esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### RQ2: Which Stimuli May Be Used to Elicit Emotions?  
#### 3.2.1 Format and Nature of Stimuli  
Pictures and images were the preferred stimulus type to induce emotional states. Besides, some studies used a combination of pictures with sounds [36], with words [52] or with videos [32]. Other studies used film clips [26; 27; 29; 33; 43; 46; 48; 51; 59; 62] and/or music videos (DEAP dataset) [26; 69]. Two studies conveyed emotions by presenting written words to the participants [45; 58].  
Twenty-nine studies included colored stimuli, whereas other studies preferred a greyscale [33; 38; 42; 61] or black and white [44] presentation, and six studies did not mention the color [27; 29; 46; 48; 51; 62].  
As regards stimuli contents, three studies presented social scenes to the participants [63; 64; 65], five showed faces [33; 38; 39; 42; 47; 61; 66], and two studies presented a combination of social, non-social scenes and faces [28; 37]. Three studies showed a combination of social and non-social scenes [31; 50; 53]. However, most studies did not specify any content.  
Thirteen studies preferred to use in-home made stimuli [27; 29; 40; 43; 45; 46; 48; 51; 59; 61; 62; 63; 64], whereas all other studies included stimuli coming from publicly available databases.
## 5 Results and Discussion
### Statistical Properties  
This study evaluates the average, range, deviation, and standard deviation of frequency upon pulses resulted among patterns. The average frequencies in a higher temperature are lower, indicating that ROs generated fewer pulses than those at low temperatures [3]. When ROs are realized using Pattern-3 at low temperatures, they produce the highest average frequency (428.28 MHz) compared to other patterns. Meanwhile, the ROs implemented using Pattern-1 resulted in the least average frequency (354.64 MHz). The vast difference has occurred because Quartus assigned different LUT inputs that form RO among patterns, as shown in Table 2. Another reason is due to the different positioning of input and output connections of LAB of different patterns, which indirectly determine average routing hotspots among the patterns. For instance, the average routing hotspots of Pattern-1, at 35.23%, is higher than Pattern-3, 29.77%. Hence, the delay of ROs concerning the pattern is varied, which leads to the difference in average frequencies.  
The range of frequencies of the ROs resulted among patterns also vary. The most extensive range resulted using Pattern-4, which is 130.05 MHz, and the narrowest range resulted using Pattern-2, which is 37.36 MHz. The narrow frequency range of Pattern-2 is due to all ROs being assigned the same input configuration, as listed in Table 2. The study estimated the frequency deviation and maximum frequency deviation of the pulse to understand RO behavior better. The frequency deviations of Pattern-1, Pattern-2, Pattern-3, Pattern-4, and Pattern-5 are 4.45 MHz, 1.29 MHz, 2.80 MHz, 4.48 MHz, and 4.15 MHz, respectively. In comparison, the maximum deviations of Pattern-1, Pattern-2, Pattern-3, Pattern-4, and Pattern-5 are 37.69 MHz, 6.78 MHz, 22.13 MHz, 58.22 MHz, and 44.95 MHz, respectively. The vast differences between maximum deviation and average deviation among patterns indicate that the number of pulses generated by the RO is due to delay and the difference of routing density.  
The study found a slight increase (1.62%) of the average frequency among patterns when scenarios are applied. Meanwhile, the frequency ranges and standard deviations decreased in most scenarios, as shown in Figure 5. There is a significant reduction of frequency range except using Pattern-2 (all scenarios) and Pattern-3 (scenario RH1 and RH2), as shown in Figure 5a. The significant change of standard deviation (31.2%) between scenarios occurred when the RO was realized using Pattern-3, Pattern-4, and Pattern-5, as shown in Figure 5b. These changes might affect the increase of uniqueness and the reduction in reliability. The list of frequency ranges and standard deviations and their change can be seen in Table 3.  
Figure 5: The shift upon four scenarios of (**a**) frequency range (**b**) standard deviation.
## 1 Introduction
[14] reported the electron heating mechanisms both in single-frequency (13.56 Mhz and 4.52 Mhz) and dual-frequency (a combination of 13.56 Mhz and 4.52 Mhz) plasmas. [PERSON] et al. [15] investigated a dual-frequency discharge at atmospheric pressure by conducting experiments as well as numerical modeling. In their work, they focused on the influence of the low-frequency voltage amplitude on the dielectric barrier discharge in an Ar-NH\({}_{3}\) mixture. They also investigated the discharge mode transition from \(\alpha\) to \(\alpha\)-\(\gamma\) mode, which resulted in an increase in the plasma density [16]. The use of Ar/O\({}_{2}\) mixtures in dielectric barrier discharges at atmospheric pressure using dual-frequency (13.56 Mhz/200 kHz) excitation was investigated by [PERSON] et al. [17]. In their studies, they analyzed the effect of each frequency component on the dual-frequency-discharge mechanism and pointed out that the dual-frequency discharge exhibited potential for use in applications in plasma-assisted surface processing and plasma-assisted, gas-phase chemical conversion. [PERSON] et al. [18] numerically investigated the characteristics of argon-dielectric-barrier-discharge excitation using dual frequencies at atmospheric pressure. When the low-frequency voltage amplitude was low, the electron flux at the dielectric surface decreased; however, the ion flux was unchanged. Although the dual-radio-frequency-driven discharge could increase the opportunities for plasma manipulation, it also increases the complexity of the discharge. In our previous studies, we investigated the effects of high-frequency, secondary-electron emission coefficient on the characteristics of helium-discharge plasma driven by dual frequencies at atmospheric pressure. When combining a low-frequency source with a high-frequency source, the effects of the low-frequency source on the basic characteristics of the Ar/O\({}_{2}\)-discharge plasma at atmospheric pressure have not been thoroughly clarified. On the foundation of our previous studies, this paper investigates the characteristics of Ar/O\({}_{2}\) atmospheric pressure discharge driven by a dual-frequency source. A one-dimensional fluid model has been used to describe a dual-frequency system in Section 2. The dependence of several discharge parameters on the basic plasma characteristics is presented and discussed in Section 3. Finally, the conclusions are summarized in the Section 4.
## 6 Conclusions  
This paper proposes a new adaptive algorithm that controls the update filter coefficient and adjusts the notch depth with respect to JSR. Moreover, the proposed method detects and cancels single and multiple interferences using an ANF based on a second IIR direct NF. The proposed algorithms operate in TD, which eliminates the need for transferred domain information and, thus, lowers hardware costs. Furthermore, by cascading MANF modules, the proposed ANF module can notch out multiple CWIs. Simulation results show that as JSR increases, the notch bandwidth decreases, as does the depth of the notch magnitude responses. Therefore, the proposed approach can detect and mitigate interference while adjusting the notch depth for any given JSR values, effectively controlling the notch depth; it also achieves better performance than full suppression for both low and high JSR values.  
Figure 21: BER vs. JSR, with \(E_{b}/N_{o}=20\) dB.  
Figure 22: The estimated optimal k1 vs. JSR.  
The results indicate that the IIR NF can adapt, track any frequency change, and reduce CWI power at the output of the NF, with less distortion of the useful signal; they also clearly show improvements over ANF in terms of overall BER, as well as a robust performance of the ANF over a range of \(\abs{\text{SR}}\) values--especially in terms of CWI. This method is able to detect and mitigate weak and the strong jamming (\(-30\) to \(40\) dB); therefore, it can be used in a DVB-S2 receiver, or in other types of wireless communication receivers.  
Conceptualization, A.E.G.; methodology, A.E.G.; writing--original draft preparation, A.E.G.; writing--review and editing, A.E.G.; supervision, R.J.L. All authors have read and agreed to the published version of the manuscript.  
The research presented in this paper is part of the LASESNA Lab's AVIO 601- Interference Mitigation in Satellite Communication project at Ecole de Technologie Superieure (ETS). This research is also supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Thales, Telesat, VIGILANT GLOBAL, CRIAQ, and ATEM Canada.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
With the implementation of dual-system fusion, real-time orbit determination precision has progressively increased, from meter-level in the early 2000s to centimeter- and even sub-centimeter-level beyond 2020. This tendency represents the ongoing technological improvement and maturation of real-time orbit determination, bringing it closer to the international standard. [PERSON] et al. (2006) used the CHAMP spacecraft to achieve an onboard GPS real-time orbit determination accuracy of 1.5-3 m [15]. [PERSON] et al. (2015) presented a \"pseudo-ambiguity\" methodology to absorb broadcast ephemeris errors, achieving an accuracy of 0.39 m utilizing dual-mode (GPS+BDS) observations from the FY3C satellite [16]. [PERSON] et al. (2022) achieved a 0.2 m precision using BDS-3 B1C/B2a readings from the HY2D satellite [17]. [PERSON] et al. (2024) used a numerical stepwise search and comparison method to optimize the random process parameters for pseudo-ambiguities. Using two low-Earth orbit satellites, GRACE-C and SWARM-A, they achieved a real-time exact orbit determination 3D position error (RMS) of around 4.62 to 7.56 cm [18]. [PERSON] et al. [19] used ultra-rapid forecast ephemerides from the ground-based IGS (International GNSS Service) to replace broadcast ephemerides and performed real-time orbit determination of LEO satellite positions and velocities. Compared to traditional airborne orbit determination methods, the sub-meter precision method suggested in this study has substantial advantages, especially in real-time orbit determination in high-dynamic settings. While other investigations, like Sentinel-3A, have demonstrated good orbit accuracy, traditional and novel approaches may perform similarly in some circumstances [20; 21; 22]. [PERSON] et al. [23] improved real-time orbit determination accuracy by reasonably setting the random model of pseudo-ambiguity parameters, achieving a roughly 10% improvement over GPS. However, the introduction of additional parameters during the numerical integration phase increased the computational load, which may make this algorithm unsuitable for spaceborne GNSS receivers. The research on sub-meter-level orbit determination discussed above has only been validated in ground-based tests and has yet to be verified on spaceborne GNSS receiver platforms. In existing gravity field models, the degree of expansion at different orders significantly impacts orbit determination accuracy. However, as the order increases, the computational load required grows exponentially [24; 25; 26]. To match the computational capabilities of spaceborne receivers, it is essential to strike the right balance between computational efficiency and orbit determination accuracy. By selecting an appropriate gravity field model order, accuracy can be ensured while minimizing the consumption of system resources as much as possible. Therefore, optimizing orbit determination accuracy under limited system resources has become a critical challenge that needs to be addressed.
## 3 Results and Discussion
### Microstructure
However, in the alloy 1 specimen, the size of the cellular substructures is significantly larger in some areas than in others. The cellular substructures in the alloy 2 specimen were not only smaller in size but also more homogeneous compared to those in the alloy 1 specimen. The average size of the cellular substructures in the alloy 2 specimen is 6 microns, which is about 45% smaller compared to cellular substructures in the alloy 1 specimen. It can be noted that the inclusion of a Cr, Mo, V, and N composite modifier refines not only the grain structure of the invar alloy specimen but also the cellular substructure within the grains. Figure 9 compares and analyzes the microstructure of the alloy 1 specimen and alloy 2 specimen using EBSD technology. The results show that the grains on the X-Y planes of alloy 1 and alloy 2 are equiaxed, and the different colored regions in Figure 9b,e represent grains with different orientations. Figure 9c,f show the distribution of grain boundary orientation differences, with the blue lines representing low-angle grain boundaries (LAGB, \(2\leq\theta\leq 15^{\circ}\)) and the red lines representing high-angle grain boundaries (HAGB, \(\theta\geq 15^{\circ}\)). Figure 8: Microstructure of grains and cellular substructures in X–Y plane: (**a**,**c**) alloy 1 specimen; (**b**,**d**) alloy 2 specimen. The proportion of low-angle grain boundaries in alloy 1 and alloy 2 samples is 22.2% and 36.3%, respectively. To a certain extent, small-angle grain boundaries can reflect the dislocation density, and the larger the proportion of small-angle grain boundaries, the greater the degree of dislocation accumulation [26].
## 4 Experimental Results
### Analysis of Support Vector Machine Model Results  
Based on the principle of structural risk minimization and statistical theory, the support vector machine (SVM) classification method finds wide application in image classification and machine learning. When solving nonlinear classification problems, SVM maps the sample space to a high-dimensional or even infinite-dimensional feature space through nonlinear mapping and seeks the optimal classification surface in the feature space. The classification function of SVM is similar to a neural network in form, where each intermediate layer node corresponds to the inner product of the input sample and a support vector, and the output is a linear combination of several intermediate layer nodes. To solve nonlinear classification problems, non-negative relaxation terms, misclassification penalty constants, and kernel functions are introduced. The kernel function transforms a completely indivisible problem into a separable or approximately separable state without increasing algorithm complexity. The commonly used kernel functions include polynomial, radial basis function (RBF), and sigmoid. The radial basis kernel function, which contains  
Figure 11: The confusion matrix based on BP network.  
fewer hyperparameters and exhibits strong classification ability, has shown good results in identifying broken wire damage in steel wire ropes. In this study, cross-validation was used to set the learning parameter misclassification penalty factor C to 0.1 and the parameter Gamma of the radial basis function to 5. The one-dimensional signal collected by the sensor was manually extracted with damage features such as the peak and width, and this feature information was used as inputs for the support vector machine model for quantitative recognition. The confusion matrix, shown in Figure 12, indicates that the accuracy of the support vector machine classification model is 64.81%.
## 2 Related Work
### Delay-Aware MAC Protocols
In [28] the authors investigate nodes synchronization in interruptible LPL protocols, _i.e._, LPL protocols that use an acknowledgment packet to interrupt the sender in its continuous transmission of data. In this paper, the authors compute for each node its new check interval by adding a delay \(T_{S}\) that is computed as the difference between the wake-up times of the communicating nodes. Further research of the same authors [29] propose to adjust the transmitter and receiver schedules from a pool of interruptible LPL protocols to meet the dynamics of the node and the network conditions. In another work [30] these authors study the dynamic adaptation of the duty cycle in interruptible LPL protocols. Firstly, they propose to adapt the duty cycle by increasing the check interval by 100 ms when five consecutive packets are successfully sent to the destination and by decreasing the check interval by 250 ms when the packet does not achieve its destination. Secondly, they optimize the check interval by using control theory in order to find a good balance between energy savings and packet delivery successes. In [31] we present preliminary work adjusting the check intervals of LPL protocols based on the communication delays suffered by the packets traveling along multi-hop paths. The present work describes improvements to the adjustment mechanism for tree topologies by taking into account the case of multiple sources; it also presents comparison results between CLAC on top of CTP/BoX-MAC and CTP/BoX-MAC alone, from the point of view of the network lifetime, connectivity, end-to-end delay and packet loss. In contrast to [28, 29] which considers single-hop scenarios, CLAC computes the delays in multi-hop scenarios. A multi-hop scenario is also considered in [30], in which the delay is estimated based on local observations (e.g., number of packets successfully sent and received) while in CLAC it is assumed that the packet transmission rate is known (as parameter of the application running on the sensors), and the delay is estimated based on the positions of the nodes within a path. Thus, CLAC may easily adapt to changes in the network topology while in other works it is assumed that the topology rarely changes.
## 2 Related Works
### Weakly Supervised Segmentation  
There are three common weak supervision methods: image level [21; 22; 23; 24], box level [25; 26; 27; 28], and scribble level [29]. Image-level supervision provides only category labels, lacking spatial details. Most existing methods build upon Class Activation Mapping (CAM) [30; 31], employing techniques such as iterative erasure, pixel affinity learning, and saliency-guided refinement [21; 22; 23; 24]. However, CAM typically highlights only the most discriminative regions for classification, which often represent just a subset of the full object, leading to incomplete segmentations.  
Box-level supervision offers richer spatial cues, including object count, size, and location [25; 26; 27; 28]. Most methods adopt a two-stage pipeline: first, pseudo-labels are generated from bounding boxes using traditional or deep segmentation algorithms; second, these labels are used to train a segmentation model. While this approach improves object coverage, the initial pseudo-labels often contain noise and inaccurate boundaries, limiting the final segmentation quality.  
Scribble-level supervision involves minimal annotation, such as lines or points [29; 32; 33]. Line annotations convey object presence and approximate shape, while point-based annotations (e.g., object extremes or random points) provide sparse but critical location and category cues. Among these, extreme-point labeling is most informative and easiest to utilize, followed by line annotations, with random point annotations posing the greatest challenge. To maximize the utility of sparse scribble cues, recent works propagate annotation signals through candidate region expansion or leverage point connectivity to estimate object shapes.
## 3 Challenges
### Sensing Techniques
#### 3.12.2 Cooperative Sensing  
CR wireless sensors may encounter incorrect judgments because radio-wave propagation through the wireless channels has adverse factors, such as multi-path fading, shadowing, and building penetration. In addition, CR wireless sensors are hardware constraints and cannot sense multiple channels simultaneously. Therefore, CR wireless sensors cooperate and share their sensing information with each other to improve the sensing performance and accuracy. Three types of cooperative sensing exist: (a) centralized cooperative spectrum sensing; (b) decentralized cooperative spectrum sensing; and (c) hybrid cooperative spectrum sensing.  
In centralized spectrum sensing techniques, each CR wireless sensor performs its own local spectrum sensing independently and makes a decision as to whether the PU's signal is present or absent on a particular channel. The CR wireless sensors then forward their decisions to a central cooperator, such as a cluster head, collector, or server. The central cooperator fuses the received decision of the CR wireless sensors and makes a final decision to infer the absence or presence of the PU. Whenever a CR wireless sensor wants to send data, it request channel information to the central cooperator.  
In decentralized cooperation, the CR wireless sensors share intra-cluster information among clusters. CR wireless sensors in a cluster perform local spectrum sensing and inform the other CR wireless sensors within the clusters of their spectrum decision. There is no central cooperator; therefore it works in a decentralized manner. This scheme requires a periodic update on the spectrum information table, hence requires more storage and computation.  
In hybrid cooperation, CR wireless sensors share information in a decentralized manner. However, the central cooperator may request a cluster head to send channel information whenever necessary. Although, cooperative sensing has advantages over non-cooperative sensing, it requires additional computation and resources, which is a challenge in hardware constraint CR wireless sensors.