esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We report a method for determining the field of view (FOV) of a dawn-dusk sun-synchronous orbit satellite based on an improved observation mode. The target trajectory distribution model in geosynchronous orbit (GEO) is established, the natural rendezvous mode is improved, and the observation mode of the satellite is determined. A scheme for determining the thresholds of cross-orbit field of view (COFOV) and the along-orbit field of view (AOFOV) was developed. The result shows that the coverage of the satellite can reach more than 95% when the improved observation mode is used to observe the GEO target. When the revisit period of the satellite is one day, the threshold of the COFOV is 15\({}^{\circ}\), and the threshold of the AOFOV is 12\({}^{\circ}\).  
dawn and dusk sun-synchronous orbit satellite; cross-orbit field of view; along-orbit field of view; observation mode; geostationary Earth orbit belt +
Footnote †: journal: Physics Letters B
## 4 Results and Analysis
### Analysis of Compliance Behavior
#### 4.1.6 Social Behaviors and Compliance  
Organizations mainly invest in technical solutions, whereas information security is a multidimensional problem (i.e., technical and behavioral). Multiple researchers provided technical solutions for employees' social factors, such as [79], who developed a fake online repository generation engine for cyber deception. The concept of deception is used for solving behavioral information security problems using a technical method. Furthermore, [80] presented a multimedia probabilistic logic graphs model for improving behavioral information security in organizations. Multiple researchers tried to solve the ISPC problem with technical controls, but technical solutions are not enough to solve behavioral information security problems. There must be administrative controls (i.e., security awareness via social techniques, top management support for enhancing social behaviors) to enhance ISP compliance [81]. Employees seek help and guidance from their social circle, and they often adopt behaviors from their peers or coworkers. Reference [22] tested their ISB framework on 400 military personnel in Malaysia. Their study showed that employee socialization and individual factors (computer self-efficacy, personal innovativeness, IS perception) have positive effects on ISB towards ISPC. Similarly, [82] demonstrated the significance of the social bonding and social cognition factors on ISP behavior. He developed a research model with social bond theory, social cognitive theory, and planned behavior theory. His
# An Efficient Method for Biomedical Entity Linking Based on Inter- and Intra-Entity Attention
#### 4.5.5 Comparisons with BERT-Based Methods  
The original BERT-based biomedical entity linking method uses the pointwise ranking model to treat the ranking problem as a classification problem. However, this Pointwise ranking model does not abstract the relevant features mentioned by the candidates and mentions well, and it is difficult to distinguish the subtle features.  
In response to the above problem, in this paper, we also try to introduce BERT and improve the original model utilizing Pairwise ranking. We propose a biomedical entity linking method (BPR) based on BioBERT and Pairwise ranking to learn better semantic representations. We introduce positive and negative entities and generate the form of triplets with mentions, then obtain semantic relevance representations by BioBERT pre-training model, respectively, and use the triplet loss function for training. Some effect improvement is achieved in the biomedical ranking task.  
We also compare the proposed lightweight model with the BERT-Base model, which has 12 layers, 768 hidden dimensions, and 12 attention heads with a total of 107 million. Despite having less than 5 million parameters, our model based on inter-entity and intra-entity attention achieves very competitive or even better results than the BERT-based SOTA model on both datasets, and the experimental results are shown in Table 6.  
To show the efficiency of the model in this paper, we also compared the complexity (parameter size) and inference time of the model with the BERT-base model, and Table 7 shows the comparison results. It is the time in the entire testing set. The comparison results show that the method in this paper has a very high CPU inference speed. The model in this paper is 9 times faster compared to the BERT-Base model and 11.8 times faster compared to the BERT(Pairwise) model, and the complexity of the model is much smaller, with about 23 times fewer model parameters. In summary, the experimental results show that the lightweight biomedical entity linking model proposed in this paper achieves performance comparable to state-of-the-art models on two benchmark datasets, with only a small number of parameters and fast inference. When speed and model size are taken into account, the method in this paper is easier and more practical to use for deployment and application.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 3 Method
### Improved SE Attention Module (CSE Module)  
In the field of target detection, the SE module has gained considerable attention for its ability to adaptively extract features and has been used by a large number of networks such as SENet and MobileNet [37]. By introducing the Squeeze-and-Excitation operation to establish a relationship between the channels, the SE module is able to adaptively learn the significance of each channel's importance and adjust the weighting of the channels according to the characteristics of the detection task, thus improving the performance of the model.  
Residual networks have been widely used in the field of image processing due to their powerful feature extraction ability. This module is based on a residual network, and the conventional residual network is shown in Figure 4a, which can be expressed as:  
\[X_{out}=X_{in}+X_{res} \tag{1}\]  
We propose the CSE module as shown in Figure 4b; first, we used lightweight grouped convolutional GConv for feature extraction of the input image and then, we used the improved SE module to obtain more important information via channel attention weighting, where the two layers of activation functions of the SE module are chosen as ReLU and H-Sigmoid, respectively. The proposed CSE module can be represented as:  
\[X_{out}=w\times X_{in}^{\prime}+X_{res} \tag{2}\]  
where the learning weights \(w\) are calculated as:  
\[r=H(R_{2}*\sigma(R_{1}*AAPool(GC(X_{in}))) \tag{3}\]  
where \(H(\cdot)\) denotes the H-Sigmoid activation function and \(\sigma(\cdot)\) denotes the ReLU activation function. First, the input feature map \(X_{in}\) is extracted using grouped convolutional GConv. Then, the features are compressed via adaptive average pooling, and the weight values of  
Figure 3: Transformer encoder block.  
the channels \(R_{1}\) and \(R_{2}\) are obtained using two \(1\times 1\) Conv, which effectively improves the expressive power of the network.  
Attention mechanisms are difficult to use for the entire network because they increase the computation and reasoning time of the algorithm. Many existing studies show that the performance of the algorithm can be better improved when the attention mechanism is at the end of the network. Therefore, we introduced our proposed color CSE module only at the end of the network, which can better achieve accuracy-speed balance.
## 5 Performance Evaluation
### Evaluation Metrics  
We choose performance indicators to evaluate the merits and demerits of the model. _MAE_, _RMSE_ and _MAP_ are selected as metrics to evaluate the prediction performance.  
\[MAE=\frac{1}{Siz}\sum_{i=1}^{Siz}|y_{i}-y| \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{Siz}\sum_{i=1}^{Siz}\left(y_{i}-y\right)^{2}} \tag{19}\]  
Figure 3: Illustration of evaluation scenario: Yan’ an Viaduct near the Shaanxi Road in front of the Shanghai Exhibition Hall\[MAPE=\frac{100\%}{Siz}\sum_{i=1}^{Siz}\left|\frac{y_{i}-y}{y}\right| \tag{20}\]  
Here, \(Siz\) is the sample size, \(y_{i}\) is the predicted values via the involved approaches, and \(y\) is the actual value. \(MAE\) (Mean Absolute Error) measures the average magnitude of the differences between predicted and actual values. It provides a straightforward indication of the model's accuracy, where lower values indicate better performance. RMSE (Root-Mean-Squared Error) calculates the square root of the average of squared differences between predicted and actual values. It penalizes larger errors more heavily than MAE and is useful when larger errors have a significant impact on the model's performance. MAPE (Mean Absolute Percentage Error) measures the average percentage difference between predicted and actual values. It provides insights into the relative performance of the model, indicating the average magnitude of errors in terms of percentages.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 2 Materials and Methods
### Fundamental Concepts
#### 2.1.2 Ideal Arrangement  
The ideal arrangement consists of TX and RX placed at the same position on the optical axis as shown in Figure 1. Both FOVs are equal in size and they fully overlap. The arrangement can be realized by using an LED. On the one hand, applying a forward-bias to the LED causes a forward-current and leads to photon generation. Biasing the PN-junction reversely enables fast photo-detection on the other hand. As a result, the transceiver only needs a single optoelectronic component for TX and RX. Data rates of up to 150 Mbit/s were demonstrated for close distances [32]. However, the LED is a rather bad PD. It has a low responsivity, small area \(A_{\text{PD}}\) and low bandwidth [32]. Although a bidirectional link is realizable, data transfer is restricted to half-duplex mode, because the link direction is switched in time domain.  
For high-speed bidirectional data transfer in full-duplex mode, TX and RX are separated. The optical arrangement becomes more challenging and design trade-offs have to be met. The following section introduces several geometrical arrangements for optical wireless rotary communication scenarios and compares their performance.
## 5 Experiments
### Quantitative Improvements of Attribution Maps  
To show that our TFT and AFT can improve the quality of input attribution, we compared the quality of attribution maps generated by the original model, TFT and AFT. For the comparison, we computed attribution quality measures widely used in model induction-based XAI research in image domains: average increase and average drop. Denoting by \(y_{i}^{c}\) the predicted probability of the original input \(x_{i}\) for the class \(c\), by \(y_{i}^{c}\) the predicted probability for the class \(c\) of the binary-masked image \((\hat{x}_{r})_{i}\) based on a thresholded attribution map \((I_{r})_{i}\), and by \(y_{i}\) the true label, we define the attribution quality measures as follows [5]:  
\[\text{Average Increase }= \frac{1}{n}\sum_{i=1}^{n}\mathbf{1}[\hat{y}_{i}^{c}>y_{i}^{c}]\] \[\text{Average Drop }= \frac{1}{n}\sum_{i=1}^{n}\max\{y_{i}^{c}-\hat{y}_{i}^{c},0\}/y_{i} ^{c}\enspace,\]  
where \(\mathbf{1}[z]\) has the value 1 if \(z\) is true and 0 otherwise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\# Train** & **\# Test** & **\# Classes** \\ \hline ImageNet [12] & 1,281,167 & 10,000 & 1000 \\ Pascal VOC 2012 [13] & 1464 & 1449 & 20 \\ \hline \end{tabular}
\end{table}
Table 2: The summary of datasets used in the experiments.  
Table 3 shows the comparison results for attribution quality. Higher values are better for the average increase, while lower values are better for the average drop. We can see that TFT improves the quality of attribution maps in all quality measures. Compared to the original cases, the attribution maps optimized by TFT have improved the average increase and average drop by \(\times 1.31\) and \(\times 1.16\) on average, respectively. Interestingly, the attribution maps generated with AFT-tuned CNN show similar improvements in the two quality measures by \(\times 1.51\) and \(\times 1.26\) on average, respectively, compared to the original attribution maps. In the case of AFT, activation tuning has been done with the optimal masks generated with the same attribution method to be used for the attribution quality assessment, and no threshold fine-tuning has been applied. Still, using AFT, we achieved the attribution maps for the attribution maps, and the attribution maps for the attribution maps.  
Figure 3: Qualitative comparison of the original attribution map and thresholded attribution map by threshold fine-tuning (TFT). The input images, the original attribution maps, and thresholded attribution maps are shown, along with the RPI curves over threshold values in \([0,1]\) with an increment of 0.1. The best threshold values are depicted with the red dotted lines in the RPI plots.  
an attribution quality similar to (sometimes better than) TFT, except for a few cases in the Pascal VOC dataset.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 4 GTCS Dataset Collection
### Lab Setup  
To generate the new dataset, benign network traffic along with malicious traffic were extracted, labeled, and stored. To mimic typical network traffic flow, we designed a complete testbed (Figure 1) composed of several normal and attacking virtual machines (VMs) that were distributed between two separate networks. The victim network consisted of a set of VMs running different versions of the most common operating systems, namely Windows Server and/or PC, Linux, and Android. The attack network was a completely separate infrastructure containing Kali 1.1 and Kali 2.0 VMs.  
To generate a large amount of realistic benign traffic, we used Ostinato [60], a flexible packet generator tool that generates normal traffic with given IPs and ports. Malicious traffic was generated using Kali Linux [61], an enterprise-ready security-auditing Linux distribution based on Debian GNU/Linux [62]. The process of generating both benign and malicious traffic took eight days. All of the attacks considered for the experiments in this paper were new. In our attack scenarios, four of the most common and up-to-date attack families were considered and are briefly described below.  
* Botnet attacks [63]: This attack type can be defined as a group of compromised network systems and devices that execute different harmful network attacks, such as sending spam, granting backdoor access to compromised systems, stealing information via keyloggers, performing phishing attacks, and so on.
* Brute force attacks [64]: This refers to a well-known network attack family in which intruders try every key combination in an attempt to guess passwords or use fuzzing methods to obtain unauthorized access to certain hidden webpages (e.g., an admin login page).
* DDoS attacks [65]: DoS attacks are a very popular type of network attack in which an attacker sends an overwhelming number of false requests to a target service or network in order to prevent legitimate users from accessing that service. DDoS attacks are a modern form of DoS attack wherein attackers use thousands of compromised systems to flood the bandwidth or resources of the target system.
* Infiltration attacks [66]: These attacks are usually executed from inside the compromised system by exploiting vulnerabilities in software applications such as Internet browsers, Adobe Acrobat Reader, and the like.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance
### Algorithmic Complexity Analysis
#### 6.1.4 Inference  
* Prediction: During inference, the complexity of a single prediction is proportional to the number of trees \(T\) and the depth of each tree \(d\), i.e., \(O(T\cdot d)\).
* Sliding Window for Testing: Similar to feature extraction, the prediction process for test data also uses a sliding window approach. The total complexity is \(O\big{(}\frac{n-w}{w-o}\cdot T\cdot d\big{)}\).
## 4 Discussions and Comparative Analysis
### Convergence Speed  
An efficient MPPT technique must be able to converge to the required voltage and current with high speed and accuracy, regardless of whether the solar irradiance changes steadily or dramatically. When compared to intelligent MPPT techniques, the offline techniques convergence speed is fast but fails to track accurate MPP, whereas online techniques track MPP faster with constant oscillations, but converges to LMPP under partial shading conditions. The offline MPPT approaches never operate at accurate MPP, and hence are not suitable for efficient systems. The online MPPT techniques have high power losses under partial shading and accuracy is dependent on the size of perturbation. Furthermore, the intelligent techniques converge the GMPP under all abnormal conditions with negligible oscillation.
# Food Processing with UHP Waterjets
## 4 Selected Cutting Applications
### Fish  
Fish file cutting has been discussed above, showing the great benefits of automated waterjet fish portioning and pin bone removal. [PERSON] [14] conducted a parametric study on cod and salmon fish cutting. She found that cutting speed is the most influential factor. The second strongest parameter was the orifice diameter where the performance of an orifice diameter of 0.12 and 0.15 mm was much better than that of the larger orifice diameters of 0.17 and 0.20 mm in terms of quality of cut and minimized waste. The waterjet pressure in the range from 200 MPa to 350 MPa affected performance but not significantly; however, higher pressures would have shown a difference. The cutting speed should be within a range from 300 and 600 mm/s for tail portions and loins and from 450 and 900 mm/s for belly flaps for optimum performance. Optimum ranges of transverse speed for each specific part were set with the goal of increasing automation and meeting certain standards of cutting quality and performance not considering connective tissues and bones.  
[PERSON] et al. [15] identified minimum pressure requirements to cut different portions of whole trout fish. Their research showed that the muscle lobe of rainbow trout  
Figure 17: Cutting meat with bone using AWJ.  
can be divided into areas of different strengths against a pressure waterjet, as shown in Figure 18. The most durable elements of the muscular lobe turned out to be inter- and intramuscular fibers located within the lateral line and caudal fin. During the cutting tests, it was observed that the waterjet has the property of selective cutting of tissues. This means that by setting a specific supply pressure value using the appropriate nozzle diameter, it is possible to cut soft tissue, that is, muscle, fat, and peritoneal membranes, without affecting harder tissues, for example, intermuscular fibers or bones. Proper use of such a property may bring great benefits to the fish processing industry.  
It must be mentioned here that instead of pressure, traverse speed may be used for selective cutting. Some areas will require a high speed while other areas will require slower speeds, as [PERSON] (2014) identified.  
[PERSON] et al. (2016) patented a process to cut frozen fish to avoid the use of knives, that can cause a significant amount of portion breakage, and cut portions from a frozen fish slab that can be economical but have cut portions with an attractive appearance that can be pictured on the wrapper of a frozen fish package with greater appeal over fish portions with only straight edges.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 2 State of the Art
### Vulnerilities in MACsec Integration  
MACsec is widely deployed and can provide confidentiality, integrity, and authentication using Layer 2 encryption. However, its application in optical transport networks poses critical issues, especially with OTU4 framing. The interfaces between router control planes and coherent optical modules are vulnerable targets for timing-based attacks as well as unauthorized access and interception [15; 16]. Often, these interfaces serve as the point of access to electronic encryption subsystems that are then used to protect information at optical transport layers, leading to vulnerabilities in the security architecture.  
Figure 1 shows how MACsec interacts with optical transport systems, as well as key vulnerability points and quantum threats. The layered diagram illustrates interaction points between the optical transport layer and MACsec at Layer 2, highlighting places vulnerable to both classical and quantum attacks.  
Moreover, in the age of quantum computing, MACsec's reliance on public key cryptographic methods acts as a major threat. Quantum attacks can be used to break intercepted MACsec communications, making these protections pointless [17; 18]. Quantum-safe cryptographic methods like Quantum Key Distribution (QKD) have theoretical security advantages but their integration with existing MACsec protocols is an active research area [15; 18].  
Figure 1: MACsec and optical transport vulnerabilities.  
These studies are valuable in understanding individual network vulnerabilities; however, a critical gap in the understanding of system-level interactions between the security layers in coherent optical networks persists. An investigation into the relationship between quantum vulnerabilities and protocol-level weaknesses is needed.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 2 Forecasting Method and Application
### Improved Multivariate Grey Model  
Compared with GM (1, 1), MGM (1, \(n\)) has the advantage of considering multiple input features at the same time. However, both the GM (1,1) and MGM (1, \(n\)) are not suitable for medium to long-term forecasting. Because the rolling prediction method can make the prediction model achieve medium to long-term forecasting, MGM (1, \(n\)) is combined with the rolling prediction method in this paper to get the improved MGM (1, \(n\)) prediction model (IMGM (1, \(n\))). The schematic diagram of IMGM (1, \(n\)) is shown in Figure 1.  
Figure 1: Schematic diagram of the improved Multivariate Grey Model (IMGM (1, \(n\))).  
In the IMGM (1, \(n\)), based on the establishing process of MGM (1, \(n\)), the rolling update of the modelling data is performed by adding the actual value corresponding to the previous step of the current prediction point and removing the first one in the previous modelling data to achieve the dynamic addition of new information. The modelling data is updated every time the model outputs one predicted value.  
Assume that \(x_{i}^{(0)}(k)\), \((i=1,2,\ldots,n;k=1,2,\ldots,m)\) is \(n\) sets of sequence data. The number of each data set input to the MGM (1, \(n\)) is \(j\), which is \(x_{i}^{(0)}(k)\), \((i=1,2,\ldots,n;k=1,2,\ldots,j\) and \(j\leq m)\). The number of cycles is \(T\) and the number of predicted steps for each cycle is one. The predicted process of IMGM (1, \(n\)) is as follows:  
* As shown in Figure 1, the original data \(\{x_{i}^{(0)}(1),x_{i}^{(0)}(2),\ldots,x_{i}^{(0)}(j)\}\), \((i=1,2,\ldots,n)\) are used to establish the MGM (1, \(n\)). The model outputs one predicted value and \(j\) fitted values. The oldest data \(x_{i}^{(0)}(1)\) is removed and the actual data \(x_{i}^{(0)}(j+1)\) is added to reconstruct the MGM (1, \(n\)). The above steps are cycled \(T\) times.
* When \(T\) cycles are finished, \(T\) predicted values after \(x_{i}^{(0)}(j)\) are obtained, and the final \(j\) fitted values of each data set output from IMGM (1, \(n\)) are the average of the fitted values in the corresponding order for all cycles.  
Thus, the IMGM (1, \(n\)) is suitable for medium-long prediction. In the application of this paper, the number \(j\) of each data set input to the MGM (1, \(n\)) model is 45. The number of \(T\) is 15. When the 15 cycles are finished, the 45 averages of the fitted values and 15 predicted values will be obtained.