esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 7 Conclusions  
This work proves that realistic Fleet Management Problem is an NP-hard problem, Apart from suggesting that exact and optimal solutions may not be realizable, it paves way to the application of heuristic and metaheuristic algorithms. This work proposed a novel methodology in optimizing fleet placement in station-based round-trip carsharing and suggests how such problems can be modeled. It is among the first to model the problem of fleet placement in carsharing and to apply a state-of-the-art optimization algorithm in attempting to determine satisfactory solutions. A set of heuristic, metaheuristic (NSGA-II), and exact (multi-criteria solver) algorithms have been applied and their performance evaluated on three instances with two objectives, i.e.,maximizing the number of carsharing users and minimizing the maximum global walking-to-the-car distance, under consideration.  
Figure 13: A solution that yields a low global walking distance, but also yields low user coverage.  
This work is also the first to use real and exact instances (instead of just an abstract) for the study. Three different instances have been used, and each has its own characteristics with different sizes and objectives to reflect real world demand. The proposed method demonstrates that NSGA-II is superior to the manual allocation by a significant margin in user coverage and in terms of approximated Pareto front, and presents a number of solutions for decision makers to choose from. Solutions from our proposed method are also more efficient in terms of both user coverage and walking distance. While a metaheuristic approach has received much attention lately, this works affirms its application in transportation and Fleet Management Problem, in particular. The model proposed ought to be a good starting point in solving similar problems for further research among transportation and logistic communities.  
Future work could apply the proposed approach to other cities such as London, Athens, and Paris. Additional objectives such as car fleet utilization, car fleet size, and the number of stations may also be included, since these are also real concerns after the initial launch of the carsharing business. A tailor-made metaheuristic algorithm may also be invented with the Fleet Management problem in mind too.  
Conceptualization, B.C., D.K. and P.B.; methodology, B.C., [PERSON], M.B. and F.G.; software, B.C.; validation, B.C., J.M. and K.L.; formal analysis, B.C., F.G. and J.M.; investigation, B.C. and D.K.; resources, D.K. and P.B.; data curation, B.C.; writing--original draft preparation, B.C., G.D. and D.K.; writing--review and editing, B.C. and K.L.; visualization, B.C.; supervision, P.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 10 Home Automation System Based on 6 LoWPAN
### Review of the 6 LoWPAN Protocol Stack  
Figure 13 shows the 6 LoWPAN protocol stack. According to the figure in Layers 1 and 2, MAC and PHY transmit the frames to the neighbors of the single hub. In Layer 3, it is necessary to fragment IPv6 packets and finally compress them; the reason for this problem is the limitation of 6 LoWPAN protocol transmission (127 MTU). On Layer 4, 6 LoWPAN neighbor discovery (6 LoWPAN-ND) [110] disseminates context information for compressing arbitrary IPv6 network prefixes. Apart from this, 6 LoWPAN-ND is a multi-hop version of IPv6 neighbor discovery and IPv6 stateless auto-configuration. Also, in Layer 4, the IPv6 Routing Protocol for Low-Power and lossy Networks (RPL) [111] routes IPv6 packets. On Layer 6, User Datagram Protocol (UDP)-based protocols, such as the Constrained Application Protocol (CoAP), are commonly employed.  
Figure 12: 6 LoWPAN network model.
## 6 Numerical Results
### Discussion and Future Research: Computing Optimal Policy  
It is important to recognize that assigning uniform weights within the reward function does not necessarily ensure the maximization of all three principles simultaneously, as conflicts may arise among them. For instance, certain scenarios may preclude the possibility of optimizing resource utilization while achieving equitable resource distribution across slices. The dynamics of their interplay warrant closer examination in future research.  
Beyond the brute force approach and [PERSON]'s iterative algorithm, alternative methods exist for finding solutions, such as neural networks. Additional techniques include linear and nonlinear programming, the asymptotic optimization method, and dynamic programming. A comparative analysis of these methods against the iterative algorithm could be beneficial in discerning their respective strengths and weaknesses.  
The solutions derived with the MDP are inherently contingent on the initial parameters. An area of ongoing investigation is the robustness of these solutions to variations in the initial data. This could involve identifying parameter ranges associated with specific albeit not necessarily optimal policies that remain satisfactory.  
Furthermore, future studies will aim to broaden the numerical analysis by exploring the determination of optimal policies using neural networks. This strategy would entail using the system's current state as input to generate the policy as output. When employing neural networks, it is crucial to consider computational demands and model intricacy. Selecting an appropriate optimizer and compiling a suitable training dataset may present challenges. Nevertheless, it is hypothesized that neural networks could yield swifter solutions for extensive datasets compared with iterative methods.
# Numerical Analysis via Mixed Inverse Hydrodynamic
## 2 Mathematical Model
### Fluid Mechanics
#### 2.2.3. Greenwood-Williamson Contact Model  
The contact force of the asperities in the contact between two rough surfaces can be easily calculated using the Greenwood-Williamson (G-W) model [20]. In the G-W model, the contact between two rough surfaces is equivalent to that of a substantially rough elastic surface and a rigid smooth surface. This rough surface is once again equivalent to the contact of a hard plate with an elastic surface composed of projections with the same radius of curvature \(R\). At this time, it is assumed that the height of the asperity follows a Gaussian distribution.  
\[\varphi(u)=\frac{1}{\sqrt{2\tau}}e^{-\frac{1}{2}u^{2}},u=\frac{z}{\sigma} \tag{13}\]  
where \(z\) is the height of the rough surface and \(\sigma\) is the standard deviation of the asperity height. Let the gap between the two surfaces be \(d\), and the dimensionless value be \(u_{h}=d/\sigma\). The force due to the contact of the asperities is calculated as follows, by applying Hertzian contact theory to the statistical equation:  
\[P_{c}=\frac{4}{3}E^{\prime}\eta R^{\frac{1}{2}}\sigma^{\frac{3}{2}}\int_{u_{h }}^{\infty}(u-u_{h})^{\frac{3}{2}}\varphi(u)du \tag{14}\]  
where \(E^{\prime}=\left(1-\
u_{1}^{2}\right)/E_{1}+\left(1-\
u_{2}^{2}\right)/E_{2}\) is the equivalent elastic modulus and \(E_{1}\), \(E_{2}\) and \(\
u_{1}\), \(\
u_{2}\) are the elastic modulus and Poisson's ratio of the two objects in relative contact, respectively. Here, \(\eta\) is the asperity density, and the projection radius \(R\) and projection density \(\eta\) can be expressed as a function of \(\sigma\) using the dimensionless values \(\beta\) and \(\gamma\)[21].  
\[R=\beta\sigma,\ \ \eta=\frac{\gamma}{\sigma^{2}} \tag{15}\]  
The oil film distribution calculated through lubrication analysis corresponds to the gap \(d\) at each position and the bearing force by the asperity contact at each position can be calculated according to Equation (14).
## 2 Materials and Methods
### Set-Up of Experiments
#### 2.4.5 System Specifications  
System specifications utilized throughout experiments were documented explicitly. Experiments were executed on an Ubuntu 24.04.2 LTS x86_64 operating system with kernel version 6.8.0-55-generic. Hardware configuration included an Intel Corp., Santa Clara, CA, USA i7-7700 CPU with eight logical cores at 4.2 GHz, 64 GB of RAM and an NVIDIA Corp., Santa Clara, CA, USA GeForce GTX 1080 GPU with 8 GB of VRAM memory, providing ample computational resources. Such explicit documentation ensured consistency and reproducibility across trials, facilitating transparent and reliable experimental outcomes. The experimental setup involved running a total of 25 epochs, with a batch size of 32 and a fixed learning rate (no scheduler) of 0.0001. The dataset comprised 20,000 samples, divided into a training and test (folded) set with samples distributed across batches. No early stopping technique was employed during training, ensuring that all 25 epochs were executed regardless of intermediate performance. Given the computing resources and the duration of the experiment, the cost implications can be significant, especially when considering the number of epochs and the extensive size of the dataset. The utilization of such advanced computational resources underlines the complexity and computational intensity of the experiments conducted.  
Figure 4: Concurrent pipeline for data ingestion followed by device-based processes. Real-time metrics capturing occurs at 0.03 s intervals, and then training threads terminate and metrics are stored.  
Figure 3: Sequential workflow focusing on image datasets, including training followed by validation where model metrics are computed, and then final results are saved.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Cobalt-Doped Manganese Oxide Films  
The cobalt-doped manganese oxide films (Co:Mn\({}_{3}\)O\({}_{4}\)) were deposited on soda lime glass substrates [166] at room temperature by using manganese (II) chloride tetrahydrate (MnCl\({}_{2}\).4H\({}_{2}\)O), ammonium hydroxide, and cobalt(II) chloride tetrahydrate (Cl\({}_{2}\)CoH\({}_{8}\)O\({}_{4}\)). The XRD results highlight that the most intense orientation changed from the (002) to the (211) plane with increasing the cobalt concentration from 0.5% to 3%.
# HD-Tree: An Efficient High-Dimensional Virtual Index Structure Using a Half Decomposition Strategy
## 1 Introduction
Although they can maintain the balance of a tree, it will consume a lot of time and space, and thus reduce the efficiency of frequent data manipulation. The spatial indexes divided by data are mainly presented as R-tree [13] and its variants, including R\({}^{*}\)-tree [14], R\({}^{+}\)-tree [15], TPR\({}^{*}\)-tree [16], RTR-tree [17], etc. These trees represent spatial objects by using the minimum bounding rectangle. The data overhead might be greater than the spatial objects themselves. Although many improved trees have solved the overlapping to some extent, the overlapping in high-dimensional space is still very serious, and thus lower the indexing efficiency. Several other methods can combine grid indexes with other indexes, for example, the combination of quadtrees [18] and hash tables. One of the most advanced methods is the decomposition tree (D-tree) [19]. D-tree adopts the principle of spatial partition and integer encoding shift operations to determine the range of multidimensional space. It does not need to store the spatial range of the node and the keywords of the child nodes, and thus no additional disk access is required when looking for leaf nodes. As a result that D-tree itself is a discrete linear structure without inner nodes, it is easy to implement parallel access and can perform excellently. However, since D-tree adopts the full decomposition strategy to allocate all spatial objects to child nodes on the principle of spatial partition, more child nodes are decomposed, which makes range queries require more disk access. In addition, when the spatial node and its upper node do not exist, a large number of lookups to the hash table are required and it will reduce efficiency sharply. In view of these shortcomings, in this paper, we proposed a high-dimensional spatial index based on D-tree--the half decomposition tree (HD-Tree). HD-tree's decomposition will generate a new node containing more spatial objects from an original node, which allows it to retain the original and new nodes based on the structure of the virtual tree. Therefore it can reduce space consumption and improve time efficiency. The experimental comparison results show that under normal circumstances, the space, time and I/O times of the HD-tree are less than those of D-tree, especially when the data are unevenly distributed. The rest of this paper is organized as follows: in the next section, we will review the decomposition strategy and indexing structure of D-tree. In Section 3, we describe the half decomposition strategy, structure and algorithms of HD-tree. In Section 4, we describe the experimental results and analysis. Finally, in Section 5, we will summarize.
## 3 Methodology  
### Artificial Neural Network  
An artificial neural network (ANN) is regarded as one of the machine learning methods which can be used to model or predict linear and nonlinear parameters, such as the ionosphere parameters. This method is less influenced by noise-infected data and has the ability to use noisy data for large amounts of observations [43]. By processing the observations entered as experimental data, the ANN detects the relationship or the law between them and transfers it to the network. Each ANN consists of a number of nodes, which are neurons, and the communication weights that connect the nodes together [44]. The input data are multiplied by their corresponding weights, and their sum is entered into neurons. Each neuron has a transfer function, which passes this input value and specifies the output value of the neuron. Then, the obtained values of neurons, which create an initial response by combining together, enter a process known as back-propagation and begin to modify the primary weights by comparing the error rate and calculating the impact of each entry in all layers. Reaching the least error rate between the output data of the model and the target data entered as input into the model is the stop condition of the process [45].
# Application of Internet of Things in a Kitchen Fire Prevention System
## 1 Introduction  
### Background  
When a fire breaks out, it can often cause major loss of life and damage to property, due to the spread of the fire. Early detection of fire sources and effective countermeasures have become important issues in fire prevention and protection of life and property [1]. As building fires tend to increase the number of casualties and property damage, it is necessary to take appropriate action accordingly. There has been an increasing effort to develop such disaster management systems worldwide by applying information communication technology (ICT), and many studies have been conducted in practice. The core idea behind the smart city is to collaborate and integrate the information from urban areas and utilize ICT, which includes cloud computing, Internet of Things and so on. The main characteristics of smart city are scalability, interoperability, limited power consumption, fast deployment, multi-modalaccess and robustness [2; 3]. The need for a basic unified architecture for smart city based applications is not new, but a proper solution that can map real world smart city applications to the standard architecture is not currently available [4]. The smart building is also an important part of the smart city. The gas stove is a type of cooking device used in most households in Taiwan. Compared with an electric stove, it saves electricity and can cook food rapidly because it produces large flames. However, users or older adults may forget they are cooking or forget to turn off the stove because they are busy or have memory problems. Double-income couples may be unsure whether they have turned off the stove and therefore feel compelled to, during their commute to work, return home and check. These problems affect numerous households.  
Business Insider, a British magazine, commissioned an online survey of 14,300 expats who represented 174 nationalities and lived in 191 countries to rate various aspects of life, including leisure options, personal happiness, travel, transport, health and well-being, and safety and security. The company published the ranking of top 21 countries with the best quality of life in January 30, 2017, and Taiwan was ranked first because of a comprehensive medical care system and affordable price level [5]. Among the aspects measured by the assessment, personal happiness, travel, and leisure options are related to food in Taiwan. Taiwanese people prefer to cook over a large flame and high heat; however, the oil in woks may catch fire because of the excessively high temperature that is produced, which could lead to kitchen fires. Therefore, devising a method to automatically shut off gas when a gas stove catches on fire is of critical importance to home safety.
## 1 Introduction
Furthermore, CCD and BBD based on RSM is an effective statistical technique for enhancing the transesterification technique that could maximize biodiesel conversion [49]. Furthermore, CCD requires more experimental trials and more computation effort, time, and cost than BBD [35; 42; 48; 50]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feedstock** & **Catalyst** & **Process Parameters** & **Optimization** & **Major Results** & **Ref.** \\ \hline Castor oil & KOH & Rt: 30-150 min, CC: 0.5-2 wt.\%, & CCD and RSM & 94.9\% yield & [51] \\ Musa acuminata & CRBP & CC: 1.5-3.5 wt.\%, OMMR: 6–14, & CCD and RSM & 98.73\% yield & [41] \\ peduncle & & Rt 40-120 min & & & \\ Waste cooking oil & NaOH & OMV: 10–1.1, Rt 10-60 s, & & & \\  & & MP: 100–400 w & & & \\ Rubber seed oil & RSS & Rt: 60–70 min, CC: 2.5-3.5 g, & & & \\  & & OMMR: 0.2–0.3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Biodiesel production via transesterification process utilizing various feedstocks. Transesterification parameters (M:O, ripe plantain fruit peel catalyst, reaction temperature, and time) that were optimized via an artificial neural network integrated with a genetic algorithm (ANN-GA) technique resulted in a maximized yield of 99.2% [57]. GA was applied to convert the microalgae oil to biodiesel yield (98.12%) subjected to optimizing the transesterification parameters (Rt, RT, and M:O) [58]. ANN models predicted the thermal conductivity of Al\({}_{2}\)O\({}_{3}\)-Cu/EG and nano-antifreeze applications [59; 60]. ANN performed better than the RSM model in predicting the yield of soybean biodiesel [61], palm kernel oil biodiesel [62], sesame oil biodiesel [63], and so on. The reason for better performance by artificial intelligence tools than RSM is summarized as follows [64; 65]: (a) Experimental data specified as per the predefined design matrix by RSM result in poor generalizability for unknown factors, and the model does not perform global optimization. RSM does not have good generalizability for unknowns and the model cannot conduct global optimization, as experimental data are defined according to the initial design matrix. (b) AI tools predict unknown data from a known set of data due to better generalizability with the essence of intelligence that establishes a system capable of responding identically to those performed by human intelligence. (c) Traditional RSM uses a deterministic search procedure with predefined rules, wherein deriving optimal solutions for multi-modal input behavior may result in local solutions. The aforementioned reasons made artificial intelligence tools outperform RSM-based optimization techniques in distinguished applications of the squeeze casting process [64], turning process [66], abrasive water jet machining [67],
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 3 Results and Discussion
### Evolution of Melt Pool Characteristics
#### 3.1.3 Evolution and Remelting of Melt Pool
(**b**) The contraction and breakage of the melt end during solidification led to the formation of balling (SED = 75 Jmm\({}^{-2}\)) (Supplementary Video ESM_2). (**c**) At a high energy density (SED = 100 Jmm\({}^{-2}\)), remelting occurred between adjacent melt tracks (Supplementary Video ESM_13). (**d**) The accumulation of melt at the end of the melt track (Supplementary Video ESM_14). All images are at a scale of 100 \(\upmu\)m.  
extend outward but instead accumulated, forming noticeable bulges at both edges of the sample perpendicular to the scanning direction (Figure 6d). The spherical particle protrusions formed by the splashing of powder or droplet spattered due to melt pool oscillation onto the already-formed surface became the main influencing factors of surface quality at this stage. The results indicate that the melt pool area, energy density, and surface quality were all directly proportional to each other. Under high-energy-density conditions, the area of the heated powder increased, leading to an increase in the length, width, and depth of the melt pool, yet the keyhole phenomenon was less frequently observed. The results of the surface roughness analysis in the subsequent Section 3.2.3 also indicate that the surface quality of the samples was good under a high energy density; thus, the relationship between the keyhole phenomenon and process parameters requires further investigation.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 2 Related Work
The Bubble Rap algorithm [23] is a representative community-division-based routing algorithm. The algorithm uses the encounter law between nodes to divide the communities, and designs two different mechanisms for forwarding messages within and between communities. The authors of [24] proposed a technique to detect the selfishness of nodes based on their historical message forwarding and discarding behaviors, and a novel credit-based scheme to motivate nodes to cooperate in forwarding messages. The authors of [25] extracted the key factors affecting friendships, including contact frequency, contact time, and regularity of contact through the analysis of social networks, and friendships in turn include direct friendships and indirect friendships, thus constructing an analytical model to evaluate the closeness of friendships. The authors of [26] predicted the mobile probability based on the social relationships and forwarding partnerships among mobile users, using a hybrid relationship matrix decomposition to predict the mobile encounter probability of users. The authors of [27] introduced a new metric, social energy, to quantify the ability of a node to forward packets to others. Social energy is generated through the encounter of nodes, shared by the community of encountering nodes, and decays over time. The authors of [28] designed a novel opportunity network mobility model for node community hierarchy to deeply evaluate the similarity and difference of nodes at different levels in the community and to implement relay node selection and destination node difference analysis in different community hierarchies. The authors of [29] combined location-based forwarding with contact-based forwarding, relying on packets based on predictable location and contact patterns. The various auxiliary information types of algorithms described above require each node to maintain real-time information from other nodes, so when the network size increases, the nodes lack the adaptive adjustment capability to reduce this part of the overhead.