esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Threats and Cryptographic Protocols for Medical Wearables
## 2 Security in Medical Devices
### Firmware Vulnerabilities  
Since the development of medical devices, wearables, and WHMS, several authors have claimed that security and privacy issues are not considered in the design and construction processes [29; 44]. These weaknesses include the absence of software/firmware protection techniques (code obfuscating/packing) [35] and the lack of authentication processes in the gateway to update the server [33]. In fact, in [45] the authors investigate the security level of different smartbands and state that their firmware vulnerabilities enable the introduction of malicious versions, compromising the integrity of patients' information and facilitating the remote control of the wearable. The authors identified that some devices did not implement a protocol to verify firmware updates, and, when included, were easy to bypass.  
One example of firmware attack can be found in [44], where an attack is implemented on the wearable medical device Nike+Fuelband, which include a USB connector to be synchronized. The authors took advantage of the lack of read and write protection on the device and designed a process to obtain the original firmware. Then, they introduced a modified version of the firmware via the USB connector, which allow them to modify the texts displayed by the wearable. Similarly, in 2016, [PERSON] et al. [36] showed how to successfully attack the fitness tracker Withings' Activite by introducing a false firmware update. They first located the local firmware address and performed reverse engineering to find the code structure in charge of firmware update verification. Once this was achieved, the authors introduced the modified firmware into the wearable as an update with the Health Mate app, gaining control of the device.  
In the work presented in [46] the authors introduce several modified versions of the firmware by building a fake gateway on a PC. The developed methodology consisted on three steps: (1) reverse engineer of the gateway app of the device, (2) analysis of the BLE communication between both elements, and (3) reverse engineer of the original firmware.  
Another example of an attack exploiting firmware vulnerabilities is [47]. In this case, with the introduction of a personalized firmware, they were able to disturb the correct functioning of the device, eliminating the encryption process and eavesdropping on the patients' information. Additionally, they were also capable of obtaining the user credentials to execute a replay attack.
# Performance Evaluation of an Imaging Radiation Portal Monitor System
## 3 RPM Detection Performance Results
### False Alarm Rate Tests  
A \"false alarm\" is defined when an alarm is triggered due to a measurement of counts in any of the measured parameters (neutrons, gamma-rays, gamma-neutron coincidences, neutron-neutron coincidences, etc.) above a preselected or automatically determined alarm level during the relevant measurement period, while no radiation source other than room background is present. The ANSI N42.35-2016 evaluation standard for RPMs does not take into account detection systems which are able to measure fast particle coincidences, as in the present case. The false alarm test requirements, for both single gamma-rays and neutrons, are less than one alarm per 1000 occupancies for systems with occupancy sensor or one alarm in two hours' measurement time in systems without occupancy sensor [9]. The KTH RPM prototype system can be operated in both modes, i.e., with or without occupancy sensor. The adopted alarm trigger thresholds were set to \(\gtrsim\)4\(\sigma\) above mean background count rates and are given in Table 2. These thresholds were decided to be optimal for an interrogation time of one second in the background conditions measured with the KTH RPM prototype system. The detection probability for one gamma-fast neutron coincidence event in a one-second interrogation time due to room background radiation was determined to be 0.003. The system was let to measure background radiation continuously for a total measurement time of 61.4 h, corresponding to a total of 221,000 individual one-second measurements. In only one case during this period were two gamma-neutron coincidence events observed within one second of each other. Consequently, the false alarm rate for fast gamma-neutron coincidence events was determined to be of the order of 4 \(\times\) 10\({}^{-6}\)s\({}^{-1}\).
# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
## 4 Experiment and Results
### Data Acquisition  
For the experiment, vision data were collected using Mask Aligner equipment. Figure 8 shows the Karl Suss MA6 Mask Aligner instrument used for data acquisition. Each plate is loaded onto the Substrate and Photomask. It is possible to monitor the alignment mark through a microscope and a microscope monitor. The stage can be manipulated in the x, y, and z directions through the controller, and alignment and exposure settings are possible. The mask used in the experiment is \(126.6\times 126.6\) (mm) in size, and there are various types of alignment marks on the mask. A cross and four block marks were used in the experiment.  
The specifications of the Karl Suss MA6 Mask Aligner are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Environment** & **Software Environment** \\ \hline CPU: Intel Core i7-8700k, 3.7 Ghz, & Window PyTorch framework \\ Six-core twelve threads 16 GB & Python 3.6 \\ GPU: GeForce GTX 1080 Ti & Darknet C++ Interface \\ \hline \hline \end{tabular}
\end{table}
Table 1: System specification.  
Figure 8: Karl Suss MA6 Mask Aligner.  
Normal data and abnormal data sets were used to test and validate the proposed model. A total of 2500 data points were used in the experiment, and Figure 9 shows the image dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contents** & **Specifications** \\ \hline Overview & UV broadband (250–450 nm), I-line (365 nm), and G-line (436 nm) wavelength available \\ Exposure methods & Flood, proximity, soft and hard contacts, low vacuum andvacuum contacts \\ Mask size & 2.5” \(\times\) 2.5”, 4” \(\times\) 4”, 5” \(\times\) 5” and 8” \(\times\) 8” \\ Wafer size for top-side alignment & up to 6” in diameter (small samples, 2”, 3”, 4”, and 6”) \\ Wafer size for bottom-side alignment & 3” and 4” chucksore \\ Maximum wafer thickness & 3 mm \\ Other & The machine is exclusively intended for use as an alignment and/or exposure device for substrates used in semiconductor and microsystems technology \\ \hline \hline \end{tabular}
\end{table}
Table 2: Karl Suss MA6 Mask Aligner’s specification.  
Figure 9: Alignment mark images collected using the vision camera of the MA6 Mask Alignment equipment.
## 4 Simulation and Experimental Results Analysis  
This article carries on the simulation experiment under the PLECS environment. The parameters of the PMSM used in the simulation are shown in Table 1. This simulation experiment mainly verifies the control performance of the system under the action of the traditional sliding mode control algorithm, integral sliding mode control algorithm, and sliding mode control algorithm based on the disturbance observer. The experimental test platform is shown in Figure 2 and the experimental platform is described as follows: (i) A three-phase PMSM (MDMA302P1G) and its main parameters are presented in Table 1, (ii) a high speed programmable dynamometer (Model DSP6001), (iii) a DC regulated power supply (IT6006C-800-25) is used as the DC power input of the system, and (iv) the switching frequency of the IGBT of the inverter is 10 KHz. Finally, the coordinate transformation as well as the proposed controller are implemented by means of a TMS320f28335 DSP control card.
# Orthofacade-Based Assisted Inspection Method for Buildings
## 3 Results
### Building Facade Survey  
Image dataset acquisition was carried out during the winter months of 2021. Before the flight, UAV take-off positions were determined and marked on the terrain in front of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**UAV Specifications** \\ \hline Size unfolded & 240 \(\times\) 175 \(\times\) 65 mm \\ Weight & 320 g \\ Max. flight time & 25 min \\ Operating temperature range & \(-\)10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C \\ Max. horizontal speed & 15 m/s \\ Max. vertical speed & 4 m/s \\ Max. transmission range & 4 km with controller \\ Max. wind resistance & 50 km/h \\ Satellite Positioning Systems & GPS \& GLONASS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the Parrot ANAFI drone used in the case study [32].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Flight Parameter** & **Performance** & **Flight Pattern** \\ \hline Flight mode & Manual \\ Flying distance from the facade & 6 m \\ Camera orientation & Perpendicular \\ GSD & 2.1 mm \\ Area covered by a single image & 9 m \(\times\) 7 m \\ Image capture intervals & 1 m \(\times\) 1 m \\ \
\begin{tabular}{c} Vertical \\ Horizontal \\ \end{tabular} & 86\% \\
\begin{tabular}{c} Horizontal \\ \end{tabular} & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the flight plan.  
the facade (V positions, Figure 3a) and at two associated building corners (D positions, Figure 3a). The facade was photographed from close range--from a distance of 6 m--and flights were controlled using the _Parrot FreeFlight_ 6 mobile application. In order to ensure horizontal and vertical image overlap of 89% and 86%, respectively, the UAV flew along 18 vertical paths, capturing images at 1 m \(\times\) 1 m intervals, with the camera oriented perpendicular to the facade surface. Two out of 18 paths (paths V1 and V18, Figure 3a) were positioned beyond the facade boundaries, in order to obtain accurate data on the edges of the facade surface.  
The time required to conduct the mission was about 2 h and 1055 facade images were collected.  
_Survey of control points_ included measurement of 3D coordinates of selected characteristic details on the facade. _Trimble S5_ robotic total station was used to determine the positions of control points. A total of 12 points, evenly distributed at different height levels of the facade, were surveyed (Figure 3b).
## 4 Experiment Results and Comparison
### Results and Discussion
#### 4.5.2 Class-Based Evaluation  
Figures 8 and 9 show the 10-fold cross-validation evaluation results of the labeling process using recall and F-measure metrics for each class separately on the NSL-KDD data set. As shown in Figure 8, this evaluation shows the comparison results of the proposed approach SAL and the baseline algorithms based on recall metric for each class. It can be seen that the proposed approach SAL consistently achieves the highest mean recall scores for labeling the normal, Dos, and probe classes, containing more than two values exceeding 95%, while PGM achieves the second highest mean recall, containing one value being above 90%. PGM achieves the highest mean recall results for R2L and U2L classes which are (83.46%) and (79.43%), respectively, while SAL obtains the second best mean recall (82.53%) and (57.82%). However, a labeling algorithm can obtain a high-precision score, while a number of observations are being missed (not labeled). Similarly, the algorithm can obtain a high recall score, while the false-positive rate is higher. Therefore, the F-measure, which is the harmonic mean of precision and recall, is a more appropriate metric to demonstrate the accuracy of the algorithm when working on classification models in which a data set is imbalanced. In the NSL-KDD data set, U2R and R2L attack types are relatively tiny fractions of the overall data set. Therefore, the proposed approach, as shown in Figure 9, demonstrates better accuracy results which are consistently higher than PGM and KNNVWC algorithms. From these results, we can conclude that the proposed approach SAL demonstrates promising results compared to baseline algorithms on the NSL-KDD data set. It can be concluded from the results that, under the assumption that the behaviour of abnormal activities mathematically or statistically differs from normal behaviours, or if they are noticeably distinguishable from the normal ones [40], the zero-day attacks can be classified as a attack type. This is because the proposed approach demonstrated significant results in differentiating among many flow types. This means the behaviour of the zero-day attacks will mathematically or statistically have similar characterisations as the known flow attack types.  
Figure 7: Overall accuracy comparison of the three semi-supervised algorithms on the both datasets using the Friedman–Nemenyi test. (**a**) Overall accuracy on the NSL-KDD dataset. (**b**) Overall accuracy on the ISCX dataset.
## 1 Introduction
The POD-based method combines the advantage of the design space reduction and variable screening and retains the flexibility of the original optimization problem [24]. [PERSON] et al. [26; 27] proposed a similar POD-based scheme for the design space reduction and demonstrated that the method could effectively reduce the number of design variables. In the present work, a POD-based dimension reduction technique coupling with an adaptive sampling method is proposed and applied to optimize a centrifugal compressor to improve its aerodynamic efficiency and reduce the processing costs. The sampling approach places more points in the regions of interest by learning the information from previous data through POD analysis. The proposed method has great potential to decrease the total sampling points required and further reduce the computational costs. The remainder of the paper is organized as follows. Section 2 presents the basic information of the researched centrifugal compressor. Section 3 describes the details of the optimization framework, which involves the geometrical parameterization, the multidisciplinary analysis, the proposed pod-based dimension reduction technique coupled with an adaptive sampling method, and the formulation of the optimization problem. Section 4 presents the validation for the proposed method. Section 5 introduces a practical engineering optimization problem for a centrifugal compressor and proves the benefits of the dimension reduction strategy for the MDO of the centrifugal compressor. Section 6 shows the experimental verification for the optimization results. The last section concludes the study and discusses the research prospects.
## 2 Materials and Methods
### Benchmarks  
Today, the scientific community has considerably promoted the development of datasets, including existing open-source datasets even for evaluating complex hardware setups like visual-inertial systems (i.e., YTU [69], WHUVID [70], and VOID [71]). In this way, there are several datasets and benchmarks available in the literature for evaluating RGB SLAM, SFM, and VO systems, like [72; 73; 74; 75; 76; 77; 78; 79; 80]. Nevertheless, only a few are suitable for pure monocular RGB systems due to the nature of image acquisition, the type of camera calibration or camera models used, and the format of the provided ground truth. Similarly, it is safe to say that among the reviewed available datasets, the following can be applied for monocular algorithms comparison:  
* The **KITTI** dataset in [74] contains 21 video sequences of a driving car, where the movement parameters are limited to forward driving. The available images have pre-rectification treatments, and the dataset provides a ground truth obtained through an assembly of GPS and INS.
* The **EUROC-MAV** dataset in [75] contains 11 inertial stereoscopic sequences of a quadcopter flying in different indoor environments providing groundtruth values of all frames and calibration parameters.
* The **TUM-Mono** dataset in [6] presents 50 sequences of indoor/outdoor environments obtained using monocular RBG cameras on monochrome uEye UI-3241 LE-M-GL cameras equipped with Lensagon BM2420 (with 148\({}^{\circ}\)\(\times\) 122\({}^{\circ}\) field of view) and Lensagon BM4018S118 (with 98\({}^{\circ}\)\(\times\) 79\({}^{\circ}\) field of view) sensors. This benchmark includes the photometric calibration parameters, the ground truth, the timestamps for the execution of each image sequence, and the calibration file for the vignetting effect in each sequence, comprising more than 190,000 frames and more than 100 min of video.
* The **ICL-NUIM** benchmark in [8] has eight sequences in conjunction with its raytracing of two environments, providing the groundtruth values of each sequence and camera intrinsics; so, no photometric calibration is required. This dataset presents degenerative and purely rotational motion sequences, which are considered demanding for monocular algorithms.  
As can be noticed, the most complete and largest dataset of the above is the TUM-Mono, which is why this dataset was applied in this comparison study. It also has the advantage of being the only dataset that was obtained purely depending on a monocular RGB setup, without depending on any additional sensor or source of information as mentioned in [6; 39; 40], making it ideal for comparing visual-only SLAM and VO systems. In addition, this benchmark provides the most complete set of metrics that can be explored to efficiently compare the selected algorithms in multiple dimensions--discussed in the following section.
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
## 4 Conclusions  
Based on the characteristics of the Zhuhai-1 hyperspectral satellite, this paper proposes a hyperspectral image geometric calibration model method and a basic product production method. The OHS image was used to perform on-orbit geometric calibration, and the spectral registration accuracy, single-view orientation accuracy, and regional network adjustment accuracy of the basic product spectrum after calibration were evaluated. The experimental results show that the spectral alignment accuracy of the OHS basic products is 0.3-0.5 pixels, which is equivalent to the spectral segment calibration accuracy. The single-view orientation accuracy should theoretically be \"no more than 2 pixels.\" The accuracy of the actual evaluation result is better than 1.5 pixels (0.8-1.5 pixels (1\(\sigma\)) and the regional network adjustment accuracy is better than 1.2 pixels (1\(\sigma\)). The generated area orthophoto image satisfies the seamless edge requirement. It is verified that the image of OHS basic products can perform regional mapping and meet the application requirements.  
Figure 17: Sketch of the left and right edge of the image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Connection Points**} & \multicolumn{3}{c}{**RMS (pixels)**} \\ \cline{3-5}  & & **x** & **y** & **Plane** \\ \hline Test area & 13,086 & 0.642 & 0.954 & 1.150 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of block adjustment.  
Figure 18: Sketch of the upper and lower edge of the image.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper; and all authors edited the paper.  
**Funding:** This research was funded by the Key Research and Development Program of the Ministry of Science and Technology (grant number 2016 YFB0500801), the National Natural Science Foundation of China (grant numbers 91538106, 41501503, 41601490, and 41501383) and the Open Foundation of Key Laboratory of Precise Engineering and the Industry Surveying of National Administration of Surveying, Mapping and Geoinformation (grant number PF2017-4).  
**Acknowledgments:** We thank the research team at Orbita for providing the OHS images. Furthermore, we would like to thank the reviewers for their helpful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors 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.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation
### Channel Estimation for Filter Bank OFDM-OQAM
These interference embedded CE techniques involve CE in the presence of ISI only, ICI only and in situations of both ICI and ISI. From the results obtained, both broadband- and narrowband-based models offer the best normalised mean square error (NMSE) performances when only ISI is considered. ICI comes next in estimation performance while estimation in the presence of both ICI and ISI offers the worst CE performance. Estimation of time-varying channels is adopted in [123] where dual optimal Kalman filters are introduced for estimating fading channel statistics as well as their unknown \(p\)th order autoregressive parameters in an OFDM-OQAM based wireless system. This proposed method estimates the fading coefficients at pilot symbol positions while linear, spline and low-pass interpolation are adopted for estimating data position fading coefficients.
## 3 System Architecture
### LTE Macro-BS Subsystem and Power Consumption Modeling  
The BS, a centrally located set of equipment used to communicate with mobile units and the backhaul network, consists of multiple transceivers that in turn consist of a power amplifier that amplifies the input power, a radio-frequency small-signal transceiver section, a baseband for system processing and coding, a DC-DC power supply, and a cooling system. More details on the BS components can be found in [21]. An LTE-macro BS type is described in [22] with a focus on the component level for three sectors; each BS contains two antennas. BS operating power is expressed as \(P_{op}=N_{TRX}\times(P_{PA}^{DC}+P_{RF}^{DC}+P_{BB}^{DC})/(1-\sigma_{DC})(1- \sigma_{cool})\), where \(N_{TRX}\) is the number of transceivers (i.e., transmit/receive antennas per site); \(P_{PA}^{DC},P_{RF}^{DC}\), and \(P_{BB}^{DC}\) are the power amplifier (PA), radio frequency (RF), and baseband power (BB), respectively. Losses incurred by the DC-DC regulator and active cooling (air conditioner) scale linearly with the power consumption of the other components and may be approximated by loss factors \(\sigma_{DC}=6\%\), and \(\sigma_{cool}=10\%\). The power consumption of the air-conditioning unit depends on the internal and ambient temperature of the BS cabinet. Typically, an internal and ambient temperature of 25 \({}^{\circ}\)C is assumed, resulting in constant power consumption for the air conditioning. \(P_{PA}^{DC}\) is a linear function of the BS transmission power \(P_{tx}^{\max}\) and is expressed as \(P_{tx}^{\max}/\eta_{PA}\), where \(\eta_{PA}\) is the PA efficiency. In general, the BS transmission power depends on the radius of coverage and the signal propagation fading. To simplify the model derivation, the macro BS transmission power is normalized as \(P_{o}=40\) W with a coverage radius of \(R_{o}=1\) km. Similarly, the BS transmission power with coverage radius \(R\) is denoted by \(P_{tx}^{\max}=P_{o}\times(R/R_{o})^{\alpha}\), where \(\alpha\) is the path loss coefficient. Therefore, the BS operating power with coverage radius \(R\) is expressed as \(P_{op}=N_{TRX}[P_{o}\cdot\left(R/R_{o}\right)^{\alpha}/\eta_{PA}+P_{RF}^{DC}+P _{BB}^{DC}]/(1-\sigma_{DC})(1-\sigma_{cool})\). In addition, most sites use a microwave backhaul (\(P_{mc}\)), and auxiliary equipment located at the BS site include lighting (\(P_{lm}\)). Then, a mathematical expression for the power consumption of a BS site is given by Equation (1):  
\[P_{BS}=\frac{N_{TRX}\bigg{(}\frac{P_{o}\big{(}\frac{R}{R_{o}}\big{)}^{\alpha}} {\eta_{PA}}+P_{RF}^{DC}+P_{BB}^{DC}\bigg{)}}{(1-\sigma_{DC})(1-\sigma_{cool})}+ P_{mc}+P_{lm} \tag{1}\]  
Table 1 summarises the power consumption of the different pieces of LTE-macro BS equipment for a 2 \(\times\) 2 multiple-input and multiple-output (MIMO) antenna configuration with three sectors.