esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Stroke is one of the main causes of long-term disability in adults [1]. The most general stroke symptom is the limitation of upper limb function which significantly impacts on a person's level of independence and daily living activities [2]. Some rehabilitation interventions which may involve different exercises, type of equipment or techniques have developed to improve the upper limb function and reduce the disability [3; 4; 5; 6]. Particularly, traditional rehabilitation techniques are effective approaches in motor function recovery. However, these methods are time-consuming, expensive, and intensive resources as well as specialized facilities are needed [7; 8]. Therefore, post-stroke rehabilitation needs new approaches that allow individuals to enhance motor function.  
Recently, mirror therapy has been considered as an effective adjunct to improve upper extremity motor function for stroke patients [9; 10]. The principle of the mirror therapy techniques is the reflective superimposition of healthy extremity movements on the affected upper limb through a mirror [11]. This induces a visual illusion of increased movement ability of the unhealthy extremity for the patient [12]. Due to the simplicity of mirror therapy systems, they become difficult to provide a perfectly synchronized mirrored movement for the patient [13].  
To address limitations of the mirror therapy, avatar images or virtual objects have been constructed by image processing techniques in virtual reality (VR) systems. These systems create a challenging and motivating environment to provide enhanced feedback of movement characteristics and improved motor learning tasks for the patients [14; 15]. However, the VR therapy systems have been widely used for the upper limb rehabilitation and have been adapted for individual patients after stroke [16]. Furthermore, some clinical trial reports figured out the effectiveness of VR in upper limb function improvement [17; 18; 19]. However, the systems could not provide a high sense of presence and embodiment to the patients due to using the rendered avatar images instead of real photos. Hence, the patients only obtain short-term effects from these systems. Meanwhile, augmented reality (AR) has emerged as one of the effective methods in rehabilitation. The AR combines real and virtual objects to run interactively in a common real-time environment [20]. Despite exhibiting advantages similar to the VR in the upper limb function recovery, the AR could provide a higher degree of presence and embodiment than the VR [21].  
In order to overcome drawbacks of the VR technology, an AR system applying the mirror therapy principle has been reported to provide a higher quality of presence and embodiment for the patients [13; 22; 23]. Moreover, the system could be feasible for long-term neuromodulation. In this study, we proposed an AR system applying mirror therapy mechanism for hemiplegic patients and evaluated its effectiveness in eliciting upper-limb motor recovery.
## 3 Proposed Method
### Class Score Fusion for Classification  
As shown in Figure 2, we fuse discriminant information characterized by multiple spectro-temporal descriptors for ASC. To this end, we estimate class membership probabilities of input sound clip using various acoustic features with probabilistic SVM, which generates the probability interpretation of distance between input data and classification hyperplane in the (kernel) feature space. The formulation can be expressed as:  
\[\min_{A,B}\frac{1}{M}\sum_{m=1}^{M}\log\left(1+\exp\left(-y_{m}(A(\mathbf{w}_{ svm}^{\prime}\Phi(\mathbf{x}_{HLAC,m}+b_{svm})+B)\right)\right) \tag{10}\]  
where \(\{\mathbf{x}_{HLAC,m},y_{m}\}\) are the HLAC feature vector extracted from \(m-\)th training clip and the corresponding label, respectively. Parameters of \(\mathbf{w}_{svm}\) and \(b_{svm}\) can be determined by quadratic programming, and logistic regression can be performed to compute A, B accordingly. Finally, we can derive class score \(l_{HLAC,m}\). In the same vein, the class probabilities of \(l_{LBP,m}\) and \(l_{HOG,m}\) can be computed by using features \(\{\mathbf{x}_{LBP,m},y_{m}\}\) and \(\{\mathbf{x}_{HOG,m},y_{m}\}\). Finally, we employed linear programming to calibrate multi-stream class scores as follows:  
\[l_{SCENE}=(\alpha_{1})\times l_{HLAC}+(\alpha_{2})\times l_{LBP}+(\alpha_{3}) \times l_{HOG},\ 0\leq\alpha_{i}\leq 1\,i\in[1,2,3],\quad\sum_{i=1}^{3}\alpha_{i}=1 \tag{11}\]  
in which the conditional fusion weights \(\alpha_{i}\) can be tuned explicitly at the training/validation stage. The estimated score fusion formula is anticipated to achieve higher accuracy comparing to the case of simple majority voting.
## 4 Conclusions  
(1) Since the special structure form of the lock bottom weld and the crack originated from the lock step, the well-known fact in the field of oscillating laser welding that the stirring effect can generate more equiaxed grains and hence increase the strength of the weld, cannot fully explain the fracture behavior of the lock bottom weld. In order to reveal the fracture behavior of the lock bottom weld under the influence of the lock step, this paper studied the oscillating laser welded 5A06 aluminum alloy lock butt joint.  
(2) Compared with lock bottom welds without beam oscillation, the temperature gradient of the molten pool decreased and growth of columnar grains was inhibited by beam oscillation, resulting in the reduction of width of the columnar grain zone (CGZ). The trend was strengthened with the increase of oscillation frequency (_f_), while the angle between the lock step and fusion line was increased.  
(3) With the increase of \(f\), the fracture angle between lock step and crack propagation line decreased from 90\({}^{\circ}\) to 45\({}^{\circ}\). The fracture area during tensile tests transferred from equiaxed grain zone to CGZ and then to the heat affected zone when the width of the CGZ was narrow enough. The ultimate tensile strength and elongation of weld were increased up to 308 MPa and 18.2% respectively, showing an improvement of 36.3% and 203.3%.  
Y.L.: Conceptualization, Methodology, Writing-original draft. J.L.: Visualization, Investigation, Formal analysis. J.P.: Methodology, Software, Formal analysis, Data curation. X.L.: Visualization, Project administration. C.Z.: Writing-review & editing, Supervision. M.G.: Funding acquisition, Writing-review & editing, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Natural Science Foundation of China (52205465, and 52205360) and the Aviation Science Foundation of China (20200054079001).
## 4 Experiment and Results
### Evaluation Metrics and Visualization
#### 4.3.3 PCA  
High-dimensional data, such as image data, can be visualized in two dimensions using the PCA. Figure 11 shows an example of a two-dimensional (2D) PCA image. Fire data points were colored red, Smoke data points were colored blue, and Normal data points were colored green. Two-dimensional PCA reduces the data to two principal components to identify the two axes that can explain the most variance in the high-dimensional space; the data are then transformed into 2D data based on these axes. This method allows visualization of the distribution of the data of each class in a 2D space to grasp how well the model separates each class and intuitively understand the data structure and distribution. PCA visualization allows us to analyze the model-trained data and evaluate the model performance from different perspectives. Thus, the performance of the proposed fire classification model can be evaluated and improved.
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### Reconstruction Quality Evaluation
In cases when the test point cloud contains a large number of spurious points and reconstructed points from background objects, point cloud alignment using the traditional iterative closest point algorithm will likely fail. Facial feature points may be detected in 2D images with high confidence. Additionally, faces will be detected in multiple images, and this will lead to higher localization precision of facial landmarks. Knowing the parameters of the reconstructed cameras, feature points may be transferred from 2D images to the reconstructed 3D model. After transferring landmarks to the 3D model, multiple coordinates representing the same facial landmark are averaged after removing outliers. Facial feature points were detected in the images using the _FaceLandmarkImg.exe_ tool from the facial behavior analysis toolkit in OpenFace (version 2.2.0) ([[https://github.com/[PERSON]/OpenFace](https://github.com/[PERSON]/OpenFace)]([https://github.com/[PERSON]/OpenFace](https://github.com/[PERSON]/OpenFace)) accessed on 9 August 2021). The description of the landmark detection algorithm may be found in [90; 91]. The detection of landmarks was not performed in the highly off-angle (profile) images. An example of the detected facial feature point locations on a 2D face and their locations on the 3D model is shown in Figure 6.
In addition, due to its performance and preferability in drought modeling studies, we hybridized SVM and PSO (SVM-PSO) and compared them with other models. The development and comparison of these diverse hybrid models represent a novel contribution to drought forecasting research. Droughts, characterized by prolonged periods of below-average precipitation, pose significant challenges to water resource management, agriculture, and ecosystems [20]. Accurate and timely drought forecasting is crucial for mitigating these impacts, enabling proactive planning and adaptation measures [44]. This study comprehensively evaluates the drought prediction capabilities of five distinct methods, encompassing both machine learning and optimization techniques. The goal is to determine their strengths, weaknesses, and suitability for different drought forecasting contexts in the Troms region of Norway with a focus on developing a soft computing approach. The five methods under investigation are ANFIS-PSO [33; 45], ANFIS-GA [32; 33], ANFIS-GWO [37; 46], ANFIS-ABC [33; 47], and SVM-PSO [42; 48]. The aim of this study is to explore the potential of these hybrid architecture for predicting drought on a monthly timescale. The performance of ANFIS models combined with each of four optimization algorithms given above will be compared to that of the SVM-PSO model. Additionally, this study will investigate the influence of data feature selection and the optimization techniques on the accuracy and efficiency of each method. By comparing the performance of these five methods, this research seeks to contribute to the advancement of drought forecasting methodologies and provide valuable insights for selecting the most suitable approach for specific drought prediction contexts. The findings will be relevant for water resource managers, agricultural planners, dam management, risk assessors, and policymakers seeking to enhance drought preparedness and mitigation strategies.
## 2 Related Works
### Emerging Techniques: Contrastive Learning, Weak Supervision, and Adaptive Fusion  
To overcome data noise and scalability limits, recent approaches adopt contrastive learning and adaptive architectures.  
Contrastive models such as CLIP [7] and ALIGN [11] align text-image embeddings in a shared semantic space. [PERSON] et al. [1] fine-tuned CLIP on CrisisMMD, achieving a 2.33% gain over late fusion. [PERSON] et al. [3] fused attention with contrastive loss to achieve 91.5% F1.  
Beyond contrastive learning, recent LLM-driven multimodal models have further advanced cross-modal alignment. [PERSON] [29] introduced a \"frozen LLM + trainable adapter\" paradigm for few-shot multimodal learning, while BLIP-2 [30] optimized vision-to-LLM alignment via lightweight querying mechanisms. Kosmos-2 [31] extended this by unifying language, vision, and spatial understanding. However, these models prioritize general-domain performance and often overlook the unique challenges of crisis data--such as 20% textual noise and 40% cross-modal misalignment--relying on clean, curated inputs.  
Weak supervision reduces annotation cost. TweetDIS [32] and AUM-ST-Mixup [33] reached competitive performance with minimal labels. AUM-ST-Mixup achieved 0.076 expected calibration error, 30% lower than baseline MixMatch.  
In 2024-2025, crisis informatics has seen advancements in two key directions: LLM-based text classification and multimodal ambiguous tweet analysis. [PERSON] et al. [34] proposed CrisisSense-LLM, an instruction-finetuned LLM for multi-label disaster text classification, demonstrating strong performance on textual crisis data but lacking visual integration. [PERSON] & [PERSON] (2025) [26] addressed ambiguous tweet classification via multimodal attention but did not explicitly handle modality noise.  
Adaptive fusion dynamically weights modality contributions. [PERSON] et al. [35] used graph attention for reliable content filtering. [PERSON] and [PERSON] [36] applied multimodal LLMs to filter crisis-related video content. Similarly, CrisisSense-LLM [34] excelled in weakly supervised text classification, but lacks visual integration, limiting multimodal potential.  
These findings motivate our framework's combination of contrastive pre-training, adaptive modality weighting, and robust cross-modal refinement.
## 2 Materials and Methods
### Datasets  
#### 2.2.1 Spatial Data  
In this study, we utilized several types of spatial data to analyze the Nagavali and Vamsadhara watersheds. These included a digital elevation model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM) through the USGS Earth Explorer website, a Land Use Land Cover (LULC) dataset from NRSC Bhuvan, and a soil map from the International Soil Reference and Information Centre (ISRIC). The SRTM DEM was used to define the watersheds, classify slopes, and extract stream networks in the two river basins. We considered 0-2%, 2-8%, and more than 8% slope bands in both watersheds. The LULC data from 2005 were used to identify the various land cover types present in the watersheds. In the Nagavali watershed, agricultural land accounted for 43% of the area, followed by 34% forest, 19% barren land, 2.91% water bodies, and 1.15% built-up land. In the Vamsadhara watershed, 51.1% of the area was covered by forest, 29.3% was agricultural land, 17.23% was barren land, 1.84% was water bodies, and 0.44% was built-up land. We compared the LULC data from 2005 with those of 2015 and identified an increase of 2-3% in agricultural land and a corresponding decrease of 2-3% in barren land in both basins. This change can be attributed to shifting cultivation by tribal people. The 1 km resolution soil map provided information on the different soil textures present in the basins, which included sandy loam, loam, clay loam, sandy clay loam, and clay soil.
## 3 Results and Discussion
### Gas Concentrations
#### 3.2.1 Ammonia Monitoring Results
Following aeration that occurred during the week, the temperature of the undisturbed manure at weekends remained higher, leading to increased NH\({}_{3}\) concentrations during the warmer daytimes and lower NH\({}_{3}\) concentrations during the cooler nights. The agitation events can be clearly recognized in the raw NH\({}_{3}\) concentration dataset based on the internal sampling points (Figure 5). Three periods of raised concentrations are apparent, representing the agitator travelling along each of the three windows. The first period of raised concentrations with the highest peaks (at 275-500 mg m\({}^{-3}\)) shows the agitation of the window closest to the internal sampling points and distinctly displays sequential concentration peaks that arose as the agitator passed each of the four sampling points. External NH\({}_{3}\) concentrations were generally an order of magnitude lower than internal concentrations. Average NH\({}_{3}\) concentrations at 3 m height above ground level adjacent to the manure agitation hall window were 3.64 \(\pm\) 0.06 mg m\({}^{-3}\) across the whole monitoring period, while average external NH\({}_{3}\) concentrations at ground level were slightly lower at 2.43 \(\pm\) 0.16 mg m\({}^{-3}\) (Figure 6; Table 2). There was no substantial diurnal/weekday-weekend variation in external NH\({}_{3}\) concentrations at 3 m height, although at ground level, weekday daytime NH\({}_{3}\) concentrations (3.1 mg m\({}^{-3}\)) were approximately 50% higher than during the other time periods (1.7-2.1 mg m\({}^{-3}\)). Although the data showed that the NH\({}_{3}\) emitted by the facility was low compared with internal concentrations, increases in external NH\({}_{3}\) concentrations were detected coincident with increases in internal concentrations, demonstrating that some leakage did occur. In addition, external NH\({}_{3}\) concentrations at 3 m height were comparable to average concentrations of 3.7-5 mg m\({}^{-3}\) that were measured inside a Chinese commercial manure-belt layer house [31], and as such, they are not negligible. It was expected that the external sampling point at 3 m height would record higher average NH\({}_{3}\) concentrations than the ground-level sampling point, due to the  
Figure 5: Spikes of NH\({}_{3}\) trends following agitation, at four sampling points (18 June 2022). potential for emissions through the window fitting. However, external ground-level NH\({}_{3}\) concentrations, although lower on average, regularly displayed greater peak concentrations of NH\({}_{3}\) during manure agitation activity (Figure 6), although it is unclear why this might have been the case.
## 3 Verification and Validation of Condensation Heat Transfer Model
### Model Validation via Wisconsin Condensation Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Pressure** & **Molar Fraction of** & **Wall Temperature** & **Gas Temperature** \\  & **(kPa)** & **Noncondensable Gas (\%)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** \\ \hline
1 & 100.0 & 0.795 & 28.60 & 60.65 \\
2 & 100.0 & 0.699 & 29.40 & 69.23 \\
3 & 100.0 & 0.702 & 29.40 & 69.04 \\
4 & 100.0 & 0.713 & 29.20 & 68.15 \\
5 & 100.0 & 0.533 & 34.00 & 79.68 \\
6 & 100.0 & 0.420 & 28.43 & 85.12 \\
7 & 100.0 & 0.417 & 29.76 & 85.25 \\
8 & 100.0 & 0.433 & 29.47 & 84.55 \\
9 & 100.0 & 0.312 & 30.60 & 89.53 \\
10 & 100.0 & 0.307 & 30.30 & 89.72 \\
11 & 100.0 & 0.308 & 29.96 & 89.68 \\
12 & 100.0 & 0.498 & 27.76 & 81.46 \\
13 & 100.0 & 0.511 & 31.74 & 80.80 \\
14 & 100.0 & 0.516 & 29.58 & 80.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial condition of condensation heat transfer. Figure 3: _Cont._  
The calculation results for the vertical condensing plate in cases 9, 10 and 11 show a slightly larger deviation from the experimental data, with the maximum difference of 29.1477%, but are still within the acceptable deviation range of engineering. The other cases show smaller deviations. The average error of the heat transfer coefficient of horizontal condensation is about 2.5481%, which is in good agreement with the experimental data. The comparison results indicate that the wall condensation model used in the PISAA can simulate the physical process of wall condensation scientifically and accurately.
# Review of Dew Point Evaporative Cooling Technology for Air Conditioning Applications
## 1 Introduction  
Primary energy consumption is increasing year by year, mainly due to economic development and the growth of the human population in the world [1]. The construction sector accounts for 36% of global energy consumption [2]. The International Energy Agency reports that the energy intensity for space cooling increased by 2.7%, while an improvement for space heating (\(-2\)%) and lighting (\(-1.4\)%) was observed. The energy intensity for water heating, cooking, and appliances remained steady. Space cooling is the fastest-growing consumer of electricity in buildings; its energy intensity has been constantly rising since 2014 [2]. It is caused by economic and population growth and intensive urbanization. The cooling energy needed in the summer period is in most cases generated by electrically-powered compressor systems. Therefore, alternative methods of producing cooling energy, which will allow operating costs of air-conditioning systems to be minimized while ensuring thermal comfort, are being constantly searched for. A proven solution is evaporative cooling, which can provide cheap cooling energy because of its effective evaporation process in wet channels of the dew point indirect evaporative coolers (DPIEC) used to cool the air. The dew point evaporative coolers have been extensively studied by many researchers in recent years. This paper discusses selected studies in the field of dew point evaporative cooling. A literature review was performed to present current directions of development of this technology to highlight the challenges that await scientists aiming to develop an alternative cooling system based on dew point evaporative cooling. In recent years, many publications have appeared that have set new directions for research in this field. The literature review was carried out according to the thematic division presented in Figure 1. The division covers the most important areas of the field of evaporative dew point cooling. The researchers focus on indirect evaporative cooling(IEC) development by improving its design, geometry, water distribution, and new porous materials implementation. To evaluate performance of the new types of DPIEC, different methods and software were used. Finally, the optimized devices were studied in terms of their performance in different systems, such as hybrid and desiccant systems. It follows from the fact that DPIEC does not decrease the humidity ratio for entire operational conditions (ambient airflow), hence in humid and moderate conditions the air needs to be dehumidified in an additional device (desiccant system or cooling coil). For some indirect evaporative coolers, the condensation of water vapor in a product channel may occur. This phenomenon is also considered in this study.
## 2 Methods
### Fractional Norms  
The use of Euclidean distance faces significant limitations in high-dimensional spaces. High dimensionality relates to the similarity space's separability, geometry, topology, and density. It refers to a high number of input variables in the dataset. As the dimensionality of data increases, the data size grows proportionally, leading to data dispersion that complicates the data analysis [30]. This phenomenon is known as the curse of dimensionality.  
Distance concentration and hubness are two phenomena that occur in high-dimensional datasets and have a direct negative impact on the nearest neighbor calculation. On the one hand, distance concentration is the characteristic present in high-dimensional spaces where all points seem to be almost the same distance from other points in that space [31]. This makes using distance metrics to discriminate between similar examples ineffective. Meanwhile, hubness is described by [PERSON] et al. [32] as the tendency for specific instances to appear frequently in the list of nearest neighbors of other instances.  
[PERSON] et al. [33] discuss how distances based on the norm-p, with fractional coefficients, i.e., Minkowski distance metric, can help mitigate distance concentration in high dimensionality. Formally, the Minkowski distance for \(\mathbf{x}\) and \(\mathbf{y}\) is defined as  
\[d(\mathbf{x},\mathbf{y}):=\left(\sum_{i=1}^{n}|x_{i}-y_{i}|^{p}\right)^{\frac {1}{p}}\quad for\quad p>1 \tag{1}\]  
where \(p\) for the fractional distances or quasi-distances take values between 0 and 1. Consequently, fractional distances are not formally defined as distance metrics due to the violation of the triangle inequality [34].  
Although it has been demonstrated that fractional distances can mitigate distance concentration [33], selecting the appropriate \(p\) is a complex process [31; 34; 35; 36].