esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Waste Comparison  
In this section, we present the comparison among the three waste functions regarding quality and time. The waste functions detect the amount of wasted space of any object. For the computational test, the algorithm uses three waste functions. We evaluate the performance and efficiency to determine the best waste function using the [PERSON] and the Wilcoxon tests. The waste functions are \(w^{1}_{ij}\), \(w^{2}_{ij}\), and \(w^{3}_{ij}\) (see Equations (5)-(7)).  
Figure 7 describes the quality result obtained from the different waste area functions. This comparison indicates that waste function \(w^{1}_{ij}\) obtains lower heights than \(w^{2}_{ij}\), and \(w^{3}_{ij}\). However, \(w^{3}_{ij}\) obtains lower heights than \(w^{2}_{ij}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Autor** & **Total** & \(n\) \\ \hline
2 lcvrp & [PERSON] [30] & 180 & 15–255 \\ [PERSON]/cgcut & [PERSON] \& [PERSON] [31] & 3 & 10–70 \\ Brk/N1-13 & [PERSON] [32] & 13 & 10–500 \\ [PERSON]/beng & [PERSON] [33] & 10 & 20–200 \\ Htu/ht & [PERSON] and [PERSON] [34] & 114 & 16–28 \\ Hop & [PERSON] and [PERSON] [35] & 350 & 17–199 \\ [PERSON]/gcut-ngcut & [PERSON] [36; 37] & 25 & 10–22 \\ Class 1–4 & [PERSON] and [PERSON] [19] & 200 & 20–100 \\ Class 5–10 & [PERSON] and [PERSON] [38] & 300 & 20–100 \\
50 cx–15,000 cx & [PERSON] and [PERSON] [39] & 6 & 50–15,000 \\ P1 & [PERSON] [40] & 1 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Used instances.  
A Friedman test showed that \(w^{1}_{ij}\) is the best-ranked waste function, followed by \(w^{3}_{ij}\), and \(w^{2}_{ij}\) in last place. Additionally, it shows that there is a statistically significant difference among them. Finally, the Wilcoxon test indicates that waste function \(w^{1}_{ij}\) has statistically better quality than \(w^{3}_{ij}\) and \(w^{2}_{ij}\).  
Regarding efficiency, most of the tests showed a computing time close to one second, which is not suitable for graphics. Here, the waste function \(w^{3}_{ij}\) showed the lowest times. A Friedman test confirmed that the best ranked waste function is \(w^{3}_{ij}\), followed by \(w^{1}_{ij}\), and \(w^{2}_{ij}\) in the last place. Finally, the Wilcoxon test indicated that there is no statistical difference between \(w^{1}_{ij}\) and \(w^{3}_{ij}\). However, there is a significant difference between \(w^{1}_{ij}\) and \(w^{2}_{ij}\). Therefore, \(w^{1}_{ij}\) produces solutions with low computational times and the highest quality. On the other hand, \(w^{2}_{ij}\) produces solutions with high computational times and low quality. Therefore, our proposed algorithm uses the waste function \(w^{1}_{ij}\) for the rest of the computational test.
# A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices
## 3 Proposed Crypto-Steganography Approach
Algorithm 2 presents the ransomware extraction process that takes as an input the stego HEVC frame (_stego_frame_), user-defined secret key (_user_SK_), and the generated hop number (_hop_num_). The extraction process implements the same steps described in the embedding process but in a reverse manner. Initially, all the required credentials (\(IV\) and \(AES\_SK\)) are generated to be further used in the AES decryption algorithm. Then, the encrypted ransomware data is extracted from the cover video frame. Finally, the accumulated encrypted ransomware data is decrypted using AES algorithm to get the original ransomware data.
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Determination of Emulsion Optical Properties  
Since many applications require that emulsions are optically clear (e.g., eye drop formulations), transparency is an important parameter considered at the product development stage. Optical properties of emulsions, such as transparency, opacity, turbidity, and color, depend on the degree of absorption and scattering of light passing through the sample [41]. The changes in optical properties can be visually observed or quantitatively measured using colorimeters, refracometers, and UV-vis spectrophotometers.  
Colorimeters quantify parameters, such as brightness, hue, and saturation, to measure the color and appearance of emulsions, while spectrophotometers measure the absorption and reflection of light by emulsions across a wide range of wavelengths. Spectrophotometers record the emulsion absorbance spectra by detecting the amount of light absorbed while passing through a sample. Refractometers are used to measure the refractive index of emulsions. During measurement, a drop of emulsion is placed onto a slide and introduced into a refracometer. The refractive index describes how fast light travels through the analyzed sample and is expressed as the ratio of the speed of light in a vacuum and the phase velocity of light in the assessed medium. The calculated value is compared to the refractive index of the reference medium, e.g., distilled water, which is considered the most transmittable liquid. If the refractive index of the emulsion is equal to or close to that of water (1.333), the emulsion is considered transparent [67; 113].  
Analysis of optical properties is non-destructive and can provide real-time monitoring of emulsion stability, making it a valuable tool in quality control and product development. As the appearance of emulsions is strongly influenced by droplet concentration, size, and distribution, measuring the optical properties of emulsions over time can provide valuable information on destabilization processes that may occur in the system [114]. For example, as the emulsion undergoes coalescence, the droplet size distribution changes, leading to alterations in the overall appearance of the emulsion. As larger droplets form due to coalescence, the emulsion becomes less transparent, more opaque, and turbid. Additionally, the color intensity may increase due to the merging of the droplets [77]. When flocculation occurs [115], the formation of flocs or droplet aggregates leads to increased light scattering and reduced transparency of the emulsion. This increase in light scattering contributes to the higher turbidity of the emulsion, making it appear cloudy or hazy. The color intensity of the emulsion may also change due to a different optical density or color of the flocs compared to the individual droplets. Similar changes can be observed when emulsion separates through Ostwald ripening. As Ostwald ripening progresses, the average droplet size in the emulsion increases, leading to changes in light scattering and absorption and a decrease in transparency.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 1 Introduction
To alleviate this burden, a novel approach was proposed [5], employing a trusted third party called a cryptographic server (CS). In this protocol, the CS receives data from clients and handles the encryption on their behalf, thereby enhancing client convenience. Nonetheless, this protocol comes with several limitations:  
* The protocol lacks data deduplication functionality, resulting in an inefficient usage of cloud storage space. * The protocol requires trust in the CS as it exposes the client's plaintext to the CS. * The protocol does not consider specifically updating ownership information concerning stored data in cloud storage. In the context of cloud storage, data ownership refers to the right of a client to access data stored in the cloud. Clients acquire ownership by uploading data to cloud storage. Furthermore, after storing their data in the cloud, clients can request modifications or deletions. In cases where a client requests data modification or deletion, the cloud storage can remove the client's information from the group that owns the data. Particularly, the reason for removing the client from the data owner group when a modification request occurs is because cloud storage recognizes the original and modified data as distinct entities. Therefore, to prevent the client from accessing the data before modification, cloud storage needs to delete the client's information from the group, necessitating a change in the client's ownership status. Data deduplication requires changing ownership information, which is called a dynamic ownership update. Such changes typically occur in two scenarios: firstly, when clients delete or modify their stored data, leading to a revocation of ownership; and, secondly, when ownership is acquired by uploading data already existing in cloud storage. Dynamic ownership updates in deduplication guarantee that revoked clients cannot access the data and newly added clients are prevented from gaining access to old data [6]. These ownership updates can occur frequently in cloud services, necessitating effective ownership management.
## 6 Experiment  
### Experimental Datasets  
To verify the effectiveness of our proposed spectrogram and dual-channel complementary model, we tested it on three widely used datasets: the Berlin dataset of German emotional speech (EMO-DB) [54], the Surrey Audio-Visual Expressed Emotion dataset (SAVEE) [55], and the Ryerson audiovisual dataset of emotional speech and song (RAVDESS) [56].  
The EMO-DB database was created by researchers at the University of Berlin and is an emotional database in Germany. This database contains 535 utterances produced by  
Figure 8: The structure of the SSAE.  
10 professional actors, including 5 males and 5 females), who produced 49, 58, 43, 38, 55, 35, 61, 69, 56, and 71 utterances respectively. These data are made up of seven different emotions: happiness, neutral, anger, boredom, disgust, sadness, and fear. The Sampling rate is 48 kHz (compressed to 16 kHz), and 16-bit quantization is adopted. It belongs to acted, and discrete type, access type is open, and modalities is audio.  
The SAVEE database records the data of four native English-speaking males ([PERSON], [PERSON], [PERSON], [PERSON]), graduate students and researchers from Surrey University, aged from 27 to 31. It contains a total of 480 utterances, each of which produces 120 utterances. The average length of each utterance is 4 s with a sampling rate of 44.1 kHz. This database consists of seven different emotions: happiness, sadness, anger, disgust, fear, neutral and surprise. It belongs to acted, and discrete type, access type is free, and modalities is audio/visual.  
The RAVDESS database is an English emotional dataset, which consists of 1440 utterances produced by 24 actors, including 12 males and 12 females, who expressed eight different emotions: happiness, sadness, surprise, anger, calmness, disgust, fearfulness, and neutral. The database is widely used for emotional song and speech recognition, with a sampling rate of 48 kHz. It belongs to evoked and discrete types; access type is free and modalities are audio/visual.  
In the above databases, emotion is expressed in different languages and cultures; for example, pronunciation characteristics of German and English differ; however, they have some common characteristics. Low-arousal emotions, such as sadness and disgust, have low energy and the speed of speech is generally slow. High-arousal emotions, such as happiness and anger, have high energy and the emotions are expressed strongly. For all datasets, the Mel spectrogram and IMel spectrogram are always complimentary.
# Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks
## 3 Numerical Study
### Results and Discussion
#### 3.3.3 Ballated vs. Slab Track  
Comparing the dynamic response of the two track systems due to wheel flats, it appeared that for the same traffic conditions: (1) The dynamic force on the slab track was higher than on the ballated system; (2) The maximum vertical displacement was higher on the ballated track than on the slab system for vehicle speeds equal to or greater than 120 km/h; (3) The force on the fastening systems was higher on the slab track than on the ballated one due to the higher slab track stiffness, as shown by the receptance frequency response of the two rail tracks.  
Figure 14: Dynamic amplification factor for the force on the fastening system: perfect wheel vs. flat wheel (\(d=2\) mm), slab track.  
Figure 13: Dynamic amplification factor for the (rail) vertical displacement: perfect wheel vs. flat wheel (\(d=2\) mm), slab track.  
In conclusion, the higher overall stiffness and different frequency responses of the slab track, in comparison to the the ballasted track, contributed to the higher wheel-rail impact load. Thus, the effect of the wheel flats is relevant to the vehicle track dynamic behavior, and for that reason, it should not be neglected either for ballasted tracks or slab systems, especially for high-speed railway lines. Additional numerical studies and experimental measurements should be performed in order to correlate the impact forces due to wheel flats with the train speed and track stiffness to propose future regulations on the limit values for wheel flats.
## 3 Results
### CNN-SVM Prediction Model Improved by the Black-Winged Kite Algorithm  
#### 3.3.1 Training Process  
This study employs the BKA for optimization, leveraging its robust search capabilities and balance between local and global optima. The BKA iterates within the CNN-SVM network to identify three optimal parameters: the best mini-batch size (best1), the best initial learning rate (best2), and the best learning rate decay factor (best3) [55]. The BKA iterates according to the parameters in Table 5, with the loss function recording the match between predicted and actual values. As shown in Figure 8, the loss fluctuates significantly in the early stages but generally decreases, stabilizing after the 60 th generation, indicating that the model converges to the true values.  
Figure 7: Different modeling evaluation indexes under SG noise reduction. (**a**) shows the details of R\({}^{2}\), MARE and MBE. (**b**) shows the details of RMSE, MAE and RPIQ.  
After the BKA iterations, the optimal parameters selected were best1 = 164, best2 = 0.0720, and best3 = 0.0026. These three parameters were incorporated into the CNN network training. The CNN was set with a maximum of 800 training iterations, a mini-batch size of best1, an initial learning rate of best2, a learning rate decay factor of best3, and an L2 regularization coefficient of 0.1. The training set was shuffled for each training session. The specific network structure is detailed in Table 6.  
The optimal parameters were obtained by traversing the values of the penalty factor (c) and the Gamma function (g) using the grid search cross-validation method. The initial  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Definition** & **Parameters** \\ \hline Input & 199, \(1\times 1\) \\ Conv1 & 16, \(3\times 1\) \\ BN1 & - \\ Relu1 & - \\ Maxpool1 & \(2\times 1\) \\ Conv2 & 32, \(3\times 1\) \\ BN2 & - \\ Relu2 & - \\ Maxpool2 & \(2\times 1\) \\ Dropout & 0.5 \\ Fc & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CNN structure settings.  
Figure 8: BKA iteration. (**a**) shows the change of the fitness value. (**b**) shows the change of the Loss.  
value of the penalty factor (c) for the SVM model was set to 0.5 with a step size of 0.1; the initial value of the Gamma function (g) was set to 0.05 with a step size of 0.01, and the SG-1 DR data were trained. As shown in Figure 9, the model results were optimal when (c) was 1.1 and (g) was 0.11. The final SVM parameter settings were based on the optimal (c) and (g) values and the commonly used parameters for SVM modeling of soil spectral data [22; 23]. The SVM type selected was e-SVR, with a penalty factor (c) of 1.1 and a loss function (p) of 0.01; the kernel function was the RBF function, and the Gamma function in the kernel function was set to 0.11.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 3 Parameterized Modeling of Optimal DES Planning in Active Distribution Networks (ADNs)
### DES Operation Constraint  
\[\sqrt{\left(P_{s,t,i,m}^{\rm DES}\right)^{2}+\left(Q_{s,t,i,m}^{\rm DES} \right)^{2}}\leq y_{i,m}S_{m}^{\rm unit} \tag{15}\]  
\[P_{s,t,i,m}^{\rm DES,L}=A_{m}^{\rm DES}\sqrt{\left(P_{s,t,i,m}^{\rm DES} \right)^{2}+\left(Q_{s,t,i,m}^{\rm DES}\right)^{2}} \tag{16}\]  
\[E_{s,t+\Delta t,i,m}^{\rm DES}=E_{s,t,i,m}^{\rm DES}-\left(P_{s,i,i,m}^{\rm DES }+P_{s,i,i,m}^{\rm DES,L}\right)\Delta t \tag{17}\]  
\[z_{i,m}E_{m}^{\rm unit}SOC_{m}^{\rm min}\leq E_{s,t,i,m}\leq z_{i,m}E_{m}^{ \rm unit}SOC_{m}^{\rm max} \tag{18}\]  
\[\frac{\sum_{t\in\rm NT}\left|P_{s,t,i,m}^{\rm DES}\right|\Delta t}{2z_{i,m}E_{ m}^{\rm unit}}\leq\frac{1}{365}\frac{N_{m}^{\rm M}}{y_{m}} \tag{19}\]  
\[E_{s,i,m,t=T}^{\rm DES}=z_{i,m}E_{m}^{\rm unit}SOC_{s,m,t=0} \tag{20}\]  
where \(P_{s,t,i,m}^{\rm DES}\) and \(Q_{s,t,i,m}^{\rm DES}\) are the active and reactive power injection by the \(m\)th DES at node \(i\) in the \(s\)th scenario. \(E_{s,t,i,m}^{\rm DES}\) is the energy stored of the \(m\)th DES at node \(i\) in the \(s\)th scenario. \(A_{m}^{\rm DES}\) is the loss coefficient of the \(m\)th DES. \(SOC_{m}^{\rm min}\) and \(SOC_{m}^{\rm max}\) are the minimum and maximum state of charge limit of the \(m\)th DES. \(N_{m}^{\rm M}\) is the cycle life of the \(m\)th DES.  
Constraint (15) represents the converter capacity limit of DES. The power loss of DES is considered in constraint (16). Constraint (17) determines that the energy stored in DES in period \((t+1)\) depends on the previous energy stored in DES and the charge/discharge power of the time interval. Constraint (18) represents the maximum and minimum state of charge (SOC) limits. Weighted energy throughput method is adopted to describe the cycle life of DES in constraint (19) [31]. The constraint relies on the principle that an electrochemical cell can exchange a finite amount of charge during its lifespan. This value can be assessed through cycling tests and calculated as the charge that pass through a cell during a complete discharge-charge cycle multiplied by the total number of cycles that a battery can perform before depletion. Constraint (20) implies that the energy stored in DES at the end of one day has to equal its initial value. It is noteworthy that the power of DES is determined by the minimum power of the energy storage equipment and the power converter. To make the DES modeling more generalized and simplified, an ideal power matching between the energy storage device and its power converter is assumed in this paper. Therefore, constraint (15) can be considered as the power limit of the entire energy storage system.
## 2 The European ENTSO-E RG-CE System
### Advantages of the Interconnected ENTSO-E System
#### 2.1.2 Economic Advantages  
When a power system operates autonomously, it schedules and operates its own generation stations in a way to serve its customers as economically as possible. Interconnected systems, however, can enter into a variety of mutually beneficial agreements, if the power transfer constraints are imposed by the transmission network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Regional Group (RG)** & **Participating Countries** \\ \hline \multirow{5}{*}{Continental Europe} & Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark (west), France, Germany, Greece, Italy, Hungary, Luxembourg, Montenegro, The Netherlands, Poland, Portugal, Romania, Serbia, Slovakia, Spain, Switzerland, North Macedonia, Ukraine * \\ \hline North Europe & Denmark (East), Norway, Sweden, Finland \\ \hline Baltic & Estonia, Latvia, Lithuania \\ \hline United Kingdom (U.K.) & Great Britain \\ \hline Ireland & Ireland, Great Britain \\ \hline \hline \end{tabular}  
* The TSOs of Continental Europe decided to begin the trial synchronization of the Continental European Electricity System with the power systems of Moldova and Ukraine on 16 March 2022.  
\end{table}
Table 2: Participating countries in the Regional Groups (RG) of ENTSO-E.
###### Abstract  
The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today's IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a \"place-and-forget\" IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.  
Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT 16 1160. [[https://doi.org/10.3390/su16031160](https://doi.org/10.3390/su16031160)]([https://doi.org/10.3390/su16031160](https://doi.org/10.3390/su16031160))  
19 December 2023  
16 January 2024