esa-sceva/satcom-chunk-collection huggingface dataset:
# Role of Exposure on the Microbial Consortium on Historical Rural Granite Buildings
## 4 Conclusions  
The combination of culture-dependent techniques and next-generation sequencing enabled characterization, for the first time, of the microbial consortium present on historical granite buildings in rural areas. In the three buildings examined, the biofilms were mainly composed of bacteria, fungi (lichenized and non-lichenized), green algae and to a lesser extent cyanobacteria. Overall, this composition is very similar to that of biofilms on historical granite buildings in urban settings, especially in terms of fungal diversity. However, some differences were observed regarding the presence of algae, which were more abundant on urban buildings, and of lichenized fungi, which were more abundant in the rural environment.  
Orientation also proved to be an important factor regarding both the diversity and abundance of microorganisms on the walls, with environmental factors associated with the north orientation favoring a higher diversity of fungi and green algae, and environmental factors associated with the west orientation determining the abundance of lichenized fungi. Among the two most abundant species of green algae, _Tentepophilia_ cf. _umbrina_, mainly occurred on the N-facing wall, while _Desmococcus olivacueus_ predominated on the W-facing wall.  
These findings support the recommendation to characterize the microbial consortium forming the biofilms developed on the granite cultural heritage. The observed differences in both rural and urban environments, as well as regarding the orientation of different walls and the consequent exposure to different climatic factors, should be considered in selecting techniques to remove the colonizing microorganisms (such as the use of biocides), and when making any decisions related to the conservation of the buildings.  
Conceptualization, B.P.; methodology, B.P. and R.C.; software, R.C.; validation, E.F. and R.C.; formal analysis, E.F.; investigation, R.C., B.P. and E.F.; resources, B.P.; data curation, E.F. and R.C.; writing--original draft preparation, E.F.; writing--review and editing, B.P.; visualization, B.P.; supervision, B.P.; project administration, B.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.  
This study was partly financed through project CGL2016-79778-R (AEI/FEDER, UE) and Xunta de Galicia (ED431 2018/32). [PERSON] was financially supported by a PhD fellowship contract, [PERSON] (BES-2017-079927).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
In this algorithm, the detection node first uses the linear prediction method to estimate the signal characteristics of the SUs in the next period. Then, in the spectrum sensing, the result of subtracting the estimated value from the received signal is used as the judgment criterion to determine whether the PU exists or not. In [15], the authors add a feedback time slot in the center-based spectrum allocation algorithm architecture, so that the number of sampling points can be adjusted automatically according to the feedback information. However, in the case of low signal-to-noise ratio (SNR), it cannot guarantee the reliable transmission of the feedback information which allows for the possibility of over-regulation which will reduce the detection probability of the PU. At the same time, the introduction of the feedback time slot will also reduce the time utilization rate of the time resources to a certain extent. Referring to the ideas on feedback information proposed in [15], we constructed a cognitive wireless communication system model with an additional signaling channel. The model utilizes additional frequency resources rather than time resources to feedback the state information of the data transmission which can characterize the time-varying channel characteristics in the data transmission. So, on this basis, an improved adaptive silence period management (IA-SPM) algorithm is proposed. The algorithm combines the results of the previous perception period with the data channel communication result feedback from the signaling channel to adaptively adjust the relevant parameters to be used in the current sensing period. The signaling channel can not only reduce the interference of noise, but it can also ensure the reliability of the transmission through coding error correction. Meanwhile, we used the discrete Markov analysis method to construct a mathematical simulation model of the system and analyze the detection probability of the proposed algorithm. As a result, in the case of low SNR, the detection probability of our algorithm was 10-15% higher than that of the algorithm proposed in [16].
## 4 Discussion
### The SIF-GPP Relationship on Different Timescales
SM and VPD had different influences on SIF and GPP on the daily scale, instead, SIF-GPP are mainly driven by PAR and have a significantly positive correlation with PAR on the 30 min timescale, so their linear correlation on the 30 min timescale was weakened with aggregation. SM is the most directly absorbed water by plant roots, which has a decisive effect on the physiological state of plants [62]. This has a strong influence on SIF on the seasonal scale, reflected in a significant positive correlation with SIF, which is consistent with a previous study in the Great Plains [63]. SIF is very sensitive in monitoring vegetation drought [64; 60], especially early soil drought [5], and is an important indicator of plant stress and physiological status [62]. With an increase in SM, GPP also shows an upward trend, but there was no significant correlation. As the total photosynthetic production of the vegetation canopy, GPP has a relatively sensitive response to VPD [33; 65], and there was an obvious negative correlation on the seasonal scale. This can be largely explained that with the increase of VPD and the intensification of the air drought stress, the stomata closed to reduce the loss of water [66] and caused a significant decrease in gas exchange rate [59], which affected the response of GPP to PAR. VPD also has a slight impact on the seasonal variation of SIF, but the impact is not significant. As SM and VPD have different impacts on SIF and GPP, respectively, they lead to a weakening of the relationship of SIF-GPP on a seasonal scale.
## 3 Results and Discussion
### Chemical-Physical and Morphological Characterization of the SLS Manufactured Foams
From both curves, a main thermal event is noticed, consisting of a first-order transition showing large thermal hysteresis, as indicated by the peak maximum recorded at 91.2 \(\pm\) 0.3 \({}^{\circ}\)C and 166 \(\pm\) 1.2 \({}^{\circ}\)C upon cooling and heating, respectively. This transition is related to the melt crystallization and fusion of TPU hard segment crystallites [40]. The calculated melting enthalpy value was as low as 5.4 \(\pm\) 0.4 J/g, indicating that only a small fraction of the material was able to crystallize [41]. In the heating thermogram, it is also worth noting the occurrence of the glass transition temperature (\(T_{\mathrm{g}}\)) at \(-\)18 \({}^{\circ}\)C.  
Figure 5a also reports the DSC thermogram of TPU/GE as representative of the thermal behavior of the SLS processed TPU/GE composite materials. Crystallization and melting peaks were detected at 65.47 \(\pm\) 0.40 \({}^{\circ}\)C and 144.65 \(\pm\) 1.01 \({}^{\circ}\)C, respectively, indicating that the addition of GE hindered TPU crystallization, also decreasing the crystalline size of the TPU fraction [40; 42]. In addition, the value of melting enthalpy was about 4.9 \(\pm\) 0.3 J/g, showing that an even smaller amount of hard segments  
Figure 4: Raman spectra of GE powder (black line), and TPU (green line) and GE (red line) in the TPU/GE G40 composite after SLS processing. crystallized in comparison with the plain TPU. Finally, GE also caused a significant increase in \(T_{\rm g}\), which was detected at \(-\)11 \({}^{\circ}\)C in the composites. The rise in \(T_{\rm g}\) shows that the presence of the carbonaceous filler was able to mediate the H-bonding interactions between TPU chains, reducing the mobility of the polymer soft segments, as already reported for graphene/TPU composites [43]. Similar results were obtained for all porous systems regardless of geometry and porosity. GE also affected the thermal stability of the TPU foam. In Figure 5b, the thermogravimetric curves of TPU and TPU/GE are compared. TPU degradation occurs with a two-step mechanism. The first process, attributed to the cleavage of urethane bonds of TPU [44], starts at about 280 \({}^{\circ}\)C, with a maximum rate at 308.30 \(\pm\) 1.20 \({}^{\circ}\)C, and accounts for about 30% mass loss. The second weight loss step, related to the decomposition of soft segments of TPU, had a maximum rate at 388.12 \(\pm\) 1.23 \({}^{\circ}\)C, leading to a residual char value of 1.2%. The presence of 1 wt% GE significantly retarded the degradation onset, which occurred at about 301.22 \(\pm\) 3.37 \({}^{\circ}\)C, also shifting the degradation rate maximum at 342.74 \(\pm\) 2.32 \({}^{\circ}\)C. Therefore, the addition of GE brings about an improvement of thermal stability of TPU, as the large-area graphene sheets increase the tortuous path for the volatile products to be released, also resulting in a higher amount of residual char (8.5%) [45].
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
###### Abstract  
Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network's lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim-Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network's lifetime. It also achieves a high packet delivery rate with lower energy consumption.  
underwater sensor networks; energy balance; energy efficiency; power control
## 4 Experimental Results and Analysis
### Filtering Modal Components  
The IMF component of the bearing signal obtained by IGVMD contains noise signals, and to extract effective fault features, this paper uses the correlations to filter the IMF component for signal denoising. The correlation mainly reflects the correlation degree between two signals and is commonly used in the analysis of vibration signals [39]. Its algorithm is expressed as follows:  
\[\rho XY=Corr(X,Y)=\frac{Cov(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}},|\rho XY|\leq 1 \tag{26}\]  
where \(Corr(X,Y)\) represents the covariance, and \(\sqrt{D(X)}\) and \(\sqrt{D(Y)}\) are variances of signals \(X\) and \(Y\), respectively. The closer the correlation coefficient is to 0, the lower the correlation of the signal; the closer the absolute value of the correlation coefficient is to 1, the higher the correlation of the signal.  
In this paper, by selecting the modal components with a correlation threshold greater than 0.3 with the original signal for signal reconstruction, signal noise reduction is effectively achieved, and the impulse characteristics of the fault are retained to a great extent. To verify the extraction effect of the weak signal under the background noise of the improved VMD, the reconstructed signal of the improved VMD was analyzed and compared with the reconstructed signal of the EMD and the conventional VMD in the envelope spectrum, and the envelope spectrum of the reconstructed signal of each method is shown in Figure 13. Figure 13 clearly shows that, although fault frequency and twofold frequency exist in the envelope signal after EMD, the other multiples are disturbed by noise and the fault characteristics are not obvious; Figure 13b,c clearly show that, in the analysis of traditional VMD and improved VMD envelope spectrum, the feature frequencies and their multiples in the improved VMD envelope had more obvious peaks. The experiment shows that the improved VMD can more accurately decompose the signal, which is more conducive to extracting early faint fault features in the noise background.
## 6 Numerical Results Discussion and Optimization Analysis
### Sensitivity Analysis  
The main goal of this part is to perform a numerical examination of the efficiency of the analytical results presented previously, while showing the influence of each system parameter \((c,\ \lambda,\mu_{1},\ \alpha,\ \eta,\ \delta,\ \mu_{w},\ \
u,\ b\ )\) on the system performance indices \(\left(\overline{O},\ P_{v},\ P_{y},\ P_{d},\ U\right)\). We consider the following cases:  
**Case 1.**\(c=[4;6;8],\ \lambda=[1:0.1:5],\ \alpha=3,\ \
u=10,\ \eta=0.3,\ \mu_{1}=3,\ \mu_{2}=1.5,\ \mu_{w}=1,\delta=0.1,\ \gamma=1,\ b_{b}=0.8,\ b_{r}=0.6,\ b_{v}=0.6\).  
**Case 2.**\(c=[4;6;8],\ \lambda=4,\ \alpha=[0:0.2:5],\ \
u=10,\ \eta=0.3,\ \mu_{1}=3,\ \mu_{2}=1.5,\ \mu_{w}=1,\ \delta=0.1,\ \gamma=1,\ b_{b}=0.8,\ b_{r}=0.6,\ b_{v}=0.6\).  
**Case 3.**\(c=[4;6;8],\ \lambda=4,\ \alpha=3,\ \
u=10,\ \eta=0.3,\ \mu_{1}=[1:0.2:5],\ \mu_{2}=1.5,\ \mu_{w}=1,\ \delta=0.1,\ \gamma=1,\ b_{b}=0.8,\ b_{r}=0.6,\ b_{v}=0.6\).  
Figure 1: \(\overline{\Omega}\) for different values of \(c\) by varying (**a**) \(\lambda\), (**b**) \(\alpha\), (**c**) \(H_{1}\), (**d**) \(\mu_{w}\), (**e**) \(\eta\), (**f**) \(\delta\), (**g**) \(v\), and (**h**) \(b\).  
Figure 2: \(P_{y},\ P_{b},\ P_{d}\), and \(U\) by varying (a) \(\lambda\), (b) \(\alpha\), (c) \(\mu_{1}\), (d) \(\mu_{w}\), (e) \(\eta\), (f) \(\delta\), (g) \(\
u\), and (h) \(b\).
## Materials and Methods
### Films Characterization
#### 2.3.4 Thermal Analysis  
Differential scanning calorimetry (DSC) tests were performed using a TA Instruments Q2000 equipment (New Castle, DE, USA) under N\({}_{2}\) atmosphere (50 mL min\({}^{-1}\)). Films (5.0 mg) were introduced in 70 \(\upmu\)L aluminum pans and they were submitted to the following thermal program: Heating from 25 to 115 \({}^{\circ}\)C (3 min hold), cooling to \(-90\)\({}^{\circ}\)C (5 min hold) and heating to 150 \({}^{\circ}\)C, all steps at 10 \({}^{\circ}\)C min\({}^{-1}\). Thermal parameters were determined from the second heating scan. Crystallization and melting temperatures (T\({}_{\rm c}\) and T\({}_{\rm m}\)) were determined at peak temperatures of the corresponding transitions, while the crystallization and fusion enthalpies (\(\Delta\)H\({}_{\rm c}\) and \(\Delta\)H\({}_{\rm m}\)) were calculated from the area of the corresponding peaks. The crystallization content (X\({}_{\rm c}\)) of LDPE was calculated using the expression according to the following equation using the first DSC heating cycle to express the thermal history because of film processing [17]:  
\[X_{\rm c}=(\Delta\)H\({}_{\rm m}\)/\(\Delta\)H\({}^{\circ}\)\({}_{\rm m}\)W\({}_{\rm LDPE})*100 \tag{2}\]  
where \(\Delta\)H\({}_{\rm m}\) is the melting enthalpy per unit mass of the sample, \(\Delta\)H\({}^{\circ}\)\({}_{\rm m}\) the theoretical value of the melting enthalpy per unit mass of 100% crystalline PE (293 J/g) [27] and W\({}_{\rm LDPE}\) is the weight fraction of LDPE matrix.  
Thermogravimetric analyses were carried out by using TGA/SDTA851e/SF/1100 Mettler Toledo (Schwarzenbach, Switzerland) equipment. Films (4.0 mg) were heated from 30 to 700 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) under N\({}_{2}\) atmosphere (50 mL min\({}^{-1}\)). The initial degradation temperature (T\({}_{\rm ini}\)) calculated at 5% of weight loss and the temperature of maximum decomposition rate (T\({}_{\rm max}\)) were determined. All analysis described above were performed in triplicate.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 1 Introduction
Moreover, other power sources, such as solar energy, are not available in the ocean depths. The unbalanced consumption of power will cause an early shortage of energy. This will affect the whole network and will impair the network's integrity. As a result, balanced power consumption for each sensor node becomes essential in underwater circumstances and can prolong the lifetime of the network. The consumption of power in a way that results in the exhaustion of all sensors at the same time is desirable so that the sensors' batteries can be replaced together [18]. Minimization of energy consumption is considered one of the most important issues to be solved [19]. Once an underwater sensor network is deployed, it is usually hard to change the power source (batteries) after it has been spent. Furthermore, alternative power sources, like solar energy, are not available in the dark depths of the seas [20; 21]. In order to make an underwater sensor network as efficient as possible, Medium Access Control (MAC) and routing protocols should be designed in a smart way, based on the network topology, in order to overcome this issue and create an optimal system [12; 19]. MAC protocols are classified into deterministic and non-deterministic methods. Examples of deterministic methods are Time Division Multiple Access (TDMA), Frequency-Division Multiple Access (FDMA), and Code Division Multiple Access (CDMA) [22]. Non-deterministic protocols include random access methods (e.g., ALOHA, slotted ALOHA, and Carrier Sense Multiple Access (CSMA)) and Collision Avoidance (CA) approaches (e.g., CSMA/CA) [23]. In the CSMA/CA protocol, the node waits before sending data or acknowledgment (ACK) packets, in order to avoid collisions [24]. To solve the hidden node problem, the CSMA/CA technique can be enhanced to perform Request to Send (RTS)/Clear to Send (CTS) handshaking [25]. Designing an energy-efficient MAC protocol should take into consideration the issues that cause waste in sensor energy, i.e., overheads, collisions, idle listening, and overhearing. The main contribution of this paper is proposing an energy-efficient technique for underwater sensor networks, named EE-UWSNs, that overcomes the limitations of recent related works. It is based on five principles to save sensor energy and to prolong the lifetime of UWSNs. The rest of this paper is organized as follows. Section 2 presents recent related MAC and routing protocols that are designed for UWSNs. The proposed EE-UWSNs protocol is explained in detail in Section 3. Section 4 presents the performance analysis of the proposed EE-UWSNs protocol in detail. Finally, Section 5 gives the conclusion of the paper and highlights suggested future work.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 2 Study Area and Data  
### Study Area  
The study area includes Jilin Province and Liaoning Province in Northeast China (Figure 1). It is located in a high latitude area of China (\(38.5^{\circ}\) N-\(46.5^{\circ}\) N and \(118.5^{\circ}\) E-\(131.5^{\circ}\) E) and belongs to the temperate continental monsoon climate. It has low temperature and dry conditions in winter, and warm and humid conditions in summer, thus exhibiting obvious seasonal characteristics. The terrain of this area is low in the northwest and high in the southeast, which includes three farming areas: the coastal area of Eastern Liaoning, the Changbai Mountain Area, and the southern Songliao Plain area. The main planting system in the study is one crop per year, and the maize planting area accounts for 40% of the total grain planting area. It is one of main maize producing areas in China. The agricultural cultivation in Jilin Province and Liaoning Province is mainly dependent on rainfall [43]. The temperature rise in this area is significant, and the degree of drought has increased over the last 30 years [44; 45]. The frequency of extreme climate events is higher than that in other regions of China [46; 47]. These climatic factors are important factors driving crop yield fluctuations in the region. Therefore, the establishment of a yield estimation model driven by climate factors would be useful for predicting the impact of climate change factors on agricultural production in the region.
## 5 ICOREs for Sequence Analysis
### Sequence Comparison with an Affine Gap Model  
#### 5.1.1 Standard Affine Gap Model  
We refine the simple edit distance approach with an affine gap model, such as introduced in [PERSON]'s algorithm [19]. The idea of affine gap scoring is that a gap of length \(n\) should be charged a cost of type \(o+(n-1)e\), where \(o\) is a (high) initial \"opening\" charge while \(e\) is a smaller charge for gap extension. This model is motivated, for example, by gene comparison, where breakage of the DNA is rare, but once it is broken, an insertion or deletion of any length is likely. For that, we need to introduce new operators in the core signature, \(\mathtt{open\_del}\) and \(\mathtt{open\_ins}\), while we retain \(\mathtt{del}\) and \(\mathtt{ins}\) which now designate gap extension.  
Figure 1: Overview of sequence analysis problems addressed in Section 5. Solid arrow lines indicate transformations between icores that are detailed in the text. Dashed arrow lines indicate transformations that are left as exercise for the reader.  
ICORE Affine is our first case where it is convenient to use a tree grammar for defining precisely the candidate space. The grammar ensures that each gap begins indeed with a gap opening, followed possibly by extension operations. One can note that the dependency graph (Recall the definition of a _dependency graph_ of a grammar: the nodes are the non-terminal symbols. There is an edge from the node labeled by the non-terminal \(S\) to the node labeled by the non-terminal \(T\) if there is a production of the form \(S\to c(T)\)) of the grammar Affi has the same architecture as the finite state transducer usually associated to alignments with affine gap costs, such as displayed in a classical textbook ([20], chapter 2). This transducer takes as input the first string, and outputs the second string. The edit script is obtained as the list of states visited, and each transition arc can be assigned a score. Figure 2a shows this transducer, and 2b shows the dependency graph of Affi.  
**Example 3**.: For the pair of sequences \"GCTGTCCA\" and \"ACGAATGCA\" the core term  
\[c=\texttt{rep}(\texttt{G},\texttt{A},\texttt{rep}(\texttt{C}, \texttt{C},\texttt{open\_ins}(G,\texttt{ins}(\texttt{A},\texttt{ins}(\texttt{A},\texttt{rep}(\texttt{T},\texttt{T},\texttt{rep}(\texttt{G},\texttt{G},\] \[\texttt{open\_del}(\texttt{T},\texttt{del}(\texttt{C}, \texttt{rep}(\texttt{C},\texttt{C},\texttt{rep}(\texttt{A},\texttt{A}, \texttt{mty}))))))))))))\]  
encodes the alignment  
\[\texttt{G}\texttt{C}\texttt{-}\texttt{-}\  
allow for this case-it will never score optimally, so why care. However, should we be interested also in near-optimal solutions, it is more systematic to keep malformed candidates out of the search space. Our grammar rules out this candidate solution.