esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Security and Privacy of Baby Monitor Apps
## 6 Discussion & Conclusions
### Security Issues and Their Impact on Video Confidentiality  
Our security analysis covered threats to the privacy of the app users, mainly focusing on identifying security issues threatening the confidentiality of monitoring video streams. We reviewed utilized network security measures and performed an automated static code analysis using the MobSF framework, supplemented by a manual code inspection of the investigated app's source code. The most impactful security issue was uncovered during dynamic network analysis. The Babyam app does not encrypt video streams when transferring video footage over the Internet. In contrast, all the other apps examined encrypt video streams, ensuring that potential eavesdroppers cannot compromise confidentiality. Using automated static analysis, we found no security issues directly threatening the confidentiality of video streams. However, the usage of cryptographic primitives should be improved, e.g., MD5 and SHA1 hash functions, as well as PKCB5 padding, are long known to be vulnerable. Furthermore, issues such as raw SQL queries and user-controlled code in WebView implementations should be inspected and fixed to close potential attack vectors. Our additional manual code review was valuable, as several security issues threatening the users' privacy could be identified. As Table 11 shows, we mainly identified security issues in the establishment of secure channels, correct peer authentication, and authorization. Although we focused on investigating the network security measures that protect sensitive video streams, future work may explore different attack vectors. The proprietary Dormi protocol may be susceptible to downgrade attacks, as legacy app versions that do not use encryption are supported. Due to the proprietary nature of the protocol, flaws may be revealed when fully reverse engineering the protocol. Furthermore, attacks on device pairing that allow for session hijacking may be possible, particularly when there is no user confirmation during the device pairing workflow.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 4 Experiments and Performance Analysis
### Tamper Detection and Localization  
Generally, the digital image might be manipulated illegally in the process of transmission or storage. To test the performance of the proposed algorithm under malicious attacks, some classical attacks are performed on the watermarked images such as image cropping, copy-paste attack, and so on. It should be noted that the attacks used in this paper are all finished by Adobe Photoshop CS3. Figure 7 illustrates the watermarked images forged by the corresponding attacks. Without prior knowledge, the tampered images cannot be distinguished from the original host images by human eyes. The tamper localization maps obtained by the proposed watermarking scheme are shown in Figure 8. To remove noise effect and improve the localization accuracy, the median filter is applied to the detection map. From Figure 8, we can see that the falsified areas are clearly identified. It suggests that the proposed algorithm achieves good tamper identification and location results for various malicious attacks.  
Figure 6: Watermarked images: (**a**) Lena; (**b**) Airplane; (**c**) Boat; (**d**) Couple; (**e**) Lax; (**f**) Best_cha.  
Figure 7: Tampered images: (**a**) Image cropping; (**b**) Copy–paste attack; (**c**) Text addition; (**d**) Collage attack; (**e**) Content-based attack; (**f**) Constant-average attack.
# Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing
## 4 Results and Discussion  
### Differences with Long-Time Solutions  
The long-time solutions of ground-couple heat transfer were developed mostly in previous studies, which ignored the initial temperature of the soil. In the current study, the initial conditions were also considered. The temperature at two positions of \(x=-0.05\) m and \(x=-1.0\) m were picked to represent the floor temperature distribution. Referring to Figure 6a, floor surface temperature had almost uniformly distributed in space, except for a small distance near walls (\(x=0\)). Figure 8 shows the floor surface temperature in temporary housing built at different seasons. When buildings were built in the cold season, for example, on \(1\) January (Figure 8a), floor temperatures at the initial stage were lower than the long-time solution. In contrast, when buildings were built in the hot season, e.g., on \(1\) July (Figure 8c), the long-time solution underestimated the floor temperature at the initial stage. A visible temperature difference between the two solutions occurred if buildings were built in hot summer and cold winter. However, no obvious difference was found if temporary housing was built in transition seasons, seen in Figure 8b,d.  
Figure 9 compares the floor surface heat fluxes between the current calculation and the long-time solution. Set \(1\) January and \(1\) July as examples of the cold and hot climates, respectively. Heat fluxes transferred into the indoor environment were defined as positive. It showed that there were obvious differences in the face and edge heat fluxes. Edge heat fluxes along with the wall were overestimated by the long-time solution both in cold  
Figure 8: Floor temperatures in temporary housing built (**a**) on \(1\) January; (**b**) on \(1\) April; (**c**) on \(1\) July; (**d**) on \(1\) October.  
and hot conditions. Moreover, the face heat fluxes of the two methods are directed in opposite directions.  
Long-time solutions to ground-coupled heat conduction have been commonly investigated over the past several decades. Firstly, the initial transient was unimportant for air-conditioning calculation in conventional buildings. Secondly, it is quite difficult to solve the three-dimensional transient equations analytically. Nonetheless, some researchers have discussed the influence of initial soil conditions [38]. [PERSON] et al. [39] found that more than 200 h of indoor temperature data had been significantly affected by the initial soil temperature conditions. [PERSON]'s calculation showed that it took more than three years before ground-coupled heat transfer reached a steady annual periodic cycle for a rectangular slab of 100 ft \(\times\) 30 ft [23; 40]. The finding of this study was closer to [PERSON]'s result. Given that a bigger slab size was calculated by [PERSON], it could be deduced that larger ground thermal storage capacities could result in bigger calculation differences. Moreover, floor insulation and extreme outside temperature conditions did matter.
## 6 GA-Based Slot Assignment Algorithm
### Selection and Fitness Function  
After the crossover, mutation, and repair operations, there are \(N_{indiv}+2\) individuals. The first \(N_{indiv}\) individuals, which have higher fitness values, are selected and transferred to the next generation. The fitness values of chromosomes are calculated from our fitness function (Equation (13)).  
One of our objectives is to uniform the data traffic generation from a central node to nodes. In our proposal, each slot in a QF generates at most one query to collect data and the slot type chromosome represents how many queries are generated in a QF. At the same time, our scheduler tries to maximize communication success rate. When the fitness value is calculated, the communication success rate of all nodes are also calculated and checked. If the success rate of all nodes of a child chromosome does not satisfy our target probability, the individual is discarded.  
Finally, when the fitness values of the best 5 slot type chromosomes are not changed after crossover operation, and the best fitness value is less than a target threshold value, we consider the GA operation is convergent. After the maximum iteration, we also consider that the GA operation will not be converged. At that time, we return to the initialization procedure. Our experiments showed that after 100 experiments, the average execution time of slot assignment for 10 nodes was 1544 milliseconds. After the convergence, the best slot type chromosome that is ranked in the top 5 and that is expected the highest average communication success rate of all nodes, is selected as the best schedule.
## 3 Results and Discussion  
### Compressive Strength  
Figure 6 presents the simulated and measured compressive strength of cement samples at hydration times of 1, 3, 7, 28, and 140 days, in which the error bar represents the standard deviation. As can be seen from Figure 6, the compressive strength of the cement stone decreased with the increasing _w/c_ ratio from 0.40 to 0.50 and increased with the increasing hydration time at the same _w/c_ ratio.  
Figure 5: The flowchart of pore structure test and analysis using micro-CT.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Phase**} & \multicolumn{1}{c}{**C\({}_{3}\)S**} & \multicolumn{1}{c}{**C\({}_{2}\)S**} & \multicolumn{1}{c}{**C\({}_{3}\)A**} & \multicolumn{1}{c}{**C\({}_{4}\)AF**} & \multicolumn{1}{c}{**GOC**} \\ \hline Hydration & \(K_{0}\) (\(\upmu\)m/h) & 0.0713 & 0.0047 & 0.0644 & 0.02 & 0.0503 \\ parameters & \(\delta_{\text{tr}}\) (\(\upmu\)m/h) & 2.66 & 3.07 & 3.58 & 1.19 & 2.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The hydration parameters of GOC.  
With the increase of _w/c_ ratio from 0.40 to 0.44, the compressive strength of the cement stone at 1, 3, 7, 28, and 140 days decreased by 24.30%, 22.17%, 9.41%, 4.12%, and 1.75%, respectively. With the increase of the _w/c_ ratio from 0.44 to 0.50, the compressive strength of the cement stone at 1, 3, 7, 28, and 140 days decreased by 33.49%, 20.24%, 26.94%, 36.22%, and 24.86%, respectively. When the _w/c_ ratio was 0.40, the daily growth rates of compressive strength of the cement paste from 1 to 3 days, 1 to 7 days, 1 to 28 days, and 1 to 140 days were 84.74%, 38.33%, 12.31%, and 2.82%, respectively, and the daily growth rate of compressive strength markedly decreased with the hydration time.  
It also can be seen from Figure 6 that, when the _w/c_ ratio is 0.40, 0.44, and 0.50, respectively, the relative deviation between the simulated results and measured results is 1.5-10.7%, 0.5-9.2%, and 0.6-9.1%, respectively. The maximum relative deviation between the simulated results and measured results is 10.7%, which indicates that the simulated results are in good agreement with the measured results.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Energy Saving Behavior  
As already mentioned in Section 2.3.2, there is a need for extra energy to change the direction of flight, besides the energy needed to continue flying. This increment in the consumption depends on how much the direction of flight changes. Moreover, there is a reduction of velocity when the agent changes its flight direction (see Equation (6)), which will have an impact on the performance. In Figure 5a, the energy costs \(C_{E_{g}}\) for each cell \(g\) considered has been shown. As we will see, since those values will be multiplied by a coefficient, their absolute values are not relevant, but only the relationship between them.
# Machine-Vision-Based Algorithm for Blockage Recognition of Jittering Sieve in Corn Harvester
## 5 Conclusions  
We have proposed a sieve dividing algorithm and a blockage recognition algorithm. The dividing algorithm, that is, Algorithm 1, is used to recognize sub-sieves and their edges from a jittering sieve. Algorithm 2 is used to recognize kernel blockages based on the dividing results obtained from Algorithm 1. For recognition, each pixel of the sub-sieves is first judged through the thresholding strategy, and then the extending operation is executed to determine the blocking area. We have also proposed the metric, the blocking ratio, to evaluate the blocking level by computing the area proportion of a blockage in a sub-sieve.  
The performance of proposed algorithms and the metric have been verified based on the cleaning apparatus of the John Deere 3316 harvester with the jittering sieve in green. For the proposed sieve dividing algorithm, the determination of row edges and column edges is based on the luminance that is reflected by the F-norm of the corresponding matrix (see Step 4 of Algorithm 1). For the accuracy of blockage recognition, it is suggested to distinguish the sieve color from the kernel color.  
The experimental results demonstrate all sub-sieves, as well as their row edges and column edges, can be correctly determined by using Algorithm 1. For all test scenes, the success ratio of edge determination reaches 100%. The results have also indicated the high correlation between the actual and recognized blocking areas. The mean cross-correlation coefficient of the blockage levels and the actual numbers of blocked kernels is 0.932. Indicated by the results, there is no obvious influence of the rotational ratio on recognizing accuracy. Overall, the proposed method has the potential to achieve automatic blockage detection for corn harvesters at work. We believe this study could also provide the basis for intelligent blockage clearing.  
Conceptualization, J.F.; Funding acquisition, X.T., Z.C. and L.R.; Methodology, J.F. and R.Z.; Project administration, R.Z.; Resources, J.W.; Software, H.Y.; Writing--original draft, H.Y.; Writing--review & editing, Z.C. and J.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the China Postdoctoral Science Foundation under Grant 2019M661215. The authors declare no conflict of interest.
# Electrospun Nanofibrous Membranes for Tissue Engineering and Cell Growth
## 5 Chitosan and Chitin
This finding is similar to those reported in other papers, where 3D structures and increased surface areas generally supported cell growth due to the improved possibility of cell infiltration and migration into the scaffold. The authors mentioned that for this effect, the pores in the electrospun nanofiber mats do not have to be larger than the cell diameters, but cells were found to perform amoeboid movements to migrate even through smaller pores by pushing  
Figure 2: SEM images of hMSCs attached on the surfaces of CMC/PVA scaffolds after (a) 12 h; (b) 24 h; and (c) 48 h of incubation. Reprinted from [83], with permission from Elsevier. the neighboring fibers away. Instead, hydrophilicity and biochemical signals were found to be more important to promote cell ingrowth than perfectly fitting pore sizes. [PERSON] et al. used an electrospinning method based on two separate syringes to prepare PLGA nanofibers with chitin electrospray nanoparticles [86]. They seeded normal human oral keratinocytes and normal human epidermal keratinocytes on these nanofiber composites and found the PLGA/chitin nano-composite fibers to be well suitable for tissue engineering scaffolds, due to their biomimetic 3D structure, similar to the collagen/glycosaminoglycan composite structure in the extracellular matrix. Another blend was investigated by [PERSON] et al., who used electrospinning to prepare chitin/silk fibroin (SF) blends from HFIP as solvent [87]. Normal human epidermal keratinocytes (NHEK) and fibroblasts (NHEF) were seeded onto these nanofiber mats after crystallization of the SF fraction in water vapor. While for attachment of NHEK cells, the reference polystyrene substrate performed best, cell spreading of NHEF cells was superior on chitin/SF nanofiber mats, as visible in Figure 3. This finding was explained by the high biocompatibility and other supportive properties of chitin and SF. As mentioned before, chitosan combines many of the positive physical and chemical properties of chitin with water-solubility, making it more easily electrospinning. This is why diverse groups investigated electrospun chitosan or chitosan blend fibers. A recent review of electrospinning-based solutions for tissue engineering was given by [PERSON] et al. [88]. Some exemplary blend partners for chitosan are poly(ethylene oxide) PEO [39; 89], collagen [90], PVA [91; 92], PCL [93], and hydroxyapatite [94]. After this brief overview of materials correlated to the extracellular matrix, the next sections will show progress in using electrospun man-made polymers as scaffolds in tissue engineering. Figure 3: **(A)** Photographs and **(B)** numbers of NHEK and NHEK adhered on the substrates; **(C)** percentage of cell spreading (\(n=4\)). Reprinted from [87], with permission from Elsevier.
# Deep Morphological Anomaly Detection Based on Angular Margin Loss
## 5 Experimental Results and Discussion  
In this section, deep anomaly detection experiments were performed to verify the performance of the proposed algorithms on several datasets, including MVTec [5], MNIST [10], and Fashion-MNIST [3]. Following [7; 9], we learn the representations by transformation prediction from scratch with ResNet-34 [31]. The classes in the proposed algorithm are \(\mathcal{M}=\{\oplus,\ominus\}\), \(\mathcal{W}=\{1,4,8,12\}\), \(\mathcal{H}=\{1,4,8,12\}\), and \(\mathcal{R}=\{0^{\circ},90^{\circ},180^{\circ},270^{\circ}\}\). We set the classes in \(\mathcal{R}\) by following the geometric rotation classes in [7; 8; 9]. Additionally, we follow [28] to set rescale parameter \(r\), multiplicative angular margin \(m_{1}\), additive angular margin \(m_{2}\), and additive cosine margin \(m_{3}\) to 64, 0.9, 0.4, and 0.15, respectively. All experimental results are reported as the area under the receiver operating characteristic (AUROC), which is a useful performance metric to measure the quality of the trade-off of \(g(\mathbf{A})\) in (8). Because MNIST and Fashion-MNIST are not designed for anomaly detection, we trained the model for only one class as a \"normal\" class, and the performance was evaluated for the entire test dataset--classes other than the trained class were assumed to be \"abnormal.\" The proposed algorithm was actualized using PyTorch in a GPU implementation. We performed experiments with an RTX 2080 Ti 11 GB graphical processing unit and an Intel i7 processor. To validate the experimental results using statistical methods, we conducted all experiments five times and then calculated the average and variance.
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 4 Development of the Abstract Ontology
In our abstract OWL ontology, four aspects are captured:  
* Domain concepts (rolling stock fleet and depot, vehicles and components);
* Time;
* Space (maintenance workspace--the physical depot);
* Maintenance activities and resources (including tools, staff, fixed plant, parts, and consumables). Figure 2 gives a pictorial abstraction of the ontology, which is formalised in the standard OWL language. For example, this asserts that a Depot has one or more Sheds having one or more roads. A Depot has a set of available Resources that can be Workers, Materials, Consumables, or Tools. A Depot is also equipped with Machinery covering the set of the machines required for servicing, inspection, and maintenance. A fleet is usually linked to a Depot for periodic preventive maintenance. The ontology is then linked to the rolling stock ontology using the MaintenanceActivity class, where a Depot Road is considered a fixed depot resource to perform maintenance activities. Existing community ontologies as detailed above were re-used to define a high-level rolling stock ontology. Railway experts helped to refine the Bogie fragment of the ontology. Railway standards (such as GMRT2466 Iss 5 [27]) and external proprietary resources were also used to define the Bogie subcomponent attributes and maintenance-activities-related concepts. Some components are overhauled based on age mileage, such as the vehicle's engine. Maintenance rules, including tasks and maximum permitted periodicity between tasks, are also included in the ontology with links to the relevant rolling stock component and depot resources. The following section describes the second step of the proposed approach used to populate the abstract ontology with case-specific information.
# Multidisciplinary Planning in Orthognathic Surgery for Prosthetic Patients
## 3 Results  
For each case, instrumental evaluations are performed: orthopanoramics (OPT), Later-Lateral (LL) and Antero-Posterior (AP) teleraliographs, 3D Cone Beam CT (CBCT). More specifically, the CBCT scans were performed both with and without the diagnostic prostheses: this was done to allow for the 3D evaluation of the relationship between the prostheses and the skeletal bases before the surgical phase (if necessary). Lastly, a CT of the condyles in both open and closed positions was performed. VTO and cephalometric analysis are always performed before and after surgery [29; 30] (Figure 3). The preliminary study and design phase was carried out digitally using 3D software (Dolphin Orthodontics software) while the fixed prosthetic rehabilitation using optical impressions (digital scanner) and CAD/CAM equipment. The surgical phase, on the other hand, was carried out according to traditional techniques.  
Figure 3: VTO and cephalometric analysis: N—nasion; S—sella; Po—portion; Or—Orbital point; CC—craniocranical point; Ba—basion; A—subspinal point; B—supramental point; Pg—pogonion. Different color lines have been used to make the image easier to read.  
In the case of total dedentulous, the fracture of a plaque is observed during the sedimentation phase, and this causes a recurrent displacement of the mandible; however, a first dental class of Angle is obtained.  
In one out of four patients with fixed crowns, prosthetic rehabilitation is not completed for both arches because the patient died prematurely during the finalization stages due to non-oral comorbidities. For each case, a complete surgery of LeFort I osteotomy and BSSO is performed (Figure 4). In 12 out of 17 cases, prosthetic work is finalized with implant-supported prostheses (implants used are \"Zimmer Trabecular Metal\" manufactured by Zimmer Dental Inc. 1900 Aston Avenue Carlsbad, CA, USA). The remaining five cases are finalized with removable prostheses (Figures 5-7).  
Figure 5: A 45-year female patient with III dento-skeletal malocclusion, bilateral cross-bite, and TMJ’s dysfunction: VTO and intraoral views before (**a–c**) and after orthodontic surgery and new prosthetic rehabilitation (**A–C**). Different colored lines were used to facilitate comparison between the two pre- and post-treatment images.  
Figure 6: \(Cont.\)  
During the surgical and prosthetic rehabilitation phases, no significant difference was found for the purposes of the result in relation to gender and age. Clinical manifestations following orthognathic surgery, such as swelling, may have varied in individual patients. However, these common complications remained within a normal range and therefore were not sufficient to compromise or complicate healing and the following rehabilitation.  
All patients are monitored over time, so an average follow-up of 24 months is recorded from the end of the prosthesis without surgical or prosthetic complications. Three of the five patients who received the removable prosthesis expressed a desire to perform a fixed prosthesis.