esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Computing Architectures for Video Querying  
Computing architecture for video querying refers to the physical integration between producers, processors, and consumers. In general, there are three computing architectures: (a) compute everything on a powerful server, (b) compute where the data is produced, or (c) distribute computing across hierarchical nodes. Each approach has advantages and disadvantages.  
The most common architecture used over time is the server-only architecture, also called client-cloud configuration or cloud computing. In this architecture, developers consider distributing a powerful server or cluster with many CPUs, GPUs, memory, and bandwidth across producers (e.g., mobile, IoT devices, video databases) and the cloud to process all data [30; 40; 41; 42; 43; 3, 4]. The advantages are simplicity, powerful hardware support, and low internal latency. However, the main disadvantage depends on the external Internet connection between the video source and the system, which can be very slow.  
The edge-first architecture, also called client-first or server-less, is almost the opposite of server-only. In this architecture, the focus of query processing is on the edge devices and local edge servers rather than high-performance cloud systems [31; 44; 45; 46; 47]. This architecture is particularly suitable for applications such as autonomous vehicles and real-time monitoring that cannot tolerate internet latency and offloading of processing. The disadvantages are related to the hardware limitations of edge computing devices.  
In the fully distributed architecture, all data flow is distributed through a three-stage pipeline coordinated by an extensive scheduling algorithm that determines when each task is processed [48; 42]. For example, [49] proposed the mobile-cloudlet-cloud architecture, a proof-of-concept system to distribute the computing of the face recognition task on mobile devices, cloudlet server, and cloud in order to minimize the response time, given the diverse latencies and computing resources. Similarly, [42] focused on distributing DNN processes between edge devices and the cloud, considering online and offline contexts. Furthermore, [48] proposed an optimization model to maximize data throughput in a more extensive cluster system. Because of these characteristics, it is the most complex architecture, as it depends on many internal factors (e.g., scheduling) and external factors (e.g., network connectivity).
# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Feature Extraction and Selection
\begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Dataset** & **Feature Extraction Method and Selected Features** \\ \hline
[30] & Test score & The study extracted features manually based on cognitive and evaluation test results. These included an IQ test, rapid naming test, evaluation of short-term memory, sequencing skills, and non-word reading to evaluate phonological coding skills. \\ \hline
[32] & Using a kernel density estimation process, brain activity features were extracted from EGG signals (353 features). The ksdensity () function in MATLAB was used to derive features depending on the normal kernel function. \\ \hline
[35] & To extract features from the EGG signals, the study used SSA, which divides the raw signal into additive components representing various oscillatory manners. Five components were generated that might be utilized to explain the data. For each component, Pearson’s correlation among various channels of the PSD of each singular SSA component was calculated. Pearson’s correlation represents the degree of similarity between two channels, thus helping to differentiate between dyslexia and normal functioning. \\ \hline
[33] & EGG \\ \hline
[34] & EGG \\ \hline
[33] & EGG \\ \hline
[33] & The study used discrete wavelet transform techniques to extract the most beneficial ERP signals from EGG, which have a waveform linked both in frequency and time domain. The signal was divided into low pass and high pass. A group of temporal features were extracted from the low-pass portion such as latency, absolute amplitude, positive area, and entropy, while a group of statistical and spectral features were extracted from the high-pass portion such as mean, skewness ratios, standard deviation, and zero crossing rate. Moreover, some features relevant to the frequencies’ structure were extracted such as spectral flatness measure, spectral centroid as well as power spectral density. \\ \hline
[41] & The various eye movement events for preprocessed data have been analyzed, such as saccades and fixations. Different features relevant to these events have been extracted using major statistical measures, dispersion, and approaches velocity-based. Two algorithms for feature selection have been used in this study, which are Recursive feature Elimination with Cross-Validation (RFE-CV) and PCA. \\ \hline
[40] & In this study, the CNN model extracted, implicitly, substantial features scattered either in time or frequency from preprocessed eye-tracking data and nonlinearly bound them using machine learning to minimize detection error. \\ \hline
[43] & The velocity features which extracted from eye-tracking events discriminate between dyslexic and control. \\ \hline \end{tabular}
\end{table} 
Table 2: Feature extraction methods for dyslexia disorder detection utilizing Deep Learning Methods.
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Optical Activity in Solids: a-Quartz  
#### 4.2.1 Method I  
Measurements on an \(\alpha\)-cut quartz crystal are included as an example of a solid with only circular birefringence. A transmission Mueller matrix measured on a sample with a nominal thickness of 1 mm is shown in Figure 4. The profound oscillations in elements \(m_{22}\), \(m_{23}\), \(m_{32}\), and \(m_{33}\) indicate that the rotation of polarization is larger than that of the glucose solution. The polarization effects are confined to these four elements showing that the sample is a circular diattenuator (rotator).  
The remaining elements differ less than 0.002 from their expected values of zero or unity in most of the spectral range but with deviations up to 0.01 for shorter wavelengths in some elements. From the data in Figure 4, we have \((m_{23}-m_{32})/2=0.727\) at \(\lambda=589\) nm. If we use Equation (2), we find the rotation angle \(\alpha=\arcsin\left(0.727\right)/2=23.32^{\circ}\), and, if we use the reference value of \(21.73^{\circ}\)[18], we obtain a sample thickness of \(d=23.32^{\circ}/21.73^{\circ}\) mm\(=1.073\) mm.
## 3 Image Processing Technique Used
### Objects Splitting  
Before labeling each object in the image, the program has to separate the objects that are counted as one when two or more objects are connected and not well separated while thresholding and erosion/dilation. No matter how intelligently the thresholds are chosen by the program, the original  
Figure 6: A series of segmentation process: (**a**) applying edge detection on the image of Figure 4b; (**b**) image map presenting different threshold values; (**c**) a binary image after threshold.  
Figure 7: Morphological operations: (**a**) a gray-scale image; (**b**) after threshold; (**c**) watershed splitting filter with erosion/dilation for the splitting of contacts.  
scanning image that has a limited number of grayscale levels creates a misclassification of the edges of the individual object, which causes some artificial particles to touch each other. In those cases, the separation of objects was done through a technique called the \"watershed splitting filter\", which is provided in the MATLAB software. In this technique, the objects do not come together again via dilation after separation by erosion. Performing this operation in an uncontrolled way causes many single objects to be internally split. Coding several steps of erosion and dilation in a limited watershed split minimized the internal splitting of objects while maximizing the true separation of touching objects. It was recognized that this operation, while necessary for preparing object images so that the individual objects can be recognized, creates some small loss of information and a corresponding increase in the uncertainty of the particle shape. Figure 8 shows an image after segmentation, and the resultant image after watershed splitting. Clearly, some artifacts remain or have been caused by the image operation (small appendages on objects). However, these are minimal, changing the object volumes and the surface areas in the order of a few percentage points.
## 4 Conclusions  
In this study, extreme temperature thermal shock test from \(-196\)\({}^{\circ}\)C to \(150\)\({}^{\circ}\)C was conducted on QFP assemblies with Sn-37 Pb solder joints to investigate the evolution and growth behavior of interfacial IMCs and their effect on the pull strength and fracture behavior of Sn-37 Pb solder joints under extreme temperature environment. The following conclusions were obtained:  
1. A continuous (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMC layer was formed at the interface between the Cu lead and the Sn-37 Pb solder after reflowing, and the IMCs formed between the solder and Ni-P layer was (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMCs. A thin layer of Cu\({}_{3}\)Sn IMCs was formed between the Cu lead and (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMC layer after 150 cycles.
2. The morphology of interfacial (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMCs changed from scallop-type to plane-type, and the morphology of interfacial (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMCs evolved from needle-type to scallop-type, and finally to plane-type during extreme temperature thermal shock.
3. The thickness of the interfacial IMCs both at the Cu lead side and at the Ni-P layer side gradually increased with extreme temperature thermal shock. The growth of interfacial IMCs at the Cu lead side was found to be much faster than that at the Ni-P layer side. The growth of the interfacial IMCs at the lead side was dominated by bulk diffusion, and the growth of the interfacial (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMCs at the Ni-P layer side was dominated by grain-boundary diffusion.
4. With increasing thermal shock cycles, the pull strength of the joints decreased, and the fracture mechanism changed from ductile fracture to mixed ductile-brittle fracture. The pull strength was reduced about 31.54% after 300 cycles.  
**Author Contributions:** [PERSON] conceived and performed the experiments, analyzed the data, and wrote the paper. [PERSON] and [PERSON] provided the experimental materials and laboratory equipment, and directed the research.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (Grant No. 51522503), the National Natural Science Foundation of China (Grant No. 51775141), and the Program for New Century Excellent Talents in University (NCET-13-0175).  
Figure 10: EDS analysis results of: (a) (Cu, Ni)\({}_{6}\)Sn\({}_{5}\) IMCs; and (b) (Ni, Cu)\({}_{3}\)Sn\({}_{4}\) IMCs on the fracture surface.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 3 The Proposed AdaAL-MID Algorithm
### Overall Framework and Process of the AdaAL-MID
Symbols: ensemble classifiers, \(E\); number of processed instances, \(p\); number of labeled instances, \(l\); minimum margin threshold matrix, \(M_{1}\)[C][C]; maximum margin threshold matrix, \(M_{2}\)[C][C]; random selection ratio, R; instance sliding window, \(\textit{Win}[\textit{C}][\textit{sizeW}/\textit{C}]\); and current class distribution, \(cd\)[C]. Output: final ensemble classifier, \(E\), and prediction result. Process:
1. While(\(S\).hasNext()) do
2.\(x=S\).nextInstance()
3.\(p=p+1\). // processed instances plus one. 4. labeling = false. 5. generate a random number \(\delta\in[0,1]\)// random strategy
6. if(\(p<\textit{sizeW}\) or \(\delta<\textit{R}\)) then
7. labeling = true. 8. update the current class distribution \(cd\) based on Equation (3)
9. elseif(\(\textit{LOCinating}\textit{strategy}(E,x,cd,M_{1},M_{2})\)) // uncertainty strategy labeling = true. 11. if(labeling and \((l/p)<b\)) // surplus in the label budget
12.\(y=\textit{classvalue}\). // request the real label \(y\) of instance \(x\)
13.\(l=l+1\). // number of instances labeled plus one
14.\(Win[y]\leftarrow\textit{Win}[y]\cup(x,y)\)// store instances \((x,y)\) in the corresponding instance sliding window \(\textit{Win}[y]\) based on label \(y\)
15. for\(j\in E\)do// adaptive sampling strategy
16. calculate the sampling weight \(SW_{j}\) on instance \((x,y)\) using Equation (7). 17. calculate the retraining times \(k\sim poisson(SW_{j})\)
18. calculate the classifier weight \(CW_{j}\) on instance \((x,y)\) using Equation (8). 19. for\(n\in\{1,\ldots,k\}\)do
20. update classifier \(j\) on instance \((x,y)\) with the weights of the classifier \(CW_{j}\). 21. ifADWIN detect changes
22. create a new classifier \(C_{\text{new}}\) and initialize it on \(\textit{Win}[\textit{SizeW}/\textit{C}]\). 23. replace the worst classifier in ensemble \(E\) with \(C_{\text{new}}\)
24. change the current label budget \(b\) using Equation (1). // dynamically adjust label budget based on drift level
25. end while
```  
**Algorithm 2** AdaAL-MID
## 5 Discussion  
We introduced an experimental setup to test two QLC SSD models (Model A and Model B) at 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 50 \({}^{\circ}\)C, observing throughput, IOPS, and latency. Although both drives are nominally suitable for some automotive or embedded contexts, they showed varying degrees of thermal resilience. Specifically, Model A's sharper performance dips indicate that consumer-grade drives may be more vulnerable to high ambient temperatures encountered in vehicles, while Model B (intended for industrial-grade environments) retained more consistent performance.  
Our findings align with prior literature [5; 7; 12; 13; 14; 15], which similarly report significant SSD performance impacts under thermal stress. However, few of these studies focus on the demanding, real-time conditions of vehicular edges. By demonstrating how random and sequential operations degrade differently, and by quantifying the scale of decline, our results underscore that consistent thermal management is essential for automotive reliability.  
We also highlight the need for broader environmental testing that includes sub-zero conditions, repeated thermal cycles, and system-level implications for tasks like sensor fusion and real-time analytics. This discussion provides an initial comparative insight rather than a comprehensive statement on all flash-based automotive storage.  
In summary, the advances made here show that temperature can be a deciding factor for in-vehicle storage performance. Designing future vehicular storage solutions should therefore involve a balance between cost, thermal management, and data reliability.  
A preliminary version of this work was presented at the 2024 IEEE MOST Conference [31]. This article incorporates substantial enhancements and extensions. First, we have expanded the experimental design to include long-duration stress testing and additional environmental factors, such as humidity and vibration. Second, we have conducted a more comprehensive statistical analysis of device behavior under prolonged thermal load, incorporating degradation percentages and \(p\)-value validation across multiple performance metrics. Third, we have added a discussion on the implications of SSD behavior for vehicular edge applications and potential mitigation strategies, including cooling solutions and firmware optimization. These enhancements strengthen this article.
# Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
## 2 Backgrounds
### Volumes of Convex Bodies and SMT(LIA) Formulas  
In this subsection, we explain the differences between the volume computation problem of a convex body and that of a SMT(LIA) formula. A convex body is defined by a conjunction of several LIA constraints. For example, the area with mosaics in Figure 2a is a convex body, and it is defined by:  
\[(x_{1}\geq-128)\land(x_{1}\leq 127)\land(x_{2}\geq-128)\land(x_{2}\leq 127) \land(-x_{1}+x_{2}<0)\land(x_{1}+x_{2}>5).\]  
Note that the boundaries of \(x_{1}\) and \(x_{2}\) ([\(-\)128, 127]) are indicated by a square with broken lines, and the volume of the body is the number of value pairs for \(x_{1}\) and \(x_{2}\) that satisfy all the LIA constraints.  
Figure 1: Example program for _Hot/Cold path_ analysis.  
Figure 2: Demonstration of volumes of a convex body and a SMT(LIA) formula: (**a**) Volume of a convex body; (**b**) Volume of a SMT(LIA) formula which implies two convex bodies.  
In contrast, a SMT(LIA) formula may imply multiple convex bodies, depending on the underlying logical structure. For example, with the same boundaries for \(x_{1}\) and \(x_{2}\), a formula  
\[((-x_{1}+x_{2}<0)\wedge(x_{1}+x_{2}>5))\vee((-x_{1}+x_{2}>0)\wedge(x_{1}+x_{2}< 5)).\]  
implies two convex bodies as shown in Figure 2b. In general, a SMT(LIA) formula may imply \(m^{\prime}\) convex bodies, and the maximal value of \(m^{\prime}\) is: \(2^{m}-1\) where \(m\) is the number of Boolean variables involved. However, the idea to break a SMT(LIA) formula into a set of convex bodies and then take the sum of the corresponding volumes is not feasible. One reason is that this idea possibly results in an exponential number of convex bodies, and the other reason is there may exist overlaps among the convex bodies [8].
## 2 Theoretical Background
### Elliptic Curve Groups  
Let an integer \(p\) be a prime number, and \(\mathbb{F}_{p}\) represent a field of integers modulo \(p\). An elliptic curve \(E\) over \(\mathbb{F}_{p}\) can be denoted by an equation of the form defined below.  
\[y^{2}=x^{3}+ax+b \tag{1}\]Where terms \(a\) and \(b\) are integer constants such that \(a,b\ \in\mathbb{F}_{p}\) and satisfy the condition in Equation (2).  
\[4a^{3}+27b^{2}\
ot\equiv 0\ (mod\ p) \tag{2}\]  
A pair \((x,y)\) on the curve \(\mathbf{E}\) such \(x,y\ \in\mathbb{F}_{p}\), is a point on the curve if \((\mathsf{x},\mathsf{y})\) satisfies the equation. The set of all the points on \(\mathrm{E}\) is expressed as denoted by \(\mathbf{E}(\mathbb{F}_{p})\).  
To implement discrete logarithm systems, cyclic subgroups of a point \(P(x,y)\) on an elliptic curve \(\mathbf{E}(\mathbb{F}_{p})\) can be used.
## 3 LBMR: Load Balancing Multipath Routing  
As medical applications grow rapidly, medical sensors may deliver intensive and critical data to the gateway so reliable medical routing service is highly desirable. Current solutions, such as Zigbee and related works mentioned in Section 1, cannot avoid bottlenecks, which may paralyze the entire network if the network traffic grows and congestion occurs near the gateway. To solve this problem, we propose LBMR to provide a reliable routing service for medical applications, especially data-intensive applications. Compared with AODV's improved multipath version, AOMDV, LBMR has the same advantage of multipath routing but has better traffic load distribution and network reliability. In order to achieve load balancing, routing robustness, and routing reliability, LBMR consists of three main designs: LRC, LEA, and RM mechanism. LBMR is introduced from these three aspects in the following.  
In AODV and AOMDV, nodes require flooding RREQ to the gateway and receiving RREP from the gateway, which is usually several hops away from nodes. In LRC, nodes can simply receive the beacons from the upper-layer nodes, which are neighbors of the nodes, to establish the routes to the gateway, so the route establishment time is shorter in LMBR than the time in AODV and AOMDV. Based on LEA, LMBR accomplishes the load balancing service, which AODV and AOMDV does not provide. In AODV and AOMDV, nodes broadcast RERR to recover routes and this mechanism generates heavy message overhead and recovery time. RM in LMBR reuses beacons in LRC to announce and recover the failed routes. The recovery process in LMBR is very quick and requires no message overhead. Therefore, LMBR provides more reliable routing service with additional loading balancing feature than AOMDV and AODV, which are adapted by the Zigbee stack.
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
###### Abstract  
Continuous Descent Approach (CDA) is the flight technique for aircraft to continuously descend from cruise altitude with an idle thrust setting and without level-offs, contrary to the staircase-like Step-down Descent Approach (SDA). Important for air transportation sustainability, using CDA reduces noise, fuel consumption, and pollution. Nevertheless, CDA has been limited to low traffic levels at airports, often at night, because it requires more separation distance between aircraft arrivals and, thus, could decrease throughput. Insufficient attention has been given to helping air traffic controllers decide when CDA may be used. In this paper, we calculate the probability that an aircraft arriving during a particular brief period of time (e.g., 15 min) will need to revert to SDA when the controller tentatively plans to permit CDA for all aircrafts arriving during that time period. If this probability is low enough, the controller may plan to permit CDA during that time period. We utilize an analytical approach and queueing theory framework that considers factors such traffic and weather conditions to estimate the probability. We also provide the number of aircrafts that can be accommodated within the airport's stacking space using CDA. This number provides insight into whether a particular aircraft may use CDA.  
green transport; continuous descent approach; optimized profile descent; climate change; terminal maneuvering area; environmental impact; applied queueing theory; air traffic management; air transportation sustainability +
Footnote †: journal: Applied  
Article  
Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports