esa-sceva/satcom-chunk-collection huggingface dataset:
# FirepanIF: High Performance Host-Side Flash Cache Warm-Up Method in Cloud Computing
## 5 Conclusions  
This study developed and proposed the use of Cachemior, Firepan, and FirepanIF to discover an efficient method for host-side flash cache warm-up in order to enable high performance VM migration. Cachemior warms up the host-side flash cache in the destination node with a separate thread after VM migration. However, it causes storage and network congestion on the shared storage server, thus achieving a marginal performance gain. As Firepan copies all the cache contents from the source to the destination node, it exhibits higher performance than Cachemior. However, for reasons of synchronization, Firepan does not allow the migrated VM to resume before the completion of flash cache migration. Finally, FirepanIF overcomes the limitation of Firepan by resuming the VM migration while the flash cache migration procedure is in progress. This is possible due to the IF, which traces I/O requests from the migrated VM and records write accesses in the invalidation table. FirepanIF can improve the performance of the I/O workload by up to 21.87% when the size of the flash cache is 20 GB.  
The work presented here was completed in collaboration between all authors, conceptualization, H.P.; validation, M.L.; formal analysis, H.P. and C.-H.H.; investigation, H.P. and C.-H.H.; writing-original draft preparation, H.P., M.L., and C.-H.H.; writing-review and editing, C.-H.H.; supervision, C.-H.H.; funding acquisition, H.P. and C.-H.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF-2019R1C1C1011068 and No. NRF-2017R1C1B5016000) and research funds for newly appointed professors of Jeonbuk National University in 2016.  
The authors would like to thank the anonymous reviewers of Applied Sciences journal for their valuable comments and suggestions to improve the quality of the paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  &
\begin{tabular}{c} **NoCache** \\ **Migration (Baseline)** \\ \end{tabular} & **Cachemior** & **Firepan** & **FirepanIF** \\ \hline
5 GB & 43.62 & 44.55 (+2.14\%) & 46.23 (+5.99\%) & 46.23 (+5.97\%) \\ \hline
10 GB & 50.21 & 52.69 (+4.94\%) & 56.98(+13.49\%) & 58.58 (+16.68\%) \\ \hline
20 GB & 48.75 & 48.02 (-1.51\%) & 56.61 (+16.11\%) & 59.41 (+21.87\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average bandwidth during 400 s after VM migration.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  &
\begin{tabular}{c} **NoCache** \\ **Migration (Baseline)** \\ \end{tabular} & **Cachemior** & **Firepan** & **FirepanIF** \\ \hline
5 GB & 45.64 & 46.36 (+1.57\%) & 47.17 (+3.34\%) & 46.97 (+2.92\%) \\ \hline
10 GB & 53.72 & 57.48 (+7.00\%) & 59.05 (+9.93\%) & 60.16 (+12.00\%) \\ \hline
20 GB & 54.86 & 57.93 (+5.60\%) & 61.89 (+12.80\%) & 62.79 (+14.45\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average bandwidth during 800 s after VM migration.
## 3 Results
### Experimental Results and Comparisons
#### 3.2.5 The Results of the Proposed Defect Detection Method  
The practical application effects of the two sub-methods of the proposed defect detection method (image segmentation method and stem/calyx recognition method) are presented above, respectively. Finally, the defect detection method was deployed to the embedded image processing device OpenMV for experiments to verify the effectiveness and practicality of the method. Firstly, after all the defective areas were segmented by the  
Figure 16: Texture characterization data for each type of defect sample. (**a**) R-component map stem/calyx texture data; (**b**) R-component map scar texture data; (**c**) SSR image enhanced stem/calyx texture data; (**d**) SSR image enhanced scar texture data; (**e**) stem/calyx texture data corrected by SSR and gamma; (**f**) scar texture data corrected by SSR and gamma.  
proposed image segmentation algorithm, the built-in \"find_blobs color block\" function of OpenMV was used to mark the defective areas with ROI regions of interest, extract the texture featured, and then send them to the support vector machine for binary classification. According to the classification result, the stem/calyx was eliminated and only the scar area was retained as the execution result of the proposed method. In this way, on the one hand, there was no need to open a separate storage space to set labels for the classification results of the defective areas, which reduced redundant operations. On the other hand, the scar detection results could be directly displayed graphically, which was more intuitive than the form of tables, convenient to further summarize some qualities of the scars, and conducive to judging the health of the apples. Some of the test results are shown in Figure 17.  
Table 2 shows the defect detection results for 280 pairs of apple images. Using the criteria that scars were not missed and the stem/calyx was not misclassified as a scar, healthy and diseased fruits were distinguished by whether the number of scars detected was zero or not. For the browning heavier defect type, although the detection effect was better, it was found in practical application that when the disease rot spread over a large area, the internal rotted degree would appear obvious grading or even white mold, resulting in the brightness decomposition results being affected, which led to over-segmentation. In addition, in this paper, only scars with an area larger than 20 pixels (the resolution of the experimental image was 160 \(\times\) 120) were counted as the detection results, and the surface spots on the apples themselves (usually with an area of less than five pixels) were ignored.
## 2 Related Work
### Multimodal ERC Datasets  
Table 1 summarizes some of the most notable datasets relevant to multimodal sentiment analysis and emotion recognition in conversations, which are pertinent to this study. Early research in multimodal sentiment analysis often overlooked scenarios with contextual interactions, as exemplified by CMU-MOSEI [12], CMU-MOSI [13], and CH-SIMS [14]. These datasets primarily originate from non-dialogue contexts, such as TV show clips and short videos. Consequently, they are better suited for sentiment judgment in independent contexts, lacking conversational interactions. However, this data collection approach limits their utility for conversational sentiment analysis as it often necessitates considering contextual information and emotional dynamics during interactions. Therefore, subsequent research should explore developing multimodal datasets explicitly designed for conversation emotion recognition. This would enable more accurate capture and analysis of emotional changes in dialogues, enhance the application of sentiment analysis in dialogue settings, and better address our emotional interaction needs. Multimodal emotion recognition in conversation is a rapidly growing field that aims to predict emotions for each utterance in a dialogue by integrating multimodal data. Speakers convey emotions through various modalities, including textual expression, facial gestures, vocal tone, and other cues. Key datasets in this field include IEMOCAP, MELD, and M3 ED. The IEMOCAP dataset, collected by the SAIL laboratory at the University of Southern California, comprises dyadic dialogues performed by ten professional actors, totaling 7433 utterances. It covers various dialogue roles and includes six emotion labels; however, typically four are widely used for training and recognition due to their ease of learning and distinctiveness. The MELD dataset expands on EmotionLines and is derived from the TV show _Friends_. It contains 1433 multi-party dialogues and 13,708 utterances, annotated with seven emotion labels. MELD offers explicit facial expressions and speech emotions, but its script-based content can complicate emotion recognition. M3 ED encompasses 990 emotional dyadic dialogue video clips from 56 TV shows, containing 9082 utterances and annotated with seven emotion labels. Its inter-annotator agreement score is 0.59, significantly higher than those of MELD (0.43) and IEMOCAP (0.48). Moreover, M3 ED is China's first multimodal interactive dataset and represents a valuable contribution to emotion computing.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 2 Snapshot Receivers and Beamforming
### Acquisition Signal Model
Typically, the longer the integration time \(\tau_{\text{int}}\) is, the more processing gain is achieved, which results in better performance and noise suppression. However, this is limited by the bit-transitions of the modulated data \(d_{i}(t)\). In the worst case, a bit-transition can result in deconstructive self-interference, which will completely remove the signal during the correlation stage. As such, either the bit-transitions need to be estimated or incoherent integration is required. As the PRN sequence \(r(t)\) is periodic intime, the correlation output \(y_{i,j}(t)\) is also quasi-periodic: the magnitude of the correlation function is periodic in time, but the phase is not, due to the modulated bit-stream. To combine these values incoherently in time, the squared values are summed:  
\[y_{i,j}^{N_{\text{inco}}}(t)=\sum_{l=0}^{N_{\text{inco}}-1}y_{i,j}(t-l\cdot T_{ \text{rep}})\times y_{i,j}^{*}(t-l\cdot T_{\text{rep}})=\sum_{l=0}^{N_{\text{ inco}}-1}\left\|y_{i,j}(t-l\cdot T_{\text{rep}})\right\|^{2}\,, \tag{5}\]  
where \(N_{\text{inco}}\) is the number of temporal incoherent epochs to use, \(T_{\text{rep}}\) is the repetition interval for the PRN code, \((\cdot)^{*}\) is the complex conjugate of the function, and \(\left\|\cdot\right\|\) is the magnitude function. As these values are added incoherently, longer effective integration times can be achieved. However, due to the incoherent addition, there is also a squaring loss. This makes this method less effective in comparison to temporal coherent integration, but it is significantly more robust to bit-transitions.
## 1 Introduction
### Literature Review
#### 1.1.2.
Recent study carried out by [PERSON] and [PERSON] study [30], describes application of shop floor management model as presented by [PERSON], and defined a guideline dealing with sustainability of lean tools and philosophies in a manufacturing environment. Another study presented by [PERSON] and [PERSON] [31], describes implementation of value stream mapping in a manufacturing company which is producing automotive suspension and fastening components and proved process improvement in terms of cycle time and level of work in process. Application of lean tools for waste reduction in steel industry is described in case study by [PERSON] and [PERSON] [32]. A case study of lean implementation in plastic industry, specifically application of SMED (single minute exchange of die), lean tool for reduction of preparation time is presented by [PERSON] et al. [33]. The authors of a study conducted in Mexico also argue that lean is still one of the most important approaches in improving production [34]. They developed an instrument based on critical success factors to evaluate the implementation of lean tools, specifically in this study, for transportation equipment manufacturing subsector. Research about importance of implementation of lean and critical factors for implementing lean in Iranian industry was presented by [PERSON] [35]. Lean has proven successful also in construction and shipbuilding industry. The authors of [36] have described the case study of implementation of lean in the bidding phase of construction process and found that it can benefit from lean. Lean has also found its application in the shipbuilding industry. [PERSON] et al. have presented the study of possibilities of lean thinking implementation in construction industry. The objective was to improve building productivity. Results of productivity improvements with the approach presented were more sustainable as well as the cost and waste reduction [37]. The case study of introducing lean practice in the Croatian shipbuilding industry, by value stream mapping is presented in [38]. What the barriers of implantation of lean in shipbuilding industry are, specifically on the example on the shipbuilding industry in Singapore is well described in research paper [39]. Authors tried to explain the barriers to lean manufacturing as well as provide guidance for adopting lean practice in the shipbuilding industry. In order to help various industries in implementing lean, a very interesting question is which lean tools and methods help the most to achieve the organizational goal. Thus, [40] have conducted review of 70 articles in 21 journals and have found that value stream mapping and DMAIC are the most widely used tools in all types of industries. They are followed by SMED and 5S. DMAIC is basically Six Sigma tool, while all other mentioned are lean tools. These findings could help other companies in deciding which direction to take when considering implementing lean [40].
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.7 Distance to Powerlines  
The distance to powerlines is a vital criterion for selecting suitable locations for PV farms, as it directly affects the cost and feasibility of connecting the solar farms to the electrical grid. Proximity to powerlines reduces the expense and complexity of the infrastructure needed to transmit generated electricity, making areas closer to existing powerlines more attractive for solar energy projects. The study classifies areas based on their distance from existing powerlines. In this study, OpenStreetMap powerline data were obtained and analyzed [51]. Regions within 2 km of powerlines are considered highly suitable due to the lower connection costs and minimal energy losses during transmission. Conversely, areas beyond 10 km from powerlines may be determined to be unsuitable, as extending transmission infrastructure to these areas involves significant investment and logistical challenges [7; 53].
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Membrane Properties and Module Configurations
#### 5.1.4 Fiber Diameter  
A smaller HF diameter has been experimentally shown to improve the performance of submerged HF systems [15; 16; 187] due to the attendant greater fiber mobility [15], which can be further enhanced by bubbling [16] or turbulence in general [187]. However, theoretical analysis shows that a smaller HF diameter leads to a higher pressure drop along the fiber lumen, such that a higher suction pressure is required for a given average imposed flux [187]. On the other hand, simulations show that the average surface shear was 67% higher for fibers with a diameter of 1.3 mm relative to fibers with a diameter of 1.0 mm that had an identical Young's modulus and looseness [193]. The interplay of these two effects suggests an optimal fiber diameter that balances flux enhancement with flux-distribution and suction-pressure considerations [187].
## 3 Results
### Evaluation of the Accuracy of the GPP Estimation Model
#### 3.2.2 Response of \(\mathrm{GPP_{RF}}\) to Drought at Different Time Scales  
We analysed the correlation between the \(\mathrm{GPP_{RF}}\) and SPEI temporal scales (SPEI01, SPEI03, SPEI06, SPEI09, and SPEI12) during the 2009 growing season. We aimed to comprehensively assess the differential impacts of short- and long-term drought on GPP. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **May** & **June** & **July** & **August** & **September** & **October** \\ \hline \(\mathrm{GPP_{Mean}}\) & 48.299 & 69.358 & 82.189 & 77.352 & 52.481 & 27.464 \\ \hline \end{tabular}
\end{table}
Table 4: Patterns of change in GPP averages, May–October.  
Figure 5: (**a**) Trend changes in SPEI12 before and after standardised anomalies (2001–2020); (**b**) Trend changes in mean GOSIF values during the growing season (2009–2011) (the red box represents the most anomalous year, 2009).  
Figure 6: Spatial distribution of GPP during the growing season (May–October) of 2009.  
study showed that the mean values of GPP and SPEI at different timescales were SPEI09 (0.210), SPEI12 (\(-\)0.061), SPEI06 (\(-\)0.216), SPEI01 (\(-\)0.240), and SPEI03 (\(-\)0.241). Notably, the highest mean correlation between the GPP and SPEI during the growing season was observed for SPEI09. This finding indicates that the SPEI09 can sensitively reflect water surplus and deficit conditions during Xinjiang's vegetation growing season. The regions with high correlations between SPEI09 and GPP were primarily concentrated in ecologically sensitive areas such as the Altai Mountains, the southern part of Tacheng, the Tianshan Mountain Range, the Ili Valley, and the oases around the Tarim Basin. In summary, the SPEI09 is pivotal for elucidating the patterns by which vegetation GPP responds to drought in Xinjiang.  
We analysed the correlation between GPP\({}_{\text{RF}}\) and SPEI09 for different vegetation types in Xinjiang (Figure 7f). Our findings highlighted that the mean correlation coefficient linking grass land GPP with SPEI09 reached a notable level of 0.244, whereas forest land GPP and SPEI09 exhibited the second-highest correlation at 0.224. In contrast, cultivated land GPP had the weakest correlation with SPEI09 at 0.105. In summary, this study advances our understanding of the complex effects of drought on different vegetation types in Xinjiang and their responses to drought at different timescales, thereby providing insights into the mechanisms that influence GPP.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 7 Future Directions and Challenges
### Emerging Technologies  
Advancements in technology present emerging opportunities for the improvement of volumetric assessment methods. One area of development is the integration of artificial intelligence (AI) and machine-learning algorithms [88]. AI can enhance the accuracy and efficiency of volumetric measurements by automating the segmentation of edematous tissue and reducing inter-observer variability [38; 88]. Machine-learning algorithms can also assist in the identification of patterns and predictive models for treatment outcomes, helping to personalize therapeutic interventions [2; 38].  
Furthermore, wearable devices and smartphone applications hold promise for convenient and accessible volumetric assessments [89; 90]. These technologies can provide patients with tools for self-monitoring and facilitate remote monitoring by healthcare providers. By leveraging sensors and imaging capabilities in smartphones or wearable devices, volumetric measurements can be obtained outside clinical settings, enhancing patient engagement and enabling real-time data collection [38; 89; 90].
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 3 Experiment
### DeepLab  
DeepLabV3 [41] is a cutting-edge convolutional neural network architecture tailored for semantic image segmentation. It leverages atrous convolution to capture multi-scale contextual information efficiently. Key features include a pyramid network for hierarchical feature extraction, atrous spatial pyramid pooling for multi-scale feature aggregation, and efficient up-sampling methods for high-resolution segmentation maps. Our DeepLabV3+ model (Figure 8) utilized an EfficientNetB0 backbone for feature extraction without fully connected layers, serving as the encoder. This encoder processes input images and extracts features, which are then passed on to the decoder. The decoder consists of an atrous spatial pyramid pooling (ASPP) module followed by global average pooling and low-level feature  
Figure 7: SegNet block diagram with backbone encoder–decoder CNN architecture.  
concatenation. The ASPP module employs convolutional layers with varying dilation rates to capture multi-scale contextual information. Additionally, global average pooling is performed to capture global context information. The decoder then combines ASPP outputs, global context, and low-level features using concatenation. Further convolutional layers refine the features, followed by up-sampling layers to restore spatial information. Finally, a \(1\times 1\) convolutional layer with SoftMax activation generates pixel-wise predictions for semantic segmentation. The model is compiled using the Adam optimizer and categorical cross-entropy loss and accuracy function for metrics.  
We implemented U-Net, SegNet, and DeepLabV3 segmentation models for the semantic segmentation of satellite images. Initially, the image and mask datasets underwent preprocessing by subdividing them into smaller patches to facilitate training. Each patch was then scaled using min-max scaling, and the RGB masks were converted into integer labels based on predefined RGB values. These models were constructed using the EfficientNetB0 backbone, with and without encoder and decoder paths. A SoftMax activation function was applied to the output layer for multi-class segmentation. Following compilation with the Adam optimizer and categorical cross-entropy loss function, the model was trained for 100 epochs with batch size 16. The evaluation was made using accuracy and IoU metrics on both training and validation datasets, and the trained model was then saved for future reference. Performance metrics, including accuracy, precision, recall, F1 score, and Jaccard coefficient, were computed, and the segmentation performance was visually demonstrated on randomly selected test images.
## 2 Velocity Estimation
### Raim-Fde  
RAIM algorithms are user-level algorithms able to provide integrity information, i.e., the trust that can be placed in the GNSS solution. RAIM algorithms are based on a consistency check on redundant measurements [32]; and if RAIM algorithms are also able to identify and reject anomalous measurements, they are referred to as FDE algorithms.  
In this work, the adopted RAIM-FDE algorithm is called \"Subset Testing\" [28], and it is based on the analysis of residuals, \(\underline{r}\), whose expression is as follows:  
\[\underline{r}=\underline{y}-H\underline{\hat{x}} \tag{16}\]  
where \(\underline{y}\) is the measurement vector, \(H\) is the design matrix, and \(\underline{\hat{x}}\) is the vector of the estimated unknowns.  
A decision variable \(V\), depending on the residuals and on the weighting matrix \(W\), is defined as follows:  
\[V=\underline{r}^{T}W\underline{r} \tag{17}\]  
\(V\) is compared with a threshold, whose value depends on the measurement redundancy and on the probability of a false alarm; if \(V\) exceeds the threshold, the presence of at least one anomalous measurement is reported. In subset testing, such a test is carried out on each possible subset of measurements, until a blunder-free subset is identified; in this way, the possible anomalous measurements are excluded, and they do not contribute to the solution computation. If an inconsistency is revealed but a blunder-free subset is not identified, the solution is indicated as not reliable.  
In this study, the RAIM-FDE block is used also for detecting cycle slips; in that context, a cycle slip is considered as a simple blunder affecting the carrier-phase measurements and is rejected by the FDE part of the integrity algorithm. No repair action is performed when a cycle slip is detected.