esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Classification Schemes and Accuracy Assessment
The search range for learning_rate was set to 0.05 to 0.5 with a search step of 0.05, the search range for max_features was set to 5 to 50 with a search step of 5, and the search range for n_estimators was set to 10 to 300 with a search step of 10. The final parameters for the three were determined to be 0.15, 25, and 150, respectively. The Cart classifier performs classification tasks based on the generated tree decision rules. It has the advantages of a simple structure and easy interpretation. Therefore, it is widely used in crop identification [61; 62]. The algorithm consists of a root node, a series of internal nodes, and leaf nodes; Each internal node represents an attribute judgment, each branch represents a judgment result output, and each leaf node represents a classification result. However, this method also has some drawbacks. Small changes in the training data may affect the tree structure, and a single decision tree is often prone to overfitting and poor generalization. When classifying images, the max_depth is set to 20, the standard is set to \"Gini\", the min_samples_leaf is set to 35, and the min_sample_split is set to 10. The SVM classifier has good generalization ability when the number of samples is limited, and the dimensionality of the feature variables is high [63; 64; 65]. Therefore, it is widely used to solve classification problems. The principle is to find the optimal hyperplane (decision boundary) to separate the various input training samples (support vectors) from each class. The setting of the kernel type, gamma coefficient, and penalty parameter C in this classifier significantly affects the model performance. The types of kernel functions include \"RBF\", \"linear\", \"sigmoid\", and \"poly\". The search values for gamma coefficients include 0.1, 0.2, 0.5, 0.75, and 1, and the search values for C include 1, 2, 4, 8, and 10. The final three were RBF, 0.5, and 4. (3) Accuracy Assessment  
To quantitatively assess the accuracy of the above four classifiers in identifying crops in different periods, the image classification results based on the confusion matrix established from the field validation data were evaluated. The overall accuracy (OA), kappa coefficient, producer accuracy (PA), user accuracy (UA), and \(F_{1}\)-score were selected to reflect the classification accuracy of the images from different aspects [66]. The formula for \(F_{1}\)-score is:  
\[F_{1}=2\times\frac{PA*UA}{PA+UA} \tag{4}\]
## 3 Results and Discussion
### Physicochemical Properties
#### 3.2.2 pH Value  
The final pH values of all samples (pH 4.3-4.5) were below the range reported for traditional meat-based salamis, which tends to be pH 4.8-5.5 [66; 67]. Similar to the drying kinetics, pH development was primarily influenced by pectin concentration and only slightly by water content. Higher pectin concentrations led to lower pH values, probably caused by the pectin's native pH when dissolved in water (pH 4.2 \(\pm\) 0.5). The dextrose content might also have had an influence, since pectin is standardized with it by the manufacturer. The latter provides a more readily available substrate for lactic acid fermentation, potentially contributing to a lower pH. Additionally, an increased gluten content led to slightly higher pH values. This could have been caused by the lower a\({}_{\mathrm{w}}\) value associated with gluten addition in these batches and thus decreased metabolic activity of the microorganisms, combined with a different buffering capacity of gluten. Glutamic acid makes up a large part of gluten (\(\sim\)37%) [68], and with its pKa2 at pH 4.25 [69], where the strongest buffering effect of the amino acid can be expected, the total buffering capacity of gluten in this pH range can also be expected to make a contribution.
# Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors
## 6 Experimental Evaluation  
In this section, we evaluate the proposed pattern-based decoding algorithm through indoor testbed experiments. For comparison with conventional threshold-based decoding, we considered two methods to configure a threshold: (1) the average of all samples of a received Wi-Fi frame (total mean), and (2) the average of the samples of a sliding window (sliding). In the sliding method, window size was set as the same as a slot length.
# Numerical Association Rule Mining from a Defined Schema Using the VMO Algorithm
## 4 Methodology
### Rule Scheme  
The rule scheme is a template from which a population's individuals are generated, similarly to the definition of a class and its instantiated objects. This implies that the optimization procedure is applied to each population derived from its respective template. For its representation, it is necessary to start from the following definition. Let \(R\) be a rule, such that \(R\) is described with the conditions \(c_{1}\) and \(c_{2}\). So, we have the denomination \(c_{1}\to c_{2}\), where \(c_{1}\) is the condition and \(c_{2}\) is the conclusion. An element of the rule can be considered as an expression \(A=v\) if \(A\) is a variable of the categorical type. However, if this is numeric, we have \(A=[l,u]\), where \(l\) and \(u\) are the lower and upper thresholds, respectively.  
Table 1 outlines the logical structure of the elements of a rule and clearly differentiates between the extreme left and the right. This first partition corresponds to the antecedent and the consequent in the indicated order. Additionally, this table presents the attributes that are part of each end of the rule.  
The scheme of a rule can be built randomly through the controlled combination of each of the attributes that make it up or from the user's specifications, the latter making necessary an interface that allows the entry of these parameters. The generation of three classification rule schemes based on a definition that could be established by a user is detailed below.  
Let \(a_{1}\) and \(a_{2}\) be two numerical attributes and let \(b_{1}\) be a categorical attribute; \(l_{x}\) and \(h_{x}\) are, respectively, called the lower and upper thresholds of the \(x\) attribute, so the first scheme can be established.  
\[Rule=a_{1}\in[l_{1},h_{1}]\wedge a_{2}\in[l_{2},h_{2}]\to b_{1}=C_{1}\]  
Each combination of the attributes allows one to obtain a new template that will be entered into the optimization process. An example of possible derivations is shown below.  
\[Rule_{1}=a_{1}\in[l_{1},h_{1}]\to b_{1}=C_{1}\] \[Rule_{2}=a_{2}\in[l_{2},h_{2}]\to b1=C_{1}\] \[Rule_{3}=a_{1}\in[l_{1},h_{1}]\ \wedge\ a_{2}\in[l_{2},h_{2}]\to b_{1}=C_{1}\]  
An optimization process based on population algorithms is part of the definition of an initial population of rules, and the objects of improvement are the intervals of each numerical attribute. As in all convergence, a quality evaluation function that covers the most interesting criteria is required.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
The main additions of this paper are (a) the final version of the ONTO-SAFE ontology that is able to semantically represent the weather anomalies of fire weather index (FWI), drought, duff moisture, fine fuel moisture, the weather conditions of the prescribed burning process, and the generated alerts and recommendations, and (b) an extended ontology-based decision support module which offers pertinent recommendations taking weather conditions and forecast-related variables into account. The main contributions of this work can be summarized in the following:  
* We present an updated version of the ONTO-SAFE ontology that offers enhanced semantic capabilities, enabling it to effectively capture various situations associated with forest fires. * We introduce the functionalities of the ONTO-SAFE framework, which includes the ability to identify anomalies in factors including drought, moisture, and FWI, and the appropriate conditions to conduct the prescribed burning process, promoting effective fire management strategies. * We propose an improved semantic reasoning mechanism within the ONTO-SAFE framework, leading to real-time alerts and valuable recommendations with efficient response time, effectively assisting decision makers during forest fire emergencies. * We demonstrate practical use cases focusing on weather and forecast anomalies, along with the prescribed burning process. These use cases showcase how our framework actively assists decision makers in forest fire emergency situations, empowering them with valuable insights and support. The rest of the paper is structured as follows. Section 2 describes the background and related work regarding semantic web technologies, ontologies, decision support systems, and their contribution to emergency management and disaster planning. An overview of the architectural design of the ONTO-SAFE framework is presented in Section 3. Section 4 illustrates the ONTO-SAFE ontology with the relative additions and the semantic representation of fundamental concepts. Semantic reasoning for decision support is demonstrated through simulation examples in Section 5. In Section 6, the evaluation results are presented, and Section 7 discusses the results obtained from the simulation examples. Finally, Section 8 follows with the conclusions of the paper.
## 5 Methodology
### Metrics  
In this section, we present, define, and describe the evaluation metrics used to assess the system's performance, including the bit error rate, spectral efficiency, and energy.
## 3 Results  
### Seasonal Climatology of SSI  
Figure 2 shows the seasonal climatology of the simulated SSI for the two coupled vegetation-runoff systems with respect to the SRB for: winter (December-January-February; DJF), spring (March-April-May; MAM), summer (June-July-August; JJA) and autumn (September-October-November; SON). In general, the RegCM4 reproduces well the spatial pattern of SSI in comparison with the SRB in all seasons. Furthermore, both simulations tend to overestimate the SSI over large parts of the Sahara region with biases about 20-60 W m\({}^{-2}\). Over tropical Africa, the simulated biases vary across the seasons. For instance in the MAM season; both CN-TOP and CN-VIC underestimate SSI by 20 W m\({}^{-2}\) over the region of (7-15\({}^{\circ}\) N) and underestimate by 40-50 W\(\cdot\)m\({}^{-2}\) over the Evergreen Forest (Congo basin; 5\({}^{\circ}\) N-5\({}^{\circ}\) S, 10-40\({}^{\circ}\) E). Over the aforementioned regions, the CN-VIC underestimates the SSI by 25 W m\({}^{-2}\). In JJA and SON, both CN-TOP and CN-VIC show a similar performance particularly over the Congo Basin. Over the region 15-30\({}^{\circ}\) S, 10-40\({}^{\circ}\) E, both coupled systems overestimate the SSI (by 10-40 W m\({}^{-2}\)). In addition, the SSI simulated biases by CN-VIC (in the DJF) were about 10 W m\({}^{-2}\) lower than those found with the CN-TOP. In comparison with a study conducted by [11], the difference between the two vegetation  
Figure 1: This figure shows the model domain topography (in meters) including three sub-area averages. The domain was customized with permission from Ref. [3].  
runoff systems (CN-TOP and CN-VIC) is larger than the induced vegetation cover/fraction changes. Therefore, vegetation-runoff systems play a more important role in the constraint of the SSI of tropical Africa than if only vegetation cover/fraction changes are considered. A simple flow chart explains the role of vegetation-runoff systems (in simulating the SSI) is shown in Figure 3.  
Figure 2: This figure shows the Surface Solar Irradiance (SSI) over the period 1998–2007 (in W\(\cdot\)m\({}^{-2}\)) for: MAM season in the first row **(a–f)**; JJA in the second **(g–l)**; SON in the third **(m–r)**, DJF in the fourth **(s–x)**. For each row, CN-TOP is on the left, followed by CN-VIC, SRB is in the third from left, CN-TOP minus SRB, CN-VIC minus SRB and the difference between CN-VIC and CN-TOP. Significant model bias is indicated in black dots using Student’s \(t\)-test with \(\alpha\) equals 5%.
## 3 Results
### Hydrometeorological Conditions in the Catchment of the River Swierszcz
#### 3.1.4 Fluctuations in the Groundwater Table in the Catchment Area of the River Swierszcz  
At the beginning of the hydrological year 2020/2021, the depth of the groundwater table in the catchment area of the River Swierszcz was \(-\)1725 cm below ground level. In January 2021, the groundwater table dropped slightly to \(-\)1733 cm below ground level to then rise again starting from March 2021. Over the next three months, water levels rose quite quickly at about 20 cm per month. In June 2021, the water table reached the level of \(-\)1675 cm below ground level. Very heavy rains at the turn of June and July 2021 contributed to the rapid rise of the water table by as much as 130 cm to its maximum level in the hydrological year 2020/2021. On 11 July 2021, the depth of the water table was \(-\)1544 cm below ground level. In the following months of the study, the groundwater level fluctuated slightly, falling to \(-\)1613 cm below ground level at the end of October 2021. The annual amplitude of groundwater table fluctuations in the hydrological year 2020/2021 was very high at 190 cm. The average annual water table level in that year was 16 cm lower than the average for the years 2012-2020. It is worth noting that the depth of the water table at the end of the hydrological year 2020/2021 was as much as 112 cm higher than at its beginning. This demonstrates that high precipitation increased the amount of groundwater reserves in the catchment area of the River Swierszcz.  
In the hydrological year 2021/2022, the annual amplitude of the groundwater table was lower at 81 cm. From January to April 2022, a slow rise in the water table was observed. In April, the water level reached a maximum of \(-\)1590 cm below ground level. From May 2022, a slow drop in the groundwater level was observed to \(-\)1671 cm below ground level in December.  
The data reported above show that temperature and precipitation had a significant impact on the fluctuations in the groundwater table and water flows in the River Swierszcz in the RNP. A slightly delayed response of the groundwater table to the high amounts of rainfall was observed in the summer months of 2021. It can also be claimed that the uniform precipitation throughout 2022 was reflected in the smaller fluctuations of the groundwater table and water flows in the River Swierszcz. Owing to the relatively high precipitation in 2021/2022, the water level of the Cretaceous layer in the park was replenished.  
The Quality of Rainwater Compared with the Quality of Surface Water and Groundwater and the Possibility of their Use
## 3 Materials and Methods
### Deep Neural Network Architecture  
A typical deep neural network (DNN) consists of an input layer, multiple hidden layers, and an output layer. Each layer consists of neurons that are activated based on input from the previous layer using an activation function. In this paper, we use the long-short-term memory network (LSTM) [39]. LSTM stands out as a powerful variant of recurrent neural networks (RNNs) that is widely recognized for its capabilities in processing and predicting sequence data, with a specific emphasis on time-series data. The LSTM unit encompasses four essential components, as visually depicted in Figure 3. These components include a specialized cell that is designed to store and retain information over extended periods. Furthermore, the cell incorporates three important gates: the input gate, the output gate, and the forget gate. These gates serve as vital mechanisms for controlling the flow of information into and out of the cell. In our specific context, we train the LSTM to intelligently preserve quality information from previous segments while disregarding historical details such as bandwidth and buffer state information.
# Optimization and Experimental Verification of Lower
## 2 Optimization Model for Lower Articulation
### Design Variable of Lower Articulation
#### 2.2.2 Design Variable  
Based on the basic design of the lower articulation, the design factors were derived from the results of the structural analysis. These factors, which can influence both the strength and weight of the articulation, were determined by ensuring that there is no interference between the components. The design variable parameters were calculated based on the structural analysis. These parameters include \(x_{1}\) (Middle Frame), \(x_{2}\) (Frame Hold Width), \(x_{3}\) (Frame Edge Width), \(x_{4}\) (Frame Neck Length), \(x_{5}\) (Frame Width), \(x_{6}\) (Core Upper Length), \(x_{7}\) (Core Lower Length), and \(x_{8}\) (Core Radius). The design variables are illustrated in Figure 6. A total of eight variables affecting the characteristics of the lower articulation were selected. A range of conditions were established for each derived design factor, with the criteria based on considerations such as shape interference, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variable** & **Min** & **Max** \\ \hline \(x_{1}\) (Middle Frame) & 5 & 15 \\ \(x_{2}\) (Frame Hold Width) & 70 & 90 \\ \(x_{3}\) (Frame Edge Width) & 25 & 45 \\ \(x_{4}\) (Frame Neck Length) & 54 & 64 \\ \(x_{5}\) (Frame Width) & 120 & 140 \\ \(x_{6}\) (Core Upper Length) & 25.5 & 31.5 \\ \(x_{7}\) (Core Lower Length) & 33.5 & 39.5 \\ \(x_{8}\) (Core Radius) & 58 & 66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design variable.  
Figure 5: Stress contour of lower articulation: (**a**) tensile case; (**b**) compression case.  
Figure 6: Design variable of lower articulation.
## Methods
### LiDAR Data Acquisition and Processing  
Airborne LiDAR data were acquired on 5 November 2014 using a Leica LiDAR50-II flown at 1850 m altitude on a Dornier 228-201 travelling at 135 knots. The LiDAR sensor emitted pulses at 83.1 Hz with a field of view of 12.0\({}^{\circ}\), and a footprint of about 40 cm diameter. The average pulse density was 7.3/m\({}^{2}\). The Leica LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. Accurate georeferencing of LiDAR point cloud was ensured by incorporating data from a Leica base station running in the study area concurrently to the flight. The LiDAR data were pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools [35]. Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells (Figure 3b).  
Figure 3: (**a**) A monochromatic image from the Pléiades satellite constellation and (**b**) an airborne LiDAR image of a 1-ha plot to show a typical oil palm plantation canopy. The legend refers to height in metres of individual palms displayed on the LiDAR image.  
Figure 2: Boxplots contrasting the structural attributes of oil palm plantations of two different ages (\(x\)-axis), based on measurements taken in 27 1-ha plots. ACD is aboveground carbon density (Mg C ha\({}^{-1}\)).
# A Survey of the Literature on Order-Picking Systems by Combining Planning Problems
## 5 Discussion
### Performance Measure
#### 5.2.5 Makespan  
To minimize the order-picking makespan, it is necessary to simultaneously solve order batching, assignment and pickers routing. Table 9 shows six research works aimed at makespan minimization.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Auditors** & **Assignment** & **Routing** & **Batching** & **Sequencing** & **Tardiness** & **Others** \\ \hline [PERSON] \& [PERSON] [40], [PERSON] et al. [61], [PERSON] \& & & & & & \\ [PERSON] [48], [PERSON] et al. [78], [PERSON] et al. [54], [PERSON] [79], [PERSON] \& [PERSON] [51], [PERSON] \& [PERSON] [80], & & & & \\ [PERSON] et al. [42] & [PERSON] et al. [29], [PERSON] et al. [81], [PERSON] \& [PERSON] [82] & & & & \\ [PERSON] et al. [1], [PERSON] \& [PERSON] [64], [PERSON] et al. [49], [PERSON] \& [PERSON] [41], [PERSON] et al. [70] & & & \(\checkmarkmark\) & & \\ [PERSON] et al. [62], [PERSON] et al. [71] & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & \\ [PERSON] et al. [24] & & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ [PERSON] et al. [36] & & & \(\checkmark\) & & & \(\checkmark\) \\ [PERSON] et al. [18] & \(\checkmark\) & & & & & \\ [PERSON] et al. [52] & & & & \(\checkmark\) & & \\ [PERSON] et al. [4], [PERSON] et al. [57], [PERSON] et al. [72], [PERSON] et al. [32], [PERSON] \& [PERSON] [74], [PERSON] & & & & \\ et al. [28], [PERSON] et al. [65], [PERSON] et al. [66], [PERSON] et al. [9], [PERSON] \& [PERSON] [68], [PERSON] et al. [69], [PERSON] \& [PERSON] [76], [PERSON] & & & \\ et al. [2], [PERSON] et al. [77] & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Overview of the reviewed articles on Space optimization.  
According to Table 9, [PERSON] et al. [46] studied the simultaneous analysis of assignment, routing, batching, and sequencing. The authors studied an on-line order batching problem where orders become available over time on a dynamic basis and the total completion time of all orders in a certain time period needed to be minimized [46]. It is clear that there are a few studies that consider makespan as the objective function.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation  
Alongside the physical modelling techniques, numerous data-driven methodologies for obtaining personalised HRTFs have been explored, spanning a spectrum of approaches that differ in both complexity and accuracy. These methods can be broadly defined as anthropocentric and morphological, with further subdivisions shown in Figure 4. Anthropometric approaches are based on the idea that the HRTF of an individual is influenced by somatic features of parts of the body like the size and shape of the torso, head, and pinnae. Additionally, the use of anthropometric data enables a less intrusive and more scalable approach, where individualised HRTFs can be derived.  
Many of the early studies on HRTF personalisation based on anthropometric features have relied on a sparse set of measures introduced in the CIPIC dataset [147]. This dataset includes measurements from 45 individuals, with a total of 37 anthropometric features recorded for each subject. Specifically, 17 of these features pertain to the upper body, while 10 additional measurements are dedicated to each ear, encompassing a comprehensive range of parameters relevant to auditory perception. Furthermore, the subsequent HUTUBS dataset [89], published later in 2019, utilises the same 37 measurements. Over the years, researchers have developed methodologies that leverage either all or a subset of these parameters to personalize HRTFs more effectively. These approaches vary in complexity, aiming to balance accuracy with computational efficiency. Table 1 provides a detailed overview of the key anthropometric parameters from the CIPIC dataset.  
Somatic features can also be derived from a set of landmarks corresponding to acoustically significant points of the body extracted from 2D images, or even learnt automatically from a 3D representation using machine learning techniques. Each of these methods presents distinct advantages and challenges. In the following sections, we will examine these approaches in detail, exploring their specific use cases and potential for personalised auditory rendering. Anthropometric approaches to HRTF personalisation try to exploit these data to individualize an already available, standardised HRTF or to generate a new one starting from the available data. The main approaches include anthropometry matching, adaptation, and regression.