esa-sceva/satcom-chunk-collection huggingface dataset:
# The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process
## 3 Energy Efficiency Analysis
### Energy Efficiency  
In this paper, we define the energy efficiency as the ratio of the achievable rate per unit area \(\tau\) to the network power consumption \(P\), in bps/Hz/W, which is expressed as:  
\[\eta_{EE}=\frac{\tau}{P}(bps/Hz/W) \tag{37}\]  
The achievable rate per unit area of a two-tier HetNets is defined as the product of the base station deployment density, the coverage probability (Formula (17)), and the average achievable rate of a typical user (Formulas (20) and (27)) [52], which can be written as:  
\[\tau=\lambda_{m}P_{C\_m}\tau_{m}+\lambda_{p}P_{C\_p}\tau_{p}^{PHP} \tag{38}\]  
To study the EE of HetNets, we should pay attention to the power consumption of the base station (referred to as \"power consumption\"), which consists of static power consumption and dynamic power consumption. The dynamic power consumption mainly comes from the information transmitted by the base station, and the static power consumption is mainly composed of a power amplifier, refrigerator, signal processing, backup battery, etc. In this paper, the power consumption models of base stations in two-tier heterogeneous networks can be given by:  
\[P_{m}=\xi_{m}\mu_{m}+P_{m0} \tag{39}\]  
\[P_{p}=\xi_{p}\mu_{p}+P_{p0} \tag{40}\]  
where \(\mu_{m}\) and \(\mu_{p}\) are the transmit powers of the base station of two-tier heterogeneous network, and \(P_{m0}\) and \(P_{p0}\) are the static power consumptions of the base station, which are independent of the transmit power of the base station. \(\xi_{m}\) and \(\xi_{p}\) are the load-related power consumption coefficients, representing the amount of power consumption proportional to the service load of the MBS and the PBS, while \(N_{m}\) and \(N_{p}\) are the numbers of users served by the MBS and the PBS, respectively. Therefore, \(\xi_{m}\) and \(\xi_{p}\) can be replaced by \(N_{m}\) and \(N_{p}\). So, the power consumption of the base station in the two-tier heterogeneous network can be obtained as follows:  
\[P_{m}=N_{m}\mu_{m}+P_{m0} \tag{41}\]  
\[P_{p}=N_{p}\mu_{p}+P_{p0} \tag{42}\]  
where, according to the literature [49], \(N_{m}\) and \(N_{p}\) are:  
\[N_{m}=\frac{A_{m}\lambda_{u}}{\lambda_{m}} \tag{43}\]  
\[N_{p}=\frac{A_{p}\lambda_{u}}{\lambda_{p}} \tag{44}\]Therefore, the total power consumption of the two-tier HetNets is written as:  
\[P=\lambda_{m}P_{m}+\lambda_{p}P_{p} \tag{45}\]  
By substituting Formulas (38) and (45) into Formula (37), we obtain the network EE as follows:  
\[\eta_{EE}=\frac{\tau}{P}=\frac{\lambda_{m}P_{\text{C}_{-}m}\tau_{m}+\lambda_{p} P_{\text{C}_{-}p}\tau_{p}^{PHP}}{\lambda_{m}P_{m}+\lambda_{p}P_{p}} \tag{46}\]
# CCFNet: Collaborative Cross-Fusion Network for Medical Image Segmentation
## 3 Method
### Transformer Branch  
The CNN branch obtains rich local features under a limited receptive field through convolution operations, while the Transformer branch performs global self-attention through attention mechanisms. The Transformer branch has the same input image \(x\in R^{H\times W\times D_{0}}\) as the CNN branch. Following [13; 16], in the patch embedding we first divide \(x\) into an \(N=\frac{H}{P}\times\frac{W}{P}\) sequence of patches, the size of each patch is \(P\times P\), and the default setting is 16. After splitting the input images into small patches, the patches are flattened to a sequence  
Figure 3: The architecture of the proposed CCFNet. (**a**) Shows the convolution process of SEConv; (**b**) shows the structure of the CCFM, there are two branches in the module whose inputs are \(C_{i-1}\) and \(T_{i-1}\). The Transformer branch is composed of collaborative self-attention fusion blocks, and the CNN branch is composed of detail feature extractor blocks and spatial feature injector blocks.  
of 2D patches \(\{x_{p}^{i}\in R^{p^{2}\cdot D_{0}}|i=1,\ldots,N\}\) and fed to a trainable linear layer, which converts the vectorized patches \(x_{p}\) into a sequence embedding space with an output dimension of \(D_{4}\), and then, in order to facilitate the fusion with the CNN branch, the reshape operation is used to generate image \(T_{0}\in R^{\frac{H}{6}\times\frac{W}{6}\times D_{4}}\), which can be expressed as  
\[T_{0}=\text{Reshape}([x_{p}^{1}E;x_{p}^{2}E;\ldots;x_{p}^{N}E]), \tag{3}\]  
where \(E\in R^{(p^{2}\cdot D_{0})\times D_{4}}\) is the patch embedding projection. The Transformer branch in the parallel fusion layer is connected to six CSF blocks of attention operations, and the CSF block consists of a ConvAttention and a CMLP (convolution multi-layer perceptron). The feature map output \(T_{i}\) of each layer has the same resolution size (\(\frac{H}{16}\), \(\frac{W}{16}\), \(D_{4}\)). Therefore, the output of the \(i\)-th layer can be written as follows:  
\[T_{i}^{\prime}=\text{ConvAttention}(\text{Normal}(C_{i-1}^{\prime}),\text{ Normal}(T_{i-1}))+T_{i-1}, \tag{4}\]  
\[T_{i}=\text{CMLP}(\text{Normal}(T_{i}^{\prime}))+T_{i}^{\prime}, \tag{5}\]  
where \(C_{i-1}^{\prime}\) and \(T_{i-1}\) are the two inputs of the CSF block, \(C_{i-1}^{\prime}\) is the intermediate output of the \(i\)-th layer DFE module on the CNN branch, which has the same resolution as \(T_{i-1}\), and \(T_{i}\) is the encoded image representation. The structure of a CSF block is illustrated in Figure 2(b). More detailed operations are described in the parallel fusion layer.
# Self-Learning Power Control in Wireless Sensor Networks
## 4 Method
### Q-Learning Transmission Power Control (QL-TPC)
#### 4.3.1 MDP Parameters Definition  
In our system, the episodes are the transmission of \(N\) packets in a window \(W\). Therefore, the state-action-state transition and reward acquirement happen after each \(k\)-th window. During one window, the same action is taken. The actions are chosen following the \(\epsilon\)-greedy strategy, based on the exploration factor \(\epsilon\in[0,1]\). If \(\epsilon\gg 0\), the actions are taken randomly (\(a\in U(a_{min},a_{max})\)) with high probability, in order to learn how the environment reacts to different available decisions. Otherwise, when \(\epsilon\ll 1\), the system exploits the knowledge acquired by selecting the actions that have given the maximum action-values. More specifically, the actions are chosen by comparing a random value \(x\in U[0,1]\) with the parameter \(\epsilon\), which can vary in time:  
\[a=\begin{cases}U(a_{min},a_{max})&\text{if }x\leq\epsilon\\ \arg\max_{a}\ Q_{k}(s_{k},a)&\text{if }x>\epsilon\end{cases}\]  
The actions represent the available transmission power levels: \(a_{k}=p_{tx,k}\). The state is a combination of rounded averaged retransmissions and Clear Channel Assessments (CCA) attempts, over \(W\). The state describes the status of a link, providing an evaluation of interference at both the transmitter (i.e., CCA) and receiver sides (i.e., retransmissions). The state is expressed as:  
\[s_{k}=\|\overline{retr_{k}}\|+\|\overline{cca_{k}}\|\cdot(n_{retr}+1), \tag{8}\]  
where \(n_{retr}\) is the maximum number of retransmissions, \(\overline{retr_{k}}\) and \(\overline{cca_{k}}\) are the number of retransmissions and CCA attempts, at the \(k\)-th episode, respectively. Each retransmission is attempted for a number of maximum CCA, \(n_{CCA}\), which is reset after the packet is sent. Therefore, the total maximum CCA for one packet transmission is \(n_{CCA}^{tot}=n_{CCA}\cdot(n_{retr}+1)\). The rewards are defined as a combination of PRR, quantized linearly over a \(m_{prr}\)-level scale, and \(n_{prx}\) power levels; higher reward suggests higher PRR and lower power level, whereas lower reward suggests lower PRR and higher power level. The calculation of the reward is given by:  
\[r_{k}=\Delta\cdot[(prr_{k}^{q}-1)\cdot n_{ptx}+(n_{ptx}-p_{tx,k})-\frac{m_{r}} {2}], \tag{9}\]  
where \(\Delta\) is the quantization step size factor between two consecutive quantization levels. \(prr_{k}^{q}\) and \(p_{tx,k}\) are the quantized PRR and power levels at the \(k\)-th window of transmitted packets. \(m_{r}\) is the number of quantization levels of the reward and is expressed as \(m_{r}=m_{prr}\cdot n_{ptx}\), with \(m_{prr}\) the number of quantized PRR levels.  
The nodes generate a Q-matrix that contains the Q-values of state-action combinations, where the states and actions are the rows and columns of the matrix, respectively. The Q-values are initialized with null values. The transmission power is initialized with the minimum value.
## 2 Methods
### Screening  
We conducted the review according to guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for screening articles during the systematic review process (Figure 3).  
Two researchers independently screened articles for relevance and subsequently assessed the quality and relevance of the articles for inclusion in this review. Any conflicting decisions were resolved by discussion.  
We first searched for records on three databases: PubMed, Web of Science, and Scopus. This yielded 123, 360, and 297 articles, respectively, for a total of 780. From these 780 articles, we removed 210 duplicate records, resulting in 570 articles. We screened these 570 articles by examining their title and abstracts to determine whether they fit the topic of our review. To determine that the article fits the topic, we determined if the article was on the topic of environmental psychology in VR or, in other words, that it involved each of the three key concepts: (1) VR, (2) nature, and (3) psychological effects. Thus, we excluded 45 articles  
\begin{table}
\begin{tabular}{c c} \hline
**Concept** & **Keywords** \\ \hline Nature & (nature OR forest OR park OR “natural environment” OR “urban green space” OR green) \\ \hline Mental & mental OR psycho * OR mood OR restoration OR recovery OR stress OR attention \\ \hline Real and virtual & (virtual NEAR/5 real) OR (virtual NEAR/5 physical) \\ \hline \end{tabular}
\end{table}
Table 1: Keywords used to represent each of the concept for database search.  
Figure 3: Flow chart of screening and selection process. We retrieved articles from PubMed, Web of Science, and Scopus. Duplicates were removed and the remaining articles were screened for relevance to environmental psychology and virtual reality (VR). Finally, the full texts of relevant articles are retrieved and screened, resulting in 12 articles for review.  
that are reviews and 513 articles that are on unrelated topics. Many of these articles used the keywords \"nature\" and \"real\" in other contexts.  
For all articles with relevant topics, we retrieved the full text for screening. We screened the full text to determine that the study involved both a VR experiment and a real-world experiment. We excluded three articles that used only a VR experiment but had no physical real-world experiment.  
One study [32] used images of nature displayed on a computer in the VR condition. As it was the only study to use 2D images, we excluded it as it was in a less immersive mode than either the 360-degree video or 3D virtual environment, which was what most other studies used.  
One article [33] only involved the virtual experiment. However, they modeled the virtual environment based on the physical environment of a previous study and compared the effects of both virtual and physical environments, which fit the other studies being reviewed and we chose to include it.
# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 3 Results and Discussions
### Thematic Maps of Groundwater Contamination Conditioning Factors  
The present study used eight conditioning factors for mapping a contamination probability map. The maps of the used conditioning factors are shown in Figure 5a-h. The central area of the buffer zone is very close to the Hunza River; therefore, the distance is not very far at the center, as can be seen from the map shown in Figure 5a.  
The TWI map shown in Figure 5b demonstrates that a considerable percentage of the deliberated area has relatively high levels of wetness. The elevation values in the study area vary between 1294 m to 7330 m, and most of the high elevation regions are on the north side of the study area, as shown in Figure 5c. In addition, the study area has considerably diverse slopes due to its topography, as shown in Figure 5d. Figure 5e demonstrates six classes of land use in the region, including agriculture, bare areas, shrubs, urban area, snow, and water bodies, with a considerable proportion of bare areas and shrubs. Figure 5f shows that the soil of the considered study region is comprised of clay, loam sand, sandy clay, and silt, where silt is the most dominant soil type present. In addition, metamorphic rocks and granite gneiss are present in most study areas, as demonstrated in Figure 5g. The drainage density map in Figure 5h shows that the drainage density is considerably high in the central part of the buffer zone, where the Hunza River runs alongside the KKH, while traversing it at certain points.  
Figure 4: Groundwater nitrate concentration map for the study area.  
Figure 5: Groundwater conditioning factors: (**a**) distance to river, (**b**) twi, (**c**) elevation, (**d**) slope, (**e**) land use, (**f**) soil type, (**g**) lithology, (**h**) drainage density.
## 2 Evolution, Virulence and Pathogenicity of _E. coli_ O157:H7
### Transmission of E. coli O157:H7 to Humans and Animals
#### 2.1.3 Person-To-Person Transmission  
The transmission of _E. coli_ O157:H7 infection most often occurs through the faecal-oral route, primarily in settings where infected individuals come in close contact with others, such as in households, daycare centres, and healthcare facilities [64; 65]. Children are more vulnerable than adults due to their immunological immaturity and limited understanding of appropriate hygiene practices [66]. In numerous instances, there has been a correlation between the consumption of shared contaminated food or water and the occurrence of an outbreak, thereby emphasizing the importance of implementing strict control measures in healthcare facilities and hygiene practice amongst people living in shared apartments.
## 4 Experimental Result Analysis
### Influence of Stud Spacing and Arrangement  
The arrangement and spacing of the studs significantly impacted the group stud effect and the overall load-bearing capacity of the connection. The experimental results showed that in the more densely arranged specimens (Z-40 and Z-32), the reduction in shear capacity was more pronounced. This was due to the greater influence of the group stud effect in densely packed arrangements, where the outer studs bore more shear force than those located in the center, leading to an uneven distribution of load across the studs.  
The strain data further confirmed this observation. The load distribution pattern, characterized by \"higher strain at both ends and lower strain in the middle,\" persisted throughout the loading process for specimens Z-40 and Z-32. This non-uniform distribution of shear forces is a key characteristic of the group stud effect and directly affects the failure mode and load-bearing capacity of the studs.  
In contrast, the diagonal arrangement of studs in specimen Z-16 resulted in a more uniform load distribution, thereby mitigating the group stud effect. The load-bearing capacity and ductility of this specimen were significantly better than those of the specimens with parallel stud arrangements. Similarly, the diagonal arrangement used in the scaled specimen S-13 yielded similar results, although the smaller size of the specimen further reduced the group stud effect.
## 3 Results
### Effect of Item Difficulty and Discrimination on Model Performance  
The impact of item difficulty (\(\beta\)) and discrimination index (DI) on model performance was analyzed using logistic regression, correlation analysis, non-parametric comparisons, and categorical testing.  
Logistic regression (Supplementary Table S2) revealed that item difficulty was a consistent and significant negative predictor of the model accuracy. For instance, GPT-4o at T = 1 had an odds ratio of 0.07 (95% CI: [0.01, 0.44]; \(p\) = 0.005), indicating a marked decline in correct responses for harder items. In contrast, DI was not a reliable predictor; although some models, like Deepseek-R1, showed high odds ratios (e.g., OR = 40.5), these lacked statistical significance.  
[PERSON]'s correlation analysis (Supplementary Tables S3 and S4) further confirmed these findings. Models such as LLaMA 3.2 450B, Anthropic Haiku, Owen 2.5, and Gemini 1.5 demonstrated strong correlations between item difficulty and accuracy (r = 0.48-0.60; \(p\) < 0.001), while GPT-o1 and GPT-4o showed moderate but significant correlations (r \(\approx\) 0.33-0.40). Other models, including Deepseek-R1, LLaMA 3 70B, and Anthropic Sonnet, showed negligible associations, indicating a lower sensitivity to difficulty.  
These results were mirrored by the Mann\(-\)Whitney U tests, which identified significant differences in item difficulty between correct and incorrect responses for models with high correlations (\(p\) < 0.01; [PERSON]'s d > 1.0). Models with weak correlations, such as GPT-4o (T = 1) and Deepseek-R1, showed no significant differences and small effect sizes, confirming the variability in difficulty sensitivity across models.  
Chi-square tests (Supplementary Table S5) using categorical groupings of item difficulty (easy, moderate, hard) showed significant associations with accuracy in most models (e.g., GPT-4o: \(\chi^{2}\) = 16.97, \(p\) < 0.001; Cramer's V = 0.52), indicating that harder items were more likely to be answered incorrectly. In contrast, the discrimination category had no significant effect on model performance (all \(p\) > 0.5), with consistently low Cramer's V values (<0.25), suggesting that LLMs are generally insensitive to item discrimination.  
Figure 2: Comparison of model accuracies and Rasch-based abilities, reinforcing the observed performance disparities.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 2 Fault Models for Delay Defects
### Advantage of LOC over LOS  
The major difference between LOC and LOS lies in the launch of transition. In LOC, the transition is launched at the at-speed functional launch clock cycle, but in LOS, the transition is launched at the last shift cycle. The LOS scheme is difficult to implement for high operating frequency because of the complexity of generating a high-speed switching scan enable (SE) signal. Figure 2a illustrates the waveform of the LOC scheme for pattern generation. Here the SE signal is de-asserted before the launch clock edge. Hence avoiding the need for high-speed switching SE signal. Figure 2b illustrates the waveform for the LOS scheme. Here the clock for launching the transition is in shift mode, and the SE signal has to be switched between the launch and capture clock cycle, which demands a high-speed SE signal.  
Figures 3 and 4 shows the result comparison of LOS and LOC based test pattern generation scheme for delay faults that evolved over time [14]. Figure 3 compares the test coverage of both the schemes produced by the commercial ATPG tool. It can be inferred that the LOC scheme has evolved over time and provides better test coverage. Figure 4 represents the pattern count for both the schemes generated by the commercial ATPG tool over the years. By comparing both Figures 3 and 4. It can be inferred that the test coverage of LOC has been improving at the expense of pattern count. As compared to 2006, the test coverage of LOC has improved and is very close to that of LOS in 2012. But the pattern count is more. In 2019 noticeable test coverage increase is found in the case of LOC than in LOS, but still, the pattern count is higher. This is the case when the test coverage of LOC is more than the LOS. But in [14], it is reported that the pattern count will be almost equal for the same test coverage in the year 2019. This shows that the LOC scheme has been evolving in commercial tools in terms of test coverage. But in the case of run time, the LOS scheme remains better. Still, the LOC scheme-based pattern generation can be adopted, especially for low-cost testers.  
Figure 1: (**a**) TDF based pattern generation to propagate the fault at node A; (**b**) PDF based pattern generation for the path A to D.  
Figure 3: Result comparison of LOC and LOS based test coverage (commercial ATPG tool) with the evolution of years [14].  
Figure 2: (**a**) Waveform for LOC based scheme for pattern generation; (**b**) Waveform for LOS based scheme for pattern generation.
## 2 Materials and Methods  
### Instrumentation  
**Scanning electron microscopy.** Elemental analyses and SEM images of the morphology of the metal-BPs collected with a JOEL JSM-6390 LV electron microscope.  
**Single crystal X-ray diffraction.** Measured crystals were prepared under inert conditions immersed in perfluoropolyether as protecting oil for manipulation. Suitable crystals were mounted on MiTeGen Micromounts\({}^{\text{TM}}\), and these samples were used for data collection. Data for Ca-RIS and Sr-RIS were collected with a Bruker D8 Venture diffractometer with graphite monochromated CuK\(\alpha\) (\(\lambda\) = 1.54178 A, for Ca-RIS) or AgK\(\alpha\) (\(\lambda\) = 0.56086 A, for Sr-RIS). The data were processed with APEX3 suite [13]. The structures were solved by intrinsic phasing using the ShelXT program [14], which revealed the position of all non-hydrogen atoms. These atoms were refined on F\({}^{2}\) by a full-matrix least-squares procedure, using the anisotropic displacement parameter [15]. All hydrogen atoms were located in difference Fourier maps and included as fixed contributions riding on attached atoms with isotropic thermal displacement parameters 1.2- or 1.5-times those of the respective atom. The Olex2 software was used as a graphical interface [16]. Molecular graphics were generated using mercury [17]. The crystallographic data for the reported structure was deposited with the Cambridge Crystallographic Data Center as supplementary publication nos. CCDC 2118543 and 2118546. Copies of the data can be obtained free of charge at [[http://www.ccdc.cam.ac.uk/products/csd/request](http://www.ccdc.cam.ac.uk/products/csd/request)]([http://www.ccdc.cam.ac.uk/products/csd/request](http://www.ccdc.cam.ac.uk/products/csd/request)).  
**Powder X-ray diffraction.** The powder X-ray diffraction (XRD) patterns were performed on PANalytical X'Pert Pro diffractometer, a configuration of the Bragg-Brentano, equipped with monochromator Ge(111) (Cu K\({}_{\alpha 1}\)) and detector X'Celerator.  
**Hydrothermal high-throughput synthesis setup.** The hydrothermal syntheses of metal-BPs were conducted in a laboratory oven, in a custom-made autoclave system composed of 36 Teflon-lined reaction chambers (volume 5 mL). The syntheses were carried out under autogenous pressure in parallel. The chambers were covered by a thin Teflon sheet, which was enclosed inside the aluminum autoclave, in order to avoid any solvent leaks.
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 2 Characteristics and SRP Models for BeiDou-3 I2-S Satellite  
The details of the optical and geometrical properties, as well as the attitude of satellite bus and solar panels (SP), are essential for the modeling of non-gravitational forces. In this section, these data as well as SRP models for BeiDou-3 I2-S satellite will be presented.