esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Service Discovery Mechanism
### Service Discovery  
When a node wants to use a service, it first looks for that service in its local service table. If it is not there, it looks for the service in its remote service table in order to know if some other node in the network offers the service. If there is not an entry for this type of service in its remote service table, the service will not be available at this moment. Otherwise, if there is an entry for that service, we check if the node offering the service remains available, as explained below.  
To guarantee that the node that offers the service remains available in the network and to decrease the false service discovery ratio, we check the information the node stores in the _Topology Information Base_. This information is updated with the information received in TC messages in the _Advertising Remote Router Set_, _Router Topology Set_ and _Routable Address Topology Set_ fields. Moreover, each node keeps a _Routing Set_ updated with the information stored in _Topology Information Base_, _Local Information Base_ and _Neighbor Information Base_.  
The first set, _Advertising Remote Router Set_, stores information about what remote nodes transmit TC messages. Each entry has the following format: AR_orig_addr, AR_seq_number, AR_time. When the time defined in the AR_time parameter expires, the entry is deleted. In the _Router Topology Set_, information about link topology between MANET nodes is stored. Each entry has the following data: TR_from_orig_addr, TR_to_orig_addr, TR_seq_number, TR_metric, TR_time. TR_from_orig_addr is the node's address that can reach the node TR_to_orig_addr in one hop. In this case, the entry is deleted when the TR_time has finished. In the _Routable Address Topology Set_, topology information about routes in the network is stored; it contains data about through which nodes a destination is reachable: TA_from_orig_addr, TA_dest_addr, TA_seq_number, TA_metric, TA_time. TA_from_orig_addr is the node's source address that can reach the node TA_dest_addr in one hop. Furthermore, this entry is deleted when the TA_time has finished. Finally, the _Routing Set_ stores information about the first  
Figure 10: Remote service table of each node.  
hop of a path to each destination node reachable by this node: R_dest_addr, R_next_iface_addr, R_local_iface_addr, R_dist, R_metric. R_dest_addr is the destination address, and R_next_iface_addr is the network address of the \"next hop\" in the destination's path. R_local_iface_addr is the source address used to send the packet to the destination. R_dist is the number of hops to the destination. This information is updated (entries are added or deleted) with information obtained in _Link Sets_, _Neighbor Sets_ and _Routable address Topology Set_.  
At the same time, if an entry is deleted in these sets, this means that this node, or the route to it, is no longer available. In this case, we also delete the services offered by this node from its remote service table.
# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 2 Proposed Methodology
### Inception-V3 Model  
An upgraded variety of the Inception-v1 [31] model is called the Inception-v3 model [31]. For greater model adaptability, the network is optimized in several ways by the Inception-v3 model. Its network is larger than that of the Inception-v1 and v2 models. It is a low-configuration computer that was used to train a deep CNN model. Training can be difficult and time-consuming; it may even take several days at times. This issue is resolved by transfer learning, which preserves the model's last layer for application to new categories. The 48 layers of the Inception-V3 model comprise newly added layers based on our dataset classes, and all the model's upper layers are frozen, as shown in Figure 4.  
Figure 3: Proposed diagram of fully connected layers.  
Figure 2: Data augmentation techniques, such as flipping, rotation, zooming, and cropping, employed to augment the training dataset and improve the model performance.  
Moreover, complex DL problems benefit from the use of a model such as Inception-V3. Other Deep learning models employ a basic stack of convolutional layers, pooling layers, and fully connected layers, like Vgg16, Vgg19, and AlexNet, among others. The Inception-V3 models apply \(1\times 1\) convolutions, also referred to as point-wise convolutions, and then apply convolutional layers with varying kernel sizes simultaneously. This results in an increased number of hidden layers. This means that Inception-V3 models perform better for complex tasks or extracting features. For more complicated issues, Inception is utilized because it enables it to learn more complex features.
# Towards High Usability in Gamified Systems: A Systematic Review of Key Concepts and Approaches
## 2 Methods  
A systematic review is performed following the recommendations outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) [12] methodology.  
This review aims to examine the state of research in usability and related issues in the context of gamified systems. Three research questions were raised to further specify the review goals:  
1. RQ1. What is the nature of specific usability recommendations for gamified systems?
2. RQ2. Do gamified systems usability recommendations depend on the gamification target audience?
3. RQ3. Do gamified systems usability recommendations depend on the system application domain?  
The above questions influenced the key literature search criteria. Additionally, publication date, type, and other similar attributes were also defined. The full list of constraints applied to basic properties, as well as the titles and abstracts of papers, is given below.  
Paper inclusion criteria:  
1. Publishing date 2008 to the first quarter of 2022;
2. Document type is article and/or conference paper;
3. Usability, user experience, human-centred design, user-centred design guidelines for gamified systems are given;
4. Discussion is related to software gamified systems (i.e., not just gamification per se). Paper exclusion criteria:  
1. Publishing language is not English;
2. Paper is not in the field of computer science;
3. Full text unavailable.  
The literature search was carried out between February 2022 and March 2022 in five databases: Web of Science, Scopus, IEEE Xplore, ScienceDirect, and ACM Digital Library. The relevant papers were identified by searching in papers' metadata (title, abstract, keywords), using keyword search queries. The keywords identified from the research questions were as follows: gamification; usability; user experience; human-centred design; user-centred design; guidelines; and their synonyms. The search queries and number of search results for each database can be found in Table 1.  
The following data about the identified papers were imported into the reference management tool: publication type; year of publication; title, abstract; author(s); publication language. Based on the eligibility criteria, the titles and abstracts were manually screened to determine which of the papers were relevant, irrelevant, or indeterminate and thus requiring full-text screening. The manual screening of both the abstracts and full-text was performed by two of the authors in a pair and, in case of disagreement, the paper under consideration was discussed to reach a consensus.
# Targeted Protein Profiling of In Vivo NIPP-Treated Tissues Using DigiWest Technology
## Materials and Methods
### Multiplex Protein Profiling via DigiWest  
DigiWest was performed as described previously [18]. Briefly, the NuPAGE system (Life Technologies, Carlsbad, CA, USA) with a 4-12% Bis-Tris gel was used for gel electrophoresis and Western blotting onto PVDF membranes. After washing with PBST, proteins were biotinylated by adding 50 \(\upmu\)M of NHS-PEG12-Biotin in PBST for 1 h to the membrane. After washing in PBST, membranes were dried overnight. Each Western blot lane was cut into 96 strips of 0.5 mm each. Strips of one Western blot lane were sorted into a 96-well plate (Greiner Bio-One, Frickenhausen, Germany) according to their molecular weight. Protein elution was performed using 10 \(\upmu\)L of elution buffer (8 M urea, 1% Triton-X100 in 100 mM of Tris-HCl with a pH of 9.5). Neutravidin-coated MagPlex beads (Luminex, Austin, TX, USA) of a distinct color ID were added to the proteins of a distinct molecular weight fraction, and coupling was performed overnight. Leftover binding sites were blocked by adding 500 \(\upmu\)M of deactivated NHS-PEG12-Biotin for 1 h. To reconstruct the original Western blot lane, the beads were pooled, at which point the color IDs represent the molecular weight fraction of the proteins.  
For antibody incubation, 5 \(\upmu\)L aliquots of the DigiWest bead mixes were added to 50 \(\upmu\)L of an assay buffer (blocking reagent for ELISA (Roche, Rotkreuz, Switzerland) supplemented with 0.2% milk powder, 0.05% Tween-20, and 0.02% sodium azide) or PVXC buffer (0.1% casein, 0.5% PVA, 0.8% PVP, and 0.05% Tween-20 in PBS) in a 96-well plate. The buffer was discarded and 30 \(\upmu\)L of primary antibody diluted in assay buffer or PVXC buffer was added per well. Primary antibodies were incubated overnight at 15 \({}^{\circ}\)C on a shaker. Subsequently, primary antibodies were discarded and beads were washed twice with PBST. After washing, 30 \(\upmu\)L of species-specific secondary antibody diluted in an assay buffer or PVXC buffer labeled with phycoerithrin was added and incubation took place for 1 h at 23 \({}^{\circ}\)C. Before the readout on a Luminex FlexMAP 3D, beads were washed twice with PBST. Protein bands were depicted as peaks by plotting the molecular weight to the corresponding median signal intensity. An Excel macro-based algorithm was used to identify peaks at the provided molecular weight of each antibody. After subtracting the local background integrals of the area of a peak was calculated. The resulting signals were normalized to the total amount of protein that was loaded onto the beads (Figure S1b).
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results  
This section introduces the evaluation setup, including the database description and shows the main results derived from the evaluation of the proposed method and the effects of the D2D distance, motion direction, amount of D2D measurements, among others.
# Pulsed Thermography Applied to the Study of Cultural Heritage
## 3 Results
### Recovery of Hidden or Lost Texts in Ancient Books/Documents  
In the last years, several techniques have been used for the recovery of lost and/or hidden writings and, in particular, for the investigation of erased and overwritten texts in palimpsest [64]. For instance, ultraviolet fluorescence (UVF) [65], hyperspectral imaging (HSI) [66], and multispectral imaging (MSI) [67; 68; 69] have been applied, respectively, to the recovery of the readability of damaged or censored texts, to the study of burnt papyri and parchments, and to the recovery of the contrast of erased iron gall inks on parchment [65]. Among the imaging techniques, IRR has been successfully used to examine faded texts written with carbon-based inks, improving the contrast between the ink and the support [70]. However, none of the mentioned techniques have proven to be successful in investigating texts buried inside the structure of ancient books. In fact, such written elements are often not accessible and, for this reason, a technique that could provide the possibility to detect and characterize them without dismantling the bookbinding is extremely useful. Such a topic has only been faced in recent times, using IRT and spectroscopic techniques, like a macro-Xray fluorescence (XRF) scanner [71; 72].  
Here, the applications of IRT to the analysis of texts buried in historical bookbindings, generally belonging to scraps of older books reused in the assembly of a new book, are shown. Moreover, this article also reports on the IRT investigation of ancient manuscripts and documents affected by different forms of deterioration and physical degradation phenomena which cause the loss of readability of the textual elements laying on the leaves surface.  
Figure 6: [PERSON], left rear paw. Thermograms (**A**,**B**) obtained by the front face heating mode where the arrows indicate some pseudo-spherical cavities; (**C**) time dependence of the contrast coefficients obtained over the cavities of Figure 6B. The vertical dashed lines intercept the \(t_{\rm m/2}\) values (see text) for the various curves [48; 49].
###### Abstract
In the operation of UAMs, additional depot locations are not required, and all vehicles can travel to all locations, meaning that existing routing problems are not suitable for application to UAM operation management. Therefore, we proposed a new routing problem formulation to be utilized in UAM vertiport-to-vertiport locations by introducing new constraints. However, scheduling is required for safe operation with an increasing number of vehicles and depot locations. Most airline scheduling and assignment problems already specify a time window for departure from and arrival at the airport [7,8], which is known as static scheduling [9]. In UAM operation, landings and departures are assigned to different runways and are sequenced in advance based on their start times, which is known as dynamic arrival and departure scheduling (dynamic AADS) [9]. Unlike other airline scheduling problems [7,8,10], instead of static scheduling, we introduced new constraints for dynamic scheduling, which is controlled by vehicles' travel speeds and waiting time constants, enabling generation of dynamic arrival and departure scheduling so that UAMs can operate with no collisions with an increasing numbers of vehicles.
## 3 Results and Discussion
### Implications for Engineering Applications  
According to the above research results, an increase in holding time in the discontinuous cyclic load increased the degree of damage inside the coal body. For small coal pillars of the gob-side entry, the expansion of internal fissures reduced the supporting strength of the coal pillars and reduced their bearing performance significantly under long-term ground stress. The increase in mesopores and macropores (cracks) inside the coal pillar increased the coal permeability and reduced the tightness of the coal pillar. The probability of gas and water in the goat that flowed into the working face increased significantly. Therefore, it is necessary to grout the coal pillars to improve the bearing capacity of the coal pillars and reduce the coal permeability.  
The most widely used cement slurry (superfine cement) has a minimum pourable width of only \(\sim\)0.1 mm [33]. The microcracks in the coal were relatively developed, and the average pore diameter and dominant pore diameter of the matrix system were at the nanometer level. The cement slurry had little effect on the sealing of a micro-matrix system, and could only close the macrocrack. Silica sol is a nanoscale grouting material with a particle size of 8-12 nm, a D\({}_{95}\) of 62 nm [34], an ultra-low viscosity (the fluidity is equivalent to water), an ultra-strong permeability (permeable to 0.02 \(\upmu\)m gaps) and is the best permeability grouting material. However, the silica sol strength is low, and the UCS after curing for 500 days is only 320 kPa (A:B = 4:1) [35].  
Therefore, a multi-component and multi-granularity coal pillar grouting reinforcement method based on \"silica sol-cement slurry\" was proposed. The construction process is as follows: (1) Select a suitable position to drill several vertical holes in the coal pillar before mining. (2) Use the grouting pipeline to inject PO 425 cement slurry to cement the broken coal and fill the macrocracks. Improve the strength of the broken coal. (3) After the cement slurry has solidified, use drilling equipment to sweep the hole at the original drilling position and re-insert the grouting pipeline. (4) Inject silica sol at a low pressure and keep it at low pressure for an extended time. The silica sol diffuses fully under the action of a capillary adsorption force to fill the fine cracks in the coal body, which can improve the coal pillar tightness.  
Figure 10: Failure form of coal samples: (**a**) 0 min; (**b**) 5 min; (**c**) 10 min; (**d**) 15 min.  
The method uses cement as the main binding material, improves the overall strength of the crushed coal body, and can improve the bearing performance of the coal pillar. Silica sol was used to seal the microcracks in coal and cement, which reduces the coal permeability, and isolates water and gas infiltration in the goat. The materials used were non-toxic and non-heating, and have good application prospects.
## 3 Results
### Effect of Focal Point Position  
Negative defocusing was used to study whether decrease in beam density at the workpiece surface has a favorable effect on the formation of the weld bead. The influence of focal point position on the weld profile was investigated by changing the defocusing distance in steps of 2 mm. Beam offset from the flange was selected as 1 mm and 1.5 mm (for the 300 \(\upmu\)m and 600 \(\upmu\)m setups, respectively) to increase the likelihood of full penetration. The focal point was moved along the beam propagation direction inside the material in 2 mm steps. Figure 5 shows cross-sectional macrographs of the welds produced.  
It can be seen from the images presented in Figure 5 that decreasing the focal point position leads to a slight decrease in penetration. F\({}_{\text{PP}}-4\) mm resulted in full penetration in the set-up with the 600 \(\upmu\)m process fiber, while none of the welds produced with the 300 \(\upmu\)m fiber had complete penetration at the weld root.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 3 Problem Description
Therefore, the first objective is to transform raw data into reliable and safe information of the system so that they can be used in fault diagnosis, prognosis, and for the control and optimal management of LNG regasification satellite plants. The second objective is to develop a new methodology for the prognosis of remaining useful life to refill of LNG the tank of the plant and to compare it with one of the popular time series forecasting methods. The new methodology is based on fuzzy inductive reasoning using historical data for model training.
## 2 Near-Field Multi-Focusing Spot Generation  
One approach to generate a near-field multi-focusing area is based on increasing the number of feeds, so that each feed is associated with one focused spot. If the elements are considered ideal phase shifts, the amplitude of the transmitted field of a transmitarray only depends on the amplitude of the incident field, whereas the phase on two factors: (1) the phase shift of the cell and (2) the phase of the incident field [19]. Therefore, if the tapers of the different incident fields are nearly similar and the cells are designed to ensure a good angular stability, the distance and the direction of the focused spot are given by the phase response of the cell and the phase of the incident field, respectively. If a transmitarray is designed to focus using a central feed configuration, whenever the additional feeds radiate a similar incident field over the antenna surface, different spots will be created with a direction specified by each incident phase. Hence, it must be guaranteed that the distance between the phase center of each feed and the transmitarray is preserved independently of the feed position. A location that satisfies this condition independently of the number of feeds is a parabolic distribution. However, in this case, it is simplified to an arc defined by the midpoint of each transmitarray edge and the central feed, as is shown in Figure 1. The error considering this arc approach is low since the parabola is close to a circular-arc path within the proposed angles. Note that the feeds are always pointing to the geometric center of the transmitarray to ensure a similar amplitude on the incident field.  
The distance between adjacent feeds is defined by the angle \(\alpha\) and \(\theta\) (see Figure 1) for the cut \(xoz\) and \(yoz\), respectively. Setting the center of the coordinates at the geometric center of the transmitarray, the central feed is at \((\hat{x},\hat{y},\hat{z})=(0,0,-r)\) mm, and the other feeds are located at:  
\[\begin{split}(\hat{x},\hat{y},\hat{z})&=(-r\sin\theta,0,-r\cos \theta)\text{ for the }xoz\text{ cut}\\ (\hat{x},\hat{y},\hat{z})&=(0,-r\sin\alpha,-r\cos\alpha)\text{ for the }yoz\text{ cut}\end{split} \tag{1}\]  
where \(r\) is the radius of the arc where the feeds are located, defined by the \(\hat{z}\) coordinate of the central feed, and \(\theta\) and \(\alpha\) are the angles described previously.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 6 Smooth Pulses
### Chirped Smooth Rectangular Pulse  
The case of the chirped rectangular pulse can be generalized to the chirped smooth rectangular one. That is, if the initial pulse's profile is  
\[A(z=0,t)=\frac{A_{0}}{2}\exp\Bigl{(}-iq^{2}t\Bigr{)}\left[erfc\left(\frac{t- \theta/2}{\Delta}\right)-erfc\left(\frac{t+\theta/2}{\Delta}\right)\right] \tag{72}\]  
then, after a certain distance in the dispersive medium, the pulse's profile will be  
\[A(z>0,t)=(1+2\beta_{2}zq)^{-1/2}\exp\Bigl{(}-i\frac{t^{2}}{2\beta_{2}z+1/q} \Bigr{)}\frac{A_{0}}{2}\left[erfc\left(\frac{\frac{t-\theta/2}{\Delta}-\theta /2}{\sqrt{\frac{-2\beta_{2}z+1}{1+2\beta_{2}z+1}+\Delta^{2}}}\right)-erfc \left(\frac{\frac{t}{1+2\beta_{2}z+1}+\theta/2}{\sqrt{\frac{-2\beta_{2}z+1}{1 +2\beta_{2}z+1}+\Delta^{2}}}\right)\right] \tag{73}\]  
A spatiotemporal presentation of the pulse's intensity is presented in Figure 19. The fact that the initial pulse is smooth eliminates the ripples that appear in Figure 8.  
Figure 19: Same as Figure 8 but for a smooth rectangular pulse with \(\Delta=0.25\theta\).  
Figure 18: A comparison between smooth rectangular pulses (Equation (63), solid curves) and super-Gaussian pulses (Equation (69), dashed curves) for \(n=4\) (right) and \(n=14\) (left). In these plots, only the real part of the fields is presented. The imaginary part is zero.
## 2 Optimal Design Consideration and Analytical Modeling
### COM Modeling of the SAW Sensor Platform  
A two-port SAW delay line with a center frequency of 222 MHz was designed on a 128\({}^{\circ}\) YX LiNbO\({}_{3}\) piezoelectric substrate using coupling of mode (COM) modeling. The SAW-based sensor platform involves several parameters, including substrate, center frequency, IDT pair, aperture length, metal selection, metal thickness, cavity length, and so on. By modulating these parameters, the optimal design specifications are derived. Figure 2 shows the S\({}_{21}\) scattering parameter as a function of frequency, obtained using the optimized parameters listed in Table 2. The S\({}_{21}\) characteristics show low insertion loss at the center frequency of 222 MHz, high side lobes, and minimal parasitic conduction at high frequencies. The sensing material is applied to the cavity region between the two IDTs. No significant change in the central frequency was observed due to the minimal mass loading of the monolayer sensing material. Based on this, the sensor was fabricated, and the experimental results closely matched the analytical modeling predictions. The detailed process of COM modeling has been thoroughly explained in our previous research [27; 28; 29]; therefore, this study omits that explanation.