esa-sceva/satcom-chunk-collection huggingface dataset:
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 3 SoDa Architecture
### Workflow Meta-Language
#### 3.6.3 Automated Workflow Construction  
To compute the following interesting query  
_Q: Find all genes implicated in obesity related male infertility using the gene list in the table crerews_.  
Using a set of genes (gene symbols) in a table called _crerews_ (with scheme _crerews_(_Symbol_)) and the MiKDB [31] and DisGeNET databases, we proceed as follows. We first collect a set of genes (gene symbols) from MiKDB database for a specific phenotype (e.g., teratozospermia). We then submit those genes (Entrez IDs) to the DisGeNET database to find the genes in the set that are also associated with obesity. The technical issue is that MiKDB returns the gene list in the form of gene symbols, and DisGeNET requires the gene list in the form of Entrez GeneIDs, necessitating an ID conversion step, which we carry out using MapBase. The entire workflow query generated by SoDa is as follows:  
select _Disease_, _Type_, _N_genes_, _Score_gda_, _EL_gda_, _N_PMIDs_, _First_Ref_  
from (extract _Type_, _N_genes_, _Score_gda_, _EL_gda_, _N_PMIDs_, _First_Ref_  
using matcher _S-match_ wrapper _Web-Prospector_  
from _[[https://www.disgenet.org/browser/1/1/0/_](https://www.disgenet.org/browser/1/1/0/_)]([https://www.disgenet.org/browser/1/1/0/_](https://www.disgenet.org/browser/1/1/0/_))  
submit (extract _GeneID_  
using matcher _S-match_ wrapper _Web-Prospector_  
from _[[https://www.mapbase.smartdblab.org_](https://www.mapbase.smartdblab.org_)]([https://www.mapbase.smartdblab.org_](https://www.mapbase.smartdblab.org_))  
submit (with _mikgene_ as (extract _Symbol_, _Phenotyoe_  
using matcher _S-match_ wrapper _Web-Prospector_  
from _[[http://mik.bcinirrh.res.in/mtip.php_](http://mik.bcinirrh.res.in/mtip.php_)]([http://mik.bcinirrh.res.in/mtip.php_](http://mik.bcinirrh.res.in/mtip.php_))  
select _crrews_Symbol_  
from _crrews_ natural join _mikgenes_  
where _Phenotype = \"teratozospermia\"_  
where _Disease = 'obesity'_ and _Score_gda > 0.01_;  
The execution of this query in SoDa will return the partial table shown in Figure 10. In the above statement, S-Match [32] is a schema matcher, and Web-Prospector [33] is a wrapper. An interesting observation is that SQL's select statements and BioFlow's extract statements both uniformly accept each other where a table is expected, and thus allows nesting, as in this query. In the event that the query needs to be broken down into smaller queries and then strung together, SQL's create view construct can be used in the usual way.
# Development of an Autonomous Underwater Helicopter with High Maneuverability
## 4 Mechatronics Design of AUH Prototype
### General Layout  
Considering the high maneuverability of the AUH disc shape and the zero-radius rotation characteristic of low damping, the symmetrical distribution of mass in the plane can achieve stability, and the arrangement of the large mass near the center is also beneficial to reducing the moment of inertia. This can further improve the maneuverability of AUH.  
The AUH layout concept diagram and 3D model diagram are shown in Figure 6. It mainly includes two symmetrical cylindrical cabins, a battery cabin and a control system cabin, four propellers, iUSBL equipment for positioning and navigation, buoyancy material, a skeleton for connecting and supporting, and a disc-shaped hull. Each cabin passed the 100 m pressure seal test.  
The prototype is 1 m in diameter, 45 cm high and weighs 42 kg. The design speed is 0.8 m/s and the design depth is 100 m. Since the experimental prototype produced this time is only a prototype for verifying the working principle of the circular dish AUV, considering the test and iteration cost, the design depth is selected as 100 m, so that the design cost would remain low and the experimental environment would easier to find. In addition, the design speed is the estimated average flow rate in the working environment, considering that it should have a certain resistance to flow in order to achieve hovering.
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Database  
To construct the dictionary, we used the Physikalisch-Technische Bundesanstalt (PTB) database, compiled by the National Metrology Institute of Germany for research, algorithmic benchmarking and teaching purposes [29]. The ECGs were collected from healthy volunteers and patients with different heart diseases by Prof. [PERSON], at the Dep. of Cardiology of Univ. Clinic Benjamin Franklin in Berlin (Germany), and can be freely downloaded from Physionet [27]. ([[https://www.physionet.org/physiobank/database/ptbdb/](https://www.physionet.org/physiobank/database/ptbdb/)]([https://www.physionet.org/physiobank/database/ptbdb/](https://www.physionet.org/physiobank/database/ptbdb/))). The database contains 549 records from 290 subjects (aged 17-87 years) composed of 15 simultaneously measured signals: the 12 standard leads plus the 3 Frank lead ECGs [1; 2]. Each signal lasts approximately 2 min and is digitized using a sampling frequency \(f_{s}=1000\) Hz with a 16 bit resolution. Out of the 268 subjects for which the clinical summary is available, we selected channel 10 (lead V4) of the first recording of the \(Q=51\) healthy patients available in order to build the dictionary.
## 4 Protocol Description
### Routing Reconfiguration
The energy consumption is the average energy consumed for each sensor during the simulation. Within the simulated area, a fire breaks out 30 seconds after the simulation is started which means the first 30 seconds of the simulation. The node in the network is static. At 30 seconds after the simulation \begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline Propagation model & Shadowing \\ Shadowing deviation & 4.0 \\ Reference distance & 1.0 \\ PhyType & Phy/WirelessPhy/802\_15\_4 \\ MacType & Mac/802\_15\_4 \\ CSThresh\_ (carrier sense threshold) & 5.29754e-11 \\ RXThresh\_ (reception threshold) & 5.29754e-11 \\ Pt\_ (transmit power) & 5.35395e-05/0.000214 [88/0.000487865] \\ Freq\_ & & \\ Traffic & & CBR\_ \\ Traffic packetSize\_ & & \\ Traffic Interval\_ & & 10969 \\ Node Initial energy & & 3.8 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 4: Simulation grid. begins, a fire occurs randomly in the network area and then spreads to its neighbors continuously every 10 seconds. When the fire reaches a sensor node, it will lead to a terminal node failure after 10 seconds. We compare our protocol with minimal hop count routing and RPAR protocol to make performance evaluation. The two comparing routing mechanisms are operated with the initial power as the default transmission power in EAR. RPAR is a real-time power-aware routing mechanism that achieves this by dynamically adapting transmission power and routing decisions based on packet velocity calculated by geographical distance and time left.
## 3 Results and Discussion
### Nitrogen Metabolism  
The transcriptomic response of _L. plantarum_ to HXT involved the differential expression of genes encompassed by the GlnR regulon. This response included the downregulation of genes involved in the influx and production of glutamine and ammonium, i.e., _lp_1581_ or _glnA_ (glutamine synthetase),\(lp\_0822\) (glutamine-fructose-6-P transaminase), genes coding for two Gln ABC transporters (\(lp\_0802\) and \(lp\_0803\)) (\(lp\_2110\) and \(lp\_2111\)), \(lp\_2830\) (aspartate ammonia lyase which produces fumarate from Asp with the production of ammonium), \(lp\_0956\) or \(asnC\) (Asn-tRNA ligase) and \(lp\_0349\) or \(amtB\) (NH4\({}^{+}\) transport protein). In addition, \(lp\_2738\), coding for L-asparaginase was upregulated. This response prevents the influx and simultaneously limits the intracellular production of ammonia and glutamine. It allows for a tight control and impairment of efflux of ammonia levels which in turn are required to ensure sufficient biosynthetic nitrogen assimilation for growth. The observed profile regarding nitrogen metabolism is very well conserved among the responses of \(L\). _plantarum_ WCFS1 to other phenolic compounds, including gallic acid [11], resveratrol [13], oleuropein [14], and p-coumaric acid [7].
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 3 Experimental Equipment and Procedures
### Test LUT Airfoil Model  
In this study, the test LUT airfoil was designed by the authors. The cross-section width of the LUT model is the same as that specified above, the chord of the LUT is 200 mm, and the span is 400 mm. As shown in Figure 6, to investigate the aerodynamic performance of the LUT airfoil under a static condition, 50 pressure taps with a diameter of 0.7 mm are distributed on the LUT airfoil model, where the distribution average density of the pressure taps is 0.7:0.19:0.4 from the leading edge to the trailing edge of the LUT model.
## 2 Methodology
### Data Analysis and Processing
#### 2.1.1 Data Acquisition and Correlation Analysis
The wind direction, atmospheric pressure, and rainfall have very weak correlations with the heat load and can be ignored. The correlation coefficients for the minimum and maximum indoor set temperatures range from 0.2 to 0.3, indicating a weak correlation. The personnel occupancy rate and housing occupancy rate have correlation coefficients between 0.5 and 0.6, showing a moderate correlation with the heat load. Ultimately, outdoor weather factors such as the dry bulb temperature and solar radiation are selected as the primary influencing factors, while the relative humidity and wind speed are considered secondary influencing factors. For indoor factors, the hotel's housing occupancy rate and personnel occupancy rate are chosen as the primary influencing factors. These influencing factors, along with the historical heat load data, are then used as input features for the model.
# To Cache or Not to Cache
## 2 Background and Motivation
### Non-Datapath Caching Algorithms  
Non-datapath caching algorithms have received attention in recent years, starting with the LARC [14] work. LARC is a static policy that admits items into the cache only upon their second miss. Since the LARC filter first accesses absolutely, it can prevent important items from entering the cache in a timely fashion. mARC, on the other hand, employs a dynamic policy and learns about workload states to allow updates or not into the cache. It uses a three state machine with _unstable_, _stable_, and _unique access_ states, and seven distinct state transition conditions to learn the workload's state and adapt admission into the cache accordingly. mARC builds its mechanisms upon the classic ARC [3] datapath cache replacement algorithm. However, as Figure 1 illustrates, mARC's cache hit-rate compared to its datapath counterpart algorithm can at times be over 20% lower when such sub-optimal state transition or a delayed reaction to state change is encountered. Finally, TinyLFU [17] is a CDN cache admission policy that improves performance by keeping the items with the highest frequency within the cache.
## 2 Computational Simulation Models
### LV AC Network  
The hybrid AC/DC network modeled in this work also comprises an LV AC section, as represented in Figure 8. The LV AC network section comprises controllable PV generation units, controllable ESS and other non-controllable DC loads connected in different nodes.  
The feeders of the AC networks were modeled considering predominantly resistive cables, as it is the typical nature of LV AC distribution feeders. Non-controllable loads are modeled in the LV AC network in order to evaluate the exploitation of its voltage sensitivity for the provision of frequency support to the main power system. The feasibility of exploiting load voltage sensitivity in AC networks for the provision of frequency support to the main power system is already explored in the literature [42; 43]. Moreover, the estimation of load voltage sensitivity in AC networks using the ST itself is already addressed in the literature [41; 44]. Given this, the representation of the non-controllable load present in the LV AC network is based in one of the case-studies already addressed in the literature, namely in [41], represented by Equation (19).  
Figure 7: Diagram of the developed computational model for the Medium-Voltage (MV) and LV DC networks constituting the hybrid distribution grid.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Use of Separating Families for Construction of Authentication Tags  
In this section, we draft a strategy for creating tags to authenticate multicast messages. We assume that \(R\) is a multicast group with \(r\) receivers.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 2 Algorithm Description  
The flowchart of this article is shown in Figure 1, where we first detect the burst signal to find the frame header of the received signal. In the sequel, we exploit the above correlation value to estimate the frequency offset, which is compensated for the true carrier frequency and closely followed by the phase offset estimation part. Once again, we apply the interpolation method to the correlation peak in the frame header detection to obtain the timing error.
## 2 Materials and Methods
### Causality Analysis
#### 2.1.2 Developing VECM Models  
The major steps for understanding causal relationships among the dependent and independent variables include (i) examining the existence of unit roots in the data set, (ii) testing the co-integration among variables, and (iii) developing a VECM based on unit root and co-integration test results.  
Both the augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test were performed to examine the existence of unit roots in a variable. The presence of unit roots indicates the non-stationarity of a time series variable [41] and one of the preconditions for using a time series variable in a vector error correction model (VECM) is that the variable needs to be non-stationary at the level and stationary at the first difference [42]. The ADF test was deployed in this study because it is one of the most widely used unit root tests [43]. On the other hand, the PP test is adopted along with the ADF test because the strength of an ADF test to reject a unit root is often limited [44]. Moreover, [PERSON] et al. [41] confirmed that the combination of both the tests is likely to provide robust results.  
Based on the results of the unit root, this study adopted the [PERSON]'s test of co-integration to understand if any of the two variables (both dependent and independent variables) were co-integrated. The precondition for developing a VECM model was that at least two variables had to be co-integrated among each other. To investigate the existence of co-integration among different variables, trace statistics (TS) and maximum eigenvalue statistics (MES) were considered. It was assumed that the use of two different statistics would provide reliable results. Finally, based on the unit test and co-integration test results, this study developed the VECM for each emission type and performs the Granger causality test to understand the short- and long-run causal relationship between solid waste emissions and their drivers.
# Joints Trajectory Planning of Robot Based on Slime Mould Whale Optimization Algorithm
## 4 Trajectory Optimization of Joints Based on SMWOA
### Optimal Experiment of Path Planning
The blue imaginary line shows the change of the corresponding joint-angular jerk, which is denoted by \(j_{i}\). It can be seen that the optimized angular velocity and angular acceleration are in a gentle and stable state, and the angular jerk of joints \(\theta_{1}\), \(\theta_{2}\), \(\theta_{4}\) and \(\theta_{6}\) only appear as a cusp in the whole time range, with the remaining parts in a basically smooth state. Figure 4: \(Cont.\)  
The proposed algorithm was compared with the SMA and WOA in the optimizing joint trajectory to verify the effectiveness of the SMWOA. The total time and spent time for each segment were recorded and compared, and the results are shown in Table 6. The segment of trajectory from the \(i\)th node to the \(i\)+1 th node is represented as \(\mathbf{h}_{i}\). The experimental results show that the optimization performance of the proposed SMWOA algorithm is superior to the SMA and WOA in joint trajectory planning. Figure 4: Movements of joints after optimization based on the SMWOA. (**a**) Movement of base joint \(\theta_{1}\); (**b**) Movement of shoulder joint \(\theta_{2}\); (**c**) Movement of elbow joint \(\theta_{3}\); (**d**) Movement of wrist joint \(\theta_{4}\); (**e**) Movement of wrist joint \(\theta_{5}\); (**f**) Movement of wrist joint \(\theta_{6}\).
## 4 Simulation Results
### Simulation Settings  
Ten nodes are deployed in a 1000 m \(\times\) 1000 m \(\times\) 1000 m region including 3 anchors and 7 unknown ordinary sensor nodes. The anchors' positions are known and accurate before the localization process. The estimation period \(T_{0}=100t_{0}\) in the simulation experiments.  
Figure 5: The implementation of DRSL algorithm.  
As it can be seen in Figure 8, the localization error increases with the growth of the observation error. Meanwhile, compared with the non-optimal reference selection scheme, the proposed optimal reference selection scheme can reduce the localization error of DUANs effectively.  
Since the topology of DUANs is changing over time, the dynamic reference selection scheme proposed in this paper is more appropriate for DUANs with mobile sensor nodes. The localization performance of dynamic reference selection scheme will be illustrated in the next section.
# Exploring ISAC: Information-Theoretic Insights
## 2 Pre-ISAC: Sensing (Radar) vs. Communication
### Coexisting Communication and Radar Systems
Thus, non-overlapping resource allocation can be performed in the time, frequency, or spatial domains, as illustrated also in Figure 1.  
From an information-theoretic perspective, we examine pre-ISAC and non-overlapping schemes through two baseline approaches: the basic time-sharing (TS) scheme and the improved time-sharing (TS) scheme. The basic TS scheme represents the non-overlapping  
Figure 1: The red cubes demonstrate the communication waveform and the blue cubes demonstrate the sensing waveform. (**a**) Time-sharing. (**b**) Frequency-sharing. (**c**) Spatial-sharing. resource allocation strategy, which divides its resources (time, bandwidth, or spatial dimensions) between the following two modes:  
* _Sensing mode_ The system aims to design a suitable waveform to attain the minimum possible distortion. In this model, the waveform is translated into an input distribution; thus, the input probability mass function (pmf) \(P_{X}\) is chosen to minimize the distortion, and hence, the minimum distortion is achieved. The communication rate is zero. * _Communication mode_ The system is designed to transfer as much reliable data as possible. Therefore, the input distribution is chosen to maximize the rate and communicates rate equals channel capacity. The estimator is set to a constant value regardless of the feedback and the input signals. The mode thus suffers from a large distortion. The improved TS scheme still performs a sort of non-overlapped resource allocation, but resources are not exclusively dedicated to only sensing or only communication. It is simply that one of these tasks is prioritized. The second baseline scheme is improved TS scheme and can simultaneously perform the communication and sensing tasks. This scheme time-shares between the following modes. * _Sensing mode with communication_ The input pmf \(P_{X}\) is chosen to achieve minimum distortion. At the same time, the transmitter is also equipped with a communication encoder. It uses this input pmf to simultaneously transmit data at the rate given by the input-output mutual information of the system. * _Communication mode with sensing_ The input distribution is chosen to maximize the communication rate, i.e., achieve the capacity of the channel. The transmitter is, however, also equipped with a radar estimation device that optimally guesses the state sequence based on the transmitted and backscattered signals.
## 4 Problem Formulation  
The _VM_ request is in the form of a triplet (\(f,r,e\)), where \(f\in F\) represents the reserved frequency, \(r\in R\) represents the resource requirement, and \(e\in I\) represents the execution interval. Consider \(M\) heterogeneous servers, with each containing discrete frequencies (\(f_{0}\),\(f_{1}\),\(f_{2}\),\(f_{3}\),\(f_{4}\),... \(f_{k}\)) with utilization (\(U_{0}\), \(U_{1}\), \(U_{2}\), \(U_{3}\)..., \(U_{k}\)),where \(U_{0}\)= 0% (idle), \(U_{k}\)= 100%, and fixed dynamic power consumption (\(P_{0}\),\(P_{1}\), \(P_{2}\)\(P_{3}\)\(P_{4}\)..., \(P_{k}\)). Here, \(U_{0}\) is considered as the idle state, with power consumption \(P_{0}\). Let \(S=\{S_{1}\), \(S_{2}\), \(S_{3}\)... \(S_{M}\)\(I\) represent \(M\) servers for each \(S_{j}\), where \(j\epsilon\) [\(I\),\(M\)] with utilization (\(U_{j,0}\), \(U_{j,1}\), \(U_{j,2}\), \(U_{j,3}\)..., \(U_{j,k}\)), and power consumption (\(P_{j,0}\), \(P_{j,1}\), \(P_{j,2}\), \(P_{j,3}\), \(P_{j,4}\)..., \(P_{j,k}\)) can be characterized as a triplet (\(CU_{j}\), \(CP_{j}\), \(C_{j}\)); where \(CU_{j}\) is the current utilization of server \(S_{j}\), \(CP_{j}\) is the power consumption of server \(S_{j}\) with utilization state \(CU_{j}\), and \(C_{j}\) is the total processing capacity of \(S_{j}\).  
The relation \(R\) between the \(j^{\text{th}}\)\(PM\) and \(i^{\text{th}}\)\(VM\) indicates whether \(VM_{i}\) is placed in \(PM_{j}\), as below:  
\[R_{j,i}=\left\{\begin{array}{cc}1&VM_{i}\text{ \emph{is allocated to PM}}_{j}\\ 0&\text{ \emph{otherwise}}\end{array}\right\} \tag{1}\]  
The service level agreement (SLA) is measured using the ratio of virtual machine acceptance (_RVA_), calculated as:  
\[RVA\ (V)=T(R)/N \tag{2}\]  
Figure 1: System model.  
where \(N\) represents the total number of \(VM\) requests submitted and \(T(R)\) represents the total number of \(VM\) requests accepted and mapped to available PMs. This is derived as:  
\[T(R)=\sum_{j=1}^{M}\sum_{i=1}^{N}R_{j,i} \tag{3}\]