esa-sceva/satcom-chunk-collection huggingface dataset:
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
## 3 Results and Discussion
### Preliminary Cleaning Testing  
After reviewing the different cleaning procedures reported in the literature [50; 51; 52; 53], we selected three methods: (i) mechanical cleaning with liquid nitrogen, (ii) cleaning with sodium hypochlorite, and (iii) cleaning with hydrogen peroxide. They were tested in biofilms from Sala del Aguila.  
The cleaning with liquid nitrogen on biofilms was performed with a brush. Liquid nitrogen provided a cleaning technique that combined mechanical removal with freezing. This protocol, in the case of a biofilm composed mainly of the bryophyte _E. verticillatum_, or cyanobacteria and green algae, was much less effective than when using chemicals, because it was not able to completely remove the green biofilms.  
The cleaning with sodium hypochlorite on the biofilm of cyanobacteria and algae was effective, and was maintained for a long time (Figure 7). This treatment has been applied in many caves all over the world for removing lampeniflora [52]. Two months later, the rock presented a whiter hue than at the end of the cleaning, indicating that the chemical had a residual effect.  
Figure 7: Cleaning of phototrophic biofilms with sodium hypochlorite. (**A**) Before treatment. (**B**) After treatment.  
Cleaning with hydrogen peroxide on _E. verticillatum_ showed that the hydrogen peroxide applied directly with a brush was effective. In the case of abundant colonization of bryophytes, it is preferable to use the mechanical removal of the organisms, that can be done easily, and to only apply hydrogen peroxide to the residual protonema and rhizids that remain on the rock. Likewise, in the areas on which bryophytes are growing, lighting should be removed (Figure 8).  
The cleaning with hydrogen peroxide on biofilms of cyanobacteria, algae and bryophytes was very effective as it eliminated the phototrophic community without leaving residues (Figure 9). This was considered an environmentally acceptable procedure for removing cave biofilms [51]. Cleaning efficiency can be maintained after treatment if the lighting is removed after cleaning. In fact, under these environmental conditions, the cleaned areas should not develop new biofilms and will guarantee the persistence of the cleaning effect.  
From the preliminary tests carried out in the cave, it can be inferred that the most suitable methods for the cleaning and removal of the extensive green biofilms coating the cave surface were the use of hydrogen peroxide and sodium hypochlorite. The first should be used preferentially for its innocuousness. The second can leave residue, and odor can remain for a short time. Because of its complicated transport and execution, as well as possible danger to the applicator and for not having shown greater effectiveness than the other two treatments, the use of liquid nitrogen is not recommended.  
Figure 8: Cleaning with hydrogen peroxide on _Eucladium verticillatum_. (**A**) Before cleaning. (**B**) After cleaning.  
Figure 9: Cleaning of biofilms with hydrogen peroxide. (**A**) Before cleaning. (**B**) After cleaning.
## 1 Introduction
In Section 2, we briefly describe the signals transmitted and receive by the OTFS system and provide a discrete-time formulation of the OTFS channel in V2X scenarios. Then, the channel estimation is formulated as a sparse signal recovery problem, and a GAMP-SBL solution is proposed in Section 3. Simulation results and complexity analysis are given in Section 4. Finally, conclusions are drawn in Section 5.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
All original studies written in English focusing on the detection of genetic variations, including haplotypes, microsatellites, copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) in patients with schizophrenia, schizoaffective or delusional disorder, and who were also suffering from sleep disorders including antipsychotic treatment-induced sleep disturbances, were considered eligible to enter the review. According to the latest edition of the International Classificationof Sleep Disorders (ICSD), sleep disorders are classified in seven major categories, namely insomnia disorders, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep-wake disorders, sleep-related movement disorders, parasomnias, and other sleep disorders [13]. We only included studies in which schizophrenia and sleep disorders diagnosis was based on a comprehensive psychiatric assessment and the use of validated instruments (semi-structured interviews or questionnaires) according to the diagnostic criteria of the Diagnostic Statistical Manual (DSM-5) or the International Classification of Diseases (ICD-10) [16; 17]. We excluded studies failing to provide adequate demographic, clinical and genetic data for their participants. We also decided to exclude studies of genetic variations in loci known to be implicated in schizophrenia or sleep pathophysiology--such as genome-wide association studies (GWAS), whose methodology did not include the clinical evaluation of both schizophrenia symptoms and sleep patterns--in an attempt to focus exclusively on findings derived from clinical populations. We used a data collection report to extract data for each study, including name of first author, year of study and publication, country, number and demographic data of participants, clinical characteristics of the sample, genotyping method, genes and genetic variations examined, and major outcomes.  
Moreover, a network of the relevant genes was generated through the use of Ingenuity Pathway Analysis (IPA) in order to highlight possible correlations. IPA is a web-based software developed by QIAGEN which can identify gene associations, as well as subnetworks and canonical pathways relevant to a specific dataset (QIAGEN Inc., [[https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis](https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis)]([https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis](https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis))).
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 4 Latent Heat Measurements of PR Data
### Vertical Profiles of the LH over Land and Oceans  
Because of the well-known land-sea difference, some systematic differences were revealed for vertical profiles and diurnal variations of the LH over land and over oceans. Two peaks were found at about 3 km and 7 km in LH profiles over oceans [93; 94], while only one peak at about 7 km over land [94; 95; 96]. [PERSON] et al. [93] discovered that the upper peak was associated with a high precipitation rate and the lower peak was associated with a low precipitation rate. [PERSON] et al. [94] noticed that the upper peak was associated with mesoscale convective systems (MCS) while the lower peak was associated with small shallow precipitation. [PERSON] et al. [95] and [PERSON] et al. [94] measured the difference in the fractional contribution of the LH at an altitude over different land areas including central Africa, South America and United States, and the difference might be attributed to differences in convection intensity and in the size of the MCSs (Figure 5). Regarding the diurnal cycles, the LH was much stronger in the late afternoon over land and was a little stronger in the early morning over oceans [94]. However, it was found that the LH showed higher values along the southern slopes of the Himalayas during the night and early morning than during the afternoon [96] and this exception might be attributable to the impact of the Himalayas on the latitudinal distribution of the LH.
## 3 Development of Prediction Model of Ground Subsidence
### Description of Prediction Model  
In this study, we constructed a model for predicting the amount of ground subsidence due to liquefaction using extreme gradient boosting (XGBoost) [55] and LightGBM [56] algorithms on existing data. Specifically, we utilized XGBoost version 1.7.3 and LightGBM version 4.1.0, operating on a 64-bit Windows 11 Pro system. Gradient boosting reduces loss at each step based on the gradient vector function [30]. In this process, gradient-boosting decision trees are integrated into XGBoost and LightGBM, functioning to create an ensemble method that combines multiple weak learners to create a strong model [57].  
XGBoost is an ensemble-learning algorithm based on decision trees and is known for its high accuracy. It employs a level-wise approach to tree growth, simultaneously expanding all tree levels. This method ensures balanced growth of trees at each step, leading to more evenly developed structures. XGBoost offers numerous features, including handling of missing values and regularization, to prevent overfitting. It supports distributed and parallel computing, making it suitable for large datasets, although the computation time tends to increase proportionally with the data size.  
LightGBM, developed by Microsoft, is an efficient gradient-boosting technique. It adopts a leaf-wise growth approach, prioritizing branches from the leaves that contribute the most to losses. This method can create imbalanced trees, but often leads to faster and more accurate model development owing to greater reductions in loss during splits. LightGBM is specialized for the efficient processing of large datasets and enhances computational efficiency using techniques such as gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB).
## 3 Result Analysis
### Time Differences Analysis
#### 3.1.2 Time Differences Analysis Based on Empirical Mode Decomposition Method  
Through the above analysis process, we have a preliminary understanding of the trend of the public support intention and implementation intention over time. In order to further clarify the law of its internal response over time, this paper innovatively introduces the empirical mode decomposition (EMD) method for research. The EMD method is a signal decomposition method in the Hilber-Huang transform, which is appropriate for nonlinear and non-stationary time series. It can decompose complex signals into several intrinsic mode functions (IMF) with different scales, stationarity, and periodic fluctuation characteristics, and a trend term [45]. The functional relationship is shown in Equation (1).  
\[\mathrm{s_{t}} =\sum_{\mathrm{i=1}}^{n}\mathrm{imf_{i}(t)+r_{m}(t)}, \tag{1}\]  
st: Original signal; \(\mathrm{imf_{i}(t)}\): The i-th IMF component; \(\mathrm{r_{m}(t)}\): The trend term.  
The EMD method has been widely used in celestial body observation data, seismic record analysis, and mechanical fault diagnosis, etc. In recent years, the EMD method has also been used to analyze price changes, such as agricultural product price changes [32] and crude oil price changes [46].  
The change of public emotion time series is a complex process. We calculate the average value of the public support intention and implementation intention by week from 11 August 2020 to 18 October 2020. A simple line chart is drawn accordingly (Figure 3a). It is found that the time series of the public support intention and implementation intention are nonlinear. Based on this, this study uses the EMD method to decompose the public support intention and implementation intention.  
The experimental process is realized by MATLAB R2019b v9.7.0 software, and the results are shown in Figure 3b. In terms of the support intention, the data are decomposed into two IMF and a trend term by the EMD method. Through observing the original data signal, the support intention has no obvious variety regulation. However, analysis of the IMF term and trend term obtained by decomposition shows a change in IMF from the highest mode to the lowest mode. This is evidenced by an increase in amplitude, a gradual decrease in the frequency of fluctuations and an increasing period of fluctuation. In addition, the trend term is decreasing, which indicates that the support intention tends to decrease in the period after the release-style communication.  
In terms of the implementation intention, the data are decomposed into two IMF and a trend term through the EMD method. There is no apparent change rule of the implementation intention based on the original data signal. However, the analysis of the IMF term and trend term shows that the fluctuation period of IMF1 is short and frequent, while the fluctuation period of IMF2 is gentle and long. Furthermore, the trend term is an upward trend, which reflects the implementation intention increases after release-style communication.
## 3 Results and Discussions
### Pozzolanic Reactivity
#### 3.3.3 Frattini Test  
The Frattini test is a widely recognized technique for evaluating pozzolanic reactivity of blended cements. After titration, the Frattini test results are presented as Ca\({}^{2+}\) and OH\({}^{-}\). A lime solubility curve is used to separate the pozzolanic area from the non-pozzolanic. A plot of the OH\({}^{-}\) against CaO in the form of Ca\({}^{2+}\) on the solubility curve gives an indication of the pozzolanic reactivity or otherwise of the blended cement. Table 3 presents the Frattini  
Figure 6: Portlandite content in hydrated calcined clay-silica fume blended cement pastes.  
test results data. Figure 7 shows the pozzolanic reactivity of the blended cement pastes at 28 days, as determined by Frattini test showing a plot of CaO against OH\({}^{-}\)[46]. Silica sand (inert material), known for its unreactivity, was used as a reference in determining the pozzolanic reactivity of the blended cements pastes. As demonstrated by the portlandite consumption in Figure 6, a specimen containing only calcined clay was found just below the lime solubility curve. This is an indication of pozzolanic reactivity, however small. The specimen containing 30% silica fume, on the other hand, was positioned far below the curve, outperforming all the blended cement pastes. This demonstrates the consumption of portlandite, confirming the material's pozzolanic properties. It is observed that specimens containing greater amounts of calcined clay showed relatively weaker reactivity due to lower kaolinite content. Silica sand is positioned within the over saturated region above the curve, signifying the absence of pozzolanic reactivity. This trend of reactivity is similar to observations reported by [PERSON] et al. [19].
# Evaluating the Impact of the COVID-19 Pandemic on Residential Energy Use in Los Angeles
###### Abstract  
The 2020 COVID-19 pandemic provided an opportunity to assess energy use during times of emergency that disrupt daily and seasonal patterns. The authors present findings from a regional evaluation in the city of Los Angeles (California, USA) with broad application to other areas and demonstrate an approach for isolating and analyzing residential loads from community-level electric utility feeder data. The study addresses effects on residential energy use and the implications for future energy use models, energy planning, and device energy standards and utility program development. In this study we review changes in residential energy use during the progression of the COVID-19 pandemic from four residential communities across Los Angeles covering approximately 6603 households within two microclimate sub regional areas (Los Angeles Basin and San Fernando Valley). Analyses address both absolute and seasonal temperature-corrected energy use changes while assessing estimated changes on energy usage from both temperature-sensitive loads (e.g., air conditioning and electric heating) and non-temperature-sensitive loads (e.g., consumer electronics and major appliance use). An average 5.1% increase in total residential energy use was observed for non-temperature sensitive loads during the pandemic period compared to a 2018-2019 baseline. During mid-spring when shelter in place activity was highest a peak monthly energy use of 20.9% increase was seen compared to a 2018-2019 composite baseline. Considering an average of the top five warmest summer days, a 9.5% increase in energy use was observed for events during summer 2020 compared to summer 2018 (a year with similar magnitude summer high heat events). Based on these results, a potential trend is identified for increased residential load during pandemics and other shelter-in-place disruptions, net of any temperature-sensitive load shifts with greater impacts expected for lower-income communities.  
residential energy modeling; COVID-19; coronavirus pandemic; temperature sensitivity; energy security +
Footnote †: journal: Medical Energy  
1 California Pug Load Research Center (CalPitug),  
California Institute of Telecommunications and Information Technology (Calit2), University of California,  
Irvine, CA 92697, USA; [EMAIL_ADDRESS] (M.J.K.); [EMAIL_ADDRESS] (J.E.P.)  
2 Los Angeles Department of Water and Power (LADWP), Los Angeles, CA 90051, USA;  
[PERSON]SS] (A.S.); [PERSON]ESS] (A.T.); [EMAIL_ADDRESS] (D.J.)
# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 1 Introduction  
Nowadays, the most critical procedures of corporations are already being conducted through Software as a Service (SaaS) products. To this direction, the critical procedure of hiring has been successfully ported to the cloud. Hiring can be modelled as a multistep process that starts with composing and advertising job ads and ends with successful hires. To accomplish their mission more effectively, hiring managers rely on a wide range of cloud-based solutions, namely the Applicant Tracking Systems (ATS).  
On the downside, the increasing adoption of ATS has also attracted the interest of scammers. In most cases, this phenomenon (a) jeopardizes job seekers' privacy; (b) leads to financial loses; and (c) vitiates the credibility of organizations. Today, as detailed in Section 3, job frauds have become rather resourceful. Phony content is hard to be distinguished from benign, so countermeasures are usually ad-hoc and their practical value is often questionable. Furthermore, as discussed in Section 4, the peculiarities of this particular field render the application of existing solutions developed for relevant problems difficult to adapt. Specifically, although considerable work has been carried out to tackle relevant problems such as email spam [1; 2; 3], phishing [4], Wikipedia vandalism [5; 6], cyber bullying [7; 8], trolling [9], and opinion fraud [10]. Nevertheless, when it comes to employmentscam, the proposed solution have proven inadequate in practice. Our preliminary study leads to the conclusion that text and metadata based classification can only provide a stepping stone towards an effective scam prediction system. A fully-fledged solution will require a composite approach that involves deep inspection and analysis of user, organization and network data.  
The contribution of this work is twofold: on one hand, we examine the diverse aspects of Online Recruitment Fraud (ORF) and more specifically employment scam, underlining its severity and distinct characteristics. Major similarities and differences between the investigated problem and related ones are also identified. On the other hand, this work offers the first of its kind publicly available dataset [11] containing real-life legitimate and fraudulent job ads, published in English spoken countries. Moreover, we offer an exploratory evaluation of the dataset by different means, including empirical evaluation, bag of words modeling, and machine learning analysis.  
The remainder of this paper is comprised of the following sections: Section 2 elucidates the value of ATS. Section 3 describes the problem at hand and elaborates on its consequences. Section 4 outlines the relevant problems and solutions proposed in the literature so far. The contributed dataset is described in full detail in Section 5. Section 6 contains the experiments conducted using this dataset. The final section contains the conclusions and closing remarks.
## 4 Performance Evaluation
### Simulation Setup  
The experiments are implemented in MATLAB R2021a (MathWorks Inc., Natick, MA, USA) on a laptop for simulation results. The hardware platform comprises the Intel(R) Core(TM) i7 9750H CPU 2.59 GHz processor (Intel Corporation, San Francisco, CA, USA) with 12 GB RAM and GPU of NVIDIA GeForce GTX 1650 (NVidia Corporation, Santa Clara, CA, USA) in Microsoft Windows 11 (Microsoft, WA, USA).
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 3 Results
### Method Robustness  
With the increasing demand for satellite communication in modern life, limited spectrum resources have become one of the important factors limiting the development of communication services; however, increasing the transmission signal bandwidth or using broadband signals cannot guarantee the demand for the continuous development of  
Figure 10: Sum rate of space compression.  
Figure 9: The effect of channel conditions on scheme performance.  
communication services. Therefore, in the background of current spectrum resources, the effective use of spectrum resources and the improvement of transmission efficiency are the research directions that communication researchers have been paying close attention to.  
The relay node receives the signals sent by multiple nodes at the same time and broadcasts the forwarding signal to multiple nodes in the next stage. Therefore, the performance of the relay node becomes the key that restricts the performance of the entire transmission system. According to the rank-zero theorem, the dimension of the right null space of the existing scheme is 1. This means that there is only one alignment signal vector available between each interacting pair of users. Therefore, the aligned signal vectors are all fixed. In most cases, these vectors are not optimal. The bit error rate of a relay system is determined by the minimum distance to align the signal constellation points. However, the traditional scheme cannot flexibly coordinate and align the positions of the signal constellation points according to the signal space structure. To increase the distance between alignment signals, the alignment space needs to be reconstructed. Utilizing the redundant degrees of freedom introduced by the additional antennas enables the optimal selection of aligned signal vectors in the redundant space. There are more and more kinds of remote sensing images that can be obtained by satellite remote sensing. The information contained in remote sensing images also tends to be diversified and complex. Figure 11 shows the performance comparison. The method proposed in this paper is used to process remote sensing data acquisition and transmission to realize the fusion of remote sensing images. Figure 11b greatly improves the effectiveness and reliability of transmission by optimizing the spatial structure of the signal and suppressing interference signals. It is found that Figure 11b can obtain more detailed remote sensing images than Figure 11a.