esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In this paper, we proposed and validated an intelligent measurement method for strabismus deviation in digital videos, based on the cover test. The algorithms were applied to video recordings by near-infrared cameras, while the subject performed the cover test for a diagnosis of strabismus. In particular, we focused on the automated algorithms for the identification of the extent to which the eyes involuntarily move when a stimulus occurs. We validated the proposed method using the manual ground truth of deviations in prism diopters, from our database. Experimental results suggest that our automated system can perform a high accuracy of evaluation of strabismus deviation.  
Although the proposed intelligent evaluation system for strabismus could achieve a satisfying accuracy, there are still some aspects to be further improved in our future work. First, for the acquisition of data, there are obvious changes in the video brightness, due to the cover of the occluder. For example, almost half of the light was blocked when one eye was covered completely. This might bring a perturbation for the algorithm, especially for the pupil detection. Therefore, our system needed to be further upgraded to reduce this interference. Second, the subjects were required to remain motionless while the cover test is performed. In fact, a slight movement of the head that is not detectable to humans will cause a certain deviation in the detection of eye position, thus, reducing the accuracy of the final evaluation. To develop a fine eye localization, eliminating slight movements would improve the result. Additionally, our system can also be used for an automatic diagnosis of strabismus, in the future.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data and wrote the manuscript. [PERSON], [PERSON], [PERSON]-[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to revising the manuscript.  
The research was fully funded by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Reference No.: UGC/FDS13/E01/17).  
We would like to thank the Hong Kong Association of Squint and Double Vision Sufferers for their cooperation in collecting the experimental data.  
The authors declare no conflict of interest.  
Figure 10: The correlation of the deviation between the ground truth and predicted results. The first row shows the horizontal axis for different cover tests, and the second row shows the vertical direction.
# Validation of a Modal Work Approach for Forced Response Analysis of Bladed Disks
## 3 Results and Comparisons  
The validation of the tool described in Section 2.2 was preliminarily performed on a cyclic dummy test case reproducing a simplified bladed disk and finally on an aeronautical LPT (low-pressure turbine) bladed rotor, comparing the displacements computed by Tremor with the values obtained by the harmonic cyclic symmetry analysis performed in ANSYS Mechanical.  
For the dummy test case, various arbitrary rotating forcings were imposed to different mode shape families reproducing resonance crossings, whereas when studying the forced response of the bladed rotor, the forcing functions were directly extracted from an unsteady multi-row simulation. The latter test case consists of an IGV (representative of a turbine center frame) and a downstream stage (stator + rotor). In this configuration, the rotor is subjected to the wakes generated by the two upstream static components (IGV and stator) at their blade passing frequencies. In detail, this configuration based on realistic profile geometries was modified in terms of rotor rotation speed and blade count to experience a resonance crossing corresponding to the 1 xBPF of the IGV and no crossing for the stator passing frequency at the first bending mode. In this way, the tool can be tested both in resonance and out of resonance conditions. As already mentioned, only the first bending mode family is considered for the validation.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 3 VLC Receiver Technology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Receiver Type** & **Bandwidth** & **Data Rate** & **Responsivity** & **Chip Material** & **Ref.** \\  & & **(MHz)** & **(Gbps)** & **(A/W)** & & **(108)** \\ \hline
2018 & PD & 0.82 & N/A & 1.2 1 & Triple-cation perovskite & [108] \\
2019 & scintillating-fibers & 86.13 & 0.25 (OOK) & N/A & N/A & [109] \\
2019 & SD-APD & N/A & 0.5 (PAM4) & N/A & silicon & [110] \\
2019 & APD & 155 & N/A & 0.35 2 & silicon & [105] \\
2020 & Micro-PD & 300 & 7.4 (OFDM) & 0.11 3 & Semipolar InGaN/GaN PIN & [111] \\
2020 & PD & Rise/fall time: 0.65/2.13 ms & \(\sim\)100 3 & Black silicon & [112] \\
2020 & Micro-PD & 347 & 1.55 (OOK) & 0.191 3 & Semipolar InGaN/GaN PIN & [107] \\
2020 & Solar cell & 0.114\(\sim\)0.586 & 0.056 & N/A & Triple-cation perovskite & [106] \\
2021 & APD & 890 & 2 (OOK) & 0.45 4 & silicon & [113] \\ \hline \hline \end{tabular}  
* At 500 nm; 2 at 450 nm; 3 at 400 nm; 4 at 675 nm.  
\end{table} 
Table 3: Recently proposed receiver for VLC system.
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 4 Experiments
### Main Results
#### 4.3.3 Embedding vs. Back-End
This, in turn, supports the premise that the embeddings extracted by the pre-trained network maintain sufficient discriminant strength in terms of speakers; otherwise, the simple clustering cannot generate such high-quality pseudo labels. * C-LDA vs. A-PLDA (10.66% vs. 10.86%) vs. CORAL+ (10.54%): In comparison, C-LDA is simpler than the other two PLDA-based domain adaptation methods, both in the adaptation process and scoring process, but obtained similar or even better performance. This suggests that if the distributional distortion can be well alleviated, a simple cosine scoring is sufficient to gain good performance. Considering the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adaptation/Scoring** & **Method** & **Supervision** & **Knowledge** & **EER(\%)** & **minDCF** \\  & & **Label** & & **(P\({}_{tar}\) = 0.05)** \\ \hline  & Pretrain & - & - & 14.22 & 0.5137 \\ \hline \multirow{2}{*}{Embedding/Cosine} & S-LDA & Speaker & - & 9.75 & 0.4047 \\  & C-LDA & - & - & 10.66 & 0.4122 \\ \hline \multirow{4}{*}{Back-End/PLDA} & S-PLDA & Speaker & - & 8.87 & 0.3684 \\  & A-PLDA & - & Covariance & 10.86 & 0.4198 \\ \cline{1-1}  & CORAL+ & - & Covariance & 10.54 & 0.4299 \\ \cline{1-1}  & C-PLDA & - & - & 10.11 & 0.3979 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between C-LDA and other back-end adaptation methods on CNC1.eval. ‘C-’ and ‘S-’ signify ‘clustering’ and ‘supervised’, respectively. simplicity and the good acceptance of cosine scoring, _C-LDA is preferable compared to A-PLDA, CORAL+, and even to the more powerful but also more complex C-PLDA_. This argument will be further strengthened in the single-domain adaptation experiments presented shortly.
# A Method for Generation of a Sizing System and Representative Models for a Facial Mask Design
## 2 Development of a Sizing System of a Pilot Oxygen Mask for KAF Pilots  
### Facial Anthropometric Data of KAF Pilots  
3D facial anthropometric data (Table 1) of 336 KAF pilots (male: 278, female: 58) collected by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [5] were used for the development of a mask sizing system in this study. In their study [5], the number of participants were determined based on the formula for estimating minimum sample size provided in ISO 15535 and the gender composition of the KAF pilot population. 3D facial images of the pilots were obtained by scanning their faces using a Rexcan 560 3D scanner (Solutionix Co., South Korea) and processed by the ezScan software accompanied with the 3D scanner. The scanned 3D facial images were edited using image alignment, hole-filling, and smoothing functions in the ezScan software. Fourteen anthropometric landmarks were marked with stickers on a face by palpation before the face was scanned, then those stickers were identified automatically by the ezScan software. Eighteen facial dimensions (eight vertical length dimensions, one horizontal length dimension, six width dimensions, and three circumference and arc dimensions) closely related to pilot oxygen mask design were measured based on the facial landmarks. Descriptive statistics (e.g., mean, SD, minimum, maximum, and percentiles) of [PERSON] et al.'s study regarding the 18 facial dimensions of the KAF pilots are presented in Table 1.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 5 Implementation
### Presentation Layer  
In addition, to create a more user-friendly environment, it is possible to add an integration with a service such as that provided by TagoIO, so that the user can interact with different devices, sensors, or actuators through the construction of dashboards.  
The part of the network formed by VLC comprises a transmitter module and a receiver module. For the correct operation of both endpoints, it is necessary to add external circuits to the modules used for VLC communication.
## 2 Materials and Methods
Figure 6: Detailed results depicting velocity values calculated for AE waveform generated in mineral oil at various frequencies and measured at various distances. Figure 7: Detailed results depicting the median velocity values calculated for AE waveform generated in the considered dielectrics, in function of generation frequencies. As mentioned in previous section, the results obtained refer to the median values calculated over 32 samples for each distance and modulation frequency. In order to determine a specific numerical value, the median over all considered frequencies (see Figure 7) and distances (see Figure 8) was analyzed. Based on these charts, the differences are clearly recognizable. Another significant issue that may be noticed is the rapid increase in AE speed up to 100 Hz, which was confirmed by the power function applied for the regression analysis \(F_{1}(f)\), and a rapid decrease in these values at the smallest distances between the applied sensors, which was confirmed by the exponential function \(F_{2}(d)\). Both regression analyses were performed achieving high goodness parameters (see Tables 2 and 3), e.g., Rsquare values exceeding 0.89 for all liquids. The value of the coefficient \(a_{3}\) is closely related to the stabilized velocity value, regarding the dependency from modulation frequency. It can therefore be stated that the stable AE velocity equals to 1372 m/s in the synthetic ester, 1424 m/s in the mineral oil, and 1477 m/s in the natural ester. In the case of dependency from the distance between the source and receiver, the AE velocity may be determined from the value of coefficient \(b_{3}\), which differs slightly, with amounts of 1377 m/s in the synthetic ester, 1422 m/s in the mineral oil, and 1487 m/s in the natural ester. It must also be stated that the measured AE speed values do not correlate directly with the densities of the dielectric liquids (see Table 1). From the mathematical dependencies \(F_{1}(f)\) and \(F_{2}(d)\), it can be predicted that a further increase in modulation frequency or distance between the sensors will not significantly influence the AE velocity, as both models indicate stabilized tail values. Nevertheless, it may be beneficial to consider obtaining further measurements, including higher distances, frequencies and temperature influences on the velocities of AE waves in the considered liquids. This research was funded by National Science Centre in Poland, grant number: 2017/01/X/ST8/00263. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols
#### 3.2.1 Decentralized Predictive MAC Protocol  
The decentralized Predictive MAC protocol (P-MAC) [73] is a synchronized MAC protocol for multi-channels and multi-handsets. A dedicated CCC is assumed to be available, and each CR device is furnished with dual half-duplex transceivers, called the switch and the information transceivers. To determine in the whole network the spectrum opportunity, P-MAC executes distributed sensing. Time synchronization is employed to delay or to stop the auxiliary transmission and decides the system-wide spectral occurrences. P-MAC utilizes the Timer Synchronization Function (TSF), similar to the IEEE 802.11 MAC protocol, for harmonization. P-MAC uses an innovative algorithm for channel selection, which depends on historical prediction model (HPM). In P-MAC, the identifying data are shared by the nodes and conserve the information of the state of the channels in the entire system for sensing and to keep the rights of the PUs.  
Similar to IEEE 802.22, P-MAC combines two sensing techniques: quick sensing, which takes little time and has low exactness, and fine sensing, which enhances precision, diminishes the rate of false caution and expands the QoS. The data transceiver is additionally in charge of fine detecting and quick detecting. The data transceiver stays in the snooze state at points where there is no information to send and receive. To conserve more energy, the transceiver stays active for 60% of the time and goes to sleep mode for the remaining 40%. Figure 5 shows the P-MAC time-channel area structure.
# Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT +
## 3 A Three Step MUSIC-Taylor-Based Localization Approach
### JAEDE-Based Localization  
In the final step of localizing the target MS using a single BS, the output AoAz, AoAE, and ToA are utilized. Among all the multipath components, it is highly likely that the path with the lowest time delay corresponds to the LOS path. For exceptional cases, specialized methods such as the PEak TRacking Algorithm (PETRA) can be employed to distinguish the LOS path from the NLOS multipath clusters [22]. However, these methods are beyond the scope of this discussion. Let \((\tilde{\theta}_{l},\tilde{\phi}_{l},\tilde{\tau}_{l},)\) represent the estimated LOS AoAz, AoAe, and ToA, as illustrated in Figure 2. The coordinates of the MS can be estimated using the following expression:  
\[\begin{split}\tilde{x_{\mathbf{n}}}&=x_{\mathbf{b} }+\tilde{\tau}_{l}v_{c}\sin\tilde{\phi}_{l}\cos\tilde{\theta}_{l},\\ \tilde{y_{\mathbf{n}}}&=y_{\mathbf{b}}+\tilde{\tau} _{l}v_{c}\sin\tilde{\phi}_{l}\sin\tilde{\theta}_{l},\\ \tilde{z_{\mathbf{n}}}&=z_{\mathbf{b}}+\tilde{\tau} _{l}v_{c}\cos\tilde{\phi}_{l}.\end{split} \tag{24}\]  
Algorithm 1 and the flow chart in Figure 4 summarize the full proposed localization technique step-by-step.  
It is important to note that the constraints of the algorithm should be considered when discussing the limitations and challenges faced in the proposed JAEDE approach. In this context, two key points can be highlighted. Firstly, accurate location estimation heavily depends on the determination of LOS. Any errors in LOS determination can significantly impact the accuracy of localization results. Therefore, improving LOS estimation techniques  
Figure 4: Flow chart of the MUSIC-Taylor-based Single BS Localization algorithm.  
is essential for enhancing the overall performance of localization algorithms, particularly for IIoT scenarios where multipath effects can be severe. Secondly, the initial step of MUSIC-based coarse JAEDE acts as a crucial starting point that sets the stage for the entire algorithm, laying a robust foundation for its overall performance. The results of the first step should align closely with the grid points and be faithful to the available grid points, although grid spacing may be sparse. Otherwise, subsequent steps may not be able to effectively reduce the grid bias. There exists a performance bar for the Taylor expansion used in the second step, and surpassing this bar can be a potential direction for future research.
# Sensitivity Estimation for Differentially Private Query Processing
## 5 Experiments
### Experimental Setup
#### 5.1.2 Datasets  
We tested our method based on two datasets, including TPC-H and Facebook ego-network.  
_TPC-H dataset_ ([[https://www.tpc.org/tpch/](https://www.tpc.org/tpch/)]([https://www.tpc.org/tpch/](https://www.tpc.org/tpch/)), accessed on 1 May 2025). The TPC-H dataset provides the schema for the TPC Benchmark H, which is designed to measure the performance of complex decision support systems. It consists of eight tables (nation, region, part, customer, lineitem, orders, partsupp, and supplier) with varying data sizes. In our implementation, we treat the first three tables as public relations and the remaining five as private relations. For practical purposes, we extracted the key join attributes from these tables to create corresponding representations.  
_Facebook ego-network dataset._ ([[https://snap.stanford.edu/data/ego-Facebook.html](https://snap.stanford.edu/data/ego-Facebook.html)]([https://snap.stanford.edu/data/ego-Facebook.html](https://snap.stanford.edu/data/ego-Facebook.html)), accessed on 1 May 2025). Since the TPC-H datasets follow uniform distributions, we additionally evaluated the Facebook ego-network dataset (4039 nodes, 176,467 edges) to test skewness handling. Following the methodology in [11], we organized this graph dataset into five binary-relation tables (each containing 'from' and 'to' attributes) to maintain compatibility with our relational query processing framework. This transformation preserves the original graph structure while enabling join operations that are critical for sensitivity analysis.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 5 Experiments and Evaluation  
### Experiment Settings  
The satellite images used in this paper are derived from a multi-view benchmark dataset provided by Applied Physics Laboratory in Johns Hopkins University [2]. The dataset includes 50 high-resolution images taken by the World-View3 satellite from different viewpoints for the same geographic area between Nov 2014 and Jan 2016. The orbit of World-View3 and the target region are shown in Figure 5, as well as the other auxiliary satellites simulated in this paper for constellation task planning. The yellow rectangle on the right side enlarges the rectangular region (about 10 square kilometers) to be observed in Figure 5. Each image is accompanied by detailed meta-parameters such as acquisition time, off-nadir angle, solar elevation angle, solar azimuth angle, etc. The features for an image pair can be constructed as described in Section 3.2.1, and a part of the features are listed in Table 2.  
To align the satellite images with the corresponding time windows in the orbit, we simulate the satellite movements according to its orbit TLE (Two Line Elements) data. By performing visible time-window calculations according to the geolocation of the imaging region, additional imaging opportunities can be identified which significantly increases the number of potential time windows. In total, 150 time windows are localized within the same dates when the satellite images are actually captured in the dataset. For each  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline avg\_in & average intersection angle \\ max\_n & maximum off-nadir angle \\ s\_e\_min & minimum sun elevation angle \\ s\_e\_diff & difference of sun elevation angle \\ s\_a\_diff & difference of sun azimuth angle \\ s\_diff & root of sum of squares (s\_a\_diff and s\_e\_diff) \\ mon\_diff & time interval \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some typical features used for quality prediction.  
obtained time window, its corresponding meta-parameters are calculated for further model training and planning. The generated dataset is then divided with 80% for training and 20% for testing.  
The hyperparameters are chosen as follows: the number of weak learners _n_estimators_ in the estimation model is set to 630, meaning the model will construct 630 decision trees to balance performance and computational cost; _learning_rate_, which controls how much each tree contributes to the final predicted result, is set to 0.1; the maximum depth per tree _max_depth_ is set to 7 to avoid overly complex models; the percentage of training data used for each iteration _subsample_ is set to 55%.
# Evaluating the Capacity Coordination in the Urban Multimodal Transport Network
## 4 Numerical Example  
To test the feasibility and effectiveness of the proposed method, Nanjing city was selected as a case study. Figure 3 shows the research scope of Nanjing city, which is the main commuter corridor from suburban railway station LIUZHOUDONGLU to the central railway station XINJIEKOU. At present, there are more than 100 road links, more than 20 bus links, more than 20 rail links, and two transfer hubs in this scope. The transport network structure is composed of a roadway network, railway network, and bus transit network. Travelers in this area can choose unimodal transport modes including metro, bus, and car or combined modes including bus-to-metro and car-to-metro. The OD demand in this case was assumed to be 7000 during the morning commuter peak. Table 2 provides the detailed supply facilities and the number of corresponding links in the area. \begin{table}
\begin{tabular}{c c c c} \hline
**Evaluation Functions** & **Indicators** & **Criteria** & **Results** \\ \hline \multirow{4}{*}{LOS of multimodal transport network} & \multirow{4}{*}{\(LOS_{j}=\frac{\
abla(V_{B}/C_{C}\times L_{j})}{\sum L_{i}}\)} & LOS \(\leq\) 0.3 & A \\ \cline{3-4}  & & 0.3 \(<\) LOS \(\leq\) 0.6 & B \\ \cline{3-4}  & & 0.6 \(<\) LOS \(\leq\) 0.9 & C \\ \cline{3-4}  & & LOS \(>\) 0.9 & D \\ \hline \multirow{4}{*}{Gini coefficient of transport network} & \multirow{4}{*}{\(G_{init_{LOS}}=A/(A+B)=1-2\int_{0}^{1}f(x_{i})d(x_{i})\)} & GiniLOS \(<\) 0.2 & A \\ \cline{3-4}  & & 0.2 \(\leq\) GiniLOS \(<\) 0.3 & B \\ \cline{3-4}  & & 0.3 \(\leq\) GiniLOS \(<\) 0.4 & C \\ \cline{3-4}  & & 0.4 \(\leq\) GiniLOS \(<\) 0.5 & D \\ \cline{3-4}  & & GiniLOS \(\geq\) 0.5 & E \\ \hline \multirow{4}{*}{Mode share of transport modes} & \multirow{4}{*}{\(ModeRatio=\frac{V_{B}+V_{M}}{V_{B}+V_{M}+V_{C}}\times 100\%\)} & ModeShare \(\geq\) 40\% & A \\ \cline{3-4}  & & 30\% \(\leq\) ModeShare \(<\) 40\% & B \\ \cline{1-1} \cline{3-4}  & & 20\% \(\leq\) ModeShare \(<\) 30\% & C \\ \cline{1-1} \cline{3-4}  & & ModeShare \(<\) 20\% & D \\ \hline \end{tabular}
\end{table} 
Table 1: Evaluation indicators and criteria. According to the topology presented before, the 69 links introduced above including the connection network, roadway network, and transit networks were abstracted into a topology structure, where the transfer hubs were regarded as the virtual links, and the nodes were numbered. The final network topology of the actual urban multimodal transport network in this example is shown in Figure 4.  
To calculate the impedance function of the 69 links above, it was necessary to calibrate the relevant parameters of the impedance function presented before. The specific values of parameters were set as described in Table 3 according to [18] and the actual status.