esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 1 Introduction
### Segment Anything Model  
The Segment Anything Model (SAM) [8] is an instance segmentation model based on the vision transformer architecture [13]. It is an advanced model for segmenting arbitrary entities out of photographs. It stands out primarily for its high quality, robustness, and minimal required user input. One of its notable features is the ability to be queried using a variety of prompts, allowing it to segment a RGB input image with a spatial resolution up to \(1024\times 1024\) pixels into multiple segments in one inference call. SAM supports prompts in various forms such as seed points (point prompts), bounding boxes, brush masks (dense prompts), and text prompts.  
Furthermore, SAM allows the generation of multiple output masks for each input prompt, hence enabling image segmentations at varying hierarchical levels of granularity. Another advancement presented by the SAM is the extensive training data-set SA-1B, which has been iteratively collected and refined through prior versions of SAM during its own training process.  
A multitude of studies and publications are currently emerging, which aim to apply SAM as a foundation model across a diverse range of fields, testing its segmentation quality. The application domains are varied. For instance, [PERSON] et al. [14] assess SAM for GeoAI vision tasks particularly in permafrost mapping. Alternatively, [PERSON] et al. [15] utilise SAM to introduce a new approach for tracking black cattle on photographs. Another application  
Figure 1: Rendered example of instance segmentation (**c**), of a sub-volume of size \(512\times 512\times 512\) voxels (**b**), from the XXL-CT Me 163 data-set with a data resolution of 10,000 \(\times\) 10,000 \(\times\) 8000 voxels (**a**). The objective of instance segmentation is to generate a plausible segmentation of individual objects or instances, as depicted in (**c**), from an input sub-volume such as that shown in (**b**), applicable to data-sets of any size, akin to the one demonstrated in (**a**).  
within the domain of so-called \"Precision Agriculture\" is investigated by [PERSON] et al. [16], where mapping of crop features by automated mechanisms is conducted. In the field of NDT, the work by [PERSON] et al. [17] examines how SAM can distinguish various segments in CT volume slices through unsupervised learning techniques. However, the direct application of SAM for instance segmentation is not the only focus of resent research. For example, [PERSON] et al. [18] explored how an expanded data-set computed via SAM can be used to train an object detection network to improve license plate detection under severe weather conditions. Similarly, [PERSON] [19] employed SAM to optimise road sign detection by using the model for background pixel exclusion in the data-set. In all these named studies, SAM exhibits a performance ranging from high quality to mixed results, which are strongly influenced by the data-set and specific problem domain under investigation.
# Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
## 2 Theoretical Background
### Methodologies for Multimodal Data Fusion  
A modality refers to something that can be experienced in the environment. It is a type of information that can be felt and is stored. Some examples could be-text information, image information, smell, taste, auditory, video, and touch. Multimodal Sensor Fusion refers to combining sensor data from different sources to produce more consistent, accurate, and useful information than individual sensors to reduce false positives and false negatives. The fusion architectures can be of three types: early fusion, late fusion, and hybrid fusion [27; 28]. Early fusion combines the raw data or the features extracted from the raw data [29]. This is a suitable technique when there exists a high correlation between modalities. The feature extracting algorithms are applied to the individual modalities and then fused together using the process of concatenation to get the final feature vector. A classifier model is trained using this feature vector, and final predictions are made. In this method, the fusion is performed before the classification, which allows the interaction of features at a low level. In late fusion, the decisions are taken based on the individual modalities separately. Predictions from individual modalities are then combined the using statistical method like mean, mode, median, etc. As it is a combination of decisions, it is also known as Decision Fusion Technique. This technique is preferred when there exists a time relationship between the modalities. Hybrid fusion combines the advantages of early and late fusion for better fusion of features as well as decisions.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 2 Related Works
### Fall Detection Based on WiFi Signals
The following improvements to the LSTM model are made: * CSI data noise is eliminated by using the Hampel filter. * This paper first combines the CNN model with the short-duration memory neural network architecture of LSTM to improve the LSTM model's prediction accuracy and reduce the algorithm's complexity. * The improved LSTM model's object is changed to amplitude analysis of 90 CSI signals in the dataset, and the model's output is changed to the result of determine the corresponding signal's target posture. 3. When the target is detected as a fall posture by the improved YOLOv5s or improved LSTM model, a discriminator is designed to improve the accuracy of judgment, which combines the detected result from the models with the physiological data of the target to deliver the final target posture. The key contributions of this work are as follows:  
* The proposed dual fall detection model can detect the target's posture whether it is visible or not, simultaneously solving the problem of fall detection in different scenarios. * A small-scale database suitable for the home environment is established, and YOLOv5s, the most lightweight of the YOLOv5 series, is improved and applied to human fall detection in a home environment, improving detection accuracy and reducing the detection system's complexity. * The CNN model and LSTM neural network architecture are combined to process images from CSI human posture data, capturing long-term dependencies in sequence data and effectively extracting local CSI data features, providing a more accurate and stable fall detection model. * Image data, CSI data, and target physiological data are combined to further improve the fall judgment accuracy and detect the target's health status in real time, providing elderly people with a healthy and comfortable living environment. The overall composition of this article is as follows. The proposed YOLOv5s-based fall detection algorithm is presented in Section 3, with an introduction of the YOLOv5s model and the improved YOLOv5s model. Section 4 describes the general framework of the proposed fall detection with WiFi signal based on the LSTM model, exploring WiFi signal sensing, the fall prediction principle based on WiFi signal, CSI data acquisition, CSI data preprocessing, and the principle of the proposed WiFi signal-based fall detection algorithm. Section 5 presents the structure and principle of the discriminator. The experiment deployment and experiment results are given in Section 6. Section 7 presents the conclusion.
## 4 Discussion
### Complications  
Adverse effects were specifically reported and analyzed in seven reviews [21,28,29,31,34,35,36]. These are often reported as local edema, erythema, paresthesia, bruise and pain (during and a few days after the intervention). One systematic review by [PERSON], [PERSON] et al. [48] specifically focused on the complications related to SW treatment: it included 39 studies and highlighted that pain during treatment was the most reported side effect (255 out of 1820 participants), more frequently reported in high-intensity rather than low-intensity treatment. Two severe adverse events (out of 2229 participants) were reported: one of skin infection and one of precordial pain. Both were judged as not likely to be related to ESWT treatment.  
In our review, adverse effects were specifically compared between treatment groups in two studies [28,29]: [PERSON], [PERSON] et al. [29] reported a major incidence of calcaneal pain and erythema in ESWT group, while [PERSON], [PERSON] et al. [28] reported major incidence in the control group. [PERSON], [PERSON] et al. [36] reported that one patient withdrew from an RCT included after a loss of consciousness due to the magnitude of pain. [PERSON], [PERSON] et al. [34] reported four patients with severe migraine or headache in one of the included RCTs. However, no other severe adverse effects, such as infections, tendon rupture or abnormal musculoskeletaletals, were reported in the studies included.  
Future research should focus on the design of more trials with stronger methodological quality, assessing the effectiveness on PF of the different therapies: in particular, more attention has to be paid to the type and the parameters of the SW administered. More articles with similar and more objective outcome measures are needed to perform new meta-analysis and systematic reviews assessing the effectiveness of treatments for PF.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 4 Experiments
### Limitations  
Although our method yields promising results, it still has several limitations.  
First, while our Gaussian Splatting pruning approach can be applied to complex datasets such as Tanks and Temples (TnT), it requires manual adjustment of the number of clusters. Different cluster settings can significantly affect the rendered artistic style--larger clusters tend to produce coarser, more abstract results, whereas smaller clusters preserve finer details. An automatic method for determining an appropriate number of clusters would further improve usability and consistency.  
Second, because Gaussian Splatting primitives inherently treat the left-right and up-down directions symmetrically, our texture mapping may produce inverted or inconsistent details in some rendered frames, leading to minor artifacts in video sequences. We show the results of such inconsistencies in Figure 9, presenting four consecutive frames from \(i-1\) to \(i+2\). The red rectangular area highlights an individual GS primitive that flips upside down, while most of the surrounding primitives remain stable. This issue only occurs when the affected primitive is aligned perfectly horizontally or vertically, making it sensitive to directional ambiguity during projection. A potential solution is to track the orientation of each 3 DGS primitive across consecutive frames during rendering and use the orientation from the previous frame as a temporal constraint to correct inconsistencies in subsequent frames. This can help mitigate abrupt flipping or directional artifacts by ensuring the consistent projection of texture and stroke direction over time.  
Finally, our method does not yet support text-based editing, a functionality that many artists find essential for enabling more flexible and creative workflows.  
\begin{table}
\begin{tabular}{l|c c c c c c|c} \hline \hline
**StyleScene** & **Chair** & **Ficus** & **Hotdog** & **Lego** & **Materials** & **Mic** & **Mean** \\ \hline Ground truth & 99.95 & 99.61 & 99.07 & 99.32 & 95.46 & 93.51 & 97.82 \\ StyleRF (swirl) & 69.71 & 2.54 & 2.09 & 60.74 & 34.1 & 15.14 & 30.72 \\ ARF (swirl) & 99.02 & **98.73** & 23.36 & **99.46** & 32.89 & **99.51** & 75.49 \\ Ours (swirl) & **99.61** & 96.78 & **96.88** & 68.16 & **60.83** & 64.45 & **81.11** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preservation of scene semantics: CLIP similarity \(\uparrow\) comparison.  
Figure 9: Inconsistent frame demonstration. The Gaussian Splatting primitive highlighted by the red box flips upside down in frame i, while remaining stable in adjacent frames.
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 2 Experiment 1
### Results
#### 2.2.5. Perceived Competence  
The analysis on perceived competence revealed no significant main effect of ambient sounds (\(F\)(1,31) = 1.58, \(p\) = 0.22, and \({\eta_{p}}^{2}\) = 0.05) or character sounds (\(F\)(1,31) = 0.09, \(p\) = 0.22, and \({\eta_{p}}^{2}\) = 0.05). The interaction between the two showed a marginally significant trend (\(F\)(1,31) = 3.97, \(p\) = 0.06, and \({\eta_{p}}^{2}\) = 0.11). The perceived competence was higher with ambient sounds on compared to off when character sounds were turned on (\(M\) = 3.51 vs. 3.30 on a scale from 1 to 5)--\(t\)(31) = 2.27, \(p\) = 0.03, and [PERSON]'s \(d\) = 0.40--but no such difference was found when character sounds were turned off--\(t\)(31) = \(-\)0.41, \(p\) = 0.68, [PERSON]'s \(d\) = \(-\)0.07.
###### Abstract  
The purpose of this study was to evaluate the accuracy of intraoral scanners in 10 abutments (five premolars and five molars) obtained in a dental clinic and to analyze the impacts of the volume and area of abutments on scanning accuracy. Abutment casts were scanned five times with a 3D contact scanner (DS10; Renishaw plc). The five scan files were lined up and then merged, and one high-resolution computer-aided design reference model (CRM) was obtained. To obtain a computer-aided design test model (CTM), three types of intraoral scanners (CS3600 (Carestream Dental), 1500 (Medit), and EZIS PO (DDS)) and one type of laboratory scanner (E1; 3 Shape) were employed. Using 3D analysis software (Geomagic control X; 3D Systems), the accuracy of the scanners was evaluated, including optimal overlap by optimal alignment. The conformity of the overlapped data was calculated by the root mean square (RMS) value, using the 3D compare function for evaluation. As for statistical analysis, testing was conducted, using one-way and two-way ANOVA and the Tukey HSD test (\(\alpha\) = 0.05) for the comparison of the groups. To analyze the correlations of the volume and area of the abutments with accuracy, Pearson's correlation analysis was conducted (\(\alpha\) = 0.00625). Both premolar and molar abutments showed a lower RMS value on the laboratory scanner than on the intraoral scanners, and the RMS value was lower in premolars than in molars (\(p\) < 0.001). In the intraoral scanner group, CS3600 showed the best accuracy (\(p\) < 0.001). There were significant positive correlations for the volume and area of the abutments with accuracy (\(p\) < 0.001). The type, volume, and area of the clinically applicable abutments may affect the accuracy of intraoral scanners; however, the scanners used in the present study showed a clinically acceptable accuracy range, regardless of the type of abutment.  
intraoral scanner; accuracy; digital workflow; tooth preparation; abutment +
Footnote †: journal: Applied Sciences
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 10 Performance of the Multimode Tree Coder
### Performance with Different CNG Codes  
We compare the PESQ of the MMT with perceptual pre- and post-weighting and backward pitch prediction (MMT-WP) for Voiced (V) and Onset (ON) using 3 core bits/sample and Voiced (V) and Onset (ON) using 2 core bits/sample. The Unvoiced (UV) and Hangover (H) are coded at 2 core bits/sample.  
The PESQ-MOS [4] results of the MMT-WP for narrowband sequences using 2 core bits/sample on Voiced, Onset are shown in Table 3. Compared with the results of 3 core bits/sample for Voiced and Onset in Table 4, the PESQs of 2 core bits/sample for Voiced and Onset in Table 3 are lower than those in Table 4 since the bitrate is lower. However, comparing the performance of MMT with weighting and pitch predictor with the performance of MMT without pitch prediction from prior studies, the PESQ for 2 bits/sample increases more than the PESQ for 3 bits/sample on Voiced and Onset. It shows that the pitch predictor is very important when the bitrate is low.
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
## 2 Overview and Related Works
### Packet Scheduler for LTE Network  
LTE uses orthogonal frequency division multiplexing (OFDM) for the downlink and single carrier frequency division multiple access (SC-FDMA) in the uplink [9]. The latter has the advantage of enhancing the power efficiency of users, thanks to its low peak to average power ratio (PAPR) compared to OFDM. In LTE, both the uplink and downlink transmissions are divided into frames of duration of 10 ms, where each frame is composed of 10 sub-frames (1 ms). Each sub-frame contains two slots (0.5 ms), where each slot can contain N resource blocks (from 6 to 110 RBs) [9] depending on the bandwidth allocation. The resource element is the smallest modulation structure and consists of one symbol of 15 kHz. These resource elements are grouped to form a resource block, RB (Figure 1).  
All the allocations of RBs are handled by a scheduling function at the medium access control (MAC) layer of the eNB. When a user has data to transmit, it sends an uplink scheduling request to the base station and reports information about buffered data sizes. The scheduler in the eNB receives in input the matrix M, which has N rows corresponding to the number of active users in the cell and N\({}_{\text{RB}}\) columns according to the number of RBs which can be scheduled. Each element, M\({}_{\text{ij}}\), of this matrix represents a metric value that is achieved from the utility function, where M\({}_{\text{ij}}\) denotes the metric value for user \(i\) and RB\({}_{\text{j}}\). Figure 2 shows the UE-RB metric matrix, where UE (user equipment) designates user, which can be an H2H user or M2M device. The scheduler allocates \(m\) RBs to \(n\) users according to a defined algorithm. Each RB can be allocated only to one user and each user can be assigned to a set of contiguous RBs because of the use of SC-FDMA. The scheduler in the eNB sends the allocation map to active users over the physical downlink control channel (PDCCH). When receiving the permission to transmit, the user sends its packets as required in the allocation map which specifies the transmission time and the assigned RBs.  
The LTE standard does not specify how the allocation of resource blocks is carried out. Consequently, there are many scheduling algorithms with different objectives that are proposed in the literature.  
In LTE, in order to reduce complexity, most schedulers can be divided into two steps:  
* Time domain packet scheduling (TDPS): In the first step, a group of devices are selected to be scheduled based on diverse metrics: delay, channel quality identifier (CQI) reports, buffer size and so forth. TDPS does not allocate resource blocks to devices.
* Frequency domain packet scheduling (FDPS): In the second step, the select users are assigned contiguous RBs using a defined algorithm.
## 3 Materials and Methods
### UPLC-MS/MS Chromatography  
An ultra-performance liquid chromatography (UPLC) system (Agilent series 1290 RRLC equipment, Agilent, Santa Clara, CA, USA) was used to separate the targeted phenolic compounds using a Fortis C18 column measuring 100 \(\times\) 2.1 mm with 1.7 m particle size (Fortis Technologies Ltd., Neston, UK). [PERSON] et al. (2013) have previously provided descriptions of gradient settings and eluent compositions. A 64,300 QQ mass spectrometer (Agilent) was used for the identification and quantification of the phenolic compounds in both ionization modes. In summary, ESI ion source ionized the analytes at a flow rate of 11 L h\({}^{-1}\) and a temperature of 300 \({}^{\circ}\)C while using N\({}_{2}\) as desolvation and collision gas. Capillary voltage was set at +4 and \(-\)3.5 kV and nebulizer pressure at 40 psi. Instrument control and data analysis were performed using Agilent MassHunter Workstation Software (v. B.04.01, Agilent, Santa Clara, CA, USA). The calibration curves of the standards served as the basis for identification and quantitative determination: myricetin, rutin, caffeic acid, gallic acid, ferulic acid, protocatechuic acid, syringic acid, rosmarinic acid, chlorogenic acid, \(p\)-coumaric acid, quercetin-3-glucoside, kaempfer-3-glucose, catechin, epigallocatechin gallate, epicatechin gallate, apigenin, proxyanidin B2, and luteolin. The identification of the compounds for which there are no reference standards was based on their mass spectral data and mass fragmentation pattern reports published in the literature, while quantification was performed as follows: kaempfer-3-rutinoside, kaempfer-3-O-hexoside, kaempfer-3-O-deoxyhexoside and kaempfer-3-O-pentoside were calculated corresponding to kaempfer-3-glucoside, apigenin-6-C-(O-deoxyhexosyl)-hexoside corresponding to apigenin, luteolin-6-C-hexoside according to luteolin, isorhamentin-3-hexoside, quercetin-3-rhammoside and quercetin-3-pentoside corresponding to quercetin-3-glucoside, epicatechin corresponding to catechchin, 3,4-dihydroxybenzoic acid hexoside according to protocatehuic acid, while \(p\)-hydroxybenzoic acid was calculated as gallic acid equivalent. All analyses were performed in duplicate, and the concentrations of analyzed compounds are expressed as mean values \(\pm\) standard error of mg L\({}^{-1}\) of the sample (\(n\) = 2 replicates).
## 4 Conclusions  
This study demonstrated the potential of state-of-the-art chemometrics approach to have an increased insight into volatile changes during shelf life of PEF, HPP and thermal pasteurized apple juices. Immediately after processing, thermal processing lowered the amount of odor-active ester and aldehyde volatiles in comparison to PEF and HPP technologies. Consequently, at the end of storage, the amount of these aroma relevant volatiles appears to be still higher in PEF and HPP pasteurized juices compared to their conventional counterparts. Hence, mild pasteurization by PEF or HPP seems to provide a better retention of aroma-relevant volatiles during apple juice storage. Based on these  
Figure 3: Change in the relative peak areas (peak area of compound/peak area of internal standard) of hexyl acetate, butyl propanoate and (_E_)-2-hexenal as a function of storage time at 4 °C in thermal (72 °C for 15 s) (\(\blacktriangle\)), HPP (600 MPa for 3 min) and PEF (15.5 kV/cm and specific energy of 158 kJ/L) (\(\blacklozenge\)) pasteurized apple juices. A standard deviation of two replication is included.  
results, it is difficult to evaluate how the observed modification of the volatile fraction will affect the overall apple juice aroma. Therefore, there is a need for a sensory analysis to understand how these changes will be appreciated.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], S.Y.L., V.K., Q.M. and K.A. performed the experiments; B.K. analyzed the data; B.K., G.T.E. and N.H. contributed analysis tools; and B.K. wrote the paper.  
The authors acknowledge Royal Society of New Zealand funding for the NZ-Germany Science and Technology Program on Non-thermal Processing (FRG13-10). This research was supported by the Riddet Institute, a New Zealand Centre of Research Excellence, funded by the Tertiary Education Commission. This work was also carried out as part of the Food Industry Enabling Technologies (FliET) program funded by the New Zealand Ministry of Business, Innovation and Employment (contract MAUX1402).  
The authors thank the staffs at the University of Otago and the Auckland University of Technology for their technical support in this study.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.