esa-sceva/satcom-chunk-collection huggingface dataset:
# Reference Mapping Considering Swaps of Adjacent Bases
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Paired-End Reads**} & \\ \cline{2-4}
**Tools** & **Runtime (s)** & **Number of Aligned Reads (Ratio)** & **Difference from SOAP3-dp** & **GPU-Based** \\ \hline SOAP3-swap & \multirow{2}{*}{641.47} & 31,514,424 & \multirow{2}{*}{\(+\)1253} & \multirow{2}{*}{Yes} \\ (Full SA, \(S_{SW}=-1\)) & & (81.07\%) & & \\ \hline SOAP3-swap & \multirow{2}{*}{624.95} & 31,513,625 & \multirow{2}{*}{\(+\)454} & \multirow{2}{*}{Yes} \\ (Full SA, \(S_{SW}=-2\)) & & (81.07\%) & & \\ \hline SOAP3-swap & \multirow{2}{*}{623.67} & 31,513,300 & \multirow{2}{*}{\(+\)129} & \multirow{2}{*}{Yes} \\ (Full SA, \(S_{SW}=-3\)) & & (81.07\%) & & \\ \hline SOAP3-dp & \multirow{2}{*}{563.83} & 31,513,171 & \multirow{2}{*}{\(\cdot\)} & \multirow{2}{*}{Yes} \\ (Full SA) & & (81.07\%) & & \\ \hline SOAP3 & \multirow{2}{*}{698.08} & 24,409,682 & \multirow{2}{*}{\(-\)7,103,489} & \multirow{2}{*}{Yes} \\ (62.79\%) & & & \\ \hline BarraCUDA & \multirow{2}{*}{1131.08} & 30,380,166 & \multirow{2}{*}{\(-\)1,133,005} & \multirow{2}{*}{Yes} \\ (8.15\%) & & (78.15\%) & & \\ \hline Bowtie2 & \multirow{2}{*}{1261.55} & 30,602,667 & \multirow{2}{*}{\(-\)910,504} & \multirow{2}{*}{No} \\ (Sensitive) & & (78.72\%) & & \\ \hline CUSHAW2 & \multirow{2}{*}{2697.97} & 30,099,066 & \multirow{2}{*}{\(-\)1,414,105} & \multirow{2}{*}{No} \\ (77.43\%) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of SOAP3-swap and other tools for the drosophila genome.
## 2 Previous Work
### Converting 3D Data to 2D Data  
To make 3D classification similar to 2D image classification, some people have used multiple views to represent the 3D model [9]. A group of 2D images represents a 3D model by taking pictures at different angles to represent a multi-view model. Several models exploit the features of such pictures to grasp the features for 3D data [10]. For example, [PERSON] et al. developed a model (MVCNN) [9] that inputs each picture into a convolution neural network (CNN) and aggregates all features through another CNN.  
Another way of dealing with multi-view images was proposed by [PERSON] et al. (GVCNN) [11], which gives each picture discrimination scores before processing. However, multi-view methods require heavy computation since they require an algorithm to take pictures from different angles and it is difficult to capture all the features from just a few pictures.  
In addition to using multi-view representations of the data, some attempts have also had models use projections on 3D models. For instance, [PERSON] et al. [12] proposed a framework that uses an auto-encoder, an encoder-decoder model with the same output shape as the input, to classify 3D shapes via feature learning after projecting them into 2D space.  
Converting 3D data into 2D data is beneficial because commonly used machine learning models, like CNNs, are good at processing 2D shapes. However, using finite screenshots of 3D data might only catch part of the picture of the 3D model, as there are infinitely many angles to take such screenshots, and those images likely only tell part of the story. Therefore, works focusing on the raw 3D data are presented.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
###### Abstract  
With the widespread use of lithium-ion batteries in various application fields, accurate prediction of battery state of health (SOH) has become an important research topic to ensure battery performance and safety. To improve the accuracy of SOH prediction, this paper proposes a novel approach that combines multidimensional feature extraction and a transformer-LSTM fusion model. This method extracts time domain, frequency domain, and time dimension features from voltage, energy, and temperature curves. It evaluates feature importance, removes redundancy, and focuses on key features most relevant to SOH. Then, using the self-attention mechanism of transformer and the long-term dependency capture ability of LSTM, an efficient fusion model is constructed to further improve the accuracy and stability of SOH prediction. The proposed method is validated based on the cycling data from 124 commercial lithium iron phosphate/graphite batteries under fast-charging conditions. Compared with existing methods, the proposed approach effectively extracts key features closely related to SOH and builds models based on these features. It achieves a prediction accuracy exceeding 50% and demonstrates superior generalization performance relative to current methods.  
m +
Footnote †: journal: Applied Sciences  
ultidimensional features; transformer-LSTM fusion model; lithium-ion battery; state of health
## 5 Conclusions  
Though the concept of DT is decades old, its real impact has only come into being in recent years. The digital market is and will continue to grow as more and more end-use industries adapt the technology owing to its potential in reducing operational costs and time, increasing the productivity of the existing system, improving maintenance, easing accessibility, creating a safer work environment, and other purposes yet to be realized.  
DT technology, when combined with the other technologies such as AR/VR, mixed reality, additive manufacturing, 3D printing, etc., as well as with other DTs, will open doors for new applications and potential. Although this technology comes with its own challenges, the benefits are far greater. This paper identified different types of DTs to better understand and distinguish what can and cannot be described as DT to edge closer to consensus on its definition and limitations. Identifying and understanding the potential of DT in any sector and complementing it with the pertinent type of DT provides an opportunity for developing Industry 4.0 tools that offer numerous advantages, from simulation and prediction capabilities to record-keeping and forensic troubleshooting. This paper looked at the advantages offered by implementing this technology and contrasted them with the challenges. The benefits involve reductions in cost, increased outputs, remote access, and streamlining of services and operations, all of which can be obtained by operating the system digitally instead of physically without any additional material or investment cost. It also gave a glimpse of the future of different industries with DT as one of their core technologies.  
DT as a technology is in its infancy, meaning it is far from reaching its full potential. Identifying and addressing the challenges of DT is crucial in leveraging the technology in different sectors. A lot of challenges associated with DT can be attributed to the novelty of the technology: lack of consensus on its definition and its value, lack of standards and regulations, lack of competent engineers and technicians, and lack of supporting software. There are issues of data security and ownership associated with DT that demand more attention since data serve as the foundation for DT. In addition to addressing the present challenges related to DT, there is also a need to broaden our horizons and identify the problems that DT as a technology can face or create in the future. Understanding the holistic view on DT--which involves what qualifies as DT, its characteristics, its advantages, how can it be implemented, and its challenges--is essential if we want to unleash the true potential of the technology.  
Conceptualization, M.S. and E.F.; writing--original draft preparation, M.S.; writing--review and editing, E.F. and E.P.H.; writing--review, Y.Q. and D.D.; supervision, E.F., D.D., and N.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number 16/RC/3918 and the APC was funded by Science Foundation Ireland.  
Not Applicable.  
The authors declare no conflict of interest.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 3 Preliminaries
### Operations on Elliptic Curve  
An elliptic curve \(E\) defined over finite field \(F_{q}\) is expressed by the famous Weierstrass Equation:  
\[y^{2}=x^{3}+Ax+B \tag{1}\]where \(A,\ B\in F_{q}\) are constants that satisfy the equation:  
\[4A^{3}+27B^{2}\
eq 0 \tag{2}\]  
The points on the elliptic curve \(E\) are given by:  
\[E(Fq)=\ \{O|\text{U}\left\{(x,\ y)\in F_{q}\ X\ F_{q}\ :\ y^{2}\ =\ x^{3}+Ax+B\right\} \tag{3}\]  
where \(O\) is the point at infinity. The properties and operations satisfied by \(E\big{(}F_{q}\big{)}\) are listed below.  
* Identity--For each point existing on the elliptic curve \(E\), i.e., \(Q\in E\big{(}F_{q}\big{)},\ Q+O\ =\ O+Q\ =\ Q\).
* Negative--Assuming a point \(Q=(x,\ y\in E\big{(}F_{q}\big{)}\), then the negative of point \(Q\) is represented by \(-Q=(x,-y)\). The property is \(Q+(-Q)=O\). Moreover, \(-O=O\).
* Elliptic Curve Point Addition--Assuming the two points \(Q=(x_{1},\ y_{1})\) and \(R=(x_{2},\ y_{2})\) on \(E\big{(}F_{q}\big{)}\) with \(Q\
eq\pm R\). The elliptic curve point addition of \(Q\) and \(R\) is expressed by another third point \(Q+R\ =\ (x,y)\in E\big{(}F_{q}\big{)}\). The coordinates \(x\) and \(y\) are given by: \[x=\ \rho^{2}-x_{1}\ -x_{2}\ \text{and}\ y=\ \lambda(x_{1}-x)-y_{1}\] (4) where: \[\rho=\ \frac{y_{2}-y_{1}}{x_{2}-x_{1}},\ \text{if}\ Q\
eq R\ \text{and}\ =\frac{3{x_{1}}^{2}+A}{2y_{1}},\ \text{if}\ Q=R\] (5)
* Elliptic Curve Point Multiplication--Consider a point \(Q\in E\big{(}F_{q}\big{)}\), the elliptic curve point multiplication is expressed as \(kQ\ =\ Q+Q+\ldots+Q(k\ times)\).
* XOR Operation--The XOR operation between two points on the elliptic curve has been computed by performing XOR operation between the corresponding x and y coordinates of both the points.
# Green Corridors and Social Connectivity with a Sustainable Approach in the City of Cuzco in Peru
## 2 Materials and Methods
### Diagnosis of Study Area  
#### 2.3.1 Urban Analysis  
* Road Analysis  
In Figure 6, the road diagram marks the different types of roads present in the city of Cuzco, among which Av. La Cultura stands out for crossing the department of Cuzco, and in magenta lines, the streets that interconnect with the terrain are highlighted. Due to its broken plate morphology, the city of Cuzco presents a geometry of irregular blocks, which influences the easy vehicular congestion of the arterial roads. The roads adjacent to the terrain are the Expressway, Av. Velasco Astete, Daniel Estrada Street, and Chachacomo Street, and other closer and more important ones are the Bypass, Av. de la Cultura, and a railway network [35].  
Figure 5: Location of the city of Cuzco.  
* Vulnerability Analysis  
In Figure 7, the vulnerability of Cuzco due to its location in the valley and the proximity of the Huatanay River is highlighted. The areas near the river are especially prone to flooding, which presents a significant risk to inhabited areas and infrastructure. Additionally, the terrain of the Wanchaq airport contains organic soil and is affected by landslides in some areas.  
The different types of soils present in Cuzco include fine soil, gravel, and rock, each with specific characteristics and challenges. The presence of geological faults and alluviums is also noted, which represent additional risks. Faults can concentrate tectonic stresses, increasing vulnerability to earthquakes, while alluviums can drastically modify the landscape and affect infrastructure [36].  
Figure 6: Urban context of the intervention area.  
Figure 7: Vulnerability and soil analysis in Cuzco.  
Environmental Analysis  
In Figure 8, it can be observed that Cuzco faces several immediate environmental challenges that affect both its ecosystem and the quality of life of its inhabitants. Mining activity is concentrated in areas far from the urban zone, which reduces the direct exposure of the population to pollution. However, the risks associated with mining, such as soil degradation, biodiversity loss, and water source contamination, remain significant and require proper attention and regulation.  
Additionally, the figure shows a higher risk of forest fires in the wooded areas of Cuzco, highlighting the need for preventive and control measures to protect forest resources and maintain the ecological balance of the region. Finally, the issue of water pollution along the Huatanay River is highlighted, affecting water quality and the health of aquatic ecosystems.
# Robust Registration of Medical Images in the Presence of Spatially-Varying Noise
## 3 Results
### Evaluation of the Proposed Algorithms on Retina Images

Table 2: Registration performance of different approaches in the application of retina images. Figure 9: Registration accuracy of different similarity measures in three approaches of intensity-based, LR-EMD, and AFR-EMD. (**A**). SSD, (**B**). CC, (**C**). MI, (**D**). RC. of our proposed registration methods. Both images were distorted via strong additive spatially-varying intensity noise. Figure 10A illustrates two levels of the IMFs and residuals for each image. The residuals contain a significant part of the spatially varying noise and the IMFs contain denoised patterns. Therefore, similar to brain MR images, the EMD-based features have decomposed the signal and noise into two separate components. Figure 10B demonstrates the performance of registration based on LR-EMD, AFR-EMD as well as the benchmark intensity-based algorithm for these retina images. Here, SSD is used as the similarity measure. From the figure, both LR-EMD, AFR-EMD visually outperform the intensity-based registration. This is demonstrated via the composite view through contour overlap after the registration in Figure 10B. In particular, the red circle shows the vascular pattern for which the intensity-based approach has failed to accurately register. Both LR-EMD and AFR-EMD are successful in registering this vascular pattern. This visual inspection suggests that both of the proposed methods are robust against the additive spatially varying intensity distortion in the retina images.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Static Load Test  
Finite element modeling that reflects the behavior of an actual anchorage device is important to establish the stress and strain characteristics occurring at a specific location of the mono anchor head. Therefore, the static load test of the anchoring system using  
Figure 2: Behavioral mechanism of mono-anchor head.  
the anchor head of the shape shown in Figure 3 and Table 1 was performed in advance to understand the behavior characteristics of the actual mono-anchor head. The test was performed, and the deformation characteristics of the anchor head according to the applied load were identified by attaching a strain gauge to the outer surface of the mono-anchor head. As shown in Figure 4a, a three-piece wedge and a mono-anchor head were used at both ends of a 2360 MPa strand with a free length of 1500 mm. The load was measured on the universal test machine, and the displacement was measured as the movement of the UTM crosshead. The strain gauge attached to the mono-anchor head measured the strain in the hoop direction as shown in Figure 4b. In order to eliminate undesired initial set-up error, the test specimens were loaded and unloaded three times up to 50 kN and then loaded until the strand failed. Two specimens were prepared and tested to establish reliable behavioral characteristics for all specimen groups.  
Figure 4: Static load test.  
Figure 3: Detail of mono-anchor head.  
The load-displacement curve for each specimen is shown in Figure 5. When the load-displacement curves of all specimens are compared, as shown in Figure 5e, the behavior characteristics according to the configuration of the mono-anchor head cannot be discriminated, and it can be confirmed that only the dominant behavior appears in the strand. On the other hand, the results of strain measurement in the hoop direction measured to specify the behavior of the mono-anchor head are shown in Figure 6, according to the load applied to the strand. It can be confirmed that the individual strains attached to the same specimen show non-constant result values. This is considered to be the effect of the wedge placed inside the anchor head, and should be specifically considered through analysis when investigating the behavior according to the design variables of the anchor head.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen** & **D (mm)** & **H (mm)** & **\(\theta\) (\({}^{\circ}\))** \\ \hline D50H50-66.3 & 50 & 50 & \\ D50H60-66.3 & 50 & 60 & \\ D60H50-66.3 & 60 & 50 & 6.3 \\ D60H60-66.3 & 60 & 60 & \\ \hline \end{tabular}
\end{table}
Table 1: Anchor head specifications for the static load test.  
Figure 5: _Cont._
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Algorithm
#### 3.3.6 Step 6--DR, Fractional CFO, and Fractional STO Compensation  
The DR is compensated in the preamble to allow accurate estimation of \(\
u\) and \(\lambda\). In the payload, the receiver has to compensate fractional STO, perform the down-sampling at \(f_{s_{min}}\), and then compensate the DR and \(\
u\).  
To this end, based on the estimate \(\hat{\lambda}\), the timing alignment is done in the decimation chain of the receiver's digital front-end. Before the samples are produced at the minimum sampling frequency \(f_{s_{min}}\), an oversampling \(f_{s}^{\prime}=\alpha\times f_{s_{min}}\) is considered. Indeed, the compensation of the fractional STO \(\lfloor\alpha\times\hat{\lambda}\rceil\) can be easily done by shifting the decimation operator's input by a corresponding number of undecimated samples. After that, the payload is sub-sampled at the frequency rate \(f_{s_{min}}\). Thus, the symbols can be easily estimated as depicted in (10), where the FFTs are computed as in (32) after the compensation of \(\hat{v}\) and the DR.  
Finally, before starting the payload decoding, the receiver has to verify the accuracy of our synchronization algorithm by finding the two special modulated symbols of the _sync word_, as depicted in Figure 3, which summarizes the architecture of our proposed receiver.  
In the next section, we provide simulation results to demonstrate the efficiency of our DCSS receiver.
## 4 Application Characteristics
### Application Cases  
Through network observation of HN-1 01/02 and WC-1 01/02 satellites and utilizing the obtained remote sensing data and AIS information, application services have been launched in Hainan province, including monitoring of ships, analysis and prediction of target travel direction and monitoring of high-risk targets in the surrounding areas. It can meet the spatial data needs in key fields, such as the marine environment, maritime emergency search and rescue, and fishery information services.  
Currently, the applications cases are mainly in Hainan province. But it is obvious that the applications of the HSC can be broadened to other low-latitude regions or even to global ocean monitoring, enhancing its applicability.
## 2 Recent Evolution in Actor-Critic Frameworks
### Hybrid Structure-Based Frameworks
#### 2.3.1 RNN-Based Actor and Multi-Head Attention-Based Critic  
This type of actor-critic framework employs either LSTM or GRU in the actor network to capture the sequential behavior of dynamic states. Subsequently, utilizes a multi-head cross attention-based critic network to precisely estimate the value function. In [2], the authors proposed a behavior-based mobility integrated LSTM-based actor and multi-head cross-attention-based critic framework to maximize a multi-metric link utility to deal with packet routing problems in dynamic UAVSNs. They have considered the cross-layer design consisting of a large hybrid action space, which includes collaborative trajectory control, frequency allocation, and next-hop selection. The model is trained using distributed MADDPG consisting of two-hop neighbor information-based POMDP. The actor network is constructed based on an LSTM-based three-layer state encoding layer and an FCN. LSTM-based state encoding layers extracted key features from dynamic topology by detecting temporal continuity utilizing current and historical information. Subsequently, a multi-head cross-attention-based critic network determines the precise value function to train the actor network by paying attention to the neighboring agent's observation-action space according to their degree of influence. It provides training stability and overcomes environmental nonstationary, training adaptivity, and algorithm convergence.  
In [1], a swarming-behavior-integrated GRU-based actor and a multi-head attention-based critic network are proposed for an air-ground integrated network. Here, mobile edge computing-enabled UAVSN acts as agents to jointly minimize the task execution delay for mobile GUs and energy consumption for UAVs by controlling UAV's trajectory, UAV-GUs association, partial task off-loading ratio, computing, and bandwidth allocation for G2U links under several key constraints. This framework introduced behavior-based motion models and adaptive penalty terms to enhance decision-making. The GRU-based actor network is capable of coping with the dynamic observation of each agent. Similarly, the multi-head attention-based critic network utilizes a cross-attention mechanism to identify the influence of other neighboring agent's dynamic observation-action space for each individual UAV agent. Moreover, it provides training adaptivity to different numbers of UAVs and GUs, system scalability, and helps to avoid environmental nonstationary.