esa-sceva/satcom-chunk-collection huggingface dataset:
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### CNN-Based Methods  
Convolutional neural networks (CNNs) [24] have achieved remarkable performance in the field of computer vision, such as object detection [53] and image segmentation [54]. The core operation of CNN is to extract different feature maps of input data through convolution kernels. Formally, the feature value at location \((i,j)\) in the \(k\)-th feature map is calculated by:  
\[z_{i,j,k}=W_{k}x_{i,j}+b_{k}, \tag{2}\]  
where \(W_{k}\) and \(b_{k}\) are the weight matrix and bias vector of the \(k\)-th convolution kernel, respectively, and \(x_{i,j}\) denotes the overlapped input data centered at location \((i,j)\).  
[PERSON] et al. [29] propose a context-aware TKGC method (CATKGC), which consists of two modules: one for context selection and the other for modeling temporal interactions. Specifically, the context selection module decomposes a fact into two parts, i.e., relation and entities, and measures the usefulness of the context for each part, respectively. Then, a CNN is used to encode context and extract features of temporal interactions, based on which a fully connected layer is adopted to learn the temporal consistency score. Finally, the consistency score is used together with a plausibility score to predict the correctness of a fact.  
[PERSON] et al. [30] propose a query-aware (a query is an incomplete quadruple) embedding model based on a convolutional neural network, named ConvTKG, which has an encoder-decoder architecture. In the encoder, it converts the timestamp into a sequence of atomic-level temporal tokens. Then, these tokens are fed into a gate recurrent unit (GRU) layer and an attention layer to obtain query-aware representations of timestamps. In the decoder, multiple one-dimensional kernels are employed to extract global features among elements in the query, yielding various feature maps that are then reduced to feature vectors through multi-layer perception (MLP). Finally, the feature vectors of a query are computed with the candidate entity embeddings to produce the plausibility scores of candidate entities.  
SANe [31] extends CNN to map the facts with different timestamps into different latent spaces. Essentially, it turns TKGC into static KGC by handling different temporal snapshotsin separate spaces. In particular, a time-aware parameter generator (TaPG) is designed to constrain the overlap of latent spaces according to the distance of the timestamps, which endows the CNN with specific parameters in terms of the context of timestamps. [PERSON] et al. (2020) present an expanded version of SANe by introducing an adaptive parameter generator (AdaPG), which overcomes the limitations of TaPG in handling unbalanced TKGs through allocating parameters based on the number of facts instead of timestamps.  
Methods in this category use CNNs to model temporal information and its interactions, which have more powerful feature learning capabilities than the above LSTM-based methods but which are still unable to model the graph structure of TKGs.
## 2 Related Works  
Current studies have shown that there has not been a precise definition of a unified standard for the threshold between different wave components within a singular spectrum. To mitigate this, [PERSON] et al. [27] proposed the singular value truncation, which separates diffraction wavefields by setting the larger singular values and some smaller singular values to zero based on the composition characteristics of the wavefield and then reconstructing the diffraction using the remaining singular values. Other researchers employed the mean value method and cumulative energy method to define the singular value threshold [10; 27]. The two methods involve obtaining the mean value and total energy of all singular values in the singular spectrum [10; 27]. in regard to GPR data processing, the complexity of the wavefield often actually determines the number of singular values to be selected. The task of choosing the appropriate number of singular values becomes more difficult when the number of data acquisition traces increases, for instance, as the number of singular values obtained through SVD increases. In contrast to seismic data processing, GPR data are commonly observed with strong direct waves and complicated crosstalk, resulting in a first singular value in the singular spectrum that is substantially larger than the subsequent ones.  
Figure 1a,b illustrate the synthetic dataset and its singular spectrum at the dominant frequency position, which was determined by the SVD result of GPR data. When employing methods such as mean value method or cumulative energy approach [27] to analyze the singular spectrum, it becomes evident that even the first singular value alone accounts for more than 98% of the total sum of singular values or cumulative energy. More specifically, for example, the singular values that are larger than 0.3 times of the mean value (i.e., the first ten singular values that are highlighted in red in Figure 1b) are set to zero based on the cumulative energy ideology that these ten value components actually account for 99.9995% of the total energy in singular spectrum [10; 27]. However, the separated diffraction profiles still present multi-reflective and crosstalk features in terms of oblique events with considerable energy appearing between 10 ns-15 ns and 25 ns-30 ns, at the point where the two undulating interfaces are designed and located, as shown in Figure 1c,d. This disproportionate contribution (i.e., those faint but vital residual diffraction components and  
those intense but coda reflection components) could significantly limit the practicality of these seismic mainstream approaches in analyzing the singular spectrum of electromagnetic data for the GPR data processing community.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 5 Experimental Evaluation
### Plm-Icd-C
#### 5.3.2 Performance Metrics  
Table 11 shows the performance metrics for PLM-ICD and PLM-ICD-C.  
For PLM-ICD, the threshold of 0.2 has a lower precision (0.46282) than the threshold of 0.4 (0.60596), but a much better recall (from 0.38264 to 0.47680), which translates into a slightly better F1-score.  
For PLM-ICD-C, model (1) has a better precision (0.62031) than (2) (0.50008), but (2) has a much better recall (from 0.37370 to 0.45316), which translates into a better F1 for (2) (from 0.46641 to 0.47547).  
In general, PLM-ICD-C (2) has the best F1-score (0.47547), in which it is slightly better than PLM-ICD threshold 0.2 (0.46970), but a much better precision (from 0.46282 to 0.50008), which may improve the code suggestions.  
It is shown that the three rules defined for PLM-ICD-C improve the results. In the case of rule number 1, if PLM-ICD finds a code with high probability of being a positive, there is no need to screen it again, being immediately accepted as a positive. In the case of rule number 2, ICS is called to disambiguate, because PLM-ICD's confidence is just borderline between positive or negative. Finally, rule number 3 determines that when PLM-ICD gives a very low probability of being positive to a code, it is not screened by ICS, being accepted as a negative candidate.
###### Abstract  
Mitoxantrone (MTX) is a drug employed in breast cancer treatment, but its application is largely limited due to side effects. A controlled delivery approach can potentially reduce the side effects. In this study, two zirconium (Zr)-based MOFs, UiO-66 and UiO-66-NH\({}_{2}\), were studied for a more controlled delivery of MTX with a 40% and 21% loading capacity, respectively. Characterisation via powder X-ray diffraction, thermogravimetric analysis, Fourier transform infrared spectrometry, scanning electron microscopy, and dynamic light scattering confirmed the integrity of structure post-MTX loading. UV-vis spectrophotometry revealed distinctive release profiles, with UiO-66-MTX exhibiting a 25% cumulative release after 96 h in water and 120 h in PBS +10% FBS. UiO-66-NH2-MTX displayed a more sustained release, reaching 62% in water and 47% in PBS +10% FBS after 168 h. The interaction between MTX and the MOFs was also proposed based on computational modelling, suggesting a stronger interaction of UiO-66 NH2 and MTX, and an optimised interaction of MTX in the tetrahedral and octahedral pores of the MOFs. The study also reports the release profile of the drug and antiproliferative activity against a panel of breast cancer cell lines (MDA-MB-231, MDA-MB-468, and MCF7) and a normal breast epithelial cell line (MCF10A). MTX-encapsulated MOFs were thoroughly characterised, and their biological activity was assessed in vitro. MTT cell viability assay indicated a higher IC\({}_{50}\) value for MTX-loaded MOFs compared to free MTX in physiological conditions, albeit with a slower release profile. These findings suggest the potential of these MTX-loaded MOFs as an alternative avenue for formulation to mitigate side effects.  
metal-organic frameworks; breast cancer; UiO-66; UiO-66-NH\({}_{2}\); Mitoxantrone +
Footnote †: journal: Applied Sciences
## 4 Performance Analysis of Multi-Constellation Kinematic PPP  
### Experimental Data Description  
To assess the availability of multi-constellation kinematic PPP applied to dynamic objects, a multi-constellation kinematic PPP test was conducted in Huainan, China on 18 January 2015. Kinematic GNSS observations were collected over 2 h from three receivers mounted on a vehicle. The speed of the vehicle was approximately 20 km/h. The receivers were Hi-Target V8 (TRM59800.00 NONE) devices, which can collect GPS, BDS, and GLONASS observations. The sampling interval was 1 s. Figure 1 shows the vehicle's route, which started from the \"start point\" and went through each section twice. To investigate the accuracy of the PPP result, we also set a base station with the same type of receiver and antenna at the start point to determine the coordinates of the rover station with cm-level accuracy using the double-difference real-time kinematic (RTK) approach. The surrounding environment was generally good for observations, and the sky visibility was very high. Multi-constellation mixed precise satellite orbit and clock products provided by the Deutsches GeoForschungsZentrum (GFZ) (Germany) were adopted for PPP data processing. It should be noted that no BDS receiver antenna PCO and PCV correction file was available for the antenna mentioned above. Consequently, the BDS PPP positioning results contained a systemic error.
# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 4 Real-Time Fault Detection in Edge Computing  
We propose a holistic framework for real-time and robust hydraulic system fault detection, as illustrated in Figure 2. The process for real-time detection is described in Algorithm 1. In lines 1-3, we conduct offline learning at the cloud server using historical data consisting of selecting the feature subsets, training the fault detection model or classifier (CLF), and deploying the model to the edge server. We use our proposed correlation and redundancy-aware feature selection to select on most relevant and less redundant features that described in detail in the Section 6. By performing the feature selection, we determine which features are essential during the fault detection process. These smaller feature subsets are directed to the edge and used for real-time detection. We pre-train an LSTM-AE, then put and tune the encoder part with a classifier layer to obtain a classifier model. These learning models will be further depicted in Section 7. By deploying the classifier model on the edge server with the features selected, the urgency of time-sensitive applications, such as fault detection, is supported. All feature data can still be sent to the cloud for collection as historical data and further analysis. Hence, the cooperation between the cloud and edge servers established in this framework provides efficient real-time fault detection for a hydraulic system.  
```
Input Historical data with all features \(f\) Output Predicted output \(c\) \(\triangleright\) Offline learning at Cloud server
1: Feature selection \(f_{\text{s}}\) based on Algorithm 2
2: Train LSTM-AE using \(f_{\text{s}}\) based on Algorithm 3
3: Deploy trained classifier \(CLF\) to Edge server \(\triangleright\) Real-time detection at Edge server
4: Initialize queue \(Q=\{\}\) with size \(w\)
5:for all incoming data \(x\) with feature subset \(f_{\text{s}}\)do
6: Calculate mean and normalize \(x\)
7: Push \(x\) to \(Q\)
8:if number of elements in \(Q==w\)then
9: Predict \(Q\) using \(CLF\) model
10: Pop top element in \(Q\)
```  
**Algorithm 1** Real-time time-series fault detection.  
Within a real-world scenario, the sensor data stream arrives sequentially, so these incoming data are queued, and the detection is performed on these queued elements. The window queue functions accommodate the data stream that is then fed into the LSTM model, which learns the temporal relationships between these sequence data. The incoming data include the selected features resulting  
Figure 2: Architecture of the proposed real-time fault detection with edge computing.  
from the CRFS algorithm. Therefore, our framework is suitable for general industrial systems that conduct simultaneous condition monitoring in time cycles. These processes are shown in lines 4-10.
### Addressing The Issue of Data Storing and Sharing Security  
The conventional ITS frameworks rely on cloud servers to collect information and store data. Such a centralized system architecture is prone to single-point failures. For example, successful distributed denial of service (DDoS) attacks to control servers make important ITS services unavailable. In addition, the in-vehicle driving data also contain critical evidence for forensics scenarios, such as traffic accident investigation and vehicle fault analysis [9]. Therefore, ensuring the data's audibility and traceability is critical in case of disputes about reliable data service.  
With attractive characteristics in decentralization, immutability, and transparency, blockchain has demonstrated great potential to construct a trust-free and secure network infrastructure for complex and heterogeneous IoT ecosystems [10]. Distributed ledger technology (DLT) promises to ensure the public auditability and traceability of IoV data. Blockchain promotes the migration from centralized to decentralized IoV frameworks, thereby reducing the risks of single-point failures and improving the availability and resilience of data service in IoV networks.  
To improve the availability and residence of video data services atop IoV networks with high heterogeneity and dynamics, we propose a blockchain-based video frames authentication (BloVA) scheme using blockchain and distributed storage technologies. To address the limitations of the traditional monolithic service-oriented architecture (SOA) that lacks flexibility, a microservices-oriented architecture (MOA) is adopted for video data delivery and security schemes, which bring attractive features, such as scalability, fine granularity, loose coupling, and continuous delivery [2]. MOA improves system availability from a service architecture aspect. In addition, we also adopt a hybrid on-chain and off-chain storage [11] to build a distributed content delivery fabric for IoV networks. This can improve the efficiency of integrating blockchain and ensure privacy preservation by not directly exposing video data and other sensitive information on a transparent distributed ledger.
# The Long and Winding Road to Cardiac Regeneration
## 2 Stem Cell Therapy
### Adult Stem Cells
#### 2.1.4 Mesenchymal Stem Cells  
Mesenchymal stem cells (MSCs) are multipotent cells that can be derived from various sources, including bone marrow, adipose tissue, umbilical cord blood, and placenta. Their ability to differentiate into multiple cell lineages, along with their immunomodulatory and paracrine effects, make them an attractive option for treating cardiac disorders [116].  
One of the key advantages of MSCs is their relatively easy isolation and expansion in vitro, allowing for large-scale production. Additionally, they exhibit low immunogenicity, reducing the risk of immune rejection upon transplantation [117]. MSCs possess anti-inflammatory properties, which can help attenuate the detrimental immune response associated with cardiac injury. Moreover, they secrete trophic factors that promote angiogenesis, modulate fibrosis, and enhance the survival of existing cardiomyocytes [118; 119].  
Despite these advantages, several limitations must be considered. The heterogeneity of MSC populations obtained from different sources can affect their therapeutic potential and consistency [120]. The propensity of MSCs to undergo senescence during expansion limits their long-term viability and functionality [121]. Furthermore, the unpredictable fate and limited engraftment of transplanted MSCs within the injured heart pose challenges to achieving sustainable cardiac regeneration [122]. Additionally, optimizing the delivery methods and timing of MSC transplantation remains an area of active research.
# Complete Genome Report of a Hydrocarbon-Degrading _Sphingobium yanoikuyae_ S72
## 3 Results
Three copies of the genes encoding ferredoxin--NAD (+) reductase (EC:1.18.1.3), seven copies of acyltransferases (EC:2.3.1), three copies of carboxymethylenebutenolidase, and one copy of muconate cycloisomerase are present in S72's genome. S72 relatedness to other _Sphingobium_ strain (105) was evaluated on PYANI ([PERSON] et al. 2016) using the average nucleotide identity mummer (ANIm) comparison measure. The ANIm result showed that S72 is closely related to _S. yanokiyue_ strain UBA2097 sharing 97% average identity with it (Figure 3). The analysis of S72 pan genome showed that it shared 1734 core genome with other _Sphingobium_ species and possesses 403 unique genes. We noted that 126 of the unique genes are associated with the catabolism of xenobiotics. Twenty of the unique genes have been previously reported to be involved in the degradation of toluene, xylene, ethylbenzene, biphenyl, benzoate, naphthalene, anthracene, tetrachloroethene, 1,4-dichlorobenzene, bisphenol, and trinitrotoluene in bacteria. These genes are involved in both the catabolism of central aromatic intermediate and the peripheral catabolic pathway for aromatic hydrocarbon (Table S1). Horizontal gene transfer is a common phenomenon through which bacteria often acquire some genes that are essential for their survival [3]. The acquired genes are commonly  
Figure 3: ANIm % Identity of 106 Sphingobium Strains. ANI analysis based on MUMmer alignment of the genome sequences was performed using PYANI. found on the genomic islands in bacteria or as mobile genetic elements. The analysis of the S72's genome with the online based Island viewer 4.0 revealed that it has 37 genomic islands (Figure 4). Five large regions were found in the genomic island with regions II and III being the largest consisting of 317,308 kb and 579,060 kb in length, respectively. An in-depth look into the genomic island showed the presence of many genes that are associated with the degradation of xenobotics and hydrocarbons. Among such genes are SDR family NAD(P)-dependent oxidoreductase (A6768_07840), aldo/keto reductase (A6768_07825), cytochrome P450 (A6768_11830), 4-hydroxybenzoate 3-monooxygenase (A6768_12975), and aromatic alcohol reductase (A6768_RS13080). The other hydrocarbon degrading genes found on the genomic island can be seen in the Supplementary File.
## 3 Theoretical Framework of Compressed Sensing
### Reconstruction Algorithm  
A signal reconstruction algorithm is the core of compressed sensing theory and refers to the reconstruction of the sparse signal \(x\) with a length of \(N\) from the measurement vector \(y\) with a length of M. Since \(\mathbf{y}=\mathbf{\Phi}\mathbf{x}\) and the dimension of \(y\) is far smaller than that of \(x\), the equation has infinitely many solutions and the signal cannot be reconstructed. However, if the original signal is \(K\)-sparse and the measurement matrix satisfies certain conditions, the signal \(x\) can be accurately reconstructed from the measured value \(y\) through the solution of the \(l_{0}\) norm. The reconstruction was proved theoretically and can be written as follows:  
\[\hat{\mathbf{x}}=\arg\min\left\|\mathbf{x}\right\|_{0}\quad s.t.\quad\mathbf{\Phi}\mathbf{x}= \mathbf{y} \tag{3}\]  
where \(\left\|\cdot\right\|_{0}\) denotes the vector's \(l_{0}\) norm, which is the number nonzero elements in the vector \(x\).
# Application of Elliptic Jerk Motion Profile to Cartesian Space Position Control of a Serial Robot
## 4 Elliptic Jerk Motion Profile
It can be understood graphically considering that the integral of the jerk is the acceleration; consequently, if the semi-elliptical areas delimited by the jerk profiles of phases #1 and #3 are equal, the acceleration is null at the beginning of phase #4 (constant velocity); similarly, if the semi-elliptical areas delimited by the jerk profiles of phases #5 and #7 are equal, the acceleration is null at the end of the rest-to-rest motion. A profile fulfilling these conditions is named a _symmetric elliptic jerk profile_ (Figure 4). For a symmetric elliptic jerk profile in dimensionless formulation, the number of independent parameters is four (\(t_{\rm ad,pa}\), \(t_{\rm ad,papi}=t_{\rm ad,papi}\), \(t_{\rm ad,na}\), \(t_{\rm ad,napi}=t_{\rm ad,napi}\)) [10]. In [10] the elliptic jerk motion profile (EJ) has been compared to other motion laws discussed in the scientific literature: the trapezoidal velocity profile (TV, [5]), the trapezoidal acceleration profile or S-curve (TA, [5]), the cycloidal profile (CY, [5]), the sinusoidal jerk profile (SJ, [20]), and the modified sinusoidal jerk profile (MSJ, [21]). Since the CY law cannot have finite phases with a null jerk, in order to include it in the comparison the durations of the phases of the EJ profile with a null jerk (#2, #4, #6) are imposed null. Moreover, the durations of the remaining phases (#1, #3, #5, #7) are imposed equally (1/4) [10]. With these assumptions, all the previously cited motion profiles are fully defined in dimensionless formulation, except the MSJ profile [10]. As a matter of fact, the MSJ profile is similar to the EJ profile, but each phase with a semi-elliptical jerk with a duration of 1/4 is replaced by a triplet of subphases: sinusoidal, constant, and sinusoidal [21]. In the comparison, these subphases have respective durations of 1/16, 1/8, and 1/16, with a total  
Figure 4: Symmetric elliptic jerk profile. duration of \(1/4\). Overall, imposing all the discussed hypotheses, the comparison of the six motion profiles is shown in Figure 5 in terms of dimensionless jerk, acceleration, velocity and position. It is possible to note that the SJ profile has the highest jerk peak, 50.27, excluding the TV profile, which has infinite jerk, not represented. The EJ profile has a maximum jerk of 40.74, slightly higher than the CY, 39.48, and the MS], 39.11, while the TA profile has the lowest maximum jerk, 32.00. On the other hand, the jerk of the TA profile is discontinuous. Regarding the acceleration, the EJ, TA, SJ, and MS] profiles have the same maximum, 8, while the CY and TV profiles have lower maxima, respectively, 2\(\pi\) and 4. With the considered hypotheses, all the profiles have the same maximum velocity, 2. In [10] the six profiles are compared with reference to a SISO second-order linear system. In the following, the same profiles will be applied to the rest-to-rest free motions of the RRFbR manipulator.