esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 3 Support Vector Machine Model  
### Problem Descriptionof Freeway Travel Time Prediction  
Freeway travel time is strongly consistent in a certain time range. That is, there are some complex functional relationships between the current travel time and the past travel time. By analyzing the changes in travel time, we can obtain rules and establish a real-time prediction model updated every five minutes. Then, the accuracy and reliability of the predicted results can be improved by using an optimization algorithm to find the optimal solution of the model.  
The change in the freeway travel time in different time periods is not a simple linear relationship, and it will neither increase indefinitely, nor decrease indefinitely, but it will only change continuously within a floating interval. Therefore, using a simple least squares regression prediction or similar methods is not sufficient to predict the travel time. The SVM nonlinear regression theory can be employed to solve this problem.  
SVM uses nonlinear transformation to map the original variables to a high-dimensional feature space so that the problem of nonlinear separability in the original sample space is transformed into high-dimensional feature space. The linear separable problem and the application of expansion theorem of the kernel function in the calculation process do not require the explicit expression of nonlinear mapping. In addition, since the linear learning machine is established in the high-dimensional feature space, it can be compared to the linear model. The comparison not only increases the complexity of the calculation, but also solves the problem of \"dimensional disaster\".  
Owing to changes in the traffic environment, sudden traffic accidents, weather, and other special events, the SVM algorithm will eventually be transformed into a quadratic programming problem. In theory, a global optimal solution can be obtained, thus solving the traditional neural network. The network can avoid the local optimal problem and should adequately accommodate the influencing factors caused by sudden changes to improve the accuracy of the travel time prediction result.  
Therefore, this study used the nonlinear support vector machine regression theory [30].
# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 7 Results
### Crowdsensing
#### 7.1.1 VRP Model  
The optimization model is implemented in Python, based on Google OR-Tools framework, which provides a general formulation of the VRP with multiple layouts and constraints. To evaluate the VRP load balancing, i.e., task allocation, we considered a set of 10 locations in Bucharest, and three players that correspond to the simulated behavior: accurate reporting, mostly accurate reporting and false reporting. The routing module is initialized with the distance matrix, number of players and location of depots (starting locations).  
The selection of optimal routes is based on a distance function and the defined consumer demands, while vehicle capacities (player reputation) and fuel level (travel distance) are modeled as capacity constraint dimensions. A penalty function is added, allowing to exclude nodes in order to solve the optimization problem, whereas no feasible solution could be found within the given conditions (e.g., total consumer demand exceeds the capacity of the fleet). The optimization algorithm solves for optimal cost within the provided constraints and time limit, returning the optimal path for each vehicle, which provides the initial assignment of tasks to nearby players.  
To evaluate the effect on the distribution of tasks according to the player reputation, we defined the capacities in the context of VRP as follows: accurate reporting (100), mostly accurate reporting (80) and false reporting (50). Then, we proceed to evaluate how the relative importance of the issues found in the locations scattered across the city should be related to the player reputation to achieve optimal load balancing. We considered a fixed distribution of associated demands and a reward factor that is applied for each demand.  
For the experiment, we evaluated the effect of this reward factor in the range of 1 to 15, where a factor of 5 results in a balanced overall distribution (total rewards vs. total reputation). As the starting location of players may affect the accuracy of the results, we considered a random initialization using a bounding circle. The VRP is evaluated for 1000 iterations, and the average load (resource allocation) is shown in Figure 11(a) for each player type.  
In this evaluation, the optimal reward factor should be 5 or greater, as there is an increase in standard deviation for lower values, as shown in Figure 11(b), which accounts for less predictable results. In fact, for a reward factor of 1, the optimal allocation of tasks is not satisfied, whereas a factor of 5 or greater results in more predictable distributions while the proposed objective is achieved.  
According to the results, while the optimization problem is solved either way, the optimal reward factor should be defined to achieve the desired outcome for the incentive mechanism. Therefore, there should be a balance between the reputation of players and the rewards allocated for each available sensing task.
# Evaluation of Horizontal Shear Performance of Composite Girders Using Separable Shear Connectors
## 3 Evaluation of Horizontal Shear Performance
### Review of Reconstructability of Precast Decks  
This section analyzes the experimental results for the dismantling and reinstallation of the deck by using an embedded separable stud bolt (DY stud). Figure 10 displays the reassembly simulation and failure photographs of S-DY-s3, which visualize the deck dismantling and reinstallation processes. In the S-DY model, the s3 specimens were designed to facilitate the separation of the precast deck by pouring mortar only up to the bottom of the T-sleeve head during the composite action, facilitating bolt dismantling. Initially subjected to repeated loadings of up to 180 kN (one cycle), the stud bolts were dismantled and reassembled, and loading was applied until failure in a phased manner (reloading). Figure 8c illustrates the load-slip displacement behavior.  
The ultimate load of S-DY-s3 was 345.7 kN, which is approximately 97.7% of the average ultimate load of S-DY at 353.9 kN. This phenomenon confirms the presence of adequate horizontal shear strength even after reinstallation. Therefore, when deploying the proposed separable stud bolts in actual field conditions, no reduction in strength is anticipated, because a new non-shrink mortar is used to install a new deck. Although using a limited number of specimens reduces the reliability of the results, the proposed embedded separable shear connectors achieve the intended performance for detachment and reconstructability to a certain extent.  
Figure 9: Fracture of test specimens (**(Left)**: Rebar; **(Middle)**: M-DY; **(Right)**: S-DY).
# Design of a Variable-Stiffness Compliant Skin for a Morphing Leading Edge
## 3 Structural Concept of Morphing Leading Edge  
In the VCAN program, the design objective is a two-dimensional morphing wing physical mockup with a span size of 3 m, which will be used for ground and wind tunnel tests. Figure 4 presents the digital mockup concept of the morphing wing. The mockup integrates a morphing leading edge and a morphing trailing edge to be drooped seamlessly, smoothly, and precisely to an aerodynamic target profile. Due to the scalability of the concept and a lower budget of the physical mock-up manufacturing, this study extracts a morphing leading edge with a spanwise length of 350 mm as the design object, including only one set of internal actuating mechanism.  
As shown in Figure 5, the structural concept of the morphing leading edge is inspired by the research of [PERSON] et al. (2018), which also includes a compliant skin and an internal kinematic mechanism. The flexible skin is connected to a front spar to form a seamless and smooth profile. Four stringers are integrated to transfer the aerodynamic load to inner kinematic mechanisms. The mechanism is used not only to deflect the entire compliant skin but also to withstand the outer aerodynamic force. Obviously, the final deflected profile of the morphing leading edge is determined by the stiffness distribution of the compliant skin and the position of the stringers according to the classical spline theory. If the end-to-end compliant skin is constant in stiffness, the drooped profile will be uniquely determined by the interface displacement transferred by the internal mechanism at the four interface points, which seem like four control points of a spline curve. Therefore, it is necessary to use a variable-stiffness compliant skin and design it elaborately to obtain an aerodynamically optimal profile. The following two sections present the proposed  
Figure 4: Two-dimensional morphing wing digital mockup with morphing leading and trailing edge.  
Figure 3: Curves of the lift coefficient versus deflection angle.  
two-step design method and illustrate how it takes the manufacturing constraints into account collaboratively.
## 4 Preliminaries
### Secret Sharing Scheme  
Secret sharing is a crucial security primitive of MPC, and it is the foundation of various two-party and three-party protocols. Two-party frameworks such as Cryptflow2 and SecureML use a 2-out-of-2 additive secret sharing scheme. Three-party framework such as SecureNN also uses 2-out-of-2 additive secret sharing between two parties with a third assist party that only provides relevant randomness for two-party protocols.  
A t-out-of-n threshold secret sharing scheme (TSSS) [31] consists of two algorithms: Share and Reconstruct. Share scheme means a randomized algorithm that takes a message \(x\) in \(\mathbb{Z}_{L}\) as the input and outputs a sequence \(a=(a_{1},a_{2},\ldots,a_{n})\) of _shares_. The reconstruct scheme means a deterministic algorithm that takes a collection of t or more _shares_ of \(a\) as the input, and outputs the message \(x\). In a word, TSSS is that among \(n\) parties that hold secret shares, and at least \(t\) of them are able to restore the secret.  
FALCON combines SecureNN and ABY3 [5] frameworks using 2-out-of-3 secret sharing scheme, which can tolerate at most corruption of one party. The RSS scheme can help three-party protocols be more efficient and allow more offline computing. \([x]^{m}\) denotes an RSS of \(x\) with module \(m\). In fact, \([x]^{m}\) demonstrates that \((x_{0},x_{1})\) is held by \(P_{0}\), \((x_{1},x_{2})\) is held by \(P_{1}\), and \((x_{2},x_{0})\) is held by \(P_{2}\). The paper assumes that there are two private inputs \(\{x,y\}\), the process of converting the 3-out-of-3 secret sharing to the 2-out-of-3 RSS shows in Figure 2.
## 3 Materials and Methods
### Resistivity Data  
Resistivity data plays an important role in identifying and mapping potential groundwater areas [85; 86]. Apparent resistivity is a measure of the ability of a material to withstand the passage of an electric current. Areas of low resistivity may indicate the presence of saturated groundwater, while areas of high resistivity may correspond to more resistant rock or soil [86; 87]. In this study, the apparent resistivity map at a frequency of 7200 Hz at a scale of 1:100,000 was used to determine the negative and positive anomalies of the Larb a Misti geological formations. The apparent resistivity anomaly values were compared with those represented on the gravity anomaly maps. It was also used to assess the productivity of the drill holes drilled. This map was provided by the Geology Directorate of the Moroccan Ministry of Energy and Mines in late 2001. In general, low frequencies penetrate deeper, but provide a less detailed resolution of shallow features. In this study, the depth of investigation is determined using Equation (5) and based on the average resistivity of the geological formations in the area [88; 89; 90; 91; 92].  
\[\delta=503\sqrt{\frac{\rho}{f}} \tag{5}\]  
where 503, \(\rho\) and \(f\) are constant, the average resistivity (Ohm.m) and Frequency (Hz), respectively.
## 3 Doppler OCT for Otology Applications
### In Vivo Experiments  
As clinical applications of Doppler OCT to otology, various in vivo experiments from mouse to human have been presented.
## 5 Impact of Color Conflicts Caused by Late Arrivals, Node Mobility or Topology Changes
### Should Color Conflicts Always be Solved?  
In this section, we summarize our recommendations concerning color conflicts. Color conflicts caused by the use of bad links should always be avoided: only links of good quality should be used for routing and association. For other color conflicts, if they are not detected, no specific action is taken, because no data is lost. If they are detected, nothing is done as long as the packet loss is tolerated by the application. As soon as it is no longer the case, SERENA is able to react in order to solve them.  
The benefits brought by this approach are:  
* If there is no color conflict, TDMA/CA does not introduce any delay and acts in this sense like any other TDMA approach.
* Even if there is a color conflict, TDMA/CA does not notify SERENA as long as the desired frames can be captured.
* TDMA/CA approach allows collision avoidance: color conflicts can be tolerated as long as the number of collisions does not exceed a threshold.
* TDMA/CA is able to detect locally any color conflict generated by SERENA.
## 3 Problem Formulation and Solutions  
### Problem Formulation  
For maximizing the sum rate (as in (16)) within the proposed network, here, the authors have addressed the issue of joint optimization of the PS, PA, and digital as well as analog RF precoders. The statement of the problem can be formulated as  
\[\max_{p_{d,k},\,p_{d,k},\,\mathbf{f}_{d}^{BB},\,\text{F}^{RF}}R_{sum} \Bigg{(}R_{sum}=\sum_{d=1}^{D}\sum_{k=1}^{|\Xi_{d}|}R_{d,k}\Bigg{)} \tag{17a}\] \[s.t.R_{d,k}\geq R_{d,k}^{min},\,\forall d,k,\] (17b) \[p_{d,k}\geq 0,\,\forall d,k,\] (17c) \[\sum_{d=1}^{D}\sum_{k=1}^{|\Xi_{d}|}p_{d,k}\leq P,\] (17d) \[P_{d,k}^{EH}\geq P_{d,k}^{min}\forall d,k,\] (17e) \[\left|\text{F}^{RF}_{i,j}\right|^{2}=\frac{1}{N_{T}},1\leq i\leq N _{T},1\leq j\leq N_{RF},\] (17f) \[\left\|\text{F}^{RF}\mathbf{f}_{d}^{BB}\right\|_{2}=1\,\forall d, \tag{17g}\]  
Here, constraint (17b) ensures the minimum desirable data rate for the \(k^{th}\) UE within the \(d^{th}\) beam. The constraints (17c) and (17d) pertain to the transmitted power. The transmitted power at the BS associated with every UE ought to be positive yet should not surpass the permitted level P. Constraint (17e) represents the fact that each \(k^{th}\) UE within the \(d^{th}\) beam is required to harvest minimum energy of \(P_{d,k}^{min}\). Here, (17f) represents the constant-modulus constraint related to the AP. The HP matrix's unit power restriction is given by (17g). It turns out that the optimization problem as given in (17a)-(17g) is a non-convex optimization dilemma, making it challenging to find a globally optimal solution.
## 4 Fatigue Damage Estimation Based on a Large Number of Sensors
To reduce the influence of the non-stationary characteristics of the measured response on fatigue damage estimation, one possible approach is to divide the recorded response time series into shorter segments and then to employ the POD or MPR methods on those shorter segments. 4. Generally, when a large number of sensors is available, as is the case with the NDP model riser, all five of the empirical methods estimate fatigue damage rates over the entire riser length quite well; the variability factors are typically in the range from 0.3 to 3.0, with lowest and highest estimations of 0.16 and 14.54, respectively. Among the five methods, the hybrid method (Figure 8e) which combines the MPR and the modified WWA procedures, is the most accurate for fatigue damage estimation. In Section 3, it is stated that the variability factor is a better indicator of the inaccuracy or error in the reconstructed rise response than is the strain-RMS ratio or the displacement-RMS ratio. This is the reason of choosing the variability factor as the parameter to evaluate and compare the different empirical methods. In order to illustrate this point, the ratio of the fatigue damage rates  
Figure 8: Variability factors estimated by different empirical methods at all locations along the NDP model riser, using twenty-three stains as inputs: (**a**) WWA; (**b**) Modified WWA; (**c**) POD; (**d**) MPR; and (**e**) Hybrid: MPR + modified WWA. (i.e., the defined variability factor), the ratio of RMS values of strain, and the ratio of RMS values of displacement are all calculated using the WWA procedure with 23 input sensors for the NDP2350 data set. Figure 9 shows that the variability factor (or damage ratio) falls in the range of 0.36 to 14.54, while the strain-RMS ratio falls in the range of 0.60 to 2.24 and the displacement-RMS ratio falls in the range of 0.34 to 2.14. The variability factor variation indicates that it describes the widest range (i.e., it is most sensitive to errors) and, thus, serves as the best indicator of the accuracy of any method's reconstructed riser response. In addition to the NDP data sets, two sets of synthetic riser motion data are simulated for the evaluation of the empirical response reconstruction methods. One set contains three standing waves of different frequencies, amplitudes and wavelengths; another set contains one standing wave and two traveling waves. The riser physical properties and sensor arrangement are assumed to be identical to the NDP model riser. Using measurements at sensor locations with each empirical method, the riser response is reconstructed and, then, compared with the true/simulated response over its entire span. Similar observations as with the NDP data sets were obtained with the simulated data sets. Generally, given a sufficient number of sensors, all five of the empirical methods can be employed to reconstruct the riser response and, then, to estimate the fatigue damages quite well, despite different underlying assumptions and advantages/disadvantages associated with each of them.
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 2 Related Work
### Attention Mechanisms  
In recent years, attention mechanisms have been shown to play a superior role in computer vision tasks, including image classification [25; 28; 30; 31; 32], semantic segmentation [26], objection detection [33; 34], and image super-resolution [26; 29]. We introduce the work related to attention mechanisms from two aspects: channel attention and spatial attention.  
**Channel Attention.** [PERSON] et al. [30] proposed a squeeze-and-excitation network that introduces a novel channel attention mechanism to significantly improve image classification accuracy. Although this network allows for reassigning the weights of channel features, it only employs one feature statistic through global average pooling, thus lacking further excavation of features. [PERSON] et al. [32] proposed a deep residual single-image super-resolution network which uses a channel attention mechanism. [PERSON] et al. [31] proposed a context-encoding module to capture the semantic context of senses and selectively channel class-dependent feature maps. [PERSON] et al. [33] proposed a channel normalization layer to reduce the associated computational complexity and the number of parameters. [PERSON] et al. [34] proposed a second-order attention network to explore the feature correlations of intermediate layers for image super-resolution. [PERSON] et al. [35] proposed a novel multi-spectral channel attention involving the pre-processing of a channel attention mechanism in the frequency domain.  
**Spatial Attention.** [PERSON] et al. [36] proposed a non-local operation to compute the response as a weighted sum of the features at all positions. [PERSON] et al. [37] presented an object relation module for object detection tasks that processes a set of objects by computing the reasoned relations between them simultaneously, instead of using individual recognition. [PERSON] et al. [38] proposed a graph-based global reasoning network that captures global relations between relation-aware features. [PERSON] et al. [39] proposed a novel method that views object detection as a direct set prediction, from which the use of many hand-designed components and anchor generation are removed. This method interprets the relations between objects and the global image context to predict the final output directly. [PERSON] et al. [40] proposed a texture transformer network for image super-resolution, in which the spatial attention between a low-resolution image and a reference low-resolution image is calculated to further transfer the texture information.  
Although current SSR algorithms utilize attention mechanisms to enhance the learning ability of the models, these methods lack further optimization of the spectral structure. To address this problem, we propose a spectral transformer network to enable the network to search for and learn spectral structure information.
## 5 Experiments and Results  
In this section, the experiments conducted are introduced and the resulting performance is analyzed. First, the path-planning alterations from what is presented in [1] are discussed to facilitate the capture of PFB imagery. Then, the simulations of deep learning classification are introduced, including the created dataset, augmentation and training parameters, and transfer learning. Finally, the necessary comparative metrics are discussed along with each of the introduced classification architectures' performance on the proposed dataset.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
###### Abstract  
A new approach to GPS positioning is described in which the post-processing of ultra-short sequences of captured GPS signal data can produce an estimate of receiver location. The algorithm, called 'FastFix', needs only 2-4 ms of stored L1-band data sampled at \(\sim\)16 MHz. The algorithm uses a least-squares optimization to estimate receiver position and GPS time from measurements of the relative codephase, and Doppler-shift of GNSS satellite signals. A practical application of this algorithm is demonstrated in a small, lightweight, low-power tracking tag that periodically wakes-up, records and stores 4 ms of GPS L1-band signal and returns to a low-power state--reducing power requirements by a factor of \(\sim\)10,000 compared to typical GPS devices. Stationary device testing shows a median error of 27.7 m with a small patch antenna. Results from deployment of this tag on adult Royal Albatross show excellent performance, demonstrating lightweight, solar-powered, long-term tracking of these remarkable birds. This work was performed on the GPS system; however, the algorithm is applicable to other GNSS systems.  
GPS; GNSS; navigation; codephase; wildlife tracking +
Footnote †: journal: Sensors