esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species
## 2 Post-Harvest Classification  
### Datasets  
This study focused on the oka classification problem. Okra is divided into two quality types, A and B, according to the Japan Agricultural Cooperatives' (JA) standards. Table 1 lists the requirements for these two quality types. Type A refers to oka that meets the highest requirements and is sold at the highest price. Type B refers to low- to mid-range oka sold at lower prices targeting different customers.  
As mentioned previously, the oka image dataset used in this study comprises two quality types: A and B. Their numbers depend on the harvesting results; usually, the number of type A images is greater than that of B. The dataset used in this study was obtained under two conditions. Images of different oka products were captured at different dates and times. The different conditions required to obtain image data affect the classification  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quality** & **Size** & **Length (cm)** & **Bent (cm)** & **Dots** & **Color** \\ \cline{2-6}
**Type** & **Class** & & & & \\ \hline \multirow{3}{*}{A} & L & 9.5 to 10.5 & \multirow{3}{*}{\(\leq\)0.5} & \multirow{3}{*}{no dots} & \multirow{3}{*}{dark green} \\ \cline{2-6}  & M & 8.0 to 9.5 & & & \\ \cline{1-1} \cline{2-6}  & L & 9.5 to 10.5 & & & \\ \cline{1-1} \cline{2-6} B & M & 8.0 to 9.5 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oka quality types and size classes (relying on JA specification).  
process. Therefore, to evaluate this effect, we distinguished the image datasets captured under different conditions using the notations DS1 and DS2.  
Table 2 summarizes the data for each quality type used in the experiment. Only minor, but significant, differences were observed between the two subsets. For example, the oka orientation is vertical on DS2 and horizontal on DS1. The lighting conditions also differed between the two subsets. Additionally, oka colorization was different among the two subsets.  
The images of oka had 640 \(\times\) 480 pixels, regardless of their object orientation. Furthermore, oka was not necessarily centered within the image frame. All oka images were taken from the same distance and camera angles. Moreover, a white tray was used as a unified background to limit noise and enhance the oka features. Practically, it is difficult to obtain images under the same conditions at different dates and times. To avoid this bias, we generated a new dataset from the raw dataset. In addition, the dataset contains multiple types of background noise. In our experiment, shade constituted a major issue, as it was often mistaken for oka. The shades of the objects in the shooting environment were also included in specific images (Figure 1). These residues in the images are also considered noise because they can potentially interfere with image processing. The data conversion is explained later.
# Effect of Methylammonium Iodide (MACI) on MAPbI\({}_{3}\)-Based Perovskite UV-C Photodetectors
## 3 Results and Discussion
### Ultraviolet (UV) Detection Performance Evaluation of the Fabricated Photodetection Device
If the signal input to the manufactured photodetector changes with time, a delay occurs until it reaches the output of the device, and this is the response time. There is an inverse correlation between response speed and response time and these are important factors in evaluating PD performance. Rise/decay times show rise/decay times of 46.1 ms/44.3 ms, 39.2 ms/45.9 ms and 36 ms/32 ms. Figure 8b shows the ON/OFF repeatability of the MAPbI3 sensor with 0.10 M MACI added at a 254 nm light source with a light intensity of 0.774 mW/cm\({}^{2}\). For \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Name** & **Responsivity (R)** & **Detectivity (D)** & **EQE** \\  & **(mA/W)** & **(Jones)** & **(\%)** \\ \hline FABr 0 & 24.5 (21.1) & 1.82 \(\times\) 1012 (1.74 \(\times\) 1012) & 19 (16) \\ \hline FABr 5 & 37.4 (34.4) & 5.12 \(\times\) 1012 (4.98 \(\times\) 1012) & 28 (27) \\ \hline FABr 10 & 50.7 (46.7) & 2.61 \(\times\) 1013 (2.38 \(\times\) 1013) & 39 (36) \\ \hline FABr 15 & 60.9 (56.8) & 3.07 \(\times\) 1013 (2.89 \(\times\) 1013) & 47 (44) \\ \hline FABr 20 & 76.7 (72.2) & 4.89 \(\times\) 1013 (4.67 \(\times\) 1013) & 60 (56) \\ \hline FABr 25 & 53.9 (50.2) & 2.67 \(\times\) 1013 (2.44 \(\times\) 1013) & 42 (39) \\ \hline \hline \end{tabular} () is the value after going through the anti-solvent process without adding MACI.  
\end{table} 
Table 2: Performance parameters of the PD according to the amount of FABr added before and after. Figure 7: (a) Calculated R as a function of voltage and D\({}^{\prime}\), and (b) EQE. the first time, the photocurrent was 2.19 \(\mu\)A, and after 100 repetitions, the photocurrent was 2.53 \(\mu\)A. It can be seen that reproducibility is continuously maintained compared to the initial photocurrent. It demonstrates the stability of the PD.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 3 Methods
### Data Pre-Processing  
For each data set, we employed brightness and contrast jitter to augment the training data. We used \(\alpha\) and \(\beta\) to control the contrast and brightness \(f(x)\) of a pixel at position \(x\) by \(g(x)=\alpha f(x)+\beta\). Specifically, random brightness and contrast were applied with a factor of 0.2. The value of \(\alpha\) was randomly chosen from a uniform distribution in [0.8, 1.2], and the value of \(\beta\) from [\(-0.2\frac{1}{N_{x}}\sum_{x}f(x)\), 0.2\(\frac{1}{N_{x}}\sum_{x}f(x)\)], with \(N_{x}\) denoting the total number of pixels and \(\frac{1}{N_{x}}\sum_{x}f(x)\) denoting the mean pixel value.  
The _smallNORB_[37] data set contains 24,300 images at 96 \(\times\) 96 resolution, classified into 5 classes. Pre-processing, as per [4], involved down-sampling to 48 \(\times\) 48, normalization and, for training, a contrast factor of 0.2, random shifts of 0.2, padding to 56 \(\times\) 56, and random cropping to 32 \(\times\) 32. For testing, images were centrally cropped to 32 \(\times\) 32.  
The _Fashion-MNIST_[38] data set contains 70,000 images with a resolution of 28 \(\times\) 28 pixels, categorized into 10 classes. The data set was split into training and testing sets with a ratio of 6:1. During training, various data augmentation techniques were applied to the images, including random brightness adjustments, random flips with a 0.5 probability, and contrast adjustments with a factor of 0.2. Additionally, the images were padded to a size of 36 \(\times\) 36 pixels, then randomly cropped to a final size of 32 \(\times\) 32 pixels.  
The _CIFAR-10_[39] data set consists of a collection of 60,000 color images, each measuring 32 \(\times\) 32 pixels, and is divided into 10 different classes, with each class representing a distinct object or category. Each class contains 6000 images. The training and testing sets were divided with a 5:1 ratio. In the training phase, random adjustments to brightness and contrast were implemented with a factor of 0.2. Padding to achieve a size of 40 \(\times\) 40 was conducted, then random cropping was applied to obtain a 32 \(\times\) 32 portion. Additionally, random horizontal flips were introduced with a 0.5 probability.  
The _SVHN_[40] data set comprises 10 digit classes represented as 32 \(\times\) 32 real-world house numbers. The core training set, consisting of 73,257 images, was used for training, while evaluation was performed on the 26,032 test set images. Throughout training, random brightness and contrast adjustments with a factor of 0.2 were applied. Images were padded to 40 \(\times\) 40, and random 32 \(\times\) 32 crops were extracted for further processing.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## Materials and Methods  
### Field Observations  
Field reflectance spectra of rock and soil substrates, green and senescent vegetation, and clouds were collected using an ASD HandHeld2 field spectrometer (Figure 1E,F). These Visible through Near Infrared (VNIR) field spectra were used for a comparison of sub-meter scale reflectance of specific land covers with the aggregated reflectance measurements that were derived from the MODIS radiance observations. The details of this comparison are given by [40]. Land cover and vegetation type were field validated by the authors, with over 25,000 geotagged photographs and dashcam video of over 2500 km of driving and walking transects in February and March of 2016 and 2017. Field photos and spectra were used for scaling comparison and validation of meter-resolution (Quickbird, Worldview) and simultaneously acquired decameter-resolution (Landsat 8, Sentinel-2) satellite imagery--also described in detail by [40].
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 3 The Proposed DSAH Framework
### Asymmetric Learning Framework  
We briefly review the related work Supervised Hashing with Kernels (KSH) [51], the symmetric learning framework can be formulated as  
\[\min_{\mathbf{B}} ||\mathbf{B}^{\top}\mathbf{B}-k\mathbf{S}||_{F}^{2} \tag{5}\] \[s.t.\ \mathbf{B}\in\{-1,1\}^{k\times n},\]  
where \(\mathbf{B}\) is the learned hash codes. However, there are two major problems of Equation (5): (1) It is very time-consuming to directly compute \(\mathbf{S}\), as the similarity information \(\mathbf{S}\) is a \(n\times n\) matrix. (2) Some works [54; 55] show that the use of an asymmetric learning framework can not only solve the problem of high time consumption, but also improves retrieval accuracy, because the value range of the asymmetric learning framework is wider than that of symmetric learning. Therefore, in this paper, we construct an asymmetric learning framework to learn the compact hash codes, that is,  
\[\min_{\mathbf{B},\mathbf{P}_{1},\mathbf{P}_{2}} ||(\mathbf{P}_{1}\phi(\mathbf{X}^{(1)}))^{\top}\mathbf{B}_{1}-k \mathbf{S}||_{F}^{2}+||(\mathbf{P}_{2}\phi(\mathbf{X}^{(2)}))^{\top}\mathbf{ B}_{2}-k\mathbf{S}||_{F}^{2} \tag{6}\] \[+\alpha\sum_{i=1}^{2}||\mathbf{B}_{i}-\mathbf{P}_{i}\phi(\mathbf{ X}^{(i)})||_{F}^{2},\] \[s.t.\ \mathbf{B}_{i}\in\{-1,1\}^{k\times n}\]  
The advantages of Equation (6) are as follows:  
1. The learning mode uses an efficient asymmetric learning architecture instead of a time-consuming symmetric one.
2. The use of the real-valued embeddings instead of the binary embeddings produces a close semantic similarity relation, and the value of the objective function is smaller.
3. The last term is used to reduce the quantization errors, which leads to a better retrieval performance.  
However, there is a limitation of Equation (6) that is for the purpose of cross-modal retrieval tasks, we need to obtain a unified hash codes. Therefore, we need to consider another discrete constraint, i.e., \(\min_{\mathbf{B}_{1},\mathbf{B}_{2}}||\mathbf{B}_{1}-\mathbf{B}_{2}||_{F}^{2}\). In order to make the optimization easy, we set a unified hash code \(\mathbf{B}=\mathbf{B}_{1}=\mathbf{B}_{2}\) instead of minimizing the constraint \(\underset{\mathbf{B}_{1},\mathbf{B}_{2}}{\text{min}}||\mathbf{B}_{1}-\mathbf{B }_{2}||_{F}^{2}\), then Equation (6) can be rewritten as  
\[\underset{\mathbf{B},\mathbf{P}_{1},\mathbf{P}_{2}}{\text{min}} ||(\mathbf{P}_{1}\phi(\mathbf{X}^{(1)}))^{\top}\mathbf{B}-k \mathbf{S}||_{F}^{2}+||(\mathbf{P}_{2}\phi(\mathbf{X}^{(2)}))^{\top}\mathbf{B} -k\mathbf{S}||_{F}^{2}\] \[+\alpha||\mathbf{B}-0.5\Big{(}\mathbf{P}_{1}\phi(\mathbf{X}^{(1)} )+\mathbf{P}_{2}\phi(\mathbf{X}^{(2)})\Big{)}||_{F}^{2}, \tag{7}\] \[s.t.\ \mathbf{B}\in\{-1,1\}^{k\times n}\]  
where \(\alpha\) is the balance parameter.
###### Abstract  
In today's fast-paced and dynamic business environment, investment decision making is becoming increasingly complex due to the inherent uncertainty and ambiguity of the financial data. Traditional decision-making models that rely on crisp and precise data are no longer sufficient to address these challenges. Fuzzy logic-based models that can handle uncertain and imprecise data have become popular in recent years. However, they still face limitations when dealing with complex, multi-criteria decision-making problems. To overcome these limitations, in this paper, we propose a novel three-way group decision model that incorporates decision-theoretic rough sets and intuitionistic hesitant fuzzy sets to provide a more robust and accurate decision-making approach for selecting an investment policy. The decision-theoretic rough set theory is used to reduce the information redundancy and inconsistency in the group decision-making process. The intuitionistic hesitant fuzzy sets allow the decision makers to express their degrees of hesitancy in making a decision, which is not possible in traditional fuzzy sets. To combine the group opinions, we introduce novel aggregation operators under intuitionistic hesitant fuzzy sets (IHFSs), including the IHF Aczel-Alsina average \((IHF\mathcal{A}_{A}WA_{0})\) operator, the IHF Aczel-Alsina ordered weighted average \((IHF\mathcal{A}_{A}OWA_{0})\) operator, and the IHF Aczel-Alsina hybrid average \((IHF\mathcal{A}_{A}HA_{0})\) operator. These operators have desirable properties such as idempotency, boundedness, and monotonicity, which are essential for a reliable decision-making process. A mathematical model is presented as a case study to evaluate the effectiveness of the proposed model in selecting an investment policy. The results show that the proposed model is effective and provides more accurate investment policy recommendations compared to existing methods. This research can help investors and financial analysts in making better decisions and achieving their investment goals.  
intuitionistic fuzzy sets; intuitionistic hesitant fuzzy sets; three-way decision; decision-theoretic rough sets; Aczel-Alsina aggregation operators; decision making +
Footnote †: journal: Applied Sciences
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 4 Obb-Srp-Phat
### Offline Database Construction
DPC is based on two assumptions:the local density of the center is bigger than that of the neighboring sample points; the distance between any two centers of different clusters is far away. To find the centers satisfying the two assumptions simultaneously, DPC introduces the local density. We assume that the local density of \(x_{i}\) is \(\rho_{i}\). \(\rho_{i}\) is a Gaussian kernel function as:  
\[\rho_{i}=\sum_{j\in I_{5}\backslash\{i\}}\exp{(-(\frac{d_{ij}}{d_{c}})^{2})} \tag{10}\]  
where \(d_{ij}\) is the distance between \(x_{i}\) and \(x_{j}\), and \(d_{c}\) is the cutoff distance. We designate \(\delta_{j}\) as the distance from \(x_{i}\) to the closest sample point \(x_{j}\) with a bigger density than \(x_{i}\),  
\[\delta_{j}=\min_{j\
eq j>\rho_{i}}\big{(}d_{ij}\big{)} \tag{11}\]  
For the sample point with the biggest local density, its \(\delta_{j}\) is as follows  
\[\delta_{i}=\max_{j}\big{(}d_{ij}\big{)}. \tag{12}\]  
By constructing the decision graph of \(\delta_{j}\) versus \(\rho_{i}\), allocating the sample points, and deleting noise points, the clustering results can be obtained. The algorithm flow of DPC is shown in Algorithm 1. ```
Step 1. Calculating the distance between any two sample points Step 2. Calculating the local density \(\rho_{i}\) of any sample point \(x_{i}\) according to the cutoff distance Step 3. Computing \(\delta_{i}\) for any sample point \(x_{i}\) Step 4. Plotting the decision graph of \(\delta_{j}\) versus \(\rho_{i}\) Step 5. In the decision graph, designating the points with big values of both \(\rho_{i}\) and \(\delta_{j}\) as the centers of clusters, and the points with big \(\delta_{j}\) and small \(\rho_{i}\) as noise Step 6. Allocating the remaining points, with each of them being assigned to the closest cluster in which the points have a larger local density
```  
**Algorithm 1** The Flow of DPC. According to the above steps, the cluster center, i.e., the actual location \((\theta_{i},\varphi_{i})\) of each sound source position, is finally obtained and stored in the ODB.
## 1 Introduction  
Lateral ankle sprains (LAS) are one of the most common musculoskeletal injuries consequent to physical activity and are related to a high rate of recurrence after the first episode [1]. The most commonly affected anatomical structures are the anterior latofibrular ligament (ATFL), the calcaneofibrular ligament (CFL) and, less frequently, the posterior talofibrular ligament (PTFL), which stabilize the lateral side of the talocrural joint [2].  
Up to 40% of affected individuals experience the development of a condition known as chronic ankle instability (CAI), when symptoms such as local pain, tenderness, swelling, feeling of instability and frequent falls last for more than 3 months [3].  
The initial treatment of CAI is based on a conservative approach with an adequate physiotherapy program, while surgical indication should be proposed in the case of persistence of pain and instability after at least 3 months of treatment [4]. The instability that requires surgery can be functional and/or mechanical: the first type is reported by the patient as a feeling of instability and loss of strength due to neuromuscular and proprioceptive deficits, while the latter can be identified during physical examination as laxity and local tenderness [5]. As this condition can affect young patients, the surgical approach is also proposed for children and adolescents under 18 years old [6]. The surgical procedures range from repairs to non-anatomic tenodesis or anatomic reconstructions using autografts, allografts or fibular periosteal flaps [7; 8; 9]. Ankle arthroscopy can be performed in conjunction with these procedures in order to diagnose and treat eventual concomitant lesions such as osteochondral lesions, impingement, syndesmosis lesions or loose intra-articular bodies [10].  
As far as anatomic reconstruction is concerned, open techniques have good clinical results but present complications regarding wound healing, post-operative fibrosis and stiffness [11]. As a consequence, minimally invasive techniques are nowadays an interesting solution for the treatment of CAI with a demonstrated reduction in such complications [12; 13].  
A minimally invasive reconstruction of the lateral ligament complex using semi-tendinosus tendon autograft in chronic lateral ankle instability has been described, and demonstrated satisfying clinical outcomes and return to sport [14]. The purpose of the present study is to retrospectively assess clinical outcomes, return to sport and recurrence rate after minimally invasive reconstruction of the lateral ligament complex using semi-tendinosus tendon autograft at a mid-term follow up. Our hypothesis is that this study will confirm the promising outcomes and complication rate at a 5-year minimum follow up.
# SimEx: A Tool for the Rapid Evaluation of the Effects of Explosions
###### Abstract  
The dynamic response of structural elements subjected to blast loading is a problem of growing interest in the field of defense and security. In this work, a novel computational tool for the rapid evaluation of the effects of explosions, hereafter referred to as SimEx, is presented and discussed. The classical correlations for the reference chemical (1 kg of TNT) and nuclear (10\({}^{6}\) kg of TNT) explosions, both spherical and hemispherical, are used together with the blast wave scaling laws and the International Standard Atmosphere (ISA) to compute the dynamic response of Single-Degree-of-Freedom (SDOF) systems subject to blast loading. The underlying simplifications in the analysis of the structural response follow the directives established by UFC 3-340-02 and the Protective Design Center Technical Reports of the US Army Corps of Engineers. This offers useful estimates with a low computational cost that enable in particular the computation of damage diagrams in the Charge Weight-Standoff distance (CW-S) space for the rapid screening of component (or building) damage levels. SimEx is a computer application based on Matlab and developed by the Fluid Mechanics Research Group at University Carlos III of Madrid (UC3M). It has been successfully used for both teaching and research purposes in the Degree in Security Engineering, taught to the future Guardia Civil officers at the Spanish University Center of the Civil Guard (CUGC). This dual use has allowed the development of the application well beyond its initial objective, testing on one hand the implemented capacities by undergraduate cadets with the end-user profile, and implementing new functionalities and utilities by Masters and PhD students. With this experience, the application has been continuously growing since its initial inception in 2014 both at a visual and a functional level, including new effects in the propagation of the blast waves, such as clearing and confinement, and incorporating new calculation assistants, such as those for the thermochemical analysis of explosive mixtures; crater formation; fragment mass distributions, ejection speeds and ballistic trajectories; and the statistical evaluation of damage to people due to overpressure, body projection, and fragment injuries.  
Keywords:effects of explosions; blast loading; SDOF systems; thermochemistry of explosives; fragments; crater formation; damage to people +
Footnote †: journal: Applied Sciences
## 3 Coverage Control Using Memetic Algorithm
### The CoCMA Proposed for Cluster-based WSNs
#### 3.2.3 Genetic Operations  
The GA operations encompass selection, crossover and mutation. There are several kinds of strategies for the selection operation; the most popular ones are the proportional fitness, roulette wheel and tournament. In the CoCMA, we adopt the tournament selection strategy that seeks the optimal fitness of each generation more efficiently. The tournament selection runs a tournament among randomly chosen individuals and then selects the winner for crossover. According to the genetic representation for the CoCMA described above, every allele represents the status of a sensor node: 1-active or 0-inactive. The chromosome with higher fitness implies that the CWSN has a better schedule for sensor nodes to cope with SCP while applying CoCMA. In the process of tournament selection, ten chromosomes are randomly selected from the population, and the elitist chromosome is then selected as parent chromosome 1. The second best chromosome is selected as parent chromosome 2. Both the selected parent chromosomes will breed the new chromosomes of offspring by crossover.  
In order to maintain the diversity of a population, determining an appropriate crossover rate is important. The crossover rate \(R_{c}\) specifies the probability of producing two new chromosomes from two selected chromosomes by crossover. For the crossover task, the CoCMA uses a single-point crossover.  
Mutation is a genetic operator that helps the entire GA to prevent the population from being trapped in a local optimal solution. It alters one or more values in any possible gene allele. Thus, the new individual is added to the original gene pool. The mutation of GA operations usually occurs after crossover. The mutation rate \(R_{m}\) specifies the probability of altering the value of an allele from its original value. In general, \(R_{m}\) is set to a low value to prevent the search process from turning into a brute-force one. Through the iterative operations of crossover and mutation, the final offspring have a higher fitness on average. According to the paper presented by [PERSON] _et al._[33], the crossover rate and mutation rate can be optimized by attaching the parameters to individuals during the evolutionaryprocess. However, we set both of them as fixed values in order to simplify our study. For detailed descriptions regarding the GA, please refer to [35].