esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Techno-Economic Analysis
tomentosa_ without biochar does not achieve a positive return within the 20-year lifecycle of Scenario 1 or the 14-year lifecycle of Scenario 2; (ii) Scenario 1 is less efficient than Scenario 2, as only the 5.0 and 10% biochar treatments showed positive revenue starting from the 8 th and 12 th years (or the 2 nd and 3 rd harvests), respectively (Figure 6). However, only 10% biochar application rate reaches the point of breaking-even within the 20-year lifecycle; (iii) valorising post-phytoremediation _P. tomentosa_ biomass via Scenario 2 allows to determine the optimal biochar application rate to be 5% since its pay-off period is only 1 year later than the 10% biochar application rate (Figure 7). In addition, valorisation of _P. tomentosa_ biomass grown in 2.5, 5.0, and 10% biochar-amended DS through the biomass-biogas-electricity value chain will generate total revenues of EUR 26,556 ha\({}^{-1}\), EUR 49,610 ha\({}^{-1}\), and EUR 85,869 ha\({}^{-1}\), respectively, over the 14-year lifecycle (Figure 7). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Operation**} & \multirow{2}{*}{**Labour**} & \multirow{2}{*}{**Diesel**} & \multirow{2}{*}{**Materials**} & \multirow{2}{*}{**Total Inputs**} \\ \cline{3-4}  & & **EUR ha\({}^{-1}\)** & & & & **EUR ha\({}^{-1}\)** & **EUR ha\({}^{-1}\)** & **EUR 200 M m\({}^{-3}\)** \\ \hline \multirow{2}{*}{Tillage} & Ploughing & 15.9 & 37.0 & \(-\) & 52.9 & 529 \\  & Harrowing & 10.2 & 4.93 & \(-\) & 15.2 & 152 \\ \hline \multirow{3}{*}{Soil amending} & Ctrl & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\  & Biochar 2.5\% & 186 & \(-\) & 749 & 935 & 9347 \\  & Biochar 5.0\% & 186 & \(-\) & 1497 & 1683 & 16,832 \\  & Biochar 10\% & 186 & \(-\) & 2994 & 3180 & 31,802 \\ \hline \multicolumn{2}{c}{Planting} & 186 & \(-\) & 8160 & 8346 & 83,462 \\ \hline \multicolumn{2}{c}{Weed control} & 23.8 & 33.3 & 74.5 & 132 & 1316 \\ \hline \multicolumn{2}{c}{Harvest} & \(-\) & \(-\) & \(-\) & 600 & 6000 \\ \hline \multicolumn{2}{c}{Total investments} & & & & \\ \hline \multirow{3}{*}{1 st year} & Ctrl & 236 & 75.2 & 8235 & 8546 & 85,458 \\  & Biochar 2.5\% & 422 & 75.2 & 8983 & 9481 & 94,805 \\  & Biochar 5.0\% & 422 & 75.2 & 9732 & 10,229 & 102,290 \\  & Biochar 10\% & 422 & 75.2 & 11,229 & 11,726 & 117,260 \\ \hline \multicolumn{2}{c}{2 nd and following years} & 23.8 & 33.3 & 74.5 & 732 & 7316 \\ \hline \end{tabular}
\end{table} 
Table 3: The economic costs of valorising _P. tomentosa_ biomass via the “biomass–biogas–electricity” value chain (Scenario 2).
## 3 Results and Discussion
### Precipitation of Silica  
The synthesis method of silica powder followed the chemical precipitation method introduced in Refs. [36; 37; 38]. Generally, the precipitation uses the strong acid (H\({}_{2}\)SO\({}_{4}\), HCl) as a precipitant to move the Na\({}_{2}\)SiO\({}_{3}\) reaction in the solution. Alternatively, in this study, the carbonation method [39] was used for the silica precipitation, where CO\({}_{2}\) gas was introduced into the water washing solution from the activated CFA sample containing sodium silicate. The switch of precipitant made the process cheaper, less corrosive, and recoverable in the calcination of Na\({}_{2}\)CO\({}_{3}\). The experimental reaction is shown as follows:  
\[\mathrm{Na_{2}SiO_{3}\cdot nH_{2}O+CO_{2}Na_{2}CO_{3}+SiO_{2}\cdot nH_{2}O\downarrow} \tag{5}\]  
The average yield of precipitated silica products prepared in the experiment was approximately 257.58 g\(\cdot\)kg\({}^{-1}\). Figure 3 shows the TEM image of the silica product obtained. It is found that a certain degree of agglomeration occurred in the silica product with its size smaller than 0.5 \(\upmu\)m, while the size of the single SiO\({}_{2}\) particle can be as small as 40 nm, implying the nanometer level of the precipitated silica.  
The purity analysis of silica products in parallel experiments using the XRF analysis was shown in Table 4, indicating that the average content of SiO\({}_{2}\) in the product reached 93.43%. The trace elements of Cu, Fe, and Mn were within the allowable range, implying that its quality conforms with the Standard HG/T 3061-2009. As a value-added byproduct, the size of nanoscale silica is crucial when extracting rare earth elements. The smaller the particle size of nanoscale silicon dioxide, the larger its specific surface area, the larger its surface energy, the higher its dispersity, and the stronger its adsorption capacity and reinforcing property. The precipitated nano-silica powder in this study was found to be loose in packing, white in color, and good in dispersibility. By weighing the material and energy costs of the whole process, the BET analysis showed that its specific surface area can be achieved by 338.0512 m\({}^{2}\cdot\)g\({}^{-1}\), which belongs to the class A (>191 m\({}^{2}\cdot\)g\({}^{-1}\)) commercial white carbon black single crystal.  
Figure 3: TEM diagram of precipitated silica synthesized by the carbonation method.
# Smart Factory Web--A Blueprint Architecture for Open Marketplaces for Industrial Production
## 3 State of the Art
### Manufacturing as a Service (MaaS)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MaaS Platform** & **URL** & **Range of Manufacturing** & **Main Market** \\ \hline fictiv & www.fictiv.com & 3D printing, CNC machining, injection molding and urethane casting & USA, China \\ \hline Fractory & fractory.com & Sheet metal fabrication & UK \\ \hline Haizol & www.haizol.com/en & Machining, molding, fabrication, casting, stamping & Asia \\ \hline Hubs & www.hubs.com & CNC machining, 3D printing, sheet metal fabrication, injection molding & Worldwide \\ \hline Istawerk & www.instawerk.de & CNC turning and milling & Germany \\ \hline laserhub & laserhub.com & Laser cutting, metal forming, CNC milling & Germany, Austria, France \\ \hline macrofab & macrofab.com & Printed circuit board assembly & North America \\ \hline mipart (of BAM & mipart.com & CNC turning and milling, rapid prototyping, small series & Focus Germany \\ \hline Plethora & www.plethora.com & CNC machining (mainly for military and defence) & USA \\ \hline spanflug & spanflug.de & CNC turning and milling & Focus Germany \\ \hline up2 parts & www.up2 parts.com & Wide range of processes & Germany \\ \hline Weerg & www.weerg.it & 3D printing, CNC machining, finishing & Italy \\ \hline Xometry & www.xometry.com & Wide range of processes & Worldwide, focus USA \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MaaS platform examples and references (accessed on 16 July 2021).
## 2 Communication Framework Description
### Security and Data Integrity  
Although the primary focus of the WBAN system is efficient data transmission, security and data integrity are paramount. The system offers a ground for the discussion of  
Figure 2: The diagram presented illustrates the token-based communication protocol, which is divided into three integral phases: initialization, path discovery, and communication routine. The red striped lines connecting all devices in the diagram represent the ‘wired’ HBC connection within the Body Area Network, showcasing a supplementary channel for transmitting broadcast signals between the devices or dedicated messages for each sensor node.  
sophisticated algorithms and encryption techniques to protect data during transmission as a general example in the On-Body health sector. This includes measures to verify data integrity and authenticity, ensuring that the medical information transmitted is both accurate and secure from unauthorized access.  
The combination of ESP-NOW protocol efficiency, strategic data transmission scheduling, and the HBC method makes the WBAN system a robust and promising framework for medical data communication. It addresses key challenges in digital healthcare, such as real-time monitoring, data security, and efficient resource utilization, making it an indispensable technology in modern healthcare infrastructure.
## 4 Proposed Approach  
Considering the high complexity of Smart City systems and the event-based nature of many WSN monitoring scenarios, this article proposes an innovative mechanism to automatically detect, classify and assign sensing priorities to source nodes according to information posted on predefined social media (_Twitter_). This new approach, TwitterSensing, is described in this section.  
Typically, sensor nodes will be deployed to retrieve scalar or multimedia information from the monitored field. Actually, the information gathered and processed by sensor nodes are transmitted toward one or more sink nodes [4; 20], depending on the network topology, which may be connected to the Internet. In such way, it is natural to expect that information extracted from the Internet can also be inserted into wireless sensor networks, allowing different kinds of optimizations. This realistic scenario is the basis for the proposed approach and it exemplified in Figure 2.  
The main goal of the proposed approach is to assign relevance levels (in the form of priorities indexes) to (source) sensor nodes, according to the detection of events of interest from the social media _Twitter_. To accomplish that goal, the overall solution is divided into two logical blocks: (a) Priority Computing Unit (PCU); and (b) Priority Assignment Unit (PAU), which may be executed centrally or not. Both logical blocks of the proposed approach have well-defined inputs and outputs, leading to the final goal of the solution. In such way, considering that TwitterSensing is a logical box that can be employed in different Smart City scenarios and that it is expected to be executed constantly, the input  
Figure 2: Abstract view of the proposed approach.  
of the system as a whole is a _Twitter_ data flow formatted in the JSON standard [52], while the output of the system are messages containing priority information to be inserted (broadcasted) into one or more WSN. Figure 3 presents the overall operation of TwitterSensing.  
The PCU block accesses a data flow from _Twitter_, detecting events of interest and classifying them according to their significance for the considered sensing application. For that, the TwitterSensing approach is designed to be located outside the target WSN, as an external system, but it must to be connected both to that wireless sensor network (through its gateway/sink) and to the Internet. The same principle is valid if more than one WSN is considered at once, for a defined Smart City scenario and the same _Twitter_ data flow. After computing priority indexes, the PAU block associates sensor nodes to priority indexes and delivers a specialized message to the considered WSN, which should broadcast it to the sensor nodes using any protocol. As the TwitterSensing approach is focused on computing priority indexes to be adopted by source nodes, the way WSN will broadcast priority configuration messages is out of the scope of this article, making the overall solution easier to be adopted by any wireless sensor network technologies.
# Evaluation of Glottal Inverse Filtering Techniques on
## 4 Results
### Vowels
As discussed before, ST-QCP and QCP clearly outperform the three IAIF-based approaches. In Figure 6a these differences become apparent, with ST-QCP being the method that attains a greater degree of similarity to the ground-truth signal. In Figure 6b these differences are more salient because the normalised glottal flow output signals of IAIF-based approaches present more noisy behaviour than their QCP-based counterparts. When it comes to the spectral representations of the glottal flow estimations, Figure 6c shows estimations that denote fewer differences in spectral tilt with regard to the ground-truth signal, while in Figure 6d, these differences are visibly higher, specially for the three IAIF variants.
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 2 Background
### AloT Implementation  
In light of previous considerations, the present SLR looks at studies where the implementation of AIoT systems leverages ML methods. The latter comprise the well-known training and inference two-stage process. The first stage uses data from IoT devices to train the model, while the second stage uses the built ML model to make inferences from the actual input data. Figure 2 shows the two alternatives to implement the AIoT. Figure 1(a) depicts the Cloud-based path, where both model building and model inferencing are performed by the server on the Cloud. On the other side of the IoT network, IoT devices upload data and receive decisions from the Cloud. Figure 1(b) depicts the Edge Cloud computing scenario where IoT devices are connected to the edge servers close to them. Model building is still performed in the Cloud; then, the model is delivered to the edge where inferencing takes place in near real time. Again, the decisions are returned to the IoT devices. It is worth noticing that initially the edge still must send to the Cloud a meaningful quantity of data sensed by the IoT devices for building the model, but while this step usually takes place once, the model is used several times to make inferences. An Edge Cloud collaborative computing platform (called Sophon Edge) for building AIoT applications is described in [20]. Specifically, [PERSON] et al. present, in sequence, the platform computing model, its architecture, and how the evolution of the ML model is supported.
# Advancing in RGB-D Salient Object Detection: A Survey
## 2 Classification and Comparison of Cross-Modal Fusion Strategies  
The evolution of SOD has marked a significant progression from simple feature-based methods to complex deep learning architectures. Initially, SOD predominantly relied on contrast-based techniques that used color and brightness to distinguish the salient regions from their surroundings [4]. [PERSON] et al. [5] utilized basic feature contrasts to create saliency maps. With the advent of machine learning, particularly deep learning, methods evolved to incorporate more advanced features and semantic information, significantly enhancing the accuracy and robustness of detection models [18].  
In RGB-D SOD, the challenge of cross-modal fusion involves effectively combining complementary information from RGB and depth modalities to enhance detection performance. Key issues in this process include the following: (1) balancing the contribution of each modality during fusion, and (2) designing fusion strategies to extract more effective salient features. Research and improvement of these strategies are crucial for advancing the field.
## 4 Application
### Evaluation Metrics  
This paper focuses on using the AUC as the main evaluation metric. The AUC value is obtained from the ROC curve, which is a path constructed using different pairs of specificity and sensitivity.  
**Sensitivity and Specificity.** The notion of sensitivity is interchangeable with recall or true positive rate. In a simple two-class classification problem, the goal is to investigate covariate matrix \(X\) in order to produce an estimated value of \(Y\). From the output of a NN model, the predicted values are always between zero and one, which acts as a probabilistic statement to describe the chance an observation is class one or zero. Given a threshold between zero and one, we can compute sensitivity as:  
\[\text{Sensitivity} = \frac{\text{True Positive}}{\text{Positive}} \tag{19}\] \[= \frac{\text{\# of Images Classified COVID-19 Correctly}}{\text{\# of COVID-19 Images}}\]  
Specificity is also used to create a ROC curve. Given a certain threshold between zero and one, we can compute specificity as:  
\[\text{Specificity} = \frac{\text{True Negative}}{\text{Negative}} \tag{20}\] \[= \frac{\text{\# of Images Classified Non-COVID Correctly}}{\text{\# of Non-COVID Images}}\]  
Given different thresholds, a list of pairs of sensitivity and specificity can be created. The AUC is the area under the path plotted using pairs of sensitivity and specificity that is generated using different thresholds. An example of using Specificity and Sensitivity to draw Receiver Operating Curve (ROC) is presented in Figure 7.  
**Area under the curve (AUC).** The AUC value is a single number derived from a predicted probability by a classification rule and the true label [32]. Given a vector of the true label \(Y\) and a vector of the predicted probability \(\hat{Y}\), we can use statistical package pROC (The package is called Display and Analyze ROC Curves. Source: [[https://github.com/xrobin/pROC](https://github.com/xrobin/pROC)]([https://github.com/xrobin/pROC](https://github.com/xrobin/pROC)) (2 November 2021)) to assist this computation. The package uses automatically generated thresholds to convert \(\hat{Y}\) into binary format. For example, we can use a threshold of \(t_{1}=0.3\) to convert a vector of the predicted probabilities \(\hat{Y}=[0,0.2,0.4,0.8]\) into binary form by writing \(\hat{Y}_{t_{1}}=\mathbb{\mathcal{K}}(\hat{Y}>t_{1})=[0,0,1,1]\). Let us assume the true label to be \(Y=[0,0,1,1]\). Thus, we can compute specificity as one and sensitivity as one. We can then change threshold to a different value to compute another pair of specificity and sensitivity. By tracking all pairs of specificity and sensitivity, we can generate a curve called the ROC curve [32]. The value of the AUC is exactly the area under the ROC. Assume the predicted probability contains some mistakes. In other words, let us assume the predicted probability vector is \(\hat{Y}=[0,0.2,0.2,0.8]\). It is not possible for two observations to have the same prediction probability when they come from different classes. Therefore, there must be a mistake in one of them. This information is reflected using the same procedure (Table 7).
# Ensemble-Based Knowledge Distillation for Video Anomaly Detection
## 3 Proposed Approaches
### Equally Weighted Combination (EWC) Approach  
In the EWC approach, the distillation process is formulated as below to provide the AR-Net student model with an equal-weighted distillation signal from both AR-Net and GCN teacher models (Figure 2):  
\[L_{Total}=(1-\alpha)*L_{GT}+L_{EWC} \tag{1}\]  
\[L_{EWC}=\frac{\alpha}{2}*H(\sigma(Z_{t_{1}};T=\tau),\sigma(Z_{s};T=\tau))+\]  
\[\frac{\alpha}{2}*H(\sigma(Z_{t_{2}};T=\tau),\sigma(Z_{s};T=\tau)) \tag{2}\]  
where \(Z_{t_{1}}\) and \(Z_{t_{2}}\) represent the end output logits of GCN and AR-Net teacher models, respectively, and \(Z_{s}\) represents the end output logit of the AR-Net student model. \(H\) represents the cross entropy function and \(\sigma\) represents the sigmoid function. \(L_{GT}\) represents the specific cross-entropy-based ground-truth loss function used within the context of the AR-Net student model. Finally, \(\alpha\) is the balancing hyperparameter between \(L_{GT}\) and \(L_{EWC}\) and \(T\) is the temperature constant.  
The EWC approach uses equal weight values (\(\frac{\alpha}{2}\)) for merging the two feedback signals from AR-Net and GCN teacher models in the training process. In this way, the student model can use multiple information feedback from different teacher networks and utilize different interpretations of a sample with respect to each teacher model's respective output. Within our first approach, we decided to especially observe how naively distributing the \(\alpha\) parameter value equally affects the student model's performance in our task.  
Compared to the standard response-based knowledge distillation strategy, this formulation allows equally weighted different perspective-based information transfer from multiple different model teacher models to student models by utilizing a combined distillation loss function.
## 2 Materials and Methods
### Mathematical Model Development
In(c) = \(-\)5.10 + 0.4167 \(\times\) A \(-\)1.16 \(\times\) B + 0.9810 \(\times\) C[1] \(-\)0.0698 \(\times\) C[2] \(-\)0.1206 \(\times\) AB \(-\)0.4700 \(\times\) AC[1] \(-\)0.3907 \(\times\) AC[2] + 0.6025 \(\times\) BC[1] \(-\)0.2519 \(\times\) BC[2]  
A Pareto analysis of variance (ANOVA) was performed to analyse the experimental results. The higher F-values of the model (42.66, 5.88, and 48.88 for the measured electrolyte \(T\), pH, and \(c\)) and the associated lower \(p\)-values show the validity of the developed mathematical models (Table 6). Furthermore, all \(p\)-values below 0.0500 indicate that the model terms are significant, while \(p\)-values above 0.1000 indicate that the model terms are insignificant. For temperature, B, C, and BC are significant model terms (AB and AC are not significant); for electrolyte pH, A and AB are significant model terms (B, C, AC, and BC are not significant, which is to be expected since electrolyte pH depends on electrolyte temperature); and for the change in electrolyte c, A, B, C, AC, and BC are significant model terms (AB is not significant). The goodness of fit statistics for \(T\) show that the predicted R\({}^{2}\) (Pre-R\({}^{2}\)) of 0.8639 is in reasonable agreement with the adjusted R\({}^{2}\) (Adj-R\({}^{2}\)) of 0.9518, i.e., the difference is less than 0.2. Adequate precision (Adeq. Pre.) measures the signal-to-noise ratio. A ratio of more than 6 is desirable. The achieved ratio of 19.352 indicates a reasonable signal, which means that the model can be used for navigation in the design space. Similarly, for the change in c, the Pre-R\({}^{2}\) of 0.8070 is in reasonable agreement with the Adj-R\({}^{2}\) of 0.9578 (the difference is less than 0.2). The Adeq. Pre. ratio of 18.9734 indicates a reasonable signal. However, the negative Pre-R\({}^{2}\) of \(-\)0.1692 for electrolyte pH suggests that the overall mean may predict the response better than the current model. The higher-order models may also provide better prediction in some cases. The Adeq. Pre. of 8.1390 is a reasonable signal, indicating that the model can be used to navigate the design space.