esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer
## 1 Introduction  
With the development of financial technology, big data and artificial intelligence technology have been paid increasingly more attention by financial enterprises. For financial enterprises, such as banks and P2P lending platforms, the most important risk is credit risk, that is, user default risk. Therefore, an increasing number of enterprises are trying to apply artificial intelligence technology, i.e., deep learning, to user credit risk assessment so as to reduce the loan default rate and to improve the ability of enterprises to resist risks [1; 2]; this problem has attracted increasing attention.  
Credit scoring is essentially a classification problem in machine learning. With the help of a credit risk assessment model, applicants can be divided into \"good\" customers and \"bad\" customers. Financial institutions can make loan approval decisions and risk pricing based on the credit scoring results.  
With the development of financial technology, some loan businesses are carried out on online platforms, from basic websites to the current mobile application (APP), which has accumulated massive amounts of user online behavioral data, such as data on user registration behavior, user login behavior, user click behavior, and user authentication behavior. These online behavioral data have important mining value. In recent years, with the maturity of deep learning technology, it has become feasible to mine these data.  
Based on the online behavioral data of users and the credit data of financial enterprises, this study proposed an end-to-end transformer credit scoring system, which can accurately predict users' default risk.  
The main contributions of this study are as follows:  
1. This paper introduces transformer into the field of credit scoring based on user online behavioral data, and the experimental results show that the transformer used in this study outperforms LSTM and traditional machine learning models.  
We make use of credit feature data and user behavioral data and develop a novel end-to-end deep learning credit scoring framework. The framework is composed of two parts, a wide part and a deep part, and it can automatically learn from user behavioral data and feature data.  
The structure of this study is as follows: Section 2 summarizes the literature relevant to this study, Section 3 introduces the relevant theories and the transformer method proposed in this study, Section 4 analyzes the experimental results, and Section 5 summarizes this study.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Sharing Phase
#### 5.5.2 Sharing Process  
The sharing process described in this subsection focuses on the second scenario described above.  
Assume that a user \(U_{rec}\) in a healthcare organization \(HO_{j}\) as a DU needs to use the medical data in the system. After the authentication and role verification of \(U_{rec}\) is passed, the data index \(TreatmentData\) on the blockchain is accessed based on the request to obtain the corresponding \(\{N_{EMR_{\text{label}}},CID,H_{i},MD_{EMR}\}\). Send the shared request with \(\{N_{EMR_{\text{label}}},CID,MD_{EMR}\}\) to hospital \(H_{i}\). When \(H_{i}\) receives the sharing request, \(H_{i}\) queries the IPFS to obtain the medical data ciphertext \(C_{EMR}\) based on the CID. Calculate the digest of \(C_{EMR}\): \(MD^{\prime}_{EMR}\) and compare it with \(MD_{EMR}\) to verify whether the data have been tampered with and to ensure the data integrity.  
After verifying integrity, \(H_{i}\) decrypts \(C_{EMR}\) to obtain plaintext \(M_{EMR}\). \(H_{i}\) randomly generates a shared symmetric key \(KeyGen(H_{i},U_{rec})\overset{AES}{\rightarrow}k_{AES}\) based on the AES algorithm. In using this key, \(H_{i}\) encrypts the medical data plaintext \(M_{EMR}\) and the main information in the \(TreatmentData\) record to obtain the shared ciphertext \(C_{share(EMR)}=Enc_{k_{AES}}(M_{EMR},H_{i},N_{EMR_{\text{label}}})\). Data sharing security is protected through AES-PRE.  
After receiving the data forwarded by the IPFS, \(U_{rec}\) decrypts it using private key \(sk_{rec}\) to obtain the AES key \(k_{AES}\). With \(k_{AES}\), \(U_{rec}\) decrypts \(C_{share(EMR)}\) to obtain the plaintext medical data \(M_{EMR}\), along with other information such as \(N_{EMR_{\text{label}}}\) and \(H_{i}\). Upon obtaining the plaintext medical data, \(U_{rec}\) can encrypt them using the public key of \(H_{i}\), calculate the message digest \(MD^{\prime}_{EMR}\), and sign it as \(sign=Sign(MD^{\prime}_{EMR},U_{rec})\). In querying the blockchain using \(N_{EMR_{\text{label}}}\) to retrieve the corresponding \(TreatmentData\), \(U_{rec}\) verifies the integrity of the medical data by comparing the stored \(MD_{EMR}\) in the index. This allows \(U_{rec}\) to determine whether the data have been tampered with by the sender. Upon successful verification, \(U_{rec}\) sends \(N_{EMR_{\text{label}}}\), \(MD^{\prime}_{EMR}\), and \(sign\) to the IPFS.  
The IPFS uploads the constructed data structure for \(U_{rec}\)'s medical data usage record to the blockchain network for persistent storage as follows: \(DataUsageRecord=\{N_{EMR_{\text{label}}}\), \(UID_{rec},MD_{EMR}^{\prime},sign,HO_{j},H_{i},t\}\).
## 3 Calibration Methodologies & Dataset Used  
The scientific community investigates [(10)] and provides post-launch calibration and validation of satellite altimeter products from the early beginning of the altimetry era [(11; 12)]. Following the technological advances in satellite altimetry, several techniques have been implemented to provide both absolute and relative calibration of satellite altimeters: distributed tide-gauge network (relative), crossover analysis (relative), sea-surface calibration using tide gauges and/or GNSS buoys (absolute indirect), and active transponder calibration (absolute direct). Three of these Cal/Val techniques are put into action at the PFAC, and presented hereafter.  
Figure 5: The operational status of the instrumentation (tide gauges, GNSS receivers) at the Gavdos Cal/Val site as of 2004.  
Figure 6: The regional GNSS network in Gavdos and west Crete, Greece (**left**) and availability of GNSS observations (**right**).
## 4 Discussion  
### Strength Increase Rate  
Figure 9 shows the effect of various curing temperatures (\(-\)10, 5, 40, and 60 \({}^{\circ}\)C) on the rate of strength increase for polysulfide polymer concrete. Here, the rate of strength increase is defined as the ratio of the strength increase to the standard value of strength at 20 \({}^{\circ}\)C. The strength at 20 \({}^{\circ}\)C was selected because the average of temperatures in all seasons except winter, in which fewer construction projects are undertaken in Korea, is 19.3 \({}^{\circ}\)C, and because most standard test methods specify 20 \({}^{\circ}\)C as the standard curing temperature.  
Figure 8: Effect of curing temperature on average bond strength.  
Figure 9: Comparison of rates of increase in compressive, flexural, and bond strengths as a function of curing temperature.  
The results plotted in Figure 9 show that the rates of increase in the compressive, flexural, and bond strengths become higher with a decrease in the curing temperature, and vice versa. This tendency can be attributed to the strong dependence of these strengths on the curing temperature. The maximum rate of increase in the compressive and flexural strengths was found to occur at \(-10\)\({}^{\circ}\)C, and that in the bond strength was found to occur at \(5\)\({}^{\circ}\)C. The maximum rates for the compressive strength, flexural strength, and bond strength were \(36.3\%\), \(17.2\%\), and \(67.0\%\), respectively. Conversely, the maximum rates of decrease for the compressive strength, flexural strength, and bond strength were all observed at \(60\)\({}^{\circ}\)C, with values of \(-61.2\%\), \(-73.7\%\), and \(-58.5\%\), respectively. These results suggest that it would be impossible to improve the strength of polysulfide polymer concrete by employing a curing temperature higher than \(20\)\({}^{\circ}\)C.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 3 Mine Planning Using GISs
### Ore Reserve Estimation
The Getis-Ord Gi* statistic is calculated by comparing the local sum for a feature and its neighbors to the total sum as follows:  
\[G_{i}^{*}=\frac{\sum_{j=1}^{n}w_{i,j}x_{j}-\overline{X}\sum_{j=1}^{n}w_{i,j}}{ S\sqrt{\frac{\left[n\sum_{j=1}^{n}w_{i,j}^{2}\left(\sum_{j=1}^{n}w_{i,j} \right)\right]^{2}}{n-1}}}, \tag{1}\]  
where \(x_{j}\) is the attribute value for feature \(j\), \(w_{i,j}\) is the spatial weight between feature \(i\) and \(j\), \(n\) is equal to\[S=\sqrt{\frac{\sum_{j=1}^{n}x_{j}^{2}}{n}-\left(\overline{X}\right)^{2}}. \tag{3}\]  
The resultant Getis-Ord Gi* statistic is a z-score. For statistically significant positive z-scores, the larger the z-score, the more intense the clustering of high values (i.e., a hot spot). When the local sum is very different from the expected local sum, and that difference is too large to be the result of random chance, a z-score is statistically significant. Figure 1 shows a schematic diagram of a 3D hotspot analysis for borehole data. Figure 2 shows the results of kriging using different top-cut methods for Au deposits. The proposed method can alleviate the overestimation or underestimation that can occur when applying existing methods.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 2 Track Condition Monitoring System
### Car-Body Vibration Measurement in a Regional Railway  
An actual vehicle test trial was conducted by installing an onboard sensing device in in-service vehicles on a regional railway for continuous measurements from October 2020 to August 2022. Owing to the general inspection of vehicles and software updates of the onboard sensing devices, almost no data were obtained between January 2022 and June 2022. In this study, we evaluated track conditions using the vertical and lateral acceleration data acquired during daily trials.
## 6 Numerical Simulation of Harvesters with ITDs
### Generated Power  
The most important characteristic of a piezoelectric harvester is the power that it can generate at the various frequencies. In order to calculate the generated power, load resistance was set equal to the optimal value for the first resonance peak Equation (15). The harmonic response with a base acceleration of 10 ms\({}^{-2}\) was simulated. Calculated results are represented in Figure 16, and the powers generated by the harvesters with ITDs are compared with the powers generated by the harvester alone and by the harvester with a tip mass that trims the harvester to the same frequency.  
The power generated by the harvester with ITD1 at the low frequency resonance (89.3 Hz) is 1.10 mW. This value is lower than the power generated by PPA 1001 alone at 124.5 Hz, however, the harvester with ITD1 generates 0.41 mW in correspondence of the high frequency resonance (154.0 Hz).  
Figure 16: Numerical results, power generated by PPA 1001 equipped with trimming devices, optimal load resistance, base acceleration 10 ms\({}^{-2}\) (1 g). (**a**) ITD1, (**b**) ITD2.  
Figure 15: Numerical results, strains in the piezo layer of the harvester with ITD2. (**a**) Modal shape at the first resonance (89.7 Hz). (**b**) Strain modulus at the first resonance (89.7 Hz). (**c**) Strain phase at the first resonance (89.7 Hz). (**d**) Modal shape at the second resonance (179.5 Hz). (**e**) Strain modulus at the second resonance (179.5 Hz). (**f**) Strain phase at the second resonance (179.5 Hz).  
The harvester with ITD2 is suited to operate in the presence a periodic excitation with the first harmonic at 89.5 Hz (fundamental frequency), because it generates 0.95 mW at the fundamental frequency and 0.42 mW at the frequency of the second harmonic.  
Finally, the harvester with tip mass generates the largest power at 87.3 Hz.
## 3 Results
### Porosity  
Figure 5a shows the porosity of the MWNT/NS-incorporated cement composites as a function of the NS content, and the composites were grouped as MWNT0, MWNT0.3, MWNT0.6, and MWNT1.0. The porosity tended to decrease with the addition of NS in the MWNT0, MWNT0.3, and MWNT0.6 groups. It is noteworthy that the enhancements of compressive and flexural strengths in the MWNT0, MWNT0.3, and MWNT0.6 groups shown in Figures 3a and 4a were closely related to the decrease in the porosity. Meanwhile, the decrease in the porosity for the MWNT1.0 group was not pronounced. This was likely because the densification of the microstructure developed by NS addition was minor, particularly for the MWNT1.0 group.  
Figure 4: Flexural strength results of the MWNT/NS-incorporated cement composites after 28 days of curing: (**a**) NS content varied and (**b**) MWNT content varied.  
Figure 5b shows the porosity results as a function of the MWNT content. The porosity tended to decrease with the addition of MWNTs in the NS0, NS1, and NS3 groups. In contrast, the porosity tended to increase with the MWNT content in the NS5 group, which was prepared with a relatively large amount of NS. This outcome was closely related to the compressive and flexural strength results of the NS5 group shown in Figures 3b and 4b, respectively, which represented a decrease in strength due to the addition of 1% MWNT.
## 3 Experiments and Analysis
### Ablation Study  
This study conducted ablation experiments on the Seq. 3 scenario, analyzing TRC, RIA, GELCM, and their various combinations as shown in Figure 10. From the experimental results, it can be observed that although the GELCM performs slightly better than TRC and RIA individually, the combinations of TRC+GELCM or RIA+GELCM significantly outperform the standalone methods. The proposed method achieves the best performance, highlighting the superior effectiveness of the approach and underscoring the rationality and robustness of the STFD design.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Seq** & **Metric** & **DNGM** & **TLLCM** & **SSTLCM** & **FKRW** & **TVF** & **IPI** & **IMNNLWC** & **Proposed** \\ \hline \multirow{2}{*}{Seq3} & Runtime & 0.384 & 25.806 & 11.88 & 9.344 & 0.025 & 59.239 & 167.324 & 1.950 \\  & Memory & 5.12 & 13.64 & 33.37 & 16.55 & 4.82 & 6.57 & 291.48 & 24.74 \\ \hline \multirow{2}{*}{Seq4} & Runtime & 1.838 & 124.114 & 18.40 & 16.924 & 0.091 & 1266.426 & 675.276 & 9.757 \\  & Memory & 216.64 & 218.73 & 267.56 & 218.91 & 226.42 & 213.72 & 292.39 & 171.14 \\ \hline \multirow{2}{*}{Seq.6} & Runtime & 1.838 & 128.059 & 17.76 & 19.38 & 0.092 & 522.474 & 689.988 & 9.893 \\  & Memory & 216.26 & 218.72 & 267.33 & 291.11 & 178.99 & 213.68 & 293.05 & 170.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Runtime and memory cost comparison (Seconds/MB).
## 5 Experimental Results
### Analysis
#### 5.4.2 Parameter Efficient Fine-Tuning  
Parameter-Efficient Fine-Tuning (PEFT) is a type of model tuning that selectively fine-tunes only a subset of a model's parameters. This approach is beneficial because it requires only a small fraction of the total number of parameters in the backbone model to achieve a substantial improvement in performance. In some cases, PEFT can even surpass traditional fine-tuning methods. PEFT encompasses various techniques including P-tuning [8], prompt-tuning [60], and LoRA [52].  
PEFT's popularity stems from its convenience in reducing the computational overhead associated with fine-tuning very large models, a feature particularly relevant to LLMs. However, there are drawbacks with PEFT including the inability to utilise all of the parameters in a model to be learnt; this may limit its ability to match the performance of traditional fine-tuning methods. Numerous studies, for example see [17], provide a comparative analysis to compare the performance of PEFT to that of full fine-tuning; studies have viewed this as a trade-off between computational cost and model accuracy and quality.  
Retrospectively examining these concerns, we can draw a comparison between _Expert-B_ and Phoenix that while Phoenix has undergone complete fine-tuning, _Expert-B_ has prioritised optimising computational cost by utilising LoRA. Consequently, the limited data available may not permit the exploitation of all learning parameters, leading to inferior performance by _Expert-B_ for in the Vietnamese benchmark Vicuna [55] evaluation dataset compared to Phoenix.
###### Abstract  
In this work, we demonstrate the co-catalytic modification of ZnO films via the photodeposition of palladium (Pd) to enhance the photocatalytic degradation of doxycyclineine (DC). Pristine ZnO films were synthesized using a sol-gel method and deposited onto glass substrates via dip-coating. The films were subsequently modified with Pd through chemical photodeposition under UV light, which facilitated the photoreduction of an aqueous 5 \(\times\) 10\({}^{-3}\) M Pd\({}^{2+}\) precursor. The influence of varying UV photodeposition doses (2.5, 5, and 10 J/cm\({}^{2}\)) on the morphology and chemical composition of the Pd-modified films was investigated to control Pd surface coverage and chemical state. Characterization techniques included scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS). At low UV doses (2.5 J/cm\({}^{2}\)), approximately 1.6 at.% of Pd was photodeposited, primarily as PdO, while higher UV doses (5-10 J/cm\({}^{2}\)) increased the metallic Pd\({}^{0}\) content. The photocatalytic degradation of DC was evaluated in both distilled and tap water, where Pd/ZnO films demonstrated significantly higher removal efficiency (40-380% higher) than pristine ZnO films, with those containing higher Pd\({}^{0}\) levels exhibiting the greatest activity. Across all samples, removal efficiency in tap water was approximately double that in distilled water.  
ol-gel ZnO; photodeposition; Pd functionalization; photocatalysis; doxycycline; pharmaceutical photodegradation 202515609
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADCs & Analog-to-digital converters \\ AF & Amplify-and-forward \\ BER & Bit error rate \\ BS & Base station \\ cdf & Cumulative (probability measure) distribution function \\ CDMA & Code division multiple access \\ CSI & Channel state information \\ C-NOMA & Cooperative NOMA \\ ch.f. & Characteristic function \\ CSI & Channel state information \\ DF & Decode-and-forward \\ ER & Ergodic rate \\ FD & Full duplex \\ FDMA & Frequency division multiple access \\ HD & Half duplex \\ i.i.d. & Independent and identically distributed \\ LI & Loopback interference \\ NOMA & Non-orthogonal multiple access \\ OFDMA & Orthogonal frequency division multiple access \\ OMA & Orthogonal multiple access \\ OP & Outage probability \\ Qos & Quality of service \\ pdf & Probability density function \\ RF & Radio frequency \\ r.v. & Random variable \\ SER & Symbol error rate \\ SIC & Successive interference cancellation \\ SINR & Signal-to-interference-plus-noise ratio \\ SNR & Signal-to-noise ratio \\ TDMA & Time division multiple access \\ UE & User equipment \\ \end{tabular}
###### Abstract  
Nano-polydopamine-graphene oxide-TiO\({}_{2}\) (nano-PDA@GO-TiO\({}_{2}\)) composites were prepared by dopamine modified graphene oxide (GO) and loaded nano-TiO\({}_{2}\) on the surface of GO. The structure and morphology of nano-PDA@GO-TiO\({}_{2}\) composites were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Raman, X-ray photoelectron spectroscopy (XPS), Scanning electron microscope (SEM), and Transmission electron microscope (TEM) Results demonstrate that the introduction of dopamine to functionalize the GO could self-polymerize polydopamine (PDA) on the surfaces of the GO and endow abundant chemical groups reduce the GO. The interaction between the GO and nano-TiO\({}_{2}\) particles could prevent graphene nanosheets from restacking and nano-TiO\({}_{2}\) particles from agglomeration. Nano-PDA@GO-TiO\({}_{2}\) composite material was used as the nano-filler, and nano-PDA@GO-TiO\({}_{2}\) composites waterborne epoxy resin coatings (PGT/WEP) were prepared by dispersing a different content of nano-PDA@GO-TiO\({}_{2}\) composites into waterborne epoxy resin with the help of ultrasonic dispersion and mechanical agitation. The physical properties of PGT/WEP coatings, such as hardness, impact resistance, and adhesion, were tested and the electrochemical performance was evaluated. The results show that dispersing 2% nano-PDA@GO-TiO\({}_{2}\) composites in waterborne epoxy resin could significantly improve the physical properties and corrosion resistance of waterborne epoxy resin coating when compared with pure waterborne epoxy coating.  
Keywords:dopamine; graphene oxide; nano-TiO\({}_{2}\); composite materials; nanofiller; epoxy coating; physical properties; corrosion resistance +
Footnote †: journal: Applied Physics Letters
# ERACE: Toward Facilitating Exploit Generation for Kernel Race Vulnerabilities
## 2 Background and Challenges  
It is challenging to exploit kernel race vulnerabilities. First, it requires researchers to be equipped with comprehensive security knowledge and experience such as knowledge of the underlying operating system, binary reverse engineering, heap, stack, etc. Second, the indeterminacy of a race vulnerability decreases our analysis efficiency. For common vulnerabilities such as stack overflows, we can set accurate breakpoints to figure out whether the vulnerability has been triggered. However, for race vulnerabilities, it is unknown whether every execution can satisfy the race condition, further increasing the difficulty of debugging and analysis. Moreover, even senior security researchers have to spend a lot of time on reverse analysis to find useful exploit primitives or heap-spraying structures. This process is quite complicated and boring.  
Many researchers have defined race vulnerabilities. [PERSON] et al. (2010) defined a data race, which is a behavior in which the output is dependent on the sequence or the timing of other non-deterministic events. If two memory-access instructions meet the following three conditions, a data race may occur: (i) they access the same memory location; (ii) at least one instruction is a write instruction; and (iii) they can be executed concurrently. A data race is one type of concurrent vulnerability. The concept of concurrency vulnerabilities is broader. However, most of the exploitable and user-controllable vulnerabilities belong to the data race type. Therefore, the definition of a data race by [PERSON] is more relevant to our research. As shown in Figure 1, when a race occurs, C1, C2, and C3 are the instructions that access the same memory location. C3 is a write instruction. C1 and C3, and C2 and C3 can be executed concurrently and satisfy all three conditions.