esa-sceva/satcom-chunk-collection huggingface dataset:
# Airline Network Planning Considering Climate Impact: Assessing New Operational Improvements
## 2 Materials and Methods
### Flight Performance and Climate Impact Assessment
#### 2.2.2 Flight Performance Assessment and Emissions Calculation  
DLR's trajectory calculation module is used for every mission to calculate the required trajectories. Based on BADA4 aircraft performance data, a total energy model approach is applied to calculate the aircraft's state in every simulation step [16; 31; 32]. This course evaluates the central performance characteristics such as speed, thrust, and fuel flow along the mission.  
This is the basis for the following evaluation of emission quantities. While CO\({}_{2}\) and H\({}_{2}\)O emissions quantities are directly derived from their proportionate correlation to fuel flow, nitrogen oxides (NO\({}_{x}\)) are calculated following the DLR fuel flow correlation method [33]. Hydrocarbon (HC) and carbon monoxide (CO) emissions are assessed with the Boeing fuel flow correlation method [34]. The SO\({}_{2}\) emission index varies regionally according to the local fuel sulphur content [35] and soot emissions are derived with a method by [PERSON] [36]. ICAO engine emission data bank's information is applied in the aforementioned methods [37].  
In the modelling process, the following assumptions are taken:  
* An average European flight LF of 0.84 for the selected reference year of 2018 is assumed for every mission [38].
* We exclude both time- and location-specific weather conditions and wind effects as we follow a climatological approach to assess the full reference year's effects.
* On-ground times and emissions are assumed to follow ICAO's landing take-off cycle [37].
* We restrict considered cruise altitudes to either fuel-optimal flight altitudes, including step climbs, or a constant reduced cruise flight level of 31,000 ft. Flight speeds follow the given schedule as per BADA4 data.  
This assessment step leads to the fuel consumption, flight time, and emission quantities for CO\({}_{2}\) as well as non-CO\({}_{2}\) emission species used as input in the _integrated network planning model_.
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 3 Proposed Approach
### Feature Extraction  
#### 3.1.1 Shallow Feature Extraction  
To effectively combine complementary local features, we use four different types of handcrafted feature descriptors to extract image information from different aspects. SIFT [23] extracts the structural information from image patches, GIST [49] obtains the scales and orientation information from different parts of images as an envelope of the image, LBP [24] enables the texture information extraction, and HOG [25] counts the occurrence of gradient orientation in the localized portions of an image resulting in the high feasibility for object detection problems.  
* SIFT: We decompose our SIFT algorithms into four stages. First, using the feature point detection step based on the property of scale invariance, we can find features under various image sizes. After detecting some key point features found in the scale space, which are poorly localized, a subpixel localization refines the positions of these feature points to pinpoint subpixels while removing any poor features. Next, the gradient orientations of sample points within a region form an orientation histogram.
* A set of 16 histograms--a \(4\times 4\) spatial grid with 8 orientation bins resulting in a feature vector containing 128 elements--is used.
* GIST: We convolve the image with 32 Gabor filters in 8 orientations and 4 scales. With a \(4\times 4\) grid, we divide each feature map into 16 regions and then average the values of the features of each region. Finally, we concatenate the 16 values with 32 feature maps producing a 512 GIST descriptor.
* LBP: By comparing each pixel with its surrounding pixels, LBP allows us to compute the local representation of texture in the image. First, LBP converts the image into grayscale. For each pixel of the grayscale image, a neighborhood size surrounding the center pixel is selected. If the value of neighbor is less than the value of the center pixel, written as \"1\"; otherwise, \"0\" is written. The conversion from binary number in to decimal number in the range of 0-255 is then applied. Finally, we divide the image into multiple \(8\times 8\) grids to generate each histogram associated with each region and a feature vector of 256 positions. The final histogram contains 1 6384 positions.
* HOG: HOG decomposes an image into small squares in a dense manner, computes the histogram of oriented gradients, normalizes the obtained results by a block-wise pattern, concatenates the \(3\times 3\) grid cells, and returns the HOG descriptor at each grid location.
## 3 Results and Discussion
### Experimental Study of the Ag Adsorption by Atomic Absorption Spectrometry (AAS)
#### 3.2.2 Adsorption Efficiency of Ag Ions and AgNPs on the Zeolite  
Figure 4a shows the AgNP adsorption by clinoptiolite. The highest rates of adsorption are observed in the sample with a concentration of 2 mg\(\cdot\)L\({}^{-1}\). The value of maximum adsorption is 90% and is conducted over a time of 150 min. For subsequent values of 150 min and 4 mg\(\cdot\)L\({}^{-1}\) no relevant changes are presented in the adsorption rates. This result suggests that zeolite saturation depends directly on the effective diameter of the nanoparticles and, consequently, the volume of AgNPs used for adsorption.  
Figure 4: Graphs of the experimental data corresponding to the adsorption efficiency of (**a**) AgNPs and (**b**) AgNPs and Ag ions onto the zeolite.  
Figure 3: AAS calibration curves associated with the Ag ions and Ag nanoparticles. Experimental data and linear fit.  
Some theoretical models are presented below to investigate the mechanism of AgNP sorption onto clinoptiolite. Moreover, the calibration curves obtained for Ag ions and AgNPs show a very similar behavior in both cases (Figure 4b). This can be attributed to the fact that silver nanoparticles with pH values over 6 tend to ionize and, consequently, their adsorption behavior is very similar to that exhibited by the Ag ions [16]. In order to compare the adsorption efficiency of the AgNPs and Ag ions, Figure 4b shows a graph of the adsorption efficiency of these two species. In this case, it can be seen that, for all adsorption times (30-150 min), the Ag ion solutions have a higher adsorption efficiency on the zeolite compared with AgNPs.
# A Novel Approach for a Inverse Kinematics Solution of a Redundant Manipulator
## 3 Inverse Kinematic Model and Computing Algorithm  
The inverse kinematic model serves to find such vector of generalized variables \(\text{q}\in\mathbb{R}^{\text{n}}\), (n--number of DOF) in the joint space, by which the end-effector of the manipulator reaches the required position in the task space \(\text{x}\in\mathbb{R}^{\text{m}}\). The vector of generalized variables is defined as \(\text{q}=[\text{q}_{1},\,\text{q}_{2},\dots,\text{q}_{\text{n}}]^{\text{T}}\). The solution of the inverse kinematic model is significantly more difficult to obtain than that of the direct kinematic model. In many cases there are no analytical solutions. This especially holds in cases of kinematically-redundant manipulators [28]. In such cases, a numerical  
Figure 5: Planned path using potential field method.  
solution of inverse kinematics has to be applied. The solution arises from the following Equation (6), which represents the relation between joint space and task space:  
\[\dot{\mathrm{x}}\ =\mathrm{J}\ \dot{\mathrm{q}} \tag{6}\]  
where \(\dot{\mathrm{x}}\) is vector of end-effector velocity, \(\mathrm{q}\) is vector of generalized variables-joint velocities, and \(\mathrm{J}\in\mathrm{R}^{\mathrm{m}\times\mathrm{n}}\) is the Jacobian matrix. The indices \(\mathrm{m}\) and \(\mathrm{n}\) represent the dimension of task space and the dimension of joint space, respectively. The inverse kinematics are usually based on the numerical solution of:  
\[\dot{\mathrm{q}}=\mathrm{J}^{-1}\dot{\mathrm{x}} \tag{7}\]  
Equation (7) can be solved when the Jacobian matrix is symmetric. For non-symmetric Jacobian matrices, any method developed for the purpose of Equation (7) solving has to be applied, such as the pseudo-inverse of the Jacobian matrix, or its transposition. In this study, the damped least squares method is used. The Equation (7) includes primary solution-finding, such as \(\mathrm{q}\), by which the end-effector reaches the required position in the task space. Subsequently, the advantage of kinematically-redundant manipulators can be applied. This advantage relates to the use of the redundant degrees of freedom for optimization tasks. From the viewpoint of mathematics, kinematically-redundant manipulators can reach the desired position of end-effector in an infinite amount of ways. In other words, the required position \(\mathrm{x}\) can be reached by an infinite amount of generalized variable configurations.  
The optimization tasks solved in this study are a joint limit avoidance task, a kinematic singularity avoidance task, and an obstacle avoidance task. These tasks are so-called secondary tasks which can be completed while the primary task is also performed. There are several methods to solve these optimization tasks. This study uses a method which is part of the class of Jacobian-based methods class. This method considers weight matrices in order to prioritize particular tasks. Very often, some constraints cannot be satisfied simultaneously, although they can be satisfied separately [29]; accordingly, some compromise has to be made.
## 6 Software Architecture of the Multi-Sensor Platform
### Application Phase
#### 6.2.1 Sensor Task  
The sensor interface tasks are interrupt-triggered tasks, whereby the external sensor data, after configuration, place their data array on the memory, which triggers the task to gather the data and conditions and sets it up for transmission. The onboard sensor task is time-triggered with periodicity to gather the platform diagnostic data and packs it for transmission.
## 3 Results and Discussion
### Macroscale Optimization and Characterization
#### 3.1.3 Optical Analysis of CRANAD-2 Binding with Amyloid-Beta  
The next phase of experiments focused on CRANAD-2's interaction with A\(\beta\). CRANAD-2 fluorescence was measured immediately after mixing with oligomeric and fibrillar forms of A\(\beta\) at concentrations ranging from 100 nM to 1000 nM. This range is particularly relevant for measuring absolute baseline levels of A\(\beta\) observed in adult control groups, where concentrations fall within or above this threshold [34; 35]. The oligomers, presented in Figure 4a, exhibit an inverse relationship with fluorescence intensity, where an increase in concentration results in a decrease in fluorescence intensity. This comes in contrast to the behavior observed for A\(\beta\) fibrils, where the binding to A\(\beta\) fibrils resulted in increasing fluorescence intensity, as depicted in Figure 4b. A\(\beta\) fibrils showcased significantly more luminescence intensity, which confirms the manufacture indication that CRANAD-2 has a higher affinity for fibrillated A\(\beta\) structures than for oligomeric conformation. The binding of CRANAD-2 to A\(\beta\) was further validated by the red shift of the main peak (710-740 nm). This shift is directly illustrated in the Supplementary Materials (Figure S3).
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
###### Abstract  
High capacity and ultra-reliable vehicular communication are going to be important aspects of beyond 5G communication networks. However, the vehicular communication problem becomes complex at a large scale when vehicles are roaming on the road, while simultaneously communicating with each other. Moreover, at higher frequencies (like 28 GHz), the dynamics of vehicular communication completely shift towards unpredictability and low-reliability. These factors may result in high packet error and a large amount of interference, resulting in regular disruptions in communications. A thorough understanding of performance variations is the key to moving towards the next generation of vehicular networks. With this intent, this article aims to provide a comprehensive interference analysis, wherein the closed-form expressions of packet error probability (PEP) and ergodic capacity are derived. Using the expression of the PEP, diversity analysis is provided which unveils the impact of channel nonlinearities on the performance of interference-constrained vehicular networks. The insights provided here are expected to pave the way for reliable and high capacity vehicular communication networks.  
Keywords:beyond 5G communications; high capacity; ultra-reliable; vehicle-to-vehicle (V2V) communications +
Footnote †: journal: Electronics
## 6 Investigating Several RL-Based Routing Methods
### [PERSON] et al. in [61] have introduced the Q-learning-based load balancing routing (Q-LBR) scheme for VANETs. This method uses unmanned aerial vehicles (UAVs) in the routing operation. In this method, it is assumed that the UAVs can monitor the congestion level on the ground network. It has also designed a Q-learning-based congestion control system  
Figure 15: Learning structure in QTAR.  
to balance the load on the network. In this method, UAVs estimate the network load based on the queue status of each vehicle in the ground network. This technique has a low routing overhead. Q-LBR consists of four main steps: route discovery and maintenance, network load estimation, Q-learning-based load balancing, and routing decision. In the route discovery and maintenance phase, Q-LBR, like the source-based multi-path routing protocol, disseminates RREQs to discover paths. Finally, the destination node responds to this request by sending back the route reply (RREP) message for optimal and suboptimal paths. In this process, UAVs also receive RREQ messages from ground vehicles. Therefore, the created paths may also include UAVs as relay nodes. The network load estimation includes two steps: identifying the ground network congestion level and identifying the UAV congestion level. Each vehicle estimates the ground network congestion identifier (GNCI) parameter based on its queue load and sends it to the corresponding UAV through a hello message. As a result, the UAV can estimate the average congestion level of the ground network. Next, each UAV calculates the UAV relay congestion identification (URCI) parameter based on its buffer queue. GNCI and URCI are considered as the state space in the Q-learning-based load balancing methodology, and UAV acts as an learning agent. According to the current state, UAV must select its action from the action space, which includes the upper UAV routing policy area (\(URPA_{upper}\)) and the lower UAV routing policy area (\(URPA_{lower}\)). The reward value is obtained from GNCI and URCI. This learning structure is displayed in Figure 16. Finally, the URPA parameter is sent to the ground nodes. In the routing decision phase, when the source vehicle obtains several RREP messages, it must choose the best route for the data transmission process from those created paths. These routes include ground routes and the routes having UAV nodes. If a route includes UAVs as relay nodes and has less routing cost than the ground routes, and the congestion level of the UAV nodes is less than a congestion threshold, the source node selects this path for sending data packets. Otherwise, the ground route will be selected to send data packets.
## 2 Materials and Methods
### Online Expert Survey  
Decision-making should involve multiple expert opinions. In this study, an expert refers to a person with extensive knowledge of flood vulnerability analysis, acquired through field experience or education [47].  
Therefore, an online survey was structured to include the perceptions and priorities of a group of experts concerning flooding in the vulnerability assessment. Experts usually have different attitudes, interests, and knowledge about the problem, which can be reflected in a certain inconsistency in the decision-making process. Therefore, the participation of several experts in the weighting process is considered essential. The online survey was carried out with 25 people (56% from the academic world and 44% from the non-academic world) selected from among flood experts from governmental organisations (8% national laboratories, 12% civil protection authorities, and 8% municipalities), universities (37% professors and 19% researchers from the fields of natural hazards, geographic information, hydrology and hydraulics, and social sciences), NGOs (4%) and private companies (12%), distributed all over Portugal. The composition of this panel allows for different points of view, rather than deriving a single solution based on a single opinion. The number of members of the panel can be considered sufficient, as the experts have to be part of a comprehensive questionnaire. Just as important as the number of members of the group is the variability of the sectors from which they come and their geographical distribution. The survey was conducted online, and the respondents do not know each other. Their participation is limited to the first round of responses. There are three reasons why we did not encourage a meeting between the participants in our methodology: (i) the gathering of group members takes a lot of time; (ii) if the experts are together in a group, this can give some bias to the results. Group dynamics can have some pitfalls, such as members often follow the opinion of the majority or of a more persuasive group member, or they may be concerned with their reputation within the group; and (iii) the method relies on a mathematical validation that, for each criterion, gives higher weights to the most consistent answers. Therefore, it is not possible to follow a group consensus approach and the weighting of each expert in the final priority vector is completed without the intervention of the experts.  
Throughout the questionnaire, the AHP pairwise comparisons were made by asking the experts: \"Which of the two criteria contributes more to the vulnerability? The questionnaire contains fourteen comparison questions (nine questions at the fourth level, four questions at the third level and one question at the second level). The response options were the nine-point numerical scale of [PERSON] [15] (Table 2).  
As an example, the second level questions are shown in Figure 4.  
Figure 4: Example of a Google forms second level question (translated from the original questionnaire in Portuguese).
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 3 Results  
### Improved Change Detection Approach  
The empirical improved change detection approach has a double objective, taking into account the effect of vegetation and limiting the effect of surface geometry. The calibration of \(\alpha\) and \(\beta\) parameters were conducted by using 70% of the dataset selected randomly (23869 samples). For the validation, the remaining 10,229 samples of the dataset were used.  
Figure 2 illustrate the validations of the different algorithms described in Section 3.2 tested with ISMN data. The proposed results show an improvement in accuracy by considering the effect of vegetation cover in the tested relationships. The \(RMSE\) (\(R\)) values decrease (increase) from 0.074 m\({}^{3}\)/m\({}^{3}\) (0.58) from the change detection approach (Equation (2), Figure 2a) to 0.073 m\({}^{3}\)/m\({}^{3}\) (0.59) for the improved change detection approach (Equation (7)) when the VH/VV ratio is used as the V1 parameter and to 0.068 m\({}^{3}\)/m\({}^{3}\) (0.63) when NDVI is used as the \(V1\) (Figure 2b).
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### An Upper-Bound for the Sum-Capacity  
Again, we can use the Jensen's inequality as [1]  
\[\mathbb{E}\left[\log_{2}(1+z)\right]\leq\log_{2}\left(1+\mathbb{E}\left[z \right]\right), \tag{20}\]  
for \(z>0\). Therefore, Equation (9) can be rewritten as  
\[\begin{split} C&=K\mathbb{E}\left[\log_{2}\left(1+ \frac{\rho M\eta}{d_{k}^{2}}\right)\right]\\ &\leq K\log_{2}\left(1+\rho M\eta\mathbb{E}\left[\frac{1}{d_{k} ^{2}}\right]\right).\end{split} \tag{21}\]  
Remembering that \(f_{d}(r)=\frac{1}{R-R_{\min}},\ R_{\min}\leq r\leq R\), then  
\[\mathbb{E}\left[\frac{1}{d_{k}^{2}}\right]=\frac{1}{RR_{min}}. \tag{22}\]  
For proof of Equation (22), see Appendix C. Therefore, Equation (21) can be re-expressed as\[C\leq K\log_{2}\left(1+\frac{\rho M\eta}{RR_{min}}\right). \tag{23}\]
###### Abstract  
In order to analyze the effect of temperature changes on the shock initiation performance of HMX-based booster explosive, which consists of 95% HMX and 5% FPM\({}_{2602}\) by weight, a temperature calibration test of acceptor was designed. The temperature changes in the booster at low and elevated temperatures under the constraint of steel sleeve were obtained. Based on the temperature calibration results, polymethyl methacrylate (PMMA) was selected as gap material to conduct a small scale gap test (SSGT) of HMX-based booster under different temperature conditions. The corresponding critical gap thickness was tested. Based on SSGT results at different temperatures, the shock initiation processes were simulated by adjusting parameters of ignition and growth reactive rate model. The critical gap thickness and critical initiation pressure of HMX-based booster at different temperatures were numerically predicted. By combining SSGT experimental data and simulation results, the attenuation law and fitting prediction formula of the critical initiation pressure of HMX-based booster were proposed. The mechanism of temperature effect on the shock sensitivity of HMX-based booster explosive was analyzed. The research results indicate that the critical gap thickness of HMX-based booster gradually increases with the rise in temperature, and the critical initiation pressure gradually decreases during the shock initiation process under the heating temperature conditions. In addition, the simulation results show that the heated HMX-based booster under steel constraints becomes more sensitive at high temperatures (>120 \({}^{\circ}\)C), while the cooled booster is more insensitive, but its critical initial pressure does not change significantly between 88 \({}^{\circ}\)C and 120 \({}^{\circ}\)C. The experimental and numerical prediction results are important for the shock initiation safety and design of an insensitive booster explosive.  
small-scale gap test; booster; shock sensitivity; numerical prediction; simulation; safety Article