esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case Study 1: PC-Based Interface  
The demographic information of the participants was as follows: 70.00% of the participants were female, while the remaining 30.00% were male. The age range was intentionally broad, with 30.00% of participants between 18 and 40 years old, 50.00% between 40 and 55 years old, and 20.00% over 70 years old. In terms of education level, 20.00% indicated basic studies, 30.00% indicated bachelor's studies, and 50.00% indicated post-graduate studies. Additionally, 60.00% of participants had previous experience with metaverse services, while 40.00% had no prior experience. Furthermore, 60.00% of participants indicated that they regularly used the internet to organize their trips, while 40.00% did not rely on the internet for trip planning. Moreover, only 20.00% of participants reported always using guided visits during their trips, 60.00% indicated occasional use, and 20.00% never used guided visits. Finally, 70.00% of participants reported being users of PC peripheral devices (e.g., keyboards, mouse, joystick, gamepads), while 10.00% occasionally or never used such devices.  
Figure 4 presents the results of the PQ. Specifically, for participants who used the PC-based interface, Figure 4 displays the mean standard deviation for each PQ question, while Figure 4 illustrates, for each PQ subscale, the total percentage.  
Figure 4: PQ results for case studies 1 and 2. (**a**) Case study 1: mean and standard deviation per question. (**b**) Case study 1: subscales results. (**c**) Case study 2: mean and standard deviation per question. (**d**) Case study 2: subscales results.  
Note that the Involvement score was 77.14%, indicating that participants paid close attention to the virtual reality environment and actively participated in most aspects. The Sensor Fidelity score was 78.10%, signifying that users were able to observe from multiple perspectives and interact with elements in the virtual environment readily and with no issues. The Immersion score for PC-based participants was 84.29%, indicating that participants quickly adapted to the virtual environments and performed the tasks without distractions. Moreover, the Interface Quality score was 73.33%, suggesting that users perceived some failures or malfunctions in the applications during the tasks.  
With respect to the SUS questionnaire, the global perceived usability was 74.00 out of 100 (min: 62.5, max: 100; SD: 9.80), indicating that the proposed interfaces achieved a good level of usability. Figure 5 presents the results obtained for the SUS questions, see Table 3. Most users expressed a willingness to frequently use the interface and found it easy to use. They also felt that the functionalities of the interface are well-integrated. Additionally, the users felt confident using the interface and found it consistent.
## 2 Mathematical Model Formulation
### The CP Model  
CP decision variables, objective function and the constraints are presented in the following equations using the OPL API of CP Optimizer. To express the processing times of the jobs on the machines, the model uses interval variables denoted as _JobInt_. In addition, sequence variables for the machines are defined in the model as _Machine_ which collects all these interval variables.  
**Decision Variables:**  
\(JobInt_{i,k}\): Interval variable for job \(i\) on machine \(k\) with duration \(p_{i,k}\)  
\(Machine_{k}\): Sequence variable for machine \(k\) over \(\left\{JobInt_{i,k}\middle|1\leq i\leq n\right\}\).  
CP Model: Objective and Constraints:  
\[Min\left(\max_{i\in I}(endOf(Joblnt_{i,m}))\right) \tag{13}\]  
\[endBeforeStart(Joblnt_{i,k},Joblnt_{i,k+1})\forall i=1,\ldots,n,\ \forall k=1,\ldots,m-1 \tag{14}\]  
\[noOverlap(Machine_{k})\forall k=1,\ldots,m \tag{15}\]  
\[sameSequence(Machine_{1},Machine_{k})\forall k=2,\ldots,m. \tag{16}\]  
The CP model minimizes the makespan by computing the maximum end date of each job on the last machine (13). Constraint (14) impose the precedence constraints between the consecutive operations of each job on the sequence of machines. Machines are the disjunctive resources and can process only one job at a time, which is expressed by the _noOverlap_ Constraint (15) over the sequence variables associated with machines. The relationship between sequence variables and the interval variables are provided while defining the decision variables. The last constraint _sameSequence_ (16) guarantees that all the jobs are processed in the same order on each machine. Therefore, the permutation of the jobs will be the same for each machine.
# Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine
## 3 Pairwise Support Vector Machine  
Although various categorization algorithms can be used, in this research, a pairwise Support Vector Machine (SVM) is utilized for its simplicity. Pairwise SVM takes two inputs and defines if they are from the same class, whereas standard SVM takes only one input and seeks to determine its class. Assume \(R=\{H_{1}^{1},\cdots,H_{i}^{j},\cdots,H_{N}^{M}\}\) be a training set of chainlets where \(H_{i}^{j}\) links to the \(j\)-th training image of the \(i\)-th person. The pairwise decision function between \(H_{i}^{j}\) and the inquiry chainlet \(H_{p}^{q}\) can then be written as:  
\[D(H_{i}^{j},H_{p}^{q})=\sum_{(m,n)}\alpha_{mn}y_{mn}K((H_{m},H_{n}),(H_{i}^{j},H_{p}^{q}))+\gamma \tag{4}\]  
where \(\alpha\) and \(K\) represent the learned weights and the kernel function, respectively, and \(\gamma\) indicates the learned bias, and:  
\[y_{mn}=\begin{cases}+1,m=n\\ -1,m\
eq n\end{cases} \tag{5}\]  
In this paper, the kernel \(K\) represents the direct sum pairwise kernel, i.e.,  
\[K((a,b),(c,d)):=k(a,c)+k(b,d) \tag{6}\]  
where \(k\) indicates a standard kernel; in this instance, the linear kernel:  
\[k(a,c)=a^{T}c \tag{7}\]  
In this paper, the parameters alpha and gamma were learned as explained in [21]. Moreover, the first two ear images of each subject in the dataset were employed for training the algorithm and the rest of its images were utilized for testing the algorithm.
## 3 Results  
### Classifier Performance with Different Window Lengths  
An overall average classification F1-score of about 0.90 was achieved for the physical activity classifier in this study, depending on the used window length, sampling frequency, feature set, and classified activity type. To evaluate how each of these parameters affected the classifier individually, classifier F1-scores were averaged over other parameters.  
Figure 4 shows the classification F1-score of activity types for the different window lengths when averaged over different sampling frequencies (50 Hz, 25 Hz, 13 Hz) and feature sets (110 features, 43 features, 28 features, 13 features, and SFS feature set). The classifier had better performance with the average F1-score over 0.9 classifying static, walking and running activity types. Window lengths of 5 s and 3 s had similar results with the average F1-scores of 0.92 \(\pm\) 0.02 and 0.91 \(\pm\) 0.02, while the result with 1 s was 0.87 \(\pm\) 0.02.  
Statistically significant differences (marked with an asterisk in Figure 4) were found in moderate intensity and rhythmical intensity activity types between window lengths of 5 s and 3 s. Window length of 1 s had a statistical difference classifying every activity type other than running compared to both 5 s and 3 s window length.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
###### Abstract  
Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they cannot preserve the multi-way signal characteristics as the dimension of arrays expands. In this paper, we propose a structural-missing tensor completion algorithm for robust DOA estimation with uniform rectangular array (URA), which exhibits a high robustness to non-ideal sensor failure conditions. Specifically, the signals received at the impaired URA are represented as a three-dimensional incomplete tensor, which contains whole fibers or slices of missing elements. Due to this structural-missing pattern, the conventional low-rank tensor completion becomes ineffective. To resolve this issue, a spatio-temporal dimension augmentation method is developed to transform the structural-missing tensor signal into a six-dimensional Hankel tensor with dispersed missing elements. The augmented Hankel tensor can then be completed with a low-rank regularization by solving a Hankel tensor nuclear norm minimization problem. As such, the inverse Hankelization on the completed Hankel tensor recovers the tensor signal of an unimpaired URA. Accordingly, a completed covariance tensor can be derived and decomposed for robust DOA estimation. Simulation results verify the effectiveness of the proposed algorithm.  
robust DOA estimation; structural-missing tensor completion; sensor failure; spatio-temporal dimension augmentation
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 5 Challenges and Future Research Directions
### Energy Harvesting and Cognitive Radio  
Saving energy in D2D networks is an essential requirement for prolonging network lifetime. ML-based energy harvesting and stochastic optimization schemes are urgently required to mitigate the harvested energy outage. The concept of CR can be intelligently implemented with the aid of ML.
## 5 Conclusions  
DCIM has natural advantages in high-precision computing, but there are still challenges in terms of power consumption and area overhead. This paper proposed an all-digital CIM Macro based on SOT-MRAM (**MDCIM**), which can perform FP64 FMA instructions, with CSA and a pipeline MAC array to optimize digital computing circuits for  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **ISSCC’19** & **JSSC’20** & **VLSI’21** & **ESSCIRC’22** & **This Work** \\  & [26] & [27] & [25] & [24] & \\ \hline Memory array & 28 nm SRAM & 28 nm SRAM & 28 nm SRAM & 28 nm SRAM & SOT-MRAM \\ \hline Supply voltage (V) & 0.6–1.1 & 0.6–1.1 & 0.76–1.1 & 0.5–0.9 & 0.9–1.32 \\ \hline Supported floating-point format & FP8 & FP8 & BF16 & FP32 & FP64 \\ \hline Macro size (mm\({}^{2}\)) & 2.55 (Chip) & 2.7 (Die) & 5.832 (Die) & 0.0439 & 0.051814 \\ \hline Frequency (MHz) & 475 & 114 & 250 & 400 & 150 \\ \hline GOPS/W/FP64 & 8.59 & 4.86 & 47.5 & 24.75 & 26.9 \\ \hline GOPS/mm\({}^{2}\)/FP64 & 0.354 & 0.426 & 1.28 & 1.21 & 0.322 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed **MDCIM** with previous works.  
reducing power consumption and latency. This work firstly proposed the application of digital CIM with SOT-MRAM in FP64 computing. However, the high bit width and large computation volume brought by floating-point calculations still pose a challenge for on-chip deployment. Currently, introducing approximate computing into floating point could significantly enhance energy efficiency [28; 29]. Approximate computing can substantially accelerate the speed of operations and reduce the complexity of circuit. At the same time, for binary matrices where elements are only zero and one, the precision loss brought about by approximate matrices is within an acceptable degree. In MAC operations, we can choose suitable approximate computing methods based on the sparsity of the matrices involved in the operations. This could potentially reduce the circuit area used for computations in digital memory computation macros. Therefore, approximate floating-point computation will become a very important research approach in the near future.  
Conceptualization, L.L., L.T. and B.P.; methodology, L.T. and B.P.; software, L.T. and J.G.; validation, L.L. and L.T.; formal analysis, J.G.; investigation, Z.L. and B.P.; resources, B.P. and L.L.; data curation, L.T. and B.P.; writing--original draft, L.T., L.L. and B.P.; writing--review and editing, L.L., B.P. and J.Z.; visualization, L.T. and J.Z.; supervision, B.P. and Z.L.; project administration, B.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.  
This work is supported partly by the Laboratory Open Fund of Beijing Smart-chip Microelectronics Technology Co., Ltd., partly by National Natural Science Foundation of China (62001019), and partly by the Fundamental Research Funds for the Central Universities (YWF-23-L-1241).  
Not applicable.  
We currently have no additional data.  
The authors declare no conflict of interest.
# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 5 Conclusions
### Limitations of the Review  
While this review offers important insights into GenAI adoption, certain methodological constraints and literature gaps must be acknowledged.  
First, several relevant dimensions remain underexplored in the reviewed studies. These include ethical concerns (e.g., data privacy, bias), the role of teacher professional development, the impact of student autonomy, and contextual factors such as institutional policies and socio-cultural dynamics. These elements deserve further attention to understand how they interact with adoption predictors.  
Second, the review exclusively used the Web of Science Core Collection to ensure access to high-quality, peer-reviewed literature. While this increased methodological rigor, it may have excluded relevant studies from other databases or grey literature--particularly those representing emerging markets or non-English-speaking regions. As such, the findings may not fully capture the global diversity of GenAI adoption.  
Third, although the Boolean search strategy supported precision, it may have omitted studies using alternative terminologies or conceptual framings. Moreover, the review focused primarily on studies reporting significant adoption predictors, placing less emphasis on conflicting or null findings, which may limit insights into barriers or resistance to adoption.  
Future research should address these limitations by incorporating a broader range of sources, including non-English publications and studies from underrepresented regions. Greater attention to barriers, ethical considerations, and diverse contextual variables will offer a more balanced and comprehensive understanding of GenAI integration in education.
### Contributions  
* We designed a framework for spectrum sensing with multi-class hypotheses in cognitive radio networks. The classifications of the hypotheses are obtained based on the quantized regions of received signal energy range and the discrete levels of residual energy in a node battery. The results show that the proposed scheme successfully improves the throughput performance of a secondary link.
* Generally, an increase in the dimensions of input vector to the SVM-based classifier increases the complexity of the system. Therefore, the input to classifier is given in form of statistical features extracted from the input signal rather than inputting the original 300-dimensional signal. This feature extraction technique minimizes the computational cost of a classifier yet acquiring satisfactory performance in terms of accuracy. In this paper, three statistical features are proposed for this specific application, which reduce the input signal of 300 elements to a three-dimensional observation. To the best of authors' knowledge, this is the first work to consider a combination of these features, and the third feature in specific, for spectrum sensing classification. The results presented in this paper shows that an SVM with these features can achieve satisfactory classification performance for spectrum sensing.
* The performance of an SVM-based classifier is analyzed for the proposed multi-class hypothesis-based spectrum sensing algorithm. The classifier applied in a _one-versus-rest_ manner is trained with respect to three kernel functions: linear, polynomial, and radial-basis function (RBF), separately. The results show that the polynomial kernel-based classifier achieves the best results in terms of accuracy.
* Finally, the throughput performance of the proposed scheme with polynomial kernel-based classifier is examined. The conventional two-class hypothesis-based spectrum sensing with similar characteristics of SVM-based classifier is utilized for comparison. The results show that throughput increased reasonably under the proposed scheme.  
The rest of the paper is organized as follows. Section 2 presents a theoretical explanation of the proposed system model. Simulation results along with discussions are presented in Section 3. Finally, the conclusion is given in Section 4.
## 2 Experimental Setup
### Electrical Diagnostics  
The diagnostic probes used are coaxially mounted capacitive voltage dividers, CVD, and Rogowski probes, see Figure 3. The Rogowski coils and CVDs are designed, manufactured, and calibrated at the Texas Tech University Center for Pulsed Power and Power Electronics. These diagnostics permit the passive measurement of large amplitude voltage (10's-100's kV) and current (1 kA-100's kA) transients over a wide frequency range [20; 21; 22; 23; 24; 25].  
An RF design rule of thumb stipulates that a signal 10% to 90% risetime, \(\tau_{10/90}\), has a frequency bandwidth, \(f_{BW}\), equivalent to \(f_{BW}=0.35/\tau_{10/90}\)[21]. Thus, a risetime of 1 ns requires a frequency bandwidth of 350 MHz. The CVD and Rogowski probes' transfer function, \(H\), was measured using a vector network analyzer, VNA, and calculated using Equation (1) [23]. The CVD and Rogowski probes are calibrated on a 100-m-long pool of 2121 cable. The 2121 cable has a characteristic impedance of 59 \(\Omega\) and is terminated with a matched load when used for calibration.  
\[H=\frac{S_{21}}{1-S_{11}}\ . \tag{1}\]  
The transfer function of the high-side probes is shown in Figure 4. The CVD probe follows a slope of 20 dB per decade until it levels off. The transition region of the CVD is the \(-3\) dB point of the transfer function. Before the \(-3\) dB point, CVD operates in a derivative mode. After the \(-3\) dB point, the CVD is self-integrating [21]. Fourier transform signal corrections permit the CVD to have a bandwidth of 2.5 GHz, resulting in a minimum  
Figure 3: A cross-section representation of the CVD and Rogowski probes on the setup, not to scale. \(C_{1}\) is the capacitance between the inner conductor and copper tape. Kapton tape produces the dielectric barrier for the second capacitance, \(C_{2}\), between the copper and brass electrodes. \(L_{1}\) is the inductance of the inner conductor, which couples to \(L_{2}\) of the Rogowski coil. The Rogowski coil uses an azimuthal slit to mitigate the capacitive coupling of \(L_{2}\) to \(L_{1}\). The case of both probes is made out of brass.  
possible resolution of 140 ps, see Figure 4. The Rogowski probe transfer function has a valley around 850 MHz, which limits the bandwidth, see Figure 4. The bandwidth used in data processing is 600 MHz, resulting in a minimum possible resolution of 583 ps for the Rogowski probes. All the other CVD and Rogowski probes on the setup had similar transfer functions and bandwidths.  
The transfer function of the diagnostic probes is used to calculate the input signal measured by the probes using discrete Fourier transform analysis. The oscilloscopes are in a Faraday cage some distance from the pressure chamber to reduce noise. All probes are connected to the oscilloscopes via 12.2-m RG223 cables. To account for the high-frequency losses of these cable lengths, the cable transfer function was measured on the VNA in situ, similar to other works [22; 26].
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 3 The Smart Collaborative Routing Protocol for IoT
### Model Establishment  
In order to better understand the working mechanism and performance optimization of the GIN protocol, we first establish a network model, and the specific structures of each layer are introduced. As shown in Figure 4, the network model of the GIN protocol is divided into three layers, i.e., application layer, middle layer and perceptual layer.  
Figure 4: The network model establishment of GIN protocol.  
Figure 3: Cluster Head (CH) node checks the Inspecting Node (IN).  
In the application layer, the managers can learn about the related status of the event regions by collecting data. After the collected information being analyzed, they will take corresponding measures to deal with relevant conditions. Moreover, different monitoring software is added to the application layer according to various service requirements, including energy management, mobile management and assignment management. They are used for monitoring energy usage, nodes movement and task assignment respectively. In terms of the energy management, when the energy of nodes is exhausted, they will broadcast such situation to the surrounding nodes. Upon receiving the information, the surrounding nodes will understand the energy shortage in data forwarding. For the mobile management, the relevant information of nodes can be recorded. For the assignment management, it can coordinate the perceptual tasks of the event area.  
The middle layer contains a lot of sink nodes and multi-access nodes. The multi-access nodes are used to receive the information collected from the perceptual layer, and send them to the sink nodes. Meanwhile, these multi-access nodes can also forward the query commands from the upper layer to the bottom layer, while the sink nodes can conduct data fusion and analysis from multi-access nodes, the refined data is sent to the upper layer. Furthermore, the sink nodes will also forward the query commands from the upper layer to the multi-access nodes.  
In the perceptual layer, it is the basis of the WSN. It consists of massive scattered sensor nodes. The various sensors can obtain related packets of the event area (such as temperature, air quality, pressure, light, vibration, humidity, etc.) and deliver the collected data to the middle layer. They follow the query command of the upper layer to gather the corresponding information. In the GIN protocol, three kinds of nodes are deployed in the perceptual layer, i.e., MN, CH, and IN nodes. They are supervised by each other and related geographic location messages are exchanged among them.
# Advanced Medical Image Segmentation Enhancement:
## 2 Related Works
### Traditional Segmentation Methods  
Traditional segmentation methods have historically served as the building blocks of medical image analysis [32; 34]. These methods include thresholding, region growing, and edge detection [35; 36; 37]. Researchers conducted a comparative study of thresholding techniques for medical image segmentation [34; 38] and highlighted their simplicity and limitations in handling complex intensity variations [39]. One facet of the research community has explored the challenges and prospects of region-growing-based segmentation [40], particularly in complex structures like brain tumor images [41; 42]. This exploration has led to a recognized need for more robust techniques to address the subtleties and complexities of such images [43].  
Furthermore, foundational knowledge of digital image processing has underpinned many traditional segmentation methods [44]. However, it is crucial to acknowledge that these traditional techniques often face difficulties when handling noisy or non-uniformly illuminated images [45; 46]. This recognition has driven the exploration for more advanced and adaptable approaches.