esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this study is to retrospectively investigate the effectiveness of outpatient ductal steroid irrigations (DSIs) as an adjuvant treatment in recurrent inflammatory obstructive sialadentis (RIOS). A retrospective chart review of prospectively recruited RIOS patients was randomly assigned to group A (i.e., interventional sialendoscopy) or group B (i.e., interventional sialendoscopy with outpatient DSIs). The statistical analysis detected any postoperative difference between groups in terms of the number and severity (attested by 0-10 VAS pain) of episodes of swelling. Interventional sialendoscopy and DSIs were effective and well tolerated in all 122 patients, and a significantly reduced number of salivary gland swelling episodes (\(p\)-value = 0.01) was documented in group B compared to group A at any follow-up assessment. When the specific aetiology was considered, repeated DSIs were more effective than interventional sialendoscopy alone in patients with type 1 or 3 stenoses and in those without mucous plugs. Our results confirm the safety and effectiveness of interventional sialendoscopy (both as a single-modality and a multimodal approach) in a large series of adult patients with RIOS at short-, medium-, and long-term analyses. The superiority of a combined therapeutic protocol, with a positive effect still detectable 12 months after the end of the treatment, was attested as well.  
sialendoscopy; sialadentis; salivary gland; infection +
Footnote †: journal: applied sciences  
0000-0001-8202-4820]Cited for updates  
[PERSON]; [PERSON]  
[PERSON]; [PERSON]  
[PERSON]; [PERSON]  
[PERSON]; [PERSON]  
[PERSON]; C.; C.  
rate of untoward effects [11]. In addition, (interventional) sialendoscopy has been also proven to be economically viable, as it has been found to be more cost-effective both as a diagnostic and therapeutic option compared with multiple combined diagnostic modalities and traditional medical management in patients with radioiodine-induced sialadenitis [12].  
Interventional sialendoscopy combined with repeated ductal steroid irrigations (DSIs) can be used in an integrated therapeutic protocol for the treatment of patients with recurrent inflammatory obstructive sialadenitis (RIOS) [7; 9; 13], but the actual therapeutic gain offered by DSIs compared to interventional sialendoscopy alone is difficult to quantify. Based on this, we previously documented the positive effect of interventional sialendoscopy combined with DSIs in 54 adult patients with recurrent sine causa sialadenitis, with the finding of better results in terms of reduction in the mean number of salivary gland swelling episodes 6 months after the procedure in the group receiving combined treatment compared to patients undergoing interventional sialendoscopy alone [7].  
The aim of this retrospective study is to evaluate the impact of outpatient DSIs as an adjuvant treatment to interventional sialendoscopy in a larger cohort of patients with RIOS and its effectiveness in the medium- and long-term.
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 4 Experiments
### Results of Model Prediction  
The test uses the PF-Informer model to train the multilayer thick target models, and the single-time input and multi-element inputs are compared. The prediction effect is measured by the MSE and the MAE. The results of comparisons conducted in several prediction situations are shown in Table 2, and the visual data are shown in Figures 10 and 11.  
Figure 9: Local time series prediction effect obtained for thick targets. (**a**) First single timing prediction; (**b**) second single time series prediction; (**c**) first multivariate element prediction; (**d**) second multivariate element prediction.  
Figure 11: MAE performance achieved on the test set. (**a**) First test; (**b**) second test.  
Figure 10: MSE performance achieved on the test set. (**a**) First test; (**b**) second test.  
By analyzing the data in Table 2 and the figure, we can see that the prediction accuracy of the multivariate input is far better than that of the time-only input. Slight optimization is achieved for multilayer targets, but the effect is remarkable on multivariate thick targets, which demonstrates the importance of multivariate parameters in long time series prediction.
## 4 Full State Reconstruction Using the Ae-Dmdc Reduced-Order Model
### Kalman Filter  
For comparison purposes, the state estimation by using the Kalman Filter [21; 22] is considered. The combination of DMDc models with Kalman Filters has recently been shown to yield quite convincing results [3; 17; 18; 19; 20]. One big potential of the Kalman Filter relies on the fact that it provides a minimum estimation error covariance based on _a priori_ statistics of the model and measurement uncertainties, which are considered as white noise processes with covariances \(Q\in\mathbb{R}^{r_{2}\times r_{2}}\) and \(r\in\mathbb{R}\) in the present context, i.e.,  
\[\mathbf{z}(t+1) =\bar{A}\mathbf{z}(t)+\bar{\mathbf{b}}u(t)+w(t),\quad w(t)\sim\mathcal{N} (\mathbf{0},Q) \tag{7a}\] \[y(t) =\bar{\mathbf{c}}^{\mathrm{T}}\mathbf{z}(t)+v(t),\quad v(t)\sim\mathcal{N }(0,r). \tag{7b}\]  
Under these assumptions, the minimum estimation error covariance state estimator is given by the following prediction-correction (innovation) scheme (see, e.g., [21])  
\[\text{Prediction}\] \[\hat{\mathbf{z}}_{p}(t+1) =\bar{A}\mathbf{z}(t)+\bar{\mathbf{b}}u(t)\] (8a) \[P_{p}(t+1) =\bar{A}P(t)\bar{A}^{\mathrm{T}}+Q\] (8b) Kalman gain \[L(t+1) =P_{p}(t+1)\bar{\mathbf{c}}(\bar{\mathbf{c}}^{\mathrm{T}}P_{p}(t+1)\bar{ \mathbf{c}}+r)  
with predicted state \(\hat{z}_{p}\), error covariance \(P_{p}\), Kalman (correction) gain \(L\), state estimate \(\hat{z}\) and error covariance \(P\).  
Note that the implementation of the Kalman Filter implies the memory usage of a total of \(r_{2}^{2}+r_{2}\) floating point variables for storing (and for updating) the error covariance matrix \(P(t)\in\mathbb{R}^{r_{2}\times r_{2}}\) and the state estimate \(\hat{z}(t)\in\mathbb{R}^{r_{2}}\). For the case study with \(r_{2}=17\) this yields 306 dynamic variables, while the original pde approximation is based on only 100. On the other hand it should be noticed that the Kalman Filter provides a minimum estimation error covariance, which is in particular useful if one takes into account the stochastic process and measurement noise perturbations. Given the approximative nature of the model (in full-scale with \(N=100\) state variables as well as in reduced scale with \(r_{2}=17\) state variables, this uncertainty rejection property can eventually compensate modeling errors up to some point.
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 8 Results and Discussion
### Results
* (_p_ < 0.05) denotes statistically significant results and indicates no significant improvement was achieved when combined with other languages during MTL training. 5-class metric in Latinized Bulgarian. Similarly, for the 3-class metric, Bulgarian (Latin) + English, Bulgarian (Latin) + Polish, and Bulgarian (Latin) + Croatian perform significantly better than others. Three-class metrics for Czech and Polish, two high-resource languages, did not improve from their initial scores. In the case of Polish and Czech, the addition of the English dataset had no positive effect. While all other languages, i.e., Bulgarian, Croatian, Russian, Slovak, and Slovene, had improved performance with a large English dataset, we noticed that combining Slavic languages had slightly lower performance than English combinations. It was also observed that combining multiple languages (such as Bulgarian, Croatian, Slovene, and Slovak) did not outperform the five-class metric. A further observation is that, except for Russian (Latin) and Slovak, none of the model combinations that scored over 80% on the three-class F1 value utilized English during their training phase. The performance of Bulgarian (Latin) is inferior to that of the Cyrillic version. In contrast, Russian (Latin) achieved the highest scores in each of the four metric classes. This may be due to the lack of training data in Bulgarian. When the languages are combined with English, it has resulted in superior performance in the majority of instances. Slovene was found to be the dataset /language with the lowest performance. This is because the Slovene dataset is derived from informal sources, such as news commentaries, which are noisy in nature. The Slovak dataset has fewer examples for training than the Slovene dataset, but these examples come from customer reviews. The scores that were previously reported in the respective target language are presented in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Language** & **Metric** & **5-Class** & **3-Class** & **2-Class** \\ \hline Bulgarian & MSE & 0.666 & 0.141 & \\ Croatian [68] & F1 & & 91.1 \\ Czech [122] & F1 & & 87.08 \(\pm\) 0.11 & 96.00 \(\pm\) 0.02 \\ English [110] & ACC & 56.5 & & \\ Russian [82] & F1 & & 72.69 & 87.04 \\ Slovak [123] & & & & 81.5 \\ sentimentu, accessed on (23 April 2023)) & & & & \\ Slovene [90] & F1 & & 65.7 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Previously reported results for the languages in the study. ACC—Accuracy.
## 4 Results
### Comparison of Fertilization Decision Strategies
#### 4.2.2 Yield Comparison  
We employed the fertilization plan generated by our model to guide the fertilization operations and compared the yields from this treatment with those obtained in the field experiments, as illustrated in Figure 9. Our results indicate that our approach successfully optimized the field fertilization strategy, leading to a significant increase in lint cotton yield. Although the seed cotton yield did not increase compared to the highest field experimental group and was slightly lower (a decrease of 0.7%), the lint cotton yield increased by 10.9%. Given the greater economic benefit of lint cotton over seed cotton in cotton production, these results not only validate the effectiveness of our proposed strategy but also enhance the fiber quality. Therefore, the optimized nitrogen fertilization plan (\(N_{2}\)) recommended in this study is deemed entirely feasible.  
The comparative analysis reveals distinct performance differentials between the DSSAT + GA-optimized fertilization strategy and experimental field treatments across cotton quality metrics (Table 8). The DSSAT + GA approach achieved a lint cotton yield of 5025.6 kg/ha, surpassing all experimental groups by 9.9-37.2%, with the largest margin observed relative to the 2023-Tr-1 group (3155.55 kg/ha). Notably, while the 2024-Tr-2 treatment produced the highest seed cotton yield (9756.09 kg/ha), its lint conversion efficiency (46.0%) underperformed the DSSAT + GA system's 51.9% lint turnout rate. Fiber quality metrics further highlight the superiority of the optimized strategy, registering a 0.51 score that exceeds the 0.45-0.47 range of field trials, indicating enhanced fiber maturity and uniformity.  
Figure 9: Comparison of field yields between our proposed plan and the experimental groups. The two subplots represent the yields of seed cotton and lint cotton, respectively.  
Interannual comparisons show incremental improvements in 2024 treatments over 2023 cohorts, particularly in seed cotton yields (7076.08-9756.09 kg/ha versus 7848.2-9756.09 kg/ha). However, this yield gain did not translate proportionally to lint production or fiber quality, underscoring systemic inefficiencies in conventional fertilization regimes. The DSSAT + GA framework demonstrates balanced optimization, simultaneously maximizing lint yield and fiber attributes without compromising metabolic partitioning efficiency--a critical advancement for sustainable cotton production systems.
# Sentiment Interaction Distillation Network for Image Sentiment Analysis
###### Abstract  
Sentiment is a high-level abstraction, and it is a challenging task to accurately extract sentimental features from visual contents due to the \"affective gap\". Previous works focus on extracting more concrete sentimental features of individual objects by introducing saliency detection or instance segmentation into their models, neglecting the interaction among objects. Inspired by the observation that interaction among objects can impact the sentiment of images, we propose the Sentiment Interaction Distillation (SID) Network, which utilizes object sentimental interaction to guide feature learning. Specifically, we first utilize a panoptic segmentation method to obtain objects in images; then, we propose a sentiment-related edge generation method and employ Graph Convolution Network to aggregate and propagate object relation representation. In addition, we propose a knowledge distillation framework to utilize interaction information guiding global context feature learning, which can avoid noisy features introduced by error propagation and a varying number of objects. Experimental results show that our method outperforms the state-of-the-art algorithm, e.g., about 1.2% improvement on the Flickr dataset and 1.7% on the most challenging subset of Twitter I. It is demonstrated that the reasonable use of interaction features can improve the performance of sentiment analysis.  
sentiment classification; knowledge distillation; visual sentiment analysis; convolutional neural networks +
Footnote †: journal: Medical Imaging  
12
## 4 Discussion
### Reflection on the Used Design  
Installing a novel lighting system in the houses of people with dementia does not guarantee that the lighting system will be used in a proper way. For example, the bedroom light worked with Wi-Fi and a Bluetooth connection, and sometimes it was accidently disconnected or turned off by the participants. Problem solving required physically reconnecting the system, which was only possible during home visits.  
The wearable sensors that objectively measured the amount of light regularly registered negative or unrealistically low values. Perhaps the sensor was partly or completely covered by clothing at such times or worn too close to the face so that there was a shadow on the sensor. Furthermore, people sometimes forgot to wear the sensor. Probably due to attentiveness of the caregivers, no sensors disappeared, were hidden, or were thrown away. Negative or unrealistically low values can be detected in data analysis, as it is highly unlikely that the participants received no light intensity and, at the same time, were registered as moving or physically active. Therefore, the study design must take into account substantial amounts of missing data by ensuring sufficient power and/or redundancy. The used SCED design with the ABAB setup seems to be appropriate to fill these gaps [29; 30; 31].  
Some caregivers reported that the strange new lights in the house sometimes caused some confusion among the participants and that this did not always make them sit down in their familiar places. The caregivers sometimes tried to support or encourage the participants to continue with their usual preferred daily routine. Some the caregivers encountered behavioral problems or agitation at these moments. These symptoms are common in people with dementia, and their prevalence is high also among persons still living at home. For instance, in a cross-sectional study by [PERSON] et al. [48], 80% of the population scored positively on the behavioral and psychological symptoms of dementia (BPSD) spectrum at the onset of the disease, and even 97.5% in the moderate stage of dementia. Nevertheless, they appreciated the lighting system as indicated in the user evaluation.  
Finally, our study is a field experiment in a very heterogeneous, vulnerable, and hard-to-reach population. The results of our study have high ecological validity, much more than a fully controlled experimental design, yet confounding variables may influence the results and could have made it hard to find a statistically significant effect at the group level. However, this is an inevitable property of the population and setting. The fact that an effect of the lighting system was found despite these confounding variables shows that the effect of the lighting system is rather robust and may therefore be a real benefit in practice and the real-life setting of people with dementia at home.
## 2 ROV Modeling
### Complete ROV State Space Model  
The complete mathematical model of an ROV is represented using a state space formulation that integrates the coordinate system and dynamic model. By combining Equations (1) and (5), we obtain a state space model.  
\[\dot{\eta} =R(\psi)V, \tag{12}\] \[\dot{V} =M^{-1}[-C(V)v-D(V)V-g(\eta)-g_{r}+\tau+\tau_{env}],\]  
where, as in Table 1, it has 4-DOF; when moving on the anchor chain, the transverse rolling and pitching movements do not need to be considered. The transformation matrix \(J(\eta)\) is simplified to a pure rotational matrix \(R(\psi)\).
## 4 Discussion
### Comparison of Least Squares and FDD  
After comparing the two sensor types, the least squares approach is evaluated against the FDD. Both methods generally result in very similar linear regressions. However, the frequency mean is slightly lower when calculated by the FDD. This difference is caused by the large time window of several minutes, which contains multiple vehicle crossings. Most importantly, the time window also includes the crossing itself with the added weight of the vehicle. For small bridges, the added weight significantly lowers the natural frequency, which is observed indirectly through the averaging effect of the FDD. Another effect of the time window is the significantly lower number of frequency estimations compared to the least squares approach. This effect causes more significant variation in the linear regression, as shown in Figures 7-9. The simulations demonstrate that the least squares approach allows shorter measurements and a narrower temperature band than the FDD. For example, a real-world monitoring scenario could be constructed with only one measurement during the summer and one during the winter. The resulting temperature range would be sufficient to estimate the linear relationship with the least squares approach and perform damage detection on new measurements. However, it is important to note that the simulations only provide qualitative comparisons between the two methods and cannot assess the accuracy of the linear regression itself. For example, biases caused by an unequal temperature distribution are not considered since the temperature is measured at only one location on the exterior of the structure.
## 3 Results
### Crop Level, Thinning Performance and Grape Juice Composition  
#### 3.6.1 Crop Level  
Across three years, VSP training system had the lowest yield of all treatments. On average, crop load in SMPH was about 61% higher compared to VSP (Table 3). Compared to SMPH, in SMPH ST, SMPH GA and SMPH BT yield was reduced by 33.1%, 18.3% and 37.3% respectively. Best thinning performance was observed for SMPH ST and SMPH BT between 2017 and 2019. On average, both treatments reduced yield to a crop load level of VSP. As crop level did alternate annually (alternate bearing), interaction effects in vintage and treatment occurred for almost all grape juice parameters. Effect of alternate bearing was examined by coefficient of variation between 2017 and 2019, which was highest for SMPH GA (37%), SMPH ST (36%) and VSP (34%). Lowest coefficient of variation was calculated for SMPH and SMPH BT (22%) and hence showed less alternate bearing (Table S5).  
Figure 4: Mean values of incidence (**A**) and severity (**B**) of _Botrytis cinerea_ infections [%] for cv. [PERSON] trained in Vertical Shoot Positioning (VSP ) and Semi-Minimal-Pruned Hedge (SMPH) with non-thinned control (SMPH ), shoot thinning (SMPH ST ), biotechnological thinning (SMPH GA ) and bunch thinning using a harvester (SMPH BT ) at harvest in season 2017 and 2019. Evaluation for SMPH was conducted 13 days later than for VSP in 2017 and 21 days later in 2019. Different letters within vintages indicate statistically significant differences between the treatments. Incidence: 2017: \(p\) = 0.146, 2019: \(p\) < 0.001. Severity: 2017: \(p\) = 0.002, 2019: \(p\) < 0.001. Error bars are representing \(\pm\)SD.
## 4 Electrodes and Electrolytes for SSTFBs
### Thin-film Electrodes  
The materials for the electrodes in SSTFBs play a crucial role in determining the overall performance of the battery. Thus, discovering new electrode materials and improving conventional electrode materials by structural engineering are critical to develop high-performance SSTFBs. To date, significant performance improvements in electrode materials were accomplished with the development of materials in thin-film form. For example, thin-film electrodes are capable of accommodating the strain induced by lithium insertion and removal, improving the cycle life. Thin-film electrodes also provide the increased contact area between the electrode and the electrolyte, resulting in high charge and discharge rates. However, the cathode materials still need to be further developed due to the lower theoretical capacity compared to the anode materials.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 7 Performance Assessments
### Assessment Metrics
#### 7.1.1 Effectiveness Evaluation of Service Area  
Maximum service area (MSA): This refers to the largest area that a satellite can cover, primarily determined by its orbit height. This area is relatively fixed and serves as the upper limit for the satellite's coverage.  
Actual service area (ASA): After planning the service area, the specific area served by each SBS can be determined.  
This area can be calculated using a grid-based approach and represents the subset of the maximum service area that is actually covered by the SBS.