esa-sceva/satcom-chunk-collection huggingface dataset:
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 3 Mathematical Formulation of the Model
### Mathematical Model  
Let us denote the utility of a path passing through nodes i and j; demand originating from m to n is formulated as \(U_{mnij}\). The proportion of demand originating from m to n choosing i and j as their O-D can be calculated as  
\[\frac{U_{mnij}}{\sum_{o,d}U_{mmod}s_{o}s_{d}} \tag{1}\]  
As occurs in the real world, not all the demand can be satisfied, especially once we decide not to locate a facility for every demand point. As a result, the sum of all utilities is not necessarily summed up to 1, leaving some \"lost demand\", which is calculated as  
\[\sum_{m,n}P_{mn}-\sum_{i,j}\sum_{m,n}P_{mm}\frac{U_{mnij}\cdot s_{i}s_{j}}{ \sum_{o,d}U_{mmod}s_{o}s_{d}} \tag{2}\]Let us denote the following model as HSLTP-01:  
\[\begin{array}{c}\underset{i}{min}\underset{i}{\sum}\underset{j\
eq i}{\sum} \underset{k}{\sum}\underset{l\
eq k}{\sum}\alpha w_{ij}C_{kl}x_{ijkl}+\underset{ i}{\sum}\underset{j\
eq i}{\sum}\underset{k\
eq i,j}{\sum}w_{ij}C_{ik}a_{ijk}\\ \hskip 14.226378 pt+\underset{i}{\sum}\underset{j\
eq i}{\sum}\underset{k\
eq i,j}{\sum}w_{ij}C_{kj}b_{ijk}+\underset{i}{\sum}\underset{j\
eq i}{\sum} \underset{j\
eq i}{\sum}w_{ij}C_{ij}e_{ij}+\underset{k}{\sum}F_{k}h_{k}\\ \hskip 14.226378 pt+\underset{k}{\sum}D_{k}s_{k}+\underset{k}{\sum} \underset{l>k}{\sum}\underset{l_{l}}{l_{l}}y_{kl}+B*\left(\underset{m,n}{P_{mn }}-\underset{m,n}{\sum}w_{mn}\right)\end{array} \tag{3}\]  
Subject to  
\[y_{kl}\leq h_{k},y_{kl}\leq h_{l}\hskip 28.452756 ptk,l\
eq k \tag{4}\]  
\[h_{k}\leq s_{k}\hskip 28.452756 pt\forall k \tag{5}\]  
\[\underset{l\
eq i}{x_{ijil}}+\underset{l\
eq i,j}{\sum}a_{ijl}+e_{ij}=z, \hskip 28.452756 pti,j\
eq i \tag{6}\]  
\[\underset{l\
eq i}{x_{ijl}}+\underset{l\
eq i,j}{\sum}b_{ijl}+e_{ij}=z, \hskip 28.452756 pti,j\
eq i \tag{7}\]  
\[\underset{i,j}{\sum}w_{ij}\leq z*M,\hskip 28.452756 pti,k\
eq j \tag{8}\]  
\[\underset{l\
eq k,i}{\sum}x_{ijkl}+b_{ijk}=\underset{l\
eq k,j}{\sum}x_{ijlk} +a_{ijk},\hskip 28.452756 pti,j\
eq i,k\
eq i,j \tag{9}\]  
\[x_{ijl}\leq y_{kl},i,\hskip 28.452756 pti\
eq i,k,l \tag{10}\]  
\[\underset{l\
eq k}{\sum}x_{ikjl}\leq h_{k},\hskip 28.452756 pti,k\
eq j \tag{11}\]  
\[\underset{k\
eq l}{\sum}x_{ikl}\leq h_{l},\hskip 28.452756 pti,l\
eq i \tag{12}\]  
\[a_{ijk}\leq 1-h_{i},\hskip 28.452756 pti,j\
eq i,k\
eq i,j \tag{13}\]  
outgoing (incoming) flow from (to) a hub facility that is not traversed via a hub link should not be destined to (originated from) a hub facility. The flow between i and j would not pass through any other node on its path unless the node is a hub node. Constraints (15) and (16) ensure such happens. In addition, the path between i and j has three (3) possible combinations: either both i and j are hub facilities, one of the two is a hub facility, or none of them are a hub facility, as shown in constraint (17). The budget constraint is formulated in (18), and the utility is used to calculate the demand values, as shown in (19). Finally, constraint (20) is a domain constraint.
# Adsorption of Coxsackievirus in Sediments: Influencing Factors, Kinetics, and Isotherm Modeling
## 3 Results and Discussion
### Effect of Different Factors on Virus Adsorption
#### 3.1.3 pH Values  
According to the observations in Figure 3c, the pH of the aqueous environment significantly influences CA16 adsorption by clay. The graph shows higher adsorption efficiency of the sediment under acidic conditions, diminishing notably in alkaline environments. With the pH increasing from 5.5 to 9.5, there is a visible decline in clay's adsorption capacity for CA16. At pH 5.5, the reduction in CA16 concentration after reaching equilibrium is 2.93 log. Conversely, at pH 9.5, the reduction is 2.33 log, indicating a 24.7% decrease in CA16 concentration reduction as pH rises from 5.5 to 9.5. These findings confirm pH sensitivity in CA16 adsorption, favoring higher efficiency under acidic conditions. This can be explained by pH fluctuations impacting the surface charges of the virus and clay. Changes in pH may modify the surface charges, affecting their adsorption capacities. Generally, high pH supports free viruses, while low pH encourages virus adsorption. However, the isoelectric points of the virus and surface may also contribute to this interaction [49].
## 2 System Description and Equations of Motion
### Study of the Behavior of the System  
The mathematical model of the system is verified using numerical simulation. The model used in the simulation consists of 5 segments of 0.01 m length and 0.001 kg mass. There are no external forces acting on the system (\(Q_{no-pot}=0\)) and the initial conditions are \(\alpha_{1}=\alpha_{2}=...=\alpha_{n}=0\) rad and \(\dot{\alpha}_{1}=\dot{\alpha}_{2}=...=\dot{\alpha}_{n}=0.1\) rad/s.  
As can be seen from the simulation results in Figure 4, the solution of nonlinear equations contains oscillations. The oscillations in the system occur in the form of beats, in which energy is cyclically transferred from one rod to the other.
# Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability
## 3 Experiment and Results  
### Experiment Setup  
Figure 2 shows experimental setup for 112 Gbps optical interconnects. In the transmitter, 112 Gbps pseudo-random binary sequence (PRBS)-11 PAM-4 signals are generated by an arbitrary waveform generator (AWG), which is Keysight M8195A with 64-Gsa/s sampling rate and 25-GHz 3 dB bandwidth. Please noted that the waveform generator is bandwidth limited for generating 112 Gbps PAM4 signals, but this inherent bandwidth limitation can be mitigated by pre-distortion and equalization. PRBS-11 is used due to memory limitation of our AWG. High-speed data-streaming combined with DC bias are applied on an 850 nm multimode VCSEL bare die through a high-speed probe. Light-current-voltage  
(LIV) curve and 3-dB bandwidth of VCSEL can be found in the inset of the Figure 2. Lights from VCSEL are coupled into a 100 m OM3 multimode fiber through a three-dimension coupling station. Through a variable optical attenuator (VOA), lights are detected by a photodetector (PD, New Focus 1484-A-50) integrated with a transimpedance amplifier (TIA). At the receiver, detected signals are sampled and captured by a real-time digital storage oscilloscope (DSO, Keysight DSOZ592A) with 160-GSa/s sampling rate and 59-GHz analog bandwidth for offline digital signal processing. The captured data are re-sampled to one sample per symbol, followed by nonlinear equalization and bit error rate (BER) calculation.  
To avoid the risk of performance over-estimation from nonlinear equalization, we randomly disrupt the signals to create a sequence without periodicity before the received signals are fed into equalizer and conducted mini-batch training [12]. After such process, a disrupted sequence, with an ultra-long unrepeated pattern, is used to train equalizer, which enables equalizer to learn system characteristics instead of generation rules of PRBS.
## Results and Discussion
### Chemometrics Approaches to Identify Chemical Descriptors of Strawberry Cultivar
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Varieties Investigated** & **Findings** & **Reference** \\ \hline
35 & Thirty-one volatile compounds correlated to strawberry flavor intensity, particularly esters, terpenes and furans. & [1] \\ \hline
4 & Key odorants identified were furanenol, y-decalactone, ethyl butanoate, ethyl hexanoate, ethyl 3-methylbutanoate, diacetyl and hexanoic acid. The aroma of Fuentepina and Candonga varieties presented mainly green notes, whereas the aromatic notes in Camarosa and Sabrina varieties were mainly sweet. & [7] \\ \hline
12 & The most abundant volatile sulfur compounds in strawberry are methanethiol, dimethyl sulfide, dimethyl disulfide and dimethyl trisulfide, being methanethiol the predominant aromatic compound. Festival and Florida Radiance presented higher thicker concentrations, whereas Dover, Rosa Linda and Florida Belle were characterized by relatively high sulfide and low thioester concentrations. & [10] \\ \hline
9 & Esters, such as methyl butanoate, pentyl acetate and methyl hexanoate, characterized the aroma of ripe strawberries, and allow discriminating between cultivars. & [11] \\ \hline
16 & Great diversity of the volatile patterns in F. vesca accessions in comparison with F. x ananassas cultivars. & [33] \\ \hline
5 & The content of volatiles varied depending on the cultivars, but in general ethyl butanoate, mesifurane, ethyl hexanoate, ethyl 3-methylbutanoate, hexyl acetate and \(\gamma\) -dodecalactone had the highest odor activity values. & [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of previous works investigating the influence of the variety in the strawberry volatile profile. This LDA model yielded satisfactory classification (92.18%) and prediction (75.68%) rates, thereby allowing a clear discrimination among the study groups, as illustrated in Figure 5. The first discriminant function enabled the separation of the three varieties, mainly Festival and Camarosa cultivars with negative and positive loadings for root 1, respectively. The primary variables responsible for the discrimination of Festival strawberries with respect to the other two varieties were geraniol and hexyl hexanoate, with negative loadings in root 1 (Table 3). On the other hand, Camonga samples were orthogonally separated in the second root. This second discriminant function was positively associated with geraniol and negatively with trans-2-hexen-1-al, which could therefore serve as chemical descriptors for the Camonga cultivar.
## 1 Introduction
MASW and ERT show advantages and limitations in what concerns the reached exploration depth and the resolution of the subsurface geological structures. MASW provides higher exploration depths than ERT but less detailed resolution, whereas ERT is highly responsive to detailed subsurface EC changes related to geological and hydrological heterogeneities [23, 29, 31, 38]. The limited capability of ERT for geological definition contrasts with its ability to identify temporal groundwater changes. This way, time-lapse ERT can be used to disambiguate steady features of geological media (e.g., bulk density, clay content, and organic matter content) from transient features of saturated media (e.g., groundwater storage, dissolved ions). These transient features result from the variable combination of the non-evaporative fractions of precipitation [6] and atmospheric salinity rates [39] reaching the water table over time. This research covered the natural year 2014 and involved a geophysical survey characterized by one MASW campaign and four (quarterly) ERT campaigns. Besides this, two other complementary tasks were: a geological survey to define the local faulting and its correspondence to the regional structural setting; and aquifer monitoring, with quarterly piezometry measurements to draw water table fluctuation along the preferential groundwater flow path, and some EC measurements to support the hydrogeological interpretation of ERT surveys. Since the contributing area to the SAL is too large for making up these tasks in detail, only the Cascalheira Stream Basin (CSB), the largest and most representative tributary, was selected to define the geological structure and the preliminary hydrological functioning of the Upper Miocene--Quaternary sedimentary body hydraulically connected to the SAL. For a fluent reading, a description of acronyms used is in Table 1.
# Software Architecture for Mobile Cloud Computing Systems
## 7 Past and Emerging Trends of Research on Architecting Mobile Cloud Software
### Past Trends of Research on Architecture for MCC Software  
We now aim to answer RQ-5 to investigate the past trends that highlight a historical progression of the research. Based on the mapping of the research, we have considered the years 2006-2012 as historical research because during those years, the research has emerged and established itself before its tremendous growth in the later years. Moreover, during these years, the research on software  
Figure 7: Overview of the research progression—three phases of research.  
architecture for mobile cloud computing enabled (i) the establishment of the theoretical foundation of the research and (ii) fundamental research on mobile cloud computing that later gained momentum.  
**Pay-per-use SaaS for Mobile Computing**: During the foundational phase, the research focused on software service-driven architectures for mobile computing environments. In comparison with (pre-built and embedded) software components or modules for mobile computing, software services provided loosely coupled and dynamically discovered mobile software that can be acquired or released instantly [S1]. During this phase, the research focused on exploiting the dynamically composable and pay-per-use services to perform computation and provide the mobile recommender systems [S12]. The software as a service model also inspired the early research where computation [S11] or security [S18] of a mobile device can be offloaded to the cloud servers. Such offloading proved vital to compensate for the resource poverty of mobile devices with virtually unlimited cloud storage and processing. After the foundation phase, during the years 2010-2012, the earliest research on architecting mobile cloud software started to mature. The maturation is reflected in a significant growth of research and the emergence of new challenges and solutions.  
**Computational Offloading for Mobile Devices:** The idea of computational offloading gained attention and solutions have been developed that not only support energy efficiency [S15; S27; S55], but also computational, storage, and data analytics efficiency [S7; S10; S43; S46].  
During this phase, the cloud-based servers were exploited for analytics of big data effectively and efficiently. The integration of the mobile cloud resulted in the development of mobile sensing systems or mobile crowd sensing [S10]. The portable and context-aware mobile devices have been used as a front-end system to sense the contextual information such as environment, noise, traffic, and pollution levels [S10; S46]. The sensed data from the mobile device are shared with the cloud-server for analytics and recommendations. The research on software architecture for mobile cloud systems is growing rapidly. Such growth demands the identification of active and innovative solutions to guide futuristic solutions.
## 4 Results and Discussion
### Performance Comparison with State-of-the-Art Approaches  
Table 11 presents the comparison of the proposed model in terms of accuracy with state-of-the-art approaches from the literature. It can be seen that the proposed RFMLP outperforms the other approaches regarding each category of attacks such as DoS, Probe, R2L, and U2R. Despite combining different approaches like PCA+MCA, SVM-ANN, DT-RFE to improve the performance of classifiers for intrusion detection, RFMLP shows better performance than those models. It can be noticed that the proposed model has shown slightly lower accuracy than SVM-ANN [14] for the 'Prob' class on the NSL-KDD dataset. The proposed model is based on RF and MLP. RF works by combining multiple decision trees and using a bootstrap dataset for training. Occasionally, the bootstrap subset cannot extract the significant features from data due to data scarcity which reduces the performance. However, this problem can be solved by applying upsampling techniques like the synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) sampling approaches. The performance of all other classifiers is degraded for R2L and U2R attack types specifically. But the proposed RFMLP shows robust results for the detection of R2L and U2R attack type that shows robustness and generalizability of the approach. Despite this, RFMLP has shown 100% accuracy for DoS and U2R on the NSL-KDD dataset. The proposed RFMLP model is superior in the sense that it is simple with low computational complexity and suitable for both terrestrial and satellite networks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Estimated Time** \\ \hline \multirow{3}{*}{RF} & KDD-CUP 99 & 35 s \\  & NSL-KDD & 37 s \\  & STIN & 41 s \\ \hline \multirow{3}{*}{SVM} & KDD-CUP 99 & 79 s \\  & NSL-KDD & 93 s \\  & STIN & 135 s \\ \hline \multirow{3}{*}{LR} & KDD-CUP 99 & 21 s \\  & NSL-KDD & 33 s \\  & STIN & 47 s \\ \hline \multirow{3}{*}{MLP} & KDD-CUP 99 & 38 s \\  & NSL-KDD & 43 s \\  & STIN & 47 s \\ \hline \multirow{3}{*}{RFMLP} & KDD-CUP 99 & 43 s \\  & NSL-KDD & 51 s \\ \cline{1-1}  & STIN & 55 s \\ \hline \hline \end{tabular}
\end{table}
Table 10: Estimated execution time of all classifiers on all three datasets.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 5 Accuracy Analysis
### Deformation Monitoring Accuracy Analysis  
We utilize the DEM obtained by interferometric ArcSAR to assist imaging, which can significantly improve the imaging quality. The improvement of image quality also helps to improve the deformation monitoring accuracy. In this part, the contribution of the proposed method to the improvement of deformation monitoring accuracy is analyzed. As a comparison, the impact of traditional ArcSAR imaging algorithm (the imaging method with the image on the reference plane) on the deformation monitoring accuracy is also discussed. With reference to Section 5.1, defocusing causes two types of phase error. They will affect the accuracy of deformation monitoring. We define the deformation monitoring accuracy as \(\sigma_{\rm d}\), which can be expressed as [7]:  
\[\sigma_{\rm d}=\frac{\lambda}{4\pi}\Big{(}\big{|}\sigma_{\rm ps}\big{|}+\big{|} \sigma_{\rm pd}\big{|}\Big{)} \tag{35}\]  
According to Equation (35), Figures 13 and 15, the \(h\)-\(\sigma_{\rm d}\) curve can be acquired, as shown in Figure 17.  
Figure 16: \(h\)-\(\sigma_{h}\) curve.  
Figure 17: \(h\)-\(\sigma_{\rm d}\) curve.  
For the convenience of analysis, we take the deformation monitoring accuracy at \(h=100\) m as the example. Referring to the curve in Figure 17, the deformation monitoring accuracy of the traditional ArcSAR imaging algorithm is \(1.78\times 10^{-4}\) m. If we use the proposed imaging method, the target in the scenes will be imaged on its real position. According to Figure 16, the DEM accuracy at h = 100 m is \(\sigma_{h}\)(100 m) = 0.27 m. Therefore, the deformation monitoring accuracy of the proposed method can reach to \(\sigma_{d}\)(0.27 m) = \(0.47\times 10^{-4}\) m, as marked in Figure 17. Through the above analysis, we can conclude that, compared with the traditional ArcSAR imaging algorithm, the imaging method proposed in this paper can effectively improve the deformation monitoring accuracy.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
###### Abstract  
Micro-nano satellites have provided a large amount of remote sensing images for many earth observation applications. However, the hysteresis of satellite-ground mutual communication of massive remote sensing images and the low efficiency of traditional information processing flow have become the bottlenecks for the further development of micro-nano satellites. To solve this problem, this paper proposes an on-board ship detection scheme based on deep learning and Commercial Off-The-Shelf (COTS) component, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. The on-board ship detection algorithm based on deep learning consists of a feature extraction network, Region Proposal Network (RPN) with square anchors, Global Average Pooling (GAP), and Bigger-Left Non-Maximum Suppression (BL-NMS). With the help of high performance COTS components, the proposed scheme can extract target patches and valuable information from remote sensing images quickly and accurately. A ground demonstration and verification system is built to verify the feasibility and effectiveness of our scheme. Our method achieves the performance with 95.9% recall and 80.5% precision in our dataset. Experimental results show that the scheme has a good application prospect in micro-nano satellites with limited power and computing resources.  
on-board processing; ship detection; micro-nano satellite; deep learning; COTS component 1
# A Study on PF-IFF-Based Diagnosis Model of Plant Equipment Failure
## 4 Case Study
### Identifying Abnormal Patterns Related to Each Failure Mode
#### 4.3.3 Identifying Abnormal Patterns Related to Each Failure Mode  
This step identified the associated anomalous pattern based on the PF-IFF value for each failure mode. PF-IFF has a high value when the pattern frequently occurs at a specific failure mode. The bold in Table 4 indicates three patterns having high values associated with each failure mode, for example, pattern 12, pattern 9, and pattern 17 for bearing damage; pattern 10, pattern 3, and pattern 13 for leakage; pattern 14, pattern 5, and pattern 15 for misalignment. Except for the anomalous patterns selected, as above, the others are considered normal patterns.  
In this study, we assumed that the number of normal patterns is larger than the number of abnormal patterns in all collected data and limited the maximum number of abnormal patterns in each failure mode to 3. Therefore, the classifier was trained for a total of 27 cases by varying the number of abnormal patterns in each failure type, and its performance was checked.  
Figure 10 depicts several variables to see how feature vectors associated with failure modes are distributed when there is one associated pattern in each failure mode. Misalignment (green color) seems to have a high value in vibration. Leakage (red color) seems to have a relatively high value at temperature and bearing damage (black color) seems to have a high value in axial movement.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pattern** & **Bearing Damage** & **Leakage** & **Misalignment** \\ \hline
1 & 0.532 * & 0.458 & 0.793 \\
2 & 0.494 & 0.458 & 0.54 \\
3 & 0.693 & **1.232** & 0.693 \\
4 & 0.49 & 0.458 & 0.546 \\
5 & 0.464 & 0.458 & **0.916** \\
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 7 Transport of LPS  
### MsbA-Mediated Transport of LPS Across the Inner Membrane  
After the completion of core-lipid A synthesis, LPS is flipped across the IM by the essential ATP-binding cassette MsbA transporter. Several structures have been determined, which show that MsbA exists as a functional dimer, wherein core-lipid A binds the cytoplasmic open conformation [41; 42]. Upon ATP binding, conformational change in transmembrane (TM) domains results into change from the cytoplasmic open to the periplasmic open state causing flipping of core-lipid A into the periplasmic leaflet of the IM. MsbA can return to the cytoplasmic open state after release of phosphate [42]. MsbA has a higher preference for hexaacylated lipid A derivatives, thereby providing an early checkpoint to prevent transport of early intermediates of LPS biosynthesis. Recent structural analysis of MsbA-core lipid A revealed that MsbA recognizes a bivalent phosphoglucosamine headgroup and correct acylation of lipid A to achieve substrate selectivity over competing bulk membrane phospholipids [43]. The tight packing of hexaacaylated LPS in the hydrophobic pocket suggests that MsbA packs acyl chains of correct length and number, providing the basis of this selectivity [43].