esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 5 Conclusions  
In this work, we propose a lossy compression scheme for image sequences, based on the AVQ algorithm, which we denoted as AVQ\({}_{\text{IS}}\). Our approach uses a shared dictionary to exploit the temporal correlation of image sequences.  
The experimental results that we achieved show slight improvements, on average, in terms of compression performances, when the AVQ\({}_{\text{IS}}\) algorithm is compared with the AVQ algorithm. However, we have designed our approach in order for it to be easily implementable and it does not affect the execution time. The compression performances are dependent anyway on the temporal correlation among the frames that compose the input image sequence.  
Future research directions will include the testing on collections of bi-level images [13] and on 3D medical images [14]. Moreover, we will focus on the design of new heuristics and on the design of parallel implementations through adequate frameworks (e.g., the OpenCL framework [15], etc.) of the AVQ\({}_{\text{IS}}\) algorithm (e.g., based on the technique addressed in [16]). In addition, we will focus on a possible pre-processing based on a frame ordering (similar to the one highlighted in [17]), which could improve the compression performances.  
**Acknowledgments:** The authors would like to thank students [PERSON] and [PERSON], who have implemented and tested a preliminary version of the AVQ\({}_{\text{IS}}\) algorithm.  
Figure 14: Graphical comparison of the achieved PSNR between the AVQ algorithm (green line) and the AVQ\({}_{\text{IS}}\) algorithm (magenta), by using a dictionary of 8192 (\(2^{13}\)) entries, for (**a**) the _Dune_ sequence; (**b**) the _Trees_ sequence; and (**c**) the _UN__amp_ sequence.  
Figure 13: Graphical comparison of the achieved PSNR between the AVQ algorithm (green line) and the AVQ\({}_{\text{IS}}\) algorithm (magenta line), by using a dictionary of 4096 (\(2^{12}\)) entries, for (**a**) the _Dune_ sequence; (**b**) the _Trees_ sequence; and (**c**) the _UN__amp_ sequence.  
**Author Contributions:** [PERSON] designed and performed the experiments. [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Evaluation
### Security Enhancement Validation  
#### 5.4.1 Attack Detection Performance  
SMART DShot demonstrates superior attack detection capabilities across all six threat scenarios, achieving substantial improvements over baseline approaches.  
The ROC analysis confirms excellent discrimination with AUC values: Stealth Timing (0.91), Desynchronization (0.88), Burst (0.89), Replay (0.91), Coordination (0.90), and Firmware (0.81), averaging 0.88 across all attacks.  
Table 6 summarizes the quantitative results of SMART DShot's attack detection performance in six major threat scenarios, highlighting improvements in detection accuracy and CVSS score reductions compared to baseline methods.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Attack Type** & **Baseline Detection** & **SMART DShot** & **CVSS Reduction** \\ \hline Stealth Timing & 0.0\% & 91.0\% & 6.8 \(\to\) 3.1 \\ Desynchronization & 0.0\% & 88.0\% & 9.0 \(\to\) 2.8 \\ Burst Interference & 15.0\% & 89.0\% & 5.4 \(\to\) 2.4 \\ Replay Attack & 73.0\% & 91.0\% & 7.1 \(\to\) 3.3 \\ Multi-Channel Coordination & 0.0\% & 90.0\% & 8.3 \(\to\) 3.0 \\ Firmware Compromise & 0.0\% & 81.0\% & 7.2 \(\to\) 3.5 \\ \hline Average Performance & 14.7\% & 88.3\% & 7.3 \(\to\) 3.1 \\ False Positive Rate & N/A & 2.3\% & \(-\) \\ Recovery Time & N/A & \textless{}35 ms & \(-\) \\ \hline \hline \end{tabular} \({}^{\text{N/A}}\) indicates that the metric does not apply to the baseline, which does not perform active detection or recovery, making false positive rate and recovery time because these features are only applicable by ML feature at SMART DShot.  
\end{table}
Table 6: Security enhancement and attack detection performance.  
The 88.3% average detection rate (95% CI: [87.1%, 89.5%]) represents a 520% improvement over baseline CRC-only approaches, while maintaining an acceptably low 2.3% false positive rate. CVSS score reductions from High severity (7.3) to Low severity (3.1) demonstrate substantial vulnerability mitigation across all attack categories.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 1 Introduction
World Radiocommunication Conference 2019 (WRC-19) adopted the topic of \"Promoting People's Access to Broadband Applications Provided through High Altitude Platform Stations\" and proposed a new 38 GHz-39.5 GHz frequency band for HAP globally under a fixed business division [28]. Therefore, studying the HAP communication characteristics in the mmWave band is also very important [29]. As the frequency increases to the mmWave band, HAP requires a wider bandwidth. The model proposed in [30] was extended to a wideband channel model. The statistical characteristics of non-stationary wideband HAP-MIMO channel models were analyzed in [31; 32]. However, none of the above models have taken into account the non-stationary characteristics of wide bandwidth in the frequency domain. To address these challenges, we propose a non-stationary 3D wideband GBSM where scatterers are distributed on the surface of a cylinder following Markov birth-death processes. Large-scale parameters, such as free space loss and rainfall fading, are considered to enhance the accuracy of the channel model. Furthermore, to analyze the non-stationary behavior of the channel, we derive the temporal auto-correlation function (ACF), spatial cross-correlation function (CCF), and frequency correlation function (FCF), and conduct simulation verification. We also consider the impact of different channel parameters on temporal ACF. The contributions of our research can be summarized as follows. * A wideband non-stationary 3D GBSM for HAP-MIMO communication systems at mmWave bands is established, which consider the impact of large-scale parameters on the channel. * The dynamic birth-death behavior of scatterers is modeled through Markov processes. Based on the proposed non-stationary 3D GBSM, some important statistical properties, i.e., temporal ACF, spatial CCF, and FCF, are derived and analyzed. * The impacts of some parameters on ACF are compared, and the non-stationary characteristics in time, space, and frequency domains are investigated thoroughly by simulations. The remainder of this paper is organized as follows. Section 2 introduces the composition and transmission characteristics of the HAP-MIMO channel model. Section 3 derives the channel statistics, i.e., the ACF, CCF, and FCF formula. The simulation settings and results are shown in Section 4. Conclusions are drawn in Section 5.
# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 5 Implementation
### Technologies
#### 5.3.1 Client Side  
In accordance with the requirements set for the software solution, the client side (i.e., the _Presentation Layer_) will run in a web browser. On that basis, we have put together a set of technologies that, at this point in time, are commonly used when building the front-end of a web solution. Such technologies are centered around the JavaScript programming language.  
The details are as follows: the front-end is based on the JavaScript library React [29] that relates itself to the JavaScript runtime environment Node.js. The user interface components are built with Material-UI, which provides React components to build the user interface with a consistent look and feel [30]. The maps rely on the JavaScript library Leaflet [31], while charts are created with the help of libraries built on top of React and the JavaScript library D3.js. Notice that D3 is very popular in the data visualization field [32].  
An aspect worth mentioning is that all the rendering of information is carried out in the client side. We put a great deal of workload in this component as, for instance, to be able to minimise the number of connections to the server side and speeding up the response time, so MNOs can have a richer user experience.
###### Abstract  
Unlike the winding structure of existing three-phase single winding motors, the winding structure of the dual winding motor (DWM) contains a master part and two slave parts. Thus, when the master part fails, it can be driven using the remaining slave part. It is applicable to various electric parts driving the integrated electric brake (IEB) system; thereby securing the high reliability of vehicle parts. However, in the existing DWM, there is an overheating problem owing to the increase in current because it operates with half the motor during the faulty mode. Therefore, a compensation method for the increase in current in a faulty mode was employed by increasing the stacking length of the DWM. However, although it solves the overheating problem of the DWM in the faulty mode, the motor output performance and braking performance of the IEB system are degraded in the normal mode because of the change in the motor control parameters. Thus, in this paper, we propose a DWM with winding switching (WS) to simultaneously solve the DWM overheating problem in the faulty mode and the DWM's performance degradation problem in the normal mode. The output performance of the DWM with an increased stack length and the previously developed base model are compared to verify the performance of the proposed DWM with WS. As a result of the comparison, unlike the DWM with an increased stack length, in which the maximum speed is reduced by 20.5% compared to the existing base model during quick braking in the normal mode, the proposed DWM with WS has no performance degradation. In other words, it is confirmed that the proposed DWM with WS effectively solves the overheating problem during faulty mode, while simultaneously solving the performance degradation problem of the IEB system during the normal mode.  
integrated brake system; permanent magnet synchronous motor; dual winding motor; winding switching +
Footnote †: journal: Applied Sciences
###### Abstract  
Detection and assessment of cracks in civil engineering structures such as roads, bridges, dams and pipelines are crucial tasks for maintaining the safety and cost-effectiveness of those concrete structures. With the recent advances in machine learning, the development of ANN- and CNN-based algorithms has become a popular approach for the automated detection and identification of concrete cracks. However, most of the proposed models are trained on images taken in ideal conditions and are only capable of achieving high accuracy when applied to the concrete images devoid of irregular illumination conditions, shadows, shading, llemishes, etc. An overview of challenges related to the automatic detection of concrete cracks in the presence of shadows is presented in this paper. In particular, difficulties associated with the application of deep learning-based methods for the classification of concrete images with shadows are demonstrated. Moreover, the limitations of the shadow removal techniques for the improvement of the crack detection accuracy are discussed.  
concrete-crack detection; shadows; deep learning; convolutional neural networks (CNN); structural health monitoring +
Footnote †: journal: Applied Sciences  
Article  
An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows
## 3 Results and Discussion
### Simulated Images with MLT and Short-Lag F-DMAS
Particularly, when only short-lag signal couples are used to generate the image, the PSF main lobe is narrower and the resolution is higher, but side-lobe and artifact suppression is not as good as in the full F-DMAS formulation case, which negatively affects image contrast. This is probably because in the short-lag region signals are highly correlated, being recorded by elements separated by a small distance in the probe, but the inclusion of higher-lag components in the F-DMAS summation provides further useful information for a better off-axis artifact rejection. Albeit the presented analysis does not suggest an absolute criterion for the choice of an optimum maximum-lag value that could maximize all the analyzed performance parameters in F-DMAS images, it provides valuable indications to select a maximum-lag value (and thus a certain trade-off between contrast, resolution, and speckle SNR) based on the specific requirements of the considered application, also relating it to the frame-rate. For example, in those cases in which the contrast achieved by simple DAS could be sufficient but a higher resolution is required, like tissue Doppler applications [28], short-lag F-DMAS with low maximum-lag values can be employed; on the other hand, high maximum-lag values should be used when a high contrast is particularly required (e.g., for an effective segmentation of cardiac walls in ultrasound images [29]).
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 3 Results
### Flaring Estimates in Russia by Major Regions and Companies
#### 3.2.2 Company-Level Estimates
A significant portion is also related to oil fields operated by these companies. Among the independent oil producers, high flaring volumes (exceeding 1 BCM since 2016 for each) are observed for companies operating in Irkutsk Oblast, Irkutsk Oil Company and Dulisma.
## 2 Materials and Methods
### CO\({}_{2}\) Flux Measurement and Calculation  
The CO\({}_{2}\) flux of the _S. senanensis_ community was measured by using a static chamber system (Figure 2) at four measurement points at each site. The chamber system is described in detail in [PERSON] et al. (2021). In brief, it consists of a lid, a body, and a bottom frame (Figure 2). We used a new small multiple sensors for CO\({}_{2}\), temperature, and RH with a datalogger (HOBO MX1102A, Onset Computer), whose measurement accuracy and response time were verified beforehand. The bottom frame was set into the soil right after the snow melted at each site (2 April in the evergreen coniferous forest and mixed forest, 7 May in the canopy area and gap area in the deciduous broadleaved forest), and the lid and body were placed only during CO\({}_{2}\) flux measurement.  
CO\({}_{2}\) uptake from the atmosphere within the chamber was defined as positive, and emission as negative. To estimate the GPP at the community scale and to obtain GPP-light response curves at each measurement point, right after we measured the CO\({}_{2}\) flux under 100% light (CO\({}_{2}\)Flight), we measured it under 0% light (CO\({}_{2}\)Fdark) by placing a black plastic sheet over the chamber and measuring PPFD outside the chamber. All data were stored at 2 s intervals, and each measurement took 3 to 5 min to hold the increase of TA within 2 \({}^{\circ}\)C during the measurements. To maintain data quality, we excluded measurements in which T\({}_{\rm A}\) increased by <2 \({}^{\circ}\)C or decreased or RH decreased. We calculated CO\({}_{2}\)Flight and CO\({}_{2}\)Fdark (\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) as [12]:  
\[\rm CO_{2}Flight\ or\ CO_{2}Fdark=\Delta CO_{2}/\Delta t\times(P/(R(273.15+T)))V/A \tag{1}\]  
where \(\Delta\)CO\({}_{2}\)/\(\Delta t\) is the slope of the chamber CO\({}_{2}\) concentration against time (\(\mu\)mol CO\({}_{2}\) mol\({}^{-1}\) s\({}^{-1}\)), P is the atmospheric pressure held constant (kPa), R is the gas constant (8.314 kPa m\({}^{-2}\) K\({}^{-1}\) mol\({}^{-1}\)), T is the average temperature during the measurement (\({}^{\circ}\)C), V is the chamber volume (m\({}^{3}\)), and A is the surface area under the chamber (m\({}^{2}\)). The slope (S = \(\Delta\)CO\({}_{2}\)/\(\Delta t\)) was obtained by fitting a linear model (C = St + b) to the sampling data, where C is the momentary CO\({}_{2}\) concentration (\(\mu\)mol mol\({}^{-1}\)) at time t, and b is the intercept.  
Measurements were taken from 28 April to 2 May (spring), 31 August to 3 September (summer), and 26 to 29 October (autumn) in 2021 in the evergreen coniferous forest and mixed forest, and from 22 to 25 May, 28 to 31 July, and 22 to 25 October in the canopy area and gap area in the deciduous broadleaved forest.  
Figure 2: Measurement of the _Sasa_ community GPP under (**A**) 100% light and (**B**) 0% light. (**C**) Schematic illustration of the static chamber system: (a) sensor for CO\({}_{2}\) concentration, temperature, and relative humidity with data logger, (b) mini-fan, and (c) quantum sensor.
## 4 Discussion
### Limitations and Future Work  
There are a number of limitations to this review. First, our search is potentially over-constrained. Our inclusion criterion regarding only considering apps with English as a language option might have led us to miss out on relevant apps that do not have this option.  
Also, searching only the App Store and Google Play might have missed apps exclusive to other app stores, such as Microsoft Store. The search strings that we used may have missed relevant apps even in the stores that we searched; the fact that we found far more relevant apps in the App Store than in Google Play may suggest imperfections in our search strings. There really might be far fewer relevant apps in Google Play, but we think this is rather unlikely. Second, some of the apps that we screened out may have been relevant after all, but only ignored due to human error or subjective opinion. Third, there may have been new relevant apps introduced in between the time of the original search (15 May 2024) and the time that this paper was completed, or the apps we found may have been updated within this time frame and had their characteristics altered. Fourth, some of the apps' characteristics that we described were possibly subjective, and even our claims of which ones were subjective and how much could have been subjective, too, given that we made them based on agreement rates and Cohen's Kappa. The significant relationships we found involving these characteristics are also debatable, since we split some characteristics' values into groups while not doing the same for some others. Doing this for more or fewer characteristics would likely alter the number and nature of significant relationships found. Even some of the significant relationships had small sample sizes; increasing sample sizes would make them more trustworthy. Future scoping/systematic reviews may seek to account for these four limitations, such as by including more opinions during screening and characteristic evaluation. Fifth, we did not analyze any of the review comments that players made about the games; in future work, we plan to analyze these reviews to gain more insight into the findings presented in this paper, which will enable us to provide useful design guidelines that can be beneficial to the development of mnemonics serious games. Sixth, despite addressing how [PERSON] and [PERSON] [35] did not discuss combining mnemonics serious games for retrieval practice with mnemonics creation tools for knowledge construction and retention, we did not discuss it either in this paper; future research efforts could focus on this subject matter.
# Investigation on the Mass Flow Rate of a Refrigerator Compressor Based on the p-V Diagram
## 2 Experimental Setup
### Data Acquisition System  
The data acquisition system for the experiment in this paper is shown in Figure 3. The NI-9205 (National Instruments, Austin, TX, USA) was used for the acquisition of pressure signals from a KULITE pressure sensor (Kulite Semiconductor Products, Inc., Leonia, NJ, USA) with sampling rate set to 50 kHz; measurement accuracy was 0.1% (full scale). The mass flow of the compressor was measured by a Coriolis mass flowmeter (CMF) (Emerson, St. Louis, MO, USA) with a 0.1% reading measurement accuracy.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 7 Disclosure: Crime Reporting Bias and Ethical Considerations
### Feature Importance and Systematic Bias Analysis  
To quantitatively assess whether specific socioeconomic features disproportionately influenced the model's output--and to evaluate the potential algorithmic bias--we used SHAP (Shapley additive explanations) analysis on the final, trained model. As shown in Figure 11, variables such as population percentage below the poverty threshold, inter national migration, and unemployment rate yielded the highest average absolute SHAP values, indicating they exerted the greatest influence on the model's predictions.  
While these variables are indeed relevant indicators of socioeconomic status, their prominence necessitates ethical scrutiny. The SHAP analysis helped us detect whether sensitive features like income or migration may dominate predictions in ways that could lead to biased outcomes. This diagnostic approach provides transparency in terms of feature contributions and supports efforts to minimize unintended discriminatory effects.  
To further safeguard against systemic bias, we applied fairness considerations during preprocessing, such as feature standardization, exploratory disparity checks across subgroups, and avoiding those variables directly linked to protected attributes. While we did not implement reweighing or adversarial debiasing in this study, we recognize their value and plan to integrate them in future model iterations. Although advanced bias mitigation techniques like bias-aware reweighing or adversarial debiasing were not implemented in this version of the model, we recognize their importance. As part of our future work, we plan to incorporate these fairness-aware algorithms, especially in scenarios where predictive models may influence policy or decision-making. Our current SHAP analysis \(11\) provides a transparent view of feature contributions and flags potentially sensitive variables for ongoing fairness monitoring.
###### Abstract  
In this paper, a novel high-sensitivity temperature sensor with two sensing regions based on optical microfiber coupler interferometer (OMCI) for ocean application is proposed. The OMCI sensor is constructed by connecting Faraday mirrors to the two ports of the microfiber coupler respectively. Its sensing characteristics analysis and experimental test are conducted. Using a broad-spectrum light source as input light, temperature sensor demodulation can be implemented by tracking the drift of the characteristic wavelength (dip wavelength or peak wavelength) of the reflection spectrum. Experimental results show that the temperature sensitivity of the OMCI sensor can reach 1007.4 pm/\({}^{\circ}\)C and the detection dynamic range up to 17.6 \({}^{\circ}\)C. Besides, due to the two sensing regions in OMCI, self-calibration of seawater temperature sensing and optimization of multi-parameter cross-sensitive demodulation are performed by affecting the non-equal-arm interferometer through a specific package design of the external environment. The sensor has the advantages of high sensitivity, large dynamic range, small size, easy to manufacture, which will play an important role in the practical application of marine environment monitoring.  
Keywords:optical microfiber coupler; interferometer; temperature sensing; optical fiber sensor; self-calibration +
Footnote †: journal: Applied Sciences