esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Anticipated and Episodic UX of Tasks in Social Networks
## 1 Introduction
While if UX is measured some time after the use of an artifact, the user can remember more positive things and suppress the negative ones [29]. In this way, a study that considers more than one period could be more enriching. While EUX is simply the experience that is obtained after having used a system, product, or service [30], AUX has to do with attitudes and experiences that the user assumes to happen when envisioning using an artifact [31]. Thus, the goal of an AUX assessment is recognizing whether a determined idea offers the type of UX anticipated by developers for potential users [32]. Making AUX trials has been established worthily, even if there are not many research works on this subject [27; 33; 34; 35]. This paper gives continuity and expands the research that we have previously published on this topic [36; 37]. The objective of the present work was to know whether there are and which are the differences between User eXpectations (AUX) and the experiences they find on social networks (EUX). Identifying these contrast elements could help improve the design of the user-tools. Thus, we propose the following hypothesis for our study:  
* _There is no significant difference in perceived UX between the prototypes imagined by the participants and the actual social networks performing the same tasks._  
To confirm or refute this hypothesis, we propose a method that allows us to assess the AUX and EUX of daily tasks on social networks: sending messages, sharing multimedia, and doing searches. Our participants (\(N=20\)) completed these tasks in two phases. In the former phase, they had to make a paper prototype with the elements they considered necessary to solve the task; once their prototype was finished, they evaluated it with the AttrakDiff questionnaire [38]. In the latter phase, the participants solved the task in real social networks and, in the same way, they evaluated it with AttrakDiff. Our main finding is that user expectations are mainly composed of pragmatic aspects. The organization of this article is as follows. First, we present a brief analysis of related works (Section 2). After describing the research methodology that we follow to develop our proposal (Section 3), we explain our assessment method (Section 4). Subsequently, we report all the details of our tests (Section 5), followed by the results of said tests (Section 6). After that, we depict the discussion of our results, as well as the implications and limitations of our study (Section 7). Finally, we expose our conclusions and some proposals for future work (Section 8).
## 4 Three-Dimensional Fractal Model
### Total Load and Total Stiffness  
From Equations (19), (39) and (40), the total load in the elastic-plastic first stage can be derived as \[{P_{e1}}^{\prime} =\int_{\frac{1}{b}\frac{1}{D-1}a_{c}}^{a_{c}}P_{1}n(a)da \tag{42}\] \[=15.7636\cdot 2^{0.225-1.275D}DEG^{0.85D-1.7}a_{l}^{0.5D}\phi^{0.15} k_{\mu}{}^{0.15}\pi^{0.425D-1.7}(\ln\gamma)^{0.425}\] \[\cdot\frac{6\frac{(0.952D-1.85)a_{c}}{D-1}-a_{c}(1.85-0.925D)}{D-2}\]  
From Equations (21), (39) and (40), the total load in the elastic-plastic second stage can be derived as  
\[{P_{e2}}^{\prime} =\int_{\frac{1}{b}\frac{1}{D-1}a_{c}}^{\frac{1}{D-1}a_{c}}P_{2}n(a )da \tag{43}\] \[=6.8692\cdot 2^{0.0012-0.708D}DE\xi^{0.472D-0.944}a_{l}^{0.5D} \phi^{0.528}k_{\mu}{}^{0.528}\pi^{0.236D-0.944}(\ln\gamma)^{0.236}\] \[\cdot\frac{110\frac{(0.736D-1.472)a_{c}}{D-1}-6\frac{(0.736D-1.47 2)a_{c}}{D-1}}{D-2}\]  
The total normal load \(P^{\prime}\) of the joint is given as  
\[P^{\prime}={P_{e}}^{\prime}+{P_{e1}}^{\prime}+{P_{e2}}^{\prime}+{P_{l}}^{\prime}\]  
From Equations (32), (39) and (40), the total stiffness in the elastic-plastic first stage can be derived as  
\[\begin{array}{ll}K_{1}&=\int_{\frac{1}{b}\frac{1}{D-1}a_{c}}^{a_{c}}{k_{e1}} ^{\prime}n(a)da\\ &=12.9865\cdot 2^{0.2725+0.225D}DEG^{0.3-0.15D}a_{l}^{0.5D}\phi^{0.15} k_{\mu}{}^{0.15}\\ &\cdot\frac{6\frac{(0.175D-0.435)a_{c}}{D-1}-a_{c}(0.35-0.175D)}{(17D-14)\pi^{0. 075D+0.2}(\ln\gamma)^{0.075}}\end{array} \tag{44}\]  
From Equations (34), (39) and (40), the total stiffness in the elastic-plastic second stage can be derived as  
\[\begin{array}{ll}K_{2}&=\int_{\frac{1}{b}\frac{1}{D-1}a_{c}}^{\frac{1}{D-1} a_{c}}{k_{e2}}^{\prime}n(a)da\\ &=24.4098\cdot 2^{0.512+0.792D}DE\xi^{0.056-0.528D}a_{l}^{0.5D}\phi^{0.528} k_{\mu}{}^{0.528}\pi^{0.556-0.264D}\\ &\cdot\frac{110\frac{(0.236D+0.028)a_{c}}{D-1}-6\frac{(0.236D+0.008)a_{c}}{D-1 }}{D-2}\end{array} \tag{45}\]  
The total normal stiffness of joint \(K\) is given as  
\[K=K_{e}+K_{1}+K_{2}\]
## 3 Proposed Method
### Reduction of Cluster Head Broadcast  
To reduce the number of rebroadcasts and also to reduce the message overhead in the cluster maintenance, the cluster heads are categorized as active or passive. After selection of gateway nodes, each gateway node '\(u\)' sends its neighbourhood information (\(N_{u}\)) to its \(CH\) neighbours. Each \(CH\) checks if all its cluster members can be covered by one or more of the GW nodes. If so, it becomes a passive cluster head and will not participate in the rebroadcasting of messages. Otherwise, it remains as an active cluster head and will take part in rebroadcasting the messages.  
Figure 3 depicts a snapshot of the vehicle traffic on a four-lane road. For clarity, the vehicles are shown as solid circles and are labelled. Dotted circles represent the transmission ranges of vehicles \(H1\), \(H2\), and \(H3\). Solid edges are drawn between the cluster head, and its members and dotted black, dark edges are drawn between the cluster head and candidate gateway nodes. The dotted blue thin edges are drawn between the gateway and its members.  
After the initial cluster formation and gateway selection, \(H1\), \(H2\), and \(H3\) are selected as cluster heads, and vehicles \(M4\) & \(M6\) are chosen as gateways. Upon receiving the neighborhood table of \(M4\) and \(M6\), the cluster head \(H2\) marks itself as passive since all its cluster members are covered by the gateways. To maintain stability in the network, the cluster head state of \(H2\) is not withdrawn. For example, if \(H2\) withdraws its state from cluster head, then \(M4\) and \(M6\) cannot continue to be a gateway and M5 becomes un-clustered which results in reconstructing the cluster again.
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 4 Optimization for Tandem Blades
### Optimization for a 2D Tandem Blade Model  
#### 4.1.1 The Optimization Procedure  
The simulation results for a baseline 2D tandem blade indicate that the typical CD airfoil achieved acceptable overall performance. However, some potential, improving directions are still being reflected in the comparison of isentropic Mach number distributions in single-blade and tandem configurations. It should be noted that the design parameters, such as AO/PP, solidity ratio, and camber ratio, were selected during a proper interval in the preliminary design, and they are kept fixed during the optimizing process. The camber line and blade angle were modified to adapt to aerodynamic interaction better in tandem blades, with the objective of reducing total pressure loss while maintaining the aerodynamic load. Based on proper preliminary design parameters and the CD airfoil, the modification process for tandem blades and the critical aerodynamic criteria can be summarized in Figure 14. The modification concepts for FB and AB will be discussed in the following parts, respectively.  
Figure 13: Comparison of Mach contours in single-blade and tandem configurations. FB incidence +5\({}^{\circ}\); FB inflow Mach number 0.8. (**a**) AB single-blade configuration; (**b**) tandem configuration.  
* Modification for the forward blade As mentioned in the previous analysis, the FB diffusion process is influenced by the AB pressure field in the tandem configuration, as characterized by the increase of the aerodynamic load near the FB trailing edge. To better adapt to the aerodynamic interaction, a negative camber is adopted near the FB trailing edge, which leads to a reduction in the local aerodynamic load. Normalized camber line distributions of the modification blade are shown in Figure 15; the normalized camber increases in correspondence with the normalized chord length, varying from 0 to 0.5. Additionally, a negative camber line is used from 0.7 to 1.0 normalized chord length.
* Modification for the aft blade Due to AB being located in the rear of FB, the impact of gap flow is more significant. Additionally, the aerodynamic interaction should not be ignored during the modification process. Firstly, to make the AB incidence adapt to the influence of gap flow, the modified AB inlet metal angle decreases by  
Figure 14: Modification process for tandem blades based on a CD airfoil.  
Figure 15: Comparison of camber line between baseline and modified blades.  
\(5^{\circ}\). The criterion of \"AB incidence modification\" can be estimated by the AB surface isentropic Mach number; that is, AB should form a \"zero incidence\" distribution in the tandem configuration. Secondly, to ensure a stronger converging passage between FB and AB, the normalized camber increases more rapidly before 30 percent of the chord length, which is beneficial for preventing the separation near the AB trailing edge. A comparison of baseline and modified blade geometry is shown in Figure 16.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 2 Methodology
### Acquisition
Similarly, different hypotheses need to be made for other signals as well depending on their specific code structures. Thus, the CAF needs to be modified taking into account these data bit hypotheses. For the example of GPS LICA signal with 20 ms integration time, the CAF can be represented as follows, assuming a data bit transition happening after the \(u\)-th code period:  
\[ \begin{split} Y_{I}(\tau,F_{D},u)=\sum_{v=1}^{20}H_{v}^{(u)}\cdot Y _{Iv}(\tau,F_{D}),\\ Y_{Q}(\tau,F_{D},u)=\sum_{v=1}^{20}H_{v}^{(u)}\cdot Y_{Qv}(\tau, F_{D}).\end{split}  \tag{3}\]This equation includes the parameter \(H_{v}^{(u)}\) that represents the assigned bits for the \(v\)-th code period under the \(u\)-th hypothesis, where \(u\) is a value between \(1\) and the total number of hypotheses \(N_{h}\). This value is defined as [18]:  
\[H_{v}^{(u)}=\left\{ \begin{array}{ll}1,&v\leq u\\ -1,&v>u\end{array} \right.. \tag{4}\]  
Note that the expression of the assigned data bits \(H_{v}^{(u)}\) can become more complicated when longer integration times are considered. This is due to the fact that multiple bit transitions can, and likely will, take place for integration times longer than a single bit period. The total number of hypotheses \(N_{h}\) will also grow in such scenarios. By searching for the CAF's maximum magnitude, the code delay, Doppler offset and navigation bit hypothesis can be determined. In addition, an interpolation step needs to be implemented for both the code delay and Doppler offset in order to obtain sufficiently precise measurements [7] for the SRTK navigation filter; this results in the final estimates of code delay (\(\hat{\tau}\)) and Doppler-shift (\(\hat{F}_{D}\)). In addition, the carrier phase at the beginning of the collected signal can be computed by the following equation based on these code delay and Doppler offset estimates:  
\[\hat{\varphi}=\arctan\left(\frac{Y_{Q}(\hat{\tau},\hat{F}_{D},u)}{Y_{I}(\hat{ \tau},\hat{F}_{D},u)}\right). \tag{5}\]
## 3 Results
### Sustainability and Stability Analysis of WUEc  
From 2002 to 2022, the Hurst index of WUEc in Xinjiang fluctuated between 0.13 and 0.89, with a mean value of 0.52. Of these, regions with a Hurst index greater than 0.5 accounted for 71% of the study area and were predominantly characterized by anti-persistence (Figure 5a). To further analyze the future trend of WUEc in Xinjiang, the results from the Mann-Kendall trend analysis were combined with the Hurst analysis outcomes to forecast future WUEc changes, with all change categories being mutually exclusive. In the future, Xinjiang's WUEc is predominantly characterized by a decreasing trend (62.4% of the region). Within this, areas experiencing a persistent decline account for 41.1% of the study area, primarily located in Karamay and Tacheng; regions expected to experience future decline account for 21.3%, primarily in Aksu, Bazhou, and central Yili; regions with an increasing trend are concentrated in Yili and Changji, making up 32.3% of the region. Meanwhile, regions with a stable and unchanging trend account for only 5.3% of the region (Figure 5b). The mean CV of WUEc in Xinjiang is 0.23, indicating low variability and high stability. In terms of distribution, the CV gradually increases from north to south. Specifically, 41.9% of the CV values fall within the range of 0 to 0.2, primarily concentrated in the southern part of the study area (e.g., Aksu, Hetian, and Tacheng). Meanwhile, 46.9% fall within the range of 0.2 to 0.3, mostly in central Yili, Changji, and northern Tacheng. Only 11.2% of the areas have higher CV values (ranging from 0.3 to 1.9), mainly located  
Figure 4: Trends in annual mean (**a**) and Sen + MK test (**b**) for WUEc in Xinjiang, 2002–2022.  
in the central part of the study area, where human activities are more frequent, such as in Urumqi, Turpan, and Karamay (Figure 5c).
# Traffic Sign Detection Based on the Improved YOLOv5
###### Abstract  
With the advancement of intelligent driving technology, researchers are paying more and more attention to the identification of traffic signs. Although a detection method of traffic signs based on color or shape can achieve recognition of large categories of signs such as prohibitions and warnings, the recognition categories are few, and the accuracy is not high. A traffic sign detection algorithm based on color or shape is small in computation and good in real-time, but the color features are greatly affected by light and weather. For the questions raised above, this paper puts forward an improved YOLOv5 method. The method uses the SloU loss function to take the place of the loss function in the YOLOv5 model, which optimizes the training model, and the convolutional block attention model (CBAM) is fused with the CSP1_3 model in YOLOv5 to form a new CSP1_3 CBAM model, which enhances YOLOv5's feature extraction ability and improves the accuracy regarding traffic signs. In addition, the ACONC is introduced as the activation function of YOLOv5, which promotes YOLOv5's generalization ability through adaptive selection of activation by linear-nonlinear switching factors. The research results on the TT100k dataset show that the improved YOLOv5 precision rate increased from 73.2% to 81.9%, an increase of 8.7%; the recall rate increased from 74.2% to 77.2%, an increase of 3.0%; and the mAP increased from 75.7% to 81.9%, an increase of 6.2%. The FPS also increased from 26.88 to 30.42 frames per second. The same training was carried out on the GTSDB traffic sign dataset, and the mAP increased from 90.2% to 92.5%, which indicates that the algorithm has good generalization ability.  
intelligent driving; traffic sign detection; YOLOv5; attention model; SloU; ACONC
## 3 Inverse Kinematics  
### General Spherical Joint  
To solve the inverse kinematics and illustrate the essence of the kinematics for the \(2n\)-DOF redundant manipulator with \(n\) identical UJs, a novel concept named as \"general spherical joint\" (GSI) is introduced.  
General spherical joint is inspired by the homogeneous transformation between any two adjacent frames. Notice that the rotation component of the homogeneous transformation matrix is obtained by multiplying three basic rotation matrices about three orthonormal axes. Based on this fact, the joint between any two adjacent frames can be regard as a spherical joint, i.e., the new concept general spherical joint. In accordance with the spherical wrist, GSI also possesses the property of three consecutive revolute joint axes intersecting at a common point. These three revolute joint axes include the two axes of \(i\)-th UJ and the central axis of Link \(i\), as shown in Figure 2. The revolution about the central axis of Link \(i\) is embodied in the constant angle \(\alpha_{i}\) between link Frame \(i\) and link Frame \(i-1\). Thus, this revolution is virtualized as the third DOF of the GSI to form the three revolute axes intersecting at a common point.  
Figure 2: General spherical joint.  
Based on the GSJ, the studied \(2n\)-DOF manipulator can be regard as a serial robot, whose links are connected by \(n\) identical GSJs. Simultaneously, the GSJ can also realize the decoupling between the position and orientation just as the spherical joint. Thus, considering the GSJ \(i-1\) in Figure 2, the relationship between these two adjacent locations \(\mathbf{p}_{i-1}\) and \(\mathbf{p}_{i}\) can be given by  
\[\mathbf{p}_{i-1}=\mathbf{p}_{i}-l_{i}\mathbf{a}_{i} \tag{4}\]  
The manipulator studied in this paper is a strange robot. Most series robot commonly has only one spherical joint, which often acts as the last joint. This joint is called as spherical wrist, which can decouple the solutions for the position and for the orientation of end-effector. However, according to Equation (4), all joints of the studied manipulator can realize the decoupling between position of this joint and orientation of the corresponding end-effector. This fact is essential for the following inverse kinematics derivation of the whole manipulator.
## 3 Results and Discussion
### Iceberg Drift
#### 3.1.1 2018 Deployments  
Beacon deployments made during the 2018 field season were focused on the areas of Talbot Inlet and waters adjacent to Bylot Island and southern Baffin Island (Figure 3). The recorded iceberg drift tracks are shown in Figure 6. In terms of cumulative distance travelled, the most notable was beacon 300434063415110, which was deployed on a very large tabular iceberg near Bylot Island on 27 August 2018. After remaining grounded for several months, the iceberg became dislodged during the winter of 2019 and travelled over 4000 km southwards before grounding and breaking up off the coast of Labrador in May 2019. Beacon 300434063419120 was deployed on 28 August 2018 on an ice island with a surface area of 5 km\({}^{2}\) that calved from the Petermann Glacier in 2017 and remained in the general area of northern Baffin Bay until the summer of 2020 when it became trapped in a gyre and eventually broke apart. Satellite imagery confirmed that the beacon remained on a smaller iceberg fragment that continued to drift around Jones Sound before breaking apart on the coast of Devon Island. Beacon 300434063418130 was deployed on an iceberg near southern Baffin Island, which drifted through several fiords before breaking apart in the winter of 2019. Beacons 300434063416060 and 300434063415160, which were deployed on icebergs near Qikiqarjuaq, Nunavut, were unable to escape the local shallow coastal area  
Figure 5: Example of a typical Cryologger ITB deployment: (**a**) Hand deployment of ITB 300434063418130 on 3 September 2018. (**b**) Aerial photo of iceberg target located on the southern coast of Baffin Island, Nunavut (approx. 600 m \(\times\) 330 m); (**c**) Measurement of the magnetic compass heading of ITB at time of deployment.  
and frequently became grounded. Beacon 300434063415160 eventually travelled further into a nearby fiord and washed up onshore after becoming dislodged from the iceberg as it deteriorated. This beacon provided an opportunity to test the durability of the electronics and capacity of the battery pack and continued to operate until the battery was depleted in August 2022. This ITB was set to transmit every 3 h, and from the long times it took to transmit, it was evident the beacon had difficulty transmitting from the shore, which was likely due to snow and sand cover, as well as a reduced sky view from its location in a fiord with steep sidewalls. Despite this, it was still able to achieve 4 years of continuous operation in less-than-ideal conditions.
## 3 Materials and Methods
### Integrated Model (GRU-LR-TCN)  
TCN and LSTM are two different types of neural networks used to process time series data. Since they have their characteristics and advantages, better prediction results can be obtained by combining them through adaptive weighted fusion to construct an integrated prediction model [38]. Inspired by the TCN-LSTM integrated prediction model, since LR-TCN is better than the TCN in capturing local and global patterns in the time series, GRU is superior to LSTM in handling long-term dependencies. For this reason, the GRU-LR-TCN integrated prediction model was proposed in this paper, which fuses the prediction results of the GRU prediction model and the LR-TCN prediction model and combines the characteristics and advantages of the two models to enhance the prediction accuracy of the model. The GRU-LR-TCN integrated prediction model is shown in Figure 5.  
The GRU-LR-TCN integrated prediction model used the root mean square error of the validation sets of the two models for the weight calculation of the adaptive weighted fusion. If the RMSE of the model is smaller, its weight in the combined prediction model is greater. Assuming that both models predict the nth time step concurrently, the weighting algorithms are the following Equations (6)-(8):  
\[W_{n}^{T}=\frac{\frac{1}{S_{t}^{n}}}{\frac{1}{S_{t}^{n}}+\frac{1}{S_{t}^{n}}} \tag{6}\]  
Figure 4: LR-TCN structure.  
Figure 5: Flowchart of the GRU-LR-TCN integrated predicting model.  
\[W_{n}^{G}=\frac{\frac{\frac{1}{S_{n}^{G}}}{\frac{1}{S_{n}^{G}}+\frac{1}{S_{n}^{G}}}}{ \frac{1}{S_{n}^{G}}+\frac{1}{S_{n}^{G}}} \tag{7}\]  
\[y_{n}=W_{n}^{T}\otimes y_{n}^{T}+W_{n}^{G}\otimes y_{n}^{G} \tag{8}\]  
where \(S_{n}^{T}\) is the LR-TCN validation set RMSE metric, \(S_{n}^{G}\) is the GRU validation set RMSE metric, \(W_{n}^{T}\) is the LR-TCN prediction result weight, \(W_{n}^{G}\) is the GRU prediction result weight, \(y_{n}\) is the fusion prediction result, and the operator '\(\otimes\)' denotes the sequential multiplication of array elements.