esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 5 Discussion  
### Flow Pattern Between Communities  
As depicted in Figure 7, the flow patterns between communities reveal distinct characteristics on weekdays (a) and weekends (b). On weekdays, there is a pronounced unidirectional convergence of flows from suburbs to the central regions (Type I). This stems from the necessity for residents to travel to central hubs for work, business meetings, or commuting to office buildings, commercial zones, or transportation hubs. Such a pattern  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Embedding Model** & **Distribution Learning** & \(d_{KL}\downarrow\) & \(d_{cos}\uparrow\) & \(d_{L1}\downarrow\) & **Avg. Rank** \\ \hline GCN & & 0.025 & 0.832 & 61.3 & 2.0 \\ GCL & MLP & 0.032 & 0.784 & 66.6 & 3.0 \\ Proposed & & 0.022 & 0.843 & 56.1 & 1.0 \\ \hline GCN & & 0.024 & 0.842 & 60.8 & 2.0 \\ GCL & Proposed & 0.030 & 0.793 & 65.9 & 3.0 \\ Proposed & & 0.021 & 0.859 & 55.8 & 1.  
underscores the central area's dominant role and the significant reliance of suburban residents on its employment and commercial resources.  
On weekends, the flow between suburbs significantly increases, and more bidirectional arrows appear. This indicates that during weekends, people have more leisure time for diverse activities, no longer limited to traveling to the central area. There may be  
Figure 7: Flow patterns between communities, with the size of the circle representing the relative scale of challenging communities.  
more recreational, entertainment, commercial, or social activities between peripheral nodes. For example, on weekends, people may choose to engage in consumption and leisure activities in surrounding commercial streets, parks, or cultural venues, which enhances the interaction between peripheral nodes. It shows the potential for functional transformation in suburban spatial structures on non-working days.  
The change from (a) to (b) shows that the flow pattern on weekdays is more concentrated and singular, while the flow pattern on weekends is more dispersed and diversified. This reflects the different living rhythms and activity demands of urban residents on weekdays and weekends, which is consistent with the trend of functional diversification in suburban and fringe areas within urban spatial forms. With urban development, suburban areas are gradually transitioning from a single residential function to a multifunctional one, increasing the supply of leisure, entertainment, and commercial facilities. The transformation not only relieves pressure on the central area but also promotes the balanced development of the urban spatial structure. However, this balanced development is still in its early stages, and there is still a significant gap in the degree of functional improvement in suburban areas compared to the central urban areas. This is particularly evident in the travel patterns of Type IV and Type V communities.
## 2 Results and Discussion
### Algorithm
#### Step 4: Superposition  
After completion of the above iteration we superimpose the resulting matching sets from \(A\) and \(B\) in Cartesian space so that the RMSD is minimal using established methods [12]. This step has two purposes. Firstly, the property of forming a maximum common subgraph is not sufficient for having a spatial arrangement with similar physico-chemical properties. For instance in the Figure 3 it is possible to rotate atom groups corresponding to the subgraph pair \(a_{4}b_{2}\), \(a_{7}b_{9}\), and \(a_{5}b_{1}\) around atoms \(a_{4}b_{2}\) so that the spatial arrangement of all atoms is changed while the overall graph property is untouched. In contrast, low RMSD means similar spatial arrangement and thus similar physico-chemical properties. Secondly, it is possible that the two matched subgraphs have opposite chiralities; such unwanted matches usually can be removed easily since they will usually give high RMSD-values after optimal superposition.  
Step 4 results in the maximum common subgraphs that have the lowest RMSD-values after optimal superposition with the query epitope. In the simplest case this is one match, but we frequently encounter cases with several matches of equal size and comparable RMSD-values, e.g. in multimeric complexes.  
Step 5: Selection of Pareto-optimal matches  
Steps 1 to 4 are carried out for all pairings of query epitope \(A\) with the 3D-structures \(B_{i}\) in a database. Sizes of maximum common subgraphs (i.e. numbers of matched node pairs) and corresponding RMSD-values are collected for all these pairings. The set of matches that are not surpassed both in terms of low RMSD and high number of pairs constitute the set of Pareto-optimal matches or Pareto-front (Figure 4). Based on the structure of the Pareto-front, the user can then decide which matches are considered for further work. E.g. if there is a large gap in the Pareto-front between a low and a high RMSD-population but otherwise similar sizes of matched graphs, the user will prefer the matches with low RMSD. The plane shown in Figure 4 can be supplemented by accumulated \(P\)-scores from Step 3 as third dimension, resulting in a two-dimensional Pareto-front. In that case it is sensible to re-calibrate the \(P\)-scores with  
Figure 3: Schematic of situation in Step 3. Two cliques have been identified at the distance tolerance of Step 2, the first given by node pairs \(a_{1}b_{3}\), \(a_{2}b_{4}\), \(a_{3}b_{5}\), \(a_{4}b_{2}\), and the second by \(a_{4}b_{2}\), \(a_{7}b_{9}\), \(a_{5}b_{1}\). The two cliques have node pair \(a_{4}b_{2}\) in common and are therefore merged into the single graphs shown here.  
the maximum \(S_{max}\) encountered in the database search.
## 4 Results
### Impact of RAG and Fine-Tuning on Model Performance
#### Researcher input (question)  
A 3-month-old infant is brought to a consultation at the family health unit due to abnormal and repetitive side-to-side eye movements. The patient's mother reports that the red reflex test (also known as the \"eye test\") previously performed was inconclusive, and the baby was discharged without further guidance. The pregnancy and delivery were uneventful. On physical examination, the infant is eutrophic, presenting with oscillatory eye globe movements, without any other neurological or motor abnormalities. Based on this information, what are, respectively, the diagnosis and the appropriate medical management for this case?  
(A) Congenital cataract; repeat the red reflex test.  
(B) Strabismus; reassure the patient's mother and schedule an early follow-up.  
(C) Nystagmus; refer the patient to an ophthalmologist and a paediatric neurologist.  
(D) Xerophthalmia; urgently request a vitamin A level for the patient.  
\[LLM\ output\ (answer\ and\ explanation)\]  
The described condition suggests nystagmus, an involuntary, rhythmic, and repetitive eye movement that may be associated with various ophthalmologic or neurologic conditions. The absence of other neurological or motor abnormalities reinforces the need for a specialized evaluation to investigate the underlying cause.  
**Reasoning used to reach the answer:**  
1. Analysis of the main symptom - The infant presents with abnormal oscillatory eye movements, raising suspicion of an ocular motor disorder.  
2. Exclusion of other conditions -  
- Congenital cataract could explain the inconclusive red reflex test, but it typically leads to leukocoria (white pupillary reflex), not necessarily to nystagmus.  
- Strabismus involves misalignment of the eyes, not repetitive oscillatory movement.  
- Xerophthalmia is caused by vitamin A deficiency and manifests with signs of ocular dryness and night blindness, which are not consistent with the condition described.  
3. Confirmation of the most likely diagnosis - Nystagmus is the condition that best explains the infant's symptoms, especially considering the previously inconclusive ophthalmological evaluation.  
4. Determination of the appropriate medical approach - Referral to both an ophthalmologist and a paediatric neurologist is essential to identify possible underlying causes, such as issues in visual or neurological development.  
**Correct alternative:**  
**C) Nystagmus; refer the patient to an ophthalmologist and a paediatric neurologist.**  
Figure 10: Number of correct answers on the 2024 Revalida exam for GPT-4o (with/without RAG) and LLAMA 3.1 (with/without fine-tuning).  
Figure 9: Example of a prompt, question, answer, and explanation given by GPT-4o, translated from PT-BR into English with maximum possible fidelity.
# A Bench Evaluation Test for Refrigeration Oils in a Refrigeration System Using a Screw Compressor
###### Abstract  
Refrigeration oil has a large effect on the performance of refrigeration systems. However, the physical and chemical indexes of fresh refrigeration oils often fail to reflect the actual operating characteristics, especially with respect to the degradation of refrigeration oils. In this paper, a bench evaluation test of refrigeration oils was carried out to investigate the degradation of synthetic refrigeration oils used with the refrigerant R134a, in a purpose-built, full-scale refrigeration system utilizing a screw compressor. To accelerate the degradation process of the refrigeration oil, the discharge temperature at the exit of the compressor was turned to a high level. Comparison tests of a mature refrigeration oil type A and a newly developed oil type B were performed under the same working conditions with 500 h of operating time. The performance of the screw compressor and refrigeration system was analyzed. The abrasion of the screw rotors and carbon deposition at the discharge port was investigated. Results showed that the bench evaluation test successfully predicted the degradation process of the refrigeration oils. The evolution analysis of the refrigeration oil viscosity and acid value during the bench evaluation test indicated that 15% of the physical and chemical indexes for refrigeration oil drain were too strict. Research work in this paper showed a more practical method to evaluate the performance of refrigeration oils through the bench evaluation test.  
refrigeration oil; screw compressor; degradation; bench evaluation +
Footnote †: journal: Applied Sciences
###### Abstract  
Nowadays, time-domain features see wide use in speech enhancement (SE) networks such as frequency-domain features to achieve excellent performance in eliminating noise from input utterances. This study primarily investigates how to extract information from time-domain utterances to create more effective features in SE. We extend our recent work by employing sub-signals which dwell in multiple acoustic frequency bands in the time domain and integrating them into a unified time-domain feature set. The discrete wavelet transform (DWT) is applied to decompose each input frame signal to obtain sub-band signals, and a projection fusion process is performed on these signals to create the ultimate features. The corresponding fusion strategy is either bi-projection fusion (BPF) or multiple projection fusion (MPF). In short, MPF exploits the softmax function to replace the sigmoid function in order to create ratio masks for multiple feature sources. The concatenation of fused DWT features and time features serves as the encoder output of two celebrated SE frameworks, the fully convolutional time-domain audio separation network (Conv-TasNet) and the dual-path transformer network (DPTNet), to estimate the mask and then produce the enhanced time-domain utterances. The evaluation experiments are conducted on the VoiceBank-DEMAND and VoiceBank-QUT tasks, and the results reveal that the proposed method achieves higher speech quality and intelligibility than the original Conv-TasNet that uses time features only, indicating that the fusion of DWT features created from the input utterances can benefit time features to learn a superior Conv-TasNet/DPTNet network in SE.  
speech enhancement; discrete wavelet transform; cross-domain; Conv-TasNet; bi-projection fusion; multiple projection fusion Article
# Improving Multi-Label Learning by Correlation Embedding
## 2 Related Works
Through the overview of dimension reduction [44], dimension reduction can basically be divided into three categories, i.e, dimension reduction of the feature space, dimension reduction of the label space, and dimension reduction of the label and feature spaces simultaneously. PCA [45] is a method of dimension reduction in feature space based on label-independence. DCR [46] is a new multi-label feature selection method by combining feature relevance and label relevance. In [47], the authors propose a dimension reduction method DSE to learn the sparse weight matrix by projecting the original sample into a low-dimensional subspace. MDDM [48] is a multi-label dimension reduction approach based on maximizing the dependency between feature descriptions and relevant class labels. CLEMS [49] performs the dimension reduction of the label space through embedded instances. In addition, some methods are proposed to reduce dimension of the label space, such as [50; 51]. GIMC [52] learns a nonlinear mapping of the features by reducing the instance features and labels. In the environment of big data, the feature space of data sets becomes larger and larger, adopting dimension reduction, which can help to get rid of redundant features and obtain a more compact feature space, and further improve the performance of a model. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. We learn an embedding matrix from the pre-defined label correlation graph by graph embedding and utilize the embedding matrix as the model coefficients.
## 4 Computational and Mathematical Aspects
### Structural Modeling Techniques for Masonry Structures  
As pointed out above, masonry is in fact a heterogeneous material comprising masonry units and mortar. Due to the existence of mortar joints as planes of weakness, a masonry wall exhibits distinct directional mechanical properties. Therefore, a masonry wall can be treated as an orthotropic material. Depending on the orientation of the joints to the stress directions and the applied normal stress level, four different failure modes are feasible for unreinforced masonry walls according to FEMA 356, 2000 [94]. Namely, pier diagonal tension, toe crushing, bed joint-sliding, and rocking failure can occur. The first two failure modes are considered force-controlled (brittle failure) and hence no plastic deformation capacity is considered. The last two failure modes are considered deformation-controlled (ductile failure) and a plastic deformation capacity is taken into account.  
To model the structural behavior of a masonry wall, there are some influencing factors such as material properties of masonry units and mortar, dimension of the units, mortar thickness and the brittle behavior of masonry units, which should be taken into account properly. If these factors are incorporated suitably into a structural model of a masonry wall, the four above-mentioned failure modes can be simulated.  
Depending on the required level of accuracy and simplicity, there are different structural modeling strategies for masonry wall elements as follows.
## 3 Methods
### The MODIS GPP Product  
The MOD17 algorithm is essentially a big-leaf light use efficiency model that uses both remote sensing data and meteorological data to model vegetation GPP. The MOD17 algorithm applies environmental scalar functions to account for the constraints of minimum air temperature and vapor pressure deficit on photosynthesis [12]. The operational GPP product, the 8-day 500 m MODIS GPP Version 6 product (MOD17A2H), was produced using the MOD17 algorithm with large scale from the NASA Global Modeling and Assimilation Office. MOD17A2H was also obtained from the website of the Land Processes Distributed Active Archive Center. Details on the MOD17 algorithm and the MOD17A2H product can be found in the user guide of the MODIS GPP product [39]. Note that the MOD17A2H product provides 46 GPP summations for each 8-day time period in one calendar year, and, therefore, the extracted GPP data were divided by 8 for the first 45 values in a year and by 5 (or 6 in a leap year) for the last value to obtain daily values. The obtained 8-day 500 m GPP was then spatially averaged to 1000 m resolution for subsequent analysis.
## 2 Survey of Commercial Small Platforms
### Passive Microwave  
#### 3.1.1 Microwave Imagers (MWIm)  
The main applications of Microwave Imagers (MWIm) are atmospheric (X, K, Ka, and milimiter waves bands), oceanographic (C, X, K, and Ka bands), vegetation and soil moisture monitoring (P, L, S, C and X bands). High frequency microwave radiometers are particularly well suited for small platforms because of the antenna size constraints. These types of instruments can measure: wind speed [26; 27], sea ice thickness [28; 29], and sea ice cover [30], among other variables. Table A4 presents the features of some microwave radiometers, in terms of frequency bands, spatial resolution, antenna size, swath, mass, power consumption, and data rate. Assuming only one payload per platform, the affordable platforms (nano, micro, mini, and large) for the instruments are identified according to the power and mass requirements. This information is valuable in order to choose the potential instruments that will complement the Copernicus Space segment, trying to make them compatible with the smallest possible platforms, while fulfilling the user requirements. The measurement gaps that can be covered with this technology are: horizontal wind speed over the sea surface (MWIm with channels around 7, 10, 19, 37 GHz or 19 and 37 GHz), sea ice monitoring (cover, type, drift, MWIm with channels around 7, 10, 19, 37, and 90 GHz), sea ice thickness (MWIm with channels around 1.4 GHz), soil moisture (MWI with channels around 1.4 GHz, or 7 GHz, or 11 GHz), and sea surface temperature (MWIm with channels around 7 and/or 10 GHz).  
According to Table A4, two microwave imagers capable of measuring the variables with gaps have been identified. These are selected because they are suitable for small platforms and present good data quality, to cover the user requirements.  
* A Tropical Rainfall Measuring Mission Microwave Imager (TMI) like instrument is capable of measuring wind speed (at 10.65, 19.35 and 37 GHz), sea ice cover (at 19.35, 37, and 85.5 GHz), and sea surface temperature (at 10.65 GHz). Modified versions of TMI for micro- or mini-platforms achieving a 10 km spatial resolution using an aperture size (inflatable antenna) of 3.4 m @ 10.65 GHz from 600 km height will suit LEO polar Sun-Synchronous Orbit (SSO, \(\sim\)14 orbits/day) reducing the revisit time to 3 h in the Polar Regions. The required number of satellites was optimized in [31].
* The available L-Band microwave sensors, such as Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) and Soil Moisture Active-Passive (SMAP) are suitable for mini-platforms. L-band microwave radiometers are capable of measuring the variables with the detected gaps, such as sea ice thickness and soil moisture. Sea ice thickness presents gaps in the revisit and latency times. The revisit time required is 24 h, and a latency time of 1 h. Surface soil moisture monitoring presents gaps in the accuracy 0.01 m\({}^{3}\)/m\({}^{3}\) and the latency time 1 h.
###### Abstract  
The prediction of fuel consumption and Carbon Intensity Index (CII) of ships is crucial for optimizing decarbonization strategies in the maritime industry. This study proposes a ship fuel consumption prediction model based on the Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM). The model is applied to a container ship of 2400 TEU to predict its hourly fuel consumption, hourly CII, and annual CII rating. Four different feature sets are selected from these data sources and are used as inputs for SA-LSTM and another ten models. The results demonstrate that the SA-LSTM model outperforms the other models in prediction accuracy. Specifically, the Mean Absolute Percentage Error (MAPE) for fuel consumption predictions using the SA-LSTM model is reduced by up to 20% compared to the XGBoost and by up to 12% compared to the LSTM model. Additionally, the SA-LSTM model achieves the highest accuracy in annual CII predictions.  
ship carbon intensity prediction; deep learning; decarbonization; self-attention mechanism; LSTM; multi-source heterogeneous data Article
# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
## 5 Discussion
### Limitations  
Firstly, we compared established ML models frequently applied in healthcare research that were considered suitable for our dataset dimensions. However, recent research has extended prediction models to the application of deep learning (DL) algorithms. To increase the performance of ML-based models for ED disposition, we plan to apply DL algorithms in future studies.  
Secondly, this study did not include an independent dataset for external validation. Instead, five-fold cross-validation was applied for internal validation, the efficiency of which has been reported to be similar to that of independent external validation.  
The utilization of diverse imputation techniques for missing data potentially resulted in improvements in the predictive performance of emergency admission. Therefore, future research could incorporate imputation strategies to further refine the predictive accuracy of the ML models.  
This study also includes the limitations of retrospective file review studies, the use of triple triage classification, the discharge practice decided by the emergency physician alone, and the fact that respiratory rate parameters could not be evaluated among vital signs.  
AI-based systems created to make decisions alone may cause legal and ethical problems. The aim of such AI-based systems is to help experts in the field and enable them to make more accurate decisions. In the future, it is thought that AI-based prediction models will take more responsibility in the decision-making process, particularly after the establishment of legal regulations.
###### Abstract
Quantification of dry matter and protein in yam bean using NIRS was reported with an excellent prediction performance of 0.94 and a SEP of 1.2 [20]. This technology could be a useful tool in the breeding of improved yam varieties where thousands of accessions are maintained. Traits such as the physicochemical characteristics of the tubers are vital because they allow the identification of parents as well as the screening of generated progenies with suitable tuber quality. Due to its rapid, cost-effective, and non-destructive features, the NIRS application will be beneficial for detecting tuber quality attributes in the yam crop. Sample preparation is a prerequisite for the prediction of quality traits by NIRS and sometimes is a big challenge when large populations of breeding materials are to be evaluated. The effects of sample preparations on the prediction performances of NIRS have been reported on other materials such as livestock slurry and forages [21; 22; 23]. Few, if any, research studies have examined the effects of different sampling techniques on the application of NIRS for nutritional traits in yam. The current study has evaluated different methods of sample preparation for the feasibility of using NIRS as an alternative to wet chemical analysis for the prediction of quality parameters in fresh yam tubers.