esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Ap4A represents both a challenge and an opportunity in cancer therapy. Its role in promoting tumor cell survival under stress suggests that targeting its pathways could enhance therapeutic efficacy, yet its involvement in fundamental processes like DNA repair and metabolic adaptation necessitates a careful approach to avoid unintended consequences. Modulating Ap4A's interaction with purinergic receptors in the TME could offer new strategies to enhance immune surveillance or inhibit tumor progression. Similarly, its role in alternative RNA capping presents a potential avenue for disrupting stress-induced translation programs that drive treatment resistance. In ovarian and melanoma cancers, Ap4A functions largely as a suppressor of proliferation and transcriptional activation, whereas in gastrointestinal cancers, dysregulation of its metabolism through loss of FHIT may promote tumor progression. These mechanisms underscore Ap4A's potential as both a biomarker and therapeutic target, particularly in strategies aiming to modulate its synthesis, degradation, or receptor-mediated signaling.  
To fully harness Ap4A's therapeutic potential, future research must elucidate the molecular mechanisms regulating its synthesis, degradation, and cellular interactions. This will enable the development of precise modulators with minimal off-target effects, allowing for tailored interventions based on cancer type and disease stage. Additionally, clarifying whether ApnA (n = number of phosphates) molecules act as functional regulators or merely accumulate as metabolic by-products is essential for determining their true significance in tumor biology.  
In summary, Ap4A's multifaceted roles make it a compelling target for future cancer therapies. Addressing the current controversies and unanswered questions will be critical to translating its biological complexity into actionable clinical strategies, potentially paving the way for innovative treatments that improve patient outcomes.  
Conceptualization, C.R.; writing--original draft preparation, C.R., T.B. and K.T.; writing--review and editing, K.T. and T.B.; visualization, K.T.; supervision, C.R. and T.B.; project administration, C.R.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
The work was partially supported by the Italian Ministry of Health (Fondi Ricerca Corrente FA706).
## 3 Mathematical Model
### Formulation of the Mathematical Model
#### Energy cost (EC)  
Energy costs are related to the source of energy, i.e., electricity (\(ECC\)), solar energy (\(SEC\)), diesel fuel (\(DC\)), and gasoline (\(GC\)), and the in-house manufacturing, reworking, and outsourcing operations. The basic energy requirement is fulfilled by regular grid-based electricity and renewable energy from PV, while diesel and gasoline are auxiliary types of energy resources. These individual costs are represented as in Equations (12)-(15), respectively [49]:  
\[EC = \sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{1}\times EP_{j}\times(MQ_{jt} +CQ_{jt}+\sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{1}\times ER_{j}\times OQ_{jit} \times(1-\alpha_{ijt})\times\beta_{ijt}, \tag{12}\] \[SEC = \sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{4}\times SP_{j}\times(MQ_{jt} +CQ_{jt})+\sum_{i=1}^{J}\sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{4}\] (13) \[\times SR_{j}\times OQ_{ijt}\times(1-\alpha_{ijt})\times\beta_{ijt},\] \[DC = \sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{2}\times DP_{j}\times(MQ_{jt} +CQ_{jt})+\sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{2}\] (14) \[\times DR_{j}\times OQ_{jit}\times(1-\alpha_{ijt})\times\beta_{ijt},\] \[GC = \sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{3}\times GP_{j}\times(MQ_{jt} +CQ_{jt})+\sum_{i=1}^{J}\sum_{j=1}^{J}\sum_{t=1}^{T}\gamma_{3}\] (15) \[\times\ GR_{j}\times OQ_{ijt}\times(1-\alpha_{ijt})\times\beta_{ ijt}.\]
# High-Order Aberrations: A Key Factor in Accommodative Dysfunctions
###### Abstract  
This study investigated the relationship between high-order aberrations (HOAs) and accommodative dysfunctions by analyzing their changes with accommodation. Understanding this relationship is important for understanding the mechanisms underlying these conditions. Sixty-three subjects were divided into five groups: control, infacility of accommodation (INFA), excess of accommodation (EA), insufficiency of accommodation (INSA), and symptomatic without dysfunction (SWD). Variations in root-mean-square (RMS) of the 3 rd, 4 th, 5 th, and 6 th orders and HOAs, and fluctuations of RMS HOAs, were measured using a Shack-Hartmann aberrometer at different accommodative stimuli and during residual accommodation after their removal, in the following order: 0.00 D, 1.00 D, 0.00 D, 2.45 D, 0.00 D, 4.73 D and 0.00 D. The SWD group showed a significant increase in RMS HOAs during accommodation and residual accommodation. In contrast, the EA group showed an improvement in the ocular optical quality at higher stimuli. Different patterns of changes in the 3 rd, 4 th, 5 th, and 6 th orders were observed across all groups, and fluctuations of RMS HOAs increased significantly in the SWD group during accommodation and residual accommodation. These distinct patterns of aberration changes in different accommodative dysfunctions suggest a potential link between their underlying mechanisms, providing insights that may aid their earlier diagnosis and improved management.  
high-order aberrations; accommodation; accommodative dysfunctions +
Footnote †: journal:  
as not all subjects exposed to similar conditions, such as prolonged near-vision activities, develop accommodative dysfunctions [10].  
As mentioned above, there is a close relationship between HOAs and accommodation, and some ocular aberrations have an important impact on the accommodative response [6]. Therefore, the presence of certain aberrations, their signal or how they change during accommodation in certain subjects may lead to dysfunctional accommodative responses. However, this relationship between HOAs and accommodative dysfunctions has not yet been explored. This study seeks to improve the understanding of these conditions, investigating how HOAs change in subjects with accommodative dysfunctions and with symptoms associated with near vision tasks but not yet diagnosed for any accommodative dysfunction. Studying the association between HOAs and accommodative dysfunctions may provide new insights into the underlying mechanisms involved in these conditions and help to understand why some individuals develop accommodative dysfunctions while others do not. Identifying the distinct patterns of aberrations' changes during accommodation associated with each accommodative dysfunction could also aid in early detection and diagnosis, providing a more targeted approach to managing these conditions.
## 4 Experiment  
In order to validate the performance of the mentioned method, experiments were conducted on the sample with the photoresist film layer. The system was performed by referring to Figure 1, with a scanning stage (PI, the resolution is \(\pm\) 0.5 nm), a microscopy objective (Olympus, 100X, NA = 0.9), a CCD camera (WAT-902H, 576 \(\times\) 768 pixels, pixel size 6.25 \(\times\) 6.25 um), a 1024 \(\times\) 768 pixels DMD (TI) with the pixel resolution 13.6 um, an LED broadband light source (central wavelength is 580 nm, bandwidth is 160 nm), and two tube lenses provided by DAHENG Optics. The diagrams of the layer structures on the samples are shown in Figure 7.  
In the experiment, the film was made by a photoresist RZJ-304 on the wafer using a spin coater. The refractive index provided by Filmetrics was 1.674. The pattern was formed on the silicon substrate through the photolithography process. The PZT stage scanned 4 um with the stepping pace of 50 nm in the z-direction. For each step, the pattern with the period of 16 pixels was shifted by \(\pi/4\) phase to calculate the modulation distribution, and the experimental value of \(FWHM_{1}\) was calculated as 1443 nm. Then, the peak location of the MDR for each pixel was extracted using the IPS method.  
Figure 8a shows the captured image at the scanning distance of 1700 nm. The black line in Figure 8b is the original MDR curve of a pixel at the photoresist area. Although the fitting curve (red and plus-marked) obtained by the IPS-SIM does not entirely match the simulation, the peaks (blue and yellow star) are successfully separated. The thickness at the point obtained by the presented method is 1382.47 nm.  
Figure 6: The influence of the initial peak position.  
To confirm the accuracy of the developed method, we made a comparison between the commercial Profilometer and the IPS-SIM. The measurement provided by the commercial Profilometer is 1364.60 nm, as presented in Figure 9a. This result demonstrates that the relative uncertainty of the IPS-SIM is less than 17.80 nm. The 3D structure of the sample is reconstructed, as shown in Figure 9b. The cross-section profile at X = 70 is presented in Figure 9c. Furthermore, five repetitive tests were carried out to estimate the repeatability of the proposed method. Figure 9d describes the measured result at the same point five times, which indicates that the repeatability of the measurements is less than 2 nm.  
Figure 8: (**a**) The image of the patterned sample captured by CCD at the scanning position of 1700 nm. (**b**) The normalized MDR curve (black) at the pixel (162,72) and the IPS-SIM fitting result.  
Figure 7: The schematic of the measurement system.
# Secure Internal Data Markets
## 3 Security Issues of Internal Data Markets
### Data Locality  
Some global data markets store data locally on dedicated databases; others, such as Data Market Austria (DMA), solely act as brokering platforms, i.e., they only store meta information on the data that are required to handle the brokering. However, the data transfer itself is completed outside of the market, basically leaving many issues, especially those related to security, to the users. In an internal market, especially when considering a small institution, data are typically local, regardless of whether the actual transfer function is an integral part of the market software, or is simply done through other means. This also opens up attack vectors through the database administrator responsible for the data store; thus, measures that take malicious actions from this side into account need to be put in place, including securing the Audit and Control mechanisms (see Section 4.2). Furthermore, this locality might also result in other security-related implications, especially in fields that require obeying certain regulations, such as BASEL or Sarbanes-Oxley (SOX) [31]. Contrary to a global market that acts data agnostic and only trades links, these issues have to be considered when designing a secure internal data market, as well as considering issues regarding the secure storage and transfer of the data itself.
###### Abstract  
The sparse matrix-vector product (SpMV), considered one of the seven dwarfs (numerical methods of significance), is essential in high-performance real-world scientific and analytical applications requiring solution of large sparse linear equation systems, where SpMV is a key computing operation. As the sparsity patterns of sparse matrices are unknown before runtime, we used machine learning-based performance optimization of the SpMV kernel by exploiting the structure of the sparse matrices using the Block Compressed Sparse Row (BCSR) storage format. As the structure of sparse matrices varies across application domains, optimizing the block size is important for reducing the overall execution time. Manual allocation of block sizes is error prone and time consuming. Thus, we propose AAQAL, a data-driven, machine learning-based tool that automates the process of data distribution and selection of near-optimal block sizes based on the structure of the matrix. We trained and tested the tool using different machine learning methods--decision tree, random forest, gradient boosting, ridge regressor, and AdaBoost--and nearly 700 real-world matrices from 43 application domains, including computer vision, robotics, and computational fluid dynamics. AAQAL achieved 93.47% of the maximum attainable performance with a substantial difference compared to in practice manual or random selection of block sizes. This is the first attempt at exploiting matrix structure using BCSR, to select optimal block sizes for the SpMV computations using machine learning techniques.  
sparse matrices; artificial intelligence; machine learning; compressed sparse row (CSR); block CSR; decision trees; random forest gradient boosting; ada boost; high performance computing (HFC)
## 5 Performance Analysis
### Probability of Error from Source to Relay Node
However, with IFP, CF can be implemented without the limitation of any self-noise adding to the effective noise. However, the penalty that is paid is the extra power required at the transmitter. We analyze the effect of this penalty on performance in terms of outage probability in the next section.
## 6 Experimental Results and Discussion
### Influence of Fusion of Different Measuring Points  
From the previous section, when the number of measuring points (sensors) increases, the diagnostic accuracy is not necessarily improved. In addition, considering the calculation complexity, the larger number of measuring points means the increase of the model parameters, which will increase the calculation time. Therefore, it is necessary to study how many measuring points can make the calculation time effectively shorten under the condition of ensuring high recognition accuracy in the next section. Further, under the condition that the number of measuring points is fixed, our research can choose which measuring points can have better recognition accuracy.
## 3 Data and Methodology
### Modeling Methods
#### 3.4.3 Logistic Regression  
Logistic regression (LR) tries to fit the most suitable regression model to describe the relationship between several independent variables and the dependent variable [71]. The output of the LR is an approximate logarithmic probability of real classification, which is actually a value transformed from the result of linear regression by the logarithmic function [72]. The calculation of the LR is shown as follows:  
\[p=\frac{1}{1+e^{-(B_{0}+B_{1}x_{1}+B_{2}x_{2}+\ldots+B_{n}x_{n})}} \tag{5}\]  
where \(p\) is the probability of the landslide occurrence, \(n\) represents the number of landslide conditioning factors, \(B_{0}\) is a constant which represents the intercept of the fitting model, and \(B_{i}(i=0,1\ldots 1)\) are the regression coefficients.
## 2 Materials and Methods
### TL and OSL/IRSL Dating  
For the TL and OSL/IRSL dating investigation, the material was sampled with a low-speed drill from the inner part of the bricks, and all the procedures were followed under dim red light according to the fine-grain protocol [36]. The obtained polymineral fraction (4-11 \(\upmu\)m) was settled onto stainless steel discs and used for both TL and OSL/IRSL measurements. This fraction, mainly consisting of quartz and feldspars, can be excited using both blue (OSL) and infrared radiations (IRSL), the latter being effective only on feldspars.  
TL/OSL/IRSL measurements were performed with a Rise TL-DA-20 reader, using blue-LEDs (470 \(\pm\) 30 nm, -72 mW/cm\({}^{2}\)) for OSL and infra-red LEDs (\(\uplambda\) = 850 nm, -270 mW/cm\({}^{2}\)) for IRSL, detecting the emitted photons with an Electron Tube PDM 9107Q-AP-TTL-03, coupled with a U-340 filter for OSL and a Schott BG3/BG39 filter combination for IRSL measurements. The system is equipped with an internal \({}^{90}\)Sr-\({}^{90}\)Y beta source (dose rate: 0.127 \(\pm\) 0.05 Gy/s).  
To evaluate the Equivalent Dose (E.D.), two different techniques were applied: the Multiple-Aliquot Additive protocol (MAAD, Aitken [20] was used for TL, and the Single Aliquot Regenerative dose (SAR, ref. [37] protocol was used for OSL and IRSL.  
The annual dose rate was indirectly derived from the measurement of the radioactivity of the samples. The U and Th concentrations were obtained with total alpha counting using ZnS scintillator discs, assuming a Th/U concentration ratio equal to 3.16 ([PERSON], 1985). The contribution due to \({}^{40}\)K content was deduced from the total concentration of K obtained using flame photometry. For IRSL, the effect of the internal K-content of feldspar was considered. Alpha particles are less effective in inducing luminescence than beta and gamma radiation. This was considered introducing the a-value determination, which is based on the comparison of the luminescence signals induced by alpha and beta laboratory irradiations. Because water absorbed part of the radiation that would otherwise reach the sample, the saturation water content was evaluated for each brick. The F value (i.e., the fraction of saturation to which the assumed average water content corresponds) was considered equal (75 \(\pm\) 10)% for all samples, considering the available information on humidity in the area. The cosmic rays' contribution to the final dose rate was added [38]. The TL/OSL/IRSL dating was performed in the LAMBDA Lab of the Department of Material Science of the University of Milano-Bicocca, Milano, Italy [39].
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## Appendix D  
By the definition of association probability,  
\[\mathcal{Q}_{NC_{k}} =\mathbb{E}_{R_{i}}\left[\mathbb{P}\left(R_{1}\;>\;D\,,P_{k}R_{k} ^{-\alpha_{k}}/P_{i}R_{i}^{-\alpha_{i}}\;\geqslant\;\beta_{i},\;\forall i\
eq k \right)\right]\] \[=\mathbb{E}_{R_{i}}\left[\mathbb{P}\left(R_{1}\;>\;D\,,P_{k}R_{k} ^{-\alpha_{k}}\;\geqslant\;\max_{i,i\
eq k}\beta_{i}P_{i}R_{i}^{-\alpha_{i}} \right)\right]\] \[=\mathbb{E}_{R_{i}|R_{1}>D}\,\left[\prod_{i=1,i\
eq k}^{K} \mathbb{P}\left(P_{k}R_{k}^{-\alpha_{k}}\;\geqslant\;\beta_{i}P_{i}R_{i}^{- \alpha_{i}}\right)\right]\times\mathbb{P}\left(R_{1}\;>\;D\right)\]  
and  
\[\mathcal{Q}_{C_{ij}} =\mathbb{E}_{R_{i}}\left[\mathbb{P}\left(R_{1}>D\&\beta_{i}P_{i} R_{i}^{-\alpha_{i}}/P_{k}R_{k}^{-\alpha_{k}}<\beta_{k}\
u_{i}\
eq k\&1<\beta_{j}P_{j}R_{j}^{- \alpha_{j}}/P_{k}R_{k}^{-\alpha_{k}},j\
eq k\right)\right]\] \[=\mathbb{E}_{R_{i}}\left[\mathbb{P}\left(R_{1}>D,P_{k}R_{k}^{- \alpha_{k}}<\max_{i,i\
eq k}\beta_{i}P_{i}R_{i}^{-\alpha_{i}}<\beta_{k}P_{k}R_{ k}^{-\alpha_{k}},1<\beta_{j}P_{j}R_{j}^{-\alpha_{j}}/P_{k}R_{k}^{-\alpha_{k}},j \
eq k\right)\right]\] \[=\mathbb{E}_{R_{i}|R_{1}>D}\,\left[1\left(i=j\right)\mathbb{P} \left(P_{k}R_{k}^{-\alpha_{k}}<\beta_{i}P_{i}R_{i}^{-\alpha_{i}}<\beta_{k}P_{k}R_{ k}^{-\alpha_{k}}\right)\prod_{i=1,i\
eq k,i\
eq j}^{K}\mathbb{P}\left(\beta_{i}P_{i}R_{ i}^{-\alpha_{i}}<\beta_{k}P_{k}R_{k}^{-\alpha_{k}}\right)\right]\] \[\times\mathbb{P}\left(R_{1}>D\right).\]  
The above two equations use the conditional PDFs of \(R_{i}\), which are:  
\[f_{R_{i}|R_{1}>D}\left(r\right)=\left\{\begin{aligned} & 2\pi\lambda_{1}r\exp\left(-\pi \lambda_{1}r^{2}\right)\Big{/}\exp\left(-\pi\lambda_{1}D^{2}\right),\;r>D\;for \;i=1\\ & 2\pi\lambda_{i}r\exp\left(-\pi\lambda_{i}r^{2}\right)\Big{/} \exp\left(-\pi\lambda_{i}D^{2}\right),\;r>0\;for\;i>1\end{aligned}\right.\]  
The remaining derivation of \(\mathcal{Q}_{NC_{k}}\) and \(\mathcal{Q}_{C_{k}}\) follows along the same lines as that of Lemma 1, and it is hence skipped.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Non-Destructive Testing Methods  
The damage occurring on large and complex bridges is difficult to detect through visual inspection, and in many cases, it is not feasible to conduct extensive vibration tests during the service life of the bridge to assess its health condition. Therefore, non-destructive testing (NDT) provides the possibility of extending the life of bridges equipped with BHM systems. NDT is a testing and analysis procedure designed to evaluate the quality of components, materials, or systems, and detect damage or defects in materials or structures without causing any physical damage [202]. The working principle of NDT depends on the measured parameters, structure type, and its physical properties [203].  
[PERSON] et al. [204] proposed a self-referencing NDT method based on pulse response testing for detecting and estimating the damage degree of RC bridge decks. Ultra-high-pressure hydro-blasting was performed on the deck, and infrared data analysis was conducted to observe potential damage. [PERSON] et al. [205] developed an acoustic emission monitoring method for detecting RC bridge decks, and they used the method to study acoustic emission signals caused by heavy rain, which revealed severe cracking deep inside the deck, indicating the effectiveness of the proposed method. [PERSON] et al. [206] proposed a bridge maintenance method that combines visual inspection and NDT. In tests on six bridges in Croatia, the proposed method accurately detected steel corrosion on structural elements. [PERSON] and [PERSON] [207] studied a damage detection method for steel components on steel bridges. They used the DL method to identify the results of infrared thermography imaging of bridges, and the proposed method accurately identified corrosion and delamination on the steel surface in 200 thermal images. [PERSON] et al. [208] studied the application of NDT  
Figure 9: Relationship between the first-grade natural frequency of the No. 1 beam and the No. 2 beam with temperature change [190].  
methods in detecting cable defects on bridges. They developed a quantitative identification method based on magnetic flux detection and validated and evaluated the proposed method on FEM and laboratory models. NDT appears to be the best choice for detecting ancient bridges with preservation value. [PERSON] et al. (2019) conducted damage detection research on the underwater foundation of ancient stone arch bridges in China using sonar-based technology. They first measured the riverbed terrain with a multibeam echosounder to analyze the scouring condition of the underwater foundation and then scanned it using sonar imaging technology. The results clearly showed the damage to the bridge structure underwater.  
As the service life of large bridges that are already constructed continues to increase, higher demands will be placed on NDT techniques in the future. Updating existing detection methods and instruments is crucial for the development of NDT technology. The NDT methods mentioned above are listed in Table 6.