esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments and Results
### Experiment B  
Experiment B compares the proposed metaheuristic hybrid algorithm against two well-known metaheuristic algorithms: simulated annealing and tabu search.  
To prove if the difference in the startup time of each algorithm is significant, a hypothesis test was conducted. To do this test, experiments were carried out for a randomly generated initial solution and a feasible solution that was generated according to the procedure shown in Figure 6. A total of 10 experiments were performed for each algorithm as shown in Table 5. In either algorithm, each experiment was run with 1000 iterations as stopping condition, which was shown to guarantee convergence in all cases.  
The test aimed at proving whether there was a significant difference in the average startup time obtained by each of the algorithms. To prove that, we used the t-test and the mean start up time of 10 experiments. The start-up time means were considered significantly different when \(p\leq 0.05\). Conversely, start-up times means were not considered significantly different if \(p>0.05\). In all the experiments, the result was \(p<0.01\), indicating that in all the experiments the start-up times means were considered significantly different.  
Figure 14: Changes of water level obtained with the benchmark solution (red line), the micro genetic algorithm solution (black line) and the metaheuristic hybrid algorithm solution (green line).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Solution** & **Hybrid Algorithm** & **Simulated Annealing** & **Tabu Search** \\ \hline Randomly Solution & 10 experiments & 10 experiments & 10 experiments \\ Feasible Solution & 10 experiments & 10 experiments & 10 experiments \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiment design for experiment B.  
For the time to complete the start-up, the following hypothesis test was carried out, which sought to verify or refute if there was a significant difference between the solution generated by the metaheuristic hybrid algorithm and the tabu search algorithm; and between the metaheuristic hybrid algorithm and the simulated annealing algorithm:  
\[h_{0}:\mu_{x}-\mu_{y}=0\;versus\;h_{1}:\mu_{x}-\mu_{y}\
eq 0 \tag{13}\]  
where \(\mu_{x}\) is the average of the startup time obtained with the metaheuristic hybrid algorithm and \(\mu_{y}\) is the average of the startup time obtained with either simulated annealing or tabu search.  
Tables 6 and 7 show the mean and standard deviation of the 10 experiments for each method. Table 8 shows the results obtained with the application of the \(t\)-test in experiment B.  
and tabu search algorithm, but it was not better than the computational time of the micro genetic algorithm. However, in any case, the solutions proposed by the micro genetic algorithm were better than the proposed by the metaheuristic hybrid algorithm in terms of the start-up time.
## 3 Results and Discussion  
In this work, SVR and RFs were used to build prediction models for the AQI of Beijing and the NO\({}_{\rm{X}}\) of an Italian city, respectively.  
RMSE, \(r\), and \(R^{2}\) were used to evaluate the performance of the regression models. To obtain a good regression model, the following criteria could be used as references: (1) low RMSE, (2) high \(r\), and (3) high \(R^{2}\). The mathematical expressions of the parameters are shown in Equations (4)-(6), where \(y_{i}\) is the label of the \(i\)-th sample, \(\hat{y_{i}}\) is the predicted value of the \(i\)-th sample, and the superscript horizontal line indicates the average value. RMSE represents the sample's standard deviation of the differences between the predicted values and the observed values. The correlation coefficient (\(r\)) is a number that quantifies the type of correlation and dependence, implying the statistical relationships between two  
Figure 2: The schematic diagram of random forest regression (RFR).  
or more values in fundamental statistics. The coefficient of determination (\(R^{2}\)) is proportional to the variance in the dependent variable that is predictable from the independent variable(s) [33].  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{({{\hat{y}}_{i}}-{y_{i}})} ^{2}}}}} \tag{4}\]  
\[\text{r}=\frac{{\sum\limits_{i=1}^{n}{{{({y_{i}}-\overline{y})}({{\hat{y}}_{i} }-\overline{\hat{y}})}}}}{{\sqrt{\sum\limits_{i=1}^{n}{{{({y_{i}}-\overline{y} )}^{2}}}}\sqrt{\sum\limits_{i=1}^{n}{{{({{\hat{y}}_{i}}-\overline{\hat{y}})}^{2 }}}}}} \tag{5}\]  
\[\text{R}^{2}=1-\frac{{\sum\limits_{i}^{n}{{{({y_{i}}-{{\hat{y}}_{i}})}^{2}}}} }{{\sum\limits_{i}^{n}{{{({y_{i}}-\overline{y})}^{2}}}}} \tag{6}\]  
We know that the AQI and the concentration of air pollutants (CAP) are affected by many factors. In particular, when gas diffusion conditions are poor, the concentration of various pollutants increases, so there are correlations between the concentration of each pollutant. Before the regression model was established, we studied the correlation of various indicators.  
Figure 3a,b shows the correlation between the 6 and 11 air pollution indicators, respectively; we found that these indicators are highly correlated. Therefore, when one or more indicators is missing, it is feasible to use the remaining indicators to predict missing indicators. Taking the AQI prediction and NO\({}_{\text{X}}\) concentration prediction as examples, we constructed a regression model to verify the feasibility of our proposed method.  
Figure 3: Cont.
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 4 Implementation
### Recovery Mechanisms  
When the controller detects a link from a path is not available, it examines the rest of links seeking for alternative routes, according to the definition of eTorii, as described in Figure 6. If an alternative route exists, the new link to be used should be associated with a specific prefix and, thus, it implies the modification of the HLMAC prefix in the frame, accordingly. Afterwards, the frame in sent through the associated port. Additionally, the source edge switch will remove the rule associated to the old prefix and will include the new one, so that future packets from the same flow are directly forwarded through the newer alternative path. Finally, the old prefix is excluded from the set of available prefixes until the link is back to normal again. In parallel, the HLMAC association may be updated triggered by a topology change detected by LLDP.  
Figure 6: Recovery logic in the eTorii implementation.  
The previous procedure is applied in the case of unicast routing. In the case of broadcast forwarding, the tree should be recalculated every time one of its links is affected, as previously mentioned.
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 3 Methodology
### Knowledge Supervised Sentences Selection
#### 3.2.2 Knowledge Attention over Sentences  
Given a sentence bag \(B_{i}=\{S_{1},S_{2},...,S_{n}\}\) with \(n\) sentences, all the sentences refer to a common entity pair. The embeddings of each sentence \(\{\mathbf{s}_{1},\mathbf{s}_{2},...,\mathbf{s}_{n}\}\) in the bag can be obtained by using the sentence representation module, as described in Section 3.1. However, the sentence bag are obtained by using the distant supervision algorithm, which contains some vague and wrong nematic components. Thus, we argue that some sentences may contribute more to the final textual relation representation. In order to discriminately aggregate sentence-level representations into bag-level representation, the multi-instance learning that use a selective attention mechanism is an intuitive choice. The selective attention algorithm generates a weight distribution over all sentences in the bag to alleviate the noise problem. However, when there is only one sentence in the bag, even the only sentence is a noise instance (wrong labeled instance), the selective attention mechanism will be useless. It is worth noting that in the commonly used distant supervision relation extraction corpus, almost 80% of the bags contain only one sentence, and many of the them are even wrong labeled. To address this problem, we use the KG relation embedding \(r_{kg}\) as knowledge attention over sentences to augment the contribution of positive instances and reduce the impact of negative instances, as shown in Figure 3. In this way, the wrongly labeled sentence will be dynamically assigned with low weight score to prevent the propagation of noise representation.  
Figure 3: Knowledge attention guided selection of multiple valid sentences.  
Instead of generating a weight probability distribution for all sentences in the bag, we calculate a confidence score for each sentence based on the prior knowledge \(\mathbf{r}_{kg}\). For the \(i\)-th sentence in the bag, the scoring function is formally defined as  
\[\begin{split} u_{i}&=\text{sigmoid}(\mathbf{v}_{a}^{ \intercal}\text{tanh}(\mathbf{W}_{a}[\mathbf{s}_{i};\mathbf{r}_{t}])),\\ \mathbf{r}_{t}&=\mathbf{W}_{t}\mathbf{r}_{kg}+\mathbf{b}_{t},\end{split} \tag{13}\]  
where \(u_{i}\) is the confidence score for the \(i\)-th sentence, and \([;]\) denotes the concatenation operation. \(\mathbf{W}_{t}\in\mathbb{R}^{d_{w}\times d_{w}}\), \(\mathbf{W}_{a}\in\mathbb{R}^{d_{a}\times d_{a}}\), \(\mathbf{v}_{a}\in\mathbb{R}^{d_{a}\times 1},andd_{a}=d+d_{w}\), are parameters learned in the training stage. The mean aggregation operation is performed over sentence in the bag to form the bag-level vector representation for further relation classification, which is obtained by  
\[\mathbf{r}_{b}=\frac{1}{n}\sum_{i}^{n}u_{i}\cdot\mathbf{s}_{i}, \tag{14}\]  
where \(\mathbf{r}_{b}\in\mathbb{R}^{d}\) is the final representation of the sentence bag.
# An Overview of CubeSat Missions and Applications
## 7 CubeSats as Deep-Space Exploration Platforms
### CubeSat Missions for Planetary Exploration
The second payload will be a multispectral, Linear Variable Filter (LVF) imager operating between 320 nm and 570 nm wavelengths and featuring a 4 nm spectral resolution, this payload would provide high-resolution UV image data. Both instruments are expected to offer a spatial resolution of 3 km from an altitude of 66,000 km. The satellite is intended to be placed in an elliptical orbit around Venus and once in orbit, the expected CUVE mission duration is 6 months [276]. Two more proposed CubeSat-based planetary exploration missions are Aeolus and the Chariot to the Moons of Mars [246], with both missions having received funding through NASA's PSDS3 program [13]. According to the mission concept, Aeolus will be a 24U CubeSat developed by NASA's Ames Research Center that will study the vertically resolved global winds of the Martian atmosphere and its planetary-wide energy balance. The satellite's proposed mission payload consists of three components. One of these payloads will be the Surface Radiometric Sensor Package (SuRSeP), which would monitor surface temperatures of Mars with an accuracy of 5 K, as well as the total amount of solar radiation that is reflected by the planet's surface. Furthermore, the satellite will also be equipped with the Spatial Heterodyne Spectrometers (SHS) which, with the help of two orthogonal viewing telescopes, will be used for the measurement the atmospheric wind of the Martian atmosphere through Doppler shift in atmospheric O\({}_{2}\) airglow emission. The SHS will be capable of measuring wind speeds between 5 m/s and 230 m/s with an accuracy of 5 m/s. Aeolus is also expected to carry the Thermal Limb Sounder (TLS), a payload that will be tasked with the measurement of the atmospheric temperature of the planet (with an accuracy of 3 K) and the detection of aerosols. If successful, Aeolus will be the first mission ever to directly measure the winds of the Martian atmosphere [277]. The Chariot to the Moons of Mars mission on the other hand is developed by Purdue University and aims to utilize a 12U CubeSat platform that would characterize and map the composition of the surfaces of the two natural satellites of Mars, Deimos and Phobos. It would achieve this by observing the surfaces of the two moons in both VNIR and TIR spectra using a multi-spectral TIR imager, a NIR point-spectrometer, and a visible spectrum optical imager. For communication with the Earth, the CubeSat is intended to be equipped with the same communication hardware suite as the CubeSats of the MarCO mission. The data collected by this mission would greatly help the scientific community in the generation of more accurate hypotheses and models about the creation of Deimos and Phobos [278].
## 2 Dynamic Response of a Multi-Story STMF with SMAs
### Dynamic Time History Analysis
Taking into consideration the above remarks for each earthquake record, the following assumptions can be observed: For all earthquake records, except the Landers earthquake, the incorporation of SMAs according to the maximum expected vertical shear strength (\(V_{ne}\)) decreased the drift at upper floors and especially of the 9 th floor, compared to the corresponding drift of the conventional structure. SMAs responded to the limits of superelasticity for all earthquakes on all floors except for the SMAs on the 9 th floor for the Northridge earthquake, which slightly exceeded the 6% strain limit. The incorporation of SMAs with half the calculated diameter of Table 1 led to larger drifts and larger SMA deformations that, in some cases, led to SMA failure and more damages, as expected. It should be noted that an integrated in-depth mechanism analysis of the dynamic response for all cases considered is not feasible, since it depends on the following varying parameters:  
1. The distribution, placement, and dimensioning of SMA X-bars in each special segment and on each floor, with a direct effect also on the free vibration characteristics of each frame configuration, i.e., on the eigenfrequencies and eigenmodes;
2. The seismic excitation features (duration and PGA);
3. The energy dissipated by each floor component, starting from ground one and ending at the roof. All the above remarks are also valid for the alternative design approach that follows. However, the foregoing difficult task has been extensively investigated by the second author in her Ph.D. thesis [2].
## 2 Problem Formulation
### Analysis of the Influence of the Centroid Shift on HSV Motion
#### 2.2.1 Variation of Inertial Matrix  
As for the \(\Delta J\in R^{3\times 3}\), it represents an uncertain portion of \(J\) due to the centroid shift (from \(O_{b}\) to \(O_{b}^{\prime}\), as shown in Figure 1). Details are given as follows:  
\[J=J^{*}+\Delta J=\left[\begin{array}{ccc}J_{xx}&0&-J_{xz}\\ 0&J_{yy}&0\\ -J_{xz}&0&J_{zz}\end{array}\right]+\left[\begin{array}{ccc}\Delta J_{xx}&-J_ {xy}&0\\ -J_{xy}&\Delta J_{yy}&-J_{yz}\\ 0&-J_{yz}&\Delta J_{zz}\end{array}\right] \tag{8}\]  
where the details of \(\Delta J\) are shown as (A5). It is worth pointing out that changes in the inertia matrix of the system (\(J^{*}+\Delta J\)), due to the potential irreversibility of (\(J^{*}+\Delta J\)), will pose challenges to the controller that depends on the inverse matrix of the inertia matrix of the system. Such as in sliding mode control [16], robust adaptive control [27], and so on, resulting in the singularity of the controller or giving rise to the underactuated characteristic of the system. Existing controllers struggle to function properly.
# Recent Advances in the Synthesis of Graphene-Based Nanomaterials for Controlled Drug Delivery
## 3 Drug Delivery with Graphene-Based Nanomaterials
### Graphene-Biomacromolecule Nanohybrids  
Due to their rich functions, good biocompatibility, and fine stability in human body environment, biomacromolecules like protein, peptide, DNA, and others, attracted more attentions in drug delivery in recent years. [PERSON] et al. [85] synthesized photo-responsive protein-graphene-protein (PGP) capsules by anchoring rGO on one-component protein shell through a double emulsion method (Figure 9a). The PGP capsules showed good biocompatibility and high loading capacity for DOX (9.43 umol g\({}^{-1}\)). The fabricated capsules could eradicate subcutaneous tumors in 10 days following a single 5 min near-infrared irradiation (NIR) without distal damage. It was found that with the drug loading onto the PGP capsules, drugs could be released from PGP by NIR therapy, and the live cancer cells could be killed effectively, as shown in Figure 9b.  
Figure 8: (**a**) Main components of tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL)/DOX-FGO; (**b**) Site-specific delivery of TRAIL to cell membrane and DOX to nuclei for enhanced synergism cancer treatment. Reproduced with permission from [54]. Copyright Wiley-VCH, 2014.  
[PERSON] and co-workers conjugated GO nanosheets with chlorotoxin for glioma therapy [86]. The maximum loading of DOX on target peptide chlorotoxin-conjugated GO (CTX-GO) is 570 mg g\({}^{-1}\). The DOX-loaded CTX-GO could release DOX under the tumor pH microenvironment and kill more glioma cells than both free DOX and DOX-loaded GO. [PERSON] et al., functionalized GO with two single-stranded DNA molecules and adenosine-5\({}^{\prime}\)-triphosphate (ATP) for ATP-responsive anticancer drug delivery [27]. The conjugated ATP aptamer not only effectively inhibited the DOX release from GO nanosheets, but also promoted the release of DOX in a high ATP concentration environment, such as cytosol, when compared with that in the ATP-deficient fluid. [PERSON] and co-workers developed a peptide-GO hybrid hydrogel for DOX delivery and pulse-triggered release [87]. In the prepared hybrid hydrogel, the peptide was linked with GO by \(\pi\)-\(\pi\) interaction and the peptide molecules were connected either by hydrogen bonds or direct covalent cross-linking. The strong interactions (e.g., covalent cross-linking) could serve as permanent cross-links that warrant the high stability of the hydrogel, whereas the weak interactions (e.g., \(\pi\)-\(\pi\) interaction) could be temporarily broken upon external triggering. Therefore, the created peptide-GO hydrogel possessed high mechanic and chemical stability and were suitable for being triggered by NIR for controlled drug delivery and therapy.
## 2 Materials and Methods
### Test Stand  
Empirical tests were conducted on an engine dynamometer. The bench was based on a 4-cylinder Perkins 854E-E34 TA compression ignition engine. The test stand is equipped with a SCHENCK brake for measuring engine torque. The accuracy of the torque measurement is \(\pm\)2 Nm. As part of the tests, it is also possible to measure fuel consumption with an accuracy of 1% and also to measure the engine's crankshaft speed. In addition, AVL IndiSmart 612 (software version 2010) software was used to circle the engine cylinder pressure. The test stand and measuring apparatus are shown in Figure 1.  
Technical data of the engine are shown in Table 1. Measurements of the toxic components of the exhaust gas were carried out on AVL CEB II and Horiba Mexa 1230 PM analyzers. Table 2 shows the measurement ranges and measurement errors of toxic exhaust components on the AVL CEB II analyzer. Table 3 shows the error associated in AVL CEB II.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Mode** & **Unit** & **Engine Speed** \\ \hline Cylinder arrangement & - & in-line \\ Number of cylinders & - & 4 \\ Type of injection & - & direct \\ Compression ratio & - & 17 \\ Cylinder diameter & mm & 99 \\ Piston stroke & mm & 110 \\ Engine displacement & dm\({}^{3}\) & 3.4 \\ Engine type & - & compression ignition \\ Nominal power & kW & 86 \\ Nominal power speed & rpm & 2200 \\ Maximum torque & Nm & 450 \\ Speed at maximum torque & rpm & 1400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of Perkins 854E-E34 TA engine [41].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Species** & **Range** & **Analyzer Error** \\ \hline \multirow{2}{*}{NO\({}_{\text{x}}\)} & Low: 30–5000 & \(\pm\)1 ppm \\  & High: 50–10,000 & \(\pm\)2 ppm \\ \hline \hline \end{tabular}
\end{table}
Table 3: The error associated with the AVL CEB II [42].  
Particulate matter (PM) was determined by a real-time continuous analyzer. Its measuring range was 0-300 mg/m\({}^{3}\) (measurement accuracy was 1 mg/m\({}^{3}\)).
# Secure Internal Data Markets
## 3 Security Issues of Internal Data Markets
### Security Personal  
Large infrastructures handling potentially critical material, such as global data markets that deal with valuable data and provide payment channels in order to reimburse for data proliferation, typically have a team of security experts dedicated to monitoring the platform and the surrounding ecosystem for (potential) threats and attacks, as well as constantly improving the security measures of the platform. Internal data markets, on the other hand, especially when introduced into smaller institutions, often do not have these resources. There might be a security team on hand, especially when the internal data market is set up inside a larger company in order to handle data between different divisions; however, the data market is not the focus of this team, being just another application that needs to be handled.
## 2 Related Work
### Compressed Domain Image Classification  
Neural networks are employed for image compression using CNN architecture [28], RNN architecture [29], and GAN architecture [30]. In another way, the neural network can determine compressed data either by decompressing the data or directly on the compressed data. [PERSON] et al. [8] provided the first theoretical results for compressed domain inference, demonstrating that learning directly in the compressed domain is possible. They provided bounds demonstrating that a linear SVM in the compressed domain performs similarly to the best linear classifier in the uncompressed domain and that classifiers can be learned directly in the compressed domain. The \"smashed\" filter was used by [PERSON] et al. [22], which used a random sensing matrix and a 1-nearest-neighbor classifier. Later, the SPC system was used to perform classification directly in compressed data using learned patterns via data-dependent \"secant projections\". Prof. [PERSON] and his team created a rate-adaptive neural network for compressed domain classification recently [21].  
Several studies had been conducted on frequency domain learning problems. DCT is utilized by JPEG, the most frequently used compression standard, and DCT coefficients are fed into CNNs for picture classification, object identification, and instance segmentation [25; 26; 27; 28; 29; 30; 31]. Similar to DCT, wavelet coefficients can also be directly used in the CNN training [29; 30].  
[PERSON] et al. [32] present an effective training technique for a neural network with dynamic rate property, which allows a single neural network to categorize any measurement rate (MR) within the range of interest using a given sensing matrix that represents a SPC process. Only a few selected MRs are used in this training approach, yet the trained neural network is valid throughout the whole range of MRs of relevance. They also demonstrated that the dynamic-rate training scheme may be thought of as a universal strategy that works with a variety of sensing matrices and neural network designs, and it is a significant step forward in the use of neural networks for compressive inference and other compressive sensing applications.