esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
## 4 Phase 1: Building the Data Set
### Labeling Part of the Data Set for Training  
After having a complete Spanish data set from different media and characteristics, we selected a subset to create an appropriate training data set, which must be labeled according to the presence of sexism. This was essential to train our supervised learning system to eventually be able to automatically identify sexism in new comments.  
In order to avoid an unbalanced training data set, we selected comments from approximately one third of each type of social media source (posts/hashtags, videos, and pieces of news). Besides that, we selected comments that assured a balanced representa  
Figure 2: Characteristics of the comments in the data set.  
tion of the gender of the protagonist (half of each), number of protagonists (one third of each), and context (one third of each). At the end, we obtained 4389 balanced comments, approximately 5% of the global data set.  
We need to highlight that, for this proof of concept, the _anti-sexism alert_ system does not have contextual information of the comment to be classified. Therefore, we proceeded with the manual labeling process according to four categories:  
* Yes (sexist comment): clearly the comment has a sexist connotation.
* No (sexist comment): clearly the comment does not have a sexist connotation.
* Discard: the comment cannot be used for the analysis because of the following reasons: comments with a large number of spelling or grammatical errors; comments with a high presence of non-alphanumeric characters; comments without a clear intention or with a doubtful presence of sarcasm; comments in which we do not fully understand what the person wanted to say; duplicate comments; comments in a language other than Spanish; incomplete comments; and comments that are associated with other sources of information, such as images.
* Depends on the context: it is not possible to decide if the comment has a sexist connotation or not without knowing the context or conversation.  
The labeling process was performed by four different persons, of both genders. A comment was labeled as \"sexist\" only if the majority agreed. The few times that there was a tie, we decided to label the comment as \"depends on the context\".  
After the labeling process, we obtained the results that are summarized in Figure 3. In the training set, a clearly higher proportion of sexist comments are related to content where women are the main protagonist (11.3%) and also when the content is related to a personal aspect (9.1%). Sexist comments are also more frequent in newspapers and video comments, than on Twitter, and also they are clearly higher when the content is related to a collective (8.7%), rather than when it refers to an individual woman.
# Study of the Impact of PV-Thermal and Nanofluids on the Desalination Process by Flashing
## 3 Nanofluid Heat Transfer Fluid  
The different equations reported in [16; 17; 18] were used to calculate the thermophysical properties of the nanofluids, such as the specific heat, thermal conductivity, viscosity and density using the law of mixtures. The thermophysical, thermodynamic and heat transfer properties of nanofluids are determined in terms of the volumetric concentration of the nano particles as per the following equation:  
\[\alpha_{\text{ total}}=\alpha_{\text{ particles}}+\alpha_{\text{ base fluid}} \tag{12}\]  
where \(\alpha\) represents the thermophysical property of a particular nanofluid.  
The nanofluid thermal and thermophysical properties, \(\alpha_{\text{ total}}\), can be calculated as follows:  
\[\alpha_{\text{ total}}=\alpha_{\text{ base fluid}}+\alpha_{\text{particles}} \left(\Phi\right) \tag{13}\]  
where \(\Phi\) represents the nano particles volumetric concentration.  
The thermal conductivity and thermal diffusivity and density of the nanofluids are related as follows [16; 17; 18]:  
\[\lambda=\alpha\;\delta\;C_{p} \tag{14}\]  
where \(C_{p}\) is the specific heat, \(\alpha\) is the thermal diffusivity, \(\lambda\) and \(\delta\) represent the thermal conductivity and density, respectively.  
Readers interested in further details on the calculations of the nanofluids thermophysical and thermodynamic properties are advised to consult references [16; 17; 18; 19; 20; 21; 22; 23; 24]. In particular, these references discussed the impact of the nanofluid concentrations on the thermophysical properties of the nanofluids used in this study. Moreover, the scope of this study is rather focused on the discussion of the MSF process using the PV-Thermal with nanofluids as heat transfer fluids than the thermophysical properties of the nanofluids, since they are discussed elsewhere: [16; 17; 18; 19; 20; 21; 22; 23; 24].  
Finally, the availability and irreversibility of the flashing process are calculated as:  
\[A_{q}=\Sigma(1-T_{amb}/T_{J})\;Q_{J} \tag{15}\]  
where \(A_{q}\) is the flow availability change under steady state condition per stage, and \(A_{q}\) is the availability transfer due to \(Q_{J}\), the heat transfer between the control volume and its surroundings. \(T_{amb}\) and \(T_{J}\) are the ambient temperature and the Flashing chamber temperature, respectively.  
The availability destruction i.e., the irreversibility of the flashing process per stage can be determined as follows:  
\[I_{j}=T_{amb}\;x\;S_{j} \tag{16}\]where \(S_{J}\) represents the entropy of the flashing process at each stage.
###### Abstract  
The main aim of the paper is to show how, and how many, simulations carried out using the Land Surface Model UTOPIA (University of TOrino model of land Process Interaction with Atmosphere) are representative of the micro-meteorological conditions and exchange processes at the atmosphere/biosphere interface, with a particular focus on heat and hydrologic transfers, over an area of the Piemonte (Piedmont) region, NW Italy, which is characterized by the presence of many vineyards. Another equally important aim is to understand how much the quality of the simulation outputs was influenced by the input data, whose measurements are often unavailable for long periods over country areas at an hourly basis. Three types of forcing data were used: observations from an experimental campaign carried out during the 2008, 2009, and 2010 vegetative seasons in three vineyards, and values extracted from the freely available Global Land Data Assimilation System (GLDAS, versions 2.0 and 2.1). Since GLDAS also contains the outputs of the simulations performed using the Land Surface Model NOAH, an additional intercomparison between the two models, UTOPIA and NOAH, both driven by the same GLDAS datasets, was performed. The intercomparisons were performed on the following micro-meteorological variables: net radiation, sensible and latent turbulent heat fluxes, and temperature and humidity of soil. The results of this study indicate that the methodology of employing land surface models driven by a gridded database to evaluate variables of micro-meteorological and agronomic interest in the absence of observations is suitable and gives satisfactory results, with uncertainties comparable to measurement errors, thus, allowing us to also evaluate some time trends. The comparison between GLDAS2.0 and GLDAS2.1 indicates that the latter generally produces simulation outputs more similar to the observations than the former, using both UTOPIA and NOAH models.  
land-surface; UTOPIA; NOAH; GLDAS; micrometeorology; exchanges processes; vineyards +
Footnote †: journal: Applied Soft. 2019, 9, 3880
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 4 Trajectory Optimization Based on GWO
### Trajectory Optimization  
From the geometric meaning of parameters \(e\) and \(f\) as shown in Figure 3, it can be established that the shape of the Lame curve will be vary with different parameters of \(e\) and \(f\), which will result in different pick-and-place trajectories for the Par4 parallel robot. Then, the question arises of which set of parameters \(e\) and \(f\) is more suitable for the Par4 parallel robot. Here, the GWO algorithm will be introduced to find the optimum values of parameters \(e\) and \(f\), and the optimization will be measured by the energy consumption of the Par4 parallel robot.
## 4 Security
### Confidentiality of Information  
The evaluation begins with data confidentiality including protection against eavesdropping and traffic analysis. The most efficient way to protect the industrial WSNs against eavesdropping is to encrypt the exchanged data. The WirelessHART, the ISA100.11a as well as the ZigBee standards use the 128-bit AES encryption [44] coupled with different keys depending on the layer of encryption. For example, WirelessHART uses the session key to encrypt the message at transport layer, while link and network keys are used at data link layer and at network layer respectively in the ZigBee standard. As mentioned by [45], AES remains an efficient mechanism to keep the data secret. Moreover, its efficiency is increased by the utilization of keys with short lifetime and unique for each device such as the session key used in WirelessHART. Eavesdropping is consequently made difficult or even impossible in networks running the three considered standards. However, the confidentiality is not ensured at all layers. For example, even if the packets are encrypted at transport layer, header and payload of packets sent at network layer are transmitted unencrypted in the WirelessHART standard [45]. An eavesdropper can therefore discover the crucial information, such as source and destination addresses that are contained in the network header, and perform traffic analysis easily afterwards. Nonetheless, the traffic analysis attack can also be performed without any message decryption [39].
## 3 Application Description and Requirements
### Core Features
#### 3.2.1 Authentication  
Users must be able to create individual accounts seamlessly. These user credentials will be used to create and interact with text documents and other data. Credentials should be securely stored and properly formatted to avoid exposing sensitive information.
## 3 Results and Discussion
### Determination of Lager Aging Characteristic Aroma  
The comparison of aldehydes in lagers before and after aging is shown in Table 10: 2-furaldehyde, acetaldehyde, isobutyraldehyde, glutaraldehyde, phenylacetaldehyde, and 3-methylbutyraldehyde. 2-Furaldehyde was the main heat load indicator and was not detected in commercially available lager, sugar cane lager, or OWBL before aging, while 2-furaldehyde increased to varying degrees in all lagers after aging, which can be attributed to residual carbohydrate reactions, with sugar cane lager having significantly lower residual sugar than OWBL, which had the lowest production of furfuraldehyde. From the data in the table, it can be seen that the degree of accumulation of 2-furaldehyde in sugarcane lager was lower than those of OWBL and commercially available lager, i.e., sugarcane lager had a higher level of resistance to aging than ordinary lager, which contributed to its flavor stability. However, in an experiment to measure the antioxidant power of beer, we found that the antioxidant power of LS was about 10% lower than that of OWBL. It is hypothesized that this was due to the fact that the free amino acid residue at the completion of fermentation of LS was smaller than that of OWBL, resulting in a weaker oxidation of amino acids. The increase in phenylacetaldehyde in sugarcane lager from 1.861 ug/L to 10.744 ug/L was higher due to Strecker degradation of amino acids to form aldehydes, such as 2-methylpropionaldehyde (isobutyraldehyde) and phenylacetaldehyde [32]. But, whether it is the effect of sugarcane syrup remains to be studied. The increase in glutaraldehyde during lager aging is then due to the autoxidation of linoleic acid. During forced aging, the fruity aroma of the lager gradually fades at first and the hops flavor gradually disappears, followed by a strong \"currant flavor\" and \"soy sauce flavor\", and the longer the aging time, the heavier the \"currant flavor\". [PERSON] [33] described the aging flavors after sensory panel evaluation as \"cardboard taste\", \"metallic taste\", solvent taste\", \"old hops taste\", \"cool chestnut taste\", \"merlot taste\", \"thioether taste \", \"acetaldehyde\" (\"greenapple\"), and \"white wine\". The main substance of \"cardboard flavor\" is trans-2-nonenal (T2N) [23], but it was not detected in the present study. Some researchers have suggested that aging flavors differ by lager type [34], and the \"cardboard taste\" in lagers is not the only characteristic aging taste of lager.
# Statistical Model for Mobile User Positioning Based on Social Information
## 2 Related Work
Due to this, it is considered as another source of information for understanding user behavior. The work presented in [24] provides an analysis of the tourist flows in a city, combining both social media information and mobile positioning. At the same time, in Reference [25], Twitter information is applied to study home-work urban mobility. In Reference [26], a collaborative positioning methodology is proposed, employing social information to estimate position of all users based on the GPS information of a subset of the devices. The relation between social networks activity and mobile user positioning is a demonstrated fact [27], and thus, it has been greatly studied. Some works aim to apply social network information to provide context awareness to crowdsensing applications [28; 29], in order to improve the richness of the collected data. Other approaches are focused on applying the social network data to model different aspects of cellular networks. For example, activity on social networks can be used to predict cellular traffic, regardless of radio access technologies or network providers [30; 31]. Similarly, areas of poor signal coverage or service performance (i.e., black spots) can be detected by processing geotagged text messages in social networks [32]. At the same time, social event information obtained from browser results or open data repositories can be used to explain abnormal network behavior during troubleshooting procedures [33], detect anomalies [34; 35] or predict the network performance [36]. From the network operators' point of view, a users' exact position is not as relevant in maintenance tasks. Instead, accurate knowledge of the spatial distribution of UEs in the scenario and how they perform is key in SON tasks (e.g., detecting areas with capacity problems). For this purpose, geolocated information from social networks can be an interesting data source. However, no study has been found in the literature that combines social networks and users' context information to estimate the spatial distribution of users in the network.
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Operations of AMG-Buffer
#### 3.2.2 Tuple Migration  
When loading a target page into the F-Buffer, we need to perform a migrating operation to move a victim page into the P-Buffer or the S-Buffer for releasing the space of the F-Buffer. When the clustering rate of the victim page is below the threshold, i.e., the page has a low clustering rate, we perform a tuple migration operation to move the hot or dirty tuples in the victim page to the S-Buffer. In our implementation, we use the widely-used LRU policy to select a victim page in the F-Buffer. LRU is a popular and efficient buffer replacement algorithm that has been adopted by Oracle, MySQL, PostgreSQL, etc. We identify the hot or dirty tuples by checking the _VisitBitmap_. If a tuple is hot (frequently accessed) or dirty, the bit in the _VisitBitmap_ that belongs to the page descriptor of the page containing the tuple is set to 1. Thus, by checking the _VisitBitmap_, we can quickly know whether the tuple needs to be migrated or not. If the bit is 1, we copy the tuple to the S-Buffer and add a _<SlotID, Addr>_ pair into the _SlotArray_ in the page descriptor.  
Figure 2: The structure of page descriptor.  
```
Input :PageID \(page\_id\), SlodID \(slot\_id\) Output :Tuple
1\(desc\) = FindDescriptor(\(page\_id\)) ;
2 if\(desc\)!=NULLthen
3 if\(desc\)\(\rightarrow\)\(page\_type\) is Full_Pagethen
4\(page\) = PagePool.getPage(\(desc\)\(\rightarrow\)\(buff\_id\));
5\(t\) = \(page\)\(\rightarrow\) getTuple(\(slot\_id\));
6\(desc\)\(\rightarrow\)\(visit\_bitmap\).set(\(slot\_id\));
7 return\(t\);
8 else
9 if\(desc\)\(\rightarrow\)\(visit\_bitmap\).get(\(slot\_id\)) is Truethen
10\(t\) = \(desc\)\(\rightarrow\)\(slot\_array\).getTuple(\(slot\_id\));
11 return\(t\);
12
13 end if
14
15 end if /* Load the page into memory */ \(desc\) = PagePool.loadPage(\(page\_id\));
16\(page\) = PagePool.getPage(\(desc\)\(\rightarrow\)\(buff\_id\));
17\(t\) = \(page\)\(\rightarrow\) getTuple(\(slot\_id\));
18\(desc\)\(\rightarrow\)\(visit\_bitmap\).set(\(slot\_id\));
19 return\(t\);
```  
**Algorithm 1**Read Tuple
## 3 Materials and Methods
### Field Image Acquisitions and Calibration  
Thermal infrared (TIR) and visible-near infrared (VIS-NIR) images were acquired by a suspended platform carrying an optical PowerShotG6 digital camera (Canon Inc., Tokyo, Japan), a multispectral ADC-lite Tetracam (Tetracam Inc., Losangeles, CA, USA) and a thermal FLIR-A330 (FLIR Inc., Wilson Weil, OR, USA) camera (Figure 1a,b). Black and white reference markers were positioned to allow calibrating images by means of proximity reflectance measurements with a Field Spec Pro handled spectro-radiometer (Analytical Spectral Device, ASD, Inc., Longmont, CO, USA).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Layer** & \(SWC_{s}\) & \(SWC_{r}\) & \(\alpha\) & \(m\) & \(n\) & \(K_{s}\) & \(\rho\) & \(e\) \\ \hline
[m] & [m\({}^{3}\) m\({}^{-3}\)] & [m\({}^{3}\) m\({}^{-3}\)] & [m\({}^{-1}\)] & [-] & [-] & [m h\({}^{-1}\)] & [kg m\({}^{-3}\)] & [m\({}^{3}\) m\({}^{-3}\)] \\
0-0.3 & 0.46 & 0.05 & 0.000114 & 0.20 & 1.25 & 0.167 & 1370 & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil physical properties of the layer 0–0.3 m (legend: saturated, \(SWC_{s}\), and residual, \(SWC_{r}\), soil water content; van Genuchten fitting parameters \(\alpha\), \(m\) and \(n\); saturated hydraulic conductivity, \(K_{s}\), soil bulk density, \(\rho\), and porosity, \(e\)).  
Acquisition times are listed in Table 2. The thermal images from 12 July were acquired during the increasing thermal transient, whereas the images on the 18 th and 30 th were acquired during the decreasing thermal transient (after midday). The value of albedo (\(\alpha_{SW}\)), obtained by processing shortwave reflectances, was interpolated in order to estimate its noontime value.  
A number of radiometric temperature and spectro-radiometric measurements were acquired on artificial surfaces (e.g., black and white panels). The shortwave images were calibrated to spectral reflectance and corrected for atmospheric influence by applying the empirical line method, which required ground reflectance measurements over artificial targets taken simultaneously with the images [44]. The thermal images were converted into surface radiometric temperatures by means of a linear regression with in situ temperature measurements using emissivity maps of bare soil. The soil emissivity was inferred by linearly rescaling the day-night temperature variations between a minimum and a maximum value. Minimum and maximum emissivity were assumed about 0.94 (for dry soil) and 0.97 (for wet soil), respectively, as indicated by [45] for compacted sandy loam soil. Radiometric calibration was validated on natural surfaces with homogeneous radiometric characteristics (bare soils).
# DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
## 4 Experiments and Results
### Ablation Experiments
#### 4.2.2 AFC Module Number  
The decoder is the core part of the DIR-Net algorithm, while the AFC is the significant unit that constructs the decoder, for which the number of modules plays a crucial role in the performance of the decoding. In this section, we investigate the impact of the number of AFC modules on the performance of the DIR-Net algorithm. In the experiment, (16, 8) polar codes were selected as training and test data, i.e., the code length \(N=16\), and the information bit length \(K=8\). In addition, Gaussian white noise was added to the encoded data as input of the network. At the same time, the corresponding original codeword was used as the training label. The network structure in our experiment was the same as that introduced in the previous section, consisting of a denoiser and a decoder. The denoiser was an attention-based residual network, and the decoder was composed of a series of AFC units.  
In our experiment, five network structures were adopted for comparison where the only difference among them was the number of AFC units in the decoder. Thus, 1-5 AFC units were utilized to investigate the impact of AFC module number on decoding performance. In the training process, all \(2^{K}\) codewords were involved in training. Each network structure was trained until convergence. The BER, BLER, and loss curves of five AFC modules converged with the number of iterations increasing, as shown in Figure 8. It is obvious that the network was finally convergent. When the AFC number was less than five, the network converged more quickly.  
Figure 8: The (**a**) BER, (**b**) BLER, and (**c**) loss curves with epoch.  
Figure 7: Probability density function with and without denoiser under different signal-to-noise ratios.  
The effects of different AFC module numbers are shown in Figure 9. When the number of AFC units was one, its bit error rate was still relatively high although the network could complete the task of decoding. Especially in the case of high SNR, compared with other network structures, the decoding performance still needed to be improved. The network performance tended to be saturated when the number of AFC units increased to four, and the decoding performance improvement gradually became smaller. When the number of AFC units increased to five, the decoding performance deteriorated. Comparing the bit error rate curves of four and five AFC units, it can be seen that, when the signal-to-noise ratio was less than 7 dB, the two were very close. The advantages of the decoder network depth began to appear when the signal-to-noise ratio was located at 7-8 dB. In addition, the performance gap of each network gradually increased with the SNR, indicating that the deep network had better generalization capabilities.
# Generalized Chirp Spread Spectrum for Underwater Acoustic Communications
###### Abstract  
In this paper, we propose a generalized-chirp spread spectrum (G-CSS) that can have various modulation orders and offers superior bit error rate (BER) performance to improve the communication performance of existing chirp-based modulation/demodulation schemes. The proposed G-CSS sets frequency bins with different modulation orders at the start and end points of the symbol and selects frequency bins based on the bits to be transmitted, modulating the signal using corresponding chirps. Therefore, the proposed method allows for independent design of modulation orders at the start and end of the symbol, enabling signal design and flexible transmission tailored to the transmission rate required by the system. Through computer simulations and practical ocean experiments, we compared and analyzed the BER performances of the proposed G-CSS with existing up/down-CSS and long-range CSS (LoRa-CSS). The proposed G-CSS demonstrated a superior BER performance at the same transmission rate.  
generalized chirp spread spectrum; chirp; underwater communication; long range communication 2025202514964  
202514964