esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Regression and Causality Analysis
This allows us to estimate the coefficients of the system described by Equation (9) after the individual effects removed. Robustness test shows that the PVAR model is reasonable, as shown in Figure 7. Table 2 shows the results of the model with three variables, from the columns of which we find that the impact of \(\ln\text{GDP}\) with one period lag on \(\ln\text{GDP}\), DfG and ECI are significant for all three different panel VARs, the impact of DfG with one period lag on DfG and ECI are significant, and the impact of ECI with one period lag on DfG and ECI are significant. However, impacts of DfG and ECI with one period lag on In GDP are not significant. On the other hand, impact of DfG with one period lag on DfG is positive but negative on ECI, while the impacts of ECI with one period lag on both DfG and ECI are positive. Finally, we have examined the statistical causality among the three variables based on PVAR by using panel Granger causality Wald test. The results are shown in Table 3. We \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Response of**} & \multicolumn{2}{c}{**Response to**} & \\ \cline{2-4}  & **In GDP(t-1)** & **DfG(t-1)** & **ECI(t-1)** \\ \hline \multirow{2}{*}{\(\ln\text{GDP}_{(t)}\)} & 0.961 *** & \(-\)0.313 & 0.027 \\  & (130.16) & (\(-\)1.40) & (0.6) \\ \multirow{2}{*}{\(\text{DfG}_{(t)}\)} & \(-\)0.008 *** & 0.734 *** & 0.027 *** \\  & (\(-\)7.96) & (24.23) & (4.41) \\ \multirow{2}{*}{\(\text{ECI}_{(t)}\)} & 0.292 *** & \(-\)4.864 *** & 0.874 *** \\  & (15.10) & (\(-\)8.31) & (7.43) \\ \hline \multicolumn{4}{l}{Observations} & 1440 \\ \multicolumn{4}{l}{N countries} & 60 \\ \hline \hline \end{tabular}  
* **Note: \(p<0.01\).  
\end{table} 
Table 2: Main results of a three-variables panel VAR. Figure 7: Robustness test of Panel VAR. observe that the \(\ln\) GDP is not the Granger cause of DfG and ECI at the 5% level, while the DfG and ECI are the Granger cause of \(\ln\) GDP at the 1% level. This result is as anticipated.
## 4 Results and Discussion
### Rotating Speed and Scratch Sizes Detected by CD and DVD Pick-Up Heads  
A speed variation of the rotating disk controlled by a new designed microcontroller has to be studied to find an optimistic speed for CD and DVD pick-up heads. A speed of 500-1500 rpm is varied and the results are shown in Table 4, while the error compared to the SEM results are shown in Figure 10.  
The disk rotation speeds of 500-1500 rpm are used to detect the width of the three scratches. Then, scratches are detected by the CD/DVD pick-up head at different speeds. In Table 4, it is shown that the measured size of scratches for both CD/DVD pick-up heads are enlarged when the rotation speed  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Sizes Measured (\(\mu\)m)**} \\ \hline  & \multicolumn{4}{c|}{**1 st Scratch (55 \(\mu\)m)**} & \multicolumn{4}{c|}{**2 nd Scratch (94 \(\mu\)m)**} & \multicolumn{4}{c}{**3 rd Scratch (332 \(\mu\)m)**} \\ \hline
**Speed (\(\mathrm{\textbf{rpm}}\))** & 500 & 750 & 1000 & 1250 & 1500 & 500 & 750 & 1000 & 1250 & 1500 & 500 & 750 & 1000 & 1250 & 1500 \\ \hline
**CD** & 58.12 & 60.51 & 63.19 & 68.23 & 68.97 & 94.83 & 96.11 & 96.88 & 100.00 & 110.35 & 330.36 & 331.05 & 330.81 & 332.08 & 337.18 \\ \hline
**DVD** & 55.13 & 57.03 & 59.50 & 67.86 & 68.94 & 94.95 & 96.24 & 97.74 & 99.18 & 110.30 & 330.82 & 331.52 & 331.45 & 334.11 & 337.82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of rotating speed and scratch sizes detected by CD and DVD pick-up heads.  
Figure 10: Error compared of three scratches to SEM by CD and DVD pick-up head at speeds of 500–1500 rpm.  
Figure 9: Shape characteristic measured by SEM of **(a)** 55 \(\mu\)m, **(b)** 94 \(\mu\)m, **(c)** 332 \(\mu\)m scratch.  
increased. The error is also increased when the velocity is fast. This is possible due to the high speed resulting in a loss of focus on the disk and the unstable easily [14]. Thus, an optimizing scratch can be obtained at low speed of disk rotation.
# Biochar Amended Soils and Water Systems: Investigation of Physical and Structural Properties
## 3 Results and Discussion
### Aggregate Stability Index  
Volume of drainable pores, \(\Delta U_{g}\), the modal suction, \(\mid\tau_{d}\mid\), and the SI values obtained for the three BC amended and non-amended soils. \(\Delta U_{g}\) significantly increased in the loamy sand + BC and in loam + BC compared to the soils without BC but decreased in the clay + BC compared to the clay (Table 5).  
The modal suction, \(\mid\tau_{d}\mid\), describing the water potential corresponding to the liquid constrained in the most common pore size diameter, significantly decreased in the loamy sand + BC and in the loam + BC soil, indicating an increase in the most frequent pore diameter, \(D\), and thus a pores' enlargement, but significantly increased in the clay + BC, indicating a reduction in the most frequent \(D\).  
Compared to the original soils, the SI values increased by about 60 times in both the loamy sand + BC and the loam + BC, but decreased by 0.48 times for the clay + BC,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Loamy Sand** & **Loamy Sand + BC** & **Loam** & **Loam + BC** & **Clay** & **Clay + BC** \\ \hline \(\mid\tau_{d}\mid\) (cm) & 121.2 a & 15.3 b & 156.5 a & 7.1 b & 78.0 a & 125.1 b \\ \hline \(\Delta U_{g}\) (g g\({}^{-1}\)) & 0.061 a & 0.460 b & 0.204 a & 0.567 b & 0.273 a & 0.213 b \\ \hline SI & 0.0005 a & 0.0300 b & 0.0013 a & 0.0802 b & 0.0035 a & 0.0017 b \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modal suction, \(\mid\tau_{d}\mid\), volume of drainable pores, \(\Delta U_{g}\), structural index (SI), for the three soils amended and not amended with biochar. Within each row, values followed by the same letter are not significantly different per a two-tailed \(t\)-test (\(p<0.05\)).  
indicating an improvement in the aggregate stability condition for the loamy sand and for the loam, but not for the clay.  
The diameter, \(D\) (\(\mu\)m), corresponding to \(\mid\tau_{d}\mid\), increased from 25 to 200 for the loamy sand, and from 20 to 433 for the loam and, according to the Greenland classification, shifted from the range of storage pores (0.5 \(\mu\)m \(<D<\) 50 \(\mu\)m) to the range of transmission pores (\(D\geq\) 50 \(\mu\)m), showing that BC induced formation of water stable macro-aggregates, which stored more water than small aggregates, as also shown by the increased \(\Delta U_{S}\). Similar results were reported by [PERSON] et al. and by [PERSON] et al. [31; 38], for a desert sandy soil. Instead, for the clay, \(D\) (\(\mu\)m) decreased from 39 to 24, falling in the range of storage pores, and indicating that BC induced formation of aggregates characterized by smaller inter-aggregate pores than in the original soil, as also shown by the decreased \(\Delta U_{S}\).  
These results indicated that BC did not improve the aggregate stability condition of the clay soil, appearing consistent with those obtained by analysing the \(\theta\)(\(l\)) functions obtained before and after BC addition.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 7 Conclusions  
NOMA is one of the most promising techniques to enable massive connectivity in 5G systems and enhance data rates in future mobile communication systems. This work offers a novel user allocation algorithm (USFMA) that builds on the advantages of other existing user allocation algorithms. We also proposed implementing a DC programming approach to assign power to superimposed users on the same sub-channel to maximize the system's energy efficiency. The fundamental concept is to use the DC programming method to convert non-convex problems into convex problems. We also used Matlab to simulate the proposed novel technique. The results of the simulation show that the NOMA system outperforms the OFDM system, and the proposed USFMA outperforms the existing USMA and CSS-PA. For future exploration, we suggest the integration of USMA with emerging technologies like massive MIMO, millimeter wave communications, and device-to-device communications for enhanced performance and capabilities.  
[PERSON] as the principal investigator takes the primary responsibility for this research and analyzed the results. [PERSON] and [PERSON] conceived the study and participated in its design and coordination and helped to draft the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the CHIST-ERA grant SAMBAS (CHIST-ERA-20-SICT-003) funded by FWO, ANR, NKFIH and UKRI.  
The data used to support the findings of this study are available from the corresponding author upon request.  
This research was supported by Tempus Public Foundation, Stipendium Hungaricum Scholarship Programme and High-Speed Networks Lab, Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics.  
The authors declare no conflicts of interest.
## 4 Experiments
### Model Comparison Experiment  
In this section, we analyze the performance of single models and the proposed multi-model synergetic framework in recognizing RRBs in children with autism through ablation experiments. The experiments primarily evaluate the performance of the action recognition model UniformV2, the MLLM InternVL, and the proposed multi-model synergetic method MS-RRBR on the ARRBD dataset. The model performance was assessed by calculating the accuracy of the test set to validate the advantages of the MS-RRBR in improving recognition effectiveness. The experimental results are shown in Table 4.  
The Top-1 accuracy of UniformV2 on the test set was 86.08%, while the Top-5 accuracy reached 100%. These results indicate that UniformV2 effectively captures spatiotemporal dependencies in videos for autism behavior recognition tasks. However, there is still room for improvement in accuracy, particularly when dealing with complex RRBs in the dataset. In contrast, InternVL, as a general-purpose MLLM, performed significantly worse than UniformV2 on the ARRBD dataset, achieving an accuracy of only 55.70%. This result suggests that although InternVL possesses powerful multimodal understanding capabilities, its visual encoder lacks the ability to capture critical details for autism behavior recognition, particularly in the absence of task-specific training or fine-tuning.  
Our proposed multi-model synergetic method, MS-RRBR, achieved an accuracy of 94.94% on the test set, demonstrating a significant improvement in Top-1 accuracy compared to single models. This result highlights the advantage of combining multiple models to leverage their complementary strengths for enhanced behavior recognition performance.  
As shown in Table 5, further analysis reveals the advantages of MS-RRBR in recognizing specific behavior types. While retaining the efficient performance of UniformV2 on certain action recognition tasks, MS-RRBR delivers significant improvements in both precision and recall for multiple action categories. For example, the recall for \"arm flapping\", \"hand action\", and \"object manipulation\" increases to 100%. As for the action \"clapping\", UniformV2 fails to recognize it correctly, whereas MS-RRBR accurately identifies all test instances. These findings indicate that MS-RRBR demonstrates superior performance in fine-grained behavior analysis, which is closely tied to the multi-model collaborative strategy employed. Moreover, this further supports the potential of MS-RRBR for application in autism-related behavioral recognition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline UniformV2 & Top-1: 86.08\%; Top-5: 100\% \\ InternVL & 55.70\% \\ Ours (MS-RRBR) & 94.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of overall accuracy.
## 4 Discussion  
### Terpenoid Backbone Biosynthesis  
The results of previous studies show that bilobalide is a sesquiterpene that contains three lactone units [35]. The terpenoid lactones from ginkgo, including bilobalide and ginkgolides, have antitumor, antileukemia, and autoxidation activities [7]. Herein, extremely significant differences in the total ginkgolide and lactone contents were found between the TH and TL samples (Figure 1a). Therefore, we selected groups with different lactone contents (high and low total lactone contents) to investigate the terpenoid synthesis pathway. In this study, 56 unigenes encoding enzymes involved in the terpenoid backbone biosynthesis pathway that are expressed in ginkgo were identified by RNA-Seq [3]. Multiple unigenes were annotated as the same enzyme, and these unigenes might represent different alternative splicing transcripts or members of a gene family [36]. In the terpenoid backbone biosynthesis pathway, two _HMGS_ genes exhibited differential mRNA expression, indicating that the encoded enzyme might be important in terpenoid biosynthesis. We compared the _GbHMGS1_ and _GbHMGS2_ gene expression values in ginkgo leaves and selected the most highly expressed _GbHMGS1_ for further function and metabolism studies. In this gymnosperm of ginkgo, HDR was described to produce an exceptionally high ratio of IPP and DMAPP compared to what up to now was commonly accepted as a general 5:1 ratio based mostly on studies done on microorganisms and angiosperms [10]. However, [PERSON] et al. [11] reported the existence of two isoforms of HDR in the coniferous gymnosperm _Picea Sitchensis_ showing opposite IPP:DMAPP ratio production in in vitro assays and suggesting the existence of two paralogs with functional specialization. Hence,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Metabolite Name** & **Class** & **Sub Class** & **CK** & _GbHMGS1_ \\ \hline Lanosterol & Prenyl lipid & Triterpenoid & 78.89 \(\pm\) 2.33 & 126.37 \(\pm\) 14.34 * \\ Dehydroabietic acid & Prenyl lipid & Diterpenoid & 0.07 \(\pm\) 0.03 & 0.17 \(\pm\) 0.02 * \\ Phytol & Prenyl lipid & Diterpenoid & 0.00 \(\pm\) 0.00 & 0.05 \(\pm\) 0.02 * \\ \hline \end{tabular}
\end{table}
Table 2: Terpenoid contents in nontransgenic wild-type and transgenic plants. There is no unit for the expression value of the peak area of metabolites after internal standard normalization. * represents \(p\) < 0.05, as determined by one-way AVOVA with [PERSON]’s multiple range tests.  
we can further explore the regulation of terpenoid content synthesis by other key genes or important transcription factors in the pathway in the future.
## 4 Mitigating Quantization Quality Degradation Based on the Observation  
To address the quantization bottleneck, our approach is based on the deterministic occurrence patterns of activation spikes. First, we utilize the observation that bottlenecks occur at a few specific layers. This implies that the naive full quantization of LLMs is affected by these bottlenecks. Second, we exploit the phenomenon that the activation spike is derived from the first occurrence of specific tokens. Thus, the planned occurrence prevents recurrence in the subsequent and possibly future tokens.
###### Abstract
The relationship between the amount of radiation reaching the satellite sensor and the intensity (and the type) of surface rain is nonlinear and non-Gaussian, then it is well-matched to NNs estimation capabilities. Additionally, NNs can handle huge amount of data in an automatic and quick way without the need of new training sessions. On the other hand, physically based models often require a great quantity of computational resources, then NNs might take a considerable advantage in an operational context. Lastly, noisy data are well managed by NNs, fault tolerance is indeed another advantage of using such an approach [13]. On the other hand, the use of NN algorithms has been often considered suitable for parameter retrieval in atmospheric remote sensing [14; 15] and neural techniques for rainfall monitoring involving some data fusion approach have been applied in the past [16; 17]. More in detail, if we restrict the research to the use of MSG data, a first attempt of using NN and a fusion technique for rainfall retrieval can be found in [18]. In this case only the IR10.8 SEVIRI channel was considered together with SSM/I measurements. More recently, in [19] a NN approach using MSG and ground based radar measurements is presented to estimate rainfall over north of Algeria. In this study, a novel NN architecture using as inputs MSG SEVIRI multispectral VIS and IR data and capable to generate rainfall maps at the MSG SEVIRI time resolution is presented. For the training phase, the possibility to use LEO PMW data from all in-orbit satellites, \"fusing\" data from six different sensors has been explored. The methodology is based on the design of two neural architectures, the first one to extract rainy pixels,the second one to provide the corresponding estimated rainfall value. The resolution issues are handled by means of a rigorous and accurate collocation procedure and by considering mean and standard deviation values over 3 \(\times\) 3 and 5 \(\times\) 5 pixel neighborhood. The algorithm performance is tested through comparison with a more conventional one, such as PR-OBS-3A, the EUMETSAT Hydrological Satellite Application Facility (HSAF) precipitation product. It has to be noted that both daylight and night time scenarios have been addressed. These latter characteristics together with the concurrent richness of the data used, either in terms of MSG bands considered as NN inputs or in terms of the microwave information exploited in the training phase, represent an additional significant element of innovation in this field.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 2 Materials and Methods  
### Space-Time Graph Model  
Studying the identification of key nodes in a satellite network is the basis of network vulnerability analysis, and it is also an important step in attacking LSCNs with DDoS. As a spatial information network, LSCNs are dynamic and connected, and traditional static graph models cannot model their highly dynamic network topology. Therefore, the concept of topology snapshot was formally proposed. Suppose there are \(N\) satellite nodes in a satellite network, \(V=\{v_{1},v_{2},..,v_{N}\}\) represents the set of all satellite nodes. Divide the satellite network into time slots \(\{1,2,\ldots,T\}\) with the same interval in a period; then, in the time slot \(t\), the network topology of the satellite can be expressed as \(G^{t}=\{V^{t},E^{t}\}\). In this time slot, \(V^{t}\) represents the set of all satellite nodes, and \(E^{t}\) represents the set of all links. So the set of satellite network topology snapshot sequences can be represented by \(\{G^{1},G^{2},\ldots,G^{T}\}\). However, there is no end-to-end path between some node pairs in the snapshot, and the network topology composed of topological snapshot sequences is not connected.  
In order to solve the connectivity problem, we transform the satellite network topology snapshot sequence \(\{G^{1},G^{2},\ldots,G^{T}\}\) into a space-time graph model \(\mathcal{G}=(\mathcal{V},\mathcal{E})\)[23; 24]. In the space-time graph model, the satellite network is divided into \(T+1\) layers, the satellite nodes of each layer are \(V=\{v_{1},v_{2},..,v_{N}\}\), and so the entire space-time graph contains \(N\times(T+1)\) nodes. Each adjacent layer can be connected by a space link and a time link. If a directed link \(\vec{v_{i}^{t}v_{j}^{t}}\ \in E^{t}\) exists, a space link \(\vec{v_{i}^{t-1}v_{j}^{t}}\ \wedge\ (i\
eq j)\) will be added between the adjacent layers, indicating that the node \(v_{i}\) can send data to \(v_{j}\) during time period \([t-1,t)\). The red path represents the space-time path for data sent from node \(v_{2}\) to reach node \(v_{5}\) through 4 time slots. Compared with other models, the space-time graph model can analyze the topology of a satellite dynamic network from the time dimension and space dimension and identify the key nodes in the constructed network. The space-time graph model is shown in Figure 1.
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 1 Introduction
### Principle of 3D Position Measurement Using Ultrasonic Sensors: 3D Ultrasonic Sensor Module
#### 2.1.2 Extension of Directional Angle of View and Detection Area: 3D Ultrasonic Sensor System  
In the 3D ultrasonic sensor module of Figure 4, the angle of view and the detection area exist in only one direction, as shown in Figure 5a,b due to the characteristics of the ultrasonic sensor. This disadvantage can be improved upon by adding additional sensor modules on top, bottom, left, and right, as shown in Figure 5c,d, called the \"3D ultrasonic sensor system\" in this paper.  
In Figure 5, each detection area from #1 to #5 has its own local coordinate frame, from {1} to {5}, respectively. In these frames, the \(z\)-axes represent directional angles of view at the corresponding detection areas. Each sensor module measures the relative position of an object based on its own local coordinate frame. For example, the object in detection area #2 is at (\(p_{x2}\), \(p_{y2}\), \(p_{z2}\)) of frame {2} and the object in detection area #3 is at (\(p_{x3}\), \(p_{y3}\), \(p_{z3}\)) of frame {3}.  
Therefore, all local positions have to be converted to global positions (\(p_{x3}\), \(p_{y}\), \(p_{z3}\)) of global coordinate frame {S} in Figure 5. This transformation utilizes a homogeneous transformation matrix. Let \(i=1,2,\cdots,5\), then the homogeneous transformation matrix \(T_{s}^{i}\) from frame {S} to {\(i\)} is derived as \[\mathbf{T}_{s}^{1} =\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right],\] \[\mathbf{T}_{s}^{2} =\left[\begin{array}{cccc}-\cos\theta&0&-\sin\theta&-\frac{l}{2} (1+\cos\theta)\\ 0&-1&0&0\\ -\sin\theta&0&\cos\theta&-\frac{l}{2}\sin\theta\\ 0&0&0&1\end{array}\right],\] \[\mathbf{T}_{s}^{3} =\left[\begin{array}{cccc}1&0&0&0\\ 0&\cos\theta&\sin\theta&\frac{l}{2}(1+\cos\theta)\\ 0&-\sin\theta&\cos\theta&-\frac{l}{2}\sin\theta\\ 0&0&0&1\end{array}\right] \tag{7}\] \[\mathbf{T}_{s}^{4} =\left[\begin{array}{cccc}\cos\theta&0&\sin\theta&\frac{l}{2} (1+\cos\theta)\\ 0&1&0&0\\ -\sin\theta&0&\cos\theta&-\frac{l}{2}\sin\theta\\ 0&0&0&1\end{array}\right],\] \[\mathbf{T}_{s}^{5} =\left[\begin{array}{cccc}1&0&0&0\\ 0&-\cos\theta&-\sin\theta&-\frac{l}{2}(1+\cos\theta)\\ 0&-\sin\theta&\cos\theta&-\frac{l}{2}\sin\theta\\ 0&0&0&1\end{array}\right]\]  
Using Equation (7), a relative position \(P_{i}\) with respect to frame \(\{i\}\) can be transformed into a global position \(P_{\mathrm{s}}\) with respect to frame \(\{\mathrm{S}\}\) as  
\[P_{s}=T_{s}^{i}P_{i} \tag{8}\]  
where \(P_{s}=\left[p_{xs}\ p_{ys}\ p_{zs}\ 1\right]^{T}\) and \(P_{i}=\left[p_{xi}\ p_{yi}\ p_{zi}\ 1\right]^{T}\).