esa-sceva/satcom-chunk-collection huggingface dataset:
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 4 Experiments
### Experiment Results
#### 4.2.3 Ablation Study: Comparison with Backbone and Query Drop Setting  
In this study, we conducted an ablation study to deeply analyze the performance changes of the SQ-DETR model according to the backbone network. In particular, we compared the object detection performance difference when applying ResNet-18 (R-18) and ResNet-50 (R-50) backbones to SQ-DETR and aimed to clarify the impact of the backbone network on model performance.  
Table 4 summarizes the best SQ-DETR results for the ResNet-18 and ResNet-50 backbones under several _drop ratio_ configurations, that is, the number of object queries discarded at each decoder layer.  
SQ-DETR Based on ResNet-18 Backbone (SQ-DETR-R18-Dec\({}^{3}\))  
The SQ-DETR-R18-Dec\({}^{3}\) variant (ResNet-18 backbone, three-layer decoder) was evaluated under several _drop ratio_ configurations. The best result was obtained with the '50, 50, 50' setting, which discards 50 queries at every decoder layer.  
Even with the lightweight ResNet-18 backbone, this configuration achieved a strong AP of 47.3.  
SQ-DETR Based on ResNet-50 Backbone (SQ-DETR-R50-Dec\({}^{6}\))  
The SQ-DETR-R50\({}^{6}\) variant (ResNet-50 backbone, six-layer decoder) was evaluated under several _drop ratio_ configurations. The best result was obtained with the '25, 25, 25, 25, 25' setting, which discards 25 queries at every decoder layer.  
The SQ-DETR-R50\({}^{6}\) model achieved a higher optimal AP score compared to SQ-DETR-R18-Dec\({}^{3}\). Notably, under certain settings, it also reached similar AP performance in fewer training epochs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Backbone** & **Layers** & **Drop Ratio** & **Epoch** & \begin{tabular}{c} **Dec.** \\ **FLOPs** \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} & \begin{tabular}{c} _AP_ \\ \end{tabular} \\ \hline ResNet-18 & 3 & \
\begin{tabular}{c} 50, 50, 50 \\ 100, 35, 15 \\ \end{tabular} & 72 & 9.5 & 47.3 & 64.6 & 51.0 & 29.7 & 51.1 & 62.8 \\ \hline ResNet-50 & 6 &
\begin{tabular}{c} 25, 25, 25, 25, 25, 25 \\ 20, 20, 20, 30, 30, 30 \\ 50, 50, 50, 0, 0 \\ \end{tabular} & 72 & 14.1 & 52.6 & 71.3 & 56.7 & 34.6 & 57.1 & 70.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with backbone and query drop ratio.  
Figure 4: SQ-DETR inference results.  
In conclusion, the results of this ablation study demonstrate that SQ-DETR is well applied to both ResNet-18 and ResNet-50 backbones. Considering its advantages of lightweightness, the ResNet-18 backbone shows potential value for the efficient implementation of SQ-DETR. In future research, we plan to further explore various lightweight and performance enhancement techniques, focusing on the ResNet-18 backbone-based SQ-DETR model, to further expand its applicability in the field of object detection.
### Fifth-Generation (5G) Technology  
With its emphasis on record-breaking data rates, extremely low latency, and widespread device connection, fifth-generation (5G) technology marks a substantial advancement in wireless communication. Fifth-generation promises data speeds up to 100 times faster than 4G by operating in higher frequency bands and leveraging cutting-edge technology like Massive MIMO and beamforming. Real-time applications like augmented reality, remote surgery, and driverless cars are improved by this technology. The network slicing capabilities of the 5G architecture make it possible to build virtual networks that are specialized for certain use cases. Although it is still being implemented, 5G opens the door for revolutionary applications that make the Internet of Things (IoT) and smart cities more practical [7].
## 3 Results and Discussion
### Fluorescence Intensity Results  
The fluorescence intensity classification method (described in Section 2.4) was analyzed using all images in the public AIDA database (2080 IIF images)--1498 positive images and 582 negative images. In terms of performance, the system showed a sensitivity in the recognition of positive images equal to 92.9%, while with regard to the ability to identify the negatives, the system showed a specificity of 70.5%. Sensitivity and specificity were analyzed by varying the threshold value, in this way the ROC (Receiver Operating Characteristic) curve shown in Figure 9 was obtained. The area under the curve value obtained was: A\({}_{Z}\) = 0.914+/\(-\)0.007. The accuracy value obtained was: Acc = 87%.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **C** & \(\gamma\) \\ \hline homogenous & 256.0 & 0.125 \\ speckled & 32.0 & 0.25 \\ centromere & 256.0 & 0.25 \\ Nucleolar & 128.0 & 0.50 \\ nuclear dots & 256.0 & 0.0156 \\ nuclear membrane & 128.0 & 0.0625 \\ \hline \end{tabular}
\end{table}
Table 3: The C and \(\gamma\) parameters obtained for each one-against-all(OAA) classifier.  
Figure 9: Receiver Operating Characteristic (ROC) curve of the fluorescence intensity classification method.  
Since the analysis of the fluorescence intensity of the HEp-2 images has not been sufficiently addressed as a research activity, there is only a limited number of studies with which a comparison can be made. Table 4 shows the results obtained and the performance comparison with other notable intensity fluorescence classification methods, proposed in the literature, in the last years; the table reports (when present)--the number of images on which the analysis was performed, sensitivity, specificity, accuracy, and A\({}_{\text{Z}}\).  
As the intensity method works on the full slide and not on the individual HEp-2 cells, the performances did not depend on the cell segmentation method. Table 4 shows--also due to the greater statistical weight of our result--that our method of fluorescence intensity classification is, at least, comparable with the other methods analyzed.
## 1 Introduction
We develop the event based model to present a method to obtain more reliable solutions for partnership integration in a dynamic and changing supply chain environment. It considers unforeseen events of orders to reduce uncertainties to provide accurate and up-to-date information for enterprises to improve efficiencies. By implementing event information synergy about orders, trustworthiness, products received, delivery, and cost in supply chain, the concordance of supply chain can be realized from the precise knowledge about how to adjust the sourcing, manufacturing, and distribution plans across the parties in the supply chain. A supply chain partnership integration problem has multiple criteria and needs sophisticated decision support, and we propose a hybrid algorithm by integrating particle swarm optimization (PSO) and ant colony optimization (ACO) to solve the multi-objective problem. PSO is good at global optimization, whereas it has the disadvantages of easily trapping in a local optimum [17]. ACO finds better solutions of effective feedback and distributed computation, although it has shortcomings of long-searching time and an initial lack of pheromone [18]. By fusing these two algorithm, we can improve the convergence rate and avoid trapping into local optimum [19]. In the hybrid algorithm, PSO is employed for global search and ACO is used for local optimization. That is, we can use the distribution of pheromone from initial PSO searching and feed into ACO to improve efficiency and reach an optimal solution. The major contributions of the study are summarized below. * This is a work regarding the design of an event-based framework in a supply chain partnership integration problem, which is based on a clear understanding of key events in a dynamic supply chain process and focuses on how events can be used to reveal factors that influence partnership in supply chain operations. * The hybrid algorithm, PSACO, which utilizes the functions of PSO and ACO, is proposed. In the algorithm, a linear decreasing inertia weight schema is introduced to achieve relatively high performance for PSO, and a new pheromone strategy is used for ACO. * In order to improve the precision of experiment solutions, experimental analyses are carried out to select the optimal parameters of ACO. * Experimental results demonstrate that PSACO can obtain better performance than traditional PSO and ACO. The rest of the paper is organized as follows: the related literatures about supply chain partnership integration and events are reviewed in Section 2. Section 3 presents the event-based supply chain partnership integration and the mathematical optimization model. Section 4 introduces the background of particle swarm optimization and ant colony optimization, and the key idea of a hybrid algorithm is described. Section 5 presents the process of the hybrid algorithm in solving the multi-objective supply chain partnership integration problem. Section 6 explores the optimization of the algorithm coefficiency, and compares the performance of PSACO with PSO and ACO. Finally, Section 7 presents the conclusions and further research directions.
## 3 Model Description
### Information Integration  
#### 3.3.1 Record the Motion Trajectory  
In the \(\mathcal{CIST}\)MD model, we apply movement information channel outputs to track the locations of small traits and record the movement trajectories of small traits. The process of recording the motion trajectory is described in Figure 5. We give an identification criterion \(\theta\), and the small feature position is determined by comparing the threshold \(\vartheta\) with the motion detection channel outputs. For the threshold \(\vartheta\) and fixed time point \(t_{0}\), when the motion detection channel outputs \(D(x_{0},y_{0},t_{0})\) at location \((x_{0},y_{0})\) in time \(t_{0}\) satisfy \(D(x_{0},y_{0},t_{0})>\vartheta\), then, we conclude that a little feature is tracked on site \((x_{0},y_{0})\). Analogously, at the next time point \(t_{1}\), the another location \((x_{1},y_{1})\) can be tracked and recorded. In particular, when position \((x_{1},y_{1})\) at time point \(t_{1}\) is in a small area of position \((x_{0},y_{0})\) at time point \(t_{0}\), we judge that position \((x_{1},y_{1})\) and position \((x_{0},y_{0})\) belong to the same trajectory. Iterating the aforementioned steps, a movement trajectory can be obtained throughout a duration. The movement traces \(TRC\) are depicted by  
\[TRC=(x(t),y(t)),t\in[t_{0},t_{i}]. \tag{15}\]  
Here, \((x(t),y(t))\) represents the coordinates of the pixel as it moves over time \(t\). \(t_{0}\) and \(t_{i}\) denote the initial and present times of the motion, respectively.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
### Recoding of Batched Network Coding  
The simplest recoding scheme for BNC is known as the baseline recoding, which generates the same number of recoded packets for all batches regardless of their ranks. Due to its simple and deterministic structure, it appears in many BNC designs and analyses such as [69; 70; 71]. However, the throughput cannot be optimized [72] as the scheme assigns too many recoded packets for those low-rank batches. Adaptive recoding [64; 73; 74] is an optimization framework for deciding the number of recoded packets to be sent in order to enhance the throughput. The framework depends only on the local information at each node; thus, it is capable of being applied distributively and adopted for more advanced formulations [75; 76].  
On the other hand, systematic recoding [77; 72] is a subsidiary recoding scheme that forwards the received linearly independent recoded packets at a node (i.e., those packets that carry innovative rank to the node) and regards them as the recoded packets generated by the node itself. This way, fewer recoded packets have to be generated and transmitted later on, thus potentially reducing the delay of receiving a batch at the next node as well as the time taken for decoding. The number of extra recoded packets to be generated is independent of systematic recoding but is related to the results of baseline or adaptive recoding. Theoretically speaking, the throughput of systematic recoding is better than generating all recoded packets by RLNC, though the benefit is indistinguishable in practice [77]. The actual enhancement in decoding time, however, is not well studied.  
Although many works focus on the recoding of BNC, they consider a fire-and-forget approach: the reception status of the recoded packets generated by the current node is not taken into consideration. This fire-and-forget approach can be useful in extreme applications when feedback is expensive or not available, such as deep-space [78; 79; 80] and underwater communications [81; 82; 83]. However, the use of feedback is very common in daily networking applications. For fire-and-forget adaptive recoding, feedback can be used to update the channel condition for further transmission in adaptive recoding [74]. Yet, it was also shown in [74] that although the throughput can be enhanced, the gain is insignificant. Therefore, another better way of making use of feedback for BNC has to be sorted out.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.2.2 Second Method: Clusters with at Least 25% Attack Data as Attack Clusters  
In the second method, clusters were labeled as attack clusters if they contained at least 25% of attack data. Table 3 presents the results of the runs for clusters with at least 25% of attack data. In this set of runs, since a seed of 1 produced an error, a seed of 2 was used. At 400 clusters the results were very close to that of 300 clusters, but performance did not improve significantly, hence the runs were stopped at 400. Again, though runs were done for all clusters between 2 and 400, only results for 2, 10, 50, 100, 200, 300, and 400 clusters are presented.  
Analysis of the results for the Second Method:  
* Effectiveness from the beginning
* Since K-means naturally grouped 91.97% of the attacks into one cluster at two clusters, this method was able to label one cluster as an attack and the other as normal, leading to high precision from the start and a much lower number of false positives compared to the first method. Two clusters also had the lowest computation time.
* Best Performance at 300 Clusters
* Though the computation time went up quite a bit, the highest precision (98.74%) and recall (98.71%) were achieved at 300 clusters, where clusters were well-separated with minimal misclassification. These results are bolded in Table 3.
* At 400 clusters, the numbers were very close to those at 300, but performance did not improve significantly. While no significant increase in false positives was observed, there was no additional benefit in further segmentation at this stage. The computation time also increases with the number of clusters.
* Summary of the Second Method
* The Second Method had higher average accuracy, precision, recall, F1 score, and AUC than the First Method.
* Both the First and Second Methods had the best results at 300 clusters, but the computation time of the Second Method was slightly higher. In general, the average computation time was also higher with the second method.
# Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist
## 7 Experiments
### Comparative Study on One Synthetic Dataset  
To study the impact of copiers in crowdsourcing, we conducted a comparison study on a synthetic dataset with copiers. By tuning the proportion of copiers in the dataset from 10% to 30%, we obtained three datasets. We ran all eight methods on those datasets. All method results are shown in Table 3. From Figure 3, we can see that when the proportion of copiers increased, the MAEs of different methods increased to varying degrees, but our method (SWWC) could still maintain the lowest MAE. The errors of five methods, i.e., SWWC-NoCopier, D&S, ARE, MDC, and CDAS, increased linearly when the proportion of copiers increased, while the error of RandomMV increased exponentially. Among all the methods, ASKIT! showed the worst performance when copiers existed in the dataset. ASKIT! determines which task to assign through uncertainty. It does not involve selecting appropriate workers to complete it, so copiers have a great impact on it. Because SWWC identifies the copiers and removes the copiers from the task assignment, each task can be assigned to highly reliable workers. Thus, the accuracy of the answer is improved. For this reason, our method maintains the error at the lowest level. Experimental results showed that our method can effectively perform task assignment and truth discovery in crowdsourcing with copiers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Anime (Domain 1)**} & \multicolumn{4}{c}{**Anime (Domain 2)**} \\ \hline  & **MAE** & **RMSE** & **Time (s)** & **MAE** & **RMSE** & **Time (s)** \\ \hline SWWC & 0.3043 & 1.1036 & 3614.597 & 0.3243 & 1.0234 & 3524.965 \\ SWWC-NoCopier & **0.2875** & 1.0368 & **5.611** & **0.3075** & 0.9521 & **5.483** \\ RandomMV & 0.8882 & 1.1077 & 48.123 & 0.6779 & 0.8865 & 52.328 \\ D&S & 0.4850 & 0.6799 & 46.375 & 0.4971 & **0.6459** & 50.762 \\ ASKIT! & 0.7605 & 0.9749 & 1507.95 & 0.7605 & 0.9749 & 1507.95 \\ CDAS & 0.3097 & 1.0935 & 39.32 & 0.3097 & 1.0935 & 39.32 \\ ARE & 1.2089 & 1.6919 & 519.29 & 1.2757 & 1.6080 & 502.35 \\ MDC & 0.3663 & **0.4879** & 489.131 & 0.3662 & 1.1027 & 482.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different methods on two real-world datasets (2).  
Figure 3: MAEs of different methods on one synthetic dataset.
Numerical simulations are also carried out using finite element (FE) modelling to evaluate whether or not the new technique enhances the accuracy of the analytical results. Moreover, experimental data points from previous studies are used for validation of the proposed technique.
## 2 Methods
### Geometric Model of the Air-Gap with Mixed Eccentricity
 \tag{24}\]  
It can be found in Figure 2 that the air-gap length can be described as  
\[g(\alpha,t)=\|AO_{R}\|-R_{r} \tag{25}\]  
and the ratio of the static eccentricity can be described as  
\[\varepsilon_{S}=\frac{d_{SE}}{g_{0}} \tag{26}\]while the ratio of dynamic eccentricity can be described as  
\[\varepsilon_{D}=\frac{d_{DE}}{g_{0}} \tag{27}\]  
where \(g_{0}\) is the length of air-gap in normal state. The air-gap length of mixed eccentricity can be obtained by combining Equations (19)-(27) and can be described as  
\[g(\alpha,t)=\left\{ \begin{array}{ll}\sqrt{{R_{s}}^{2}-\sin^{2}\left[\alpha- \omega t-\arccos\left(\frac{\varepsilon_{D}+\varepsilon_{S}\cos(\delta-\omega t )}{\sqrt{\varepsilon_{s}}^{2}+\varepsilon_{D}^{2}+2\varepsilon_{S}\cdot \varepsilon_{D}\cos(\delta-\omega t)}\right)}\right]\\ \sqrt{\cdot g_{0}^{2}\left[\varepsilon_{S}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)\right]}\hskip 11.381102 pt, \omega t-\delta<2k\pi\\ -R_{r}-g_{0}\sqrt{\varepsilon_{S}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)}\\ \cdot\cos\left[\alpha-\omega t-\arccos\left(\frac{\varepsilon_{D}+\varepsilon _{S}\cos(\delta-\omega t)}{\sqrt{\varepsilon_{s}}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)}\right)\right]\\ \sqrt{\cdot g_{0}^{2}\left[\varepsilon_{S}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)\right]}\hskip 11.381102 pt, \ 2k\pi\leq\omega t-\delta<(2k+1)\pi\\ -R_{r}-g_{0}\sqrt{\varepsilon_{S}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)}\\ \cdot\cos\left[\alpha t-\arccos\left(\frac{\varepsilon_{D}+\varepsilon_{S} \cos(\delta-\omega t)}{\sqrt{\varepsilon_{s}}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)}\right)\right]\\ \sqrt{\cdot g_{0}^{2}\left[\varepsilon_{S}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)\right]}\hskip 11.381102 pt, \ (2k+1)\pi\leq\omega t-\delta<2(k+1)\pi\\ -R_{r}-g_{0}\sqrt{\varepsilon_{S}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)}\\ \cdot\cos\left[\alpha-\omega t-\arccos\left(\frac{\varepsilon_{D}+\varepsilon _{S}\cos(\delta-\omega t)}{\sqrt{\varepsilon_{s}}^{2}+\varepsilon_{D}^{2}+2 \varepsilon_{S}\cdot\varepsilon_{D}\cos(\delta-\omega t)}\right)\right] \end{array} \right. \tag{28}\]  
Obviously, Equation (28) is a general expression suitable for static eccentricity, dynamic eccentricity, and mixed eccentricity. By substituting \(g(\alpha,t)\) calculated from Equation (28) into Equations (16) and (17), the air-gap length becomes a function of \(\alpha\) and \(t\). The analytical model reflecting the mixed eccentric air-gap magnetic field is finally obtained.
# Flow-Based Programming for Machine Learning
## 2 Background  
### Machine Learning  
In classical instruction-based programming, a machine processes datasets based on predefined rules. However, in ML programming, machines are trained on large datasets to discover the inherent pattern and thereby auto-create the data processing rules. ML programming involves the usage of an input _dataset_, _features_ or pieces of information in the dataset useful for problem-solving and an _ML model_. The features when passed to an ML algorithm for learning purposes outputs an ML model. ML algorithms have been broadly classified into two categories of _supervised_ and _unsupervised_ learning algorithms. In supervised learning, the system learns from the training dataset with labelled data to predict future events. Examples include _classification_ and _regression_. Regression provides continuous prediction while classification does non-continuous prediction. Unsupervised machine learning algorithms work on unlabelled data to find unknown patterns in the data. Unsupervised learning outputs data as clusters. Clusters are a grouping of similar data from the unlabelled input. Creation of an ML model involves specific steps like datacollection and data processing, choosing a relevant ML algorithm, training the model on the training dataset, evaluating the model on testing dataset for prediction accuracy, tuning the parameters to enhance the prediction accuracy of the model and finally using the model to make predictions on new input data.  
ML is often confused with Deep Learning (DL) [8]. The necessary steps involved to create a model are the same for both ML and DL. Nevertheless, there are many subtle differences between the two. First, we rely on a single algorithm in ML to predict while DL uses multiple ML algorithms in a layered network to make predictions. Second, ML typically works with structured data, while DL can work with unstructured data too. Third, in ML, manual intervention is required in the form of model parameter tuning to improve prediction accuracy while DL self-improves to minimise error and increase prediction accuracy. Fourth, DL is suited well with the availability of massive amounts of data and is a more sophisticated technique in comparison to ML.