esa-sceva/satcom-chunk-collection huggingface dataset:
# Discriminating Pattern Mining for Diagnosing Reading Disorders +
###### Abstract
Computer science contribution for speech disorders is witnessing a great interest [10; 11; 12; 13] and, to the best to our knowledge, this is the first work trying to help therapists which employ tachistoscopes in diagnosing by automatically collecting and analysing information. A challenging problem from a computer science point of view is that of suggesting characteristics of the words that discriminate, for a patient, between correct and erroneous answer, namely, characteristics shared by erroneously answered words and low-frequently present in the set of correctly answered words. We address the problem by borrowing the results on non-negative matrix factorization [14; 15; 16], a widely employed basic technique able to decompose an input matrix into two lower rank matrices. This method has been successful for the extraction of concepts/topics from unstructured text [17], for speak verification [18], for feature extraction, for clustering, for classification and many other fields [19]. The technique analyzes outcomes of tachistoscope sessions by firstly vectorizing words and secondly extracting prototype of errors. For this second part, nnmf is exploited, and its objective function is relevantly changed to allow us to achieve two purposes, approximating input vectors and discriminating the correctness. Furthermore, contexts are analyzed to provide indications about problematic scenarios and, equipped with the information of encoded pathologies, the framework is able to suggest the presence of known diseases or of not yet structured ones. The work provides then a contribution in the following aspects (_i_) to the best of our knowledge; this is the first attempt to automatically diagnose reading pathologies starting by tachistoscope output (_ii_) a nonnegative matrix factorization technique able to take simultaneously into account two sets of words is provided; (_iii_) an ad-hoc discrimination power is embedded in the formulation of the _nnmf_ problem; (_iv_) the analysis of the contexts potentially influencing disorders is performed as a phase of the technique; (_v_) the system is able to individuate an already encoded pathology and/or unencoded ones. The paper is organized as follows: Section 2 reports the notation employed throughout the paper and preliminary notions; Section 3 describes the proposed technique; and Section 4 illustrates the experimental campaign conducted on both real and synthetic data. Finally, Section 5 depicts conclusions.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 1 Introduction
### Background
Through prolonged exploration, several optimal or near-optimal LHZ FHS sets have been successfully developed (see [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], for example). To sum up, there are several optimal construction methods: constructions based on \(m\)-sequences [13; 14; 15], constructions based on interleaving techniques [16; 17; 18; 19], constructions based on Cartesian products [15; 20], and constructions based on cyclotomic theory [21; 22].
## 3 Results
### Activity on the PET Powder  
The extracellular fraction (ECF) containing the cutinase-like enzyme activity against the semicrystalline PET powder was assessed after 72 h of reaction at 40 \({}^{\circ}\)C and a pH of 9. The product (BHET, MHET, and TPA) peaks obtained using HPLC were regarded as the terephthalalic acid equivalents (TPAeq), assuming that they all had the same molar extinction coefficient e = 17.000 M\({}^{-1}\) cm\({}^{-1}\) due to the presence of the aromatic ring. Thus, the total amount of the degraded polymer was calculated by the TPAeq and the molecular weight 192 g/mol, corresponding to the PET repeating unit [17]. DMSO was used as the co-solvent to maximize the solubility of the depolymerized products. No products were detected in the negative controls (DMSO, PET, and the buffer) under the same conditions. The enzyme concentration in the extracellular medium was estimated from the total protein concentration and the enzyme fraction (58 kDa band) using SDS-PAGE. The PET depolymerization yield was 22.5%, with the main product being MHET, followed by TPA, and a small fraction of BHET (Table 5). Interestingly, these results were comparable with those of _Ideonella sakaiensis IsPETase_ and superior to those of the thermostable cutinase HiCut from _Humicola insolens_ (Table 5).  
Figure 10: Analysis of the degradation products of the PET bottle flakes using the cutinase-like enzyme. (**A**) Chromatogram of the product’s profile. (**B**) Mass spectra confirmed the identity of TA, MHET, and BHET.  
The crystallinity and thermal properties of the treated (with the ECF) and untreated PET powders were analyzed using DSC (Figure 11 and Table 6). The glass transition of the untreated PET powder was clearly observed in the first heating curve (\(T_{g}\) \(\sim\)83 \({}^{\circ}\)C). In the case of the treated PET powder, the \(T_{g}\) was not clearly observed on the first heating curve, probably due to overlapping with the onset of the cold crystallization process (-85 \({}^{\circ}\)C). The \(T_{g}\) of the treated PET powder was observed in the second heating curve (Supplementary Materials, Figure S3). It was observed that the temperature for the cold crystallization peak (\(T_{cc}\)) became lower after the ECF treatment (110 \({}^{\circ}\)C compared to 127 \({}^{\circ}\)C for the untreated PET powder), which was accompanied by a lowered enthalpy for the cold crystallization (\(\Delta H_{cc}\)\(\sim\)2.9 J/g compared to 9.0 J/g for the pristine PET powder, Table 6). In addition, an increased \(T_{m}\) (247 \({}^{\circ}\)C) and crystallinity (21.1%) were observed after the PET powder was treated with the ECF (compared to the \(T_{m}\) of \(\sim\)242 \({}^{\circ}\)C and crystallinity of 19.3% for the pristine PET powder). This result suggested that the degradation of the bulk PET materials presumably occurred (or occurred faster) in the amorphous region of the PET powder, which increased the relative content of the crystalline regions.
## 3 Landing Trajectory Data Correction
### Landing Trajectory Correction  
We first visualize the landing trajectory of a flight with its latitude and longitude parameters. Figure 1(a) shows the visualization of the trajectory obtained with the Folium map visualization tool ([[https://python-visualization.github.io/folium/](https://python-visualization.github.io/folium/)]([https://python-visualization.github.io/folium/](https://python-visualization.github.io/folium/)), accessed on 25 August 2023). In the visualization, the green location marker represents the touchdown moment of the aircraft, and the red location marker represents the last trajectory point of the 120 s interval. The trajectory points are sampled at a frequency of 1 Hz, meaning that the time interval between any two adjacent points is 1 s. Generally, the overall shape of the landing trajectory fits the runway. However, when the trajectory is enlarged, as shown in Figure 1(b), it can be observed that there are local deviations from the runway in terms of latitude and longitude. Since the landing trajectory information is crucial for the analysis of runway overrun risk, it is necessary to correct the landing trajectory to meet the analysis requirements. In this paper, we correct the landing trajectory data through the following steps.
## 2 Materials and Methods
### Case Study  
Sicily is an autonomous region of Italy and the largest island in the Mediterranean Sea (Figure 1), covering approximately 26,000 km\({}^{2}\). It has a unique climate pattern characterised by hot/dry summers and mild/wet winters. These conditions are beneficial to renewable energy production using agro-industrial residues obtained from typical activities: Cultivation first of all of citrus, and grape, olive oil, wheat and other products, and sheep and cattle breeding. Therefore, it was chosen as the study area. In Sicily, agriculture has a decisive position in the regional economy. Like in the other parts of southern Italy, employees in agriculture are plentiful but they have low wages. Furthermore, in Sicily, about 3.9 million metric tons of matrix are yearly produced by agro-industrial activities and are available and potentially reusable in biogas plants. These biomasses include byproducts from the agro-industrial chains (i.e., whey, citrus pulps, olive pomace residues and others), wastes from livestock (i.e., cattle manure), agricultural crop residues (i.e., cereal straw and others field and process residues) and some dedicated feedstocks obtained from energy crops. Among them, agro-industrial biomasses and livestock manure represent more than 60% of the total biomasses produced [42]. Sicily is the Italian region with the greatest extension of agricultural land [42]. Considering the area dedicated to agriculture, Sicily excels in cereal production and is the first Italian producer of oranges with half (52%) of the entire national output. By elaborating on data from the Italian National Statistical Institute (Istat) database it was found that, in the last five years (from 2015 to 2019), with reference to the horticultural production area (both horticultural open field crops and greenhouses), in Italy an average of 356,089 ha/year were cultivated, while in Sicily about 58,357 ha/year (about 16%) were cultivated [41]. Table 1 shows the trend of the analysed period and the mean values, subdivided for the specific cultivation of horticultural open field crops and greenhouses ones, for the nine provinces of the study area. Within the selected interval, overall, as shown in Table 1, the areas cultivated with horticultural crops registered a slight increase (+1.9%) by rising from 57,949 ha to 59,069 ha. Instead of, by considering greenhouse horticulture, leaving out the data for the provinces of Catania and Messina, which are outliers, there is a common trend across the region: Greenhouse horticulture areas decreased from 2015 to 2019. Only the province of Trapani recorded a slight increase in greenhouse cultivated areas: From 1104 ha in 2015 to 1114 ha in 2019. Figure 1: Geographic position of the study area. Regarding open field horticultural areas, however, there is no common generalised trend: Some provinces have recorded a contraction of areas while others have recorded a marked increase (first of all Messina). Greenhouse cultivation is mainly concentrated in the province of Ragusa, in the coastal area. More than 56% of the Sicilian greenhouse surface is located in this territorial area.
# Welding Image Data Augmentation Method Based on LRGAN Model
###### Abstract  
This study focuses on the data bottleneck issue in the training of deep learning models during the intelligent welding control process and proposes an improved model called LRGAN (loss reconstruction generative adversarial networks). First, a five-layer spectral normalization neural network was designed as the discriminator of the model. By incorporating the least squares loss function, the gradients of the model parameters were constrained within a reasonable range, which not only accelerated the convergence process but also effectively limited drastic changes in model parameters, alleviating the vanishing gradient problem. Next, a nine-layer residual structure was introduced in the generator to optimize the training of deep networks, preventing the mode collapse issue caused by the increase in the number of layers. The final experimental results show that the proposed LRGAN model outperforms other generative models in terms of evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Frechet inception distance (FID). It provides an effective solution to the small sample problem in the intelligent welding control process.  
Keywords:intelligent welding; data augmentation; spectral normalization discriminator; residual structure +
Footnote †: journal: applied sciences
## 4 Case Study
### Calculating the Location of Parking Lots and Sizing  
The location of parking lots, the number of EVs, and the benefit of loss reduction (Equation (10)) are obtained using the PSO algorithm. At the end, the benefit of providing peak power (net income - required cost) and total benefit is calculated using Equation (1).  
Additionally, in order to see the voltage enhancement in the presence of V2G, the voltage profile is examined for three different scenarios, both with and without V2G power.
earthquake have shown that the first 3 days or so after an earthquake are very critical for the smooth operation of rescue and relief. So, the first 72 h after an earthquake are often referred to as the \"golden hours\" for rescue. After three days, the chances of survival decrease significantly due to dehydration, lack of food, and exposure to the elements. For example, the 1999 Izmit earthquake in Turkiye was one of the deadliest earthquakes in recent history, with over 17,000 people killed and many more injured. During the first 72 h after the earthquake, search and rescue teams from around the world worked together to locate and save survivors [5]. Many of those rescued during this time had been trapped in small spaces without access to food, water, medical attention, and other essential resources. A similar situation happened in 2015 after an earthquake in Nepal, where thousands of people were buried under collapsed buildings and rubble [6]. In the first 72 h, rescue teams from around the world worked tirelessly to pull survivors from the wreckage [7]. Many of those saved during this time were found trapped under rubble in remote areas that were difficult to access.  
In recent years, there has been significant progress in the development of new types of sensors that can be used in wide areas of earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection [8]. One particularly exciting development in this field is the use of sensor networks to create real-time earthquake monitoring systems. These systems can quickly alert populations in affected areas to incoming earthquakes, giving them precious moments to take protective actions.  
However, there are gaps between sensors' development and earthquake response practice, especially from monitoring to rescuing. For example, scientists have made great strides in their ability to provide more detailed information about the characteristics of earthquakes using equipment such as satellite sensors and drone-based sensors. Such kinds of information and technologies are practiced in different earthquake disaster scenarios. However, these kinds of practices are scattered, and comprehensive review literatures are scanty. Therefore, this article reviewed all available earthquake response practices to provide their strengths and limitations in disaster scenarios.
# K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics
## 2 Classification Law of High-Potassium Glass and Lead-Barium Glass
### Sample Data Acquisition and Exploratory Spatial Data Analysis
#### 2.1.2 Exploratory Data Analysis  
Based on the chemical composition of the artifact samples and other testing means, the high-potassium glass and lead-barium glass were classified from a total of 67 data samples (including ornamentation, color, weathering degree, the proportion of the main components, etc.). The data distribution of sample color and the data distribution of color based on type are shown in Figure 1.  
The samples are marked 01-67, including 18 high-potassium and 49 lead-barium samples. To see how much information the basic data provided, a Kendall correlation coefficient matrix was established to initially observe the correlations among the four qualitative variables, and a correlation diagram was drawn, as shown in Figure 2. Additionally, because in the later section we recover the original chemical compositions of weathered points, we took a brief look at the linear relationship between weathering degree and other variables (including 3 qualitative variables and 14 continuous variables) by linear regression. This aimed to provide insight into how well we could do to discover the nonlinear clusters and distinguish weathered data from unweathered data using other variables. In the regression result shown in Figure 3, we obtained adjusted an R-squared value of 0.75. In Figure 4, we can see the positive/negative correlations between weathering degree and certain chemical compositions. From the model diagnostics graph shown in Figure 5, we conclude that there is a nonlinear relationship (from Residuals vs. Fitted), the residual is statistically normally distributed (from Normal Q-Q), heterogeneity appears in the model (from Scale-Location), and there are no global outliers in the data (from Residuals vs. Leverage).  
Figure 1: Samples color (type-based) data distribution.  
Figure 2: Correlation diagram among columns.
## 2 Materials and Methods
### Design of Experiments
#### 2.2.2 Setup of Actual Experiments  
At the beginning of the experiments, the initial setting (in the SolidCAM program) was:  
* Cutting speed as the circumferential speed of the milling cutting tool, \(v_{c}\) = 94 m\(\cdot\)min\({}^{-1}\);
* Constant cutting conditions, namely, feed per tooth: \(f_{z}\) = 0.1 mm;
* The depth of cut was identical to the depth of the groove;
* Dimensions of the machined groove (width 20 mm, length 55 mm, depth 10 mm) (Figure 8);
* Trochoid radius \(R_{p}\) = 5 mm (the same for all experiments);
* Trochoid step \(s\) = 0.1, 0.6 and 1 mm (the same for three triples of experiments);
* Engagement angle \(\alpha\) = 10\({}^{\circ}\), 30\({}^{\circ}\) and 60\({}^{\circ}\) (the same for three triples of experiments).  
Selected trochoidal parameters were set according to DoE principles--response surface. As a potentially suitable model, we chose the central composite plan with three levels of adjustment.  
A total of nine grooves were milled, with two varied parameters for each partial experiment: parameter \(s\)--trochoid step, and parameter \(\alpha\)--engagement angle. The individual experiments were divided into three levels according to the 3-level plan (Table 6).  
During the milling of each groove, the components of the machining forces were measured and recorded. The procedure was repeated according to Table 6, with the same chip volume being taken for each groove.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & \(s\) **(mm)** & \(\alpha\) (\({}^{\circ}\))** \\ \hline
1 & 0.2 & 10 \\
2 & 0.2 & 30 \\
3 & 0.2 & 60 \\
4 & 1.0 & 10 \\
5 & 1.0 & 30 \\
6 & 1.0 & 60 \\
7 & 0.6 & 10 \\
8 & 0.6 & 30 \\
9 & 0.6 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Three-level division of conventional trochoidal milling experiments.  
Figure 8: An example of a finished groove sample: the detail of one of the milled grooves with a noticeably higher surface waviness.
## 4 Experimental Results
### Addressing RQ2: Impact of PR Behaviors on Contributor Engagement in GitHub Projects
#### 4.3.2 Probability of Immediate PR Processing and Average Time Step to Process PR
As the time interval between branch creation and PR submission increases, the SHAP value decreases, suggesting a moderate negative impact on the likelihood of long-term engagement. This implies that participants who take longer to submit PRs after creating a branch may be slightly less engaged or less committed to the project. Conversely, participants who submit PRs more promptly after creating a branch appear to have a higher likelihood of sustained participation. This highlights the importance of maintaining momentum in the development process, where delays in submitting contributions could signal decreased activity or interest. Figure 10b shows the SHAP dependence plot for the pr_to_create variable, which represents the time taken from PR submission to the creation of a new branch. However, as observed, the SHAP values for pr_to_create display high variability, making it difficult to derive clear conclusions regarding its relationship with participation duration. The lack of a consistent trend in the SHAP values suggests that the time taken to create a new branch after submitting a PR may not be as strongly correlated with participant engagement as other factors. However, it is worth noting that core participants tend to cluster around shorter times (close to 0), indicating that they are more likely to create new branches soon after submitting PRs. This could reflect their efficiency and active involvement in multiple project workflows. The results indicate that while the time taken from branch creation to PR submission (create_to_pr) has a noticeable effect on contributor participation, the time between PR submission and the creation of a new branch (pr_to_create) does not show the same level of influence. This difference might suggest that the urgency in submitting contributions (as reflected by create_to_pr) is a more significant factor in determining long-term engagement compared to the subsequent activity of creating new branches.
## 3 SDN/VNF HTS Architecture
### Agent Learning Goal  
User communications occur as a result of requests originated from the clients with data flowing in either or both directions. The routing goal is to minimize the completion time of these requests (i.e., the response time) and their loss ratio. The agents need not be aware of the actual path of the communications but can determine the resulting performance of each routing decision. This is achieved by testing the state of the feeder links at regular intervals. The problem is formulated as a Markov decision process (MDP), which models the feeder link selection process. There are two variants of reinforcement learning investigated for this task: Q-Learning and actor-critic (learning automata--LA).  
Learning Automata (LA) is a decision-making agent working in a random environment that selects an optimal action from a set of actions based on their performance [17]. The environment reacts to the selected action either with a favorable response or with an unfavorable response. The output from the environment is feedback to the LA as an input to assist in deciding the next action. When an action is selected, it has a certain penalty probability \(c_{i}\) to produce an unfavorable response and a reward probability \(d_{i}=1-c_{i}\) to produce a favorable response. The reward and penalty probabilities vary over time and depend on the environment. Each action is selected based on a probability distribution. If the action resulted in a favorable response, its selection probability will be increased, otherwise decreased.  
An LA is described by a six-tuple, \(\{\phi,X,\lambda,P,A,G\}\). Table 1 indicates the model parameters. The number of \(r\) actions is always less than or equal to the the number of internal states \(s\).  
The diverse types of LA are classified based on the kind of input provided to the agent:  
1. _P-model_: binary input (either 0 or 1),
2. _Q-model_: finite values in the interval \([0,1]\),
3. _S-model_: continuous values in the interval \([0,1]\).  
The S-model has been applied to network communication problems due to its adaptive learning nature (see for example [18; 19; 20]) and is the model adopted in this work.  
With Q-Learning, each action is linked to a certain reward (or Q-value). The agent's goal is to maximize the long-term reward associated with its selected actions: at state \(s\), the agent receives an immediate reward by choosing an action \(a\), based on a policy \(\pi\). The agent's long-term reward is then given by the total discounted reward from that state \(s\)[21]. Q-Learning has been applied to different routing problems in the past (see for example [22; 23]).