esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PIESAT-01 Multiple Satellites Interferometric Imaging
### Synchronous Data Processing
#### 3.1.1 Bistatic SAR Time Synchronization  
Temporal errors are caused by system atomic clock inaccuracies and system delay errors [28]. The time-varying component of temporal errors accumulated due to clock inaccuracies leads to the range cell migration (RCM) and defocusing of SAR images.  
By extracting the peak position of the received signals after range pulse compression, the arrival time of the direct wave can be determined separately at each sensor. The time synchronization error between two receivers can be estimated as follows:  
\[\left\{\begin{array}{l}\Delta t_{ij}=\frac{t_{p,ji}-t_{p,ji}}{2}\\ t_{p,ji}=-\Delta t_{ij}+t_{0}\\ t_{p,ji}=\Delta t_{ij}+\tau_{ij}+t_{0}\\ \end{array}\right. \tag{1}\]  
where \(t_{p,ij}\) and \(t_{p,ji}\) represent the peak moments of the direct waves, \(\Delta t_{ij}=t_{i}-t_{j}\) denotes the clock bias between the two antennas, \(\tau_{ij}\) signifies the time taken for the electromagnetic wave to propagate from the radar \(i\) to the radar \(j\) in free space, and \(t_{0}\) represents the starting time of the transmission signal generated by the radar \(i\) and the starting time of the demodulation signal generated by the radar \(j\).  
In the context of bistatic imaging, estimated temporal error refers to the uncertainty in the synchronization between the transmission and reception of signals at two separate locations, and even millisecond-level synchronization errors can lead to significant positional errors. The estimated temporal error can be compensated for by shifting range cells or multiplying by a linear phase term of range frequency in the distance domain. After bistatic SAR time synchronization, the signal envelope model received by radar \(j\) from radar \(i\) is as follows:  
\[s_{TCij}\big{(}t_{j};t_{0}\big{)}=\sin c\bigg{(}\frac{t_{j}-\tau_{ij}-t_{0}}{ \rho_{t}}\bigg{)} \tag{2}\]  
where \(\rho_{t}\) represents the fast time resolution after range pulse compression.
## 5 Experimental Results
### Analysis of the Proposed Scheme
#### 5.3.2 Keyword Detection  
Here, \(N^{\text{KW}}\) is seven, and we evaluate the performance of the proposed keyword-detection algorithm. Figure 10 also shows the results of the proposed scheme for keyword detection. In the right columns of Figure 10, the dashed rectangle is the result of the proposed scheme for keyword detection. The local results are also shown in Figure 11. The threshold of IoU is set to 0.5. As we can see in Figure 10, some keywords, e.g., \"\(\mathbb{H}\)\" (Name), \"\(\mathbb{H}\)\(\mathbb{H}\)\" (Address), in Figure 10b1 and some keywords, e.g., \"\(\mathbb{H}\)\(\mathbb{H}\)\(\mathbb{J}\)\" (Applicant), Figure 10b4,b5, are arranged in the horizontal direction. These keywords, such as \"\(\mathbb{H}\)\(\mathbb{H}\)\" (Name), \"\(\mathbb{H}\)\(\mathbb{H}\)\" (Address), and \"\(\mathbb{H}\)\(\mathbb{H}\)\(\mathbb{J}\)\" (Applicant), can be located and recognized well. Furthermore, \"\(\mathbb{H}\)\(\mathbb{H}\)\(\mathbb{Z}\)\" (Property Owner), in Figure 10b1, is arranged in the vertical direction. The keyword \"\(\mathbb{H}\)\(\mathbb{H}\)\(\mathbb{Z}\)\" (Property Owner) can also be detected well. This means that the lexicon analysis is useful to detect keywords in horizontal and vertical directions.  
Figure 14 illustrates the AP results of the proposed keyword-detection algorithm. As shown in Figure 14, most AP rates are higher than 0.89, except for two keywords, \"\(\mathbb{H}\)\(\mathbb{H}\)\(\mathbb{J}\)\" (Applicant) and \"\(\mathbb{S}\)\" (Building). According to Figures 10-13, the main reason is that the proposed scheme may not detect the two key characters, \"\(\mathbb{H}\)\" (Apply) and \"\(\mathbb{S}\)\" (Building). According to Figure 10-13, the main reason is that the proposed scheme may not detect the two key characters, \"\(\mathbb{H}\)\" (Apply) and \"\(\mathbb{S}\)\" (Building).  
(Building), of poor quality, as well as the two keywords, \"HHR/\(\lambda\)\" (Applicant) and \"HHR\" (Building). In addition, the mAP of the proposed scheme is 85.84%, as shown in Figure 14. This means that the experimental result shows that the lexicon analysis can effectively find some suitable key characters, and then the keywords can be obtained by analyzing the combination of these key characters. Therefore, the experimental results demonstrate that the proposed keyword-detection algorithm can effectively locate and recognize the keywords in document images with different image qualities.  
As for the execution time, the proposed scheme can deal with each document image about 0.2 frames per second. It is expected that the execution time of the proposed scheme can be reduced if our program is optimized or a powerful graphics card is used.
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 5 Pegasus--Third-Generation Optically Enabled Data Center System
### Optical End Node Connector Interface  
To connect the storage or compute end nodes we developed a proprietary electro-optical connector [20] (Figure 13). The connector was based on an SFF-type hard disk drive connector, allowing it to be integrated onto a hard disk drive. This offers the potential for backward-compatible connectivity between current and future generations of disk drives. The connector retains power and high-speed electronic data interfaces; however,  
Figure 12: Pegasus—optically enabled, object-oriented converged switch, storage and compute platform: (**a**) photo of Pegasus system [8], (**b**) optically enabled controller card with mezzanine transceiver cards, (**c**) electro-optical midplane with 192-fiber flexplane [8], (**d**) schematic view of interconnect topology.  
one section is reserved for high-density optical communication. This section includes a receptacle for a partially floating PrizmMT ferrule, which can accommodate up to 64 optical channels in a small space.  
Figure 13: Optical end node connector and interface with proprietary optical end node connector assembled onto a hard disk drive, (**a**) CAD view of vertical interface cards, (**b**) CAD view of horizontal interface card, (**c**) photo of vertical and horizontal interface cards connected, (**d**) microserver end node with optical interface, (**e**) data storage end node with optical interface, (**f**) Ethernet disk drive with proprietary electro-optical connector.  
We designed a special two-part interface card (Figure 13c) to fit into a 3.5\({}^{\prime\prime}\) disk drive carrier in order to convert Ethernet data from optical signals from the midplane to electrical signals to the storage or compute end node. These comprised a vertical section holding a 2 + 2 Murata FOT optical transceiver (Figure 13a) and a horizontal section (Figure 13b) holding the electro-optical connector. Two varieties of optically interchangeable end node device were developed: (1) a storage device comprising a 3.5\({}^{\prime\prime}\) hard disk drive with an Ethernet communications interface (Figure 13d) and (2) a compute device, which comprised a microserver platform with an Ethernet communications interface (Figure 13e). Both included the optical interface card and electro-optical connector allowing them to be optically pluggable and interchangeable.
###### Abstract  
This paper examines the growing adoption of laser communication (lasercom) in space missions and payloads for identifying emerging trends and key technology drivers of future optical communications satellite systems. It also presents a comprehensive overview of commercially available and custom-designed lasercom terminals, outlining their characteristics and specifications to meet the evolving demands of global satellite networks. The analysis explores the technical considerations and challenges associated with integrating lasercom terminals into LEO constellations and the Inter-satellite communications service provision in LEO due to their power, size, and weight constraints. By analyzing advancements in CubeSat lasercom technology designed to cater for the emergence of future mega constellations of interacting small satellites, the paper underscores its promising role in establishing high-performance satellite communication networks for future space exploration and data transmission. In addition, a brief overview of our ALIGN planned mission is provided, which highlights the main key operational features in terms of PAT and link budget analysis.  
free-space optical communication; satellites; low-earth orbit; geostationary orbit +
Footnote †: journal: Aerospace
## 3 Results and Discussion  
### CMF Characterization  
Table 1 lists the properties of CMF used in this study. Figure 1 shows a CMF image by SEM analysis in which the microfibers can be observed. The diameter distribution of CMF was measured, obtaining an average diameter of 105 nm and a median of 90 nm. These results indicate that the cellulose structure is microfibrillated with a low content of nanofibers, as expected, in CMF obtained without pretreatment ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2015).
## 2 Related Work
### Feature-Importance-Based RF  
In addition to its classification capabilities, Random Forest provides an internal measure of feature importance by scoring the features during classification. These scores are based on the features' contribution to the classification process within the Random Forest model. They can be utilized to select the most relevant feature subset that highly impacts the model and most strongly influences the predictions.  
The RF algorithm calculates feature importance from the training model using the mean decrease in accuracy (MDA) and the mean decrease in Gini (MDG). MDA quantifies how much model accuracy decreases when the values of a feature are randomly shuffled (feature values are changed). MDG is the mean of a feature's total reduction in node impurity, weighted by the proportion of observations reaching that node in every decision tree. The larger the decrease in both methods, the more important the feature is considered [36].  
Random Forest's feature importance measures offer a distinct advantage over univariate screening methods because they evaluate the impact of each feature both individually and in interaction with other features [37].
## 2 Navigation and Localization
### Dead-Reckoning and Inertial Navigation
Currently, damping techniques, using water speed measurements, are used to control velocity and position errors caused by uncorrected vertical deflection and inertial sensor errors [30]. However, this is at the cost of introducing an additional error source (the water-speed/ground-speed difference caused by ocean currents). Another alternative to reduce these effects is the use of maps of vertical deflection compensation values, as a function of latitude and longitude, to compensate the measured accelerations.
## 3 Optimization Algorithms
### Particle Swarm Optimization  
PSO is a heuristic search method inspired by social interaction of animals living in groups. PSO begins with a group (swarm) of randomly generated particles, each of which represents a possible solution for the optimization problem. Each particle is characterized by two main properties: position and velocity. The position of a particle is the evaluation of the optimization's fitness function, which represents the particles distance from the ideal optimum solution (_i.e._, zero). Similarly, each particle's velocity dictates the motion of the particle in the solution space.  
The social behaviour of the swarm is represented by two important variables, _pbest_ and _gbest_. _pbest_ represents the personal best position that a particle can achieve through the iterations of the optimization, while the best position achieved by the whole swarm is represented by _gbest_. Both variables do not change in every iteration, but only gain new values if the swarm or the particles have achieved better positions in the current iteration that the stored _gbest_ and _pbest_. In a sense, _gbest_ represents the goal position for the particles of the swarm while _pbest_ represents the memory of each particle [19].  
In each operation, the position and velocity of each particle are calculated as follows:  
\[x_{i}^{k+1}=\ x_{i}^{k}+\
u_{i}^{k+1} \tag{12}\]  
\[\
u_{i}^{k+1}=\ \phi^{k}\
u_{i}^{k}+\alpha_{1}\gamma_{1,i}\big{(}pbest_{i}-x_{i }^{k}\big{)}+\alpha_{2}\gamma_{2,i}\big{(}gbest_{i}-x_{i}^{k}\big{)} \tag{13}\]  
where \(x_{i}^{k}\) and \(\
u_{i}^{n}\) are the position and velocity of the \(i\)th particle in the \(k\)th iteration, \(\gamma_{1,i}\) and \(\gamma_{2,i}\) are uniformly distributed parameters of the \(i\)th particle, and \(\alpha_{1}\), \(\alpha_{2}\) are acceleration constants.  
The acceleration parameters \(\alpha_{1}\), and \(\alpha_{2}\) of Equation (19) have a great impact on the convergence of the optimization. \(\alpha_{1}\) represents the swarm's cognitive acceleration parameter, which determines the \"confidence\" of each particle in itself, while \(\alpha_{2}\) represents the influence of the _gbest_ position on every particle of the swam and is named social acceleration parameter.  
Additionally, parameter \(\phi\) represents the particle inertia that produces a certain momentum for the swarm and is defined as:  
\[\phi^{k}=\frac{\phi_{b}-\phi_{a}}{K}\cdot(k-1)+\phi_{a} \tag{14}\]  
where K is the maximum number of iterations and \(\phi_{b},\phi_{a}\) are inertia constants defined as [20]:  
\[\begin{cases}\phi_{a}=1-\varepsilon\\ \phi_{b}=\frac{\alpha_{1}+\ \alpha_{2}}{2}-1+\varepsilon\end{cases}, \varepsilon\ll 1 \tag{15}\]
## 3 Results and Discussion
### Assessment of the PCB Concentration in the Water of Lake Baikal
#### 3.3.4 Toxicity Equivalence for Dioxin-like PCBs
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **Year** & **PCBs in the Water, ng/L** & **References** \\ \hline Arctic & 2011 & \(\sum_{13}\)PCBs 0.0013–0.021 & [9] \\ Arctic Norway & 1999 & \(\sum_{12}\)PCBs 0.023–0.129 & [74] \\ Canadian Archipelago & 2018 & \(\sum\)?PCBs 0.65–46 & [42] \\ Tropical Atlantic Ocean & 2021 & \(\sum\)?PCBs 0.0041 ng/L & [12] \\ Antarctic, Victoria Land & 2011–2012 & \(\sum_{12}\)?PCBs 0.046–0.143 & [74] \\ European alpine lakes & 2000–2001 & \(\sum\)?PCBs 0.048–0.123 & [75] \\ Himalayas & 2007 & \(\sum\)?SPCBs 0.02–0.45 & [76] \\ Lake Baikal, Russia & 2015–2021 & \(\sum_{34-34}\)PCBs 1.4–9.7 & This study \\ Lake Como, Italy & 2007 & \(\sum\)?PCBs 0.30 & [77] \\ Caspian Sea, Russia & 2002 & \(\sum\)?PCBs 0.10–7.3 & [78] \\ Lake Baiyangdian, China & 2008 & \(\sum_{39}\)PCBs 19–132 & [79] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The concentrations of PCB congeners in the water from background sites and in the tissues of fish chosen as PCB bioindicators (ng/g, ww). In the muscle tissues of _C. migratorius_, the total toxicity of four dl-congeners 118, 105, 123, and 156 did not exceed the WHO-TEQ (2005) pg/g (ww) value of 0.06. The higher toxicity level of the detected dl-congeners, up to the WHO-TEQ (2005) pg/g (ww) value of 4.7, was observed in the individuals of the older age group (eight years and older) due to the accumulation of congener 126. In the fish from the Baltic Sea, the total toxicity equivalent of dl-congeners was by two orders of magnitude higher than in _C. migratorius_ (WHO-TEQ (1997) pg/g (ww) from 0.64 to 6.0) [80].
## 3 Results and Discussion
### Methodology for Relative Assessment of Sustainability of Construction Solutions (MARS-SC)
The equation converts the value of each parameter into a dimensionless scale, where zero corresponds to the level of worst practice and one corresponds to the level of best practice. Then, the overall environmental performance (EI) was calculated by the sum of each normalised value multiplied by the weights presented above. When considering the normalised values, the hep fibre solution is consolidated as the option with the lowest environmental performance, closely followed by the straw insulation solution. Unexpectedly, corkboard and rock wool insulation solutions ranked lowest in environmental performance, as shown in Table 15.  
Although the analysis shows that the cork insulation solution is worse than the XPS insulation solution, it does not mean XPS is better for the environment than cork as a material; as cork requires a greater thickness to the same thermal \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Construction Solutions** & **GWP** & **ODP** & **AP** & **POCP** & **EP** & **NRE** & **EI** \\ \hline Solution 01—XPS insulation & 0.009 & 0.937 & 0.934 & 0.961 & 0.979 & 0.000 & 0.480 \\ Solution 02—EPS insulation & 0.012 & 0.867 & 0.885 & 0.931 & 0.972 & 0.354 & 0.504 \\ Solution 03—Rock wool insulation & 0.000 & 1.000 & 0.298 & 0.892 & 0.922 & 0.297 & 0.427 \\ Solution 04—Glass wool insulation & 0.026 & 0.605 & 0.735 & 0.597 & 0.781 & 0.871 & 0.452 \\ Solution 05—Corkboard insulation & 1.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.488 & 0.439 \\ Solution 06—Hermp and flax insulation & 0.052 & 0.875 & 0.922 & 0.965 & 0.936 & 0.818 & 0.581 \\ Solution 07—Hemp fibre insulation & 0.072 & 0.953 & 1.000 & 1.000 & 1.000 & 0.952 & 0.636 \\ Solution 08—Straw insulation & 0.115 & 0.845 & 0.901 & 0.989 & 0.912 & 1.000 & 0.621 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: A summary of the environmental performance level of the construction solutions. have influenced the values for higher impact. Additionally, the better performance in the GWP category does not compensate for the worst performance at the level of the ODP, AP, POCP, and EP impact categories, which led to this solution having final values similar to those of conventional solutions. The MARS-SC tool enables the comparison of several solutions and allows for a more detailed analysis since it is based on EPDs, and each environmental impact category can be individually assessed. Despite some adversities, solutions with fast-growing bio-based insulation materials are consistently among the more sustainable solutions, considering only the environmental dimension.
###### Abstract  
The aim of this study was to investigate the application of [18]FFDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18]FJFDG PET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly,in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18]FJDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.  
colon; cancer; radiomics; artificial intelligence; positron emission tomography-computed tomography; nuclear medicine
## 3 Materials, Methods, and Findings
### K-Nearest Neighbor (K-NN)  
The K-NN algorithm is structured with the hyperparameter of n_neighbors = 5n. This algorithm is used to classify a data point according to its k neighbors. The model makes decisions using distance metrics in the feature space. Normalization or scaling, especially in data preprocessing steps, improved the accuracy of this algorithm. Figure 5 shows the complexity matrix of the K-NN algorithm.  
The model correctly classified 87 instances in the \"no verbal violence\" class and incorrectly labelled only 15 instances as \"verbal violence present\" (false positive). However, while it correctly predicted 98 instances in the \"verbal violence present\" class, it incorrectly classified 10 instances as \"verbal violence absent\" (false negative). These results show that the K-NN algorithm has a high overall accuracy rate but exhibits a higher false negative rate (9.26%) in the \"verbal violence present\" class. The high rate of false negatives may limit the sensitivity (recall) of the model to detect verbal violence. However, the low false positive rate (14.71%) suggests that the model provides a more reliable performance in the \"no verbal violence\" class.
## 3 Results
### Factor Analysis and Clustering of Complete Datasets  
Five factors explain 79% of total geochemical variance of complete dataset TSP (Table S4) and their loadings significant at \(p\) < 0.01 are listed in parentheses: F1\({}_{\text{TSP}}\) (Rb, Al, Si, Zr, K, Mg, Mn, Ti, Ca, Cu, \(-\)Cr, \(-\)Fe), F2\({}_{\text{TSP}}\) (P, Sr, Ca), F3\({}_{\text{TSP}}\) (Br, S, Cl), F4\({}_{\text{TSP}}\) (Zn, V, Cu, Ti, \(-\)Mg), F5\({}_{\text{TSP}}\) (Pb, As, Cr, Mn). Cluster analysis of such dataset demonstrates two main branches of dendrogram joining together at a linkage distance (LD) 3.5 (Figure 7). The left branch consists of four clusters, i.e., (Br-S)-Cl (L1), V-(Ni-(Zn-Cu)) (L2), Sr-P (L3), and Cr-Fe (L4). The right branch is composed of three clusters, i.e., Pb-As (R1), Mg-Ca (R2), and (Mn-Ti)-(K-(Si-(Zr-(Rb-Al)))) (R3). The same nine elements Rb, Al, Si, Zr, K, Mg, Mn, Ti, and Ca which are in clusters R2 and R3 have the highest positive loadings (>0.69) on F1\({}_{\text{TSP}}\) factor. Part of R3 elements, i.e., Al, Rb, Zr, Si, K, show very close association (LD < 0.25) and have the highest (>0.85) loadings on F1\({}_{\text{TSP}}\) (Table S4).  
Quite different relationship was revealed in topsoil background sites of Klaipeda (Table S4): Nine elements which determine the variance of factor F1\({}_{\text{TSP}}\) were distributed in the loadings of three factors of background topsoil: F1\({}_{\text{BT}}\), F2\({}_{\text{BT}}\), and F3\({}_{\text{BT}}\). Factor F1\({}_{\text{BT}}\) showed marked antagonism between Si (indicator of sandy soils) and tracers of clayey soils in unpolluted (background) areas Al, Mg, Ti, and Fe. A similar separation of Si from elements clayey component indicators was characteristic of European topsoils and was visible from cluster analysis dendrograms [85]. The variance of factor F2\({}_{\text{BT}}\) was determined by other three of aforementioned nine elements, i.e., Zr, Rb, and K, while of F3\({}_{\text{BT}}\) by remaining Ca and partly (at \(p\) < 0.05) also Mg. In background topsoil, factor F1\({}_{\text{BT}}\) was loaded not only by Al, Mg, Ti, Fe, and Cu, but also by Ni, V, and Cr. However, in dust factor F1\({}_{\text{TSP}}\), Cr as well as Fe were obvious antagonists of Al, Mg, and Ti, while V, Ni and Cu as well as Zn and Ti had positive loadings on F4\({}_{\text{TSP}}\) and were antagonists to Mg.