esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of a New Bulk Packaging Container for the Ripening of Feta Cheese
## 2 Materials and Methods  
### Cheese Preparation  
Feta cheese was prepared as part of the commercial production process, in the cheese plant of Dodoni S.A. in Thebes, Greece, in the early spring of 2020 using local ewe and goat milk. Raw ewe and goat milk and a mixture of the two were pasteurized at 72 \({}^{\circ}\)C for 15 s; a 70/30 \(v/v\) ratio was used for Feta production, according to the Hellenic Legislation [6]. After cooling to 35 \({}^{\circ}\)C, artisanal kid and lamb rennet (Ipiotopoula S.A, Ioannina, Greece) (1:15.000 activity) was added at a rate of 7.5 mL/kg so as to achieve coagulation in about 50 min. The CHOOZIT MT1 LYO 10 DC freeze-dried adjunct starter culture ([PERSON], Denmark) was also added at a rate of 0.75 g/kg. The adjunct culture was composed of _Lactococcus lactis_ subsp. _Lactis, Lactococcus lactis_ subsp. _Cremoris, Streptococcus thermophilus_, and _Lactobacillus delbrueckii_ subsp. _Bulgarcius_. After coagulation, the cheese curd was cut into cubes of dimensions 2 cm \(\times\) 2 cm \(\times\) 2 cm and transferred into parallelepiped or cylindrical molds. After draining, the curd from the parallelepiped molds was placed in tin cans (capacity, 17 kg) and stainless-steel tanks lined with wood (capacity 100 kg) (Supplementary Materials Figure S1), while the curd from the cylindrical molds was placed in wooden barrels (capacity, 50 kg). A 70.0 g/kg NaCl solution was added to the cheeses in the tin cans and stainless-steel tanks. Granular NaCl was added to the cheese in the wooden barrels at a concentration of 25.0 g/kg of cheese for a period of 24 h. All containers were then sealed and left to ripen at 16-18 \({}^{\circ}\)C for ca. 15 days. Subsequently, the cheeses were transferred into storage/ripening rooms operating at 3-4 \({}^{\circ}\)C and kept there for up to 100 days. All cheeses were analyzed for their physico-chemical composition, proteolysis,volatile compounds, and mechanical and sensory properties on days 1, 2, 7, 40, 60, 80, and 100 of storage.
## 4 Novel \(O(\log_{2}N)\) fully-balanced Redistribution
### General Overview  
The algorithm consists of two phases. In the first phase, we want the elements in **ncopies** to be _nearly sorted_, a property which is defined as follows.  
**Definition 2**.: _A sequence of \(N\) non-negative integers, **ncopies**, is nearly sorted in descending order when it has the following shape:_  
\[\mathbf{ncopies}=\left[\lambda^{0},\lambda^{1},...,\lambda^{m-1},0,...,0\right] \tag{9}\]  
_where \(\mathbf{ncopies}^{i}>0\) (marked with \(\lambda s\) in (9)) \(\forall i=0,1,...,m-1\) and \(0\leq m\leq N\). On the other hand, \(\mathbf{ncopies}\) is an ascending nearly sorted sequence if the last \(m\) elements are positive and the first are \(0\)._  
In this paper, **ncopies** is nearly sorted in descending order. We also note that, as the elements in **ncopies** are progressively shifted to achieve (9), the related particles in \(\mathbf{x}\) are consequently also shifted. The main purpose of this phase is to separate all particles that must be duplicated (i.e., those for which \(\mathbf{ncopies}^{i}>0\)) from those that must be deleted. Here, we prove that (9) can be achieved by using Rotational Nearly Sort, an \(O(\log_{2}N)\) alternative to the \(O((\log_{2}N)^{2})\) Nearly Sort step in [7] (as also described in Appendix A). We denote that (9) can also be achieved with sort, as carried out in [25] by using Bitonic Sort. In theory, one could also employ \(O(\log_{2}N)\) sorting networks [32; 33]. However, the constant time of these networks is notoriously prohibitive in practice, even with some of the most recent advances [34]. Our constant time is significantly smaller; because of that, we do not consider AKS-like sorting networks as a practical approach for (9).  
In the second phase, we want to achieve two goals: the first is to make room on the right of each particle that has to be copied; the second is for the \(P\) cores to have the same number of particles in their private memory. The first goal easily translates to shifting the particles to the right until **ncopies** has the following new shape:  
\[\mathbf{ncopies}=\left[\lambda^{0},0,...,0,\lambda^{1},0,...,0,\lambda^{m-1},0,...,0\right] \tag{10}\]  
where, for each \(\mathbf{ncopies}^{i}>0\) (again marked with \(\lambda\)s in (10)), \(\mathbf{ncopies}^{i}-1\) zeros follow. The second can be expressed as follows:  
\[\sum_{i=p\frac{N}{P}}^{(p+1)\frac{N}{P}-1}\mathbf{ncopies}^{i}=\frac{N}{P}\ \ \ \forall p=0,...,P-1 \tag{11}\]  
which is essentially (5) applied locally. In the next section, we prove it is possible to achieve both (10) and (11) in \(O(\log_{2}N)\) parallel time by using a single algorithm, which we refer to as Rotational Split. After that, the cores are completed by using S-R independently to redistribute the particles within their private memory.
# Review of Physical Layer Security in 5G Wireless Networks
## 5 Future Research
### Exploiting Interference  
Research the use of interference for physical layer security. By intentionally interfering with eavesdroppers' ability to listen in on wireless communications, artificial noise techniques, for instance, can be further refined to increase their secrecy. To reach a compromise between security and system performance, investigate the best configuration and distribution of generated noise in various network settings.
# Deep Image Compression with Residual Learning
## 1 Introduction
### Deep Channel Residual Learning  
Since quantization is necessary for compression, it also introduces a lot of errors. Assuming that the shape of the quantized bits is \(n\times h\times w\), the quantization errors exist in these coded bits. Motivated by denoising algorithm in traditional signal processing, a variation of \(\Delta\Sigma\) modulation similar to residual learning is introduced. Utilizing the residual information between the adjacent channel can eliminate errors effectively. Given the input is \(x\), the quantizer is \(sign\) function, and the quantized bits is \(\hat{q}\). The residual is \(r\), i.e. the difference between current channel \(y_{i}\) and previous quantized channel \(\hat{q_{i}}\). The reconstruction \(z\) is accumulated by residual \(r\). The errors existed between channels can be eliminated in the process of calculating the residual and accumulation of the information. The specific process of channel residual learning is depicted as Algorithm 1.  
```
Input: Up-sampled representations \(y_{1},y_{2},...,y_{n}\) Output: Quantized bits \(\hat{q_{1}},\hat{q_{2}},...,\hat{q_{n}}\) \(r\Leftarrow 0,z\Leftarrow y_{1}\) for i = 1,2,...,n do \(z\Leftarrow z+r\) \(\hat{q_{i}}\Leftarrow sign(z)\) \(r\Leftarrow y_{i}-\hat{q_{i}}\) endfor
```  
**Algorithm 1**Deep channel residual learning.
## 8 IoMT Security Model
### Machine Learning Model  
In order to predict the different patterns of attacks in deep brain stimulation (DBS), [PERSON] et al. [87] built a full prototype of an embedded system to lessen the attacks on these devices. This system does not only distinguish real alarms from fake ones, but it also classifies the different attacks using deep learning and Raspberry Pi3. Deep learning has been proved to indicate an accuracy of 97% in learning and predicting false signals. The feasibility of real-time attack-detection can be clearly demonstrated when this system is deployed on a cloud.  
[PERSON] et al. [88] proposed an anomaly in terms of data detection and a separation approach designed for smartphone healthcare, called AUDIT. A pre-processing phase and a real-time processing step were used in the study. Using PCA (Principal Component Analysis) and correlation coefficient, the feature was selected and extracted. This allows the system to detect erroneous physiological measurements and to distinguish between real and false medical features.  
[PERSON] et al. [89] classified networked assaults using a hybrid PCA-GWO (Principal Component Analysis-Grey Wolf Optimization) technique for selecting features and a deep neural network (DNN) classifier. The proposed solution is suitable for IoMT devices with a single IP address. The input data was preprocessed using the One-Hop coding strategy. The two algorithms were then applied successively for data reduction, followed by prediction using well-known classifiers. The proposed model outperformed and outclassed other current learning algorithms, with a 15% gain in detection accuracy and a 32% reduction in training and classification time.  
As a system to detect possible intrusions, [PERSON] et al. [90] proposed a Deep Belief Network (DBN) algorithm model. In that study, the metrics they used were F1 score, precision, recall, and detection rate, as well as accuracy. In comparison to other techniques, the proposed model obtained positive results for all variables. This model is claimed to have the possibility of being extended to detect different attack patterns against both various databases and IoT devices. For the normal class, the proposed method obtained 99.37% accuracy; for the Botnet class, it reached 97.93%; for the Dos/DDoS class, the percentage was 96.67%; as for the Port scan class, it was 97.71%; 97.71% for the Brute Force class, 96.37% for the Infiltration class; and finally, the method was able to obtain 98.37% for the Web attack.  
To obtain features from the ECG signal, and thus reduce the computational cost, [PERSON] et al. [91] used only the fiducial points detected during signal capture. For classification, a number of machine-learning approaches were applied. The results of the evaluation reveal that the suggested solution is efficient because of its accuracy of more than 98.2% in the continuous authentication and identification scenario and reduced complexity using less than 10 features. This appears to be a viable approach for improving the security of many critical services and applications.
## 1 Introduction
Furthermore, the current trend in flood susceptibility mapping is to create hybrid or ensemble machine learning models in order to achieve an even better accuracy of the resulting model [39; 40; 41]. The multi-layer perceptron neural network (MLP), which was used in this study, belongs to the artificial neural network techniques, having one of the highest prediction powers and efficiency in modelling [42; 43]. This technique is capable of universal modelling, but mainly it is used for analyzing nonlinear, multivariate, and complex processes in the real world [44]. The MLP neural network has been used for different types of natural hazards, such as landslide assessment [45; 46], land subsidence [47], or for determining the soil consolidation and compression coefficient [44; 48]. Despite the advantages of this method, its application in flash-flood or flood susceptibility studies is limited to only few studies [49; 50; 51; 52]. Although the abovementioned methods and models reach a high predictive capability, it is still difficult to accurately predict flash floods, as well as to provide an answer to the increase in flash floods frequency and its connection to the landscape properties, such as land-use. Therefore, the aim of this study is to investigate the correlation between the land-use change and the flash-flood potential changes in the Zabala catchment (Romania) between the years 1989 and 2019 using GIS, remote sensing, and machine learning algorithms. The procedural steps of the applied workflow are the following:  
(i) A bi-temporal analysis of two satellite scenes, Landsat 5 for 1989 and Landsat 8 for 2019, using the supervised classifications in order to derive the land covers. These two years were selected in order to reflect the land-use changes before and after the change in political regime of the country. (ii) The land-use change analysis where land-use/land-cover changes were summarized in the Markov matrix and subsequently quantized by the total relative difference - synthetic dynamic land-use index (TRDSDLUI). (iii) Processing the flash-flood inventory, consisting of 127 flash-flood locations and 127 non-flash-flood locations, and 10 flash-flood conditioning factors. (iv) Application of the multilayer perceptron (MLP) neural network model for deriving the flash-flood potential index (FFPI) for both years (1989 and 2019). (v) Validation of the MLP model using 30% of flash-flood locations (testing dataset). (vi) Calculation of the changes in flash-flood potential between 1989 and 2019 using the relative difference for flash-flood potential index (RDFFPI). (vii) Statistical correlation between TRDSDLUI and RDFFPI using a geographically weighted regression (GWR). From the methodological point of view, the importance of this study can be seen in an integrated approach of the connection between land-use/land-cover changes and flash-flood potential.
## 4 Methodology
### Data Augmentation Module  
Various data augmentation strategies were used to develop a more adaptable model with increased detection accuracy. A common issue associated with the use of computer vision in traffic safety applications is the need for a long recording period due to sample-size concerns and data quality. Data augmentation can be used to address this issue, with methods such as: blur, mosaic, flipping, rotation, and flipping. One method of performing augmentation is to rotate the original picture at various angles, while flipping produces a mirror image that may be either horizontally or vertically oriented. The blur method uses a filter to lessen the sharpness of the picture. A random section is then chosen from the mosaic picture and utilized as the final enhanced image. In contrast, the mosaic process resizes four separate photos and merges them. The advantage of this method is that it improves the visual complexity of the photos, giving the model a more demanding and realistic environment to recognize. These methods allow the model to process a wider variety of pictures, which improves the accuracy of identifying the classes of interest in the dataset. The videos used contain a wide range of variations, such as different camera angles, lighting conditions, and rider styles. By applying data augmentation techniques such as random cropping, horizontal flipping, and color jittering, we created a larger and more diverse dataset as shown in Figure 3, which can help the model to learn more robust and discriminative features.  
Figure 3: Sample of the augmented frames using resize, hue, flip, crop, saturation, blur, exposure, and rotate distortions.
## 4 Results and Discussion  
Two case studies were selected from literature to evaluate the performance of this work's optimization. The first was for a non-optimized dome SOE, and the second was for a single-surfaced spherical SOE that was analytically optimized with ray tracing analysis.
## 3 Using IC-BEVS for AAV Production
### Advantages of rAAV Production Using Bevs  
The method of rAAV production based on baculovirus expression system can effectively support the replication of the rAAV genome, provide the necessary auxiliary functions, and significantly facilitate the production of rAAV vectors, which has three main advantages. Firstly, it has a high-yield production capacity. In the large-scale production of AAV, baculoviruses are able to express recombinant proteins efficiently by binding to insect cells (e.g., Sf9 or High Five cell lines), which results in the high-yield production of AAV particles in a relatively short period of time [78]. Secondly, there is less risk of viral contamination. During the production process, insect cells do not carry human viruses, which avoids the problem of human viral contamination that may exist in mammalian cells, and therefore reduces the risk of cross-contamination during the production process [79; 80]. Finally, it is easy to operate and more cost-effective. Compared with mammalian cell systems, the culture conditions of insect cells are more relaxed, without complex medium or growth factors, and the operation is simpler [81]. In the production process, the baculovirus vector contains all the required auxiliary functions, and does not require additional auxiliary virus infection or supplementary auxiliary genes, so it shows higher economic benefits in large-scale production [82]. In addition, efficient and stable delivery of cis and trans elements required for rAAV production by infecting production cells with viral vectors also helps to improve the overall production process.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
###### Abstract  
Delay-tolerant networks face challenges in efficiently utilizing network resources and real-time sensing of node and message statuses due to the dynamic changes in their topology. In this paper, we propose a Multi-Decision Dynamic Intelligent (MDDI) routing protocol based on double Q-learning, node relationships, and message attributes to achieve efficient message transmission. In the proposed protocol, the entire network is considered a reinforcement learning environment, with all mobile nodes treated as intelligent agents. Each node maintains two Q-tables, which store the Q-values corresponding to when a node forwards a message to a neighboring node. These Q-values are also related to the network's average latency and average hop count. Additionally, we introduce node relationships to further optimize route selection. Nodes are categorized into three types of relationships: friends, colleagues, and strangers, based on historical interaction information, and message forwarding counts and remaining time are incorporated into the decision-making process. This protocol comprehensively takes into account the attributes of various resources in the network, enabling the dynamic adjustment of message-forwarding decisions as the network evolves. Simulation results show that the proposed multi-decision dynamic intelligent routing protocol achieves the highest message delivery rate as well as the lowest latency and overhead in all states of the network compared with other related routing protocols for DTNs.  
delay-tolerant network; double Q-learning; node relationships; network resources +
Footnote †: journal: Medical Informatics
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 3 Numerical Simulations
### The Sensitivity Test of the Extra key \(\alpha\)  
The parameter \(\alpha\) in FrFT can be severed as an extra key for this cryptosystem. To test the sensitivity of the parameter \(\alpha\), an experiment was designed as follows. In the encryption approach, \(\alpha\) is set as 0.5. In the decryption process, suppose that the main key is known, the additional key \(\alpha\) is changed around 0.5, and the corresponding PSNR curve is illustrated in Figure 5. In calculation, the sampling step length of \(\alpha\) is 0.01 between 0.25 and 0.75. As we can see from Figure 5, the 24 th band and 26 th band of the decrypted image are the noise pattern; even the values of \(\alpha\) are so close to the correct value 0.5. In the decryption result, only the 25 th band image is decrypted successful. The experimental result indicates that the parameter \(\alpha\) is sensitive and can protect the secret hyperspectral image well.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### MQTT Encryption  
As described in the original paper, TLS-encrypted MQTT can be substituted for a plain-text implementation of MQTT. For maximum security, this would be implemented using MQTT over TLS, using the ITU (International Telecommunications Union) X.509 certificate authority approach [13]. Whilst this would add an additional processing overhead (which may not be well suited for very simple devices designed around very low-power microcontrollers), it is a means to provide full end-to-end security of the connection between the device and the server for applications and systems where this is required. With modern microcontrollers such as the Espressif ESP32 [14] now fully supporting TLS, even low-power devices in the near-future would be able to fully utilize this approach.
# An Empirical Study on Ensemble of Segmentation Approaches
## 3 Results
### Metrics  
The system has been evaluated using two standard metrics: dice score and intersection over union (IoU). In the following formula, TP, TN, FP, and FN correspond to the true positives, true negatives, false positives, and false negatives, respectively. A is the predicted mask (TP + FP) and B is the ground truth map (TP + FN).  
**Dice score** (which is equivalent to F1 score in binary classification tasks) is a weighted average of precision and recall. Formally, it is defined as:  
\[F1 score=Dice=\frac{|A\;\cap\;B|}{|A|+|B|}=\frac{2\cdot\text{TP}}{2\cdot\text{ TP }\;+\;\text{FP}\;+\;\text{FN}} \tag{18}\]  
**Intersection over union (IoU)** defines the shared area between two masks, divided by the area of the union between the two maps. Formally, it is defined as:  
\[\text{IoU}\;=\;\frac{|A\;\cap\;B|}{|A\;\cup\;B|}=\frac{\text{TP}}{\text{TP }\;+\;\text{FP}\;+\;\text{FN}} \tag{19}\]  
In the experiments, image size has been modified due to the input size of the models. In these cases, we always change back to the original dimension the predicted masks.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
## 3 Results and Discussion
### Identifying the Development of Tensile and Shear Cracks under Biaxial Compression Test
#### 3.1.2 Tensile and Shear Cracks Development under Biaxial Compression Test  
The tensile and shear crack development during rock failure under biaxial conditions was determined as follows. Occurring sporadically throughout the recording time during the biaxial test, the cracking sound signals are divided into sections with a time interval of 20 ms. After obtaining all the intercepted sound signals, the spectrograms are extracted to construct the prediction sample. Then, the deep characteristics of the predicted spectrograms are extracted from the pool5 layer of the pre-trained ResNet-18; the corresponding operational process is described in Section 3.2. The characteristics are used as input to a trained GPC to obtain the final recognition results of the crack types. The related parameters of trained GPC during identification are set as follows. The training sample comprises 48 spectrograms. The GPC initial hyper-parameter is \([-2;2]\). The number of repetitions used to optimize the adaptive hyper-parameter is 30. The GPC kernel function is covNNone.  
Figure 17: Stress–strain curve of tested granite specimen from the biaxial compression test.  
Figure 18: Waveform of the sound signal obtained from the biaxial compression test.  
The variation of tensile and shear sound signals obtained using the proposed method is shown in Figure 19. In the figure, the abscissa indicates the time when the sound signal of the crack occurs, and the ordinate indicates the predicted probability of GPC. When the predicted probability of GPC is greater than or equal to 0.5, a tensile crack is predicted, and when it is less than 0.5, a shear crack is predicted. The proportions of tensile and shear sound signals in different deformation stages are shown in Table 5. During the initial compaction stage (0-28.8 s) and the elastic stage (28.8-56.743 s), there is no crack sound appearance. In the elastic-plastic stage (56.743-109.44 s), the cracking activity inside the rock increases, and the number of sound signals increases. The first cracking sounds occur at 56.753 s. During this stage, the sound signals are mainly tensile type, and only a small amount of shear sound signals occur; tensile sound signals account for 85%. During the plastic stage (109.44-119.4 s), the number of shear sound signals continually increases, the proportion of the shear sound signals accounts for more than 75%.  
The tensile and shear crack development during rock failure under biaxial conditions is shown in Figure 20. The rock specimen undergoes a typical process from tensile crack development on both sides to internal shear cracks, which is consistent with previous research [35]. Consequently, it can be seen that the proposed method is feasible.