esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
In addition, there are other techniques for reducing redundant traffic in HSR networks, including the HSR SwitchBox technique [18], the integration of HSR and OpenFlow (HSE + OF) [19], the reducing multicast traffic (RMT) [20], the cost-effective topology design for HSR resilient mesh networks [21], and the latency and traffic reduction technique for process-level network [22]. The HSR SwitchBox technique defines a new switching node in HSR networks that forwards HSR frames based on looking up of media access control (MAC) tables instead of flooding the frames. The HSE + OF approach aims to manage HSR networks by means of the software-defined networking (SDN) paradigm. The approach defines new HSE + OF nodes whose control plane is managed by an OpenFlow controller. In other words, this approach is an implementation of HSR in SDN. The RMT technique was proposed to reduce multicast traffic in HSR networks by limiting the spreading of the multicast traffic to only the rings that have members associated with that traffic instead of spreading the traffic into all the network parts. The study of traffic reduction for process-level networks [22] presented two enhanced solutions and fully implemented QR to HSR for improving the latency and reducing the traffic volume in a process-level network. The main idea of these two enhanced solutions is to reduce the minimum number of hops required for the delivery of messages to destinations for reducing the maximum end-to-end latency. EFA is one of the most effective filtering techniques. EFA reduces redundant unicast traffic in HSR networks by solving all issues faced in HSR [13]. EFA uses the PL and FQR techniques for filtering unicast traffic for nondestination DANH rings and passive QuadBox rings, respectively. In addition, EFA uses QR in HSR networks to prevent traffic frames from being duplicated and circulated in rings. However, EFA has to forward the first sent frame to all DANH rings to check nondestination DANH rings. Additionally, EFA uses FQR to discover passive QuadBox rings by broadcasting a control message. In this paper, we propose an enhanced version of EFA, called enhanced-EFA (eEFA), to overcome these two issues. The main purpose of eEFA is to reduce more redundant unicast traffic compared to EFA by filtering the first sent frame for nondestination DANH rings and by avoiding using a control message to discover passive QuadBox rings for bidirectional communications, thus saving network bandwidth and improving the network performance. The remainder of this paper is organized as follows: In Section 2, we briefly introduce EFA. In Section 3, we describe eEFA. The traffic performance of eEFA is analyzed in Section 4. Several simulations are conducted, and their results are presented in Section 5. Finally, Section 6 presents the conclusions of this study.
## 4 Simulation Results and Discussion
### Case Studies  
#### 4.1.1 Case Study 1 (4 \(\Omega\) Shunt Resistor)  
In this case, the shunt resistor was set to 4 \(\Omega\). The fault current and corresponding voltage profile of the single line-to-ground fault are depicted in Figure 3. Without using the r-SFCL, the current exhibited first and second peak values of 6.25 and 4.64 kA, respectively. However, the r-SFCL with a 4 \(\Omega\) shunt resistor could limit these values to 3.9 and 3.44 kA, respectively, representing a reduction of 37.6% for the first peak. The voltage dropped to about 8.061 kV\({}_{\mathrm{rms}}\) when using the 4 \(\Omega\) shunt resistor, compared to zero without the r-SFCL.  
The temperature of the r-SFCL is shown in Figure 3c. The temperature peaked at 195 K in this case study. The r-SFCL completely recovered 3.77 s after the fault occurred, i.e., at T = 5.77 s, and returned to the coolant boiling temperature of 77 K. Before the fault occurred, the current passed through the r-SFCL while the shunt resistor's current was zero, as shown in the current distribution in Figure 3d. After the fault occurred, the temperature increased, and the r-SFCL's resistance increased in turn, resulting in most of the current passing through the shunt resistor, leaving a small fraction to still pass through the r-SFCL. When the r-SFCL fully recovered at T = 5.77 s, its total current flow returned, with zero current flowing through the shunt resistor.
## 1 Introduction  
Diabetes mellitus [1] is one of the pandemics characterized by chronic hyperglycemia [2], which seriously threatens human physical and mental health and quality of life. Since no cure has been developed for this disease, maintaining euglycemia through diet, exercise and pharmacological management is a way to prevent diabetic complications [3; 4; 5]. Currently, continuous glucose monitoringsystems (CGMS) are increasingly popular, since they can reflect fluctuation of blood glucose (BG), easily perceive asymptomatic hypoglycemia and relieve the pain of sticking fingers frequently for people with diabetes [2,6,7].  
The minimally invasive implantable glucose sensor is a key module that affects the performance of CGMS [8]. The sensitivity, biocompatibility, stability and linear measuring range of the implantable sensors are important to the entire system [5]. Glucose oxidase (GOD) can be easily prepared, and has strong specificity to glucose, high activity and stability, which is widely applied in amperometric glucose sensor [9]. GOD catalyzes glucose into the gluconic acid and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). Then the H\({}_{2}\)O\({}_{2}\) undergoes an electrochemical reaction on the electrode, generating a response current. The reaction equations are displayed below:  
\[\text{glucose}+O_{2}+H_{2}O\overset{GOD}{\rightarrow}\text{ }\text{ }\text{ }\text{ }  
Single membrane is not enough to meet the multiple requirements of implantable glucose sensors [17; 18]. Herein, the aim of this study is to introduce a novel simple method for preparing an implantable glucose sensor with high sensitivity, wide linear measurement range and good stability. SHPM-OS membrane consisting of PU, PEG and Cat sensing layer was designed. The modification of electrode surface would enlarge sensing area and improve sensor selectivity. The porous inner PU of SHPM-OS would accelerate the diffusion rate of oxygen and H\({}_{2}\)O\({}_{2}\), thus improving response time. Cat immobilization in sandwich layer could convert H\({}_{2}\)O\({}_{2}\) to oxygen, which would attenuate oxygen deficiency during glucose monitoring, and also prevent the accumulation of H2O2 around the glucose electrode. The porous inner and outer PU composited hierarchical structure would balance the sensitivity and linearity of glucose sensors. Moreover, directly depositing SHPM-OS membrane at room temperature would be helpful to maintain the high enzyme activity. The SHPM-OS and the AiTi health management platform (developed by our group) can form CGMS system, thus helping more users to achieve the effective glucose self-management.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 6 Design and Implementation of the Autonomic Feature Management Controller
### Controller Implementation
#### 6.2.3 Feature Monitoring  
For each feature that we select, we monitor whether the feature is supported by observing the cluster configuration states. To ensure that the controller monitor features in a vendor-agnostic way, we only use the native API endpoints exposed by the API-server to view the cluster configuration states. The relevant configuration states for each selected feature are as follows:  
* API server: The API server has an endpoint /apis to list all installed API resources. The controller interacts with this endpoint and see if the desired API resource is supported;
* Kubelet: The Kubelet of a worker node exposes the endpoint /configz to get its configurations. However, the credential to access Kubelet endpoints is only available in the API server, which is managed by vendors for a cluster of a hosted product type. Therefore, we should use the API server's endpoint /proxy to redirect our requests to Kubelet. The Kubelet returns its settings in JSON format, and then the controller can inspect the current CPU manager policy;
* Network plugin: We found that although the network plugin mode is one of the Kubelet configurations, it is not returned when we call the Kubelet configz endpoints. Therefore, the controller inspects the network plugin mode by directly observing the name of the Daemonset that is installed by the CNI plugin.  
However, observing the cluster configuration states through the endpoints of the API server is not robust enough to verify that a feature is actually correctly implemented. For example, the CNI network plugin may be installed in the cluster, but the Kubelet is misconfigured and still use the original Kubenet plugin. In this case, the controller would mistakenly think the cluster is running with a CNI plugin. To address this issue, we can utilize the end-to-end testing suite provided by the open-source distribution of Kubernetes to run conformance tests to validate if a feature is supported in a cluster. However, the feature coverage of the test suite is insufficient [35]. Improving the coverage of this test suite is beyond the scope of this paper.
## Data  
### OHS Data Introduction  
The Orbita hyperspectral remote sensing satellite constellation, consisting of four hyperspectral satellites and one video satellite, was successfully launched on April 26, 2018, and it realized the networking of hyperspectral satellites with a strong capability of obtaining hyperspectral data. Table 1 gives the main parameters of OHS.
# A Simple Estimation Method of Weibull Modulus and Verification with Strength Data
## 4 Material Strength Data
### Ceramics and Glasses
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ceramics and Glasses** & **\textless{}0\textgreater{}** & **S** & **\textless{}0\textgreater{}/SD** & **m\textgreater{}** & **m\textgreater{}** & **N** & **m\textgreater{}/m\textgreater{}** & **Note** & **D/V** & **Ref** \\ \hline Porcelain & 57.0 & 3.6 & 15.66 & 16.30 & 17.23 & 30 & 0.946 & porcelain glazed & V & [64] \\ Porcelain & 52.0 & 5.3 & 9.77 & 10.50 & 10.75 & 30 & 0.977 & 1000 grit polish & V & [64] \\ Porcelain & 48.0 & 4.7 & 10.28 & 13.30 & 11.31 & 30 & 1.176 & 600 grit polish & V & [64] \\ Porcelain & 46.2 & 4.7 & 9.89 & 10.80 & 10.88 & 30 & 0.992 & 100 grit polish & V & [64] \\ Zirconia & 757.0 & 79.0 & 9.58 & 11.40 & 10.54 & 40 & 1.082 & Maximum likelihood & V & [65] \\ Zirconia & 1077.0 & 113.0 & 9.53 & 9.60 & 10.48 & 40 & 0.916 & Maximum likelihood & V & [65] \\ Zirconia & 891.0 & 115.0 & 7.75 & 9.40 & 8.52 & 40 & 1.103 & Maximum likelihood & V & [65] \\ Zirconia & 1126.1 & 114.0 & 9.88 & 10.30 & 10.86 & 40 & 0.948 & Maximum likelihood & V & [65] \\ Zirconia & 835.0 & 102.0 & 8.19 & 10.90 & 9.00 & 40 & 1.210 & Maximum likelihood & V & [65] \\ Zirconia & 1322.0 & 214.0 & 6.18 & 7.90 & 6.80 & 40 & 1.163 & Maximum likelihood & V & [65] \\ Graphite & 19.1 & 1.7 & 11.38 & 11.54 & 12.51 & 108 & 0.922 & NBIG IS Graphite & V & [66] \\ Graphite & 21.1 & 1.6 & 13.35 & 14.77 & 14.68 & 140 & 1.006 & NBIG IS Graphite & V & [66] \\ Graphite & 18.9 & 1.8 & 10.44 & 10.73 & 11.49 & 56 & 0.934 & NBIG IS Graphite & V & [66] \\ Dental Ceramic E1 & 84.5 & 14.6 & 5.79 & 5.20 & 6.37 & 20 & 0.817 & Flexure strength & V & [67] \\ Dental Ceramic E2 & 215.0 & 40.1 & 5.36 & 5.40 & 5.90 & 20 & 0.916 & Flexure strength & V & [67] \\ Dental Ceramic ES & 239.0 & 36.3 & 6.58 & 7.20 & 7.24 & 20 & 0.994 & Flexure strength & V & [67] \\ Dental Ceramic CV & 63.8 & 5.8 & 11.00 & 14.10 & 12.10 & 20 & 1.165 & Flexure strength & V & [67] \\ Dental Ceramic ES-G & 231.0 & 45.0 & 5.13 & 5.00 & 5.65 & 20 & 0.885 & Flexure strength & V & [67] \\ Dental Ceramic ES-GV-G & 238.0 & 40.5 & 5.88 & 6.10 & 6.46 & 20 & 0.944 & Flexure strength & V & [67] \\ Dental Ceramic ES & 285.0 & 48.9 & 5.83 & 6.20 & 6.41 & 20 & 0.967 & Flexure strength & V & [67] \\ Hydroxyapatite & 110.0 & 18.5 & 5.95 & 5.82 & 6.54 & 30 & 0.890 & Flexure strength & V & [68] \\ Hydroxyapatite & 18.6 & 2.5 & 7.44 & 7.24 & 8.18 & 30 & 0.885 & Flexure strength & V & [68] \\ Hydroxyapatite & 70.9 & 8.8 & 8.06 & 8.67 & 8.86 & 30 & 0.978 & Compression & V & [68] \\ Hydroxyapatite & 21.8 & 2.3 & 9.48 & 10.30 & 10.43 & 30 & 0.988 & Compression & V & [68] \\ Hydroxyapatite & 91.0 & 16.0 & 5.69 & 6.80 & 6.26 & 20 & 1.087 & 1360 \({}^{\circ}\) 240 min & V & [69] \\ Hydroxyapatite & 69.0 & 10.0 & 6.90 & 8.40 & 7.59 & 24 & 1.107 & 1360 \({}^{\circ}\) 12 min & V & [69] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Laboratory Test of a Vehicle Active Noise-Control System Based on an Adaptive Step Size Algorithm
## 2 Methods
### ExLMS Algorithm  
As the core content of the feed-forward active control system, the adaptive algorithm follows the traditional feedforward FxLMS algorithm system principle shown in Figure 2, where \(X(n)\) is the input reference signal, \(P(z)\) and \(S(z)\) are the transfer functions of the primary and secondary path channels, respectively, \(\hat{S}(z)\) is the estimate of the secondary path, \(S(z)\), and \(W(z)\) is the update weight controller. The \(d(n)\) is the noise signal in the cancellation area, \(e(n)\) is the error signal after cancellation, and \(y^{\prime}(n)\) represents the cancellation signal.  
Assuming that the number of filter taps is \(L\), the input reference signal and weight coefficient at time \(n\) are expressed as vectors:  
\[\textbf{X}(n)=\begin{bmatrix}x(n)&x(n-1)&\dots&x(n-L+1)\end{bmatrix} \tag{1}\]  
\[\textbf{W}(n)=\begin{bmatrix}w_{1}(n)&w_{2}(n)&\dots&w_{L}(n)\end{bmatrix} \tag{2}\]  
\(y(n)\) is the filter output signal:  
\[y(n)=\textbf{W}(n)\textbf{X}^{T}(n)=\sum_{l=1}^{L}w_{l}(n)x(n-l+1) \tag{3}\]  
\(y(n)\) passes through the secondary channel to reach the position where the sound waves cancel, and the cancellation signal \(y^{\prime}(n)\) is obtained:  
\[y^{\prime}(n)=S(n)*y(n). \tag{4}\]  
The error signal is obtained after the offset signal and noise signal are superimposed:  
\[e(n)=d(n)-y^{\prime}(n)=d(n)-s(n)*\textbf{W}(n)\textbf{X}^{T}(n) \tag{5}\]  
where \(*\) is the convolution operation. According to the stochastic gradient descent method and the least mean square error criterion, the gradient of the \(l\) tap weight of the filter can be expressed as the partial derivative of the squared error signal, with respect to the \(l\) tap weight:  
\[\
abla_{l}=\frac{\partial e^{2}(n)}{\partial w_{l}(n)}=2\frac{\partial e(n)}{ \partial w_{l}(n)}e(n)=2e(n)*\textbf{S}(n)x(n-l+1)=2e(n)x(n-l+1). \tag{6}\]  
When using the FxLMS algorithm, the filter weights are updated as follows:  
\[\textbf{W}(n+1)=\textbf{W}(n)+\frac{1}{2}\mu\cdot\
abla=\textbf{W}(n)+\mu e(n )\textbf{X}^{\prime}(n) \tag{7}\]  
where \(\mu\) is the step factor and \(\textbf{X}^{\prime}(n)\) is the tap vector of the filtered reference signal.  
Figure 2: Schematic diagram of the feed-forward FxLMS algorithm.
# On the Effect of a Rate-Dependent Work of Adhesion in the Detachment of a Dimpled Surface
## 1 Introduction
The above studies have considered the work of adhesion (the work needed to separate two flat surfaces from their equilibrium position up to infinity) as a constant that depends on the contact pair. Nevertheless, it is well-known that soft materials show rate-dependent properties. In particular, the dependence of the work of adhesion \(w\) on the velocity of the peeling \(v_{p}\) (the velocity at the crack tip) is well documented both experimentally [23; 24] and theoretically [25; 26]. Although with some variants in the nomenclature, most of the authors agree that the dependence of the work of adhesion on the peeling velocity is well captured by a power law form (Reference [27])  
\[w=w_{0}\bigg{[}1+\bigg{(}\frac{v_{p}}{V_{0}}\bigg{)}^{n}\bigg{]} \tag{1}\]  
Figure 1: (**a**) SEM images of a surface pattered with dimples (adapted from [9]). (**b**) Geometry of the dimple surface considered in this study. The model is axysimmetric. where \(w_{0}\) is the adiabatic (or thermodynamic) work of adhesion for vanishing peeling velocity, while \(V_{0}\) and \(n\) are material properties with \(V_{0}\) a reference velocity, and \(n\) an exponent usually in a range from 0.1 to 0.9 for realistic materials. Here, we revise the model of [PERSON] et al. [17] by accounting for the effect of the rate at which the external load is applied. It will be shown that the dimple still behaves as a pressure-sensitive adhesive, but the \"weak\" adhesive state is strongly influenced by the unloading rate so that the pull-off stress in partial contact and the interfacial toughness can largely increase for high unloading velocity. The enhancement effect is greatly dependent on the exponent \(n\), larger exponents leading to larger strengthening of the interface, but it is only marginally affected by the level of preload.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Packet Collision Sensing
#### 3.2.2 Feedback Provider Qualification Based on Received Signal Strength Indicator
In contrast, false positives will cause the transmitter to reselect resources unnecessarily, increasing the unpredictability in SPS and leading to more packet collisions [8].
## 4 Materials and Methods
### Model Selection and Training
#### 4.2.2 Model Architecture  
* RF: the RF classifier is created with 100 trees in the forest. The maximum depth for each tree was not set, allowing it to grow until all the leaves were pure or the node contained insufficient samples for splitting. The model parameters specified two samples minimum for internal node splitting and one sample minimum per leaf. These settings were chosen for the purpose of balancing the models complexity and its ability to generalize well.
* SVM: it uses a regularization parameter (C) of 1.0 to balance low error on the training data and weight norm optimization. A linear kernel was used, making the decision boundary a straight line. The influence of a single training example is determined by the gamma parameter, which was set to its default value of 1 /(number of features).
* KNN: the KNN model's algorithm type was left at auto to allow the model to choose the optimum method for the dataset's structure. To generate predictions, k values (3, 5, 7) of neighbors were tested. A uniform weight function was used so that all locations in each neighborhood weigh equally, regardless of their distance from the goal.
* LSTM: the LSTM networks' ability to learn and recall over extended sequences makes them ideal when it comes to finding patterns in time-series data connected with DDoS attacks. The sequential architecture employs four LSTM layers containing 50, 50, 100, and 100 units, respectively, interspersed with dropout layers for regularization, followed by a final dense layer with 15 output neurons for multi-class DDoS detection.
* CNN: the detailed structure is made up of convolutional, pooling, and fully connected layers that have been tailored to the dataset's specific attributes. CNNs are able to detect complex trends in network traffic data because they easily identify spatial hierarchies in the data. The model is made up of five one-dimensional convolutional layers, using 32, 64, 128, 128, and 128 filters, respectively. Each convolutional layer is followed by a max pooling layer to reduce the feature size. After these layers, the output is flattened and passed through two fully connected (dense) layers: first with 64 neurons, then with 15 neurons, which produce the final classification.
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 2 Materials and Methods
### Preprocessing Methods  
Data preprocessing methods are essential to separate signal from noise and improve the signal-to-noise ratio in Vis-NIR spectra. The multiplicative scatter correction (MSC) method was introduced by [PERSON] et al. [26], which is one of the most widely applied NIR preprocessing techniques. The primary objective of MSC is to mitigate spectral discrepancies associated with varying scattering levels present in the acquired spectral data, enhancing the correlation between spectral data and the target variables. By fitting a first-order function between the recorded spectra \(x_{raw}\) and a reference standard \(x_{ref}\), the MSC technique can eliminate additive and multiplicative linear imperfections:  
\[x_{raw}=b_{0}+b_{1}\cdot x_{ref}+e \tag{2}\]  
Figure 4: Histograms for the five quality parameters of the 118 tangerines. (**a**) SSC, the bins are set at 0.1 \({}^{\circ}\)Brix, (**b**) TA, the bins are set at 0.03%, (**c**) A/S, the bins are set at 0.8, (**d**) Firmness, the bins are set at 0.01 kg, (**e**) VC, the bins are set at 10 (mg/100 g).  
where \(b_{0}\) represents the additive part and \(b_{1}\) the multiplicative part. Generally, the reference spectrum employed is the average spectrum obtained from the sample set. The scatter-corrected spectra \(x_{corr}\) is thus calculated as:  
\[x_{corr}=\frac{x_{raw}-b_{0}}{b_{1}} \tag{3}\]  
To prevent the amplification of noise within imperfect data, Savitzky-Golay (SG) derivatization [27] was used in most cases. This method involves fitting a symmetric polynomial function around neighboring data for each point in the spectrum, generating a smoothed spectrum that retains its characteristic features. In this experiment, SG smoothing parameters were set to second-degree polynomial and 11 smoothing points, and the second-order derivative was computed. StandardScaler (SS) is a preprocessing technique commonly used in machine-learning and neural-network applications. SS transforms the input data so that it has zero mean and unit variance. This is carried out by subtracting the mean of each feature and dividing it by its standard deviation.  
The most suitable preprocessing methods in this study are performed in this order: Savitzky-Golay 2 nd-derivatization (SG), multiplicative scatter correction (MSC), and standard scaling (SS). Through preprocessing, the impact of noise disturbances can be effectively reduced.
# Tire Defect Detection via 3D Laser Scanning Technology
## 3 Experiments and Discussion
### Evaluation on the 3D Tire Dataset

Table 1: Comparison of state-of-the-art methods on RGB images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **mAP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline Faster R-CNN [31] & 59.2 & **89.8** & 65.5 & 23.6 & 52.8 & 73.0 \\ FCOS [21] & 29.6 & 73.0 & 21.1 & 0.7 & 28.0 & 37.5 \\ RetinaNet [32] & 49.2 & 87.4 & 50.0 & 7.1 & 47.3 & 60.0 \\ Deformable-DETR [8] & 42.5 & 88.6 & 39.6 & 8.3 & 29.9 & 72.0 \\ Conditional DETR [34] & 50.1 & 88.2 & 51.2 & 10.0 & 47.2 & 65.0 \\ DAB-DETR [33] & 55.3 & 88.4 & 63.2 & 18.3 & 52.0 & 70.2 \\ \hline Ours & **69.1** & **89.8** & **74.1** & **35.7** & **59.2** & **89.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of state-of-the-art methods on rendered images. * **Experiment on depth images**  
Lastly, we experimented on the depth image set. The quantitative results are shown in Table 3. The evaluation results clearly show that our detection method achieved much better results than those of other methods (14.5% better than that of the second-ranked DAB-DETR [8] in terms of mAP). In particular, our method achieved 0.082 AP\({}_{\text{S}}\), whereas other methods almost failed to detect tiny defects, indicating that our method can detect very tiny defects and justifying the novel designs of our detector. The qualitative results are shown in Figure 8. Similar to the experiment in the rendered image set, we visualized the top-1 bounding box of each method. The visual results further proved the advanced performance of our