esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sorption Properties
#### 3.2.6 Application to Ore Raffinate
The SC values are globally better at pH\({}_{\text{eq}}\) 4.15, which represents a good compromise, taking into account both the sorption capacities and efficiencies, as well as the selectivity parameters. The exception is the selectivity of MC-UR/S against Pb(II), which is maximal at pH 2.19, considering both Ce(III) and Nd(III). This work focused on the recovery of cerium from processed leachate using MC-UR/S. It would be easy to recover the REE through acidic desorption (as illustrated by Section 3.2.5) and then process the eluate by specific precipitation. Indeed, the metal could readily be recovered using oxalic acid for the precipitation of the REE under controlled pH conditions (i.e., pH 1.5) [82; 83]. [PERSON] and [PERSON] [84] reported that at a low H\({}_{2}\)SO\({}_{4}\) concentration (0.1-0.2 M), \(\text{CeSO}_{4}\)\({}^{+}\) and \(\text{Ce(SO}_{4})_{2}\)\({}^{-}\) may coexist, while at a higher concentration (\(>\)1 M), the only species present in the solution is the anionic form. Obviously, the environmental conditions may affect the affinity of the sorbent for cerium through variations in the metal speciation. Under the conditions selected for the tests on the processed ore leachate, cerium may be present at least partially under a different speciation than that in the preceding study (where the metal exists as CeCl\({}^{2+}\)). Therefore, the decrease in the sorption performance, compared with the synthetic solutions, may be due to the cross-effects of the competition with other metals for binding on the reactive groups and the change in the speciation (charge of the cerium species).
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 4 Evaluation
### Experiment I: Direction Recognition
#### 4.1.3 Results  
The results obtained are presented in the confusion matrix in Table 1. The average recognition rates were 100%, 97.78%, 88.89%, 90%, and 100% for **F**, **B**, **L**, **R**, and **S**, respectively.  
Note the high recognition rates. These are due to an optimized tactile rendering approach that tackles two perception issues identified in previous experiments: (1) Missed vibrations: the first three  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Answered (\%)**} \\ \hline  & & **Forward** & **Backward** & **Left** & **Right** & **Stop** \\ \hline \multirow{4}{*}{**Presented**} & Forward & 100 & 0 & 0 & 0 & 0 \\  & Backward & 2.22 & 97.78 & 0 & 0 & 0 \\ \cline{1-1}  & Left & 0 & 0 & 88.89 & 11.11 & 0 \\ \cline{1-1}  & Right & 1.67 & 1.67 & 6.66 & 90 & 0 \\ \cline{1-1}  & Stop & 0 & 0 & 0 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Direction recognition rates with the user interface and the proposed tactile rendering approach.  
Figure 4: (**a**) Schedule of activation of the vibrating actuators for the direction recognition task—example for ‘forward’; (**b**) the corresponding vibrotactile pattern: the whole pattern is 7 s.  
vibrations prevent users from missing a vibration (because of distraction, performing another activity, etc.) and failing at direction identification; and (2) Inaccurate discrimination: the fourth vibration points to the opposite direction. This provides a reliable reference to identify points of vibration when users cannot accurately distinguish which actuator is actually vibrating. The fifth vibration allows reconfirming of the correct direction. Displaying both correct and opposite directions in the same tactile pattern actually eases their recognition [45].
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 4 Discussion
### Beyond Incremental Change  
Classical table olive graders rely on bespoke CNNs or hand-tuned morphology that must be retrained, re-lit and re-calibrated whenever the cultivar, camera or conveyor geometry changes. In contrast, the pipeline proposed here leverages a frozen foundation model (SAM) that transfers zero-shot to two morphologically distinct cultivars (Gordal vs. Hojiblanca) without additional labels, delivers sub-pixel geometric accuracy (IoU \(\geq\) 0.94) under harsh specular lighting and integrates seamlessly with deterministic post-processing to recover millimetric morpho-metrics (area, ellipse, major axis orientation). The ability to generalize across cultivars, cup designs and illumination regimes--while maintaining real-time throughput--positions SAM not as an incremental replacement for U-Net or Mask R-CNN but as a paradigm shift: segmentation moves from task-specific model engineering to prompt-based configuration. This decouples algorithm development from plant maintenance, lowers the entry barrier for small processors and creates a reusable foundation for digital-twin feedback loops and predictive control across the broader agri-food sector.
## 3 Results and Discussion
### Hardness  
Figure 10 shows the hardness measurements across the welds prepared with different welding speeds (3 mm/s, 4 mm/s, and 5 mm/s). A reduction in hardness in the fusion zone is evident in all conditions compared to a base metal. The welds prepared with 5 mm/s welding speed (low heat input) exhibit the highest hardness compared with 4 mm/s and 3 mm/s welding speeds. This is attributed to the fine equiaxed dendritic grains and \(\beta\)-precipitates present in the fusion zone. Fine-grain size is critical in magnesium alloys, and is responsible for grain boundary strengthening [38]. The grain boundaries become the primary impediment to the dislocation motion, and materials with fine grain sizes display higher hardness and strength. In all conditions, the equiaxed zone exhibits slightly higher hardness than the columnar zone within the fusion zone. The lower hardness value in the  
Figure 8: Weld transverse cross-section comparing weld metal, heat-affected zone (HAZ), and base metal micro-structures of AZ31 welded joint prepared with 4 mm/s speed.  
HAZ region, compared to the base metal, is attributed to the coarse grains in all conditions. The width of the HAZ decreases with increasing welding speed because of low heat input. However, the base metal shows the highest hardness values compared to the HAZ and fusion zone in all conditions, and this is attributed to the cold-worked microstructure and fine grain size.
## 4 Case Study
### Training Results  
In the current study, the total number of time steps used for training the models was fixed to 200,000, and the simulation time of FlexSim model was fixed to 5000 s. In addition, all three different RL algorithm hyperparameters were set to the default values provided via Stable-Baselines3. In fact, the hyperparameters were left unmodified since a good performance was expected with the default hyperparameter values.  
Figure 4 shows the average reward obtained during the training process for three different RL algorithms, where the horizontal and vertical axes represent the timesteps of the training process and the obtained average reward, respectively. Notice that for every RL algorithm, as the training time advances, the average reward tends to increase, finally reaching an average reward of around 85. This trend indicates that the agent has learned how to place items based on the received reward, until the reward cannot be further increased. The differences in learning behavior observed among the three RL algorithms can be attributed to the underlying design and optimization strategies of the algorithms. The A2C algorithm's behavior seems the most efficient, as in the initial phase of the training process, there is a very short descending trend in the average reward, corresponding to the exploration phase. Subsequently, as the training time advances, the average reward increases steadily, quickly reaching a plateau for the rest of the training process. Similarly, the PPO algorithm exhibits a short exploration phase in the initial training process, and as the training time advances, the average reward increases steadily. However, the average reward oscillates in the later stages of training, indicating that the PPO algorithm incorporates some exploration during the exploitation phase. In contrast, the DQN algorithm behaves somewhat differently from the other RL algorithms. The ample descending trend in the mean reward in the initial phase of the training process can be attributed to a long exploration phase, where the agent explores suboptimal actions and learns from its mistakes. Once it has collected sufficient data, the algorithm starts  
exploiting the learned Q-values and improves its performance. This is not surprising, as DQN is a value-based algorithm that learns to approximate the optimal Q-value function using a deep neural network. This approach generally requires more training time than A2C and PPO due to the algorithm's need to explore the state-action space to learn the optimal Q-values.
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 4 Experimental Validation
### Model Validation
#### 4.3.2 Validation of Coupled View Cases  
In coupled view imaging cases, the camera is placed at bottom and in front of the front plate, just as in the bottom-front coupled view and side-front coupled view cases where the yaw angles and wind directions are both regulated. For different coupled view cases, the practical yaw angle and wind direction are simulated by adjusting REV1# in the range of 0 to 90\({}^{\circ}\) and REV2# in the range of \(\pm\)90\({}^{\circ}\), in steps of 15\({}^{\circ}\). In each imaging case, the yaw angle and wind direction are calculated according to Equations (38) and (39) based on the given REV1# and REV2#.  
The camera is fixed in the same position as in the pure deviated bottom view, for bottom-front coupled view imaging case, and as in the pure deviated side view, for the side-front coupled view imaging case. Detected yaw angles and wind directions are calculated based on the stick's imaging length and orientation, respectively, according to Equations (6) and (12) for bottom-front coupled view, and according to Equations (27) and (28) for side-front coupled view. The detected results are denoted as black-edged white circles and compared with the 3-D contour curve surface of the expected yaw angle and wind direction acquired from REV1# and REV2# in a dense space of 0.5\({}^{\circ}\), as shown in Figure 22a, 21b, 21c, 21d, respectively, for yaw angle detection in bottom-front and side-front coupled view, and wind direction detection in bottom-front and side-front coupled view. The absolute errors between detected and expected results are accordingly given in terms of 3-D contoured bars in Figure 23a, 22b, 22c, 22d, respectively, for the above cases.  
It can be observed that all the detected results agree well with the expected cases, both in terms of yaw angle and wind direction detections, as depicted in the 3-D surface plots. The maximum absolute error for yaw angle detections is 4.0\({}^{\circ}\) within the limit range and climbs to 18.1\({}^{\circ}\) beyond the limit in bottom-front coupled view, compared to a smaller maximum absolute error of 2.9\({}^{\circ}\) in side-front coupled view validation. This is because the validated yaw angle range in side-front coupled view covers all validated cases, whereas in bottom-front coupled view, it exceeds the limit range above the 75\({}^{\circ}\). In wind direction detection, a similar trend is seen as in bottom-front coupled view where the maximum absolute error is no more than 3.0\({}^{\circ}\) within the limit range but climbs to 5.9\({}^{\circ}\) above the limit. The maximum error is 3.4\({}^{\circ}\) for side-front coupled view, which is proximal to the bottom-front view within its yaw angle limit range. In all, the results show that the proposed method measures both yaw angle and wind direction with good accuracy, provided that the limit range of measurement is not exceeded.
## 4 Conclusions  
Damage identification in cable force is of great significance to maintain the structural safety of cable-stayed bridges. The existing static methods for cable force damage identification generally require knowledge of an accurate magnitude of static force. This increases the test cost and time for static methods. To overcome this shortcoming, an improved residual force algorithm was proposed in this paper, which is independent of static load vector. By combining two different static loading modes, a new damage indicator vector was defined in the method for damage localization and quantification. Compared with existing static residual force methods, the outstanding advantage of the proposed algorithm is that the specific value of static load is not required in the damage identification process. The feasibility of the proposed method was verified by the example of a single tower cable-stayed bridge. Even if the static load is unknown, the proposed method can successfully identify the damages to cable forces. It was shown that the proposed method has good application prospects in engineering practice. The proposed method is also applicable to damage identification in other types of linear structures. More numerical and experimental studies should be carried out for applications of the proposed method in the future.  
Conceptualization, R.F. and W.W.; formal analysis, Q.Y.; writing--original draft preparation, Y.W. and Q.Y.; writing--review and editing, L.N. and P.J.; project administration, Q.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Deep Learning of GNSS Acquisition
## 2 GNSS Signal Model and Acquisition
### CAF Evaluation  
The CAF is therefore a function which depends on the delay \(\tau\) and the Doppler frequency \(f_{d}\) of the local replica. The optimization in (6) is performed over a grid of possible \(\tau\) and \(f_{d}\) values, typically evaluating the CAF on a set of discrete values. Such a bidimensional grid is referred to as the search space. The search space consists of a set of cells which include the different value of delay and Doppler, which we gather in vectors \(\boldsymbol{\tau}\in\mathbb{R}^{n_{\tau}}\) and \(\mathbf{f}_{d}\in\mathbb{R}^{n_{f}}\), respectively. Typically, we have that \(n_{\tau}\gg n_{f}\). The evaluation of this grid can be performed following several strategies that trade off search speed and performance. Three searching strategies are typically considered: maximum search, serial search, and hybrid search strategies [10].  
1. Maximum: This strategy evaluates the CAF all over the search space \(\mathbb{R}^{n_{\tau}}\times\mathbb{R}^{n_{f}}\), such that each cell corresponds to a CAF value at the corresponding delay/Doppler pair. The overall maximum value of the ambiguity function is then selected and compared with the threshold \(\beta\), if the maximum's value is greater than \(\beta\), the satellite is considered acquired, with the estimated code delay and Doppler frequency corresponding to those of the maximum's cell.  
Serial: In this strategy, the ambiguity function is evaluated serially cell by cell. In each cell, when the ambiguity function (9) is computed, it is immediately compared with the threshold. If the value exceeds the threshold, the acquisition process stops, and the value of the estimated code delay and Doppler frequency are matched to those from the cell under the test. This strategy has the benefit of reducing the number of CAF evaluations, at the expense of some performance degradation.
3. Hybrid: This strategy evaluates the ambiguity function row by row (or column by column), and at the end of each row (column), the values of the computed ambiguity functions are compared with the threshold. As soon as the maximum value in the current row (column) exceeds the threshold, the acquisition process stops, and the estimated code delay and Doppler frequency are set to the corresponding cell. This strategy brings in a balance between the two approaches above.  
In this work, we consider the maximum search strategy, both for the standard GNSS acquisition revisited in this section and the DNN approach proposed in the upcoming Section 3.
## 1 Introduction
The main contributions of this article are as follows:  
Initially, a novel non-coherent communication scheme, termed the reference signal scrambling transformation short reference differential chaos shift keying (RSST-SR-DCSK) communication system and method, is proposed to address the security vulnerabilities of SR-DCSK systems. The article delineates the design of the signal frame structure, modulator, and receiver for this system. Secondly, the paper derived the data-energy-to-bit-energy ratio (DBR) and the theoretical bit error rate of the RSST-SR-DCSK system in Gaussian channels, analyzing its energy efficiency. Compared to DCSK and SR-DCSK systems, the RSST-SR-DCSK system conserves transmitted bit energy and increases data rates. Finally, through a simulation analysis, the article explores the optimal replication times and semi-spreading factors for the RSST-SR-DCSK system. It is found that under specific signal-to-noise ratios, the RSST-SR-DCSK system outperforms SR-DCSK and DCSK systems in terms of transmission data rates and bit error rates.
## 1 Introduction
Another study showed that changing the milling conditions affects the quality and accuracy of materials; moreover, surface roughness is widely used as an index for evaluating machining accuracy [17; 18; 19]. [PERSON] et al. suggested that the milling speed is the most significant factor affecting surface roughness and that optimum surface roughness can be obtained by reducing the feed rate and increasing the spindle speed when the hardness of the material is high [17]. [PERSON] et al. showed that, in addition to speed, the depth of the cut in the radial direction as well as the interaction between the radial and axial depths of the cut affect surface roughness; furthermore, the smaller the depth of the cut, the better the surface roughness [18]. Similarly, [PERSON] et al. found that a combination of high milling speed, low feed rate, and small depth of cut comprised the optimal conditions for minimal adequate surface roughness [19]. Although milled dentures ideally have a high accuracy, parameters such as the type of milling tool, rotation speed, and feed rate during milling can affect the actual surface roughness of the material [20]. Rough milling is possible using a large-diameter tool and fine milling is possible with a narrow-diameter tool. Similarly, for milled denture fabrication, tools with a larger diameter enable faster fabrication; however, the surface becomes rougher and requires more polishing after fabrication. In contrast, tools with a narrower diameter enable more precise milling of surfaces and less polishing; however, there is an increase in the milling duration as well as the wear and tear of tools [21]. No studies have evaluated the effects of milling tools and conditions on machining accuracy and the time required for denture base fabrication using PMMA disks; further, the optimal milling conditions for the fabrication of milled dentures remain unclear. Therefore, the objective of this study was to determine the effects of different milling conditions on the surface properties and milling duration of PMMA disks for denture base fabrication using a milling machine. The null hypothesis was that the surface roughness and milling duration would not differ under different milling conditions.
# Ground-Borne Vibration Due to Construction Works with Respect to Brownfield Areas
## 3 Experimental Setup and Results of SEISMIC Measurements
### Vibrated Gravel Piles  
The first processed measurement was the response of the rock environment from the vibrated gravel piles. The source of harmonic oscillation was a 20-ton vibrator with a predominant vibration frequency of 50 Hz. In the vicinity of the geotechnical works carried out, the following geology was found: landfill of 2 m thickness, followed by clays and marlite of 4 m thickness, and then weathered to unweathered marl. Measurements ranged from 5 to 13 m from the dynamic load source shown in Figure 4. The outputs of the seismic measurements are wave images of recordings of vibrated gravel piles. An example of the wave image of the record is shown in Figure 5 (peak particle velocity, PPV). The figure shows the vertical component /SHZ/, as well as the horizontal radial /SHN/ and horizontal transverse /SHE/ components. Time is displayed on the horizontal axis.  
Figure 4: Measurement situation—vibrated gravel piles.  
Figure 3: Example photos of measurements—vibrated gravel piles (**left**) and vibrating roller (**right**).  
From the wave images, the peak particle velocity (PPV) was subtracted from the vibrating piles at individual distances, and attenuation curves for three perpendicular directions (Figure 6) were constructed from these values (this means that for five different distances, we obtain fifteen maximum values of oscillation velocity). To obtain the attenuation curves, an exponential curve was chosen with respect to the shape of the amplitude attenuation equation.