esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In diesel engines, fuel mixing is an important process in determining the combustion efficiency and emissions level. One of the measures used to achieve fuel mixing is controlling the nature and behavior of the fuel spray by shaping the injection rate. The mechanism underlying the behavior of the spray with varying injection rates before the start of combustion is not fully understood. Therefore, in this research, the fuel injection rate shape is investigated to assess the spraying and mixing behavior. Diesel sprays with different ambient temperatures and injection pressures are modeled using the CONVERGE-CFD software. The validation is performed based on experimental data from an Engine Combustion Network (ECN). The verified models are then used to analyze the characteristics of the diesel spray before and after the end-of-injection (EOI) with four fuel injection rate shapes, including a rectangular injection rate shape (RECT), a quick increase gradual decrease injection rate shape (QIGD), a gradual increase gradual decrease injection rate shape (GIGD), and a gradual increase quick decrease injection rate shape (GIQD). The spray vapor penetrations, liquid lengths, evaporation ratios, Sauter mean diameter (SMDs), distributions of turbulence kinetic energy, temperatures, and equivalence ratios were compared under different injection rate shapes. The results show that the QIGD injection rate shape can enhance mixing during injection, while the GIQD injection rate shape can achieve better mixing after the EOI.  
diesel spray; spray mixing; varying injection rate; numerical simulation +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Biomass Concentration of Yeasts After Cultivation in Banana Peel Hydrolysates
This difference could be due to the different methods of preparing the hydrolysates and the breeding conditions (including strain and time). The biomass yield obtained, quantified as grams of dry yeast mass per gram of sugars present in the medium, ranged from 0.36 to 0.43 g DM/g. Comparable values have been recorded by [PERSON] et al. [76], who reported a yield of 0.52 g/g for _M. guillermondii_. For the _S. stipitis_ strain cultivated in brewer's spent grain hydrolysate, [PERSON] et al. [77] reported a Yx/s of 0.46 g/g. The sugar-to-biomass conversion results obtained in this study are economically promising and surpass those reported by [PERSON] et al. [78], who observed the conversion ratios of 0.08-0.17 g/g for glucose and 0.13-0.19 g/g for arabinose after 60 h of cultivation of _M. guillermondii_ in bagasse hydrolysate. [PERSON] et al. [64] reported a conversion ratio of 0.17 g/g for _S. cerevisiae_ and 0.55 g/g for S. _stipitis_ in aerobic batch cultivation with 20 g/L glucose. As sugars are also used for energy production and various metabolic by-products, yeast technologists consider the Yx/s value of 0.5 g/g very high [79].
# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
###### Abstract  
Recommender systems aim to forecast users' rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user's past purchase behavior consists of the user's feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology.  
cold-start problem; sparsity; recommender systems; discriminant frequent patterns +
Footnote †: journal: 2023, 16, 182. [[https://doi.org/10.3390/a16040182](https://doi.org/10.3390/a16040182)]([https://doi.org/10.3390/a16040182](https://doi.org/10.3390/a16040182))
Sensors_**2011**, _11_, 2056-2076; doi:10.3390/s110202056  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Design and Implementation of A CMOS Light Pulse Receiver Cell Array for Spatial Optical Communications  
**[PERSON]. [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2,**  
**[PERSON] 2, [PERSON] 1 and [PERSON] 1,* 1**  
Research Institute of Electronics, Shizuoka University, 3-5-1 Johoku, Nakaku, Hamamatsu, Shizuoka 432-8011, Japan;  
E-Mails: [EMAIL_ADDRESS] (M.S.Z.S.); [EMAIL_ADDRESS] (S.I.);  
[EMAIL_ADDRESS] (M.H.); [EMAIL_ADDRESS] (K.Y.)  
Toyota Central R&D Labs., Inc. 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan;  
E-Mails: [EMAIL_ADDRESS] (I.T.); [EMAIL_ADDRESS] (M.A.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-53-478-1342; Fax: +81-53-412-5481.  
_Received: 4 January 2011 / Accepted: 6 February 2011 / Published: 10 February 2011_  
**Abstract:** A CMOS light pulse receiver (LPR) cell for spatial optical communications is designed and evaluated by device simulations and a prototype chip implementation. The LPR cell consists of a pinned photodiode and four transistors. It works under sub-threshold region of a MOS transistor and the source terminal voltage which responds to the logarithm of the photo current are read out with a source follower circuit. For finding the position of the light spot on the focal plane, an image pixel array is embedded on the same plane of the LPR cell array. A prototype chip with 640 \(\times\) 240 image pixels and 640 \(\times\) 240 LPR cells is implemented with 0.18 \(\upmu\)m CMOS technology. A proposed model of the transient response of the LPR cell agrees with the result of the device simulations and measurements. Both imaging at 60 fps and optical communication at the carrier frequency of 1 MHz are successfully performed. The measured signal amplitude and the calculation results of photocurrents show that the spatial optical communication up to 100 m is feasible using a 10 \(\times\)10 LED array.  
**Keywords:** spatial optical communication; CMOS image sensor; LED; light pulse receiver
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
###### Abstract  
The location of user equipments (UEs) allows application developers to customize the services for users to perceive an enhanced experience. In addition, this UE location enables network operators to develop location-aware solutions to optimize network resource management. Moreover, the combination of location-aware approaches and new network features introduced by 5G enables to further improve the network performance. In this sense, dual connectivity (DC) allows users to simultaneously communicate with two nodes. The basic strategy proposed by 3 GPP to select these nodes is based only on the power received by the users. However, the network performance could be enhanced if an alternative methodology is proposed to make this decision. This paper proposes, instead of power-based selection, to choose the nodes that provide the highest quality of experience (QoE) to the user. With this purpose, the proposed system uses the UE location as well as multiple network metrics as inputs. A dense urban scenario is assumed to test the solution in a system-level simulation tool. The results show that the optimal selection varies depending on the UE location, as well as the increase in the QoE perceived by users of different services.  
multi-radio dual connectivity; location-awareness; cellular network node management +
Footnote †: journal: Sensors
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 7 Experiment of the Interference of the Human Body
### Experiment  
The overview of the experiment is shown in Figure 17. The experimental procedure is as follows. In the same place used in the experiment shown in the previous section, an IMES transmitter is installed on the ceiling. The experimenter who holds the receiver antenna at the height of 800 mm from the ground moves from the position just below the transmitter antenna (origin of the coordinate system shown in Figure 17) to the position of \(y=4000\) mm. At the points of every 500 mm on the \(y\)-axis, the experimenter rotates around the receiver antenna with the step size of \(30^{\circ}\) and acquires the CNR for each angle for 50 epochs while standing still. The positional relation where the experimenter is between the transmitter and receiver antennas corresponds to \(0^{\circ}\) and \(360^{\circ}\), and the positional relation where the transmitter antenna, the receiver antenna and the experimenter are on the same line in this order corresponds to \(180^{\circ}\).  
The relation between the CNR and the experimenter's body angle for each measurement position is shown in Figure 18. As seen in the graph, in the case that the experimenter does not come between the transmitter and receiver antennas (around \(180^{\circ}\)), the CNR is high; on the other hand, in the case of the interference of the experimenter's body (zero or \(360^{\circ}\)), the CNR decreases. The range of CNR variation according to the experimenter's body angle is about 5-15 dB. As also can be seen in the graph, the CNR does not greatly vary around the position below the transmitter antenna, but it widely varies in the place far from the transmitter.  
Figure 17: Experimental setup for human body interference.  
Figure 18: Experimental result of human body interference.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 3 Classification of Multi-Objective Evolutionary Algorithms from the View of Search Strategy  
### Decomposition-Based MOEA Algorithms  
#### 3.1.1 Weighted Summation Approach  
The weighted sum approach is a commonly used linear multi-objective aggregation method [69]. Assuming that the multi-objective problem to be optimized has \(m\) total objectives, the function converts MOP into a single objective sub-vector by weighting each objective with a non-negative weight vector \(\lambda=(\lambda_{1}\), \(\lambda_{2}\),..., \(\lambda_{m})\). The mathematical expression is as shown in Equation (2).  
\[\min g^{\text{ns}}(x\mid\overset{\rightarrow}{\lambda})=\sum_{i=1}^{m}\lambda_ {i}f_{i}(x)\quad\text{s.t.}x\in\Omega. \tag{2}\]  
The vector \(\lambda=(\lambda_{1}\), \(\lambda_{2}\),..., \(\lambda_{m})\) is a set of weight vectors. Each weight component \(\lambda_{i}\) corresponds to the i-th target vector, respectively, for \(\lambda_{i}\geq 0\) and the sum of \(\lambda_{i}\) is 1.  
According to the mathematical expression, \(\lambda_{i}\star f_{i}\) can be regarded as the projection of point \(f_{i}\) in the direction of \(\lambda\), and finds the minimum value. From the fitness landscape analysis, some contours can be drawn which are perpendicular to \(\lambda\), so that we can see that the distance from the origin to the right angle points is the projection of \(F_{i}\) in the direction of \(\lambda\). Consequently, discovering that point \(A\) is the shortest distance point is not difficult. In the same way, the shortest distance points are found and together produce a different set of Pareto optimal vectors. Nevertheless, the weight summation method has limitations. The standard weight summation method cannot deal with non-convex problems, as shown in Figure 2. For non-convex problems, the perpendicular of each reference vector cannot be tangent to its front edge.
## 4 Results and Discussion
### Uncertainty Analysis  
Following [PERSON] and [PERSON] [57], the uncertainty in a calculated quantity \(U_{Y}\), which depends on the values of the measured quantities \(X_{i}\) with their own uncertainties \(U_{X_{i}}\), is given by:  
\[U_{Y}=\sqrt{\sum_{i}\left(\frac{\partial Y}{\partial X_{i}}\right)^{2}U_{X_{i}} ^{2}} \tag{16}\]  
The uncertainties in \(h_{i}\) calculated in this work varied from 7.1% to 13.9%.
# Engineering Vehicle Detection Based on Improved YOLOv6
## 3 Methods
### Loss Functions  
Object detection involves both classification and localization, where classification pertains to the classification loss and localization refers to the loss associated with predicting the bounding boxes. To address classification losses, YOLOv6 utilizes VariFocal Loss, which reduces the weight of negative examples and concentrates training on high-quality positive examples. For box regression losses, YOLOv6 utilizes GIoU for intersection over union (IoU) losses and distribution focal loss for probability losses [37].
## 3 Fusion of FL and Medical Domain
### Architecture of FL in Medical Domain  
Figure 4 depicts the generic architecture of FL in the medical domain, where multiple hospitals/institutes jointly train the AI model for certain medical tasks.  
The key steps in the overall process are task definition (e.g., image analysis or motion detection), client selection (hospitals or medical institutes), desired objectives (detection or classification), AI model selection, AI model parameters, etc. The model training process (local training, aggregation, and distribution of global models) is akin to the traditional FL process. Usually, the global model is trained on diverse data owned by many hospitals, and the training process stops when the desired accuracy is achieved, or loss converges. The trained AI model can be deployed in real settings for prediction/classification tasks. In FL-based medical applications, the AI model is exposed to diverse medical data, and therefore, the models are more generalizable and robust.
## 8 Future Challenges for the Enhancement of ED Limitations
### Research Challenges Related to Noise Uncertainty
Previous related works have mostly analysed the adaptation of the DT through simulations with different system parameters assumed to be unchanged in simulations. Since parameters can change over time, the assessment of new algorithms for ED must be performed in practice.
## 4 Taxonomy: Classification of GT Approaches in Network Virtualization
### Game Goals
#### 4.2.1 Goal 1: Maximize the Bandwidth Allocation
Similar to the previous research, the authors in [19] proposed a game-based framework for the provisioning and the management of the bandwidth resources in the virtualized network environment. The interaction between the SPs and the InPs is modeled as a two-stage non-cooperative game. The first stage is a non-cooperative negotiation game aiming at modeling the interaction between the InP and the SP. The second stage models the resource request from multiple SPs and a single InP. Resource negotiation game: During the game, the SP starts by reclaiming the resource from the InP; then, it chooses to lease the resource from InP or to leave and to look for another one depending on the required QoS. Thus, the InP can admit or reject the request based on the resource sufficiency. The SP has a contract with an InP, so any direction to another InP can apply a penalty cost. When the two players are satisfied, the resource is allocated to the SP. However, if both players are dissatisfied, SP can lease the resource from another InP without any charged monetary penalty. Then, in case of only InP being satisfied, the SP must pay a penalty. The last case occurs when the SP is the only satisfied player; consequently, it is redirected to another InP without any charge. The players aim at maximizing their gains by reaching the Nash equilibrium. * Node and link allocation games: two game models are proposed to fairly allocate the node and link resources for the SPs. The authors consider the SPs as a competitive player with selfish behaviors which aim at maximizing their utility function by consuming the maximum of the resources. The optimal solution for all players is achieved by the Nash equilibrium for each game. Authors in [25] proposed a hierarchical Stackelberg game model to solve the bandwidths allocation competition problem between the VNs. The data center is presented as the game leader that announces bandwidths prices for the followers (VNs), attempting to maximize their profits based on willingness-to-pay strategy. They formulate the optimization problem using the following formula:  
\[P1:Max\sum_{i=1}^{N}\sum_{l=1}^{L_{i}}U_{i}(b_{il}) \tag{3}\]  
where \(N\) defines the VN number to allocate \(L\) links, \(b_{il}\) is the vector of allocated bandwidths, and \(U(b_{il})\)is the utility function for the allocation process.Finally, an optimal solution is proposed by reaching the Nash equilibrium point.
# The Influence of UV on the Production of Free Trepenes in _Vitis vinifera_ cv. [PERSON]
###### Abstract  
Trepenes contribute to the desirable flavour and aroma of grapes and wine. The biosynthesis of these plant secondary metabolites is influenced by both physiological and environmental factors, such as grapevine phenological stage and sunlight exposure. In this study, we investigated the influence of ultraviolet (UV) at different grapevine phenological stages on free terpen in grape at harvest. Two types of transparent polymer films were applied to grape bunches to eliminate both UV-A and UV-B or only eliminate UV-B, followed by the identification and quantification of terpen using headspace solid-phase microextraction with gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis. In all, 27 free terpen were identified, including eight monoterpenes/monoterpenoids, four norisoprenoids and fifteen sesquiterpenes. Higher concentrations of \(\gamma\)-terpinene, linalool and \(\beta\)-damascenone were observed in grapes with UV-B attenuation compared to the naturally exposed grape bunches. Elevated \(\alpha\)-muvrolene was observed in UV-attenuated grapes from pre-veraison to harvest, while higher concentrations of \(\gamma\)-cadinene were observed in naturally exposed grapes. The impacts of UV exclusion on grape terpen at harvest were specific to phenological stages, where applying UV films from veraison to intermediate ripeness reduced the concentrations of key terpen in grape harvest and UV attenuation from intermediate ripeness to harvest promoted the accumulation of \(\alpha\)-muvrolene and \(\gamma\)-cadinene. This study provides information for viticulturists to better manage grape terpene composition through UV shading.  
t  
t  
terence; sesquiterpene; ultraviolet radiation; grape; HS-SPME-GC-MS
## 3 Results and Discussion
### Effects of Temperature Gradient
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Series**} & \multirow{2}{*}{**Case ID**} & **Waste** & **Initial Matric** & **LFG Influx** \\  & & **Temperature(\({}^{\circ}\)C)** & **Suction(kPa)** & **(mol m\({}^{-2}\) d\({}^{-1}\))** \\ \hline \multirow{3}{*}{1} & WT20 & 20 & \multirow{3}{*}{33} & \multirow{3}{*}{10.625} \\  & WT35 & 35 & & \\  & WT50 & 50 & & \\ \hline \multirow{3}{*}{2} & MS33 & & 33 & \\  & MS200 & 200 & 200 & \\  & MS500 & 500 & 500 & \\  & MS1500 & & 1500 & \\ \hline \multirow{3}{*}{3} & LG0.25 & & & 2.656 \\  & LG0.5 & & & 5.313 \\ \cline{1-1}  & LG1 & 50 & 33 & 10.625 \\ \cline{1-1}  & LG2 & & 21.25 & \\ \cline{1-1}  & LG4 & & 42.5 \\ \hline \hline \end{tabular} Note: The Case IDs WT, MS, and LG in the table refer to waste temperature, initial matric suction, and LFG influx, respectively. The LG values are based on a typical reference rate of 10.625 mol m\({}^{-2}\) d\({}^{-1}\), with the corresponding Case IDs representing values ranging from 0.25 to 4 times this reference rate.  
\end{table} 
Table 2: Summary of parametric study. Figure 3: Influence of temperature gradient on CH\({}_{4}\) oxidation efficiency. Figure 2: Influence of temperature gradient on (**a**) temperature, (**b**) volumetric water content, and (**c**) evaporation.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 2 Background
### Sensitivity Analysis  
A system can be described as a black box that receives a set of inputs (parameters) and generates a set of outputs (results). When we change the parameters, the system can produce new results or not. Observing this, the sensitivity analysis checks which parameters (inputs) interfere in the results (output) of the system [9].  
An uncomplicated way to perform the sensitivity analysis is changing one parameter at a time while keeping the other constant. Therefore, it is possible to compare the results according to the modified parameter. Besides that, experiment design techniques (Design of Experiments--DoE) can be used to determine (simultaneously) the different effects and interaction between various parameters that can affect the output [14]. In DoE, parameters and their values are referred as factors and levels, respectively. To experiment using DoE techniques, it is necessary to define the factors and their levels, create and evaluate all possible combinations. The number of combinations (\(C\)) is calculated as follows [14]:  
\[C=\prod_{i=1}^{n}level(f_{i}) \tag{3}\]  
where \(n\) is the total number of factors, and _level(\(f_{i}\))_ is a function that returns the amount of levels of factor \(f_{i}\). Depending on the number of factors and levels, the sensitivity analysis may require a lot of processing and time, which can derail the study. An alternative is to limit the number of factors and/or levels.  
We can do a sensitivity analysis with only two levels and with \(k\) factors. This type of experiment is called \(2^{k}\) design and the number of combinations is equal to \(2^{k}\). Nevertheless, depending on the number  
Figure 2: RBD organized in (**a**) series, (**b**) parallel and (**c**) combined.  
of factors, the number of combinations can be large. After obtaining the results, we can create a ranking of the factors and the interactions between them that influenced the results. Therefore, it is necessary to calculate the impact (\(S\)) of one factor (\(\theta\)) in a particular output (\(Y\)). The effect of factor \(\theta\) is the difference between the averages of the first (\(\theta_{-1}\)) and the second levels (\(\theta_{+1}\)), as shown in Equation (4).  
\[S_{a}(Y)=T_{Y}(\theta_{+1})-T_{Y}(\theta_{-1}), \tag{4}\]  
where \(T\) is a function that returns output \(Y\) of the average obtained with the first level (\(\theta_{-1}\)) and the second level (\(\theta_{+1}\)) of factor \(\theta\). Besides that, we must use this equation to calculate the interaction between two factors. For example, the interaction between factors \(A\) and \(B\) is calculated as follows:  
\[S_{a}(Y)=\frac{T_{Y}(A_{+1},B_{+1})+T_{Y}(A_{-1},B_{-1})}{2}-\frac{T_{Y}(A_{+1 },B_{-1})+T_{Y}(A_{-1},B_{+1})}{2} \tag{5}\]  
where function \(T_{Y}(A_{+1},B_{+1})\) returns the average of the output \(Y\) when the value of the factors \(A\) and \(B\) is the second level; function \(T_{Y}(A_{-1},B_{-1})\), when the value of the factors \(A\) and \(B\) is the first level; and so on.
# Storing Set Families More Compactly with Top ZDDs
## 3 Top Tree and Top DAG
### Top DAG Compression  
Top DAG compression is a compression scheme for labelled rooted trees by converting the input tree into top tree [21] and then compress it by DAG compression [18; 19; 20]. Top DAG compression can compress repeated sub-structures (not only subtrees). DAG compression can share identical subtrees, but it cannot share similar structures at different heights. This is made possible by top DAG compression. For example, a path of length \(n\) with identical labels cannot be compressed at all by DAG compression but can be represented by a top DAG with \(\mathrm{O}(\log n)\) nodes. Also, for a tree with \(n\) nodes, top DAG supports the following operations in \(\mathrm{O}(\log n)\) time: accessing a node label, computing the subtree size, and tree navigational operations such as first child and parent. Here we explain the top tree and its greedy construction algorithm. We also explain operations on top DAGs.