esa-sceva/satcom-chunk-collection huggingface dataset:
# Strength Analysis of Real-Life Passwords Using Markov Models
## 1 Introduction  
Authentication is the core of today's Web experience. The online services, social networks (e.g., Facebook, Twitter etc.) and websites require an authentication so that users can create a profile, post messages and comments, and tailor the website's content so it can match their interests. In an information security sense, authentication is the process of verifying someone's identity and typically it can be classified into three main categories: knowledge-based authentication-\"what you know\" (e.g., textual or graphical passwords), biometrics authentication-\"what you are\" (e.g., retina, iris, voice, and fingerprint scans), and token-based authentication-\"what you have\" (e.g., smart cards, mobile phones or other tokens). Lately, another alternative authentication method is becoming more available-the two-step verification. We focus on the first category and in particular, on the textual passwords and their security simply because the username-password combination used to be [1, 2] and still is the most widely used method for authentication [3], due to their simplicity and cost effectiveness. The problems related to textual passwords and password security are not new. [PERSON] and [PERSON] [4] were first to identify textual passwords as a weak point in information system's security. More than three decades ago, they conducted experiments about typical users' habits about how they choose their passwords. They reported that many UNIX-users have chosen passwords that were very weak: short, contained only lower-case letters or digits, or appeared in various dictionaries. The aforementioned problems still exist today and are still being made to solve them. However, users fail to implement the behaviours necessary to stay safe and secure, even though they are aware of the security issues. They create the easiest-to-remember passwords regardless of any recommendations or instructions and tend to trade security for memorability. Some important literature [5, 6, 7, 8, 9] proposes the use of a proactive password checker as a method (beyond simple dictionary lookup and composition rules) for preventing users from entering simple and easy-to-guess passwords into a computer system. The coreproperty of a proactive password checker to be more effective and more prudent is the ability to check the probability of a given password to be chosen by the user and hence the probability to be chosen by an attacker. Some passwords are more likely to be chosen than others since certain letters and combinations of letters in any given language occur with varying frequencies.
## 5 Results
### Inter-Channel Spatial/Temporal Correlation at Plot Scale
Significant correlations are highlighted in light-grey; strong and significant correlations are also bolded; very strong and significant correlations are highlighted in dark-grey with values in bold; very weak correlations are not reported. Figure 8: \(r_{\rm s}\) domain for plot 460 at three selected dates: 19 April (left panel), 7 June (central panel) and 26 July (right panel). The dichromatic color ramp describes very strong negative to very strong positive correlations (from blue to grey). Values of \(r_{\rm s}\) rounded to first decimal place are also reported. On 13 June, the higher correlations among channels of the same frequency are observable. More specifically, moderate to very strong correlations occur among X, C and L frequency channels. Conversely, very weak to weak correlations characterized inter-frequency channels (e.g., C vs. L frequencies). On 21 June, \(m_{\rm v}\) considerably increased (with almost constant \(F_{\rm v}\)) causing a radical change in the C vs. L region; indeed, positive weak became negative weak correlations. On 26 July the \(m_{\rm v}\) and \(F_{\rm v}\) decreased to their minimum values causing the considerably increase of all \(r_{\rm s}\) values. Based on the above mentioned evidences, it could be hypnotized that \(r_{\rm s}\) and SVV time series are correlated (temporal). Thus, for 9 selected channel pairs the \(r_{\rm s}\) vs. SVV were computed over both bare soil (Table 7) and vegetated plots (Tables 8-11) to explore the determinability of SVV through \(r_{\rm s}\). Results are shown in terms of correlation coefficients, \(r_{\rm t}\). Over bare soils, a total of 36 correlations (9 band pairs, 2 soil variables, 2 crops) were evaluated. Only 13 (\(\approx\)30%) \(r_{\rm t}\) are moderate (>10.61); most of them (6 cases) involve multi-frequency pairs. Conversely to the single regression approach, the inter-channel spatial/temporal correlation analysis pointed out three quasi-significant and very strong \(r_{\rm s}\) vs. _h'_ correlations (involving X\({}_{\rm HH}\)-X\({}_{\rm VV}\), C\({}_{\rm HH}\)-L\({}_{\rm HH}\), X\({}_{\rm HH}\)-L\({}_{\rm HH}\) pairs). Best correlations will be further analyzed within the discussion section (Tables 12 and 13).
## 3 Results  
### VOC Profile of the Tested Bottle Gourd Accessions and Derivatives  
GC-MS analysis in combination with headspace-SPME and a comparison of the mass spectra with the NIST 14 library resulted in the identification of 60 VOCs in the four bottle gourd accessions. The analysis of variance showed that there was not a significant difference between the tested lines and the variable abundance, with F = 0.67 and \(p\) = 0.572. The identified VOCs belonged to the following categories: aliphatic alcohols (twelve), aliphatic aldehydes (fourteen), aliphatic ketones (five), amines (two), aliphatic alkanes (eleven), aliphatic alkenes (two), aromatic compounds (four), monoterpenoids (three), furans (four) and other VOCs (four) (Table 1).  
Among them, the aliphatic aldehydes constituted a major part of the identified VOCs, with an RT ranging from 12.09 to 39.52 (Table 1, Figures 2 and 3). The aldehydes were the leading group, consisting of decanal 3-methyl-butanol, 2-methyl-butanol, pentanal, (Z)-3-hexenal, hexanal, (E)-2-hexenal, (E,E)-2,4-hexadienal, octanal nonanal, (E,E)-2,6-nonadien, (E)-2-nonenal and 2-methyl-propanol (Table 1). Similarly, the aliphatic ketones consisted of a total of five compounds, including acetone, 1-penten-3-one, 2,3-pentanedione, 4-methyl-2-heptanone and 6-methyl-5-hepten-2-one (Table 1). The aliphatic alcohols consisted of a total of twelve compounds, including ethanol, 2-methyl-2-propanol, 2-methyl-1-propanol, 1-penten-3-ol, 3-methyl-3-buten-1-ol, 2-methyl-1-butanol, (Z)-2-penten-1-ol, 3-methyl-2-buten-1-ol, (E)-2-hexen-1-ol, 1-hexanol, 2-ethyl-1-hexanol and 2-methylbutan-2-ol (Table 1). Similarly, the aliphatic alkanes consisted of a total of 11 VOCs, propyl-cyclopentane, 2-methoxy-2-methylpropane, 2-ethoxy-2-methyl propane, 3,3,4-trimethyl-decane, 4-methyl-heptane, 3-methylheptane, 2,3-dimethyl-heptane, 2,4-dimethyl-heptane, 3,3,5-trimethyl-heptane,2,2,5-trimethyl-hexane, 2,4-dimethyl-1-heptene and 3,3-dimethyl-octane (Table 1). The aliphatic alkenes consisted of only two compounds, 1-nonene and 2,4-dimethyl-1-heptene (Table 1). Similarly, the amines also included two compounds, 9-phenanthrenamine and N-methylallyamine. The aromatic compounds constituted a total of four compounds, including benzaldehyde, ben eneacetaldehyde, 1-ethyl-2,3-dimethyl-benzene and toluene (Table 1). The monoterpenoids consisted of three compounds 1,8-cineol Eucalyptustol, D-limonene and \(\alpha\)-pinene. A total of four compounds constituted the furans, including 2-Methylfuran, Tetrahydro-furan, 2-pentyl-furan and 2-ethylfuran. Other miscellaneous compounds (2-methoxy-3-(1-methylpropyl)-pyrazine, Methoxy-phenyl-oxime, ethyl 2-cyanocrotonate and 2,3,6-trimethyl-carbazole) were also found to contribute to the VOC profile of bottle gourd accessions (Table 1).  
Figure 2: Abundance (total ion spectra) of compositions of the major classes of volatile organic compounds (VOCs) in the fruit of the tested bottle gourd lines, USVL10 CYN, USVL5, USVL10A and USVL10B.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 5 Experiment Evaluation
### Threats to Validity  
The validity of our controlled experiments may be subject to the following threats:  
**Construct validity.** This kind of validity analyzes the relationship between theory and observation. The above experiment was conducted to evaluate the effectiveness and efficiency of the created templates. The concepts of \"effectiveness\" and \"efficiency\" are broad and general and are prone to construct validity. To minimize this threat, the effectiveness was examined from the template reusability, and the corresponding evaluation metrics were concrete and quantifiable; while the efficiency was evaluated with the modeling and analysis time of the SAS applications, which was also a quantitative metric.  
**Internal validity.** As for internal validity, the manual assessment of tasks in the fifth step of the experiment (i.e., results analysis in Table 3) is prone to errors and bias. To reduce this kind of threat, the task assessment was conducted by two domain experts who were not directly involved in our project. In addition, the assessment followed a blind procedure.  
**External validity.** External validity refers to the generalizability of the experiment conclusions. In this study, we performed the experiments with five classical self-adaptive software scenarios, and the experiment results are satisfactory. However, the backgrounds of the experiments' subjects are limited. In the above experiments, we chose 20 graduate students as subjects, but the modeling and quantitative analysis templates of the Timed-SAS approach is designed not only for students but also for industrial software developers. In the future, we plan to conduct more experiments with subjects from the industrial community.
## 2 Materials and Methods
### Case Studies Design
#### 2.3.3 Software  
For each case study of an autonomous system, an optimization took place based on hourly simulations of the system. For the rest, hourly simulations were performed. The software platform used, consisting of TRNSYS, GenOPT, and TRNOPT allows for the simultaneous optimization of up to 100 parameters and a combination of discrete and continuous variables. This platform has been used successfully in the past for microgrids and desalination systems [28; 44]. Particle swarm optimization (PSO) is used for the optimization. The parameters of the PSO utilized are presented in Table 2 and is a set that has been utilized extensively with success for microgrids and desalination systems powered by renewables [48; 49; 50; 51]. The included sub-routines in TRNSYS for the weather processor, the PVs, the wind turbine, and the batteries were used. The model presented in [18] for the desalination plant was updated with the data presented in Figure 3. As far as the geothermal system is concerned the models developed and used in [44] were utilized.  
The optimizations are techno-economic ones. The designed systems needed to meet the needs in water at 100% throughout the year. As such, the optimization process aims to design the system with the lowest net present cost (NPC) which at the same time fulfils this technical constraint. As such, the optimization process aims to design the system with the lowest net present cost (NPC) while fulfilling at the same time all the technical constraints. The technical constraint is monetized using a simple process. Moreover, a penalty was included to make certain that the water in the tank in the beginning of the year is equal or less than the water in the tank at the end of the year. For each timestep if a single technical constraint is not met, a very large monetary penalty of USD 1,000,000 is added to the net present cost, while the tank penalty is only added at the end of the simulation. When the technical constraints are met for all the time-steps of the yearly simulation, then the penalty related to them is zero. The optimization cost function (CF) is thus formed as follows:  
\[CF=NPC+\sum_{t=1}^{8760}P_{W}(t)+P_{S} \tag{1}\]  
where \(NPC\) is the net present cost for a 20-year period (\(\mathbf{\xi}\)); \(P_{w}\) is the water penalty equal to USD 1,000,000; and \(P_{S}\) is the tank penalty equal to USD 1,000,000.  
The above equation has been modelled in TRNSYS with new subroutines written in order to realize the specific penalties (water and tank). GenOPT has been used for implementing the particle swarm optimization process. GenOPT is written in Java and the code is available. The GenOPT software is provided under a modified BSD license [52].  
In order to calculate the net present cost of water per m\({}^{3}\), the net present cost of the system was divided by the amount of water considered to be utilized by the consumers for the 20-year period.
## 3 Results
### IOD of Observations of Space Debris Related to the COSMOS 1408 Satellite
#### 3.3.2 IOD of Unmatched Arcs of Space Objects  
First, the observation data is tried to match with TLE to judge whether the observation arc is from a new object or a cataloged object. The SMA, eccentricity, and inclination of the COSMOS 1408 satellite orbit are 6,862,203.9 m, 0.00285, and 82.57\({}^{\circ}\), respectively.  
For the part of the observation data of the past two days that cannot be successfully matched with the TLE, the IOD is carried out. Due to the approaching full moon, object imaging was affected to a certain extent, and the amount of data on 16 November was relatively small. Due to the weather, there were no observations on 17 November. In the initial orbit results, the orbit parameters whose SMA is between 6800-6950 km and the inclination angles between 75\({}^{\circ}\)-90\({}^{\circ}\) are screened out. Counting the results of three days, respectively, it was found that only the COSMOS 1408 satellites were in this interval on 14 November, and 64 and 9 initial orbits were in this interval on 15 November and 16 November, respectively. The orbit parameter distribution is shown in Figure 7.  
The number of objects observed near the orbital altitude and inclination of the COSMOS 1408 satellite is analyzed. The observation arcs that have been successfully matched with the TLE object are excluded. According to Figure 7, before the event, there was no object in the related sky area on the night of 14 November. Following the incident, 64 objects appeared in the airspace on the night of 15 November, and 9 objects appeared on the night of 16 November. Therefore, the observations on the 15 th-16 th are likely to be debris generated by the COSMOS 1408 event. The 64 initial orbits were correlated. One pair was successfully correlated, and the orbit-determination results of the two arc segments showed that the inclination angle was about 82.33\({}^{\circ}\).  
Further analysis of the initial orbit results on 16 November shows that there are a large number of objects with an inclination angle of 53\({}^{\circ}\) and an SMA between 6880 km and 6950 km (Figure 8). It is noted that 53 Star-link satellites were launched by SpaceX at 12:40 (UTC) on 12 November 2021 before the ASAT event. But, the TLE of these satellites was not public at that time, so they failed to match with TLEs.  
Figure 8: IOD parameters of observations for Star-link satellites on 16 November 2021.  
Figure 7: IOD parameters of observations on 15 November 2021 and 16 November 2021.
# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 1 Introduction
By using a loss function that combines spectral and spatial information loss in training, the entire network can obtain fusion results with accurate spectral information and clear spatial details. (3) The experiments conducted on simulated datasets and showed that the fusion results of IFNMPH were superior to other advanced methods from both qualitative and quantitative aspects. The rest of this article is organized as follows: Section 2 introduces the current state of knowledge on remote sensing image fusion; Section 3 provides a detailed introduction to IFNMPH; Section 4 verifies the effectiveness and superiority of the IFNMPH network compared to other methods using a common dataset, and also proves the effectiveness of the combined loss function; and, Section 5 summarizes IFNMPH and reviews its shortcomings.
## 3 Results and Discussion
### Cell Viability--Toxicity Evaluation  
Cell viability was evaluated at two different concentrations: 100 ppm and 1000 ppm. For this purpose, previous solutions of TCD were made in PBS (1\(\times\)) at 200 ppm and 2000 ppm. Control solution was made out of PBS (1\(\times\)). Then, 50 uL of these solutions was added to the wells containing growing cells with 50 uL of complete cell medium. Cell viability and cytotoxicity were evaluated using the cell proliferation kit and according to the manufacturer's instructions. As shown in Figure 7, neither concentration of TCDs evaluated affected healthy dermal fibroblasts growth up to 72 h, showing a similar response to control cells. At longer times (96 h), a certain toxicity can be observed, especially using the highest concentration. However, TCDs have toxic activity against A549 lung carcinoma cells, even at times as short as 24 h at either of the tested concentrations; the growth inhibition effect becomes stronger as the contact time becomes higher. This different behavior is likely due to the faster metabolism of cancer cells, which can incorporate TCDs at a higher rate than healthy cells. On the other hand, the high content in ROS produced by tumor cells compared to those present in the healthy ones [14] may cause a longer prevalence of the free radical form of TCD (Figure 6), producing a synergic effect and thus inducing higher toxicity.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Body Temperature  
Body temperature rises due to physical exertion and relates to other physiological parameters, therefore, it can also be considered when monitoring physical exercise intensity [60]. Thermal imaging is the technology that allows for measuring the body temperature without any contact sensors.  
Thermal imaging captures images of an object based on the infrared radiation emanated from it [56; 62; 86]. Its use in medical and sports environments is widespread [54]. However, it seems that body location is important in terms of assessment quality as thermal emission might differ depending on the body part aimed to be analyzed [57]. [PERSON] [54] presented a critical comparison between the main methods used to obtain body temperature from images, and also proposed an alternative. It was found that the temperature obtained within an ROI selection of a well-defined area can be considered as the most reliable.  
[PERSON] [55] proposed an approach to investigate the validity and reliability of skin temperature measurement using a Telemetry Thermistor system (TT) and thermal camera during exercise in a hot and humid environment. Another similar study was presented by [56] to compare data loggers (skin adhesive), thermal imaging, and wired electrodes for the measurement of skin temperature during exercise in a similar environment. The authors concluded that data logger and thermal imaging can be used as alternative measures for skin temperature in exercising, especially on higher temperatures and humidity.  
Another study explored temperature in several different body parts pre, during and after moderate aerobic exercise using infrared thermography, proposed by [PERSON] [98]. The authors concluded that there are significant distinctions in the skin temperature distribution during exercise depending on the body part.  
The changes in body temperature during endurance work-outs in highly trained male sprinters were analyzed by [PERSON] [58] using a thermal camera. The aim was to characterize experiments: before the session (pre-exercise), warm-up, specific drills for athletes' warming up by comparing body temperature in the four phases of the sprinting techniques, and endurance exercise. Significant differences were found between the temperature of athletes' backs and from body profiles, as well as significant changes before and after exercise. However, the thermography results were not compared to ground-truth measurements of temperature.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Method Based on Deep Learning
#### 2.3.2 Pulse Coupled Neural Network  
The pulse coupled neural network model [60] is proposed based on the analysis of synchronous pulse oscillations of visual cortex neurons in cats and has been widely used in image fusion. Each neuron in the PCNN model corresponds to a pixel, whose definition is determined by the firing times of the neuron. The more the firing times are, the clearer the  
Figure 8: The typical structure diagram of CNN [58]. Reprinted/adapted with permission from Ref. [58]. 2020, MDPI.  
corresponding pixel points would be. As shown in Figure 9, PCNN consists of three parts: feeding input field, modulation field, and firing subsystem. The stimulus is received by the feeding input field and fed back to the firing subsystem through modulation field.  
To make the fused image clearer, [PERSON] et al. [27] proposed a multi-focus image fusion method based on PCNN and random walk. The technique used PCNN to measure the sharpness of the source image and constructed an initial fusion image. The random walk method was then used to improve the accuracy of fusion region detection, and the final fusion image was generated according to the probability calculated by a random walk. An improved PCNN method [28] was proposed to fuse the source image with the guide filter. The improved PCNN was excited by the intermediate fusion image to generate the fusion image. This method created fusion images several times and fused the fusion images with PCNN to make the fusion results more accurate.  
The PCNN model can extract local details effectively and recognize image content well. However, the massive iterative calculation and configuration parameters make this method high coupling and time-consuming.  
In addition to the above two models, a generative adversarial network (GAN) has also been used in multi-focus image fusion. [PERSON] et al. [29] proposed a multi-focus image fusion method based on least square GAN. In the fusion process, the final fusion decision graph was obtained by binary segmentation and the refinement of the focus graph.