esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilded pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green-Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilded pine trees. The results showed that the proposed method effectively distinguishes wilded pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user's accuracy (81.2% vs. 67.7%) while maintaining the same level of producer's accuracy (84.7% vs. 82.6%).  
p +
Footnote †: journal: remote sensing  
1
Footnote 1: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (B.Z.); [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (Z.H.)  
Footnote †: journal: remote sensing
# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 3 The LoRaUAV System  
The objective of LoRaUAV is to provide coverage to mobile GNs using a fleet of UAVs, whose purpose is to relay the collected data to a BS through an ad hoc network established between its members. Each UAV must be able to adjust its position to reflect the changing topology of the GNs and, at the same time, maintain connectivity with the BS. Disconnections, although difficult to avoid, must be minimized, and recovery measures must be set up to recover the connectivity. LoRaUAV provides mechanisms to tackle all these problems. Some assumptions are made:  
1. All UAVs fly at the same altitude in a 2D plane;
2. All UAVs can move with constant speed in any direction;
3. UAVs periodically exchange their position and the list of covered GNs with their neighbors;
4. UAVs have access to the received power of neighboring UAVs and GNs or can estimate it based on their position;
5. The position of the BS is known by all UAVs;
6. The minimum data rate (and hence maximum SF) acceptable for each LoRaWAN link is a configuration parameter.
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## 3 Prebiotics
### Prebiotics and Ophthalmic Related Diseases  
Gut dysbiosis has been demonstrated in many inflammatory and autoimmune ocular diseases such as uveitis, which is an inflammatory condition of ocular tissues. A recent clinical study compared the gut microbiome of people with uveitis and control group, showing a reduction of gut microbiome diversity in people with uveitis. This reduction was shown to be more significant in anti-inflammatory bacterial strains [101].  
Sjogren syndrome (SS) is an autoimmune disease with a variety of ocular complications. This disease affects lacimal glands, the primary source of production of watery layer of the tear film and triggers DED and other ocular surface disorders. The microbiome shifts in SS have been previously reported, with suggestions for both prebiotic and probiotic interventions [102].  
Apart from the extensive literature on omega-3 PUFAs in treatment of DED (in which studies the mechanism in not generally ascribed to be prebiotic), there are few reports addressing the use of prebiotics for DED. However, in a recent review of diet and rosacea (which included ocular symptoms) [103] a prebiotic diet was recommended.  
In relation to dry eye, there has been robust investigation into the oral consumption of omega-3 fatty acids in the treatment of DED with mixed results initiated by the observation from the 32,000 participant Women Health study that drew an association between DED and low consumption of omega 3 PUFA [104]. By way of illustration of the controversy surrounding omega 3 PUFA and dry eye, a recent meta-analysis of omega 3 PUFA supplementation for DED, including results from randomized clinical trials comprising a total of 3363 subjects, concluded supplementation led to significant improvement in dry eye signs and symptoms [99], while the large multicentre NIH sponsored DREAM study, one of the most comprehensive so far, concluded there was no difference in dry eye outcome measures between test and placebo [105]. Both these and other studies report on the difficulties of direct comparison of results given variations in eligibility criteria, dose, placebo content, duration and so forth.
# Effect of the Insulating Layer on the Properties of SMC Inductors
## 4 Experimental Results
### RLC Meter Measurement  
The obtained inductors, together with the ferrite one, were then measured by means of a fluke PM6306 RLC meter in the frequency range of 50 Hz-1 MHz and at a 20 mA excitation current. The inductance, resistance, and quality factor \(Q\) were directly acquired from the instrument. The DC resistance value was also recorded for every wound sample and then subtracted from the measured AC resistance values. This approach allowed us to neglect the series resistance due to the copper wire, precisely detecting only the iron losses. The final quality factor \(Q\), obtained from the corrected resistance values, is related to the effective properties of the core materials, reflecting the proper ratio between the reactive and the dissipated energy.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 4 Results
### Model  
We employed a binary classification, since it produces better results than a continuous classification [44]. The sentiment value, low and high price, volume, and adjusted price are all input features. The target feature, on the other hand, is the stock change, which has two separate values (\(-1\) and \(1\)). Therefore, it has two numbers that describe the state of either selling or buying the stock.  
Table 5 shows the distribution of the target variable for StockTwits and Twitter, as well as the SA tools TextBlob and VADER. In all four scenarios, the number of buyers outnumbers the number of sellers, suggesting that most individuals prefer to acquire Microsoft stock and sell it less frequently.  
The input and target variables are displayed over time in the figures below. The blue line shows the target variable, which is the change in the stock price, and the red line indicates the input variable, which is public opinion towards the Microsoft stock.  
Particularly, Figure 5 shows that the two lines are associated from July to August. When sentiment is favorable, stock change rises, meaning that when individuals are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Buy Count** & **Sell Count** \\ \hline StockTwits with TextBlob & 51 & 39 \\ StockTwits with VADER & 49 & 42 \\ Twitter with TextBlob & 55 & 45 \\ Twitter with VADER & 53 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stock movement distribution per data set.  
optimistic, they tend to purchase Microsoft shares. From September through October, the trend is similar to the preceding period, though with some notable variances.  
On the other hand, there is no apparent trend for August (Figure 6). Instead, it appears that sentiment in July is tied to market movement. When the mood is good, stock exchange prices rise, and vice versa. The two lines are connected for several days in September and October, although there are many variances.  
When Twitter data are utilized and evaluated with the TextBlob and VADER, as illustrated in Figures 7 and 8, the majority of tweets have a neutral sentiment. There are many highs and lows that are very near to zero. This suggests that utilizing Twitter data to anticipate stock prices may be unreliable, as emotion and stock movement are unrelated.  
Figure 5: Sentiment and stock change for ‘StockTwits with TextBlob’ dataset.  
Figure 6: Sentiment and stock change for ‘StockTwits with VADER’ data set.
## 1 Introduction  
Temporomandibularbular joint spaces (TMJSs) have been investigated to determine the condyle-fossa relationships. The mean ratios of AS (anterior space) to SS (superior space) to PS (posterior space) in the sagittal view and LS (lateral space) to CS (central space) to MS (medial space) were evaluated. SS was the greatest and AS was the least in the sagittal view. CS was the greatest and LS was the least in the coronal view [1, 2, 3].  
TMJSs were not significantly different according to skeletal patterns in our previous study [3], while the other studies showed that the condyles were positioned more anteriorly [4], posteriorly [5], and superiorly [6] in the patients with a high angle skeletal pattern than in others, and Class III patients showed more anterior position of the condyles than other groups [7]. However, the condyle position in the TMJ did not correlate with the temporomandibular disorder (TMD) [8].  
In skeletal Class III malocclusions, the dimensions of the anteroposterior glenoid fossa and AS were smaller than normal, which suggests that the mandible displaced anteriorly due to its relationship to the condylar protrusion, with a relative mediolateral elongation of the condyle within a relatively smaller glenoid fossa [9]. TMJSs were symmetrical in both condyles of the patients with facial Class III deformity and an indication for orthognathic surgery [10]. However, the incidence of internal derangement in asymmetrical patients with dentofacial Class III deformity was associated with variations in the TMJ morphology, including the disc position on both sides [11].  
Mandibular prognathism with Class III skeletal deformity can be corrected by orthothathic surgery, including a sagittal split ramus osteotomy (SSRO) and intraoral vertical ramus osteotomy (IVRO) with or without maxillary surgery. Although both surgical procedures can be biologically sound, IVRO may have more favorable effects on TMJ due to condylar adaptive remodeling and anterior-inferior displacement of the condyle after surgery [12; 13], and the improved anterior disc displacement in the initial postsurgical period [14].  
Several factors are related to changes in the TMJS throughout the orthodontic treatment combined with orthognathic surgery. These are anterior disc displacement [15], TMJ dysfunction [16], rigid fixation procedure [17], technical problems [18], magnitude of setback [19; 20], vertical bony step [21], condylar repositioning devices [22; 23; 24], biomechanical stress on the condyle [25], and vertical and horizontal skeletal patterns [3; 4; 5; 6; 7]. Additionally, other factors such as gender, side, number of surgery sites, type of surgery, asymmetry, genioplasty, and overbite should be considered when planning orthognathic surgery for skeletal Class III deformities.  
The authors hypothesized that orthognathic surgery does not influence the TMJS in treating skeletal Class III patients. The aim of this study was to identify the effects of orthognathic surgery on TMJS throughout the treatment of skeletal Class III deformities.
## 4 Conclusions  
This study examined the potential of post-hardening treatment using Sr\({}^{2+}\) for modifying the properties of hydrated cement composites. The effects of treatment with a 30% strontium nitrate aqueous solution on the strength characteristics of cement composites were investigated. Additionally, durability aspects such as surface abrasion resistance, water sorptivity, and porosity were evaluated in the cement paste specimens treated with the strontium nitrate solution. Microscopic morphology changes in the hardened cement hydrates due to strontium cations were analyzed using SEM, EDS, and XRD. Based on the testing results, the following conclusions can be drawn:  
Treating hardened cement paste composites with a 30% strontium nitrate solution enhanced both compressive and flexural strengths. Notably, flexural strength showed a remarkable improvement, with over 70% enhancement in early stage strontium-treated specimens compared to those cured in deionized water for the same period.  
The penetration of the 30% strontium nitrate solution into the 28-day cured cement paste specimens reached up to 6.5 mm over an additional 7 days. Most penetration appears to occur in the early stages of the treatment process. This penetration likely alters the hydrated cement components into new forms, contributing to the observed strength enhancements.  
Specimens treated with the strontium showed improved surface abrasion resistance and reduced water absorption. Additionally, a significant reduction in porosity was noted in the strontium-treated specimens. These results led to enhanced surface durability, suggesting a denser and more durable surface layer. The strontium treatment process rapidly improved the surface abrasion resistance characteristics, while porosity and water sorptivity gradually decreased over time.  
SEM, EDS, and XRD analyses revealed a denser microstructure in the hydrated cement paste composites and confirmed the presence of strontium throughout the treated layer. New hydrate shapes, not typically found in normal hydrated cement composites, were identified and are believed to be formed by strontium. Several types of zeolite structures were identified, accompanied by an observed decrease in the portlandite phase. These changes are thought to be responsible for the improved mechanical and durability performance of the strontium-treated specimens.  
In conclusion, treatment with strontium cations offers a promising approach for enhancing the durability of hydrated cement composites post-hardening, along with improved mechanical characteristics. This highlights the potential of strontium cation treatment in extending the service life of cement concrete structures.  
Figure 14: XRD analysis.  
**Funding:** This research was funded by 2021 and 2022 Research Grants from Sangmyung University (2021-A000-0011 and 2022-A000-0099).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available from the authors upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
However, these analyses are limited by the availability, quality, granularity, and applicability of data describing what is the real perception of a pedestrian, or a building occupant, as his/her surroundings can drastically modify the perception of climatic and air quality distress [22; 23]. Moreover, integrated and multi-hazard analysis at the meso-scale is hampered by the distribution of sensors and the fact that, mostly, the data collection location does not always gather more than one parameter category (e.g., weather and pollutants), which makes the superposition of the effects hardly applicable. Therefore, this work intends to propose and perform a preliminary multi-hazard risk analysis of northern Italy (i.e., Milan), using available open-source climate and air quality databases supported by the Lombardy Regional Environmental Protection Agency (ARPA Lombardia) [24]. Milan was considered a relevant location where a large population density is found (large risk exposure) and both considerable increasing temperatures and the decay of air quality and the increase of pollution converge (large risk hazard), based on the preliminary assessment analysis carried out by [PERSON] et al. [25] using demographic data from the Italian National Institute of Statistics (ISTAT) [26] and historical temperature and particulate matter measurements from EEA [27; 28]. In particular, the study presented in this paper is concentrated on assessing the overall increasing temperatures and air pollution SLODs' risk frequency for the least resilient population: demographic groups that are more prone to suffer severe health consequences when exposed to adverse environmental conditions. Moreover, critical hazard risk time-frames (including daily profiles) are established to acknowledge and communicate those moments of the year in which people are and probably will continue to be critically at risk of their health being affected if exposed to these conditions.
## 3 Methodology
### Feature Selection
#### 3.2.5 Support Vector Machine-Based Recursive Feature Elimination  
Recursive Feature Elimination (RFE) is a wrapper-based feature selection method that requires an existing classifier to guide its selection process [41]. In this case, the SVM served as the underlying model, assessing the importance of each feature based on its contribution to the SVM's performance. This evaluation fueled RFE's iterative removal of non-discriminative features, ultimately leading to a curated set of features ideal for SVM classification [42].  
This approach, known as SVM-RFE, is a backward selection algorithm based on the SVM principle. Leveraging the strengths of SVMs in identifying informative features, it iteratively refines the feature space to retain only those that contribute most significantly to model performance. Initially, an SVM is trained on the entire training set. The coefficients of the trained SVM, which represent the relative importance of each feature in defining the decision boundary, are then analyzed. The features with the smallest absolute coefficient magnitudes are removed from the dataset. This process continues iteratively, retraining the SVM on the reduced feature set and pruning features based on their updated coefficients until the desired number of features remains. By progressively focusing on features that maximize the margin between classes, SVM-RFE efficiently improves the model's generalizability and reduces overfitting [43]. The SVM-RFE was implemented using RFE from Scikit-learn.  
Figure 7 illustrates the absolute coefficient values of the top 100 features selected using SVM-RFE. Features with larger coefficients indicate greater importance in defining the model's decision boundary.
# New Possibilities of Using DEMATEL and ERPN in the New PFMEA Hybrid Model
## 2 Materials and Methods
### Determination of ERPN  
It is an extended risk number that is created by including internal and external costs as a result of failure modes in the model. We used the methodology proposed by [PERSON] [8]. This methodology [8] was used to develop the model on which we built our research (Section 2.7).  
Internal Failure Cost (_IFC_) is the cost incurred as a result of product quality non-conformance, assuming that defective products are detected at the production stage. If no defective product is detected, then _IFC_ = 0 [8].  
External Failure Costs (_EFC_) are the costs that are incurred by a non-conformity detected by the customer. If they do not occur, _EFC_ = 0. _EFC_ is divided into two parts:  
1. Without-casualty costs (_WoC_): field servicing and handling complaints; recalls, returns, and replacements; warranty; other indirect costs, etc.;
2. Casualty costs (_CC_): the cost of compensating customers for damage caused by a defective product. Then, _IFC_ = _WoC_ + _CC_.  
Additionally, _IFC_ and _EFC_ can be converted to SI and SE as follows [8]:  
1. We follow up on the conventional PFMEA and the determined RPN.
2. Internal costs are determined--_IFC_ (for 1 defective product).  
External costs--_EFC_ are determined, considering _WoC_ costs and _CC_ costs (per one defective product).
* The probability of occurrence of the mode--_PO_ is determined. _PO_ (Equation (8) is based on the probability of occurrence of _O_: \[PO=\frac{O}{10}\] (8)
* We determine the detection probability of the mode--_PD_. The _PD_ (Equation (9)) is based on the detection parameter \(D\). \[PD=\frac{(10-D)}{9}\] (9)
* The internal costs of the _IFC_ will be taken into account when determining the severity level (_SI_) (Equation (10)).  
\[SI=\frac{IFC}{FCmin} \tag{10}\]  
where _FCmin_ response minimum costs by all failures and causes.
* The _IFC_'s internal expenditures will be taken into consideration when establishing a severity level (_SI_) (Equation (10)).  
\[SE=\frac{WoC}{FCmin} \tag{11}\]
* The external costs of the _CC_ will be taken into account when establishing a severity level (_SC_) (Equation (12)).  
\[SC=\frac{CC}{FCmin} \tag{12}\]
* We determine the poc--the probability of the failure mode. For _WoC_ costs, we consider poc = 0. For _CC_ costs, we consider _poc = 1_.
* The _ERPN_ calculation for the extended FMEA is calculated according to Equation (13): \[ERPN=PO.ST.[PD.SI+(1-PD).(poc.SC+(1-poc)SE)]\] (13)
* The comparison of the RPN calculated for conventional PFMEA with the calculated _ERPN_.
# Grouping Bilinear Pooling for Fine-Grained Image Classification
## 4 Experiment
### Comparing with Other Compact Bilinear Pooling
Compared with [19], although the performance of GBP based on VGG-16 on the CUB dataset is lower, the pooling computation of GBP is reduced by two orders of magnitude, and the dimension of feature representation obtained by GBP is lower. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **FBP [15]** & **iFBP [32]** & **CBP-TS [17]** & **CBP-RM [17]** & **LRBP [19]** & **Intra-GBP** & **Inter-GBP** \\ \hline \hline
**CUB** & 84.01 & **85.80** & 84.00 & 83.86 & 84.21 & 83.64 & 83.66 \\
**(\%)** & 91.18 & 92.10 & 90.19 & 89.54 & 90.92 & 91.42 & **92.49** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performances of different compact bilinear pooling methods on CUB dataset and Stanford Cars dataset. (Based on VGG-16). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dimension**} & \multicolumn{4}{c}{**Computing**} & \multicolumn{2}{c}{**Parameters**} \\ \cline{3-8}  & & & **Pooling** & **Classifying** & **Total** & **Projection** & **Classifier** & **Total** \\ \hline \multirow{8}{*}{VGG-16} & FBP [15] & \(c^{2}\)[262 K] & O (\(hvc^{2}\)) & O (\(Nc^{2}\)) & 257,949,696 & 0 & \(Nc^{2}\) & 200 MB \\  & iFBP [32] & \(c^{2}\)[262 K] & O (\(hvc^{2}\)) & O (\(Nc^{2}\)) & 257,949,696 & 0 & \(Nc^{2}\) & 200 MB \\  & CBP-TS [17] & \(d\)[10 K] & O (\(hvc+dlogd\)) & O (\(Nd\)) & 85,532,672 & \(2c\) & \(Nd\) & 8 MB \\  & CBP-RM [17] & \(d\)[10 K] & O (\(hvc\)) & O (\(Nd\)) & 3,289,972,736 & \(2 cd\) & \(Nd\) & 48 MB \\  & LRBP-I [19] & \(mInv\)[78 K] & O (\(hvc\)) & O (\(Nvm\)) & 165,580,800 & \(cm\) & \(Nvm\) & 0.8 MB \\  & LRBP-II [19] & \(m^{2}\)[10 K] & O (\(Inv\)(cm + \(m^{2}\))) & O (\(Nvm^{2}\)) & 63,980,800 & \(cm\) & \(Nvm\) & 0.8 MB \\  & Intra-GBP (\(ours\)) & \(c^{2}/g\)[2 K] & O (\(hvc^{2}/g^{2}\)) & O (\(Nc^{2}/g\)) & 422,144 & 0 & \(Nc^{2}/g\) & 1.6 MB \\  & Inter-GBP (\(ours\)) & \(c^{2}/2g\)[1 K] & O (\(hvc^{2}/2g^{2}\)) & O (\(Nc^{2}/2g\)) & 211,072 & 0 & \(Nc^{2}/2g\) & 0.8 MB \\ \hline \multirow{2}{*}{ResNet-50} & FBP [15] & \(c^{2}\)[4194 K] & O (\(hvc^{2}\)) & O (\(Nc^{2}\)) & 1,660,944,384 & 0 & \(Nc^{2}\) & 3200 MB \\  & Intra-GBP (\(ours\)) & \(c^{2}/g\)[4 K] & O (\(hvc^{2}/g^{2}\)) & O (\(Nc^{2}/g\)) & 819,984 & 0 & \(Nc^{2}/g\) & 3.2 MB \\ \cline{1-1}  & Inter-GBP (\(ours\)) & \(c^{2}/2g\)[2 K] & O (\(hvc^{2}/2g\)) & O (\(Nc^{2}/2g\)) & 409,992 & 0 & \(Nc^{2}/2g\) & 1.6 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different compact bilinear pooling methods. We used VGG-16 and ResNet-50 as the backbone respectively to compare the computational complexity, representation dimensions and the parameters needed to be learned (excluding the backbone network).