esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 1 Introduction  
Many advanced applications can benefit from electrospun materials with superior mechanical and dielectric properties, especially in the fields of composite reinforcement and energy. Aligned electrospun fibers, more specifically, have applications in structural reinforcement of materials and energy storage devices. For these applications, it is paramount to understand the effects of electrospun fiber alignment on mechanical and dielectric properties. Though adding appropriate fillers to the polymers changes mechanical and dielectric properties, better fiber alignment alone improves these properties and keeps the composition uniform throughout. Mechanical and dielectric properties depend on the density and porosity of nanofiber mats, as well as the fiber morphology, including the fiber diameter, and the effect of degree of alignment [1; 2; 3]. Therefore, it is necessary to understand the knowledge on both mechanical and dielectric properties of polymer mats together. Mechanical and dielectric properties are among the most important parameters to determine the performance of the polymeric nanomaterials [4; 5]. Electrospinning influences both mechanical and dielectric properties of nanofiber membranes [4]. Electrospinning is the process of producing micro- and nanofibers, using a polymer solutionwith a syringe pump, syringe, needle, collector, and high-voltage power supply. The typical setup of an electrospinning apparatus is either horizontal electrospinningning or vertical electrospinningning [6]. Figure 1 shows the schematic setup of both types.
# Performance of Molasses Waste as a Cement Replacement to Study Concrete Compressive Strength
## 4 Discussion  
The results indicated that the addition of a small amount of molasses, 0.25% wt. of cement (28.85 g), positively influenced the compressive strength of concrete for all test periods (7, 14, and 28 days). The increase in compressive strength at 0.25% wt. of cement (28.85 g) was due to the presence of the coagulation property which is inherent in molasses.  
However, as the percentage of molasses replacement increased beyond 0.25% (28.85 g), the compressive strength decreased. This decrease can be attributed to factors such as the dilution effect, variations in molasses composition, and the potential hindrance of proper hydration and bond formation in the concrete. As in previous studies, the setting time of the molasses-containing concrete was prolonged, as in the case of specimens with a 0.25% replacement ratio (28.85 g). Similarly, the molasses-containing concrete achieved the maximum compressive strength at an optimum replacement ratio, which in this study was at 0.25% wt. of cement (28.85 g). To fully assess the potential of molasses waste as a cement replacement material, long-term testing is crucial, such as evaluating the compressive strength at 28 days or beyond.  
The results emphasize the importance of carefully selecting the appropriate molasses replacement ratio to achieve the desired compressive strength. A replacement ratio of 0.25% (28.85 g) showed favorable results in terms of improved compressive strength, while higher replacement ratios led to diminished strength values. Engineers and researchers should consider this optimal replacement ratio when using molasses waste as a cement replacement material in concrete applications. Furthermore, this research can be extended by considering the determination of the compressive strength at 56 days to fully understand the properties of the concrete and the effect of a higher percentage of molasses. Compressive strength is a crucial parameter; it is essential to evaluate other concrete properties, such as workability and durability, and the influence of molasses waste on different aspects of concrete performance.
## 4 Improved PSO-Optimized Fuzzy-BPNN Control Algorithm
### Fuzzy-BPNN Optimal Control Algorithm Based on Improved Particle Swarm  
The main principle of the improved particle swarm-based Fuzzy-BPNN-PID control algorithm is that the initial weights of the BP neural network can be optimized accurately and quickly by introducing an improved particle swarm algorithm with an inertia weight adjustment strategy to improve the search efficiency of the network. At the same time, the state variables at the input of the neural network are fuzzily preprocessed to reduce the output of the network, which is insensitive to the input and easy to saturate. The steps are as follows:1. Initialization of particle swarm, where parameters such as population size, particle dimension, number of iteration steps, initial inertia weight and learning factor are set according to neural network factors.
2. Using the global search capability of the particle swarm algorithm, the approximate optimal solution of the network weight parameters is preferentially obtained, and the particles are updated according to the particle fitness. If the particle swarm meets the requirements of the optimal fitness value, the corresponding network parameters are transmitted to the BP network.
3. The BP neural network uses the network weights of the improved particle swarm optimization to process the fuzzy preprocessed signal, updates the network parameters through back propagation, and finally, outputs the optimal \(k_{p}\), \(k_{i}\) and \(k_{d}\), which are calculated according to the incremental PID formula that also calculates the control signal \(u(k)\). Its control principle diagram is shown in Figure 10.  
When the IPSO algorithm is used to obtain the network weight parameter matrix, the particle swarm population size is M = 20, the number of iteration steps is N = 100, the learning factor is \(c_{1}=c_{2}=2\), the initial inertia weight is \(\omega_{0}\) = 0.9, the particle velocity interval is set to [\(-\)5, 5], the structure of the neural network in this paper is 3-9-3 and the number of adjustable network parameters is 2 \(\times\) 3 \(\times\) 9 = 54; therefore, the particle dimension D = 54 is set, and each dimension represents a variable-adjusted parameter.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 3 Materials and Methods
### Generative Language Models
#### 3.3.2 Transformer-Based Model (GPT-2)  
From the family of transformer models, we use GPT-2 [6]. Prior work showed promising results using GPT-2 for the generation of English EHR [8]. To the best of our knowledge, there is no Dutch GPT-2 model for the clinical domain which we could re-use. However, prior work showed that pre-trained English models can be adapted to the Dutch language with smaller computational demand than training from scratch [28]. The intuition is, that the Dutch and English language share similar language rules and even (sub-)words. Below, we provide a summary of this fine-tuning process.  
**Adapting the vocabulary:** We train a byte-pair-encoding (BPE) tokenizer on our Dutch EHR corpus. All sub-word embeddings are randomly initialized. To benefit from the pre-trained English GPT-2 model (small variant) [6], we copy embeddings that are shared between the English and Dutch tokenizer. To account for the in-text annotations, we add a tokenization rule to not split PHI tags into sub-words.  
**Fine-tuning the model:** The layers of the pre-trained GPT-2 model represent text at different abstraction levels. For transfer learning, the key is to take advantage of the previously learned information that is relevant for the current task, but adjust representations such that they are suitable for the new language and domain-specific terminology. To do  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PHI Tag** & **Count** & **\% of Total** \\ \hline Name & 782,499 & 59.74 \\ Date & 202,929 & 15.49 \\ Initials & 181,811 & 13.88 \\ Address & 46,387 & 3.54 \\ Care Institute & 38,669 & 2.95 \\ Organization & 37,284 & 2.85 \\ Internal Location & 6977 & 0.53 \\ Phone/Fax & 3843 & 0.29 \\ Age & 3350 & 0.26 \\ Email & 2539 & 0.19 \\ Hospital & 2425 & 0.19 \\ Profession & 537 & 0.04 \\ URL/IP & 326 & 0.02 \\ ID & 232 & 0.02 \\ Other & 105 & 0.01 \\ SSN & 6 & 0.00 \\ \hline Total & 1,309,919 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of PHI tags in the 52 million token corpus used to develop the language models (i.e., real data). PHI was tagged by an automatic de-identification routine.  
so, layers are split into groups and we use gradual unfreezing with differential learning rates, such that the last layer group (with corpus-specific information) is changed more than the first ones, where learned representations can be re-used. To train layer groups on our data, we used the one-cycle-policy [29], where learning rates are scheduled with cosine annealing. Our GPT-2 model was split into four layer groups which were trained in 5 epochs. We provide additional details on model and fine-tuning steps in Table 2 and Appendix A.
## 3 Results and Discussion
### Operative Parameters of MW-Based Plant Prototype  
As reported in the previous section, the electric motor was equipped with a PLC-controlled VFD system to adjust motor frequency and the resulting helix rotation speed, the discharge speed (flow rate) and the residence time of the almonds in the treatment chamber. Figure 8 shows the trend of mass flow rate and the residence time as a function of motor frequencies. The increase in the engine motor frequency affects both the mass flow rate and the residence time, showing a nonlinear positive and negative relationship, respectively. At the minimum and maximum engine motor frequency, the mass flow rate varied from 1.9 to 5.4 kg/min and the residence time from 200 to 80 s, respectively, easily meeting the quantitative performance objectives set in the design step.  
Figure 7: (**a**) MW-based plant prototype and (**b**) detail illustrating the magnetron generator, power supply, and control system.  
A second-order polynomial regression was applied for all data points, obtaining an R-squared equal to 0.989.  
As the frequency of the electric motor and the rotation speed of the helix inside the treatment tube increased, the residence time decreased, and the mass flow rate increased proportionally in the first half of the trend while in the second section it tended to saturate. This means that, although the helix inside the tube mixes the almonds well, the corresponding mass transport from the supply section to the discharge one does not occur with the same efficiency. In fact, once a certain rpm value of the helix has been exceeded, the almonds do not advance proportionally to the rotation speed of the helix.  
Figure 8: Mass flow rate and residence time versus the electric motor frequency.  
Figure 9: Trend of residence time versus mass flow rate.
## 2 Theoretical Aspects
### Centre of Mass  
According to the second law of dynamics, inertial force is proportional with mass, so it is very important to know the distribution of masses in a structure both in plan and vertical directions. In the case of multi-storey buildings, the inertial forces will be generated at each storey and it will act at a point where the mass of the entire floor may be assumed to be concentrated [3]. This is the CM of that floor. CM is a position defined as the average position of all the parts of the system, weighted according to their masses. The distribution of mass is balanced around the CM and the average of the weighted position coordinates of the distributed mass defines its coordinates [3]. To determine the CM at every floor, one must know the mass and position of CM corresponding to each structural component element. In order to solve the problem, section meshing of the elements in triangles is used. Using Equations (1) and (2) for every element, due to the irregular geometric form in plan and Equation (2) for all elements of the involved floor, one may obtain CM of the storey [15]:  
\[X_{CM}\ =\ \frac{\sum_{i=\,1}^{n}A_{i}x_{Ci}}{\sum_{i=\,1}^{n}A_{i}}; \tag{1}\]\[Y_{CM}\ =\ \frac{\sum_{i\ =\ 1}^{n}A_{i}y_{Ci}}{\sum_{i\ =\ 1}^{n}A_{i}}, \tag{2}\]  
where \(x_{Ci},\ y_{Ci}\) are coordinates of the area \(A_{i}\).  
During an earthquake, acceleration-induced inertia forces will be generated at each floor level, which acts at a point where the mass of the entire floor may be assumed to be concentrated. In a building with a uniform distribution of mass, the position of the CM corresponding to each floor is not different from one floor to another floor. However, irregular mass distribution over the height of a building may result in a variation of the position of CM at various floors [15]. In this paper, the symmetrical distribution of the positions of CM from one floor to another floor is investigated.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Access Models for IoT-over-Satellite
#### 3.3.2 Case II: Scheduled Access  
To improve packet delivery rate, scheduled random access has been widely used for IoT networks [50]. In many applications, IoT devices are required to remain in sleep mode to prolong the battery lifespan and only wake up to transmit based on different scheduling algorithms which may be periodical or event triggered. In this case, we assume that the IoT devices are scheduled to transmit only when at least one satellite is capable of receiving IoT packets. In other words, an IoT device is capable of transmitting its signal when a satellite is available. Case II in Figure 9 illustrates the transmission pattern under scheduled random access, where the IoT payload (yellow blocks) is only transmitted when at least one satellite is available (green blocks). Therefore, we expect packet collisions to be the sole factor behind high PERs.
## 2 Materials and Methods
### Modeling Approach  
Physical parameters of the railway embankment slope were obtained using laboratory testing. A cross-section of the railway embankment slope (Figure 2c) [48] and lake water level measurements were collated. A 2D finite element model of the railway embankment slope was produced using the soil material parameters from experimental data using GeoStudio 2012 software [49]. Currently, there is no information on groundwater/surface water interaction or lag times for the study area. Therefore, an instantaneous change in pore water pressure with variations in lake levels was employed to establish the model boundary conditions for both rising (0.1 m/day) and falling (0.2 m/day) scenarios. Scenarios 1 and 2 are as follows: increasing water (53.5 m to 71.2 m and 53.5 m to 58 m) used historical field data of the lake water levels. These were observed to be critical within 20 days in the summer and autumn, and hence, seepage analysis was conducted for 20 days under various drawdown scenarios. Previous research shows that water level and antecedent precipitation changes most affect slope stability after 5 days [50; 51; 52]. The Plackett-Burman experimental design is used to screen nine parameters affecting railway embankment slope stability; the most critical parameters were chosen, and minor factors were excluded. The BBD-RSM test design evaluated the main and interaction effects of selected significant factors from the Plackett-Burman test. Components with considerable sensitivity to the railway slope stability coefficient were identified. The BBD-RSM dataset was used to build an ANN model using JMP Pro 15.0 software [48]. Finally, BBD-RSM and ANN findings were compared to assess their stability factor sensitivity analysis performance. In this study, nine external and internal elements affecting railway embankment stability were screened. Two unique levels were selected by the One Factor at a Time process for each factor. Table 1 show the factors analyzed and their greater (+) and lower (\(-\)) values. A full matrix of a normal 12-run PBD (Table 2) was constructed using Minitab 19.0 [17]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Factor Level** & **v (m/s)** & \(\rho_{\mathbf{s}}\) (kg/m\({}^{3}\)) & **c (Kpa)** & \(\varphi\) (\({}^{\circ}\)) & **L (KN)** & \(\kappa_{\mathbf{s}}\) (m/s)** & \(\mu\) & **_E (Mpa)_ & **H (m)** \\ \hline + & 0.22 & 2090 & 15.4 & 37.4 & 143 & 5.83 \(\times\) 10\({}^{-6}\) & 0.33 & 25.85 & 18.7 \\ \hline
0 & 0.2 & 1900 & 14 & 34 & 130 & 5.30 \(\times\) 10\({}^{-6}\) & 0.3 & 23.5 & 17.0 \\ - & 0.18 & 1710 & 12.6 & 30.6 & 117 & 4.77 \(\times\) 10\({}^{-6}\) & 0.27 & 21.15 & 15.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental levels for nine parameters using Plackett–Burman and BBD-RSM.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 2 Methodology
### Grasp Algorithm  
The following pseudo-codes summarize the approaches taken by the grasping method. Algorithm 1 shows the information extraction process from an image via feature detection and image segmentation.  
```
1:Detect features \(E\), \(C\), form abstract shape
2:Calculate \(P_{e}\), \(A_{e}\), \((x_{ce},y_{ce})\) from abstract shape
3:Image segmentation detects \(P_{g}\), \(A_{g}\), \((x_{cg},y_{cg})\)
4:if unable to detect then
5: Use \(P_{e}\), \(A_{e}\), \((x_{ce},y_{ce})\)
6:else
7: Compare \(A_{g}\) and \(A_{e}\) to evaluate detections
```  
**Algorithm 1** Feature and parameter extraction  
Algorithm 2 shows the steps in searching for a suitable pair of parallel straight edges closest to the object centroid.  
Figure 13: **(a)** A slight oval with 2 detected corners. (**b**) Lines are drawn through the object centroid from one end to another at varying angles. If any of these lines intersect the bounding box of any corner, these lines are eliminated. (**c**) The shortest amongst the remaining lines is chosen as the grasp position.  
```
1:\(d_{max}(px)=physicalmax(cm)\times\frac{focal(px)}{depth(cm)}\)
2:if\(n\geq 2\)then
3: Calculate distance \(D_{i}\) for all \(E_{i}\) from \((x_{c},y_{c})\)
4: Sort \(E_{i}\) by \(D_{i}\) (ascending)
5:for\(\theta_{max}=min\) to \(max\)do\(\triangleright\) user-defined min/max
6:for\(i=1\) to \(n-1\)do
7:for\(j=i+1\) to \(n\)do
8:\(\Delta\theta=|\theta_{E_{i}}-\theta_{E_{j}}|\)
9:if\(\Delta\theta\leq\theta_{max}\)then
10: Draw \(line\)\(d_{i}\) between \(E_{i},E_{j}\)
11: Check \(|\theta_{line}-\theta_{i}|\geq\theta_{diff\_min}\)
12: Check \(d_{i}\leq d_{max}\)
13:if suitable then
14: Break
15:else
16:\(j=j+1\)
17:if no suitable pair then
18:\(i=i+1\)
19:if no suitable pair then
20:\(\theta_{max}=\theta_{max}+step\)\(\triangleright\) user-defined step
21:if pair found then
22: Optimize grasp position
23:else
24: Sort \(E_{i}\) by length \(l_{E_{i}}\) (descending)
25:for\(i=1\) to \(n\)do
26:\(K=\) Project \((x_{E_{i}},y_{E_{i}})\) to other side of \(P\)
27: Distance \(K\) to \((x_{E_{i}},y_{E_{i}})\leq d_{max}\)
28:if suitable then
29: Break
30:else
31:\(i=i+1\)
```  
**Algorithm 2** Search for parallel straight edges  
Algorithm 3 shows the steps in deducing the grasp position for objects which have no suitable parallel edges for grasping.  
The full source code for the algorithm can be found here: ([[https://github.com/khor054/FeatureDetectionRobotGrasping/](https://github.com/khor054/FeatureDetectionRobotGrasping/)]([https://github.com/khor054/FeatureDetectionRobotGrasping/](https://github.com/khor054/FeatureDetectionRobotGrasping/)) (accessed on 1 July 2024)).
## 3 Q2: What Are the Challenges of the Current UIoT System
### Technical Challenges of UioT
#### 3.2.4. High Cost  
As shown in Figure 1 of Section 1, in the UIoT environment, the sensor nodes are devices that are sparsely deployed. Additionally, the products are from different vendors. Therefore, it is too costly to install, monitor and manage the network and devices in the UIoT environment [2; 3; 4].
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 5 Analysis
### Variance  
Since the residual energy varies after each energy consumption when sending a packet, and since the hop-count distribution varies after the update of the tree, the expected data forwarding probability given by Equation (19) varies by the variance \(\sigma_{i}^{2}\), given by Equation (20) for the source node \(n_{i}\).  
\[\sigma_{i}^{2}=\sum_{j=1}^{m_{i}}\tilde{\varphi}_{i,j}\Big{(}\ \phi_{i,j}-EX_{i}\Big{)}^{2}=\sum_{j=1}^{m_{i}}\ \ \frac{\omega_{i,j}}{\rho_{i}}\left[\ \frac{\omega_{i,j}}{\partial_{i}}-\frac{1}{ \partial_{i}\rho_{i}}\sum_{j=1}^{m_{i}}{\omega_{i,j}}^{2}\right]^{2}. \tag{20}\]
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
###### Abstract  
Efficient human resource management is critical to public sector performance, particularly in dynamic environments where traditional systems struggle to adapt to fluctuating workloads. The increasing complexity of public sector operations and the need for equitable task allocation highlight the limitations of conventional evaluation methods, which often fail to account for variations in employee performance and workload demands. This study addresses these challenges by optimizing load distribution through predicting employee capability using data-driven approaches, ensuring efficient resource utilization and enhanced productivity. Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. Performance is assessed through ten evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), ensuring a comprehensive assessment of accuracy, robustness, and bias. The results demonstrate ANFIS as the superior model, consistently outperforming other algorithms across all metrics. By synergizing fuzzy logic's capacity to model uncertainty with neural networks' adaptive learning, ANFIS effectively captures non-linear relationships and variations in employee performance, enabling precise capability predictions in dynamic environments. This research highlights the transformative potential of machine learning in public sector workforce management, underscoring the role of data-driven decision-making in improving task allocation, operational efficiency, and resource utilization.  
workload management; public sector; load control; human resources; productivity; ranking; efficiency; task allocation; personnel selection +
Footnote †: journal: Future Internet
## 3 Results and Discussion
### Analysis of Diversity Performance
#### 3.1.3 Mean Effective Gain  
The measurement of the power received by the diversity antenna in relation to the power received by an isotropic antenna in a fading environment is known as MEG (Mean effective gain) [30]. Equation (5) is used for carrying out the calculation, and the corresponding graph is presented in Figure 19.  
\[\text{MEG}_{\text{i}}=0.5\left\{1-\sum_{j=1}^{k}\left|\text{S}_{\text{ij}} \right|^{2}\right\} \tag{5}\]  
Figure 17: Simulated and measured ECC curve.  
Figure 18: Simulated and measured diversity gain (DG) curve.  
where i designates the particular antenna under consideration and \(k\) specifies the total number of antennas. In this particular scenario, the value of \(k\) corresponds to 2 for the two-port antenna. Consequently, we have  
\[\text{MEG}_{1}=0.5\,\left\{1-\left|\text{S}_{11}\right|^{2}-\left|\text{S}_{12} \right|^{2}\,\right\}\]  
\[\text{MEG}_{2}=0.5\,\left\{1-\left|\text{S}_{21}\right|^{2}-\left|\text{S}_{22} \right|^{2}\,\right\}\]  
According to reference [31], the ratio of MEG1/MEG2 for the suggested two-port element antenna should be less than 3 dB in order to obtain excellent diversity performance. The validation of the MEG readings being less than 1.2 dB can be observed from Figure 16. The performance data for the suggested antenna are shown in Table 7. The outcomes indicate that the antenna exhibits favorable diversity properties, making it highly suitable for millimeter wave applications.