esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 3 Materials and Methods
### Determining T Optimal Number of Topics  
The main challenge facing topic modeling is to determine the optimal number of _latent_ topics in a group of documents. This issue is still one of the main difficulties faced by researchers during topic modeling. For this reason, there is not a fixed methodology to follow [25; 26; 27; 28]. Our first approach was the implementation of a Markov Chain Monte Carlo (MCMC) using Gibbs Sampling to obtain the optimal T topic model, which maximizes the log-likelihood value (log P(w|T)). This approach was driven following [PERSON] model [28].  
Finally, the optimal number of topics in our model was found by performing different LDA implementations to achieve an optimal value of Topic Coherence. Topic Coherence measures score a single topic by measuring the degree of semantic similarity between high scoring words in the topic [29]. In this work, the \(c\_v\) Topic Coherence measure was selected, and it provides values from the range [0, 1].  
For each of the models defined, several pre-processing techniques (Section 3.2) were performed: (a) corpus lemmatization without stemming nor bigrams; (b) lemmatization and stemming for reducing the granularity in our dataset; and (c) lemmatization, stemming and the introduction of variations in the frequency to form bigrams.  
We implemented LDA [24] in each of the proposed models, iterating T from 1 topic to 40 topics to determine which T in each model has the maximum \(c\_v\) measure. It is important to clarify that \(c\_v\) is a measure that helps to determine the optimal number of topics, but this measurement requires human interpretability to determine if the T topics selected are appropriate. For more extensive information on Topic Coherence measures, we encourage the reader to see [29; 30].  
Finally, pyLDAvis [31] Python's library was implemented in the T highest \(c\_v\) measure of each model to understand visually how words in each document were fitted among the different topics declared. This tool was very useful for giving a name to each of the topics extracted and checking the above-mentioned human interpretability.
## 4 Results and Discussion
### Parametric Analyses of Responses
#### 4.2.2 Analysis of Surface Roughness  
The main effects plot of means for surface roughness (Ra) is illustrated in Figure 9. It is clear that (A) has a more significant impact on (Ra), and (Ra) decreases with an increase in (A). The decrease in roughness of the surface of the printed parts via exposure to UV light indicates a higher level of chemical bonds at the surface, which is associated with cross-linking and/or oxidation [41]. Enhancing the bonds between layers can lead to a reduction in the visibility of layer lines and surface irregularities on the printed product, hence yielding a more refined surface texture. Also, the application of a polymer surface treatment technique results in a decrease in surface roughness upon exposure to ultraviolet (UV) light, hence potentially enhancing the glossiness of the polymer surface [42]. A notable correlation exists between gloss and surface roughness, whereby an increase in gloss is accompanied by a decrease in surface roughness [43].  
Similarly, the increase in (B) results in a smaller (Ra) value. This is the same reason as that mentioned above for (A), which can surely be applied here.  
Lastly, Figure 9 clarifies that the increase in bottom layers (C) decreases surface roughness (Ra); these layers act as support structures and are necessary to prevent the part from warping or collapsing (changing orientation) during the printing process. The (Ra) of a  
Figure 9: Main effects plot for surface roughness. (**A**: Normal exposure time, **B**: Bottom exposure time, and C: Bottom layers).  
stereolithographic (SLA) printed part can vary depending on its orientation, which can result in marks being left on the surface during the printing process [25]. For example, if the part is printed with a flat surface parallel to the build platform, the resin layers are deposited horizontally, resulting in a smoother surface finish. On the other hand, if the part is printed with a flat surface perpendicular to the build platform, the resin layers are deposited vertically, resulting in a rougher surface finish with visible layer lines. However, increasing the number of bottom layers may be necessary to provide additional support and prevent warping or deformation during the printing process [19].
## 3 Results and Discussion
### Effect of DES-Lignin on the Antioxidant Activity of Fabricated Membranes  
Antioxidative activity expressed as DPPH and ABTS radical scavenging activity of pure and modified membranes is presented in Table 1. In both antioxidant assays, as can be seen in the data in Table 1, the radical-scavenging activity of the fabricated membranes increased significantly (\(p<0.05\)) as the DES-lignin content in the casting solution increased. In the membrane containing 1 wt % DES-lignin, the DPPH and ABTS antioxidant scavenging activity increased about 2.6- and 1.1-fold (the difference between antioxidant scavenging activities of membrane #4 and membrane #1 divided by antioxidant scavenging activity of membrane #1), respectively, in relation to the pure membrane sample. The better antioxidant activity of the DES-lignin membranes can be attributed to the presence of phenolic OH in the molecular structure of the DES-lignin, which can act as a hydrogen donor antioxidant [38]. The pure membrane also shows antioxidant activity to some extent, which may be attributed to the presence of the sulfone (O=S=O) group in the structure of the polyethersulfone  
Figure 5: SEM cross-sectional images showing the effect of different amounts of added DES-lignin; (**A**) 0 wt %, (**B**) 0.25 wt %, (**C**) 0.5 wt %, (**D**) 1 wt %. The cross sections are on the left and the zoom sections of marked top layers are shown on the right.  
membrane [53], which can act as electron donor in order to neutralize the free radicals by pairing the DPPH odd electron by electron donation.
## 4 Discussion
### OpSCC HPV DNA Positive and HPV RNA Positive  
Current HPV detection methods in OPSCC have varying sensitivity and specificity. Many authors included HPV RNA testing in their studies. [PERSON] et al. analyzed, in Canada, more than 200 patients diagnosed with OPSCC, using RNA in situ hybridization (ISH) for HPV16 and 18, beside other biomarkers. For the samples that they tested, the authors detected a higher sensitivity for HPV testing in comparison to p16, and they concluded that HPV RNA ISH testing could be used as a routine analysis [39]. Another team of researchers from the USA used mRNA in situ hybridization (mRNA ISH) for HPV detection in OPSCC and detected a sensitivity and specificity of 100% for both in comparison to p16 analysis, which are ideal parameters for any laboratory test [40]. [PERSON] et al. simultaneously tested HPV RNA ISH and p16 IHC on the same slide, for both cervical cancer and OPSCC samples. The results obtained by this double testing was similar with the results previously obtained by classical p16 and HPV RNA testing. The authors are underlying the ease of applicability of this assay on FFPE samples, with complete automation, and possibility for implementation in clinical laboratories [41]. Researchers from Finland, with recognized experience in the HNSCC field, analyzed OPSCC samples by different methods, including HPV mRNA ISH. From all assays used, the ISH for high-risk HPV E6/E7 mRNA had the highest sensitivity (93%). The authors correlated the characteristics of this test with the necessity of de-escalating the therapy for HPV-positive tumors, at safe doses to maintain the favorable outcome [42]. [PERSON] et al. developed a study starting from the lack of concordance between p16 and HPV RNA testing. Despite this, the authors carried out a large contemporary retrospective study (nearly 500 patients), in the USA, in which they analyzed samples of OPSCC by these two assays. They concluded that it would be more efficient to confirm cases by HPV mRNA testing, as they identified that p16 status is not consistent with tumor morphology [43]. A very interesting study evaluated the prognostic utility, in HPV16-associated OPSCC, of HPV16 E6/E7 expression in circulating tumor cells. This expression was correlated with HPV16 DNA status and with p16. The authors concluded that this non-invasive liquid biopsy test for circulating tumor cells together with UICC staging could improve risk discrimination at the onset of disease [44]. This study is based on the same principle of microchimerism analyzed in other tumors and clinical circumstances [45]. A team of researchers from Ireland found that using the HPV RNA-ISH method was a more specific test in confirming HPV status, in comparison to the p16 assay [46].
## 3 Results
### Evaluation of Model 2: Settlement_2 Subgroup
Finally, classification error was assessed for the combined footprints. Overall accuracy was reported at 88.18% with 1097 out of 1244 locations of known land cover classified correctly (Table 5). Omission error within the primary cropland class was slightly higher than commission error but both reasonably low. Errors of commission were high for the savanna class at 19.25% however, almost 66% of the error is attributable to confusion with the forest class. Of the 72 non-savanna plots classified as savanna, 43 were forest and 23 cropland. The largest recorded error was found in the settlement class, the majority of which was attributable to settlement misclassified as cropland. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Reference** & **Classified** & **No.** & \multirow{2}{*}{**\(\varepsilon\)Omission**} & \multirow{2}{*}{**\(\varepsilon\)Commission**} & **Abs. Change** & **Abs. Change** \\  & **Total** & **Total** & **Correct** & & & **\(\varepsilon\)Omission** & **\(\varepsilon\)Omission** & **\(\varepsilon\)C Commission** \\ \hline Forest & 108 & 116 & 104 & 3.70 & 10.34 & \(-\)1.86 & \(-\)10.59 \\ Cropland & 152 & 142 & 132 & 13.16 & 7.04 & \(-\)3.40 & \(-\)17.24 \\ Savanna & 152 & 155 & 132 & 13.16 & 14.84 & \(-\)28.78 & 3.08 \\ Settlement & 40 & 40 & 40 & 0.00 & 0.00 & 0.00 & \(-\)28.57 \\ Water & 25 & 24 & 24 & 4.00 & 0.00 & 4 & 0.00 \\ Total & 477 & 477 & 432 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Error assessment of thematic land cover map at Path: 172 and Row: 071. Absolute changes in errors of omission and commission are reported in the last two columns. These values reflect the impact of error correction measures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Reference** & **Classified** & **No.** & \multirow{2}{*}{**\(\varepsilon\)Omission**} & \multirow{2}{*}{**\(\varepsilon\)Commission**} \\  & **Total** & **Total** & **Correct** & & \\ \hline Forest & 288 & 298 & 265 & 7.99 & 11.07 \\ Cropland & 421 & 410 & 370 & 12.11 & 9.76 \\ Savanna & 349 & 374 & 302 & 13.47 & 19.25 \\ Settlement & 100 & 80 & 78 & 22.00 & 2.50 \\ Water & 86 & 82 & 82 & 4.65 & 0.00 \\ Total & 1244 & 1244 & 1097 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error assessment of combined thematic maps from Path: 071 Row: 072, Path: 072 Row: 072, and Path: 073 Row: 071. Errors of omission and commission are reported in the body of the table, overall accuracy at the bottom.
## 6 Results and Discussion
### Influencing Factors on Framework Validity  
While the framework demonstrated high expert agreement and robust statistical support, its validity is influenced by several interrelated factors. First, the expertise and background diversity of evaluators play a critical role in shaping feedback quality; professionals with industry experience may prioritize practicality, while academics may emphasize theoretical coherence. Second, project typology and scale, residential status, infrastructure, or mega-projects, can affect the relevance and adaptability of specific Lean, BIM, or Technology tools integrated within the framework. Third, regional context, including technological infrastructure, digital maturity, and regulatory requirements, impacts implementation feasibility. For instance, regions with limited digital adoption may encounter integration challenges despite the framework's structured approach. Lastly, the stage of the project lifecycle at which the framework is applied may influence its perceived utility, with higher impact expected when adopted early (Plan or Design phases). These factors must be considered when interpreting the framework's applicability and generalizability.
# Review on Soft Mobility Infrastructure Design Codes
## 2 Codes for Soft Mobility Infrastructure
### Design Features
#### 2.3.3 Horizontal Geometrics  
The radius of horizontal curvature influences the stability of bicycles and the driver's speed choice [49]. Among the 17 cases viewed, only HK and TW give out specific limits on horizontal radii. HK regulations prefer minimum horizontal radii of 5 m, but in difficult conditions, radii of 2 m may be acceptable. TW specifies the minimum horizontal radii into the design speed of the bicycle paths, 3 m for 10 km/h, 15 m for 20 km/h, and 30 m for 30 km/h, which uses the same concept for motorway design.  
The superelevation for bicycle lanes is largely similar to the need for superelevation for motor vehicle lanes, but there are some specific considerations. By setting appropriate superelevation for bicycle lanes, the stability of cyclists in curves can be improved and the possibility of cyclists falling or losing balance in curves can be reduced, thereby reducing the incidence of cycling accidents, as shown in Figure 7. At the same time, the superelevation of the bicycle lane can allow cyclists to pass curves faster, increasing the riding speed and the traffic capacity of the bicycle lane. However, among the 17 cases we have information on, only TW has relevant requirements for bicycle lanes superelevation with the optimal value of the superelevation of the bicycle lane being 2%, and the maximum cannot exceed 3%.
## 3 Results
### fNiRS: HbO Data
No significant effect of group was revealed (\(p>0.05\)). A similar analysis conducted for the right DLPFC channel that showed high beta-values with the SIN condition also revealed no significant effect of group (\(p>0.05\)). As large within-subject variability was observed in the SRTn performance for the CI group (Table 4), we further examined activation levels in this group by dividing it based on SRTn, compared to the mean SRTn of the NHV group (mean SRTn = \(-\)2.14 dB). This division yielded two subgroups: \"Good CI\"--participants with SRTn \(<-\)2.14 dB (n = 6), and \"Poor CI\"--participants with SRTn \(>-\)2.14 dB (n = 8). Note that all the poor CI had SRTn \(>0\) dB, which was the SNR in the SIN condition. Next, we reanalyzed the MTG ROI-data of the four groups using two-way RM-ANOVA with Condition (SO, SIN) and Side (right, left) as the within-subject variables and Group (NH, NHV, good CI, poor CI) as the between-subject variable. The results showed a significant effect of Subgroup [F]1, 40) = 3.003, \(p\) = 0.042, \(\eta^{2}\) = 0.184], with a larger response (i.e., higher beta values) for the NH group compared to the poor CI group (\(p\) = 0.046). Figure 5c,d demonstrate that whereas smaller but positive beta values were shown for the poor CI compared to the NH with the SO condition, the opposite behavior was shown with the SIN condition. Specifically, large positive beta values were shown for the NH, as opposed to negative values for the poor CI. No significant differences were observed between the two CI subgroups (\(p>0.05\)), possibly because of the small number of participants included in each subgroup. There were no significant effects of Condition or Side, and no significant interactions (\(p>0.05\)).
## 4 Discussion
If the clinician takes pre-processing steps to obtain the measured values by taking or tracing the landmark points with the cephalometric radiographs, the precision or measured value of the landmark point detection may vary depending on the clinician's ability. However, diagnosing with the image itself reduces the chances of such an intervention. Moreover, the information that has not been considered previously can be considered together, which means that the meaning and effect of deep learning can be obtained more reliably. One of the biggest diagnostic differences between indications of orthognathic surgery and orthodontic treatment is determining whether the skeletal differences between the maxilla and mandible can be overcome [4]. In this study, Grad-CAM was used to determine whether the deep learning AI model considered and evaluated the correct region. Grad-CAM is a generalized version of CAM that can be applied to AI models without global average pooling [29; 30]. The highlighted CAM gives us insight into what it is seeing and evaluating why it failed. This was not different from the real situation that the actual clinician was seeing and evaluating. The limitation of this study was that comparison of the performance was limited due to the lack of data. When assessed with more data, different results can be obtained, which will need to be addressed in future studies. Additionally, future studies should analyze the impact of better model construction and their comparison with other models. For example, in the Resnet model, we can compare the accuracy difference according to the number of blocks. Compared to conventional machine learning, potential issues that deep learning may have include image quality problems, differences in X-ray devices, and image distortion caused by disturbing factors such as dental prostheses. The significance of this study is that it is the first study to perform differential diagnosis of the indications of orthognathic surgery and orthodontic treatment based on image, not measurements, and it showed a significant success rate. Compared with previous studies conducted with ANNs, the diagnostic performance of orthognathic surgery increased from 95.8% to 96.4% in total [33]. Most of the artificial intelligence research in the field of orthodontics is still limited to ANNs. AI models have been applied to the determination of extraction or not, extraction patterns, and anchorage patterns [23; 24]. The image feature analysis and classification technique using DCNNs has had an enormous impact on the entire medical field, and the same is true in the field of orthodontics and oral surgery.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 3 Pedestrian Dead Reckoning
### The Uncertainty of PDR  
We test edour PDR system to estimate the performance of the PDR system. In the experiment, we tested the basic pedometer and positioning performance of the algorithm by holding a smartphone and walking clockwise along the pedestrian walkway back to the location near the starting point. The test results are shown in Figure 3.  
In Figure 3, the red curve represents the actual route of pedestrian walking, while the black curve represents the pedestrian walking trajectory calculated by the PDR algorithm. The pedestrian walked for totally 55 m with 82 steps while the result of PDR is 81 steps. The distance between the end point of pedestrian position estimated by PDR and the end point of actual pedestrian position is 1.24 m, thus the positioning error is about 2.3% of the travel distance. The detailed results are shown in Table 1.  
Due to the short walking distance of pedestrians and the accumulated error of inertial sensors during walking, it is foreseeable that, as the walking distance continues to increase, the positioning error of pedestrian dead reckoning will also gradually increase. In a short distance (about 50 m), the error is within 3%. This means that, in short-distance operations, we can regard the PDR algorithm results as correct. We need to find a way not to modify the PDR all the time, but only to modify the location after the PDR has been running for a long time.
## 3 Results
### Clearing Prices
#### 3.1.4 The Algorithm  
The considerations made above allow us to present an algorithm for finding an optimal solution for the UDEFP with metric substitutability and clearing prices. The procedure is described in the following Algorithm 1.  
```
1: Perform a permutation \(\pi\) such that \(v_{\pi^{-1}(1)}\leq v_{\pi^{-1}(2)}\leq\ldots\leq v_{\pi^{-1}(n)}\)
2:\(p_{\pi^{-1}(1)}\leftarrow\tau_{\pi^{-1}(1)}\)
3:\(p_{\pi^{-1}(i)}\leftarrow\min_{1\leq j<i}\{v_{\pi^{-1}(i)},v_{\pi^{-1}(j)}+ \delta_{\pi^{-1}(i)\pi^{-1}(j)}\}\) for all \(2\leq i\leq n\)
```  
**Algorithm 1** Find an optimal clearing envy-free pricing.  
This algorithm allows us to state the following theorem:  
**Theorem 1**.: _The UDEFP with metric substitutability and clearing prices can be solved optimally in \(O(n^{2})\) time._  
Proof.: Lemma 1 ensures that Line 2 returns an optimal price for consumer \(\pi^{-1}(1)\), and Lemma 2 ensures that Line 3 returns optimal prices for the others. The permutation in Line 1 can be performed in \(O(n^{2})\) with any classical sorting algorithm, and Line 3 performs  
Figure 3: The network constructed to find the envy-free prices.  
\(n-1\) times \(O(n)\) operations. Therefore, the overall time complexity of the algorithm is \(O(n^{2})\).
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 2 Materials and Methods
### CO\({}_{2}\) Flux Measurements and Supporting Meteorological Data  
#### 2.4.1 Instrumentation  
Fluxes of CO\({}_{2}\) were measured continuously using the eddy-covariance (EC) technique and reported for 30-min averaging intervals [76; 77; 78]. The EC system at Auchencorth Moss has been in operation since 2002 [79], and currently consists of: (i) a closed-path infrared gas analyser (LI-COR 7000, LI-COR Environmental, Inc., Lincoln, NE, USA) measuring water vapour and CO\({}_{2}\) concentrations at 10 Hz (effective height 3.5 m, 20-m long inlet tube, outside diameter \(1/4^{\prime\prime}\)); and (ii) a 3D ultrasonic anemometer (WindMaster Pro installed at a 3.6-m height was used until June 2019, and then replaced by a HS-50 installed at a 4.1-m height (both Gill Instruments, Lymington, UK)) operating at 20 Hz. Both setups have the same separation (30 cm vertical; 20 cm horizontal) from the inlet of the sampling line and all data were logged to a PC using an in-house LabView\({}^{\text{TM}}\) program (National Instruments, Austin, TX, USA). In addition to EC measurements, the site is supported by an adjacent meteorological station (see Figure 1; ref. [80]), providing a comprehensive suite of meteorological observations. These measurements include temperature (fine wire type-E thermocouple), water-table depth (WTD, Druck PDCR 1830, Druck Ltd, Leicester, UK), photosynthetically active radiation (PAR, SKP215, Skye Instruments Ltd., Llandrinodod, UK) and rainfall (tipping bucket rain gauge). Reference in the text to average meteorological conditions describes long-term values for the period 2002-2017 for all variables except WTD, for which recording began in 2007. Finally, multiple dry spells were observed over the study period, defined here as periods longer than 7 days with WTD > 5 cm below the surface.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 3 Methodology
### Hand Gesture Recognition (HGR)  
This section introduces a straightforward and efficient HGR approach. For pitch control, Section 3.3.1 describes a method to measure the hand distance from the RGB camera without employing a distance sensor. Section 3.3.2 outlines the estimation of the hand rotation angle for yaw control. Finally, a simple network is presented for the classification of the posture-based hand gestures, enabling the manipulation of the remaining movements.