esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 2 Related Work
### Entity and Relation Extraction
Therefore, they constructed separate entity and relation models, trained them independently, used entity features as inputs to connect the two models, and reverted to the pipeline method. This approach achieved good results. [PERSON] et al. [27] proposed a PFN model that includes a partition-filtering network encoder and two task units. It divides the cells into entity cells, relation cells, and shared cells, filters and stores feature information, and then inputs the feature information into the task units. This model achieved good results, demonstrating the benefits of relation extraction for entity extraction and refuting the viewpoint that the pipeline method is more efficient. [PERSON] et al. [28] created an encoding framework for each token and fused the entity and relations using BIES encoding. They applied a position attention to identify the entities at the current position and their corresponding relations. However, this method requires repeated encoding for each sentence, resulting in higher complexity. [PERSON] et al. [29] proposed the TPLink model, which uses three types of matrix markings to label the entities and an upper triangular matrix to represent them, thereby addressing the issue of matrix sparsity. Finally, it performs entity decoding for each relation. The labeling strategy of this model effectively addresses the challenges of nested entity and relation overlap, although it increases the computational cost by increasing the sentence length from N to N-squared, making it unsuitable for processing large sentences. Lastly, [PERSON] et al. [30] introduced the OneRel model, which extracts all triplets in a single step. It uses a score-based classifier to evaluate the correctness of triplets and assigns specific labels to them, then decodes the triplets based on relation-specific Horn clauses to obtain the final triplet results. Joint entity and relation extraction is the core task of natural language processing and knowledge engineering. A joint model can connect the subtasks of entity extraction and relation extraction, as there are opportunities for information sharing between them. Relation extraction can provide valuable feedback for entity extraction. To address the challenges of relation overlap and nested entities, in this paper we propose joint Enhanced Span and Gate Mechanism (ESGM)-based extraction of entities and relations.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 3 Background  
### DNS Tunneling  
Protocol tunneling is an attack technique commonly used to maximize the time in which the infection remains undetected in a targeted network. In this context, the DNS protocol is usually abused in order to bypass security gateways and, then, to tunnel malware and other data through a client-server model [7]. Figure 1 depicts a typical DNS tunneling scenario: firstly, an attacker registers a malicious domain (e.g., attacker.com, accessed on 17 October 2023) on a C&C center managed by her; at that point, assuming that the attacker has already taken control over a machine inside the targeted network and violated its security perimeter, the infected computer sends a query to the malicious domain. Since DNS requests are typically allowed to move in and out of the network, the query through the DNS resolver reaches the attacker's C&C center, where the tunneling program is installed. This established tunnel can be used either to exfiltrate data and sensitive information or for other malicious purposes.
# Heterogeneously Integrated Multicore Fibers for Smart Oilfield Applications
## 3 Heterogeneously Integrated Multicore Fibers  
The proposed heterogeneously integrated multicore fiber could simultaneously support digital signal transmission, analog signal transmission, and temperature and vibration measurement. The schematic of the designed fiber is shown in Figure 2. It can be seen that different cores are responsible for different functions. To maintain the stability of digital signals and depress the bending loss, cores for digital transmission are set in the center of the cladding, while cores for sensing and analog transmission are arranged alternately in  
Figure 1: Schematic of the smart oilfields, which are consisted of production area, data distribution area, and office area.  
the outside circle. The numbers and types of different cores are determined by the demand of data rates and application scenarios. In addition, the pitch between adjacent cores is another key parameter needing to be optimized due to the trade-off between crosstalk and bending loss with a limited diameter of cladding. The cladding diameter should not be larger than 200 \(\upmu\)m to guarantee mechanical reliability [29]. To maintain a crosstalk lower than \(-\)50 dB/km, the core pitch is set as 50 \(\upmu\)m. To reduce micro bending loss, the outer cladding thickness is set as 40 \(\upmu\)m. Therefore, the cladding diameter is 180 \(\upmu\)m.  
In general, there are three types of fibers: step-index (SI) fibers, grade-index (GI) fibers, and trench-assisted (TA) fibers. From the perspective of the manufacturing process, SI fibers and GI fibers are easier to fabricate than TA fibers due to their relatively simple structures. However, TA fibers could be designed by optimizing the width and depth of the trench and other parameters to obtain high performance, such as low DMGD or large A\({}_{\text{eff}}\). Figure 3 reveals the refractive index profiles of various types of fibers. r\({}_{\text{core}}\), w\({}_{1}\), and w\({}_{2}\) are the core radius, the distance between the core and the trench, and the width of the trench, respectively. \(\alpha\) is the gradient parameter determined by the shape of the refractive index profile.
## 3 Materials and Methods
### Description of Ideal Pixel Symmetry  
We start with the simplest case: the object is symmetric in the pixel plane. This situation occurs when the camera is directly on the front side of the object. Suppose the symmetry axis of the object on the image is \(l_{s}=[1,0,-c_{x}]^{T}\), then, for any pixel point \(p=[p_{x},p_{y},1]^{T}\) on the image, its symmetry point is \(p^{\prime}=[2c_{x}-p_{x},p_{y},1]^{T}\). Let  
\[\mathbb{S}=\left[\begin{array}{ccc}-1&0&2c_{x}\\ 0&1&0\\ 0&0&1\end{array}\right], \tag{1}\]  
we have \(p^{\prime}=\mathbb{S}p\). Ideally, as Figure 2 shows, the pixels in the object detection box of this frame should be strictly symmetric: the textures (e.g., gray-scale values, RGB values) at \(p\) and \(p^{\prime}\) should be equal.  
For robustness and speed in practical use, we choose the HSV (hue, saturation, value) color space to describe the pixel \(p\) itself, noted as \(\mathrm{H}(p),\mathbb{S}(p),\mathbb{V}(p)\). We use the DT (distance transform) values as a kind of texture descriptor near the pixel \(p\), noted as \(\mathrm{DT}(p)\). The combined descriptors are constructed as:  
\[Sym(p)=\alpha\mathrm{H}(p)+\beta\mathbb{S}(p)+\delta\mathbb{V}(p)+\eta\mathrm{ DT}(p) \tag{2}\]  
where \(\alpha=\beta=0.5\delta=2\eta\). So, in the ideal front-view case, we should have:  
\[f_{sym}=\sum_{i}\lVert Sym(p_{i})-Sym(p^{\prime}_{i})\rVert=0 \tag{3}\]
## 4 Results and Discussion
### ESC Voltage Reversal during the W wafer De-Clucking Step  
A reverse voltage was used to neutralize the remaining charge in the wafer de-chucking step. The effect of different reverse voltages in the neutralization step on particle deposition was investigated. The reverse voltage was set as \(-2500\), \(-1000\), or \(-100\) V. The opening time was set as \(5\) s, and the wafer residual charge was neutralized by applying a reverse voltage. Figure 17 illustrates the neutralization process in the wafer de-chucking step.  
Figure 18 reveals that a reverse voltage of \(-100\) V resulted in marginally higher particle deposition than did the other reverse voltages. Nevertheless, the particle deposition results for the three reverse voltages were similar, and whether particle counts are correlated with changes in the reverse voltage is unclear.  
In accordance with the aforementioned results, the reverse voltage was set as \(-1000\) V, and the voltage application time was set as \(1\), \(5\), or \(10\) s to analyze the effects of this time on the particle distribution on the wafer surface. Figure 19 reveals that a longer application time for the ESC reverse voltage resulted in higher wafer particle deposition. A longer application time for an ESC reverse voltage caused higher potential changes, and the particles were first suspended before adhering to the wafer.  
Figure 17: Neutralization process in the wafer de-chucking step.  
Figure 18: Particle distribution results under different reverse ESC voltages.
## 4 Results and Discussion
### The Interfacial Tension Effect  
The interfacial tension between the displacing and displaced fluid is known to play an important role at the pore scale for two-phase displacement through a porous media. The same viscosity ratio (\(\lambda=1\)) was used and the contact angle was fixed at 120\({}^{\circ}\). Figure 7 shows the fluid distributions at breakthrough for the interfacial tensions of 0.03 mu-ts\({}^{-2}\), 0.015 mu-ts\({}^{-2}\), and 0.0005 mu-ts\({}^{-2}\). From this figure, it is seen that when the interfacial tension is as low as 0.0005 mu-ts\({}^{-2}\), the viscous force prevails, the main fingers get thinner and even break up, and many fingers only occupy partial pores or throats that they pass by. Also a number of small blobs of displaced fluid are left behind at low levels of interfacial tension. When the interfacial tension increases to 0.015 mu-ts\({}^{-2}\), larger blobs of the displaced fluid are trapped by the displacing fluid, and the displacing fluid flows into the large pore more easily due to higher capillary pressure, as highlighted by the black elliptical circles in Figure 7. As the interfacial tension is further increased to 0.03 mu-ts\({}^{-2}\), we surprisingly found that the front of the displacing fluid can be flat inside some pores, e.g., the one surrounded by the black rectangle box in Figure 7, though most interfaces remain curved due to large capillary pressure. Meanwhile, much less of the displaced phase is left behind as the invading phase continues to move forward.  
Figure 8 shows the evolution of the displacing fluid saturation for an interfacial tension of 0.0005 mu-ts\({}^{-2}\), 0.015 mu-ts\({}^{-2}\) and 0.03 mu-ts\({}^{-2}\). It is seen that generally higher interfacial tension results in a higher saturation. The linear relationship between saturation and the evolving time is again observed.  
Figure 6: Displacing fluid saturation as a function of viscosity ratio for the four typical cases listed in Table 1.  
Figure 7: Fluid distributions at breakthrough for a wetting fluid invading a porous medium initially saturated with a non-wetting fluid. The wetting fluid (indicated in blue) is injected from the left side and the values of the interfacial tension are 0.03 mu-ts\({}^{-2}\), 0.015 mu-ts\({}^{-2}\), and 0.0005 mu-ts\({}^{-2}\) from the left to right.
# Stochastic Extensions of the Elo Rating System
## 2 Preliminaries
### Basis of the Elo Adjustment Formula  
So far, we have worked assuming Equation (2) holds and each player has a constant \"true\" rating \(r_{j}\). However, in practice, the strength of chess players or soccer teams changes over time, and hence the need for a system that updates the ratings with each new game and not with the whole history.  
This is the reasoning behind the update formula in Equation (1): if we consider only one match between \(A\) and \(B\), with estimated ratings \(\hat{r}_{A},\hat{r}_{B}\) and real ratings \(r_{A},r_{B}\) (and for simplicity, we pick \(\hat{r}_{A}+\hat{r}_{B}=r_{A}+r_{B}=0\)), then using [PERSON]'s expansion, \(\exists\xi\in\mathbb{R}\) such that  
\[\mathbb{E}[\hat{r}_{A}^{\prime}-\hat{r}_{A}]=K\cdot\mathbb{E}[p_{A}-F_{X}(\hat {r}_{A}-\hat{r}_{B})]=K\cdot F_{X}(2r_{A})-K\cdot F_{X}(2\hat{r}_{A})=2K(r_{A}- \hat{r}_{A})f_{X}(\xi) \tag{11}\]  
Therefore, for a small enough \(K\), in particular for \(K<(2\max_{x\in\mathbb{R}}f_{X}(x))^{-1}\), this gives  
\[|\mathbb{E}[\hat{r}_{A}^{\prime}-r_{A}]|=|\mathbb{E}[\hat{r}_{A}^{\prime}-\hat {r}_{A}]+\hat{r}_{A}-r_{A}|=|1-2 Kf(\xi)||\hat{r}_{A}-r_{A}|<|\hat{r}_{A}-r_{A}| \tag{12}\]  
and thus, if \(r_{A}\) is fixed, \(E[\hat{r}_{A}]\) converges to \(r_{A}\) as \(\hat{r}_{A}\) is updated over an increasing number of games. In practice, \(K\) is chosen much smaller than \(\frac{1}{2\max f(\mathbb{R})}\)--for instance, the FIDE uses \(K=10\) for players with ratings above 2400, and in that case, \(F_{X}(x)=\frac{1}{1+10^{-x/400}}\), so a better \(K\) for accelerating the convergence of the expectancy would be \(\frac{1}{2f_{X}(0)}=\frac{4\cdot 400}{2\log(10)}\approx 347\).  
The reason for the difference is that faster convergence is at the expense of a much higher variance of \(\hat{r}_{A}^{\prime}\) (the variance is proportional to \(K^{2}\)). If we apply the update formula over an infinite number of games, even if the true ratings of the model stay constant, the computed ratings do not converge, and they approach a limiting distribution [11], the variance of which we would like to minimize.  
Therefore, the choice of \(K\) depends on the dynamic properties of the strength of the players. If we expect this strength to change significantly from one game to the next, \(K\) should be bigger, and if we expect the skill of the players to be relatively stable (for instance, if many games are played in a small lapse of time), we choose a lower \(K\). The FIDE uses \(K=40\) for younger players, who may improve quickly, and \(K=10\) as said above, for players with rating above 2400, which are usually grandmasters and do not improve their play that fast.
## 2 Methods  
### BRIR Measurement  
Seven pairs of BRIRs were measured with a low-noise dummy head KEMAR 45 BC-12 at azimuth angles of \(-90^{\circ}\), \(-60^{\circ}\), \(-30^{\circ}\), \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\) relative to the dummy head in a listening room (area of \(\approx\)31.6 m\({}^{2}\), height of \(\approx\)3.2 m), which was designed under the ITU-R BS.1116 standard and has a broadband reverberation time \(T_{60}\) of about 260 ms [23]. Seven equally-calibrated Neumann KH 120 Aloudspeakers located at the respective incident angles were used as sound sources for the recording of non-individual BRIRs. The distance between each loudspeaker and the dummy head was 1.9 m. A five second-long exponential sweep [24] from 20 Hz-20 kHz was used as an excitation signal, and the BRIR measurement for each azimuth angle was repeated five times. The BRIRs recorded were then truncated by a 260 ms-long time window. The measurements were performed at a sampling frequency of 44.1 kHz using a Fireface UFX+ audio interface. Figure 2 shows an illustration of the measurement setup.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Ship Detection Based on One-Stage Object Detection Model
#### 4.1.3 Activation Functions and Loss Functions  
A narrow waterway and heavy vessel traffic limit the available navigation space, leading to frequent vessel overlaps in certain sections. However, traditional loss functions present limitations in object detection, particularly when handling small objects, highly overlapped targets, or significant bounding-box deformations. These challenges can impede the model's convergence speed and generalization capability, especially in such unpredictable scenarios.  
To address these challenges, this paper proposes replacing the loss function in the algorithm with Focal-EloU. Focal-EloU is an enhanced object detection loss function that combines the advantages of focal loss and EloU, aiming to improve the performance of object detection models, particularly in challenging scenarios. It is specifically designed to overcome the limitations of traditional loss functions in small object detection and high-density target environments. The Focal-EloU loss function is as follows:  
\[\rm{L_{EloU}}\ =\rm{L_{4 oU}}+\rm{L_{dis}}\ +\rm{L_{asp}}=1-\rm{IoU}+\frac{ \rm{\rho^{2}(b,b^{gt})}}{c^{2}}+\frac{\rm{\rho^{2}(w,wgt)}}{c_{w}^{2}}+\frac{ \rm{\rho^{2}\big{(}h,h^{gt}\big{)}}}{c_{h}^{2}} \tag{7}\]  
Furthermore, this study replaces the activation function in the detection algorithm with RReLU (randomized ReLU). RReLU is a variant of the ReLU activation function that introduces a random slope in the negative value range to mitigate the vanishing gradient problem. During training, RReLU randomly selects a slope value, enabling the network to maintain a larger gradient in the negative region and thereby enhancing the model's learning capacity. Compared to traditional ReLU, RReLU effectively prevents gradient vanishing in the negative range and introduces randomness into the model, which improves its generalization ability and accelerates convergence. The formula is as follows:  
\[\rm{RReLU(x)}=\left\{\begin{array}{ll}\rm{x}&\rm{if\ x\geq 0}\\ \rm{ax}&\rm{otherwise}\end{array}\right. \tag{8}\]  
In this context, \(\rm{a}\ \sim\rm{Uniform(l,\ n)}\) indicates that the slope a is randomly sampled from the interval \(\rm{[l,n]}\). To address the complex dynamic background and deformation issues in the inland waterway scenario, this study enhances the dynamic adjustment mechanism for the negative slope of RReLU. The adjustment is based on the absolute value distribution of the input features:  
\[\rm{a}=1+\frac{\rm{\sum_{x_{i}<0}}|x_{i}|}{\rm{\sum x_{i}}}\ (\rm{u}\ -\rm{ l}) \tag{9}\]  
In this context, \(\rm{x_{i}}\) represents the input feature value of the current batch. The higher the proportion of negative values in the absolute value distribution, the larger the random slope. This mechanism helps maintain gradient propagation in the negative region under dynamic backgrounds, thereby enhancing stability and accelerating convergence.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 2 System Model
Considering the related true and time-invariant nature, the SI channel residual echo can be expressed as [14,15]  
\[\sqrt{P_{2}}t\left[i\right]=\beta r\left[i-\tau\right]. \tag{2}\]3  
where \(\varepsilon\) denotes the relative estimation error, and the echo cancellation performance can be measured as [15]  
\[\sqrt{P_{2}}t\left[i\right]=\beta r\left[i-\tau\right]. \tag{2}\]4  
Based on the relationship in (10), (11) is equal to  
\[\sqrt{P_{2}}t\left[i\right]=\beta r\left[i-\tau\right]. \tag{2}\]5  
As can be observed in (12), the cancellation performance degrades significantly even with low errors in the estimation of the coupling magnitude, which can be shown in Table 1 (As proved in [19] for the reference conceived to exchange information between two ends through a satellite, the on-borad digital cancellation can target cancellation levels about 30 dB in a concept known commercially as Paired-Carrier Multiple Access or DoubleTalk Carrier-in-Carrier. ).
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 3 Ext-LOUDS  
### Data Preprocessing  
Data preprocessing maps or transforms data according to a specific logic, which can help to improve the index construction efficiency and superset query efficiency. Element ordering and set ordering are the two common preprocessing methods for sets. The former sorts elements in a set in a certain order, which further includes element value (EV: sort elements in a set by value) ordering, element frequency (EF: sort elements in a set by their frequency in the entire dataset) ordering and element frequency-value [9] (EFV: map elements to their positions in the frequency sequence) ordering. Set ordering sorts sets in a certain order, which includes set length (SL: sort sets according to their lengths) ordering, set dictionary (SD: view a collection of sets as strings and sort them alphabetically) ordering. For the example dataset in Figure 1a, the processed dataset by EFV and SD is shown in Figure 2.
## 2 Source Seismic Records  
Many factors may influence the character of the near-fault earthquake motion, including the mechanism of the fault rupture, the complex soil and rock condition around the source of rupture, the relative direction with respect to the seismic station, and the closest distance-to-fault of the seismic station. The Chi-Chi event (1999-September-20 17:47:16 UTC) with a 7.6 magnitude was chosen as the single target earthquake event in this paper, to alleviate the influence of those complex aspects. All of its 221 sets of records collected from the Strong-Motion Virtual Data Center (VDC; www.strongmotioncenter.org), were used as source data. The Chi-Chi event in Taiwan was just a numerical example in this paper. The proposed method could also be applied to identify the pulse-like seismic motion from other earthquake events.  
Figure 1 shows the relative frequency distribution of the closest distance-to-fault for these source records. The majority of these traces were recorded within a distance range of 130 km, among which 36.65% were located within a distance range of 30 km. The method proposed in this paper was applied to these records to identify the unfavorable direction, and extract the dominant pulse components embedded in the seismic records. The pulse parameters for each identified set of pulse-like earthquake records were also obtained, such as the identified horizontal direction, number of dominant pulses, the mother wavelet for each pulse, and the frequency for each pulse. These pulse parameters will help in the interpretation of characters from the identified pulse-like earthquake motions.
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 2 Background  
Both unsupervised and supervised learning paradigms are involved in the design of the proposed approach. Indeed, unsupervised learning, specifically clustering, is utilized to learn the underlying structure of the data and split it automatically into homogeneous subgroups. Alternatively, supervised learning, particularly classification, is exploited to devise a set of countermeasures suitable for each subgroup.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 6 Experiments and Results
### Fault Detection Experiment
In this experiment, different complexity analysis algorithms are calculated for all 2400 segments of the vibration signal sample set and their average running times are shown in Table 4. As shown in Table 4, the permutation entropy, fuzzy entropy, and SampEn are able to detect the fault in the rolling bearing signal and the fast SampEn has an enormous advantage in terms of algorithm speed. Therefore, the fast SampEn has the potential to be a real-time method for the fault detection of rolling bearings.