esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
###### Abstract  
As pre-launch antenna calibrations are not available for GPS and GLONASS satellites, the high correlation between the terrestrial scale and phase center offset (PCO) prevents a reliable estimation of the terrestrial scale with GNSS (Global Navigation Satellite System) technology. Fortunately, the ground calibrated PCO values for Galileo, BeiDou navigation satellite system (BDS), and QZSS have been released, making a reliable estimation of the terrestrial scale with GNSS possible. In the third reprocess (repro3) of International GNSS Service (IGS), the terrestrial scale derived with Galileo, has been used. To evaluate the consistency of the terrestrial scale derived from the BDS-released FCOs as well as Galileo-released ones, and to incorporate BDS into IGS repro3 as well as operational legacy analysis, the phase center variations (PCV) and PCO for BDS medium earth orbit (MEO) and inclined geostationary orbit (IGSO) satellites are estimated to be consistent with GPS/GLONASS antenna offsets in two frames, i.e., IGb14 and IGS R3, considering robot calibrations of the ground receiver antenna models for BDS released by Geo++. We observe that the average offset of Z-PCOs achieves +98.8 mm between BDS official released and the estimated PCOs in IGb14 frame for BDS-3 MEO satellites, whereas the average offset for Z-PCO is about +174.1 mm (about \(-1.27\) ppb at the height of BDS MEO satellites) between the solutions in IGSR3 and IGb14 frame. The phase center solutions are evaluated with orbit boundary disclosures (OBD) as well as the global station coordinates. The orbit consistency benefits from the PCO/PCV estimates, particularly for BDS-2 MEO satellites, of which the 3D RMS (root mean square) OBD is reduced by 50%, whereas 3D OBD achieves about 90.0 mm for BDS-3 MEO satellites. Moreover, the scale bias between BDS-derived station coordinates and IGS legacy solutions in IGb14 frame is reduced from +0.446 \(\pm\) 0.153 ppb to +0.012 \(\pm\) 0.112 ppb using PCO/PCV estimates in IGb14, instead of the BDS official released values. Additionally, the residuals of the BDS-derived station heights (after the Helmert transformation) are slightly reduced from 9.65 to 8.62 mm. On the other hand, about +0.226 \(\pm\) 0.175 ppb is observed between BDS-only coordinate solutions derived from PCO/PCV estimates in IGSR3 frame and the IGS repro3 initial combination. These results demonstrate that the scale inconsistency derived from BDS and Galileo released PCOs is about +1.854 \(\pm\) 0.191 ppb, and a good consistency of PCO/PCC estimates for BDS in IGb14 and IGSR3 frame with other systems of GPS/ GLONASS antenna offsets is achieved.  
phase center offset; phase center variation; BDS; IGb14; IGSR3
# Physical Layer Security for Military IoT Links Using MIMO-Beamforming at 60 GHz
## 4 Conclusions  
In this paper, we have illustrated methods for the design of MIMO-beamforming systems for PHY security enhancements in cellular IoT links. Simulation results were presented for directed beams generated by a 5 cm \(\times\) 5 cm ULA, with \(\lambda/2\)--spacing, for 12, 40, and 400 elements among others. Results show that highly directed or pencil beams can be formed with antenna elements as few as 40, but 400 elements achieve the highest  
Figure 8: Received power from a UE transmitting 23 dBm (or 200 W) at 60 GHz using the transmitting antenna arrays in Figures 5–7.  
Figure 9: Received power from a BS transmitting 43 dBm (or 20 W) at 60 GHz using the transmitting antenna arrays in Figures 5–7.  
directivity or gain. The secure useful transmission range, as well as the intercept range possible when employing these beamforming arrays, were estimated using contemporary UE and BS parameters, as it is currently specified in 4G/5G cellular systems. These results provide a useful guide as to the range of secure transmissions possible when deploying IoT devices employing beamforming arrays.  
Conceptualization, A.I.S.; formal analysis, A.I.S. and C.H.; investigation, writing--original draft preparation, A.I.S. and C.H.; writing--review and editing, A.I.S. and C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by an internal research grant from Embry-Riddle Aeronautical University.  
The authors thank the anonymous reviewers for their useful comments.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Partial Least Squares (PLS) Analysis
Figure 3: Multivariate correlation analysis of measured variables. The experimental outcomes and the predictions of the PLS model show remarkable concordance, demonstrated by the high correlation coefficients of 0.9984 and 0.9991, and the substantial determination coefficients (R\({}^{2}\)) of 0.9968 and 0.9983 for the pulp and peel of OM extracts, respectively. The \(p\)-values of less than 0.0001 for both pulp and peel suggest that the differences between the observed and predicted values are not statistically significant.
## 3 Description of the Simulation Model  
### Programming Platform  
The proposed simulation approach is implemented in MATLAB (ver. R2020a), using its discrete event simulation tool SimEvents (based on SimuLink ver. 10.1/ R2020a). As per [19], SimEvents provides a discrete-event simulation engine and component library for analyzing event-driven system models and optimizing performance characteristics. A set of predefined blocks, including queues, servers, switches, supports the modelling work.
## 4 Results and Discussion
### Performance Analysis for CICIDS2017
Tables 3 and 4 illustrate the performance analysis between the proposed and existing models for CICIDS2017 [27] and UNSW-NB15 [28]. The ROC value of 98.5 for the proposed model is explained in Figure 12, which means that the model differentiates between true positive and false positive values precisely. This can be attributed to the fact that this model has proven very effective in its ability to identify cyber-attacks in the first place, while at the same time ensuring that it issues very few false \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Single LSTM Layer & 97.7\% & 0.00\% & 0.00\% & 96.9\% \\ \hline Bi-directional LSTM & 97.7\% & 0.00\% & 0.00\% & 97.6\% \\ \hline CNN-LSTM & 93.0\% & 86.4\% & 76.8\% & 81.3\% \\ \hline Proposed & 99.12\% & 98.6\% & 98.2\% & 98.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of proposed and existing techniques in the CICIDS2017 dataset. Figure 11: Confusion matrix for the CICIDS2017 dataset. Figure 12: Performance analysis between the proposed and existing model. alarms. The extended performance analysis in Table 4 confirms that the proposed model achieves superior performance over existing models on both CICIDS2017 datasets. Thus, the results show that the proposed model has a significantly higher detection capability and accuracy and can be considered an improvement over current cyber-attack detection. Table 4 shows the performance comparison of the different models on the CICIDS2017 dataset. Compared to other methods, the proposed model yields a higher accuracy of 99.12%, a precision of 98.6%, a recall of 98.2%, and an F1-score of 98.8%. However, the single LSTM layer and bi-directional LSTM models show 97.7% accuracy and F1-scores of 96.9% and 97.6%, respectively. The proposed CNN-LSTM also has comparatively lesser accuracy at 93.0% with an F1-score of 81.3%, which signifies that the model has relatively low performance. These findings are valuable, because the model-based approach achieves higher accuracy and detection rates. This is evident from the high values of precision, recall, and F1-score, which shows that it can accurately identify the positive samples as true positives while minimizing the number of false negatives. Finally, owing to the proposed model's high accuracy and comprehensive performance coefficients, there is a significant indication of its higher efficacy against various cyber-attacks in contrast to the existing model.
_Article_  
**Microalgal Growth in Paper Industry Effluent: Coupling Biomass Production with Nutrients Removal**  
**[PERSON] 1,2, [PERSON] 3,*,**,**, **[PERSON] 3, [PERSON] 1(c)**,  
**[PERSON] 1(c)**,  
**[PERSON] 2 and [PERSON] 3(c)**  
Footnote 1: [[http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci)]([http://www.mdpi.com/journal/applsci](http://www.mdpi.com/journal/applsci))  
Received: 31 March 2020; Accepted: 24 April 2020; Published: 26 April 2020  
**Abstract:** Paper and pulp industries produce effluents with high phosphorus concentrations, which need to be treated before their discharge in watercourses. The use of microalgae for this purpose has attracted the attention of researchers because: (i) microalgae can assimilate phosphorus (one of the main nutrients for their growth); and (ii) growing on effluents can significantly reduce the costs and environmental impact of microalgal biomass production. This study evaluated the growth and ability of _Chlorella vulgaris_ to remove the phosphorus from a secondary-treated effluent of a Portuguese paper company. Batch experiments were performed for 11 days using different dilutions of the effluent to evaluate its inhibitory effect on microalgae. Results showed that the non-diluted effluent inhibited microalgal growth, indicating that this bioremediation process is possible after a previous dilution of the effluent. Regarding phosphorus removal, promising results were achieved, especially in the experiments conducted with the most diluted effluent: removal efficiencies obtained in these conditions were (54 \(\pm\) 1)%. Another interesting finding of this study was microalgal growth in flakes' form (mainly due to the compounds present in the effluent and to the pH values achieved), which can be an important economic advantage for biomass recovery after the remediation step.  
**Keywords:** biomass production; _Chlorella vulgaris_; microalgae; nutrients removal; paper industry effluent; effluent treatment
## 4 Efficacy Studies for Water  
Nanoparticles have been applied in several environmental contamination remediation processes. The spectrum of application is very broad, depending upon the nanoparticles, their functionalization, and the form of catalysis employed. As an example, mesoporous silica (mSiO\({}_{2}\)) in conjunction with bismuth vanadate (BiVO\({}_{4}\)) nanoparticles were used as photocatalysts for the degradation of methylene blue (MB) and brilliant green (BG) dyes as a function of mSiO\({}_{2}\) content [70]. Additionally, pure and modified mesoporous TiO\({}_{2}\) nanoparticles with different loadings of NiO were applied for photodegradation and hydrogen evolution [71].  
In the present investigation, most of the materials are procured from commercial vendors, or synthesized using processes that are not environmentally friendly and/or do not follow green synthesis principles [12; 13]. In our ongoing investigation, we have used agricultural waste to produce nanoparticles, using green processes to remove contaminants from the aquatic environment. This process is not only environmentally friendly but also environmentally responsible. Although there is a cost associated with agricultural waste removal, for these experiments, the waste was made freely available at no cost to the authors. These nanoparticles produced using the procedure as described here, serve as adsorbents to capture contaminates from the aquatic environment. It is worth noting that with the exception of our research group, only limited studies exist in the literature that focus on the synthesis of nanoparticles that are synthesized from agricultural wastes, using green chemistry principles, and are used for environmental contamination remediation. It is, therefore, critical that efficacy studies are conducted on SNPs produced using agricultural waste to study their effectiveness for the intended application. Several efficacy studies were carried out to mitigate long-chain hydrocarbons, including petroleum, and heavy metals in aquatic environments. This is an ongoing investigation and as the scope of applications expands further, this present set of investigations serves as the underlying basis for further investigations.
## 4 Diagnosing with AI
### Other Orthopaedic Related Diagnosis  
[PERSON] et al. [11] developed a deep learning system aimed to diagnostic distal radial fracture (DRF) using anteroposterior and lateral view radiographs. To build a model, an image analysis software (ViDi Suite Version 2.0) was used. After testing different models with various hyper-parameter combinations, two models (referred to as M1 and M2 from now on) were selected, as they gave the highest AUC values. Both the resulting models had to assign a value ranging from 1 to 0 where the first extreme meant that a defect area was found while 0 meant that nothing suspicious was noticed on the image by the machine. This score was coupled with a heat map on the image indicating the ROI, where the red zone was the one where the machine thought the probability of finding the fracture would be the highest. The resulting area was, as usual, compared to the ROI outlined by two expert radiologists and two test data sets were used, an internal data set with radiographs coming from the institution and an external data set comprising radiographs coming from different institutions. The deep learning models had a good tolerance against non-pathologic alterations of the radius and were capable of detecting fractures of DRF with near-human performance [11] with M1 performing with an AUC of 93% when using anteroposterior views, 94% when using the lateral views and an AUC of 95% when a combined version of the two views was used to train the machine, while M2 performed similarly with an AUC of 95%, 94% and 96% respectively. The AUC and also sensitivity and specificity were slightly lower for an external test data set, meaning that the model had more problems diagnosing DRF on radiographs coming from other institutions. Radiologists performed slightly better on the external data set but that could be also caused by a selection bias since external images were selected whether they were high quality or not.  
Another diagnosing model for distal radial fractures was developed by [PERSON] et al. [22]. Firstly, a Region Based CNN was trained to identify the distal radiuses on anteroposterior wrist radiographs as the region of interest (ROI) and then validated by two expert radiologists. Then the cropped and augmented images were sent to the Inception-V4 model that trained on a data set of 2040 images and tested on a set of 300 images equally divided between radiographs with presence of DRFs and absence of DRFs. The results were an AUC of 0.96, an accuracy of 93% and a sensitivity of 90% a similar diagnostic capability to that of the tested orthopedists and superior performance to that of the tested radiologists. Many of the mistakes were committed on anteroposterior radiographs that displayed an absence of apparent fracture traits, features that were instead clearly visible from lateral radiographs corresponding to the anteroposterior images [22]. That is why the authors claim that the sensitivity will increase when the model will be given both types of radiographs.
# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 2 Materials and Methods
### Screw Detection Accuracy Methodology  
The testing matrix is presented in Table 2. All the laptops were run at four different velocities (5, 10, 15, or 20 mm/s), with 15 and 25 passes over the laptop, and with and without an LED light. With these parameters, each laptop was run through the machine 16 times (112 total trials), and each of these 112 trials was processed through the two YOLO neural networks of different sizes (S or XL) using a confidence threshold of 0.5 for positive detections resulting in a total of 224 tests (32 per laptop). Due to the number of trials, a naming system was devised, which will be referred to in the results. An example of the conceived naming system is V10_S15_XL_wl, in which each operating condition is labeled and separated with an underscore (velocity--10 mm/s, scans--15, neural network size--xl, and lighting condition--with light).  
Figure 3: The movement pattern performed by the robot during trials.  
Figure 2: The backs of all the laptops tested in the trials. The laptops were identified according to their manufacturers (Compaq, Dell, Gateway, HP, and Fujitsu) and a number.  
To address the possibility of detecting the same screw multiple times in consecutive frames as the robot moves over the laptop and continuously captures images, the software employs a method to determine the screw's position relative to a screw coordinate frame established when the robot is initially homed. This coordinate system's configuration at the home position is illustrated in Figure 4. The location of a screw within the screw coordinate frame can be calculated using Equations (1) and (2). Here, \(x_{px}\) and \(y_{px}\) represent the pixel coordinates of the screw's location, while \(X_{robot}\) and \(Y_{robot}\) denote the robot's current position. It is worth noting that the camera utilized in this setup has a resolution of \(1280\times 720\) px, which corresponds to an image with a field of view measuring \(49.5\) mm \(\times\) 28.5 mm. Additionally, the camera's origin was offset from the screw's origin by \(52.25\) mm in the \(X\_Screw\) direction and \(-25.75\) mm in the \(Y\_Screw\) direction.  
\[X_{screw}=\frac{x_{px}}{1280}\times 49.5+(-X_{robot}+52.25) \tag{1}\]  
\[Y_{screw}=-\frac{y_{px}}{720}\times 28.5+(-Y_{robot}-25.75) \tag{2}\]
## 4 Results
### Simulated Data
#### 4.1.1 First Experiment  
In the first scenario (Figure 3), the first two targets move vertically from bottom to top and the third target moves vertically from top to bottom. The initial coordinates of the targets are target one: x = \(-3\) m, y = \(-140\) m, target two: x = 0 m, y = \(-140\) m and target three: x = 3 m, y = 0 m. The positions of the three targets at different moments are shown in Figure 3a. Applying the Hungarian data association algorithm in conjunction with the Kalman filtering, the trajectories, in Figure 3b, are obtained. After applying the explicit polynomial fitting procedure to the data corresponding to each target, we obtain the results represented in Figure 3c. The estimated trajectories are identical to the initial trajectories.  
Figure 3: Partial and final results of the first experiment with simulated data, [6]: (**a**) spatial spreading of targets; (**b**) the cumulative effect of data association and Kalman filtering; (**c**) the effect of the explicit polynomial fitting method.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 6 Evaluations  
We conducted experiments in two environments to demonstrate the performance of the proposed communication optimization scheme. In the first experiment, a network is constructed with 1 Gbit Ethernet to evaluate the performance of the communication optimization scheme in a low bandwidth network environment. In the second experiment, a network is constructed with 56 Gbit Ethernet to evaluate the performance of the communication optimization scheme in a high performance computing environment.
## 6 Conclusions  
This study introduces Enhanced Tree Matching (ETM), a novel evaluation metric for Text-to-SQL that overcomes several limitations of the previous metrics, Execution (EXE) and Exact Set Matching (ESM). Our findings indicate that ETM offers a substantial improvement by reducing the occurrences of both false positives and false negatives that commonly plague the earlier metrics. By adopting a more rigorous approach and incorporating verifiable equivalence rules to allow query diversity, ETM can discern more granular distinctions in query correctness, allowing for a more accurate measurement of the semantic accuracy of the generated queries and a better understanding of LLMs' true capabilities in generating SQL queries. While EKE may be suitable for applications with few edge cases, domains where errors must be avoided demand a more rigorous metric like ETM.  
Moving forward, we plan to extend the list of verifiable rules to strengthen ETM. We invite the community to help in building equivalence rules and updating ETM, thereby increasing its robustness in evaluating complex SQL query structures. With our framework, adding new rules to strengthen our metric is much easier than improving ESM or EKE. As we continue to refine and enhance ETM, our goal is to establish a new standard for evaluating Text-to-SQL models that can accurately represent their practical utility and technical proficiency in real-world applications. Once this evaluation is more robust, Text-to-SQL could even be used as a standard in LLM evaluation pipelines that are used to evaluate LLM strengths and weaknesses. Text-to-SQL is particularly important for chatbots that may rely on data that is present in relational databases. With the introduction of ETM, we hope that more PLM-based methods will tackle multi-turn datasets like CoSQL, which is currently evaluated with ESM, since they will no longer be constrained by the lack of variation enforced by ESM.  
Conceptualization, B.G.A. and J.D.C.; methodology, B.G.A. and J.D.C.; formal analysis, B.G.A. and Y.S.R.K.; investigation, B.G.A., software, B.G.A. and Y.S.R.K., writing--original draft prepareation, B.G.A. and J.D.C., writing--review and editing, B.G.A. and J.D.C., supervision, J.D.C., funding acquisition, J.D.C. All authors have read and agreed to the published version of the manuscript.  
Figure 8: A question and gold query pair from Spider that assumes that every student has graduated. This assumption is not verifiable.  
Funding: This research received no external funding. Data Availability Statement: Our released evaluation script is publicly available under the Apache 2.0 license on [[https://github.com/emorynlp/ETM/](https://github.com/emorynlp/ETM/)]([https://github.com/emorynlp/ETM/](https://github.com/emorynlp/ETM/)), accessed on 17 July 2025.  
We greatly acknowledge the support of Emory University. Conflicts of Interest: The authors declare no conflict of interest.