dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this study, the performance of CNNs for automatic segmentation of individual muscles was evaluated. 2D U-Net, 3D U-Net, transUNet, and HRNet were selected from the state of the art to perform this task. The models were tested on a large database heterogeneous in degree of fat infiltration, composed of patients from three different NMDs and a population of controls. A comparison of the results was made according to the similarity of the predicted segmentations with the manual references (DSC, AR, and OR) as well as the agreement between the MRI quantities measured with the segmentations of the CNNs and the references (FF, MTR, T2, volume). The results of the best model, HRNet, were studied against the degree of infiltration, that is, FF. Finally, the muscle population was divided into two infiltration subgroups (G20À, G20+) to perform a statistical comparison of HRNet performance on each group.
The accuracy of the automatically predicted segmentations, illustrated by DSC values, was slightly better than the values reported in the literature using CNNs (from 0. 77 to 0. 93)  21, 23, 24, 26  and similar to the values reported using non learning semi-automatic methods (0. 90 AE 0. 03). 27 It is noteworthy that for the latter method, manual segmentation of a few slices was needed as a preliminary step, and this prerequisite might be seen as a limitation for large clinical applications. 18 Our method was consistent, as the results obtained on our heterogeneous database in fatty infiltration (ranging from 1. 87% to 64. 34%) were comparable with the scores reported in the literature on a slightly infiltrated set of subjects (<20%). 24, 32 Moreover, our results were superior to those obtained on a set of severely infiltrated subjects in the studies of Rohm et al (0.
23, 24 While Ding et al reported a 0. 17% systematic bias, Chen et al reported a confidence interval of [0. 56%, 0. 49%] for the thigh, and [0. 71%, 0. 84%] for the leg muscles. 23, 24 FF errors computed in our entire database were slightly larger than theirs with a systematic bias of 0. 28% and a CI of [2. 12%, 1. 49%]. However, it should be kept in mind that the infiltration range in our database (<64%) was much larger than those in the quoted studies (<20%) and this is likely to have a detrimental effect on the quality of segmentations. By selecting only muscles with 0%-20% infiltration, the biomarker estimation results (systematic bias: 0. 20%, CI: [1. 38%, 0. 976%]) were closer to the values reported by Ding et al and Chen et al. 24, 32  Comparative analysis between systematic errors obtained in muscles with FF values below (G20À) and above 20% (G20+) conducted in our database indicated that the segmentation and accuracy of the corresponding biomarker were negatively affected by high FF values.
Limitations This study was performed with data from a single center and a single scanner. It would be relevant to compare the performance of the CNNs on another database, from another center, and on other neuromuscular diseases (eg facioscapulohumeral muscular dystrophy). As the accuracy of deep learning methods is highly dependent on the nature of the training data, a transfer learning approach might be required to achieve the same results. 39 any neural networks could have been used in this study. Among the great variety of CNNs, we have chosen U-Net, the standard for medical image segmentation, as well as variants using a transformer module and 3D processing. To diversify our approach, we have chosen to test a different architecture from the encoder-decoder scheme with HRNet. We believe that the choice of these four networks was sufficient to evaluate the effect of fat infiltration on the automatic segmentation.
2023). Periods and false-alarm probabilities are provided in Appendix A. The same processing and selections as for the first set were applied, resulting in 38 k sources with periods in the range 10-500 d, as shown. For the first and second dataset, we show in Fig. 17  the Baluev A25, page 13 of 52  Holl, B. , et al. : A&A 674, A25 (2023)  false-alarm probability (FAP;  Baluev 2009). The FAP shows that a significant fraction of the peaks is highly significant. As a result of the initial blind source selection, both sets will contain a mix of truly photometric variable objects and spurious variables (for example galaxies and close pairs) due to induced scan-angledependent signals or other disturbances in the Gaia data. Most sources of each data set will not exhibit any (periodic) variability at all, however.
5 • radius around the Andromeda Galaxy (that is, 2260 simulations for each even k). Because we compared our simulations with the observed data samples introduced in Sect. 4. 1, the representability for the two all-sky samples might be enhanced by weighting the output of our uniform all-sky simulation as function of the expected sky density. We avoided adding this complexity (and choice of prior) because the result already represents the data very well, as shown in the following sections. In these simulations, we made the important implicit assumption that the (main) source of scan-angle-dependent signals arises because (close) pair stars around the resolving limit of Gaia have a fixed position angle on the sky for the duration of the Gaia mission data. For galaxies, we showed in Sect. 3. 1.
Detection of scan-angle-dependent signal With the knowledge that potential scan-angle-dependent signals might affect the time series of some sources, the obvious question arises how they might be identified. Because photometric time series are available for a large sample of sources in Gaia DR3, we provide here several photometric diagnostics, and if they are not yet available in the Gaia DR3 archive, we publish them in a special archive table (see Appendix A) for all sources with available photometric time series. Because scan-angle-dependent signals also exist in astrometric and radial velocity time series, we are developing related diagnostics for these data that will be used in Gaia DR4 processing. In this section, we first introduce two (partially complementary) correlation statistics: r ipd described in Sect.
2. 1), varying time series that do not have a scan-angle-dependent signal will create an irregular signal when ordered by scan angle and thus will have an r ipd close to zero. This is even more true for data of a constant source with randomly fluctuating noise. The table published with this paper (Appendix A) contains the IPD correlation computed for all available photometric time series with respect to G, G BP , and G RP , resulting in r ipd,G , r ipd,BP , and r ipd,RP. Observations during the ecliptic-pole scanning law (BJD-2455197. 5 < 1693. 14) and those rejected by the variability analyses (variability_flag_g_reject = true in the epoch photometry) were excluded from the computation. The resulting number of observations is listed in N noEpsl,G/BP/RP. Suggestions for minimum values for secure analyses are discussed in Appendix A.
Scan-angle-dependent signals in a nutshell In Sect. 3 and Appendix B, we discuss that the Gaia instrument data reduction can introduce modelling errors (referred to as scan-angle-dependent signals) when on-sky source structure is present. This structure can be interpreted in a very broad way, covering: (1) multiple close-pair ( 1 ) sources of a pointlike nature; (2) extended or non-symmetric sources (for example cores of galaxies or tidally distorted stars); (3) sources with a (much) brighter neighbour close by (>1 ); and (4) combinations of any of the above. In principle, the structure can either be static, like a galaxy core or very long-period binaries (P 5 y), or dynamic, like a short-term orbital binary. Subsequently, there are constraints on the data acquisition of sources by the Gaia instruments: (1) observations consist of pixel values extracted in a limited window around each source; (2) observations are made with a large variety of on-sky scan angles due to the scanning law of the spacecraft (Sect.
2); (3) pixels have an aspect ratio of roughly 1:3, the highest resolution of which is in the along-scan direction, that is, along the scan angle; and (4) for sources with G ≥ 13, all the data in the across-scan direction are collapsed into one-dimensional along-scan pixel counts, which enhances the effect of scan-angle-dependent signals. The combined effect of structure, data acquisition, and the challenge of processing these data can cause scan-angle-dependent signals to appear in derived time-series values of Gaia DR3. In this paper, we abundantly demonstrate this to be the case in publicly available photometric G-band time series, but it equally exists in the (unpublished) astrometry and radial velocity time series (see Table  1  for examples).
Rejection of affected sources from a study First, a possible rejection depends importantly on which parameters from the Gaia data are used. Especially when a period derived from Gaia data or other parameters is to be used that depends on the (phase-folded) shape of the time-series signal, we recommend to avoid sources that are affected in any significant way by using the statistics discussed in Sect. 6. If this information is not needed, it needs to be determined whether the targets might exhibit some source structure (see definition Sect. 7. 1) that might produce a scan-angle-dependent signal. When the targets have no source structure, then we again recommend to avoid sources that appear to be affected by scanangle-dependent signals (at the cost of some loss of completeness due to imperfect filtering statistics) because otherwise, they might cause contaminate the sample.
The full SEAPipe analysis will be described in  Harrison et al. (in prep. )  and is planned to be included in Gaia DR4. As mentioned in Sect. 3. 1. 1, none of these methods will be used in the IPD centroid or flux estimation. However, they can nonetheless be used for a post-Gaia re-analysis of the window data accounting for (some) of the source structure and environment, together with publicly available large-scale imaging survey data. This will allow improving the amount and accuracy of the information that can be extracted. Through the improved modelling, this might then also lead to the disappearance of scanangle-dependent signals in the derived photometric and astrometric time series data. It seems likely that this might be beneficial for the subset of sources that were identified to be particularly non-point-like in the DPAC Gaia analyses.
Conclusions We have presented an overview of the origin and background of scan-angle-dependent signals in the Gaia DR3 data for the different instruments, which are generally caused by non-point-like object structure (mainly multiplicity or extendedness) or contamination from nearby (brighter) stars. We qualitatively demonstrated that specific spurious periodic signals can be propagated into the photometric and astrometric time series when a dominating scan-angle-dependent signal is present at the right phase given the sky-position-dependent observation sampling; not all scan-angle-dependent signals therefore cause spurious periods. We would like to caution that the reverse is also true: not all features at the location of spurious periods are due to scanangle-dependent signals, as these frequencies are specifically connected to regularities in the NSL that dictate the sampling and window function of the observations of each source.
For example, BH-1, the first nearby black hole companion discovered in Gaia DR3 data  (El-Badry et al. 2023; Chakrabarti et al. 2022) , falls directly into a spurious period range with its 186 d orbital period and was therefore not mentioned in Gaia Collaboration (2023a). This does not detract from our conclusion that the majority of spurious period peaks is caused by scan-angle-dependent signals originating from fixed-orientation optical pairs with a separation of <0. 5 (including binaries with P 5 y) and (cores of) distant galaxies, as they are already well reproduced with our simple models. For the sample of sources with published epoch photometry in Gaia DR3, several statistics are published with this paper to help identify sources that might be affected, and thus reveal information of sub-arcsecond scale structure, especially in terms of binarity.
Although the vast majority of sources that are affected by these signals have been filtered out of the Gaia DR3 archive nss_two_body_orbit and several vari tables, a certain fraction remains. Its existence should therefore be acknowledged (no sources were filtered from gaia_source). Improved modelling in the data processing will likely reduce the effect in future data releases. Appendix A: Gaia archive table with photometric correlation coefficients and a small-separation binary model fit for all sources with published time series As part of this paper, the table vari_spurious_signals is published in the Gaia DR3 archive for all 11 754 237 sources with published photometric time series, that is, for sources in gaia_source with has_epoch_photometry=true. Of these, 10 509 536 sources are variables  (Eyer et al.
4. 1. The additional Figs. A. 2, A. 3, A. 4, A. 5, and A. 6  are presented here to support discussions in the body of this paper that are based on the data published with this paper. A25, page 28 of 52  Holl, B. , et al. : A&A 674, A25 (2023)  Variable stars Gaia Andromeda Photometric Survey (GAPS)    which corresponds to a source with just a two-parameter solution from AGIS, a very high r exf,G and r ipd,G Spearman correlations (see Sect. 6), quite high IPD GoF harmonic amplitude, but a modest fraction of IPD multiple peaks. Fig. 7  shows that the epoch G magnitude strongly varies with the scan angle, whereas the G BP and G RP magnitudes both remain mostly constant. When we verified the IDU pre-DR4 data, we found that this DR3 source indeed is a close pair with a very small separation of just 130 mas, with one-dimensional windows and nearly the same magnitude in both sources.
Acknowledgements. We thank the anonymous referee and  Floor van Leeuwen  for their detailed feedback and suggestions that improved this paper. This work has, in part, been carried out within the framework of the  National Centre for Competence in Research PlanetS  supported by  SNSF. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. Full acknowledgements are given in Appendix E. This work made use of software from Java (https://www.
Anders), the  Severo Ochoa Centre of Excellence Programme  ( SEV2015-0493 ), and  MICIN/AEI/10. 13039/501100011033 (and the  European Union  through  European Regional Development Fund 'A way of making Europe')  through grant  RTI2018-095076-B-C21 , the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu ') through grant  CEX2019-000918-M , the  University of Barcelona 's official doctoral pro-gramme for the development of an  R+D+i  project through an  Ajuts de Personal Investigador en Formació (APIF) grant , the  Spanish Virtual Observatory  through project  AyA2017-84089 , the  Galician Regional Government, Xunta de Galicia , through grants  ED431B-2021/36 ,  ED481A-2019/155 , and  ED481A-2021/296 , the  Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) , funded by the  Xunta de Galicia  and the  European Union  ( European Regional Development Fund -Galicia 2014-2020 Programme ), through grant  ED431G-2019/01 , the  Red Española de Supercomputación (RES) computer resources at MareNostrum , the  Barcelona Supercomputing Centre -Centro Nacional de Supercomputación (BSC-CNS)  through activities  AECT-2017-2-0002 ,  AECT-2017-3-0006 ,  AECT-2018-1-0017 ,  AECT-2018-2-0013 ,  AECT-2018-3-0011 ,  AECT-2019-1-0010 ,  AECT-2019-2-0014 ,  AECT-2019-3-0003 ,  AECT-2020-1-0004 , and  DATA-2020-1-0010 , the  Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya  through grant  2014-SGR-1051  for project ' Models de Programació i Entorns d'Execució Parallels' (MPEXPAR) , and  Ramon y Cajal Fellowship   RYC2018-025968-I  funded by  MICIN/AEI/10.
IoT: background & overview The remarkable rise of IoT devices and domains, requires a significant innovation that improves the wireless range, overall performance, easiness of use, efficiency, and interoperability, to solve key issues related to traffic and transportation, energy, and waste management without affecting the resource/power budget. In fact, IoT is based on smart equipped devices with embedded technologies that allow them to either automatically or selectively collect information from IoT servers and databases before aggregating them to connect and communicate with other users' end-devices via device-to-device communications using sensor technology and actuators  [25]. As a result, IoT 2. 0 may as well become the new replacement for traditional IoT by becoming the "Intelligence of Things"  [26].
Hence, it is very important to adopt the Ethical Hacking for IoT approach to detect security and vulnerability gaps to reduce the likelihood of these events from occurring  [44]. Industrial Systems: especially cyber-physical systems such as smart factories, smart grids, power grids, smart enterprises and smart cities, which rely on constant data transmission between these IoT distributed systems, made them prone to a variety of cyber-attacks (i. e worm) mostly linked to sabotage and espionage acts such as the case of targeting the US grids, as well as the ongoing cyber-war between Israel and Iran  [45, 46] , Russia and Ukraine  [47, 48] , including attacks against Ukrainian nuclear facilities,  [49, 50]. This made the Industrial Control Systems (ICS) and other smart devices, systems, and servers the most vulnerable to these attacks.
It is mainly done to conduct cyber-espionage operation(s), or a covert information gathering to retrieve essential information about their targets for further exploitation and further cyber-attacks (i. e Espionage/Reconnaissance)  [84]. In fact, they can also be employed by rival organisations to conduct industrial cyber-espionage for industrial data theft. Cyber-Heist: Cyber-heist is usually achieved when the attackers try to steal as much money as possible from a large number of known online bank accounts, in the least amount of time, mainly targeting Internet of Financial Things (IoFT). Such attacks are usually achieved either through hacking or even phishing, where they are cashed out in a single unique operation, targeting either the customer of a given bank, or the bank itself  [85].
In fact, they can be easily exploited by any hacker through a malicious code injection or an SQL injection attack. As a result, this malicious script will be running on the victim's device without their knowledge  [146]. Therefore, registering keystrokes or/and retrieving all of the users credentials including emails, passwords, credit card numbers, along with cookies, browsers, and the device's/user's ID. SQL Injection Attacks: The impact of SQL injection attacks varies depending on the objective and aim of a given hacker  [147]. Such an aim is based on either manipulating database information, gathering sensitive data, or even executing a DoS attack. In Ref. [148] , SQL injections were divided into three different categories. These categories include three order attacks. The first order attack, is based on the attacker entering a malicious string to modify the executed code.
In fact, recent attacks were able to leverage session IDs by relying on JavaScripts functions on other websites to inject them into different pages. This included online banking, instant messaging and emails. Therefore, being capable of stealing a user's session which in turn, can be used to steal stored web cookies. This allows the attacker to replicate a user's session on a different machine  [152, 153]. Another form of XSS is based on the attacker sending HTTP requests at his own will from his victim's machine, or sending false counterfeited requests relying on the use of an especially sophisticated tool(s). Cross-Site Request Forgery: Cross-Site Request Forgery (CSRF) takes advantage of any possible lack of authorisation that is either absent or weakly employed in poorly designed web applications.
Once activated, Trojans can steal, spy, exploit, implement backdoors (rootkit) to delete, block, modify, disrupt and modify data  [159]. Trojans can take many forms: Trojan-Banker (steal data's account), Trojan-DDoS (denying service), Trojan-Downloader (download/install new malicious program versions), Trojan-Dropper (install viruses), Trojan-FakeAV (fake Anti-Virus), Trojan-Game-Thief (steals online gamers' accounts), Trojan-IM (steals users' usernames/passwords of Skype, Yahoo, Messenger, etc), Trojan-Ransom (modify computer's data as part of ransomware attack), Trojan-SMS (sending text messages from their victims' phones), Trojan-Spy (implement keystroke, keep users under surveillance), and Trojan-Mailfinder (harvest email addresses). Botnet: bots or zombies tend to exploit IoT devices that suffer from the same vulnerability  [160] , and can also be used to launch DoS and DDoS attacks by being ordered from a command and control (C&C) software.
Real case vulnerability exploitation Several real case studies are mentioned and briefly discussed to highlight how such vulnerabilities were exploited to conduct cyberattacks including: Adobe Flash Incident: in 2013, Adobe suffered a breach  [248]. In running on Equifax's web servers was exploited due to a security failure caused by Equifax's administrators not applying least privilege controls. After accessing the web server, the attackers successfully sneaked into the database, held and siphoned private information of 143 million consumers including social security names, addresses, dates of birth, social security, and credit card numbers, costing around $3 billions to sort the breach. Hence, the importance of a zerotrust approach to Privileged Access Management (PAM)  [249]. Exploit -CVE Variants Incidents: CVE variants were prone to various exploits  [250].
Thus, their identification and mitigation process remains as a serious challenge. This is all due to their ability to avoid and evade being detected by intrusion detection systems, firewalls and anti-viruses. Data Management Challenges: especially when it comes to processing the intercepted data, which depends on its volume, size, type, compatibility, processing speed, homogeneous/heterogeneous nature, validation, consistency, trust, and location, especially in case where ethical hackers are dealing with big data  [283, 284]. This also depends on data compatibility with the in-use ethical hacking tools to ensure that the collected data would not result into it being damaged, altered or modified in a way that damages the IoT system, server, application, etc, nor would result into further security breach nor information/data leaks.
Application and Web Application penetration testing focuses on reviewing the authorisation, data protection and security configuration mechanisms. Internal & External IoT Network Penetration Testing: aims to detect the main IoT network vulnerabilities  [311]  by conducting a series of simulation attack scenarios using the Kali Linux Operating System platform  [312]. It tries to identify the exploited vulnerability(ies) or/and security gap(s) along the path of the attack in order to assess its likelihood and impact. Thus, offering the necessary recommendations to protect a network both internally and externally. Cloud Penetration Testing: is applied to the cloud infrastructure domains as part of pen testing in clouds or cloud pen testing  [313]  and includes both corporate applications and integrated networks  [314].