esa-sceva/satcom-chunk-collection huggingface dataset:
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation  
[PERSON]  
1 Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Chemical and Biological Engineering, University of Colorado-Boulder, Boulder, CO 80303, USA; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA; [EMAIL_ADDRESS]  
Footnote 1: email: [EMAIL_ADDRESS]; Tel.: +1-979-845-5571  
Footnote 2: email: [EMAIL_ADDRESS]; Tel.: +1-979-845-5571  
Footnote 3: email: [EMAIL_ADDRESS]; Tel.: +1-979-845-5571  
Footnote 4: email: [EMAIL_ADDRESS]; Tel.: +1  
concentration increases via the regulation of potassium transporters [11; 12], and the accumulation of osmoprotectants (e.g., trehalose, glycine betaine, proline, etc.) also increases [13; 14; 15; 16]. In addition to the regulation of intracellular osmolality, _E. coli_ has been found to counter osmotic stress by accumulating ubiquinone-8 to enhance its cytoplasmic membrane stability [17]. Based on existing knowledge, there have been prior attempts to rationally improve osmotolerance in _E. coli_[18; 19]. However, the levels of improvement [20; 21; 22; 23] achieved have been modest, potentially due to limited knowledge of the genetic determinants and mechanisms involved.  
We had previously identified an 84 bp in-frame duplication (K370_A396 dup) in the RNA polymerization domain of RpoC of the RNA polymerase in two independently evolved osmotolerant mutants [24], which suggests that this _rpoC_ mutation confers osmotic tolerance and warrants further study. We reconstructed this _rpoC_ mutation in a wild-type BW25113 background and confirmed the beneficial effect of this mutation on osmotic tolerance. The reconstructed _rpoC_ mutant exhibited improved growth in minimal media under osmotic stress compared with the wild-type strain. Since the _rpoC_ mutation was selected during evolution in media supplemented with tryptophan [24], the impact of tryptophan supplementation was also investigated and found to confer increased osmotic tolerance, which has not been reported previously. Subsequent metabolite analysis, membrane damage analysis and transcriptional analysis of the mutant revealed potential mechanisms of how this specific _rpoC_ mutation confers tolerance to osmotic stress.
## 5 Case Study: HFECS in Neighborhood Area Networks
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Time-Frequency** & **Residual** &
\begin{tabular}{c} **Remaining** \\ **Buffer** \\ **Capacity** \\ \end{tabular} & **LQR** & **Distance to BS** & **Nsupport** \\ \hline Interval & [0,100] & [0,2.5] & [0,100] & [10,15] & [150,10] & [0,40] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interval for each attribute used for data standardization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Time-Frequency** & **Residual** &
\begin{tabular}{c} **Remaining** \\ **Parameter** \\ \end{tabular} & **LQR** & **Distance to BS** & **Nsupport** \\ \hline S1 & [70,65,60] & [2,18,1.6] & [78,75,73] & [11.5,11,10.5] & [110] & [30,27,25] \\ S2 & [65,60,55] & [1,9.185,183] & [65,63,62,60] & [12] & [50,49] & [29,28,27] \\ S3 & [65,60] & [1,9,1,7,1,5,13] & [90,87,84,82] & [12,5,11.9] & [45] & [29,26,24] \\ S4 & [54,51,48,45] & [2,2,2,1.8] & [80,78,77] & [13,5,13,12.8] & [42,40] & [33,30] \\ S5 & [67,63,59,55] & [2,1,1,9] & [95,92,91] & [14,13,8,13.5] & [39,38,36] & [31.5,29] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured value after screening matrix. Figure 2: Hesitant fuzzy entropy (HFE) based opportunistic clustering for neighborhood area networks. Moreover, the average values of all attributes in Table 2 and entropy weight coefficients in Table 6 were used to compute the threshold attribute values using Equation (14). As we have six attributes in total, so the six threshold attribute values in our case study are \(D_{F_{1}}=59.6252\), \(D_{F_{2}}=1.8748\), \(D_{F_{3}}=81.8583\), \(D_{F_{4}}=12.5952\), \(D_{F_{5}}=54.7839\), \(D_{F_{6}}=28.9909\). Consequently, we compare the measured attribute value of each sensor with that of theshold attribute value in Table 7. Here, we considered the attributes with the three highest entropy coefficient weights, i.e., \(TF_{ViV_{j}}\), \(D_{tBS}\) and \(N_{support}\). According to Table 7, only three sensors fulfill our criteria after comparing the measured attribute values with threshold attribute values. The BS puts the sensor nodes with highest \((\alpha_{o(i)}>D_{Fi})\) in CH election set and then invokes those sensor nodes in the CH election set. Hence, in this case study, only S5 will be selected as the CH, but in reality, the whole process is much more complex than the one mentioned in this case study due to interference, overhead and energy constraints.
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
## 4 Experimental Work  
### Geometric Dimensioning of Test Girders  
Three single cell box girder test specimens are used for this study. The geometry of the curved box girders is shown in Figure 7. The length is 6.6 m and the radius of curvature is 8 m. The box girders have two or three intermediate steel diaphragms with a thickness of 8 mm. The results of material tests are given in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \(R\) **(m)** & \(h\) **(mm)** & \(a\)/\(h\) & \(t\) **(mm)** & \(d\)/\(t\) & \(L\)/2** & \(\tau_{TEA}^{e}\) **(Mpa)** & \(\tau_{cr}^{e}\) **(Mpa)** & \(\tau_{TEA}^{e}/\tau_{cr}^{e}\) \\ \hline \multirow{8}{*}{CSW900} & \multirow{8}{*}{30} & \multirow{8}{*}{1250} & \multirow{8}{*}{0.2} & \multirow{8}{*}{8} & 5\(q\) & 1080.1 & 1069 & 1.01 \\  & & & & & 10\(q\) & 1074.1 & 1069 & 1.00 \\  & & & & & 15\(q\) & 1073.8 & 1069 & 1.00 \\  & & & & & 20\(q\) & 1073.8 & 1069 & 1.00 \\  & & & & & 25\(q\) & 1073.7 & 1069 & 1.00 \\ \cline{2-10}  & & & & & & 5\(q\) & 1334.4 & 1064.4 & 1.26 \\  & & & & & 10\(q\) & 1312.4 & 1064.4 & 1.23 \\  & & & & & 12\(q\) & 1308.3 & 1064.4 & 1.23 \\  & & & & & 20\(q\) & 1306.5 & 1064.4 & 1.23 \\  & & & & & 25\(q\) & 1304.6 & 1064.4 & 1.23 \\ \hline \multirow{8}{*}{CSW1200} & \multirow{8}{*}{30} & \multirow{8}{*}{1650} & \multirow{8}{*}{0.2} & \multirow{8}{*}{8} & 5\(q\) & 656.3 & 613.5 & 1.07 \\  & & & & & 10\(q\) & 655.9 & 613.5 & 1.07 \\  & & & & & 15\(q\) & 655.7 & 613.5 & 1.07 \\  & & & & & 20\(q\) & 655.6 & 613.5 & 1.07 \\  & & & & & 25\(q\) & 655.6 & 613.5 & 1.
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 4 Experiment Settings  
### ERA-Interim Dataset  
ERA-Interim is a dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), an objective re-analysis of a global atmospheric variable from 1 January 1979 to 31 August 2019. The dataset includes a 4-dimensional variational analysis (4D-Var): latitude, longitude, air pressure (height), and time. The monthly means of daily means was used as a time resolution. To validate the proposed method performance, we chose two climate variables from ERA-Interim: geopotential at 300 hPa (Z300) and sea surface temperature (SST). In addition, the two datasets used the same periods of training, validation, and test set (1979-2015, 2016, and 2017-2018, respectively). Figure 4 shows the sample of datasets.  
Z300 is at the same height as the jet stream, a strong upper-tropospheric wind axis 9-12 km above sea level. The jet stream wind separates the high and low pressure systems representing hot and cold weather, respectively.Atmospheric variation in the upper troposphere is used for weather prediction at the surface because the air circulation can be a precursor to weather prediction [47]. Moreover, an upper troposphere change can cause extreme winter cold [48] over northern Europe and cold air outflows in the Northern Hemisphere [49]. In this paper, Z300 is converted to a geopotential height at pressure level 300 hPa (ZH300) divided by gravitational acceleration (9.8 m/s\({}^{2}\)). Then we extracted the data to a 90 (latitude) \(\times\) 360 (longitude) grid size that covered the Northern Hemisphere.  
Sea surface temperature (SST) plays an important role in the interaction between the earth's surface and atmosphere. It is an important parameter in energy balancing at the earth's surface and is also a critical indicator of the heat of sea water. We chose a global SST with a 1 degree grid resolution, which is the spatial size of 180 (latitude) \(\times\) 360 (longitude).
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 3 Preliminaries
### Security Definition of Spe-Cks  
To prove the security of the SPE-CKS scheme, we introduce a security game called indistinguishability of ciphertext from random against chosen keyword attacks (IND-CR-CKA) defined by [PERSON] and [PERSON] [5]. IND-CR-CKA is a variant of security game called indistinguishability of ciphertext from ciphertext (ICC) defined by [PERSON] et al. [13] for a symmetric key system. The essential of IND-CR-CKA of SPE-CKS is that the scheme should ensure that the encrypted indices of two challenge keyword sets cannot be distinguished by any adversaries.  
The IND-CR-CKA game is as follows:  
1. Setup: the challenger \(\mathbb{C}\) runs the \(KeyGen_{R}(1^{n})\) and \(KeyGen_{S}(1^{n})\) algorithms to generate \(pk_{R},sk_{R}\), \(pk_{S}\) and \(sk_{S}\) and gives \(pk_{R}\) and \(pk_{S}\) to the attacker \(\mathbb{A}\).
2. Phase 1: the attacker \(\mathbb{A}\) can adaptively ask the challenger \(\mathbb{C}\) for the trapdoor \(T_{Q}\) for any query Q of his choice. Moreover, \(\mathbb{A}\) can adaptively ask \(\mathbb{C}\) for the encrypted index for any keyword set of his choice.
3. Challenge: \(\mathbb{A}\) selects a target keyword set \(W^{*}\) and sends it to the \(\mathbb{C}\). \(\mathbb{C}\) selects a random keyword set \(R\). The restrictions are that the secure indices of \(W^{*}\) and \(R\) have not been obtained in the previous phase and the trapdoor queried in previous phase can not distinguish \(W^{*}\) from \(R\). Then, \(\mathbb{C}\) picks a random bit \(\beta\in\{0,1\}\). Suppose that \(W_{0}=W^{*}\) and \(W_{1}=R\), \(\mathbb{C}\) produces \(I_{\beta}=IndexBuild(pk_{R},sk_{S},pk_{S},W_{\beta})\) and sends \(\{I_{\beta},W_{0},W_{1}\}\) to \(\mathbb{A}\).
4. Phase 2: \(\mathbb{A}\) can continue asking for trapdoor \(T_{Q}\) and index \(I_{W}\) for any query \(Q\) and keyword set \(W\) of his choice. The restrictions in this phase are the same as that in the challenge phase.
5. Response:the attacker \(\mathbb{A}\) outputs \(\beta^{{}^{\prime}}\in\{0,1\}\) and wins the game if \(\beta^{{}^{\prime}}=\beta\). So, \(\mathbb{A}\)'s advantage can be expressed as a function of the security parameter (\(1^{n}\)): \[Adv_{\mathbb{A}}^{IND-CR-CKA}(1^{n})=|Pr[\beta^{{}^{\prime}}=\beta]-\frac{1}{2}|\]  
Based on the game above, the security definition is described as follows:  
Definition 2: We say that a SPE-CKS is IND-CR-CKA secure if for any PPT attacker \(\mathbb{A}\) according to the game IND-CR-CKA, the function \(Adv_{\mathbb{A}}^{IND-CR-CKA}(1^{n})\) is negligible.  
[PERSON] et al. [25] pointed that the consistency implies insecurity of a SPE scheme against keyword guessing attacks, since the public key in the tradition SPE scheme can be accessed by anyone and anyone can create the secure index. When trapdoors are obtained by attackers, attackers can run keyword guessing attack to guess the keywords contained in the indices and trapdoors. Inspired by the idea in Reference [11], by limiting the adversary's ability to generate the index, our scheme can defend against the offline guessing attack.
## 6 Performance Analysis
### Timing Performance  
Timing performance of a routing protocol can be measured in terms of the time required for the data to be received by the DS from the SN after sending the data request message. The data request message has two main components: processing time (including message decoding, data encryption, and data packet processing) required for the specific protocol and the TOA of the wireless technology used. For the LoRa wireless network used with the proposed LDAP, the processing time (around 7 ms as measured for the prototype SN) is around 5% compared with the LoRa TOA (135 mS).
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Architectural Layers
#### 3.4.9 The Messaging Layer  
The messaging layer is used primarily to facilitate the transmission of data from one system to another. One example of such usage in the context of streaming architectures is use as a sink or output for real-time data from IOT devices and as a source or input for big data processing frameworks. Likewise, the messaging layer could be configured as a sink for the result of the data processing performed by the big data framework and as a source for subsequent processing by the same or a different framework.  
As MC-BDP is a reference architecture for big data stream processing, it is recommended that the messaging technology be distributed and scalable in order to avoid bottlenecks. It should also be fault-tolerant so that processing can continue with minimal disruption in the event of a node, container, or even region failure. If savings derived from economies of scale are to be maximised, it is recommended that the messaging layer be implemented as a set of containerised services. Finally, in order to facilitate the handling of backpressure without loss of data by the big data framework, it is advisable that the messaging technology be capable of a buffering or persisting state for a short (configurable) amount of time. Technologies such as Google Cloud's Pub/Sub [89], Amazon Kinesis [90], or Apache Kafka [91] are suitable choices to fulfil these requirements. The latter was selected for the prototype implementation of MC-BDP since the others are cloud services and carry an inherent risk of vendor lock-in.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 6 Evaluation of SDM Ontology
### Evaluation by Ontology Evaluation Index  
[PERSON] et al. [54] defined 12 indicators for evaluating and measuring ontologies. In their Internet of Musical Things Ontology [33], [PERSON] et al. used the following indicators, which were modified from some of the above indicators, for ontology evaluation.  
* Indicators of knowledge coverage and popularity
* The number of classes in the target ontology.
* The number of properties in the target ontology.
* The number of individuals in the target ontology.
* The number of times the target ontology is used by external ontologies. In general, the more mature the ontology, the larger the value of this indicator.
* Number of external ontologies used The number of times the target ontology uses external ontologies.
* Indicators for data structure
* Minimum number of triples The minimum number of triples required to describe the data for which the target ontology exists.
* Maximum number of triples The maximum number of triples that can be used to describe the data for which the ontology of interest exists.  
Table 1 presents the performance of the SDM Ontology based on the indicators of knowledge coverage and popularity among these indicators. Because the SDM Ontology is a new ontology, it has not yet been used by external ontologies. External ontologies referenced by the SDM Ontology are ignf, Dublin Core, and schema-org. Only a basic vocabulary has been defined for using an external ontology. We expect this number to increase as more detailed definitions are added in the future.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 5 Validation of the Models and Power Consumption Analysis
### Idle Mode Power Consumption  
All measured idle mode power consumption parameters presented in Section 3 are summarized in Table 2. The ECG sensor cannot be put in fast advertising and slow advertising modes manually. It alternates between the two advertising modes automatically, and therefore the measurements of individual advertising modes have to be differentiated based on the readings of the multimeter. Both advertising periods are long enough for the values on the multimeter to settle, and reliable current values can be read. The overall average advertising current consumption \(P_{adv\_tag}\) can be calculated from both of the measured current consumptions--\(P_{adv\_fast}\) and \(P_{adv\_slow}\)--as presented with Equation (4).  
In Table 3, both measured values and model-based values for the idle mode of the ECG sensor are shown.The \(P_{measured}\) and \(I_{measured}\) values represent the measured power and current consumption, respectively, while \(P_{model}\) and \(I_{model}\) represent the model-based calculated power and current consumption values, respectively. The results show that the measured and calculated values are very similar, which presents a very good model fit to the actual power and current consumption values, with less than 2% maximal error in current consumption, while average advertising current consumption fits perfectly. The error is calculated as deviation of the modeled value from the measured value, and expressed as percentage of the measured value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(T_{adv\_slow}\) & 300 s \\ \hline \(T_{adv\_fast}\) & 30 s \\ \hline \(tp_{adv\_slow}\) & 1000 ms \\ \hline \(tp_{adv\_fast}\) & 100 ms \\ \hline \(tp_{tick}\) & 1000 ms \\ \hline \(t_{adv}\) & 4.038 ms \\ \hline \(t_{tick}\) & 0.133 ms \\ \hline \(P_{sleep\_alle}\) & 1.095 mW \\ \hline \(e_{adv}\) & 120.65 mJ \\ \hline \(e_{tick}\) & 1.85 mJ \\ \hline \hline \end{tabular}
\end{table}
Table 2: BLE idle mode evaluation parameters.
## 4 Experimental Results
### Results
#### 4.2.1 Effectiveness
(4) Space complexity of the abstraction model. This metric refers to the scale of storage cost by the learned model PFA/DFA. In practical situations, the abstraction model can be represented by an adjacency matrix. Intuitively speaking, the fewer states and edges there are, the lower its complexity. After adjusting the number of clusters and making their abstraction errors below 0.4, we counted the total number of states in the six abstraction models. Table 4 shows the number of states obtained from different abstraction techniques on the IMDB sentiment dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abstracting** & **IMDB-Re** & **DLRV-Re** & **Tomita-Re** \\
**Technique** & & & \\ \hline BSL1 & 0.43 & 0.39 & 0.62 \\ BSL2 & 0.46 & 0.36 & 0.65 \\ PSO4 RNN & 0.67 & 0.47 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification restoration degree of the original model on the test datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Consumption** & **BSL1** & **BSL2** & **PSO4 RNN** \\ \hline Tomita-LSTM & 0.23 h & 0.12 h & 0.11 h \\ IMDB-LSTM & 0.34 h & 0.17 h & 0.13 h \\ DLRV-LSTM & 0.37 h & 0.23 h & 0.21 h \\ \hline \hline \end{tabular} Note: the unit of time cost is hours (h).  
\end{table} 
Table 3: Time consumption of the three techniques for LSTM abstraction learning. Figure 9: Error change curves of three models. It can be seen that, while ensuring an abstraction error below 0.4, the scale of the abstract model is much smaller than the RNN model. PSO4 RNN requires fewer state nodes to keep the behavior of the original RNN. This also indicates that the probabilistic model learned by PSO4 RNN is more general, and its abstraction effect is more competitive. DFA learned by BSL1 has a higher number of states because DFA lacks information on probability distribution.
# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 2 Materials and Methods
### Data
#### 2.2.2 Future Climate Data  
The future climatic forcing (precipitation, surface air temperature, and surface downwelling shortwave radiation) from five global climate models (BCC-CSM2-MR, CAS-ESM2-0, FGOALS-f3-L, GFDL-ESM4, and MRI-ESM2-0- all r1i1p1f1 ensemble members) were selected to predict the NDVI of the study area under three shared socioeconomic pathways (SSPs), namely SSP126, SSP245, and SSP585 ([PERSON], 2015). These models belong to the scenario model intercomparison (ScenarioMIP), which is part of the Coupled Model Intercomparison Project Phase 6 (CMIP6, [[https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)]([https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)), accessed on 04 December 2021). Notably, SSPs work in harmony with representative concentration pathways (RCPs) through shared policy assumptions, implying that future scenarios are more reasonable ([PERSON], 2015). Bias correction was conducted through the relationship between observed values and historical values by linear regression for precipitation, surface air temperature, and shortwave radiation, respectively, from 1979 to 2014. The slope of the regression was used to rescale the future data from 2020 to 2100. Whether NDVI will continue to increase in the future without artificial afforestation was evaluated using future scenarios from 2020 to 2100. Information regarding the models is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Producer** & **Nation** & **Grid Number** \\ \hline BCC-CSM2-MR & Beijing climate center & China & 160 \(\times\) 320 \\ CAS-ESM2-0 & Chinese academy sciences & China & 128 \(\times\) 256 \\ FGOALS-f3-L & Chinese academy of sciences & China & 180 \(\times\) 288 \\ GFDL-ESM4 & Geophysical fluid dynamics & USA & 180 \(\times\) 288 \\  & laboratory & USA & 180 \(\times\) 288 \\ MRI-ESM2-0 & MRI (meteorological research & Japan & 160 \(\times\) 320 \\  & institute) & Japan & 160 \(\times\) 320 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of global climate models of CMIP6 used in this study.