esa-sceva/satcom-chunk-collection huggingface dataset:
# Geological Influences on Wine Quality: Analyzing Nebbiolo Grapes from Northern Italy
## 3 Results
### Musts Analyses  
Table 5 presents the data obtained from the analysis of 16 must samples to evaluate the variation in the quantities of individual elements throughout the production processes. The choice of the Nebbiolo vine is particularly interesting because, due to its unique characteristics and late ripening, it is typically used to produce high-value wines that are unified almost exclusively as single variatals without the addition of other cultivars. This specific production aspect facilitates the monitoring of potential mineral transfer at every stage without variations arising from factors unrelated to anthropic intervention.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & **Ca** & \(\pm\)**SD** & **K** & \(\pm\)**SD** & **Mg** & \(\pm\)**SD** & **Al** & \(\pm\)**SD** & **Ba** & \(\pm\)**SD** & **Cu** & \(\pm\)**SD** & **Fe** & \(\pm\)**SD** \\ \hline  
\end{table}
Table 5: Concentrations of elements in musts, expressed as mg element/Kg musts. Standard deviation was calculated on values obtained from replicated samples.  
Figure 3: (**a**) PCA of the complete dataset of elements’ concentrations in grapes. (**b**) PCA performed only on ‘_meso elements’_. (Variance PC1 46.56%, PC2 27.35%, PC3 13.09%).  
The first observation was the sharp reduction in the concentrations of Ca, K, and Mg from the berries to the must, likely due to the loss of the solid fractions of skins, seeds, and pulp. Continuing the analysis with the _meso elements_, we noted a similar trend to what was partially observed in the grapes, although with absolute values lower than those previously recorded. The concentration variations were somewhat pronounced but could be weakly correlated with factors related to growth conditions. A similar conclusion could be drawn  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **Mn** & \(\pm\)**SD** & **Sr** & \(\pm\)**SD** & **Zn** & \(\pm\)**SD** & **Co** & \(\pm\)**SD** & **Cr** & \(\pm\)**SD** & **Li** & \(\pm\)**SD** \\ \hline  
\end{tabular} \({}^{*}\)nd = not detectable.  
\end{table}
Table 5: _Cont._from the analysis of _micro elements_, which show less variability among the different samples and may, therefore, be less discriminative for our investigation.  
Applying multivariate analysis solely to the dataset concerning the _meso elements_ (Figure 4) confirmed these observations, as PCA revealed a distribution of samples that correctly groups many from the Canavese (red oval), some from the Langhe (green square), and those from the Valtellina (blue oval). Notably, the must samples NM6, NM11, NM12, NM9, and NM10 are exceptions; however, the last two, also from the Langhe, are positioned closely together.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 4 Simulation Experiment Settings
### Throughput  
The cumulative number of packets received by the recipient divided by the time between the first and last packet received is known as throughput. The throughput is measured in bytes per second (bps). However, to work with kilo bytes per second (kbps), a factor of 8/1000 must be multiplied by the standard figure. The formula to calculate throughput was adopted from [15] and is shown in Equation (14).  
\[Throuput\;(kbps)=\left[\frac{\sum_{i=1}^{n}R_{i}}{FP_{t}-LP_{t}}\right]\times \left(\frac{8}{1000}\right) \tag{14}\]  
where \(R\) is the packet received and \((FP_{t}-LP_{t})\) is the duration elapsed from the first to the last packet received.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 5 Eco-Friendly _Lysinibacillus sphaericus_ and _Bacillus thuringiensis_ subsp. _israelensis_  
The two well-known bacteria used in mosquito control programs worldwide are _Lysinibacillus sphaericus_ (Ls) and _Bacillus thuringiensis_ subsp. _israelensis_ (Bti). These Gram-positive bacteria are spore-forming aerobic to facultative anaerobic bacilli that occur naturally in soil. Although they produce several different proteinaceous and non-proteinaceous toxins during vegetative growth (e.g., Mtx, Vip), they are best known for the proteinaceous parasporal crystalline inclusions (e.g., Cry, Cyt, Tpp) they synthesize during the sporulation phase of growth [220]. The parasporal inclusions of Ls and Bti are composed of different types of protoxins, but, nevertheless, they share a common feature in that when ingested by mosquito larvae, they solubilize in the alkaline environment of the midgut where they elicit substantial damage to the epithelial lining leading to larval death [221].
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 3 Methodology
### SPS Encoder  
To preserve the spatial characteristics of the clouds and make the framework ignore as little as possible the small-sized clouds that occupy fewer pixels, a structure that contains only a few down-samplings was designed, as shown in the Figure 2. The image of size \(H\times W\) is first fed into the encoder, and the feature maps are obtained by convolution and pooling. Then, the feature maps are fed into the pyramid structure. The three dilated convolutions with different expansion rates are used to extract features and improve the global receptive field while maintaining the resolution. In addition, the pyramid structure extracts features from multiple-scale spaces to enhance the expressive information of the clouds and enhance the mining of the hidden relationships between pixels. To ensure that the characteristic signal can densely cover more areas, we concatenate the features extracted from differently scaled spaces. Next, the SupA encoder further extracts the information and adjusts the number of channels by convolution. From this framework, a set of feature maps \(\ell_{deep}\) containing rich information can be obtained as:  
\[\ell_{deep}\in\mathbb{R}^{C_{\ell}\times\frac{H}{8^{m}}\times\frac{W}{8^{m}}} \tag{1}\]  
where \(C_{\ell}\) is the number of channels, \(S\) is stride, and \(n\) means the down-sampling frequency. These feature maps will be the basis of pixel-level analysis and mask-level analysis of the whole framework.  
Figure 1: There are four key components in the framework: SPS Encoder, SupA Decoder, Transformer module, and mask segmentation. The remote-sensing data are input into the SPS encoder to extract the feature of the cloud through convolution. Then the feature is sent to the SupA decoder for pixel-level decoding. The feature will also be sent to the Transformer module to use the attention mechanism for further calculations to obtain mask information and classification embeddings of information. Finally, the two parts of information are combined in the mask segmentation to obtain the prediction result.
## 4 Experimental Results
### Experiment I: Receiver Operating Characteristic (ROC) Curves SSRDS  
The ROC curve shows the performance of the system in terms of the area that is covered under it. The larger the area under the curve, the better the performance and vice versa. For the evaluation of the proposed SSRDC system, we plotted the ROC curves for every class of all the datasets that our system was dealing with. Figure 9 depicts the ROC curves for MobiAct v2.0.  
Figure 9 shows that the average area under the ROC curve was 0.93, which is a very good number regarding the performance of the proposed model over Mobiact_v2.0. According to Figure 9, the smallest area was covered by \"front-knees lying\", i.e., 0.77, and the largest covered area, 1.00, was achieved by the \"sitting\" and \"car step-out\" classes. Following a similar approach, we plotted the ROC curves for Real-World HAR, which are shown in Figure 10.  
Figure 10 shows that the average area under the ROC curve was 0.95, which advocates SSRDC system's performance over that of Real-World HAR. According to Figure 10, the smallest area was covered by \"stairs-down\", i.e., 0.67, and the largest covered area, 1.00, was achieved by the \"jumping\", \"lying\", \"sitting\", \"standing\", and \"walking\" classes. The ROC curves for Real-Life HAR are given in Figure 11. According to Figure 11, the average area under the curves for Real-Life HAR was 0.96, which was the greatest number achieved by our system. The proposed SSRDC system performed in the best manner over  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial No.** & **MobiAct_v2.0** & **Real-World HAR** & **Real-Life HAR** \\ \hline
1 & Walking & Stairs Climb-up & Inactive \\
2 & Stairs-up & Stairs Climb-down & Active \\
3 & Stairs-down & Jumping & Walking \\
4 & Stand-to-sit & Lying & Driving \\
5 & Sit-to-stand & Running & - \\
6 & Car step-in & Sitting & - \\
7 & Car step-out & Standing & - \\
8 & Jogging & Walking & - \\
9 & Jumping & - & - \\
10 & Standing & - & - \\
11 & Sitting & - & - \\
12 & Forward lying & - & - \\
13 & Back-sitting chair & - & - \\
14 & Front-knees lying & - & - \\
15 & Sideward lying & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Human locomotion activities provided by all three datasets.  
the Real-Life HAR with a minimum area of 0.90 under the curve for the class \"active\" and the maximum area was 1.00, which was commonly attained by the \"walking\" and \"driving\" classes.  
Figure 10: Real-World HAR: individual ROC curves for all the classes, class 1 = stairs-up, class 2 = stairs-down, class 3 = jumping, class 4 = lying, class 5 = running, class 6 = sitting, class 7 = standing, class 8 = walking.  
Figure 9: MobiAct_v2.0: individual ROC curves for all the classes, class 1 = walking, class 2 = stairs-up, class 3 = stairs-down, class 4 = stand-to-sit, class 5 = sit-to-stand, class 6 = car step-in, class 7 = car step-out, class 8 = forward lying, class 9 = back-sitting chair, class 10 = front-knees lying, class 11 = sideward lying, class 12 = jogging, class 13 = jumping, class 14 = standing, class 15 = sitting.
# Computer Vision as a Tool to Support Quality Control and Robotic Handling of Fruit: A Case Study
## 3 Results
### Method Implementation and Results
#### 3.1.2 Tomato Detection and Classification Model with Two Classes
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IoU** & **Tomato\_Healthy AP** & **Tomato\_Rotten AP** & **mAP (\%)** \\ \hline
0.50 & 72.32 & 90.54 & 81.43 \\
0.55 & 70.02 & 90.09 & 80.055 \\
0.60 & 69.84 & 89.53 & 79.685 \\
0.65 & 69.23 & 89.05 & 79.14 \\
0.70 & 68.21 & 87.43 & 77.82 \\
0.75 & 67.01 & 84.61 & 75.81 \\
0.80 & 60.12 & 80.76 & 70.44 \\
0.85 & 55.84 & 72.93 & 64.385 \\
0.90 & 21.04 & 46.91 & 33.975 \\
0.95 & 1.32 & 6.32 & 3.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: TensorFlow Lite model results—tomato detection and classification model—two classes. For the numerical analysis of the training of the two-class tomato detection and classification model, the accuracy metrics show greater effectiveness when working with large images (83.6%) and lower performance for medium (62.7%) and small (6.5%) images. Recovery shows similar behavior, with higher results for large images (79.2%), followed by medium (73.4%) and small images (22.2%). The model shows high accuracy (91.5%) with an IoU threshold of 0.50, indicating effectiveness in detecting objects with low overlap. As expected, the accuracy decreases at higher thresholds (82.5%). In terms of recovery, the model has a good ability to detect up to 10 objects (67.5%) and an excellent ability to detect up to 100 objects (79.2%). Converting to TensorFlow Lite shows the variation in average precision (mAP) expected between the two classes of tomatoes. With an IoU threshold of 0.50, the average accuracy achieved is 81.43%, with an AP of 72.32% for healthy tomatoes and 90.54% for rotten tomatoes. As the IoU threshold increases, there is a drop in mAP, with values of 80.055% (IoU 0.55), 79.685% (IoU 0.60), 79.14% (IoU 0.65), 77.82% (IoU 0.70), and so on, until it reaches just 3.82% at an IoU of 0.95. Thus, the mAP value for IoU [0.5:0.95] is 64.66%. These results are valid in real applications, as can be seen in Figure 18.  
The results for the two size models are in line with the nature of the database used, where most of the samples have large image annotations. For the system tests, all the samples have areas larger than 96\({}^{2}\) pixels, due to the distance from the camera to the work area, the resolution of the camera, and the nature of the type of fruit being analyzed. Therefore, the results to be analyzed for this system are always large images, as shown in Figure 19.  
Figure 19: Demonstration of the size of the object area in pixels. Figure 18: Two-class tomato detection and classification model applied to images outside their original database. Both models showed good results for images outside the nature of their database, making them valid models for real-life use.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Data Preprocessing  
Raw data from the MSAD dataset underwent preprocessing to ensure data quality and consistency. This involved video frame resizing using a bilinear interpolation method and normalization using means and standard deviations. Here, bilinear interpolation [28] for y (height) and x (width) contains four coordinate points \(A(y_{1},x_{1})\), \(B(y_{1},x_{2})\), \(C(y_{2},x_{1})\), and \(D(y_{2},x_{2})\). A formula for computation of X and Y for width dimension is given by:  
\[X=A(1-w_{x})+Bw_{x} \tag{1}\]  
\[Y=C\left(1-w_{x}\right)+Dw_{x}, \tag{2}\]  
Figure 1: Block diagram of video anomaly detection using MAML.  
Then linear interpolation between the two interpolated values \(X\) and \(Y\) in the height dimension is given by:  
\[Z=X\left(1-\mathrm{w}_{\mathrm{y}}\right)+Y\mathrm{w}_{\mathrm{y}}, \tag{3}\]  
\[=A(1-\mathrm{w}_{\mathrm{x}})(1-\mathrm{w}_{\mathrm{y}})+B\mathrm{w}_{\mathrm{x }}(1-\mathrm{w}_{\mathrm{y}})+C\left(1-\mathrm{w}_{\mathrm{x}}\right)\mathrm{w }_{\mathrm{y}}+D\mathrm{w}_{\mathrm{x}}\mathrm{w}_{\mathrm{y}} \tag{4}\]  
where \(\mathrm{w}\mathrm{x}=(\mathrm{x}-\mathrm{x}_{1})/(\mathrm{x}_{2}-\mathrm{x}_{ 1})\) and \(\mathrm{w}_{\mathrm{y}}=(\mathrm{y}-\mathrm{y}_{1})/(\mathrm{y}_{2}-\mathrm{y}_ {1})\)  
Using the above equations, images were resized to the required dimension, i.e., a 224 \(\times\) 224 RGB image. Here, PIL images with [H \(\times\) W \(\times\) C] in range [0, 255] with an unsigned int type were converted to Tensor of float type with shape [C \(\times\) H \(\times\) W] in the range of [0, 1]. In this case, PIL images belong to RGB mode. The normalization of resized images was done to standardize the images based on their mean and standard deviation, ensuring stable and consistent model performance.  
For this study, the dataset was initially partitioned into training, validation, and testing sets based on different scenarios. For the initial training phase of the model, nine distinct scenarios were utilized, encompassing shops, front doors, offices, parking lots, pedestrian streets, street highviews, warehouses, roads, and parks, each containing both anomalous and normal video data. Two different scenarios, malls and trains, which also included both anomalous and normal videos, were allocated for the initial validation of the model. Finally, the initial testing of the model employed data from two separate scenarios, restaurants and sidewalks, both containing anomalous and normal video examples.
## 4 Experiments and Results
### Sentiment Analysis Model: Training Setup
#### 4.2.2 Configurations  
Table 3 shows the features employed to build the aspect detection module. Their values were obtained using 5-fold cross-validation.  
As suggested in [24], a domain adaptation mechanism was used to enlarge the features used to train this module. Given an aspect category \(a\), two copies were made of each word \(x\). They are called \(x\#\)_general_ and \(x\#a\). The former refers to the general use of the word, whereas the latter represents the usage of the word when referring to a particular aspect. In that way, the classifier may learn that a certain word is positive in general (e.g., \"splendid\"), and it also may learn that a word is positive in a particular context (e.g., \"tasty\" is a very good word when referring to the quality of food).  
As described in Section 3.2, first the sentences of the reviews are separated into words and POS-tagged. After that, the system calculates all the features mentioned in the previous section, except the n-grams based on characters: word 3-grams (both general and aspect-based), Part-Of-Speech tags and the cluster and lexicon-based features (with the three lexicons mentioned above).
## 1 Introduction
More recent randomized controlled trials (RCTs) and quasi-experimental research have shown promising improvements in strength, functional mobility, and balance following strength and balance exercise [18; 19] as well as dance [19; 20]. These mixed results demonstrate the need for further research to clarify the role of exercise in improving cognitive-motor integration in PWD. PWD are often inactive and sedentary [21]. Therefore, further research is needed to determine if exercise is a viable strategy to improve dual-task mobility impairments due to the lack of research examining the effect of exercise on cognitive-motor integration in PWD and the need for tailored intervention [12]. There are no evidence-based guidelines regarding adapted exercise programs, frequency, duration, and type of exercise in PWD [22; 23]; only general exercise recommendations for PWD from the American College of Sports Medicine [22] and the National Institute on Aging [24]. There have been several other recommendations for physical activity for PWD, including dance, mirroring exercises, and escorting PWD to exercise, which helps foster adherence [23; 25]. The Otago Exercise Program (OEP), which was developed in New Zealand as a 6-month home-based exercise program consisting of muscle strength and balancing exercises as well as walking to prevent falls in frail community-dwelling older adults without dementia, may be a solution [26]. The OEP has been shown to reduce falls by 33% in older adults without dementia, which reduces expenditures for healthcare and rehabilitation [27; 28]. The OEPcan likely be modified to meet the needs of PWD, but further research is needed. Promising pilot RCTs have examined the effectiveness of the OEP on health outcomes among PWD. Specifically, it has improved upper limb muscle strength and range of motion as well as functional ability in people living with early-stage dementia [29]. The OEP has also elicited improvements in balance and functional mobility in people living with mild cognitive impairment and PWD [30]. Other work has shown that the OEP improved lower body strength, balance, and aerobic ability in PWD [31]. While preliminary evidence suggests that the OEP decreases fall risk in PWD, these pilot trials have included small sample sizes, and the OEP was unsustainable after the intervention period due to the number of staff members required to monitor and aid the participants [25]. To our knowledge, no studies have examined the effect of the OEP on dual-tasking in PWD. The aim of the strENgth And BaLance exercise on Executive function in people living with Dementia (ENABLEED) study was to conduct a pilot 6-month assessor-blinded RCT to determine if the adapted physical therapist-led OEP, combined with usual care, improved single- and dual-task mobility when compared to usual care alone among PWD who reside in residential care facilities. We hypothesized that exercise combined with usual care would improve single- and dual-task mobility relative to usual care alone among PWD.
## 3 Drone Routing Algorithms
### Security Algorithms Used in Drone Routing
#### 3.3.1 Blockchain-Based Secure Data Transaction Scheme (Covadel)  
For designing a drone-based delivery systems, multiple constraints must be considered, such as the physical resource status, energy, and the operational capability of the hardware. For designing drone routing techniques, the algorithm needs to be adaptive in emergency situations to consider different routes due to unexpected events. Thus, if the drone has insufficient energy for the diverted route, it cannot sustain the flight. Considering these issues, in [56], the authors suggested a blockchain-based delivery service for delivering essential materials or medicines in COVID-19 scenario. To enable secure data transaction between drones and between drones and base station, the drones are first categorized in terms of the current resource and computational capacity before being selected for the delivery mission. This categorization phase allows selecting the drones that have sufficient capability in terms of weight, fitness, payload, and battery level. Second, because the network might face congestion while communicating with an increasing number of drones in a particular area, the authors leveraged the fire-fly optimization technique to maximize the quality of service by designing an effective communication approach via time-slot mechanism. Moreover, the authors leveraged the light intensity parameter for optimizing drone communication, which depends on accurate beam positioning. The simulation study shows that the proposed method can maximize service quality by maximizing throughput and minimizing end-to-end delays.
## 3 Current Data
### Passive Microwave  
Continuity with SMMR data products has been provided by the operation of the Special Sensor Microwave Imager (SSM/I) carried on the Defense Meteorological Satellite Program (DMSP) series of satellites since 1987. The SSM/I is a seven-channel instrument, operating between 19.35 and 85.5 GHz and achieving a spatial resolution of around 70 km to 16 km, respectively. The SSMIS (Special Sensor Microwave Imager Sounder) instrument has been operated in tandem with the SSM/I since 2005. It has more channels and a frequency range up to 183 GHz, which allows for much more accurate sounding of (and correction for) atmospheric properties. Gridded data products from both of these instruments are available from NSIDC and also include brightness temperature data ([[http://nsidc.org/data/polar_stereo/data_summaries.html](http://nsidc.org/data/polar_stereo/data_summaries.html)]([http://nsidc.org/data/polar_stereo/data_summaries.html](http://nsidc.org/data/polar_stereo/data_summaries.html)) and [[http://nsidc.org/data/ease/data_summaries.html](http://nsidc.org/data/ease/data_summaries.html)]([http://nsidc.org/data/ease/data_summaries.html](http://nsidc.org/data/ease/data_summaries.html))) [54, 57, 74, 75, 76, 77, 78, 79, 80, 81, 82].  
The other principal recent open-access source of passive microwave data from the polar regions is the AMSR-E (Advanced Microwave Sounding Radiometer-Earth Observing System), which was operational from the Aqua satellite between 2002 and 2011. This is a slightly enhanced version of the AMSR that was operational from the ADEOS-II/Midori 2 satellite from 2002 to 2003. AMSR-E is a 12-channel instrument (six frequencies between 6.9 and 89 GHz, each measured in two polarizations), with a spatial resolution of around 56 km to 5.4 km. Gridded data products, accessible from NSIDC at [[http://nsidc.org/data/amsre/data_summaries/index.html](http://nsidc.org/data/amsre/data_summaries/index.html)]([http://nsidc.org/data/amsre/data_summaries/index.html](http://nsidc.org/data/amsre/data_summaries/index.html)) include brightness temperatures, land and sea surface temperatures, sea ice concentration and motion, snow water equivalent and depth, soil moisture, rainfall rate and total monthly precipitation [79, 83, 84, 85, 86, 87, 88, 89].  
Other sources of passive microwave data exist, notably through satellites operated by the European Space Agency. These are generally, at the time of writing (early 2014), not as straightforward to access as the data from the instruments described here.