esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction
## 8 Conclusions  
HDP is an efficient technique for constructing a suitable defect prediction model when the source and target projects have heterogeneous metrics, potentially decreasing the data cost of the target project, and enhancing the efficiency of software testing, as well as improving software quality.  
In this paper, we presented a new multi-source heterogeneous kernel mapping (MSHKM) algorithm for HDP. To analyze the effects of multiple-source projects on the prediction results, two strategies, based on MSHKM, for multi-source HDP were proposed. In addition, the number of source projects in each experiment was changed to determine the rules that affect the prediction performance. Furthermore, we compared the proposed MSHKM with existing HDP methods, including CCA+, HDP-KS, CTKCCA, and MHCDPP, as well as comparing MSHKM with WPDP.  
Experiments were performed on 28 data sets from five widely used projects, using three common measures to evaluate the considered methods. We further evaluated the experimental results by performing a non-parametric Friedman test with the Nemenyi post hoc test and the Cliff's delta effect size test. The experimental results demonstrated that (1) in the multi-source HDP scenario, strategy 2 is superior to strategy 1; (2) for MSHKM, the lower the number of source projects, the better the results obtained under strategy \(1\), while \(n=4\) was the optimal number for strategy 2; (3) MSHKM can outperform state-of-the-art HDP methods; and (4) MSHKM also performs better than WPDP. From the above, it can be stated that the proposed MSHKM approach may improve software quality obviously, as well as decreasing both time and human costs.  
For future work, we plan to employ other effective classifiers and evaluation measures for comparison. Additionally, the similarity between heterogeneous source and target projects will also be studied to optimize source project selection, as well as the weight parameters for multi-source projects in the MSHKM algorithm.  
Conceptualization, Y.W. and J.Y.; methodology, J.Y.; software, J.Y.; validation, J.Y.; formal analysis, J.Y.; investigation, J.Y.; resources, J.Y.; data curation, J.Y.; writing--original draft preparation, J.Y. and Z.L.; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, B.L.; project administration, [PERSON]; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National defense research foundation of China, grant number JZXY20220142200501.  
Not applicable.  
Data Availability Statement: Data used in this article is publicly available data. Information about the data is contained within the text.  
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there
## 2 System Model
### Wireless Resource Slicing  
The 3 GPP standard specifies 56 flexible slot formats [25]. For a scenario where the downlink traffic demand is greater than the uplink traffic demand, the BS can operate a configuration of slot format that assigns more RB and Orthogonal Frequency Division Multiplexing (OFDM) symbols for downlink transmission. In this paper, we consider a mode where each TDD pattern has only one downlink-to-uplink switch point. To align with the actual deployment, the entire spectrum of the NR band uses the same TDD pattern, that is, all slices switch synchronously between downlink and uplink. Therefore, the downlink and uplink bandwidths are both \(W^{nr}\) in the frequency domain. The TDD pattern can be depicted by parameter \(\lambda\), which represents the ratio of downlink duration to a TDD pattern duration. \(\lambda\) is constrained by  
\[0<\lambda<1. \tag{4}\]  
Let \(\tau\) denote the duration of a TDD pattern, which contains \(N^{dl}+N^{ul}\) OFDM symbols. \(N^{dl}\) and \(N^{ul}\) denote the number of OFDM symbols for downlink and uplink, respectively. Here, we simplified the structures of pilots and special subframe, including ignoring DwPTS (Downlink Pilot Time Slot), GP (Guard Period), and UpPTS (Uplink Pilot Time Slot), which have no significant effect on the results of the experiment. Based on (4), \(\lambda\) can be specifically defined as \(\lambda=\frac{N^{dl}}{N^{dl}+N^{ul}}\). The TDD pattern \(\lambda\) divides \(\tau\) into downlink duration \(\lambda\tau\) and uplink duration \((1-\lambda)\tau\). The BS adjusts the allocation of downlink and uplink duration through \(\lambda\), depending on the fluctuating slice load. For example, with the increasing need  
Figure 1: System Model. A 5G TDD BS is enabled with network slicing and SUL transmission. The edge UEs of the coverage can use the SUL band for uplink transmission. UEs in different slices are marked with different colors.  
for downlink, \(\lambda\) becomes larger, which means that more resources are allocated to downlink. On the contrary, with the increasing need for uplink, \(\lambda\) becomes smaller, which means that more resources are allocated to uplink. As shown in Figure 2, every time a period of time \(T\) passes, \(\lambda\) can be dynamically reconfigured to adapt to the new traffic ratio.  
For the NR band, each slice is assigned a mutually orthogonal bandwidth for full isolation, i.e., hard slicing. Furthermore, the SUL band can be used by all slices as a common band, which is called soft slicing. The resource allocation diagram is shown in Figure 2. Let \(w_{m}^{d}\) denote the bandwidth of slice \(m\) assigned in the NR band. The hard slicing scheme can be expressed as a tuple \(\mathbf{w}=\{w_{m}^{d}|w_{m}^{d}>0,\sum\limits_{m\in\mathcal{M}}w_{m}^{d}=W^{ mr},m\in\mathcal{M},d\in\mathcal{D}\}\). At the beginning of the period of resource-slicing, the BS decides the SUL admission for user association, determines the TDD pattern for the whole NR spectrum, and allocates the NR bandwidth to all slices.
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## 4 Conclusions  
This study proved that the stability of CsPbBr\({}_{3}\) in air is a crucial point for the industrial development of perovskite thin-film-based devices. Surprisingly, the sensitivity of CsPbBr\({}_{3}\) toward these environmental agents was not only amenable to structural defects, crystallite size, and crystalline orientation as previously reported, but also the nature of the substrate on which the perovskites were grown, which affected their stability and endurance. The thin films grown on amorphous (glasses) substrates and ITO displayed better performances, whereas the films deposited onto oriented gold resulted much more moisture-sensitive. The results of this preliminary investigation state the role of the substrate, as well as indicate a possible way of designing more stable and long-performing devices.  
Figure 7: Compositional trends of the perovskite component as a function of exposure time for different substrates: (**a**) silica glass, (**b**) ITO, (**c**) soda-lime glass, and (**d**) gold. The red dashed lines at 20% and 60% represent the theoretical amounts of cesium and lead and of bromine, respectively, in CsPbBr\({}_{3}\).  
**Author Contributions:** The authors equally contributed to this research paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by Fondazione CR Firenze within the \"Bando Ricerca Scientifica 2019\" call, project n\({}^{\circ}\) 24047, acronym: \"Supporto\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Comparative Analysis of Various PCA Set Sizes  
This section explores the effect of PCA on various contribution rates on a comparative analysis of statistically weighted dimension features, along with three classification algorithms; (1) logistic regression, (2) KNN, and (3) SVM (RBF). To verify the effectiveness of PCA with statistically weighted dimension features, an experiment is conduct on three datasets: MNIST, E-MNIST, and F-MNIST. The statistically weighted exponent values are determined in the same manner as in Table 4. To investigate the effect of contribution rate, the test rate is set to 0.5% to 1.0% with a step size of 0.05%, and 0.99 for PCA in the other general experiment setting of this paper. Figure 2a,c,e illustrates the performance of classification accuracy, and Figure 2b,d,f depicts the number of components in various contribution rates. The legends represent the six types of result that are obtained after applying the different algorithms without PCA or along with PCA.  
The legends provide complete information about the datasets (MNIST, E-MNIST, and F-MNIST), classification algorithms (logistic regression (LR), k-nearest neighbour (KNN), and support vector machine (SVM) with RBF kernel), and the methods for weighting (PCA: BASE-LINE weighting with PCA, and statistically weighted dimension with PCA SWD-PCA). For example, the notation \"MNIST KNN PCA\" indicates that this experiment is performed on the test set of the MNIST dataset that is classified by KNN using BASE-LINE weighting with PCA. The classification results show that all test sets in statistically weighted dimensions with PCA (SWD PCA) give a contribution rate of PCA of around 0.90% to 0.95%, which is effective for high performance and may minimize the number of components greater than them fluctuating from 75% to 90%. The classification results demonstrate the similar performance of PCA methods to the simulation studies in test set SWD PCA that slightly increased/improved the classification rate over the standard of BASE-LINE. The results fully indicate that SVM outperforms than other algorithm.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 3 PQ-DPoL
### Delegation
PQ-DPoLmitigates this computational load by selecting a small subset of nodes, called delegate nodes, to handle block generation and verification. Selecting delegate nodes is performed in Algorithm 4. This significantly reduces the overall processing demand on the network, even as the number of nodes grows. This delegation ensures that the performance impact remains low even as the network grows. While iVRF involves an initial key exchange during network setup, this is a one-time overhead that does not affect regular operations. Once the initial exchange is completed, it does not introduce further delays during block generation or verification. ```
1:Input:\((R_{A},\pi_{A})\) from Node A, \(PK_{A}\) (public key of Node A)
2:Output: Validity of \((R_{A},\pi_{A})\)
3:for each \((R_{i},\pi_{i})\) received from node \(i\)do
4:if Verify\((PK_{i},input,R_{i},\pi_{i})\
eq\) true then
5: Reject \((R_{i},\pi_{i})\)
6:endif
7:endfor
```  
**Algorithm 2** Verifying iVRF values  
```
1:Input:\(H_{prev}\) (previous block hash), \((R_{i},\pi_{i})\) (verified iVRF values from all nodes)
2:Output:\(NodeScores\)
3:\(NodeScores\leftarrow\) empty list
4:for each \(R_{i}\) in the set of verified iVRF values do
5:\(score_{i}\leftarrow\text{abs}(H_{prev}-R_{i})\)
6: Append \((score_{i},node_{i})\) to \(NodeScores\)
7:endfor
```  
**Algorithm 3** Comparing iVRF values  
The nodes with scores closest to the previous block's hash are selected as delegate nodes, which will be responsible for generating and validating blocks in the next phase. ```
1:Input:\(NodeScores\)
2:Output:\(SelectedNodes\)
3:Sort \(NodeScores\) by \(score\) in ascending order
4:\(SelectedNodes\leftarrow\) first \(n\) nodes from \(NodeScores\)
5:return\(SelectedNodes\)
```  
**Algorithm 4** Selecting delegate nodes
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 2 Geodetic and Satellites Input Data
### GPS Accessory Data at Viedma Glacier  
Surface elevation of the glacier lower tongue was also surveyed in April 2015 with a Trimble 5700 dual-frequency GPS receiver using a Differential GPS (DGPS) mode linked to the CHLT station.
# Comparison of Modern Deep Learning Models for Speaker Verification
## 4 Models Overview
### TitaNet  
TitaNet is a model for a speaker representation extraction, which employs 1D depthwise convolutions with Squeeze-and-Excitation (SE) layers that integrate global context, and a channel attention-based statistics pooling layer that maps variable-length utterances to fixed-length embeddings (t-vectors) [42]. This model achieves state-of-the-art results in the speaker verification task [27; 43]. Authors of TitaNet use the encoder of the ContextNet model as a top-level feature extractor and feed the output to the attentive pooling layer. This layer computes attention features across channel dimensions to capture the time-independent utterance-level speaker representations. The output speaker representation has a fixed size of 192. The training dataset for TitaNet consists of the data from VoxCeleb1 and VoxCeleb2, NIST SRE, Switchboard-Cellular2, Fisher and LibriSpeech. Combined, these datasets consist of approximately 4.8 M utterances from 16.6K speakers with a total duration of 3.3K h. Additionally, the training process is enhanced with RIR impulse corpora, speed perturbation and spectral augmentation.
## 2 Literature Review  
### Related Work  
#### 2.1.1 Research on Weather Delay Index  
Since the late 1990s, airport delay studies in the context of severe weather have been at the forefront of research, with pioneering work led by organizations such as MITRE, MIT Lincoln Laboratory, and NASA. Early studies focused on the Weather Impacted Index (WITI) developed by MITRE, along with related initiatives like MITRE's Flow Contingency Management (FCM), the application of Ensemble Weather Forecasts (EWF) to quantify weather uncertainty, and the FAA-led Air Traffic Management-Weather Integration study.  
In terms of WITI, a great deal of research has been done by scholars at home and abroad. [PERSON] et al. [1] introduced WITI in 2001 to quantify how unfavorable weather affects the National Airspace System, particularly for large-scale or long-duration scenarios. Between 2007 and 2009, [PERSON] and [PERSON] [2, 3] developed regression models that incorporated weather conditions, flight traffic, and airport size to more comprehensively analyze daily national airspace delays. [PERSON] et al. [4, 5] further refined the concept by proposing the enroute WITI (E-WITI), the terminal area WITI (T-WITI), queuing delays, and a predictive WITI FA index, and they introduced a scanline-based method (2008-2009) for assessing sector availability under convective weather forecasts [6, 7]. In 2009, [PERSON] et al. [8] quantified the weather's effect on air traffic via WITI. [PERSON] et al. [9] used WITI to classify weather days and compare weather-induced rerouting with National Advanced Planning Manual guidelines [10] developed a WITI-based method to evaluate sector capacity under severe weather.
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 3 Automotive Ethernet Security Protocol
### Design of Data Encryption and Authentication Algorithms for In-Vehicle Multimedia Signals  
The existing encryption algorithms, while secure, may not be optimized for the computational constraints of real-time automotive applications. The hypothesis is that these algorithms may be mathematically inefficient in their current form, leading to increased latency and reduced communication efficiency. The proposed optimizations aim to address these inefficiencies without compromising security.
## 3 Results and Discussion
### Cu Tube Expansion Analysis
In this correlation analysis, Type 4 was excluded because the tube's inner groove peaks do not contact the expansion ball surface. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Product**} & **Measurement Method** & \multirow{2}{*}{**Group**} & **Expansion Force** & \multicolumn{2}{c}{**Tube’s Inner Groove**} \\  & & & **(N)** & **Deformation (\(\Delta\)P2 \(-\)\(\Delta\)P1) (mm)** \\ \hline \multirow{4}{*}{Aluminum tubes} & Expansion experiments & G1N (\(n=3\)) & \(518.5\pm 46.6\) & - \\  & Expansion experiments & S1N (\(n=4\)) & \(561.3\pm 44.8\) & - \\  & Expansion experiments & S1A (\(n=10\)) & \(461.8\pm 5.3\) & - \\  & Expansion experiments & S1H (\(n=10\)) & \(442.1\pm 8.9\) & - \\  & Expansion experiments & S05H (\(n=10\)) & \(438.5\pm 17.2\) & - \\ \hline \multirow{4}{*}{Copper tubes} & \multirow{4}{*}{Finite element analysis} & Type 1 ball & 104.8 & \(-0.007\) \\  & & Type 2 ball & 270.5 & \(-0.003\) \\ \cline{1-1}  & & Type 3 ball & 138.3 & \(-0.004\) \\ \cline{1-1}  & & Type 4 ball & 131.8 & \(-0.001\) \\ \cline{1-1}  & & Type 5 ball & 115.5 & \(-0.005\) \\ \cline{1-1}  & & Type 6 ball & 91.6 & \(-0.008\) \\ \cline{1-1}  & & Type 7 ball & 88.9 & \(-0.006\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Expansion force measured from Al tube expansion experiments and Cu tube expansion analyses. Figure 10: (**a**) Expansion and radial forces from Types 1–7 except Type 4, where the tube’s inner groove does not contact the expansion ball; and (**b**) correlation between the deformation of the tube’s inner grooves and the radial force divided by the expansion force.
## 5 \(p\)-Value Computation
### MHE to SMPC Protocol  
In Ref. [17], [PERSON] et al. present an encryption to secret share (Enc2 Share) protocol for the BFV scheme and use it for multiplication triple generation. In the protocol, each party \(P_{i}\) (other than \(P_{1}\)) samples its own secret share \(M_{i}\in R_{i}\) (where \(R_{i}\) is the plaintext space of the BFV scheme) and sends a partial decryption of the ciphertext with its own secret share subtracted to \(P_{1}\). \(P_{1}\) fully decrypts the partial decryptions to get \(M_{1}=m-\sum_{i=2}^{N}M_{i}\) where \(m\) is the plaintext and \(N\) is the number of parties.  
While this method works for integer-based HE schemes such as BFV where the plaintext space is small, allowing \(M_{i}\) to be easily sampled uniformly from \(R_{t}\), in approximate arithmetic schemes such as CKKS, the size of the plaintext space presents a challenge to the Enc2 Share protocol. Nevertheless, a solution is presented in the lattigo library that leverages the noise smudging approach of [PERSON] et al. [24] to construct secret shared polynomials in the plaintext space, whose sum is the target plaintext but are statistically indistinguishable from random polynomials.  
However, this results in secret shared polynomials that have very large coefficients, and it is not clear how such a protocol can be used in practice to switch between the ciphertext encrypting a vector of real numbers \([v_{1},v_{2},\ldots,v_{d}]\) and a vector of secret shared fixed-point real numbers \([[v_{1}],[v_{2}],\ldots,[v_{d}]]\). A naive method for doing so would be to sum the plaintext polynomials and perform the decoding fully using SMPC, which would result in huge communication costs due to the large degree of the plaintext polynomials.  
Instead, in our method, each party \(P_{i}\) instead decodes the polynomials first using the Fast Fourier Transform but using high precision arithmetic (128 bit floating point) to arrive at a vector of floating point numbers \([v_{1}^{(i)},\ldots,v_{d}^{(i)}]\). We note that since the smudging lemma was used in the generation of the secret shares, each element in the vector can in fact be summed across the parties to arrive at the expected value, i.e., \(\forall k\ v_{k}=\sum_{l=1}^{N}v_{k}^{(i)}\). We can then continue our computation in SMPC by taking in the necessary \(v_{k}^{(i)}\)s as \"real\" valued inputs from each party and adding them up by using SMPC to arrive at the secret shared representation of the original encryption.
## 4 Implementation Details
### Dataset Description and Preprocessing  
The network presented in the previous section was applied to the task of the multiple cardiovascular structures from the Kaggle Dataset [31,43]. This data was collected on patients from the everyday clinical environment. It has various qualities to preserve the robustness of the developed algorithms when it comes to real clinical usage. The cardiac CTA data are obtained using 64-slice CT scanners, and images are reconstructed at 0.50 mm thickness. The datasets include the following eleven cardiovascular structures: coronary sinus (CS), descending aorta (DA), inferior vena cava (IVC), left atrial appendage (LAA), left atrial wall (LAW), papillary muscle (PM), posterior mitral leaflet (PML), proximal ascending aorta (PAA), pulmonary aorta (PA), right ventricular wall (RVW), and superior vena cava (SVC). Dataset details regarding the number of patients and the total number of 2D images are expressed in Table 1. The PAA begins in the plane corresponding to the nadirs of all three aortic valve cusps, which is also the plane closest to the origin of the brachiocephalic artery. The DA begins distal to the origin of the left subclavian artery and extends to the lowest axial disc. The vena cava are venous veins that run through the right middle mediastum adjacent to and to the right of the trachea and PAA and empty into the right atrium. The main left and right pulmonary arteries were all included in the PA. The CS is a venous structure that runs from the great cardiac vein through the atrioventricular groove near the left circumflex coronary artery and empties into the right atrium. The RVW is the volume of myocardial tissue in the right ventricle calculated by delineating the endocardial and epicardial boundaries of the ventricle, omitting the papillary muscles.
## 4 Discussion
Limitations The limitations of this research study are:
* In Romania, the development and implementation of the SCADA system for the control of the railway transport system is just beginning, there are very few dispatching points and railway stations that are outfitted with this technology. For the quick resolution of this situation, it is recommended to implement the solution proposed here, mobile phone terminals and GSM modem based control;
* The control system was tested on a single railway. However, considering the comprehensive description of the GSM module, it is plausible that the proposed control system can be replicated by implementing the same system on other railways that possess decommissioned stations. Such an approach would enable a comparative analysis of the outcomes achieved, thus strengthening the generalizability of the findings. * The research did not include the automation of train circulation, and for this reason there was the need to maintain operative service personnel in railway stations, authorized for the execution of maneuvers related to the effective movement of trains;
* The research could not be performed on a larger sample of railway stations, due to the lack of a digitized system providing a database with the necessary information. Ideally, the energy cost would be monitored using a dedicated procedure and module, as previously described [23];
* Although during the research period the limitations mentioned above were discovered, comparative analysis of the two situations shows that the solution developed and proposed in this paper requires a minimum financial effort to obtain important economic results.