esa-sceva/satcom-chunk-collection huggingface dataset:
# Misconfiguration in Firewalls and Network Access Controls: Literature Review
## 3 Misconfigurations
### FlowBit Misconfiguration  
This final misconfiguration has the most variety in exactly what it is and how it occurs. In advanced firewalls and in IDS/IPS such as Snort, there is a feature that allows the user to create rules based on the state of the application protocol. One such example of this is Snort's flowbit feature, which is capable of keeping track of an application state. A flowbit can keep track of whether a user is logged in, what they are doing inside an application, etc. The exact implementation is different, depending on the platform and even the version of the platform. While different devices may refer to this feature by different names, we will refer to this feature as \"flowbits\", Snort's implementation.  
In Snort, flowbits replaced the now-depreciated activate and dynamic rules. Flowbits allow Snort to keep track of what has happened in a session. Rules checking flowbits will only fire if the associated flowbit is set on that session. As [39] explains, there are multiple ways a flowbit rule can be misconfigured. A flowbit could be set to check for a connection state that is impossible, such as being logged in and not being logged at the same time. There is also the issue of not checking for enough connection states. It is possible that any given attack can be carried out in different ways that would have different connection states. While no single rule would be misconfigured, the policy as a whole is misconfigured as it did not take into account other means of that same attack occurring that one or more of its rules are attempting to block. There is also the issue of checking for flowbits that are too specific or broad. Checking for overly broad conditions, say being logged in, may result in many false positives as normal traffic triggers the alerts. Alternatively, too specific conditions could allow an attack to sneak by because they did not meet everything that the flowbits were flagging.  
Checking for a connection state can be fairly tricky and thus could be easy to misconfigure. The nature of flowbits often means that rules that check flowbits or their counterparts in other systems will often be looking for a few specific attacks. The result of a flowbit rule being misconfigured will leave a network either vulnerable or label good traffic as that type of attack. As stated above, the exact impact is determined by how the misconfiguration occurs. It can result in the attack slipping by entirely or causing benign traffic to trigger the rule.
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
###### Abstract  
Image inpainting and colorization are crucial tasks in image restoration, yet existing methods typically address them independently, limiting their applicability to complex real-world scenarios. Conventional inpainting techniques struggle with maintaining structural consistency in arbitrarily masked regions, while colorization models often assume fully restored grayscale inputs. To bridge this gap, we propose a unified framework that seamlessly integrates inpainting and colorization into a single pipeline. Our approach leverages three state-of-the-art models: the segment anything model (SAM) for detecting arbitrary masked regions, the mask-aware transformer (MAT) for structurally coherent inpainting, and the dual-decoder colorization model (DDColor) for producing perceptually consistent colorized outputs. By integrating these models, our framework effectively restores both structure and color, handling arbitrary masks with greater robustness than existing methods. Experimental results on diverse datasets demonstrate superior performance in both qualitative and quantitative evaluations. Our findings highlight the advantages of a unified approach to image restoration and provide a foundation for future advancements in multi-stage deep learning frameworks.  
Keywords:image restoration; inpainting; colorization; deep learning +
Footnote †: journal: Applied sciences
# Filament X-ray Tube Current Control Method Using Indirect Filament Temperature Estimation
## 2 Principle of Operation
### Tungsten Thermal Properties  
The electrical energy is converted into thermal energy when the tungsten filament applies as the load, as expressed in Equation (4). Here, \(V\) denotes the load voltage, \(I\) indicates the load current, and \(R_{T}\) represents the resistance value at absolute temperature \(T\). Based on Equation (4), the heating value of the tungsten filament can be obtained, as shown in Equation (5). Here, \(m\) denotes the mass of the tungsten filament, \(c_{p}\) indicates the heat capacity, and \(\Delta T\) represents the absolute temperature change.  
\[P=VI=I^{2}R_{T} \tag{4}\]  
\[Q=\int_{t_{1}}^{t_{2}}Pdt=mc_{p}\Delta T \tag{5}\]  
The temperature is maintained constant at the point where the input energy matches the heat loss. However, as the temperature of the metal varies, thermal properties, such as the heat capacity, conductivity, and radiation coefficient, change owing to the variations in the internal energy and vibrational motion of the metal atoms [16; 17; 18].  
The heat losses in the filament of an X-ray vacuum tube are primarily classified into radiation and conduction losses, which can be expressed as indicated in Equations (6) and (7), respectively. Here, \(P_{con}\) represents the conduction heat loss; \(k_{con}\) denotes the thermal conductivity of the material; \(A\) indicates the heat transit area; \(\Delta T\) represents the absolute temperature change; \(\Delta x\) denotes the conduction distance; \(P_{rad}\) indicates the radiation heat loss; \(\sigma\) represents the Stefan-Boltzmann constant; \(\varepsilon\) denotes the emissivity of the material; \(A\) indicates the surface area of the object; and \(T\) represents the temperature of the metal.  
\[P_{con}=k_{con}A\Delta T/\Delta x \tag{6}\]  
\[P_{rad}=\varepsilon\sigma A\tau^{4} \tag{7}\]  
The relationship between the tungsten temperature, heat capacity, thermal conductivity, and emissivity is presented in Equations (8)-(10) [16].  
\[c_{p}=135.76+\Big{(}9.1159\times 10^{-3}\Big{)}T+\Big{(}2.3134\times 10^{-9} \Big{)}T^{3}-\Big{(}6.5233\times 10^{5}\Big{)}T^{-2} \tag{8}\]  
\[k_{con}=108.34-\Big{(}1.052\times 10^{-2}\Big{)}T+23419.9/T \tag{9}\]  
\[\varepsilon_{T}=-2.6875\times 10^{-2}+\Big{(}1.819696\times 10^{-4}\Big{)}T- \Big{(}2.1946163\times 10^{-8}\Big{)}T^{2} \tag{10}\]  
Figure 3 illustrates the thermal properties of filament considering the temperature based on Equations (8)-(10).  
Equations (4)-(10) indicate the difference in heat loss based on the temperature of the tungsten filament. When the approximate loss is calculated at 300 and 3000 K, the conduction loss increases from 7.4 mW to 1.579 W, and the radiation loss from 0.1644 mW to 2.0661 W, which varies non-linearly with temperature. Thus, temperature control is essential to maintain it constant considering the non-linear changes of the thermal properties, based on the temperature, of the tungsten filament.
# iVRPM: Conceptual Proposal of an Immersive Virtual Reality Pedagogical Model
## 3 Methodology  
The study is organized within a design-based research (DBR) methodological approach, which is \"a practical research methodology that could effectively bridge the chasm between research and practice in formal education\" [30]. According to [PERSON] [31], DBR focuses on examining a specific intervention with the goal of designing and testing learning environments or technological interventions through a continuous process of design, implementation, analysis, and redesign. This process seeks to explain and understand issues related to teaching and learning in a real-world context.  
Based on this approach, the study is organized into three phases: (1) a systematic literature review, which has already been completed and published separately as \"The Use of Immersive Virtual Reality in Educational Practices in Higher Education: A Systematic Review\" [8], identified gaps, opportunities, and challenges related to the use of immersive technologies in education. It provided insights into the learning process in immersive environments and key components to be included in the proposed framework. (2) The construction of the pedagogical framework, which is the focus of this article. This phase presents a structure that seeks to align the affordances offered by iVR environments with pedagogical principles. (3) The development and validation of an educational prototype with higher education students, which is planned for future implementation. The methodological design of the study is illustrated in Figure 1.  
The main purpose of this article is to present the construction of the pedagogical framework (Phase 2), which seeks to identify and understand the elements that operate in an immersive virtual learning environment. This framework aims to support the planning of immersive educational experiences by aligning the affordances offered by iVR environments with educational objectives.  
The development of the pedagogical framework was based on theoretical and empirical insights obtained during the systematic literature review (Phase 1) [8], which analyzed a corpus of 39 academic publications selected following the PRISMA guidelines, and guided by two foundational models: the cognitive affective model of immersive learning [5] and the XR ABC framework [33]. These models provided conceptual guidelines for integrating technological affordances, such as interactivity, immersion, and embodiment, into a coherent structure aligned with educational objectives. Additionally, the revised [PERSON]'s taxonomy [15] was employed to relate the technological affordances of iVR environments to the cognitive and psychomotor dimensions of learning.  
The framework emphasizes the role of student involvement as a pivotal element in immersive learning, identified as a critical factor in the literature review. By integrating technological affordances with pedagogical strategies, the framework aims to create educational experiences that facilitate learning.
# Phytoglycoproteins and Human Health: Current Knowledge and Future Applications
## 3 Bioactive Properties of Phytoglycoproteins
### Wound Healing Property  
_Dioscorea_ Detac is traditionally used in Asia to treat stomach disorders, and this inspired studies on the wound healing activity of its PGPs. For example, treatment of human intestinal epithelial (INT-407) cells with 100 ug/mL of DBP glycoproteins enhanced the cell mobility by improving the activities of PKC and p38-MAPK, leading to the activation of NF-kB signaling pathway and the downstream effects such as upregulation of expression of profilin-1 (PFN1), \(\alpha\)-actinin and F-actin [7]. In a mouse model of colitis (induced using dextran sulfate sodium), DBP glycoprotein was further demonstrated to attenuate colitis by reducing the disease activity index (via promotion of intestinal wound healing) and preventing histological alterations in the intestines. Similarly, a previous study demonstrated that SNP glycoprotein (20 mg/kg) prevented colitis in A/J mice by inhibiting LDH release, and nitric oxide and TBARS formation in serum, as well as downregulation of NF-kB, AP-1, iNOS and COX-2 gene expression in colonic tissues [56]. Taken together, these findings demonstrate that PGPs may be major active components mediating the traditional medicinal uses of _D. batatas_ rhizomes and _S. nigrum_ fruits, and support further exploration of the glycoproteins for the management and treatment of inflammatory bowel disease.
## 2 Materials and Methods
### Experiments
#### 2.2.3 Data Acquisition  
We evaluated the hysteresis and sensitivity of the sensing materials utilising a microcomputer-controlled electronic universal testing machine (FBS200N, Forbes Testing Equipment Co., Ltd., located in Xinglong Road, Xiamen, China) and an LCR digital bridge (UC2836B, U-Tech Electronic Technology Co., Ltd., located in Pan Yang Road, Longhu Tang Street, Changzhou, China). The sample was positioned between two copper plates of the universal testing equipment, and to simultaneously measure pressure and resistance, the compression speed was established at a constant rate of 1 N/s. During the analysis of the hysteresis characteristics, the testing apparatus applied a steadily increasing uniaxial pressure on the sensing material, ranging from 0 to 30 kPa during the loading phase, followed by a uniform reduction in pressure from 30 kPa to zero during the unloading phase. The resistance values throughout the forward and reverse strokes, at identical pressure levels, were documented via the data gathering program. During the sensitivity performance analysis, the tester incrementally delivered uniaxial pressure from 0 to 30 kPa on the sensing material, halting at this pressure to record the resistance measurement.  
In this study, SEM images were acquired using the Carl Zeiss GeminiSEM 360 field emission scanning electron microscope. This device, manufactured by Carl Zeiss AG, located at No. 1636, Zhi Siri Avenue, Guangzhou, China, offers high-resolution imaging and analysis capabilities for a wide range of materials. It features a resolution of 0.7 nm at 15 kV and 1.2 nm at 1 kV, with a maximum probe current of 20 nA.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Content (g)** \\ \hline Fe\({}_{3}\)O\({}_{4}\) & 0.4/0.6/0.8 \\ CNT & 0.8/1/1.2 \\ MLG & 0.7/0.9/1.1 \\ KH550 & 1.8 \\ DSO & 2.25 \\ CA & 0.675 \\ RTV & 45 \\ \hline \end{tabular}
\end{table}
Table 1: Material composition and content.  
Figure 1: Procedure for fabricating flexible sensing materials.
## 5 Results
### Classification Results
#### 5.3.3 Transmission Error  
The scores of the classifications with different feature selections using different classification methods are shown in Table 7.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature Selection** & \begin{tabular}{l} **Number of** \\ **Features** \\ \end{tabular} & \begin{tabular}{l} **Accuracy** \\ **Bayes** \\ \end{tabular} & \
\begin{tabular}{l} **Accuracy** \\ **SVM** \\ \end{tabular} &
\begin{tabular}{l} **Accuracy** \\ **KNN** \\ \end{tabular} \\ \hline MANOVA RS + STFT & 1 & 0.60 & 0.53 & 0.55 \\ \hline MANOVA RS + CWT & 1 & 0.60 & 0.67 & 0.62 \\ \hline MANOVA RS + DWT & 1 & 0.68 & 0.69 & 0.62 \\ \hline MANOVA RS + WPT with 15 coeff. & 1 & 0.83 & 0.88 & 0.83 \\ \hline MANOVA RS + WPT with 128 coeff. & 1 & 0.83 & 0.83 & 0.85 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + STFT & 60 & 1.00 & 1.00 & 1.00 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + CWT & 60 & 1.00 & 1.00 & 1.00 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + DWT & 60 & 0.97 & 1.00 & 1.00 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + WPT with 15 coeff. & 60 & 0.70 & 1.00 & 0.97 \\ \hline ANOVA + \(\tau_{\text{s}}\) RS + WPT with 128 coeff. & 60 & 0.98 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification model evaluations for tool tumbling classification.  
Figure 12: Best two features and confusion matrix for tool tumble classification (feature selection: ANOVA+rs from raw signal and STFT).  
The SVM achieves the highest accuracy of 0.88 using the MANOVA selected features from the raw signal and the STFT. The highest-scoring two-way combination of the four selected features and the confusion matrix of the model are shown in Figure 13. When the trained model misclassifies data points, they are in adjacent classes to the true class. The KNN method, which uses a combination of features extracted from the raw signal, and the WPT, with 15 coefficients using ANOVA and the rank correlation coefficient, is the worst at predicting the OTE. It has an accuracy of 0.36 and assigns misclassified data points to distant classes.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 4 Use of USVs in Environmental Disasters with Contaminants
### Chemical Hazards
#### 4.1.3 Oil Spill Ensnaring and Removing
While booms can be used to control the spreading of oil by confining it to a specific area, skimmers are used in _mechanical cleanup methods_ to recover the oil from the water surface. Such robotic system reduces the enormous effort involved in manual skimming operations. [PERSON] et al. [122] and later, [PERSON] and [PERSON] [123] examine the behavior of a robotic swarm concept for the active confrontation of oil spills. Their system consists of some identical autonomous robotic units thatrecover oil mechanically employing skimming brushes and can communicate with each other. In a similar approach, [PERSON] et al. [120] use two USVs towing a floating rope to improve oil skimming by increasing the containment area. Skimmer booms are modeled as a flexible, floating rope of constant length and as a discrete segment model. Equations governing the rope dynamics are derived and tested through simulations trying to maximize skimming efficiency. Experiments to verify the dynamics of a flexible rope being pulled by two USVs are performed using OceanScience QBoat-I hull USVs in Echo Park Lake, Los Angeles. Adapting an approach already in use by manned surface vehicles to USVs, [PERSON] et al. [127] have devised a USV named \"HaiTeng 01\" that can move at 40 knots with an inclined plane skimmer at the front of the vehicle to collect oil from the water effectively. Unlike most USVs presented in this work, HaiTeng 01 has the capacity to store up to 1000 liters of spilled oil. Experiments to investigate the influences of the oil recovery apparatus located on the front deck in the performance of USV at high speed were performed in Shenzhen Bay, China. [PERSON] et al. [126] develop a Porous Unmanned Ship (PUS), a USV with aligned ZnO nanorod arrays on the surface of the porous stainless steel wire mesh with properties of superhydrophobicity and superoleophilicity. Hence, when the PUS contacts with the oil, it is quickly pulled toward and penetrates the PUS automatically. Experiments are performed showing that the superhydrophobicity and low water adhesion force of the mesh surface endow the PUS with high oil/water separation capacity (above 94%), illustrating the importance of the vessel design for the given task.
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 2 Literature Review
### Handoff Techniques
#### 2.1.2 Cluster-Based Handoff Scheme  
In cluster-based handoff, vehicles are moving into a gathering or a cluster in highway and each group contains a cluster head. Cluster head straightforwardly communicates with BS and another vehicle communicates with the cluster head. Reference [31] mentions that cluster heads are responsible for maintaining the whole network, so a cluster head must choose the basic metrics like storage capacity, communication range, energy, etc. In [32] authors proposed the CBHBCK in which a predefined backup mobile edge-node and cluster head were used. Backup node prepares the handoff and updates the list of BSs while cluster head performs the physical handoff.  
The overhead of a cluster node decreases, but the drawback is if the backup mobile edge-node or cluster head stops working then the communication can be distorted.
## 3 Results and Discussion
### Analysis of Volatile Compounds in Muffins
Heterocyclic compounds containing sulphur and nitrogen atoms are mainly formed through the interaction of Maillard reaction intermediates with lipid decomposition products such as 2,4-decadienal and hexanal. Substituted-furans such as 2-furancarboxaldehyde, 2-furanmethanol, 2(5H)-furanone and 2-pentylfuran were also detected (Table 4). These compounds are formed through the complex processes of Maillard reaction and sugar thermal degradation during baking [58]. 2-pentylfuran is the only furan derivative detected in all muffin samples (t\({}_{\rm R}\) = 18.84 min., analyte 19, Table 4). The formation of 2-pentylfuran could be attributed both to Maillard reaction and lipid oxidation [59]. Hydrocarbon compounds like toluene, 1,3-dimethylbenzene, decane, undecane and dodecane detected in all muffin samples (Table 4), could come from the oxidative decomposition of lipids and are often associated with grassy and fatty flavors [60]. Among the volatile components that contribute to the aroma of all analyzed muffin samples we also found 1-octen-3-ol (t\({}_{\rm R}\) = 18.36, analyte 18, Table 4), a compound with a strong mushroom note that is associated with the enzymatic degradation of linoleic acid [61]. Other detected alcohols are 1-pentanol (sample T_MUF and C_MUF) and 2-ethyl-1-hexanol (sample C_MUF). Some volatile molecules like 1-hydroxy-2-propanone, methyl pyrazine, 2-furancarboxaldehyde, 2-ethyl-1-hexanol, octanal, and tetradecane (Table 4) were detected exclusively in the extract of C_MUF. In the GC profile of the control (C_MUF) is observed the presence of a higher number of volatile components compared to T_MUF and M_MUF muffins. The obtained results show that volatile compounds that derive from lipid oxidation and thermal degradation of sugars and free amino acids affect the flavor of these products.
## 2 Preliminaries  
### Contraction Hierarchies  
The contraction hierarchies approach [5] computes an overlay graph in which so-called shortcut edges span large sections of shortest paths. This reduces the hop length of shortest paths and therefore allows a variant of [PERSON]'s algorithm to answer queries more efficiently.  
The preprocessing is based on the so-called _node contraction_ operation. Here, a node \(v\) as well as its adjacent edges are removed from the graph. In order not to affect shortest path distances between the remaining nodes, shortcut edges are inserted between two neighbors \(u\) and \(w\) of \(v\), if and only if \(uvw\) was a shortest path (which can easily be checked via a Dijkstra run). The cost of the new shortcut edge \((u,w)\) is the sum of costs of \((u,v)\) and \((v,w)\). In the preprocessing phase, all nodes are contracted one-by-one in some order. The rank of the node in this contraction order is also called the _level_ of the node.  
Having contracted all nodes, the new graph \(G^{+}(V,E^{+})\) contains all original edges of \(G\) as well as all shortcuts that were inserted in the contraction process. An edge \(e=(v,w)\)--original or shortcut--is called upwards, if the level of \(v\) is smaller than the level of \(w\), and downwards otherwise. By construction, the following property holds: For every pair of nodes \(s,t\in V\), there exists a shortest path in \(G^{+}\) that consists of a sequence of upward edges followed by a sequence of downward edges. This property allows us to search for the optimal path with a bidirectional Dijkstra only considering upwards edges in the search starting at \(s\), and only downwards edges in the reverse search starting in \(t\). This reduces the search space significantly and allows for answering of shortest path queries within the _milliseconds_ range compared to _seconds_ on a country-sized road network. We call this algorithm the CH-Dijkstra.  
For our lower bound later on, the notion of _direct search space_ (\(DSS\)) (as defined in [13]) is useful. A node \(w\) is in \(DSS(v)\), if on the shortest path from \(v\) to \(w\) all nodes have level at most \(level(w)\). Therefore, \(w\) will be settled with the correct distance \(d(v,w)\) in the CH-Dijkstra run. As a remark, the set of nodes considered during a CH-Dijkstra is usually a superset of \(DSS(v)\) as also nodes on monotonously increasing (w.r.t. level) but non-shortest paths are considered. Our construction will lower bound the size of \(DSS(v)\) which obviously also lower bounds the number of nodes that have to be considered during CH-Dijkstra.