esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 State-of-the-Art Review
### Related Projects, Systems and Applications
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|} \hline
**Reference** & **Organization** & **Country** & **Year** & **Application** & **Sensing** & **Communica-Ares** & **Buoy** & **Energy** & **Testing \& **Main Features** & **Imployment** \\ \hline [PERSON] _et al._[4] & Universidad Politecnica de Cartagen & Spain & 2011 & Ocean sensing \& monitoring & Temperature, pressure, salinity, nitrates, velocity, turbidity & GPSBee & Special \& buoy & Two solar panels & Deployed in the harbor of Cartagen & LabVIEW-based user interface using Google Maps; Solar energy harvesting; Special buoy \\ \hline [PERSON] _et al._[23] & Swedish Institute _et al._[23] & Sweden; of Computer Sci. \& University at Berlin & Sweden; Germany & 2007 & Ocean sensing \& monitoring & Temperature, motion, vibration and sound & GPRS & Simple buoy and king’s & Batteries & Tested in Baltic Sea & Design of an advanced low-cost buoy system \\ \[PERSON] _et al._[39] & Penn State _et al._[39] & University & USA & 2002 & Water quality monitoring & pH & RF transceiver and acoustic transducer & PVC & Two rechargebal with 5 nodes & Lab testing with 5 nodes & The design of various interface circuits and the use of five air-based sensor nodes \\ \[PERSON] _et al._[40] & UC Santa Cruz & USA & 2007 & Ocean sensing \& monitoring & Temperature, wave and location & 900 MHz & mobile & Battery minibuoy & Prototype & An autonomous mini-buoy prototype; GPS and in a PID scheme control \\ \[PERSON] _et al._[41] & UC Santa Cruz & USA & 2007 & Coral reefs monitoring & Temperature, pH, light, pressure, and conductivity & 900 MHz & Watertight housing & Battery & Monterey Bay & Programmable Oceanic Device (POD) with a 4-mode scheduler to serve energy \\ \[PERSON] _et al._[42] & University of Notre _et al._[42] & USA & 2007 & Water quality monitoring & Temperature, pH, and DO & 433 MHz & Box and polyethylene & 12 volt & Tested a marine & Tested a prototype in a small lake & Tested a and an altered sampling strategy \\ \[PERSON] _et al._[43] & Dublin City _et al._[43] & Ireland & 2009 & Water quality monitoring & Temperature, pH, turbidity, DO and conductivity & ZigBee & Insbore sensor & Solar panel and power & Deployed in five sites on & A real-time heterogeneous water quality monitoring; Lee, Ireland & A real-time \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of WSN-based marine environment monitoring projects, systems and applications.
Thus, we consider 8-level PAM (PAM-8) as a candidate modulation format for 200 Gbps transmission to have a reduced baud rate (i.e., around 66.67 Gbaud for 200 Gbps bit rate). In [13], a 200 Gbps PAM-4 communication with a 29 dB ODN loss was experimentally demonstrated by using directly modulated laser (DML), direct detection and Raman amplification over a 21 km fiber. However, very high transmitter and receiver bandwidth devices, i.e., a 65 GHz DML and 70 GHz P-type-intrinsic-N-type diodes (PIN), were used in the experiments. Moreover, the strong Raman pumping used in the experiments is not suited for a cost-effective short-reach PON. In this paper, we demonstrate over 29 dB of ODN loss using 50G O/E devices without Raman amplification. In a 50G PON, the maximum launched power into ODN is +7, +9, +11 and +11 dBm for budget class N1, N2, E1 and E2, respectively [24]. For 50G PON with a higher budget class, for example Class C+ (32 dB) and beyond, a minimum launched power of +10 dBm might be required. In PONs, a launch power a few dB higher than the minimum one can be tolerated [25]. Transmitted optical power (TOP) could be increased to achieve the required link power budget. An electro absorption modulated laser (EML) boosted by an external semiconductor optical amplifier (SOA) can be placed at the TX side to increase launch power in PONs for a wide wavelength range of 1200-1650 nm [26]. For a 50G PON, EML + SOA was exploited DS to support high budget classes. In [25], a 14 dBmlaunch power was emulated by using EML + SOA. In [27], an integrated EML + SOA with a 13 dBm output power was experimentally demonstrated to achieve a 35 dB link budget. In our previous works for 100 Gbps transmission, we used a TOP of 11 dBm. In this paper, we increase it to 15 dBm (when the VNLE is applied) to achieve the 29 dB power-budget requirements for 200 Gbps transmission. The paper is organized as follows: the details of the simulation setup and simulated parameters are described in Section 2. In Section 3, we analyze the proposed system performance through simulations with variable transceiver bandwidth and for different RX DSP options. The complexity of different RX DSP options is compared in terms of multiplication per sample (MPS). Lastly, we discuss the results and draw some conclusions in Section 4.
## 4 Results
### No Feature Selection and Transformation  
The CN2 Rule Inducer and Random Forest algorithm exhibited a performance rate of 85.1%, as shown in Table 3. Further investigating the performance metrics of the CN2 Rule Inducer showed good sensitivity, levelling-out the lower specificity and giving a good F-measure score. When no data transformation and no feature selection were undertaken, the CN2 rule inducer showed the highest classification accuracy.
# Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring
## 2 Materials and Methods  
The most important challenge affecting the WSN is energy consumption, which can be optimized by using an effective routing protocol. Flat, location-based, and hierarchical routing protocols are the three basic types of routing protocols suggested for WSNs [3]. The multi-hop approach is utilized in flat routing, where each sensor node performs identical functions [1]. The sensing mission is conducted by sensor nodes cooperating. Instead of sending data across the whole network, location-based routing uses sensor node position information to distribute data to a specific region. The network is separated into clusters in hierarchical routing, and sensor nodes with higher energy oversee data processing and transmission. In terms of energy efficiency, hierarchical routing techniques deliver the best results [29].  
The hierarchical routing technique uses clustering mechanisms; clustering techniques can be energy- and scalability-efficient [8]. They use a clustering technique to greatly reduce the amount of energy consumed in collecting and disseminating (fusion and aggregation) data. The hierarchical routing technique reduces energy consumption by grouping nodes into distinct clusters [1].  
A CH election process is based on selecting a node as a leader node within the cluster [27]. The CH keeps information related to its community. This information includes a list of each node's cluster nodes and path. Choosing a particular node as a CH is not only difficult but also a very critical task. Various considerations for selecting the best node as a CH can be considered, such as the position of the node relative to other nodes' positions, mobility, energy, confidence, and node throughput.  
There are many clustering techniques from which we chose hierarchical clustering and partitioned clustering [29]. There is a wide range of commonly used partitioning techniques. We studied LEACH, as it is known as the simplest hierarchical clustering technique, and the K-means algorithm, as an example of the partitioned clustering algorithm to prolong network lifetime and enhance network performance, as is illustrated in the Simulation Section [5; 23].
# Roadway Backfill Mining with Super-High-Water Material to Protect Surface Buildings: A Case Study
## 3 Design for Roadway Backfill Mining under Surface Buildings
### Design of the Technical Parameters
#### 3.2.3 Determination of Filling Rate  
As a key index of the filling effect, the filling rate is directly related to the quality of surface subsidence control. Under the same conditions, with a higher filling rate, the larger gob space is occupied by filling materials and the smaller activity space of rock deformation, then there is a better control effect. Conversely, the control effect is poor. Therefore, the reasonable filling rate is particularly important. According to the definition of limited coal height mining, the calculation of the filling rate is shown as follows in Equation (9):  
\[\eta\geq 1-\frac{[\varepsilon]H}{1.52 mbq\tan\beta\cos\alpha\times 1000} \tag{10}\]  
where \(\eta\) is the filling rate; m is the coal seam thickness, 2.5 m; \(b\) is the displacement factor, 0.28; \(q\) is the subsidence factor, 0.79; tan\(\beta\) is the tangent of the major influence angle, 2.17; \(\alpha\) is the dip angle of the coal seam, \(2^{\circ}\); \([\varepsilon]\) is the maximum allowable horizontal deformation of ground structures, 2 mm/m; and \(H\) is the mining depth, 175 m.  
Therefore, the filling rate \(\eta\geq 80.8\%\). However, in order to protect the surface buildings better, the filling rate should be improved as much as possible.  
In order to verify the reliability of filling rate and ensure the control effect of backfill mining on the surface, assuming that the filling rate is 80% when SKS 1 is broken, it can be predicted that the separation will develop to the bottom of SKS 2 based on the KS theory. According to the Equivalent Mining Height (EMH) theory, the mining height \(h=2.5\times(1-80\%)=0.5\) m. Meanwhile, it can be seen from Figure 1 that the strata height controlled by SKS 1 is 39.93 m, assuming that the bulking coefficient of overlying strata controlled by SKS 1 is 1.002, the separation height under SKS 2 is \(h_{1}=0.5-39.93\times 0.002=0.42\) m. Then, according to the calculation of the above subsidence factor \(q\), the surface subsidence value is \(s=0.42\times 0.79=0.33\) m. Considering the deformation resistance of the building itself and the mining experience of other working faces, the buildings are less affected by mining. Therefore, the filling rate can effectively control the overburden movement, and can achieve the purpose of protecting surface buildings.
## 2 Methodology  
### Overall Framework  
The overall GDSW identification method consists of four steps, as shown in Figure 1. (1) Original website acquisition and pre-processing. We used a series of keywords related to geospatial data to obtain the original website list L through the Baidu search engine. A large number of websites in the website list exist in the same domain name, so we performed the domain name extraction first. Then, we downloaded the website documents and performed feature extraction to obtain the main information of the website. (2) Geospatial data website filtering based on multi-source semantic information. We calculated the similarity value between multi-source semantic information and websites to remove unrelated websites and obtain the potential geospatial data website list W. Multi-source semantic information contains geospatial data semantics and website feature semantics. The geospatial data semantics include geospatial data content ontology, source ontology, and morphology ontology (see Section 2.3 for details). (3) Precise identification of GDSW based on the machine learning method. With website lists L and W as sample data, respectively, we used machine learning [31] for classification and identification. The data were manually labeled before classification. (4) Evaluation and analysis of the geospatial data website identification precision. We designed two comparative experiments to evaluate the precision, recall, F1-score, precision-recall, and receiver operating characteristic curve (ROC curve) of classification results, using original data and multi-source semantic information filtering.  
The first experiment divided L into geospatial data sharing websites (GDSW) and non-geospatial data sharing websites (non-GDSW). In GDSW and non-GDSW, we selected the data as training sets according to a certain proportion, respectively, and used the rest as test sets. W was also divided into training set and test set according to the same method and used for the second experiment. To fully verify the robustness and generalizability of applying multi-source semantic information to solve the imbalance of sample data, we conducted the third set of experiments and evaluated the results using different sample data from those mentioned above.
## Appendix A
### TCN Model Definition
It contains an arbitrary number of inner blocks \(i,i\in\mathbb{N}\) and own parameters \((N_{l_{i-1}},N_{l_{i}},S_{l_{i}})\) such that the temporal block is a mapping:  
\[\psi_{l}:\mathbb{R}^{N_{l-1}\times T_{l-  
A temporal convolution network \(f(X,\theta)\) is a composite of \(L\) layers of temporal blocks parameterised by \(\theta:=(\theta_{1},...\theta_{L})\). Propagating an original input tensor \(X\) of time dimension \(T_{0}\) through all temporal block layers \(l\) requires an output time dimension \(T_{L}\) of an \(L\)-layered TCN to be at least 1:  
\[T_{L}=T_{0}-\sum_{l=1}^{L}S_{l};T_{L}\geq 1 \tag{11}\]  
Finally, let \(f(X;N_{0},N_{L+1},K,D,\theta)\) be a temporal convolution network, a non-linear mapping through a network of temporal blocks \(\psi_{l},l=\{1,\dots,L\}\)  
\[f:\mathbb{R}^{N_{0}\times T_{0}}\rightarrow\mathbb{R}^{N_{L+1}\times T_{L}}, f(X,\theta)=w_{L+1}\circ\psi_{L}\circ...\circ\psi_{1}(X) \tag{12}\]  
where \(w_{L+1}\in\mathbb{R}^{N_{L}\times T_{L}}\rightarrow\mathbb{R}^{N_{L+1}\times T _{L+1}}\) is a weight tensor used to apply a final \(1\times 1\) (i.e., \(K=1\), \(D=1\)) convolution layer. The receptive field size for the TCN is given by:  
\[RFS(f(X;N_{0},N_{L+1},L,D,K))=1+T_{L}=1+\sum_{l=1}^{L}S_{l} \tag{13}\]  
In the special case where the kernel size for temporal block layer \(l\), \(K_{l}\) is constant, \(K_{l}=K,\forall l=1,\dots,L\), and the dilation factor \(D\geq 2\) at layer \(l\) is given by \(D_{l}=D^{l}\), then the TCN is said to be a _vanilla TCN_. For a vanilla TCN with growing dilation factor \(D^{l}\) for temporal block \(l\) and constant \(B\) inner blocks for each temporal block,  
\[RFS(f(X;N_{0},N_{L+1},L,D,K))=1+(K-1)\cdot\left(\frac{B\cdot(D^{L}-1)}{D-1}\right) \tag{14}\]  
Finally, the TCN network can include skip layers such as those used in the ResNet [47] architecture to mitigate vanishing gradient problems in learning through deep neural architectures. Figure A4 below explains the topology. Let \(N_{skip}\in\mathbb{N}\) be the number of neurons in a skip connection. Define the joint mapping \(\gamma_{l}\) from the input of temporal block \(l\) to the output of temporal block \(\psi_{l}\) and skip temporal block output \(\eta_{l}\) such that:  
\[\gamma_{l}:\mathbb{R}^{N_{l-1}\times T_{l-1}}\rightarrow\mathbb{R}^{N_{l}\times T _{l}}\times\mathbb{R}^{N_{skip}\times T_{L}} \tag{11}\]  
\[\gamma_{l}(X_{l-1})=(\psi_{l}(X_{l-1}),\eta_{l}(X_{l-1}))=(X_{l},H_{l}) \tag{12}\]  
where \(X_{l-1}\) are inputs to layer \(l\), \(\psi_{l}\) is the temporal block for layer \(l\) such that \(X_{l}=\psi_{l}(X_{l-1})\), and \(H_{l}=\eta_{l}(X_{l-1})\) is the skip connection for layer \(l\) accumulated to the final layer \(L\) such that:  
\[Y=f(X_{0};N_{0},N_{L+1},K,B,D,\theta)=\sum_{l=1}^{L}H_{l}=\sum_{l=1}^{L}\eta_{ l}(X_{l-1}) \tag{13}\]  
Parameters for the skip layers \(\theta_{\eta_{l}},l=\{L,L-1,\ldots 1\}\) are found through recursion.
# Courtyard Sound Field Characteristics by Bell Sounds in Han Chinese Buddhist Temples
## 3 Results and Discussion
### Courtyard Partition Walls  
Compared to other types of traditional courtyards, courtyards of Han Chinese Buddhist temples have an important feature; that is, there is no separate partition wall between courtyards. This not only allows visitors to choose a tour path, but also increases the viewing angle of the main Buddhist halls, which enriches the temple's landscape structure [1]. This study simulated courtyard sound fields with and without courtyard partition walls, and courtyard walls were set to a 3 m height. Figure 7a illustrates each courtyard's SPL(A) changes. It shows that SPL(A) did not differ significantly with and without partition walls in the first and second courtyards. It also shows that SPL(A) was significantly smaller with a partition wall than without one in the third and fourth courtyards, decreasing by 4.7 and 6.0 dBA, respectively. This significant decrease in SPL(A) was analysed using a sound-ray diagram (Figure 8), which revealed that the decrease was mainly due to courtyard partition walls blocking direct sound. In addition, the results showed that additional courtyard partition walls would not lead to significant changes in T\({}_{30\text{m}}\). After courtyard partition walls were introduced into the model, there were no significant changes in EDT\({}_{\text{m}}\) in the first courtyard; however, EDT\({}_{\text{m}}\) of the second through fourth courtyards increased by 0.51 s (52%), 0.58 s (42%), and 0.35 s (21%), respectively (Figure 7b). As seen in Figure 7c, after the introduction of partition walls, the C\({}_{80}\) of the second through fourth courtyards decreased by 4.8 dB, 2.9 dB, and 6.7 dB. Figure 7d shows that there was no significant change in (1-IACC\({}_{\text{E3}}\)) in the first through third courtyards, regardless of partition walls; however, the fourth courtyard exhibited a decrease in the value of (1-IACC\({}_{\text{E3}}\)) by 0.09 after the introduction of the partition wall. These results show that the absence of courtyard partition walls would not lead to significant SPL(A) changes in the first and second courtyards, but it would increase the SPL(A) and (1-IACC\({}_{\text{E3}}\)) values in the third and fourth courtyards. Moreover, the absence of courtyard partition walls would decrease the EDT\({}_{\text{m}}\) of the second, third, and fourth courtyards while increasing their C\({}_{80}\), making music clearer. In short, the absence of courtyard partition walls will have an impact on courtyard sound fields.
## 3 Results
### Validation of FDR Measurements
#### 3.1.2 Calibration Testing  
Based on the calibration dataset, a two-step testing approach was adopted. First, we validated the standard models suggested by the manufacturer by simply relating the measured \(\theta_{\rm v}\) at a specific moment with the observed output period of the FDR sensor at that moment, for each of the 60 soil layers. In Figure 2a these observed data-pairs are plotted along with the standard function for the M12 layer of profile B on the HOE site. Clearly the standard function underestimates the measured \(\theta_{\rm v}\) (negative bias of \(-0.0534\) m\({}^{3}\) m\({}^{-3}\), data in Table S5). In the second step, we recalibrated the model in order to remove the bias, and used this recalibrated model for predicting \(\theta_{\rm FDR}\) using the continuous FDR output period values. Note that with calibration the accuracy improved (RMSPE decreased from 0.057 to 0.019 m\({}^{3}\) m\({}^{-3}\)) and the index of agreement substantially increased from 0.67 to 0.91. Figure 2b illustrates that the monthly measured real soil water content is much better predicted when the recalibrated function is applied.  
Figure 2: Example of (re)calibration of FDR response functions. (**a**) Measured values with response curve of standard function and recalibrated function for plot HOE, profile B and layer M12; (**b**) time series of predicted \(\theta_{\rm FDR}\) by standard function and by recalibrated function with indication of 26 monthly calibration points. Ticks on the \(X\) axis indicate months, vertical gridlines indicate years. Green, cyan, and red horizontal lines indicate respectively saturated water content, field capacity and permanent wilting point (Table 2).  
The standard response remains permanently below field capacity whereas the recalibrated response during winter is situated between \(\theta_{\rm s}\) and \(\theta_{\rm fc}\) as expected. In summertime standard response drops below \(\theta_{\rm pwp}\) in contrast to recalibrated response and the calibration measurements.  
Note that the calibration measurements on the HOE plot started in February when soils were above field capacity, then followed a drying phase until September and a fast-rewetting phase in October/November. To improve calibration for the HOE plot, more calibration pairs would be beneficial for \(\theta_{\rm v}\) between 0.25 and 0.35 m\({}^{3}\) m\({}^{-3}\), roughly situated between May and the end of November.  
For each plot, layer and profile, graphs as in Figure 2b were produced to illustrate all calibration results of this study (Figures A1-A5), with indication of measured \(\theta_{\rm fc}\) and \(\theta_{\rm pwp}\).
## 3 Results and Discussions
### Role of Pore Structure and Surface Chemistry in the Adsorption Performance of Rice Husk Biochar
The peak at 791.2 cm\({}^{-1}\) is attributed to Si-H stretching vibrations but could also be linked to C-H bonds [68; 69]. Lastly, the peak at 459.9 cm\({}^{-1}\) represents Si-H bending vibrations, consistent with previous studies [68]. The observed reduction in NH\({}_{3}\) emissions is attributed to the oxygen-containing functional groups in the rice husk biochar, particularly carbonyl groups (peak at 1588.1 cm\({}^{-1}\)), which interact with NH\({}_{4}\)\({}^{+}\) through electrostatic interactions. The dipole moment of the carbonyl group enables its oxygen atom to engage with the positively charged NH\({}_{4}\)\({}^{+}\) ion [70; 71; 72]. In addition to these chemical interactions, physical adsorption within the biochar's porous structure further enhances NH\({}_{3}\) retention. On the other hand, the presence of silica (SiO\({}_{2}\)) on the biochar surface can negatively impact its adsorption capacity. While SiO\({}_{2}\) helps maintain the hierarchical porous structure of rice husk during pyrolysis by providing structural support, it can also hinder pore formation on the biochar surface [73; 74; 75; 76]. Rice husk ash typically contains around 90% SiO\({}_{2}\)[77; 78; 79]. [PERSON] et al. [80] reported that rice husk biochar pyrolyzed at 900 \({}^{\circ}\)C for 30 min contains about 40% wt% silicon (Si) due to the high SiO\({}_{2}\) content in rice husk. The FTIR spectrum confirms the presence of siloxane (Si-O-Si) groups [66; 68; 69], which form the structural framework of SiO\({}_{2}\)[81; 82; 83]. Additional peaks further support the existence of silicon-containing structures on the biochar surface. SEM-EDS analysis also confirms a  
Figure 6: FTIR spectrum of rice husk biochar. 14.5% atomic percentage of Si on the biochar surface (Figure 7). [PERSON] et al. [84] conducted an XRD analysis using the same biochar and identified typical silica characteristics, such as cristobalite, at 22\({}^{\circ}\)[85]. [PERSON] et al. [86] reported that etching silica using an alkali-silica reaction with NaOH significantly altered the material's surface properties. After etching with NaOH, the silica content in a rice husk adsorbent was significantly reduced to 2.7%, resulting in a more ordered surface graphitic structure. Similarly, [PERSON] et al. [87] observed that KOH activation of rice husk biochar reduced the presence of Si-O-Si functional groups and increased its porosity. These structural characteristics and potential limitations imposed by silica indicate  
Figure 7: SEM (**a**) and EDS (**b**) result of rice husk biochar. that optimizing the biochar's surface chemistry and enhancing its porosity can significantly improve its adsorption capacity for odorous compounds.