esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets and Methods  
### Datasets  
The backscatter data contained in Level 1B (L1B) or Level 2A (L2A) products from Ku-band scatterometers onboard the spacecraft of HY-2B, HY-2C, HY-2D, QuikSCAT, and ISS were used. Table 1 provides brief information on these data products. We note that the \(\sigma^{0}\) data from HY-2 scatterometers L1B products are used hereafter with an initial correction (provided in Table 1). The initial constant gain corrections are crudely estimated for each HSCAT instrument, and more precise and inter-calibrated corrections will be achieved based on this.  
In addition, the NWP winds are from the European Centre for Medium-Range Weather Forecasts (ECMWFs) operational forecast model. The forecast time steps of +3 h, +6 h,..., +18 h are used in this study. The NWP data are quadratically interpolated with respect to time and bi-linearly interpolated with respect to geolocation. Note that small systematic differences exist between these standard ECMWF NWP 10-m winds and the more representative stress-equivalent 10-m winds [21], while these will be globally similar for all HY-2 wind datasets.
_Algorithms_**2013**, \(6\), 1-11; doi:10.3390/a6010001  
_algorithms_  
ISSN 1999-4893  
www.mdpi.com/journal/algorithms  
_Article_  
**Maximum Disjoint Paths on Edge-Colored Graphs: Approximability and Tractability**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{2,\ast}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Computer Systems and Communication, University of Milan-Bicocca, Viale Sarca 336, Milan, Italy; E-Mails: [PERSON]DRESS] (P.B.); [PERSON]_ADDRESS] (Y.P.)  
\({}^{2}\) Department of Humanities and Social Sciences, University of Bergamo, Via Donizzetti 3, Bergamo, Italy  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-03520-52423  
_Received: 31 October 2012; in revised form: 13 December 2012 / Accepted: 18 December 2012 / Published: 27 December 2012_  
**Abstract:** The problem of finding the maximum number of vertex-disjoint uni-color paths in an edge-colored graph has been recently introduced in literature, motivated by applications in social network analysis. In this paper we investigate the approximation and parameterized complexity of the problem. First, we show that, for any constant \(\varepsilon>0\), the problem is not approximable within factor \(c^{1-\varepsilon}\), where \(c\) is the number of colors, and that the corresponding decision problem is W[1]-hard when parametrized by the number of disjoint paths. Then, we present a fixed-parameter algorithm for the problem parameterized by the number and the length of the disjoint paths.  
**Keywords:** social networks; disjoint paths; fixed-parameter algorithms; hardness of approximation
# 3R Composites: Knockdown Effect Assessment and Repair Efficiency via Mechanical and NDE Testing
## 3 Results and Discussion
### Compression after Impact (CAI)  
#### 3.3.1 Knockdown Effect on Mechanical Properties  
Half of the impacted 3R specimens (at each energy level) were subsequently tested in compression after impact (CAI), and the rest of them were repaired and tested again (impact damage and CAI) in order to calculate the knockdown effect (Table 6). Regarding the laminates that were subjected to 9 J impact, CAI showed that the 3R composites exhibited mean values of maximum load of 100.59 \(\pm\) 4.75 kN and a mean maximum strength of 212.43 \(\pm\) 12.18 MPa. The conventional specimens exceeded the upper limit of the loadcell (105 kN) during mechanical testing without breaking. Therefore, the knockdown effect  
Figure 23: IR Thermography of damage on repaired 3R laminates at 9 J (**left**) and 15 J (**right**).  
Figure 22: Damage diameter comparison.  
was evaluated using an approximate value of 105 kN as the maximum load and estimated approximately at \(\approx-3.0\%\) or \(\approx-1.9\%\) using the maximum strength values. At the 15 J impact, the knockdown effect of 3R specimens was \(-4.3\%\) in terms of maximum load and \(-1.1\%\) in compression strength.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 2 Resilience in Smart Grids
### From Reliability to Resilience  
Reliability is the ability of an item (component or system) to operate under designated operating conditions for a designated period of time or number of cycles, where this ability can be formulated through a probability [66]. In electrical networks, this is equivalent to maintaining the delivery of electric services to customers in the face of routine uncertainty under operating conditions [67]. Metrics like Energy Not Supplied (ENS), Average Customer Curtailment Index (ACCI), System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (GAIFI), Customer Average Interruption Duration Index (CAIDI) are widely used to describe PDN reliability [68; 69]. System operators use such indicators to track and enhance the performance of their networks. These indices are further used by system regulators and system operators in service level agreements (SLAs), in order to define penalty thresholds and ensure that the right compensation is paid based on the experienced outages. Reliability metrics are relevant to assess the impact of recurrent events with available historical records, over which maintenance actions are applicable; excluding major hazards such as severe weather events [70]. Some of these metrics were extended to capture more severe events, where metrics like STorm Average Interruption Frequency Index (STAIFI) and STorm Average Interruption Duration Index (STAIDI) are proposed [71]. However, a demonstration was made that these two metrics are not relevant for resilience evaluation because, when used during a storm, they show large deviation that can be even greater than the values of STAIDI and STAIFI [62].  
Resilience is \"the ability to prepare and plan for, absorb, recover from, or more successfully adapt to actual or potential adverse events\" [72]. Unlike reliability, which focuses on the frequency and duration of failures \"event-agnostically,\" resilience seeks to further track the dynamics and resources of response, adaptability, and ability to restore.  
This is relevant to HILP hazards where consequences in the system need to be studied with respect to specific events, as each disruption has its distinguishing characteristics [73]. Thus, the fundamental difference resides in the scale, scope, and duration of events handled: resilience targets events with strong impact in a wide geographical area with long duration of outages, while reliability handles local impact in short duration outages [74].  
Despite this difference between the two concepts, mainly due to events each of them tackles, they remain closely related because enhancing resilience or reliability may require the same strategies, with resilience being more general, confirming that being resilient typically encompasses being reliable, but not vice versa [75].
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 3 Experimental Verification
### Simulation with Specified Traffic  
In this section, the proposed algorithm is verified by comparative analysis in the simulations. We set up a typical traffic scenario with two lanes and four traffic vehicles, where the host vehicle \(V_{H}\) take a lane change maneuver. The traffic scenario is shown in Figure 6. To examine the robustness of the proposed algorithm, three typical road events that could happen during a lane change are designed as: unexpected braking of front vehicle in current lane, unexpected braking of front vehicle in target lane, and unexpected acceleration of rear vehicle in target lane. For each scenario, simulations are conducted by different path planning methods: LC with the proposed algorithm, LC without time-variant safety margin, and LC without re-planning control. For comparison purposes, three methods are referred to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **TBRP** & **CBRP-C** & **CBRP-A** \\ \hline Numbers of LC & 280 & 277 & 283 \\ Numbers of RP & 86 & 20 & 43 \\ Average LC Time (s) & 1.85 & 1.48 & 1.40 \\ Average Acceleration (\(\mathrm{m/s^{2}}\)) & 1.9 & 1.6 & 1.7 \\ Average Speed (\(\mathrm{m/s}\)) & 22.57 & 22.54 & 22.55 \\ Total Computing Time (s) & 16768 & 2736 & 2774 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of Random Traffic Test I.  
A, B and C respectively. By comparative analysis, the proposed algorithm's ability to take appropriate approaches for different given situations is demonstrated.
# Mechanical Properties of ASTM A572 Grades 50 and 60 Steels at High Temperatures
## 3 Results and Discussion
### Proposed Reduction Factors for the Strength and Elastic Modulus
#### 3.2.1 Yield Strength (0.2% Strain Level)  
As shown in Figure 11, the MS strength reduction factors are always lower than those of HSS with an increase in the temperature. In previous studies, HSS was shown to display a characteristic vulnerability to temperature in yield strength compared to MS [13; 31]. However, using the average value of the data, the regression equation showed that MS was more vulnerable to temperature than HSS, in contrast to Figure 1. Compared to the HSS, the yield strength of the MS decreased significantly with the increasing temperature. Thus, the maximum difference in the yield strength resulted by 17% at 400 \({}^{\circ}\)C particularly. Therefore, we added the temperature range at 400 \({}^{\circ}\)C in the regression equation. To reflect these observations, the yield strength reduction factor for MS is summarized in Equations (1)-(3), whereas the yield strength reduction factor for HSS is summarized in Equations (4)-(6). The yield strengths obtained from the experiments at various strains are insufficient to present the yield strength reduction factor for each steel grade according to the strain level. To elucidate the equations for various strain levels, future studies must analyze the yield strength at additional strain levels. Therefore, in this study, the following yield strength reduction factors are proposed by considering the variables at a 0.2% strain level.  
* MS (Mild Steel): Yield Strength Reduction Factor Equation \[\begin{split}\text{For 25 }^{\circ}\text{C}\leq T_{a}\leq 400\ ^{\circ}\text{C}:\\ R_{f,Y}=-2\times 10^{-7}(T_{a})^{2}-1\times 10^{-4}(T_{a})+1. \end{split}\] (1)  
\[\begin{split}\text{For 400 }^{\circ}\text{C}<T_{a}\leq 800\ ^{\circ}\text{C}:\\ R_{f,Y}=2\times 10^{-6}(T_{a})^{2}-4.1\times 10^{-3}(T_{a})+2. 267.\end{split} \tag{2}\]  
\[\begin{split}\text{For 800 }^{\circ}\text{C}<T_{a}\leq 1000\ ^{\circ}\text{C}:\\ R_{f,Y}=3\times 10^{-8}(T_{a})^{2}-1\times 10^{-3}(T_{a})+1. \end{split} \tag{3}\]
* HSS (High-Strength Steel): Yield Strength Reduction Factor Equation \[\begin{split}\text{For 25 }^{\circ}\text{C}\leq T_{a}\leq 400\ ^{\circ}\text{C}:\\ R_{f,Y}=-1\times 10^{-6}(T_{a})^{2}-2\times 10^{-4}(T_{a})+1. \end{split}\] (4)
###### Abstract
After performing normalization and quantization, a three-dimensional polar coordinate graph was generated based on the quantized amplitude, phase, and number of symbols in each grid. Unlike most of the previously reported MFI schemes that tend to learn overall global features, the proposed scheme makes full use of effective local features of the polar coordinate graph. A total of 20 specific local zones were selected, and the number of symbols in these zones were grouped into six-dimensional (6 \(\times\) 1) features. This not only utilized the local features precisely but also reduced the computational complexity of the subsequent identification algorithm correspondingly. After that, the Gaussian weighted k-nearest neighbors (KNN) algorithm was applied to classify the features extracted from the polar coordinate graph. In order to verify the feasibility of the proposed scheme, numerical simulations of 28 GBaud polarization division multiplexed (PDM)-QPSK/-16 QAM/-32 QAM/-64 QAM/-128 QAM signals were conducted. The results show that, to achieve a 100% correct identification rate for all of the five modulation formats, the required minimum optical signal-to-noise ratio (OSNR) values were less than the thresholds corresponding to the 20% forward error correction (FEC) correcting bit error rate (BER) of 2.4 \(\times\) 10\({}^{-2}\). Furthermore, the simulated results also showed that the proposed MFI scheme was robust against residual CD, PMD, and fiber nonlinearities.
Besides the image features, we also considered the image captions, title, and even the full-text article to extract the location information of a particular historical image. In this way, we could geolocate more historical images than just well-known landmarks. The next step after geolocalization is an attractive visualization of historical content. For this purpose, we chose the computer-aided rephotography of historical images to show the time travel between two different timestamps of a given location. As the name implies, rephotography is the process of recapturing a photograph from the same point of view [28]. Rephotography is valuable for comparing historical versus recent architectural heritage related visual material [29]. Performing rephotography on historical images is notoriously difficult due to the quality of such collections and is usually carried out manually or semiautomatically. Visualizing the temporal evolution in one image can enable humanities scholars to study and analyze changes in a location over time. Some examples of manual rephotography are shown in Figure 1.  
This research's results open new directions not only for computer scientists but also for historians and cultural heritage researchers to study spatial and temporal changes in a region or place. The enrichment of metadata facilitates location-based service applications [32]. Geolocalization will be an important feature for a further georeferencing of street-level images [33]. Rephotography will also be of interest to the general public and tourists waiting for a time-based visualization of historical images.
## 4 Numerical Analysis of Prototype Structure
### Dynamic Characteristics  
The dynamic characteristics of the prototype structure are shown in Table 6. Figure 14 shows the vibration modes of first three modes. As shown in Table 6, it can be found that the accumulated participating mass coefficients of the first three vibration modes can reach above 0.97. In addition, the first fundamental period was 1.216 s and the corresponding vibration mode was torsion, as shown in Figure 14a. The plan layouts are regular and symmetric (as shown in Figure 2), however, the stiffness and mass mainly distribute on the upper part of the structure along the vertical direction, easily generate the torsional effect which is adverse to the seismic design. According to the China seismic design code [22], the period ratio between the first torsional mode and the first translation mode should not be larger than 0.85 so as to prevent excessive structural torsion. The calculated result showed that the period ratio of this structure was about 1.02, which was larger than 0.85. This indicated that the torsional effect should not be neglected in the structural design for this hybrid structure.
## 4 Evaluation
### Case Study  
We conducted a experiment in vulnerability detection to evaluate the effectiveness of BlockMatch in real-world situations. The experiment utilized BlockMatch to locate the starting position of the vulnerability CVE-2022-0778 in the suspected vulnerable function, aiming to improve the efficiency of security researchers when analyzing vulnerabilities. CVE-2022-0778 is a vulnerability in OpenSSL caused by the improper handling of non-prime moduli in the BN_mod_sqrt function, which can potentially lead to an infinite loop and result in a denial of service (DoS) issue. The affected versions range from 1.1.1 to 1.1.11.  
Figure 11(a) shows a control flow graph of the confirmed vulnerability function (CVE-2022-0778). According to the symbol tables, we have identified the start position of the vulnerability as the red block. Figure 11(b) shows the CFGs of a function affected by  
Figure 11: Results of the ablation study.  
vulnerability CVE-2022-0778. Identifying the start position of the vulnerability in a function with hundreds of basic blocks is a time-consuming task, but it is crucial for security researchers to analyze the vulnerability. The existing function-oriented BCSD tools have difficulty in solving this last-mile problem. We use our approach, BlockMatch, to quickly and accurately identify the corresponding blocks in the suspected vulnerable function by searching for blocks similar to the red block. This helps save a significant amount of time in vulnerability analysis. We compare our approach with PalmTree and SAFE in this scenario, and Table 4 shows the results, where candidate-\(k\) represents the \(k\)th candidate basic block, ranked in descending order of similarity. Note that we filter out basic blocks with a similarity lower than 0.9. PalmTree and BlockMatch can both identify the start position of the vulnerability at \(k=1\), while SAFE requires \(k=4\) to identify the start position of the vulnerability. This indicates that BlockMatch and PalmTree have higher recall compared to SAFE in identifying the start block of vulnerabilities in functions. Additionally, when retrieving the top 5 candidates, BlockMatch retrieves one true positive and two false positives, while PalmTree and SAFE retrieve one true positive and four false positives. This suggests that BlockMatch has better discrimination ability between similar and dissimilar basic blocks compared to the other two baselines, which can better alleviate the workload of security researchers during vulnerability analysis.  
Figure 12: On the left is the control flow graph of the confirmed vulnerable function (CVE-2022-0778), with the red block indicating the start position of the vulnerability. On the right is the control flow graph from the suspected vulnerable function. Through our manual confirmation, the blue block represents the start position of the vulnerability.
## 3 Proposed Framework
### Accuracy Assessment  
To assess the performance of the proposed framework, the Confusion Matrix (CM) of the classification and various CM-derived parameters were utilized. These parameters included Overall Accuracy (OA), Kappa coefficient, Producer's Accuracy (PA), and User's Accuracy (UA). Figure 11 (in Appendix A) illustrates a hypothetical CM for n classes. Equations (A1)-(A4) (in Appendix A) also present the formulas for calculating the aforementioned metrics directly from the CM. It is important to highlight that the accuracy assessment was performed using a validation dataset (30% of the reference dataset). The validation dataset was not used during the training phase or model development. It should be highlighted that the Pearson Correlation Coefficient (PCC) was also utilized to investigate the correlation between the PMs of the Pa structure within each class. This analysis was conducted to justify the need for employing the PCA technique in the proposed methodology.
_Energies_**2015**, \(8\), 4629-4646; doi:10.3390/en8054629  
_Article_  
Modeling and Performance Analysis of State Transitions for Energy-Efficient Femto Base Stations \({}^{\dagger}\)  
**[PERSON]**  
School of Electronic Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Korea; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-820-0908; Fax: +82-2-821-7653  
\({}^{\dagger}\) This paper is an extended version of our paper published in [PERSON] Performance Analysis of Energy-Efficient Home Femto Base Stations. In Proceedings of International Conference on Electrical, Computer, Electronics and Communication Engineering, Paris, France, 24-26 June 2011.  
Academic Editor: [PERSON]  
_Received: 24 March 2015 / Accepted: 8 May 2015 / Published: 21 May 2015_  
**Abstract:** Lowering the energy required by base stations (BSs) is one of the hot issues nowadays in order to achieve green cellular networks. The energy consumption of femto BSs can be reduced, by turning off the radio interface when there is no mobile station (MS) under the coverage area of the femto BSs or MSs served by the femto BSs do not transmit or receive data packets for a long time, especially late at night. In the energy-efficient femto BSs, if MSs have any data packet to transmit and the radio interface of femto BSs is in the off state, MSs wake up the radio interface of femto BSs by using an additional low-power radio interface. In this paper, active (data), idle, active (signaling), sleep entering, sleep and waking up states are defined for the state model for the energy-efficient femto BSs, and the state transitions are modeled analytically. The steady-state probability of each state is derived thoroughly using a semi-Markov approach. Then, the performance of the energy-efficient femto BSs is analyzed in detail, from the aspects of energy consumption, cumulative average delay, cost and low-power radio signaling load. From the results, the tradeoff relationship between energy consumption and cumulative average delay is analyzed in detail, and it was concluded that an appropriate inactivity timer value should be selected to balance the tradeoff.  
**Keywords:** femtocell; energy consumption; semi-Markov approach
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 9 Future Directions and Development Trends
### Privacy-Preserving Models  
The challenge for privacy-preserving models is ensuring user data privacy while guaranteeing model performance and utility. This requires a multipronged approach, as outlined below. Privacy-preserving techniques such as anonymization during data preprocessing help to protect sensitive information before using it for model training. Another approach is to perform computations directly on user devices to minimize data transfer and centralization. This reduces the privacy risks associated with data transmission. Using trusted execution environments (TEEs) such as Intel SGX or ARM TrustZone secures computations within isolated environments, which protects user data from unauthorized access. Another way to preserve user privacy is by designing privacy-preserving metrics and evaluation methodologies.  
Federated model training involves training models across decentralized devices or servers without exchanging raw data. Privacy is preserved by aggregating model updates while keeping the user data local. Differential privacy is an approach to privacy preservation where a noise or perturbation is added to the data before the training process. This prevents the extraction of sensitive information from individual data samples as the model does not memorize specific data points. Techniques such as _homomorphic encryption_ allow computation on encrypted data without decrypting them. This approach preserves data privacy throughout the computation process. Protocols such as secure multiparty computation (MPC) enable multiple parties to compute a function while keeping their inputs private. This paves the way for collaborative model training without sharing raw data.  
The model aggregation and ensemble approach aggregates predictions from multiple models without sharing individual user data. This approach enables leveraging the collective knowledge of models while preserving user privacy. The development of privacy-preserving metrics and evaluation methodologies guarantees that model evaluation processes do not compromise user privacy. Lastly, compliance with legal and ethical frameworks like GDPR protects users' privacy rights.