esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Three Layer Hybrid Clustering Mechanism for Energy Efficient Routing in IoT
## 4 Network Model of ETLHCM
\(E_{TX}ControlPacket\) and \(E_{RX}ControlPacket\) are the energy consumed in transmitting and receiving control packets. Their values can be calculated using Equations (1) and (2) respectively, given that \(l\) is equal to the control packet size. For achieving the objective function which is described in Equation (4), we split the total energy of sensor nodes into different equal portions called energy levels (EL). EL is calculated from Equation (6). \[EL=E_{o}/TL \tag{6}\]  
where \(E_{o}\) is the fixed initial energy of sensor node and \(TL\) is the total energy levels. The value of \(TL\) depends upon the frequency of energy consumption by a node, density of the network, and data packet size. As \(TL\) is inversely proportional to \(EL\), so a lower value of \(TL\) will result in a higher value of \(EL\). The reason that we cannot set very low values to \(TL\) is due to its tradeoff with balance energy consumption between all deployed sensor nodes. The value of \(TL\) in this paper is set to 10 based on different simulation experiments. Unless the residual energy of a sensor node that is selected as CH is not decreased by EL, it remains as CH, and no reselection process of CH take place. Whenever the CH residual energy decreased by EL, it broadcasts a control packet announcing end of its CH role. Thus new CH selection process start. The working procedure of the proposed mechanism is further explained in a flow chart which is shown in Figure 3.  
Figure 3: Cluster joining and cluster head (CH) selection process in the proposed mechanism.
# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 2 Materials and Methods  
This study is an extension of the ongoing project SmartSpeech, with the full title \"Smart Computing Models, Sensors, and Early diagnostic speech and language deficiencies indicators in Child Communication\", funded by the Region of Epirus and supported by the European Regional Development Fund (ERDF). Participants were recruited through public and private health and education establishments, with most of them being young children. Prior to the start of the study, parents were informed of the project's scope and protocols, provided with written consent forms, and shared information about their child's developmental and communication skills. They were also informed that the study had been approved by the University of Ioannina Research Ethics Committee, in compliance with the General Data Protection Regulation (GDPR).  
During the project's data collection, the children played a serious game (SG) that was part of the SmartSpeech system. The SG activities were designed to collect data on the children's developmental skills and biometric measurements to examine potential biomarkers for classification purposes. The game dataset included variables that were child responses quantified from two sources: hand movements on the touch screen (such as solving puzzles, manipulating items on the touchscreen, or identifying images and forms) and verbal responses to questions or executing commands (such as recalling names/events, recognizing emotions, or answering with vocal replies). It is important to note that this study only focuses on the dataset gathered from the SG activities, excluding biometric measurements [19; 43]. The children participated in a range of activities that were presented in an engaging and visually appealing manner.  
To recognize the children's verbal responses, we used the CMUSphinx voice-to-text program (Pittsburgh, PA, USA) [44]. This program is accessible, open-source, and compatible with both desktop and mobile platforms. Additionally, we designed and trained a Greek language model using this program [45].
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 2 Definition of a Coordinate System  
Goal localization by UAVs refers to the establishment of a suitable coordinate system, such as geodetic, space rectangular, geographic, airframe, and camera coordinate types [28].
## 2 Signal Model and Problem Statement
### Signal Model  
The GNSS signal is received and downconverted by each antenna of a multisensor array, along with the interference and noise. The signals from all antenna elements can be combined into a vector as  
\[\mathbf{x}(t)=\mathbf{a}_{0}s(t)+\sum_{q=1}^{Q}\mathbf{a}_{q}j_{q}(t)+\mathbf{ n}(t) \tag{1}\]  
where \(t\) is the time, \(\mathbf{x}(t)=[x_{1}(t),x_{2}(t),\ldots,x_{N}(t)]^{\mathrm{T}}\in\mathbb{C}^{N \times 1}\) is the array snapshot with \(N\) being the number of antennas, \(s(t)\) is the GNSS signal that is the signal of interest, \(j_{q}(t)\) is the interfering signal transmitted from the \(q\)-th jammer, \(Q\) is the number of jammers, \(\mathbf{a}_{0}\in\mathbb{C}^{N\times 1}\) and \(\mathbf{a}_{q}\in\mathbb{C}^{N\times 1}\) are the steering vectors of \(s(t)\) and \(j_{q}(t)\), respectively, and \(\mathbf{n}(t)\in\mathbb{C}^{N\times 1}\) is the additive noise vector. The GNSS signal, interfering signals, and noise are assumed to be mutually uncorrelated.  
The GNSS signal in Equation (1) can be expressed as  
\[s(t)=\sqrt{P}D(t-\tau)c(t-\tau)\mathrm{e}^{\mathrm{i}(2\pi(f_{\mathrm{IF}}+f_{ \mathrm{d}})t+\varphi)} \tag{2}\]  
where \(P\) is the signal power, \(D(\cdot)\) is the navigation data bit, \(c(\cdot)\) is the pseudorandom noise code, \(\tau\) is the signal delay, \(f_{\mathrm{IF}}\) is the intermediate frequency, \(f_{\mathrm{d}}\) is the Doppler frequency, and \(\varphi\) is the initial carrier phase.  
To suppress the strong interference, the PI weight is calculated as [16]  
\[\mathbf{w}_{\mathrm{PI}}=\frac{\mathbf{R}_{xx}^{-1}\mathbf{b}}{\mathbf{b}^{ \mathrm{H}}\mathbf{R}_{xx}^{-1}\mathbf{b}} \tag{3}\]  
where \(\mathbf{R}_{xx}=E[\mathbf{x}(t)\mathbf{x}^{\mathrm{H}}(t)]\) denotes the autocorrelation matrix of the array input signal, \(E[\cdot]\) stands for the expectation operation, \((\cdot)^{\mathrm{H}}\) denotes the conjugate transpose operation, and \(\mathbf{b}=[1,0,\ldots,0]^{\mathrm{T}}\) is a constraint vector.  
Then, the output signal of the antenna array is obtained as  
\[y_{\rm{PI}}(t)=\mathbf{w}_{\rm{PI}}^{\rm{H}}\mathbf{x}(t) \tag{4}\]
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 8 Numerical Simulation and Discussion
While the ergodic capacity of the system of \((N_{t},N_{r},M,N)=(2,2,2,2)\) is increased to 7.23 bits/s/Hz. Figure 7 shows the diversity-multiplexing tradeoff of the considered MIMO mixed RF/FSO system and SIMO RF/MIMO FSO relay system. Compared with the SIMO RF/MIMO FSO relay system, the MIMO system considered in this paper has obvious advantages in diversity gain and multiplexing gain, as shown in the figure. In particular, the end-to-end maximal diversity gain of the system is \(\min\Bigl{(}N_{t}N_{r},\frac{MN}{2}\min(\alpha,\beta)\Bigr{)}\), and the maximal multiplexing gain is \(\min(N_{t},N_{r},M,N)\). We can flexibly allocate diversity or multiplexing gain through different coding methods under different fading conditions. For example, we can use Vertical Bell Labs Layered Space-Time (V-BLAST) encoding to obtain a higher transmission code rate when the channel condition is good. On the other hand, we can use RC to combat channel fading when the channel condition is poor. Figure 8 presents diversity-multiplexing tradeoff (DMT) curves of different number MIMO antennas configurations of RF links and FSO links, respectively. From the figure we can find that DMT curves of FSO links are always lower than the curves of RF links under the same configuration. The reason is that the channel of the FSO link using the IM/DD method only provides one degree of freedom. While the RF channel provides complex-valued channel coefficients, which has two degrees of freedom, this greatly affects the diversity gain. Further, we find that there is an intersection point between a (3 \(\times\) 4) RF curve and (4 \(\times\) 4) FSO curve, which means that the end-to-end maximal diversity gain is determined by the FSO link and end-to-end maximal multiplexing gain is determined by the RF link, respectively. It is obvious that as the number of antennas increases, both maximal diversity gain and maximal multiplexing gain increases. Figure 6: Average ergodic capacity of different schemes of MIMO mixed RF/FSO systems. Figure 9 presents the DMT of the MIMO mixed RF/FSO system with an intersection under different conditions. Similar to Figure 8, we consider the universal case of intersection under the system we considered. Since the DMT curves of RF and FSO are both decreasing, consider the following two situations, (1) \(N_{t}N_{r}<\frac{MN}{2}\min(\alpha,\beta)\cap\min(N_{t},N_{r})>\min(M,N)\) and (2) \(N_{t}N_{r}>\frac{MN}{2}\min(\alpha,\beta)\cap\min(N_{t},N_{r})<\min(M,N)\), then the two curves have at least one intersection as shown in Figure 9a,b respectively. Figure 9a shows that maximal diversity gain depends on the RF link under situation (1). While maximal diversity gain depends on the FSO link under situation (2), as shown in Figure 9b. While the maximal multiplexing gain dependence is opposite. Figure 8: DMT of different MIMO configurations of RF link and FSO link. Figure 7: Diversity–multiplexing tradeoff of MIMO mixed RF/FSO system and single input multiple output (SIMO) RF/MIMO FSO system.
## 1 Introduction  
Cellulose is a homopolymer in which D-glucose units are linked by \(\beta\)-1.4 glycosidic bonds [1]. Cellulose, together with hemicellulose, are major constituents of the plant cell wall, which maintains the shape of plant cells and plays a role in supporting trees to stand firmly against gravity. Cellulose is the most abundant biomass on earth and is a renewable energy source that can be continuously biosynthesized using solar energy. Therefore, developing an optimal process for effectively treating and utilizing inexpensive carbon sources, such as cellulose and cellulose-containing waste material, has become of great interest to humankind, as it will help enable a fossil fuel-independent future [2].  
The breakdown of cellulose is relatively simple compared to that of hemicellulose. It is mainly broken down by cellulase enzymes produced by bacteria and fungi. Rather than acting alone, cellulasessynergize with at least three kinds of enzymes, endo-\(\beta\)-1,4-glucanase (carboxymethylcellulose, E.C. 3.2.1.4), cellobiohydrolase (avicelase, E.C. 3.2.1.91), and \(\beta\)-D-glucosidase (cellbiases, E.C. 3.2.1.21), which can effectively hydrolyze cellulose polymers into glucose units [3]. Among these enzymes, endoglucanas have long been used for a variety of purposes in the textile, detergent, leather, food, and pulp industries [4; 5]. Moreover, their use has recently been reported in fine chemical manufacturing processes, such as biomass fermentation, fiber processing, and pharmaceutical [6] and bioethanol production [7]. The application of these enzymes to these industrial fields requires the development of stable enzymes that are active at wide pH ranges and at high temperatures [8; 9]. Most research on cellulase production has been performed in fungi, such as _Trichoderma resegi_[2; 10]. However, there is currently much research on cellulase production in bacteria because of their faster growth rate compared with fungi [6].  
Many bacteria that can hydrolyze insoluble crystalline cellulose have been identified from soil, rotten wood, and industrial waste, such as molasses [6; 11]. In this context, we recently isolated a bacterium, _Cellulophaga_ sp. J9-3, from sea water and found that it can hydrolyze cellulose (glucan) as well as agar (galactan). The unique biochemical properties of the cellulase produced by _Cellulophaga_ sp. J9-3 are described in this article.
## 4 Discussion
### Effect of Organic Fertilizer on Tomato Quality at Different pH and Cultivation Types  
Organic fertilizers are essential for tomato growth, and their effect on tomato quality varies at different pH values. At a pH < 7, the increased application of organic fertilizers significantly increased the soluble solids, soluble sugars, lycopene, and VC content. This indicates that organic fertilizers improved fruit quality and increased the concentration of soluble solids, sugars, and vitamin C [55; 56]. At a pH \(\geq\) 7, the tomato lycopene content was improved more by additional organic fertilizers than at a pH < 7. This difference may be because tomato lycopene content depends not only on the growing conditions but also on the cultivation method and variety [57]. Organic fertilizers contain not only nutrients, but also organic matter required for plants. Organic matter plays an important role in plant growth and development by releasing nutrients, improving soil physical and chemical properties, and promoting root activity.  
Among the cultivation types, the greenhouse had less temperature fluctuations than open-field cultivation and higher humidity, which was more favorable for tomato growth. However, the percentage change of organic fertilizers application on tomato VC content in open-field cultivation was 1.77 times higher than the percentage change in the greenhouse. The lack of moisture in open-field cultivation resulted in higher VC content in tomatoes than in the greenhouse. These results are consistent with a previous study that observed that moisture stress can promote VC content [58; 59; 60; 61].
## 5 Discussion
### Visualization of the NGPI to Identify Groundwater Potential  
The visualization of three-dimensional (3D) results provides information about potential sites [65]. However, in agricultural eras preceding modern computer analysis, people relied on the characteristics of terrain and their personal experience of groundwater exploration. For instance, the Chinese proverbs related to the locations of GWPS describe the confluences of two rivers, intersections of two mountains, concave sides of riverbeds at the foot of mountains, and so on. It is not easy to understand these descriptions of groundwater potential from the ancient proverbs without comparing the 3D visualization of the NGPI map with a Google map (Figures 13 and 14). At site B103-02, the distribution of higher NGPI values shows a pattern consistent with the confluence of two upstream rivers. Site  
Figure 12: Geomorphic terrain near B099-11 site. (**a**) The seven categories of terrain are defined as near roof (Rf), at ridge (Rg), steep slope (Ss), flat slope (Sf), valley or creek bottom (Vb), alluvial fan of downstream the valley (Vbf), and main riverbed deposit (Vm). (**b**) The distribution of TPI and TWI.  
B102-08, located in a downstream deposited alluvial fan, is shaped into the mouth where two mountains intersect, and is also illustrated by a higher NGPI value. Site B106-02, located on meandering terrain and the concave side of a riverbed, is full of groundwater due to reduction of the flow velocity and increases in recharged water resources. Those predominant types of terrain are favorable to GWPS. Oppositely, an area composed of nearly horizontal bedding has less groundwater flow, accumulation, and potential. In order to solve the problem of water scarcity and assess the substitute water resources, the regions located in geological units of metamorphic rock, and topographic units of Vm, Vbf, and Sf have more groundwater potential for regional water supply. In addition, the RS data- and GIS-based results provide visualized and comprehensive information for groundwater exploration, which improved the accuracy of the interpretation of GWPS from 48.6% to 84.7% (Tables 5 and 7).  
Figure 13: Photo of in-situ sites and outcrops. (**a**) B103-02 GWPS (well yield: 900 L/min); (**b**) outcrop near B103-02; (**c**) B102-08 GWPS (well yield: 780 L/min); (**d**) outcrop near B102-08; (**e**) B106-02 GWPS (well yield: 966 L/min); (**f**) terrain near B106-02; (**g**) B099-15 non-GWPS (well yield: 22.8 L/min); (**h**) outcrop near B099-15.  
Figure 14: Three-dimensional (3D) visualization of NGPI map and satellite imagery at investigated sites (Figure 13). Without comparing the 3D visualization of the NGPI maps with a Google map, it is difficult to link the results of GWPS with these descriptions of groundwater potential from the ancient proverbs: (**a**) more groundwater at confluences of two rivers (at B103-02 GWPS); (**b**) more groundwater at intersection of two mountains (at B102-08 GWPS); (**c**) more groundwater at concave side of riverbed and foot of mountain (at B106-02 GWPS); (**d**) less groundwater at nearly horizontal bedding of rocks (at B099-15 non-GWPS).
## 3 Wenchuan EQ Analysis and Discussion
### Results and Discussion
#### 3.2.2 Simulation Results and Discussion
As shown in Figure 7, we solve the variation of the positive and negative particle concentrations and the atmospheric vertical electric field with height. From the data in Figure 7, the separation of positive and negative particles can be seen. As shown in Figure 7, we calculate that the atmospheric vertical electric field from the surface to 10 m is \(-\)593 to \(-\)285 V/m. Negative atmospheric electric field anomalies were observed at both Wenjiang and Pixian on 12 May, with a peak of \(-\)600 V/m at Pixian and \(-\)200 V/m at Wenjiang [19]. The data of atmospheric electric field sounding at Wenjiang station were consistent with the data obtained from the model calculation in this paper. Moreover, there were some deviations in the data of the Pixian st  
Figure 7: Variation of positive and negative particle concentration and atmospheric vertical electric field with height. consistent with those calculated by [PERSON], [PERSON], and [PERSON], which showed an anomaly of reduction in the atmospheric electric field [21]. The difference was that the calculations used as input conditions were the atmospheric electric field on fair weather and the radon ionization source within 10 cm of the ground. An analogous process was observed in the case of measuring the electric field in the underground nuclear weapons test area: the electric field drastically decreased at the time of the explosion [31]. In this case, the near-surface layer was ionized by the fission fragments emerging from the soil. This experiment was similar to the mechanism of this paper and was a direct demonstration of this paper. Since we could not obtain the detailed radon concentration detection data before the Wenchuan EQ, the radon concentration used in the calculation was based on the gradient of the natural logarithm of radon concentration with height in fair weather. The variation of radon concentration with height was obtained using the radon concentration at the ground level y = 0 km before the Wenchuan EQ. Moreover, the parameters in the equation of motion were according to the average parameters on fair weather, which might have some errors with the actual situation. Therefore, the parameters used in the simulation deviated from the actual parameters. If the actual meteorological parameters before the EQ and the variation of radon concentration with height were available in the simulation, the accuracy of the simulation would be improved. In general, these results still indicate that the mechanism of our initial exploration matches well with the measured values and indicates a good validity of our model.
## 4 Results
### Spatio-Temporal Patterns of the ESTARFM Time Series
For example, the waterbodies in Ouagadougou or the cultivated areas in the fragmented area in southern Burkina Faso cannot be delineated from the MODIS NDVI. Also the course of rivers in the Mole National Park or the irrigated fields around the dam in the southeast of the fragmented focus site cannot be adequately identified at this scale. However, the mean annual NDVI values provided by MODIS can be seen as realistic, because of the high temporal resolution of MODIS covering the entire phenological cycle. In contrast, the mean annual NDVI of the original Landsat time series (fourth row) allows the differentiation of the aforementioned features of the focus sites. On the other hand, the overall level of Landsat's mean annual NDVI is considerably lower than the one of MODIS. The reason for this is that there are only severely cloud-affected Landsat data available during the peak vegetative season between July and October (compare Figure 2) and thus this period is not well represented in the mean NDVI, thereby resulting in values that are too low in comparison to the MODIS benchmark. The ESTARFM time series (third row) combines both the high spatial detail which allows delineation of even small objects, and the high temporal resolution allowing derivation of a realistic mean NDVI (similar NDVI levels compared to MODIS, second row).
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
### Signal Sequence Design  
The concept of STAR (Simultaneous Transmission and Reception) is purposed to synchronously transmit and receive electromagnetic signals at the same space-time frequency; theoretically, it can improve the spectral efficiency and system capacity. STAR offers the possibility of simultaneous operation of SAR echo reception and data transmission systems; however, the transmitted signals may be leaked to the receiver through a low-noise amplifier, thus causing the problem of dramatic self-interference. The technologies to suppress self-interference can be divided into airspace self-interference suppression technologies, RF domain self-interference suppression technologies, and digital domain self-interference suppression technologies [27]. [PERSON] et al. designed a digital phased array, in which the aperture is divided into a transmitter subarray and a receiver subarray; at the digital end, it can eliminate the interference of the received signals going into the low-noise power amplifier, thus achieving the isolation of the received and transmitted signals with a difference of 160 dB within a 100 MHz bandwidth [28]. In addition, [PERSON] et al. [29] also eliminated interference from the transmitted signals by introducing filters in the low-noise power amplifier section. Figure 2 shows the integrated sequence of spaceborne SAR data transmission.  
Based on the STAR method, this study designed a STAR integrated mode for the synchronous operation of SAR echo reception and data transmission; this mode is a kind of cooperation mode, where the data transmission signal and the SAR pulse transmission sequence are independent of each other. The integrated system divides the bandwidth into multiple subcarrier channels. The radar system uses a part of the subcarrier channel to transmit SAR signals, the transmission and reception of which are isolated from each other. The data transmission system uses the rest of the subcarrier channel to transmit data transmission signals separately. \(T_{\text{xx}}\) denotes the transmitting and receiving time of a single SAR image; \(T_{\text{PRI}}\) denotes the duration of SAR radar pulses; \(T_{\text{yr}}\) denotes the window time of receiving the echo; and \(T_{\text{xc}}\) denotes the data transmission time of data transmission signals. Among them, \(T_{\text{xc}}=T_{\text{yr}}\). For \(T_{\text{xc}}\), when the phased array receives the echo signals, the data transmission signals will be continuously transmitted. In addition, this mode has the following advantages: high data transmission rate, high spectral efficiency, and uninterrupted SAR imaging.