esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 3 Proposed Method  
Aiming at the problem of entity relationship recognition in a small dataset, the overall framework of the RBF model proposed in this paper is illustrated in Figure 1. It mainly includes four levels: (1) the text layer, which refers to the preprocessed text; (2) Roberta layer, using the Roberta pre-training model for text feature coding; (3) the BiGRU layer, decoding text features; and (4) the FC layer, mapping the result to the corresponding relationship category, and outputting the final relationship value according to the rules.
## 5 Numerical Results and Analysis
### Proposed Scheme Focusing Performance Analysis  
As discussed in Section 3, the focusing of FDA-DM depends on range-angle dimensions, whereas the focusing of PA-DM only depends on angle. Therefore, our proposed scheme is based on FDA, and in order to improve the focusing performance, we propose random frequency offset and design the beamforming matrix \(\mathbf{D}(\mathbf{t})\). Here, we give the spatial power distribution of confidential messages and the SINR spatial distribution to evaluate the focusing performance.  
The first experiment measures the energy distribution of confidential messages without the aid of SM and MP-WFRFT modulation. In this experiment, the minimum required receiving power of all LUs are set as \(\sqrt{\varsigma_{1}}{=}\sqrt{\varsigma_{2}}{=}\sqrt{\varsigma_{3}}{=}\sqrt{ \varsigma_{4}}{=}\) - 90 dBm. In Figure 5, it can be seen that the received confidential energy of LUs is almost equal to \(-90\) dBm. Moreover, in the other region, the energy of confidential decreases as the distance increases. That means our proposed scheme can achieve confidential message energy focusing and accurate control the received power of each LU only based on RFDA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency \(f_{c}\) & \(1\) GHz \\ Number of FDA elements \(N\) & \(32\) \\ Inter-element spacing \(d\) & \(c/2f_{c}\) \\ Number of LUs \(K\) & \(4\) \\ Number of Eves \(J\) & \(4\) \\ Receive noise power,\(10\log(\sigma^{2})\) & \(-100\) dBm \\ Location of LU1,\((r_{l_{1}},\theta_{l_{1}})\) & \((1500\) m,\(30^{\circ})\) \\ Location of LU2,\((r_{l_{2}},\theta_{l_{2}})\) & \((2000\) m,\(-50^{\circ})\) \\ Location of LU3,\((r_{l_{3}},\theta_{l_{3}})\) & \((2500\) m,\(50^{\circ})\) \\ Location of LU4,\((r_{l_{4}},\theta_{l_{4}})\) & \((3000\) m,\(-30^{\circ})\) \\ MP-WFRFT parameters \((\alpha,\mathbf{m},\mathbf{c})\) & \((0.5,[1,2,3,4],[5,6,7,8])\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
In the second experiment, we simulate and analyze SINR distribution in free space. Here, we consider the case with independent LUs. The proposed approach combined with MP-WFRFT modulation to realize secure wireless communication. MP-WFRFT realizes the embedding process of \"AN\" from the modulation level of digital baseband signal. From Figure 6, it is obvious that there are four sharp peaks corresponding to each LU due to FDA with the design of beamforming matrix \(\mathbf{D}(t)\) and the perfect inverse MP-WFRFT operation on received symbol vectors. Otherwise, the SINR of users in other regions is low, due to the weak received signal that is rotated by MP-WFRFT. In general, we achieve satisfactory focusing performance of wireless communication system.
# Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si
## 5 Fabrication and Testing
### Testing Results  
The measured \(|S_{11}|\) values for the RFID tag are shown in Figure 15, bearing a close similarity with the simulated values of Figure 10. A small shift in the dip frequencies is observed, possibly due to manufacturing discrepancies and the impact of the attached cable and connector on the antenna load. Interestingly, the measured results seem to exhibit an improved behavior (i.e., better matching) in the dip regions, which may be attributed to the additional loss caused by specimen preparation (e.g., contact and soldering losses) and/or some reactance introduced by the connector used.  
The measured results for the gain \(G_{R}\) of the RFID antenna are shown in Figure 16. In all, they are several dB lower than the simulated directivity values, especially in the region of 5.8 GHz. The difference is probably due to significant resistive losses over the RFID antenna structure. Such losses are to be expected in printed antennas to a greater extent than wire antennas, while larger losses at higher frequencies are usually due to the skin effect. As a check, the radiation efficiency measurements (described above) at 2.4 and 5.8 GHz yield efficiency values of \(\eta=0.85\) and 0.6, respectively, corresponding to differences of 0.7 and 2.2 dB between the directivity and gain values at these frequencies.
## 1 Introduction
Savanna ecosystems in Brazil have been identified as biodiversity hotspots and critical carbon sinks yet they are some of the most endangered ecosystems in the world with conversion to agriculture, fires, and clearing of vegetation exceeding the rate of deforestation in the Brazilian Amazon [90; 91; 92; 93; 94; 95; 96; 97; 98]. For these reasons, studies examining tropical forest and savanna productivity in Brazil have dominated the research landscape. Each Brazilian biome, however, is a mosaic of vegetation types, and in addition to tropical forests and savannas, include deciduous broadleaf forest, grasslands, and woody savannas among others. Previous studies examining SIF in the tropics have primarily focused on filtering SIF data for the single vegetation class evergreen broadleaf forest (EBF) for comparison to SIF and other remote sensing indices from satellites, flux tower data, or leaf level measurements or response to climate events such as drought [99; 100; 22; 35; 47; 60; 90; 91; 92; 93; 94; 96; 97; 98]. These studies have taken important steps to understand satellite SIF in relation to GPP, light use efficiency (LUE), and drivers of photosynthetic function (i.e., light availability, water availability, temperature, vapor pressure deficit) and to interpret SIF data (i.e., sensor geometry, canopy structure, sensor degradation). In our approach, however, we investigated the degree to which satellite-based SIF may or may not capture differences in magnitude and seasonality among tropical vegetation classes that exist within similar climatic and geographic regions as a means to understand SIF before filtering or combining vegetation classes or making comparisons to other quantities. To our knowledge, no studies have been conducted to examine the biome-level photosynthetic activity of EBF outside the Amazon or have compared SIF of EBF to other vegetation classes in Brazil. Therefore, an examination of the spatiotemporal patterns of SIF for different vegetation classes within and among biomes is an important step to understanding the relative productivity and drivers of tropical vegetation SIF beyond the Amazonian tropical forest [47; 60; 100]. We examined three years of Orbiting Carbon Observatory-2 (OCO-2) SIF data in the six Brazilian biomes (Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa, and Pantanal) to answer the following: (1) how does SIF in vegetation classes within and across Brazilian biomes differ? (2) What is the relationship (strength and direction) of SIF with canopy temperature (T\({}_{\text{can}}\)), air temperature (T\({}_{\text{air}}\)), and vapor pressure deficit (VPD) among vegetation classes within and across biomes? (3) How does the phenology of SIF (duration and amplitude of seasonal integrated SIF) compare among vegetation classes within and across biomes?
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 2 Prior Knowledge  
[PERSON] et al. proposed an energy optimization model to deploy the minimum number of static chargers to ensure the energy requirement of the sensor nodes in a multi-hop scenario. The authors utilize a modified [PERSON]'s algorithm to build the shortest path tree to minimize the energy transfer loss during data transmission. Furthermore, the authors run a MILP model to decrease the total number of chargers required to fulfill the energy constraint of every node [13].  
[PERSON] et al. proposed an energy-efficient geometric routing protocol to minimize the energy consumption of the sensor nodes regarding maximizing the network lifetime. Their proposed solution suggests to divides the sensor network into grids and applies the single point to numerous recharging schemes and Moore curve to calculate the routing path. Their objective is to ensure the sensor nodes can be operational forever with sufficient remaining energy [14].  
[PERSON] et al. proposed a grid clustering routing algorithm to balance the network lifetime, considering the vacation time, energy consumption, and remaining energy of the network. They deployed the shortest path model to enhance the vacation time of the network and grid clustering to balance the energy consumption [15].  
[PERSON] et al. employed a dynamic optimal scheduling method to maximize the vacation time of a single mobile charger for the wireless rechargeable sensor network. Their proposed scheme divided the network into many sub-networks to prolong the network lifetime [16].  
[PERSON] al. studied the problem of user-centric energy-efficient power management in a rechargeable wireless sensor network. Their proposed algorithm adopts WET and WIT phases to provide the energy-efficient communication [17].  
[PERSON] et al. proposed an approach which enables the use of clustering such as devices of the cluster with the help of member cluster heads. Their approach can harvest and stably store energy via RF signals adopting the wireless rechargeable sensor network paradigm [18].
## 7 The Effect of Environmental Conditions on Wind Gust Measurements  
The overall accuracy of wind measurements includes the accuracy of the data acquisition system and the effect of environmental conditions. Especially in climatological studies, the overall accuracy is highly important, because the inter-decadal trends in wind speed are typically of the order of only 0.5 ms\({}^{-1}\)[31]. Standard in situ anemometers used at weather stations can provide measurements with high enough precision, but the effect of environmental conditions may become critical for the measurement accuracy. Large obstacles in the vicinity of anemometers can cause a wake that is 2.5-times the height of the obstacle in near-neutral stability conditions, but in stable conditions, the wake can reach much further (e.g., [109]).  
As pointed out already in Section 1, exposure corrections can be applied to correct mean wind speed measurements for suboptimal conditions [34; 35; 36]. However, these methods are not suitable for correcting wind gusts. Another possibility to obtain more representative measurements is to increase the measurement height from the standard 10-m level [29]. The effect of measurement height and surface roughness on the gust factor can be demonstrated by assuming neutral atmospheric conditions with a logarithmic wind profile:  
\[U(z)=\frac{u_{*}}{\kappa}\ln{(\frac{z}{z_{0}})}, \tag{23}\]  
where \(u_{*}\) is the friction velocity, \(\kappa\) the von Karman constant, \(z\) the height above surface and \(z_{0}\) the aerodynamic roughness length. Assuming \(\sigma_{u}\approx 2.5u_{*}\) and combining Equation (23) with Equations (1) and (22) yield:  
\[G=1+g_{x}\frac{1}{\ln{(z/z_{0})}}. \tag{24}\]  
The dependency of the gust factor on height in optimal conditions over flat grassland terrain and over rough forest surface is shown in Figure 10. In the latter case, also the theoretical profile based on Equation (24) is shown. In the optimal conditions (Figure 10a), the gust factor decreases with height only by about 5-15% above the 10-m level, whereas above a rough forest surface, where the average tree height is about 10-15 m and the lowest measurement height is at 30 m, the decrease from the lowest (30 m) to the second-lowest (100 m) measurement level is as much as 20-30%. The theoretical curve shows that the change is the strongest near the surface. In other words, even if the measurements were obtained at a 30-m height instead of 10 m, they are strongly influenced by the roughness elements of the underlying forest, which, for example, affects comparisons with model results, when the model gust factors are based on the average surface conditions in the grid scale, i.e., in the scale of a kilometer or more. As seen in Figure 10a, Doppler lidar can provide information on gust factor profiles and could therefore be a good tool to investigate the optimal measurement height for wind gusts when the environment surrounding the weather station is not optimal.
## 3 Results and Discussion
### Dark Fermentation Reactor Performance Evaluation  
#### 3.2.1 Startup Strategy and Reactor Stabilization Test  
The continuous reactor performance is described based on the experimental steps of reactor stabilization followed by FeNPs nanoparticle evaluation. Figure 2 illustrates the overall process of continuous operation. During the initial experimental period, the reactor was operated for 24 h to promote the adaptation of the microbiota. After 7 days of continuous operation, H\({}_{2}\) productivity reached 0.7 \(\pm\) 0.1 NL H\({}_{2}\)/L-d, with an average hydrogen content of 58.2 \(\pm\) 3.7%. In addition to H\({}_{2}\), CO\({}_{2}\) was present in the gas composition, while other gases, such as H\({}_{2}\)S and CH\({}_{4}\), were not detected. After reaching a pseudo-steady state, the HRT condition of the reactor was changed to 12 h. The reactor responded positively to this change, reaching a maximum productivity of 0.9 \(\pm\) 0.1 NL H\({}_{2}\)/L-d after 2 days; however, a steep decline in H\({}_{2}\) production was observed until the complete process collapsed. To promote the growth and activity of acidogenic communities in the reactor, a batch recovery period with lactose (as the sole carbon source) was performed for three days. After this period, the continuous operation mode was restarted, still feeding with lactose as the sole carbon and energy source, with an HRT of 12 h. During this phase, the reactor reached a maximum productivity of 5.6 NL/L-d, indicating the presence of active H\({}_{2}\)-producing communities. After two days of reactor recovery, there was a sudden drop in H\({}_{2}\) productivity associated with the replacement of the lactose-based growth medium with RS hydrolysates and the shortening of HRT to 18 h. It is important to note that lignocellulosic material subjected to acid pretreatment typically generates a significant amount of inhibitors, including furfural, HMF, and other phenolic compounds. Furfural concentrations can range from 0.03 to 8.23 g/L and HMF concentrations from 0.09 to 1.59 g/L [32], often exceeding the inhibition threshold for DF processes, which is approximately 1 g/L [32]. In this context, several techniques have been developed to remove inhibitors prior to substrate utilization in DF systems [33]. Among these methods, over-lining involves the precipitation of inhibitors with Ca(OH)\({}_{2}\). Enhanced pretreatment efficacy is achieved through a combination of high pH and temperature over a prolonged period of time. This approach has been recognized as a promising detoxification method for dilute sulfuric acid pretreated lignocellulosic biomass hydrolysate [34]. In addition, over-lining has been used to remove inhibitors from sulfuric acid hydrolysates derived from RS [28]. Based on these considerations, an over-lining treatment was applied to avoid reactor inhibition.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 3 Review of Landslide Monitoring Equipment
### Geotechnical Monitoring System  
Geotechnical monitoring system is comprised of a complex network of miniature sensors equivalent to in-field used geotechnical monitoring equipment [60]. Various researchers established different monitoring systems using available sensor types for small-scale models [42; 43; 44; 45; 46; 47; 48; 49] with a wide range of abilities--measuring soil moisture, pore pressure, displacement, suction, etc. All sensors used in the tests are constantly connected to data loggers for continuous data collection during the time of the test.
## 3 Results and Discussion
### Minimising the Effect of Contrast Lack-Of-Uniformity: Proposed Approaches
* Adaptive edge-detection would involve particularising edge detection as a func-tion of local contrast characteristics for different contour segments. This particu-larisation should consider local greyscale transition characteristics in the vicinity of a contour point candidate. The approximate location of the contour would be known, therefore a function that selects an optimal \(T\) considering the contour orientation and the layer-to-background distance would provide an adequate response to anisotropic contrast. This approach does not af-fect the physical system, nor demands multiple scans. Nevertheless, it requires a mathematical model that includes all possible factors influencing contrast anti-sotropy, such as the distance to the background, the contour orientation or the op-tical characteristics of material. This requirement would imply an additional cal-ibration step, that could include a specific treatment for local abnormalities.
## 3 Results and Discussion
### Discussion of FE Analysis
#### 3.2.2 Wind Turbine Blade FE Model  
To simulate the load acting on the blade structure over a 20-year period, we applied a load equivalent to 20 years at a position 33.825 m from the root, which corresponds to 55% of the blade length. This load was based on the constant amplitude of the static load state in the flap direction, a factor that significantly impacts the overall load distribution. We then conducted a detailed analysis of fatigue crack growth behavior in three key regions of the blade: spar cap-shear web joints 1 and spar cap-shear web joints 2, all of which began with an initial crack length of 200 mm. After the 20-year simulation, the final crack length was observed only in spar cap-shear web joints 1 and spar cap-shear web joints 2, as depicted in Figure 16.  
Figure 12: Analysis and test results of specimens at different load ratios: (**a**) \(R=0.1\); (**b**) \(R=0.3\); (c) \(R=0.5\).  
Figure 14: Analysis results of specimens at \(R=0.3\).  
Figure 13: Analysis results of specimens at \(R=0.1\).  
For each adhesive joint, we examined the fatigue crack growth length in depth. As illustrated in Figure 17, the crack growth behavior in spar cap-shear web joints 1 is primarily in the flap direction under constant load, leading to the initial crack occurring at the top of the adhesive joint based on the blade skin airfoil geometry. In contrast, spar cap-shear web joints 2 exhibited a later initiation of crack growth compared to spar cap-shear web joints 1, but eventually, both experienced similar crack growth patterns. To provide a relative analysis of these characteristics, we summarized the crack growth behaviors of spar cap-shear web joints 1 and 2 in Figure 18.  
It is worth noting that in spar cap-shear web joints 1, the initial crack growth occurs rapidly, primarily due to the flap load's influence, which is directed toward the pressure side at the beginning of the loading process. However, crack growth was not observed in the TE part of the blade. As mentioned earlier, the crack growth rate in the spar cap-shear web joints is highly influenced by the flap load, whereas the TE part experiences less impact from these loading conditions.  
Figure 16: Fatigue crack propagation shape of blade under constant load conditions.  
Figure 15: Analysis results of specimens at \(R=0.5\).
## 3 Technology Overview
### Semantic Web Services (SWS)
#### WSMO-Lite  
WSMO-Lite is used to specify the actual semantic layer ontology [17]. Languages such as RDF, RDFS and OWL are used at this layer. WSMO-Lite captures four aspects of service semantics [17]:  
* _Information model_-defines the data model of the service.
* _Functional semantics_-what does the service do when it is invoked. This can be specified by either:
* _Categorisation_-simple functionality taxonomies where functionality is organised into a hierarchy of categories. WSMO-Lite offers the RDFS class _wsl:FunctionalClassificationRoot_ to distinguish functional classification hierarchies from normal ontology hierarchies.
* _Capability_-preconditions and effects. In [17], a telecoms example has a precondition that the client has to have a minimal bandwidth before the service can be executed and the effect identifies the valid outputs as a result of successfully executing the service. WSMO-Lite offers the RDFS class _wsl:Capability_ and the properties _wsl:hasPrecondition_ and _wsl:hasEffect_ so that preconditions and effects can be setup in an ontology.
* _Non-functional semantics_-these are incidental details specific to the implementation (or running environment) of a service, independent of the central purpose of the service but necessary for successful completion. Examples would be the price of the service or QoS guarantees. Non-functional semantics are often used for ranking e.g., which service is the cheapest. Non-semantic languages such as WS-Policy are often used to express non-functional characteristics. WSMO-Lite provides the class _wsl:NonfunctionalParameter_ to mark items with non-functional semantics.
* _Behavioural semantics_-defines the sequence of operations that a client needs to follow when invoking a service. This is done in WSMO-Lite by using functional semantics (categories and/or preconditions and effects). The URI's of these pieces of functional descriptions are attached to the operation (in either SAWSDL as _modelReference_ or MicroWSMO as _model_). The client can now reason about which operation can be executed at a particular point in time (_i.e._, at a particular state in the application).
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 3 Materials and Methods
### Knowledge Transfer and Model Testing  
The TL was performed according to the following workflow: first, the top fully connected (FC) layer of the selected pre-trained model was removed and replaced with a new one. The number of neurons in the new FC layer corresponds to the number of classes in the target dataset (12 in the UCI-HAPT dataset), and the weights were set using the Glorot uniform initializer. Second, the performance of the model was evaluated by fine-tuning it on the preprocessed UCI-HAPT dataset and its subset with varying numbers of frozen layers.  
Layer freezing is a commonly employed technique in TL for overcoming the overfitting problem. The number of layers to freeze typically depends on how similar the source and target datasets are. If the datasets are comparable, it may be sufficient to freeze all the network layers except the top FC layer. Therefore, more layers of the pre-trained network must be trainable during fine-tuning as the diversity of the datasets increases.  
In this work, the number of frozen layers was picked in accordance with the architecture of the chosen model. As discussed in the later sections, the selected model has the DenseNet121 architecture, which consists of a conv block, four dense blocks, and an FC layer. Hence, the following configurations were considered: only the top FC layer is trainable; the first 308 layers are frozen (conv, dense 1, dense 2, and dense 3 blocks); the first 136 layers are frozen (conv, dense 1, dense 2 blocks); and all the layers are trainable. The described methodology is illustrated in Figure 3.  
Figure 3: The employed transfer learning methodology.