esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experience Transfer Approach for the Initial Data of Iterative Learning Control
## 3 ILC of Recombining Transform
### Simulation Test  
For the approach described above, this paper uses two systems, as shown in Equations (8) and (9), to track the desired trajectory, as shown in Equation (10), for the simulation test.  
\[\mathrm{G}_{a}(s)=\frac{1}{s^{2}+2s+1}, \tag{8}\]  
\[\mathrm{G}_{b}(s)=\frac{1}{0.1s^{2}+1.1s+1}, \tag{9}\]  
\[y(t)=0.006t^{2}(20-t)\quad\ t\in[0,20] \tag{10}\]  
Take the example of an original system \(G_{a}\), as shown in Equation (8). Suppose the inertia time constant changes due to the replacement of a unit in the system, and the transfer function of system changes as shown in Equation (9). Via the relation of unit step response, the inertia time constant \(T\) of two systems could be measured. Moreover, the unit pulse response of two systems can be figured via mathematic simulation. The control experience data obtained via iterative learning in system \(G_{a}\) are inherited and transformed according to the methods in Equations (5)-(7). Then the experience data are transferred as the data of initial iterative learning data in \(G_{b}\).  
The simulation results are shown in Table 1 and Figure 4. The top figure shows the iterative learning process of the original system on the condition of zero initial data. The middle figure shows the iterative learning process of the new system when the initial data are obtained. The dashed line in the bottom figure shows the error convergence process based on ILC of the experience transform approach proposed in this paper. The solid line is the error convergence process of ILC without experience transform. According to the comparison results in the figure, the approach based on ILC of experience transform is better than that based on zero initial data in terms of initial deviation and iteration turns required to achieve the same control accuracy, e.g., 0.1 drawn by a dotted line in the figure of RMS error, which are 9 and 20, respectively. The learning efficiency improves by 55%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Initial Error** & **Convergence Iteration** \\ \hline Original system & 4.7 & 20 \\ New system & 1.0 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effect of Recombined Experience Transform.
## 3 Design of Fault Detection and Estimation Observer  
Traditional fault detection methods are easily affected by the time-delay variation of the model in the actual scene [31], resulting in the increase of false alarm rate. Based on this, this section first proposes a robust fault detection method, and then designs an adaptive fault observer to estimate the total fault of the actuator. It is different from the observation of each sub fault in literature [9], which greatly improves the operation speed and saves memory space.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Results Analysis
#### 4.2.3 Sample-Level Analysis  
Figure 7 presents the sample-level PSNR and SSIM distributions across the evaluation set. The combined PSNR plot shows ModelU consistently achieving higher PSNR values across samples, while the SSIM plot confirms its structural fidelity. These distributions underscore ModelU's robustness and consistency compared to baseline-trained models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **PSNR (dB)** & **SSIM** \\ \hline ModelS & 14.8382 & 0.7186 \\ ModelU & 17.6018 & 0.7697 \\ ModelDA & 14.6777 & 0.7125 \\ ModelDC & 14.5749 & 0.7007 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dehazing performance comparison on the O-HAZY evaluation set.  
Figure 7: Sample-level performance: (**a**) combined PSNR across models and (**b**) combined SSIM across models.
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 4 Experiment
### Experimental Results and Analysis  
#### 4.2.1 Ablation Experiment  
To comprehensively assess the effectiveness and superiority of the WETS-Net improvement method proposed in this paper, a series of comparative experiments were designed in this section. The experiments were based on the U-Net as the benchmark model, with the encoder part initialized using a pre-trained VGG19 network. Validation was conducted on a dataset collected and annotated by ourselves. Through these experiments, we aimed to validate the effectiveness of the EAEM and MIFM modules proposed in this paper. Inparticular, we examined whether the EAEM module could effectively extract edge texture features to enhance the precise segmentation of images with weak edges.  
Ablation experiments were conducted separately on two datasets, with experimental parameters set as shown in Table 1. The results of cell classification are presented in Figure 8, where the legend numbers correspond to the experimental numbers in Table 1. As seen in Figure 8, evaluation metrics, including _mIOU_, Recall, and Precision, demonstrate the segmentation effectiveness. Analysis of the results reveals that the classification framework without EAEM, MIFM, and loss functions (Model 1) achieved only a _mIOU_ of 71.23%. In contrast, the WETS-Net model (Model 8) achieved the highest _mIOU_ value of 84.23% within the complete framework.  
To ascertain the contribution of EAEM, a comparison between Model 1 and Model 2, as well as between Model 5 and Model 8, shows an increase in accuracy ranging from 5.92% to 6.81%. This improvement is attributed to EAEM effectively extracting edge feature information from shallow regions, significantly enhancing the model's segmentation accuracy. In comparison to the baseline U-Net, WETS-Net achieved an accuracy of 96.13%, which is 9.82% higher than U-Net (86.31%). These results demonstrate the effectiveness of the method proposed in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **EAEM** & **MIFM** & **Loss Function** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) \\
2 & \(\surd\) & \(\times\) & \(\times\) \\
3 & \(\times\) & \(\surd\) & \(\times\) \\
4 & \(\times\) & \(\times\) & \(\surd\) \\
5 & \(\times\) & \(\surd\) & \(\surd\) \\
6 & \(\surd\) & \(\surd\) & \(\times\) \\
7 & \(\surd\) & \(\times\) & \(\surd\) \\
8 & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular} Note: \(\surd\) indicates that the module is used; \(\times\) indicates that the module is not used.  
\end{table}
Table 1: Parameter settings for ablation experiments.  
Figure 8: Results of ablation experiments with different parameter settings.
# The Effect of Seed Removal and Extraction Time on the Phenolic Profile of Plavac Mali Wine
## 2 Materials and Methods
### Fermentation Trials  
Regarding the vintages of 2014 and 2015, 75 kg of Plavac mali grapes were manually harvested and transported to the winery near the vineyard. The basic chemical composition of the Plavac mali 2014 was the following: initial sugar 225 g/L, total acidity as tartaric acid 6.55 g/L, and pH 3.15. As for the Plavac mali 2015 it was: initial sugar 254 g/L, total acidity as tartaric acid 6.10 g/L, and pH 3.25. The noted differences between the investigated years is the result of extremely high temperatures in the year 2015 resulting in higher sugar content and lower acidity,a well-known pattern also presented in the work by [21]. The total grape amount was divided into five different treatments (5 \(\times\) 15 kg) and each amount was destemmed, crushed separately, and distributed evenly into 20 L stainless steel fermenters. In the two treatments, approximately 85-90% of the seeds (between 480 and 500 g/15 kg of grapes) were removed. Immediately after crushing, the mash was treated with 5 mg/L of potassium metabisulfite (AEB SPA Brescia, Italy). Each mash was inoculated with 3 mg/L of rehydrated dry yeast _Saccharomyces cerevisiae_ (Lalvin ICV D254(r), Lallemand Inc., Canada). The yeast strain was added at approximately 1 \(\times\) 10\({}^{7}\) cells/mL and fermentations were carried out at 20 \({}^{\circ}\)C according to the manufacturer's instructions. The cell concentrations (haemocytometry) and viability (methylene blue staining) were determined under a light microscope. The addition of 10 mg/L of yeast supplement (Fermaid E(r), Lallemand Inc., Canada) followed on the second and the fifth day of fermentation. Each treatment was conducted in three replicates. The course of fermentation was monitored by analyzing the sugar consumption rate and it was considered complete when the residual sugar concentrations were under 4.0 g/L. In all variants, fermentation started 24 h after inoculation and lasted between 10-12 days. In that period, kinetic fermentation determined by the decomposition of sugars showed no marked difference. The control treatment implied standard seven day long maceration while the others were different prolonged maceration treatments as follows:  
* control treatment, 7 day maceration
* 21 day maceration
* 22 day maceration with seed removal
* 24 day maceration with seed removal  
During the maceration period, the cap was punched down manually three times a day. Once maceration was finished, the wines were pressed and placed in the 10 L-glass containers until the end of alcoholic fermentation where needed (reducing sugars to less than 4 g/L). The final wines were bottled in 750 mL glass bottles with screw caps and transported to the laboratory of the Department of Viticulture and Enology and the Faculty of Agriculture of the University of Zagreb for further chemical and instrumental analysis. The basic physicochemical analysis (ethanol content, tittatable acidity, volatile acidity, and pH) showed no marked differences between treatments in both the experimental years.
## 3 CSCL-WVE-UDI: Method for Identifying Unknown IoT Devices
### Algorithm for Generating Permutation Positive-Negative Sample Pairs (PNSPs)
#### 3.3.2 Generation of PNSPs  
The conventional approach for generating positive-negative sample pairs is to randomly select a pair of samples from the device-fingerprint sample set. Positive sample pairs comprise fingerprints from the same IoT device for training the model to capture the consistent features of the same device. Negative sample pairs comprise fingerprints from different IoT devices for training the model to be sensitive to the differences between different devices. Although the random selection strategy is simple and easy to implement, it may cause the repeated selection of some device-fingerprint samples, while others are potentially overlooked. In IoT device identification, such randomness cannot fully reflect the characteristics of the dataset, thus rendering it difficult to capture key traffic features.  
To address the limitations of the random selection strategy and to fully utilize each IoT device-fingerprint sample while clearly defining the relationships between sample pairs, we propose a method for generating PNSPs, as shown in Figure 4. The proposed method is based on the complete data samples generated in a previous study [4]. First, for the grayscale image fingerprint samples of each IoT device, nonredundant positive sample pairs are created to ensure that effective pairings are formed between samples of the same IoT device. During this process, each fingerprint sample can be paired with itself to enrich the number of positive sample pairs. Subsequently, each fingerprint sample of the current IoT device is paired with the samples of all other IoT devices to construct the complete set of negative sample pairs for the current device. When generating negative sample pairs, redundant pairings are avoided through meticulous attention, thus ensuring that previously paired negative samples are not rematched. For example, if \(t_{11}\) is the first fingerprint sample of the \(i\)-type IoT device and \(t_{j2}\) is the second fingerprint sample of the \(j\)-type IoT device, then the pairing of \(\left(t_{11},t_{j2}\right)\) with \(\left(t_{j2},t_{11}\right)\) essentially represents the same negative relationship, regardless of order. Therefore, when constructing negative sample pairs for the \(j\)-type IoT device, redundant sample pairs are eliminated to remove superfluous samples, thus improving the efficiency and quality of sample-pair generation.
## 2 Collisions and the Coefficient of Restitution (\(CoR\), \(e\))
### Methods for Determining the Coefficient of Restitution (CoR, e)
#### 2.2.3 Impulse-Based Method  
The present study adopts an impulse-based technique [26]. The time-resolved force signal is integrated to obtain loading and unloading impulses; this avoids errors that occur when irregular wheat kernels rebound at oblique angles and is therefore better suited to non-spherical grains.  
For a single kernel impacting an infinitely rigid plate, [PERSON]'s second law gives  
\[F=ma=\frac{d(mv)}{dt}=\frac{dp}{dt} \tag{6}\]  
Integrating over the contact duration \(t_{0}\)\(\rightarrow\)\(t_{2}\) yields the total impulse  
\[S=\int_{t_{0}}^{t_{2}}dp=\int_{t_{0}}^{t_{2}}F(t)dt \tag{7}\]  
The area under the force-time curve is separated into a loading impulse \(S_{1}\) and an unloading impulse \(S_{2}\); their ratio gives \(CoR\) (see Section 2.2). Figure 4 illustrates the impulse zones analysed in this study.  
Compared with the height-based approach, the impulse method avoids errors arising from irregular rebound trajectories and is therefore better suited to non-spherical wheat kernels.  
According to Figure 4, the total impulse of the impact can be divided into two phases. Phase 1 covers the rising part of the force curve, where local deformation spreads to the  
Figure 4: Impulse force zones [7].  
whole kernel. Phase 2 begins at the peak force and ends when the grain loses contact, while the deformation is released [27].  
When the collision is modelled as a two-phase process, the total impulse \(S\) equals the sum of the partial impulses \(S_{1}\) and \(S_{2}\):  
\[S=S_{1}+S_{2}. \tag{8}\]  
The impulse contributions for each phase can be expressed as  
\[S_{2} =\int_{t_{0}}^{t_{1}}F(t)\ dt \tag{9}\] \[S_{1} =\int_{t_{1}}^{t_{2}}F(t)\ dt \tag{10}\]  
The coefficient of restitution is then  
\[CoR=e=\frac{S_{2}}{S_{1}}=\frac{\int_{t_{0}}^{t_{1}}F(t)\ dt}{\int_{t_{1}}^{t_{ 2}}F(t)\ dt}(0\leq e\leq 1). \tag{11}\]  
Typical limits are as follows:  
* \(e=1\)--perfectly elastic: no kinetic-energy loss, full rebound;
* \(e=0\)--perfectly inelastic: the bodies stick together, and all kinetic energy is dissipated;
* \(0<e<1\)--partially elastic: some energy is lost during impact.  
This procedure is known as the impulse-based (force-impulse) method for evaluating the CoR.  
Although an impact can, to a first approximation, be represented by \(F(t)=S\delta(t)\), biological contacts last tens of microseconds. Consequently, the complete force-time curve was recorded and integrated so that both the impulse \(S\) and the deformation dynamics (peak force, loading and unloading rates) could be evaluated [4].  
A review of the literature shows only a limited number of studies on the coefficient of restitution (\(CoR\)) of wheat grain determined by force-based methods [27; 28].
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 5 Zone Geographic Forwarding Rules
### Extension of the Message Header  
Nowadays, the localization is part of the smart devices, which facilitates its exploitation [31]. Many applications are based on location information such as transport, coordination in battlefield, smart home, etc. [32]. Our algorithm assumes that every node \(u\) is aware of its position \((X_{u},Y_{u})\) inferred from the location service, while the positions of other nodes are acquired and updated periodically  
Figure 5: Default forwarding rules of OLSR.  
by the topology maintenance mechanism. Our solution acts locally on the surrounding nodes and globally on the entire network. Two fields should be added to the message header to share the location information. Figure 6 shows the new OLSR message header format with the two coordinates \(X\) and \(Y\).  
When a node \(u\) is ready to send an OLSR message, a HELLO or TC message, it contacts the location service to obtain its position \((X_{u},Y_{u})\), as shown in Figure 7. Then, it introduces it to the message header. Upon receiving an OLSR message, a node maintains a table of positions of all its surrounding nodes and positions of all MPRs in the network. This geographic information is used to put in place our algorithm.
## 6 Implementation
### Motivating Example  
We develop the CompoSecReasoner logic in Event Calculus (EC) [68], and deploy it for the evaluation of an actual IoT ecosystem. The application implements an ambient intelligence setting for the administration of a smart home system.  
In the current smart home setting, most of the utilized devices reply to any access request and do not offer any secure access control techniques. Thus, cyber-attacks are becoming feasible as these devices are connected to Internet either directly or indirectly through the user's smart phone [69]. To improve security, we deploy the secure Policy-Based Access Control (PBAC) mechanism which is proposed in [70]. At the device/node layer, this framework enforces access control based on pre-defined security configurations. The policies are securely maintained at the framework's back-end framework, which is installed on a laptop.  
Furthermore, we use the lightweight cryptographic library (ULCL) [71] to develop a service for secure storage. This service encrypts information at the device-end to safeguard data from disclosure in case where the equipment gets compromised. The standardized cipher AES is utilized for the main cryptographic operations.  
The embedded system is comprised by a BeagleBone and a BeagleBoard devices [72] (nodes with constraint computational/communication features), and a laptop (power node administrates the whole setting). The BeagleBone/BeagleBoard nodes capture environmental factors (like humidity and temperature) and manage electrical devices, such as fridge, air-conditioning, room-lights, etc. The BeagleBones/BeagleBoard audit the associated electrical equipment based on the enforced access strategies of PBAC. The laptop runs the administration framework of smart home management, gathers data from the underlying resources, and displays them to the user. The offered functionality of each device is developed as a service. The nodes interact wirelessly with WiFi and IPsec. All data are encrypted and several security levels are provided, ranging from low-energy consumption settings for \"green\" operation to high security ones.  
CompoSecReasoner runs on the laptop with the PBAC mechanism. CompoSecReasoner is implemented as the reasoning behavior of the home's SPD agent, which can also interact with other smart building agents laying in the overlay layer. The management framework forms the middleware layer which administrates the local ambient ecosystem. The devices are composed into networks. The BeagleBone/BeagleBoard and the laptop are the nodes. As an application scenario, we define a smart-home for assisting living conditions, while for the attacker's model we consider individual hackers with moderate cyber-security knowledge and low attack/computational capabilities (i.e., 1 to 5 PCs).
## 4 Discussion
### Hydraulic Energy and Seismic Energy  
To establish the relationship between input energy and output energy, the concept of hydraulic energy is adopted. The input and output energy can be described based on the conservation of energy. Therefore, the energy budget can be expressed using the following balanced formulation:  
\[E_{H}+\Delta W=E_{f}+E_{R}+E_{d}+l \tag{6}\]  
where \(E_{H}\) is the injected hydraulic energy, defined as the product of the injection fluid pressure (\(P\) Pa/s) and the injection rate (\(Q\) m\({}^{3}\)/s) (Equation (7)) [29]. The injected volume and pressure change with time. Thus, the injection energy is calculated by integrating \(E_{H}\) over the injection interval (\(t_{1}-t_{2}\)). \(\Delta W\) is the potential elastic energy. \(E_{f}\) is the frictional energy dissipated on the fault plane and is supposed to be equal to \(\Delta W\)[30]. \(E_{r}\) is radiated energy, which is also regarded as seismic energy. It is hard to monitor the radiated energy because of the limited knowledge of the radiation pattern and limited frequency band [31]. Thus this energy is calculated by establishing the relationship [32] with the seismic moment (Equation (8)). \(E_{d}\) is deformation energy; \(l\) is energy loss, ignored in this experiment.  
\[E_{H}=\int\limits_{t_{1}}^{t_{2}}PQdt \tag{7}\]  
\[\log_{10}(E_{R})=1.5M_{w}+4.8 \tag{8}\]  
According to Equation (6), each seismic event has a certain hydraulic energy (\(E_{H}\)) and radiated energy (\(E_{r}\)). The moment magnitude (\(M_{w}\)) can be calculated according to Equation (8). Therefore, the seismic energy (\(M_{0}\)) can be calculated according to Equation (4). Above this, the relationship between seismic energy and hydraulic energy is established. In Figure 9, the black line is the ratio between seismic energy and hydraulic energy. The 11 black lines represent the ratio of 100% to 0.00000001%, correspondingly. The triangles and circles in Figure 9 are data collected from several field experiments and laboratory  
Figure 8: The overpressure and understress in the experiment.  
experiments. Therefore, Figure 9 shows the relationship between seismic energy and hydraulic energy. The triangle symbols represent the triggered seismicity and the circle symbols represent the induced seismicity. The total input hydraulic energy is 2.52 J. The deformation energy, aiming to enlarge the fracture aperture, ranges from 88% to 93% of the injection energy. Fluid could flow along the aperture more smoothly after several slips. The ratio of seismic energy and hydraulic energy is defined as seismic efficiencies. For triggered seismicity, the seismic efficiency ranges from 0.1% to 10%, referencing the field tests and laboratory tests. But for the induced seismicity, this parameter ranges from \(1\times 10^{-7}\%\) to \(1\times 10^{-3}\%\). According to Equation (7), we can deduce that the tectonic stress of a fracture and the fluid flow inside the fracture determines the released seismic energy. Therefore, seismic energy could be predicted using hydraulic energy.
## 3 Results and Discussion
### LC-QToF-MS Analysis  
Analysis of phenolic compounds has identified polyphenols in the control sample (B = dark ale 6% ABV without carob), the carob beers (CB1, CB2), and carob syrup (C), as shown in Table 4. However, the carob beers were enriched with 10 more phenolic compounds, which were all ingredients of carob syrup, the initial raw material used to produce carob beer. Among phenolic acids, LC-QToF-MS analysis revealed the presence of hydroxybenzoic acids, such as gallic acid, 4-hydroxybenzoic acid, gentisic acid, protocatechu acid, syringic acid, vanillic acid, and derivatives of hydroxycinamic acid, such as p-coumaric acid, ferulic acid, abscisic acid, and _trans_-cinnamic acid. Phenolic acids are the aromatic secondary metabolites imparting color, flavor, astringency, and harshness, which contribute to the typical organoleptic characteristics of foods and are comprised of one-third of the constituents among phenolic compounds [47]. Gallic acid, which was found only in carob beers, is a strong antioxidant and free radical scavenger that can protect biological cells, tissues, and organs from damage caused by oxidative stress. There are diverse reports with regard to gallic acid for medicinal uses, such as antibacterial, anti-allergy, anti-inflammatory, and antioxidant stress [48; 49]. Other recent studies have identified gallic acid to be the most abundant phenolic compound in both ripe and unripe carob pulp extracts, and flavonoids such as myricetin, quercetin, and naringenin were detected [50]. Moreover, gallic acid was found to be the major component of carob syrup, which was probably co-extracted with carbohydrates during the preparation of carob syrup, while other polyphenols were detected in minor amounts [30; 51]. In addition, other phenolic acids identified in the carob beers were gentisic acid, which is