esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Setup  
The artificial leak scenario consists of a shot-blasted aluminum target (10 \(\times\) 10 cm\({}^{2}\)) with a central hole as the \"leak\" (diameter, 0.3 mm). Motivated by [PERSON]'s cosine law, this target ensures sufficient diffuse reflection of the incident laser radiation to mimic a so-called non-cooperative target of a real application in the vicinity of the leak. The leakage gas is 100% methane (CH4) and released through the hole or drill as the leak by a flow rate of 0.25 mL/min. As illustrated in Figure 1, a gas supply hose is connected at the back side of the leak plate. The flow rate and the leakage gas are set by a calibrated gas mixing station (HovaCAL digital 922 SP flow controller, IAS GmbH, Oberursel, Germany). Distributed feedback (DFB) lasers for the infrared wavelength regime are the best-suited choice for gas sensing. Unique technical characteristics such as continuous wave (cw), single mode, narrow line width (e.g., 5 MHz) emission and tunability over a few wavenumbers (up to 5 cm\({}^{-1}\)) are required for selective and accurate gas sensing. In this context, an interband cascade laser (ICL, Nanoplus, Meiningen, Germany) was chosen for the illumination of the leak scenario. The emission wavelength of the ICL targets a comparably strong methane absorption line around 3271 nm with an optical power of nearly 5 mW. The laser light is directed at the leakage plate at a distance of approximately 2.25 m, and the beam diameter at the target is approximately 8 cm. The backscattered infrared light is recorded by a fast and sensitive thermal imaging camera (ImageIR 8320, InfraTec GmbH, Dresden, Germany) for the mid-IR range between 2 and 5.7 \(\upmu\)m. The detector of this thermal imaging camera is a MWIR focal plane array photodetector based on InSb. This means that this camera or every pixel works as a single pixel photodetector that measures the incoming radiation. The tunable ICL is periodically wavelength-modulated across the methane absorption line by a sawtooth injection current modulation of 10 Hz. The IR camera is operated at frame rate of 470 Hz and a (reduced) frame size of 320 \(\times\) 256 pixels (corresponding to a spatial resolution below 0.5 \(\times\) 0.5 mm\({}^{2}\)). This frame rate allows a scan of the gas absorption line within an image stack of 47 images (or wavelengths) per ramp. These ramps or wavelengths scans are recorded image by image and processed offline, as described in Section 2.2.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair
### Acellular Dermal Matrix (ADM)
#### 2.1.5 Animal-Derived ADMs
Kerecis(tm) Omega3 Wound (Kerecis) is an ADM rich in omega-3 fatty acids, intended for use in burn wounds, chronic wounds, and other applications. Human skin differs from fish skin mainly because of the adaptation of the latter to the aqueous environment through the development of scales, secretion of mucus from epithelial cells, lack of superficial keratinized layers, and presence of two basement membranes [53]. Additionally, fish skin heals faster than human skin without leaving scar tissue [54]. The histological analysis of human skin after a Kerecis(tm) graft demonstrated that the ADM is incorporated into the damaged area and infiltrated by autologous cells, is resistant to bacteria, and exhibits anti-inflammatory properties because of the omega-3 fatty acids [29; 54].
## 1 Introduction
As a result, further detailed assessments of the MGEX products are in need to identify potential improvements and deficiencies. In this study, we present an assessment of the MGEX Galileo, BDS and QZSS products since 2018. After the introduction, an overview of the product availability as well as processing strategies is given. Then, quality of the precise orbit and clock products is analyzed in terms of orbit/clock comparisons and SLR validation. Thereafter, precise point positioning (PPP) performances with MGEX orbit and clock products are assessed. Finally, conclusions and discussions are given.
## 1 Introduction
### Minimizing Reader Collisions and Reading Delay  
DTS-AC prioritizes the minimization of reader collisions and reading delay to enhance network performance. By focusing on efficient resource allocation and management, DTS-AC minimizes the likelihood of reader collisions, leading to smoother operation and improved read rates.  
Additionally, the protocol's emphasis on minimizing tag reading delay is achieved through features such as Time Slot sharing among multiple readers, enabling faster and more efficient tag identification processes.  
Through comprehensive simulations, we demonstrate that DTS-AC significantly improves the performance of RFID systems, particularly in dense network environments. Its scalability benefits, based on the number of readers, channels, and Time Slots, make it a promising solution for various RFID applications.  
The remainder of this paper is structured as follows: Section 2 presents our proposed DTS-AC algorithm, Section 3 discusses related literature protocols, Section 4 presents the results of our simulation, and finally, Section 5 concludes the paper.
## 6 Simulation
#### Traffic Efficiency  
The traffic produced by an ON algorithm is an important metric, as it directly impacts power consumption. This traffic is produced by agents exchanging routing data and when forwarding messages.  
Thus, the total traffic in the network is:  
\[T_{tot}=E\times S_{R}+T\times S_{M} \tag{8}\]  
where the parameters are shown in Table 3, assuming \(1\) KB messages and \(1000\) buses.  
\(BSW\) does not attempt to learn from the network, and no routing information is exchanged. The only addition to the messages is the integer number of the number of copies.  
\(PRoPHET\) exchanges a predictability vector during each encounter. This vector contains a floating-point predictability value for each device in the network. \(r4 dtn\) does not exchange routing data; in its place, a Q-value used to update a Q-table is retrieved from the destination when forwarding. Additionally, the messages have a copies-count integer.  
The total traffic produced in the network is shown in Figure 8. \(BSW\) has a very high transmission cost, despite not producing routing data. This is related to the fact that every transmission opportunity is taken, and the maximum allowable number of replications is produced. Nevertheless, the delivery rate is poor because the replication is made very aggressively following the message generation, not achieving adequate dissemination through the network. It can be seen that \(PRoPHET\) has a high traffic consumption, the vast majority of it being routing information and not data transmissions. This signaling grows with the number of encounters (vector exchanges) and devices in the network (vector size). Furthermore, it must be noted that a considerable part of the routing information exchange might not be of interest to the actual data flows in the network. Finally, \(r4 dtn\) achieves better delivery performance with a lower data overhead.
# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
###### Abstract
However, the literature shows a lack of a comprehensive survey to categorize and characterize the papers. This research summarizes the current problems discussed in LMD vehicle routing. The results of this study can be used to identify the research gaps in LMD vehicle routing and develop future research agendas. In the literature, the same solution procedures of the conventional VRP models are used for the LMD optimization as well. This review paper does not explore details of the solution methods, except listing them for each paper in the summary tables. In-depth discussions of the solution procedures in VRP can be found in the literature including [27, 28, 29, 30]. The distinctive characteristics of vehicle routing in LMD in urban areas include more uncertainties and higher dynamicity compared with conventional VRP. The information update occurs more frequently, asking for more frequent real-time adjustments of the delivery plan. It also gives a larger number of visits per route, and the parking lot space and service time at the destination are sometimes considered. The time windows are tighter. The allowed route of a vehicle can be different for different vehicles, e.g., the size of a truck to make a right turn or its weight. Additionally, vehicle routing in LMD considers the involvement of individuals, for both the supply and demand sides. For the supply side, we can use occasional drives (outsourcing), which makes the operation more efficient at peak times. Considerations of occasional drivers add new issues of the supplier's time window, zoning, and matching of drivers and customers. For the demand side, customers are willing to provide optional delivery points and use regional parcel lockers. The common characteristics of conventional VRP and LMD include vehicle capacity and fleet options. Also, both the conventional VRP and vehicle routing in LMD problems can utilize modern technology for sidekicks. This review paper is constructed as follows. Section 2 explains the review methodology. Section 3 describes the framework used for the survey. The framework groups LMD study into four categories based on the focus of the problems: delivery with occasional drivers, delivery to parcel lockers, delivery by sidekicks, and delivery to optional points. Section 4 presents the research on LMD routing in these four subsections. Section 5 concludes the paper by summarizing the study's implications and presenting an agenda for future research.
###### Abstract  
In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.  
localization model schemes; mobile wireless sensor network; path model design; positioning 2021
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 4 Results
### Result of Binary Mask Refinement  
This section shows the results of binary mask refinement methods, which are morphological operations and dense CRF. Based on the overall accuracy of test images, as shown in Table 7, except for closing operation with cross kernel shape, all the selected methods cause slight increases in accuracy. Dense CRF can be seen to have the highest accuracy, which equals 0.9643.  
For qualitative evaluation, two test images are selected to display the results of the selected methods. This analysis observes how the selected method improves or worsens the detection of shadow regions with unclear boundaries. The results using Test Image 1 are shown in Figure 8. The second test image has a more complicated and unclear boundary, as shown in Figure 9. It can be observed that noises in the predicted mask without refinement are more visible compared to the results with refined methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{1}{c}{**Accuracy**} \\ \hline \multicolumn{2}{c}{Without refinement} & 0.9638 \\ \hline Morphological operation & Rectangle kernel shape & 0.9640 \\ opening & Cross kernel shape & 0.9639 \\ \hline Morphological operation & Rectangle kernel shape & 0.9640 \\ closing & Cross kernel shape & 0.9637 \\ \hline \multicolumn{2}{c}{Dense CRF} & 0.9643 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative analysis of binary mask refinement methods.  
Figure 8: Visual comparison of refinement methods on Test Image 1: (**a**) input image, (**b**) ground truth, (**c**) without refinement, (**d**) opening using rectangle kernel shape (**e**), opening using cross kernel shape, (**f**) closing using rectangle kernel shape, (**g**) closing using cross kernel shape, and (**h**) dense CRF. Dense CRF performs considerably well, as most of the white noise is removed.  
The second test image shown in Figure 9 has a more complicated and unclear boundary. This analysis observes how the selected method improves or worsens the detection of shadow regions with unclear boundaries. Comparing the result of opening and closing operations, the opening method manages to erase white noise, while the closing method erases black noise. Both methods maintain the boundary of the shadow, as predicted. Dense CRF seems to erase most of the noises and perform better than the other two methods, but it smoothens the boundary and fills up the wrong non-shadow area into a shadow area.
## 3 Methodology
### Random Permutation and Fuzzy Commitment
#### 3.3.1 Random Permutation  
Random permutation can shuffle the elements of binary code, which can obtain different bio-keys. On the one hand, once the generated bio-key is compromised, a new bio-key can be easily regenerated by only modifying the random permutation seed. On the other hand, it can reduce the cost-time of bio-key generation instead of retraining the DNN model. PV is firstly produced by \(RNG\) with random permutation seed for each user, let the produced \(\text{PV}=\left\{P^{1},P^{2},P^{3}\cdots P^{r},\cdots P^{l-1},P^{l}\right\}\), which satisfies \(\forall t=\left\{1\cdots l\right\},\ \exists t=P^{r}\). Given a binary code \(K\) with length \(l\) where \(K=\left\{K_{1},K_{2},K_{3}\cdots K_{r},\cdots K_{l-1},K_{l}\right\}\), a permuted binary code \(K_{R}=\left\{K_{p1},K_{p2},K_{p3}\cdots K_{pr},\cdots K_{p1-1},K_{pl}\right\}\) is obtained by randomly shuffling the elements of \(K\) according to PV. In addition, PV is stored in the database during the enrollment stage. It can be observed from random permutation that PV does not reveal any information of the binary code or bio-key. This is because that PV is only determined by random permutation seed and has no relationship with binary code. In the bio-key reconstruction stage, the permuted binary code is generated by only shuffling the elements of query binary code through the stored PV in the database. As shown in Figure 5, we give an example of random permutation when \(l=256\).  
Figure 5: Illustration of the random permutation where the length of binary code is 256.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Sample Preparation
#### 5.1.4 Derivatization
In addition, rapid acylation [111] in an aqueous medium allows some highly volatile polar metabolites, for example, alcohols or phenols [107] to be derivatized without fear of their premature evaporation (which, for example, can happen during aggressive drying before derivatization with silylating agents). Another important advantage of acylation is its amenability to a wide range of chromatographic and mass spectrometric systems due to the simple separation of the reaction products from the reagents [106]. The most popular acylating reagents are chloroformates with the simplest alkyls (methyl, ethyl, isobutyl), which are well suited for analyzing amines, phenols, and carboxylic acids. The classic version of the reaction proceeds in pyridine and requires the corresponding alkyl alcohol (methanol, ethanol, isobutanol). Identical radicals of alcohol and chloroformate are needed to eliminate the probability of the formation of different derivatives for the same acid. After adding an excess of chloroformate, the formed derivatives are extracted, for example, with chloroform [111; 112]. The main disadvantage of derivatization with alkylformates is the smaller coverage of metabolome: for example, the protocol is generally not suitable for carbonyls and amides due to their precipitation. Another critical disadvantage, especially compared with silylation, is the limited spectral databases of acylated molecules, making them challenging to identify [55; 106; 108]. Metabolomics methods employing derivatization are more complex than direct analysis without chemical modifications since the experimenter requires additional intervention. However, in most cases, the advantages of derivatization outweigh its disadvantages, and for this reason, it is still widely used in analytical practice [100]. Moreover, with the democratization of autosamplers that allow chemical derivatization immediately before analysis [12], metabolomic profiling becomes less laborious, and the results obtained become more reliable and reproducible.
## 4 Results and Discussion
Figure 7b shows that, for \(Ra^{*}=10^{11}\), thinner walls and higher \(k^{*}_{wf}\)s increased \(\varepsilon_{T\max}\) in the heating section, where the highest \(\varepsilon_{T\max}\) of 1.92 was obtained for \(t_{w}=0\), a theoretical condition. In this case, the optimized design parameters (\(k^{*}_{wf}\) and \(t_{w}\)) are not possible; however, using a thicker pipe with a high thermal conductivity is recommended. For \(k^{*}_{wf}=100\), \(t_{w}=0.1\), \(\varepsilon_{T\max}=1.83\), which represented a reduction of only 4.8% with respect to the highest theoretical \(\varepsilon_{T\max}\) (1.92). When water was used as the working fluid, \(\varepsilon_{T\max}=1\) in the loop (dotted red line in Figure 7). The working fluid was changed from pure water to a 10% PCM suspension (by volume fraction of PCM particles), and the simulations were again performed using the same parameter values. For \(Ra^{*}=10^{10}\), the pipe with \(k^{*}_{wf}\geq 1\) enhanced temperature-control effectiveness (indicated by \(\varepsilon_{T\max}\)) in the heating section: for \(k^{*}_{wf}=100\), \(t_{w}=0.4\), \(\varepsilon_{T\max}\) was enhanced by up to 21.5% over that of pure water. For \(Ra^{*}=10^{11}\) and a thin pipe (\(t_{w}\leq 0.2\)), \(k^{*}_{wf}\geq 1\) enhanced temperature-control effectiveness in the heating section, whereas for a thick pipe (\(t_{w}>0.2\)), \(k^{*}_{wf}\geq 10\), i.e., high thermal conductivity, is recommended.
# Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things
## 2 Literature Review
Their framework utilized named data networking to provide IoTC-based data, allowing numerous IoT devices to obtain data from the closest provider through a single data delivery mechanism. In addition, the framework provided mobility support for IoT devices to ensure adequate data reception. They employed a neuro-fuzzy rule-based clustering technique for cluster-based routing. In their method, the cluster creation in WSNs used energy modeling to effectively route packets using machine learning with a convolutional neural network and fuzzy rules for weight modification, extending the network lifespan. As a result, their routing algorithm exhibited better efficiency in terms of usage, packet delivery ratio, latency, and network lifespan. The technology of IoT networks with SDN architecture is being explored for the formation of smart cities. It senses the data from the targeted area and sends it to remote sites for processing and analysis. Many solutions have been recommended by researchers based on the aforementioned discussed work for coping with resource constraints with computing capabilities; however, the management of network data over the presence of huge traffic flow is one of the main research problems. Moreover, providing prompt responses on remote sites with affordable delay is also considered a significant research problem. It was also noticed that optimizing the routing process coped with the support of intelligent strategies; however, it exposed additional costs to the IoT devices as the network grew. This research proposed a reliable model for IoT technologies with the support of dynamic programming, leading to optimized efficiency. The states of the SDS-GIoT model are evaluated each time before announcing the optimal decision for data sending. Furthermore, edge computing integrates with blockchain technology to ensure data privacy and authentication. Edge computing provides an offloading method for decreasing the network burden in terms of applying security on the constraint devices.