esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 5 Full-System Load Test and Validation of the Hub Drive System
### Test Results of Acceleration Signals and Dynamic Model Validation  
During the load test, the driving speed was maintained at 500 rpm while the load torque was gradually increased from 0 to 16,500 Nm. The vibration acceleration signals of the hub drive system were recorded throughout this process. The results are shown in Figure 8.  
From Figure 8, when the system speed is constant, it can be observed that as the load torque increased, the magnitude of the vibration signals also increased, indicating that the vibration at the primary ring gear intensified. Once the load torque reached 16,500 Nm and stabilized, the vibration signal amplitude also stabilized, fluctuating within a certain range.  
To validate the accuracy of the dynamic model, the load torque in the simulation was set to 16,500 Nm and the motor input speed was set to 500 rpm, matching the boundary conditions of the bench test. The contact stiffness, friction coefficients, and constraints defined earlier were maintained in the simulation. The radial vibration acceleration of the primary ring gear in the dynamic model was recorded, as shown in Figure 9.  
Figure 6: Installation schematic of the load testing system.  
Figure 7: Schematic of unidirectional accelerometer placement.  
The results in Figure 9 indicate that, under the motor speed of 500 rpm and a load torque of 16,500 Nm, the radial vibration acceleration at the primary ring gear fluctuated within a range of \(-5\) m/s\({}^{2}\) to \(+5\) m/s\({}^{2}\). Comparing this with the experimental results in Figure 9, where the vibration amplitude ranged between \(-500\) mg and \(+500\) mg, it is evident that both the simulation and experimental results aligned closely. This demonstrates that the high-torque hub drive system's dynamic model accurately reflects real-world behavior, validating the model's accuracy.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 4 Evaluation
### Cost Model
#### 4.1.2. Range-Based Partition  
A range-based partition processes skewed data by randomly distributing skew records to the reducers and duplicating records with the same key in the remaining dataset.  
* \(|D|=|R|+|L|-\Sigma_{i=1}^{k}(|L|+\alpha_{i})+\Sigma_{i=1}^{k}(|L|\cdot\alpha_{ i}^{2}\cdot n)\)
* \(C_{reduce(1)}=C_{reduce(2)}=\cdots=C_{reduce(n)}\)
* \(\Rightarrow C_{reduce}\approx(\frac{|R^{\prime}|+|L^{\prime}|-\Sigma_{i=1}^{k} (|L|+\alpha_{i})+\Sigma_{i=1}^{k}(|L|+\alpha_{i}^{2}\cdot n)}{n})\cdot c_{d}\) (**)
* \(\Rightarrow C_{write}=n\cdot(\frac{|O|}{n})\cdot c_{r}=|O|\cdot c_{r}\)  
The value of \(\alpha_{i}\cdot n\) is equal to the number of segments with duplicate keys.  
* If \(\alpha=0\), then \(|D|=|R|+|L|\);
* If \(\alpha=10\%\) and \(\alpha=20\%\), the amount of intermediate data \(|D|\) generated is negligible;
* If \(\alpha=50\%\), the intermediate data generated are significant, but the hash-based partition algorithm cannot be performed because the nodes processing the skewed data will be overloaded or even stop working. That is, in this case, a range-based partition is more efficient than a hash-based partition. Therefore, we only need to consider the cases of moderately skewed ratios and to compare the costs of the algorithms based on the performance of the reducers.
## 2 Materials and Methods  
### Generating Arterial Walls Model  
Our training database comprises finite element simulations of different arterial geometries and boundary conditions. Our collaborators provided clinical data obtained through a two-step deep learning process for segmenting calcified coronary plaque in intravascular OCT images. This method involved combining a U-Net-based model with a GAN to effectively identify calcified plaques in OCT images. We utilized this segmentation data to construct a 3D model of a calcified artery based on OCT data [26; 27]. To streamline the process and generate various artery geometries rapidly, we first developed a 2D parametric model using Python scripting in Abaqus/Explicit software version 2019, drawing from the simplified clinical data and our observations. Python scripts in Abaqus can automate repetitive tasks, customize the software's behavior, and integrate it with other tools in a software pipeline. For example, a script could generate a series of similar simulations with varying parameters or post-process simulation results and generate customized visualizations. As shown in Figure 1, we simplified the assumption that the cross-section consists of a series of circles and splines. The specifications of the circles, including a lumen, fibrous, artery, calcium deposits, and the amount of calcium, were randomly varied according to Table 1. To produce calcification in a free-form shape, we began by selecting two circles as the inner and outer boundaries of the calcification, each with a random radius. We then randomly chose a calcification angle between 0 and 180 degrees. Next, we generated 10 points with equal intervals within this angle on the calcification's inner and outer boundaries. Finally, we utilized spline tools in Python to connect all these points and form a closed surface as the calcification. The number of calcifications may also be either one or two.
## 2 Materials and Methods
### Morphology and Mechanical Behaviour of the Composite
#### 2.3.2 Tensile Test  
Tensile behaviour of the composite samples was conducted in accordance with ASTM D3039 [49] standard. Samples were machined to a rectangular shape of dimension of 100 \(\times\) 20 \(\times\) 5 mm while a Tensometric machine M500-25 CT using a crosshead speed of 50 mm/min was used to conduct the test. Gauge length of the samples is 40 mm. Three samples were tested and average values taken.
## 2 Materials and Methods
### Training Samples Selection and Classification Analysis  
The Google Earth Engine machine learning algorithm was used to generate the training datasets along with sample points collected from field observations. Specifically, 100-200 random sample pixels were derived from Landsat for each time epoch for each land-cover class, except water, for Dedza where fewer samples were collected because of its small water surface area [44]. Google Earth exploreer imagery (2001, 2009, and 2019) was used for visual identification of land cover for signature development and as well as the 150 samples collected for ground truthing. A temporal comparison between 2018 Landsat imagery that is similar to the ground truth period and Landsat 2019 imagery for the same study location showed minimal differences (Table 2: The Random Forest supervised classification method in Google Earth Engine machine learning software was used to classify the land classes (described in Table 3). Most (80%) of the sample pixels were used as training data and the remaining 20% for assessing accuracy for each image year (2001, 2009, and 2019) [44]. Random Forest supervised classification uses a random selection with replacement to classify pixels using an ensemble of decision trees [50; 51]. Lastly, a post-classification image change analysis in ArcGIS v 10.7.1 was carried out; land-cover output maps and land-cover change maps across the land-cover classes for all years (2001, 2009, 2019) were completed. Figure 4 depicts the logical flow of the methodology used in this study.
## 2 Materials and Methods
### Simulation Studies for Gamma-ray Transmission
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class Code**} & \multicolumn{4}{c}{**mol\%**} & \multicolumn{4}{c}{**wt\%**} & \multirow{2}{*}{**Density (g/cm\({}^{3}\))**} \\ \cline{2-2} \cline{5-10}  & \(\mathbf{V_{2}O_{5}}\) & \(\mathbf{Te_{2}}\) & \(\mathbf{B_{2}O_{3}}\) & \(\mathbf{Nd_{2}O_{3}}\) & \(\mathbf{B}\) & \(\mathbf{O}\) & \(\mathbf{V}\) & \(\mathbf{Te}\) & \(\mathbf{Nd}\) \\ \hline VTBNd0.0 & 0.25 & 74.75 & 25 & 0 & 0.03941 & 0.26333 & 0.00186 & 0.69541 & 0.00000 & 4.7015 \\ VTBNd0.5 & 0.25 & 74.75 & 24.5 & 0.5 & 0.03825 & 0.26079 & 0.00184 & 0.68871 & 0.01041 & 4.9031 \\ VTBNd1.0 & 0.25 & 74.75 & 24 & 1 & 0.03711 & 0.25830 & 0.00182 & 0.68214 & 0.02063 & 4.9130 \\ VTBNd1.5 & 0.25 & 74.75 & 23.5 & 1.5 & 0.03599 & 0.2586 & 0.00180 & 0.67569 & 0.03065 & 4.9465 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical compositions and density for glass samples. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Energy (MeV)**} & \multicolumn{2}{c}{**VTBNd0.0**} & \multicolumn{2}{c}{**VTBNd0.5**} & \multicolumn{2}{c}{**VTBNd1.0**} & \multicolumn{2}{c}{**VTBNd1.5**} \\ \hline
**Energy (MeV)** & **Phy-X PSD** & **MCNPX** & **Phy-X PSD** & **MCNPX** & **Phy-X PSD** & **MCNPX** & **Phy-X PSD** & **MCNPX** \\ \hline
0.015 & 35.8866 & 38.6955 & 36.3656 & 38.8954 & 36.8356 & 39.0214 & 37.2966 & 39.1225 \\ \hline
0.02 & 16.5527 & 17.0157 & 16.7767 & 17.0954 & 16.996
## 4 Mathematical Description for Linear Bearings  
Four-point connection linear ball bearings are advanced mechanical components that enable precise linear motion with minimal friction. Their design is based on the use of rolling balls, which are supported at four contact points, ensuring an even distribution of loads and increased stability. The design of a linear ball bearing consists of rolling balls, housings to maintain the appropriate distance between the balls and raceways. The four-point connection means that each ball has four contact points with the raceways, allowing the loads to be evenly distributed along the entire length of the bearing. This allows these bearings to accommodate both axial and radial loads, which increases their versatility. The study focused only on radial loading, which plays a key role in natural frequency but also wear.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 3 E-Mqtt
### Detailed Process of End-to-End Communication
The _waited_packet_num_ and _subscriber_id(s)_ fields play a pivotal role in the broker's response aggregation process. Upon the broker's reception of a PUBREC packet from a subscriber, it immediately sends a PUBREL packet to the subscriber and stores the PUBREC packet as well as the PUBREL packet. Subsequently, the broker assesses the stored PUBLISH packet. If the _waited_packet_num_ field is 1 or more (synchronous E-MQTT), the broker proceeds to examine the _subscriber_id(s)_ field. If the field is empty, the broker performs a count of received PUBREC packets. Conversely, if the _subscriber_id(s)_ field is not empty, this implies that the publisher has specifically earmarked subscribers whose responses it is awaiting. Consequently, the broker increases the count of received PUBREC packets only if the PUBREC sender is contained in the _subscriber_id(s)_ field. The broker then compares the _waited_packet_num_ value with the count of PUBREC packets received so far. If the number of received PUBREC packet becomes the same as _waited_packet_num_, the broker transmits an aggregated PUBREC packet to the publisher. However, if the _waited_packet_num_ equals 0 (asynchronous E-MQTT), the initial PUBREC packet is promptly transmitted to the  
Figure 10: Flowcharts showing the broker-side execution flow of E-MQTT. publisher upon reception. The subsequent tasks when the broker receives the PUBREL and PUBCOMP packets are the same as that in the existing MQTT. Figure 11 illustrates the operation sequence of the subscriber within the E-MQTT. The subscriber's functionality is the same as that in MQTT QoS 2 except for the check of the QoS level field. Upon the receipt of the QoS level 3 PUBLISH packet from the broker, the subscriber stores the packet and then sends the PUBREC packet for QoS 3 as a response back to the broker. When the subscriber receives the PUBREL packet, the execution flow is the same as that of MQTT QoS 2.
# High-Power Broadband Frequency Chirped Intensity-Modulated Single-Frequency 1064-nm Laser
## 5 Experimental Results and Discussions  
### Output Power and Spectrum  
The input power to the EOM was 26 mW, and the output power of the chirped intensity modulated laser was 10.4 mW. After the first-stage optical fiber amplifier, the laser signal was amplified to 1.26 W. The output power of the second-stage amplifier versus pump power is shown in Figure 3a. A maximum output power of 29.5 W was obtained when 40 W of pump power was added to the second-stage amplifier. The optical-optical conversion efficiency was 70.6%. At the maximum output power, we measured the stability of the output power. As shown in Figure 3b, the long-term peak-to-peak power fluctuation was lower than \(\pm\)0.26% over 30 min. Because of the feedback system of the YDFA, the output power stability was high.  
Figure 3: (**a**) Output power of the second-stage amplifier versus pump power; (**b**) Power stability within 30 min.  
Figure 2: Diagram of the experimental setup of the chirped intensity-modulated laser and the fiber power amplifier.  
We measured the power spectrum for the amplifier output power by an optical spectrum analyzer (Yokogawa:AQ6317, Tokyo, Japan). As shown in Figure 4, the full width at half maximum of the spectrum is 0.019 nm, which is close to the resolution of the optical spectrum analyzer. Because the laser spectrum is very narrow, it is difficult for the spectrometer to measure the spectrum accurately.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 7 Adaptive Golomb Code and Context Adaptive Binary Arithmetic Coder
### Medium-Term Features of the Probability Distribution
The method of scaling is as follows; for \(m<1\), we assign \(m=1\) and values between 1 and 6 remain unchanged, whereas for \(m>6\), we calculate \(m:=\lfloor 1.65\cdot m\rfloor\). The last step is the approximation of the calculated \(m\) value to the nearest of those in \(\mathbf{m}\) vector. In this way, the index of the quantised value of \(m\) (hereinafter referred to as \(b_{\text{Golomb}}\)) is obtained. The \(b_{\text{Golomb}}\) value is a fragment of the context number and is invariable when encoding all bits of the next Golomb code word representing the number \(\widetilde{e}(n+1)\). This allows for a highly flexible adaptation to the local features of the probability distribution of currently coded prediction errors as well as the rate of change of features of this distribution. Then, the quantisation of the \(m\) parameter to only 40 values is a compromise approach with regard to the uninterrupted adaptation of each of the 40 probability distributions that are used in the binary arithmetic coder, by which the bit stream coming from Golomb coder is coded. The Golomb code word coding \(\widetilde{e}(n)\) value consists of two elements. The first one is the \(u_{G}=\lfloor\widetilde{e}(n)/m\rfloor\) number specifying the group number that is written in the unary form (sequence \(u_{G}\) zeros ending with one). The second element is the \(v_{G}=\widetilde{e}(n)-u_{G}\cdot m\) number, called the number of element in a group (remainder of division by \(m\)). It is coded using a phased-in binary code (which is the variant of the Huffman code for sources with \(m\) equally probable symbols [42]). Specifying the \(k=\left\lceil\log_{2}m\right\rceil\) parameter means that, in each group, the first \(l=2^{k}-m\) elements \(v_{G}\) are coded using \(k-1\) bits, and the remaining \(m-l\) elements are coded as number \(v_{G}+l\) using \(k\) bits [13].
## 4 Results and Discussion
### Limitations and Future Work  
The method proposed in this paper, based on large language models and synchronous attention mechanisms for detecting cyberattack behaviors, has been validated on multiple datasets for its effectiveness and superiority, especially in handling complex and multimodal data. However, despite the encouraging results achieved in this study, there remain a number of limitations and challenges for practical application and further research, which will be important directions for future work. First, although the synchronous attention mechanism effectively integrates and processes multimodal data, enhancing the model's detection capability for complex attack behaviors, its performance on extremely imbalanced datasets has not been fully verified. In the field of cybersecurity, attack events are often much less frequent than normal behaviors, and this class imbalance problem may affect the learning efficiency and detection performance of the model. Therefore, designing more effective data sampling strategies or optimization algorithms to improve the model's performance on imbalanced datasets is a critical issue for future research. Second, while the proposed method shows good generalization ability on certain specific datasets, such as the stock price prediction dataset, this does not mean that it will maintain the same performance across all types of tasks. The adaptability and stability of the model still need further exploration and validation, especially on tasks with high dynamics and uncertainty.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** \\ \hline Transformer & 0.78 & 0.75 & 0.77 \\ BERT & 0.80 & 0.77 & 0.79 \\ OPT-175B & 0.83 & 0.79 & 0.82 \\ LLaMA & 0.85 & 0.82 & 0.84 \\ ChatGLM3-6B & 0.88 & 0.84 & 0.87 \\ Ours & 0.91 & 0.87 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Stock Price Prediction Results.  
Thus, further enhancing the model's generalization capability and robustness to adapt to a broader range of application scenarios will be an important direction for future research. Lastly, the computational complexity of the proposed method is relatively high, especially when processing large-scale datasets, which may entail significant computational resource consumption and time costs.
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 4 Experiments
### Results and Analysis
#### 4.4.1 Experimental Results  
**PASCAL VOC (Table 2):** For the one-shot setting, our model achieved an AP75 of 42.3%, outperforming TFA and Attention-RPN but slightly lagging behind MPSR and FSCE. With two-shot samples, the model achieved an AP75 of 48.4%, surpassing TFA and Attention-RPN, while closely competing with FSCE and MPSR. As the number of shots increased, our model demonstrated a consistent improvement, achieving the highest AP75 scores in the 3-shot (55.8%), 5-shot (63.2%), and 10-shot (65.8%) settings. Notably, in the 10-shot setting, the model showed an absolute improvement of 8.0% over MPSR and achieved the highest overall AP75 among all methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Samples** & **Our Model** & **TFA [7]** & **MPSR [9]** & **FSCE [8]** & **Attention-RPN [11]** \\ \hline
1 & 42.3 & 39.8 & 41.7 & **44.2** & 35.0 \\
2 & 48.4 & 43.7 & **49.3** & 46.3 & 36.0 \\
3 & **55.8** & 44.6 & 51.4 & 52.4 & 39.1 \\
5 & **63.2** & 55.6 & 55.2 & 61.9 & 51.7 \\
10 & **65.8** & 56.0 & 57.8 & 63.4 & 55.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: AP75 results under different few-shot settings on the PASCAL VOC dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Samples** & **Our Model** & **TFA [7]** & **MPSR [9]** & **FSCE [8]** & **Attention-RPN [11]** \\ \hline
10 & **10.3** & 8.8 & 9.7 & 9.8 & 9.2 \\
30 & **15.7** & 12.0 & 14.2 & 14.2 & 14.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: AP75 results under different few-shot settings on the COCO dataset.  
**COCO dataset (Table 3):** In the 10-shot setting, our model achieved an AP75 of 10.3%, outperforming TFA and other baselines. For the 30-shot setting, the model reached an AP75 of 15.7%, achieving the highest AP75 in this configuration, with an absolute improvement of 3.7% compared to TFA. These results underscore the proposed model's robustness and adaptability in handling varying levels of few-shot data, particularly in challenging settings with limited samples.
# Cloud Based Smart City Services for Industrial Internet of Things in Software-Defined Networking
## 3 Proposed Architecture for Smart Cities in SDN-IoT
### System Model  
The communication in the network gets initiated by an user while sending request towards any one of the cluster. Considering a network scenario as shown in Table 1, each cluster say \(cluster_{i}\) is supposed to have predefined threshold value \(TH_{i}\) which defines the increased number of requests which can be served by this cluster. All the nodes in each cluster say \(I\) where \((I\to I_{1},I_{2},I_{3},\ldots)\) are connected via a local SDN POX controller. This controller is responsible for maintaining check on the number of requests currently being served within its own cluster. At particular instance, when the threshold value matches with the current number of processed requests, the POX controller does not let any new request to get served in its own cluster and hence, it transmits this request towards another cluster via GN. This request again has to be checked at newly arriving cluster for its processing. In case of availability for request processing, the POX controller allows this request to come in and transmits it to the node in its cluster based on remaining request processing availability of that node and is checked at each node using Equation (1) :  
\[current-req_{I1,cluster_{i+1}}<req_{I1,cluster_{i+1}} \tag{1}\]  
Here, \(current-req_{I1,cluster_{i+1}}\) is the current number of requests being served by node \(I1\) in \(cluster_{i+1}\), \(req_{I1,cluster_{i+1}}\) is the number of requests that can be processed by node \(I1\) corresponding to \(cluster_{i+1}\).  
In case of multiple nodes available for processing this request, it can be sent to any one of them by after increment of \(1\) to its \(current-req\) count. The number of requests that can be processed by any node \(I1\) in its cluster say \(cluster_{i+1}\) is based on its threshold value \(TH_{i+1}\) and is computed using Equation (2):  
\[req_{I1,cluster_{i+1}}=\frac{TH_{i+1}}{I} \tag{2}\]  
where \(TH_{i+1}\) is the predefined threshold of \(cluster_{i+1}\) and \(I\) is the total number of nodes in \(cluster_{i+1}\).  
Figure 4: Applications of Smart City.  
The process goes on until all the requests received from the users are not satisfied by any one of the available clusters. The complete working of the proposed architecture is represented in Table 1.