esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 6 Conclusions
Furthermore, Trimble R8s was giving a false phase solution for some time, which should be considered a concern for geodetic use. At the end, the effect on satellite's CNR has been calculated, showing that both Javads performed better by means of GLONASS reception. In the other instruments, the GLONASS reception was affected by the jammer's presence despite that particular jammer not interfering significantly with the frequency band used by GLONASS satellites. Performing experiments of this kind and interpreting the results is always limited in some ways. There are many factors in the complete workflow that influence the results. Nevertheless, the authors believe that the results and conclusions from their experiments contribute to a better understanding of the impact of issues in everyday use of GNSS in geodetic applications, as well as in civil use for navigation. To this end, further work will be focused on identifying the causes of false ambiguity fixing and the quality of jammer's localisation in several different jamming scenarios. The main research idea and the design of the experiment was conceived by [PERSON]-P., M.B. and F.D. All the authors analysed the results and wrote the original draft as well as contributing to improvements in the final manuscript. All authors have read and agreed to the published version of the manuscript. The authors acknowledge the financial support of the Slovenian Research Agency (research core funding No. P2-0227 Geoinformation Infrastructure and Sustainable Spatial Development of Slovenia, and No. P2-0394, Modelling and Simulations in Traffic and Maritime Engineering). All the tests conducted for this work were authorised by the Agency for Communication Networks and Services of the Republic of Slovenia (AKOS), whose support is highly acknowledged. The authors acknowledge the support of Geoservis d.o.o. and its director, [PERSON], MSC, for renting out the Leica GS07 instrument, as well as GDI d.o.o. ([PERSON]) and the Surveying and Mapping Authority of the Republic of Slovenia for renting out the Trimble R8s, Trimble R10 and Javad Triumph-VS to perform the tests. The authors would like to thank to the referees, who immediately reviewed the earlier version of this paper and provided valuable suggestions and comments, which improved the paper significantly. The authors declare no conflict of interest.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 1 Introduction
[19], nanosilica can significantly improve the shortcomings of poor cohesion of silt through a series of physical and chemical reactions with silt, making its structure more compact. Combined with the above studies, a new stabilizer for stabilizing silt, named slag-nanosilica stabilizer (SNS), was developed in this research. The stabilizer is composed of slag, nanosilica, and quicklime. Orthogonal test and grey correlation analysis were used to determine the optimal ratio of each component of stabilizer [20; 21] with the aim of evaluating the comprehensive properties of stabilized soil through macroscopic mechanical properties test, road performance test, and microscopic mechanism analysis.
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
###### Abstract  
Sparse code multiple access (SCMA) is a promising code domain non-orthogonal multiple-access scheme which is able to support massive connectivity and grant-free transmission in future satellite-based Internet of Things (IoT) communication networks. Traditional grant-free SCMA is based on time synchronization, which is no longer favorable in such satellite communication networks since the amount of signaling generated to keep all transmitters' time synchronized is impractical for large networks. Moreover, without centralized codebook assignment, grant-free SCMA suffers from codebook collisions which mean more than one terminal selecting the same codebook being interfered. Motivated by these issues, a novel uplink grant-free asynchronous flipped SCMA scheme named AF-SCMA is proposed in this paper. With the concept of flipped diversity, a specific SCMA-encoded packet is transmitted with its flipped replica together. Successive interference cancellation technique combined with a sliding window is adopted to resolve the packet collisions including codebook collisions at the gateway station. The performance of AF-SCMA is investigated via both mathematical analysis and simulations. Simulation results show that the proposed AF-SCMA provides remarkable performance in terms of throughput and packet loss ratio (PLR), and can benefit from the received signal power unbalance.  
satellite communication networks; IoT; massive connectivity; uplink multiple access; asynchronous system; flipped diversity Article
## 2 Theoretical Basis
### Continuity Equation  
The mass conservation equation for fluid microelement is referred to the continuity equation. That is, the velocity of fluid mass in a fixed volume region is equal to the mass difference between the inflow and the outflow, expressed by Equation (1):  
\[\frac{\partial\rho}{\partial t}+\sum\
olimits_{i=1}^{3}\frac{\partial(\rho u _{i})}{\partial x_{i}}=0\text{ or }\frac{D\rho}{Dt}+\triangledown\cdot\rho U=0, \tag{1}\]  
where \(\rho\) is the air density, \(t\) is the time, and \(u_{i}\) is the velocity component of air in the x, y, and z directions in a Cartesian coordinate system.
## 4 Discussion
### Gait Speed  
The link between gait performance and cognitive ability in aging has received increased attention due to studies that demonstrated that gait is not solely a motor activity but also involves cognitive processes [39]. In line with the aforementioned findings on dynamic balance, our results also indicate that higher gait performance was associated with better cognitive function, except for phonological fluency. On one hand, lower gait speed is strongly linked to more significant overall cognitive decline, as assessed by MMSE performance [40]. However, a recent study [34] that compared gait to other physical skills demonstrated a stronger association with dynamic balance in healthy older individuals and those with MCI. On the other hand, the relationship between gait speed and verbal fluency appears more complex. Semantic fluency was found to be correlated in one study [41] but not in another [42], while for phonological fluency [42], no significant relationship was observed, aligning with our findings. A possible explanation for the lack of connection between gait and phonological fluency might be similar to the relationship with dynamic balance. Indeed, dynamic balance and gait are closely interconnected, as gait involves effective dynamic balance. Moreover, both have an indirect link with cognitive flexibility,as the latter may influence a person's ability to adapt to changing situations where gait and dynamic balance become more crucial.  
Furthermore, the specific relationship between gait and the BNT is not consistently supported. Although gait speed is the gait parameter most strongly studied with cognitive function, regarding its association with language performance according to the BNT, [PERSON] et al. [38] observed that longer stride length was related to better language proficiency.  
In short, gait speed testing, like dynamic balance, could be a practical tool for detecting cognitive impairment in the aging. On the other side, more studies should clarify the relationship between gait speed and verbal fluency and specifically study the gait speed variable in relation to language.
# CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems
###### Abstract
Figure 2 shows the average power consumption in Watts on the Exynos 5422 MPSoC when executing different benchmark applications on different approaches. Figure 1: Percentage of average power consumed by the big CPUs, LITTLE CPUs, GPUs, memory and the rest of the components such as the fan (active cooling), hardware storage and on-chip communication network of the Exynos 5422 MPSoC when idle vs executing the streamcluster benchmark. The benchmark applications were object detection using YOLO (_yolo_) [17], the blackscholes benchmark from PARSEC [11] and fft from Splash-2 [18]. From Figure 2, it is evident that application-agnostic approaches to performing DVFS on CPU-GPU-memory might not lead to close-to-optimal power consumption and therefore indicates that DVFS in the CPU, GPU and memory in mobile MPSoCs more challenging. In this paper, we study the effect of DVFS on memory towards the total power consumption in a mobile MPSoC for different types of applications and we also propose a novel approach, called _CGM-DVFS_ (**CPU-GPU-Memory DVFS**), to perform DVFS on CPU (big and LITTLE), GPU and memory in mobile MPSoCs to cater for the performance requirements of the execution of applications while consuming the least amount of power. To this extent, the concrete contributions of this paper are as follows. 1. Studying the effect of DVFS on memory in regard to the total power consumption and performance of executing applications in a mobile MPSoC. 2. Proposing a novel approach--CGM-DVFS--to perform DVFS on CPU-GPU-memory in a mobile MPSoC to cater for the performance requirements of executing applications, while consuming the least amount power. 3. An experimental evaluation of CGM-DVFS on a real hardware platform, Odroid XU4, and a comparative study between CGM-DVFS and state-of-the-art approaches to optimise power consumption. 4. A comparative study and analysis between CGM-DVFS and state-of-the-art delayed reinforcement learning approaches to show that CGM-DVFS is better suited to achieving close-to-optimal power consumption. The rest of the paper is organised as follows. In Section 2, we show the effect of DVFS on memory in terms of power consumption as a motivational case study. In Section 3, we mention the related works, whereas in Section 4 we provide details of the hardware and software infrastructure used in this study, along with the problem formulation, on the basis of which our proposed method was designed. In Section 5, we provide details on our proposed methodology-_CGM-DVFS_, whereas in Section 6 we show the efficacy of our proposed method through an experimental evaluation, along with a comparative study with the state-of-the-art approach. Finally, we conclude the paper in Section 7.
There are many excellent examples of antimicrobial compounds isolated from plants. For example, glycoalkaloids and glucosinolates extracted from _Solanum nigrum_ and _Armoracia rusciana_ exhibit inhibitory activity against the Gram-positive bacteria _Bacillus thuringiensis_ and _B. amyloliquefaciens_[8]. Naringin and quercetin show significant antibacterial activity against _B. subtilis, Escherichia coli_, and _Staphylococcus aureus_, while flavonoids in chestnuts are able to eradicate spoilage bacteria, including _Megasphaera_ spp. and _Proteus_ spp. [9]. Polyphenols from moss species and cloves (_Syzygium aromaticum_) exhibit antibacterial activity against _E. coli_, _Proteus mirabilis, Klebsiella pneumoniae, Enterobacter cloace_, and _Salmonella_ spp. [10]. Compared with synthetic chemical compounds, plant-derived bioactive compounds typically show lower toxicity and fewer side effects. Moreover, most of these compounds have minimal influence on the environment, aligning with the goal of sustainable development. The antimicrobial activity of plant-derived bioactive compounds therefore provides an abundant and attractive natural resource for the development of new antimicrobial agents. This is reflected in the market, with the annual growth rate of the traditional agricultural chemical market being currently 5.5%, while that of the biopesticide sector is growing at a rate of 16% per year. _Ailanthus altissima_ (Mill.) Swingle, a deciduous tree belonging to the family of Simaroubaceae, is widely distributed in tropical and warm regions, and it is abundant in Northeast China [11]. The species has become an invasive alien plant in Europe and North America. The aqueous extracts of _A. altissima_ leaves and roots are used in Northeast China as a traditional form of pest control, including the control of _Pieris rapae_, _Helicoverpa armigera_, and aphids [12]. _A. altissima_ is rich in chemical compounds, and many types of specialized metabolites, including alkaloids, terpenoids, steroids, and flavonoids, have been isolated from the plant. These specialized metabolites also show diverse biological activities; for example, quassinoid glycosides derived from _A. altissima_ inhibit the multiplication of tobacco mosaic virus [13], and ailanthone, a quassinoid compound, strongly inhibits the expression of RSV protein-coding genes, thereby reducing the damage caused by rice stripe disease [14]. However, although many diverse compounds are known from _A. altissima_[15], their efficacy against invasive pathogenic bacteria and the mechanisms of any potential interactions remain unknown.
## 3 Materials and Methods
### Workflow Scheduling
However, the introduction of dependency constraints in the assignment process may cause the under-usage of certain stages. To cope with this issue, we introduced a further step of consolidation, aimed at reducing the number of stages by merging together stages without dependencies according to the available memory. The main execution flow of the IIWM scheduler is shown in Figure 3 and described by Algorithm 1. In particular, given a data-intensive workflow \(\mathcal{W}\), described as a DAG by its tasks and dependencies, and the prediction model \(\mathcal{M}\) as the input, a scheduling plan is generated in two steps: (i) building of the stages and task assignment; (ii) stage consolidation. The algorithm is divided into two main parts: in the first part (Lines 1-23), the stages are built by iteratively assigning each task according to the estimates of the prediction model; in the second part (Lines 25-34), a consolidation process is performed, trying to minimize the number of stages. The first part (Lines 1-23) starts with the initialization of an empty list of stages \(\mathcal{S}\), which will be filled according to a dictionary \(\mathcal{Q}\) that stores the in-degree of each task in the DAG, which is used for identifying the free tasks that can be scheduled. The prediction model \(\mathcal{M}\) is exploited to estimate the memory occupancy and execution time of each task in \(\mathcal{T}\), according to their dataset description (Lines 3-4). The dictionary \(\mathcal{P}_{mem}\), which collects the predicted memory occupancies, is then used to sort tasks according to the first fit decreasing strategy (Line 5). At each iteration, tasks that can be scheduled (i.e., assigned to a stage) are collected in the \(\mathcal{T}_{free}\) set. In particular, they are identified by a zero in-degree,  
Figure 3: Execution flow of the IIWM scheduler. Given a workflow and a prediction model as the input, a scheduling plan is generated in two steps: (i) building of the stages and task assignment; (ii) stage consolidation. as their execution does not depend on the others (Line 7). By virtue of the acyclicity of the DAG-based workflow representation, there will always exist a task \(t\in\mathcal{T}\) with a zero in-degree not yet scheduled, unless set \(\mathcal{T}\) is empty. Afterwards, the task with the highest memory occupancy is selected from \(\mathcal{T}_{free}\) in order to be scheduled (Line 8). At this point, a list of candidate stages (\(S_{sel}\)) for the selected task is identified according to the peak memory occupancy forecasted by the prediction model \(\mathcal{M}\) (Lines 9-10). In particular, a stage \(s_{i}\) belongs to \(\mathcal{S}_{sel}\) if it satisfies the following conditions:  
* The residual capacity \(C_{s_{i}}\) of the selected stage \(s_{i}\) is not exceeded by the addition of the task \(t\). * There does not exist a dependency between \(t\) and any task \(t^{\prime}\) belonging to \(s_{i}\) and every subsequent stage (\(s_{i+1}\cup\cdots\cup s_{k}\)), where a dependency \((t^{\prime},t)^{n}\) is identified by a path of length \(n>0\).
# Airwave Noise Identification from Seismic Data Using YOLOv5
## 5 Results  
Figure 6 shows the statistical distribution of the test dataset samples. In Figure 6a, the predicted locations of airwave noise closely align with the actual positions, with the coordinate origin at the top-left corner of the predicted image. This creates an \"inverted V\" pattern in the distribution of airwave noise. In Figure 6b, the width of the predicted bounding boxes varies linearly with height, as the boxes for airwave noise are not elongated (see Figure 1). Figure 6c illustrates the precision and recall curves as a function of the confidence threshold. Regions with confidence above the threshold are classified as containing airwave noise. A lower confidence threshold generally yields higher recall and lower precision, meaning the model may produce more false positives but is less likely to miss any detection. On the other hand, a higher confidence threshold results in lower recall but higher precision, reducing false positives while increasing the chance of missed detections.  
Figure 7 presents several prediction results from the test dataset. Overall, the model effectively identifies prominent airwave noise, with higher-confidence bounding boxes indicating more pronounced interference. The detection of interference locations is also highly accurate. These results confirm the robustness and effectiveness of the YOLOv5 model.  
Figure 5: The evolution curves of loss and precision-recall during the secondary training of YOLOv5m.  
Figure 6: Statistical distribution of samples in the test dataset: (**a**) A distribution map of predicted locations of airwave noise, where x and y represent the two spatial dimensions of the image, with the top-left corner of the image set as the coordinate origin. (**b**) Statistical distribution of widths and heights of predicted airwave noise boxes. (**c**) The relationship between recall and accuracy at different confidence levels.
## 4 Behavioral Analysis of Surface Tension, Capillarity, Cavitation, and Splash
### Cavitation and Capillarity Analysis  
Cavitation during the insertion of the SOIC package into the dielectric fluid at the initial impact speed could lead to the formation of splashes at the edges of the SOIC package. However, cavity formation becomes dependent on the physical properties of the SOIC package, the pin contours, the wettability of the material, and the impact speed. Using  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SOIC Package**} & \multicolumn{3}{c}{**Dielectric Medium**} \\ \cline{2-4}  & **FR3** & **DTE-150** & **DPMS** \\ \hline
**SOP-16** & 39.929 & 43.957 & 49.561 \\
**SOP-20** & 37.127 & 40.805 & 46.058 \\
**SOP-24** & 35.025 & 38.528 & 43.431 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Surface tension force (N/m) for each dielectric and SOIC package.  
approximations of the Froude model that describe the surface behavior of the dielectric fluid when it receives disturbances at the moment of contact with an object, we analyzed each of the SOIC packages concerning the maximum operating speed of the pick-and-place machine [40]. The nondimensional Froude's number is given by \(f_{r}=V_{Mmax}\cdot(g\cdot D)^{-1/2}\), where \(V_{Mmax}\) is the maximum velocity at the moment of contact with the dielectric fluid, \(g\) is the gravity acceleration, and \(D\) is the equivalent diameter of SOIC package. The Froude numbers exhibit variation across different SOIC packages, with values of 0.8946 for the 16-pin, 0.8561 for the 20-pin, and 0.8175 for the 24-pin.  
The air cavity's hydrodynamics and the device's geometry, resulting from vertical immersion, exhibit relatively low Froude numbers near the cavity (\(F_{r}\leq 0\)). However, the nozzle holding the SOIC package prevents the air cavity on the DUT from completely closing. Therefore, analyzing the effects of viscosity and surface tension near the air cavity closure due to capillarity on the SOIC package's surface using the Weber model (\(W_{e}\)) [41]. We evaluated the role of surface tension and the drag force of the capillarity \((C_{a})\) on the device's surface as follows:  
\[W_{e}=\frac{\rho\cdot V_{Mmax}^{2}\cdot D}{\sigma} \tag{38}\]  
\[C_{a}=\frac{W_{e}}{R_{e}}, \tag{39}\]  
where \(\rho\) is the dielectric fluid density, \(D\) is the equivalent diameter of the SOIC package, \(\sigma\) is the surface tension coefficient, and \(V_{Mmax}\) is the maximum velocity of the pick-and-place machine. Table 11 shows the nondimensional values of capillarity at the surface of SOIC packages when they change between the air interface and the dielectric fluid.  
We used the values in Table 11 to predict the cohesion between the surfaces of different SOIC packages and a similar dielectric fluid. This capillary action contributes to the formation of microdroplets and bubbles. We deduced that the maximum capillary action depends on the dielectric's viscosity below a certain microunit threshold, allowing negligible cavitation.
## 4 Results and Discussion
### Experiment Environment
#### 4.1.2 Experimental Results  
The first experiment compared the proposed framework with three mainstream object detection algorithms in a human detection task. Using human instances of the MS COCO and Teledyne FLIR dataset, this experiment validates the domain adaptation ability of the proposed algorithm. The experimental results are shown in Table 3. The proposed framework showed the best performance among other algorithms. As the relative sizes of objects are not extremely small, and there is only one object category, this task is an easier  
Figure 4: Sample images from datasets. (**a**) Images in visible light spectrum. (**b**) Images in infrared spectrum.  
task compared to generalized object detection. The YOLOv5 model shows relatively high accuracy, while faster-RCNN, which is a two-stage method, fails to yield good performance. It can be interpreted that due to multi-domain training, the model fails to learn both localization and classification cues. In the case of YOLO-Z, the model sacrifices its mid- to large-sized object detection ability in exchange for small object detection accuracy. As the size distribution of target data is rather large in this task, the performance of YOLO-Z deteriorates. The second experiment compares algorithms in the vehicle detection task. Using MS COCO, VEDAI, DOTA, and our dataset, a detection task using multiple labels of vehicles (large vehicles, small vehicles, tanks, planes, helicopters) is trained. This task consists of mainly aerial images with small object size distributions. The experimental results are shown in Table 4. Compared to the first experiment, the overall performance of each model is much worse, as the task is more difficult while the datasets are more complex. Our proposed framework achieved superior accuracy compared to conventional algorithms, indicating that it has a powerful learning ability when dealing with imbalanced, multi-domain datasets.