esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of Datasets
#### 2.1.2 Field-Measured Data  
For further testing, three field experiments were also carried out. We measured the diurnal canopy spectra of winter wheat (_Triticum aestivum_ L.) with a customized Ocean Optics QE Pro spectrometer (SR = 0.31 nm; SSI = 0.155 nm; SNR > 1000; wavelength range: 645-805 nm; Ocean Optics, Dunedin, FL, USA) at the National Precision Agriculture Demonstration Base located north of Beijing (40 deg11'N,116\({}^{\circ}\)27\({}^{\prime}\)E) from 8:00 to 17:30 (local time) on 14 April (jointing stage, with a Leaf Area Index (LAI) of 1.94 and leaf chlorophyll content (Cab) of 62.2 \(\upmu\)g/cm\({}^{2}\)), 25 April (booting stage, with an LAI of 2.40 and Cab of 61.3 \(\upmu\)g/cm\({}^{2}\)), and 20 May 2015 (filling stage, with an LAI of 2.23 and Cab of 56.7\(\upmu\)g/cm\({}^{2}\)). In order to test the algorithm at different SRs, the canopy spectra were also measured by an ASD FieldSpec4 spectrometer (SR = 3 nm; SSI = 1 nm; SNR > 4000; Analytical Spectral Devices, Boulder, CO, USA) at the same time as the measurements made with the QE Pro spectrometer (except for the experiment on 14 April, 2015). The measurements were taken every half hour under stable weather conditions. In total, 53 valid measurements were made by the QE Pro and 30 valid measurements by the ASD FieldSpec4 on the three days. The fibers of the two spectrometers were fixed on to the horizontal rod of a tripod at a height of about 2 m above the canopy for nadir observation. In order to measure the TOC down welling solar irradiance, a 40-cm by 40-cm BaSO\({}_{4}\) calibration panel was used. The LAIs were measured with an _in situ_ scanning method, while the Cab values were measured using the SPAD-502 chlorophyll meter (Konica Minolta, Tokyo, Japan) and calibrated using an empirical model [39].
## Methods
### Living Labs: Operating Context, Tasks, and Human-Machine Interfaces  
Across Germany, a variety of living labs with highly automated vehicles (HAV, SAE Level 4) have been set up. In this study, on-board operators' workspaces from three of these HAV projects, which focus on passenger transportation, were examined. The vehicles used in the investigated living labs were small electric shuttle buses with a capacity of approximately 12 passengers or less. They were developed as highly automated vehicles, thus not requiring a human fallback level on board of the vehicle in theory. However, due to legal or technical constraints in the living labs, a human operator had to accompany the shuttle buses at all times. The specific tasks the on-board operator had to execute depended on the living lab and the deployed vehicle type (Figure 1). Typical interventions the operators had to conduct are listed in Table 3.  
Being a system component that was not considered in vehicle development from the start, the workspaces of the on-board operators were retrofitted in all of the examined living labs. The shuttle services focused on transporting passengers on the \"first/last mile\", i.e., to or from a transportation hub such as a train or metro station (HEAT, RealLabHH), or on fulfilling basic mobility needs in an urban or suburban context (TaBuLa). Details on the deployment context of each living lab are listed in Table 3.  
In order to characterize the driving experience from the on-board operator's perspective, an observation study with 32 rides in all three living labs had been conducted prior to the interview study. Shuttles were accompanied and documented by researchers regarding the mental effort the on-board operators reported, the amount of time spent in automated versus manual mode, and how safety-critical the rides were, as well as the number of operator interventions per hour. See Table 4 for the results from the observation study.  
The following sections focus on the three living labs that were examined in this interview study.
## 3 Implementation and Validation
### Implementation of Transmission Reliability Gains and Validation  
The IEEE 802.11 bd standard introduces many innovative technologies to improve the data transmission reliability. These include using LDPC coding instead of BCC, introducing midambles, and enhanced beamforming, among others. However, simulations face limitations in accurately replicating physical behaviors that are heavily reliant on real-world environments, e.g., signal modulation and coding, wireless signal transmission and reception, and beamforming. Consequently, directly implementing technical improvements such as LDPC coding via programming is impractical. Instead, these enhancements can only be indirectly realized by simulating the effects (SNR gains) of these technologies on the data transmission reliability.  
Considering the significant impact of real-world wireless environments on wireless signal transmission, the reliability improvement aspect is based on the findings of [24], who concluded that the replacement of BCC with LDPC coding can result in a 1-4 dB SNR gain across multiple scenarios.  
Based on these gains, certain adjustments have been made to the Packet Error Rate (PER) calculation formulas concerning the SNR in Veins. Specifically, under different data transmission modes, higher data transfer rates result in greater SNR gains, while, under the same data transmission mode, higher SNR values lead to increased SNR gains. The corrected calculation formulas for some data transmission modes are given in Equations (4)-(6).  
\[SNR^{\prime}_{BPSK\,1/2}=SNR_{BPSK\,1/2}+\min\Bigl{(}1.0,\,5^{(SNR_{BPSK\,1/2} -2.0)/20}\Bigr{)} \tag{4}\]  
Figure 3: The simulation curve of PER with respect to SNR of IEEE 802.11 bd data transmission modes in Veins.  
\[SNR^{\prime}_{QPSK\ 1/2}=SNR_{QPSK\ 1/2}+\min\left(1.5,\ 10^{\left(SNR_{QPSK\ 1/2}-3.3 \right)/10}\right) \tag{5}\]  
\[SNR^{\prime}_{QPSK\ 3/4}=SNR_{QPSK\ 3/4}+\min\left(2.0,\ 20^{\left(SNR_{QPSK 3/4}-6.5\right)/20}\right) \tag{6}\]  
Figure 4 illustrates a comparison between the simulation curve of the PER with respect to the SNR for selected data transmission modes. This comparison includes a curve corrected based on the SNR gain and the original, uncorrected simulation curve.  
Based on Figure 4, it is evident that the modified (LDPC) simulation curve achieves a noticeable SNR gain compared to the unmodified (BCC) simulation curve. The modified curve exhibits a similar PER at a lower SNR, typically within a range of 1-2 dB, thereby enhancing the data transmission reliability in the simulation. This demonstrates the successful implementation of the new technologies in terms of their effectiveness in improving the data transmission reliability.
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 5 Experimental Results
### Result of the Statistical Approach  
We evaluated the performance of the proposed statistical approach using different values of \(c_{1}\), \(c_{2}\) and \(pen\). For experiment 3, we obtained the best detection result (Figure 15b) with \(c_{1}=1\), \(c_{2}=1.5\) and \(pen=0\). Using the images from the cooling period yielded a better outcome for both experiments (2 and 3) as illustrated in Figure 15c,d. In general, we  
Figure 14: IRT with cold stimuli; experiment 4. Blue regions inside the dashed oval show the effect of a cold stimulus behind the specimen. (**a**) The processed image. (**b**) Color mapping.  
Figure 13: ATC; Experiment 3. (**a**) Selected points. (**b**) Temperature-time graph and thermal contrast.  
recommend keeping the penalty value at 0. However, different visualizations of the defects are available with the help of the _pen_ factor. When \(pen=1\), the contour of the cavity can be shown. This feature can be useful when dealing with an automated system. By choosing a penalty around 0.5, both the contour and the surface of the cavity appear in the image but with different colors. The effectiveness of this method was also examined for experiment 2 which contains multiple cavities in one image. Using the same parameters (\(c_{1}\), \(c_{2}\), \(pen\)), the most pronounced temperature fluctuations (related to cavity 4) could be easily detected (Figure 15f). By using \(c_{1}=0.9\), \(c_{2}=1.1\), we could adjust it to visualize cavity 2. This selectivity feature of the algorithm is the outcome of extending Equation (12) to (13).
# Improving Sentence Representations via Component Focusing
## 3 Methods
### Details of the CF-BERT Model with Similarity Tasks
#### 3.2.1 Training Process of CF-BERT  
As shown in Figure 2, there are two networks, \(\text{BERT}_{\text{A}}\) and \(\text{BERT}_{\text{B}}\), which each process one of the sentences in a given pair; we solely focus on Siamese architectures with tied weights such that  
Figure 2: The overall architecture of the component focusing bidirectional encoder representations from transformers (CF-BERT) model (with semantic textual similarity tasks). The two BERT networks have tied weights (Siamese network structure).  
\(\text{BERT}_{\text{A}}=\text{BERT}_{\text{B}}\) in this work. First, we use the BERT model to map tokens in a sentence to the output embeddings from BERT (word_embedding_model). The next layer in our model is a pooling_model. We experiment with three pooling strategies: using the output of the [CLS] token, computing the mean of all output vectors (MEAN-strategy), and computing a max-over-time of the output vectors (MAX-strategy). When trained with different tasks, we observe that the pooling strategy has a large impact. There, the MEAN strategy performs better than MAX or [CLS] token strategy. Therefore, the default configuration is MEAN.  
We performed dependency parsing to obtain the component-enhanced parts of A and B, namely, \(SA_{cf}\) and \(SB_{cf}\). The basic part \(SA_{\text{{basic}}}\) and the component-enhanced part \(SA_{cf}\) of sentence A are now passed first through the word embedding model, using the \(\text{BERT}_{\text{A}}\) model to map tokens in a sentence to the output embeddings from \(\text{BERT}_{\text{A}}\), namely, \(emb\_SA_{\text{{basic}}}\) and \(emb\_SA_{cf}\). The final sentence representation \(emb\_SA\) of sentence A is calculated using Equation (1). \(emb\_SA\) is then passed through the pooling_model to obtain fixed-sized sentence vectors \(U\). Using the same process, sentence B is finally expressed as sentence vectors \(V\). We used a batch-size of 16 and 4 epochs, an Adam optimizer with a learning rate \(2\text{e}^{-5}\), and a linear learning rate warm-up over 10% of the training data.  
Given a sentence pair and a gold similarity score (either between -1 and 1 or between 0 and 1), computes the cosine similarity between the sentence embeddings \(U\) and \(V\) and minimizes the mean squared error (mse) loss. The cosine-similarity is embedded in the loss layer, and then back-propagated. The previous step returns the propagation loss to calculate the gradient. The weights of the Siamese network are then updated by using the optimizer. We ran our experiments also with negative Manhattan and negative Euclidean distances as similarity measures, but the results for all approaches remained roughly the same.  
When training is complete, the cosine similarity between all sentence pairs is computed and correlation to the gold scores is computed. The experiment automatically saves a model to embed sentences for the task. By loading the model, we can obtain the semantically meaningful embeddings of each sentence corresponding to the task.
###### Abstract  
Ultra-high-speed integrated communication and sensing (ICS) transmission techniques are highly desired for next-generation highly reliable optical transport networks (OTNs). The inherent multiple-channel advantage of uncoupled multi-core fibers (MCFs) empowers the evolution of ICS techniques. In this paper, we demonstrate an ultra-high-speed ICS OTN system utilizing 130 Gbaud probability constellation shaping 64-ary quadrature amplitude modulation (PCS-64 QAM) 1.2 Tb/s OTN transponders and polarization-based sensing technique over a field-deployed seven-core MCF cable for the first time. A real-time 267.8 Tb/s 2 \(\times\) 70.76 km transmission is achieved by only utilizing C-band signals thanks to the high-performance 1.2 Tb/s OTN transponders. Moreover, the ICS system can sense environmental impacts on the MCF cable such as shaking, striking, etc., in real time. The capacity of the transmission system can also be further enhanced by using signals in the L-band. Our work demonstrates the feasibility of simultaneously achieving ultra-high-speed data transmission and the real-time sensing of environmental disturbances over a field-deployed MCF cable, which we believe is a crucial milestone for next-generation ultra-high-speed highly reliable optical transmission networks.  
Keywords:space-division multiplexing; multi-core fiber; real-time transmission; optical network; field trial +
Footnote †: journal: Article
## 3 Validation Cases
### Validation Sites
#### 3.1.2 Jornada Experimental Range (JER)  
This area is located to the southwest of the White Sands National Park, NM, USA. There are three SoilSCAPE sites in this area, denoted as JR-1, JR-2, and JR-3. Figure 2(a) shows the location of the sites on a satellite map. The sites are within the Jornada Experimental Range [27].  
In each site, four nodes are equipped with four soil moisture sensors installed at 5 cm, 10 cm, 20 cm, and 30 cm depths. Sensors were installed in May 2022. Node JR-3-3 of the JR-3 site is located within 100 m of the US Climate Reference Network (USCRN) Las Cruces station [28]. These sites are mostly dry with minimal vegetation.  
Figure 3: JER SoilSCAPE sites: (**a**) Satellite photo; (**b**) Photo of the site taken in May 2019.  
Figure 2: Photos of WG taken in the dry season and monsoon season: (**a**) Photo was taken in May 2022 [credit: [PERSON]]; (**b**) Photo was taken in Aug 2022 [credit: [PERSON]].
# The Use of Mobile Applications for Sustainable Development of SMEs in the Context of Industry 4.0
## 3 Results  
### Background of \"Testing\" and \"Feedback\" Stages  
The proposed study is primarily aimed at evaluating the effectiveness of the mobile applications as a tool for adapting to new technologies in manufacturing SMEs. The production process in SMEs is based on workers and production managers and both are responsible for product and staff quality, which is why it was important to get feedback from both groups of users. The first stage of \"Testing\" was to give the mobile application group of specialists (top management and stakeholders of the enterprise) and then was realized \"Feedback\" collecting process by evaluating its efficiency. This group was not chosen by chance, since in DEMATEL, MCDM, and INRD [7] theories and other assessment methods, these people are an important component of decision-making on automation. The assessment was carried out using a questionnaire, where each criterion was assigned by a weighting factor as well as a scale of grades from 0 to 5, where \"5\" is the maximum value and 1 is the minimum one. To simplify the evaluation process, the original teaching method was called \"mentoring\" and the proposed was called \"M-learning\". The consistency of expert opinions on each indicator is assessed by scattering estimates around the mean (variances) using [PERSON]'s method. The average score for each indicator \"x\" was calculated by the formula:  
\[\overline{\mathrm{x}}=\frac{\sum_{i=1}^{m}\mathrm{x_{i}}}{m} \tag{1}\]  
where x\({}_{i}\)--mark is given by the i-th expert on this indicator;  
\(m\)--number of experts. There were 4 experts (project manager, welder's team leader, and two specialists from the university) and 5 criteria (Table 1). Figure 4: Smartphone users by age groups [34]. The consistency of expert opinions is assessed by the scatter of xi around the mean. To do this, it is necessary to calculate the variance of the estimate and compare the significance of its difference from the minimum possible variance according to [PERSON]'s criterion. The variance is calculated by the formula:  
\[S_{1}^{2}=\frac{\sum_{i=1}^{m}(x_{i}-\overline{\mathrm{x}})^{2}}{m-1} \tag{2}\]  
The variance for mentoring (\(S_{1}^{2}\)) was 0.08 and 0.05 for M-learning (\(S_{2}^{2}\)). The next step was the F-Test: hypothesis tests for the variances of 2 normal distributions. \[F=\frac{S_{1}^{2}}{S_{2}^{2}} \tag{3}\]  
To assess the quality of the proposed teaching methodology, it was hypothesized that M-learning is equivalent in quality to E-learning. To confirm or refute the theory, calculations were made according to the above formulas for each criterion. The average X for mentoring is 3.4 and for M-learning is 3.7 (Table 1.). The average score differs by only 0.3 which is not significant, given the assessment step of 1 point.
## 4 Experimental Results
### Quantitative Evaluation
#### 4.2.2 Validation Phase  
The quantitative evaluation demonstrated the good performance of our proposed system as justified by the reached values of the evaluation metrics for the three tasks in Table 4 below. The attained dice coefficient values are 0.98 for the lung segmentation model and 0.91 for infection segmentation model. Additionally, we achieved an accuracy of 0.95, 0.94, and 0.98 for lung segmentation, infection segmentation, and classification models, respectively.  
Figure 12 below displays fourfold obtained dice for different chosen thresholds.  
\begin{table}
\begin{tabular}{c c c} \hline
**Task** & **Accuracy** & **Dice Coefficient** \\ \hline Lung Segmentation & 0.95 & 0.98 \\ \hline Infection Segmentation & 0.94 & 0.91 \\ \hline COVID-19 Classification & 0.98 & - \\ \hline \end{tabular}
\end{table}
Table 4: Quantitative results among different tasks on testing set in terms of accuracy and Dice Coefficient values.  
Figure 11: Classification model’s loss curve.  
As the figure shows, the maximum validation dice was on the second split as the value reached 0.927999. Hence, we consider that the best threshold to be used later for the calculation of the other metrics is 0.40. The mean of all obtained dice is 0.9189. The results of the fourfold cross-validation for the different metrics are summarized in Table 5.  
The model achieved a dice coefficient of 0.91, an IoU of 0.85, a precision 0.92, a recall of 0.90, an AUROC of 0.95, and an AUPRC of 0.91. The high AUROC and AUPRC values demonstrate that our model succeeded in handling and distinguishing both infected regions and non-infected ones well.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme  
### Assumptions and Design Principles  
Each vehicle (node) is able to get its own position information and road map through positioning services. Hello messages are exchanged periodically (1 Hz by default) to share location information and velocity information between neighbors.  
The proposed scheme uses edge nodes to forward data packets. To efficiently utilize wireless resources, the scheme uses the same edge nodes for different traffic flows as far as possible. However, as different applications could expect different levels of QoS, the scheme also employs a context-aware approach to differentiate the traffic flows with different requirements. The proposed scheme generates different virtual edge nodes for different traffic flows with different context information. While the conventional routing protocols use the same route for the traffic flows with the same source-destination pairs, the proposed scheme could possibly use different routes as the edge nodes depending on the context information. We explain the proposed scheme by using three different types of communication patterns, namely end-to-end connection-dependent unicast, end-to-end connection-independent broadcast, and end-to-end connection-independent unicast communications.
## 3 Datasets
### GNSS Almanac/Trimble Software  
Accurate information for satellite positions at different times of the day was required. As previously demonstrated, a number of different methods are available that detail the orbit precisely. If kept up to date, these software tools can be used to calculate the position of any GNSS satellite at any time of day. However, this is a complicated process in terms of user training and also in terms of processing time. It was one of the aims of this study to show that an existing, free GNSS software package wasa suitable substitute, providing savings in terms of cost and time. Trimble Planning was readily available; therefore, this method could be more readily reproduced and so was selected for the project. This study was limited to GPS and GLONASS satellites only as the GNSS receiver used in the validation tests was limited to those constellations.
## 4 Fluoride Removal
### Rice Husk and Rice Husk Ash as Adsorbents for the Removal of Fluoride  
There are several sections in literature investigating the efficiency of fluoride removal from water by the different forms of rice husk (Table 8). [PERSON] [96] studied the efficiency of fluoride removal by using different adsorbent materials such as rice husk, eggshell, activated carbon, and rice husk ash, showing that the efficiency of fluoride removal was 40% by rice husk while the efficiencies for the other matrices were 61.8, 53.4, and 42.5% respectively. A wide range of treatment methods have been proposed and investigated in literature to improve the adsorption efficiency [97; 98]. [PERSON] et al. [99] prepared rice husk by chemical impregnation with nitric acid, followed by physical activation to investigate the removal of fluoride in aqueous solution. They reported that the maximum fluoride removal was 75% at pH = 2, with the fluoride uptaking capacity decreasing by increasing pH in the range 2-10. This finding was confirmed by [PERSON] and [PERSON] [37]. Therefore, authors emphasized that it is preferable to carry out the fluoride adsorption process at lower pH values. Moreover, fluoride adsorption increases with time, reaching equilibrium within 1-1.5 h. Some authors [37; 100] investigated the removal of fluoride by modifying rice husk ash by coating with aluminum hydroxide. The study showed promising results of fluoride removal capacity, and the adsorption was determined to be 910 mg/g. Other authors investigated the fluoride removal capacity by using silica nano adsorbent modified by rice husk [101]. The results illustrated that the fluoride adsorption capacity is 12 mg/g. The summary in Table 8 and Figure 8 represents a comparison of fluoride removal capacities according to different treatment methods of rice husk. By way of comparison with rice husk, according to the experiments of [PERSON] and [PERSON] [102], commercially available powdered activated carbon (2.0 g/100 mL) allowed a 94% removal of fluorides under optimal conditions (contact time: 120 min, pH: 2). As reported by [PERSON] and [PERSON] [103], some coal-based adsorbents (lignite, fine coke, and bituminous coal) showed an adsorption  
Figure 7: Source of fluoride entering to groundwater (adopted from [91; 92; 93]). capacity in the range of 6.9-7.4 mg/g for the removal of fluoride. A pH between 6 and 12 was more favorable in the case of lignite, while fine coke and bituminous coal showed a greater efficiency at acid pH.