esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Experiments
### Simulation Setting  
We implemented the proposed LFDC in Python 3.9, with PyTorch set up on a 64-bit CentOS Linux workstation with an Intel Xeon W-2155 CPU, 128 GB RAM, and 2080 Ti GPU. In the simulation, there are \(|N|=10\) BSs with \(|U|=500\) randomly distributed within the coverage area of the BSs. The cache capacity of each BS is 1 GB. The maximum coverage radius of each BS is 500 m, with a channel bandwidth of 20 MHz and a transmission power of 500 W. The path loss \(g_{u,n}\) is modeled as \(30.6+36.7 lg(dt_{u,n})dB\), where \(dt_{u,n}\) denotes the distance between UE \(u\) and \(BS_{n}\).  
Moreover, to validate the effectiveness of the proposed algorithm in practical scenarios, we obtain content from actual datasets. We collect content request records from the publicly available Movielens dataset. MovieLens consists of 27,753,444 ratings from 283,228 users on 58,098 movies, as well as related information about the movies, such as their titles and genres. We assume that the number of ratings is equal to the number of requests, and the time when a review is posted is considered to be the time when a request is initiated. We record the tags of each video as video features and divide the videos into several sub-files \(C_{f}\) of equal size, based on their duration. To simulate users' mobility, we set the mobility coefficient to 0.1 so that after each epoch, a portion of the users served by the base station (BS) will be randomly replaced. For each BS selected for local training, the initial value of \(L_{0}\) for the number of local training iterations is set to 10, with a minibatch size of 32. The optimizer used is Adam, with a step size of 0.01. We use a dataset consisting of 300 h of data for experimentation, with each epoch being one hour, and Pandas and Requests are used to retrieve and process the data. Other parameters for the experimental setup are shown in Table 2.
# Plant Disease Diagnosis in the Visible Spectrum
## 3 Materials and Methods
### Color Identification and Quantification  
#### 3.4.1 Identification of Colors Using HSV Thresholds  
HSV thresholds were established for the identification of white, black, green, yellow, and brown colors. Those colors were considered because they are strongly associated with plant diseases and the vast majority of plants are green; when a color that does not satisfy any of the threshold criteria, it is classified as \"unidentified\".
## 2 Materials and Methods  
The PSAB glasses with composition of PbO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-xCuO-yNa\({}_{2}\)CO\({}_{3}\) (x = 0.5, 1, 1.5, and 2 wt%; y = 0.5, 1, 3, and 5 wt%) were prepared by the melt-quenching method. Reagent grade chemicals of PbO (99.9%), SiO\({}_{2}\) (99.9%), Al\({}_{2}\)O\({}_{3}\) (99.99%), B\({}_{2}\)O\({}_{3}\) (99.9%), CuO (99.9%), and Na\({}_{2}\)CO\({}_{3}\) (99.9%) purchased from Alfa-Aesar were used as raw materials to fabricate the glasses. The batch chemicals (about 50 g) were weighed using a microbalance and then crushed using a ball mill with zirconia balls for 30 min to get a homogeneous mixture. Then, the well-mixed powder was transferred into an alumina crucible and heated in an electric furnace at 750 \({}^{\circ}\)C for 60 min under N\({}_{2}\) atmosphere. After one hour, the melt was cast onto a preheated brass mold and then transferred to another electric furnace to anneal the glass samples. These glasses were annealed at 350 \({}^{\circ}\)C for 3 h to get thermal stability in the glasses. The optimized PSAB glass system codoped with CuO and Na\({}_{2}\)CO\({}_{3}\) was pulled into a fiber with 180 \(\upmu\)m diameter using a draw tower in the temperature range of 500-535 \({}^{\circ}\)C.  
The amorphous nature of glasses was examined by means of X-ray diffractometer (X'pert Pro, Panalytical) with CuK\(\alpha\) (=1.542 A) used as source. Simultaneous thermal analyzer (STA 409 PC, NETZSCH) was used for thermal behavior of the glass samples. The measurement was carried out in the range of 25-900 \({}^{\circ}\)C at the heating rate of 10 \({}^{\circ}\)C/min under N\({}_{2}\) atmosphere. Thermo-mechanical analysis (TMA) was carried out using a dilatometer (DIL402F3, NETZSCH) in the range of 0-450 \({}^{\circ}\)C with a heating rate of 5 \({}^{\circ}\)C/min under N\({}_{2}\) atmosphere. The glass samples with a dimension of 5 (H) \(\times\) 5 (W) \(\times\) 8 (L) mm\({}^{3}\) were used for the measurement. The transmittance spectra were recorded with a UV-VIS-NIR spectrophotometer (Cary 500 scan, Varian H) in the range of 400-1100 nm with a spectral resolution of 1.0 nm. The glass powders were made into cylindrical pellets (diameter 10 mm and height 10 mm) by pressing. The flow button test was performed. The pellets on FTO substrates were sintered in the oven at 400-550 \({}^{\circ}\)C for 30 min. The laser sealing process was performed by a custom made laser sealer composed of a galvanometer (SG7210, Sinogalvo) and a CW diode laser with center wavelength of 810 nm. In the process, the laser beam with a spot diameter of 3 mm and maximum output power of 60 W focused to the FTO substrates placed on the hot plate. After being sealed with fiber types of sealant, the sealing strength tests were performed using a universal tensile tester (AG-X, Shimadzu) to evaluate the laser sealing quality. In this test, five measurements were taken, and the average value was given as a result.
## 3 Experiments
### Baseline Methods  
In order to comprehensively evaluate the performance of the SDNN, we set several strong baseline methods for sentiment classification, including traditional machine learning models, traditional deep learning models, deep learning models relying on specified parsing structure, and models extracting important information by attention mechanisms.  
SVM/Feature-SVM: SVM [6] is a classic machine learning method to solve sentiment classification tasks, which won first place in the SemEval-2014 Task 4 by using the following groups of features: n-grams, character n-grams, non-contiguous n-grams, POS tags, cluster n-grams, and lexicon features. In addition, we incorporated sentiment resource words into the implementation of the SVM model (denoted as Feature-SVM).  
LSTM/Bi-LSTM: LSTM/Bi-LSTM (Bidirectional Long Short-Term Memory) [12] has the capability to acquire dependencies between words in a sentence by incorporating the long short-term memory unit and the bidirectional variant into neural networks, which makes it effective in solving the problem of the long dependence of text.  
CNN: A convolutional neural network [26] is a very strong baseline for text sentiment classification tasks. It can sufficiently capture the local feature information of the text to generate task-specific sentence representation.  
BLSTM-C: BLSTM-C (Bi-LSTM combined with the generalized CNN) [27] combines CNN with Bi-LSTM networks in order to fuse the local information and sequence information of text to form feature-enhanced sentence representation for predicting the sentiment polarity of a sentence.  
Tree-LSTM: Tree-LSTM (Tree-structured long short-term memory) [12] introduced memory information into tree-structured neural networks, relying on predefined paring structures, which helps to capture the semantic relatedness.  
LR-Bi-LSTM: LR-Bi-LSTM (Linguistically Regularized-based Bidirectional Long Short-Term Memory) [20] makes use of linguistic roles with neural networks by utilizing linguistic regularization on intermediate outputs with KL divergence.  
Self-Attention: Self-attention [28] can learn structured sentence embedding with a special regularization term.
# S2p: Provenance Research for Stream Processing System
## 4 Online Provenance  
The online provenance phase aims to build the relationship between each output result data and their corresponding source stream data. Our online provenance solution only answers the question of what combination of the nearest source stream data contributes to the object result data instead of providing a complete explanation. Object data refers to the data of interest, which is selected by users for provenance analysis. Similar to the reliance in [23], s2p relies on instrumented operators. It maintains the relationship among output and input data of each operator basing on the operators' semantics.  
Reaccess DSP systems' source data is nontrivial. DSP is suitable for many areas, e.g., stock analysis and user activity analysis, which usually produce massive data at high speeds and require real-time processing. These require data to be processed instantly instead of storing them in one unified place and waiting to be processed. Some DSP applications integrate third-party systems (e.g., Kafka) to consume source data through interacting connectors. Other DSP applications ingest source data directly from sensors to consume them only once. For convenience, we assume a uniform method to access the source data, i.e., every source data bounded with a unique ID and accessing source data by its ID.  
Figure 6: A framework for our DSP provenance solution.  
Then, our online provenance works as follows. First, label every input data with a unique ID in source operators. Next, the IDs are piggybacked on each data during transformations and propagate alone the DSP pipeline. Last, when some result data are selected as object results, we can reverse their corresponding source data by extracting the source ID list attached in object result data. A more detailed description of this process is as follows.
## 3 Research Results  
### Textures and Spatial Variation of Soil pH  
According to the granulometric composition, the main part of the study area is characterized by the following textures: loamy soil and sand clay, and only a small part of it is clay, sand and alkaline soil (Figure 2). The changes in soil salinity, indicated by alkaline-expressing soil, have been identified in the area adjacent to Balkhash Lake, incl. the territory of the Ili-Balkhash State Natural Reserve. Such changes can lead to the complete degradation of the soil cover in the near future, which, in turn, will negatively affect the development of flora and fauna in the study area. The huge area, laying from the southeastern to the northwestern part, which belongs to the ancient delta, is characterized by loamy and clayey soils. Along the Ili River, from the southeastern to the southwestern part of the study area, sandy clays and sands occur.  
The results of the pH measurements of the soil cover show changes in values from 7.5 to 9.5, averaging a pH of 8.5. The alkalinity of the soils throughout the study area varies, producing slightly alkaline to strongly alkaline reactions [41; 42].  
Soil salinization or alkalization occurs due to the excessive presence of alkali metals in the soil solution (calcium, magnesium and sodium). The pH of the soil cover, determined through the water extraction, ranges between 7.8 (alkaline) and 9.5 (highly alkaline) (Figure 2). The maximum accumulation of salts is characteristic of loamy soils and clays with a dense soil structure. High alkalinity, as a rule, will lead to the weakening of the biological cycle of substances and, as a consequence, many types of plant organisms will disappear. The gene pool of terrestrial populations will decrease due to the deterioration of the living conditions required by living organisms.  
The results show that the carbonate content of soils is responsible for the averaged pH values of 7.5 in 2001; these values have increased up to a pH of 8.5 in 2021, which indicates the gradual salinization of the soils in the southern Balkhash region, as observed over a twenty-year period. It confirms the trend of soil salinization in the study area [43; 44].  
Thus, the high alkalinity of the soils, in addition to salinity, can lead to other negative consequences, such as the destruction of the soil structure, as a result of which leaching, waterlogging and alkalization will occur, leading up to complete degradation.
# AIP Bioluminescence for Rapid and Selective Detection of Bacteria and Yeasts in Wine
## 3 Results  
The ability of the bioluminescence assay to detect and discriminate microbial contamination at high and low concentrations in wine samples was investigated in this study. RLU values for the analyzed wines samples are reported in Table 3. For all the tested wines, \begin{table}
\begin{tabular}{c c} \hline \hline
**Pass/Fail Limits** & **RLU Value** \\ \hline Sterile & \(\leq\)2 times blank \\ Re-test & 2 times blank \(<\) RLU value \(<\) 3 times blank \\ Non-sterile & \(\geq\)3 times blank \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pass/fail limits for the RLU value. the filtration steps and resuscitation of cells with the appropriate medium allowed for the detection of both bacteria and yeasts at high concentration (10\({}^{7}\) CFU/mL) in 24 h (Table 3). The presence of yeasts (1.2 \(\upmu\)m filters) or bacteria (0.45 \(\upmu\)m filters) revealed by the bioluminescence assay (\(>\)3 times blank value) was confirmed by plating the resuscitation media (after growth) on the respective selective medium (Table 4). The effectiveness of filtration steps was confirmed by plating the eluates resulting from the first filtration step (1.2 \(\upmu\)m) both on YPDch agar and MRScy agar (Figure 1, top left). As expected, all eluates plated on YPDch agar resulted in the absence of colonies, proving that no yeasts passed through the filter. At the same time, all eluates plated on MRScy agar formed colonies on the agar medium, confirming the crossing of bacteria through the filter. These results were confirmed also for eluates from the first filtration (1.2 \(\upmu\)m) of samples with the lowest bacterial concentration (10\({}^{3}\) CFU/mL LAB + 10\({}^{4}\) CFU/mL yeasts). Yeast colonies were absent in all YPDch agar plates, while LAB colonies were present in 3 out 12 MRScy agar plates. These could be explained by the high dilution factor of the bacteria concentration in the eluates. After the first filtration step (1.2 \(\upmu\)m), the filter was washed with 100 mL of Ringers' solution to reduce the residual alcohol and pigments of wine, diluting the bacteria concentration in the eluate (50 mL + 100 mL). This concentration was further reduced when spread-plating 100 \(\upmu\)L of the eluate on the agar; thus, the number of expected colonies was lower than 10. In analyzing wines with lower bacterial concentration, the bioluminescence assay was able to detect yeasts in all the tested samples and bacteria in R2, R5, R6, W1, W3, W4, W5, and W6 samples. These results were in agreement with plating for all the samples but one (R2). After confirmation that the developed protocol was able to detect the microbial contamination in wines, discriminating between bacteria and yeasts, a second trial was set up, by inoculating five wine samples with the maximum tolerable microbial concentration to consider the tested wines shelf-stable, according to producers' indications. The wines and vermouth samples were thus inoculated with 50 CFU/L of yeast and 1000 CFU/L of
# Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach
## 2 Study Area
### Available Geophysical Data  
Geophysical data that are used to characterize the dynamic soil response are usually presented in terms of shear-wave velocity (Vs) distribution, mainly nearthe surface, i.e., in the upper 30 m (Vs\({}_{30}\)). [PERSON] et al. [35] used passive-source vibrations/waveforms generated by ambient noise, such as traffic, wind, and engines, to estimate the V\({}_{\rm s}\) spatial distribution in Chania. For this purpose, 24 microtremor linear array measurements (MAM), equally spaced every 500 m in the X- and Y- directions, were acquired and analyzed to construct a 3D V\({}_{\rm s}\) model of the study area (Figure 3a). Twenty-four low-frequency (4.5 Hz) geophones (with a 2 m/s sampling time, a 10 min record length, linear geometry, and 3 m geophone spacing) were deployed along each measuring line, and data were carefully collected during the night. The total survey length of deployment was about 69 m. Moreover, all the geophones were installed in similar ground conditions, and all the measurements were taken on roads and pavements. The shear-wave velocities with depth were calculated, and the spatial distribution of V\({}_{\rm s}\) at the depths of 5 and 36 m below the ground was extracted (Figure 3b,c). The results are in good agreement with those derived via an active-source vibrations/waveforms generation methodology, the so-called spectral analysis of surface waves (SASW) [21].  
Figure 3: (**a**) Wider Chania region with MAM measurement locations (black dots) and shear-wave velocities (m/s) model for (**b**) 5 m and (**c**) 36 m depths (adopted from [35]).
# A Research on Manipulator-Path Tracking Based on Deep Reinforcement Learning
## 2 Method
### Deep-Reinforcement-Learning Algorithm Combined with Manipulator
#### 2.3.2 Application of Multi-Degree-of-Freedom Manipulator
The hyperparameters were set as follows: replay buffer = 1 \(\times\) 10\({}^{6}\), discount factor = 0.99, batch size = 128, the update between the \(Q\) network and the target \(Q\) network adopted the soft update method, the soft parameter tau = 0.01, the learning rate of Actor and Critic network were both set to learning rate = 1 \(\times\) 10\({}^{-3}\), and the weight coefficient of policy entropy during the entire training process \(\alpha\) = 1 \(\times\) 10\({}^{-3}\). The reward settings for this experiment were set as Equation (15). \[r_{t}=-\left\|p_{\tau(t,n)}^{*}-p_{t}\right\|,\ \tau(t,n)=\left\{ \begin{matrix}n( \text{floor}(t/n)+1)&n>1\\ t&n=1\end{matrix} \right. \tag{15}\]  
where \(n\) is the interval distance. In addition, an experiment was terminated when the robot arm ran for 100 steps or the distance between the end point of the robot arm and the target point was within 0.05 m.
## 3 Algorithm Design and Analysis
### Ms-SCTP Method
#### 3.1.3 Algorithm Design  
The MS-SCTP method determined the primary path through the packet loss rate recorded last time in each path and the maximum packet loss rate in the link. It is shown in Algorithm 1.  
* Line 1 indicates that the switching algorithm began when the main path needed reselection.
* Line 2 indicates that the link probe needed to be performed before switching between the master and the slave paths. If the path was found unreachable, the Max array and the last array values  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Max [] & Record the maximum packet loss rate for each path \\ Last [] & Record the latest packet loss rate for each path \\ CurrentNumber & Record the current primary path number \\ PathNumber & Record the selected new primary path number \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter descriptions of the MS-SCTP method.  
Figure 6: SCTP multi-path transmission model.  
corresponding to the path were updated. When the path was not reachable, the value of the Max array and the corresponding position in the Last array was set to 101.
* Lines 3 and 4 show that the algorithm traversed the Max and the Last arrays and recorded the Max and Last values for each path.
* Line 5 indicates that the path with the smallest result was selected by calculating \(0.25\times\text{Max}+0.75\times\text{Last}\).
* Lines 6 through 14 show that if more than one alternative path was selected, the algorithm needed to confirm whether the alternative paths package included the current main path. If the alternate paths included the current primary path, it was not necessary to re-switch the primary path. If the alternative paths did not include the current main path, the scheme randomly selected one as the new main path and recorded it.
* Lines 15 through 18 indicate that when making a primary path switch, the algorithm first determined whether the recorded PathNumber variable had the same value as the current primary PathNumber. If so, then it did not have to switch. If not, the algorithm set the new primary path based on the value of the recorded path number and updated the CurrentNumber value.  
```
1:Max[], Last[], CurrentNumber
2:CurrentNumber
3:Method begin
4: Detect the reachability of the path, update the Max array and Last array
5: Search the Max array and Last array
6: Record the max value as Max and record the last value as Last
7: Calculate 0.25 * Max + 0.75 * Last and select the paths with the smallest value
8: If results have more than one path number
9: If results contains the CurrentNumber
10: Let PathNumber = CurrentNumber
11: Else Choose one at random and record the PathNumber
12: End Else
13: End If
14: Else Record the PathNumber
15: End Else
16: End If
17: If CurrentNumber == PathNumber
18: No longer switch
19: Else Set the primary path according the PathNumber
20: Let CurrentNumber = PathNumber
21: End Else
22: End If
23: End Method
```  
**Algorithm 1** **MS-SCTP Method**
## 2 EO Satellite and In Situ Observations for Earth Surface
### Ocean-Based Networks  
The ocean in situ observations coordinated under the Global Ocean Observing System (GOOS, [[http://www.goosocean.org](http://www.goosocean.org)]([http://www.goosocean.org](http://www.goosocean.org))) is a sustained observing system, unified by the Framework for Ocean Observing [61], designed to provide access to timely ocean observations. In many cases, data are telemetered to data centers in near real-time for assimilation into NWP. Higher quality data are also available in delay mode and can be used for validation and evaluation of processes. With its unique status within the United Nations system, GOOS has been able to gather a network of independently-managed and independently-funded observing elements. The network includes Argo floats [62; 63; 64], surface drifters [65], OceanSITES moorings ([[http://www.oceansites.org](http://www.oceansites.org)]([http://www.oceansites.org](http://www.oceansites.org))), and ships ([[http://www.jcommops.org/sot](http://www.jcommops.org/sot)]([http://www.jcommops.org/sot](http://www.jcommops.org/sot))). GOOS surface Essential Ocean Variables include sea state, ocean surface stress, sea ice, sea surface height, sea surface temperature, surface currents, sea surface salinity, and ocean surface heat flux, with the turbulent heat flux typically measured as a bulk flux from state variables.