esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Open Research Challenges  
Potential future directions can be identified to continue advancing the use of IoT in Martian exploration. These encompass technological advancements, standardization efforts, and testing methodologies to ensure the robustness and scalability of the proposed architecture. The following specific areas at the crossroads of interplanetary networking and direct-to-satellite IoT could be explored in future research:  
* Integration, interoperability, and standardization of delay tolerant networking (DTN) and LoRa/LoRaWAN: This integration presents an opportunity to enhance the resilience of interplanetary communication. The development of standardized protocols and interoperable systems is proposed to facilitate data transmission under varying networks and environmental conditions.
* Energy management techniques for the Martian environment: Given the unique environmental conditions on Mars, specialized energy management techniques for IoT devices are required. These could include advanced energy storage solutions and adaptive energy management algorithms to optimize the performance of IoT nodes on the Martian surface.
* Scalability and reliability testing: As the scope of IoT deployments on Mars expands, validating the robustness and effectiveness of the communication infrastructure becomes essential. Rigorous testing methodologies are needed to evaluate the system's ability to maintain reliable connectivity and scale to meet the changing demands of the Martian environment.
* Advanced interplanetary communication systems: The investigation of more advanced communication systems to handle the unique challenges of long-distance communication between Mars and Earth is proposed. This would include exploring new communication protocols, signal processing techniques, and data transmission methods to enhance the efficiency and reliability of interplanetary communication.
* Enhanced IoT applications on Mars: Expanding the scope of IoT applications on Mars is suggested by developing new sensor technologies, data collection methods, and analytical tools to support a wide range of scientific, environmental, and exploration applications on the Martian surface.
* Integration of artificial intelligence and machine learning: Integrating artificial intelligence and machine learning algorithms into the IoT framework for Martian exploration is proposed. This could enable autonomous decision-making, predictive analytics, and adaptive control systems to enhance the capabilities of Martian IoT devices and satellite constellations.
* Martian atmosphere and high-frequency communication: This involved the investigation of atmospheric effects and phenomena, like dust storms, on high-frequency communication to improve the performance of satellite communication with devices on the Martian surface.
## 4 Results
\begin{table}
\begin{tabular}{l c c c c} \hline  & \multicolumn{2}{c}{**XGBT**} & \multicolumn{2}{c}{**HGBR**} \\ \cline{2-5}
**Method** & \(\mathbf{R^{2}}\) & **MAE** & \(\mathbf{R^{2}}\) & **MAE** \\ \hline Remove missing & 0.723 (0.127) & 111.645 (20.084) & 0.740 (0.142) & 107.288 (21.944) \\ Similar fill & 0.918 (0.024) & 90.300 (10.402) & 0.922 (0.023) & 87.315 (10.608) \\ Mean values & 0.915 (0.027) & 91.202 (9.206) & 0.919 (0.027) & 87.669 (9.091) \\ Predict (LR) & 0.920 (0.025) & 88.667 (10.325) & 0.922 (0.025) & 86.593 (10.464) \\ Predict (HGBR) & 0.925 (0.024) & 85.395 (9.903) & 0.927 (0.023) & 84.427 (10.156) \\ Predict (XGBT) & 0.926 (0.022) & 84.052 (8.762) & 0.928 (0.022) & 83.031 (9.039) \\ Predict (RF) & 0.929 (0.023) & 82.050 (9.385) & 0.929 (0.022) & 81.483 (9.387) \\ Interpolation (L) & 0.918 (0.024) & 90.685 (10.332) & 0.922 (0.022) & 87.687 (10.339) \\ Interpolation (P) & 0.918 (0.024) & 90.300 (10.402) & 0.922 (0.023) & 87.315 (10.608) \\ Interpolation (S) & 0.918 (0.024) & 90.685 (10.332) & 0.922 (0.022) & 87.687 (10.339) \\ \hline \end{tabular}
\end{table} 
Table 3: Comparative results of using different preprocessing imputation methods for handling missing values, including all data in training (without removing outliers). We notice that the results presented in these tables show that there is improvement for all the imputation methods and the outlier removal technique applied, with the best value for HBGR when using RF for the imputation and OC-SVM for the removal of outliers: \(R^{2}=0.938\), \(MAE=\) 77.689, and \(MAE\%=\) \(\mathcal{E}\)7.06%. To assess the relevance of the improvement, we applied the statistical \(t\)-test between the models based on all data and each outlier model. The obtained \(p\) values for these metrics show they are statistically significant in most cases, with values of \(p<0.05\) in half the cases (as detailed in Table 8). Figures 7 and 8 present all these results for the combination of outlier method or no outlier method removal used (meaning all data is involved in constructing the model) and the two evaluation algorithms used thus far (XGBT and HGBR), for the metrics \(R^{2}\) and \(MAE\), respectively, in the form of box plots. From these we can see that based on the median value (in orange) and overall performance (minimum and maximum values, as well as the interquartile range--IQR), IF is the outlier removal method with the highest improvement on the models overall, followed by OC-SVM, for both the \(R^{2}\) and \(MAE\) metrics. The circles in the graph represent outlier values (at a distance greater than \(Q1-1.5\times IQR\) or \(Q3+1.5\times IQR\)), providing the best \(MAE\) results for LOF and OC-SVM, with the overall best performance being OC-SVM for both \(R^{2}\) and \(MAE\), while these prove to be the best approaches numerically, we are inclined to interpret them as outliers resulting form the statistical 10-fold cross-validation data split performance of five repetitions.
## 3 Property Analysis
Set \(a=\bigwedge_{k=1}^{N}f(\mathbf{x},\mathbf{u}_{k})\) and \(b=\bigvee_{k=1}^{N}f(\mathbf{x},\mathbf{u}_{k})\); Step 4. Randomly generate \(r\) in interval \([a,b]\); Step 5. If \(r\geq 0\), let \(e\gets e+m_{\lambda}\{f(\mathbf{x},\mathbf{\xi})\geq r\}\); otherwise, let \(e\gets e-m_{\lambda}\{f(\mathbf{x},\mathbf{\xi})\leq r\}\); Step 6. Repeat Step 4 to Step 5 for a total of \(N\) times; Step 7. \(E[f(\mathbf{x},\mathbf{\xi});\lambda]=a\lor 0+b\wedge 0+e\cdot(b-a)/N\). Next, we aim to give a numerical example to show the accuracy of the proposed simulation algorithm. Specifically, let us consider the sum of ten triangular fuzzy variables \(\xi_{i}=(5+i,7+i,10+i)\), \(i=1,2,\ldots,10\). If we set \(\lambda=0.8\), by using the linear property of the expected value operator, we typically have:  
\[E[\xi_{1}+\xi_{2}+\ldots+\xi_{10};0.8]=E[\xi_{1};0.8]+E[\xi_{2};0.8]+\ldots+E[ \xi_{10};0.8]=135.\]  
On the other hand, when we use the simulation algorithm, we then have \(E=134.90\) after implementing a total of 1000 cycles, which has a relative error of 0.07%. More specifically, we simulate a total of ten cases for parameter \(\lambda\) (i.e., \(\lambda=0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\)); the comparison between the exact values and simulated values is illustrated in the following Table 1. The results demonstrate the effectiveness of the proposed simulation algorithm.
# Shape Effect and Accuracy Analysis of Rock Tensile Strength Test
## 2 Analysis of Shape Effects in Splitting Tests
### Experimental Analysis  
The data in the table were plotted as scatter points and fitted using a power function, as shown in Figure 4a. The average values of the six groups of tensile strength data were then calculated and fitted again using a power function, resulting in a curve with a higher goodness of fit, as shown in Figure 4b. A comparison with the fitted curve of the numerical simulation results in Figure 4c shows that the results from the laboratory material simulation and the numerical simulation are very similar. In the numerical simulation results, the decrease in strength with decreasing diameter is less pronounced than in the laboratory tests, because the numerical model is based on homogeneous materials [23], which differs from the actual conditions.  
The results above indicate that in the splitting test, with the thickness-to-diameter ratio fixed at 0.5, the measured splitting tensile strength decreases as the disc diameter increases, and the deviation from the true strength gradually increases. This trend can be well fitted using a power function.  
Figure 4: Relationship between tensile strength and disc diameter.
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Observation Models for Combined BDS-3, GPS, and Galileo RTK
#### 4.1.1 Intra-System Double-Differenced Observations with Loosely Combined Model (LCM)  
The LCM assumes one satellite as the reference satellite for each constellation separately. All systematic errors, including the satellite-specific errors, the receiver-specific errors, and the time offset, are absent when double-differenced observations are created. The double-differenced observation equation corresponding to LCM wit \(1_{G}\) as reference satellite for GPS and \(1\), as reference satellite for constellation * is given by:  
\[\begin{split}\phi_{b,r,i}^{1_{G}\varsigma_{G}}&= \rho_{br}^{1_{G}\varsigma_{G}}+\lambda_{i}N_{br,i}^{1_{G}\varsigma_{G}}+ \varepsilon_{br,i}^{1_{G}\varsigma_{G}}\\ \phi_{br,i}^{1_{*}s_{*}}&=\rho_{br}^{1_{*}s_{*}}+ \lambda_{i}N_{br,i}^{1_{*}s_{*}}+\varepsilon_{br,i}^{1_{*}s_{*}}\\ P_{br,i}^{1_{G}\varsigma_{G}}&=\rho_{br}^{1_{G} \varsigma_{G}}+\varepsilon_{br,i}^{1_{G}\varsigma_{G}}\\ P_{br,i}^{1_{*}s_{*}}&=\rho_{br}^{1_{*}s_{*}}+ \varepsilon_{br,i}^{1_{*}s_{*}}\end{split} \tag{3}\]  
where \(s_{G}\geq 2_{G}\) and \(s_{*}\geq 2_{*}\).
## 4 Experiments, Numerical Results and Discussion
### Performance of the Developed Models  
To build multi-class models for predicting normal, depressive and anxiety tweets, we have tested around 100 models ranging from simple to hybrid models combining different types of neural network layers: convolution, recurrent, attention and bidirectional. Consequently, we found that the following hybrid multi-classifiers are the most representative typical cases of both success and failure: CNN_RNN, CNN_LSTM, CNN_GRU, CNN_BiRNN, CNN_BiLSTM and CNN_BiGRU. CNN_BiRNN, CNN_BiLSTM and CNN_BiGRU models are the best in terms of performance for all experiment instances while CNN_RNN and CNN_GRU models are the best in terms of performance improvements by involving grid search technique. Finally, CNN_LSTM model represents a failure case where the grid search technique was unable to provide performance improvements. Figure 4 show the performance of these models in terms of training accuracy and training loss, respectively. In particular, the well-performing model is CNN_BiGRU with a learning rate of 0.001.  
By setting the learning rate value to 0.001, CNN_RNN was the worst model as it recorded poor accuracy. Moreover, CNN_LSTM and CNN_GRU also showed a significant value of overfitting (red and blue curves are far from each other). However, this unwanted overfitting effect gradually disappeared by setting the learning rate value to 0.0001. In contrast, value 0.001 for the learning rate led to better performance for CNN_BiRNN, CNN_BiLSTM and CNN_BiGRU compared to 0.0001, in addition to the good behavior regarding overfitting. Figure 4 shows the associated curves (the curves on the left concern learning rate value 0.0001 while the curves on the right concern learning rate value 0.001).  
The above results suggest that changing the learning rate value of the Adam optimizer has positive or negative influence on the performance of each model. Thus, we need efficient methods to define such a value in order to provide efficient models. In this respect, we adopt grid search, which is a well-known technique serving as a Hyperparameter optimizer for each model. The results are given in Tables 7 and 8.  
According to Tables 7 and 8, the best Accuracy achieved is 93.38%; it corresponds to CNN_BiGRU model such that F1-score of the Normal class is 96%, F1-score of the Depression class is 91% and F1-score of the Anxiety class is 93%. Figure 5 illustrates the confusion matrix for both cases grid search and fixed-based learning rate values. Thus, it can be seen that the grid search could make some improvements in some cases for which the diagonal has a max of correct predictions.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 4 Reliable ICT
### Quality Assurance for ICT Technology  
In view of trustworthiness as well as of effectiveness and efficiency, quality assurance for urban ICT is essential for achieving reliable ICT components for smart cities. There has been intensive research on this topic in recent decades. Along this, the industry has established processes for quality assurance of single components, systems and of the overall systemic approach. Essentially, quality assurance addresses two main aspects: (1) the processes when developing/creating complex systems and (2) the testing of complex systems for deployment and operation, which ideally should take place in parallel to the develop ment processes. Testing comprises various validation and verification activities for ICT and constitutes the most important quality assurance method in industry [49]. In testing, it is important to address different functional and non-functional perspectives on single components as well as on complex urban ICT systems as a whole. Testing approaches include conformance testing (i.e., the conformance to standards and requirements), interoperability testing, load and performance testing, security and penetration testing as well as usability and acceptance testing [50]. All these facets need to be delivered by urban ICT infrastructures on a reasonably high level in order to guarantee the success of the intended introduction of smart city development principles. These aspects are central for the reliability and resilience of the ICT infrastructure acting as a smart city backbone.
# A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment
## 5 Experiments and Results
### Evaluation of the Explanation Results  
As we mentioned above, the explanation contains two main parts: first, the explanation decision rule(s), \(r\), and second, a set of counterfactual rules, \(\delta\) from which we can derive the counterfactual examples, \(\mathbb{C}\). These components are obtained from a set of rules, that we call the explanation model. In this section we want to compare the quality of the rules generated by the two methods. We will denote as C-LORE-F the method using typical decision trees, and we will name as DRSA the version of the same method using rules generated with Dominance-based Rough Sets. The following evaluation metrics are used to measure the quality in both cases, for the REITPROGRAM black-box method.  
* **Hit**: this metric computes the similarity between the output of the explanation model and the black-box, \(b\), for all the testing instances. It returns 1 if they are equal and 0 otherwise.  
Figure 3: The constructed rules using the explanation system for \(x\).  
* **Fidelity**: this metric measures to which extent the explanation model can accurately reproduce the black-box predictor for the particular case of instance \(x\). It answers the question of how good is the explanation model at mimicking the behaviour of the black-box by comparing its predictions and the ones of the black-box on the instances that are neighbours of \(x\), which are in \(\mathcal{D}\).
* **I-Fidelity**: it is similar to the fidelity; however, it is computed on the subset of instances from \(\mathcal{D}\) covered by the explanation rule(s), \(r\). It is used to measure to what extent this rule is good at mimicking the black-box model on similar data of the same class.
* **c-Hit**: this metric compares the predictions of the explanation model and the black-box model on all the counterfactual instances of \(x\) that are extracted from the counterfactual rules in \(\mathbb{C}\).
* **cl-Fidelity**: it is also similar to the fidelity; however, it is computed on the set of instances from \(\mathcal{D}\) covered by the counterfactual rules in \(\delta\).  
Table 2 shows the means and standard deviations of the metrics for the C-LORE-F and DRSA explanation methods on the test set. It may be seen that C-LORE-F outperforms DRSA in all metrics. Let us look at the Fidelity and I-Fidelity for the DRSA method. We can find a difference of 10% in favour of I-Fidelity, which means that most of the disagreements between RETIPROGRAM and DRSA occurred with the examples with a different outcome than the original input. So, the rules describing the opposite classes are worse in DRSA than in C-LORE-F. We can also observe that in both C-LORE-F and DRSA, the cHit and cl-Fidelity show lower performance than the other metrics. This can be attributed to the quality of the generated counterfactual examples (which are evaluated in more depth in Section 5.4).
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
## 2 Development of a Single-Tip Cathode Cold Field Emission Source
### Source Electron Optics Parameters
From the theoretical point of view, the total energy distribution (TED) of electron emission in the thermal field regime was first derived by [PERSON] based on the free-electron model as [41]:  
\[P(E)=\frac{J_{\rm FN}}{d}\left[\frac{\exp\left(\frac{E-E_{\rm f}}{d}\right)}{1 \ +\ \exp\left(\frac{E-E_{\rm f}}{KT}\right)}\right], \tag{4}\]  
where \(KT=0.155\) eV at room temperature, \(J_{\rm FN}\) is the well-known FN emission current density and \(d\) is the tunneling parameter given by: \(d=9.76\ \times\ 10^{-11}\ F/\phi^{1/2}\ t(y)\). The variable \(t(y)\) is a slowly-varyingfunction of \(y\) = 3.79 \(\times\) 10\({}^{-5}\)\(F^{1/2}/\phi\) and can be approximated by the formula \(t(y)\) = 1 + 0.1107 \(y\)[13]. The analytical formula is valid provided \(kT/d\) < 0.7 and \(y\) < 1. The longitudinal electron-electron interactions (known as Boersch effect) will add to the intrinsic source energy spread which causes an additional enlargement of the energy spread [36]. According to [PERSON]'s model of a spherical electric field around an emitter of tip radius \(r_{\text{tip}}\), the energy broadening (in eV) due to Coulomb interactions is given by [42]:  
\[\Delta E_{\text{Boersch}}=\ 15.9\frac{(I^{\prime})^{2/3}}{r_{\text{tip}}^{1/3}V_{ \text{ext}}^{1/3}}, \tag{5}\]
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### Enrollment  
The enrollment process is performed before generating CRPs from the PUF, where every possible CRP is generated and recorded in a database. It is a critical step, as the database is used to authenticate the responses of the physical PUF and locate the stable cells. For SRAM, the process involves reading out the entire contents of the SRAM device and power cycling the device. For a full enrollment and the achievement of a low BER, this process needs to happen approximately 1000 times.
# Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?
## 2 The Principle of CRISPR/Cas
### How Is CRISPR/Cas Programmable?  
One of the issues of gene editing prior to CRISPR/Cas was how to generate a programmable, targeted double-strand DNA break in the desired region. In 2012, a seminal Nobel Prize winning paper demonstrated the CRISPR/Cas system's programmability [2]. This study showed CRISPR/Cas9 could be directed to any given DNA sequence upon the use of a single-guide RNA (sgRNA) formed by the fusion of crRNA + tractRNA. The original bacteria/archae CRISPR/Cas system relies on the expression of crRNA from the CRISPR array region that interacts with tractRNA, a small 24-nt RNA that facilitates the maturation of crRNA by RNAIII nuclease [8] and interacts with Cas9. The mature form of crRNA (crRNA + tractRNA) provides the 20-nt sequence that specifically targets foreign DNA sequence, while the tractRNA recruits and activates Cas9 through conformational changes [2] (Figure 1).  
Artificial changes to link both crRNA + tractRNA structures resulted in the chimeric sgRNA that simplified the structural elements necessary for sgRNA loading in Cas9 (Figure 2). This modification resulted in a simple system in which the 20-nt DNA sequence specific for the target gene is cloned into a plasmid that transcribes the single-guide RNA (explored in the Dry Lab Section 3).  
However, when selecting the target 20-nt sequence to produce the sgRNA, an additional feature is essential, namely, the presence of protospacer-adjacent motif (PAM) sequence next to the target DNA in the genome. PAM is a short-conserved sequence motif (2-5 bp) essential for Cas9 activity that varies among species (Table 1). For example, the most commonly used Cas9, the SpCas9 from _Streptococcus pyogenes_, recognizes a PAM sequence of NGG one base pair downstream of the 20-nt target site in the non-complementary strand [2] (Figure 1). Indeed, the versatility of PAM sequences in Class 2 Type II CRISPR systems contributes to the programmability of CRISPR/Cas to virtually any sequence in the DNA.  
Figure 2: Structural differences among guide RNAs. Comparison of the crRNA associated with tractRNA and the single-guide (sgRNA) structure. The junction of crRNA + tractRNA with the linker loop resulted in the structure of single-guide RNA (sgRNA).
## 2 Proposed Time-Based Sensor
### Architecture Overview  
In the realm of electronic devices, it is common to find resistance-to-digital converters (RDCs) and capacitance-to-digital converters (CDCs) that serve the purpose of estimating input resistance and capacitance. Typically, these devices employ on-chip resistors and capacitors as internal references. Yet, inherent process variations can cause on-chip reference values for resistance and capacitance to shift, requiring the use of an external reference to convert these on-chip references into absolute values [27; 28; 29]. To overcome this limitation, we propose an in situ time-based sensor that leverages the system clock and its accuracy as a timing reference, eliminating the need for an external reference and allowing the measurement of absolute values of the time-constants without additional calibration.  
The direct measurement time-based sensor is composed of four main elements, as illustrated in Figure 2a. To begin, a time-constant generation (TCG) block enables an assessment of RC time-constant involving \(R_{metal}\) and \(C_{metal}\), which represent the resistor and capacitor of the BEOL metal routing. Subsequently, a pulse generator transforms the voltage stored on \(C_{metal}\) from the voltage domain into time domain \(Enable\) pulses. Following this, a counter acts as a quantizer in the time domain, gauging the duration of these \(Enable\) pulses. Lastly, a timing controller coordinates the functions of these elements, ensuring the proper sequence of operations. The RC time-constant can be derived from the accumulated discharge cycle, denoted as \(N_{DC}\).  
This architecture is primarily digital, with an exception of one analog subcircuit, the current source. Its predominantly digital architecture makes it synthesizable, reducing the integration effort into other circuits and across different technologies.  
Figure 1: In situ time-based sensors measuring the time-constants of BEOL metal routing for die authentication.