esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### \(\mathcal{F}\)-Deletion Problems
#### 4.6.3 Other Deletion Problems  
Chordal graphsA graph is chordal if it does not have an induced cycle of length \(\geq 4\). Chordal graphs form a subclass of perfect graphs that have been actively studied. Initially motivated by efficient kernels, approximation algorithms for Chordal Deletion have been developed recently. The current best results are a poly(Opt)-approximation [140; 266] and a \(O(\log^{2}n)\)-approximation [260].  
Edge versionsWhile this subsection focused on the vertex deletion problem, there are some results on the edge deletion, edge addition, and edge modification versions. (Edge modification allows both addition and deletion.) Cao and Sandeep [267] studied Minimum Fill-In, whose goal is to add the minimum number of edges to make a graph chordal. They gave new inapproximability results implying improved time lower bounds for parameterized algorithms. Giannopoulou et al. [268] gave \(O(1)\)-approximation algorithms for Planar \(\mathcal{H}\)-Immersion Deletion parameterized by \(\mathcal{H}\). Bliznets et al. [269] considered \(H\)-free edge modification for a forbidden induced subgraph \(H\) and give an almost complete characterization on its approximability depending on \(H\).  
Directed graphsThere is also a large body of work on parameterized algorithms for vertex deletion problems in directed graphs. While many of the known problems (including Directed FeedbackVertex Set[270]) admit an exact FPT algorithm, Lokshtanov et al. [48] studied Directed Odd Cycle Transversal, and proved that it is W[1]-hard and is unlikely to admit an PAS under the Parameterized Inapproximability Hypothesis (or Gap-ETH). They complemented the result by showing a 2-approximation algorithm running in time \(f(\textsc{Opt})n^{O(1)}\).
## 2 System Model  
### Network Topology  
Following the dual diversity model in a log-normal shadowing channel in [35], we consider a multiple-input-single-output (MISO) communication link created by two transmitters (Txs) and one receiver (Rx), as shown in Figure 2. In the figure, the two white-filled circles indicate the cooperative transmitters, the separation between which is \(\Delta_{t}\). Moreover, the receiver is indicated by the black circle, and the separation between the mid-point of the two transmitters and the receiver is \(l\). The distance from Tx\(i\) (for \(i\in\{1,2\}\)) to Rx is \(d_{i}=\sqrt{l^{2}+\Delta_{t}^{2}/4}\), which determines the path loss between Tx\(i\) to Rx.
## 3 Ambiguities
### Ambiguities Due to Signal Symmetry  
As introduced in Section 2.1, transmission lines carry an alternating current. In addition to improved filtering capabilities, this also introduces a number of ambiguities in navigation, as shown below. The magnitude and sign of the three field components measured by a three-axis magnetometer can be determined in the time domain from the samples. For this purpose, a method was proposed in [24], which is beyond the scope of the present paper. It is taken as given for the following considerations. For example, a sensor with the sample values depicted in Figure 9 would yield the measured value \(\vec{v}=(1\,mT,-200\,\upmu T,-500\,\upmu T)^{T}\).  
Figure 8: Ambiguities due to field symmetry.  
Figure 9: Sample values of a magnetometer.  
However, a half period later, the signals in Figure 9 appear shifted, and the signs of the individual field components change; i.e., the sensor now measures \(-\vec{v}\). Since technically, only signal amplitudes are used, it is as if the equivalent static current \(I\) changes its direction every half period. A direct consequence of this fact follows from Corollary 1. Under the defined constraints in Section 2.4, it allows detecting whether the UAV is flying under or above the transmission lines, since the sign of the measured \(y\)-components \(V_{iy}\) in the global frame depends only on which half plane \(z>0\) or \(z<0\) the sensors are located. However, since the direction of \(I\) changes cyclically, this is no longer possible without additional detection methods, since \(V_{iy}\) also changes sign. Such a detection could be realized, for example, by comparing the signal energy of several sensors. This could exploit the fact that the closer the sensor is to the cables, the higher the energy is. However, this is beyond the scope of this paper. The fact that it is impossible to uniquely determine the sensor position can even be seen directly from Equation (21). It contains the prefactor \(C\), which, as shown in (13), depends on the current; thus, it can be \(C>0\) or \(C<0\), leading to different solutions. Furthermore, the whole approach proposed in Theorem 3 to narrow down the solutions no longer works.
# Automatic TV Logo Identification for Advertisement Detection without Prior Data
###### Abstract  
Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.  
computer vision; logo detection; advertisement identification +
Footnote †: journal: Applied Sciences  
1
## 3 Modeling of Path Planning
### Optimization Criteria
The multi-objective optimization problem can be converted into a single-objective optimization problem by summing the weighted four cost value, as shown in the following equation:  
\[\text{min}\varphi_{\text{cost}}=\omega_{1}\cdot T_{\text{cost}}+\omega_{2}\cdot \rho_{\text{cost}}+\omega_{3}\cdot M_{\text{cost}}+\omega_{4}\cdot O_{\text{cost}} \tag{10}\]  
where \(\omega_{1},\ \omega_{2},\ \omega_{3},\ \omega_{4}\in\mathbf{R}\) are the weights of different cost values respectively. The values of weights are determined by the expert system and application demands.
## 4 Simultaneous Utilization of Multiple RANs within a 3 GPP Framework
### Simultaneous Utilization of 3 GPP-Based and Wi-Fi Access Networks over 5 GC or EPC
#### 4.1.3 ATSSS Phase 3
Furthermore, a new indicator, called Transport Mode, is introduced exclusively for MP-QUIC steering functionality. ATSSS/N4 rules specify one of three transport modes to determine how a UDP flow is transmitted between the UE and UPF according to the selected transport mode:  
* Datagram mode 1 (An extension of the mode defined in [80]): This mode encapsulates UDP packets within QUIC Datagram frames, providing unreliable transport with sequence numbering and packet reordering/duplication;
* Datagram mode 2 [80]: This mode encapsulates UDP packets within QUIC Datagram frames, offering unreliable transport without sequence numbering or packet reordering/duplication;
* Stream mode: This mode encapsulates UDP packets within QUIC Stream frames, delivering reliable transport with sequence numbering and packet reordering/duplication. It ensures strict reliability and in-order delivery with retransmission, which is particularly suitable for applications lacking their own reliability mechanism.
## 4 Conclusions  
The RSM and release kinetics of plasmid pVAX1-NH36 by disruption of _E. coli_ cells in a bead mill was studied for the very first time. The electrophoresis analysis of the _E. coli_ lysates from all the experiments was used to obtain the pDNA(sc) percentage in each sample. These percentages along with the HPLC-HIC chromatograms determinations of pDNA(t) allowed us to obtain the pDNA(sc) concentration using ImageJ. The obtained data was used to perform RSM and kinetic pDNA release analysis. RSM proved to be successful for the prediction and modeling of the release of pDNA(t) and pDNA(sc). The significant treatments for pDNA(t) release were mill frequency, bead size, and the combination of cell concentration and bead size. For pDNA(sc), the significant treatments were mill frequency, bead size, and the combination of the three parameters studied in this research. Although ANOVA for RSM demonstrated that cellular concentration per se is not a significant parameter in this operation, as it was also concluded by other authors in the disruption of _E. coli_ cells [40] and yeast cells [20], it was found in this work that it is a crucial parameter for effective cell lysis. Since the best results of the release of pDNA(sc) were obtained with a small bead size and low cell concentrations, in order to optimize the disruption of _E. coli_ for the release of pDNA(sc), we also suggest the use of a denser material with higher specific gravity than glass, e.g., zirconium beads, as reported by [PERSON] et al. [18], to increase cellular concentration and productivity. The description of pDNA(t) release by a first-order kinetic gave good fit to the data obtained in most of the experiments performed with the bead mill. The description of the pDNA(sc) release process by a first-order kinetic gave good fit to the data only at low mill frequencies. The maximum pDNA(sc) concentration values obtained in the experiments suggest a compromise between pDNA(sc) purity and plasmid recuperation in process development.  
This research was supported by a grant provided by the National Council of Science and Technology (CONACYT) under the project CB2016-257411. We also appreciate the support given by the Strengthening Program Quality in Educative Institutions (PROFOCIE) 2017 and the University of Sonora. The authors declare no conflict of interest exists.  
[PERSON] performed all experiments and contributes in the manuscript writing. [PERSON] and [PERSON] contributed reagents/materials/analysis tools and discussion for all experiments. [PERSON] and [PERSON] conceived and designed the experiments and contributed to manuscript writing.  
The authors declare no conflict of interest.
# Study on the Performance of Foam Concrete Prepared from Decarburized Fly Ash
## 3 Results and Discussions
### Compressive Strength  
Figure 8 reflects the results of the compressive strength measurement of the foam concrete with different mix ratios. From the figure, it can be seen that the compressive strength of the concrete decreases continuously with the increase in the DFA admixture, while keeping the water-to-cement ratio constant. Meanwhile, the H1, H2 and H3 groups showed a sudden drop in DFA content up to 20%, 15% and 10%. The strengths were reduced from 0.72 MPa to 0.48 MPa, 0.71 MPa to 0.52 MPa, and 0.67 MPa to 0.52 MPa, respectively, with reductions of 33.3%, 26.7%, and 22.4%. This is caused by the mineral composition of the cementitious material. The mineral composition of HBSAC is mainly anhydrous calcium sulfoaluminate and dicalcium silicate. In the early stage of the hydration reaction, Aft is mainly formed by the hydration of anhydrous calcium sulfoaluminate to improve the strength, and in the later stage, dicalcium silicate is hydrated to form hydrated calcium silicate and Ca(OH)\({}_{2}\) to provide strength [25]. Meanwhile, although the DFA with mechanical activation-flotation has higher activity, the hydration products of anhydrous calcium sulfoaluminate can further react with Ca(OH)\({}_{2}\). This means a disguised weakening of the DFA activity. Secondly, due to the relatively slow hydration rate of dicalcium silicate, when the DFA content reaches a certain level, the Ca(OH)\({}_{2}\) generated by hydration is inevitably unable to meet the amount required for the DFA to carry out the \"secondary hydration reaction\". This also results in a lot of residual un-reacted DFA inside the concrete, which not only reduces the compactness of the concrete, but also reduces the compressive strength of the concrete.  
Figure 7: The density of foam concrete with different matching ratios.  
We can also learn from the figure that an increase in the water-to-cement ratio decreases the compressive strength of concrete at the same DFA substitution rate. Taking H1-4 and H3-4 as examples, the 28 d compressive strengths of both were 0.72 MPa and 0.44 MPa, with a decrease of 38.8%. This is because, although the amount of gelling material is the same for both, H3-4 will obtain more gelling material slurry as the water-to-cement ratio increases. When the water-to-cement ratio is larger, the content of HBSAC in the same volume of cementitious material slurry will be less. In addition, as the main strength contributor, the content reduction of HBSAC will inevitably reduce the strength of the foam concrete. Secondly, due to the same volume of the mold at the time of casting, less HBSAC hydration products distributed in the same volume of space will also make the foam concrete less compact and thus reduce the compressive strength.  
According to JG/T 266-2011 \"Foam Concrete\", all the 18 groups of foam concrete prepared in this test meet the requirements of C0.3 grade foam concrete.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 6 Conclusions  
This paper presents a secure and authentic sensor cloud architecture, named SASC, for intelligent IoT system, which aims to guarantee data routing through secure and reliable communication links. The proposed solution offers lightweight cryptosystems to enhance security in terms of data confidentiality, integrity, and robust authentication between IoT sensors under the presence of malicious entities. The SASC architecture guides sensor nodes to send data packets on trustworthiness and authentic data forwarders while balancing the energy consumption over the network field. Additionally, integrating the cloud infrastructure to sensor networks, the proposed solution offers high quality and cost-effective communication services for IoT systems with minimal overhead. Furthermore, the SASC handles and processes all sensed data with the support of network scalability and integrity. The numerical results of SASC architecture reveal significant improvements in different network parameters as compared to other solutions. However, the proposed  
Figure 11: Transmission overhead in varying CBR data traffic.  
Figure 10: Transmission overhead in a varying number of nodes.  
architecture still lacks for meeting the robust and security requirements for multi-hop communications. Furthermore, non-repudiation and playback network attacks are overlooked in the proposed SASC architecture. Therefore, we aim to improve the performance of the proposed architecture by analyzing some more misbehaving threats among cluster heads and generate an efficient and trusted end-to-end routing delivery for longer communication regions.  
Conceptualization, K.H. and I.U.D.; methodology, N.I.; software, K.H.; validation, A.A. ([PERSON]), A.A. ([PERSON]); formal analysis, I.U.D.; investigation, A.A. ([PERSON]) A.A. ([PERSON]); resources, A.A. ([PERSON]); data curation, N.I.; writing--original draft preparation, K.H.; writing--review and editing, I.U.D.; visualization, N.I.; supervision, A.A. ([PERSON]); project administration, A.A. ([PERSON]); funding acquisition, A.A. ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through Vice Deanship of Scientific Research Chairs.  
The authors declare no conflict of interest.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Positron Emission Tomography (PET)
#### 4.3.1 DL Techniques and Applications to PET  
CNNs have been widely used in PET image analysis tasks. For example, U-Net [6] has been used for PET image segmentation [149]. Moreover, GANs have been used for PET image reconstruction [150] and image denoising [151]. Additionally, DL techniques have been applied to PET image registration [152] and disease classification [140].
# Influence of Posture Change on Train Running Safety under Crosswind
## 1 Introduction
Although there are many studies on train dynamics, it is still unknown whether its attitude changes will have a worse impact on safety threats in the common running on flat ground. Therefore, a three-dimensional (3D) Computational Fluid Dynamics (CFD) model of the train-wind is proposed in the present investigation to study the transient aerodynamic load of each car of the train based on the Unsteady Reynolds-Averaged Navier-Stokes (URANS) turbulence model. A validated dynamic system was used to predict the running safety of the train with and without the given specific posture. The aerodynamic responses of each car of the train passing through the crosswind region with and without the given specific posture were compared and analyzed in terms of the aerodynamic coefficient and flow patterns. The effect of the given posture on train running safety was also considered based on the train derailment coefficient (DC), wheel load reduction rate (WLRR), the time-varying running posture, and their accelerations in five degrees of freedom. The physical model, method, computational domain, boundary conditions, and computational meshes are described in Section 2; the validation is given in Section 3; Section 4 discusses the results; and finally, the conclusion is drawn in Section 5.
## 3 Results and Discussion
### Influence of LPArP on the BC Surface Morphology  
In order to check the morphological changes in the BC structure after LPArP modification, scanning electron microscopy was used. Figure 3 shows the SEM micrograph of BC surface morphology (a) and the Ar_BC after modification with LPArP for 480 s (b). The unmodified BC sample shows a typical reticulated surface of pristine BC with a well-visible ultra-fine network of microfibrils and pores [45; 46]. After modification with LPArP, the surface morphology of the BC changed significantly and presented a heterogeneous structure typical for amorphous BC. These results are consistent with the studies by [PERSON] et al. and [PERSON] et al., where they reported that the oxygen plasma treatment of BC can cause a burn of the surface which results in an increase in the surface roughness and heterogeneity due to the destruction of the BC fibril structure [47; 48].
# A Synthetic Approach for Biosynthesis of Miquelianin and Scutellarin A in _Escherichia coli_
## 3 Results and Discussion
### Synthesis of Glucuronoids in Recombinant Strains  
The codon optimized UGTs from the respective source were initially screened by feeding in recombinant strains. _E. coli_ BL21 (DE3)/piBR181-UGT88D8 and _E. coli_ BL21 (DE3)/piBR181-VvGT5 were grown in LB medium, induced and incubated at 15 \({}^{\circ}\)C for 20 h to allow functional expression of the protein, as was explained in Materials and Methods. A total of 200 \(\upmu\)M of apigenin and quercetin dissolved in dimethyl sulfoxide (DMSO) were added into the respective culture medium. After 48 h of incubation, 500 \(\upmu\)L culture broth was taken and mixed with double volume ethyl acetate and vigorously vortexed followed by centrifugation to separate the organic and aqueous layer. The dried  
Figure 2: Schematic representation of the construction of single recombinant vector with the assembly of UDP-glucuron acid pathway genes in mono-cistronic fashion. The final constructs piBR181-glk.pgm2.galU.ugd.UGT88D8 (system A) and piBR181-glk.pgm2.galU.ugd.VvGT5 (system B) contain sugar pathway genes; glucokinase (_glk_), phosphoglucomutase (_pgm2_), glucose 1-phosphate uridylyltransferase (_galU_), UDP-D-glucose dehydrogenase (_ugd_), flavonoid 3-O-glucurononsyltransferase (_VvGT5_) and flavonoid 7-O-glucurononsyltransferase (_UGT88D8_).  
supernatant organic layer was dissolved in 100 \(\upmu\)L of methanol and subjected to HPLC analysis. New peak(s) were detected at retention time (\(t_{\mathrm{R}}\)) \(\sim\) 17.5 min before the standard apigenin \(t_{\mathrm{R}}\)\(\sim\) 20.2 min. in the case of UGT88D8 at UV absorbance of 300 nm. While in VvGT5 culture sample, new peak(s) at \(t_{\mathrm{R}}\)\(\sim\) 17.5 were detected as compared to the standard quercetin \(t_{\mathrm{R}}\)\(\sim\) 19 min. These samples were further analyzed by high resolution mass analyses to confirm if the generated peaks were respective glucuronoids. The result was revealed by showing the mass spectrum of biocatalysis reaction mixture in positive mode as [apigenin 7-\(O\)-glucuronoid +H]\({}^{+}\)\(m/z\) 447.0911 (calculated mass 447.0927) and [quercetin 3-\(O\)-glucuronoid +H]\({}^{+}\)\(m/z\) 479.0821 (calculated mass 497.0826) (Figure 3). The conversion of apigenin to scutellarin A was \(\sim\)6% while that of quercetin to miquelianin was \(\sim\)13%. The conversion was unexpectedly low. Thus, we presumed that the low conversion was due to the low cytosolic pool of UDP-glucuron acid.
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
## 4 Results and Discussion  
Geophysical data of three methods (ERT, IP and SP) were used to interpret the subsurface for site suitability of engineered structures in the study area. The subsurface was interpreted for the delineation of distinct layers, fractures/faults, clay, and groundwater flow path. 2D ERT models were integrated with the borehole lithological logs to obtain a subsurface model of four distinct layers/zones with specific resistivity values range. Clay was identified by the integration of 2D IP models (with high chargeability) and 2D ERT models (with low resistivity). Groundwater flow was mapped using 2D SP models (with low or negative SP). The main fractures/faults were delineated by the integration of all geophysical models using low values of ERT, IP and SP. One of the interpreted models for three geophysical methods is shown in Figure 2. Based on the correlation between all ERT models and borehole data, the ERT model (Figure 2a) along profile B was interpreted (Figure 2b) for four layers (topsoil cover, weathered layer, weathered/unweathered transition zone (partly weathered rock) and unweathered layer) and four main fractures/faults (F2, F3, F4 and F5). The identified fractures/faults in ERT B also show consistency on IP B (Figure 2c) and SP B model (Figure 2d). The ambiguity in the water-clay distinction caused  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Profile**} & \multirow{2}{*}{**Electrodes**} & **Profile Length** & **Electrode Spacing** & **1 st Layer Depth** & **Last Layer Dept** & \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Measurements**} \\  & & **(m)** & **(m)** & **(m)** & **(m)** & & \\ \hline A & 261 & 1300 & 5 & 7.5 & 127.5 & 25 & 6525 \\ B & 101 & 500 & 5 & 7.5 & 127.5 & 25 & 2525 \\ C & 111 & 550  
by low resistivity in the ERT model (Figure 2a) was removed by integration of IP (with high chargeability) (Figure 2c). Similarly, negative SP shows the direction of groundwater flow mainly along the identified fractures/faults (Figure 2d). The details of subsurface interpretation for all geophysical profiles are given in the following sections.