esa-sceva/satcom-chunk-collection huggingface dataset:
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 3 Metal-Organic Frameworks
### MOFs as SPME Coatings for Environmental Applications
#### 3.3.3 Extraction of Organophosphorus and Organochlorine Pesticides
The coating was prepared following a two-stage deposition process: after silvo- and hydrothermal synthesis of cellulose nanocrystals functionalized with MOF-74, the material was pyrolyzed to obtain the Ni@NiO/PCs, which was deposited onto SS wires by means of a silicone sealant. The obtained coating was able to adsorb the chlorobenzenes more effectively compared to both the pristine materials and commercial coatings. The target analytes could diffuse through the surface of the material due to the presence of meso- and micropores with a size of 1.26 nm, interacting with the active sites of the coating via dipolar (thanks to the Ni-O groups) and \(\pi\)-\(\pi\) and hydrophobic interactions. The coating was tested for the DI-SPME-GC-MS analysis of eight model chlorobenzenes in tap and surface water: despite the LOQs ranging from 0.017 to 0.118 ng/L, no chlorobenzene was detected in tap water, whereas 1,3,5-trichloro-benzene (15.7, 28.9 ng/L), 1,2,3-trichloro-benzene (14.4, 10.9 ng/L), and 1,2,4,5-tetrachloro-benzene (1.4 ng/L) were detected in river water samples.
## 5 Case Study
Indeed, Figure 3 suggests that varying the terminal discharging voltage \(U_{b}^{dis}\) allows the discharging process to be controlled. More explicitly, a particular feature of these results that emerges from the numerical simulations of Equation (23) is the observation that a higher terminal voltage (i.e., 12.9 V) results in a slower discharging rate \(\alpha_{2}=1.15\), which can be advantageous when a longer battery life is desired. This variation allows the \(SOC\) to be adjusted to the desired level (i.e., 20%). In addition, setting the sensitivity coefficient (\(B\)) affects how the voltage reacts to variations in \(SOC\). A lower \(B\) value (see, for example, \(B=3.395\times 10^{-2}\)) results in lower sensitivity, giving a more stable voltage during discharging. This can be advantageous for applications requiring a steady power supply. However, a high \(\gamma_{2}\) leads to excessive discharging, as observed when \(\alpha_{2}=2.85\). This can significantly reduce battery life and damage the battery. In addition, a higher \(\alpha_{2}\) (for example, 2.85) leads to rapid discharging, resulting in over-discharging to 9% SOC after 4 h. These results are summarized in Table 4.  
Figure 2: (Color online.) The charging profile of a lithium-ion battery as a function of time for different values of charging rate \(\alpha_{1}\) and for \(V_{n}=12.8\) V, \(U_{ch}=14.6\) V, \(\beta_{ch}=80\%\), and \(\mu_{0}=20\%\).
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Performance Analysis
#### 3.3.3 The Effect of the Optimization Algorithms  
An appropriate optimization algorithm is crucial for the prediction results, which can further improve the prediction accuracy. The optimization algorithm has the effects of damping out the oscillations in the gradient descent. In the field of deep learning, optimization algorithms such as Adagrad, Adadelta, RMSprop and Adam are often used for gradient descent. This paper studied the effect of these algorithms on the prediction results. In this set of comparative experiments, the size of the TW was set to 30, and the depth of the deep CNN was seven for the FD001 dataset.  
Table 6 presents that Adam had the lowest score with the best performance, while the other algorithms spent more computational costs with higher score. The RMSE of Adam and Adagrad were almost the same, and RMSprop had the largest standard deviation. The learning rate of 0.001 was appropriate to Adam, Adagrad and RMSprop. The reduction of the learning rate will cause the training time to increase, and the larger value of the learning rate will cause the result to oscillate. However, the learning rate of 0.001 was too slow for Adadelta algorithm to converge, so the learning rate of the Adadelta was set to 0.01. According to the time spent in the training process, Adam algorithm ran on the GPU for the shortest time, with the average running time of 72.56s. While the learning rate was increased, the Adadelta algorithm took the longest time on the GPU and cost 621.15s. Adam combines the advantages of Adagrad and RMSprop, having the ability to handle sparse gradients and deal with non-stationary targets. Therefore, from the results of the computational cost and the evaluation function, Adam was the optimal gradient descent algorithm.
## 3 Methodology
### Accuracy Assessment  
Accuracy assessment is an important part in ISA estimation. The impervious/pervious surface areas that were classified by object-based SVM model were evaluated by the parameters of overall accuracy and kappa coefficient calculated from an error matrix. In this study, a total of 500 samples at 30 m resolution were randomly selected for accuracy assessment. At the same time, the results of ISA fraction estimated by object-based SVM-LSMA model and LSMA-only model were estimated by the same group of random samples. The values of samples were assigned by the synthetic impervious surfaces based on the integration of population density maps WorldPop (100 m), night-time light images (1 km) and Global Human Settlement Layers (GHSL: 250 m) [44; 45; 46; 47]. The Pearson correlation coefficient (R\({}^{2}\)) was calculated and used to compare the differences between pixel-wise and object-based ISA fraction related to WorldPop maps, night-time light images and GHSL maps. The root-mean-square error (RMSE) was then used to measure the differences between the estimated ISA fraction and the reference ISA fraction provided by GHSL maps.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 2 Related Work
### Human-Computer Interaction
#### 2.2.1 Fits' Law  
Fitts' Law can be used to understand why human behaviour demonstrates certain distinct patterns, both on computer screens and otherwise. This law states that the time required to point at an object using some form of a pointer (either on a computer screen or otherwise) is a function of the distance from the desired object to be pointed at and the size of that object. Leading on from that, it suggests that if the object is both small and far away, then it will require a longer time for someone to locate and point to the object than if it is closer and larger, as can be seen in Figure 2[30], where the graph shows how the object size interacts with the Usability Index of that object. The equation to define Fitts' Law is:  
\[T=A+Blog_{2}\bigg{(}2\frac{D}{W}\bigg{)} \tag{1}\]  
where:  
* \(T\) is the Time required to point to the object;
* \(A\) and \(B\) are empirically determined regression coefficients;
* \(D\) is the distance from the pointer to the object;
* \(W\) is the width of the object text following an equation, which need not be a new paragraph.  
Although Fitts' Law was initially established in 1954, it has developed great significance in recent times due to the desire to improve human-computer interaction and the ergonomics of the computer screen. Businesses want to promote certain aspects more than others, such as more expensive items, or make certain buttons clearer and more defined, e.g., the 'Continue' button rather than the 'Back' one. Using Fitts' principles, it has become clearer to graphical designers that command buttons should be larger and distinguishable from other features on the page. Additionally, when smaller objects are far apart, it takes longer for users to select than larger ones that are closer together, so a pie chart menu is preferable to a drop-down menu or a horizontal/vertical task bar etc. All of these features have been taken into account in recent graphical design, with the basis of Fitts' Law [31; 32].  
Figure 2: Fitts’ Law Graph–Object Size vs Usability Index [31].
## 4 Results  
### Benchmark Comparison  
Figure 3 presents a comparison of mean average precision (mAP) achieved by different methods on the Open-MIC dataset. The x-axis lists the methods, while the y-axis indicates the mAP values. The graph includes benchmark methods and our proposed methods and highlights our best model. The benchmark methods serve as a reference point, showing the progression in performance over the years, including the Baseline [31], PaSST [32], EAsT-KD + PaSST [33], and DyMN-L [34]. Our methods include various configurations of combining spectrogram features and attention mechanisms. Specifically, the \"Single Log-Mel\" approach, \"Log-Mel CST Combined Spectrogram\", and \"Log-Mel CST with Attention Layer\" configurations explore the impact of different spectrogram features on model performance. Additionally, we experimented with magnifying the spectrogram features to different extents: \"Magnified 1/4 Size\", \"Magnified 1/2 Size\", \"Magnified 3/4 Size\", and \"Magnified Full Size\".  
Figure 2: Overview of the neural network architecture used for musical instrument classification. The input is an example sample of combined spectrogram features (CST) with all components scaled to the same size and normalized to a range between 0 and 1, where brighter colors indicate values closer to 1. The model includes three residual blocks (Res-Block-1, Res-Block-2, Res-Block-3), shaded in blue and dark blue represent operations such as convolution, batch normalization, and activation functions. Each residual block is followed by a coordinate attention mechanism (Early Attention, Mid Attention, Late Attention) to enhance feature representation. After the three residual blocks and attention layers, the final fully connected (FC) layer performs the classification to generate the output.  
Among these, the \"Magnified 1/4 Size\" model achieved a mAP of 0.8445, demonstrating a performance close to the leading benchmark methods. This model's success highlights the importance of carefully scaling the spectrogram features and incorporating attention mechanisms to enhance the model's focus on the most informative parts of the input data. The magenta marker and horizontal magenta line on the graph emphasize the noteworthy performance of this model, illustrating the potential effectiveness of our approach in musical instrument recognition tasks.
## 4 Selective Image Encryption  
In general, the process of data encryption and decryption is very costly in time and computing power. Frequently, it is not possible to apply data security in some applications, especially when there are severe constraints in processing power and energy supply. For wireless sensor networks, energy efficiency leads most of the optimization efforts, usually turning security into an optimally and lowly desired issue. Additionally, this scenario may be even more stringent for security assurance when visual sensors are deployed. Nevertheless, many applications may require secure data transmissions, potentially defining a complex scenario.  
In traditional process of data encryption, all information is encrypted. However, this may not be necessary when dealing with image snapshots. Partial or selective encryption is an optimized method that provides a reasonable level of secure data transmission with reduced overhead [45, 46, 47, 48]. In short, this principle exploits characteristics of media coding algorithms to provide secrecy while reducing computational complexity [49]. In other words, selective encryption proposes the use of data encryption mechanisms for only part of the compressed data. It then creates encryption systems much more efficient than traditional encryption approaches [50]. Actually, it may be the most suitable cryptography approach for wireless image sensor networks.  
In selective encryption, the basic idea is to encode only a set of blocks of sensed images. This is possible because some compression algorithms are based on data decomposing, generating parts of the compressed data with varying relevance. In fact, in those relevant parts is concentrated more significant information from the original data [20]. Figure 2 presents a general diagram comparing traditional and selective encryption.  
In wireless image sensor networks, there are different approaches that can be employed for selective encryption. Coding techniques may be used as a reference, where blocks of data with different  
Figure 1: Taxonomy of key management in WSN [43]. LKHW, logical key hierarchy for wireless sensor networks; LEAP, Localized Encryption and Authentication Protocol.  
relevance are considered. On the other hand, segments of original images with different importance for the application, as the edges and human faces in still images, may also be considered to guide encryption [51]. Among such approaches, two coding algorithms are well suited for selective encryption: quadtree coding and wavelet coding. Quadtree-based algorithms are simpler and outperform JPEG at low bit rates, while wavelet-based algorithms have good compression performance [20]. The next subsections survey recent works related to these coding algorithms.
## 3 Parametric Methods  
Parametric methods require prior knowledge of the density distribution of the data being analysed [28]. An anomaly is assumed when a data point that has a small probability of occurring is observed. Parametric methods can use either univariate or multivariate distribution functions with the latter being more suitable for IWSN applications. The former only deals with one random variable at a time so each variable would have to be modelled independently using its own distribution function while the latter allows vectors of random variables to be modelled using the same distribution function. Parametric methods are suitable when the nodes and the distribution function will be static over the network lifetime.
## 6 Conclusions  
This paper compares the two most popular supervised ML algorithms (SVM and ANN) for lithological mapping of different rock types using RGB and SWIR multispectral remote sensing data of Sentinel-2. Training samples were collected using previously available geological maps and unsupervised techniques were applied to Sentinel 2 data for data annotation. A map of nine different lithological units and water was generated with higher accuracy and detail than the previously reported conventional band ratios/PCA map of the Jatta Gypsum applied to ASTER satellite RS data. The results show that both the algorithms map these lithologies with >95% OA and a kappa coefficient of 0.95. The accuracies also exceeded the recently reported ML lithological mapping studies; therefore, SVM with the lower computational cost is the best algorithm for this case, learning the features from limited RGB, NIR, and SWIR bands of Sentinel-2 multispectral data. In the case of ANN, the transformation of GEE data to Google Colab took 8 hrs + 3 hrs in training and, therefore, is a time-consuming process. The potential of Sentinel-2 data has been reinforced with the strength of the SVM classifier and better annotation, which can be extended to differentiate similar lithologies beyond the region of interest with high accuracy and lower financial and computational cost. In the era of Industry 4.0, the work can be further extended to lineaments mapping combined with subsurface geophysical features to develop an automated 3D geological model with AI-based spatial estimation techniques.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12231247/s1](https://www.mdpi.com/article/10.3390/app12231247/s1)]([https://www.mdpi.com/article/10.3390/app12231247/s1](https://www.mdpi.com/article/10.3390/app12231247/s1)), Figure S1: Geol Map of Kohat Plateau.  
Conceptualization, K.M., S.B. and F.E.; methodology, K.M., M.F.A.K. and F.E.; software, M.F.A.K., F.E., S.U.D. and K.M.; validation, M.H., K.M. and F.E.; formal analysis, K.M. and F.E.; investigation, S.U.D. and F.E.; resources, K.M. and S.B.; data curation, M.F.A.K., S.U.D. and F.E.; writing--original draft preparation, F.E. and K.M.; writing--review and editing, K.M., S.B. and M.H.; visualization, F.E.; supervision, K.M. and S.B.; project administration, K.M.; funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Higher Education Commission, Pakistan, grant number \"National Centre of AI\" and the APC was funded by the authors.  
Data Availability StatementData supporting reported results can be found at the Google Earth Engine website.  
The authors declare no conflict of interest.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 1 Introduction
### Wireless Communication for Linear-Based Topology Applications
Zigbee is based on the IEEE 802.15 standard, used in short-range up to 100 m and low data rate of 250 kbps. Adding more routers can increase the range [13]. * Wi-Fi  
It is designed to be used in the local area, such as homes, offices, and hospitals. It ranges up to 250 m. The data rate is high, around 600 Mbps. On the other hand, it needs a large amount of energy. Wi-Fi uses a star topology, where all communications go through the access point. This standard is easy to use and very common in IoT applications [14]. * Sigfox  
Sigfox is a wide-area low-power technology. It uses an ultra-narrow band. The Sigfox structure is similar to the cellular network, but it consumes less power and transmits small data packets [15]. The topology is a single-hop star, the area is divided into cells, and each cell has a base station. Sigfox provides low-noise communication and a wide range of up to 30 km. The data rate is around 100 bps. It is suitable for low data rate applications. Streetlights are an example of the Sigfox application [15; 16]. * DASH 7  
DASH7 Alliance supports mid-range communication where the range reaches 5 km. It requires low power and provides a 20-kbps data rate. The DASH7 network is similar to the star LoRa network; however, it commonly uses a tree topology [16]. It provides good results in applications that need low latency. DASH7 is used, for example, in the location-based application and tracking cars [17]. * LoRa  
LoRa is a low-power, long-range topology. LoRa networks consist of the node, gateway, network server, and application server. The node senses the data and sends them to the gateway, then to the network server where the data packets are processed. After that, the data are sent to the application [18]. LoRa-WAN is responsible for communication protocols. The data rate reaches up to 50 kbps. The technology provides a range of 2-15 km [19]. LoRa has many uses, such as environmental applications, healthcare, oil and gas pipelines, and smart farms [20]. Table 1 shows a comparison between the technologies. The pipeline network requirements are low power, wide range, and low cost. The most suitable technologies for pipeline monitoring are Zigbee, Sigfox, and LoRa. The Zigbee range can be extended using more routers, yet even so, it does not cover hundreds of kilometers. Sigfox can address the wide-range requirement; however, it needs subscriptions. In contrast, LoRa covers large ranges, consumes low energy, and does not require a subscription, which means there is no cost to use this technology, as it is open software.
## 5 Research Challenges and Future Directions
### Contention and Channel Access  
Contention and channel access are key issues in the design of passive wake-up radios. These issues are more prevalent when the main and wake-up radios need to share channels, and because they have dissimilar communication range, an asymmetric network is formed. This creates the need to develop protocols which are more responsive to channel variations. One way to resolve this is the use of cognitive radios with passive wake-up radios. The cognitive radios are recently being used in sensor networks as shown in [126; 10]. They are not susceptible to collision and interference as against the regular radios, which adopt fixed channel allocation. The cognitive radios are able to achieve this because they can adaptively select and use the vacant spectrums in both licensed and unlicensed band. Using cognitive radio with passive wake-up radios will improve the overall performance of the system, especially in dense networks by reducing the energy usage and eliminating packet loss and collusion. Another solution to this challenge is the use of a Wi-Fi unit alongside wake-up radio so that transmission channels are selected dynamically as proposed in [127].
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### A Mixed Graph
#### 5.14.2 Ant Colony Graph  
_Ant colony graph_ or _ants' moving disjunctive graph_[157] utilize the concept of disjunctive graphs to implement the ant colony optimization algorithm (detailed in Section 4.2.2) in shop scheduling problems.  
In addition to all the same characteristics of a disjunctive graph, Ant colony graph include ants crawling from node to node across edges, leaving pheromones which increase the likelihood of taking that path again [157; 158; 159]. The ants resemble operations such that operations look for a machine to be processed on as quickly as possible just as ants look for food as quickly as possible when foraging [50]. The incorporation of dashed edges can be seen as an alternative path for searching ants [157]. The feasible solution is the directed path which visits every node, completing every operation [160].
## 4 Conclusions  
We have demonstrated a method for the temporal shaping of ultra short pulse bursts by loss modulation using a Pockels cell. The obtained output burst shapes showed a very good agreement with the targeted shapes.  
Furthermore, the amplification of shaped bursts within a diode-pumped amplifier based on cryogenically-cooled Yb:CaF\({}_{2}\) was investigated. It was shown that the amplification will disturb the predefined shape, as the amplification within the amplifier will change due to a change of \(n\). The latter one will exponentially approach an equilibrium value, which is different for different input intensities. The characteristic decay time for this process was theoretically determined to be in the range from 204 \(\upmu\)s to 239 \(\upmu\)s for our test amplifier. This is in good agreement with the empirically found value of 240 \(\upmu\)s. Based on this result, we employed a simplified model to predict the required input pulse shape for a desired output pulse shape. Applying this method to sample bursts in the real amplifier gave a very good agreement between calculation and measurement.  
From the obtained results, the employed shaping method has proven to be a promising approach, both for the generation of application-specific temporal burst shapes and for the compensation of any temporal modulations within the burst shape introduced by an amplifier system.  
The extension of the presented shaping method to more than one amplifier is straight forward and mainly requires iterating the calculation of the burst shape. The main work will be a careful characterization of each amplifier stage. This is used to determine its specific parameters, in particular \(\tau_{eff}\). An increased accuracy for the final burst shape could be achieved by combining the presented method with a closed loop system, slightly varying the input into the system depending on the output burst shape.