esa-sceva/satcom-chunk-collection huggingface dataset:
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 1 Introduction  
Large neural network models generally require high computational and memory resources and are challenging to deploy in resource-constrained environments. Quantization [1, 2, 3] has shown great success to address this problem and achieve efficient deep learning. By reducing the precision of model parameters, quantization reduces storage and computational cost and enables the deployment of large over-parameterized models on mobile platforms such as phones or smart speakers.  
Uniform quantization is a mainstream quantization method due to its simplicity and efficiency, but it could lead to performance degradation when applied with an ultra-low bitwidth (4 bit or less). For example, directly performing W3A8 on ResNet-50 will lead to significant accuracy degradation (from 77% to 70%) [4], even with dedicated re-training. In order to preserve important components in the pre-trained model, mixed-precision quantization [3, 5] has been proposed in previous works to narrow the accuracy gap by assigning higher precision to more-sensitive layers. However, the improvement comes at the cost of a substantial demand for computational resources and non-negligible time and space consumption in hardware deployment. It should be noted that the implementation overhead is even more severe in the case of general-purpose machines (GPUs and CPUs).  
In this paper, we aimed to maintain the simplicity and meanwhile improve the accuracy with sensitivity-aware model architecture adaptation. We found that some modelsmay be more sensitive to the disturbance of quantization due to their complicated architectures and heavily fine-tuned parameters. We analyzed the sensitivity of models and automatically adjusted the model architecture accordingly to make it more compatible with uniform quantization. Specifically, the model architecture can be adapted by increasing the size of the channels in the convolution layers while making necessary accommodations to the regularization layers. The adapted architecture can then be fine-tuned to achieve better performance. To speed up this process, we formulated four initialization strategies, Halving, Zero Padding, Averaging, and Small Int. The effectiveness of different initialization strategies was evaluated based on the experimental results in Section 4. In summary, the contribution of this paper includes three aspects:  
* We propose sensitivity-aware neural architecture adaptation to automatically make the original model friendly to uniform quantization. Our model is cost-friendly compared to neural architecture search (NAS) and outperformed direct uniform quantization or mixed-precision quantization.
* We formulated four initialization strategies to quicken the quantization-aware fine-tuning process.
* Our approach provides a practical and simple solution, with the W4A8 ResNet-50-SANA model achieving 77.8% accuracy, which surpassed the 77.6% accuracy of the full-precision ResNet-50 model.
## 3 Results  
### ROC Analysis against Historical HISDAC-US Building Densities  
The ROC comparative analysis of extracted historical urban/non-urban labels and the historical building densities from the HISDAC-US BUPR dataset for the US study areas reveal notable effects of spatial constraining and post-processing the areas of the identified target clusters likely to represent urban areas (Figure 6). When detecting urban areas without spatially constraining them to the GHSL BUA\({}_{1975}\), Area-under-the-Curve (AUC) values are low (Figure 7a,d) but increase to up to 0.88 when including the spatial constraints (Figure 7b,e). The post-processing step (i.e., removing small segments of <50 pixels) further increases the AUC to values >0.9 in both study areas. Generally, the agreement between the extracted urban areas and the BUPR estimates is higher in Boston than in Atlanta, probably due to the higher complexity of the information contained in the Atlanta maps (i.e., smaller map scale, higher number of colors and individual map sheets). The choice of the number of clusters k heavily affects the results in Atlanta, but less so in the Boston study area, where the improvement stagnates when using a k > 5 (Figure 7c). This is in line with the results of the elbow analysis, based on the inertia of the detected clusters in RGB\({}_{100}\) space, suggesting that most maps (in spatially aggregated form) consist of approximately 3 to 5 main clusters (Figure A3). Herein, we use a threshold of 50 pixels for the segment removal during the post-processing step; higher thresholds do not improve the results (Figure A4).
## 4 Submitted Systems
### Speaker Diarization
#### 4.2.2 Closed-Set Condition Systems  
* G4-VG [30]. Voice Group, Advanced Technologies Application Center-CENATAV, Cuba. The submitted systems used a classic structure based on Bayesian information criterion (BIC) segmentation, hierarchical agglomerative grouping, and re-segmentation by hidden Markov models. The toolbox S4D [[https://projets-lium.univ-lemans.fr/s4d/](https://projets-lium.univ-lemans.fr/s4d/)]([https://projets-lium.univ-lemans.fr/s4d/](https://projets-lium.univ-lemans.fr/s4d/)) was used. The difference between the submitted systems was a feature extraction method ranging from classic ones as Mel frequency cepstral coefficients (MFCC), linear frequency cepstral coefficients (LFCC), and linear prediction cepstral coefficients (LPCC), to new ones such as mean Hilbert envelope coefficients [31], medium duration modulation coefficients, and power normalization cepstral coefficients [32].
* G8-AUDIAS-UAM [33]. Audio, Data Intelligence and Speech, Universidad Autonoma de Madrid, Spain. Three different systems were submitted, two based on DNN based embeddings using an architecture based on bidirectional LSTM recurrent neural network (primary and first contrastive systems) and a third one based on the classical model of total variability (second contrastive system).
* G10-VIVOLAB [34]. ViVoLAB, Universidad de Zaragoza, Spain. The system was based on the use of i-vectors with PLDA. The i-vectors were extracted from the audio in accordance with the assumption that each segment represented the intervention of a speaker. The hypothesis of diarization was obtained by grouping the i-vectors with a fully Bayesian PLDA. The number of speakers was decided by comparing multiple hypotheses according to the information provided by the PLDA. The primary system performed unsupervised PLDA adaptation, while the contrastive one did not.
* G11-ODESSA [25]. EURECOM, LIMSI, CNRS, France. The primary system was the fusion at a diarization hypothesis level of the two contrastive systems. The first contrastive system used ICMCfeatures (infinite impulse response--constant Q, Mel-frequency cepstral coefficients), one second uniform segmentation, binary key (BK) representation, and AHC, while the second one used MFCC features, bidirectional LSTM based speaker change detection, triplet-loss neural embedding representation, and affinity propagation clustering.
* G19-EML [35]. European Media Laboratory GmbH, Germany. The submitted diarization system was designed primarily for on-line applications. Every 2 s, it made a decision about the identity of the speaker without using future information. It used speaker vectors based on the transformation of a Gaussian mixture model (GMM) supervector.
* G22-JHU [29]. Center for Language and Speech Processing, Johns Hopkins University, USA. The system submitted in the closed-set condition was similar to that of the open-set condition with the difference that in this case, only embeddings based on i-vectors were used.
## 2 Materials and Methods
### Methods
#### 2.2.8 Phenolic Profile by HPLC  
The phenolic profile in PJ was determined by High-Performance Liquid Chromatography (HPLC). Curves were prepared for standard calibration by employing gallic acid, \(p\)-hydroxybenzoic acid, caffeic acid, protocatechuric acid, catechchin, \(o\)-coumaric acid, syringe acid, \(p\)-coumaric acid, \(m\)-coumaric acid, ferulic acid, myricetin, quercetin, kaempfer, and chrysin.  
The samples were filtered through a membrane filter of 0.45 \(\upmu\)m. They were then evaluated in a system with a Shimadzu HPLC (SPDM20A DAD detector, LC-20 AD pump, SIL-20A HT autosampler, DGU-20A5R degasser, CTO-10 ASVP column oven, and CMB-20A communications bus module, Shimadzu Corp., Kyoto, Japan). Separations were performed at 40 \({}^{\circ}\) C on an Intersil ODS C-18 reversed-phase column (250 mm \(\times\) 4.6 mm length, 5 \(\upmu\)m particle size). The mobile phase contained solution A (distilled water with 0.1% (_v_/_v_) acetic acid), and solution B (acetonitrile with 0.1% (_v_/_v_) acetic acid). Gradient elution was conducted as follows: 10% B (0-2 min), 10-30% B (2-27 min), 30-90% B (27-50 min), and 90-100% B (51-60 min), and at 63 min it was returned to initial conditions.  
The flow rate was 1 mL/min. Chromatograms were observed at 278, 320, and 360 nm. Identification and quantitative analyses were performed based on the retention times and the external norm curve. HPLC-DAD results were given as \(\mathrm{\SIUnitSymbolMicro g}\)/g PJ.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 7 Conclusions
The second interesting advantage is that IEEE 802.11 provides high data transfer rates which allows the transmission of all types of sensed data, even images and video content. As future work, we would like to extend our analysis to different kind of antennas and perform this same study in seawater. We are currently working on directional patch antennae (a single patch antenna and a patch antenna array) that will give us about 12-13 dBi. We think that with this type of antenna will reach larger distances. Thus, we will extend the experiment to these new antennae. Our aim is to apply all of these results in the aquaculture sector [36]. In addition, we want to test bigger volumes of air with distances between antenna and container proportional to integer values of lambda. In our next work we will try to define how the intrinsic features of signals, such as multipath and the intersymbol interference (ISI), affect its propagation. Finally, we want also to test the network performance for lower frequencies like 915 MHz. In this frequency, we will test the amount of video streams we can simultaneously send with enough quality. This work has been partially supported by the Postdoctoral Scholarship \"Contratos Postdocrales UPV 2014 (PAID-10-14)\" of the \"Universitat Politecnica de Valencia\", by the \"Program para la Formacion de Personal Investigador--(FPI-2015-S2-884)\" of the \"Universitat Politecnica de Valencia\", by _Instituto de Telecomunicacoes_, Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, by the National Funding from the FCT--_Fundacao para a Ciencia e a Tecnologia_ through the UID/EEA/500008/2013 Project, by the Government of Russian Federation, Grant 074-U01, and by Finep, with resources from Funttel, Grant No. 01.14,0231.00, under the Radiocommunication Reference Center (Centro de Referencia en Radiocommunicacoes--CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicacoes--Intel), Brazil. [PERSON] and [PERSON] took the measurements; [PERSON] and [PERSON] analyzed the data; all authors contributed writing the paper. The authors declare no conflict of interest.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 3 Results
### Validation of CCC Estimation Using Field Canopy Spectral Measurements  
Each model was evaluated by computing the RMSE between the field measurements and CCC values predicted based on the field canopy reflectance data. All of the selected VIs were calculated from field canopy spectra data that was resampled according to MERIS band settings. Figures 7-9 display the scatter plots for the predicted and true values produced by the models. As shown in Figure 7, the accuracy for MTCI \(\times\) LAI-VIs was higher than that for the MTCI, and MTCI \(\times\) MTVI2 showed the best performance (R\({}^{2}\) = 0.72, RMSE = 51.68 \(\upmu\)g cm\({}^{-2}\), Bias = \(-\)51.57 \(\upmu\)g cm\({}^{-2}\), and NRMSE = 17.60%). The results for the BLR model (Figure 8) suggest that the MTCI and LAI-VIs performed better than the MTCI alone; however, the performance was not comparable to that of the combined VIs with ULR. Both the ULR and BLR approaches universally overestimated the CCC, and this effect was most prominent with BLR. Thus, these methods cannot compensate for ill-posed VIs. Figure 9 indicates that the RFR approach performed the best with the scatter points much closer to a 1:1 linear pattern, especially for the soybeans. A comparison of all the models revealed that the random forest regression model trained using binary variables based on MTCI and MTVI2 showed the best performance (R\({}^{2}\) = 0.65, RMSE = 37.76 \(\upmu\)g cm\({}^{-2}\), Bias = \(-\)4.11 \(\upmu\)g cm\({}^{-2}\), and NRMSE = 12.89%). Therefore, the random forest regression model trained with binary variables based on the MTCI and LAI-VIs could effectively alleviate the influence of ill-posed VIs on the accuracy of CCC estimation.  
Figure 7: The CCC retrieval accuracy for soybeans and wheat when using the ULR method with field spectrum data: (**b**) MTCI \(\times\) NDVI; (**c**) MTCI \(\times\) INDVI; (**d**) MTCI \(\times\) SNDVI; (**e**) MTCI \(\times\) RDVI; (**f**) MTCI \(\times\) MTVI2. The performance of a single MTCI in a linear regression model is displayed in (**a**) for comparison.  
Figure 8: The CCC retrieval accuracy for soybeans and wheat when using the BLR method with field spectrum data: (**b**) MTCI and NDVI; (**c**) MTCI and LNDVI; (**d**) MTCI and SNDVI; (**e**) MTCI and RDVI; (**f**) MTCI and MTVI2. The performance of a single MTCI in a linear regression model is displayed in (**a**) for comparison.  
Figure 9: The CCC retrieval accuracy for soybeans and wheat when using the RFR method with field spectrum data: (**b**) MTCI and NDVI; (**c**) MTCI and LNDVI; (**d**) MTCI and SNDVI; (**e**) MTCI and RDVI; (**f**) MTCI and MTVI2. The performance of a single MTCI in a linear regression model is displayed in (**a**) for comparison.
## 6 Results and Discussions
### The Inner Loops
#### 6.1.2 Incremental Position Loop  
Figure 6 shows the force control with an inner position loop applied over two materials: a polymer (orange lines) and aluminium (blue lines). As can be seen, the force response remains around the reference force of 5 N for the first 150 s. Concerning the material in contact, the force shows higher deviations in the aluminium as a result of seizing between same materials.  
The force error at the end of the stroke (from 150 s) is mainly due to the changes produced in the cartesian stiffness. This suggests that the position loop is made with the Jacobian inverse method; therefore, the stiffness changes due to the change in position and the use of the Jacobian matrix.  
A representation of the cartesian stiffness during the trajectory is shown in Figure 7. It was calculated using the coordinate transformation in Equation (12). In the figure, the force and stiffness in the Z direction, \(\mathbf{K}_{\mathrm{z}}\), are shown. As can be seen, during the time that the end effector is within the stable force path, at 5 N, the stiffness \(\mathbf{K}_{\mathrm{z}}\) has the maximum value. However, when the robot is near the end of the trajectory, the stiffness decreases, generating errors in the force response. If the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Joint** & **k1** & **k2** & **k3** & **k4** & **k5** & **k6** \\ \hline Stiffness (N/m) & 13323 & 13323 & 4412 & 2729 & 2729 & 2729 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stiffness joint parameters.  
internal control gains were known, this effect would be compensated in the control action to avoid the observed deviations.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### Experimental and Investigations on the Proposed Method Based on the Steganography Metrics
#### 4.3.2 Capacity Feature  
Based on Table 6, it is evident that the scenario of diacritics surpasses the scenario of Hamzas significantly in terms of capacity. This is because diacritics can be added to any letter, allowing for the entire text to be utilized (maximum capacity). However, it should be noted that the Hamza scenario has a limited capacity, since Hamzas only constitute approximately 2-3% of the text, making this scenario suitable only for short messages.
###### Abstract  
A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.  
Keywords:phasor measurement unit; distribution network; compression; cost; state estimation error; supervisory control and data acquisition; multi objective model +
Footnote †: journal: Applied
## 2 The Basic Theory
### The Optimal Latin Hypercube Design  
The Latin hypercube design is used to divide each dimension coordinate interval \(\left[x_{k}^{\text{min}},x_{k}^{\text{max}}\right]\), \(k\in[1,n]\) in the n-dimensional space into m intervals, each interval is \(x_{k}^{i-1},x_{k}^{i}\), \(i\in[1,\text{m}]\). Randomly selected m points, and ensured that each level of a factor has been studied only once, leading to an n-dimensional space, the sample number m of Latin hypercube design, marked as mxn LHD [16]. The optimal Latin hypercube design is improved at the foundation of random Latin hypercube design, so all the test points are distributed as evenly as possible in the design space, so as to further enhance its space filling and equalization. Figure 3 shows test points generated by random Latin hypercube design and test points generated by optimal Latin hypercube design, respectively.
# Selective Control by _Pistacia vera_ L. and Its Carotenoid Zaxanthin on SARS-CoV-2 Virus
## 2 Results
### Inhibition of SARS-CoV-2 Variants by Zeaxanthin upon Cell Treatment  
To investigate whether zeaxanthin exhibited activity against TMPRSS2, which is selectively involved in the entry of the alpha variant but not the omicron variant, we conducted an experiment where cell monolayers were pretreated with zeaxanthin before the transduction of pseudoviruses. Specifically, A549 cells were pretreated with zeaxanthin for 2 h at 37 \({}^{\circ}\)C, as illustrated in Figure 5a. This was followed by the transduction with \(\alpha\) and o-SARS-CoV-2 pseudotype variants separately, allowing us to observe the effect of zeaxanthin on cells affecting the virus entry. The results showed that zeaxanthin inhibited the alpha entry but not the omicron variant (Figure 5b vs. Figure 5c), suggesting that it could affect the TMPRSS2-mediated internalization.  
This has significant implications for potential antiviral therapeutics, offering a ray of hope in the fight against the SARS-CoV-2 new variant different from omicron or against other viruses that use cellular proteins such as TMPRSS for entry [15].  
Figure 5: In vitro pseudovirus entry inhibition assay following zeaxanthin treatment. (**a**) Schematic diagram of pseudovirus entry inhibition assay. (**b,c**) The luciferase activity was performed in A549 cells 72 h post-infection with or without zeaxanthin (10 uM) using \(\alpha\)-SARS-CoV-2 and o-SARS-CoV-2 pseudoviruses. Experiments were performed in triplicates, and data represented the RLU of three independent experiments (n = 3). Error bars indicate standard deviation (SD), and **** indicates the significance of \(p\)-values less than 0.0001.
## 4 Results and Discussion
### Case Study 1: Flexible Beam Coupling  
In the first case, involving the coupling of two flexible beams (assembly system AB), the direct/cross-receptance matrix components were evaluated at the tip \(\mathcal{U}_{1}\) and connection point \(\mathcal{U}_{2}\). The results of the theoretical works outlined in Section 3.1 and shown in Figures 8-11 demonstrate GRCFBS's ability to capture the dynamic interactions between substructures, with close alignment to FEA at the primary vibrational modes.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 8 Proposed Framework: Multi-Layer Threat-Defense Alignment Framework
### Layered Smart Grid Architecture  
Five logical layers will make up the smart grid, each with a distinct threat surface and functional role:  
* **Perception Layer:** Is made up of data-gathering actuators, smart meters, and Internet of Things sensors.
* **Network Layer:** Is made up of gateways, routing systems, and communication protocols.
* **Control Layer:** Includes Programmable Logical Controllers (PLCs), backend controllers, and Supervisory Control and Data Acquisition (SCADA) systems for automation and decision-making.
* **Application Layer:** Makes user-facing services like demand response, billing, and analytics on energy use possible.
* **Management Layer:** Provides governance, auditing, and general security and policy enforcement.
# Assessing the Security and Privacy of Baby Monitor Apps
## 5 Mobile App Analysis
### Assessing the Confidentiality of Video Streams  
As mentioned earlier, the audiovisual footage recorded and transmitted by baby monitoring applications is sensitive. As a consequence, ensuring the confidentiality of audiovisual data should be a key concern for baby monitoring applications. However, only two baby monitoring apps state in their privacy policy that they secure their data transmissions without specifying how the data are ultimately secured. For the other apps, the use of security measures remains uncertain. Therefore, we investigated how baby monitoring apps aimed to transmit video footage securely and evaluated these mechanisms for security weaknesses (Section 3). As a result, we identified several security issues that may affect the confidentiality of the video streams, in some cases severely threatening the user's privacy.
## 3 System Architecture of the DSS
### Dialog Management  
The dialog management component furnishes users with a graphical user interface that facilitates data entry pertaining to the problem dataset, the execution of the solution procedure, and the display of the resulting solution. Additionally, the survey screen is configured as a web-based platform, ensuring accessibility for remote data processing. It enables functionalities such as data capture from the survey screen, posting to the database, and recording the information in the database system.