esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Soil Characterization  
Table 1 shows the particle size distribution of the different soil samples, with reference to the 2 mm, 250 \(\upmu\)m, and 150 \(\upmu\)m sieves.  
Sample A1 is mostly made of coarse particles, above 2 mm, which correspond to 88% weight of the overall sample, whereas the fraction below 250 \(\upmu\)m represents only 2.4% weight. In this case, the particle size fraction below 150 \(\upmu\)m was so low that it was not possible to collect an amount sufficient for carrying out the characterization and the bioaccessibility tests. Sample A2 is slightly finer, with 69% weight of the particles below 2 mm, whereas the particle size fractions below 250 \(\upmu\)m and 150 \(\upmu\)m were slightly higher than in A1, i.e., 11% and 4.3%, respectively. Samples S1 and S2, instead, had similar particle size distribution, with around 60% of the particles above 2 mm and the finer fractions representing around 4% (d \(<\) 250 \(\upmu\)m) and 2% (d \(<\) 150 \(\upmu\)m).  
Figure 1 reports for each soil sample collected in the firing range (i.e., A1, A2, S1, and S2) the metal and metalloid contents determined applying USEPA Method 3051A on the fractions with particle diameters lower than 2 mm, 250 \(\upmu\)m, and 150 \(\upmu\)m. The contents shown in Figure 1 are the mean values obtained from each triplicate. As mentioned above,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **A1** & **A2** & **S1** & **S2** \\ \hline  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline d \(>\) 2 mm & 87.8 & 31.2 & 57.7 & 60.7 \\ d \(<\) 2 mm & 12.2 & 68.6 & 42.3 & 39.3 \\ d \(<\) 250 \(\upmu\)m & 2.4 & 11 & 4.2 & 3.8 \\ d \(<\) 150 \(\upmu\)m & n.d. & 4.3 & 2.2 & 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size fractions of the soil samples collected in the firing range.  
for sample A1, the amount of the soil fraction d < 150 \(\upmu\)m was insufficient for performing the analysis.  
There were no substantial differences found between the concentrations measured in the three different soil fractions. Lead was the only inorganic that regularly presented a higher concentration in the finest fractions d < 250 \(\upmu\)m and d < 150 \(\upmu\)m than in fraction d < 2 mm. Indeed, the finer soil particles usually present a greater specific surface than the coarser fraction, and hence may sorb a higher amount of inorganic per unit weight [46]. Since this effect was observed on lead only, the enrichment of Pb observed in the finer fraction of the soil samples was linked to the firing activity carried out in the shooting range and to the bullet residues that can end up in soils after use [35]. This result is in agreement with what has also been observed in previous studies that analyzed the Pb contamination in firing ranges (e.g., [36]).
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 4 MAC/NET with Updated Genetic Algorithm--A Cross-Layer Approach (MNUG-CLA)  
The Greedy Perimeter Stateless Routing (GPSR) method is an efficient and flexible route discovery strategy for VANET that has been devised and implemented [23; 24; 25]. In fact, the GPSR method relies on the terminals' geographical positions to route the data, and it presumes that all devices have the same level of a zone. The GPSR system allowed a network to incorporate its location in header-wise packets to sends properly. Every mobile device transmits beacons, including location and identity, hence, the available nodes can recognize where it is. The networks can develop their orientation process by exchanging this control information on a regular basis. One of the benefits of such control information is that it requires less information about the immediate neighbors, which means that bandwidth is conserved. As per the packing density, GPSR uses two options for packet forwarding, such as Greedy Forwarding and Perimeter Forwarding.  
There are two major issues when using the standard GPSR methodology in urban settings, whereas greedy methods select the network segment closest to the target node when adopting the next link in a significant way. The delay in information transmission due to heavy traffic or poor density may arise. When the distant node in the specified link section is chosen as the next node, the issue of minimizing the objective function may arise.  
For our research, we focused on GPSR protocol. The major drawbacks of this protocol are discussed in the previous section. To overcome those drawbacks, we introduced a novel approach, namely MAC/NET with Updated Genetic Algorithm, a Cross-Layer Approach (MNUG-CLA). The system architecture of the proposed MNUG-CLA approach is shown in Figure 1. This approach mainly consists of three sections. They are (1) network layer model, which is the neighbor discovery process, (2) MAC layer model, which is multi-channel MAC for the prioritization process and (3) updated genetic algorithm for optimal path finding. These sections are detailed below. The system architecture is described in Figure 1 and the input output model with the system legacy is explained in Figure 2.  
Figure 2: Input/output with system legacy model.
## 2 Methods
### Control-Oriented Model  
As further described in Section 2.5, model predictive controllers require a predictive model to compute a control sequence. Since the model is used to solve an optimization problem in real-time, this control-oriented model must have a favorable structure. To do so, the best choice from a computational perspective is to use a linear model of the plant and a quadratic cost function. A simplified model was therefore derived for ACC which links the control variable \(u\), i.e., the acceleration command, to the vehicle's motion [35]:  
\[\tau\frac{d}{dt}\dot{s}(t)+\dot{s}(t)=u(t). \tag{4}\]  
Here, \(\tau\) is a constant that models the inertia between the controller command and its actuation, and its value is set to 0.5; \(\dot{s}\) and its derivatives \(\dot{s},\ddot{s}\) refer to the vehicle position, velocity, and acceleration; and \(u\) is the control variable.  
From this, the system model was cast in state-space form as follows:  
\[x_{k+1}=Ax_{k}+Bu_{k}+Gd_{k} \tag{5}\]  
where \(x\) is the vector of state variables, \(u\) are the controls, and \(d\) are the external disturbances.  
In our problem, the state variables are as follows:  
\[x=\begin{bmatrix}e_{\text{d}}\\ v\\ a\end{bmatrix} \tag{6}\]  
where \(v\) is the velocity of the ego vehicle and \(a\) is the acceleration of the ego vehicle. \(e_{\text{d}}\) is the distance error, i.e., the difference between the desired inter-vehicular distance and its desired value, as defined by the spacing policy (described in Section 2.3). The external disturbance is the velocity of the lead vehicle \(v_{\text{lead}}\), which is needed to evaluate \(e_{\text{d}}\).  
Therefore, the state-space representation for our model is as follows:  
\[x_{k+1}=\begin{bmatrix}1&-T_{s}&-Tt_{h}\\ 0&1&T_{s}\\ T_{s}&0&0\end{bmatrix}x_{k}+\begin{bmatrix}0\\ 0\\ T_{s}/\tau\end{bmatrix}u_{k}+\begin{bmatrix}T_{s}\\ 0\\ 0\end{bmatrix}d_{k}. \tag{7}\]  
where \(T_{s}\) is the sampling time.
## 5 Discussion
### Limitations  
While this study compares SAM 2 and SAM's performance when segmenting medical images and introduces a novel approach to segmenting medical image volumes, several limitations should be acknowledged. Firstly, although the study primarily focused on a single medical image segmentation task, namely lung segmentation, we have included an exploratory analysis of spleen segmentation in MRI data. This preliminary evaluation provides valuable initial insights into the applicability of the proposed methods beyond the lung and across different imaging modalities. Although it does not constitute a comprehensive assessment, it demonstrates that the results of SAM 2 improve upon those of SAM. The generalizability of the findings to a broader range of organs and imaging modalities still needs to be thoroughly tested. Future work should explore applying the proposed methods to additional segmentation tasks to determine their broader applicability. Secondly, the study employed only one prompt configuration, consisting of a bounding box with two positive points and one negative point. This configuration was chosen for its simplicity and effectiveness in lung segmentation, as demonstrated in our previous work [9], and was retained unchanged in the exploratory spleen MRI analysis. However, the performance and adaptability of other prompt configurations were not explored, potentially limiting the method's robustness and flexibility in more complex segmentation scenarios. Finally, the datasets used in this study contained COVID-19 lesions, which introduces a degree of specificity to the results. COVID-19 lesions may introduce unique challenges or biases that do not occur in other lung pathologies. Future studies should validate the approach on a broader range of lung conditions to ensure its applicability across diverse clinical scenarios. These limitations suggest clear lines for future research to enhance the generalizability and robustness of the findings presented in this study.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 2 Proposed Methodology
### Prognostic Approach: Hybrid-Driven Remaining Useful Life Prediction
#### 2.2.1 Knowledge-Based Degradation Model  
As one of the most popularly used lifetime degradation distribution models, the Weibull distribution has been widely studied in many real-world applications [43; 44; 45]. It is a versatile distribution with two parameter \(\eta\) and \(\beta\) that could fit the reasonable distribution in reliabilityengineering.  
The PDF (probability density function) of Weibull distribution is defined as:  
\[f(t)=-\frac{\beta}{\eta}\left(\frac{t}{\eta}\right)^{\beta-1}e^{-\left(\frac {t}{\eta}\right)^{\beta}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**RS Features**} & \multicolumn{2}{c}{**Energy Ratio Features**} \\ \hline \multicolumn{2}{c}{**Time-Domain**} & \multicolumn{2}{c}{**Frequency-Domain**} & \multicolumn{2}{c}{**Time-Frequency-Domain**} \\ \hline F1 & RS of 11 classical & F2 & RS of [0, 12.8 kHz] & F7 & Energy ratio of (3, 0) \\  & time-domain & F3 & RS of [0, 3.2 kHz] & F8 & Energy ratio of (3, 1) \\  & features & F4 & RS of [3.2 kHz, 6.4 kHz] & F9 & Energy ratio of (3, 2) \\  & & F5 & RS of [6.4 kHz, 9.6 kHz] & F10 & Energy ratio of (3, 3) \\  & & F6 & RS of [9.6 kHz, 12.8 kHz] & F11 & Energy ratio of (3, 4) \\  & & & F12 & Energy ratio of (3, 5) \\  & & & F13 & Energy ratio of (3, 6) \\  & & & F14 & Energy ratio of (3, 7) \\ \hline \end{tabular}
\end{table}
Table 1: Detailed features description.  
The Weibull CDF (cumulative density function) is defined as:  
\[F(t)=1-e^{-(\frac{t}{\eta})^{\beta}} \tag{5}\]  
Based on Equations (4) and (5), the reliability function (also regarded as, survival function) can be defined as below:  
\[R(t)=e^{-(\frac{t}{\eta})^{\beta}} \tag{6}\]  
Equation (6) is the predefined degradation model, which is the probability to survive of a component with \(t\) denoting usage time of the component and \(\beta\) and \(\eta\) are the Weibull parameters. The shape parameter \(\beta\) is generally considered \(\beta\geq 1\), and it describes the aging process of components over time. The case of \(\beta=1\) corresponds to process or Weibull degradation where the component never gets old. If such a component survived an arbitrarily long time \(t\) its remaining expected lifetime is determined solely by \(\eta\) (independent of \(t\)).
## 2 Materials and Methods
### Methods
#### 2.3.3 Comparison Map Profile Method (CMP)  
Spatial validation of the selected optimal scheme was conducted by comparing the ML-based GPP estimates with historical RS products. For comparative analysis, the GPP estimates spanning from 2009 to 2015 were spatially resampled to maintain consistency with the resolution of historical products. The Comparison Map Profile (CMP) method [48; 49] was used to evaluate spatial consistency. This method quantifies spatial similarity by calculating the absolute difference (\(D\)) and the cross-correlation coefficient (\(CC\)) for each pixel at multiple spatial scales. The \(D\) value represents the absolute difference, while the CC value indicates spatial similarity. For multi-spatial scale analysis, a moving window process was adopted, with the window size increasing from scale 1 (3 \(\times\) 3 pixels) to scale 20 (41 \(\times\) 41 pixels). The results from these 20 scales were averaged to produce the CMP map. Lower \(D\) values and higher \(CC\) values indicate greater similarity between the generated GPP data and historical products, thereby validating the selected optimal scheme.  
In each pixel of each moving window, \(D\) is calculated as follows [50]:  
\[D=abs(\overline{x}-\overline{y}) \tag{2}\]  
where \(\overline{x}\) and \(\overline{y}\) are the average GPP of the model estimates and the historical products within the moving window, respectively.  
\[CC=\frac{1}{N^{2}}\sum_{i=1}^{N}\sum_{j=1}^{N}\frac{\left(x_{ij}-\overline{x }\right)\times\left(y_{ij}-\overline{y}\right)}{\sigma_{x}\times\sigma_{y}} \tag{3}\]  
where \(x_{ij}\) and \(y_{ij}\) are the pixel GPP values from the model results and historical products, respectively. \(i\) and \(j\) denote the coordinates within the moving window. \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of \(x\) and \(y\) within the moving window. \(N\) is the number of pixels within each moving window, corresponding to the window size [51].
## 3 Results and Discussions
### 0.34 THz Mixers' Performances
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Frequency Range (GHz)** & **Noise Temperature (K)** & **Conversion Loss (dB)** \\ \hline
[37] & 290–310 & 2000–2600 (SSB) & 9–10 (SSB) \\
[38] & 295–345 & 900 (DSB) (minimum) & 9–13 (SSB) \\
[39] & 320–360 & 900 (DSB) & 9 (SSB) \\ This work & 325–352 & 900–1500 (DSB) & 6–9 (DSB) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of published Schottky SHM performance in a similar frequency range.
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution
#### 3.2.2 Creating Tables in AWS DynamoDB  
An AWS DynamoDB table belongs to a NoSQL database, allowing for a flexible table structure based on specific needs, using a key-value data model. To organize and access data, a table must contain the following specific mandatory and optional elements:  
* **Partition key:** Also known as the hash key, this must be defined when creating a table and is used to distribute data across different partitions. DynamoDB uses this key to calculate a hash that determines the partition (or node) where a record will be stored. This distribution mechanism is essential for horizontal scalability, allowing the database to grow and handle large volumes of data and heavy traffic by adding more nodes, ensuring a uniform distribution of data across the cluster and allowing for efficient data access through rapid localization of the partition where the sought-after data is located. All queries on a table must specify the partition key for an efficient search. * **Sort key:** This key allows storing multiple elements with the same partition key, with records subsequently sorted by this sort key. This is useful for modeling relationships and efficiently accessing groups of records. * **Global secondary index:** This is an index with a partition key and, optionally, a sort key, defined from the table's attributes. GSIs allow querying any attribute or combination of attributes in the table, not just those defined as the partition key or sort key in the main table schema. Thus, each GSI is essentially a parallel table to the main one, used to allow more flexible queries. When creating a table, it is essential to first analyze the primary attributes and their data types to determine the partition key and, if needed, the sort key from the outset. To create tables in the Amazon-managed DynamoDB database, we used the service's graphical interface. For the presentation of each table used in the system, we will highlight the table structure and its role. The explanations also present the exact names of the attributes used in the tables, along with their data type in the form of attributeName: initialDataType, where S-string, N-number, and B-boolean. Table 3 presents the created and used tables of our solution. For the system testing phase, all tables were populated using either the AWS service console or Node.js functions to streamline the data entry process. Within the system, these tables are accessed via HTTP requests or WebSocket-defined routes, ensuring that all operations--such as writing, deleting, adding, and reading--are executed exclusively through the implemented functionalities. This approach enhances data security by restricting direct access to the AWS console and simplifies user interactions by eliminating the need for manual table management.
# Water Quality Changes in Cement-Lined Water Pipe Networks
## 3 Results
### Analysis of Test Results for Multiple Day Stagnation of Water in The Setup  
The first series assumed stagnation of water in the setup until stabilisation of its properties. In this series, water samples were collected once a week for 8 consecutive weeks. Changes in the values of the most significant water quality indicators over the experimental period presented in Table 3 provides values of the basic water stability indices in subsequent weeks of stagnation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & & \multicolumn{4}{c}{**Sample**} & \multicolumn{2}{c}{**Small Risk of**} \\  & **No 1** & **No 2** & **No 3** & **No 4** & **No 5** & **Corrosion, When** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} [PERSON] index (I\({}_{\text{L}}\)) \\ Ryznar stability index (I\({}_{\text{R}}\)) \\ AWWA index (I\({}_{\text{AWWA}}\)) \\ \end{tabular} } & \(-\)0.04 & 0.08 & \(-\)0.40 & 0.23 & \(-\)0.09 & 0 \\  & 7.14 & 7.02 & 7.70 & 7.07 & 7.29 & 6.25 \(\div\) 6.75 \\  & 12.1 & 12.2 & 11.9 & 12.5 & 12.2 & \(>\)12 \\  & 14.0 & 14.0 & 14.0  
Obtained results show that over the stagnation period, water reacted with the cement coating for the first 5 weeks. Over this period, a gradual increase in pH was observed (from 7.06 to 8.11), which was accompanied by carbon dioxide disappearance and increased water turbidity, chloride content and in particular sulphate and dry residue content. During this period, alkalinity and water hardness was reported to increase to a lesser extent. Values of the Langelier Saturation Index (LSI) indicated that water introduced into the setup gradually lost its corrosive properties and the calcium-carbonate balance was observed. The above-mentioned changes in water quality reflected gradual saturation of the water with cement coating components and an increasing water capability to precipitate calcium carbonate deposits. Analyses of water stagnant in the setup over the subsequent period of the research confirmed this trend, which is especially emphasized by the reduced value of water turbidity, calcium concentration and dry residue content. It should be emphasized that water quality over the entire experimental period corresponded to the quality of water for human consumption, whereas values of the AWWA index showed that water could be safely used in contact with cement materials.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 4 Results
### Keypoint-Matching Comparison vs. SIFT/ORB  
This evaluation compares the abilities of traditional feature-based methods (SIFT and ORB) and our proposed GeoTemplateKPNet to consistently provide a sufficient number of correspondences required for geometric alignment in the synthetic template test set. The metric used is the percentage of image pairs with sufficient matches for transformation,as defined in Section 3.7.2. Table 2 presents the overall average success rates across all the template classes for different minimum required inlier (\(N_{min}\)) thresholds.  
Table 2 presents the overall average success rates in consistently providing sufficient matches in the synthetic template dataset. For traditional methods, ORB+RANSAC achieves high success rates at lower \(N_{min}\) thresholds (3 and 4), while SIFT+RANSAC performs better at \(N_{min}=5\). Our GeoTemplateKPNet method shows varying success rates, depending on the configuration. GeoTemplateKPNet (ResNet-50, K = 3) achieves a 63.36% rate in providing at least three usable keypoints, while GeoTemplateKPNet (VGG-16, K = 4) shows the highest rate (68.24%) among our configurations for providing at least four usable keypoints. VGG-16 with K = 5 has the highest rate (36.84%) for providing at least five usable keypoints.  
Comparing GeoTemplateKPNet configurations to those of traditional methods at comparable thresholds, SIFT and ORB generally show higher success rates for \(N_{min}=3\) and 4 than our method does in providing at least that many usable keypoints. However, this metric primarily indicates the model's ability to produce the minimum count of keypoints across the dataset consistently. It does not directly reflect the quality of the resulting alignment or the model's ability to handle various data transformations, which are evaluated using the ARE and qualitative results in subsequent sections. As observed experimentally, ORB+RANSAC often produces visually poor and inaccurate transformations, despite reporting a sufficient number of inliers, suggesting that the matches deemed as inliers are not geometrically accurate, which supports higher ARE values. SIFT+RANSAC, although better than ORB in terms of alignment quality (when successful), still struggles to consistently provide sufficient matches compared to ORB or the number of heatmaps predicted using our model.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
In TL, the weights of those pre-trained models are kept and subsequently applied and partially updated to the new data [41]. Then, the knowledge of the original DL model is transferred to a new but similar classification problem, even though it does not have the same feature space or distribution [42]. The TL technique has been effectively used in the agricultural domain for disease detection in grapevines [43], tomatoes [44], wheat [45], and apple leaf [46]. After training, the models can be saved in a format that is readable by edge devices that can connect to a web services platform with the required sensors (cameras, complete with other sensors etc.) and be deployed for online validation, essentially giving farmers a way to precisely monitor the health status of their crops. This interconnection between the sensors in the field and the farmer's computer or cell phone is called the Internet of Things (IoT) and has been successfully implemented in agriculture for smart irrigation [47; 48], soil management [49; 50], and pesticide management [51; 52], among others [53]. IoT is, in other words, the internet-based interconnection of embedded computing devices, which allows them to send and receive data. The problem of crop's health status monitoring is usually tackled by employing computer vision techniques with IoT [54], as in, for example, [55; 56; 57]. The this research aims to develop an integrated IoT system through validating the performance of an edge device, like the Raspberry Pi (RPi), based on DL algorithms, for the online detection of five grapevines diseases, based on symptoms in the leaves, in field conditions. The validation of the integrated system was performed in the Chatzivariti winery vineyard, where only Esca was identified. The system's results are geotagged using an onboard GPS sensor, then are reported through Amazon Web Services (AWS) cloud platform on a web-based service in order to provide an online disease alert.