esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Service-Oriented Architecture for PGIS
### Description Layer  
Description layer is responsible for describing the data, commands, and services using standard methods which machines can understand. Standardization organizations have published a series of standardizations, such as W3C (World Wide Web Consortium), OGC and ISO (International Organization for Standardization). For instance, Sensor Model Language (SensorML) includes standard models which describe sensor systems and processes information with XML Schema. SensorML also provides information needed for discovery of sensors, location of sensor observations, processing of low-level sensor observations. Those standards can be easily mapped to each other. The relationships among the standards are shown in Figure 6.  
Figure 6: The relationships among the standards.  
In data services, Web Ontology Language (OWL) [31] from W3C is used to model the ontologies providing the structure to the RDF (Resource Description Framework) information, which is available in Linked Data datasets. It is designed for applications that need to process the content of information instead of just presenting information to users supported by XML, RDF [34], and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. Figure 7 shows an example of the OWL.  
The main GISC description language used in this paper is the Business Process Execution Language (BPEL) and its extension for Web Services (BPEL4 WS). A BPEL model is an abstraction of the GISC functions that involves multiple processes. In the BPEL, each service can be described using Web Service Description Language (WSDL) in syntax and Web Ontology Language for Services (OWL-S) in semantics.  
There are three basic elements (ontologies) in the OWL-S, which are service profile, service process, and service grounding. Service profile tells \"what the service does\", in a way that is suitable for a service-seeking agent to determine whether or not the service meets its needs. Service process tells a client how to use the service, by detailing the semantic content of requests, the conditions under which particular outcomes will occur, and the step by step processes leading to those outcomes. Service grounding specifies a communication protocol, message formats, and other service-specific details such as port numbers used in contacting the service.  
Figure8 shows an example of the OWL-S structure.  
Figure 7: Code segments describing a relationship of “subClassOf” from the ISO 19115 meta-data standard using OWL.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Ablation Study
Figure 12 depicts the average values of \(ACC\), \(REC\), \(PR\), and \(F_{1}\) computed on the Flood Area dataset for different values of **(a)**\(T_{L}\) with \(T_{H}\) = 0.75, and **(b)**\(T_{H}\) with \(T_{L}\) = 0.01. In any scenario, the method's performance measured with the values of \(F_{1}\) and \(ACC\) is almost stable, while, as expected, only the values of \(REC\) and \(PR\) slightly decrease and increase, respectively. In conclusion, minor fluctuations in the threshold values do not significantly alter the method's outcome. Although these thresholds are the only parameters that users can modify, they have already been experimentally optimized. Reasonable adjustments to these values produce outcomes that remain nearly identical.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 3 Extraction and Purification Methods
### Chromatographic techniques  
Ion exchange chromatography is one of the most commonly employed of the chromatographic methods used for bromelain extraction, and has proven to be a low-cost, highly specific, scalable and reliable technique [41]. Cation exchange chromatography has been used to separate ananain in combination with affinity chromatography [42]. However, better purification (85% of the initial active proteases) has been obtained using acetone precipitation in combination with the two above-mentioned chromatographic techniques [17].  
[PERSON] et al. have demonstrated the efficacious separation of the enzymatic complex from polysaccharides using ion exchange chromatography (removal of 97.7% of the polysaccharides initially present in the crude extract) [8].  
[PERSON] et al. have separately employed salt precipitation and ionic exchange chromatography to extract bromelin from clarified pineapple juice. The latter technique led to a 10-fold enrichment in bromelin (from 58 to 590 units/mg) and gave a threefold higher yield than salt precipitation [35]. A bromelin preparation with comparable enzymatic activity (390 units/mg) has been obtained by [PERSON] et al. using a combination of cation exchange and size exclusion chromatography [11]. [PERSON] et al. have also tested the combination of cation exchange and size exclusion chromatography, and this approach resulted in the recovery of 41.15% of enzymatic activity, in relation to the initial stem extract [43].  
Affinity chromatography is another successful technique for bromelin extraction. [PERSON] et al. have managed to isolate stem bromelin from other impurities by eluting pineapple-stem extracts through agarose-\(\epsilon\)-aminocaproyl-D-tryptophan methyl ester (ACTME) [44]. Similar results have been obtained by [PERSON] et al. using a Sepharose-Ahx-Phe-GlySc column [17]. The effective separation of recombinant bromelin has also been performed using a different type of affinity chromatography, based on a nickel ligand (Ni-NTA His Bind resin) [25]. Indeed, [PERSON] et al. obtained an increased purity grade (41-fold) for the recombinant bromelin expressed in the BL21-AI E. coli strain using this method [45].  
High-speed counter-current chromatography has been tested by [PERSON] et al. in combination with a reverse-micelle solvent system to purify bromelin; 3.0 g of bromelin was generated from 5 kg of pineapple fruit [46].
## 3 System Model and Problem Formulation
### Problem Formulation  
As shown in a D2D-enabled HetNet in Figure 1, when multiple DUEs and SUEs share the same radio resources, they will cause mutual interference. Therefore, our goal is to maximize the overall system throughput under the minimum QoS requirements of all users. Therefore, the objective function and constraints for mode selection, resource allocation, and power control of resource management issues among cellular, small cell, and D2D users can be formulated mathematically as follows:\[\mathcal{P}1:\max_{\psi_{j}^{f},\psi_{k}^{f},\psi_{j}^{M},P_{i}^{C},P_{ j}^{D},P_{k}^{S}}R_{\text{overall}}=\sum_{i\in\mathbb{C}}[\text{Blog}_{2}(1+ \gamma_{i}^{M})+\] \[\sum_{j\in D}\psi_{j}^{f}\text{Blog}_{2}(1+\gamma_{j}^{D})+\sum_{k \in S}\psi_{j}^{f}\text{Blog}_{2}(1+\gamma_{i}^{S})]\] s.t. \[C1:\gamma_{i}^{M}\geq\gamma_{i,th}^{M},\quad\forall i\in C \tag{16a}\] \[C2:\gamma_{j}^{D}\geq\gamma_{j,th}^{D},\quad\forall j\in D\] (16b) \[C3:\gamma_{k}^{S}\geq\gamma_{k,th}^{S},\quad\forall k\in S\] (16c) \[C4:0\leq P_{i}^{C}\leq P_{\text{max}}^{C},\quad\forall i\in C\] (16d) \[C5:0\leq P_{j}^{D}\leq P_{\text{max}}^{D},\quad\forall j\in D\] (16e) \[C6:0\leq P_{k}^{S}\leq P_{\text{max}}^{S},\quad\forall k\in S\] (16f) \[C7:\sum_{i\in C}\psi_{j}^{f}\leq 1,\quad\psi_{j}^{f}\in\{0,1\}, \quad\forall j\in D\] (16g) \[C8:\sum_{i\in C}\psi_{k}^{f}\leq 1,\quad\psi_{k}^{f}\in\{0,1\}, \quad\forall k\in S \tag{16h}\]  
where \(\gamma_{i,th}^{C}\), \(\gamma_{j,th}^{D}\), and \(\gamma_{k,th}^{S}\) are the minimum SINR requirements for the CUE, D2D pair, and SUE communication links, respectively. \(\psi_{i}^{f}\), \(\psi_{j}^{f}\), and \(\psi_{k}^{f}\) are mode selection indicators for cellular users, D2D pairs, and small cell communication, respectively. \(\psi_{j}^{f}=\psi_{k}^{f}=1\) when D2D pairs or SUE users reuse the \(i_{th}\) CUE resource; otherwise, \(\psi_{j}^{f}=\psi_{k}^{f}=0\). Constraints (16a), (16b), and (16c) represent QoS requirements of CUEs, DUEs, and SUEs, respectively. However, constraints (16g) and (16h) ensure that DUEs or SUES can reuse at most one existing CUE resource. Finally, (16d), (16e), and (16f) constraints make sure that the transmit power of CUEs, DUEs and SUEs will be within the maximum limit.
## 4 Results and Discussion  
### Seismic Reflection  
The sedimentary cover sequence at the top of the seismic profiles produces reflectors of moderate amplitude, which we interpreted as Miocene sandstones and marks. The seismic response of the Jurassic dolomites consists of prominent reflectors of high amplitude and continuity. This different response allows us to trace both the upper and lower boundaries of the Jurassic aquifer. Under the Jurassic unit, locally discontinuous reflectors of low amplitude are associated to the Late Triassic unit, which contains clays and evaporates (gypsum and halite). At the base of the Triassic unit, continuous levels of greater amplitude are detected, which we have correlated with the basal, thick Triassic sandstones levels. The acoustic material is the Paleozoic basement, which is characterized by chaotic features and disorganized seismic facies.  
Different criteria to calculate the stratigraphic units' thickness were used. A criterion, namely the measured thicknesses of stratigraphic units, was measured in the field. The other criterion was velocity values included with the profiles. These two criteria generally coincided. Finally, surveys carried out in the area provide some direct assessment of local hydrogeological conditions.  
The seismic profile selected in this study (Figures 2 and 3A) shows Jurassic dolomites dip approximately 20\({}^{\circ}\) S-SE. The unit outcrops in the northern sector and occupies deeper positions in the southernmost sectors. The resolution of the profile is very low near the land surface, so the dolomites are not readily identifiable to the north (Figure 3A).  
Using a correction speed of 2000 m/s, the dolomites can be identified at 200 m depth near the pumping boreholes, as pointed out in Figure 3A. This depth is confirmed from borehole drilling information. Under the dolomites, higher-amplitude reflectors in the lower part of the Triassic unit correspond to sandstones. Some of the faults detected with ERT techniques affecting the sedimentary cover are also identified by seismic methods.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 4 Results
### Prediction Results of ST-GAT Neural Network  
Figure 12 shows the prediction results of the ST-GAT neural network at four different depth monitoring points along the same inclinometer. The predicted values of the model show good consistency with the actual values in the training set, indicating that the proposed model effectively captures the deformation features from historical data. Similarly, the model demonstrates strong consistency in the testing set, highlighting its excellent generalization capability on unseen data. Tables 2 and 3 evaluate the prediction results of the training and testing sets in terms of MSE, MAE, and R2. The results indicate that wall deflections 2 and 3 achieve lower MSE and MAE values, as well as higher R2 values, in both the training and testing sets, suggesting accurate predictions at these points. Specifically, wall deflection 3 exhibits the best generalization performance with the lowest MSE (0.126 mm2), MAE (0.263 mm), and a high R2 value of 0.917 in the testing set.  
In contrast, the higher errors at wall deflection 6 can be attributed to its proximity to the midpoint of the inclinometer, resulting in inherently larger deformation. Nevertheless, despite recording the highest error values, with an MSE of 0.564 mm\({}^{2}\), an MAE of 0.455 mm, and an R\({}^{2}\) of 0.913, the model still performs well overall. These results indicate that the proposed ST-GAT neural network, which utilizes spatiotemporal features across different monitoring points with varying weights, can achieve an accurate prediction for wall deflection. The effect of spatial relationships between adjacent monitoring points is explored in Section 4.2.
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 4 Experiments
### Experiment Results
#### 4.2.1 Deciding the Number of Training Inputs for Velocity Prediction Model  
We used the velocity information to train the BPNN-based velocity prediction model. The number of training inputs means the number of time slots considered when training the BPNN-based velocity prediction model. For example, if the sampling rate is one velocity data sample per 0.1 s, then using three training inputs means taking previous 0.3 s velocity data to train the model. The goal of this experiment is to determine a suitable the number of input nodes for the BPNN model.  
We calculate the error with mean squared error (MSE), as shown in Equation (6), which is used to estimate the error for training:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\biggl{(}\frac{A_{i}-P_{i}}{A_{i}}\biggr{)} ^{2} \tag{6}\]  
where \(n\) is the number of training samples, \(A_{i}\) is the actual velocity of \(i\)th training sample, and \(P_{i}\) is the predicted velocity of the \(i\)th training sample.  
The error reduces after more epochs of training, but might start to increase on the validation data set when the network starts overfitting the training data. The best performance is taken from the epoch with the lowest validation error. For example, Figure 7 shows the performance of the BPNN model with eight input nodes. The best performance is 0.05978 at epoch 147 with the lowest validation error.  
We measure the MSE while varying the number of input nodes between 1 and 9. Table 1 and Figure 8 show the performance of velocity prediction model with different input nodes. The weights did not converge for the cases of 1 and 2 input nodes, where the maximum epoch was set as 1000. We can observe that the MSE increased when the number of input nodes is less than 4 and when it is more than 5. It means that if we use too few previous velocity data, we cannot get an accurate prediction. On the other hand, if we consider too much previous velocity data, the results may be impacted by the irrelevant information from a too long historical time period. When the number of  
Figure 7: Performance of velocity prediction based on BPNN model with 8 input nodes.  
nodes are 4 or 5, the proposed prediction model has a low MSE and thus give better prediction results. Thus, the number of nodes is selected as 4 or 5 in our method.
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 5 General Discussion
### Self-Disclosure in Different Spaces and User Contexts  
Users were willing to disclose more information in the living space than in the workspace (one-user context: 44% more information; two-user context: 28% more information). However, this high self-disclosure was not mediated by a heightened private self-awareness. The private self-awareness of participants was significantly higher than their public self-awareness in both Experiment 1 (living space: t\({}_{(35)}\) = 7.12, \(p\) \(<\) 0.001; workspace: t\({}_{(35)}\) = 7.32, \(p\) \(<\) 0.001) and Experiment 2 (living space: t\({}_{(23)}\) = 4.99, \(p\) \(<\) 0.001; workspace t\({}_{(23)}\) = 4.77, \(p\) \(<\) 0.001). This finding reveals that a user's attention is directed toward themselves (e.g., physical or emotional states) rather than external factors (e.g., contextual factors and other people) regardless of the space (living space or workspace) and user context (e.g., one user or two users) when interacting with an IoT-CA. This finding supports the ideas that the IoT (ubiquitous computing) brings computers into our world and frees people to shift awareness back to themselves and their everyday life [58; 59]. IoT-CAs can enable people to return to an awareness of and focus on themselves and their life rather than other external factors.  
The higher level of information disclosure in the living space than the workspace can be explained by self-disclosure reciprocity. Self-disclosure reciprocity in social interactions refers to a situation in which one person reveals personal information to the other partner, and the other will disclose something or behave in a manner that is responsive to the initial disclosure's content in return [60; 61]. Self-disclosure reciprocity can be explained by the norm of reciprocity [62] and social exchange theory [63]; people attempt to maintain equality in their relationships or behaviors, and the violation of this makes them uncomfortable. In interacting with IoT-CAs, users sought reciprocity. The opinions about the IoT-CAs in the two spaces showed that participants had more expectations for the IoT-CA in the living space than in the office. In Experiment 1, participants (30 out of 36 participants) argued that the IoT-CA in the living space should provide more diverse services (e.g., chatting, help with cooking, housework, reminding the user to exercise and sleep, and providing energy consumption information), whereas the IoT-CA in the workspace did not need to provide many services (27 out of 36 participants). Therefore, self-disclosure reciprocity can exist in human-IoT-CA interaction.
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 3 The Proposed Model
### The Feature Optimization  
The feature optimization (FO) is actually implemented by the two branches of the deep network and the cross network. The branch of the cross network plays the role to carry out effective explicit feature crossing, which is to learn certain bounded-degree feature interactions.  
The cross network includes several cross layers, and the formula of each layer is as follows (8).  
\[x_{l+1}=x_{0}x_{l}^{T}w_{l}+b_{l}+x_{l}=f(x_{l},w_{l},b_{l})+x_{l}. \tag{8}\]  
The calculation of an intersection is shown in Figure 2, where \(x_{0}\) is the original embedding layer input and \(y\) is column vectors denoting the output from the current cross layers; \(w\), \(b\) are the weight and bias parameters of the current layer. Each cross layer adds back its input \(x\) after a feature crossing, and the mapping function just fits the residual of \(y-x\).  
From the special structure of the cross network, we can see that the degree of feature cross increases with the depth of the network layer. Because the number of parameters in the cross network may limit the capacity of the network, in order to better capture the high-order non-linear interactions of features, the model further introduces a parallel deep network, which is a fully connected feed forward neural network. The formula of each layer is as (9).  
\[h_{l+1}=f(w_{l}h_{l}+b_{l}). \tag{9}\]  
The complexity of the deep network can be estimated by the number of parameters. Assuming that the dimension of input \(x_{0}\) of deep layer is \(d\), the number of layers of deep network is \(L_{d}\), and the number of neurons in each deep layer is \(m\), the complexity of the deep network is calculated as (10).  
\[O_{d}=o(d\times m+(m^{2}+m)\times(L_{d}-1)+m). \tag{10}\]  
The model finally uses the cross entropy loss function with regularized terms as Formula (11).  
\[Loss=-\frac{1}{N}\sum_{i=1}^{N}(y_{i}log(p_{i})+(1-y_{i})log(1-p_{i}))+\lambda \sum_{l}\|w_{l}\|^{2}. \tag{11}\]  
In summary, in the input layer, SMOTE is used to synthesize minority samples to deal with the imbalance of data. In the embedding and stacking layer, sparse features of data are transformed into embedded vectors, in which embedding optimization is applied, and then embedding vectors and dense vectors are spliced together. In the cross layer, explicit features are effectively learned on how to cross. The parallel deep layer fully learns the interactions between higher-order features. The combination layer integrates the output of the cross layer and the deep layer, and the output layer gets the predicted CTR through a sigmoid function. During training, FTRL-Proximal algorithm [8] is adopted, showing greatly improved prediction performance.  
Figure 2: The calculation of an intersection.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 3 Smart Public Lighting: Infrastructure Architecture and Main Tasks
### Light Dimming
In fact, when TAI or (to greater reason) FAI are adopted, streetlights belonging to the same TAI/FAI segment continuously exchange information, and they should react to varied conditions (adopting a different dimming level) simultaneously, so as to avoid to confuse drivers with differently illuminated portions of the road. An additional feature, which is related to both security and energy saving is the so-called Constant Light Output (CLO). In this regard, it should be observed that the light flux of LED luminaires reduces over time as the diode ages and dirt accumulates, just as most of other light sources. Typically, in the absence of any dimming capability, streetlights are overdriven since the first day, in such a way to significantly exceed the required light intensity, so that a reduction over time of the luminaire efficiency does not infringe the regulation on the minimum required illumination level. This surplus margin depends on the Maintenance Factor (MF), which defines the percentage of the total light output at the start of the installation life, to which the output may eventually fall. The MF is computed taking into account the luminous flux factor (i.e., the depreciation of the luminous flux over time due to the light source aging) as well as other factors, namely the survival factors, the luminaire maintenance factor and the surface maintenance factor (see the ISO/CIE TS 22012 standard [69] for a complete description). Needless to say that this policy causes a useless waste of energy. If the CLO functionality is implemented, the luminaire itself can compensate for the light depreciation, providing a constant light output [70; 71; 72]. The LED is initially powered at a given percentage (<100%) of its lighting capability and then continuously dimmed upwards to a final 100% current supply so that the required illuminance level is kept constant until the end of the service life. Clearly, this dim programming protects the LED chip and saves electricity costs over the entire luminaire lifespan.
## 2 Materials and Methods
### Background
#### 2.2.1 Data Collection Technology
The main ones are the focal length of the camera used, the GSD (ground sample distance),and the MTF (modulation transfer function) [26]. In [23] and [27] it was concluded that the GSD should be taken into consideration when planning an information-gathering flight; In [28] it was concluded that the sharper images produce a higher quality point cloud, so the camera resolution must be considered, and sharpness can be measured through the MTF. The three variables mentioned above are defined below: (a) The focal length corresponds to the distance from the rear node to the point where the ray of light enters the lens to form a sharp image [29]. (b) GSD corresponds to the distance measured between two contiguous pixel centers that make up the surveyed surface; higher GSD values mean more diffused images [30]. (c) MTF corresponds to a Fourier transformation applied to sharpness measurement in images recorded by a digital camera [31]. Regarding the flight speed, this parameter is mainly related to the overlap required for the project and at a higher speed of flight, the speed of the photo shot should be higher. For general photogrammetry applications and overlap requirements of 80% longitudinal and transverse, the flight speeds should not exceed 5 m/s in fast shooting mode [24].