esa-sceva/satcom-chunk-collection huggingface dataset:
# Last-Mile Optimization Using Neural Networks
###### Abstract  
In the era of extensive data acquisition from manufacturing and transportation processes, the utilization of machine learning and deep learning techniques has emerged as a potent force for informed decision-making and optimized deliveries in contemporary urban landscapes. This study presents a novel approach grounded in deep learning, where product data are systematically gathered to construct a multilayer perceptron neural network model. This model proves instrumental in efficiently classifying product flows within the urban milieu. To validate its efficacy, machine learning classifiers are deployed, and their performance is juxtaposed with the neural network model. Addressing the critical question of the paper's significance, our experimental evaluation unequivocally demonstrates the superior classification accuracy of the proposed multilayer perceptron model when compared to traditional machine learning models operating on the same product dataset. This advancement is not merely a theoretical achievement but translates into tangible improvements in last-mile delivery processes, marked by significant cost reduction and the mitigation of delays. The transformative potential of our approach is further underscored by the strategic application of a deep learning algorithm for optimization and illustrative purposes. This holistic methodology not only positions our work as a noteworthy contribution to the realm of product classification but also establishes a concrete pathway for enhancing the sustainability and efficiency of urban logistics. This paper, thus, goes beyond the conventional application of machine learning models, offering a paradigm shift in the intersection of deep learning, urban logistics, and sustainable development.  
relational database; supervised machine learning; deep learning; multilayer perceptron neural network; classification; optimization
# Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
## 4 Proposed Design Evaluation
### Simulation with Step Change Wind Speed Profile
These diagrams show the load comparisons (CVR\({}_{\text{Mfa}}\) and CVR\({}_{\text{Mss}}\)) versus objective functions (IAE\({}_{\text{avg}}\) and CVR\({}_{\text{xt}}\)) and the control effort CVR\({}_{\beta}\) of the baseline control with all three controller solutions. The left spider diagram shows a comparison of the index values of the different controllers with respect to the BSC, and the right spider diagram displays the comparison for the three obtained GSC with respect to the satisfactory GSC. The results of the normalized spider diagrams with respect to the baseline control for step wind simulation show a significant improvement in the indices for the three designed GSC. The results reflect a substantial improvement of the satisfactory, optimal IAE\({}_{\text{avg}}\), and optimal CVR\({}_{\text{xt}}\) with a respective reduction of 93.3%, 93.9%, and 88.2% in the IAE\({}_{\text{avg}}\) index; 42.3%, 33.9%, and 46.1% in the CVR\({}_{\text{xt}}\) index; 37.7%, 30.4%, and 41.3% in the CVR\({}_{\text{Mfa}}\) index; and 43.6%, 35.4%, and 46.8% in the CVR\({}_{\text{Mss}}\) index with respect to the baseline control. Although at first glance it seems that the results in the indices are similar for the three designed GSC, if these controllers are compared with each other with respect to the satisfactory solutions (right spider diagram in Figure 12), the satisfactory solution achieves a good balance. The satisfactory solution reduces the IAE\({}_{\text{avg}}\) index by 75.1% with respect to the optimal CVR\({}_{\text{xt}}\) solution and by 14.5%, 11.8%, and 14.7% for the CVR\({}_{\text{xt}}\), CVR\({}_{\text{Mfa}}\), and CVR\({}_{\text{Mss}}\) with respect to optimal IAE\({}_{\text{avg}}\) solutions. Table 5 shows the previously commented results. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Controller/Indices** & **IAE\({}_{\text{avg}}\)** & **CVR\({}_{\text{x}}\)** & **CVR\({}_{\beta}\)** & **CVR\({}_{\text{Mfa}}\)** & **CVR\({}_{\text{Mss}}\)** \\ \hline
**BSC** & \(16.3\times 10^{3}\) & 10.21 & 56.39 & \(1.89\times 10^{6}\) & \(9.56\times 10^{5}\) \\
**Satisfactory GSC** & 1091.6 & 5.89 & 72.36 & \(1.18\times 10^{6}\) & \(5.39\times 10^{5}\) \\
**Optimal IAE\({}_{\text{avg}}\) GSC** & 984.5 & 6.75 & 67.87 & \(1.32\times 10^{6}\) & \(6.18\times 10^{5}\) \\
**Optimal CVR\({}_{\text{xt}}\) GSC** & 1911.1 & 5.50 & 73.13 & \(1.11\times 10^{6}\) & \(5.09\times 10^{5}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Performance indices for steps change wind speed profiles.
## 2 Methodology
### Mapping of Geological Elements  
The geological map was constructed through the visual interpretation of satellite images, employing various remote sensing techniques applied effectively in arid and semiarid terrains for distinguishing rock and soil types [29; 30; 31; 32; 33]. Image processing-based remote sensing facilitated the discrimination of lithological cover and lineament elements, both important geological criteria and environmental factors for site selection and road design [11]. The satellite data processing involved several steps:  
1. Layer stacking of the different bands in the image.
2. Subsetting of images to focus on the exact area required.
3. Construction of false-color composite images (FCC) using band combination and band ratioing techniques to achieve optimal lithologic discrimination and identify lithological cover.
4. Utilization of enhancement and merging techniques during processing, with images produced in the UTM projection (WGS84 datum, zone 36N).
5. Application of Principle Component Analysis (PCA) to transform images by compressing the dataset and reducing the number of dimensions [31].
6. Implementation of supervised classification, where distinct information categories are initially identified, is followed by an examination of their spectral separability. In contrast, the unsupervised approach involves the computer determining spectrally separable classes and defining their information value.
7. Creation of a lineament map using PC images in the Geomatics platform, with a line density layer constructed in the ArcGIS environment.
8. Conducting fieldwork and ground truthing to enhance the accuracy of the geological map.
## 2 Methods
### Data Acquisition and Processing  
Raw LD signals of 10 Hz sampling frequency were imported to MATLAB (version 9.14, 2023a, RRID: SCR_001622; The MathWorks Inc., Natick, MA, USA). For WA, a 600-s tracing of the baseline LDF signal and a 600-s PORH tracing starting from the peak of the reperfusion were considered separately (Figure 1A).  
To minimize potential artifacts, raw signals were filtered with a Hampel filter [16] which removes only sharp spikes, such as due to subject movements, but does not otherwise affect the signal.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 2 Machine-Learning-Based Path Loss Prediction
### Hyperparameter Setting and Model Training  
Hyperparameters refer to the parameters whose values are set before the learning process begins. Typical hyperparameters include the number of hidden layers and neurons in ANN, the regularization coefficients and parameters in kernel function of SVR, the tree depth and the size of the ensemble in decision-tree-based algorithms, etc. A set of optimal hyperparameters should be carefully chosen in order to optimize the performance and effectiveness of the path loss prediction. The optimization methods for hyperparameters mainly include grid search, random search, and Bayesian optimization. In this paper, the final values of hyperparameters were obtained by using grid search method. It is an exhaustive search method which takes the best performing parameters as the final result by traversing all the possible values of the parameters.  
Model parameters are those parameters learned from training samples. It is worth mentioning that different learning methods have different model parameters. During the model training process, model parameters such as weights and biases are automatically learned.
## 2 Materials and Methods
### CFD Numerical Simulation
#### 2.2.2 Computational Meshing  
In numerical simulations, an appropriate mesh is crucial for obtaining accurate computational results. Generally, increasing the mesh resolution enhances accuracy; however, excessively refined meshing significantly raises computational costs [19]. In this study, a polyhedral mesh was generated using a partitioned meshing approach, as shown in Figure 2. The external domain was uniformly divided into volume meshes, while mesh refinement was applied to the walls, PV panels, and the space inside the main transformer room. To achieve more precise calculations of ventilation airflow within the main transformer room and to better capture the details of the internal flow field, two body of influence (BOI) regions were defined to enclose the target area, as shown in Figure 3. BOI.1 employed a finer mesh to improve the accuracy of wind field calculations on the windward side of the building and in recirculation zones. BOI.2 utilized the finest mesh to ensure high-accuracy wind field calculations inside the main transformer room. After conducting a mesh independence test and analyzing the trends in pressure and velocity distribution, a grid system with 3.4 million cells was selected for this study, considering both computational accuracy and cost.  
Figure 2: Diagram of meshing (sectional view).
# Business Models for the Internet of Services: State of the Art and Research Agenda
## 6 Research Agenda
### Exploring Business Models Meta-Components for IoS Adoption  
Future research could investigate the development of business models by organizations for an effective IoS adoption. Figure 2 shows the four sub-areas of IoS proposed in the presented study (infrastructure, service operation, business applications and social applications), interlocking with the business model's four meta-components (value proposition, value creation, value delivery and value capture) [20]. The intersection is the IoS-based business model, which can be better explored in future research.  
The widely accepted Business Model Canvas [24], which covers the four areas placed in the second circle of Figure 2, is rarely associated with the IoS. In the present review, considering the papers that explore business or social applications, only [PERSON] et al. [36] (P8) presented a Business Model Canvas which is an IoT-influenced Smart Home business model. The main goal of their research was to check how the raising number of internet-connected devices influence users.  
Figure 2: IoS-based business model.  
A service ecosystems perspective on business models proposes that \"service marketing strategy be less focused on traditional marketing and instead work on the relationship and conversation among the involved stakeholders. A collaborative service-oriented approach has to be embraced to actively explore the rich strategic implication from a service ecosystem\" [8]. Consequently, studies that explore how the enterprises are innovating in their business models' value proposition, creation, and delivery for a service ecosystem, allow the expansion of this theme.  
Considering the social applications emerging as an IoS domain, another possible future study is the elaboration of research about the social motivations behind a business model innovation. Moreover, analyzing what the customers want and need becomes crucial to develop an IoS-based business model that meets the requirements for a best service delivery. Companies should offer services and solutions that more effectively address their customers' needs [67], always considering their data privacy and security to avoid creating management and governance problems.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 4 System Model and Security Model of UCP-WABE
### Security Model of UCP-WABE  
The security model of UCP-WABE is described by a selective security game between a challenger and an adversary. This game is borrowed from CP-WABE [16]. The phases of the game are the following:  
**Init:** The adversary declares the challenge weighted access policy \(\Gamma^{*}\) that he/she will try to attack and sends \(\Gamma^{*}\) to the challenger.  
**Setup:** Here, the challenger calls the **Setup** algorithm to generate and send the public parameters to the adversary.  
**Phase 1:** In this phase, the adversary can adaptively query for secret keys for the weighted attribute sets \(\mathbb{S}_{q1},\mathbb{S}_{q2},\cdots\) to the challenger. The challenge weighted access policy \(\Gamma^{*}\) must not be satisfied by any one of the queried attribute sets. The challenger responds by running the **KeyGen** algorithm to generate the corresponding secret keys \(SK_{q1},SK_{q2},\cdots\).  
**Challenge:** The adversary submits two messages of the same length \(m_{0},m_{1}\). Then, the adversary makes a weighted access policy \(\Gamma^{\prime}\) that there is a mutual access sub-policy between \(\Gamma^{*}\) and \(\Gamma^{\prime}\). \(\Gamma^{\prime}\) is sent to the challenger, as well. The challenger flips a random coin \(b\leftarrow^{R}\{0,1\}\) and encrypts \(m_{b}\) under \(\Gamma^{*}\). The ciphertext \(CT^{*}\) is given to the adversary.  
**Phase 2:** This phase is the same as **Phase 1**. The adversary asks for more secret keys, and the same restriction is that \(\Gamma^{*}\) must not be satisfied by any one of the queried attribute sets.  
**Guess:** The adversary outputs his/her guess \(b^{\prime}\) on \(b\).  
**Definition 1**.: _A UCP-WABE scheme is selectively secure if all polynomial time adversaries have at most a negligible advantage in the above security game, where the advantage of an adversary is defined as \(Adv=Pr[b^{\prime}=b]-(1/2)\)[32]._  
According to this definition, if we prove that there is no such polynomial time adversary who has a non-negligible advantage in the above security game, we can prove that UCP-WABE is selectively secure. The details of the proof will be described in Section 6.1.
## 2 Composition and Pressure Measurement Principle of the Compact Pressure Measurement Catheter
For flowing liquids, the pressure experienced by the sensor is as follows:  
\[P_{total}=P+q-\Delta P, \tag{2}\]  
where \(P_{total}\) is the total pressure experienced by the sensor, \(q\) is the dynamically varying pressure part of the pressure felt by the sensor, and \(\Delta P\) is the pressure loss caused by the fluid passing through the catheter, calculated as follows:  
\[q=\frac{1}{2}\rho v^{2}, \tag{3}\]  
where \(v\) is the velocity of the liquid flow, determined by the rate of dynamically varying pressure change at the measurement point. At this time, the pressure at the measurement point is as follows:  
\[P_{0}=P+q-\rho gh. \tag{4}\]  
When \(h\) is known or could be ignored, the difference between the pressure at the measurement point and the sensor pressure can be approximately equal to the pressure loss \(\Delta P\), which can be derived using the Darcy formula:  
\[\Delta P=\frac{\lambda L\rho v^{2}}{2D}, \tag{5}\]  
where \(\lambda\) is the friction factor, \(L\) is the length of the catheter, and \(D\) is the diameter of the catheter. Under the same conditions, the friction factor \(\lambda\) varies with different catheter materials, diameters, and lengths, and is related to the Reynolds number and the roughness and hydrophobicity of the catheter walls. The friction factor for laminar flow can be approximated using the Reynolds number:  
\[Re=\frac{\rho vD}{\mu}, \tag{6}\]  
where \(\mu\) represents the dynamic viscosity of the fluid. Substituting into Equation (5) gives the dynamically varying pressure loss model:  
\[\Delta P=\frac{32\cdot\mu vL}{D^{2}}. \tag{7}\]  
From the above formula, the theoretical analysis results can be obtained:Through the dynamically varying pressure loss model, we can analyze the relationship between dynamically varying pressure loss, \(\Delta P\), and the structure of the catheter; increasing the length, \(L\), of the catheter or reducing its diameter, \(D\), will both lead to an increase in dynamically varying pressure loss, \(\Delta P\). To reduce the dynamically varying pressure loss, \(\Delta P\), this paper utilizes a compact structure for the main body of the pressure measurement catheter: reducing the length \(L\) of the catheter as much as possible while still meeting usage conditions and increasing the diameter \(D\) of the catheter as much as possible under the same conditions. Therefore, the sensor can obtain a good dynamically varying pressure signal from the in-body measurement point with minimized dynamically varying pressure loss.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 5 Deep Reinforcement Learning--Opportunistic Routing
### State Space  
At time step \(t\), the state of the agent is denoted by \(s_{t}\), while the packet that needs to be transmitted is considered to be an agent. So, the state \(s_{t}\) depends on the location of the agent. If the vehicle \(\dot{\theta}_{i}\) holds the packet, namely the agent is located in vehicle \(\dot{\theta}_{i}\), the current state can be defined as:  
\[s_{t}=\{\dot{\theta}_{i}\cup\mathcal{C}_{i}\}, \tag{8}\]  
where \(\mathcal{C}_{i}\) is the Candidate Forwarding Nodes (CFNs) set of vehicle \(\dot{\theta}_{i}\), satisfying \(\mathcal{C}_{i}\subseteq\mathcal{M}_{i}\). Next, we will discuss how to construct a CFNs set. According to the idea of opportunistic routing, multiple neighbor nodes of the sender have received the packet simultaneously. To raise the utilization of the resources as well as to avoid broadcast storms, only some neighbor nodes are selected to forward the packet. The selected neighbor nodes are the CFNs, which form a CFNs set.  
To construct a fast and reliable routing, a CFNs set is formed by considering the three metrics: relative velocity, packet forwarding ratio, and connectivity probability. Specifically, a sender \(\dot{\theta}_{i}\) needs to construct a CFNs set \(\dot{\theta}_{i}\). If a node \(\dot{\theta}_{j}\in\mathcal{C}_{i}\) meets the following three conditions [50], the node is added to the set \(\mathcal{C}_{i}\). _Condition 1_: \(0\leq\
u_{i,j}\leq 200\); _Condition 2_:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Components** & **System** \\ \hline Learning environment & VANETs \\ \hline Agent & Each packet that needs to be transmitted \\ \hline State of the agent & Union of the Node-HP and its CFNs set \\ \hline Action & Selection of a relay node \\ \hline \hline \end{tabular}
\end{table}
Table 2: The basic components of the DRL model.  
Figure 4: An \(m\) hops path from a source node to its destination.  
\(P_{j}\geq 0.63\); Condition 3: \(PFR_{j}>0.4\). To this end, the process that the \(\theta_{j}\) is added to the set \(\mathcal{C}_{i}\) can be expressed as:  
\[\mathcal{C}_{i}\longleftarrow\mathcal{C}_{i}\cup\big{\{}\theta_{j}|v_{ij}\in[ 0200]\text{ and }P_{j}\geq 0.63\text{ and }PFR_{j}>0.4\big{\}}. \tag{9}\]
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Optimization Criteria
#### 4.3.5 Pollutant Concentration  
The last criterion guides the aircraft toward areas with the highest pollution while optimizing the positioning of the measurement path for pollution sampling. Since the precise shape of the polluted region is not known a priori and only becomes apparent after UAV-based measurements, the measurement area's shape cannot be optimized directly. Instead, it must be provided externally, for instance, by a human expert. Consequently, the task is simplified to determining the optimal placement of the measurement polygon \(\triangle_{m}\) specified as follows:  
\[\triangle_{m}=\big{\{}(x_{i},y_{i},z)\big{\}},i=3,4,\ldots,N. \tag{23}\]  
In this context, \(x_{i}\) and \(y_{i}\) represent the horizontal coordinates of consecutive vertices of the polygon, while \(z\) is the common vertical coordinate for all vertices. The geometric centroid of \(\triangle_{m}\) is denoted by \(\mathbf{w}^{m}\). The criterion assigns a cost proportional to the pollutant concentration observed at \(\mathbf{w}^{m}\). Mathematically, it can be defined as follows:  
\[c_{p}=s_{fp}\,\texttt{pollution}\,(\mathbf{w}^{m},\mathcal{M}_{m}), \tag{24}\]  
where \(s_{fp}\) acts as a scaling factor, \(\mathcal{M}_{m}\) denotes the measurement model, and \(\texttt{pollution}\,(\cdot)\) is an operator returning the pollutant concentration value at \(\mathbf{w}^{m}\). The sign of \(s_{fp}\) must be selected based on the implementation of \(\mathcal{M}m\) to ensure a positive cost function. For instance, if \(\texttt{pollution}\,(\cdot)\) outputs positive values, \(s_{fp}\) should also be positive.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Parallel Model Training and HPC Integration
#### 2.4.3 GPU-Accelerated Deep Learning  
The computationally most intensive tasks in the pipeline are the training of the Bi-LSTM and f-LSTM neural networks. We leveraged TensorFlow with CUDA to offload these operations to the GPUs. Both GPUs were utilized: we split the training load of a single model across the two GPUs using TensorFlow's built-in multi-device strategy. We enabled mixed-precision training to reduce memory usage and increase throughput on the GPU, wherein less critical operations use 16-bit floating point precision and the rest use 32-bit [20]. This technique is known to accelerate training on GPUs while maintaining model accuracy, especially on hardware with limited memory. In our case, mixed precision allowed the 4 GB GPUs to handle relatively large batch sizes and sequence lengths without running out of memory, and it provided a modest speed boost due to the reduced data transfer and computation overhead on the GPU cores [20]. We also used TensorFlow's GPU parallelism for batch processing: each training epoch processed batches of data in parallel on the GPU. The result is that model training times that would have been on the order of hours on CPU were brought down to minutes on GPU, enabling us to iterate quickly and even retrain models in near real-time as new data arrived.
## 4 Experimental Results
### Performance Comparison
#### 4.2.1 Experiment 1: Impact of Interaction Operation
Wilcoxon signed-rank test between ICAAI and PSO shows that ICAAI statistically significantly outperforms PSO on benchmark functions \(f_{3}\), \(f_{6}\) and \(f_{9}\), and that PSO statistically significantly outperforms ICAAI on benchmark functions \(f_{1},f_{2},f_{5},f_{7},f_{8}\) and \(f_{13}\). Similarly, [PERSON] signed-rank test between ICACI and PSO shows that ICACI statistically significantly outperforms PSO also on benchmark functions \(f_{3}\), \(f_{6}\) and \(f_{9}\), and that PSO statistically significantly outperforms ICACI on benchmark functions \(f_{1},f_{2},f_{4},f_{5},f_{7},f_{8},f_{10},f_{12}\) and \(f_{13}\). Thus, PSO appears to be better than both ICAAI and ICACI, according to Wilcoxon signed-rank test. However, later in this section, we show that ICAAI becomes competitive to PSO after 3000 generations, according to Wilcoxon signed-rank test. Experiment using [PERSON]'s ICA [28] has also been conducted, but its performance results are much poorer than the four methods discussed herein and thus are not included in Table 2. [PERSON] signed-rank test shows that both ICAAI and ICACI statistically significantly outperform [PERSON]'s ICA on all benchmark functions. To see the possibility of further converging to a better solution, the same experiment is extended from 1000 to 3000 generations, and the results are shown in Table 3. According to Wilcoxon signed-rank test, ICACI statistically significantly outperforms the Perturbed ICA on benchmark functions \(f_{1},f_{7},f_{8}\) and \(f_{12}\), and the Perturbed ICA statistically significantly outperforms ICACI only on benchmark function \(f_{3}\). ICAAI remains more effective than the Perturbed ICA after 3000 generations. According to [PERSON] signed-rank test, ICAAI statistically significantly outperforms the Perturbed