esa-sceva/satcom-chunk-collection huggingface dataset:
# Photo-Stimulated Luminescence Approach for Effective Identification of Irradiated Fruit
###### Abstract  
This work reports the results of a study carried out to verify the applicability of the CEN EN 13751 method, based on the photo-stimulated luminescence (PSL) technique, for the identification of irradiated fruit. A total of 15 types of fresh and dried fruit not irradiated and irradiated (0.5 and 1 KGy) were tested. Preliminary tests were performed on samples of fresh kivis, lemons, mangoes, oranges, papayas, pineapples and tangerines, dried dates, dried prunes and raisions. Successively, an inter-comparative test was organized, whereby four Italian official control laboratories analyzed eight matrices, namely, kiwi, orange, dried fig, raisin, hazelnut, peanut, pistachio and walnut. The results indicated considerable differences in the radiation PSL sensitivities of the different types of fruit as well as among the PSL responses obtained from different batches of the same fruit. The best results were obtained with some types of dried fruit, namely, pistachios, peanuts, dried figs and raisions. Irradiated fresh fruit generally gave non-positive results, mainly intermediate and even very close to the negative threshold. Tests performed on kiwi, mango and orange samples indicated that the parts of the fruit peel close to the stem, which are more wrinkled, provide better results.  
Keywords:irradiated food; photo-stimulated luminescence (PSL); fruit; nuts +
Footnote †: journal: Physics Letters A
## 4 The Algorithms
### Simple Experiments--The Dynamic Removal of Agents from the Group  
We investigated the influence of \(TRr_{\{f,k,v\}}\), \(TCr_{\{f,k,v\}}\), \(Pr_{\{f,k,v\}}\), \(Pentr_{\{j,m,i\}}\) (resp. \(Pr_{\{f,k,v\}}=Pmax_{\{f,k,v\}}\) on the removal of agents as shown in Figure 7). Again, the values in the graph represent averages acquired from five repetitions of the experiment. The sub-experiments were performed in an exploration space with the size of 10 \(\times\) 8 \(\times\) 5 cells, lasting for 30,000 iterations and starting with 50 agents exploring the space. The results indicated that lowering \(TRr_{\{f,k,v\}}\) and \(Pmax_{\{f,k,v\}}\) led to a larger population at the conclusion of the experiment. Figure 8 shows the result of the same experiment in the context of the number of explorations. The \(Y\) axis shows the number of space explorations after 30,000 iterations. There was a strong correlation between the results in Figures 7 and 8.  
The dynamics of the agent removal are shown in Figures 9 and 10 (selected values only). The simulation results show that \(Pentr_{\{k,m,f\}}\) determined the slope of the removal curve. \(TCr_{\{f,k,v\}}\) and \(TRr_{\{f,k,v\}}\) influenced the number of agents in the group after removal and the time needed to stabilize the group's population (Figure 10). If \(TCr_{\{f,k,v\}}\) and \(TCa_{\{f,k,v\}}\) are set to similar values, the adaptation process will not stabilize (the agents will continue to be added and removed). If \(TCr_{\{f,k,v\}}\) is large, many agents remain in the group during the removal process. This causes conflicts and  
Figure 7: The number of the agents in the group after 30,000 iterations; we added 50 agents to the group in the 1000 th iteration and removing agents was then turned off.  
negatively affects the number of explorations. \(TRr_{\{f,k,v\}}\) must be set to interoperate with \(TCr_{\{f,k,v\}}\). When \(TCr_{\{f,k,v\}}\) is low and \(TRr_{\{f,k,v\}}\) is high, the removal process may have a long duration because the agents meet scarcely (for example, in Figure 10 where \(TRr_{\{f,k,v\}}=400\sim 500\)). We also experimented with space sizes that were two and three times larger (Figure 11). The population size was again capable of adapting to the space size.  
Figure 8: The number of space explorations after 30,000 iterations; we added 50 agents to the group in the 1000 th iteration and removing agents was then turned off.  
Figure 9: Removing agents from the group with dependence on \(TCr_{\{f,k,v\}}\) and \(Penr_{\{j,m,i\}}\); the simulation lasted for 30,000 iterations; 50 agents were added into the group in the 1000 th iteration: \(Pmax_{\{f,k,v\}}=400\), \(TRr_{\{f,k,v\}}=200\) (**a**), and \(TRr_{\{f,k,v\}}=400\) (**b**).
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
###### Abstract  
In order to tackle the issue of carbon emissions in logistics and distribution, a vehicle routing model was proposed with the aim of minimizing the overall cost, which includes the vehicle's fixed cost, transportation costs, and carbon emission costs. An enhanced genetic algorithm, based on a modified Nearest Neighbor Construction (NNC) method, was developed to solve this model. A comparative analysis was conducted using the Solomon dataset to study the impact of carbon emissions on vehicle routing optimization, comparing scenarios with and without considering carbon emission costs. The research findings revealed that the Improved NNC (INNC) method exhibited faster convergence compared with the random generation and random insertion methods. Despite a slight increase of 0.5127% in transportation cost when factoring in carbon emission costs, there was a decrease of 4.6914% in carbon emission costs and 0.3578% in total cost. These results offer theoretical insights and empirical evidence to inform the development of models for the logistics industry in the context of a low-carbon economy.  
low carbon logistics; genetic algorithm; vehicle path optimization; nearest neighbor algorithm +
Footnote †: journal: Applied sciences  
11 March 2024  
Revised: 2 April 2024  
Accepted: 3 April 2024  
Published: 5 April 2024
# A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem
## 3 Results and Discussion
### Experimental Results of Modified FAs
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**FA**} & \multicolumn{4}{c}{**Modified FA**} \\ \hline \multirow{2}{*}{**Problems**} & \multirow{2}{*}{**Best\_Sol**} & \multirow{2}{*}{**No. of**} & \multirow{2}{*}{**CPU**} & \multirow{2}{*}{\(RPD\)} & \multirow{2}{*}{\(RDI\)} & \multirow{2}{*}{**Best\_Sol**} & \multirow{2}{*}{**No. of**} & \multirow{2}{*}{**CPU**} & \multirow{2}{*}{**RPD**} & \multirow{2}{*}{\(RDI\)} \\  & & & & & & & & & & & \\ \hline
**S1** & 138,341,162 & 25 & 27.23 & 4.61 & 43.6 & 138,302,249 & 15 & 24.85 & 3.99 & 31.99 \\
**S2** & 212,765,387 & 38 & 29.61 & 1.65 & 34.7 & 212,148,214 & 21 & 26.46 & 1.69 & 37.47 \\
**M1** & 389,423,523 & 67 & 41.62 & 2.27 & 25.06 & 386,516,977 & 33 & 36.91 & 2.23 & 24.83 \\
**M2** & 564,738,007 & 84 & 157.31 & 1.88 & 18.62 & 555,143,178 & 49 & 135.48 & 1.82 & 18.21 \\
**L1** & 711,561,530 & 102 & 662.74 & 4.84 & 38.3 & 700,814,257 & 65 & 621.81 & 4.75 & 39.03 \\
**L2** & 3,238,103,430 & 143 & 992.65 & 3.27 & 42.94 & 3,223,380,912 & 84 & 917.43 & 3.2 & 40.89 \\
**L3** & 4,943,889,047 & 211 & 1218.79 & 5.01 & 45.22 & 4,941,394,458 & 144 & 1112.66 & 4.97 & 44.75 \\
**Average** & 1,456,974,584 & 95.72 & 447.13 & 3.36 & 35.49 & 1,450,957,178 & 58.71 & 410.8 & 3.23 & 33.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performances comparison of FA and modified FA.
# Distributed Consensus for Global Matrix Formation in the Principal Component Pursuit Scenario
###### Abstract  
The aim behind principal component pursuit is to recover a low-rank matrix and a sparse matrix from a noisy signal which is the sum of both matrices. This optimization problem is a priori and non-convex and is useful in signal processing, data compression, image processing, machine learning, fluid dynamics, and more. Here, a distributed scheme described by a static undirected graph, where each agent only observes part of the noisy or corrupted matrix, is applied to achieve a consensus; then, a robust approach that can also handle missing values is applied using alternating directions to solve the convex relaxation problem, which actually solves the non-convex problem under some weak assumptions. Some examples of image recovery are shown, where the network of agents achieves consensus exponentially fast.  
Keywords:consensus; image recovery; principal component pursuit; static graph +
Footnote †: journal: Applied
# Analysis of Building Platform Inhomogeneities in PBF-LB/M Process on Alloy 718
## 3 Results and Discussion  
### Track Stability Analysis  
Figure 3 represents the criteria adopted to evaluate the track stability levels. The stability levels are as follows:  
* Not stable.
* Metastable.
* Stable.  
Figure 2: (**a**) Specim layout and dimensions; (**b**) Single Track dimensions on the top surface of the specimen.  
In Table A1, the measured track's stability level for each tested configuration is reported. Figure 4 depicts the scatterplot of the track's stability levels that are measured as a function of the Exposed Area, Position, and Differential Pressure.  
From the scatterplot as shown in Figure 4, it can be observed that the specimens located in position 1 on the building platform are affected by higher process instability with respect to the other positions, resulting in widespread unstable and metastable Single Tracks. It should be noted that the process instability is mitigated by moving to the right side of the building platform, thereby reducing the exposed area, and consequently increasing the  
Figure 4: Scatterplot of track’s stability level measured as function of Area Exposed (%), Position, and Differential Pressure.  
Figure 3: Track stability assessment adopted criteria.  
differential pressure, i.e., high gas flow velocity. For instance, considering the scatterplot for a low differential pressure at 37% of the exposed area, it is evident that the Single Track's stability level changes from an unstable condition for position 1 to a stable condition for position 3. Moreover, analyzing the scatterplot for nominal differential pressure and Position 1 condition, it is observed that the Track's stability level changes significantly as a function of the exposed area percentage: moving from an unstable condition for the higher level of exposed area (70%) to a completely stable condition for the lower level (14%). For industrial applications, where maximizing machine productivity requires the utilization of all building platforms, this first result is of remarkable interest.  
However, it is clear that the mitigation effect of differential pressure on the Track's stability is appreciable considering Position 2 with an exposed area of 70%, where the Track's stability level changes from an unstable condition to a stable one. It is important to notice that a value of exposed area higher than 50% results in a predominance of unstable or metastable conditions at all the differential pressures investigated.
## 4 Results and Discussion
### Automatic Recognition of Lexical Situation Types
#### 4.3.1 Classification Experiments Based on Linguistic Feature Vectors  
To evaluate the predictive capability of verb linguistic feature vectors extracted from different corpora for situation types, we input the two linguistic feature vector matrices into three classifiers. Utilizing the pandas library (v2.2.0) for data processing and implementing classification through models from the scikit-learn library (v1.6.0), we optimized the parameters of the classifiers using grid search, and conducted classification experiments with tenfold cross-validation. The parameter settings and experimental results for each classifier are as follows in Tables 5 and 6:  
Overall, the ANN model outperforms other models in both sets of classification experiments. The highest prediction accuracy is achieved by the ANN classifier based on the CCL corpus, reaching 77.23%, demonstrating its advantage in handling complex linguistic features. This suggests that the ANN model's nonlinear feature learning capability can improve the prediction accuracy of situation types to some extent.  
We employed the majority class baseline method to establish the baseline, which involves consistently predicting the most frequent category (activity verbs, 72.64% prevalence in the training set, 823/1133 instances) throughout all iterations of the tenfold cross-validation process. However, the best classifier only improves accuracy by less than 5% compared to the baseline, indicating that relying solely on linguistic feature vectors cannot achieve high-accuracy prediction of situation types. Therefore, we will shift the focus of our experiments to classification based on embedding vectors, aiming to search for the most suitable dataset and classifier for improving prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vector** & **Classifier** & **MNLogit** & **SVM** & **ANN** \\ \hline Linguistic feature vectors in the CCL corpus & 65.31\% & 73.70\% & 77.23\% \\ Linguistic feature vectors in the Sinica corpus & 75.64\% & 73.96\% & 75.99\% \\ baseline & 72.64\% & 72.64\% & 72.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification experiment results based on linguistic feature vectors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} activation = logistic, \\ alpha = 0.01, \\ hidden layer sizes = (50, 100, 50), \\ learning\_rate = adaptive, \\ solver = adam \\ activation = tanh, alpha = 0.0001, \\ hidden layer sizes = (50, 100, 50), \\ learning\_rate = constant, \\ solver = sgd \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings for different classifiers based on linguistic feature vectors.
## 2 Methods  
### Transfer Entropy  
Transfer entropy (TE) is an information-theoretic measure of directed interactions between two dynamical systems [14; 15]. Given two time-series \(\mathbf{x}=\{x_{t}\}_{t=1}^{T}\) and \(\mathbf{y}=\{y_{t}\}_{t=1}^{T}\), with \(t\in\mathbb{N}\) a discrete time index, \(T\in\mathbb{N}\), TE is defined as:  
\[TE(\mathbf{x}\rightarrow\mathbf{y})=\sum_{y_{t},y_{t-1}^{dy},\mathbf{x}_{t-u}^ {dx}}p\Big{(}y_{t},y_{t-1}^{dy},\mathbf{x}_{t-u}^{dx}\Big{)}\text{log}\left( \frac{p\Big{(}y_{t}|\mathbf{y}_{t-1}^{dy},\mathbf{x}_{t-u}^{dx}\Big{)}}{p\Big{(} y_{t}|\mathbf{y}_{t-1}^{dy}\Big{)}}\right), \tag{1}\]  
where \(\mathbf{x}_{t}^{dx},\mathbf{y}_{t}^{dy}\in\mathbb{R}^{D\times d}\) are time-embedded versions of \(\mathbf{x}\) and \(\mathbf{y}\), \(D=T-(\tau(d-1))\) with \(d\), \(\tau\in\mathbb{N}\) the embedding dimension and delay, respectively; \(u\in\mathbb{N}\) stands for the interaction delay between the two systems, and \(p(\cdot)\) represents a probability density function [30]. The time embeddings are defined as \(\mathbf{x}_{t}^{d}=(x(t),x(t-\tau),x(t-2\tau),\dots,x(t-(d-1)\tau))\)[31; 32]. TE, as in Equation (1), evaluates the statistical causality from \(\mathbf{x}\) to \(\mathbf{y}\) by measuring whether the information contained in the previous \(\mathbf{x}\), alongside that of the previous \(\mathbf{y}\), is better at predicting the future of \(\mathbf{y}\) than the information from the past of \(\mathbf{y}\) alone. If this is the case, then \(\mathbf{x}\) causes \(\mathbf{y}\) (in the sense of [PERSON]'s definition of causality [14]). For estimation convenience, we can also express TE as a linear combination of Shannon entropies:  
\[TE(\mathbf{x}\rightarrow\mathbf{y})=H_{\text{S}}\Big{(}\mathbf{y}_{t-1}^{dy}, \mathbf{x}_{t-u}^{dx}\Big{)}-H_{\text{S}}\Big{(}y_{t},\mathbf{y}_{t-1}^{dy}, \mathbf{x}_{t-u}^{dx}\Big{)}+H_{\text{S}}\Big{(}y_{t},\mathbf{y}_{t-1}^{dy} \Big{)}-H_{\text{S}}\Big{(}\mathbf{y}_{t-1}^{dy}\Big{)}, \tag{2}\]  
where \(H_{\text{S}}(X)=-\sum_{x}p(x)\text{log}(p(x))\), \(X\) is a discrete random variable (\(x\in X\)), and \(H_{\text{S}}(\cdot,\cdot)\), \(H_{\text{S}}(\cdot)\) stand for joint and marginal entropies.
## 2 Materials and Methods
### Proposed Method  
This study aims to design and compare movie recommender systems using Non-negative Matrix Factorization (NMF) and Deep Learning-based Personas [Figure 5]. The two proposed movie recommender systems consist of three modules: 'User Modeling', 'Recommender Model Learning', and 'Movie Recommendation'.  
The 'User Modeling' module uses user information and movie rating data to form user Personas. The 'Recommender Model Learning' module trains either the NMF or Deep Learning model based on the created user Personas. Finally, the 'Movie Recommendation' module is responsible for recommending specific movies according to user or system requests.  
The difference between the two recommender systems lies in the learning method in the 'Recommender Model Learning'. In the Deep Learning model, the 'Candidate' model first extracts candidate movie data, and then the 'Ranking' model selects a final movie to be recommended from these candidates.  
Figure 6 shows the data flow for generating a user Persona from movie rating data. First, the rating data is collected to form a user corpus. Subsequently, specific topics are assigned to the user through topic modeling, and finally, K-means clustering is used to cluster the users, completing the user Persona.  
We consider the set of movies watched by a user as a document and carries out topic modeling based on this. Figure 7 shows an example of construction by topic modeling user-specific movie data. Each movie belongs to a specific topic, for example, 'Movie 1' and 'Movie 203' are primarily included in the first topic.  
User's Movies consists of movies rated by the user, each accompanied by a rating. The topic modeling results for the 100 th user show that this user composes Topic1, Topic2, \(\ldots\), Topic20. In this way, topic modeling was performed using movie rating data for a total of 943 users.  
Figure 5: Movie Recommender System—NMF and Deep Learning Recommendation System using three modules.  
Figure 6: Composition of Recommendation Algorithm—User persona generation from movie evaluation data.  
We used topic modeling (LDA) and clustering (K-means clustering) techniques to construct user Personas using user data, specifically rating information. These constructed user Personas are trained through the NMF model and Deep Learning model, leading to the construction of a recommendation model. Each model plays the role of predicting the ratings that the user Persona will assign to a specific movie.  
The input of the model is the user Persona, and the output is the predicted rating that the user will assign to a specific movie. Based on these predicted values, the Recommender excludes movies that the user has already watched from the remaining movies and makes recommendations to the user.
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 2 LoRaWAN Main Features
### LoRaWAN MAC Layer  
LoRaWAN supports optional reliability, by means of positive acknowledgments (ACKs) and MAC layer retries. For each data frame, an ED may indicate whether an ACK is requested or not. For a message requiring an ACK (also called a _confirmed_ frame), the ACK may be received in one of the next two receive windows. If the ACK is not received, the ED retransmits the same frame up to a maximum number of retries. In most currently deployed EDs [19; 20], the first two transmission attempts use the same DR and, thereafter, the DR decreases to the next lower DR every two attempts (unless DR0 is used). Once the maximum number of transmission attempts for the same frame is reached, confirmed transmission failure is notified to the application layer. Each retransmission is performed after a timeout, which is initiated at the start time of the last second receive window, with a duration chosen randomly between 1 and 3 s, by default [17].  
The LoRaWAN MAC layer also defines the Adaptive Data Rate (ADR) mechanism, which allows the NS to control the ED parameters, such as the DR, the transmit power, and the channels used, based on the estimated link quality.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 2 Applications, Motivations, and Challenges
### Motivations for Receiver-Initiated MAC
The exposed area where the exposed nodes lie is marked in light blue in Figure 1. In the traditional handshaking protocols, the exposed nodes that overhear the \(RTS\) message have to keep silent until hearing the corresponding Clear-to-Send (\(CTS\)) message. Even though the handshake mechanism helps to mitigate the exposed terminal problem, the nodes are still exposed for one propagation delay plus the transmission time of a control packet, neither of which is negligible in UWSNs. The exposed nodes in the receiver-initiated-based protocols, however, learn the fact that it is an exposed terminal when receiving an Available-to-Send (\(ATS\)) message immediately, since the \(RTR\) message has gone earlier. Therefore, the exposed terminal problem is mitigated in the receiver-initiated-based MAC protocols. To summarize, the enabled data aggregation at the link layer, the significantly reduced overhead in the control message, and the enhanced collision avoidance capacity of receiver-initiated MAC protocols make them promising solutions for data gathering UWSNs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modem Type** & **Data Rate** & **Preamble (s)** & **Control Packet Length (s)** & **Data Packet Length (s)** \\ \hline \multirow{2}{*}{Benthos ATM-88X Modem} & 800 bps (Standard) & \multirow{2}{*}{\(\approx\)1.5} & \multirow{2}{*}{\(\approx\)1.56} & \multirow{2}{*}{\(\approx\)4.06} \\ \cline{3-3} \cline{5-5}  & 2.4 Kbps (Highest) & & & \(\approx\)1.52 & \(\approx\)2.35 \\ \hline AquaSeNT OFDM Modem & 3.045 Kbps & 0.49 & 0.66 & 1.15 \\ \hline \multirow{2}{*}{WHOI Micro Modem} & 80 bps (Standard) & \multirow{2}{*}{0.87} & \multirow{2}{*}{1.47} & \multirow{2}{*}{25.87} \\ \cline{3-3} \cline{5-5}  & 300–500 bps (PSK mode) & & & 0.88–1.03 & 1.27–7.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overhead of control message (6 bytes) and data packet (250 bytes) with different acoustic modems. Figure 1: Interference and exposed areas in handshaking-based MAC protocols with parallel reservation. (**a**) Sender-initiated handshaking; (**b**) Receiver-initiated handshaking.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 4 Methodology  
Figure 1 illustrates the overall framework of the HADRM method, which is composed of three main modules working iteratively to enhance system efficiency. (i) Task decomposition considers task dependencies and priorities to ensure the logical breakdown of complex tasks. (ii) Resource mapping and allocation integrates both static and adaptive strategies to optimize resource utilization across the system. (iii) Dynamic scheduling continuously adjusts task execution based on real-time system state updates, ensuring adaptability and efficiency. Together, these modules form a cohesive approach to optimizing task execution and resource management.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 2 Materials and Methods
### Training  
Focus & TSOM-CNN belongs to the deep-learning regression model; therefore, the mean square error (MSE) is used as the loss function for training. The definition of MSE is shown in Equation (3).  
\[MSE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}, \tag{3}\]  
where \(y_{i}\) indicates the truth value to be measured in each measurement, \(\hat{y}_{i}\) indicates the predicted value and \(n\) indicates the number of measurements.  
The batch size is set to 16 and the model is trained for 200 epochs.
## 4 Results
### Soil Salinity Prediction and Accuracy Assessment
#### 4.4.2 Soil Salinity Prediction and Accuracy Assessment Using the OK Method
Likewise, the RMSE and PBIAS values of VV are lower than HH. In the meantime, the R\({}^{2}\) values of HH and VV modes are slightly different, providing a very strong correlation between measured and predicted values.