bits/Sample** \\ \hline G.726 & 4.0 bits/sample \\ G.729 & 1.1 bits/sample \\ AMR & 1.1 bit/sample \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Codec rates to achieve PESQ/mean opinion score (MOS) of 4.0 for “We were away... ” [6]. AMR, adaptive multirate. is toward the upper end of the expected \(SPER\) of say 6 dB. In this case, the voiced component has the mutual information of \(0.5265(1.96+1.0)=1.56\), so the total mutual information is 2.8, and then subtracting from 4 bits/sample, we obtain a CELP codec rate estimate of 1.2 bits/sample to achieve an MOS = 4.0. The actual CELP rate needed by G.729 and AMR for this MOS is about 1.0 bits/sample, which constitutes good agreement.
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 2 Background
It is also shown that the utilization of a stair matrix contributes positively in obtaining a high convergence rate. In the literature, most of the existing detection techniques have been tested and investigated when BUAR is large, and thus they achieve high performance. In [16], several detectors have been studied based on a stair matrix, the GS, SOR, JA, NI, and RI methods. However, they achieved a good performance-complexity profile when the BUAR is large (BUAR = \(\frac{256}{32}=8\)). However, this is not the case when the BUAR is small. In such case, a sophisticated signal processing is required to select the optimal solution.