esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Economic Performance Comparison of TCED Processes  
In this section, the capital cost, utilities cost, and TAC for the three solvents will be compared. Table 4 presents these results for the conventional extractive distillation systems (TCED) and the hot stream heat recovery designs (SH-TCEDs).  
It is shown that in the case of DMSO solvent, the flowsheet with heat recovery from hot streams (SH1) does not lead to a reduction in capital cost because the flow rate and temperature of the solvent stream are the lowest compared to for the other solvents. This is reflected in a smaller reduction in the EDC reboiler's duty, steam consumption, and its area (see Table 3). Therefore, the effect of heat recovery from the hot DMSO solvent does not compensate for the cost of the new Heater 1 and Heater 2 of the SH flowsheet. The energy saving is only 10.45%, and the TAC decreases by only 3.54%. So, the two glycols (i.e., EG and PG) are still considered competitors from our point of view. The results from Tables 1-3 support those in Table 4, namely, EG as an extractive distillation solvent in IPA dehydration has more advantages than using PG as a solvent. The capital cost is reduced by 10.79%, the utility cost is reduced by 17.03%, and the total TAC is reduced by 13.95% when using EG as a solvent in the SH scheme compared to the conventional TCED. Even if we compare only conventional TCED, it still turns out that DMSO has higher capital, utilities, and TACs than EG.  
For the same economic factors, PG solvent indicates higher costs because its flow rate is much higher than that of the other two solvents. PG possesses several advantages, including lower toxicity to human and animal health compared to EG [23,24]; it is biodegradable [25,26] and has lower cost than EG, and the extractive distillation dehydration process offers more possibilities for full heat integration for the recovery of heat from the hot solvent stream, as shown our previous work [18].
## 4 Case Study of Data Collaboration
### Dataset Description  
The case database of this paper consists of three parts, a GPS dataset, a UAV video dataset, and a building model dataset. The GPS dataset is continuous data from 18 July 2020, to 18 August 2020, and there are about \(3\times 10^{8}\) trajectory records per day. The main fields are longitude, dimension, acquisition time, direction, velocity and acceleration. The UAV dataset is video data of seven intersections- about 1680 min. The building model dataset includes building material types and more.  
Figure 4: Data input interface. The dynamic data interface is mainly for dynamic interactive data in the data layer described in Figure 2, while the static data interface is for static modelling data; the subjective selection data interface is an interface for designers to process data according to the subjective conditions (such as threshold, filter conditions, etc.) required by practical applications.
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 3 Results
### K-Means Clustering Algorithm  
According to the traditional K-Means clustering algorithm, k value needs to be specified in advance. We in turn specified the value of k, drew the sum of squares due to error (SSE) curve, and then determined the specific value of k.  
Due to the possibility of no solution in the traditional K-Means clustering algorithm, when the K-Means algorithm was initialized, the clustering centers were randomly selected. In order to ensure the validity of the results of the K-Means clustering algorithm. We conducted 10 experiments for each k value of the K-Means algorithm, and took the average of each cluster center in 10 experiments as the final cluster center for each k, then calculated the SSE, and the SSE curve is shown as Figure 13.  
Figure 13: _Cont._According to Figure 13, it can be found that as the value of k increased, the SSE value decreased. When the value of k was 7, the SSE reached the minimum value, as the value of k continued to increase, the SSE gradually increased, so the best number of clusters was 7. The traditional K-Means algorithm clustering effect is shown in Figure 14.  
Figure 14: The results graph of the K-Means cluster algorithm: (**a**) S1; (**b**) S2; (**c**) S3; and (**d**) S4.  
Figure 13: Sum of squares due to error (SSE) curve of K-Means algorithm from 4 universities: (**a**) S1; (**b**) S2; (**c**) S3; and (**d**) S4.  
According to the SSE curve, the best value of k was 7. So, we used the traditional K-Means clustering algorithm to cluster and chose 7 as the value of k. Table 5 is the mean value of the cluster centers under multiple clustering.
## 3 Tunnel Stability Based of In-Site Monitoring  
### Layout Scheme of Monitoring Points  
To ensure tunnel stability, nine monitoring points were arranged to monitor surface settlement with the tunnel centerline as the symmetry axis, and the lateral distance of the monitoring points is 5 m. The layout of these points is shown in Figure 2. Additionally, other monitoring points are placed at the vault and upper and lower steps to analyze the deformation patterns and stability [19; 20; 21] of the surrounding rock of the tunnel during its construction. These monitoring points are shown in Figure 3. In this project, the monitoring equipment includes a leveling instrument, total station, steel ruler, and convergence gauge. The surface settlement was measured and monitored by the leveling instrument, total station, and steel ruler; subsidence of the vault was monitored by the total station, steel ruler, and convergence gauge; and horizontal convergence was measured by the total station and convergence gauge.
## 3 A Review of Evaluation Criteria
### Evaluation Measures Related to Energy Usage  
Not surprisingly, given the intrinsic constraints of WSNs, almost all evaluation strategies include some form of energy metric. Different energy related metrics that are used by the researchers are listed below.  
* The metric most commonly used to evaluate the efficiency of the sensor network is the overall energy consumption. This is measured by adding the energy dissipation by each sensor in the network.
* Another important metric is energy distribution. This is a qualitative metric, rather than quantitative one. This metric measures how evenly the energy dissipation is distributed. This is important for the sensor network to balance the energy consumption by the participating sensor nodes. If some sensors dissipate energy rapidly compared with other group of sensors, _i.e._, energy consumption is not evenly distributed, this adversely affects the system lifetime of the network.
* Average dissipated energy in an important metric. This is the ratio of total energy used per node to the number of events detected.
* A useful evaluation metric list includes \"Resource expended per packet delivered\" metric. Here \"resource expended\" refers to the numbers of connected pairs that are broken down because of nodes being depleted on its power. In other words, it is defined as the ratio between numbers of broken pairs to the total packets delivered.
* The metric \"packets before partition\" is measured by the number of data packets sent and successfully delivered before network partition (partition due to node energy depletion).
# Deadlocks Detection in Multithreaded Applications Based on Source Code Analysis
## 7 Using the Source Code Model of the Multithreaded Application in Practice
### Implementation of the Method  
To locate a conflict that causes a deadlock using the source code model of a multithreaded application, the application must be represented as an operation graph. An example implementation of the method that allows one to detect deadlocks caused by bypassing the release of the lock in conditional instructions has been published on the GitHub portal ([[http://bit.ly/2](http://bit.ly/2) lsK7 zz]([http://bit.ly/2](http://bit.ly/2) lsK7 zz)) in the resource_conflict_detector repository. Python was used to implement the method, and the created application takes, as a parameter, the path to the directory where the code of the multithreaded application written using the pthreads library is located. In the further part of the article, the application with the method implementation will be referred to as resource conflict detector (RCD). RCD first divides the multithreaded application into functions. This division facilitates the conversion of code to the multi-thread application source code model. These functions are analysed to detect the presence of threads in the application. The next step is to assign threads to the appropriate time intervals. Having a set of threads and time intervals, it is possible to determine which of the operations performed by threads are protected by locks. These operations create a tree structure, which includes, among other things, locking and unlocking operations. Therefore, to locate the resource conflict causing the deadlock, one must find a sufficient condition for it to occur.  
The current implementation has many limitations. The first is the assumption that there is a single operation on one line of the source code. Placing several operations on one line may result in skipping the operation, which will affect the process of locating the resource conflict. The application also cannot recognise locks. Therefore, a deadlock resulting from an attempt to re-establish an already established lock in the same thread will not be reported. The application's behaviour will be similar when a lock other than the one established is released.  
Some of the current limitations can be eliminated by using abstract syntax trees (AST) already described in the 1970s in \"Principles of Compiler Design\" [24]. Their use in the considered case would be redundant because a lot of information present in the application code is omitted as they do not affect the described phenomenon. In addition, some AST expressions of programming language have identical forms, which can lead to error reporting in places where errors are not found. This situation is called \"false-positive error\" and was described, among others, in work on the automated refactoring tool called CoderAider [25].
# Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
## 3 Discussion  
The mAP value results were higher than 96.6% (Table 3), indicating a high precision of the neural network model (YOLOv4) used to identify cracks in concrete. As such, it is possible to use this framework in contexts where the classes to be identified, in this case, cracks, have visual characteristics with poorly defined elements, unlike, for example, a car or a person. On the other hand, it is considered that the number of images used for training (792) is an important characteristic. Furthermore, a greater number and variety of images introduced during training can improve model performance. For example, refs. [10] and [28] used between 3000 and 3500 images, while [9] and [29] used 40,000 and 12,000 images, respectively.  
Although the YOLOv4 neural network model has been previously used in other investigations within construction processes, such as those of [4; 5; 6; 7; 8], its use for detecting cracks in concrete has only been studied to a lesser extent. Research by [3] using YOLOv5 obtained high precision (mAP = 0.976), achieved using focal loss, pruning, and data scaling techniques during image preprocessing. In the case of the present investigation, pruning was not used, in addition to a relatively limited dataset of 1132 images, with 792 used for training. Since the results obtained are (mAP between 96% and 99%) above the minimum expected threshold (mAP = 0.70), they demonstrate that the use of a model with better performance characteristics, such as YOLOv5, YOLOv6, or YOLOv7, together with a higher amount and variability of training images can achieve better model performance. The possibility that the current model suffers from underfitting (high bias and low variation) may cause the lower performance of the model; however, it can be overcome either by modifying the parameters of the model (for example, by increasing the number of epochs or stages) or by increasing the training data.  
Regarding the characterization of the fracture, in which a simple count of pixels with a chromatic value greater than 160 has been used, the results indicate the feasibility of use in certain conditions. First, the general \"whiteness\" of the negative greyscale image affects the pixel count, as can be inferred from Figure 13c, which contained several white pixels comparatively higher than the other two samples (7257 in Figure 13c versus 650 and 62 pixels in Figures 11c and 12c, respectively). This evidences the need to calculate the average \"white\" value to determine the threshold. Second, while the image size was not a variable in this study, it can affect the results if images of different sizes are compared against each other.
## 2 Data and Methods
### Climate Indices
#### 2.2.4 Pacific Decadal Oscillation (PDO)  
The PDO is a recurring pattern of ocean-atmosphere climate variability centered over the Pacific basin north of 20\({}^{\circ}\) N. During the positive phase, the west Pacific becomes cooler and part of the eastern Pacific warms. The wintertime Aleutian Low is deepened and shifted southward, advecting warm, humid air along the North American west coast. Air temperatures are higher than usual from the northwest North America to Alaska but below normal in Mexico and the southeastern United States. Winter precipitation is higher than usual in the Alaska Coast Range, Mexico, and the southwestern United States but reduced over Canada, eastern Siberia, and Australia ([30]). During a negative phase, the opposite pattern occurs.  
The PDO index is derived by performing an EOF analysis on monthly sea surface temperature anomalies over the North Pacific after the global average sea surface temperature has been removed. The PDO index is the leading principal component time series. Its monthly time series is available at: [[https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/](https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/)]([https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/](https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/)) (accessed on 15 June 2021).
## 3 Methods  
This paper proposes a method that integrates an autonomous vehicle's map database with a visual localization system to enable relocalization within the scene map. As shown in Figure 1, the process involves the following steps:First, the vehicle collects map data and obtains geographic and depth information from images via RTK and depth estimation modules. Simultaneously, feature information is extracted from the left-eye image to build the scene map database. Next, the vehicle captures the left-eye image, which serves as the input image (\(I_{q}\)). The most similar image (\(I_{k}\)) is then retrieved from the database. This retrieval is achieved through feature extraction and correlation analysis, which generates correspondences between the query image and the retrieved image. The matched image (\(I_{k}\)) is then used as a feature pair and input into the feature matching algorithm module. During feature matching, 2D feature points are extracted from both the input image (\(I_{q}\)) and the matched image (\(I_{k}\)), establishing their correspondences. Given the known geographic coordinates and camera intrinsic parameters of the retrieved image (\(I_{k}\)), along with the depth information from the database, the 2D-2D correspondences are transformed into 2D-3D representations. Finally, the global pose of the vehicle relative to the world coordinate system is calculated using the PnP and L-M algorithms, thus achieving visual relocalization.  
Figure 1: The framework of the relocalization method for autonomous vehicles, enhanced through an integrated feature matching model. The process is as follows: image data are captured using the vehicle’s onboard camera; depth information is computed using the MonSter model; features are extracted from the image using ResNet-50, and a feature database is created; querying is performed based on the image captured during the vehicle’s startup; image matching is conducted using the integrated matching model; finally, the vehicle’s pose relative to the map is determined through 2D-3D correspondences.
# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 2 State of the Art: AI Algorithms Applied or Applicable in the Global Agrifood Industry  
Artificial intelligence (AI) algorithms, encompassing a diverse array of methodologies such as supervised and unsupervised learning, neural network-based approaches, reinforcement learning, and other emergent technologies are driving profound transformations within the global agrifood sector. These sophisticated computational techniques are being deployed to surmount challenges that previously seemed insurmountable. In turn, they enhance operational efficiency and foster sustainability across the entire agrifood value chain. AI applications in this domain are multifaceted. They can go from precision crop monitoring and pest detection in agricultural practices to advanced predictive modeling and resource optimization during the industrial phases of production.  
This section undertakes a thorough review of AI algorithms that, based on an exhaustive analysis of pertinent academic literature, exhibit the highest potential for application in the agrifood industry or are already being actively used. The selection of these specific algorithms and methodological families are informed by the foundational insights presented in \"Artificial Intelligence: A Modern Approach\" [1], a groundbreaking work that outlines the fundamental principles and state-of-the-art advancements in AI. While this review is not intended to encompass an exhaustive survey of all existing AI algorithms--a task more suited to a comprehensive monograph--it highlights proven key methodologies within the agrifood sector. These include, but are not limited to, machine learning, deep learning, and optimization techniques. Each of them has demonstrated its efficacy in addressing targeted challenges within agriculture and food processing.  
The integration of these AI algorithms into the agrifood industry addresses immediate operational challenges and contributes to long term sustainability goals by promoting efficient resource utilization and reducing environmental impacts. As AI technologies continue to evolve, their applications within the agrifood sector are expected to expand, driving further innovations and enhancing the resilience of agricultural systems globally.
## 3 Results  
### Measurement of System Sensitivity to External Noises  
To demonstrate the immunity of the proposed method to external noise, we monitored temporal changes in the interference signal subject to periodic high-frequency (>100 Hz) vibration induced by a mobile phone on the optical table, in addition to intermittent artificial disturbances, caused by low-frequency (few Hz) tapping applied to the corner of the optical table, to mimic industrial noises. We compared the observations with the result from a double-arm [PERSON] interferometer used in a previous study [9] under the same conditions. The latter was implemented by adding a reference arm to our common-path set-up after removing the reference glass. We measured the intensity of the interference signal as the mean of the pixel values in a 3 \(\times\) 3 kernel (box) taken at the destructive interference waveform (see inset in Figure 2a). Figure 2a shows time-course interference signal intensities recorded for 8 s using the proposed method and the double-arm interferometer. For a certain interval during vibration (0-3 s), the double-arm system exhibited large fluctuations in magnitude (Video S1). These fluctuations resulted from the concurrent but separate vibration of the different OPLs of the two arms. The fluctuation was greater after 3 s when the optical table was repeatedly tapped by hand along with the vibration. These charts indicate that the traditional interferometric method is prone to interference from external perturbations; therefore, conventional interferometric methods are not suited to evaluate the pad surface in situ. In contrast, the common-path system exhibited relatively minute variations in the magnitude throughout (0-8 s) (Video S2). The small ripples on the waveform may have arisen from intrinsic camera noises such as shot noise. The signal fluctuation was compared with standard deviations (SDs) of the magnitudes for the interval with vibration only (0-3 s), tapping plus vibration (3-8 s), and the whole duration (0-8 s), as shown Figure 2b, in which the SDs of the double-arm system were 9-13 times greater than those of the proposed approach. The results showed that the common-path method was unaffected by any mechanical perturbations, thus resulting in noise-immune interferometry.  
Figure 2: Interference signal stabilization using the proposed method. (**a**) Time-course interference signal intensities in the presence of surrounding noise (0–3 s vibration, 3–8 s vibration + tapping). Despite the external noise, the signal magnitude (red line) is stabilized by the proposed common-path interferometer (Video S2), whereas for the existing double-arm interferometer, the magnitude (dotted line) experiences considerable fluctuations (Video S1). (**b**) Comparison of the fluctuation in magnitudes with the corresponding standard deviations for each interval.
# Phenotypic Definition and Genotype-Phenotype Correlates in PMPCA-Related Disease
## 3 Results  
### Case Report and Genetic Results  
The proband (NG2880), a 12-year-old girl, was born to healthy Italian unrelated parents; an older brother is healthy. She presented an early-onset progressive encephalopathy characterized by global developmental delay with psychomotor regression since age 18 months, intellectual disability, and microcephaly. Her neuromotor phenotype is complex, combining ataxic features, pyramidal signs, and a major hyperkinetic movement disorder with choreic, ballistic, and dystonic components. At age 9 years, she started manifesting seizures, which are only partially controlled by pharmacological treatment with Levetiracetam.  
Brain MRI at 18 months of life only showed cerebellar atrophy. A later MRI exam at age 4 years confirmed the non-progressive cerebellar atrophy but disclosed thinning of the corpus callosum, mild cerebral atrophy, and bilateral T2-hyperintensity and hypotrophy of both putamen and caudate (Figure 1A). Blood lactate and muscle biopsy were normal, and there were no lactate peaks at MRI spectroscopy, ruling out a mitochondrial encephalopathy.  
Given the phenotypic complexity and the difficulty to select a specific gene panel, the patient was addressed to a clinical exome analysis, which identified two novel heterozygous missense variants (c.833G > A, p.Trp278 Leu; c.1084A > G, p.Arg362 Gly) in the _PMPCA_ gene, both affecting highly conserved residues (Figure 1B). Variants were both classified as likely pathogenic according to ACMG guidelines. They are absent in public databases and consistently predicted as damaging by several prediction softwares, such as Polyphen2, S  
Figure 1: (**A**) Brain MRI of patient NG2880. Sagittal T2-weighted image (left panel) showing global cerebellar atrophy and thin corpus callosum; axial T1-weighted image (right panel) showing hyperintensity and hypotrophy of caudate nuclei and audate nuclei and putamen (red arrows). (**B**) Electropherograms showing the two identified variants and conservation across species.