esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Area  
The Vistula Lagoon is a coastal water body located on the southern coast of the Baltic Sea, in the eastern part of the Gulf of Gdansk (Figure 1). It is a transboundary water body shared between Poland and Russia, with a total area of 838 km\({}^{2}\), of which 328 km\({}^{2}\) belongs to Poland and 472.5 km\({}^{2}\) to Russia. The lagoon stretches approximately from 19\({}^{\circ}\)13\({}^{\prime}\)30\({}^{\prime\prime}\) E to 20\({}^{\circ}\)24\({}^{\prime}\)47\({}^{\prime\prime}\) E longitude and from 54\({}^{\circ}\)15\({}^{\prime}\)15\({}^{\prime\prime}\) N to 54\({}^{\circ}\)43\({}^{\prime}\)25\({}^{\prime\prime}\) N latitude [2; 3; 38].  
The lagoon is separated from the Baltic Sea by the Vistula Spit, which is approximately 65 km long. The total length of the lagoon is 90.7 km, with an average width of 9.2 km (maximum width of 13 km). The lagoon is relatively shallow, with an average depth of 2.0-3.0 m and a maximum depth of 5.2 m. The total water capacity of the lagoon is 2.3 km\({}^{3}\). Until 2022, the only connection between the Vistula Lagoon and the Baltic Sea was through the Strait of Baltiysk located in the Russian part. The strait is approximately 2 km long, 400 m wide, and 8-12 m deep. In September 2022, a new navigable canal (Nowy Swiat ship canal) was opened in the Polish part of the Vistula Spit [39]. The canal is 1.3 km long with a lock 25 m wide and 5 m deep [11; 37; 40].  
The shoreline of the lagoon is poorly developed, except for two major bays: Primorsk Bay in the north and Elblaska Bay in the south. The salinity of the lagoon varies spatially, decreasing with distance from the Strait of Baltiysk--from about 5.5 near the strait to 2.2 in the western part. The lagoon experiences rapid water level changes exceeding 1.0 m at its SW and NE edges, primarily driven by winds and Baltic Sea-level fluctuations. The dominant winds are from the SW direction with velocities ranging from 4 to 6 ms\({}^{-1}\) (measured at 10 m in height) [5; 41; 42].  
The hydrological regime of the Vistula Lagoon is characterized by complex water exchange between the Baltic Sea, river inflows, precipitation, and evaporation. About 75% of the water input comes from the Baltic Sea through the Strait of Baltiysk, while river inflows contribute approximately 20% of the total water budget [2; 37; 41].
## 4 Experiment
### Dataset Preparation
#### 4.1.3 Augmenting the Training Datasets  
The parameter of image augmentation techniques affects the quality of images for improving the accuracy. The parameters utilized were chosen through a trial-and-error process to generate images that are as realistic as possible. The dataset, with a detailed range of parameters, is presented in Table 1. For brightness adjustment, pixel intensity was randomly altered by adding a value between \(-\)50 and +50 to the intensity of the original images. The contrast level for the augmented images was modified by randomly varying the alpha value (\(\alpha\)) within a range from 0.3 to 2.3.  
In scale augmentation, images were independently scaled along the x and y axes to random values ranging from 80% to 120% of their original size. The perspective changes were simulated through transformation by adjusting the \"scale\" parameter, with assigned ranges for these elements from 0.03 to 2.3, applied randomly and independently to each image. Rotation augmentation involved rotating the original images by a degree between \(-\)25 to +25, with the rotation parameter denoted by \(\theta\). For translation, the extent and direction of image shifting were controlled by adjusting \(t_{x}\) and \(t_{y}\), allowing for horizontal and vertical movement within a range of \(-\)0.2 to +0.2 times the image's dimensions. Shearing augmentation was executed within a \(-\)15 to +15 degree range, introducing a skew to the image features. This was achieved by modifying the shearing parameters \(sh_{x}\) and \(sh_{y}\).  
Geometric transformations like scaling, rotation, perspective shifts, translation, and shearing can push portions of the image beyond its original boundaries, creating \"voids\" without any data. Conversely, some regions may compress and overlap, forming gaps. Because deep learning models generally require inputs in standardized dimensions, the augmented images must maintain the same size. In this work, any voids in reduced images were filled with pixels of value 255, effectively padding these areas with white pixels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Augmentation** & **Parameters** &
\begin{tabular}{c} **Ranges of** \\ **Parameter Values** \\ \end{tabular} & **Number of Images** \\ \hline Brightness & \(\beta\) & \([-50,50]\) & 1382 \\ Contrast & \(\alpha\) & \([0.3,2.0]\) & 1382 \\ Scale & \(S_{x},S_{y}\) & \([0.3,2.3]\) & 1382 \\ Perspective & \(a_{31},a_{32}\), & \([0.75,1.25]\), \([0.75,1.25]\) & 1382 \\ Rotation & \(\theta\) & \([0.02,0.17]\) & 1382 \\ Translation & \(t_{x},t_{y}\) & \([-0.2,0.2]\), \([-0.2,0.2]\) & 1382 \\ Shearing & \(\theta\) & \([-15^{\circ},15^{\circ}]\) & 1382 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The used techniques with parameter and generated images.  
As indicated in Table 1, the original training set contained 1382 images, which were expanded using a range of augmentation techniques (brightness, contrast, scaling, perspective, rotation, translation, and shearing). This process generated 9674 augmented images, bringing the total to 11,056 images when combined with the originals.
# Sustainable Transformation: A Case Study of an Engineering-to-Order Additive Manufacturing Firm
## 7 Discussions and Conclusions  
This study formulated a strategy to enhance sustainable AM practices, focusing on identifying optimisation opportunities and aligning procedures with strict sustainability standards. It validates [22] findings, supporting the notion that integrating innovation and sustainability can transform AM into a more environmentally friendly and efficient manufacturing solution, thereby shaping the future of production.  
Initially, Sus-VSM was employed to conduct a thorough analysis of the MJF process. This analysis aimed to identify and eliminate potential lean and environmental waste, enhancing the efficiency of the manufacturing process. The identified waste was related to over-processing and waiting time, both of these areas require optimisation. By concentrating efforts on these critical areas, the AM company can significantly enhance its operational efficiency and contribute to a more sustainable and environmentally friendly production process.  
The Sus-VSM analysis indicated alignment between the case study company and multiple SDGs. Notably, the company is aligned with SDGs related to decent work and economic growth, sustainable industry, innovation, infrastructure, responsible consumption, production, and partnerships. These findings suggest the company's dedication to fostering sustainability, innovation, and responsible business practices.  
The study then shifted its focus to the selection of the most pivotal PIs for an AM company, employing the AHP method. This rigorous approach ensured the identification of key metrics crucial for evaluating the company's performance and sustainability efforts.  
Lastly, the paper delves into the creation of a SSM by integrating five BSC perspectives: financial performance, customer, process excellence, people and learning, and \"Society and Environment\". The authors advocate for the expansion of the BSC framework proposed by [32] to include sustainability considerations, thereby advancing the evolution of the BSC concept as outlined by [108].  
The developed SSM aims not only to improve the company's operations but also to improve its reputation and help acquire new customers who value the well-being of current and future generations [109]. It has been shown that environmentally friendly companies have loyal customers and receive better terms from the banks that provide them with loans [98].  
However, it is important to acknowledge certain limitations of this study. Firstly, the perspectives presented here came from a limited number of experts and practitioners based in Norway. Consequently, variations in business, cultural, or environmental contexts could yield different results in future research attempts. Additionally, the environmental and societal KPIs chosen through the AHP may not universally apply to all AM companies, emphasising the need for ongoing exploration and customisation in diverse organisational settings.
## 2 Materials and Methods
### Analytical Determinations
#### 2.5.4 Product Shelf-Life  
After the treatment, the packaged fresh-cut apple samples were stored at 8 \(\pm\) 1 \({}^{\circ}\)C and analytical determinations were carried out after 0, 3, 6, and 9 days of storage, considering at least three packages each time.  
The concentration of O\({}_{2}\) and CO\({}_{2}\) (%) in the package headspace was determined by a gas analyzer (MFA III S/L gas analyzer, Witt-Gasetechnik, Witten, Germany). The measurements were performed on at least three packages for each sample.  
Microbiological analyses were performed after 1, 3, 6, and 9 days of refrigerated storage, in order to take into account the specific growth kinetics of the investigated microorganisms. Each sample was analyzed in triplicate. Microbial groups considered as spoilage indicators were total mesophilic counts (TMC), Lactobacillus spp., total yeasts, and molds. Fifty grams of cut apples were suspended 1:1 (p/v), serially diluted using physiological saline solution (0.9% NaCl), and 1 mL or 100 \(\mu\)L were included or spread in different selective or complex culture media, such as plate count agar (PCA) (Oxoid-Thermofisher, Milano, Italy) for TMC; de Man, Rogosa, and Sharpe agar (MRS) (Oxoid-Thermofisher, Milano, Italy) supplemented with cyclo-heximide (0.2% p/v) (Sigma-Aldrich, Milano, Italy) for Lactobacillus spp.; and yeast extract, peptone, dextrose agar (YPD) for total yeast and mold counts. Plates were incubated for 24 or 48 h at 30 \({}^{\circ}\)C for the counts of total mesophilic bacteria, yeasts, and molds, and at 37 \({}^{\circ}\)C for Lactobacillus load determination.  
Titractable acidity (TA) was determined by titration with NaOH 0.1 N until pH 8.1 was reached [19], and expressed in g of malic acid per 100 g of fresh weight. For each sample, TA was determined in triplicate on the juice obtained by \(\simeq\)300 g of apple slices with a food processor.  
Surface color of fresh-cut apple samples was measured with a Chroma Meter CR-400 reflectance colorimeter (Minolta Italia, Milano, Italy) using the D65 illuminant and a 10 standard observer. The lightness (L*) and red index (a*) of the CIELAB scale were measured according to the lab scale established by Commission Internationale de l'Eclairage (CIE, 2001). Results were expressed as an average of 15 readings for each set of samples.  
Texture of samples was assessed through a penetration test performed using a Texture Analyser TA-HDi500 (Stable Micro Systems, Surrey, UK) equipped with a 50-N load cell and a 6-mm diameter stainless steel cylindrical probe. The test speed was set at 0.5 mm s-1 and the maximum deformation at 90%. Fifteen measurements were carried out from each set of samples and the first peak force (N) was extracted from the acquired curves.
# DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT
## 1 Introduction
Blockchain distributed ledger storage is used to realize cross-domain authentication between trust domains. The analysis proves that the protocol is secure in the random oracle model, and the size of the signature generated by the scheme is independent of the number of group members \(N\), and only depends on the security parameter \(\lambda\), which effectively improves the operating efficiency of the protocol.
## 3 Problem Formulation
### Inner Model: Stochastic Optimal Dispatching Model of IPGES
#### 3.1.3 Natural Gas System Operating Constraints  
The natural gas system mainly consists of natural gas source, natural gas pipeline, pressure station, gas load and so on. The main operating constraints are as follows.  
(1) Supply constraint of natural gas well  
\[F_{N,j,\min}\leq F_{N,j,t}\leq F_{N,j,\max}\ \ ;\ 1\leq t\leq T,\ 1\leq j \leq N_{well} \tag{15}\]  
(2) Natural gas network flow constraints  
The IPGES involved in this paper is a high-pressure network, so the Weymouth equation of high-pressure pipeline with pressure above 0.7 MPa is used to express the flow equation of the gas network [15].  
\[F_{l,t}=F_{mn,t}=C_{mn}\text{sgn}(\pi_{m,t},\pi_{n,t})\sqrt{|\pi_{m,t}{}^{2} -\pi_{n,t}{}^{2}|}\ ;\ 1\leq t\leq T,\ 1\leq l\leq N_{pip} \tag{16}\]  
\[\text{sgn}(\pi_{m,t},\pi_{n,t})=\left\{\begin{array}{ll}1,\pi_{m,t}\geq\pi _{n,t}&\\ -1,\pi_{m,t}<\pi_{n,t}&\end{array}\ ;\ 1\leq t\leq T\right. \tag{17}\]  
(3) Gas flow constraint of the pipeline  
\[F_{l,\min}\leq F_{mn,t}\leq F_{l,\max}\ \ ;\ 1\leq t\leq T \tag{18}\]  
(4) Compressor operation constraint In order to simplify the calculation, the consumption characteristics of the compressor are not considered in this paper, and the compression ratio is constant.  
\[\pi_{m,t}=k_{t}\pi_{n,t}\ \ ;\ 1\leq t\leq T \tag{19}\]  
(5) Gas storage station operating constraint  
\[G_{S,j,min}\leq G_{S,j,t}=G_{S,j,t-1}+F_{S,j,t}^{int}-F_{S,j,t}^{out}\leq G_{S, j,max}\ \ ;\ 1\leq t\leq T,\ 1\leq j\leq N_{SG} \tag{20}\]  
\[0\leq F_{S,j,t}^{in}\leq x_{S,t}^{in}F_{S,j,max}^{in}\ \ ;\ 1\leq t\leq T,1\leq j\leq N_{SG} \tag{21}\]  
\[0\leq F_{S,j,t}^{out}\leq x_{j,t}^{out}F_{S,j,max}^{out}\ \ ;\ 1\leq t\leq T,1\leq j\leq N_{SG} \tag{22}\]  
\[x_{j,t}^{in}+x_{j,t}^{out}\leq 1\ \ ;\ 1\leq t\leq T,1\leq j\leq N_{SG} \tag{23}\]  
(6) Nodal pressure constraint  
\[\pi_{n,min}\leq\pi_{n,t}\leq\pi_{n,max}\ \ ;\ 1\leq t\leq T,1\leq n\leq N_{node} \tag{24}\]  
(7) Nodal gas flow balance constraint  
\[A_{i}^{w}F_{t}^{N}-A_{i}^{s}F_{S,t}^{in}+A_{i}^{s}F_{S,t}^{out}-A_{i}^{s}F_{ t,t}-A_{i}^{s}F_{t}-A_{i}^{s}F_{t}^{s}-A_{i}^{p2g}F_{t}^{p2g}=A_{i}^{D}F_{t}^{D}\ \ ;\ 1\leq t\leq T,1\leq i\leq N_{node} \tag{25}\]
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 5 Experimental Set-Up
### Database Description  
Firstly, for the experimental stage, five real-world remote sensing databases are chosen: Cayo, Feltwell, Satimage, Segment and 92 AV3C.The Cayo dataset comes from a particular region in the Gulf of Mexico [18]. The Feltwell dataset represents an agricultural area near the village of Feltwell (UK) [46]. The Satimage and Segment datasets are from the UCI (University of California, Irvine) Machine Learning Database Repository [47]. The 92 AV3C dataset [48] corresponds to a hyperspectral image (145 \(\times\) 145 pixels, 220 bands, 17 classes) taken over the Northwestern Indiana Indian Pines by the AVIRIS (Airborne Visible / Infrared Imaging Spectrometer) sensor. In this work, we employed a reduced version of this dataset with six classes (2, 3, 4, 6, 7 and 8) and thirty eight attributes as in [18].  
The two-class imbalance problem is only studied. We decompose the multi-class problems into multiple two-class imbalanced problems. This proceeds as follows: one class (\(c_{j}\)) is taken from the original database (\(DB\)) to integrate the minority class (\(c^{+}\)), and the rest of classes were joined to shape the majority class (\(c^{-}\)). Then, we integrate the two-class database \(DB_{j}\) (\(j=1,2,...,J\), and \(J\) is the number of classes in \(DB\)). In other words, \(DB_{j}=c^{+}\cup c^{-}\). Therefore, for each database, \(J\) two-class imbalanced datasets were obtained. The main characteristics of the new produced benchmarking datasets are shown in Table 1. This table shows that the datasets used in this work have several class imbalance levels (see the class imbalance ratio), ranging from a low to a high class imbalance ratio (for example, see 92A3 and CAY4 datasets). In addition, the ten-fold cross-validation method was applied on all datasets shown in this table.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 2 Related Work and Background
### Data Routing  
All the member nodes in a cluster sense data and transmit this information towards their concerned CHs. After collecting and fusing data from all the nodes the CHs transmit it towards the BS. An overlay network that only contains CHs and relay nodes is constructed in [29; 39]. The traditional ad-hoc and routing protocols take into account this overlay network. A greedy data forwarding scheme is discussed in [24] which is frequently considered in previous literature for this type of routing. However, the usual problem for greedy data forwarding protocol is void zones.
## 4 Case Study
### T-Intvariant Analysis of the Petri Net Model  
The presented HAN system was verified in terms of transition invariants. In order to perform this verification, firstly the PIPE tool was used. This tool offers various analytical possibilities. Initially, the basic verification of the system was performed using \"State Space Analysis\". This technique did not detect any errors. The PIPE tool returned that the analysed system was bounded, safe, and deadlock-free. However, direct computation of invariants within PIPE failed. The analysis could not be completed due to the long computation time, and the verification was stopped after one hour. Since PIPE failed to compute invariants, the system was examined using the proposed algorithm. The algorithm computed the results in 4.06 ms and informed us that there were uncovered transitions in the system. In particular, transitions \(t_{21}\), \(t_{22}\), and \(t_{28}\) were not covered by t-invariants. This result was quite surprising compared to the information gained by PIPE about boundedness, safeness, and deadlock-freeness. Nevertheless, detailed analysis of the modelled Petri net confirmed errors in the system. Indeed, due to the improper modelling of the system, the detected uncovered transitions never fire. In particular, transition \(t_{22}\) (marked by red square) cannot be executed since it contains two input arcs, from \(p_{13}\) and \(p_{25}\), but the latter one never becomes marked. Moreover, such a situation means that places \(p_{14}\) and \(p_{25}\) are never reachable. In particular, place \(p_{14}\) is crucial, since it is in response to turning the appliance off in case of high energy prices
# A Review of Time-Scale Modification of Music Signals +
## 4 TSM Based on Waveform Similarity Overlap-Add (WSOLA)
### Tricks of the Trade  
In order to assure that WSOLA can adapt to the most dominant periodic pattern in the waveform of the input signal, one frame must be able to capture at least a full period of the pattern. In addition, the tolerance parameter \(\Delta_{\text{max}}\) must be large enough to allow for an appropriate adjustment. Therefore, it should be set to at least half a period's length. Assuming that the lowest frequency that can be heard by humans is roughly 20 Hz, a common choice is a frame length \(N\) corresponding to 50 ms and a tolerance parameter of 25 ms.  
One possibility to reduce transient doubling and skipping artifacts in WSOLA is to apply a _transient preservation_ scheme. In [13], the idea is to first identify the temporal positions of transients in the input signal by using a transient detector. Then, in the WSOLA process, the analysis hopsize is temporarily fixed to be equal to the synthesis hopsize whenever an analysis frame is located in a neighborhood of an identified transient. This neighborhood, including the transient, is therefore copied without modification to the output signal, preventing WSOLA from doubling or skipping it. The deviation in the global stretching factor is compensated dynamically in the regions between transients.
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Electronic Chart Display Information System  
Security issues related to ECDIS have been deeply investigated in many studies [9; 37; 38; 39; 40]. In fact, there is a long list of flaws in ECDIS software implementations. The system is frequently run on old computers, which have no security updates available. The maps are downloaded from the Internet or manually uploaded via USB to the system, which may cause a compromise of the system while trying to update the maps. This update medium can open much room for attack [38]. The authors in [40] investigated ECDIS software and discovered several security flaws that could allow an attacker to delete or reinstall system files, as well as inject malicious content. As a result, altered sensor data can be sent to ECDIS to affect navigation judgements, thereby causing collisions [38; 39].
# A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
## 2 Bearing Fault Diagnosis Method Based on Knowledge Reasoning
### Construction of Multi-Local Fault Diagnosis Model Based on Bi-LSTM
#### 3.1.4 Multi-Local Model Decision Fusion under Conflict-Free Conditions
(4) Get the final diagnosis result according to the corresponding decision rules: After calculating the reliability interval under the combined effect of the two evidence (local diagnostic models), according to the maximum trust decision rule, absolute support decision rule, and uncertainty limit decision rule of D-S evidence theory, the corresponding diagnostic conclusions of the elements in each evidence are made. The maximum trust decision rule, the absolute support decision rule, and the uncertainty limit decision rule can be expressed as:  
\[ \begin{cases}Bel(A)=max\{Bel(A_{n})\}\\ Bel(A)-Pl(A)>a\\ m(\theta)<b\end{cases}  \tag{22}\]  
where \(A\) is the fault type of fusion diagnosis, and \(n\) is the number of bearing fault types. In addition, \(a\) and \(b\) are the parameters that are greater than 0.